df = pd.DataFrame()
# read the files and compute the mean YFP value
for i, op in enumerate(operator):
for j, strain in enumerate(rbs):
# find the file
try:
#r_file = glob.glob(datadir + str(date) + '_' + run + '*' + \
# operator + '_' + strain + '_' + str(c) + 'uM' + '*csv')
r_file = glob.glob(datadir + str(date) + '*'+ op + '_' + strain + '.csv')
print(r_file)
# read the csv file
dataframe = pd.read_csv(r_file[0])
# apply an automatic bivariate gaussian gate to the log front
# and side scatterng
data = mwc.auto_gauss_gate(dataframe, alpha,
x_val='FSC-A', y_val='SSC-A',
log=True)
# compute the mean and append it to the data frame along the
# operator and strain
df = df.append([[date, username, op, energy[i],
strain, repressors[j],
data['FITC-A'].mean()]],
ignore_index=True)
except:
pass
# rename the columns of the data_frame
df.columns = ['date', 'username', 'operator', 'binding_energy', \
'rbs', 'repressors', 'mean_YFP_A']
# initialize pandas series to save the corrected YFP value
for r in rbs:
if op in ['007', '012', '009', '013']:
conc_list = concentrations_std
elif op in ['010', '014']:
conc_list = concentrations_alt
for c in conc_list:
try:
r_file = glob.glob(datadir + str(date) + '_' + run + '*' + \
op + '_' + r + '_' + str(c) + 'uMIPTG' + '*csv')
print(r_file)
# convert to dataframe
temp_df = pd.read_csv(r_file[0])
# apply an automatic bivariate gaussian gate to the log front
# and side scattering
data = mwc.auto_gauss_gate(temp_df, alpha,
x_val='FSC-A', y_val='SSC-A',
log=True)
# compute the mean and append it to the data frame along the
# operator and strain
df = df.append([[date, username, str(op), op_dict[op],
r, rbs_dict[r], c,
data['FITC-A'].mean()]],
ignore_index=True)
except:
pass
# rename the columns of the data_frame
df.columns = ['date', 'username', 'operator', 'binding_energy', \
'rbs', 'repressors', 'IPTG_uM', 'mean_YFP_A']
mean_bgcorr_A = np.array([])
