Merge pull request #43 from PhysiCell-Models/dev

Dev to Main - final scripts for processing Painter data

.github/workflows/test-macos.yml

@@ -13,13 +13,13 @@ jobs:
     - uses: actions/checkout@v3
 
     - name: Install dependencies
-      run : brew install gcc@11
+      run : brew install gcc@13
 
     - name: Build model
       run: |
-        make PHYSICELL_CPP=g++-11
-        make invasive_carcinoma PHYSICELL_CPP=g++-11
-        make fibrosis PHYSICELL_CPP=g++-11
+        make PHYSICELL_CPP=g++-13
+        make invasive_carcinoma PHYSICELL_CPP=g++-13
+        make fibrosis PHYSICELL_CPP=g++-13
         
     - name: Run model
       run: |

python_imaging/image_processing_for_physicell.py

@@ -418,7 +418,7 @@ def create_density_histogram(self, histogram_options=None, options: dict=None):
 
             # Add a horizontal line at 95% of total count
             inset_ax.axvline(x=bin_centers[percentile_95], color='blue', linestyle='--')
-
+            print('95th percentile value:', bin_centers[percentile_95])
             # Set the same y-axis limits for the inset
             inset_ax.set_xlim(-500, 500)
             # inset_ax.set_xlim(0, inset_ax.get_xlim()[1])

scripts/data_analysis_Painter.py

@@ -0,0 +1,78 @@
+import numpy as np
+import csv
+
+
+try:
+    from pyMCDS_ECM import *
+except ImportError:
+    from pyMCDS import *
+
+
+## Adapting the code from the image processing code - create_density_histogram function. 
+## I don't need the histogram - just the 95th percentile value - converted back from bin number to position.
+
+def create_density_histogram(mcds):
+
+    # inherited from the image processing code
+    num_bins = 40
+
+    y_positions = mcds.data['discrete_cells']['position_y']
+
+    # Create a 1D histogram of the y-positions, ensuring the range spans -500 to 500
+    hist, bin_edges = np.histogram(y_positions, bins=num_bins, range=(-500, 500)) ## not automatic
+
+    # Print the histogram counts
+    print("Histogram counts for each bin:")
+    for i, count in enumerate(hist):
+        print(f"Bin {i+1}: {count} counts")
+
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+    # Calculate the cumulative sum and find the 95th percentile value
+    cumulative_counts = np.cumsum(hist)
+
+    total_count = cumulative_counts[-1]
+    percentile_95 = np.searchsorted(cumulative_counts, 0.95 * total_count)
+
+    print(f"95th percentile bin number: {percentile_95}")
+    print(f"95th percentile center of bin in um: {bin_centers[percentile_95]}")
+    # to doulbe check - print bin centers then bin edges
+    # print(bin_centers)
+    # print(bin_edges)
+
+    return bin_centers[percentile_95]
+
+percentile_centers = []
+for n in range(1, 21):
+    # folder name needs padded with 1 zero
+    if n < 10:
+        folder_name = "0" + str(n)
+    else:
+        folder_name = str(n)
+
+    #     Parameters
+    # ----------
+    # xml_name: str
+    #     String containing the name of the xml file without the path
+    # output_path: str, optional
+    #     String containing the path (relative or absolute) to the directory
+    #     where PhysiCell output files are stored (default= ".")
+
+    mcds = pyMCDS('output00000005.xml', folder_name)
+
+
+    percentile_centers.append(create_density_histogram(mcds))
+
+# add in value for the extra run (the run used for detail in the paper)
+
+percentile_centers.append(0.0) # placeholder for the extra run - these were determined by outputing the value from the image processing code. so just make the image 
+                            # processing code output the value and then copy it here.   
+
+print(percentile_centers)
+print('Mean of 95th percentile centers:')
+print(np.mean(percentile_centers))
+
+print('Standard deviation of 95th percentile centers:')
+print(np.std(percentile_centers))
+
+np.savetxt("95th_percentile_center.csv", percentile_centers, delimiter=",", fmt="%s") # rename as needed

scripts/invasive_front_vizualization_of_variance.py

@@ -0,0 +1,76 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Read the CSV files into DataFrames
+df_parallel = pd.read_csv('../Painter_data/95th_percentile_center_parallel.csv', header=None, names=['Position'])
+df_perpendicular = pd.read_csv('../Painter_data/95th_percentile_center_orthogonal.csv', header=None, names=['Position'])
+df_random = pd.read_csv('../Painter_data/95th_percentile_center_random.csv', header=None, names=['Position'])
+
+# Add a 'Category' column to each DataFrame to differentiate the data
+df_parallel['Category'] = 'Parallel'
+df_perpendicular['Category'] = 'Perpendicular'
+df_random['Category'] = 'Random'
+
+# Combine the DataFrames into a single DataFrame
+df_combined = pd.concat([df_random, df_parallel, df_perpendicular])
+
+# Assuming the positional data is in a column called 'Position' in the CSV files
+# If the column name is different, replace 'Position' with the actual name
+sns.set(style="whitegrid")
+
+# Create the beeswarm plot to show variations
+fig, ax = plt.subplots(figsize=(5, 6))
+# ax = sns.stripplot(x='Category', y='Position', data=df_combined, size=8)
+
+# # plot the mean line
+# sns.boxplot(showmeans=True,
+#             meanline=True,
+#             meanprops={'color': 'k'},
+#             medianprops={'visible': False},
+#             whiskerprops={'visible': False},
+#             zorder=10,
+#             x="Category",
+#             y="Position",
+#             data=df_combined,
+#             showfliers=False,
+#             showbox=False,
+#             showcaps=False,
+#             ax=ax)
+
+
+
+#  Boxplot to show distribution statistics, mean is shown as a green triangle
+sns.boxplot(x='Category', y='Position', data=df_combined, showmeans=True, meanline=True, width=0.3, 
+            # meanprops={"marker":".", "markerfacecolor":"red", "markeredgecolor":"red", "markersize":"5"},
+            boxprops=dict(alpha=0.7), ax=ax)
+
+sns.pointplot(x='Category', y='Position', data=df_combined, estimator=np.mean, join=False, color='red', markers='o', scale=0.75, ax=ax)
+
+# Jitter plot (strip plot) overlayed on the boxplot
+sns.stripplot(x='Category', y='Position', data=df_combined, jitter=True, size=5, color='black', alpha=0.5, ax=ax)
+
+plt.tick_params(axis='x', which='major', labelsize=14)
+plt.tick_params(axis='y', which='major', labelsize=14)
+
+# Customize the plot
+
+# Set y-axis intervals to 125
+plt.yticks(range(-500, 501, 125))
+
+# Add grid lines
+ax.set_axisbelow(True) # https://stackoverflow.com/questions/1726391/matplotlib-draw-grid-lines-behind-other-graph-elements # Not workign - but overall fig gets job done. 
+
+# plt.rc('axes', axisbelow=True)
+ax.yaxis.grid(linestyle='--', linewidth=0.7)
+
+plt.ylabel('Position (µm)', fontsize=16)
+plt.xlabel('', fontsize=2)
+plt.ylim(-500, 500)  # Set limits based on your preference
+plt.tight_layout()
+# Save figure
+plt.savefig('painter_extent_of_invasive_front.png', dpi=300)
+
+# Show the plot
+plt.show()

shell_scripts/make_all_executables.sh → scripts/make_all_executables.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 make -j4
 make fibrosis -j4
-make invasive_spheroid -j4
+make invasive_carcinoma -j4