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Bokito_GapFractionCorrelation.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 14 20:02:29 2019
@author: Fred
"""
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 30 22:59:26 2018
@author: Fred
"""
# Import the require python libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# Read the datasets in the same directory or folder where the python is save
Bok_Gapfraction = pd.read_csv('Bokito_GapFraction2016n2017.csv')
Bok_Gamma0 = pd.read_csv('Bokito_Extracted_SAR_Gamma0db.csv')
print (Bok_Gamma0.columns)
# Summary the backscatter data by the mean of each plot
Gamma0mean = Bok_Gamma0.groupby(['PlotID', 'SARdate'])[['VHgamma0', 'VVgamma0',
'VHdb', 'VVdb', 'VV-VH', 'VH-VVnorm', 'VH-VV', 'VVVHratio', 'VHVVratio',
'VV-VHnorm']].mean()
Gamma0mean.head(10) # view the first 10 rows of dataframe
# Sort the date frame in ascending order of PlotID
Gamma0mean = Gamma0mean.sort_values(by=['PlotID'], ascending=[True])
Gamma0mean.to_csv("Bok_Gamma0mean.csv")
#Gamma0mean.to_csv("Bok_Gamma0sum.csv")
print(Bok_Gapfraction.columns)
# Summarize Gap Fractions dataframe by the mean for each sample Plot
Bok_GFmean = Bok_Gapfraction.groupby(['PlotID','Year'])[['no_of_gaps', 'gap_area', 'gap_fraction']].std()
Bok_GFmean.head(10)
Bok_GFmean = Bok_GFmean.sort_values(by=['PlotID'], ascending=[True])
Bok_GFmean.to_csv("Bok_GFstd.csv")
# Merge two dataframes: SAR Gamma0 mean intensity with Gap Fracation for each plot (means)
# One-to-one merge using PlotId in both dataframes
Bok_Gamma0GF = pd.merge(left=Gamma0mean, right=Bok_GFmean, left_on='PlotID', right_on='PlotID')
print(Bok_Gamma0GF.columns)
corr = Bok_Gamma0GF.corr()
sns.heatmap(corr,
xticklabels=corr.columns.values,
yticklabels=corr.columns.values)
plt.matshow(Bok_Gamma0GF.corr())
plt.show()
# To visualize the correlation matrix using pandas styling option
df = pd.DataFrame(Bok_Gamma0GF)
correl = df.corr()
# limit the correlation value to a precision of digits
correl.style.background_gradient(cmap='coolwarm').set_precision(2)
#BACoa = Coa2017.groupby(['Origin','PlotID2','AgeG'])[['Total BA']].var()
# To show the cluster relationships, use the clustermap of seeaborn
correlate = df.corr().mul(100).astype(int)
sns.clustermap(data=correlate, annot=True, fmt='d', cmap="Greens").savefig('Gamma0meanGFstd.pdf', dpi=300)
sns.clustermap(data=correlate, annot=True, fmt='d', cmap="Greens").savefig('Gamma0meanFFstd.tiff', dpi=300)
sns.clustermap(data=correlate, annot=True, fmt='d', cmap="Greens").savefig('Gamma0meanGFstd.jpg', dpi=300)
#Bok_GmGFs = pd.read_csv('Bokito_Gamma0mean_GFsum.csv')
Bok_GmGFs = pd.read_csv('Bokito_Gamma0mean_GFstd.csv')
print(Bok_GmGFs.columns)
plt.style.use ('seaborn-whitegrid')
colors = Bok_GmGFs['VV-VH']
plt.scatter(Bok_GmGFs['gap_fraction'], Bok_GmGFs['VH-VVnorm'], c=colors, alpha=0.3, cmap='viridis')
plt.ylabel("Normalized VH-VV Backscatter (Gamma0 dB)")
plt.xlabel("Canopy Gap Fraction")
plt.colorbar();
plt.savefig("Correlation_VH-VVNormVsGapFraction.tiff", dpi=300)
plt.savefig("Correlation_VH-VVNormVsGapFraction.pdf", dpi=300)
#plt.legend()
Bok_GmGFs2 = Bok_GmGFs.drop('Year', 1)
Bok_GmGFs2 = Bok_GmGFs2.drop(Bok_GmGFs2.columns[[0, 1]], axis=1)
Bok_GmGFs2 = pd.DataFrame(Bok_GmGFs2)
pd.scatter_matrix(Bok_GmGFs2, alpha=0.2, figsize=(10, 10), diagonal='kde')
plt.show
plt.savefig("Scatter_Gamma0_Bands_GapFraction.tiff", dpi=300)
plt.savefig("Scatter_Gamma0_Bands_GapFraction.pdf", dpi=300)
pp = sns.pairplot(data = Bok_GmGFs,
y_vars =['gap_fraction'],
x_vars = ['VH-VVnorm','VVVHratio','VV-VH'])
plt.savefig("GapFraction_PairPlot_meanGamma0GFstd.tiff", dpi=300)
plt.savefig("GapFraction_PairPlot_meanGamma0GFstd.pdf", dpi=300)
Bok_GmGFs.describe() # ger summary statistics of each variable in Bok_GmGFs
'''
PlotID', 'SARdate', 'VHgamma0', 'VVgamma0', 'VHdb', 'VVdb', 'VV-VH',
'VH-VVnorm', 'VH-VV', 'VVVHratio', 'VHVVratio', 'VV-VHnorm', 'Year',
'no_of_gaps', 'gap_area', 'gap_fraction', 'Ptype'],
'''
BokEfoul_GapF = pd.read_csv('GapFraction_BokitoEfoulan_2016_17.csv')
BokEfoul_GapF.describe()
print(BokEfoul_GapF.columns)
BokEoul_GFmean = BokEfoul_GapF.groupby(['Site','PlotID','Year'])[['no_of_gaps', 'gap_area', 'gap_fraction']].mean()
BokEoul_GFmean = BokEoul_GFmean.sort_values(by=['PlotID'], ascending=[True])
BokEoul_GFmean.to_csv('BokitoEoulan_meanGapFraction.csv')
BokEoul_GFvariance = BokEfoul_GapF.groupby(['Site','PlotID','Year'])[['no_of_gaps', 'gap_area', 'gap_fraction']].std()
BokEoul_GFvariance.to_csv('BokitoEoulan_varianceGapFraction.csv')
Mean_GapF = pd.read_csv('BokitoEoulan_meanGapFraction.csv')
print(Mean_GapF.columns)
sns.distplot(Mean_GapF['gap_fraction'])
sns.distplot(Mean_GapF['gap_fraction'], hist = False, bins = 10 )
sns.catplot(y ='gap_fraction', x = 'Site', kind = "box", data = Mean_GapF)
BokEfoul_Gamma0 = pd.read_csv('BokitoEfoulan_SAR_Gamma0_extracted.csv')
print(BokEfoul_Gamma0.columns)
BokEfoul_SARmean = BokEfoul_Gamma0_pixels.groupby(['Site', 'PlotID'])[['Gamma0VH',
'Gamma0VV', 'Gamma0VHdb', 'Gamma0VVdb', 'Gamma0VV-VH', 'VH-VVonVH+VV',
'VH-VV', 'VVVHratio', 'VHVVratio', 'VV-VHonVV+VH']].mean()
BokEfoul_SARmean = BokEfoul_SARmean.sort_values(by=['PlotID'], ascending=[True])
BokEfoul_SARmean.to_csv('BokEfoul_SAR_meanGamma0.csv')
BokEfoul_Gamma0GF = pd.merge(left=BokEfoul_SARmean, right=BokEoul_GFmean, left_on='PlotID', right_on='PlotID')
print(BokEfoul_Gamma0GF.columns)
BokEfoul_Gamma0GF.to_csv('BokEfoul_Gamma0GF.csv')
BokEfoul_Gamma0GFmean = pd.read_csv('BokEfoul_Gamma0GF.csv')
print(BokEfoul_Gamma0GFmean.columns)
BokEfoul_Gamma0GF.to_csv('BokEfoul_Gamma0GF.csv')
BokEfoul_Gamma0GFmean = pd.read_csv('BokEfoul_Gamma0GF.csv')
print(BokEfoul_Gamma0GFmean.columns)
df = BokEfoul_Gamma0GFmean.copy()
df = df.drop(['no_of_gaps', 'gap_area'],axis=1)
print(df.columns)
print(df.info())
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgb
from sklearn.metrics import accuracy_score
import tensorflow as tf
x_SAR = BokEfoul_Gamma0GFmean[:, 1:11]
y_SAR = BokEfoul_Gamma0GFmean[:, -1]
# SUMMARY OF SAR BACKSCATTER FROM EXTRACTED PIXELS IN BOTH STUDY SITES
# Import Gap Fraction data
BokEoul_GFmean = pd.read_csv('BokitoEoulan_meanGapFraction.csv')
print(BokEoul_GFmean.columns)
BokEfoul_Gamma0_pixels = pd.read_csv('BokitoEfoulan_SAR_Gamma0_extractedpixels.csv')
print(BokEfoul_Gamma0_pixels.columns)
BokEfoul_pixSARmean = BokEfoul_Gamma0_pixels.groupby(['Site', 'PlotID'])[['Gamma0VH',
'Gamma0VV', 'Gamma0VHdb', 'Gamma0VVdb', 'Gamma0VV-VH', 'VH-VVonVH+VV',
'VH-VV', 'VVVHratio', 'VHVVratio', 'VV-VHonVV+VH']].mean()
BokEfoul_pixSARmean = BokEfoul_pixSARmean.sort_values(by=['PlotID'], ascending=[True])
BokEfoul_pixSARmean.to_csv('BokEfoul_SAR_meanGamma0pixels.csv')
BokEfoul_Gamma0GFpixels = pd.merge(left=BokEfoul_pixSARmean, right=BokEoul_GFmean, left_on='PlotID', right_on='PlotID')
print(BokEfoul_Gamma0GFpixels.columns)
BokEfoul_Gamma0GFpixels.to_csv('BokEfoul_Gamma0GFpixels.csv')
BokEfoul_Gamma0GFpixels = pd.read_csv('BokEfoul_Gamma0GFpixels.csv')
BokEfoul_Gamma0GFpixels = BokEfoul_Gamma0GFpixels.drop(['Unnamed: 0'],axis=1)
BokEfoul_Gamma0GFpixels.to_csv('BokEfoul_Gamma0GFpixels.csv')
BokEfoul_Gamma0GFpixels = pd.read_csv('BokEfoul_Gamma0GFpixels.csv')
print(BokEfoul_Gamma0GFpixels.columns)
dfPix = BokEfoul_Gamma0GFpixels.copy()
dfPix2 = dfPix.drop(['PlotID','Site', 'Year','no_of_gaps', 'gap_area'],axis=1)
print(dfPix2.columns)
print(dfPix2.info())
dfPix2_stats = dfPix2.describe() # Get summary statistice of dataframe
print(dfPix2_stats.shape)
#df2_stats.pop('gap_fraction')
dfPix2_stats = dfPix2_stats.transpose()
dfPix2_stats
dfPix2_stats.to_csv('Summary_PixelGamm0.csv')
sns.pairplot(dfPix[['Gamma0VH', 'Gamma0VV', 'Gamma0VHdb', 'Gamma0VVdb',
'Gamma0VV-VH', 'VH-VVonVH+VV', 'VH-VV', 'VVVHratio', 'VHVVratio',
'VV-VHonVV+VH', 'gap_fraction']], diag_kind="kde")
Pixeldf = pd.read_csv('BokEfoul_Gamma0GFpixels_Luse.csv')
# PLOT BOXPLOT OF SIGNIFICANT STEPWISE REGRESSION PREDICTORS
names = ['Gamma0VV', "Gamma0VVdb", "VVVHratio", 'VHVVratio']
fig, axes = plt.subplots(1,4, constrained_layout=True)
#plt.subplots_adjust(hspace = 3.8)
sns.set_style("whitegrid")
for i, t in enumerate(names):
sns.boxplot(y = t, x = "luse", data = Pixeldf, ax = axes[i % 4])
#set_xticklabels(rotation =45)
plt.savefig("Boxplot_StepRegressPredictorsPixel.tiff", dpi=300)
plt.savefig("Boxplot_StepRegressPredictorsPixel.pdf", dpi=300)
plt.savefig("Boxplot_StepRegressPredictorsPixel.eps", dpi=300)