def get_file_names(extension, sample='All'):
directory = get_xlsx(extension)
results = pandas.read_excel(directory[0], 0, index_col='Date')
results = results.dropna(subset=['Time'])
# generate list with file names of available results
files = []
results['Sample'] = results['Sample'] + '.csv'
if sample.upper() == 'ALL':
for file_name in results['Sample']:
files.append(
set_filename('Wimpie Data/Metrastat Results/Unfiltered/' +
file_name))
elif sample.upper() == 'CA':
for file_name in results['Sample']:
if (file_name[0:2] == 'Ca') or (file_name[0:1] == 'N'):
files.append(
set_filename('Wimpie Data/Metrastat Results/Unfiltered/' +
file_name))
else:
for file_name in results['Sample']:
if file_name[0:2] == 'Mg' or (file_name[0:1] == 'N'):
files.append(
set_filename('Wimpie Data/Metrastat Results/Unfiltered/' +
file_name))
return files
def get_csv(extension):
"file type csv"
# access database with names of available results
fname = set_filename(extension)
directory = glob.glob(os.path.join(fname, '*.csv'))
return directory
def get_csv(extension):
"file type csv"
# access database with names of available results
fname = set_filename(extension)
directory = glob.glob(os.path.join(fname, '*.csv'))
return directory
def get_file_names(extension, sample='All'):
directory = get_xlsx(extension)
results = pandas.read_excel(directory[0], 0, index_col='Date')
results = results.dropna(subset=['Time'])
# generate list with file names of available results
files = []
results['Sample'] = results['Sample'] + '.csv'
if sample.upper() == 'ALL':
for file_name in results['Sample']:
files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name))
elif sample.upper() == 'CA':
for file_name in results['Sample']:
if (file_name[0:2] == 'Ca') or (file_name[0:1] == 'N'):
files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name))
else:
for file_name in results['Sample']:
if file_name[0:2] == 'Mg' or (file_name[0:1] == 'N'):
files.append(set_filename('Wimpie Data/Metrastat Results/Unfiltered/' + file_name))
return files
YI, time = refine_YI(sample)
constants = all_constants[i]
initial = all_initial[i]
conc = odeint(metrastat, initial, time, args=(k12, constants))
conc_mat = array(conc, dtype=float)
fit = Mg[0]*conc_mat[:,1] + \
Mg[1]*conc_mat[:,5] + \
Mg[2]*conc_mat[:,6] + \
Mg[3]*conc_mat[:,5]**2 + \
Mg[4]
# plot concentration profile
_, fname = data.rsplit('/', 1)
sample_name, _ = fname.split('.')
plt.figure(i)
plt.title('Fit for sample ' + sample_name)
plt.xlabel('Time /min')
plt.ylabel('Concentration')
plt.plot(time, YI)
plt.plot(time, fit)
plt.legend(['Experimental', 'Fit'], loc=0)
# save plot
fname = set_filename('YI Profiles/' + sample_name + '.svg')
plt.savefig(fname)
plt.show()
k12 = 0
for i, data in enumerate(exp_data):
# simulate the metrastat
sample = pd.read_csv(data, index_col='Time')
sample.dropna()
YI, time = refine_YI(sample)
constants = all_constants[i]
initial = all_initial[i]
conc = odeint(metrastat, initial, time, args=(k12, constants))
conc_mat = array(conc, dtype=float)
# plot concentration profile
_, fname = data.rsplit('/', 1)
sample_name, _ = fname.split('.')
plt.figure(i)
plt.title('Fit for sample ' + sample_name)
plt.xlabel('Time /min')
plt.ylabel('Concentration')
plt.plot(time, conc_mat)
plt.legend(['HCl', 'LDH', 'Active sites', 'Radical', 'Primary Stabiliser',
'Double bonds', 'Cross-link'], loc=0)
# save plot
fname = set_filename('Concentration Profiles/' + sample_name + '.svg')
plt.savefig(fname)
plt.show()
def get_xlsx(extension):
"file type xlsl"
fname = set_filename(extension)
directory = glob.glob(os.path.join(fname, '*.xlsx'))
return directory
def get_xlsx(extension):
"file type xlsl"
fname = set_filename(extension)
directory = glob.glob(os.path.join(fname, '*.xlsx'))
return directory
