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analyze_AWS_elevs_including_ATM_v2.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 28 07:32:22 2023
@author: jason, [email protected]
compare ATM and other sources of AWS elevations
see ATM folder in this repository
for the GEUS GPS, this scripts reads output from which currently has some simple filtering of SDL and CP1 outliers
./GCN_positions_timeseries_v_thredds.py
"""
from glob import glob
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from numpy.polynomial.polynomial import polyfit
from pathlib import Path
import calendar
import simplekml
def save_kml(lat,lon,namex,opath,ofile):
kml = simplekml.Kml(open=1)
pnt = kml.newpoint(name=namex)
pnt.coords=[(lon,lat)]
pnt.style.labelstyle.color = simplekml.Color.blue # Make the text red
pnt.style.labelstyle.scale = 1.5 # text scaling multiplier
pnt.style.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png'
pnt.altitudemode = simplekml.AltitudeMode.relativetoground
kml_ofile=opath+ofile+".kml"
kml.save(kml_ofile)
# ## change to your system's login name to change dir for local work
if os.getlogin() == 'jason':
base_path = '/Users/jason/Dropbox/AWS/GCNET/GCNet_positions.stash/'
os.chdir(base_path)
# read Greenland AWS locations
meta = pd.read_csv('/Users/jason/Dropbox/AWS/GCNET/GC-Net-level-1-data-processing/L1/GC-Net_location.csv')
meta = meta.rename({'Name': 'name'}, axis=1)
meta = meta.rename({'Latitude (°N)': 'lat'}, axis=1)
meta = meta.rename({'Longitude (°E)': 'lon'}, axis=1)
meta = meta.rename({'Elevation (wgs84 m)': 'elev'}, axis=1)
meta["nickname"]=''
sites=['Swiss Camp', 'Crawford Point 1', 'CP2', 'JAR1', 'JAR2', 'JAR3',
'NASA-U', 'GITS', 'Humboldt', 'Summit', 'Tunu-N', 'DYE-2',
'Saddle', 'South Dome', 'NASA-E', 'NASA-SE', 'NGRIP', 'NEEM',
'EastGRIP', 'KAR', 'KULU', 'Aurora', 'Petermann Glacier',
'Petermann ELA']
nicknames=['SWC', 'CP1', 'CP2', 'JAR', 'JR2', 'JR3',
'NAU', 'GIT', 'HUM', 'SUM', 'TUN', 'DY2',
'SDL', 'SDM', 'NAE', 'NSE', 'NGRP', 'NEM',
'EGP', 'KAR', 'KUL', 'AUR', 'PTG',
'PET']
for ss,site in enumerate(sites):
meta["nickname"][meta["name"] == sites[ss]]=nicknames[ss]
names=['SMS-PET','SMS1','SMS2','SMS3','SMS4','SMS5','LAR1','LAR2','LAR3','Swiss Camp 10m','Roof_GEUS','LYN_T','LYN_L','CEN1','CEN2','KPC_Lv3','KPC_Uv3','THU_L2','WEG_B','ZAK_Uv3']
for name in names:
meta.drop(meta[meta.name==name].index, inplace=True) # drop original time column
print(meta.name)
meta['elev_linear_slope']=np.nan
meta['elev_linear_intercept']=np.nan
meta['elev_fit_t0']=np.nan
meta['elev_fit_t1']=np.nan
meta['elev_change_linear']=np.nan
meta['elev_change_n_years']=np.nan
meta['elev_mean_from_altimetry']=np.nan
meta['k']=np.nan
meta['N_altimetry_measurements']=np.nan
print(meta.columns)
#%%
# obtain geoidal heights using data from https://www.agisoft.com/downloads/geoids/
# EGM = earth gravitational model
import rasterio
geoids=['egm2008_25','egm96_15']
geoids=['egm96_15']
# geoids=['egm2008_25']
# geoids=['egm2008_25']
for geoid in geoids:
dat = rasterio.open(f"/Users/jason/0_dat/geoid/us_nga_{geoid}.tiff")
# read all the data from the first band
z = dat.read()[0]
#%%
# check the crs of the data
# dat.crs
# >>> CRS.from_epsg(4326)
# check the bounding-box of the data
# dat.bounds
# >>> Out[49]: BoundingBox(left=-120.0, bottom=45.0, right=-117.0, top=48.0)
# since the raster is in regular lon/lat grid (4326) we can use
# `dat.index()` to identify the index of a given lon/lat pair
# (e.g. it expects coordinates in the native crs of the data)
def getval(lon, lat):
idx = dat.index(lon, lat, precision=1E-6)
# return dat.xy(*idx), z[idx]
return z[idx]
N=len(meta)
x=np.zeros(N)
for i in range(N):
# print( getval(meta.lon.values[i], meta.lat.values[i]))
x[i]=getval(meta.lon.values[i], meta.lat.values[i])
# print(i,meta.lon.values[i], meta.lat.values[i],x)
meta[geoid]=x
#%%
df = pd.read_excel('./meta/GC-Net historical positions.xlsx')
os.system('open '+'./meta/GC-Net historical positions.xlsx')
df.drop(df[df.date == 'doc_2000'].index, inplace=True)
df.drop(df[df.date == 'ref'].index, inplace=True)
df.drop(df[df.date == 'WMO-DMI_2012'].index, inplace=True)
df.drop(df[df.date == 35596].index, inplace=True)
df.drop(df[df.elev == '-'].index, inplace=True)
df.drop(df[df.elev == np.nan].index, inplace=True)
df.loc[ df["name_long"] == "Crawford Pt. 1","name_long"] = "Crawford Point 1"
df.date=pd.to_datetime(df.date)
df['year']=df.date.dt.year
df['doy']=df.date.dt.dayofyear
df['jy']=df['year']+df['doy']/366
# print(np.array(df.date))
# print(len(df))
# df.columns
# v=np.where(((df.name_long=='JAR1')&(df.elev>940)))
# df.elev[v[0]]=np.nan
vandecrux_position_compilation=df.copy()
#%%
n_AWS=len(meta)
# meta=meta.sort_values(by='name', ascending=True)
# print(meta.columns)
# iyear=1995 ; fyear=1998
# n_years=fyear-iyear+1
# years=np.arange(iyear,fyear+1).astype(str)
years=['1995',
'1996',
'1997',
'1998',
'1999',
'2001',
'2002',
'2003',
'2005',
'2006']
years=['2001',
'2002',
'2003',
'2005',
'2006']
years=['1995',
'1996',
'1997',
'1998',
'1999',
'2001',
'2002',
'2005',
'2006',
'2007',
'2008',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015',
'2016',
'2017',
'2018',
'2019',
]
n_years=len(years)
# print(n_years)
#%%
th=1
font_size=10
# plt.rcParams['font.sans-serif'] = ['Georgia']
plt.rcParams["font.size"] = font_size
plt.rcParams['axes.facecolor'] = 'w'
plt.rcParams['axes.edgecolor'] = 'k'
plt.rcParams['axes.grid'] = True
plt.rcParams['grid.alpha'] = 1
plt.rcParams['grid.color'] = "#cccccc"
plt.rcParams["legend.facecolor"] ='w'
plt.rcParams["mathtext.default"]='regular'
plt.rcParams['grid.linewidth'] = th
plt.rcParams['axes.linewidth'] = th #set the value globally
plt.rcParams['figure.figsize'] = 17, 10
plt.rcParams["legend.framealpha"] = 0.8
plt.rcParams['figure.figsize'] = 5, 4
ms=12
sites=['Swiss Camp', 'Crawford Point 1', 'CP2', 'JAR1', 'JAR2', 'JAR3',
'NASA-U', 'GITS', 'Humboldt', 'Summit', 'Tunu-N', 'DYE-2',
'Saddle', 'South Dome', 'NASA-E', 'NASA-SE', 'NGRIP', 'NEEM',
'EastGRIP', 'KAR', 'KULU', 'Aurora', 'Petermann Glacier',
'Petermann ELA']
# choice_site=0 # SWC
# choice_site=1 # CP1
# choice_site=2 # CP2
choice_site=3 # JAR1
# choice_site=4 # JAR2
# choice_site=5 # JAR3
# choice_site=6 # NAU
# choice_site=7 # GIT
# choice_site=8 # HUM
# choice_site=9 # SUM
# choice_site=10 # TUN
# choice_site=11 # DY2
# choice_site=12 # SDL
# choice_site=13 # SDM
# choice_site=14 # NAE
# choice_site=15 # NSE
# choice_site=16 # NGP
# choice_site=17 # NEM
# choice_site=18 # EGP
# choice_site=19 # EGP
# choice_site=23 # PTE
elevs=np.zeros(n_years)
time_ATM_decimal_year=np.zeros(n_years)
time_ATM_decimal_year_v2=np.zeros(n_years)
dist=np.zeros(n_years)
n_AWS=1
# for k in range(n_AWS):
for k in [choice_site]:
#
min_tolerated_dist=1
if nicknames[k]=='HUM':
min_tolerated_dist=8
if choice_site==3: #JAR
# v=np.where(((df.name_long=='JAR1')&(df.elev>940)))
# v=np.where(df.name_long=='JAR1')
# ix=v[0]
# for i in ix:
# print(pd.to_datetime(df.date.values[i]).strftime('%Y-%m-%d'))
# datexx=pd.to_datetime(df.date.values[i]).strftime('%Y-%m-%d')
# save_kml(df.lat.values[i],df.lon.values[i],'JAR1_'+datexx,'/Users/jason/Dropbox/AWS/GCNET/GCNet_positions.stash/output/kml/','JAR1_'+datexx)
geoid_offset=-meta[geoid].values[k]
fn='./meta/Stober_et_al_2023/SWC+ST2-Alle Pegel_Koordinaten Geodätisch.xlsx'
# os.system('open '+fn)
stober=pd.read_excel(fn,skiprows=7,names=['latdeg','latmin','latsec','NS','lat','emt','londeg','lonmin','lonsec','EW','lon','elevation'],index_col=0)
stober = stober.iloc[65:]
stober_dates=[]
stober_elevs=[]
for i,temp in enumerate(stober.index):
temp=str(temp)
stake=temp[0:5]
# print(i,stake)
# temp.replace('106-ex010915','nan')
# temp.replace('ex010915','nan')
# print(i,temp)
if temp!='nan':
lat=stober.lat.values[i]
lon=-stober.lon.values[i]
datex=pd.to_datetime(temp[6:],format=('%d%m%y'))
print(stake,datex.strftime('%Y-%m-%d'),lat,lon,stober.elevation.values[i])
ST2_offset=35
if stake=='ST201':
stober_dates.append(datex.strftime('%Y-%m-%d'))
# stober_offset=-66
stober_elevs.append(stober.elevation.values[i]-1.08+geoid_offset-ST2_offset)
# save_kml(lat,lon,stake+' '+datex.strftime('%Y-%m-%d'),'/Users/jason/Dropbox/AWS/GCNET/GCNet_positions/output/kml/Stober_Swiss_Camp/',stake+' '+datex.strftime('%Y-%m-%d'))
stober2=pd.DataFrame({'date':np.array(stober_dates),
'elev':np.array(stober_elevs),
})
stober2['date']=pd.to_datetime(stober2.date)
stober2['year'] = stober2['date'].dt.year
stober2['doy'] = stober2['date'].dt.dayofyear
stober2['n_days']=365
for m in range(len(stober2)):
if calendar.isleap(stober2.year[m]):
stober2['n_days']=366
stober2['jdy']=stober2['year']+stober2['doy']/stober2['n_days']
#
if choice_site==0: # SWC
fn='./meta/Stober_et_al_2023/SWC+ST2-Alle Pegel_Koordinaten Geodätisch.xlsx'
# heights are referenced to EUREF, a height system I used since my first measurements in 1991.
# For correction to ITRF use ITRF = EUREF - 1.08 Meter.
# os.system('open '+fn)
stober=pd.read_excel(fn,skiprows=7,names=['latdeg','latmin','latsec','NS','lat','emt','londeg','lonmin','lonsec','EW','lon','elevation'],index_col=0)
stober = stober.iloc[:-32]
stober_dates=[]
stober_elev0=[]
stober_elev1=[]
stober_elev2=[]
for i,temp in enumerate(stober.index):
temp=str(temp)
pegel=temp[0:3]
temp.replace('106-ex010915','nan')
temp.replace('ex010915','nan')
# print(i)
if temp!='nan' and temp[4:]!='ex010915':
lat=stober.lat.values[i]
lon=-stober.lon.values[i]
datex=pd.to_datetime(temp[4:],format=('%d%m%y'))
# print(pegel,datex.strftime('%Y-%m-%d'),lat,lon,stober.elevation.values[i])
pegs=['106','120','121']
# oss=1.3
geoid_offset=-meta[geoid].values[k]
geoid_offset=-25
for peg in pegs:
if pegel=='106':
stober_dates.append(datex.strftime('%Y-%m-%d'))
# geoid_offset=-25
stober_elev0.append(stober.elevation.values[i]-1.08+geoid_offset)
if pegel=='120':
# geoid_offset=-41/oss
stober_elev1.append(stober.elevation.values[i]-1.08+geoid_offset)
if pegel=='121':
# geoid_offset=-44/oss
stober_elev2.append(stober.elevation.values[i]-1.08+geoid_offset)
# save_kml(lat,lon,pegel+' '+datex.strftime('%Y-%m-%d'),'/Users/jason/Dropbox/AWS/GCNET/GCNet_positions/output/kml/Stober_Swiss_Camp/',pegel+' '+datex.strftime('%Y-%m-%d'))
stober2=pd.DataFrame({'date':np.array(stober_dates),
'elev0':np.array(stober_elev0),
'elev1':np.array(stober_elev1),
'elev2':np.array(stober_elev2),
})
stober2['date']=pd.to_datetime(stober2.date)
stober2['year'] = stober2['date'].dt.year
stober2['doy'] = stober2['date'].dt.dayofyear
stober2['n_days']=365
for m in range(len(stober2)):
if calendar.isleap(stober2.year[m]):
stober2['n_days']=366
stober2['jdy']=stober2['year']+stober2['doy']/stober2['n_days']
plt.close()
plt.clf()
fig, ax = plt.subplots(figsize=(9,7))
yx=np.zeros(n_years)
ATM=pd.read_csv(f'./ATM/output/{sites[choice_site]}_v2.csv')
ATM.columns
ATM['date']=pd.to_datetime(ATM.date)
ATM['doy'] = ATM['date'].dt.dayofyear
ATM['year'] = ATM['date'].dt.year
ATM['n_days']=365
for m in range(len(ATM)):
if calendar.isleap(ATM.year[m]):
ATM['n_days']=366
ATM['jdy']=ATM['year']+ATM['doy']/ATM['n_days']
time_ATM_decimal_year=ATM['jdy'].values
elevs=ATM.elev_ATM_m-meta[geoid].values[k]
dist=ATM.distance_km
x1=np.sin(np.radians(ATM.slope_S2N_deg.values))*ATM.distance_km.values*1000
x2=np.sin(np.radians(ATM.slope_W2E_deg.values))*ATM.distance_km.values*1000
# print('slope cor',ATM.slope_S2N_deg.values,x1)
# x1=ATM.slope_S2N.values[v][0]*ATM.dist.values[v][0]*1000
# x2=ATM.slope_W2E.values[v][0]*ATM.dist[v][0]*1000
x1=ATM.slope_S2N_deg.values*ATM.distance_km.values*1000
x2=ATM.slope_W2E_deg.values*ATM.distance_km*1000
yx=ATM.elev_ATM_m.values-meta[geoid].values[k]-x1-x2
# if dist<min_tolerated_dist:
# # print(time_ATM_decimal_year[yy],"%.1f"%elevs[yy],"%.1f"%dist[yy],"%.1f"%(ATM.slope_S2N.values[v][0]*1000),"%.1f"%(ATM.slope_W2E.values[v][0]*1000),
# # "%.1f"%x1,"%.1f"%x2,"%.1f"%(x1+x2))
# print(geoid,time_ATM_decimal_year[yy],"%.1f"%elevs[yy],"%.1f"%dist[yy],"%.1f"%(ATM.slope_S2N.values[v]*1000),"%.1f"%(ATM.slope_W2E.values[v]*1000),
# "%.1f"%x1,"%.1f"%x2,"%.1f"%(x1+x2))
v=np.where(dist<min_tolerated_dist)
v=v[0]
ATM_dates=time_ATM_decimal_year[v]
elevs_ATM_within_distance_tolerance=elevs.values[v]
y2=yx.values[v]
# exclude suspicious values else plot stober
if nicknames[k]=='SWC':
# inv=np.where((x<2005)&(y<1125))
# inv=inv[0]
# y[inv]=np.nan
# inv=np.where((x>2015)&(y<1120))
# inv=inv[0]
# y[inv]=np.nan
khan=pd.read_csv('/Users/jason/Dropbox/AWS/GCNET/GCNet_positions.stash/Khan/SwissCamp.txt',skiprows=2,delim_whitespace=True,names=['jy','elev'])
# print(khan)
# #%%
# plt.plot(stober2['jdy'],(stober2['elev0']+stober2['elev1']+stober2['elev2'])/3,'s',color='k',label="GPS survey c/o M. Stober, Jan 2024, average of\nstakes 106,120,121 "+geoid+": %.1f"%np.mean(stober2['elev0'])+"±%.1f"%np.std(stober2['elev0'])+' m')
plt.plot(stober2['jdy'],stober2['elev0'],'s',color='k',label="GPS survey c/o M. Stober, Jan 2024, average of\nstakes 106 with 25m offset: %.1f"%np.mean(stober2['elev0'])+"±%.1f"%np.std(stober2['elev0'])+' m')
os_khan=1135.7
plt.plot(khan.jy,khan.elev+os_khan,'-',linewidth=th*4,color='orange',label='satellite altimetry c/o S.A. Khan, Jan. 2024\noffset to GEUS GPS data 2022 to 2023',zorder=20)
# exclude suspicious values else plot stober
if nicknames[k]=='JAR':
# inv=np.where((x<2005)&(y<1125))
# inv=inv[0]
# y[inv]=np.nan
# inv=np.where((x>2015)&(y<1120))
# inv=inv[0]
# y[inv]=np.nan
plt.plot(stober2['jdy'],stober2['elev'],'s',color='k',zorder=20,
label=f"Stober ST201 including {ST2_offset} m offset: %.1f"%np.mean(stober2['elev'])+"±%.1f"%np.std(stober2['elev'])+' m')
khan=pd.read_csv('/Users/jason/Dropbox/AWS/GCNET/GCNet_positions.stash/Khan/JAR.txt',skiprows=2,delim_whitespace=True,names=['jy','elev'])
# print(khan)
# #%%
# plt.plot(stober2['jdy'],(stober2['elev0']+stober2['elev1']+stober2['elev2'])/3,'s',color='k',label="GPS survey c/o M. Stober, Jan 2024, average of\nstakes 106,120,121 with %.1f"%geoid_offset+" m offset: %.1f"%np.mean(stober2['elev0'])+"±%.1f"%np.std(stober2['elev0'])+' m')
os_khan=927
plt.plot(khan.jy,khan.elev+os_khan,'-',linewidth=th*4,color='orange',label='satellite altimetry c/o S.A. Khan, Jan. 2024\noffset to GEUS GPS data 2022 to 2023',zorder=20)
if nicknames[k]=='CP1':
inv=np.where((ATM_dates>2005)&(elevs_ATM_within_distance_tolerance>1960))
inv=inv[0]
elevs_ATM_within_distance_tolerance[inv]=np.nan
if nicknames[k]=='CP2':
inv=np.where((ATM_dates>2005)&(elevs_ATM_within_distance_tolerance>1960))
inv=inv[0]
elevs_ATM_within_distance_tolerance[inv]=np.nan
if nicknames[k]=='JR2':
inv=np.where((ATM_dates>2010)&(elevs_ATM_within_distance_tolerance>530))
inv=inv[0]
elevs_ATM_within_distance_tolerance[inv]=np.nan
if nicknames[k]=='JR3':
inv=np.where((ATM_dates>2005)&(elevs_ATM_within_distance_tolerance<220))
inv=inv[0]
elevs_ATM_within_distance_tolerance[inv]=np.nan
xxx=1000
lab="ATM within %.1f"%min_tolerated_dist+" km: %.1f"%np.mean(elevs[v])+"±%.1f"%np.std(elevs[v])+' m, using '+geoid.split('_')[0].upper()
# print('lab',lab)
# print('elevs',elevs[v])
plt.plot(ATM_dates,elevs_ATM_within_distance_tolerance,'o', fillstyle='none',markersize=ms, mew=2,label=lab)
# plt.plot(ATM_dates,y2,'s', fillstyle='none',markersize=ms,label="ATM with slope cor: %.1f"%np.mean(yx[v])+"±%.1f"%np.std(yx[v])+' m')
# if len(x)>1:
# v=np.where(~np.isnan(y))
# v=v[0]
# x=x[v]
# y=y[v]
# N_valid=len(y)
# b, m = polyfit(x, y, 1)
# xx=[x[0],x[-1]]
# yy=[xx[0]*m+b,xx[1]*m+b]
# dy=yy[1]-yy[0]
# ny=xx[1]-xx[0]
# plt.plot(xx,yy,c='m',label=f'ATM fit averages {"%.0f"%np.mean(yy)}m')
# # print(np.mean(y),np.std(y))
# # kx=meta.ID.values[k]-1
# # kx=k-1
# # kx=np.where()
# # kx=kx[0]
# # print('hi',meta.name.values[k],k,kx)
# meta['elev_linear_slope'][meta.name==sites[k]]=m
# meta['elev_linear_intercept'][meta.name==sites[k]]=b
# meta['elev_fit_t0'][meta.name==sites[k]]=xx[0]
# meta['elev_fit_t1'][meta.name==sites[k]]=xx[1]
# meta['elev_change_linear'][meta.name==sites[k]]=dy
# meta['elev_change_n_years'][meta.name==sites[k]]=ny
# meta['elev_mean_from_altimetry'][meta.name==sites[k]]=np.mean(yy)
# meta['N_altimetry_measurements'][meta.name==sites[k]]=N_valid
# if len(x)==1:
# meta['elev_linear_slope'][meta.name==sites[k]]=np.nan
# meta['elev_linear_intercept'][meta.name==sites[k]]=np.nan
# meta['elev_fit_t0'][meta.name==sites[k]]=np.nan
# meta['elev_fit_t1'][meta.name==sites[k]]=np.nan
# meta['elev_change_linear'][meta.name==sites[k]]=np.nan
# meta['elev_change_n_years'][meta.name==sites[k]]=1
# meta['elev_mean_from_altimetry'][meta.name==sites[k]]=y
# meta['N_altimetry_measurements'][meta.name==sites[k]]=1
plt.title(sites[k]+' a.k.a. '+nicknames[k])
plt.ylabel('elevation above mean sea level, m')
year0=1989 ; year1=2024
plt.xlim(year0,year1)
v=vandecrux_position_compilation.name_long==sites[k]
y3=vandecrux_position_compilation.elev[v]
if ~np.isnan(np.std(y3)):
if nicknames[k]=='SWC':
plt.plot(vandecrux_position_compilation.jy[v][0],y3[0],'s', fillstyle='none',markersize=ms/2,c='b',label="Ohmura et al 1991: %.0f"%y3[0]+' m')
else:
plt.plot(vandecrux_position_compilation.jy[v],y3,'s', fillstyle='none',markersize=ms/2,c='b',
label="GC-Net historical positions.xlsx: %.0f"%np.mean(y3)+"±%.0f"%np.std(y3)+' m')
out=pd.DataFrame({'jdy':ATM_dates,
'elev':elevs_ATM_within_distance_tolerance,
})
suffix=''
fn = Path(f'./output/Jason/{nicknames[k]}_positions_monthly.csv')
cat_flag=0
if fn.is_file():
# print(fn)
GEUS_AWS_position=pd.read_csv(fn)
GEUS_AWS_position.elev[GEUS_AWS_position.elev==0]=np.nan
GEUS_AWS_position.elev-=1.5 # correction to height of ice surface
GEUS_AWS_position['day']=15
GEUS_AWS_position['date']=pd.to_datetime(GEUS_AWS_position[['year', 'month', 'day']])
GEUS_AWS_position['doy'] = GEUS_AWS_position['date'].dt.dayofyear
GEUS_AWS_position['n_days']=365
for m in range(len(GEUS_AWS_position)):
if calendar.isleap(GEUS_AWS_position.year[m]):
GEUS_AWS_position['n_days']=366
GEUS_AWS_position['jdy']=GEUS_AWS_position['year']+GEUS_AWS_position['doy']/GEUS_AWS_position['n_days']
plt.plot(GEUS_AWS_position.jdy,GEUS_AWS_position.elev,'s', fillstyle='none',markersize=ms,c='r',
label="GEUS GC-Net GPS: %.0f"%np.mean(GEUS_AWS_position.elev)+"±%.0f"%np.std(GEUS_AWS_position.elev)+' m')
suffix='_has_GEUS_AWS_GPS'
out2=pd.DataFrame({'jdy':GEUS_AWS_position['jdy'],
'elev':GEUS_AWS_position['elev'],
})
cat_flag=1
# vals=['t2m']
# for val in vals:
# out_Arctic[val] = out_Arctic[val].map(lambda x: '%.2f' % x)
# if cat_flag:
# out_cat = out.append(out2, ignore_index=True)
# else:
# out_cat=out.copy()
# if len(out_cat)>1:
# x=out_cat.jdy.values
# y=out_cat.elev.values
# v=~np.isnan(y)
# y=y[v]
# x=x[v]
# b, m = polyfit(x, y, 1)
# xx=[x[0],x[-1]]
# yy=[xx[0]*m+b,xx[1]*m+b]
# dy=yy[1]-yy[0]
# ny=xx[1]-xx[0]
# sign=''
# if dy>0:sign='+'
# plt.plot(xx,yy,'--',c='grey',
# label=f'ATM and GEUS AWS fit\nfrom {"%.1f"%yy[0]}m in {"%.0f"%xx[0]} to {"%.1f"%yy[1]}m in {"%.0f"%int(xx[1])}\n= {sign}{"%.1f"%dy} m elevation change over {"%.0f"%ny} years')
# out_cat.to_csv(f'./ATM/output/merged_ATM_AWS/{nicknames[k]}.csv',index=None)
plt.hlines(meta.elev.values[k],year0,year1,linestyle='--',color='grey',label="Table 4 Vandecrux er al 2023: %.0f"%meta.elev.values[k]+' m')
if nicknames[k]=='SWC':
xx=[1990.5,2005,2015,2020,2023.5]
yy=[1155.5,1135,1122,1119.2,1119.2]
dy=np.min(yy)-np.max(yy)
dx=np.max(xx)-np.min(xx)
print('elevation change %.0f'%dy+' m')
print('N years %.0f'%dx+' m')
dhdt=dy/dx
print('linear dhdt %.0f'%dhdt+' m')
plt.plot(xx,yy,'-s',c='m',label='approximation of elevation change:\n%.0f'%dy+' m, %.1f'%dhdt+' m y$^{-1}$ over %.1f'%dx+' years',zorder=30)
plt.legend(fontsize=10)
ly='x'
if ly =='x':plt.show()
if ly =='p':
plt.savefig(f'./ATM/Figs/{nicknames[k]}_{geoid}.png', bbox_inches='tight', dpi=150)
# if suffix!='':
# plt.savefig(f'./ATM/Figs/has_GEUS_AWS_GPS/{nicknames[k]}_{geoid}_{suffix}.png', bbox_inches='tight', dpi=150)
#%%
wo=0
meta.columns
outx=meta.copy()
if wo:
outx = outx.rename({'elev': 'elev_assumed_earlier'}, axis=1)
vals=['elev_mean_from_altimetry','elev_linear_intercept','elev_change_linear','elev_change_n_years']
for val in vals:
outx[val] = outx[val].map(lambda x: '%.1f' % x)
vals=['elev_linear_slope']
for val in vals:
outx[val] = outx[val].map(lambda x: '%.4f' % x)
vals=['elev_fit_t0',
'elev_fit_t1']
for val in vals:
outx[val] = outx[val].map(lambda x: '%.2f' % x)
header = ['ID', 'name', 'nickname',
'lat', 'lon', 'elev_assumed_earlier',
'elev_mean_from_altimetry',
'elev_linear_slope',
'elev_linear_intercept',
'N_altimetry_measurements',
'elev_fit_t0',
'elev_fit_t1',
'elev_change_linear',
'elev_change_n_years',
'Date of installation',
'Last valid time', 'Length of record (years)',
# 'k',
]
outx.to_csv('./ATM/output/GC-Net_elevations_solely_from_ATM_fit.csv', columns = header,index=None)