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albedoBias.py
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albedoBias.py
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# %%
import pandas as pd
import os
import glob
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
from sklearn.metrics import mean_squared_error
import numpy as np
# %%
# '''
# obtain the albedo and promice station data
# '''
# df = pd.read_csv('/data/shunan/github/Remote-Sensing-of-Albedo/script/promice/promice.csv')
# df['Longitude'] = df['Longitude'] * -1
# folderpath = "promice/multiSat60m"
# searchCriteria = "*.csv"
# globInput = os.path.join(folderpath, searchCriteria)
# csvPath = glob.glob(globInput)
# csvList = os.listdir(folderpath)
# #%%
# # hourly
# for i in range(len(csvList)):
# # promice data
# stationName = os.path.splitext(csvList[i])[0].replace("-", "*")
# index = df.index[df.Station == stationName][0]
# url = df.urlhourly[index]
# dfs = pd.read_table(url, sep=r'\s{1,}', engine='python')
# dfs = dfs[['Albedo_theta<70d', 'Year', 'MonthOfYear','DayOfMonth', 'HourOfDay(UTC)', 'CloudCover']]
# dfs['datetime'] = pd.to_datetime(dict(year=dfs.Year, month=dfs.MonthOfYear, day = dfs.DayOfMonth, hour = dfs['HourOfDay(UTC)']))
# # cloud cover less than 50% and albedo must be valid value
# dfs = dfs[(dfs['Albedo_theta<70d'] > 0) & (dfs['Albedo_theta<70d'] < 1) & (dfs['CloudCover'] < 0.5)]
# dfs['Station'] = stationName
# # satellite data
# dfr = pd.read_csv(csvPath[i])
# # dfr.datetime = pd.to_datetime(dfr.datetime).dt.date # keep only ymd
# dfr.datetime = pd.to_datetime(dfr.datetime)
# # join by datetime
# dfmerge = pd.merge_asof(dfr.sort_values('datetime'), dfs.dropna().sort_values('datetime'), on='datetime',allow_exact_matches=False, tolerance=pd.Timedelta(hours=1),direction='nearest' )
# # dfmerge = pd.merge_asof(dfr.sort_values('datetime'), dfs, on='datetime', tolerance=pd.Timedelta(hours=1) )
# if i==0:
# dfmerge.to_csv('promice/promice vs satellite60m.csv', mode='w', index=False)
# else:
# dfmerge.to_csv('promice/promice vs satellite60m.csv', mode='a', index=False, header=False)
# %%
# Plot
#
#
# # %% different scales
df = pd.read_csv('promice/promice vs satellite60m.csv').dropna()
# # if the scale is less than 60m, filter out landsat
# df = df[df.satellite == 'Sentinel2']
slope, intercept, r_value, p_value, std_err = stats.linregress(df.visnirAlbedo, df["Albedo_theta<70d"])
df['bias'] = df["visnirAlbedo"] - df["Albedo_theta<70d"]
df.datetime = pd.to_datetime(df.datetime)
df['doy'] = df['datetime'].dt.dayofyear
def nse(simulations, evaluation):
"""Nash-Sutcliffe Efficiency (NSE) as per `Nash and Sutcliffe, 1970
<https://doi.org/10.1016/0022-1694(70)90255-6>`_.
:Calculation Details:
.. math::
E_{\\text{NSE}} = 1 - \\frac{\\sum_{i=1}^{N}[e_{i}-s_{i}]^2}
{\\sum_{i=1}^{N}[e_{i}-\\mu(e)]^2}
where *N* is the length of the *simulations* and *evaluation*
periods, *e* is the *evaluation* series, *s* is (one of) the
*simulations* series, and *μ* is the arithmetic mean.
https://github.com/ThibHlln/hydroeval/tree/v0.1.0
Thibault Hallouin, 2021. hydroeval: an evaluator for streamflow time series in Python. https://doi.org/10.5281/zenodo.4709652
"""
nse_ = 1 - (
np.sum((evaluation - simulations) ** 2, axis=0, dtype=np.float64)
/ np.sum((evaluation - np.mean(evaluation)) ** 2, dtype=np.float64)
)
return nse_
def ioa(simulations, evaluation):
"""Index of agreement
"""
ioa_ = 1 - (
np.sum((evaluation - simulations) ** 2, axis=0, dtype=np.float64)
/ np.sum(
(np.abs(simulations - np.mean(evaluation)) + np.abs(evaluation - np.mean(evaluation))) ** 2,
dtype=np.float64)
)
return ioa_
def nse_modified(simulations, evaluation, j):
"""Nash-Sutcliffe Efficiency (NSE) Modified
10.5194/adgeo-5-89-2005
"""
nse_modified_ = 1 - (
np.sum(
(np.abs(evaluation - simulations)) ** j , axis=0, dtype=np.float64
)
/ np.sum(
(np.abs(evaluation - np.mean(evaluation))) ** j , dtype=np.float64
)
)
return nse_modified_
nsecoefficient = nse(df["visnirAlbedo"].values, df["Albedo_theta<70d"].values)
nsecoefficientLog = nse(np.log(df["visnirAlbedo"].values), np.log(df["Albedo_theta<70d"].values))
ioad = ioa(df["visnirAlbedo"].values, df["Albedo_theta<70d"].values)
nsem = nse_modified(df["visnirAlbedo"].values, df["Albedo_theta<70d"].values, 1)
print("nse coefficient is %.4f" % nsecoefficient)
print("nse coefficient (log) is %.4f" % nsecoefficientLog)
print("index of agreement is %.4f" % ioad)
print("nse modified is %.4f" % nsem)
#%% different scale plots
sns.set_theme(style="darkgrid", font="Arial", font_scale=2)
g = sns.jointplot(x="visnirAlbedo", y="Albedo_theta<70d", data=df, kind="hist",
height=8, xlim=(0,1), ylim=(0,1), cbar=True, vmin=0, vmax=55)
g.ax_joint.axline((0, 0), (1, 1), linewidth=1, color='k', linestyle='--')
g.plot_joint(sns.regplot, color='r', scatter=False)
g.set_axis_labels(xlabel="visnir albedo", ylabel="PROMICE albedo")
# ref https://stackoverflow.com/a/60849048/13318759
# get the current positions of the joint ax and the ax for the marginal x
pos_joint_ax = g.ax_joint.get_position()
pos_marg_x_ax = g.ax_marg_x.get_position()
# reposition the joint ax so it has the same width as the marginal x ax
g.ax_joint.set_position([pos_joint_ax.x0, pos_joint_ax.y0, pos_marg_x_ax.width, pos_joint_ax.height])
# reposition the colorbar using new x positions and y positions of the joint ax
g.fig.axes[-1].set_position([.96, pos_joint_ax.y0, .07, pos_joint_ax.height])
g.savefig("promice/albedo60m.png",
dpi=300, bbox_inches="tight")
# g.savefig("promice/albedo60m.pdf",
# dpi=300, bbox_inches="tight")
print('ALL: \ny={0:.4f}x+{1:.4f}\nr_value:{2:.2f} \np:{3:.3f} \nstd_err:{4:.4f}'
.format(slope,intercept,r_value,p_value, std_err))
print('Total RMSE is %.4f' % (mean_squared_error(df["Albedo_theta<70d"], df["visnirAlbedo"], squared=False)))
print("average bias is: %.4f" % df.bias.mean())
# %% bias plot
sns.set_theme(style="darkgrid", font="Arial", font_scale=1)
g = sns.FacetGrid(data=df, col="Station", col_wrap=4, legend_out=True)
g.map(sns.regplot, "doy", "bias")
g.add_legend()
g.refline(y=0)
# ax = g.axes[0]
# ax.annotate('n:%.0f' % (len(df.L8.values)), xy=(0.7, 0.1), xycoords='data',
# horizontalalignment='left', verticalalignment='top',
# )
g.savefig("promice/bias60m.png",
dpi=600, bbox_inches="tight")
# %% ryan 2017
df = df[(df.Station=="KAN_L") | (df.Station=="KAN_M") | (df.Station=="KAN_U") ]
# %%
sns.set_theme(style="darkgrid", font="Arial", font_scale=2)
g = sns.FacetGrid(data=df.sort_values("Station"), col="Station", col_wrap=3,
legend_out=True, size=6)
g.map(sns.regplot, "doy", "bias")
g.add_legend()
g.refline(y=0)
# %%
dfsubset = df[(df.Station=="KAN_L") | (df.Station=="KAN_M")]
# RMSE
print('Total RMSE is %.4f' % (mean_squared_error(dfsubset["Albedo_theta<70d"], dfsubset["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubset.bias.mean())
dfsubsetM = dfsubset[(dfsubset["MonthOfYear"] == 4) | (dfsubset["MonthOfYear"] == 5)]
print('April/May RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 6]
print('June RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 7]
print('July RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 8]
print('August RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubset = df[df.Station=="KAN_L"]
# RMSE
print('KAN_L RMSE is %.4f' % (mean_squared_error(dfsubset["Albedo_theta<70d"], dfsubset["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubset.bias.mean())
dfsubsetM = dfsubset[(dfsubset["MonthOfYear"] == 4) | (dfsubset["MonthOfYear"] == 5)]
print('April/May RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 6]
print('June RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 7]
print('July RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 8]
print('August RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubset = df[df.Station=="KAN_M"]
# RMSE
print('KAN_M RMSE is %.4f' % (mean_squared_error(dfsubset["Albedo_theta<70d"], dfsubset["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubset.bias.mean())
dfsubsetM = dfsubset[(dfsubset["MonthOfYear"] == 4) | (dfsubset["MonthOfYear"] == 5)]
print('April/May RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 6]
print('June RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 7]
print('July RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
dfsubsetM = dfsubset[dfsubset["MonthOfYear"] == 8]
print('August RMSE is %.4f' % (mean_squared_error(dfsubsetM["Albedo_theta<70d"], dfsubsetM["visnirAlbedo"], squared=False)))
print("mean bias is %.4f" % dfsubsetM.bias.mean())
# %% Individual AWS
dfsubset = df[df.Station=="KAN_L"]
slope, intercept, r_value, p_value, std_err = stats.linregress(dfsubset["doy"], dfsubset["bias"])
print('KAN_L: \ny={0:.4f}x+{1:.4f}\nr_value:{2:.2f} \np:{3:.3f}'.format(slope,intercept,r_value,p_value))
dfsubset = df[df.Station=="KAN_M"]
slope, intercept, r_value, p_value, std_err = stats.linregress(dfsubset["doy"], dfsubset["bias"])
print('KAN_M: \ny={0:.4f}x+{1:.4f}\nr_value:{2:.2f} \np:{3:.3f}'.format(slope,intercept,r_value,p_value))
# %%
df = df[(df.Station=="KAN_L") | (df.Station=="KAN_M")]
sns.set_theme(style="darkgrid", font="Arial", font_scale=2)
g = sns.FacetGrid(data=df.sort_values("Station"), col="Station", col_wrap=2,
legend_out=True, height=6)
g.map(sns.regplot, "doy", "bias")
g.add_legend()
g.refline(y=0)
g.savefig("promice/KANbias60m.png",
dpi=300, bbox_inches="tight")
#%%
sns.set_theme(style="darkgrid", font="Arial", font_scale=2)
g = sns.FacetGrid(data=df, col="Station", col_wrap=2, height=6)
g.map(sns.regplot, "visnirAlbedo", "bias")
g.refline(y=0)
g.savefig("promice/KANbiasLinear60m.png",
dpi=300, bbox_inches="tight")
dfsubset = df[df.Station=="KAN_L"]
slope, intercept, r_value, p_value, std_err = stats.linregress(dfsubset["visnirAlbedo"], dfsubset["bias"])
print('KAN_L: \ny={0:.4f}x+{1:.4f}\nr_value:{2:.2f} \np:{3:.3f}'.format(slope,intercept,r_value,p_value))
dfsubset = df[df.Station=="KAN_M"]
slope, intercept, r_value, p_value, std_err = stats.linregress(dfsubset["visnirAlbedo"], dfsubset["bias"])
print('KAN_M: \ny={0:.4f}x+{1:.4f}\nr_value:{2:.2f} \np:{3:.3f}'.format(slope,intercept,r_value,p_value))
# %%
sns.boxplot(data=df, x="Station", y="bias")
# %%
sns.boxplot(data=df, x="Station", y="visnirAlbedo")
# %%