Mercurial > repos > bgruening > cp_measure_image_intensity

annotate track_objects.py @ 5:bd0a391f8545 draft default tip

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 7d7a519c3a2cc612d38695b335d0f6c75a099de3"
author bgruening
date Fri, 26 Feb 2021 13:48:59 +0000
parents 5832468d6e1d
children
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1 #!/usr/bin/env python
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2
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3 import argparse
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4 import json
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5
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6 from cp_common_functions import get_json_value
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7 from cp_common_functions import get_pipeline_lines
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8 from cp_common_functions import get_total_number_of_modules
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9 from cp_common_functions import INDENTATION
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10 from cp_common_functions import update_module_count
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11 from cp_common_functions import write_pipeline
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13 MODULE_NAME = "TrackObjects"
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14 OUTPUT_FILENAME = "output.cppipe"
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17 def build_header(module_name, module_number):
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18 result = "|".join([f"{module_name}:[module_num:{module_number}",
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19 "svn_version:\\'Unknown\\'",
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20 "variable_revision_number:7",
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21 "show_window:True",
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22 "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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23 "batch_state:array(\\x5B\\x5D, dtype=uint8)",
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24 "enabled:True",
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25 "wants_pause:False]\n"])
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26 return result
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29 def build_main_block(input_params):
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30 result = INDENTATION.join([f"{INDENTATION}Choose a tracking method:{get_json_value(input_params,'con_tracking_method.tracking_method')}\n",
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31 f"Select the objects to track:{get_json_value(input_params,'object_to_track')}\n"
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32 ])
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34 tracking_method = get_json_value(input_params, 'con_tracking_method.tracking_method')
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36 obj_measurement = "None" # default value
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37 if tracking_method == "Measurements":
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38 measurement_category = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement_category')
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39 measurement = get_json_value(input_params, 'con_tracking_method.con_measurement_category.measurement')
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40
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41 if measurement_category == "Intensity" or measurement_category == "Location":
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42 img_measure = get_json_value(input_params, 'con_tracking_method.con_measurement_category.img_measure')
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43 obj_measurement = f"{measurement_category}_{measurement}_{img_measure}"
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44 else:
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45 obj_measurement = f"{measurement_category}_{measurement}"
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47 result += INDENTATION.join([f"{INDENTATION}Select object measurement to use for tracking:{obj_measurement}\n"])
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49 if tracking_method == "LAP": # no max distance required, set default for pipeline
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50 max_distance = 50
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51 else:
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52 max_distance = get_json_value(input_params, 'con_tracking_method.max_distance')
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53
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54 result += INDENTATION.join([f"{INDENTATION}Maximum pixel distance to consider matches:{max_distance}\n"])
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56 display_option = get_json_value(input_params, 'con_tracking_method.display_option')
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57
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58 output_img_name = "TrackedCells" # default value, required by cppipe regardless of its presence in UI
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59 save = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.save_coded_img')
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60 if save == "Yes":
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61 output_img_name = get_json_value(input_params, 'con_tracking_method.con_save_coded_img.name_output_img')
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62
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63 result += INDENTATION.join(
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64 [f"{INDENTATION}Select display option:{display_option}\n",
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65 f"Save color-coded image?:{save}\n",
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66 f"Name the output image:{output_img_name}\n"
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67 ])
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68
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69 # LAP method default values
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70 movement_model = "Both"
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71 no_std = 3.0
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72 radius_limit_max = 10.0
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73 radius_limit_min = 2.0
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74 radius = "2.0,10.0"
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75 run_second = "Yes"
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76 gap_closing = 40
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77 split_alt = 40
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78 merge_alt = 40
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79 max_gap_displacement = 5
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80 max_split = 50
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81 max_merge = 50
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82 max_temporal = 5
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83 max_mitosis_dist = 40
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84 mitosis_alt = 80
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85
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86 # LAP method
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87 if tracking_method == "LAP":
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88 movement_model = get_json_value(input_params, 'con_tracking_method.movement_method')
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89 no_std = get_json_value(input_params, 'con_tracking_method.no_std_radius')
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90 radius_limit_max = get_json_value(input_params, 'con_tracking_method.max_radius')
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91 radius_limit_min = get_json_value(input_params, 'con_tracking_method.min_radius')
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92 radius = f"{radius_limit_min},{radius_limit_max}"
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93
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94 run_second = get_json_value(input_params, 'con_tracking_method.con_second_lap.second_lap')
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95 if run_second == "Yes":
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96 gap_closing = get_json_value(input_params, 'con_tracking_method.con_second_lap.gap_closing')
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97 split_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.split_alt')
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98 merge_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.merge_alt')
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99 max_gap_displacement = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_gap_displacement')
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100 max_split = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_split')
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101 max_merge = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_merge')
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102 max_temporal = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_temporal')
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103 max_mitosis_dist = get_json_value(input_params, 'con_tracking_method.con_second_lap.max_mitosis_distance')
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104 mitosis_alt = get_json_value(input_params, 'con_tracking_method.con_second_lap.mitosis_alt')
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105
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106 result += INDENTATION.join(
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107 [f"{INDENTATION}Select the movement model:{movement_model}\n",
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108 f"Number of standard deviations for search radius:{no_std}\n",
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109 f"Search radius limit, in pixel units (Min,Max):{radius}\n",
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110 f"Run the second phase of the LAP algorithm?:{run_second}\n",
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111 f"Gap closing cost:{gap_closing}\n",
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112 f"Split alternative cost:{split_alt}\n",
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113 f"Merge alternative cost:{merge_alt}\n",
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114 f"Maximum gap displacement, in pixel units:{max_gap_displacement}\n",
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115 f"Maximum split score:{max_split}\n",
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116 f"Maximum merge score:{max_merge}\n",
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117 f"Maximum temporal gap, in frames:{max_temporal}\n"
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118 ])
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119
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120 # common section
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121 filter_by_lifetime = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.filter_by_lifetime')
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122 use_min = "Yes" # default
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123 min_life = 1 # default
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124 use_max = "No" # default
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125 max_life = 100 # default
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126
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127 if filter_by_lifetime == "Yes":
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128 use_min = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.use_min')
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129 if use_min == "Yes":
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130 min_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_min.min_lifetime')
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131
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132 use_max = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.use_max')
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133 if use_max == "Yes":
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134 max_life = get_json_value(input_params, 'con_tracking_method.con_filter_by_lifetime.con_use_max.max_lifetime')
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135
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136 result += INDENTATION.join(
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137 [f"{INDENTATION}Filter objects by lifetime?:{filter_by_lifetime}\n",
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138 f"Filter using a minimum lifetime?:{use_min}\n",
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139 f"Minimum lifetime:{min_life}\n",
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140 f"Filter using a maximum lifetime?:{use_max}\n",
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141 f"Maximum lifetime:{max_life}\n"
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142 ])
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143
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144 # print 2 leftover from LAP
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145 result += INDENTATION.join(
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146 [f"{INDENTATION}Mitosis alternative cost:{mitosis_alt}\n",
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147 f"Maximum mitosis distance, in pixel units:{max_mitosis_dist}\n"
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148 ])
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149
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150 # Follow Neighbors
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151 # defaults
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152 avg_cell_diameter = 35.0
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153 use_adv = "No"
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154 cost_of_cell = 15.0
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155 weight_of_area_diff = 25.0
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156
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157 if tracking_method == "Follow Neighbors":
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158 avg_cell_diameter = get_json_value(input_params, 'con_tracking_method.avg_diameter')
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159 use_adv = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.adv_parameter')
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160 if use_adv == "Yes":
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161 cost_of_cell = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.cost')
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162 weight_of_area_diff = get_json_value(input_params, 'con_tracking_method.con_adv_parameter.weight')
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163
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164 result += INDENTATION.join(
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165 [f"{INDENTATION}Average cell diameter in pixels:{avg_cell_diameter}\n",
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166 f"Use advanced configuration parameters:{use_adv}\n",
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167 f"Cost of cell to empty matching:{cost_of_cell}\n",
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168 f"Weight of area difference in function matching cost:{weight_of_area_diff}\n"
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169 ])
5
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170 result = result.rstrip("\n")
4
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171 return result
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172
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173
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174 if __name__ == "__main__":
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175 parser = argparse.ArgumentParser()
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176 parser.add_argument(
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177 '-p', '--pipeline',
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178 help='CellProfiler pipeline'
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179 )
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180 parser.add_argument(
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181 '-i', '--inputs',
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182 help='JSON inputs from Galaxy'
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183 )
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184 args = parser.parse_args()
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185
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186 pipeline_lines = get_pipeline_lines(args.pipeline)
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187 inputs_galaxy = json.load(open(args.inputs, "r"))
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188
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189 current_module_num = get_total_number_of_modules(pipeline_lines)
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190 current_module_num += 1
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191 pipeline_lines = update_module_count(pipeline_lines, current_module_num)
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192
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193 header_block = build_header(MODULE_NAME, current_module_num)
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194 main_block = build_main_block(inputs_galaxy)
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195
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196 module_pipeline = f"\n{header_block}{main_block}\n"
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197 pipeline_lines.append(module_pipeline)
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198
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199 write_pipeline(OUTPUT_FILENAME, pipeline_lines)