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Flexbrdf/hytools/plotting/__init__.py Normal file
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# -*- coding: utf-8 -*-
"""
HyTools: Hyperspectral image processing library
Copyright (C) 2021 University of Wisconsin
Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 3 of the License.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
The :mod:`hytools.plottinh` module include functions for plotting.
"""
from .brdf_diagnostics import *

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Flexbrdf/hytools/plotting/brdf_diagnostics.py Normal file
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# -*- coding: utf-8 -*-
"""
HyTools: Hyperspectral image processing library
Copyright (C) 2021 University of Wisconsin
Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 3 of the License.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Plotting functions for BRDF
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
def universal_diagno_plot(hy_obj,config_dict):
''' Generate a diagnostic plot of BRDF correction results.
'''
#Flip sign of zenith angle at minimum
sensor_zn = hy_obj.get_anc('sensor_zn',radians =False)
sensor_zn[~hy_obj.mask['no_data']] = np.nan
for i,line in enumerate(sensor_zn):
line[:np.nanargmin(line)] *= -1
sensor_zn[i] = line
sensor_zn = (sensor_zn[hy_obj.mask['calc_brdf']]//2)*2
diagno_df = pd.DataFrame()
diagno_df['sensor_zn'] =sensor_zn
bands = [hy_obj.wave_to_band(wave) for wave in config_dict['brdf']['diagnostic_waves']]
for band_num in bands:
band = hy_obj.get_band(band_num,mask='calc_brdf')
diagno_df['uncorr_%s' % band_num] = band
band = hy_obj.get_band(band_num,
corrections = hy_obj.corrections + ['brdf'],
mask='calc_brdf')
diagno_df['corr_%s' % band_num] = band
fvol, fgeo, fiso = hy_obj.brdf['coeffs'][band_num]
brdf = fvol*hy_obj.ancillary['k_vol']
brdf += fgeo*hy_obj.ancillary['k_geom']
brdf+=fiso
brdf = brdf[hy_obj.mask['calc_brdf']]
diagno_df['brdf_%s' % band_num] = brdf
# Average every 2 degrees of zenith angle
diagno_df= diagno_df.groupby(by= 'sensor_zn').mean()
fig = plt.figure(figsize= (8,6))
fig.suptitle(hy_obj.base_name)
for a,band_num in enumerate(bands,start=1):
ax = fig.add_subplot(2,2,a)
ax.plot(diagno_df.index,diagno_df['brdf_%s' % band_num],c='k',ls ='--')
ax.scatter(diagno_df.index,diagno_df['uncorr_%s' % band_num],marker ='o',fc='w',ec='k')
ax.scatter(diagno_df.index,diagno_df['corr_%s' % band_num],marker ='o',fc='k',ec='k')
ax.text(.85,.9, "%s nm" % int(hy_obj.wavelengths[band_num]), transform=ax.transAxes,
ha = 'center', fontsize = 12)
if a > 2:
ax.set_xlabel('View zenith angle')
if a in [1,3]:
ax.set_ylabel('Reflectance')
#Create legend
custom_points = []
custom_points.append(Line2D([0],[0], marker = 'o',label='Uncorrected',
markerfacecolor='w', markersize=10,lw=0,markeredgecolor='k'))
custom_points.append(Line2D([0],[0], marker = 'o',label='Corrected',
markerfacecolor='k', markersize=10,lw=0,markeredgecolor='k'))
custom_points.append(Line2D([0],[1],label='Modeled BRDF',c='k', ls ='--'))
ax.legend(handles=custom_points, loc='center',frameon=False,
bbox_to_anchor=(-.15, -.3), ncol =3,columnspacing = 1.5,labelspacing=.25)
plt.savefig("%s%s_brdf_plot.png" % (config_dict['export']['output_dir'],hy_obj.base_name),
bbox_inches = 'tight')
plt.close()
def flex_diagno_plot(hy_obj,config_dict):
''' Generate a diagnostic plot of BRDF correction results.
'''
#Flip sign of zenith angle at minimum
sensor_zn = hy_obj.get_anc('sensor_zn',radians =False)
sensor_zn[~hy_obj.mask['no_data']] = np.nan
for i,line in enumerate(sensor_zn):
line[:np.nanargmin(line)] *= -1
sensor_zn[i] = line
sensor_zn = (sensor_zn[hy_obj.mask['calc_brdf']]//2)*2
diagno_df = pd.DataFrame()
diagno_df['sensor_zn'] =sensor_zn
bands = [hy_obj.wave_to_band(wave) for wave in config_dict['brdf']['diagnostic_waves']]
for band_num in bands:
band = hy_obj.get_band(band_num,mask='calc_brdf')
diagno_df['uncorr_%s' % band_num] = band
band = hy_obj.get_band(band_num,
corrections = hy_obj.corrections + ['brdf'],
mask='calc_brdf')
diagno_df['corr_%s' % band_num] = band
# Average every 2 degrees of zenith angle
diagno_df= diagno_df.groupby(by= 'sensor_zn').mean()
fig = plt.figure(figsize= (8,6))
fig.suptitle(hy_obj.base_name)
for a,band_num in enumerate(bands,start=1):
ax = fig.add_subplot(2,2,a)
ax.scatter(diagno_df.index,diagno_df['uncorr_%s' % band_num],marker ='o',fc='w',ec='k')
ax.scatter(diagno_df.index,diagno_df['corr_%s' % band_num],marker ='o',fc='k',ec='k')
ax.text(.85,.9, "%s nm" % int(hy_obj.wavelengths[band_num]), transform=ax.transAxes,
ha = 'center', fontsize = 12)
if a > 2:
ax.set_xlabel('View zenith angle')
if a in [1,3]:
ax.set_ylabel('Reflectance')
#Create legend
custom_points = []
custom_points.append(Line2D([0],[0], marker = 'o',label='Uncorrected',
markerfacecolor='w', markersize=10,lw=0,markeredgecolor='k'))
custom_points.append(Line2D([0],[0], marker = 'o',label='Corrected',
markerfacecolor='k', markersize=10,lw=0,markeredgecolor='k'))
ax.legend(handles=custom_points, loc='center',frameon=False,
bbox_to_anchor=(-.15, -.3), ncol =2,columnspacing = 1.5,labelspacing=.25)
plt.savefig("%s%s_flexbrdf_plot.png" % (config_dict['export']['output_dir'],hy_obj.base_name),
bbox_inches = 'tight')
plt.close()