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Update riptide.py #20

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Update riptide.py #20

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37 changes: 20 additions & 17 deletions src/riptide.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import pandas
import warnings
from random import seed
from copy import deepcopy
from datetime import datetime

import cobra
Expand Down Expand Up @@ -64,15 +63,16 @@ def save_output(riptide_obj='NULL', path='NULL', file_type='JSON', silent=False)
if riptide_obj == 'NULL':
raise ValueError('ERROR: Did not provide a RIPTiDe object')

curr_wd = str(os.getcwd())
if path == 'NULL':
if silent == False:
print('WARNING: Did not provide an output directory. Using default riptide_files in working directory')
path = riptide_obj.model.id + '_' + riptide_obj.run_start
path = curr_wd + '/' + riptide_obj.model.id + '_' + riptide_obj.run_start
else:
path = path + '_' + riptide_obj.run_start
try:
# Recursively build dirs if possible
os.mkdirs(path)
os.mkdir(path)
if silent == False: print('Saving results to', path)
except:
if silent == False:
Expand Down Expand Up @@ -636,7 +636,6 @@ def _screen_tasks(model, tasks, silent):

return screened_tasks


# Create context-specific model based on transcript distribution
def contextualize(model, transcriptome = 'none', samples = 1000, silent = False, prune = True,
fraction = 0.8, minimum = False, conservative = False, objective = True, additive = False, direct = False,
Expand Down Expand Up @@ -810,7 +809,7 @@ def contextualize(model, transcriptome = 'none', samples = 1000, silent = False,
riptide_model = _prune_model(model, inactive_rxns, conservative)
riptide_object.pruned = _record_pruned_elements(model, riptide_model)
else:
riptide_model = deepcopy(model)
riptide_model = model.copy()

# Find optimal solution space based on transcription and final constraints
if silent == False:
Expand Down Expand Up @@ -1007,7 +1006,7 @@ def _weighted_expression(model, coefficients={}):
# Determine those reactions that carry flux in a pFBA objective set to a threshold of maximum
def _constrain_for_pruning(model, min_coefficients, objective, obj_fraction, tasks, task_fraction, minimum_flux, silent):

constrained_model = deepcopy(model)
constrained_model = model.copy()

# Add objective/task constraints
min_coefficient = min(list(min_coefficients.values()))
Expand All @@ -1034,7 +1033,7 @@ def _constrain_for_pruning(model, min_coefficients, objective, obj_fraction, tas
# Determine those reactions that carry flux in a pFBA objective set to a threshold of maximum
def _constrain_for_sampling(model, max_coefficients, sampling_depth, objective, obj_frac, tasks, task_frac, min_flux, silent):

constrained_model = deepcopy(model)
constrained_model = model.copy()

# Apply weigths to new expression, allow deviation
if isinstance(objective, str) == True:
Expand All @@ -1048,17 +1047,21 @@ def _constrain_for_sampling(model, max_coefficients, sampling_depth, objective,
constrained_model.solver.update()

flux_sum = constrained_model.slim_optimize(error_value=0.)
flux_sum_constraint = constrained_model.problem.Constraint(sampling_expr, lb=min_flux, ub=flux_sum)
model.add_cons_vars(flux_sum_constraint)
constrained_model.solver.update()

# Analyze flux ranges and calculate concordance
try:
flux_samples = _gapsplit(constrained_model, depth=sampling_depth)
concordance = _calc_concordance(flux_samples, max_coefficients)
except:
if flux_sum <= min_flux:
flux_samples = 'Not performed'
concordance = 'Not performed'
else:
flux_sum_constraint = constrained_model.problem.Constraint(sampling_expr, lb=min_flux, ub=flux_sum)
constrained_model.add_cons_vars(flux_sum_constraint)
constrained_model.solver.update()

# Analyze flux ranges and calculate concordance
try:
flux_samples = _gapsplit(constrained_model, depth=sampling_depth)
concordance = _calc_concordance(flux_samples, max_coefficients)
except:
flux_samples = 'Not performed'
concordance = 'Not performed'

return flux_samples, concordance

Expand Down Expand Up @@ -1087,7 +1090,7 @@ def _calc_concordance(flux_samples, coefficient_dict):
# Prune model based on blocked reactions from minimization as well as user-defined reactions
def _prune_model(model, rm_rxns, conserve):

new_model = deepcopy(model)
new_model = model.copy()

if str(new_model.id).strip() == '':
new_model.id = 'model_riptide'
Expand Down