Adding in python viz scripts that are more generic.

python_imaging/README.md

@@ -1,37 +1,19 @@
-# Python Imaging
+# Python Visualizations
 
-In an effort to create some better imaging to view cells, ECM components and other things simultaneously, these scripts have been written.
+`image_processing_for_physicell.py` contains the class `PhysiCellPlotter`. This class enables reproducible visualization with many options - including setting which layers to include (cell, ECM components, diffusing fields), positional tracking of cell movement history, and smoothly generating stills and movies sequentially. It uses a modified version of [`pyMCDS.py`](https://github.com/PhysiCell-Tools/python-loader) - `pyMCDS_ECM.py`. The modifications include loading ECM data .mat outputs and formatting that data for use in visualizations. 
 
 
 ## Scripts
 
-* `basic_cell_plot.py`: create a plot with Cells plotted.
-* `basic_oxygen_density.py`: Plot the density of oxygen.  
-* `basic_ecm_density.py`: Plot the ECM density.
-* `basic_ecm_anisotropy.py`: Plot the ECM anisotropy.
-
-## ToDo
-* Read in data
-* ECM anisotropy plot
-* ECM alignment plot
-* Overlap all elements / modularity
-* Panels
-
-## Other stuff
 
-### Helpful function to load in ECM matlab data
-```
-    import scipy.io as sci
 
-    sim_frame = 'output00001192' # The name of the simulation frame (eg. `initial`, `output00001192`).  This is not the file name
-    output_path = 'example_output' # The folder with all the output data
+Advanced scripts - using the PhysiCellPlotter class in `image_processing_for_physicell.py`. Place scripts in `output` or other directory at same level or alter path assignment. 
+* 
 
-    def load_ecm_data(file_name):
-        mat = sci.loadmat(file_name)['ECM_Data']
-        data = {"x":mat[0], "y":mat[1], "z":mat[2], "anisotropy":mat[3], "density":mat[4], "fiber_alignment_x":mat[5], "fiber_alignment_y":mat[6], "fiber_alignment_z":mat[7], "gradient_x":mat[8], "gradient_y":mat[9], "gradient_z":mat[10]}
 
-        return data
-
-    # load data
-    ecm_data = load_ecm_data(output_path + '/' + sim_frame + '_ECM.mat')
-```
+Basic scripts. Original basis for integrated plotter in `image_processing_for_physicell.py`. Included as basic original examples. Place scripts in `output` or other directory at same level or alter path assignment. 
+* `basic_cell_plot.py`: create a plot with Cells plotted.
+* `cell_plus_environment_movie_maker.py`: Generates a movie of the ECM anisotorpy and orientations with cell overlay. Alter as needed for need. 
+* `cell_plus_environment_plotter.py`: Generates a still of the ECM anisotorpy and orientations with cell overlay. Alter as needed for need. 
+* `cell_track_plotter.py`: Plots still of cells and cell positional histories. 
+* `cell_tracker_movie.py`: Generates a movie of cells and cell positional histories.

python_imaging/figure_2a_movie_script.py → python_imaging/cell_march_movie_script.py

@@ -34,14 +34,12 @@
                             }
 
 #### Right now, if you don't have None or the full contour and quiver options, it will break in the plotting ... I wonder if there
-#### is a better/more robust way to do it (kwargs???, lots of "trapping"??) but this will be handled later ... and I can ask Randy etc
-### What is up with scaling - hum ...
-
-# oof - I got different results on the two runs when I did and didn't scale by anistoropy ... yikes! How do I manage that!!
+#### is a better/more robust way to do it (kwargs???, lots of "trapping"??) but this will be handled later 
 
 mf = PhysiCellPlotter()
 
 mf.produce_movie(save_name='figure_2a_march', movie_options=movie_options_for_figure_2a, image_options=options_for_figure2a)
+
 # m2 = PhysiCellPlotter()
 # m3 = PhysiCellPlotter()
 

python_imaging/figure_2a_stills_script.py → python_imaging/cell_march_stills_script.py

@@ -27,10 +27,7 @@
                        }
 
 #### Right now, if you don't have None or the full contour and quiver options, it will break in the plotting ... I wonder if there
-#### is a better/more robust way to do it (kwargs???, lots of "trapping"??) but this will be handled later ... and I can ask Randy etc
-### What is up with scaling - hum ...
-
-# oof - I got different results on the two runs when I did and didn't scale by anistoropy ... yikes! How do I manage that!!
+#### is a better/more robust way to do it (kwargs???, lots of "trapping"??) but this will be handled later ... 
 
 mf = PhysiCellPlotter()
 # m2 = PhysiCellPlotter()
@@ -41,7 +38,8 @@
 image_list_for_figure2a = [90, 500, 1200]
 
 file_name = 'march_' + str(90)
-
+print('To make \'March\' images - must hard code both colors to blue!!!!')
+print('SEE plot_cells_from_physicell_data function!!!')
 for number in image_list_for_figure2a:
     mf.generic_plotter(starting_index=number, number_of_samples=1, options=options_for_figure2a, file_name='march_' + str(number))
 

python_imaging/figure_4_movie_script.py → python_imaging/partial_history_2_level_contour_movie.py

@@ -19,16 +19,16 @@
                         "produce_for_movie" : True,
                         "contour_options" : {'lowest_contour': 1e-14, ### I woud like this to be cleaner - but it does work!!!
                                            'upper_contour': 1.0,
-                                           'number_of_levels': 25,
+                                           'number_of_levels': 2,
                                            'color_map_name': 'Reds',
-                                           'color_bar': True
+                                           'color_bar': False
                                            },
                        }
 
 movie_options_for_figure_4 = {}
 
 movie_options_for_figure_4 = {'INCLUDE_ALL_SVGs': True,
-                            'INCLUDE_FULL_HISTORY': True
+                            'INCLUDE_FULL_HISTORY': False
                             }
 
 #### Right now, if you don't have None or the full contour and quiver options, it will break in the plotting ... I wonder if there
@@ -52,7 +52,11 @@
 
 #  starting_index: int = 0, sample_step_interval: int = 1, number_of_samples: int = 120,
 
-mf.produce_movie(save_name='figure_4_parameter_set_21_80_80_ECM_with_chemotaxsis', movie_options=movie_options_for_figure_4, image_options=options_for_figure4_movie)
+mf.produce_movie(save_name='adh_0_repulsion_0_speed_10_20_20_ECM_with_write_05', trail_length=15, movie_options=movie_options_for_figure_4, image_options=options_for_figure4_movie)
+
+# next - adh_0_repulsion_0_speed_10_20_20_ECM_with_write_05_no_following(s=0)
+
+# mf.produce_movie(save_name='figure_4_parameter_set_21_80_80_ECM_with_chemotaxsis', trail_length=1, movie_options=movie_options_for_figure_4, image_options=options_for_figure4_movie)
 
 # mf.generic_plotter(starting_index=90, number_of_samples=1, options=options_for_figure2a)
 # m2.generic_plotter(starting_index=500, number_of_samples=1, options=options_for_figure2a)

python_imaging/figure_4_stills_script.py → python_imaging/partial_history_2_level_contour_still.py

@@ -4,23 +4,23 @@
 
 from image_processing_for_physicell import *
 
-options_for_figure4 = {}
+options_for_figure5a = {}
 
-options_for_figure4 = {"output_plot" : True,
+options_for_figure5a = {"output_plot" : True,
                        "show_plot" : False,
                        "produce_for_panel" : True,
                         "plot_ECM_anisotropy" : True,
-                        "plot_ECM_orientation" : False,
+                        "plot_ECM_orientation" : True,
                         "retrieve_ECM_data": True,
                         "load_full_physicell_data" : True,
                         "plot_cells_from_SVG" : True,
                         "load_SVG_data": True,
                         'plot_cells_from_physicell_data': False,
                         "contour_options" : {'lowest_contour': 1e-14, ### I woud like this to be cleaner - but it does work!!!
                                            'upper_contour': 1.0,
-                                           'number_of_levels': 25,
+                                           'number_of_levels': 2,
                                            'color_map_name': 'Reds',
-                                           'color_bar': True
+                                           'color_bar': False
                                            },
                        }
 
@@ -36,16 +36,18 @@
 
 # image_list_for_figure2a = []
 
-# image_list_for_figure2a = [90, 500, 1200]
+image_list_for_figure5a = [60, 160]
 
 # file_name = 'march_' + str(90)
 
-# for number in image_list_for_figure2a:
-#     mf.generic_plotter(starting_index=number, number_of_samples=1, options=options_for_figure2a, file_name='march_' + str(number))
+number_of_samples = 12
+for number in image_list_for_figure5a:
+    starting_index = number-number_of_samples + 1
+    mf.generic_plotter(starting_index=starting_index, number_of_samples=number_of_samples, options=options_for_figure5a, file_name='Figure_5a' + str(number))
 
 #  starting_index: int = 0, sample_step_interval: int = 1, number_of_samples: int = 120,
 
-mf.generic_plotter( number_of_samples = 481, file_name='parameter_set_21_80_80_ECM_with_chemotaxsis', options=options_for_figure4)
+# mf.generic_plotter( number_of_samples = 481, file_name='parameter_set_21_80_80_ECM_with_chemotaxsis', options=options_for_figure4)
 
 # mf.generic_plotter(starting_index=90, number_of_samples=1, options=options_for_figure2a)
 # m2.generic_plotter(starting_index=500, number_of_samples=1, options=options_for_figure2a)