917 lines
36 KiB
Python
917 lines
36 KiB
Python
#%% Plotting EWR Flume Tests
|
|
|
|
# Alexander Rey, 2022
|
|
|
|
#%% Import
|
|
import pandas as pd
|
|
import numpy as np
|
|
import math
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.cm as cm
|
|
import geopandas as gp
|
|
gp.options.use_pygeos = True
|
|
from shapely import geometry, ops
|
|
|
|
# Map Plotting
|
|
import cartopy.crs as ccrs
|
|
import contextily as ctx
|
|
|
|
# Interpolation
|
|
import scipy as sp
|
|
from scipy.interpolate import griddata
|
|
|
|
from scipy.interpolate import LinearNDInterpolator, interp1d
|
|
|
|
from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
|
|
from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
|
|
import pathlib as pl
|
|
|
|
import datetime
|
|
#%% Read Model Log
|
|
|
|
# runLog = pd.read_excel("Y:/12828.101 English Wabigoon River/06_Models/00_Delft3D/ModelRuns.xlsx", "Sensitivity")
|
|
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "12828.101 English Wabigoon River", "06_Models", "00_Delft3D", "ModelRuns.xlsx")
|
|
runLog = pd.read_excel(pth.as_posix(), "Sensitivity")
|
|
|
|
dataPath = "FlowFM_map.nc"
|
|
|
|
#%% Read in centerline shapefile
|
|
river_centerline = gp.read_file('//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/03_Data/02_Physical/16_Waterline/Centerline_for_Modelling_UTMZ15.shp')
|
|
|
|
river_centerlineExploded = river_centerline.explode(ignore_index=True)
|
|
river_centerlineExploded.reset_index(inplace=True)
|
|
|
|
tempMulti = river_centerlineExploded.iloc[[5,0,1,2,3,4,6,7,9], 4]
|
|
# Put the sub-line coordinates into a list of sublists
|
|
outcoords = [list(i.coords) for i in tempMulti]
|
|
# Flatten the list of sublists and use it to make a new line
|
|
river_centerline_merge = geometry.LineString([i for sublist in outcoords for i in sublist])
|
|
river_centerline_merge_gpd = gp.GeoSeries(river_centerline_merge)
|
|
|
|
river_centerline_merge_gpd2 =\
|
|
gp.GeoDataFrame(geometry=gp.points_from_xy(
|
|
river_centerline_merge.xy[0], river_centerline_merge.xy[1], crs="EPSG:32615"))
|
|
|
|
# Add distance along centerline
|
|
river_centerline_merge_gpd2['DistanceFromPrevious'] = river_centerline_merge_gpd2.distance(river_centerline_merge_gpd2.shift(1))
|
|
river_centerline_merge_gpd2['RiverKM'] = river_centerline_merge_gpd2['DistanceFromPrevious'].cumsum()
|
|
river_centerline_merge_gpd2.iloc[0, 1] = 0
|
|
river_centerline_merge_gpd2.iloc[0, 2] = 0
|
|
|
|
#%% Import Validation Data
|
|
|
|
# Read in combined dataset
|
|
obs_IN = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/05_Analyses/02_Data Analysis/output/combined dataset-SGJ.csv")
|
|
# Add Datetime
|
|
obs_IN['DateTime'] = pd.to_datetime(obs_IN.Sampledate_x)
|
|
|
|
# Convert to geodataframe
|
|
obs_gdf = gp.GeoDataFrame(obs_IN, geometry=gp.points_from_xy(
|
|
obs_IN.loc[:, 'Longitude'], obs_IN.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
|
|
|
# Add centerline and reset index
|
|
obs_gdf = gp.sjoin_nearest(river_centerline_merge_gpd2, obs_gdf, how='right', max_distance=250).reset_index()
|
|
|
|
# Sediment Hg GeoDataFrame where media is Sediment
|
|
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
|
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg) Total')
|
|
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
|
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
|
|
|
obs_HgSed = obs_gdf.loc[obsMask, :]
|
|
|
|
# Adjust Units
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 1000
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
|
|
obs_HgSed.loc[obs_HgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_HgSed.Samplevalue * 0.001
|
|
|
|
obs_HgSed.to_file('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/HgSedDB2.geojson', driver="GeoJSON")
|
|
|
|
# Group sediment core data to take average/min/max
|
|
obs_HgSed_Avg = obs_HgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
|
|
'RiverKM': ['mean'],
|
|
'Longitude': ['mean'],
|
|
'Latitude': ['mean'],
|
|
'DateTime': ['mean']}).reset_index()
|
|
# Merge Index
|
|
obs_HgSed_Avg.columns = obs_HgSed_Avg.columns.map('|'.join).str.strip('|')
|
|
|
|
# Fix Names
|
|
obs_HgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
|
|
"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
|
|
|
|
# Create new GeoDataFrame
|
|
obs_HgSed_Avg = gp.GeoDataFrame(obs_HgSed_Avg, geometry=gp.points_from_xy(
|
|
obs_HgSed_Avg.loc[:, 'Longitude'], obs_HgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
|
|
|
|
|
|
|
# Water Hg GeoDataFrame where media is SurfaceWater (SW)
|
|
obsMask = (((obs_gdf.Media_x == 'SW')) &
|
|
((obs_gdf.Parameter == 'Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)-Total')
|
|
| (obs_gdf.Parameter == 'Total Mercury') | (obs_gdf.Parameter == 'Mercury (Hg)')) &
|
|
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
|
|
|
obs_HgWater = obs_gdf.loc[obsMask, :]
|
|
# Adjust Units
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
|
|
obs_HgWater.loc[obs_HgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_HgWater.Samplevalue * 1000000
|
|
|
|
|
|
# Import Sediment MeHg
|
|
# Sediment MeHg GeoDataFrame
|
|
obsMask = (((obs_gdf.Media_x == 'SED') | (obs_gdf.Media_x == 'SOIL')) &
|
|
((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
|
|
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
|
|
|
obs_MeHgSed = obs_gdf.loc[obsMask, :]
|
|
|
|
# Adjust Units
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'NG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ng/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'UG/G', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'ug/g', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 1000
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'MG/KG', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
|
|
obs_MeHgSed.loc[obs_MeHgSed.Unit == 'mg/kg', 'Sample_NG/G'] = obs_MeHgSed.Samplevalue * 0.001
|
|
|
|
# Group sediment core data to take average/min/max
|
|
obs_MeHgSed_Avg = obs_MeHgSed.groupby(['Sitecode', 'DateTime']).agg({'Sample_NG/G': ['mean', 'min', 'max'],
|
|
'RiverKM': ['mean'],
|
|
'Longitude': ['mean'],
|
|
'Latitude': ['mean'],
|
|
'DateTime': ['mean']}).reset_index()
|
|
# Merge Index
|
|
obs_MeHgSed_Avg.columns = obs_MeHgSed_Avg.columns.map('|'.join).str.strip('|')
|
|
|
|
# Fix Names
|
|
obs_MeHgSed_Avg.rename(columns={"RiverKM|mean": "RiverKM", "Longitude|mean": "Longitude",
|
|
"Latitude|mean": "Latitude", "DateTime|mean": "DateTime"}, inplace=True)
|
|
|
|
# Create new GeoDataFrame
|
|
obs_MeHgSed_Avg = gp.GeoDataFrame(obs_MeHgSed_Avg, geometry=gp.points_from_xy(
|
|
obs_MeHgSed_Avg.loc[:, 'Longitude'], obs_MeHgSed_Avg.loc[:, 'Latitude'], crs="EPSG:4326")).to_crs(crs="EPSG:32615")
|
|
|
|
|
|
|
|
|
|
# MeHg in Water
|
|
obsMask = (((obs_gdf.Media_x == 'SW')) &
|
|
((obs_gdf.Parameter == 'Methylmercury (as MeHg)') | (obs_gdf.Parameter == 'Methyl mercury')) &
|
|
(obs_gdf.DateTime > datetime.datetime(2000, 1, 1)) & obs_gdf.RiverKM.notna())
|
|
|
|
obs_MeHgWater = obs_gdf.loc[obsMask, :]
|
|
# Adjust Units
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'NG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ng/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'UG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'ug/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'MG/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
|
|
obs_MeHgWater.loc[obs_MeHgWater.Unit == 'mg/L', 'Sample_NG/L'] = obs_MeHgWater.Samplevalue * 1000000
|
|
|
|
#%% Plot Observations
|
|
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(30, 15))
|
|
|
|
axes.set_xlim(494649, 513647)
|
|
axes.set_ylim(5514653, 5525256)
|
|
|
|
# Add basemap
|
|
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
|
ctx.add_basemap(axes, source=mapbox, crs='EPSG:26915')
|
|
|
|
obs_HgSed_Avg.plot(column='Sample_NG/G|mean', axes=axes, vmin=0, vmax=10000)
|
|
|
|
plt.show()
|
|
|
|
#%% Interpolate Hg to grid
|
|
|
|
# Read in model points and roughness
|
|
bathy_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Bed_Level.xyz',
|
|
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
|
|
|
rough_xyz = pd.read_csv('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/Roughness.xyz',
|
|
names=['x', 'y', 'z'], header=0, delim_whitespace=True)
|
|
|
|
# Loop through parameters
|
|
for i in range(0, 5):
|
|
if i == 0:
|
|
dataOUT = obs_HgSed_Avg['Sample_NG/G|mean']
|
|
dataOutx = obs_HgSed_Avg.geometry.x
|
|
dataOuty = obs_HgSed_Avg.geometry.y
|
|
dateFileName = 'HgSed_meanDB'
|
|
elif i == 1:
|
|
dataOUT = obs_MeHgSed_Avg['Sample_NG/G|mean']
|
|
dataOutx = obs_MeHgSed_Avg.geometry.x
|
|
dataOuty = obs_MeHgSed_Avg.geometry.y
|
|
dateFileName = 'MeHgSed_meanDB'
|
|
elif i == 2:
|
|
dataOUT = obs_HgWater['Sample_NG/L']
|
|
dataOutx = obs_HgWater.geometry.x
|
|
dataOuty = obs_HgWater.geometry.y
|
|
dateFileName = 'HgWater_allDB'
|
|
elif i == 3:
|
|
dataOUT = obs_MeHgWater['Sample_NG/L']
|
|
dataOutx = obs_MeHgWater.geometry.x
|
|
dataOuty = obs_MeHgWater.geometry.y
|
|
dateFileName = 'MeHgWater_allDB'
|
|
elif i == 4:
|
|
# Synthetic from Reed's Sheet
|
|
dataOUT = ((10 - 1) * np.exp(-0.08 * np.array(river_centerline_merge_gpd2.loc[:, 'RiverKM']) / 1000) + 1) * 1000
|
|
dataOutx = river_centerline_merge_gpd2.geometry.x
|
|
dataOuty = river_centerline_merge_gpd2.geometry.y
|
|
dateFileName = 'ReedHgSed'
|
|
|
|
gridInterp = griddata(np.array([dataOutx, dataOuty]).T, dataOUT, np.array([bathy_xyz.x, bathy_xyz.y]).T,
|
|
method='nearest')
|
|
|
|
gridInterpOut = np.vstack((bathy_xyz.x, bathy_xyz.y, gridInterp))
|
|
gridInterpOut = np.transpose(gridInterpOut)
|
|
|
|
# Set Wetlands to zero
|
|
# Interpolate Roughness
|
|
RoughInterp = sp.interpolate.griddata(np.transpose(np.array([rough_xyz.x, rough_xyz.y])), rough_xyz.z, np.transpose(np.array([bathy_xyz.x, bathy_xyz.y])),
|
|
method='nearest')
|
|
|
|
gridInterpOut[RoughInterp==0.05, 2] = 0
|
|
|
|
np.savetxt('C:/Users/arey/files/Projects/Grassy Narrows/LocalData/' + dateFileName + '.xyz',
|
|
gridInterpOut, delimiter=" ")
|
|
|
|
|
|
|
|
#%% Import using DFM Functions
|
|
|
|
modelPlot = 27
|
|
|
|
## Degine import path
|
|
# file_nc_map = pl.Path(runLog['Run Location'][modelPlot], 'Water_Quality', 'output', 'deltashell_map.nc')
|
|
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42_WAQ.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42B_WAQ_Coverage.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
# file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42C_WAQ_Coverage2.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
#file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_42E_WAQ_DIDO.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
# file_nc_map = pl.Path("//ott-diomedes/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_B.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
file_nc_map = pl.Path("//ott-diomedes.baird.com/Projects/12828.101_EWR_Delft3FM/TR_42_WAQ/TR_43_OceanMesh5_C.dsproj_data/Water_Quality/output/deltashell_map.nc")
|
|
|
|
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
|
|
|
# Print file variables
|
|
A = get_ncvardimlist(file_nc_map.as_posix())
|
|
# print(A)
|
|
|
|
# Define extraction variables
|
|
dataVars = ['FrHgIM1', 'FrHgIM2', 'FrHgIM3', 'FrHgDOC', 'FrHgPOC', 'FrHgPHYT', 'FrHgDis',
|
|
'FrHgIM1S1', 'FrHgIM2S1', 'FrHgIM3S1', 'FrHgDOCS1', 'FrHgPOCS1', 'FrHgPHYTS1', 'FrHgDisS1',
|
|
'FrHgIM1S2', 'FrHgIM2S2', 'FrHgIM3S2', 'FrHgDOCS2', 'FrHgPOCS2', 'FrHgPHYTS2', 'FrHgDisS2',
|
|
'FrMmIM1', 'FrMmIM2', 'FrMmIM3', 'FrMmDOC', 'FrMmPOC', 'FrMmPHYT', 'FrMmDis',
|
|
'FrMmIM1S1', 'FrMmIM2S1', 'FrMmIM3S1', 'FrMmDOCS1', 'FrMmPOCS1', 'FrMmPHYTS1', 'FrMmDisS1',
|
|
'FrMmIM1S2', 'FrMmIM2S2', 'FrMmIM3S2', 'FrMmDOCS2', 'FrMmPOCS2', 'FrMmPHYTS2', 'FrMmDisS2',
|
|
'IM1', 'IM2', 'IM3', 'Hg', 'Mm', 'H0', 'POC1', 'POC2',
|
|
'IM1S1', 'IM2S1', 'IM3S1', 'HgS1', 'MmS1', 'DetCS1', 'OOCS1',
|
|
'IM1S2', 'IM2S2', 'IM3S2', 'HgS2', 'MmS2', 'DetCS2', 'OOCS2',
|
|
'fResS1IM1', 'fSedIM1', 'fResS1IM2', 'fSedIM2',
|
|
'fResS1DetC', 'fSedPOC1', 'fResS1OOC', 'fSedPOC2',
|
|
'fSedHg', 'fResS1Hg', 'fSedMm', 'fResS1Mm',
|
|
'HgHgS1O', 'MmMmS1O',
|
|
'HgMmO', 'HgS1MmS1O', 'MmHgO', 'MmS1HgS1O',
|
|
'oPhoDeg', 'oPhoOxy', 'oPhoRed',
|
|
'f_minPOC1', 'MnODCS1', 'Continuity']
|
|
|
|
# Create Pandas Output Array
|
|
dataIN_x = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
|
varname='mesh2d_agg_face_x',
|
|
silent=True)
|
|
dataIN_y = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
|
varname='mesh2d_agg_face_y',
|
|
silent=True)
|
|
|
|
dataIN_gp = gp.GeoDataFrame(geometry=gp.points_from_xy(dataIN_x, dataIN_y, crs="EPSG:32615"))
|
|
|
|
|
|
# Extract variables
|
|
for vIDX, v in enumerate(dataVars):
|
|
# Extract data from NetCDF file
|
|
dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
|
|
varname='mesh2d_agg_' + v, timestep=len(tSteps)-5,
|
|
silent=True) #, layer=0
|
|
|
|
dataIN_gp[v] = np.array(dataIN[0][0][:])
|
|
print(v)
|
|
#
|
|
|
|
#%% Create Dataframe along river centerline for plotting
|
|
dataOUT = gp.sjoin_nearest(river_centerline_merge_gpd2, dataIN_gp, how='left', max_distance=100)
|
|
dataOUT.set_index('RiverKM', inplace=True)
|
|
|
|
#%% Plot Partition Data
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
#Hg Parition in Water
|
|
ax[0].set_title('Hg Fraction in Water')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgIM1'], linewidth=3, label='IM1')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgIM2'], linewidth=3, label='IM2')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgIM3'], linewidth=3, label='IM3')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgPOC'], linewidth=3, label='POC')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgDOC'], linewidth=3, label='DOC')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgPHYT'], linewidth=3, label='PHYT')
|
|
ax[0].plot(dataOUT.index, dataOUT['FrHgDis'], linewidth=3, label='Dissolved')
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('Percentage Hg')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
#Mm Parition in Water
|
|
ax[1].set_title('MeHg Fraction in Water')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmIM1'], linewidth=3, label='IM1')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmIM2'], linewidth=3, label='IM2')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmIM3'], linewidth=3, label='IM3')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmPOC'], linewidth=3, label='POC')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmDOC'], linewidth=3, label='DOC')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmPHYT'], linewidth=3, label='PHYT')
|
|
ax[1].plot(dataOUT.index, dataOUT['FrMmDis'], linewidth=3, label='Dissolved')
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('Percentage MeHg')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
#Hg Parition in S1
|
|
ax[2].set_title('Hg Fraction in Sediment')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgIM1S1'], linewidth=3, label='IM1')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgIM2S1'], linewidth=3, label='IM2')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgIM3S1'], linewidth=3, label='IM3')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgPOCS1'], linewidth=3, label='POC')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgDOCS1'], linewidth=3, label='DOC')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgPHYTS1'], linewidth=3, label='PHYT')
|
|
ax[2].plot(dataOUT.index, dataOUT['FrHgDisS1'], linewidth=3, label='Dissolved')
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('Percentage Hg')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
#Mm Parition in S1
|
|
ax[3].set_title('MeHg Fraction in Sediment')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmIM1S1'], linewidth=3, label='IM1')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmIM2S1'], linewidth=3, label='IM2')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmIM3S1'], linewidth=3, label='IM3')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmPOCS1'], linewidth=3, label='POC')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmDOCS1'], linewidth=3, label='DOC')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmPHYTS1'], linewidth=3, label='PHYT')
|
|
ax[3].plot(dataOUT.index, dataOUT['FrMmDisS1'], linewidth=3, label='Dissolved')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('Percentage MeHg')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
plt.show()
|
|
|
|
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/Partitioning.png',
|
|
# bbox_inches='tight', dpi=200)
|
|
|
|
|
|
#%% Plot Total Data
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
#Hg in Water
|
|
ax[0].set_title('Hg in Water')
|
|
ax[0].plot(dataOUT.index, dataOUT['Hg'] * 1000000, linewidth=3, label='Hg')
|
|
ax[0].scatter(obs_HgWater.RiverKM, obs_HgWater['Sample_NG/L'], 2, label='Hg Obs', color='y')
|
|
ax[0].set_xlim([0, 100000])
|
|
ax[0].set_ylim([0, 100])
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('Hg [ng/L]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# MeHg in Water
|
|
ax[1].set_title('MeHg in Water')
|
|
ax[1].plot(dataOUT.index, dataOUT['Mm'] * 1000000, linewidth=3, label='MeHg')
|
|
ax[1].scatter(obs_MeHgWater.RiverKM, obs_MeHgWater['Sample_NG/L'], 2, label='Hg Obs', color='y')
|
|
ax[1].set_xlim([0, 100000])
|
|
ax[1].set_ylim([0, 2])
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('MeHg [ng/L]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
#Hg in Sediment
|
|
ax[2].set_title('Hg in Sediment')
|
|
ax[2].plot(dataOUT.index, dataOUT['HgS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
|
linewidth=3, label='HgS1')
|
|
# ax[2].scatter(obs_HgSed.RiverKM, obs_HgSed['Sample_NG/G'], 2, label='Hg Obs', color='y')
|
|
ax[2].scatter(obs_HgSed_Avg.RiverKM, obs_HgSed_Avg['Sample_NG/G|mean'], 2, label='Hg Obs', color='y')
|
|
ax[2].set_xlim([0, 100000])
|
|
ax[2].set_ylim([0, 20000])
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('HgS1 [ng/g]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# MeHg in Sediment
|
|
ax[3].set_title('MeHg in Sediment')
|
|
ax[3].plot(dataOUT.index, dataOUT['MmS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
|
linewidth=3, label='MeHgS1')
|
|
ax[3].scatter(obs_MeHgSed_Avg.RiverKM, obs_MeHgSed_Avg['Sample_NG/G|mean'], 2, label='MeHg Obs', color='y')
|
|
ax[3].set_ylim([0, 60])
|
|
ax[3].set_xlim([0, 100000])
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('MeHg [ng/g]')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# ax[3].set_title('Continuity')
|
|
# ax[3].plot(dataOUT.index, dataOUT['Continuity'],
|
|
# linewidth=3, label='Continuity')
|
|
# ax[3].set_ylim([0, 1])
|
|
#
|
|
# ax[3].legend()
|
|
# ax[3].set_ylabel('Continuity [-]')
|
|
# ax[3].set_xlabel('Distance Along Flume [m]')
|
|
# ax[3].set_ylim([0, 2])
|
|
|
|
plt.show()
|
|
|
|
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgTotals.png',
|
|
# bbox_inches='tight', dpi=200)
|
|
|
|
#%% Plot POC total Data
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
# POC1 in Water
|
|
ax[0].set_title('POC1 (Fast) in Water')
|
|
ax[0].plot(dataOUT.index, dataOUT['POC1'], linewidth=3, label='POC1')
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('POC1 [g/m3]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# POC2 in Water
|
|
ax[1].set_title('POC2 (Slow) in Water')
|
|
ax[1].plot(dataOUT.index, dataOUT['POC2'], linewidth=3, label='POC2')
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('POC2 [g/m3]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
#DetC in sediment
|
|
ax[2].set_title('DetC (Fast) in Sediment')
|
|
ax[2].plot(dataOUT.index, dataOUT['DetCS1']
|
|
/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1'])
|
|
, linewidth=3, label='DetCS1')
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('DetCS1 [g/g]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# OOC in Sediment
|
|
ax[3].set_title('OOC (Slow) in Sediment')
|
|
ax[3].plot(dataOUT.index, dataOUT['OOCS1']
|
|
/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
|
linewidth=3, label='OOCS1')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('OOCS1 [g/g]')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
plt.show()
|
|
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCTotals.png',
|
|
# bbox_inches='tight', dpi=200)
|
|
|
|
|
|
#%% More Total Data
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
#H0 in Water
|
|
ax[0].set_title('Hg(0) in Water')
|
|
ax[0].plot(dataOUT.index, dataOUT['H0']* 1000000, linewidth=3, label='Hg(0)')
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('Hg(0) [ng/L]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# IM1 in Water
|
|
ax[1].set_title('IM1 (Sand) in Water')
|
|
ax[1].plot(dataOUT.index, dataOUT['IM1'], linewidth=3, label='IM1')
|
|
ax[1].set_ylim([0, 50])
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('IM1 [g/m3]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# IM2 in Water
|
|
ax[2].set_title('IM2 (Silt) in Water')
|
|
ax[2].plot(dataOUT.index, dataOUT['IM2'], linewidth=3, label='IM2')
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('IM2 [g/m3]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# IM3 in Water
|
|
ax[3].set_title('IM3 (Woodchips) in Water')
|
|
ax[3].plot(dataOUT.index, dataOUT['IM3'], linewidth=3, label='IM3 (Woodchips)')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('IM3 [g/m3]')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
plt.show()
|
|
|
|
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/InorganicTotals.png',
|
|
# bbox_inches='tight', dpi=200)
|
|
|
|
#%% Plot Fluxes
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
# IM1 Sediment Flux
|
|
ax[0].set_title('IM1 (Sand) Resuspension Flux')
|
|
ax[0].plot(dataOUT.index, dataOUT['fResS1IM1'], linewidth=3, label='IM1 Resuspension')
|
|
ax[0].plot(dataOUT.index, dataOUT['fSedIM1'], linewidth=3, label='IM1 Sedimentation')
|
|
ax[0].set_ylim([0, 2000])
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('IM1 (Sand) Sedimentation and Resuspension [g/m2/d]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# POC Sediment Flux
|
|
ax[1].set_title('POC Resuspension Flux')
|
|
ax[1].plot(dataOUT.index, dataOUT['fResS1DetC'], linewidth=3, label='DetC Resuspension')
|
|
ax[1].plot(dataOUT.index, dataOUT['fResS1OOC'], linewidth=3, label='OOC Resuspension')
|
|
ax[1].plot(dataOUT.index, dataOUT['fSedPOC1'], linewidth=3, label='POC1 (Fast) Sedimentation')
|
|
ax[1].plot(dataOUT.index, dataOUT['fSedPOC2'], linewidth=3, label='POC2 (Slow) Sedimentation')
|
|
ax[1].set_ylim([0, 100])
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('POC Sedimentation and Resuspension [g/m2/d]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# Hg Sediment Flux
|
|
ax[2].set_title('Hg Resuspension Flux')
|
|
ax[2].plot(dataOUT.index, dataOUT['fResS1Hg'], linewidth=3, label='Hg Resuspension')
|
|
ax[2].plot(dataOUT.index, dataOUT['fSedHg'], linewidth=3, label='Hg Sedimentation')
|
|
ax[2].set_ylim([0, 0.01])
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('Hg Sedimentation and Resuspension [g/m2/d]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# Mm Sediment Flux
|
|
ax[3].set_title('MeHg Resuspension Flux')
|
|
ax[3].plot(dataOUT.index, dataOUT['fResS1Mm'], linewidth=3, label='MeHg Resuspension')
|
|
ax[3].plot(dataOUT.index, dataOUT['fSedMm'], linewidth=3, label='MeHg Sedimentation')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('MeHg Sedimentation and Resuspension [g/m2/d]')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
plt.show()
|
|
|
|
# fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgFluxes.png',
|
|
# bbox_inches='tight', dpi=200)
|
|
|
|
#%% Plot More Fluxes
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=4)
|
|
ax = axes.flat
|
|
|
|
# Methylation Flux
|
|
ax[0].set_title('Methylation Flux in Water')
|
|
ax[0].plot(dataOUT.index, dataOUT['HgMmO'], linewidth=3, label='Mehtylation')
|
|
ax[0].plot(dataOUT.index, dataOUT['MmHgO'], linewidth=3, label='Demehtylation')
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('Methylation Flux in Water [g/m3/d]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# Methylation Flux in Sediment
|
|
ax[1].set_title('Methylation Flux in Sediment')
|
|
ax[1].plot(dataOUT.index, dataOUT['HgS1MmS1O']
|
|
/ (dataOUT['HgS1']),
|
|
linewidth=3, label='Mehtylation')
|
|
ax[1].plot(dataOUT.index, dataOUT['MmS1HgS1O']
|
|
/ (dataOUT['MmS1']),
|
|
linewidth=3, label='Demehtylation')
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('Methylation Flux in Sediment [g/gHg/d]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# Hg Diffusion Flux
|
|
# ax[2].set_title('Diffusion Flux')
|
|
# ax[2].plot(dataOUT.index, dataOUT['HgHgS1O'], linewidth=3, label='Hg Diffusion')
|
|
# ax[2].plot(dataOUT.index, dataOUT['MmMmS1O'], linewidth=3, label='MeHg Diffusion')
|
|
#
|
|
# ax[2].legend()
|
|
# ax[2].set_ylabel('Diffusion [g/m2/d]')
|
|
# ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# POC Degradation Flux
|
|
ax[2].set_title('POC Degradation Flux in Water')
|
|
ax[2].plot(dataOUT.index, dataOUT['f_minPOC1'], linewidth=3, label='POC Degradation Water')
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('POC Degradation [g/m3/d]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# POC Degradation Flux
|
|
ax[3].set_title('POC Degradation Flux in Sediment')
|
|
ax[3].plot(dataOUT.index, dataOUT['MnODCS1']
|
|
/ (dataOUT['DetCS1'] + dataOUT['OOCS1']),
|
|
linewidth=3, label='POC Degradation Sediment')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('POC Degradation [g/gPOC/d')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
plt.show()
|
|
|
|
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCFluxes.png',
|
|
bbox_inches='tight', dpi=200)
|
|
|
|
#%% Photo Fluxes
|
|
|
|
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=4)
|
|
ax = axes.flat
|
|
|
|
# Diffusion Flux
|
|
ax[0].set_title('Diffusion Flux')
|
|
ax[0].plot(dataOUT.index, dataOUT['HgHgS1O'], linewidth=3, label='Hg Diffusion')
|
|
ax[0].plot(dataOUT.index, dataOUT['MmMmS1O'], linewidth=3, label='MeHg Diffusion')
|
|
|
|
ax[0].legend()
|
|
ax[0].set_ylabel('Diffusion [g/m2/d]')
|
|
ax[0].set_xlabel('Distance Along Flume [m]')
|
|
|
|
# Photo Oxidation Flux
|
|
ax[1].set_title('Photo Oxidation Flux')
|
|
ax[1].plot(dataOUT.index, dataOUT['oPhoOxy'], linewidth=3, label='Photo Oxidation Flux')
|
|
|
|
ax[1].legend()
|
|
ax[1].set_ylabel('Photo Oxidation Flux [g/m3/d]')
|
|
ax[1].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# Photo Degradation Flux
|
|
ax[2].set_title('Photo Degradation Flux')
|
|
ax[2].plot(dataOUT.index, dataOUT['oPhoDeg'], linewidth=3, label='Photo Degradation Flux')
|
|
|
|
ax[2].legend()
|
|
ax[2].set_ylabel('Photo Degradation Flux [g/m3/d]')
|
|
ax[2].set_xlabel('Distance Along Flume [m]')
|
|
|
|
|
|
# Photo Reduction Flux
|
|
ax[3].set_title('Photo Reduction Flux')
|
|
ax[3].plot(dataOUT.index, dataOUT['oPhoRed'], linewidth=3, label='Photo Reduction Flux')
|
|
|
|
ax[3].legend()
|
|
ax[3].set_ylabel('Photo Reduction Flux [g/m3/d]')
|
|
ax[3].set_xlabel('Distance Along Flume [m]')
|
|
|
|
plt.show()
|
|
|
|
fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/H0Fluxes.png',
|
|
bbox_inches='tight', dpi=200)
|
|
|
|
#%% Make summary table
|
|
dataTableOut = dict()
|
|
|
|
#Hg Parition in Water
|
|
dataTableOut['Hg:IM1 Fraction'] = dataOUT['FrHgIM1'].mean()
|
|
dataTableOut['Hg:IM2 Fraction'] = dataOUT['FrHgIM2'].mean()
|
|
dataTableOut['Hg:IM3 Fraction'] = dataOUT['FrHgIM3'].mean()
|
|
dataTableOut['Hg:POC Fraction'] = dataOUT['FrHgPOC'].mean()
|
|
dataTableOut['Hg:DOC Fraction'] = dataOUT['FrHgDOC'].mean()
|
|
dataTableOut['Hg:Phyt Fraction'] = dataOUT['FrHgPHYT'].mean()
|
|
dataTableOut['Hg:Dissolved Fraction'] = dataOUT['FrHgDis'].mean()
|
|
|
|
#MeHg Parition in Water
|
|
dataTableOut['MeHg:IM1 Fraction'] = dataOUT['FrMmIM1'].mean()
|
|
dataTableOut['MeHg:IM2 Fraction'] = dataOUT['FrMmIM2'].mean()
|
|
dataTableOut['MeHg:IM3 Fraction'] = dataOUT['FrMmIM3'].mean()
|
|
dataTableOut['MeHg:POC Fraction'] = dataOUT['FrMmPOC'].mean()
|
|
dataTableOut['MeHg:DOC Fraction'] = dataOUT['FrMmDOC'].mean()
|
|
dataTableOut['MeHg:Phyt Fraction'] = dataOUT['FrMmPHYT'].mean()
|
|
dataTableOut['MeHg:Dissolved Fraction'] = dataOUT['FrMmDis'].mean()
|
|
|
|
#Hg Parition in S1
|
|
dataTableOut['Hg:IM1 Fraction S1'] = dataOUT['FrHgIM1S1'].mean()
|
|
dataTableOut['Hg:IM2 Fraction S1'] = dataOUT['FrHgIM2S1'].mean()
|
|
dataTableOut['Hg:IM3 Fraction S1'] = dataOUT['FrHgIM3S1'].mean()
|
|
dataTableOut['Hg:POC Fraction S1'] = dataOUT['FrHgPOCS1'].mean()
|
|
dataTableOut['Hg:DOC Fraction S1'] = dataOUT['FrHgDOCS1'].mean()
|
|
dataTableOut['Hg:Phyt Fraction S1'] = dataOUT['FrHgPHYTS1'].mean()
|
|
dataTableOut['Hg:Dissolved Fraction S1'] = dataOUT['FrHgDisS1'].mean()
|
|
|
|
#MeHg Parition in S1
|
|
dataTableOut['MeHg:IM1 Fraction S1'] = dataOUT['FrMmIM1S1'].mean()
|
|
dataTableOut['MeHg:IM2 Fraction S1'] = dataOUT['FrMmIM2S1'].mean()
|
|
dataTableOut['MeHg:IM3 Fraction S1'] = dataOUT['FrMmIM3S1'].mean()
|
|
dataTableOut['MeHg:POC Fraction S1'] = dataOUT['FrMmPOCS1'].mean()
|
|
dataTableOut['MeHg:DOC Fraction S1'] = dataOUT['FrMmDOCS1'].mean()
|
|
dataTableOut['MeHg:Phyt Fraction S1'] = dataOUT['FrMmPHYT'].mean()
|
|
dataTableOut['MeHg:Dissolved Fraction S1'] = dataOUT['FrMmDisS1'].mean()
|
|
|
|
#Hg in Water
|
|
dataTableOut['Hg in Water [ng/L]'] = dataOUT['Hg'].mean() * 1000000
|
|
|
|
# Mm in Water
|
|
dataTableOut['MeHg in Water [ng/L]'] = dataOUT['Mm'].mean() * 1000000
|
|
|
|
#Hg in Sediment
|
|
dataTableOut['Hg in Sediment [ng/g]'] = dataOUT['HgS1'].mean() * 1*10 ** 9 /\
|
|
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
|
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
|
|
|
# Mm in Sediment
|
|
dataTableOut['MeHg in Sediment [ng/g]'] = dataOUT['MmS1'].mean() * 1*10 ** 9 /\
|
|
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
|
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
|
|
|
# Hg(0) in Water
|
|
dataTableOut['Hg(0) in Water [ng/L]'] = dataOUT['H0'].mean() * 1000000
|
|
|
|
# IM1 in Water
|
|
ax[1].set_title('IM1 (Sand) in Water')
|
|
dataTableOut['IM1 (Sand) in Water [g/m3]'] = dataOUT['IM1'].mean()
|
|
|
|
# IM2 in Water
|
|
dataTableOut['IM2 (Silt) in Water [g/m3]'] = dataOUT['IM2'].mean()
|
|
|
|
# IM3 in Water
|
|
dataTableOut['IM3 (Woodchips) in Water [g/m3]'] = dataOUT['IM3'].mean()
|
|
|
|
# POC1 in Water
|
|
dataTableOut['POC1 (Fast) in Water [g/m3]'] = dataOUT['POC1'].mean()
|
|
|
|
# POC2 in Water
|
|
dataTableOut['POC1 (Slow) in Water [g/m3]'] = dataOUT['POC2'].mean()
|
|
|
|
#DetC in sediment
|
|
dataTableOut['DetC (Fast) in Sediment [g/g]'] = dataOUT['DetCS1'].mean() /\
|
|
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
|
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
|
|
|
# OOC in Sediment
|
|
dataTableOut['OOC (Slow) in Sediment [g/g]'] = dataOUT['OOCS1'].mean() /\
|
|
(dataOUT['IM1S1'].mean() + dataOUT['IM2S1'].mean() + dataOUT['IM3S1'].mean() +
|
|
dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
|
|
|
|
|
# IM1 Resuspension
|
|
dataTableOut['IM1 (Sand) Resuspension Flux [g/m2/d]'] = dataOUT['fResS1IM1'].mean()
|
|
|
|
# IM1 Sedimentation
|
|
dataTableOut['IM1 (Sand) Sedimentation Flux [g/m2/d]'] = dataOUT['fSedIM1'].mean()
|
|
|
|
# DetC Resuspension
|
|
dataTableOut['DetC Resuspension Flux [g/m2/d]'] = dataOUT['fResS1DetC'].mean()
|
|
|
|
# OOC Resuspension
|
|
dataTableOut['OOC Resuspension Flux [g/m2/d]'] = dataOUT['fResS1OOC'].mean()
|
|
|
|
# POC1 Sedimentation
|
|
dataTableOut['POC1 (Fast) Sedimentation [g/m2/d]'] = dataOUT['fSedPOC1'].mean()
|
|
|
|
# POC2 Sedimentation
|
|
dataTableOut['POC2 (Slow) Sedimentation [g/m2/d]'] = dataOUT['fSedPOC2'].mean()
|
|
|
|
# Hg Resuspension
|
|
dataTableOut['Hg Resuspension Flux [g/m2/d]'] = dataOUT['fResS1Hg'].mean()
|
|
|
|
# Hg Sedimentation
|
|
dataTableOut['Hg Sedimentation Flux [g/m2/d]'] = dataOUT['fSedHg'].mean()
|
|
|
|
# MeHg Resuspension
|
|
dataTableOut['MeHg Resuspension Flux [g/m2/d]'] = dataOUT['fResS1Mm'].mean()
|
|
|
|
# MeHg Sedimentation
|
|
dataTableOut['MeHg Sedimentation Flux [g/m2/d]'] = dataOUT['fSedMm'].mean()
|
|
|
|
|
|
# Methylation Flux in Water
|
|
dataTableOut['Methylation Flux in Water [g/m3/d]'] = dataOUT['HgMmO'].mean()
|
|
|
|
# Demehtylation Flux in Water
|
|
dataTableOut['Demehtylation Flux in Water [g/m3/d]'] = dataOUT['MmHgO'].mean()
|
|
|
|
# Methylation Flux in Sediment
|
|
dataTableOut['Methylation Flux in Sediment [g/gHg/d]'] = dataOUT['HgS1MmS1O'].mean() / dataOUT['HgS1'].mean()
|
|
|
|
# Demehtylation Flux in Sediment
|
|
dataTableOut['Demehtylation Flux in Sediment [g/gMm/d]'] = dataOUT['MmS1HgS1O'].mean() / dataOUT['MmS1'].mean()
|
|
|
|
|
|
# POC Degradation Flux in Water
|
|
dataTableOut['POC Degradation Flux in Water [g/m3/d]'] = dataOUT['f_minPOC1'].mean()
|
|
|
|
# POC Degradation Flux in Sediment
|
|
dataTableOut['Demehtylation Flux in Water [g/gPOC/d]'] = dataOUT['MnODCS1'].mean() /\
|
|
(dataOUT['DetCS1'].mean() + dataOUT['OOCS1'].mean())
|
|
|
|
|
|
# Hg Diffusion Flux
|
|
dataTableOut['Hg Diffusion [g/m2/d]'] = dataOUT['HgHgS1O'].mean()
|
|
|
|
# MeHg Diffusion Flux
|
|
dataTableOut['MeHg Diffusion [g/m2/d]'] = dataOUT['MmMmS1O'].mean()
|
|
|
|
# Photo Oxidation Flux
|
|
dataTableOut['Hg Diffusion [g/m3/d]'] = dataOUT['oPhoOxy'].mean()
|
|
|
|
# Photo Degradation Flux
|
|
dataTableOut['MeHg Diffusion [g/m3/d]'] = dataOUT['oPhoDeg'].mean()
|
|
|
|
# Photo Reduction Flux
|
|
dataTableOut['Hg Diffusion [g/m3/d]'] = dataOUT['oPhoRed'].mean()
|
|
|
|
#%% Save Data Table as Excel
|
|
dataTableOut_df = pd.DataFrame(data=dataTableOut, index=[0])
|
|
dataTableOut_df = (dataTableOut_df.T)
|
|
|
|
dataTableOut_df.to_excel('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ModelOutputs_Sept26.xlsx')
|
|
|
|
|
|
#%% Save as geotiff
|
|
tiffPlot = dataIN_gp['HgS1']
|
|
|
|
# Blank zero sed
|
|
# tiffPlot[tiffPlot == 0] = np.nan
|
|
|
|
# Blank outside of hull
|
|
# sedPlot[~regularMask2[i]] = np.nan
|
|
|
|
# Convert to xarray dataset
|
|
tiffPlot_xr = xr.DataArray(data=tiffPlot.T,
|
|
dims=["x", "y"],
|
|
coords=dict(
|
|
x=(["x"], X2[i][0, :]),
|
|
y=(["y"], Y2[i][:, 0])))
|
|
|
|
# Transpose for geotiff writing
|
|
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
|
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
|
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
|
runLog['Run Name'][i] + '_mag.tif')
|
|
|
|
|
|
tiffPlot = shear2[i]
|
|
|
|
# Blank zero sed
|
|
tiffPlot[tiffPlot == 0] = np.nan
|
|
|
|
# Blank outside of hull
|
|
# sedPlot[~regularMask2[i]] = np.nan
|
|
|
|
# Convert to xarray dataset
|
|
tiffPlot_xr = xr.DataArray(data=tiffPlot.T,
|
|
dims=["x", "y"],
|
|
coords=dict(
|
|
x=(["x"], X2[i][0, :]),
|
|
y=(["y"], Y2[i][:, 0])))
|
|
|
|
# Transpose for geotiff writing
|
|
tiffPlot_xr = tiffPlot_xr.transpose('y', 'x')
|
|
tiffPlot_xr.rio.write_crs("epsg:32615", inplace=True)
|
|
tiffPlot_xr.rio.to_raster('C:/Users/arey/files/Projects/Grassy Narrows/Slides/' +
|
|
runLog['Run Name'][i] + '_shear.tif')
|
|
|
|
|
|
|
|
|