"""Simple use of lmfit to fit data."""
from Stoner import Data
from numpy import linspace,exp,random
#Make some data
x=linspace(0,10.0,101)
y=2+4*exp(-x/1.7)+random.normal(scale=0.2,size=101)
d=Data(x,y,column_headers=["Time","Signal"],setas="xy")
d.plot(fmt="ro") # plot our data
#Do the fitting and plot the result
fit = d.lmfit(lambda x,A,B,C:A+B*exp(-x/C),result=True,header="Fit",A=1,B=1,C=1)
d.setas="x.y"
d.labels=[]
d.plot(fmt="b-")
# Make nice label of the parameters
text=r"$y=A+Be^{-x/C}$"+"\n\n"
text+="\n".join([d.format(k,latex=True) for k in ["Model:A","Model:B","Model:C"]])
d.text(5,4,text,fontdict={"size":"x-small"})
return res, res.column_headers
###############################################################################################
#################### Important - if using multiprocessing on Windows, this block must be ######
#################### Inside a if __name__=="__main__": test. ###################################
###############################################################################################
if __name__ == "__main__":
# Load data
d = Data(join(__home__, "..", "sample-data", "FMR-data.txt"))
# Rename columns and reset plot labels
d.rename("multi[1]:y",
"Field").rename("multi[0]:y",
"Frequency").rename("Absorption::X", "FMR")
d.labels = None
# Deine x and y columns and normalise to a big number
d.setas(x="Field", y="FMR") # pylint: disable=not-callable
d.normalise(base=(-1e6, 1e6))
fldr = d.split(field_sign, "Frequency")
# Split the data file into separate files by frequencies and sign of field
fldr = PlotFolder(fldr) # Convert to a PlotFolder
fldr.template = template # Set my custom plot template
for f in fldr[-1]: # Invert the negative field side
f.x = -f.x[::-1]
f.y = -f.y[::-1]
resfldr = PlotFolder(
) # Somewhere to keep the results from +ve and -ve fields
y = 2 + 4 * exp(-x / 1.7) + random.normal(scale=0.2, size=101)
d = Data(x, y, column_headers=["Time", "Signal"], setas="xy")
d.plot(fmt="ro") # plot our data
func = lambda x, A, B, C: A + B * exp(-x / C)
# Do the fitting and plot the result
fit = d.differential_evolution(
func, result=True, header="Fit", A=1, B=1, C=1, prefix="Model", residuals=True
)
# Reset labels
d.labels = []
# Make nice two panel plot layout
ax = d.subplot2grid((3, 1), (2, 0))
d.setas = "x..y"
d.plot(fmt="g+")
d.title = ""
ax = d.subplot2grid((3, 1), (0, 0), rowspan=2)
d.setas = "xyy"
d.plot(fmt=["r.", "b-"])
d.xticklabels = [[]]
d.xlabel = ""
# Annotate plot with fitting parameters
d.annotate_fit(func, prefix="Model", x=0.7, y=0.3, fontdict={"size": "x-small"})
return res, res.column_headers
###############################################################################################
#################### Important - if using multiprocessing on Windows, this block must be ######
#################### Inside a if __name__=="__main__": test. ###################################
###############################################################################################
if __name__ == "__main__":
# Load data
d = Data(join(__home__, "..", "sample-data", "FMR-data.txt"))
# Rename columns and reset plot labels
d.rename("multi[1]:y", "Field").rename("multi[0]:y", "Frequency").rename(
"Absorption::X", "FMR"
)
d.labels = None
# Deine x and y columns and normalise to a big number
d.setas(x="Field", y="FMR")
d.normalise(base=(-1e6, 1e6))
fldr = d.split(field_sign, "Frequency")
# Split the data file into separate files by frequencies and sign of field
fldr = PlotFolder(fldr) # Convert to a PlotFolder
fldr.template = template # Set my custom plot template
for f in fldr[-1]: # Invert the negative field side
f.x = -f.x[::-1]
f.y = -f.y[::-1]
resfldr = PlotFolder() # Somewhere to keep the results from +ve and -ve fields
