Commit b3b42c64 authored by eudesyawog's avatar eudesyawog
Browse files

adding plots pdf, update existing code and news files

parent 6933432e
import pandas as pd
import numpy as np
import os
def normalize_time_series (radar_file, opt_file, norm="meanstd", radar_bands=["VV","VH"],
opt_bands=["B2","B3","B4","B8","B5","B6","B7","B8A","B11","B12"],
opt_indices = ["NDVI","NDWI","EVI","MSAVI2","GDVI","CIGreen","CIRedEdge"]):
radar_df = pd.read_csv(radar_file)
radar_array = None
for band in radar_bands :
columns = [col for col in radar_df.columns if col.startswith(band) and col.endswith("Mean")]
columns.sort()
ts_values = radar_df[columns].values
if norm == "meanstd":
ts_values = (ts_values - ts_values.mean()) / ts_values.std()
elif norm == "minmax":
ts_values = (ts_values - ts_values.min()) / (ts_values.max() - ts_values.min())
if radar_array is None :
radar_array = ts_values
else :
radar_array = np.hstack((radar_array,ts_values))
n_timestamps = len(columns)
n_bands = len(radar_bands)
radar_seq = None
for i in range(n_timestamps):
lst = []
for j in range(n_bands):
lst.append(radar_array[:,i+j*n_timestamps])
if radar_seq is None :
radar_seq = np.stack(lst, axis=1)
else :
radar_seq = np.hstack((radar_seq,np.stack(lst, axis=1)))
opt_df = pd.read_csv(opt_file)
opt_array = None
for band in opt_bands :
columns = [col for col in opt_df.columns if col.split("_")[0]==band and col.endswith("Mean")]
columns.sort()
ts_values = opt_df[columns].values
if norm == "meanstd":
ts_values = (ts_values - ts_values.mean()) / ts_values.std()
elif norm == "minmax":
ts_values = (ts_values - ts_values.min()) / (ts_values.max() - ts_values.min())
if opt_array is None :
opt_array = ts_values
else :
opt_array = np.hstack((opt_array,ts_values))
n_timestamps = len(columns)
n_bands = len(opt_bands)
opt_seq = None
for i in range(n_timestamps):
lst = []
for j in range(n_bands):
lst.append(opt_array[:,i+j*n_timestamps])
if opt_seq is None :
opt_seq = np.stack(lst, axis=1)
else :
opt_seq = np.hstack((opt_seq,np.stack(lst, axis=1)))
indices_df = pd.read_csv(opt_file)
indices_array = None
for band in opt_indices :
columns = [col for col in indices_df.columns if col.startswith(band) and col.endswith("Mean")]
columns.sort()
ts_values = indices_df[columns].values
if norm == "meanstd":
ts_values = (ts_values - ts_values.mean()) / ts_values.std()
elif norm == "minmax":
ts_values = (ts_values - ts_values.min()) / (ts_values.max() - ts_values.min())
if indices_array is None :
indices_array = ts_values
else :
indices_array = np.hstack((indices_array,ts_values))
n_timestamps = len(columns)
n_bands = len(opt_indices)
indices_seq = None
for i in range(n_timestamps):
lst = []
for j in range(n_bands):
lst.append(indices_array[:,i+j*n_timestamps])
if indices_seq is None :
indices_seq = np.stack(lst, axis=1)
else :
indices_seq = np.hstack((indices_seq,np.stack(lst, axis=1)))
ptrn = os.path.basename(radar_file).split('_')[0]+"_"+os.path.basename(radar_file).split('_')[1]
np.save("./data/{}_rad_seq.npy".format(ptrn),radar_seq)
np.save("./data/{}_opt_seq.npy".format(ptrn),opt_seq)
np.save("./data/{}_indices_seq.npy".format(ptrn),indices_seq)
try:
rdt_df = radar_df.merge(opt_df[["ID"]],on="ID")
rdt = rdt_df[["Rdt_s"]].values
np.save("./data/{}_yields.npy".format(ptrn),rdt)
except Exception as error :
print (error)
if __name__ == '__main__' :
# Niakhar 2017
radar_file = "./data/niakhar_2017_radar_notree.csv"
opt_file = "./data/niakhar_2017_opt_gapf_notree.csv"
normalize_time_series(radar_file,opt_file,norm="meanstd")
# Niakhar 2018
radar_file = "./data/niakhar_2018_radar_notree.csv"
opt_file = "./data/niakhar_2018_opt_gapf_notree.csv"
normalize_time_series(radar_file,opt_file,norm="meanstd")
# Nioro 2018
radar_file = "./data/nioro_2018_radar_notree.csv"
opt_file = "./data/nioro_2018_opt_gapf_notree.csv"
normalize_time_series(radar_file,opt_file,norm="meanstd")
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