comparison COBRAxy/ras_to_bounds.py @ 123:258ff4208e52 draft

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author luca_milaz
date Mon, 14 Oct 2024 08:29:08 +0000
parents 7e1ce04cf192
children 67a79e4f037a
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122:7e1ce04cf192 123:258ff4208e52
123 Returns: 123 Returns:
124 None 124 None
125 """ 125 """
126 for reaction in ras_row.index: 126 for reaction in ras_row.index:
127 scaling_factor = ras_row[reaction] 127 scaling_factor = ras_row[reaction]
128 if(scaling_factor not in [np.nan, None]): 128 lower_bound=model.reactions.get_by_id(reaction).lower_bound
129 lower_bound=model.reactions.get_by_id(reaction).lower_bound 129 upper_bound=model.reactions.get_by_id(reaction).upper_bound
130 upper_bound=model.reactions.get_by_id(reaction).upper_bound 130 valMax=float((upper_bound)*scaling_factor)
131 valMax=float((upper_bound)*scaling_factor) 131 valMin=float((lower_bound)*scaling_factor)
132 valMin=float((lower_bound)*scaling_factor) 132 if upper_bound!=0 and lower_bound==0:
133 if upper_bound!=0 and lower_bound==0: 133 model.reactions.get_by_id(reaction).upper_bound=valMax
134 model.reactions.get_by_id(reaction).upper_bound=valMax 134 if upper_bound==0 and lower_bound!=0:
135 if upper_bound==0 and lower_bound!=0: 135 model.reactions.get_by_id(reaction).lower_bound=valMin
136 model.reactions.get_by_id(reaction).lower_bound=valMin 136 if upper_bound!=0 and lower_bound!=0:
137 if upper_bound!=0 and lower_bound!=0: 137 model.reactions.get_by_id(reaction).lower_bound=valMin
138 model.reactions.get_by_id(reaction).lower_bound=valMin 138 model.reactions.get_by_id(reaction).upper_bound=valMax
139 model.reactions.get_by_id(reaction).upper_bound=valMax
140 pass 139 pass
141 140
142 def process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder): 141 def process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder):
143 """ 142 """
144 Process a single RAS cell, apply bounds, and save the bounds to a CSV file. 143 Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
246 245
247 # Concatenate all ras DataFrames into a single DataFrame 246 # Concatenate all ras DataFrames into a single DataFrame
248 ras_combined = pd.concat(ras_list, axis=0) 247 ras_combined = pd.concat(ras_list, axis=0)
249 # Normalize the RAS values by max RAS 248 # Normalize the RAS values by max RAS
250 ras_combined = ras_combined.div(ras_combined.max(axis=0)) 249 ras_combined = ras_combined.div(ras_combined.max(axis=0))
250 ras_combined.dropna(axis=1, how='all', inplace=True)
251 #ras_combined = ras_combined.fillna(0) 251 #ras_combined = ras_combined.fillna(0)
252 #il ras c'è per tutti o non c'è per nessuno 252 #il ras c'è per tutti o non c'è per nessuno
253 253
254 254
255 255