def fromDataFolder(folder, wantedSBs, keepErrors=False, negateNIR=True):
"""
Create a fan compiler by passing the data path. Handles looping through the
folder's sub-folders to find
:param folder: The folder to search through. Alternatively, if it's a
list/iterable, iterate through that instead. Useful if external code is
directly removing sets of data.
:return:
"""
comp = FanCompiler(wantedSBs, keepErrors, negateNIR)
# If it's a string, assume it's a single path that wants to be searached
if isinstance(folder, str):
wantFolders = hsg.natural_glob(folder, "*")
else:
# Otherwise, assume they've passed an iterable to search through
wantFolders = folder
for nirFolder in wantFolders:
if "skip" in nirFolder.lower(): continue
laserParams, rawData = hsg.hsg_combine_qwp_sweep(nirFolder,
save=False,
verbose=False,
loadNorm=False)
_, fitDict = hsg.proc_n_fit_qwp_data(rawData,
laserParams,
vertAnaDir="VAna"
in nirFolder,
series=nirFolder)
comp.addSet(fitDict)
return comp
def fan_generator(file,
observedSidebands,
crystalAngle,
saveFileName,
save_results=False,
Plot=True):
'''
This is a split off from fan_n_Tmat that just handles the fan. Apparently
some of the fan creation stops scripts from running so to avoid that we're
seperating the creation of the fan into this.
:param file: String of folder name containing 4 polarimetry scans
:param observedSidebands: np array of observed sidebands. Data will be
cropped such that these sidebands are included in everything.
:param crystalAngle: (Float) Angle of the sample from the 010 crystal face
:saveFileName: Str of what you want to call the text files to be saved
:save_results: Boolean controls if the fan diagram png is saved.
Returns:
:f: This is a fan object. I'm not really sure how to mess with these in
greater depth.
'''
# Make a list of folders for the fan data
datasets = hsg.natural_glob(file, "*")
# FanCompiler class keeps alpha and gamma data together for different pols
fanData = FanCompiler(observedSidebands)
for data in datasets:
laserParams, rawData = hsg.hsg_combine_qwp_sweep(data)
_, fitDict = hsg.proc_n_fit_qwp_data(rawData,
vertAnaDir=False,
laserParams=laserParams,
wantedSbs=observedSidebands)
# Add the new alpha and gamma sets to the fan class
fanData.addSet(fitDict)
alphaData, gammaData, _ = fanData.build()
from PyQt5 import QtWidgets
app = QtWidgets.QApplication([])
# interpolate so we have alpha and gamma for all excitation pols
resampledAlpha, resampledGamma = jonesToFans(observedSidebands, J)
# And create the fan
f = FanDiagram(resampledAlpha, resampledGamma)
# And show the fann
if Plot:
f.show()
app.exec_()
# ToDo: Add code for making the fans look nicer
# You can save the fan with
if save_results:
f.export(saveFileName + "_fanDiagram.png")
return f
def J_T_proc(file,
observedSidebands,
crystalAngle,
saveFileName,
save_results=False,
Plot=False):
"""
This function will take a folder of polarimetry scans and produce
DOP, alpha/gamma, J and T matrices, and Matrix Relations This is the same
as fan_n_Tmat but doesn't create the fan itself. Otherwise creates pretty
much everything one would need.
:param file: String of folder name containing 4 polarimetry scans
:param observedSidebands: np array of observed sidebands. Data will be
cropped such that these sidebands are included in everything.
:param crystalAngle: (Float) Angle of the sample from the 010 crystal face
:saveFileName: Str of what you want to call the text files to be saved
:save_results: Boolean controls if things are saved to txt files.
Currently saves DOP, alpha, gamma, J matrix, T matrix, Fan, and Matrix
Relations
:Plot: Boolean controls if plots of matrix relations are displayed
:return: DOP,alpha,gamma,J matrix, T matrix, Matrix Relations
"""
# Make a list of folders for the fan data
datasets = hsg.natural_glob(file, "*")
# FanCompiler class keeps alpha and gamma data together for different pols
fanData = FanCompiler(observedSidebands)
# Initialize arrays for DOP
dop00 = np.array([])
dop45 = np.array([])
dop90 = np.array([])
dopn45 = np.array([])
for data in datasets:
laserParams, rawData = hsg.hsg_combine_qwp_sweep(data)
try:
_, fitDict = hsg.proc_n_fit_qwp_data(rawData,
vertAnaDir=False,
laserParams=laserParams,
wantedSbs=observedSidebands)
# Add the new alpha and gamma sets to the fan class
except IndexError:
print('incorrect number of files in data folder ', data)
print('proceeding without fourier analysis')
_, fitDict = hsg.proc_n_fit_qwp_data(rawData,
vertAnaDir=False,
laserParams=laserParams,
wantedSbs=observedSidebands,
fourier=False)
# Add the new alpha and gamma sets to the fan class
fanData.addSet(fitDict)
# Get Stoke Parameters
s0 = fitDict['S0']
s1 = fitDict['S1']
s2 = fitDict['S2']
s3 = fitDict['S3']
# Trims the Stoke Parameters down to remove any sidebands beyond observedSidebands
# This does mean that it won't calculate the DOP for any extra sidebands, even if
# the software detected them.
while s0[-1, 0] > observedSidebands[-1]:
s0 = s0[:-1, :]
s1 = s1[:-1, :]
s2 = s2[:-1, :]
s3 = s3[:-1, :]
# This actually calculates the DOP and DOP error
dop = s0
dop[1:, 1] = np.sqrt((s1[1:, 1])**2 + (s2[1:, 1])**2 +
(s3[1:, 1])**2) / s0[1:, 1]
dop[1:,
2] = np.sqrt((s1[1:, 1]**2) * (s1[1:, 2]**2) / (s0[1:, 1]**2) /
((s1[1:, 1])**2 + (s2[1:, 1])**2 + (s3[1:, 1])**2) +
(s2[1:, 1]**2) * (s2[1:, 2]**2) / (s0[1:, 1]**2) /
((s1[1:, 1])**2 + (s2[1:, 1])**2 + (s3[1:, 1])**2) +
(s3[1:, 1]**2) * (s3[1:, 2]**2) / (s0[1:, 1]**2) /
((s1[1:, 1])**2 + (s2[1:, 1])**2 +
(s3[1:, 1])**2)) + (
(s1[1:, 1])**2 + (s2[1:, 1])**2 +
(s3[1:, 1])**2) * s0[1:, 2]**2 / s0[1:, 1]**4
# Save according to alpha. This will break if you use an alpha not in the usual
# 0,45,90,-45 range.
if laserParams['lAlpha'] == 00:
dop00 = dop[1:, :]
if laserParams['lAlpha'] == 45:
dop45 = dop[1:, :]
if laserParams['lAlpha'] == 90:
dop90 = dop[1:, :]
if laserParams['lAlpha'] == -45:
dopn45 = dop[1:, :]
# Put into appropriate form for saving
#totdop = [dop00,dop45[:,1:],dop90[:,1:],dopn45[:,1:]]
#totdop = np.hstack(totdop)
# Saves as txt file with columns, SB Order, 00 DOP and error, 45 DOP and error,
# 90 DOP and error, -45 DOP and error
# if save_results:
# np.savetxt(saveFileName + "_DOP.txt", totdop, delimiter=',', header=
# 'SB Order, 00 DOP, 00 DOP Error, 45 DOP, 45 DOP Error, 90 DOP, 90 DOP Error, -45 DOP, -45 DOP Error')
# Building the fan compiler causes it to make 2D np arrays with the alpha and
# gamma angles where each column is a different alpha_NIR excitation
alphaData, gammaData, _ = fanData.build()
# save the alpha and gamma results
if save_results:
fanData.buildAndSave(saveFileName + "_{}.txt")
# Now we need to calculate the Jones matrices.
J = findJ(alphaData, gammaData)
# Get the T matrix:
T = makeT(J, crystalAngle)
# and save the matrices
if save_results:
saveT(J, observedSidebands, "{}_JMatrix.txt".format(saveFileName))
saveT(T, observedSidebands, "{}_TMatrix.txt".format(saveFileName))
# And to look at the ratios of the T matrix directly:
if Plot:
pg.figure("Magnitudes")
pg.plot(observedSidebands,
np.abs(T[0, 0, :] / T[1, 1, :]),
'o-',
label="T++/T--")
pg.plot(observedSidebands,
np.abs(T[0, 1, :] / T[1, 0, :]),
'o-',
label="T+-/T-+")
pg.figure("Angles")
pg.plot(observedSidebands,
np.angle(T[0, 0, :] / T[1, 1, :], deg=True),
'o-',
label="T++/T--")
pg.plot(observedSidebands,
np.angle(T[0, 1, :] / T[1, 0, :], deg=True),
'o-',
label="T+-/T-+")
pg.show()
# Ok this is sort of a quick way to get what I want for Origin plotting
# the relevant T matrix values
#
# ToDo: Format the text files to fit with the standards of other txt files
tmag = np.transpose(
np.array([
observedSidebands,
np.abs(T[0, 0, :] / T[1, 1, :]),
np.abs(T[0, 1, :] / T[1, 0, :])
]))
tang = np.transpose(
np.array([
observedSidebands,
np.angle(T[0, 0, :] / T[1, 1, :], deg=True),
np.angle(T[0, 1, :] / T[1, 0, :], deg=True)
]))
if save_results:
np.savetxt(saveFileName + "_{}.txt".format("TmatrixMag"),
tmag,
delimiter=',',
header='SB Order, |T++/T--|, |T+-/T-+|')
np.savetxt(saveFileName + "_{}.txt".format("TmatrixAng"),
tang,
delimiter=',',
header='SB Order, Angle(T++/T--), Angle(T+-/T-+)')
return alphaData, gammaData, J, T, tmag, tang