586 lines
23 KiB
Python
586 lines
23 KiB
Python
## Plotting Mike21 SW results for SouthShore
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# Author: AJMR
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# December 22, 2021
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# %% Setup Project
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from mikeio import Dfsu, Mesh, Dfs2, Dfs0, Dfs1
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import pandas as pd
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import pathlib as pl
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import numpy as np
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import geopandas as gp
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import datetime as datetime
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import matplotlib.pyplot as plt
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import contextily as ctx
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import matplotlib.font_manager as fm
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# %% Read Model Log
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pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13539.101 L'Ansecoy Bay, Mustique", "06_Models",
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"Model Log Mustique.xlsx")
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runLog = pd.read_excel(pth.as_posix(), "ModelLog")
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# %% Read Model Results
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modelPlot = range(10, 11)
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curSpeed_list = [None] * (max(modelPlot) + 1)
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curElm_list = [None] * (max(modelPlot) + 1)
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curU_list = [None] * (max(modelPlot) + 1)
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curV_list = [None] * (max(modelPlot) + 1)
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curW_list = [None] * (max(modelPlot) + 1)
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dfs_list = [None] * (max(modelPlot) + 1)
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dfs2d_list = [None] * (max(modelPlot) + 1)
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ds_list = [None] * (max(modelPlot) + 1)
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wl_list = [None] * (max(modelPlot) + 1)
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wd_list = [None] * (max(modelPlot) + 1)
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bed_list = [None] * (max(modelPlot) + 1)
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z_list = [None] * (max(modelPlot) + 1)
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z_profile_list = [None] * (max(modelPlot) + 1)
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elm_list = [None] * (max(modelPlot) + 1)
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elm_df_list = [None] * (max(modelPlot) + 1)
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elm2d_list = [None] * (max(modelPlot) + 1)
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elm2d_df_list = [None] * (max(modelPlot) + 1)
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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_RevB.csv",
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delimiter=",")
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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_RevE.csv",
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delimiter=",").iloc[0:, 2:-2].values
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# readPoints = np.zeros((14, 2))
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# readPoints[0, :] = [693000, 1425500]
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# readPoints[1, :] = [697000, 1429500]
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# readPoints[2, :] = [697000, 1421500]
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# readPoints[3, :] = [701000, 1425500]
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#
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# # readPoints[4, :] = [697576.96, 1425767.10] # RBR NEAR
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# # readPoints[5, :] = [697760.89, 1426109.73] # RBR FAR
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#
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# # From Mathew
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# readPoints[4, :] = [697858.00, 1426095.00] # RBR NEAR
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# readPoints[5, :] = [697613.00, 1426509.00] # RBR FAR
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#
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# # Location Uncertainty
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# # 500 -x
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# readPoints[6, :] = [697858.00-100, 1426095.00-0] # RBR NEAR
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# readPoints[7, :] = [697613.00-150, 1426509.00+150] # RBR FAR
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# # 500 +x
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# readPoints[8, :] = [697858.00+100, 1426095.00-0] # RBR NEAR
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# readPoints[9, :] = [697613.00-50, 1426509.00+50] # RBR FAR
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# # 500 -x
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# readPoints[10, :] = [697858.00+150, 1426095.00+150] # RBR NEAR
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# readPoints[11, :] = [697613.00+150, 1426509.00+150] # RBR FAR
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# # 500 -x
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# readPoints[12, :] = [697858.00-150, 1426095.00+150] # RBR NEAR
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# readPoints[13, :] = [697613.00-150, 1426509.00+150] # RBR FAR
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#125 +125
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for m in modelPlot:
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dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain2D.dfsu').as_posix())
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dfs2d_list[m] = dfsIN
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# Read Map
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# ds_list[m] = dfs_list[m].read()
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# curU_list[m] = ds_list[m]["Depth averaged U velocity"]
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# curV_list[m] = ds_list[m]["Depth averaged V velocity"]
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# curElm_list[m] = dfs_list[m].element_coordinates
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# wl_list[m] = ds_list[m]["Surface elevation"]
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# z_list[m] = ds_list[m]["Z coordinate"]
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## Read specific points in 2D
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# Find nearest elements
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elem_ids = dfs2d_list[m].find_nearest_elements(readPoints[:, 0], readPoints[:, 1])
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# Read in data from nearest elements
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elm2d_list[m] = dfs2d_list[m].read(elements=elem_ids)
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# Convert to Pandas DataFrame
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elm2d_df_list[m] = [None] * readPoints.shape[0]
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for p in range(0, readPoints.shape[0]):
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elm2d_df_list[m][p] = elm2d_list[m].isel(idx=p).to_dataframe()
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## Read specific points 3D
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# Setup MIKE object
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dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain3D.dfsu').as_posix())
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dfs_list[m] = dfsIN
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# Find nearest elements
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elem_ids = dfs_list[m].find_nearest_profile_elements(readPoints[:, 0], readPoints[:, 1])
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# Read from elements- flatten 2d element array (xy and z) to 1d for reading
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elm_list[m] = dfs_list[m].read(elements=elem_ids.flatten().astype(int))
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elm_df_list[m] = [None] * readPoints.shape[0]
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z_profile_list[m] = [None] * readPoints.shape[0]
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# Convert to Pandas DataFrame
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# Loop through points, selecting corresponding depths
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for p in range(0, readPoints.shape[0]):
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z_profile_list[m][p] = dfs_list[m].element_coordinates[elem_ids[p, :].astype(int), 2]
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# Convert to Pandas and add zidx
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for z in range(0, z_profile_list[m][p].shape[0]):
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df_tmp = elm_list[m].isel(idx=p * 5 + z).to_dataframe()
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df_tmp['Z'] = z_profile_list[m][p][z]
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df_tmp['Z_IDX'] = z
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if z == 0:
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elm_df_list[m][p] = df_tmp
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else:
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elm_df_list[m][p] = elm_df_list[m][p].append(df_tmp)
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del df_tmp
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# %% Import RBR data
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rbr_path = "C:/Users/arey/files/Projects/Mustique/041279_20211203_1541Ruskin.xlsx"
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rbr_pd = pd.read_excel(rbr_path, sheet_name='Wave', parse_dates=True, header=1, index_col=0)
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rbr_pd['WL'] = rbr_pd['Depth'] - rbr_pd['Depth'].mean() - 0.2
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# Correct time zone
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rbr_pd.index = rbr_pd.index + datetime.timedelta(hours=4)
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# %% Import Nortek Eco data
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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"
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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"
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eco1_pd = pd.read_csv(eco_path_1, sep=',', header=[28], index_col=0,
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parse_dates=False)
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eco2_pd = pd.read_csv(eco_path_2, sep=',', header=[28], index_col=0,
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parse_dates=False)
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# Drop first row
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eco1_pd.drop(eco1_pd.index[0], inplace=True)
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eco2_pd.drop(eco2_pd.index[0], inplace=True)
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eco1_pd.index = pd.to_datetime(eco1_pd.index, format='%m/%d/%Y %I:%M:%S %p')
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eco2_pd.index = pd.to_datetime(eco2_pd.index, format='%m/%d/%Y %I:%M:%S %p')
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# Set water level to zero
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eco1_pd['WL'] = eco1_pd['Depth'].astype(float) - eco1_pd['Depth'].astype(float).mean(skipna=True) + 0.2
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eco2_pd['WL'] = eco2_pd['Depth'].astype(float) - eco2_pd['Depth'].astype(float).mean(skipna=True) + 0.2
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# Correct time zone
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eco1_pd.index = eco1_pd.index + datetime.timedelta(hours=4)
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eco2_pd.index = eco2_pd.index + datetime.timedelta(hours=4)
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# %% Read in boundary conditions
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ds_wl = []
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dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_EastBound.dfs1")
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ds_wl.append(dfs1.read())
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dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_NorthBound.dfs1")
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ds_wl.append(dfs1.read())
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dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_SouthBound.dfs1")
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ds_wl.append(dfs1.read())
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dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_WestBound.dfs1")
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ds_wl.append(dfs1.read())
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# %% Plot Water Level at a point
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fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8), sharex=True)
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# plotstart = '2021-10-01 00:00:00'
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# plotend = '2021-10-21 00:00:00'
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plotstart = '2021-10-05 00:00:00'
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plotend = '2021-10-07 00:00:00'
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for sub, p in enumerate([5, 6, 9]):
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if p == 9:
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rbr_pd['WL'].plot(ax=axes[sub], color='k', label='Obervations')
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elif p == 5:
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eco1_pd['WL'].plot(ax=axes[sub], color='k', label='Obervations')
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elif p == 6:
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eco2_pd['WL'].plot(ax=axes[sub], color='k', label='Obervations')
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elm2d_df_list[m][p].plot(y='Surface elevation', ax=axes[sub], label=readPointsName.Dataset[p])
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axes[sub].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[sub].set_ylim(-1.0, 1.0)
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plt.show()
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# %% Plot Water Level at a 3D point
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plotstart = '2021-09-14 00:00:00'
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# plotend = '2021-09-16 00:00:00'
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plotend = '2021-10-03 00:00:00'
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 8), sharex=True)
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# elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == 4, :].plot(
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# y='Z', ax=axes[0], label='Model')
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axes[0].plot(elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == 4, :].index,
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elm_list[m]['Z coordinate'][:, 30*6-1]+0.1, label='Model')
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eco1_pd['WL'].plot(ax=axes[0], color='k', label='Observations')
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axes[0].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[0].set_ylabel('Water Level Offshore (m)')
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axes[0].legend()
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# elm_df_list[m][7].loc[elm_df_list[m][7]['Z_IDX'] == 4, :].plot(
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# y='Z', ax=axes[1], label='Model')
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axes[1].plot(elm_df_list[m][7].loc[elm_df_list[m][7]['Z_IDX'] == 4, :].index,
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elm_list[m]['Z coordinate'][:, 14*6-1]+0.1, label='Model')
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eco2_pd['WL'].plot(ax=axes[1], color='k', label='Observations')
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axes[1].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[1].set_ylabel('Water Level Nearshore (m)')
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axes[1].legend()
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axes[1].set_xlabel('Date')
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plt.show()
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fig.savefig('//srv-ott3.baird.com/Projects/13539.101 L\'Ansecoy Bay, Mustique/06_Models/01_MIKE3/00_Figures/' +
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'/wl_TS.png', bbox_inches='tight')
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# %% Velocity point
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fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8), sharex=True)
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eco1_pd['Upper speed'].astype(float).plot(ax=axes[0], color='tab:red', label='Observations')
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elm_df_list[m][5]['Current Speed'] = np.sqrt(
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elm_df_list[m][5]['U velocity'] ** 2 + elm_df_list[m][5]['V velocity'] ** 2)
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elm_df_list[m][5].loc[elm_df_list[m][4]['Z_IDX'] == 4, :].plot(
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y='Current Speed', ax=axes[0], label='Model')
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axes[0].set_xlim(pd.Timestamp('2021-09-15 00:00:00'), pd.Timestamp('2021-10-3 00:00:00'))
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axes[0].set_ylabel('Nearshore Eco Current')
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axes[0].legend()
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eco2_pd['Upper speed'].astype(float).plot(ax=axes[1], color='tab:red', label='Observations')
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elm_df_list[m][5]['Current Speed'] = np.sqrt(
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elm_df_list[m][5]['U velocity'] ** 2 + elm_df_list[m][5]['V velocity'] ** 2)
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elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == 4, :].plot(
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y='Current Speed', ax=axes[1], label='Model')
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axes[1].set_xlim(pd.Timestamp('2021-09-15 00:00:00'), pd.Timestamp('2021-10-3 00:00:00'))
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axes[1].set_ylabel('Offshore Eco Current')
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plt.show()
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# %% Velocity point U and V
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# Z_IDX = 4
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# ecoVar1 = 'Upper speed U'
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# ecoVar2 = 'Upper speed V'
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# ecoVar3 = 'Upper speed'
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# ecoVar4 = 'Upper direction'
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# plotstart = '2021-09-14 00:00:00'
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# # plotend = '2021-09-16 00:00:00'
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# plotend = '2021-10-03 00:00:00'
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plotstart = '2021-10-01 00:00:00'
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# plotend = '2021-09-16 00:00:00'
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plotend = '2021-10-21 00:00:00'
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Z_IDX = 2
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ecoVar1 = 'Middle speed U'
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ecoVar2 = 'Middle speed V'
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ecoVar3 = 'Middle speed'
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ecoVar4 = 'Middle direction'
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# Z_IDX = 1
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# ecoVar1 = 'Lower speed U'
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# ecoVar2 = 'Lower speed V'
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# ecoVar3 = 'Lower speed'
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# ecoVar4 = 'Lower direction'
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fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(8, 8), sharex=True)
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eco1_pd[ecoVar1] = eco1_pd[ecoVar3].astype(float) * \
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np.sin(np.radians(eco1_pd[ecoVar4].astype(float).to_numpy()))
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eco1_pd[ecoVar1].astype(float).plot(ax=axes[0], color='k', label='Observations')
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elm_df_list[m][3].loc[elm_df_list[m][3]['Z_IDX'] == Z_IDX, :].plot(
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y='U velocity', ax=axes[0], label='Model')
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elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
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y='U velocity', ax=axes[0], label='Model Shift')
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axes[0].set_ylim(-0.5, 0.5)
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axes[0].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[0].set_ylabel('Nearshore U (m/s)')
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axes[0].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
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eco1_pd[ecoVar2] = eco1_pd[ecoVar3].astype(float) * \
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np.cos(np.radians(eco1_pd[ecoVar4].astype(float).to_numpy()))
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eco1_pd[ecoVar2].astype(float).plot(ax=axes[1], color='k', label='Observations')
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elm_df_list[m][3].loc[elm_df_list[m][3]['Z_IDX'] == Z_IDX, :].plot(
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y='V velocity', ax=axes[1], label='Model')
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elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
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y='V velocity', ax=axes[1], label='Model Shift')
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axes[1].set_ylim(-0.5, 0.5)
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axes[1].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[1].set_ylabel('Nearshore V (m/s)')
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axes[1].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
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eco2_pd[ecoVar1] = eco2_pd[ecoVar3].astype(float) * \
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np.sin(np.radians(eco2_pd[ecoVar4].astype(float).to_numpy()))
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eco2_pd[ecoVar1].astype(float).plot(ax=axes[2], color='k', label='Observations')
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elm_df_list[m][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
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y='U velocity', ax=axes[2], label='Model')
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elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == Z_IDX, :].plot(
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y='U velocity', ax=axes[2], label='Model Shift')
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axes[2].set_ylim(-1.5, 1.5)
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axes[2].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[2].set_ylabel('Offshore U (m/s)')
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axes[2].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
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eco2_pd[ecoVar2] = eco2_pd[ecoVar3].astype(float) * \
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np.cos(np.radians(eco2_pd[ecoVar4].astype(float).to_numpy()))
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eco2_pd[ecoVar2].astype(float).plot(ax=axes[3], color='k', label='Observations')
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elm_df_list[m][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
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y='V velocity', ax=axes[3], label='Model')
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elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
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y='V velocity', ax=axes[3], label='Model Shift')
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axes[3].set_ylim(-1.00, 1.00)
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axes[3].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
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axes[3].set_ylabel('Offshore V (m/s)')
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axes[3].legend(bbox_to_anchor=(0.90, 1), loc="upper left")
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axes[3].set_xlabel('Date')
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plt.show()
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# fig.savefig('//srv-ott3.baird.com/Projects/13539.101 L\'Ansecoy Bay, Mustique/06_Models/01_MIKE3/00_Figures/' +
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# '/uVel_TS.png', bbox_inches='tight')
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# %% Plotting
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# Shaded Water
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mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
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x, y, arrow_length = 0.93, 0.95, 0.12
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fontprops = fm.FontProperties(size=12)
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modelPlot = range(6, 7)
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vmin = 0
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vmax = 1
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scale = 1 # 5
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for m in modelPlot:
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if m < 4 or m == 6:
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vmax = 5
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qmax = 4
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scale = 50
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amin = 1
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else:
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vmax = 1
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qmax = 0.25
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scale = 75
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amin = 3
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fig, axes = plt.subplots(figsize=(8, 8))
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# Convert to feet and ignore missing
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cur_plot = curSpeed_list[m] * 3.28084
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cur_nan = np.isnan(cur_plot)
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axDHI = dfs_list[m].plot(cur_plot[~cur_nan], plot_type='contourf', show_mesh=False, cmap='viridis', ax=axes,
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levels=11,
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vmin=vmin, vmax=vmax, label='Current Speed (ft/s)',
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elements=dfs_list[m].element_ids[~cur_nan])
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# Plot arrows on overset grid
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overGrid = dfs_list[m].get_overset_grid(dx=50)
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interpolant = dfs_list[m].get_2d_interpolant(overGrid.xy, n_nearest=1)
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# Interpolate velocity at last time step by selecting time from dataset
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overGrid_Cur = dfs_list[m].interp2d(ds_list[m][ds_list[m].time[-1]:], *interpolant,
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shape=(overGrid.ny, overGrid.nx))
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overGrid_U = overGrid_Cur["U velocity"][-1]
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overGrid_V = overGrid_Cur["V velocity"][-1]
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# Normalize velocity above limit
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overGrid_U_Scale = overGrid_U * 3.28084
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overGrid_V_Scale = overGrid_V * 3.28084
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r = np.power(np.add(np.power(overGrid_U_Scale, 2), np.power(overGrid_V_Scale, 2)), 0.5)
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curPlotMask = np.sqrt((overGrid_U_Scale) ** 2 + (overGrid_V_Scale) ** 2) > qmax
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overGrid_U_Plot = np.zeros(overGrid_U_Scale.shape)
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overGrid_V_Plot = np.zeros(overGrid_V_Scale.shape)
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# Normalize arrows to 1
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overGrid_U_Plot = overGrid_U_Scale / r
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overGrid_V_Plot = overGrid_V_Scale / r
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# Scale arrows below qmax
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overGrid_U_Plot[~curPlotMask] = overGrid_U_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
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overGrid_V_Plot[~curPlotMask] = overGrid_V_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
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qv = axDHI.quiver(overGrid.x, overGrid.y, overGrid_U_Plot, overGrid_V_Plot, scale=scale,
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color='k', headlength=3.5, headwidth=3.5, headaxislength=3.5, width=0.0015, minlength=amin) # 200
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axes.set_xlim(left=427800, right=431000)
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axes.set_ylim(bottom=4758000, top=4762000)
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ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
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# axes.title.set_text(runLog['Short Description'][m])
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# axes.titlesize = 'x-large'
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fig.suptitle(runLog['Short Description'][m], fontsize=18)
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axes.set_xlabel('Easting [m]')
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axes.set_ylabel('Northing [m]')
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plt.show()
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fig.savefig(
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'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/04_MIKE21_HD/Images/Production/' +
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runLog['Short Description'][m] + '_RevC_Current.png', bbox_inches='tight', dpi=300)
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# %% Plot Bathymetry
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fig, axes = plt.subplots(figsize=(8, 8))
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# Convert to feet and ignore missing
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bed_plot = (bed_list[m] * 3.28084) - 578.007
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bed_nan = np.isnan(bed_plot)
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axDHI = dfs_list[m].plot(bed_plot[~bed_nan], plot_type='contourf', show_mesh=False, cmap='magma_r', ax=axes, levels=9,
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vmin=-80, vmax=0, label='Bed Elevation (ft)',
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elements=dfs_list[m].element_ids[~bed_nan])
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axes.set_xlim(left=427800, right=431000)
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axes.set_ylim(bottom=4758000, top=4762500)
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ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
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fig.suptitle('Bed Elevation', fontsize=18)
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axes.set_xlabel('Easting [m]')
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axes.set_ylabel('Northing [m]')
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plt.show()
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fig.savefig(
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'//srv-mad3/Projects/13632.101 South Shore Breakwater/10_Reports&Pres/13632.101.R3 Ph I BOD/Support files/' +
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'Bed_Elevation.png', bbox_inches='tight', dpi=300)
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# %% Read time series
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MIKEds_list = [None] * (max(modelPlot) + 1)
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MIKEdsT_list = [None] * (max(modelPlot) + 1)
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for m in modelPlot:
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dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
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runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
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dfsIN_read = dfsIN.read()
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MIKEds_list[m] = dfsIN_read.to_dataframe()
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dfsTIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
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runLog['Run Name'][m] + '.sw - Result Files', 'TransectPTS.dfs0').as_posix())
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dfsTIN_read = dfsTIN.read()
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MIKEdsT_list[m] = dfsTIN_read.to_dataframe()
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# Cleanup unnecessary variables
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del dfsIN_read
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del dfsIN
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del dfsTIN_read
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del dfsTIN
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# %% Read in Toe and Crest Shapefiles
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breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
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"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
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"20211211_Toe Extents (to Alexander)/"
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"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
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breakCrest = pd.read_csv(
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'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
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header=None, names=['x', 'y', 'z'])
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breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
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geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
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breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
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breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
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breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
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# %% Merge with data
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breakPointsOut = [None] * (max(modelPlot) + 1)
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breakTimesOut = [None] * (max(modelPlot) + 1)
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for m in modelPlot:
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breakwaterPTS_times = None
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for t in range(0, MIKEds_list[m].shape[0]):
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breakwaterPTS_merge = None
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breakwaterPTS_merge = breakwaterPTS_Crest
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for i in range(0, MIKEds_list[m].shape[1]):
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paramName = MIKEds_list[m].columns.values[i][MIKEds_list[m].columns.values[i].find('"', 5, -1) + 3:]
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if paramName not in breakwaterPTS_merge.columns:
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breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
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tmpFID = int(MIKEds_list[m].columns.values[i][1:MIKEds_list[m].columns.values[i].find('"', 1)])
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tmpIND = int(MIKEds_list[m].columns.values[i][5:MIKEds_list[m].columns.values[i].find('"', 5)])
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breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
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(breakwaterPTS_merge.vertex_ind == tmpIND)),
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paramName] = MIKEds_list[m].iloc[t, i]
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breakwaterPTS_merge['Time'] = MIKEds_list[m].index[t]
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breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
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breakTimesOut[m] = breakwaterPTS_times
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# %% Read in Breakwater Transect Points Sh
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breakwaterT = pd.read_csv("C:/Users/arey/files/Projects/SouthShore/Bathy/BreakTransectPTS_Names.csv", sep='\t')
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breakwaterT_PTS = gp.GeoDataFrame(breakwaterT, geometry=gp.points_from_xy(breakwaterT.X, breakwaterT.Y),
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crs='EPSG:32616')
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# %% Merge with data
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breakPoints_TOut = [None] * (max(modelPlot) + 1)
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breakTimes_TOut = [None] * (max(modelPlot) + 1)
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for m in modelPlot:
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breakwaterPTS_times = None
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for t in range(0, MIKEdsT_list[m].shape[0]):
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breakwaterPTS_merge = None
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breakwaterPTS_merge = breakwaterT_PTS
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for i in range(0, MIKEdsT_list[m].shape[1]):
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paramName = MIKEdsT_list[m].columns.values[i][MIKEdsT_list[m].columns.values[i].find(':', 5, -1) + 2:]
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if paramName not in breakwaterPTS_merge.columns:
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breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
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tmpName = MIKEdsT_list[m].columns.values[i][0:MIKEdsT_list[m].columns.values[i].find(':', 1)]
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breakwaterPTS_merge.loc[(breakwaterPTS_merge.Name == tmpName),
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paramName] = MIKEdsT_list[m].iloc[t, i]
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breakwaterPTS_merge['Time'] = MIKEdsT_list[m].index[t]
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breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
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breakTimes_TOut[m] = breakwaterPTS_times
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# %% Format and save
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for m in modelPlot:
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saveTmp = breakTimesOut[m].copy()
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saveTmp['X'] = saveTmp.geometry.x
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saveTmp['Y'] = saveTmp.geometry.y
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saveTmp.drop(['vertex_par', 'vertex_p_1', 'angle', 'geometry', 'index_right',
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'breakCrest_x', 'breakCrest_y', 'Length'], axis=1, inplace=True)
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saveTmp.sort_values(by=['FID', 'vertex_ind'], inplace=True, ignore_index=True)
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# Reorder columns
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colNames = saveTmp.columns.values
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saveTmp = saveTmp[['X', 'Y', *colNames[0:-2]]]
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saveTmpT = saveTmp.transpose(copy=True)
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# Transect points
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saveTmp2 = breakTimes_TOut[m].copy()
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saveTmp2.drop(['geometry'], axis=1, inplace=True)
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saveTmp2T = saveTmp2.transpose(copy=True)
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saveTmpT.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
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'Toe' + runLog['Short Description'][m] + '.csv')
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saveTmp2T.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
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'Transect' + runLog['Short Description'][m] + '.csv')
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