code refactoring
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main.py
615
main.py
@ -1,13 +1,25 @@
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import sys
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import pickle
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from pyfiglet import Figlet
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import warnings
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import numpy as np
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import xarray as xr
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import pandas as pd
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import pickle as pkl
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import cartopy.crs as ccrs
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import astropy.units as unit
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import matplotlib.pyplot as plt
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from dask import delayed
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from datetime import datetime
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starttime = datetime.now()
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print('----------------------------------------')
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ascii_banner = Figlet(font="slant")
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print(ascii_banner.renderText("BASTET"))
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print("Ver. 1.0, 2021 by Marcel Frommelt")
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print('----------------------------------------')
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print("")
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print("")
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from netCDF4 import Dataset
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from astropy.time import Time
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from scipy import interpolate
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@ -18,16 +30,19 @@ from input.natural_constants import *
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from models.gravity import grav
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from models.valving import valving
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from models.ballasting import ballasting
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from dask.diagnostics import ProgressBar
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from models.simple_atmosphere import T_air_simple, p_air_simple, rho_air_simple
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from models.sun import sun_angles_analytical, tau
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from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_sphere
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from models.transformation import visible_cells, transform, radii
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from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_PalumboMC, cd_sphere
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from models.transformation import visible_cells, transform, radii, transform2
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from multiprocessing import Process
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starttime = datetime.now()
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if not sys.warnoptions:
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warnings.simplefilter("ignore")
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data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Data_PoGo2016.xls', sheet_name='Tabelle3') # Tabelle3
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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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@ -35,43 +50,31 @@ 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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print("")
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print("Initialising simulation...")
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print("Launch location : longitude %.4f, latitude: %.4f" % (start_lon, start_lat))
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print("INITIALISING SIMULATION...")
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print("")
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print("Launch location:")
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print("longitude: %.4f deg" % (start_lon))
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print("latitude: %.4f deg" % (start_lat))
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print("Launch time: " + str(start_utc) + " (UTC)")
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print("")
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print("Reading ERA5-datasets, please wait.")
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ascend_data = xr.open_dataset("ascend_2019_kiruna.nc")
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#float_data = xr.open_dataset("float_2019.nc")
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first_file = Dataset(ERA5_float[0])
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last_file = Dataset(ERA5_float[-1])
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df = xr.open_mfdataset(['float1_2019.nc', 'float2_2019.nc', 'float3_2019.nc', 'float4_2019.nc'], combine='by_coords', concat_dim="time", parallel=True)
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tstart = int(first_file.variables['time'][0])
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tend = int(last_file.variables['time'][-1])
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startNC = Dataset('float1_2019.nc')
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endNC = Dataset('float4_2019.nc')
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start = int(startNC.variables['time'][0])
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end = int(endNC.variables['time'][-1])
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first_file.close()
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last_file.close()
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float_data = df.assign_coords(time=np.linspace(start, end, (end - start) + 1))
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single_data = xr.open_dataset("single_2019.nc")
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ERAtime = float_data.variables['time'][:] # time
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# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT)
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ERAlat0 = ascend_data.variables['latitude'][:].values # latitude [deg]
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ERAlon0 = ascend_data.variables['longitude'][:].values # longitude [deg]
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ERAz_ascend = ascend_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
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ERApress_ascend = ascend_data.variables['level'][:].values # pressure level [-]
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ERAtemp_ascend = ascend_data.variables['t'][:].values # air temperature in K
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vw_x_ascend = ascend_data.variables['u'][:].values # v_x in [m/s]
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vw_y_ascend = ascend_data.variables['v'][:].values # v_y in [m/s]
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vw_z_ascend = ascend_data.variables['w'][:].values # v_z in [m/s]
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df1 = xr.open_mfdataset(ERA5_float, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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float_data = df1.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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# ERA5 MULTI-LEVEL FLOAT (WIND + ATMOSPHERIC DATA DURING FLOAT)
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ERAtime = float_data.variables['time'][:] # time
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ERAlat1 = float_data.variables['latitude'][:].values # latitude [deg]
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ERAlon1 = float_data.variables['longitude'][:].values # longitude [deg]
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ERAz_float = float_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
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@ -81,6 +84,18 @@ vw_x_float = float_data.variables['u'][:].values # v_x in [m/s]
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vw_y_float = float_data.variables['v'][:].values # v_y in [m/s]
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vw_z_float = float_data.variables['w'][:].values # v_z in [m/s]
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first_file = Dataset(ERA5_single[0])
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last_file = Dataset(ERA5_single[-1])
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tstart = int(first_file.variables['time'][0])
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tend = int(last_file.variables['time'][-1])
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first_file.close()
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last_file.close()
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df2 = xr.open_mfdataset(ERA5_single, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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single_data = df2.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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# ERA5 SINGLE-LEVEL (RADIATIVE ENVIRONMENT)
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@ -102,6 +117,32 @@ ERAtisr = single_data.variables['tisr'][:].values # hourly accumulated TOA
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ERAstrdc = single_data.variables['strdc'][:].values # hourly accumulated surface thermal radiation downward clear-sky [J/m^2]
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ERAsp = single_data.variables['sp'][:].values # surface pressure in [Pa]
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first_file = Dataset(ERA5_ascent[0])
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last_file = Dataset(ERA5_ascent[-1])
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tstart = int(first_file.variables['time'][0])
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tend = int(last_file.variables['time'][-1])
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first_file.close()
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last_file.close()
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df3 = xr.open_mfdataset(ERA5_ascent, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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ascent_data = df3.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT)
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ERAlat0 = ascent_data.variables['latitude'][:].values # latitude [deg]
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ERAlon0 = ascent_data.variables['longitude'][:].values # longitude [deg]
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ERAz_ascent = ascent_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
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ERApress_ascent = ascent_data.variables['level'][:].values # pressure level [-]
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ERAtemp_ascent = ascent_data.variables['t'][:].values # air temperature in K
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vw_x_ascent = ascent_data.variables['u'][:].values # v_x in [m/s]
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vw_y_ascent = ascent_data.variables['v'][:].values # v_y in [m/s]
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vw_z_ascent = ascent_data.variables['w'][:].values # v_z in [m/s]
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ascent_data.close()
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print("Finished reading ERA5-datasets.")
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lon_era2d0, lat_era2d0 = np.meshgrid(ERAlon0, ERAlat0)
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@ -116,10 +157,14 @@ print("")
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tree0 = cKDTree(np.column_stack((xs0, ys0, zs0)))
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tree1 = cKDTree(np.column_stack((xs1, ys1, zs1)))
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tree2 = cKDTree(np.column_stack((xs2, ys2, zs2)))
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print("Built kd-trees")
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print("Built kd-trees.")
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print("")
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wflag1, wflag2, wflag3, wflag4, wflag5, wflag6, wflag7, wflag8, wflag9, wflag10, wflag11, wflag12, wflag13, wflag14, wflag15, wflag16, wflag17, wflag18, wflag19 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
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def ERA5Data(lon, lat, h, t, deltaT_ERA):
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flag_arr = np.zeros(20)
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def ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr):
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t_epoch = deltaT_ERA + t / 3600
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t_pre = int(t_epoch)
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@ -165,7 +210,7 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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interp4d_vw_z_post = np.ma.dot(w1, vw_z_float[t_post_ind, :, lat_ind1, lon_ind1]) / 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])
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pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20, 30]) # !!!
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pressure = 100 * pressure_hPa
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@ -176,27 +221,32 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
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except IndexError:
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print("Error: Please check time range of ERA5 data!")
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if flag_arr[18] == 0:
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print("Error: Please check time range of ERA5 data!")
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flag_arr[18] = 1
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else:
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flag_arr[18] = 1
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elif np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0:
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try:
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interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre
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interp4d_height_pre = np.ma.dot(w0, ERAz_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height_post = np.ma.dot(w0, ERAz_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height_pre = np.ma.dot(w0, ERAz_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height_post = np.ma.dot(w0, ERAz_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre
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interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre
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interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre
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interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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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(
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@ -212,7 +262,12 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
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except IndexError:
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print("Error: Check time range of ERA5 data!")
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if flag_arr[19] == 0:
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print("Error: Check time range of ERA5 data!")
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flag_arr[19] = 1
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else:
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flag_arr[19] = 1
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else:
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pass
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@ -221,8 +276,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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tcc = (tcc_post - tcc_pre) * (t_epoch - t_pre) + tcc_pre
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if isinstance(tcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"tcc\"!")
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print("Assuming simplified value for parameter \"tcc\".")
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if flag_arr[1] == 0:
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print("WARNING: Corrupt or missing ERA5 Data for parameter \"tcc\"!")
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print("Assuming simplified value for parameter \"tcc\".")
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flag_arr[1] = 1
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else:
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flag_arr[1] = 1
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tcc = cc
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cbh_pre = np.ma.dot(w2, ERAcbh[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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@ -230,8 +290,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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cbh = (cbh_post - cbh_pre) * (t_epoch - t_pre) + cbh_pre
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if isinstance(tcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"cbh\"!")
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print("Assuming simplified value for parameter \"cbh\".")
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if flag_arr[2] == 0:
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print("WARNING: Corrupt or missing ERA5 Data for parameter \"cbh\"!")
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print("Assuming simplified value for parameter \"cbh\".")
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flag_arr[2] = 1
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else:
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flag_arr[2] = 1
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cbh = 2000
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lcc_pre = np.ma.dot(w2, ERAlcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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@ -239,8 +304,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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lcc = (lcc_post - lcc_pre) * (t_epoch - t_pre) + lcc_pre
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if isinstance(lcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"lcc\"!")
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print("Assuming simplified value for parameter \"lcc\".")
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if flag_arr[3] == 0:
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print("WARNING: Corrupt or missing ERA5 Data for parameter \"lcc\"!")
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print("Assuming simplified value for parameter \"lcc\".")
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flag_arr[3] = 1
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else:
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flag_arr[3] = 1
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lcc = cc/3
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mcc_pre = np.ma.dot(w2, ERAmcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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@ -248,8 +318,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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mcc = (mcc_post - mcc_pre) * (t_epoch - t_pre) + mcc_pre
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if isinstance(mcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"mcc\"!")
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print("Assuming simplified value for parameter \"mcc\".")
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if flag_arr[4] == 0:
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print("WARNING: Corrupt or missing ERA5 Data for parameter \"mcc\"!")
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print("Assuming simplified value for parameter \"mcc\".")
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flag_arr[4] = 1
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else:
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flag_arr[4] = 1
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mcc = cc/3
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hcc_pre = np.ma.dot(w2, ERAhcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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@ -257,8 +332,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
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hcc = (hcc_post - hcc_pre) * (t_epoch - t_pre) + hcc_pre
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if isinstance(hcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"hcc\"!")
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print("Assuming simplified value for parameter \"hcc\".")
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if flag_arr[5] == 0:
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print("WARNING: Corrupt or missing ERA5 Data for parameter \"hcc\"!")
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print("Assuming simplified value for parameter \"hcc\".")
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flag_arr[5] = 1
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else:
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flag_arr[5] = 1
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hcc = cc/3
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|
||||
ssr_pre = np.ma.dot(w2, ERAssr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -270,8 +350,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
strn = ((strn_post - strn_pre) * (t_epoch - t_pre) + strn_pre) / 3600
|
||||
|
||||
if isinstance(strn, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"strn\"!")
|
||||
print("Assuming simplified value for parameter \"strn\".")
|
||||
if flag_arr[6] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"strn\"!")
|
||||
print("Assuming simplified value for parameter \"strn\".")
|
||||
flag_arr[6] = 1
|
||||
else:
|
||||
flag_arr[6] = 1
|
||||
|
||||
strn = 0
|
||||
|
||||
skt_pre = np.ma.dot(w2, ERAskt[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -279,8 +364,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
skt = ((skt_post - skt_pre) * (t_epoch - t_pre) + skt_pre)
|
||||
|
||||
if isinstance(skt, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"skt\"!")
|
||||
print("Assuming simplified value for parameter \"skt\".")
|
||||
if flag_arr[7] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"skt\"!")
|
||||
print("Assuming simplified value for parameter \"skt\".")
|
||||
flag_arr[7] = 1
|
||||
else:
|
||||
flag_arr[7] = 1
|
||||
|
||||
skt = T_ground
|
||||
|
||||
strd_pre = np.ma.dot(w2, ERAstrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -288,8 +378,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
strd = ((strd_post - strd_pre) * (t_epoch - t_pre) + strd_pre) / 3600
|
||||
|
||||
if isinstance(strd, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"strd\"!")
|
||||
print("Assuming simplified value for parameter \"strd\".")
|
||||
if flag_arr[8] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"strd\"!")
|
||||
print("Assuming simplified value for parameter \"strd\".")
|
||||
flag_arr[8] = 1
|
||||
else:
|
||||
flag_arr[8] = 1
|
||||
|
||||
strd = epsilon_ground * sigma * T_ground ** 4
|
||||
|
||||
strdc_pre = np.ma.dot(w2, ERAstrdc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -297,8 +392,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
strdc = ((strdc_post - strdc_pre) * (t_epoch - t_pre) + strdc_pre) / 3600
|
||||
|
||||
if isinstance(strdc, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"strdc\"!")
|
||||
print("Assuming simplified value for parameter \"strdc\".")
|
||||
if flag_arr[9] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"strdc\"!")
|
||||
print("Assuming simplified value for parameter \"strdc\".")
|
||||
flag_arr[9] = 1
|
||||
else:
|
||||
flag_arr[9] = 1
|
||||
|
||||
strdc = epsilon_ground * sigma * T_ground ** 4
|
||||
|
||||
ssrd_pre = np.ma.dot(w2, ERAssrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -306,14 +406,24 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
ssrd = ((ssrd_post - ssrd_pre) * (t_epoch - t_pre) + ssrd_pre) / 3600
|
||||
|
||||
if isinstance(ssrd, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"ssrd\"!")
|
||||
print("Assuming simplified value for parameter \"ssrd\".")
|
||||
if flag_arr[10] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssrd\"!")
|
||||
print("Assuming simplified value for parameter \"ssrd\".")
|
||||
flag_arr[10] = 1
|
||||
else:
|
||||
flag_arr[10] = 1
|
||||
|
||||
ssrd = 1
|
||||
ssr = 1 - Albedo
|
||||
|
||||
if isinstance(ssr, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"ssr\"!")
|
||||
print("Assuming simplified value for parameter \"ssr\".")
|
||||
if flag_arr[11] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssr\"!")
|
||||
print("Assuming simplified value for parameter \"ssr\".")
|
||||
flag_arr[11] = 1
|
||||
else:
|
||||
flag_arr[11] = 1
|
||||
|
||||
ssrd = 1
|
||||
ssr = 1 - Albedo
|
||||
|
||||
@ -326,8 +436,14 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
tisr = ((tisr_post - tisr_pre) * (t_epoch - t_pre) + tisr_pre) / 3600
|
||||
|
||||
if isinstance(tisr, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"tisr\"!")
|
||||
print("Assuming simplified value for parameter \"tisr\".")
|
||||
if flag_arr[12] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"tisr\"!")
|
||||
print("Assuming simplified value for parameter \"tisr\".")
|
||||
flag_arr[12] = 1
|
||||
else:
|
||||
flag_arr[12] = 1
|
||||
|
||||
utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0')
|
||||
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
|
||||
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
|
||||
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
|
||||
@ -335,8 +451,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
tisr = I_Sun * np.sin(np.deg2rad(ELV))
|
||||
|
||||
if isinstance(tsr, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"tsr\"!")
|
||||
print("Assuming simplified value for parameter \"tsr\".")
|
||||
if flag_arr[13] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"tsr\"!")
|
||||
print("Assuming simplified value for parameter \"tsr\".")
|
||||
flag_arr[13] = 1
|
||||
else:
|
||||
flag_arr[13] = 1
|
||||
|
||||
tsr = (1 - Albedo) * tisr
|
||||
|
||||
ttr_pre = np.ma.dot(w2, ERAttr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
||||
@ -348,31 +469,55 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
p0 = (p0_post - p0_pre) * (t_epoch - t_pre) + p0_pre
|
||||
|
||||
if isinstance(p0, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"sp\"!")
|
||||
print("Assuming simplified value for parameter \"sp\".")
|
||||
if flag_arr[14] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"sp\"!")
|
||||
print("Assuming simplified value for parameter \"sp\".")
|
||||
flag_arr[14] = 1
|
||||
else:
|
||||
flag_arr[14] = 1
|
||||
p0 = 101325.0
|
||||
|
||||
if isinstance(ttr, float) != True:
|
||||
print("WARNING: Corrupt ERA5 Data for parameter \"ttr\"!")
|
||||
print("Assuming simplified value for parameter \"ttr\".")
|
||||
if flag_arr[15] == 0:
|
||||
print("WARNING: Corrupt or missing ERA5 Data for parameter \"ttr\"!")
|
||||
print("Assuming simplified value for parameter \"ttr\".")
|
||||
flag_arr[15] = 1
|
||||
else:
|
||||
flag_arr[15] = 1
|
||||
|
||||
utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0')
|
||||
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
|
||||
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
||||
HalfConeAngle = np.arcsin(R_E / (R_E + h))
|
||||
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
|
||||
ttr = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2
|
||||
|
||||
if h > interp4d_height[0]:
|
||||
p_air = press_interp1d(interp4d_height[0])
|
||||
T_air = temp_interp1d(interp4d_height[0])
|
||||
u = vw_x_interp1d(interp4d_height[0])
|
||||
v = vw_y_interp1d(interp4d_height[0])
|
||||
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[0]) * R_air * T_air / p_air
|
||||
elif h < interp4d_height[-1]:
|
||||
p_air = press_interp1d(interp4d_height[-1])
|
||||
T_air = temp_interp1d(interp4d_height[-1])
|
||||
u = vw_x_interp1d(interp4d_height[-1])
|
||||
v = vw_y_interp1d(interp4d_height[-1])
|
||||
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[-1]) * R_air * T_air / p_air
|
||||
if h > np.amax(interp4d_height):
|
||||
if flag_arr[16] == 0:
|
||||
print("WARNING: Balloon altitude above interpolation area!")
|
||||
flag_arr[16] = 1
|
||||
else:
|
||||
flag_arr[16] = 1
|
||||
|
||||
p_air = press_interp1d(np.amax(interp4d_height))
|
||||
T_air = temp_interp1d(np.amax(interp4d_height))
|
||||
u = vw_x_interp1d(np.amax(interp4d_height))
|
||||
v = vw_y_interp1d(np.amax(interp4d_height))
|
||||
w = -1 / grav(lat, h) * vw_z_interp1d(np.amax(interp4d_height)) * R_air * T_air / p_air
|
||||
|
||||
elif h < np.amin(interp4d_height):
|
||||
if flag_arr[17] == 0:
|
||||
print("WARNING: Balloon altitude below interpolation area!")
|
||||
flag_arr[17] = 1
|
||||
else:
|
||||
flag_arr[17] = 1
|
||||
|
||||
p_air = press_interp1d(np.amin(interp4d_height))
|
||||
T_air = temp_interp1d(np.amin(interp4d_height))
|
||||
u = vw_x_interp1d(np.amin(interp4d_height))
|
||||
v = vw_y_interp1d(np.amin(interp4d_height))
|
||||
w = -1 / grav(lat, h) * vw_z_interp1d(np.amin(interp4d_height)) * R_air * T_air / p_air
|
||||
|
||||
else:
|
||||
p_air = press_interp1d(h)
|
||||
T_air = temp_interp1d(h)
|
||||
@ -384,10 +529,15 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
|
||||
|
||||
return p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt
|
||||
|
||||
t_start = Time(start_utc)
|
||||
|
||||
deltaT_ERA = (Time(start_utc).jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000
|
||||
p_air0, p00, T_air0, rho_air0, u0, v0, w0, cbh0, tcc0, lcc0, mcc0, hcc0, ssr0, strn0, strd0, strdc0, ssrd0, tsr0, ttr0, tisr0, skt0 = ERA5Data(
|
||||
start_lon, start_lat, start_height, 0, deltaT_ERA)
|
||||
m_gas_init = ((m_pl + m_film + m_bal_init) * (FreeLift / 100 + 1)) / (R_gas / R_air - 1)
|
||||
|
||||
deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000
|
||||
p_air0, p00, T_air0, rho_air0, u0, v0, w0, cbh0, tcc0, lcc0, mcc0, hcc0, ssr0, strn0, strd0, strdc0, ssrd0, tsr0, ttr0, tisr0, skt0 = ERA5Data(start_lon, start_lat, start_height, 0, deltaT_ERA, flag_arr)
|
||||
|
||||
|
||||
A_top0 = np.pi/4 * 1.383 ** 2 * (m_gas_init * R_gas * T_air0 / p_air0) ** (2/3)
|
||||
|
||||
y0 = [
|
||||
start_lon, # start longitude [deg]
|
||||
@ -404,8 +554,32 @@ y0 = [
|
||||
]
|
||||
|
||||
|
||||
def model(t, y, m_pl, m_film, c_virt):
|
||||
utc = Time(start_utc) + t * unit.second
|
||||
t_list, h_list, v_list = [], [], []
|
||||
lat_list, lon_list = [], []
|
||||
p_list, rho_list = [], []
|
||||
Temp_list, Tgas_list, T_film_list = [], [], []
|
||||
rhog_list = []
|
||||
V_b_list = []
|
||||
Q_Albedo_list = []
|
||||
Q_IREarth_list = []
|
||||
Q_Sun_list = []
|
||||
Q_IRFilm_list = []
|
||||
Q_IRout_list = []
|
||||
Q_ConvExt_list = []
|
||||
Q_ConvInt_list = []
|
||||
utc_list = []
|
||||
ssr_list = []
|
||||
ssrd_list = []
|
||||
ttr_list = []
|
||||
strd_list = []
|
||||
strn_list = []
|
||||
tisr_list = []
|
||||
tsr_list = []
|
||||
|
||||
|
||||
|
||||
def model(t, y, m_pl, m_film, c_virt, A_top0, t_start):
|
||||
utc = t_start + t * unit.second
|
||||
lon = y[0] # 1
|
||||
lat = y[1] # 2
|
||||
h = y[2] # 3
|
||||
@ -430,21 +604,61 @@ def model(t, y, m_pl, m_film, c_virt):
|
||||
else:
|
||||
lat = lat
|
||||
|
||||
if h > 53700:
|
||||
h = 53700
|
||||
elif h < 0:
|
||||
h = 0
|
||||
else:
|
||||
h = h
|
||||
|
||||
h_list.append(h)
|
||||
utc_list.append(utc)
|
||||
lat_list.append(lat)
|
||||
lon_list.append(lon)
|
||||
Tgas_list.append(T_gas)
|
||||
T_film_list.append(T_film)
|
||||
|
||||
|
||||
r_lon, r_lat = radii(lat, h) # calculate radii for velocity conversion between cartesian and Earth reference frame
|
||||
|
||||
deltaT_ERA = (Time(start_utc).jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format
|
||||
deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format
|
||||
|
||||
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
|
||||
|
||||
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
|
||||
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
|
||||
|
||||
I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2
|
||||
|
||||
HalfConeAngle = np.arcsin(R_E / (R_E + h))
|
||||
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
|
||||
|
||||
try:
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = ERA5Data(lon, lat, h, t, deltaT_ERA)
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr)
|
||||
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
||||
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
|
||||
I_SunZ = I_Sun * tau_atm
|
||||
I_Sun0 = I_Sun * tau_atm0
|
||||
except:
|
||||
# in case of solver (temporarily) exceeding interpolation area (with subsequent correction)
|
||||
# in case of solver (temporarily) exceeding interpolation area (with subsequent correction by the solver itself)
|
||||
# or permanent drift out of interpolation area
|
||||
if h >= 30000 or (np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0):
|
||||
print("solver exceeds definition area")
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = 0, 101325, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
p0 = 101325
|
||||
p_air = p_air_simple(h)
|
||||
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
||||
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
|
||||
I_SunZ = I_Sun * tau_atm
|
||||
I_Sun0 = I_Sun * tau_atm0
|
||||
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground
|
||||
else:
|
||||
print("instable trajectory!")
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = 0, 101325, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
p0 = 101325
|
||||
p_air = p_air_simple(h)
|
||||
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
||||
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
|
||||
I_SunZ = I_Sun * tau_atm
|
||||
I_Sun0 = I_Sun * tau_atm0
|
||||
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground
|
||||
|
||||
p_gas = p_air
|
||||
|
||||
@ -464,23 +678,22 @@ def model(t, y, m_pl, m_film, c_virt):
|
||||
dP_valve = grav(lat, h) * (rho_air - rho_gas) * h_valve
|
||||
dP_duct = grav(lat, h) * (rho_air - rho_gas) * h_duct
|
||||
|
||||
if m_gas > m_gas_init: # limit gas mass to plausible value
|
||||
m_gas = m_gas_init #
|
||||
elif m_gas < 0:
|
||||
if m_gas < 0: # limit gas mass to plausible value
|
||||
m_gas = 0
|
||||
else:
|
||||
pass
|
||||
|
||||
V_b = m_gas / rho_gas # calculate balloon volume from current gas mass and gas density
|
||||
rhog_list.append(rho_gas)
|
||||
|
||||
if V_b > V_design:
|
||||
c_duct = c_ducts
|
||||
elif V_b < 0:
|
||||
c_duct = 0
|
||||
V_b = 0
|
||||
V_b = 1.0
|
||||
else:
|
||||
c_duct = 0
|
||||
|
||||
V_b_list.append(V_b)
|
||||
|
||||
if ballasting(utc) == True:
|
||||
if m_bal >= 0:
|
||||
mdot = m_baldot
|
||||
@ -518,27 +731,13 @@ def model(t, y, m_pl, m_film, c_virt):
|
||||
A_surf1 = 4.94 * V_design ** (2 / 3) * (1 - np.cos(np.pi * L_goreB / L_goreDesign))
|
||||
A_eff = 0.65 * A_surf + 0.35 * A_surf1
|
||||
A_top = np.pi / 4 * d_b ** 2
|
||||
|
||||
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
|
||||
A_top0 = A_top0
|
||||
|
||||
A_proj = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * np.deg2rad(ELV))) # projected area for sun radiation
|
||||
A_drag = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * alpha)) # projected area for drag
|
||||
|
||||
# CALCULATIONS FOR THERMAL MODEL
|
||||
|
||||
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
||||
|
||||
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
|
||||
|
||||
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
|
||||
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
|
||||
I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2
|
||||
I_SunZ = I_Sun * tau_atm
|
||||
I_Sun0 = I_Sun * tau_atm0
|
||||
|
||||
HalfConeAngle = np.arcsin(R_E / (R_E + h))
|
||||
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
|
||||
|
||||
if simple == True:
|
||||
|
||||
q_IREarth = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR
|
||||
@ -680,12 +879,33 @@ def model(t, y, m_pl, m_film, c_virt):
|
||||
Q_ConvExt = HC_external * A_eff * (T_air - T_film)
|
||||
Q_ConvInt = HC_internal * A_eff * (T_film - T_gas)
|
||||
|
||||
if simple == True:
|
||||
c_d = 0.47 # sphere
|
||||
else:
|
||||
c_d = cd_PalumboHigh(Fr, Re, A_top) # Ref. xx
|
||||
Q_Albedo_list.append(Q_Albedo)
|
||||
Q_IREarth_list.append(Q_IREarth)
|
||||
Q_Sun_list.append(Q_Sun)
|
||||
Q_IRFilm_list.append(Q_IRFilm)
|
||||
Q_IRout_list.append(Q_IRout)
|
||||
Q_ConvExt_list.append(Q_ConvExt)
|
||||
Q_ConvInt_list.append(Q_ConvInt)
|
||||
|
||||
#c_d = 0.47 # cd_PalumboHigh(Fr, Re, A_top) #cd_sphere(Re) #0.47 # cd_Palumbo(Fr, Re, A_top) # 0.8 #cd_sphere(Re) # cd_palumbo(Fr, Re, A_top) # cd_sphere(Re) / 0.8 / 0.47
|
||||
ssr_list.append(ssr)
|
||||
ssrd_list.append(ssrd)
|
||||
ttr_list.append(ttr)
|
||||
strd_list.append(strd)
|
||||
strn_list.append(strn)
|
||||
tisr_list.append(tisr)
|
||||
tsr_list.append(tsr)
|
||||
|
||||
if simple == True:
|
||||
c_d = c_d
|
||||
else:
|
||||
if drag_model == 'PalumboHigh':
|
||||
c_d = cd_PalumboHigh(Fr, Re, A_top, A_top0)
|
||||
elif drag_model == 'Palumbo':
|
||||
c_d = cd_Palumbo(Fr, Re, A_top, A_top0)
|
||||
elif drag_model == 'PalumboLow':
|
||||
c_d = cd_PalumboLow(Fr, Re, A_top, A_top0)
|
||||
else:
|
||||
c_d = cd_sphere(Re)
|
||||
|
||||
D = drag(c_d, rho_air, A_drag, v_rel) # calculate drag force
|
||||
|
||||
@ -707,7 +927,12 @@ def model(t, y, m_pl, m_film, c_virt):
|
||||
eqn7 = Q_ConvInt / (gamma * c_v * m_gas) - (gamma - 1) / gamma * (rho_air * grav(lat, h)) / (rho_gas * R_gas) * v_z
|
||||
eqn8 = (Q_Sun + Q_Albedo + Q_IREarth + Q_IRFilm + Q_ConvExt - Q_ConvInt - Q_IRout) / (c_f * m_film)
|
||||
eqn9 = -(A_ducts * c_duct * np.sqrt(np.abs(2 * dP_duct * rho_gas))) - (A_valve * c2 * np.sqrt(np.abs(2 * dP_valve * rho_gas)))
|
||||
|
||||
if eqn9 > 0:
|
||||
eqn9 = 0
|
||||
|
||||
eqn10 = c2dot
|
||||
|
||||
if m_bal > 0:
|
||||
eqn11 = -mdot
|
||||
else:
|
||||
@ -733,28 +958,143 @@ def below_float(t, y, m_pl, m_film, c_virt):
|
||||
hit_ground = lambda t, x: at_ground(t, x, m_pl, m_film, c_virt)
|
||||
hit_ground.terminal = True
|
||||
hit_ground.direction = -1
|
||||
excess_ascend = lambda t, x: above_float(t, x, m_pl, m_film, c_virt)
|
||||
excess_ascend.terminal = True
|
||||
excess_ascend.direction = -1
|
||||
excess_ascent = lambda t, x: above_float(t, x, m_pl, m_film, c_virt)
|
||||
excess_ascent.terminal = True
|
||||
excess_ascent.direction = -1
|
||||
instable = lambda t, x: below_float(t, x, m_pl, m_film, c_virt)
|
||||
instable.terminal = True
|
||||
instable.direction = -1
|
||||
|
||||
t0 = 2.0
|
||||
tf = 30000
|
||||
|
||||
print("Beginning simulation")
|
||||
t0 = 0
|
||||
tf = t_sim
|
||||
|
||||
sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt), t_span=[t0, tf], y0=y0, method='BDF', events=[hit_ground, excess_ascend, instable])
|
||||
print("")
|
||||
print("BEGINNING SIMULATION")
|
||||
|
||||
# tnew = np.linspace(0, sol.t[-1], len(Vol_list))
|
||||
sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt, A_top0, t_start), t_span=[t0, tf], y0=y0, method='RK45', events=[hit_ground, excess_ascent, instable]) #, t_eval=comp_time
|
||||
|
||||
tnew = np.linspace(0, sol.t[-1], len(V_b_list))
|
||||
|
||||
print(sol.message)
|
||||
|
||||
|
||||
"""
|
||||
lonsol = sol.y[0, :]
|
||||
latsol = sol.y[1, :]
|
||||
hsol = sol.y[2, :]
|
||||
|
||||
x_sol, y_sol, z_sol = transform2(lonsol, latsol, hsol)
|
||||
x_test, y_test, z_test = transform2(comp_lon, comp_lat, comp_height)
|
||||
|
||||
delta = ((x_sol - x_test)**2 + (y_sol - y_test)**2 + (z_sol - z_test)**2)**(0.5)
|
||||
|
||||
val = 0
|
||||
i = 0
|
||||
|
||||
for x in delta:
|
||||
print(latsol[i])
|
||||
print(comp_lat[i])
|
||||
print(latsol[i] - comp_lat[i])
|
||||
print(x)
|
||||
val += x ** 2
|
||||
i += 1
|
||||
|
||||
RMS = np.sqrt(val/i)
|
||||
|
||||
print('RMS')
|
||||
print(RMS)
|
||||
"""
|
||||
|
||||
|
||||
print(datetime.now() - starttime)
|
||||
|
||||
|
||||
arr0 = np.linspace(0, sol.t[-1], len(V_b_list))
|
||||
arr1 = np.asarray(utc_list)
|
||||
arr2 = np.asarray(h_list)
|
||||
arr3 = np.asarray(lat_list)
|
||||
arr4 = np.asarray(lon_list)
|
||||
arr5 = np.asarray(Tgas_list)
|
||||
arr6 = np.asarray(T_film_list)
|
||||
arr7 = np.asarray(rhog_list)
|
||||
arr8 = np.asarray(V_b_list)
|
||||
arr9 = np.asarray(Q_Albedo_list)
|
||||
arr10 = np.asarray(Q_IREarth_list)
|
||||
arr11 = np.asarray(Q_Sun_list)
|
||||
arr12 = np.asarray(Q_IRFilm_list)
|
||||
arr13 = np.asarray(Q_IRout_list)
|
||||
arr14 = np.asarray(Q_ConvExt_list)
|
||||
arr15 = np.asarray(Q_ConvInt_list)
|
||||
arr16 = np.asarray(ssr_list)
|
||||
arr17 = np.asarray(ssrd_list)
|
||||
arr18 = np.asarray(ttr_list)
|
||||
arr19 = np.asarray(strd_list)
|
||||
arr20 = np.asarray(strn_list)
|
||||
arr21 = np.asarray(tisr_list)
|
||||
arr22 = np.asarray(tsr_list)
|
||||
|
||||
ind_list = []
|
||||
for i in range(len(arr0)):
|
||||
if arr0[i - 1] == arr0[i]:
|
||||
ind_list.append(i)
|
||||
|
||||
arr0 = np.delete(arr0, ind_list)
|
||||
arr1 = np.delete(arr1, ind_list)
|
||||
arr2 = np.delete(arr2, ind_list)
|
||||
arr3 = np.delete(arr3, ind_list)
|
||||
arr4 = np.delete(arr4, ind_list)
|
||||
arr5 = np.delete(arr5, ind_list)
|
||||
arr6 = np.delete(arr6, ind_list)
|
||||
arr7 = np.delete(arr7, ind_list)
|
||||
arr8 = np.delete(arr8, ind_list)
|
||||
arr9 = np.delete(arr9, ind_list)
|
||||
arr10 = np.delete(arr10, ind_list)
|
||||
arr11 = np.delete(arr11, ind_list)
|
||||
arr12 = np.delete(arr12, ind_list)
|
||||
arr13 = np.delete(arr13, ind_list)
|
||||
arr14 = np.delete(arr14, ind_list)
|
||||
arr15 = np.delete(arr15, ind_list)
|
||||
arr16 = np.delete(arr16, ind_list)
|
||||
arr17 = np.delete(arr17, ind_list)
|
||||
arr18 = np.delete(arr18, ind_list)
|
||||
arr19 = np.delete(arr19, ind_list)
|
||||
arr20 = np.delete(arr20, ind_list)
|
||||
arr21 = np.delete(arr21, ind_list)
|
||||
arr22 = np.delete(arr22, ind_list)
|
||||
|
||||
|
||||
|
||||
df1 = pd.DataFrame(data={
|
||||
'time [s]': arr0,
|
||||
'UTC': arr1,
|
||||
'Altitude [m]': arr2,
|
||||
'Latitude [deg]': arr3,
|
||||
'Longitude [deg]': arr4,
|
||||
'T_gas [K]': arr5,
|
||||
'T_film [K]': arr6,
|
||||
'rho_gas [kg/m^3]': arr7,
|
||||
'V_balloon [m^3]': arr8,
|
||||
'Q_Albedo [W/m^2]': arr9,
|
||||
'Q_IR_Earth [W/m^2]': arr10,
|
||||
'Q_Sun [W/m^2]': arr11,
|
||||
'Q_IRFilm [W/m^2]': arr12,
|
||||
'Q_IRout [W/m^2]': arr13,
|
||||
'Q_ConvExt [W/m^2]': arr14,
|
||||
'Q_ConvInt [W/m^2]': arr15,
|
||||
'SSR [W/m^2]': arr16,
|
||||
'SSRD [W/m^2]': arr17,
|
||||
'TTR [W/m^2]': arr18,
|
||||
'STRD [W/m^2]': arr19,
|
||||
'STRN [W/m^2]': arr20,
|
||||
'TISR [W/m^2]': arr21,
|
||||
'TSR [W/m^2]': arr22
|
||||
})
|
||||
|
||||
df1.to_excel("output.xlsx")
|
||||
|
||||
plt.plot(sol.t, sol.y[2, :], 'k--', label='Simulation')
|
||||
plt.plot(comp_time, comp_height, 'r-', label='PoGo Flight Test')
|
||||
plt.plot(comp_time, comp_height, 'r-', label='PoGo+ Flight Test')
|
||||
plt.legend()
|
||||
plt.title('high factor')
|
||||
plt.xlabel('time in s')
|
||||
@ -762,17 +1102,14 @@ plt.ylabel('Balloon Altitude in m')
|
||||
plt.show()
|
||||
|
||||
plt.clf()
|
||||
# ax = plt.axes(projection=ccrs.Mollweide())
|
||||
ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=90))
|
||||
ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=-90))
|
||||
ax.coastlines()
|
||||
ax.gridlines()
|
||||
ax.gridlines(draw_labels=True, linewidth=0.25, color='black')
|
||||
ax.stock_img()
|
||||
ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree())
|
||||
|
||||
plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic())
|
||||
plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic())
|
||||
plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic())
|
||||
# plt.xticks()
|
||||
# figname = "LatLon_%.2f_%.2f.png" % (Albedo, epsilon_ground)
|
||||
# plt.savefig(os.path.join(rootdir, figname))
|
||||
plt.show()
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue
Block a user