Mercurial > repos > bgruening > cp_measure_image_intensity

annotate track_objects.py @ 6:b64af54c5c09 draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 09:28:54 +0000
parents 8536b1ae0254
children
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1 #!/usr/bin/env python
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3 import argparse
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4 import json
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6 from cp_common_functions import (get_json_value,
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7 get_pipeline_lines,
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8 get_total_number_of_modules,
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9 INDENTATION, update_module_count,
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10 write_pipeline)
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12 MODULE_NAME = "TrackObjects"
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13 OUTPUT_FILENAME = "output.cppipe"
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16 def build_header(module_name, module_number):
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17 result = "|".join(
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18 [
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19 f"{module_name}:[module_num:{module_number}",
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20 "svn_version:\\'Unknown\\'",
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21 "variable_revision_number:7",
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22 "show_window:True",
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23 "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",
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24 "batch_state:array(\\x5B\\x5D, dtype=uint8)",
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25 "enabled:True",
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26 "wants_pause:False]\n",
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27 ]
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28 )
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29 return result
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32 def build_main_block(input_params):
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33 result = INDENTATION.join(
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34 [
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35 f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n",
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36 f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n",
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37 ]
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38 )
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39
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40 tracking_method = get_json_value(
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41 input_params, "con_tracking_method.tracking_method"
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42 )
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44 obj_measurement = "None" # default value
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45 if tracking_method == "Measurements":
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46 measurement_category = get_json_value(
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47 input_params,
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48 "con_tracking_method.con_measurement_category.measurement_category",
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49 )
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50 measurement = get_json_value(
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51 input_params, "con_tracking_method.con_measurement_category.measurement"
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52 )
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54 if measurement_category == "Intensity" or measurement_category == "Location":
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55 img_measure = get_json_value(
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56 input_params, "con_tracking_method.con_measurement_category.img_measure"
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57 )
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58 obj_measurement = f"{measurement_category}_{measurement}_{img_measure}"
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59 else:
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60 obj_measurement = f"{measurement_category}_{measurement}"
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62 result += INDENTATION.join(
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63 [
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64 f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"
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65 ]
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66 )
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68 if tracking_method == "LAP": # no max distance required, set default for pipeline
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69 max_distance = 50
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70 else:
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71 max_distance = get_json_value(input_params, "con_tracking_method.max_distance")
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73 result += INDENTATION.join(
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74 [f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"]
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75 )
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77 display_option = get_json_value(input_params, "con_tracking_method.display_option")
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79 output_img_name = "TrackedCells" # default value, required by cppipe regardless of its presence in UI
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80 save = get_json_value(
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81 input_params, "con_tracking_method.con_save_coded_img.save_coded_img"
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82 )
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83 if save == "Yes":
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84 output_img_name = get_json_value(
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85 input_params, "con_tracking_method.con_save_coded_img.name_output_img"
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86 )
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88 result += INDENTATION.join(
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89 [
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90 f"{INDENTATION}Select display option:{display_option}\n",
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91 f"Save color-coded image?:{save}\n",
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92 f"Name the output image:{output_img_name}\n",
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93 ]
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94 )
4
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95
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96 # LAP method default values
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97 movement_model = "Both"
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98 no_std = 3.0
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99 radius_limit_max = 10.0
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100 radius_limit_min = 2.0
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101 radius = "2.0,10.0"
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102 run_second = "Yes"
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103 gap_closing = 40
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104 split_alt = 40
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105 merge_alt = 40
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106 max_gap_displacement = 5
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107 max_split = 50
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108 max_merge = 50
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109 max_temporal = 5
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110 max_mitosis_dist = 40
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111 mitosis_alt = 80
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112
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113 # LAP method
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114 if tracking_method == "LAP":
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115 movement_model = get_json_value(
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116 input_params, "con_tracking_method.movement_method"
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117 )
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118 no_std = get_json_value(input_params, "con_tracking_method.no_std_radius")
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119 radius_limit_max = get_json_value(
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120 input_params, "con_tracking_method.max_radius"
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121 )
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122 radius_limit_min = get_json_value(
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123 input_params, "con_tracking_method.min_radius"
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124 )
4
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125 radius = f"{radius_limit_min},{radius_limit_max}"
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126
6
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127 run_second = get_json_value(
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128 input_params, "con_tracking_method.con_second_lap.second_lap"
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129 )
4
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130 if run_second == "Yes":
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131 gap_closing = get_json_value(
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132 input_params, "con_tracking_method.con_second_lap.gap_closing"
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133 )
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134 split_alt = get_json_value(
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135 input_params, "con_tracking_method.con_second_lap.split_alt"
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136 )
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137 merge_alt = get_json_value(
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138 input_params, "con_tracking_method.con_second_lap.merge_alt"
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139 )
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140 max_gap_displacement = get_json_value(
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141 input_params, "con_tracking_method.con_second_lap.max_gap_displacement"
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142 )
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143 max_split = get_json_value(
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144 input_params, "con_tracking_method.con_second_lap.max_split"
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145 )
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146 max_merge = get_json_value(
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147 input_params, "con_tracking_method.con_second_lap.max_merge"
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148 )
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149 max_temporal = get_json_value(
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150 input_params, "con_tracking_method.con_second_lap.max_temporal"
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151 )
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152 max_mitosis_dist = get_json_value(
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153 input_params, "con_tracking_method.con_second_lap.max_mitosis_distance"
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154 )
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155 mitosis_alt = get_json_value(
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156 input_params, "con_tracking_method.con_second_lap.mitosis_alt"
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157 )
4
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158
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159 result += INDENTATION.join(
6
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160 [
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161 f"{INDENTATION}Select the movement model:{movement_model}\n",
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162 f"Number of standard deviations for search radius:{no_std}\n",
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163 f"Search radius limit, in pixel units (Min,Max):{radius}\n",
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164 f"Run the second phase of the LAP algorithm?:{run_second}\n",
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165 f"Gap closing cost:{gap_closing}\n",
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166 f"Split alternative cost:{split_alt}\n",
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167 f"Merge alternative cost:{merge_alt}\n",
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168 f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n",
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169 f"Maximum split score:{max_split}\n",
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170 f"Maximum merge score:{max_merge}\n",
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171 f"Maximum temporal gap, in frames:{max_temporal}\n",
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172 ]
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173 )
4
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174
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175 # common section
6
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176 filter_by_lifetime = get_json_value(
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177 input_params, "con_tracking_method.con_filter_by_lifetime.filter_by_lifetime"
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178 )
4
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179 use_min = "Yes" # default
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180 min_life = 1 # default
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181 use_max = "No" # default
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182 max_life = 100 # default
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183
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184 if filter_by_lifetime == "Yes":
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185 use_min = get_json_value(
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186 input_params,
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187 "con_tracking_method.con_filter_by_lifetime.con_use_min.use_min",
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188 )
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189 if use_min == "Yes":
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190 min_life = get_json_value(
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191 input_params,
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192 "con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime",
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193 )
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194
6
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195 use_max = get_json_value(
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196 input_params,
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197 "con_tracking_method.con_filter_by_lifetime.con_use_max.use_max",
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198 )
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199 if use_max == "Yes":
6
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200 max_life = get_json_value(
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201 input_params,
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202 "con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime",
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203 )
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204
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205 result += INDENTATION.join(
6
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206 [
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207 f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n",
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208 f"Filter using a minimum lifetime?:{use_min}\n",
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209 f"Minimum lifetime:{min_life}\n",
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210 f"Filter using a maximum lifetime?:{use_max}\n",
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211 f"Maximum lifetime:{max_life}\n",
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212 ]
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213 )
4
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214
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215 # print 2 leftover from LAP
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216 result += INDENTATION.join(
6
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217 [
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218 f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n",
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219 f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n",
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220 ]
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221 )
4
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222
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223 # Follow Neighbors
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224 # defaults
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225 avg_cell_diameter = 35.0
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226 use_adv = "No"
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227 cost_of_cell = 15.0
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228 weight_of_area_diff = 25.0
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229
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230 if tracking_method == "Follow Neighbors":
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231 avg_cell_diameter = get_json_value(
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232 input_params, "con_tracking_method.avg_diameter"
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233 )
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234 use_adv = get_json_value(
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235 input_params, "con_tracking_method.con_adv_parameter.adv_parameter"
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236 )
4
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237 if use_adv == "Yes":
6
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238 cost_of_cell = get_json_value(
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239 input_params, "con_tracking_method.con_adv_parameter.cost"
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240 )
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241 weight_of_area_diff = get_json_value(
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242 input_params, "con_tracking_method.con_adv_parameter.weight"
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243 )
4
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244
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245 result += INDENTATION.join(
6
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246 [
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247 f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n",
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248 f"Use advanced configuration parameters:{use_adv}\n",
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249 f"Cost of cell to empty matching:{cost_of_cell}\n",
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250 f"Weight of area difference in function matching cost:{weight_of_area_diff}\n",
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251 ]
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252 )
5
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253 result = result.rstrip("\n")
4
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254 return result
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255
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256
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257 if __name__ == "__main__":
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258 parser = argparse.ArgumentParser()
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259 parser.add_argument("-p", "--pipeline", help="CellProfiler pipeline")
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260 parser.add_argument("-i", "--inputs", help="JSON inputs from Galaxy")
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261 args = parser.parse_args()
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262
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263 pipeline_lines = get_pipeline_lines(args.pipeline)
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264 inputs_galaxy = json.load(open(args.inputs, "r"))
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265
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266 current_module_num = get_total_number_of_modules(pipeline_lines)
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267 current_module_num += 1
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268 pipeline_lines = update_module_count(pipeline_lines, current_module_num)
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269
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270 header_block = build_header(MODULE_NAME, current_module_num)
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271 main_block = build_main_block(inputs_galaxy)
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272
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273 module_pipeline = f"\n{header_block}{main_block}\n"
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274 pipeline_lines.append(module_pipeline)
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275
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276 write_pipeline(OUTPUT_FILENAME, pipeline_lines)