New NTC scrips
This commit is contained in:
Alexander Rey
parent
91ce9fac30
commit
15c7820c1c
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@ -0,0 +1,66 @@
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#!/usr/bin/env python3
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# coding: utf8
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import argparse
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import time
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import json
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from datetime import datetime, timedelta
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from urllib.request import urlopen
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from urllib.parse import urlencode
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import math
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import time
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# Find station ID from API
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stationCode = 1700
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# Get all stations
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with urlopen("https://api-iwls.dfo-mpo.gc.ca/api/v1/stations") as url:
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stationlist = json.load(url)
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# Find station id number for API
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stationsFiltered = list(filter(lambda station: station['code'] == str(stationCode).zfill(5), stationlist))
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stationID = stationsFiltered[0]['id']
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stationName = stationsFiltered[0]['officialName']
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# Need UTC time for start and end of the date
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start_dt = datetime(2019, 7, 1)
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end_dt = datetime(2021, 12, 31)
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# Select data type
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# wlo - Observed water level
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# wlf or wlf-spine - predicted water levels (at operational stations only)
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# wlp - Predicted water levels
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#wlp-hilo - High and low sea predictions (Tide tables)
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dataType = 'wlo'
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dt_merge = []
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wl_merge = []
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# Loop though request period in 7 day segments
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for startDayCount in range(0, math.floor((end_dt-start_dt).days), 7):
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# Convert the times to ISO 8601 strings as needed for the HTTP request
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sdt = (start_dt+timedelta(days=startDayCount)).strftime("%Y-%m-%dT%H:%M:00Z")
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edt = (start_dt+timedelta(days=startDayCount+7)).strftime("%Y-%m-%dT%H:%M:00Z")
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resturl = 'https://api-iwls.dfo-mpo.gc.ca/api/v1/stations/{}/data?time-series-code={}&{}&{}'.format(
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stationID,
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dataType,
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urlencode({'from':sdt}),
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urlencode({'to':edt}))
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time.sleep(0.5)
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# Obtain JSON data from CHS
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data = {}
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with urlopen(resturl) as hf:
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data = json.loads(hf.read().decode('utf-8'))
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dt_merge.extend([datetime.strptime(x['eventDate'], "%Y-%m-%dT%H:%M:00Z") for x in data])
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wl_merge.extend([float(x['value']) for x in data])
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print(startDayCount)
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print(len(wl_merge))
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@ -147,12 +147,18 @@ df_moored_data.columns = colOUT
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### Table 3-1, PDF page 65
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### Locations change between deployment 1-9 and 10/11
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### YSI from 2021 FRMR report Pg 385
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df_moored = pd.DataFrame(
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{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM'],
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'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM'],
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'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33],
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'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90],
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'bedElev': [-16, -17, -20, -18, -20]})
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{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM',
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'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
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'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM',
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'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
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'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33,
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208809.66, 205547.85, 203870.2, 201684.6, 200860.91, 200336.12],
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'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90,
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996586.22, 1001028.85, 1004501.4, 1005027.4, 1004516.53, 1005535.44],
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'bedElev': [-16, -17, -20, -18, -20, 0, 0, 0, 0, 0, 0]})
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gdf_moored_loc = gp.GeoDataFrame(
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df_moored, geometry=gp.points_from_xy(df_moored.Easting, df_moored.Northing), crs="EPSG:2263")
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gdf_moored_loc.geometry = gdf_moored_loc.geometry.to_crs("EPSG:32118")
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@ -212,8 +212,9 @@ nearest_IDX_NGG = list(range(100))
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dsloc = list(range(100))
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dsloc_NGG = list(range(100))
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# Find nearest point
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# Find the nearest point
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for i in modelPlot:
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dataPath = pl.Path(runLog['Run Location'][i],
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'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
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@ -363,7 +364,7 @@ for i in modelPlot:
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df_wl_D3D[i].mesh2d_s1),
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label=runLog['Run'][i])
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axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
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axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
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gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048),
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np.interp(interpTimes, station_gdf.index,
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station_gdf['WSE_m']),
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@ -0,0 +1,334 @@
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"""
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@author: AJMR
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Plotting and animation script for NTC
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Febuary 01, 2023
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"""
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import os
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import pandas as pd
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import numpy as np
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import geopandas as gp
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import pytz
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import dfm_tools as dfmt
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from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
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from dfm_tools.get_nc_helpers import get_ncvarproperties, get_stationid_fromstationlist, get_varnamefromattrs
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import cartopy.crs as ccrs
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import contextily as ctx
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from shapely.geometry import Point, MultiPoint
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from shapely.ops import nearest_points
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import pathlib as pl
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import xarray as xr
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from datetime import timedelta
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import datetime as datetime
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#%% Read Model Log
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runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
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dataPath = "FlowFM_map.nc"
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histPath = "FlowFM_his.nc"
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#%% Load in Water Level Data
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# Field Gauge-EAST
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FFG_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/FieldFacility.csv")
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FFG_pd['DateTime'] = pd.to_datetime(FFG_pd.Date_time)
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FFG_pd.set_index(FFG_pd['DateTime'], inplace=True)
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# Put in UTC from Eastern Time (with DST!)
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FFG_pd = FFG_pd.tz_localize(
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pytz.timezone('US/Eastern'), ambiguous='infer').tz_convert(pytz.utc)
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# National Grid Gauge-WEST
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NGG1_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid1.csv")
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NGG1_pd['DateTime'] = pd.to_datetime(NGG1_pd.Date_time)
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NGG1_pd.set_index(NGG1_pd['DateTime'], inplace=True)
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# Put in UTC from Eastern Time (with DST!)
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NGG1_pd = NGG1_pd.tz_localize(
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pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
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# Drop ambiguous times
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NGG1_pd = NGG1_pd.dropna()
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NGG2_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid2.csv")
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NGG2_pd['DateTime'] = pd.to_datetime(NGG2_pd.datetime)
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NGG2_pd.set_index(NGG2_pd['DateTime'], inplace=True)
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# Put in UTC from Eastern Time (with DST!)
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NGG2_pd = NGG2_pd.tz_localize(
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pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
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# Drop ambiguous times
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NGG2_pd = NGG2_pd.dropna()
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#%% Read in time series
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modelPlot = [102, 104]
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Delft_WL = [None] * (max(modelPlot)+1)
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# Note, this uses the standard netcdf lib + xarray
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for i in modelPlot:
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file_nc_hist = pl.Path(runLog['Run Location'][i],
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'FlowFM', 'dflowfm', 'output', 'FlowFM_his.nc')
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# Hist file path
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file_nc_hist = file_nc_hist.as_posix()
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# Open hist file dataset
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data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
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# Get Variables
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vars_pd = get_ncvarproperties(data_xr)
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# Get stations
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stations_pd = data_xr['stations'].to_dataframe()
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# Convert to Pandas
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Delft_WL[i] = data_xr.waterlevel.sel(stations=['West_WL', 'East_WL']).to_pandas()
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# Put in UTC if needed
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if i == 104:
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Delft_WL[i] = Delft_WL[i].tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc)
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else:
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Delft_WL[i] = Delft_WL[i].tz_localize(
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pytz.timezone('UTC'))
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#%% Read in EFDC Data to dataframe
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efdc_df = pd.read_csv(
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'//ott-athena.baird.com/D/11934.201_Newtown_Creek_TPP/EFDC_MNR/2015_8/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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#%% Read in EFDC grid
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efdc_grid_df = pd.read_csv('//ott-athena.baird.com/D/11934.201_Newtown_Creek_TPP/EFDC_MNR/GRID_FILES/NC_ER_lxly_20140129.inp',
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delim_whitespace=True, skiprows=4,
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names=['I', 'J', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc_grid_gdf = gp.GeoDataFrame(
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efdc_grid_df, geometry=gp.points_from_xy(efdc_grid_df.X, efdc_grid_df.Y), crs="EPSG:32118")
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#%% Merge EFDC outputs with grid locations and add datetimes
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efdc_merged = pd.merge(efdc_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc_merged['date'] = pd.to_datetime([datetime.datetime(2015, 8, 1, 0, 0, 0) +
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timedelta(hours=h) for h in efdc_merged['ST_hr']])
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efdc_merged.set_index('date', inplace=True)
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#Convert to UTC
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efdc_merged.index = efdc_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc)
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efdc_merged_gdf = gp.GeoDataFrame(
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efdc_merged, geometry=efdc_merged['geometry'], crs="EPSG:32118")
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del efdc_merged
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#%% Find nearest grid point to station FFG
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nearest_geoms = nearest_points(Point(
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data_xr.waterlevel.sel(stations=['East_WL']).station_x_coordinate.values[0],
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data_xr.waterlevel.sel(stations=['East_WL']).station_y_coordinate.values[0]),
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MultiPoint(efdc_grid_gdf.geometry))
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stationFFG_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry'] == nearest_geoms[1]]
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#%% Find nearest grid point to station NGG
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nearest_geoms = nearest_points(Point(
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data_xr.waterlevel.sel(stations=['West_WL']).station_x_coordinate.values[0],
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data_xr.waterlevel.sel(stations=['West_WL']).station_y_coordinate.values[0]),
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MultiPoint(efdc_grid_gdf.geometry))
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stationNGG_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry'] == nearest_geoms[1]]
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#%% Plot Time Series
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modelPlot = [102, 104]
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 8),
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sharex=True)
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# Plot Data
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FFG_pd['Water Surface Elevation (ft)'].shift(
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0, timedelta(days=1)).multiply(0.3048).plot(ax=axes[0],
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color='k', label='Field Facility Gauge')
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#Plot EFDC
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stationFFG_gdf['WSE_m'].plot(ax=axes[0], label='EFDC 2019')
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# Plot Delft3D
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for i in modelPlot:
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Delft_WL[i]['East_WL'].plot(ax=axes[0],
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label='Delft3D: ' + runLog['Plotting Legend Entry'][i])
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# Set axis
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axes[0].set_xlim(Delft_WL[modelPlot[0]].index[0],
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Delft_WL[modelPlot[0]].index[-1])
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axes[0].set_ylim(-1.5, 2.5)
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axes[0].legend(loc='upper right')
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# Plot Data
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NGG2_pd['water_surface_elevation'].shift(
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0, timedelta(hours=1)).multiply(0.3048).plot(ax=axes[1],
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color='k', label='National Grid Gauge')
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#Plot EFDC
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stationNGG_gdf['WSE_m'].plot(ax=axes[1], label='EFDC')
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# Plot Delft
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for i in modelPlot:
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Delft_WL[i]['West_WL'].plot(ax=axes[1],
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label='Delft3D: ' + runLog['Plotting Legend Entry'][i])
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# Set axis
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axes[1].set_xlim(Delft_WL[modelPlot[0]].index[0],
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Delft_WL[modelPlot[0]].index[-1])
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axes[1].set_ylim(-1.5, 2.5)
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axes[1].legend(loc='upper right')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Newtown/Figures2023/TS_2023.png',
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bbox_inches='tight', dpi=400)
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#%% Plot scatters
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fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))
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fig.patch.set_facecolor('white')
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interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
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Delft_WL[modelPlot[0]].index[-1],
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freq="20min")
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# Compare EFDC and Data
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axes[0].scatter(np.interp(interpTimes, FFG_pd.index,
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FFG_pd['Water Surface Elevation (ft)']*0.3048),
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np.interp(interpTimes, stationFFG_gdf.index,
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stationFFG_gdf['WSE_m']), s=1,
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label='EFDC 2019')
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for i in modelPlot:
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# Compare Delft3D and Data
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axes[0].scatter(np.interp(interpTimes, FFG_pd.index,
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FFG_pd['Water Surface Elevation (ft)']*0.3048),
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np.interp(interpTimes, Delft_WL[i].index,
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Delft_WL[i]['East_WL']), s=1,
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label='Delft3D: ' + runLog['Plotting Legend Entry'][i])
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linepts = np.array([-1.5, 1.5])
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axes[0].plot(linepts, linepts, color='k')
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axes[0].legend(loc='lower right')
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axes[0].set_xlim(-1.5, 1.5)
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axes[0].set_ylim(-1.5, 1.5)
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axes[0].set_aspect('equal', 'box')
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# Compare EFDC and Data
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modelInterp = np.interp(interpTimes, stationNGG_gdf.index.shift(
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0, timedelta(hours=1)),
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stationNGG_gdf['WSE_m'])
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dataInterp = np.interp(interpTimes, NGG2_pd.index,
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NGG2_pd['water_surface_elevation']*0.3048)
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axes[1].scatter(dataInterp, modelInterp, s=1,
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label='EFDC 2019')
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print(np.sqrt(np.mean((modelInterp-dataInterp)**2)))
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corr_matrix = np.corrcoef(dataInterp, modelInterp)
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print(corr_matrix[0, 1]**2)
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for i in modelPlot:
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# Compare Delft3D and Data
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modelInterp = np.interp(interpTimes, Delft_WL[i].index.shift(
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0, timedelta(hours=1)),
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Delft_WL[i]['West_WL'])
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dataInterp = np.interp(interpTimes, NGG2_pd.index,
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NGG2_pd['water_surface_elevation']*0.3048)
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axes[1].scatter(dataInterp, modelInterp, s=1,
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label='Delft3D: ' + runLog['Plotting Legend Entry'][i])
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print(np.sqrt(np.mean((modelInterp-dataInterp)**2)))
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corr_matrix = np.corrcoef(dataInterp, modelInterp)
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print(corr_matrix[0, 1]**2)
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axes[1].plot(linepts, linepts, color='k')
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axes[1].legend(loc='lower right')
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axes[1].set_aspect('equal', 'box')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Newtown/Figures2023/FFG_Scatter2023.png',
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bbox_inches='tight', dpi=400)
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#%% Read in YSI ADCP
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# %% Load in moored data
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df_moored_data = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NC_CurrentMeter_All_Phase1_all_data_2013_07_16/NC_CurrentMeter_All_Phase1_all_moored_2013_05_20.xlsx',
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sheet_name='mag_all_moored')
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# Shift col names to put second back in
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colIN = list(df_moored_data.columns)
|
||||
colOUT = colIN[0:6] + ['second'] + colIN[6:-1]
|
||||
df_moored_data.columns = colOUT
|
||||
|
||||
### Moored current meter locations from here:
|
||||
### file://srv-ott3/Projects/11934.201%20Newtown%20Creek%20TPP%20%E2%80%93%20Privileged%20and%20Confidential/03_Data/01_BkgrdReports/NC_DRAFT_DSR_Submittal_No_3_2013-07-03.pdf
|
||||
### Table 3-1, PDF page 65
|
||||
### Locations change between deployment 1-9 and 10/11
|
||||
|
||||
### YSI from 2021 FRMR report Pg 385
|
||||
|
||||
df_moored = pd.DataFrame(
|
||||
{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33,
|
||||
208809.66, 205547.85, 203870.2, 201684.6, 200860.91, 200336.12],
|
||||
'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90,
|
||||
996586.22, 1001028.85, 1004501.4, 1005027.4, 1004516.53, 1005535.44],
|
||||
'bedElev': [-16, -17, -20, -18, -20, 0, 0, 0, 0, 0, 0]})
|
||||
gdf_moored_loc = gp.GeoDataFrame(
|
||||
df_moored, geometry=gp.points_from_xy(df_moored.Easting, df_moored.Northing), crs="EPSG:2263")
|
||||
gdf_moored_loc.geometry = gdf_moored_loc.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
# for d in range(1,10):
|
||||
# depMask = df_moored_data['deployment'] == ('dep' + str(d))
|
||||
# for stationIDX, station in enumerate(df_moored['depOrig']):
|
||||
# stationMask = (df_moored_data['station'] == station) & depMask
|
||||
#
|
||||
# # Assign geography to station plus deployment
|
||||
# df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
# df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
# Loop through Station IDs for deployments 10-11 and assign locations
|
||||
for d in range(1,12):
|
||||
depMask = df_moored_data['deployment'] == 'dep' + str(d)
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_moored = gp.GeoDataFrame(
|
||||
df_moored_data, geometry=gp.points_from_xy(df_moored_data.Easting, df_moored_data.Northing), crs="EPSG:2263")
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf_moored.geometry = gdf_moored.geometry.to_crs("EPSG:32118")
|
||||
|
||||
gdf_moored['date'] = pd.to_datetime(gdf_moored['year'].astype(str) + '-' +
|
||||
gdf_moored['month'].astype(str).str.zfill(2) + '-' +
|
||||
gdf_moored['day'].astype(str).str.zfill(2) + ' ' +
|
||||
gdf_moored['hour'].astype(str).str.zfill(2) + ':' +
|
||||
gdf_moored['minute'].astype(str).str.zfill(2))
|
||||
|
||||
|
||||
|
||||
#%% Velocity validation
|
||||
|
|
@ -1,18 +1,21 @@
|
|||
#%% Read and Process NWS Alerts
|
||||
# Created on: 2023-01-17
|
||||
import datetime
|
||||
|
||||
# Import Modules
|
||||
import os
|
||||
import geopandas as gp
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
from netCDF4 import Dataset
|
||||
|
||||
import nwswx
|
||||
import requests
|
||||
|
||||
from shapely.geometry import Polygon
|
||||
import shapely.wkt
|
||||
|
||||
import re
|
||||
|
||||
#%% Setup NWS ID
|
||||
nws = nwswx.WxAPI('api@alexanderrey.ca')
|
||||
|
||||
|
|
@ -34,7 +37,7 @@ while 'pagination' in alertsIN:
|
|||
alertsIN = result.json()
|
||||
nws_alerts.extend(alertsIN['features'])
|
||||
|
||||
print('AWS Alerts: ', len(nws_alerts))
|
||||
print('AWS Alerts: ', len(nws_alerts))
|
||||
|
||||
#%% Create NWS Alerts DataFrame
|
||||
nws_alert_df = pd.DataFrame.from_records(nws_alerts)
|
||||
|
|
@ -123,7 +126,7 @@ lons, lats = np.meshgrid(xs, ys)
|
|||
gridPointsSeries = gp.GeoSeries.from_xy(lons.flatten(), lats.flatten())
|
||||
|
||||
#%% Loop through each NWS Alert and find points within polygon
|
||||
alertData_1D = ['' for i in range(len(gridPointsSeries))]
|
||||
alertData_1D = [None for i in range(len(gridPointsSeries))]
|
||||
|
||||
# Loop through NWS Alerts
|
||||
for alertIDX in nws_alert_gdf.index:
|
||||
|
|
@ -138,13 +141,26 @@ for alertIDX in nws_alert_gdf.index:
|
|||
# Get existing alert data and add new alert
|
||||
alertString = alertData_1D[alertPoint]
|
||||
|
||||
alertString = alertString + '[' + nws_alert_gdf.loc[alertIDX, 'headline'] + \
|
||||
nws_alert_gdf.loc[alertIDX, 'description'] + \
|
||||
nws_alert_gdf.loc[alertIDX, 'areaDesc'] + \
|
||||
nws_alert_gdf.loc[alertIDX, 'onset'] + \
|
||||
nws_alert_gdf.loc[alertIDX, 'expires'] + \
|
||||
nws_alert_gdf.loc[alertIDX, 'severity'] + \
|
||||
nws_alert_gdf.loc[alertIDX, '@id'] + ']'
|
||||
if alertString == None:
|
||||
alertString = ''
|
||||
|
||||
alertDescription = nws_alert_gdf.loc[alertIDX, 'description']
|
||||
|
||||
if alertDescription == None:
|
||||
alertDescription = nws_alert_gdf.loc[alertIDX, 'headline']
|
||||
|
||||
if nws_alert_gdf.loc[alertIDX, 'ends'] == None:
|
||||
alertEndTime = nws_alert_gdf.loc[alertIDX, 'expires']
|
||||
else:
|
||||
alertEndTime = nws_alert_gdf.loc[alertIDX, 'ends']
|
||||
|
||||
alertString = alertString + '[' + nws_alert_gdf.loc[alertIDX, 'headline'] + '}' \
|
||||
'{' + alertDescription + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'areaDesc'] + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'onset'] + '}' + \
|
||||
'{' + alertEndTime + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'severity'] + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, '@id'] + ']'
|
||||
|
||||
alertData_1D[alertPoint] = alertString
|
||||
|
||||
|
|
@ -160,8 +176,31 @@ list_alerts_2d_np = list_alerts_1d_np.reshape(lons.shape)
|
|||
grid_alerts_xr = xr.DataArray(data=list_alerts_2d_np,
|
||||
dims=["x", "y"],
|
||||
coords={"lon": (("x", "y"), lons),
|
||||
"lat": (("x", "y"), lats)},
|
||||
"lat": (("x", "y"), lats)}
|
||||
)
|
||||
|
||||
# Save as netcdf
|
||||
grid_alerts_xr.to_netcdf('grid_alerts_contains3.nc')
|
||||
grid_alerts_xr.to_netcdf('grid_alerts_contains8.nc', engine="h5netcdf",
|
||||
encoding={"__xarray_dataarray_variable__": {'gzip': True, "compression_opts": 9}})
|
||||
|
||||
#%% Read in netcdf
|
||||
grid_alerts_nc = Dataset('grid_alerts_contains5.nc')
|
||||
grid_alert_pt = grid_alerts_nc['__xarray_dataarray_variable__'][176, 300]
|
||||
print(grid_alerts_nc['lon'][176, 300])
|
||||
print(grid_alerts_nc['lat'][176, 300])
|
||||
|
||||
#%% Process alert data string
|
||||
|
||||
alertPattern = '\[([^]]+)\]'
|
||||
alertList = re.findall(alertPattern, grid_alert_pt)
|
||||
|
||||
# Loop through each alert
|
||||
for alert in alertList:
|
||||
# Extract alert details
|
||||
alertDetails = alert.split('}{')
|
||||
print(alertDetails)
|
||||
alertTime = datetime.datetime.strptime(alertDetails[3], '%Y-%m-%dT%H:%M:%S%z')
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue