def metrastat_model(exp_data,
initial_conditions,
rate_constants,
refine_extended=False,
one_set_params=False):
start = time()
# refine data to remove faulty data points, and store in a single list
all_times = []
all_YI = []
for data in exp_data:
sample = pandas.read_csv(data, index_col='Time')
sample = sample.dropna()
if refine_extended:
new_YI, new_time = refine_YI(sample, extended=True)
else:
new_YI, new_time = refine_YI(sample)
# update the single list that will be used in the fitting
all_times.append(new_time)
all_YI.append(new_YI)
# fit using only one set of parameters
if one_set_params:
quadratic, params, error = fit_experimental(all_times,
all_YI,
initial_conditions,
rate_constants,
one_set_params=True)
# fit each curve individually
else:
params = []
error = []
quadratic = []
for i in range(len(exp_data)):
quadratic_temp, params_temp, error_temp = \
fit_experimental(all_times[i], all_YI[i],
initial_conditions[i], rate_constants[i])
# update results
params.append(params_temp)
error.append(error_temp)
quadratic.append(quadratic_temp)
stop = time()
print "elapsed time = %f min" % ((stop - start) / 60)
return [params, error, quadratic, all_times, all_YI]
def metrastat_model(exp_data, initial_conditions, rate_constants,
refine_extended=False, one_set_params=False):
start = time()
# refine data to remove faulty data points, and store in a single list
all_times = []
all_YI = []
for data in exp_data:
sample = pandas.read_csv(data, index_col='Time')
sample = sample.dropna()
if refine_extended:
new_YI, new_time = refine_YI(sample, extended=True)
else:
new_YI, new_time = refine_YI(sample)
# update the single list that will be used in the fitting
all_times.append(new_time)
all_YI.append(new_YI)
# fit using only one set of parameters
if one_set_params:
quadratic, params, error = fit_experimental(all_times, all_YI,
initial_conditions,
rate_constants,
one_set_params=True)
# fit each curve individually
else:
params = []
error = []
quadratic = []
for i in range(len(exp_data)):
quadratic_temp, params_temp, error_temp = \
fit_experimental(all_times[i], all_YI[i],
initial_conditions[i], rate_constants[i])
# update results
params.append(params_temp)
error.append(error_temp)
quadratic.append(quadratic_temp)
stop = time()
print "elapsed time = %f min" % ((stop - start)/60)
return [params, error, quadratic, all_times, all_YI]
# load initial conditions
all_initial = get_initial('Reinhard Fit/')
# load rate constants
all_constants = get_constants('Reinhard Fit/')
k12 = 0
Mg = [20.1, 110.4, 90.7, -39, -3.2]
Ca = [27.6, 101.6, 78.4, -34.1, 18.3]
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)
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)
from data_refine import refine_YI, get_file_names
from matplotlib import pyplot as plt
import pandas as pd
exp_data = get_file_names('Wimpie Data/Rheomix Results/', sample='Mg')
legend = []
evaluate = list(exp_data[i] for i in (0, 2, 6, 10))
for data in evaluate:
sample = pd.read_csv(data, index_col='Time')
sample = sample.dropna()
YI, Time = refine_YI(sample)
_, fname = data.rsplit('/', 1)
name, _ = fname.split('.')
legend.append(name)
plt.plot(Time, YI, label=name)
plt.legend(legend, loc=0)