Azure translate and others
This commit is contained in:
Alexander Rey
parent
704977b42f
commit
91ce9fac30
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@ -0,0 +1,8 @@
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
|
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/httpRequests/
|
||||
# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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@ -0,0 +1,8 @@
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<?xml version="1.0" encoding="UTF-8"?>
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||||
<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="inheritedJdk" />
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||||
<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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|
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@ -0,0 +1,13 @@
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<component name="InspectionProjectProfileManager">
|
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<profile version="1.0">
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<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
|
||||
<option name="ignoredIdentifiers">
|
||||
<list>
|
||||
<option value="bool.*" />
|
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<option value="geopandas.io.file" />
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</list>
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</option>
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</inspection_tool>
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</profile>
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</component>
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@ -0,0 +1,6 @@
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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@ -0,0 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (geopandas_forge_mustique)" project-jdk-type="Python SDK" />
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</project>
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@ -0,0 +1,8 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/Azure.iml" filepath="$PROJECT_DIR$/.idea/Azure.iml" />
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</modules>
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</component>
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</project>
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@ -0,0 +1,6 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
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</component>
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</project>
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@ -0,0 +1,36 @@
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# Sample script to translate a document on azure
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import requests
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endpoint = "https://translateazuretesting.cognitiveservices.azure.com/translator/text/batch/v1.0"
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key = '0cad779367054a3cb47d374de2ad110c'
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path = '/batches'
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constructed_url = endpoint + path
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payload= {
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"inputs": [
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{
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"source": {
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"sourceUrl": "https://translatedatastore.blob.core.windows.net/translate-source?sp=rl&st=2023-01-10T20:06:57Z&se=2024-01-11T04:06:57Z&spr=https&sv=2021-06-08&sr=c&sig=payaBXqpPpVZGG4y4h8Bncjlfzf5T3JN2HFbtCCXMeI%3D",
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"storageSource": "AzureBlob",
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"language": "fr"
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},
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"targets": [
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{
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"targetUrl": "https://translatedatastore.blob.core.windows.net/translate-output?sp=rwl&st=2023-01-10T20:07:41Z&se=2024-01-11T04:07:41Z&spr=https&sv=2021-06-08&sr=c&sig=C8Ivgssa4W5AaeKtE4%2B3xZElNPa%2FJxiXo93897dpBxI%3D",
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"storageSource": "AzureBlob",
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"category": "general",
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"language": "en"
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}
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]
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}
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]
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}
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headers = {
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'Ocp-Apim-Subscription-Key': key,
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'Content-Type': 'application/json'
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}
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response = requests.post(constructed_url, headers=headers, json=payload)
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print(f'response status code: {response.status_code}\nresponse status: {response.reason}\nresponse headers: {response.headers}')
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@ -2,7 +2,7 @@
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
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<orderEntry type="jdk" jdkName="Python 3.8 (dfm_tools_env)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyDocumentationSettings">
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@ -1,4 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (D3DFM)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (dfm_tools_env)" project-jdk-type="Python SDK" />
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</project>
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@ -5,12 +5,20 @@
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#%% Import
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import pandas as pd
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import numpy as np
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import math
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import geopandas as gp
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gp.options.use_pygeos = True
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from shapely import geometry, ops
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# Map Plotting
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import cartopy.crs as ccrs
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import contextily as ctx
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# Interpolation
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import scipy as sp
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from scipy.interpolate import griddata
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from scipy.interpolate import LinearNDInterpolator, interp1d
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@ -18,6 +26,7 @@ from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
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from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
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import pathlib as pl
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import datetime
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#%% Read Model Log
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# runLog = pd.read_excel("Y:/12828.101 English Wabigoon River/06_Models/00_Delft3D/ModelRuns.xlsx", "Sensitivity")
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@ -49,13 +58,205 @@ river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFr
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river_centerline_merge_gpd2.iloc[0, 1] = 0
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river_centerline_merge_gpd2.iloc[0, 2] = 0
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#%% Import Validation Data
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# Read in combined dataset
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obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
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# Add Datetime
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obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x)
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# Convert to geodataframe
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obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
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obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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# Add centerline and reset index
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obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
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# Sediment Hg GeoDataFrame where media is Sediment
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obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
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((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
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| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
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(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
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obs_HgSed = obs_gdf.loc[obsMask, :]
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# Adjust Units
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obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue
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obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue
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obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
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obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
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obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
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obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
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obs_HgSed.to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/HgSedDB2.geojson', driver="GeoJSON")
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# Group sediment core data to take average/min/max
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obs_HgSed_Avg = obs_HgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
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'RiverKM': ['mean'],
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'Longitude': ['mean'],
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'Latitude': ['mean'],
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'DateTime': ['mean']}).reset_index()
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# Merge Index
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obs_HgSed_Avg.columns = obs_HgSed_Avg.columns.map('|'.join).str.strip('|')
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# Fix Names
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obs_HgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
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"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
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# Create new GeoDataFrame
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obs_HgSed_Avg = gp.GeoDataFrame(obs_HgSed_Avg, geometry=gp.points_from_xy(
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obs_HgSed_Avg.loc[:, 'Longitude'], obs_HgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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# Water Hg GeoDataFrame where media is SurfaceWater (SW)
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obsMask = (((obs_gdf.Media_x == 'SW')) &
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((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total')
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| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
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(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
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obs_HgWater = obs_gdf.loc[obsMask, :]
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# Adjust Units
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obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
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obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
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obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
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obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
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obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
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obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
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# Import Sediment MeHg
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# Sediment MeHg GeoDataFrame
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obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
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((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
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(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
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obs_MeHgSed = obs_gdf.loc[obsMask, :]
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# Adjust Units
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
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obs_MeHgSed.loc[obs_MeHgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
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# Group sediment core data to take average/min/max
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obs_MeHgSed_Avg = obs_MeHgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
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'RiverKM': ['mean'],
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'Longitude': ['mean'],
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'Latitude': ['mean'],
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'DateTime': ['mean']}).reset_index()
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# Merge Index
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obs_MeHgSed_Avg.columns = obs_MeHgSed_Avg.columns.map('|'.join).str.strip('|')
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# Fix Names
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obs_MeHgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
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"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
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# Create new GeoDataFrame
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obs_MeHgSed_Avg = gp.GeoDataFrame(obs_MeHgSed_Avg, geometry=gp.points_from_xy(
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obs_MeHgSed_Avg.loc[:, 'Longitude'], obs_MeHgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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# MeHg in Water
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obsMask = (((obs_gdf.Media_x == 'SW')) &
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((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
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(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
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obs_MeHgWater = obs_gdf.loc[obsMask, :]
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# Adjust Units
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
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obs_MeHgWater.loc[obs_MeHgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
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#%% Plot Observations
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(30, 15))
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axes.set_xlim(494649, 513647)
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axes.set_ylim(5514653, 5525256)
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# Add basemap
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mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
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ctx.add_basemap(axes, source=mapbox, crs='EPSG:26915')
|
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obs_HgSed_Avg.plot(column='Sample_NG/G|mean', axes=axes, vmin=0, vmax=10000)
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plt.show()
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#%% Interpolate Hg to grid
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# Read in model points and roughness
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bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
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names=['x', 'y', 'z'], header=0, delim_whitespace=True)
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rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
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names=['x', 'y', 'z'], header=0, delim_whitespace=True)
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# Loop through parameters
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for i in range(0, 5):
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if i == 0:
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dataOUT = obs_HgSed_Avg['Sample_NG/G|mean']
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dataOutx = obs_HgSed_Avg.geometry.x
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dataOuty = obs_HgSed_Avg.geometry.y
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dateFileName = 'HgSed_meanDB'
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elif i == 1:
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dataOUT = obs_MeHgSed_Avg['Sample_NG/G|mean']
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dataOutx = obs_MeHgSed_Avg.geometry.x
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dataOuty = obs_MeHgSed_Avg.geometry.y
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dateFileName = 'MeHgSed_meanDB'
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elif i == 2:
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dataOUT = obs_HgWater['Sample_NG/L']
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dataOutx = obs_HgWater.geometry.x
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dataOuty = obs_HgWater.geometry.y
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dateFileName = 'HgWater_allDB'
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elif i == 3:
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dataOUT = obs_MeHgWater['Sample_NG/L']
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dataOutx = obs_MeHgWater.geometry.x
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dataOuty = obs_MeHgWater.geometry.y
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dateFileName = 'MeHgWater_allDB'
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elif i == 4:
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# Synthetic from Reed's Sheet
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dataOUT = ((10 - 1) * np.exp(-0.08 * np.array(river_centerline_merge_gpd2.loc[:, 'RiverKM']) / 1000) + 1) * 1000
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dataOutx = river_centerline_merge_gpd2.geometry.x
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dataOuty = river_centerline_merge_gpd2.geometry.y
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dateFileName = 'ReedHgSed'
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gridInterp = griddata(np.array([dataOutx, dataOuty]).T, dataOUT, np.array([bathy_xyz.x, bathy_xyz.y]).T,
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method='nearest')
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gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterp))
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gridInterpOut = np.transpose(gridInterpOut)
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# Set Wetlands to zero
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# Interpolate Roughness
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RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
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method='nearest')
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||||
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||||
gridInterpOut[RoughInterp==0.05, 2] = 0
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np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/' + dateFileName + '.xyz',
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gridInterpOut, delimiter=" ")
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#%% Import using DFM Functions
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modelPlot = 27
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## Degine import path
|
||||
# file_nc_map = pl.Path(runLog['Run Location'][modelPlot], 'Water_Quality', 'output', 'deltashell_map.nc')
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||||
file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42_WAQ.dsproj_data/Water_Quality/output/deltashell_map.nc")
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||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42_WAQ.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42B_WAQ_Coverage.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42C_WAQ_Coverage2.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
#file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42E_WAQ_DIDO.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_B.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_C.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
|
|
@ -79,14 +280,14 @@ dataVars = ['FrHgIM1', 'FrHgIM2', 'FrHgIM3', 'FrHgDOC', 'FrHgPOC', 'FrHg
|
|||
'HgHgS1O', 'MmMmS1O',
|
||||
'HgMmO', 'HgS1MmS1O', 'MmHgO', 'MmS1HgS1O',
|
||||
'oPhoDeg', 'oPhoOxy', 'oPhoRed',
|
||||
'f_minPOC1', 'MnODCS1']
|
||||
'f_minPOC1', 'MnODCS1', 'Continuity']
|
||||
|
||||
# Create Pandas Output Array
|
||||
dataIN_x = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_face_x',
|
||||
varname='mesh2d_agg_face_x',
|
||||
silent=True)
|
||||
dataIN_y = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_face_y',
|
||||
varname='mesh2d_agg_face_y',
|
||||
silent=True)
|
||||
|
||||
dataIN_gp = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="EPSG:32615"))
|
||||
|
|
@ -96,15 +297,16 @@ dataIN_gp = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="
|
|||
for vIDX, v in enumerate(dataVars):
|
||||
# Extract data from NetCDF file
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_' + v, timestep=len(tSteps)-1,
|
||||
silent=True, layer=0)
|
||||
varname='mesh2d_agg_' + v, timestep=len(tSteps)-5,
|
||||
silent=True) #, layer=0
|
||||
|
||||
dataIN_gp[v] = np.array(dataIN[0][0][:])
|
||||
print(v)
|
||||
#
|
||||
|
||||
#%% Create Dataframe along river centerline for plotting
|
||||
dataOUT = gp.sjoin_nearest(river_centerline_merge_gpd2, dataIN_gp, how='left', max_distance=100)
|
||||
|
||||
dataOUT.set_index('RiverKM', inplace=True)
|
||||
|
||||
#%% Plot Partition Data
|
||||
|
||||
|
|
@ -188,15 +390,21 @@ ax = axes.flat
|
|||
#Hg in Water
|
||||
ax[0].set_title('Hg in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['Hg'] * 1000000, linewidth=3, label='Hg')
|
||||
ax[0].scatter(obs_HgWater.RiverKM, obs_HgWater['Sample_NG/L'], 2, label='Hg Obs', color='y')
|
||||
ax[0].set_xlim([0, 100000])
|
||||
ax[0].set_ylim([0, 100])
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Hg [ng/L]')
|
||||
ax[0].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
|
||||
# Mm in Water
|
||||
# MeHg in Water
|
||||
ax[1].set_title('MeHg in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['Mm'] * 1000000, linewidth=3, label='MeHg')
|
||||
ax[1].scatter(obs_MeHgWater.RiverKM, obs_MeHgWater['Sample_NG/L'], 2, label='Hg Obs', color='y')
|
||||
ax[1].set_xlim([0, 100000])
|
||||
ax[1].set_ylim([0, 2])
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('MeHg [ng/L]')
|
||||
|
|
@ -206,24 +414,42 @@ ax[1].set_xlabel('Distance Along Flume [m]')
|
|||
ax[2].set_title('Hg in Sediment')
|
||||
ax[2].plot(dataOUT.index, dataOUT['HgS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='HgS1')
|
||||
# ax[2].scatter(obs_HgSed.RiverKM, obs_HgSed['Sample_NG/G'], 2, label='Hg Obs', color='y')
|
||||
ax[2].scatter(obs_HgSed_Avg.RiverKM, obs_HgSed_Avg['Sample_NG/G|mean'], 2, label='Hg Obs', color='y')
|
||||
ax[2].set_xlim([0, 100000])
|
||||
ax[2].set_ylim([0, 20000])
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('HgS1 [ng/g]')
|
||||
ax[2].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
|
||||
# Mm in Sediment
|
||||
# MeHg in Sediment
|
||||
ax[3].set_title('MeHg in Sediment')
|
||||
ax[3].plot(dataOUT.index, dataOUT['MmS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='MeHgS1')
|
||||
ax[3].scatter(obs_MeHgSed_Avg.RiverKM, obs_MeHgSed_Avg['Sample_NG/G|mean'], 2, label='MeHg Obs', color='y')
|
||||
ax[3].set_ylim([0, 60])
|
||||
ax[3].set_xlim([0, 100000])
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('MeHg [ng/g]')
|
||||
ax[3].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
# ax[3].set_title('Continuity')
|
||||
# ax[3].plot(dataOUT.index, dataOUT['Continuity'],
|
||||
# linewidth=3, label='Continuity')
|
||||
# ax[3].set_ylim([0, 1])
|
||||
#
|
||||
# ax[3].legend()
|
||||
# ax[3].set_ylabel('Continuity [-]')
|
||||
# ax[3].set_xlabel('Distance Along Flume [m]')
|
||||
# ax[3].set_ylim([0, 2])
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgTotals.png',
|
||||
bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot POC total Data
|
||||
|
||||
|
|
@ -272,8 +498,8 @@ ax[3].set_xlabel('Distance Along Flume [m]')
|
|||
|
||||
|
||||
plt.show()
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCTotals.png',
|
||||
bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
#%% More Total Data
|
||||
|
|
@ -295,7 +521,7 @@ ax[0].set_xlabel('Distance Along Flume [m]')
|
|||
# IM1 in Water
|
||||
ax[1].set_title('IM1 (Sand) in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['IM1'], linewidth=3, label='IM1')
|
||||
|
||||
ax[1].set_ylim([0, 50])
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('IM1 [g/m3]')
|
||||
ax[1].set_xlabel('Distance Along Flume [m]')
|
||||
|
|
@ -318,8 +544,8 @@ ax[3].set_xlabel('Distance Along Flume [m]')
|
|||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/InorganicTotals.png',
|
||||
bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/InorganicTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot Fluxes
|
||||
|
||||
|
|
@ -332,6 +558,7 @@ ax = axes.flat
|
|||
ax[0].set_title('IM1 (Sand) Resuspension Flux')
|
||||
ax[0].plot(dataOUT.index, dataOUT['fResS1IM1'], linewidth=3, label='IM1 Resuspension')
|
||||
ax[0].plot(dataOUT.index, dataOUT['fSedIM1'], linewidth=3, label='IM1 Sedimentation')
|
||||
ax[0].set_ylim([0, 2000])
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('IM1 (Sand) Sedimentation and Resuspension [g/m2/d]')
|
||||
|
|
@ -343,6 +570,7 @@ ax[1].plot(dataOUT.index, dataOUT['fResS1DetC'], linewidth=3, label='DetC Resusp
|
|||
ax[1].plot(dataOUT.index, dataOUT['fResS1OOC'], linewidth=3, label='OOC Resuspension')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fSedPOC1'], linewidth=3, label='POC1 (Fast) Sedimentation')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fSedPOC2'], linewidth=3, label='POC2 (Slow) Sedimentation')
|
||||
ax[1].set_ylim([0, 100])
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('POC Sedimentation and Resuspension [g/m2/d]')
|
||||
|
|
@ -352,6 +580,7 @@ ax[1].set_xlabel('Distance Along Flume [m]')
|
|||
ax[2].set_title('Hg Resuspension Flux')
|
||||
ax[2].plot(dataOUT.index, dataOUT['fResS1Hg'], linewidth=3, label='Hg Resuspension')
|
||||
ax[2].plot(dataOUT.index, dataOUT['fSedHg'], linewidth=3, label='Hg Sedimentation')
|
||||
ax[2].set_ylim([0, 0.01])
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('Hg Sedimentation and Resuspension [g/m2/d]')
|
||||
|
|
@ -368,8 +597,8 @@ ax[3].set_xlabel('Distance Along Flume [m]')
|
|||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgFluxes.png',
|
||||
bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgFluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot More Fluxes
|
||||
|
||||
|
|
@ -636,3 +865,52 @@ dataTableOut_df = pd.DataFrame(data=dataTableOut, index=[0])
|
|||
dataTableOut_df = (dataTableOut_df.T)
|
||||
|
||||
dataTableOut_df.to_excel('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ModelOutputs_Sept26.xlsx')
|
||||
|
||||
|
||||
#%% Save as geotiff
|
||||
tiffPlot = dataIN_gp['HgS1']
|
||||
|
||||
# Blank zero sed
|
||||
# tiffPlot[tiffPlot == 0] = np.nan
|
||||
|
||||
# Blank outside of hull
|
||||
# sedPlot[~regularMask2[i]] = np.nan
|
||||
|
||||
# Convert to xarray dataset
|
||||
tiffPlot_xr = xr.DataArray(data=tiffPlot.T,
|
||||
dims=["x", "y"],
|
||||
coords=dict(
|
||||
x=(["x"], X2[i][0, :]),
|
||||
y=(["y"], Y2[i][:, 0])))
|
||||
|
||||
# Transpose for geotiff writing
|
||||
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
||||
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
||||
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
||||
runLog['Run Name'][i] + '_mag.tif')
|
||||
|
||||
|
||||
tiffPlot = shear2[i]
|
||||
|
||||
# Blank zero sed
|
||||
tiffPlot[tiffPlot == 0] = np.nan
|
||||
|
||||
# Blank outside of hull
|
||||
# sedPlot[~regularMask2[i]] = np.nan
|
||||
|
||||
# Convert to xarray dataset
|
||||
tiffPlot_xr = xr.DataArray(data=tiffPlot.T,
|
||||
dims=["x", "y"],
|
||||
coords=dict(
|
||||
x=(["x"], X2[i][0, :]),
|
||||
y=(["y"], Y2[i][:, 0])))
|
||||
|
||||
# Transpose for geotiff writing
|
||||
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
||||
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
||||
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
||||
runLog['Run Name'][i] + '_shear.tif')
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -36,7 +36,8 @@ runLog = pd.read_excel(pth.as_posix(), "Test runs", skiprows=1)
|
|||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
#%% Import using DFM functions
|
||||
modelPlot = [24]
|
||||
modelPlot = [18, 35]
|
||||
|
||||
|
||||
# stormTime = [datetime.datetime(2005, 8, 7, 0, 0, 0)]
|
||||
|
||||
|
|
@ -57,8 +58,8 @@ mag2 = [None] * (max(modelPlot)+1)
|
|||
shear2 = [None] * (max(modelPlot)+1)
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_map = os.path.join(runLog['Run Location'][i], runLog['Run Name'][i], runLog['Run Name'][i][0:4] + '.dsproj_data',
|
||||
'FlowFM', 'output', 'FlowFM_map.nc')
|
||||
file_nc_map = os.path.join(runLog['Run Location'][i],
|
||||
'output','FlowFM_map.nc')
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
|
|
@ -102,8 +103,8 @@ for i in modelPlot:
|
|||
|
||||
# Create a list of grid points where the water depth is greater than zero
|
||||
wd_mask = (wd[i][:] > 0)
|
||||
alphaPoints = list(map(tuple, np.column_stack((facex[i][wd_mask.tolist()],
|
||||
facey[i][wd_mask.tolist()])).data))
|
||||
alphaPoints = list(map(tuple, np.column_stack((facex[i][wd_mask[0].tolist()],
|
||||
facey[i][wd_mask[0].tolist()])).data))
|
||||
|
||||
# Find the concave hull
|
||||
alphaPoly = alphashape.alphashape(alphaPoints, 0.02)
|
||||
|
|
@ -120,14 +121,14 @@ for i in modelPlot:
|
|||
# maskland_dist=50, method='nearest')
|
||||
|
||||
X2[i], Y2[i], mag2[i] = scatter_to_regulargrid(xcoords=facex[i], ycoords=facey[i],
|
||||
ncellx=8000, ncelly=6000, values=mag_mean[i][0, :],
|
||||
ncellx=4000, ncelly=3000, values=mag_mean[i][0, :],
|
||||
maskland_dist=50, method='nearest')
|
||||
X2[i], Y2[i], shear2[i] = scatter_to_regulargrid(xcoords=facex[i], ycoords=facey[i],
|
||||
ncellx=8000, ncelly=6000, values=shear[i][0, :],
|
||||
ncellx=4000, ncelly=3000, values=shear[i][0, :],
|
||||
maskland_dist=50, method='nearest')
|
||||
|
||||
#%% Save as geotiff
|
||||
modelPlot = [24]
|
||||
modelPlot = [18, 35]
|
||||
|
||||
sedIDX = 0
|
||||
|
||||
|
|
@ -151,7 +152,7 @@ for i in modelPlot:
|
|||
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
||||
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
||||
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
||||
runLog['Run Name'][i] + '_mag.tif')
|
||||
runLog['Run Title'][i] + '_mag.tif')
|
||||
|
||||
|
||||
tiffPlot = shear2[i]
|
||||
|
|
@ -173,7 +174,7 @@ for i in modelPlot:
|
|||
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
||||
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
||||
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
||||
runLog['Run Name'][i] + '_shear.tif')
|
||||
runLog['Run Title'][i] + '_shear.tif')
|
||||
|
||||
#%% Plot using DFM functions
|
||||
modelPlot = [24]
|
||||
|
|
@ -231,3 +232,5 @@ for i in modelPlot:
|
|||
# fig.savefig(
|
||||
# 'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
|
||||
# 'StormVelDisWide.png', bbox_inches='tight', dpi=400)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,305 @@
|
|||
#%% Plotting script for LSU D3D data
|
||||
|
||||
# Alexander Rey, 2022
|
||||
import os
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import netCDF4 as nc
|
||||
import math
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
import matplotlib.cm as cm
|
||||
|
||||
import datetime as datetime
|
||||
|
||||
from scipy.interpolate import LinearNDInterpolator, interp1d
|
||||
|
||||
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
|
||||
from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
import pathlib as pl
|
||||
|
||||
import alphashape
|
||||
|
||||
import rioxarray
|
||||
import xarray as xr
|
||||
|
||||
from shapely import geometry, ops
|
||||
|
||||
#%% Read in centerline shapefile
|
||||
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
||||
|
||||
river_centerlineExploded = river_centerline.explode(ignore_index=True)
|
||||
river_centerlineExploded.reset_index(inplace=True)
|
||||
|
||||
tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
|
||||
# Put the sub-line coordinates into a list of sublists
|
||||
outcoords = [list(i.coords) for i in tempMulti]
|
||||
# Flatten the list of sublists and use it to make a new line
|
||||
river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
|
||||
river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
|
||||
|
||||
river_centerline_merge_gpd2 =\
|
||||
gp.GeoDataFrame(geometry=gp.points_from_xy(
|
||||
river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
|
||||
|
||||
# Add distance along centerline
|
||||
river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
|
||||
river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
|
||||
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
||||
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
||||
|
||||
#%% Save River Centerline as Shapefile
|
||||
river_centerline_merge_gpd2.to_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15_RiverKM.shp')
|
||||
|
||||
#%% Read Model Log
|
||||
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "12828.101 English Wabigoon River", "06_Models", "00_Delft3D", "ModelRuns.xlsx")
|
||||
|
||||
runLog = pd.read_excel(pth.as_posix(), "Test runs", skiprows=1)
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
#%% Import using DFM functions
|
||||
#modelPlot = [35, 36, 37, 39, 40, 41, 42, 43, 44, 45]
|
||||
modelPlot = [18, 35]
|
||||
|
||||
dataIN_gp = [None] * (max(modelPlot)+1)
|
||||
dataOUT = [None] * (max(modelPlot)+1)
|
||||
|
||||
for i in modelPlot:
|
||||
|
||||
if (i == 39) or (i == 40) or (i == 41) or (i == 42) or (i == 43) or (i == 44) or (i == 45):
|
||||
Flume = True
|
||||
else:
|
||||
Flume = False
|
||||
|
||||
# Find Map File in Output Folder
|
||||
outputFiles = os.listdir(os.path.join(runLog['Run Location'][i],
|
||||
'output'))
|
||||
for file in outputFiles:
|
||||
if file.endswith("map.nc"):
|
||||
mapFile = file
|
||||
|
||||
file_nc_map = os.path.join(runLog['Run Location'][i],
|
||||
'output', mapFile)
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
# Find nearest time step to desired time
|
||||
# tStep = []
|
||||
# for s in stormTime:
|
||||
# abs_deltas_from_target_date = np.absolute(tSteps - s)
|
||||
# tStep.append(np.argmin(abs_deltas_from_target_date))
|
||||
|
||||
# Otherwise, define a timestep
|
||||
tStep = len(tSteps)-2
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map)
|
||||
|
||||
|
||||
# Define extraction variables
|
||||
# if (i == 37) or (i == 39):
|
||||
# dataVars = ['s1', 'waterdepth', 'taus', 'ucmaga']
|
||||
# else:
|
||||
|
||||
dataVars = ['s1', 'waterdepth', 'taus', 'ucmag']
|
||||
|
||||
# Create Pandas Output Array
|
||||
dataIN_x = get_ncmodeldata(file_nc=file_nc_map,
|
||||
varname='mesh2d_face_x',
|
||||
silent=True)
|
||||
dataIN_y = get_ncmodeldata(file_nc=file_nc_map,
|
||||
varname='mesh2d_face_y',
|
||||
silent=True)
|
||||
|
||||
dataIN_gp[i] = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="EPSG:32615"))
|
||||
|
||||
# Extract variables
|
||||
for vIDX, v in enumerate(dataVars):
|
||||
# Extract data from NetCDF file
|
||||
if len(vars_pd.loc['mesh2d_' + v, 'shape']) == 3:
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map,
|
||||
varname='mesh2d_' + v, timestep=len(tSteps) - 1,
|
||||
silent=True, layer='all')
|
||||
dataIN = dataIN.mean(axis=2)
|
||||
else:
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map,
|
||||
varname='mesh2d_' + v, timestep=len(tSteps) - 1,
|
||||
silent=True) # , layer=0
|
||||
|
||||
|
||||
dataIN_gp[i][v] = np.array(dataIN[0][:])
|
||||
print(v)
|
||||
|
||||
|
||||
# Extract Bed Level
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map,
|
||||
varname='mesh2d_flowelem_bl',
|
||||
silent=True) # , layer=0
|
||||
|
||||
dataIN_gp[i]['mesh2d_flowelem_bl'] = np.array(dataIN[:])
|
||||
print('mesh2d_flowelem_bl')
|
||||
|
||||
|
||||
# Create Dataframe along river centerline for plotting
|
||||
if Flume == True:
|
||||
dataOUT[i] = dataIN_gp[i]
|
||||
dataOUT[i]['RiverKM'] = dataIN_x
|
||||
else:
|
||||
dataOUT[i] = gp.sjoin_nearest(river_centerline_merge_gpd2, dataIN_gp[i], how='left', max_distance=100)
|
||||
|
||||
dataOUT[i].set_index('RiverKM', inplace=True)
|
||||
|
||||
#%% Key Profile Sites
|
||||
KeySites = dict()
|
||||
KeySites['Dryden'] = 0
|
||||
KeySites['Rugby Creek'] = 2650
|
||||
KeySites['Eagle River'] = 44200
|
||||
KeySites['Beaver Creek'] = 38600
|
||||
KeySites['Clay Lake'] = 90000
|
||||
KeySites['Gullwing River'] = 20100
|
||||
KeySites['Cowamula'] = 57400
|
||||
# KeySites['Buller Creek'] = 66000
|
||||
|
||||
# Structure Sites
|
||||
StructureSites = dict()
|
||||
StructureSites['Mutrie Lake'] = 54214
|
||||
StructureSites['Highway 105'] = 64998
|
||||
StructureSites['Quibell'] = 70000
|
||||
StructureSites['Wainwright Dam'] = 6013
|
||||
|
||||
#%% Plot Data along centerline
|
||||
fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(9, 9), sharex=True)
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
modelPlot = [35, 37, 40, 41, 42, 43, 44]
|
||||
modelPlot = [40, 41, 42, 43, 44]
|
||||
modelPlot = [35, 37, 40, 45]
|
||||
modelPlot = [18, 35]
|
||||
|
||||
|
||||
for i in modelPlot:
|
||||
# S1
|
||||
ax[0].set_title('Water Level')
|
||||
ax[0].plot(dataOUT[i].index, dataOUT[i]['s1'], linewidth=3, label=runLog['Run Title'][i])
|
||||
ax[0].set_ylim([330, 345])
|
||||
# ax[0].set_xlim([0, 10000])
|
||||
ax[0].set_xlim([0, 90000])
|
||||
|
||||
ax[0].legend(loc='upper right')
|
||||
ax[0].set_ylabel('Water Level [m]')
|
||||
|
||||
# Shear
|
||||
ax[1].set_title('Shear Stress')
|
||||
ax[1].plot(dataOUT[i].index, dataOUT[i]['taus'], linewidth=3, label=runLog['Run Title'][i])
|
||||
ax[1].set_ylim([0, 2])
|
||||
|
||||
ax[1].legend(loc='upper right')
|
||||
ax[1].set_ylabel('Shear Stress [N/m^2]')
|
||||
|
||||
# Water Depth
|
||||
ax[2].set_title('Water Depth')
|
||||
ax[2].plot(dataOUT[i].index, dataOUT[i]['waterdepth'], linewidth=3, label=runLog['Run Title'][i])
|
||||
ax[2].set_ylim([0, 20])
|
||||
|
||||
ax[2].legend(loc='upper right')
|
||||
ax[2].set_ylabel('Water Depth [m]')
|
||||
|
||||
# Bed Level
|
||||
ax[3].set_title('Bed Level')
|
||||
ax[3].plot(dataOUT[i].index, dataOUT[i]['mesh2d_flowelem_bl'], linewidth=3, label=runLog['Run Title'][i])
|
||||
# ax[3].set_ylim([320, 340])
|
||||
|
||||
ax[3].legend(loc='upper right')
|
||||
ax[3].set_ylabel('Bed Level [m]')
|
||||
|
||||
# Velocity
|
||||
ax[4].set_title('Depth Averaged Velocity')
|
||||
ax[4].plot(dataOUT[i].index, dataOUT[i]['ucmag'], linewidth=3, label=runLog['Run Title'][i])
|
||||
ax[4].set_ylim([0, 2])
|
||||
|
||||
ax[4].legend(loc='upper right')
|
||||
ax[4].set_ylabel('Velocity [m/s]')
|
||||
ax[4].set_xlabel('Distance Along River [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/DomainHDCompare.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/07_PostProcessing/HD_Profile_Compare.png',
|
||||
bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot select data along centerline
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 6), sharex=True)
|
||||
ax = axes.flat
|
||||
|
||||
# fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 4), sharex=True)
|
||||
# ax[0] = axes
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# Expand plot to allow space for legend
|
||||
# fig.subplots_adjust(left=-2)
|
||||
|
||||
|
||||
modelPlot = [18, 35]
|
||||
|
||||
for i in modelPlot:
|
||||
|
||||
# Velocity
|
||||
ax[0].set_title('Depth Averaged Velocity Along River Centerline')
|
||||
ax[0].plot(dataOUT[i].index / 1000, dataOUT[i]['ucmag'], linewidth=3, label=runLog['Run Title'][i], zorder=1)
|
||||
ax[0].set_ylim([0, 2])
|
||||
ax[0].set_xlim([0, 90])
|
||||
ax[0].set_ylabel('Velocity [m/s]')
|
||||
|
||||
# Shear
|
||||
ax[1].set_title('Shear Stress Along River Centerline')
|
||||
ax[1].plot(dataOUT[i].index/1000, dataOUT[i]['taus'], linewidth=3, label=runLog['Run Title'][i], zorder=1)
|
||||
ax[1].set_ylim([0, 2])
|
||||
ax[1].set_xlim([0, 90])
|
||||
ax[1].set_ylabel('Shear Stress [N/$\mathregular{m^2}$]')
|
||||
|
||||
ax[1].set_xlabel('Distance Along River [km]')
|
||||
|
||||
# Add in structure sites and key sites
|
||||
for site in KeySites.keys():
|
||||
for axID in range(0, 2):
|
||||
if site == 'Dryden':
|
||||
ax[axID].scatter(KeySites[site]/1000, 1, marker='^', color='k', s=30, label='Inflow', zorder=2)
|
||||
else:
|
||||
ax[axID].scatter(KeySites[site]/1000, 1, marker='^', color='k', s=30, zorder=10)
|
||||
|
||||
ax[axID].text(KeySites[site]/1000, 1.1, site, rotation=45)
|
||||
|
||||
for structureSite in StructureSites.keys():
|
||||
for axID in range(0, 2):
|
||||
if structureSite == 'Mutrie Lake':
|
||||
ax[axID].scatter(StructureSites[structureSite]/1000, 1, marker='s', color='k', label='Hydraulic Structure',
|
||||
s=30, zorder=10)
|
||||
else:
|
||||
ax[axID].scatter(StructureSites[structureSite]/1000, 1, marker='s', color='k', s=30, zorder=10)
|
||||
|
||||
|
||||
ax[axID].text(StructureSites[structureSite] / 1000, 1.1, structureSite, rotation=45)
|
||||
|
||||
# Add remobilization threshold lines
|
||||
ax[0].plot([0, 90000], [0.1, 0.1], linestyle='--', linewidth=1, label='Silt Remobilization Threshold', zorder=5)
|
||||
ax[0].plot([0, 90000], [1.3, 1.3], linestyle='--', linewidth=1, label='Sand Remobilization Threshold', zorder=5)
|
||||
ax[0].legend(bbox_to_anchor=(1.01, 0.5), loc="upper left")
|
||||
|
||||
fig.tight_layout(pad=1)
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/07_PostProcessing/HD_Profile_Compare_DAV.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/07_PostProcessing/HD_Profile_Compare_RevD.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
|
|
@ -0,0 +1,999 @@
|
|||
#%% Plotting EWR Flume Tests
|
||||
|
||||
# Alexander Rey, 2022
|
||||
|
||||
#%% Import
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.cm as cm
|
||||
import geopandas as gp
|
||||
gp.options.use_pygeos = True
|
||||
from shapely import geometry, ops
|
||||
|
||||
# Map Plotting
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
|
||||
# Interpolation
|
||||
import scipy as sp
|
||||
from scipy.interpolate import griddata
|
||||
|
||||
from scipy.interpolate import LinearNDInterpolator, interp1d
|
||||
|
||||
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
|
||||
from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
|
||||
import pathlib as pl
|
||||
|
||||
import datetime
|
||||
#%% Read Model Log
|
||||
|
||||
# runLog = pd.read_excel("Y:/12828.101 English Wabigoon River/06_Models/00_Delft3D/ModelRuns.xlsx", "Sensitivity")
|
||||
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "12828.101 English Wabigoon River", "06_Models", "00_Delft3D", "ModelRuns.xlsx")
|
||||
runLog = pd.read_excel(pth.as_posix(), "Sensitivity")
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
#%% Read in centerline shapefile
|
||||
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
||||
|
||||
river_centerlineExploded = river_centerline.explode(ignore_index=True)
|
||||
river_centerlineExploded.reset_index(inplace=True)
|
||||
|
||||
tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
|
||||
# Put the sub-line coordinates into a list of sublists
|
||||
outcoords = [list(i.coords) for i in tempMulti]
|
||||
# Flatten the list of sublists and use it to make a new line
|
||||
river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
|
||||
river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
|
||||
|
||||
river_centerline_merge_gpd2 =\
|
||||
gp.GeoDataFrame(geometry=gp.points_from_xy(
|
||||
river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
|
||||
|
||||
# Add distance along centerline
|
||||
river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
|
||||
river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
|
||||
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
||||
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
||||
|
||||
#%% Import Validation Data
|
||||
|
||||
# Read in combined dataset
|
||||
obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
||||
# Add Datetime
|
||||
obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x)
|
||||
|
||||
# Convert to geodataframe
|
||||
obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
|
||||
obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
||||
|
||||
# Add centerline and reset index
|
||||
obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
|
||||
|
||||
# Sediment Hg GeoDataFrame where media is Sediment
|
||||
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgSed = obs_gdf.loc[obsMask, :]
|
||||
|
||||
# Adjust Units
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
|
||||
|
||||
# obs_HgSed.to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/HgSedDB2.geojson', driver="GeoJSON")
|
||||
|
||||
# Group sediment core data to take average/min/max
|
||||
obs_HgSed_Avg = obs_HgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
|
||||
'RiverKM': ['mean'],
|
||||
'Longitude': ['mean'],
|
||||
'Latitude': ['mean'],
|
||||
'DateTime': ['mean']}).reset_index()
|
||||
# Merge Index
|
||||
obs_HgSed_Avg.columns = obs_HgSed_Avg.columns.map('|'.join).str.strip('|')
|
||||
|
||||
# Fix Names
|
||||
obs_HgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
|
||||
"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
|
||||
|
||||
# Create new GeoDataFrame
|
||||
obs_HgSed_Avg = gp.GeoDataFrame(obs_HgSed_Avg, geometry=gp.points_from_xy(
|
||||
obs_HgSed_Avg.loc[:, 'Longitude'], obs_HgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
||||
|
||||
|
||||
|
||||
# Water Hg GeoDataFrame where media is SurfaceWater (SW)
|
||||
obsMask = (((obs_gdf.Media_x == 'SW')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgWater = obs_gdf.loc[obsMask, :]
|
||||
# Adjust Units
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
|
||||
obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
|
||||
|
||||
|
||||
# Import Sediment MeHg
|
||||
# Sediment MeHg GeoDataFrame
|
||||
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_MeHgSed = obs_gdf.loc[obsMask, :]
|
||||
|
||||
# Adjust Units
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
|
||||
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
|
||||
|
||||
# Group sediment core data to take average/min/max
|
||||
obs_MeHgSed_Avg = obs_MeHgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
|
||||
'RiverKM': ['mean'],
|
||||
'Longitude': ['mean'],
|
||||
'Latitude': ['mean'],
|
||||
'DateTime': ['mean']}).reset_index()
|
||||
# Merge Index
|
||||
obs_MeHgSed_Avg.columns = obs_MeHgSed_Avg.columns.map('|'.join).str.strip('|')
|
||||
|
||||
# Fix Names
|
||||
obs_MeHgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
|
||||
"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
|
||||
|
||||
# Create new GeoDataFrame
|
||||
obs_MeHgSed_Avg = gp.GeoDataFrame(obs_MeHgSed_Avg, geometry=gp.points_from_xy(
|
||||
obs_MeHgSed_Avg.loc[:, 'Longitude'], obs_MeHgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
||||
|
||||
|
||||
|
||||
|
||||
# MeHg in Water
|
||||
obsMask = (((obs_gdf.Media_x == 'SW')) &
|
||||
((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_MeHgWater = obs_gdf.loc[obsMask, :]
|
||||
# Adjust Units
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
|
||||
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
|
||||
|
||||
#%% Plot Observations
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(30, 15))
|
||||
|
||||
axes.set_xlim(494649, 513647)
|
||||
axes.set_ylim(5514653, 5525256)
|
||||
|
||||
# Add basemap
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:26915')
|
||||
|
||||
obs_HgSed_Avg.plot(column='Sample_NG/G|mean', axes=axes, vmin=0, vmax=10000)
|
||||
|
||||
plt.show()
|
||||
|
||||
#%% Plot Profiles
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 7))
|
||||
obs_HgSed.plot.scatter('RiverKM', 'TopDepth_x', 12, 'Sample_NG/G', ax=axes, vmax=20000)
|
||||
|
||||
axes.invert_yaxis()
|
||||
axes.set_xlabel('Distance Along River [m]')
|
||||
axes.set_ylabel('Sample Depth [m]')
|
||||
axes.set_xlim([0, 100000])
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/Figures/HgDepthWide.png",
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
|
||||
#%% Interpolate Hg to grid
|
||||
|
||||
# Read in model points and roughness
|
||||
bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
# Loop through parameters
|
||||
for i in range(0, 5):
|
||||
if i == 0:
|
||||
dataOUT = obs_HgSed_Avg['Sample_NG/G|mean']
|
||||
dataOutx = obs_HgSed_Avg.geometry.x
|
||||
dataOuty = obs_HgSed_Avg.geometry.y
|
||||
dateFileName = 'HgSed_meanDB'
|
||||
elif i == 1:
|
||||
dataOUT = obs_MeHgSed_Avg['Sample_NG/G|mean']
|
||||
dataOutx = obs_MeHgSed_Avg.geometry.x
|
||||
dataOuty = obs_MeHgSed_Avg.geometry.y
|
||||
dateFileName = 'MeHgSed_meanDB'
|
||||
elif i == 2:
|
||||
dataOUT = obs_HgWater['Sample_NG/L']
|
||||
dataOutx = obs_HgWater.geometry.x
|
||||
dataOuty = obs_HgWater.geometry.y
|
||||
dateFileName = 'HgWater_allDB'
|
||||
elif i == 3:
|
||||
dataOUT = obs_MeHgWater['Sample_NG/L']
|
||||
dataOutx = obs_MeHgWater.geometry.x
|
||||
dataOuty = obs_MeHgWater.geometry.y
|
||||
dateFileName = 'MeHgWater_allDB'
|
||||
elif i == 4:
|
||||
# Synthetic from Reed's Sheet
|
||||
dataOUT = ((10 - 1) * np.exp(-0.08 * np.array(river_centerline_merge_gpd2.loc[:, 'RiverKM']) / 1000) + 1) * 1000
|
||||
dataOutx = river_centerline_merge_gpd2.geometry.x
|
||||
dataOuty = river_centerline_merge_gpd2.geometry.y
|
||||
dateFileName = 'ReedHgSed'
|
||||
|
||||
gridInterp = griddata(np.array([dataOutx, dataOuty]).T, dataOUT, np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterp))
|
||||
gridInterpOut = np.transpose(gridInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
# Interpolate Roughness
|
||||
RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
|
||||
method='nearest')
|
||||
|
||||
gridInterpOut[RoughInterp==0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/' + dateFileName + '.xyz',
|
||||
gridInterpOut, delimiter=" ")
|
||||
|
||||
|
||||
|
||||
#%% Import using DFM Functions
|
||||
|
||||
modelPlot = 27
|
||||
|
||||
## Degine import path
|
||||
# file_nc_map = pl.Path(runLog['Run Location'][modelPlot], 'Water_Quality', 'output', 'deltashell_map.nc')
|
||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42_WAQ.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42B_WAQ_Coverage.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42C_WAQ_Coverage2.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
#file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42E_WAQ_DIDO.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//ott-diomedes/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_B.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("D:/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_D.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
#file_nc_map = pl.Path("D:/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_E.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
# file_nc_map = pl.Path("//oak-inundation/D/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_44_OceanMesh5.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
|
||||
# file_nc_map = pl.Path("//oak-inundation/D/Projects/12828.101_EWR_Delft3FM/Flume/RiverFlumeHD50.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
||||
file_nc_map = pl.Path("//oak-inundation/D/Projects/12828.101_EWR_Delft3FM/Flume/RiverFlumeArg_Test2/deltashell_map.nc")
|
||||
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
# Print file variables
|
||||
A = get_ncvardimlist(file_nc_map.as_posix())
|
||||
# print(A)
|
||||
|
||||
# Define extraction variables
|
||||
dataVars = ['FrHgIM1', 'FrHgIM2', 'FrHgIM3', 'FrHgDOC', 'FrHgPOC', 'FrHgPHYT', 'FrHgDis',
|
||||
'FrHgIM1S1', 'FrHgIM2S1', 'FrHgIM3S1', 'FrHgDOCS1', 'FrHgPOCS1', 'FrHgPHYTS1', 'FrHgDisS1',
|
||||
'FrHgIM1S2', 'FrHgIM2S2', 'FrHgIM3S2', 'FrHgDOCS2', 'FrHgPOCS2', 'FrHgPHYTS2', 'FrHgDisS2',
|
||||
'FrMmIM1', 'FrMmIM2', 'FrMmIM3', 'FrMmDOC', 'FrMmPOC', 'FrMmPHYT', 'FrMmDis',
|
||||
'FrMmIM1S1', 'FrMmIM2S1', 'FrMmIM3S1', 'FrMmDOCS1', 'FrMmPOCS1', 'FrMmPHYTS1', 'FrMmDisS1',
|
||||
'FrMmIM1S2', 'FrMmIM2S2', 'FrMmIM3S2', 'FrMmDOCS2', 'FrMmPOCS2', 'FrMmPHYTS2', 'FrMmDisS2',
|
||||
'IM1', 'IM2', 'IM3', 'Hg', 'Mm', 'H0', 'POC1', 'POC2',
|
||||
'IM1S1', 'IM2S1', 'IM3S1', 'HgS1', 'MmS1', 'DetCS1', 'OOCS1',
|
||||
'IM1S2', 'IM2S2', 'IM3S2', 'HgS2', 'MmS2', 'DetCS2', 'OOCS2',
|
||||
'fResS1IM1', 'fSedIM1', 'fResS1IM2', 'fSedIM2',
|
||||
'fResS1DetC', 'fSedPOC1', 'fResS1OOC', 'fSedPOC2',
|
||||
'fSedDM', 'fResS1DM', 'fDigS1DM', 'fBurS1DM', 'fDigS2DM', 'fBurS2DM',
|
||||
'fSedHg', 'fResS1Hg', 'fSedMm', 'fResS1Mm',
|
||||
'HgHgS1O', 'MmMmS1O',
|
||||
'HgMmO', 'HgS1MmS1O', 'MmHgO', 'MmS1HgS1O',
|
||||
'fBurS1Hg', 'fDigS1Hg', 'fBurS1Mm', 'fDigS1Mm',
|
||||
'oPhoDeg', 'oPhoOxy', 'oPhoRed',
|
||||
'f_minPOC1', 'MnODCS1', 'Continuity',
|
||||
'volume', 'Depth']
|
||||
|
||||
# Define if sediment or water variable
|
||||
# Read depth average for water and bottom layer for sediment
|
||||
sedTerms = ['S1', 'S2', 'Sed', 'Res', 'Bur', 'Dig']
|
||||
|
||||
# Create Pandas Output Array
|
||||
dataIN_x = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_face_x',
|
||||
silent=True)
|
||||
dataIN_y = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_face_y',
|
||||
silent=True)
|
||||
|
||||
dataIN_gp = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="EPSG:32615"))
|
||||
|
||||
|
||||
# Extract variables
|
||||
for vIDX, v in enumerate(dataVars):
|
||||
# Extract data from NetCDF file
|
||||
|
||||
# If sediment variable read full 3d variable and then use the values from the bottom layer
|
||||
if any(s in v for s in sedTerms):
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_' + v, timestep=len(tSteps) - 2,
|
||||
silent=True) # , #mesh2d_agg_2d_
|
||||
|
||||
dataIN_gp[v] = np.array(dataIN[0][5][:])
|
||||
|
||||
# Else (Water) read 2d variable
|
||||
else:
|
||||
dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
||||
varname='mesh2d_2d_' + v, timestep=len(tSteps) - 2,
|
||||
silent=True) # , #mesh2d_agg_2d_
|
||||
|
||||
dataIN_gp[v] = np.array(dataIN[0][:])
|
||||
|
||||
# Add data to Pandas Array
|
||||
# For 2D Runs
|
||||
# dataIN_gp[v] = np.array(dataIN[0][0][:])
|
||||
|
||||
# For 3D Runs
|
||||
# dataIN_gp[v] = np.array(dataIN[0][:])
|
||||
|
||||
print(v)
|
||||
#
|
||||
|
||||
#%% Create Dataframe along river centerline for plotting
|
||||
dataOUT = gp.sjoin_nearest(river_centerline_merge_gpd2, dataIN_gp, how='left', max_distance=100)
|
||||
dataOUT.set_index('RiverKM', inplace=True)
|
||||
|
||||
#%% Create Dataframe along Flume for plotting
|
||||
dataOUT = dataIN_gp
|
||||
dataOUT['RiverKM'] = dataIN_x
|
||||
dataOUT.set_index('RiverKM', inplace=True)
|
||||
|
||||
#%% Read in river sections shapefile
|
||||
river_sections_shp = 'C:/Users/arey/files/Projects/Grassy Narrows/GIS/RiverSegmentsModel.shp'
|
||||
river_sections = gp.read_file(river_sections_shp)
|
||||
|
||||
river_sections_names = ['1_Dryden', '2_RugbyCreek', 'Eagle_River', 'Full_Domain']
|
||||
|
||||
#%% Sum data over area
|
||||
# Identify 2d flux variables
|
||||
fluxVars2d = ['fSedHg', 'fResS1Hg', 'fSedMm', 'fResS1Mm',
|
||||
'fBurS1Hg', 'fDigS1Hg', 'fBurS1Mm', 'fDigS1Mm',
|
||||
'HgS1MmS1O', 'MmS1HgS1O',
|
||||
'fSedDM', 'fResS1DM',
|
||||
'fDigS1DM', 'fBurS1DM', 'fDigS2DM', 'fBurS2DM']
|
||||
|
||||
# Create geoDataFrame for summing
|
||||
dataSum_2d_gp = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="EPSG:32615"))
|
||||
|
||||
# Area = Volumne / Depth
|
||||
dataSum_2d_gp['Area'] = dataIN_gp['volume'] / dataIN_gp['Depth']
|
||||
|
||||
# Create dict for summing
|
||||
dataSum_2d = dict()
|
||||
|
||||
# Loop through river sections shapefile
|
||||
for rIDX, rSHP in enumerate(river_sections.geometry):
|
||||
# Create dict for summing
|
||||
dataSum_2d[river_sections_names[rIDX]] = dict()
|
||||
|
||||
for vIDX, v in enumerate(fluxVars2d):
|
||||
# Change from g/day to ng/day
|
||||
dataSum_2d[river_sections_names[rIDX]][v] = sum(dataIN_gp.loc[dataIN_gp.within(rSHP), v] *
|
||||
dataSum_2d_gp.loc[dataIN_gp.within(rSHP), 'Area']) * 1e9
|
||||
|
||||
# Convert to Pandas DataFrame
|
||||
dataSum_2d_pd = pd.DataFrame(dataSum_2d)
|
||||
# dataSum_2d_pd.to_excel('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ModelOutput/GrassyNarrows_2dFluxes.xlsx')
|
||||
|
||||
#%% Sum data over water volumne
|
||||
|
||||
# Identify 3d flux variables
|
||||
fluxVars3d = ['oPhoOxy', 'oPhoRed', 'oPhoDeg',
|
||||
'HgMmO', 'MmHgO']
|
||||
|
||||
# Create dict for summing
|
||||
dataSum_3d = dict()
|
||||
# Loop through river sections shapefile
|
||||
for rIDX, rSHP in enumerate(river_sections.geometry):
|
||||
# Create dict for summing
|
||||
dataSum_3d[river_sections_names[rIDX]] = dict()
|
||||
|
||||
for vIDX, v in enumerate(fluxVars3d):
|
||||
# Change from g/day to ng/day
|
||||
dataSum_3d[river_sections_names[rIDX]][v] = sum(dataIN_gp.loc[dataIN_gp.within(rSHP), v] /
|
||||
dataIN_gp.loc[dataIN_gp.within(rSHP), 'volume'])* 1e9
|
||||
|
||||
# Convert to Pandas DataFrame
|
||||
dataSum_3d_pd = pd.DataFrame(dataSum_3d)
|
||||
# dataSum_3d_pd.to_excel('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ModelOutput/GrassyNarrows_3dFluxes.xlsx')
|
||||
|
||||
#%% Plot Partition Data
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
#Hg Parition in Water
|
||||
ax[0].set_title('Hg Fraction in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgIM1'], linewidth=3, label='IM1')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgIM2'], linewidth=3, label='IM2')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgIM3'], linewidth=3, label='IM3')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgPOC'], linewidth=3, label='POC')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgDOC'], linewidth=3, label='DOC')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgPHYT'], linewidth=3, label='PHYT')
|
||||
ax[0].plot(dataOUT.index, dataOUT['FrHgDis'], linewidth=3, label='Dissolved')
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Percentage Hg')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
ax[0].set_ylim([0, 1])
|
||||
|
||||
|
||||
#Mm Parition in Water
|
||||
ax[1].set_title('MeHg Fraction in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmIM1'], linewidth=3, label='IM1')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmIM2'], linewidth=3, label='IM2')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmIM3'], linewidth=3, label='IM3')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmPOC'], linewidth=3, label='POC')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmDOC'], linewidth=3, label='DOC')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmPHYT'], linewidth=3, label='PHYT')
|
||||
ax[1].plot(dataOUT.index, dataOUT['FrMmDis'], linewidth=3, label='Dissolved')
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('Percentage MeHg')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
ax[1].set_ylim([0, 1])
|
||||
|
||||
#Hg Parition in S1
|
||||
ax[2].set_title('Hg Fraction in Sediment')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgIM1S1'], linewidth=3, label='IM1')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgIM2S1'], linewidth=3, label='IM2')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgIM3S1'], linewidth=3, label='IM3')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgPOCS1'], linewidth=3, label='POC')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgDOCS1'], linewidth=3, label='DOC')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgPHYTS1'], linewidth=3, label='PHYT')
|
||||
ax[2].plot(dataOUT.index, dataOUT['FrHgDisS1'], linewidth=3, label='Dissolved')
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('Percentage Hg')
|
||||
ax[2].set_xlabel('Distance Along River [m]')
|
||||
ax[2].set_ylim([0, 1])
|
||||
|
||||
#Mm Parition in S1
|
||||
ax[3].set_title('MeHg Fraction in Sediment')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmIM1S1'], linewidth=3, label='IM1')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmIM2S1'], linewidth=3, label='IM2')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmIM3S1'], linewidth=3, label='IM3')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmPOCS1'], linewidth=3, label='POC')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmDOCS1'], linewidth=3, label='DOC')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmPHYTS1'], linewidth=3, label='PHYT')
|
||||
ax[3].plot(dataOUT.index, dataOUT['FrMmDisS1'], linewidth=3, label='Dissolved')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('Percentage MeHg')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
ax[3].set_ylim([0, 1])
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/Partitioning.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
#%% Plot Total Data
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
#Hg in Water
|
||||
ax[0].set_title('Hg in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['Hg'] * 1000000, linewidth=3, label='Hg')
|
||||
ax[0].scatter(obs_HgWater.RiverKM, obs_HgWater['Sample_NG/L'], 5, label='Hg Obs', color='k')
|
||||
ax[0].set_xlim([0, 100000])
|
||||
ax[0].set_ylim([0, 100])
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Hg [ng/L]')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
|
||||
|
||||
# MeHg in Water
|
||||
ax[1].set_title('MeHg in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['Mm'] * 1000000, linewidth=3, label='MeHg')
|
||||
ax[1].scatter(obs_MeHgWater.RiverKM, obs_MeHgWater['Sample_NG/L'], 5, label='Hg Obs', color='k')
|
||||
ax[1].set_xlim([0, 100000])
|
||||
ax[1].set_ylim([0, 3])
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('MeHg [ng/L]')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
|
||||
#Hg in Sediment
|
||||
ax[2].set_title('Hg in Sediment')
|
||||
ax[2].plot(dataOUT.index, dataOUT['HgS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='HgS1')
|
||||
ax[2].scatter(obs_HgSed_Avg.RiverKM, obs_HgSed_Avg['Sample_NG/G|mean'], 5, label='Hg Obs', color='k')
|
||||
ax[2].set_xlim([0, 100000])
|
||||
ax[2].set_ylim([0, 20000])
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('HgS1 [ng/g]')
|
||||
ax[2].set_xlabel('Distance Along River [m]')
|
||||
|
||||
|
||||
# MeHg in Sediment
|
||||
ax[3].set_title('MeHg in Sediment')
|
||||
ax[3].plot(dataOUT.index, dataOUT['MmS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='MeHgS1')
|
||||
ax[3].scatter(obs_MeHgSed_Avg.RiverKM, obs_MeHgSed_Avg['Sample_NG/G|mean'], 5, label='MeHg Obs', color='k')
|
||||
ax[3].set_ylim([0, 60])
|
||||
ax[3].set_xlim([0, 100000])
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('MeHg [ng/g]')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# ax[3].set_title('Continuity')
|
||||
# ax[3].plot(dataOUT.index, dataOUT['Continuity'],
|
||||
# linewidth=3, label='Continuity')
|
||||
# ax[3].set_ylim([0, 1])
|
||||
#
|
||||
# ax[3].legend()
|
||||
# ax[3].set_ylabel('Continuity [-]')
|
||||
# ax[3].set_xlabel('Distance Along Flume [m]')
|
||||
# ax[3].set_ylim([0, 2])
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/HgTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot POC total Data
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
# POC1 in Water
|
||||
ax[0].set_title('POC1 (Fast) in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['POC1'], linewidth=3, label='POC1')
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('POC1 [g/m3]')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
ax[0].set_ylim([0, 1])
|
||||
ax[0].set_xlim([0, 100000])
|
||||
|
||||
# POC2 in Water
|
||||
ax[1].set_title('POC2 (Slow) in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['POC2'], linewidth=3, label='POC2')
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('POC2 [g/m3]')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
ax[1].set_ylim([0, 1])
|
||||
ax[1].set_xlim([0, 100000])
|
||||
|
||||
|
||||
#DetC in sediment
|
||||
ax[2].set_title('DetC (Fast) in Sediment')
|
||||
ax[2].plot(dataOUT.index, dataOUT['DetCS1']
|
||||
/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1'])
|
||||
, linewidth=3, label='DetCS1')
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('DetCS1 [g/g]')
|
||||
ax[2].set_xlabel('Distance Along River [m]')
|
||||
ax[2].set_ylim([0, 0.02])
|
||||
ax[2].set_xlim([0, 100000])
|
||||
|
||||
|
||||
# OOC in Sediment
|
||||
ax[3].set_title('OOC (Slow) in Sediment')
|
||||
ax[3].plot(dataOUT.index, dataOUT['OOCS1']
|
||||
/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='OOCS1')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('OOCS1 [g/g]')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
ax[3].set_ylim([0, 0.02])
|
||||
ax[3].set_xlim([0, 100000])
|
||||
|
||||
plt.show()
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/POCTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
#%% More Total Data
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
#H0 in Water
|
||||
ax[0].set_title('Hg(0) in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['H0']* 1000000, linewidth=3, label='Hg(0)')
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Hg(0) [ng/L]')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
ax[0].set_ylim([0, 0.35])
|
||||
ax[0].set_xlim([0, 100000])
|
||||
|
||||
|
||||
# IM1 in Water
|
||||
ax[1].set_title('IM1 (Sand) in Water')
|
||||
ax[1].plot(dataOUT.index, dataOUT['IM1'], linewidth=3, label='IM1')
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('IM1 [g/m3]')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
ax[1].set_ylim([0, 20])
|
||||
ax[1].set_xlim([0, 100000])
|
||||
|
||||
# IM2 in Water
|
||||
ax[2].set_title('IM2 (Silt) in Water')
|
||||
ax[2].plot(dataOUT.index, dataOUT['IM2'], linewidth=3, label='IM2')
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('IM2 [g/m3]')
|
||||
ax[2].set_xlabel('Distance Along Flume [m]')
|
||||
ax[2].set_ylim([0, 10])
|
||||
ax[2].set_xlim([0, 100000])
|
||||
|
||||
# IM3 in Water
|
||||
ax[3].set_title('IM3 (Woodchips) in Water')
|
||||
ax[3].plot(dataOUT.index, dataOUT['IM3'], linewidth=3, label='IM3 (Woodchips)')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('IM3 [g/m3]')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
# ax[3].set_ylim([0, 0.02])
|
||||
ax[3].set_xlim([0, 100000])
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/InorganicTotals.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot Fluxes
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
# IM1 Sediment Flux
|
||||
ax[0].set_title('IM1 (Sand) Resuspension Flux')
|
||||
ax[0].plot(dataOUT.index, dataOUT['fResS1IM1'], linewidth=3, label='IM1 Resuspension')
|
||||
ax[0].plot(dataOUT.index, dataOUT['fSedIM1'], linewidth=3, label='IM1 Sedimentation')
|
||||
ax[0].set_ylim([0, 200])
|
||||
ax[0].set_xlim([0, 100000])
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('IM1 (Sand) Sedimentation and Resuspension [g/m2/d]')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# POC Sediment Flux
|
||||
ax[1].set_title('POC Resuspension Flux')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fResS1DetC'], linewidth=3, label='DetC Resuspension')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fResS1OOC'], linewidth=3, label='OOC Resuspension')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fSedPOC1'], linewidth=3, label='POC1 (Fast) Sedimentation')
|
||||
ax[1].plot(dataOUT.index, dataOUT['fSedPOC2'], linewidth=3, label='POC2 (Slow) Sedimentation')
|
||||
ax[1].set_ylim([0, 2])
|
||||
ax[1].set_xlim([0, 100000])
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('POC Sedimentation and Resuspension [g/m2/d]')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# Hg Sediment Flux
|
||||
ax[2].set_title('Hg Resuspension Flux')
|
||||
ax[2].plot(dataOUT.index, dataOUT['fResS1Hg'], linewidth=3, label='Hg Resuspension')
|
||||
ax[2].plot(dataOUT.index, dataOUT['fSedHg'], linewidth=3, label='Hg Sedimentation')
|
||||
ax[2].set_ylim([0, 0.002])
|
||||
ax[2].set_xlim([0, 100000])
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('Hg Sedimentation and Resuspension [g/m2/d]')
|
||||
ax[2].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# Mm Sediment Flux
|
||||
ax[3].set_title('MeHg Resuspension Flux')
|
||||
ax[3].plot(dataOUT.index, dataOUT['fResS1Mm'], linewidth=3, label='MeHg Resuspension')
|
||||
ax[3].plot(dataOUT.index, dataOUT['fSedMm'], linewidth=3, label='MeHg Sedimentation')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('MeHg Sedimentation and Resuspension [g/m2/d]')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
ax[3].set_xlim([0, 100000])
|
||||
ax[3].set_ylim([0, 0.0001])
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgFluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/HgFluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Plot More Fluxes
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=4)
|
||||
ax = axes.flat
|
||||
|
||||
# Methylation Flux
|
||||
ax[0].set_title('Methylation Flux in Water')
|
||||
ax[0].plot(dataOUT.index, dataOUT['HgMmO'], linewidth=3, label='Mehtylation')
|
||||
ax[0].plot(dataOUT.index, dataOUT['MmHgO'], linewidth=3, label='Demehtylation')
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Methylation Flux in Water [g/m3/d]')
|
||||
ax[0].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# Methylation Flux in Sediment
|
||||
ax[1].set_title('Methylation Flux in Sediment')
|
||||
ax[1].plot(dataOUT.index, dataOUT['HgS1MmS1O'] * 1000000,
|
||||
linewidth=3, label='Mehtylation')
|
||||
ax[1].plot(dataOUT.index, dataOUT['MmS1HgS1O'] * 1000000,
|
||||
linewidth=3, label='Demehtylation')
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('Methylation Flux in Sediment [ug/m2/d]')
|
||||
ax[1].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# Hg Diffusion Flux
|
||||
# ax[2].set_title('Diffusion Flux')
|
||||
# ax[2].plot(dataOUT.index, dataOUT['HgHgS1O'], linewidth=3, label='Hg Diffusion')
|
||||
# ax[2].plot(dataOUT.index, dataOUT['MmMmS1O'], linewidth=3, label='MeHg Diffusion')
|
||||
#
|
||||
# ax[2].legend()
|
||||
# ax[2].set_ylabel('Diffusion [g/m2/d]')
|
||||
# ax[2].set_xlabel('Distance Along River [m]')
|
||||
|
||||
# POC Degradation Flux
|
||||
ax[2].set_title('POC Degradation Flux in Water')
|
||||
ax[2].plot(dataOUT.index, dataOUT['f_minPOC1'], linewidth=3, label='POC Degradation Water')
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('POC Degradation [g/m3/d]')
|
||||
ax[2].set_xlabel('Distance Along River [m]')
|
||||
|
||||
|
||||
# POC Degradation Flux
|
||||
ax[3].set_title('POC Degradation Flux in Sediment')
|
||||
ax[3].plot(dataOUT.index, dataOUT['MnODCS1']
|
||||
/ (dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
||||
linewidth=3, label='POC Degradation Sediment')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('POC Degradation [g/gPOC/d')
|
||||
ax[3].set_xlabel('Distance Along River [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCFluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
# fig.savefig('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/12_WAQ/FullDomainFigures/POCFluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Photo Fluxes
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=4)
|
||||
ax = axes.flat
|
||||
|
||||
# Diffusion Flux
|
||||
ax[0].set_title('Diffusion Flux')
|
||||
ax[0].plot(dataOUT.index, dataOUT['HgHgS1O'], linewidth=3, label='Hg Diffusion')
|
||||
ax[0].plot(dataOUT.index, dataOUT['MmMmS1O'], linewidth=3, label='MeHg Diffusion')
|
||||
|
||||
ax[0].legend()
|
||||
ax[0].set_ylabel('Diffusion [g/m2/d]')
|
||||
ax[0].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
# Photo Oxidation Flux
|
||||
ax[1].set_title('Photo Oxidation Flux')
|
||||
ax[1].plot(dataOUT.index, dataOUT['oPhoOxy'], linewidth=3, label='Photo Oxidation Flux')
|
||||
|
||||
ax[1].legend()
|
||||
ax[1].set_ylabel('Photo Oxidation Flux [g/m3/d]')
|
||||
ax[1].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
|
||||
# Photo Degradation Flux
|
||||
ax[2].set_title('Photo Degradation Flux')
|
||||
ax[2].plot(dataOUT.index, dataOUT['oPhoDeg'], linewidth=3, label='Photo Degradation Flux')
|
||||
|
||||
ax[2].legend()
|
||||
ax[2].set_ylabel('Photo Degradation Flux [g/m3/d]')
|
||||
ax[2].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
|
||||
# Photo Reduction Flux
|
||||
ax[3].set_title('Photo Reduction Flux')
|
||||
ax[3].plot(dataOUT.index, dataOUT['oPhoRed'], linewidth=3, label='Photo Reduction Flux')
|
||||
|
||||
ax[3].legend()
|
||||
ax[3].set_ylabel('Photo Reduction Flux [g/m3/d]')
|
||||
ax[3].set_xlabel('Distance Along Flume [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/H0Fluxes.png',
|
||||
# bbox_inches='tight', dpi=200)
|
||||
|
||||
#%% Make summary table
|
||||
dataTableOut = dict()
|
||||
|
||||
#Hg Parition in Water
|
||||
dataTableOut['Hg:IM1 Fraction'] = dataOUT['FrHgIM1'].mean()
|
||||
dataTableOut['Hg:IM2 Fraction'] = dataOUT['FrHgIM2'].mean()
|
||||
dataTableOut['Hg:IM3 Fraction'] = dataOUT['FrHgIM3'].mean()
|
||||
dataTableOut['Hg:POC Fraction'] = dataOUT['FrHgPOC'].mean()
|
||||
dataTableOut['Hg:DOC Fraction'] = dataOUT['FrHgDOC'].mean()
|
||||
dataTableOut['Hg:Phyt Fraction'] = dataOUT['FrHgPHYT'].mean()
|
||||
dataTableOut['Hg:Dissolved Fraction'] = dataOUT['FrHgDis'].mean()
|
||||
|
||||
#MeHg Parition in Water
|
||||
dataTableOut['MeHg:IM1 Fraction'] = dataOUT['FrMmIM1'].mean()
|
||||
dataTableOut['MeHg:IM2 Fraction'] = dataOUT['FrMmIM2'].mean()
|
||||
dataTableOut['MeHg:IM3 Fraction'] = dataOUT['FrMmIM3'].mean()
|
||||
dataTableOut['MeHg:POC Fraction'] = dataOUT['FrMmPOC'].mean()
|
||||
dataTableOut['MeHg:DOC Fraction'] = dataOUT['FrMmDOC'].mean()
|
||||
dataTableOut['MeHg:Phyt Fraction'] = dataOUT['FrMmPHYT'].mean()
|
||||
dataTableOut['MeHg:Dissolved Fraction'] = dataOUT['FrMmDis'].mean()
|
||||
|
||||
#Hg Parition in S1
|
||||
dataTableOut['Hg:IM1 Fraction S1'] = dataOUT['FrHgIM1S1'].mean()
|
||||
dataTableOut['Hg:IM2 Fraction S1'] = dataOUT['FrHgIM2S1'].mean()
|
||||
dataTableOut['Hg:IM3 Fraction S1'] = dataOUT['FrHgIM3S1'].mean()
|
||||
dataTableOut['Hg:POC Fraction S1'] = dataOUT['FrHgPOCS1'].mean()
|
||||
dataTableOut['Hg:DOC Fraction S1'] = dataOUT['FrHgDOCS1'].mean()
|
||||
dataTableOut['Hg:Phyt Fraction S1'] = dataOUT['FrHgPHYTS1'].mean()
|
||||
dataTableOut['Hg:Dissolved Fraction S1'] = dataOUT['FrHgDisS1'].mean()
|
||||
|
||||
#MeHg Parition in S1
|
||||
dataTableOut['MeHg:IM1 Fraction S1'] = dataOUT['FrMmIM1S1'].mean()
|
||||
dataTableOut['MeHg:IM2 Fraction S1'] = dataOUT['FrMmIM2S1'].mean()
|
||||
dataTableOut['MeHg:IM3 Fraction S1'] = dataOUT['FrMmIM3S1'].mean()
|
||||
dataTableOut['MeHg:POC Fraction S1'] = dataOUT['FrMmPOCS1'].mean()
|
||||
dataTableOut['MeHg:DOC Fraction S1'] = dataOUT['FrMmDOCS1'].mean()
|
||||
dataTableOut['MeHg:Phyt Fraction S1'] = dataOUT['FrMmPHYT'].mean()
|
||||
dataTableOut['MeHg:Dissolved Fraction S1'] = dataOUT['FrMmDisS1'].mean()
|
||||
|
||||
#Hg in Water
|
||||
dataTableOut['Hg in Water [ng/L]'] = dataOUT['Hg'].mean() * 1000000
|
||||
|
||||
# Mm in Water
|
||||
dataTableOut['MeHg in Water [ng/L]'] = dataOUT['Mm'].mean() * 1000000
|
||||
|
||||
#Hg in Sediment
|
||||
dataTableOut['Hg in Sediment [ng/g]'] = dataOUT['HgS1'].mean() * 1*10 ** 9 /\
|
||||
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
||||
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
||||
|
||||
# Mm in Sediment
|
||||
dataTableOut['MeHg in Sediment [ng/g]'] = dataOUT['MmS1'].mean() * 1*10 ** 9 /\
|
||||
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
||||
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
||||
|
||||
# Hg(0) in Water
|
||||
dataTableOut['Hg(0) in Water [ng/L]'] = dataOUT['H0'].mean() * 1000000
|
||||
|
||||
# IM1 in Water
|
||||
ax[1].set_title('IM1 (Sand) in Water')
|
||||
dataTableOut['IM1 (Sand) in Water [g/m3]'] = dataOUT['IM1'].mean()
|
||||
|
||||
# IM2 in Water
|
||||
dataTableOut['IM2 (Silt) in Water [g/m3]'] = dataOUT['IM2'].mean()
|
||||
|
||||
# IM3 in Water
|
||||
dataTableOut['IM3 (Woodchips) in Water [g/m3]'] = dataOUT['IM3'].mean()
|
||||
|
||||
# POC1 in Water
|
||||
dataTableOut['POC1 (Fast) in Water [g/m3]'] = dataOUT['POC1'].mean()
|
||||
|
||||
# POC2 in Water
|
||||
dataTableOut['POC1 (Slow) in Water [g/m3]'] = dataOUT['POC2'].mean()
|
||||
|
||||
#DetC in sediment
|
||||
dataTableOut['DetC (Fast) in Sediment [g/g]'] = dataOUT['DetCS1'].mean() /\
|
||||
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
||||
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
||||
|
||||
# OOC in Sediment
|
||||
dataTableOut['OOC (Slow) in Sediment [g/g]'] = dataOUT['OOCS1'].mean() /\
|
||||
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
||||
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
||||
|
||||
|
||||
# IM1 Resuspension
|
||||
dataTableOut['IM1 (Sand) Resuspension Flux [g/m2/d]'] = dataOUT['fResS1IM1'].mean()
|
||||
|
||||
# IM1 Sedimentation
|
||||
dataTableOut['IM1 (Sand) Sedimentation Flux [g/m2/d]'] = dataOUT['fSedIM1'].mean()
|
||||
|
||||
# DetC Resuspension
|
||||
dataTableOut['DetC Resuspension Flux [g/m2/d]'] = dataOUT['fResS1DetC'].mean()
|
||||
|
||||
# OOC Resuspension
|
||||
dataTableOut['OOC Resuspension Flux [g/m2/d]'] = dataOUT['fResS1OOC'].mean()
|
||||
|
||||
# POC1 Sedimentation
|
||||
dataTableOut['POC1 (Fast) Sedimentation [g/m2/d]'] = dataOUT['fSedPOC1'].mean()
|
||||
|
||||
# POC2 Sedimentation
|
||||
dataTableOut['POC2 (Slow) Sedimentation [g/m2/d]'] = dataOUT['fSedPOC2'].mean()
|
||||
|
||||
# Hg Resuspension
|
||||
dataTableOut['Hg Resuspension Flux [g/m2/d]'] = dataOUT['fResS1Hg'].mean()
|
||||
|
||||
# Hg Sedimentation
|
||||
dataTableOut['Hg Sedimentation Flux [g/m2/d]'] = dataOUT['fSedHg'].mean()
|
||||
|
||||
# MeHg Resuspension
|
||||
dataTableOut['MeHg Resuspension Flux [g/m2/d]'] = dataOUT['fResS1Mm'].mean()
|
||||
|
||||
# MeHg Sedimentation
|
||||
dataTableOut['MeHg Sedimentation Flux [g/m2/d]'] = dataOUT['fSedMm'].mean()
|
||||
|
||||
|
||||
# Methylation Flux in Water
|
||||
dataTableOut['Methylation Flux in Water [g/m3/d]'] = dataOUT['HgMmO'].mean()
|
||||
|
||||
# Demehtylation Flux in Water
|
||||
dataTableOut['Demehtylation Flux in Water [g/m3/d]'] = dataOUT['MmHgO'].mean()
|
||||
|
||||
# Methylation Flux in Sediment
|
||||
dataTableOut['Methylation Flux in Sediment [g/gHg/d]'] = dataOUT['HgS1MmS1O'].mean() / dataOUT['HgS1'].mean()
|
||||
|
||||
# Demehtylation Flux in Sediment
|
||||
dataTableOut['Demehtylation Flux in Sediment [g/gMm/d]'] = dataOUT['MmS1HgS1O'].mean() / dataOUT['MmS1'].mean()
|
||||
|
||||
|
||||
# POC Degradation Flux in Water
|
||||
dataTableOut['POC Degradation Flux in Water [g/m3/d]'] = dataOUT['f_minPOC1'].mean()
|
||||
|
||||
# POC Degradation Flux in Sediment
|
||||
dataTableOut['Demehtylation Flux in Water [g/gPOC/d]'] = dataOUT['MnODCS1'].mean() /\
|
||||
(dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
||||
|
||||
|
||||
# Hg Diffusion Flux
|
||||
dataTableOut['Hg Diffusion [g/m2/d]'] = dataOUT['HgHgS1O'].mean()
|
||||
|
||||
# MeHg Diffusion Flux
|
||||
dataTableOut['MeHg Diffusion [g/m2/d]'] = dataOUT['MmMmS1O'].mean()
|
||||
|
||||
# Photo Oxidation Flux
|
||||
dataTableOut['Hg Diffusion [g/m3/d]'] = dataOUT['oPhoOxy'].mean()
|
||||
|
||||
# Photo Degradation Flux
|
||||
dataTableOut['MeHg Diffusion [g/m3/d]'] = dataOUT['oPhoDeg'].mean()
|
||||
|
||||
# Photo Reduction Flux
|
||||
dataTableOut['Hg Diffusion [g/m3/d]'] = dataOUT['oPhoRed'].mean()
|
||||
|
||||
#%% Save Data Table as Excel
|
||||
dataTableOut_df = pd.DataFrame(data=dataTableOut, index=[0])
|
||||
dataTableOut_df = (dataTableOut_df.T)
|
||||
|
||||
dataTableOut_df.to_excel('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ModelOutputs_Sept26.xlsx')
|
||||
|
|
@ -0,0 +1,207 @@
|
|||
# This file may be used to create an environment using:
|
||||
# $ conda create --name <env> --file <this file>
|
||||
# platform: win-64
|
||||
@EXPLICIT
|
||||
https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2022.9.24-h5b45459_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2022.1.0-h57928b3_3787.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.11-hd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.20348.0-h57928b3_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.29.30139-h890b9b1_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/vc-14.2-hb210afc_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h8ffe710_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/expat-2.4.9-h1537add_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/geos-3.11.0-h39d44d4_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/icu-70.1-h0e60522_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/jpeg-9e-h8ffe710_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.0.9-h8ffe710_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.14-hcfcfb64_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-h8ffe710_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libogg-1.3.4-h8ffe710_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libspatialindex-1.9.3-h39d44d4_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.39.3-hcfcfb64_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.2.4-h8ffe710_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.12-hcfcfb64_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.9.3-h8ffe710_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/openssl-1.1.1q-h8ffe710_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pcre-8.45-h0e60522_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pixman-0.40.0-h8ffe710_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/snappy-1.1.9-h82413e6_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/tbb-2021.6.0-h91493d7_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/tk-8.6.12-h8ffe710_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/xerces-c-3.2.3-h0e60522_5.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/freexl-1.0.6-h67ca5e6_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/gettext-0.19.8.1-h5728263_1009.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/hdf4-4.2.15-h0e5069d_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/krb5-1.19.3-h1176d77_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.0.9-h8ffe710_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.0.9-h8ffe710_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libclang13-14.0.6-default_h77d9078_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.38-h19919ed_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/librttopo-1.1.0-h2842628_11.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libssh2-1.10.0-h680486a_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libvorbis-1.3.7-h0e60522_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libxml2-2.10.2-h99b13fb_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libzip-1.9.2-hfed4ece_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/mkl-2022.1.0-h6a75c08_874.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pcre2-10.37-hdfff0fc_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/sqlite-3.39.3-hcfcfb64_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/zlib-1.2.12-hcfcfb64_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.2-h7755175_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/blosc-1.21.1-h74325e0_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/boost-cpp-1.78.0-h9f4b32c_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.0.9-h8ffe710_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/freetype-2.12.1-h546665d_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-16_win64_mkl.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libclang-14.0.6-default_h77d9078_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libcurl-7.83.1-h789b8ee_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libglib-2.74.0-h79619a9_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libpq-14.5-hfcc5ef8_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libtiff-4.4.0-h8e97e67_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/python-3.8.13-h9a09f29_0_cpython.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.9-hcd874cb_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/affine-2.3.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/attrs-22.1.0-pyh71513ae_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/brotli-1.0.9-h8ffe710_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/certifi-2022.9.24-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cfitsio-4.1.0-h5a969a9_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-2.1.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.5-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/curl-7.83.1-h789b8ee_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-1.0.1-py_1001.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.14.0-h720f74d_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/fsspec-2022.8.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/geographiclib-1.52-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.74.0-h12be248_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/hdf5-1.12.2-nompi_h2a0e4a3_100.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/heapdict-1.0.1-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/lcms2-2.12-h2a16943_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-16_win64_mkl.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libkml-1.3.0-hf2ab4e4_1015.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-16_win64_mkl.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libxcb-1.13-hcd874cb_1004.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/networkx-2.8.6-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.0-hc9384bd_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/ply-3.11-py_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/postgresql-14.5-h1c22c4f_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/proj-9.0.1-h1cfcee9_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pycparser-2.21-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.0.9-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyshp-2.3.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/python_abi-3.8-2_cp38.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pytz-2022.2.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/setuptools-65.4.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/tblib-1.7.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.1.0-pyh8a188c0_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.3.0-pyha770c72_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/wheel-0.37.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/xyzservices-2022.9.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cairo-1.16.0-hd694305_1014.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cffi-1.15.1-py38hd8c33c5_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/click-8.1.3-py38haa244fe_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cytoolz-0.12.0-py38h294d835_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopy-2.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/geotiff-1.7.1-h714bc5f_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/glib-2.74.0-h12be248_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/kealib-1.4.15-hdf81f3a_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.4-py38hbd9d945_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libnetcdf-4.8.1-nompi_h85765be_104.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libspatialite-5.0.1-hd9530bf_19.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/lz4-4.0.0-py38he18b7d8_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/markupsafe-2.1.1-py38h294d835_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/msgpack-python-1.0.4-py38hbd9d945_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/munch-2.5.0-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/numpy-1.23.3-py38h8503d5b_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/openpyxl-3.0.10-py38h91455d4_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/partd-1.3.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pillow-9.2.0-py38h37aa274_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pip-22.2.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/psutil-5.9.2-py38h91455d4_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyproj-3.4.0-py38h5a72d38_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0-py38h294d835_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/rtree-1.0.0-py38h8b54edf_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/tiledb-2.11.3-h5689973_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/tornado-6.1-py38h294d835_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/unicodedata2-14.0.0-py38h294d835_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/win_inet_pton-1.1.0-py38haa244fe_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/zict-2.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/bottleneck-1.3.5-py38hbdcd294_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/brotlipy-0.7.0-py38h294d835_1004.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cftime-1.6.2-py38hbaf524b_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/click-log-0.4.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/contourpy-1.0.5-py38hb1fd069_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cryptography-37.0.4-py38hb7941b4_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.9.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/fonttools-4.37.3-py38h91455d4_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/gstreamer-1.20.3-h6b5321d_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/mercantile-1.2.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pandas-1.5.0-py38h5846ac1_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/poppler-22.04.0-hb57f792_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pygeos-0.13-py38h8678f91_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyh0701188_6.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/scipy-1.9.1-py38h91810f7_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/shapely-1.8.4-py38h91759cc_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/sip-6.6.2-py38h885f38d_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/trimesh-3.15.2-pyh1a96a4e_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/branca-0.5.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopandas-base-0.11.1-pyha770c72_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/gst-plugins-base-1.20.3-h001b923_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/libgdal-3.5.2-hc386656_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.5.3-py38h3268a40_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/netcdf4-1.6.1-nompi_py38h78680c8_100.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyqt5-sip-12.11.0-py38h885f38d_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.1.2-py38hc27f28a_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/xarray-2022.6.0-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cartopy-0.21.0-py38h395e5b8_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/gdal-3.5.2-py38h658b046_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.4.3-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.6-hf0cf448_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/rasterio-1.3.2-py38h8b19869_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.11-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/distributed-2022.9.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/fiona-1.8.21-py38h4ea64ce_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyqt-5.15.7-py38h75e37d8_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.12.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/contextily-1.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/dask-2022.9.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/folium-0.12.1.post1-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.5.3-py38haa244fe_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyepsg-0.4.0-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopandas-0.11.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/alphashape-1.3.1-pyh44b312d_0.tar.bz2
|
||||
|
|
@ -0,0 +1,207 @@
|
|||
# This file may be used to create an environment using:
|
||||
# $ conda create --name <env> --file <this file>
|
||||
# platform: win-64
|
||||
@EXPLICIT
|
||||
https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2022.9.24-h5b45459_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2022.1.0-h57928b3_3787.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.11-hd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.20348.0-h57928b3_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.29.30139-h890b9b1_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/vc-14.2-hb210afc_7.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h8ffe710_4.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/expat-2.4.9-h1537add_0.tar.bz2
|
||||
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||||
https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.5.3-py38h3268a40_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/netcdf4-1.6.1-nompi_py38h78680c8_100.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyqt5-sip-12.11.0-py38h885f38d_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.1.2-py38hc27f28a_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/xarray-2022.6.0-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cartopy-0.21.0-py38h395e5b8_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/gdal-3.5.2-py38h658b046_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.4.3-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.6-hf0cf448_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/rasterio-1.3.2-py38h8b19869_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.11-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/distributed-2022.9.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/fiona-1.8.21-py38h4ea64ce_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/pyqt-5.15.7-py38h75e37d8_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.12.2-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/contextily-1.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/dask-2022.9.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/folium-0.12.1.post1-pyhd8ed1ab_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.5.3-py38haa244fe_2.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyepsg-0.4.0-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopandas-0.11.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/alphashape-1.3.1-pyh44b312d_0.tar.bz2
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="jdk" jdkName="Python 3.9 (py39)" jdkType="Python SDK" />
|
||||
<orderEntry type="jdk" jdkName="Python 3.8 (geopandas_forge_mustique)" jdkType="Python SDK" />
|
||||
<orderEntry type="sourceFolder" forTests="false" />
|
||||
</component>
|
||||
<component name="PyDocumentationSettings">
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (py39)" project-jdk-type="Python SDK" />
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (geopandas_forge_mustique)" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
## Process CFSv2 Wind NetCDF Data
|
||||
# AJMR: December 2022
|
||||
|
||||
import xarray as xr
|
||||
import netCDF4 as nc
|
||||
import numpy as np
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
from datetime import datetime
|
||||
|
||||
#%% Setup paths
|
||||
cdfv2Paths = os.listdir('C:/Users/arey/Downloads/wnd10m.cdas1.201104-gdas.201103grb2.nc/')
|
||||
|
||||
# Add path to each file
|
||||
cdfv2Paths = ['C:/Users/arey/Downloads/wnd10m.cdas1.201104-gdas.201103grb2.nc/' + i for i in cdfv2Paths]
|
||||
|
||||
#%% Set up multiple file dataset
|
||||
cfsv2_mf = nc.MFDataset(cdfv2Paths)
|
||||
|
||||
#%% Read in NC data
|
||||
cfsv2_windU = cfsv2_mf.variables['U_GRD_L103']
|
||||
cfsv2_windV = cfsv2_mf.variables['V_GRD_L103']
|
||||
|
||||
#%% Process time from NetCDF file
|
||||
nctimes = cfsv2_mf.variables['valid_date_time'][:] # get values
|
||||
# Convert numpy char array to list of strings
|
||||
nctimesStr = [''.join(row) for row in nctimes.astype(str)]
|
||||
|
||||
ncDatetime = [datetime.strptime(date, "%Y%m%d%H") for date in nctimesStr]
|
||||
|
||||
#%% Plot
|
||||
|
||||
# Set up figure
|
||||
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
|
||||
|
||||
# Plot U and V wind speeds
|
||||
# ax.plot(ncDatetime, np.sqrt(cfsv2_windU[:, 0, 0]**2 + cfsv2_windV[:, 0, 0]**2), label='CFSv2 Wind Magnitude')
|
||||
ax.plot(ncDatetime)
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -2,14 +2,23 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"pycharm": {
|
||||
"name": "#%% Setup\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\arey\\Anaconda3\\envs\\py39\\lib\\site-packages\\geopandas\\_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.3-CAPI-1.16.1). Conversions between both will be slow.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#import jupyter\n",
|
||||
"import pandas as pd\n",
|
||||
|
|
@ -702,11 +711,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# sc = axes.scatter(EWR_gdf_filt_UTM.loc[(EWR_gdf_filt_UTM['param_name'].isin(plotvars)) &\n",
|
||||
|
|
@ -949,8 +954,6 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'\n",
|
||||
"\n",
|
||||
"fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 14))\n",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,150 @@
|
|||
## Process EWR Hg Data into a surface
|
||||
# AJMR: September 2022
|
||||
|
||||
#%% Setup
|
||||
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import gpxpy
|
||||
import gpxpy.gpx
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import contextily as ctx
|
||||
import datetime
|
||||
|
||||
import scipy as sp
|
||||
from scipy.interpolate import griddata
|
||||
|
||||
from shapely import geometry, ops
|
||||
|
||||
|
||||
#%% Read in consolidated dataset
|
||||
df_in = pd.read_csv('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/mercury sediment subset-SGJ.csv')
|
||||
|
||||
# Convert to geopandas
|
||||
dataIN_gp = gp.GeoDataFrame(df_in, geometry=gp.points_from_xy(df_in.Longitude, df_in.Latitude, crs="EPSG:4326"))
|
||||
# Convert to UTM
|
||||
dataIN_gp.to_crs(crs='EPSG:32615', inplace=True)
|
||||
|
||||
#%% Read in centerline shapefile
|
||||
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
||||
|
||||
river_centerlineExploded = river_centerline.explode(ignore_index=True)
|
||||
river_centerlineExploded.reset_index(inplace=True)
|
||||
|
||||
tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
|
||||
# Put the sub-line coordinates into a list of sublists
|
||||
outcoords = [list(i.coords) for i in tempMulti]
|
||||
# Flatten the list of sublists and use it to make a new line
|
||||
river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
|
||||
river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
|
||||
|
||||
river_centerline_merge_gpd2 =\
|
||||
gp.GeoDataFrame(geometry=gp.points_from_xy(
|
||||
river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
|
||||
|
||||
# Add distance along centerline
|
||||
river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
|
||||
river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
|
||||
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
||||
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
||||
|
||||
#%% Add riverkm distances to dataset
|
||||
dataOUT = gp.sjoin_nearest(river_centerline_merge_gpd2, dataIN_gp, how='left', max_distance=100)
|
||||
|
||||
#%% Take only surface values
|
||||
dataOUTmax = dataOUT.groupby(['Samplesiteid'], as_index=False)['Samplevalue'].max()
|
||||
# Merge geometry and river KM
|
||||
dataOUTmax = pd.merge(dataOUTmax, dataOUT[['Samplesiteid', 'RiverKM']], on='Samplesiteid', how='left')
|
||||
dataOUTmax = pd.merge(dataOUTmax, dataOUT[['Samplesiteid', 'geometry']], on='Samplesiteid', how='left')
|
||||
|
||||
#%% Set River KM as index
|
||||
dataOUT.set_index('RiverKM', inplace=True)
|
||||
dataOUTmax.set_index('RiverKM', inplace=True)
|
||||
|
||||
|
||||
#%% River Profile Plotting
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
axes.scatter(dataOUT.index, dataOUT.Samplevalue)
|
||||
axes.scatter(dataOUTmax.index, dataOUTmax.Samplevalue)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
#%% Hg Map Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Full system limits
|
||||
# axes.set_xlim(350000, 520000)
|
||||
# axes.set_ylim(5510000, 5580000)
|
||||
|
||||
# # Clay Lake Limits
|
||||
# axes.set_xlim(466418, 515846)
|
||||
# axes.set_ylim(5513437, 5545362)
|
||||
|
||||
# Dryden Limits
|
||||
axes.set_xlim(466418, 515846)
|
||||
axes.set_ylim(5513437, 5545362)
|
||||
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
|
||||
# Plot Hg
|
||||
# dataOUTmax.plot(ax=axes, column='Samplevalue', legend=True, vmin=0, vmax=20000)
|
||||
|
||||
# Plot Hg At surface
|
||||
dataOUT.loc[((dataOUT.TopDepth == 0) | (dataOUT.TopDepth.isna()))].plot(
|
||||
ax=axes, column='Samplevalue', legend=True, vmin=0, vmax=10000)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
#%% Interpolate Hg
|
||||
bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
HgInterp = griddata(np.array([dataOUTmax.geometry.x, dataOUTmax.geometry.y]).T,
|
||||
np.array(dataOUTmax.Samplevalue).T, np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, HgInterp))
|
||||
HgInterpOut = np.transpose(HgInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
# Interpolate Roughness
|
||||
RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut[RoughInterp==0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceInterFiltDB.xyz',
|
||||
HgInterpOut, delimiter=" ")
|
||||
|
||||
|
||||
#%% Hg Interp Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Clay Lake Limits
|
||||
axes.set_xlim(466418, 515846)
|
||||
axes.set_ylim(5513437, 5545362)
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
# Plot Hg
|
||||
axes.scatter(HgInterpOut[:, 0], HgInterpOut[:, 1], 5, HgInterpOut[:, 2],
|
||||
vmin=0, vmax=5000)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,189 @@
|
|||
## Process EWR Hg Data into a surface
|
||||
# AJMR: September 2022
|
||||
|
||||
#%% Setup
|
||||
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import gpxpy
|
||||
import gpxpy.gpx
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import contextily as ctx
|
||||
import datetime
|
||||
|
||||
import scipy as sp
|
||||
from scipy.interpolate import griddata
|
||||
|
||||
from shapely import geometry, ops
|
||||
|
||||
#%% Read in centerline shapefile
|
||||
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
||||
|
||||
river_centerlineExploded = river_centerline.explode(ignore_index=True)
|
||||
river_centerlineExploded.reset_index(inplace=True)
|
||||
|
||||
tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
|
||||
# Put the sub-line coordinates into a list of sublists
|
||||
outcoords = [list(i.coords) for i in tempMulti]
|
||||
# Flatten the list of sublists and use it to make a new line
|
||||
river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
|
||||
river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
|
||||
|
||||
river_centerline_merge_gpd2 =\
|
||||
gp.GeoDataFrame(geometry=gp.points_from_xy(
|
||||
river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
|
||||
|
||||
# Add distance along centerline
|
||||
river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
|
||||
river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
|
||||
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
||||
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
||||
|
||||
|
||||
#%% Read in combined dataset
|
||||
obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
||||
# Add Datetime
|
||||
obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x)
|
||||
|
||||
#%% Process combined datsaset
|
||||
# Convert to geodataframe
|
||||
obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
|
||||
obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
||||
# Add centerline and reset index
|
||||
obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
|
||||
|
||||
# Sediment Hg GeoDataFrame where media is Sediment
|
||||
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() &
|
||||
((obs_gdf.TopDepth_x == 0) | (obs_gdf.TopDepth_x.isna())))
|
||||
# obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
||||
# ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
||||
# | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
||||
# (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
||||
|
||||
obs_HgSed = obs_gdf.loc[obsMask, :].copy()
|
||||
|
||||
# Adjust Units
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Samplevalue']
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Samplevalue']
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Samplevalue'] * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Samplevalue'] * 1000
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Samplevalue'] * 0.001
|
||||
obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Samplevalue'] * 0.001
|
||||
|
||||
#%% River Profile Plotting
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
|
||||
axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
|
||||
axes.set_ylim(0, 20000)
|
||||
axes.set_xlim(0, 100000)
|
||||
axes.set_xlabel('Distance Along River [m]')
|
||||
axes.set_ylabel('Surface Sediment Hg [ng/g]')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_SurfaceProfile.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Plot Vertical Profiles
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 7))
|
||||
obs_HgSed.plot.scatter('RiverKM', 'TopDepth_x', 12, 'Sample_NG/G',
|
||||
ax=axes, vmax=20000, cmap='viridis')
|
||||
|
||||
axes.invert_yaxis()
|
||||
axes.set_xlabel('Distance Along River [m]')
|
||||
axes.set_ylabel('Sample Depth [cm]')
|
||||
axes.set_xlim([0, 100000])
|
||||
|
||||
# Get colorbar axis to set a legend
|
||||
cax = fig.get_axes()[1]
|
||||
#and we can modify it, i.e.:
|
||||
cax.set_ylabel('Sediment Hg (ng/g)')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_Vertical.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Hg Map Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Full system limits
|
||||
# axes.set_xlim(350000, 520000)
|
||||
# axes.set_ylim(5510000, 5580000)
|
||||
|
||||
# # Clay Lake Limits
|
||||
# axes.set_xlim(466418, 515846)
|
||||
# axes.set_ylim(5513437, 5545362)
|
||||
|
||||
# Dryden Limits
|
||||
axes.set_xlim(497697, 515846)
|
||||
axes.set_ylim(5513437, 5525166)
|
||||
|
||||
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
|
||||
# Plot Hg At surface
|
||||
obs_HgSed.plot(ax=axes, column='Samplevalue', legend=True,
|
||||
vmin=0, vmax=5000, markersize=2, cmap='viridis',
|
||||
legend_kwds={'label': 'Surface Sediment Hg (ng/g)'})
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceHgMap.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
#%% Interpolate Hg
|
||||
bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
|
||||
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
||||
|
||||
HgInterp = griddata(np.array([dataOUTmax.geometry.x, dataOUTmax.geometry.y]).T,
|
||||
np.array(dataOUTmax.Samplevalue).T, np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, HgInterp))
|
||||
HgInterpOut = np.transpose(HgInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
# Interpolate Roughness
|
||||
RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
|
||||
method='nearest')
|
||||
|
||||
HgInterpOut[RoughInterp==0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceInterFiltDB.xyz',
|
||||
HgInterpOut, delimiter=" ")
|
||||
|
||||
|
||||
#%% Hg Interp Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
# Clay Lake Limits
|
||||
axes.set_xlim(466418, 515846)
|
||||
axes.set_ylim(5513437, 5545362)
|
||||
# Add Basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
|
||||
# Plot Hg
|
||||
axes.scatter(HgInterpOut[:, 0], HgInterpOut[:, 1], 5, HgInterpOut[:, 2],
|
||||
vmin=0, vmax=5000)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -24,11 +24,11 @@ import matplotlib.font_manager as fm
|
|||
|
||||
import os
|
||||
import sys
|
||||
sys.path.append('C:\\Users\\arey\\Repo\\Python_Baird\\Mustique\\adcptool')
|
||||
sys.path.append('C:/Users/arey/Repo/Python_Baird/Mustique')
|
||||
sys.path.append('C:/Users/arey/Repo/Python_Baird/Mustique/adcptool')
|
||||
from adcploader import *
|
||||
|
||||
|
||||
|
||||
# %% Read Model Log
|
||||
|
||||
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13509.101 NCC New Edinburgh Ottawa",
|
||||
|
|
@ -128,7 +128,7 @@ plt.show()
|
|||
|
||||
|
||||
# %% Read Model Results at point
|
||||
modelPlot = [1]
|
||||
modelPlot = [5]
|
||||
|
||||
ds_list = [None] * (max(modelPlot) + 1)
|
||||
dsT = [None] * (15)
|
||||
|
|
@ -167,23 +167,23 @@ readPoints = readPoints[~np.isnan(readPoints).any(axis=1)]
|
|||
|
||||
for m in modelPlot:
|
||||
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'Run01_2DOut.dfsu').as_posix())
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'NCC_2Dout.dfsu').as_posix())
|
||||
dfs2d_list[m] = dfsIN
|
||||
|
||||
# # Read Map
|
||||
ds_list[m] = dfs2d_list[m].read()
|
||||
|
||||
# Read in transects
|
||||
dsT[4] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T4.dfsu').as_posix())
|
||||
dsT[5] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T5.dfsu').as_posix())
|
||||
dsT[6] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T6.dfsu').as_posix())
|
||||
dsT[7] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T7.dfsu').as_posix())
|
||||
dsT[8] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T8.dfsu').as_posix(), time=90)
|
||||
dsT[9] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T9.dfsu').as_posix())
|
||||
dsT[10] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T10.dfsu').as_posix())
|
||||
dsT[11] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T11.dfsu').as_posix())
|
||||
dsT[13] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T13.dfsu').as_posix())
|
||||
dsT[14] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T14.dfsu').as_posix())
|
||||
# dsT[4] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T4.dfsu').as_posix())
|
||||
# dsT[5] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T5.dfsu').as_posix())
|
||||
# dsT[6] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T6.dfsu').as_posix())
|
||||
# dsT[7] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T7.dfsu').as_posix())
|
||||
# dsT[8] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T8.dfsu').as_posix(), time=90)
|
||||
# dsT[9] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T9.dfsu').as_posix())
|
||||
# dsT[10] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T10.dfsu').as_posix())
|
||||
# dsT[11] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T11.dfsu').as_posix())
|
||||
# dsT[13] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T13.dfsu').as_posix())
|
||||
# dsT[14] = mikeio.read(pl.Path(runLog['Run Location'][m], 'Run01_T14.dfsu').as_posix())
|
||||
|
||||
# ## Read specific points in 2D
|
||||
# # Find nearest elements
|
||||
|
|
@ -235,7 +235,7 @@ mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb
|
|||
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
fontprops = fm.FontProperties(size=12)
|
||||
modelPlot = [1]
|
||||
modelPlot = [5]
|
||||
# Set thinning factor
|
||||
thin_factor = 10
|
||||
|
||||
|
|
@ -246,12 +246,17 @@ for m in modelPlot:
|
|||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
|
||||
# Plot Velocity
|
||||
# Select salinity data at last time step
|
||||
# Plot all selected values at a given later and not nan
|
||||
axDHI = ds_list[m]['Current speed'][-1, :].plot(plot_type='contourf',
|
||||
|
||||
# Filter Out Land Values (no velocity)
|
||||
dhiFilt = ds_list[m]['Current speed'].sel(time="2018-01-01 02:30:00").values > 0
|
||||
dhiFiltIDX = [i for i, x in enumerate(dhiFilt) if x]
|
||||
|
||||
# Plot Velocity at last time step for elements greater than zero
|
||||
axDHI = ds_list[m]['Current speed'].sel(time="2018-01-01 02:30:00").isel(element=dhiFiltIDX).plot.contourf(
|
||||
show_mesh=False, cmap='inferno', ax=axes,
|
||||
vmin=vmin, vmax=vmax, label='Current Speed (m/s)')
|
||||
|
||||
|
||||
# Add ADCP data
|
||||
for adcp_to_plot in range(0, len(adcpDat)):
|
||||
# position of ensembles
|
||||
|
|
|
|||
|
|
@ -69,7 +69,14 @@ importPaths = ['//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Resear
|
|||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220530 WQ sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220709 WQ sampling/Great House',
|
||||
None,
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220709 WQ sampling/Old Queens Fort']
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220709 WQ sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220812 WQ sampling/Great House',
|
||||
None,
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220812 WQ sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20221115 WQ sampling/Great House',
|
||||
None,
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20221115 WQ sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20221127 WQ Sampling/Crane']
|
||||
|
||||
siteNames = ['Great House',
|
||||
'Greensleeves',
|
||||
|
|
@ -97,7 +104,14 @@ siteNames = ['Great House',
|
|||
'Old Queens Fort',
|
||||
'Great House',
|
||||
None,
|
||||
'Old Queens Fort']
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
None,
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
None,
|
||||
'Old Queens Fort',
|
||||
'Crane']
|
||||
|
||||
timeLabels= ['Before Construction',
|
||||
'Before Construction',
|
||||
|
|
@ -125,7 +139,14 @@ timeLabels= ['Before Construction',
|
|||
'May',
|
||||
'July',
|
||||
None,
|
||||
'July']
|
||||
'July',
|
||||
'August',
|
||||
None,
|
||||
'August',
|
||||
'November',
|
||||
None,
|
||||
'November',
|
||||
'November']
|
||||
|
||||
wave_bts_file = [
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
|
|
@ -154,11 +175,20 @@ wave_bts_file = [
|
|||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None]
|
||||
|
||||
# %% Read in site shapefile
|
||||
siteShp = gp.read_file('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/SitePolygons_Crane.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
|
||||
for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
||||
for s in [33]: #range(15, 27):
|
||||
## Define master import path
|
||||
importPath = importPaths[s]
|
||||
siteName = siteNames[s]
|
||||
|
|
@ -187,7 +217,7 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
Obs_dat =Obs_dat[Obs_dat['CH1:Temperature(degC)'].notna()]
|
||||
|
||||
# Set Time Zone for Sensor. Sometimes it is set to UTC, sometimes it is set to EST
|
||||
if s < 15:
|
||||
if s < 15 or s == 30 or s == 32 or s == 33:
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('America/Barbados').dt.tz_convert('UTC')
|
||||
else:
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('UTC')
|
||||
|
|
@ -203,7 +233,10 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
#convert GPS data to geodataframe
|
||||
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs="EPSG:4326"))
|
||||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))
|
||||
if s == 30 or s == 32 or s == 33:
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date1'].astype(str) + ' ' + GPS_gdf['Time1'].astype(str))
|
||||
else:
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))
|
||||
|
||||
|
||||
elif GPS_Type == 'xls':
|
||||
|
|
@ -223,11 +256,6 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
# Convert to UTM
|
||||
GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
|
||||
|
||||
#%% Read in site shapefile
|
||||
siteShp = gp.read_file('C:/Users/arey/files/Projects/West Coast/SitePolygons.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
|
||||
#%% Merge GPS to RBR
|
||||
# Process RBR into datetime
|
||||
if OBS_File is not None:
|
||||
|
|
@ -261,6 +289,12 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
GFS_Lon = -59.6419
|
||||
GFS_Lat = 13.1960
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[0, 1])
|
||||
elif siteName == 'Crane':
|
||||
axXlim = (234835, 235507)
|
||||
axYlim = (1449966, 1450580)
|
||||
GFS_Lon = -59.442
|
||||
GFS_Lat = 13.1075
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[3, 1])
|
||||
|
||||
|
||||
# Set min and max times using conductivity
|
||||
|
|
@ -295,6 +329,9 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
# Set times to download
|
||||
startdate = metDate - timedelta(hours=18)
|
||||
enddate = metDate + timedelta(hours=6)
|
||||
# startdate = metDate - timedelta(hours=36)
|
||||
# enddate = metDate + timedelta(hours=-12)
|
||||
|
||||
date_list = pd.date_range(startdate, enddate, freq='6H')
|
||||
|
||||
# Loop through dates
|
||||
|
|
@ -309,27 +346,36 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
|
||||
# Format the query parameters
|
||||
ncss = best_ds.subset()
|
||||
query = ncss.query()
|
||||
|
||||
ncss_precp = best_ds_precp.subset()
|
||||
query_precp = ncss_precp.query()
|
||||
|
||||
# Extract data from specific point
|
||||
query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
|
||||
query.accept('netcdf')
|
||||
query.variables(var[0], var[1], var[2], var[3])
|
||||
query.vertical_level(10)
|
||||
# Loop through variables and request
|
||||
data = dict()
|
||||
for v in var:
|
||||
# Setup query
|
||||
query = ncss.query()
|
||||
|
||||
data = ncss.get_data(query)
|
||||
data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')
|
||||
# Extract data from specific point
|
||||
query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
|
||||
query.accept('netcdf')
|
||||
|
||||
query.variables(v)
|
||||
|
||||
# IF there are multiple vertical levels, select the desired one
|
||||
if v == 'u-component_of_wind_height_above_ground' or v == 'v-component_of_wind_height_above_ground':
|
||||
query.vertical_level(10)
|
||||
|
||||
dataIN = ncss.get_data(query)
|
||||
data[v] = xr.open_dataset(NetCDF4DataStore(dataIN), drop_variables='height_above_ground4')
|
||||
|
||||
query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))
|
||||
query_precp.accept('netcdf')
|
||||
query_precp.variables(var_precp[0])
|
||||
|
||||
|
||||
data_precp = ncss_precp.get_data(query_precp)
|
||||
data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))
|
||||
dataIN = ncss_precp.get_data(query_precp)
|
||||
data_precp = dict()
|
||||
data_precp[var_precp[0]] = xr.open_dataset(NetCDF4DataStore(dataIN))
|
||||
|
||||
temp_3d = data[var[0]]
|
||||
gust_3d = data[var[1]]
|
||||
|
|
@ -338,11 +384,12 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
prep_3d = data_precp[var_precp[0]]
|
||||
|
||||
# Read the individual point (with units) and append to the list
|
||||
temp_1d.append(temp_3d.metpy.unit_array.squeeze())
|
||||
gust_1d.append(gust_3d.metpy.unit_array.squeeze())
|
||||
wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())
|
||||
wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())
|
||||
prep_1d.append(prep_3d.metpy.unit_array.squeeze())
|
||||
temp_1d.append(temp_3d.metpy.quantify().Temperature_surface.data.squeeze())
|
||||
gust_1d.append(gust_3d.metpy.quantify().Wind_speed_gust_surface.data.squeeze())
|
||||
wndu_1d.append(wndu_3d.metpy.quantify()['u-component_of_wind_height_above_ground'].data.squeeze())
|
||||
wndv_1d.append(wndv_3d.metpy.quantify()['v-component_of_wind_height_above_ground'].data.squeeze())
|
||||
prep_1d.append(prep_3d.metpy.quantify()['Total_precipitation_surface_6_Hour_Accumulation'].data.squeeze())
|
||||
|
||||
time_1d.append(find_time_var(temp_3d))
|
||||
|
||||
#%% Process Met Data
|
||||
|
|
@ -411,7 +458,7 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
|
||||
# paramMax = [1.0, 36.0, 34.0, 31.0, 20.0, 1.0]
|
||||
paramMin = [0.0, 32.0, 100, 26.0, 0, 0]
|
||||
paramMax = [1.0, 36.0, 130, 34.0, 75.0, 12000]
|
||||
paramMax = [6, 36.0, 130, 34.0, 150.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
|
|
@ -444,13 +491,13 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label='1 Minute Average', color='black',
|
||||
ax=axes[paramIDX], label='10 Second Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=6, mode='wrap')[0])
|
||||
np.greater_equal, order=8, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
|
|
@ -478,7 +525,7 @@ for s in [15, 16, 17, 18, 19, 20, 21, 23, 24, 26]: #range(15, 27):
|
|||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper left')
|
||||
axes[paramIDX].legend(loc='upper right')
|
||||
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
|
|
@ -664,8 +711,8 @@ plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]'
|
|||
for i in range(2, 3):
|
||||
summTable = None
|
||||
# plotIDXs = np.arange(i, 27, 3)
|
||||
plotIDXs = [2, 5, 8, 14, 17, 20, 23, 26]
|
||||
#plotIDXs = [0, 3, 6, 12, 15, 18, 21, 24]
|
||||
#plotIDXs = [2, 5, 8, 14, 17, 20, 23, 26, 29, 32]
|
||||
plotIDXs = [0, 3, 6, 12, 15, 18, 21, 24, 27, 30]
|
||||
|
||||
plotDates = []
|
||||
plotTable = np.empty([10, len(plotIDXs)])
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
EFDC_compare.ipynb
|
||||
|
|
@ -0,0 +1,424 @@
|
|||
#%%
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@author: AJMR
|
||||
|
||||
Script to compare EFDC results against observations
|
||||
"""
|
||||
#%% Import
|
||||
import os
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.dates as mdates
|
||||
import numpy as np
|
||||
import geopandas as gp
|
||||
gp.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'
|
||||
from shapely.ops import nearest_points
|
||||
from shapely.geometry import LineString
|
||||
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
from shapely.ops import nearest_points
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
|
||||
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
|
||||
from dfm_tools.get_nc_helpers import get_timesfromnc
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
from pathlib import Path
|
||||
import netCDF4 as nc
|
||||
|
||||
import xarray as xr
|
||||
import pytz
|
||||
import pathlib as pl
|
||||
#%% Function to find nearest point
|
||||
def get_nearest_values(row, other_gdf, point_column='geometry', value_column="geometry"):
|
||||
"""Find the nearest point and return the corresponding value from specified value column."""
|
||||
|
||||
# Create an union of the other GeoDataFrame's geometries:
|
||||
other_points = other_gdf["geometry"].unary_union
|
||||
|
||||
# Find the nearest points
|
||||
nearest_geoms = nearest_points(row[point_column], other_points)
|
||||
|
||||
# Get corresponding values from the other df
|
||||
nearest_data = other_gdf.loc[other_gdf["geometry"] == nearest_geoms[1]]
|
||||
|
||||
nearest_value = nearest_data[value_column].get_values()[0]
|
||||
|
||||
return nearest_value
|
||||
|
||||
#%% Load in Water Level Data
|
||||
# Gauge Locations from map
|
||||
# gdf_gaugeLoc = gp.read_file('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/NTC6.kml')
|
||||
gdf_gaugeLoc = gp.read_file('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/NTC6.kml')
|
||||
gdf_gaugeLocUSSP = gdf_gaugeLoc.to_crs("EPSG:32118")
|
||||
|
||||
# All in Feet NAVD88
|
||||
# df_wl_FFG = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
|
||||
df_wl_FFG = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
|
||||
sheet_name='Field_Facility_Gauge')
|
||||
df_wl_FFG.set_index('Date_time', inplace=True)
|
||||
df_wl_FFG.index = df_wl_FFG.index.tz_localize(
|
||||
pytz.timezone('America/New_York')).tz_convert(pytz.utc) #Convert to UTC
|
||||
|
||||
gdf_wl_FFG = gp.GeoDataFrame(df_wl_FFG,
|
||||
geometry=gp.points_from_xy(np.ones([len(df_wl_FFG), 1]) * gdf_gaugeLoc['geometry'].x[2],
|
||||
np.ones([len(df_wl_FFG), 1]) * gdf_gaugeLoc['geometry'].y[2]), crs="EPSG:4326")
|
||||
gdf_wl_FFG.geometry = gdf_wl_FFG.geometry.to_crs("EPSG:32118")
|
||||
|
||||
|
||||
df_wl_NGG1 = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
|
||||
sheet_name='National_Grid_Gauge')
|
||||
df_wl_NGG1.set_index('Date_time', inplace=True)
|
||||
df_wl_NGG1.index = df_wl_NGG1.index.tz_localize(
|
||||
pytz.timezone('America/New_York'), ambiguous=True).tz_convert(pytz.utc) #Convert to UTC
|
||||
|
||||
gdf_wl_NGG1 = gp.GeoDataFrame(df_wl_NGG1,
|
||||
geometry=gp.points_from_xy(np.ones([len(df_wl_NGG1), 1]) * gdf_gaugeLoc['geometry'].x[3],
|
||||
np.ones([len(df_wl_NGG1), 1]) * gdf_gaugeLoc['geometry'].y[3]), crs="EPSG:4326")
|
||||
gdf_wl_NGG1.geometry = gdf_wl_NGG1.geometry.to_crs("EPSG:32118")
|
||||
|
||||
|
||||
# Also includes temperature
|
||||
df_wl_NGG2 = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP2_NG_TideGauge_20160113/NCP2_NG_TideGauge_20160113.xlsx',
|
||||
sheet_name='Sheet1')
|
||||
gdf_wl_NGG2 = gp.GeoDataFrame(df_wl_NGG2,
|
||||
geometry=gp.points_from_xy(np.ones([len(df_wl_NGG2), 1]) * gdf_gaugeLoc['geometry'].x[3],
|
||||
np.ones([len(df_wl_NGG2), 1]) * gdf_gaugeLoc['geometry'].y[3]), crs="EPSG:4326")
|
||||
gdf_wl_NGG2.geometry = gdf_wl_NGG2.geometry.to_crs("EPSG:32118")
|
||||
|
||||
#%% Read in EFDC Data to dataframe
|
||||
# efdc6_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon6/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
# efdc7_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon7/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
# efdc8_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon8/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
# efdc9_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon9/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
|
||||
efdc12_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon12/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
|
||||
#%% Read in EFDC grid
|
||||
efdc_grid_df = pd.read_csv('//Ott-flavius/j/INPUT_FILES/GRID_FILES/NC_ER_lxly_20140129.inp',
|
||||
delim_whitespace=True, skiprows=4,
|
||||
names=['I', 'J', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
|
||||
efdc_grid_gdf = gp.GeoDataFrame(
|
||||
efdc_grid_df, geometry=gp.points_from_xy(efdc_grid_df.X, efdc_grid_df.Y), crs="EPSG:32118")
|
||||
#%% Merge EFDC outputs with grid locations and add datetimes
|
||||
efdc6_merged = pd.merge(efdc6_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
|
||||
right_on = ['I','J']).drop(columns=[
|
||||
' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
efdc6_merged['date'] = pd.to_datetime([datetime(2012, 6, 1, 00, 00, 00) +
|
||||
timedelta(hours=h) for h in efdc6_merged['ST_hr']])
|
||||
efdc6_merged.set_index('date', inplace=True)
|
||||
efdc6_merged.index = efdc6_merged.index.tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
|
||||
del efdc6_df
|
||||
|
||||
|
||||
efdc7_merged = pd.merge(efdc7_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
|
||||
right_on = ['I','J']).drop(columns=[
|
||||
' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
efdc7_merged['date'] = pd.to_datetime([datetime(2012, 7, 1, 00, 00, 00) +
|
||||
timedelta(hours=h) for h in efdc7_merged['ST_hr']])
|
||||
efdc7_merged.set_index('date', inplace=True)
|
||||
efdc7_merged.index = efdc7_merged.index.tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
|
||||
del efdc7_df
|
||||
|
||||
|
||||
efdc8_merged = pd.merge(efdc8_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
|
||||
right_on = ['I','J']).drop(columns=[
|
||||
' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
efdc8_merged['date'] = pd.to_datetime([datetime(2012, 8, 1, 00, 00, 00) +
|
||||
timedelta(hours=h) for h in efdc8_merged['ST_hr']])
|
||||
efdc8_merged.set_index('date', inplace=True)
|
||||
efdc8_merged.index = efdc8_merged.index.tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
|
||||
del efdc8_df
|
||||
|
||||
|
||||
efdc9_merged = pd.merge(efdc9_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
|
||||
right_on = ['I','J']).drop(columns=[
|
||||
' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
efdc9_merged['date'] = pd.to_datetime([datetime(2012, 9, 1, 00, 00, 00) +
|
||||
timedelta(hours=h) for h in efdc9_merged['ST_hr']])
|
||||
efdc9_merged.set_index('date', inplace=True)
|
||||
efdc9_merged.index = efdc9_merged.index.tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
|
||||
del efdc9_df
|
||||
#%%
|
||||
efdc12_merged = pd.merge(efdc12_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
|
||||
right_on = ['I','J']).drop(columns=[
|
||||
' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
|
||||
efdc12_merged['date'] = pd.to_datetime([datetime(2012, 12, 1, 00, 00, 00) +
|
||||
timedelta(hours=h) for h in efdc12_merged['ST_hr']])
|
||||
efdc12_merged.set_index('date', inplace=True)
|
||||
efdc12_merged.index = efdc12_merged.index.tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
|
||||
del efdc12_df
|
||||
#%% Merge EFDC dataframes and convert to geodataframe
|
||||
efdc_merged = pd.concat([efdc6_merged, efdc7_merged, efdc8_merged, efdc9_merged])
|
||||
del efdc6_merged
|
||||
del efdc7_merged
|
||||
del efdc8_merged
|
||||
del efdc9_merged
|
||||
|
||||
efdc_merged_gdf = gp.GeoDataFrame(
|
||||
efdc_merged, geometry=efdc_merged['geometry'], crs="EPSG:32118")
|
||||
del efdc_merged
|
||||
#%%
|
||||
efdc_merged = efdc12_merged
|
||||
|
||||
efdc_merged_gdf = gp.GeoDataFrame(
|
||||
efdc_merged, geometry=efdc_merged['geometry'], crs="EPSG:32118")
|
||||
del efdc_merged
|
||||
|
||||
#%% Find nearest grid point to station FFG
|
||||
nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(efdc_grid_gdf.geometry))
|
||||
station_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
|
||||
#%% Find nearest grid point to station BGG
|
||||
nearest_geoms = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(efdc_grid_gdf.geometry))
|
||||
stationNGG_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
|
||||
|
||||
# efdc_grid_gdf.loc[efdc_grid_gdf['geometry']== nearest_geoms[1]].value
|
||||
#%% Save EFDC as Parquet for faster importing
|
||||
efdc_merged_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/efdc_merged_Dec.parquet',
|
||||
index=True, compression='gzip')
|
||||
|
||||
station_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/station_gdf_Dec.parquet',
|
||||
index=True, compression='gzip')
|
||||
|
||||
stationNGG_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/stationNGG_gdf_Dec.parquet',
|
||||
index=True, compression='gzip')
|
||||
#%% Read in EFDC as Paraquet
|
||||
stationNGG_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/stationNGG_gdf.parquet')
|
||||
station_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/station_gdf.parquet')
|
||||
|
||||
efdc_merged_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/efdc_merged.parquet')
|
||||
#%% Read Model Log
|
||||
runLog = pd.read_excel('C:/Users/arey/files\Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
#%% Import Delft3D results
|
||||
modelPlot = [31]
|
||||
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
data_frommap_wl = [None] * (max(modelPlot)+1)
|
||||
facex = [None] * (max(modelPlot)+1)
|
||||
facey = [None] * (max(modelPlot)+1)
|
||||
|
||||
nearest_IDX = list(range(100))
|
||||
nearest_IDX_NGG = list(range(100))
|
||||
dsloc = list(range(100))
|
||||
dsloc_NGG = list(range(100))
|
||||
|
||||
# Find nearest point
|
||||
for i in modelPlot:
|
||||
dataPath = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
|
||||
|
||||
file_nc_map = dataPath.as_posix()
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
tStep = len(tSteps)-1
|
||||
|
||||
ugrid_all[i] = get_netdata(file_nc=file_nc_map)#,multipart=False)
|
||||
data_frommap_wl[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_s1', timestep=tStep)
|
||||
facex[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_x')
|
||||
facey[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_y')
|
||||
|
||||
# Find matching Delft3D index
|
||||
s = gp.GeoSeries(map(Point, zip(facex[i], facey[i])))
|
||||
nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(s))
|
||||
nearest_IDX[i] = np.where(s==nearest_geoms[1])
|
||||
|
||||
nearest_geoms = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(s))
|
||||
nearest_IDX[i] = np.where(s==nearest_geoms[1])
|
||||
|
||||
#%% Read in time series
|
||||
modelPlot = [31]
|
||||
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
data_frommap_wl = [None] * (max(modelPlot)+1)
|
||||
facex = [None] * (max(modelPlot)+1)
|
||||
facey = [None] * (max(modelPlot)+1)
|
||||
|
||||
nearest_IDX = list(range(100))
|
||||
nearest_IDX_NGG = list(range(100))
|
||||
dsloc = list(range(100))
|
||||
dsloc_NGG = list(range(100))
|
||||
|
||||
# Note, this uses the standard netcdf lib + xarray
|
||||
for i in modelPlot:
|
||||
dataPath = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
|
||||
|
||||
file_nc_map = dataPath.as_posix()
|
||||
|
||||
ds = xr.open_dataset(file_nc_map)
|
||||
|
||||
# Find nearest point
|
||||
ds_x = ds['mesh2d_face_x']
|
||||
ds_y = ds['mesh2d_face_y']
|
||||
|
||||
s = gp.GeoSeries(map(Point, zip(ds_x, ds_y)))
|
||||
nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(s))
|
||||
nearest_IDX[i] = np.where(s==nearest_geoms[1])
|
||||
|
||||
nearest_geoms_NGG = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(s))
|
||||
nearest_IDX_NGG[i] = np.where(s==nearest_geoms_NGG[1])
|
||||
|
||||
dsloc[i] = ds['mesh2d_s1'].sel(mesh2d_nFaces=nearest_IDX[i][0])
|
||||
dsloc_NGG[i] = ds['mesh2d_s1'].sel(mesh2d_nFaces=nearest_IDX_NGG[i][0])
|
||||
#%% Convert D3D Timezone
|
||||
modelPlot = [31]
|
||||
|
||||
df_wl_D3D = list(range(100))
|
||||
df_wl_D3D_NGG = list(range(100))
|
||||
|
||||
d3dTzones = list(range(100))
|
||||
d3dTzones[25] = 'EST'
|
||||
d3dTzones[26] = 'EST'
|
||||
d3dTzones[31] = 'EST'
|
||||
|
||||
for i in modelPlot:
|
||||
df_wl_D3D[i] = dsloc[i].to_dataframe()
|
||||
df_wl_D3D[i].index = df_wl_D3D[i].index.droplevel(-1)
|
||||
|
||||
df_wl_D3D[i].index = df_wl_D3D[i].index.tz_localize(
|
||||
pytz.timezone(d3dTzones[i])).tz_convert(pytz.utc) #Convert to UTC
|
||||
|
||||
df_wl_D3D_NGG[i] = dsloc_NGG[i].to_dataframe()
|
||||
df_wl_D3D_NGG[i].index = df_wl_D3D_NGG[i].index.droplevel(-1)
|
||||
|
||||
df_wl_D3D_NGG[i].index = df_wl_D3D_NGG[i].index.tz_localize(
|
||||
pytz.timezone(d3dTzones[i])).tz_convert(pytz.utc) #Convert to UTC
|
||||
#%% Plot water levels at nearest point
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 5))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
for week in range(0, 2):
|
||||
weekOffset = 0
|
||||
axes[week+weekOffset].plot(station_gdf.index, station_gdf['WSE_m'], label='EFDC Model')
|
||||
|
||||
axes[week+weekOffset].plot(gdf_wl_FFG.index+timedelta(hours=0),
|
||||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048,
|
||||
label='Field Facility Gauge', color='k')
|
||||
|
||||
for i in modelPlot:
|
||||
axes[week+weekOffset].plot(df_wl_D3D[i].index, df_wl_D3D[i].mesh2d_s1, label=runLog['Run'][i])
|
||||
|
||||
axes[week+weekOffset].set_xlim(datetime(2012, 12, 15, 0, 0, 0) + timedelta(days=week*7),
|
||||
datetime(2012, 12, 15+7, 0, 0, 0) + timedelta(days=week*7))
|
||||
axes[week+weekOffset].set_ylim(-1, 2.5)
|
||||
|
||||
|
||||
axes[week+weekOffset].set_ylabel('Water Surface Elevation [m]')
|
||||
|
||||
axes[0].legend(loc='upper left')
|
||||
axes[0].title.set_text('Field Facility Gauge')
|
||||
|
||||
plt.show()
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/DecTS_FFG.png',
|
||||
bbox_inches='tight')
|
||||
#%% Plot water levels at National Grid
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 5))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
for week in range(0, 2):
|
||||
weekOffset = 0
|
||||
axes[week+weekOffset].plot(stationNGG_gdf.index, stationNGG_gdf['WSE_m'], label='EFDC Model')
|
||||
|
||||
axes[week+weekOffset].plot(gdf_wl_NGG1.index+timedelta(hours=0),
|
||||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048,
|
||||
label='National Grid Gauge', color='k')
|
||||
|
||||
for i in modelPlot:
|
||||
axes[week+weekOffset].plot(df_wl_D3D_NGG[i].index, df_wl_D3D_NGG[i].mesh2d_s1,
|
||||
label=runLog['Run'][i])
|
||||
|
||||
axes[week+weekOffset].set_xlim(datetime(2012, 12, 15, 0, 0, 0) + timedelta(days=week*7),
|
||||
datetime(2012, 12, 15+7, 0, 0, 0) + timedelta(days=week*7))
|
||||
axes[week+weekOffset].set_ylim(-1, 2.5)
|
||||
|
||||
axes[week+weekOffset].set_ylabel('Water Surface Elevation [m]')
|
||||
|
||||
axes[0].legend(loc='upper left')
|
||||
axes[0].title.set_text('National Grid Gauge')
|
||||
|
||||
plt.show()
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/DecTS_NGG.png',
|
||||
bbox_inches='tight')
|
||||
#%% Plot scatters
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
interpTimes = pd.date_range("2012-06-02T00:00:00", "2012-10-01T00:00:00", freq="60min")
|
||||
interpTimes = pd.date_range("2012-07-07T00:00:00", "2012-07-27T00:00:00", freq="60min")
|
||||
|
||||
for i in modelPlot:
|
||||
axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
|
||||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048),
|
||||
np.interp(interpTimes, df_wl_D3D[i].index,
|
||||
df_wl_D3D[i].mesh2d_s1),
|
||||
label=runLog['Run'][i])
|
||||
|
||||
axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
|
||||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048),
|
||||
np.interp(interpTimes, station_gdf.index,
|
||||
station_gdf['WSE_m']),
|
||||
label='EFDC')
|
||||
|
||||
linepts = np.array([-1.0, 1.5])
|
||||
|
||||
plt.plot(linepts, linepts, color='k')
|
||||
|
||||
axes.set_xlim(-1.0, 1.5)
|
||||
axes.set_ylim(-1.0, 1.5)
|
||||
axes.set_aspect('equal')
|
||||
axes.set_title('Field Facility Gauge')
|
||||
axes.legend(loc='upper left')
|
||||
axes.set_ylabel('Model Water Surface Elevation [m]')
|
||||
axes.set_xlabel('Observed Water Surface Elevation [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/FFG_ScatterUpdated.pdf',
|
||||
bbox_inches='tight')
|
||||
|
||||
#%% Plot scatters NGG
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
interpTimes = pd.date_range("2012-06-02T00:00:00", "2012-10-01T00:00:00", freq="60min")
|
||||
interpTimes = pd.date_range("2012-07-07T00:00:00", "2012-07-27T00:00:00", freq="60min")
|
||||
|
||||
for i in modelPlot:
|
||||
axes.scatter(np.interp(interpTimes, gdf_wl_NGG1.index,
|
||||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048),
|
||||
np.interp(interpTimes, df_wl_D3D_NGG[i].index,
|
||||
df_wl_D3D_NGG[i].mesh2d_s1),
|
||||
label=runLog['Run'][i])
|
||||
|
||||
axes.scatter(np.interp(interpTimes, gdf_wl_NGG1.index,
|
||||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048),
|
||||
np.interp(interpTimes, stationNGG_gdf.index,
|
||||
stationNGG_gdf['WSE_m']),
|
||||
label='EFDC')
|
||||
|
||||
linepts = np.array([-1.0, 1.5])
|
||||
|
||||
plt.plot(linepts, linepts, color='k')
|
||||
|
||||
axes.set_xlim(-1.0, 1.5)
|
||||
axes.set_ylim(-1.0, 1.5)
|
||||
axes.set_aspect('equal')
|
||||
axes.set_title('National Grid Gauge')
|
||||
axes.legend(loc='upper left')
|
||||
axes.set_ylabel('Model Water Surface Elevation [m]')
|
||||
axes.set_xlabel('Observed Water Surface Elevation [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/NGG_ScatterUpdated.pdf',
|
||||
bbox_inches='tight')
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
# Default ignored files
|
||||
/shelf/
|
||||
/workspace.xml
|
||||
# Editor-based HTTP Client requests
|
||||
/httpRequests/
|
||||
# Datasource local storage ignored files
|
||||
/dataSources/
|
||||
/dataSources.local.xml
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<profile version="1.0">
|
||||
<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
|
||||
<option name="ignoredIdentifiers">
|
||||
<list>
|
||||
<option value="bool.*" />
|
||||
<option value="geopandas.io.file" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (rasterProcess)" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/rasterProcess.iml" filepath="$PROJECT_DIR$/.idea/rasterProcess.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="inheritedJdk" />
|
||||
<orderEntry type="sourceFolder" forTests="false" />
|
||||
</component>
|
||||
</module>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
# Default ignored files
|
||||
/shelf/
|
||||
/workspace.xml
|
||||
# Editor-based HTTP Client requests
|
||||
/httpRequests/
|
||||
# Datasource local storage ignored files
|
||||
/dataSources/
|
||||
/dataSources.local.xml
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<profile version="1.0">
|
||||
<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
|
||||
<option name="ignoredIdentifiers">
|
||||
<list>
|
||||
<option value="bool.*" />
|
||||
<option value="geopandas.io.file" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
|
||||
</component>
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (rasterProcess2)" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/rasterProcess2.iml" filepath="$PROJECT_DIR$/.idea/rasterProcess2.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="PySciProjectComponent">
|
||||
<option name="PY_SCI_VIEW" value="true" />
|
||||
<option name="PY_SCI_VIEW_SUGGESTED" value="true" />
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="inheritedJdk" />
|
||||
<orderEntry type="sourceFolder" forTests="false" />
|
||||
</component>
|
||||
<component name="PyDocumentationSettings">
|
||||
<option name="renderExternalDocumentation" value="true" />
|
||||
</component>
|
||||
</module>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,167 @@
|
|||
#%% Read and Process NWS Alerts
|
||||
# Created on: 2023-01-17
|
||||
|
||||
# Import Modules
|
||||
import os
|
||||
import geopandas as gp
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
|
||||
import nwswx
|
||||
import requests
|
||||
|
||||
from shapely.geometry import Polygon
|
||||
import shapely.wkt
|
||||
#%% Setup NWS ID
|
||||
nws = nwswx.WxAPI('api@alexanderrey.ca')
|
||||
|
||||
#%% Read NWS Alerts
|
||||
# All alerts
|
||||
# alertsIN = nws.alerts(return_format=nwswx.formats.JSONLD)
|
||||
|
||||
# Active alerts
|
||||
alertsIN = nws.active_alerts(return_format=nwswx.formats.JSONLD)
|
||||
|
||||
nws_alerts = alertsIN['@graph']
|
||||
|
||||
# If more than one page of alerts, loop through an append
|
||||
while 'pagination' in alertsIN:
|
||||
# Get the next page of alerts
|
||||
result = requests.get(alertsIN['pagination']['next'])
|
||||
|
||||
# Convert to JSON
|
||||
alertsIN = result.json()
|
||||
nws_alerts.extend(alertsIN['features'])
|
||||
|
||||
print('AWS Alerts: ', len(nws_alerts))
|
||||
|
||||
#%% Create NWS Alerts DataFrame
|
||||
nws_alert_df = pd.DataFrame.from_records(nws_alerts)
|
||||
|
||||
#%% Read in NWS Polygon shapefiles as GeoDataFrame
|
||||
z_shp = gp.read_file("C:/Users/arey/Downloads/z_13se22/z_13se22.shp")
|
||||
fz_shp = gp.read_file("C:/Users/arey/Downloads/fz13se22/fz13se22.shp")
|
||||
oz_shp = gp.read_file("C:/Users/arey/Downloads/oz22mr22/oz22mr22.shp")
|
||||
mz_shp = gp.read_file("C:/Users/arey/Downloads/mz13se22/mz13se22.shp")
|
||||
w_shp = gp.read_file("C:/Users/arey/Downloads/w_22mr22/w_22mr22.shp")
|
||||
# ba_shp = gp.read_file("C:/Users/arey/Downloads/ba12my15/ba12my15.shp")
|
||||
# hs_shp = gp.read_file("C:/Users/arey/Downloads/hs08mr23/hs08mr23.shp")
|
||||
# s_shp = gp.read_file("C:/Users/arey/Downloads/s_22mr22/s_22mr22.shp")
|
||||
|
||||
|
||||
#%% Match NWS Alerts to NWS Polygons
|
||||
|
||||
# Create empty list
|
||||
nws_alert_polygons = []
|
||||
nws_alert_data = []
|
||||
nws_alert_debug = []
|
||||
|
||||
# Loop through each NWS Alert and match to NWS Polygon using UGC
|
||||
for i in range(len(nws_alert_df)):
|
||||
|
||||
# If alert contains geometry as a string
|
||||
if type(nws_alert_df['geometry'][i]) == str:
|
||||
nws_alert_polygons.append(shapely.wkt.loads(nws_alert_df['geometry'][i]))
|
||||
nws_alert_data.append(nws_alert_df.iloc[i, :])
|
||||
nws_alert_debug.append(i)
|
||||
|
||||
# Geometry Array
|
||||
elif type(nws_alert_df['geometry'][i]) == gp.array.GeometryArray:
|
||||
nws_alert_polygons.append(nws_alert_df['geometry'][i][0])
|
||||
nws_alert_data.append(nws_alert_df.iloc[i, :])
|
||||
nws_alert_debug.append(i)
|
||||
else:
|
||||
# Get the UGC code for the alert
|
||||
UGCs = nws_alert_df['geocode'][i]['UGC']
|
||||
|
||||
# Loop through each UGC code and find matching polygon
|
||||
for ugc in UGCs:
|
||||
# Zone based warning
|
||||
if (ugc[2] == 'Z') & (ugc[0:2] in z_shp['STATE'].unique()):
|
||||
alert_poly = z_shp[(z_shp['STATE'] == ugc[0:2]) & (z_shp['ZONE'] == ugc[3:6])].geometry
|
||||
# Fire Weather Zone based warning
|
||||
elif (ugc[2] == 'C') & (ugc[0:2] in z_shp['STATE'].unique()):
|
||||
alert_poly = fz_shp[(fz_shp['STATE'] == ugc[0:2]) & (fz_shp['ZONE'] == ugc[3:6])].geometry
|
||||
# Marine Zone based warning
|
||||
elif ugc in mz_shp['ID'].unique():
|
||||
alert_poly = mz_shp[mz_shp['ID'] == ugc].geometry
|
||||
# Offshore Zone based warning
|
||||
elif ugc in oz_shp['ID'].unique():
|
||||
alert_poly = oz_shp[oz_shp['ID'] == ugc].geometry
|
||||
# elif ugc[0] == 'W':
|
||||
# nws_alert_df['geometry'][i] = w_shp[w_shp['UGC'] == ugc].geometry.values
|
||||
|
||||
else:
|
||||
print('No polygon found for UGC: ', ugc)
|
||||
print('Alert: ', nws_alert_df['headline'][i])
|
||||
continue
|
||||
|
||||
# If no matches found
|
||||
if len(alert_poly) == 0:
|
||||
print('No polygon found for UGC: ', ugc)
|
||||
print('Alert: ', nws_alert_df['headline'][i])
|
||||
continue
|
||||
|
||||
|
||||
# Append polygon to list
|
||||
nws_alert_polygons.append(alert_poly.values[0])
|
||||
nws_alert_data.append(nws_alert_df.iloc[i, :])
|
||||
nws_alert_debug.append(i)
|
||||
|
||||
#%% Create GeoDataFrame of alert polygons
|
||||
nws_alert_gdf = gp.GeoDataFrame(nws_alert_data, geometry=nws_alert_polygons)
|
||||
nws_alert_gdf.reset_index(inplace=True)
|
||||
|
||||
#%% Create grid of points
|
||||
xs = np.arange(-127, -65, 0.075)
|
||||
ys = np.arange(24, 50, 0.075)
|
||||
|
||||
lons, lats = np.meshgrid(xs, ys)
|
||||
|
||||
# Create GeoSeries of Points
|
||||
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))]
|
||||
|
||||
# Loop through NWS Alerts
|
||||
for alertIDX in nws_alert_gdf.index:
|
||||
alertString = ''
|
||||
# Find h3 index for point
|
||||
|
||||
# Identify which points are within an alert polygon
|
||||
alertPoints = np.where(nws_alert_gdf.loc[alertIDX, 'geometry'].contains(gridPointsSeries))[0]
|
||||
|
||||
# Loop through points and add alert data
|
||||
for alertPoint in alertPoints:
|
||||
# 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'] + ']'
|
||||
|
||||
alertData_1D[alertPoint] = alertString
|
||||
|
||||
print(alertIDX)
|
||||
|
||||
#%% Create xarray of alert data
|
||||
# Convert list to numpy
|
||||
list_alerts_1d_np = np.array(alertData_1D)
|
||||
# Reshape back to 2d array
|
||||
list_alerts_2d_np = list_alerts_1d_np.reshape(lons.shape)
|
||||
|
||||
# Convert to xarray
|
||||
grid_alerts_xr = xr.DataArray(data=list_alerts_2d_np,
|
||||
dims=["x", "y"],
|
||||
coords={"lon": (("x", "y"), lons),
|
||||
"lat": (("x", "y"), lats)},
|
||||
)
|
||||
|
||||
# Save as netcdf
|
||||
grid_alerts_xr.to_netcdf('grid_alerts_contains3.nc')
|
||||
Loading…
Reference in New Issue