Added scripts to reproduce files in paper

examples/Configs/Figure8Reproduce.ncfg

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+settings:
+  format: NWB
+  analysis_mode: Single Unit
+  mode_id: 0
+  graphic_format: pdf
+  unit_no: 1
+  lfp_file: C:\Users\smartin5\Recordings\NC_eg\112512_1.hdf5+/processing/Neural Continuous/LFP/eeg
+  cell_type: ''
+  spike_file: C:\Users\smartin5\Recordings\NC_eg\112512_1.hdf5+/processing/Shank/5
+  spatial_file: C:\Users\smartin5\Recordings\NC_eg\112512_1.hdf5+/processing/Behavioural/Position
+  nwb_file: C:\Users\smartin5\Recordings\NC_eg\112512_1.hdf5
+  excel_file: ''
+  data_directory: ''
+analyses:
+  ang_vel: false
+  border: false
+  burst: false
+  gradient: false
+  grid: false
+  hd_rate: false
+  hd_shuffle: false
+  hd_time_lapse: false
+  hd_time_shift: false
+  inter_depend: false
+  isi: true
+  isi_corr: true
+  lfp_spectrum: true
+  lfp_spike_causality: true
+  loc_rate: false
+  loc_shuffle: false
+  loc_time_lapse: false
+  loc_time_shift: false
+  multiple_regression: false
+  phase_lock: true
+  spatial_corr: false
+  speed: false
+  spike_phase: true
+  theta_cell: true
+  theta_skip_cell: false
+  wave_property: false
+parameters:
+  ang_vel_bin: 10
+  ang_vel_cutoff: 10
+  ang_vel_kern_len: 3
+  ang_vel_max: 200
+  ang_vel_min: -200
+  ang_vel_rate_kern_len: 3
+  border_ang_bin: 3
+  border_firing_thresh: 0.1
+  border_stair_steps: 5
+  burst_thresh: 5
+  grad_asymp_lim: 0.25
+  grad_displace_lim: 0.25
+  grad_growth_rate_lim: 0.5
+  grid_ang_bin: 3
+  grid_ang_tol: 2
+  hd_ang_vel_cutoff: 10
+  hd_bin: 5
+  hd_rate_kern_len: 5
+  hd_shift_max: 10
+  hd_shift_min: -10
+  hd_shift_step: 1
+  hd_shuffle_bins: 100
+  hd_shuffle_limit: 0
+  hd_shuffle_total: 500
+  ibi_thresh: 50
+  isiLogLength: 350
+  isiLogNoBins: 70
+  isi_bin: 1
+  isi_corr_bin_long: 1
+  isi_corr_bin_short: 1
+  isi_corr_len_long: 350
+  isi_corr_len_short: 10
+  isi_length: 200
+  isi_refractory: 2.0
+  lfp_prefilt_highcut: 40
+  lfp_prefilt_lowcut: 1.5
+  lfp_prefilt_order: 5
+  lfp_pwelch_freq_max: 40
+  lfp_pwelch_nfft: 1024
+  lfp_pwelch_overlap: 1.0
+  lfp_pwelch_seg_size: 2.0
+  lfp_stft_freq_max: 40
+  lfp_stft_nfft: 1024
+  lfp_stft_overlap: 1.0
+  lfp_stft_seg_size: 2.0
+  loc_chop_bound: 5
+  loc_colormap: viridis
+  loc_field_smooth: false
+  loc_field_thresh: 0.2
+  loc_pixel_size: 3
+  loc_rate_filter: Box
+  loc_rate_kern_len: 5
+  loc_shift_max: 10
+  loc_shift_min: -10
+  loc_shift_step: 1
+  loc_shuffle_limit: 0
+  loc_shuffle_nbins: 100
+  loc_shuffle_total: 500
+  loc_style: contour
+  mra_episode: 120
+  mra_interval: 0.1
+  mra_nrep: 1000
+  phase_amp_thresh: 0.15
+  phase_bin: 7
+  phase_freq_max: 12.0
+  phase_freq_min: 6.0
+  phase_loc_freq_max: 40
+  phase_loc_nfft: 640
+  phase_loc_win_low: -0.4
+  phase_loc_win_up: 0.4
+  phase_power_thresh: 0.1
+  phase_raster_bin: 2
+  rot_corr_bin: 3
+  spatial_corr_filter: Box
+  spatial_corr_kern_len: 5
+  spatial_corr_min_obs: 20
+  speedKernLen: 3
+  speed_bin: 1
+  speed_max: 40
+  speed_min: 0
+  speed_rate_kern_len: 3
+  spikesToBurst: 2
+  theta_cell_freq_max: 12.0
+  theta_cell_freq_min: 6.0
+  theta_cell_freq_start: 6.0
+  theta_cell_tau1_max: 5.0
+  theta_cell_tau1_start: 0.1
+  theta_cell_tau2_max: 0.05
+  theta_cell_tau2_start: 0.05

examples/reproduce_figure_6.py

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+"""
+Use this file to reproduce Figure 6 in the NeuroChaT paper
+Please change line 13 to reflect the directory you saved 040512_1.hdf5 in
+"""
+import os
+import numpy as np
+from neurochat.nc_data import NData
+import neurochat.nc_plot as nc_plot
+
+
+def load_h5_data():
+    # Change this to the directory where the file 040513_1.hdf5 is saved
+    data_dir = r'C:\Users\smartin5\Recordings\NC_eg'
+    main_file = "040513_1.hdf5"
+    spike_file = "/processing/Shank/6"
+    pos_file = "/processing/Behavioural/Position"
+    lfp_file = "/processing/Neural Continuous/LFP/eeg"
+    unit_no = 3
+
+    def m_file(x): return os.path.join(data_dir, main_file + "+" + x)
+    ndata = NData()
+    ndata.set_data_format(data_format='NWB')
+    ndata.set_spatial_file(m_file(pos_file))
+    ndata.set_spike_file(m_file(spike_file))
+    ndata.set_lfp_file(m_file(lfp_file))
+    ndata.load()
+    ndata.set_unit_no(unit_no)
+
+    return ndata
+
+
+def main():
+    ndata = load_h5_data()
+    graph_data = ndata.place()
+    fig = nc_plot.loc_firing(
+        graph_data, style="digitized", colormap="default")
+    fig.savefig("Figure6a_place.png")
+
+    graph_data = ndata.hd_rate()
+    figs = nc_plot.hd_firing(graph_data)
+    figs[1].savefig("Figure6a_hd.png")
+
+    graph_data = ndata.multiple_regression()
+    fig = nc_plot.multiple_regression(graph_data)
+    fig.savefig("Figure6b.png")
+
+    graph_data = ndata.loc_shuffle(bins=50)
+    fig = nc_plot.loc_shuffle(graph_data)
+    fig.savefig("Figure6c.png")
+
+    graph_data = ndata.loc_shift(shift_ind=np.arange(-10, 21))
+    figs = nc_plot.loc_time_shift(
+        graph_data)
+    figs[0].savefig("Figure6d.png")
+
+
+if __name__ == "__main__":
+    main()

examples/reproduce_figure_8.py

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+"""
+Use this file to reproduce Figure 8 in the NeuroChaT paper.
+Please replace all instances of 
+C:\\Users\\smartin5\\Recordings\\NC_eg\\ with the folder
+that you downloaded 112512_1.hdf5 to
+"""
+
+import os
+import logging
+from neurochat.nc_config import Configuration
+from neurochat.nc_control import NeuroChaT
+
+
+def main():
+    nc = NeuroChaT()
+    config = Configuration()
+
+    root = os.path.dirname(os.path.realpath(__file__))
+    name = "Figure8Reproduce.ncfg"
+    config_loc = os.path.join(root, "Configs", name)
+    config.set_config_file(config_loc)
+    config.load_config()
+
+    nc.set_configuration(config)
+    nc.run()
+
+    excel_file = nc._pdf_file[:-3] + "xlsx"
+    try:
+        results = nc.get_results()
+        results.to_excel(excel_file)
+        logging.info("Analysis results exported to: " +
+                     excel_file.rstrip("\n\r").split(os.sep)[-1])
+    except:
+        logging.error('Failed to export results!')
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    mpl_logger = logging.getLogger("matplotlib")
+    mpl_logger.setLevel(level=logging.WARNING)
+    main()