161 lines
6.2 KiB
Python
161 lines
6.2 KiB
Python
#%% Plotting script for LSU D3D data
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# Alexander Rey, 2022
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import os
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import pandas as pd
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import geopandas as gp
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import netCDF4 as nc
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import math
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import matplotlib.cm as cm
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import datetime as datetime
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from scipy.interpolate import LinearNDInterpolator, interp1d
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from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
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from dfm_tools.get_nc_helpers import get_timesfromnc, get_ncfilelist, get_hisstationlist, get_ncvardimlist, get_timesfromnc
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import cartopy.crs as ccrs
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import contextily as ctx
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from dfm_tools.regulargrid import scatter_to_regulargrid
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import pathlib as pl
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from shapely.geometry import Point, MultiPoint
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import alphashape
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import rioxarray
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import xarray as xr
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#%% Read Model Log
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pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13522.101 LSU Lakes", "06_Models", "Delft3DFM", "13522.678.W.SBV.Rev0_LSU Lakes D3D Model Log.xlsx")
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runLog = pd.read_excel(pth.as_posix(), "ModelLog")
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dataPath = "FlowFM_map.nc"
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#%% Import using DFM functions
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modelPlot = range(30, 46)
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# stormTime = [datetime.datetime(2005, 8, 29, 14, 0, 0),
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# datetime.datetime(2005, 9, 24, 14, 0, 0),
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# datetime.datetime(2005, 12, 28, 14, 0, 0)]
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# stormTime = [datetime.datetime(2005, 1, 14, 18, 0, 0),
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# datetime.datetime(2005, 2, 3, 18, 0, 0),
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# datetime.datetime(2005, 2, 10, 18, 0, 0)]
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#stormTime = [datetime.datetime(2005, 12, 31, 18, 0, 0)]
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#stormTime = [datetime.datetime(2005, 8, 7, 0, 0, 0)]
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ugrid_all = [None] * (max(modelPlot)+1)
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X2 = [None] * (max(modelPlot)+1)
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Y2 = [None] * (max(modelPlot)+1)
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sed2 = [None] * (max(modelPlot)+1)
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facex = [None] * (max(modelPlot)+1)
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facey = [None] * (max(modelPlot)+1)
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ux_mean = [None] * (max(modelPlot)+1)
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uy_mean = [None] * (max(modelPlot)+1)
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ux = [None] * (max(modelPlot)+1)
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uy = [None] * (max(modelPlot)+1)
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sed = [None] * (max(modelPlot)+1)
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bed = [None] * (max(modelPlot)+1)
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bed2 = [None] * (max(modelPlot)+1)
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wd = [None] * (max(modelPlot)+1)
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regularMask2 = [None] * (max(modelPlot)+1)
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meshMask = [None] * (max(modelPlot)+1)
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alphaPolyList = [None] * (max(modelPlot)+1)
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sedIDX = 0
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tPlot = 0
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for i in modelPlot:
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#file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'output', 'FlowFM_map.nc')
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file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
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tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
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# Find nearest time step to desired time
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stormTime = [runLog['End Date'][i]]
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tStep = []
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for s in stormTime:
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abs_deltas_from_target_date = np.absolute(tSteps - s)
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tStep.append(np.argmin(abs_deltas_from_target_date))
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# Get Var info
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vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map)
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facex[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_x')
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facey[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_y')
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sed[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_bodsed',
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timestep=tStep, station='all')
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bed[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_flowelem_bl')
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wd[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_waterdepth',
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timestep=tStep)
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# Create a list of grid points
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alphaPoints = list(map(tuple, np.column_stack((facex[i][wd[i][0, :] != 0],
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facey[i][wd[i][0, :] != 0])).data))
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# Find the concave hull
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alphaPoly = alphashape.alphashape(alphaPoints, 0.05)
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alphaPolyList[i] = alphaPoly
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plottingMask = (facex[i] > 676470) & (facex[i] < 677021) & \
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(facey[i] > 3365939) & (facey[i] < 3366617)
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# Convert to regular grid for interpolation
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X2[i], Y2[i], sed2[i] = scatter_to_regulargrid(xcoords=facex[i][plottingMask], ycoords=facey[i][plottingMask],
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ncellx=500, ncelly=500, values=sed[i][tPlot, plottingMask, sedIDX],
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maskland_dist=25, method='linear')
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X2[i], Y2[i], bed2[i] = scatter_to_regulargrid(xcoords=facex[i][plottingMask], ycoords=facey[i][plottingMask],
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ncellx=750, ncelly=750, values=bed[i][plottingMask],
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maskland_dist=25, method='cubic')
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# Create mask of regular grid inside concave hull
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regularMask2[i] = [alphaPoly.contains(Point(i[0], i[1]))
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for i in np.array([X2[i].flatten(), Y2[i].flatten()]).T]
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# Reshape back to regular grid for mask
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regularMask2[i] = np.array(regularMask2[i]).reshape(X2[i].shape)
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# %% Setup forebay polygon
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# forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPoly.shp')
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forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPolyExpand.shp')
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# Setup mask. Can only do this once if grids are the same
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i = 45
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forebayPolyMask = [forebayPoly.iloc[0, 1].contains(Point(i[0], i[1]))
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for i in np.array([facex[i], facey[i]]).T]
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#%% Plot Sediment DFM functions
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modelPlot = range(30, 46)
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sedIDX = 0
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for tPlot in range(0, 1):
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for i in modelPlot:
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sedPlot = bed2[i]
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# Blank zero sed
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sedPlot[sedPlot == 0] = np.nan
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# Blank outside of hull
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sedPlot[~regularMask2[i]] = np.nan
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# Convert to xarray dataset
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sed2_xr = xr.DataArray(data=sedPlot.T,
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dims=["x", "y"],
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coords=dict(
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x=(["x"], X2[i][0, :]),
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y=(["y"], Y2[i][:, 0])))
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# Transpose for geotiff writing
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sed2_xr = sed2_xr.transpose('y', 'x')
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sed2_xr.rio.write_crs("epsg:26915", inplace=True)
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sed2_xr.rio.to_raster('C:/Users/arey/files/Projects/LSU/Modelling/GeoTiffBed/' +
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runLog['Run Name'][i] + 'B.tif')
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