def test_split_data(self):
'''
test if splitting on temperature was successful
'''
data = process.split_data(self.data)
assert (len([i for i in data]) == 11)
assert (all(len(item) == 26 for key, item in data.items()))
def setUp(self):
self.test_input = 'testdata/d1.dat'
self.parsed = parse.parse_file(self.test_input)
self.data = process.subset(parse.parse_data(self.parsed['data']))
self.split_data = process.split_data(self.data)
# constants
self.e_mass = 51.2
self.moles = 1.80563E-05
self.dmc = -0.00049552
self.y_mass = 0
def execute(target_path, out_path):
parsed = parse.parse_file(target_path)
header = parse.parse_header(parsed['header'])
data = parse.parse_data(parsed['data'])
split_data = process.split_data(data)
# write the output
if not os.path.exists(out_path):
os.makedirs(out_path)
if not os.path.exists(os.path.join(out_path, 'tsvs')):
os.makedirs(os.path.join(out_path, 'tsvs'))
for temp, df in split_data.items():
df = df[['Field (Oe)', 'Temperature (K)', "m' (emu)", 'm\" (emu)', 'Wave Frequency (Hz)']]
df = process.calculate_chi(df, 51.2, 0, 1.80573e-05, -0.00049552)
print(df.columns)
df.to_csv(os.path.join(out_path, 'tsvs', "T{}.tsv".format(temp)),
sep="\t",
index=None,
quoting=3)
def tempsplit(target_path, outdir):
outdir, target_path = os.path.normpath(outdir), os.path.normpath(target_path)
parsed = parse.parse_file(target_path)
header = parse.parse_header(parsed['header'])
data = parse.parse_data(parsed['data'])
split_data = process.split_data(data)
# write the output
if not os.path.exists(outdir):
os.makedirs(outdir)
if not os.path.exists(os.path.join(outdir, 'tsvs')):
os.makedirs(os.path.join(outdir, 'tsvs'))
for temp, df in split_data.items():
print('Processing temperature: {}'.format(temp))
df = process.subset(df)
df.to_csv(os.path.join(outdir, 'tsvs', "T{}.tsv".format(temp)),
sep="\t",
index=None,
quoting=3)
print("Completed")
return 0
def ika():
'''
Entry point
'''
parser = argparse.ArgumentParser()
parser.add_argument("datafile", type=str, help="MS data file to split by temperature")
parser.add_argument("out_path", type=str, help="Output path")
parser.add_argument("-em", "--e_mass", type=str, help="Mass of E", default='')
parser.add_argument("-ym", "--y_mass", type=str, help="Mass of Y complex", default='')
parser.add_argument("-mo", "--moles", type=str, help="Moles of Sample", default='')
parser.add_argument("-ma", "--mass", type=str, help="Mass of Sample", default='')
parser.add_argument("-mm", "--mol_mass", type=str, help="Molecular mass of Sample", default='')
parser.add_argument("-dmc", "--diamagnetic_correction", type=str, help="Diamagnetic Correction", default='')
args = parser.parse_args()
# Check inputs and generate/calculate as needed
params = hi.ika_in(args)
print('ika_in: ', params)
print([params[i] for i in ["e_mass", "y_mass", "diamagnetic_correction", "moles"]])
# pop a GUI if all input not provided
if not all(params[i] != '' for i in ["e_mass", "y_mass", "diamagnetic_correction", "moles"]):
fields = ["mass", "mol_mass", "e_mass", "moles", "diamagnetic_correction", "y_mass"]
print('PARAMS: ', params)
params = gui.param_input_loop(prefill=[params[i] for i in fields])
# Handle that horrible keying of the GUI input field. REFACTOR THIS
params = [params[i] for i in ['Mass_E', 'Mass_y', 'Diamagnetic_corr', 'moles']]
# make all params floats
for key, item in params.items():
params[key] = float(item)
e_mass, y_mass = params['e_mass'], params['y_mass']
moles, dmc = params['moles'], params['diamagnetic_correction']
# Parse the datafile and extract data
parsed = parse.parse_file(args.datafile)
data = parse.parse_data(parsed['data'])
data = process.subset(data)
data = process.split_data(data)
# Calculate secondary values, optimize and get the total error squared and the x value
processed = process.process_datafile(args.datafile,
e_mass, y_mass, moles, dmc)
print(processed)
# Prepare output file
shutil.copy('data/template.xlsx', args.out_path)
wb = openpyxl.load_workbook(args.out_path)
taus = []
errors = []
for temp in arange(5, 10.5, 0.5):
error, (chi_t, chi_s, alpha, tau) = processed[temp]
# Fill in raw data
df = data[temp]
sheet = wb.copy_worksheet(wb['Sheet1'])
sheet.title = "ac_varT-{}K".format(temp)
process.insert_df_excel(df, sheet, 5, 1, skip_n=1)
# Insert fit params
df = pd.DataFrame({"fit_params": [chi_t, chi_s, alpha, tau]})
process.insert_df_excel(df, sheet, 5, 16, skip_n=1)
# Fill in the baseline values (e_mass et al.)
sheet.cell(row=3, column=2, value=moles)
sheet.cell(row=3, column=4, value=dmc)
sheet.cell(row=2, column=6, value=e_mass)
sheet.cell(row=3, column=6, value=y_mass)
# Record the tau, error value for next plot
taus.append(tau)
errors.append(error)
# Create a new sheet for the tau values
sheet = wb.create_sheet('Arhenius_plot')
df = pd.DataFrame({'err^2': errors,
'Tau': taus,
'T': [str(temp) for temp in arange(5, 10.5, 0.5)],
'Ln(tau)': [log(tau) for tau in taus],
'1/T': [1 / temp for temp in arange(5, 10.5, 0.5)]})
df = df[['err^2', 'Tau', 'T', 'Ln(tau)', '1/T']]
process.insert_df_excel(df, sheet, 2, 1)
wb.save(args.out_path)
def test_process_data(self):
parsed = parse.parse_file(self.test_input)
data = parse.parse_data(parsed['data'])
splat = process.split_data(data)
assert (sum(len(item) for key, item in splat.items()) == 286)