May 8 commit
This commit is contained in:
Alexander Rey
parent
15c7820c1c
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#%% Plotting EWR Flume Tests
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# Alexander Rey, 2022
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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 matplotlib.colors as mcolors
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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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# Lowess interpolation
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import statsmodels.api as sm
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import pathlib as pl
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import datetime
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#%% Read in centerline shapefile
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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')
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river_centerlineExploded = river_centerline.explode(ignore_index=True)
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river_centerlineExploded.reset_index(inplace=True)
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tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
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# Put the sub-line coordinates into a list of sublists
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outcoords = [list(i.coords) for i in tempMulti]
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# Flatten the list of sublists and use it to make a new line
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river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
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river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
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river_centerline_merge_gpd2 =\
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gp.GeoDataFrame(geometry=gp.points_from_xy(
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river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
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# Add distance along centerline
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river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
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river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
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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 Observations from database
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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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# Add months
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obs_IN['Month'] = obs_IN['DateTime'].dt.month
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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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# Adjust Units
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obs_gdf.loc[obs_gdf.Unit == 'NG/G', 'Sample_NG/G'] = obs_gdf.Samplevalue
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obs_gdf.loc[obs_gdf.Unit == 'ng/g', 'Sample_NG/G'] = obs_gdf.Samplevalue
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obs_gdf.loc[obs_gdf.Unit == 'UG/G', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'ug/g', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'MG/KG', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'mg/kg', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'NG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue
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obs_gdf.loc[obs_gdf.Unit == 'ng/L', 'Sample_NG/L'] = obs_gdf.Samplevalue
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obs_gdf.loc[obs_gdf.Unit == 'UG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'ug/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000
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obs_gdf.loc[obs_gdf.Unit == 'MG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000000
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obs_gdf.loc[obs_gdf.Unit == 'mg/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000000
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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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# Sediment Hg GeoDataFrame for surface 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_gdf.TopDepth_x < 5) | (obs_gdf.TopDepth_x.isna())))
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obs_HgSurfaceSed = obs_gdf.loc[obsMask, :]
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# obs_HgSurfaceSed.to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgSurfaceSed.geojson', driver="GeoJSON")
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obs_HgSurfaceSed.loc[:, ['Sample_NG/G', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgSurfaceSed2.shp')
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# Group sediment core data to take average/min/max
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obs_HgSurfaceSed_Avg = obs_HgSurfaceSed.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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# Flatten Multiindex
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obs_HgSurfaceSed_Avg.columns = ["_".join(a) for a in obs_HgSurfaceSed_Avg.columns.to_flat_index()]
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# Rename for shapefile
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obs_HgSurfaceSed_Avg.rename(columns={"Sample_NG/G_mean": "Mean_NG/G",
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"Sample_NG/G_max": "Max_NG/G",
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"Sample_NG/G_min": "Min_NG/G"}, inplace=True)
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# Convert to geodataframe and save
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obs_HgSurfaceSed_Avg_gdf = gp.GeoDataFrame(obs_HgSurfaceSed_Avg, geometry=gp.points_from_xy(
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obs_HgSurfaceSed_Avg.loc[:, 'Longitude_mean'], obs_HgSurfaceSed_Avg.loc[:, 'Latitude_mean'],
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crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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obs_HgSurfaceSed_Avg_gdf.loc[:, ['Mean_NG/G', 'Max_NG/G', 'Min_NG/G', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgSurfaceSed_Avg.shp')
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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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# Save
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# obs_HgWater.loc[:, ['Sample_NG/L', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgWater.shp')
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# Sediment surface 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_gdf.TopDepth_x < 5) | (obs_gdf.TopDepth_x.isna())))
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obs_MeHgSed = obs_gdf.loc[obsMask, :]
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# Save
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# obs_MeHgSed.loc[:, ['Sample_NG/G', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_MeHgSed.shp')
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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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# Flatten Multiindex
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obs_MeHgSed_Avg.columns = ["_".join(a) for a in obs_MeHgSed_Avg.columns.to_flat_index()]
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# Rename for shapefile
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obs_MeHgSed_Avg.rename(columns={"Sample_NG/G_mean": "Mean_NG/G",
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"Sample_NG/G_max": "Max_NG/G",
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"Sample_NG/G_min": "Min_NG/G"}, inplace=True)
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# Create new GeoDataFrame
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obs_MeHgSed_Avg_gdf = gp.GeoDataFrame(obs_MeHgSed_Avg, geometry=gp.points_from_xy(
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obs_MeHgSed_Avg.loc[:, 'Longitude_mean'], obs_MeHgSed_Avg.loc[:, 'Latitude_mean'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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obs_MeHgSed_Avg_gdf.loc[:, ['Mean_NG/G', 'Max_NG/G', 'Min_NG/G', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_MeHgSed_Avg.shp')
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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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# Save
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obs_MeHgWater.loc[:, ['Sample_NG/L', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_MeHgWater.shp')
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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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#%% Plot River Profile
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name = "Set1"
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cmap = cm.get_cmap(name)
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colors = cmap.colors
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# Create a HSV colormap sifted by 180 degrees
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# create the "hsv" colormap
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hsv = plt.get_cmap('hsv')
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# shift the colormap so that red is in the middle
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shifted_hsv = mcolors.LinearSegmentedColormap.from_list('shifted_hsv',
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np.roll(hsv(np.linspace(0.0, 1.0, 256)), 128, axis=0))
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interp_xvals = np.linspace(0, 100000, num=1000)
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# Bin averaging function
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def average_in_bins(df, bin_width):
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binned_df = pd.cut(df.index, bins=range(0, int(df.index.max() + bin_width), bin_width), right=False)
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return df.groupby(binned_df).mean()
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fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(8, 12), sharex=True)
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axes[0].set_prop_cycle(color=colors)
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axes[1].set_prop_cycle(color=colors)
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axes[2].set_prop_cycle(color=colors)
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axes[3].set_prop_cycle(color=colors)
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# Plot surface Hg
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# Plot using a different color for each month
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obs_HgSurfaceSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[0], c='Month',
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label='All Samples', vmin=1, vmax=12, cmap=shifted_hsv)
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# obs_HgSurfaceSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[0], color='grey', label='All Samples')
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# obs_HgSurfaceSed_Avg_gdf.plot.scatter('RiverKM_mean', 'Mean_NG/G', ax=axes[0], color='magenta', label='Site Averaged')
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# obs_HgSurfaceSed_Index = obs_HgSurfaceSed.copy()
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# obs_HgSurfaceSed_Index['RiverKM2'] = obs_HgSurfaceSed['RiverKM']
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# obs_HgSurfaceSed_Index = obs_HgSurfaceSed_Index.set_index('RiverKM')
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#
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# binwidths = [10, 100, 1000, 10000]
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# for binWidthIDX, binWidth in enumerate(binwidths):
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# average_in_bins(obs_HgSurfaceSed_Index, binWidth).dropna(subset=['Sample_NG/G']).\
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# plot.line('RiverKM2', 'Sample_NG/G', ax=axes[0],
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# label=str(binWidth) + ' m binned Samples')
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# Find and plot Lowess regression for each season
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SeasonMonths = [[12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]
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SeasonNames = ['Winter', 'Spring', 'Summer', 'Fall']
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SeasonColors = ['tab:blue', 'tab:green', 'darkred', 'tab:orange']
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for seasonIDX, season in enumerate(SeasonMonths):
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# Skip Winter
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if seasonIDX == 0:
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continue
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seasonMask = obs_HgSurfaceSed['DateTime'].dt.month.isin(season)
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lowess = sm.nonparametric.lowess(obs_HgSurfaceSed.loc[seasonMask, 'Sample_NG/G'],
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obs_HgSurfaceSed.loc[seasonMask, 'RiverKM'],
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frac=0.5, xvals=interp_xvals, it=2)
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axes[0].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX],
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linewidth=4, color=SeasonColors[seasonIDX])
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# lowess = sm.nonparametric.lowess(obs_HgSurfaceSed['Sample_NG/G'], obs_HgSurfaceSed['RiverKM'],
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# frac=0.3, xvals=interp_xvals, it=2)
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# axes[0].plot(interp_xvals, lowess, label='Lowess regression', linewidth=4, color='black')
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axes[0].set_ylabel('Surface Sediment Total Hg [ng/g]')
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axes[0].set_ylim([0, 20000])
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axes[0].set_title('Surface Sediment Total Hg')
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axes[0].legend()
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obs_MeHgSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[1], c='Month',
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label='All Samples', vmin=1, vmax=12, cmap=shifted_hsv)
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# obs_MeHgSed_Avg.plot.scatter('RiverKM_mean', 'Mean_NG/G', ax=axes[1], color='magenta', label='Site Averaged')
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axes[1].set_ylabel('Surface Sediment Total MeHg [ng/g]')
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# obs_MeHgSed_Index = obs_MeHgSed.copy()
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# obs_MeHgSed_Index['RiverKM2'] = obs_MeHgSed_Index['RiverKM']
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# obs_MeHgSed_Index = obs_MeHgSed_Index.set_index('RiverKM')
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#
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# binwidths = [10, 100, 1000, 10000]
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# for binWidthIDX, binWidth in enumerate(binwidths):
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# average_in_bins(obs_MeHgSed_Index, binWidth).dropna(subset=['Sample_NG/G']).\
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# plot.line('RiverKM2', 'Sample_NG/G', ax=axes[1],
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# label=str(binWidth) + ' m binned Samples')
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# Find and plot Lowess regression
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# lowess = sm.nonparametric.lowess(obs_MeHgSed['Sample_NG/G'], obs_MeHgSed['RiverKM'],
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# frac=0.2, xvals=interp_xvals, it=2)
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# axes[1].plot(interp_xvals, lowess, label='Lowess regression', linewidth=4)
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# Find and plot Lowess regression for each season
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for seasonIDX, season in enumerate(SeasonMonths):
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# Skip Winter
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if seasonIDX == 0:
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continue
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seasonMask = obs_MeHgSed['DateTime'].dt.month.isin(season)
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lowess = sm.nonparametric.lowess(obs_MeHgSed.loc[seasonMask, 'Sample_NG/G'],
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obs_MeHgSed.loc[seasonMask, 'RiverKM'],
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frac=0.5, xvals=interp_xvals, it=2)
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axes[1].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX],
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linewidth=4, color=SeasonColors[seasonIDX])
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# axes[0].set_ylim([0, 20000])
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axes[1].set_title('Surface Sediment Total MeHg')
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axes[1].legend()
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obs_HgWater.plot.scatter('RiverKM', 'Sample_NG/L', ax=axes[2], c='Month',
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label='All Samples', vmin=1, vmax=12,
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cmap=shifted_hsv)
|
||||
# Find and plot Lowess regression
|
||||
# lowess = sm.nonparametric.lowess(obs_HgWater['Sample_NG/L'], obs_HgWater['RiverKM'],
|
||||
# frac=0.2, xvals=interp_xvals, it=2)
|
||||
# axes[2].plot(interp_xvals, lowess, label='Lowess regression', linewidth=4)
|
||||
# Find and plot Lowess regression for each season
|
||||
for seasonIDX, season in enumerate(SeasonMonths):
|
||||
# Skip Winter
|
||||
if seasonIDX == 0:
|
||||
continue
|
||||
|
||||
seasonMask = obs_HgWater['DateTime'].dt.month.isin(season)
|
||||
lowess = sm.nonparametric.lowess(obs_HgWater.loc[seasonMask, 'Sample_NG/L'],
|
||||
obs_HgWater.loc[seasonMask, 'RiverKM'],
|
||||
frac=0.4, xvals=interp_xvals, it=2)
|
||||
axes[2].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX],
|
||||
linewidth=4, color=SeasonColors[seasonIDX])
|
||||
|
||||
axes[2].set_ylabel('Water Total Hg [ng/L]')
|
||||
axes[2].set_ylim([0, 50])
|
||||
axes[2].set_title('Water Total Hg')
|
||||
axes[2].legend()
|
||||
|
||||
obs_MeHgWater.plot.scatter('RiverKM', 'Sample_NG/L', ax=axes[3], c='Month',
|
||||
label='All Samples', vmin=1, vmax=12, cmap=shifted_hsv)
|
||||
|
||||
# Find and plot Lowess regression
|
||||
# lowess = sm.nonparametric.lowess(obs_MeHgWater['Sample_NG/L'], obs_MeHgWater['RiverKM'],
|
||||
# frac=0.2, xvals=interp_xvals, it=2)
|
||||
# axes[3].plot(interp_xvals, lowess, label='Lowess regression', linewidth=4)
|
||||
for seasonIDX, season in enumerate(SeasonMonths):
|
||||
# Skip Winter
|
||||
if seasonIDX == 0:
|
||||
continue
|
||||
|
||||
seasonMask = obs_MeHgWater['DateTime'].dt.month.isin(season)
|
||||
lowess = sm.nonparametric.lowess(obs_MeHgWater.loc[seasonMask, 'Sample_NG/L'],
|
||||
obs_MeHgWater.loc[seasonMask, 'RiverKM'],
|
||||
frac=0.25, xvals=interp_xvals, it=2)
|
||||
axes[3].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX],
|
||||
linewidth=4, color=SeasonColors[seasonIDX])
|
||||
|
||||
axes[3].set_ylabel('Water Total MeHg [ng/L]')
|
||||
axes[3].set_ylim([0, 5])
|
||||
axes[3].set_title('Water Total MeHg')
|
||||
axes[3].legend()
|
||||
|
||||
|
||||
axes[3].set_xlim([0, 100000])
|
||||
axes[3].set_xlabel('Distance Along River [m]')
|
||||
|
||||
|
||||
plt.show()
|
||||
fig.savefig("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/Figures/RiverDataProfiles_Lowess_Months_RevB.png",
|
||||
bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
#%% 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/HgDepthMonth.png",
|
||||
bbox_inches='tight', dpi=200)
|
||||
|
||||
|
||||
#%% Interpolate lowess to grid for hg
|
||||
# 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)
|
||||
|
||||
|
||||
gridInterpNearest = griddata(np.array([obs_HgSurfaceSed.geometry.x, obs_HgSurfaceSed.geometry.y]).T,
|
||||
obs_HgSurfaceSed['Sample_NG/G'], np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterpNearest))
|
||||
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/Hg_Nearest.xyz',
|
||||
gridInterpOut, delimiter=" ")
|
||||
|
||||
# Linear interpolation
|
||||
gridInterpNearest = griddata(np.array([obs_HgSurfaceSed.geometry.x, obs_HgSurfaceSed.geometry.y]).T,
|
||||
obs_HgSurfaceSed['Sample_NG/G'], np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='linear')
|
||||
|
||||
gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterpNearest))
|
||||
gridInterpOut = np.transpose(gridInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
gridInterpOut[RoughInterp == 0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_Linear.xyz',
|
||||
gridInterpOut, delimiter=" ")
|
||||
|
||||
# Find and plot Lowess regression
|
||||
lowess = sm.nonparametric.lowess(obs_HgSurfaceSed['Sample_NG/G'], obs_HgSurfaceSed['RiverKM'],
|
||||
frac=0.3, it=2)
|
||||
|
||||
gridInterpLowess= griddata(np.array([obs_HgSurfaceSed.geometry.x, obs_HgSurfaceSed.geometry.y]).T,
|
||||
lowess[:, 1], np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
||||
method='nearest')
|
||||
|
||||
gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterpLowess))
|
||||
gridInterpOut = np.transpose(gridInterpOut)
|
||||
|
||||
# Set Wetlands to zero
|
||||
gridInterpOut[RoughInterp == 0.05, 2] = 0
|
||||
|
||||
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_Lowess.xyz',
|
||||
gridInterpOut, delimiter=" ")
|
||||
|
||||
#%% 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=" ")
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -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 +1,8 @@
|
|||
<?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 +1,13 @@
|
|||
<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 (geopandas_forge_mustique)" 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/MEDS.iml" filepath="$PROJECT_DIR$/.idea/MEDS.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -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>
|
||||
|
|
@ -76,7 +76,12 @@ importPaths = ['//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Resear
|
|||
'//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']
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20221127 WQ Sampling/Crane',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Great House',
|
||||
None,
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Crane',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20230212 WQ Sampling/Caribee']
|
||||
|
||||
siteNames = ['Great House',
|
||||
'Greensleeves',
|
||||
|
|
@ -111,7 +116,12 @@ siteNames = ['Great House',
|
|||
'Great House',
|
||||
None,
|
||||
'Old Queens Fort',
|
||||
'Crane']
|
||||
'Crane',
|
||||
'Great House',
|
||||
None,
|
||||
'Old Queens Fort',
|
||||
'Crane',
|
||||
'Caribee']
|
||||
|
||||
timeLabels= ['Before Construction',
|
||||
'Before Construction',
|
||||
|
|
@ -146,7 +156,12 @@ timeLabels= ['Before Construction',
|
|||
'November',
|
||||
None,
|
||||
'November',
|
||||
'November']
|
||||
'November',
|
||||
'February',
|
||||
None,
|
||||
'February',
|
||||
'February',
|
||||
'February']
|
||||
|
||||
wave_bts_file = [
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
|
|
@ -182,13 +197,18 @@ wave_bts_file = [
|
|||
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 = gp.read_file('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/SitePolygons_Crane_Caribee.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
for s in [33]: #range(15, 27):
|
||||
for s in [34, 36, 37, 38]: #range(15, 27):
|
||||
## Define master import path
|
||||
importPath = importPaths[s]
|
||||
siteName = siteNames[s]
|
||||
|
|
@ -217,7 +237,7 @@ for s in [33]: #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 or s == 30 or s == 32 or s == 33:
|
||||
if s < 15 or s >= 30:
|
||||
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')
|
||||
|
|
@ -233,7 +253,7 @@ for s in [33]: #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"))
|
||||
|
||||
if s == 30 or s == 32 or s == 33:
|
||||
if s >= 30:
|
||||
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))
|
||||
|
|
@ -295,7 +315,12 @@ for s in [33]: #range(15, 27):
|
|||
GFS_Lon = -59.442
|
||||
GFS_Lat = 13.1075
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[3, 1])
|
||||
|
||||
elif siteName == 'Caribee':
|
||||
axXlim = (217929, 218345)
|
||||
axYlim = (1446528, 1446818)
|
||||
GFS_Lon = -59.442
|
||||
GFS_Lat = 13.1075
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[4, 1])
|
||||
|
||||
# Set min and max times using conductivity
|
||||
if Obs_geo['inArea'].any():
|
||||
|
|
@ -457,8 +482,8 @@ for s in [33]: #range(15, 27):
|
|||
parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
|
||||
# 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 = [6, 36.0, 130, 34.0, 150.0, 12000]
|
||||
paramMin = [0.0, 32.0, 100, 24.0, 0, 0]
|
||||
paramMax = [6, 36.5, 130, 34.0, 150.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
|
|
@ -496,8 +521,9 @@ for s in [33]: #range(15, 27):
|
|||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
##### Adjust "order" to control the number of turbidity points that are selected
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=8, mode='wrap')[0])
|
||||
np.greater_equal, order=12, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
|
|
@ -676,7 +702,11 @@ plotIDXsLoop = []
|
|||
for i in range(0, 1):
|
||||
summTable = None
|
||||
# plotIDXs = plotIDXsLoop[i]
|
||||
plotIDXs = np.arange(i, 25, 3)
|
||||
# plotIDXs = np.arange(i, 25, 3)
|
||||
|
||||
#plotIDXs = [0, 3, 6, 12, 15, 18, 21, 24, 27, 30, 34] # Great House
|
||||
# plotIDXs = [2, 5, 8, 14, 17, 20, 23, 26, 29, 32, 36] # Old Queen's Fort
|
||||
plotIDXs = [1, 4, 7, 10, 13, 16, 19] # Greensleevs
|
||||
|
||||
for s, plotIDX in enumerate(plotIDXs):
|
||||
## Define master import path
|
||||
|
|
@ -712,7 +742,9 @@ for i in range(2, 3):
|
|||
summTable = None
|
||||
# plotIDXs = np.arange(i, 27, 3)
|
||||
#plotIDXs = [2, 5, 8, 14, 17, 20, 23, 26, 29, 32]
|
||||
plotIDXs = [0, 3, 6, 12, 15, 18, 21, 24, 27, 30]
|
||||
# plotIDXs = [2, 5, 8, 14, 17, 20, 23, 26, 29, 32, 36] # Old Queen's Fort
|
||||
plotIDXs = [1, 4, 7, 10, 13, 16, 19] # Greensleevs
|
||||
|
||||
|
||||
plotDates = []
|
||||
plotTable = np.empty([10, len(plotIDXs)])
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
EFDC_compare.ipynb
|
||||
NTC_PlottingD3D_2023.py
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="jdk" jdkName="Python 3.8 (D3DFM)" jdkType="Python SDK" />
|
||||
<orderEntry type="jdk" jdkName="Python 3.9 (dfm_tools_23)" 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 3.8 (D3DFM)" project-jdk-type="Python SDK" />
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (dfm_tools_23)" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
|
|
@ -12,16 +12,21 @@ 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'
|
||||
# gp.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'
|
||||
|
||||
import scipy as sp
|
||||
import scipy.ndimage
|
||||
#from astropy.convolution import Gaussian2DKernel
|
||||
import contextily as ctx
|
||||
import os
|
||||
from shapely.geometry import Point
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
from shapely.ops import nearest_points
|
||||
|
||||
import datetime as dt
|
||||
import pytz
|
||||
|
||||
import pickle
|
||||
|
||||
# %% Read in data
|
||||
dataPath = '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/05_Analyses/07 ADCP/NC_CurrentMeter_All_Phase1_all_data_2012_05_20/'
|
||||
|
||||
|
|
@ -134,67 +139,71 @@ gdf.loc[:, df.columns != 'geometry'].to_xarray().to_netcdf(
|
|||
'C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/Transects.nc')
|
||||
|
||||
# %% Load in moored data
|
||||
df_moored_data = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NC_CurrentMeter_All_Phase1_all_data_2013_07_16/NC_CurrentMeter_All_Phase1_all_moored_2013_05_20.xlsx',
|
||||
sheet_name='mag_all_moored')
|
||||
gdf_moored = dict()
|
||||
|
||||
# Shift col names to put second back in
|
||||
colIN = list(df_moored_data.columns)
|
||||
colOUT = colIN[0:6] + ['second'] + colIN[6:-1]
|
||||
df_moored_data.columns = colOUT
|
||||
# Loop through directions
|
||||
for direction in ['u_all_moored', 'u_all_moored', 'mag_all_moored' , 'dir_all_moored']:
|
||||
df_moored_data = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NC_CurrentMeter_All_Phase1_all_data_2013_07_16/NC_CurrentMeter_All_Phase1_all_moored_2013_05_20.xlsx',
|
||||
sheet_name=direction)
|
||||
|
||||
### Moored current meter locations from here:
|
||||
### file://srv-ott3/Projects/11934.201%20Newtown%20Creek%20TPP%20%E2%80%93%20Privileged%20and%20Confidential/03_Data/01_BkgrdReports/NC_DRAFT_DSR_Submittal_No_3_2013-07-03.pdf
|
||||
### Table 3-1, PDF page 65
|
||||
### Locations change between deployment 1-9 and 10/11
|
||||
# Shift col names to put second back in
|
||||
colIN = list(df_moored_data.columns)
|
||||
colOUT = colIN[0:6] + ['second'] + colIN[6:-1]
|
||||
df_moored_data.columns = colOUT
|
||||
|
||||
### YSI from 2021 FRMR report Pg 385
|
||||
### Moored current meter locations from here:
|
||||
### file://srv-ott3/Projects/11934.201%20Newtown%20Creek%20TPP%20%E2%80%93%20Privileged%20and%20Confidential/03_Data/01_BkgrdReports/NC_DRAFT_DSR_Submittal_No_3_2013-07-03.pdf
|
||||
### Table 3-1, PDF page 65
|
||||
### Locations change between deployment 1-9 and 10/11
|
||||
|
||||
df_moored = pd.DataFrame(
|
||||
{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33,
|
||||
208809.66, 205547.85, 203870.2, 201684.6, 200860.91, 200336.12],
|
||||
'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90,
|
||||
996586.22, 1001028.85, 1004501.4, 1005027.4, 1004516.53, 1005535.44],
|
||||
'bedElev': [-16, -17, -20, -18, -20, 0, 0, 0, 0, 0, 0]})
|
||||
gdf_moored_loc = gp.GeoDataFrame(
|
||||
df_moored, geometry=gp.points_from_xy(df_moored.Easting, df_moored.Northing), crs="EPSG:2263")
|
||||
gdf_moored_loc.geometry = gdf_moored_loc.geometry.to_crs("EPSG:32118")
|
||||
### YSI from 2021 FRMR report Pg 385
|
||||
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
# for d in range(1,10):
|
||||
# depMask = df_moored_data['deployment'] == ('dep' + str(d))
|
||||
# for stationIDX, station in enumerate(df_moored['depOrig']):
|
||||
# stationMask = (df_moored_data['station'] == station) & depMask
|
||||
#
|
||||
# # Assign geography to station plus deployment
|
||||
# df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
# df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
df_moored = pd.DataFrame(
|
||||
{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33,
|
||||
208809.66, 205547.85, 203870.2, 201684.6, 200860.91, 200336.12],
|
||||
'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90,
|
||||
996586.22, 1001028.85, 1004501.4, 1005027.4, 1004516.53, 1005535.44],
|
||||
'bedElev': [-16, -17, -20, -18, -20, 0, 0, 0, 0, 0, 0]})
|
||||
gdf_moored_loc = gp.GeoDataFrame(
|
||||
df_moored, geometry=gp.points_from_xy(df_moored.Easting, df_moored.Northing), crs="EPSG:2263")
|
||||
gdf_moored_loc.geometry = gdf_moored_loc.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# Loop through Station IDs for deployments 10-11 and assign locations
|
||||
for d in range(1,12):
|
||||
depMask = df_moored_data['deployment'] == 'dep' + str(d)
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
for d in range(1, 11):
|
||||
depMask = df_moored_data['deployment'] == ('dep' + str(d))
|
||||
for stationIDX, station in enumerate(df_moored['depOrig']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_moored = gp.GeoDataFrame(
|
||||
df_moored_data, geometry=gp.points_from_xy(df_moored_data.Easting, df_moored_data.Northing), crs="EPSG:2263")
|
||||
# Loop through Station IDs for deployments 10-11 and assign locations
|
||||
for d in range(10,12):
|
||||
depMask = df_moored_data['deployment'] == 'dep' + str(d)
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf_moored.geometry = gdf_moored.geometry.to_crs("EPSG:32118")
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
gdf_moored['date'] = pd.to_datetime(gdf_moored['year'].astype(str) + '-' +
|
||||
gdf_moored['month'].astype(str).str.zfill(2) + '-' +
|
||||
gdf_moored['day'].astype(str).str.zfill(2) + ' ' +
|
||||
gdf_moored['hour'].astype(str).str.zfill(2) + ':' +
|
||||
gdf_moored['minute'].astype(str).str.zfill(2))
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_moored[direction] = gp.GeoDataFrame(
|
||||
df_moored_data, geometry=gp.points_from_xy(df_moored_data.Easting, df_moored_data.Northing), crs="EPSG:2263")
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf_moored[direction].geometry = gdf_moored[direction].geometry.to_crs("EPSG:32118")
|
||||
|
||||
gdf_moored[direction]['date'] = pd.to_datetime(gdf_moored[direction]['year'].astype(str) + '-' +
|
||||
gdf_moored[direction]['month'].astype(str).str.zfill(2) + '-' +
|
||||
gdf_moored[direction]['day'].astype(str).str.zfill(2) + ' ' +
|
||||
gdf_moored[direction]['hour'].astype(str).str.zfill(2) + ':' +
|
||||
gdf_moored[direction]['minute'].astype(str).str.zfill(2))
|
||||
|
||||
|
||||
# %% ADCP1
|
||||
|
|
@ -214,7 +223,7 @@ fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(16, 8))
|
|||
fig.tight_layout(pad=2)
|
||||
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
for d in range(1,12):
|
||||
for d in range(11):
|
||||
depMask = gdf_moored['deployment'] == ('dep' + str(d))
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (gdf_moored['station'] == station) & depMask
|
||||
|
|
@ -244,6 +253,112 @@ plt.show()
|
|||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/ADCP_2012.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Read in report obs
|
||||
reportObs = pd.read_csv('C:/Users/arey/files/Projects/Newtown/NTC_Obs_NC_086_Report.csv',
|
||||
header=None, parse_dates=[0], index_col=0)
|
||||
|
||||
# Avergae duplicate dates
|
||||
reportObs = reportObs.groupby(reportObs.index).mean()
|
||||
|
||||
#%% Read in report EFDC
|
||||
reportEFDC = pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/06_Models/02_EFDC/04_Model_Comparisons/NC086_DAV_Report_2016_EFDC.csv',
|
||||
header=None, parse_dates=[0], index_col=0)
|
||||
|
||||
|
||||
#%% Read in EFDC data
|
||||
efdc7_df = pd.read_csv(
|
||||
'//ott-athena.baird.com/D/11934.201_Newtown_Creek_TPP/EFDC_Results/RevisedEFDC/2012_Mon7/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
|
||||
|
||||
# Read in EFDC grid
|
||||
efdc_grid_df = pd.read_csv('//ott-athena.baird.com/F/2019_FMRM_Deliverable/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")
|
||||
|
||||
|
||||
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([dt.datetime(2012, 7, 1, 00, 00, 00) +
|
||||
dt.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')) #Convert to UTC
|
||||
del efdc7_df
|
||||
|
||||
efdc_merged_gdf = gp.GeoDataFrame(
|
||||
efdc7_merged, geometry=efdc7_merged['geometry'], crs="EPSG:32118")
|
||||
del efdc7_merged
|
||||
|
||||
#%% Find nearest grid point to EFDC ADCP Station
|
||||
station = 'NC086CM'
|
||||
stationLoc = gdf_moored_loc.loc[0, 'geometry']
|
||||
nearest_geoms = nearest_points(stationLoc, MultiPoint(efdc_grid_gdf.geometry))
|
||||
station_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
|
||||
|
||||
#%% Plot single deplyment for comparision
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 8), sharex=True)
|
||||
|
||||
station = 'NC086CM'
|
||||
stationMask = (gdf_moored['mag_all_moored']['station'] == station)
|
||||
# axes.plot(gdf_moored.loc[stationMask, 'date'],
|
||||
# gdf_moored.iloc[:, 8:50].loc[stationMask].mean(axis=1).multiply(3.28084), 'k')
|
||||
|
||||
|
||||
# Plot vector component in arbitrary direction
|
||||
for plotDir in [250]:
|
||||
ADCPtime = pd.to_datetime(gdf_moored['mag_all_moored'].loc[stationMask, 'date'].values).tz_localize(
|
||||
pytz.timezone('EST'))
|
||||
axes[0].plot(ADCPtime, gdf_moored['mag_all_moored'].iloc[:, 8:50].loc[stationMask].mean(
|
||||
axis=1).multiply(3.28084).multiply(
|
||||
np.cos(np.deg2rad(gdf_moored['dir_all_moored'].iloc[:, 8:50].loc[
|
||||
stationMask].mean(axis=1)) - np.deg2rad(plotDir))), label =
|
||||
'ADCP Obs at ' + str(plotDir) + ' deg')
|
||||
|
||||
# axes.plot(gdf_moored.loc[stationMask, 'date'],
|
||||
# gdf_moored.iloc[:, 8:50].loc[stationMask].mean(axis=1).multiply(3.28084), 'k')
|
||||
|
||||
# Plot EFDC
|
||||
axes[1].plot(station_gdf.index,
|
||||
np.sqrt(station_gdf.loc[:, ['U_VEL1', 'U_VEL5', 'U_VEL10']].mean(axis=1)**2+
|
||||
station_gdf.loc[:, ['V_VEL1', 'V_VEL5', 'V_VEL10 ']].mean(axis=1)**2)*3.28084 *
|
||||
np.cos(np.arctan2(station_gdf.loc[:,
|
||||
['U_VEL1', 'U_VEL5', 'U_VEL10']].mean(axis=1),
|
||||
station_gdf.loc[:,
|
||||
['V_VEL1', 'V_VEL5', 'V_VEL10 ']].mean(axis=1)) - np.deg2rad(plotDir)),
|
||||
'r', label='EFDC Model at ' + str(plotDir) + ' deg')
|
||||
|
||||
axes[1].plot(station_gdf.index,
|
||||
np.sqrt(station_gdf.loc[:, ['U_VEL5']].mean(axis=1)**2+
|
||||
station_gdf.loc[:, ['V_VEL5']].mean(axis=1)**2)*3.28084 *
|
||||
np.cos(np.arctan2(station_gdf.loc[:,
|
||||
['U_VEL5']].mean(axis=1),
|
||||
station_gdf.loc[:,
|
||||
['V_VEL5']].mean(axis=1)) - np.deg2rad(plotDir)),
|
||||
label='EFDC Model Layer 5 at ' + str(plotDir) + ' deg')
|
||||
|
||||
|
||||
# Plot report obs
|
||||
axes[0].plot(reportObs.index.tz_localize(
|
||||
pytz.timezone('EST')), reportObs[1], 'k', label='Report Obs')
|
||||
|
||||
# plot report efdc
|
||||
axes[1].plot(reportEFDC.index.tz_localize(
|
||||
pytz.timezone('EST')) - dt.timedelta(hours=0), reportEFDC[1], 'k', label='Report EFDC')
|
||||
|
||||
|
||||
# Add legend
|
||||
axes[0].legend()
|
||||
axes[1].legend()
|
||||
|
||||
axes[1].set_xlim([dt.datetime(2012, 7, 17, 12, 0, 0), dt.datetime(2012, 7, 24, 12, 0, 0)])
|
||||
axes[0].set_ylim([-1, 1])
|
||||
axes[1].set_ylim([-1, 1])
|
||||
|
||||
plt.show()
|
||||
|
||||
# %% Load in ADCP2 data
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,887 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@author: aforsythe
|
||||
|
||||
Copied from "P:/11934.201 Newtown Creek TPP – Privileged and Confidential/05_Analyses/07 ADCP/NC_CurrentMeter_All_Phase1_all_data_2012_05_20/ADCP_Plot_v4.py"
|
||||
_AJMR
|
||||
"""
|
||||
|
||||
|
||||
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'
|
||||
|
||||
import scipy as sp
|
||||
import scipy.ndimage
|
||||
#from astropy.convolution import Gaussian2DKernel
|
||||
import contextily as ctx
|
||||
import os
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
from shapely.ops import nearest_points
|
||||
|
||||
import datetime as dt
|
||||
import pytz
|
||||
|
||||
import pickle
|
||||
|
||||
# %% Read in data
|
||||
dataPath = '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/05_Analyses/07 ADCP/NC_CurrentMeter_All_Phase1_all_data_2012_05_20/'
|
||||
|
||||
df = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NC_CurrentMeter_All_Phase1_all_data_2013_07_16/NC_CurrentMeter_All_Phase1_all_mobile_2013_05_02.xlsx',
|
||||
sheet_name='mag_all_mobile')
|
||||
|
||||
# Convert to geodataframe
|
||||
gdf = gp.GeoDataFrame(df, geometry=gp.points_from_xy(df.Longitude, df.Latitude, crs="EPSG:4326"))
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf.geometry = gdf.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# Add new cols to geodataframe
|
||||
gdf['Distance'] = np.zeros([len(gdf), 1])
|
||||
gdf['DistanceSmth'] = np.zeros([len(gdf), 1])
|
||||
gdf['dist'] = np.zeros([len(gdf), 1])
|
||||
gdf['date'] = np.zeros([len(gdf), 1])
|
||||
|
||||
# Set Date
|
||||
gdf['date'] = gdf['year'].astype(str) + '-' + gdf['mon'].astype(str).str.zfill(2) + '-' + gdf['day'].astype(
|
||||
str).str.zfill(2)
|
||||
|
||||
# Column names to distances data for plotting
|
||||
depths1 = gdf.columns[11:43] # ADCP sensor depth reading column names to list
|
||||
depths = np.array([float(d.replace('m', '')) for d in depths1]) * -1
|
||||
|
||||
#loop through all transects
|
||||
|
||||
transectCount = 0
|
||||
transects = gdf['transect'].unique()
|
||||
|
||||
kernel = Gaussian2DKernel(x_stddev=0.25)
|
||||
|
||||
for transect_id in range(1, 2): #transects:
|
||||
# Select a given trasect
|
||||
tMask = (gdf['transect']==transect_id)
|
||||
# Remove rows without locations
|
||||
tMask[pd.isna(gdf['Latitude'])] = False
|
||||
|
||||
# Find distance betwen points in m
|
||||
gdf.loc[tMask, 'Distance'] = gdf[tMask].distance(gdf[tMask].shift())
|
||||
|
||||
# Many of the points are recorded as being at the same location...
|
||||
# Where the distance is zero, set it to half of the following distance
|
||||
|
||||
# Find zeros and iloc after zeros
|
||||
zeroDist = (gdf['Distance'] == 0) & tMask
|
||||
zeroDistShift = zeroDist.shift()
|
||||
zeroDistShift.iloc[0] = False
|
||||
distShift = gdf['Distance'].shift(-1)
|
||||
distShift.iloc[-1] = False
|
||||
gdf.loc[tMask, 'DistanceSmth'] = gdf['Distance'][tMask]
|
||||
|
||||
# Set zeros to half of following
|
||||
gdf.loc[zeroDist, 'DistanceSmth'] = distShift[zeroDist]/2
|
||||
# Set following to half
|
||||
gdf.loc[zeroDistShift, 'DistanceSmth'] = gdf['Distance']/2
|
||||
|
||||
# Set initial to zero
|
||||
gdf.loc[gdf[tMask].index[0], 'DistanceSmth'] = 0
|
||||
|
||||
# If final location is duplicate, set to previous value
|
||||
if gdf.loc[gdf[tMask].index[-1], 'Distance']==0:
|
||||
gdf.loc[gdf[tMask].index[-1], 'DistanceSmth'] = gdf.loc[gdf[tMask].index[-2], 'DistanceSmth']
|
||||
|
||||
# Get cumulative sum of distances
|
||||
gdf.loc[tMask, 'dist'] = gdf.loc[tMask, 'DistanceSmth'].cumsum()
|
||||
|
||||
# get velocity data all rows columns 11-43
|
||||
V = gdf.values[tMask, 11:43].astype(float)
|
||||
|
||||
# #__________________________________________________________________________________________________________________________
|
||||
# %% Plotting
|
||||
if transectCount == 0:
|
||||
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(16, 8))
|
||||
fig.tight_layout(pad=2.5)
|
||||
ax = axes.flat
|
||||
|
||||
colormap = 'jet'
|
||||
vmin=0
|
||||
vmax=0.5
|
||||
|
||||
VS = sp.ndimage.filters.gaussian_filter(V, [1, 1], mode='nearest')
|
||||
|
||||
# vDatEnd = (~np.isnan(V)).cumsum(1).argmax(1).astype(int) + 1
|
||||
# VS = convolve(V, kernel)
|
||||
# for i in range(0, len(VS)):
|
||||
# VS[i, vDatEnd[i]:-1] = np.nan
|
||||
|
||||
pltDat = ax[transectCount].pcolormesh(gdf.loc[tMask, 'dist'], depths, np.transpose(VS),
|
||||
shading='auto', vmin=vmin, vmax=vmax, cmap=colormap)
|
||||
|
||||
cbar = fig.colorbar(pltDat, ax=ax[transectCount],
|
||||
shrink=0.95,aspect=5)
|
||||
cbar.set_label('Magnitude [m/s]')
|
||||
ax[transectCount].set_xlabel('Distance Along Transect [m]')
|
||||
ax[transectCount].set_ylabel('Depth below WSL [m]')
|
||||
stationStart = next((i for i, j in enumerate(tMask) if j), None)
|
||||
ax[transectCount].set_title(gdf.loc[stationStart, 'station'])
|
||||
ax[transectCount].set_ylim(-6, 0)
|
||||
|
||||
transectCount = transectCount + 1
|
||||
|
||||
plt.show()
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/Transects221-241.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Save Transects
|
||||
gdf.loc[:, df.columns != 'geometry'].to_xarray().to_netcdf(
|
||||
'C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/Transects.nc')
|
||||
|
||||
# %% Load in moored data
|
||||
gdf_moored = dict()
|
||||
|
||||
# Loop through directions
|
||||
for direction in ['u_all_moored', 'u_all_moored', 'mag_all_moored' , 'dir_all_moored']:
|
||||
df_moored_data = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NC_CurrentMeter_All_Phase1_all_data_2013_07_16/NC_CurrentMeter_All_Phase1_all_moored_2013_05_20.xlsx',
|
||||
sheet_name=direction)
|
||||
|
||||
# Shift col names to put second back in
|
||||
colIN = list(df_moored_data.columns)
|
||||
colOUT = colIN[0:6] + ['second'] + colIN[6:-1]
|
||||
df_moored_data.columns = colOUT
|
||||
|
||||
### Moored current meter locations from here:
|
||||
### file://srv-ott3/Projects/11934.201%20Newtown%20Creek%20TPP%20%E2%80%93%20Privileged%20and%20Confidential/03_Data/01_BkgrdReports/NC_DRAFT_DSR_Submittal_No_3_2013-07-03.pdf
|
||||
### Table 3-1, PDF page 65
|
||||
### Locations change between deployment 1-9 and 10/11
|
||||
|
||||
### YSI from 2021 FRMR report Pg 385
|
||||
|
||||
df_moored = pd.DataFrame(
|
||||
{'depOrig': ['NC083CM', 'NC081CM', 'NC082CM', 'NC086CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'depCurr': ['NC086CM', 'NC087CM', 'NC088CM', 'NC089CM', 'EK023CM',
|
||||
'NC310', 'NC313', 'NC316', 'NC318', 'EK108', 'EB043'],
|
||||
'Northing': [208519.95, 206083.24, 203835.10, 201381.55, 200827.33,
|
||||
208809.66, 205547.85, 203870.2, 201684.6, 200860.91, 200336.12],
|
||||
'Easting': [996198.97, 1000959.45, 1004387.42, 1005283.07, 1004644.90,
|
||||
996586.22, 1001028.85, 1004501.4, 1005027.4, 1004516.53, 1005535.44],
|
||||
'bedElev': [-16, -17, -20, -18, -20, 0, 0, 0, 0, 0, 0]})
|
||||
gdf_moored_loc = gp.GeoDataFrame(
|
||||
df_moored, geometry=gp.points_from_xy(df_moored.Easting, df_moored.Northing), crs="EPSG:2263")
|
||||
gdf_moored_loc.geometry = gdf_moored_loc.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
for d in range(1, 11):
|
||||
depMask = df_moored_data['deployment'] == ('dep' + str(d))
|
||||
for stationIDX, station in enumerate(df_moored['depOrig']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
# Loop through Station IDs for deployments 10-11 and assign locations
|
||||
for d in range(10,12):
|
||||
depMask = df_moored_data['deployment'] == 'dep' + str(d)
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (df_moored_data['station'] == station) & depMask
|
||||
|
||||
# Assign geography to station plus deployment
|
||||
df_moored_data.loc[stationMask, 'Northing'] = df_moored.loc[stationIDX, 'Northing']
|
||||
df_moored_data.loc[stationMask, 'Easting'] = df_moored.loc[stationIDX, 'Easting']
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_moored[direction] = gp.GeoDataFrame(
|
||||
df_moored_data, geometry=gp.points_from_xy(df_moored_data.Easting, df_moored_data.Northing), crs="EPSG:2263")
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf_moored[direction].geometry = gdf_moored[direction].geometry.to_crs("EPSG:32118")
|
||||
|
||||
gdf_moored[direction]['date'] = pd.to_datetime(gdf_moored[direction]['year'].astype(str) + '-' +
|
||||
gdf_moored[direction]['month'].astype(str).str.zfill(2) + '-' +
|
||||
gdf_moored[direction]['day'].astype(str).str.zfill(2) + ' ' +
|
||||
gdf_moored[direction]['hour'].astype(str).str.zfill(2) + ':' +
|
||||
gdf_moored[direction]['minute'].astype(str).str.zfill(2))
|
||||
|
||||
|
||||
# %% ADCP1
|
||||
gdf_moored.iloc[:, 1:-2].to_xarray().to_netcdf(
|
||||
'C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/ADCP1.nc')
|
||||
|
||||
# %% Plot moored data
|
||||
# Column names to distances data for plotting
|
||||
depths1 = gdf_moored.columns[8:51] # ADCP sensor depth reading column names to list
|
||||
depths_moored = np.array([float(d.replace('m', '')) for d in depths1])
|
||||
|
||||
colormap = 'jet'
|
||||
vmin = 0
|
||||
vmax = 0.2
|
||||
|
||||
fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(16, 8))
|
||||
fig.tight_layout(pad=2)
|
||||
|
||||
# Loop through Station IDs for deployments 1-9 and assign locations
|
||||
for d in range(11):
|
||||
depMask = gdf_moored['deployment'] == ('dep' + str(d))
|
||||
for stationIDX, station in enumerate(df_moored['depCurr']):
|
||||
stationMask = (gdf_moored['station'] == station) & depMask
|
||||
|
||||
V = gdf_moored.values[stationMask, 8:51].astype(float)
|
||||
|
||||
if len(gdf_moored.loc[stationMask, 'date']) != 0:
|
||||
pltDat = axes[stationIDX].pcolormesh(gdf_moored.loc[stationMask, 'date'],
|
||||
depths_moored, np.transpose(V),
|
||||
shading='auto', vmin=vmin, vmax=vmax, cmap=colormap)
|
||||
|
||||
axes[stationIDX].set_ylim(0, 7)
|
||||
fmt_half_year = mdates.MonthLocator(interval=1)
|
||||
axes[stationIDX].xaxis.set_major_locator(fmt_half_year)
|
||||
axes[stationIDX].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
|
||||
|
||||
if d == 1:
|
||||
axes[stationIDX].set_ylabel('Elevation [m]')
|
||||
axes[stationIDX].set_title(station)
|
||||
cbar = fig.colorbar(pltDat, ax=axes[stationIDX],
|
||||
shrink=1.1, aspect=3)
|
||||
cbar.set_label('Magnitude [m/s]')
|
||||
pltDat.set_clim([0, 0.2])
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/ADCP_2012.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Read in report obs
|
||||
reportObs = pd.read_csv('C:/Users/arey/files/Projects/Newtown/NTC_Obs_NC_086_Report.csv',
|
||||
header=None, parse_dates=[0], index_col=0)
|
||||
|
||||
# Avergae duplicate dates
|
||||
reportObs = reportObs.groupby(reportObs.index).mean()
|
||||
|
||||
#%% Read in report EFDC
|
||||
reportEFDC = pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/06_Models/02_EFDC/04_Model_Comparisons/NC086_DAV_Report_2016_EFDC.csv',
|
||||
header=None, parse_dates=[0], index_col=0)
|
||||
|
||||
|
||||
#%% Read in EFDC data
|
||||
efdc7_df = pd.read_csv(
|
||||
'//ott-athena.baird.com/D/11934.201_Newtown_Creek_TPP/EFDC_Results/RevisedEFDC/2012_Mon7/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
|
||||
|
||||
|
||||
# Read in EFDC grid
|
||||
efdc_grid_df = pd.read_csv('//ott-athena.baird.com/F/2019_FMRM_Deliverable/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")
|
||||
|
||||
|
||||
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([dt.datetime(2012, 7, 1, 00, 00, 00) +
|
||||
dt.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')) #Convert to UTC
|
||||
del efdc7_df
|
||||
|
||||
efdc_merged_gdf = gp.GeoDataFrame(
|
||||
efdc7_merged, geometry=efdc7_merged['geometry'], crs="EPSG:32118")
|
||||
del efdc7_merged
|
||||
|
||||
#%% Find nearest grid point to EFDC ADCP Station
|
||||
station = 'NC086CM'
|
||||
stationLoc = gdf_moored_loc.loc[0, 'geometry']
|
||||
nearest_geoms = nearest_points(stationLoc, MultiPoint(efdc_grid_gdf.geometry))
|
||||
station_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
|
||||
|
||||
#%% Plot single deplyment for comparision
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 8), sharex=True)
|
||||
|
||||
station = 'NC086CM'
|
||||
stationMask = (gdf_moored['mag_all_moored']['station'] == station)
|
||||
# axes.plot(gdf_moored.loc[stationMask, 'date'],
|
||||
# gdf_moored.iloc[:, 8:50].loc[stationMask].mean(axis=1).multiply(3.28084), 'k')
|
||||
|
||||
|
||||
# Plot vector component in arbitrary direction
|
||||
for plotDir in [250]:
|
||||
ADCPtime = pd.to_datetime(gdf_moored['mag_all_moored'].loc[stationMask, 'date'].values).tz_localize(
|
||||
pytz.timezone('EST'))
|
||||
axes[0].plot(ADCPtime, gdf_moored['mag_all_moored'].iloc[:, 8:50].loc[stationMask].mean(
|
||||
axis=1).multiply(3.28084).multiply(
|
||||
np.cos(np.deg2rad(gdf_moored['dir_all_moored'].iloc[:, 8:50].loc[
|
||||
stationMask].mean(axis=1)) - np.deg2rad(plotDir))), label =
|
||||
'ADCP Obs at ' + str(plotDir) + ' deg')
|
||||
|
||||
# axes.plot(gdf_moored.loc[stationMask, 'date'],
|
||||
# gdf_moored.iloc[:, 8:50].loc[stationMask].mean(axis=1).multiply(3.28084), 'k')
|
||||
|
||||
# Plot EFDC
|
||||
axes[1].plot(station_gdf.index,
|
||||
np.sqrt(station_gdf.loc[:, ['U_VEL1', 'U_VEL5', 'U_VEL10']].mean(axis=1)**2+
|
||||
station_gdf.loc[:, ['V_VEL1', 'V_VEL5', 'V_VEL10 ']].mean(axis=1)**2)*3.28084 *
|
||||
np.cos(np.arctan2(station_gdf.loc[:,
|
||||
['U_VEL1', 'U_VEL5', 'U_VEL10']].mean(axis=1),
|
||||
station_gdf.loc[:,
|
||||
['V_VEL1', 'V_VEL5', 'V_VEL10 ']].mean(axis=1)) - np.deg2rad(plotDir)),
|
||||
'r', label='EFDC Model at ' + str(plotDir) + ' deg')
|
||||
|
||||
axes[1].plot(station_gdf.index,
|
||||
np.sqrt(station_gdf.loc[:, ['U_VEL5']].mean(axis=1)**2+
|
||||
station_gdf.loc[:, ['V_VEL5']].mean(axis=1)**2)*3.28084 *
|
||||
np.cos(np.arctan2(station_gdf.loc[:,
|
||||
['U_VEL5']].mean(axis=1),
|
||||
station_gdf.loc[:,
|
||||
['V_VEL5']].mean(axis=1)) - np.deg2rad(plotDir)),
|
||||
label='EFDC Model Layer 5 at ' + str(plotDir) + ' deg')
|
||||
|
||||
|
||||
# Plot report obs
|
||||
axes[0].plot(reportObs.index.tz_localize(
|
||||
pytz.timezone('EST')), reportObs[1], 'k', label='Report Obs')
|
||||
|
||||
# plot report efdc
|
||||
axes[1].plot(reportEFDC.index.tz_localize(
|
||||
pytz.timezone('EST')) - dt.timedelta(hours=0), reportEFDC[1], 'k', label='Report EFDC')
|
||||
|
||||
|
||||
# Add legend
|
||||
axes[0].legend()
|
||||
axes[1].legend()
|
||||
|
||||
axes[1].set_xlim([dt.datetime(2012, 7, 17, 12, 0, 0), dt.datetime(2012, 7, 24, 12, 0, 0)])
|
||||
axes[0].set_ylim([-1, 1])
|
||||
axes[1].set_ylim([-1, 1])
|
||||
|
||||
plt.show()
|
||||
|
||||
# %% Load in ADCP2 data
|
||||
|
||||
# Read in locations
|
||||
df_adcp2_locs = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/NCP2_ADCP_D1-D3/AQ_LocationsSO_ADCP_ADV_overview_Coords_20150126_AJMR.xlsx',
|
||||
sheet_name='ADCP')
|
||||
gdf_adcp2_locs = gp.GeoDataFrame(df_adcp2_locs,
|
||||
geometry=gp.points_from_xy(df_adcp2_locs.X_NYSPLI, df_adcp2_locs.Y_NYSPLI), crs="EPSG:2263")
|
||||
gdf_adcp2_locs = gdf_adcp2_locs.to_crs("EPSG:32118")
|
||||
|
||||
|
||||
adcp2_data_path = '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/'
|
||||
|
||||
adcp2_paths = ['NCP2_ADCP_D1-D3/ADCP_D1_070314_090814', 'NCP2_ADCP_D1-D3/ADCP_D2_090914_110214',
|
||||
'NCP2_ADCP_D1-D3/ADCP_D3_110414_010615', 'NCP2_ADCP_D4-D5/D4_010715_030315',
|
||||
'NCP2_ADCP_D4-D5/D5_030415_050515']
|
||||
|
||||
adcp2_gdfs = dict()
|
||||
|
||||
for depIDX, adcp2_path in enumerate(adcp2_paths):
|
||||
adcp2_files = os.listdir(adcp2_data_path + adcp2_path) # returns list of files in adv folder
|
||||
|
||||
for adcp2_file in adcp2_files:
|
||||
if '.xls' in adcp2_file or '.csv' in adcp2_file:
|
||||
if '.xls' in adcp2_file:
|
||||
df_in = pd.read_excel(adcp2_data_path + adcp2_path + '/' + adcp2_file)
|
||||
else:
|
||||
df_in = pd.read_csv(adcp2_data_path + adcp2_path + '/' + adcp2_file)
|
||||
|
||||
for stationIDX, station in enumerate(df_adcp2_locs['parent_loc_code']):
|
||||
advStrIDX = adcp2_file.find('_')+1
|
||||
if adcp2_file[advStrIDX:advStrIDX+3] in station:
|
||||
adcp2_geo_x = np.ones([len(df_in), 1]) * df_adcp2_locs.X_NYSPLI[stationIDX]
|
||||
adcp2_geo_y = np.ones([len(df_in), 1]) * df_adcp2_locs.Y_NYSPLI[stationIDX]
|
||||
|
||||
|
||||
df_in['date'] = pd.to_datetime(df_in['Year'].astype(str) + '-' + df_in['Month'].astype(
|
||||
str).str.zfill(2) + '-' + df_in['Day'].astype(
|
||||
str).str.zfill(2) + ' ' + df_in['Hour'].astype(
|
||||
str).str.zfill(2) + ':' + df_in['Minute'].astype(
|
||||
str).str.zfill(2) + ':' + df_in['Second'].astype(str).str.zfill(2))
|
||||
|
||||
gdf_in = gp.GeoDataFrame(
|
||||
df_in, geometry=gp.points_from_xy(adcp2_geo_x, adcp2_geo_y), crs="EPSG:2263")
|
||||
|
||||
if adcp2_file[advStrIDX:advStrIDX + 3] not in adcp2_gdfs:
|
||||
adcp2_gdfs[adcp2_file[advStrIDX:advStrIDX + 3]] = dict()
|
||||
|
||||
if adcp2_file[0:advStrIDX-1] not in adcp2_gdfs[adcp2_file[advStrIDX:advStrIDX + 3]]:
|
||||
adcp2_gdfs[adcp2_file[advStrIDX:advStrIDX + 3]][adcp2_file[0:advStrIDX - 1]] = dict()
|
||||
|
||||
if depIDX+1 not in adcp2_gdfs[adcp2_file[advStrIDX:advStrIDX + 3]][adcp2_file[0:advStrIDX - 1]]:
|
||||
adcp2_gdfs[adcp2_file[advStrIDX:advStrIDX + 3]][adcp2_file[0:advStrIDX - 1]][depIDX+1] = gdf_in
|
||||
# %% ADCP2
|
||||
for stationIDX, stat in enumerate(adcp2_gdfs):
|
||||
for varIDX, var in enumerate(adcp2_gdfs[stat]):
|
||||
for depIDX, depdat in enumerate(adcp2_gdfs[stat][var]):
|
||||
ncDat = adcp2_gdfs[stat][var][depdat].iloc[:, 1:-2]
|
||||
ncDat.to_xarray().to_netcdf(
|
||||
'C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/ADCP2/' +
|
||||
stat + '_' + var + '_d' + str(depdat) + '.nc')
|
||||
|
||||
gdf_adcp2_locs.to_csv('C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/ADCP2/ADCP2locations.csv')
|
||||
|
||||
# %% Plot ADCP2 Data
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(9, 8))
|
||||
fig.tight_layout(pad=2)
|
||||
|
||||
colormap = 'jet'
|
||||
vmin = 0
|
||||
vmax = 0.2
|
||||
|
||||
for stationIDX, stat in enumerate(adcp2_gdfs):
|
||||
for depIDX, depdat in enumerate(adcp2_gdfs[stat]['mag']):
|
||||
depths1 = adcp2_gdfs[stat]['mag'][depdat].columns[6:-2] # ADCP sensor depth reading column names to list
|
||||
depths_moored = np.array([float(d.replace('m', '')) for d in depths1])
|
||||
|
||||
V = adcp2_gdfs[stat]['mag'][depdat].values[:, 6:-2].astype(float)
|
||||
|
||||
pltDat = axes[stationIDX].pcolormesh(adcp2_gdfs[stat]['mag'][depdat].loc[:, 'date'],
|
||||
depths_moored, np.transpose(V),
|
||||
shading='auto', vmin=vmin, vmax=vmax, cmap=colormap)
|
||||
|
||||
axes[stationIDX].set_ylim(0, 7)
|
||||
axes[stationIDX].set_xlim(pd.to_datetime("2014-07-01"), pd.to_datetime('2015-05-15'))
|
||||
#axes[stationIDX, depIDX].format_xdata = mdates.DateFormatter('%Y-%m')
|
||||
fmt_half_year = mdates.MonthLocator(interval=1)
|
||||
axes[stationIDX].xaxis.set_major_locator(fmt_half_year)
|
||||
axes[stationIDX].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
|
||||
|
||||
axes[stationIDX].set_title(str(stat))
|
||||
|
||||
axes[stationIDX].set_ylabel('Elevation [m]')
|
||||
|
||||
cbar = fig.colorbar(pltDat, ax=axes[stationIDX],
|
||||
shrink=1.1, aspect=3)
|
||||
cbar.set_label('Magnitude [m/s]')
|
||||
pltDat.set_clim([0, 0.2])
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/ADCP_2014.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% 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_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',
|
||||
sheet_name='Field_Facility_Gauge')
|
||||
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/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')
|
||||
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/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")
|
||||
|
||||
gdf_wl_FFG.to_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/FieldFacility.csv')
|
||||
gdf_wl_NGG1.to_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid1.csv')
|
||||
gdf_wl_NGG2.to_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid2.csv')
|
||||
|
||||
|
||||
# %% Plot Water Level Data
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 4))
|
||||
|
||||
axes.plot(gdf_wl_FFG.Date_time,
|
||||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048,
|
||||
label='Field Facility Gauge')
|
||||
|
||||
axes.plot(gdf_wl_NGG1.Date_time,
|
||||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048,
|
||||
label='National Grid Gauge Deployment 1')
|
||||
|
||||
axes.plot(gdf_wl_NGG2.datetime,
|
||||
gdf_wl_NGG2['water_surface_elevation']*0.3048,
|
||||
label='National Grid Gauge Deployment 2')
|
||||
|
||||
fmt_half_year = mdates.MonthLocator(interval=6)
|
||||
axes.xaxis.set_major_locator(fmt_half_year)
|
||||
axes.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
|
||||
axes.set_ylabel('Water Surface Elevation [mNAVD88]')
|
||||
axes.set_title('Water Surface Elevation')
|
||||
|
||||
axes.legend()
|
||||
|
||||
fig.show()
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/WaterLevel.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
|
||||
|
||||
# %% Load temperature data
|
||||
df_tdat = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/10_Salinity/tData.xlsx',
|
||||
skiprows=[1])
|
||||
|
||||
# Day Month order is reversed
|
||||
df_tdat['date'] = pd.to_datetime(df_tdat['CollectionDate'])
|
||||
|
||||
df_tsample = pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/10_Salinity/tSampleLocation.csv')
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_tsample = gp.GeoDataFrame(
|
||||
df_tsample, geometry=gp.points_from_xy(df_tsample.EastCoordinate, df_tsample.NorthCoordinate), crs="EPSG:2263")
|
||||
|
||||
#convert data to CRS of D3D
|
||||
gdf_tsample.geometry = gdf_tsample.geometry.to_crs("EPSG:32118")
|
||||
|
||||
tdat_geo = list()
|
||||
tdat_facility = list()
|
||||
|
||||
for i in range(0, len(df_tdat)):
|
||||
#sampleID = df_tdat['LocationID'][i][0:df_tdat['LocationID'][i].find('_')]
|
||||
#geoFind = df_tsample.loc[df_tsample['ParentLocationID'] == sampleID]
|
||||
geoFind = df_tsample.loc[df_tsample['LocationID'] == df_tdat['LocationID'][i]].geometry.values
|
||||
|
||||
if len(geoFind) !=0:
|
||||
tdat_geo.append(df_tsample.loc[df_tsample['LocationID'] == df_tdat['LocationID'][i]].geometry.values[0])
|
||||
tdat_facility.append(df_tsample.loc[df_tsample['LocationID'] == df_tdat['LocationID'][i]].SourceArea.values[0])
|
||||
else:
|
||||
tdat_geo.append(Point())
|
||||
tdat_facility.append('')
|
||||
|
||||
# Create geodataframe
|
||||
gdf_tdat = gp.GeoDataFrame(df_tdat, geometry=tdat_geo, crs="EPSG:32118")
|
||||
gdf_tdat.loc[:, 'SourceArea'] = tdat_facility
|
||||
|
||||
gdf_tdat.loc[gdf_tdat.loc[:, 'DepthUnit']=='ft', 'DepthM'] = gdf_tdat.loc[gdf_tdat.loc[:, 'DepthUnit']=='ft', 'BeginDepth']*0.3048
|
||||
gdf_tdat.loc[gdf_tdat.loc[:, 'DepthUnit']=='cm', 'DepthM'] = gdf_tdat.loc[gdf_tdat.loc[:, 'DepthUnit']=='cm', 'BeginDepth']*0.01
|
||||
|
||||
|
||||
# Import spring observations
|
||||
df_springDat = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/11_Temperature/NC_Spr2012_Benthic_Water_Quality_FieldData&Observations_20121022.xlsx')
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_springDat = gp.GeoDataFrame(
|
||||
df_springDat, geometry=gp.points_from_xy(df_springDat.x_coord_as_numeric, df_springDat.y_coord_as_numeric), crs="EPSG:2263")
|
||||
gdf_springDat.geometry = gdf_springDat.geometry.to_crs("EPSG:32118")
|
||||
|
||||
|
||||
# Import Summer observations
|
||||
df_summerDat = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/11_Temperature/NC_Sum2012_Benthic_Water_Quality_FieldData&Observations_20130205.xlsx')
|
||||
|
||||
# Create geodataframe and convert to USSP
|
||||
gdf_summerDat = gp.GeoDataFrame(
|
||||
df_summerDat, geometry=gp.points_from_xy(df_summerDat.x_coord_as_numeric, df_summerDat.y_coord_as_numeric), crs="EPSG:2263")
|
||||
gdf_summerDat.geometry = gdf_summerDat.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# Merged
|
||||
df_SpringSummerDat = pd.concat([df_springDat, gdf_summerDat], ignore_index=True)
|
||||
gdf_SpringSummerDat = gp.GeoDataFrame(
|
||||
df_SpringSummerDat, geometry=gp.points_from_xy(df_SpringSummerDat.x_coord_as_numeric, df_SpringSummerDat.y_coord_as_numeric), crs="EPSG:2263")
|
||||
gdf_SpringSummerDat.geometry = gdf_SpringSummerDat.geometry.to_crs("EPSG:32118")
|
||||
|
||||
# %% Save temperature and salinity data
|
||||
gdf_SpringSummerDat.to_csv('C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/TempSal/Spring_Summer.csv')
|
||||
|
||||
# %% Plot Salinity Time series
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 8))
|
||||
plotMaskStation = (gdf_tdat.SourceArea == 'Newtown Creek') | (gdf_tdat.SourceArea == 'East Branch of Newtown Creek')
|
||||
plotMaskWater = (gdf_tdat.SampleMedium == 'Surface Water')
|
||||
plotMask = plotMaskStation & plotMaskWater
|
||||
|
||||
pltDat = axes.scatter(gdf_tdat.loc[plotMask, 'date'],
|
||||
gdf_tdat.loc[plotMask, 'DepthM'], 10,
|
||||
gdf_tdat.loc[plotMask, 'NumericResult'])
|
||||
axes.set_ylim(10, 0)
|
||||
axes.set_ylabel('Depth below water surface [m]')
|
||||
axes.set_title('Surface Water Salinity Samples')
|
||||
axes.set_xlabel('Date')
|
||||
|
||||
fmt_half_year = mdates.MonthLocator(interval=12)
|
||||
axes.xaxis.set_major_locator(fmt_half_year)
|
||||
axes.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
|
||||
|
||||
cbar = fig.colorbar(pltDat, ax=axes, shrink=0.95)
|
||||
cbar.set_label('Salinity [PSU]')
|
||||
pltDat.set_clim([5, 40])
|
||||
|
||||
fig.show()
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/Salinity.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
|
||||
# %% Plot Temperature Time series
|
||||
## Additional salinity obs are here!
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(6, 8))
|
||||
fig.tight_layout(pad=2)
|
||||
|
||||
for i, miles in enumerate(np.arange(0, 3, 0.5)):
|
||||
plotMaskDistance = (gdf_SpringSummerDat.miles_from_NC_mouth > miles) & (
|
||||
gdf_SpringSummerDat.miles_from_NC_mouth < miles + 0.5)
|
||||
plotMaskStation = (gdf_SpringSummerDat.subfacility_code == 'Newtown Creek')
|
||||
plotMaskVar = (gdf_SpringSummerDat.chemical_name == 'Temperature (field)')# Temperature (field)'
|
||||
|
||||
plotMask = plotMaskDistance & plotMaskStation & plotMaskVar
|
||||
|
||||
|
||||
pltDat = axes[i].scatter(gdf_SpringSummerDat.loc[plotMask, 'location_start_date'],
|
||||
gdf_SpringSummerDat.loc[plotMask, 'elev']*0.3048, 10,
|
||||
gdf_SpringSummerDat.loc[plotMask, 'result_value'])
|
||||
|
||||
axes[i].set_ylim(-10, 0)
|
||||
axes[i].set_ylabel('Elevation [m]')
|
||||
|
||||
cbar = fig.colorbar(pltDat, ax=axes[i], shrink=0.95)
|
||||
cbar.set_label('Temp [degC]')
|
||||
pltDat.set_clim([5, 30])
|
||||
|
||||
fmt_half_year = mdates.MonthLocator(interval=1)
|
||||
axes[i].xaxis.set_major_locator(fmt_half_year)
|
||||
axes[i].xaxis.set_major_formatter(mdates.DateFormatter('%m-%Y'))
|
||||
|
||||
axes[0].set_title('Surface Water Temperature Samples')
|
||||
axes[i].set_xlabel('Date')
|
||||
|
||||
fig.show()
|
||||
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/Temperature.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Load in ADV data
|
||||
|
||||
# Read in locations
|
||||
df_adv_locs = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/Velocity_Data_Compiled/Phase2/Locations/AQ_LocationsSO_ADCP_ADV_overview_Coords_20150126_AJMR.xlsx',
|
||||
sheet_name='ADV2')
|
||||
gdf_adv_locs = gp.GeoDataFrame(df_adv_locs,
|
||||
geometry=gp.points_from_xy(df_adv_locs.X_NYSPLI, df_adv_locs.Y_NYSPLI), crs="EPSG:2263")
|
||||
gdf_adv_locs = gdf_adv_locs.to_crs("EPSG:32118")
|
||||
|
||||
|
||||
adv_data_path = '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/05 Currents/Velocity_Data_Compiled/Phase2/Raw'
|
||||
|
||||
adv_paths = ['NCP2_ADV_D1-D2/ADV_D1_070914_080214', 'NCP2_ADV_D1-D2/ADV_D2_080214_090814',
|
||||
'NCP2_ADV_D3-D4/ADV_D3_090914_100614', 'NCP2_ADV_D3-D4/ADV_D4_100714_110214',
|
||||
'NCP2_ADV_D5-D6/ADV_D5_110414_120214', 'NCP2_ADV_D5-D6/ADV_D6_120414_010615']
|
||||
|
||||
adv_gdfs = dict()
|
||||
adv_dfs = dict()
|
||||
|
||||
for depIDX, adv_path in enumerate(adv_paths):
|
||||
adv_files = os.listdir(adv_data_path + '/' + adv_path) # returns list of files in adv folder
|
||||
|
||||
for adv_file in adv_files:
|
||||
if '.txt' in adv_file and 'Readme' not in adv_file:
|
||||
df_in = pd.read_csv(adv_data_path + '/' + adv_path + '/' + adv_file, delim_whitespace=True, skipinitialspace=True)
|
||||
|
||||
for stationIDX, station in enumerate(gdf_adv_locs['parent_loc_code']):
|
||||
if adv_file[-9:-6] in station:
|
||||
# adv_geo_x = np.ones([len(df_in), 1]) * df_adv_locs.X_NYSPLI[stationIDX]
|
||||
# adv_geo_y = np.ones([len(df_in), 1]) * df_adv_locs.Y_NYSPLI[stationIDX]
|
||||
|
||||
|
||||
df_in['date'] = pd.to_datetime(df_in['Year'].astype(str) + '-' + df_in['Month'].astype(
|
||||
str).str.zfill(2) + '-' + df_in['Day'].astype(
|
||||
str).str.zfill(2) + ' ' + df_in['Hour'].astype(
|
||||
str).str.zfill(2) + ':' + df_in['Minute'].astype(
|
||||
str).str.zfill(2) + ':' + df_in['Second'].astype(str).str.zfill(2))
|
||||
df_in.set_index('date', inplace=True)
|
||||
|
||||
df_in.drop(columns=['Year', 'Month', 'Day', 'Hour', 'Minute', 'Second'], inplace=True)
|
||||
df_in.dropna(how='all', axis=1, inplace=True)
|
||||
|
||||
colName = df_in.columns
|
||||
df_in[colName] = df_in[colName].astype('float32')
|
||||
|
||||
# Location
|
||||
if adv_file[-9:-6] not in adv_gdfs:
|
||||
adv_gdfs[adv_file[-9:-6]] = dict()
|
||||
adv_dfs[adv_file[-9:-6]] = dict()
|
||||
# D1-6
|
||||
if adv_file[-6:-4] not in adv_gdfs[adv_file[-9:-6]]:
|
||||
adv_gdfs[adv_file[-9:-6]][adv_file[-6:-4]] = gdf_adv_locs.iloc[stationIDX]
|
||||
adv_dfs[adv_file[-9:-6]][adv_file[-6:-4]] = df_in
|
||||
else:
|
||||
adv_gdfs[adv_file[-9:-6]][adv_file[-6:-4]] = gdf_adv_locs.iloc[stationIDX]
|
||||
adv_dfs[adv_file[-9:-6]][adv_file[-6:-4]].loc[:, colName] = df_in
|
||||
|
||||
print('ADV:' + adv_file[-9:-6] +
|
||||
'; ' + adv_file[-6:-4] +
|
||||
'; var:' + adv_file[3:6])
|
||||
|
||||
# with open('ADV.pickle', 'wb') as f:
|
||||
# pickle.dump(adv_dfs, f)
|
||||
# %% Save ADV to NetCDF
|
||||
for stationIDX, stat in enumerate(adv_dfs):
|
||||
for depIDX, depdat in enumerate(adv_dfs[stat]):
|
||||
ncDat = adv_dfs[stat][depdat]
|
||||
ncDat.columns = ncDat.columns.str.replace(r"[()]", "_")
|
||||
ncDat.columns = ncDat.columns.str.replace(r"[/]", "_")
|
||||
|
||||
ncDat.to_xarray().to_netcdf(
|
||||
'C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/ADV/' + stat + '_' + depdat + '.nc')
|
||||
|
||||
gdf_adv_locs.to_csv('C:/Users/arey/files/Projects/Newtown/DataFigs/NetCDF/ADV/ADVlocations.csv')
|
||||
|
||||
# %% Plot ADV Data
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(9, 8))
|
||||
fig.tight_layout(pad=2)
|
||||
|
||||
for stationIDX, stat in enumerate(adv_dfs):
|
||||
for depIDX, depdat in enumerate(adv_dfs[stat]):
|
||||
|
||||
# adv_dfs[stat][depdat]['vel'].iloc[::60, :].plot(ax=axes[stationIDX])
|
||||
if 'Velocity' in adv_dfs[stat][depdat].columns:
|
||||
plotingDat = adv_dfs[stat][depdat].loc[:, 'Velocity'].resample('1s').mean()
|
||||
else:
|
||||
plotingDat = adv_dfs[stat][depdat].loc[:, 'Velocity_m_s_'].resample('1s').mean()
|
||||
|
||||
axes[stationIDX].plot(plotingDat.index, plotingDat)
|
||||
|
||||
# axes[stationIDX].set_ylim(0, 7)
|
||||
axes[stationIDX].set_xlim(pd.to_datetime("2014-07-01"), pd.to_datetime('2015-02-01'))
|
||||
# #axes[stationIDX, depIDX].format_xdata = mdates.DateFormatter('%Y-%m')
|
||||
fmt_half_year = mdates.MonthLocator(interval=1)
|
||||
axes[stationIDX].xaxis.set_major_locator(fmt_half_year)
|
||||
axes[stationIDX].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
|
||||
|
||||
|
||||
axes[stationIDX].set_title(str(stat))
|
||||
#
|
||||
axes[stationIDX].set_ylabel('ADV Velocity [m/s]')
|
||||
|
||||
# cbar.set_label('Velocity Magnitude [m/s]')
|
||||
# pltDat.set_clim([0, 0.2])
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/ADV_raw.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Plot Map
|
||||
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=(8, 8))
|
||||
|
||||
locTableNames = []
|
||||
locTableX = []
|
||||
locTableY = []
|
||||
|
||||
axes.set_xlim(303500, 306500)
|
||||
axes.set_ylim(61000, 63750)
|
||||
gdf.plot(ax=axes, markersize=10, color='blue', label='Mobile ADCP')
|
||||
for x, y, label in zip(gdf.drop_duplicates(subset="station", keep='last').geometry.x,
|
||||
gdf.drop_duplicates(subset="station", keep='last').geometry.y,
|
||||
gdf.drop_duplicates(subset="station", keep='last').station):
|
||||
axes.annotate(label, xy=(x, y), xytext=(20, 3), textcoords="offset points", color='blue',
|
||||
bbox=dict(boxstyle="square,pad=0.3", fc="white", ec="k", lw=0))
|
||||
|
||||
|
||||
gdf_SpringSummerDat.plot(ax=axes, markersize=12, color='magenta', label='Temperature & Salinity')
|
||||
for x, y, label in zip(gdf_SpringSummerDat.drop_duplicates(subset="loc_name", keep='last').geometry.x,
|
||||
gdf_SpringSummerDat.drop_duplicates(subset="loc_name", keep='last').geometry.y,
|
||||
gdf_SpringSummerDat.drop_duplicates(subset="loc_name", keep='last').loc_name):
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
gdf_moored_loc.plot(ax=axes, markersize=20, color='red', label='Moored ADCP 2012')
|
||||
for x, y, label in zip(gdf_moored_loc.drop_duplicates(subset="depCurr", keep='last').geometry.x,
|
||||
gdf_moored_loc.drop_duplicates(subset="depCurr", keep='last').geometry.y,
|
||||
gdf_moored_loc.drop_duplicates(subset="depCurr", keep='last').depCurr):
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
gdf_adcp2_locs.plot(ax=axes, markersize=20, color='orange', label='Moored ADCP 2014')
|
||||
for x, y, label in zip(gdf_adcp2_locs.drop_duplicates(subset="parent_loc_code", keep='last').geometry.x,
|
||||
gdf_adcp2_locs.drop_duplicates(subset="parent_loc_code", keep='last').geometry.y,
|
||||
gdf_adcp2_locs.drop_duplicates(subset="parent_loc_code", keep='last').parent_loc_code):
|
||||
axes.annotate(label, xy=(x, y), xytext=(-65, 3), textcoords="offset points", color='orange',
|
||||
bbox=dict(boxstyle="square,pad=0.3", fc="white", ec="k", lw=0))
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
gdf_adv_locs.plot(ax=axes, markersize=20, color='green', label='Moored ADV 2014')
|
||||
for x, y, label in zip(gdf_adv_locs.geometry.x,
|
||||
gdf_adv_locs.geometry.y,
|
||||
gdf_adv_locs.parent_loc_code):
|
||||
axes.annotate(label, xy=(x, y), xytext=(-30, -30), textcoords="offset points", color='green',
|
||||
bbox=dict(boxstyle="square,pad=0.3", fc="white", ec="k", lw=0))
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
gdf_gaugeLocUSSP.loc[2:3, 'geometry'].plot(ax=axes, markersize=20, color='yellow', label='Water Level Gauge')
|
||||
for x, y, label in zip(gdf_gaugeLocUSSP.loc[2:3, 'geometry'].x,
|
||||
gdf_gaugeLocUSSP.loc[2:3, 'geometry'].y,
|
||||
gdf_gaugeLocUSSP.loc[2:3, 'Name']):
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
for x, y, label in zip(gdf.geometry.x,
|
||||
gdf.geometry.y,
|
||||
gdf.station + '_' + gdf.transect.astype(str) + gdf['min'].astype(str) + gdf.second.astype(str)):
|
||||
locTableNames.append(label)
|
||||
locTableX.append(x)
|
||||
locTableY.append(y)
|
||||
|
||||
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32118')
|
||||
axes.set_xlabel('New York State Plane Easting [m]')
|
||||
axes.set_ylabel('New York State Plane Northing [m]')
|
||||
axes.legend()
|
||||
|
||||
# axes[1].set_xlim(303500, 306500)
|
||||
# axes[1].set_ylim(61000, 63750)
|
||||
# gdf_SpringSummerDat.plot(ax=axes[1], markersize=12, color='magenta', label='Temperature & Salinity')
|
||||
# axes[1].set_xlabel('New York State Plane Easting [m]')
|
||||
# axes[1].legend()
|
||||
# ctx.add_basemap(axes[1], source=mapbox, crs='EPSG:32118')
|
||||
|
||||
fig.show()
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/DataFigs/DataMap_ADV.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
# %% Import grid shapefile and find cells
|
||||
delftGrid = gp.read_file('C:/Users/arey/files/Projects/Newtown/Topology data of 2D network.shp')
|
||||
delftGrid = delftGrid.set_crs("EPSG:32118")
|
||||
delftGrid['centroid'] = delftGrid.geometry.centroid
|
||||
|
||||
obsPts = gp.GeoDataFrame(locTableNames, geometry=gp.points_from_xy(locTableX, locTableY), crs="EPSG:32118")
|
||||
|
||||
joinPTS = gp.sjoin(obsPts, delftGrid, op='within')
|
||||
|
||||
uniqueJoinPTS = joinPTS.index_right.unique()
|
||||
groupdObsLabels = joinPTS.groupby(by='index_right').agg({0:lambda x:list(x)})
|
||||
|
||||
uniqueDelftGrid = delftGrid.iloc[uniqueJoinPTS, :]
|
||||
uniqueDelftGrid['Station Names'] = groupdObsLabels
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
|
||||
axes.set_xlim(303500, 306500)
|
||||
axes.set_ylim(61000, 63750)
|
||||
delftGrid.plot(ax=axes, markersize=10, color='gray', label='Mobile ADCP')
|
||||
delftGrid.loc[uniqueJoinPTS, 'geometry'].plot(ax=axes, markersize=10, color='blue', label='Mobile ADCP')
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32118')
|
||||
axes.set_xlabel('New York State Plane Easting [m]')
|
||||
axes.set_ylabel('New York State Plane Northing [m]')
|
||||
fig.show()
|
||||
|
||||
uniqueDelftGrid.to_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/06_Models/06_Delft3DFM/RectConnect2_Obs_PTS.xlsx')
|
||||
|
|
@ -0,0 +1,427 @@
|
|||
"""
|
||||
@author: AJMR
|
||||
|
||||
Plotting and animation script for NTC sediment tests
|
||||
|
||||
April 17, 2023
|
||||
"""
|
||||
|
||||
import os
|
||||
import pandas as pd
|
||||
import geopandas as gpd
|
||||
import netCDF4 as nc
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
import matplotlib as mpl
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.interpolate import LinearNDInterpolator, interp1d
|
||||
import matplotlib.animation as animation
|
||||
|
||||
import dfm_tools as dfmt
|
||||
|
||||
import xarray as xr
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
# Colormaps
|
||||
import cm_xml_to_matplotlib as cm
|
||||
|
||||
#%% Read Model Log
|
||||
|
||||
runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
|
||||
|
||||
dataPath = "FlowFM/dflowfm/output/FlowFM_map.nc"
|
||||
histPath = "FlowFM/dflowfm/output/FlowFM_his.nc"
|
||||
|
||||
#%% Import Model results for static images
|
||||
|
||||
# Define which models to import
|
||||
modelPlot = [120, 121, 129, 130, 131, 132, 133, 134]
|
||||
|
||||
d3dfm_DataArray = [None] * (max(modelPlot)+1)
|
||||
sedMass = [None] * (max(modelPlot)+1) # Available Mass of Sediment
|
||||
erodeSed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
initialBed = [None] * (max(modelPlot)+1) # Erosion and deposition
|
||||
modelCRS = [None] * (max(modelPlot)+1) # Model CRS
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_map = Path(runLog['Run Location'][i]) / dataPath
|
||||
|
||||
# Open Map file as xarrray Dataset
|
||||
d3dfm_DataArray[i] = dfmt.open_partitioned_dataset(file_nc_map.as_posix())
|
||||
|
||||
# Get Var info
|
||||
vars_pd = dfmt.get_ncvarproperties(d3dfm_DataArray[i])
|
||||
|
||||
# Get last timestep
|
||||
tStep = d3dfm_DataArray[i].time[-1].values
|
||||
|
||||
# Import sand sediment class
|
||||
sedMass[i] = d3dfm_DataArray[i].mesh2d_bodsed[-1, :, 2].compute()
|
||||
initialBed[i]= d3dfm_DataArray[i].mesh2d_s1[0, :] - d3dfm_DataArray[i].mesh2d_waterdepth[0, :]
|
||||
finalBed = d3dfm_DataArray[i].mesh2d_s1[-1, :] - d3dfm_DataArray[i].mesh2d_waterdepth[-1, :]
|
||||
|
||||
erodeSed[i] = finalBed - initialBed[i]
|
||||
erodeSed[i] = erodeSed[i].compute()
|
||||
initialBed[i] = initialBed[i].compute()
|
||||
|
||||
modelCRS[i] = vars_pd['EPSG_code']['projected_coordinate_system']
|
||||
|
||||
#%% Define axis limits
|
||||
axLim_Xmin = []
|
||||
axLim_Xmax = []
|
||||
|
||||
axLim_Ymin = []
|
||||
axLim_Ymax = []
|
||||
|
||||
# Set axis limits
|
||||
# Full domain
|
||||
axLim_Xmin.append(298500)
|
||||
axLim_Xmax.append(306800)
|
||||
|
||||
axLim_Ymin.append(58500)
|
||||
axLim_Ymax.append(68000)
|
||||
|
||||
# Zoom North River
|
||||
axLim_Xmin.append(303500)
|
||||
axLim_Xmax.append(305500)
|
||||
|
||||
axLim_Ymin.append(66000)
|
||||
axLim_Ymax.append(68000)
|
||||
|
||||
# Zoom Tribs
|
||||
axLim_Xmin.append(305500)
|
||||
axLim_Xmax.append(306800)
|
||||
|
||||
axLim_Ymin.append(60000)
|
||||
axLim_Ymax.append(62200)
|
||||
|
||||
# Zoom far trib
|
||||
axLim_Xmin.append(305800)
|
||||
axLim_Xmax.append(305900)
|
||||
|
||||
axLim_Ymin.append(60150)
|
||||
axLim_Ymax.append(60400)
|
||||
|
||||
|
||||
#%% Plot using DFM functions
|
||||
|
||||
for i in modelPlot:
|
||||
# Create figure
|
||||
fig, axesFig = plt.subplots(1, 4, figsize=(8, 2))
|
||||
|
||||
axes = axesFig.flatten()
|
||||
|
||||
# Loop through axis and plot with different limits
|
||||
for subIDX in range(0, 4):
|
||||
|
||||
# # Plot Sediment Mass
|
||||
# smp = sedMass[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='turbo',
|
||||
# vmin=0, vmax=2000, add_colorbar=False)
|
||||
|
||||
# # Plot Erosion
|
||||
# smp = erodeSed[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='coolwarm',
|
||||
# vmin=-1, vmax=1, add_colorbar=False)
|
||||
|
||||
# Plot Bed
|
||||
smp = initialBed[i].ugrid.plot(ax=axes[subIDX], edgecolors='face', cmap='nipy_spectral',
|
||||
vmin=-25, vmax=0, add_colorbar=False)
|
||||
|
||||
|
||||
# Set axis labels
|
||||
axes[subIDX].set_xlabel('')
|
||||
axes[subIDX].set_ylabel('')
|
||||
axes[subIDX].set_title('')
|
||||
axes[subIDX].set_xticks([])
|
||||
axes[subIDX].set_yticks([])
|
||||
|
||||
axes[subIDX].set_xlim(axLim_Xmin[subIDX], axLim_Xmax[subIDX])
|
||||
axes[subIDX].set_ylim(axLim_Ymin[subIDX], axLim_Ymax[subIDX])
|
||||
|
||||
# Add basemap
|
||||
# ctx.add_basemap(ax=axes, source=ctx.providers.Esri.WorldImagery, crs=modelCRS[i], attribution=False)
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
ctx.add_basemap(axes[subIDX], source=mapbox, crs=modelCRS[i])
|
||||
|
||||
|
||||
# cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Sand Mass [kg/m2]')
|
||||
# cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Erosion/ deposition [m]')
|
||||
cbar = plt.colorbar(smp, ax=axesFig.ravel().tolist(), label='Initial Bed [mNAVD88]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/SedMass_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/ErodeDep_' + runLog['Run'][i] + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs2/InitialBed_' + runLog['Run'][i] + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Plot using DFM functions
|
||||
#Cmap Path
|
||||
cmap_path = 'C:/Users/arey/Repo/MATLAB_Q/Downloads/KeyColormaps/'
|
||||
|
||||
for i in modelPlot:
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, subplot_kw={'projection': ccrs.epsg(32118)}, figsize=(6, 6))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
fig.tight_layout(pad=3.0)
|
||||
|
||||
# Load cmaps
|
||||
cmap = 'AJMR_Sed5_RevE.xml'
|
||||
plotcmap = cm.make_cmap(cmap_path + cmap)
|
||||
|
||||
pc = plot_netmapdata(ugrid_all[i].verts, values=modelMaxShear[i][0, :],
|
||||
ax=axes, linewidth=0.5, cmap=plotcmap)
|
||||
|
||||
axes.set_xlim(305900, 306400)
|
||||
axes.set_ylim(61500, 62200)
|
||||
|
||||
# axes.quiver(modelX[i], modelX[i], U[i], V[i], color='w', scale=1.00)
|
||||
|
||||
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:32118')
|
||||
|
||||
extent = axes.get_extent()
|
||||
axes.set_xticks(np.linspace(extent[0], extent[1], 4))
|
||||
axes.set_yticks(np.linspace(extent[2], extent[3], 4))
|
||||
|
||||
pc.set_clim([0, 0.145])
|
||||
cbarTicks = [0, 0.0378, 0.063, 0.0826, 0.11, 0.145]
|
||||
cbar = fig.colorbar(pc, ax=axes, shrink=0.95,
|
||||
ticks=cbarTicks)
|
||||
cbar.set_label('Total Max Shear Stress [N/m^2]')
|
||||
|
||||
# Add label on colorbar
|
||||
cbar.ax.text(0.08, (cbarTicks[0]+cbarTicks[1])/2, 'CLAY', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[1]+cbarTicks[2])/2, 'FSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[2]+cbarTicks[3])/2, 'MSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[3]+cbarTicks[4])/2, 'CSILT', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
cbar.ax.text(0.08, (cbarTicks[4]+cbarTicks[5])/2, 'FSAND', ha='center', va='center',
|
||||
rotation=90, fontweight='bold')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/SedFigs/Shear_Class_' + runLog['Run'][i] + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
#%% Import Model results for animations
|
||||
|
||||
# modelPlot = [20, 22]
|
||||
# modelPlot = [18, 20, 21, 22]
|
||||
# modelPlot = [18, 21]
|
||||
modelPlot = [25, 26]
|
||||
|
||||
modelSedConc = [None] * (max(modelPlot)+1)
|
||||
modelMaxShear_animate = [None] * (max(modelPlot)+1)
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
tSteps = [None] * (max(modelPlot)+1)
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_map = Path(runLog['Run Location'][i]) / dataPath
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map.as_posix())
|
||||
|
||||
# Import
|
||||
tSteps[i] = get_timesfromnc(file_nc=file_nc_map.as_posix(), varname='time')
|
||||
|
||||
ugrid_all[i] = get_netdata(file_nc=file_nc_map.as_posix())
|
||||
modelSedConc[i] = get_ncmodeldata(file_nc=file_nc_map.as_posix(), varname='mesh2d_sedfrac_concentration',
|
||||
timestep='all', station=0, layer=0) #timestep='all' OR timestep=range(0, 30)
|
||||
modelMaxShear_animate[i] = get_ncmodeldata(file_nc=file_nc_map.as_posix(), varname='mesh2d_tausmax',
|
||||
timestep='all')
|
||||
|
||||
|
||||
# %% Import water levels from hist
|
||||
modelHistWL = [None] * (max(modelPlot)+1)
|
||||
modelHistTime = [None] * (max(modelPlot)+1)
|
||||
# tSteps = [None] * (max(modelPlot)+1)
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_hist = Path(runLog['Run Location'][i]) / histPath
|
||||
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_hist.as_posix())
|
||||
|
||||
# Get station names
|
||||
histStations = get_hisstationlist(file_nc=file_nc_hist.as_posix(), varname='waterlevel')
|
||||
|
||||
# Import
|
||||
modelHistTime[i] = get_timesfromnc(file_nc=file_nc_hist.as_posix(), varname='time')
|
||||
modelHistWL[i] = get_ncmodeldata(file_nc=file_nc_hist.as_posix(), varname='waterlevel',
|
||||
timestep='all', station=1)
|
||||
|
||||
|
||||
#%% Sediment animations
|
||||
plt.rcParams['animation.ffmpeg_path'] = \
|
||||
'C:/Users/arey/Local/ffmpeg-2022-02-14-git-59c647bcf3-full_build/bin/ffmpeg.exe'
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckex9vtri0o6619p55sl5qiyv/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
# fontprops = fm.FontProperties(size=12)
|
||||
|
||||
# Set model to plot
|
||||
# modelPlot = [20, 22]
|
||||
# modelPlot = [18, 20, 21, 22]
|
||||
modelPlot = [25, 26]
|
||||
cmap_path = 'C:/Users/arey/Repo/MATLAB_Q/Downloads/KeyColormaps/'
|
||||
|
||||
for m in modelPlot:
|
||||
vmax = 0.75
|
||||
vmin = 0
|
||||
# vmax = 0.145
|
||||
# vmin = 0
|
||||
|
||||
cbarTicks = [0, 0.0378, 0.063, 0.0826, 0.11, 0.145]
|
||||
|
||||
cmapName = 'turbo'
|
||||
cmap = mpl.cm.turbo
|
||||
# cmapName = 'AJMR_Sed5_RevE.xml'
|
||||
# cmap = cm.make_cmap(cmap_path + cmapName)
|
||||
|
||||
|
||||
# Zoom limits
|
||||
# xLimits = [305900, 306500]
|
||||
# yLimits = [61400, 62200]
|
||||
|
||||
# Wide Limits
|
||||
xLimits = [302719.4, 306934.2]
|
||||
yLimits = [60263.7, 64194.8]
|
||||
|
||||
# Setup Video
|
||||
metadata = dict(title='NTC Sediment Animation', artist='Matplotlib',
|
||||
comment='AJMR June 28, 2022')
|
||||
writer = animation.FFMpegWriter(fps=2, metadata=metadata, codec='h264', bitrate=5000)
|
||||
fig, axes = plt.subplots(figsize=(6, 6))
|
||||
|
||||
writer.setup(fig, '//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
'06_Models/04_Delft3D/Figures/SedConc/HQ3_Wide_RevC_Long_Sed_Cond_'
|
||||
+ runLog['Run'][m] + '.mp4')
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
for i in np.arange(1, len(modelSedConc[m])-1, 1): #289
|
||||
# Clear axes for video
|
||||
axes.cla()
|
||||
|
||||
# Create new figure for stills
|
||||
# fig, axes = plt.subplots(figsize=(6, 6))
|
||||
|
||||
# Clear inset WL Plot
|
||||
# if i > 1:
|
||||
# axin1.cla()
|
||||
|
||||
# Plot sediment
|
||||
pc = plot_netmapdata(ugrid_all[m].verts, values=modelSedConc[m][i, 0, :, 0],
|
||||
ax=axes, cmap=cmapName,
|
||||
antialiaseds=False)
|
||||
|
||||
# Plot shear
|
||||
# pc = plot_netmapdata(ugrid_all[m].verts, values=modelMaxShear_animate[m][i,:],
|
||||
# ax=axes, cmap=cmap,
|
||||
# antialiaseds=False)
|
||||
|
||||
# Set map limits
|
||||
axes.set_xlim(xLimits[0], xLimits[1])
|
||||
axes.set_ylim(yLimits[0], yLimits[1])
|
||||
|
||||
# Add basemap
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32118')
|
||||
|
||||
# Set map ticks
|
||||
axes.set_xticks(np.linspace(xLimits[0], xLimits[1], 4))
|
||||
axes.set_yticks(np.linspace(yLimits[0], yLimits[1], 4))
|
||||
|
||||
# Color Limits
|
||||
pc.set_clim([vmin, vmax])
|
||||
|
||||
# Add title
|
||||
axes.title.set_text('Bottom Sediment Concentration at: ' + tSteps[m][i].strftime("%Y/%m/%d %H:%M"))
|
||||
# axes.title.set_text('Total Max Shear Stress at: ' + str(tSteps[m][i]))
|
||||
|
||||
# Add inset wl plot
|
||||
axin1 = axes.inset_axes([0.1, 0.1, 0.4, 0.15])
|
||||
axin1.plot(modelHistTime[m], modelHistWL[m])
|
||||
# Add current point
|
||||
# Find closest time
|
||||
abs_deltas_from_target_date = np.absolute(modelHistTime[m] - tSteps[m][i])
|
||||
axin1.scatter(modelHistTime[m][np.argmin(abs_deltas_from_target_date)], modelHistWL[m][np.argmin(abs_deltas_from_target_date)], 10, 'r')
|
||||
|
||||
# axin1.set_xticks([modelHistTime[m][0], modelHistTime[m][len(modelHistTime[m])-1]])
|
||||
axin1.set_xticks([])
|
||||
axin1.set_yticks([])
|
||||
|
||||
plt.setp(axin1.get_xticklabels(), backgroundcolor="white")
|
||||
plt.setp(axin1.get_yticklabels(), backgroundcolor="white")
|
||||
|
||||
if i == 1: #i < 1000
|
||||
fig.subplots_adjust(right=0.8)
|
||||
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
|
||||
|
||||
# Create colorbar axis
|
||||
# cax = plt.axes([0.82, 0.25, 0.04, 0.5])
|
||||
cax = plt.axes([0.85, 0.15, 0.04, 0.7])
|
||||
|
||||
# Add colorbar with designated tick marks
|
||||
# cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
# norm=norm,
|
||||
# orientation='vertical',
|
||||
# ticks=cbarTicks)
|
||||
|
||||
# Add colorbar without designated tick marks
|
||||
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
norm=norm,
|
||||
orientation='vertical')
|
||||
|
||||
# Colorbar lavel
|
||||
cb1.set_label('Sediment Concentration [$kg/m^3$]')
|
||||
# cb1.set_label('Total Max Shear Stress [N/m^2]')
|
||||
|
||||
# Add label on colorbar
|
||||
# cb1.ax.text(0.08, (cbarTicks[0] + cbarTicks[1]) / 2, 'CLAY', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[1] + cbarTicks[2]) / 2, 'FSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[2] + cbarTicks[3]) / 2, 'MSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[3] + cbarTicks[4]) / 2, 'CSILT', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
# cb1.ax.text(0.08, (cbarTicks[4] + cbarTicks[5]) / 2, 'FSAND', ha='center', va='center',
|
||||
# rotation=90, fontweight='bold')
|
||||
|
||||
|
||||
# Change label font size for only the primary axis
|
||||
for item in ([axes.xaxis.label, axes.yaxis.label] +
|
||||
axes.get_xticklabels() + axes.get_yticklabels()):
|
||||
item.set_fontsize(4)
|
||||
|
||||
|
||||
# Save video frame
|
||||
writer.grab_frame()
|
||||
plt.show()
|
||||
print(i)
|
||||
|
||||
# Save stills
|
||||
fig.savefig('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
'06_Models/04_Delft3D/Figures/SedConc/Wide_RevC_SedConc_' +
|
||||
runLog['Run'][m] + '_Frame_' + str(i) + '.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
# fig.savefig('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/' +
|
||||
# '06_Models/04_Delft3D/Figures/ShearStress/Wide_ShearStress_' +
|
||||
# runLog['Run'][m] + '_Frame_' + str(i) + '.png',
|
||||
# bbox_inches='tight', dpi=300)
|
||||
|
||||
# Save video
|
||||
writer.finish()
|
||||
|
||||
|
|
@ -0,0 +1,411 @@
|
|||
#%%
|
||||
#Project: 11934.201 Newtown Creek TPP - Privileged and Confidential
|
||||
#Confidentiality Note: For internal discussion only.
|
||||
#Description: This code creates plots for comparing temperature, salinity between the EFDC model, Delft3DFM, and the measured data.
|
||||
#To run, ensure that the model log is saved locally, and change the path in the code.
|
||||
|
||||
#%% # -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Feb 8 11:14:28 2023
|
||||
|
||||
@author: mrobinson
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy
|
||||
from scipy.interpolate import interp1d
|
||||
from scipy import interpolate
|
||||
|
||||
from numpy import trapz
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from matplotlib import dates as mpl_dates
|
||||
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
|
||||
|
||||
import os
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.metrics import mean_squared_error
|
||||
import geopandas as gp
|
||||
import pytz
|
||||
|
||||
import matplotlib as mpl
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.animation as animation
|
||||
|
||||
import dfm_tools as dfmt
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
from shapely.ops import nearest_points
|
||||
|
||||
import pathlib as pl
|
||||
import xarray as xr
|
||||
|
||||
from datetime import timedelta
|
||||
import datetime as datetime
|
||||
|
||||
|
||||
#%% Load in Measured data
|
||||
|
||||
dfIN=pd.read_csv('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/21_YSI Data/D1-D9_Combined/Combined_with_2015/All_Combine.csv')
|
||||
|
||||
# Filter out invalid data
|
||||
df1 = dfIN
|
||||
df1.loc[df1['Temp'] < -30, 'Temp'] = np.nan
|
||||
df1.loc[df1['Sal'] < 0, 'Sal'] = np.nan
|
||||
df1['DateTime'] = pd.to_datetime(df1['Date Time Local'])
|
||||
df1.set_index('DateTime', inplace=True)
|
||||
|
||||
df1.index = df1.index.tz_localize(pytz.timezone('America/New_York'),
|
||||
ambiguous='NaT', nonexistent='NaT')
|
||||
|
||||
df1.index = df1.index.tz_convert(pytz.utc)
|
||||
|
||||
# Convert to Dataframe
|
||||
YSI_df = pd.DataFrame(df1, columns=['Station', 'Temp', 'Sal', 'Depth'])
|
||||
# Set index as DateTime
|
||||
|
||||
#Change Station of interest (EB043, EK108, NC310, NC313, NC316, NC318)
|
||||
Station=['EB043','EK108','NC310','NC313','NC316','NC318']
|
||||
x=4
|
||||
i=Station[x]
|
||||
|
||||
|
||||
Station_interest=i
|
||||
Top=str(Station_interest)+str('A')
|
||||
Bot=str(Station_interest)+str('C')
|
||||
|
||||
|
||||
#%% Load in EFDC Output
|
||||
FILE=r"//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/06_Models/02_EFDC/06_EFDC_Outputs/2014-2015/EFDC_Compiled_2014_2015.xlsx"
|
||||
|
||||
EFDC_dat = pd.read_excel(FILE, None)
|
||||
|
||||
# Loop through stations and add to dataframe
|
||||
Stations=['EB043', 'EK108', 'NC310', 'NC313', 'NC316', 'NC318']
|
||||
for Stat in Stations:
|
||||
# Set index as DateTime
|
||||
EFDC_dat[Stat].set_index('date fix', inplace=True)
|
||||
EFDC_dat[Stat].index = pd.to_datetime(EFDC_dat[Stat].index).tz_localize(
|
||||
pytz.timezone('UTC'))
|
||||
|
||||
#%% Load in Telemac Output
|
||||
|
||||
FILE_T=r"C:/Users/arey/Downloads/Results_Summarized_Telemac.xlsx"
|
||||
|
||||
TM_dat = pd.read_excel(FILE_T, None)
|
||||
|
||||
# Loop through stations and adjust times
|
||||
Stations=['EB043', 'EK108', 'NC310', 'NC313', 'NC316', 'NC318']
|
||||
for Stat in Stations:
|
||||
# Set index as DateTime
|
||||
TM_dat[Stat].set_index('Date_Time', inplace=True)
|
||||
TM_dat[Stat].index = pd.to_datetime(TM_dat[Stat].index).tz_localize(
|
||||
pytz.timezone('UTC'))
|
||||
|
||||
#%% Load in Water Level Data
|
||||
# Field Gauge-EAST
|
||||
FFG_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/FieldFacility.csv")
|
||||
FFG_pd['DateTime'] = pd.to_datetime(FFG_pd.Date_time)
|
||||
FFG_pd.set_index(FFG_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
FFG_pd = FFG_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='infer').tz_convert(pytz.utc)
|
||||
|
||||
# National Grid Gauge-WEST
|
||||
NGG1_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid1.csv")
|
||||
NGG1_pd['DateTime'] = pd.to_datetime(NGG1_pd.Date_time)
|
||||
NGG1_pd.set_index(NGG1_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
NGG1_pd = NGG1_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
|
||||
# Drop ambiguous times
|
||||
NGG1_pd = NGG1_pd.dropna()
|
||||
|
||||
NGG2_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid2.csv")
|
||||
NGG2_pd['DateTime'] = pd.to_datetime(NGG2_pd.datetime)
|
||||
NGG2_pd.set_index(NGG2_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
NGG2_pd = NGG2_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
|
||||
# Drop ambiguous times
|
||||
NGG2_pd = NGG2_pd.dropna()
|
||||
|
||||
|
||||
#%% Load in Delft Results
|
||||
runLog = pd.read_excel('C:/Users/arey/files/Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
|
||||
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
histPath = "FlowFM_his.nc"
|
||||
|
||||
#read in time series
|
||||
modelPlot = [105]#, 106]
|
||||
|
||||
Delft_WL = [None] * (max(modelPlot)+1)
|
||||
Delft_T_B = [None] * (max(modelPlot)+1)
|
||||
Delft_S_B = [None] * (max(modelPlot)+1)
|
||||
Delft_T_T = [None] * (max(modelPlot)+1)
|
||||
Delft_S_T = [None] * (max(modelPlot)+1)
|
||||
|
||||
|
||||
# Note, this uses the standard netcdf lib + xarray
|
||||
for i in modelPlot:
|
||||
file_nc_hist = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'dflowfm', 'output', 'FlowFM_his.nc')
|
||||
# file_nc_hist = "C:/Users/mrobinson/OneDrive - W.F. Baird & Associates Coastal Engineers Ltd/Documents/11934.202 NTC/06 Models/06 Delft3DFM/Results/Run129/FlowFM_his.nc"
|
||||
|
||||
|
||||
# Hist file path
|
||||
#file_nc_hist = file_nc_hist.as_posix()
|
||||
|
||||
# Open hist file dataset
|
||||
data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
|
||||
|
||||
# Get stations
|
||||
stations_pd = data_xr['stations'].to_dataframe()
|
||||
|
||||
# Get observations
|
||||
#observations_pd=data_xr['stations'].to_dataframe()
|
||||
|
||||
# Convert to Pandas
|
||||
Delft_WL[i] = data_xr.waterlevel.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108','West_WL']).to_pandas()
|
||||
|
||||
#Temp
|
||||
Delft_T_B[i] = data_xr.temperature.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108'], laydim=0).to_pandas()
|
||||
Delft_T_T[i] = data_xr.temperature.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108'], laydim=9).to_pandas()
|
||||
|
||||
#Salinity
|
||||
Delft_S_B[i] = data_xr.salinity.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108'], laydim=0).to_pandas()
|
||||
Delft_S_T[i] = data_xr.salinity.sel(stations=['NC310', 'NC313','NC316','NC318','EB043','EK108'], laydim=9).to_pandas()
|
||||
|
||||
# Put in UTC if needed
|
||||
if i == 104 or i == 105:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
|
||||
Delft_T_B[i] = Delft_T_B[i].tz_localize(pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
Delft_T_T[i] = Delft_T_T[i].tz_localize(pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
|
||||
Delft_S_B[i] = Delft_S_B[i].tz_localize(pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
Delft_S_T[i] = Delft_S_T[i].tz_localize(pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
else:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('UTC'))
|
||||
|
||||
|
||||
#%% Plot Water Levels
|
||||
i = 105
|
||||
|
||||
# Create Figure
|
||||
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(18, 8), sharex=False, sharey=True)
|
||||
fig.suptitle('Comparison of Predicted and Measured Water Level at NGG', fontsize=18)
|
||||
|
||||
# Plot Oberservations
|
||||
NGG2_pd['water_surface_elevation'].multiply(0.3048).plot(label='FFG', color='k', linewidth=2)
|
||||
|
||||
# Plot Delft
|
||||
Delft_WL[i]['West_WL'].plot(label='Delft', color='r', linewidth=2)
|
||||
|
||||
# Plot YSI
|
||||
YSI_df.loc[YSI_df['Station'] == 'NC318A', 'Depth'].add(-1.3).plot(
|
||||
label='YSI', color='b', linewidth=2, ax=ax)
|
||||
|
||||
# Plot EFDC
|
||||
EFDC_dat['NC318A'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
|
||||
# Plot Telemac
|
||||
TM_dat['NC318'].loc[:, 'WSE_m'].plot(label='EFDC', color='m', linewidth=2, ax=ax)
|
||||
|
||||
|
||||
# Set Time Bounds
|
||||
dateMin = pd.to_datetime(datetime.datetime(2014, 11, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2014, 11, 13, 0, 0, 0, 0), utc=True)
|
||||
|
||||
# Set X Axis
|
||||
ax.set_xlim(dateMin, dateMax)
|
||||
|
||||
# Set Y Axis
|
||||
ax.set_ylim(-1.5, 2.5)
|
||||
|
||||
plt.show()
|
||||
|
||||
#%% Plot of Temperature AJMR
|
||||
def resampleToObs(model, obs, obsMatchFreq):
|
||||
# Resample to observation time
|
||||
modelSampled = model.resample(obsMatchFreq).interpolate().reindex(
|
||||
obs.index)
|
||||
return modelSampled
|
||||
|
||||
def syncModelRMSE(model, obs):
|
||||
rmseOut = mean_squared_error(obs.loc[model.notna() & obs.notna()].dropna(
|
||||
).values, model.loc[model.notna() & obs.notna()].dropna(
|
||||
).values,squared=False)
|
||||
return rmseOut
|
||||
|
||||
degree_sign = u'\N{DEGREE SIGN}'
|
||||
|
||||
|
||||
# Create Figure
|
||||
fig, ax = plt.subplots(nrows=2, ncols=6, figsize=(16, 7), sharex=True, sharey=True)
|
||||
# fig.suptitle('Comparison of Predicted and Measured Temperature at NC316', fontsize=18)
|
||||
|
||||
# Set Time Bounds for all axis since linked
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 28, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 8, 7, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2014, 7, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 10, 1, 0, 0, 0, 0), utc=True)
|
||||
|
||||
|
||||
# Loop through stations and plot
|
||||
for StatIDX, Stat in enumerate(Stations):
|
||||
ax[0, StatIDX].set_title(Stat)
|
||||
|
||||
# Resample to 5 minute data, reindex to observed and calculate RMSD
|
||||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'A', 'Temp']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_T_T[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Top Layer Temperature
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[0, StatIDX])
|
||||
ax[0, StatIDX].plot(Delft_WL[i].index, Delft_T_T[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + degree_sign + 'C')
|
||||
EFDC_dat[Stat].loc[:, 'TEM10'].plot(linewidth=1, ax=ax[0, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + degree_sign + 'C')
|
||||
# TM_dat[Stat].loc[:, 'TEM10'].plot(linewidth=1, ax=ax[0, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + degree_sign + 'C')
|
||||
|
||||
ax[0, StatIDX].legend(loc='lower right')
|
||||
|
||||
|
||||
# Plot Bottom Layer Temperature
|
||||
# Resample to 5 minute data, reindex to observed and calculate RMSD
|
||||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'C', 'Temp']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_T_B[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'TEM1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'TEM1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[1, StatIDX])
|
||||
ax[1, StatIDX].plot(Delft_WL[i].index, Delft_T_B[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + degree_sign + 'C')
|
||||
EFDC_dat[Stat].loc[:, 'TEM1'].plot(linewidth=1, ax=ax[1, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + degree_sign + 'C')
|
||||
# TM_dat[Stat].loc[:, 'TEM1'].plot(linewidth=1, ax=ax[1, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + degree_sign + 'C')
|
||||
|
||||
# Add Legend and Set X Axis label
|
||||
ax[1, StatIDX].legend(loc='lower right')
|
||||
ax[1, StatIDX].set_xlabel('UTC Date')
|
||||
|
||||
|
||||
# Set X Axis
|
||||
ax[0, 0].set_xlim(dateMin, dateMax)
|
||||
ax[0, 0].xaxis.set_major_formatter(
|
||||
mpl_dates.ConciseDateFormatter(ax[0, 0].xaxis.get_major_locator()))
|
||||
|
||||
# Set Y Axis
|
||||
# ax[0, 0].set_ylim(18, 28)
|
||||
ax[0, 0].set_ylim(-10, 30)
|
||||
ax[0, 0].set_ylabel('Temperature Top (' + degree_sign + 'C)')
|
||||
ax[1, 0].set_ylabel('Temperature Bottom (' + degree_sign + 'C)')
|
||||
|
||||
# Reduce spacing between subplots
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
# Save figure
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Temperature_All.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
|
||||
#%% Plot Salinity AJMR
|
||||
Stations=['NC310', 'NC313', 'NC316', 'NC318', 'EB043', 'EK108']
|
||||
|
||||
# Create Figure
|
||||
fig, ax = plt.subplots(nrows=2, ncols=6, figsize=(16, 7), sharex=True, sharey=True)
|
||||
|
||||
|
||||
# Set Time Bounds for all axis since linked
|
||||
# dateMin = pd.to_datetime(datetime.datetime(2015, 7, 15, 0, 0, 0, 0), utc=True)
|
||||
# dateMax = pd.to_datetime(datetime.datetime(2015, 9, 1, 0, 0, 0, 0), utc=True)
|
||||
dateMin = pd.to_datetime(datetime.datetime(2015, 7, 28, 0, 0, 0, 0), utc=True)
|
||||
dateMax = pd.to_datetime(datetime.datetime(2015, 8, 7, 0, 0, 0, 0), utc=True)
|
||||
|
||||
|
||||
# Loop through stations and plot
|
||||
for StatIDX, Stat in enumerate(Stations):
|
||||
ax[0, StatIDX].set_title(Stat)
|
||||
|
||||
# Resample to 5 minute data, reindex to observed and calculate RMSE
|
||||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'A', 'Sal']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_S_T[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL10'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL10'].resample('15T').nearest(), ObsDat, '5T')
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Top Layer Salinity
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[0, StatIDX])
|
||||
ax[0, StatIDX].plot(Delft_S_T[i].index, Delft_S_T[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + ' psu')
|
||||
EFDC_dat[Stat].loc[:, 'SAL10'].plot(linewidth=1, ax=ax[0, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + ' psu')
|
||||
# TM_dat[Stat].loc[:, 'SAL10'].plot(linewidth=1, ax=ax[0, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + ' psu')
|
||||
|
||||
ax[0, StatIDX].legend(loc='lower right')
|
||||
|
||||
# Resample to 5 minute data, reindex to observed and calculate RMSD
|
||||
ObsDat = YSI_df.loc[YSI_df['Station'] == Stat + 'C', 'Sal']
|
||||
ObsDat = ObsDat.loc[(ObsDat.index > dateMin) & (ObsDat.index <= dateMax)]
|
||||
|
||||
DelftInterp = resampleToObs(Delft_S_B[i][Stat], ObsDat, '5T')
|
||||
EFDCInterp = resampleToObs(EFDC_dat[Stat].loc[:, 'SAL1'].resample('10T').nearest(), ObsDat, '5T')
|
||||
# TMInterp = resampleToObs(TM_dat[Stat].loc[:, 'SAL1'].resample('15T').nearest(), ObsDat, '5T')
|
||||
|
||||
RMSE_D = syncModelRMSE(DelftInterp, ObsDat)
|
||||
RMSE_E = syncModelRMSE(EFDCInterp, ObsDat)
|
||||
# RMSE_T = syncModelRMSE(TMInterp, ObsDat)
|
||||
|
||||
# Plot Bottom Layer Salinity
|
||||
ObsDat.plot(label='YSI', color='k', linewidth=2, ax=ax[1, StatIDX])
|
||||
ax[1, StatIDX].plot(Delft_S_B[i].index, Delft_S_B[i][Stat], linestyle='-', label='Delft3D: ' + str(round(RMSE_D, 2)) + ' psu')
|
||||
EFDC_dat[Stat].loc[:, 'SAL1'].plot(linewidth=1, ax=ax[1, StatIDX], label='EFDC: ' + str(round(RMSE_E, 2)) + ' psu')
|
||||
# TM_dat[Stat].loc[:, 'SAL1'].plot(linewidth=1, ax=ax[1, StatIDX], label='Telemac: ' + str(round(RMSE_T, 2)) + 'psu')
|
||||
|
||||
# Add Legend and Set X Axis label
|
||||
ax[1, StatIDX].legend(loc='lower right')
|
||||
ax[1, StatIDX].set_xlabel('UTC Date')
|
||||
|
||||
# Set X Axis
|
||||
ax[0, 0].set_xlim(dateMin, dateMax)
|
||||
ax[0, 0].xaxis.set_major_formatter(
|
||||
mpl_dates.ConciseDateFormatter(ax[0, 0].xaxis.get_major_locator()))
|
||||
|
||||
# Set Y Axis
|
||||
ax[0, 0].set_ylim(12, 27)
|
||||
ax[0, 0].set_ylabel('Salinity Top (psu)')
|
||||
ax[1, 0].set_ylabel('Salinity Bottom (psu)')
|
||||
|
||||
# Reduce spacing between subplots
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
# Save figure
|
||||
fig.savefig('C:/Users/arey/files/Projects/Newtown/TempSalFigs/Salinity_2015_Zoom.png',
|
||||
bbox_inches='tight', dpi=300)
|
||||
|
||||
|
|
@ -0,0 +1,455 @@
|
|||
#%%
|
||||
#Project: 11934.201 Newtown Creek TPP - Privileged and Confidential
|
||||
#Confidentiality Note: For internal discussion only.
|
||||
#Description: This code plots the water levels at Field Facility Gage, and National Grid.
|
||||
#To run,
|
||||
|
||||
|
||||
#%% # -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Feb 16 11:54:29 2023
|
||||
|
||||
@author: mrobinson
|
||||
"""
|
||||
|
||||
#%%Packages
|
||||
import os
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import geopandas as gp
|
||||
import pytz
|
||||
import matplotlib as mpl
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.animation as animation
|
||||
import dfm_tools as dfmt
|
||||
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
|
||||
from dfm_tools.get_nc_helpers import get_ncvarproperties, get_stationid_fromstationlist, get_varnamefromattrs
|
||||
from dfm_tools.get_nc_helpers import get_timesfromnc
|
||||
import contextily as ctx
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
from shapely.ops import nearest_points
|
||||
import pathlib as pl
|
||||
import xarray as xr
|
||||
from datetime import timedelta
|
||||
import datetime as datetime
|
||||
from matplotlib import dates as mpl_dates
|
||||
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
|
||||
#%% Read Model Log
|
||||
|
||||
runLog = pd.read_excel("C:/Users/mrobinson/OneDrive - W.F. Baird & Associates Coastal Engineers Ltd/Documents/11934.202 NTC/Model Log NTC.xlsx", 'ModelLog')
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
histPath = "FlowFM_his.nc"
|
||||
|
||||
#%% Load in Water Level Data
|
||||
# Field Gauge-EAST
|
||||
FFG_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/FieldFacility.csv")
|
||||
FFG_pd['DateTime'] = pd.to_datetime(FFG_pd.Date_time)
|
||||
FFG_pd.set_index(FFG_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
FFG_pd = FFG_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='infer').tz_convert(pytz.utc)
|
||||
|
||||
# National Grid Gauge-WEST
|
||||
NGG1_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid1.csv")
|
||||
NGG1_pd['DateTime'] = pd.to_datetime(NGG1_pd.Date_time)
|
||||
NGG1_pd.set_index(NGG1_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
NGG1_pd = NGG1_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
|
||||
# Drop ambiguous times
|
||||
NGG1_pd = NGG1_pd.dropna()
|
||||
|
||||
NGG2_pd = pd.read_csv("//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/00_ProcessingCode/NetCDF/Gauge/NationalGrid2.csv")
|
||||
NGG2_pd['DateTime'] = pd.to_datetime(NGG2_pd.datetime)
|
||||
NGG2_pd.set_index(NGG2_pd['DateTime'], inplace=True)
|
||||
# Put in UTC from Eastern Time (with DST!)
|
||||
NGG2_pd = NGG2_pd.tz_localize(
|
||||
pytz.timezone('US/Eastern'), ambiguous='NaT').tz_convert(pytz.utc)
|
||||
# Drop ambiguous times
|
||||
NGG2_pd = NGG2_pd.dropna()
|
||||
|
||||
#%% Read in time series
|
||||
modelPlot = [105, 106, 107, 108]
|
||||
|
||||
Delft_WL = [None] * (max(modelPlot)+1)
|
||||
|
||||
# Note, this uses the standard netcdf lib + xarray
|
||||
for i in modelPlot:
|
||||
file_nc_hist = pl.Path(runLog['Run Location'][i],
|
||||
'FlowFM', 'dflowfm', 'output', 'FlowFM_his.nc')
|
||||
|
||||
# Hist file path
|
||||
file_nc_hist = file_nc_hist.as_posix()
|
||||
|
||||
# Open hist file dataset
|
||||
data_xr = xr.open_mfdataset(file_nc_hist, preprocess=dfmt.preprocess_hisnc)
|
||||
|
||||
# Get Variables
|
||||
vars_pd = get_ncvarproperties(data_xr)
|
||||
|
||||
# Get stations
|
||||
stations_pd = data_xr['stations'].to_dataframe()
|
||||
|
||||
# Convert to Pandas
|
||||
Delft_WL[i] = data_xr.waterlevel.sel(stations=['West_WL', 'East_WL']).to_pandas()
|
||||
|
||||
# Put in UTC if needed
|
||||
if i == 104:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('EST')).tz_convert(pytz.utc)
|
||||
else:
|
||||
Delft_WL[i] = Delft_WL[i].tz_localize(
|
||||
pytz.timezone('UTC'))
|
||||
|
||||
#%%
|
||||
modelPlot=[107]
|
||||
i=107
|
||||
|
||||
interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
Delft_WL[modelPlot[0]].index[-1],
|
||||
freq="20min")
|
||||
|
||||
|
||||
|
||||
df1=Delft_WL[i]
|
||||
df1_index=df1.index.shift(periods=1,freq='0H')
|
||||
|
||||
df2=pd.DataFrame(index=df1_index)
|
||||
df2['West_WL']=df1['West_WL']
|
||||
df2['East_WL']=df1['East_WL']
|
||||
df2['West_WL']=df2['West_WL'].shift(30)
|
||||
df2['East_WL']=df2['East_WL'].shift(30)
|
||||
|
||||
x=(np.interp(interpTimes,FFG_pd.index,FFG_pd['Water Surface Elevation (ft)']*0.3048))
|
||||
x[x==-1.15332]=np.nan
|
||||
x[0:2584]=np.NAN
|
||||
|
||||
x2= np.interp(interpTimes,NGG1_pd.index,NGG1_pd['Water Surface Elevation (ft)']*0.3048)
|
||||
x2[0:2638]=np.NAN
|
||||
|
||||
|
||||
#RMSE Values
|
||||
predNGG=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
rms = mean_squared_error(predNGG[2638:], x2[2638:], squared=False)
|
||||
print(rms)
|
||||
|
||||
predFG=np.interp(interpTimes,df2.index, df2['East_WL'])
|
||||
|
||||
rms2 = mean_squared_error(predFG[2584:], x[2584:], squared=False)
|
||||
print(rms2)
|
||||
|
||||
#Plot
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
|
||||
fig.patch.set_facecolor('white')
|
||||
#interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
# Delft_WL[modelPlot[0]].index[-1],
|
||||
# freq="20min")
|
||||
|
||||
|
||||
axes[0].title.set_text('National Grid Gauge')
|
||||
axes[0].scatter(np.interp(interpTimes,df2.index, df2['West_WL']),
|
||||
x2,s=1,
|
||||
label='RMSE =' + str(round(rms,2)))
|
||||
|
||||
|
||||
axes[1].title.set_text('Field Facility Gauge')
|
||||
axes[1].scatter(np.interp(interpTimes,df2.index, df2['East_WL']), x,s=1,
|
||||
label='RMSE =' + str(round(rms2,2)))
|
||||
|
||||
|
||||
axes[1].set_xlim(-2, 2)
|
||||
axes[1].set_ylim(-2, 2)
|
||||
axes[0].set_xlim(-2, 2)
|
||||
axes[0].set_ylim(-2, 2)
|
||||
|
||||
|
||||
linepts = np.array([-3, 3])
|
||||
axes[0].plot(linepts, linepts, color='k')
|
||||
axes[1].plot(linepts, linepts, color='k')
|
||||
|
||||
axes[0].set_xlabel('Modelled water level (m)')
|
||||
axes[0].set_ylabel('Measured water level (m)')
|
||||
|
||||
axes[1].set_xlabel('Modelled water level (m)')
|
||||
axes[1].set_ylabel('Measured water level (m)')
|
||||
|
||||
axes[1].grid()
|
||||
axes[0].grid()
|
||||
|
||||
fig.suptitle(runLog['Plotting Legend Entry'][i])
|
||||
|
||||
axes[1].legend(loc='lower left')
|
||||
axes[0].legend(loc='lower left')
|
||||
|
||||
#%%
|
||||
modelPlot=[106]
|
||||
i=106
|
||||
|
||||
#interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
# Delft_WL[modelPlot[0]].index[-1],
|
||||
# freq="20min")
|
||||
|
||||
#Note For 106 Run, period is from 2012-03-01 to 2012-10-30
|
||||
|
||||
interpTimes = pd.date_range("2012-03-01", "2012-10-30", freq="20min")
|
||||
|
||||
|
||||
df1=Delft_WL[i]
|
||||
df1_index=df1.index.shift(periods=1,freq='0H')
|
||||
|
||||
df2=pd.DataFrame(index=df1_index)
|
||||
df2['West_WL']=df1['West_WL']
|
||||
df2['East_WL']=df1['East_WL']
|
||||
df2['West_WL']=df2['West_WL'].shift(30)
|
||||
df2['East_WL']=df2['East_WL'].shift(30)
|
||||
|
||||
x=(np.interp(interpTimes,FFG_pd.index,FFG_pd['Water Surface Elevation (ft)']*0.3048))
|
||||
x[x==-1.15332]=np.nan
|
||||
x[0:2584]=np.NAN
|
||||
|
||||
x2= np.interp(interpTimes,NGG1_pd.index,NGG1_pd['Water Surface Elevation (ft)']*0.3048)
|
||||
x2[0:2638]=np.NAN
|
||||
|
||||
|
||||
#RMSE Values
|
||||
predNGG=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
rms = mean_squared_error(predNGG[2638:], x2[2638:], squared=False)
|
||||
print(rms.round(3))
|
||||
|
||||
predFG=np.interp(interpTimes,df2.index, df2['East_WL'])
|
||||
|
||||
rms2 = mean_squared_error(predFG[2584:], x[2584:], squared=False)
|
||||
print(rms2.round(3))
|
||||
|
||||
#Plot
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18, 8))
|
||||
fig.patch.set_facecolor('white')
|
||||
plt.rcParams['font.size'] = 16
|
||||
#interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
# Delft_WL[modelPlot[0]].index[-1],
|
||||
# freq="20min")
|
||||
|
||||
|
||||
axes[0].title.set_text('National Grid Gauge')
|
||||
axes[0].scatter(np.interp(interpTimes,df2.index, df2['West_WL']),
|
||||
x2,s=1,
|
||||
label='RMSE =' + str(round(rms,3)))
|
||||
|
||||
|
||||
axes[1].title.set_text('Field Facility Gauge')
|
||||
axes[1].scatter(np.interp(interpTimes,df2.index, df2['East_WL']), x,s=1,
|
||||
label='RMSE =' + str(round(rms2,3)))
|
||||
|
||||
|
||||
axes[1].set_xlim(-2, 2)
|
||||
axes[1].set_ylim(-2, 2)
|
||||
axes[0].set_xlim(-2, 2)
|
||||
axes[0].set_ylim(-2, 2)
|
||||
|
||||
|
||||
linepts = np.array([-3, 3])
|
||||
axes[0].plot(linepts, linepts, color='k')
|
||||
axes[1].plot(linepts, linepts, color='k')
|
||||
|
||||
axes[0].set_xlabel('Modelled water level (m)')
|
||||
axes[0].set_ylabel('Measured water level (m)')
|
||||
|
||||
axes[1].set_xlabel('Modelled water level (m)')
|
||||
axes[1].set_ylabel('Measured water level (m)')
|
||||
|
||||
axes[1].grid()
|
||||
axes[0].grid()
|
||||
|
||||
axes[0].set_aspect('equal')
|
||||
axes[1].set_aspect('equal')
|
||||
|
||||
fig.suptitle(runLog['Plotting Legend Entry'][i])
|
||||
|
||||
axes[1].legend(loc='lower left')
|
||||
axes[0].legend(loc='lower left')
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(20,10))
|
||||
|
||||
axes[0].plot(FFG_pd.index,FFG_pd['Water Surface Elevation (ft)']*0.3048,color='red')
|
||||
axes[0].plot(df2.index,df2['East_WL'])
|
||||
|
||||
|
||||
axes[1].plot(NGG1_pd['DateTime'],NGG1_pd['Water Surface Elevation (ft)']*0.3048,color='red')
|
||||
axes[1].plot(df2.index,df2['West_WL'])
|
||||
|
||||
axes[0].set_xlim([np.datetime64('2012-01-01'), np.datetime64('2013-01-01')])
|
||||
|
||||
|
||||
#%%
|
||||
modelPlot=[105]
|
||||
i=105
|
||||
|
||||
|
||||
|
||||
interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
Delft_WL[modelPlot[0]].index[-1],
|
||||
freq="20min")
|
||||
|
||||
df1=Delft_WL[i]
|
||||
df1_index=df1.index.shift(periods=1,freq='0H')
|
||||
|
||||
df2=pd.DataFrame(index=df1_index)
|
||||
df2['West_WL']=df1['West_WL']
|
||||
df2['East_WL']=df1['East_WL']
|
||||
df2['West_WL']=df2['West_WL'].shift(30)
|
||||
df2['East_WL']=df2['East_WL'].shift(30)
|
||||
|
||||
x=(np.interp(interpTimes,FFG_pd.index,FFG_pd['Water Surface Elevation (ft)']*0.3048))
|
||||
x2=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
|
||||
pred=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
meas=np.interp(interpTimes,NGG2_pd.index,NGG2_pd['water_surface_elevation']*0.3048)
|
||||
|
||||
#RMSE Values
|
||||
rms3=mean_squared_error(meas[15:],pred[15:], squared=False)
|
||||
print(rms3)
|
||||
"""
|
||||
predNGG=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
rms = mean_squared_error(predNGG[2638:], x2[2638:], squared=False)
|
||||
print(rms)
|
||||
|
||||
predFG=np.interp(interpTimes,df2.index, df2['East_WL'])
|
||||
|
||||
rms2 = mean_squared_error(predFG[2584:], x[2584:], squared=False)
|
||||
print(rms2)
|
||||
"""
|
||||
|
||||
# Plot
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||||
fig.patch.set_facecolor('white')
|
||||
interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
Delft_WL[modelPlot[0]].index[-1],
|
||||
freq="20min")
|
||||
|
||||
|
||||
axes.title.set_text('National Grid Gauge')
|
||||
axes.scatter(np.interp(interpTimes,df2.index, df2['West_WL']),
|
||||
np.interp(interpTimes,NGG2_pd.index,NGG2_pd['water_surface_elevation']*0.3048),s=1,
|
||||
label='RMSE =' + str(round(rms3,2)))
|
||||
|
||||
|
||||
|
||||
axes.set_xlim(-2, 2)
|
||||
axes.set_ylim(-2, 2)
|
||||
linepts = np.array([-3, 3])
|
||||
axes.plot(linepts, linepts, color='k')
|
||||
axes.set_xlabel('Modelled water level (m)')
|
||||
axes.set_ylabel('Measured water level (m)')
|
||||
axes.grid()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
fig.suptitle(runLog['Plotting Legend Entry'][i])
|
||||
|
||||
|
||||
#%%
|
||||
modelPlot=[105]
|
||||
i=105
|
||||
|
||||
interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
Delft_WL[modelPlot[0]].index[-1],
|
||||
freq="20min")
|
||||
df1=Delft_WL[i]
|
||||
df1_index=df1.index.shift(periods=1,freq='0H')
|
||||
|
||||
df2=pd.DataFrame(index=df1_index)
|
||||
df2['West_WL']=df1['West_WL']
|
||||
df2['East_WL']=df1['East_WL']
|
||||
df2['West_WL']=df2['West_WL'].shift(30)
|
||||
df2['East_WL']=df2['East_WL'].shift(30)
|
||||
|
||||
x=(np.interp(interpTimes,FFG_pd.index,FFG_pd['Water Surface Elevation (ft)']*0.3048))
|
||||
x2=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
|
||||
pred=np.interp(interpTimes,df2.index, df2['West_WL'])
|
||||
meas=np.interp(interpTimes,NGG2_pd.index,NGG2_pd['water_surface_elevation']*0.3048)
|
||||
|
||||
#RMSE Values
|
||||
rms3=mean_squared_error(meas[15:],pred[15:], squared=False)
|
||||
print(rms3)
|
||||
|
||||
# Plot
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||||
fig.patch.set_facecolor('white')
|
||||
interpTimes = pd.date_range(Delft_WL[modelPlot[0]].index[0],
|
||||
Delft_WL[modelPlot[0]].index[-1],
|
||||
freq="20min")
|
||||
|
||||
axes.title.set_text('National Grid Gauge')
|
||||
axes.scatter(np.interp(interpTimes,df2.index, df2['West_WL']),
|
||||
np.interp(interpTimes,NGG2_pd.index,NGG2_pd['water_surface_elevation']*0.3048),s=1,
|
||||
label='RMSE =' + str(round(rms3,3)))
|
||||
|
||||
axes.set_xlim(-2, 2)
|
||||
axes.set_ylim(-2, 2)
|
||||
linepts = np.array([-3, 3])
|
||||
axes.plot(linepts, linepts, color='k')
|
||||
axes.set_xlabel('Modelled water level (m)')
|
||||
axes.set_ylabel('Measured water level (m)')
|
||||
axes.grid()
|
||||
axes.legend(loc='lower left')
|
||||
|
||||
axes.set_aspect('equal')
|
||||
fig.suptitle(runLog['Plotting Legend Entry'][i])
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,205 @@
|
|||
# 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/git-2.39.1-h57928b3_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2022.12.7-h5b45459_0.conda
|
||||
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-2023.0.0-h57928b3_25922.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2
|
||||
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https://conda.anaconda.org/conda-forge/noarch/requests-2.28.2-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/bottleneck-1.3.6-py39hc266a54_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/cdsapi-0.5.1-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/cftime-1.6.2-py39hc266a54_1.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/contourpy-1.0.7-py39h1f6ef14_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/folium-0.14.0-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/gdal-3.6.2-py39h3be0312_3.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/numba-0.56.4-py39h99ae161_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/pandas-1.5.3-py39h2ba5b7c_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pydap-3.3.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/pyepsg-0.4.0-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/shapely-2.0.1-py39h7c5f289_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-py_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/dask-2023.1.1-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/fiona-1.9.0-py39hb5a1417_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopandas-base-0.12.2-pyha770c72_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.6.3-py39haf65ace_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/netcdf4-1.6.2-nompi_py39h34fa13a_100.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/rasterio-1.3.4-py39hce277b7_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/scipy-1.10.0-py39hfbf2dce_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/xarray-2023.1.0-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/win-64/cartopy-0.21.1-py39h25ee47b_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/contextily-1.2.0-pyhd8ed1ab_0.tar.bz2
|
||||
https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.2.1-py39h6fe01c0_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/xugrid-0.2.0-pyhd8ed1ab_0.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.5.0-pyhd8ed1ab_1.conda
|
||||
https://conda.anaconda.org/conda-forge/noarch/geopandas-0.12.2-pyhd8ed1ab_0.conda
|
||||
|
|
@ -0,0 +1,212 @@
|
|||
name: dfm_tools_23
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- affine=2.4.0=pyhd8ed1ab_0
|
||||
- appdirs=1.4.4=pyh9f0ad1d_0
|
||||
- attrs=22.2.0=pyh71513ae_0
|
||||
- beautifulsoup4=4.11.1=pyha770c72_0
|
||||
- blosc=1.21.3=hdccc3a2_0
|
||||
- bokeh=2.4.3=pyhd8ed1ab_3
|
||||
- boost-cpp=1.78.0=h9f4b32c_1
|
||||
- bottleneck=1.3.6=py39hc266a54_0
|
||||
- branca=0.6.0=pyhd8ed1ab_0
|
||||
- brotli=1.0.9=hcfcfb64_8
|
||||
- brotli-bin=1.0.9=hcfcfb64_8
|
||||
- brotlipy=0.7.0=py39ha55989b_1005
|
||||
- bzip2=1.0.8=h8ffe710_4
|
||||
- ca-certificates=2022.12.7=h5b45459_0
|
||||
- cairo=1.16.0=hd694305_1014
|
||||
- cartopy=0.21.1=py39h25ee47b_0
|
||||
- cdsapi=0.5.1=pyhd8ed1ab_0
|
||||
- certifi=2022.12.7=pyhd8ed1ab_0
|
||||
- cffi=1.15.1=py39h68f70e3_3
|
||||
- cfitsio=4.2.0=h9ebe7e4_0
|
||||
- cftime=1.6.2=py39hc266a54_1
|
||||
- charset-normalizer=2.1.1=pyhd8ed1ab_0
|
||||
- click=8.1.3=win_pyhd8ed1ab_2
|
||||
- click-plugins=1.1.1=py_0
|
||||
- cligj=0.7.2=pyhd8ed1ab_1
|
||||
- cloudpickle=2.2.1=pyhd8ed1ab_0
|
||||
- colorama=0.4.6=pyhd8ed1ab_0
|
||||
- contextily=1.2.0=pyhd8ed1ab_0
|
||||
- contourpy=1.0.7=py39h1f6ef14_0
|
||||
- cryptography=39.0.0=py39hb6bd5e6_0
|
||||
- curl=7.87.0=h68f0423_0
|
||||
- cycler=0.11.0=pyhd8ed1ab_0
|
||||
- cytoolz=0.12.0=py39ha55989b_1
|
||||
- dask=2023.1.1=pyhd8ed1ab_0
|
||||
- dask-core=2023.1.1=pyhd8ed1ab_0
|
||||
- distributed=2023.1.1=pyhd8ed1ab_0
|
||||
- docopt=0.6.2=py_1
|
||||
- et_xmlfile=1.1.0=pyhd8ed1ab_0
|
||||
- expat=2.5.0=h1537add_0
|
||||
- fiona=1.9.0=py39hb5a1417_0
|
||||
- folium=0.14.0=pyhd8ed1ab_0
|
||||
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
|
||||
- font-ttf-inconsolata=3.000=h77eed37_0
|
||||
- font-ttf-source-code-pro=2.038=h77eed37_0
|
||||
- font-ttf-ubuntu=0.83=hab24e00_0
|
||||
- fontconfig=2.14.2=hbde0cde_0
|
||||
- fonts-conda-ecosystem=1=0
|
||||
- fonts-conda-forge=1=0
|
||||
- fonttools=4.38.0=py39ha55989b_1
|
||||
- freetype=2.12.1=h546665d_1
|
||||
- freexl=1.0.6=h67ca5e6_1
|
||||
- fsspec=2023.1.0=pyhd8ed1ab_0
|
||||
- gdal=3.6.2=py39h3be0312_3
|
||||
- geographiclib=1.52=pyhd8ed1ab_0
|
||||
- geopandas=0.12.2=pyhd8ed1ab_0
|
||||
- geopandas-base=0.12.2=pyha770c72_0
|
||||
- geopy=2.3.0=pyhd8ed1ab_0
|
||||
- geos=3.11.1=h1537add_0
|
||||
- geotiff=1.7.1=h720ab47_5
|
||||
- gettext=0.21.1=h5728263_0
|
||||
- git=2.39.1=h57928b3_0
|
||||
- hdf4=4.2.15=h1b1b6ef_5
|
||||
- hdf5=1.12.2=nompi_h57737ce_101
|
||||
- heapdict=1.0.1=py_0
|
||||
- icu=70.1=h0e60522_0
|
||||
- idna=3.4=pyhd8ed1ab_0
|
||||
- intel-openmp=2023.0.0=h57928b3_25922
|
||||
- jinja2=3.1.2=pyhd8ed1ab_1
|
||||
- joblib=1.2.0=pyhd8ed1ab_0
|
||||
- jpeg=9e=h8ffe710_2
|
||||
- kealib=1.5.0=h61be68b_0
|
||||
- kiwisolver=1.4.4=py39h1f6ef14_1
|
||||
- krb5=1.20.1=heb0366b_0
|
||||
- lcms2=2.14=ha5c8aab_1
|
||||
- lerc=4.0.0=h63175ca_0
|
||||
- libaec=1.0.6=h63175ca_1
|
||||
- libblas=3.9.0=16_win64_mkl
|
||||
- libbrotlicommon=1.0.9=hcfcfb64_8
|
||||
- libbrotlidec=1.0.9=hcfcfb64_8
|
||||
- libbrotlienc=1.0.9=hcfcfb64_8
|
||||
- libcblas=3.9.0=16_win64_mkl
|
||||
- libcurl=7.87.0=h68f0423_0
|
||||
- libdeflate=1.17=hcfcfb64_0
|
||||
- libffi=3.4.2=h8ffe710_5
|
||||
- libgdal=3.6.2=h060c9ed_3
|
||||
- libglib=2.74.1=he8f3873_1
|
||||
- libhwloc=2.8.0=h039e092_1
|
||||
- libiconv=1.17=h8ffe710_0
|
||||
- libjpeg-turbo=2.1.4=hcfcfb64_0
|
||||
- libkml=1.3.0=hf2ab4e4_1015
|
||||
- liblapack=3.9.0=16_win64_mkl
|
||||
- libnetcdf=4.8.1=nompi_h8c042bf_106
|
||||
- libpng=1.6.39=h19919ed_0
|
||||
- libpq=15.1=ha9684e8_3
|
||||
- librttopo=1.1.0=he22b5cd_12
|
||||
- libspatialindex=1.9.3=h39d44d4_4
|
||||
- libspatialite=5.0.1=h07bf483_22
|
||||
- libsqlite=3.40.0=hcfcfb64_0
|
||||
- libssh2=1.10.0=h9a1e1f7_3
|
||||
- libtiff=4.5.0=hf8721a0_2
|
||||
- libwebp-base=1.2.4=h8ffe710_0
|
||||
- libxcb=1.13=hcd874cb_1004
|
||||
- libxml2=2.10.3=hc3477c8_0
|
||||
- libzip=1.9.2=h519de47_1
|
||||
- libzlib=1.2.13=hcfcfb64_4
|
||||
- llvmlite=0.39.1=py39hd28a505_1
|
||||
- locket=1.0.0=pyhd8ed1ab_0
|
||||
- lz4=4.2.0=py39hf617134_0
|
||||
- lz4-c=1.9.4=hcfcfb64_0
|
||||
- m2w64-gcc-libgfortran=5.3.0=6
|
||||
- m2w64-gcc-libs=5.3.0=7
|
||||
- m2w64-gcc-libs-core=5.3.0=7
|
||||
- m2w64-gmp=6.1.0=2
|
||||
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
|
||||
- mapclassify=2.5.0=pyhd8ed1ab_1
|
||||
- markupsafe=2.1.2=py39ha55989b_0
|
||||
- matplotlib-base=3.6.3=py39haf65ace_0
|
||||
- mercantile=1.2.1=pyhd8ed1ab_0
|
||||
- mkl=2022.1.0=h6a75c08_874
|
||||
- msgpack-python=1.0.4=py39h1f6ef14_1
|
||||
- msys2-conda-epoch=20160418=1
|
||||
- munch=2.5.0=py_0
|
||||
- munkres=1.1.4=pyh9f0ad1d_0
|
||||
- netcdf4=1.6.2=nompi_py39h34fa13a_100
|
||||
- networkx=3.0=pyhd8ed1ab_0
|
||||
- numba=0.56.4=py39h99ae161_0
|
||||
- numba_celltree=0.1.6=pyhd8ed1ab_0
|
||||
- numpy=1.23.5=py39hbccbffa_0
|
||||
- openjpeg=2.5.0=ha2aaf27_2
|
||||
- openpyxl=3.1.0=py39ha55989b_0
|
||||
- openssl=3.0.7=hcfcfb64_2
|
||||
- packaging=23.0=pyhd8ed1ab_0
|
||||
- pandas=1.5.3=py39h2ba5b7c_0
|
||||
- partd=1.3.0=pyhd8ed1ab_0
|
||||
- pcre2=10.40=h17e33f8_0
|
||||
- pillow=9.4.0=py39h9767c21_0
|
||||
- pip=23.0=pyhd8ed1ab_0
|
||||
- pixman=0.40.0=h8ffe710_0
|
||||
- pooch=1.6.0=pyhd8ed1ab_0
|
||||
- poppler=22.12.0=h183ae7b_1
|
||||
- poppler-data=0.4.11=hd8ed1ab_0
|
||||
- postgresql=15.1=hd87cd2b_3
|
||||
- proj=9.1.0=heca977f_1
|
||||
- psutil=5.9.4=py39ha55989b_0
|
||||
- pthread-stubs=0.4=hcd874cb_1001
|
||||
- pthreads-win32=2.9.1=hfa6e2cd_3
|
||||
- pycparser=2.21=pyhd8ed1ab_0
|
||||
- pydap=3.3.0=pyhd8ed1ab_0
|
||||
- pyepsg=0.4.0=py_0
|
||||
- pyopenssl=23.0.0=pyhd8ed1ab_0
|
||||
- pyparsing=3.0.9=pyhd8ed1ab_0
|
||||
- pyproj=3.4.1=py39h9727d73_0
|
||||
- pyshp=2.3.1=pyhd8ed1ab_0
|
||||
- pysocks=1.7.1=pyh0701188_6
|
||||
- python=3.9.15=h4de0772_0_cpython
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python_abi=3.9=3_cp39
|
||||
- pytz=2022.7.1=pyhd8ed1ab_0
|
||||
- pyyaml=6.0=py39ha55989b_5
|
||||
- rasterio=1.3.4=py39hce277b7_0
|
||||
- requests=2.28.2=pyhd8ed1ab_0
|
||||
- rtree=1.0.1=py39h09fdee3_1
|
||||
- scikit-learn=1.2.1=py39h6fe01c0_0
|
||||
- scipy=1.10.0=py39hfbf2dce_0
|
||||
- setuptools=66.1.1=pyhd8ed1ab_0
|
||||
- shapely=2.0.1=py39h7c5f289_0
|
||||
- six=1.16.0=pyh6c4a22f_0
|
||||
- snappy=1.1.9=hfb803bf_2
|
||||
- snuggs=1.4.7=py_0
|
||||
- sortedcontainers=2.4.0=pyhd8ed1ab_0
|
||||
- soupsieve=2.3.2.post1=pyhd8ed1ab_0
|
||||
- sqlite=3.40.0=hcfcfb64_0
|
||||
- tbb=2021.7.0=h91493d7_1
|
||||
- tblib=1.7.0=pyhd8ed1ab_0
|
||||
- threadpoolctl=3.1.0=pyh8a188c0_0
|
||||
- tiledb=2.13.2=h3132609_0
|
||||
- tk=8.6.12=h8ffe710_0
|
||||
- toolz=0.12.0=pyhd8ed1ab_0
|
||||
- tornado=6.2=py39ha55989b_1
|
||||
- tqdm=4.64.1=pyhd8ed1ab_0
|
||||
- typing_extensions=4.4.0=pyha770c72_0
|
||||
- tzdata=2022g=h191b570_0
|
||||
- ucrt=10.0.22621.0=h57928b3_0
|
||||
- unicodedata2=15.0.0=py39ha55989b_0
|
||||
- urllib3=1.26.14=pyhd8ed1ab_0
|
||||
- vc=14.3=hb6edc58_10
|
||||
- vs2015_runtime=14.34.31931=h4c5c07a_10
|
||||
- webob=1.8.7=pyhd8ed1ab_0
|
||||
- wheel=0.38.4=pyhd8ed1ab_0
|
||||
- win_inet_pton=1.1.0=pyhd8ed1ab_6
|
||||
- xarray=2023.1.0=pyhd8ed1ab_0
|
||||
- xerces-c=3.2.4=h63175ca_1
|
||||
- xorg-libxau=1.0.9=hcd874cb_0
|
||||
- xorg-libxdmcp=1.1.3=hcd874cb_0
|
||||
- xugrid=0.2.0=pyhd8ed1ab_0
|
||||
- xyzservices=2022.9.0=pyhd8ed1ab_0
|
||||
- xz=5.2.6=h8d14728_0
|
||||
- yaml=0.2.5=h8ffe710_2
|
||||
- zict=2.2.0=pyhd8ed1ab_0
|
||||
- zlib=1.2.13=hcfcfb64_4
|
||||
- zstd=1.5.2=h12be248_6
|
||||
- pip:
|
||||
- dfm-tools==0.10.3
|
||||
- hydrolib-core==0.4.2
|
||||
- lxml==4.9.2
|
||||
- meshkernel==2.0.0
|
||||
- pydantic==1.10.4
|
||||
prefix: C:\Users\arey\Anaconda3\envs\dfm_tools_23
|
||||
|
|
@ -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 3.10 (pyextremes)" 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/pyextremes.iml" filepath="$PROJECT_DIR$/.idea/pyextremes.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 +1,144 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Feb 27 10:12:19 2023
|
||||
|
||||
@author: usaied
|
||||
"""
|
||||
|
||||
|
||||
import datetime
|
||||
#import calendar
|
||||
#import math
|
||||
#import re
|
||||
import pandas as pd
|
||||
#import seaborn as sns
|
||||
import matplotlib.dates as mdates
|
||||
#import dateutil
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
#import statsmodels.api as sm
|
||||
#from statsmodels.tsa.statespace.sarimax import SARIMAX
|
||||
#from statsmodels.tsa.seasonal import STL
|
||||
#import sklearn
|
||||
import scipy
|
||||
#pip install pyextremes
|
||||
|
||||
if __name__ == '__main__':
|
||||
from pyextremes import get_extremes, get_return_periods
|
||||
from pyextremes import EVA
|
||||
from pyextremes.plotting import plot_extremes
|
||||
|
||||
|
||||
# Read Data and Convert Datetime string to Datetime object
|
||||
d_parser = lambda x: datetime.datetime.strptime(x,'%Y-%m-%d %H:%M')
|
||||
WL_hourly = pd.read_csv("//srv-oak3.baird.com/Projects/13471.101 Ontario Place Therme/05_Analyses/37_EVA PyExtremes/input_WL.csv", skiprows = 16, parse_dates = ['Datetime'], date_parser = d_parser)
|
||||
|
||||
|
||||
#%% Fix for multiprocessing pool with PyCharm
|
||||
__file__ = 'WL_Extremes.py'
|
||||
|
||||
|
||||
#%% =========Data Processing and preparation================
|
||||
# Delete rows with no water level values
|
||||
WL_hourly = WL_hourly.drop(WL_hourly[(WL_hourly.WL < -900)].index)
|
||||
WL_hourly.dropna() # drop nan values
|
||||
# Reset index to Datetime
|
||||
WL_hourly.set_index('Datetime', inplace=True)
|
||||
# Hourly resample with linear interpolation
|
||||
WL_hourly = WL_hourly.asfreq('h') # This will generate missing values
|
||||
WL_hourly = WL_hourly.interpolate(method = 'linear') #Linear interpolation
|
||||
# Drop rows from extended WL dataframe
|
||||
StartDate = WL_hourly.index.min()
|
||||
EndDate = WL_hourly.index.max()
|
||||
Length_of_record = (EndDate-StartDate).days/365.2425 # in years
|
||||
#WL_hr_POR = WL_hourly.drop(WL_hourly[(WL_hourly.index < StartDate) | (WL_hourly.index > EndDate)].index)
|
||||
#df1['WL'] = WL_hr_POR['WL']
|
||||
|
||||
# Resample for Monthly average water levels
|
||||
# Resample with monthly freq.
|
||||
WaterLevels_MM = WL_hourly.resample('M').mean() # Monthly resample
|
||||
WaterLevels_MM = WaterLevels_MM.interpolate(method='linear')
|
||||
WaterLevels_MM.rename(columns = {'WL':'WL_MM'}, inplace = True)
|
||||
|
||||
##### Gaussian Kernel - Develop a continous variable for EVA ###
|
||||
Length = 30 # 30 Days average
|
||||
x = np.array(WL_hourly['WL'])
|
||||
y_static = scipy.ndimage.gaussian_filter(x, sigma=Length*6, order=0) # Note the Gaussian kernel is applied applied over 4 Sigma by default - Therfore multiplying Length (days) by 24hr/4
|
||||
|
||||
WL_hourly['Static_WL'] = y_static
|
||||
WL_hourly['Surge'] = WL_hourly['WL'] - WL_hourly['Static_WL']
|
||||
|
||||
Max_Surge = WL_hourly['Surge'].max()
|
||||
|
||||
|
||||
###### Peak Over Threshold - Surge Analysis #####
|
||||
Threshold = 0.15 # Surge threshold
|
||||
Storm_Duration = "48H" # Hours
|
||||
if __name__ == '__main__':
|
||||
Surge = get_extremes(WL_hourly.Surge, "POT", threshold = 0.14, r="48H")
|
||||
model = EVA(WL_hourly.Surge)
|
||||
model.get_extremes(method = "POT", threshold = 0.14, r="48H")
|
||||
model.plot_extremes()
|
||||
|
||||
#Fit Distribution: By default the distribution is selected automatically as best between
|
||||
#'genextreme' (GEV) and 'gumbel_r' for 'BM' extremes and
|
||||
#'genpareto' and 'expon' for 'POT' extremes.
|
||||
#Best distribution is selected using the AIC metric.
|
||||
|
||||
#model.fit_model(model='MLE', distribution='genpareto')
|
||||
if __name__ == '__main__':
|
||||
model.fit_model()
|
||||
RP_Surge = model.get_summary(return_period=[2, 5, 10, 25, 50, 100, 200, 500, 1000], alpha=0.95, n_samples=500)
|
||||
fig, ax = model.plot_diagnostic(alpha=0.95)
|
||||
# plt.savefig('Surge_EVA.png')
|
||||
|
||||
plt.show()
|
||||
|
||||
#%%
|
||||
|
||||
######## Annual Maxima EVA (BM) Smoothed Static WL Signal ####
|
||||
Static_WL = get_extremes(WL_hourly.Static_WL, "BM", block_size="365.2425D")
|
||||
model = EVA(WL_hourly.Static_WL)
|
||||
model.get_extremes(method = "BM", block_size="365.2425D")
|
||||
model.plot_extremes()
|
||||
model.fit_model()
|
||||
RP_Static = model.get_summary(return_period=[2, 5, 10, 25, 50, 100, 200, 500, 1000], alpha=0.95, n_samples=1000)
|
||||
fig, ax = model.plot_diagnostic(alpha=0.95)
|
||||
plt.savefig('Static_EVA.png')
|
||||
|
||||
######## Annual Maxima EVA (BM) Hourly WL Signal ####
|
||||
Combined_WL = get_extremes(WL_hourly.WL, "BM", block_size="365.2425D")
|
||||
model = EVA(WL_hourly.WL)
|
||||
model.get_extremes(method = "BM", block_size="365.2425D")
|
||||
model.plot_extremes()
|
||||
model.fit_model()
|
||||
RP_Combined = model.get_summary(return_period=[2, 5, 10, 25, 50, 100, 200, 500, 1000], alpha=0.95, n_samples=1000)
|
||||
fig, ax = model.plot_diagnostic(alpha=0.95)
|
||||
plt.savefig('Combined_EVA.png')
|
||||
|
||||
|
||||
|
||||
# Time Series Plot
|
||||
fig, ax = plt.subplots(2, figsize=(30,10), dpi=400)
|
||||
ax[0].plot(WL_hourly.index, WL_hourly['WL'], linewidth=0.5, label='Water Level (m IGLD85)')
|
||||
ax[0].plot(WL_hourly.index, WL_hourly['Static_WL'], linewidth=1.5, color='black', label='Static Water Level (m IGLD85)')
|
||||
ax[0].legend()
|
||||
ax[0].xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
|
||||
ax[0].xaxis.set_major_locator(mdates.YearLocator(1, month=1, day=1))
|
||||
ax[0].set_xlim([StartDate, EndDate])
|
||||
ax[0].set_ylabel("Water Level (m IGLD85)", fontsize=10)
|
||||
ax[0].grid(color = 'grey', linestyle = '--', linewidth = 0.5)
|
||||
|
||||
ax[1].plot(WL_hourly.index, WL_hourly['Surge'], linewidth=0.5, label='Surge (m)')
|
||||
ax[1].legend()
|
||||
ax[1].xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
|
||||
ax[1].xaxis.set_major_locator(mdates.YearLocator(1, month=1, day=1))
|
||||
ax[1].set_xlim([StartDate, EndDate])
|
||||
ax[1].set_ylabel("Surge (m)", fontsize=10)
|
||||
ax[1].grid(color = 'grey', linestyle = '--', linewidth = 0.5)
|
||||
|
||||
|
||||
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
|
||||
plt.gca().xaxis.set_major_locator(mdates.YearLocator(1, month=1, day=1))
|
||||
plt.tight_layout()
|
||||
plt.savefig('Water_Level_TS.png')
|
||||
|
|
@ -16,6 +16,8 @@ import shapely.wkt
|
|||
|
||||
import re
|
||||
|
||||
import urllib.parse
|
||||
|
||||
#%% Setup NWS ID
|
||||
nws = nwswx.WxAPI('api@alexanderrey.ca')
|
||||
|
||||
|
|
@ -154,13 +156,16 @@ for alertIDX in nws_alert_gdf.index:
|
|||
else:
|
||||
alertEndTime = nws_alert_gdf.loc[alertIDX, 'ends']
|
||||
|
||||
alertURL = 'https://alerts-v2.weather.gov/#/?id=' + \
|
||||
urllib.parse.quote(nws_alert_gdf.loc[alertIDX, 'id'])
|
||||
|
||||
alertString = alertString + '[' + nws_alert_gdf.loc[alertIDX, 'headline'] + '}' \
|
||||
'{' + alertDescription + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'areaDesc'] + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'onset'] + '}' + \
|
||||
'{' + alertEndTime + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, 'severity'] + '}' + \
|
||||
'{' + nws_alert_gdf.loc[alertIDX, '@id'] + ']'
|
||||
'{' + alertURL + ']'
|
||||
|
||||
alertData_1D[alertPoint] = alertString
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,186 @@
|
|||
name: rasterProcess2
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- appdirs=1.4.4=pyh9f0ad1d_0
|
||||
- attrs=22.2.0=pyh71513ae_0
|
||||
- blosc=1.21.3=hdccc3a2_0
|
||||
- bokeh=2.4.3=pyhd8ed1ab_3
|
||||
- boost-cpp=1.78.0=h9f4b32c_1
|
||||
- bottleneck=1.3.5=py38hbaf524b_1
|
||||
- branca=0.6.0=pyhd8ed1ab_0
|
||||
- brotli=1.0.9=hcfcfb64_8
|
||||
- brotli-bin=1.0.9=hcfcfb64_8
|
||||
- brotlipy=0.7.0=py38h91455d4_1005
|
||||
- bzip2=1.0.8=h8ffe710_4
|
||||
- ca-certificates=2022.12.7=h5b45459_0
|
||||
- cairo=1.16.0=hd694305_1014
|
||||
- cffi=1.15.1=py38h57701bc_3
|
||||
- cfitsio=4.2.0=h9ebe7e4_0
|
||||
- cftime=1.6.2=py38hbaf524b_1
|
||||
- charset-normalizer=2.1.1=pyhd8ed1ab_0
|
||||
- click=8.1.3=win_pyhd8ed1ab_2
|
||||
- click-plugins=1.1.1=py_0
|
||||
- cligj=0.7.2=pyhd8ed1ab_1
|
||||
- cloudpickle=2.2.0=pyhd8ed1ab_0
|
||||
- colorama=0.4.6=pyhd8ed1ab_0
|
||||
- contourpy=1.0.7=py38hb1fd069_0
|
||||
- cryptography=39.0.0=py38h95f5157_0
|
||||
- curl=7.87.0=h68f0423_0
|
||||
- cycler=0.11.0=pyhd8ed1ab_0
|
||||
- cytoolz=0.12.0=py38h91455d4_1
|
||||
- dask=2023.1.0=pyhd8ed1ab_0
|
||||
- dask-core=2023.1.0=pyhd8ed1ab_0
|
||||
- distributed=2023.1.0=pyhd8ed1ab_0
|
||||
- expat=2.5.0=h1537add_0
|
||||
- fiona=1.8.22=py38hc97cbf3_5
|
||||
- folium=0.14.0=pyhd8ed1ab_0
|
||||
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
|
||||
- font-ttf-inconsolata=3.000=h77eed37_0
|
||||
- font-ttf-source-code-pro=2.038=h77eed37_0
|
||||
- font-ttf-ubuntu=0.83=hab24e00_0
|
||||
- fontconfig=2.14.1=hbde0cde_0
|
||||
- fonts-conda-ecosystem=1=0
|
||||
- fonts-conda-forge=1=0
|
||||
- fonttools=4.38.0=py38h91455d4_1
|
||||
- freetype=2.12.1=h546665d_1
|
||||
- freexl=1.0.6=h67ca5e6_1
|
||||
- fsspec=2022.11.0=pyhd8ed1ab_0
|
||||
- gdal=3.6.2=py38h5a6f081_3
|
||||
- geopandas=0.12.2=pyhd8ed1ab_0
|
||||
- geopandas-base=0.12.2=pyha770c72_0
|
||||
- geos=3.11.1=h1537add_0
|
||||
- geotiff=1.7.1=h720ab47_5
|
||||
- gettext=0.21.1=h5728263_0
|
||||
- hdf4=4.2.15=h1b1b6ef_5
|
||||
- hdf5=1.12.2=nompi_h57737ce_101
|
||||
- heapdict=1.0.1=py_0
|
||||
- icu=70.1=h0e60522_0
|
||||
- idna=3.4=pyhd8ed1ab_0
|
||||
- intel-openmp=2023.0.0=h57928b3_25922
|
||||
- jinja2=3.1.2=pyhd8ed1ab_1
|
||||
- joblib=1.2.0=pyhd8ed1ab_0
|
||||
- jpeg=9e=h8ffe710_2
|
||||
- kealib=1.5.0=h61be68b_0
|
||||
- kiwisolver=1.4.4=py38hb1fd069_1
|
||||
- krb5=1.20.1=heb0366b_0
|
||||
- lcms2=2.14=ha5c8aab_1
|
||||
- lerc=4.0.0=h63175ca_0
|
||||
- libaec=1.0.6=h63175ca_1
|
||||
- libblas=3.9.0=16_win64_mkl
|
||||
- libbrotlicommon=1.0.9=hcfcfb64_8
|
||||
3ibbrotlidec=1.0.9=hcfcfb64_8
|
||||
- libbrotlienc=1.0.9=hcfcfb64_8
|
||||
- libcblas=3.9.0=16_win64_mkl
|
||||
- libcurl=7.87.0=h68f0423_0
|
||||
- libdeflate=1.17=hcfcfb64_0
|
||||
- libffi=3.4.2=h8ffe710_5
|
||||
- libgdal=3.6.2=h060c9ed_3
|
||||
- libglib=2.74.1=he8f3873_1
|
||||
- libhwloc=2.8.0=h039e092_1
|
||||
- libiconv=1.17=h8ffe710_0
|
||||
- libjpeg-turbo=2.1.4=hcfcfb64_0
|
||||
- libkml=1.3.0=hf2ab4e4_1015
|
||||
- liblapack=3.9.0=16_win64_mkl
|
||||
- libnetcdf=4.8.1=nompi_h8c042bf_106
|
||||
- libpng=1.6.39=h19919ed_0
|
||||
- libpq=15.1=ha9684e8_3
|
||||
- librttopo=1.1.0=he22b5cd_12
|
||||
- libspatialindex=1.9.3=h39d44d4_4
|
||||
- libspatialite=5.0.1=h07bf483_22
|
||||
- libsqlite=3.40.0=hcfcfb64_0
|
||||
- libssh2=1.10.0=h9a1e1f7_3
|
||||
- libtiff=4.5.0=hf8721a0_2
|
||||
- libwebp-base=1.2.4=h8ffe710_0
|
||||
- libxcb=1.13=hcd874cb_1004
|
||||
- libxml2=2.10.3=hc3477c8_1
|
||||
- libzip=1.9.2=h519de47_1
|
||||
- libzlib=1.2.13=hcfcfb64_4
|
||||
- locket=1.0.0=pyhd8ed1ab_0
|
||||
- lz4=4.2.0=py38hb6d8784_0
|
||||
- lz4-c=1.9.3=h8ffe710_1
|
||||
- m2w64-gcc-libgfortran=5.3.0=6
|
||||
- m2w64-gcc-libs=5.3.0=7
|
||||
- m2w64-gcc-libs-core=5.3.0=7
|
||||
- m2w64-gmp=6.1.0=2
|
||||
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
|
||||
- mapclassify=2.5.0=pyhd8ed1ab_1
|
||||
- markupsafe=2.1.1=py38h91455d4_2
|
||||
- matplotlib-base=3.6.2=py38h528a6c7_0
|
||||
- mkl=2022.1.0=h6a75c08_874
|
||||
- msgpack-python=1.0.4=py38hb1fd069_1
|
||||
- msys2-conda-epoch=20160418=1
|
||||
- munch=2.5.0=py_0
|
||||
- munkres=1.1.4=pyh9f0ad1d_0
|
||||
- netcdf4=1.6.2=nompi_py38h78680c8_100
|
||||
- networkx=3.0=pyhd8ed1ab_0
|
||||
- numpy=1.24.1=py38h90ce339_0
|
||||
- openjpeg=2.5.0=ha2aaf27_2
|
||||
- openssl=3.0.7=hcfcfb64_1
|
||||
- packaging=23.0=pyhd8ed1ab_0
|
||||
- pandas=1.5.2=py38h5846ac1_2
|
||||
- partd=1.3.0=pyhd8ed1ab_0
|
||||
- pcre2=10.40=h17e33f8_0
|
||||
- pillow=9.4.0=py38h409c3de_0
|
||||
- pip=22.3.1=pyhd8ed1ab_0
|
||||
- pixman=0.40.0=h8ffe710_0
|
||||
- pooch=1.6.0=pyhd8ed1ab_0
|
||||
- poppler=22.12.0=h183ae7b_1
|
||||
- poppler-data=0.4.11=hd8ed1ab_0
|
||||
- postgresql=15.1=hd87cd2b_3
|
||||
- proj=9.1.0=heca977f_1
|
||||
- psutil=5.9.4=py38h91455d4_0
|
||||
- pthread-stubs=0.4=hcd874cb_1001
|
||||
- pthreads-win32=2.9.1=hfa6e2cd_3
|
||||
- pycparser=2.21=pyhd8ed1ab_0
|
||||
- pyopenssl=23.0.0=pyhd8ed1ab_0
|
||||
- pyparsing=3.0.9=pyhd8ed1ab_0
|
||||
- pyproj=3.4.1=py38hac5b721_0
|
||||
- pysocks=1.7.1=pyh0701188_6
|
||||
- python=3.8.15=h4de0772_0_cpython
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python_abi=3.8=3_cp38
|
||||
- pytz=2022.7.1=pyhd8ed1ab_0
|
||||
- pyyaml=6.0=py38h91455d4_5
|
||||
- requests=2.28.2=pyhd8ed1ab_0
|
||||
- rtree=1.0.1=py38h8b54edf_1
|
||||
- scikit-learn=1.2.0=py38h69724d7_0
|
||||
- scipy=1.10.0=py38h0f6ee2a_0
|
||||
- setuptools=66.0.0=pyhd8ed1ab_0
|
||||
- shapely=2.0.0=py38h9c0aba1_0
|
||||
- six=1.16.0=pyh6c4a22f_0
|
||||
- snappy=1.1.9=hfb803bf_2
|
||||
- sortedcontainers=2.4.0=pyhd8ed1ab_0
|
||||
- sqlite=3.40.0=hcfcfb64_0
|
||||
- tbb=2021.7.0=h91493d7_1
|
||||
- tblib=1.7.0=pyhd8ed1ab_0
|
||||
- threadpoolctl=3.1.0=pyh8a188c0_0
|
||||
- tiledb=2.13.2=h3132609_0
|
||||
- tk=8.6.12=h8ffe710_0
|
||||
- toolz=0.12.0=pyhd8ed1ab_0
|
||||
- tornado=6.2=py38h91455d4_1
|
||||
- typing_extensions=4.4.0=pyha770c72_0
|
||||
- ucrt=10.0.22621.0=h57928b3_0
|
||||
- unicodedata2=15.0.0=py38h91455d4_0
|
||||
- urllib3=1.26.14=pyhd8ed1ab_0
|
||||
- vc=14.3=hb6edc58_10
|
||||
- vs2015_runtime=14.34.31931=h4c5c07a_10
|
||||
- wheel=0.38.4=pyhd8ed1ab_0
|
||||
- win_inet_pton=1.1.0=pyhd8ed1ab_6
|
||||
- xarray=2022.12.0=pyhd8ed1ab_0
|
||||
- xerces-c=3.2.4=h63175ca_1
|
||||
- xorg-libxau=1.0.9=hcd874cb_0
|
||||
- xorg-libxdmcp=1.1.3=hcd874cb_0
|
||||
- xyzservices=2022.9.0=pyhd8ed1ab_0
|
||||
- xz=5.2.6=h8d14728_0
|
||||
- yaml=0.2.5=h8ffe710_2
|
||||
- zict=2.2.0=pyhd8ed1ab_0
|
||||
- zlib=1.2.13=hcfcfb64_4
|
||||
- zstd=1.5.2=h7755175_4
|
||||
- pip:
|
||||
- capparselib==0.6.6
|
||||
- certifi==2022.12.7
|
||||
- h3==4.0.0b2
|
||||
- lxml==4.6.3
|
||||
- nwswx==0.1.1
|
||||
prefix: C:\Users\arey\Anaconda3\envs\rasterProcess2
|
||||
Loading…
Reference in New Issue