424 lines
18 KiB
Python
424 lines
18 KiB
Python
#%%
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# -*- coding: utf-8 -*-
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"""
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@author: AJMR
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Script to compare EFDC results against observations
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"""
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#%% Import
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import os
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import geopandas as gp
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gp.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'
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from shapely.ops import nearest_points
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from shapely.geometry import LineString
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from shapely.geometry import Point, MultiPoint
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from shapely.ops import nearest_points
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from datetime import datetime, timedelta
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from dfm_tools.get_nc import get_netdata, get_ncmodeldata, plot_netmapdata
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from dfm_tools.get_nc_helpers import get_timesfromnc
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from dfm_tools.regulargrid import scatter_to_regulargrid
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from pathlib import Path
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import netCDF4 as nc
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import xarray as xr
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import pytz
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import pathlib as pl
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#%% Function to find nearest point
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def get_nearest_values(row, other_gdf, point_column='geometry', value_column="geometry"):
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"""Find the nearest point and return the corresponding value from specified value column."""
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# Create an union of the other GeoDataFrame's geometries:
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other_points = other_gdf["geometry"].unary_union
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# Find the nearest points
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nearest_geoms = nearest_points(row[point_column], other_points)
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# Get corresponding values from the other df
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nearest_data = other_gdf.loc[other_gdf["geometry"] == nearest_geoms[1]]
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nearest_value = nearest_data[value_column].get_values()[0]
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return nearest_value
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#%% Load in Water Level Data
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# Gauge Locations from map
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# gdf_gaugeLoc = gp.read_file('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/NTC6.kml')
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gdf_gaugeLoc = gp.read_file('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/NTC6.kml')
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gdf_gaugeLocUSSP = gdf_gaugeLoc.to_crs("EPSG:32118")
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# All in Feet NAVD88
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# df_wl_FFG = pd.read_excel('//srv-ott3/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
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df_wl_FFG = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
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sheet_name='Field_Facility_Gauge')
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df_wl_FFG.set_index('Date_time', inplace=True)
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df_wl_FFG.index = df_wl_FFG.index.tz_localize(
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pytz.timezone('America/New_York')).tz_convert(pytz.utc) #Convert to UTC
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gdf_wl_FFG = gp.GeoDataFrame(df_wl_FFG,
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geometry=gp.points_from_xy(np.ones([len(df_wl_FFG), 1]) * gdf_gaugeLoc['geometry'].x[2],
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np.ones([len(df_wl_FFG), 1]) * gdf_gaugeLoc['geometry'].y[2]), crs="EPSG:4326")
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gdf_wl_FFG.geometry = gdf_wl_FFG.geometry.to_crs("EPSG:32118")
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df_wl_NGG1 = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP1_Gauge_Elev_Data_20130521/NC_Gauge_Elev_Data_20130521.xlsx',
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sheet_name='National_Grid_Gauge')
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df_wl_NGG1.set_index('Date_time', inplace=True)
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df_wl_NGG1.index = df_wl_NGG1.index.tz_localize(
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pytz.timezone('America/New_York'), ambiguous=True).tz_convert(pytz.utc) #Convert to UTC
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gdf_wl_NGG1 = gp.GeoDataFrame(df_wl_NGG1,
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geometry=gp.points_from_xy(np.ones([len(df_wl_NGG1), 1]) * gdf_gaugeLoc['geometry'].x[3],
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np.ones([len(df_wl_NGG1), 1]) * gdf_gaugeLoc['geometry'].y[3]), crs="EPSG:4326")
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gdf_wl_NGG1.geometry = gdf_wl_NGG1.geometry.to_crs("EPSG:32118")
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# Also includes temperature
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df_wl_NGG2 = pd.read_excel('//srv-ott3.baird.com/Projects/11934.201 Newtown Creek TPP – Privileged and Confidential/03_Data/02_Physical/04 Gauge Data/NCP2_NG_TideGauge_20160113/NCP2_NG_TideGauge_20160113.xlsx',
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sheet_name='Sheet1')
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gdf_wl_NGG2 = gp.GeoDataFrame(df_wl_NGG2,
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geometry=gp.points_from_xy(np.ones([len(df_wl_NGG2), 1]) * gdf_gaugeLoc['geometry'].x[3],
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np.ones([len(df_wl_NGG2), 1]) * gdf_gaugeLoc['geometry'].y[3]), crs="EPSG:4326")
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gdf_wl_NGG2.geometry = gdf_wl_NGG2.geometry.to_crs("EPSG:32118")
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#%% Read in EFDC Data to dataframe
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# efdc6_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon6/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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# efdc7_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon7/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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# efdc8_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon8/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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# efdc9_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon9/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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efdc12_df = pd.read_csv('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/2012_Mon12/READ_FROM_EFDC_GRAPHICS_OUT_BIN.CSV')
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#%% Read in EFDC grid
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efdc_grid_df = pd.read_csv('//Ott-flavius/j/INPUT_FILES/GRID_FILES/NC_ER_lxly_20140129.inp',
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delim_whitespace=True, skiprows=4,
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names=['I', 'J', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc_grid_gdf = gp.GeoDataFrame(
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efdc_grid_df, geometry=gp.points_from_xy(efdc_grid_df.X, efdc_grid_df.Y), crs="EPSG:32118")
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#%% Merge EFDC outputs with grid locations and add datetimes
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efdc6_merged = pd.merge(efdc6_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc6_merged['date'] = pd.to_datetime([datetime(2012, 6, 1, 00, 00, 00) +
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timedelta(hours=h) for h in efdc6_merged['ST_hr']])
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efdc6_merged.set_index('date', inplace=True)
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efdc6_merged.index = efdc6_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
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del efdc6_df
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efdc7_merged = pd.merge(efdc7_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc7_merged['date'] = pd.to_datetime([datetime(2012, 7, 1, 00, 00, 00) +
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timedelta(hours=h) for h in efdc7_merged['ST_hr']])
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efdc7_merged.set_index('date', inplace=True)
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efdc7_merged.index = efdc7_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
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del efdc7_df
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efdc8_merged = pd.merge(efdc8_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc8_merged['date'] = pd.to_datetime([datetime(2012, 8, 1, 00, 00, 00) +
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timedelta(hours=h) for h in efdc8_merged['ST_hr']])
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efdc8_merged.set_index('date', inplace=True)
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efdc8_merged.index = efdc8_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
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del efdc8_df
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efdc9_merged = pd.merge(efdc9_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc9_merged['date'] = pd.to_datetime([datetime(2012, 9, 1, 00, 00, 00) +
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timedelta(hours=h) for h in efdc9_merged['ST_hr']])
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efdc9_merged.set_index('date', inplace=True)
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efdc9_merged.index = efdc9_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
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del efdc9_df
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#%%
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efdc12_merged = pd.merge(efdc12_df, efdc_grid_df, how='left', left_on=['I_MOD','J_MOD'],
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right_on = ['I','J']).drop(columns=[
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' DUMPID', 'END_hr', 'I_MOD', 'J_MOD', 'I_MOD', 'X', 'Y', 'CUE', 'CVE', 'CUN', 'CVN'])
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efdc12_merged['date'] = pd.to_datetime([datetime(2012, 12, 1, 00, 00, 00) +
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timedelta(hours=h) for h in efdc12_merged['ST_hr']])
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efdc12_merged.set_index('date', inplace=True)
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efdc12_merged.index = efdc12_merged.index.tz_localize(
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pytz.timezone('EST')).tz_convert(pytz.utc) #Convert to UTC
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del efdc12_df
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#%% Merge EFDC dataframes and convert to geodataframe
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efdc_merged = pd.concat([efdc6_merged, efdc7_merged, efdc8_merged, efdc9_merged])
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del efdc6_merged
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del efdc7_merged
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del efdc8_merged
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del efdc9_merged
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efdc_merged_gdf = gp.GeoDataFrame(
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efdc_merged, geometry=efdc_merged['geometry'], crs="EPSG:32118")
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del efdc_merged
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#%%
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efdc_merged = efdc12_merged
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efdc_merged_gdf = gp.GeoDataFrame(
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efdc_merged, geometry=efdc_merged['geometry'], crs="EPSG:32118")
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del efdc_merged
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#%% Find nearest grid point to station FFG
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nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(efdc_grid_gdf.geometry))
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station_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
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#%% Find nearest grid point to station BGG
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nearest_geoms = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(efdc_grid_gdf.geometry))
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stationNGG_gdf = efdc_merged_gdf.loc[efdc_merged_gdf['geometry']==nearest_geoms[1]]
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# efdc_grid_gdf.loc[efdc_grid_gdf['geometry']== nearest_geoms[1]].value
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#%% Save EFDC as Parquet for faster importing
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efdc_merged_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/efdc_merged_Dec.parquet',
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index=True, compression='gzip')
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station_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/station_gdf_Dec.parquet',
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index=True, compression='gzip')
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stationNGG_gdf.to_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/stationNGG_gdf_Dec.parquet',
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index=True, compression='gzip')
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#%% Read in EFDC as Paraquet
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stationNGG_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/stationNGG_gdf.parquet')
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station_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/station_gdf.parquet')
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efdc_merged_gdf = gp.read_parquet('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/efdc_merged.parquet')
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#%% Read Model Log
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runLog = pd.read_excel('C:/Users/arey/files\Projects/Newtown/Model Log NTC.xlsx', 'ModelLog')
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dataPath = "FlowFM_map.nc"
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#%% Import Delft3D results
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modelPlot = [31]
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ugrid_all = [None] * (max(modelPlot)+1)
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data_frommap_wl = [None] * (max(modelPlot)+1)
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facex = [None] * (max(modelPlot)+1)
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facey = [None] * (max(modelPlot)+1)
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nearest_IDX = list(range(100))
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nearest_IDX_NGG = list(range(100))
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dsloc = list(range(100))
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dsloc_NGG = list(range(100))
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# Find nearest point
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for i in modelPlot:
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dataPath = pl.Path(runLog['Run Location'][i],
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'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
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file_nc_map = dataPath.as_posix()
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tSteps = get_timesfromnc(file_nc=file_nc_map, varname='time')
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tStep = len(tSteps)-1
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ugrid_all[i] = get_netdata(file_nc=file_nc_map)#,multipart=False)
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data_frommap_wl[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_s1', timestep=tStep)
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facex[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_x')
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facey[i] = get_ncmodeldata(file_nc=file_nc_map, varname='mesh2d_face_y')
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# Find matching Delft3D index
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s = gp.GeoSeries(map(Point, zip(facex[i], facey[i])))
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nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(s))
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nearest_IDX[i] = np.where(s==nearest_geoms[1])
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nearest_geoms = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(s))
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nearest_IDX[i] = np.where(s==nearest_geoms[1])
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#%% Read in time series
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modelPlot = [31]
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ugrid_all = [None] * (max(modelPlot)+1)
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data_frommap_wl = [None] * (max(modelPlot)+1)
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facex = [None] * (max(modelPlot)+1)
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facey = [None] * (max(modelPlot)+1)
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nearest_IDX = list(range(100))
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nearest_IDX_NGG = list(range(100))
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dsloc = list(range(100))
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dsloc_NGG = list(range(100))
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# Note, this uses the standard netcdf lib + xarray
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for i in modelPlot:
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dataPath = pl.Path(runLog['Run Location'][i],
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'FlowFM', 'dflowfm', 'output', 'FlowFM_map.nc')
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file_nc_map = dataPath.as_posix()
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ds = xr.open_dataset(file_nc_map)
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# Find nearest point
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ds_x = ds['mesh2d_face_x']
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ds_y = ds['mesh2d_face_y']
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s = gp.GeoSeries(map(Point, zip(ds_x, ds_y)))
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nearest_geoms = nearest_points(Point(gdf_wl_FFG.iloc[0,:].geometry), MultiPoint(s))
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nearest_IDX[i] = np.where(s==nearest_geoms[1])
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nearest_geoms_NGG = nearest_points(Point(gdf_wl_NGG1.iloc[0,:].geometry), MultiPoint(s))
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nearest_IDX_NGG[i] = np.where(s==nearest_geoms_NGG[1])
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dsloc[i] = ds['mesh2d_s1'].sel(mesh2d_nFaces=nearest_IDX[i][0])
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dsloc_NGG[i] = ds['mesh2d_s1'].sel(mesh2d_nFaces=nearest_IDX_NGG[i][0])
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#%% Convert D3D Timezone
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modelPlot = [31]
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df_wl_D3D = list(range(100))
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df_wl_D3D_NGG = list(range(100))
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d3dTzones = list(range(100))
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d3dTzones[25] = 'EST'
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d3dTzones[26] = 'EST'
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d3dTzones[31] = 'EST'
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for i in modelPlot:
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df_wl_D3D[i] = dsloc[i].to_dataframe()
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df_wl_D3D[i].index = df_wl_D3D[i].index.droplevel(-1)
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df_wl_D3D[i].index = df_wl_D3D[i].index.tz_localize(
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pytz.timezone(d3dTzones[i])).tz_convert(pytz.utc) #Convert to UTC
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df_wl_D3D_NGG[i] = dsloc_NGG[i].to_dataframe()
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df_wl_D3D_NGG[i].index = df_wl_D3D_NGG[i].index.droplevel(-1)
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df_wl_D3D_NGG[i].index = df_wl_D3D_NGG[i].index.tz_localize(
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pytz.timezone(d3dTzones[i])).tz_convert(pytz.utc) #Convert to UTC
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#%% Plot water levels at nearest point
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 5))
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fig.patch.set_facecolor('white')
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for week in range(0, 2):
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weekOffset = 0
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axes[week+weekOffset].plot(station_gdf.index, station_gdf['WSE_m'], label='EFDC Model')
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axes[week+weekOffset].plot(gdf_wl_FFG.index+timedelta(hours=0),
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gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048,
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label='Field Facility Gauge', color='k')
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for i in modelPlot:
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axes[week+weekOffset].plot(df_wl_D3D[i].index, df_wl_D3D[i].mesh2d_s1, label=runLog['Run'][i])
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axes[week+weekOffset].set_xlim(datetime(2012, 12, 15, 0, 0, 0) + timedelta(days=week*7),
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datetime(2012, 12, 15+7, 0, 0, 0) + timedelta(days=week*7))
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axes[week+weekOffset].set_ylim(-1, 2.5)
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axes[week+weekOffset].set_ylabel('Water Surface Elevation [m]')
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axes[0].legend(loc='upper left')
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axes[0].title.set_text('Field Facility Gauge')
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plt.show()
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fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/DecTS_FFG.png',
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bbox_inches='tight')
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#%% Plot water levels at National Grid
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(9, 5))
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fig.patch.set_facecolor('white')
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for week in range(0, 2):
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weekOffset = 0
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axes[week+weekOffset].plot(stationNGG_gdf.index, stationNGG_gdf['WSE_m'], label='EFDC Model')
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axes[week+weekOffset].plot(gdf_wl_NGG1.index+timedelta(hours=0),
|
||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048,
|
||
label='National Grid Gauge', color='k')
|
||
|
||
for i in modelPlot:
|
||
axes[week+weekOffset].plot(df_wl_D3D_NGG[i].index, df_wl_D3D_NGG[i].mesh2d_s1,
|
||
label=runLog['Run'][i])
|
||
|
||
axes[week+weekOffset].set_xlim(datetime(2012, 12, 15, 0, 0, 0) + timedelta(days=week*7),
|
||
datetime(2012, 12, 15+7, 0, 0, 0) + timedelta(days=week*7))
|
||
axes[week+weekOffset].set_ylim(-1, 2.5)
|
||
|
||
axes[week+weekOffset].set_ylabel('Water Surface Elevation [m]')
|
||
|
||
axes[0].legend(loc='upper left')
|
||
axes[0].title.set_text('National Grid Gauge')
|
||
|
||
plt.show()
|
||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/DecTS_NGG.png',
|
||
bbox_inches='tight')
|
||
#%% Plot scatters
|
||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||
fig.patch.set_facecolor('white')
|
||
|
||
interpTimes = pd.date_range("2012-06-02T00:00:00", "2012-10-01T00:00:00", freq="60min")
|
||
interpTimes = pd.date_range("2012-07-07T00:00:00", "2012-07-27T00:00:00", freq="60min")
|
||
|
||
for i in modelPlot:
|
||
axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
|
||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048),
|
||
np.interp(interpTimes, df_wl_D3D[i].index,
|
||
df_wl_D3D[i].mesh2d_s1),
|
||
label=runLog['Run'][i])
|
||
|
||
axes.scatter(np.interp(interpTimes, gdf_wl_FFG.index,
|
||
gdf_wl_FFG['Water Surface Elevation (ft)']*0.3048),
|
||
np.interp(interpTimes, station_gdf.index,
|
||
station_gdf['WSE_m']),
|
||
label='EFDC')
|
||
|
||
linepts = np.array([-1.0, 1.5])
|
||
|
||
plt.plot(linepts, linepts, color='k')
|
||
|
||
axes.set_xlim(-1.0, 1.5)
|
||
axes.set_ylim(-1.0, 1.5)
|
||
axes.set_aspect('equal')
|
||
axes.set_title('Field Facility Gauge')
|
||
axes.legend(loc='upper left')
|
||
axes.set_ylabel('Model Water Surface Elevation [m]')
|
||
axes.set_xlabel('Observed Water Surface Elevation [m]')
|
||
|
||
plt.show()
|
||
|
||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/FFG_ScatterUpdated.pdf',
|
||
bbox_inches='tight')
|
||
|
||
#%% Plot scatters NGG
|
||
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
|
||
fig.patch.set_facecolor('white')
|
||
|
||
interpTimes = pd.date_range("2012-06-02T00:00:00", "2012-10-01T00:00:00", freq="60min")
|
||
interpTimes = pd.date_range("2012-07-07T00:00:00", "2012-07-27T00:00:00", freq="60min")
|
||
|
||
for i in modelPlot:
|
||
axes.scatter(np.interp(interpTimes, gdf_wl_NGG1.index,
|
||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048),
|
||
np.interp(interpTimes, df_wl_D3D_NGG[i].index,
|
||
df_wl_D3D_NGG[i].mesh2d_s1),
|
||
label=runLog['Run'][i])
|
||
|
||
axes.scatter(np.interp(interpTimes, gdf_wl_NGG1.index,
|
||
gdf_wl_NGG1['Water Surface Elevation (ft)']*0.3048),
|
||
np.interp(interpTimes, stationNGG_gdf.index,
|
||
stationNGG_gdf['WSE_m']),
|
||
label='EFDC')
|
||
|
||
linepts = np.array([-1.0, 1.5])
|
||
|
||
plt.plot(linepts, linepts, color='k')
|
||
|
||
axes.set_xlim(-1.0, 1.5)
|
||
axes.set_ylim(-1.0, 1.5)
|
||
axes.set_aspect('equal')
|
||
axes.set_title('National Grid Gauge')
|
||
axes.legend(loc='upper left')
|
||
axes.set_ylabel('Model Water Surface Elevation [m]')
|
||
axes.set_xlabel('Observed Water Surface Elevation [m]')
|
||
|
||
plt.show()
|
||
|
||
fig.savefig('C:/Users/arey/files/Projects/Newtown/RevisedEFDC/Figures/NGG_ScatterUpdated.pdf',
|
||
bbox_inches='tight') |