def plotsummary(results, positions,deciX=1,deciY=1,title='',savefile=None):
""" Plot results
Inputs:
1. results, a pandas DataFrame
2. Positions, a list of depth positions
3. use every deciX sample along depth
4. use every deciY sample along time
5. title for the plots
6. save file name
"""
import pyradi.ryplot as ryplot
from matplotlib import cm
df = results.loc[positions,:]
Z = results.T.values
Z = Z[:-1:deciY,:-1:deciX]
Xv = np.asarray(results.index[0:-1:deciX])
Yv = np.asarray(results.columns[0:-1:deciY])
maxtime = np.max(Yv)
if maxtime > 50*24*60*60:
timeStr = 'Time Years'
Yv = Yv / (365.25*24*60*60)
elif maxtime > 20*24*60*60:
timeStr = 'Time Weeks'
Yv = Yv / (7*24*60*60)
elif maxtime > 3*24*60*60:
timeStr = 'Time Days'
Yv = Yv / (24*60*60)
elif maxtime > 3*60*60:
timeStr = 'Time Hours'
Yv = Yv / (60*60)
elif maxtime > 3*60:
timeStr = 'Time Minutes'
Yv = Yv / (60)
else :
timeStr = 'Time Seconds'
X, Y = np.meshgrid(Xv, Yv)
samples = df.values[:,0:-1:deciY]
p = ryplot.Plotter(1,1,1,figsize=(10,16),doWarning=False)
p.mesh3D(1,X,Y,Z,title,'Depth m',timeStr,'Temperature K',xInvert=True,yInvert=True,cmap=cm.seismic)
if savefile is not None:
fname = savefile.replace('.png','-3D.png')
p.saveFig(f'{fname}')
miny = np.min(samples)
maxy = np.max(samples)
q = ryplot.Plotter(2,1,1,figsize=(10,6),doWarning=False)
for ix,(index, row) in enumerate(df.iterrows()):
q.plot(1,Yv,samples[ix,:],title,timeStr,'Temperature K',
pltaxis=[0,np.max(Yv),miny,maxy],label=[f'depth={positions[ix]:0.2f} m'],xAxisFmt='%.2f',maxNX=11)
if savefile is not None:
fname = savefile.replace('.png','-T.png')
q.saveFig(f'{fname}')
def plotTau(alts, dirs):
#first plot the different altitudes for each atmosphere
for i, directory in enumerate(dirs):
p = ryplot.Plotter(i, 1, 1, figsize=(12, 6))
for alt in alts:
datadir = os.path.join('.', directory, '{}'.format(alt))
filename = '{}-{}m.1km'.format(directory, alt)
data = np.loadtxt(os.path.join(datadir, filename), skiprows=1)
p.plot(1,
data[:, 0],
data[:, 1],
'{} {} 1 km transmittance, 135 deg zenith'.format(
directory, directory),
'Wavelength $\mu$m',
'Transmittance',
label=['{} m'.format(alt)],
legendAlpha=0.5)
p.saveFig(os.path.join('.', directory, '{}.png'.format(directory)))
#now plot the different altitudes for each altitude
for alt in alts:
p = ryplot.Plotter(i, 1, 1, figsize=(12, 6))
for i, directory in enumerate(dirs):
datadir = os.path.join('.', directory, '{}'.format(alt))
filename = '{}-{}m.1km'.format(directory, alt)
data = np.loadtxt(os.path.join(datadir, filename), skiprows=1)
p.plot(
1,
data[:, 0],
data[:, 1],
'{} m altitude, 135 deg zenith 1 km transmittance'.format(alt),
'Wavelength $\mu$m',
'Transmittance',
label=['{} {}'.format(directory, directory)],
legendAlpha=0.5)
p.saveFig(os.path.join('.', 'AllScen-{}m.png'.format(alt)))
print(rgb)
print('xy values from sRGB RGB values:')
print(xy)
rgbn = CIExy2rgb(xy,system='sRGB') * np.max(rgb)
print('RGB values (recomputed from xy):')
print(rgbn.astype('int'))
print(60*'*')
if doAll:
## ----------------------load ciebar -----------------------------------
ciebarwl = loadCIEbar(wavelength, stype='wl')
ciebarwn = loadCIEbar(wavenum, stype='wn')
cietriplt = ryplot.Plotter(1, 2, 2, figsize=(12,6))
cietriplt.plot(1, ciebarwl[:,0], ciebarwl[:,1:4], "CIE tristimulus values, wl input",
r'Wavelength $\mu$m', r'Response', plotCol = ['r','g','b'],
label=['$\\bar{x}$', '$\\bar{y}$', '$\\bar{z}$'],legendAlpha=0.5);
cietriplt.plot(2, 1e4/ciebarwl[:,0], ciebarwl[:,1:4], "CIE tristimulus values, wl input",
r'Wavenumber cm$^{-1}$', r'Response', plotCol = ['r','g','b'],
label=['$\\bar{x}$', '$\\bar{y}$', '$\\bar{z}$'],legendAlpha=0.5,maxNX=5);
cietriplt.plot(3, 1e4/ciebarwn[:,0], ciebarwn[:,1:4], "CIE tristimulus values, wn input",
r'Wavelength $\mu$m', r'Response', plotCol = ['r','g','b'],
label=['$\\bar{x}$', '$\\bar{y}$', '$\\bar{z}$'],legendAlpha=0.5);
cietriplt.plot(4, ciebarwn[:,0], ciebarwn[:,1:4], "CIE tristimulus values, wn input",
r'Wavenumber cm$^{-1}$', r'Response', plotCol = ['r','g','b'],
label=['$\\bar{x}$', '$\\bar{y}$', '$\\bar{z}$'],legendAlpha=0.5,maxNX=5);
cietriplt.saveFig('cieBAR'+figtype)
"data/tape7-05",
['FREQ', 'TOT_TRANS', 'PTH_THRML', 'SURF_EMIS', 'TOTAL_RAD'])
np.savetxt('tape7-05.txt', tape7, fmt=str('%.6e'))
tape7 = loadtape7(
"data/tape7-05b",
['FREQ', 'TOT_TRANS', 'PTH_THRML', 'SURF_EMIS', 'TOTAL_RAD'])
np.savetxt('tape7-05b.txt', tape7, fmt=str('%.6e'))
if doAll:
colSelect = ['FREQ_CM-1', 'COMBIN_TRANS', 'H2O_TRANS', 'UMIX_TRANS', \
'O3_TRANS', 'H2O_CONT', 'MOLEC_SCAT', 'AER+CLD_TRANS']
tape7 = loadtape7("data/tape7VISNIR5kmTrop23Vis", colSelect)
wavelen = ryutils.convertSpectralDomain(tape7[:, 0], type='nl')
mT = ryplot.Plotter(1, 1, 1,"Modtran Tropical, 23 km Visibility (Rural)"\
+ ", 5 km Path Length",figsize=(12,6))
mT.plot(1,
wavelen,
tape7[:, 1:],
"",
"Wavelength [$\mu$m]",
"Transmittance",
label=colSelect[1:],
legendAlpha=0.5,
pltaxis=[0.4, 1, 0, 1],
maxNX=10,
maxNY=4,
powerLimits=[-4, 4, -5, 5])
mT.saveFig('ModtranPlot.png')
#mT.saveFig('ModtranPlot.eps')
def analyse_HDF5_image(imghd5,plotfile,gwidh=12,gheit=8):
r"""Summarise the image properties and statistics
Args:
| imghd5 (handle to an open hdf5 file): file to be analysed
| plotfile(string): filename for plot graphics
| gwidh (float): graph width in inches
| gheit (float): graph height in inches
Returns:
| nothing: as a side effect a set properties are written and graphs created
Raises:
| No exception is raised.
Author: CJ Willers
"""
from scipy import stats
import pyradi.ryplot
#calculate and display values of these variables
elements = ['image/imageFilename','image/imageName','image/filename','image/rad_dynrange',
'image/rad_min','image/irrad_dynrange','image/irrad_min','image/disk_diameter','image/blur',
'image/blur','image/steps','image/imtype','image/imageSizePixels','image/pixelPitch',
'image/imageSizeRows','image/imageSizeCols','image/imageSizeDiagonal',
'image/equivalentSignalType','image/equivalentSignalUnit','image/LinUnits','image/EinUnits',
'image/saveNoiseImage','image/saveEquivImage','image/joule_per_photon',
'image/conversion',
]
for item in elements:
if item in imghd5:
print('{:30s} : {}'.format(item,imghd5[item].value))
# wavelength as scalar or vector
print(imghd5)
if isinstance( imghd5['image/wavelength'].value, float):
print('{:30s} : {}'.format('wavelength',imghd5['image/wavelength'].value))
else:
print('{:30s} : {}'.format('wavelength (mean)',np.mean(imghd5['image/wavelength'].value)))
#calculate and display statistics of these variables
elements = ['image/PhotonRateRadianceNoNoise','image/PhotonRateRadiance',
'image/PhotonRateIrradianceNoNoise','image/PhotonRateIrradiance','image/equivalentSignal'
]
for item in elements:
if item in imghd5:
print('\nStatistics for {}:'.format(item))
print(stats.describe(imghd5[item].value,axis=None))
# plot the images
p = ryplot.Plotter(1,3,1,plotfile, figsize=(gwidh,gheit))
for item in ['image/PhotonRateRadianceNoNoise','image/PhotonRateIrradianceNoNoise']:
if item in imghd5:
p.showImage(1,imghd5[item].value,item,cbarshow=True)
for item in ['image/PhotonRateRadiance','image/PhotonRateIrradiance']:
if item in imghd5:
p.showImage(2,imghd5[item].value,item,cbarshow=True)
if 'image/equivalentSignal' in imghd5:
p.showImage(3,imghd5['image/equivalentSignal'].value,'image/equivalentSignal',cbarshow=True)
p.saveFig('{}.png'.format(plotfile))
# plot interpolation function
if 'image/interpolate_x' in imghd5:
q = ryplot.Plotter(1,1,1,plotfile, figsize=(12,6))
q.plot(1,imghd5['image/interpolate_x'].value,imghd5['image/interpolate_y'].value)
q.saveFig('{}-lookup.png'.format(plotfile))
print(50*'='+'\n\n')
################################################################################
if __name__ == '__init__':
pass
if __name__ == '__main__':
pass
"""
In the model application, the user must define all the detector and
semiconductor parameters. Each material type has its own paramenters,
"""
import pyradi.ryplot as ryplot
#calculate the theoretical D* for a spectrally flat detector
Tenvironment = np.linspace(1, 600, 100)
Tdetector = [0, 77, 195, 290]
dstarP = ryplot.Plotter(1, 1, 1, figsize=(5, 2))
for Tdetec in Tdetector:
dStar = DstarSpectralFlatPhotonLim(Tdetec, Tenvironment, 1)
dstarP.semilogY(1,Tenvironment,dStar,'',\
r'Environmental temperature [K]','D* [cm.\sqrt{Hz}/W]',
pltaxis=[0, 600, 1e9, 1e13])
currentP = dstarP.getSubPlot(1)
for xmaj in currentP.xaxis.get_majorticklocs():
currentP.axvline(x=xmaj, ls='-')
for ymaj in currentP.yaxis.get_majorticklocs():
currentP.axhline(y=ymaj, ls='-')
dstarP.saveFig('dstarFlat.eps')
######################################################################
# tempBack = np.asarray([1])
tempBack = np.asarray([1, 2, 4, 10, 25, 77, 195, 300, 500])
rows = 100
cols = 100
vartype = np.uint16
imagefile = 'data/sensornoise.raw'
outfilename = 'sensornoise.txt'
# load images
framesToLoad = list(range(1, 21, 1))
frames, img = ryfiles.readRawFrames(imagefile, rows, cols, vartype,
framesToLoad)
if frames > 0:
# show something
P = ryplot.Plotter(1, 1, 1, 'Simulated noise', figsize=(12, 8))
P.showImage(1, img[0])
#P.getPlot().show()
P.saveFig('rawframe0.png')
outfile = open(outfilename, 'w')
outfile.write('\n{0} Frames read from {1}\n'.format(frames, imagefile))
outfile.write('\nImage average S : {0:10.3f} \n'.format(
getS(img)))
outfile.write('Total system noise : {0:10.3f} \n\n'.format(
getTotal(img)))
outfile.write(
'Fixed/spatial noise | Temporal noise | Variation effect\n')
outfile.write(
'---------------------|---------------------|-----------------\n')
outfile.write(
center=4.33,
width=0.45,
exponent=6,
taupass=0.8,
taustop=0.1)
#construct the sensor filter from parameters
sfilter = ryutils.sfilter(wavel,
center=4.3,
width=0.8,
exponent=12,
taupass=0.9,
taustop=0.0001)
#plot the data
plot1 = ryplot.Plotter(1, 2, 2, 'Flame sensor', figsize=(24, 16))
plotdata = detR
plotdata = numpy.hstack((plotdata, emis))
plotdata = numpy.hstack((plotdata, sfilter))
plotdata = numpy.hstack((plotdata, tauA))
label = ['Detector', 'Emissivity', 'Filter', 'Atmosphere transmittance']
plot1.plot(1,
wavel,
plotdata,
"Spectral",
"Wavelength [$\mu$m]",
"Relative magnitude",
label=label,
maxNX=10,
maxNY=10)
# see also reference images at http://sipi.usc.edu/database/
import numpy as np
import Image
import pyradi.ryplot as ryplot
import pyradi.ryutils as ryutils
if __name__ == '__main__':
# im = Image.open('lena512color.png')
im = Image.open('600px-Siemens_star-blurred.png').convert('RGB')
# im = im.convert('RGB')
data = np.array(im)
"""Plots an image reprojected into polar coordinages with the origin
at "origin" (a tuple of (x0, y0), defaults to the center of the image)"""
print(data.shape)
origin = None #(300,350)
pim = ryplot.ProcessImage()
polar_grid, r, theta = pim.reproject_image_into_polar(data, origin, False)
p = ryplot.Plotter(1, 1, 2)
p.showImage(1, data, ptitle='Image')
p.showImage(2,
polar_grid,
ptitle='Image in Polar Coordinates',
xlabel='Angle',
ylabel='Radial')
p.saveFig('warpedStar.png')
print('done')
