elif model == 2:
pic50, hill = params
if hill <= hill_lower or hill > hill_upper or pic50 <= pic50_lower:
return 1e9
else:
predicted = dr.dose_response_model(concs, hill, dr.pic50_to_ic50(pic50))
return np.sum((responses-predicted)**2)
data_file = "../input/crumb_data.csv"
run_all = True
dr.setup(data_file)
drugs_to_run, channels_to_run = dr.list_drug_channel_options(run_all)
all_figs_dir = "../output/all_best_fits/"
if not os.path.exists(all_figs_dir):
os.makedirs(all_figs_dir)
#drug = "Amitriptyline"
#channel = "Cav1.2"
#drug = "Amiodarone"
#channel = "hERG"
num_models = 2
def do_plot(drug_channel):
global concs, responses
help='plot data points on top of predicted curves',
default=False)
parser.add_argument(
"--save-pdf",
action='store_true',
help=
'save dose-response curves figure as pdf (in addition to png), but the file will probably be massive',
default=False)
args = parser.parse_args()
dr.define_model(args.model)
temperature = 1
num_params = dr.num_params
dr.setup(args.data_file)
drugs_to_run, channels_to_run = dr.list_drug_channel_options(args.all)
num_x_pts = 50
alpha = 0.002 # this is the lowest value I've found that actually shows anything
for drug, channel in it.product(drugs_to_run, channels_to_run):
try:
num_expts, experiment_numbers, experiments = dr.load_crumb_data(
drug, channel)
except:
print "\nCan't load experimental data for {} + {} --- skipping\n".format(
drug, channel)
continue
drug, channel, chain_file, images_dir = dr.nonhierarchical_chain_file_and_figs_dir(
args.model, drug, channel, temperature)
hill = 1
elif model == 2:
pic50, hill = params
if hill <= hill_lower or hill > hill_upper or pic50 <= pic50_lower:
return 1e9
else:
predicted = dr.dose_response_model(concs, hill,
dr.pic50_to_ic50(pic50))
return np.sum((responses - predicted)**2)
data_file = "../input/crumb_data.csv"
run_all = True
dr.setup(data_file)
drugs_to_run, channels_to_run = dr.list_drug_channel_options(run_all)
all_figs_dir = "../output/all_best_fits/"
if not os.path.exists(all_figs_dir):
os.makedirs(all_figs_dir)
#drug = "Amitriptyline"
#channel = "Cav1.2"
#drug = "Amiodarone"
#channel = "hERG"
num_models = 2
def do_plot(drug_channel):
global concs, responses