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main.py
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# <codecell>
# run current file in interactive window to see the frontend
from pathlib import Path # see https://docs.python.org/3/library/pathlib.html#basic-use
import os.path
## Define paths
# Directory containing the data:
data_dir = Path('./data/')
# Directory containing the useful hdf5 files (cleaned)
useful_dir = Path('./data/useful/')
# Directory containing the background-subtracted hdf5 files
bgsubtracted_dir = Path('./data/bgsubtracted/')
# Directory for local temporary files:
scratch_dir = Path('./scratch/')
if os.path.isdir(scratch_dir) == False:
if os.path.isdir(scratch_dir.parent.absolute()) == False:
os.mkdir(scratch_dir.parent.absolute())
os.mkdir(scratch_dir)
results_dir = Path('./results/')
# prebgsubtracted_dir
# bgsubtracted_dir = Path.joinpath('/content/gdrive/MyDrive/PhD/coherence/data/scratch_cc/','bgsubtracted')
# <codecell>
# imports
# install missing packages --> see https://stackoverflow.com/a/63096701
import sys
import subprocess
import pkg_resources
required = {'numpy', 'pandas', 'lmfit', 'wget', 'scipy', 'h5py', 'ipywidgets'}
installed = {pkg.key for pkg in pkg_resources.working_set}
missing = required - installed
if missing:
# implement pip as a subprocess:
subprocess.check_call([sys.executable, '-m', 'pip', 'install', *missing])
import time
from datetime import datetime
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.patches as patches
import matplotlib.image as mpimg
from pathlib import Path # see https://docs.python.org/3/library/pathlib.html#basic-use
import collections
from ipywidgets import (
interact,
interactive,
fixed,
interact_manual,
Button,
VBox,
HBox,
interactive,
interactive_output,
GridspecLayout
)
import ipywidgets as widgets
# import bqplot as bq
import h5py
import math
import scipy
import pandas as pd
from lmfit import Model
import warnings
# everything for deconvolution method
# Garbage Collector - use it like gc.collect() from https://stackoverflow.com/a/61193594
import gc
from scipy.signal import convolve2d as conv2
from skimage import color, data, restoration
from scipy import fftpack
from scipy.optimize import curve_fit
from scipy.optimize import brenth
from scipy.optimize import minimize_scalar
import scipy.optimize as optimize
from IPython.display import display, clear_output
from coherencefinder.deconvolution_module import calc_sigma_F_gamma_um, deconvmethod, deconvmethod_v1, normalize, chi2_distance
from coherencefinder.fitting_module import Airy, find_sigma, fit_profile_v1, fit_profile_v2, gaussian
# import pickle as pl
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
# %% settings for figures and latex
# download stixfonts with wget module if missing --> see https://stackoverflow.com/a/28313383
import wget
fonts_dir = './fonts'
if os.path.isfile(os.path.join(fonts_dir,'static_otf.zip')) == False:
url='https://github.com/stipub/stixfonts/raw/master/zipfiles/static_otf.zip'
if os.path.isdir(fonts_dir) == False:
os.mkdir(fonts_dir)
wget.download(url,out='./fonts')
# unzip --> see https://stackoverflow.com/a/3451150
import zipfile
if os.path.isdir(os.path.join(fonts_dir,'static_otf')) == False:
with zipfile.ZipFile(os.path.join(fonts_dir,'static_otf.zip'), 'r') as zip_ref:
zip_ref.extractall('./fonts/')
# add stixfonts -> see https://stackoverflow.com/a/65841091
from matplotlib import font_manager as fm
font_files = fm.findSystemFonts(fonts_dir)
for font_file in font_files:
fm.fontManager.addfont(font_file)
# from
# # https://www.dmcdougall.co.uk/publication-ready-the-first-time-beautiful-reproducible-plots-with-matplotlib
# WIDTH = 350.0 # the number latex spits out
WIDTH = 379.41753 # optics express
# FACTOR = 0.45 # the fraction of the width you'd like the figure to occupy
FACTOR = 0.9 # the fraction of the width you'd like the figure to occupy
# FACTOR = 1 # the fraction of the width you'd like the figure to occupy
fig_width_pt = WIDTH * FACTOR
inches_per_pt = 1.0 / 72.27
golden_ratio = (np.sqrt(5) - 1.0) / 2.0 # because it looks good
fig_width_in = fig_width_pt * inches_per_pt # figure width in inches
fig_height_in = fig_width_in * golden_ratio # figure height in inches
fig_dims = [fig_width_in, fig_height_in] # fig dims as a list
# adapted from https://tex.stackexchange.com/questions/391074/how-to-use-the-siunitx-package-within-python-matplotlib?noredirect=1 to make siunitx work with pdf
rcparams_with_latex_stix = { # setup matplotlib to use latex for output
"mathtext.fontset": 'stix',
"font.family": "serif",
"font.serif": ['STIX Two Text'], # not working in texmode, just uses cm
"font.sans-serif": ['Helvetica'], # to inherit fonts from the document
"font.monospace": [],
"axes.labelsize": 9, # LaTeX default is 10pt font.
"font.size": 9,
"legend.fontsize": 8, # Make the legend/label fonts
"xtick.labelsize": 8, # a little smaller
"ytick.labelsize": 8,
"figure.figsize": fig_dims, # default fig size of 0.9 textwidth
"figure.dpi": 300,
"text.latex.preamble": [
# r"\usepackage[utf8x]{inputenc}", # use utf8 fonts
r"\usepackage[T1]{fontenc}", # plots will be generated
r'\usepackage{lmodern}', # otherwise savefig to pdf will produce an error!!
r"\usepackage[detect-all,locale=US]{siunitx}",
# r"\usepackage{amsmath}",
# r"\usepackage{stix}"
# r"\usepackage{stix2-type1}"
], # using this preamble
"text.usetex": True, # use LaTeX to write all text
}
#mpl.rcParams.update(pgf_with_latex)
# mpl.rcParams.update(rcparams_with_latex_stix)
# adapted from https://tex.stackexchange.com/questions/391074/how-to-use-the-siunitx-package-within-python-matplotlib?noredirect=1 to make siunitx work with pdf
rcparams_without_latex = { # setup matplotlib to use latex for output
"mathtext.fontset": 'stix',
"font.family": "serif",
"font.serif": ['STIX Two Text'], # blank entries should cause plots
"font.sans-serif": ['stixsans'], # to inherit fonts from the document
"font.monospace": [],
"axes.labelsize": 9, # LaTeX default is 10pt font.
"font.size": 9,
"legend.fontsize": 8, # Make the legend/label fonts
"xtick.labelsize": 8, # a little smaller
"ytick.labelsize": 8,
"figure.figsize": fig_dims, # default fig size of 0.9 textwidth
"figure.dpi": 150,
"text.usetex": False, # use LaTeX to write all text
}
#mpl.rcParams.update(pgf_with_latex)
mpl.rcParams.update(rcparams_without_latex)
# %%
# move this to a common module:
def get_sep_and_orient(pinholes):
pinholes = pinholes[0:2]
choices = {'1a': (50, 'vertical'), '1b': (707, 'vertical'),'1c': (50, 'horizontal'), '1d': (707, 'horizontal'),
'2a': (107, 'vertical'), '2b': (890, 'vertical'), '2c': (107, 'horizontal'), '2d': (890, 'horizontal'),
'3a': (215, 'vertical'), '3b': (1047, 'vertical'), '3c': (215, 'horizontal'), '3d': (1047, 'horizontal'),
'4a': (322, 'vertical'), '4b': (1335, 'vertical'), '4c': (322, 'horizontal'), '4d': (1335, 'horizontal'),
'5a': (445, 'vertical'), '5b': (1570, 'vertical'), '5c': (445, 'horizontal'), '5d': (1570, 'horizontal')}
(sep, orient) = choices.get(pinholes,(np.nan,'bg'))
return sep, orient
"""# Load dph settings and combinations"""
datasets_py_file = str(Path.joinpath(data_dir, "datasets.py"))
# datasets_py_file = str(Path.joinpath(data_dir, "datasets_deconvolution_failing.py"))
# datasets_py_file = str(Path.joinpath(data_dir, "datasets_fitting_failing.py"))
# Commented out IPython magic to ensure Python compatibility.
# %run -i $dph_settings_py # see https://stackoverflow.com/a/14411126 and http://ipython.org/ipython-doc/dev/interactive/magics.html#magic-run
# see also https://stackoverflow.com/questions/4383571/importing-files-from-different-folder to import as a module,
# requires however that it is located in a folder with an empty __init__.py
exec(open(datasets_py_file).read())
dph_settings_py_file = str(Path.joinpath(data_dir, "dph_settings.py"))
# Commented out IPython magic to ensure Python compatibility.
# %run -i $dph_settings_py # see https://stackoverflow.com/a/14411126 and http://ipython.org/ipython-doc/dev/interactive/magics.html#magic-run
# see also https://stackoverflow.com/questions/4383571/importing-files-from-different-folder to import as a module,
# requires however that it is located in a folder with an empty __init__.py
exec(open(dph_settings_py_file).read())
# %%
# import sys
# sys.path.append('g:\\My Drive\\PhD\\coherence\data\\dph_settings_package\\')
# from dph_settings_package import dph_settings_module
datasets_widget_layout = widgets.Layout(width="30%")
datasets_widget = widgets.Dropdown(options=list(datasets), layout=datasets_widget_layout, description='Dataset:')
# settings_widget.observe(update_settings, names='value')
# display(dph_settings_widget)
# initialize a dictionary holding a selection of measurements
datasets_selection_py_files = sorted(list(data_dir.glob("datasets_selection*.py")), reverse=True)
datasets_selection_py_files_widget_layout = widgets.Layout(width="30%")
datasets_selection_py_files_widget = widgets.Dropdown(
options=datasets_selection_py_files,
value=datasets_selection_py_files[0], # use newest available file per default
layout=datasets_selection_py_files_widget_layout,
description='Datasets selection file::'
)
datasets_selection_py_file = datasets_selection_py_files_widget.value
if os.path.isfile(datasets_selection_py_file):
exec(open(datasets_selection_py_file).read())
create_new_datasets_selection_py_file_widget = widgets.ToggleButton(
value=False,
description='create new file',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='save df_fits to new csv file',
icon='check'
)
def create_new_datasets_selection_py_file(change):
datasets_selection_py_file = datasets_selection_py_files_widget.value
new_datasets_selection_py_file = Path.joinpath(data_dir,str('datasets_selection_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.py'))
with open(datasets_selection_py_file) as f:
text = f.read()
with open(new_datasets_selection_py_file,'w') as f:
f.write(text)
datasets_selection_py_files = sorted(list(data_dir.glob("datasets_selection*.py")), reverse=True)
datasets_selection_py_files_widget.options = datasets_selection_py_files
datasets_selection_py_files_widget.value = datasets_selection_py_files[0]
create_new_datasets_selection_py_file_widget.value = False
create_new_datasets_selection_py_file_widget.observe(create_new_datasets_selection_py_file, names='value')
# else:
# datasets_selection = datasets.copy()
# dph_settings_widget_layout = widgets.Layout(width="100%")
# dph_settings_widget = widgets.Dropdown(options=dph_settings, layout=dph_settings_widget_layout)
# settings_widget.observe(update_settings, names='value')
# display(dph_settings_widget)
# dph_settings_bgsubtracted = list(bgsubtracted_dir.glob("*.h5"))
dph_settings_bgsubtracted = []
for pattern in ['*'+ s + '.h5' for s in datasets[datasets_widget.value]]:
dph_settings_bgsubtracted.extend(bgsubtracted_dir.glob(pattern))
dph_settings_bgsubtracted_widget_layout = widgets.Layout(width="50%")
dph_settings_bgsubtracted_widget = widgets.Dropdown(
options=dph_settings_bgsubtracted,
layout=dph_settings_bgsubtracted_widget_layout,
description='Measurement:'
# value=dph_settings_bgsubtracted[3], # workaround, because some hdf5 files have no proper timestamp yet
)
# settings_widget.observe(update_settings, names='value')
measurements_selection_files = []
for pattern in ['*'+ s + '.h5' for s in datasets_selection[datasets_widget.value]]:
measurements_selection_files.extend(bgsubtracted_dir.glob(pattern))
measurements_selection_widget_layout = widgets.Layout(width="100%")
measurements_selection_widget = widgets.SelectMultiple(
options=dph_settings_bgsubtracted,
value=measurements_selection_files,
layout=measurements_selection_widget_layout,
description='Measurement:'
# value=dph_settings_bgsubtracted[3], # workaround, because some hdf5 files have no proper timestamp yet
)
# just hdf5_filename_bg_subtracted so we can use it to search in the dataframe
# dph_settings_bgsubtracted_widget.value.name
# how to get the hdf5_filename ?
# with h5py.File(dph_settings_bgsubtracted_widget.label, "r") as hdf5_file:
# hdf5_file_useful_name = hdf5_file["/hdf5_file_useful_name"][0]
# print(hdf5_file_useful_name)
"""# Load dataframes from csv"""
# dataframe of extracted from all available useful hdf5 files
df_all = pd.read_csv(Path.joinpath(data_dir, "df_all.csv"), index_col=0)
# maybe rename to df_hdf5_files? and then use df instead of df0?
df_all["imageid"] = df_all.index
# dataframe based on the dph_settings dictionary inside dph_settings.py
# del df_settings
dph_settings_keys = []
hdf5_file_name = []
hdf5_file_name_background = []
setting_wavelength_nm = []
setting_energy_uJ = []
setting_undulators = []
KAOS = []
separation_um = []
pinholes = []
background = []
for idx in range(len(dph_settings.keys())):
dph_settings_keys.append(list(dph_settings.keys())[idx])
hdf5_file_name.append(dph_settings[list(dph_settings.keys())[idx]][2])
hdf5_file_name_background.append(dph_settings[list(dph_settings.keys())[idx]][0])
setting_wavelength_nm.append(float(list(dph_settings.keys())[idx].split()[1][:-2]))
setting_energy_uJ.append(int(list(dph_settings.keys())[idx].split()[2][:-2]))
setting_undulators.append(int(list(dph_settings.keys())[idx].split()[3][:-4]))
KAOS.append(list(dph_settings.keys())[idx].split()[4][5:])
separation_um.append(int(list(dph_settings.keys())[idx].split()[5][:-2]))
pinholes.append((dph_settings[list(dph_settings.keys())[idx]][3][2]))
background.append((dph_settings[list(dph_settings.keys())[idx]][1][2]))
df_settings = pd.DataFrame(
{
"dph_settings": dph_settings_keys,
"hdf5_file_name": hdf5_file_name,
"hdf5_file_name_background": hdf5_file_name_background,
"setting_wavelength_nm": setting_wavelength_nm,
"setting_energy_uJ": setting_energy_uJ,
"setting_undulators": setting_undulators,
"KAOS": KAOS,
"separation_um": separation_um,
"pinholes": pinholes,
"background": background,
}
)
# dph_settings_bgsubtracted_widget.value.name.split('.h5')[0]
df_settings[df_settings['dph_settings'] == dph_settings_bgsubtracted_widget.value.name.split('.h5')[0]]
# df_settings
# merge dataframe of hdf5files with dataframe of settings
df_temp = []
df_temp = pd.merge(df_all, df_settings)
df_temp["timestamp_pulse_id"] = df_temp["timestamp_pulse_id"].astype("int64")
# store this instead of df_all?
# definition of fits header columns
# needed in case we want to add new columns?
# preparation parameter and results
fits_header_list1 = [
"bgfactor",
"pixis_rotation",
"pixis_centerx_px",
"pixis_centery_px",
"pinholes_centerx_px",
"pinholes_centery_px",
"pixis_profile_centerx_px_fit",
"pixis_profile_centery_px_fit",
"pinholes_cm_x_px",
"pinholes_cm_y_px",
]
fits_header_list2 = [
"pixis_image_minus_bg_rot_cropped_counts",
"phcam_scalex_um_per_px",
"phcam_scaley_um_per_px",
"phap_diam_um",
"phap_xc_px",
"phap_yc_px",
"phap_width_px",
"phap_height_px",
"pinholes_bg_avg_phi",
"pinholes_bg_avg_xc_um",
"pinholes_bg_avg_yc_um",
"pinholes_bg_avg_sx_um",
"pinholes_bg_avg_sy_um",
]
# CDC results
fits_header_list3 = [
"xi_um_fit",
"xi_x_um_fit",
"xi_y_um_fit",
"zeta_x",
"zeta_x_fit",
"zeta_y",
"zeta_y_fit",
]
# fitting parameter
fits_header_list4 = [
'pixis_profile_avg_width',
'crop_px',
'shiftx_um',
'shiftx_um_range_0',
'shiftx_um_range_1',
'shiftx_um_do_fit',
'wavelength_nm',
'wavelength_nm_range_0',
'wavelength_nm_range_1',
'wavelength_nm_do_fit',
'z_mm',
'z_mm_range_0',
'z_mm_range_1',
'z_mm_do_fit',
'd_um',
'd_um_range_0',
'd_um_range_1',
'd_um_do_fit',
'gamma',
'gamma_range_0',
'gamma_range_1',
'gamma_do_fit',
'w1_um',
'w1_um_range_0',
'w1_um_range_1',
'w1_um_do_fit',
'w2_um',
'w2_um_range_0',
'w2_um_range_1',
'w2_um_do_fit',
'I_Airy1',
'I_Airy1_range_0',
'I_Airy1_range_1',
'I_Airy1_do_fit',
'I_Airy2',
'I_Airy2_range_0',
'I_Airy2_range_1',
'I_Airy2_do_fit',
'x1_um',
'x1_um_range_0',
'x1_um_range_1',
'x1_um_do_fit',
'x2_um',
'x2_um_range_0',
'x2_um_range_1',
'x2_um_do_fit',
'normfactor',
'normfactor_range_0',
'normfactor_range_1',
'normfactor_do_fit'
]
fits_header_list4_v1 = []
for header in fits_header_list4:
fits_header_list4_v1.append(header + '_v1')
fits_header_list4_v2 = []
for header in fits_header_list4:
fits_header_list4_v2.append(header + '_v2')
# fitting parameter of version 2
fits_header_list5 = [
'mod_sigma_um',
'mod_sigma_um_range_0',
'mod_sigma_um_range_1',
'mod_sigma_um_do_fit',
'mod_shiftx_um',
'mod_shiftx_um_range_0',
'mod_shiftx_um_range_1',
'mod_shiftx_um_do_fit'
]
fits_header_list5_v2 = []
for header in fits_header_list5:
fits_header_list5_v2.append(header + '_v2')
# fitting results
fits_header_list6a = [
"shiftx_um_fit",
"wavelength_nm_fit",
"z_mm_fit",
"d_um_fit",
"d_um_at_detector", # extra?
"gamma_fit",
"w1_um_fit",
"w2_um_fit",
"I_Airy1_fit",
"I_Airy2_fit",
"x1_um_fit",
"x2_um_fit",
'chi2distance_fitting'
]
fits_header_list6a_v1 = []
for header in fits_header_list6a:
fits_header_list6a_v1.append(header + '_v1')
# fitting results
fits_header_list6b = [
# fitting results of version 2
'mod_sigma_um_fit',
'mod_shiftx_um_fit',
'gamma_fit_v2', # at center
'xi_um_fit_v2' # at center
]
# deconvolution parameter
fits_header_list7 = [
'balance',
'snr_db',
'pixis_profile_avg_width',
'crop_px',
'xi_um_guess',
'sigma_x_F_gamma_um_multiplier',
'xatol'
]
# deconvolution parameter v1
fits_header_list7_v1 = [
'pixis_profile_avg_width',
'crop_px',
'sigma_x_F_gamma_um_min',
'sigma_x_F_gamma_um_max',
'sigma_x_F_gamma_um_stepsize',
'sigma_y_F_gamma_um_min',
'sigma_y_F_gamma_um_max',
'sigma_y_F_gamma_um_stepsize'
]
# deconvolution_1d results
fits_header_list8 = [
"xi_um",
"chi2distance_deconvmethod_1d"
]
# deconvolution_2d results
fits_header_list9 = [
"sigma_F_gamma_um_opt",
"xi_x_um",
"xi_y_um",
"chi2distance_deconvmethod_2d"
]
fits_header_list9_v1 = []
for header in fits_header_list9:
fits_header_list9_v1.append(header + '_v1')
fits_header_list8_v2 = []
for header in fits_header_list9:
fits_header_list8_v2.append(header + '_v2')
fits_header_list9_v2 = []
for header in fits_header_list9:
fits_header_list9_v2.append(header + '_v2')
fits_header_list8_v3 = []
for header in fits_header_list9:
fits_header_list8_v3.append(header + '_v3')
fits_header_list9_v3 = []
for header in fits_header_list9:
fits_header_list9_v1.append(header + '_v3')
fits_header_list = fits_header_list1 + fits_header_list2 + fits_header_list3 + fits_header_list4 + fits_header_list5 + fits_header_list6a + fits_header_list6b + fits_header_list7 + fits_header_list8 + fits_header_list9
# fits_header_list1 already exists in saved csv, only adding fits_header_list2, only initiate when
initiate_df_fits = True
# if initiate_df_fits == True:
# df0 = df0.reindex(columns = df0.columns.tolist() + fits_header_list)
# df_fits = df0[['timestamp_pulse_id'] + fits_header_list]
# load saved df_fits from csv
df_fits_csv_files = sorted(list(results_dir.glob("df_fits*.csv")), reverse=True) # newest on top
df_fits_csv_file = df_fits_csv_files[0] # use the newest
df_fits = pd.read_csv(df_fits_csv_file, index_col=0)
df_fits_clean = df_fits[df_fits["pixis_rotation"].notna()].drop_duplicates()
df_fits = df_fits_clean
df_fits = df_fits.reindex(columns = df_fits.columns.tolist() + list(set(fits_header_list) - set(df_fits.columns.tolist())) )
df0 = pd.merge(df_temp, df_fits, on="timestamp_pulse_id", how="outer")
# %% default values per measurement
measurement_arr = []
dataset_arr = []
for dataset in list(datasets):
for measurement in datasets[dataset]:
measurement_arr.append(measurement)
dataset_arr.append(dataset)
df_fitting_v1_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_fitting_v2_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_deconvmethod_2d_v1_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_deconvmethod_1d_v2_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_deconvmethod_2d_v2_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_deconvmethod_1d_v3_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
df_deconvmethod_2d_v3_measurement_default = pd.DataFrame({'dataset' : dataset_arr,
'measurement' : measurement_arr})
fitting_v1_measurement_default_headers = []
for header in fits_header_list4:
fitting_v1_measurement_default_headers.append(header + '_measurement_default')
fitting_v2_measurement_default_headers = []
for header in fits_header_list4 + fits_header_list5:
fitting_v2_measurement_default_headers.append(header + '_measurement_default')
deconvmethod_2d_v1_measurement_default_headers = []
for header in fits_header_list7_v1:
deconvmethod_2d_v1_measurement_default_headers.append(header + '_measurement_default')
deconvmethod_1d_v2_measurement_default_headers = []
for header in fits_header_list7:
deconvmethod_1d_v2_measurement_default_headers.append(header + '_measurement_default')
deconvmethod_2d_v2_measurement_default_headers = []
for header in fits_header_list7:
deconvmethod_2d_v2_measurement_default_headers.append(header + '_measurement_default')
deconvmethod_1d_v3_measurement_default_headers = []
for header in fits_header_list7:
deconvmethod_1d_v3_measurement_default_headers.append(header + '_measurement_default')
deconvmethod_2d_v3_measurement_default_headers = []
for header in fits_header_list7:
deconvmethod_2d_v3_measurement_default_headers.append(header + '_measurement_default')
df_fitting_v1_measurement_default = df_fitting_v1_measurement_default.reindex(columns = df_fitting_v1_measurement_default.columns.tolist() + list(set(fitting_v1_measurement_default_headers) - set(df_fitting_v1_measurement_default.columns.tolist())) )
df_fitting_v2_measurement_default = df_fitting_v2_measurement_default.reindex(columns = df_fitting_v2_measurement_default.columns.tolist() + list(set(fitting_v2_measurement_default_headers) - set(df_fitting_v2_measurement_default.columns.tolist())) )
df_deconvmethod_2d_v1_measurement_default = df_deconvmethod_2d_v1_measurement_default.reindex(columns = df_deconvmethod_2d_v1_measurement_default.columns.tolist() + list(set(deconvmethod_2d_v1_measurement_default_headers) - set(df_deconvmethod_2d_v1_measurement_default.columns.tolist())) )
df_deconvmethod_1d_v2_measurement_default = df_deconvmethod_1d_v2_measurement_default.reindex(columns = df_deconvmethod_1d_v2_measurement_default.columns.tolist() + list(set(deconvmethod_1d_v2_measurement_default_headers) - set(df_deconvmethod_1d_v2_measurement_default.columns.tolist())) )
df_deconvmethod_2d_v2_measurement_default = df_deconvmethod_2d_v2_measurement_default.reindex(columns = df_deconvmethod_2d_v2_measurement_default.columns.tolist() + list(set(deconvmethod_2d_v2_measurement_default_headers) - set(df_deconvmethod_2d_v2_measurement_default.columns.tolist())) )
df_deconvmethod_1d_v3_measurement_default = df_deconvmethod_1d_v3_measurement_default.reindex(columns = df_deconvmethod_1d_v3_measurement_default.columns.tolist() + list(set(deconvmethod_1d_v3_measurement_default_headers) - set(df_deconvmethod_1d_v3_measurement_default.columns.tolist())) )
df_deconvmethod_2d_v3_measurement_default = df_deconvmethod_2d_v3_measurement_default.reindex(columns = df_deconvmethod_2d_v3_measurement_default.columns.tolist() + list(set(deconvmethod_2d_v3_measurement_default_headers) - set(df_deconvmethod_2d_v3_measurement_default.columns.tolist())) )
# create empyt csv, will cause errors since there are no values
# df_measurement_default_file = Path.joinpath(results_dir, 'df_fitting_v2_measurement_default.csv')
# df_fitting_v2_measurement_default.to_csv(df_measurement_default_file, columns=['dataset','measurement']+fitting_v2_measurement_default_headers)
# store also 'measurement' into df_fits to be able to cross-correlate!
df_measurement_default_file = Path.joinpath(results_dir, 'df_fitting_v1_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_fitting_v1_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_fitting_v2_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_fitting_v2_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_deconvmethod_2d_v1_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_deconvmethod_2d_v1_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_deconvmethod_1d_v2_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_deconvmethod_1d_v2_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_deconvmethod_2d_v2_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_deconvmethod_2d_v2_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_deconvmethod_1d_v3_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_deconvmethod_1d_v3_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_measurement_default_file = Path.joinpath(results_dir, 'df_deconvmethod_2d_v3_measurement_default.csv')
if os.path.isfile(df_measurement_default_file):
df_deconvmethod_2d_v3_measurement_default = pd.read_csv(df_measurement_default_file,index_col=0)
df_fitting_v1_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + fits_header_list4 + fits_header_list5 + fits_header_list6a_v1 )
df_fitting_v2_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + fits_header_list4 + fits_header_list5 + fits_header_list6a + fits_header_list6b )
df_deconvmethod_2d_v1_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + fits_header_list7_v1 + fits_header_list9_v1)
df_deconvmethod_1d_v2_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + list(set(fits_header_list7) - set(['xatol'])) + fits_header_list8_v2)
# create fits_header_list_v2 and v3????
df_deconvmethod_2d_v2_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + fits_header_list7 + fits_header_list9_v2)
df_deconvmethod_1d_v3_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + list(set(fits_header_list7) - set(['xatol'])) + fits_header_list8_v3)
df_deconvmethod_2d_v3_results = pd.DataFrame(columns=['measurement','timestamp_pulse_id','imageid','separation_um'] + fits_header_list7 + fits_header_list9_v3)
df_CDC_results = pd.DataFrame(columns=[
'dataset',
'orientation',
'fittingmethod',
'deconvmethod',
'undulators',
'wavelength_nm',
'sigma_B_um', 'sigma_B_err_um',
'xi_fitting_um', 'xi_fitting_std_um',
'xi_deconv_um', 'xi_deconv_std_um',
'zeta_fitting_um', 'zeta_fitting_std_um',
'zeta_deconv_um', 'zeta_deconv_std_um'
]
)
# load previously determined beamsizes
df_beamsize_file = Path.joinpath(data_dir, 'df_beamsize.csv')
if os.path.isfile(df_beamsize_file):
df_beamsize = pd.read_csv(df_beamsize_file,index_col=0)
# %%
# creating frontend
# Widget definitions
n = 1024 # number of sampling point # number of pixels
fittingprogress_widget = widgets.IntProgress(
value=0,
min=0,
max=10,
step=1,
description="Progress:",
bar_style="success", # 'success', 'info', 'warning', 'danger' or ''
orientation="horizontal",
)
statustext_widget = widgets.Text(value="", placeholder="status", description="", disabled=False)
do_plot_fitting_v1_widget = widgets.Checkbox(value=False, description='', tooltip="do_fitting_v1", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_fitting_v2_widget = widgets.Checkbox(value=False, description='', tooltip="do_fitting_v2", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_deconvmethod_2d_v1_widget = widgets.Checkbox(value=False, description='', tooltip="deconvmethod_2d_v1", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_deconvmethod_1d_v2_widget = widgets.Checkbox(value=False, description='', tooltip="deconvmethod_1d_v2", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_deconvmethod_2d_v2_widget = widgets.Checkbox(value=False, description='', tooltip="deconvmethod_2d_v2", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_deconvmethod_1d_v3_widget = widgets.Checkbox(value=False, description='', tooltip="deconvmethod_1d_v3", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
do_plot_deconvmethod_2d_v3_widget = widgets.Checkbox(value=False, description='', tooltip="deconvmethod_2d_v3", disabled=False, indent = False, layout=widgets.Layout(width='auto'))
timestamp_pulse_id_widget_layout = widgets.Layout(width="auto")
timestamp_pulse_id_widget = widgets.Dropdown(
options=[],
description="timestamp_pulse_id:",
disabled=False,
layout=timestamp_pulse_id_widget_layout,
indent = False
)
imageid_widget_layout = widgets.Layout(width="auto")
imageid_widget = widgets.Dropdown(
options=[],
description="imageid:",
disabled=False,
layout=imageid_widget_layout,
indent = False
)
imageid_index_widget_layout = widgets.Layout(width="auto")
imageid_index_widget = widgets.BoundedIntText(
options=[],
description="idx",
disabled=False,
layout=imageid_index_widget_layout,
indent = False
)
savefigure_profile_fit_widget = widgets.Checkbox(value=False, description="savefigure", disabled=False)
# dataframe and csv widgets
save_to_df_widget = widgets.Checkbox(value=False, description="save_to_df", disabled=False)
load_from_df_widget = widgets.Checkbox(value=False, description="load_from_df", disabled=False)
df_fits_csv_files = sorted(list(results_dir.glob("df_fits*.csv")), reverse=True)
df_fits_csv_files_widget_layout = widgets.Layout(width="50%")
df_fits_csv_files_widget = widgets.Dropdown(
options=df_fits_csv_files,
value=df_fits_csv_files[0], # use newest available file per default
layout=df_fits_csv_files_widget_layout,
description='csv file:'
)
scan_for_df_fits_csv_files_widget = widgets.ToggleButton(
value=False,
description='scan for df_fits*.csv',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='scan for df_fits*.csv',
icon='check'
)
def update_df_fits_csv_files_widget(change):
df_fits_csv_files = sorted(list(results_dir.glob("df_fits*.csv")), reverse=True)
df_fits_csv_files_widget.options=df_fits_csv_files
scan_for_df_fits_csv_files_widget.value = False
scan_for_df_fits_csv_files_widget.observe(update_df_fits_csv_files_widget)
load_csv_to_df_widget = widgets.ToggleButton(
value=False,
description='csv-->df',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='load from csv to dataframe',
icon='check'
)
def update_load_csv_to_df_widget(change):
global df0
global df_fitting_v1_results
global df_fitting_v2_results
# global df_deconvmethod_1d_v1_results
global df_deconvmethod_2d_v1_results
global df_deconvmethod_1d_v2_results
global df_deconvmethod_2d_v2_results
global df_deconvmethod_1d_v3_results
global df_deconvmethod_2d_v3_results
df_fits_csv_file = df_fits_csv_files_widget.value
df_fits = pd.read_csv(df_fits_csv_file, index_col=0)
df_fits_clean = df_fits[df_fits["pixis_rotation"].notna()].drop_duplicates()
df_fits = df_fits_clean
df0 = pd.merge(df_temp, df_fits, on="timestamp_pulse_id", how="outer")
datestring = os.path.splitext(os.path.basename(df_fits_csv_files_widget.value))[0].split('df_fits_')[1]
df_fitting_v1_results_file = Path.joinpath(results_dir,str('df_fitting_v1_results_'+datestring+'.csv'))
df_fitting_v1_results = pd.read_csv(df_fitting_v1_results_file, index_col=0)
df_fitting_v2_results_file = Path.joinpath(results_dir,str('df_fitting_v2_results_'+datestring+'.csv'))
df_fitting_v2_results = pd.read_csv(df_fitting_v2_results_file, index_col=0)
# df_deconvmethod_1d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v1_results_'+datestring+'.csv'))
# df_deconvmethod_1d_v1_results = pd.read_csv(df_deconvmethod_1d_v1_results_file, index_col=0)
df_deconvmethod_2d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v1_results_'+datestring+'.csv'))
df_deconvmethod_2d_v1_results = pd.read_csv(df_deconvmethod_2d_v1_results_file, index_col=0)
df_deconvmethod_1d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v2_results_'+datestring+'.csv'))
df_deconvmethod_1d_v2_results = pd.read_csv(df_deconvmethod_1d_v2_results_file, index_col=0)
df_deconvmethod_2d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v2_results_'+datestring+'.csv'))
df_deconvmethod_2d_v2_results = pd.read_csv(df_deconvmethod_2d_v2_results_file, index_col=0)
df_deconvmethod_1d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v3_results_'+datestring+'.csv'))
df_deconvmethod_1d_v3_results = pd.read_csv(df_deconvmethod_1d_v3_results_file, index_col=0)
df_deconvmethod_2d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v3_results_'+datestring+'.csv'))
df_deconvmethod_2d_v3_results = pd.read_csv(df_deconvmethod_2d_v3_results_file, index_col=0)
load_csv_to_df_widget.value = False
load_csv_to_df_widget.observe(update_load_csv_to_df_widget)
df_fits_csv_save_widget = widgets.ToggleButton(
value=False,
description='df-->csv',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='save df_fits to csv',
icon='check'
)
def update_df_fits_csv_save_widget(change):
if df_fits_csv_save_widget.value == True:
df_fits = df0[['timestamp_pulse_id'] + list(set(fits_header_list) - set(['chi2distance_fitting', 'chi2distance_deconvmethod_1d', 'chi2distance_deconvmethod_2d']))]
df_fits_csv_file = df_fits_csv_files_widget.value
df_fits.to_csv(df_fits_csv_file)
datestring = os.path.splitext(os.path.basename(df_fits_csv_files_widget.value))[0].split('df_fits_')[1]
df_fitting_v1_results_file = Path.joinpath(results_dir,str('df_fitting_v1_results_'+datestring+'.csv'))
df_fitting_v1_results.to_csv(df_fitting_v1_results_file)
df_fitting_v2_results_file = Path.joinpath(results_dir,str('df_fitting_v2_results_'+datestring+'.csv'))
df_fitting_v2_results.to_csv(df_fitting_v2_results_file)
# df_deconvmethod_1d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v1_results_'+datestring+'.csv'))
# df_deconvmethod_1d_v1_results.to_csv(df_deconvmethod_1d_v1_results_file)
df_deconvmethod_2d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v1_results_'+datestring+'.csv'))
df_deconvmethod_2d_v1_results.to_csv(df_deconvmethod_2d_v1_results_file)
df_deconvmethod_1d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v2_results_'+datestring+'.csv'))
df_deconvmethod_1d_v2_results.to_csv(df_deconvmethod_1d_v2_results_file)
df_deconvmethod_2d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v2_results_'+datestring+'.csv'))
df_deconvmethod_2d_v2_results.to_csv(df_deconvmethod_2d_v2_results_file)
df_deconvmethod_1d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v3_results_'+datestring+'.csv'))
df_deconvmethod_1d_v3_results.to_csv(df_deconvmethod_1d_v3_results_file)
df_deconvmethod_2d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v3_results_'+datestring+'.csv'))
df_deconvmethod_2d_v3_results.to_csv(df_deconvmethod_2d_v3_results_file)
df_fits_csv_save_widget.value = False
df_fits_csv_save_widget.observe(update_df_fits_csv_save_widget, names='value')
create_new_csv_file_widget = widgets.ToggleButton(
value=False,
description='df-->new csv',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='save df_fits to new csv file',
icon='check'
)
def create_new_csv_file(change):
df_fits_csv_file = Path.joinpath(results_dir,str('df_fits_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_fits = df0[['timestamp_pulse_id'] + list(set(fits_header_list) - set(['chi2distance_fitting', 'chi2distance_deconvmethod_1d', 'chi2distance_deconvmethod_2d']))]
df_fits.to_csv(df_fits_csv_file)
df_fits_csv_files = sorted(list(results_dir.glob("df_fits*.csv")), reverse=True)
df_fits_csv_files_widget.options=df_fits_csv_files
df_fits_csv_files_widget.value = df_fits_csv_file
df_fitting_v1_results_file = Path.joinpath(results_dir,str('df_fitting_v1_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_fitting_v1_results.to_csv(df_fitting_v1_results_file)
df_fitting_v2_results_file = Path.joinpath(results_dir,str('df_fitting_v2_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_fitting_v2_results.to_csv(df_fitting_v2_results_file)
# df_deconvmethod_1d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v1_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
# df_deconvmethod_1d_v1_results.to_csv(df_deconvmethod_1d_v1_results_file)
df_deconvmethod_2d_v1_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v1_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_deconvmethod_2d_v1_results.to_csv(df_deconvmethod_2d_v1_results_file)
df_deconvmethod_1d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v2_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_deconvmethod_1d_v2_results.to_csv(df_deconvmethod_1d_v2_results_file)
df_deconvmethod_2d_v2_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v2_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_deconvmethod_2d_v2_results.to_csv(df_deconvmethod_2d_v2_results_file)
df_deconvmethod_1d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_1d_v3_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_deconvmethod_1d_v3_results.to_csv(df_deconvmethod_1d_v3_results_file)
df_deconvmethod_2d_v3_results_file = Path.joinpath(results_dir,str('df_deconvmethod_2d_v3_results_'+datetime.now().strftime("%Y-%m-%d--%Hh%M")+'.csv'))
df_deconvmethod_2d_v3_results.to_csv(df_deconvmethod_2d_v3_results_file)
create_new_csv_file_widget.value = False
create_new_csv_file_widget.observe(create_new_csv_file, names='value')
# run widgets
## run_over_all_images_widget
run_over_all_images_widget = widgets.ToggleButton(