Updated Mustique and SouthShore plotting
This commit is contained in:
Alexander Rey
parent
0dea526cd3
commit
b08bca6217
|
|
@ -0,0 +1,290 @@
|
||||||
|
## Plotting Mike21 SW results for SouthShore
|
||||||
|
# Author: AJMR
|
||||||
|
# December 22, 2021
|
||||||
|
|
||||||
|
# %% Setup Project
|
||||||
|
from mikeio import Dfsu, Mesh, Dfs2, Dfs0
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pathlib as pl
|
||||||
|
import numpy as np
|
||||||
|
import geopandas as gp
|
||||||
|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import contextily as ctx
|
||||||
|
import matplotlib.font_manager as fm
|
||||||
|
|
||||||
|
# %% Read Model Log
|
||||||
|
|
||||||
|
pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakwater", "06_Models",
|
||||||
|
"13632.101.W.AJMR.Rev0_ModelLog.xlsx")
|
||||||
|
runLog = pd.read_excel(pth.as_posix(), "HD")
|
||||||
|
|
||||||
|
# %% Read Model Results
|
||||||
|
modelPlot = range(6, 7)
|
||||||
|
|
||||||
|
curSpeed_list = [None] * (max(modelPlot) + 1)
|
||||||
|
curElm_list = [None] * (max(modelPlot) + 1)
|
||||||
|
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],
|
||||||
|
runLog['Run Name'][m] + '.m21fm - Result Files', 'FullDomain.dfsu').as_posix())
|
||||||
|
|
||||||
|
dfs_list[m] = dfsIN
|
||||||
|
ds_list[m] = dfs_list[m].read()
|
||||||
|
curSpeed_list[m] = ds_list[m]["Current speed"][-1]
|
||||||
|
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
|
||||||
|
# mikeMeshIN_list = [p for p in mikeMeshIN]
|
||||||
|
|
||||||
|
# Add to list
|
||||||
|
# hmo_list[m] = gp.GeoDataFrame(hm0, geometry=mikeMeshIN_list)
|
||||||
|
|
||||||
|
# Cleanup unnecessary variables
|
||||||
|
# del mikeMeshIN
|
||||||
|
# del mikeMeshIN_list
|
||||||
|
del dfsIN
|
||||||
|
|
||||||
|
# %% 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
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# 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]
|
||||||
|
|
||||||
|
# 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')
|
||||||
|
# axes.title.set_text(runLog['Short Description'][m])
|
||||||
|
# axes.titlesize = 'x-large'
|
||||||
|
fig.suptitle(runLog['Short Description'][m], fontsize=18)
|
||||||
|
axes.set_xlabel('Easting [m]')
|
||||||
|
axes.set_ylabel('Northing [m]')
|
||||||
|
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# %% 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)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
|
||||||
|
dfsIN_read = dfsIN.read()
|
||||||
|
MIKEds_list[m] = dfsIN_read.to_dataframe()
|
||||||
|
|
||||||
|
dfsTIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'TransectPTS.dfs0').as_posix())
|
||||||
|
dfsTIN_read = dfsTIN.read()
|
||||||
|
MIKEdsT_list[m] = dfsTIN_read.to_dataframe()
|
||||||
|
|
||||||
|
# Cleanup unnecessary variables
|
||||||
|
del dfsIN_read
|
||||||
|
del dfsIN
|
||||||
|
del dfsTIN_read
|
||||||
|
del dfsTIN
|
||||||
|
|
||||||
|
# %% Read in Toe and Crest Shapefiles
|
||||||
|
breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
|
||||||
|
"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
|
||||||
|
"20211211_Toe Extents (to Alexander)/"
|
||||||
|
"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
|
||||||
|
breakCrest = pd.read_csv(
|
||||||
|
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
|
||||||
|
header=None, names=['x', 'y', 'z'])
|
||||||
|
breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
|
||||||
|
geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
|
||||||
|
breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
|
||||||
|
|
||||||
|
breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
|
||||||
|
breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
|
||||||
|
|
||||||
|
# %% Merge with data
|
||||||
|
breakPointsOut = [None] * (max(modelPlot) + 1)
|
||||||
|
breakTimesOut = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEds_list[m].shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterPTS_Crest
|
||||||
|
|
||||||
|
for i in range(0, MIKEds_list[m].shape[1]):
|
||||||
|
paramName = MIKEds_list[m].columns.values[i][MIKEds_list[m].columns.values[i].find('"', 5, -1) + 3:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
tmpFID = int(MIKEds_list[m].columns.values[i][1:MIKEds_list[m].columns.values[i].find('"', 1)])
|
||||||
|
tmpIND = int(MIKEds_list[m].columns.values[i][5:MIKEds_list[m].columns.values[i].find('"', 5)])
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
|
||||||
|
(breakwaterPTS_merge.vertex_ind == tmpIND)),
|
||||||
|
paramName] = MIKEds_list[m].iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEds_list[m].index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakTimesOut[m] = breakwaterPTS_times
|
||||||
|
|
||||||
|
# %% Read in Breakwater Transect Points Sh
|
||||||
|
breakwaterT = pd.read_csv("C:/Users/arey/files/Projects/SouthShore/Bathy/BreakTransectPTS_Names.csv", sep='\t')
|
||||||
|
|
||||||
|
breakwaterT_PTS = gp.GeoDataFrame(breakwaterT, geometry=gp.points_from_xy(breakwaterT.X, breakwaterT.Y),
|
||||||
|
crs='EPSG:32616')
|
||||||
|
|
||||||
|
# %% Merge with data
|
||||||
|
breakPoints_TOut = [None] * (max(modelPlot) + 1)
|
||||||
|
breakTimes_TOut = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEdsT_list[m].shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterT_PTS
|
||||||
|
|
||||||
|
for i in range(0, MIKEdsT_list[m].shape[1]):
|
||||||
|
paramName = MIKEdsT_list[m].columns.values[i][MIKEdsT_list[m].columns.values[i].find(':', 5, -1) + 2:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
tmpName = MIKEdsT_list[m].columns.values[i][0:MIKEdsT_list[m].columns.values[i].find(':', 1)]
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[(breakwaterPTS_merge.Name == tmpName),
|
||||||
|
paramName] = MIKEdsT_list[m].iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEdsT_list[m].index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakTimes_TOut[m] = breakwaterPTS_times
|
||||||
|
|
||||||
|
|
||||||
|
#%% Format and save
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
saveTmp = breakTimesOut[m].copy()
|
||||||
|
saveTmp['X'] = saveTmp.geometry.x
|
||||||
|
saveTmp['Y'] = saveTmp.geometry.y
|
||||||
|
saveTmp.drop(['vertex_par', 'vertex_p_1', 'angle', 'geometry', 'index_right',
|
||||||
|
'breakCrest_x', 'breakCrest_y', 'Length'], axis=1, inplace=True)
|
||||||
|
saveTmp.sort_values(by=['FID', 'vertex_ind'], inplace=True, ignore_index=True)
|
||||||
|
|
||||||
|
# Reorder columns
|
||||||
|
colNames = saveTmp.columns.values
|
||||||
|
saveTmp = saveTmp[['X', 'Y', *colNames[0:-2]]]
|
||||||
|
saveTmpT = saveTmp.transpose(copy=True)
|
||||||
|
|
||||||
|
# Transect points
|
||||||
|
saveTmp2 = breakTimes_TOut[m].copy()
|
||||||
|
saveTmp2.drop(['geometry'], axis=1, inplace=True)
|
||||||
|
saveTmp2T = saveTmp2.transpose(copy=True)
|
||||||
|
|
||||||
|
|
||||||
|
saveTmpT.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||||
|
'Toe' + runLog['Short Description'][m] + '.csv')
|
||||||
|
|
||||||
|
saveTmp2T.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||||
|
'Transect' + runLog['Short Description'][m] + '.csv')
|
||||||
|
|
@ -0,0 +1,585 @@
|
||||||
|
## Plotting Mike21 SW results for SouthShore
|
||||||
|
# Author: AJMR
|
||||||
|
# December 22, 2021
|
||||||
|
|
||||||
|
# %% Setup Project
|
||||||
|
from mikeio import Dfsu, Mesh, Dfs2, Dfs0, Dfs1
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pathlib as pl
|
||||||
|
import numpy as np
|
||||||
|
import geopandas as gp
|
||||||
|
import datetime as datetime
|
||||||
|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import contextily as ctx
|
||||||
|
import matplotlib.font_manager as fm
|
||||||
|
|
||||||
|
# %% Read Model Log
|
||||||
|
|
||||||
|
pth = pl.Path("//srv-ott3.baird.com/", "Projects", "13539.101 L'Ansecoy Bay, Mustique", "06_Models",
|
||||||
|
"Model Log Mustique.xlsx")
|
||||||
|
|
||||||
|
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||||
|
|
||||||
|
# %% Read 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)
|
||||||
|
z_list = [None] * (max(modelPlot) + 1)
|
||||||
|
z_profile_list = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
elm_list = [None] * (max(modelPlot) + 1)
|
||||||
|
elm_df_list = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
elm2d_list = [None] * (max(modelPlot) + 1)
|
||||||
|
elm2d_df_list = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
readPointsName = pd.read_csv("//srv-ott3.baird.com/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/Dataset locations_RevB.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",
|
||||||
|
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())
|
||||||
|
dfs2d_list[m] = dfsIN
|
||||||
|
|
||||||
|
# Read Map
|
||||||
|
# ds_list[m] = dfs_list[m].read()
|
||||||
|
# curU_list[m] = ds_list[m]["Depth averaged U velocity"]
|
||||||
|
# curV_list[m] = ds_list[m]["Depth averaged V velocity"]
|
||||||
|
# curElm_list[m] = dfs_list[m].element_coordinates
|
||||||
|
# wl_list[m] = ds_list[m]["Surface elevation"]
|
||||||
|
# z_list[m] = ds_list[m]["Z coordinate"]
|
||||||
|
|
||||||
|
## Read specific points in 2D
|
||||||
|
# Find nearest elements
|
||||||
|
elem_ids = dfs2d_list[m].find_nearest_elements(readPoints[:, 0], readPoints[:, 1])
|
||||||
|
# Read in data from nearest elements
|
||||||
|
elm2d_list[m] = dfs2d_list[m].read(elements=elem_ids)
|
||||||
|
|
||||||
|
# Convert to Pandas DataFrame
|
||||||
|
elm2d_df_list[m] = [None] * readPoints.shape[0]
|
||||||
|
for p in range(0, readPoints.shape[0]):
|
||||||
|
elm2d_df_list[m][p] = elm2d_list[m].isel(idx=p).to_dataframe()
|
||||||
|
|
||||||
|
|
||||||
|
## Read specific points 3D
|
||||||
|
# Setup MIKE object
|
||||||
|
dfsIN = Dfsu(pl.Path(runLog['Run Location'][m], 'fullDomain3D.dfsu').as_posix())
|
||||||
|
dfs_list[m] = dfsIN
|
||||||
|
|
||||||
|
# Find nearest elements
|
||||||
|
elem_ids = dfs_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_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]
|
||||||
|
|
||||||
|
# Convert to Pandas and add zidx
|
||||||
|
for z in range(0, z_profile_list[m][p].shape[0]):
|
||||||
|
df_tmp = elm_list[m].isel(idx=p * 5 + z).to_dataframe()
|
||||||
|
df_tmp['Z'] = z_profile_list[m][p][z]
|
||||||
|
df_tmp['Z_IDX'] = z
|
||||||
|
if z == 0:
|
||||||
|
elm_df_list[m][p] = df_tmp
|
||||||
|
else:
|
||||||
|
elm_df_list[m][p] = elm_df_list[m][p].append(df_tmp)
|
||||||
|
|
||||||
|
del df_tmp
|
||||||
|
|
||||||
|
# %% Import RBR data
|
||||||
|
rbr_path = "C:/Users/arey/files/Projects/Mustique/041279_20211203_1541Ruskin.xlsx"
|
||||||
|
rbr_pd = pd.read_excel(rbr_path, sheet_name='Wave', parse_dates=True, header=1, index_col=0)
|
||||||
|
rbr_pd['WL'] = rbr_pd['Depth'] - rbr_pd['Depth'].mean() - 0.2
|
||||||
|
|
||||||
|
# Correct time zone
|
||||||
|
rbr_pd.index = rbr_pd.index + datetime.timedelta(hours=4)
|
||||||
|
|
||||||
|
# %% Import Nortek Eco data
|
||||||
|
eco_path_1 = "//srv-ott3/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/02_Nortek ECO/Eco61_20210910184606.csv"
|
||||||
|
eco_path_2 = "//srv-ott3/Projects/13539.101 L'Ansecoy Bay, Mustique/03_Data/03_Field/01_September 2021 trip/02_Nortek ECO/Eco214_20210902120703.csv"
|
||||||
|
|
||||||
|
eco1_pd = pd.read_csv(eco_path_1, sep=',', header=[28], index_col=0,
|
||||||
|
parse_dates=False)
|
||||||
|
|
||||||
|
eco2_pd = pd.read_csv(eco_path_2, sep=',', header=[28], index_col=0,
|
||||||
|
parse_dates=False)
|
||||||
|
|
||||||
|
# Drop first row
|
||||||
|
eco1_pd.drop(eco1_pd.index[0], inplace=True)
|
||||||
|
eco2_pd.drop(eco2_pd.index[0], inplace=True)
|
||||||
|
|
||||||
|
eco1_pd.index = pd.to_datetime(eco1_pd.index, format='%m/%d/%Y %I:%M:%S %p')
|
||||||
|
eco2_pd.index = pd.to_datetime(eco2_pd.index, format='%m/%d/%Y %I:%M:%S %p')
|
||||||
|
|
||||||
|
# Set water level to zero
|
||||||
|
eco1_pd['WL'] = eco1_pd['Depth'].astype(float) - eco1_pd['Depth'].astype(float).mean(skipna=True) + 0.2
|
||||||
|
eco2_pd['WL'] = eco2_pd['Depth'].astype(float) - eco2_pd['Depth'].astype(float).mean(skipna=True) + 0.2
|
||||||
|
|
||||||
|
# Correct time zone
|
||||||
|
eco1_pd.index = eco1_pd.index + datetime.timedelta(hours=4)
|
||||||
|
eco2_pd.index = eco2_pd.index + datetime.timedelta(hours=4)
|
||||||
|
|
||||||
|
# %% Read in boundary conditions
|
||||||
|
ds_wl = []
|
||||||
|
|
||||||
|
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_EastBound.dfs1")
|
||||||
|
ds_wl.append(dfs1.read())
|
||||||
|
|
||||||
|
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_NorthBound.dfs1")
|
||||||
|
ds_wl.append(dfs1.read())
|
||||||
|
|
||||||
|
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_SouthBound.dfs1")
|
||||||
|
ds_wl.append(dfs1.read())
|
||||||
|
|
||||||
|
dfs1 = Dfs1("//oak-spillway.baird.com/D/13539.101 L'Ansecoy Bay, Mustique/MIKE3/02_Bounds/NCOM2_WestBound.dfs1")
|
||||||
|
ds_wl.append(dfs1.read())
|
||||||
|
|
||||||
|
# %% Plot Water Level at a point
|
||||||
|
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8), sharex=True)
|
||||||
|
# plotstart = '2021-10-01 00:00:00'
|
||||||
|
# plotend = '2021-10-21 00:00:00'
|
||||||
|
plotstart = '2021-10-05 00:00:00'
|
||||||
|
plotend = '2021-10-07 00:00:00'
|
||||||
|
|
||||||
|
for sub, p in enumerate([5, 6, 9]):
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
elm2d_df_list[m][p].plot(y='Surface elevation', ax=axes[sub], label=readPointsName.Dataset[p])
|
||||||
|
|
||||||
|
axes[sub].set_xlim(pd.Timestamp(plotstart), pd.Timestamp(plotend))
|
||||||
|
axes[sub].set_ylim(-1.0, 1.0)
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
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()
|
||||||
|
|
||||||
|
|
||||||
|
# %% Velocity point U and V
|
||||||
|
# Z_IDX = 4
|
||||||
|
# ecoVar1 = 'Upper speed U'
|
||||||
|
# ecoVar2 = 'Upper speed V'
|
||||||
|
# ecoVar3 = 'Upper speed'
|
||||||
|
# ecoVar4 = 'Upper direction'
|
||||||
|
|
||||||
|
# plotstart = '2021-09-14 00:00:00'
|
||||||
|
# # plotend = '2021-09-16 00:00:00'
|
||||||
|
# plotend = '2021-10-03 00:00:00'
|
||||||
|
|
||||||
|
plotstart = '2021-10-01 00:00:00'
|
||||||
|
# plotend = '2021-09-16 00:00:00'
|
||||||
|
plotend = '2021-10-21 00:00:00'
|
||||||
|
|
||||||
|
Z_IDX = 2
|
||||||
|
ecoVar1 = 'Middle speed U'
|
||||||
|
ecoVar2 = 'Middle speed V'
|
||||||
|
ecoVar3 = 'Middle speed'
|
||||||
|
ecoVar4 = 'Middle direction'
|
||||||
|
|
||||||
|
# Z_IDX = 1
|
||||||
|
# ecoVar1 = 'Lower speed U'
|
||||||
|
# ecoVar2 = 'Lower speed V'
|
||||||
|
# ecoVar3 = 'Lower speed'
|
||||||
|
# ecoVar4 = 'Lower direction'
|
||||||
|
|
||||||
|
fig, axes = plt.subplots(nrows=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
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# 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]
|
||||||
|
|
||||||
|
# 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')
|
||||||
|
# axes.title.set_text(runLog['Short Description'][m])
|
||||||
|
# axes.titlesize = 'x-large'
|
||||||
|
fig.suptitle(runLog['Short Description'][m], fontsize=18)
|
||||||
|
axes.set_xlabel('Easting [m]')
|
||||||
|
axes.set_ylabel('Northing [m]')
|
||||||
|
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# %% 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)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
|
||||||
|
dfsIN_read = dfsIN.read()
|
||||||
|
MIKEds_list[m] = dfsIN_read.to_dataframe()
|
||||||
|
|
||||||
|
dfsTIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'TransectPTS.dfs0').as_posix())
|
||||||
|
dfsTIN_read = dfsTIN.read()
|
||||||
|
MIKEdsT_list[m] = dfsTIN_read.to_dataframe()
|
||||||
|
|
||||||
|
# Cleanup unnecessary variables
|
||||||
|
del dfsIN_read
|
||||||
|
del dfsIN
|
||||||
|
del dfsTIN_read
|
||||||
|
del dfsTIN
|
||||||
|
|
||||||
|
# %% Read in Toe and Crest Shapefiles
|
||||||
|
breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
|
||||||
|
"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
|
||||||
|
"20211211_Toe Extents (to Alexander)/"
|
||||||
|
"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
|
||||||
|
breakCrest = pd.read_csv(
|
||||||
|
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
|
||||||
|
header=None, names=['x', 'y', 'z'])
|
||||||
|
breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
|
||||||
|
geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
|
||||||
|
breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
|
||||||
|
|
||||||
|
breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
|
||||||
|
breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
|
||||||
|
|
||||||
|
# %% Merge with data
|
||||||
|
breakPointsOut = [None] * (max(modelPlot) + 1)
|
||||||
|
breakTimesOut = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEds_list[m].shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterPTS_Crest
|
||||||
|
|
||||||
|
for i in range(0, MIKEds_list[m].shape[1]):
|
||||||
|
paramName = MIKEds_list[m].columns.values[i][MIKEds_list[m].columns.values[i].find('"', 5, -1) + 3:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
tmpFID = int(MIKEds_list[m].columns.values[i][1:MIKEds_list[m].columns.values[i].find('"', 1)])
|
||||||
|
tmpIND = int(MIKEds_list[m].columns.values[i][5:MIKEds_list[m].columns.values[i].find('"', 5)])
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
|
||||||
|
(breakwaterPTS_merge.vertex_ind == tmpIND)),
|
||||||
|
paramName] = MIKEds_list[m].iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEds_list[m].index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakTimesOut[m] = breakwaterPTS_times
|
||||||
|
|
||||||
|
# %% Read in Breakwater Transect Points Sh
|
||||||
|
breakwaterT = pd.read_csv("C:/Users/arey/files/Projects/SouthShore/Bathy/BreakTransectPTS_Names.csv", sep='\t')
|
||||||
|
|
||||||
|
breakwaterT_PTS = gp.GeoDataFrame(breakwaterT, geometry=gp.points_from_xy(breakwaterT.X, breakwaterT.Y),
|
||||||
|
crs='EPSG:32616')
|
||||||
|
|
||||||
|
# %% Merge with data
|
||||||
|
breakPoints_TOut = [None] * (max(modelPlot) + 1)
|
||||||
|
breakTimes_TOut = [None] * (max(modelPlot) + 1)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEdsT_list[m].shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterT_PTS
|
||||||
|
|
||||||
|
for i in range(0, MIKEdsT_list[m].shape[1]):
|
||||||
|
paramName = MIKEdsT_list[m].columns.values[i][MIKEdsT_list[m].columns.values[i].find(':', 5, -1) + 2:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
tmpName = MIKEdsT_list[m].columns.values[i][0:MIKEdsT_list[m].columns.values[i].find(':', 1)]
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[(breakwaterPTS_merge.Name == tmpName),
|
||||||
|
paramName] = MIKEdsT_list[m].iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEdsT_list[m].index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakTimes_TOut[m] = breakwaterPTS_times
|
||||||
|
|
||||||
|
# %% Format and save
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
saveTmp = breakTimesOut[m].copy()
|
||||||
|
saveTmp['X'] = saveTmp.geometry.x
|
||||||
|
saveTmp['Y'] = saveTmp.geometry.y
|
||||||
|
saveTmp.drop(['vertex_par', 'vertex_p_1', 'angle', 'geometry', 'index_right',
|
||||||
|
'breakCrest_x', 'breakCrest_y', 'Length'], axis=1, inplace=True)
|
||||||
|
saveTmp.sort_values(by=['FID', 'vertex_ind'], inplace=True, ignore_index=True)
|
||||||
|
|
||||||
|
# Reorder columns
|
||||||
|
colNames = saveTmp.columns.values
|
||||||
|
saveTmp = saveTmp[['X', 'Y', *colNames[0:-2]]]
|
||||||
|
saveTmpT = saveTmp.transpose(copy=True)
|
||||||
|
|
||||||
|
# Transect points
|
||||||
|
saveTmp2 = breakTimes_TOut[m].copy()
|
||||||
|
saveTmp2.drop(['geometry'], axis=1, inplace=True)
|
||||||
|
saveTmp2T = saveTmp2.transpose(copy=True)
|
||||||
|
|
||||||
|
saveTmpT.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||||
|
'Toe' + runLog['Short Description'][m] + '.csv')
|
||||||
|
|
||||||
|
saveTmp2T.to_csv('//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/06_Models/02_Mike21SW/Results/' +
|
||||||
|
'Transect' + runLog['Short Description'][m] + '.csv')
|
||||||
|
|
@ -0,0 +1,765 @@
|
||||||
|
#%% Project Setup
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import geopandas as gp
|
||||||
|
from scipy.signal import argrelextrema
|
||||||
|
import numpy as np
|
||||||
|
import math
|
||||||
|
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import matplotlib.font_manager as fm
|
||||||
|
import matplotlib as mpl
|
||||||
|
|
||||||
|
import cartopy.crs as ccrs
|
||||||
|
import contextily as ctx
|
||||||
|
import cmocean.cm as cmo
|
||||||
|
from shapely.geometry import Point, LineString
|
||||||
|
|
||||||
|
from xarray.backends import NetCDF4DataStore
|
||||||
|
import xarray as xr
|
||||||
|
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
from netCDF4 import num2date
|
||||||
|
from metpy.units import units
|
||||||
|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import cartopy.crs as ccrs
|
||||||
|
import cartopy.feature as cfeature
|
||||||
|
from metpy.plots import ctables
|
||||||
|
|
||||||
|
from siphon.catalog import TDSCatalog
|
||||||
|
|
||||||
|
#%% Helper function for finding proper time variable
|
||||||
|
|
||||||
|
def find_time_var(var, time_basename='time'):
|
||||||
|
for coord_name in var.coords:
|
||||||
|
if coord_name.startswith(time_basename):
|
||||||
|
return var.coords[coord_name]
|
||||||
|
raise ValueError('No time variable found for ' + var.name)
|
||||||
|
|
||||||
|
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
importPaths = ['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/']
|
||||||
|
|
||||||
|
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']
|
||||||
|
|
||||||
|
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):
|
||||||
|
## Define master import path
|
||||||
|
importPath = importPaths[s]
|
||||||
|
siteName = siteNames[s]
|
||||||
|
timeLabel = timeLabels[s]
|
||||||
|
importFiles = os.listdir(importPath)
|
||||||
|
|
||||||
|
# Initialize import variables
|
||||||
|
RBR_File = None
|
||||||
|
JFE_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]:
|
||||||
|
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)
|
||||||
|
|
||||||
|
#%% JFE Import Data
|
||||||
|
if JFE_File is not None:
|
||||||
|
JFE_Obs = pd.read_csv(importPath + JFE_File, skiprows=30)
|
||||||
|
|
||||||
|
#%% 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'])
|
||||||
|
#convert GPS data to geodataframe
|
||||||
|
GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs="EPSG:4326"))
|
||||||
|
|
||||||
|
GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))
|
||||||
|
|
||||||
|
GPS_gdf.set_index('DateTime', 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")
|
||||||
|
|
||||||
|
|
||||||
|
#%% 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)
|
||||||
|
|
||||||
|
# Merge with GPS as dataframe
|
||||||
|
RBR_Obs_geo = pd.merge_asof(RBR_Obs, 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")
|
||||||
|
|
||||||
|
#%% Find and setup key plotting details
|
||||||
|
|
||||||
|
# Shaded Water
|
||||||
|
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||||
|
# Sat water
|
||||||
|
# mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckekcw3pn08am19qmqbhtq8sb/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||||
|
|
||||||
|
if siteName == 'Great House':
|
||||||
|
axXlim = (213210.7529575412, 213562.64172686986)
|
||||||
|
axYlim = (1464769.2243017585, 1465135.2219089477)
|
||||||
|
GFS_Lon = -59.6441
|
||||||
|
GFS_Lat = 13.2372
|
||||||
|
RBR_Obs_geo['inArea'] = RBR_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])
|
||||||
|
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])
|
||||||
|
|
||||||
|
|
||||||
|
# Set min and max times using conductivity
|
||||||
|
# if JFE_File is None:
|
||||||
|
if RBR_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]
|
||||||
|
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)
|
||||||
|
|
||||||
|
metDate = minTime - timedelta(
|
||||||
|
hours = minTime.hour % 6,
|
||||||
|
minutes=minTime.minute,
|
||||||
|
seconds=minTime.second,
|
||||||
|
microseconds=minTime.microsecond)
|
||||||
|
|
||||||
|
|
||||||
|
#%% GFS Met Import
|
||||||
|
var = ['Temperature_surface', 'Wind_speed_gust_surface',
|
||||||
|
'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground']
|
||||||
|
var_precp = ['Total_precipitation_surface_6_Hour_Accumulation']
|
||||||
|
|
||||||
|
temp_1d = []
|
||||||
|
gust_1d = []
|
||||||
|
wndu_1d = []
|
||||||
|
wndv_1d = []
|
||||||
|
prep_1d = []
|
||||||
|
time_1d = []
|
||||||
|
|
||||||
|
# Set times to download
|
||||||
|
startdate = metDate - timedelta(hours=18)
|
||||||
|
enddate = metDate + timedelta(hours=6)
|
||||||
|
date_list = pd.date_range(startdate, enddate, freq='6H')
|
||||||
|
|
||||||
|
# Loop through dates
|
||||||
|
for date in date_list:
|
||||||
|
# Base URL for 0.5 degree GFS data
|
||||||
|
best_gfs = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/model-gfs-g4-anl-files/' +
|
||||||
|
date.strftime('%Y%m') + '/' + date.strftime('%Y%m%d') + '/' + 'catalog.xml')
|
||||||
|
|
||||||
|
# Generate URLs for specific grib file
|
||||||
|
best_ds = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_000.grb2']
|
||||||
|
best_ds_precp = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_006.grb2']
|
||||||
|
|
||||||
|
# Format the query parameters
|
||||||
|
ncss = best_ds.subset()
|
||||||
|
query = ncss.query()
|
||||||
|
|
||||||
|
ncss_precp = best_ds_precp.subset()
|
||||||
|
query_precp = ncss_precp.query()
|
||||||
|
|
||||||
|
# Extract data from specific point
|
||||||
|
query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
|
||||||
|
query.accept('netcdf')
|
||||||
|
query.variables(var[0], var[1], var[2], var[3])
|
||||||
|
query.vertical_level(10)
|
||||||
|
|
||||||
|
data = ncss.get_data(query)
|
||||||
|
data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')
|
||||||
|
|
||||||
|
query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))
|
||||||
|
query_precp.accept('netcdf')
|
||||||
|
query_precp.variables(var_precp[0])
|
||||||
|
|
||||||
|
|
||||||
|
data_precp = ncss_precp.get_data(query_precp)
|
||||||
|
data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))
|
||||||
|
|
||||||
|
temp_3d = data[var[0]]
|
||||||
|
gust_3d = data[var[1]]
|
||||||
|
wndu_3d = data[var[2]]
|
||||||
|
wndv_3d = data[var[3]]
|
||||||
|
prep_3d = data_precp[var_precp[0]]
|
||||||
|
|
||||||
|
# Read the individual point (with units) and append to the list
|
||||||
|
temp_1d.append(temp_3d.metpy.unit_array.squeeze())
|
||||||
|
gust_1d.append(gust_3d.metpy.unit_array.squeeze())
|
||||||
|
wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())
|
||||||
|
wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())
|
||||||
|
prep_1d.append(prep_3d.metpy.unit_array.squeeze())
|
||||||
|
time_1d.append(find_time_var(temp_3d))
|
||||||
|
|
||||||
|
#%% Process Met Data
|
||||||
|
# 24h Precipitation Total
|
||||||
|
# Time weighted average of last two points for everything else
|
||||||
|
|
||||||
|
met_prep = prep_1d[0] + prep_1d[1] + prep_1d[2] + prep_1d[3]
|
||||||
|
|
||||||
|
timeWeight1 = minTime-metDate
|
||||||
|
timeWeight2 = (metDate+timedelta(hours=6))-minTime
|
||||||
|
|
||||||
|
timeWeight1 = timeWeight1.seconds/21600
|
||||||
|
timeWeight2 = timeWeight2.seconds/21600
|
||||||
|
|
||||||
|
met_gust = gust_1d[3] * timeWeight1 + gust_1d[4] * timeWeight2
|
||||||
|
met_temp = temp_1d[3] * timeWeight1 + temp_1d[4] * timeWeight2
|
||||||
|
met_wind = math.sqrt((wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2) ** 2 +
|
||||||
|
(wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2) **2 )
|
||||||
|
met_wdir = math.degrees(math.atan2(
|
||||||
|
wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2,
|
||||||
|
wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2)) % 360
|
||||||
|
|
||||||
|
#%% Read in wave conditions from BTS file
|
||||||
|
if wave_bts_file[s] is not None:
|
||||||
|
if siteName == 'Great House':
|
||||||
|
wave_bts = pd.read_csv(wave_bts_file[s],
|
||||||
|
names=['date', 'time', 'HM0', 'TP', 'TM', 'MWD', 'DPK', 'HSWL', 'TSWL', 'DPSWL', 'HSEA', 'TSEA', 'DPSEA'],
|
||||||
|
header=0, skiprows=22, delim_whitespace=True)
|
||||||
|
wave_bts['datetime'] = pd.to_datetime(wave_bts['date'] + ' ' + wave_bts['time'])
|
||||||
|
wave_bts.set_index('datetime', inplace=True)
|
||||||
|
|
||||||
|
|
||||||
|
met_hmo = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].HM0
|
||||||
|
met_tp = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].TP
|
||||||
|
met_mwd = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].MWD
|
||||||
|
else:
|
||||||
|
met_hmo = -999
|
||||||
|
met_tp = -999
|
||||||
|
met_mwd = -999
|
||||||
|
|
||||||
|
|
||||||
|
#%% 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]
|
||||||
|
|
||||||
|
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]
|
||||||
|
|
||||||
|
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]
|
||||||
|
|
||||||
|
|
||||||
|
fig.patch.set_facecolor('white')
|
||||||
|
# fig.tight_layout(pad=1.05)
|
||||||
|
|
||||||
|
fontprops = fm.FontProperties(size=25)
|
||||||
|
|
||||||
|
dataTable[0, 0] = met_temp.m.item(0)-272.15
|
||||||
|
dataTable[1, 0] = met_wind
|
||||||
|
dataTable[2, 0] = met_wdir
|
||||||
|
dataTable[3, 0] = met_prep.m.item(0)
|
||||||
|
dataTable[4, 0] = met_hmo
|
||||||
|
dataTable[5, 0] = met_tp
|
||||||
|
dataTable[6, 0] = met_mwd
|
||||||
|
|
||||||
|
ilocs_max = []
|
||||||
|
ilocs_max_pts = []
|
||||||
|
RBR_mask = []
|
||||||
|
JFE_mask = []
|
||||||
|
|
||||||
|
for paramIDX, param in enumerate(RBRparam):
|
||||||
|
RBR_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])))
|
||||||
|
|
||||||
|
RBR_smoothed = RBR_Obs_geo.loc[RBR_mask[paramIDX], param].rolling(
|
||||||
|
60, win_type='nuttall',center=True).mean()
|
||||||
|
|
||||||
|
RBR_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])
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# 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')
|
||||||
|
for a in range(0, len(ilocs_max[paramIDX])):
|
||||||
|
axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 30), 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)
|
||||||
|
|
||||||
|
axes[paramIDX].set_ylabel(RBRparamName[paramIDX])
|
||||||
|
axes[paramIDX].set_title('RBR: ' + RBRparamName[paramIDX])
|
||||||
|
axes[paramIDX].set_xlabel('')
|
||||||
|
axes[paramIDX].set_ylim(RBRparamMin[paramIDX], RBRparamMax[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'))
|
||||||
|
|
||||||
|
# Formate Data Table
|
||||||
|
dataTableFormat_mean = []
|
||||||
|
dataTableFormat_maxmin = []
|
||||||
|
for d in range(0, len(roundIDX)):
|
||||||
|
dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
|
||||||
|
if dataTable[d, 3] == 0:
|
||||||
|
dataTableFormat_maxmin.append('--')
|
||||||
|
else:
|
||||||
|
dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
|
||||||
|
|
||||||
|
|
||||||
|
dfOut = pd.DataFrame(dataTable[:, :])
|
||||||
|
dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
|
||||||
|
|
||||||
|
# Change the column names
|
||||||
|
dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
|
||||||
|
dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
|
||||||
|
np.array(['Mean', 'Min / Max'])]
|
||||||
|
# Change the row indexes
|
||||||
|
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]
|
||||||
|
|
||||||
|
|
||||||
|
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')
|
||||||
|
|
||||||
|
fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
|
||||||
|
bbox_inches='tight')
|
||||||
|
fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
|
||||||
|
bbox_inches='tight', dpi=500)
|
||||||
|
|
||||||
|
|
||||||
|
#%% 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.patch.set_facecolor('white')
|
||||||
|
# fig.tight_layout(pad=1.05)
|
||||||
|
|
||||||
|
fontprops = fm.FontProperties(size=25)
|
||||||
|
x, y, arrow_length = 0.95, 0.93, 0.20
|
||||||
|
plt.rcParams.update({'font.size': 22})
|
||||||
|
|
||||||
|
axXlimTT = (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)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
|
||||||
|
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
||||||
|
|
||||||
|
# Add time labels
|
||||||
|
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||||
|
# ilocs_max, :].geometry.x,
|
||||||
|
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
||||||
|
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
||||||
|
|
||||||
|
if (not 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)
|
||||||
|
|
||||||
|
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].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)
|
||||||
|
|
||||||
|
# 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')
|
||||||
|
|
||||||
|
fig.savefig(importPath + '/Figures/SummaryMap_' + siteName + '.pdf',
|
||||||
|
bbox_inches='tight')
|
||||||
|
|
||||||
|
fig.savefig(importPath + '/Figures/SummaryMap_' + siteName + '.png',
|
||||||
|
bbox_inches='tight', dpi=500)
|
||||||
|
|
||||||
|
#%% Summary Sheet
|
||||||
|
plotIDXsLoop = []
|
||||||
|
plotIDXsLoop.append([0, 3, 6, 9])
|
||||||
|
plotIDXsLoop.append([1, 4, 7, 10])
|
||||||
|
plotIDXsLoop.append([2, 5, 8, 11])
|
||||||
|
|
||||||
|
for i in range(0, 3):
|
||||||
|
summTable = None
|
||||||
|
plotIDXs = plotIDXsLoop[i]
|
||||||
|
|
||||||
|
for s, plotIDX in enumerate(plotIDXs):
|
||||||
|
## Define master import path
|
||||||
|
importPath = importPaths[plotIDX]
|
||||||
|
siteName = siteNames[plotIDX]
|
||||||
|
|
||||||
|
obsStatsIN = pd.read_excel(importPath + '/Summary_StatsFormat_' + siteName + '.xlsx', header=[0,1], index_col=0)
|
||||||
|
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||||
|
obsStatsIN.rename({'Turbidity [NTU]': 'Turbidity [FTU]'}, inplace=True)
|
||||||
|
|
||||||
|
if s == 0:
|
||||||
|
summTable = obsStatsIN
|
||||||
|
else:
|
||||||
|
summTable = summTable.join(obsStatsIN)
|
||||||
|
|
||||||
|
# Remove -999 with nan
|
||||||
|
summTable.replace(-999, np.nan, inplace=True)
|
||||||
|
|
||||||
|
summTable.to_excel('//srv-ott3.baird.com/Projects/13033.201 Great House - Coastal Structures/05_Analyses/01_WQ Monitoring/CombinedStats/Summary_StatsMerge_' + siteName + '.xlsx')
|
||||||
|
|
||||||
|
|
||||||
|
#%% Summary Plot
|
||||||
|
|
||||||
|
plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
||||||
|
'Significant Wave Height [m]', 'Salinity [PSU]',
|
||||||
|
'Dissolved O₂ [%]', 'Temperature [degC]',
|
||||||
|
'Turbidity [FTU]', 'Chl-Flu. [ppb]']
|
||||||
|
|
||||||
|
|
||||||
|
plotIDXsLoop = []
|
||||||
|
plotIDXsLoop.append([0, 3, 6, 9])
|
||||||
|
plotIDXsLoop.append([1, 4, 7, 10])
|
||||||
|
plotIDXsLoop.append([2, 5, 8, 11])
|
||||||
|
|
||||||
|
for i in range(0, 3):
|
||||||
|
summTable = None
|
||||||
|
plotIDXs = plotIDXsLoop[i]
|
||||||
|
plotDates = []
|
||||||
|
plotTable = np.empty([10, len(plotIDXs)])
|
||||||
|
|
||||||
|
for s, plotIDX in enumerate(plotIDXs):
|
||||||
|
## Define master import path
|
||||||
|
importPath = importPaths[plotIDX]
|
||||||
|
siteName = siteNames[plotIDX]
|
||||||
|
|
||||||
|
obsStatsIN = pd.read_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')
|
||||||
|
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
||||||
|
plotTable[0:3, s] = obsStatsIN.iloc[0:3, 1]
|
||||||
|
plotTable[8, s] = obsStatsIN.iloc[7, 1]
|
||||||
|
plotDates.append(datetime.strptime(obsStatsIN.iloc[8, 1], '%B %d, %Y'))
|
||||||
|
else:
|
||||||
|
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 12], 1]
|
||||||
|
plotDates.append(datetime.strptime(obsStatsIN.iloc[14, 1], '%B %d, %Y'))
|
||||||
|
|
||||||
|
|
||||||
|
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(19, 25), sharex=True)
|
||||||
|
fig.patch.set_facecolor('white')
|
||||||
|
fig.tight_layout(pad=3)
|
||||||
|
ax = axes.flat
|
||||||
|
|
||||||
|
# Repalce zero with nan
|
||||||
|
plotTable[plotTable < 0.00001] = np.nan
|
||||||
|
|
||||||
|
for v in range(0, 10):
|
||||||
|
ax[v].scatter(plotDates, plotTable[v, :], 250)
|
||||||
|
ax[v].set_ylabel(plotvars[v])
|
||||||
|
|
||||||
|
fig.suptitle(siteName, fontsize=35)
|
||||||
|
plt.gcf().autofmt_xdate()
|
||||||
|
plt.gcf().align_ylabels()
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
fig.savefig('//srv-ott3.baird.com/Projects/13033.201 Great House - Coastal Structures/05_Analyses/01_WQ Monitoring/CombinedStats/' + siteName + '.pdf',
|
||||||
|
bbox_inches='tight')
|
||||||
|
fig.savefig('//srv-ott3.baird.com/Projects/13033.201 Great House - Coastal Structures/05_Analyses/01_WQ Monitoring/CombinedStats/' + siteName + '.png',
|
||||||
|
bbox_inches='tight', dpi=500)
|
||||||
|
|
@ -0,0 +1,225 @@
|
||||||
|
## Wave transmission through a Breakwater
|
||||||
|
# Author: AJMR
|
||||||
|
# December 14, 2021
|
||||||
|
|
||||||
|
# %% Setup Project
|
||||||
|
from mikeio import Dfs0, Dfs1
|
||||||
|
from mikeio.eum import ItemInfo, EUMUnit, EUMType
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pathlib as pl
|
||||||
|
import numpy as np
|
||||||
|
import geopandas as gp
|
||||||
|
import math
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import contextily as ctx
|
||||||
|
|
||||||
|
# %% Read Model Log
|
||||||
|
|
||||||
|
pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakwater", "06_Models",
|
||||||
|
"13632.101.W.AJMR.Rev0_ModelLog.xlsx")
|
||||||
|
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||||
|
|
||||||
|
# %% Read in Point Sh
|
||||||
|
breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
|
||||||
|
"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
|
||||||
|
"20211211_Toe Extents (to Alexander)/"
|
||||||
|
"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
|
||||||
|
|
||||||
|
# %% Read in Breakwater crest
|
||||||
|
breakCrest = pd.read_csv(
|
||||||
|
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
|
||||||
|
header=None, names=['x', 'y', 'z'])
|
||||||
|
breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
|
||||||
|
geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
|
||||||
|
breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
|
||||||
|
|
||||||
|
breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
|
||||||
|
breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
|
||||||
|
|
||||||
|
# %% Read Model Results
|
||||||
|
modelPlot = range(12, 20)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
|
||||||
|
|
||||||
|
dfsIN_read = dfsIN.read()
|
||||||
|
|
||||||
|
MIKEds = dfsIN_read.to_dataframe()
|
||||||
|
|
||||||
|
# %% Merge with dataframe
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEds.shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterPTS_Crest
|
||||||
|
|
||||||
|
for i in range(0, MIKEds.shape[1]):
|
||||||
|
paramName = MIKEds.columns.values[i][MIKEds.columns.values[i].find('"', 5, -1) + 3:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
# MIKEds_dict[paramName] = []
|
||||||
|
# MIKEid_dict[paramName] = []
|
||||||
|
tmpFID = int(MIKEds.columns.values[i][1:MIKEds.columns.values[i].find('"', 1)])
|
||||||
|
tmpIND = int(MIKEds.columns.values[i][5:MIKEds.columns.values[i].find('"', 5)])
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
|
||||||
|
(breakwaterPTS_merge.vertex_ind == tmpIND)),
|
||||||
|
paramName] = MIKEds.iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEds.index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakwaterPTS_times.rename(columns={'index_right': 'index_right_old'}, inplace=True)
|
||||||
|
|
||||||
|
# %% 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))
|
||||||
|
|
||||||
|
# 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] *\
|
||||||
|
transmit_paramA
|
||||||
|
breakwaterPTS_times['transmitted'][breakMask_C] = 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.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.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
|
||||||
|
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'])
|
||||||
|
innerBoundSouth.sort_values(by=['x'], inplace=True)
|
||||||
|
innerBoundSouth_gdf = gp.GeoDataFrame(innerBoundSouth, crs='EPSG:32616',
|
||||||
|
geometry=gp.points_from_xy(innerBoundSouth.x, innerBoundSouth.y))
|
||||||
|
innerBoundSouth_gdf = innerBoundSouth_gdf.sjoin_nearest(breakwaterPTS_times.loc[breakwaterPTS_end, :])
|
||||||
|
|
||||||
|
# %% Save as DFS1
|
||||||
|
boundDataNorth = pd.DataFrame(
|
||||||
|
innerBoundNorth_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataNorth.set_index('Time', inplace=True)
|
||||||
|
boundDataNorth.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerNorth1.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataNorth['Significant wave height'], boundDataNorth['Significant wave height'])),
|
||||||
|
np.array((boundDataNorth['Peak wave period'], boundDataNorth['Peak wave period'])),
|
||||||
|
np.array((boundDataNorth['Mean wave direction'], boundDataNorth['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataNorth.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
datetimes=None,
|
||||||
|
items=items,
|
||||||
|
title=None)
|
||||||
|
|
||||||
|
|
||||||
|
# %% Save as DFS1- Mid
|
||||||
|
boundDataMid = pd.DataFrame(
|
||||||
|
innerBoundMid_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataMid.set_index('Time', inplace=True)
|
||||||
|
boundDataMid.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerMid.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataMid['Significant wave height'], boundDataMid['Significant wave height'])),
|
||||||
|
np.array((boundDataMid['Peak wave period'], boundDataMid['Peak wave period'])),
|
||||||
|
np.array((boundDataMid['Mean wave direction'], boundDataMid['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataMid.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
items=items)
|
||||||
|
|
||||||
|
# %% Save as DFS1- South
|
||||||
|
boundDataSouth = pd.DataFrame(
|
||||||
|
innerBoundSouth_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataSouth.set_index('Time', inplace=True)
|
||||||
|
boundDataSouth.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerSouth.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataSouth['Significant wave height'], boundDataSouth['Significant wave height'])),
|
||||||
|
np.array((boundDataSouth['Peak wave period'], boundDataSouth['Peak wave period'])),
|
||||||
|
np.array((boundDataSouth['Mean wave direction'], boundDataSouth['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataSouth.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
items=items)
|
||||||
|
|
||||||
|
|
||||||
|
# %% Test Plot
|
||||||
|
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||||
|
|
||||||
|
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 9), constrained_layout=True)
|
||||||
|
|
||||||
|
breakwaterPTS_merge.plot(ax=axes, column='Sign. Wave Height', cmap='viridis')
|
||||||
|
# breakwaterPTS_merge.plot(ax=axes, column='Crest', cmap='viridis', legend=True)
|
||||||
|
innerBound_gdf.plot(ax=axes, column='transmitted', cmap='viridis')
|
||||||
|
|
||||||
|
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
|
||||||
|
|
||||||
|
plt.show()
|
||||||
|
|
@ -0,0 +1,257 @@
|
||||||
|
## Wave transmission through a Breakwater
|
||||||
|
# Author: AJMR
|
||||||
|
# December 14, 2021
|
||||||
|
|
||||||
|
# %% Setup Project
|
||||||
|
from mikeio import Dfs0, Dfs1
|
||||||
|
from mikeio.eum import ItemInfo, EUMUnit, EUMType
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pathlib as pl
|
||||||
|
import numpy as np
|
||||||
|
import geopandas as gp
|
||||||
|
import math
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import contextily as ctx
|
||||||
|
|
||||||
|
# %% Read Model Log
|
||||||
|
|
||||||
|
pth = pl.Path("//srv-mad3.baird.com/", "Projects", "13632.101 South Shore Breakwater", "06_Models",
|
||||||
|
"13632.101.W.AJMR.Rev0_ModelLog.xlsx")
|
||||||
|
runLog = pd.read_excel(pth.as_posix(), "ModelLog")
|
||||||
|
|
||||||
|
# %% Read in Point Sh
|
||||||
|
breakwaterPTS = gp.read_file("//srv-mad3.baird.com/Projects/"
|
||||||
|
"13632.101 South Shore Breakwater/08_CADD/Outgoing/"
|
||||||
|
"20211211_Toe Extents (to Alexander)/"
|
||||||
|
"20211211_Toe_Extents_NAD83_WISStatePlaneSZn_USFt_Lines_OffshoreClipSimple_10m_vertexUTM.shp")
|
||||||
|
|
||||||
|
# %% Read in Breakwater crest
|
||||||
|
breakCrest = pd.read_csv(
|
||||||
|
'//srv-mad3.baird.com/Projects/13632.101 South Shore Breakwater/08_CADD/Outgoing/20211214_Crest Points (to Alexander)/20211214_Crest_Points_m.csv',
|
||||||
|
header=None, names=['x', 'y', 'z'])
|
||||||
|
breakCrest_gdf = gp.GeoDataFrame(breakCrest, crs='EPSG:32154',
|
||||||
|
geometry=gp.points_from_xy(breakCrest.x, breakCrest.y))
|
||||||
|
breakCrest_gdf.to_crs('EPSG:32616', inplace=True)
|
||||||
|
|
||||||
|
breakwaterPTS_Crest = breakwaterPTS.sjoin_nearest(breakCrest_gdf)
|
||||||
|
breakwaterPTS_Crest.rename(columns={'x': 'breakCrest_x', 'y': 'breakCrest_y', 'z': 'Crest'}, inplace=True)
|
||||||
|
|
||||||
|
# %% 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(31, 32)
|
||||||
|
|
||||||
|
for m in modelPlot:
|
||||||
|
dfsIN = Dfs0(pl.Path(runLog['Run Location'][m], str(runLog['Number'][m]) + '_' +
|
||||||
|
runLog['Run Name'][m] + '.sw - Result Files', 'BreakPts.dfs0').as_posix())
|
||||||
|
|
||||||
|
dfsIN_read = dfsIN.read()
|
||||||
|
|
||||||
|
MIKEds = dfsIN_read.to_dataframe()
|
||||||
|
|
||||||
|
# %% Merge with dataframe
|
||||||
|
breakwaterPTS_times = None
|
||||||
|
|
||||||
|
for t in range(0, MIKEds.shape[0]):
|
||||||
|
breakwaterPTS_merge = None
|
||||||
|
breakwaterPTS_merge = breakwaterPTS_Crest
|
||||||
|
|
||||||
|
for i in range(0, MIKEds.shape[1]):
|
||||||
|
paramName = MIKEds.columns.values[i][MIKEds.columns.values[i].find('"', 5, -1) + 3:]
|
||||||
|
if paramName not in breakwaterPTS_merge.columns:
|
||||||
|
breakwaterPTS_merge[paramName] = np.full([breakwaterPTS_merge.shape[0], 1], np.nan)
|
||||||
|
# MIKEds_dict[paramName] = []
|
||||||
|
# MIKEid_dict[paramName] = []
|
||||||
|
tmpFID = int(MIKEds.columns.values[i][1:MIKEds.columns.values[i].find('"', 1)])
|
||||||
|
tmpIND = int(MIKEds.columns.values[i][5:MIKEds.columns.values[i].find('"', 5)])
|
||||||
|
|
||||||
|
breakwaterPTS_merge.loc[((breakwaterPTS_merge.FID == tmpFID) &
|
||||||
|
(breakwaterPTS_merge.vertex_ind == tmpIND)),
|
||||||
|
paramName] = MIKEds.iloc[t, i]
|
||||||
|
|
||||||
|
breakwaterPTS_merge['Time'] = MIKEds.index[t]
|
||||||
|
|
||||||
|
breakwaterPTS_times = pd.concat([breakwaterPTS_times, breakwaterPTS_merge], ignore_index=True)
|
||||||
|
|
||||||
|
breakwaterPTS_times.rename(columns={'index_right': 'index_right_old'}, inplace=True)
|
||||||
|
|
||||||
|
# %% 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))
|
||||||
|
|
||||||
|
# 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]
|
||||||
|
|
||||||
|
# %% 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'])
|
||||||
|
|
||||||
|
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_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 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'])
|
||||||
|
innerBoundSouth.sort_values(by=['x'], inplace=True)
|
||||||
|
innerBoundSouth_gdf = gp.GeoDataFrame(innerBoundSouth, crs='EPSG:32616',
|
||||||
|
geometry=gp.points_from_xy(innerBoundSouth.x, innerBoundSouth.y))
|
||||||
|
innerBoundSouth_gdf = innerBoundSouth_gdf.sjoin_nearest(breakwaterPTS_times.loc[breakwaterPTS_end, :])
|
||||||
|
|
||||||
|
# %% Save as DFS1
|
||||||
|
boundDataNorth = pd.DataFrame(
|
||||||
|
innerBoundNorth_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataNorth.set_index('Time', inplace=True)
|
||||||
|
boundDataNorth.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerNorth1.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataNorth['Significant wave height'], boundDataNorth['Significant wave height'])),
|
||||||
|
np.array((boundDataNorth['Peak wave period'], boundDataNorth['Peak wave period'])),
|
||||||
|
np.array((boundDataNorth['Mean wave direction'], boundDataNorth['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataNorth.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
datetimes=None,
|
||||||
|
items=items,
|
||||||
|
title=None)
|
||||||
|
|
||||||
|
|
||||||
|
# %% Save as DFS1- Mid
|
||||||
|
boundDataMid = pd.DataFrame(
|
||||||
|
innerBoundMid_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataMid.set_index('Time', inplace=True)
|
||||||
|
boundDataMid.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerMid.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataMid['Significant wave height'], boundDataMid['Significant wave height'])),
|
||||||
|
np.array((boundDataMid['Peak wave period'], boundDataMid['Peak wave period'])),
|
||||||
|
np.array((boundDataMid['Mean wave direction'], boundDataMid['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataMid.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
items=items)
|
||||||
|
|
||||||
|
# %% Save as DFS1- South
|
||||||
|
boundDataSouth = pd.DataFrame(
|
||||||
|
innerBoundSouth_gdf.loc[:, ['Time', 'transmitted', 'Peak Wave Period', 'Mean Wave Direction']])
|
||||||
|
boundDataSouth.set_index('Time', inplace=True)
|
||||||
|
boundDataSouth.rename(columns={'transmitted': 'Significant wave height', 'Peak Wave Period': 'Peak wave period',
|
||||||
|
'Mean Wave Direction': 'Mean wave direction'},
|
||||||
|
inplace=True)
|
||||||
|
|
||||||
|
dfsfilename = '//oak-spillway.baird.com/F/13632.101 South Shore Breakwater/SetupFiles/InnerBounds/' + \
|
||||||
|
runLog['Run Name'][m] + 'ProdInnerSouth.dfs1'
|
||||||
|
|
||||||
|
items = [ItemInfo("Significant wave height", EUMType.Significant_wave_height, EUMUnit.meter),
|
||||||
|
ItemInfo("Peak wave period", EUMType.Wave_period, EUMUnit.second),
|
||||||
|
ItemInfo("Mean wave direction", EUMType.Mean_Wave_Direction, EUMUnit.degree),
|
||||||
|
ItemInfo("Directional Standard Deviation, n", EUMType.Directional_Standard_Deviation, EUMUnit.degree)]
|
||||||
|
|
||||||
|
dfs = Dfs1()
|
||||||
|
dfs.write(filename=dfsfilename,
|
||||||
|
data=[np.array((boundDataSouth['Significant wave height'], boundDataSouth['Significant wave height'])),
|
||||||
|
np.array((boundDataSouth['Peak wave period'], boundDataSouth['Peak wave period'])),
|
||||||
|
np.array((boundDataSouth['Mean wave direction'], boundDataSouth['Mean wave direction'])),
|
||||||
|
np.ones((2, boundDataSouth.shape[0])) * 23.28],
|
||||||
|
start_time=breakwaterPTS_times['Time'].min(),
|
||||||
|
dt=86400,
|
||||||
|
items=items)
|
||||||
|
|
||||||
|
|
||||||
|
# %% Test Plot
|
||||||
|
mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
|
||||||
|
|
||||||
|
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 9), constrained_layout=True)
|
||||||
|
|
||||||
|
breakwaterPTS_merge.plot(ax=axes, column='Sign. Wave Height', cmap='viridis')
|
||||||
|
# breakwaterPTS_merge.plot(ax=axes, column='Crest', cmap='viridis', legend=True)
|
||||||
|
innerBound_gdf.plot(ax=axes, column='transmitted', cmap='viridis')
|
||||||
|
|
||||||
|
ctx.add_basemap(axes, source=mapbox, crs='EPSG:32616')
|
||||||
|
|
||||||
|
plt.show()
|
||||||
Loading…
Reference in New Issue