elif isinstance(data_combi, np.ndarray):
data2save = data_combi
np.savetxt(dir_name + '/response_diff.txt',
data2save,
fmt='%f',
delimiter=',')
plt.figure(11)
plt.plot(data[0][:, 0], data_p1, '-r', data[0][:, 0], data_p2, '-b')
plt.show()
return data_combi
wpu._mpl_settings_4_nice_graphs(otheroptions={'lines.linewidth': 2})
# the path to the cap sensor data for response function
path_cap = '/Users/zhiqiao/Documents/GitHub/wavepytools/wavepytools/imaging/single_grating/exp_data/response_test/cali'
use_cap = True
ref_p = [0, -50]
# if the perfect wavefront is used as the target wavefront
target_perfect = True
voltage4response = 1.00
phenergy = 1e20
wavelength = wpu.hc / phenergy
# here is to choose which collum in the csv fitting data is used for the response function calculation
profilenumber = 1
wgi.plot_harmonic_peak(img, [pv, ph], isFFT=False)
plt.savefig('FFT_peaks_{:.0f}mm.png'.format(zvec[i] * 1e3))
plt.show(block=False)
plt.close()
return wgi.visib_1st_harmonics(img, [pv, ph],
searchRegion=searchRegion,
unFilterSize=unFilterSize)
# =============================================================================
# %% Script starts
# =============================================================================
wpu._mpl_settings_4_nice_graphs()
# =============================================================================
# %% Load Image
# =============================================================================
originalDir = os.getcwd()
samplefileName = easyqt.get_file_names("Choose one of the scan files")[0]
data_dir = samplefileName.rsplit('/', 1)[0]
os.chdir(data_dir)
try:
os.mkdir(data_dir + '/output/')
except:
import numpy as np
import matplotlib
import os
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from wavepy.utils import easyqt
import wavepy.utils as wpu
from scipy.ndimage.filters import uniform_filter1d
from matplotlib.patches import Rectangle
wpu._mpl_settings_4_nice_graphs(otheroptions={
'axes.formatter.use_mathtext': True,
'axes.formatter.limits': '-3, 4'
})
# %%
def _n_profiles_H_V(arrayH,
arrayV,
virtual_pixelsize,
zlabel=r'z',
titleH='Horiz',
titleV='Vert',
nprofiles=5,
filter_width=0,
remove2ndOrder=False,
saveFileSuf='',
saveFigFlag=True):
import numpy as np
import matplotlib
import os
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import easygui_qt as easyqt
import wavepy.utils as wpu
from scipy.ndimage.filters import uniform_filter1d
from matplotlib.patches import Rectangle
wpu._mpl_settings_4_nice_graphs(otheroptions={'axes.formatter.use_mathtext':True,
'axes.formatter.limits': '-3, 4'})
# %%
def _n_profiles_H_V(arrayH, arrayV, virtual_pixelsize,
zlabel=r'z',
titleH='Horiz', titleV='Vert',
nprofiles=5, filter_width=0,
remove1stOrderDPC=False,
saveFileSuf='',
saveFigFlag=True):
xxGrid, yyGrid = wpu.grid_coord(arrayH, virtual_pixelsize)
fit_coefs = [[], []]
data2saveH = None
curves1.append(figx.axes[0].lines[i].get_data())
curves1 = np.asarray(curves1)
curves2 = []
for i in range(len(figx2.axes[0].lines)):
curves2.append(figx2.axes[0].lines[i].get_data())
curves2 = np.asarray(curves2)
# %% Sandbox
import wavepy.utils as wpu
wpu._mpl_settings_4_nice_graphs(fs=20)
plt.figure(figsize=(10,8))
plt.plot(curves1[0,0,:], curves1[0,1,:]/np.max(curves1[0,1,:]), '-ok', label= 'Direct beam')
plt.plot(curves2[0,0,:], curves2[0,1,:]/np.max(curves2[0,1,:]), '-og', label= 'Reflected beam')
plt.xlabel(r'Distance [mm]')
plt.ylabel(r'Visibility')
plt.legend()
plt.show()
# %%
def main_terminal(data_dir,
zvec_from,
startDist,
step_z_scan,
image_per_point,
strideFile,
pixelSize=0.65e-6,
gratingPeriod=4.8e-6,
pattern='Diagonal',
sourceDistanceV=-1,
sourceDistanceH=32,
unFilterSize=1,
searchRegion=20,
idx4crop=[0, -1, 0, -1],
darkRegionSelctionFlag=True):
'''
*** all unit in [m]
data_dir: data folder path
zvec_from: distance type:
'Calculated'
'Tabled'
startDist: started distance postion
step_z_scan: step size
image_per_point: images number for every distance
strideFile: Stride (Use only every XX files)
pixelSize: Pixel Size
gratingPeriod: CB Grating Period
pattern: grating pattern
'Diagonal' or 'Edge']
sourceDistanceV: Distance to Source
in the VERTICAL [m]
sourceDistanceH: Distance to Source
in the Horizontal [m]
unFilterSize: Size for Uniform Filter [Pixels]
default_value 1
searchRegion: Size of Region for Searching
the Peak [in Pixels]
default_value=20
idx4crop: crop area
[low_y, high_y, low_x, high_x ]
darkRegionSelctionFlag: use dark region [0, 20, 0, 20]?
'''
wpu._mpl_settings_4_nice_graphs()
# =============================================================================
# %% Load Image
# =============================================================================
originalDir = os.getcwd()
# samplefileName = easyqt.get_file_names("Choose one of the scan files")[0]
# data_dir = samplefileName.rsplit('/', 1)[0]
os.chdir(data_dir)
try:
os.mkdir(data_dir + '/output/')
except:
pass
fname2save = data_dir + '/output/' + 'zscan'
# wpu.print_blue('MESSAGE: Loading files ' +
# samplefileName.rsplit('_', 1)[0] + '*.tif')
wpu.print_blue('MESSAGE: Loading files ' + data_dir + '/*.tif')
# listOfDataFiles = glob.glob(samplefileName.rsplit('_', 2)[0] + '*.tif')
listOfDataFiles = glob.glob(os.path.join(data_dir, '*.tif'))
listOfDataFiles.sort()
nfiles = len(listOfDataFiles)
# zvec_from = easyqt.get_choice(message='z distances is calculated or from table?',
# title='Title',
# choices=['Calculated', 'Tabled'])
# %%
if zvec_from == 'Calculated':
# startDist = easyqt.get_float('Starting distance scan [mm]',
# title='Title',
# default_value=20)*1e-3
# step_z_scan = easyqt.get_float('Step size scan [mm]',
# title='Title',
# default_value=5)*1e-3
# image_per_point = easyqt.get_int('Number of images by step',
# title='Title',
# default_value=1)
zvec = np.linspace(
startDist,
startDist + step_z_scan * (nfiles / image_per_point - 1),
int(nfiles / image_per_point))
zvec = zvec.repeat(image_per_point)
# strideFile = easyqt.get_int('Stride (Use only every XX files)',
# title='Title',
# default_value=1)
listOfDataFiles = listOfDataFiles[0::strideFile]
zvec = zvec[0::strideFile]
print(zvec)
elif zvec_from == 'Tabled':
zvec = np.loadtxt(
easyqt.get_file_names("Table with the z distance values in mm")
[0]) * 1e-3
step_z_scan = np.mean(np.diff(zvec))
if step_z_scan > 0:
pass
else:
listOfDataFiles = listOfDataFiles[::-1]
zvec = zvec[::-1]
img = dxchange.read_tiff(listOfDataFiles[0])
# =============================================================================
# %% Experimental parameters
# =============================================================================
# pixelSize = easyqt.get_float("Enter Pixel Size [um]",
# title='Experimental Values',
# default_value=.6500, decimals=5)*1e-6
# gratingPeriod = easyqt.get_float("Enter CB Grating Period [um]",
# title='Experimental Values',
# default_value=4.8)*1e-6
# pattern = easyqt.get_choice(message='Select CB Grating Pattern',
# title='Title',
# choices=['Diagonal', 'Edge'])
# # choices=['Edge', 'Diagonal'])
# sourceDistanceV = easyqt.get_float("Enter Distance to Source\n in the VERTICAL [m]",
# title='Experimental Values',
# default_value=-0.73)
# sourceDistanceH = easyqt.get_float("Enter Distance to Source\n in the Horizontal [m]",
# title='Experimental Values',
# default_value=34.0)
# unFilterSize = easyqt.get_int("Enter Size for Uniform Filter [Pixels]\n" +
# " (Enter 1 to NOT use the filter)",
# title='Experimental Values',
# default_value=1)
# searchRegion = easyqt.get_int("Enter Size of Region for Searching\n the Peak [in Pixels]",
# title='Experimental Values',
# default_value=20)
os.chdir(originalDir)
# =============================================================================
# %% Crop
# =============================================================================
idx4crop = [0, -1, 0, -1]
# [colorlimit,
# cmap] = wpu.plot_slide_colorbar(img,
# title='SELECT COLOR SCALE,\n' +
# 'Raw Image, No Crop',
# xlabel=r'x [$\mu m$ ]',
# ylabel=r'y [$\mu m$ ]',
# extent=wpu.extent_func(img,
# pixelSize)*1e6)
# idx4crop = wpu.graphical_roi_idx(img, verbose=True,
# kargs4graph={'cmap': cmap,
# 'vmin': colorlimit[0],
# 'vmax': colorlimit[1]})
wpu.print_blue("MESSAGE: idx for cropping")
wpu.print_blue(idx4crop)
# =============================================================================
# %% Dark indexes
# =============================================================================
# darkRegionSelctionFlag = easyqt.get_yes_or_no('Do you want to select ' +
# 'region for dark calculation?\n' +
# 'Press ESC to use [0, 20, 0, 20]')
print(darkRegionSelctionFlag)
if darkRegionSelctionFlag:
idx4cropDark = wpu.graphical_roi_idx(img,
verbose=True,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
else:
idx4cropDark = [0, 20, 0, 20]
# dark_im = dxchange.read_tiff(listOfDataFiles[0])*0.0 + avgDark
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# ==============================================================================
# %% Harmonic Periods
# ==============================================================================
if pattern == 'Diagonal':
period_harm_Vert = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[1])
elif pattern == 'Edge':
period_harm_Vert = np.int(2 * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(2 * pixelSize / gratingPeriod * img.shape[1])
# Obtain harmonic periods from images
(period_harm_Vert,
_) = wgi.exp_harm_period(img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['1', '0'],
searchRegion=40,
isFFT=False,
verbose=True)
(_, period_harm_Horz) = wgi.exp_harm_period(
img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['0', '1'],
searchRegion=40,
isFFT=False,
verbose=True)
wpu.log_this('Input folder: ' + data_dir, preffname=fname2save)
wpu.log_this('\nNumber of files : ' + str(nfiles))
wpu.log_this('Stride : ' + str(strideFile))
print(zvec_from)
wpu.log_this('Z distances is ' + zvec_from)
if zvec_from == 'Calculated':
wpu.log_this('Step zscan [mm] : {:.4g}'.format(step_z_scan * 1e3))
wpu.log_this('Start point zscan [mm] : {:.4g}'.format(startDist * 1e3))
wpu.log_this('Pixel Size [um] : {:.4g}'.format(pixelSize * 1e6))
wpu.log_this('Grating Period [um] : {:.4g}'.format(gratingPeriod * 1e6))
wpu.log_this('Grating Pattern : ' + pattern)
wpu.log_this('Crop idxs : ' + str(idx4crop))
wpu.log_this('Dark idxs : ' + str(idx4cropDark))
wpu.log_this('Vertical Source Distance: ' + str(sourceDistanceV))
wpu.log_this('Horizontal Source Distance: ' + str(sourceDistanceH))
wpu.log_this('Uniform Filter Size : {:d}'.format(unFilterSize))
wpu.log_this('Search Region : {:d}'.format(searchRegion))
# =============================================================================
# %% Calculate everything
# =============================================================================
# =============================================================================
# %% multiprocessing
# =============================================================================
ncpus = cpu_count()
wpu.print_blue("MESSAGE: %d cpu's available" % ncpus)
tzero = time.time()
p = Pool(ncpus - 2)
indexes = range(len(listOfDataFiles))
parameters = []
for i in indexes:
parameters.append([
i, listOfDataFiles, zvec, idx4cropDark, idx4crop, period_harm_Vert,
sourceDistanceV, period_harm_Horz, sourceDistanceH, searchRegion,
unFilterSize
])
res = p.map(_func, parameters)
p.close()
wpu.print_blue('MESSAGE: Time spent: {0:.3f} s'.format(time.time() -
tzero))
'''
res = []
for i in range(len(listOfDataFiles)):
res.append(_func(i))
print(res)
'''
# =============================================================================
# %% Sorting the data
# =============================================================================
contrastV = np.asarray([x[0] for x in res])
contrastH = np.asarray([x[1] for x in res])
p0 = np.asarray([x[2] for x in res])
pv = np.asarray([x[3] for x in res])
ph = np.asarray([x[4] for x in res])
pattern_period_Vert_z = pixelSize / (pv[:, 0] - p0[:, 0]) * img.shape[0]
pattern_period_Horz_z = pixelSize / (ph[:, 1] - p0[:, 1]) * img.shape[1]
# =============================================================================
# %% Save csv file
# =============================================================================
outputfname = wpu.get_unique_filename(fname2save, 'csv')
wpu.save_csv_file(np.c_[zvec.T, contrastV.T, contrastH.T,
pattern_period_Vert_z.T, pattern_period_Horz_z.T],
outputfname,
headerList=[
'z [m]', 'Vert Contrast', 'Horz Contrast',
'Vert Period [m]', 'Horz Period [m]'
])
wpu.log_this('\nOutput file: ' + outputfname)
# =============================================================================
# %% Plot
# =============================================================================
# contrast vs z
fig = plt.figure(figsize=(10, 7))
plt.plot(zvec * 1e3, contrastV * 100, '-ko', label='Vert')
plt.plot(zvec * 1e3, contrastH * 100, '-ro', label='Hor')
plt.xlabel(r'Distance $z$ [mm]', fontsize=14)
plt.ylabel(r'Visibility $\times$ 100 [%]', fontsize=14)
plt.title('Visibility vs detector distance', fontsize=14, weight='bold')
plt.legend(fontsize=14, loc=0)
wpu.save_figs_with_idx(fname2save)
plt.show(block=False)
# =============================================================================
# %% Plot Harmonic position and calculate source distance
# =============================================================================
from wavepytools.diag.coherence.fit_singleGratingCoherence_z_scan import fit_period_vs_z
#xshi 20190719
#from fit_singleGratingCoherence_z_scan import fit_period_vs_z
(sourceDistance_from_fit_V,
patternPeriodFromData_V) = fit_period_vs_z(zvec,
pattern_period_Vert_z,
contrastV,
direction='Vertical',
threshold=.002,
fname4graphs=fname2save)
(sourceDistance_from_fit_H,
patternPeriodFromData_H) = fit_period_vs_z(zvec,
pattern_period_Horz_z,
contrastH,
direction='Horizontal',
threshold=0.0005,
fname4graphs=fname2save)