NTC Mustique EWR Code Updates
This commit is contained in:
Alexander Rey
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#%% Plotting EWR Flume Tests
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# Alexander Rey, 2022
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#%% Import
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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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 pathlib as pl
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#%% Read Model Log
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# runLog = pd.read_excel("Y:/12828.101 English Wabigoon River/06_Models/00_Delft3D/ModelRuns.xlsx", "Sensitivity")
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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(), "Sensitivity")
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dataPath = "FlowFM_map.nc"
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#%% Import using DFM Functions
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modelPlot = 27
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file_nc_map = pl.Path(runLog['Run Location'][modelPlot], 'Water_Quality', 'output', 'deltashell_map.nc')
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tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
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# Print file variables
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A = get_ncvardimlist(file_nc_map.as_posix())
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# print(A)
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# Define extraction variables
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dataVars = ['FrHgIM1', 'FrHgIM2', 'FrHgIM3', 'FrHgDOC', 'FrHgPOC', 'FrHgPHYT', 'FrHgDis',
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'FrHgIM1S1', 'FrHgIM2S1', 'FrHgIM3S1', 'FrHgDOCS1', 'FrHgPOCS1', 'FrHgPHYTS1', 'FrHgDisS1',
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'FrHgIM1S2', 'FrHgIM2S2', 'FrHgIM3S2', 'FrHgDOCS2', 'FrHgPOCS2', 'FrHgPHYTS2', 'FrHgDisS2',
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'FrMmIM1', 'FrMmIM2', 'FrMmIM3', 'FrMmDOC', 'FrMmPOC', 'FrMmPHYT', 'FrMmDis',
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'FrMmIM1S1', 'FrMmIM2S1', 'FrMmIM3S1', 'FrMmDOCS1', 'FrMmPOCS1', 'FrMmPHYTS1', 'FrMmDisS1',
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'FrMmIM1S2', 'FrMmIM2S2', 'FrMmIM3S2', 'FrMmDOCS2', 'FrMmPOCS2', 'FrMmPHYTS2', 'FrMmDisS2',
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'IM1', 'IM2', 'IM3', 'Hg', 'Mm', 'H0', 'POC1', 'POC2',
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'IM1S1', 'IM2S1', 'IM3S1', 'HgS1', 'MmS1', 'DetCS1', 'OOCS1',
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'IM1S2', 'IM2S2', 'IM3S2', 'HgS2', 'MmS2', 'DetCS2', 'OOCS2',
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'fResS1IM1', 'fSedIM1', 'fResS1IM2', 'fSedIM2',
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'fResS1DetC', 'fSedPOC1', 'fResS1OOC', 'fSedPOC2',
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'fSedHg', 'fResS1Hg', 'fSedMm', 'fResS1Mm',
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'HgHgS1O', 'MmMmS1O',
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'HgMmO', 'HgS1MmS1O', 'MmHgO', 'MmS1HgS1O',
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'oPhoDeg', 'oPhoOxy', 'oPhoRed',
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'f_minPOC1', 'MnODCS1']
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# Create Pandas Output Array
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dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
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varname='mesh2d_face_x',
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silent=True)
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dataOUT = pd.DataFrame(index=np.array(dataIN[2:-2]))
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# Extract variables
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for vIDX, v in enumerate(dataVars):
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# Extract data from NetCDF file
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dataIN = get_ncmodeldata(file_nc=file_nc_map.as_posix(),
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varname='mesh2d_' + v, timestep=len(tSteps)-1,
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silent=True, layer=0)
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dataOUT[v] = np.array(dataIN[0][0][2:-2])
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print(v)
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#%% Plot Partition Data
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=3)
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ax = axes.flat
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#Hg Parition in Water
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ax[0].set_title('Hg Fraction in Water')
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ax[0].plot(dataOUT.index, dataOUT['FrHgIM1'], linewidth=3, label='IM1')
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ax[0].plot(dataOUT.index, dataOUT['FrHgIM2'], linewidth=3, label='IM2')
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ax[0].plot(dataOUT.index, dataOUT['FrHgIM3'], linewidth=3, label='IM3')
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ax[0].plot(dataOUT.index, dataOUT['FrHgPOC'], linewidth=3, label='POC')
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ax[0].plot(dataOUT.index, dataOUT['FrHgDOC'], linewidth=3, label='DOC')
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ax[0].plot(dataOUT.index, dataOUT['FrHgPHYT'], linewidth=3, label='PHYT')
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ax[0].plot(dataOUT.index, dataOUT['FrHgDis'], linewidth=3, label='Dissolved')
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ax[0].legend()
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ax[0].set_ylabel('Percentage Hg')
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ax[0].set_xlabel('Distance Along Flume [m]')
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#Mm Parition in Water
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ax[1].set_title('MeHg Fraction in Water')
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ax[1].plot(dataOUT.index, dataOUT['FrMmIM1'], linewidth=3, label='IM1')
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ax[1].plot(dataOUT.index, dataOUT['FrMmIM2'], linewidth=3, label='IM2')
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ax[1].plot(dataOUT.index, dataOUT['FrMmIM3'], linewidth=3, label='IM3')
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ax[1].plot(dataOUT.index, dataOUT['FrMmPOC'], linewidth=3, label='POC')
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ax[1].plot(dataOUT.index, dataOUT['FrMmDOC'], linewidth=3, label='DOC')
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ax[1].plot(dataOUT.index, dataOUT['FrMmPHYT'], linewidth=3, label='PHYT')
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ax[1].plot(dataOUT.index, dataOUT['FrMmDis'], linewidth=3, label='Dissolved')
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ax[1].legend()
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ax[1].set_ylabel('Percentage MeHg')
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ax[1].set_xlabel('Distance Along Flume [m]')
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#Hg Parition in S1
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ax[2].set_title('Hg Fraction in Sediment')
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ax[2].plot(dataOUT.index, dataOUT['FrHgIM1S1'], linewidth=3, label='IM1')
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ax[2].plot(dataOUT.index, dataOUT['FrHgIM2S1'], linewidth=3, label='IM2')
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ax[2].plot(dataOUT.index, dataOUT['FrHgIM3S1'], linewidth=3, label='IM3')
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ax[2].plot(dataOUT.index, dataOUT['FrHgPOCS1'], linewidth=3, label='POC')
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ax[2].plot(dataOUT.index, dataOUT['FrHgDOCS1'], linewidth=3, label='DOC')
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ax[2].plot(dataOUT.index, dataOUT['FrHgPHYTS1'], linewidth=3, label='PHYT')
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ax[2].plot(dataOUT.index, dataOUT['FrHgDisS1'], linewidth=3, label='Dissolved')
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ax[2].legend()
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ax[2].set_ylabel('Percentage Hg')
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ax[2].set_xlabel('Distance Along Flume [m]')
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#Mm Parition in S1
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ax[3].set_title('MeHg Fraction in Sediment')
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ax[3].plot(dataOUT.index, dataOUT['FrMmIM1S1'], linewidth=3, label='IM1')
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ax[3].plot(dataOUT.index, dataOUT['FrMmIM2S1'], linewidth=3, label='IM2')
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ax[3].plot(dataOUT.index, dataOUT['FrMmIM3S1'], linewidth=3, label='IM3')
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ax[3].plot(dataOUT.index, dataOUT['FrMmPOCS1'], linewidth=3, label='POC')
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ax[3].plot(dataOUT.index, dataOUT['FrMmDOCS1'], linewidth=3, label='DOC')
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ax[3].plot(dataOUT.index, dataOUT['FrMmPHYTS1'], linewidth=3, label='PHYT')
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ax[3].plot(dataOUT.index, dataOUT['FrMmDisS1'], linewidth=3, label='Dissolved')
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ax[3].legend()
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ax[3].set_ylabel('Percentage MeHg')
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ax[3].set_xlabel('Distance Along Flume [m]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/Partitioning.png',
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bbox_inches='tight', dpi=200)
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#%% Plot Total Data
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=3)
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ax = axes.flat
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#Hg in Water
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ax[0].set_title('Hg in Water')
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ax[0].plot(dataOUT.index, dataOUT['Hg'] * 1000000, linewidth=3, label='Hg')
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ax[0].legend()
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ax[0].set_ylabel('Hg [ng/L]')
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ax[0].set_xlabel('Distance Along Flume [m]')
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# Mm in Water
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ax[1].set_title('MeHg in Water')
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ax[1].plot(dataOUT.index, dataOUT['Mm'] * 1000000, linewidth=3, label='MeHg')
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ax[1].legend()
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ax[1].set_ylabel('MeHg [ng/L]')
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ax[1].set_xlabel('Distance Along Flume [m]')
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#Hg in Sediment
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ax[2].set_title('Hg in Sediment')
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ax[2].plot(dataOUT.index, dataOUT['HgS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
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linewidth=3, label='HgS1')
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ax[2].legend()
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ax[2].set_ylabel('HgS1 [ng/g]')
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ax[2].set_xlabel('Distance Along Flume [m]')
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# Mm in Sediment
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ax[3].set_title('MeHg in Sediment')
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ax[3].plot(dataOUT.index, dataOUT['MmS1'] * 1*10 ** 9 / (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
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linewidth=3, label='MeHgS1')
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ax[3].legend()
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ax[3].set_ylabel('MeHg [ng/g]')
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ax[3].set_xlabel('Distance Along Flume [m]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgTotals.png',
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bbox_inches='tight', dpi=200)
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#%% Plot POC total Data
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=3)
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ax = axes.flat
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# POC1 in Water
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ax[0].set_title('POC1 (Fast) in Water')
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ax[0].plot(dataOUT.index, dataOUT['POC1'], linewidth=3, label='POC1')
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ax[0].legend()
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ax[0].set_ylabel('POC1 [g/m3]')
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ax[0].set_xlabel('Distance Along Flume [m]')
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# POC2 in Water
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ax[1].set_title('POC2 (Slow) in Water')
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ax[1].plot(dataOUT.index, dataOUT['POC2'], linewidth=3, label='POC2')
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ax[1].legend()
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ax[1].set_ylabel('POC2 [g/m3]')
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ax[1].set_xlabel('Distance Along Flume [m]')
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#DetC in sediment
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ax[2].set_title('DetC (Fast) in Sediment')
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ax[2].plot(dataOUT.index, dataOUT['DetCS1']
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/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1'])
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, linewidth=3, label='DetCS1')
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ax[2].legend()
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ax[2].set_ylabel('DetCS1 [g/g]')
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ax[2].set_xlabel('Distance Along Flume [m]')
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# OOC in Sediment
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ax[3].set_title('OOC (Slow) in Sediment')
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ax[3].plot(dataOUT.index, dataOUT['OOCS1']
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/ (dataOUT['IM1S1'] + dataOUT['IM2S1'] + dataOUT['IM3S1'] + dataOUT['DetCS1'] + dataOUT['OOCS1']),
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linewidth=3, label='OOCS1')
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ax[3].legend()
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ax[3].set_ylabel('OOCS1 [g/g]')
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ax[3].set_xlabel('Distance Along Flume [m]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/POCTotals.png',
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bbox_inches='tight', dpi=200)
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#%% More Total Data
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=3)
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ax = axes.flat
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#H0 in Water
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ax[0].set_title('Hg(0) in Water')
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ax[0].plot(dataOUT.index, dataOUT['H0']* 1000000, linewidth=3, label='Hg(0)')
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ax[0].legend()
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ax[0].set_ylabel('Hg(0) [ng/L]')
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ax[0].set_xlabel('Distance Along Flume [m]')
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# IM1 in Water
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ax[1].set_title('IM1 (Sand) in Water')
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ax[1].plot(dataOUT.index, dataOUT['IM1'], linewidth=3, label='IM1')
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ax[1].legend()
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ax[1].set_ylabel('IM1 [g/m3]')
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ax[1].set_xlabel('Distance Along Flume [m]')
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# IM2 in Water
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ax[2].set_title('IM2 (Silt) in Water')
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ax[2].plot(dataOUT.index, dataOUT['IM2'], linewidth=3, label='IM2')
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ax[2].legend()
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ax[2].set_ylabel('IM2 [g/m3]')
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ax[2].set_xlabel('Distance Along Flume [m]')
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# IM3 in Water
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ax[3].set_title('IM3 (Woodchips) in Water')
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ax[3].plot(dataOUT.index, dataOUT['IM3'], linewidth=3, label='IM3 (Woodchips)')
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ax[3].legend()
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ax[3].set_ylabel('IM3 [g/m3]')
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ax[3].set_xlabel('Distance Along Flume [m]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/InorganicTotals.png',
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bbox_inches='tight', dpi=200)
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#%% Plot Fluxes
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=3)
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ax = axes.flat
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# IM1 Sediment Flux
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ax[0].set_title('IM1 (Sand) Resuspension Flux')
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ax[0].plot(dataOUT.index, dataOUT['fResS1IM1'], linewidth=3, label='IM1 Resuspension')
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ax[0].plot(dataOUT.index, dataOUT['fSedIM1'], linewidth=3, label='IM1 Sedimentation')
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ax[0].legend()
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ax[0].set_ylabel('IM1 (Sand) Sedimentation and Resuspension [g/m2/d]')
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ax[0].set_xlabel('Distance Along Flume [m]')
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# POC Sediment Flux
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ax[1].set_title('POC Resuspension Flux')
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ax[1].plot(dataOUT.index, dataOUT['fResS1DetC'], linewidth=3, label='DetC Resuspension')
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ax[1].plot(dataOUT.index, dataOUT['fResS1OOC'], linewidth=3, label='OOC Resuspension')
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ax[1].plot(dataOUT.index, dataOUT['fSedPOC1'], linewidth=3, label='POC1 (Fast) Sedimentation')
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ax[1].plot(dataOUT.index, dataOUT['fSedPOC2'], linewidth=3, label='POC2 (Slow) Sedimentation')
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ax[1].legend()
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ax[1].set_ylabel('POC Sedimentation and Resuspension [g/m2/d]')
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ax[1].set_xlabel('Distance Along Flume [m]')
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# Hg Sediment Flux
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ax[2].set_title('Hg Resuspension Flux')
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ax[2].plot(dataOUT.index, dataOUT['fResS1Hg'], linewidth=3, label='Hg Resuspension')
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ax[2].plot(dataOUT.index, dataOUT['fSedHg'], linewidth=3, label='Hg Sedimentation')
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ax[2].legend()
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ax[2].set_ylabel('Hg Sedimentation and Resuspension [g/m2/d]')
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ax[2].set_xlabel('Distance Along Flume [m]')
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# Mm Sediment Flux
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ax[3].set_title('MeHg Resuspension Flux')
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ax[3].plot(dataOUT.index, dataOUT['fResS1Mm'], linewidth=3, label='MeHg Resuspension')
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ax[3].plot(dataOUT.index, dataOUT['fSedMm'], linewidth=3, label='MeHg Sedimentation')
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ax[3].legend()
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ax[3].set_ylabel('MeHg Sedimentation and Resuspension [g/m2/d]')
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ax[3].set_xlabel('Distance Along Flume [m]')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Grassy Narrows/Modelling/ProcessFigures/HgFluxes.png',
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bbox_inches='tight', dpi=200)
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#%% Plot More Fluxes
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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 9))
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fig.patch.set_facecolor('white')
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fig.tight_layout(pad=4)
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ax = axes.flat
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# Methylation Flux
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ax[0].set_title('Methylation Flux in Water')
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ax[0].plot(dataOUT.index, dataOUT['HgMmO'], linewidth=3, label='Mehtylation')
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ax[0].plot(dataOUT.index, dataOUT['MmHgO'], linewidth=3, label='Demehtylation')
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ax[0].legend()
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ax[0].set_ylabel('Methylation Flux in Water [g/m3/d]')
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ax[0].set_xlabel('Distance Along Flume [m]')
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# Methylation Flux in Sediment
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ax[1].set_title('Methylation Flux in Sediment')
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ax[1].plot(dataOUT.index, dataOUT['HgS1MmS1O']
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/ (dataOUT['HgS1']),
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linewidth=3, label='Mehtylation')
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ax[1].plot(dataOUT.index, dataOUT['MmS1HgS1O']
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/ (dataOUT['MmS1']),
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linewidth=3, label='Demehtylation')
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ax[1].legend()
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ax[1].set_ylabel('Methylation Flux in Sediment [g/gHg/d]')
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ax[1].set_xlabel('Distance Along Flume [m]')
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|
||||
# 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')
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
#%% Process EWR Data for use in OceanMesh2D
|
||||
|
||||
# Alexander Rey, July 12 2022
|
||||
|
||||
# %% Import
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
import matplotlib.cm as cm
|
||||
|
||||
from shapely.geometry import Point, MultiPoint, Polygon
|
||||
import alphashape
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
|
||||
# %% Import Bathymetry Points to find Concave Hull
|
||||
|
||||
bathyPoints = pd.read_csv("//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/00_Grid/OceanMesh/TR_Bathy.xyz",
|
||||
header=None, names=["x", "y", "z"], delim_whitespace=True)
|
||||
|
||||
# Convert to Geopandas
|
||||
bathyPoints_gdf = gp.GeoDataFrame(bathyPoints, geometry=gp.points_from_xy(bathyPoints.x, bathyPoints.y),
|
||||
crs="epsg:32615")
|
||||
|
||||
|
||||
# %% Find Concave Hull
|
||||
|
||||
alphaPoints = list(map(tuple, np.column_stack((bathyPoints_gdf['x'], bathyPoints_gdf['y']))))
|
||||
|
||||
# Find the concave hull using the alphashape package
|
||||
alphaPoly = alphashape.alphashape(alphaPoints, 0.02)
|
||||
|
||||
|
||||
# %% Test Plot of Concave Hull
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, subplot_kw={'projection': ccrs.epsg(32615)}, figsize=(10, 10))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
axes.set_xlim(468545, 511043)
|
||||
axes.set_ylim(5514782, 5546325)
|
||||
|
||||
# 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:32615')
|
||||
|
||||
bathyPoints_gdf.plot('.', ax=axes, color='y', markersize=0.1)
|
||||
axes.plot(*alphaPoly.exterior.xy, color='k', linewidth=1)
|
||||
|
||||
plt.show()
|
||||
|
||||
# %% Save Concave Hull to Shapefile
|
||||
dataBounds = gp.GeoDataFrame(index=[0], geometry=[alphaPoly], crs='EPSG:32615')
|
||||
dataBounds_wgs = dataBounds.to_crs('EPSG:4326')
|
||||
|
||||
dataBounds_wgs.to_file(
|
||||
'//srv-ott3.baird.com/Projects/12828.101 English Wabigoon River/06_Models/00_Delft3D/00_Grid/OceanMesh/ModelBoundsWGS.shp')
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,526 @@
|
|||
#%% Plotting script for LSU D3D data
|
||||
|
||||
# Alexander Rey, 2022
|
||||
import os
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import netCDF4 as nc
|
||||
import math
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
import matplotlib.cm as cm
|
||||
|
||||
import datetime as datetime
|
||||
|
||||
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 cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
import pathlib as pl
|
||||
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
import alphashape
|
||||
|
||||
import rasterio
|
||||
from rasterio.plot import show as rioshow
|
||||
from osgeo import osr #Spatial reference module
|
||||
#%% Read Model Log
|
||||
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")
|
||||
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
#%% Import using DFM functions
|
||||
modelPlot = range(45, 46) #30
|
||||
|
||||
# stormTime = [datetime.datetime(2005, 8, 29, 14, 0, 0),
|
||||
# datetime.datetime(2005, 9, 24, 14, 0, 0),
|
||||
# datetime.datetime(2005, 12, 28, 14, 0, 0)]
|
||||
# stormTime = [datetime.datetime(2005, 1, 14, 18, 0, 0),
|
||||
# datetime.datetime(2005, 2, 3, 18, 0, 0),
|
||||
# datetime.datetime(2005, 2, 10, 18, 0, 0)]
|
||||
#stormTime = [datetime.datetime(2005, 12, 31, 18, 0, 0)]
|
||||
#stormTime = [datetime.datetime(2005, 8, 7, 0, 0, 0)]
|
||||
|
||||
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
X = [None] * (max(modelPlot)+1)
|
||||
Y = [None] * (max(modelPlot)+1)
|
||||
U = [None] * (max(modelPlot)+1)
|
||||
V = [None] * (max(modelPlot)+1)
|
||||
facex = [None] * (max(modelPlot)+1)
|
||||
facey = [None] * (max(modelPlot)+1)
|
||||
ux_mean = [None] * (max(modelPlot)+1)
|
||||
uy_mean = [None] * (max(modelPlot)+1)
|
||||
ux = [None] * (max(modelPlot)+1)
|
||||
uy = [None] * (max(modelPlot)+1)
|
||||
sed = [None] * (max(modelPlot)+1)
|
||||
area = [None] * (max(modelPlot)+1)
|
||||
wd = [None] * (max(modelPlot)+1)
|
||||
regularMask = [None] * (max(modelPlot)+1)
|
||||
meshMask = [None] * (max(modelPlot)+1)
|
||||
alphaPolyList = [None] * (max(modelPlot)+1)
|
||||
|
||||
|
||||
for i in modelPlot:
|
||||
#file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'output', 'FlowFM_map.nc')
|
||||
file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
# Find nearest time step to desired time
|
||||
stormTime = [runLog['End Date'][i]]
|
||||
tStep = []
|
||||
for s in stormTime:
|
||||
abs_deltas_from_target_date = np.absolute(tSteps - s)
|
||||
tStep.append(np.argmin(abs_deltas_from_target_date))
|
||||
|
||||
# tStep = 50
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map)
|
||||
|
||||
ugrid_all[i] = get_netdata(file_nc=file_nc_map)#,multipart=False)
|
||||
facex[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_x')
|
||||
facey[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_y')
|
||||
# ux_mean[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucxa', timestep=tStep)
|
||||
# uy_mean[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucya', timestep=tStep)
|
||||
|
||||
ux[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucx',
|
||||
timestep=tStep, layer='all')
|
||||
uy[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_ucy',
|
||||
timestep=tStep, layer='all')
|
||||
|
||||
sed[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_bodsed',
|
||||
timestep=tStep, station='all')
|
||||
area[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_flowelem_ba')
|
||||
wd[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_waterdepth',
|
||||
timestep=tStep)
|
||||
|
||||
ux_mean[i] = ux[i].mean(axis=2)
|
||||
uy_mean[i] = uy[i].mean(axis=2)
|
||||
|
||||
X[i],Y[i],U[i] = scatter_to_regulargrid(xcoords=facex[i], ycoords=facey[i],
|
||||
ncellx=50, ncelly=70, values=ux_mean[i][0, :],
|
||||
maskland_dist=25, method='linear')
|
||||
X[i],Y[i],V[i] = scatter_to_regulargrid(xcoords=facex[i], ycoords=facey[i],
|
||||
ncellx=50, ncelly=70, values=uy_mean[i][0, :],
|
||||
maskland_dist=25, method='linear')
|
||||
# Find convex hull of pcolor data
|
||||
# Convert grid to multipoint
|
||||
|
||||
# Create a list of grid points
|
||||
# alphaPoints = list(map(tuple, np.column_stack((facex[i][:], facey[i][:])).data))
|
||||
alphaPoints = list(map(tuple, np.column_stack((facex[i][wd[i][0, :] != 0],
|
||||
facey[i][wd[i][0, :] != 0])).data))
|
||||
|
||||
# Find the concave hull
|
||||
alphaPoly = alphashape.alphashape(alphaPoints, 0.05)
|
||||
alphaPolyList[i] = alphaPoly
|
||||
|
||||
# Create mask of regular grid inside concave hull
|
||||
regularMask[i] = [alphaPoly.contains(Point(i[0], i[1]))
|
||||
for i in np.array([X[i].flatten(), Y[i].flatten()]).T]
|
||||
|
||||
# Create mask of mesh grid points inside concave hull
|
||||
meshMask[i] = [alphaPoly.contains(Point(i[0], i[1]))
|
||||
for i in np.array([facex[i], facey[i]]).T]
|
||||
|
||||
|
||||
# Reshape back to regular grid for mask
|
||||
regularMask[i] = np.array(regularMask[i]).reshape(X[i].shape)
|
||||
|
||||
|
||||
#%% Plot using DFM functions
|
||||
modelPlot = [24]
|
||||
# stormDirs = [306, 135, 250]
|
||||
stormDirs = [3, 346, 342]
|
||||
|
||||
# fig, axes = plt.subplots(nrows=1, ncols=3,subplot_kw={'projection': ccrs.epsg(26915)}, figsize=(13, 6))
|
||||
fig, axes = plt.subplots(nrows=1, ncols=2, subplot_kw={'projection': ccrs.epsg(26915)}, figsize=(10, 6))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plotCount = 1
|
||||
|
||||
vmin = 0 # Legend min
|
||||
vmax = 0.05 # Legend max
|
||||
scale = 60 # Size of arrows, smaller is bigger #40 # 15
|
||||
qmax = 0.025 # Scaling threshold. Above this value, arrows are scaled to 1 #0.1
|
||||
amin = 1 # Minimum arrow length. A dot is used below this value
|
||||
|
||||
for tPlot in range(1, 2):
|
||||
|
||||
for i in modelPlot:
|
||||
vel_mask = meshMask[i]
|
||||
|
||||
pc = plot_netmapdata(ugrid_all[i].verts[vel_mask],
|
||||
values=np.sqrt(ux_mean[i][tPlot, :][vel_mask]**2 +\
|
||||
uy_mean[i][tPlot, :][vel_mask]**2),
|
||||
ax=axes[plotCount], linewidth=0.5, cmap="viridis")
|
||||
|
||||
axes[plotCount].set_xlim(675524, 677027)
|
||||
axes[plotCount].set_ylim(3365191, 3367182)
|
||||
# axes[plotCount].set_xlim(676470, 677021)
|
||||
# axes[plotCount].set_ylim(3365939, 3366617)
|
||||
|
||||
# axes.quiver(X[i], Y[i], U[i], V[i], color='w', scale=8)
|
||||
|
||||
# Normalize velocity above limit
|
||||
|
||||
# Using fixed grid
|
||||
overGrid_U_Scale = U[i]
|
||||
overGrid_V_Scale = V[i]
|
||||
|
||||
# Using model grid
|
||||
# overGrid_U_Scale = ux_mean[i][tPlot, :]
|
||||
# overGrid_V_Scale = uy_mean[i][tPlot, :]
|
||||
|
||||
r = np.power(np.add(np.power(overGrid_U_Scale, 2), np.power(overGrid_V_Scale, 2)), 0.5)
|
||||
curPlotMask = np.sqrt((overGrid_U_Scale) ** 2 + (overGrid_V_Scale) ** 2) > qmax
|
||||
|
||||
overGrid_U_Plot = np.zeros(overGrid_U_Scale.shape)
|
||||
overGrid_V_Plot = np.zeros(overGrid_V_Scale.shape)
|
||||
|
||||
# Normalize arrows to 1 where not zero
|
||||
overGrid_U_Plot[r != 0] = overGrid_U_Scale[r != 0] / r[r != 0]
|
||||
overGrid_V_Plot[r != 0] = overGrid_V_Scale[r != 0] / r[r != 0]
|
||||
|
||||
# Scale arrows below qmax
|
||||
overGrid_U_Plot[~curPlotMask] = overGrid_U_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
|
||||
overGrid_V_Plot[~curPlotMask] = overGrid_V_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
|
||||
|
||||
# Using fixed grid
|
||||
# Remove arrows outside of convex hull
|
||||
overGrid_U_Plot[~regularMask[i]] = np.nan
|
||||
overGrid_V_Plot[~regularMask[i]] = np.nan
|
||||
|
||||
qv = axes[plotCount].quiver(X[i], Y[i], overGrid_U_Plot, overGrid_V_Plot, scale=scale,
|
||||
color='k', headlength=3.5, headwidth=3.5, headaxislength=3.5, width=0.0015, minlength=amin) #200
|
||||
|
||||
# # Using model grid
|
||||
# # Remove arrows outside convex hull
|
||||
# overGrid_U_Plot[~np.array(meshMask[i])] = np.nan
|
||||
# overGrid_V_Plot[~np.array(meshMask[i])] = np.nan
|
||||
# qv = axes[plotCount].quiver(facex[i], facey[i], overGrid_U_Plot, overGrid_V_Plot, scale=scale,
|
||||
# color='k', headlength=3.5, headwidth=3.5, headaxislength=3.5, width=0.0015, minlength=amin) #200
|
||||
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
ctx.add_basemap(axes[plotCount], source=mapbox, crs='EPSG:26915')
|
||||
|
||||
# extent = ax[plotCount].get_extent()
|
||||
# ax[plotCount].set_xticks(np.linspace(extent[0], extent[1], 4))
|
||||
# ax[plotCount].set_yticks(np.linspace(extent[2], extent[3], 4))
|
||||
|
||||
|
||||
# Add model bounds line
|
||||
axes[plotCount].plot(*alphaPoly.exterior.xy, color='k', linewidth=1)
|
||||
|
||||
pc.set_clim([vmin, vmax])
|
||||
if plotCount == 1:
|
||||
cbar = fig.colorbar(pc, ax=axes, shrink=0.95)
|
||||
cbar.set_label('Depth Averaged Velocity [m/s]')
|
||||
|
||||
axes[plotCount].set_title('Wind Direction: ' + str(stormDirs[tPlot]) + ' Degrees')
|
||||
|
||||
# Add rectangle to show storm arrow
|
||||
xLimDist = axes[plotCount].get_xlim()[1] - axes[plotCount].get_xlim()[0]
|
||||
yLimDist = axes[plotCount].get_ylim()[1] - axes[plotCount].get_ylim()[0]
|
||||
|
||||
axes[plotCount].add_patch(patches.Rectangle((axes[plotCount].get_xlim()[0] + 0.7 * xLimDist,
|
||||
axes[plotCount].get_ylim()[0] + 0.8 * yLimDist - xLimDist * 0.1),
|
||||
xLimDist * 0.2, xLimDist * 0.2,
|
||||
facecolor='white', edgecolor='w', linewidth=5))
|
||||
|
||||
|
||||
# Add wind arrow
|
||||
# Set at 80% of axis limits
|
||||
axes[plotCount].arrow(axes[plotCount].get_xlim()[0] + 0.8 * xLimDist,
|
||||
axes[plotCount].get_ylim()[0] + 0.8 * yLimDist,
|
||||
xLimDist * 0.03 * math.sin(math.radians(stormDirs[tPlot])),
|
||||
xLimDist * 0.03 * math.cos(math.radians(stormDirs[tPlot])),
|
||||
width=xLimDist * 0.015,
|
||||
color='k')
|
||||
|
||||
plotCount = plotCount + 1
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig(
|
||||
'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
|
||||
'StormVelDisWide.png', bbox_inches='tight', dpi=400)
|
||||
|
||||
|
||||
# %% Setup forebay polygon
|
||||
# forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPoly.shp')
|
||||
forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPolyExpand.shp')
|
||||
|
||||
# Setup mask. Can only do this once if grids are the same
|
||||
i = 45
|
||||
forebayPolyMask = [forebayPoly.iloc[0, 1].contains(Point(i[0], i[1]))
|
||||
for i in np.array([facex[i], facey[i]]).T]
|
||||
|
||||
|
||||
# %% Read in hist file
|
||||
#
|
||||
modelPlot = range(45, 46)
|
||||
|
||||
cumSedCS = [None] * (max(modelPlot)+1)
|
||||
cumSedCS_B = [None] * (max(modelPlot)+1)
|
||||
SedCS_B = [None] * (max(modelPlot)+1)
|
||||
cs_Names = [None] * (max(modelPlot)+1)
|
||||
cumDis = [None] * (max(modelPlot)+1)
|
||||
cumDisVol = [None] * (max(modelPlot)+1)
|
||||
|
||||
|
||||
for i in modelPlot:
|
||||
file_nc_hist = os.path.join(runLog['Run Location'][i], 'FlowFM', 'dflowfm', 'output', 'FlowFM_his.nc')
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_hist)
|
||||
|
||||
# cumSedCS[i] = get_ncmodeldata(file_nc=file_nc_hist, varname='cross_section_cumulative_Sediment',
|
||||
# timestep='all', station='all')
|
||||
# SedCS_B[i] = get_ncmodeldata(file_nc=file_nc_hist, varname='cross_section_suspended_sediment_transport',
|
||||
# timestep='all', station='all')
|
||||
# cumSedCS_B[i] = SedCS_B[i].cumsum(axis=1)
|
||||
#
|
||||
# cs_Names[i] = get_ncmodeldata(file_nc=file_nc_hist, varname='cross_section_name',
|
||||
# station='all')
|
||||
cumDis[i] = get_ncmodeldata(file_nc=file_nc_hist, varname='source_sink_prescribed_discharge',
|
||||
timestep='all', station='all')
|
||||
|
||||
cumDisVol[i] = cumDis[i] * 5 * 60 * 129.6
|
||||
cumDisVol[i] = cumDisVol[i].sum()
|
||||
|
||||
# Get Time info
|
||||
# tStepsHist = get_timesfromnc(file_nc=file_nc_hist, varname='time')
|
||||
|
||||
|
||||
#%% Plot Sediment DFM functions
|
||||
# modelPlot = [30, 31, 32, 33, 34, 35]
|
||||
#modelPlot = [30, 35]
|
||||
# modelPlot = [35]
|
||||
# stormDirs = [306, 135, 250]
|
||||
modelPlot = range(45, 46)
|
||||
|
||||
# fig, axes = plt.subplots(nrows=1, ncols=3,subplot_kw={'projection': ccrs.epsg(26915)}, figsize=(13, 6))
|
||||
# fig, axes = plt.subplots(nrows=1, ncols=3,
|
||||
# subplot_kw={'projection': ccrs.epsg(26915)},
|
||||
# figsize=(6, 6))
|
||||
# fig.patch.set_facecolor('white')
|
||||
# plt.tight_layout()
|
||||
|
||||
plotCount = 1
|
||||
sedIDX = 0
|
||||
Trapping_List = [None] * (max(modelPlot)+1)
|
||||
|
||||
for tPlot in range(0, 1):
|
||||
for i in modelPlot:
|
||||
# Plot as seperate figures
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1,
|
||||
subplot_kw={'projection': ccrs.epsg(26915)},
|
||||
figsize=(6, 6))
|
||||
fig.patch.set_facecolor('white')
|
||||
|
||||
vel_mask = meshMask[i]
|
||||
|
||||
pc = plot_netmapdata(ugrid_all[i].verts[vel_mask],
|
||||
values=sed[i][tPlot, :, sedIDX][vel_mask],
|
||||
ax=axes, linewidth=0.5,
|
||||
cmap="viridis")
|
||||
# pc = plot_netmapdata(ugrid_all[i].verts[vel_mask],
|
||||
# values=area[i][:][vel_mask],
|
||||
# ax=axes, linewidth=0.5,
|
||||
# cmap="viridis")
|
||||
|
||||
# axes[plotCount].set_xlim(675524, 677027)
|
||||
# axes[plotCount].set_ylim(3365191, 3367182)
|
||||
axes.set_xlim(676470, 677021)
|
||||
axes.set_ylim(3365939, 3366617)
|
||||
|
||||
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')
|
||||
|
||||
# extent = ax[plotCount].get_extent()
|
||||
# ax[plotCount].set_xticks(np.linspace(extent[0], extent[1], 4))
|
||||
# ax[plotCount].set_yticks(np.linspace(extent[2], extent[3], 4))
|
||||
|
||||
|
||||
# Add model bounds line
|
||||
alphaPoly = alphaPolyList[i]
|
||||
axes.plot(*alphaPoly.exterior.xy, color='k', linewidth=1)
|
||||
|
||||
# Add forebay bounds line
|
||||
forebayPoly.plot(ax=axes, linewidth=2,
|
||||
facecolor='none', edgecolor='r')
|
||||
|
||||
if i in [44, 45, 36, 37]:
|
||||
pc.set_clim([0, 2000])
|
||||
else:
|
||||
pc.set_clim([0, 200])
|
||||
|
||||
cbar = fig.colorbar(pc, ax=axes, shrink=0.95)
|
||||
cbar.set_label('Available mass of sediment [$kg/m^2$]')
|
||||
|
||||
# Calculate sediment in polygon
|
||||
massSed = sed[i][0, :, 0] * area[i]
|
||||
massSedSum = np.sum(massSed)
|
||||
|
||||
|
||||
massSed_FB = sed[i][0, forebayPolyMask, 0]\
|
||||
* area[i][forebayPolyMask]
|
||||
massSedSum_FB = np.sum(massSed_FB)
|
||||
|
||||
# Add title
|
||||
# axes.set_title('Existing Condition: ' + "{:0.1f}".format(
|
||||
# (massSedSum_FB / massSedSum) * 100) + '% Trapping')
|
||||
# axes.set_title(runLog['Short Description'][i] + ': ' + "{:0.1f}".format(
|
||||
# (massSedSum_FB / massSedSum) * 100) + '% Trapping')
|
||||
|
||||
|
||||
plt.title(runLog['Short Description'][i] + ": {:0.1f}".format(
|
||||
(massSedSum_FB / cumDisVol[i]) * 100) + '% Trapping')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
Trapping_List[i] = (massSedSum_FB / cumDisVol[i])
|
||||
|
||||
# # Add rectangle to show storm arrow
|
||||
# xLimDist = axes.get_xlim()[1] - axes.get_xlim()[0]
|
||||
# yLimDist = axes.get_ylim()[1] - axes.get_ylim()[0]
|
||||
#
|
||||
# axes.add_patch(patches.Rectangle((axes.get_xlim()[0] + 0.7 * xLimDist,
|
||||
# axes.get_ylim()[0] + 0.8 * yLimDist - xLimDist * 0.1),
|
||||
# xLimDist * 0.2, xLimDist * 0.2,
|
||||
# facecolor='white', edgecolor='w', linewidth=5))
|
||||
#
|
||||
#
|
||||
# # Add wind arrow
|
||||
# # Set at 80% of axis limits
|
||||
# axes.arrow(axes.get_xlim()[0] + 0.8 * xLimDist,
|
||||
# axes.get_ylim()[0] + 0.8 * yLimDist,
|
||||
# xLimDist * 0.03 * math.sin(math.radians(stormDirs[tPlot])),
|
||||
# xLimDist * 0.03 * math.cos(math.radians(stormDirs[tPlot])),
|
||||
# width=xLimDist * 0.015,
|
||||
# color='k')
|
||||
|
||||
plotCount = plotCount + 1
|
||||
plt.show()
|
||||
# fig.savefig(
|
||||
# 'C:/Users/arey/files/Projects/LSU/Modelling/FiguresExpand/' +
|
||||
# runLog['Run Name'][i] + '.png', bbox_inches='tight', dpi=400)
|
||||
|
||||
# plt.show()
|
||||
|
||||
# fig.savefig(
|
||||
# 'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
|
||||
# 'SedZoomPercent.png', bbox_inches='tight', dpi=400)
|
||||
|
||||
# %% Create summary table
|
||||
modelPlot = range(30, 46)
|
||||
trappingSumTable = pd.DataFrame()
|
||||
|
||||
trappingSumTable['Description'] = runLog['Short Description'][modelPlot]
|
||||
trappingSumTable['Trapping'] = np.array(Trapping_List)[modelPlot]
|
||||
|
||||
|
||||
# %% Calculate the total sediment discharged
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6,6))
|
||||
modelPlot = [27]
|
||||
|
||||
sourceSinkDat = []
|
||||
for i in modelPlot:
|
||||
for sourceSink in range(1, 5):
|
||||
|
||||
# Read in the sediment discharge file (.tim)
|
||||
sourceSinkFile = os.path.join(runLog['Run Location'][i], 'FlowFM', 'input', 'SourceSink0' + str(sourceSink) + '.tim')
|
||||
sourceSinkDat.append(pd.read_csv(sourceSinkFile, sep='\s+', header=None, names=['time', 'Q', 'conc_sed', 'conc_fluff']))
|
||||
|
||||
# Add datetimes
|
||||
sourceSinkDat[-1].index = pd.to_datetime(sourceSinkDat[-1].time, unit='m', origin=pd.Timestamp('2005-01-01'))
|
||||
|
||||
# Vol sediment at each time step, multiplied by the time step
|
||||
sourceSinkDat[-1]['SedVol'] = sourceSinkDat[-1]['Q'] *\
|
||||
sourceSinkDat[-1].loc[:, 'time'].diff() * 60 *\
|
||||
sourceSinkDat[-1]['conc_sed']
|
||||
|
||||
# Fix first time step
|
||||
sourceSinkDat[-1].iloc[0, -1] = 0
|
||||
|
||||
# Sediment cumlative sum
|
||||
sourceSinkDat[-1].loc[:, 'CumSed'] = sourceSinkDat[-1].loc[:, 'SedVol'].cumsum()
|
||||
|
||||
sourceSinkDat[-1].loc[:, 'Q'].rolling(24*4).sum().plot()
|
||||
|
||||
plt.show()
|
||||
|
||||
# %% Read in 15 minute precipitation data
|
||||
prep_data = pd.read_excel('//srv-mad3/Projects/13522.101 LSU Lakes/06_Models/PCSWMM Model/DataforHistoricRuns/15min_precip_formatted.xlsx',
|
||||
sheet_name='Sheet2')
|
||||
|
||||
prep_data['DateTime'] = pd.to_datetime(prep_data['DT'])
|
||||
prep_data.set_index('DateTime', inplace=True)
|
||||
|
||||
# %% Plot 24h precipitationto find events
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(12, 6))
|
||||
|
||||
prep_data.loc[:, 'per 15min'].rolling(24 * 4).sum().plot()
|
||||
axes.set_ylabel('24h Precipiation (in)')
|
||||
|
||||
axes.set_xlim((pd.to_datetime('20050701'), pd.to_datetime('20060101')))
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig(('//srv-mad3/Projects/13522.101 LSU Lakes/06_Models/PCSWMM Model/DataforHistoricRuns/24h_PrecpSum.png')
|
||||
|
||||
|
||||
# %% Plot the sediment discharge and cumlative transport
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6,6))
|
||||
|
||||
csIDX = 3
|
||||
|
||||
# Calculate the total sediment discharged
|
||||
# SS 1/2 is SouthWest, 3/4 is SouthEast
|
||||
sourceSinkDat[0].loc[:, 'CumSed'].multiply(2).plot(
|
||||
label='Discharged Sediment SW') # in kg
|
||||
sourceSinkDat[2].loc[:, 'CumSed'].multiply(2).plot(
|
||||
label='Discharged Sediment NE') # in kg
|
||||
|
||||
totalSedDis = sourceSinkDat[0].loc[:, 'CumSed'].multiply(2) +\
|
||||
sourceSinkDat[2].loc[:, 'CumSed'].multiply(2)
|
||||
|
||||
# Plot the total sediment discharge
|
||||
totalSedDis.plot(label='Total Discharged Sediment')
|
||||
|
||||
axes.plot(tStepsHist, cumSedCS[i][:, csIDX]*2650,
|
||||
label='Cross Section Sediment Transport', color='k') # In M3. Multiple by 2650 to get kg
|
||||
# axes.plot(tStepsHist, cumSedCS_B[i][:, csIDX],
|
||||
# label='Cross Section Sediment Transport B', color='r') # In M3. Multiple by 2650 to get kg
|
||||
|
||||
axes.set_xlabel('Date')
|
||||
axes.set_ylabel('Sediment [kg]')
|
||||
|
||||
trappedSed = (1 - (cumSedCS[i][-1, csIDX]*2650) /
|
||||
totalSedDis[-1])*100
|
||||
trappedSedB = (1 - (cumSedCS_B[i][-1, csIDX]) /
|
||||
totalSedDis[-1])*100
|
||||
|
||||
axes.set_title('Sediment Transport: ' + "{:0.1f}".format(trappedSed) + '% Trapping')
|
||||
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
# fig.savefig(
|
||||
# 'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
|
||||
# 'SedTrap_Concept_NE.png', bbox_inches='tight', dpi=400)
|
||||
# fig.savefig(
|
||||
# 'C:/Users/arey/files/Projects/LSU/Modelling/Figures/' +
|
||||
# 'SedTrap_Concept.png', bbox_inches='tight', dpi=400)
|
||||
|
|
@ -0,0 +1,184 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Script to comapre EFDC boundry conditions against observations
|
||||
"""
|
||||
|
||||
import noaa_coops as noaa
|
||||
from solarpy import beam_irradiance, irradiance_on_plane
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.dates as mdates
|
||||
import numpy as np
|
||||
import geopandas as gp
|
||||
gp.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'
|
||||
|
||||
import scipy as sp
|
||||
import scipy.ndimage
|
||||
#from astropy.convolution import Gaussian2DKernel
|
||||
import contextily as ctx
|
||||
import os
|
||||
from shapely.geometry import Point
|
||||
|
||||
import pickle
|
||||
|
||||
# %% Import NOAA CO-OPS data at the battery
|
||||
theBattery = noaa.Station(8518750)
|
||||
|
||||
df_air_temperature = theBattery.get_data(
|
||||
begin_date="20120101",
|
||||
end_date="20121231",
|
||||
product="air_temperature",
|
||||
units="metric",
|
||||
time_zone="gmt")
|
||||
|
||||
df_air_temperature['DateTime'] = df_air_temperature.index
|
||||
df_air_temperature['DateTime'] = df_air_temperature['DateTime'].dt.tz_localize('UTC')
|
||||
|
||||
|
||||
df_water_temperature = theBattery.get_data(
|
||||
begin_date="20120101",
|
||||
end_date="20121231",
|
||||
product="water_temperature",
|
||||
units="metric",
|
||||
time_zone="gmt")
|
||||
|
||||
df_water_temperature['DateTime'] = df_water_temperature.index
|
||||
df_water_temperature['DateTime'] = df_water_temperature['DateTime'].dt.tz_localize('UTC')
|
||||
|
||||
# %% Read in EFDC met bounds
|
||||
# WEATHER DATA FOR TIME SERIES N
|
||||
# TASER(M,N) = TIME WHEN THE DATA WAS COLLECTED, IT IS IN DAY IF TCASER(N) IS 86400.
|
||||
# PATM(M,N) = ATMOSPHERIC [PRESSURE MILLIBAR]
|
||||
# TDRY(M,N) = DRY BULB ATMOSPHERIC TEMPERATURE [DEGREE C) ISTOPT(2)=1 OR EQUIL TEMP
|
||||
# ISTOPT(2)=2; AQ VERSION SETS ISTOPT(2)=1.
|
||||
# TWET(M,N) = WET BULB ATM TEMP IF IRELH=0, RELATIVE HUMIDITY (BETWEEN 0 AND 1) IF
|
||||
# IRELH=1
|
||||
# RAIN(M,N) = RAIN FALL RATE LENGTH/TIME, IT IS INCH/DAY IF RAINCVT IS SET TO 7.055560e-006
|
||||
# (=0.0254/3600.)
|
||||
# EVAP(M,N) = EVAPORATION RATE IF EVAPCVT>0.
|
||||
# SOLSWR(M,N) = SOLAR SHORT WAVE RADIATION AT WATER SURFACE ENERGY FLUX/UNIT AREA.
|
||||
# IT IS IN W/M2 IF SOLRCVT = 1.
|
||||
# CLOUD(M,N) = FRACTIONAL CLOUD COVER
|
||||
efdc_air_in = pd.read_csv('//ott-athena/E/INPUT_FILES/BC_WEATHER/20191206/files/NC_bc_weather_2012_191206.inp',
|
||||
delim_whitespace=True, skiprows=10,
|
||||
names=['TASER', 'PATM', 'TDRY', 'TWET', 'RAIN', 'EVAP', 'SOLSWR', 'CLOUD'])
|
||||
|
||||
# Remove final row
|
||||
efdc_air_in.drop(efdc_air_in.tail(1).index, inplace=True)
|
||||
# Correct type
|
||||
efdc_air_in['TASER'] = efdc_air_in['TASER'].astype(float)
|
||||
|
||||
# Create datetime index
|
||||
efdc_air_in['DateTime'] = pd.to_datetime(efdc_air_in['TASER'], unit='D', origin=pd.Timestamp('2012-01-01')).\
|
||||
dt.tz_localize('EST').dt.tz_convert('UTC')
|
||||
|
||||
efdc_air_in.set_index('DateTime', inplace=True)
|
||||
|
||||
|
||||
# %% Read in EFDC Water Temperature Bounds
|
||||
|
||||
efdc_water_in = pd.read_csv('//ott-athena/E/INPUT_FILES/TEMPERATURE_FILES/RM_North_RM_South/190423/NC_bc_temp_Yr2012_20190423.inp',
|
||||
delim_whitespace=True, skiprows=16,
|
||||
names=['TASER', 'L1', 'L2', 'L3', 'L4', 'L5', 'L6', 'L7', 'L8', 'L9', 'L10'])
|
||||
|
||||
# Find row when the next boundry is reached and create new df
|
||||
efdc_water_South = efdc_water_in.iloc[0:np.where(efdc_water_in['TASER'] == 'AQEA_QA_QC')[0][0], :].copy()
|
||||
efdc_water_North = efdc_water_in.iloc[np.where(efdc_water_in['TASER'] == 'AQEA_QA_QC')[0][0]+2:
|
||||
np.where(efdc_water_in['TASER'] == 'AQEA_QA_QC')[0][1], :].copy()
|
||||
|
||||
# Correct type
|
||||
efdc_water_South.iloc[:, 0] = efdc_water_South.iloc[:, 0].astype(float)
|
||||
efdc_water_North.iloc[:, 0] = efdc_water_North.iloc[:, 0].astype(float)
|
||||
|
||||
# Create datetime index
|
||||
efdc_water_South['DateTime'] = pd.to_datetime(efdc_water_South['TASER'], unit='D', origin=pd.Timestamp('2012-01-01')).\
|
||||
dt.tz_localize('EST').dt.tz_convert('UTC')
|
||||
|
||||
efdc_water_South.set_index('DateTime', inplace=True)
|
||||
|
||||
efdc_water_North['DateTime'] = pd.to_datetime(efdc_water_North['TASER'], unit='D', origin=pd.Timestamp('2012-01-01')).\
|
||||
dt.tz_localize('EST').dt.tz_convert('UTC')
|
||||
efdc_water_North.set_index('DateTime', inplace=True)
|
||||
|
||||
del efdc_water_in
|
||||
|
||||
# %% Import Clear Sly radiation
|
||||
|
||||
vnorm = np.array([0, 0, -1])
|
||||
h = 0 # sea-level
|
||||
# date = np.arange(datetime(2012, 1, 1), datetime(2012, 12, 31), timedelta(hours=1)).astype(datetime)
|
||||
solar_date = [datetime(2012, 1, 1) + timedelta(minutes=i) for i in range(0, 60 * 24 * 365, 60)]
|
||||
solar_date_utc = pd.date_range(start=datetime(2012, 1, 1), end=datetime(2012, 12, 31), freq='1H').tz_localize('EST')
|
||||
|
||||
lat = 40.7128 # southern hemisphere
|
||||
|
||||
# solar_rad = [beam_irradiance(h, i, lat) for i in solar_date]
|
||||
solar_rad = [irradiance_on_plane(vnorm, h, i, lat) for i in solar_date]
|
||||
|
||||
|
||||
# %% Merge the dataframes
|
||||
|
||||
# met_merged = pd.merge(df_air_temperature, efdc_air_in, how='outer', left_index=True, right_index=True)
|
||||
met_merged = pd.merge_ordered(df_air_temperature, efdc_air_in, on='DateTime',
|
||||
how='outer', fill_method='ffill')
|
||||
|
||||
waterTempSouth_merged = pd.merge_ordered(df_water_temperature, efdc_water_South, on='DateTime',
|
||||
how='outer', fill_method='ffill')
|
||||
|
||||
waterTempNorth_merged = pd.merge_ordered(df_water_temperature, efdc_water_North, on='DateTime',
|
||||
how='outer', fill_method='ffill')
|
||||
|
||||
|
||||
#%% Plot air temperature
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
# efdc_air_in['TDRY'].plot(ax=axes, color='black', label='EFDC Air Temperature')
|
||||
# df_air_temperature['air_temp'].plot(ax=axes, color='blue', label='Obs at The Battery')
|
||||
# axes.set_xlim(pd.to_datetime('2012-08-01'), pd.to_datetime('2012-08-31'))
|
||||
|
||||
# efdc_water_North['L10'].astype(float).plot(ax=axes, color='blue', label='EFDC North Boundry L10')
|
||||
# efdc_water_North['L5'].astype(float).plot(ax=axes, color='red', label='EFDC North Boundry L5')
|
||||
# efdc_water_North['L1'].astype(float).plot(ax=axes, color='green', label='EFDC North Boundry L1')
|
||||
|
||||
efdc_water_South['L10'].astype(float).plot(ax=axes, color='blue', label='EFDC South Boundry L10')
|
||||
efdc_water_South['L5'].astype(float).plot(ax=axes, color='red', label='EFDC South Boundry L5')
|
||||
efdc_water_South['L1'].astype(float).plot(ax=axes, color='green', label='EFDC South Boundry L1')
|
||||
|
||||
df_water_temperature['water_temp'].plot(ax=axes, color='black', label='Obs at The Battery')
|
||||
axes.set_xlim(pd.to_datetime('2012-08-01'), pd.to_datetime('2012-08-31'))
|
||||
|
||||
axes.set_ylabel('Temperature (deg C)')
|
||||
axes.set_xlabel('Date')
|
||||
axes.legend()
|
||||
|
||||
# axes.scatter(met_merged['TDRY'], met_merged['air_temp'])
|
||||
# axes.scatter(waterTempSouth_merged['water_temp'], waterTempSouth_merged['L5'])
|
||||
# axes.scatter(waterTempNorth_merged['water_temp'], waterTempSouth_merged['L5'])
|
||||
|
||||
plt.show()
|
||||
|
||||
#%% Plot Solar Radiation
|
||||
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
|
||||
|
||||
efdc_air_in['SOLSWR'].plot(ax=axes, color='black', label='EFDC Air Temperature')
|
||||
axes.plot(solar_date_utc[0:-1], solar_rad, color='blue', label='Solar Radiation')
|
||||
|
||||
axes.set_xlim(pd.to_datetime('2012-08-01'), pd.to_datetime('2012-08-07'))
|
||||
|
||||
axes.set_ylabel('Solar Radiation (w/m^2)')
|
||||
axes.set_xlabel('Date')
|
||||
axes.legend()
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,160 @@
|
|||
#%% Plotting script for LSU D3D data
|
||||
|
||||
# Alexander Rey, 2022
|
||||
import os
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
import netCDF4 as nc
|
||||
import math
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
import matplotlib.cm as cm
|
||||
|
||||
import datetime as datetime
|
||||
|
||||
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 cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
from dfm_tools.regulargrid import scatter_to_regulargrid
|
||||
import pathlib as pl
|
||||
|
||||
from shapely.geometry import Point, MultiPoint
|
||||
import alphashape
|
||||
|
||||
import rioxarray
|
||||
import xarray as xr
|
||||
#%% Read Model Log
|
||||
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")
|
||||
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||
|
||||
dataPath = "FlowFM_map.nc"
|
||||
|
||||
#%% Import using DFM functions
|
||||
modelPlot = range(30, 46)
|
||||
|
||||
# stormTime = [datetime.datetime(2005, 8, 29, 14, 0, 0),
|
||||
# datetime.datetime(2005, 9, 24, 14, 0, 0),
|
||||
# datetime.datetime(2005, 12, 28, 14, 0, 0)]
|
||||
# stormTime = [datetime.datetime(2005, 1, 14, 18, 0, 0),
|
||||
# datetime.datetime(2005, 2, 3, 18, 0, 0),
|
||||
# datetime.datetime(2005, 2, 10, 18, 0, 0)]
|
||||
#stormTime = [datetime.datetime(2005, 12, 31, 18, 0, 0)]
|
||||
#stormTime = [datetime.datetime(2005, 8, 7, 0, 0, 0)]
|
||||
|
||||
|
||||
ugrid_all = [None] * (max(modelPlot)+1)
|
||||
X2 = [None] * (max(modelPlot)+1)
|
||||
Y2 = [None] * (max(modelPlot)+1)
|
||||
sed2 = [None] * (max(modelPlot)+1)
|
||||
facex = [None] * (max(modelPlot)+1)
|
||||
facey = [None] * (max(modelPlot)+1)
|
||||
ux_mean = [None] * (max(modelPlot)+1)
|
||||
uy_mean = [None] * (max(modelPlot)+1)
|
||||
ux = [None] * (max(modelPlot)+1)
|
||||
uy = [None] * (max(modelPlot)+1)
|
||||
sed = [None] * (max(modelPlot)+1)
|
||||
bed = [None] * (max(modelPlot)+1)
|
||||
bed2 = [None] * (max(modelPlot)+1)
|
||||
wd = [None] * (max(modelPlot)+1)
|
||||
regularMask2 = [None] * (max(modelPlot)+1)
|
||||
meshMask = [None] * (max(modelPlot)+1)
|
||||
alphaPolyList = [None] * (max(modelPlot)+1)
|
||||
|
||||
sedIDX = 0
|
||||
tPlot = 0
|
||||
|
||||
for i in modelPlot:
|
||||
#file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'output', 'FlowFM_map.nc')
|
||||
file_nc_map = os.path.join(runLog['Run Location'][i], 'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
|
||||
|
||||
tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
|
||||
|
||||
# Find nearest time step to desired time
|
||||
stormTime = [runLog['End Date'][i]]
|
||||
tStep = []
|
||||
for s in stormTime:
|
||||
abs_deltas_from_target_date = np.absolute(tSteps - s)
|
||||
tStep.append(np.argmin(abs_deltas_from_target_date))
|
||||
|
||||
|
||||
# Get Var info
|
||||
vars_pd, dims_pd = get_ncvardimlist(file_nc=file_nc_map)
|
||||
|
||||
facex[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_x')
|
||||
facey[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_y')
|
||||
|
||||
sed[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_bodsed',
|
||||
timestep=tStep, station='all')
|
||||
bed[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_flowelem_bl')
|
||||
wd[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_waterdepth',
|
||||
timestep=tStep)
|
||||
|
||||
# Create a list of grid points
|
||||
alphaPoints = list(map(tuple, np.column_stack((facex[i][wd[i][0, :] != 0],
|
||||
facey[i][wd[i][0, :] != 0])).data))
|
||||
|
||||
# Find the concave hull
|
||||
alphaPoly = alphashape.alphashape(alphaPoints, 0.05)
|
||||
alphaPolyList[i] = alphaPoly
|
||||
|
||||
plottingMask = (facex[i] > 676470) & (facex[i] < 677021) & \
|
||||
(facey[i] > 3365939) & (facey[i] < 3366617)
|
||||
|
||||
# Convert to regular grid for interpolation
|
||||
X2[i], Y2[i], sed2[i] = scatter_to_regulargrid(xcoords=facex[i][plottingMask], ycoords=facey[i][plottingMask],
|
||||
ncellx=500, ncelly=500, values=sed[i][tPlot, plottingMask, sedIDX],
|
||||
maskland_dist=25, method='linear')
|
||||
|
||||
X2[i], Y2[i], bed2[i] = scatter_to_regulargrid(xcoords=facex[i][plottingMask], ycoords=facey[i][plottingMask],
|
||||
ncellx=750, ncelly=750, values=bed[i][plottingMask],
|
||||
maskland_dist=25, method='cubic')
|
||||
|
||||
|
||||
# Create mask of regular grid inside concave hull
|
||||
regularMask2[i] = [alphaPoly.contains(Point(i[0], i[1]))
|
||||
for i in np.array([X2[i].flatten(), Y2[i].flatten()]).T]
|
||||
|
||||
# Reshape back to regular grid for mask
|
||||
regularMask2[i] = np.array(regularMask2[i]).reshape(X2[i].shape)
|
||||
|
||||
|
||||
# %% Setup forebay polygon
|
||||
# forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPoly.shp')
|
||||
forebayPoly = gp.read_file('C:/Users/arey/files/Projects/LSU/ForebayPolyExpand.shp')
|
||||
|
||||
# Setup mask. Can only do this once if grids are the same
|
||||
i = 45
|
||||
forebayPolyMask = [forebayPoly.iloc[0, 1].contains(Point(i[0], i[1]))
|
||||
for i in np.array([facex[i], facey[i]]).T]
|
||||
|
||||
#%% Plot Sediment DFM functions
|
||||
modelPlot = range(30, 46)
|
||||
|
||||
sedIDX = 0
|
||||
|
||||
for tPlot in range(0, 1):
|
||||
for i in modelPlot:
|
||||
sedPlot = bed2[i]
|
||||
|
||||
# Blank zero sed
|
||||
sedPlot[sedPlot == 0] = np.nan
|
||||
|
||||
# Blank outside of hull
|
||||
sedPlot[~regularMask2[i]] = np.nan
|
||||
|
||||
# Convert to xarray dataset
|
||||
sed2_xr = xr.DataArray(data=sedPlot.T,
|
||||
dims=["x", "y"],
|
||||
coords=dict(
|
||||
x=(["x"], X2[i][0, :]),
|
||||
y=(["y"], Y2[i][:, 0])))
|
||||
|
||||
# Transpose for geotiff writing
|
||||
sed2_xr = sed2_xr.transpose('y', 'x')
|
||||
sed2_xr.rio.write_crs("epsg:26915", inplace=True)
|
||||
sed2_xr.rio.to_raster('C:/Users/arey/files/Projects/LSU/Modelling/GeoTiffBed/' +
|
||||
runLog['Run Name'][i] + 'B.tif')
|
||||
|
|
@ -0,0 +1,682 @@
|
|||
## Plotting Mike21 SW results for SouthShore
|
||||
# Author: AJMR
|
||||
# December 22, 2021
|
||||
|
||||
# %% Setup Project
|
||||
from mikeio import Dfsu, Mesh, Dfs2, Dfs0, Dfs1
|
||||
|
||||
import pandas as pd
|
||||
import pathlib as pl
|
||||
import numpy as np
|
||||
import geopandas as gp
|
||||
import datetime as datetime
|
||||
|
||||
import matplotlib as mpl
|
||||
import matplotlib.pyplot as plt
|
||||
import time
|
||||
import matplotlib.animation as animation
|
||||
import contextily as ctx
|
||||
import matplotlib.font_manager as fm
|
||||
|
||||
# %% Read Model Log
|
||||
|
||||
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13539.101 L'Ansecoy Bay, Mustique", "06_Models",
|
||||
"Model Log Mustique.xlsx")
|
||||
|
||||
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||
# %% Read Map Model Results
|
||||
modelPlot = [8, 10, 11, 12, 13, 14]
|
||||
modelPlot = [15, 16, 17]
|
||||
modelPlot = [18]
|
||||
|
||||
dfs_list = [None] * (max(modelPlot) + 1)
|
||||
ds_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain3D.dfsu').as_posix())
|
||||
|
||||
dfs_list[m] = dfsIN
|
||||
ds_list[m] = dfs_list[m].read()
|
||||
|
||||
|
||||
# %% Read Model Results at point
|
||||
modelPlot = [12]
|
||||
|
||||
dfs2d_list = [None] * (max(modelPlot) + 1)
|
||||
dfs3d_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
z_list = [None] * (max(modelPlot) + 1)
|
||||
z_profile_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
elm_list = [None] * (max(modelPlot) + 1)
|
||||
elm_df_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
elm2d_list = [None] * (max(modelPlot) + 1)
|
||||
elm2d_df_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
readPointsName = pd.read_csv("//srv-ott3.baird.com/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/Dataset locations_RevF.csv",
|
||||
delimiter=",")
|
||||
readPoints = pd.read_csv("//srv-ott3.baird.com/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/Dataset locations_RevF.csv",
|
||||
delimiter=",").iloc[0:, 2:-2].values
|
||||
|
||||
disPipePtsin = gp.read_file('C:/Users/arey/files/Projects/Mustique/DisPipeA_Points6.shp')
|
||||
disPipePtsin_np = np.array([disPipePtsin.geometry.x.values, disPipePtsin.geometry.y.values])
|
||||
readPoints = np.vstack((readPoints,disPipePtsin_np.T))
|
||||
|
||||
disPipePtsin = gp.read_file('C:/Users/arey/files/Projects/Mustique/DisPipeB_Points2.shp')
|
||||
disPipePtsin_np = np.array([disPipePtsin.geometry.x.values, disPipePtsin.geometry.y.values])
|
||||
readPoints = np.vstack((readPoints,disPipePtsin_np.T))
|
||||
|
||||
for m in modelPlot:
|
||||
|
||||
# dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain2D.dfsu').as_posix())
|
||||
# dfs2d_list[m] = dfsIN
|
||||
#
|
||||
# # Read Map
|
||||
# # ds_list[m] = dfs_list[m].read()
|
||||
# # curU_list[m] = ds_list[m]["Depth averaged U velocity"]
|
||||
# # curV_list[m] = ds_list[m]["Depth averaged V velocity"]
|
||||
# # curElm_list[m] = dfs_list[m].element_coordinates
|
||||
# # wl_list[m] = ds_list[m]["Surface elevation"]
|
||||
# # z_list[m] = ds_list[m]["Z coordinate"]
|
||||
#
|
||||
# ## Read specific points in 2D
|
||||
# # Find nearest elements
|
||||
# elem_ids = dfs2d_list[m].find_nearest_elements(readPoints[:, 0], readPoints[:, 1])
|
||||
# # Read in data from nearest elements
|
||||
# elm2d_list[m] = dfs2d_list[m].read(elements=elem_ids)
|
||||
#
|
||||
# # Convert to Pandas DataFrame
|
||||
# elm2d_df_list[m] = [None] * readPoints.shape[0]
|
||||
# for p in range(0, readPoints.shape[0]):
|
||||
# elm2d_df_list[m][p] = elm2d_list[m].isel(idx=p).to_dataframe()
|
||||
|
||||
|
||||
## Read specific points 3D
|
||||
# Setup MIKE object
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain3D.dfsu').as_posix())
|
||||
dfs3d_list[m] = dfsIN
|
||||
|
||||
# Find nearest elements
|
||||
elem_ids = dfs3d_list[m].find_nearest_profile_elements(readPoints[:, 0], readPoints[:, 1])
|
||||
# Read from elements- flatten 2d element array (xy and z) to 1d for reading
|
||||
elm_list[m] = dfs3d_list[m].read(elements=elem_ids.flatten().astype(int))
|
||||
|
||||
elm_df_list[m] = [None] * readPoints.shape[0]
|
||||
z_profile_list[m] = [None] * readPoints.shape[0]
|
||||
# Convert to Pandas DataFrame
|
||||
# Loop through points, selecting corresponding depths
|
||||
for p in range(0, readPoints.shape[0]):
|
||||
z_profile_list[m][p] = dfs3d_list[m].element_coordinates[elem_ids[p, :].astype(int), 2]
|
||||
|
||||
# Convert to Pandas and add zidx
|
||||
for z in range(0, z_profile_list[m][p].shape[0]):
|
||||
df_tmp = elm_list[m].isel(idx=p * 5 + z).to_dataframe()
|
||||
df_tmp['Z'] = z_profile_list[m][p][z]
|
||||
df_tmp['Z_IDX'] = z
|
||||
if z == 0:
|
||||
elm_df_list[m][p] = df_tmp
|
||||
else:
|
||||
elm_df_list[m][p] = elm_df_list[m][p].append(df_tmp)
|
||||
|
||||
del df_tmp
|
||||
|
||||
# %% Import RBR data
|
||||
rbr_path = "C:/Users/arey/files/Projects/Mustique/041279_20211203_1541Ruskin.xlsx"
|
||||
rbr_pd = pd.read_excel(rbr_path, sheet_name='Wave', parse_dates=True, header=1, index_col=0)
|
||||
rbr_pd['WL'] = rbr_pd['Depth'] - rbr_pd['Depth'].mean() - 0.2
|
||||
|
||||
# Correct time zone
|
||||
rbr_pd.index = rbr_pd.index + datetime.timedelta(hours=4)
|
||||
|
||||
# %% Import Nortek Eco data
|
||||
eco_path_1 = "//srv-ott3/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/02_Nortek ECO/Eco61_20210910184606.csv"
|
||||
eco_path_2 = "//srv-ott3/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/02_Nortek ECO/Eco214_20210902120703.csv"
|
||||
|
||||
eco1_pd = pd.read_csv(eco_path_1, sep=',', header=[28], index_col=0,
|
||||
parse_dates=False)
|
||||
|
||||
eco2_pd = pd.read_csv(eco_path_2, sep=',', header=[28], index_col=0,
|
||||
parse_dates=False)
|
||||
|
||||
# Drop first row
|
||||
eco1_pd.drop(eco1_pd.index[0], inplace=True)
|
||||
eco2_pd.drop(eco2_pd.index[0], inplace=True)
|
||||
|
||||
eco1_pd.index = pd.to_datetime(eco1_pd.index, format='%m/%d/%Y %I:%M:%S %p')
|
||||
eco2_pd.index = pd.to_datetime(eco2_pd.index, format='%m/%d/%Y %I:%M:%S %p')
|
||||
|
||||
# Set water level to zero
|
||||
eco1_pd['WL'] = eco1_pd['Depth'].astype(float) - eco1_pd['Depth'].astype(float).mean(skipna=True) + 0.2
|
||||
eco2_pd['WL'] = eco2_pd['Depth'].astype(float) - eco2_pd['Depth'].astype(float).mean(skipna=True) + 0.2
|
||||
|
||||
# Correct time zone
|
||||
eco1_pd.index = eco1_pd.index + datetime.timedelta(hours=4)
|
||||
eco2_pd.index = eco2_pd.index + datetime.timedelta(hours=4)
|
||||
|
||||
# %% Read in boundary conditions
|
||||
ds_wl = []
|
||||
|
||||
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_EastBound.dfs1")
|
||||
ds_wl.append(dfs1.read())
|
||||
|
||||
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_NorthBound.dfs1")
|
||||
ds_wl.append(dfs1.read())
|
||||
|
||||
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_SouthBound.dfs1")
|
||||
ds_wl.append(dfs1.read())
|
||||
|
||||
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_WestBound.dfs1")
|
||||
ds_wl.append(dfs1.read())
|
||||
|
||||
# %% Plot Water Level at a point
|
||||
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8), sharex=True)
|
||||
plotstart = '2021-10-01 00:00:00'
|
||||
plotend = '2021-10-15 00:00:00'
|
||||
# plotstart = '2021-10-05 00:00:00'
|
||||
# plotend = '2021-10-07 00:00:00'
|
||||
modelPlot = [13]
|
||||
|
||||
for m in modelPlot:
|
||||
for sub, p in enumerate([5, 9, 6]):
|
||||
|
||||
if p == 9:
|
||||
rbr_pd['WL'].plot(ax=axes[sub], color='k', label='Nearshore East Obervations')
|
||||
elif p == 5:
|
||||
eco1_pd['WL'].plot(ax=axes[sub], color='k', label='Offshore Obervations')
|
||||
elif p == 6:
|
||||
eco2_pd['WL'].plot(ax=axes[sub], color='k', label='Nearshore West Obervations')
|
||||
|
||||
elm2d_df_list[m][p].plot(y='Surface elevation', ax=axes[sub], label='Model')
|
||||
axes[sub].legend(loc="upper left")
|
||||
|
||||
axes[sub].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[sub].set_ylim(-1.0, 1.0)
|
||||
axes[sub].set_ylabel('Water Level (m)')
|
||||
|
||||
axes[sub].set_xlabel('Date')
|
||||
|
||||
plt.show()
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/13539.101 L\'Ansecoy Bay, Mustique/06_Models/01_MIKE3/00_Figures/' +
|
||||
'/wl_TS_RevB.png', bbox_inches='tight')
|
||||
|
||||
# %% Velocity point U and V
|
||||
# Z_IDX = 4
|
||||
# ecoVar1 = 'Upper speed U'
|
||||
# ecoVar2 = 'Upper speed V'
|
||||
# ecoVar3 = 'Upper speed'
|
||||
# ecoVar4 = 'Upper direction'
|
||||
|
||||
# plotstart = '2021-09-14 00:00:00'
|
||||
# # plotend = '2021-09-16 00:00:00'
|
||||
# plotend = '2021-10-03 00:00:00'
|
||||
|
||||
plotstart = '2021-10-01 00:00:00'
|
||||
# plotend = '2021-09-16 00:00:00'
|
||||
plotend = '2021-10-21 00:00:00'
|
||||
|
||||
# plotstart = '2021-10-05 00:00:00'
|
||||
# plotend = '2021-10-07 00:00:00'
|
||||
|
||||
Z_IDX = 2
|
||||
ecoVar1 = 'Middle speed U'
|
||||
ecoVar2 = 'Middle speed V'
|
||||
ecoVar3 = 'Middle speed'
|
||||
ecoVar4 = 'Middle direction'
|
||||
|
||||
# Z_IDX = 1
|
||||
# ecoVar1 = 'Lower speed U'
|
||||
# ecoVar2 = 'Lower speed V'
|
||||
# ecoVar3 = 'Lower speed'
|
||||
# ecoVar4 = 'Lower direction'
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 4), sharex=True)
|
||||
modelPlot = [13]
|
||||
|
||||
for m in modelPlot:
|
||||
eco1_pd[ecoVar1] = eco1_pd[ecoVar3].astype(float) * \
|
||||
np.sin(np.radians(eco1_pd[ecoVar4].astype(float).to_numpy()))
|
||||
|
||||
eco1_pd[ecoVar1].astype(float).plot(ax=axes[0], color='k', label='Observations')
|
||||
elm_df_list[m][3].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[0], label='Model')
|
||||
|
||||
axes[0].set_ylim(-0.5, 0.5)
|
||||
axes[0].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[0].set_ylabel('Nearshore U (m/s)')
|
||||
axes[0].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
|
||||
|
||||
eco1_pd[ecoVar2] = eco1_pd[ecoVar3].astype(float) * \
|
||||
np.cos(np.radians(eco1_pd[ecoVar4].astype(float).to_numpy()))
|
||||
|
||||
eco1_pd[ecoVar2].astype(float).plot(ax=axes[1], color='k', label='Observations')
|
||||
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[1], label='Model')
|
||||
|
||||
|
||||
axes[1].set_ylim(-0.5, 0.5)
|
||||
axes[1].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[1].set_ylabel('Nearshore V (m/s)')
|
||||
axes[1].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
|
||||
|
||||
# eco2_pd[ecoVar1] = eco2_pd[ecoVar3].astype(float) * \
|
||||
# np.sin(np.radians(eco2_pd[ecoVar4].astype(float).to_numpy()))
|
||||
# eco2_pd[ecoVar1].astype(float).plot(ax=axes[2], color='k', label='Observations')
|
||||
# elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == Z_IDX, :].plot(
|
||||
# y='U velocity', ax=axes[2], label='Model')
|
||||
|
||||
# axes[2].set_ylim(-1.5, 1.5)
|
||||
# axes[2].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
# axes[2].set_ylabel('Offshore U (m/s)')
|
||||
# axes[2].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
|
||||
#
|
||||
# eco2_pd[ecoVar2] = eco2_pd[ecoVar3].astype(float) * \
|
||||
# np.cos(np.radians(eco2_pd[ecoVar4].astype(float).to_numpy()))
|
||||
# eco2_pd[ecoVar2].astype(float).plot(ax=axes[3], color='k', label='Observations')
|
||||
# elm_df_list[m][5].loc[elm_df_list[m][6]['Z_IDX'] == Z_IDX, :].plot(
|
||||
# y='V velocity', ax=axes[3], label='Model')
|
||||
#
|
||||
# axes[3].set_ylim(-1.00, 1.00)
|
||||
# axes[3].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
# axes[3].set_ylabel('Offshore V (m/s)')
|
||||
# axes[3].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
|
||||
|
||||
axes[1].set_xlabel('Date')
|
||||
|
||||
plt.show()
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/13539.101 L\'Ansecoy Bay, Mustique/06_Models/01_MIKE3/00_Figures/' +
|
||||
'/uVel_TS_RevC.png', bbox_inches='tight')
|
||||
|
||||
|
||||
# %% Subplot Map Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckex9vtri0o6619p55sl5qiyv/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
fontprops = fm.FontProperties(size=12)
|
||||
modelPlot = [18]
|
||||
|
||||
tStepsPlot = np.zeros((5, 6))
|
||||
tStepsPlot[0, :] = range(110, 116)
|
||||
tStepsPlot[1, :] = range(158, 164)
|
||||
tStepsPlot[2, :] = range(206, 212)
|
||||
tStepsPlot[3, :] = range(255, 261)
|
||||
tStepsPlot[4, :] = range(138, 144)
|
||||
|
||||
for m in modelPlot:
|
||||
vmax = 34.0
|
||||
vmin = 33.7
|
||||
cmap = 'inferno'
|
||||
|
||||
if m == 8 or m == 17:
|
||||
disPlot = 7
|
||||
elif m == 11 or m == 15 or m == 18:
|
||||
disPlot = 13
|
||||
elif m == 14 or m == 16:
|
||||
disPlot = 8
|
||||
|
||||
for a in range(0, 5):
|
||||
fig, axes = plt.subplots(nrows=2, ncols=3, sharey=True, sharex=True, figsize=(16, 9))
|
||||
ax = axes.flatten()
|
||||
for t in range(0, 6):
|
||||
# Plot salinity
|
||||
# Select salinity data at selected time step
|
||||
plotDat = ds_list[m]['Salinity'][int(tStepsPlot[a, t]), :]
|
||||
# Identify nan values
|
||||
plot_nan = np.isnan(plotDat)
|
||||
|
||||
# Add additional nan values to show bed
|
||||
plot_nan = plot_nan | (plotDat < 33.725)
|
||||
|
||||
# Plot all selected values at a given later and not nan
|
||||
axDHI = dfs_list[m].plot(plotDat[(dfs_list[m].layer_ids == 0) & (~plot_nan)], plot_type='contourf',
|
||||
show_mesh=False, cmap=cmap, ax=ax[t], add_colorbar=False,
|
||||
vmin=vmin, vmax=vmax,
|
||||
elements=dfs_list[m].element_ids[(dfs_list[m].layer_ids == 0) & (~plot_nan)])
|
||||
# Add discharge point
|
||||
ax[t].scatter(readPoints[disPlot, 0], readPoints[disPlot, 1], marker='^', color='r', s=20)
|
||||
|
||||
# ax[t].set_xlim(left=696680.7, right=698252.7)
|
||||
# ax[t].set_ylim(bottom=1425547.5, top=1426681.8)
|
||||
ax[t].set_xlim(left=696508.3, right=698252.7)
|
||||
ax[t].set_ylim(bottom=1425267.0, top=1426681.8)
|
||||
|
||||
ctx.add_basemap(ax[t], source=mapbox, crs='EPSG:32620')
|
||||
ax[t].title.set_text(str(ds_list[m].time[int(tStepsPlot[a, t])]))
|
||||
ax[t].xaxis.set_major_locator(plt.MaxNLocator(4))
|
||||
# axes.titlesize = 'x-large'
|
||||
# fig.suptitle(runLog['Short Description'][m], fontsize=18)
|
||||
# ax[t].set_xlabel('Easting [m]')
|
||||
# ax[t].set_ylabel('Northing [m]')
|
||||
|
||||
fig.subplots_adjust(right=0.8)
|
||||
cmap = mpl.cm.inferno
|
||||
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
|
||||
cax = plt.axes([0.85, 0.2, 0.05, 0.6])
|
||||
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
norm=norm,
|
||||
orientation='vertical')
|
||||
cb1.set_label('Salinity (PSU)')
|
||||
plt.show()
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/13539.101 L\'Ansecoy Bay, Mustique/06_Models/01_MIKE3/00_Figures/' +
|
||||
'/Plume_6_Plot_' + runLog['Run'][m][0:-20] +'_Alt' + str(a) + '.png', bbox_inches='tight')
|
||||
|
||||
# %% Map Plotting
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckex9vtri0o6619p55sl5qiyv/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
fontprops = fm.FontProperties(size=12)
|
||||
modelPlot = range(8, 9)
|
||||
|
||||
for m in modelPlot:
|
||||
vmax = 34.2
|
||||
vmin = 34
|
||||
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
|
||||
# Plot salinity
|
||||
# Select salinity data at last time step
|
||||
plotDat = ds_list[m]['Salinity'][-1, :]
|
||||
# Identify nan values
|
||||
plot_nan = np.isnan(plotDat)
|
||||
|
||||
# Add additional nan values to show bed
|
||||
plot_nan = plot_nan | (plotDat<34.05)
|
||||
|
||||
# Plot all selected values at a given later and not nan
|
||||
axDHI = dfs_list[m].plot(plotDat[(dfs_list[m].layer_ids == 1) & (~plot_nan)], plot_type='contourf',
|
||||
show_mesh=False, cmap='inferno', ax=axes,
|
||||
vmin=vmin, vmax=vmax, label='Salinity (PSU)',
|
||||
elements=dfs_list[m].element_ids[(dfs_list[m].layer_ids == 1) & (~plot_nan)])
|
||||
|
||||
|
||||
axes.set_xlim(left=696680.7, right=698252.7)
|
||||
axes.set_ylim(bottom=1425547.5, top=1426681.8)
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32620')
|
||||
# axes.title.set_text(runLog['Short Description'][m])
|
||||
# axes.titlesize = 'x-large'
|
||||
# fig.suptitle(runLog['Short Description'][m], fontsize=18)
|
||||
axes.set_xlabel('Easting [m]')
|
||||
axes.set_ylabel('Northing [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
# %% Animated map
|
||||
plt.rcParams['animation.ffmpeg_path'] = 'C:/Users/arey/Local/ffmpeg-2022-02-14-git-59c647bcf3-full_build/bin/ffmpeg.exe'
|
||||
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckex9vtri0o6619p55sl5qiyv/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
x, y, arrow_length = 0.93, 0.95, 0.12
|
||||
fontprops = fm.FontProperties(size=12)
|
||||
|
||||
# Set model to plot
|
||||
modelPlot = [15, 16, 17]
|
||||
m = 16
|
||||
|
||||
# m = 11
|
||||
vmax = 34.0
|
||||
vmin = 33.7
|
||||
cmap = 'inferno'
|
||||
|
||||
metadata = dict(title='Movie Test', artist='Matplotlib',
|
||||
comment='Movie support')
|
||||
writer = animation.FFMpegWriter(fps=2, metadata=metadata, codec='h264')
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
|
||||
writer.setup(fig, 'C:/Users/arey/files/Projects/Mustique/' +
|
||||
runLog['Run'][m][0:-20] + 'Intake.mp4')
|
||||
|
||||
# Animation function
|
||||
for i in np.arange(1, 384, 1): #384
|
||||
if m == 8 or m == 17:
|
||||
disPlot = 7
|
||||
elif m == 11 or m == 15:
|
||||
disPlot = 13
|
||||
elif m == 14 or m == 16:
|
||||
disPlot = 8
|
||||
|
||||
# fig, axes = plt.subplots(figsize=(8, 8))
|
||||
axes.cla()
|
||||
|
||||
# Plot salinity
|
||||
# Select salinity data at last time step
|
||||
plotDat = ds_list[m]['Salinity'][i, :]
|
||||
# Identify nan values
|
||||
plot_nan = np.isnan(plotDat)
|
||||
|
||||
# Add additional nan values to show bed
|
||||
plot_nan = plot_nan | (plotDat<33.725)
|
||||
|
||||
# Plot all selected values at a given later and not nan
|
||||
axDHI = dfs_list[m].plot(plotDat[(dfs_list[m].layer_ids == 0) & (~plot_nan)], plot_type='contourf',
|
||||
show_mesh=False, cmap='inferno', ax=axes, add_colorbar=False,
|
||||
vmin=vmin, vmax=vmax, label='Salinity (PSU)', levels=24,
|
||||
elements=dfs_list[m].element_ids[(dfs_list[m].layer_ids == 0) & (~plot_nan)])
|
||||
|
||||
axes.scatter(readPoints[disPlot, 0], readPoints[disPlot, 1], marker='^', color='r', s=20)
|
||||
# axes.set_xlim(left=696680.7, right=698252.7)
|
||||
# axes.set_ylim(bottom=1425547.5, top=1426681.8)
|
||||
axes.set_xlim(left=696508.3, right=698252.7)
|
||||
axes.set_ylim(bottom=1425267.0, top=1426681.8)
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32620')
|
||||
axes.set_xlabel('Easting [m]')
|
||||
axes.set_ylabel('Northing [m]')
|
||||
|
||||
axes.title.set_text(str(ds_list[m].time[i]))
|
||||
|
||||
if i == 1:
|
||||
fig.subplots_adjust(right=0.8)
|
||||
cmap = mpl.cm.inferno
|
||||
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
|
||||
cax = plt.axes([0.82, 0.25, 0.04, 0.5])
|
||||
cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
|
||||
norm=norm,
|
||||
orientation='vertical')
|
||||
cb1.set_label('Salinity (PSU)')
|
||||
|
||||
|
||||
writer.grab_frame()
|
||||
# plt.show()
|
||||
print(i)
|
||||
|
||||
writer.finish()
|
||||
|
||||
|
||||
# %% Plot Bathymetry
|
||||
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
# Convert to feet and ignore missing
|
||||
axDHI = dfs_list[m].plot(bed_plot[~bed_nan], plot_type='contourf', show_mesh=False, cmap='magma_r', ax=axes, levels=9,
|
||||
vmin=-80, vmax=0, label='Bed Elevation (ft)',
|
||||
elements=dfs_list[m].element_ids[~bed_nan])
|
||||
|
||||
axes.set_xlim(left=427800, right=431000)
|
||||
axes.set_ylim(bottom=4758000, top=4762500)
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32620')
|
||||
|
||||
fig.suptitle('Bed Elevation', fontsize=18)
|
||||
axes.set_xlabel('Easting [m]')
|
||||
axes.set_ylabel('Northing [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
# fig.savefig(
|
||||
# '//srv-mad3/Projects/13632.101 South Shore Breakwater/10_Reports&Pres/13632.101.R3 Ph I BOD/Support files/' +
|
||||
# 'Bed_Elevation.png', bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Read time series
|
||||
MIKEds_list = [None] * (max(modelPlot) + 1)
|
||||
MIKEdsT_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||
runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
|
||||
dfsIN_read = dfsIN.read()
|
||||
MIKEds_list[m] = dfsIN_read.to_dataframe()
|
||||
|
||||
dfsTIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||
runLog['Run Name'][m] + '.sw - Result Files', 'TransectPTS.dfs0').as_posix())
|
||||
dfsTIN_read = dfsTIN.read()
|
||||
MIKEdsT_list[m] = dfsTIN_read.to_dataframe()
|
||||
|
||||
# Cleanup unnecessary variables
|
||||
del dfsIN_read
|
||||
del dfsIN
|
||||
del dfsTIN_read
|
||||
del dfsTIN
|
||||
|
||||
# %% Read in Toe and Crest Shapefiles
|
||||
breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
|
||||
"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
|
||||
"20211211_Toe Extents (to Alexander)/"
|
||||
"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
|
||||
breakCrest = pd.read_csv(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
|
||||
header=None, names=['x', 'y', 'z'])
|
||||
breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
|
||||
geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
|
||||
breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
|
||||
|
||||
breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
|
||||
breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
|
||||
|
||||
# %% Merge with data
|
||||
breakPointsOut = [None] * (max(modelPlot) + 1)
|
||||
breakTimesOut = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
breakwaterPTS_times = None
|
||||
|
||||
for t in range(0, MIKEds_list[m].shape[0]):
|
||||
breakwaterPTS_merge = None
|
||||
breakwaterPTS_merge = breakwaterPTS_Crest
|
||||
|
||||
for i in range(0, MIKEds_list[m].shape[1]):
|
||||
paramName = MIKEds_list[m].columns.values[i][MIKEds_list[m].columns.values[i].find('"', 5, -1) + 3:]
|
||||
if paramName not in breakwaterPTS_merge.columns:
|
||||
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||
tmpFID = int(MIKEds_list[m].columns.values[i][1:MIKEds_list[m].columns.values[i].find('"', 1)])
|
||||
tmpIND = int(MIKEds_list[m].columns.values[i][5:MIKEds_list[m].columns.values[i].find('"', 5)])
|
||||
|
||||
breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
|
||||
(breakwaterPTS_merge.vertex_ind == tmpIND)),
|
||||
paramName] = MIKEds_list[m].iloc[t, i]
|
||||
|
||||
breakwaterPTS_merge['Time'] = MIKEds_list[m].index[t]
|
||||
|
||||
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||
|
||||
breakTimesOut[m] = breakwaterPTS_times
|
||||
|
||||
# %% Read in Breakwater Transect Points Sh
|
||||
breakwaterT = pd.read_csv("C:/Users/arey/files/Projects/SouthShore/Bathy/BreakTransectPTS_Names.csv", sep='\t')
|
||||
|
||||
breakwaterT_PTS = gp.GeoDataFrame(breakwaterT, geometry=gp.points_from_xy(breakwaterT.X, breakwaterT.Y),
|
||||
crs='EPSG:32616')
|
||||
|
||||
# %% Merge with data
|
||||
breakPoints_TOut = [None] * (max(modelPlot) + 1)
|
||||
breakTimes_TOut = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
breakwaterPTS_times = None
|
||||
|
||||
for t in range(0, MIKEdsT_list[m].shape[0]):
|
||||
breakwaterPTS_merge = None
|
||||
breakwaterPTS_merge = breakwaterT_PTS
|
||||
|
||||
for i in range(0, MIKEdsT_list[m].shape[1]):
|
||||
paramName = MIKEdsT_list[m].columns.values[i][MIKEdsT_list[m].columns.values[i].find(':', 5, -1) + 2:]
|
||||
if paramName not in breakwaterPTS_merge.columns:
|
||||
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||
tmpName = MIKEdsT_list[m].columns.values[i][0:MIKEdsT_list[m].columns.values[i].find(':', 1)]
|
||||
|
||||
breakwaterPTS_merge.loc[(breakwaterPTS_merge.Name == tmpName),
|
||||
paramName] = MIKEdsT_list[m].iloc[t, i]
|
||||
|
||||
breakwaterPTS_merge['Time'] = MIKEdsT_list[m].index[t]
|
||||
|
||||
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||
|
||||
breakTimes_TOut[m] = breakwaterPTS_times
|
||||
|
||||
# %% Format and save
|
||||
|
||||
for m in modelPlot:
|
||||
saveTmp = breakTimesOut[m].copy()
|
||||
saveTmp['X'] = saveTmp.geometry.x
|
||||
saveTmp['Y'] = saveTmp.geometry.y
|
||||
saveTmp.drop(['vertex_par', 'vertex_p_1', 'angle', 'geometry', 'index_right',
|
||||
'breakCrest_x', 'breakCrest_y', 'Length'], axis=1, inplace=True)
|
||||
saveTmp.sort_values(by=['FID', 'vertex_ind'], inplace=True, ignore_index=True)
|
||||
|
||||
# Reorder columns
|
||||
colNames = saveTmp.columns.values
|
||||
saveTmp = saveTmp[['X', 'Y', *colNames[0:-2]]]
|
||||
saveTmpT = saveTmp.transpose(copy=True)
|
||||
|
||||
# Transect points
|
||||
saveTmp2 = breakTimes_TOut[m].copy()
|
||||
saveTmp2.drop(['geometry'], axis=1, inplace=True)
|
||||
saveTmp2T = saveTmp2.transpose(copy=True)
|
||||
|
||||
saveTmpT.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||
'Toe' + runLog['Short Description'][m] + '.csv')
|
||||
|
||||
saveTmp2T.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||
'Transect' + runLog['Short Description'][m] + '.csv')
|
||||
|
||||
|
||||
# %% Setup path along pipe transect
|
||||
m = 12
|
||||
|
||||
df_time_rows = elm_df_list[m][0].Z.drop_duplicates()
|
||||
df_time_rowsidx = np.where(df_time_rows.index.values[0] == elm_df_list[m][0].index.values)[0]
|
||||
|
||||
tSteps = range(100, 2000)
|
||||
|
||||
|
||||
u_plotarray = np.zeros((2000, 5, 81))
|
||||
for layer in range(0, 5):
|
||||
for tStep in tSteps:
|
||||
u_plotarray[tStep, layer, :] = [x.iloc[df_time_rowsidx[layer] + tStep, 0] for x in elm_df_list[m]]
|
||||
|
||||
v_plotarray = np.zeros((2000, 5, 81))
|
||||
for layer in range(0, 5):
|
||||
for tStep in tSteps:
|
||||
v_plotarray[tStep, layer, :] = [x.iloc[df_time_rowsidx[layer] + tStep, 0] for x in elm_df_list[m]]
|
||||
|
||||
mag_plotarray = np.sqrt(u_plotarray ** 2 + v_plotarray ** 2)
|
||||
|
||||
max_plotarray = np.max(mag_plotarray, axis=0)
|
||||
|
||||
# Find layer depths along transect and interpolate along vertical profile
|
||||
layer_depth = np.zeros((5, 81))
|
||||
max_plot_interp = np.zeros((60, 81))
|
||||
interp_depths = np.arange(-6, 0, 0.1)
|
||||
|
||||
for chain in range(14, 81):
|
||||
layer_depth[:, chain] = elm_df_list[m][chain].Z.unique()
|
||||
max_plot_interp[:, chain] = np.interp(interp_depths, layer_depth[:, chain], max_plotarray[:, chain])
|
||||
# Set points below bed as nan
|
||||
max_plot_interp[interp_depths<layer_depth[0, chain], chain] = np.nan
|
||||
|
||||
# %% Plot along transect
|
||||
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
# plt.pcolor(np.arange(0, 67*5, 5), elm_df_list[m][0].Z.unique(), max_plotarray[:, 14:], cmap='jet')
|
||||
# for chain in range(15, 81):
|
||||
# plt.pcolor([(chain-15) * 5, (chain-14) * 5], layer_depth[:, chain-1:chain+1], max_plotarray[:, chain-1:chain+1], cmap='jet')
|
||||
|
||||
axes.set_ylabel('Depth (m)')
|
||||
axes.set_xlabel('Distance along discharge pipe (m). Existing Pipe at 170 m')
|
||||
|
||||
plt.pcolor(np.arange(0, 67*5, 5), interp_depths, max_plot_interp[:, 14:], cmap='jet')
|
||||
plt.colorbar(label="Maximum Current Speed (m/s)")
|
||||
plt.show()
|
||||
|
||||
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13539.101 L'Ansecoy Bay, Mustique", "06_Models", "01_MIKE3", "00_Figures",
|
||||
"Discharge Velocity.png")
|
||||
|
||||
fig.savefig(pth.as_posix(), bbox_inches='tight', dpi=300)
|
||||
|
|
@ -0,0 +1,560 @@
|
|||
#%% Project Setup
|
||||
|
||||
import os
|
||||
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
from scipy.signal import argrelextrema
|
||||
import numpy as np
|
||||
import math
|
||||
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.font_manager as fm
|
||||
import matplotlib as mpl
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
import cmocean.cm as cmo
|
||||
from shapely.geometry import Point, LineString
|
||||
|
||||
from xarray.backends import NetCDF4DataStore
|
||||
import xarray as xr
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from netCDF4 import num2date
|
||||
from metpy.units import units
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import cartopy.crs as ccrs
|
||||
import cartopy.feature as cfeature
|
||||
from metpy.plots import ctables
|
||||
|
||||
from siphon.catalog import TDSCatalog
|
||||
|
||||
import gsw as gsw
|
||||
|
||||
#%% Helper function for finding proper time variable
|
||||
|
||||
def find_time_var(var, time_basename='time'):
|
||||
for coord_name in var.coords:
|
||||
if coord_name.startswith(time_basename):
|
||||
return var.coords[coord_name]
|
||||
raise ValueError('No time variable found for ' + var.name)
|
||||
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
importPath = '//srv-ott3.baird.com/Projects/13711.101 Caribee (Indigo) Hotel, Barbados/03_Data/03_Field/20220613_EnvData'
|
||||
|
||||
siteName = 'Caribee Hotel'
|
||||
|
||||
timeLabel= 'June 13, 2022'
|
||||
|
||||
importFiles = os.listdir(importPath)
|
||||
|
||||
# Initialize import variables
|
||||
OBS_File = None
|
||||
GPS_File = None
|
||||
|
||||
for i in range(0, len(importFiles)):
|
||||
if '.csv' in importFiles[i] and 'Summary' not in importFiles[i] and 'export' not in importFiles[i]:
|
||||
OBS_File = importFiles[i]
|
||||
elif '.csv' in importFiles[i] and 'export' in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'CSV'
|
||||
elif '.xls' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'xls'
|
||||
|
||||
#%% Obs Import Data
|
||||
if OBS_File is not None:
|
||||
Obs_dat = pd.read_csv(importPath + '/' + OBS_File, skiprows=0, header=0)
|
||||
|
||||
# Drop rows with metadata
|
||||
Obs_dat =Obs_dat[Obs_dat['CH1:Temperature(degC)'].notna()]
|
||||
|
||||
# Set Time Zone for Sensor. Sometimes it is set to UTC, sometimes it is set to EST
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('America/Barbados')
|
||||
|
||||
# Set Index
|
||||
Obs_dat.set_index('DateTime', inplace=True)
|
||||
|
||||
#%% GPS Import Data
|
||||
if GPS_File is not None:
|
||||
if GPS_Type == 'CSV':
|
||||
GPS = pd.read_csv(importPath + '/' + GPS_File,
|
||||
header=None, names=['Index', 'Date1', 'Time1', 'Date2', 'Time2', 'Northing', 'North', 'Easting', 'East', 'Var1', 'Var2'])
|
||||
#convert GPS data to geodataframe
|
||||
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs="EPSG:4326"))
|
||||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str)).dt.tz_localize('America/Barbados')
|
||||
|
||||
|
||||
elif GPS_Type == 'xls':
|
||||
GPS = pd.read_excel(importPath + '/' + GPS_File, header=0)
|
||||
|
||||
#convert GPS data to geodataframe
|
||||
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(GPS.x, GPS.y, crs="EPSG:4326"))
|
||||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['time']).dt.tz_localize('UTC')
|
||||
|
||||
# Set Datetime as index
|
||||
GPS_gdf.set_index('DateTime', inplace=True)
|
||||
|
||||
# Sort by time
|
||||
GPS_gdf.sort_index(inplace=True)
|
||||
|
||||
# Convert to UTM
|
||||
GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
|
||||
|
||||
#%% Read in site shapefile
|
||||
siteShp = gp.read_file('//srv-ott3.baird.com/Projects/13711.101 Caribee (Indigo) Hotel, Barbados/03_Data/03_Field/20220613_EnvData\SiteBounds_Caribee.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
|
||||
#%% Merge GPS to RBR
|
||||
# Process RBR into datetime
|
||||
if OBS_File is not None:
|
||||
# Merge with GPS as dataframe
|
||||
Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
|
||||
left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
|
||||
Obs_geo = gp.GeoDataFrame(Obs_geo, geometry=Obs_geo.geometry, crs="EPSG:32621")
|
||||
|
||||
#%% Find and setup key plotting details
|
||||
# Shaded Water
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
# Sat water
|
||||
# mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckekcw3pn08am19qmqbhtq8sb/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
axXlim = (218050, 218345)
|
||||
axYlim = (1446635, 1446802)
|
||||
GFS_Lon = -59.5992624
|
||||
GFS_Lat = 13.0733928
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[3, 1])
|
||||
|
||||
|
||||
# Set min and max times using conductivity
|
||||
if Obs_geo['inArea'].any():
|
||||
# First and last times from area in shapefile
|
||||
minTime = Obs_geo[Obs_geo['inArea']==True].index[0]
|
||||
maxTime = Obs_geo[Obs_geo['inArea']==True].index[-1]
|
||||
else:
|
||||
# First and last times if no GPS data
|
||||
minTime = Obs_geo.index[20]
|
||||
maxTime = Obs_geo.index[-20]
|
||||
|
||||
metDate = minTime.tz_convert('UTC') - timedelta(
|
||||
hours = minTime.hour % 6,
|
||||
minutes=minTime.minute,
|
||||
seconds=minTime.second,
|
||||
microseconds=minTime.microsecond)
|
||||
|
||||
|
||||
#%% GFS Met Import
|
||||
var = ['Temperature_surface', 'Wind_speed_gust_surface',
|
||||
'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground']
|
||||
var_precp = ['Total_precipitation_surface_6_Hour_Accumulation']
|
||||
|
||||
temp_1d = []
|
||||
gust_1d = []
|
||||
wndu_1d = []
|
||||
wndv_1d = []
|
||||
prep_1d = []
|
||||
time_1d = []
|
||||
|
||||
# Set times to download
|
||||
|
||||
### USED TO BE -18/ +6 CHECK ###
|
||||
|
||||
startdate = metDate - timedelta(hours=30)
|
||||
enddate = metDate - timedelta(hours=6)
|
||||
date_list = pd.date_range(startdate, enddate, freq='6H')
|
||||
|
||||
# Loop through dates
|
||||
for date in date_list:
|
||||
# Base URL for 0.5 degree GFS data
|
||||
best_gfs = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/model-gfs-g4-anl-files/' +
|
||||
date.strftime('%Y%m') + '/' + date.strftime('%Y%m%d') + '/' + 'catalog.xml')
|
||||
|
||||
# Generate URLs for specific grib file
|
||||
best_ds = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_000.grb2']
|
||||
best_ds_precp = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_006.grb2']
|
||||
|
||||
# Format the query parameters
|
||||
ncss = best_ds.subset()
|
||||
query = ncss.query()
|
||||
|
||||
ncss_precp = best_ds_precp.subset()
|
||||
query_precp = ncss_precp.query()
|
||||
|
||||
# Extract data from specific point
|
||||
query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
|
||||
query.accept('netcdf')
|
||||
query.variables(var[0], var[1], var[2], var[3])
|
||||
query.vertical_level(10)
|
||||
|
||||
data = ncss.get_data(query)
|
||||
data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')
|
||||
|
||||
query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))
|
||||
query_precp.accept('netcdf')
|
||||
query_precp.variables(var_precp[0])
|
||||
|
||||
|
||||
data_precp = ncss_precp.get_data(query_precp)
|
||||
data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))
|
||||
|
||||
temp_3d = data[var[0]]
|
||||
gust_3d = data[var[1]]
|
||||
wndu_3d = data[var[2]]
|
||||
wndv_3d = data[var[3]]
|
||||
prep_3d = data_precp[var_precp[0]]
|
||||
|
||||
# Read the individual point (with units) and append to the list
|
||||
temp_1d.append(temp_3d.metpy.unit_array.squeeze())
|
||||
gust_1d.append(gust_3d.metpy.unit_array.squeeze())
|
||||
wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())
|
||||
wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())
|
||||
prep_1d.append(prep_3d.metpy.unit_array.squeeze())
|
||||
time_1d.append(find_time_var(temp_3d))
|
||||
|
||||
#%% Process Met Data
|
||||
# 24h Precipitation Total
|
||||
# Time weighted average of last two points for everything else
|
||||
|
||||
met_prep = prep_1d[0] + prep_1d[1] + prep_1d[2] + prep_1d[3]
|
||||
|
||||
timeWeight1 = minTime-metDate
|
||||
timeWeight2 = (metDate+timedelta(hours=6))-minTime
|
||||
|
||||
timeWeight1 = timeWeight1.seconds/21600
|
||||
timeWeight2 = timeWeight2.seconds/21600
|
||||
|
||||
met_gust = gust_1d[3] * timeWeight1 + gust_1d[4] * timeWeight2
|
||||
met_temp = temp_1d[3] * timeWeight1 + temp_1d[4] * timeWeight2
|
||||
met_wind = math.sqrt((wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2) ** 2 +
|
||||
(wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2) **2 )
|
||||
met_wdir = math.degrees(math.atan2(
|
||||
wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2,
|
||||
wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2)) % 360
|
||||
|
||||
#%% Add PSU and depth units
|
||||
Obs_geo['PSU'] = gsw.conversions.SP_from_C(Obs_geo['CH3:Conductivity(mS/cm)'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values)
|
||||
|
||||
|
||||
Obs_geo['Depth'] = (Obs_geo['CH0:Pressure(dbar)'].astype(float)*10000)/(gsw.rho(Obs_geo['PSU'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values) * 9.81)
|
||||
|
||||
|
||||
#%% Plot time series for Geo data
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(19, 25), constrained_layout=True)
|
||||
dataTable = np.zeros([14, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]
|
||||
|
||||
params = ['Depth', 'PSU', 'CH8:Oxygen_Saturation(%)', 'CH1:Temperature(degC)',
|
||||
'CH6:Turbidity(NTU)', 'CH2:Chlorophyll_a(ppb)']
|
||||
paramName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ sat [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]']
|
||||
|
||||
parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
|
||||
# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
|
||||
# paramMax = [1.0, 36.0, 34.0, 31.0, 20.0, 1.0]
|
||||
paramMin = [0.0, 31.0, 100, 26.0, 0, 0]
|
||||
paramMax = [1.0, 34.0, 130, 30.0, 600.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
dataTable[0, 0] = met_temp.m.item(0)-272.15
|
||||
dataTable[1, 0] = met_wind
|
||||
dataTable[2, 0] = met_wdir
|
||||
dataTable[3, 0] = met_prep.m.item(0)
|
||||
|
||||
ilocs_max = []
|
||||
ilocs_max_pts = []
|
||||
OBS_mask = []
|
||||
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name
|
||||
|
||||
# Create mask for RBR data based on time and parameter minimum
|
||||
OBS_mask.append(((Obs_geo.index>minTime) &
|
||||
(Obs_geo.index<maxTime) &
|
||||
(Obs_geo[param]>paramMin[paramIDX])))
|
||||
|
||||
OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
|
||||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label='1 Minute Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=6, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
# ilocs_max[-1] = len(OBS_smoothed.values)
|
||||
|
||||
ilocs_max_pts.append(OBS_smoothed.iloc[ilocs_max[paramIDX]].index.values)
|
||||
|
||||
# Add labels if GPS data is available
|
||||
if GPS_File is not None:
|
||||
# axes[paramIDX].scatter(OBS_smoothed.iloc[
|
||||
# ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
|
||||
# color='blue')
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 50), fontsize=30)
|
||||
else:
|
||||
ilocs_max.append(None)
|
||||
ilocs_max_pts.append(None)
|
||||
|
||||
dataTable[paramIDX+7, 0] = OBS_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+7, 1] = OBS_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+7, 2] = max(OBS_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+7, 3] = OBS_smoothed.max(skipna=True)
|
||||
|
||||
axes[paramIDX].set_ylabel(paramName[paramIDX])
|
||||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper left')
|
||||
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
# Formate Data Table
|
||||
dataTableFormat_mean = []
|
||||
dataTableFormat_maxmin = []
|
||||
for d in range(0, len(roundIDX)):
|
||||
dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
|
||||
if dataTable[d, 3] == 0:
|
||||
dataTableFormat_maxmin.append('--')
|
||||
else:
|
||||
dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
|
||||
|
||||
|
||||
dfOut = pd.DataFrame(dataTable[:, :])
|
||||
dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
|
||||
|
||||
# Change the column names
|
||||
dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
|
||||
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||
np.array(['Mean', 'Min / Max'])]
|
||||
# Change the row indexes
|
||||
dfOut.iloc[13, 0] = minTime.strftime('%B %#d, %Y')
|
||||
rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',
|
||||
'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ saturation [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]', 'Observation Date']
|
||||
|
||||
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
|
||||
fig.suptitle(siteName + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
# outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPath
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
dfOut.to_excel(outPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
dfOutFormat.to_excel(outPath + '/Summary_StatsFormat_' + siteName + '.xlsx')
|
||||
|
||||
dfOut.to_csv(outPath + '/Summary_Stats_' + siteName + '.csv')
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
|
||||
#%% Plot Maps
|
||||
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(25, 19), constrained_layout=True)
|
||||
ax = axes.flat
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||
plt.rcParams.update({'font.size': 22})
|
||||
|
||||
axXlimTT = (Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
|
||||
axYlimTT = (Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.y.max()+200)
|
||||
|
||||
plt.setp(axes, xlim=axXlim, ylim=axYlim)
|
||||
# plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)
|
||||
|
||||
# Plot the RBR observations
|
||||
# Salinity
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX], vmin=paramMin[paramIDX], vmax=paramMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': paramName[paramIDX]},
|
||||
cmap=parmCmap[paramIDX], markersize=20)
|
||||
|
||||
|
||||
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
||||
|
||||
# Add time labels
|
||||
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.x,
|
||||
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
||||
|
||||
if (not Obs_geo.geometry.isnull().all()) &\
|
||||
(GPS_File is not None) & (param == 'CH6:Turbidity(NTU)'):
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
ax[paramIDX].annotate(str(a + 1), (Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.x,
|
||||
Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
||||
|
||||
ax[paramIDX].set_title(paramName[paramIDX])
|
||||
# ax[paramIDX].set_ylabel('UTM 21N [m]')
|
||||
# ax[paramIDX].set_xlabel('UTM 21N [m]')
|
||||
ax[paramIDX].locator_params(axis='y', nbins=3)
|
||||
ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')
|
||||
|
||||
ax[paramIDX].get_xaxis().set_ticks([])
|
||||
ax[paramIDX].get_yaxis().set_ticks([])
|
||||
|
||||
#Add scale-bar
|
||||
scalebar = AnchoredSizeBar(ax[paramIDX].transData,
|
||||
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
||||
ax[paramIDX].add_artist(scalebar)
|
||||
ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
||||
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
||||
ha='center', va='center', fontsize=35,
|
||||
xycoords=ax[paramIDX].transAxes)
|
||||
|
||||
fig.suptitle(siteName + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
#outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPath
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabel + '_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabel + '_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
#%% Summary Sheet
|
||||
plotIDXsLoop = []
|
||||
# plotIDXsLoop.append([0, 3, 6, 9])
|
||||
# plotIDXsLoop.append([1, 4, 7, 10])
|
||||
# plotIDXsLoop.append([2, 5, 8, 11])
|
||||
# plotIDXsLoop.append([np.arange(0, 21*3, 3)])
|
||||
# plotIDXsLoop.append([np.arange(1, 21*3, 3)])
|
||||
# plotIDXsLoop.append([np.arange(2, 21*3, 3)])
|
||||
|
||||
for i in range(0, 3):
|
||||
summTable = None
|
||||
# plotIDXs = plotIDXsLoop[i]
|
||||
plotIDXs = np.arange(i, 21, 3)
|
||||
|
||||
for s, plotIDX in enumerate(plotIDXs):
|
||||
## Define master import path
|
||||
importPath = importPaths[plotIDX]
|
||||
siteName = siteNames[plotIDX]
|
||||
|
||||
obsStatsIN = pd.read_excel(importPath + '/Summary_StatsFormat_' + siteName + '.xlsx', header=[0, 1], index_col=0)
|
||||
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||
obsStatsIN.rename({'Turbidity [NTU]': 'Turbidity [FTU]'}, inplace=True)
|
||||
if plotIDX > 11:
|
||||
obsStatsIN.rename({'Chl-a [ppb]': 'Chl-a [ug/l]'}, inplace=True)
|
||||
|
||||
if s == 0:
|
||||
summTable = obsStatsIN
|
||||
else:
|
||||
summTable = summTable.join(obsStatsIN)
|
||||
|
||||
# Remove -999 with nan
|
||||
summTable.replace(-999, np.nan, inplace=True)
|
||||
|
||||
summTable.to_excel('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/Summary_StatsMerge_' + siteName + '.xlsx')
|
||||
|
||||
|
||||
#%% Summary Plot
|
||||
|
||||
plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a. [ug/L]']
|
||||
|
||||
|
||||
for i in range(0, 3):
|
||||
summTable = None
|
||||
plotIDXs = np.arange(i, 21, 3)
|
||||
|
||||
plotDates = []
|
||||
plotTable = np.empty([10, len(plotIDXs)])
|
||||
|
||||
for s, plotIDX in enumerate(plotIDXs):
|
||||
## Define master import path
|
||||
importPath = importPaths[plotIDX]
|
||||
siteName = siteNames[plotIDX]
|
||||
|
||||
obsStatsIN = pd.read_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||
plotTable[0:3, s] = obsStatsIN.iloc[0:3, 1]
|
||||
plotTable[8, s] = obsStatsIN.iloc[7, 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[8, 1], '%B %d, %Y'))
|
||||
elif plotIDX<12:
|
||||
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 13], 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[14, 1], '%B %d, %Y'))
|
||||
else:
|
||||
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 12], 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[13, 1], '%B %d, %Y'))
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(19, 25), sharex=True)
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
# Repalce zero with nan
|
||||
plotTable[plotTable < 0.00001] = np.nan
|
||||
|
||||
for v in range(0, 10):
|
||||
ax[v].scatter(plotDates, plotTable[v, :], 250)
|
||||
ax[v].set_ylabel(plotvars[v])
|
||||
|
||||
fig.suptitle(siteName, fontsize=35)
|
||||
plt.gcf().autofmt_xdate()
|
||||
plt.gcf().align_ylabels()
|
||||
plt.show()
|
||||
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
|
@ -0,0 +1,685 @@
|
|||
#%% Project Setup
|
||||
|
||||
import os
|
||||
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
from scipy.signal import argrelextrema
|
||||
import numpy as np
|
||||
import math
|
||||
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.font_manager as fm
|
||||
import matplotlib as mpl
|
||||
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
import cmocean.cm as cmo
|
||||
from shapely.geometry import Point, LineString
|
||||
|
||||
from xarray.backends import NetCDF4DataStore
|
||||
import xarray as xr
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from netCDF4 import num2date
|
||||
from metpy.units import units
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import cartopy.crs as ccrs
|
||||
import cartopy.feature as cfeature
|
||||
from metpy.plots import ctables
|
||||
|
||||
from siphon.catalog import TDSCatalog
|
||||
|
||||
import gsw as gsw
|
||||
|
||||
#%% Helper function for finding proper time variable
|
||||
|
||||
def find_time_var(var, time_basename='time'):
|
||||
for coord_name in var.coords:
|
||||
if coord_name.startswith(time_basename):
|
||||
return var.coords[coord_name]
|
||||
raise ValueError('No time variable found for ' + var.name)
|
||||
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
importPaths = ['//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211006 WQ Pre_Construction/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211006 WQ Pre_Construction/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211006 WQ Pre_Construction/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211021 WQ During Construction/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211021 WQ During Construction/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211021 WQ During Construction/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211104 WQ Post Construction Monitoring/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211104 WQ Post Construction Monitoring/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211104 WQ Post Construction Monitoring/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211125 WQ Post Construction Monitoring/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211125 WQ Post Construction Monitoring/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20211125 WQ Post Construction Monitoring/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220208 WQ February/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220208 WQ February/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220208 WQ February/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220318 WQ sampling by LNW with WiMo/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220318 WQ sampling by LNW with WiMo/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220318 WQ sampling by LNW with WiMo/Old Queens Fort',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220414 WQ sampling/Great House',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220414 WQ sampling/Greensleeves',
|
||||
'//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/20220414 WQ sampling/Old Queens Fort']
|
||||
|
||||
siteNames = ['Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort',
|
||||
'Great House',
|
||||
'Greensleeves',
|
||||
'Old Queens Fort']
|
||||
|
||||
timeLabels= ['Before Construction',
|
||||
'Before Construction',
|
||||
'Before Construction',
|
||||
'During Construction',
|
||||
'During Construction',
|
||||
'During Construction',
|
||||
'After Construction',
|
||||
'After Construction',
|
||||
'After Construction',
|
||||
'Monitoring',
|
||||
'Monitoring',
|
||||
'Monitoring',
|
||||
'February',
|
||||
'February',
|
||||
'February',
|
||||
'March',
|
||||
'March',
|
||||
'March',
|
||||
'April',
|
||||
'April',
|
||||
'April']
|
||||
|
||||
wave_bts_file = [
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/holetown_mid_15m',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/holetown_mid_15m',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/spawnee_mid_27m.bts',
|
||||
'T:/Alexander/WestCoast/Barbados Nowcast 2021-09-15 to 2021-11-15/holetown_mid_15m',
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None]
|
||||
|
||||
|
||||
|
||||
for s in range(12, 21):
|
||||
## Define master import path
|
||||
importPath = importPaths[s]
|
||||
siteName = siteNames[s]
|
||||
timeLabel = timeLabels[s]
|
||||
importFiles = os.listdir(importPath)
|
||||
|
||||
# Initialize import variables
|
||||
OBS_File = None
|
||||
GPS_File = None
|
||||
|
||||
for i in range(0, len(importFiles)):
|
||||
if '.csv' in importFiles[i] and 'Summary' not in importFiles[i] and 'export' not in importFiles[i]:
|
||||
OBS_File = importFiles[i]
|
||||
elif '.csv' in importFiles[i] and 'export' in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'CSV'
|
||||
elif '.xls' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
GPS_Type = 'xls'
|
||||
|
||||
#%% Obs Import Data
|
||||
if OBS_File is not None:
|
||||
Obs_dat = pd.read_csv(importPath + '/' + OBS_File, skiprows=0, header=0)
|
||||
|
||||
# Drop rows with metadata
|
||||
Obs_dat =Obs_dat[Obs_dat['CH1:Temperature(degC)'].notna()]
|
||||
|
||||
# Set Time Zone for Sensor. Sometimes it is set to UTC, sometimes it is set to EST
|
||||
if s < 15:
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('America/Barbados').dt.tz_convert('UTC')
|
||||
else:
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('UTC')
|
||||
|
||||
# Set Index
|
||||
Obs_dat.set_index('DateTime', inplace=True)
|
||||
|
||||
#%% GPS Import Data
|
||||
if GPS_File is not None:
|
||||
if GPS_Type == 'CSV':
|
||||
GPS = pd.read_csv(importPath + '/' + GPS_File,
|
||||
header=None, names=['Index', 'Date1', 'Time1', 'Date2', 'Time2', 'Northing', 'North', 'Easting', 'East', 'Var1', 'Var2'])
|
||||
#convert GPS data to geodataframe
|
||||
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs="EPSG:4326"))
|
||||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))
|
||||
|
||||
|
||||
elif GPS_Type == 'xls':
|
||||
GPS = pd.read_excel(importPath + '/' + GPS_File, header=0)
|
||||
|
||||
#convert GPS data to geodataframe
|
||||
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(GPS.x, GPS.y, crs="EPSG:4326"))
|
||||
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['time']).dt.tz_localize('UTC')
|
||||
|
||||
# Set Datetime as index
|
||||
GPS_gdf.set_index('DateTime', inplace=True)
|
||||
|
||||
# Sort by time
|
||||
GPS_gdf.sort_index(inplace=True)
|
||||
|
||||
# Convert to UTM
|
||||
GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
|
||||
|
||||
#%% Read in site shapefile
|
||||
siteShp = gp.read_file('C:/Users/arey/files/Projects/West Coast/SitePolygons.shp')
|
||||
siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
|
||||
|
||||
|
||||
#%% Merge GPS to RBR
|
||||
# Process RBR into datetime
|
||||
if OBS_File is not None:
|
||||
# Merge with GPS as dataframe
|
||||
Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
|
||||
left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
|
||||
Obs_geo = gp.GeoDataFrame(Obs_geo, geometry=Obs_geo.geometry, crs="EPSG:32621")
|
||||
|
||||
#%% Find and setup key plotting details
|
||||
# Shaded Water
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
# Sat water
|
||||
# mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckekcw3pn08am19qmqbhtq8sb/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
if siteName == 'Great House':
|
||||
axXlim = (213210.7529575412, 213562.64172686986)
|
||||
axYlim = (1464769.2243017585, 1465135.2219089477)
|
||||
GFS_Lon = -59.6441
|
||||
GFS_Lat = 13.2372
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[2, 1])
|
||||
elif siteName == 'Greensleeves':
|
||||
axXlim = (213269.99233348924, 213648.1643157148)
|
||||
# axYlim = (1463378.1020314451, 1463843.5442048472)
|
||||
axYlim = (1463378.1020314451, 1463950.5442048472)
|
||||
GFS_Lon = -59.6428
|
||||
GFS_Lat = 13.2289
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[1, 1])
|
||||
elif siteName == 'Old Queens Fort':
|
||||
axXlim = (213368.59866770002, 213745.6997016811)
|
||||
axYlim = (1460192.707288096, 1460672.371780407)
|
||||
GFS_Lon = -59.6419
|
||||
GFS_Lat = 13.1960
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[0, 1])
|
||||
|
||||
|
||||
# Set min and max times using conductivity
|
||||
if Obs_geo['inArea'].any():
|
||||
# First and last times from area in shapefile
|
||||
minTime = Obs_geo[Obs_geo['inArea']==True].index[0]
|
||||
maxTime = Obs_geo[Obs_geo['inArea']==True].index[-1]
|
||||
else:
|
||||
# First and last times if no GPS data
|
||||
minTime = Obs_geo.index[20]
|
||||
maxTime = Obs_geo.index[-20]
|
||||
|
||||
metDate = minTime - timedelta(
|
||||
hours = minTime.hour % 6,
|
||||
minutes=minTime.minute,
|
||||
seconds=minTime.second,
|
||||
microseconds=minTime.microsecond)
|
||||
|
||||
|
||||
#%% GFS Met Import
|
||||
var = ['Temperature_surface', 'Wind_speed_gust_surface',
|
||||
'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground']
|
||||
var_precp = ['Total_precipitation_surface_6_Hour_Accumulation']
|
||||
|
||||
temp_1d = []
|
||||
gust_1d = []
|
||||
wndu_1d = []
|
||||
wndv_1d = []
|
||||
prep_1d = []
|
||||
time_1d = []
|
||||
|
||||
# Set times to download
|
||||
startdate = metDate - timedelta(hours=18)
|
||||
enddate = metDate + timedelta(hours=6)
|
||||
date_list = pd.date_range(startdate, enddate, freq='6H')
|
||||
|
||||
# Loop through dates
|
||||
for date in date_list:
|
||||
# Base URL for 0.5 degree GFS data
|
||||
best_gfs = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/model-gfs-g4-anl-files/' +
|
||||
date.strftime('%Y%m') + '/' + date.strftime('%Y%m%d') + '/' + 'catalog.xml')
|
||||
|
||||
# Generate URLs for specific grib file
|
||||
best_ds = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_000.grb2']
|
||||
best_ds_precp = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_006.grb2']
|
||||
|
||||
# Format the query parameters
|
||||
ncss = best_ds.subset()
|
||||
query = ncss.query()
|
||||
|
||||
ncss_precp = best_ds_precp.subset()
|
||||
query_precp = ncss_precp.query()
|
||||
|
||||
# Extract data from specific point
|
||||
query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
|
||||
query.accept('netcdf')
|
||||
query.variables(var[0], var[1], var[2], var[3])
|
||||
query.vertical_level(10)
|
||||
|
||||
data = ncss.get_data(query)
|
||||
data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')
|
||||
|
||||
query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))
|
||||
query_precp.accept('netcdf')
|
||||
query_precp.variables(var_precp[0])
|
||||
|
||||
|
||||
data_precp = ncss_precp.get_data(query_precp)
|
||||
data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))
|
||||
|
||||
temp_3d = data[var[0]]
|
||||
gust_3d = data[var[1]]
|
||||
wndu_3d = data[var[2]]
|
||||
wndv_3d = data[var[3]]
|
||||
prep_3d = data_precp[var_precp[0]]
|
||||
|
||||
# Read the individual point (with units) and append to the list
|
||||
temp_1d.append(temp_3d.metpy.unit_array.squeeze())
|
||||
gust_1d.append(gust_3d.metpy.unit_array.squeeze())
|
||||
wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())
|
||||
wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())
|
||||
prep_1d.append(prep_3d.metpy.unit_array.squeeze())
|
||||
time_1d.append(find_time_var(temp_3d))
|
||||
|
||||
#%% Process Met Data
|
||||
# 24h Precipitation Total
|
||||
# Time weighted average of last two points for everything else
|
||||
|
||||
met_prep = prep_1d[0] + prep_1d[1] + prep_1d[2] + prep_1d[3]
|
||||
|
||||
timeWeight1 = minTime-metDate
|
||||
timeWeight2 = (metDate+timedelta(hours=6))-minTime
|
||||
|
||||
timeWeight1 = timeWeight1.seconds/21600
|
||||
timeWeight2 = timeWeight2.seconds/21600
|
||||
|
||||
met_gust = gust_1d[3] * timeWeight1 + gust_1d[4] * timeWeight2
|
||||
met_temp = temp_1d[3] * timeWeight1 + temp_1d[4] * timeWeight2
|
||||
met_wind = math.sqrt((wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2) ** 2 +
|
||||
(wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2) **2 )
|
||||
met_wdir = math.degrees(math.atan2(
|
||||
wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2,
|
||||
wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2)) % 360
|
||||
|
||||
#%% Read in wave conditions from BTS file
|
||||
if wave_bts_file[s] is not None:
|
||||
if siteName == 'Great House':
|
||||
wave_bts = pd.read_csv(wave_bts_file[s],
|
||||
names=['date', 'time', 'HM0', 'TP', 'TM', 'MWD', 'DPK', 'HSWL', 'TSWL', 'DPSWL', 'HSEA', 'TSEA', 'DPSEA'],
|
||||
header=0, skiprows=22, delim_whitespace=True)
|
||||
wave_bts['datetime'] = pd.to_datetime(wave_bts['date'] + ' ' + wave_bts['time'])
|
||||
wave_bts.set_index('datetime', inplace=True)
|
||||
|
||||
|
||||
met_hmo = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].HM0
|
||||
met_tp = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].TP
|
||||
met_mwd = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].MWD
|
||||
else:
|
||||
met_hmo = -999
|
||||
met_tp = -999
|
||||
met_mwd = -999
|
||||
|
||||
#%% Add PSU and depth units
|
||||
Obs_geo['PSU'] = gsw.conversions.SP_from_C(Obs_geo['CH3:Conductivity(mS/cm)'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values)
|
||||
|
||||
|
||||
Obs_geo['Depth'] = (Obs_geo['CH0:Pressure(dbar)'].astype(float)*10000)/(gsw.rho(Obs_geo['PSU'].values,
|
||||
Obs_geo['CH1:Temperature(degC)'].values,
|
||||
Obs_geo['CH0:Pressure(dbar)'].values) * 9.81)
|
||||
|
||||
|
||||
#%% Plot time series for Geo data
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(19, 25), constrained_layout=True)
|
||||
dataTable = np.zeros([14, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]
|
||||
|
||||
params = ['Depth', 'PSU', 'CH8:Oxygen_Saturation(%)', 'CH1:Temperature(degC)',
|
||||
'CH6:Turbidity(NTU)', 'CH2:Chlorophyll_a(ppb)']
|
||||
paramName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ sat [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]']
|
||||
|
||||
parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
|
||||
# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
|
||||
# paramMax = [1.0, 36.0, 34.0, 31.0, 20.0, 1.0]
|
||||
paramMin = [0.0, 32.0, 100, 26.0, 0, 0]
|
||||
paramMax = [1.0, 36.0, 130, 30.0, 75.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
|
||||
dataTable[0, 0] = met_temp.m.item(0)-272.15
|
||||
dataTable[1, 0] = met_wind
|
||||
dataTable[2, 0] = met_wdir
|
||||
dataTable[3, 0] = met_prep.m.item(0)
|
||||
dataTable[4, 0] = met_hmo
|
||||
dataTable[5, 0] = met_tp
|
||||
dataTable[6, 0] = met_mwd
|
||||
|
||||
ilocs_max = []
|
||||
ilocs_max_pts = []
|
||||
OBS_mask = []
|
||||
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name
|
||||
|
||||
# Create mask for RBR data based on time and parameter minimum
|
||||
OBS_mask.append(((Obs_geo.index>minTime) &
|
||||
(Obs_geo.index<maxTime) &
|
||||
(Obs_geo[param]>paramMin[paramIDX])))
|
||||
|
||||
OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
|
||||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label='1 Minute Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'CH6:Turbidity(NTU)':
|
||||
ilocs_max.append(argrelextrema(OBS_smoothed.values,
|
||||
np.greater_equal, order=6, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
# ilocs_max[-1] = len(OBS_smoothed.values)
|
||||
|
||||
ilocs_max_pts.append(OBS_smoothed.iloc[ilocs_max[paramIDX]].index.values)
|
||||
|
||||
# Add labels if GPS data is available
|
||||
if GPS_File is not None:
|
||||
# axes[paramIDX].scatter(OBS_smoothed.iloc[
|
||||
# ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
|
||||
# color='blue')
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 12), fontsize=30)
|
||||
else:
|
||||
ilocs_max.append(None)
|
||||
ilocs_max_pts.append(None)
|
||||
|
||||
dataTable[paramIDX+7, 0] = OBS_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+7, 1] = OBS_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+7, 2] = max(OBS_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+7, 3] = OBS_smoothed.max(skipna=True)
|
||||
|
||||
axes[paramIDX].set_ylabel(paramName[paramIDX])
|
||||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper left')
|
||||
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
# Formate Data Table
|
||||
dataTableFormat_mean = []
|
||||
dataTableFormat_maxmin = []
|
||||
for d in range(0, len(roundIDX)):
|
||||
dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
|
||||
if dataTable[d, 3] == 0:
|
||||
dataTableFormat_maxmin.append('--')
|
||||
else:
|
||||
dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
|
||||
|
||||
|
||||
dfOut = pd.DataFrame(dataTable[:, :])
|
||||
dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
|
||||
|
||||
# Change the column names
|
||||
dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
|
||||
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||
np.array(['Mean', 'Min / Max'])]
|
||||
# Change the row indexes
|
||||
dfOut.iloc[13, 0] = minTime.strftime('%B %#d, %Y')
|
||||
rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',
|
||||
'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ saturation [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]', 'Observation Date']
|
||||
|
||||
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
# outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
dfOut.to_excel(outPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
dfOutFormat.to_excel(outPath + '/Summary_StatsFormat_' + siteName + '.xlsx')
|
||||
|
||||
dfOut.to_csv(outPath + '/Summary_Stats_' + siteName + '.csv')
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
|
||||
#%% Plot Maps
|
||||
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(19, 25), constrained_layout=True)
|
||||
ax = axes.flat
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
# fig.tight_layout(pad=1.05)
|
||||
|
||||
fontprops = fm.FontProperties(size=25)
|
||||
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||
plt.rcParams.update({'font.size': 22})
|
||||
|
||||
axXlimTT = (Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
|
||||
axYlimTT = (Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
||||
Obs_geo.loc[minTime:maxTime].geometry.y.max()+100)
|
||||
|
||||
plt.setp(axes, xlim=axXlim, ylim=axYlim)
|
||||
# plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)
|
||||
|
||||
# Plot the RBR observations
|
||||
# Salinity
|
||||
for paramIDX, param in enumerate(params):
|
||||
Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX], vmin=paramMin[paramIDX], vmax=paramMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': paramName[paramIDX]},
|
||||
cmap=parmCmap[paramIDX], markersize=20)
|
||||
|
||||
|
||||
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
||||
|
||||
# Add time labels
|
||||
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.x,
|
||||
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
||||
|
||||
if (not Obs_geo.geometry.isnull().all()) &\
|
||||
(GPS_File is not None) & (param == 'CH6:Turbidity(NTU)'):
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
ax[paramIDX].annotate(str(a + 1), (Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.x,
|
||||
Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
||||
|
||||
ax[paramIDX].set_title(paramName[paramIDX])
|
||||
# ax[paramIDX].set_ylabel('UTM 21N [m]')
|
||||
# ax[paramIDX].set_xlabel('UTM 21N [m]')
|
||||
ax[paramIDX].locator_params(axis='y', nbins=3)
|
||||
ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')
|
||||
|
||||
ax[paramIDX].get_xaxis().set_ticks([])
|
||||
ax[paramIDX].get_yaxis().set_ticks([])
|
||||
|
||||
#Add scale-bar
|
||||
scalebar = AnchoredSizeBar(ax[paramIDX].transData,
|
||||
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
||||
ax[paramIDX].add_artist(scalebar)
|
||||
ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
||||
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
||||
ha='center', va='center', fontsize=35,
|
||||
xycoords=ax[paramIDX].transAxes)
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%b %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
#outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
||||
outPath = importPaths[s]
|
||||
|
||||
if not os.path.exists(outPath):
|
||||
os.mkdir(outPath)
|
||||
|
||||
if not os.path.exists(outPath + '/Figures'):
|
||||
os.mkdir(outPath + '/Figures')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
|
||||
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabels[s] + '_' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
|
||||
#%% Summary Sheet
|
||||
plotIDXsLoop = []
|
||||
# plotIDXsLoop.append([0, 3, 6, 9])
|
||||
# plotIDXsLoop.append([1, 4, 7, 10])
|
||||
# plotIDXsLoop.append([2, 5, 8, 11])
|
||||
# plotIDXsLoop.append([np.arange(0, 21*3, 3)])
|
||||
# plotIDXsLoop.append([np.arange(1, 21*3, 3)])
|
||||
# plotIDXsLoop.append([np.arange(2, 21*3, 3)])
|
||||
|
||||
for i in range(0, 3):
|
||||
summTable = None
|
||||
# plotIDXs = plotIDXsLoop[i]
|
||||
plotIDXs = np.arange(i, 21, 3)
|
||||
|
||||
for s, plotIDX in enumerate(plotIDXs):
|
||||
## Define master import path
|
||||
importPath = importPaths[plotIDX]
|
||||
siteName = siteNames[plotIDX]
|
||||
|
||||
obsStatsIN = pd.read_excel(importPath + '/Summary_StatsFormat_' + siteName + '.xlsx', header=[0, 1], index_col=0)
|
||||
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||
obsStatsIN.rename({'Turbidity [NTU]': 'Turbidity [FTU]'}, inplace=True)
|
||||
if plotIDX > 11:
|
||||
obsStatsIN.rename({'Chl-a [ppb]': 'Chl-a [ug/l]'}, inplace=True)
|
||||
|
||||
if s == 0:
|
||||
summTable = obsStatsIN
|
||||
else:
|
||||
summTable = summTable.join(obsStatsIN)
|
||||
|
||||
# Remove -999 with nan
|
||||
summTable.replace(-999, np.nan, inplace=True)
|
||||
|
||||
summTable.to_excel('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/Summary_StatsMerge_' + siteName + '.xlsx')
|
||||
|
||||
|
||||
#%% Summary Plot
|
||||
|
||||
plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||
'Significant Wave Height [m]', 'Salinity [PSU]',
|
||||
'Dissolved O₂ [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a. [ug/L]']
|
||||
|
||||
|
||||
for i in range(0, 3):
|
||||
summTable = None
|
||||
plotIDXs = np.arange(i, 21, 3)
|
||||
|
||||
plotDates = []
|
||||
plotTable = np.empty([10, len(plotIDXs)])
|
||||
|
||||
for s, plotIDX in enumerate(plotIDXs):
|
||||
## Define master import path
|
||||
importPath = importPaths[plotIDX]
|
||||
siteName = siteNames[plotIDX]
|
||||
|
||||
obsStatsIN = pd.read_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||
plotTable[0:3, s] = obsStatsIN.iloc[0:3, 1]
|
||||
plotTable[8, s] = obsStatsIN.iloc[7, 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[8, 1], '%B %d, %Y'))
|
||||
elif plotIDX<12:
|
||||
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 13], 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[14, 1], '%B %d, %Y'))
|
||||
else:
|
||||
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 12], 1]
|
||||
plotDates.append(datetime.strptime(obsStatsIN.iloc[13, 1], '%B %d, %Y'))
|
||||
|
||||
|
||||
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(19, 25), sharex=True)
|
||||
fig.patch.set_facecolor('white')
|
||||
fig.tight_layout(pad=3)
|
||||
ax = axes.flat
|
||||
|
||||
# Repalce zero with nan
|
||||
plotTable[plotTable < 0.00001] = np.nan
|
||||
|
||||
for v in range(0, 10):
|
||||
ax[v].scatter(plotDates, plotTable[v, :], 250)
|
||||
ax[v].set_ylabel(plotvars[v])
|
||||
|
||||
fig.suptitle(siteName, fontsize=35)
|
||||
plt.gcf().autofmt_xdate()
|
||||
plt.gcf().align_ylabels()
|
||||
plt.show()
|
||||
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.png',
|
||||
bbox_inches='tight', dpi=500)
|
||||
Loading…
Reference in New Issue