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0c_correction_firncover.py
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0c_correction_firncover.py
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# -*- coding: utf-8 -*-
"""
@author: bav@geus.dk
tip list:
%matplotlib inline
%matplotlib qt
import pdb; pdb.set_trace()
"""
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.interpolate import interp1d
import progressbar
import matplotlib.pyplot as plt
import firn_temp_lib as ftl
import time
import os
import xarray as xr
import time
def interp_pandas(s, kind="quadratic"):
# A mask indicating where `s` is not null
m = s.notna().values
s_save = s.copy()
# Construct an interpolator from the non-null values
# NB 'kind' instead of 'method'!
kw = dict(kind=kind, fill_value="extrapolate")
f = interp1d(s[m].index, s.loc[m].values.reshape(1, -1)[0], **kw)
# Apply this to the indices of the nulls; reconstruct a series
s[~m] = f(s[~m].index)[0]
plt.figure()
s.plot(marker="o", linestyle="none")
s_save.plot(marker="o", linestyle="none")
# plt.xlim(0, 60)
return s
print("Loading FirnCover")
time.sleep(0.2)
filepath = os.path.join("Data/FirnCover/FirnCoverData_2.0_2021_07_30.h5")
sites = ["Summit", "KAN-U", "NASA-SE", "Crawford", "EKT", "Saddle", "EastGrip", "DYE-2"]
statmeta_df, sonic_df, rtd_df, rtd_dep, metdata_df = ftl.load_metadata(filepath, sites)
statmeta_df["elevation"] = [1840, 2119, 2361, 2370, 2456, 1942, 3208, 2666]
rtd_df = rtd_df.reset_index()
rtd_df = rtd_df.set_index(["sitename", "date"])
site = "DYE-2"
df_firncover = rtd_df.xs(site, level="sitename")
# Achim Dye-2
print("Loading Achim Dye-2")
time.sleep(0.2)
ds = xr.open_dataset("Data/Achim/T_firn_DYE-2_16.nc")
df = ds.to_dataframe()
df = df.reset_index(1).groupby("level").resample("D").mean()
df.reset_index(0, inplace=True, drop=True)
df.reset_index(inplace=True)
df_achim = pd.DataFrame()
df_achim["date"] = df.loc[df["level"] == 1, "time"]
for i in range(1, 9):
df_achim["rtd" + str(i)] = df.loc[df["level"] == i, "Firn temperature"].values
for i in range(1, 9):
df_achim["depth_" + str(i)] = df.loc[df["level"] == i, "depth"].values
df_achim = df_achim.set_index("date")
print("loading Samiras data")
df_samira = pd.read_excel("Data/too shallow/Samira 1.60m deep/pitB_temp.xlsx")
df_samira = df_samira.set_index("date")
df_samira_a = pd.read_excel("Data/too shallow/Samira 1.60m deep/pitA_temp.xlsx")
df_samira_a = df_samira_a.set_index("date")
print("loading GC-Net surface height")
ds = xr.open_dataset(
"C:/Users/bav/OneDrive - Geological survey of Denmark and Greenland/Data/AWS/GC-Net/20190501_jaws/08c.dat_Req1957.nc"
)
df = ds.to_dataframe()
df = df.reset_index(1)
df = df.loc[df.nbnd == 0][["snh1", "snh2"]]
df = df.loc["2015-01-01":]
# height_gcnet = df['Depth']-df.Depth.iloc[0]-0.5
plt.figure()
# df.snh1.plot()
(df.snh2 - 1).plot()
df_firncover["depth_1"].plot()
df_firncover = df_firncover.loc["2016-05-01":"2016-12-31"]
df_samira = df_samira.loc["2016-05-01":"2016-12-31"]
df_samira_a = df_samira_a.loc["2016-05-01":"2016-12-31"]
# %%
plt.close("all")
fig, ax = plt.subplots(1, 2)
i = 2
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print("firncover", i, df_firncover["depth_" + str(i)].loc["2016-05-11"])
i = 5
df_samira["depth_" + str(i)].plot(ax=ax[0])
df_samira["temp_" + str(i)].plot(ax=ax[1], label="Samira - B")
print("samira-b", i, df_samira["depth_" + str(i)].loc["2016-05-11"].iloc[0])
i = 3
df_samira_a["depth_" + str(i)].plot(ax=ax[0])
df_samira_a["temp_" + str(i)].plot(ax=ax[1], label="Samira - A")
print("samira-a", i, df_samira_a["depth_" + str(i)].loc["2016-05-11"].iloc[0])
plt.legend()
plt.tight_layout()
ax[0].set_ylabel("Depth (m)")
ax[1].set_ylabel("Temperature (degC)")
# %% sensor 3 of firncover matches with sensor 4 in samira-a
fig, ax = plt.subplots(1, 2)
i = 3
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print("firncover", i, df_firncover["depth_" + str(i)].loc["2016-05-11"])
i = 2
df_achim["depth_" + str(i)].plot(ax=ax[0])
df_achim["rtd" + str(i)].plot(ax=ax[1], label="Achim")
print("achim", i, df_achim["depth_" + str(i)].loc["2016-05-11"])
i = 4
df_samira_a["depth_" + str(i)].plot(ax=ax[0])
df_samira_a["temp_" + str(i)].plot(ax=ax[1], label="Samira - A")
print("samira-a", i, df_samira_a["depth_" + str(i)].loc["2016-05-11"].iloc[0])
plt.legend()
plt.tight_layout()
ax[0].set_ylabel("Depth (m)")
ax[1].set_ylabel("Temperature (degC)")
# %
fig, ax = plt.subplots(1, 2)
i = 4
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print("firncover", i, df_firncover["depth_" + str(i)].loc["2016-05-11"])
i = 5
df_samira_a["depth_" + str(i)].plot(ax=ax[0])
df_samira_a["temp_" + str(i)].plot(ax=ax[1], label="Samira - A")
print("samira-a", i, df_samira_a["depth_" + str(i)].loc["2016-05-11"].iloc[0])
plt.legend()
plt.tight_layout()
ax[0].set_ylabel("Depth (m)")
ax[1].set_ylabel("Temperature (degC)")
# %
fig, ax = plt.subplots(1, 2)
i = 5
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print("firncover", i, df_firncover["depth_" + str(i)].loc["2016-05-11"])
i = 3
df_achim["depth_" + str(i)].plot(ax=ax[0])
df_achim["rtd" + str(i)].plot(ax=ax[1], label="Achim")
print("achim", i, df_achim["depth_" + str(i)].loc["2016-05-11"])
plt.tight_layout()
ax[0].set_ylabel("Depth (m)")
ax[1].set_ylabel("Temperature (degC)")
# %
fig, ax = plt.subplots(1, 2)
i = 7
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print("firncover", i, df_firncover["depth_" + str(i)].loc["2016-05-11"])
i = 4
df_achim["depth_" + str(i)].plot(ax=ax[0])
df_achim["rtd" + str(i)].plot(ax=ax[1], label="Achim")
print("achim", i, df_achim["depth_" + str(i)].loc["2016-05-11"])
plt.tight_layout()
ax[0].set_ylabel("Depth (m)")
ax[1].set_ylabel("Temperature (degC)")
#%%
df_firncover = df_firncover.resample("D").mean().loc["2016-05-11":"2016-09-16"]
df_achim = df_achim.resample("D").mean().loc["2016-05-11":"2016-09-16"]
df_samira_a = df_samira_a.resample("D").mean().loc["2016-05-11":"2016-09-16"]
df_samira = df_samira.resample("D").mean().loc["2016-05-11":"2016-09-16"]
X = np.array([])
Y = np.array([])
source = np.array([])
i = 2
X = np.append(X, df_firncover["rtd" + str(i)].values)
X = np.append(X, df_firncover["rtd" + str(i)].values)
i = 5
Y = np.append(Y, df_samira["temp_" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 3)
i = 3
Y = np.append(Y, df_samira_a["temp_" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 2)
i = 3
X = np.append(X, df_firncover["rtd" + str(i)].values)
X = np.append(X, df_firncover["rtd" + str(i)].values)
i = 2
Y = np.append(Y, df_achim["rtd" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 1)
i = 4
Y = np.append(Y, df_samira_a["temp_" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 2)
i = 4
X = np.append(X, df_firncover["rtd" + str(i)].values)
i = 5
Y = np.append(Y, df_samira_a["temp_" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 2)
i = 5
X = np.append(X, df_firncover["rtd" + str(i)].values)
i = 3
Y = np.append(Y, df_achim["rtd" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 1)
i = 7
X = np.append(X, df_firncover["rtd" + str(i)].values)
i = 4
Y = np.append(Y, df_achim["rtd" + str(i)].values)
source = np.append(source, df_samira["temp_" + str(i)].values * 0 + 1)
#%%
z = np.polyfit(X, Y, 1)
p = np.poly1d(z)
fig, ax = plt.subplots(1, 2)
ax[0].plot(X, Y, "o", linestyle="None", color="k")
ax[0].plot([-18, 2], [-18, 2], color="r", label="1:1 line")
ax[0].plot(
np.arange(-18, 2, 0.5), p(np.arange(-18, 2, 0.5)), label="correction function"
)
ax[0].set_xlabel("FirnCover temperatures ($^o$C)")
ax[0].set_ylabel("Reference temperatures ($^o$C)")
ax[0].set_xlim(-18, 2)
ax[0].set_ylim(-18, 2)
ax[0].legend()
ax[0].set_title("before correction")
ax[0].annotate(
"ME = %0.2f\nRMSE = %0.2f" % (np.mean(X - Y), np.sqrt(np.mean((X - Y) ** 2))),
(-16, -2.5),
)
print(z)
# [ 1.03093649 -0.49950273]
# after correction
ax[1].plot(p(X), Y, "o", linestyle="None", color="k")
ax[1].plot([-18, 2], [-18, 2], color="r")
ax[1].set_xlabel("FirnCover temperatures ($^o$C)")
ax[1].set_ylabel("Reference temperatures ($^o$C)")
ax[1].set_xlim(-18, 2)
ax[1].set_ylim(-18, 2)
ax[1].set_title("after correction")
ax[1].annotate(
"ME = %0.2f\nRMSE = %0.2f"
% (np.round(np.mean(p(X) - Y), 2), np.sqrt(np.mean((p(X) - Y) ** 2))),
(-16, -2.5),
)
plt.tight_layout()
# %%
plt.close("all")
for site in sites:
df_firncover = rtd_df.xs(site, level="sitename")
fig, ax = plt.subplots(1, 2)
i = 0
for i in range(23):
if (df_firncover["depth_" + str(i)].iloc[0] < 0.1) | (
np.isnan(df_firncover["depth_" + str(i)].iloc[0])
):
continue
df_firncover["depth_" + str(i)].plot(ax=ax[0])
df_firncover["rtd" + str(i)].plot(ax=ax[1], label="FirnCover")
print(site, i, df_firncover["depth_" + str(i)].iloc[0])
plt.title(site)
break