Add plots

_includes/chart_contribution.html

@@ -0,0 +1,2 @@
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_pages/home.md

@@ -9,4 +9,10 @@ header:
 
 # coneScenes Dataset
 
-Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+
+{% include chart_contribution.html %}
+
+{% include chart_range_distribution.html %}
+
+{% include chart_2d_distribution.html %}

tools/compute_scene_specs.py

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+import os
+import pandas as pd
+
+# Path to the directory containing the scene files
+scene_dir = '/Users/bertaveira/Documents/CFS/dv/CFS_dataset/awesome_dataset'
+
+# Open existing csv file
+df = pd.read_csv(os.path.join(os.getcwd(),'tools', 'coneScenes_stats.csv'))
+
+# Convert the 'Date' column to datetime
+df['Date'] = pd.to_datetime(df['Date'])
+current_num_scans = df['NumScans'].iloc[-1]
+current_num_new_cones = df['NumNewCones'].iloc[-1]
+
+# Get all txt files in the scene directory
+label_files = [os.path.join(scene_dir, 'labels', f) for f in os.listdir(os.path.join(scene_dir, 'labels')) if f.endswith('.txt')]
+num_scans = len(label_files)
+
+num_new_cones = 0
+distribution = []
+for label_file in label_files:
+    # Open the label file
+    with open(label_file, 'r') as f:
+        # Count number of non empty lines
+        # Read first 3 numbers of each line
+        for line in f:
+            if line.strip() != '':
+                num_new_cones += 1
+
+                numbers = line.split(' ')
+                # Compute euclidean distance in 3d
+
+                if numbers[0] == '0' and numbers[1] == '0' and numbers[2] == '0':
+                    print(f"Skipping cone at origin: {label_file}")
+                    continue
+
+                distribution.append((float(numbers[0]), float(numbers[1]), (float(numbers[0]) ** 2 + float(numbers[1]) ** 2 + float(numbers[2]) ** 2) ** 0.5))
+
+# Add new row to the dataframe with current date and number of new cones
+new_row = {
+    'Date': pd.to_datetime('today'),
+    'NumScans': current_num_scans + num_scans,
+    'NumNewCones': current_num_new_cones + num_new_cones
+}
+
+print(f"New row: {new_row}")
+
+df = pd.concat([df, pd.DataFrame(new_row, index=[0])], ignore_index=True)
+
+# Save the updated dataframe to the csv file
+df.to_csv(os.path.join('tools', 'coneScenes_stats.csv'), index=False)
+
+# Append to distribution csv
+csv_file = os.path.join('tools', 'coneScenes_distribution.csv')
+distribution_df = pd.DataFrame(distribution)
+distribution_df.columns = ['X', 'Y', 'Distance']
+
+distribution_df = pd.concat([pd.read_csv(csv_file), distribution_df], ignore_index=True)
+distribution_df.to_csv(os.path.join('tools', 'coneScenes_distribution.csv'), index=False, float_format='%.1f')
+
+# Distribution plot
+# import plotly.express as px
+
+# fig = px.histogram(distribution_df, x=0, histnorm='density', title='Distribution of cone distances', template='plotly_white')
+# fig.update_layout(width=800, height=400)
+# fig.update_xaxes(title_text='Distance (m)')
+# fig.update_yaxes(title_text='Count')
+# fig.show()
+
+import numpy as np
+import plotly.figure_factory as ff
+import plotly.express as px
+
+pos = np.array(distribution)
+x, y = pos[:, 0], pos[:, 1]
+
+import plotly.graph_objects as go
+
+import numpy as np
+
+fig = go.Figure()
+fig.add_trace(go.Histogram2dContour(
+        x = y,
+        y = x,
+        colorscale = 'Blues',
+        reversescale = True,
+        xaxis = 'y',
+        yaxis = 'y'
+    ))
+fig.add_trace(go.Histogram(
+        y = x,
+        xaxis = 'x2',
+        marker = dict(
+            color = 'rgba(0,0,0,1)'
+        )
+    ))
+fig.add_trace(go.Histogram(
+        x = y,
+        yaxis = 'y2',
+        marker = dict(
+            color = 'rgba(0,0,0,1)'
+        )
+    ))
+
+fig.update_layout(
+    autosize = False,
+    xaxis = dict(
+        zeroline = False,
+        domain = [0,0.85],
+        showgrid = False
+    ),
+    yaxis = dict(
+        zeroline = False,
+        domain = [0,0.85],
+        showgrid = False
+    ),
+    xaxis2 = dict(
+        zeroline = False,
+        domain = [0.85,1],
+        showgrid = False
+    ),
+    yaxis2 = dict(
+        zeroline = False,
+        domain = [0.85,1],
+        showgrid = False
+    ),
+    height = 600,
+    width = 600,
+    bargap = 0,
+    hovermode = 'closest',
+    showlegend = False
+)
+
+fig.show()