561 lines
20 KiB
Python
561 lines
20 KiB
Python
#%% Project Setup
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import os
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import pandas as pd
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import geopandas as gp
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from scipy.signal import argrelextrema
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import numpy as np
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import math
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from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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import matplotlib as mpl
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import cartopy.crs as ccrs
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import contextily as ctx
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import cmocean.cm as cmo
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from shapely.geometry import Point, LineString
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from xarray.backends import NetCDF4DataStore
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import xarray as xr
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from datetime import datetime, timedelta
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from netCDF4 import num2date
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from metpy.units import units
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import matplotlib.pyplot as plt
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import cartopy.crs as ccrs
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import cartopy.feature as cfeature
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from metpy.plots import ctables
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from siphon.catalog import TDSCatalog
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import gsw as gsw
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#%% Helper function for finding proper time variable
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def find_time_var(var, time_basename='time'):
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for coord_name in var.coords:
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if coord_name.startswith(time_basename):
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return var.coords[coord_name]
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raise ValueError('No time variable found for ' + var.name)
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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importPath = '//srv-ott3.baird.com/Projects/13711.101 Caribee (Indigo) Hotel, Barbados/03_Data/03_Field/20220613_EnvData'
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siteName = 'Caribee Hotel'
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timeLabel= 'June 13, 2022'
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importFiles = os.listdir(importPath)
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# Initialize import variables
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OBS_File = None
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GPS_File = None
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for i in range(0, len(importFiles)):
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if '.csv' in importFiles[i] and 'Summary' not in importFiles[i] and 'export' not in importFiles[i]:
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OBS_File = importFiles[i]
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elif '.csv' in importFiles[i] and 'export' in importFiles[i]:
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GPS_File = importFiles[i]
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GPS_Type = 'CSV'
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elif '.xls' in importFiles[i] and 'Summary' not in importFiles[i]:
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GPS_File = importFiles[i]
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GPS_Type = 'xls'
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#%% Obs Import Data
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if OBS_File is not None:
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Obs_dat = pd.read_csv(importPath + '/' + OBS_File, skiprows=0, header=0)
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# Drop rows with metadata
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Obs_dat =Obs_dat[Obs_dat['CH1:Temperature(degC)'].notna()]
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# Set Time Zone for Sensor. Sometimes it is set to UTC, sometimes it is set to EST
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Obs_dat['DateTime'] = pd.to_datetime(Obs_dat['Timestamp(Standard)']).dt.tz_localize('America/Barbados')
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# Set Index
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Obs_dat.set_index('DateTime', inplace=True)
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#%% GPS Import Data
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if GPS_File is not None:
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if GPS_Type == 'CSV':
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GPS = pd.read_csv(importPath + '/' + GPS_File,
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header=None, names=['Index', 'Date1', 'Time1', 'Date2', 'Time2', 'Northing', 'North', 'Easting', 'East', 'Var1', 'Var2'])
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#convert GPS data to geodataframe
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GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs="EPSG:4326"))
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GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str)).dt.tz_localize('America/Barbados')
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elif GPS_Type == 'xls':
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GPS = pd.read_excel(importPath + '/' + GPS_File, header=0)
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#convert GPS data to geodataframe
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GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(GPS.x, GPS.y, crs="EPSG:4326"))
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GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['time']).dt.tz_localize('UTC')
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# Set Datetime as index
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GPS_gdf.set_index('DateTime', inplace=True)
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# Sort by time
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GPS_gdf.sort_index(inplace=True)
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# Convert to UTM
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GPS_gdf.geometry = GPS_gdf.geometry.to_crs("EPSG:32621")
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#%% Read in site shapefile
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siteShp = gp.read_file('//srv-ott3.baird.com/Projects/13711.101 Caribee (Indigo) Hotel, Barbados/03_Data/03_Field/20220613_EnvData\SiteBounds_Caribee.shp')
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siteShp.geometry = siteShp.geometry.to_crs("EPSG:32621")
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#%% Merge GPS to RBR
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# Process RBR into datetime
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if OBS_File is not None:
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# Merge with GPS as dataframe
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Obs_geo = pd.merge_asof(Obs_dat, GPS_gdf,
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left_index=True, right_index=True, direction='nearest', tolerance=pd.Timedelta('300s'))
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Obs_geo = gp.GeoDataFrame(Obs_geo, geometry=Obs_geo.geometry, crs="EPSG:32621")
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#%% Find and setup key plotting details
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# Shaded Water
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mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
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# Sat water
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# mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckekcw3pn08am19qmqbhtq8sb/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'
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axXlim = (218050, 218345)
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axYlim = (1446635, 1446802)
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GFS_Lon = -59.5992624
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GFS_Lat = 13.0733928
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Obs_geo['inArea'] = Obs_geo.within(siteShp.iloc[3, 1])
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# Set min and max times using conductivity
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if Obs_geo['inArea'].any():
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# First and last times from area in shapefile
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minTime = Obs_geo[Obs_geo['inArea']==True].index[0]
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maxTime = Obs_geo[Obs_geo['inArea']==True].index[-1]
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else:
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# First and last times if no GPS data
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minTime = Obs_geo.index[20]
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maxTime = Obs_geo.index[-20]
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metDate = minTime.tz_convert('UTC') - timedelta(
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hours = minTime.hour % 6,
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minutes=minTime.minute,
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seconds=minTime.second,
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microseconds=minTime.microsecond)
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#%% GFS Met Import
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var = ['Temperature_surface', 'Wind_speed_gust_surface',
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'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground']
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var_precp = ['Total_precipitation_surface_6_Hour_Accumulation']
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temp_1d = []
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gust_1d = []
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wndu_1d = []
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wndv_1d = []
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prep_1d = []
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time_1d = []
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# Set times to download
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### USED TO BE -18/ +6 CHECK ###
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startdate = metDate - timedelta(hours=30)
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enddate = metDate - timedelta(hours=6)
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date_list = pd.date_range(startdate, enddate, freq='6H')
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# Loop through dates
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for date in date_list:
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# Base URL for 0.5 degree GFS data
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best_gfs = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/model-gfs-g4-anl-files/' +
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date.strftime('%Y%m') + '/' + date.strftime('%Y%m%d') + '/' + 'catalog.xml')
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# Generate URLs for specific grib file
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best_ds = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_000.grb2']
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best_ds_precp = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_006.grb2']
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# Format the query parameters
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ncss = best_ds.subset()
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query = ncss.query()
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ncss_precp = best_ds_precp.subset()
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query_precp = ncss_precp.query()
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# Extract data from specific point
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query.lonlat_point(GFS_Lon, GFS_Lat).time(date)
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query.accept('netcdf')
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query.variables(var[0], var[1], var[2], var[3])
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query.vertical_level(10)
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data = ncss.get_data(query)
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data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')
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query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))
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query_precp.accept('netcdf')
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query_precp.variables(var_precp[0])
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data_precp = ncss_precp.get_data(query_precp)
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data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))
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temp_3d = data[var[0]]
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gust_3d = data[var[1]]
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wndu_3d = data[var[2]]
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wndv_3d = data[var[3]]
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prep_3d = data_precp[var_precp[0]]
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# Read the individual point (with units) and append to the list
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temp_1d.append(temp_3d.metpy.unit_array.squeeze())
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gust_1d.append(gust_3d.metpy.unit_array.squeeze())
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wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())
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wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())
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prep_1d.append(prep_3d.metpy.unit_array.squeeze())
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time_1d.append(find_time_var(temp_3d))
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#%% Process Met Data
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# 24h Precipitation Total
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# Time weighted average of last two points for everything else
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met_prep = prep_1d[0] + prep_1d[1] + prep_1d[2] + prep_1d[3]
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timeWeight1 = minTime-metDate
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timeWeight2 = (metDate+timedelta(hours=6))-minTime
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timeWeight1 = timeWeight1.seconds/21600
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timeWeight2 = timeWeight2.seconds/21600
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met_gust = gust_1d[3] * timeWeight1 + gust_1d[4] * timeWeight2
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met_temp = temp_1d[3] * timeWeight1 + temp_1d[4] * timeWeight2
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met_wind = math.sqrt((wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2) ** 2 +
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(wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2) **2 )
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met_wdir = math.degrees(math.atan2(
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wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2,
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wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2)) % 360
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#%% Add PSU and depth units
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Obs_geo['PSU'] = gsw.conversions.SP_from_C(Obs_geo['CH3:Conductivity(mS/cm)'].values,
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Obs_geo['CH1:Temperature(degC)'].values,
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Obs_geo['CH0:Pressure(dbar)'].values)
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Obs_geo['Depth'] = (Obs_geo['CH0:Pressure(dbar)'].astype(float)*10000)/(gsw.rho(Obs_geo['PSU'].values,
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Obs_geo['CH1:Temperature(degC)'].values,
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Obs_geo['CH0:Pressure(dbar)'].values) * 9.81)
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#%% Plot time series for Geo data
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fontprops = fm.FontProperties(size=25)
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fig, axes = plt.subplots(nrows=6, ncols=1, figsize=(19, 25), constrained_layout=True)
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dataTable = np.zeros([14, 4])
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roundIDX = [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]
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params = ['Depth', 'PSU', 'CH8:Oxygen_Saturation(%)', 'CH1:Temperature(degC)',
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'CH6:Turbidity(NTU)', 'CH2:Chlorophyll_a(ppb)']
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paramName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ sat [%]', 'Temperature [degC]',
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'Turbidity [FTU]', 'Chl-a [ppb]']
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parmCmap = [cmo.deep, 'cividis', cmo.dense, cmo.thermal, cmo.turbid, cmo.algae]
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# paramMin = [0.0, 34.0, 32.5, 25.0, 0, 0]
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# paramMax = [1.0, 36.0, 34.0, 31.0, 20.0, 1.0]
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paramMin = [0.0, 31.0, 100, 26.0, 0, 0]
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paramMax = [1.0, 34.0, 130, 30.0, 600.0, 12000]
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fig.patch.set_facecolor('white')
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# fig.tight_layout(pad=1.05)
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fontprops = fm.FontProperties(size=25)
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dataTable[0, 0] = met_temp.m.item(0)-272.15
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dataTable[1, 0] = met_wind
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dataTable[2, 0] = met_wdir
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dataTable[3, 0] = met_prep.m.item(0)
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ilocs_max = []
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ilocs_max_pts = []
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OBS_mask = []
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for paramIDX, param in enumerate(params):
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Obs_geo.loc[minTime:maxTime, param].plot(
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ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name
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# Create mask for RBR data based on time and parameter minimum
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OBS_mask.append(((Obs_geo.index>minTime) &
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(Obs_geo.index<maxTime) &
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(Obs_geo[param]>paramMin[paramIDX])))
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OBS_smoothed = Obs_geo.loc[OBS_mask[paramIDX], param].rolling(
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12, win_type='nuttall',center=True).mean()
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OBS_smoothed.plot(
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ax=axes[paramIDX], label='1 Minute Average', color='black',
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linewidth=3)
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# Find the local maximums for Turbidity
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if param == 'CH6:Turbidity(NTU)':
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ilocs_max.append(argrelextrema(OBS_smoothed.values,
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np.greater_equal, order=6, mode='wrap')[0])
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# Add start and end points?
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# ilocs_max = np.insert(ilocs_max, 0, 10)
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# ilocs_max[-1] = len(OBS_smoothed.values)
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ilocs_max_pts.append(OBS_smoothed.iloc[ilocs_max[paramIDX]].index.values)
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# Add labels if GPS data is available
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if GPS_File is not None:
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# axes[paramIDX].scatter(OBS_smoothed.iloc[
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# ilocs_max[paramIDX]].index, np.ones(len(ilocs_max[paramIDX])) * 30, 75,
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# color='blue')
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for a in range(0, len(ilocs_max[paramIDX])):
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axes[paramIDX].annotate(str(a+1), (ilocs_max_pts[paramIDX][a], 50), fontsize=30)
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else:
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ilocs_max.append(None)
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ilocs_max_pts.append(None)
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dataTable[paramIDX+7, 0] = OBS_smoothed.mean(skipna=True)
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dataTable[paramIDX+7, 1] = OBS_smoothed.std(skipna=True)
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dataTable[paramIDX+7, 2] = max(OBS_smoothed.min(skipna=True), 0)
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dataTable[paramIDX+7, 3] = OBS_smoothed.max(skipna=True)
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axes[paramIDX].set_ylabel(paramName[paramIDX])
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axes[paramIDX].set_title(paramName[paramIDX])
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axes[paramIDX].set_xlabel('')
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axes[paramIDX].set_ylim(paramMin[paramIDX], paramMax[paramIDX])
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axes[paramIDX].legend(loc='upper left')
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# axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))
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# Formate Data Table
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dataTableFormat_mean = []
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dataTableFormat_maxmin = []
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for d in range(0, len(roundIDX)):
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dataTableFormat_mean.append(round(dataTable[d, 0], roundIDX[d]))
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if dataTable[d, 3] == 0:
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dataTableFormat_maxmin.append('--')
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else:
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dataTableFormat_maxmin.append(str(round(dataTable[d, 2], roundIDX[d])) + ' / ' + str(round(dataTable[d, 3], roundIDX[d])))
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dfOut = pd.DataFrame(dataTable[:, :])
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dfOutFormat = pd.DataFrame([dataTableFormat_mean, dataTableFormat_maxmin]).transpose()
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# Change the column names
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dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']
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dfOutFormat.columns = [np.array([minTime.strftime('%B %#d, %Y'), minTime.strftime('%B %#d, %Y')]),
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np.array(['Mean', 'Min / Max'])]
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# Change the row indexes
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dfOut.iloc[13, 0] = minTime.strftime('%B %#d, %Y')
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rowNames = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
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'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',
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'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',
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'Dissolved O₂ saturation [%]', 'Temperature [degC]',
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'Turbidity [FTU]', 'Chl-a [ppb]', 'Observation Date']
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dfOut.index = rowNames
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dfOutFormat.index = rowNames[0:-1]
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fig.suptitle(siteName + ' (' + timeLabel + ')', fontsize=30)
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plt.show()
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# outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
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outPath = importPath
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if not os.path.exists(outPath):
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os.mkdir(outPath)
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dfOut.to_excel(outPath + '/Summary_Stats_' + siteName + '.xlsx')
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dfOutFormat.to_excel(outPath + '/Summary_StatsFormat_' + siteName + '.xlsx')
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dfOut.to_csv(outPath + '/Summary_Stats_' + siteName + '.csv')
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if not os.path.exists(outPath + '/Figures'):
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os.mkdir(outPath + '/Figures')
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fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',
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bbox_inches='tight')
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fig.savefig(outPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',
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bbox_inches='tight', dpi=500)
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#%% Plot Maps
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fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(25, 19), constrained_layout=True)
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ax = axes.flat
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fig.patch.set_facecolor('white')
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# fig.tight_layout(pad=1.05)
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fontprops = fm.FontProperties(size=25)
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x, y, arrow_length = 0.95, 0.93, 0.20
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plt.rcParams.update({'font.size': 22})
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axXlimTT = (Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,
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Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)
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axYlimTT = (Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,
|
|
Obs_geo.loc[minTime:maxTime].geometry.y.max()+200)
|
|
|
|
plt.setp(axes, xlim=axXlim, ylim=axYlim)
|
|
# plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)
|
|
|
|
# Plot the RBR observations
|
|
# Salinity
|
|
for paramIDX, param in enumerate(params):
|
|
Obs_geo.loc[minTime:maxTime].plot(
|
|
column=param, ax=ax[paramIDX], vmin=paramMin[paramIDX], vmax=paramMax[paramIDX],
|
|
legend=True, legend_kwds={'label': paramName[paramIDX]},
|
|
cmap=parmCmap[paramIDX], markersize=20)
|
|
|
|
|
|
ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')
|
|
|
|
# Add time labels
|
|
# plt.scatter(RBR_Obs_geo.loc[RBR_mask].iloc[
|
|
# ilocs_max, :].geometry.x,
|
|
# RBR_Obs_geo.loc[RBR_mask].iloc[
|
|
# ilocs_max, :].geometry.y, 75, marker='o', color='black')
|
|
|
|
if (not Obs_geo.geometry.isnull().all()) &\
|
|
(GPS_File is not None) & (param == 'CH6:Turbidity(NTU)'):
|
|
for a in range(0, len(ilocs_max[paramIDX])):
|
|
ax[paramIDX].annotate(str(a + 1), (Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
|
ilocs_max[paramIDX][a], :].geometry.x,
|
|
Obs_geo.loc[OBS_mask[paramIDX]].iloc[
|
|
ilocs_max[paramIDX][a], :].geometry.y), fontsize=30)
|
|
|
|
ax[paramIDX].set_title(paramName[paramIDX])
|
|
# ax[paramIDX].set_ylabel('UTM 21N [m]')
|
|
# ax[paramIDX].set_xlabel('UTM 21N [m]')
|
|
ax[paramIDX].locator_params(axis='y', nbins=3)
|
|
ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')
|
|
|
|
ax[paramIDX].get_xaxis().set_ticks([])
|
|
ax[paramIDX].get_yaxis().set_ticks([])
|
|
|
|
#Add scale-bar
|
|
scalebar = AnchoredSizeBar(ax[paramIDX].transData,
|
|
100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)
|
|
ax[paramIDX].add_artist(scalebar)
|
|
ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
|
|
arrowprops=dict(facecolor='black', width=6, headwidth=30),
|
|
ha='center', va='center', fontsize=35,
|
|
xycoords=ax[paramIDX].transAxes)
|
|
|
|
fig.suptitle(siteName + ' (' + timeLabel + ')', fontsize=30)
|
|
|
|
plt.show()
|
|
|
|
#outPath = '//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + timeLabels[s]
|
|
outPath = importPath
|
|
|
|
if not os.path.exists(outPath):
|
|
os.mkdir(outPath)
|
|
|
|
if not os.path.exists(outPath + '/Figures'):
|
|
os.mkdir(outPath + '/Figures')
|
|
|
|
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabel + '_' + siteName + '.pdf',
|
|
bbox_inches='tight')
|
|
|
|
fig.savefig(outPath + '/Figures/SummaryMap_' + timeLabel + '_' + siteName + '.png',
|
|
bbox_inches='tight', dpi=500)
|
|
|
|
#%% Summary Sheet
|
|
plotIDXsLoop = []
|
|
# plotIDXsLoop.append([0, 3, 6, 9])
|
|
# plotIDXsLoop.append([1, 4, 7, 10])
|
|
# plotIDXsLoop.append([2, 5, 8, 11])
|
|
# plotIDXsLoop.append([np.arange(0, 21*3, 3)])
|
|
# plotIDXsLoop.append([np.arange(1, 21*3, 3)])
|
|
# plotIDXsLoop.append([np.arange(2, 21*3, 3)])
|
|
|
|
for i in range(0, 3):
|
|
summTable = None
|
|
# plotIDXs = plotIDXsLoop[i]
|
|
plotIDXs = np.arange(i, 21, 3)
|
|
|
|
for s, plotIDX in enumerate(plotIDXs):
|
|
## Define master import path
|
|
importPath = importPaths[plotIDX]
|
|
siteName = siteNames[plotIDX]
|
|
|
|
obsStatsIN = pd.read_excel(importPath + '/Summary_StatsFormat_' + siteName + '.xlsx', header=[0, 1], index_col=0)
|
|
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
|
obsStatsIN.rename({'Turbidity [NTU]': 'Turbidity [FTU]'}, inplace=True)
|
|
if plotIDX > 11:
|
|
obsStatsIN.rename({'Chl-a [ppb]': 'Chl-a [ug/l]'}, inplace=True)
|
|
|
|
if s == 0:
|
|
summTable = obsStatsIN
|
|
else:
|
|
summTable = summTable.join(obsStatsIN)
|
|
|
|
# Remove -999 with nan
|
|
summTable.replace(-999, np.nan, inplace=True)
|
|
|
|
summTable.to_excel('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/Summary_StatsMerge_' + siteName + '.xlsx')
|
|
|
|
|
|
#%% Summary Plot
|
|
|
|
plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',
|
|
'Significant Wave Height [m]', 'Salinity [PSU]',
|
|
'Dissolved O₂ [%]', 'Temperature [degC]',
|
|
'Turbidity [FTU]', 'Chl-a. [ug/L]']
|
|
|
|
|
|
for i in range(0, 3):
|
|
summTable = None
|
|
plotIDXs = np.arange(i, 21, 3)
|
|
|
|
plotDates = []
|
|
plotTable = np.empty([10, len(plotIDXs)])
|
|
|
|
for s, plotIDX in enumerate(plotIDXs):
|
|
## Define master import path
|
|
importPath = importPaths[plotIDX]
|
|
siteName = siteNames[plotIDX]
|
|
|
|
obsStatsIN = pd.read_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')
|
|
if any((plotIDX == 9, plotIDX == 10, plotIDX == 11)):
|
|
plotTable[0:3, s] = obsStatsIN.iloc[0:3, 1]
|
|
plotTable[8, s] = obsStatsIN.iloc[7, 1]
|
|
plotDates.append(datetime.strptime(obsStatsIN.iloc[8, 1], '%B %d, %Y'))
|
|
elif plotIDX<12:
|
|
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 13], 1]
|
|
plotDates.append(datetime.strptime(obsStatsIN.iloc[14, 1], '%B %d, %Y'))
|
|
else:
|
|
plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 12], 1]
|
|
plotDates.append(datetime.strptime(obsStatsIN.iloc[13, 1], '%B %d, %Y'))
|
|
|
|
|
|
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(19, 25), sharex=True)
|
|
fig.patch.set_facecolor('white')
|
|
fig.tight_layout(pad=3)
|
|
ax = axes.flat
|
|
|
|
# Repalce zero with nan
|
|
plotTable[plotTable < 0.00001] = np.nan
|
|
|
|
for v in range(0, 10):
|
|
ax[v].scatter(plotDates, plotTable[v, :], 250)
|
|
ax[v].set_ylabel(plotvars[v])
|
|
|
|
fig.suptitle(siteName, fontsize=35)
|
|
plt.gcf().autofmt_xdate()
|
|
plt.gcf().align_ylabels()
|
|
plt.show()
|
|
|
|
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.pdf',
|
|
bbox_inches='tight')
|
|
fig.savefig('//srv-ott3.baird.com/Projects/INNOVATION/Phase97 Barbados Research/05_Analyses/' + siteName + '.png',
|
|
bbox_inches='tight', dpi=500)
|