239 lines
10 KiB
Python
239 lines
10 KiB
Python
## Process EWR Hg Data into a surface
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# AJMR: September 2022
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#%% Setup
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import pandas as pd
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import geopandas as gp
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import gpxpy
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import gpxpy.gpx
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import numpy as np
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import matplotlib.pyplot as plt
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import contextily as ctx
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import datetime
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import scipy as sp
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from scipy.interpolate import griddata
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from shapely import geometry, ops
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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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#%% 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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# obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/combined dataset.csv")
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# obs_IN = pd.read_csv("C:/Users/arey/Downloads/ExportDownloads_EwrrpDataExport_rharris_2023-05-12_104911/EwrrpDataExport_rharris_2023-05-12_104911.txt", sep='\t', encoding='UTF-16 LE')
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df = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Results.txt", sep = '\t', encoding='UTF-16 LE')
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locations = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Sites.txt", sep = '\t', encoding='UTF-16 LE')
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samples = pd.read_csv(r"P:\12828.101 English Wabigoon River\03_Data\02_Physical\30_Site Sampling Database\EwrrpDataExport_rharris_2022-08-23_102403_Samples.txt", sep = '\t', encoding='UTF-16 LE')
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#%% Merge locations and results
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obs_IN = pd.merge(left=df, right=locations, left_on= 'Samplesiteid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
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obs_IN = pd.merge(left=obs_IN, right=samples, left_on= 'Sampleid', right_on= 'Id', how = 'left') # 1096 locations w/Hg
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# Add Datetime
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obs_IN['Sampledate'] = pd.to_datetime(obs_IN.Sampledate)
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#%% Process combined datsaset
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# Convert to geodataframe
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obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
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obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
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# Add centerline and reset index
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obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
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# Sediment Hg GeoDataFrame where media is Sediment
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obsMask = (((obs_gdf.Media == 'SED') | (obs_gdf.Media == '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.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() &
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((obs_gdf.TopDepth == 0) | (obs_gdf.TopDepth.isna())))
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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, :].copy()
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# Adjust Units
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obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Samplevalue']
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obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Samplevalue']
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obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Samplevalue'] * 1000
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obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Samplevalue'] * 1000
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obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Samplevalue'] * 0.001
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obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Samplevalue'] * 0.001
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# Save subset of columns as a shapefile
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obs_HgSed.loc[:, ['Sample_NG/G', 'Sampledate_x', 'RiverKM', 'Latitude', 'Longitude', 'geometry',
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'Surveyname','Sitecode','Parameter']].to_file(
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'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgSed.shp')
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#%% Extract Hg data from surface water samples
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# Water Hg GeoDataFrame where media is SurfaceWater (SW)
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obsMask = (((obs_gdf.Media == '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.Sampledate > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
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obs_HgWater = obs_gdf.loc[obsMask, :].copy()
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# Adjust Units
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obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Samplevalue'] = obs_HgWater.Samplevalue
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obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Samplevalue'] = obs_HgWater.Samplevalue
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obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
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obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000
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obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
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obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Samplevalue'] = obs_HgWater.Samplevalue * 1000000
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# Drop where no result
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obs_HgWater = obs_HgWater.dropna(subset=['Samplevalue'])
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# Save subset of columns as a shapefile
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obs_HgWater.loc[:, ['Samplevalue', 'Sampledate', 'RiverKM', 'geometry',
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'Contributor', 'Sitecode', 'Parameter']].to_file(
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'C:/Users/arey/files/Projects/Grassy Narrows/Wood English-Wabigoon Export_alldata_2017-current/BairdDB_HgWater_RevB.shp')
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#%% River Profile Plotting
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
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axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
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axes.scatter(obs_HgSed.RiverKM, obs_HgSed.Samplevalue)
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axes.set_ylim(0, 20000)
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axes.set_xlim(0, 100000)
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axes.set_xlabel('Distance Along River [m]')
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axes.set_ylabel('Surface Sediment Hg [ng/g]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_SurfaceProfile.png',
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bbox_inches='tight', dpi=300)
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#%% Plot Vertical Profiles
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 7))
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obs_HgSed.plot.scatter('RiverKM', 'TopDepth_x', 12, 'Sample_NG/G',
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ax=axes, vmax=20000, cmap='viridis')
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axes.invert_yaxis()
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axes.set_xlabel('Distance Along River [m]')
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axes.set_ylabel('Sample Depth [cm]')
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axes.set_xlim([0, 100000])
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# Get colorbar axis to set a legend
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cax = fig.get_axes()[1]
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#and we can modify it, i.e.:
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cax.set_ylabel('Sediment Hg (ng/g)')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Hg_Vertical.png',
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bbox_inches='tight', dpi=300)
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#%% Hg Map Plotting
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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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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
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fig.patch.set_facecolor('white')
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# Full system limits
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# axes.set_xlim(350000, 520000)
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# axes.set_ylim(5510000, 5580000)
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# # Clay Lake Limits
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# axes.set_xlim(466418, 515846)
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# axes.set_ylim(5513437, 5545362)
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# Dryden Limits
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axes.set_xlim(497697, 515846)
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axes.set_ylim(5513437, 5525166)
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# Add Basemap
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ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
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# Plot Hg At surface
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obs_HgSed.plot(ax=axes, column='Samplevalue', legend=True,
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vmin=0, vmax=5000, markersize=2, cmap='viridis',
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legend_kwds={'label': 'Surface Sediment Hg (ng/g)'})
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceHgMap.png',
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bbox_inches='tight', dpi=300)
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#%% Interpolate Hg
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bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
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names=['x', 'y', 'z'], header=0, delim_whitespace=True)
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rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
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names=['x', 'y', 'z'], header=0, delim_whitespace=True)
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HgInterp = griddata(np.array([dataOUTmax.geometry.x, dataOUTmax.geometry.y]).T,
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np.array(dataOUTmax.Samplevalue).T, np.array([bathy_xyz.x, bathy_xyz.y]).T,
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method='nearest')
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HgInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, HgInterp))
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HgInterpOut = np.transpose(HgInterpOut)
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# Set Wetlands to zero
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# Interpolate Roughness
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RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
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method='nearest')
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HgInterpOut[RoughInterp==0.05, 2] = 0
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np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/SurfaceInterFiltDB.xyz',
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HgInterpOut, delimiter=" ")
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#%% Hg Interp Plotting
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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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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 7))
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fig.patch.set_facecolor('white')
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# Clay Lake Limits
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axes.set_xlim(466418, 515846)
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axes.set_ylim(5513437, 5545362)
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# Add Basemap
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ctx.add_basemap(axes, source=mapbox, crs='EPSG:32615')
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# Plot Hg
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axes.scatter(HgInterpOut[:, 0], HgInterpOut[:, 1], 5, HgInterpOut[:, 2],
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vmin=0, vmax=5000)
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plt.show()
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