157 lines
5.9 KiB
Python
157 lines
5.9 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, Polygon
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import alphashape
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#%% Read Model Log
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pth = pl.Path("//srv-ott3.baird.com/", "Projects", "12828.101 English Wabigoon River", "06_Models", "00_Delft3D", "ModelRuns.xlsx")
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runLog = pd.read_excel(pth.as_posix(), "Test runs", skiprows=1)
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dataPath = "FlowFM_map.nc"
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#%% Import using DFM functions
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modelPlot = [19, 22]
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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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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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wl = [None] * (max(modelPlot)+1)
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wd = [None] * (max(modelPlot)+1)
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pol_shp_list = [None] * (max(modelPlot)+1)
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alphaPolyList = [None] * (max(modelPlot)+1)
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for i in modelPlot:
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file_nc_map = os.path.join(runLog['Run Location'][i], runLog['Run Name'][i], runLog['Run Name'][i][0:4] + '.dsproj_data',
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'FlowFM', '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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# 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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# Otherwise, define a timestep
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tStep = len(tSteps)-2
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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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ugrid_all[i] = get_netdata(file_nc=file_nc_map)#,multipart=False)
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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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ux_mean[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucx', timestep=tStep)
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uy_mean[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucy', timestep=tStep)
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uy_mean[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucy', timestep=tStep)
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wl[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_s1', timestep=tStep)
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wd[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_waterdepth', timestep=tStep)
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# Setup polygons for shapefile export
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pol_shp_list[i] = []
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#partly from dfm_tools.ugrid.polygon_intersect()
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for iP, pol_data in enumerate(ugrid_all[i].verts):
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pol_data_nonan = pol_data[~np.isnan(pol_data).all(axis=1)]
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pol_shp = Polygon(pol_data_nonan)
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pol_shp_list[i].append(pol_shp)
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print('Polygon Complete')
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# Find convex hull of pcolor data
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# Convert grid to multipoint
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# Create a list of grid points where the water depth is greater than zero
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wd_mask = (wd[i][:] > 0)
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alphaPoints = list(map(tuple, np.column_stack((facex[i][wd_mask.tolist()],
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facey[i][wd_mask.tolist()])).data))
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# Find the concave hull
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alphaPoly = alphashape.alphashape(alphaPoints, 0.02)
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alphaPolyList[i] = alphaPoly
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#%% Plot using DFM functions
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modelPlot = [19, 22]
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for i in modelPlot:
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fig, axes = plt.subplots(nrows=1, ncols=1, subplot_kw={'projection': ccrs.epsg(32615)}, figsize=(14, 10))
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fig.patch.set_facecolor('white')
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plt.tight_layout()
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plotCount = 1
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vel_mask = (wd[i][:] > 0)
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# pc = plot_netmapdata(ugrid_all[i].verts[vel_mask.tolist()],
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# values=np.sqrt(ux_mean[i][vel_mask]**2 +\
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# uy_mean[i][vel_mask]**2),
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# ax=axes, linewidth=0.5, cmap="viridis")
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pc = plot_netmapdata(ugrid_all[i].verts[vel_mask.tolist()],
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values=wd[i][vel_mask],
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ax=axes, linewidth=0.5, cmap="viridis")
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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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# Add model bounds line
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alphaPoly = alphaPolyList[i]
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# for p in range(0, len(alphaPoly.geoms)):
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# axes.plot(*alphaPoly.geoms[p].exterior.xy, color='k', linewidth=1)
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axes.plot(*alphaPoly.exterior.xy, color='k', linewidth=1)
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plt.show()
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# # Save as shapefile
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dataShape = gp.GeoDataFrame()
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dataShape['geometry'] = pol_shp_list[i]
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dataShape.set_crs(epsg=32615)
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dataShape['WaterLevel'] = wl[i].data.flatten()
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dataShape['WaterDepth'] = wd[i].data.flatten()
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dataShape.to_file('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/Flooding/' + runLog['Run Name'][i] + '.shp')
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# dataBounds = gp.GeoDataFrame
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# dataBounds['geometry'] = alphaPoly.geoms
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features = [i for i in range(len(alphaPoly))]
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dataBounds = gp.GeoDataFrame(index=[0], geometry=[alphaPoly], crs='EPSG:32615')
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dataBounds.to_file('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/Flooding/Bounds_' + runLog['Run Name'][i] + '.shp')
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# fig.savefig(
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# 'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
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# 'StormVelDisWide.png', bbox_inches='tight', dpi=400) |