diff track_objects.py @ 4:ff1a86a87364 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 09:37:26 +0000
parents 6100a22bf4f2
children
line wrap: on
line diff
--- a/track_objects.py	Fri Feb 26 14:06:51 2021 +0000
+++ b/track_objects.py	Sun Nov 05 09:37:26 2023 +0000
@@ -3,68 +3,95 @@
 import argparse
 import json
 
-from cp_common_functions import get_json_value
-from cp_common_functions import get_pipeline_lines
-from cp_common_functions import get_total_number_of_modules
-from cp_common_functions import INDENTATION
-from cp_common_functions import update_module_count
-from cp_common_functions import write_pipeline
+from cp_common_functions import (get_json_value,
+                                 get_pipeline_lines,
+                                 get_total_number_of_modules,
+                                 INDENTATION, update_module_count,
+                                 write_pipeline)
 
 MODULE_NAME = "TrackObjects"
 OUTPUT_FILENAME = "output.cppipe"
 
 
 def build_header(module_name, module_number):
-    result = "|".join([f"{module_name}:[module_num:{module_number}",
-                       "svn_version:\\'Unknown\\'",
-                       "variable_revision_number:7",
-                       "show_window:True",
-                       "notes:\\x5B\\'Track the embryos across images using the Overlap method\\x3A tracked objects are identified by the amount of frame-to-frame overlap. Save an image of embryos labeled with a unique number across time.\\'\\x5D",
-                       "batch_state:array(\\x5B\\x5D, dtype=uint8)",
-                       "enabled:True",
-                       "wants_pause:False]\n"])
+    result = "|".join(
+        [
+            f"{module_name}:[module_num:{module_number}",
+            "svn_version:\\'Unknown\\'",
+            "variable_revision_number:7",
+            "show_window:True",
+            "notes:\\x5B\\'Track the embryos across images using the Overlap method\\x3A tracked objects are identified by the amount of frame-to-frame overlap. Save an image of embryos labeled with a unique number across time.\\'\\x5D",
+            "batch_state:array(\\x5B\\x5D, dtype=uint8)",
+            "enabled:True",
+            "wants_pause:False]\n",
+        ]
+    )
     return result
 
 
 def build_main_block(input_params):
-    result = INDENTATION.join([f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n",
-                               f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n"
-                               ])
+    result = INDENTATION.join(
+        [
+            f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n",
+            f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n",
+        ]
+    )
 
-    tracking_method = get_json_value(input_params, 'con_tracking_method.tracking_method')
+    tracking_method = get_json_value(
+        input_params, "con_tracking_method.tracking_method"
+    )
 
     obj_measurement = "None"  # default value
     if tracking_method == "Measurements":
-        measurement_category = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement_category')
-        measurement = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement')
+        measurement_category = get_json_value(
+            input_params,
+            "con_tracking_method.con_measurement_category.measurement_category",
+        )
+        measurement = get_json_value(
+            input_params, "con_tracking_method.con_measurement_category.measurement"
+        )
 
         if measurement_category == "Intensity" or measurement_category == "Location":
-            img_measure = get_json_value(input_params, 'con_tracking_method.con_measurement_category.img_measure')
+            img_measure = get_json_value(
+                input_params, "con_tracking_method.con_measurement_category.img_measure"
+            )
             obj_measurement = f"{measurement_category}_{measurement}_{img_measure}"
         else:
             obj_measurement = f"{measurement_category}_{measurement}"
 
-    result += INDENTATION.join([f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"])
+    result += INDENTATION.join(
+        [
+            f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"
+        ]
+    )
 
     if tracking_method == "LAP":  # no max distance required, set default for pipeline
         max_distance = 50
     else:
-        max_distance = get_json_value(input_params, 'con_tracking_method.max_distance')
+        max_distance = get_json_value(input_params, "con_tracking_method.max_distance")
 
-    result += INDENTATION.join([f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"])
+    result += INDENTATION.join(
+        [f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"]
+    )
 
-    display_option = get_json_value(input_params, 'con_tracking_method.display_option')
+    display_option = get_json_value(input_params, "con_tracking_method.display_option")
 
     output_img_name = "TrackedCells"  # default value, required by cppipe regardless of its presence in UI
-    save = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.save_coded_img')
+    save = get_json_value(
+        input_params, "con_tracking_method.con_save_coded_img.save_coded_img"
+    )
     if save == "Yes":
-        output_img_name = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.name_output_img')
+        output_img_name = get_json_value(
+            input_params, "con_tracking_method.con_save_coded_img.name_output_img"
+        )
 
     result += INDENTATION.join(
-        [f"{INDENTATION}Select display option:{display_option}\n",
-         f"Save color-coded image?:{save}\n",
-         f"Name the output image:{output_img_name}\n"
-         ])
+        [
+            f"{INDENTATION}Select display option:{display_option}\n",
+            f"Save color-coded image?:{save}\n",
+            f"Name the output image:{output_img_name}\n",
+        ]
+    )
 
     # LAP method default values
     movement_model = "Both"
@@ -85,67 +112,113 @@
 
     # LAP method
     if tracking_method == "LAP":
-        movement_model = get_json_value(input_params, 'con_tracking_method.movement_method')
-        no_std = get_json_value(input_params, 'con_tracking_method.no_std_radius')
-        radius_limit_max = get_json_value(input_params, 'con_tracking_method.max_radius')
-        radius_limit_min = get_json_value(input_params, 'con_tracking_method.min_radius')
+        movement_model = get_json_value(
+            input_params, "con_tracking_method.movement_method"
+        )
+        no_std = get_json_value(input_params, "con_tracking_method.no_std_radius")
+        radius_limit_max = get_json_value(
+            input_params, "con_tracking_method.max_radius"
+        )
+        radius_limit_min = get_json_value(
+            input_params, "con_tracking_method.min_radius"
+        )
         radius = f"{radius_limit_min},{radius_limit_max}"
 
-        run_second = get_json_value(input_params, 'con_tracking_method.con_second_lap.second_lap')
+        run_second = get_json_value(
+            input_params, "con_tracking_method.con_second_lap.second_lap"
+        )
         if run_second == "Yes":
-            gap_closing = get_json_value(input_params, 'con_tracking_method.con_second_lap.gap_closing')
-            split_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.split_alt')
-            merge_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.merge_alt')
-            max_gap_displacement = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_gap_displacement')
-            max_split = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_split')
-            max_merge = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_merge')
-            max_temporal = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_temporal')
-            max_mitosis_dist = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_mitosis_distance')
-            mitosis_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.mitosis_alt')
+            gap_closing = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.gap_closing"
+            )
+            split_alt = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.split_alt"
+            )
+            merge_alt = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.merge_alt"
+            )
+            max_gap_displacement = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.max_gap_displacement"
+            )
+            max_split = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.max_split"
+            )
+            max_merge = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.max_merge"
+            )
+            max_temporal = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.max_temporal"
+            )
+            max_mitosis_dist = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.max_mitosis_distance"
+            )
+            mitosis_alt = get_json_value(
+                input_params, "con_tracking_method.con_second_lap.mitosis_alt"
+            )
 
     result += INDENTATION.join(
-        [f"{INDENTATION}Select the movement model:{movement_model}\n",
-         f"Number of standard deviations for search radius:{no_std}\n",
-         f"Search radius limit, in pixel units (Min,Max):{radius}\n",
-         f"Run the second phase of the LAP algorithm?:{run_second}\n",
-         f"Gap closing cost:{gap_closing}\n",
-         f"Split alternative cost:{split_alt}\n",
-         f"Merge alternative cost:{merge_alt}\n",
-         f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n",
-         f"Maximum split score:{max_split}\n",
-         f"Maximum merge score:{max_merge}\n",
-         f"Maximum temporal gap, in frames:{max_temporal}\n"
-         ])
+        [
+            f"{INDENTATION}Select the movement model:{movement_model}\n",
+            f"Number of standard deviations for search radius:{no_std}\n",
+            f"Search radius limit, in pixel units (Min,Max):{radius}\n",
+            f"Run the second phase of the LAP algorithm?:{run_second}\n",
+            f"Gap closing cost:{gap_closing}\n",
+            f"Split alternative cost:{split_alt}\n",
+            f"Merge alternative cost:{merge_alt}\n",
+            f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n",
+            f"Maximum split score:{max_split}\n",
+            f"Maximum merge score:{max_merge}\n",
+            f"Maximum temporal gap, in frames:{max_temporal}\n",
+        ]
+    )
 
     # common section
-    filter_by_lifetime = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.filter_by_lifetime')
+    filter_by_lifetime = get_json_value(
+        input_params, "con_tracking_method.con_filter_by_lifetime.filter_by_lifetime"
+    )
     use_min = "Yes"  # default
     min_life = 1  # default
     use_max = "No"  # default
     max_life = 100  # default
 
     if filter_by_lifetime == "Yes":
-        use_min = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.use_min')
+        use_min = get_json_value(
+            input_params,
+            "con_tracking_method.con_filter_by_lifetime.con_use_min.use_min",
+        )
         if use_min == "Yes":
-            min_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime')
+            min_life = get_json_value(
+                input_params,
+                "con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime",
+            )
 
-        use_max = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.use_max')
+        use_max = get_json_value(
+            input_params,
+            "con_tracking_method.con_filter_by_lifetime.con_use_max.use_max",
+        )
         if use_max == "Yes":
-            max_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime')
+            max_life = get_json_value(
+                input_params,
+                "con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime",
+            )
 
     result += INDENTATION.join(
-        [f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n",
-         f"Filter using a minimum lifetime?:{use_min}\n",
-         f"Minimum lifetime:{min_life}\n",
-         f"Filter using a maximum lifetime?:{use_max}\n",
-         f"Maximum lifetime:{max_life}\n"
-         ])
+        [
+            f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n",
+            f"Filter using a minimum lifetime?:{use_min}\n",
+            f"Minimum lifetime:{min_life}\n",
+            f"Filter using a maximum lifetime?:{use_max}\n",
+            f"Maximum lifetime:{max_life}\n",
+        ]
+    )
 
     # print 2 leftover from LAP
     result += INDENTATION.join(
-        [f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n",
-         f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n"
-         ])
+        [
+            f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n",
+            f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n",
+        ]
+    )
 
     # Follow Neighbors
     # defaults
@@ -155,32 +228,36 @@
     weight_of_area_diff = 25.0
 
     if tracking_method == "Follow Neighbors":
-        avg_cell_diameter = get_json_value(input_params, 'con_tracking_method.avg_diameter')
-        use_adv = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.adv_parameter')
+        avg_cell_diameter = get_json_value(
+            input_params, "con_tracking_method.avg_diameter"
+        )
+        use_adv = get_json_value(
+            input_params, "con_tracking_method.con_adv_parameter.adv_parameter"
+        )
         if use_adv == "Yes":
-            cost_of_cell = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.cost')
-            weight_of_area_diff = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.weight')
+            cost_of_cell = get_json_value(
+                input_params, "con_tracking_method.con_adv_parameter.cost"
+            )
+            weight_of_area_diff = get_json_value(
+                input_params, "con_tracking_method.con_adv_parameter.weight"
+            )
 
     result += INDENTATION.join(
-        [f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n",
-         f"Use advanced configuration parameters:{use_adv}\n",
-         f"Cost of cell to empty matching:{cost_of_cell}\n",
-         f"Weight of area difference in function matching cost:{weight_of_area_diff}\n"
-         ])
+        [
+            f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n",
+            f"Use advanced configuration parameters:{use_adv}\n",
+            f"Cost of cell to empty matching:{cost_of_cell}\n",
+            f"Weight of area difference in function matching cost:{weight_of_area_diff}\n",
+        ]
+    )
     result = result.rstrip("\n")
     return result
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '-p', '--pipeline',
-        help='CellProfiler pipeline'
-    )
-    parser.add_argument(
-        '-i', '--inputs',
-        help='JSON inputs from Galaxy'
-    )
+    parser.add_argument("-p", "--pipeline", help="CellProfiler pipeline")
+    parser.add_argument("-i", "--inputs", help="JSON inputs from Galaxy")
     args = parser.parse_args()
 
     pipeline_lines = get_pipeline_lines(args.pipeline)