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ued_analysis.py
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ued_analysis.py
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import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.patches as patch
import numpy as np
import scipy as sp
from scipy.optimize import curve_fit
from scipy.misc import face
import glob
import csv
import re
import sys
import os
import copy
import time
import h5py
import skimage.feature
import skimage.filters
import skimage.measure
import socket
import itertools
import math
from matplotlib.patches import Polygon
from IPython.display import clear_output
from PIL import Image, ImageOps
C = 299792458 #m/s
def h(t, t0 = 0):
"""returns 0 until time t passes t0 then it returns 1"""
return [item>=t0 for item in t]
def exp_fit(x, t0, a, b, t1, t2):
"""linear combination of exponentials to fit function points"""
x = np.array(x)
y = np.reshape(h(x, t0)*np.array([a*np.exp(-x/t1)+b*np.exp(-x/t2)]), len(x))
return y
def float_to_int(num):
"""Changes floats to ints, even when float is in a single value tuple."""
num = str(num)
num = num.split('.')
return int(num[0])
def mm_to_ps(mm, zero=0, direction=-1):
"""changes mm to ps"""
ps = direction * (np.array(mm)-np.array(zero))*2 / C * 1e9
return ps
def ps_to_mm(ps, zero=0, direction=-1):
"""changes ps to mm"""
mm = direction * (ps * C * 1e-9) / 2 + zero
return mm
def gaus(x,a,x0,sigma,y0=0,k=0):
"""Gaussian Function as a python function"""
x= np.array(x)
return a*np.exp(-(x-x0)**2/(2*sigma**2)) + y0 + k*x
def checks_data_sizes(fnames, fnames_I0, delays):
"""
Checks the shape of the fnames, fnames_I0 and delays arrays
to make sure each data shape matches data file sizes. If not,
will return a data shape that works and a warning describing
the issue.
"""
x = len(fnames)
y = len(fnames_I0)
z = len(delays)
delays_n = delays
if z != y or z != x:
# if fnames_I0 is less than or equal to fnames
if y < x:
print("WARNING: Mismatched Data shape: fnames_I0 is less than fnames; reducing fnames and delays accordingly.")
delays_n = []
fnames_n = []
for i in range(y):
delays_n += [np.float64(fnames_I0[i].split('\\')[-1].split('_')[-2])]
for j in range(len(delays_n)):
fnames_n += [fnames[j]]
return delays_n, fnames_n, fnames_I0
# if fnames is less than or equal to fnames_I0
elif x < y:
print("WARNING: Mismatched Data shape: fnames is less than fnames_I0; reducing fnames_I0 and delays accordingly.")
delays_n = []
fnames_I0_n = []
for i in range(x):
delays_n += [np.float64(fnames[i].split('\\')[-1].split('_')[1].split('-')[-1])]
for j in range(len(delays_n)):
fnames_I0_n += [fnames_I0[j]]
return delays_n, fnames, fnames_I0_n
print("All Data shapes match")
return delays, fnames, fnames_I0
#image loading and processing
def find_coords(image, coord, roisize=50, returnall=False, showerrors = True):
"""
Takes in an image, coordinates, and returns either coordinates
or Coordinates, Amplitude, and Sigma, depending on if returnall
is true or false. This function fits peaks with gaussians, if
unable to fit a peak, the function returns an error message,
and sets the peak to zero.
"""
#fitting the peak
coord = list(np.array(coord,dtype=int))
subimg = image.T[coord[0]-roisize/2:coord[0]+roisize/2,coord[1]-roisize/2:coord[1]+roisize/2]
try:
xproj = np.sum(subimg,axis=1)
xpxl = range(coord[0]-roisize/2,coord[0]+roisize/2)
mean = xpxl[np.argmax(xproj)] #sp.ndimage.measurements.center_of_mass(xproj) +np.min(xpxl)#sum(xpxl)/len(xpxl)
sigma = 15
y0 = min(xproj)
a = max(xproj) - y0
k=0
poptx,pcovx = curve_fit(gaus,xpxl,xproj,p0=[a,mean,sigma,y0,k])
yproj = np.sum(subimg,axis=0)
ypxl = range(coord[1]-roisize/2,coord[1]+roisize/2)
mean = ypxl[np.argmax(yproj)]#sp.ndimage.measurements.center_of_mass(yproj) +np.min(ypxl)
#mean = sum(ypxl)/len(ypxl)
sigma = 15
y0 = min(yproj)
a = max(yproj) - y0
k=0
popty,pcovy = curve_fit(gaus,ypxl,yproj,p0=[a,mean,sigma,y0,k])
amplitude = np.mean([poptx[0], popty[0]])
sigma = np.mean([poptx[2], popty[2]])
coords = np.array([poptx[1],popty[1]])
except:
if showerrors == True:
print('Fitting Failed!!!! for coords {};{}'.format(coord[0],coord[1]))
amplitude = 0
sigma = 0
coords = np.array([0,0])
if returnall == True:
return coords, amplitude, sigma
else:
return coords
def fit_image(fname, fname_I0, indexes, roicoord, roinames, roisize=60, correct_t0 = False):
"""
This function takes in fname, fname_I0, indexes, roicoord, roinames, and returns a
data dictionary comprised of i0_pos, i0_sigma, i0_centroid, i0, i0_sum, delaycorrection,
delay, center, amplitudes, sigma, length, coordinates, pixelsum.
"""
#fitting the peaks from {indexes}
data = dict()
image = load_img(fname)
image = np.array(image, dtype=float)
#bg subtraction
image -= np.mean([image[0:64,0:64],image[-64:,0:64],image[0:64,-64:],image[-64:,-64:]])
#I normalization
i0 = load_img(fname_I0)
pos_fitted,amplitude,sigma = find_coords(i0,[280,226],roisize=200, returnall=True, showerrors=False)
centroid = sp.ndimage.measurements.center_of_mass(i0.T[249:325,179:262])
data['i0_pos'] = pos_fitted
data['i0_sigma'] = sigma
data['i0_centroid'] = centroid
data['i0'] = amplitude * sigma
data['i0_sum'] = np.mean(i0.T[249:325,179:262]) - np.mean(i0.T[0:200,0:100])
data['delaycorrection'] = (57.5-pos_fitted[1]) *0.0093
data['delay'] = float(fname.split('\\')[-1].split('-')[-1].split('_')[-2])
try: delays2 = np.array([float(fname.split('\\')[-1].split('-')[-3].split('_')[0]) for fname in fnames])
except: delays2 = [-np.inf]
totalI = np.sum(image)
image /= totalI
data['totalI'] = np.sum(image)
#fitting first Bragg peaks
roicoord_new = [find_coords(image, coord, roisize=50, returnall=False, showerrors = True) for coord in roicoord]
#center recentered
center = np.mean(roicoord_new, axis=0)
try: a = (roicoord_new[roinames.index('a')] - center) / 2
except: a = np.array([0,0])
try: b = (roicoord_new[roinames.index('b')] - center) / 2
except: b = np.array([0,0])
try: c = (roicoord_new[roinames.index('c')] - center) / 2
except: c = np.array([0,0])
data['center'] = center
#now calculating positions for all other peaks and then fitting them
data['amplitudes'] = []
data['sigma'] = []
data['length'] = []
data['coordinates'] = []
data['pixelsum'] = []
count = 0
for num, index in enumerate(indexes):
pos = index[0] * a + index[1] * b + index[2] * c + center
pos_fitted,amplitude,sigma = find_coords(image,pos, roisize=roisize, returnall=True)
pos_fitted = [float_to_int(pos_fitted[0]),float_to_int(pos_fitted[1])]
#print('pos_fitted:', pos_fitted[0], pos_fitted[1])
pixelsum = np.mean(image.T[pos_fitted[0]-roisize/2:pos_fitted[0]+roisize/2,pos_fitted[1]-roisize/2:pos_fitted[1]+roisize/2])
data['amplitudes'].append(amplitude)
data['sigma'].append(sigma)
data['length'].append(np.linalg.norm(pos_fitted - center))
data['coordinates'].append(pos_fitted)
data['pixelsum'].append(pixelsum)
data['center_all'] = np.mean(data['coordinates'],axis=0)
return data
def closest_point(points, x0,y0,x1,y1):
"""Finds closest point on the line for a selection of points"""
line_direction = np.array([x1 - x0, y1 - y0], dtype=float)
line_length = np.linalg.norm(line_direction)
line_direction /= line_length
n_bins = int(np.ceil(line_length))
# project points on line
#projections = np.array([(p[0] * line_direction[0] + p[1] * line_direction[1]) for p in points])
projections = np.array([(p[0] * line_direction[0] + p[1] * line_direction[1]) for p in points])
# normalize projections so that they can be directly used as indices
projections -= np.min(projections)
projections *= (n_bins - 1) / np.max(projections)
return np.floor(projections).astype(int), n_bins
def rect_profile(x0, y0, x1, y1, width):
"""
Takes in four points and a width and returns a rectangular
polygon, as well as 8 y and x points.
"""
xd = x1 - x0
yd = y1 - y0
length = np.sqrt(xd**2 + yd**2)
y00 = y0 + xd * width / length
x00 = x0 - yd * width / length
y01 = y0 - xd * width / length
x01 = x0 + yd * width / length
y10 = y1 - xd * width / length
x10 = x1 + yd * width / length
y11 = y1 + xd * width / length
x11 = x1 - yd * width / length
poly_points = [x00, x01, x10, x11], [y00, y01, y10, y11]
poly = Polygon(((y00, x00), (y01, x01), (y10, x10), (y11, x11)))
return poly, poly_points
def averaged_profile(image, x0, y0, x1, y1, width):
"""
Takes in an image, 4 points and a width, and returns an
averaged set of data,perpendicular to the profile created
by the rect_profile function.
"""
num = np.sqrt((x1-x0)**2 + (y1-y0)**2)
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)
coords = list(zip(x, y))
# Get all points that are in Rectangle
poly, poly_points = rect_profile(x0, y0, x1, y1, width)
points_in_poly = []
for point in itertools.product(range(image.shape[0]), range(image.shape[1])):
if poly.get_path().contains_point(point, radius=1) == True:
points_in_poly.append((point[1], point[0]))
# Finds closest point on line for each point in poly
neighbours, n_bins = closest_point(points_in_poly, x0, y0, x1, y1)
# Add all phase values corresponding to closest point on line
data = [[] for _ in range(n_bins)]
for idx in enumerate(points_in_poly):
index = neighbours[idx[0]]
data[index].append(image[idx[1][1], idx[1][0]])
# Average data perpendicular to profile
for i in enumerate(data):
data[i[0]] = np.nanmean(data[i[0]])
'''
# Plot
fig, axes = plt.subplots(figsize=(10, 5), nrows=1, ncols=2)
axes[0].imshow(image)
axes[0].plot([poly_points[0][0], poly_points[0][1]], [poly_points[1][0], poly_points[1][1]], 'yellow')
axes[0].plot([poly_points[0][1], poly_points[0][2]], [poly_points[1][1], poly_points[1][2]], 'yellow')
axes[0].plot([poly_points[0][2], poly_points[0][3]], [poly_points[1][2], poly_points[1][3]], 'yellow')
axes[0].plot([poly_points[0][3], poly_points[0][0]], [poly_points[1][3], poly_points[1][0]], 'yellow')
axes[0].axis('image')
axes[1].plot(data)'''
return data
def calc_diffuse(data):
"""Takes in a set of data, and returns Bragg Peak Profiles for the diffuse scattering between peaks."""
diffuse_profiles = [[] for _ in data['diffuse_sets']]
braggcoords = data['braggcoords']
braggnames = data['braggnames']
for profnum, difuse_profile in enumerate(data['diffuse_sets']):
tmp_prof = []
for linenum, line in enumerate(difuse_profile):
[x0, y0] = braggcoords[braggnames==line[0]][0]
[x1, y1] = braggcoords[braggnames==line[1]][0]
print('Image {}. profile {}'.format(data['indx'], linenum))
#profile = averaged_profile(data['image'], x0, y0, x1, y1, data['halfwidth'])
profile = skimage.measure.profile_line(data['image'].T,(x0,y0),(x1,y1),linewidth = data['halfwidth'])
#multiply by scalling factor to account for the need of the profile width proportional to length
#profile = np.dot(profile, 1/sp.linalg.norm((x0-x1,y0-y1)))
#print(profile)
x = np.linspace(-1, 1, len(profile))
profile_interp = sp.interpolate.interp1d(x, profile, bounds_error=False)
x_interpolated = np.linspace(-1, 1, data['npoints'])
profile = np.array([profile_interp(x) for x in x_interpolated])
tmp_prof.append(profile)
diffuse_profiles[profnum] = np.mean(tmp_prof, axis=0)
return diffuse_profiles
def generate_fnames (datafolder, delay2=-np.inf, minscannum=1, maxscannum=np.inf, ftype='tif'):
"""Takes data folders as strings, and returns lists of filenames fnames and fnames_I0"""
try: i = np.shape(datafolder)[0]
except: datafolder = [datafolder]
fnames = []
fnames_I0 = []
for folder in datafolder:
totalscans = len(glob.glob('{}/scan*/'.format(folder)))
for scannum in range(max(minscannum,1),min(maxscannum,totalscans)+1):
if delay2 < -1E10:
fnames.extend(sorted(glob.glob('{}/scan{:03d}/images-ANDOR1/*ANDOR1_*.{}'.format(folder, scannum,ftype))))
fnames_I0.extend(sorted(glob.glob('{}/scan{:03d}/I0/*ANDOR2_*.{}'.format(folder, scannum,ftype))))
else:
fnames.extend(sorted(glob.glob('{}/scan{:03d}/images-ANDOR1/*ANDOR1_longDelay-*-{:.8f}_2nd_pulse_delay-*.{}'.format(folder, scannum,delay2,ftype))))
fnames_I0.extend(sorted(glob.glob('{}/scan{:03d}/I0/*ANDOR2_longDelay-*-0{:.4f}_*.{}'.format(folder, scannum,delay2,ftype))))
#print(len(fnames))
return fnames, fnames_I0
def load_img(fname):
"""takes in a fname and returns the image that corresponds to that fname."""
if fname.split('.')[-1] == 'npy': return np.load(fname)
else: return sp.ndimage.imread(fname)
#d-w analysis
def unique_values(name_list):
"""
Takes in list of values, returns list of all unique values.
"""
uniq_vals = []
for i in range(len(name_list)):
x = name_list[i]
if x not in uniq_vals:
uniq_vals += [x]
return uniq_vals
def grouped_indices(name_list):
"""
Takes in list of values, and returns a 2-D list
with the indexes of identical values grouped into separate lists.
"""
grouped_ind = []
values = unique_values(name_list)
for i in range(len(values)):
y = []
for j in range(len(name_list)):
if values[i] == name_list[j]:
y += [j]
grouped_ind += [y]
return grouped_ind
def averaged_amplitudes(name_list, amplitudes):
"""
Takes in a list of values and a list of amplitudes that have been
grouped by peak and returns a nested list where the first index of
each is the grouping label, and the second index is the averaged amplitudes.
"""
indices = grouped_indices(name_list)
averaged_amplitudes = []
labels = []
peak_amplitudes = []
#organizing amplitude data points by delay orders
for i in range(len(name_list)):
x = []
for j in range(len(amplitudes)):
x += [amplitudes[j][i]]
peak_amplitudes += [x]
#grouping full amplitude data sets by momentum transfer
for group in indices:
amps = []
for i in range(len(group)):
amps += [peak_amplitudes[group[i]]]
amps = np.true_divide(np.sum(amps, axis = 0), len(group))
averaged_amplitudes += [amps]
labels += [name_list[group[i]]]
return averaged_amplitudes, labels
def g_2(pt, amplitudes, name_list, a, indexes):
"""
Takes in a list of values and a list of amplitudes,
a time range and a coefficient b and returns
a slice of averaged of amplitudes and a list of
corresponding g2 values for plotting
"""
#defining physical scale value for amplitudes
ind = indexes[1]
ang_dist = ind[0]**2+ind[1]**2+ind[2]**2
label = int(name_list[1])
scale_factor = ((ang_dist/a**2)/(label**2))
b = (8*math.pi/3)*scale_factor
pt = unique_values(pt)
#initializing variables
g2 = []
new_amps = []
#performing naturual log on amplitudes
for i in range(len(amplitudes)):
new_amp = []
for j in range(len(amplitudes[i])):
new_amp += [-math.log(amplitudes[i][j])]
new_amps += [new_amp]
g2_amp = []
g2_amp_all = []
#separating single points along certain time interval
for i in range(len(amplitudes[1])):
g2_amp = []
for j in range(len(new_amps)):
g2_amp += [new_amps[j][i]]
g2_amp_all += [g2_amp]
#creating q^2 points
pt = (unique_values(pt))
for k in range(len(amplitudes)):
g2 += [(b*(pt[k]**2))]
return g2_amp_all, g2
#ued data class
class Data:
"""Class for electron diffraction data analysis, represents and analyzes a set of data files."""
#Defined Attributes
minscannum = 1
maxscannum = np.inf
imcontrast = 10
T0 = 0
roicoord = [[640,520],
[490,670],
[352,520],
[490,370]]
roicoord = np.array(roicoord)
#directions of first two rois
roinames = ['a', 'b']
rcolors = ['r','g','y','m','r','g','y','m']
#Initializer / Instance Attributes
def __init__(self, data_path, zero, zero2, a = 1, roisize = 60, ftype = 'tif', imcontrast = imcontrast, maxorder = [4,4,0], roicoord = roicoord, roinames = roinames, rcolors = rcolors):
self.data_path = data_path
self.ftype = ftype
self.data = dict()
self.a = a
self.zero = zero
self.zero2 = zero2
self.T0 = 0
self.imcontrast = imcontrast
self.maxorder = maxorder
self.roisize = roisize
self.roicoord = roicoord
self.roinames = roinames
self.rcolors = rcolors
#print(roisize, roicoord, roinames, rcolors, maxorder)
self.minscannum = 1
self.maxscannum = np.inf
#filling self.images and self.data_libs
try:runnum = data_path.split('\\')[0]
except:runnum = '{}-{}'.format(data_path[0].split('\\')[-2],data_path[-1].split('\\')[-2])
self.runnum = runnum
self.fnames, self.fnames_I0 = generate_fnames(datafolder = data_path, ftype = self.ftype)
self.delays = np.array([float(fname.split('\\')[-1].split('-')[-1].split('_')[-2]) for fname in self.fnames])
self.delays_ps = mm_to_ps(self.delays,zero=zero)
try: self.delays2 = np.array([float(fname.split('\\')[-1].split('-')[-3].split('_')[0]) for fname in self.fnames])
except: self.delays2 = [-np.inf]
image = np.mean([load_img(self.fnames[i]) for i in range(min(20, len(self.fnames)))], axis = 0)
image = np.log(np.array(image, dtype = float) / np.mean(image))
for indx, coord in enumerate(self.roicoord):
rect = patch.Rectangle(coord - self.roisize*2/2, self.roisize*2, self.roisize*2, linewidth = 1, edgecolor = self.rcolors[indx], facecolor = 'none')
self.roicoord[indx] = find_coords(image, coord, roisize =2*self.roisize, returnall = False, showerrors = False)
rect = patch.Rectangle(self.roicoord[indx] - self.roisize/2, self.roisize, self.roisize, linewidth = 1, edgecolor = self.rcolors[indx], facecolor = 'none')
#dictionary of images
self.images = np.array([load_img(self.fnames[i]) for i in range(len(self.fnames))])
center = np.mean(self.roicoord, axis = 0)
self.center = center
#defining reciprocal lattice
try: a = (roicoord[roinames.index('a')] - center) / 2
except: a = np.array([0,0])
try: b = (roicoord[roinames.index('b')] - center) / 2
except: b = np.array([0,0])
try: c = (roicoord[roinames.index('c')] - center) / 2
except: c = np.array([0,0])
#Lists for peak characterization
bragg = []
name = []
indexes = []
pt = []
pt_hk = []
#adding peak marks and labels to images
maxorderi = maxorder[0]
maxorderj = maxorder[1]
maxorderk = maxorder[2]
#recording preliminal peak labeling data
for i in range(-maxorderi, maxorderi+1):
for j in range(-maxorderj, maxorderj+1):
for k in range(-maxorderk, maxorderk+1):
if (i==0 and j==0 and k==0) or (abs(i)+abs(j)+abs(k)) % 2 != 0:
continue
pos = (i*a + j*b + k*c) + center
q_hk = ((i*a)**2 + (j*b)**2 + (k*c)**2)**.5
q = ((q_hk[0])**2 + (q_hk[1])**2)**.5
pos_fitted = find_coords(image, pos, roisize = self.roisize)
#adding boxes and labels
if sp.linalg.norm(pos_fitted) == 0: pass
else:
bragg.append(pos)
name.append('{}'.format((float_to_int(q))))
indexes.append([i,j,k])
pt_hk.append(q_hk)
pt.append(q)
self.bragg = np.array(bragg)
self.name = np.array(name)
self.indexes = np.array(indexes)
self.pt = np.array(pt)
self.pt_hk = np.array(pt_hk)
def __repr__(self):
"""returns pertinent characteristics of the data object."""
print("Runnum: {}".format(self.runnum))
print("# of Images: {}".format(len(self.images)))
print("Fnames: {}".format(len(self.fnames)))
print("Fnames_I0: {}".format(len(self.fnames_I0)))
print("Delays: {}".format(len(self.delays)))
return '# of Peaks {}'.format(len(self.pt))
def show_sample(self):
"""
Displays a marked and labeled image from the collected data that is an average of 20 images from the image data.
"""
image = np.mean([load_img(self.fnames[i]) for i in range(min(20, len(self.fnames)))], axis = 0)
image = np.log(np.array(image, dtype = float) / np.mean(image))
fig, ax = plt.subplots(1,1,figsize=(9.8, 8), tight_layout = True)
vmax = np.max(image)/self.imcontrast
ax.imshow(image, vmax = vmax)
#defining reciprocal lattice
try: a = (self.roicoord[self.roinames.index('a')] - self.center) / 2
except: a = np.array([0,0])
try: b = (self.roicoord[self.roinames.index('b')] - self.center) / 2
except: b = np.array([0,0])
try: c = (self.roicoord[self.roinames.index('c')] - self.center) / 2
except: c = np.array([0,0])
maxorderi = self.maxorder[0]
maxorderj = self.maxorder[1]
maxorderk = self.maxorder[2]
for i in range(-maxorderi,maxorderi+1):
for j in range(-maxorderj,maxorderj+1):
for k in range(-maxorderk,maxorderk+1):
if (i==0 and j==0 and k==0) or (abs(i)+abs(j)+abs(k)) % 2 !=0:
continue
self.pos = (i*a + j*b + k*c) + self.center
self.q_hk = ((i*a)**2 + (j*b)**2 + (k*c)**2)**.5
self.q = ((self.q_hk[0])**2 + (self.q_hk[1])**2)**.5
self.pos_fitted = find_coords(image, self.pos, roisize = self.roisize)
#adding boxes and labels
rect = patch.Rectangle(self.pos - self.roisize/2, self.roisize, self.roisize, linewidth=1, edgecolor='g', facecolor='none')
ax.add_patch(rect)
#ax.text(self.q_hk[0]+10,self.q_hk[1]+10, '{}'.format(float_to_int(self.q)), color='w', fontsize=7)
fig.show()
return fig
def fit_data(self):
"""Fits data and returns data libraries for all data and all delays"""
#labeling peaks
#storing averaged data:
alldata = dict()
alldelays = dict()
#storing NOT averaged data:
alldata_na = dict()
alldelays_na = dict()
alldata[self.runnum] = dict()
alldelays[self.runnum] = dict()
alldata_na[self.runnum] = dict()
alldelays_na[self.runnum] = dict()
for delay2 in np.unique(self.delays2):
self.fnames, self.fnames_I0 = generate_fnames(datafolder = self.data_path, delay2 = delay2, ftype = self.ftype)
self.delays = np.array([float(fname.split('//')[-1].split('-')[-1].split('_')[-2]) for fname in self.fnames])
#guaranteeing data shapes
self.delays, self.fnames, self.fnames_I0 = checks_data_sizes(fnames = self.fnames, fnames_I0 = self.fnames_I0, delays = self.delays)
self.delays_ps = mm_to_ps(self.delays, zero = self.zero)
self.delay2_ps = np.round(mm_to_ps(delay2, zero = self.zero2, direction = 1), 3)
#for cluster implementation
#pushdata = dict(delays = self.delays, fnames = self.fnames, fnames_I0 = self.fnames_I0, ftype = self.ftype, indexes = self.indexes, roicoord = self.roicoord, roinames = self.roinames, roisize = self.roisize)
#tmp = dv.map(lambda delaynum: fit_image(self.fnames[delaynum], fnames_I0[delaynum], self.indexes, self.roicoord, self.roinames, roisize = self.roisize), range(len(self.delays)))
tmp = map(lambda delaynum: fit_image(self.fnames[delaynum], self.fnames_I0[delaynum], self.indexes, self.roicoord, self.roinames, roisize = self.roisize), range(len(self.delays)))
keylist = tmp[0].keys()
for key in keylist:
for dnum, item in enumerate(tmp):
if dnum == 0:
self.data[key] = []
self.data[key].append(tmp[dnum][key])
for key in self.data.keys():
self.data[key] = np.array(self.data[key])
print('Images before filtering: {}'.format(len(self.data['i0'])))
#max deviations from avg values
deviations = dict()
deviations['i0'] = 0.8
deviations['i0_centroid'] = 0.8
deviations['center'] = 0.1
avg_values = dict()
filt = []
for key, item in deviations.items():
avg_values[key] = np.mean(self.data[key], axis = 0)
tmp = np.logical_and(self.data[key] >= avg_values[key] * (1-deviations[key]), self.data[key] <= avg_values[key]*(1 + deviations[key]))
try:
_=np.shape(tmp)[0]
tmp = [np.prod(tmp_element, axis = 0) for tmp_element in tmp]
except: pass
filt.append(tmp)
filt = np.array(np.prod(filt, axis = 0), dtype = bool)
#Removing all bad data:
for key, item in self.data.items(): self.data[key] = np.array(item)[filt]
self.delays = np.array(self.delays)[filt]
self.delays_ps = np.array(self.delays_ps)[filt]
select_neg_full = self.delays_ps < self.T0
self.fnames = np.array(self.fnames)[filt]
self.fnames_I0 = np.array(self.fnames_I0)[filt]
print('Images after filtering: {}'.format(len(self.data['i0'])))
#filtering by deviation from neg time
deviations_rel = dict()
deviations_rel['length'] = 0.1
deviations_rel['sigma'] = 0.1
neg_values = dict()
filt = []
for key, item in deviations_rel.items():
neg_values[key] = np.mean(self.data[key][select_neg_full], axis = 0)
tmp = np.logical_and(self.data[key] >= neg_values[key] * (1 - deviations_rel[key]), self.data[key] <= neg_values[key] * (1+ deviations_rel[key]))
try:
_=np.shape(tmp)[0]
tmp = [np.prod(tmp_element, axis = 0) for tmp_element in tmp]
except: pass
filt.append(tmp)
filt = np.array(np.prod(filt, axis = 0), dtype = bool)
for key, item in self.data.items():
self.data[key] = np.array(item)[filt]
self.delays = np.array(self.delays)[filt]
self.delays_ps = np.array(self.fnames)[filt]
select_neg_full = self.delays_ps < self.T0
self.fnames = np.array(self.fnames)[filt]
self.fnames_I0 = np.array(self.fnames_I0)[filt]
print('Images after filtering stage 2: {}'.format(len(self.data['i0'])))
#average same delays
data_avg = dict()
delays_avg = np.unique(self.data['delay'])
delays_ps_avg = mm_to_ps(delays_avg, zero = self.zero)
for key,item in self.data.items():
if key in []:
continue
try:
i = np.shape(self.data[key])[2]
data_avg[key] = np.zeros((len(delays_ps_avg), len(self.indexes),i))
except:
try:
i = np.shape(self.data[key])[1]
data_avg[key] = np.zeros((len(delays_ps_avg),i))
except:
data_avg[key] = np.zeros(len(delays_ps_avg))
for delaynum, delay in enumerate(delays_avg):
data_avg[key][delaynum] = np.mean(self.data[key][self.data['delay'] == delay], axis = 0)
#normalize to negative delays
data_avg_norm = copy.deepcopy(data_avg)
for key, item in data_avg_norm.items():
if key in ['i0','i0_pos','i0_centroid','center','coordinates','delay','totalI']:
continue
select_neg = delays_ps_avg < self.T0
if (select_neg.any() == True):
factor = np.mean(item[select_neg], axis = 0)
else:
factor = n.mean(item, axis = 0)
if factor.all > 0:
pass
else:
factor = item[-1]
for delaypoint in item:
delaypoint /= factor
alldelays[self.runnum][self.delay2_ps] = copy.deepcopy(delays_ps_avg)
alldata[self.runnum][self.delay2_ps] = copy.deepcopy(data_avg_norm)
alldelays_na[self.runnum][self.delay2_ps] = copy.deepcopy(self.delays_ps)
alldata_na[self.runnum][self.delay2_ps] = copy.deepcopy(self.data)
self.alldelays = alldelays
self.alldata = alldata
self.alldelays_na = alldelays_na
self.alldata_na = alldelays_na
print('Done.')
return alldelays, alldata, alldelays_na, alldata_na
def display_prelim_data(self):
"""Returning data plots for amplitudes, sigma, pixelsum, and vector length"""
fig_i, ax_i = plt.subplots(2, 2, figsize = (9.8, 8), tight_layout = True)
selectpeaks = self.name
select_array = np.array([item in selectpeaks for item in self.name])
for key, element in self.alldata[self.runnum].items():
ax_i[0,0].plot(self.alldelays[self.runnum][key], element['amplitudes'][::,select_array])
ax_i[0,1].plot(self.alldelays[self.runnum][key], element['sigma'][::,select_array])
ax_i[1,0].plot(self.alldelays[self.runnum][key], element['pixelsum'][::,select_array])
ax_i[1,1].plot(self.alldelays[self.runnum][key], element['length'][::,select_array])
ax_i[0,0].set_title('Intensity')
ax_i[0,0].set_ylabel('I/I0')
ax_i[0,1].set_title('Sigma')
ax_i[0,1].set_ylabel('S/S0')
ax_i[1,0].set_title('Pixel Sum')
ax_i[1,0].set_ylabel('I/I0')
ax_i[1,1].set_title('Vector Length')
ax_i[1,1].set_ylabel('L/L0')
fig_i.suptitle('Run {}'.format(self.runnum))
fig_i.show()
def display_d_w(self, display_count = 5, showall = False, num_bins = 10, t0 = 0):
"""Returns Debye-Waller Analysis plots and statistics"""
selectpeaks = self.name
select_array = np.array([item in selectpeaks for item in self.name])
for key, element in self.alldata[self.runnum].items():
amp_avg, labels = averaged_amplitudes(self.name, element['amplitudes'][::, select_array])
amps, g2 = g_2(self.pt, amp_avg, self.name, self.a, self.indexes)
t_delays = np.array(self.alldelays[self.runnum][key])
fig_j, ax_j = plt.subplots(1, 3, figsize = (9.8, 3), tight_layout = True)
for key, element in self.alldata[self.runnum].items():
for i in range(len(amp_avg)):
ax_j[0].plot(self.alldelays[self.runnum][key], amp_avg[i])
ax_j[0].legend(labels, loc = 1, prop = {'size': 6})
ax_j[0].set_ylabel('I/I0')
ax_j[0].set_xlabel('Delays [ps]')
y = []
for i in range(0, display_count):
y += [round(self.alldelays[self.runnum][key][i], 4)]
y = np.array(y)
for i in range(0, display_count):
x = np.array(amps[i])
x[0::] += i*.1
z = np.polyfit(g2, x, 1)
poly_fit = np.array([z[1] + z[0]*xi for xi in g2])
ax_j[1].scatter(g2, x)
ax_j[1].legend(y, loc = 1, prop = {'size': 6})
for i in range(0, display_count):
x = np.array(amps[i])
x[0::] += i*.1
z = np.polyfit(g2, x, 1)
poly_fit = np.array([z[1] + z[0]*xi for xi in g2])
ax_j[1].plot(g2, poly_fit)
ax_j[1].set_ylabel('-ln(I/I0)')
ax_j[1].set_xlabel('G_2 [A^-2]')
amplitude_slopes = [(np.polyfit(g2, i, 1))[0] for i in amps]
covarr = [((np.polyfit(g2, i, 1, cov = True)[1][0][0])**.5) for i in amps]
a = .015
b = .015
t1 = 15
t2 = 25
popt, pcov = curve_fit(exp_fit, t_delays, amplitude_slopes, p0 = np.array([t0, a, b, t1, t2]))
x = np.linspace(-5, 100, 1051)
t0, a, b, t1, t2 = popt
nonlin_fit = exp_fit(x, t0, a, b, t1, t2)
'''amplitude_slopes = [(np.polyfit(g2, i, 1))[0] for i in amps]
covarr = [((np.polyfit(g2, i, 1, cov = True)[1][0][0])**.5) for i in amps]'''
ax_j[2].errorbar(self.alldelays[self.runnum][key], amplitude_slopes, yerr = covarr, fmt = 'o')
ax_j[2].plot(x, nonlin_fit)
ax_j[2].set_ylabel('<u^2> [A^-2]')
ax_j[2].set_xlabel('Delay [ps]')
fig_j.suptitle('Run {}'.format(self.runnum))
fig_j.show()
if showall == True:
fig_k, ax_k = plt.subplots(1, 2, figsize = (7.8, 3), tight_layout = True)
for key, element in self.alldata[self.runnum].items():
#plotting averaged d-w linear curve
poly_fits = []
avgd_t = np.array(np.true_divide(np.sum(amps, axis = 0), len(amps)))
for i in range(len(amps)):
z = np.polyfit(g2, amps[i], 1)
poly_fits += [[z[1] + z[0]*xi for xi in g2]]
poly_fits1 = np.array(np.true_divide(np.sum(poly_fits, axis = 0), len(amps)))
ax_k[0].scatter(g2, avgd_t)
ax_k[0].plot(g2, poly_fits1)
ax_k[0].set_ylabel('-ln(I/I0)')
ax_k[0].set_xlabel('G_2 [A^-2]')
ax_k[0].set_title('Average')
#generating r^2 gistogram
hist = []
for i in range(len(amps)):
z = np.polyfit(g2, amps[i], 1, full = True)
avg_z = np.polyfit(g2, np.mean(amps, axis = 0), 1, full = True)
hist += [1 - ((z[0][1]**2)/(avg_z[0][1]**2))]
ax_k[1].hist(hist, num_bins, facecolor = 'blue', alpha = .5)
ax_k[1].set_xlabel('R^2')
ax_k[1].set_title('R Squared Histogram')
fig_k.show()