For example I have data:
x y1 y2 y1
---------------------
1 5 8 -
2 4 - 4
3 7 7 10
4 9 4 12
5 10 - 20
6 15 - 21
Where x is x axis and y1, y2, y3 are three different data sets, which are fitted together.
For the sake of simplicity, heres reduced version of fitting code:
def gauss_dataset(params, i, x):
"""calc gaussian from params for data set i
using simple, hardwired naming convention"""
x = params['x_%i' % (i+1)].value
y = params['x_%i' % (i+1)].value
return x + y
def objective(params, x, data):
""" calculate total residual for fits to several data sets held
in a 2-D array, and modeled by Gaussian functions"""
ndata, nx = data.shape
resid = 0.0*data[:]
# make residual per data set
for i in range(ndata):
resid[i, :] = data[i, :] - gauss_dataset(params, i, x)
# now flatten this to a 1D array, as minimize() needs
return resid.flatten()
x = np.linspace(0, 50, 50)
data = []
...
# Rearange data
for col in range(0, data_sets):
for row in range (0, size_rows):
data[col][row] = intens[row][col+1]
# create 5 sets of parameters, one per data set
fit_params = Parameters()
for iy, y in enumerate(data):
fit_params.add( 'x_%i' % (iy+1))
fit_params.add( 'y_%i' % (iy+1))
# run the global fit to all the data sets
minimize(objective, fit_params, args=(x, data))
in minimize(objective, fit_params, args=(x, data)):
data is data[y][z]: y - data set, z - data in that data set.
and x is x axis.
How do I modify my python script lmfit minimize to ignore missing data points or rewrite my script, so each data has its own x axis?:
x1 y1 x2 y2 x3 y3
-----------------------------
1 5 1 8 2 4-
2 4 3 7 3 10
3 7 4 4 4 12
4 9 5 20
5 10 6 21
6 15
also I cannot use multiple minimize fits (script is actually more complicated than shown above), so first = minimize(objective, fit_params, args=(x1, y1)), second = minimize(objective, fit_params, args=(x2, y2)) is not valid answer.
