updated environment
This commit is contained in:
parent
5963606d05
commit
a7ee25fba7
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@ -1,4 +1,4 @@
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name: BASTET
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name: MasterThesis
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channels:
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- jmcmurray
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- anaconda
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@ -17,11 +17,11 @@ dependencies:
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- branca=0.4.2=pyhd8ed1ab_0
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- brotlipy=0.7.0=py38hfa6e2cd_1000
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- bzip2=1.0.8=he774522_3
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- ca-certificates=2020.12.5=h5b45459_0
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- ca-certificates=2021.5.30=h5b45459_0
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- cartopy=0.18.0=py38hd77ba2b_0
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- cartopy_offlinedata=0.2.3=pyh9f0ad1d_0
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- cdsapi=0.2.7=py_0
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- certifi=2020.12.5=py38haa244fe_1
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- certifi=2021.5.30=py38haa244fe_0
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- cffi=1.14.3=py38h0e640b1_1
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- cfgrib=0.9.8.4=py_0
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- cftime=1.2.1=py38h1e00858_1
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@ -41,6 +41,7 @@ dependencies:
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- distributed=2021.3.0=py38haa244fe_0
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- eccodes=2.17.0=h37af81a_0
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- entrypoints=0.3=py38h32f6830_1002
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- et_xmlfile=1.0.1=py_1001
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- expat=2.2.9=h33f27b4_2
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- folium=0.12.0=pyhd8ed1ab_1
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- freeglut=3.0.0=h6538335_1005
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@ -58,6 +59,7 @@ dependencies:
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- ipython=7.19.0=py38hc5df569_0
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- ipython_genutils=0.2.0=py_1
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- jasper=2.0.14=hdc05fd1_1
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- jdcal=1.4.1=py_0
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- jedi=0.17.2=py38h32f6830_1
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- jinja2=2.11.2=pyh9f0ad1d_0
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- joblib=1.0.1=pyhd8ed1ab_0
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@ -100,6 +102,7 @@ dependencies:
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- numpy=1.19.4=py38h0cc643e_0
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- olefile=0.46=pyh9f0ad1d_1
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- openjpeg=2.3.1=h57dd2e7_3
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- openpyxl=3.0.7=pyhd8ed1ab_0
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- openssl=1.1.1k=h8ffe710_0
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- os=0.1.4=0
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- owslib=0.20.0=py_0
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@ -120,6 +123,7 @@ dependencies:
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- pthreads-win32=2.9.1=hfa6e2cd_3
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- pycparser=2.20=pyh9f0ad1d_2
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- pyepsg=0.4.0=py_0
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- pyfiglet=0.8.post1=py_0
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- pygments=2.7.2=py_0
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- pygrib=2.0.5=py38hbf9c9a7_0
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- pykdtree=1.3.1=py38h1e00858_1004
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170
DataRequest.py
170
DataRequest.py
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@ -1,23 +1,58 @@
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import os
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import sys
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import cdsapi
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from input.user_input import *
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from datetime import datetime
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from datetime import date
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import numpy as np
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import xarray as xr
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from netCDF4 import Dataset
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from dask.diagnostics import ProgressBar
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# start_lat = 78.22 Svalbard
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folder = "ERA5"
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ident = "McMurdo"
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north_lim, south_lim, east_lim, west_lim = 90, 45, 180, -180 # Northern Polar Region
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# north_lim, south_lim, east_lim, west_lim = -45, -90, 180, -180 # Southern Polar Region
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start_lat = 67.887382 # Kiruna
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start_lon = 21.081452
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# SOME START LOCATIONS:
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# start_lat = 78.22 # Svalbard
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# start_lon = 15.65
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#
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# start_lat = -77.8535 # McMurdo
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# start_lon = 167.2022
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# start_lat = 67.887382 Kiruna
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# start_lon = 21.081452
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startdate = '2019-12-15'
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enddate = '2020-01-10'
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startdays = ['23', '24', '25', '26', '27', '28', '29', '30', '31']
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days = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31']
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endascent = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22']
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endfloat = ['1']
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try:
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os.makedirs(folder)
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except FileExistsError:
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pass
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def ERAsingle(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
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start = datetime.fromisoformat(startdate)
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end = datetime.fromisoformat(enddate)
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#"""
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# DOWNLOAD OF ERA5-DATA:
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startdays = [str(start.day+i).zfill(2) for i in range((date(start.year, start.month + 1, 1) - date(start.year, start.month, 1)).days - start.day + 1)]
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endascent = [str(i+1).zfill(2) for i in range(30 - len(startdays))]
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days = [str(i+1).zfill(2) for i in range(31)]
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endfloat = [str(i+1).zfill(2) for i in range(end.day)]
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def ERAsingle(year, month, days, nlim, slim, elim, wlim, name):
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single = cdsapi.Client().retrieve(
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'reanalysis-era5-single-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'cloud_base_height', 'high_cloud_cover', 'low_cloud_cover', 'medium_cloud_cover',
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'skin_temperature', 'surface_net_solar_radiation', 'surface_net_thermal_radiation',
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@ -40,24 +75,23 @@ def ERAsingle(year, month, days, north_lim, south_lim, east_lim, west_lim, name)
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'month': str(month),
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'day': days,
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'area': [
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north_lim, west_lim, south_lim, # north_lim, west_lim, south_lim, # North, West, South 72, -111, 67,
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east_lim, # east_lim, # East 22,
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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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single.download(name)
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def ERAlevelAscend(year, month, days, start_lat, start_lon, name):
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north_lim = start_lat + 10.0
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south_lim = start_lat - 10.0
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east_lim = start_lon - 10.0
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west_lim = start_lon + 10.0
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def ERAlevelAscent(year, month, dayrange, start_lat, start_lon, name):
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nlim = start_lat + 10.0
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slim = start_lat - 10.0
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elim = start_lon + 10.0
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wlim = start_lon - 10.0
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ascend = cdsapi.Client().retrieve(
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ascent = cdsapi.Client().retrieve(
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'reanalysis-era5-pressure-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'geopotential', 'temperature', 'u_component_of_wind',
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'v_component_of_wind', 'vertical_velocity',
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@ -79,7 +113,7 @@ def ERAlevelAscend(year, month, days, start_lat, start_lon, name):
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],
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'year': str(year),
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'month': str(month),
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'day': days,
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'day': dayrange,
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'time': [
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'00:00', '01:00', '02:00',
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'03:00', '04:00', '05:00',
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@ -91,19 +125,18 @@ def ERAlevelAscend(year, month, days, start_lat, start_lon, name):
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'21:00', '22:00', '23:00',
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],
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'area': [
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north_lim, west_lim, south_lim,
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east_lim,
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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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ascend.download(name)
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ascent.download(name)
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def ERAlevelFloat(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
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def ERAlevelFloat(year, month, dayrange, nlim, slim, elim, wlim, name):
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floating = cdsapi.Client().retrieve(
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'reanalysis-era5-pressure-levels',
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{
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'product_type': 'reanalysis',
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'format': 'netcdf',
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'variable': [
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'geopotential', 'temperature', 'u_component_of_wind',
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'v_component_of_wind', 'vertical_velocity',
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@ -115,7 +148,7 @@ def ERAlevelFloat(year, month, days, north_lim, south_lim, east_lim, west_lim, n
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],
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'year': str(year),
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'month': str(month),
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'day': days,
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'day': dayrange,
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'time': [
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'00:00', '01:00', '02:00',
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'03:00', '04:00', '05:00',
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@ -127,28 +160,83 @@ def ERAlevelFloat(year, month, days, north_lim, south_lim, east_lim, west_lim, n
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'21:00', '22:00', '23:00',
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],
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'area': [
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north_lim, west_lim, south_lim,
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east_lim,
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nlim, wlim, slim, elim,
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],
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'format': 'netcdf',
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})
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floating.download(name)
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##ERAsingle(2018, 5, startdays, 90, 45, 180, -180, "single_2018_1.nc")
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##ERAsingle(2018, 6, days, 90, 45, 180, -180, "single_2018_2.nc")
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##ERAsingle(2018, 7, days, 90, 45, 180, -180, "single_2018_3.nc")
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##ERAsingle(2018, 8, endfloat, 90, 45, 180, -180, "single_2018_4.nc")
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ERAlevelAscent(start.year, start.month, startdays, start_lat, start_lon, os.path.join(folder, "ascent1.nc"))
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ERAlevelAscent(start.year, start.month + 1, endascent, start_lat, start_lon, os.path.join(folder, "ascent2.nc"))
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##ERAlevelAscend(2018, 5, startdays, start_lat, start_lon, "ascend_2018_kiruna_1.nc") # start_lat = 67.887382 Kiruna
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ERAlevelAscend(2018, 6, endascent, start_lat, start_lon, "ascend_2018_kiruna_2.nc") # start_lon = 21.081452
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ERAsingle(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "single" + str(start.month) + ".nc"))
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ERAlevelFloat(start.year, start.month, startdays, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "float" + str(start.month) + ".nc"))
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ERAlevelFloat(2018, 5, startdays, 90, 45, -180, 180, "float_2018_1.nc")
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ERAlevelFloat(2018, 6, days, 90, 45, -180, 180, "float_2018_2.nc")
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ERAlevelFloat(2018, 7, days, 90, 45, -180, 180, "float_2018_3.nc")
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ERAlevelFloat(2018, 8, endfloat, 90, 45, -180, 180, "float_2018_4.nc")
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for m in range(end.month - start.month - 1):
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ERAsingle(start.year, start.month + m + 1, days, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "single" + str(start.month + m + 1) + ".nc"))
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ERAlevelFloat(start.year, start.month + m + 1, days, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "float" + str(start.month + m + 1) + ".nc"))
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#ERAlevelAscend(2016, 7, ['12', '13', '14'], start_lat, start_lon, "ascend_2016_kiruna_new.nc")
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#ERAsingle(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, 180, -180, "single_2016_new.nc")
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#ERAlevelFloat(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, -180, 180, "float_2016_new.nc")
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ERAsingle(start.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "single" + str(end.month) + ".nc"))
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ERAlevelFloat(start.year, end.month, endfloat, north_lim, south_lim, east_lim, west_lim, os.path.join(folder, "float" + str(end.month) + ".nc"))
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#"""
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# STITCHING OF MULTIPLE *.NC-FILES TO ONE:
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floatfiles = []
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singlefiles = []
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ascentfiles = []
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for (root, dirs, files) in os.walk("ERA5"):
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for name in files:
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if name.startswith("float"):
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floatfiles.append(os.path.join(folder, str(name)))
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elif name.startswith("radiation"):
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singlefiles.append(os.path.join(folder, str(name)))
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else:
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ascentfiles.append(os.path.join(folder, str(name)))
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startfile = Dataset(floatfiles[0], "r", format="NETCDF4")
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endfile = Dataset(floatfiles[-1], "r", format="NETCDF4")
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tstart = int(startfile.variables['time'][0])
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tend = int(endfile.variables['time'][-1])
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startfile.close()
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endfile.close()
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df1 = xr.open_mfdataset(floatfiles, chunks={'time': 100}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True)
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df1 = df1.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df1.to_netcdf(os.path.join(folder, "FLOAT_" + str(ident) + "_" + str(start.year) + "_" + str(start.month) + "to" + str(end.year) + "_" + str(end.month) + ".nc"), mode='w', format="NETCDF4", engine="netcdf4", encoding={"z": {"dtype": "float32"}, "t": {"dtype": "float32"}, "u": {"dtype": "float32"}, "v": {"dtype": "float32"}, "w": {"dtype": "float32"}})
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df2 = xr.open_mfdataset(singlefiles, chunks={'time': 500}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True)
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df2 = df2.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df2.to_netcdf(os.path.join(folder, "SINGLE_" + str(ident) + "_" + str(start.year) + "_" + str(start.month) + "to" + str(end.year) + "_" + str(end.month) + ".nc"), mode='w', format="NETCDF4", engine="netcdf4", encoding={"cbh": {"dtype": "float32"}, "hcc": {"dtype": "float32"}, "lcc": {"dtype": "float32"}, "mcc": {"dtype": "float32"}, "skt": {"dtype": "float32"}, "ssr": {"dtype": "float32"}, "str": {"dtype": "float32"}, "sp": {"dtype": "float32"}, "ssrd": {"dtype": "float32"}, "strdc": {"dtype": "float32"}, "strd": {"dtype": "float32"}, "tisr": {"dtype": "float32"}, "tsr": {"dtype": "float32"}, "ttr": {"dtype": "float32"}, "tcc": {"dtype": "float32"}, "fdir": {"dtype": "float32"}})
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startfile = Dataset(ascentfiles[0], "r", format="NETCDF4")
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endfile = Dataset(ascentfiles[-1], "r", format="NETCDF4")
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tstart = int(startfile.variables['time'][0])
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tend = int(endfile.variables['time'][-1])
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startfile.close()
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endfile.close()
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df3 = xr.open_mfdataset(ascentfiles, chunks={'time': 800}, combine="nested", engine='netcdf4', concat_dim="time", parallel=True)
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df3 = df3.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df3.to_netcdf(os.path.join(folder, "ASCENT_" + str(ident) + "_" + str(start.year) + "_" + str(start.month) + ".nc"), mode='w', format="NETCDF4", engine="netcdf4", encoding={"z": {"dtype": "float32"}, "t": {"dtype": "float32"}, "u": {"dtype": "float32"}, "v": {"dtype": "float32"}, "w": {"dtype": "float32"}})
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# DELETING ORIGINAL FILES:
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"""
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for (root, dirs, files) in os.walk("ERA5"):
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for name in files:
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if name in floatfiles + singlefiles + ascentfiles:
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os.remove(name)
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else:
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pass
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"""
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@ -44,12 +44,14 @@ starttime = datetime.now()
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if not sys.warnoptions:
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warnings.simplefilter("ignore")
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"""
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data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Data_PoGo2016.xls', sheet_name='SuperTIGER2') # Tabelle3
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comp_time = pd.DataFrame(data, columns=['Time']).to_numpy().squeeze()
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comp_height = pd.DataFrame(data, columns=['Height']).to_numpy().squeeze()
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comp_lat = pd.DataFrame(data, columns=['Latitude']).to_numpy().squeeze()
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comp_lon = pd.DataFrame(data, columns=['Longitude']).to_numpy().squeeze()
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"""
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print("")
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print("INITIALISING SIMULATION...")
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@ -184,7 +186,7 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr):
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interp4d_vw_z_post = np.ma.dot(w1, vw_z_float.vindex[t_post_ind, :, lat_ind1, lon_ind1].compute()) / np.sum(w1)
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interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre
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pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20, 30]) # !!!
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pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20]) # !!!
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pressure = 100 * pressure_hPa
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@ -1068,7 +1070,7 @@ df1 = pd.DataFrame(data={
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df1.to_excel("output.xlsx")
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plt.plot(sol.t, sol.y[2, :], 'k--', label='Simulation')
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plt.plot(comp_time, comp_height, 'r-', label='PoGo+ Flight Test')
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# plt.plot(comp_time, comp_height, 'r-', label='PoGo+ Flight Test')
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plt.legend()
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plt.title('high factor')
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plt.xlabel('time in s')
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@ -1084,6 +1086,6 @@ ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree())
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plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic())
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plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic())
|
||||
plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic())
|
||||
# plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic())
|
||||
# plt.savefig(os.path.join(rootdir, figname))
|
||||
plt.show()
|
||||
|
|
@ -12,6 +12,9 @@ matplotlib 3.3.2
|
|||
astropy 4.1
|
||||
netcdf4 1.3.3
|
||||
cdsapi 0.2.7 (*)
|
||||
dask 2.20
|
||||
openpyxl 3.0.4
|
||||
pyfiglet 0.8
|
||||
|
||||
|
||||
Alternatively, use ANACONDA and the environment(*.yml)-file in this repository.
|
||||
|
|
|
|||
Loading…
Reference in New Issue