def save_params_to_file(self, filepath=None):
if filepath is None:
data_file = df.create_file(set_current=False, mode='a')
else:
data_file = df.DataFile(filepath)
data_file.create_dataset(name='AndorSettings', data=[], attrs=self.get_andor_parameters())
data_file.close()
def main(filepath, debug=0):
f = df.DataFile(filepath, "r")
g = f["calibration"]
keys = list(g.keys())
center_wavelengths = []
for k in keys:
center_wavelengths = center_wavelengths + [
g[k].attrs["center_wavelength"]
]
center_wavelengths = sorted(np.unique(center_wavelengths))
print("main:max center wavelength:", np.max(center_wavelengths))
dataset = []
for cw in center_wavelengths:
entry = (cw, [], [])
for k in keys:
if g[k].attrs["center_wavelength"] == cw:
entry[2].append(g[k].attrs["laser_wavelength"])
entry[1].append(np.array(g[k]))
dataset.append(entry)
# print "-="*10
# for d in dataset:
# print d
# print "-="*10
mapper = scan_fit(dataset, debug=debug)
return mapper
def initialize_datafile(path):
"""
This function defines the format of the group structure
Parameters are path on filesystem
"""
f = df.DataFile(path, 'a')
return f
def load_params_from_file(self, filepath=None):
if filepath is None:
data_file = df.open_file(set_current=False, mode='r')
else:
data_file = df.DataFile(filepath)
if 'AndorSettings' in list(data_file.keys()):
self.set_andor_parameters(dict(data_file['AndorSettings'].attrs))
else:
self._logger.error('Load settings failed as "AndorSettings" does not exist')
data_file.close()
def initialize_measurement():
f = df.DataFile("ir_calibration.hdf5", "a")
g = f.require_group("calibration")
print("Starting..")
print("Pixis...")
p = Pixis(debug=1)
p.StartUp()
print("Acton...")
act = Acton("COM7", debug=1)
print("Done...")
pacton = Pacton(pixis=p, acton=act)
print("Measuring...")
fig, ax = plt.subplots(1)
p.SetExposureTime(500)
pacton.get_pixel_response_calibration_spectrum()
return pacton, g
def main(debug=0):
#Measurements and Calibration files
measurement_file = df.DataFile("measured_spectrum.hdf5", "r")
reference_file = df.DataFile(
"maxwell_room_light_spectrum_calibration.hdf5", "r")
#Load reference within given range
ref_xs, ref_ys = load_reference_data(reference_file,
lower_wavelength=430,
upper_wavelength=714)
#Load measured data from file
data = load_measured_data(measurement_file)
center_wavelengths = [d[0] for d in data]
#Load the raw counts from the measured data
#Merge individual spectra (using median) and normalise to set minimum value to zero
signal_spectrum, mapper_1 = median_spectrum(data, debug=1)
signal_spectrum = zero_min(signal_spectrum)
#handcrafted truncation to "valid range" where we can see peaks & that matches the reference spectrum
signal_spectrum = signal_spectrum[1550:10570]
#Apply smoothing to the measured signal - want to eliminate most false peaks
signal_spectrum = SUREShrink(signal_spectrum)
signal_spectrum, _, _ = convex_smooth(signal_spectrum, 1.0)
#rescale the reference and fit a line to the wavelength range [nm]
ref_xs, ref_ys, gradient, offset = rescale_reference(
xs=ref_xs,
ys=ref_ys,
max_size=np.nanmax(signal_spectrum),
N=len(signal_spectrum),
debug=1)
#Get peaks from the signal, with thresholding to eliminate low order maxima/minima
signal_peaks = get_peaks(signal_spectrum, threshold=1.0)
ref_peaks = get_peaks(ref_ys, threshold=2.0)
#Link peaks, handcrated to ignore false peaks
interpolator_function, interpolator_bounds, lines = link_peaks(
signal_peaks=signal_peaks,
reference_peaks=ref_peaks,
ignored_signal_indices=[3, 8, 11, 13, 19, 22, 23],
ignored_reference_indices=[11, 14, 18],
debug=debug)
if debug > 0:
fig, ax = plt.subplots(1, figsize=(12, 10))
ax.set_title("fname: main, debug 0")
ax.plot(ref_ys, "-", label="Reference")
ax.plot(signal_spectrum, label="Signal")
ax.plot(ref_peaks[0, :], ref_peaks[1, :], "o", label="Reference peaks")
t1s = annotate_points(ref_peaks[0, :], ref_peaks[1, :], ax)
ax.plot(signal_peaks[0, :],
signal_peaks[1, :],
"o",
label="Signal peaks")
t2s = annotate_points(signal_peaks[0, :], signal_peaks[1, :], ax)
for (xs, ys) in lines:
ax.plot(xs, ys, "-", color="red")
ax.set_xlabel("Array index")
ax.set_ylabel("Counts [arb. units]")
ax.legend()
def make_mapper2(interpolator_function, gradient, offset):
def index_to_wavelength(index, with_correction=True):
if with_correction:
alignment_correction = int(
np.round(interpolator_function(index)))
index = index - alignment_correction
#this out linear model
wavelength = index * gradient + offset
return wavelength
return index_to_wavelength
index_to_wavelength = mapper_2 = make_mapper2(interpolator_function,
gradient, offset)
if debug > 0:
fig, ax = plt.subplots(1)
ax.set_title("fname: main, debug plot 1")
xs = list(
range(np.min(interpolator_bounds), np.max(interpolator_bounds)))
ys = [index_to_wavelength(x) for x in xs]
plt.plot(xs, ys)
fig, ax = plt.subplots(1)
ax.set_title("fname: main, debug plot 2")
wavelengths_reference = [
index_to_wavelength(x, with_correction=False)
for x in range(len(ref_ys))
]
ax.plot(wavelengths_reference,
ref_ys,
label="Reference spectrum (rescaled)")
xs = list(range(interpolator_bounds[0], interpolator_bounds[1]))
ax.plot([index_to_wavelength(x, with_correction=True) for x in xs],
[signal_spectrum[x] for x in xs],
label="Stitched signal (corrected)")
ax.plot([index_to_wavelength(x, with_correction=False) for x in xs],
[signal_spectrum[x] for x in xs],
alpha=0.4,
label="Stitched signal (uncorrected)")
ax.set_ylim(0)
ax.set_xlabel("Wavelength [nm]")
ax.set_ylabel("Intensity (arb. units)")
ax.set_title(
"Alignment to reference spectrum\n Reference: Ocean Optics Spectrometer\n Signal: Acton+Pixis"
)
ax.legend()
# mapper_1 : maps from (center_wavelength, pixel_index) to index in spectrum array
# mapper_2 : maps from index in spectrum array to wavelenegth
def mapper(center_wavelength, pixel_index):
try:
array_index = mapper_1(center_wavelength, pixel_index)
wavelength = mapper_2(array_index)
return wavelength
except:
return np.nan
if debug > 0:
plt.show()
#This tests the data
plot_layers(center_wavelengths, data, mapper, show_plot=True)
return mapper
from skimage import feature, filters
import cv2
import math
from skimage.filters import gaussian
from skimage.segmentation import active_contour
from scipy.signal import convolve2d, correlate2d
import scipy.misc
from PIL import Image
import re
import sys
import scipy.ndimage as ndimage
import skimage
FILEPATH = "/home/ilya/Desktop/2018-02-17.h5"
FOLDERPATH = "ParticleScannerScan_2"
datafile = datafile.DataFile(FILEPATH, mode="r")
TAG = "Raman_White_Light_0Order.*"
TAGS = [
"Infinity3_Bias_Image", "Infinity3_FirstBkgndWhiteLight_Image",
"Infinity3_FirstWhiteLight_Image", "Infinity3_SecondWhiteLight_Image",
"Infinity3_SecondWhiteLight_atBkgndLoc_Image", "Raman_Bias_0Order_int",
"Raman_Bias_Spectrum_int", "Raman_Bias_Spectrum_wl",
"Raman_Laser_0Order_atBkgndLoc_int", "Raman_Laser_0Order_int",
"Raman_Laser_Spectrum_atBkgndLoc_int",
"Raman_Laser_Spectrum_atBkgndLoc_wl", "Raman_Laser_Spectrum_int",
"Raman_Laser_Spectrum_wl", "Raman_White_Light_0Order_int",
"Raman_White_Light_Bkgnd_0Order_int",
"Raman_White_Light_Bkgnd_Spectrum_int",
"Raman_White_Light_Bkgnd_Spectrum_wl", "Raman_White_Light_Spectrum_int",
"Raman_White_Light_Spectrum_wl"
return pacton, g
def plot_measurement(pacton, center_wavelength):
fig, ax = plt.subplots(1)
spectrum, _ = pacton.get_spectrum(center_wavelength,
subtract_background=True,
roi=[0, 1024, 600, 800],
debug=1)
ax.plot(spectrum)
plt.show()
if __name__ == "__main__":
f = df.DataFile("ir_calibration_1200gmm.hdf5", "a")
g = f.require_group("calibration")
print("Starting..")
print("Pixis...")
p = Pixis(debug=0)
p.StartUp()
print("Acton...")
act = Acton("COM5", debug=0)
print("Done...")
pacton = Pacton(pixis=p, acton=act)
print("Setting grating...")
pacton.acton.set_grating(1) # 1 : 1200g/mm, 2: 300g/mm
# print pacton.acton.read_grating()
from __future__ import division
from __future__ import print_function
from builtins import range
from past.utils import old_div
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal
from nplab import datafile as df
from nplab.analysis.smoothing import convex_smooth
from nplab.instrument import Instrument
f = df.DataFile("maxwell_room_light_spectrum_calibration.hdf5", "r")
spectrum = np.array(f["calibration"]["spectrum_1"])
CALIBRATION_WAVELENGTHS = spectrum[0, :]
CALIBRATION_COUNTS = spectrum[1, :]
def spectrum_pixel_offset(spectrum_1, spectrum_2):
sp1 = spectrum_1
sp2 = spectrum_2
sp1 = sp1 - np.mean(sp1)
sp2 = sp2 - np.mean(sp2)
#compute cross correlation to match
xcs = np.correlate(sp1, sp2, mode="same")
#perform fftshift to center zero offset on 0th position
xcs = np.fft.fftshift(xcs)
return pacton, g
def plot_measurement(pacton, center_wavelength):
fig, ax = plt.subplots(1)
spectrum, _ = pacton.get_spectrum(center_wavelength,
subtract_background=True,
roi=[0, 1024, 600, 800],
debug=1)
ax.plot(spectrum)
plt.show()
if __name__ == "__main__":
f = df.DataFile("ir_calibration.hdf5", "a")
g = f.require_group("calibration")
print("Starting..")
print("Pixis...")
p = Pixis(debug=1)
p.StartUp()
print("Acton...")
act = Acton("COM7", debug=1)
print("Done...")
pacton = Pacton(pixis=p, acton=act)
print("Measuring...")
fig, ax = plt.subplots(1)
p.SetExposureTime(500)
pacton.get_pixel_response_calibration_spectrum()
from nplab.instrument.camera.Picam.pixis import Pixis
from .Pacton import Pacton
print("Starting..")
print("Pixis...")
p = Pixis(debug=0)
p.StartUp()
print("Acton...")
act = Acton("COM6", debug=0)
print("Done...")
pacton = Pacton(pixis=p, acton=act, debug=0)
print("Measuring...")
from nplab import datafile as df
output_file = df.DataFile("measured_spectrum.hdf5", "w")
dg = output_file.require_group("spectra")
fig, axarr = plt.subplots(3)
p.SetExposureTime(500)
pacton.get_pixel_response_calibration_spectrum()
measured_ys = []
measured_xs = []
from nplab.analysis.signal_alignment import correlation_align
for wl in range(400, 800, 4):
print(p.GetExposureTime())
spectrum, wavelengths = pacton.get_spectrum(wl,
subtract_background=True,
roi=[0, 1024, 600, 800],
debug=1)
# -*- coding: utf-8 -*- """ Created on Mon Dec 05 17:41:32 2016 @author: Will A Python file that allows you to run the databrowser from cmd line on a h5 file """ import qtpy import sys import nplab.datafile as df import h5py file_path = sys.argv[1] #Take the file location from sys.argv list data_file = h5py.File(file_path, mode='r') #Open the file using h5py data_file = df.DataFile(data_file) #convert the file to nplab.datafile type data_file.show_gui() #Show data browser
