SouthShore transmission and plotting
This commit is contained in:
Alexander Rey
parent
b08bca6217
commit
df32b0901a
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@ -3,12 +3,13 @@
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# December 22, 2021
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# %% Setup Project
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from mikeio import Dfsu, Mesh, Dfs2, Dfs0
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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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@ -16,48 +17,342 @@ import matplotlib.font_manager as fm
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# %% Read Model Log
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pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakwater", "06_Models",
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"13632.101.W.AJMR.Rev0_ModelLog.xlsx")
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runLog = pd.read_excel(pth.as_posix(), "HD")
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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(6, 7)
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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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dfs_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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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],
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runLog['Run Name'][m] + '.m21fm - Result Files', 'FullDomain.dfsu').as_posix())
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dfs_list[m] = dfsIN
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ds_list[m] = dfs_list[m].read()
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curSpeed_list[m] = ds_list[m]["Current speed"][-1]
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curU_list[m] = ds_list[m]["U velocity"][-1]
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curV_list[m] = ds_list[m]["V velocity"][-1]
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curElm_list[m] = dfs_list[m].element_coordinates
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wl_list[m] = ds_list[m]["Surface elevation"][-1]
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wd_list[m] = ds_list[m]["Total water depth"][-1]
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bed_list[m] = wl_list[m] - wd_list[m]
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# # Read Mesh for first model
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# mikeMeshIN = dfsIN.to_shapely()
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# # Convert to list of polygons
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# mikeMeshIN_list = [p for p in mikeMeshIN]
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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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# Add to list
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# hmo_list[m] = gp.GeoDataFrame(hm0, geometry=mikeMeshIN_list)
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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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# Cleanup unnecessary variables
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# del mikeMeshIN
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# del mikeMeshIN_list
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del dfsIN
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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) * \
|
||||
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][3].loc[elm_df_list[m][3]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[1], label='Model')
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[1], label='Model Shift')
|
||||
|
||||
|
||||
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][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[2], label='Model')
|
||||
elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[2], label='Model Shift')
|
||||
|
||||
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][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[3], label='Model')
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[3], label='Model Shift')
|
||||
|
||||
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[3].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.png', bbox_inches='tight')
|
||||
|
||||
# %% Plotting
|
||||
# Shaded Water
|
||||
|
|
@ -66,45 +361,47 @@ x, y, arrow_length = 0.93, 0.95, 0.12
|
|||
fontprops = fm.FontProperties(size=12)
|
||||
modelPlot = range(6, 7)
|
||||
|
||||
vmin = 0
|
||||
vmax = 1
|
||||
scale = 1 #5
|
||||
vmin = 0
|
||||
vmax = 1
|
||||
scale = 1 # 5
|
||||
|
||||
for m in modelPlot:
|
||||
if m < 4 or m == 6:
|
||||
vmax = 5
|
||||
qmax = 4
|
||||
vmax = 5
|
||||
qmax = 4
|
||||
scale = 50
|
||||
amin = 1
|
||||
amin = 1
|
||||
else:
|
||||
vmax = 1
|
||||
qmax = 0.25
|
||||
vmax = 1
|
||||
qmax = 0.25
|
||||
scale = 75
|
||||
amin = 3
|
||||
amin = 3
|
||||
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
|
||||
# Convert to feet and ignore missing
|
||||
cur_plot = curSpeed_list[m]*3.28084
|
||||
cur_nan = np.isnan(cur_plot)
|
||||
cur_plot = curSpeed_list[m] * 3.28084
|
||||
cur_nan = np.isnan(cur_plot)
|
||||
|
||||
axDHI = dfs_list[m].plot(cur_plot[~cur_nan], plot_type='contourf', show_mesh=False, cmap='viridis', ax=axes, levels=11,
|
||||
vmin=vmin, vmax=vmax, label='Current Speed (ft/s)',
|
||||
elements=dfs_list[m].element_ids[~cur_nan])
|
||||
axDHI = dfs_list[m].plot(cur_plot[~cur_nan], plot_type='contourf', show_mesh=False, cmap='viridis', ax=axes,
|
||||
levels=11,
|
||||
vmin=vmin, vmax=vmax, label='Current Speed (ft/s)',
|
||||
elements=dfs_list[m].element_ids[~cur_nan])
|
||||
|
||||
# Plot arrows on overset grid
|
||||
overGrid = dfs_list[m].get_overset_grid(dx=50)
|
||||
interpolant = dfs_list[m].get_2d_interpolant(overGrid.xy, n_nearest=1)
|
||||
overGrid = dfs_list[m].get_overset_grid(dx=50)
|
||||
interpolant = dfs_list[m].get_2d_interpolant(overGrid.xy, n_nearest=1)
|
||||
|
||||
# Interpolate velocity at last time step by selecting time from dataset
|
||||
overGrid_Cur = dfs_list[m].interp2d(ds_list[m][ds_list[m].time[-1]:], *interpolant, shape=(overGrid.ny, overGrid.nx))
|
||||
overGrid_Cur = dfs_list[m].interp2d(ds_list[m][ds_list[m].time[-1]:], *interpolant,
|
||||
shape=(overGrid.ny, overGrid.nx))
|
||||
|
||||
overGrid_U = overGrid_Cur["U velocity"][-1]
|
||||
overGrid_V = overGrid_Cur["V velocity"][-1]
|
||||
|
||||
# Normalize velocity above limit
|
||||
overGrid_U_Scale = overGrid_U*3.28084
|
||||
overGrid_V_Scale = overGrid_V*3.28084
|
||||
overGrid_U_Scale = overGrid_U * 3.28084
|
||||
overGrid_V_Scale = overGrid_V * 3.28084
|
||||
|
||||
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
|
||||
|
|
@ -121,7 +418,7 @@ for m in modelPlot:
|
|||
overGrid_V_Plot[~curPlotMask] = overGrid_V_Plot[~curPlotMask] * (r[~curPlotMask] / qmax)
|
||||
|
||||
qv = axDHI.quiver(overGrid.x, overGrid.y, 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
|
||||
color='k', headlength=3.5, headwidth=3.5, headaxislength=3.5, width=0.0015, minlength=amin) # 200
|
||||
|
||||
axes.set_xlim(left=427800, right=431000)
|
||||
axes.set_ylim(bottom=4758000, top=4762000)
|
||||
|
|
@ -166,7 +463,7 @@ fig.savefig(
|
|||
'Bed_Elevation.png', bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Read time series
|
||||
MIKEds_list = [None] * (max(modelPlot) + 1)
|
||||
MIKEds_list = [None] * (max(modelPlot) + 1)
|
||||
MIKEdsT_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
|
|
@ -203,7 +500,7 @@ breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z
|
|||
|
||||
# %% Merge with data
|
||||
breakPointsOut = [None] * (max(modelPlot) + 1)
|
||||
breakTimesOut = [None] * (max(modelPlot) + 1)
|
||||
breakTimesOut = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
breakwaterPTS_times = None
|
||||
|
|
@ -227,17 +524,17 @@ for m in modelPlot:
|
|||
|
||||
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||
|
||||
breakTimesOut[m] = breakwaterPTS_times
|
||||
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 = 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)
|
||||
breakTimes_TOut = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
breakwaterPTS_times = None
|
||||
|
|
@ -259,32 +556,30 @@ for m in modelPlot:
|
|||
|
||||
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||
|
||||
breakTimes_TOut[m] = breakwaterPTS_times
|
||||
breakTimes_TOut[m] = breakwaterPTS_times
|
||||
|
||||
|
||||
#%% Format and save
|
||||
# %% Format and save
|
||||
|
||||
for m in modelPlot:
|
||||
saveTmp = breakTimesOut[m].copy()
|
||||
saveTmp['X'] = saveTmp.geometry.x
|
||||
saveTmp['Y'] = saveTmp.geometry.y
|
||||
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)
|
||||
colNames = saveTmp.columns.values
|
||||
saveTmp = saveTmp[['X', 'Y', *colNames[0:-2]]]
|
||||
saveTmpT = saveTmp.transpose(copy=True)
|
||||
|
||||
# Transect points
|
||||
saveTmp2 = breakTimes_TOut[m].copy()
|
||||
saveTmp2 = breakTimes_TOut[m].copy()
|
||||
saveTmp2.drop(['geometry'], axis=1, inplace=True)
|
||||
saveTmp2T = saveTmp2.transpose(copy=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')
|
||||
'Transect' + runLog['Short Description'][m] + '.csv')
|
||||
|
|
|
|||
|
|
@ -11,7 +11,10 @@ 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
|
||||
|
||||
|
|
@ -21,21 +24,26 @@ pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13539.101 L'Ansecoy Bay, Mus
|
|||
"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]
|
||||
|
||||
# %% Read Model Results
|
||||
modelPlot = range(10, 11)
|
||||
|
||||
curSpeed_list = [None] * (max(modelPlot) + 1)
|
||||
curElm_list = [None] * (max(modelPlot) + 1)
|
||||
curU_list = [None] * (max(modelPlot) + 1)
|
||||
curV_list = [None] * (max(modelPlot) + 1)
|
||||
curW_list = [None] * (max(modelPlot) + 1)
|
||||
dfs_list = [None] * (max(modelPlot) + 1)
|
||||
dfs2d_list = [None] * (max(modelPlot) + 1)
|
||||
ds_list = [None] * (max(modelPlot) + 1)
|
||||
wl_list = [None] * (max(modelPlot) + 1)
|
||||
wd_list = [None] * (max(modelPlot) + 1)
|
||||
bed_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 = [4, 10, 12, 13]
|
||||
|
||||
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)
|
||||
|
||||
|
|
@ -45,41 +53,12 @@ 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_RevB.csv",
|
||||
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_RevE.csv",
|
||||
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
|
||||
|
||||
|
||||
# readPoints = np.zeros((14, 2))
|
||||
# readPoints[0, :] = [693000, 1425500]
|
||||
# readPoints[1, :] = [697000, 1429500]
|
||||
# readPoints[2, :] = [697000, 1421500]
|
||||
# readPoints[3, :] = [701000, 1425500]
|
||||
#
|
||||
# # readPoints[4, :] = [697576.96, 1425767.10] # RBR NEAR
|
||||
# # readPoints[5, :] = [697760.89, 1426109.73] # RBR FAR
|
||||
#
|
||||
# # From Mathew
|
||||
# readPoints[4, :] = [697858.00, 1426095.00] # RBR NEAR
|
||||
# readPoints[5, :] = [697613.00, 1426509.00] # RBR FAR
|
||||
#
|
||||
# # Location Uncertainty
|
||||
# # 500 -x
|
||||
# readPoints[6, :] = [697858.00-100, 1426095.00-0] # RBR NEAR
|
||||
# readPoints[7, :] = [697613.00-150, 1426509.00+150] # RBR FAR
|
||||
# # 500 +x
|
||||
# readPoints[8, :] = [697858.00+100, 1426095.00-0] # RBR NEAR
|
||||
# readPoints[9, :] = [697613.00-50, 1426509.00+50] # RBR FAR
|
||||
# # 500 -x
|
||||
# readPoints[10, :] = [697858.00+150, 1426095.00+150] # RBR NEAR
|
||||
# readPoints[11, :] = [697613.00+150, 1426509.00+150] # RBR FAR
|
||||
# # 500 -x
|
||||
# readPoints[12, :] = [697858.00-150, 1426095.00+150] # RBR NEAR
|
||||
# readPoints[13, :] = [697613.00-150, 1426509.00+150] # RBR FAR
|
||||
|
||||
#125 +125
|
||||
|
||||
for m in modelPlot:
|
||||
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain2D.dfsu').as_posix())
|
||||
|
|
@ -108,19 +87,19 @@ for m in modelPlot:
|
|||
## Read specific points 3D
|
||||
# Setup MIKE object
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain3D.dfsu').as_posix())
|
||||
dfs_list[m] = dfsIN
|
||||
dfs3d_list[m] = dfsIN
|
||||
|
||||
# Find nearest elements
|
||||
elem_ids = dfs_list[m].find_nearest_profile_elements(readPoints[:, 0], readPoints[:, 1])
|
||||
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] = dfs_list[m].read(elements=elem_ids.flatten().astype(int))
|
||||
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] = dfs_list[m].element_coordinates[elem_ids[p, :].astype(int), 2]
|
||||
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]):
|
||||
|
|
@ -184,84 +163,34 @@ 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 = '2021-10-21 00:00:00'
|
||||
plotstart = '2021-10-05 00:00:00'
|
||||
plotend = '2021-10-07 00:00:00'
|
||||
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 sub, p in enumerate([5, 6, 9]):
|
||||
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='Obervations')
|
||||
elif p == 5:
|
||||
eco1_pd['WL'].plot(ax=axes[sub], color='k', label='Obervations')
|
||||
elif p == 6:
|
||||
eco2_pd['WL'].plot(ax=axes[sub], color='k', label='Obervations')
|
||||
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=readPointsName.Dataset[p])
|
||||
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_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[sub].set_ylim(-1.0, 1.0)
|
||||
axes[sub].set_ylabel('Water Level (m)')
|
||||
|
||||
plt.show()
|
||||
|
||||
# %% Plot Water Level at a 3D point
|
||||
plotstart = '2021-09-14 00:00:00'
|
||||
# plotend = '2021-09-16 00:00:00'
|
||||
plotend = '2021-10-03 00:00:00'
|
||||
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 8), sharex=True)
|
||||
|
||||
# elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == 4, :].plot(
|
||||
# y='Z', ax=axes[0], label='Model')
|
||||
|
||||
axes[0].plot(elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == 4, :].index,
|
||||
elm_list[m]['Z coordinate'][:, 30*6-1]+0.1, label='Model')
|
||||
eco1_pd['WL'].plot(ax=axes[0], color='k', label='Observations')
|
||||
axes[0].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[0].set_ylabel('Water Level Offshore (m)')
|
||||
axes[0].legend()
|
||||
|
||||
|
||||
# elm_df_list[m][7].loc[elm_df_list[m][7]['Z_IDX'] == 4, :].plot(
|
||||
# y='Z', ax=axes[1], label='Model')
|
||||
axes[1].plot(elm_df_list[m][7].loc[elm_df_list[m][7]['Z_IDX'] == 4, :].index,
|
||||
elm_list[m]['Z coordinate'][:, 14*6-1]+0.1, label='Model')
|
||||
eco2_pd['WL'].plot(ax=axes[1], color='k', label='Observations')
|
||||
axes[1].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||
axes[1].set_ylabel('Water Level Nearshore (m)')
|
||||
axes[1].legend()
|
||||
|
||||
axes[1].set_xlabel('Date')
|
||||
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.png', bbox_inches='tight')
|
||||
|
||||
# %% Velocity point
|
||||
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8), sharex=True)
|
||||
|
||||
eco1_pd['Upper speed'].astype(float).plot(ax=axes[0], color='tab:red', label='Observations')
|
||||
elm_df_list[m][5]['Current Speed'] = np.sqrt(
|
||||
elm_df_list[m][5]['U velocity'] ** 2 + elm_df_list[m][5]['V velocity'] ** 2)
|
||||
elm_df_list[m][5].loc[elm_df_list[m][4]['Z_IDX'] == 4, :].plot(
|
||||
y='Current Speed', ax=axes[0], label='Model')
|
||||
|
||||
axes[0].set_xlim(pd.Timestamp('2021-09-15 00:00:00'), pd.Timestamp('2021-10-3 00:00:00'))
|
||||
axes[0].set_ylabel('Nearshore Eco Current')
|
||||
axes[0].legend()
|
||||
|
||||
eco2_pd['Upper speed'].astype(float).plot(ax=axes[1], color='tab:red', label='Observations')
|
||||
elm_df_list[m][5]['Current Speed'] = np.sqrt(
|
||||
elm_df_list[m][5]['U velocity'] ** 2 + elm_df_list[m][5]['V velocity'] ** 2)
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == 4, :].plot(
|
||||
y='Current Speed', ax=axes[1], label='Model')
|
||||
|
||||
axes[1].set_xlim(pd.Timestamp('2021-09-15 00:00:00'), pd.Timestamp('2021-10-3 00:00:00'))
|
||||
axes[1].set_ylabel('Offshore Eco Current')
|
||||
|
||||
plt.show()
|
||||
|
||||
'/wl_TS_RevB.png', bbox_inches='tight')
|
||||
|
||||
# %% Velocity point U and V
|
||||
# Z_IDX = 4
|
||||
|
|
@ -278,6 +207,9 @@ 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'
|
||||
|
|
@ -290,159 +222,262 @@ ecoVar4 = 'Middle direction'
|
|||
# ecoVar3 = 'Lower speed'
|
||||
# ecoVar4 = 'Lower direction'
|
||||
|
||||
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(8, 8), sharex=True)
|
||||
|
||||
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][3]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[0], label='Model')
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[0], label='Model Shift')
|
||||
|
||||
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][3].loc[elm_df_list[m][3]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[1], label='Model')
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[1], label='Model Shift')
|
||||
|
||||
|
||||
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][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[2], label='Model')
|
||||
elm_df_list[m][6].loc[elm_df_list[m][6]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='U velocity', ax=axes[2], label='Model Shift')
|
||||
|
||||
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][4].loc[elm_df_list[m][4]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[3], label='Model')
|
||||
elm_df_list[m][5].loc[elm_df_list[m][5]['Z_IDX'] == Z_IDX, :].plot(
|
||||
y='V velocity', ax=axes[3], label='Model Shift')
|
||||
|
||||
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[3].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.png', bbox_inches='tight')
|
||||
|
||||
# %% Plotting
|
||||
# Shaded Water
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/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(6, 7)
|
||||
|
||||
vmin = 0
|
||||
vmax = 1
|
||||
scale = 1 # 5
|
||||
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 4), sharex=True)
|
||||
modelPlot = [13]
|
||||
|
||||
for m in modelPlot:
|
||||
if m < 4 or m == 6:
|
||||
vmax = 5
|
||||
qmax = 4
|
||||
scale = 50
|
||||
amin = 1
|
||||
else:
|
||||
vmax = 1
|
||||
qmax = 0.25
|
||||
scale = 75
|
||||
amin = 3
|
||||
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 = [15, 16, 17]
|
||||
|
||||
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:
|
||||
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))
|
||||
|
||||
# Convert to feet and ignore missing
|
||||
cur_plot = curSpeed_list[m] * 3.28084
|
||||
cur_nan = np.isnan(cur_plot)
|
||||
# Plot salinity
|
||||
# Select salinity data at last time step
|
||||
plotDat = ds_list[m]['Salinity'][-1, :]
|
||||
# Identify nan values
|
||||
plot_nan = np.isnan(plotDat)
|
||||
|
||||
axDHI = dfs_list[m].plot(cur_plot[~cur_nan], plot_type='contourf', show_mesh=False, cmap='viridis', ax=axes,
|
||||
levels=11,
|
||||
vmin=vmin, vmax=vmax, label='Current Speed (ft/s)',
|
||||
elements=dfs_list[m].element_ids[~cur_nan])
|
||||
# Add additional nan values to show bed
|
||||
plot_nan = plot_nan | (plotDat<34.05)
|
||||
|
||||
# Plot arrows on overset grid
|
||||
overGrid = dfs_list[m].get_overset_grid(dx=50)
|
||||
interpolant = dfs_list[m].get_2d_interpolant(overGrid.xy, n_nearest=1)
|
||||
# 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)])
|
||||
|
||||
# Interpolate velocity at last time step by selecting time from dataset
|
||||
overGrid_Cur = dfs_list[m].interp2d(ds_list[m][ds_list[m].time[-1]:], *interpolant,
|
||||
shape=(overGrid.ny, overGrid.nx))
|
||||
|
||||
overGrid_U = overGrid_Cur["U velocity"][-1]
|
||||
overGrid_V = overGrid_Cur["V velocity"][-1]
|
||||
axes.set_xlim(left=696680.7, right=698252.7)
|
||||
axes.set_ylim(bottom=1425547.5, top=1426681.8)
|
||||
|
||||
# Normalize velocity above limit
|
||||
overGrid_U_Scale = overGrid_U * 3.28084
|
||||
overGrid_V_Scale = overGrid_V * 3.28084
|
||||
|
||||
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
|
||||
overGrid_U_Plot = overGrid_U_Scale / r
|
||||
overGrid_V_Plot = overGrid_V_Scale / r
|
||||
|
||||
# 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)
|
||||
|
||||
qv = axDHI.quiver(overGrid.x, overGrid.y, 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
|
||||
|
||||
axes.set_xlim(left=427800, right=431000)
|
||||
axes.set_ylim(bottom=4758000, top=4762000)
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
|
||||
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)
|
||||
# fig.suptitle(runLog['Short Description'][m], fontsize=18)
|
||||
axes.set_xlabel('Easting [m]')
|
||||
axes.set_ylabel('Northing [m]')
|
||||
|
||||
plt.show()
|
||||
|
||||
fig.savefig(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/04_MIKE21_HD/Images/Production/' +
|
||||
runLog['Short Description'][m] + '_RevC_Current.png', bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% 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
|
||||
bed_plot = (bed_list[m] * 3.28084) - 578.007
|
||||
bed_nan = np.isnan(bed_plot)
|
||||
|
||||
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])
|
||||
|
|
@ -450,7 +485,7 @@ axDHI = dfs_list[m].plot(bed_plot[~bed_nan], plot_type='contourf', show_mesh=Fal
|
|||
axes.set_xlim(left=427800, right=431000)
|
||||
axes.set_ylim(bottom=4758000, top=4762500)
|
||||
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
|
||||
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32620')
|
||||
|
||||
fig.suptitle('Bed Elevation', fontsize=18)
|
||||
axes.set_xlabel('Easting [m]')
|
||||
|
|
@ -458,9 +493,9 @@ 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)
|
||||
# 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)
|
||||
|
|
|
|||
|
|
@ -31,6 +31,8 @@ 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'):
|
||||
|
|
@ -42,62 +44,25 @@ def find_time_var(var, time_basename='time'):
|
|||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
importPaths = ['C:/Users/arey/files/Projects/West Coast/Pre_Construction/Great House/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Pre_Construction/Greensleeves/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Pre_Construction/Old Queens Fort/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Construction/Great House/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Construction/Greensleeves/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Construction/Old Queens Fort/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Post_Construction/Great House/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Post_Construction/Greensleeves/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Post_Construction/Old Queens Fort/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Great House/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Greensleeves/',
|
||||
'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Old Queens Fort/']
|
||||
importPaths = ['C:/Users/arey/files/Projects/West Coast/Monitor_Feb/Great House',
|
||||
'C:/Users/arey/files/Projects/West Coast/Monitor_Feb/Greensleeves',
|
||||
'C:/Users/arey/files/Projects/West Coast/Monitor_Feb/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']
|
||||
|
||||
timeLabels= ['Before Construction',
|
||||
'Before Construction',
|
||||
'Before Construction',
|
||||
'During Construction',
|
||||
'During Construction',
|
||||
'During Construction',
|
||||
'After Construction',
|
||||
'After Construction',
|
||||
'After Construction',
|
||||
'Monitoring',
|
||||
'Monitoring',
|
||||
'Monitoring']
|
||||
timeLabels= ['February',
|
||||
'February',
|
||||
'February']
|
||||
|
||||
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]
|
||||
|
||||
|
||||
|
||||
for s in range(9,13):
|
||||
for s in range(0, 3):
|
||||
## Define master import path
|
||||
importPath = importPaths[s]
|
||||
siteName = siteNames[s]
|
||||
|
|
@ -105,49 +70,43 @@ for s in range(9,13):
|
|||
importFiles = os.listdir(importPath)
|
||||
|
||||
# Initialize import variables
|
||||
RBR_File = None
|
||||
JFE_File = None
|
||||
OBS_File = None
|
||||
GPS_File = None
|
||||
|
||||
for i in range(0, len(importFiles)):
|
||||
if '.xlsx' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
RBR_File = importFiles[i]
|
||||
elif '_A.csv' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
JFE_File = importFiles[i]
|
||||
elif '.csv' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
if '.csv' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
OBS_File = importFiles[i]
|
||||
elif '.xls' in importFiles[i] and 'Summary' not in importFiles[i]:
|
||||
GPS_File = importFiles[i]
|
||||
|
||||
#%% RBR Import Data
|
||||
if RBR_File is not None:
|
||||
RBR_Obs = pd.read_excel(importPath + RBR_File,
|
||||
sheet_name='Data', skiprows=0, header=1)
|
||||
#%% Obs Import Data
|
||||
if OBS_File is not None:
|
||||
Obs_dat = pd.read_csv(importPath + '/' + OBS_File, skiprows=0, header=0)
|
||||
|
||||
#%% JFE Import Data
|
||||
if JFE_File is not None:
|
||||
JFE_Obs = pd.read_csv(importPath + JFE_File, skiprows=30)
|
||||
# Drop rows with metadata
|
||||
Obs_dat =Obs_dat[Obs_dat['CH1:Temperature(degC)'].notna()]
|
||||
|
||||
# Set Time Zone
|
||||
Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('America/Barbados').dt.tz_convert('UTC')
|
||||
|
||||
# Set Index
|
||||
Obs_dat.set_index('DateTime', inplace=True)
|
||||
#%% GPS Import Data
|
||||
if GPS_File is not None:
|
||||
GPS = pd.read_csv(importPath + GPS_File,
|
||||
header=None, names=['Index', 'Date1', 'Time1', 'Date2', 'Time2', 'Northing', 'North', 'Easting', 'East', 'Var1', 'Var2'])
|
||||
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.Easting, GPS.Northing, crs="EPSG:4326"))
|
||||
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['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))
|
||||
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['time']).dt.tz_localize('UTC')
|
||||
|
||||
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")
|
||||
|
||||
else:
|
||||
# Synthesize GPS data for great house
|
||||
GPS_gdf = gp.read_file('C:/Users/arey/files/Projects/West Coast/GreatHousePath_R3.shp')
|
||||
GPS_gdf['DateTime'] = pd.date_range(pd.to_datetime(RBR_Obs['Time'].iloc[0]),
|
||||
pd.to_datetime(RBR_Obs['Time'].iloc[-1]),
|
||||
periods=len(GPS_gdf)).values
|
||||
GPS_gdf.set_index('DateTime', inplace=True)
|
||||
|
||||
#%% 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")
|
||||
|
|
@ -155,27 +114,13 @@ for s in range(9,13):
|
|||
|
||||
#%% Merge GPS to RBR
|
||||
# Process RBR into datetime
|
||||
if RBR_File is not None:
|
||||
RBR_Obs['DateTime'] = pd.to_datetime(RBR_Obs['Time'])
|
||||
RBR_Obs.set_index('DateTime', inplace=True)
|
||||
|
||||
if OBS_File is not None:
|
||||
# Merge with GPS as dataframe
|
||||
RBR_Obs_geo = pd.merge_asof(RBR_Obs, GPS_gdf,
|
||||
Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
|
||||
left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
|
||||
RBR_Obs_geo = gp.GeoDataFrame(RBR_Obs_geo, geometry=RBR_Obs_geo.geometry, crs="EPSG:32621")
|
||||
|
||||
#%% Merge GPS to JFE
|
||||
if JFE_File is not None:
|
||||
# Process JFE into datetime
|
||||
JFE_Obs['DateTime'] = pd.to_datetime(JFE_Obs['Date'])
|
||||
JFE_Obs.set_index('DateTime', inplace=True)
|
||||
|
||||
# Merge with GPS as dataframe
|
||||
JFE_Obs_geo = pd.merge_asof(JFE_Obs, GPS_gdf, left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('60s'))
|
||||
JFE_Obs_geo = gp.GeoDataFrame(JFE_Obs_geo, geometry=JFE_Obs_geo.geometry, crs="EPSG:32621")
|
||||
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
|
||||
|
|
@ -186,44 +131,31 @@ for s in range(9,13):
|
|||
axYlim = (1464769.2243017585, 1465135.2219089477)
|
||||
GFS_Lon = -59.6441
|
||||
GFS_Lat = 13.2372
|
||||
RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[2, 1])
|
||||
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
|
||||
RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[1, 1])
|
||||
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
|
||||
RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[0, 1])
|
||||
Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[0, 1])
|
||||
|
||||
|
||||
# Set min and max times using conductivity
|
||||
# if JFE_File is None:
|
||||
if RBR_Obs_geo['inArea'].any():
|
||||
if Obs_geo['inArea'].any():
|
||||
# First and last times from area in shapefile
|
||||
minTime = RBR_Obs_geo[RBR_Obs_geo['inArea']==True].iloc[0, 0]
|
||||
maxTime = RBR_Obs_geo[RBR_Obs_geo['inArea']==True].iloc[-1, 0]
|
||||
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 = RBR_Obs_geo.iloc[20, 0]
|
||||
maxTime = RBR_Obs_geo.iloc[-20, 0]
|
||||
# else:
|
||||
# minTime = (RBR_Obs['Conductivity ']>5).idxmax()
|
||||
# minTime = minTime + timedelta(seconds=30)
|
||||
# maxTime = ((RBR_Obs['Conductivity ']<5) & (RBR_Obs['Time']>minTime)).idxmax()
|
||||
# maxTime = maxTime - timedelta(seconds=30)
|
||||
# obsPeriod = maxTime-minTime
|
||||
#
|
||||
# if (obsPeriod.seconds<180) | (maxTime<minTime):
|
||||
# minTime = ((RBR_Obs['Conductivity ']>5) & (RBR_Obs['Time']>(minTime+timedelta(seconds=180)))).idxmax()
|
||||
# minTime = minTime + timedelta(seconds=30)
|
||||
# maxTime = ((RBR_Obs['Conductivity ']<5) & (RBR_Obs['Time']>minTime)).idxmax()
|
||||
# maxTime = maxTime - timedelta(seconds=30)
|
||||
minTime = Obs_geo.iloc[20, 0]
|
||||
maxTime = Obs_geo.iloc[-20, 0]
|
||||
|
||||
metDate = minTime - timedelta(
|
||||
hours = minTime.hour % 6,
|
||||
|
|
@ -335,38 +267,35 @@ for s in range(9,13):
|
|||
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)
|
||||
|
||||
if JFE_File is None:
|
||||
fig, axesTMP = plt.subplots(nrows=1, ncols=1, figsize=(19, 5), constrained_layout=True)
|
||||
|
||||
RBRparam = ['Turbidity ']
|
||||
RBRparamName = ['Turbidity [NTU]']
|
||||
RBRparmCmap = [cmo.turbid]
|
||||
RBRparamMin = [0.0]
|
||||
RBRparamMax = [60.0]
|
||||
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]
|
||||
|
||||
dataTable = np.zeros([9, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 1, 1]
|
||||
axes = []
|
||||
axes.append(axesTMP)
|
||||
else:
|
||||
fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(19, 25), constrained_layout=True)
|
||||
dataTable = np.zeros([15, 4])
|
||||
roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 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 saturation [%]', 'Temperature [degC]',
|
||||
'Turbidity [FTU]', 'Chl-a [ppb]']
|
||||
|
||||
RBRparam = ['Depth ', 'Salinity ', 'Dissolved O₂ saturation ', 'Temperature ']
|
||||
RBRparamName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ saturation [%]', 'Temperature [degC]']
|
||||
RBRparmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal]
|
||||
RBRparamMin = [0.0, 34.0, 32.5, 29.0]
|
||||
RBRparamMax = [1.0, 36.0, 34.0, 31.0]
|
||||
|
||||
JFEparam = ['Turb. -M[FTU]', 'Chl-Flu.[ppb]', 'Chl-a[ug/l]']
|
||||
JFEparamName = ['Turbidity [FTU]', 'Chl-Flu. [ppb]', 'Chl-a [ug/l]']
|
||||
JFEparamMin = [0.0, 0.0, 0.0]
|
||||
JFEparamMax = [20.0, 1.0, 1.0]
|
||||
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, 34.0, 100, 26.0, 0, 0]
|
||||
paramMax = [1.0, 36.0, 130, 29.0, 50.0, 12000]
|
||||
|
||||
|
||||
fig.patch.set_facecolor('white')
|
||||
|
|
@ -384,119 +313,58 @@ for s in range(9,13):
|
|||
|
||||
ilocs_max = []
|
||||
ilocs_max_pts = []
|
||||
RBR_mask = []
|
||||
JFE_mask = []
|
||||
OBS_mask = []
|
||||
|
||||
for paramIDX, param in enumerate(RBRparam):
|
||||
RBR_Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
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
|
||||
RBR_mask.append(((RBR_Obs_geo.index>minTime) &
|
||||
(RBR_Obs_geo.index<maxTime) &
|
||||
(RBR_Obs_geo[param]>RBRparamMin[paramIDX])))
|
||||
OBS_mask.append(((Obs_geo.index>minTime) &
|
||||
(Obs_geo.index<maxTime) &
|
||||
(Obs_geo[param]>paramMin[paramIDX])))
|
||||
|
||||
RBR_smoothed = RBR_Obs_geo.loc[RBR_mask[paramIDX], param].rolling(
|
||||
60, win_type='nuttall',center=True).mean()
|
||||
OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
|
||||
12, win_type='nuttall',center=True).mean()
|
||||
|
||||
RBR_smoothed.plot(
|
||||
OBS_smoothed.plot(
|
||||
ax=axes[paramIDX], label='1 Minute Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'Turbidity ':
|
||||
ilocs_max.append(argrelextrema(RBR_smoothed.values,
|
||||
np.greater_equal, order=40, mode='wrap')[0])
|
||||
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(RBR_smoothed.values)-10
|
||||
ilocs_max_pts.append(RBR_smoothed.iloc[ilocs_max[paramIDX]].index.values)
|
||||
# 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(RBR_smoothed.iloc[
|
||||
ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
|
||||
color='blue')
|
||||
# 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], 30), fontsize=30)
|
||||
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] = RBR_Obs_geo.loc[minTime:maxTime, param].mean(skipna=True)
|
||||
# dataTable[paramIDX+7, 1] = RBR_Obs_geo.loc[minTime:maxTime, param].std(skipna=True)
|
||||
# dataTable[paramIDX+7, 2] = max(RBR_Obs_geo.loc[minTime:maxTime, param].min(skipna=True), 0)
|
||||
# dataTable[paramIDX+7, 3] = RBR_Obs_geo.loc[minTime:maxTime, param].max(skipna=True)
|
||||
dataTable[paramIDX+7, 0] = RBR_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+7, 1] = RBR_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+7, 2] = max(RBR_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+7, 3] = RBR_smoothed.max(skipna=True)
|
||||
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(RBRparamName[paramIDX])
|
||||
axes[paramIDX].set_title('RBR: ' + RBRparamName[paramIDX])
|
||||
axes[paramIDX].set_ylabel(paramName[paramIDX])
|
||||
axes[paramIDX].set_title(paramName[paramIDX])
|
||||
axes[paramIDX].set_xlabel('')
|
||||
axes[paramIDX].set_ylim(RBRparamMin[paramIDX], RBRparamMax[paramIDX])
|
||||
axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
|
||||
axes[paramIDX].legend(loc='upper right')
|
||||
|
||||
if JFE_File is not None:
|
||||
for paramIDX, param in enumerate(JFEparam):
|
||||
JFE_Obs_geo.loc[minTime:maxTime, param].plot(
|
||||
ax=axes[paramIDX+4], label='15 Second Observations', color='lightgrey')
|
||||
|
||||
JFE_mask.append(((JFE_Obs_geo.index > minTime) &
|
||||
(JFE_Obs_geo.index < maxTime) &
|
||||
(JFE_Obs_geo[param] > JFEparamMin[paramIDX])))
|
||||
|
||||
JFE_smoothed = JFE_Obs_geo.loc[JFE_mask[paramIDX], param].rolling(
|
||||
20, win_type='nuttall', center=True).mean()
|
||||
|
||||
JFE_smoothed.plot(
|
||||
ax=axes[paramIDX+4], label='1 Minute Average', color='black',
|
||||
linewidth=3)
|
||||
|
||||
|
||||
# Find the local maximums for Turbidity
|
||||
if param == 'Turb. -M[FTU]':
|
||||
ilocs_max.append(argrelextrema(JFE_smoothed.values,
|
||||
np.greater_equal, order=60, mode='wrap')[0])
|
||||
|
||||
# Add start and end points?
|
||||
# ilocs_max = np.insert(ilocs_max, 0, 10)
|
||||
# ilocs_max[-1] = len(RBR_smoothed.values)-10
|
||||
ilocs_max_pts.append(JFE_smoothed.iloc[ilocs_max[paramIDX+4]].index.values)
|
||||
|
||||
# Add labels if GPS data is available
|
||||
if GPS_File is not None:
|
||||
axes[paramIDX+4].scatter(JFE_smoothed.iloc[
|
||||
ilocs_max[paramIDX+4]].index, np.ones(len(ilocs_max[paramIDX+4])) * 10, 75,
|
||||
color='blue')
|
||||
for a in range(0, len(ilocs_max[paramIDX+4])):
|
||||
axes[paramIDX+4].annotate(str(a + 1), (ilocs_max_pts[paramIDX+4][a], 10), fontsize=30)
|
||||
else:
|
||||
ilocs_max.append(None)
|
||||
ilocs_max_pts.append(None)
|
||||
|
||||
# dataTable[paramIDX+4+7, 0] = JFE_Obs_geo.loc[minTime:maxTime, param].mean(skipna=True)
|
||||
# dataTable[paramIDX+4+7, 1] = JFE_Obs_geo.loc[minTime:maxTime, param].std(skipna=True)
|
||||
# dataTable[paramIDX+4+7, 2] = max(JFE_Obs_geo.loc[minTime:maxTime, param].min(skipna=True), 0)
|
||||
# dataTable[paramIDX+4+7, 3] = JFE_Obs_geo.loc[minTime:maxTime, param].max(skipna=True) dataTable[paramIDX+4+7, 0] = JFE_Obs_geo.loc[minTime:maxTime, param].mean(skipna=True)
|
||||
|
||||
dataTable[paramIDX+4+7, 0] = JFE_smoothed.mean(skipna=True)
|
||||
dataTable[paramIDX+4+7, 1] = JFE_smoothed.std(skipna=True)
|
||||
dataTable[paramIDX+4+7, 2] = max(JFE_smoothed.min(skipna=True), 0)
|
||||
dataTable[paramIDX+4+7, 3] = JFE_smoothed.max(skipna=True)
|
||||
|
||||
axes[paramIDX+4].set_ylabel(JFEparamName[paramIDX])
|
||||
axes[paramIDX+4].set_title('JFE: ' + JFEparamName[paramIDX])
|
||||
axes[paramIDX+4].set_xlabel('')
|
||||
|
||||
axes[paramIDX+4].set_ylim(JFEparamMin[paramIDX], JFEparamMax[paramIDX])
|
||||
axes[paramIDX+4].legend(loc='upper right')
|
||||
|
||||
axes[paramIDX+4].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
else:
|
||||
axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
|
||||
|
||||
# Formate Data Table
|
||||
dataTableFormat_mean = []
|
||||
|
|
@ -517,33 +385,29 @@ for s in range(9,13):
|
|||
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||
np.array(['Mean', 'Min / Max'])]
|
||||
# Change the row indexes
|
||||
if JFE_File is not None:
|
||||
dfOut.iloc[14, 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-Flu. [ppb]', 'Chl-a [ug/l]', 'Observation Date']
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
else:
|
||||
dfOut.iloc[8, 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]',
|
||||
'Turbidity [NTU]', 'Observation Date']
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
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']
|
||||
|
||||
|
||||
fig.suptitle(siteName + ', ' +minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
dfOut.index = rowNames
|
||||
dfOutFormat.index = rowNames[0:-1]
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
|
||||
plt.show()
|
||||
|
||||
dfOut.to_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||
dfOutFormat.to_excel(importPath + '/Summary_StatsFormat_' + siteName + '.xlsx')
|
||||
|
||||
dfOut.to_csv(importPath + '/Summary_Stats_' + siteName + '.csv')
|
||||
|
||||
if not os.path.exists(importPath + '/Figures'):
|
||||
os.mkdir(importPath + '/Figures')
|
||||
|
||||
fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
|
||||
bbox_inches='tight')
|
||||
fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
|
||||
|
|
@ -551,14 +415,8 @@ for s in range(9,13):
|
|||
|
||||
|
||||
#%% Plot Maps
|
||||
if JFE_File is None:
|
||||
# Only Turbidity Data
|
||||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 9), constrained_layout=True)
|
||||
ax = []
|
||||
ax.append(axes)
|
||||
else:
|
||||
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(19, 25), constrained_layout=True)
|
||||
ax = axes.flat
|
||||
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)
|
||||
|
|
@ -567,108 +425,58 @@ for s in range(9,13):
|
|||
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||
plt.rcParams.update({'font.size': 22})
|
||||
|
||||
axXlimTT = (RBR_Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
|
||||
RBR_Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
|
||||
axYlimTT = (RBR_Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
||||
RBR_Obs_geo.loc[minTime:maxTime].geometry.y.max()+100)
|
||||
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(RBRparam):
|
||||
if RBR_File is not None:
|
||||
|
||||
# Draw thick black line to show approx path
|
||||
if GPS_File is None:
|
||||
ax[paramIDX].scatter(RBR_Obs_geo.loc[minTime:maxTime].geometry.x,
|
||||
RBR_Obs_geo.loc[minTime:maxTime].geometry.y,
|
||||
150, marker='o', color='black', label='Approximate Path')
|
||||
plt.legend(loc='upper left')
|
||||
|
||||
RBR_Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX], vmin=RBRparamMin[paramIDX], vmax=RBRparamMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': RBRparamName[paramIDX]},
|
||||
cmap=RBRparmCmap[paramIDX], markersize=20)
|
||||
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')
|
||||
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')
|
||||
# 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 RBR_Obs_geo.geometry.isnull().all()) & (GPS_File is not None) & (param == 'Turbidity '):
|
||||
for a in range(0, len(ilocs_max[paramIDX])):
|
||||
ax[paramIDX].annotate(str(a + 1), (RBR_Obs_geo.loc[RBR_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.x,
|
||||
RBR_Obs_geo.loc[RBR_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
||||
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(RBRparamName[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].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([])
|
||||
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)
|
||||
#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)
|
||||
|
||||
# Plot Plot JFE Points
|
||||
JFEparam = ['Turb. -M[FTU]', 'Chl-Flu.[ppb]']
|
||||
JFEparamName = ['Turbidity [FTU]', 'Chl-Flu. [ppb]']
|
||||
JFEparamCmp = [cmo.turbid, cmo.algae]
|
||||
JFEparamMin = [0.0, 0.0]
|
||||
JFEparamMax = [10.0, 1.0]
|
||||
if JFE_File is not None:
|
||||
for paramIDX, param in enumerate(JFEparam):
|
||||
if JFE_File is not None:
|
||||
JFE_Obs_geo.loc[minTime:maxTime].plot(
|
||||
column=param, ax=ax[paramIDX+4], vmin=JFEparamMin[paramIDX], vmax=JFEparamMax[paramIDX],
|
||||
legend=True, legend_kwds={'label': JFEparamName[paramIDX]},
|
||||
cmap=JFEparamCmp[paramIDX], markersize=20) # Note the space in the col name
|
||||
ctx.add_basemap(ax[paramIDX+4], source=mapbox, crs='EPSG:32621')
|
||||
|
||||
# Add time labels
|
||||
if (not JFE_Obs_geo.geometry.isnull().all()) & (GPS_File is not None) & (param == 'Turb. -M[FTU]'):
|
||||
for a in range(0, len(ilocs_max[paramIDX+4])):
|
||||
ax[paramIDX+4].annotate(str(a + 1), (JFE_Obs_geo.loc[JFE_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX+4][a], :].geometry.x,
|
||||
JFE_Obs_geo.loc[JFE_mask[paramIDX]].iloc[
|
||||
ilocs_max[paramIDX+4][a], :].geometry.y), fontsize=30)
|
||||
|
||||
ax[paramIDX+4].set_title(JFEparamName[paramIDX])
|
||||
# ax[paramIDX+4].set_ylabel('UTM 21N [m]')
|
||||
# ax[paramIDX+4].set_xlabel('UTM 21N [m]')
|
||||
ax[paramIDX+4].locator_params(axis='y', nbins=3)
|
||||
ax[paramIDX+4].ticklabel_format(useOffset=False, style='plain', axis='both')
|
||||
ax[paramIDX+4].get_xaxis().set_ticks([])
|
||||
ax[paramIDX+4].get_yaxis().set_ticks([])
|
||||
|
||||
#Add scale-bar
|
||||
scalebar = AnchoredSizeBar(ax[paramIDX+4].transData,
|
||||
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
||||
ax[paramIDX+4].add_artist(scalebar)
|
||||
ax[paramIDX+4].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
||||
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
||||
ha='center', va='center', fontsize=25,
|
||||
xycoords=ax[paramIDX+4].transAxes)
|
||||
|
||||
fig.suptitle(siteName + ', ' + minTime.strftime('%b %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)
|
||||
plt.show()
|
||||
|
||||
if not os.path.exists(importPath + '/Figures'):
|
||||
os.mkdir(importPath + '/Figures')
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,102 @@
|
|||
## Read, process, and merge bathymetry data
|
||||
# Author: AJMR
|
||||
# Date: 2022-02-10
|
||||
|
||||
#%% Import
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
from mikeio import Mesh
|
||||
import math
|
||||
|
||||
from scipy.spatial import cKDTree
|
||||
from shapely.geometry import Point
|
||||
|
||||
import numpy as np
|
||||
import cartopy.crs as ccrs
|
||||
import contextily as ctx
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
#%% Functions
|
||||
def ckdnearest(gdA, gdB):
|
||||
|
||||
nA = np.array(list(gdA.geometry.apply(lambda x: (x.x, x.y))))
|
||||
nB = np.array(list(gdB.geometry.apply(lambda x: (x.x, x.y))))
|
||||
btree = cKDTree(nB)
|
||||
dist, idx = btree.query(nA, k=1)
|
||||
gdB_nearest = gdB.iloc[idx].drop(columns="geometry").reset_index(drop=True)
|
||||
gdf = pd.concat(
|
||||
[
|
||||
gdA.reset_index(drop=True),
|
||||
gdB_nearest,
|
||||
pd.Series(dist, name='dist')
|
||||
],
|
||||
axis=1)
|
||||
|
||||
return gdf
|
||||
#%% Setup Paths
|
||||
dataPath = "C:/Users/arey/files/Projects/SouthShore/Bathy/"
|
||||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||
|
||||
#%% Read in Source 1
|
||||
S1 = pd.read_csv(dataPath + '211102_05 South MLW Breakwall inner Breakwall SBES and Median MBES NAD83 NAVD88 1x1.xyz',
|
||||
delimiter='\s+', header=None, names=['x', 'y', 'z'])
|
||||
|
||||
#%% Read in Source 2
|
||||
S2 = pd.read_csv(dataPath + '211102_05 South MLW Breakwall sturcture Median MBES NAD83 NAVD88 1x1.xyz',
|
||||
delimiter='\s+', header=None, names=['x', 'y', 'z'])
|
||||
|
||||
#%% Read in Source 3
|
||||
S3 = pd.read_csv(dataPath + '211102_05 South MLW Breakwall outer Breakwall Median MBES NAD83 NAVD88 1x1.xyz',
|
||||
delimiter='\s+', header=None, names=['x', 'y', 'z'])
|
||||
|
||||
#%% Merge Sources
|
||||
bathy_merged = pd.concat([S1, S2, S3])
|
||||
|
||||
# Convert to GeoPandas
|
||||
bathy_gdf = gp.GeoDataFrame(bathy_merged, crs='EPSG:2289',
|
||||
geometry=gp.points_from_xy(bathy_merged.x, bathy_merged.y))
|
||||
bathy_gdf.to_crs('EPSG:32616', inplace=True)
|
||||
|
||||
bathy_gdf.loc[:, 'z'] = bathy_gdf.loc[:, 'z'] * 0.3048
|
||||
|
||||
del S1
|
||||
del S2
|
||||
del S3
|
||||
del bathy_merged
|
||||
|
||||
bathy_gdf.drop(columns=['x', 'y'], inplace=True)
|
||||
|
||||
#%% Read in Previous Bathymetry
|
||||
previousBath = gp.read_file("//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/SSB_Mesh_v40_Nodes.shp")
|
||||
|
||||
|
||||
#%% Read in new mesh
|
||||
currenMesh = Mesh("//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/southshore_mesh2_v3InnerBounds.mesh")
|
||||
|
||||
#%% Make Geodataframe of mesh
|
||||
currenMesh_gdf = gp.GeoDataFrame(currenMesh.node_coordinates, crs='EPSG:32616',
|
||||
geometry=gp.points_from_xy(currenMesh.node_coordinates[:, 0],
|
||||
currenMesh.node_coordinates[:, 1]))
|
||||
#%% Find Nearest Points to All
|
||||
Bathy_nearest = ckdnearest(currenMesh_gdf, bathy_gdf)
|
||||
|
||||
# Save to file
|
||||
Bathy_nearest.rename(columns={0: 'x'}, inplace=True)
|
||||
Bathy_nearest.rename(columns={1: 'y'}, inplace=True)
|
||||
Bathy_nearest.rename(columns={2: 'z_old'}, inplace=True)
|
||||
Bathy_nearest.to_parquet("C:/Users/arey/Files/Projects/SouthShoreNearest.parquet")
|
||||
Bathy_nearest.to_file("C:/Users/arey/Files/Projects/SouthShoreNearest.shp")
|
||||
#%% Update Mesh
|
||||
newMesh = currenMesh
|
||||
|
||||
newMesh.node_coordinates[Bathy_nearest.dist<100, 2] = Bathy_nearest.loc[Bathy_nearest.dist<100, 'z']
|
||||
|
||||
newMesh.write("//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/southshore_mesh2_v3InnerBounds_Bathy.mesh")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -37,8 +37,31 @@ 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)
|
||||
|
||||
# %% Modify Breakwater Crest to new elevation
|
||||
# From email
|
||||
breakSegments = np.zeros((4, 3))
|
||||
breakSegments[0, :] = [2534702.7417, 372246.1099, 0] # 6+85
|
||||
breakSegments[1, :] = [2535059.6902, 371628.1622, 180] # 14+00
|
||||
breakSegments[2, :] = [2535910.4820, 370655.9834, 181.1] # 27+00
|
||||
breakSegments[3, :] = [2536821.5682, 369722.4850, 180.5] # 40+05
|
||||
|
||||
breakSegments_gdf = gp.GeoDataFrame(breakSegments,
|
||||
geometry=gp.points_from_xy(breakSegments[:, 0], breakSegments[:, 1]),
|
||||
crs='EPSG:8796')
|
||||
breakSegments_gdf.to_crs('EPSG:32616', inplace=True)
|
||||
|
||||
for idx in range(0, breakwaterPTS_Crest.shape[0]):
|
||||
|
||||
# Check where point is (based on x coordinate) and assign new crest elevation
|
||||
if breakSegments_gdf.geometry.x[0] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[1]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[1, 2]
|
||||
elif breakSegments_gdf.geometry.x[1] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[2]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[2, 2]
|
||||
elif breakSegments_gdf.geometry.x[2] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[3]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[3, 2]
|
||||
|
||||
# %% Read Model Results
|
||||
modelPlot = range(12, 20)
|
||||
modelPlot = range(31, 32)
|
||||
|
||||
for m in modelPlot:
|
||||
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||
|
|
@ -94,32 +117,41 @@ for m in modelPlot:
|
|||
(transmit_paramA > transmit_paramB))
|
||||
|
||||
# Updated approach
|
||||
breakwaterPTS_times['transmitted'][breakMask_A] = breakwaterPTS_times['Sign. Wave Height'][breakMask_A] * 0.09 * gamma
|
||||
breakwaterPTS_times['transmitted'][breakMask_B] = breakwaterPTS_times['Sign. Wave Height'][breakMask_B] *\
|
||||
breakwaterPTS_times.loc[breakMask_A, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_A] * 0.09 * gamma
|
||||
breakwaterPTS_times.loc[breakMask_B, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_B] *\
|
||||
transmit_paramA
|
||||
breakwaterPTS_times['transmitted'][breakMask_C] = breakwaterPTS_times['Sign. Wave Height'][breakMask_C] * \
|
||||
breakwaterPTS_times.loc[breakMask_C, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_C] * \
|
||||
transmit_paramB[breakMask_C]
|
||||
|
||||
# %% Merge with breakwater transformed waves
|
||||
breakwaterPTS_end = breakwaterPTS_times['Time'] == breakwaterPTS_times['Time'].max()
|
||||
|
||||
# %% Read Inner Boundary Points North
|
||||
# innerBoundNorth = pd.read_csv(
|
||||
# '//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_North.txt',
|
||||
# delimiter='\s+', header=None, names=['x', 'y'])
|
||||
innerBoundNorth = pd.read_csv(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_North.txt',
|
||||
delimiter='\s+', header=None, names=['x', 'y'])
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_Mesh2_North.txt',
|
||||
delimiter='\s+', header=None, names=['node', 'val', 'x', 'y'])
|
||||
|
||||
innerBoundNorth.sort_values(by=['x'], inplace=True)
|
||||
innerBoundNorth_gdf = gp.GeoDataFrame(innerBoundNorth, crs='EPSG:32616',
|
||||
geometry=gp.points_from_xy(innerBoundNorth.x, innerBoundNorth.y))
|
||||
innerBoundNorth_gdf = innerBoundNorth_gdf.sjoin_nearest(breakwaterPTS_times.loc[breakwaterPTS_end, :])
|
||||
# %% Read Inner Boundary Points Mid
|
||||
# innerBoundMid = pd.read_csv(
|
||||
# '//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_Mid.txt',
|
||||
# delimiter='\s+', header=None, names=['x', 'y'])
|
||||
innerBoundMid = pd.read_csv(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_Mid.txt',
|
||||
delimiter='\s+', header=None, names=['x', 'y'])
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_Mesh2_Mid.txt',
|
||||
delimiter='\s+', header=None, names=['node', 'val', 'x', 'y'])
|
||||
|
||||
innerBoundMid.sort_values(by=['x'], inplace=True)
|
||||
innerBoundMid_gdf = gp.GeoDataFrame(innerBoundMid, crs='EPSG:32616',
|
||||
geometry=gp.points_from_xy(innerBoundMid.x, innerBoundMid.y))
|
||||
innerBoundMid_gdf = innerBoundMid_gdf.sjoin_nearest(breakwaterPTS_times.loc[breakwaterPTS_end, :])
|
||||
# %% Read Inner Boundary Points North
|
||||
|
||||
# %% Read Inner Boundary Points South
|
||||
innerBoundSouth = pd.read_csv(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_South.txt',
|
||||
delimiter='\s+', header=None, names=['x', 'y'])
|
||||
|
|
|
|||
|
|
@ -37,13 +37,18 @@ 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)
|
||||
|
||||
# Add in Default width and slope
|
||||
breakwaterPTS_Crest['Width'] = 5.0
|
||||
breakwaterPTS_Crest['Slope'] = 2.6
|
||||
|
||||
# %% Modify Breakwater Crest to new elevation
|
||||
# From email
|
||||
breakSegments = np.zeros((4, 3))
|
||||
breakSegments[0, :] = [2534702.7417, 372246.1099, 0] # 6+85
|
||||
breakSegments[1, :] = [2535059.6902, 371628.1622, 180] # 14+00
|
||||
breakSegments[2, :] = [2535910.4820, 370655.9834, 181.1] # 27+00
|
||||
breakSegments[3, :] = [2536821.5682, 369722.4850, 180.5] # 40+05
|
||||
# X, Y, Crest, Slope, Cotangent
|
||||
breakSegments = np.zeros((4, 5))
|
||||
breakSegments[0, :] = [2534702.7417, 372246.1099, 0, 0, 0] # 6+85
|
||||
breakSegments[1, :] = [2535059.6902, 371628.1622, 180.5, 5.5, 2.6] # 14+00
|
||||
breakSegments[2, :] = [2535910.4820, 370655.9834, 181.1, 6.7, 2.6] # 27+00
|
||||
breakSegments[3, :] = [2536821.5682, 369722.4850, 180.5, 5.5, 2.6] # 40+05
|
||||
|
||||
breakSegments_gdf = gp.GeoDataFrame(breakSegments,
|
||||
geometry=gp.points_from_xy(breakSegments[:, 0], breakSegments[:, 1]),
|
||||
|
|
@ -55,13 +60,19 @@ for idx in range(0, breakwaterPTS_Crest.shape[0]):
|
|||
# Check where point is (based on x coordinate) and assign new crest elevation
|
||||
if breakSegments_gdf.geometry.x[0] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[1]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[1, 2]
|
||||
breakwaterPTS_Crest.loc[idx, 'Width'] = breakSegments[1, 3]
|
||||
breakwaterPTS_Crest.loc[idx, 'Slope'] = breakSegments[1, 4]
|
||||
elif breakSegments_gdf.geometry.x[1] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[2]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[2, 2]
|
||||
breakwaterPTS_Crest.loc[idx, 'Width'] = breakSegments[2, 3]
|
||||
breakwaterPTS_Crest.loc[idx, 'Slope'] = breakSegments[2, 4]
|
||||
elif breakSegments_gdf.geometry.x[2] <= breakwaterPTS_Crest.geometry.x[idx] <= breakSegments_gdf.geometry.x[3]:
|
||||
breakwaterPTS_Crest.loc[idx, 'Crest'] = breakSegments[3, 2]
|
||||
breakwaterPTS_Crest.loc[idx, 'Width'] = breakSegments[3, 3]
|
||||
breakwaterPTS_Crest.loc[idx, 'Slope'] = breakSegments[3, 4]
|
||||
|
||||
# %% Read Model Results
|
||||
modelPlot = range(31, 32)
|
||||
modelPlot = range(33, 40)
|
||||
|
||||
for m in modelPlot:
|
||||
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||
|
|
@ -99,37 +110,53 @@ for m in modelPlot:
|
|||
|
||||
# %% Conversion
|
||||
breakwaterPTS_times['transmitted'] = np.full([breakwaterPTS_times.shape[0], 1], np.nan)
|
||||
gamma = 1.2
|
||||
|
||||
transmit_paramA = 0.81 * gamma
|
||||
transmit_paramB = 0.45 * gamma - 0.3 * gamma * \
|
||||
(breakwaterPTS_times['Crest'] - breakwaterPTS_times['Surface elevation']) / \
|
||||
breakwaterPTS_times['Sign. Wave Height']
|
||||
|
||||
# Identify different overtopping regimes
|
||||
breakMask_A = (breakwaterPTS_times['Crest'] - breakwaterPTS_times['Surface elevation']) / \
|
||||
breakwaterPTS_times['Sign. Wave Height'] >= 1.2
|
||||
breakMask_B = (((breakwaterPTS_times['Crest'] - breakwaterPTS_times['Surface elevation']) /
|
||||
breakwaterPTS_times['Sign. Wave Height'] < 1.2) &
|
||||
(transmit_paramA < transmit_paramB))
|
||||
breakMask_C = (((breakwaterPTS_times['Crest'] - breakwaterPTS_times['Surface elevation']) /
|
||||
breakwaterPTS_times['Sign. Wave Height'] < 1.2) &
|
||||
(transmit_paramA > transmit_paramB))
|
||||
breakMask_A = breakwaterPTS_times['Sign. Wave Height'] < 0
|
||||
breakMask_B = (breakwaterPTS_times['Width'] / breakwaterPTS_times['Sign. Wave Height']) <=8
|
||||
breakMask_C = (breakwaterPTS_times['Width'] / breakwaterPTS_times['Sign. Wave Height']) >=12
|
||||
breakMask_Da = (breakwaterPTS_times['Width'] / breakwaterPTS_times['Sign. Wave Height']) >8
|
||||
breakMask_Db = (breakwaterPTS_times['Width'] / breakwaterPTS_times['Sign. Wave Height']) <12
|
||||
breakMask_D = breakMask_Da & breakMask_Db
|
||||
|
||||
# Updated approach
|
||||
breakwaterPTS_times.loc[breakMask_A, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_A] * 0.09 * gamma
|
||||
breakwaterPTS_times.loc[breakMask_B, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_B] *\
|
||||
transmit_paramA
|
||||
breakwaterPTS_times.loc[breakMask_C, 'transmitted'] = breakwaterPTS_times['Sign. Wave Height'][breakMask_C] * \
|
||||
transmit_paramB[breakMask_C]
|
||||
breakwaterPTS_times.loc[breakMask_A, 'transmitted'] = 0
|
||||
breakwaterPTS_times.loc[breakMask_B, 'transmitted'] = breakwaterPTS_times.loc[breakMask_B, 'Sign. Wave Height']*\
|
||||
np.maximum(0, np.minimum(0.8, (-0.4 * (breakwaterPTS_times.loc[breakMask_B, 'Crest'] - breakwaterPTS_times.loc[breakMask_B, 'Surface elevation']) /
|
||||
breakwaterPTS_times.loc[breakMask_B, 'Sign. Wave Height'] + 0.64 * (breakwaterPTS_times.loc[breakMask_B, 'Width'] /
|
||||
breakwaterPTS_times.loc[breakMask_B, 'Sign. Wave Height']) ** (-0.31) * (1 - np.exp(-0.5 * (1 / breakwaterPTS_times.loc[breakMask_B, 'Slope'] /
|
||||
np.sqrt(2 * math.pi * breakwaterPTS_times.loc[breakMask_B, 'Sign. Wave Height'] / 9.81 / breakwaterPTS_times.loc[breakMask_B, 'Peak Wave Period'] ** 2)))))))
|
||||
|
||||
breakwaterPTS_times.loc[breakMask_C, 'transmitted'] = breakwaterPTS_times.loc[breakMask_C, 'Sign. Wave Height'] * \
|
||||
np.maximum(0.05, np.minimum(np.maximum(0, -0.006 * breakwaterPTS_times.loc[breakMask_C, 'Width'] / breakwaterPTS_times.loc[breakMask_C, 'Sign. Wave Height'] + 0.93),
|
||||
(-0.35 * (breakwaterPTS_times.loc[breakMask_C, 'Crest'] - breakwaterPTS_times.loc[breakMask_C, 'Surface elevation']) /
|
||||
breakwaterPTS_times.loc[breakMask_C, 'Sign. Wave Height'] + 0.51 * (breakwaterPTS_times.loc[breakMask_C, 'Width'] /
|
||||
breakwaterPTS_times.loc[breakMask_C, 'Sign. Wave Height']) ** (-0.65) * (1 - np.exp(-0.41 * (1 / breakwaterPTS_times.loc[breakMask_C, 'Slope'] /
|
||||
np.sqrt(2 * math.pi * breakwaterPTS_times.loc[breakMask_C, 'Sign. Wave Height'] / 9.81 / breakwaterPTS_times.loc[breakMask_C, 'Peak Wave Period'] ** 2)))))))
|
||||
|
||||
breakwaterPTS_times.loc[breakMask_D, 'transmitted'] = breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] * \
|
||||
((np.maximum(0.05, np.minimum(np.maximum(0, -0.006 * breakwaterPTS_times.loc[breakMask_D, 'Width'] / breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] + 0.93),
|
||||
(-0.35 * (breakwaterPTS_times.loc[breakMask_D, 'Crest'] - breakwaterPTS_times.loc[breakMask_D, 'Surface elevation']) /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] + 0.51 * (breakwaterPTS_times.loc[breakMask_D, 'Width'] /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height']) ** (-0.65) * (1 - np.exp(-0.41 * (1 / breakwaterPTS_times.loc[breakMask_D, 'Slope'] /
|
||||
np.sqrt(2 * math.pi * breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] / 9.81 / breakwaterPTS_times.loc[breakMask_D, 'Peak Wave Period'] ** 2))))))) -
|
||||
np.maximum(0, np.minimum(0.8, (-0.4 * (breakwaterPTS_times.loc[breakMask_D, 'Crest'] - breakwaterPTS_times.loc[breakMask_D, 'Surface elevation']) /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] + 0.64 * (breakwaterPTS_times.loc[breakMask_D, 'Width'] /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height']) ** (-0.31) * (1 - np.exp(-0.5 * (1 / breakwaterPTS_times.loc[breakMask_D,'Slope'] /
|
||||
np.sqrt(2 * math.pi * breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] / 9.81 / breakwaterPTS_times.loc[breakMask_D,'Peak Wave Period'] ** 2)))))))) *
|
||||
(breakwaterPTS_times.loc[breakMask_D,'Width'] / breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] - 8) / (12 - 8) +\
|
||||
np.maximum(0, np.minimum(0.8, (-0.4 * (breakwaterPTS_times.loc[breakMask_D, 'Crest'] - breakwaterPTS_times.loc[breakMask_D, 'Surface elevation']) /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] + 0.64 * (breakwaterPTS_times.loc[breakMask_D, 'Width'] /
|
||||
breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height']) ** (-0.31) * (1 - np.exp(-0.5 * (1 / breakwaterPTS_times.loc[breakMask_D, 'Slope'] /
|
||||
np.sqrt(2 * math.pi * breakwaterPTS_times.loc[breakMask_D, 'Sign. Wave Height'] / 9.81 / breakwaterPTS_times.loc[breakMask_D, 'Peak Wave Period'] ** 2))))))))
|
||||
|
||||
|
||||
|
||||
|
||||
# %% Merge with breakwater transformed waves
|
||||
breakwaterPTS_end = breakwaterPTS_times['Time'] == breakwaterPTS_times['Time'].max()
|
||||
|
||||
# %% Read Inner Boundary Points North
|
||||
# innerBoundNorth = pd.read_csv(
|
||||
# '//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_North.txt',
|
||||
# delimiter='\s+', header=None, names=['x', 'y'])
|
||||
innerBoundNorth = pd.read_csv(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/InnerBound_Mesh2_North.txt',
|
||||
delimiter='\s+', header=None, names=['node', 'val', 'x', 'y'])
|
||||
|
|
@ -169,7 +196,7 @@ for m in modelPlot:
|
|||
inplace=True)
|
||||
|
||||
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||
runLog['Run Name'][m] + 'ProdInnerNorth1.dfs1'
|
||||
runLog['Run Name'][m] + 'TestInnerNorth_RevB.dfs1'
|
||||
|
||||
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||
|
|
@ -199,7 +226,7 @@ for m in modelPlot:
|
|||
inplace=True)
|
||||
|
||||
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||
runLog['Run Name'][m] + 'ProdInnerMid.dfs1'
|
||||
runLog['Run Name'][m] + 'TestInnerMid_RevB.dfs1'
|
||||
|
||||
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakw
|
|||
runLog = pd.read_excel(pth.as_posix(), "HD")
|
||||
|
||||
# %% Read Model Results
|
||||
modelPlot = range(2, 6)
|
||||
modelPlot = range(6, 7)
|
||||
|
||||
curSpeed_list = [None] * (max(modelPlot) + 1)
|
||||
curElm_list = [None] * (max(modelPlot) + 1)
|
||||
|
|
@ -29,6 +29,9 @@ curU_list = [None] * (max(modelPlot) + 1)
|
|||
curV_list = [None] * (max(modelPlot) + 1)
|
||||
dfs_list = [None] * (max(modelPlot) + 1)
|
||||
ds_list = [None] * (max(modelPlot) + 1)
|
||||
wl_list = [None] * (max(modelPlot) + 1)
|
||||
wd_list = [None] * (max(modelPlot) + 1)
|
||||
bed_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
for m in modelPlot:
|
||||
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m],
|
||||
|
|
@ -40,7 +43,9 @@ for m in modelPlot:
|
|||
curU_list[m] = ds_list[m]["U velocity"][-1]
|
||||
curV_list[m] = ds_list[m]["V velocity"][-1]
|
||||
curElm_list[m] = dfs_list[m].element_coordinates
|
||||
|
||||
wl_list[m] = ds_list[m]["Surface elevation"][-1]
|
||||
wd_list[m] = ds_list[m]["Total water depth"][-1]
|
||||
bed_list[m] = wl_list[m] - wd_list[m]
|
||||
# # Read Mesh for first model
|
||||
# mikeMeshIN = dfsIN.to_shapely()
|
||||
# # Convert to list of polygons
|
||||
|
|
@ -59,14 +64,14 @@ for m in modelPlot:
|
|||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/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(2, 6)
|
||||
modelPlot = range(6, 7)
|
||||
|
||||
vmin = 0
|
||||
vmax = 1
|
||||
scale = 1 #5
|
||||
|
||||
for m in modelPlot:
|
||||
if m < 4:
|
||||
if m < 4 or m == 6:
|
||||
vmax = 5
|
||||
qmax = 4
|
||||
scale = 50
|
||||
|
|
@ -134,6 +139,32 @@ for m in modelPlot:
|
|||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/04_MIKE21_HD/Images/Production/' +
|
||||
runLog['Short Description'][m] + '_RevC_Current.png', bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Plot Bathymetry
|
||||
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
# Convert to feet and ignore missing
|
||||
bed_plot = (bed_list[m] * 3.28084) - 578.007
|
||||
bed_nan = np.isnan(bed_plot)
|
||||
|
||||
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:32616')
|
||||
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakw
|
|||
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||
|
||||
# %% Read Model Results
|
||||
modelPlot = range(20, 29)
|
||||
modelPlot = range(40, 47)
|
||||
hmo_list = [None] * (max(modelPlot) + 1)
|
||||
dfs_list = [None] * (max(modelPlot) + 1)
|
||||
|
||||
|
|
@ -53,7 +53,8 @@ for m in modelPlot:
|
|||
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/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(28, 29)
|
||||
modelPlot = range(40, 47)
|
||||
|
||||
for m in modelPlot:
|
||||
fig, axes = plt.subplots(figsize=(8, 8))
|
||||
|
||||
|
|
@ -88,7 +89,7 @@ for m in modelPlot:
|
|||
plt.show()
|
||||
|
||||
fig.savefig(
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Images/Production/' +
|
||||
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Images/Production-Mesh2/' +
|
||||
runLog['Short Description'][m] + '_HM0.png', bbox_inches='tight', dpi=300)
|
||||
|
||||
# %% Read time series
|
||||
|
|
|
|||
Loading…
Reference in New Issue