This notebook forms part of a series on computational optical radiometry. The notebooks can be downloaded from Github. These notebooks are constantly revised and updated, please revisit from time to time.
The date of this document and module versions used in this document are given at the end of the file.
Feedback is appreciated: neliswillers at gmail dot com.
The pyradi rystare module models the signal and noise in CCD and CMOS staring array sensors. The code is originally based on the Matlab code described in 'High-level numerical simulations of noise in solid-state photosensors: review and tutorial' by Mikhail Konnik and James Welsh, arXiv:1412.4031v1 [astro-ph.IM], available here.
An earlier version (20161020) of a Python version of the model was presented at the Fourth SMEOS conference and was published in SPIE Proc 10036 (2016). The PDF is available here:
https://github.com/NelisW/pyradi/blob/master/pyradi/documentation/SM200-30-staring-array-modeling.pdf
Good additional references for this work include James R Janesick's books 'Photon Transfer' and 'Scientific Charge-Coupled Devices'
This notebook provides a description of the application of the model to a low light level visual sensor. Notebooks 09b and 09d provide an overview of the model and its application to infrared sensors.
In [1]:
import numpy as np
import scipy as sp
import pandas as pd
import re
import os.path
from scipy.optimize import curve_fit
import datetime
from matplotlib import cm as mcm
import matplotlib.mlab as mlab
import scipy.constants as const
import collections
%matplotlib inline
# %reload_ext autoreload
# %autoreload 2
# import xlsxwriter
import pyradi.ryplot as ryplot
import pyradi.ryfiles as ryfiles
import pyradi.rystare as rystare
import pyradi.ryutils as ryutils
import pyradi.rypflux as rypflux
import pyradi.ryplanck as ryplanck
from IPython.display import HTML
from IPython.display import Image
from IPython.display import display
from IPython.display import FileLink, FileLinks
#make pngs at stated dpi
import matplotlib as mpl
plotdpi = 72
mpl.rc("savefig", dpi=plotdpi)
mpl.rc('figure', figsize=(10,8))
# %config InlineBackend.figure_format = 'svg'
pd.set_option('display.max_columns', 80)
pd.set_option('display.width', 200)
pd.set_option('display.max_colwidth', 150)
Two visual-band image files are available: (1) a 300 mlux maximum value [a little brighter than full moon] and (2) a 25 lux maximum value [dusk, before twilight]. Each image comprise a staircase from some non-zero low value to the maximum value in 40 steps. The 25 lux scenario uses an integration time of 10 ms, to achieve full well fill. The low light case uses a 35 ms integration time, to get maximum signal, but does not achieve full well fill.
In [2]:
#set up the parameters for this run and open the HDF5 file
outfilename = 'Output'
prefix = 'PA'
githubprefix = 'https://github.com/NelisW/pyradi/raw/master/pyradi/data/'
# set this True to use the low light imput image (300 mlux)
if True:
integrationtime = 0.035
imagedatafilename = 'image-Stairslin-LowLight-40-100-520.hdf5'
else:
integrationtime = 0.0105
magedatafilename = 'image-Stairslin-40-100-520.hdf5'
pathtoimage = ryfiles.downloadFileUrl('{}{}'.format(githubprefix,imagedatafilename),
saveFilename=imagedatafilename)
In [3]:
#open the file to create data structure and store the results, remove if exists
hdffilename = '{}{}.hdf5'.format(prefix, outfilename)
if os.path.isfile(hdffilename):
os.remove(hdffilename)
print(hdffilename)
print(pathtoimage)
strh5 = ryfiles.open_HDF(hdffilename)
# Optics parameters to convert from radiance to irradiance in the image plane
strh5['rystare/fnumber'] = 3.2
# solid angle = pi*sin^2(theta), sin(theta) = 1 / (2*fno)
# solid angle optics from detector
strh5['rystare/fnumberConeSr'] = np.pi / ((2. * strh5['rystare/fnumber'].value)**2.)
strh5['rystare/pixelPitch'] = [5e-6,5e-6]
#sensor parameters
strh5['rystare/sensortype'] = 'CCD' # CMOS/CCD must be in capitals
# Light Noise parameters
strh5['rystare/photonshotnoise/activate'] = True #photon shot noise.
#detector parameters
strh5['rystare/photondetector/operatingtemperature'] = 300. # operating temperature, [K]
strh5['rystare/photondetector/geometry/fillfactor'] = 0.95 # Pixel Fill Factor for full-frame CCD photo sensors.
strh5['rystare/photondetector/integrationtime'] = integrationtime # Exposure/Integration time, [sec].
strh5['rystare/photondetector/externalquantumeff'] = 0.8 # external quantum efficiency, fraction not reflected.
strh5['rystare/photondetector/quantumyield'] = 1. # number of electrons absorbed per one photon into material bulk
# light photo response non-uniformity noise (PRNU), or also called light Fixed Pattern Noise (light FPN)
strh5['rystare/photondetector/lightPRNU/activate'] = True
strh5['rystare/photondetector/lightPRNU/seed'] = 362436069
strh5['rystare/photondetector/lightPRNU/model'] = 'Janesick-Gaussian'
strh5['rystare/photondetector/lightPRNU/sigma'] = 0.01 # sigma [about 1\% for CCD and up to 5% for CMOS];
# detector material bandgap properties
strh5['rystare/photondetector/varshni/Egap0'] = 1.166 #bandgap energy for 0 degrees of K. [For Silicon, eV]
strh5['rystare/photondetector/varshni/varA'] = 5.5e-04 #Si material parameter, [eV/K].
strh5['rystare/photondetector/varshni/varB'] = 636. #Si material parameter, [K].
# Dark Current Noise parameters
strh5['rystare/photondetector/darkcurrent/activate'] = True
strh5['rystare/photondetector/darkcurrent/densityAm2'] = 1. * 1e-9 * 1e4 # dark current density [A/m2].
strh5['rystare/photondetector/darkcurrent/ca'] = 4.31e5 # for density in m2
strh5['rystare/photondetector/darkcurrent/ed'] = 2.
# dark current shot noise
strh5['rystare/photondetector/darkcurrent/shotnoise/activate'] = True # dark current shot noise
strh5['rystare/photondetector/darkcurrent/shotnoise/seed'] = 6214069
strh5['rystare/photondetector/darkcurrent/shotnoise/model'] = 'Gaussian'
#dark current Fixed Pattern uniformity
strh5['rystare/photondetector/darkcurrent/fixedPatternNoise/activate'] = True
strh5['rystare/photondetector/darkcurrent/fixedPatternNoise/seed'] = 362436128
strh5['rystare/photondetector/darkcurrent/fixedPatternNoise/model'] = 'LogNormal' #suitable for long exposures
strh5['rystare/photondetector/darkcurrent/fixedPatternNoise/sigma'] = 0.4 #lognorm_sigma.
#sense node charge to voltage
strh5['rystare/sensenode/gain'] = 50e-06 # Sense node gain, A_SN [V/e]
strh5['rystare/sensenode/vrefreset'] = 3.1 # Reference voltage to reset the sense node. [V] typically 3-10 V.
strh5['rystare/sensenode/vsnmin'] = 0.5 # Minimum voltage on sense node, max well charge [V] typically < 1 V.
strh5['rystare/sensenode/gainresponse/type'] = 'linear'
strh5['rystare/sensenode/gainresponse/k1'] = 1.090900000e-14 # nonlinear capacitance is given by C = k1/V
if strh5['rystare/sensenode/gainresponse/type'] in ['nonlinear']:
strh5['rystare/sensenode/fullwellelectronselection/fullwellelectrons'] = \
-(strh5['rystare/sensenode/gainresponse/k1'].value/const.e) * \
np.log(strh5['rystare/sensenode/vsnmin'].value/strh5['rystare/sensenode/vrefreset'].value)
else:
strh5['rystare/sensenode/fullwellelectronselection/fullwellelectrons'] = 2e4 # full well of the pixel (how many electrons can be stored in one pixel), [e]
strh5['rystare/sensenode/resetnoise/activate'] = True
strh5['rystare/sensenode/resetnoise/seed'] = 2154069
strh5['rystare/sensenode/resetnoise/model'] = 'Gaussian'
strh5['rystare/sensenode/resetnoise/factor'] = 0.8 #[0,1]the compensation factor of the Sense Node Reset Noise:
#source follower
strh5['rystare/sourcefollower/gain'] = 1. # Source follower gain, [V/V], lower means amplify the noise.
strh5['rystare/sourcefollower/dataclockspeed'] = 20e6 #Hz data rate clocking speed.
strh5['rystare/sourcefollower/freqsamplingdelta'] = 10000. #Hz spectral sampling spacing
strh5['rystare/sourcefollower/nonlinearity/activate'] = True
strh5['rystare/sourcefollower/nonlinearity/ratio'] = 1.05 # > 1 for lower signal, < 1 for higher signal
strh5['rystare/sourcefollower/noise/activate'] = True
strh5['rystare/sourcefollower/noise/seed'] = 6724069
strh5['rystare/sourcefollower/noise/flickerCornerHz'] = 1e6 #flicker noise corner frequency $f_c$ in [Hz], where power spectrum of white and flicker noise are equal [Hz].
strh5['rystare/sourcefollower/noise/whitenoisedensity'] = 15e-9 #thermal white noise [\f$V/Hz^{1/2}\f$, typically \f$15 nV/Hz^{1/2}\f$ ]
strh5['rystare/sourcefollower/noise/deltaindmodulation'] = 1e-8 #[A] source follower current modulation induced by RTS [CMOS ONLY]
strh5['rystare/sourcefollower/fpoffset/activate'] = True
strh5['rystare/sourcefollower/fpoffset/model'] = 'Janesick-Gaussian'
strh5['rystare/sourcefollower/fpoffset/sigma'] = 0.0005 # percentage of (V_REF - V_SN)
strh5['rystare/sourcefollower/fpoffset/seed'] = 362436042
strh5['rystare/sourcefollower/CDS/gain'] = 1. # CDS gain, [V/V], lower means amplify the noise.
strh5['rystare/sourcefollower/CDS/sampletosamplingtime'] = 1e-6 #CDS sample-to-sampling time [sec].
# Analogue-to-Digital Converter (ADC)
strh5['rystare/ADC/num-bits'] = 12. # noise is more apparent on high Bits
strh5['rystare/ADC/offset'] = 0. # Offset of the ADC, in DN
strh5['rystare/ADC/nonlinearity/activate'] = False
strh5['rystare/ADC/nonlinearity/ratio'] = 1.1
#For testing and measurements only:
strh5['rystare/darkframe'] = False # True if no signal, only dark
strh5.flush()
strh5.close()
In [ ]:
Download the image file from the GitHub directory. Normally you will prepare this file for your specific application. The format for this file is described here.
In [4]:
if pathtoimage is not None:
imghd5 = ryfiles.open_HDF(pathtoimage)
else:
imghd5 = None
The input data has been set up, now execute the model. To test the model performance at the limits, uncomment one of the two scaleInput lines. The first line provides a signal with small SNR whereas the second line provides a signal that saturates the well.
In [ ]:
In [5]:
##
# scaleInput = 0.01 # for low SNR for Disk image
# scaleInput = 5 # saturates the well to full capacity for Disk image
scaleInput = 1
if imghd5 is not None:
strh5 = ryfiles.open_HDF(hdffilename)
#images must be in photon rate irradiance units q/(s.m2)
strh5['rystare/equivalentSignal'] = scaleInput * imghd5['image/equivalentSignal'].value
strh5['rystare/signal/photonRateRadianceNoNoise'] = scaleInput * imghd5['image/PhotonRateRadianceNoNoise'].value
strh5['rystare/signal/photonRateRadiance'] = scaleInput * imghd5['image/PhotonRateRadiance'].value
strh5['rystare/signal/photonRateIrradianceNoNoise'] = scaleInput * strh5['rystare/fnumberConeSr'].value * \
imghd5['image/PhotonRateRadianceNoNoise'].value
strh5['rystare/signal/photonRateIrradiance'] = scaleInput * strh5['rystare/fnumberConeSr'].value * \
imghd5['image/PhotonRateRadiance'].value
if 'image/pixelPitch' in imghd5:
strh5['rystare/pixelPitch'] = imghd5['image/pixelPitch'].value
strh5['rystare/imageName'] = imghd5['image/imageName'].value
strh5['rystare/imageFilename'] = imghd5['image/imageFilename'].value
strh5['rystare/imageSizePixels'] = imghd5['image/imageSizePixels'].value
strh5['rystare/wavelength'] = imghd5['image/wavelength'].value
strh5['rystare/imageSizeRows'] = imghd5['image/imageSizeRows'].value
strh5['rystare/imageSizeCols'] = imghd5['image/imageSizeCols'].value
pixelPitch = strh5['rystare/pixelPitch'].value
numPixels = imghd5['image/imageSizePixels']
strh5['rystare/imageSizeDiagonal'] = np.sqrt((pixelPitch[0] * numPixels[0]) ** 2. + (pixelPitch[1] * numPixels[1]) ** 2)
# strh5['rystare/imageSizeDiagonal'] = imghd5['image/imageSizeDiagonal'].value
strh5['rystare/equivalentSignalUnit'] = imghd5['image/equivalentSignalUnit'].value
strh5['rystare/equivalentSignalType'] = imghd5['image/equivalentSignalType'].value
if 'image/EinUnits' in imghd5:
strh5['rystare/EinUnits'] = imghd5['image/EinUnits'].value
if 'W' in imghd5['image/EinUnits'].value:
strh5['rystare/LinUnits'] = 'W/(m2.sr)'
elif 'q' in imghd5['image/EinUnits'].value:
strh5['rystare/LinUnits'] = 'q/(s.m2.sr)'
else:
strh5['rystare/LinUnits'] = imghd5['image/LinUnits'].value
if 'W' in imghd5['image/LinUnits'].value:
strh5['rystare/EinUnits'] = 'W/(m2)'
elif 'q' in imghd5['image/LinUnits'].value:
strh5['rystare/EinUnits'] = 'q/(s.m2)'
#calculate the noise and final images
strh5 = rystare.photosensor(strh5) # here the Photon-to-electron conversion occurred.
strh5.flush()
strh5.close()
Print some statistics of the sensor, noise and image
In [6]:
print(rystare.get_summary_stats(hdffilename))
The source follower DCS noise is shown next.
In [7]:
#open the sensor data file
%matplotlib inline
strh5 = ryfiles.open_HDF(hdffilename)
p = ryplot.Plotter(1,1,1,figsize=(10,4))
p.logLog(1, strh5['rystare/sourcefollower/noise/spectralfreq'].value.T,
strh5['rystare/sourcefollower/noise/cdsgain'].value.T,'CDS Response',
'Frequency Hz','Transfer function')
q = ryplot.Plotter(2,1,1,figsize=(10,4))
q.logLog(1, strh5['rystare/sourcefollower/noise/spectralfreq'].value.T,
np.sqrt(strh5['rystare/sourcefollower/noise/spectrumwhiteflicker'].value.T),label=['White and 1/f in'])
if strh5['rystare/sensortype'].value in ['CMOS']:
q.logLog(1, strh5['rystare/sourcefollower/noise/spectralfreq'].value.T,
np.sqrt(strh5['rystare/sourcefollower/noise/spectrumRTN'].value.T), label=['Burst / RTN in'])
q.logLog(1, strh5['rystare/sourcefollower/noise/spectralfreq'].value.T,
np.sqrt(strh5['rystare/sourcefollower/noise/spectrumtotal'].value.T),label=['Total in'])
q.logLog(1, strh5['rystare/sourcefollower/noise/spectralfreq'].value.T,
np.sqrt(strh5['rystare/sourcefollower/noise/spectrumtotal'].value.T) * strh5['rystare/sourcefollower/noise/cdsgain'].value.T,'CDS Noise',
'Frequency Hz','Spectral density V/rtHz',label=['Total out'])
strh5.flush()
strh5.close()
In [8]:
def plotResults(imghd5, hdffilename, arrayname, units='xx', bins=50, plotscale=1.0, logscale=False):
if imghd5 is not None and hdffilename is not None:
mpl.rc("savefig", dpi=plotdpi)
#open the sensor data file
strh5 = ryfiles.open_HDF(hdffilename)
#get the prescribed array
arr = strh5['{}'.format(arrayname)].value
maxarr = np.max(arr)
arr = strh5['{}'.format(arrayname)].value * plotscale
arr = np.where(arr > maxarr, maxarr,arr) / plotscale
if logscale:
arr = np.log10(arr + 0.5)
ptitle = "log('{}'+0.5)".format(arrayname.replace('rystare/',''))
else:
ptitle = "'{}'".format(arrayname.replace('rystare/',''))
if np.max(arr) > 1000. or np.max(arr) < 1e-2:
ptitle = '{} [{}] ({:.2e} : {:.2e})'.format(ptitle, units,np.min(arr),np.max(arr) )
else:
ptitle = '{} [{}] ({:.4f} : {:.4f})'.format(ptitle, units,np.min(arr),np.max(arr) )
his, binh = np.histogram(arr,bins=bins)
if np.min(arr) != np.max(arr):
arrshift = arr - np.min(arr)
arrshift = 255 * arrshift/np.max(arrshift)
p = ryplot.Plotter(1,1,1,figsize=(8, 2))
p.showImage(1, arr, ptitle=ptitle, cmap=mcm.jet, cbarshow=True);
q = ryplot.Plotter(2,1,1,figsize=(8, 2))
q.showImage(1, arrshift, ptitle=ptitle, cmap=mcm.gray, cbarshow=False);
if not logscale:
r = ryplot.Plotter(3,1,1,figsize=(4,4))
r.plot(1, (binh[1:]+binh[:-1])/2, his,
'{}, {} bins'.format(arrayname.replace('rystare/',''), bins),
'Magnitude','Counts / bin',maxNX=5)
strh5.flush()
strh5.close()
The graph in the rest of the document shows signals at the stations marked in the signal flow diagram shown below.
In [9]:
display(Image(filename='images/camerascheme-horiz.png', width=1000))
The following description follows the signal through the processing chain, step by step, showing the relevant signal or noise at that step.
In [10]:
##
plotResults(imghd5, hdffilename, 'rystare/signal/photonRateIrradianceNoNoise', units='q/(s.m2)', bins=120)
In [11]:
##
plotResults(imghd5, hdffilename, 'rystare/signal/photonRateIrradiance', units='q/(s.m2)', bins=120)
In [12]:
##
plotResults(imghd5, hdffilename, 'rystare/photondetector/lightPRNU/value', units='-', bins=120)
In [13]:
##
plotResults(imghd5, hdffilename, 'rystare/signal/photonRateIrradianceNU', units='q/(s.m2)', bins=120)
In [14]:
##
plotResults(imghd5, hdffilename, 'rystare/signal/electronRateIrradiance', units='e/(s.m2)', bins=120)
In [15]:
plotResults(imghd5, hdffilename, 'rystare/signal/electronRate', units='e/s', bins=120)
In [16]:
plotResults(imghd5, hdffilename, 'rystare/signal/lightelectronsnoshotnoise', units='e', bins=120)
In [17]:
plotResults(imghd5, hdffilename, 'rystare/signal/lightelectrons', units='e', bins=120)
In [18]:
##
strh5 = ryfiles.open_HDF(hdffilename)
print('Average dark current {:.3e} electrons'.format(strh5['rystare/darkcurrentelectronsnonoise'].value))
strh5.flush()
strh5.close()
In [19]:
plotResults(imghd5, hdffilename, 'rystare/signal/darkcurrentelectrons', units='e', bins=120)
strh5 = ryfiles.open_HDF(hdffilename)
print('Before dark FPN:')
print('Minimum dark current {:.3e} electrons'.format(np.min(strh5['rystare/signal/darkcurrentelectronsnoDFPN'].value)))
print('Average dark current {:.3e} electrons'.format(np.mean(strh5['rystare/signal/darkcurrentelectronsnoDFPN'].value)))
print('Maximum dark current {:.3e} electrons'.format(np.max(strh5['rystare/signal/darkcurrentelectronsnoDFPN'].value)))
print('After dark FPN:')
print('Minimum dark current {:.3e} electrons'.format(np.min(strh5['rystare/signal/darkcurrentelectrons'].value)))
print('Average dark current {:.3e} electrons'.format(np.mean(strh5['rystare/signal/darkcurrentelectrons'].value)))
print('Maximum dark current {:.3e} electrons'.format(np.max(strh5['rystare/signal/darkcurrentelectrons'].value)))
strh5.flush()
strh5.close()
In [20]:
plotResults(imghd5, hdffilename, 'rystare/photondetector/darkcurrent/fixedPatternNoise/value', units='-', bins=120)
In [21]:
plotResults(imghd5, hdffilename, 'rystare/signal/darkcurrentelectrons', units='e', bins=120)
In [22]:
plotResults(imghd5, hdffilename, 'rystare/signal/electrons', units='e', bins=120)
In [23]:
plotResults(imghd5, hdffilename, 'rystare/signal/electronsWell', units='e', bins=120)
In [24]:
plotResults(imghd5, hdffilename, 'rystare/signal/sensenodevoltageLinear', units='V', bins=120)
In [25]:
plotResults(imghd5, hdffilename, 'rystare/noise/sn_reset/resetnoise', units='V', bins=120)
In [26]:
plotResults(imghd5, hdffilename, 'rystare/noise/sn_reset/vrefresetpluskTC', units='V', bins=120)
In [27]:
plotResults(imghd5, hdffilename, 'rystare/sensenode/vrefreset', units='V', bins=120)
In [28]:
plotResults(imghd5, hdffilename, 'rystare/signal/voltage', units='V', bins=120)
In [29]:
plotResults(imghd5, hdffilename, 'rystare/sourcefollower/gainA', units='-', bins=120)
In [30]:
plotResults(imghd5, hdffilename, 'rystare/signal/voltageAfterSF', units='V', bins=120)
In [31]:
plotResults(imghd5, hdffilename, 'rystare/sourcefollower/source_follower_noise', units='V', bins=120)
In [32]:
plotResults(imghd5, hdffilename, 'rystare/signal/voltageAfterSFnoise', units='V', bins=120)
In [33]:
plotResults(imghd5, hdffilename, 'rystare/sourcefollower/fpoffset/value', units='V', bins=120)
In [34]:
plotResults(imghd5, hdffilename, 'rystare/signal/voltagebeforecds', units='V', bins=120)
In [35]:
plotResults(imghd5, hdffilename, 'rystare/signal/voltageaftercds', units='V', bins=120)
In [36]:
plotResults(imghd5, hdffilename, 'rystare/ADC/gainILE', units='-', bins=120)
In [37]:
plotResults(imghd5, hdffilename, 'rystare/ADC/gain', units='DN/V', bins=120)
In [38]:
plotResults(imghd5, hdffilename, 'rystare/signal/DN', units='DN', bins=120)
In [39]:
plotResults(imghd5, hdffilename, 'rystare/signal/DN', units='DN', bins=120, plotscale=3.6)
Signal converted to digital counts (location 27 in the diagram). This graph shows the log-base10 values in the image. The image is offset by half a digital count to prevent log(0) errors.
In [40]:
plotResults(imghd5, hdffilename, 'rystare/signal/DN', units='DN', bins=120, plotscale=1, logscale=True)
Next we will attempt to calculate the photon transfer function and signal to noise ratio for the various parts of the image.
In [41]:
tplZones = collections.namedtuple('tplZones', ['ein', 'count','mean','var','std','snr','stf'], verbose=False)
lstZones = []
if imghd5 is not None:
strh5 = ryfiles.open_HDF(hdffilename)
#get the list of unique zones in the image
arrnn = strh5['rystare/equivalentSignal'].value
# arrnn = strh5['rystare/PhotonRateIrradianceNoNoise'].value
arr = strh5['rystare/signal/DN'].value
uniqueZs, uCnts = np.unique(arrnn, return_counts=True)
# print(uniqueZs, uCnts)
for uniqueZ,ucnt in zip(uniqueZs,uCnts):
zone = arr[arrnn==uniqueZ]
mean = np.mean(zone)
lstZones.append(tplZones(ein=uniqueZ, count=ucnt, mean=mean,
var=np.var(zone-mean), std=np.std(zone-mean),
snr=np.mean(zone)/np.std(zone-mean),
stf=np.mean(zone)/uniqueZ))
equivalentSignalLabel = '{} {}'.format(strh5['rystare/equivalentSignalType'].value,
strh5['rystare/equivalentSignalUnit'].value)
strh5.flush()
strh5.close()
# print(lstZones)
#build numpy arrays of the results
ein = np.asarray([x.ein for x in lstZones])
mean = np.asarray([x.mean for x in lstZones])
var = np.asarray([x.var for x in lstZones])
std = np.asarray([x.std for x in lstZones])
snr = np.asarray([x.snr for x in lstZones])
stf = np.asarray([x.stf for x in lstZones])
figsize = (6,4)
p = ryplot.Plotter(1,1,1,figsize=figsize);
p.plot(1,ein,mean,'Signal DN vs irradiance',equivalentSignalLabel,'Digital count');
q = ryplot.Plotter(2,1,1,figsize=figsize);
q.plot(1,ein[2:],stf[2:],'Signal transfer function',equivalentSignalLabel,'STF [DN/lux]',maxNX=5);
r = ryplot.Plotter(3,1,1,figsize=figsize);
r.plot(1,ein,std,'Noise vs irradiance',equivalentSignalLabel,'RMS noise [DN]',maxNX=5);
s = ryplot.Plotter(4,1,1,figsize=figsize);
s.logLog(1,ein[:-1],snr[:-1],'Signal to noise ratio',equivalentSignalLabel,'Signal to noise ratio');
# s.logLog(1,mean[:-1],snr[:-1],'SNR [-] vs mean [DN]');
In [1]:
# to get software versions
# https://github.com/rasbt/watermark
# https://github.com/rasbt/watermark/blob/master/docs/watermark.ipynb
# you only need to do this once
# pip install watermark
%load_ext watermark
# %watermark -v -m -p numpy,scipy,pyradi -g -d
%watermark -v -m -p numpy,scipy,pyradi,pandas -g -r -u -d -b -i
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