diff --git a/docs/.buildinfo b/docs/.buildinfo index e3fbbb98..f453256f 100644 --- a/docs/.buildinfo +++ b/docs/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 1abb69093b22996e21358efdfe7002eb +config: 92f6ef0f99d20f0325aa38e0bfd88664 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_modules/index.html b/docs/_modules/index.html index c42574b0..4330aed1 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -3,10 +3,10 @@ - - Overview: module code — segregation v1.3.0 Manual - + + Overview: module code — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -138,7 +138,7 @@

All modules for which code is available

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/aspatial/aspatial_indexes.html b/docs/_modules/segregation/aspatial/aspatial_indexes.html index 3e14a478..696594a6 100644 --- a/docs/_modules/segregation/aspatial/aspatial_indexes.html +++ b/docs/_modules/segregation/aspatial/aspatial_indexes.html @@ -3,10 +3,10 @@ - - segregation.aspatial.aspatial_indexes — segregation v1.3.0 Manual - + + segregation.aspatial.aspatial_indexes — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -2159,7 +2159,7 @@

Source code for segregation.aspatial.aspatial_indexes

Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/aspatial/multigroup_aspatial_indexes.html b/docs/_modules/segregation/aspatial/multigroup_aspatial_indexes.html index 728e3544..09a8577e 100644 --- a/docs/_modules/segregation/aspatial/multigroup_aspatial_indexes.html +++ b/docs/_modules/segregation/aspatial/multigroup_aspatial_indexes.html @@ -3,10 +3,10 @@ - - segregation.aspatial.multigroup_aspatial_indexes — segregation v1.3.0 Manual - + + segregation.aspatial.multigroup_aspatial_indexes — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -1327,7 +1327,7 @@

Source code for segregation.aspatial.multigroup_aspatial_indexes

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/compute_all/compute_all.html b/docs/_modules/segregation/compute_all/compute_all.html index 3ddc4908..fe7265e8 100644 --- a/docs/_modules/segregation/compute_all/compute_all.html +++ b/docs/_modules/segregation/compute_all/compute_all.html @@ -3,10 +3,10 @@ - - segregation.compute_all.compute_all — segregation v1.3.0 Manual - + + segregation.compute_all.compute_all — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -475,7 +475,7 @@

Source code for segregation.compute_all.compute_all

Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/decomposition/decompose_segregation.html b/docs/_modules/segregation/decomposition/decompose_segregation.html index 288319b2..4bcbc09c 100644 --- a/docs/_modules/segregation/decomposition/decompose_segregation.html +++ b/docs/_modules/segregation/decomposition/decompose_segregation.html @@ -3,10 +3,10 @@ - - segregation.decomposition.decompose_segregation — segregation v1.3.0 Manual - + + segregation.decomposition.decompose_segregation — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -121,18 +121,19 @@

Source code for segregation.decomposition.decompose_segregation

import warnings -from segregation.util.util import _generate_counterfactual, _dep_message, DeprecationHelper +import seaborn as sns +import matplotlib.pyplot as plt +import pandas as pd +from segregation.util.util import _generate_counterfactual # Including old and new api in __all__ so users can use both -__all__ = ['Decompose_Segregation', - 'DecomposeSegregation'] +__all__ = ["DecomposeSegregation"] # The Deprecation calls of the classes are located in the end of this script # -def _decompose_segregation(index1, - index2, - counterfactual_approach='composition'): + +def _decompose_segregation(index1, index2, counterfactual_approach="composition"): """Decompose segregation differences into spatial and attribute components. Given two segregation indices of the same type, use Shapley decomposition @@ -164,14 +165,17 @@

Source code for segregation.decomposition.decompose_segregation

df1 = index1.core_data.copy() df2 = index2.core_data.copy() - assert index1._function == index2._function, "Segregation indices must be of the same type" + assert ( + index1._function == index2._function + ), "Segregation indices must be of the same type" counterfac_df1, counterfac_df2 = _generate_counterfactual( df1, df2, - 'group_pop_var', - 'total_pop_var', - counterfactual_approach=counterfactual_approach) + "group_pop_var", + "total_pop_var", + counterfactual_approach=counterfactual_approach, + ) seg_func = index1._function @@ -182,12 +186,14 @@

Source code for segregation.decomposition.decompose_segregation

G_S2_A2 = index2.statistic # index for spatial 1 attribute 2 (counterfactual population for structure 1) - G_S1_A2 = seg_func(counterfac_df1, 'counterfactual_group_pop', - 'counterfactual_total_pop')[0] + G_S1_A2 = seg_func( + counterfac_df1, "counterfactual_group_pop", "counterfactual_total_pop" + )[0] # index for spatial 2 attribute 1 (counterfactual population for structure 2) - G_S2_A1 = seg_func(counterfac_df2, 'counterfactual_group_pop', - 'counterfactual_total_pop')[0] + G_S2_A1 = seg_func( + counterfac_df2, "counterfactual_group_pop", "counterfactual_total_pop" + )[0] # take the average difference in spatial structure, holding attributes constant C_S = 1 / 2 * (G_S1_A1 - G_S2_A1 + G_S1_A2 - G_S2_A2) @@ -195,12 +201,18 @@

Source code for segregation.decomposition.decompose_segregation

# take the average difference in attributes, holding spatial structure constant C_A = 1 / 2 * (G_S1_A1 - G_S1_A2 + G_S2_A1 - G_S2_A2) - results = {'s1_a1': G_S1_A1, - 's1_a2': G_S1_A2, - 's2_a1': G_S2_A1, - 's2_a2': G_S2_A2 } + results = {"s1_a1": G_S1_A1, "s1_a2": G_S1_A2, "s2_a1": G_S2_A1, "s2_a2": G_S2_A2} - return C_S, C_A, df1, df2, counterfac_df1, counterfac_df2, counterfactual_approach, results + return ( + C_S, + C_A, + df1, + df2, + counterfac_df1, + counterfac_df2, + counterfactual_approach, + results, + )
[docs]class DecomposeSegregation: @@ -210,43 +222,35 @@

Source code for segregation.decomposition.decompose_segregation

to measure whether the differences between index measures arise from differences in spatial structure or population structure - Parameters - ---------- - index1 : segregation.SegIndex class - First SegIndex class to compare. - index2 : segregation.SegIndex class - Second SegIndex class to compare. - counterfactual_approach : str, one of - ["composition", "share", "dual_composition"] - The technique used to generate the counterfactual population - distributions. - - Attributes - ---------- - - c_s : float - Shapley's Spatial Component of the decomposition - - c_a : float - Shapley's Attribute Component of the decomposition - - Methods - ---------- - - plot : Visualize features of the Decomposition performed - plot_type : str, one of ['cdfs', 'maps'] - - 'cdfs' : visualize the cumulative distribution functions of the compositions/shares - 'maps' : visualize the spatial distributions for original data and counterfactuals generated and Shapley's components (only available for GeoDataFrames) - Examples -------- Several examples can be found at https://github.com/pysal/segregation/blob/master/notebooks/decomposition_wrapper_example.ipynb. - """ -
[docs] def __init__(self, index1, index2, counterfactual_approach='composition'): - +
[docs] def __init__(self, index1, index2, counterfactual_approach="composition"): + """Decompose segregation differences into spatial and attribute components. + + Given two segregation indices of the same type, use Shapley decomposition + to measure whether the differences between index measures arise from + differences in spatial structure or population structure + + Parameters + ---------- + index1 : segregation.SegIndex class + First SegIndex class to compare. + index2 : segregation.SegIndex class + Second SegIndex class to compare. + counterfactual_approach : str, one of {"composition", "share", "dual_composition"} + The technique used to generate the counterfactual population + distributions. + + Attributes + ---------- + c_s : float + Shapley's Spatial Component of the decomposition + c_a : float + Shapley's Attribute Component of the decomposition + """ aux = _decompose_segregation(index1, index2, counterfactual_approach) self.c_s = aux[0] @@ -258,172 +262,163 @@

Source code for segregation.decomposition.decompose_segregation

self._counterfactual_approach = aux[6] self.indices = aux[7]
- def plot(self, plot_type='cdfs'): - """ - Plot the Segregation Decomposition Profile + def plot( + self, + plot_type="cdfs", + figsize=None, + city_a=None, + city_b=None, + cmap="OrRd", + scheme="equalinterval", + k=10, + suptitle_size=16, + title_size=12, + savefig=None, + dpi=300, + ): + """Plot maps or CDFs of urban contexts used in calculating the Decomposition class. + + Parameters + ---------- + plot_type : str, {'cdfs, 'maps'} + which type of plot to generate. Options include `cdfs` and `maps` by default "cdfs" + figsize : tuple, optional + figsize parameter passed to matplotlib.pyplot + city_a : pysal.segregation spatial or aspatial index class, required + Fitted segregation class for first urban context + city_b : pysal.segregation spatial or aspatial index class, required + Fitted segregation class for second urban context, required + cmap : str, optional + matplotlib colormap used to shade the map, by default "OrRd" + scheme : str, optional + pysal.mapclassify classification scheme used to shade the map, by default "equalinterval" + k : int, optional + number of classes in pysal.mapclassify classification scheme, by default 10 + suptitle_size : int, optional + size parameter passed to `matplotlib.Figure.suptitle`, by default 16 + title_size : int, optional + size parameter passed to `matplotlib.Axes.set_title`, by default 12 + savefig : str, optional + Location to save the figure if desired. If None, fig will not be saved + dpi : int, optional + dpi parameter passed to matplotlib.pyplot, by default 300 + + Returns + ------- + None + Generates a new matplotlib.Figure instance and optionally saves to disk """ - try: - import matplotlib.pyplot as plt - except ImportError: - warnings.warn('This method relies on importing `matplotlib`') - - if (plot_type == 'cdfs'): - if (self._counterfactual_approach == 'composition'): - plt.suptitle( - 'Spatial Component = {}, Attribute Component: {}'.format( - round(self.c_s, 3), round(self.c_a, 3)), - size=20) - plt.step( - self._counterfac_df1['group_composition'].sort_values(), - self._counterfac_df1['group_composition'].rank( - pct=True).sort_values(), - label='First Context Group Composition') - - plt.step( - self._counterfac_df2['group_composition'].sort_values(), - self._counterfac_df2['group_composition'].rank( - pct=True).sort_values(), - label='Second Context Group Composition') - plt.legend() - - if (self._counterfactual_approach == 'share'): - plt.suptitle( - 'Spatial Component = {}, Attribute Component: {}'.format( - round(self.c_s, 3), round(self.c_a, 3)), - size=20) - plt.step((self._df1['group_pop_var'] / - self._df1['group_pop_var'].sum()).sort_values(), - (self._df1['group_pop_var'] / - self._df1['group_pop_var'].sum()).rank( - pct=True).sort_values(), - label='First Context Group Share') - - plt.step((self._df2['group_pop_var'] / - self._df2['group_pop_var'].sum()).sort_values(), - (self._df2['group_pop_var'] / - self._df2['group_pop_var'].sum()).rank( - pct=True).sort_values(), - label='Second Context Group Share') - - plt.step( - ((self._df1['total_pop_var'] - self._df1['group_pop_var']) - / (self._df1['total_pop_var'] - - self._df1['group_pop_var']).sum()).sort_values(), - ((self._df1['total_pop_var'] - self._df1['group_pop_var']) - / (self._df1['total_pop_var'] - - self._df1['group_pop_var']).sum()).rank( - pct=True).sort_values(), - label='First Context Complementary Group Share') - - plt.step( - ((self._df2['total_pop_var'] - self._df2['group_pop_var']) - / (self._df2['total_pop_var'] - - self._df2['group_pop_var']).sum()).sort_values(), - ((self._df2['total_pop_var'] - self._df2['group_pop_var']) - / (self._df2['total_pop_var'] - - self._df2['group_pop_var']).sum()).rank( - pct=True).sort_values(), - label='Second Context Complementary Group Share') - plt.legend() - - if (self._counterfactual_approach == 'dual_composition'): - plt.suptitle( - 'Spatial Component = {}, Attribute Component: {}'.format( - round(self.c_s, 3), round(self.c_a, 3)), - size=20) - plt.step( - self._counterfac_df1['group_composition'].sort_values(), - self._counterfac_df1['group_composition'].rank( - pct=True).sort_values(), - label='First Context Group Composition') - - plt.step( - self._counterfac_df2['group_composition'].sort_values(), - self._counterfac_df2['group_composition'].rank( - pct=True).sort_values(), - label='Second Context Group Composition') - - plt.step( - (1 - - self._counterfac_df1['group_composition']).sort_values(), - (1 - self._counterfac_df1['group_composition']).rank( - pct=True).sort_values(), - label='First Context Complementary Group Composition') - - plt.step( - (1 - - self._counterfac_df2['group_composition']).sort_values(), - (1 - self._counterfac_df2['group_composition']).rank( - pct=True).sort_values(), - label='Second Context Complementary Group Composition') - - plt.legend() - - if (plot_type == 'maps'): - if (str(type(self._df1)) != - '<class \'geopandas.geodataframe.GeoDataFrame\'>'): - raise TypeError( - 'data is not a GeoDataFrame and, therefore, maps cannot be draw.' - ) - - # Subplots - fig, axs = plt.subplots(2, 2, figsize=(10, 10)) - - fig.suptitle( - 'Spatial Component = {}, Attribute Component: {}'.format( - round(self.c_s, 3), round(self.c_a, 3)), - size=20) - fig.subplots_adjust(hspace=1.25, wspace=0.2, - top=0.95) # hspace space between lines - fig.tight_layout(rect=[ - 0, 0, 1, 0.925 - ]) # rect is to position the suptitle above the subplots + if not city_a: + city_a = "City A" + if not city_b: + city_b = "City B" + + if plot_type == "cdfs": + if not figsize: + figsize = (10, 10) + fig, ax = plt.subplots(figsize=figsize) + plt.suptitle( + f"Decomposing differences between\n{city_a} and {city_b}", + size=suptitle_size, + ) + plt.title( + f"Spatial Component = {round(self.c_s, 3)}, Attribute Component: {round(self.c_a, 3)}", + size=title_size, + ) + + temp_a = self._counterfac_df1.copy() + temp_a["Location"] = city_a + temp_b = self._counterfac_df2.copy() + temp_b["Location"] = city_b + df = pd.concat([temp_a, temp_b]) + + if self._counterfactual_approach == "composition": + sns.ecdfplot(data=df, x="group_composition", hue="Location", ax=ax) + return ax + + elif self._counterfactual_approach == "share": + f = sns.ecdfplot(data=df, x="share", hue="Location", ax=ax) + return f + + elif self._counterfactual_approach == "dual_composition": + df["compl"] = 1 - df.group_composition + f = sns.ecdfplot(data=df, x="group_composition", hue="Location", ax=ax) + f2 = sns.ecdfplot(data=df, x="compl", hue="Location", ax=ax) + if savefig: + plt.savefig(savefig, dpi=dpi) + + if plot_type == "maps": + if not figsize: + figsize = (20, 20) + fig, axs = plt.subplots(2, 2, figsize=figsize) + plt.suptitle( + f"Decomposing differences between\n{city_a} and {city_b}", + size=suptitle_size, + ) + plt.title( + f"Spatial Component = {round(self.c_s, 3)}, Attribute Component: {round(self.c_a, 3)}" + ) # Original First Context (Upper Left) - self._counterfac_df1.plot(column='group_composition', - cmap='OrRd', - legend=True, - ax=axs[0, 0]) - axs[0, 0].title.set_text('Original First Context Composition') - axs[0, 0].axis('off') + self._counterfac_df1.plot( + column="group_composition", + cmap=cmap, + legend=True, + scheme=scheme, + k=k, + ax=axs[0, 0], + ) + axs[0, 0].set_title( + f"{city_a}\nOriginal Composition", fontdict={"fontsize": title_size} + ) + axs[0, 0].axis("off") # Counterfactual First Context (Bottom Left) - self._counterfac_df1.plot(column='counterfactual_composition', - cmap='OrRd', - legend=True, - ax=axs[1, 0]) - axs[1, 0].title.set_text( - 'Counterfactual First Context Composition') - axs[1, 0].axis('off') + self._counterfac_df1.plot( + column="counterfactual_composition", + cmap=cmap, + scheme=scheme, + k=k, + legend=True, + ax=axs[1, 0], + ) + axs[1, 0].set_title( + f"{city_a}\nCounterfactual Composition", + fontdict={"fontsize": title_size}, + ) + axs[1, 0].axis("off") # Counterfactual Second Context (Upper Right) - self._counterfac_df2.plot(column='counterfactual_composition', - cmap='OrRd', - legend=True, - ax=axs[0, 1]) - axs[0, 1].title.set_text( - 'Counterfactual Second Context Composition') - axs[0, 1].axis('off') + self._counterfac_df2.plot( + column="counterfactual_composition", + cmap=cmap, + scheme=scheme, + k=k, + legend=True, + ax=axs[0, 1], + ) + axs[0, 1].set_title( + f"{city_b}\nCounterfactual Composition", + fontdict={"fontsize": title_size}, + ) + axs[0, 1].axis("off") # Original Second Context (Bottom Right) - self._counterfac_df2.plot(column='group_composition', - cmap='OrRd', - legend=True, - ax=axs[1, 1]) - axs[1, 1].title.set_text('Original Second Context Composition') - axs[1, 1].axis('off')
- - - - - - - - -# Deprecation Calls - -msg = _dep_message("Decompose_Segregation", "DecomposeSegregation") -Decompose_Segregation = DeprecationHelper(DecomposeSegregation, message=msg) + self._counterfac_df2.plot( + column="group_composition", + cmap=cmap, + scheme=scheme, + k=k, + legend=True, + ax=axs[1, 1], + ) + axs[1, 1].set_title( + f"{city_b}\nOriginal Composition", fontdict={"fontsize": title_size} + ) + axs[1, 1].axis("off") + if savefig: + plt.savefig(savefig, dpi=dpi) + return axs
@@ -441,7 +436,7 @@

Source code for segregation.decomposition.decompose_segregation

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/inference/inference_wrappers.html b/docs/_modules/segregation/inference/inference_wrappers.html index f414702d..14d5d423 100644 --- a/docs/_modules/segregation/inference/inference_wrappers.html +++ b/docs/_modules/segregation/inference/inference_wrappers.html @@ -3,10 +3,10 @@ - - segregation.inference.inference_wrappers — segregation v1.3.0 Manual - + + segregation.inference.inference_wrappers — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -872,7 +872,7 @@

Source code for segregation.inference.inference_wrappers

Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/local/local_indexes.html b/docs/_modules/segregation/local/local_indexes.html index dce60cbb..cfb4f6f8 100644 --- a/docs/_modules/segregation/local/local_indexes.html +++ b/docs/_modules/segregation/local/local_indexes.html @@ -3,10 +3,10 @@ - - segregation.local.local_indexes — segregation v1.3.0 Manual - + + segregation.local.local_indexes — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -871,7 +871,7 @@

Source code for segregation.local.local_indexes

<

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_modules/segregation/network/network.html b/docs/_modules/segregation/network/network.html index 5dfcc57b..cb0aa018 100644 --- a/docs/_modules/segregation/network/network.html +++ b/docs/_modules/segregation/network/network.html @@ -3,10 +3,10 @@ - - segregation.network.network — segregation v1.3.0 Manual - + + segregation.network.network — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -265,7 +265,7 @@

Source code for segregation.network.network

     

     

         © Copyright 2018, pysal developers.

-      Created using Sphinx 3.1.1.

+      Created using Sphinx 3.3.0.
diff --git a/docs/_modules/segregation/spatial/spatial_indexes.html b/docs/_modules/segregation/spatial/spatial_indexes.html index c388dd80..1bbfccb8 100644 --- a/docs/_modules/segregation/spatial/spatial_indexes.html +++ b/docs/_modules/segregation/spatial/spatial_indexes.html @@ -3,10 +3,10 @@ - - segregation.spatial.spatial_indexes — segregation v1.3.0 Manual - + + segregation.spatial.spatial_indexes — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -3656,7 +3656,7 @@

Source code for segregation.spatial.spatial_indexes

Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/_sources/generated/segregation.aspatial.Atkinson.rst.txt b/docs/_sources/generated/segregation.aspatial.Atkinson.rst.txt index 49191784..fb75436d 100644 --- a/docs/_sources/generated/segregation.aspatial.Atkinson.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.Atkinson.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.Atkinson +segregation.aspatial.Atkinson ============================= .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt b/docs/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt index 077f0cca..2c5597b6 100644 --- a/docs/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.BiasCorrectedDissim +segregation.aspatial.BiasCorrectedDissim ======================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.ConProf.rst.txt b/docs/_sources/generated/segregation.aspatial.ConProf.rst.txt index 77cf0417..15d2149d 100644 --- a/docs/_sources/generated/segregation.aspatial.ConProf.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.ConProf.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.ConProf +segregation.aspatial.ConProf ============================ .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.CorrelationR.rst.txt b/docs/_sources/generated/segregation.aspatial.CorrelationR.rst.txt index d83560db..2724ca5e 100644 --- a/docs/_sources/generated/segregation.aspatial.CorrelationR.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.CorrelationR.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.CorrelationR +segregation.aspatial.CorrelationR ================================= .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt b/docs/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt index d0b14d62..18ca28c7 100644 --- a/docs/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.DensityCorrectedDissim +segregation.aspatial.DensityCorrectedDissim =========================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.Dissim.rst.txt b/docs/_sources/generated/segregation.aspatial.Dissim.rst.txt index 60945e10..837fdf0f 100644 --- a/docs/_sources/generated/segregation.aspatial.Dissim.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.Dissim.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.Dissim +segregation.aspatial.Dissim =========================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.Entropy.rst.txt b/docs/_sources/generated/segregation.aspatial.Entropy.rst.txt index f4e0e658..6f7039ef 100644 --- a/docs/_sources/generated/segregation.aspatial.Entropy.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.Entropy.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.Entropy +segregation.aspatial.Entropy ============================ .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.Exposure.rst.txt b/docs/_sources/generated/segregation.aspatial.Exposure.rst.txt index d2928c40..869bd466 100644 --- a/docs/_sources/generated/segregation.aspatial.Exposure.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.Exposure.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.Exposure +segregation.aspatial.Exposure ============================= .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.GiniSeg.rst.txt b/docs/_sources/generated/segregation.aspatial.GiniSeg.rst.txt index 2ed90dbb..111329bc 100644 --- a/docs/_sources/generated/segregation.aspatial.GiniSeg.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.GiniSeg.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.GiniSeg +segregation.aspatial.GiniSeg ============================ .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.Isolation.rst.txt b/docs/_sources/generated/segregation.aspatial.Isolation.rst.txt index 977a014d..a3237179 100644 --- a/docs/_sources/generated/segregation.aspatial.Isolation.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.Isolation.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.Isolation +segregation.aspatial.Isolation ============================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MinMax.rst.txt b/docs/_sources/generated/segregation.aspatial.MinMax.rst.txt index b5068c2b..3b1cb22e 100644 --- a/docs/_sources/generated/segregation.aspatial.MinMax.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MinMax.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MinMax +segregation.aspatial.MinMax =========================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt b/docs/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt index 33b9fca0..8273507d 100644 --- a/docs/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.ModifiedDissim +segregation.aspatial.ModifiedDissim =================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt b/docs/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt index 4cb1ffcb..856c3743 100644 --- a/docs/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.ModifiedGiniSeg +segregation.aspatial.ModifiedGiniSeg ==================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiDissim.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiDissim.rst.txt index 831b917b..8c5be314 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiDissim.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiDissim +segregation.aspatial.MultiDissim ================================ .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt index 5b78231e..cd7356ba 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiDivergence +segregation.aspatial.MultiDivergence ==================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt index f868a052..91bd519b 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiDiversity +segregation.aspatial.MultiDiversity =================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt index fec16f29..f5179aff 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiGiniSeg +segregation.aspatial.MultiGiniSeg ================================= .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt index 0161d2b6..10fb9d02 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiInformationTheory +segregation.aspatial.MultiInformationTheory =========================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt index 90daa95e..8e64ddba 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiNormalizedExposure +segregation.aspatial.MultiNormalizedExposure ============================================ .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt index 43898059..906dc991 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiRelativeDiversity +segregation.aspatial.MultiRelativeDiversity =========================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt b/docs/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt index aa58c52e..f396d717 100644 --- a/docs/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.MultiSquaredCoefficientVariation +segregation.aspatial.MultiSquaredCoefficientVariation ===================================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt b/docs/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt index 3756049d..85b52a88 100644 --- a/docs/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.SimpsonsConcentration +segregation.aspatial.SimpsonsConcentration ========================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt b/docs/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt index dbc312bf..e1e12c73 100644 --- a/docs/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt +++ b/docs/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt @@ -1,4 +1,4 @@ -segregation.aspatial.SimpsonsInteraction +segregation.aspatial.SimpsonsInteraction ======================================== .. currentmodule:: segregation.aspatial diff --git a/docs/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt b/docs/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt index 4b5dd82e..90eb149c 100644 --- a/docs/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt +++ b/docs/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt @@ -1,4 +1,4 @@ -segregation.compute\_all.ComputeAllAspatialSegregation +segregation.compute\_all.ComputeAllAspatialSegregation ====================================================== .. currentmodule:: segregation.compute_all diff --git a/docs/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt b/docs/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt index 7ea9d749..bea02eef 100644 --- a/docs/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt +++ b/docs/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt @@ -1,4 +1,4 @@ -segregation.compute\_all.ComputeAllSegregation +segregation.compute\_all.ComputeAllSegregation ============================================== .. currentmodule:: segregation.compute_all diff --git a/docs/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt b/docs/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt index 055b788a..8d66df04 100644 --- a/docs/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt +++ b/docs/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt @@ -1,4 +1,4 @@ -segregation.compute\_all.ComputeAllSpatialSegregation +segregation.compute\_all.ComputeAllSpatialSegregation ===================================================== .. currentmodule:: segregation.compute_all diff --git a/docs/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt b/docs/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt index a68d0b0c..e87f753a 100644 --- a/docs/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt +++ b/docs/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt @@ -1,4 +1,4 @@ -segregation.decomposition.DecomposeSegregation +segregation.decomposition.DecomposeSegregation ============================================== .. currentmodule:: segregation.decomposition diff --git a/docs/_sources/generated/segregation.inference.SingleValueTest.rst.txt b/docs/_sources/generated/segregation.inference.SingleValueTest.rst.txt index 3f97decb..421372c2 100644 --- a/docs/_sources/generated/segregation.inference.SingleValueTest.rst.txt +++ b/docs/_sources/generated/segregation.inference.SingleValueTest.rst.txt @@ -1,4 +1,4 @@ -segregation.inference.SingleValueTest +segregation.inference.SingleValueTest ===================================== .. currentmodule:: segregation.inference diff --git a/docs/_sources/generated/segregation.inference.TwoValueTest.rst.txt b/docs/_sources/generated/segregation.inference.TwoValueTest.rst.txt index 013da514..cfc06cd1 100644 --- a/docs/_sources/generated/segregation.inference.TwoValueTest.rst.txt +++ b/docs/_sources/generated/segregation.inference.TwoValueTest.rst.txt @@ -1,4 +1,4 @@ -segregation.inference.TwoValueTest +segregation.inference.TwoValueTest ================================== .. currentmodule:: segregation.inference diff --git a/docs/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt b/docs/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt index b914ad6a..2b1c6f58 100644 --- a/docs/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt +++ b/docs/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt @@ -1,4 +1,4 @@ -segregation.local.LocalRelativeCentralization +segregation.local.LocalRelativeCentralization ============================================= .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt b/docs/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt index ab3d3a9b..759d3967 100644 --- a/docs/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt +++ b/docs/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt @@ -1,4 +1,4 @@ -segregation.local.MultiLocalDiversity +segregation.local.MultiLocalDiversity ===================================== .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt b/docs/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt index b2f70db1..336e7567 100644 --- a/docs/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt +++ b/docs/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt @@ -1,4 +1,4 @@ -segregation.local.MultiLocalEntropy +segregation.local.MultiLocalEntropy =================================== .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt b/docs/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt index a9883ab4..ac682acc 100644 --- a/docs/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt +++ b/docs/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt @@ -1,4 +1,4 @@ -segregation.local.MultiLocalSimpsonConcentration +segregation.local.MultiLocalSimpsonConcentration ================================================ .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt b/docs/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt index 6f660da2..ae86e631 100644 --- a/docs/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt +++ b/docs/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt @@ -1,4 +1,4 @@ -segregation.local.MultiLocalSimpsonInteraction +segregation.local.MultiLocalSimpsonInteraction ============================================== .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt b/docs/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt index 38e696dd..496f4093 100644 --- a/docs/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt +++ b/docs/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt @@ -1,4 +1,4 @@ -segregation.local.MultiLocationQuotient +segregation.local.MultiLocationQuotient ======================================= .. currentmodule:: segregation.local diff --git a/docs/_sources/generated/segregation.network.calc_access.rst.txt b/docs/_sources/generated/segregation.network.calc_access.rst.txt index 22fc2013..aa4ab112 100644 --- a/docs/_sources/generated/segregation.network.calc_access.rst.txt +++ b/docs/_sources/generated/segregation.network.calc_access.rst.txt @@ -1,4 +1,4 @@ -segregation.network.calc\_access +segregation.network.calc\_access ================================ .. currentmodule:: segregation.network diff --git a/docs/_sources/generated/segregation.network.get_osm_network.rst.txt b/docs/_sources/generated/segregation.network.get_osm_network.rst.txt index 02c6a6c4..93dfcd8e 100644 --- a/docs/_sources/generated/segregation.network.get_osm_network.rst.txt +++ b/docs/_sources/generated/segregation.network.get_osm_network.rst.txt @@ -1,4 +1,4 @@ -segregation.network.get\_osm\_network +segregation.network.get\_osm\_network ===================================== .. currentmodule:: segregation.network diff --git a/docs/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt b/docs/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt index f56f28b1..bb608e37 100644 --- a/docs/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt +++ b/docs/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.AbsoluteCentralization +segregation.spatial.AbsoluteCentralization ========================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt b/docs/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt index 5944bc0d..9733f8c6 100644 --- a/docs/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt +++ b/docs/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.AbsoluteClustering +segregation.spatial.AbsoluteClustering ====================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt b/docs/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt index e540d2e6..9ac60c39 100644 --- a/docs/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt +++ b/docs/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.AbsoluteConcentration +segregation.spatial.AbsoluteConcentration ========================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt b/docs/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt index 59516f46..5af34bef 100644 --- a/docs/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt +++ b/docs/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.BoundarySpatialDissim +segregation.spatial.BoundarySpatialDissim ========================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.Delta.rst.txt b/docs/_sources/generated/segregation.spatial.Delta.rst.txt index 0d9568c9..cf24aba0 100644 --- a/docs/_sources/generated/segregation.spatial.Delta.rst.txt +++ b/docs/_sources/generated/segregation.spatial.Delta.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.Delta +segregation.spatial.Delta ========================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt b/docs/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt index 8600ea86..57980d81 100644 --- a/docs/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt +++ b/docs/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.DistanceDecayExposure +segregation.spatial.DistanceDecayExposure ========================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt b/docs/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt index ed2733e1..21359442 100644 --- a/docs/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt +++ b/docs/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.DistanceDecayIsolation +segregation.spatial.DistanceDecayIsolation ========================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt b/docs/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt index 2f6e4b99..50f56444 100644 --- a/docs/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt +++ b/docs/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.PerimeterAreaRatioSpatialDissim +segregation.spatial.PerimeterAreaRatioSpatialDissim =================================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt b/docs/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt index 677bc0b7..75ae1aab 100644 --- a/docs/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt +++ b/docs/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.RelativeCentralization +segregation.spatial.RelativeCentralization ========================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.RelativeClustering.rst.txt b/docs/_sources/generated/segregation.spatial.RelativeClustering.rst.txt index 2ef580ac..24281f66 100644 --- a/docs/_sources/generated/segregation.spatial.RelativeClustering.rst.txt +++ b/docs/_sources/generated/segregation.spatial.RelativeClustering.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.RelativeClustering +segregation.spatial.RelativeClustering ====================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt b/docs/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt index 4a58676b..1fba3256 100644 --- a/docs/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt +++ b/docs/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.RelativeConcentration +segregation.spatial.RelativeConcentration ========================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.SpatialDissim.rst.txt b/docs/_sources/generated/segregation.spatial.SpatialDissim.rst.txt index c631e6e5..29071836 100644 --- a/docs/_sources/generated/segregation.spatial.SpatialDissim.rst.txt +++ b/docs/_sources/generated/segregation.spatial.SpatialDissim.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.SpatialDissim +segregation.spatial.SpatialDissim ================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt b/docs/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt index f948a80a..7a07bf6e 100644 --- a/docs/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt +++ b/docs/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.SpatialMinMax +segregation.spatial.SpatialMinMax ================================= .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt b/docs/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt index a26e0038..c7560ad0 100644 --- a/docs/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt +++ b/docs/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.SpatialProxProf +segregation.spatial.SpatialProxProf =================================== .. currentmodule:: segregation.spatial diff --git a/docs/_sources/generated/segregation.spatial.SpatialProximity.rst.txt b/docs/_sources/generated/segregation.spatial.SpatialProximity.rst.txt index 02ff5a0a..ba44fa0e 100644 --- a/docs/_sources/generated/segregation.spatial.SpatialProximity.rst.txt +++ b/docs/_sources/generated/segregation.spatial.SpatialProximity.rst.txt @@ -1,4 +1,4 @@ -segregation.spatial.SpatialProximity +segregation.spatial.SpatialProximity ==================================== .. currentmodule:: segregation.spatial diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 56f5efc6..24a49f09 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -15,6 +15,12 @@ div.clearer { clear: both; } +div.section::after { + display: block; + content: ''; + clear: left; +} + /* -- relbar ---------------------------------------------------------------- */ div.related { @@ -328,8 +334,8 @@ p.sidebar-title { font-weight: bold; } -div.admonition, div.topic, pre, div[class|="highlight"] { - clear: both; +div.admonition, div.topic, blockquote { + clear: left; } /* -- topics ---------------------------------------------------------------- */ @@ -338,7 +344,6 @@ div.topic { border: 1px solid #ccc; padding: 7px; margin: 10px 0 10px 0; - overflow-x: auto; } p.topic-title { @@ -353,7 +358,6 @@ div.admonition { margin-top: 10px; margin-bottom: 10px; padding: 7px; - overflow-x: auto; } div.admonition dt { @@ -378,6 +382,15 @@ div.admonition > :last-child { margin-bottom: 0; } +div.sidebar::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + /* -- tables ---------------------------------------------------------------- */ table.docutils { @@ -513,28 +526,31 @@ ol.upperroman { list-style: upper-roman; } -ol > li:first-child > :first-child, -ul > li:first-child > :first-child { +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { margin-top: 0px; } -ol ol > li:first-child > :first-child, -ol ul > li:first-child > :first-child, -ul ol > li:first-child > :first-child, -ul ul > li:first-child > :first-child { - margin-top: revert; +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; } -ol > li:last-child > :last-child, -ul > li:last-child > :last-child { - margin-bottom: 0px; +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; } -ol ol > li:last-child > :last-child, -ol ul > li:last-child > :last-child, -ul ol > li:last-child > :last-child, -ul ul > li:last-child > :last-child { - margin-bottom: revert; +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; } dl.footnote > dt, @@ -677,6 +693,10 @@ pre { overflow-y: hidden; /* fixes display issues on Chrome browsers */ } +pre, div[class*="highlight-"] { + clear: both; +} + span.pre { -moz-hyphens: none; -ms-hyphens: none; @@ -684,7 +704,7 @@ span.pre { hyphens: none; } -div[class^="highlight-"] { +div[class*="highlight-"] { margin: 1em 0; } @@ -744,6 +764,7 @@ div.code-block-caption code { } table.highlighttable td.linenos, +span.linenos, div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ user-select: none; } diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js index daccd209..7d88f807 100644 --- a/docs/_static/doctools.js +++ b/docs/_static/doctools.js @@ -285,9 +285,10 @@ var Documentation = { initOnKeyListeners: function() { $(document).keydown(function(event) { var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea + // don't navigate when in search box, textarea, dropdown or button if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { + && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey + && !event.shiftKey) { switch (event.keyCode) { case 37: // left var prevHref = $('link[rel="prev"]').prop('href'); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 7cf0629b..d9d4c291 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.3.0', + VERSION: '1.4.0', LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css index 20c4814d..d14395ef 100644 --- a/docs/_static/pygments.css +++ b/docs/_static/pygments.css @@ -1,5 +1,10 @@ +pre { line-height: 125%; margin: 0; } +td.linenos pre { color: #000000; background-color: #f0f0f0; padding: 0 5px 0 5px; } +span.linenos { color: #000000; background-color: #f0f0f0; padding: 0 5px 0 5px; } +td.linenos pre.special { color: #000000; background-color: #ffffc0; padding: 0 5px 0 5px; } +span.linenos.special { color: #000000; background-color: #ffffc0; padding: 0 5px 0 5px; } .highlight .hll { background-color: #ffffcc } -.highlight { background: #eeffcc; } +.highlight { background: #eeffcc; } .highlight .c { color: #408090; font-style: italic } /* Comment */ .highlight .err { border: 1px solid #FF0000 } /* Error */ .highlight .k { color: #007020; font-weight: bold } /* Keyword */ diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js index ab564996..261ecaa9 100644 --- a/docs/_static/searchtools.js +++ b/docs/_static/searchtools.js @@ -59,10 +59,10 @@ var Search = { _pulse_status : -1, htmlToText : function(htmlString) { - var htmlElement = document.createElement('span'); - htmlElement.innerHTML = htmlString; - $(htmlElement).find('.headerlink').remove(); - docContent = $(htmlElement).find('[role=main]')[0]; + var virtualDocument = document.implementation.createHTMLDocument('virtual'); + var htmlElement = $(htmlString, virtualDocument); + htmlElement.find('.headerlink').remove(); + docContent = htmlElement.find('[role=main]')[0]; if(docContent === undefined) { console.warn("Content block not found. Sphinx search tries to obtain it " + "via '[role=main]'. Could you check your theme or template."); @@ -166,8 +166,7 @@ var Search = { objectterms.push(tmp[i].toLowerCase()); } - if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) || - tmp[i] === "") { + if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") { // skip this "word" continue; } diff --git a/docs/api.html b/docs/api.html index 8d81dc3e..cde24817 100644 --- a/docs/api.html +++ b/docs/api.html @@ -3,10 +3,10 @@ - - API reference — segregation v1.3.0 Manual - + + API reference — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -395,7 +395,7 @@

NetworkSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.Atkinson.html b/docs/generated/segregation.aspatial.Atkinson.html index 4bd0d2ac..2a427a68 100644 --- a/docs/generated/segregation.aspatial.Atkinson.html +++ b/docs/generated/segregation.aspatial.Atkinson.html @@ -3,10 +3,10 @@ - - segregation.aspatial.Atkinson — segregation v1.3.0 Manual - + + segregation.aspatial.Atkinson — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -179,7 +179,7 @@

segregation.aspatial.Atkinson
-__init__(self, data, group_pop_var, total_pop_var, b=0.5)[source]
+__init__(data, group_pop_var, total_pop_var, b=0.5)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -190,7 +190,7 @@

segregation.aspatial.Atkinson -

__init__(self, data, group_pop_var, …[, b])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -219,7 +219,7 @@

segregation.aspatial.AtkinsonSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.BiasCorrectedDissim.html b/docs/generated/segregation.aspatial.BiasCorrectedDissim.html index 05293e0e..e28b6eb0 100644 --- a/docs/generated/segregation.aspatial.BiasCorrectedDissim.html +++ b/docs/generated/segregation.aspatial.BiasCorrectedDissim.html @@ -3,10 +3,10 @@ - - segregation.aspatial.BiasCorrectedDissim — segregation v1.3.0 Manual - + + segregation.aspatial.BiasCorrectedDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -180,7 +180,7 @@

segregation.aspatial.BiasCorrectedDissim
-__init__(self, data, group_pop_var, total_pop_var, B=500)[source]
+__init__(data, group_pop_var, total_pop_var, B=500)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -191,7 +191,7 @@

segregation.aspatial.BiasCorrectedDissim -

__init__(self, data, group_pop_var, …[, B])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -220,7 +220,7 @@

segregation.aspatial.BiasCorrectedDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.ConProf.html b/docs/generated/segregation.aspatial.ConProf.html index 4199e018..70d06a8b 100644 --- a/docs/generated/segregation.aspatial.ConProf.html +++ b/docs/generated/segregation.aspatial.ConProf.html @@ -3,10 +3,10 @@ - - segregation.aspatial.ConProf — segregation v1.3.0 Manual - + + segregation.aspatial.ConProf — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -189,14 +189,14 @@

segregation.aspatial.ConProf -

plot(self)

+

plot()

Plot the Concentration Profile

-__init__(self, data, group_pop_var, total_pop_var, m=1000)[source]
+__init__(data, group_pop_var, total_pop_var, m=1000)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -207,10 +207,10 @@

segregation.aspatial.ConProf -

__init__(self, data, group_pop_var, …[, m])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

-

plot(self)

+

plot()

Plot the Concentration Profile

@@ -239,7 +239,7 @@

segregation.aspatial.ConProfSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.CorrelationR.html b/docs/generated/segregation.aspatial.CorrelationR.html index 4edb94f1..32267da3 100644 --- a/docs/generated/segregation.aspatial.CorrelationR.html +++ b/docs/generated/segregation.aspatial.CorrelationR.html @@ -3,10 +3,10 @@ - - segregation.aspatial.CorrelationR — segregation v1.3.0 Manual - + + segregation.aspatial.CorrelationR — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -177,7 +177,7 @@

segregation.aspatial.CorrelationR
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -188,7 +188,7 @@

segregation.aspatial.CorrelationR -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -217,7 +217,7 @@

segregation.aspatial.CorrelationRSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.DensityCorrectedDissim.html b/docs/generated/segregation.aspatial.DensityCorrectedDissim.html index b51a9441..8eb6ff20 100644 --- a/docs/generated/segregation.aspatial.DensityCorrectedDissim.html +++ b/docs/generated/segregation.aspatial.DensityCorrectedDissim.html @@ -3,10 +3,10 @@ - - segregation.aspatial.DensityCorrectedDissim — segregation v1.3.0 Manual - + + segregation.aspatial.DensityCorrectedDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -179,7 +179,7 @@

segregation.aspatial.DensityCorrectedDissim
-__init__(self, data, group_pop_var, total_pop_var, xtol=1e-05)[source]
+__init__(data, group_pop_var, total_pop_var, xtol=1e-05)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -190,7 +190,7 @@

segregation.aspatial.DensityCorrectedDissim -

__init__(self, data, group_pop_var, …[, xtol])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -219,7 +219,7 @@

segregation.aspatial.DensityCorrectedDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.Dissim.html b/docs/generated/segregation.aspatial.Dissim.html index 36347429..404d2c9c 100644 --- a/docs/generated/segregation.aspatial.Dissim.html +++ b/docs/generated/segregation.aspatial.Dissim.html @@ -3,10 +3,10 @@ - - segregation.aspatial.Dissim — segregation v1.3.0 Manual - + + segregation.aspatial.Dissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -178,7 +178,7 @@

segregation.aspatial.Dissim
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -189,7 +189,7 @@

segregation.aspatial.Dissim -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -218,7 +218,7 @@

segregation.aspatial.DissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.Entropy.html b/docs/generated/segregation.aspatial.Entropy.html index a8cf1fcc..07bac2d8 100644 --- a/docs/generated/segregation.aspatial.Entropy.html +++ b/docs/generated/segregation.aspatial.Entropy.html @@ -3,10 +3,10 @@ - - segregation.aspatial.Entropy — segregation v1.3.0 Manual - + + segregation.aspatial.Entropy — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -177,7 +177,7 @@

segregation.aspatial.Entropy
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -188,7 +188,7 @@

segregation.aspatial.Entropy -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -217,7 +217,7 @@

segregation.aspatial.EntropySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.Exposure.html b/docs/generated/segregation.aspatial.Exposure.html index 9bd5d971..3f38d5ca 100644 --- a/docs/generated/segregation.aspatial.Exposure.html +++ b/docs/generated/segregation.aspatial.Exposure.html @@ -3,10 +3,10 @@ - - segregation.aspatial.Exposure — segregation v1.3.0 Manual - + + segregation.aspatial.Exposure — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -179,7 +179,7 @@

segregation.aspatial.Exposure
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -190,7 +190,7 @@

segregation.aspatial.Exposure -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -219,7 +219,7 @@

segregation.aspatial.ExposureSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.GiniSeg.html b/docs/generated/segregation.aspatial.GiniSeg.html index 0310dbb7..61f61dbb 100644 --- a/docs/generated/segregation.aspatial.GiniSeg.html +++ b/docs/generated/segregation.aspatial.GiniSeg.html @@ -3,10 +3,10 @@ - - segregation.aspatial.GiniSeg — segregation v1.3.0 Manual - + + segregation.aspatial.GiniSeg — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -173,7 +173,7 @@

segregation.aspatial.GiniSeg
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -184,7 +184,7 @@

segregation.aspatial.GiniSeg -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -213,7 +213,7 @@

segregation.aspatial.GiniSegSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.Isolation.html b/docs/generated/segregation.aspatial.Isolation.html index f4a337ef..af342a7a 100644 --- a/docs/generated/segregation.aspatial.Isolation.html +++ b/docs/generated/segregation.aspatial.Isolation.html @@ -3,10 +3,10 @@ - - segregation.aspatial.Isolation — segregation v1.3.0 Manual - + + segregation.aspatial.Isolation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -179,7 +179,7 @@

segregation.aspatial.Isolation
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -190,7 +190,7 @@

segregation.aspatial.Isolation -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -219,7 +219,7 @@

segregation.aspatial.IsolationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MinMax.html b/docs/generated/segregation.aspatial.MinMax.html index b52283cc..5771fe18 100644 --- a/docs/generated/segregation.aspatial.MinMax.html +++ b/docs/generated/segregation.aspatial.MinMax.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MinMax — segregation v1.3.0 Manual - + + segregation.aspatial.MinMax — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -151,7 +151,7 @@

segregation.aspatial.MinMax
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -162,7 +162,7 @@

segregation.aspatial.MinMax -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -191,7 +191,7 @@

segregation.aspatial.MinMaxSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.ModifiedDissim.html b/docs/generated/segregation.aspatial.ModifiedDissim.html index 6163d2cc..bc604943 100644 --- a/docs/generated/segregation.aspatial.ModifiedDissim.html +++ b/docs/generated/segregation.aspatial.ModifiedDissim.html @@ -3,10 +3,10 @@ - - segregation.aspatial.ModifiedDissim — segregation v1.3.0 Manual - + + segregation.aspatial.ModifiedDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -180,7 +180,7 @@

segregation.aspatial.ModifiedDissim
-__init__(self, data, group_pop_var, total_pop_var, iterations=500)[source]
+__init__(data, group_pop_var, total_pop_var, iterations=500)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -191,7 +191,7 @@

segregation.aspatial.ModifiedDissim -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -220,7 +220,7 @@

segregation.aspatial.ModifiedDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.ModifiedGiniSeg.html b/docs/generated/segregation.aspatial.ModifiedGiniSeg.html index 1caf756e..e6163f12 100644 --- a/docs/generated/segregation.aspatial.ModifiedGiniSeg.html +++ b/docs/generated/segregation.aspatial.ModifiedGiniSeg.html @@ -3,10 +3,10 @@ - - segregation.aspatial.ModifiedGiniSeg — segregation v1.3.0 Manual - + + segregation.aspatial.ModifiedGiniSeg — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -180,7 +180,7 @@

segregation.aspatial.ModifiedGiniSeg
-__init__(self, data, group_pop_var, total_pop_var, iterations=500)[source]
+__init__(data, group_pop_var, total_pop_var, iterations=500)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -191,7 +191,7 @@

segregation.aspatial.ModifiedGiniSeg -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -220,7 +220,7 @@

segregation.aspatial.ModifiedGiniSegSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiDissim.html b/docs/generated/segregation.aspatial.MultiDissim.html index ea4ab14a..8e75742a 100644 --- a/docs/generated/segregation.aspatial.MultiDissim.html +++ b/docs/generated/segregation.aspatial.MultiDissim.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiDissim — segregation v1.3.0 Manual - + + segregation.aspatial.MultiDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiDissim
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiDissim -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiDivergence.html b/docs/generated/segregation.aspatial.MultiDivergence.html index bab159b5..d23d7b01 100644 --- a/docs/generated/segregation.aspatial.MultiDivergence.html +++ b/docs/generated/segregation.aspatial.MultiDivergence.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiDivergence — segregation v1.3.0 Manual - + + segregation.aspatial.MultiDivergence — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiDivergence
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiDivergence -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiDivergenceSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiDiversity.html b/docs/generated/segregation.aspatial.MultiDiversity.html index 14cf54ae..61ef7d96 100644 --- a/docs/generated/segregation.aspatial.MultiDiversity.html +++ b/docs/generated/segregation.aspatial.MultiDiversity.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiDiversity — segregation v1.3.0 Manual - + + segregation.aspatial.MultiDiversity — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -174,7 +174,7 @@

segregation.aspatial.MultiDiversity
-__init__(self, data, groups, normalized=False)[source]
+__init__(data, groups, normalized=False)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -185,7 +185,7 @@

segregation.aspatial.MultiDiversity -

__init__(self, data, groups[, normalized])

+

__init__(data, groups[, normalized])

Initialize self.

@@ -214,7 +214,7 @@

segregation.aspatial.MultiDiversitySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiGiniSeg.html b/docs/generated/segregation.aspatial.MultiGiniSeg.html index ca666fa2..bcad5aae 100644 --- a/docs/generated/segregation.aspatial.MultiGiniSeg.html +++ b/docs/generated/segregation.aspatial.MultiGiniSeg.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiGiniSeg — segregation v1.3.0 Manual - + + segregation.aspatial.MultiGiniSeg — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiGiniSeg
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiGiniSeg -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiGiniSegSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiInformationTheory.html b/docs/generated/segregation.aspatial.MultiInformationTheory.html index ad2f7560..3ebf1b09 100644 --- a/docs/generated/segregation.aspatial.MultiInformationTheory.html +++ b/docs/generated/segregation.aspatial.MultiInformationTheory.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiInformationTheory — segregation v1.3.0 Manual - + + segregation.aspatial.MultiInformationTheory — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiInformationTheory
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiInformationTheory -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiInformationTheorySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiNormalizedExposure.html b/docs/generated/segregation.aspatial.MultiNormalizedExposure.html index 966874b4..13335fc0 100644 --- a/docs/generated/segregation.aspatial.MultiNormalizedExposure.html +++ b/docs/generated/segregation.aspatial.MultiNormalizedExposure.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiNormalizedExposure — segregation v1.3.0 Manual - + + segregation.aspatial.MultiNormalizedExposure — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiNormalizedExposure
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiNormalizedExposure -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiNormalizedExposureSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiRelativeDiversity.html b/docs/generated/segregation.aspatial.MultiRelativeDiversity.html index 91b12c43..ab6920c8 100644 --- a/docs/generated/segregation.aspatial.MultiRelativeDiversity.html +++ b/docs/generated/segregation.aspatial.MultiRelativeDiversity.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiRelativeDiversity — segregation v1.3.0 Manual - + + segregation.aspatial.MultiRelativeDiversity — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.aspatial.MultiRelativeDiversity
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.aspatial.MultiRelativeDiversity -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -207,7 +207,7 @@

segregation.aspatial.MultiRelativeDiversitySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.MultiSquaredCoefficientVariation.html b/docs/generated/segregation.aspatial.MultiSquaredCoefficientVariation.html index a90a83c6..a05cc6b8 100644 --- a/docs/generated/segregation.aspatial.MultiSquaredCoefficientVariation.html +++ b/docs/generated/segregation.aspatial.MultiSquaredCoefficientVariation.html @@ -3,10 +3,10 @@ - - segregation.aspatial.MultiSquaredCoefficientVariation — segregation v1.3.0 Manual - + + segregation.aspatial.MultiSquaredCoefficientVariation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -166,7 +166,7 @@

segregation.aspatial.MultiSquaredCoefficientVariation
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -177,7 +177,7 @@

segregation.aspatial.MultiSquaredCoefficientVariation -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -206,7 +206,7 @@

segregation.aspatial.MultiSquaredCoefficientVariation

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.SimpsonsConcentration.html b/docs/generated/segregation.aspatial.SimpsonsConcentration.html index b51cf5a0..6b70ffc3 100644 --- a/docs/generated/segregation.aspatial.SimpsonsConcentration.html +++ b/docs/generated/segregation.aspatial.SimpsonsConcentration.html @@ -3,10 +3,10 @@ - - segregation.aspatial.SimpsonsConcentration — segregation v1.3.0 Manual - + + segregation.aspatial.SimpsonsConcentration — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -169,7 +169,7 @@

segregation.aspatial.SimpsonsConcentration
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -180,7 +180,7 @@

segregation.aspatial.SimpsonsConcentration -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -209,7 +209,7 @@

segregation.aspatial.SimpsonsConcentrationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.aspatial.SimpsonsInteraction.html b/docs/generated/segregation.aspatial.SimpsonsInteraction.html index 616f2ad5..611a0c2a 100644 --- a/docs/generated/segregation.aspatial.SimpsonsInteraction.html +++ b/docs/generated/segregation.aspatial.SimpsonsInteraction.html @@ -3,10 +3,10 @@ - - segregation.aspatial.SimpsonsInteraction — segregation v1.3.0 Manual - + + segregation.aspatial.SimpsonsInteraction — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -169,7 +169,7 @@

segregation.aspatial.SimpsonsInteraction
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -180,7 +180,7 @@

segregation.aspatial.SimpsonsInteraction -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -209,7 +209,7 @@

segregation.aspatial.SimpsonsInteractionSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.compute_all.ComputeAllAspatialSegregation.html b/docs/generated/segregation.compute_all.ComputeAllAspatialSegregation.html index 5c10318a..2d0dab33 100644 --- a/docs/generated/segregation.compute_all.ComputeAllAspatialSegregation.html +++ b/docs/generated/segregation.compute_all.ComputeAllAspatialSegregation.html @@ -3,10 +3,10 @@ - - segregation.compute_all.ComputeAllAspatialSegregation — segregation v1.3.0 Manual - + + segregation.compute_all.ComputeAllAspatialSegregation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.compute_all.ComputeAllAspatialSegregation
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.compute_all.ComputeAllAspatialSegregation -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -207,7 +207,7 @@

segregation.compute_all.ComputeAllAspatialSegregation

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.compute_all.ComputeAllSegregation.html b/docs/generated/segregation.compute_all.ComputeAllSegregation.html index 2473d277..89038733 100644 --- a/docs/generated/segregation.compute_all.ComputeAllSegregation.html +++ b/docs/generated/segregation.compute_all.ComputeAllSegregation.html @@ -3,10 +3,10 @@ - - segregation.compute_all.ComputeAllSegregation — segregation v1.3.0 Manual - + + segregation.compute_all.ComputeAllSegregation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.compute_all.ComputeAllSegregation
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.compute_all.ComputeAllSegregation -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -207,7 +207,7 @@

segregation.compute_all.ComputeAllSegregationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.compute_all.ComputeAllSpatialSegregation.html b/docs/generated/segregation.compute_all.ComputeAllSpatialSegregation.html index b96585b3..1b21bfae 100644 --- a/docs/generated/segregation.compute_all.ComputeAllSpatialSegregation.html +++ b/docs/generated/segregation.compute_all.ComputeAllSpatialSegregation.html @@ -3,10 +3,10 @@ - - segregation.compute_all.ComputeAllSpatialSegregation — segregation v1.3.0 Manual - + + segregation.compute_all.ComputeAllSpatialSegregation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.compute_all.ComputeAllSpatialSegregation
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.compute_all.ComputeAllSpatialSegregation -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -207,7 +207,7 @@

segregation.compute_all.ComputeAllSpatialSegregation

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.decomposition.DecomposeSegregation.html b/docs/generated/segregation.decomposition.DecomposeSegregation.html index 09641e3d..70f6d7d7 100644 --- a/docs/generated/segregation.decomposition.DecomposeSegregation.html +++ b/docs/generated/segregation.decomposition.DecomposeSegregation.html @@ -3,10 +3,10 @@ - - segregation.decomposition.DecomposeSegregation — segregation v1.3.0 Manual - + + segregation.decomposition.DecomposeSegregation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -126,27 +126,41 @@

segregation.decomposition.DecomposeSegregation +

Examples

+

Several examples can be found at https://github.com/pysal/segregation/blob/master/notebooks/decomposition_wrapper_example.ipynb.

+

Methods

+ ++++ + + + + + +

plot([plot_type, figsize, city_a, city_b, …])

Plot maps or CDFs of urban contexts used in calculating the Decomposition class.

+
+
+__init__(index1, index2, counterfactual_approach='composition')[source]
+

Decompose segregation differences into spatial and attribute components.

+

Given two segregation indices of the same type, use Shapley decomposition +to measure whether the differences between index measures arise from +differences in spatial structure or population structure

+
Parameters
-
+
index1segregation.SegIndex class

First SegIndex class to compare.

index2segregation.SegIndex class

Second SegIndex class to compare.

-
counterfactual_approachstr, one of
-

[“composition”, “share”, “dual_composition”]

-
-

The technique used to generate the counterfactual population +

counterfactual_approachstr, one of {“composition”, “share”, “dual_composition”}

The technique used to generate the counterfactual population distributions.

-
-

Examples

-

Several examples can be found at https://github.com/pysal/segregation/blob/master/notebooks/decomposition_wrapper_example.ipynb.

-
-
Attributes
-
+
Attributes
+
c_sfloat

Shapley’s Spatial Component of the decomposition

c_afloat

Shapley’s Attribute Component of the decomposition

@@ -154,22 +168,6 @@

segregation.decomposition.DecomposeSegregationMethods

- ---- - - - - - -

plot(self[, plot_type])

Plot the Segregation Decomposition Profile

-
-
-__init__(self, index1, index2, counterfactual_approach='composition')[source]
-

Initialize self. See help(type(self)) for accurate signature.

Methods

@@ -179,11 +177,11 @@

segregation.decomposition.DecomposeSegregation -

__init__(self, index1, index2[, …])

-

Initialize self.

+

__init__(index1, index2[, …])

+

Decompose segregation differences into spatial and attribute components.

-

plot(self[, plot_type])

-

Plot the Segregation Decomposition Profile

+

plot([plot_type, figsize, city_a, city_b, …])

+

Plot maps or CDFs of urban contexts used in calculating the Decomposition class.

@@ -211,7 +209,7 @@

segregation.decomposition.DecomposeSegregationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.inference.SingleValueTest.html b/docs/generated/segregation.inference.SingleValueTest.html index df0d1ed2..4f0c38ef 100644 --- a/docs/generated/segregation.inference.SingleValueTest.html +++ b/docs/generated/segregation.inference.SingleValueTest.html @@ -3,10 +3,10 @@ - - segregation.inference.SingleValueTest — segregation v1.3.0 Manual - + + segregation.inference.SingleValueTest — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -169,14 +169,14 @@

segregation.inference.SingleValueTest -

plot(self[, ax])

+

plot([ax])

Plot the Infer_Segregation class

-__init__(self, seg_class, iterations_under_null=500, null_approach='systematic', two_tailed=True, **kwargs)[source]
+__init__(seg_class, iterations_under_null=500, null_approach='systematic', two_tailed=True, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -187,10 +187,10 @@

segregation.inference.SingleValueTest -

__init__(self, seg_class[, …])

+

__init__(seg_class[, iterations_under_null, …])

Initialize self.

-

plot(self[, ax])

+

plot([ax])

Plot the Infer_Segregation class

@@ -219,7 +219,7 @@

segregation.inference.SingleValueTestSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.inference.TwoValueTest.html b/docs/generated/segregation.inference.TwoValueTest.html index 6bbccabb..01a88b37 100644 --- a/docs/generated/segregation.inference.TwoValueTest.html +++ b/docs/generated/segregation.inference.TwoValueTest.html @@ -3,10 +3,10 @@ - - segregation.inference.TwoValueTest — segregation v1.3.0 Manual - + + segregation.inference.TwoValueTest — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -163,14 +163,14 @@

segregation.inference.TwoValueTest -

plot(self[, ax])

+

plot([ax])

Plot the Compare_Segregation class

-__init__(self, seg_class_1, seg_class_2, iterations_under_null=500, null_approach='random_label', **kwargs)[source]
+__init__(seg_class_1, seg_class_2, iterations_under_null=500, null_approach='random_label', **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -181,10 +181,10 @@

segregation.inference.TwoValueTest -

__init__(self, seg_class_1, seg_class_2[, …])

+

__init__(seg_class_1, seg_class_2[, …])

Initialize self.

-

plot(self[, ax])

+

plot([ax])

Plot the Compare_Segregation class

@@ -213,7 +213,7 @@

segregation.inference.TwoValueTestSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.LocalRelativeCentralization.html b/docs/generated/segregation.local.LocalRelativeCentralization.html index 7c225bf5..ec781bd1 100644 --- a/docs/generated/segregation.local.LocalRelativeCentralization.html +++ b/docs/generated/segregation.local.LocalRelativeCentralization.html @@ -3,10 +3,10 @@ - - segregation.local.LocalRelativeCentralization — segregation v1.3.0 Manual - + + segregation.local.LocalRelativeCentralization — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -168,7 +168,7 @@

segregation.local.LocalRelativeCentralization
-__init__(self, data, group_pop_var, total_pop_var, k_neigh=5)[source]
+__init__(data, group_pop_var, total_pop_var, k_neigh=5)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -179,7 +179,7 @@

segregation.local.LocalRelativeCentralization -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -208,7 +208,7 @@

segregation.local.LocalRelativeCentralizationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.MultiLocalDiversity.html b/docs/generated/segregation.local.MultiLocalDiversity.html index 64023d20..372e81ef 100644 --- a/docs/generated/segregation.local.MultiLocalDiversity.html +++ b/docs/generated/segregation.local.MultiLocalDiversity.html @@ -3,10 +3,10 @@ - - segregation.local.MultiLocalDiversity — segregation v1.3.0 Manual - + + segregation.local.MultiLocalDiversity — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.local.MultiLocalDiversity
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.local.MultiLocalDiversity -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -207,7 +207,7 @@

segregation.local.MultiLocalDiversitySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.MultiLocalEntropy.html b/docs/generated/segregation.local.MultiLocalEntropy.html index c3833716..e1f9f5df 100644 --- a/docs/generated/segregation.local.MultiLocalEntropy.html +++ b/docs/generated/segregation.local.MultiLocalEntropy.html @@ -3,10 +3,10 @@ - - segregation.local.MultiLocalEntropy — segregation v1.3.0 Manual - + + segregation.local.MultiLocalEntropy — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

segregation.local.MultiLocalEntropy
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -178,7 +178,7 @@

segregation.local.MultiLocalEntropy -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -207,7 +207,7 @@

segregation.local.MultiLocalEntropySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.MultiLocalSimpsonConcentration.html b/docs/generated/segregation.local.MultiLocalSimpsonConcentration.html index 56c49e6a..83f3a959 100644 --- a/docs/generated/segregation.local.MultiLocalSimpsonConcentration.html +++ b/docs/generated/segregation.local.MultiLocalSimpsonConcentration.html @@ -3,10 +3,10 @@ - - segregation.local.MultiLocalSimpsonConcentration — segregation v1.3.0 Manual - + + segregation.local.MultiLocalSimpsonConcentration — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -170,7 +170,7 @@

segregation.local.MultiLocalSimpsonConcentration
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -181,7 +181,7 @@

segregation.local.MultiLocalSimpsonConcentration -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -210,7 +210,7 @@

segregation.local.MultiLocalSimpsonConcentrationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.MultiLocalSimpsonInteraction.html b/docs/generated/segregation.local.MultiLocalSimpsonInteraction.html index a2327a3e..82f45f29 100644 --- a/docs/generated/segregation.local.MultiLocalSimpsonInteraction.html +++ b/docs/generated/segregation.local.MultiLocalSimpsonInteraction.html @@ -3,10 +3,10 @@ - - segregation.local.MultiLocalSimpsonInteraction — segregation v1.3.0 Manual - + + segregation.local.MultiLocalSimpsonInteraction — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -170,7 +170,7 @@

segregation.local.MultiLocalSimpsonInteraction
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -181,7 +181,7 @@

segregation.local.MultiLocalSimpsonInteraction -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -210,7 +210,7 @@

segregation.local.MultiLocalSimpsonInteractionSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.local.MultiLocationQuotient.html b/docs/generated/segregation.local.MultiLocationQuotient.html index 35667121..b19d4ad7 100644 --- a/docs/generated/segregation.local.MultiLocationQuotient.html +++ b/docs/generated/segregation.local.MultiLocationQuotient.html @@ -3,10 +3,10 @@ - - segregation.local.MultiLocationQuotient — segregation v1.3.0 Manual - + + segregation.local.MultiLocationQuotient — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -170,7 +170,7 @@

segregation.local.MultiLocationQuotient
-__init__(self, data, groups)[source]
+__init__(data, groups)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -181,7 +181,7 @@

segregation.local.MultiLocationQuotient -

__init__(self, data, groups)

+

__init__(data, groups)

Initialize self.

@@ -210,7 +210,7 @@

segregation.local.MultiLocationQuotientSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.network.calc_access.html b/docs/generated/segregation.network.calc_access.html index d9138d9f..d4ba0ce5 100644 --- a/docs/generated/segregation.network.calc_access.html +++ b/docs/generated/segregation.network.calc_access.html @@ -3,10 +3,10 @@ - - segregation.network.calc_access — segregation v1.3.0 Manual - + + segregation.network.calc_access — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -178,7 +178,7 @@

segregation.network.calc_accessSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.network.get_osm_network.html b/docs/generated/segregation.network.get_osm_network.html index 2f165485..ff3a8af0 100644 --- a/docs/generated/segregation.network.get_osm_network.html +++ b/docs/generated/segregation.network.get_osm_network.html @@ -3,10 +3,10 @@ - - segregation.network.get_osm_network — segregation v1.3.0 Manual - + + segregation.network.get_osm_network — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -172,7 +172,7 @@

segregation.network.get_osm_networkSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.AbsoluteCentralization.html b/docs/generated/segregation.spatial.AbsoluteCentralization.html index 03767e2e..84c0b34c 100644 --- a/docs/generated/segregation.spatial.AbsoluteCentralization.html +++ b/docs/generated/segregation.spatial.AbsoluteCentralization.html @@ -3,10 +3,10 @@ - - segregation.spatial.AbsoluteCentralization — segregation v1.3.0 Manual - + + segregation.spatial.AbsoluteCentralization — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -204,7 +204,7 @@

segregation.spatial.AbsoluteCentralization
-__init__(self, data, group_pop_var, total_pop_var, center='mean', metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, center='mean', metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -215,7 +215,7 @@

segregation.spatial.AbsoluteCentralization -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -244,7 +244,7 @@

segregation.spatial.AbsoluteCentralizationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.AbsoluteClustering.html b/docs/generated/segregation.spatial.AbsoluteClustering.html index df77225e..ca11c560 100644 --- a/docs/generated/segregation.spatial.AbsoluteClustering.html +++ b/docs/generated/segregation.spatial.AbsoluteClustering.html @@ -3,10 +3,10 @@ - - segregation.spatial.AbsoluteClustering — segregation v1.3.0 Manual - + + segregation.spatial.AbsoluteClustering — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -190,7 +190,7 @@

segregation.spatial.AbsoluteClustering
-__init__(self, data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -201,7 +201,7 @@

segregation.spatial.AbsoluteClustering -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -230,7 +230,7 @@

segregation.spatial.AbsoluteClusteringSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.AbsoluteConcentration.html b/docs/generated/segregation.spatial.AbsoluteConcentration.html index cdf6622c..75dd5cbc 100644 --- a/docs/generated/segregation.spatial.AbsoluteConcentration.html +++ b/docs/generated/segregation.spatial.AbsoluteConcentration.html @@ -3,10 +3,10 @@ - - segregation.spatial.AbsoluteConcentration — segregation v1.3.0 Manual - + + segregation.spatial.AbsoluteConcentration — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -189,7 +189,7 @@

segregation.spatial.AbsoluteConcentration
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -200,7 +200,7 @@

segregation.spatial.AbsoluteConcentration -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -229,7 +229,7 @@

segregation.spatial.AbsoluteConcentrationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.BoundarySpatialDissim.html b/docs/generated/segregation.spatial.BoundarySpatialDissim.html index 3a777e72..b38feb3d 100644 --- a/docs/generated/segregation.spatial.BoundarySpatialDissim.html +++ b/docs/generated/segregation.spatial.BoundarySpatialDissim.html @@ -3,10 +3,10 @@ - - segregation.spatial.BoundarySpatialDissim — segregation v1.3.0 Manual - + + segregation.spatial.BoundarySpatialDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -194,7 +194,7 @@

segregation.spatial.BoundarySpatialDissim
-__init__(self, data, group_pop_var, total_pop_var, standardize=False)[source]
+__init__(data, group_pop_var, total_pop_var, standardize=False)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -205,7 +205,7 @@

segregation.spatial.BoundarySpatialDissim -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -234,7 +234,7 @@

segregation.spatial.BoundarySpatialDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.Delta.html b/docs/generated/segregation.spatial.Delta.html index 9cfb5d32..98438f6a 100644 --- a/docs/generated/segregation.spatial.Delta.html +++ b/docs/generated/segregation.spatial.Delta.html @@ -3,10 +3,10 @@ - - segregation.spatial.Delta — segregation v1.3.0 Manual - + + segregation.spatial.Delta — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -189,7 +189,7 @@

segregation.spatial.Delta
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -200,7 +200,7 @@

segregation.spatial.Delta -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -229,7 +229,7 @@

segregation.spatial.DeltaSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.DistanceDecayExposure.html b/docs/generated/segregation.spatial.DistanceDecayExposure.html index b9f88112..b7b61609 100644 --- a/docs/generated/segregation.spatial.DistanceDecayExposure.html +++ b/docs/generated/segregation.spatial.DistanceDecayExposure.html @@ -3,10 +3,10 @@ - - segregation.spatial.DistanceDecayExposure — segregation v1.3.0 Manual - + + segregation.spatial.DistanceDecayExposure — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -198,7 +198,7 @@

segregation.spatial.DistanceDecayExposure
-__init__(self, data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -209,7 +209,7 @@

segregation.spatial.DistanceDecayExposure -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -238,7 +238,7 @@

segregation.spatial.DistanceDecayExposureSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.DistanceDecayIsolation.html b/docs/generated/segregation.spatial.DistanceDecayIsolation.html index 34d7fb63..f727e568 100644 --- a/docs/generated/segregation.spatial.DistanceDecayIsolation.html +++ b/docs/generated/segregation.spatial.DistanceDecayIsolation.html @@ -3,10 +3,10 @@ - - segregation.spatial.DistanceDecayIsolation — segregation v1.3.0 Manual - + + segregation.spatial.DistanceDecayIsolation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -198,7 +198,7 @@

segregation.spatial.DistanceDecayIsolation
-__init__(self, data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -209,7 +209,7 @@

segregation.spatial.DistanceDecayIsolation -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -238,7 +238,7 @@

segregation.spatial.DistanceDecayIsolationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.html b/docs/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.html index 2c0d8406..e466a8c2 100644 --- a/docs/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.html +++ b/docs/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.html @@ -3,10 +3,10 @@ - - segregation.spatial.PerimeterAreaRatioSpatialDissim — segregation v1.3.0 Manual - + + segregation.spatial.PerimeterAreaRatioSpatialDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -195,7 +195,7 @@

segregation.spatial.PerimeterAreaRatioSpatialDissim
-__init__(self, data, group_pop_var, total_pop_var, standardize=True)[source]
+__init__(data, group_pop_var, total_pop_var, standardize=True)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -206,7 +206,7 @@

segregation.spatial.PerimeterAreaRatioSpatialDissim -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -235,7 +235,7 @@

segregation.spatial.PerimeterAreaRatioSpatialDissim

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.RelativeCentralization.html b/docs/generated/segregation.spatial.RelativeCentralization.html index c4a0a1b4..00688ca4 100644 --- a/docs/generated/segregation.spatial.RelativeCentralization.html +++ b/docs/generated/segregation.spatial.RelativeCentralization.html @@ -3,10 +3,10 @@ - - segregation.spatial.RelativeCentralization — segregation v1.3.0 Manual - + + segregation.spatial.RelativeCentralization — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -207,7 +207,7 @@

segregation.spatial.RelativeCentralization
-__init__(self, data, group_pop_var, total_pop_var, center='mean', metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, center='mean', metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -218,7 +218,7 @@

segregation.spatial.RelativeCentralization -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -247,7 +247,7 @@

segregation.spatial.RelativeCentralizationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.RelativeClustering.html b/docs/generated/segregation.spatial.RelativeClustering.html index 9646a6ce..04d19013 100644 --- a/docs/generated/segregation.spatial.RelativeClustering.html +++ b/docs/generated/segregation.spatial.RelativeClustering.html @@ -3,10 +3,10 @@ - - segregation.spatial.RelativeClustering — segregation v1.3.0 Manual - + + segregation.spatial.RelativeClustering — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -197,7 +197,7 @@

segregation.spatial.RelativeClustering
-__init__(self, data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -208,7 +208,7 @@

segregation.spatial.RelativeClustering -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -237,7 +237,7 @@

segregation.spatial.RelativeClusteringSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.RelativeConcentration.html b/docs/generated/segregation.spatial.RelativeConcentration.html index 5405cc80..897654c0 100644 --- a/docs/generated/segregation.spatial.RelativeConcentration.html +++ b/docs/generated/segregation.spatial.RelativeConcentration.html @@ -3,10 +3,10 @@ - - segregation.spatial.RelativeConcentration — segregation v1.3.0 Manual - + + segregation.spatial.RelativeConcentration — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -189,7 +189,7 @@

segregation.spatial.RelativeConcentration
-__init__(self, data, group_pop_var, total_pop_var)[source]
+__init__(data, group_pop_var, total_pop_var)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -200,7 +200,7 @@

segregation.spatial.RelativeConcentration -

__init__(self, data, group_pop_var, …)

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -229,7 +229,7 @@

segregation.spatial.RelativeConcentrationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.SpatialDissim.html b/docs/generated/segregation.spatial.SpatialDissim.html index 490c2478..e0584872 100644 --- a/docs/generated/segregation.spatial.SpatialDissim.html +++ b/docs/generated/segregation.spatial.SpatialDissim.html @@ -3,10 +3,10 @@ - - segregation.spatial.SpatialDissim — segregation v1.3.0 Manual - + + segregation.spatial.SpatialDissim — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -213,7 +213,7 @@

segregation.spatial.SpatialDissim
-__init__(self, data, group_pop_var, total_pop_var, w=None, standardize=False)[source]
+__init__(data, group_pop_var, total_pop_var, w=None, standardize=False)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -224,7 +224,7 @@

segregation.spatial.SpatialDissim -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -253,7 +253,7 @@

segregation.spatial.SpatialDissimSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.SpatialMinMax.html b/docs/generated/segregation.spatial.SpatialMinMax.html index 2a0732d3..8877b2f8 100644 --- a/docs/generated/segregation.spatial.SpatialMinMax.html +++ b/docs/generated/segregation.spatial.SpatialMinMax.html @@ -3,10 +3,10 @@ - - segregation.spatial.SpatialMinMax — segregation v1.3.0 Manual - + + segregation.spatial.SpatialMinMax — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -173,7 +173,7 @@

segregation.spatial.SpatialMinMax
-__init__(self, data, group_pop_var, total_pop_var, network=None, w=None, decay='linear', distance=2000, precompute=True)[source]
+__init__(data, group_pop_var, total_pop_var, network=None, w=None, decay='linear', distance=2000, precompute=True)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -184,7 +184,7 @@

segregation.spatial.SpatialMinMax -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -213,7 +213,7 @@

segregation.spatial.SpatialMinMaxSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.SpatialProxProf.html b/docs/generated/segregation.spatial.SpatialProxProf.html index d43759b9..4889de53 100644 --- a/docs/generated/segregation.spatial.SpatialProxProf.html +++ b/docs/generated/segregation.spatial.SpatialProxProf.html @@ -3,10 +3,10 @@ - - segregation.spatial.SpatialProxProf — segregation v1.3.0 Manual - + + segregation.spatial.SpatialProxProf — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -200,14 +200,14 @@

segregation.spatial.SpatialProxProf -

plot(self)

+

plot()

Plot the Spatial Proximity Profile

-__init__(self, data, group_pop_var, total_pop_var, m=1000)[source]
+__init__(data, group_pop_var, total_pop_var, m=1000)[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -218,10 +218,10 @@

segregation.spatial.SpatialProxProf -

__init__(self, data, group_pop_var, …[, m])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

-

plot(self)

+

plot()

Plot the Spatial Proximity Profile

@@ -250,7 +250,7 @@

segregation.spatial.SpatialProxProfSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/generated/segregation.spatial.SpatialProximity.html b/docs/generated/segregation.spatial.SpatialProximity.html index e275998a..89a88401 100644 --- a/docs/generated/segregation.spatial.SpatialProximity.html +++ b/docs/generated/segregation.spatial.SpatialProximity.html @@ -3,10 +3,10 @@ - - segregation.spatial.SpatialProximity — segregation v1.3.0 Manual - + + segregation.spatial.SpatialProximity — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -197,7 +197,7 @@

segregation.spatial.SpatialProximity
-__init__(self, data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]
+__init__(data, group_pop_var, total_pop_var, alpha=0.6, beta=0.5, metric='euclidean')[source]

Initialize self. See help(type(self)) for accurate signature.

@@ -208,7 +208,7 @@

segregation.spatial.SpatialProximity -

__init__(self, data, group_pop_var, …[, …])

+

__init__(data, group_pop_var, total_pop_var)

Initialize self.

@@ -237,7 +237,7 @@

segregation.spatial.SpatialProximitySphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/genindex.html b/docs/genindex.html index 578bc5bf..23cf63d7 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -3,10 +3,10 @@ - - Index — segregation v1.3.0 Manual - + + Index — segregation v1.4.0 Manual + @@ -38,7 +38,7 @@ segregation - 1.3.0 + 1.4.0
@@ -457,7 +457,7 @@

T

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/index.html b/docs/index.html index b9013375..9bb9e549 100644 --- a/docs/index.html +++ b/docs/index.html @@ -3,10 +3,10 @@ - - Welcome to segregation’s documentation! — segregation v1.3.0 Manual - + + Welcome to segregation’s documentation! — segregation v1.4.0 Manual + @@ -39,7 +39,7 @@ segregation - 1.3.0 + 1.4.0
@@ -201,7 +201,7 @@
Local segregation measures

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/installation.html b/docs/installation.html index 455068c9..61e52fce 100644 --- a/docs/installation.html +++ b/docs/installation.html @@ -3,10 +3,10 @@ - - Installation — segregation v1.3.0 Manual - + + Installation — segregation v1.4.0 Manual + @@ -40,7 +40,7 @@ segregation - 1.3.0 + 1.4.0
@@ -167,7 +167,7 @@

InstallationSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/objects.inv b/docs/objects.inv index 36d86f67..9440275a 100644 Binary files a/docs/objects.inv and b/docs/objects.inv differ diff --git a/docs/references.html b/docs/references.html index 8728f0a4..bc776913 100644 --- a/docs/references.html +++ b/docs/references.html @@ -3,10 +3,10 @@ - - References — segregation v1.3.0 Manual - + + References — segregation v1.4.0 Manual + @@ -39,7 +39,7 @@ segregation - 1.3.0 + 1.4.0
@@ -200,7 +200,7 @@

ReferencesSphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/search.html b/docs/search.html index 04a01377..567a1e58 100644 --- a/docs/search.html +++ b/docs/search.html @@ -3,10 +3,10 @@ - - Search — segregation v1.3.0 Manual - + + Search — segregation v1.4.0 Manual + @@ -44,7 +44,7 @@ segregation - 1.3.0 + 1.4.0
@@ -156,7 +156,7 @@

Search

© Copyright 2018, pysal developers.
- Created using Sphinx 3.1.1.
+ Created using Sphinx 3.3.0.

diff --git a/docs/searchindex.js b/docs/searchindex.js index c2035df8..94a5f003 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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to segregation\u2019s 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When it is not found, a full rebuild will be done. -config: 1abb69093b22996e21358efdfe7002eb -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docsrc/_build/html/_modules/index.html b/docsrc/_build/html/_modules/index.html deleted file mode 100644 index c42574b0..00000000 --- a/docsrc/_build/html/_modules/index.html +++ /dev/null @@ -1,146 +0,0 @@ - - - - - - - Overview: module code — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

All modules for which code is available

- - -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/aspatial/aspatial_indexes.html b/docsrc/_build/html/_modules/segregation/aspatial/aspatial_indexes.html deleted file mode 100644 index 3e14a478..00000000 --- a/docsrc/_build/html/_modules/segregation/aspatial/aspatial_indexes.html +++ /dev/null @@ -1,2167 +0,0 @@ - - - - - - - segregation.aspatial.aspatial_indexes — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.aspatial.aspatial_indexes

-"""
-Aspatial based Segregation Metrics
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"
-
-import numpy as np
-import pandas as pd
-import warnings
-import geopandas as gpd
-
-from scipy.stats import norm
-from scipy.optimize import minimize
-
-from segregation.util.util import _dep_message, DeprecationHelper, _nan_handle
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = ['Dissim', 
-           
-           'Gini_Seg',
-           'GiniSeg',
-           
-           'Entropy', 
-           'Isolation',
-           'Exposure',
-           'Atkinson',
-           
-           'Correlation_R',
-           'CorrelationR',
-           
-           'Con_Prof',
-           'ConProf',
-           
-           'Modified_Dissim',
-           'ModifiedDissim',
-           
-           'Modified_Gini_Seg',
-           'ModifiedGiniSeg',
-           
-           'Bias_Corrected_Dissim',
-           'BiasCorrectedDissim',
-           
-           'Density_Corrected_Dissim',
-           'DensityCorrectedDissim',
-           
-           'MinMax']
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-
-
-def _min_max(data, group_pop_var, total_pop_var):
-    """
-    Calculation of the Aspatial version of SpatialMinMax
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                MinMax Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on O'Sullivan & Wong (2007). A Surface‐Based Approach to Measuring Spatial Segregation.
-    Geographical Analysis 39 (2). https://doi.org/10.1111/j.1538-4632.2007.00699.x
-
-    Reference: :cite:`osullivanwong2007surface`.
-    
-    We'd like to thank @AnttiHaerkoenen for this contribution!
-    
-    """
-    
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-        
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    if any(data.total_pop_var < data.group_pop_var):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    data['group_2_pop_var'] = data['total_pop_var'] - data['group_pop_var']
-    
-    data['group_1_pop_var_norm'] = data['group_pop_var'] / data['group_pop_var'].sum()
-    data['group_2_pop_var_norm'] = data['group_2_pop_var'] / data['group_2_pop_var'].sum()
-    
-    density_1 = data['group_1_pop_var_norm'].values
-    density_2 = data['group_2_pop_var_norm'].values
-    densities = np.vstack([
-        density_1,
-        density_2
-    ])
-    v_union = densities.max(axis=0).sum()
-    v_intersect = densities.min(axis=0).sum()
-    
-    MM = 1 - v_intersect / v_union
-    
-    if not isinstance(data, gpd.GeoDataFrame):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return MM, core_data
-
-
-
[docs]class MinMax:
-    """
-    Calculation of the Aspatial version of SpatialMinMax
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                MinMax Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on O'Sullivan & Wong (2007). A Surface‐Based Approach to Measuring Spatial Segregation.
-    Geographical Analysis 39 (2). https://doi.org/10.1111/j.1538-4632.2007.00699.x
-
-    Reference: :cite:`osullivanwong2007surface`.
-    
-    We'd like to thank @AnttiHaerkoenen for this contribution!
-    
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _min_max(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _min_max

-
-
-
-
-
-def _dissim(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Dissimilarity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-        
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    T = t.sum()
-    P = x.sum() / T
-    
-    # If a unit has zero population, the group of interest frequency is zero
-    pi = np.where(t == 0, 0, x / t)
-    
-    D = (((t * abs(pi - P)))/ (2 * T * P * (1 - P))).sum()
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return D, core_data
-
-
-
[docs]class Dissim:
-    """
-    Classic Dissimilarity Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Dissimilarity Index
-        
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the degree of dissimilarity (D) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import Dissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> dissim_index = Dissim(df, 'tractid', 'pop10')
-    >>> dissim_index.statistic
-    0.31565682496226544
-    
-    The interpretation of this value is that 31.57% of the non-hispanic black population would have to move to reach eveness in the Riverside County.
-        
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _dissim(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _dissim

-        
-        
-        
-def _gini_seg(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Gini Segregation index
-    
-    Parameters
-    ----------
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    Returns
-    ----------
-    statistic : float
-                Gini Segregation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    if any(data.total_pop_var < data.group_pop_var):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    T = data.total_pop_var.sum()
-    P = data.group_pop_var.sum() / T
-    
-    # If a unit has zero population, the group of interest frequency is zero
-    data = data.assign(ti = data.total_pop_var,
-                       pi = np.where(data.total_pop_var == 0, 0, data.group_pop_var/data.total_pop_var))
-    
-    num = (np.matmul(np.array(data.ti)[np.newaxis].T, np.array(data.ti)[np.newaxis]) * abs(np.array(data.pi)[np.newaxis].T - np.array(data.pi)[np.newaxis])).sum()
-    den = (2 * T**2 * P * (1-P))
-    G = num / den
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return G, core_data
-
-
-
[docs]class GiniSeg:
-    """
-    Classic Gini Segregation Index
-    
-    Parameters
-    ----------
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    Attributes
-    ----------
-    statistic : float
-                Gini Segregation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Examples
-    --------
-    In this example, we will calculate the Gini Segregation Index (G) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import GiniSeg
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> gini_seg_index = GiniSeg(df, 'tractid', 'pop10')
-    >>> gini_seg_index.statistic
-    0.44620350030600087
-       
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _gini_seg(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _gini_seg

-        
-        
-        
-def _entropy(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Entropy index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Entropy Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    T = t.sum()
-    P = x.sum() / T
-    
-    # If a unit has zero population, the group of interest frequency is zero
-    pi = np.where(t == 0, 0, x / t)
-    
-    E = P * np.log(1 / P) + (1 - P) * np.log(1 / (1 - P))
-    Ei = pi * np.log(1 / pi) + (1 - pi) * np.log(1 / (1 - pi))
-    H = np.nansum(t * (E - Ei) / (E * T)) # If some pi is zero, numpy will treat as zero
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return H, core_data
-
-
-
[docs]class Entropy:
-    """
-    Classic Entropy Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Entropy Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the Entropy (H) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import Entropy
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> entropy_index = Entropy(df, 'tractid', 'pop10')
-    >>> entropy_index.statistic
-    0.08636489348167173
-       
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _entropy(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _entropy

-        
-        
-        
-        
-def _isolation(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Isolation index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Isolation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Notes
-    -----
-    The group of interest is labelled as group X.
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    X = x.sum()
-    xPx = np.nansum((x / X) * (x / t))
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return xPx, core_data
-
-
-
[docs]class Isolation:
-    """
-    Classic Isolation Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Isolation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.   
-                
-    Examples
-    --------
-    In this example, we will calculate the Isolation Index (xPx) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import Isolation
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> isolation_index = Isolation(df, 'tractid', 'pop10')
-    >>> isolation_index.statistic
-    0.11321482777341298
-    
-    The interpretation of this number is that if you randomly pick a X member of a specific area, there is 11.32% of probability that this member shares a unit with another X member.
-    
-    Notes
-    -----
-    The group of interest is labelled as group X.
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _isolation(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _isolation

-        
-        
-        
-def _exposure(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Exposure index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Exposure Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    The group of interest is labelled as group X, whilst Y is the complementary group. Groups X and Y are mutually excludent.
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    yi = t - x
-    
-    X = x.sum()
-    xPy = np.nansum((x / X) * (yi / t))
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return xPy, core_data
-
-
-
[docs]class Exposure:
-    """
-    Classic Exposure Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Exposure Index
-
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Examples
-    --------
-    In this example, we will calculate the Exposure Index (xPy) for the Riverside County using the census tract data of 2010.
-    The group of interest (X) is non-hispanic black people which is the variable nhblk10 in the dataset and the Y group is the other part of the population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import Exposure
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> exposure_index = Exposure(df, 'tractid', 'pop10')
-    >>> exposure_index.statistic
-    0.886785172226587
-    
-    The interpretation of this number is that if you randomly pick a X member of a specific area, there is 88.68% of probability that this member shares a unit with a Y member.
-    
-    Notes
-    -----
-    The group of interest is labelled as group X, whilst Y is the complementary group. Groups X and Y are mutually excludent.
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _exposure(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _exposure

-
-
-
-
-def _atkinson(data, group_pop_var, total_pop_var, b = 0.5):
-    """
-    Calculation of Atkinson index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    b             : float
-                    The shape parameter, between 0 and 1, that determines how to weight the increments to segregation contributed by different portions of the Lorenz curve.
-
-    Returns
-    ----------
-
-    statistic : float
-                Atkinson Index
-    
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if (not isinstance(b, float)):
-        raise ValueError('The parameter b must be a float.')
-        
-    if ((b < 0) or (b > 1)):
-        raise ValueError('The parameter b must be between 0 and 1.')
-        
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    T = t.sum()
-    P = x.sum() / T
-    
-    # If a unit has zero population, the group of interest frequency is zero
-    pi = np.where(t == 0, 0, x / t)
-    
-    A = 1 - (P / (1-P)) * abs((((1 - pi) ** (1-b) * pi ** b * t) / (P * T)).sum()) ** (1 / (1 - b))
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return A, core_data
-
-
-
[docs]class Atkinson:
-    """
-    Classic Atkinson Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    b             : float
-                    The shape parameter, between 0 and 1, that determines how to weight the increments to segregation contributed by different portions of the Lorenz curve.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Atkison Index
-    
-    core_data : a pandas DataFrame
-            A pandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the Atkinson Index (A) with the shape parameter (b) equals to 0.5 for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import Atkinson
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> atkinson_index = Atkinson(df, 'tractid', 'pop10', b = 0.5)
-    >>> atkinson_index.statistic
-    0.16722406110274002
-       
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, b = 0.5):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _atkinson(data, group_pop_var, total_pop_var, b)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _atkinson

-        
-        
-        
-def _correlationr(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Correlation Ratio index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Correlation Ratio Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-
-    X = x.sum()
-    T = t.sum()
-    P = X / T
-    
-    xPx = np.nansum((x / X) * (x / t))
-
-    V = (xPx - P) / (1 - P)
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return V, core_data
-
-
-
[docs]class CorrelationR:
-    """
-    Classic Correlation Ratio Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest (X)
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Correlation Ratio Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the Correlation Ratio Index (V) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import CorrelationR
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> correlationr_index = CorrelationR(df, 'tractid', 'pop10')
-    >>> correlationr_index.statistic
-    0.048716810856363923
-    
-    Notes
-    -----
-    
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _correlationr(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _correlationr

-        
-        
-def _conprof(data, group_pop_var, total_pop_var, m = 1000):
-    """
-    Calculation of Concentration Profile
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    m             : int
-                    a numeric value indicating the number of thresholds to be used. Default value is 1000. 
-                    A large value of m creates a smoother-looking graph and a more precise concentration profile value but slows down the calculation speed.
-
-    Returns
-    ----------
-
-    statistic : float
-                Concentration Profile Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    Based on Hong, Seong-Yun, and Yukio Sadahiro. "Measuring geographic segregation: a graph-based approach." Journal of Geographical Systems 16.2 (2014): 211-231.
-
-    Reference: :cite:`hong2014measuring`.
-
-    """
-    if(type(m) is not int):
-        raise TypeError('m must be a string.')
-        
-    if(m < 2):
-        raise ValueError('m must be greater than 1.')
-    
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < x):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-   
-    def calculate_vt(th):
-        g_t_i = np.where(x / t >= th, 1, 0)
-        v_t = (g_t_i * x).sum() / x.sum()
-        return v_t
-    
-    grid = np.linspace(0, 1, m)
-    curve = np.array(list(map(calculate_vt, grid)))
-    
-    threshold = x.sum() / t.sum()
-    R = ((threshold - ((curve[grid < threshold]).sum() / m - (curve[grid >= threshold]).sum()/ m)) / (1 - threshold))
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return R, grid, curve, core_data
-
-
-
[docs]class ConProf:
-    """
-    Concentration Profile Index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    m             : int
-                    a numeric value indicating the number of thresholds to be used. 
-                    A large value of m creates a smoother-looking graph and a more precise concentration profile value but slows down the calculation speed.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Concentration Profile Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the concentration profile (R) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import ConProf
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> conprof_index = ConProf(df, 'tractid', 'pop10')
-    >>> conprof_index.statistic
-    0.06393365660089256
-    
-    You can plot the profile curve with the plot method.
-    
-    >>> conprof_index.plot()
-        
-    Notes
-    -----
-    Based on Hong, Seong-Yun, and Yukio Sadahiro. "Measuring geographic segregation: a graph-based approach." Journal of Geographical Systems 16.2 (2014): 211-231.
-    
-    Reference: :cite:`hong2014measuring`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, m = 1000):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _conprof(data, group_pop_var, total_pop_var, m)
-
-        self.statistic = aux[0]
-        self.grid      = aux[1]
-        self.curve     = aux[2]
-        self.core_data = aux[3]
-        self._function = _conprof

-
-    def plot(self):
-        """
-        Plot the Concentration Profile
-        """
-        try:
-            import matplotlib.pyplot as plt
-        except ImportError:
-            warnings.warn('This method relies on importing `matplotlib`')
-        graph = plt.scatter(self.grid, self.curve, s = 0.1)
-        return graph

-    
-    
-    
-    
-def _modified_dissim(data, group_pop_var, total_pop_var, iterations = 500):
-    """
-    Calculation of Modified Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    iterations    : int
-                    The number of iterations the evaluate average classic dissimilarity under eveness. Default value is 500.
-
-    Returns
-    ----------
-
-    statistic : float
-                Modified Dissimilarity Index (Dissimilarity from Carrington and Troske (1997))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Notes
-    -----
-    Based on Carrington, William J., and Kenneth R. Troske. "On measuring segregation in samples with small units." Journal of Business & Economic Statistics 15.4 (1997): 402-409.
-    
-    Reference: :cite:`carrington1997measuring`.
-
-    """
-    if(type(iterations) is not int):
-        raise TypeError('iterations must be an integer')
-        
-    if(iterations < 2):
-        raise TypeError('iterations must be greater than 1.')
-   
-    D = _dissim(data, group_pop_var, total_pop_var)[0]
-    
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    # core_data has to be in the beggining of the call because assign methods will be used later
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    p_null = x.sum() / t.sum()
-    
-    Ds = np.empty(iterations)
-    
-    for i in np.array(range(iterations)):
-
-        freq_sim = np.random.binomial(n = np.array([t.tolist()]), 
-                                      p = np.array([[p_null] * data.shape[0]]), 
-                                      size = (1, data.shape[0])).tolist()[0]
-        data = data.assign(group_pop_var = freq_sim)
-        aux = _dissim(data, 'group_pop_var', 'total_pop_var')[0]
-        Ds[i] = aux
-        
-    D_star = Ds.mean()
-    
-    if (D >= D_star):
-        Dct = (D - D_star)/(1 - D_star)
-    else:
-        Dct = (D - D_star)/D_star
-    
-    return Dct, core_data
-
-
-
[docs]class ModifiedDissim:
-    """
-    Calculation of Modified Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    iterations    : int
-                    The number of iterations the evaluate average classic dissimilarity under eveness. Default value is 500.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Modified Dissimilarity Index (Dissimilarity from Carrington and Troske (1997))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.    
-                
-    Examples
-    --------
-    In this example, we will calculate the Modified Dissimilarity Index (Dct) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import ModifiedDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> np.random.seed(1234)
-    >>> modified_dissim_index = ModifiedDissim(df, 'tractid', 'pop10')
-    >>> modified_dissim_index.statistic
-    0.30009504639081996
-     
-    Notes
-    -----
-    Based on Carrington, William J., and Kenneth R. Troske. "On measuring segregation in samples with small units." Journal of Business & Economic Statistics 15.4 (1997): 402-409.
-
-    Reference: :cite:`carrington1997measuring`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, iterations = 500):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _modified_dissim(data, group_pop_var, total_pop_var, iterations)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _modified_dissim

-        
-        
-def _modified_gini_seg(data, group_pop_var, total_pop_var, iterations = 500):
-    """
-    Calculation of Modified Gini Segregation index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    iterations    : int
-                    The number of iterations the evaluate average classic gini segregation under eveness. Default value is 500.
-
-    Returns
-    ----------
-
-    statistic : float
-                Modified Gini Segregation Index (Gini from Carrington and Troske (1997))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-                
-    Notes
-    -----
-    Based on Carrington, William J., and Kenneth R. Troske. "On measuring segregation in samples with small units." Journal of Business & Economic Statistics 15.4 (1997): 402-409.
-    
-    Reference: :cite:`carrington1997measuring`.
-
-    """
-    if(type(iterations) is not int):
-        raise TypeError('iterations must be an integer')
-        
-    if(iterations < 2):
-        raise TypeError('iterations must be greater than 1.')
-   
-    G = _gini_seg(data, group_pop_var, total_pop_var)[0]
-    
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    # core_data has to be in the beggining of the call because assign methods will be used later
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    p_null = x.sum() / t.sum()
-    
-    Gs = np.empty(iterations)
-    
-    for i in np.array(range(iterations)):
-
-        freq_sim = np.random.binomial(n = np.array([t.tolist()]), 
-                                      p = np.array([[p_null] * data.shape[0]]), 
-                                      size = (1, data.shape[0])).tolist()[0]
-        data = data.assign(group_pop_var = freq_sim)
-        aux = _gini_seg(data, 'group_pop_var', 'total_pop_var')[0]
-        Gs[i] = aux
-        
-    G_star = Gs.mean()
-    
-    if (G >= G_star):
-        Gct = (G - G_star)/(1 - G_star)
-    else:
-        Gct = (G - G_star)/G_star
-
-    return Gct, core_data
-
-
-
[docs]class ModifiedGiniSeg:
-    """
-    Calculation of Modified Gini Segregation index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    iterations    : int
-                    The number of iterations the evaluate average classic gini segregation under eveness. Default value is 500.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Modified Gini Segregation Index (Gini from Carrington and Troske (1997))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.     
-                
-    Examples
-    --------
-    In this example, we will calculate the Modified Gini Segregation Index (Gct) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import ModifiedGiniSeg
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> np.random.seed(1234)
-    >>> modified_gini_seg_index = ModifiedGiniSeg(df, 'tractid', 'pop10')
-    >>> modified_gini_seg_index.statistic
-    0.4280279611418648
-     
-    Notes
-    -----
-    Based on Carrington, William J., and Kenneth R. Troske. "On measuring segregation in samples with small units." Journal of Business & Economic Statistics 15.4 (1997): 402-409.
-    
-    Reference: :cite:`carrington1997measuring`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, iterations = 500):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _modified_gini_seg(data, group_pop_var, total_pop_var, iterations)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _modified_gini_seg

-        
-        
-        
-        
-def _bias_corrected_dissim(data, group_pop_var, total_pop_var, B = 500):
-    """
-    Calculation of Bias Corrected Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    B             : int
-                    The number of iterations to calculate Dissimilarity simulating randomness with multinomial distributions. Default value is 500.
-
-    Returns
-    ----------
-
-    statistic : float
-                Dissimilarity with Bias-Correction (bias correction from Allen, Rebecca et al. (2015))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    Based on Allen, Rebecca, et al. "More reliable inference for the dissimilarity index of segregation." The econometrics journal 18.1 (2015): 40-66.
-    
-    Reference: :cite:`allen2015more`.
-
-    """
-    if(type(B) is not int):
-        raise TypeError('B must be an integer')
-        
-    if(B < 2):
-        raise TypeError('B must be greater than 1.')
-   
-    D = _dissim(data, group_pop_var, total_pop_var)[0]
-    
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    other_group_pop = t - x
-    
-    # Group 0: minority group
-    p0_i = x / x.sum()
-    n0   = x.sum()
-    sim0 = np.random.multinomial(n0, p0_i, size = B)
-    
-    # Group 1: complement group
-    p1_i = other_group_pop / other_group_pop.sum()
-    n1   = other_group_pop.sum()
-    sim1 = np.random.multinomial(n1, p1_i, size = B)
-
-    
-    Dbcs = np.empty(B)
-    for i in np.array(range(B)):
-        data_aux = {'simul_group': sim0[i].tolist(), 'simul_tot': (sim0[i] + sim1[i]).tolist()}
-        df_aux = pd.DataFrame.from_dict(data_aux)
-        Dbcs[i] = _dissim(df_aux, 'simul_group', 'simul_tot')[0]
-        
-    Db = Dbcs.mean()
-    
-    Dbc = 2 * D - Db
-    Dbc # It expected to be lower than D, because D is upwarded biased
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-        
-    return Dbc, core_data
-
-
-
[docs]class BiasCorrectedDissim:
-    """
-    Calculation of Bias Corrected Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    B             : int
-                    The number of iterations to calculate Dissimilarity simulating randomness with multinomial distributions. Default value is 500.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Dissimilarity with Bias-Correction (bias correction from Allen, Rebecca et al. (2015))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the Dissimilarity with Bias Correction (Dbc) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import BiasCorrectedDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> np.random.seed(1234)
-    >>> bias_corrected_dissim_index = BiasCorrectedDissim(df, 'tractid', 'pop10')
-    >>> bias_corrected_dissim_index.statistic
-    0.31484636081876954
-     
-    Notes
-    -----
-    Based on Allen, Rebecca, et al. "More reliable inference for the dissimilarity index of segregation." The econometrics journal 18.1 (2015): 40-66.
-    
-    Reference: :cite:`allen2015more`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, B = 500):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _bias_corrected_dissim(data, group_pop_var, total_pop_var, B)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _bias_corrected_dissim

-        
-        
-def _density_corrected_dissim(data, group_pop_var, total_pop_var, xtol = 1e-5):
-    """
-    Calculation of Density Corrected Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    xtol          : float
-                    The degree of tolerance in the optimization process of returning optimal theta_j
-
-    Returns
-    ----------
-
-    statistic : float
-                Dissimilarity with Density-Correction (density correction from Allen, Rebecca et al. (2015))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-
-    Notes
-    -----
-    Based on Allen, Rebecca, et al. "More reliable inference for the dissimilarity index of segregation." The econometrics journal 18.1 (2015): 40-66.
-    
-    Reference: :cite:`allen2015more`.
-
-    """
-    if((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-    
-    if ((group_pop_var not in data.columns) or (total_pop_var not in data.columns)):    
-        raise ValueError('group_pop_var and total_pop_var must be variables of data')
-        
-    data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                total_pop_var: 'total_pop_var'})
-    
-    g = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-    
-    if any(t < g):    
-        raise ValueError('Group of interest population must equal or lower than the total population of the units.')
-    
-    other_group_pop = t - g
-    
-    # Group 0: minority group
-    p0_i = g / g.sum()
-    n0 = g.sum()
-    
-    # Group 1: complement group
-    p1_i = other_group_pop / other_group_pop.sum()
-    n1 = other_group_pop.sum()
-    
-    sigma_hat_j = np.sqrt(((p1_i * (1 - p1_i)) / n1) + ((p0_i * (1 - p0_i)) / n0))
-    theta_hat_j = abs(p1_i - p0_i) / sigma_hat_j
-    
-    # Constructing function that returns $n(\hat{\theta}_j)$
-    def return_optimal_theta(theta_j):
-        
-        def fold_norm(x):
-            y = (-1) * (norm.pdf(x - theta_j) + norm.pdf(x + theta_j))
-            return y
-        
-        initial_guesses = np.array(0)
-        res = minimize(fold_norm, 
-                       initial_guesses, 
-                       method='nelder-mead',
-                       options = {'xtol': xtol})
-        return res.final_simplex[0][1][0]
-        
-    optimal_thetas = pd.Series(data = theta_hat_j).apply(return_optimal_theta)
-
-    Ddc = np.multiply(sigma_hat_j, optimal_thetas).sum() / 2
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        core_data = data[['group_pop_var', 'total_pop_var']]
-    
-    else:    
-        core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-    
-    return Ddc, core_data
-
-
-
[docs]class DensityCorrectedDissim:
-    """
-    Calculation of Density Corrected Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    xtol          : float
-                    The degree of tolerance in the optimization process of returning optimal theta_j
-                                  
-    Attributes
-    ----------
-
-    statistic : float
-                Dissimilarity with Density-Correction (density correction from Allen, Rebecca et al. (2015))
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate. 
-        
-    Examples
-    --------
-    In this example, we will calculate the Dissimilarity with Density Correction (Ddc) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.aspatial import DensityCorrectedDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['pop10','tractid']]
-    
-    The value is estimated below.
-    
-    >>> density_corrected_dissim_index = DensityCorrectedDissim(df, 'tractid', 'pop10')
-    >>> density_corrected_dissim_index.statistic
-    0.29350643204887517
-     
-    Notes
-    -----
-    Based on Allen, Rebecca, et al. "More reliable inference for the dissimilarity index of segregation." The econometrics journal 18.1 (2015): 40-66.
-    
-    Reference: :cite:`allen2015more`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, xtol = 1e-5):        
-        
-        if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-            data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-        else:
-            data = _nan_handle(data[[group_pop_var, total_pop_var]])
-        
-        aux = _density_corrected_dissim(data, group_pop_var, total_pop_var, xtol)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _density_corrected_dissim

-        
-
-
-
-
-
-
-
-
-
-
-
-
-# Deprecation Calls
-        
-msg = _dep_message("Gini_Seg", "GiniSeg")
-Gini_Seg = DeprecationHelper(GiniSeg, message=msg)
-
-msg = _dep_message("Correlation_R", "CorrelationR")
-Correlation_R = DeprecationHelper(CorrelationR, message=msg)
-
-msg = _dep_message("Con_Prof", "ConProf")
-Con_Prof = DeprecationHelper(ConProf, message=msg)
-
-msg = _dep_message("Modified_Dissim", "ModifiedDissim")
-Modified_Dissim = DeprecationHelper(ModifiedDissim, message=msg)
-
-msg = _dep_message("Modified_Gini_Seg", "ModifiedGiniSeg")
-Modified_Gini_Seg = DeprecationHelper(ModifiedGiniSeg, message=msg)
-
-msg = _dep_message("Bias_Corrected_Dissim", "BiasCorrectedDissim")
-Bias_Corrected_Dissim = DeprecationHelper(BiasCorrectedDissim, message=msg)
-
-msg = _dep_message("Density_Corrected_Dissim", "DensityCorrectedDissim")
-Density_Corrected_Dissim = DeprecationHelper(DensityCorrectedDissim, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/aspatial/multigroup_aspatial_indexes.html b/docsrc/_build/html/_modules/segregation/aspatial/multigroup_aspatial_indexes.html deleted file mode 100644 index 728e3544..00000000 --- a/docsrc/_build/html/_modules/segregation/aspatial/multigroup_aspatial_indexes.html +++ /dev/null @@ -1,1335 +0,0 @@ - - - - - - - segregation.aspatial.multigroup_aspatial_indexes — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.aspatial.multigroup_aspatial_indexes

-"""
-Multigroup Aspatial based Segregation Metrics
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"
-
-import numpy as np
-from sklearn.metrics.pairwise import manhattan_distances
-
-from segregation.util.util import _dep_message, DeprecationHelper, _nan_handle
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = [
-    'Multi_Dissim', 
-    'MultiDissim',
-    
-    'Multi_Gini_Seg', 
-    'MultiGiniSeg', 
-    
-    'Multi_Normalized_Exposure',
-    'MultiNormalizedExposure',
-    
-    'Multi_Information_Theory', 
-    'MultiInformationTheory', 
-    
-    'Multi_Relative_Diversity',
-    'MultiRelativeDiversity',
-    
-    'Multi_Squared_Coefficient_of_Variation', 
-    'MultiSquaredCoefficientVariation',
-    
-    'Multi_Diversity',
-    'MultiDiversity',
-    
-    'Simpsons_Concentration', 
-    'SimpsonsConcentration', 
-    
-    'Simpsons_Interaction', 
-    'SimpsonsInteraction', 
-    
-    'Multi_Divergence',
-    'MultiDivergence'
-]
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-# suppress numpy divide by zero warnings because it occurs a lot during the
-# calculation of many indices
-np.seterr(divide='ignore', invalid='ignore')
-
-
-def _multi_dissim(data, groups):
-    """
-    Calculation of Multigroup Dissimilarity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Dissimilarity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Sakoda, James M. "A generalized index of dissimilarity." Demography 18.2 (1981): 245-250.
-    
-    Reference: :cite:`sakoda1981generalized`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    n = df.shape[0]
-    K = df.shape[1]
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-
-    Is = (Pk * (1 - Pk)).sum()
-
-    multi_D = 1 / (2 * T * Is) * np.multiply(
-        abs(pik - Pk),
-        np.repeat(ti, K, axis=0).reshape(n, K)).sum()
-
-    return multi_D, core_data, groups
-
-
-
[docs]class MultiDissim:
-    """
-    Calculation of Multigroup Dissimilarity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Dissimilarity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiDissim
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiDissim(input_df, groups_list)
-    >>> index.statistic
-    0.41340872573177806
-
-    Notes
-    -----
-    Based on Sakoda, James M. "A generalized index of dissimilarity." Demography 18.2 (1981): 245-250.
-    
-    Reference: :cite:`sakoda1981generalized`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_dissim(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_dissim

-
-
-def _multi_gini_seg(data, groups):
-    """
-    Calculation of Multigroup Gini Segregation index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Gini Segregation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    K = df.shape[1]
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-    Is = (Pk * (1 - Pk)).sum()
-
-    elements_sum = np.empty(K)
-    for k in range(K):
-        aux = np.multiply(np.outer(ti, ti),
-                          manhattan_distances(pik[:, k].reshape(-1, 1))).sum()
-        elements_sum[k] = aux
-
-    multi_Gini_Seg = elements_sum.sum() / (2 * (T**2) * Is)
-
-    return multi_Gini_Seg, core_data, groups
-
-
-
[docs]class MultiGiniSeg:
-    """
-    Calculation of Multigroup Gini Segregation index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Gini Segregation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiGiniSeg
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiGiniSeg(input_df, groups_list)
-    >>> index.statistic
-    0.5456349992598081
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_gini_seg(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_gini_seg

-
-
-def _multi_normalized_exposure(data, groups):
-    """
-    Calculation of Multigroup Normalized Exposure index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Normalized Exposure Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-
-    MNE = ((ti[:, None] * (pik - Pk)**2) / (1 - Pk)).sum() / T
-
-    return MNE, core_data, groups
-
-
-
[docs]class MultiNormalizedExposure:
-    """
-    Calculation of Multigroup Normalized Exposure index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Normalized Exposure Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiNormalizedExposure
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiNormalizedExposure(input_df, groups_list)
-    >>> index.statistic
-    0.18821879029994157
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_normalized_exposure(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_normalized_exposure

-
-
-def _multi_information_theory(data, groups):
-    """
-    Calculation of Multigroup Information Theory index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Information Theory Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-
-    # The natural logarithm is used, but this could be used with any base following Footnote 3 of pg. 37
-    # of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    E = (Pk * np.log(1 / Pk)).sum()
-
-    MIT = np.nansum(ti[:, None] * pik * np.log(pik / Pk)) / (T * E)
-
-    return MIT, core_data, groups
-
-
-
[docs]class MultiInformationTheory:
-    """
-    Calculation of Multigroup Information Theory index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Information Theory Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiInformationTheory
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiInformationTheory(input_df, groups_list)
-    >>> index.statistic
-    0.1710160297858887
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_information_theory(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_information_theory

-
-
-def _multi_relative_diversity(data, groups):
-    """
-    Calculation of Multigroup Relative Diversity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Relative Diversity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Reardon, Sean F. "Measures of racial diversity and segregation in multigroup and hierarchically structured populations." annual meeting of the Eastern Sociological Society, Philadelphia, PA. 1998.
-
-    High diversity means less segregation.
-    
-    Reference: :cite:`reardon1998measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-    Is = (Pk * (1 - Pk)).sum()
-
-    MRD = (ti[:, None] * (pik - Pk)**2).sum() / (T * Is)
-
-    return MRD, core_data, groups
-
-
-
[docs]class MultiRelativeDiversity:
-    """
-    Calculation of Multigroup Relative Diversity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Relative Diversity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiRelativeDiversity
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiRelativeDiversity(input_df, groups_list)
-    >>> index.statistic
-    0.15820019878220337
-
-    Notes
-    -----
-    Based on Reardon, Sean F. "Measures of racial diversity and segregation in multigroup and hierarchically structured populations." annual meeting of the Eastern Sociological Society, Philadelphia, PA. 1998.
-
-    High diversity means less segregation.
-    
-    Reference: :cite:`reardon1998measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_relative_diversity(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_relative_diversity

-
-
-def _multi_squared_coefficient_of_variation(data, groups):
-    """
-    Calculation of Multigroup Squared Coefficient of Variation index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Squared Coefficient of Variation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    K = df.shape[1]
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-
-    C = ((ti[:, None] * (pik - Pk)**2) / (T * (K - 1) * Pk)).sum()
-
-    return C, core_data, groups
-
-
-
[docs]class MultiSquaredCoefficientVariation:
-    """
-    Calculation of Multigroup Squared Coefficient of Variation index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Squared Coefficient of Variation Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiSquaredCoefficientVariation
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiSquaredCoefficientVariation(input_df, groups_list)
-    >>> index.statistic
-    0.11875484641127525
-    
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_squared_coefficient_of_variation(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_squared_coefficient_of_variation

-
-
-def _multi_diversity(data, groups, normalized=False):
-    """
-    Calculation of Multigroup Diversity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic  : float
-                 Multigroup Diversity Index
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-                
-    normalized : bool. Default is False.
-                 Wheter the resulting index will be divided by its maximum (natural log of the number of groups)
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67 and Theil, Henry. "Statistical decomposition analysis; with applications in the social and administrative sciences". No. 04; HA33, T4.. 1972.
-    
-    This is also know as Theil's Entropy Index (Equation 2 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67)
-    
-    High diversity means less segregation.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    Pk = df.sum(axis=0) / df.sum()
-
-    E = -(Pk * np.log(Pk)).sum()
-
-    if normalized:
-        K = df.shape[1]
-        E = E / np.log(K)
-
-    return E, core_data, groups
-
-
-
[docs]class MultiDiversity:
-    """
-    Calculation of Multigroup Diversity index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Diversity Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiDiversity
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiDiversity(input_df, groups_list)
-    >>> index.statistic
-    0.9733112243997906
-    
-    You can also fit the normalized version of the multigroup diversity index.
-    
-    >>> normalized_index = Multi_Diversity(input_df, groups_list, normalized = True)
-    >>> normalized_index.statistic
-    0.7020956383415715
-
-    Notes
-    -----
-    Based on Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67 and Theil, Henry. "Statistical decomposition analysis; with applications in the social and administrative sciences". No. 04; HA33, T4.. 1972.
-    
-    This is also know as Theil's Entropy Index (Equation 2 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67)
-    
-    High diversity means less segregation.
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups, normalized=False):
-
-        aux = _multi_diversity(data, groups, normalized)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_diversity

-
-
-def _simpsons_concentration(data, groups):
-    """
-    Calculation of Simpson's Concentration index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic  : float
-                 Simpson's Concentration Index
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Simpson, Edward H. "Measurement of diversity." nature 163.4148 (1949): 688.
-    
-    Simpson's concentration index (Lambda) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.
-
-    Higher values means higher segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`simpson1949measurement`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    Pk = df.sum(axis=0) / df.sum()
-
-    Lambda = (Pk * Pk).sum()
-
-    return Lambda, core_data, groups
-
-
-
[docs]class SimpsonsConcentration:
-    """
-    Calculation of Simpson's Concentration index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic  : float
-                 Simpson's Concentration Index
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import SimpsonsConcentration
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = SimpsonsConcentration(input_df, groups_list)
-    >>> index.statistic
-    0.49182413151957904
-    
-    Notes
-    -----
-    Based on Simpson, Edward H. "Measurement of diversity." nature 163.4148 (1949): 688.
-    
-    Simpson's concentration index (Lambda) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.
-
-    Higher values means higher segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`simpson1949measurement`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _simpsons_concentration(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _simpsons_concentration

-
-
-def _simpsons_interaction(data, groups):
-    """
-    Calculation of Simpson's Interaction index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic  : float
-                 Simpson's Interaction Index
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's interaction index (I) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    Pk = df.sum(axis=0) / df.sum()
-
-    I = (Pk * (1 - Pk)).sum()
-
-    return I, core_data, groups
-
-
-
[docs]class SimpsonsInteraction:
-    """
-    Calculation of Simpson's Interaction index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic  : float
-                 Simpson's Interaction Index
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import SimpsonsInteraction
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = SimpsonsInteraction(input_df, groups_list)
-    >>> index.statistic
-    0.508175868480421
-    
-    Notes
-    -----
-    Based on Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's interaction index (I) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _simpsons_interaction(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _simpsons_interaction

-
-
-def _multi_divergence(data, groups):
-    """
-    Calculation of Multigroup Divergence index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistic : float
-                Multigroup Divergence Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Roberto, Elizabeth. "The Divergence Index: A Decomposable Measure of Segregation and Inequality." arXiv preprint arXiv:1508.01167 (2015).
-    
-    Reference: :cite:`roberto2015divergence`.
-
-    """
-
-    core_data = data[groups]
-    data = _nan_handle(core_data)
-
-    df = np.array(core_data)
-
-    T = df.sum()
-
-    ti = df.sum(axis=1)
-    pik = df / ti[:, None]
-    Pk = df.sum(axis=0) / df.sum()
-
-    Di = np.nansum(pik * np.log(pik / Pk), axis=1)
-
-    Divergence_Index = ((ti / T) * Di).sum()
-
-    return Divergence_Index, core_data, groups
-
-
-
[docs]class MultiDivergence:
-    """
-    Calculation of Multigroup Divergence index
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Multigroup Divergence Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.multigroup_aspatial import MultiDivergence
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiDivergence(input_df, groups_list)
-    >>> index.statistic
-    0.16645182134289443
-
-    Notes
-    -----
-    Based on Roberto, Elizabeth. "The Divergence Index: A Decomposable Measure of Segregation and Inequality." arXiv preprint arXiv:1508.01167 (2015).
-    
-    Reference: :cite:`roberto2015divergence`.
-
-    """
-
-
[docs]    def __init__(self, data, groups):
-
-        aux = _multi_divergence(data, groups)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._groups   = aux[2]
-        self._function = _multi_divergence

-
-
-
-
-# Deprecation Calls
-
-msg = _dep_message("Multi_Dissim", "MultiDissim")
-Multi_Dissim = DeprecationHelper(MultiDissim, message=msg)
-
-msg = _dep_message("Multi_Gini_Seg", "MultiGiniSeg")
-Multi_Gini_Seg = DeprecationHelper(MultiGiniSeg, message=msg)
-
-msg = _dep_message("Multi_Normalized_Exposure", "MultiNormalizedExposure")
-Multi_Normalized_Exposure = DeprecationHelper(MultiNormalizedExposure, message=msg)
-
-msg = _dep_message("Multi_Information_Theory", "MultiInformationTheory")
-Multi_Information_Theory = DeprecationHelper(MultiInformationTheory, message=msg)
-
-msg = _dep_message("Multi_Relative_Diversity", "MultiRelativeDiversity")
-Multi_Relative_Diversity = DeprecationHelper(MultiRelativeDiversity, message=msg)
-
-msg = _dep_message("Multi_Squared_Coefficient_of_Variation", "MultiSquaredCoefficientVariation")
-Multi_Squared_Coefficient_of_Variation = DeprecationHelper(MultiSquaredCoefficientVariation, message=msg)
-
-msg = _dep_message("Multi_Diversity", "MultiDiversity")
-Multi_Diversity = DeprecationHelper(MultiDiversity, message=msg)
-
-msg = _dep_message("Simpsons_Concentration", "SimpsonsConcentration")
-Simpsons_Concentration = DeprecationHelper(SimpsonsConcentration, message=msg)
-
-msg = _dep_message("Simpsons_Interaction", "SimpsonsInteraction")
-Simpsons_Interaction = DeprecationHelper(SimpsonsInteraction, message=msg)
-
-msg = _dep_message("Multi_Divergence", "MultiDivergence")
-Multi_Divergence = DeprecationHelper(MultiDivergence, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/compute_all/compute_all.html b/docsrc/_build/html/_modules/segregation/compute_all/compute_all.html deleted file mode 100644 index 3ddc4908..00000000 --- a/docsrc/_build/html/_modules/segregation/compute_all/compute_all.html +++ /dev/null @@ -1,483 +0,0 @@ - - - - - - - segregation.compute_all.compute_all — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.compute_all.compute_all

-"""
-Compute Several Segregation measures at once
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu> and Sergio J. Rey <sergio.rey@ucr.edu>"
-
-import pandas as pd
-from segregation.aspatial import *
-from segregation.spatial import *
-
-__all__ = [
-    'ComputeAllAspatialSegregation', 
-    'ComputeAllSpatialSegregation',
-    'ComputeAllSegregation'
-]
-
-
-def _compute_all_aspatial_segregation(data, group_pop_var, total_pop_var):
-    '''
-    Perform point estimation of selected aspatial segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Returns
-    -------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-    
-    Notes
-    -----
-    Currently, works with the default input parameters of the functions.
-    
-    '''
-
-    D = Dissim(data, group_pop_var, total_pop_var)
-    G = GiniSeg(data, group_pop_var, total_pop_var)
-    H = Entropy(data, group_pop_var, total_pop_var)
-    A = Atkinson(data, group_pop_var, total_pop_var)
-    xPy = Exposure(data, group_pop_var, total_pop_var)
-    xPx = Isolation(data, group_pop_var, total_pop_var)
-    R = ConProf(data, group_pop_var, total_pop_var)
-    Dbc = BiasCorrectedDissim(data, group_pop_var, total_pop_var)
-    Ddc = DensityCorrectedDissim(data, group_pop_var, total_pop_var)
-    V = CorrelationR(data, group_pop_var, total_pop_var)
-    Dct = ModifiedDissim(data, group_pop_var, total_pop_var)
-    Gct = ModifiedGiniSeg(data, group_pop_var, total_pop_var)
-    MM = MinMax(data, group_pop_var, total_pop_var)
-
-    dictionary = {
-        'Dissimilarity': D.statistic,
-        'Gini': G.statistic,
-        'Entropy': H.statistic,
-        'Atkinson': A.statistic,
-        'Exposure': xPy.statistic,
-        'Isolation': xPx.statistic,
-        'Concentration Profile': R.statistic,
-        'Bias Corrected Dissimilarity': Dbc.statistic,
-        'Density Corrected Dissimilarity': Ddc.statistic,
-        'Correlation Ratio': V.statistic,
-        'Modified Dissimilarity': Dct.statistic,
-        'Modified Gini': Gct.statistic,
-        'Minimun-Maximum': MM.statistic
-    }
-    
-    d = {'Measure': list(dictionary.keys()), 'Value': list(dictionary.values())}
-
-    computed = pd.DataFrame(data = d)
-
-    return computed
-
-
-
[docs]class ComputeAllAspatialSegregation:
-    '''
-    Perform point estimation of selected Aspatial segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Attributes
-    ----------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-    
-    Examples
-    --------
-    The Compute_All comprises simple and quick functions to assess multiple segregation measures at once in a dataset. It uses all the default parameters and returns an object that has an attribute (.computed) of a dictionary with summary of all values fitted.
-
-    Firstly, we need to import the libraries and functions to be used.
-    
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> import libpysal
-    >>> from segregation.util import ComputeAllAspatialSegregation
-    
-    Then it's time to load some data to estimate segregation. We use the data of 2000 Census Tract Data for the metropolitan area of Sacramento, CA, USA.
-
-    We use a geopandas dataframe available in PySAL examples repository.
-
-    For more information about the data: https://github.com/pysal/libpysal/tree/master/libpysal/examples/sacramento2
-    
-    >>> s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    
-    The data have several demographic variables. We are going to assess the segregation of the Hispanic Population (variable 'HISP_'). For this, we only extract some columns of the geopandas dataframe.
-    
-    >>> gdf = s_map[['geometry', 'HISP_', 'TOT_POP']]
-    
-    Now the measures are fitted.
-    
-    >>> aspatial_fit = ComputeAllAspatialSegregation(gdf, 'HISP_', 'TOT_POP')
-    >>> aspatial_fit.computed
-    
-    '''
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-
-        aux = _compute_all_aspatial_segregation(data, group_pop_var,
-                                            total_pop_var)
-
-        self.computed = aux

-
-
-def _compute_all_spatial_segregation(data, group_pop_var, total_pop_var):
-    '''
-    Perform point estimation of selected spatial segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Attributes
-    ----------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-                  
-    '''
-
-    SD = SpatialDissim(data, group_pop_var, total_pop_var)
-    PARD = PerimeterAreaRatioSpatialDissim(data, group_pop_var,
-                                               total_pop_var)
-    BSD = BoundarySpatialDissim(data, group_pop_var, total_pop_var)
-    ACE = AbsoluteCentralization(data, group_pop_var, total_pop_var)
-    ACO = AbsoluteConcentration(data, group_pop_var, total_pop_var)
-    DEL = Delta(data, group_pop_var, total_pop_var)
-    RCE = RelativeCentralization(data, group_pop_var, total_pop_var)
-    ACL = AbsoluteClustering(data, group_pop_var, total_pop_var)
-    RCL = RelativeClustering(data, group_pop_var, total_pop_var)
-    RCO = RelativeConcentration(data, group_pop_var, total_pop_var)
-    DDxPy = DistanceDecayExposure(data, group_pop_var, total_pop_var)
-    DDxPx = DistanceDecayIsolation(data, group_pop_var, total_pop_var)
-    SPP = SpatialProxProf(data, group_pop_var, total_pop_var)
-    SP = SpatialProximity(data, group_pop_var, total_pop_var)
-    SMM = SpatialMinMax(data, group_pop_var, total_pop_var)
-
-    dictionary = {
-        'Spatial Dissimilarity': SD.statistic,
-        'Absolute Centralization': ACE.statistic,
-        'Absolute Clustering': ACL.statistic,
-        'Absolute Concentration': ACO.statistic,
-        'Delta': DEL.statistic,
-        'Relative Centralization': RCE.statistic,
-        'Relative Clustering': RCL.statistic,
-        'Relative Concentration': RCO.statistic,
-        'Distance Decay Exposure': DDxPy.statistic,
-        'Distance Decay Isolation': DDxPx.statistic,
-        'Spatial Proximity Profile': SPP.statistic,
-        'Spatial Proximity': SP.statistic,
-        'Boundary Spatial Dissimilarity': BSD.statistic,
-        'Perimeter Area Ratio Spatial Dissimilarity': PARD.statistic,
-        'Spatial Minimun-Maximum': SMM.statistic
-    }
-
-    d = {'Measure': list(dictionary.keys()), 'Value': list(dictionary.values())}
-
-    computed = pd.DataFrame(data = d)
-
-    return computed
-
-
-
[docs]class ComputeAllSpatialSegregation:
-    '''
-    Perform point estimation of selected spatial segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Attributes
-    ----------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-    
-    Examples
-    --------
-    The Compute_All comprises simple and quick functions to assess multiple segregation measures at once in a dataset. It uses all the default parameters and returns an object that has an attribute (.computed) of a dictionary with summary of all values fitted.
-
-    Firstly, we need to import the libraries and functions to be used.
-    
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> import libpysal
-    >>> from segregation.util import ComputeAllSpatialSegregation
-    
-    Then it's time to load some data to estimate segregation. We use the data of 2000 Census Tract Data for the metropolitan area of Sacramento, CA, USA.
-
-    We use a geopandas dataframe available in PySAL examples repository.
-
-    For more information about the data: https://github.com/pysal/libpysal/tree/master/libpysal/examples/sacramento2
-    
-    >>> s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    
-    The data have several demographic variables. We are going to assess the segregation of the Hispanic Population (variable 'HISP_'). For this, we only extract some columns of the geopandas dataframe.
-    
-    >>> gdf = s_map[['geometry', 'HISP_', 'TOT_POP']]
-    
-    Now the measures are fitted.
-    
-    >>> spatial_fit = ComputeAllSpatialSegregation(gdf, 'HISP_', 'TOT_POP')
-    >>> spatial_fit.computed
-    
-    '''
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-
-        aux = _compute_all_spatial_segregation(data, group_pop_var, total_pop_var)
-
-        self.computed = aux

-
-
-def _compute_all_segregation(data, group_pop_var, total_pop_var):
-    '''
-    Perform point estimation of selected segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Attributes
-    ----------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-    
-    '''
-
-    x = ComputeAllAspatialSegregation(data, group_pop_var,
-                                     total_pop_var).computed
-    y = ComputeAllSpatialSegregation(data, group_pop_var, total_pop_var).computed
-
-    z = pd.concat([x, y], ignore_index = True)
-
-    return z
-
-
-
[docs]class ComputeAllSegregation:
-    '''
-    Perform point estimation of selected segregation measures at once
-
-    Parameters
-    ----------
-
-    data          : a pandas DataFrame
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    Attributes
-    ----------
-
-    computed      : a pandas DataFrame containing the name of the measure and the point estimation.
-    
-    Examples
-    --------
-    The Compute_All comprises simple and quick functions to assess multiple segregation measures at once in a dataset. It uses all the default parameters and returns an object that has an attribute (.computed) of a dictionary with summary of all values fitted.
-
-    Firstly, we need to import the libraries and functions to be used.
-    
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> import libpysal
-    >>> from segregation.util import ComputeAllSegregation
-    
-    Then it's time to load some data to estimate segregation. We use the data of 2000 Census Tract Data for the metropolitan area of Sacramento, CA, USA.
-
-    We use a geopandas dataframe available in PySAL examples repository.
-
-    For more information about the data: https://github.com/pysal/libpysal/tree/master/libpysal/examples/sacramento2
-    
-    >>> s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    
-    The data have several demographic variables. We are going to assess the segregation of the Hispanic Population (variable 'HISP_'). For this, we only extract some columns of the geopandas dataframe.
-    
-    >>> gdf = s_map[['geometry', 'HISP_', 'TOT_POP']]
-    
-    Now the measures are fitted.
-    
-    >>> segregation_fit = ComputeAllSegregation(gdf, 'HISP_', 'TOT_POP')
-    >>> segregation_fit.computed
-    
-    '''
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-
-        aux = _compute_all_segregation(data, group_pop_var, total_pop_var)
-
-        self.computed = aux

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/decomposition/decompose_segregation.html b/docsrc/_build/html/_modules/segregation/decomposition/decompose_segregation.html deleted file mode 100644 index 288319b2..00000000 --- a/docsrc/_build/html/_modules/segregation/decomposition/decompose_segregation.html +++ /dev/null @@ -1,449 +0,0 @@ - - - - - - - segregation.decomposition.decompose_segregation — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.decomposition.decompose_segregation

-"""
-Decomposition Segregation based Metrics
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu>, Elijah Knaap <elijah.knaap@ucr.edu>, and Sergio J. Rey <sergio.rey@ucr.edu>"
-
-
-import warnings
-from segregation.util.util import _generate_counterfactual, _dep_message, DeprecationHelper
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = ['Decompose_Segregation',
-           'DecomposeSegregation']
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-def _decompose_segregation(index1,
-                           index2,
-                           counterfactual_approach='composition'):
-    """Decompose segregation differences into spatial and attribute components.
-
-    Given two segregation indices of the same type, use Shapley decomposition
-    to measure whether the differences between index measures arise from
-    differences in spatial structure or population structure
-
-    Parameters
-    ----------
-    index1 : segregation.SegIndex class
-        First SegIndex class to compare.
-    index2 : segregation.SegIndex class
-        Second SegIndex class to compare.
-    counterfactual_approach : str, one of
-                              ["composition", "share", "dual_composition"]
-        The technique used to generate the counterfactual population
-        distributions.
-
-    Returns
-    -------
-    tuple
-        (shapley spatial component,
-         shapley attribute component,
-         core data of index1,
-         core data of index2,
-         data with counterfactual variables for index1,
-         data with counterfactual variables for index2)
-
-    """
-    df1 = index1.core_data.copy()
-    df2 = index2.core_data.copy()
-
-    assert index1._function == index2._function, "Segregation indices must be of the same type"
-
-    counterfac_df1, counterfac_df2 = _generate_counterfactual(
-        df1,
-        df2,
-        'group_pop_var',
-        'total_pop_var',
-        counterfactual_approach=counterfactual_approach)
-
-    seg_func = index1._function
-
-    # index for spatial 1, attribute 1
-    G_S1_A1 = index1.statistic
-
-    # index for spatial 2, attribute 2
-    G_S2_A2 = index2.statistic
-
-    # index for spatial 1 attribute 2 (counterfactual population for structure 1)
-    G_S1_A2 = seg_func(counterfac_df1, 'counterfactual_group_pop',
-                       'counterfactual_total_pop')[0]
-
-    # index for spatial 2 attribute 1 (counterfactual population for structure 2)
-    G_S2_A1 = seg_func(counterfac_df2, 'counterfactual_group_pop',
-                       'counterfactual_total_pop')[0]
-
-    # take the average difference in spatial structure, holding attributes constant
-    C_S = 1 / 2 * (G_S1_A1 - G_S2_A1 + G_S1_A2 - G_S2_A2)
-
-    # take the average difference in attributes, holding spatial structure constant
-    C_A = 1 / 2 * (G_S1_A1 - G_S1_A2 + G_S2_A1 - G_S2_A2)
-
-    results = {'s1_a1': G_S1_A1,
-            's1_a2': G_S1_A2,
-            's2_a1': G_S2_A1,
-            's2_a2': G_S2_A2 }
-
-    return C_S, C_A, df1, df2, counterfac_df1, counterfac_df2, counterfactual_approach, results
-
-
-
[docs]class DecomposeSegregation:
-    """Decompose segregation differences into spatial and attribute components.
-
-    Given two segregation indices of the same type, use Shapley decomposition
-    to measure whether the differences between index measures arise from
-    differences in spatial structure or population structure
-
-    Parameters
-    ----------
-    index1 : segregation.SegIndex class
-        First SegIndex class to compare.
-    index2 : segregation.SegIndex class
-        Second SegIndex class to compare.
-    counterfactual_approach : str, one of
-                              ["composition", "share", "dual_composition"]
-        The technique used to generate the counterfactual population
-        distributions.
-
-    Attributes
-    ----------
-
-    c_s : float
-        Shapley's Spatial Component of the decomposition
-
-    c_a : float
-        Shapley's Attribute Component of the decomposition
-
-    Methods
-    ----------
-
-    plot : Visualize features of the Decomposition performed
-        plot_type : str, one of ['cdfs', 'maps']
-
-        'cdfs' : visualize the cumulative distribution functions of the compositions/shares
-        'maps' : visualize the spatial distributions for original data and counterfactuals generated and Shapley's components (only available for GeoDataFrames)
-
-    Examples
-    --------
-    Several examples can be found at https://github.com/pysal/segregation/blob/master/notebooks/decomposition_wrapper_example.ipynb.
-
-    """
-
-
[docs]    def __init__(self, index1, index2, counterfactual_approach='composition'):
-
-        aux = _decompose_segregation(index1, index2, counterfactual_approach)
-
-        self.c_s = aux[0]
-        self.c_a = aux[1]
-        self._df1 = aux[2]
-        self._df2 = aux[3]
-        self._counterfac_df1 = aux[4]
-        self._counterfac_df2 = aux[5]
-        self._counterfactual_approach = aux[6]
-        self.indices = aux[7]

-
-    def plot(self, plot_type='cdfs'):
-        """
-        Plot the Segregation Decomposition Profile
-        """
-        try:
-            import matplotlib.pyplot as plt
-        except ImportError:
-            warnings.warn('This method relies on importing `matplotlib`')
-
-        if (plot_type == 'cdfs'):
-            if (self._counterfactual_approach == 'composition'):
-                plt.suptitle(
-                    'Spatial Component = {}, Attribute Component: {}'.format(
-                        round(self.c_s, 3), round(self.c_a, 3)),
-                    size=20)
-                plt.step(
-                    self._counterfac_df1['group_composition'].sort_values(),
-                    self._counterfac_df1['group_composition'].rank(
-                        pct=True).sort_values(),
-                    label='First Context Group Composition')
-
-                plt.step(
-                    self._counterfac_df2['group_composition'].sort_values(),
-                    self._counterfac_df2['group_composition'].rank(
-                        pct=True).sort_values(),
-                    label='Second Context Group Composition')
-                plt.legend()
-
-            if (self._counterfactual_approach == 'share'):
-                plt.suptitle(
-                    'Spatial Component = {}, Attribute Component: {}'.format(
-                        round(self.c_s, 3), round(self.c_a, 3)),
-                    size=20)
-                plt.step((self._df1['group_pop_var'] /
-                          self._df1['group_pop_var'].sum()).sort_values(),
-                         (self._df1['group_pop_var'] /
-                          self._df1['group_pop_var'].sum()).rank(
-                              pct=True).sort_values(),
-                         label='First Context Group Share')
-
-                plt.step((self._df2['group_pop_var'] /
-                          self._df2['group_pop_var'].sum()).sort_values(),
-                         (self._df2['group_pop_var'] /
-                          self._df2['group_pop_var'].sum()).rank(
-                              pct=True).sort_values(),
-                         label='Second Context Group Share')
-
-                plt.step(
-                    ((self._df1['total_pop_var'] - self._df1['group_pop_var'])
-                     / (self._df1['total_pop_var'] -
-                        self._df1['group_pop_var']).sum()).sort_values(),
-                    ((self._df1['total_pop_var'] - self._df1['group_pop_var'])
-                     / (self._df1['total_pop_var'] -
-                        self._df1['group_pop_var']).sum()).rank(
-                            pct=True).sort_values(),
-                    label='First Context Complementary Group Share')
-
-                plt.step(
-                    ((self._df2['total_pop_var'] - self._df2['group_pop_var'])
-                     / (self._df2['total_pop_var'] -
-                        self._df2['group_pop_var']).sum()).sort_values(),
-                    ((self._df2['total_pop_var'] - self._df2['group_pop_var'])
-                     / (self._df2['total_pop_var'] -
-                        self._df2['group_pop_var']).sum()).rank(
-                            pct=True).sort_values(),
-                    label='Second Context Complementary Group Share')
-                plt.legend()
-
-            if (self._counterfactual_approach == 'dual_composition'):
-                plt.suptitle(
-                    'Spatial Component = {}, Attribute Component: {}'.format(
-                        round(self.c_s, 3), round(self.c_a, 3)),
-                    size=20)
-                plt.step(
-                    self._counterfac_df1['group_composition'].sort_values(),
-                    self._counterfac_df1['group_composition'].rank(
-                        pct=True).sort_values(),
-                    label='First Context Group Composition')
-
-                plt.step(
-                    self._counterfac_df2['group_composition'].sort_values(),
-                    self._counterfac_df2['group_composition'].rank(
-                        pct=True).sort_values(),
-                    label='Second Context Group Composition')
-
-                plt.step(
-                    (1 -
-                     self._counterfac_df1['group_composition']).sort_values(),
-                    (1 - self._counterfac_df1['group_composition']).rank(
-                        pct=True).sort_values(),
-                    label='First Context Complementary Group Composition')
-
-                plt.step(
-                    (1 -
-                     self._counterfac_df2['group_composition']).sort_values(),
-                    (1 - self._counterfac_df2['group_composition']).rank(
-                        pct=True).sort_values(),
-                    label='Second Context Complementary Group Composition')
-
-                plt.legend()
-
-        if (plot_type == 'maps'):
-            if (str(type(self._df1)) !=
-                    '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                raise TypeError(
-                    'data is not a GeoDataFrame and, therefore, maps cannot be draw.'
-                )
-
-            # Subplots
-            fig, axs = plt.subplots(2, 2, figsize=(10, 10))
-
-            fig.suptitle(
-                'Spatial Component = {}, Attribute Component: {}'.format(
-                    round(self.c_s, 3), round(self.c_a, 3)),
-                size=20)
-            fig.subplots_adjust(hspace=1.25, wspace=0.2,
-                                top=0.95)  # hspace space between lines
-            fig.tight_layout(rect=[
-                0, 0, 1, 0.925
-            ])  # rect is to position the suptitle above the subplots
-
-            # Original First Context (Upper Left)
-            self._counterfac_df1.plot(column='group_composition',
-                                      cmap='OrRd',
-                                      legend=True,
-                                      ax=axs[0, 0])
-            axs[0, 0].title.set_text('Original First Context Composition')
-            axs[0, 0].axis('off')
-
-            # Counterfactual First Context (Bottom Left)
-            self._counterfac_df1.plot(column='counterfactual_composition',
-                                      cmap='OrRd',
-                                      legend=True,
-                                      ax=axs[1, 0])
-            axs[1, 0].title.set_text(
-                'Counterfactual First Context Composition')
-            axs[1, 0].axis('off')
-
-            # Counterfactual Second Context (Upper Right)
-            self._counterfac_df2.plot(column='counterfactual_composition',
-                                      cmap='OrRd',
-                                      legend=True,
-                                      ax=axs[0, 1])
-            axs[0, 1].title.set_text(
-                'Counterfactual Second Context Composition')
-            axs[0, 1].axis('off')
-
-            # Original Second Context (Bottom Right)
-            self._counterfac_df2.plot(column='group_composition',
-                                      cmap='OrRd',
-                                      legend=True,
-                                      ax=axs[1, 1])
-            axs[1, 1].title.set_text('Original Second Context Composition')
-            axs[1, 1].axis('off')

-
-
-
-
-
-
-
-
-# Deprecation Calls
-
-msg = _dep_message("Decompose_Segregation", "DecomposeSegregation")
-Decompose_Segregation = DeprecationHelper(DecomposeSegregation, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/inference/inference_wrappers.html b/docsrc/_build/html/_modules/segregation/inference/inference_wrappers.html deleted file mode 100644 index f414702d..00000000 --- a/docsrc/_build/html/_modules/segregation/inference/inference_wrappers.html +++ /dev/null @@ -1,880 +0,0 @@ - - - - - - - segregation.inference.inference_wrappers — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.inference.inference_wrappers

-"""
-Inference Wrappers for Segregation measures
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu> Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"
-
-import numpy as np
-import pandas as pd
-import geopandas as gpd
-import warnings
-from tqdm.auto import tqdm
-from segregation.util.util import _generate_counterfactual, _dep_message, DeprecationHelper
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = [
-    'Infer_Segregation', 'SingleValueTest', 'Compare_Segregation',
-    'TwoValueTest'
-]
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-
-def _infer_segregation(seg_class,
-                       iterations_under_null=500,
-                       null_approach="systematic",
-                       two_tailed=True,
-                       **kwargs):
-    '''
-    Perform inference for a single segregation measure
-
-    Parameters
-    ----------
-
-    seg_class                    : a PySAL segregation object
-    
-    iterations_under_null        : number of iterations under null hyphothesis
-    
-    null_approach : argument that specifies which type of null hypothesis the inference will iterate. Please take a look at Notes (1).
-    
-        "systematic"             : assumes that every group has the same probability with restricted conditional probabilities p_0_j = p_1_j = p_j = n_j/n (multinomial distribution).
-        "bootstrap"              : generates bootstrap replications of the units with replacement of the same size of the original data.
-        "evenness"               : assumes that each spatial unit has the same global probability of drawing elements from the minority group of the fixed total unit population (binomial distribution).
-        
-        "permutation"            : randomly allocates the units over space keeping the original values.
-        
-        "systematic_permutation" : assumes absence of systematic segregation and randomly allocates the units over space.
-        "even_permutation"       : assumes the same global probability of drawning elements from the minority group in each spatial unit and randomly allocates the units over space.
-    
-    two_tailed    : boolean. Please take a look at Notes (2).
-                    If True, p_value is two-tailed. Otherwise, it is right one-tailed.
-    
-    **kwargs      : customizable parameters to pass to the segregation measures. Usually they need to be the same input that the seg_class was built.
-    
-    Attributes
-    ----------
-
-    p_value     : float
-                  Pseudo One or Two-Tailed p-value estimated from the simulations
-    
-    est_sim     : numpy array
-                  Estimates of the segregation measure under the null hypothesis
-                  
-    statistic   : float
-                  The point estimation of the segregation measure that is under test
-                
-    Notes
-    -----
-    
-    1) The different approaches for the null hypothesis affect directly the results of the inference depending on the combination of the index type of seg_class and the null_approach chosen.
-    Therefore, the user needs to be aware of how these approaches are affecting the data generation process of the simulations in order to draw meaningful conclusions. 
-    For example, the Modified Dissimilarity (ModifiedDissim) and  Modified Gini (ModifiedGiniSeg) indexes, rely exactly on the distance between evenness through sampling which, therefore, the "evenness" value for null approach would not be the most appropriate for these indexes.
-    
-    2) The one-tailed p_value attribute might not be appropriate for some measures, as the two-tailed. Therefore, it is better to rely on the est_sim attribute.
-    
-    '''
-    if not null_approach in [
-            'systematic', 'bootstrap', 'evenness', 'permutation',
-            'systematic_permutation', 'even_permutation'
-    ]:
-        raise ValueError(
-            'null_approach must one of \'systematic\', \'bootstrap\', \'evenness\', \'permutation\', \'systematic_permutation\', \'even_permutation\''
-        )
-
-    if (type(two_tailed) is not bool):
-        raise TypeError('two_tailed is not a boolean object')
-
-    point_estimation = seg_class.statistic
-    data = seg_class.core_data.copy()
-
-    aux = str(type(seg_class))
-    _class_name = aux[1 + aux.rfind(
-        '.'):-2]  # 'rfind' finds the last occurence of a pattern in a string
-
-    Estimates_Stars = np.empty(iterations_under_null)
-
-    ##############
-    # SYSTEMATIC #
-    ##############
-    if (null_approach == "systematic"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-
-            data['other_group_pop'] = data['total_pop_var'] - data['group_pop_var']
-            p_j = data['total_pop_var'] / data['total_pop_var'].sum()
-    
-            # Group 0: minority group
-            p0_i = p_j
-            n0 = data['group_pop_var'].sum()
-            sim0 = np.random.multinomial(n0, p0_i, size=iterations_under_null)
-    
-            # Group 1: complement group
-            p1_i = p_j
-            n1 = data['other_group_pop'].sum()
-            sim1 = np.random.multinomial(n1, p1_i, size=iterations_under_null)
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    data_aux = {
-                        'simul_group': sim0[i].tolist(),
-                        'simul_tot': (sim0[i] + sim1[i]).tolist()
-                    }
-                    df_aux = pd.DataFrame.from_dict(data_aux)
-    
-                    if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                        df_aux = gpd.GeoDataFrame(df_aux)
-                        df_aux['geometry'] = data['geometry']
-    
-                    Estimates_Stars[i] = seg_class._function(df_aux, 'simul_group', 'simul_tot', **kwargs)[0]
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class))):
-            raise ValueError('Not implemented for MultiGroup indexes.')
-
-    #############
-    # BOOTSTRAP #
-    #############
-    if (null_approach == "bootstrap"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-    
-                    sample_index = np.random.choice(data.index, size=len(data), replace=True)
-                    df_aux = data.iloc[sample_index]
-                    Estimates_Stars[i] = seg_class._function(df_aux, 'group_pop_var', 'total_pop_var', **kwargs)[0]
-    
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-
-        if ('multigroup' in str(type(seg_class))):
-            
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-    
-                    sample_index = np.random.choice(data.index, size=len(data), replace=True)
-                    df_aux = data.iloc[sample_index]
-                    Estimates_Stars[i] = seg_class._function(df_aux, seg_class._groups, **kwargs)[0]
-    
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-
-    ############
-    # EVENNESS #
-    ############
-    if (null_approach == "evenness"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-
-            p_null = data['group_pop_var'].sum() / data['total_pop_var'].sum()
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    sim = np.random.binomial(n=np.array([data['total_pop_var'].tolist()]),p=p_null)
-                    data_aux = {
-                        'simul_group': sim[0],
-                        'simul_tot': data['total_pop_var'].tolist()
-                    }
-                    df_aux = pd.DataFrame.from_dict(data_aux)
-    
-                    if (str(type(data)) == '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                        df_aux = gpd.GeoDataFrame(df_aux)
-                        df_aux['geometry'] = data['geometry']
-    
-                    Estimates_Stars[i] = seg_class._function(df_aux, 'simul_group', 'simul_tot', **kwargs)[0]
-    
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class))):
-            
-            df = np.array(seg_class.core_data)
-            global_prob_vector = df.sum(axis = 0) / df.sum()
-            t = df.sum(axis = 1)
-            
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-            
-                    simul = map(lambda i: list(np.random.multinomial(i, global_prob_vector)), t)
-                    df_simul = pd.DataFrame(list(simul), columns = seg_class._groups)
-                    
-                    Estimates_Stars[i] = seg_class._function(df_simul, seg_class._groups, **kwargs)[0]
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-
-    ###############
-    # PERMUTATION #
-    ###############
-    if (null_approach == "permutation"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-
-            if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                raise TypeError('data is not a GeoDataFrame, therefore, this null approach does not apply.')
-    
-            with tqdm(total=iterations_under_null) as pbar:
-    
-                for i in np.array(range(iterations_under_null)):
-                    data = data.assign(geometry=data['geometry'][list(np.random.choice(data.shape[0], data.shape[0],replace=False))].reset_index()['geometry'])
-                    df_aux = data
-                    Estimates_Stars[i] = seg_class._function(df_aux, 'group_pop_var', 'total_pop_var', **kwargs)[0]
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class))):
-            raise ValueError('Not implemented for MultiGroup indexes.')
-            
-    ##########################
-    # SYSTEMATIC PERMUTATION #
-    ##########################
-    if (null_approach == "systematic_permutation"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-
-            if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                raise TypeError('data is not a GeoDataFrame, therefore, this null approach does not apply.')
-    
-            data['other_group_pop'] = data['total_pop_var'] - data['group_pop_var']
-            p_j = data['total_pop_var'] / data['total_pop_var'].sum()
-    
-            # Group 0: minority group
-            p0_i = p_j
-            n0 = data['group_pop_var'].sum()
-            sim0 = np.random.multinomial(n0, p0_i, size=iterations_under_null)
-    
-            # Group 1: complement group
-            p1_i = p_j
-            n1 = data['other_group_pop'].sum()
-            sim1 = np.random.multinomial(n1, p1_i, size=iterations_under_null)
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    data_aux = {
-                        'simul_group': sim0[i].tolist(),
-                        'simul_tot': (sim0[i] + sim1[i]).tolist()
-                    }
-                    df_aux = pd.DataFrame.from_dict(data_aux)
-                    df_aux = gpd.GeoDataFrame(df_aux)
-                    df_aux['geometry'] = data['geometry']
-                    df_aux = df_aux.assign(geometry=df_aux['geometry'][list(
-                        np.random.choice(
-                            df_aux.shape[0], df_aux.shape[0],
-                            replace=False))].reset_index()['geometry'])
-                    Estimates_Stars[i] = seg_class._function(
-                        df_aux, 'simul_group', 'simul_tot', **kwargs)[0]
-    
-                    pbar.set_description(
-                        'Processed {} iterations out of {}'.format(
-                            i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class))):
-            raise ValueError('Not implemented for MultiGroup indexes.')
-            
-    ########################
-    # EVENNESS PERMUTATION #
-    ########################
-    if (null_approach == "even_permutation"):
-        
-        if ('multigroup' not in str(type(seg_class))):
-
-            if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-                raise TypeError('data is not a GeoDataFrame, therefore, this null approach does not apply.')
-    
-            p_null = data['group_pop_var'].sum() / data['total_pop_var'].sum()
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    sim = np.random.binomial(n=np.array(
-                        [data['total_pop_var'].tolist()]),
-                                             p=p_null)
-                    data_aux = {
-                        'simul_group': sim[0],
-                        'simul_tot': data['total_pop_var'].tolist()
-                    }
-                    df_aux = pd.DataFrame.from_dict(data_aux)
-                    df_aux = gpd.GeoDataFrame(df_aux)
-                    df_aux['geometry'] = data['geometry']
-                    df_aux = df_aux.assign(geometry=df_aux['geometry'][list(
-                        np.random.choice(
-                            df_aux.shape[0], df_aux.shape[0],
-                            replace=False))].reset_index()['geometry'])
-                    Estimates_Stars[i] = seg_class._function(
-                        df_aux, 'simul_group', 'simul_tot', **kwargs)[0]
-                    pbar.set_description(
-                        'Processed {} iterations out of {}'.format(
-                            i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class))):
-            raise ValueError('Not implemented for MultiGroup indexes.')
-            
-    # Check and, if the case, remove iterations_under_null that resulted in nan or infinite values
-    if any((np.isinf(Estimates_Stars) | np.isnan(Estimates_Stars))):
-        warnings.warn(
-            'Some estimates resulted in NaN or infinite values for estimations under null hypothesis. These values will be removed for the final results.'
-        )
-        Estimates_Stars = Estimates_Stars[~(np.isinf(Estimates_Stars)
-                                            | np.isnan(Estimates_Stars))]
-
-    if not two_tailed:
-        p_value = sum(
-            Estimates_Stars > point_estimation) / iterations_under_null
-    else:
-        aux1 = (point_estimation < Estimates_Stars).sum()
-        aux2 = (point_estimation > Estimates_Stars).sum()
-        p_value = 2 * np.array([aux1, aux2]).min() / len(Estimates_Stars)
-
-    return p_value, Estimates_Stars, point_estimation, _class_name
-
-
-
[docs]class SingleValueTest:
-    '''
-    Perform inference for a single segregation measure
-
-    Parameters
-    ----------
-
-    seg_class                    : a PySAL segregation object
-    
-    iterations_under_null        : number of iterations under null hyphothesis
-    
-    null_approach : argument that specifies which type of null hypothesis the inference will iterate. Please take a look at Notes (1).
-    
-        "systematic"             : assumes that every group has the same probability with restricted conditional probabilities p_0_j = p_1_j = p_j = n_j/n (multinomial distribution).
-        "bootstrap"              : generates bootstrap replications of the units with replacement of the same size of the original data.
-        "evenness"               : assumes that each spatial unit has the same global probability of drawing elements from the minority group of the fixed total unit population (binomial distribution).
-        
-        "permutation"            : randomly allocates the units over space keeping the original values.
-        
-        "systematic_permutation" : assumes absence of systematic segregation and randomly allocates the units over space.
-        "even_permutation"       : assumes the same global probability of drawning elements from the minority group in each spatial unit and randomly allocates the units over space.
-    
-    two_tailed    : boolean. Please take a look at Notes (2).
-                    If True, p_value is two-tailed. Otherwise, it is right one-tailed.
-    
-    **kwargs      : customizable parameters to pass to the segregation measures. Usually they need to be the same input that the seg_class was built.
-    
-    Attributes
-    ----------
-
-    p_value     : float
-                  Pseudo One or Two-Tailed p-value estimated from the simulations
-    
-    est_sim     : numpy array
-                  Estimates of the segregation measure under the null hypothesis
-                  
-    statistic   : float
-                  The point estimation of the segregation measure that is under test
-                
-    Notes
-    -----
-    
-    1) The different approaches for the null hypothesis affect directly the results of the inference depending on the combination of the index type of seg_class and the null_approach chosen.
-    Therefore, the user needs to be aware of how these approaches are affecting the data generation process of the simulations in order to draw meaningful conclusions. 
-    For example, the Modified Dissimilarity (ModifiedDissim) and  Modified Gini (ModifiedGiniSeg) indexes, rely exactly on the distance between evenness through sampling which, therefore, the "evenness" value for null approach would not be the most appropriate for these indexes.
-    
-    2) The one-tailed p_value attribute might not be appropriate for some measures, as the two-tailed. Therefore, it is better to rely on the est_sim attribute.
-    
-    Examples
-    --------
-    Several examples can be found here https://github.com/pysal/segregation/blob/master/notebooks/inference_wrappers_example.ipynb.
-    
-    '''
-
-
[docs]    def __init__(self,
-                 seg_class,
-                 iterations_under_null=500,
-                 null_approach="systematic",
-                 two_tailed=True,
-                 **kwargs):
-
-        aux = _infer_segregation(seg_class, iterations_under_null,
-                                 null_approach, two_tailed, **kwargs)
-
-        self.p_value = aux[0]
-        self.est_sim = aux[1]
-        self.statistic = aux[2]
-        self._class_name = aux[3]

-
-    def plot(self, ax=None):
-        """
-        Plot the Infer_Segregation class
-        """
-        try:
-            import matplotlib.pyplot as plt
-            import seaborn as sns
-        except ImportError:
-            warnings.warn(
-                'This method relies on importing `matplotlib` and `seaborn`')
-
-        f = sns.distplot(self.est_sim,
-                         hist=True,
-                         color='darkblue',
-                         hist_kws={'edgecolor': 'black'},
-                         kde_kws={'linewidth': 2},
-                         ax=ax)
-        plt.axvline(self.statistic, color='red')
-        plt.title('{} (Value = {})'.format(self._class_name,
-                                           round(self.statistic, 3)))
-        return f

-
-
-def _compare_segregation(seg_class_1,
-                         seg_class_2,
-                         iterations_under_null=500,
-                         null_approach="random_label",
-                         **kwargs):
-    '''
-    Perform inference comparison for a two segregation measures
-
-    Parameters
-    ----------
-
-    seg_class_1           : a PySAL segregation object to be compared to seg_class_2
-    
-    seg_class_2           : a PySAL segregation object to be compared to seg_class_1
-    
-    iterations_under_null : number of iterations under null hyphothesis
-    
-    null_approach: argument that specifies which type of null hypothesis the inference will iterate.
-    
-        "random_label"               : random label the data in each iteration
-        
-        "counterfactual_composition" : randomizes the number of minority population according to both cumulative distribution function of a variable that represents the composition of the minority group. The composition is the division of the minority population of unit i divided by total population of tract i.
-
-        "counterfactual_share" : randomizes the number of minority population and total population according to both cumulative distribution function of a variable that represents the share of the minority group. The share is the division of the minority population of unit i divided by total population of minority population.
-        
-        "counterfactual_dual_composition" : applies the "counterfactual_composition" for both minority and complementary groups.
-
-    **kwargs : customizable parameters to pass to the segregation measures. Usually they need to be the same as both seg_class_1 and seg_class_2  was built.
-    
-    Attributes
-    ----------
-
-    p_value        : float
-                     Two-Tailed p-value
-    
-    est_sim        : numpy array
-                     Estimates of the segregation measure differences under the null hypothesis
-                  
-    est_point_diff : float
-                     Point estimation of the difference between the segregation measures
-                
-    Notes
-    -----
-    This function performs inference to compare two segregation measures. This can be either two measures of the same locations in two different points in time or it can be two different locations at the same point in time.
-    
-    The null hypothesis is H0: Segregation_1 is not different than Segregation_2.
-    
-    Based on Rey, Sergio J., and Myrna L. Sastré-Gutiérrez. "Interregional inequality dynamics in Mexico." Spatial Economic Analysis 5.3 (2010): 277-298.
-
-    '''
-
-    if not null_approach in [
-            'random_label', 'counterfactual_composition',
-            'counterfactual_share', 'counterfactual_dual_composition'
-    ]:
-        raise ValueError(
-            'null_approach must one of \'random_label\', \'counterfactual_composition\', \'counterfactual_share\', \'counterfactual_dual_composition\''
-        )
-
-    if (type(seg_class_1) != type(seg_class_2)):
-        raise TypeError('seg_class_1 and seg_class_2 must be the same type/class.')
-
-    point_estimation = seg_class_1.statistic - seg_class_2.statistic
-
-    aux = str(type(seg_class_1))
-    _class_name = aux[1 + aux.rfind('.'):-2]  # 'rfind' finds the last occurence of a pattern in a string
-
-    data_1 = seg_class_1.core_data.copy()
-    data_2 = seg_class_2.core_data.copy()
-    
-    est_sim = np.empty(iterations_under_null)
-
-    ################
-    # RANDOM LABEL #
-    ################
-    if (null_approach == "random_label"):
-        
-        data_1['grouping_variable'] = 'Group_1'
-        data_2['grouping_variable'] = 'Group_2'
-        
-        if ('multigroup' not in str(type(seg_class_1))):
-            
-            # This step is just to make sure the each frequecy column is integer for the approaches and from the same type in order to be able to stack them
-            data_1['group_pop_var'] = round(data_1['group_pop_var']).astype(int)
-            data_1['total_pop_var'] = round(data_1['total_pop_var']).astype(int)
-        
-            data_2['group_pop_var'] = round(data_2['group_pop_var']).astype(int)
-            data_2['total_pop_var'] = round(data_2['total_pop_var']).astype(int)
-            
-            stacked_data = pd.concat([data_1, data_2], ignore_index=True)
-    
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    
-                    stacked_data['grouping_variable'] = np.random.permutation(stacked_data['grouping_variable'])
-    
-                    stacked_data_1 = stacked_data.loc[stacked_data['grouping_variable'] == 'Group_1']
-                    stacked_data_2 = stacked_data.loc[stacked_data['grouping_variable'] == 'Group_2']
-    
-                    simulations_1 = seg_class_1._function(stacked_data_1,'group_pop_var','total_pop_var',**kwargs)[0]
-                    simulations_2 = seg_class_2._function(stacked_data_2,'group_pop_var','total_pop_var',**kwargs)[0]
-    
-                    est_sim[i] = simulations_1 - simulations_2
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-                    
-        if ('multigroup' in str(type(seg_class_1))):
-            
-            groups_list = seg_class_1._groups
-            
-            for i in range(len(groups_list)):
-                data_1[groups_list[i]] = round(data_1[groups_list[i]]).astype(int)
-                data_2[groups_list[i]] = round(data_2[groups_list[i]]).astype(int)
-            
-            if (seg_class_1._groups != seg_class_2._groups):
-                raise ValueError('MultiGroup groups should be the same')
-                
-            stacked_data = pd.concat([data_1, data_2], ignore_index=True)
-            
-            with tqdm(total=iterations_under_null) as pbar:
-                for i in np.array(range(iterations_under_null)):
-                    
-                    stacked_data['grouping_variable'] = np.random.permutation(stacked_data['grouping_variable'])
-    
-                    stacked_data_1 = stacked_data.loc[stacked_data['grouping_variable'] == 'Group_1']
-                    stacked_data_2 = stacked_data.loc[stacked_data['grouping_variable'] == 'Group_2']
-    
-                    simulations_1 = seg_class_1._function(stacked_data_1, groups_list, **kwargs)[0]
-                    simulations_2 = seg_class_2._function(stacked_data_2, groups_list, **kwargs)[0]
-    
-                    est_sim[i] = simulations_1 - simulations_2
-                    pbar.set_description('Processed {} iterations out of {}'.format(i + 1, iterations_under_null))
-                    pbar.update(1)
-    
-    
-    ##############################
-    # COUNTERFACTUAL COMPOSITION #
-    ##############################
-    if (null_approach in ['counterfactual_composition', 'counterfactual_share','counterfactual_dual_composition']):
-        
-        if ('multigroup' in str(type(seg_class_1))):
-            raise ValueError('Not implemented for MultiGroup indexes.')
-
-        internal_arg = null_approach[15:]  # Remove 'counterfactual_' from the beginning of the string
-
-        counterfac_df1, counterfac_df2 = _generate_counterfactual(
-            data_1,
-            data_2,
-            'group_pop_var',
-            'total_pop_var',
-            counterfactual_approach=internal_arg)
-
-        if (null_approach in [
-                'counterfactual_share', 'counterfactual_dual_composition'
-        ]):
-            data_1['total_pop_var'] = counterfac_df1[
-                'counterfactual_total_pop']
-            data_2['total_pop_var'] = counterfac_df2[
-                'counterfactual_total_pop']
-        with tqdm(total=iterations_under_null) as pbar:
-            for i in np.array(range(iterations_under_null)):
-
-                data_1['fair_coin'] = np.random.uniform(size=len(data_1))
-                data_1['test_group_pop_var'] = np.where(
-                    data_1['fair_coin'] > 0.5, data_1['group_pop_var'],
-                    counterfac_df1['counterfactual_group_pop'])
-
-                # Dropping to avoid confusion in the internal function
-                data_1_test = data_1.drop(['group_pop_var'], axis=1)
-
-                simulations_1 = seg_class_1._function(data_1_test,
-                                                      'test_group_pop_var',
-                                                      'total_pop_var',
-                                                      **kwargs)[0]
-
-                # Dropping to avoid confusion in the next iteration
-                data_1 = data_1.drop(['fair_coin', 'test_group_pop_var'],
-                                     axis=1)
-
-                data_2['fair_coin'] = np.random.uniform(size=len(data_2))
-                data_2['test_group_pop_var'] = np.where(
-                    data_2['fair_coin'] > 0.5, data_2['group_pop_var'],
-                    counterfac_df2['counterfactual_group_pop'])
-
-                # Dropping to avoid confusion in the internal function
-                data_2_test = data_2.drop(['group_pop_var'], axis=1)
-
-                simulations_2 = seg_class_2._function(data_2_test,
-                                                      'test_group_pop_var',
-                                                      'total_pop_var',
-                                                      **kwargs)[0]
-
-                # Dropping to avoid confusion in the next iteration
-                data_2 = data_2.drop(['fair_coin', 'test_group_pop_var'],
-                                     axis=1)
-
-                est_sim[i] = simulations_1 - simulations_2
-
-                pbar.set_description(
-                    'Processed {} iterations out of {}'.format(
-                        i + 1, iterations_under_null))
-                pbar.update(1)
-
-    # Check and, if the case, remove iterations_under_null that resulted in nan or infinite values
-    if any((np.isinf(est_sim) | np.isnan(est_sim))):
-        warnings.warn(
-            'Some estimates resulted in NaN or infinite values for estimations under null hypothesis. These values will be removed for the final results.'
-        )
-        est_sim = est_sim[~(np.isinf(est_sim) | np.isnan(est_sim))]
-
-    # Two-Tailed p-value
-    # Obs.: the null distribution can be located far from zero. Therefore, this is the the appropriate way to calculate the two tailed p-value.
-    aux1 = (point_estimation < est_sim).sum()
-    aux2 = (point_estimation > est_sim).sum()
-    p_value = 2 * np.array([aux1, aux2]).min() / len(est_sim)
-
-    return p_value, est_sim, point_estimation, _class_name
-
-
-
[docs]class TwoValueTest:
-    '''
-    Perform inference comparison for a two segregation measures
-
-    Parameters
-    ----------
-
-    seg_class_1           : a PySAL segregation object to be compared to seg_class_2
-    
-    seg_class_2           : a PySAL segregation object to be compared to seg_class_1
-    
-    iterations_under_null : number of iterations under null hyphothesis
-    
-    null_approach : argument that specifies which type of null hypothesis the inference will iterate.
-    
-        "random_label"      : random label the data in each iteration
-        
-        "counterfactual_composition" : randomizes the number of minority population according to both cumulative distribution function of a variable that represents the composition of the minority group. The composition is the division of the minority population of unit i divided by total population of tract i.
-
-        "counterfactual_share" : randomizes the number of minority population and total population according to both cumulative distribution function of a variable that represents the share of the minority group. The share is the division of the minority population of unit i divided by total population of minority population.
-        
-        "counterfactual_dual_composition" : applies the "counterfactual_composition" for both minority and complementary groups.
-
-    **kwargs : customizable parameters to pass to the segregation measures. Usually they need to be the same as both seg_class_1 and seg_class_2  was built.
-    
-    Attributes
-    ----------
-
-    p_value        : float
-                     Two-Tailed p-value
-    
-    est_sim        : numpy array
-                     Estimates of the segregation measure differences under the null hypothesis
-                  
-    est_point_diff : float
-                     Point estimation of the difference between the segregation measures
-                
-    Notes
-    -----
-    This function performs inference to compare two segregation measures. This can be either two measures of the same locations in two different points in time or it can be two different locations at the same point in time.
-    
-    The null hypothesis is H0: Segregation_1 is not different than Segregation_2.
-    
-    Based on Rey, Sergio J., and Myrna L. Sastré-Gutiérrez. "Interregional inequality dynamics in Mexico." Spatial Economic Analysis 5.3 (2010): 277-298.
-    
-    Examples
-    --------
-    Several examples can be found here https://github.com/pysal/segregation/blob/master/notebooks/inference_wrappers_example.ipynb.
-
-    '''
-
-
[docs]    def __init__(self,
-                 seg_class_1,
-                 seg_class_2,
-                 iterations_under_null=500,
-                 null_approach="random_label",
-                 **kwargs):
-
-        aux = _compare_segregation(seg_class_1, seg_class_2,
-                                   iterations_under_null, null_approach,
-                                   **kwargs)
-
-        self.p_value = aux[0]
-        self.est_sim = aux[1]
-        self.est_point_diff = aux[2]
-        self._class_name = aux[3]

-
-    def plot(self, ax=None):
-        """
-        Plot the Compare_Segregation class
-        """
-        try:
-            import matplotlib.pyplot as plt
-            import seaborn as sns
-        except ImportError:
-            warnings.warn(
-                'This method relies on importing `matplotlib` and `seaborn`')
-
-        f = sns.distplot(self.est_sim,
-                         hist=True,
-                         color='darkblue',
-                         hist_kws={'edgecolor': 'black'},
-                         kde_kws={'linewidth': 2},
-                         ax=ax)
-        plt.axvline(self.est_point_diff, color='red')
-        plt.title('{} (Diff. value = {})'.format(self._class_name,
-                                                 round(self.est_point_diff,
-                                                       3)))
-        return f

-
-
-# Deprecation Calls
-
-msg = _dep_message("Infer_Segregation", "SingleValueTest")
-Infer_Segregation = DeprecationHelper(SingleValueTest, message=msg)
-
-msg = _dep_message("Compare_Segregation", "TwoValueTest")
-Compare_Segregation = DeprecationHelper(TwoValueTest, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/local/local_indexes.html b/docsrc/_build/html/_modules/segregation/local/local_indexes.html deleted file mode 100644 index dce60cbb..00000000 --- a/docsrc/_build/html/_modules/segregation/local/local_indexes.html +++ /dev/null @@ -1,879 +0,0 @@ - - - - - - - segregation.local.local_indexes — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.local.local_indexes

-"""
-Local based Segregation Metrics
-
-Important: all classes that start with "Multi_" expects a specific type of input of multigroups since the index will be calculated using many groups.
-On the other hand, other classes expects a single group for calculation of the metrics.
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu>, Elijah Knaap <elijah.knaap@ucr.edu> and Sergio J. Rey <sergio.rey@ucr.edu>"
-
-import numpy as np
-import libpysal as lps
-
-from segregation.spatial import RelativeCentralization
-
-from segregation.util.util import _dep_message, DeprecationHelper
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = [
-    'Multi_Location_Quotient',
-    'MultiLocationQuotient',
-    
-    'Multi_Local_Diversity',
-    'MultiLocalDiversity',
-    
-    'Multi_Local_Entropy',
-    'MultiLocalEntropy',
-    
-    'Multi_Local_Simpson_Interaction',
-    'MultiLocalSimpsonInteraction',
-    
-    'Multi_Local_Simpson_Concentration',
-    'MultiLocalSimpsonConcentration',
-    
-    'Local_Relative_Centralization',
-    'LocalRelativeCentralization'
-]
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-
-
-
-# suppress numpy divide by zero warnings because it occurs a lot during the
-# calculation of many indices
-np.seterr(divide='ignore', invalid='ignore')
-
-def _multi_location_quotient(data, groups):
-    """
-    Calculation of Location Quotient index for each group and unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings of length k.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistics : np.array(n,k)
-                 Location Quotient values for each group and unit.
-                 Column k has the Location Quotient of position k in groups.
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Isard, Walter. Methods of regional analysis. Vol. 4. Рипол Классик, 1967.
-    
-    Reference: :cite:`isard1967methods`.
-
-    """
-    
-    core_data = data[groups]
-    
-    df = np.array(core_data)
-    
-    n = df.shape[0]
-    K = df.shape[1]
-    
-    T = df.sum()
-    
-    ti = df.sum(axis = 1)
-    Xk = df.sum(axis = 0)
-    
-    multi_LQ = (df / np.repeat(ti, K, axis = 0).reshape(n,K)) / (Xk / T)
-    
-    return multi_LQ, core_data
-
-
-
[docs]class MultiLocationQuotient:
-    """
-    Calculation of Location Quotient index for each group and unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings of length k.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistics : np.array(n,k)
-                 Location Quotient values for each group and unit.
-                 Column k has the Location Quotient of position k in groups.
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-                 
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import MultiLocationQuotient
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiLocationQuotient(input_df, groups_list)
-    >>> index.statistics[0:3,0:3]
-    array([[1.36543221, 0.07478049, 0.16245651],
-           [1.18002164, 0.        , 0.14836683],
-           [0.68072696, 0.03534425, 0.        ]])
-
-    Important to note that column k has the Location Quotient (LQ) of position k in groups. Therefore, the LQ of the first unit of 'WHITE_' is 1.36543221.
-    
-    Notes
-    -----
-    Based on Isard, Walter. Methods of regional analysis. Vol. 4. Рипол Классик, 1967.
-    
-    Reference: :cite:`isard1967methods`.
-
-    """
-    
-
[docs]    def __init__(self, data, groups):
-        
-        aux = _multi_location_quotient(data, groups)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _multi_location_quotient

-        
-        
-        
-
-def _multi_local_diversity(data, groups):
-    """
-    Calculation of Local Diversity index for each group and unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings of length k.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistics : np.array(n,k)
-                 Local Diversity values for each group and unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Theil, Henry. Statistical decomposition analysis; with applications in the social and administrative sciences. No. 04; HA33, T4.. 1972.
-    
-    Reference: :cite:`theil1972statistical`.
-
-    """
-    
-    core_data = data[groups]
-    
-    df = np.array(core_data)
-    
-    ti = df.sum(axis = 1)
-    pik = df/ti[:,None]
-    
-    multi_LD = - np.nansum(pik * np.log(pik), axis = 1)
-    
-    return multi_LD, core_data
-
-
-
[docs]class MultiLocalDiversity:
-    """
-    Calculation of Local Diversity index for each group and unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings of length k.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistics : np.array(n,k)
-                 Local Diversity values for each group and unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-                 
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import MultiLocalDiversity
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiLocalDiversity(input_df, groups_list)
-    >>> index.statistics[0:10] # Values of first 10 units
-    array([0.34332326, 0.56109229, 0.70563225, 0.29713472, 0.22386084,
-           0.29742517, 0.12322789, 0.11274579, 0.09402405, 0.25129616])
-
-    Notes
-    -----
-    Based on Theil, Henry. Statistical decomposition analysis; with applications in the social and administrative sciences. No. 04; HA33, T4.. 1972.
-    
-    Reference: :cite:`theil1972statistical`.
-
-    """
-    
-
[docs]    def __init__(self, data, groups):
-        
-        aux = _multi_local_diversity(data, groups)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _multi_local_diversity

-        
-        
-def _multi_local_entropy(data, groups):
-    """
-    Calculation of Local Entropy index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistics : np.array(n)
-                 Local Entropy values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Eq. 6 of pg. 139 (individual unit case) of Reardon, Sean F., and David O’Sullivan. "Measures of spatial segregation." Sociological methodology 34.1 (2004): 121-162.
-    
-    Reference: :cite:`reardon2004measures`.
-
-    """
-    
-    core_data = data[groups]
-    
-    df = np.array(core_data)
-    
-    K = df.shape[1]
-    
-    ti = df.sum(axis = 1)
-    pik = df/ti[:,None]
-    
-    multi_LE = - np.nansum((pik * np.log(pik)) / np.log(K), axis = 1)
-    
-    return multi_LE, core_data
-
-
-
[docs]class MultiLocalEntropy:
-    """
-    Calculation of Local Entropy index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistics : np.array(n)
-                 Local Entropy values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import MultiLocalEntropy
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiLocalEntropy(input_df, groups_list)
-    >>> index.statistics[0:10] # Values of first 10 units
-    array([0.24765538, 0.40474253, 0.50900607, 0.21433739, 0.16148146,
-           0.21454691, 0.08889013, 0.08132889, 0.06782401, 0.18127186])
-
-    Notes
-    -----
-    Based on Eq. 6 of pg. 139 (individual unit case) of Reardon, Sean F., and David O’Sullivan. "Measures of spatial segregation." Sociological methodology 34.1 (2004): 121-162.
-    
-    Reference: :cite:`reardon2004measures`.
-
-    """
-    
-
[docs]    def __init__(self, data, groups):
-        
-        aux = _multi_local_entropy(data, groups)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _multi_local_entropy

-        
-
-
-def _multi_local_simpson_interaction(data, groups):
-    """
-    Calculation of Local Simpson Interaction index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistics : np.array(n)
-                 Local Simpson Interaction values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on the local version of Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's interaction index can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-    
-    core_data = data[groups]
-    
-    df = np.array(core_data)
-    
-    ti = df.sum(axis = 1)
-    pik = df/ti[:,None]
-    
-    local_SI = np.nansum(pik * (1 - pik), axis = 1)
-    
-    return local_SI, core_data
-
-
-
[docs]class MultiLocalSimpsonInteraction:
-    """
-    Calculation of Local Simpson Interaction index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistics : np.array(n)
-                 Local Simpson Interaction values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import MultiLocalSimpsonInteraction
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiLocalSimpsonInteraction(input_df, groups_list)
-    >>> index.statistics[0:10] # Values of first 10 units
-    array([0.15435993, 0.33391595, 0.49909747, 0.1299449 , 0.09805056,
-           0.13128178, 0.04447356, 0.0398933 , 0.03723054, 0.11758548])
-
-    Notes
-    -----
-    Based on the local version of Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's interaction index can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-    
-
[docs]    def __init__(self, data, groups):
-        
-        aux = _multi_local_simpson_interaction(data, groups)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _multi_local_simpson_interaction

-        
-        
-def _multi_local_simpson_concentration(data, groups):
-    """
-    Calculation of Local Simpson concentration index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Returns
-    -------
-
-    statistics : np.array(n)
-                 Local Simpson concentration values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on the local version of Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's concentration index can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-    
-    core_data = data[groups]
-    
-    df = np.array(core_data)
-    
-    ti = df.sum(axis = 1)
-    pik = df/ti[:,None]
-    
-    local_SC = np.nansum(pik * pik, axis = 1)
-    
-    return local_SC, core_data
-
-
-
[docs]class MultiLocalSimpsonConcentration:
-    """
-    Calculation of Local Simpson concentration index for each unit
-
-    Parameters
-    ----------
-
-    data   : a pandas DataFrame of n rows
-    
-    groups : list of strings.
-             The variables names in data of the groups of interest of the analysis.
-
-    Attributes
-    ----------
-
-    statistics : np.array(n)
-                 Local Simpson concentration values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The groups of interest are White, Black, Asian and Hispanic population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import MultiLocalSimpsonConcentration
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    >>> groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
-    
-    The value is estimated below.
-    
-    >>> index = MultiLocalSimpsonConcentration(input_df, groups_list)
-    >>> index.statistics[0:10] # Values of first 10 units
-    array([0.84564007, 0.66608405, 0.50090253, 0.8700551 , 0.90194944,
-           0.86871822, 0.95552644, 0.9601067 , 0.96276946, 0.88241452])
-
-    Notes
-    -----
-    Based on the local version of Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. "Measures of multigroup segregation." Sociological methodology 32.1 (2002): 33-67.
-    
-    Simpson's concentration index can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to belong to the same group.
-
-    Higher values means lesser segregation.
-    
-    Simpson's Concentration + Simpson's Interaction = 1
-    
-    Reference: :cite:`reardon2002measures`.
-
-    """
-    
-
[docs]    def __init__(self, data, groups):
-        
-        aux = _multi_local_simpson_concentration(data, groups)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _multi_local_simpson_concentration

-        
-        
-def _local_relative_centralization(data, group_pop_var, total_pop_var, k_neigh = 5):
-    """
-    Calculation of Local Relative Centralization index for each unit
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    k_neigh       : integer greater than 0. Default is 5.
-                    Number of assumed neighbors for local context (it uses k-nearest algorithm method)
-                    
-    Returns
-    -------
-
-    statistics : np.array(n)
-                 Local Relative Centralization values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Folch, David C., and Sergio J. Rey. "The centralization index: A measure of local spatial segregation." Papers in Regional Science 95.3 (2016): 555-576.
-    
-    Reference: :cite:`folch2016centralization`.
-    """
-    
-    data = data.copy()
-    
-    core_data = data[[group_pop_var, total_pop_var, 'geometry']]
-
-    c_lons = data.centroid.map(lambda p: p.x)
-    c_lats = data.centroid.map(lambda p: p.y)
-    
-    points = list(zip(c_lons, c_lats))
-    kd = lps.cg.kdtree.KDTree(np.array(points))
-    wnnk = lps.weights.KNN(kd, k = k_neigh)
-    
-    local_RCEs = np.empty(len(data))
-    
-    for i in range(len(data)):
-    
-        x = list(wnnk.neighbors.values())[i]
-        x.append(list(wnnk.neighbors.keys())[i]) # Append in the end the current unit i inside the local context
-
-        local_data = data.iloc[x,:].copy()
-        
-        # The center is given by the last position (i.e. the current unit i)
-        local_RCE = RelativeCentralization(local_data, group_pop_var, total_pop_var, center = len(local_data) - 1)
-        
-        local_RCEs[i] = local_RCE.statistic
-        
-    return local_RCEs, core_data
-
-
-
[docs]class LocalRelativeCentralization:
-    """
-    Calculation of Local Relative Centralization index for each unit
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-    
-    k_neigh       : integer greater than 0. Default is 5.
-                    Number of assumed neighbors for local context (it uses k-nearest algorithm method)
-                    
-    Returns
-    -------
-
-    statistics : np.array(n)
-                 Local Relative Centralization values for each unit
-                
-    core_data  : a pandas DataFrame
-                 A pandas DataFrame that contains the columns used to perform the estimate.
-                 
-    Examples
-    --------
-    In this example, we are going to use 2000 Census Tract Data for Sacramento MSA, CA. The group of interest is Black population.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import libpysal
-    >>> import geopandas as gpd
-    >>> from segregation.local import LocalRelativeCentralization
-    
-    Then, we read the data and create an auxiliary list with only the necessary columns for fitting the index.
-    
-    >>> input_df = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
-    
-    The value is estimated below.
-    
-    >>> index = LocalRelativeCentralization(input_df, 'BLACK_', 'TOT_POP')
-    >>> index.statistics[0:10] # Values of first 10 units
-    array([ 0.03443055, -0.29063264, -0.19110976,  0.24978919,  0.01252249,
-            0.61152941,  0.78917647,  0.53129412,  0.04436346, -0.20216325])
-
-    Notes
-    -----
-    Based on Folch, David C., and Sergio J. Rey. "The centralization index: A measure of local spatial segregation." Papers in Regional Science 95.3 (2016): 555-576.
-    
-    Reference: :cite:`folch2016centralization`.
-    """
-    
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, k_neigh = 5):
-        
-        aux = _local_relative_centralization(data, group_pop_var, total_pop_var, k_neigh)
-
-        self.statistics = aux[0]
-        self.core_data  = aux[1]
-        self._function  = _local_relative_centralization

-        
-        
-        
-
-
-
-
-# Deprecation Calls
-
-msg = _dep_message("Multi_Location_Quotient", "MultiLocationQuotient")
-Multi_Location_Quotient = DeprecationHelper(MultiLocationQuotient, message=msg)
-
-msg = _dep_message("Multi_Local_Diversity", "MultiLocalDiversity")
-Multi_Local_Diversity = DeprecationHelper(MultiLocalDiversity, message=msg)
-
-msg = _dep_message("Multi_Local_Entropy", "MultiLocalEntropy")
-Multi_Local_Entropy = DeprecationHelper(MultiLocalEntropy, message=msg)
-
-msg = _dep_message("Multi_Local_Simpson_Interaction", "MultiLocalSimpsonInteraction")
-Multi_Local_Simpson_Interaction = DeprecationHelper(MultiLocalSimpsonInteraction, message=msg)
-
-msg = _dep_message("Multi_Local_Simpson_Concentration", "MultiLocalSimpsonConcentration")
-Multi_Local_Simpson_Concentration = DeprecationHelper(MultiLocalSimpsonConcentration, message=msg)
-
-msg = _dep_message("Local_Relative_Centralization", "LocalRelativeCentralization")
-Local_Relative_Centralization = DeprecationHelper(LocalRelativeCentralization, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/network/network.html b/docsrc/_build/html/_modules/segregation/network/network.html deleted file mode 100644 index 5dfcc57b..00000000 --- a/docsrc/_build/html/_modules/segregation/network/network.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - segregation.network.network — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.network.network

-"""Calculate street network-based segregation measures."""
-
-__author__ = "Elijah Knaap <elijah.knaap@ucr.edu> Renan X. Cortes <renanc@ucr.edu> and Sergio J. Rey <sergio.rey@ucr.edu>"
-
-import numpy as np
-import pandas as pd
-from warnings import warn
-from segregation.util import project_gdf
-import os
-import sys
-
-
-# This class allows us to hide the diagnostic messages from urbanaccess if the `quiet` flag is set
-class _HiddenPrints:  # from https://stackoverflow.com/questions/8391411/suppress-calls-to-print-python
-    def __enter__(self):
-        self._original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        sys.stdout.close()
-        sys.stdout = self._original_stdout
-
-
-
[docs]def get_osm_network(geodataframe, maxdist=5000, quiet=True, **kwargs):
-    """Download a street network from OSM.
-
-    Parameters
-    ----------
-    geodataframe : geopandas.GeoDataFrame
-        geopandas.GeoDataFrame of the study area.
-        Coordinate system should be in WGS84
-    maxdist : int
-        Total distance (in meters) of the network queries you may need.
-        This is used to buffer the network to ensure theres enough to satisfy
-        your largest query, otherwise there may be edge effects.
-    quiet: bool
-        If True, diagnostic messages from urbanaccess will be suppressed
-    **kwargs : dict
-        additional kwargs passed through to
-        urbanaccess.ua_network_from_bbox
-
-    Returns
-    -------
-    pandana.Network
-        A pandana Network instance for use in accessibility calculations or
-        spatial segregation measures that include a distance decay
-
-    """
-    try:
-        import pandana as pdna
-        from urbanaccess.osm.load import ua_network_from_bbox
-    except ImportError:
-        raise ImportError(
-            "You need pandana and urbanaccess to work with segregation's network module\n"
-            "You can install them with  `pip install urbanaccess pandana` "
-            "or `conda install -c udst pandana urbanaccess`")
-
-    gdf = geodataframe.copy()
-    gdf = project_gdf(gdf)
-    gdf = gdf.buffer(maxdist)
-    bounds = gdf.to_crs(epsg=4326).total_bounds
-
-    if quiet:
-        print('Downloading data from OSM. This may take awhile.')
-        with _HiddenPrints():
-            net = ua_network_from_bbox(bounds[1], bounds[0], bounds[3],
-                                       bounds[2], **kwargs)
-    else:
-        net = ua_network_from_bbox(bounds[1], bounds[0], bounds[3], bounds[2],
-                                   **kwargs)
-    print("Building network")
-    network = pdna.Network(net[0]["x"], net[0]["y"], net[1]["from"],
-                           net[1]["to"], net[1][["distance"]])
-
-    return network

-
-
-
[docs]def calc_access(geodataframe,
-                network,
-                distance=2000,
-                decay="linear",
-                variables=None,
-                precompute=True):
-    """Calculate access to population groups.
-
-    Parameters
-    ----------
-    geodataframe : geopandas.GeoDataFrame
-        geodataframe with demographic data
-    network : pandana.Network
-        pandana.Network instance. This is likely created with `get_osm_network`
-        or via helper functions from OSMnet or UrbanAccess.
-    distance : int
-        maximum distance to consider `accessible` (the default is 2000).
-    decay : str
-        decay type pandana should use "linear", "exp", or "flat"
-        (which means no decay). The default is "linear".
-    variables : list
-        list of variable names present on gdf that should be calculated
-    precompute: bool (default True)
-        whether pandana should precompute the distance matrix. It can only be
-        precomputed once, so If you plan to pass the same network to this
-        function several times, you should set precompute=False for later runs
-
-    Returns
-    -------
-    pandas.DataFrame
-        DataFrame with two columns, `total_population` and `group_population`
-        which represent the total number of each group that can be reached
-        within the supplied `distance` parameter. The DataFrame is indexed
-        on node_ids
-
-    """
-    if precompute:
-        network.precompute(distance)
-
-    geodataframe["node_ids"] = network.get_node_ids(geodataframe.centroid.x,
-                                                    geodataframe.centroid.y)
-
-    access = []
-    for variable in variables:
-        network.set(geodataframe.node_ids,
-                    variable=geodataframe[variable],
-                    name=variable)
-
-        access_pop = network.aggregate(distance,
-                                       type="sum",
-                                       decay=decay,
-                                       name=variable)
-
-        access.append(access_pop)
-    names = ["acc_" + variable for variable in variables]
-    access = pd.DataFrame(dict(zip(names, access)))
-
-    return access

-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_modules/segregation/spatial/spatial_indexes.html b/docsrc/_build/html/_modules/segregation/spatial/spatial_indexes.html deleted file mode 100644 index c388dd80..00000000 --- a/docsrc/_build/html/_modules/segregation/spatial/spatial_indexes.html +++ /dev/null @@ -1,3664 +0,0 @@ - - - - - - - segregation.spatial.spatial_indexes — segregation v1.3.0 Manual - - - - - - - - - - - - - - - - - - - - - - - - -
-
-
- -

Source code for segregation.spatial.spatial_indexes

-"""
-Spatial based Segregation Metrics
-"""
-
-__author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"
-
-import numpy as np
-import pandas as pd
-import geopandas as gpd
-import warnings
-import libpysal
-
-from libpysal.weights import Queen, Kernel, lag_spatial
-from libpysal.weights.util import fill_diagonal
-from numpy import inf
-from sklearn.metrics.pairwise import manhattan_distances, euclidean_distances, haversine_distances
-from scipy.ndimage.interpolation import shift
-
-from scipy.sparse.csgraph import floyd_warshall
-from scipy.sparse import csr_matrix
-
-from segregation.aspatial.aspatial_indexes import _dissim, MinMax
-from segregation.aspatial.multigroup_aspatial_indexes import MultiInformationTheory, MultiDivergence
-from segregation.network import calc_access
-from libpysal.weights.util import attach_islands
-
-from segregation.util.util import _dep_message, DeprecationHelper, _nan_handle
-
-# Including old and new api in __all__ so users can use both
-
-__all__ = [
-
-    'Spatial_Prox_Prof', 
-    'SpatialProxProf',
-    
-    'Spatial_Dissim', 
-    'SpatialDissim',
-    
-    'Boundary_Spatial_Dissim',
-    'BoundarySpatialDissim',
-    
-    'Perimeter_Area_Ratio_Spatial_Dissim', 
-    'PerimeterAreaRatioSpatialDissim',
-    
-    'SpatialMinMax',
-    
-    'Distance_Decay_Isolation',
-    'DistanceDecayIsolation',
-    
-    'Distance_Decay_Exposure', 
-    'DistanceDecayExposure', 
-    
-    'Spatial_Proximity', 
-    'SpatialProximity',
-    
-    'Absolute_Clustering',
-    'AbsoluteClustering',
-    
-    'Relative_Clustering', 
-    'RelativeClustering', 
-    
-    'Delta', 
-    
-    'Absolute_Concentration',
-    'AbsoluteConcentration',
-    
-    'Relative_Concentration', 
-    'RelativeConcentration', 
-    
-    'Absolute_Centralization',
-    'AbsoluteCentralization',
-    
-    'Relative_Centralization', 
-    'RelativeCentralization', 
-    
-    'SpatialInformationTheory',
-    'SpatialDivergence',
-
-    'compute_segregation_profile'
-]
-
-# The Deprecation calls of the classes are located in the end of this script #
-
-# suppress numpy divide by zero warnings because it occurs a lot during the
-# calculation of many indices
-np.seterr(divide='ignore', invalid='ignore')
-
-
-def _build_local_environment(data, groups, w):
-    """Convert observations into spatially-weighted sums.
-
-    Parameters
-    ----------
-    data : DataFrame
-        dataframe with local observations
-    w : libpysal.weights object
-        weights matrix defining the local environment
-
-    Returns
-    -------
-    DataFrame
-        Spatialized data
-
-    """
-    new_data = []
-    w = fill_diagonal(w)
-    for y in data[groups]:
-        new_data.append(lag_spatial(w, data[y]))
-    new_data = pd.DataFrame(dict(zip(groups, new_data)))
-    return new_data
-
-
-def _return_length_weighted_w(data):
-    """
-    Returns a PySAL weights object that the weights represent the length of the common boundary of two areal units that share border.
-    Author: Levi Wolf <levi.john.wolf@gmail.com>. 
-    Thank you, Levi!
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a 'geometry' column.
-
-    Notes
-    -----
-    Currently it's not making any projection.
-
-    """
-
-    w = libpysal.weights.Rook.from_dataframe(
-        data, ids=data.index.tolist(), geom_col=data._geometry_column_name)
-
-    if (len(w.islands) == 0):
-        w = w
-    else:
-        warnings('There are some islands in the GeoDataFrame.')
-        w_aux = libpysal.weights.KNN.from_dataframe(
-            data,
-            ids=data.index.tolist(),
-            geom_col=data._geometry_column_name,
-            k=1)
-        w = attach_islands(w, w_aux)
-
-    adjlist = w.to_adjlist()
-    islands = pd.DataFrame.from_records([{
-        'focal': island,
-        'neighbor': island,
-        'weight': 0
-    } for island in w.islands])
-    merged = adjlist.merge(data.geometry.to_frame('geometry'), left_on='focal',
-                           right_index=True, how='left')\
-                    .merge(data.geometry.to_frame('geometry'), left_on='neighbor',
-                           right_index=True, how='left', suffixes=("_focal", "_neighbor"))\
-
-    # Transforming from pandas to geopandas
-    merged = gpd.GeoDataFrame(merged, geometry='geometry_focal')
-    merged['geometry_neighbor'] = gpd.GeoSeries(merged.geometry_neighbor)
-
-    # Getting the shared boundaries
-    merged['shared_boundary'] = merged.geometry_focal.intersection(
-        merged.set_geometry('geometry_neighbor'))
-
-    # Putting it back to a matrix
-    merged['weight'] = merged.set_geometry('shared_boundary').length
-    merged_with_islands = pd.concat((merged, islands))
-    length_weighted_w = libpysal.weights.W.from_adjlist(
-        merged_with_islands[['focal', 'neighbor', 'weight']])
-    for island in w.islands:
-        length_weighted_w.neighbors[island] = []
-        del length_weighted_w.weights[island]
-
-    length_weighted_w._reset()
-
-    return length_weighted_w
-
-
-def _spatial_prox_profile(data, group_pop_var, total_pop_var, m=1000):
-    """
-    Calculation of Spatial Proximity Profile
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    m             : int
-                    a numeric value indicating the number of thresholds to be used. Default value is 1000. 
-                    A large value of m creates a smoother-looking graph and a more precise spatial proximity profile value but slows down the calculation speed.
-
-    Returns
-    ----------
-
-    statistic : float
-                Spatial Proximity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Hong, Seong-Yun, and Yukio Sadahiro. "Measuring geographic segregation: a graph-based approach." Journal of Geographical Systems 16.2 (2014): 211-231.
-    
-    Reference: :cite:`hong2014measuring`.
-
-    """
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if (type(m) is not int):
-        raise TypeError('m must be a string.')
-
-    if (m < 2):
-        raise ValueError('m must be greater than 1.')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    if any(data.total_pop_var < data.group_pop_var):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    # Create the shortest distance path between two pair of units using Shimbel matrix. This step was well discussed in https://github.com/pysal/segregation/issues/5.
-    w_libpysal = Queen.from_dataframe(data)
-    graph = csr_matrix(w_libpysal.full()[0])
-    delta = floyd_warshall(csgraph=graph, directed=False)
-
-    def calculate_etat(t):
-        g_t_i = np.where(data.group_pop_var / data.total_pop_var >= t, True,
-                         False)
-        k = g_t_i.sum()
-
-        # i and j only varies in the units subset within the threshold in eta_t of Hong (2014).
-        sub_delta_ij = delta[g_t_i, :][:, g_t_i]
-
-        den = sub_delta_ij.sum()
-        eta_t = (k**2 - k) / den
-        return eta_t
-
-    grid = np.linspace(0, 1, m)
-    aux = np.array(list(map(calculate_etat, grid)))
-    aux[aux == inf] = 0
-    aux[aux == -inf] = 0
-    curve = np.nan_to_num(aux, 0)
-
-    threshold = data.group_pop_var.sum() / data.total_pop_var.sum()
-    SPP = ((threshold - ((curve[grid < threshold]).sum() / m -
-                         (curve[grid >= threshold]).sum() / m)) /
-           (1 - threshold))
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return SPP, grid, curve, core_data
-
-
-
[docs]class SpatialProxProf:
-    """
-    Calculation of Spatial Proximity Profile
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    m             : int
-                    a numeric value indicating the number of thresholds to be used. Default value is 1000. 
-                    A large value of m creates a smoother-looking graph and a more precise spatial proximity profile value but slows down the calculation speed.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Spatial Proximity Profile Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the spatial proximity profile (SPP) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import SpatialProxProf
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    >>> spat_prox_index = SpatialProxProf(gdf, 'nhblk10', 'pop10')
-    >>> spat_prox_index.statistic
-    0.11217269612149207
-    
-    You can plot the profile curve with the plot method.
-    
-    >>> spat_prox_index.plot()
-        
-    Notes
-    -----
-    Based on Hong, Seong-Yun, and Yukio Sadahiro. "Measuring geographic segregation: a graph-based approach." Journal of Geographical Systems 16.2 (2014): 211-231.
-    
-    Reference: :cite:`hong2014measuring`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, m=1000):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _spatial_prox_profile(data, group_pop_var, total_pop_var, m)
-
-        self.statistic = aux[0]
-        self.grid = aux[1]
-        self.curve = aux[2]
-        self.core_data = aux[3]
-        self._function = _spatial_prox_profile

-
-    def plot(self):
-        """
-        Plot the Spatial Proximity Profile
-        """
-        try:
-            import matplotlib.pyplot as plt
-        except ImportError:
-            warnings.warn('This method relies on importing `matplotlib`')
-        graph = plt.scatter(self.grid, self.curve, s=0.1)
-        return graph

-
-
-def _spatial_dissim(data,
-                    group_pop_var,
-                    total_pop_var,
-                    w=None,
-                    standardize=False):
-    """
-    Calculation of Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    w             : W
-                    A PySAL weights object. If not provided, Queen contiguity matrix is used.
-                    
-    standardize   : boolean
-                    A condition for row standardisation of the weights matrices. If True, the values of cij in the formulas gets row standardized.
-                    For the sake of comparison, the seg R package of Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-                    works by default with row standardization.
-        
-    Returns
-    ----------
-
-    statistic : float
-                Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-                
-    Notes
-    -----
-    Based on Morrill, R. L. (1991) "On the Measure of Geographic Segregation". Geography Research Forum.
-    
-    Reference: :cite:`morrill1991measure`.
-
-    """
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if (type(standardize) is not bool):
-        raise TypeError('std is not a boolean object')
-
-    if w is None:
-        w_object = Queen.from_dataframe(data)
-    else:
-        w_object = w
-
-    if (not issubclass(type(w_object), libpysal.weights.W)):
-        raise TypeError('w is not a PySAL weights object')
-
-    D = _dissim(data, group_pop_var, total_pop_var)[0]
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    # If a unit has zero population, the group of interest frequency is zero
-    pi = np.where(t == 0, 0, x / t)
-
-    if not standardize:
-        cij = w_object.full()[0]
-    else:
-        cij = w_object.full()[0]
-        cij = cij / cij.sum(axis=1).reshape((cij.shape[0], 1))
-
-    # Inspired in (second solution): https://stackoverflow.com/questions/22720864/efficiently-calculating-a-euclidean-distance-matrix-using-numpy
-    # Distance Matrix
-    abs_dist = abs(pi[..., np.newaxis] - pi)
-
-    # manhattan_distances used to compute absolute distances
-    num = np.multiply(abs_dist, cij).sum()
-    den = cij.sum()
-    SD = D - num / den
-    SD
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return SD, core_data
-
-
-
[docs]class SpatialDissim:
-    """
-    Calculation of Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    w             : W
-                    A PySAL weights object. If not provided, Queen contiguity matrix is used.
-    
-    standardize   : boolean
-                    A condition for row standardisation of the weights matrices. If True, the values of cij in the formulas gets row standardized.
-                    For the sake of comparison, the seg R package of Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-                    works by default with row standardization.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.   
-                
-    Examples
-    --------
-    In this example, we will calculate the degree of spatial dissimilarity (D) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset. The neighborhood contiguity matrix is used.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import SpatialDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> spatial_dissim_index = SpatialDissim(gdf, 'nhblk10', 'pop10')
-    >>> spatial_dissim_index.statistic
-    0.2864885055405311
-        
-    To use different neighborhood matrices:
-        
-    >>> from libpysal.weights import Rook, KNN
-    
-    Assuming K-nearest neighbors with k = 4
-    
-    >>> knn = KNN.from_dataframe(gdf, k=4)
-    >>> spatial_dissim_index = Spatial_Dissim(gdf, 'nhblk10', 'pop10', w = knn)
-    >>> spatial_dissim_index.statistic
-    0.28544347200877285
-    
-    Assuming Rook contiguity neighborhood
-    
-    >>> roo = Rook.from_dataframe(gdf)
-    >>> spatial_dissim_index = Spatial_Dissim(gdf, 'nhblk10', 'pop10', w = roo)
-    >>> spatial_dissim_index.statistic
-    0.2866269198707091
-            
-    Notes
-    -----
-    Based on Morrill, R. L. (1991) "On the Measure of Geographic Segregation". Geography Research Forum.
-    
-    Reference: :cite:`morrill1991measure`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 w=None,
-                 standardize=False):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _spatial_dissim(data, group_pop_var, total_pop_var, w,
-                              standardize)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _spatial_dissim

-
-
-def _boundary_spatial_dissim(data,
-                             group_pop_var,
-                             total_pop_var,
-                             standardize=False):
-    """
-    Calculation of Boundary Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    standardize   : boolean
-                    A condition for row standardisation of the weights matrices. If True, the values of cij in the formulas gets row standardized.
-                    For the sake of comparison, the seg R package of Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-                    works by default without row standardization. That is, directly with border length.
-        
-    Returns
-    ----------
-
-    statistic : float
-                Boundary Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-                
-    Notes
-    -----
-    The formula is based on Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-    
-    Original paper by Wong, David WS. "Spatial indices of segregation." Urban studies 30.3 (1993): 559-572.
-    
-    References: :cite:`hong2014implementing` and :cite:`wong1993spatial`.
-
-    """
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if (type(standardize) is not bool):
-        raise TypeError('std is not a boolean object')
-
-    D = _dissim(data, group_pop_var, total_pop_var)[0]
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    # If a unit has zero population, the group of interest frequency is zero
-    data = data.assign(
-        pi=np.where(data.total_pop_var == 0, 0, data.group_pop_var /
-                    data.total_pop_var))
-
-    if not standardize:
-        cij = _return_length_weighted_w(data).full()[0]
-    else:
-        cij = _return_length_weighted_w(data).full()[0]
-        cij = cij / cij.sum(axis=1).reshape((cij.shape[0], 1))
-
-    # manhattan_distances used to compute absolute distances
-    num = np.multiply(manhattan_distances(data[['pi']]), cij).sum()
-    den = cij.sum()
-    BSD = D - num / den
-    BSD
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return BSD, core_data
-
-
-
[docs]class BoundarySpatialDissim:
-    """
-    Calculation of Boundary Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    standardize   : boolean
-                    A condition for row standardisation of the weights matrices. If True, the values of cij in the formulas gets row standardized.
-                    For the sake of comparison, the seg R package of Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-                    works by default without row standardization. That is, directly with border length.
-        
-
-    Attributes
-    ----------
-
-    statistic : float
-                Boundary Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-         
-    Examples
-    --------
-    In this example, we will calculate the degree of boundary spatial dissimilarity (D) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import BoundarySpatialDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> boundary_spatial_dissim_index = BoundarySpatialDissim(gdf, 'nhblk10', 'pop10')
-    >>> boundary_spatial_dissim_index.statistic
-    0.28869903953453163
-            
-    Notes
-    -----
-    The formula is based on Hong, Seong-Yun, David O'Sullivan, and Yukio Sadahiro. "Implementing spatial segregation measures in R." PloS one 9.11 (2014): e113767.
-    
-    Original paper by Wong, David WS. "Spatial indices of segregation." Urban studies 30.3 (1993): 559-572.
-    
-    References: :cite:`hong2014implementing` and :cite:`wong1993spatial`.
-    
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, standardize=False):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _boundary_spatial_dissim(data, group_pop_var, total_pop_var,
-                                       standardize)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _boundary_spatial_dissim

-
-
-def _perimeter_area_ratio_spatial_dissim(data,
-                                         group_pop_var,
-                                         total_pop_var,
-                                         standardize=True):
-    """
-    Calculation of Perimeter/Area Ratio Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    standardize   : boolean
-                    A condition for standardisation of the weights matrices. 
-                    If True, the values of cij in the formulas gets standardized and the overall sum is 1.
-
-    Returns
-    ----------
-
-    statistic : float
-                Perimeter/Area Ratio Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-                
-    Notes
-    -----
-    Originally based on Wong, David WS. "Spatial indices of segregation." Urban studies 30.3 (1993): 559-572.
-    
-    However, Tivadar, Mihai. "OasisR: An R Package to Bring Some Order to the World of Segregation Measurement." Journal of Statistical Software 89.1 (2019): 1-39.
-    points out that in Wong’s original there is an issue with the formula which is an extra division by 2 in the spatial interaction component.
-    This function follows the formula present in the first Appendix of Tivadar, Mihai. "OasisR: An R Package to Bring Some Order to the World of Segregation Measurement." Journal of Statistical Software 89.1 (2019): 1-39.
-
-    References: :cite:`wong1993spatial` and :cite:`tivadar2019oasisr`.
-        
-    """
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if (type(standardize) is not bool):
-        raise TypeError('std is not a boolean object')
-
-    D = _dissim(data, group_pop_var, total_pop_var)[0]
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    # If a unit has zero population, the group of interest frequency is zero
-    data = data.assign(
-        pi=np.where(data.total_pop_var == 0, 0, data.group_pop_var /
-                    data.total_pop_var))
-
-    if not standardize:
-        cij = _return_length_weighted_w(data).full()[0]
-    else:
-        cij = _return_length_weighted_w(data).full()[0]
-        cij = cij / cij.sum()
-
-    peri = data.length
-    ai = data.area
-
-    aux_sum = np.add(
-        np.array(list((peri / ai))),
-        np.array(list((peri / ai))).reshape((len(list((peri / ai))), 1)))
-
-    max_pa = max(peri / ai)
-
-    num = np.multiply(np.multiply(manhattan_distances(data[['pi']]), cij),
-                      aux_sum).sum()
-    den = 2 * max_pa
-
-    PARD = D - (num / den)
-    PARD
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return PARD, core_data
-
-
-
[docs]class PerimeterAreaRatioSpatialDissim:
-    """
-    Calculation of Perimeter/Area Ratio Spatial Dissimilarity index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    standardize   : boolean
-                    A condition for standardisation of the weights matrices. 
-                    If True, the values of cij in the formulas gets standardized and the overall sum is 1.
-        
-    Attributes
-    ----------
-
-    statistic : float
-                Perimeter/Area Ratio Spatial Dissimilarity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.      
-                
-    Examples
-    --------
-    In this example, we will calculate the degree of perimeter/area ratio spatial dissimilarity (PARD) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import PerimeterAreaRatioSpatialDissim
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> perimeter_area_ratio_spatial_dissim_index = PerimeterAreaRatioSpatialDissim(gdf, 'nhblk10', 'pop10')
-    >>> perimeter_area_ratio_spatial_dissim_index.statistic
-    0.31260876347432687
-            
-    Notes
-    -----
-    Originally based on Wong, David WS. "Spatial indices of segregation." Urban studies 30.3 (1993): 559-572.
-    
-    However, Tivadar, Mihai. "OasisR: An R Package to Bring Some Order to the World of Segregation Measurement." Journal of Statistical Software 89.1 (2019): 1-39.
-    points out that in Wong’s original there is an issue with the formula which is an extra division by 2 in the spatial interaction component.
-    This function follows the formula present in the first Appendix of Tivadar, Mihai. "OasisR: An R Package to Bring Some Order to the World of Segregation Measurement." Journal of Statistical Software 89.1 (2019): 1-39.
-    
-    References: :cite:`wong1993spatial` and :cite:`tivadar2019oasisr`.
-    
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var, standardize=True):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _perimeter_area_ratio_spatial_dissim(data, group_pop_var,
-                                                   total_pop_var, standardize)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _perimeter_area_ratio_spatial_dissim

-        
-
-
-
[docs]class SpatialMinMax(MinMax):
-    """Spatial MinMax Index.
-
-    This class calculates the spatial version of the MinMax
-    index. The data are "spatialized" by converting each observation
-    to a "local environment" by creating a weighted sum of the focal unit with
-    its neighboring observations, where the neighborhood is defined by a
-    libpysal weights matrix or a pandana Network instance.
-
-    Parameters
-    ----------
-    data : geopandas.GeoDataFrame
-        geodataframe with
-    group_pop_var : string
-        The name of variable in data that contains the population size of the group of interest
-    total_pop_var : string
-        The name of variable in data that contains the total population of the unit
-    w   : libpysal.W
-        distance-based PySAL spatial weights matrix instance
-    network : pandana.Network
-        pandana.Network instance. This is likely created with `get_osm_network`
-        or via helper functions from OSMnet or UrbanAccess.
-    distance : int
-        maximum distance to consider `accessible` (the default is 2000).
-    decay : str
-        decay type pandana should use "linear", "exp", or "flat"
-        (which means no decay). The default is "linear".
-    precompute: bool
-        Whether the pandana.Network instance should precompute the range
-        queries.This is true by default, but if you plan to calculate several
-        indices using the same network, then you can set this
-        parameter to `False` to avoid precomputing repeatedly inside the
-        function
-        
-    Attributes
-    ----------
-
-    statistic : float
-                SpatialMinMax Index
-                
-    core_data : a pandas DataFrame
-                A pandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on O'Sullivan & Wong (2007). A Surface‐Based Approach to Measuring Spatial Segregation.
-    Geographical Analysis 39 (2). https://doi.org/10.1111/j.1538-4632.2007.00699.x
-
-    Reference: :cite:`osullivanwong2007surface`.
-    
-    We'd like to thank @AnttiHaerkoenen for this contribution!
-    
-    """
-
-
[docs]    def __init__(self, 
-                 data, 
-                 group_pop_var, 
-                 total_pop_var,
-                 network=None,
-                 w=None,
-                 decay='linear',
-                 distance=2000,
-                 precompute=True):
-        
-        data = data.rename(columns={group_pop_var: 'group_pop_var', 
-                                    total_pop_var: 'total_pop_var'})
-    
-        data['group_2_pop_var'] = data['total_pop_var'] - data['group_pop_var']
-        
-        groups = ['group_pop_var', 'group_2_pop_var']
-        
-        if w is None and network is None:
-            points = [(p.x, p.y) for p in data.centroid]
-            w = Kernel(points)
-
-        if w and network:
-            raise (
-                "must pass either a pandana network or a pysal weights object\
-                 but not both")
-        elif network:
-            df = calc_access(data,
-                             variables=groups,
-                             network=network,
-                             distance=distance,
-                             decay=decay,
-                             precompute=precompute)
-            groups = ["acc_" + group for group in groups]
-        else:
-            df = _build_local_environment(data, groups, w)
-        
-        df['resulting_total'] = df['group_pop_var'] + df['group_2_pop_var']
-        
-        super().__init__(df, 'group_pop_var', 'resulting_total')

-
-
-
-def _distance_decay_isolation(data,
-                              group_pop_var,
-                              total_pop_var,
-                              alpha=0.6,
-                              beta=0.5,
-                              metric='euclidean'):
-    """
-    Calculation of Distance Decay Isolation index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Returns
-    ----------
-
-    statistic : float
-                Distance Decay Isolation Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    It may be interpreted as the probability that the next person a group member meets anywhere in space is from the same group.
-    
-    Based on Morgan, Barrie S. "A distance-decay based interaction index to measure residential segregation." Area (1983): 211-217.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`morgan1983distance`.
-
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    if (alpha < 0):
-        raise ValueError('alpha must be greater than zero.')
-
-    if (beta < 0):
-        raise ValueError('beta must be greater than zero.')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    X = x.sum()
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if (metric == 'euclidean'):
-        dist = euclidean_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))
-
-    if (metric == 'haversine'):
-        dist = haversine_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))  # This needs to be latitude first!
-
-    c = np.exp(-dist)
-    
-    if c.sum() < 10 ** (-15): 
-        raise ValueError('It not possible to determine accurately the exponential of the negative distances. This is probably due to the large magnitude of the centroids numbers. It is recommended to reproject the geopandas DataFrame. Also, if this is a not lat-long CRS, it is recommended to set metric to \'haversine\'')
-    
-    np.fill_diagonal(c, val = np.exp(-(alpha * data.area)**(beta)))
-    
-    Pij = np.multiply(c, t) / np.sum(np.multiply(c, t), axis=1)
-        
-    DDxPx = (np.array(x / X) *
-             np.nansum(np.multiply(Pij, np.array(x / t)), axis=1)).sum()
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return DDxPx, core_data
-
-
-
[docs]class DistanceDecayIsolation:
-    """
-    Calculation of Distance Decay Isolation index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Distance Decay Isolation Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the distance decay isolation index (DDxPx) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import DistanceDecayIsolation
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> spatial_isolation_index = DistanceDecayIsolation(gdf, 'nhblk10', 'pop10')
-    >>> spatial_isolation_index.statistic
-    0.07214112078134231
-            
-    Notes
-    -----
-    It may be interpreted as the probability that the next person a group member meets anywhere in space is from the same group.
-    
-    Based on Morgan, Barrie S. "A distance-decay based interaction index to measure residential segregation." Area (1983): 211-217.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`morgan1983distance`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 alpha=0.6,
-                 beta=0.5,
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _distance_decay_isolation(data, group_pop_var, total_pop_var,
-                                        alpha, beta, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _distance_decay_isolation

-
-
-def _distance_decay_exposure(data,
-                             group_pop_var,
-                             total_pop_var,
-                             alpha=0.6,
-                             beta=0.5,
-                             metric='euclidean'):
-    """
-    Calculation of Distance Decay Exposure index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Returns
-    ----------
-
-    statistic : float
-                Distance Decay Exposure Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    It may be interpreted as the probability that the next person a group member meets anywhere in space is from the other group.
-    
-    Based on Morgan, Barrie S. "A distance-decay based interaction index to measure residential segregation." Area (1983): 211-217.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`morgan1983distance`.
-
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    if (alpha < 0):
-        raise ValueError('alpha must be greater than zero.')
-
-    if (beta < 0):
-        raise ValueError('beta must be greater than zero.')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    y = t - x
-    X = x.sum()
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if (metric == 'euclidean'):
-        dist = euclidean_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))
-
-    if (metric == 'haversine'):
-        dist = haversine_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))  # This needs to be latitude first!
-
-    c = np.exp(-dist)
-    
-    if c.sum() < 10 ** (-15): 
-        raise ValueError('It not possible to determine accurately the exponential of the negative distances. This is probably due to the large magnitude of the centroids numbers. It is recommended to reproject the geopandas DataFrame. Also, if this is a not lat-long CRS, it is recommended to set metric to \'haversine\'')
-    
-    np.fill_diagonal(c, val = np.exp(-(alpha * data.area)**(beta)))
-    
-    Pij = np.multiply(c, t) / np.sum(np.multiply(c, t), axis=1)
-    
-    DDxPy = (x / X * np.nansum(np.multiply(Pij, y / t), axis=1)).sum()
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return DDxPy, core_data
-
-
-
[docs]class DistanceDecayExposure:
-    """
-    Calculation of Distance Decay Exposure index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Attributes
-    ----------
-
-    statistic : float
-                Distance Decay Exposure Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the distance decay exposure index (DDxPy) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import DistanceDecayExposure
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> spatial_exposure_index = DistanceDecayExposure(gdf, 'nhblk10', 'pop10')
-    >>> spatial_exposure_index.statistic
-    0.9605053172501217
-            
-    Notes
-    -----
-    It may be interpreted as the probability that the next person a group member meets anywhere in space is from the other group.
-    
-    Based on Morgan, Barrie S. "A distance-decay based interaction index to measure residential segregation." Area (1983): 211-217.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`morgan1983distance`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 alpha=0.6,
-                 beta=0.5,
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _distance_decay_exposure(data, group_pop_var, total_pop_var,
-                                       alpha, beta, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _distance_decay_exposure

-
-
-def _spatial_proximity(data,
-                       group_pop_var,
-                       total_pop_var,
-                       alpha=0.6,
-                       beta=0.5,
-                       metric='euclidean'):
-    """
-    Calculation of Spatial Proximity index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Returns
-    ----------
-    statistic : float
-                Spatial Proximity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    if (alpha < 0):
-        raise ValueError('alpha must be greater than zero.')
-
-    if (beta < 0):
-        raise ValueError('beta must be greater than zero.')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    if any(data.total_pop_var < data.group_pop_var):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    T = data.total_pop_var.sum()
-
-    data = data.assign(xi=data.group_pop_var,
-                       yi=data.total_pop_var - data.group_pop_var,
-                       ti=data.total_pop_var)
-
-    X = data.xi.sum()
-    Y = data.yi.sum()
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if (metric == 'euclidean'):
-        dist = euclidean_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))
-
-    if (metric == 'haversine'):
-        dist = haversine_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))  # This needs to be latitude first!
-
-    c = np.exp(-dist)
-    
-    if c.sum() < 10 ** (-15): 
-        raise ValueError('It not possible to determine accurately the exponential of the negative distances. This is probably due to the large magnitude of the centroids numbers. It is recommended to reproject the geopandas DataFrame. Also, if this is a not lat-long CRS, it is recommended to set metric to \'haversine\'')
-    
-    np.fill_diagonal(c, val = np.exp(-(alpha * data.area)**(beta)))
-    
-    Pxx = ((np.array(data.xi) * c).T * np.array(data.xi)).sum() / X**2
-    Pyy = ((np.array(data.yi) * c).T * np.array(data.yi)).sum() / Y**2
-    Ptt = ((np.array(data.ti) * c).T * np.array(data.ti)).sum() / T**2
-    SP = (X * Pxx + Y * Pyy) / (T * Ptt)
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return SP, core_data
-
-
-
[docs]class SpatialProximity:
-    """
-    Calculation of Spatial Proximity index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Attributes
-    ----------
-    statistic : float
-                Spatial Proximity Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the degree of spatial proximity (SP) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import SpatialProximity
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> spatial_prox_index = SpatialProximity(gdf, 'nhblk10', 'pop10')
-    >>> spatial_prox_index.statistic
-    1.002191883006537
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 alpha=0.6,
-                 beta=0.5,
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _spatial_proximity(data, group_pop_var, total_pop_var, alpha,
-                                 beta, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _spatial_proximity

-
-
-def _absolute_clustering(data,
-                         group_pop_var,
-                         total_pop_var,
-                         alpha=0.6,
-                         beta=0.5,
-                         metric='euclidean'):
-    """
-    Calculation of Absolute Clustering index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Returns
-    ----------
-    statistic : float
-                Absolute Clustering Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    if (alpha < 0):
-        raise ValueError('alpha must be greater than zero.')
-
-    if (beta < 0):
-        raise ValueError('beta must be greater than zero.')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    if any(data.total_pop_var < data.group_pop_var):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    data = data.assign(xi=data.group_pop_var,
-                       yi=data.total_pop_var - data.group_pop_var)
-
-    X = data.xi.sum()
-
-    x = np.array(data.xi)
-    t = np.array(data.total_pop_var)
-    n = len(data)
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if (metric == 'euclidean'):
-        dist = euclidean_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))
-
-    if (metric == 'haversine'):
-        dist = haversine_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))  # This needs to be latitude first!
-
-    c = np.exp(-dist)
-    
-    if c.sum() < 10 ** (-15): 
-        raise ValueError('It not possible to determine accurately the exponential of the negative distances. This is probably due to the large magnitude of the centroids numbers. It is recommended to reproject the geopandas DataFrame. Also, if this is a not lat-long CRS, it is recommended to set metric to \'haversine\'')
-    
-    np.fill_diagonal(c, val = np.exp(-(alpha * data.area)**(beta)))
-    
-    ACL = ((((x/X) * (c * x).sum(axis = 1)).sum()) - ((X / n**2) * c.sum())) / \
-          ((((x/X) * (c * t).sum(axis = 1)).sum()) - ((X / n**2) * c.sum()))
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return ACL, core_data
-
-
-
[docs]class AbsoluteClustering:
-    """
-    Calculation of Absolute Clustering index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Attributes
-    ----------
-    statistic : float
-                Absolute Clustering Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the absolute clustering measure (ACL) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['trtid10', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'trtid10')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> absolute_clust_index = Absolute_Clustering(gdf, 'nhblk10', 'pop10')
-    >>> absolute_clust_index.statistic
-    0.20979814508119624
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 alpha=0.6,
-                 beta=0.5,
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _absolute_clustering(data, group_pop_var, total_pop_var, alpha,
-                                   beta, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _absolute_clustering

-
-
-def _relative_clustering(data,
-                         group_pop_var,
-                         total_pop_var,
-                         alpha=0.6,
-                         beta=0.5,
-                         metric='euclidean'):
-    """
-    Calculation of Relative Clustering index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Returns
-    ----------
-    statistic : float
-                Relative Clustering Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    if (alpha < 0):
-        raise ValueError('alpha must be greater than zero.')
-
-    if (beta < 0):
-        raise ValueError('beta must be greater than zero.')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    if any(data.total_pop_var < data.group_pop_var):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    data = data.assign(xi=data.group_pop_var,
-                       yi=data.total_pop_var - data.group_pop_var)
-
-    X = data.xi.sum()
-    Y = data.yi.sum()
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if (metric == 'euclidean'):
-        dist = euclidean_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))
-
-    if (metric == 'haversine'):
-        dist = haversine_distances(
-            pd.DataFrame({
-                'c_lats': c_lats,
-                'c_lons': c_lons
-            }))  # This needs to be latitude first!
-
-    c = np.exp(-dist)
-    
-    if c.sum() < 10 ** (-15): 
-        raise ValueError('It not possible to determine accurately the exponential of the negative distances. This is probably due to the large magnitude of the centroids numbers. It is recommended to reproject the geopandas DataFrame. Also, if this is a not lat-long CRS, it is recommended to set metric to \'haversine\'')
-    
-    np.fill_diagonal(c, val = np.exp(-(alpha * data.area)**(beta)))
-    
-    Pxx = ((np.array(data.xi) * c).T * np.array(data.xi)).sum() / X**2
-    Pyy = ((np.array(data.yi) * c).T * np.array(data.yi)).sum() / Y**2
-    RCL = Pxx / Pyy - 1
-    
-    if np.isnan(RCL):
-        raise ValueError('It not possible to determine the distance between, at least, one pair of units. This is probably due to the magnitude of the number of the centroids. We recommend to reproject the geopandas DataFrame.')
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return RCL, core_data
-
-
-
[docs]class RelativeClustering:
-    """
-    Calculation of Relative Clustering index
-    
-    Parameters
-    ----------
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    alpha         : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.6
-    
-    beta          : float
-                    A parameter that estimates the extent of the proximity within the same unit. Default value is 0.5
-                    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-                    
-    Attributes
-    ----------
-    statistic : float
-                Relative Clustering Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the relative clustering measure (RCL) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import RelativeClustering
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> relative_clust_index = RelativeClustering(gdf, 'nhblk10', 'pop10')
-    >>> relative_clust_index.statistic
-    0.12418089857347714
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    The pairwise distance between unit i and itself is (alpha * area_of_unit_i) ^ beta.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 alpha=0.6,
-                 beta=0.5,
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _relative_clustering(data, group_pop_var, total_pop_var, alpha,
-                                   beta, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _relative_clustering

-
-
-def _delta(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Delta index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Delta Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    area = np.array(data.area)
-
-    X = x.sum()
-    A = area.sum()
-
-    DEL = 1 / 2 * abs(x / X - area / A).sum()
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return DEL, core_data
-
-
-
[docs]class Delta:
-    """
-    Calculation of Delta index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Delta Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-        
-    Examples
-    --------
-    In this example, we will calculate the delta index (D) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import Delta
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> delta_index = Delta(gdf, 'nhblk10', 'pop10')
-    >>> delta_index.statistic
-    0.8367330649317353
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-    
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _delta(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _delta

-
-
-def _absolute_concentration(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Absolute Concentration index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Absolute Concentration Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-                
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    area = np.array(data.area)
-
-    X = x.sum()
-    T = t.sum()
-
-    # Create the indexes according to the area ordering
-    des_ind = (-area).argsort()
-    asc_ind = area.argsort()
-
-    # A discussion about the extraction of n1 and n2 can be found in https://github.com/pysal/segregation/issues/43
-    n1 = np.where(((np.cumsum(t[asc_ind]) / T) < X / T) == False)[0][0] + 1
-    n2_aux = np.where(((np.cumsum(t[des_ind]) / T) < X / T) == False)[0][0] + 1
-    n2 = len(data) - n2_aux
-
-    n = data.shape[0]
-    T1 = t[asc_ind][0:n1].sum()
-    T2 = t[asc_ind][n2:n].sum()
-
-    ACO = 1- ((((x[asc_ind] * area[asc_ind] / X).sum()) - ((t[asc_ind] * area[asc_ind] / T1)[0:n1].sum())) / \
-          (((t[asc_ind] * area[asc_ind] / T2)[n2:n].sum()) - ((t[asc_ind] * area[asc_ind]/T1)[0:n1].sum())))
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return ACO, core_data
-
-
-
[docs]class AbsoluteConcentration:
-    """
-    Calculation of Absolute Concentration index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Absolute Concentration Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-                
-    Examples
-    --------
-    In this example, we will calculate the absolute concentration index (ACO) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import AbsoluteConcentration
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> absolute_concentration_index = AbsoluteConcentration(gdf, 'nhblk10', 'pop10')
-    >>> absolute_concentration_index.statistic
-    0.9577607171503524
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _absolute_concentration(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _absolute_concentration

-
-
-def _relative_concentration(data, group_pop_var, total_pop_var):
-    """
-    Calculation of Relative Concentration index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Returns
-    ----------
-
-    statistic : float
-                Relative Concentration Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    area = np.array(data.area)
-
-    y = t - x
-
-    X = x.sum()
-    Y = y.sum()
-    T = t.sum()
-
-    # Create the indexes according to the area ordering
-    des_ind = (-area).argsort()
-    asc_ind = area.argsort()
-
-    # A discussion about the extraction of n1 and n2 can be found in https://github.com/pysal/segregation/issues/43
-    n1 = np.where(((np.cumsum(t[asc_ind]) / T) < X / T) == False)[0][0] + 1
-    n2_aux = np.where(((np.cumsum(t[des_ind]) / T) < X / T) == False)[0][0] + 1
-    n2 = len(data) - n2_aux
-
-    n = data.shape[0]
-    T1 = t[asc_ind][0:n1].sum()
-    T2 = t[asc_ind][n2:n].sum()
-
-    RCO = ((((x[asc_ind] * area[asc_ind] / X).sum()) / ((y[asc_ind] * area[asc_ind] / Y).sum())) - 1) / \
-          ((((t[asc_ind] * area[asc_ind])[0:n1].sum() / T1) / ((t[asc_ind] * area[asc_ind])[n2:n].sum() / T2)) - 1)
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    return RCO, core_data
-
-
-
[docs]class RelativeConcentration:
-    """
-    Calculation of Relative Concentration index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    Attributes
-    ----------
-
-    statistic : float
-                Relative Concentration Index
-                
-    core_data : a geopandas DataFrame
-                A geopandas DataFrame that contains the columns used to perform the estimate.
-       
-    Examples
-    --------
-    In this example, we will calculate the relative concentration index (RCO) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import RelativeConcentration
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> relative_concentration_index = RelativeConcentration(gdf, 'nhblk10', 'pop10')
-    >>> relative_concentration_index.statistic
-    0.5204046784837685
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self, data, group_pop_var, total_pop_var):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _relative_concentration(data, group_pop_var, total_pop_var)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self._function = _relative_concentration

-
-
-def _absolute_centralization(data,
-                             group_pop_var,
-                             total_pop_var,
-                             center="mean",
-                             metric='euclidean'):
-    """
-    Calculation of Absolute Centralization index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    center        : string, two-dimension values (tuple, list, array) or integer.
-                    This defines what is considered to be the center of the spatial context under study.
-
-                    If string, this can be set to:
-                        
-                        "mean": the center longitude/latitude is the mean of longitudes/latitudes of all units. 
-                        "median": the center longitude/latitude is the median of longitudes/latitudes of all units. 
-                        "population_weighted_mean": the center longitude/latitude is the mean of longitudes/latitudes of all units weighted by the total population.
-                        "largest_population": the center longitude/latitude is the centroid of the unit with largest total population. If there is a tie in the maximum population, the mean of all coordinates will be taken.
-                    
-                    If tuple, list or array, this argument should be the coordinates of the desired center assuming longitude as first value and latitude second value. Therefore, in the form (longitude, latitude), if tuple, or [longitude, latitude] if list or numpy array.
-                    
-                    If integer, the center will be the centroid of the polygon from data corresponding to the integer interpreted as index. 
-                    For example, if `center = 0` the centroid of the first row of data is used as center, if `center = 1` the second row will be used, and so on.
-
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Returns
-    ----------
-
-    statistic     : float
-                    Absolute Centralization Index
-                
-    core_data     : a geopandas DataFrame
-                    A geopandas DataFrame that contains the columns used to perform the estimate.
-    
-    center_values : list
-                    The center, in the form [longitude, latitude], values used for the calculation of the centralization distances.
-    
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    A discussion of defining the center in this function can be found in https://github.com/pysal/segregation/issues/18.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    area = np.array(data.area)
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if isinstance(center, str):
-        if not center in [
-                'mean', 'median', 'population_weighted_mean',
-                'largest_population'
-        ]:
-            raise ValueError(
-                'The center string must one of \'mean\', \'median\', \'population_weighted_mean\', \'largest_population\''
-            )
-
-        if (center == "mean"):
-            center_lon = c_lons.mean()
-            center_lat = c_lats.mean()
-
-        if (center == "median"):
-            center_lon = np.median(c_lons)
-            center_lat = np.median(c_lats)
-
-        if (center == "population_weighted_mean"):
-            center_lon = np.average(c_lons, weights=t)
-            center_lat = np.average(c_lats, weights=t)
-
-        if (center == "largest_population"):
-            center_lon = c_lons[np.where(t == t.max())].mean()
-            center_lat = c_lats[np.where(t == t.max())].mean()
-
-    if isinstance(center, tuple) or isinstance(center, list) or isinstance(
-            center, np.ndarray):
-        if np.array(center).shape != (2, ):
-            raise ValueError('The center tuple/list/array must have length 2.')
-
-        center_lon = center[0]
-        center_lat = center[1]
-
-    if isinstance(center, int):
-        if (center > len(data) - 1) or (center < 0):
-            raise ValueError('The center index must by in the range of data.')
-
-        center_lon = data.iloc[[center]].centroid.x.values[0]
-        center_lat = data.iloc[[center]].centroid.y.values[0]
-
-    X = x.sum()
-    A = area.sum()
-
-    dlon = c_lons - center_lon
-    dlat = c_lats - center_lat
-
-    if (metric == 'euclidean'):
-        center_dist = np.sqrt((dlon)**2 + (dlat)**2)
-
-    if (metric == 'haversine'):
-        center_dist = 2 * np.arcsin(np.sqrt(np.sin(dlat/2)**2 + np.cos(center_lat) * np.cos(c_lats) * np.sin(dlon/2)**2))
-    
-    if np.isnan(center_dist).sum() > 0:
-        raise ValueError('It not possible to determine the center distance for, at least, one unit. This is probably due to the magnitude of the number of the centroids. We recommend to reproject the geopandas DataFrame.')
-    
-    asc_ind = center_dist.argsort()
-
-    Xi = np.cumsum(x[asc_ind]) / X
-    Ai = np.cumsum(area[asc_ind]) / A
-
-    ACE = np.nansum(shift(Xi, 1, cval=np.NaN) * Ai) - \
-          np.nansum(Xi * shift(Ai, 1, cval=np.NaN))
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    center_values = [center_lon, center_lat]
-
-    return ACE, core_data, center_values
-
-
-
[docs]class AbsoluteCentralization:
-    """
-    Calculation of Absolute Centralization index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-                    
-    center        : string, two-dimension values (tuple, list, array) or integer.
-                    This defines what is considered to be the center of the spatial context under study.
-
-                    If string, this can be set to:
-                        
-                        "mean": the center longitude/latitude is the mean of longitudes/latitudes of all units. 
-                        "median": the center longitude/latitude is the median of longitudes/latitudes of all units. 
-                        "population_weighted_mean": the center longitude/latitude is the mean of longitudes/latitudes of all units weighted by the total population.
-                        "largest_population": the center longitude/latitude is the centroid of the unit with largest total population. If there is a tie in the maximum population, the mean of all coordinates will be taken.
-                    
-                    If tuple, list or array, this argument should be the coordinates of the desired center assuming longitude as first value and latitude second value. Therefore, in the form (longitude, latitude), if tuple, or [longitude, latitude] if list or numpy array.
-                    
-                    If integer, the center will be the centroid of the polygon from data corresponding to the integer interpreted as index. 
-                    For example, if `center = 0` the centroid of the first row of data is used as center, if `center = 1` the second row will be used, and so on.
-
-    Attributes
-    ----------
-
-    statistic     : float
-                    Absolute Centralization Index
-                
-    core_data     : a geopandas DataFrame
-                    A geopandas DataFrame that contains the columns used to perform the estimate.
-    
-    center_values : list
-                    The center, in the form [longitude, latitude], values used for the calculation of the centralization distances.
-                
-    Examples
-    --------
-    In this example, we will calculate the absolute centralization index (ACE) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import AbsoluteCentralization
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> absolute_centralization_index = AbsoluteCentralization(gdf, 'nhblk10', 'pop10')
-    >>> absolute_centralization_index.statistic
-    0.6416113799795511
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    A discussion of defining the center in this function can be found in https://github.com/pysal/segregation/issues/18.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 center="mean",
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _absolute_centralization(data, group_pop_var, total_pop_var,
-                                       center, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self.center_values = aux[2]
-        self._function = _absolute_centralization

-
-
-def _relative_centralization(data,
-                             group_pop_var,
-                             total_pop_var,
-                             center="mean",
-                             metric='euclidean'):
-    """
-    Calculation of Relative Centralization index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    center        : string, two-dimension values (tuple, list, array) or integer.
-                    This defines what is considered to be the center of the spatial context under study.
-
-                    If string, this can be set to:
-                        
-                        "mean": the center longitude/latitude is the mean of longitudes/latitudes of all units. 
-                        "median": the center longitude/latitude is the median of longitudes/latitudes of all units. 
-                        "population_weighted_mean": the center longitude/latitude is the mean of longitudes/latitudes of all units weighted by the total population.
-                        "largest_population": the center longitude/latitude is the centroid of the unit with largest total population. If there is a tie in the maximum population, the mean of all coordinates will be taken.
-                    
-                    If tuple, list or array, this argument should be the coordinates of the desired center assuming longitude as first value and latitude second value. Therefore, in the form (longitude, latitude), if tuple, or [longitude, latitude] if list or numpy array.
-                    
-                    If integer, the center will be the centroid of the polygon from data corresponding to the integer interpreted as index. 
-                    For example, if `center = 0` the centroid of the first row of data is used as center, if `center = 1` the second row will be used, and so on.
-
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Returns
-    ----------
-
-    statistic     : float
-                    Relative Centralization Index
-                
-    core_data     : a geopandas DataFrame
-                    A geopandas DataFrame that contains the columns used to perform the estimate.
-    
-    center_values : list
-                    The center, in the form [longitude, latitude], values used for the calculation of the centralization distances.
-
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    A discussion of defining the center in this function can be found in https://github.com/pysal/segregation/issues/18.
-
-    """
-    
-    if not metric in ['euclidean', 'haversine']:
-        raise ValueError('metric must one of \'euclidean\', \'haversine\'')
-    
-    if (str(type(data)) != '<class \'geopandas.geodataframe.GeoDataFrame\'>'):
-        raise TypeError(
-            'data is not a GeoDataFrame and, therefore, this index cannot be calculated.'
-        )
-
-    if ('geometry' not in data.columns):
-        data['geometry'] = data[data._geometry_column_name]
-        data = data.drop([data._geometry_column_name], axis=1)
-        data = data.set_geometry('geometry')
-
-    if ((type(group_pop_var) is not str) or (type(total_pop_var) is not str)):
-        raise TypeError('group_pop_var and total_pop_var must be strings')
-
-    if ((group_pop_var not in data.columns)
-            or (total_pop_var not in data.columns)):
-        raise ValueError(
-            'group_pop_var and total_pop_var must be variables of data')
-
-    data = data.rename(columns={
-        group_pop_var: 'group_pop_var',
-        total_pop_var: 'total_pop_var'
-    })
-
-    x = np.array(data.group_pop_var)
-    t = np.array(data.total_pop_var)
-
-    if any(t < x):
-        raise ValueError(
-            'Group of interest population must equal or lower than the total population of the units.'
-        )
-
-    y = t - x
-
-    c_lons = np.array(data.centroid.x)
-    c_lats = np.array(data.centroid.y)
-
-    if isinstance(center, str):
-        if not center in [
-                'mean', 'median', 'population_weighted_mean',
-                'largest_population'
-        ]:
-            raise ValueError(
-                'The center string must one of \'mean\', \'median\', \'population_weighted_mean\', \'largest_population\''
-            )
-
-        if (center == "mean"):
-            center_lon = c_lons.mean()
-            center_lat = c_lats.mean()
-
-        if (center == "median"):
-            center_lon = np.median(c_lons)
-            center_lat = np.median(c_lats)
-
-        if (center == "population_weighted_mean"):
-            center_lon = np.average(c_lons, weights=t)
-            center_lat = np.average(c_lats, weights=t)
-
-        if (center == "largest_population"):
-            center_lon = c_lons[np.where(t == t.max())].mean()
-            center_lat = c_lats[np.where(t == t.max())].mean()
-
-    if isinstance(center, tuple) or isinstance(center, list) or isinstance(
-            center, np.ndarray):
-        if np.array(center).shape != (2, ):
-            raise ValueError('The center tuple/list/array must have length 2.')
-
-        center_lon = center[0]
-        center_lat = center[1]
-
-    if isinstance(center, int):
-        if (center > len(data) - 1) or (center < 0):
-            raise ValueError('The center index must by in the range of data.')
-
-        center_lon = data.iloc[[center]].centroid.x.values[0]
-        center_lat = data.iloc[[center]].centroid.y.values[0]
-
-    X = x.sum()
-    Y = y.sum()
-
-    dlon = c_lons - center_lon
-    dlat = c_lats - center_lat
-
-    if (metric == 'euclidean'):
-        center_dist = np.sqrt((dlon)**2 + (dlat)**2)
-
-    if (metric == 'haversine'):
-        center_dist = 2 * np.arcsin(
-            np.sqrt(
-                np.sin(dlat / 2)**2 +
-                np.cos(center_lat) * np.cos(c_lats) * np.sin(dlon / 2)**2))
-
-    if np.isnan(center_dist).sum() > 0:
-        raise ValueError('It not possible to determine the center distance for, at least, one unit. This is probably due to the magnitude of the number of the centroids. We recommend to reproject the geopandas DataFrame.')
-
-    asc_ind = center_dist.argsort()
-
-    Xi = np.cumsum(x[asc_ind]) / X
-    Yi = np.cumsum(y[asc_ind]) / Y
-
-    RCE = np.nansum(shift(Xi, 1, cval=np.NaN) * Yi) - \
-          np.nansum(Xi * shift(Yi, 1, cval=np.NaN))
-
-    core_data = data[['group_pop_var', 'total_pop_var', 'geometry']]
-
-    center_values = [center_lon, center_lat]
-
-    return RCE, core_data, center_values
-
-
-
[docs]class RelativeCentralization:
-    """
-    Calculation of Relative Centralization index
-
-    Parameters
-    ----------
-
-    data          : a geopandas DataFrame with a geometry column.
-    
-    group_pop_var : string
-                    The name of variable in data that contains the population size of the group of interest
-                    
-    total_pop_var : string
-                    The name of variable in data that contains the total population of the unit
-
-    center        : string, two-dimension values (tuple, list, array) or integer.
-                    This defines what is considered to be the center of the spatial context under study.
-
-                    If string, this can be set to:
-                        
-                        "mean": the center longitude/latitude is the mean of longitudes/latitudes of all units. 
-                        "median": the center longitude/latitude is the median of longitudes/latitudes of all units. 
-                        "population_weighted_mean": the center longitude/latitude is the mean of longitudes/latitudes of all units weighted by the total population.
-                        "largest_population": the center longitude/latitude is the centroid of the unit with largest total population. If there is a tie in the maximum population, the mean of all coordinates will be taken.
-                    
-                    If tuple, list or array, this argument should be the coordinates of the desired center assuming longitude as first value and latitude second value. Therefore, in the form (longitude, latitude), if tuple, or [longitude, latitude] if list or numpy array.
-                    
-                    If integer, the center will be the centroid of the polygon from data corresponding to the integer interpreted as index. 
-                    For example, if `center = 0` the centroid of the first row of data is used as center, if `center = 1` the second row will be used, and so on.
-    
-    metric        : string. Can be 'euclidean' or 'haversine'. Default is 'euclidean'.
-                    The metric used for the distance between spatial units. 
-                    If the projection of the CRS of the geopandas DataFrame field is in degrees, this should be set to 'haversine'.
-
-    Attributes
-    ----------
-
-    statistic     : float
-                    Relative Centralization Index
-            
-    core_data     : a geopandas DataFrame
-                    A geopandas DataFrame that contains the columns used to perform the estimate.
-    
-    center_values : list
-                    The center, in the form [longitude, latitude], values used for the calculation of the centralization distances.
-        
-    Examples
-    --------
-    In this example, we will calculate the relative centralization index (RCE) for the Riverside County using the census tract data of 2010.
-    The group of interest is non-hispanic black people which is the variable nhblk10 in the dataset.
-    
-    Firstly, we need to perform some import the modules and the respective function.
-    
-    >>> import pandas as pd
-    >>> import geopandas as gpd
-    >>> import segregation
-    >>> from segregation.spatial import RelativeCentralization
-    
-    Secondly, we need to read the data:
-    
-    >>> # This example uses all census data that the user must provide your own copy of the external database.
-    >>> # A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb
-    >>> # After the user download the LTDB_Std_All_fullcount.zip and extract the files, the filepath might be something like presented below.
-    >>> filepath = '~/data/LTDB_Std_2010_fullcount.csv'
-    >>> census_2010 = pd.read_csv(filepath, encoding = "ISO-8859-1", sep = ",")
-    
-    Then, we filter only for the desired county (in this case, Riverside County):
-    
-    >>> df = census_2010.loc[census_2010.county == "Riverside County"][['tractid', 'pop10','nhblk10']]
-    
-    Then, we read the Riverside map data using geopandas (the county id is 06065):
-    
-    >>> map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'
-    >>> map_gpd = gpd.read_file(map_url)
-    
-    It is necessary to harmonize the data type of the dataset and the geopandas in order to work the merging procedure.
-    Later, we extract only the columns that will be used.
-    
-    >>> map_gpd['INTGEOID10'] = pd.to_numeric(map_gpd["GEOID10"])
-    >>> gdf_pre = map_gpd.merge(df, left_on = 'INTGEOID10', right_on = 'tractid')
-    >>> gdf = gdf_pre[['geometry', 'pop10', 'nhblk10']]
-    
-    The value is estimated below.
-    
-    >>> relative_centralization_index = RelativeCentralization(gdf, 'nhblk10', 'pop10')
-    >>> relative_centralization_index.statistic
-    0.18550429720565376
-            
-    Notes
-    -----
-    Based on Massey, Douglas S., and Nancy A. Denton. "The dimensions of residential segregation." Social forces 67.2 (1988): 281-315.
-    
-    A discussion of defining the center in this function can be found in https://github.com/pysal/segregation/issues/18.
-    
-    Reference: :cite:`massey1988dimensions`.
-
-    """
-
-
[docs]    def __init__(self,
-                 data,
-                 group_pop_var,
-                 total_pop_var,
-                 center="mean",
-                 metric='euclidean'):
-        
-        data = _nan_handle(data[[group_pop_var, total_pop_var, data._geometry_column_name]])
-
-        aux = _relative_centralization(data, group_pop_var, total_pop_var,
-                                       center, metric)
-
-        self.statistic = aux[0]
-        self.core_data = aux[1]
-        self.center_values = aux[2]
-        self._function = _relative_centralization

-
-
-class SpatialInformationTheory(MultiInformationTheory):
-    """Spatial Multigroup Information Theory Index.
-
-    This class calculates the spatial version of the multigroup information
-    theory index. The data are "spatialized" by converting each observation
-    to a "local environment" by creating a weighted sum of the focal unit with
-    its neighboring observations, where the neighborhood is defined by a
-    libpysal weights matrix or a pandana Network instance.
-
-    Parameters
-    ----------
-    data : geopandas.GeoDataFrame
-        geodataframe with
-    groups : list
-        list of columns on gdf representing population groups for which the SIT
-        index should be calculated
-    network : pandana.Network
-        pandana.Network instance. This is likely created with `get_osm_network`
-        or via helper functions from OSMnet or UrbanAccess.
-    w   : libpysal.W
-        distance-based PySAL spatial weights matrix instance
-    distance : int
-        maximum distance to consider `accessible` (the default is 2000).
-    decay : str
-        decay type pandana should use "linear", "exp", or "flat"
-        (which means no decay). The default is "linear".
-    precompute: bool
-        Whether the pandana.Network instance should precompute the range
-        queries.This is true by default, but if you plan to calculate several
-        indices using the same network, then you can set this
-        parameter to `False` to avoid precomputing repeatedly inside the
-        function
-
-    """
-
-    def __init__(self,
-                 data,
-                 groups,
-                 network=None,
-                 w=None,
-                 decay='linear',
-                 distance=2000,
-                 precompute=True):
-
-        if w and network:
-            raise (
-                "must pass either a pandana network or a pysal weights object\
-                 but not both")
-        elif network:
-            df = calc_access(data,
-                             variables=groups,
-                             network=network,
-                             distance=distance,
-                             decay=decay,
-                             precompute=precompute)
-            groups = ["acc_" + group for group in groups]
-        else:
-            df = _build_local_environment(data, groups, w)
-        super().__init__(df, groups)
-
-
-class SpatialDivergence(MultiDivergence):
-    """Spatial Multigroup Divergence Index.
-
-    This class calculates the spatial version of the multigroup divergence
-    index. The data are "spatialized" by converting each observation
-    to a "local environment" by creating a weighted sum of the focal unit with
-    its neighboring observations, where the neighborhood is defined by a
-    libpysal weights matrix or a pandana Network instance.
-
-    Parameters
-    ----------
-    data : geopandas.GeoDataFrame
-        geodataframe with
-    groups : list
-        list of columns on gdf representing population groups for which the
-        divergence index should be calculated
-    w   : libpysal.W
-        distance-based PySAL spatial weights matrix instance
-    network : pandana.Network
-        pandana.Network instance. This is likely created with `get_osm_network`
-        or via helper functions from OSMnet or UrbanAccess.
-    distance : int
-        maximum distance to consider `accessible` (the default is 2000).
-    decay : str
-        decay type pandana should use "linear", "exp", or "flat"
-        (which means no decay). The default is "linear".
-    precompute: bool
-        Whether the pandana.Network instance should precompute the range
-        queries.This is true by default, but if you plan to calculate several
-        indices using the same network, then you can set this
-        parameter to `False` to avoid precomputing repeatedly inside the
-        function
-    """
-
-    def __init__(self,
-                 data,
-                 groups,
-                 network=None,
-                 w=None,
-                 decay='linear',
-                 distance=2000,
-                 precompute=True):
-
-        if w and network:
-            raise (
-                "must pass either a pandana network or a pysal weights object\
-                 but not both")
-        elif network:
-            df = calc_access(data,
-                             variables=groups,
-                             network=network,
-                             distance=distance,
-                             decay=decay,
-                             precompute=precompute)
-            groups = ["acc_" + group for group in groups]
-        else:
-            df = _build_local_environment(data, groups, w)
-        super().__init__(df, groups)
-
-
-def compute_segregation_profile(gdf,
-                                groups=None,
-                                distances=None,
-                                network=None,
-                                decay='linear',
-                                function='triangular',
-                                precompute=True):
-    """Compute multiscalar segregation profile.
-
-    This function calculates several Spatial Information Theory indices with
-    increasing distance parameters.
-
-    Parameters
-    ----------
-    gdf : geopandas.GeoDataFrame
-        geodataframe with rows as observations and columns as population
-        variables. Note that if using a network distance, the coordinate
-        system for this gdf should be 4326. If using euclidian distance,
-        this must be projected into planar coordinates like state plane or UTM.
-    groups : list
-        list of variables .
-    distances : list
-        list of floats representing bandwidth distances that define a local
-        environment.
-    network : pandana.Network (optional)
-        A pandana.Network likely created with
-        `segregation.network.get_osm_network`.
-    decay : str (optional)
-        decay type to be used in pandana accessibility calculation (the
-        default is 'linear').
-    function: 'str' (optional)
-        which weighting function should be passed to libpysal.weights.Kernel
-        must be one of: 'triangular','uniform','quadratic','quartic','gaussian'
-    precompute: bool
-        Whether the pandana.Network instance should precompute the range
-        queries.This is true by default, but if you plan to calculate several
-        segregation profiles using the same network, then you can set this
-        parameter to `False` to avoid precomputing repeatedly inside the
-        function
-
-    Returns
-    -------
-    dict
-        dictionary with distances as keys and SIT statistics as values
-
-    Notes
-    -----
-    Based on Sean F. Reardon, Stephen A. Matthews, David O’Sullivan, Barrett A. Lee, Glenn Firebaugh, Chad R. Farrell, & Kendra Bischoff. (2008). The Geographic Scale of Metropolitan Racial Segregation. Demography, 45(3), 489–514. https://doi.org/10.1353/dem.0.0019.
-
-    Reference: :cite:`Reardon2008`.
-
-    """
-    gdf = gdf.copy()
-    gdf[groups] = gdf[groups].astype(float)
-    indices = {}
-    indices[0] = MultiInformationTheory(gdf, groups).statistic
-
-    if network:
-        if not gdf.crs.name == 'WGS 84':
-            gdf = gdf.to_crs(epsg=4326)
-        groups2 = ['acc_' + group for group in groups]
-        if precompute:
-            maxdist = max(distances)
-            network.precompute(maxdist)
-        for distance in distances:
-            distance = np.float(distance)
-            access = calc_access(gdf,
-                                 network,
-                                 decay=decay,
-                                 variables=groups,
-                                 distance=distance,
-                                 precompute=False)
-            sit = MultiInformationTheory(access, groups2)
-            indices[distance] = sit.statistic
-    else:
-        for distance in distances:
-            w = Kernel.from_dataframe(gdf,
-                                      bandwidth=distance,
-                                      function=function)
-            sit = SpatialInformationTheory(gdf, groups, w=w)
-            indices[distance] = sit.statistic
-    return indices
-
-
-# Deprecation Calls
-
-msg = _dep_message("Spatial_Prox_Prof", "SpatialProxProf")
-Spatial_Prox_Prof = DeprecationHelper(SpatialProxProf, message=msg)
-
-msg = _dep_message("Spatial_Dissim", "SpatialDissim")
-Spatial_Dissim = DeprecationHelper(SpatialDissim, message=msg)
-
-msg = _dep_message("Boundary_Spatial_Dissim", "BoundarySpatialDissim")
-Boundary_Spatial_Dissim = DeprecationHelper(BoundarySpatialDissim, message=msg)
-
-msg = _dep_message("Perimeter_Area_Ratio_Spatial_Dissim",
-                   "PerimeterAreaRatioSpatialDissim")
-Perimeter_Area_Ratio_Spatial_Dissim = DeprecationHelper(
-    PerimeterAreaRatioSpatialDissim, message=msg)
-
-msg = _dep_message("Distance_Decay_Isolation", "DistanceDecayIsolation")
-Distance_Decay_Isolation = DeprecationHelper(DistanceDecayIsolation,
-                                             message=msg)
-
-msg = _dep_message("Distance_Decay_Exposure", "DistanceDecayExposure")
-Distance_Decay_Exposure = DeprecationHelper(DistanceDecayExposure, message=msg)
-
-msg = _dep_message("Spatial_Proximity", "SpatialProximity")
-Spatial_Proximity = DeprecationHelper(SpatialProximity, message=msg)
-
-msg = _dep_message("Absolute_Clustering", "AbsoluteClustering")
-Absolute_Clustering = DeprecationHelper(AbsoluteClustering, message=msg)
-
-msg = _dep_message("Relative_Clustering", "RelativeClustering")
-Relative_Clustering = DeprecationHelper(RelativeClustering, message=msg)
-
-msg = _dep_message("Absolute_Concentration", "AbsoluteConcentration")
-Absolute_Concentration = DeprecationHelper(AbsoluteConcentration, message=msg)
-
-msg = _dep_message("Relative_Concentration", "RelativeConcentration")
-Relative_Concentration = DeprecationHelper(RelativeConcentration, message=msg)
-
-msg = _dep_message("Absolute_Centralization", "AbsoluteCentralization")
-Absolute_Centralization = DeprecationHelper(AbsoluteCentralization,
-                                            message=msg)
-
-msg = _dep_message("Relative_Centralization", "RelativeCentralization")
-Relative_Centralization = DeprecationHelper(RelativeCentralization, message=msg)
-
- -
- -
-
- - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/api.rst.txt b/docsrc/_build/html/_sources/api.rst.txt deleted file mode 100644 index 6ef466bf..00000000 --- a/docsrc/_build/html/_sources/api.rst.txt +++ /dev/null @@ -1,106 +0,0 @@ -.. _api_ref: - -.. currentmodule:: segregation - -API reference -============= - -Aspatial Indices ---------------------- -.. autosummary:: - :toctree: generated/ - - aspatial.Dissim - aspatial.GiniSeg - aspatial.Entropy - aspatial.Isolation - aspatial.Exposure - aspatial.Atkinson - aspatial.CorrelationR - aspatial.ConProf - aspatial.ModifiedDissim - aspatial.ModifiedGiniSeg - aspatial.BiasCorrectedDissim - aspatial.DensityCorrectedDissim - aspatial.MinMax - -Spatial Indices ---------------------- -.. autosummary:: - :toctree: generated/ - - spatial.SpatialProxProf - spatial.SpatialDissim - spatial.BoundarySpatialDissim - spatial.PerimeterAreaRatioSpatialDissim - spatial.DistanceDecayIsolation - spatial.DistanceDecayExposure - spatial.SpatialProximity - spatial.AbsoluteClustering - spatial.RelativeClustering - spatial.Delta - spatial.AbsoluteConcentration - spatial.RelativeConcentration - spatial.AbsoluteCentralization - spatial.RelativeCentralization - spatial.SpatialMinMax - -Multigroup Indices ---------------------- -.. autosummary:: - :toctree: generated/ - - aspatial.MultiDissim - aspatial.MultiGiniSeg - aspatial.MultiNormalizedExposure - aspatial.MultiInformationTheory - aspatial.MultiRelativeDiversity - aspatial.MultiSquaredCoefficientVariation - aspatial.MultiDiversity - aspatial.SimpsonsConcentration - aspatial.SimpsonsInteraction - aspatial.MultiDivergence - -Local Indices ---------------------- -.. autosummary:: - :toctree: generated/ - - local.MultiLocationQuotient - local.MultiLocalDiversity - local.MultiLocalEntropy - local.MultiLocalSimpsonInteraction - local.MultiLocalSimpsonConcentration - local.LocalRelativeCentralization - -Batch Compute Wrappers ---------------------- -.. autosummary:: - :toctree: generated/ - - compute_all.ComputeAllAspatialSegregation - compute_all.ComputeAllSpatialSegregation - compute_all.ComputeAllSegregation - -Inference Wrappers ---------------------- -.. autosummary:: - :toctree: generated/ - - inference.SingleValueTest - inference.TwoValueTest - -Decomposition ---------------------- -.. autosummary:: - :toctree: generated/ - - decomposition.DecomposeSegregation - -Network ---------------------- -.. autosummary:: - :toctree: generated/ - - network.get_osm_network - network.calc_access diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.Atkinson.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.Atkinson.rst.txt deleted file mode 100644 index 49191784..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.Atkinson.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.Atkinson -============================= - -.. currentmodule:: segregation.aspatial - -.. autoclass:: Atkinson - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Atkinson.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt deleted file mode 100644 index 077f0cca..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.BiasCorrectedDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.BiasCorrectedDissim -======================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: BiasCorrectedDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~BiasCorrectedDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.ConProf.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.ConProf.rst.txt deleted file mode 100644 index 77cf0417..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.ConProf.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -segregation.aspatial.ConProf -============================ - -.. currentmodule:: segregation.aspatial - -.. autoclass:: ConProf - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ConProf.__init__ - ~ConProf.plot - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.CorrelationR.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.CorrelationR.rst.txt deleted file mode 100644 index d83560db..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.CorrelationR.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.CorrelationR -================================= - -.. currentmodule:: segregation.aspatial - -.. autoclass:: CorrelationR - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~CorrelationR.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt deleted file mode 100644 index d0b14d62..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.DensityCorrectedDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.DensityCorrectedDissim -=========================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: DensityCorrectedDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~DensityCorrectedDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.Dissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.Dissim.rst.txt deleted file mode 100644 index 60945e10..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.Dissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.Dissim -=========================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: Dissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Dissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.Entropy.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.Entropy.rst.txt deleted file mode 100644 index f4e0e658..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.Entropy.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.Entropy -============================ - -.. currentmodule:: segregation.aspatial - -.. autoclass:: Entropy - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Entropy.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.Exposure.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.Exposure.rst.txt deleted file mode 100644 index d2928c40..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.Exposure.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.Exposure -============================= - -.. currentmodule:: segregation.aspatial - -.. autoclass:: Exposure - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Exposure.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.GiniSeg.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.GiniSeg.rst.txt deleted file mode 100644 index 2ed90dbb..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.GiniSeg.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.GiniSeg -============================ - -.. currentmodule:: segregation.aspatial - -.. autoclass:: GiniSeg - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~GiniSeg.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.Isolation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.Isolation.rst.txt deleted file mode 100644 index 977a014d..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.Isolation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.Isolation -============================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: Isolation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Isolation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MinMax.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MinMax.rst.txt deleted file mode 100644 index b5068c2b..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MinMax.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MinMax -=========================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MinMax - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MinMax.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt deleted file mode 100644 index 33b9fca0..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.ModifiedDissim -=================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: ModifiedDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ModifiedDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt deleted file mode 100644 index 4cb1ffcb..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.ModifiedGiniSeg.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.ModifiedGiniSeg -==================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: ModifiedGiniSeg - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ModifiedGiniSeg.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDissim.rst.txt deleted file mode 100644 index 831b917b..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiDissim -================================ - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt deleted file mode 100644 index 5b78231e..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDivergence.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiDivergence -==================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiDivergence - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiDivergence.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt deleted file mode 100644 index f868a052..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiDiversity.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiDiversity -=================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiDiversity - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiDiversity.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt deleted file mode 100644 index fec16f29..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiGiniSeg.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiGiniSeg -================================= - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiGiniSeg - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiGiniSeg.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt deleted file mode 100644 index 0161d2b6..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiInformationTheory.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiInformationTheory -=========================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiInformationTheory - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiInformationTheory.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt deleted file mode 100644 index 90daa95e..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiNormalizedExposure.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiNormalizedExposure -============================================ - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiNormalizedExposure - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiNormalizedExposure.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt deleted file mode 100644 index 43898059..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiRelativeDiversity.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiRelativeDiversity -=========================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiRelativeDiversity - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiRelativeDiversity.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt deleted file mode 100644 index aa58c52e..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.MultiSquaredCoefficientVariation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.MultiSquaredCoefficientVariation -===================================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: MultiSquaredCoefficientVariation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiSquaredCoefficientVariation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt deleted file mode 100644 index 3756049d..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsConcentration.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.SimpsonsConcentration -========================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: SimpsonsConcentration - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SimpsonsConcentration.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt b/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt deleted file mode 100644 index dbc312bf..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.aspatial.SimpsonsInteraction.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.aspatial.SimpsonsInteraction -======================================== - -.. currentmodule:: segregation.aspatial - -.. autoclass:: SimpsonsInteraction - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SimpsonsInteraction.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt deleted file mode 100644 index 4b5dd82e..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllAspatialSegregation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.compute\_all.ComputeAllAspatialSegregation -====================================================== - -.. currentmodule:: segregation.compute_all - -.. autoclass:: ComputeAllAspatialSegregation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ComputeAllAspatialSegregation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt deleted file mode 100644 index 7ea9d749..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSegregation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.compute\_all.ComputeAllSegregation -============================================== - -.. currentmodule:: segregation.compute_all - -.. autoclass:: ComputeAllSegregation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ComputeAllSegregation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt deleted file mode 100644 index 055b788a..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.compute_all.ComputeAllSpatialSegregation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.compute\_all.ComputeAllSpatialSegregation -===================================================== - -.. currentmodule:: segregation.compute_all - -.. autoclass:: ComputeAllSpatialSegregation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~ComputeAllSpatialSegregation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt deleted file mode 100644 index a68d0b0c..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.decomposition.DecomposeSegregation.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -segregation.decomposition.DecomposeSegregation -============================================== - -.. currentmodule:: segregation.decomposition - -.. autoclass:: DecomposeSegregation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~DecomposeSegregation.__init__ - ~DecomposeSegregation.plot - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.inference.SingleValueTest.rst.txt b/docsrc/_build/html/_sources/generated/segregation.inference.SingleValueTest.rst.txt deleted file mode 100644 index 3f97decb..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.inference.SingleValueTest.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -segregation.inference.SingleValueTest -===================================== - -.. currentmodule:: segregation.inference - -.. autoclass:: SingleValueTest - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SingleValueTest.__init__ - ~SingleValueTest.plot - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.inference.TwoValueTest.rst.txt b/docsrc/_build/html/_sources/generated/segregation.inference.TwoValueTest.rst.txt deleted file mode 100644 index 013da514..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.inference.TwoValueTest.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -segregation.inference.TwoValueTest -================================== - -.. currentmodule:: segregation.inference - -.. autoclass:: TwoValueTest - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~TwoValueTest.__init__ - ~TwoValueTest.plot - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt deleted file mode 100644 index b914ad6a..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.LocalRelativeCentralization.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.LocalRelativeCentralization -============================================= - -.. currentmodule:: segregation.local - -.. autoclass:: LocalRelativeCentralization - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~LocalRelativeCentralization.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt deleted file mode 100644 index ab3d3a9b..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalDiversity.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.MultiLocalDiversity -===================================== - -.. currentmodule:: segregation.local - -.. autoclass:: MultiLocalDiversity - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiLocalDiversity.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt deleted file mode 100644 index b2f70db1..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalEntropy.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.MultiLocalEntropy -=================================== - -.. currentmodule:: segregation.local - -.. autoclass:: MultiLocalEntropy - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiLocalEntropy.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt deleted file mode 100644 index a9883ab4..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonConcentration.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.MultiLocalSimpsonConcentration -================================================ - -.. currentmodule:: segregation.local - -.. autoclass:: MultiLocalSimpsonConcentration - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiLocalSimpsonConcentration.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt deleted file mode 100644 index 6f660da2..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocalSimpsonInteraction.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.MultiLocalSimpsonInteraction -============================================== - -.. currentmodule:: segregation.local - -.. autoclass:: MultiLocalSimpsonInteraction - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiLocalSimpsonInteraction.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt b/docsrc/_build/html/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt deleted file mode 100644 index 38e696dd..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.local.MultiLocationQuotient.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.local.MultiLocationQuotient -======================================= - -.. currentmodule:: segregation.local - -.. autoclass:: MultiLocationQuotient - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~MultiLocationQuotient.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.network.calc_access.rst.txt b/docsrc/_build/html/_sources/generated/segregation.network.calc_access.rst.txt deleted file mode 100644 index 22fc2013..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.network.calc_access.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -segregation.network.calc\_access -================================ - -.. currentmodule:: segregation.network - -.. autofunction:: calc_access \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.network.get_osm_network.rst.txt b/docsrc/_build/html/_sources/generated/segregation.network.get_osm_network.rst.txt deleted file mode 100644 index 02c6a6c4..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.network.get_osm_network.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -segregation.network.get\_osm\_network -===================================== - -.. currentmodule:: segregation.network - -.. autofunction:: get_osm_network \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt deleted file mode 100644 index f56f28b1..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteCentralization.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.AbsoluteCentralization -========================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: AbsoluteCentralization - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~AbsoluteCentralization.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt deleted file mode 100644 index 5944bc0d..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteClustering.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.AbsoluteClustering -====================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: AbsoluteClustering - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~AbsoluteClustering.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt deleted file mode 100644 index e540d2e6..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.AbsoluteConcentration.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.AbsoluteConcentration -========================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: AbsoluteConcentration - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~AbsoluteConcentration.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt deleted file mode 100644 index 59516f46..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.BoundarySpatialDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.BoundarySpatialDissim -========================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: BoundarySpatialDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~BoundarySpatialDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.Delta.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.Delta.rst.txt deleted file mode 100644 index 0d9568c9..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.Delta.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.Delta -========================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: Delta - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Delta.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt deleted file mode 100644 index 8600ea86..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayExposure.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.DistanceDecayExposure -========================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: DistanceDecayExposure - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~DistanceDecayExposure.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt deleted file mode 100644 index ed2733e1..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.DistanceDecayIsolation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.DistanceDecayIsolation -========================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: DistanceDecayIsolation - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~DistanceDecayIsolation.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt deleted file mode 100644 index 2f6e4b99..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.PerimeterAreaRatioSpatialDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.PerimeterAreaRatioSpatialDissim -=================================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: PerimeterAreaRatioSpatialDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~PerimeterAreaRatioSpatialDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt deleted file mode 100644 index 677bc0b7..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeCentralization.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.RelativeCentralization -========================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: RelativeCentralization - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~RelativeCentralization.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeClustering.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeClustering.rst.txt deleted file mode 100644 index 2ef580ac..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeClustering.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.RelativeClustering -====================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: RelativeClustering - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~RelativeClustering.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt deleted file mode 100644 index 4a58676b..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.RelativeConcentration.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.RelativeConcentration -========================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: RelativeConcentration - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~RelativeConcentration.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialDissim.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialDissim.rst.txt deleted file mode 100644 index c631e6e5..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialDissim.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.SpatialDissim -================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: SpatialDissim - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialDissim.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt deleted file mode 100644 index f948a80a..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialMinMax.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.SpatialMinMax -================================= - -.. currentmodule:: segregation.spatial - -.. autoclass:: SpatialMinMax - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialMinMax.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt deleted file mode 100644 index a26e0038..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProxProf.rst.txt +++ /dev/null @@ -1,23 +0,0 @@ -segregation.spatial.SpatialProxProf -=================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: SpatialProxProf - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialProxProf.__init__ - ~SpatialProxProf.plot - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProximity.rst.txt b/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProximity.rst.txt deleted file mode 100644 index 02ff5a0a..00000000 --- a/docsrc/_build/html/_sources/generated/segregation.spatial.SpatialProximity.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -segregation.spatial.SpatialProximity -==================================== - -.. currentmodule:: segregation.spatial - -.. autoclass:: SpatialProximity - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialProximity.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/_build/html/_sources/index.rst.txt b/docsrc/_build/html/_sources/index.rst.txt deleted file mode 100644 index b88dff18..00000000 --- a/docsrc/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,79 +0,0 @@ -Welcome to segregation's documentation! -======================================= - -Open Source Segregation Analytics in PySAL! - -The PySAL `segregation` package provides a suite of tools for measuring segregation over time and space. - - -.. raw:: html - -
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Fit multiple indexes at once
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Single Value and Comparative Inference
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- -
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- - -
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Decompose Segregation
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- -
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- - -
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Multigroup segregation measures
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- -
-
- - -
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Street network based segregation measures
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- -
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- - -
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Local segregation measures
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-
- - - -.. toctree:: - :hidden: - :maxdepth: 3 - :caption: Contents: - - Installation - API - References diff --git a/docsrc/_build/html/_sources/installation.rst.txt b/docsrc/_build/html/_sources/installation.rst.txt deleted file mode 100644 index 17c90498..00000000 --- a/docsrc/_build/html/_sources/installation.rst.txt +++ /dev/null @@ -1,25 +0,0 @@ -.. Installation - -Installation -=============== - -Note: segregation supports python 3.5 and 3.6 only. Please make sure that you are operating in a python 3 environment. - -i) `pip` directly running in the prompt:: - - pip install segregation - -ii) Using the `conda-forge` channel as described in https://github.com/conda-forge/segregation-feedstock:: - - conda config --add channels conda-forge - conda install segregation - -iii) Another recommended method for installing segregation is with anaconda (https://www.anaconda.com/download/). Clone this repository or download it manually then `cd` into the directory and run the following commands (this will install the development version):: - - conda env create -f environment.yml - source activate segregation - python setup.py develop - -iv) `pip` directly from this repository running in the prompt (if you experience an issue trying to install this way, take a look at this discussion: https://github.com/pysal/segregation/issues/15):: - - pip install git+https://github.com/pysal/segregation diff --git a/docsrc/_build/html/_sources/references.rst.txt b/docsrc/_build/html/_sources/references.rst.txt deleted file mode 100644 index 09d2529e..00000000 --- a/docsrc/_build/html/_sources/references.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -.. reference for the docs - -References -========== - -.. bibliography:: _static/references.bib - :cited: diff --git a/docsrc/_build/html/_static/basic.css b/docsrc/_build/html/_static/basic.css deleted file mode 100644 index 56f5efc6..00000000 --- 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> th:first-child, -.table-bordered tbody:first-child tr:first-child > td:first-child, -.table-bordered tbody:first-child tr:first-child > th:first-child { - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; -} - -.table-bordered thead:first-child tr:first-child > th:last-child, -.table-bordered tbody:first-child tr:first-child > td:last-child, -.table-bordered tbody:first-child tr:first-child > th:last-child { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; -} - -.table-bordered thead:last-child tr:last-child > th:first-child, -.table-bordered tbody:last-child tr:last-child > td:first-child, -.table-bordered tbody:last-child tr:last-child > th:first-child, -.table-bordered tfoot:last-child tr:last-child > td:first-child, -.table-bordered tfoot:last-child tr:last-child > th:first-child { - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; -} - -.table-bordered thead:last-child tr:last-child > th:last-child, -.table-bordered tbody:last-child tr:last-child > td:last-child, -.table-bordered tbody:last-child tr:last-child > th:last-child, -.table-bordered tfoot:last-child tr:last-child > td:last-child, -.table-bordered tfoot:last-child tr:last-child > th:last-child { - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-bottomright: 4px; -} - -.table-bordered tfoot + tbody:last-child tr:last-child td:first-child { - -webkit-border-bottom-left-radius: 0; - border-bottom-left-radius: 0; - -moz-border-radius-bottomleft: 0; -} - -.table-bordered tfoot + tbody:last-child tr:last-child td:last-child { - -webkit-border-bottom-right-radius: 0; - border-bottom-right-radius: 0; - -moz-border-radius-bottomright: 0; -} - -.table-bordered caption + thead tr:first-child th:first-child, -.table-bordered caption + tbody tr:first-child td:first-child, -.table-bordered colgroup + thead tr:first-child th:first-child, -.table-bordered colgroup + tbody tr:first-child td:first-child { - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; -} - -.table-bordered caption + thead tr:first-child th:last-child, -.table-bordered caption + tbody tr:first-child td:last-child, -.table-bordered colgroup + thead tr:first-child th:last-child, -.table-bordered colgroup + tbody tr:first-child td:last-child { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; -} - -.table-striped tbody > tr:nth-child(odd) > td, -.table-striped tbody > tr:nth-child(odd) > th { - background-color: #f9f9f9; -} - -.table-hover tbody tr:hover > td, -.table-hover tbody tr:hover > th { - background-color: #f5f5f5; -} - -table td[class*="span"], -table th[class*="span"], -.row-fluid table td[class*="span"], -.row-fluid table th[class*="span"] { - display: table-cell; - float: none; - margin-left: 0; -} - -.table td.span1, -.table th.span1 { - float: none; - width: 44px; - margin-left: 0; -} - -.table td.span2, -.table th.span2 { - float: none; - width: 124px; - margin-left: 0; -} - -.table td.span3, -.table th.span3 { - float: none; - width: 204px; - margin-left: 0; -} - -.table td.span4, -.table th.span4 { - float: none; - width: 284px; - margin-left: 0; -} - -.table td.span5, -.table th.span5 { - float: none; - width: 364px; - margin-left: 0; -} - -.table td.span6, -.table th.span6 { - float: none; - width: 444px; - margin-left: 0; -} - -.table td.span7, -.table th.span7 { - float: none; - width: 524px; - margin-left: 0; -} - -.table td.span8, -.table th.span8 { - float: none; - width: 604px; - margin-left: 0; -} - -.table td.span9, -.table th.span9 { - float: none; - width: 684px; - margin-left: 0; -} - -.table td.span10, -.table th.span10 { - float: none; - width: 764px; - margin-left: 0; -} - -.table td.span11, -.table th.span11 { - float: none; - width: 844px; - margin-left: 0; -} - -.table td.span12, -.table th.span12 { - float: none; - width: 924px; - margin-left: 0; -} - -.table tbody tr.success > td { - background-color: #dff0d8; -} - -.table tbody tr.error > td { - background-color: #f2dede; -} - -.table tbody tr.warning > td { - background-color: #fcf8e3; -} - -.table tbody tr.info > td { - background-color: #d9edf7; -} - -.table-hover tbody tr.success:hover > td { - background-color: #d0e9c6; -} - -.table-hover tbody tr.error:hover > td { - background-color: #ebcccc; -} - -.table-hover tbody tr.warning:hover > td { - background-color: #faf2cc; -} - -.table-hover tbody tr.info:hover > td { - background-color: #c4e3f3; -} - -[class^="icon-"], -[class*=" icon-"] { - display: inline-block; - width: 14px; - height: 14px; - margin-top: 1px; - *margin-right: .3em; - line-height: 14px; - vertical-align: text-top; - background-image: url("../img/glyphicons-halflings.png"); - background-position: 14px 14px; - background-repeat: no-repeat; -} - -/* White icons with optional class, or on hover/focus/active states of certain elements */ - -.icon-white, -.nav-pills > .active > a > [class^="icon-"], -.nav-pills > .active > a > [class*=" icon-"], -.nav-list > .active > a > [class^="icon-"], -.nav-list > .active > a > [class*=" icon-"], -.navbar-inverse .nav > .active > a > [class^="icon-"], -.navbar-inverse .nav > .active > a > [class*=" icon-"], -.dropdown-menu > li > a:hover > [class^="icon-"], -.dropdown-menu > li > a:focus > [class^="icon-"], -.dropdown-menu > li > a:hover > [class*=" icon-"], -.dropdown-menu > li > a:focus > [class*=" icon-"], -.dropdown-menu > .active > a > [class^="icon-"], -.dropdown-menu > .active > a > [class*=" icon-"], -.dropdown-submenu:hover > a > [class^="icon-"], -.dropdown-submenu:focus > a > [class^="icon-"], -.dropdown-submenu:hover > a > [class*=" icon-"], -.dropdown-submenu:focus > a > [class*=" icon-"] { - background-image: url("../img/glyphicons-halflings-white.png"); -} - -.icon-glass { - background-position: 0 0; -} - -.icon-music { - background-position: -24px 0; -} - -.icon-search { - background-position: -48px 0; -} - -.icon-envelope { - background-position: -72px 0; -} - -.icon-heart { - background-position: -96px 0; -} - -.icon-star { - background-position: -120px 0; -} - -.icon-star-empty { - background-position: -144px 0; -} - -.icon-user { - background-position: -168px 0; -} - -.icon-film { - background-position: -192px 0; -} - -.icon-th-large { - background-position: -216px 0; -} - -.icon-th { - background-position: -240px 0; -} - -.icon-th-list { - background-position: -264px 0; -} - -.icon-ok { - background-position: -288px 0; -} - -.icon-remove { - background-position: -312px 0; -} - -.icon-zoom-in { - background-position: -336px 0; -} - -.icon-zoom-out { - background-position: -360px 0; -} - -.icon-off { - background-position: -384px 0; -} - -.icon-signal { - background-position: -408px 0; -} - -.icon-cog { - background-position: -432px 0; -} - -.icon-trash { - background-position: -456px 0; -} - -.icon-home { - background-position: 0 -24px; -} - -.icon-file { - background-position: -24px -24px; -} - -.icon-time { - background-position: -48px -24px; -} - -.icon-road { - background-position: -72px -24px; -} - -.icon-download-alt { - background-position: -96px -24px; -} - -.icon-download { - background-position: -120px -24px; -} - -.icon-upload { - background-position: -144px -24px; -} - -.icon-inbox { - background-position: -168px -24px; -} - -.icon-play-circle { - background-position: -192px -24px; -} - -.icon-repeat { - background-position: -216px -24px; -} - -.icon-refresh { - background-position: -240px -24px; -} - -.icon-list-alt { - background-position: -264px -24px; -} - -.icon-lock { - background-position: -287px -24px; -} - -.icon-flag { - background-position: -312px -24px; -} - -.icon-headphones { - background-position: -336px -24px; -} - -.icon-volume-off { - background-position: -360px -24px; -} - -.icon-volume-down { - background-position: -384px -24px; -} - -.icon-volume-up { - background-position: -408px -24px; -} - -.icon-qrcode { - background-position: -432px -24px; -} - -.icon-barcode { - background-position: -456px -24px; -} - -.icon-tag { - background-position: 0 -48px; -} - -.icon-tags { - background-position: -25px -48px; -} - -.icon-book { - background-position: -48px -48px; -} - -.icon-bookmark { - background-position: -72px -48px; -} - -.icon-print { - background-position: -96px -48px; -} - -.icon-camera { - background-position: -120px -48px; -} - -.icon-font { - background-position: -144px -48px; -} - -.icon-bold { - background-position: -167px -48px; -} - -.icon-italic { - background-position: -192px -48px; -} - -.icon-text-height { - background-position: -216px -48px; -} - -.icon-text-width { - background-position: -240px -48px; -} - -.icon-align-left { - background-position: -264px -48px; -} - -.icon-align-center { - background-position: -288px -48px; -} - -.icon-align-right { - background-position: -312px -48px; -} - -.icon-align-justify { - background-position: -336px -48px; -} - -.icon-list { - background-position: -360px -48px; -} - -.icon-indent-left { - background-position: -384px -48px; -} - -.icon-indent-right { - background-position: -408px -48px; -} - -.icon-facetime-video { - background-position: -432px -48px; -} - -.icon-picture { - background-position: -456px -48px; -} - -.icon-pencil { - background-position: 0 -72px; -} - -.icon-map-marker { - background-position: -24px -72px; -} - -.icon-adjust { - background-position: -48px -72px; -} - -.icon-tint { - background-position: -72px -72px; -} - -.icon-edit { - background-position: -96px -72px; -} - -.icon-share { - background-position: -120px -72px; -} - -.icon-check { - background-position: -144px -72px; -} - -.icon-move { - background-position: -168px -72px; -} - -.icon-step-backward { - background-position: -192px -72px; -} - -.icon-fast-backward { - background-position: -216px -72px; -} - -.icon-backward { - background-position: -240px -72px; -} - -.icon-play { - background-position: -264px -72px; -} - -.icon-pause { - background-position: -288px -72px; -} - -.icon-stop { - background-position: -312px -72px; -} - -.icon-forward { - background-position: -336px -72px; -} - -.icon-fast-forward { - background-position: -360px -72px; -} - -.icon-step-forward { - background-position: -384px -72px; -} - -.icon-eject { - background-position: -408px -72px; -} - -.icon-chevron-left { - background-position: -432px -72px; -} - -.icon-chevron-right { - background-position: -456px -72px; -} - -.icon-plus-sign { - background-position: 0 -96px; -} - -.icon-minus-sign { - background-position: -24px -96px; -} - -.icon-remove-sign { - background-position: -48px -96px; -} - -.icon-ok-sign { - background-position: -72px -96px; -} - -.icon-question-sign { - background-position: -96px -96px; -} - -.icon-info-sign { - background-position: -120px -96px; -} - -.icon-screenshot { - background-position: -144px -96px; -} - -.icon-remove-circle { - background-position: -168px -96px; -} - -.icon-ok-circle { - background-position: -192px -96px; -} - -.icon-ban-circle { - background-position: -216px -96px; -} - -.icon-arrow-left { - background-position: -240px -96px; -} - -.icon-arrow-right { - background-position: -264px -96px; -} - -.icon-arrow-up { - background-position: -289px -96px; -} - -.icon-arrow-down { - background-position: -312px -96px; -} - -.icon-share-alt { - background-position: -336px -96px; -} - -.icon-resize-full { - background-position: -360px -96px; -} - -.icon-resize-small { - background-position: -384px -96px; -} - -.icon-plus { - background-position: -408px -96px; -} - -.icon-minus { - background-position: -433px -96px; -} - -.icon-asterisk { - background-position: -456px -96px; -} - -.icon-exclamation-sign { - background-position: 0 -120px; -} - -.icon-gift { - background-position: -24px -120px; -} - -.icon-leaf { - background-position: -48px -120px; -} - -.icon-fire { - background-position: -72px -120px; -} - -.icon-eye-open { - background-position: -96px -120px; -} - -.icon-eye-close { - background-position: -120px -120px; -} - -.icon-warning-sign { - background-position: -144px -120px; -} - -.icon-plane { - background-position: -168px -120px; -} - -.icon-calendar { - background-position: -192px -120px; -} - -.icon-random { - width: 16px; - background-position: -216px -120px; -} - -.icon-comment { - background-position: -240px -120px; -} - -.icon-magnet { - background-position: -264px -120px; -} - -.icon-chevron-up { - background-position: -288px -120px; -} - -.icon-chevron-down { - background-position: -313px -119px; -} - -.icon-retweet { - background-position: -336px -120px; -} - -.icon-shopping-cart { - background-position: -360px -120px; -} - -.icon-folder-close { - width: 16px; - background-position: -384px -120px; -} - -.icon-folder-open { - width: 16px; - background-position: -408px -120px; -} - -.icon-resize-vertical { - background-position: -432px -119px; -} - -.icon-resize-horizontal { - background-position: -456px -118px; -} - -.icon-hdd { - background-position: 0 -144px; -} - -.icon-bullhorn { - background-position: -24px -144px; -} - -.icon-bell { - background-position: -48px -144px; -} - -.icon-certificate { - background-position: -72px -144px; -} - -.icon-thumbs-up { - background-position: -96px -144px; -} - -.icon-thumbs-down { - background-position: -120px -144px; -} - -.icon-hand-right { - background-position: -144px -144px; -} - -.icon-hand-left { - background-position: -168px -144px; -} - -.icon-hand-up { - background-position: -192px -144px; -} - -.icon-hand-down { - background-position: -216px -144px; -} - -.icon-circle-arrow-right { - background-position: -240px -144px; -} - -.icon-circle-arrow-left { - background-position: -264px -144px; -} - -.icon-circle-arrow-up { - background-position: -288px -144px; -} - -.icon-circle-arrow-down { - background-position: -312px -144px; -} - -.icon-globe { - background-position: -336px -144px; -} - -.icon-wrench { - background-position: -360px -144px; -} - -.icon-tasks { - background-position: -384px -144px; -} - -.icon-filter { - background-position: -408px -144px; -} - -.icon-briefcase { - background-position: -432px -144px; -} - -.icon-fullscreen { - background-position: -456px -144px; -} - -.dropup, -.dropdown { - position: relative; -} - -.dropdown-toggle { - *margin-bottom: -3px; -} - -.dropdown-toggle:active, -.open .dropdown-toggle { - outline: 0; -} - -.caret { - display: inline-block; - width: 0; - height: 0; - vertical-align: top; - border-top: 4px solid #000000; - border-right: 4px solid transparent; - border-left: 4px solid transparent; - content: ""; -} - -.dropdown .caret { - margin-top: 8px; - margin-left: 2px; -} - -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - list-style: none; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - *border-right-width: 2px; - *border-bottom-width: 2px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} - -.dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.dropdown-menu .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: normal; - line-height: 20px; - color: #333333; - white-space: nowrap; -} - -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus, -.dropdown-submenu:hover > a, -.dropdown-submenu:focus > a { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - outline: 0; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #999999; -} - -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.open { - *z-index: 1000; -} - -.open > .dropdown-menu { - display: block; -} - -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} - -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} - -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - border-top: 0; - border-bottom: 4px solid #000000; - content: ""; -} - -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 1px; -} - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu > .dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - -webkit-border-radius: 0 6px 6px 6px; - -moz-border-radius: 0 6px 6px 6px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover > .dropdown-menu { - display: block; -} - -.dropup .dropdown-submenu > .dropdown-menu { - top: auto; - bottom: 0; - margin-top: 0; - margin-bottom: -2px; - -webkit-border-radius: 5px 5px 5px 0; - -moz-border-radius: 5px 5px 5px 0; - border-radius: 5px 5px 5px 0; -} - -.dropdown-submenu > a:after { - display: block; - float: right; - width: 0; - height: 0; - margin-top: 5px; - margin-right: -10px; - border-color: transparent; - border-left-color: #cccccc; - border-style: solid; - border-width: 5px 0 5px 5px; - content: " "; -} - -.dropdown-submenu:hover > a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left > .dropdown-menu { - left: -100%; - margin-left: 10px; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.dropdown .dropdown-menu .nav-header { - padding-right: 20px; - padding-left: 20px; -} - -.typeahead { - z-index: 1051; - margin-top: 2px; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} - -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} - -.well-large { - padding: 24px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.well-small { - padding: 9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -moz-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} - -.fade.in { - opacity: 1; -} - -.collapse { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition: height 0.35s ease; - -moz-transition: height 0.35s ease; - -o-transition: height 0.35s ease; - transition: height 0.35s ease; -} - -.collapse.in { - height: auto; -} - -.close { - float: right; - font-size: 20px; - font-weight: bold; - line-height: 20px; - color: #000000; - text-shadow: 0 1px 0 #ffffff; - opacity: 0.2; - filter: alpha(opacity=20); -} - -.close:hover, 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6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - border-bottom-right-radius: 6px; - -moz-border-radius-topright: 6px; - -moz-border-radius-bottomright: 6px; -} - -.btn-group > .btn:hover, -.btn-group > .btn:focus, -.btn-group > .btn:active, -.btn-group > .btn.active { - z-index: 2; -} - -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} - -.btn-group > .btn + .dropdown-toggle { - *padding-top: 5px; - padding-right: 8px; - *padding-bottom: 5px; - padding-left: 8px; - -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group > .btn-mini + .dropdown-toggle { - *padding-top: 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-.nav > li > a > img { - max-width: none; -} - -.nav > .pull-right { - float: right; -} - -.nav-header { - display: block; - padding: 3px 15px; - font-size: 11px; - font-weight: bold; - line-height: 20px; - color: #999999; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - text-transform: uppercase; -} - -.nav li + .nav-header { - margin-top: 9px; -} - -.nav-list { - padding-right: 15px; - padding-left: 15px; - margin-bottom: 0; -} - -.nav-list > li > a, -.nav-list .nav-header { - margin-right: -15px; - margin-left: -15px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); -} - -.nav-list > li > a { - padding: 3px 15px; -} - -.nav-list > .active > a, -.nav-list > .active > a:hover, -.nav-list > .active > a:focus { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); - background-color: #0088cc; -} - -.nav-list [class^="icon-"], -.nav-list [class*=" icon-"] { - margin-right: 2px; -} - -.nav-list .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.nav-tabs, -.nav-pills { - *zoom: 1; -} - -.nav-tabs:before, -.nav-pills:before, -.nav-tabs:after, -.nav-pills:after { - display: table; - line-height: 0; - content: ""; -} - -.nav-tabs:after, -.nav-pills:after { - clear: both; -} - -.nav-tabs > li, -.nav-pills > li { - float: left; -} - -.nav-tabs > li > a, -.nav-pills > li > a { - padding-right: 12px; - padding-left: 12px; - margin-right: 2px; - line-height: 14px; -} - -.nav-tabs { - border-bottom: 1px solid #ddd; -} - -.nav-tabs > li { - margin-bottom: -1px; -} - -.nav-tabs > li > a { - padding-top: 8px; - padding-bottom: 8px; - line-height: 20px; - border: 1px solid transparent; - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -.nav-tabs > li > a:hover, -.nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #dddddd; -} - -.nav-tabs > .active > a, -.nav-tabs > .active > a:hover, 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.dropdown-toggle:focus .caret { - border-top-color: #005580; - border-bottom-color: #005580; -} - -/* move down carets for tabs */ - -.nav-tabs .dropdown-toggle .caret { - margin-top: 8px; -} - -.nav .active .dropdown-toggle .caret { - border-top-color: #fff; - border-bottom-color: #fff; -} - -.nav-tabs .active .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.nav > .dropdown.active > a:hover, -.nav > .dropdown.active > a:focus { - cursor: pointer; -} - -.nav-tabs .open .dropdown-toggle, -.nav-pills .open .dropdown-toggle, -.nav > li.dropdown.open.active > a:hover, -.nav > li.dropdown.open.active > a:focus { - color: #ffffff; - background-color: #999999; - border-color: #999999; -} - -.nav li.dropdown.open .caret, -.nav li.dropdown.open.active .caret, -.nav li.dropdown.open a:hover .caret, -.nav li.dropdown.open a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; - opacity: 1; - filter: alpha(opacity=100); -} - -.tabs-stacked .open > a:hover, -.tabs-stacked .open > a:focus { - border-color: #999999; -} - -.tabbable { - *zoom: 1; -} - -.tabbable:before, -.tabbable:after { - display: table; - line-height: 0; - content: ""; -} - -.tabbable:after { - clear: both; -} - -.tab-content { - overflow: auto; -} - -.tabs-below > .nav-tabs, -.tabs-right > .nav-tabs, -.tabs-left > .nav-tabs { - border-bottom: 0; -} - -.tab-content > .tab-pane, -.pill-content > .pill-pane { - display: none; -} - -.tab-content > .active, -.pill-content > .active { - display: block; -} - -.tabs-below > .nav-tabs { - border-top: 1px solid #ddd; -} - -.tabs-below > .nav-tabs > li { - margin-top: -1px; - margin-bottom: 0; -} - -.tabs-below > .nav-tabs > li > a { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} - -.tabs-below > .nav-tabs > li > a:hover, -.tabs-below > .nav-tabs > li > a:focus { - border-top-color: #ddd; - border-bottom-color: transparent; -} - -.tabs-below > .nav-tabs > .active > a, -.tabs-below > .nav-tabs > .active > a:hover, -.tabs-below > .nav-tabs > .active > a:focus { - border-color: transparent #ddd #ddd #ddd; -} - -.tabs-left > .nav-tabs > li, -.tabs-right > .nav-tabs > li { - float: none; -} - -.tabs-left > .nav-tabs > li > a, -.tabs-right > .nav-tabs > li > a { - min-width: 74px; - margin-right: 0; - margin-bottom: 3px; -} - -.tabs-left > .nav-tabs { - float: left; - margin-right: 19px; - border-right: 1px solid #ddd; -} - -.tabs-left > .nav-tabs > li > a { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.tabs-left > .nav-tabs > li > a:hover, -.tabs-left > .nav-tabs > li > a:focus { - border-color: #eeeeee #dddddd #eeeeee #eeeeee; -} - -.tabs-left > .nav-tabs .active > a, -.tabs-left > .nav-tabs .active > a:hover, -.tabs-left > .nav-tabs .active > a:focus { - border-color: #ddd transparent #ddd #ddd; - *border-right-color: #ffffff; -} - -.tabs-right > .nav-tabs { - float: right; - margin-left: 19px; - border-left: 1px solid #ddd; -} - -.tabs-right > .nav-tabs > li > a { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.tabs-right > .nav-tabs > li > a:hover, -.tabs-right > .nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #eeeeee #dddddd; -} - -.tabs-right > .nav-tabs .active > a, -.tabs-right > .nav-tabs .active > a:hover, -.tabs-right > .nav-tabs .active > a:focus { - border-color: #ddd #ddd #ddd transparent; - *border-left-color: #ffffff; -} - -.nav > .disabled > a { - color: #999999; -} - -.nav > .disabled > a:hover, -.nav > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; -} - -.navbar { - *position: relative; - *z-index: 2; - margin-bottom: 20px; - overflow: visible; -} - -.navbar-inner { - min-height: 40px; - padding-right: 20px; - padding-left: 20px; - background-color: #fafafa; - background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); - background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); - background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); - background-repeat: repeat-x; - border: 1px solid #d4d4d4; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); - *zoom: 1; - -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); -} - -.navbar-inner:before, -.navbar-inner:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-inner:after { - clear: both; -} - -.navbar .container { - width: auto; -} - -.nav-collapse.collapse { 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text-decoration: none; - background-color: #e5e5e5; - -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); -} - -.navbar .btn-navbar { - display: none; - float: right; - padding: 7px 10px; - margin-right: 5px; - margin-left: 5px; - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #ededed; - *background-color: #e5e5e5; - background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5)); - background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5); - background-repeat: repeat-x; - border-color: #e5e5e5 #e5e5e5 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: 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border-radius: 1px; - -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); -} - -.btn-navbar .icon-bar + .icon-bar { - margin-top: 3px; -} - -.navbar .nav > li > .dropdown-menu:before { - position: absolute; - top: -7px; - left: 9px; - display: inline-block; - border-right: 7px solid transparent; - border-bottom: 7px solid #ccc; - border-left: 7px solid transparent; - border-bottom-color: rgba(0, 0, 0, 0.2); - content: ''; -} - -.navbar .nav > li > .dropdown-menu:after { - position: absolute; - top: -6px; - left: 10px; - display: inline-block; - border-right: 6px solid transparent; - border-bottom: 6px solid #ffffff; - border-left: 6px solid transparent; - content: ''; -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:before { - top: auto; - bottom: -7px; - border-top: 7px solid #ccc; - border-bottom: 0; - border-top-color: rgba(0, 0, 0, 0.2); -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:after { - top: auto; - bottom: -6px; - border-top: 6px solid #ffffff; - border-bottom: 0; -} - -.navbar .nav li.dropdown > a:hover .caret, -.navbar .nav li.dropdown > a:focus .caret { - border-top-color: #333333; - border-bottom-color: #333333; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle, -.navbar .nav li.dropdown.active > .dropdown-toggle, -.navbar .nav li.dropdown.open.active > .dropdown-toggle { - color: #555555; - background-color: #e5e5e5; -} - -.navbar .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #777777; - border-bottom-color: #777777; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.navbar .pull-right > li > .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:before, -.navbar .nav > li > .dropdown-menu.pull-right:before { - right: 12px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:after, -.navbar .nav > li > .dropdown-menu.pull-right:after { - right: 13px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { - right: 100%; - left: auto; - margin-right: -1px; - margin-left: 0; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.navbar-inverse .navbar-inner { - background-color: #1b1b1b; - background-image: -moz-linear-gradient(top, #222222, #111111); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); - background-image: -webkit-linear-gradient(top, #222222, #111111); - background-image: -o-linear-gradient(top, #222222, #111111); - background-image: linear-gradient(to bottom, #222222, #111111); - background-repeat: 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a9969993..00000000 Binary files a/docsrc/_build/html/_static/bootstrap-2.3.2/img/glyphicons-halflings.png and /dev/null differ diff --git a/docsrc/_build/html/_static/bootstrap-2.3.2/js/bootstrap.js b/docsrc/_build/html/_static/bootstrap-2.3.2/js/bootstrap.js deleted file mode 100644 index 638bb187..00000000 --- a/docsrc/_build/html/_static/bootstrap-2.3.2/js/bootstrap.js +++ /dev/null @@ -1,2287 +0,0 @@ -/* =================================================== - * bootstrap-transition.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#transitions - * =================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CSS TRANSITION SUPPORT (http://www.modernizr.com/) - * ======================================================= */ - - $(function () { - - $.support.transition = (function () { - - var transitionEnd = (function () { - - var el = document.createElement('bootstrap') - , transEndEventNames = { - 'WebkitTransition' : 'webkitTransitionEnd' - , 'MozTransition' : 'transitionend' - , 'OTransition' : 'oTransitionEnd otransitionend' - , 'transition' : 'transitionend' - } - , name - - for (name in transEndEventNames){ - if (el.style[name] !== undefined) { - return transEndEventNames[name] - } - } - - }()) - - return transitionEnd && { - end: transitionEnd - } - - })() - - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-alert.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#alerts - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* ALERT CLASS DEFINITION - * ====================== */ - - var dismiss = '[data-dismiss="alert"]' - , Alert = function (el) { - $(el).on('click', dismiss, this.close) - } - - Alert.prototype.close = function (e) { - var $this = $(this) - , selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = $(selector) - - e && e.preventDefault() - - $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent()) - - $parent.trigger(e = $.Event('close')) - - if (e.isDefaultPrevented()) return - - $parent.removeClass('in') - - function removeElement() { - $parent - .trigger('closed') - .remove() - } - - $.support.transition && $parent.hasClass('fade') ? - $parent.on($.support.transition.end, removeElement) : - removeElement() - } - - - /* ALERT PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.alert - - $.fn.alert = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('alert') - if (!data) $this.data('alert', (data = new Alert(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.alert.Constructor = Alert - - - /* ALERT NO CONFLICT - * ================= */ - - $.fn.alert.noConflict = function () { - $.fn.alert = old - return this - } - - - /* ALERT DATA-API - * ============== */ - - $(document).on('click.alert.data-api', dismiss, Alert.prototype.close) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-button.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#buttons - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* BUTTON PUBLIC CLASS DEFINITION - * ============================== */ - - var Button = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.button.defaults, options) - } - - Button.prototype.setState = function (state) { - var d = 'disabled' - , $el = this.$element - , data = $el.data() - , val = $el.is('input') ? 'val' : 'html' - - state = state + 'Text' - data.resetText || $el.data('resetText', $el[val]()) - - $el[val](data[state] || this.options[state]) - - // push to event loop to allow forms to submit - setTimeout(function () { - state == 'loadingText' ? - $el.addClass(d).attr(d, d) : - $el.removeClass(d).removeAttr(d) - }, 0) - } - - Button.prototype.toggle = function () { - var $parent = this.$element.closest('[data-toggle="buttons-radio"]') - - $parent && $parent - .find('.active') - .removeClass('active') - - this.$element.toggleClass('active') - } - - - /* BUTTON PLUGIN DEFINITION - * ======================== */ - - var old = $.fn.button - - $.fn.button = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('button') - , options = typeof option == 'object' && option - if (!data) $this.data('button', (data = new Button(this, options))) - if (option == 'toggle') data.toggle() - else if (option) data.setState(option) - }) - } - - $.fn.button.defaults = { - loadingText: 'loading...' - } - - $.fn.button.Constructor = Button - - - /* BUTTON NO CONFLICT - * ================== */ - - $.fn.button.noConflict = function () { - $.fn.button = old - return this - } - - - /* BUTTON DATA-API - * =============== */ - - $(document).on('click.button.data-api', '[data-toggle^=button]', function (e) { - var $btn = $(e.target) - if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') - $btn.button('toggle') - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-carousel.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#carousel - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CAROUSEL CLASS DEFINITION - * ========================= */ - - var Carousel = function (element, options) { - this.$element = $(element) - this.$indicators = this.$element.find('.carousel-indicators') - this.options = options - this.options.pause == 'hover' && this.$element - .on('mouseenter', $.proxy(this.pause, this)) - .on('mouseleave', $.proxy(this.cycle, this)) - } - - Carousel.prototype = { - - cycle: function (e) { - if (!e) this.paused = false - if (this.interval) clearInterval(this.interval); - this.options.interval - && !this.paused - && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) - return this - } - - , getActiveIndex: function () { - this.$active = this.$element.find('.item.active') - this.$items = this.$active.parent().children() - return this.$items.index(this.$active) - } - - , to: function (pos) { - var activeIndex = this.getActiveIndex() - , that = this - - if (pos > (this.$items.length - 1) || pos < 0) return - - if (this.sliding) { - return this.$element.one('slid', function () { - that.to(pos) - }) - } - - if (activeIndex == pos) { - return this.pause().cycle() - } - - return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos])) - } - - , pause: function (e) { - if (!e) this.paused = true - if (this.$element.find('.next, .prev').length && $.support.transition.end) { - this.$element.trigger($.support.transition.end) - this.cycle(true) - } - clearInterval(this.interval) - this.interval = null - return this - } - - , next: function () { - if (this.sliding) return - return this.slide('next') - } - - , prev: function () { - if (this.sliding) return - return this.slide('prev') - } - - , slide: function (type, next) { - var $active = this.$element.find('.item.active') - , $next = next || $active[type]() - , isCycling = this.interval - , direction = type == 'next' ? 'left' : 'right' - , fallback = type == 'next' ? 'first' : 'last' - , that = this - , e - - this.sliding = true - - isCycling && this.pause() - - $next = $next.length ? $next : this.$element.find('.item')[fallback]() - - e = $.Event('slide', { - relatedTarget: $next[0] - , direction: direction - }) - - if ($next.hasClass('active')) return - - if (this.$indicators.length) { - this.$indicators.find('.active').removeClass('active') - this.$element.one('slid', function () { - var $nextIndicator = $(that.$indicators.children()[that.getActiveIndex()]) - $nextIndicator && $nextIndicator.addClass('active') - }) - } - - if ($.support.transition && this.$element.hasClass('slide')) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $next.addClass(type) - $next[0].offsetWidth // force reflow - $active.addClass(direction) - $next.addClass(direction) - this.$element.one($.support.transition.end, function () { - $next.removeClass([type, direction].join(' ')).addClass('active') - $active.removeClass(['active', direction].join(' ')) - that.sliding = false - setTimeout(function () { that.$element.trigger('slid') }, 0) - }) - } else { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $active.removeClass('active') - $next.addClass('active') - this.sliding = false - this.$element.trigger('slid') - } - - isCycling && this.cycle() - - return this - } - - } - - - /* CAROUSEL PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.carousel - - $.fn.carousel = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('carousel') - , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) - , action = typeof option == 'string' ? option : options.slide - if (!data) $this.data('carousel', (data = new Carousel(this, options))) - if (typeof option == 'number') data.to(option) - else if (action) data[action]() - else if (options.interval) data.pause().cycle() - }) - } - - $.fn.carousel.defaults = { - interval: 5000 - , pause: 'hover' - } - - $.fn.carousel.Constructor = Carousel - - - /* CAROUSEL NO CONFLICT - * ==================== */ - - $.fn.carousel.noConflict = function () { - $.fn.carousel = old - return this - } - - /* CAROUSEL DATA-API - * ================= */ - - $(document).on('click.carousel.data-api', '[data-slide], [data-slide-to]', function (e) { - var $this = $(this), href - , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - , options = $.extend({}, $target.data(), $this.data()) - , slideIndex - - $target.carousel(options) - - if (slideIndex = $this.attr('data-slide-to')) { - $target.data('carousel').pause().to(slideIndex).cycle() - } - - e.preventDefault() - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-collapse.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#collapse - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* COLLAPSE PUBLIC CLASS DEFINITION - * ================================ */ - - var Collapse = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.collapse.defaults, options) - - if (this.options.parent) { - this.$parent = $(this.options.parent) - } - - this.options.toggle && this.toggle() - } - - Collapse.prototype = { - - constructor: Collapse - - , dimension: function () { - var hasWidth = this.$element.hasClass('width') - return hasWidth ? 'width' : 'height' - } - - , show: function () { - var dimension - , scroll - , actives - , hasData - - if (this.transitioning || this.$element.hasClass('in')) return - - dimension = this.dimension() - scroll = $.camelCase(['scroll', dimension].join('-')) - actives = this.$parent && this.$parent.find('> .accordion-group > .in') - - if (actives && actives.length) { - hasData = actives.data('collapse') - if (hasData && hasData.transitioning) return - actives.collapse('hide') - hasData || actives.data('collapse', null) - } - - this.$element[dimension](0) - this.transition('addClass', $.Event('show'), 'shown') - $.support.transition && this.$element[dimension](this.$element[0][scroll]) - } - - , hide: function () { - var dimension - if (this.transitioning || !this.$element.hasClass('in')) return - dimension = this.dimension() - this.reset(this.$element[dimension]()) - this.transition('removeClass', $.Event('hide'), 'hidden') - this.$element[dimension](0) - } - - , reset: function (size) { - var dimension = this.dimension() - - this.$element - .removeClass('collapse') - [dimension](size || 'auto') - [0].offsetWidth - - this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') - - return this - } - - , transition: function (method, startEvent, completeEvent) { - var that = this - , complete = function () { - if (startEvent.type == 'show') that.reset() - that.transitioning = 0 - that.$element.trigger(completeEvent) - } - - this.$element.trigger(startEvent) - - if (startEvent.isDefaultPrevented()) return - - this.transitioning = 1 - - this.$element[method]('in') - - $.support.transition && this.$element.hasClass('collapse') ? - this.$element.one($.support.transition.end, complete) : - complete() - } - - , toggle: function () { - this[this.$element.hasClass('in') ? 'hide' : 'show']() - } - - } - - - /* COLLAPSE PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.collapse - - $.fn.collapse = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('collapse') - , options = $.extend({}, $.fn.collapse.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('collapse', (data = new Collapse(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.collapse.defaults = { - toggle: true - } - - $.fn.collapse.Constructor = Collapse - - - /* COLLAPSE NO CONFLICT - * ==================== */ - - $.fn.collapse.noConflict = function () { - $.fn.collapse = old - return this - } - - - /* COLLAPSE DATA-API - * ================= */ - - $(document).on('click.collapse.data-api', '[data-toggle=collapse]', function (e) { - var $this = $(this), href - , target = $this.attr('data-target') - || e.preventDefault() - || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 - , option = $(target).data('collapse') ? 'toggle' : $this.data() - $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed') - $(target).collapse(option) - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-dropdown.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#dropdowns - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* DROPDOWN CLASS DEFINITION - * ========================= */ - - var toggle = '[data-toggle=dropdown]' - , Dropdown = function (element) { - var $el = $(element).on('click.dropdown.data-api', this.toggle) - $('html').on('click.dropdown.data-api', function () { - $el.parent().removeClass('open') - }) - } - - Dropdown.prototype = { - - constructor: Dropdown - - , toggle: function (e) { - var $this = $(this) - , $parent - , isActive - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - clearMenus() - - if (!isActive) { - if ('ontouchstart' in document.documentElement) { - // if mobile we we use a backdrop because click events don't delegate - $('
').insertBefore($(this)).on('click', clearMenus) - } - $parent.toggleClass('open') - } - - $this.focus() - - return false - } - - , keydown: function (e) { - var $this - , $items - , $active - , $parent - , isActive - , index - - if (!/(38|40|27)/.test(e.keyCode)) return - - $this = $(this) - - e.preventDefault() - e.stopPropagation() - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - if (!isActive || (isActive && e.keyCode == 27)) { - if (e.which == 27) $parent.find(toggle).focus() - return $this.click() - } - - $items = $('[role=menu] li:not(.divider):visible a', $parent) - - if (!$items.length) return - - index = $items.index($items.filter(':focus')) - - if (e.keyCode == 38 && index > 0) index-- // up - if (e.keyCode == 40 && index < $items.length - 1) index++ // down - if (!~index) index = 0 - - $items - .eq(index) - .focus() - } - - } - - function clearMenus() { - $('.dropdown-backdrop').remove() - $(toggle).each(function () { - getParent($(this)).removeClass('open') - }) - } - - function getParent($this) { - var selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = selector && $(selector) - - if (!$parent || !$parent.length) $parent = $this.parent() - - return $parent - } - - - /* DROPDOWN PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.dropdown - - $.fn.dropdown = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('dropdown') - if (!data) $this.data('dropdown', (data = new Dropdown(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.dropdown.Constructor = Dropdown - - - /* DROPDOWN NO CONFLICT - * ==================== */ - - $.fn.dropdown.noConflict = function () { - $.fn.dropdown = old - return this - } - - - /* APPLY TO STANDARD DROPDOWN ELEMENTS - * =================================== */ - - $(document) - .on('click.dropdown.data-api', clearMenus) - .on('click.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) - .on('click.dropdown.data-api' , toggle, Dropdown.prototype.toggle) - .on('keydown.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown) - -}(window.$jqTheme || window.jQuery); -/* ========================================================= - * bootstrap-modal.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#modals - * ========================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================= */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* MODAL CLASS DEFINITION - * ====================== */ - - var Modal = function (element, options) { - this.options = options - this.$element = $(element) - .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) - this.options.remote && this.$element.find('.modal-body').load(this.options.remote) - } - - Modal.prototype = { - - constructor: Modal - - , toggle: function () { - return this[!this.isShown ? 'show' : 'hide']() - } - - , show: function () { - var that = this - , e = $.Event('show') - - this.$element.trigger(e) - - if (this.isShown || e.isDefaultPrevented()) return - - this.isShown = true - - this.escape() - - this.backdrop(function () { - var transition = $.support.transition && that.$element.hasClass('fade') - - if (!that.$element.parent().length) { - that.$element.appendTo(document.body) //don't move modals dom position - } - - that.$element.show() - - if (transition) { - that.$element[0].offsetWidth // force reflow - } - - that.$element - .addClass('in') - .attr('aria-hidden', false) - - that.enforceFocus() - - transition ? - that.$element.one($.support.transition.end, function () { that.$element.focus().trigger('shown') }) : - that.$element.focus().trigger('shown') - - }) - } - - , hide: function (e) { - e && e.preventDefault() - - var that = this - - e = $.Event('hide') - - this.$element.trigger(e) - - if (!this.isShown || e.isDefaultPrevented()) return - - this.isShown = false - - this.escape() - - $(document).off('focusin.modal') - - this.$element - .removeClass('in') - .attr('aria-hidden', true) - - $.support.transition && this.$element.hasClass('fade') ? - this.hideWithTransition() : - this.hideModal() - } - - , enforceFocus: function () { - var that = this - $(document).on('focusin.modal', function (e) { - if (that.$element[0] !== e.target && !that.$element.has(e.target).length) { - that.$element.focus() - } - }) - } - - , escape: function () { - var that = this - if (this.isShown && this.options.keyboard) { - this.$element.on('keyup.dismiss.modal', function ( e ) { - e.which == 27 && that.hide() - }) - } else if (!this.isShown) { - this.$element.off('keyup.dismiss.modal') - } - } - - , hideWithTransition: function () { - var that = this - , timeout = setTimeout(function () { - that.$element.off($.support.transition.end) - that.hideModal() - }, 500) - - this.$element.one($.support.transition.end, function () { - clearTimeout(timeout) - that.hideModal() - }) - } - - , hideModal: function () { - var that = this - this.$element.hide() - this.backdrop(function () { - that.removeBackdrop() - that.$element.trigger('hidden') - }) - } - - , removeBackdrop: function () { - this.$backdrop && this.$backdrop.remove() - this.$backdrop = null - } - - , backdrop: function (callback) { - var that = this - , animate = this.$element.hasClass('fade') ? 'fade' : '' - - if (this.isShown && this.options.backdrop) { - var doAnimate = $.support.transition && animate - - this.$backdrop = $('
') - .appendTo(document.body) - - this.$backdrop.click( - this.options.backdrop == 'static' ? - $.proxy(this.$element[0].focus, this.$element[0]) - : $.proxy(this.hide, this) - ) - - if (doAnimate) this.$backdrop[0].offsetWidth // force reflow - - this.$backdrop.addClass('in') - - if (!callback) return - - doAnimate ? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (!this.isShown && this.$backdrop) { - this.$backdrop.removeClass('in') - - $.support.transition && this.$element.hasClass('fade')? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (callback) { - callback() - } - } - } - - - /* MODAL PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.modal - - $.fn.modal = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('modal') - , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('modal', (data = new Modal(this, options))) - if (typeof option == 'string') data[option]() - else if (options.show) data.show() - }) - } - - $.fn.modal.defaults = { - backdrop: true - , keyboard: true - , show: true - } - - $.fn.modal.Constructor = Modal - - - /* MODAL NO CONFLICT - * ================= */ - - $.fn.modal.noConflict = function () { - $.fn.modal = old - return this - } - - - /* MODAL DATA-API - * ============== */ - - $(document).on('click.modal.data-api', '[data-toggle="modal"]', function (e) { - var $this = $(this) - , href = $this.attr('href') - , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7 - , option = $target.data('modal') ? 'toggle' : $.extend({ remote:!/#/.test(href) && href }, $target.data(), $this.data()) - - e.preventDefault() - - $target - .modal(option) - .one('hide', function () { - $this.focus() - }) - }) - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-tooltip.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tooltips - * Inspired by the original jQuery.tipsy by Jason Frame - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TOOLTIP PUBLIC CLASS DEFINITION - * =============================== */ - - var Tooltip = function (element, options) { - this.init('tooltip', element, options) - } - - Tooltip.prototype = { - - constructor: Tooltip - - , init: function (type, element, options) { - var eventIn - , eventOut - , triggers - , trigger - , i - - this.type = type - this.$element = $(element) - this.options = this.getOptions(options) - this.enabled = true - - triggers = this.options.trigger.split(' ') - - for (i = triggers.length; i--;) { - trigger = triggers[i] - if (trigger == 'click') { - this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) - } else if (trigger != 'manual') { - eventIn = trigger == 'hover' ? 'mouseenter' : 'focus' - eventOut = trigger == 'hover' ? 'mouseleave' : 'blur' - this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) - this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) - } - } - - this.options.selector ? - (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : - this.fixTitle() - } - - , getOptions: function (options) { - options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options) - - if (options.delay && typeof options.delay == 'number') { - options.delay = { - show: options.delay - , hide: options.delay - } - } - - return options - } - - , enter: function (e) { - var defaults = $.fn[this.type].defaults - , options = {} - , self - - this._options && $.each(this._options, function (key, value) { - if (defaults[key] != value) options[key] = value - }, this) - - self = $(e.currentTarget)[this.type](options).data(this.type) - - if (!self.options.delay || !self.options.delay.show) return self.show() - - clearTimeout(this.timeout) - self.hoverState = 'in' - this.timeout = setTimeout(function() { - if (self.hoverState == 'in') self.show() - }, self.options.delay.show) - } - - , leave: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - - if (this.timeout) clearTimeout(this.timeout) - if (!self.options.delay || !self.options.delay.hide) return self.hide() - - self.hoverState = 'out' - this.timeout = setTimeout(function() { - if (self.hoverState == 'out') self.hide() - }, self.options.delay.hide) - } - - , show: function () { - var $tip - , pos - , actualWidth - , actualHeight - , placement - , tp - , e = $.Event('show') - - if (this.hasContent() && this.enabled) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $tip = this.tip() - this.setContent() - - if (this.options.animation) { - $tip.addClass('fade') - } - - placement = typeof this.options.placement == 'function' ? - this.options.placement.call(this, $tip[0], this.$element[0]) : - this.options.placement - - $tip - .detach() - .css({ top: 0, left: 0, display: 'block' }) - - this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element) - - pos = this.getPosition() - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - switch (placement) { - case 'bottom': - tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'top': - tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'left': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} - break - case 'right': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} - break - } - - this.applyPlacement(tp, placement) - this.$element.trigger('shown') - } - } - - , applyPlacement: function(offset, placement){ - var $tip = this.tip() - , width = $tip[0].offsetWidth - , height = $tip[0].offsetHeight - , actualWidth - , actualHeight - , delta - , replace - - $tip - .offset(offset) - .addClass(placement) - .addClass('in') - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - if (placement == 'top' && actualHeight != height) { - offset.top = offset.top + height - actualHeight - replace = true - } - - if (placement == 'bottom' || placement == 'top') { - delta = 0 - - if (offset.left < 0){ - delta = offset.left * -2 - offset.left = 0 - $tip.offset(offset) - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - } - - this.replaceArrow(delta - width + actualWidth, actualWidth, 'left') - } else { - this.replaceArrow(actualHeight - height, actualHeight, 'top') - } - - if (replace) $tip.offset(offset) - } - - , replaceArrow: function(delta, dimension, position){ - this - .arrow() - .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '') - } - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - - $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) - $tip.removeClass('fade in top bottom left right') - } - - , hide: function () { - var that = this - , $tip = this.tip() - , e = $.Event('hide') - - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - - $tip.removeClass('in') - - function removeWithAnimation() { - var timeout = setTimeout(function () { - $tip.off($.support.transition.end).detach() - }, 500) - - $tip.one($.support.transition.end, function () { - clearTimeout(timeout) - $tip.detach() - }) - } - - $.support.transition && this.$tip.hasClass('fade') ? - removeWithAnimation() : - $tip.detach() - - this.$element.trigger('hidden') - - return this - } - - , fixTitle: function () { - var $e = this.$element - if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { - $e.attr('data-original-title', $e.attr('title') || '').attr('title', '') - } - } - - , hasContent: function () { - return this.getTitle() - } - - , getPosition: function () { - var el = this.$element[0] - return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : { - width: el.offsetWidth - , height: el.offsetHeight - }, this.$element.offset()) - } - - , getTitle: function () { - var title - , $e = this.$element - , o = this.options - - title = $e.attr('data-original-title') - || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) - - return title - } - - , tip: function () { - return this.$tip = this.$tip || $(this.options.template) - } - - , arrow: function(){ - return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow") - } - - , validate: function () { - if (!this.$element[0].parentNode) { - this.hide() - this.$element = null - this.options = null - } - } - - , enable: function () { - this.enabled = true - } - - , disable: function () { - this.enabled = false - } - - , toggleEnabled: function () { - this.enabled = !this.enabled - } - - , toggle: function (e) { - var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this - self.tip().hasClass('in') ? self.hide() : self.show() - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - } - - - /* TOOLTIP PLUGIN DEFINITION - * ========================= */ - - var old = $.fn.tooltip - - $.fn.tooltip = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tooltip') - , options = typeof option == 'object' && option - if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tooltip.Constructor = Tooltip - - $.fn.tooltip.defaults = { - animation: true - , placement: 'top' - , selector: false - , template: '
' - , trigger: 'hover focus' - , title: '' - , delay: 0 - , html: false - , container: false - } - - - /* TOOLTIP NO CONFLICT - * =================== */ - - $.fn.tooltip.noConflict = function () { - $.fn.tooltip = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-popover.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#popovers - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * =========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* POPOVER PUBLIC CLASS DEFINITION - * =============================== */ - - var Popover = function (element, options) { - this.init('popover', element, options) - } - - - /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js - ========================================== */ - - Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { - - constructor: Popover - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - , content = this.getContent() - - $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) - $tip.find('.popover-content')[this.options.html ? 'html' : 'text'](content) - - $tip.removeClass('fade top bottom left right in') - } - - , hasContent: function () { - return this.getTitle() || this.getContent() - } - - , getContent: function () { - var content - , $e = this.$element - , o = this.options - - content = (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) - || $e.attr('data-content') - - return content - } - - , tip: function () { - if (!this.$tip) { - this.$tip = $(this.options.template) - } - return this.$tip - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - }) - - - /* POPOVER PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.popover - - $.fn.popover = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('popover') - , options = typeof option == 'object' && option - if (!data) $this.data('popover', (data = new Popover(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.popover.Constructor = Popover - - $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { - placement: 'right' - , trigger: 'click' - , content: '' - , template: '

' - }) - - - /* POPOVER NO CONFLICT - * =================== */ - - $.fn.popover.noConflict = function () { - $.fn.popover = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-scrollspy.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#scrollspy - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* SCROLLSPY CLASS DEFINITION - * ========================== */ - - function ScrollSpy(element, options) { - var process = $.proxy(this.process, this) - , $element = $(element).is('body') ? $(window) : $(element) - , href - this.options = $.extend({}, $.fn.scrollspy.defaults, options) - this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process) - this.selector = (this.options.target - || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - || '') + ' .nav li > a' - this.$body = $('body') - this.refresh() - this.process() - } - - ScrollSpy.prototype = { - - constructor: ScrollSpy - - , refresh: function () { - var self = this - , $targets - - this.offsets = $([]) - this.targets = $([]) - - $targets = this.$body - .find(this.selector) - .map(function () { - var $el = $(this) - , href = $el.data('target') || $el.attr('href') - , $href = /^#\w/.test(href) && $(href) - return ( $href - && $href.length - && [[ $href.position().top + (!$.isWindow(self.$scrollElement.get(0)) && self.$scrollElement.scrollTop()), href ]] ) || null - }) - .sort(function (a, b) { return a[0] - b[0] }) - .each(function () { - self.offsets.push(this[0]) - self.targets.push(this[1]) - }) - } - - , process: function () { - var scrollTop = this.$scrollElement.scrollTop() + this.options.offset - , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight - , maxScroll = scrollHeight - this.$scrollElement.height() - , offsets = this.offsets - , targets = this.targets - , activeTarget = this.activeTarget - , i - - if (scrollTop >= maxScroll) { - return activeTarget != (i = targets.last()[0]) - && this.activate ( i ) - } - - for (i = offsets.length; i--;) { - activeTarget != targets[i] - && scrollTop >= offsets[i] - && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) - && this.activate( targets[i] ) - } - } - - , activate: function (target) { - var active - , selector - - this.activeTarget = target - - $(this.selector) - .parent('.active') - .removeClass('active') - - selector = this.selector - + '[data-target="' + target + '"],' - + this.selector + '[href="' + target + '"]' - - active = $(selector) - .parent('li') - .addClass('active') - - if (active.parent('.dropdown-menu').length) { - active = active.closest('li.dropdown').addClass('active') - } - - active.trigger('activate') - } - - } - - - /* SCROLLSPY PLUGIN DEFINITION - * =========================== */ - - var old = $.fn.scrollspy - - $.fn.scrollspy = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('scrollspy') - , options = typeof option == 'object' && option - if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.scrollspy.Constructor = ScrollSpy - - $.fn.scrollspy.defaults = { - offset: 10 - } - - - /* SCROLLSPY NO CONFLICT - * ===================== */ - - $.fn.scrollspy.noConflict = function () { - $.fn.scrollspy = old - return this - } - - - /* SCROLLSPY DATA-API - * ================== */ - - $(window).on('load', function () { - $('[data-spy="scroll"]').each(function () { - var $spy = $(this) - $spy.scrollspy($spy.data()) - }) - }) - -}(window.$jqTheme || window.jQuery); -/* ======================================================== - * bootstrap-tab.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tabs - * ======================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ======================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TAB CLASS DEFINITION - * ==================== */ - - var Tab = function (element) { - this.element = $(element) - } - - Tab.prototype = { - - constructor: Tab - - , show: function () { - var $this = this.element - , $ul = $this.closest('ul:not(.dropdown-menu)') - , selector = $this.attr('data-target') - , previous - , $target - , e - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - if ( $this.parent('li').hasClass('active') ) return - - previous = $ul.find('.active:last a')[0] - - e = $.Event('show', { - relatedTarget: previous - }) - - $this.trigger(e) - - if (e.isDefaultPrevented()) return - - $target = $(selector) - - this.activate($this.parent('li'), $ul) - this.activate($target, $target.parent(), function () { - $this.trigger({ - type: 'shown' - , relatedTarget: previous - }) - }) - } - - , activate: function ( element, container, callback) { - var $active = container.find('> .active') - , transition = callback - && $.support.transition - && $active.hasClass('fade') - - function next() { - $active - .removeClass('active') - .find('> .dropdown-menu > .active') - .removeClass('active') - - element.addClass('active') - - if (transition) { - element[0].offsetWidth // reflow for transition - element.addClass('in') - } else { - element.removeClass('fade') - } - - if ( element.parent('.dropdown-menu') ) { - element.closest('li.dropdown').addClass('active') - } - - callback && callback() - } - - transition ? - $active.one($.support.transition.end, next) : - next() - - $active.removeClass('in') - } - } - - - /* TAB PLUGIN DEFINITION - * ===================== */ - - var old = $.fn.tab - - $.fn.tab = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tab') - if (!data) $this.data('tab', (data = new Tab(this))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tab.Constructor = Tab - - - /* TAB NO CONFLICT - * =============== */ - - $.fn.tab.noConflict = function () { - $.fn.tab = old - return this - } - - - /* TAB DATA-API - * ============ */ - - $(document).on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { - e.preventDefault() - $(this).tab('show') - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-typeahead.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#typeahead - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function($){ - - "use strict"; // jshint ;_; - - - /* TYPEAHEAD PUBLIC CLASS DEFINITION - * ================================= */ - - var Typeahead = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.typeahead.defaults, options) - this.matcher = this.options.matcher || this.matcher - this.sorter = this.options.sorter || this.sorter - this.highlighter = this.options.highlighter || this.highlighter - this.updater = this.options.updater || this.updater - this.source = this.options.source - this.$menu = $(this.options.menu) - this.shown = false - this.listen() - } - - Typeahead.prototype = { - - constructor: Typeahead - - , select: function () { - var val = this.$menu.find('.active').attr('data-value') - this.$element - .val(this.updater(val)) - .change() - return this.hide() - } - - , updater: function (item) { - return item - } - - , show: function () { - var pos = $.extend({}, this.$element.position(), { - height: this.$element[0].offsetHeight - }) - - this.$menu - .insertAfter(this.$element) - .css({ - top: pos.top + pos.height - , left: pos.left - }) - .show() - - this.shown = true - return this - } - - , hide: function () { - this.$menu.hide() - this.shown = false - return this - } - - , lookup: function (event) { - var items - - this.query = this.$element.val() - - if (!this.query || this.query.length < this.options.minLength) { - return this.shown ? this.hide() : this - } - - items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source - - return items ? this.process(items) : this - } - - , process: function (items) { - var that = this - - items = $.grep(items, function (item) { - return that.matcher(item) - }) - - items = this.sorter(items) - - if (!items.length) { - return this.shown ? this.hide() : this - } - - return this.render(items.slice(0, this.options.items)).show() - } - - , matcher: function (item) { - return ~item.toLowerCase().indexOf(this.query.toLowerCase()) - } - - , sorter: function (items) { - var beginswith = [] - , caseSensitive = [] - , caseInsensitive = [] - , item - - while (item = items.shift()) { - if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) - else if (~item.indexOf(this.query)) caseSensitive.push(item) - else caseInsensitive.push(item) - } - - return beginswith.concat(caseSensitive, caseInsensitive) - } - - , highlighter: function (item) { - var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') - return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { - return '' + match + '' - }) - } - - , render: function (items) { - 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.btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n background-color: #265a88;\n background-image: none;\n}\n.btn-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n background-color: #419641;\n background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n background-color: #419641;\n border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n background-color: #419641;\n background-image: none;\n}\n.btn-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n 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progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n .btn-styles(@btn-default-bg);\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css b/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css deleted file mode 100644 index 2a69f48c..00000000 --- a/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v3.4.1 (https://getbootstrap.com/) - * Copyright 2011-2019 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 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.btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default.disabled,\n.btn-primary.disabled,\n.btn-success.disabled,\n.btn-info.disabled,\n.btn-warning.disabled,\n.btn-danger.disabled,\n.btn-default[disabled],\n.btn-primary[disabled],\n.btn-success[disabled],\n.btn-info[disabled],\n.btn-warning[disabled],\n.btn-danger[disabled],\nfieldset[disabled] .btn-default,\nfieldset[disabled] .btn-primary,\nfieldset[disabled] .btn-success,\nfieldset[disabled] .btn-info,\nfieldset[disabled] .btn-warning,\nfieldset[disabled] .btn-danger {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n text-shadow: none;\n}\n.btn:active,\n.btn.active {\n background-image: none;\n}\n.btn-default {\n background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));\n background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #dbdbdb;\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n background-color: #e0e0e0;\n background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n background-color: #e0e0e0;\n border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n background-color: #e0e0e0;\n background-image: none;\n}\n.btn-primary {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));\n background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n background-color: #265a88;\n background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n background-color: #265a88;\n border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n background-color: #265a88;\n background-image: none;\n}\n.btn-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));\n background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n background-color: #419641;\n background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n background-color: #419641;\n border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n background-color: #419641;\n background-image: none;\n}\n.btn-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#ffffff), to(#f8f8f8));\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n .btn-styles(@btn-default-bg);\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap.css b/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap.css deleted file mode 100644 index fcab4155..00000000 --- a/docsrc/_build/html/_static/bootstrap-3.4.1/css/bootstrap.css +++ /dev/null @@ -1,6834 +0,0 @@ -/*! - * Bootstrap v3.4.1 (https://getbootstrap.com/) - * Copyright 2011-2019 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */ -/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ -html { - font-family: sans-serif; - -ms-text-size-adjust: 100%; - -webkit-text-size-adjust: 100%; -} -body { - margin: 0; -} -article, -aside, -details, -figcaption, -figure, -footer, -header, -hgroup, -main, -menu, -nav, -section, -summary { - display: block; -} -audio, -canvas, -progress, -video { - display: inline-block; - vertical-align: baseline; -} -audio:not([controls]) { - display: none; - height: 0; -} -[hidden], -template { - display: none; -} -a { - background-color: transparent; -} -a:active, -a:hover { - outline: 0; -} -abbr[title] { - border-bottom: none; - text-decoration: underline; - -webkit-text-decoration: underline dotted; - -moz-text-decoration: underline dotted; - text-decoration: underline dotted; -} -b, -strong { - font-weight: bold; -} -dfn { - font-style: italic; -} -h1 { - font-size: 2em; - margin: 0.67em 0; -} -mark { - background: #ff0; - color: #000; -} -small { - font-size: 80%; -} -sub, -sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; -} -sup { - top: -0.5em; -} -sub { - bottom: -0.25em; -} -img { - border: 0; -} -svg:not(:root) { - overflow: hidden; -} -figure { - margin: 1em 40px; -} -hr { - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; - height: 0; -} -pre { - overflow: auto; -} -code, -kbd, -pre, -samp { - font-family: monospace, monospace; - font-size: 1em; -} -button, -input, -optgroup, -select, -textarea { - color: inherit; - font: inherit; - margin: 0; -} -button { - overflow: visible; -} -button, -select { - text-transform: none; -} -button, -html input[type="button"], -input[type="reset"], -input[type="submit"] { - -webkit-appearance: button; - cursor: pointer; -} -button[disabled], -html input[disabled] { - cursor: default; -} -button::-moz-focus-inner, -input::-moz-focus-inner { - border: 0; - padding: 0; -} -input { - line-height: normal; -} -input[type="checkbox"], -input[type="radio"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - padding: 0; -} -input[type="number"]::-webkit-inner-spin-button, -input[type="number"]::-webkit-outer-spin-button { - height: auto; -} -input[type="search"] { - -webkit-appearance: textfield; - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; -} -input[type="search"]::-webkit-search-cancel-button, -input[type="search"]::-webkit-search-decoration { - -webkit-appearance: none; -} -fieldset { - border: 1px solid #c0c0c0; - margin: 0 2px; - padding: 0.35em 0.625em 0.75em; -} -legend { - border: 0; - padding: 0; -} -textarea { - overflow: auto; -} -optgroup { - font-weight: bold; -} -table { - border-collapse: collapse; - border-spacing: 0; -} -td, -th { - padding: 0; -} -/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */ -@media print { - *, - *:before, - *:after { - color: #000 !important; - text-shadow: none !important; - background: transparent !important; - -webkit-box-shadow: none !important; - box-shadow: none !important; - } - a, - a:visited { - text-decoration: underline; - } - a[href]:after { - content: " (" attr(href) ")"; - } - abbr[title]:after { - content: " (" attr(title) ")"; - } - a[href^="#"]:after, - a[href^="javascript:"]:after { - content: ""; - } - pre, - blockquote { - border: 1px solid #999; - page-break-inside: avoid; - } - thead { - display: table-header-group; - } - tr, - img { - page-break-inside: avoid; - } - img { - max-width: 100% !important; - } - p, - h2, - h3 { - orphans: 3; - widows: 3; - } - h2, - h3 { - page-break-after: avoid; - } - .navbar { - display: none; - } - .btn > .caret, - .dropup > .btn > .caret { - border-top-color: #000 !important; - } - .label { - border: 1px solid #000; - 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background-color: #e4b9b9; -} -.page-header { - padding-bottom: 9px; - margin: 40px 0 20px; - border-bottom: 1px solid #eeeeee; -} -ul, -ol { - margin-top: 0; - margin-bottom: 10px; -} -ul ul, -ol ul, -ul ol, -ol ol { - margin-bottom: 0; -} -.list-unstyled { - padding-left: 0; - list-style: none; -} -.list-inline { - padding-left: 0; - list-style: none; - margin-left: -5px; -} -.list-inline > li { - display: inline-block; - padding-right: 5px; - padding-left: 5px; -} -dl { - margin-top: 0; - margin-bottom: 20px; -} -dt, -dd { - line-height: 1.42857143; -} -dt { - font-weight: 700; -} -dd { - margin-left: 0; -} -@media (min-width: 768px) { - .dl-horizontal dt { - float: left; - width: 160px; - clear: left; - text-align: right; - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - } - .dl-horizontal dd { - margin-left: 180px; - } -} -abbr[title], -abbr[data-original-title] { - cursor: help; -} -.initialism { - font-size: 90%; - text-transform: uppercase; -} -blockquote { - padding: 10px 20px; - margin: 0 0 20px; - font-size: 17.5px; - border-left: 5px solid #eeeeee; -} -blockquote p:last-child, -blockquote ul:last-child, -blockquote ol:last-child { - margin-bottom: 0; -} -blockquote footer, -blockquote small, -blockquote .small { - display: block; - font-size: 80%; - line-height: 1.42857143; - color: #777777; -} -blockquote footer:before, -blockquote small:before, -blockquote .small:before { - content: "\2014 \00A0"; -} -.blockquote-reverse, -blockquote.pull-right { - padding-right: 15px; - padding-left: 0; - text-align: right; - border-right: 5px solid #eeeeee; - border-left: 0; -} -.blockquote-reverse footer:before, -blockquote.pull-right footer:before, -.blockquote-reverse small:before, -blockquote.pull-right small:before, -.blockquote-reverse .small:before, -blockquote.pull-right .small:before { - content: ""; -} -.blockquote-reverse footer:after, -blockquote.pull-right footer:after, -.blockquote-reverse small:after, -blockquote.pull-right small:after, -.blockquote-reverse .small:after, -blockquote.pull-right .small:after { - content: "\00A0 \2014"; -} -address { - margin-bottom: 20px; - font-style: normal; - line-height: 1.42857143; -} -code, -kbd, -pre, -samp { - font-family: Menlo, Monaco, Consolas, "Courier New", monospace; -} -code { - padding: 2px 4px; - font-size: 90%; - color: #c7254e; - background-color: #f9f2f4; - border-radius: 4px; -} -kbd { - padding: 2px 4px; - font-size: 90%; - color: #fff; - background-color: #333; - border-radius: 3px; - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); -} -kbd kbd { - padding: 0; - font-size: 100%; - font-weight: 700; - -webkit-box-shadow: none; - box-shadow: none; -} -pre { - display: block; - padding: 9.5px; - margin: 0 0 10px; - font-size: 13px; - line-height: 1.42857143; - color: #333333; - word-break: break-all; - word-wrap: break-word; - background-color: #f5f5f5; - border: 1px solid #ccc; - border-radius: 4px; -} -pre code { - padding: 0; - font-size: inherit; - color: inherit; - white-space: pre-wrap; - background-color: transparent; - border-radius: 0; -} -.pre-scrollable { - max-height: 340px; - overflow-y: scroll; -} -.container { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -@media (min-width: 768px) { - .container { - width: 750px; - } -} -@media (min-width: 992px) { - .container { - width: 970px; - } -} -@media (min-width: 1200px) { - .container { - width: 1170px; - } -} -.container-fluid { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -.row { - margin-right: -15px; - margin-left: -15px; -} -.row-no-gutters { - margin-right: 0; - margin-left: 0; -} -.row-no-gutters [class*="col-"] { - padding-right: 0; - padding-left: 0; -} -.col-xs-1, -.col-sm-1, -.col-md-1, -.col-lg-1, -.col-xs-2, -.col-sm-2, -.col-md-2, -.col-lg-2, -.col-xs-3, -.col-sm-3, -.col-md-3, -.col-lg-3, -.col-xs-4, -.col-sm-4, -.col-md-4, -.col-lg-4, -.col-xs-5, -.col-sm-5, -.col-md-5, -.col-lg-5, -.col-xs-6, -.col-sm-6, -.col-md-6, -.col-lg-6, -.col-xs-7, -.col-sm-7, -.col-md-7, -.col-lg-7, -.col-xs-8, -.col-sm-8, -.col-md-8, -.col-lg-8, -.col-xs-9, -.col-sm-9, -.col-md-9, -.col-lg-9, -.col-xs-10, -.col-sm-10, -.col-md-10, -.col-lg-10, -.col-xs-11, -.col-sm-11, -.col-md-11, -.col-lg-11, -.col-xs-12, -.col-sm-12, -.col-md-12, -.col-lg-12 { - position: relative; - min-height: 1px; - padding-right: 15px; - padding-left: 15px; -} -.col-xs-1, -.col-xs-2, -.col-xs-3, -.col-xs-4, -.col-xs-5, -.col-xs-6, -.col-xs-7, -.col-xs-8, -.col-xs-9, -.col-xs-10, -.col-xs-11, -.col-xs-12 { - float: left; -} -.col-xs-12 { - width: 100%; -} -.col-xs-11 { - width: 91.66666667%; -} -.col-xs-10 { - width: 83.33333333%; -} -.col-xs-9 { - width: 75%; -} -.col-xs-8 { - width: 66.66666667%; -} -.col-xs-7 { - width: 58.33333333%; -} -.col-xs-6 { - width: 50%; -} -.col-xs-5 { - width: 41.66666667%; -} -.col-xs-4 { - width: 33.33333333%; -} -.col-xs-3 { - width: 25%; -} -.col-xs-2 { - width: 16.66666667%; -} -.col-xs-1 { - width: 8.33333333%; -} -.col-xs-pull-12 { - right: 100%; -} -.col-xs-pull-11 { - right: 91.66666667%; -} -.col-xs-pull-10 { - right: 83.33333333%; -} -.col-xs-pull-9 { - right: 75%; -} -.col-xs-pull-8 { - right: 66.66666667%; -} -.col-xs-pull-7 { - right: 58.33333333%; -} -.col-xs-pull-6 { - right: 50%; -} -.col-xs-pull-5 { - right: 41.66666667%; -} -.col-xs-pull-4 { - right: 33.33333333%; -} -.col-xs-pull-3 { - right: 25%; -} -.col-xs-pull-2 { - right: 16.66666667%; -} -.col-xs-pull-1 { - right: 8.33333333%; -} -.col-xs-pull-0 { - right: auto; -} -.col-xs-push-12 { - left: 100%; -} -.col-xs-push-11 { - left: 91.66666667%; -} -.col-xs-push-10 { - left: 83.33333333%; -} -.col-xs-push-9 { - left: 75%; -} -.col-xs-push-8 { - left: 66.66666667%; -} -.col-xs-push-7 { - left: 58.33333333%; -} -.col-xs-push-6 { - left: 50%; -} -.col-xs-push-5 { - left: 41.66666667%; -} -.col-xs-push-4 { - left: 33.33333333%; -} -.col-xs-push-3 { - left: 25%; -} -.col-xs-push-2 { - left: 16.66666667%; -} -.col-xs-push-1 { - left: 8.33333333%; -} -.col-xs-push-0 { - left: auto; -} -.col-xs-offset-12 { - margin-left: 100%; -} -.col-xs-offset-11 { - margin-left: 91.66666667%; -} -.col-xs-offset-10 { - margin-left: 83.33333333%; -} -.col-xs-offset-9 { - margin-left: 75%; -} -.col-xs-offset-8 { - margin-left: 66.66666667%; -} -.col-xs-offset-7 { - margin-left: 58.33333333%; -} -.col-xs-offset-6 { - margin-left: 50%; -} -.col-xs-offset-5 { - margin-left: 41.66666667%; -} -.col-xs-offset-4 { - margin-left: 33.33333333%; -} -.col-xs-offset-3 { - margin-left: 25%; -} -.col-xs-offset-2 { - margin-left: 16.66666667%; -} -.col-xs-offset-1 { - margin-left: 8.33333333%; -} -.col-xs-offset-0 { - margin-left: 0%; -} -@media (min-width: 768px) { - .col-sm-1, - .col-sm-2, - .col-sm-3, - .col-sm-4, - .col-sm-5, - .col-sm-6, - .col-sm-7, - .col-sm-8, - .col-sm-9, - .col-sm-10, - .col-sm-11, - .col-sm-12 { - float: left; - } - .col-sm-12 { - width: 100%; - } - .col-sm-11 { - width: 91.66666667%; - } - .col-sm-10 { - width: 83.33333333%; - } - .col-sm-9 { - width: 75%; - } - .col-sm-8 { - width: 66.66666667%; - } - .col-sm-7 { - width: 58.33333333%; - } - .col-sm-6 { - width: 50%; - } - .col-sm-5 { - width: 41.66666667%; - } - .col-sm-4 { - width: 33.33333333%; - } - .col-sm-3 { - width: 25%; - } - .col-sm-2 { - width: 16.66666667%; - } - .col-sm-1 { - width: 8.33333333%; - } - .col-sm-pull-12 { - right: 100%; - } - .col-sm-pull-11 { - right: 91.66666667%; - } - .col-sm-pull-10 { - right: 83.33333333%; - } - .col-sm-pull-9 { - right: 75%; - } - .col-sm-pull-8 { - right: 66.66666667%; - } - .col-sm-pull-7 { - right: 58.33333333%; - } - .col-sm-pull-6 { - right: 50%; - } - .col-sm-pull-5 { - right: 41.66666667%; - } - .col-sm-pull-4 { - right: 33.33333333%; - } - .col-sm-pull-3 { - right: 25%; - } - .col-sm-pull-2 { - right: 16.66666667%; - } - .col-sm-pull-1 { - right: 8.33333333%; - } - .col-sm-pull-0 { - right: auto; - } - .col-sm-push-12 { - left: 100%; - } - .col-sm-push-11 { - left: 91.66666667%; - } - .col-sm-push-10 { - left: 83.33333333%; - } - .col-sm-push-9 { - left: 75%; - } - .col-sm-push-8 { - left: 66.66666667%; - } - .col-sm-push-7 { - left: 58.33333333%; - } - .col-sm-push-6 { - left: 50%; - } - .col-sm-push-5 { - left: 41.66666667%; - } - .col-sm-push-4 { - left: 33.33333333%; - } - .col-sm-push-3 { - left: 25%; - } - .col-sm-push-2 { - left: 16.66666667%; - } - .col-sm-push-1 { - left: 8.33333333%; - } - .col-sm-push-0 { - left: auto; - } - .col-sm-offset-12 { - margin-left: 100%; - } - .col-sm-offset-11 { - margin-left: 91.66666667%; - } - .col-sm-offset-10 { - margin-left: 83.33333333%; - } - .col-sm-offset-9 { - margin-left: 75%; - } - .col-sm-offset-8 { - margin-left: 66.66666667%; - } - .col-sm-offset-7 { - margin-left: 58.33333333%; - } - .col-sm-offset-6 { - margin-left: 50%; - } - .col-sm-offset-5 { - margin-left: 41.66666667%; - } - .col-sm-offset-4 { - margin-left: 33.33333333%; - } - .col-sm-offset-3 { - margin-left: 25%; - } - .col-sm-offset-2 { - margin-left: 16.66666667%; - } - .col-sm-offset-1 { - margin-left: 8.33333333%; - } - .col-sm-offset-0 { - margin-left: 0%; - } -} -@media (min-width: 992px) { - .col-md-1, - .col-md-2, - .col-md-3, - .col-md-4, - .col-md-5, - .col-md-6, - .col-md-7, - .col-md-8, - .col-md-9, - .col-md-10, - .col-md-11, - .col-md-12 { - float: left; - } - .col-md-12 { - width: 100%; - } - .col-md-11 { - width: 91.66666667%; - } - .col-md-10 { - width: 83.33333333%; - } - .col-md-9 { - width: 75%; - } - .col-md-8 { - width: 66.66666667%; - } - .col-md-7 { - width: 58.33333333%; - } - .col-md-6 { - width: 50%; - } - .col-md-5 { - width: 41.66666667%; - } - .col-md-4 { - width: 33.33333333%; - } - .col-md-3 { - width: 25%; - } - .col-md-2 { - width: 16.66666667%; - } - .col-md-1 { - width: 8.33333333%; - } - .col-md-pull-12 { - right: 100%; - } - .col-md-pull-11 { - right: 91.66666667%; - } - .col-md-pull-10 { - right: 83.33333333%; - } - .col-md-pull-9 { - right: 75%; - } - .col-md-pull-8 { - right: 66.66666667%; - } - .col-md-pull-7 { - right: 58.33333333%; - } - .col-md-pull-6 { - right: 50%; - } - .col-md-pull-5 { - right: 41.66666667%; - } - .col-md-pull-4 { - right: 33.33333333%; - } - .col-md-pull-3 { - right: 25%; - } - .col-md-pull-2 { - right: 16.66666667%; - } - .col-md-pull-1 { - right: 8.33333333%; - } - .col-md-pull-0 { - right: auto; - } - .col-md-push-12 { - left: 100%; - } - .col-md-push-11 { - left: 91.66666667%; - } - .col-md-push-10 { - left: 83.33333333%; - } - .col-md-push-9 { - left: 75%; - } - .col-md-push-8 { - left: 66.66666667%; - } - .col-md-push-7 { - left: 58.33333333%; - } - .col-md-push-6 { - left: 50%; - } - .col-md-push-5 { - left: 41.66666667%; - } - .col-md-push-4 { - left: 33.33333333%; - } - .col-md-push-3 { - left: 25%; - } - .col-md-push-2 { - left: 16.66666667%; - } - .col-md-push-1 { - left: 8.33333333%; - } - .col-md-push-0 { - left: auto; - } - .col-md-offset-12 { - margin-left: 100%; - } - .col-md-offset-11 { - margin-left: 91.66666667%; - } - .col-md-offset-10 { - margin-left: 83.33333333%; - } - .col-md-offset-9 { - margin-left: 75%; - } - .col-md-offset-8 { - margin-left: 66.66666667%; - } - .col-md-offset-7 { - margin-left: 58.33333333%; - } - .col-md-offset-6 { - margin-left: 50%; - } - .col-md-offset-5 { - margin-left: 41.66666667%; - } - .col-md-offset-4 { - margin-left: 33.33333333%; - } - .col-md-offset-3 { - margin-left: 25%; - } - .col-md-offset-2 { - margin-left: 16.66666667%; - } - .col-md-offset-1 { - margin-left: 8.33333333%; - } - .col-md-offset-0 { - margin-left: 0%; - } -} -@media (min-width: 1200px) { - .col-lg-1, - .col-lg-2, - .col-lg-3, - .col-lg-4, - .col-lg-5, - .col-lg-6, - .col-lg-7, - .col-lg-8, - .col-lg-9, - .col-lg-10, - .col-lg-11, - .col-lg-12 { - float: left; - } - .col-lg-12 { - width: 100%; - } - .col-lg-11 { - width: 91.66666667%; - } - .col-lg-10 { - width: 83.33333333%; - } - .col-lg-9 { - width: 75%; - } - .col-lg-8 { - width: 66.66666667%; - } - .col-lg-7 { - width: 58.33333333%; - } - .col-lg-6 { - width: 50%; - } - .col-lg-5 { - width: 41.66666667%; - } - .col-lg-4 { - width: 33.33333333%; - } - .col-lg-3 { - width: 25%; - } - .col-lg-2 { - width: 16.66666667%; - } - .col-lg-1 { - width: 8.33333333%; - } - .col-lg-pull-12 { - right: 100%; - } - .col-lg-pull-11 { - right: 91.66666667%; - } - .col-lg-pull-10 { - right: 83.33333333%; - } - .col-lg-pull-9 { - right: 75%; - } - .col-lg-pull-8 { - right: 66.66666667%; - } - .col-lg-pull-7 { - right: 58.33333333%; - } - .col-lg-pull-6 { - right: 50%; - } - .col-lg-pull-5 { - right: 41.66666667%; - } - .col-lg-pull-4 { - right: 33.33333333%; - } - .col-lg-pull-3 { - right: 25%; - } - .col-lg-pull-2 { - right: 16.66666667%; - } - .col-lg-pull-1 { - right: 8.33333333%; - } - .col-lg-pull-0 { - right: auto; - } - .col-lg-push-12 { - left: 100%; - } - .col-lg-push-11 { - left: 91.66666667%; - } - .col-lg-push-10 { - left: 83.33333333%; - } - .col-lg-push-9 { - left: 75%; - } - .col-lg-push-8 { - left: 66.66666667%; - } - .col-lg-push-7 { - left: 58.33333333%; - } - .col-lg-push-6 { - left: 50%; - } - .col-lg-push-5 { - left: 41.66666667%; - } - .col-lg-push-4 { - left: 33.33333333%; - } - .col-lg-push-3 { - left: 25%; - } - .col-lg-push-2 { - left: 16.66666667%; - } - .col-lg-push-1 { - left: 8.33333333%; - } - .col-lg-push-0 { - left: auto; - } - .col-lg-offset-12 { - margin-left: 100%; - } - .col-lg-offset-11 { - margin-left: 91.66666667%; - } - .col-lg-offset-10 { - margin-left: 83.33333333%; - } - .col-lg-offset-9 { - margin-left: 75%; - } - .col-lg-offset-8 { - margin-left: 66.66666667%; - } - .col-lg-offset-7 { - margin-left: 58.33333333%; - } - .col-lg-offset-6 { - margin-left: 50%; - } - .col-lg-offset-5 { - margin-left: 41.66666667%; - } - .col-lg-offset-4 { - margin-left: 33.33333333%; - } - .col-lg-offset-3 { - margin-left: 25%; - } - .col-lg-offset-2 { - margin-left: 16.66666667%; - } - .col-lg-offset-1 { - margin-left: 8.33333333%; - } - .col-lg-offset-0 { - margin-left: 0%; - } -} -table { - background-color: transparent; -} -table col[class*="col-"] { - position: static; - display: table-column; - float: none; -} -table td[class*="col-"], -table th[class*="col-"] { - position: static; - display: table-cell; - float: none; -} -caption { - padding-top: 8px; - padding-bottom: 8px; - color: #777777; - text-align: left; -} -th { - text-align: left; -} -.table { - width: 100%; - max-width: 100%; - margin-bottom: 20px; -} -.table > thead > tr > th, -.table > tbody > tr > th, -.table > tfoot > tr > th, -.table > thead > tr > td, -.table > tbody > tr > td, -.table > tfoot > tr > td { - padding: 8px; - line-height: 1.42857143; - vertical-align: top; - border-top: 1px solid #ddd; -} -.table > thead > tr > th { - vertical-align: bottom; - border-bottom: 2px solid #ddd; -} -.table > caption + thead > tr:first-child > th, -.table > colgroup + thead > tr:first-child > th, -.table > thead:first-child > tr:first-child > th, -.table > caption + thead > tr:first-child > td, -.table > colgroup + thead > tr:first-child > td, -.table > thead:first-child > tr:first-child > td { - border-top: 0; -} -.table > tbody + tbody { - border-top: 2px solid #ddd; -} -.table .table { - background-color: #fff; -} -.table-condensed > thead > tr > th, -.table-condensed > tbody > tr > th, -.table-condensed > tfoot > tr > th, -.table-condensed > thead > tr > td, -.table-condensed > tbody > tr > td, -.table-condensed > tfoot > tr > td { - padding: 5px; -} -.table-bordered { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > tbody > tr > th, -.table-bordered > tfoot > tr > th, -.table-bordered > thead > tr > td, -.table-bordered > tbody > tr > td, -.table-bordered > tfoot > tr > td { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > thead > tr > td { - border-bottom-width: 2px; -} -.table-striped > tbody > tr:nth-of-type(odd) { - background-color: #f9f9f9; -} -.table-hover > tbody > tr:hover { - background-color: #f5f5f5; -} -.table > thead > tr > td.active, -.table > tbody > tr > td.active, -.table > tfoot > tr > td.active, -.table > thead > tr > th.active, -.table > tbody > tr > th.active, -.table > tfoot > tr > th.active, -.table > thead > tr.active > td, -.table > tbody > tr.active > td, -.table > tfoot > tr.active > td, -.table > thead > tr.active > th, -.table > tbody > tr.active > th, -.table > tfoot > tr.active > th { - background-color: #f5f5f5; -} -.table-hover > tbody > tr > td.active:hover, -.table-hover > tbody > tr > th.active:hover, -.table-hover > tbody > tr.active:hover > td, -.table-hover > tbody > tr:hover > .active, -.table-hover > tbody > tr.active:hover > th { - background-color: #e8e8e8; -} -.table > thead > tr > td.success, -.table > tbody > tr > td.success, -.table > tfoot > tr > td.success, -.table > thead > tr > th.success, -.table > tbody > tr > th.success, -.table > tfoot > tr > th.success, -.table > thead > tr.success > td, -.table > tbody > tr.success > td, -.table > tfoot > tr.success > td, -.table > thead > tr.success > th, -.table > tbody > tr.success > th, -.table > tfoot > tr.success > th { - background-color: #dff0d8; -} -.table-hover > tbody > tr > td.success:hover, -.table-hover > tbody > tr > th.success:hover, -.table-hover > tbody > tr.success:hover > td, -.table-hover > tbody > tr:hover > .success, -.table-hover > tbody > tr.success:hover > th { - background-color: #d0e9c6; -} -.table > thead > tr > td.info, -.table > tbody > tr > td.info, -.table > tfoot > tr > td.info, -.table > thead > tr > th.info, -.table > tbody > tr > th.info, -.table > tfoot > tr > th.info, -.table > thead > tr.info > td, -.table > tbody > tr.info > td, -.table > tfoot > tr.info > td, -.table > thead > tr.info > th, -.table > tbody > tr.info > th, -.table > tfoot > tr.info > th { - background-color: #d9edf7; -} -.table-hover > tbody > tr > td.info:hover, -.table-hover > tbody > tr > th.info:hover, -.table-hover > tbody > tr.info:hover > td, -.table-hover > tbody > tr:hover > .info, -.table-hover > tbody > tr.info:hover > th { - background-color: #c4e3f3; -} -.table > thead > tr > td.warning, -.table > tbody > tr > td.warning, -.table > tfoot > tr > td.warning, -.table > thead > tr > th.warning, -.table > tbody > tr > th.warning, -.table > tfoot > tr > th.warning, -.table > thead > tr.warning > td, -.table > tbody > tr.warning > td, -.table > tfoot > tr.warning > td, -.table > thead > tr.warning > th, -.table > tbody > tr.warning > th, -.table > tfoot > tr.warning > th { - background-color: #fcf8e3; -} -.table-hover > tbody > tr > td.warning:hover, -.table-hover > tbody > tr > th.warning:hover, -.table-hover > tbody > tr.warning:hover > td, -.table-hover > tbody > tr:hover > .warning, -.table-hover > tbody > tr.warning:hover > th { - background-color: #faf2cc; -} -.table > thead > tr > td.danger, -.table > tbody > tr > td.danger, -.table > tfoot > tr > td.danger, -.table > thead > tr > th.danger, -.table > tbody > tr > th.danger, -.table > tfoot > tr > th.danger, -.table > thead > tr.danger > td, -.table > tbody > tr.danger > td, -.table > tfoot > tr.danger > td, -.table > thead > tr.danger > th, -.table > tbody > tr.danger > th, -.table > tfoot > tr.danger > th { - background-color: #f2dede; -} -.table-hover > tbody > tr > td.danger:hover, -.table-hover > tbody > tr > th.danger:hover, -.table-hover > tbody > tr.danger:hover > td, -.table-hover > tbody > tr:hover > .danger, -.table-hover > tbody > tr.danger:hover > th { - background-color: #ebcccc; -} -.table-responsive { - min-height: 0.01%; - overflow-x: auto; -} -@media screen and (max-width: 767px) { - .table-responsive { - width: 100%; - margin-bottom: 15px; - overflow-y: hidden; - -ms-overflow-style: -ms-autohiding-scrollbar; - border: 1px solid #ddd; - } - .table-responsive > .table { - margin-bottom: 0; - } - .table-responsive > .table > thead > tr > th, - .table-responsive > .table > tbody > tr > th, - .table-responsive > .table > tfoot > tr > th, - .table-responsive > .table > thead > tr > td, - .table-responsive > .table > tbody > tr > td, - .table-responsive > .table > tfoot > tr > td { - white-space: nowrap; - } - .table-responsive > .table-bordered { - border: 0; - } - .table-responsive > .table-bordered > thead > tr > th:first-child, - .table-responsive > .table-bordered > tbody > tr > th:first-child, - .table-responsive > .table-bordered > tfoot > tr > th:first-child, - .table-responsive > .table-bordered > thead > tr > td:first-child, - .table-responsive > .table-bordered > tbody > tr > td:first-child, - .table-responsive > .table-bordered > tfoot > tr > td:first-child { - border-left: 0; - } - .table-responsive > .table-bordered > thead > tr > th:last-child, - .table-responsive > .table-bordered > tbody > tr > th:last-child, - .table-responsive > .table-bordered > tfoot > tr > th:last-child, - .table-responsive > .table-bordered > thead > tr > td:last-child, - .table-responsive > .table-bordered > tbody > tr > td:last-child, - .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; - } - .table-responsive > .table-bordered > tbody > tr:last-child > th, - .table-responsive > .table-bordered > tfoot > tr:last-child > th, - .table-responsive > .table-bordered > tbody > tr:last-child > td, - .table-responsive > .table-bordered > tfoot > tr:last-child > td { - border-bottom: 0; - } -} -fieldset { - min-width: 0; - padding: 0; - margin: 0; - border: 0; -} -legend { - display: block; - width: 100%; - padding: 0; - margin-bottom: 20px; - font-size: 21px; - line-height: inherit; - color: #333333; - border: 0; - border-bottom: 1px solid #e5e5e5; -} -label { - display: inline-block; - max-width: 100%; - margin-bottom: 5px; - font-weight: 700; -} -input[type="search"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -input[type="radio"], -input[type="checkbox"] { - margin: 4px 0 0; - margin-top: 1px \9; - line-height: normal; -} -input[type="radio"][disabled], -input[type="checkbox"][disabled], -input[type="radio"].disabled, -input[type="checkbox"].disabled, -fieldset[disabled] input[type="radio"], -fieldset[disabled] input[type="checkbox"] { - cursor: not-allowed; -} -input[type="file"] { - display: block; -} -input[type="range"] { - display: block; - width: 100%; -} -select[multiple], -select[size] { - height: auto; -} -input[type="file"]:focus, -input[type="radio"]:focus, -input[type="checkbox"]:focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -output { - display: block; - padding-top: 7px; - font-size: 14px; - line-height: 1.42857143; - color: #555555; -} -.form-control { - display: block; - width: 100%; - height: 34px; - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857143; - color: #555555; - background-color: #fff; - background-image: none; - border: 1px solid #ccc; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; -} -.form-control:focus { - border-color: #66afe9; - outline: 0; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6); -} -.form-control::-moz-placeholder { - color: #999; - opacity: 1; -} -.form-control:-ms-input-placeholder { - color: #999; -} -.form-control::-webkit-input-placeholder { - color: #999; -} -.form-control::-ms-expand { - background-color: transparent; - border: 0; -} -.form-control[disabled], -.form-control[readonly], -fieldset[disabled] .form-control { - background-color: #eeeeee; - opacity: 1; -} -.form-control[disabled], -fieldset[disabled] .form-control { - cursor: not-allowed; -} -textarea.form-control { - height: auto; -} -@media screen and (-webkit-min-device-pixel-ratio: 0) { - input[type="date"].form-control, - input[type="time"].form-control, - input[type="datetime-local"].form-control, - input[type="month"].form-control { - line-height: 34px; - } - input[type="date"].input-sm, - input[type="time"].input-sm, - input[type="datetime-local"].input-sm, - input[type="month"].input-sm, - .input-group-sm input[type="date"], - .input-group-sm input[type="time"], - .input-group-sm input[type="datetime-local"], - .input-group-sm input[type="month"] { - line-height: 30px; - } - input[type="date"].input-lg, - input[type="time"].input-lg, - input[type="datetime-local"].input-lg, - input[type="month"].input-lg, - .input-group-lg input[type="date"], - .input-group-lg input[type="time"], - .input-group-lg input[type="datetime-local"], - .input-group-lg input[type="month"] { - line-height: 46px; - } -} -.form-group { - margin-bottom: 15px; -} -.radio, -.checkbox { - position: relative; - display: block; - margin-top: 10px; - margin-bottom: 10px; -} -.radio.disabled label, -.checkbox.disabled label, -fieldset[disabled] .radio label, -fieldset[disabled] .checkbox label { - cursor: not-allowed; -} -.radio label, -.checkbox label { - min-height: 20px; - padding-left: 20px; - margin-bottom: 0; - font-weight: 400; - cursor: pointer; -} -.radio input[type="radio"], -.radio-inline input[type="radio"], -.checkbox input[type="checkbox"], -.checkbox-inline input[type="checkbox"] { - position: absolute; - margin-top: 4px \9; - margin-left: -20px; -} -.radio + .radio, -.checkbox + .checkbox { - margin-top: -5px; -} -.radio-inline, -.checkbox-inline { - position: relative; - display: inline-block; - padding-left: 20px; - margin-bottom: 0; - font-weight: 400; - vertical-align: middle; - cursor: pointer; -} -.radio-inline.disabled, -.checkbox-inline.disabled, -fieldset[disabled] .radio-inline, -fieldset[disabled] .checkbox-inline { - cursor: not-allowed; -} -.radio-inline + .radio-inline, -.checkbox-inline + .checkbox-inline { - margin-top: 0; - margin-left: 10px; -} -.form-control-static { - min-height: 34px; - padding-top: 7px; - padding-bottom: 7px; - margin-bottom: 0; -} -.form-control-static.input-lg, -.form-control-static.input-sm { - padding-right: 0; - padding-left: 0; -} -.input-sm { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-sm { - height: 30px; - line-height: 30px; -} -textarea.input-sm, -select[multiple].input-sm { - height: auto; -} -.form-group-sm .form-control { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.form-group-sm select.form-control { - height: 30px; - line-height: 30px; -} -.form-group-sm textarea.form-control, -.form-group-sm select[multiple].form-control { - height: auto; -} -.form-group-sm .form-control-static { - height: 30px; - min-height: 32px; - padding: 6px 10px; - font-size: 12px; - line-height: 1.5; -} -.input-lg { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-lg { - height: 46px; - line-height: 46px; -} -textarea.input-lg, -select[multiple].input-lg { - height: auto; -} -.form-group-lg .form-control { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.form-group-lg select.form-control { - height: 46px; - line-height: 46px; -} -.form-group-lg textarea.form-control, -.form-group-lg select[multiple].form-control { - height: auto; -} -.form-group-lg .form-control-static { - height: 46px; - min-height: 38px; - padding: 11px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.has-feedback { - position: relative; -} -.has-feedback .form-control { - padding-right: 42.5px; -} -.form-control-feedback { - position: absolute; - top: 0; - right: 0; - z-index: 2; - display: block; - width: 34px; - height: 34px; - line-height: 34px; - text-align: center; - pointer-events: none; -} -.input-lg + .form-control-feedback, -.input-group-lg + .form-control-feedback, -.form-group-lg .form-control + .form-control-feedback { - width: 46px; - height: 46px; - line-height: 46px; -} -.input-sm + .form-control-feedback, -.input-group-sm + .form-control-feedback, -.form-group-sm .form-control + .form-control-feedback { - width: 30px; - height: 30px; - line-height: 30px; -} -.has-success .help-block, -.has-success .control-label, -.has-success .radio, -.has-success .checkbox, -.has-success .radio-inline, -.has-success .checkbox-inline, -.has-success.radio label, -.has-success.checkbox label, -.has-success.radio-inline label, -.has-success.checkbox-inline label { - color: #3c763d; -} -.has-success .form-control { - border-color: #3c763d; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-success .form-control:focus { - border-color: #2b542c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; -} -.has-success .input-group-addon { - color: #3c763d; - background-color: #dff0d8; - border-color: #3c763d; -} -.has-success .form-control-feedback { - color: #3c763d; -} -.has-warning .help-block, -.has-warning .control-label, -.has-warning .radio, -.has-warning .checkbox, -.has-warning .radio-inline, -.has-warning .checkbox-inline, -.has-warning.radio label, -.has-warning.checkbox label, -.has-warning.radio-inline label, -.has-warning.checkbox-inline label { - color: #8a6d3b; -} -.has-warning .form-control { - border-color: #8a6d3b; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-warning .form-control:focus { - border-color: #66512c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; -} -.has-warning .input-group-addon { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #8a6d3b; -} -.has-warning .form-control-feedback { - color: #8a6d3b; -} -.has-error .help-block, -.has-error .control-label, -.has-error .radio, -.has-error .checkbox, -.has-error .radio-inline, -.has-error .checkbox-inline, -.has-error.radio label, -.has-error.checkbox label, -.has-error.radio-inline label, -.has-error.checkbox-inline label { - color: #a94442; -} -.has-error .form-control { - border-color: #a94442; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-error .form-control:focus { - border-color: #843534; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; -} -.has-error .input-group-addon { - color: #a94442; - background-color: #f2dede; - border-color: #a94442; -} -.has-error .form-control-feedback { - color: #a94442; -} -.has-feedback label ~ .form-control-feedback { - top: 25px; -} -.has-feedback label.sr-only ~ .form-control-feedback { - top: 0; -} -.help-block { - display: block; - margin-top: 5px; - margin-bottom: 10px; - color: #737373; -} -@media (min-width: 768px) { - .form-inline .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .form-inline .form-control-static { - display: inline-block; - } - .form-inline .input-group { - display: inline-table; - vertical-align: middle; - } - .form-inline .input-group .input-group-addon, - .form-inline .input-group .input-group-btn, - .form-inline .input-group .form-control { - width: auto; - } - .form-inline .input-group > .form-control { - width: 100%; - } - .form-inline .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio, - .form-inline .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio label, - .form-inline .checkbox label { - padding-left: 0; - } - .form-inline .radio input[type="radio"], - .form-inline .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .form-inline .has-feedback .form-control-feedback { - top: 0; - } -} -.form-horizontal .radio, -.form-horizontal .checkbox, -.form-horizontal .radio-inline, -.form-horizontal .checkbox-inline { - padding-top: 7px; - margin-top: 0; - margin-bottom: 0; -} -.form-horizontal .radio, -.form-horizontal .checkbox { - min-height: 27px; -} -.form-horizontal .form-group { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .form-horizontal .control-label { - padding-top: 7px; - margin-bottom: 0; - text-align: right; - } -} -.form-horizontal .has-feedback .form-control-feedback { - right: 15px; -} -@media (min-width: 768px) { - .form-horizontal .form-group-lg .control-label { - padding-top: 11px; - font-size: 18px; - } -} -@media (min-width: 768px) { - .form-horizontal .form-group-sm .control-label { - padding-top: 6px; - font-size: 12px; - } -} -.btn { - display: inline-block; - margin-bottom: 0; - font-weight: normal; - text-align: center; - white-space: nowrap; - vertical-align: middle; - -ms-touch-action: manipulation; - touch-action: manipulation; - cursor: pointer; - background-image: none; - border: 1px solid transparent; - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857143; - border-radius: 4px; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; -} -.btn:focus, -.btn:active:focus, -.btn.active:focus, -.btn.focus, -.btn:active.focus, -.btn.active.focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -.btn:hover, -.btn:focus, -.btn.focus { - color: #333; - text-decoration: none; -} -.btn:active, -.btn.active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); -} -.btn.disabled, -.btn[disabled], -fieldset[disabled] .btn { - cursor: not-allowed; - filter: alpha(opacity=65); - opacity: 0.65; - -webkit-box-shadow: none; - box-shadow: none; -} -a.btn.disabled, -fieldset[disabled] a.btn { - pointer-events: none; -} -.btn-default { - color: #333; - background-color: #fff; - border-color: #ccc; -} -.btn-default:focus, -.btn-default.focus { - color: #333; - background-color: #e6e6e6; - border-color: #8c8c8c; -} -.btn-default:hover { - color: #333; - background-color: #e6e6e6; - border-color: #adadad; -} -.btn-default:active, -.btn-default.active, -.open > .dropdown-toggle.btn-default { - color: #333; - background-color: #e6e6e6; - background-image: none; - border-color: #adadad; -} -.btn-default:active:hover, -.btn-default.active:hover, -.open > .dropdown-toggle.btn-default:hover, -.btn-default:active:focus, -.btn-default.active:focus, -.open > .dropdown-toggle.btn-default:focus, -.btn-default:active.focus, -.btn-default.active.focus, -.open > .dropdown-toggle.btn-default.focus { - color: #333; - background-color: #d4d4d4; - border-color: #8c8c8c; -} -.btn-default.disabled:hover, -.btn-default[disabled]:hover, -fieldset[disabled] .btn-default:hover, -.btn-default.disabled:focus, -.btn-default[disabled]:focus, -fieldset[disabled] .btn-default:focus, -.btn-default.disabled.focus, -.btn-default[disabled].focus, -fieldset[disabled] .btn-default.focus { - background-color: #fff; - border-color: #ccc; -} -.btn-default .badge { - color: #fff; - background-color: #333; -} -.btn-primary { - color: #fff; - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary:focus, -.btn-primary.focus { - color: #fff; - background-color: #286090; - border-color: #122b40; -} -.btn-primary:hover { - color: #fff; - background-color: #286090; - border-color: #204d74; -} -.btn-primary:active, -.btn-primary.active, -.open > .dropdown-toggle.btn-primary { - color: #fff; - background-color: #286090; - background-image: none; - border-color: #204d74; -} -.btn-primary:active:hover, -.btn-primary.active:hover, -.open > .dropdown-toggle.btn-primary:hover, -.btn-primary:active:focus, -.btn-primary.active:focus, -.open > .dropdown-toggle.btn-primary:focus, -.btn-primary:active.focus, -.btn-primary.active.focus, -.open > .dropdown-toggle.btn-primary.focus { - color: #fff; - background-color: #204d74; - border-color: #122b40; -} -.btn-primary.disabled:hover, -.btn-primary[disabled]:hover, -fieldset[disabled] .btn-primary:hover, -.btn-primary.disabled:focus, -.btn-primary[disabled]:focus, -fieldset[disabled] .btn-primary:focus, -.btn-primary.disabled.focus, -.btn-primary[disabled].focus, -fieldset[disabled] .btn-primary.focus { - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary .badge { - color: #337ab7; - background-color: #fff; -} -.btn-success { - color: #fff; - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success:focus, -.btn-success.focus { - color: #fff; - background-color: #449d44; - border-color: #255625; -} -.btn-success:hover { - color: #fff; - background-color: #449d44; - border-color: #398439; -} -.btn-success:active, -.btn-success.active, -.open > .dropdown-toggle.btn-success { - color: #fff; - background-color: #449d44; - background-image: none; - border-color: #398439; -} -.btn-success:active:hover, -.btn-success.active:hover, -.open > .dropdown-toggle.btn-success:hover, -.btn-success:active:focus, -.btn-success.active:focus, -.open > .dropdown-toggle.btn-success:focus, -.btn-success:active.focus, -.btn-success.active.focus, -.open > .dropdown-toggle.btn-success.focus { - color: #fff; - background-color: #398439; - border-color: #255625; -} -.btn-success.disabled:hover, -.btn-success[disabled]:hover, -fieldset[disabled] .btn-success:hover, -.btn-success.disabled:focus, -.btn-success[disabled]:focus, -fieldset[disabled] .btn-success:focus, -.btn-success.disabled.focus, -.btn-success[disabled].focus, -fieldset[disabled] .btn-success.focus { - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success .badge { - color: #5cb85c; - background-color: #fff; -} -.btn-info { - color: #fff; - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info:focus, -.btn-info.focus { - color: #fff; - background-color: #31b0d5; - border-color: #1b6d85; -} -.btn-info:hover { - color: #fff; - background-color: #31b0d5; - border-color: #269abc; -} -.btn-info:active, -.btn-info.active, -.open > .dropdown-toggle.btn-info { - color: #fff; - background-color: #31b0d5; - background-image: none; - border-color: #269abc; -} -.btn-info:active:hover, -.btn-info.active:hover, -.open > .dropdown-toggle.btn-info:hover, -.btn-info:active:focus, -.btn-info.active:focus, -.open > .dropdown-toggle.btn-info:focus, -.btn-info:active.focus, -.btn-info.active.focus, -.open > .dropdown-toggle.btn-info.focus { - color: #fff; - background-color: #269abc; - border-color: #1b6d85; -} -.btn-info.disabled:hover, -.btn-info[disabled]:hover, -fieldset[disabled] .btn-info:hover, -.btn-info.disabled:focus, -.btn-info[disabled]:focus, -fieldset[disabled] .btn-info:focus, -.btn-info.disabled.focus, -.btn-info[disabled].focus, -fieldset[disabled] .btn-info.focus { - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info .badge { - color: #5bc0de; - background-color: #fff; -} -.btn-warning { - color: #fff; - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning:focus, -.btn-warning.focus { - color: #fff; - background-color: #ec971f; - border-color: #985f0d; -} -.btn-warning:hover { - color: #fff; - background-color: #ec971f; - border-color: #d58512; -} -.btn-warning:active, -.btn-warning.active, -.open > .dropdown-toggle.btn-warning { - color: #fff; - background-color: #ec971f; - background-image: none; - border-color: #d58512; -} -.btn-warning:active:hover, -.btn-warning.active:hover, -.open > .dropdown-toggle.btn-warning:hover, -.btn-warning:active:focus, -.btn-warning.active:focus, -.open > .dropdown-toggle.btn-warning:focus, -.btn-warning:active.focus, -.btn-warning.active.focus, -.open > .dropdown-toggle.btn-warning.focus { - color: #fff; - background-color: #d58512; - border-color: #985f0d; -} -.btn-warning.disabled:hover, -.btn-warning[disabled]:hover, -fieldset[disabled] .btn-warning:hover, -.btn-warning.disabled:focus, -.btn-warning[disabled]:focus, -fieldset[disabled] .btn-warning:focus, -.btn-warning.disabled.focus, -.btn-warning[disabled].focus, -fieldset[disabled] .btn-warning.focus { - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning .badge { - color: #f0ad4e; - background-color: #fff; -} -.btn-danger { - color: #fff; - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger:focus, -.btn-danger.focus { - color: #fff; - background-color: #c9302c; - border-color: #761c19; -} -.btn-danger:hover { - color: #fff; - background-color: #c9302c; - border-color: #ac2925; -} -.btn-danger:active, -.btn-danger.active, -.open > .dropdown-toggle.btn-danger { - color: #fff; - background-color: #c9302c; - background-image: none; - border-color: #ac2925; -} -.btn-danger:active:hover, -.btn-danger.active:hover, -.open > .dropdown-toggle.btn-danger:hover, -.btn-danger:active:focus, -.btn-danger.active:focus, -.open > .dropdown-toggle.btn-danger:focus, -.btn-danger:active.focus, -.btn-danger.active.focus, -.open > .dropdown-toggle.btn-danger.focus { - color: #fff; - background-color: #ac2925; - border-color: #761c19; -} -.btn-danger.disabled:hover, -.btn-danger[disabled]:hover, -fieldset[disabled] .btn-danger:hover, -.btn-danger.disabled:focus, -.btn-danger[disabled]:focus, -fieldset[disabled] .btn-danger:focus, -.btn-danger.disabled.focus, -.btn-danger[disabled].focus, -fieldset[disabled] .btn-danger.focus { - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger .badge { - color: #d9534f; - background-color: #fff; -} -.btn-link { - font-weight: 400; - color: #337ab7; - border-radius: 0; -} -.btn-link, -.btn-link:active, -.btn-link.active, -.btn-link[disabled], -fieldset[disabled] .btn-link { - background-color: transparent; - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-link, -.btn-link:hover, -.btn-link:focus, -.btn-link:active { - border-color: transparent; -} -.btn-link:hover, -.btn-link:focus { - color: #23527c; - text-decoration: underline; - background-color: transparent; -} -.btn-link[disabled]:hover, -fieldset[disabled] .btn-link:hover, -.btn-link[disabled]:focus, -fieldset[disabled] .btn-link:focus { - color: #777777; - text-decoration: none; -} -.btn-lg, -.btn-group-lg > .btn { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.btn-sm, -.btn-group-sm > .btn { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-xs, -.btn-group-xs > .btn { - padding: 1px 5px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-block { - display: block; - width: 100%; -} -.btn-block + .btn-block { - margin-top: 5px; -} -input[type="submit"].btn-block, -input[type="reset"].btn-block, -input[type="button"].btn-block { - width: 100%; -} -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} -.fade.in { - opacity: 1; -} -.collapse { - display: none; -} -.collapse.in { - display: block; -} -tr.collapse.in { - display: table-row; -} -tbody.collapse.in { - display: table-row-group; -} -.collapsing { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition-property: height, visibility; - -o-transition-property: height, visibility; - transition-property: height, visibility; - -webkit-transition-duration: 0.35s; - -o-transition-duration: 0.35s; - transition-duration: 0.35s; - -webkit-transition-timing-function: ease; - -o-transition-timing-function: ease; - transition-timing-function: ease; -} -.caret { - display: inline-block; - width: 0; - height: 0; - margin-left: 2px; - vertical-align: middle; - border-top: 4px dashed; - border-top: 4px solid \9; - border-right: 4px solid transparent; - border-left: 4px solid transparent; -} -.dropup, -.dropdown { - position: relative; -} -.dropdown-toggle:focus { - outline: 0; -} -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - font-size: 14px; - text-align: left; - list-style: none; - background-color: #fff; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.15); - border-radius: 4px; - -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); - box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); -} -.dropdown-menu.pull-right { - right: 0; - left: auto; -} -.dropdown-menu .divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: 400; - line-height: 1.42857143; - color: #333333; - white-space: nowrap; -} -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus { - color: #262626; - text-decoration: none; - background-color: #f5f5f5; -} -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #fff; - text-decoration: none; - background-color: #337ab7; - outline: 0; -} -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #777777; -} -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: not-allowed; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.open > .dropdown-menu { - display: block; -} -.open > a { - outline: 0; -} -.dropdown-menu-right { - right: 0; - left: auto; -} -.dropdown-menu-left { - right: auto; - left: 0; -} -.dropdown-header { - display: block; - padding: 3px 20px; - font-size: 12px; - line-height: 1.42857143; - color: #777777; - white-space: nowrap; -} -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - content: ""; - border-top: 0; - border-bottom: 4px dashed; - border-bottom: 4px solid \9; -} -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 2px; -} -@media (min-width: 768px) { - .navbar-right .dropdown-menu { - right: 0; - left: auto; - } - .navbar-right .dropdown-menu-left { - right: auto; - left: 0; - } -} -.btn-group, -.btn-group-vertical { - position: relative; - display: inline-block; - vertical-align: middle; -} -.btn-group > .btn, -.btn-group-vertical > .btn { - position: relative; - float: left; -} -.btn-group > .btn:hover, -.btn-group-vertical > .btn:hover, -.btn-group > .btn:focus, -.btn-group-vertical > .btn:focus, -.btn-group > .btn:active, -.btn-group-vertical > .btn:active, -.btn-group > .btn.active, -.btn-group-vertical > .btn.active { - z-index: 2; -} -.btn-group .btn + .btn, -.btn-group .btn + .btn-group, -.btn-group .btn-group + .btn, -.btn-group .btn-group + .btn-group { - margin-left: -1px; -} -.btn-toolbar { - margin-left: -5px; -} -.btn-toolbar .btn, -.btn-toolbar .btn-group, -.btn-toolbar .input-group { - float: left; -} -.btn-toolbar > .btn, -.btn-toolbar > .btn-group, -.btn-toolbar > .input-group { - margin-left: 5px; -} -.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { - border-radius: 0; -} -.btn-group > .btn:first-child { - margin-left: 0; -} -.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn:last-child:not(:first-child), -.btn-group > .dropdown-toggle:not(:first-child) { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group > .btn-group { - float: left; -} -.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} -.btn-group > .btn + .dropdown-toggle { - padding-right: 8px; - padding-left: 8px; -} -.btn-group > .btn-lg + .dropdown-toggle { - padding-right: 12px; - padding-left: 12px; -} -.btn-group.open .dropdown-toggle { - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); -} -.btn-group.open .dropdown-toggle.btn-link { - -webkit-box-shadow: none; - box-shadow: none; -} -.btn .caret { - margin-left: 0; -} -.btn-lg .caret { - border-width: 5px 5px 0; - border-bottom-width: 0; -} -.dropup .btn-lg .caret { - border-width: 0 5px 5px; -} -.btn-group-vertical > .btn, -.btn-group-vertical > .btn-group, -.btn-group-vertical > .btn-group > .btn { - display: block; - float: none; - width: 100%; - max-width: 100%; -} -.btn-group-vertical > .btn-group > .btn { - float: none; -} -.btn-group-vertical > .btn + .btn, -.btn-group-vertical > .btn + .btn-group, -.btn-group-vertical > .btn-group + .btn, -.btn-group-vertical > .btn-group + .btn-group { - margin-top: -1px; - margin-left: 0; -} -.btn-group-vertical > .btn:not(:first-child):not(:last-child) { - border-radius: 0; -} -.btn-group-vertical > .btn:first-child:not(:last-child) { - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn:last-child:not(:first-child) { - border-top-left-radius: 0; - border-top-right-radius: 0; - border-bottom-right-radius: 4px; - border-bottom-left-radius: 4px; -} -.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.btn-group-justified { - display: table; - width: 100%; - table-layout: fixed; - border-collapse: separate; -} -.btn-group-justified > .btn, -.btn-group-justified > .btn-group { - display: table-cell; - float: none; - width: 1%; -} -.btn-group-justified > .btn-group .btn { - width: 100%; -} -.btn-group-justified > .btn-group .dropdown-menu { - left: auto; -} -[data-toggle="buttons"] > .btn input[type="radio"], -[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], -[data-toggle="buttons"] > .btn input[type="checkbox"], -[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { - position: absolute; - clip: rect(0, 0, 0, 0); - pointer-events: none; -} -.input-group { - position: relative; - display: table; - border-collapse: separate; -} -.input-group[class*="col-"] { - float: none; - padding-right: 0; - padding-left: 0; -} -.input-group .form-control { - position: relative; - z-index: 2; - float: left; - width: 100%; - margin-bottom: 0; -} -.input-group .form-control:focus { - z-index: 3; -} -.input-group-lg > .form-control, -.input-group-lg > .input-group-addon, -.input-group-lg > .input-group-btn > .btn { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-group-lg > .form-control, -select.input-group-lg > .input-group-addon, -select.input-group-lg > .input-group-btn > .btn { - height: 46px; - line-height: 46px; -} -textarea.input-group-lg > .form-control, -textarea.input-group-lg > .input-group-addon, -textarea.input-group-lg > .input-group-btn > .btn, -select[multiple].input-group-lg > .form-control, -select[multiple].input-group-lg > .input-group-addon, -select[multiple].input-group-lg > .input-group-btn > .btn { - height: auto; -} -.input-group-sm > .form-control, -.input-group-sm > .input-group-addon, -.input-group-sm > .input-group-btn > .btn { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-group-sm > .form-control, -select.input-group-sm > .input-group-addon, -select.input-group-sm > .input-group-btn > .btn { - height: 30px; - line-height: 30px; -} -textarea.input-group-sm > .form-control, -textarea.input-group-sm > .input-group-addon, -textarea.input-group-sm > .input-group-btn > .btn, -select[multiple].input-group-sm > .form-control, -select[multiple].input-group-sm > .input-group-addon, -select[multiple].input-group-sm > .input-group-btn > .btn { - height: auto; -} -.input-group-addon, -.input-group-btn, -.input-group .form-control { - display: table-cell; -} -.input-group-addon:not(:first-child):not(:last-child), -.input-group-btn:not(:first-child):not(:last-child), -.input-group .form-control:not(:first-child):not(:last-child) { - border-radius: 0; -} -.input-group-addon, -.input-group-btn { - width: 1%; - white-space: nowrap; - vertical-align: middle; -} -.input-group-addon { - padding: 6px 12px; - font-size: 14px; - font-weight: 400; - line-height: 1; - color: #555555; - text-align: center; - background-color: #eeeeee; - border: 1px solid #ccc; - border-radius: 4px; -} -.input-group-addon.input-sm { - padding: 5px 10px; - font-size: 12px; - border-radius: 3px; -} -.input-group-addon.input-lg { - padding: 10px 16px; - font-size: 18px; - border-radius: 6px; -} -.input-group-addon input[type="radio"], -.input-group-addon input[type="checkbox"] { - margin-top: 0; -} -.input-group .form-control:first-child, -.input-group-addon:first-child, -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group > .btn, -.input-group-btn:first-child > .dropdown-toggle, -.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), -.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.input-group-addon:first-child { - border-right: 0; -} -.input-group .form-control:last-child, -.input-group-addon:last-child, -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group > .btn, -.input-group-btn:last-child > .dropdown-toggle, -.input-group-btn:first-child > .btn:not(:first-child), -.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.input-group-addon:last-child { - border-left: 0; -} -.input-group-btn { - position: relative; - font-size: 0; - white-space: nowrap; -} -.input-group-btn > .btn { - position: relative; -} -.input-group-btn > .btn + .btn { - margin-left: -1px; -} -.input-group-btn > .btn:hover, -.input-group-btn > .btn:focus, -.input-group-btn > .btn:active { - z-index: 2; -} -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group { - margin-right: -1px; -} -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group { - z-index: 2; - margin-left: -1px; -} -.nav { - padding-left: 0; - margin-bottom: 0; - list-style: none; -} -.nav > li { - position: relative; - display: block; -} -.nav > li > a { - position: relative; - display: block; - padding: 10px 15px; -} -.nav > li > a:hover, -.nav > li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} -.nav > li.disabled > a { - color: #777777; -} -.nav > li.disabled > a:hover, -.nav > li.disabled > a:focus { - color: #777777; - text-decoration: none; - cursor: not-allowed; - background-color: transparent; -} -.nav .open > a, -.nav .open > a:hover, -.nav .open > a:focus { - background-color: #eeeeee; - border-color: #337ab7; -} -.nav .nav-divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.nav > li > a > img { - max-width: none; -} -.nav-tabs { - border-bottom: 1px solid #ddd; -} -.nav-tabs > li { - float: left; - margin-bottom: -1px; -} -.nav-tabs > li > a { - margin-right: 2px; - line-height: 1.42857143; - border: 1px solid transparent; - border-radius: 4px 4px 0 0; -} -.nav-tabs > li > a:hover { - border-color: #eeeeee #eeeeee #ddd; -} -.nav-tabs > li.active > a, -.nav-tabs > li.active > a:hover, -.nav-tabs > li.active > a:focus { - color: #555555; - cursor: default; - background-color: #fff; - border: 1px solid #ddd; - border-bottom-color: transparent; -} -.nav-tabs.nav-justified { - width: 100%; - border-bottom: 0; -} -.nav-tabs.nav-justified > li { - float: none; -} -.nav-tabs.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-tabs.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-tabs.nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs.nav-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs.nav-justified > .active > a, -.nav-tabs.nav-justified > .active > a:hover, -.nav-tabs.nav-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs.nav-justified > .active > a, - .nav-tabs.nav-justified > .active > a:hover, - .nav-tabs.nav-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.nav-pills > li { - float: left; -} -.nav-pills > li > a { - border-radius: 4px; -} -.nav-pills > li + li { - margin-left: 2px; -} -.nav-pills > li.active > a, -.nav-pills > li.active > a:hover, -.nav-pills > li.active > a:focus { - color: #fff; - background-color: #337ab7; -} -.nav-stacked > li { - float: none; -} -.nav-stacked > li + li { - margin-top: 2px; - margin-left: 0; -} -.nav-justified { - width: 100%; -} -.nav-justified > li { - float: none; -} -.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs-justified { - border-bottom: 0; -} -.nav-tabs-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs-justified > .active > a, -.nav-tabs-justified > .active > a:hover, -.nav-tabs-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs-justified > .active > a, - .nav-tabs-justified > .active > a:hover, - .nav-tabs-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.tab-content > .tab-pane { - display: none; -} -.tab-content > .active { - display: block; -} -.nav-tabs .dropdown-menu { - margin-top: -1px; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar { - position: relative; - min-height: 50px; - margin-bottom: 20px; - border: 1px solid transparent; -} -@media (min-width: 768px) { - .navbar { - border-radius: 4px; - } -} -@media (min-width: 768px) { - .navbar-header { - float: left; - } -} -.navbar-collapse { - padding-right: 15px; - padding-left: 15px; - overflow-x: visible; - border-top: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); - -webkit-overflow-scrolling: touch; -} -.navbar-collapse.in { - overflow-y: auto; -} -@media (min-width: 768px) { - .navbar-collapse { - width: auto; - border-top: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-collapse.collapse { - display: block !important; - height: auto !important; - padding-bottom: 0; - overflow: visible !important; - } - .navbar-collapse.in { - overflow-y: visible; - } - .navbar-fixed-top .navbar-collapse, - .navbar-static-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - padding-right: 0; - padding-left: 0; - } -} -.navbar-fixed-top, -.navbar-fixed-bottom { - position: fixed; - right: 0; - left: 0; - z-index: 1030; -} -.navbar-fixed-top .navbar-collapse, -.navbar-fixed-bottom .navbar-collapse { - max-height: 340px; -} -@media (max-device-width: 480px) and (orientation: landscape) { - .navbar-fixed-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - max-height: 200px; - } -} -@media (min-width: 768px) { - .navbar-fixed-top, - .navbar-fixed-bottom { - border-radius: 0; - } -} -.navbar-fixed-top { - top: 0; - border-width: 0 0 1px; -} -.navbar-fixed-bottom { - bottom: 0; - margin-bottom: 0; - border-width: 1px 0 0; -} -.container > .navbar-header, -.container-fluid > .navbar-header, -.container > .navbar-collapse, -.container-fluid > .navbar-collapse { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .container > .navbar-header, - .container-fluid > .navbar-header, - .container > .navbar-collapse, - .container-fluid > .navbar-collapse { - margin-right: 0; - margin-left: 0; - } -} -.navbar-static-top { - z-index: 1000; - border-width: 0 0 1px; -} -@media (min-width: 768px) { - .navbar-static-top { - border-radius: 0; - } -} -.navbar-brand { - float: left; - height: 50px; - padding: 15px 15px; - font-size: 18px; - line-height: 20px; -} -.navbar-brand:hover, -.navbar-brand:focus { - text-decoration: none; -} -.navbar-brand > img { - display: block; -} -@media (min-width: 768px) { - .navbar > .container .navbar-brand, - .navbar > .container-fluid .navbar-brand { - margin-left: -15px; - } -} -.navbar-toggle { - position: relative; - float: right; - padding: 9px 10px; - margin-right: 15px; - margin-top: 8px; - margin-bottom: 8px; - background-color: transparent; - background-image: none; - border: 1px solid transparent; - border-radius: 4px; -} -.navbar-toggle:focus { - outline: 0; -} -.navbar-toggle .icon-bar { - display: block; - width: 22px; - height: 2px; - border-radius: 1px; -} -.navbar-toggle .icon-bar + .icon-bar { - margin-top: 4px; -} -@media (min-width: 768px) { - .navbar-toggle { - display: none; - } -} -.navbar-nav { - margin: 7.5px -15px; -} -.navbar-nav > li > a { - padding-top: 10px; - padding-bottom: 10px; - line-height: 20px; -} -@media (max-width: 767px) { - .navbar-nav .open .dropdown-menu { - position: static; - float: none; - width: auto; - margin-top: 0; - background-color: transparent; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-nav .open .dropdown-menu > li > a, - .navbar-nav .open .dropdown-menu .dropdown-header { - padding: 5px 15px 5px 25px; - } - .navbar-nav .open .dropdown-menu > li > a { - line-height: 20px; - } - .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-nav .open .dropdown-menu > li > a:focus { - background-image: none; - } -} -@media (min-width: 768px) { - .navbar-nav { - float: left; - margin: 0; - } - .navbar-nav > li { - float: left; - } - .navbar-nav > li > a { - padding-top: 15px; - padding-bottom: 15px; - } -} -.navbar-form { - padding: 10px 15px; - margin-right: -15px; - margin-left: -15px; - border-top: 1px solid transparent; - border-bottom: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - margin-top: 8px; - margin-bottom: 8px; -} -@media (min-width: 768px) { - .navbar-form .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .navbar-form .form-control-static { - display: inline-block; - } - .navbar-form .input-group { - display: inline-table; - vertical-align: middle; - } - .navbar-form .input-group .input-group-addon, - .navbar-form .input-group .input-group-btn, - .navbar-form .input-group .form-control { - width: auto; - } - .navbar-form .input-group > .form-control { - width: 100%; - } - .navbar-form .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio, - .navbar-form .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio label, - .navbar-form .checkbox label { - padding-left: 0; - } - .navbar-form .radio input[type="radio"], - .navbar-form .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .navbar-form .has-feedback .form-control-feedback { - top: 0; - } -} -@media (max-width: 767px) { - .navbar-form .form-group { - margin-bottom: 5px; - } - .navbar-form .form-group:last-child { - margin-bottom: 0; - } -} -@media (min-width: 768px) { - .navbar-form { - width: auto; - padding-top: 0; - padding-bottom: 0; - margin-right: 0; - margin-left: 0; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } -} -.navbar-nav > li > .dropdown-menu { - margin-top: 0; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { - margin-bottom: 0; - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.navbar-btn { - margin-top: 8px; - margin-bottom: 8px; -} -.navbar-btn.btn-sm { - margin-top: 10px; - margin-bottom: 10px; -} -.navbar-btn.btn-xs { - margin-top: 14px; - margin-bottom: 14px; -} -.navbar-text { - margin-top: 15px; - margin-bottom: 15px; -} -@media (min-width: 768px) { - .navbar-text { - float: left; - margin-right: 15px; - margin-left: 15px; - } -} -@media (min-width: 768px) { - .navbar-left { - float: left !important; - } - .navbar-right { - float: right !important; - margin-right: -15px; - } - .navbar-right ~ .navbar-right { - margin-right: 0; - } -} -.navbar-default { - background-color: #f8f8f8; - border-color: #e7e7e7; -} -.navbar-default .navbar-brand { - color: #777; -} -.navbar-default .navbar-brand:hover, -.navbar-default .navbar-brand:focus { - color: #5e5e5e; - background-color: transparent; -} -.navbar-default .navbar-text { - color: #777; -} -.navbar-default .navbar-nav > li > a { - color: #777; -} -.navbar-default .navbar-nav > li > a:hover, -.navbar-default .navbar-nav > li > a:focus { - color: #333; - background-color: transparent; -} -.navbar-default .navbar-nav > .active > a, -.navbar-default .navbar-nav > .active > a:hover, -.navbar-default .navbar-nav > .active > a:focus { - color: #555; - background-color: #e7e7e7; -} -.navbar-default .navbar-nav > .disabled > a, -.navbar-default .navbar-nav > .disabled > a:hover, -.navbar-default .navbar-nav > .disabled > a:focus { - color: #ccc; - background-color: transparent; -} -.navbar-default .navbar-nav > .open > a, -.navbar-default .navbar-nav > .open > a:hover, -.navbar-default .navbar-nav > .open > a:focus { - color: #555; - background-color: #e7e7e7; -} -@media (max-width: 767px) { - .navbar-default .navbar-nav .open .dropdown-menu > li > a { - color: #777; - } - .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { - color: #333; - background-color: transparent; - } - .navbar-default .navbar-nav .open .dropdown-menu > .active > a, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #555; - background-color: #e7e7e7; - } - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #ccc; - background-color: transparent; - } -} -.navbar-default .navbar-toggle { - border-color: #ddd; -} -.navbar-default .navbar-toggle:hover, -.navbar-default .navbar-toggle:focus { - background-color: #ddd; -} -.navbar-default .navbar-toggle .icon-bar { - background-color: #888; -} -.navbar-default .navbar-collapse, -.navbar-default .navbar-form { - border-color: #e7e7e7; -} -.navbar-default .navbar-link { - color: #777; -} -.navbar-default .navbar-link:hover { - color: #333; -} -.navbar-default .btn-link { - color: #777; -} -.navbar-default .btn-link:hover, -.navbar-default .btn-link:focus { - color: #333; -} -.navbar-default .btn-link[disabled]:hover, -fieldset[disabled] .navbar-default .btn-link:hover, -.navbar-default .btn-link[disabled]:focus, -fieldset[disabled] .navbar-default .btn-link:focus { - color: #ccc; -} -.navbar-inverse { - background-color: #222; - border-color: #080808; -} -.navbar-inverse .navbar-brand { - color: #9d9d9d; -} -.navbar-inverse .navbar-brand:hover, -.navbar-inverse .navbar-brand:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-text { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a:hover, -.navbar-inverse .navbar-nav > li > a:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-nav > .active > a, -.navbar-inverse .navbar-nav > .active > a:hover, -.navbar-inverse .navbar-nav > .active > a:focus { - color: #fff; - background-color: #080808; -} -.navbar-inverse .navbar-nav > .disabled > a, -.navbar-inverse .navbar-nav > .disabled > a:hover, -.navbar-inverse .navbar-nav > .disabled > a:focus { - color: #444; - background-color: transparent; -} -.navbar-inverse .navbar-nav > .open > a, -.navbar-inverse .navbar-nav > .open > a:hover, -.navbar-inverse .navbar-nav > .open > a:focus { - color: #fff; - background-color: #080808; -} -@media (max-width: 767px) { - .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { - border-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu .divider { - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { - color: #9d9d9d; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { - color: #fff; - background-color: transparent; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #fff; - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #444; - background-color: transparent; - } -} -.navbar-inverse .navbar-toggle { - border-color: #333; -} -.navbar-inverse .navbar-toggle:hover, -.navbar-inverse .navbar-toggle:focus { - background-color: #333; -} -.navbar-inverse .navbar-toggle .icon-bar { - background-color: #fff; -} -.navbar-inverse .navbar-collapse, -.navbar-inverse .navbar-form { - border-color: #101010; -} -.navbar-inverse .navbar-link { - color: #9d9d9d; -} -.navbar-inverse .navbar-link:hover { - color: #fff; -} -.navbar-inverse .btn-link { - color: #9d9d9d; -} -.navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link:focus { - color: #fff; -} -.navbar-inverse .btn-link[disabled]:hover, -fieldset[disabled] .navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link[disabled]:focus, -fieldset[disabled] .navbar-inverse .btn-link:focus { - color: #444; -} -.breadcrumb { - padding: 8px 15px; - margin-bottom: 20px; - list-style: none; - background-color: #f5f5f5; - border-radius: 4px; -} -.breadcrumb > li { - display: inline-block; -} -.breadcrumb > li + li:before { - padding: 0 5px; - color: #ccc; - content: "/\00a0"; -} -.breadcrumb > .active { - color: #777777; -} -.pagination { - display: inline-block; - padding-left: 0; - margin: 20px 0; - border-radius: 4px; -} -.pagination > li { - display: inline; -} -.pagination > li > a, -.pagination > li > span { - position: relative; - float: left; - padding: 6px 12px; - margin-left: -1px; - line-height: 1.42857143; - color: #337ab7; - text-decoration: none; - background-color: #fff; - border: 1px solid #ddd; -} -.pagination > li > a:hover, -.pagination > li > span:hover, -.pagination > li > a:focus, -.pagination > li > span:focus { - z-index: 2; - color: #23527c; - background-color: #eeeeee; - border-color: #ddd; -} -.pagination > li:first-child > a, -.pagination > li:first-child > span { - margin-left: 0; - border-top-left-radius: 4px; - border-bottom-left-radius: 4px; -} -.pagination > li:last-child > a, -.pagination > li:last-child > span { - border-top-right-radius: 4px; - border-bottom-right-radius: 4px; -} -.pagination > .active > a, -.pagination > .active > span, -.pagination > .active > a:hover, -.pagination > .active > span:hover, -.pagination > .active > a:focus, -.pagination > .active > span:focus { - z-index: 3; - color: #fff; - cursor: default; - background-color: #337ab7; - border-color: #337ab7; -} -.pagination > .disabled > span, -.pagination > .disabled > span:hover, -.pagination > .disabled > span:focus, -.pagination > .disabled > a, -.pagination > .disabled > a:hover, -.pagination > .disabled > a:focus { - color: #777777; - cursor: not-allowed; - background-color: #fff; - border-color: #ddd; -} -.pagination-lg > li > a, -.pagination-lg > li > span { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.pagination-lg > li:first-child > a, -.pagination-lg > li:first-child > span { - border-top-left-radius: 6px; - border-bottom-left-radius: 6px; -} -.pagination-lg > li:last-child > a, -.pagination-lg > li:last-child > span { - border-top-right-radius: 6px; - border-bottom-right-radius: 6px; -} -.pagination-sm > li > a, -.pagination-sm > li > span { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; -} -.pagination-sm > li:first-child > a, -.pagination-sm > li:first-child > span { - border-top-left-radius: 3px; - border-bottom-left-radius: 3px; -} -.pagination-sm > li:last-child > a, -.pagination-sm > li:last-child > span { - border-top-right-radius: 3px; - border-bottom-right-radius: 3px; -} -.pager { - padding-left: 0; - margin: 20px 0; - text-align: center; - list-style: none; -} -.pager li { - display: inline; -} -.pager li > a, -.pager li > span { - display: inline-block; - padding: 5px 14px; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 15px; -} -.pager li > a:hover, -.pager li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} -.pager .next > a, -.pager .next > span { - float: right; -} -.pager .previous > a, -.pager .previous > span { - float: left; -} -.pager .disabled > a, -.pager .disabled > a:hover, -.pager .disabled > a:focus, -.pager .disabled > span { - color: #777777; - cursor: not-allowed; - background-color: #fff; -} -.label { - display: inline; - padding: 0.2em 0.6em 0.3em; - font-size: 75%; - font-weight: 700; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: baseline; - border-radius: 0.25em; -} -a.label:hover, -a.label:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.label:empty { - display: none; -} -.btn .label { - position: relative; - top: -1px; -} -.label-default { - background-color: #777777; -} -.label-default[href]:hover, -.label-default[href]:focus { - background-color: #5e5e5e; -} -.label-primary { - background-color: #337ab7; -} -.label-primary[href]:hover, -.label-primary[href]:focus { - background-color: #286090; -} -.label-success { - background-color: #5cb85c; -} -.label-success[href]:hover, -.label-success[href]:focus { - background-color: #449d44; -} -.label-info { - background-color: #5bc0de; -} -.label-info[href]:hover, -.label-info[href]:focus { - background-color: #31b0d5; -} -.label-warning { - background-color: #f0ad4e; -} -.label-warning[href]:hover, -.label-warning[href]:focus { - background-color: #ec971f; -} -.label-danger { - background-color: #d9534f; -} -.label-danger[href]:hover, -.label-danger[href]:focus { - background-color: #c9302c; -} -.badge { - display: inline-block; - min-width: 10px; - padding: 3px 7px; - font-size: 12px; - font-weight: bold; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: middle; - background-color: #777777; - border-radius: 10px; -} -.badge:empty { - display: none; -} -.btn .badge { - position: relative; - top: -1px; -} -.btn-xs .badge, -.btn-group-xs > .btn .badge { - top: 0; - padding: 1px 5px; -} -a.badge:hover, -a.badge:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.list-group-item.active > .badge, -.nav-pills > .active > a > .badge { - color: #337ab7; - background-color: #fff; -} -.list-group-item > .badge { - float: right; -} -.list-group-item > .badge + .badge { - margin-right: 5px; -} -.nav-pills > li > a > .badge { - margin-left: 3px; -} -.jumbotron { - padding-top: 30px; - padding-bottom: 30px; - margin-bottom: 30px; - color: inherit; - background-color: #eeeeee; -} -.jumbotron h1, -.jumbotron .h1 { - color: inherit; -} -.jumbotron p { - margin-bottom: 15px; - font-size: 21px; - font-weight: 200; -} -.jumbotron > hr { - border-top-color: #d5d5d5; -} -.container .jumbotron, -.container-fluid .jumbotron { - padding-right: 15px; - padding-left: 15px; - border-radius: 6px; -} -.jumbotron .container { - max-width: 100%; -} -@media screen and (min-width: 768px) { - .jumbotron { - padding-top: 48px; - padding-bottom: 48px; - } - .container .jumbotron, - .container-fluid .jumbotron { - padding-right: 60px; - padding-left: 60px; - } - .jumbotron h1, - .jumbotron .h1 { - font-size: 63px; - } -} -.thumbnail { - display: block; - padding: 4px; - margin-bottom: 20px; - line-height: 1.42857143; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 4px; - -webkit-transition: border 0.2s ease-in-out; - -o-transition: border 0.2s ease-in-out; - transition: border 0.2s ease-in-out; -} -.thumbnail > img, -.thumbnail a > img { - margin-right: auto; - margin-left: auto; -} -a.thumbnail:hover, -a.thumbnail:focus, -a.thumbnail.active { - border-color: #337ab7; -} -.thumbnail .caption { - padding: 9px; - color: #333333; -} -.alert { - padding: 15px; - margin-bottom: 20px; - border: 1px solid transparent; - border-radius: 4px; -} -.alert h4 { - margin-top: 0; - color: inherit; -} -.alert .alert-link { - font-weight: bold; -} -.alert > p, -.alert > ul { - margin-bottom: 0; -} -.alert > p + p { - margin-top: 5px; -} -.alert-dismissable, -.alert-dismissible { - padding-right: 35px; -} -.alert-dismissable .close, -.alert-dismissible .close { - position: relative; - top: -2px; - right: -21px; - color: inherit; -} -.alert-success { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.alert-success hr { - border-top-color: 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.table-bordered > tfoot > tr > th:last-child, -.panel > .table-bordered > thead > tr > td:last-child, -.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, -.panel > .table-bordered > tbody > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, -.panel > .table-bordered > tfoot > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; -} -.panel > .table-bordered > thead > tr:first-child > td, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, -.panel > .table-bordered > tbody > tr:first-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, -.panel > .table-bordered > thead > tr:first-child > th, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, -.panel > .table-bordered > tbody > tr:first-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { - border-bottom: 0; -} -.panel > .table-bordered > tbody > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, -.panel > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-bordered > tbody > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, -.panel > .table-bordered > tfoot > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { - border-bottom: 0; -} -.panel > .table-responsive { - margin-bottom: 0; - border: 0; -} -.panel-group { - margin-bottom: 20px; -} -.panel-group .panel { - margin-bottom: 0; - border-radius: 4px; -} -.panel-group .panel + .panel { - margin-top: 5px; -} -.panel-group .panel-heading { - border-bottom: 0; -} -.panel-group .panel-heading + .panel-collapse > .panel-body, -.panel-group .panel-heading + .panel-collapse > .list-group { - border-top: 1px solid #ddd; -} -.panel-group .panel-footer { - border-top: 0; -} -.panel-group .panel-footer + .panel-collapse .panel-body { - border-bottom: 1px solid #ddd; -} -.panel-default { - border-color: #ddd; -} -.panel-default > .panel-heading { - color: #333333; - background-color: #f5f5f5; - border-color: #ddd; -} -.panel-default > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ddd; -} -.panel-default > .panel-heading .badge { - color: #f5f5f5; - background-color: #333333; -} -.panel-default > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ddd; -} -.panel-primary { - border-color: #337ab7; -} -.panel-primary > .panel-heading { - color: #fff; - background-color: #337ab7; - border-color: #337ab7; -} -.panel-primary > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #337ab7; -} -.panel-primary > .panel-heading .badge { - color: #337ab7; - background-color: #fff; -} -.panel-primary > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #337ab7; -} -.panel-success { - border-color: #d6e9c6; -} -.panel-success > .panel-heading { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.panel-success > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #d6e9c6; -} -.panel-success > .panel-heading .badge { - color: #dff0d8; - background-color: #3c763d; -} -.panel-success > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #d6e9c6; -} -.panel-info { - border-color: #bce8f1; -} -.panel-info > .panel-heading { - color: #31708f; - background-color: #d9edf7; - border-color: #bce8f1; -} -.panel-info > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #bce8f1; -} -.panel-info > .panel-heading .badge { - color: #d9edf7; - background-color: #31708f; -} -.panel-info > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #bce8f1; -} -.panel-warning { - border-color: #faebcc; -} -.panel-warning > .panel-heading { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #faebcc; -} -.panel-warning > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #faebcc; -} -.panel-warning > .panel-heading .badge { - color: #fcf8e3; - background-color: #8a6d3b; -} -.panel-warning > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #faebcc; -} -.panel-danger { - border-color: #ebccd1; -} -.panel-danger > .panel-heading { - color: #a94442; - background-color: #f2dede; - border-color: #ebccd1; -} -.panel-danger > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ebccd1; -} -.panel-danger > .panel-heading .badge { - color: #f2dede; - background-color: #a94442; -} -.panel-danger > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ebccd1; -} -.embed-responsive { - position: relative; - display: block; - height: 0; - padding: 0; - overflow: hidden; -} -.embed-responsive .embed-responsive-item, -.embed-responsive iframe, -.embed-responsive embed, -.embed-responsive object, -.embed-responsive video { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 100%; - height: 100%; - border: 0; -} -.embed-responsive-16by9 { - padding-bottom: 56.25%; -} -.embed-responsive-4by3 { - padding-bottom: 75%; -} -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} -.well-lg { - padding: 24px; - border-radius: 6px; -} -.well-sm { - padding: 9px; - border-radius: 3px; -} -.close { - float: right; - font-size: 21px; - font-weight: bold; - line-height: 1; - color: #000; - text-shadow: 0 1px 0 #fff; - filter: alpha(opacity=20); - opacity: 0.2; -} -.close:hover, -.close:focus { - color: #000; - text-decoration: none; - cursor: pointer; - filter: alpha(opacity=50); - opacity: 0.5; -} -button.close { - padding: 0; - cursor: pointer; - background: transparent; - border: 0; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -.modal-open { - overflow: hidden; -} -.modal { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1050; - display: none; - overflow: hidden; - -webkit-overflow-scrolling: touch; - outline: 0; -} -.modal.fade .modal-dialog { - -webkit-transform: translate(0, -25%); - -ms-transform: translate(0, -25%); - -o-transform: translate(0, -25%); - transform: translate(0, -25%); - -webkit-transition: -webkit-transform 0.3s ease-out; - -o-transition: -o-transform 0.3s ease-out; - transition: -webkit-transform 0.3s ease-out; - transition: transform 0.3s ease-out; - transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out, -o-transform 0.3s ease-out; -} -.modal.in .modal-dialog { - -webkit-transform: translate(0, 0); - -ms-transform: 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text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - font-size: 12px; - filter: alpha(opacity=0); - opacity: 0; -} -.tooltip.in { - filter: alpha(opacity=90); - opacity: 0.9; -} -.tooltip.top { - padding: 5px 0; - margin-top: -3px; -} -.tooltip.right { - padding: 0 5px; - margin-left: 3px; -} -.tooltip.bottom { - padding: 5px 0; - margin-top: 3px; -} -.tooltip.left { - padding: 0 5px; - margin-left: -3px; -} -.tooltip.top .tooltip-arrow { - bottom: 0; - left: 50%; - margin-left: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-left .tooltip-arrow { - right: 5px; - bottom: 0; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-right .tooltip-arrow { - bottom: 0; - left: 5px; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.right .tooltip-arrow { - top: 50%; - left: 0; - margin-top: -5px; - border-width: 5px 5px 5px 0; - border-right-color: #000; -} -.tooltip.left .tooltip-arrow { - top: 50%; - right: 0; - margin-top: -5px; - border-width: 5px 0 5px 5px; - border-left-color: #000; -} -.tooltip.bottom .tooltip-arrow { - top: 0; - left: 50%; - margin-left: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-left .tooltip-arrow { - top: 0; - right: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-right .tooltip-arrow { - top: 0; - left: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip-inner { - max-width: 200px; - padding: 3px 8px; - color: #fff; - text-align: center; - background-color: #000; - border-radius: 4px; -} -.tooltip-arrow { - position: absolute; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover { - position: absolute; - top: 0; - left: 0; - z-index: 1060; - display: none; - max-width: 276px; - padding: 1px; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-style: normal; - font-weight: 400; - line-height: 1.42857143; - line-break: auto; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - font-size: 14px; - background-color: #fff; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); -} -.popover.top { - margin-top: -10px; -} -.popover.right { - margin-left: 10px; -} -.popover.bottom { - margin-top: 10px; -} -.popover.left { - margin-left: -10px; -} -.popover > .arrow { - border-width: 11px; -} -.popover > .arrow, -.popover > .arrow:after { - position: absolute; - display: block; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover > .arrow:after { - content: ""; - border-width: 10px; -} -.popover.top > .arrow { - bottom: -11px; - left: 50%; - margin-left: -11px; - border-top-color: #999999; - border-top-color: rgba(0, 0, 0, 0.25); - border-bottom-width: 0; -} -.popover.top > .arrow:after { - bottom: 1px; - margin-left: -10px; - content: " "; - border-top-color: #fff; - border-bottom-width: 0; -} -.popover.right > .arrow { - top: 50%; - left: -11px; - margin-top: -11px; - border-right-color: #999999; - border-right-color: rgba(0, 0, 0, 0.25); - border-left-width: 0; -} -.popover.right > .arrow:after { - bottom: -10px; - left: 1px; - content: " "; - border-right-color: #fff; - border-left-width: 0; -} -.popover.bottom > .arrow { - top: -11px; - left: 50%; - margin-left: -11px; - border-top-width: 0; - border-bottom-color: #999999; - border-bottom-color: rgba(0, 0, 0, 0.25); -} -.popover.bottom > .arrow:after { - top: 1px; - margin-left: -10px; - content: " "; - border-top-width: 0; - border-bottom-color: #fff; -} -.popover.left > .arrow { - top: 50%; - right: -11px; - margin-top: -11px; - border-right-width: 0; - border-left-color: #999999; - border-left-color: rgba(0, 0, 0, 0.25); -} -.popover.left > .arrow:after { - right: 1px; - bottom: -10px; - content: " "; - border-right-width: 0; - border-left-color: #fff; -} -.popover-title { - padding: 8px 14px; - margin: 0; - font-size: 14px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - border-radius: 5px 5px 0 0; -} -.popover-content { - padding: 9px 14px; -} -.carousel { - position: relative; -} -.carousel-inner { - position: relative; - width: 100%; - overflow: hidden; -} -.carousel-inner > .item { - position: relative; - display: none; - -webkit-transition: 0.6s ease-in-out left; - -o-transition: 0.6s ease-in-out left; - transition: 0.6s ease-in-out left; -} -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - line-height: 1; -} -@media all and (transform-3d), (-webkit-transform-3d) { - .carousel-inner > .item { - -webkit-transition: -webkit-transform 0.6s ease-in-out; - -o-transition: -o-transform 0.6s ease-in-out; - transition: -webkit-transform 0.6s ease-in-out; - transition: transform 0.6s ease-in-out; - transition: transform 0.6s ease-in-out, -webkit-transform 0.6s ease-in-out, -o-transform 0.6s ease-in-out; - -webkit-backface-visibility: hidden; - backface-visibility: hidden; - -webkit-perspective: 1000px; - perspective: 1000px; - } - .carousel-inner > .item.next, - .carousel-inner > .item.active.right { - -webkit-transform: translate3d(100%, 0, 0); - transform: translate3d(100%, 0, 0); - left: 0; - } - .carousel-inner > .item.prev, - .carousel-inner > .item.active.left { - -webkit-transform: translate3d(-100%, 0, 0); - transform: translate3d(-100%, 0, 0); - left: 0; - } - .carousel-inner > .item.next.left, - .carousel-inner > .item.prev.right, - .carousel-inner > .item.active { - -webkit-transform: translate3d(0, 0, 0); - transform: translate3d(0, 0, 0); - left: 0; - } -} -.carousel-inner > .active, -.carousel-inner > .next, -.carousel-inner > .prev { - display: block; -} -.carousel-inner > .active { - left: 0; -} -.carousel-inner > .next, -.carousel-inner > .prev { - position: absolute; - top: 0; - width: 100%; -} -.carousel-inner > .next { - left: 100%; -} -.carousel-inner > .prev { - left: -100%; -} -.carousel-inner > .next.left, -.carousel-inner > .prev.right { - left: 0; -} -.carousel-inner > .active.left { - left: -100%; -} -.carousel-inner > .active.right { - left: 100%; -} -.carousel-control { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 15%; - font-size: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); - background-color: rgba(0, 0, 0, 0); - filter: alpha(opacity=50); - opacity: 0.5; -} -.carousel-control.left { - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.5)), to(rgba(0, 0, 0, 0.0001))); - background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); - background-repeat: repeat-x; -} -.carousel-control.right { - right: 0; - left: auto; - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.0001)), to(rgba(0, 0, 0, 0.5))); - background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - filter: 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.icon-next:before { - content: "\203a"; -} -.carousel-indicators { - position: absolute; - bottom: 10px; - left: 50%; - z-index: 15; - width: 60%; - padding-left: 0; - margin-left: -30%; - text-align: center; - list-style: none; -} -.carousel-indicators li { - display: inline-block; - width: 10px; - height: 10px; - margin: 1px; - text-indent: -999px; - cursor: pointer; - background-color: #000 \9; - background-color: rgba(0, 0, 0, 0); - border: 1px solid #fff; - border-radius: 10px; -} -.carousel-indicators .active { - width: 12px; - height: 12px; - margin: 0; - background-color: #fff; -} -.carousel-caption { - position: absolute; - right: 15%; - bottom: 20px; - left: 15%; - z-index: 10; - padding-top: 20px; - padding-bottom: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); -} -.carousel-caption .btn { - text-shadow: none; -} -@media screen and (min-width: 768px) { - .carousel-control .glyphicon-chevron-left, - .carousel-control 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0.67em 0;\n}\nmark {\n background: #ff0;\n color: #000;\n}\nsmall {\n font-size: 80%;\n}\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\nsup {\n top: -0.5em;\n}\nsub {\n bottom: -0.25em;\n}\nimg {\n border: 0;\n}\nsvg:not(:root) {\n overflow: hidden;\n}\nfigure {\n margin: 1em 40px;\n}\nhr {\n box-sizing: content-box;\n height: 0;\n}\npre {\n overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit;\n font: inherit;\n margin: 0;\n}\nbutton {\n overflow: visible;\n}\nbutton,\nselect {\n text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n}\noptgroup {\n font-weight: bold;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd,\nth {\n padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n *,\n *:before,\n *:after {\n color: #000 !important;\n text-shadow: none !important;\n background: transparent !important;\n box-shadow: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n h3 {\n page-break-after: avoid;\n }\n .navbar {\n display: none;\n }\n .btn > .caret,\n .dropup > .btn > .caret {\n border-top-color: #000 !important;\n }\n .label {\n border: 1px solid #000;\n }\n .table {\n border-collapse: collapse !important;\n }\n .table td,\n .table th {\n background-color: #fff !important;\n }\n .table-bordered th,\n .table-bordered td {\n border: 1px solid #ddd !important;\n }\n}\n@font-face {\n font-family: \"Glyphicons Halflings\";\n src: url(\"../fonts/glyphicons-halflings-regular.eot\");\n src: url(\"../fonts/glyphicons-halflings-regular.eot?#iefix\") format(\"embedded-opentype\"), url(\"../fonts/glyphicons-halflings-regular.woff2\") format(\"woff2\"), url(\"../fonts/glyphicons-halflings-regular.woff\") format(\"woff\"), url(\"../fonts/glyphicons-halflings-regular.ttf\") format(\"truetype\"), url(\"../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular\") format(\"svg\");\n}\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: \"Glyphicons Halflings\";\n font-style: normal;\n font-weight: 400;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n content: \"\\002a\";\n}\n.glyphicon-plus:before {\n content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n content: \"\\270f\";\n}\n.glyphicon-glass:before {\n content: \"\\e001\";\n}\n.glyphicon-music:before {\n content: \"\\e002\";\n}\n.glyphicon-search:before {\n content: \"\\e003\";\n}\n.glyphicon-heart:before {\n content: \"\\e005\";\n}\n.glyphicon-star:before {\n content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n content: \"\\e007\";\n}\n.glyphicon-user:before {\n content: \"\\e008\";\n}\n.glyphicon-film:before {\n content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n content: \"\\e010\";\n}\n.glyphicon-th:before {\n content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n content: \"\\e012\";\n}\n.glyphicon-ok:before {\n content: \"\\e013\";\n}\n.glyphicon-remove:before {\n content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n content: \"\\e016\";\n}\n.glyphicon-off:before {\n content: \"\\e017\";\n}\n.glyphicon-signal:before {\n content: \"\\e018\";\n}\n.glyphicon-cog:before {\n content: \"\\e019\";\n}\n.glyphicon-trash:before {\n content: \"\\e020\";\n}\n.glyphicon-home:before {\n content: \"\\e021\";\n}\n.glyphicon-file:before {\n content: \"\\e022\";\n}\n.glyphicon-time:before {\n content: \"\\e023\";\n}\n.glyphicon-road:before {\n content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n content: \"\\e025\";\n}\n.glyphicon-download:before {\n content: \"\\e026\";\n}\n.glyphicon-upload:before {\n content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n content: \"\\e032\";\n}\n.glyphicon-lock:before {\n content: \"\\e033\";\n}\n.glyphicon-flag:before {\n content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n content: \"\\e040\";\n}\n.glyphicon-tag:before {\n content: \"\\e041\";\n}\n.glyphicon-tags:before {\n content: \"\\e042\";\n}\n.glyphicon-book:before {\n content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n content: \"\\e044\";\n}\n.glyphicon-print:before {\n content: \"\\e045\";\n}\n.glyphicon-camera:before {\n content: \"\\e046\";\n}\n.glyphicon-font:before {\n content: \"\\e047\";\n}\n.glyphicon-bold:before {\n content: \"\\e048\";\n}\n.glyphicon-italic:before {\n content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n content: \"\\e055\";\n}\n.glyphicon-list:before {\n content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n content: \"\\e059\";\n}\n.glyphicon-picture:before {\n content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n content: \"\\e063\";\n}\n.glyphicon-tint:before {\n content: \"\\e064\";\n}\n.glyphicon-edit:before {\n content: \"\\e065\";\n}\n.glyphicon-share:before {\n content: \"\\e066\";\n}\n.glyphicon-check:before {\n content: \"\\e067\";\n}\n.glyphicon-move:before {\n content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n content: \"\\e070\";\n}\n.glyphicon-backward:before {\n content: \"\\e071\";\n}\n.glyphicon-play:before {\n content: \"\\e072\";\n}\n.glyphicon-pause:before {\n content: \"\\e073\";\n}\n.glyphicon-stop:before {\n content: \"\\e074\";\n}\n.glyphicon-forward:before {\n content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n content: \"\\e077\";\n}\n.glyphicon-eject:before {\n content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n content: \"\\e101\";\n}\n.glyphicon-gift:before {\n content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n content: \"\\e103\";\n}\n.glyphicon-fire:before {\n content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n content: \"\\e107\";\n}\n.glyphicon-plane:before {\n content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n content: \"\\e109\";\n}\n.glyphicon-random:before {\n content: \"\\e110\";\n}\n.glyphicon-comment:before {\n content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n content: \"\\e122\";\n}\n.glyphicon-bell:before {\n content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n content: \"\\e134\";\n}\n.glyphicon-globe:before {\n content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n content: \"\\e137\";\n}\n.glyphicon-filter:before {\n content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n content: \"\\e143\";\n}\n.glyphicon-link:before {\n content: \"\\e144\";\n}\n.glyphicon-phone:before {\n content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n content: \"\\e146\";\n}\n.glyphicon-usd:before {\n content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n content: \"\\e149\";\n}\n.glyphicon-sort:before {\n content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n content: \"\\e157\";\n}\n.glyphicon-expand:before {\n content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n content: \"\\e161\";\n}\n.glyphicon-flash:before {\n content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n content: \"\\e164\";\n}\n.glyphicon-record:before {\n content: \"\\e165\";\n}\n.glyphicon-save:before {\n content: \"\\e166\";\n}\n.glyphicon-open:before {\n content: \"\\e167\";\n}\n.glyphicon-saved:before {\n content: \"\\e168\";\n}\n.glyphicon-import:before {\n content: \"\\e169\";\n}\n.glyphicon-export:before {\n content: \"\\e170\";\n}\n.glyphicon-send:before {\n content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n content: \"\\e179\";\n}\n.glyphicon-header:before {\n content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n content: \"\\e183\";\n}\n.glyphicon-tower:before {\n content: \"\\e184\";\n}\n.glyphicon-stats:before {\n content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n content: \"\\e200\";\n}\n.glyphicon-cd:before {\n content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n content: \"\\e204\";\n}\n.glyphicon-copy:before {\n content: \"\\e205\";\n}\n.glyphicon-paste:before {\n content: \"\\e206\";\n}\n.glyphicon-alert:before {\n content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n content: \"\\e210\";\n}\n.glyphicon-king:before {\n content: \"\\e211\";\n}\n.glyphicon-queen:before {\n content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n content: \"\\e214\";\n}\n.glyphicon-knight:before {\n content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n content: \"\\e216\";\n}\n.glyphicon-tent:before {\n content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n content: \"\\e218\";\n}\n.glyphicon-bed:before {\n content: \"\\e219\";\n}\n.glyphicon-apple:before {\n content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n content: \"\\e227\";\n}\n.glyphicon-btc:before {\n content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n content: \"\\e227\";\n}\n.glyphicon-yen:before {\n content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n content: \"\\e232\";\n}\n.glyphicon-education:before {\n content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n content: \"\\e237\";\n}\n.glyphicon-oil:before {\n content: \"\\e238\";\n}\n.glyphicon-grain:before {\n content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n content: \"\\e253\";\n}\n.glyphicon-console:before {\n content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n content: \"\\e260\";\n}\n* {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n*:before,\n*:after {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-size: 14px;\n line-height: 1.42857143;\n color: #333333;\n background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\na {\n color: #337ab7;\n text-decoration: none;\n}\na:hover,\na:focus {\n color: #23527c;\n text-decoration: underline;\n}\na:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\nfigure {\n margin: 0;\n}\nimg {\n vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n display: block;\n max-width: 100%;\n height: auto;\n}\n.img-rounded {\n border-radius: 6px;\n}\n.img-thumbnail {\n padding: 4px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: all 0.2s ease-in-out;\n -o-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n display: inline-block;\n max-width: 100%;\n height: auto;\n}\n.img-circle {\n border-radius: 50%;\n}\nhr {\n margin-top: 20px;\n margin-bottom: 20px;\n border: 0;\n border-top: 1px solid #eeeeee;\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n[role=\"button\"] {\n cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n font-family: inherit;\n font-weight: 500;\n line-height: 1.1;\n color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n font-weight: 400;\n line-height: 1;\n color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n margin-top: 20px;\n margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n margin-top: 10px;\n margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n font-size: 75%;\n}\nh1,\n.h1 {\n font-size: 36px;\n}\nh2,\n.h2 {\n font-size: 30px;\n}\nh3,\n.h3 {\n font-size: 24px;\n}\nh4,\n.h4 {\n font-size: 18px;\n}\nh5,\n.h5 {\n font-size: 14px;\n}\nh6,\n.h6 {\n font-size: 12px;\n}\np {\n margin: 0 0 10px;\n}\n.lead {\n margin-bottom: 20px;\n font-size: 16px;\n font-weight: 300;\n line-height: 1.4;\n}\n@media (min-width: 768px) {\n .lead {\n font-size: 21px;\n }\n}\nsmall,\n.small {\n font-size: 85%;\n}\nmark,\n.mark {\n padding: 0.2em;\n background-color: #fcf8e3;\n}\n.text-left {\n text-align: left;\n}\n.text-right {\n text-align: right;\n}\n.text-center {\n text-align: center;\n}\n.text-justify {\n text-align: justify;\n}\n.text-nowrap {\n white-space: nowrap;\n}\n.text-lowercase {\n text-transform: lowercase;\n}\n.text-uppercase {\n text-transform: uppercase;\n}\n.text-capitalize {\n text-transform: capitalize;\n}\n.text-muted {\n color: #777777;\n}\n.text-primary {\n color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n color: #286090;\n}\n.text-success {\n color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n color: #2b542c;\n}\n.text-info {\n color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n color: #245269;\n}\n.text-warning {\n color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n color: #66512c;\n}\n.text-danger {\n color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n color: #843534;\n}\n.bg-primary {\n color: #fff;\n background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n background-color: #286090;\n}\n.bg-success {\n background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n background-color: #c1e2b3;\n}\n.bg-info {\n background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n background-color: #afd9ee;\n}\n.bg-warning {\n background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n background-color: #f7ecb5;\n}\n.bg-danger {\n background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n background-color: #e4b9b9;\n}\n.page-header {\n padding-bottom: 9px;\n margin: 40px 0 20px;\n border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n margin-top: 0;\n margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n margin-bottom: 0;\n}\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n.list-inline {\n padding-left: 0;\n list-style: none;\n margin-left: -5px;\n}\n.list-inline > li {\n display: inline-block;\n padding-right: 5px;\n padding-left: 5px;\n}\ndl {\n margin-top: 0;\n margin-bottom: 20px;\n}\ndt,\ndd {\n line-height: 1.42857143;\n}\ndt {\n font-weight: 700;\n}\ndd {\n margin-left: 0;\n}\n@media (min-width: 768px) {\n .dl-horizontal dt {\n float: left;\n width: 160px;\n clear: left;\n text-align: right;\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n }\n .dl-horizontal dd {\n margin-left: 180px;\n }\n}\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n}\n.initialism {\n font-size: 90%;\n text-transform: uppercase;\n}\nblockquote {\n padding: 10px 20px;\n margin: 0 0 20px;\n font-size: 17.5px;\n border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n display: block;\n font-size: 80%;\n line-height: 1.42857143;\n color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n content: \"\\2014 \\00A0\";\n}\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n text-align: right;\n border-right: 5px solid #eeeeee;\n border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n content: \"\";\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n content: \"\\00A0 \\2014\";\n}\naddress {\n margin-bottom: 20px;\n font-style: normal;\n line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: #c7254e;\n background-color: #f9f2f4;\n border-radius: 4px;\n}\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: #fff;\n background-color: #333;\n border-radius: 3px;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n padding: 0;\n font-size: 100%;\n font-weight: 700;\n box-shadow: none;\n}\npre {\n display: block;\n padding: 9.5px;\n margin: 0 0 10px;\n font-size: 13px;\n line-height: 1.42857143;\n color: #333333;\n word-break: break-all;\n word-wrap: break-word;\n background-color: #f5f5f5;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\npre code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n}\n.pre-scrollable {\n max-height: 340px;\n overflow-y: scroll;\n}\n.container {\n padding-right: 15px;\n padding-left: 15px;\n margin-right: auto;\n margin-left: auto;\n}\n@media (min-width: 768px) {\n .container {\n width: 750px;\n }\n}\n@media (min-width: 992px) {\n .container {\n width: 970px;\n }\n}\n@media (min-width: 1200px) {\n .container {\n width: 1170px;\n }\n}\n.container-fluid {\n padding-right: 15px;\n padding-left: 15px;\n margin-right: auto;\n margin-left: auto;\n}\n.row {\n margin-right: -15px;\n margin-left: -15px;\n}\n.row-no-gutters {\n margin-right: 0;\n margin-left: 0;\n}\n.row-no-gutters [class*=\"col-\"] {\n padding-right: 0;\n padding-left: 0;\n}\n.col-xs-1,\n.col-sm-1,\n.col-md-1,\n.col-lg-1,\n.col-xs-2,\n.col-sm-2,\n.col-md-2,\n.col-lg-2,\n.col-xs-3,\n.col-sm-3,\n.col-md-3,\n.col-lg-3,\n.col-xs-4,\n.col-sm-4,\n.col-md-4,\n.col-lg-4,\n.col-xs-5,\n.col-sm-5,\n.col-md-5,\n.col-lg-5,\n.col-xs-6,\n.col-sm-6,\n.col-md-6,\n.col-lg-6,\n.col-xs-7,\n.col-sm-7,\n.col-md-7,\n.col-lg-7,\n.col-xs-8,\n.col-sm-8,\n.col-md-8,\n.col-lg-8,\n.col-xs-9,\n.col-sm-9,\n.col-md-9,\n.col-lg-9,\n.col-xs-10,\n.col-sm-10,\n.col-md-10,\n.col-lg-10,\n.col-xs-11,\n.col-sm-11,\n.col-md-11,\n.col-lg-11,\n.col-xs-12,\n.col-sm-12,\n.col-md-12,\n.col-lg-12 {\n position: relative;\n min-height: 1px;\n padding-right: 15px;\n padding-left: 15px;\n}\n.col-xs-1,\n.col-xs-2,\n.col-xs-3,\n.col-xs-4,\n.col-xs-5,\n.col-xs-6,\n.col-xs-7,\n.col-xs-8,\n.col-xs-9,\n.col-xs-10,\n.col-xs-11,\n.col-xs-12 {\n float: left;\n}\n.col-xs-12 {\n width: 100%;\n}\n.col-xs-11 {\n width: 91.66666667%;\n}\n.col-xs-10 {\n width: 83.33333333%;\n}\n.col-xs-9 {\n width: 75%;\n}\n.col-xs-8 {\n width: 66.66666667%;\n}\n.col-xs-7 {\n width: 58.33333333%;\n}\n.col-xs-6 {\n width: 50%;\n}\n.col-xs-5 {\n width: 41.66666667%;\n}\n.col-xs-4 {\n width: 33.33333333%;\n}\n.col-xs-3 {\n width: 25%;\n}\n.col-xs-2 {\n width: 16.66666667%;\n}\n.col-xs-1 {\n width: 8.33333333%;\n}\n.col-xs-pull-12 {\n right: 100%;\n}\n.col-xs-pull-11 {\n right: 91.66666667%;\n}\n.col-xs-pull-10 {\n right: 83.33333333%;\n}\n.col-xs-pull-9 {\n right: 75%;\n}\n.col-xs-pull-8 {\n right: 66.66666667%;\n}\n.col-xs-pull-7 {\n right: 58.33333333%;\n}\n.col-xs-pull-6 {\n right: 50%;\n}\n.col-xs-pull-5 {\n right: 41.66666667%;\n}\n.col-xs-pull-4 {\n right: 33.33333333%;\n}\n.col-xs-pull-3 {\n right: 25%;\n}\n.col-xs-pull-2 {\n right: 16.66666667%;\n}\n.col-xs-pull-1 {\n right: 8.33333333%;\n}\n.col-xs-pull-0 {\n right: auto;\n}\n.col-xs-push-12 {\n left: 100%;\n}\n.col-xs-push-11 {\n left: 91.66666667%;\n}\n.col-xs-push-10 {\n left: 83.33333333%;\n}\n.col-xs-push-9 {\n left: 75%;\n}\n.col-xs-push-8 {\n left: 66.66666667%;\n}\n.col-xs-push-7 {\n left: 58.33333333%;\n}\n.col-xs-push-6 {\n left: 50%;\n}\n.col-xs-push-5 {\n left: 41.66666667%;\n}\n.col-xs-push-4 {\n left: 33.33333333%;\n}\n.col-xs-push-3 {\n left: 25%;\n}\n.col-xs-push-2 {\n left: 16.66666667%;\n}\n.col-xs-push-1 {\n left: 8.33333333%;\n}\n.col-xs-push-0 {\n left: auto;\n}\n.col-xs-offset-12 {\n margin-left: 100%;\n}\n.col-xs-offset-11 {\n margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n margin-left: 75%;\n}\n.col-xs-offset-8 {\n margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n margin-left: 50%;\n}\n.col-xs-offset-5 {\n margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n margin-left: 25%;\n}\n.col-xs-offset-2 {\n margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n margin-left: 0%;\n}\n@media (min-width: 768px) {\n .col-sm-1,\n .col-sm-2,\n .col-sm-3,\n .col-sm-4,\n .col-sm-5,\n .col-sm-6,\n .col-sm-7,\n .col-sm-8,\n .col-sm-9,\n .col-sm-10,\n .col-sm-11,\n .col-sm-12 {\n float: left;\n }\n .col-sm-12 {\n width: 100%;\n }\n .col-sm-11 {\n width: 91.66666667%;\n }\n .col-sm-10 {\n width: 83.33333333%;\n }\n .col-sm-9 {\n width: 75%;\n }\n .col-sm-8 {\n width: 66.66666667%;\n }\n .col-sm-7 {\n width: 58.33333333%;\n }\n .col-sm-6 {\n width: 50%;\n }\n .col-sm-5 {\n width: 41.66666667%;\n }\n .col-sm-4 {\n width: 33.33333333%;\n }\n .col-sm-3 {\n width: 25%;\n }\n .col-sm-2 {\n width: 16.66666667%;\n }\n .col-sm-1 {\n width: 8.33333333%;\n }\n .col-sm-pull-12 {\n right: 100%;\n }\n .col-sm-pull-11 {\n right: 91.66666667%;\n }\n .col-sm-pull-10 {\n right: 83.33333333%;\n }\n .col-sm-pull-9 {\n right: 75%;\n }\n .col-sm-pull-8 {\n right: 66.66666667%;\n }\n .col-sm-pull-7 {\n right: 58.33333333%;\n }\n .col-sm-pull-6 {\n right: 50%;\n }\n .col-sm-pull-5 {\n right: 41.66666667%;\n }\n .col-sm-pull-4 {\n right: 33.33333333%;\n }\n .col-sm-pull-3 {\n right: 25%;\n }\n .col-sm-pull-2 {\n right: 16.66666667%;\n }\n .col-sm-pull-1 {\n right: 8.33333333%;\n }\n .col-sm-pull-0 {\n right: auto;\n }\n .col-sm-push-12 {\n left: 100%;\n }\n .col-sm-push-11 {\n left: 91.66666667%;\n }\n .col-sm-push-10 {\n left: 83.33333333%;\n }\n .col-sm-push-9 {\n left: 75%;\n }\n .col-sm-push-8 {\n left: 66.66666667%;\n }\n .col-sm-push-7 {\n left: 58.33333333%;\n }\n .col-sm-push-6 {\n left: 50%;\n }\n .col-sm-push-5 {\n left: 41.66666667%;\n }\n .col-sm-push-4 {\n left: 33.33333333%;\n }\n .col-sm-push-3 {\n left: 25%;\n }\n .col-sm-push-2 {\n left: 16.66666667%;\n }\n .col-sm-push-1 {\n left: 8.33333333%;\n }\n .col-sm-push-0 {\n left: auto;\n }\n .col-sm-offset-12 {\n margin-left: 100%;\n }\n .col-sm-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-sm-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-sm-offset-9 {\n margin-left: 75%;\n }\n .col-sm-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-sm-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-sm-offset-6 {\n margin-left: 50%;\n }\n .col-sm-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-sm-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-sm-offset-3 {\n margin-left: 25%;\n }\n .col-sm-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-sm-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-sm-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 992px) {\n .col-md-1,\n .col-md-2,\n .col-md-3,\n .col-md-4,\n .col-md-5,\n .col-md-6,\n .col-md-7,\n .col-md-8,\n .col-md-9,\n .col-md-10,\n .col-md-11,\n .col-md-12 {\n float: left;\n }\n .col-md-12 {\n width: 100%;\n }\n .col-md-11 {\n width: 91.66666667%;\n }\n .col-md-10 {\n width: 83.33333333%;\n }\n .col-md-9 {\n width: 75%;\n }\n .col-md-8 {\n width: 66.66666667%;\n }\n .col-md-7 {\n width: 58.33333333%;\n }\n .col-md-6 {\n width: 50%;\n }\n .col-md-5 {\n width: 41.66666667%;\n }\n .col-md-4 {\n width: 33.33333333%;\n }\n .col-md-3 {\n width: 25%;\n }\n .col-md-2 {\n width: 16.66666667%;\n }\n .col-md-1 {\n width: 8.33333333%;\n }\n .col-md-pull-12 {\n right: 100%;\n }\n .col-md-pull-11 {\n right: 91.66666667%;\n }\n .col-md-pull-10 {\n right: 83.33333333%;\n }\n .col-md-pull-9 {\n right: 75%;\n }\n .col-md-pull-8 {\n right: 66.66666667%;\n }\n .col-md-pull-7 {\n right: 58.33333333%;\n }\n .col-md-pull-6 {\n right: 50%;\n }\n .col-md-pull-5 {\n right: 41.66666667%;\n }\n .col-md-pull-4 {\n right: 33.33333333%;\n }\n .col-md-pull-3 {\n right: 25%;\n }\n .col-md-pull-2 {\n right: 16.66666667%;\n }\n .col-md-pull-1 {\n right: 8.33333333%;\n }\n .col-md-pull-0 {\n right: auto;\n }\n .col-md-push-12 {\n left: 100%;\n }\n .col-md-push-11 {\n left: 91.66666667%;\n }\n .col-md-push-10 {\n left: 83.33333333%;\n }\n .col-md-push-9 {\n left: 75%;\n }\n .col-md-push-8 {\n left: 66.66666667%;\n }\n .col-md-push-7 {\n left: 58.33333333%;\n }\n .col-md-push-6 {\n left: 50%;\n }\n .col-md-push-5 {\n left: 41.66666667%;\n }\n .col-md-push-4 {\n left: 33.33333333%;\n }\n .col-md-push-3 {\n left: 25%;\n }\n .col-md-push-2 {\n left: 16.66666667%;\n }\n .col-md-push-1 {\n left: 8.33333333%;\n }\n .col-md-push-0 {\n left: auto;\n }\n .col-md-offset-12 {\n margin-left: 100%;\n }\n .col-md-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-md-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-md-offset-9 {\n margin-left: 75%;\n }\n .col-md-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-md-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-md-offset-6 {\n margin-left: 50%;\n }\n .col-md-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-md-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-md-offset-3 {\n margin-left: 25%;\n }\n .col-md-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-md-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-md-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 1200px) {\n .col-lg-1,\n .col-lg-2,\n .col-lg-3,\n .col-lg-4,\n .col-lg-5,\n .col-lg-6,\n .col-lg-7,\n .col-lg-8,\n .col-lg-9,\n .col-lg-10,\n .col-lg-11,\n .col-lg-12 {\n float: left;\n }\n .col-lg-12 {\n width: 100%;\n }\n .col-lg-11 {\n width: 91.66666667%;\n }\n .col-lg-10 {\n width: 83.33333333%;\n }\n .col-lg-9 {\n width: 75%;\n }\n .col-lg-8 {\n width: 66.66666667%;\n }\n .col-lg-7 {\n width: 58.33333333%;\n }\n .col-lg-6 {\n width: 50%;\n }\n .col-lg-5 {\n width: 41.66666667%;\n }\n .col-lg-4 {\n width: 33.33333333%;\n }\n .col-lg-3 {\n width: 25%;\n }\n .col-lg-2 {\n width: 16.66666667%;\n }\n .col-lg-1 {\n width: 8.33333333%;\n }\n .col-lg-pull-12 {\n right: 100%;\n }\n .col-lg-pull-11 {\n right: 91.66666667%;\n }\n .col-lg-pull-10 {\n right: 83.33333333%;\n }\n .col-lg-pull-9 {\n right: 75%;\n }\n .col-lg-pull-8 {\n right: 66.66666667%;\n }\n .col-lg-pull-7 {\n right: 58.33333333%;\n }\n .col-lg-pull-6 {\n right: 50%;\n }\n .col-lg-pull-5 {\n right: 41.66666667%;\n }\n .col-lg-pull-4 {\n right: 33.33333333%;\n }\n .col-lg-pull-3 {\n right: 25%;\n }\n .col-lg-pull-2 {\n right: 16.66666667%;\n }\n .col-lg-pull-1 {\n right: 8.33333333%;\n }\n .col-lg-pull-0 {\n right: auto;\n }\n .col-lg-push-12 {\n left: 100%;\n }\n .col-lg-push-11 {\n left: 91.66666667%;\n }\n .col-lg-push-10 {\n left: 83.33333333%;\n }\n .col-lg-push-9 {\n left: 75%;\n }\n .col-lg-push-8 {\n left: 66.66666667%;\n }\n .col-lg-push-7 {\n left: 58.33333333%;\n }\n .col-lg-push-6 {\n left: 50%;\n }\n .col-lg-push-5 {\n left: 41.66666667%;\n }\n .col-lg-push-4 {\n left: 33.33333333%;\n }\n .col-lg-push-3 {\n left: 25%;\n }\n .col-lg-push-2 {\n left: 16.66666667%;\n }\n .col-lg-push-1 {\n left: 8.33333333%;\n }\n .col-lg-push-0 {\n left: auto;\n }\n .col-lg-offset-12 {\n margin-left: 100%;\n }\n .col-lg-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-lg-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-lg-offset-9 {\n margin-left: 75%;\n }\n .col-lg-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-lg-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-lg-offset-6 {\n margin-left: 50%;\n }\n .col-lg-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-lg-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-lg-offset-3 {\n margin-left: 25%;\n }\n .col-lg-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-lg-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-lg-offset-0 {\n margin-left: 0%;\n }\n}\ntable {\n background-color: transparent;\n}\ntable col[class*=\"col-\"] {\n position: static;\n display: table-column;\n float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n position: static;\n display: table-cell;\n float: none;\n}\ncaption {\n padding-top: 8px;\n padding-bottom: 8px;\n color: #777777;\n text-align: left;\n}\nth {\n text-align: left;\n}\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n padding: 8px;\n line-height: 1.42857143;\n vertical-align: top;\n border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n border-top: 0;\n}\n.table > tbody + tbody {\n border-top: 2px solid #ddd;\n}\n.table .table {\n background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n padding: 5px;\n}\n.table-bordered {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n background-color: #f5f5f5;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n background-color: #ebcccc;\n}\n.table-responsive {\n min-height: 0.01%;\n overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n .table-responsive {\n width: 100%;\n margin-bottom: 15px;\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid #ddd;\n }\n .table-responsive > .table {\n margin-bottom: 0;\n }\n .table-responsive > .table > thead > tr > th,\n .table-responsive > .table > tbody > tr > th,\n .table-responsive > .table > tfoot > tr > th,\n .table-responsive > .table > thead > tr > td,\n .table-responsive > .table > tbody > tr > td,\n .table-responsive > .table > tfoot > tr > td {\n white-space: nowrap;\n }\n .table-responsive > .table-bordered {\n border: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:first-child,\n .table-responsive > .table-bordered > tbody > tr > th:first-child,\n .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n .table-responsive > .table-bordered > thead > tr > td:first-child,\n .table-responsive > .table-bordered > tbody > tr > td:first-child,\n .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:last-child,\n .table-responsive > .table-bordered > tbody > tr > th:last-child,\n .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n .table-responsive > .table-bordered > thead > tr > td:last-child,\n .table-responsive > .table-bordered > tbody > tr > td:last-child,\n .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n }\n .table-responsive > .table-bordered > tbody > tr:last-child > th,\n .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n .table-responsive > .table-bordered > tbody > tr:last-child > td,\n .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n border-bottom: 0;\n }\n}\nfieldset {\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: 20px;\n font-size: 21px;\n line-height: inherit;\n color: #333333;\n border: 0;\n border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n display: inline-block;\n max-width: 100%;\n margin-bottom: 5px;\n font-weight: 700;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n -webkit-appearance: none;\n appearance: none;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9;\n line-height: normal;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n cursor: not-allowed;\n}\ninput[type=\"file\"] {\n display: block;\n}\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\nselect[multiple],\nselect[size] {\n height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\noutput {\n display: block;\n padding-top: 7px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n}\n.form-control {\n display: block;\n width: 100%;\n height: 34px;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n background-color: #fff;\n background-image: none;\n border: 1px solid #ccc;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n border-color: #66afe9;\n outline: 0;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n color: #999;\n opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n color: #999;\n}\n.form-control::-webkit-input-placeholder {\n color: #999;\n}\n.form-control::-ms-expand {\n background-color: transparent;\n border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n background-color: #eeeeee;\n opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n cursor: not-allowed;\n}\ntextarea.form-control {\n height: auto;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n input[type=\"date\"].form-control,\n input[type=\"time\"].form-control,\n input[type=\"datetime-local\"].form-control,\n input[type=\"month\"].form-control {\n line-height: 34px;\n }\n input[type=\"date\"].input-sm,\n input[type=\"time\"].input-sm,\n input[type=\"datetime-local\"].input-sm,\n input[type=\"month\"].input-sm,\n .input-group-sm input[type=\"date\"],\n .input-group-sm input[type=\"time\"],\n .input-group-sm input[type=\"datetime-local\"],\n .input-group-sm input[type=\"month\"] {\n line-height: 30px;\n }\n input[type=\"date\"].input-lg,\n input[type=\"time\"].input-lg,\n input[type=\"datetime-local\"].input-lg,\n input[type=\"month\"].input-lg,\n .input-group-lg input[type=\"date\"],\n .input-group-lg input[type=\"time\"],\n .input-group-lg input[type=\"datetime-local\"],\n .input-group-lg input[type=\"month\"] {\n line-height: 46px;\n }\n}\n.form-group {\n margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n position: relative;\n display: block;\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n cursor: not-allowed;\n}\n.radio label,\n.checkbox label {\n min-height: 20px;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: 400;\n cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n position: absolute;\n margin-top: 4px \\9;\n margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n position: relative;\n display: inline-block;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: 400;\n vertical-align: middle;\n cursor: pointer;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n cursor: not-allowed;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n margin-top: 0;\n margin-left: 10px;\n}\n.form-control-static {\n min-height: 34px;\n padding-top: 7px;\n padding-bottom: 7px;\n margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n padding-right: 0;\n padding-left: 0;\n}\n.input-sm {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-sm {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n height: auto;\n}\n.form-group-sm .form-control {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.form-group-sm select.form-control {\n height: 30px;\n line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n height: auto;\n}\n.form-group-sm .form-control-static {\n height: 30px;\n min-height: 32px;\n padding: 6px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.input-lg {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-lg {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n height: auto;\n}\n.form-group-lg .form-control {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.form-group-lg select.form-control {\n height: 46px;\n line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n height: auto;\n}\n.form-group-lg .form-control-static {\n height: 46px;\n min-height: 38px;\n padding: 11px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.has-feedback {\n position: relative;\n}\n.has-feedback .form-control {\n padding-right: 42.5px;\n}\n.form-control-feedback {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 2;\n display: block;\n width: 34px;\n height: 34px;\n line-height: 34px;\n text-align: center;\n pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n width: 46px;\n height: 46px;\n line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n width: 30px;\n height: 30px;\n line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n color: #3c763d;\n}\n.has-success .form-control {\n border-color: #3c763d;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n border-color: #2b542c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n color: #8a6d3b;\n}\n.has-warning .form-control {\n border-color: #8a6d3b;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n border-color: #66512c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n color: #a94442;\n}\n.has-error .form-control {\n border-color: #a94442;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n border-color: #843534;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n color: #a94442;\n background-color: #f2dede;\n border-color: #a94442;\n}\n.has-error .form-control-feedback {\n color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n top: 0;\n}\n.help-block {\n display: block;\n margin-top: 5px;\n margin-bottom: 10px;\n color: #737373;\n}\n@media (min-width: 768px) {\n .form-inline .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .form-inline .form-control-static {\n display: inline-block;\n }\n .form-inline .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .form-inline .input-group .input-group-addon,\n .form-inline .input-group .input-group-btn,\n .form-inline .input-group .form-control {\n width: auto;\n }\n .form-inline .input-group > .form-control {\n width: 100%;\n }\n .form-inline .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio,\n .form-inline .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio label,\n .form-inline .checkbox label {\n padding-left: 0;\n }\n .form-inline .radio input[type=\"radio\"],\n .form-inline .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .form-inline .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n padding-top: 7px;\n margin-top: 0;\n margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n min-height: 27px;\n}\n.form-horizontal .form-group {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .control-label {\n padding-top: 7px;\n margin-bottom: 0;\n text-align: right;\n }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n right: 15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-lg .control-label {\n padding-top: 11px;\n font-size: 18px;\n }\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-sm .control-label {\n padding-top: 6px;\n font-size: 12px;\n }\n}\n.btn {\n display: inline-block;\n margin-bottom: 0;\n font-weight: normal;\n text-align: center;\n white-space: nowrap;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none;\n border: 1px solid transparent;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n border-radius: 4px;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n color: #333;\n text-decoration: none;\n}\n.btn:active,\n.btn.active {\n background-image: none;\n outline: 0;\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n cursor: not-allowed;\n filter: alpha(opacity=65);\n opacity: 0.65;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n pointer-events: none;\n}\n.btn-default {\n color: #333;\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n color: #333;\n background-color: #e6e6e6;\n border-color: #8c8c8c;\n}\n.btn-default:hover {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n color: #333;\n background-color: #e6e6e6;\n background-image: none;\n border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n color: #333;\n background-color: #d4d4d4;\n border-color: #8c8c8c;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default .badge {\n color: #fff;\n background-color: #333;\n}\n.btn-primary {\n color: #fff;\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n color: #fff;\n background-color: #286090;\n border-color: #122b40;\n}\n.btn-primary:hover {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n color: #fff;\n background-color: #286090;\n background-image: none;\n border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n color: #fff;\n background-color: #204d74;\n border-color: #122b40;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.btn-success {\n color: #fff;\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n color: #fff;\n background-color: #449d44;\n border-color: #255625;\n}\n.btn-success:hover {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n color: #fff;\n background-color: #449d44;\n background-image: none;\n border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n color: #fff;\n background-color: #398439;\n border-color: #255625;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success .badge {\n color: #5cb85c;\n background-color: #fff;\n}\n.btn-info {\n color: #fff;\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n color: #fff;\n background-color: #31b0d5;\n border-color: #1b6d85;\n}\n.btn-info:hover {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n color: #fff;\n background-color: #31b0d5;\n background-image: none;\n border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n color: #fff;\n background-color: #269abc;\n border-color: #1b6d85;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info .badge {\n color: #5bc0de;\n background-color: #fff;\n}\n.btn-warning {\n color: #fff;\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n color: #fff;\n background-color: #ec971f;\n border-color: #985f0d;\n}\n.btn-warning:hover {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n color: #fff;\n background-color: #ec971f;\n background-image: none;\n border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n color: #fff;\n background-color: #d58512;\n border-color: #985f0d;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning .badge {\n color: #f0ad4e;\n background-color: #fff;\n}\n.btn-danger {\n color: #fff;\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n color: #fff;\n background-color: #c9302c;\n border-color: #761c19;\n}\n.btn-danger:hover {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n color: #fff;\n background-color: #c9302c;\n background-image: none;\n border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n color: #fff;\n background-color: #ac2925;\n border-color: #761c19;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger .badge {\n color: #d9534f;\n background-color: #fff;\n}\n.btn-link {\n font-weight: 400;\n color: #337ab7;\n border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n background-color: transparent;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n color: #23527c;\n text-decoration: underline;\n background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n color: #777777;\n text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n padding: 1px 5px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-block {\n display: block;\n width: 100%;\n}\n.btn-block + .btn-block {\n margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n width: 100%;\n}\n.fade {\n opacity: 0;\n -webkit-transition: opacity 0.15s linear;\n -o-transition: opacity 0.15s linear;\n transition: opacity 0.15s linear;\n}\n.fade.in {\n opacity: 1;\n}\n.collapse {\n display: none;\n}\n.collapse.in {\n display: block;\n}\ntr.collapse.in {\n display: table-row;\n}\ntbody.collapse.in {\n display: table-row-group;\n}\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n -webkit-transition-property: height, visibility;\n transition-property: height, visibility;\n -webkit-transition-duration: 0.35s;\n transition-duration: 0.35s;\n -webkit-transition-timing-function: ease;\n transition-timing-function: ease;\n}\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: 4px dashed;\n border-top: 4px solid \\9;\n border-right: 4px solid transparent;\n border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n position: relative;\n}\n.dropdown-toggle:focus {\n outline: 0;\n}\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: 1000;\n display: none;\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0;\n font-size: 14px;\n text-align: left;\n list-style: none;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.15);\n border-radius: 4px;\n -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n}\n.dropdown-menu.pull-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu .divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: 400;\n line-height: 1.42857143;\n color: #333333;\n white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n color: #262626;\n text-decoration: none;\n background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n color: #fff;\n text-decoration: none;\n background-color: #337ab7;\n outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n text-decoration: none;\n cursor: not-allowed;\n background-color: transparent;\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n display: block;\n}\n.open > a {\n outline: 0;\n}\n.dropdown-menu-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu-left {\n right: auto;\n left: 0;\n}\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: 12px;\n line-height: 1.42857143;\n color: #777777;\n white-space: nowrap;\n}\n.dropdown-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 990;\n}\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n content: \"\";\n border-top: 0;\n border-bottom: 4px dashed;\n border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n .navbar-right .dropdown-menu {\n right: 0;\n left: auto;\n }\n .navbar-right .dropdown-menu-left {\n right: auto;\n left: 0;\n }\n}\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n position: relative;\n float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n margin-left: -1px;\n}\n.btn-toolbar {\n margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n.btn-group > .btn:first-child {\n margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n padding-right: 8px;\n padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-right: 12px;\n padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn .caret {\n margin-left: 0;\n}\n.btn-lg .caret {\n border-width: 5px 5px 0;\n border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n display: table-cell;\n float: none;\n width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0, 0, 0, 0);\n pointer-events: none;\n}\n.input-group {\n position: relative;\n display: table;\n border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.input-group .form-control {\n position: relative;\n z-index: 2;\n float: left;\n width: 100%;\n margin-bottom: 0;\n}\n.input-group .form-control:focus {\n z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle;\n}\n.input-group-addon {\n padding: 6px 12px;\n font-size: 14px;\n font-weight: 400;\n line-height: 1;\n color: #555555;\n text-align: center;\n background-color: #eeeeee;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\n.input-group-addon.input-sm {\n padding: 5px 10px;\n font-size: 12px;\n border-radius: 3px;\n}\n.input-group-addon.input-lg {\n padding: 10px 16px;\n font-size: 18px;\n border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n.input-group-btn {\n position: relative;\n font-size: 0;\n white-space: nowrap;\n}\n.input-group-btn > .btn {\n position: relative;\n}\n.input-group-btn > .btn + .btn {\n margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n z-index: 2;\n margin-left: -1px;\n}\n.nav {\n padding-left: 0;\n margin-bottom: 0;\n list-style: none;\n}\n.nav > li {\n position: relative;\n display: block;\n}\n.nav > li > a {\n position: relative;\n display: block;\n padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n color: #777777;\n text-decoration: none;\n cursor: not-allowed;\n background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n background-color: #eeeeee;\n border-color: #337ab7;\n}\n.nav .nav-divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.nav > li > a > img {\n max-width: none;\n}\n.nav-tabs {\n border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n float: left;\n margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n margin-right: 2px;\n line-height: 1.42857143;\n border: 1px solid transparent;\n border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n color: #555555;\n cursor: default;\n background-color: #fff;\n border: 1px solid #ddd;\n border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n width: 100%;\n border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n float: none;\n}\n.nav-tabs.nav-justified > li > a {\n margin-bottom: 5px;\n text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-tabs.nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs.nav-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs.nav-justified > .active > a,\n .nav-tabs.nav-justified > .active > a:hover,\n .nav-tabs.nav-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.nav-pills > li {\n float: left;\n}\n.nav-pills > li > a {\n border-radius: 4px;\n}\n.nav-pills > li + li {\n margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n color: #fff;\n background-color: #337ab7;\n}\n.nav-stacked > li {\n float: none;\n}\n.nav-stacked > li + li {\n margin-top: 2px;\n margin-left: 0;\n}\n.nav-justified {\n width: 100%;\n}\n.nav-justified > li {\n float: none;\n}\n.nav-justified > li > a {\n margin-bottom: 5px;\n text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs-justified {\n border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs-justified > .active > a,\n .nav-tabs-justified > .active > a:hover,\n .nav-tabs-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.tab-content > .tab-pane {\n display: none;\n}\n.tab-content > .active {\n display: block;\n}\n.nav-tabs .dropdown-menu {\n margin-top: -1px;\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.navbar {\n position: relative;\n min-height: 50px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n .navbar {\n border-radius: 4px;\n }\n}\n@media (min-width: 768px) {\n .navbar-header {\n float: left;\n }\n}\n.navbar-collapse {\n padding-right: 15px;\n padding-left: 15px;\n overflow-x: visible;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n overflow-y: auto;\n}\n@media (min-width: 768px) {\n .navbar-collapse {\n width: auto;\n border-top: 0;\n box-shadow: none;\n }\n .navbar-collapse.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0;\n overflow: visible !important;\n }\n .navbar-collapse.in {\n overflow-y: visible;\n }\n .navbar-fixed-top .navbar-collapse,\n .navbar-static-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n padding-right: 0;\n padding-left: 0;\n }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: 1030;\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n .navbar-fixed-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n max-height: 200px;\n }\n}\n@media (min-width: 768px) {\n .navbar-fixed-top,\n .navbar-fixed-bottom {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0;\n border-width: 1px 0 0;\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .container > .navbar-header,\n .container-fluid > .navbar-header,\n .container > .navbar-collapse,\n .container-fluid > .navbar-collapse {\n margin-right: 0;\n margin-left: 0;\n }\n}\n.navbar-static-top {\n z-index: 1000;\n border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n .navbar-static-top {\n border-radius: 0;\n }\n}\n.navbar-brand {\n float: left;\n height: 50px;\n padding: 15px 15px;\n font-size: 18px;\n line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n text-decoration: none;\n}\n.navbar-brand > img {\n display: block;\n}\n@media (min-width: 768px) {\n .navbar > .container .navbar-brand,\n .navbar > .container-fluid .navbar-brand {\n margin-left: -15px;\n }\n}\n.navbar-toggle {\n position: relative;\n float: right;\n padding: 9px 10px;\n margin-right: 15px;\n margin-top: 8px;\n margin-bottom: 8px;\n background-color: transparent;\n background-image: none;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.navbar-toggle:focus {\n outline: 0;\n}\n.navbar-toggle .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n margin-top: 4px;\n}\n@media (min-width: 768px) {\n .navbar-toggle {\n display: none;\n }\n}\n.navbar-nav {\n margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: 20px;\n}\n@media (max-width: 767px) {\n .navbar-nav .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n }\n .navbar-nav .open .dropdown-menu > li > a,\n .navbar-nav .open .dropdown-menu .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n .navbar-nav .open .dropdown-menu > li > a {\n line-height: 20px;\n }\n .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-nav .open .dropdown-menu > li > a:focus {\n background-image: none;\n }\n}\n@media (min-width: 768px) {\n .navbar-nav {\n float: left;\n margin: 0;\n }\n .navbar-nav > li {\n float: left;\n }\n .navbar-nav > li > a {\n padding-top: 15px;\n padding-bottom: 15px;\n }\n}\n.navbar-form {\n padding: 10px 15px;\n margin-right: -15px;\n margin-left: -15px;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n margin-top: 8px;\n margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n .navbar-form .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .navbar-form .form-control-static {\n display: inline-block;\n }\n .navbar-form .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .navbar-form .input-group .input-group-addon,\n .navbar-form .input-group .input-group-btn,\n .navbar-form .input-group .form-control {\n width: auto;\n }\n .navbar-form .input-group > .form-control {\n width: 100%;\n }\n .navbar-form .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio,\n .navbar-form .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio label,\n .navbar-form .checkbox label {\n padding-left: 0;\n }\n .navbar-form .radio input[type=\"radio\"],\n .navbar-form .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .navbar-form .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n@media (max-width: 767px) {\n .navbar-form .form-group {\n margin-bottom: 5px;\n }\n .navbar-form .form-group:last-child {\n margin-bottom: 0;\n }\n}\n@media (min-width: 768px) {\n .navbar-form {\n width: auto;\n padding-top: 0;\n padding-bottom: 0;\n margin-right: 0;\n margin-left: 0;\n border: 0;\n -webkit-box-shadow: none;\n box-shadow: none;\n }\n}\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.navbar-btn {\n margin-top: 8px;\n margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n margin-top: 14px;\n margin-bottom: 14px;\n}\n.navbar-text {\n margin-top: 15px;\n margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n .navbar-text {\n float: left;\n margin-right: 15px;\n margin-left: 15px;\n }\n}\n@media (min-width: 768px) {\n .navbar-left {\n float: left !important;\n }\n .navbar-right {\n float: right !important;\n margin-right: -15px;\n }\n .navbar-right ~ .navbar-right {\n margin-right: 0;\n }\n}\n.navbar-default {\n background-color: #f8f8f8;\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n color: #5e5e5e;\n background-color: transparent;\n}\n.navbar-default .navbar-text {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n color: #333;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n color: #777;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #333;\n background-color: transparent;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n }\n}\n.navbar-default .navbar-toggle {\n border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-link {\n color: #777;\n}\n.navbar-default .navbar-link:hover {\n color: #333;\n}\n.navbar-default .btn-link {\n color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n color: #ccc;\n}\n.navbar-inverse {\n background-color: #222;\n border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n color: #fff;\n background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n color: #fff;\n background-color: #080808;\n}\n@media (max-width: 767px) {\n .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n border-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n color: #9d9d9d;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #fff;\n background-color: transparent;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n }\n}\n.navbar-inverse .navbar-toggle {\n border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n border-color: #101010;\n}\n.navbar-inverse .navbar-link {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n color: #fff;\n}\n.navbar-inverse .btn-link {\n color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n color: #444;\n}\n.breadcrumb {\n padding: 8px 15px;\n margin-bottom: 20px;\n list-style: none;\n background-color: #f5f5f5;\n border-radius: 4px;\n}\n.breadcrumb > li {\n display: inline-block;\n}\n.breadcrumb > li + li:before {\n padding: 0 5px;\n color: #ccc;\n content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n color: #777777;\n}\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: 20px 0;\n border-radius: 4px;\n}\n.pagination > li {\n display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n position: relative;\n float: left;\n padding: 6px 12px;\n margin-left: -1px;\n line-height: 1.42857143;\n color: #337ab7;\n text-decoration: none;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n z-index: 2;\n color: #23527c;\n background-color: #eeeeee;\n border-color: #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n margin-left: 0;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n z-index: 3;\n color: #fff;\n cursor: default;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n color: #777777;\n cursor: not-allowed;\n background-color: #fff;\n border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n border-top-left-radius: 6px;\n border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n border-top-right-radius: 6px;\n border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n border-top-left-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n border-top-right-radius: 3px;\n border-bottom-right-radius: 3px;\n}\n.pager {\n padding-left: 0;\n margin: 20px 0;\n text-align: center;\n list-style: none;\n}\n.pager li {\n display: inline;\n}\n.pager li > a,\n.pager li > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n color: #777777;\n cursor: not-allowed;\n background-color: #fff;\n}\n.label {\n display: inline;\n padding: 0.2em 0.6em 0.3em;\n font-size: 75%;\n font-weight: 700;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: 0.25em;\n}\na.label:hover,\na.label:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.label:empty {\n display: none;\n}\n.btn .label {\n position: relative;\n top: -1px;\n}\n.label-default {\n background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n background-color: #5e5e5e;\n}\n.label-primary {\n background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n background-color: #286090;\n}\n.label-success {\n background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n background-color: #449d44;\n}\n.label-info {\n background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n background-color: #31b0d5;\n}\n.label-warning {\n background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n background-color: #ec971f;\n}\n.label-danger {\n background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n background-color: #c9302c;\n}\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: 12px;\n font-weight: bold;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: middle;\n background-color: #777777;\n border-radius: 10px;\n}\n.badge:empty {\n display: none;\n}\n.btn .badge {\n position: relative;\n top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n top: 0;\n padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.list-group-item > .badge {\n float: right;\n}\n.list-group-item > .badge + .badge {\n margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n margin-left: 3px;\n}\n.jumbotron {\n padding-top: 30px;\n padding-bottom: 30px;\n margin-bottom: 30px;\n color: inherit;\n background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n color: inherit;\n}\n.jumbotron p {\n margin-bottom: 15px;\n font-size: 21px;\n font-weight: 200;\n}\n.jumbotron > hr {\n border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n padding-right: 15px;\n padding-left: 15px;\n border-radius: 6px;\n}\n.jumbotron .container {\n max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n .jumbotron {\n padding-top: 48px;\n padding-bottom: 48px;\n }\n .container .jumbotron,\n .container-fluid .jumbotron {\n padding-right: 60px;\n padding-left: 60px;\n }\n .jumbotron h1,\n .jumbotron .h1 {\n font-size: 63px;\n }\n}\n.thumbnail {\n display: block;\n padding: 4px;\n margin-bottom: 20px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: border 0.2s ease-in-out;\n -o-transition: border 0.2s ease-in-out;\n transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n margin-right: auto;\n margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n border-color: #337ab7;\n}\n.thumbnail .caption {\n padding: 9px;\n color: #333333;\n}\n.alert {\n padding: 15px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.alert h4 {\n margin-top: 0;\n color: inherit;\n}\n.alert .alert-link {\n font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n margin-bottom: 0;\n}\n.alert > p + p {\n margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n}\n.alert-success {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.alert-success hr {\n border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n color: #2b542c;\n}\n.alert-info {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.alert-info hr {\n border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n color: #245269;\n}\n.alert-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.alert-warning hr {\n border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n color: #66512c;\n}\n.alert-danger {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.alert-danger hr {\n border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n@keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n.progress {\n height: 20px;\n margin-bottom: 20px;\n overflow: hidden;\n background-color: #f5f5f5;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: 12px;\n line-height: 20px;\n color: #fff;\n text-align: center;\n background-color: #337ab7;\n -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n -webkit-transition: width 0.6s ease;\n -o-transition: width 0.6s ease;\n transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n -webkit-animation: progress-bar-stripes 2s linear infinite;\n -o-animation: progress-bar-stripes 2s linear infinite;\n animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n margin-top: 15px;\n}\n.media:first-child {\n margin-top: 0;\n}\n.media,\n.media-body {\n overflow: hidden;\n zoom: 1;\n}\n.media-body {\n width: 10000px;\n}\n.media-object {\n display: block;\n}\n.media-object.img-thumbnail {\n max-width: none;\n}\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n.media-middle {\n vertical-align: middle;\n}\n.media-bottom {\n vertical-align: bottom;\n}\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n.list-group {\n padding-left: 0;\n margin-bottom: 20px;\n}\n.list-group-item {\n position: relative;\n display: block;\n padding: 10px 15px;\n margin-bottom: -1px;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n margin-bottom: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n color: #777777;\n cursor: not-allowed;\n background-color: #eeeeee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n z-index: 2;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n color: #c7ddef;\n}\na.list-group-item,\nbutton.list-group-item {\n color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n color: #555;\n text-decoration: none;\n background-color: #f5f5f5;\n}\nbutton.list-group-item {\n width: 100%;\n text-align: left;\n}\n.list-group-item-success {\n color: #3c763d;\n background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n color: #3c763d;\n background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n color: #fff;\n background-color: #3c763d;\n border-color: #3c763d;\n}\n.list-group-item-info {\n color: #31708f;\n background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n color: #31708f;\n background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n color: #fff;\n background-color: #31708f;\n border-color: #31708f;\n}\n.list-group-item-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n color: #8a6d3b;\n background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n color: #fff;\n background-color: #8a6d3b;\n border-color: #8a6d3b;\n}\n.list-group-item-danger {\n color: #a94442;\n background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n color: #a94442;\n background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n color: #fff;\n background-color: #a94442;\n border-color: #a94442;\n}\n.list-group-item-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.list-group-item-text {\n margin-bottom: 0;\n line-height: 1.3;\n}\n.panel {\n margin-bottom: 20px;\n background-color: #fff;\n border: 1px solid transparent;\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n padding: 15px;\n}\n.panel-heading {\n padding: 10px 15px;\n border-bottom: 1px solid transparent;\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n color: inherit;\n}\n.panel-title {\n margin-top: 0;\n margin-bottom: 0;\n font-size: 16px;\n color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n color: inherit;\n}\n.panel-footer {\n padding: 10px 15px;\n background-color: #f5f5f5;\n border-top: 1px solid #ddd;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n border-width: 1px 0;\n border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n border-top: 0;\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n border-bottom: 0;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n border-top-width: 0;\n}\n.list-group + .panel-footer {\n border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n padding-right: 15px;\n padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n border-bottom: 0;\n}\n.panel > .table-responsive {\n margin-bottom: 0;\n border: 0;\n}\n.panel-group {\n margin-bottom: 20px;\n}\n.panel-group .panel {\n margin-bottom: 0;\n border-radius: 4px;\n}\n.panel-group .panel + .panel {\n margin-top: 5px;\n}\n.panel-group .panel-heading {\n border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n border-bottom: 1px solid #ddd;\n}\n.panel-default {\n border-color: #ddd;\n}\n.panel-default > .panel-heading {\n color: #333333;\n background-color: #f5f5f5;\n border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n color: #f5f5f5;\n background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ddd;\n}\n.panel-primary {\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #337ab7;\n}\n.panel-success {\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n color: #dff0d8;\n background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #d6e9c6;\n}\n.panel-info {\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n color: #d9edf7;\n background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #bce8f1;\n}\n.panel-warning {\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n color: #fcf8e3;\n background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #faebcc;\n}\n.panel-danger {\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n color: #f2dede;\n background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n position: relative;\n display: block;\n height: 0;\n padding: 0;\n overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n position: absolute;\n top: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 0;\n}\n.embed-responsive-16by9 {\n padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n padding-bottom: 75%;\n}\n.well {\n min-height: 20px;\n padding: 19px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border: 1px solid #e3e3e3;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n border-color: #ddd;\n border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n padding: 24px;\n border-radius: 6px;\n}\n.well-sm {\n padding: 9px;\n border-radius: 3px;\n}\n.close {\n float: right;\n font-size: 21px;\n font-weight: bold;\n line-height: 1;\n color: #000;\n text-shadow: 0 1px 0 #fff;\n filter: alpha(opacity=20);\n opacity: 0.2;\n}\n.close:hover,\n.close:focus {\n color: #000;\n text-decoration: none;\n cursor: pointer;\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\nbutton.close {\n padding: 0;\n cursor: pointer;\n background: transparent;\n border: 0;\n -webkit-appearance: none;\n appearance: none;\n}\n.modal-open {\n overflow: hidden;\n}\n.modal {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1050;\n display: none;\n overflow: hidden;\n -webkit-overflow-scrolling: touch;\n outline: 0;\n}\n.modal.fade .modal-dialog {\n -webkit-transform: translate(0, -25%);\n -ms-transform: translate(0, -25%);\n -o-transform: translate(0, -25%);\n transform: translate(0, -25%);\n -webkit-transition: -webkit-transform 0.3s ease-out;\n -moz-transition: -moz-transform 0.3s ease-out;\n -o-transition: -o-transform 0.3s ease-out;\n transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n -webkit-transform: translate(0, 0);\n -ms-transform: translate(0, 0);\n -o-transform: translate(0, 0);\n transform: translate(0, 0);\n}\n.modal-open .modal {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.modal-dialog {\n position: relative;\n width: auto;\n margin: 10px;\n}\n.modal-content {\n position: relative;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #999;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n outline: 0;\n}\n.modal-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1040;\n background-color: #000;\n}\n.modal-backdrop.fade {\n filter: alpha(opacity=0);\n opacity: 0;\n}\n.modal-backdrop.in {\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\n.modal-header {\n padding: 15px;\n border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n margin-top: -2px;\n}\n.modal-title {\n margin: 0;\n line-height: 1.42857143;\n}\n.modal-body {\n position: relative;\n padding: 15px;\n}\n.modal-footer {\n padding: 15px;\n text-align: right;\n border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n margin-bottom: 0;\n margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n margin-left: 0;\n}\n.modal-scrollbar-measure {\n position: absolute;\n top: -9999px;\n width: 50px;\n height: 50px;\n overflow: scroll;\n}\n@media (min-width: 768px) {\n .modal-dialog {\n width: 600px;\n margin: 30px auto;\n }\n .modal-content {\n -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n }\n .modal-sm {\n width: 300px;\n }\n}\n@media (min-width: 992px) {\n .modal-lg {\n width: 900px;\n }\n}\n.tooltip {\n position: absolute;\n z-index: 1070;\n display: block;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: 400;\n line-height: 1.42857143;\n line-break: auto;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n letter-spacing: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n white-space: normal;\n font-size: 12px;\n filter: alpha(opacity=0);\n opacity: 0;\n}\n.tooltip.in {\n filter: alpha(opacity=90);\n opacity: 0.9;\n}\n.tooltip.top {\n padding: 5px 0;\n margin-top: -3px;\n}\n.tooltip.right {\n padding: 0 5px;\n margin-left: 3px;\n}\n.tooltip.bottom {\n padding: 5px 0;\n margin-top: 3px;\n}\n.tooltip.left {\n padding: 0 5px;\n margin-left: -3px;\n}\n.tooltip.top .tooltip-arrow {\n bottom: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n right: 5px;\n bottom: 0;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n bottom: 0;\n left: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n top: 50%;\n left: 0;\n margin-top: -5px;\n border-width: 5px 5px 5px 0;\n border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n top: 50%;\n right: 0;\n margin-top: -5px;\n border-width: 5px 0 5px 5px;\n border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n top: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n top: 0;\n right: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n top: 0;\n left: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip-inner {\n max-width: 200px;\n padding: 3px 8px;\n color: #fff;\n text-align: center;\n background-color: #000;\n border-radius: 4px;\n}\n.tooltip-arrow {\n position: absolute;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1060;\n display: none;\n max-width: 276px;\n padding: 1px;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: 400;\n line-height: 1.42857143;\n line-break: auto;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n letter-spacing: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n white-space: normal;\n font-size: 14px;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n margin-top: -10px;\n}\n.popover.right {\n margin-left: 10px;\n}\n.popover.bottom {\n margin-top: 10px;\n}\n.popover.left {\n margin-left: -10px;\n}\n.popover > .arrow {\n border-width: 11px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n position: absolute;\n display: block;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover > .arrow:after {\n content: \"\";\n border-width: 10px;\n}\n.popover.top > .arrow {\n bottom: -11px;\n left: 50%;\n margin-left: -11px;\n border-top-color: #999999;\n border-top-color: rgba(0, 0, 0, 0.25);\n border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n bottom: 1px;\n margin-left: -10px;\n content: \" \";\n border-top-color: #fff;\n border-bottom-width: 0;\n}\n.popover.right > .arrow {\n top: 50%;\n left: -11px;\n margin-top: -11px;\n border-right-color: #999999;\n border-right-color: rgba(0, 0, 0, 0.25);\n border-left-width: 0;\n}\n.popover.right > .arrow:after {\n bottom: -10px;\n left: 1px;\n content: \" \";\n border-right-color: #fff;\n border-left-width: 0;\n}\n.popover.bottom > .arrow {\n top: -11px;\n left: 50%;\n margin-left: -11px;\n border-top-width: 0;\n border-bottom-color: #999999;\n border-bottom-color: rgba(0, 0, 0, 0.25);\n}\n.popover.bottom > .arrow:after {\n top: 1px;\n margin-left: -10px;\n content: \" \";\n border-top-width: 0;\n border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n top: 50%;\n right: -11px;\n margin-top: -11px;\n border-right-width: 0;\n border-left-color: #999999;\n border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n right: 1px;\n bottom: -10px;\n content: \" \";\n border-right-width: 0;\n border-left-color: #fff;\n}\n.popover-title {\n padding: 8px 14px;\n margin: 0;\n font-size: 14px;\n background-color: #f7f7f7;\n border-bottom: 1px solid #ebebeb;\n border-radius: 5px 5px 0 0;\n}\n.popover-content {\n padding: 9px 14px;\n}\n.carousel {\n position: relative;\n}\n.carousel-inner {\n position: relative;\n width: 100%;\n overflow: hidden;\n}\n.carousel-inner > .item {\n position: relative;\n display: none;\n -webkit-transition: 0.6s ease-in-out left;\n -o-transition: 0.6s ease-in-out left;\n transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n .carousel-inner > .item {\n -webkit-transition: -webkit-transform 0.6s ease-in-out;\n -moz-transition: -moz-transform 0.6s ease-in-out;\n -o-transition: -o-transform 0.6s ease-in-out;\n transition: transform 0.6s ease-in-out;\n -webkit-backface-visibility: hidden;\n -moz-backface-visibility: hidden;\n backface-visibility: hidden;\n -webkit-perspective: 1000px;\n -moz-perspective: 1000px;\n perspective: 1000px;\n }\n .carousel-inner > .item.next,\n .carousel-inner > .item.active.right {\n -webkit-transform: translate3d(100%, 0, 0);\n transform: translate3d(100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.prev,\n .carousel-inner > .item.active.left {\n -webkit-transform: translate3d(-100%, 0, 0);\n transform: translate3d(-100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.next.left,\n .carousel-inner > .item.prev.right,\n .carousel-inner > .item.active {\n -webkit-transform: translate3d(0, 0, 0);\n transform: translate3d(0, 0, 0);\n left: 0;\n }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n display: block;\n}\n.carousel-inner > .active {\n left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n position: absolute;\n top: 0;\n width: 100%;\n}\n.carousel-inner > .next {\n left: 100%;\n}\n.carousel-inner > .prev {\n left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n left: 0;\n}\n.carousel-inner > .active.left {\n left: -100%;\n}\n.carousel-inner > .active.right {\n left: 100%;\n}\n.carousel-control {\n position: absolute;\n top: 0;\n bottom: 0;\n left: 0;\n width: 15%;\n font-size: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n background-color: rgba(0, 0, 0, 0);\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\n.carousel-control.left {\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n background-repeat: repeat-x;\n}\n.carousel-control.right {\n right: 0;\n left: auto;\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n color: #fff;\n text-decoration: none;\n outline: 0;\n filter: alpha(opacity=90);\n opacity: 0.9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n position: absolute;\n top: 50%;\n z-index: 5;\n display: inline-block;\n margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n left: 50%;\n margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n right: 50%;\n margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n width: 20px;\n height: 20px;\n font-family: serif;\n line-height: 1;\n}\n.carousel-control .icon-prev:before {\n content: \"\\2039\";\n}\n.carousel-control .icon-next:before {\n content: \"\\203a\";\n}\n.carousel-indicators {\n position: absolute;\n bottom: 10px;\n left: 50%;\n z-index: 15;\n width: 60%;\n padding-left: 0;\n margin-left: -30%;\n text-align: center;\n list-style: none;\n}\n.carousel-indicators li {\n display: inline-block;\n width: 10px;\n height: 10px;\n margin: 1px;\n text-indent: -999px;\n cursor: pointer;\n background-color: #000 \\9;\n background-color: rgba(0, 0, 0, 0);\n border: 1px solid #fff;\n border-radius: 10px;\n}\n.carousel-indicators .active {\n width: 12px;\n height: 12px;\n margin: 0;\n background-color: #fff;\n}\n.carousel-caption {\n position: absolute;\n right: 15%;\n bottom: 20px;\n left: 15%;\n z-index: 10;\n padding-top: 20px;\n padding-bottom: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-prev,\n .carousel-control .icon-next {\n width: 30px;\n height: 30px;\n margin-top: -10px;\n font-size: 30px;\n }\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .icon-prev {\n margin-left: -10px;\n }\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-next {\n margin-right: -10px;\n }\n .carousel-caption {\n right: 20%;\n left: 20%;\n padding-bottom: 30px;\n }\n .carousel-indicators {\n bottom: 20px;\n }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n display: table;\n content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n clear: both;\n}\n.center-block {\n display: block;\n margin-right: auto;\n margin-left: auto;\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n font: 0/0 a;\n color: transparent;\n text-shadow: none;\n background-color: transparent;\n border: 0;\n}\n.hidden {\n display: none !important;\n}\n.affix {\n position: fixed;\n}\n@-ms-viewport {\n width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n display: none !important;\n}\n@media (max-width: 767px) {\n .visible-xs {\n display: block !important;\n }\n table.visible-xs {\n display: table !important;\n }\n tr.visible-xs {\n display: table-row !important;\n }\n th.visible-xs,\n td.visible-xs {\n display: table-cell !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-block {\n display: block !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline {\n display: inline !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm {\n display: block !important;\n }\n table.visible-sm {\n display: table !important;\n }\n tr.visible-sm {\n display: table-row !important;\n }\n th.visible-sm,\n td.visible-sm {\n display: table-cell !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-block {\n display: block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline {\n display: inline !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md {\n display: block !important;\n }\n table.visible-md {\n display: table !important;\n }\n tr.visible-md {\n display: table-row !important;\n }\n th.visible-md,\n td.visible-md {\n display: table-cell !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-block {\n display: block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline {\n display: inline !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg {\n display: block !important;\n }\n table.visible-lg {\n display: table !important;\n }\n tr.visible-lg {\n display: table-row !important;\n }\n th.visible-lg,\n td.visible-lg {\n display: table-cell !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-block {\n display: block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline {\n display: inline !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline-block {\n display: inline-block !important;\n }\n}\n@media (max-width: 767px) {\n .hidden-xs {\n display: none !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .hidden-sm {\n display: none !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .hidden-md {\n display: none !important;\n }\n}\n@media (min-width: 1200px) {\n .hidden-lg {\n display: none !important;\n }\n}\n.visible-print {\n display: none !important;\n}\n@media print {\n .visible-print {\n display: block !important;\n }\n table.visible-print {\n display: table !important;\n }\n tr.visible-print {\n display: table-row !important;\n }\n th.visible-print,\n td.visible-print {\n display: table-cell !important;\n }\n}\n.visible-print-block {\n display: none !important;\n}\n@media print {\n .visible-print-block {\n display: block !important;\n }\n}\n.visible-print-inline {\n display: none !important;\n}\n@media print {\n .visible-print-inline {\n display: inline !important;\n }\n}\n.visible-print-inline-block {\n display: none !important;\n}\n@media print {\n .visible-print-inline-block {\n display: inline-block !important;\n }\n}\n@media print {\n .hidden-print {\n display: none !important;\n }\n}\n/*# sourceMappingURL=bootstrap.css.map */","// stylelint-disable\n\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n// without disabling user zoom.\n//\n\nhtml {\n font-family: sans-serif; // 1\n -ms-text-size-adjust: 100%; // 2\n -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block; // 1\n vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// 1. Remove the bottom border in Chrome 57- and Firefox 39-.\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n//\n\nabbr[title] {\n border-bottom: none; // 1\n text-decoration: underline; // 2\n text-decoration: underline dotted; // 2\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n background: #ff0;\n color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsup {\n top: -0.5em;\n}\n\nsub {\n bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n box-sizing: content-box;\n height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n// Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit; // 1\n font: inherit; // 2\n margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n// and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n// `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button; // 2\n cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box; // 1\n padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n -webkit-appearance: textfield; // 1\n box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n border: 0; // 1\n padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\n\ntd,\nth {\n padding: 0;\n}\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type\n\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n *,\n *:before,\n *:after {\n color: #000 !important; // Black prints faster: h5bp.com/s\n text-shadow: none !important;\n background: transparent !important;\n box-shadow: none !important;\n }\n\n a,\n a:visited {\n text-decoration: underline;\n }\n\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n\n // Don't show links that are fragment identifiers,\n // or use the `javascript:` pseudo protocol\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n\n thead {\n display: table-header-group; // h5bp.com/t\n }\n\n tr,\n img {\n page-break-inside: avoid;\n }\n\n img {\n max-width: 100% !important;\n }\n\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n\n h2,\n h3 {\n page-break-after: avoid;\n }\n\n // Bootstrap specific changes start\n\n // Bootstrap components\n .navbar {\n display: none;\n }\n .btn,\n .dropup > .btn {\n > .caret {\n border-top-color: #000 !important;\n }\n }\n .label {\n border: 1px solid #000;\n }\n\n .table {\n border-collapse: collapse !important;\n\n td,\n th {\n background-color: #fff !important;\n }\n }\n .table-bordered {\n th,\n td {\n border: 1px solid #ddd !important;\n }\n }\n}\n","// stylelint-disable value-list-comma-newline-after, value-list-comma-space-after, indentation, declaration-colon-newline-after, font-family-no-missing-generic-family-keyword\n\n//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// Star\n\n// Import the fonts\n@font-face {\n font-family: \"Glyphicons Halflings\";\n src: url(\"@{icon-font-path}@{icon-font-name}.eot\");\n src: url(\"@{icon-font-path}@{icon-font-name}.eot?#iefix\") format(\"embedded-opentype\"),\n url(\"@{icon-font-path}@{icon-font-name}.woff2\") format(\"woff2\"),\n url(\"@{icon-font-path}@{icon-font-name}.woff\") format(\"woff\"),\n url(\"@{icon-font-path}@{icon-font-name}.ttf\") format(\"truetype\"),\n url(\"@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}\") format(\"svg\");\n}\n\n// Catchall baseclass\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: \"Glyphicons Halflings\";\n font-style: normal;\n font-weight: 400;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk { &:before { content: \"\\002a\"; } }\n.glyphicon-plus { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil { &:before { content: \"\\270f\"; } }\n.glyphicon-glass { &:before { content: \"\\e001\"; } }\n.glyphicon-music { &:before { content: \"\\e002\"; } }\n.glyphicon-search { &:before { content: \"\\e003\"; } }\n.glyphicon-heart { &:before { content: \"\\e005\"; } }\n.glyphicon-star { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty { &:before { content: \"\\e007\"; } }\n.glyphicon-user { &:before { content: \"\\e008\"; } }\n.glyphicon-film { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large { &:before { content: \"\\e010\"; } }\n.glyphicon-th { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list { &:before { content: \"\\e012\"; } }\n.glyphicon-ok { &:before { content: \"\\e013\"; } }\n.glyphicon-remove { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out { &:before { content: \"\\e016\"; } }\n.glyphicon-off { &:before { content: \"\\e017\"; } }\n.glyphicon-signal { &:before { content: \"\\e018\"; } }\n.glyphicon-cog { &:before { content: \"\\e019\"; } }\n.glyphicon-trash { &:before { content: \"\\e020\"; } }\n.glyphicon-home { &:before { content: \"\\e021\"; } }\n.glyphicon-file { &:before { content: \"\\e022\"; } }\n.glyphicon-time { &:before { content: \"\\e023\"; } }\n.glyphicon-road { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt { &:before { content: \"\\e025\"; } }\n.glyphicon-download { &:before { content: \"\\e026\"; } }\n.glyphicon-upload { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt { &:before { content: \"\\e032\"; } }\n.glyphicon-lock { &:before { content: \"\\e033\"; } }\n.glyphicon-flag { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode { &:before { content: \"\\e040\"; } }\n.glyphicon-tag { &:before { content: \"\\e041\"; } }\n.glyphicon-tags { &:before { content: \"\\e042\"; } }\n.glyphicon-book { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark { &:before { content: \"\\e044\"; } }\n.glyphicon-print { &:before { content: \"\\e045\"; } }\n.glyphicon-camera { &:before { content: \"\\e046\"; } }\n.glyphicon-font { &:before { content: \"\\e047\"; } }\n.glyphicon-bold { &:before { content: \"\\e048\"; } }\n.glyphicon-italic { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify { &:before { content: \"\\e055\"; } }\n.glyphicon-list { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video { &:before { content: \"\\e059\"; } }\n.glyphicon-picture { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust { &:before { content: \"\\e063\"; } }\n.glyphicon-tint { &:before { content: \"\\e064\"; } }\n.glyphicon-edit { &:before { content: \"\\e065\"; } }\n.glyphicon-share { &:before { content: \"\\e066\"; } }\n.glyphicon-check { &:before { content: \"\\e067\"; } }\n.glyphicon-move { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward { &:before { content: \"\\e070\"; } }\n.glyphicon-backward { &:before { content: \"\\e071\"; } }\n.glyphicon-play { &:before { content: \"\\e072\"; } }\n.glyphicon-pause { &:before { content: \"\\e073\"; } }\n.glyphicon-stop { &:before { content: \"\\e074\"; } }\n.glyphicon-forward { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward { &:before { content: \"\\e077\"; } }\n.glyphicon-eject { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign { &:before { content: \"\\e101\"; } }\n.glyphicon-gift { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf { &:before { content: \"\\e103\"; } }\n.glyphicon-fire { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign { &:before { content: \"\\e107\"; } }\n.glyphicon-plane { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar { &:before { content: \"\\e109\"; } }\n.glyphicon-random { &:before { content: \"\\e110\"; } }\n.glyphicon-comment { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn { &:before { content: \"\\e122\"; } }\n.glyphicon-bell { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down { &:before { content: \"\\e134\"; } }\n.glyphicon-globe { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks { &:before { content: \"\\e137\"; } }\n.glyphicon-filter { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty { &:before { content: \"\\e143\"; } }\n.glyphicon-link { &:before { content: \"\\e144\"; } }\n.glyphicon-phone { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin { &:before { content: \"\\e146\"; } }\n.glyphicon-usd { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp { &:before { content: \"\\e149\"; } }\n.glyphicon-sort { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked { &:before { content: \"\\e157\"; } }\n.glyphicon-expand { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in { &:before { content: \"\\e161\"; } }\n.glyphicon-flash { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window { &:before { content: \"\\e164\"; } }\n.glyphicon-record { &:before { content: \"\\e165\"; } }\n.glyphicon-save { &:before { content: \"\\e166\"; } }\n.glyphicon-open { &:before { content: \"\\e167\"; } }\n.glyphicon-saved { &:before { content: \"\\e168\"; } }\n.glyphicon-import { &:before { content: \"\\e169\"; } }\n.glyphicon-export { &:before { content: \"\\e170\"; } }\n.glyphicon-send { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery { &:before { content: \"\\e179\"; } }\n.glyphicon-header { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt { &:before { content: \"\\e183\"; } }\n.glyphicon-tower { &:before { content: \"\\e184\"; } }\n.glyphicon-stats { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1 { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1 { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1 { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous { &:before { content: \"\\e200\"; } }\n.glyphicon-cd { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up { &:before { content: \"\\e204\"; } }\n.glyphicon-copy { &:before { content: \"\\e205\"; } }\n.glyphicon-paste { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer { &:before { content: \"\\e210\"; } }\n.glyphicon-king { &:before { content: \"\\e211\"; } }\n.glyphicon-queen { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop { &:before { content: \"\\e214\"; } }\n.glyphicon-knight { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula { &:before { content: \"\\e216\"; } }\n.glyphicon-tent { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard { &:before { content: \"\\e218\"; } }\n.glyphicon-bed { &:before { content: \"\\e219\"; } }\n.glyphicon-apple { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin { &:before { content: \"\\e227\"; } }\n.glyphicon-btc { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt { &:before { content: \"\\e227\"; } }\n.glyphicon-yen { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted { &:before { content: \"\\e232\"; } }\n.glyphicon-education { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window { &:before { content: \"\\e237\"; } }\n.glyphicon-oil { &:before { content: \"\\e238\"; } }\n.glyphicon-grain { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top { &:before { content: \"\\e253\"; } }\n.glyphicon-console { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// https://getbootstrap.com/docs/3.4/getting-started/#third-box-sizing\n* {\n .box-sizing(border-box);\n}\n*:before,\n*:after {\n .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nbody {\n font-family: @font-family-base;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @text-color;\n background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\n\n// Links\n\na {\n color: @link-color;\n text-decoration: none;\n\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n }\n\n &:focus {\n .tab-focus();\n }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n margin: 0;\n}\n\n\n// Images\n\nimg {\n vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n padding: @thumbnail-padding;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(all .2s ease-in-out);\n\n // Keep them at most 100% wide\n .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n margin-top: @line-height-computed;\n margin-bottom: @line-height-computed;\n border: 0;\n border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: https://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n &:active,\n &:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n cursor: pointer;\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n // WebKit-specific. Other browsers will keep their default outline style.\n // (Initially tried to also force default via `outline: initial`,\n // but that seems to erroneously remove the outline in Firefox altogether.)\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n","// stylelint-disable media-feature-name-no-vendor-prefix, media-feature-parentheses-space-inside, media-feature-name-no-unknown, indentation, at-rule-name-space-after\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n display: @display;\n max-width: 100%; // Part 1: Set a maximum relative to the parent\n height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n background-image: url(\"@{file-1x}\");\n\n @media\n only screen and (-webkit-min-device-pixel-ratio: 2),\n only screen and ( min--moz-device-pixel-ratio: 2),\n only screen and ( -o-min-device-pixel-ratio: 2/1),\n only screen and ( min-device-pixel-ratio: 2),\n only screen and ( min-resolution: 192dpi),\n only screen and ( min-resolution: 2dppx) {\n background-image: url(\"@{file-2x}\");\n background-size: @width-1x @height-1x;\n }\n}\n","// stylelint-disable selector-list-comma-newline-after, selector-no-qualifying-type\n\n//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n font-family: @headings-font-family;\n font-weight: @headings-font-weight;\n line-height: @headings-line-height;\n color: @headings-color;\n\n small,\n .small {\n font-weight: 400;\n line-height: 1;\n color: @headings-small-color;\n }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n margin-top: @line-height-computed;\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 65%;\n }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n margin-top: (@line-height-computed / 2);\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 75%;\n }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n margin-bottom: @line-height-computed;\n font-size: floor((@font-size-base * 1.15));\n font-weight: 300;\n line-height: 1.4;\n\n @media (min-width: @screen-sm-min) {\n font-size: (@font-size-base * 1.5);\n }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n padding: .2em;\n background-color: @state-warning-bg;\n}\n\n// Alignment\n.text-left { text-align: left; }\n.text-right { text-align: right; }\n.text-center { text-align: center; }\n.text-justify { text-align: justify; }\n.text-nowrap { white-space: nowrap; }\n\n// Transformation\n.text-lowercase { text-transform: lowercase; }\n.text-uppercase { text-transform: uppercase; }\n.text-capitalize { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n color: @text-muted;\n}\n.text-primary {\n .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n // Given the contrast here, this is the only class to have its color inverted\n // automatically.\n color: #fff;\n .bg-variant(@brand-primary);\n}\n.bg-success {\n .bg-variant(@state-success-bg);\n}\n.bg-info {\n .bg-variant(@state-info-bg);\n}\n.bg-warning {\n .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n padding-bottom: ((@line-height-computed / 2) - 1);\n margin: (@line-height-computed * 2) 0 @line-height-computed;\n border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n margin-top: 0;\n margin-bottom: (@line-height-computed / 2);\n ul,\n ol {\n margin-bottom: 0;\n }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n .list-unstyled();\n margin-left: -5px;\n\n > li {\n display: inline-block;\n padding-right: 5px;\n padding-left: 5px;\n }\n}\n\n// Description Lists\ndl {\n margin-top: 0; // Remove browser default\n margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n line-height: @line-height-base;\n}\ndt {\n font-weight: 700;\n}\ndd {\n margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n dd {\n &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n }\n\n @media (min-width: @dl-horizontal-breakpoint) {\n dt {\n float: left;\n width: (@dl-horizontal-offset - 20);\n clear: left;\n text-align: right;\n .text-overflow();\n }\n dd {\n margin-left: @dl-horizontal-offset;\n }\n }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n}\n\n.initialism {\n font-size: 90%;\n .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n padding: (@line-height-computed / 2) @line-height-computed;\n margin: 0 0 @line-height-computed;\n font-size: @blockquote-font-size;\n border-left: 5px solid @blockquote-border-color;\n\n p,\n ul,\n ol {\n &:last-child {\n margin-bottom: 0;\n }\n }\n\n // Note: Deprecated small and .small as of v3.1.0\n // Context: https://github.com/twbs/bootstrap/issues/11660\n footer,\n small,\n .small {\n display: block;\n font-size: 80%; // back to default font-size\n line-height: @line-height-base;\n color: @blockquote-small-color;\n\n &:before {\n content: \"\\2014 \\00A0\"; // em dash, nbsp\n }\n }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n text-align: right;\n border-right: 5px solid @blockquote-border-color;\n border-left: 0;\n\n // Account for citation\n footer,\n small,\n .small {\n &:before { content: \"\"; }\n &:after {\n content: \"\\00A0 \\2014\"; // nbsp, em dash\n }\n }\n}\n\n// Addresses\naddress {\n margin-bottom: @line-height-computed;\n font-style: normal;\n line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n color: @color;\n a&:hover,\n a&:focus {\n color: darken(@color, 10%);\n }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n background-color: @color;\n a&:hover,\n a&:focus {\n background-color: darken(@color, 10%);\n }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: @code-color;\n background-color: @code-bg;\n border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: @kbd-color;\n background-color: @kbd-bg;\n border-radius: @border-radius-small;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n\n kbd {\n padding: 0;\n font-size: 100%;\n font-weight: 700;\n box-shadow: none;\n }\n}\n\n// Blocks of code\npre {\n display: block;\n padding: ((@line-height-computed - 1) / 2);\n margin: 0 0 (@line-height-computed / 2);\n font-size: (@font-size-base - 1); // 14px to 13px\n line-height: @line-height-base;\n color: @pre-color;\n word-break: break-all;\n word-wrap: break-word;\n background-color: @pre-bg;\n border: 1px solid @pre-border-color;\n border-radius: @border-radius-base;\n\n // Account for some code outputs that place code tags in pre tags\n code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n max-height: @pre-scrollable-max-height;\n overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n .container-fixed();\n\n @media (min-width: @screen-sm-min) {\n width: @container-sm;\n }\n @media (min-width: @screen-md-min) {\n width: @container-md;\n }\n @media (min-width: @screen-lg-min) {\n width: @container-lg;\n }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n .make-row();\n}\n\n.row-no-gutters {\n margin-right: 0;\n margin-left: 0;\n\n [class*=\"col-\"] {\n padding-right: 0;\n padding-left: 0;\n }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n padding-right: ceil((@gutter / 2));\n padding-left: floor((@gutter / 2));\n margin-right: auto;\n margin-left: auto;\n &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n margin-right: floor((@gutter / -2));\n margin-left: ceil((@gutter / -2));\n &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n float: left;\n width: percentage((@columns / @grid-columns));\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-sm-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-offset(@columns) {\n @media (min-width: @screen-sm-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-push(@columns) {\n @media (min-width: @screen-sm-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-pull(@columns) {\n @media (min-width: @screen-sm-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-md-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-offset(@columns) {\n @media (min-width: @screen-md-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-push(@columns) {\n @media (min-width: @screen-md-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-pull(@columns) {\n @media (min-width: @screen-md-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-lg-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-offset(@columns) {\n @media (min-width: @screen-lg-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-push(@columns) {\n @media (min-width: @screen-lg-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-pull(@columns) {\n @media (min-width: @screen-lg-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n // Common styles for all sizes of grid columns, widths 1-12\n .col(@index) { // initial\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n position: relative;\n // Prevent columns from collapsing when empty\n min-height: 1px;\n // Inner gutter via padding\n padding-right: floor((@grid-gutter-width / 2));\n padding-left: ceil((@grid-gutter-width / 2));\n }\n }\n .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n .col(@index) { // initial\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n float: left;\n }\n }\n .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n .col-@{class}-@{index} {\n width: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n .col-@{class}-push-@{index} {\n left: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n .col-@{class}-push-0 {\n left: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n .col-@{class}-pull-@{index} {\n right: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n .col-@{class}-pull-0 {\n right: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n .col-@{class}-offset-@{index} {\n margin-left: percentage((@index / @grid-columns));\n }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n .calc-grid-column(@index, @class, @type);\n // next iteration\n .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n .float-grid-columns(@class);\n .loop-grid-columns(@grid-columns, @class, width);\n .loop-grid-columns(@grid-columns, @class, pull);\n .loop-grid-columns(@grid-columns, @class, push);\n .loop-grid-columns(@grid-columns, @class, offset);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-no-qualifying-type\n\n//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n background-color: @table-bg;\n\n // Table cell sizing\n //\n // Reset default table behavior\n\n col[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n display: table-column;\n float: none;\n }\n\n td,\n th {\n &[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n display: table-cell;\n float: none;\n }\n }\n}\n\ncaption {\n padding-top: @table-cell-padding;\n padding-bottom: @table-cell-padding;\n color: @text-muted;\n text-align: left;\n}\n\nth {\n text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: @line-height-computed;\n // Cells\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-cell-padding;\n line-height: @line-height-base;\n vertical-align: top;\n border-top: 1px solid @table-border-color;\n }\n }\n }\n // Bottom align for column headings\n > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid @table-border-color;\n }\n // Remove top border from thead by default\n > caption + thead,\n > colgroup + thead,\n > thead:first-child {\n > tr:first-child {\n > th,\n > td {\n border-top: 0;\n }\n }\n }\n // Account for multiple tbody instances\n > tbody + tbody {\n border-top: 2px solid @table-border-color;\n }\n\n // Nesting\n .table {\n background-color: @body-bg;\n }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-condensed-cell-padding;\n }\n }\n }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n border: 1px solid @table-border-color;\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n border: 1px solid @table-border-color;\n }\n }\n }\n > thead > tr {\n > th,\n > td {\n border-bottom-width: 2px;\n }\n }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n > tbody > tr:nth-of-type(odd) {\n background-color: @table-bg-accent;\n }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n > tbody > tr:hover {\n background-color: @table-bg-hover;\n }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n min-height: .01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n overflow-x: auto;\n\n @media screen and (max-width: @screen-xs-max) {\n width: 100%;\n margin-bottom: (@line-height-computed * .75);\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid @table-border-color;\n\n // Tighten up spacing\n > .table {\n margin-bottom: 0;\n\n // Ensure the content doesn't wrap\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n white-space: nowrap;\n }\n }\n }\n }\n\n // Special overrides for the bordered tables\n > .table-bordered {\n border: 0;\n\n // Nuke the appropriate borders so that the parent can handle them\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th:first-child,\n > td:first-child {\n border-left: 0;\n }\n > th:last-child,\n > td:last-child {\n border-right: 0;\n }\n }\n }\n\n // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n // chances are there will be only one `tr` in a `thead` and that would\n // remove the border altogether.\n > tbody,\n > tfoot {\n > tr:last-child {\n > th,\n > td {\n border-bottom: 0;\n }\n }\n }\n\n }\n }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n // Exact selectors below required to override `.table-striped` and prevent\n // inheritance to nested tables.\n .table > thead > tr,\n .table > tbody > tr,\n .table > tfoot > tr {\n > td.@{state},\n > th.@{state},\n &.@{state} > td,\n &.@{state} > th {\n background-color: @background;\n }\n }\n\n // Hover states for `.table-hover`\n // Note: this is not available for cells or rows within `thead` or `tfoot`.\n .table-hover > tbody > tr {\n > td.@{state}:hover,\n > th.@{state}:hover,\n &.@{state}:hover > td,\n &:hover > .@{state},\n &.@{state}:hover > th {\n background-color: darken(@background, 5%);\n }\n }\n}\n","// stylelint-disable selector-no-qualifying-type, property-no-vendor-prefix, media-feature-name-no-vendor-prefix\n\n//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n // so we reset that to ensure it behaves more like a standard block element.\n // See https://github.com/twbs/bootstrap/issues/12359.\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: @line-height-computed;\n font-size: (@font-size-base * 1.5);\n line-height: inherit;\n color: @legend-color;\n border: 0;\n border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n display: inline-block;\n max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n margin-bottom: 5px;\n font-weight: 700;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\ninput[type=\"search\"] {\n // Override content-box in Normalize (* isn't specific enough)\n .box-sizing(border-box);\n\n // Search inputs in iOS\n //\n // This overrides the extra rounded corners on search inputs in iOS so that our\n // `.form-control` class can properly style them. Note that this cannot simply\n // be added to `.form-control` as it's not specific enough. For details, see\n // https://github.com/twbs/bootstrap/issues/11586.\n -webkit-appearance: none;\n appearance: none;\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9; // IE8-9\n line-height: normal;\n\n // Apply same disabled cursor tweak as for inputs\n // Some special care is needed because