#%% Plotting EWR Flume Tests # Alexander Rey, 2022 #%% Import import pandas as pd import numpy as np import math import matplotlib.pyplot as plt import matplotlib.cm as cm import matplotlib.colors as mcolors import geopandas as gp gp.options.use_pygeos = True from shapely import geometry, ops # Map Plotting import cartopy.crs as ccrs import contextily as ctx # Interpolation import scipy as sp from scipy.interpolate import griddata from scipy.interpolate import LinearNDInterpolator, interp1d # Lowess interpolation import statsmodels.api as sm import pathlib as pl import datetime #%% Read in centerline shapefile river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp') river_centerlineExploded = river_centerline.explode(ignore_index=True) river_centerlineExploded.reset_index(inplace=True) tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4] # Put the sub-line coordinates into a list of sublists outcoords = [list(i.coords) for i in tempMulti] # Flatten the list of sublists and use it to make a new line river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist]) river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge) river_centerline_merge_gpd2 =\ gp.GeoDataFrame(geometry=gp.points_from_xy( river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615")) # Add distance along centerline river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1)) river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum() river_centerline_merge_gpd2.iloc[0, 1] = 0 river_centerline_merge_gpd2.iloc[0, 2] = 0 #%% Import Observations from database # Read in combined dataset obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv") # Add Datetime obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x) # Add months obs_IN['Month'] = obs_IN['DateTime'].dt.month # Convert to geodataframe obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy( obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615") # Adjust Units obs_gdf.loc[obs_gdf.Unit == 'NG/G', 'Sample_NG/G'] = obs_gdf.Samplevalue obs_gdf.loc[obs_gdf.Unit == 'ng/g', 'Sample_NG/G'] = obs_gdf.Samplevalue obs_gdf.loc[obs_gdf.Unit == 'UG/G', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'ug/g', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'MG/KG', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'mg/kg', 'Sample_NG/G'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'NG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue obs_gdf.loc[obs_gdf.Unit == 'ng/L', 'Sample_NG/L'] = obs_gdf.Samplevalue obs_gdf.loc[obs_gdf.Unit == 'UG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'ug/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000 obs_gdf.loc[obs_gdf.Unit == 'MG/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000000 obs_gdf.loc[obs_gdf.Unit == 'mg/L', 'Sample_NG/L'] = obs_gdf.Samplevalue * 1000000 # Add centerline and reset index obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index() # Sediment Hg GeoDataFrame where media is Sediment obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) & ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total') | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) & (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna()) obs_HgSed = obs_gdf.loc[obsMask, :] # Sediment Hg GeoDataFrame for surface sediment obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) & ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total') | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) & (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & (obs_gdf.RiverKM.notna()) & ((obs_gdf.TopDepth_x < 5) | (obs_gdf.TopDepth_x.isna()))) obs_HgSurfaceSed = obs_gdf.loc[obsMask, :] # obs_HgSurfaceSed.to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgSurfaceSed.geojson', driver="GeoJSON") obs_HgSurfaceSed.loc[:, ['Sample_NG/G', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgSurfaceSed2.shp') # Group sediment core data to take average/min/max obs_HgSurfaceSed_Avg = obs_HgSurfaceSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'], 'RiverKM': ['mean'], 'Longitude': ['mean'], 'Latitude': ['mean'], 'DateTime': ['mean']}).reset_index() # Flatten Multiindex obs_HgSurfaceSed_Avg.columns = ["_".join(a) for a in obs_HgSurfaceSed_Avg.columns.to_flat_index()] # Rename for shapefile obs_HgSurfaceSed_Avg.rename(columns={"Sample_NG/G_mean": "Mean_NG/G", "Sample_NG/G_max": "Max_NG/G", "Sample_NG/G_min": "Min_NG/G"}, inplace=True) # Convert to geodataframe and save obs_HgSurfaceSed_Avg_gdf = gp.GeoDataFrame(obs_HgSurfaceSed_Avg, geometry=gp.points_from_xy( obs_HgSurfaceSed_Avg.loc[:, 'Longitude_mean'], obs_HgSurfaceSed_Avg.loc[:, 'Latitude_mean'], crs="EPSG:4326")).to_crs(crs="EPSG:32615") 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') # Group sediment core data to take average/min/max obs_HgSed_Avg = obs_HgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'], 'RiverKM': ['mean'], 'Longitude': ['mean'], 'Latitude': ['mean'], 'DateTime': ['mean']}).reset_index() # Merge Index obs_HgSed_Avg.columns = obs_HgSed_Avg.columns.map('|'.join).str.strip('|') # Fix Names obs_HgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude", "Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True) # Create new GeoDataFrame obs_HgSed_Avg = gp.GeoDataFrame(obs_HgSed_Avg, geometry=gp.points_from_xy( obs_HgSed_Avg.loc[:, 'Longitude'], obs_HgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615") # Water Hg GeoDataFrame where media is SurfaceWater (SW) obsMask = (((obs_gdf.Media_x == 'SW')) & ((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total') | (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) & (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna()) obs_HgWater = obs_gdf.loc[obsMask, :] # Save # obs_HgWater.loc[:, ['Sample_NG/L', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_HgWater.shp') # Sediment surface MeHg GeoDataFrame obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) & ((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) & (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna() & ((obs_gdf.TopDepth_x < 5) | (obs_gdf.TopDepth_x.isna()))) obs_MeHgSed = obs_gdf.loc[obsMask, :] # Save # obs_MeHgSed.loc[:, ['Sample_NG/G', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_MeHgSed.shp') # Group sediment core data to take average/min/max obs_MeHgSed_Avg = obs_MeHgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'], 'RiverKM': ['mean'], 'Longitude': ['mean'], 'Latitude': ['mean'], 'DateTime': ['mean']}).reset_index() # Flatten Multiindex obs_MeHgSed_Avg.columns = ["_".join(a) for a in obs_MeHgSed_Avg.columns.to_flat_index()] # Rename for shapefile obs_MeHgSed_Avg.rename(columns={"Sample_NG/G_mean": "Mean_NG/G", "Sample_NG/G_max": "Max_NG/G", "Sample_NG/G_min": "Min_NG/G"}, inplace=True) # Create new GeoDataFrame obs_MeHgSed_Avg_gdf = gp.GeoDataFrame(obs_MeHgSed_Avg, geometry=gp.points_from_xy( obs_MeHgSed_Avg.loc[:, 'Longitude_mean'], obs_MeHgSed_Avg.loc[:, 'Latitude_mean'], crs="EPSG:4326")).to_crs(crs="EPSG:32615") 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') # MeHg in Water obsMask = (((obs_gdf.Media_x == 'SW')) & ((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) & (obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna()) obs_MeHgWater = obs_gdf.loc[obsMask, :] # Save obs_MeHgWater.loc[:, ['Sample_NG/L', 'Samplenumber_x', 'geometry']].to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/obs_MeHgWater.shp') #%% Plot Observations fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(30, 15)) axes.set_xlim(494649, 513647) axes.set_ylim(5514653, 5525256) # Add basemap mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw' ctx.add_basemap(axes, source=mapbox, crs='EPSG:26915') obs_HgSed_Avg.plot(column='Sample_NG/G|mean', axes=axes, vmin=0, vmax=10000) plt.show() #%% Plot River Profile name = "Set1" cmap = cm.get_cmap(name) colors = cmap.colors # Create a HSV colormap sifted by 180 degrees # create the "hsv" colormap hsv = plt.get_cmap('hsv') # shift the colormap so that red is in the middle shifted_hsv = mcolors.LinearSegmentedColormap.from_list('shifted_hsv', np.roll(hsv(np.linspace(0.0, 1.0, 256)), 128, axis=0)) interp_xvals = np.linspace(0, 100000, num=1000) # Bin averaging function def average_in_bins(df, bin_width): binned_df = pd.cut(df.index, bins=range(0, int(df.index.max() + bin_width), bin_width), right=False) return df.groupby(binned_df).mean() fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(8, 12), sharex=True) axes[0].set_prop_cycle(color=colors) axes[1].set_prop_cycle(color=colors) axes[2].set_prop_cycle(color=colors) axes[3].set_prop_cycle(color=colors) # Plot surface Hg # Plot using a different color for each month obs_HgSurfaceSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[0], c='Month', label='All Samples', vmin=1, vmax=12, cmap=shifted_hsv) # obs_HgSurfaceSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[0], color='grey', label='All Samples') # obs_HgSurfaceSed_Avg_gdf.plot.scatter('RiverKM_mean', 'Mean_NG/G', ax=axes[0], color='magenta', label='Site Averaged') # obs_HgSurfaceSed_Index = obs_HgSurfaceSed.copy() # obs_HgSurfaceSed_Index['RiverKM2'] = obs_HgSurfaceSed['RiverKM'] # obs_HgSurfaceSed_Index = obs_HgSurfaceSed_Index.set_index('RiverKM') # # binwidths = [10, 100, 1000, 10000] # for binWidthIDX, binWidth in enumerate(binwidths): # average_in_bins(obs_HgSurfaceSed_Index, binWidth).dropna(subset=['Sample_NG/G']).\ # plot.line('RiverKM2', 'Sample_NG/G', ax=axes[0], # label=str(binWidth) + ' m binned Samples') # Find and plot Lowess regression for each season SeasonMonths = [[12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]] SeasonNames = ['Winter', 'Spring', 'Summer', 'Fall'] SeasonColors = ['tab:blue', 'tab:green', 'darkred', 'tab:orange'] for seasonIDX, season in enumerate(SeasonMonths): # Skip Winter if seasonIDX == 0: continue seasonMask = obs_HgSurfaceSed['DateTime'].dt.month.isin(season) lowess = sm.nonparametric.lowess(obs_HgSurfaceSed.loc[seasonMask, 'Sample_NG/G'], obs_HgSurfaceSed.loc[seasonMask, 'RiverKM'], frac=0.5, xvals=interp_xvals, it=2) axes[0].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX], linewidth=4, color=SeasonColors[seasonIDX]) # lowess = sm.nonparametric.lowess(obs_HgSurfaceSed['Sample_NG/G'], obs_HgSurfaceSed['RiverKM'], # frac=0.3, xvals=interp_xvals, it=2) # axes[0].plot(interp_xvals, lowess, label='Lowess regression', linewidth=4, color='black') axes[0].set_ylabel('Surface Sediment Total Hg [ng/g]') axes[0].set_ylim([0, 20000]) axes[0].set_title('Surface Sediment Total Hg') axes[0].legend() obs_MeHgSed.plot.scatter('RiverKM', 'Sample_NG/G', ax=axes[1], c='Month', label='All Samples', vmin=1, vmax=12, cmap=shifted_hsv) # obs_MeHgSed_Avg.plot.scatter('RiverKM_mean', 'Mean_NG/G', ax=axes[1], color='magenta', label='Site Averaged') axes[1].set_ylabel('Surface Sediment Total MeHg [ng/g]') # obs_MeHgSed_Index = obs_MeHgSed.copy() # obs_MeHgSed_Index['RiverKM2'] = obs_MeHgSed_Index['RiverKM'] # obs_MeHgSed_Index = obs_MeHgSed_Index.set_index('RiverKM') # # binwidths = [10, 100, 1000, 10000] # for binWidthIDX, binWidth in enumerate(binwidths): # average_in_bins(obs_MeHgSed_Index, binWidth).dropna(subset=['Sample_NG/G']).\ # plot.line('RiverKM2', 'Sample_NG/G', ax=axes[1], # label=str(binWidth) + ' m binned Samples') # Find and plot Lowess regression # lowess = sm.nonparametric.lowess(obs_MeHgSed['Sample_NG/G'], obs_MeHgSed['RiverKM'], # frac=0.2, xvals=interp_xvals, it=2) # axes[1].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_MeHgSed['DateTime'].dt.month.isin(season) lowess = sm.nonparametric.lowess(obs_MeHgSed.loc[seasonMask, 'Sample_NG/G'], obs_MeHgSed.loc[seasonMask, 'RiverKM'], frac=0.5, xvals=interp_xvals, it=2) axes[1].plot(interp_xvals, lowess, label='Lowess:' + SeasonNames[seasonIDX], linewidth=4, color=SeasonColors[seasonIDX]) # axes[0].set_ylim([0, 20000]) axes[1].set_title('Surface Sediment Total MeHg') axes[1].legend() obs_HgWater.plot.scatter('RiverKM', 'Sample_NG/L', ax=axes[2], c='Month', label='All Samples', vmin=1, vmax=12, 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=" ")