from post_gnome.plotting import geo_plots reload(geo_plots) import matplotlib.pyplot as plt import datetime plt.clf() ax = geo_plots.add_map(bbox=(-125.2, -124.2, 47.7, 48.3), bna="coast_wa.bna") # if bbox not specified, this will use map bounds from bna # add particles at one time t0 = datetime.datetime(2016, 8, 18, 12) ax = geo_plots.plot_particles(ax, "WA_particles.nc", t0, color="b") t1 = t0 + datetime.timedelta(hours=24) ax = geo_plots.plot_particles(ax, "WA_particles.nc", t1, color="g") ax.legend() # add initial location t = datetime.datetime(2016, 8, 18, 1) geo_plots.plot_particles(ax, "WA_particles.nc", t, marker="+", markersize=8) plt.show()
from post_gnome.plotting import geo_plots
reload(geo_plots)
import matplotlib.pyplot as plt
import datetime
import cartopy.crs as ccrs
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
import cartopy.feature as cfeature
import os
plt.clf()
#ax = geo_plots.add_map(bbox=(-89,-85,27.5,31))
ax = geo_plots.add_map(bbox=(-90, -84, 27.5, 31))
#land_10m = cfeature.NaturalEarthFeature('physical','land','50m',\
# edgecolor='face',facecolor='0.75')
##ax.add_feature(cfeature.LAND, facecolor='0.75')
#ax.add_feature(land_10m)
#if bbox not specified, this will use map bounds from bna
bathy_file = os.path.join('gom_bathy','Bathymetry.shp')
bathy = Reader(bathy_file)
for rec,geo in zip(bathy.records(),bathy.geometries()):
if rec.attributes['DEPTH_METR'] in ['100m','500m','1000m']:
shape_feature = ShapelyFeature(geo,ccrs.PlateCarree(), facecolor='none')
ax.add_feature(shape_feature)
#add particles at one time
t = datetime.datetime(2016, 9, 21, 1)
from post_gnome.plotting import geo_plots reload(geo_plots) import matplotlib.pyplot as plt import datetime plt.clf() ax = geo_plots.add_map(bbox=(-125.2,-124.2,47.7,48.3), bna='coast_wa.bna') #if bbox not specified, this will use map bounds from bna #add vectors from HYCOM.nc t0 = datetime.datetime(2016,8,18,12) ax = geo_plots.add_vectors(ax,'HYCOM.nc',t0,bbox=(-125.2,-124.2,47.7,48.3)) #add particles at one time ax = geo_plots.plot_particles(ax,'WA_particles.nc',t0,color='b') t1 = t0 + datetime.timedelta(hours=24) ax = geo_plots.plot_particles(ax,'WA_particles.nc',t1,color='g') ax.legend() #add initial location t = datetime.datetime(2016,8,18,1) ax = geo_plots.plot_particles(ax,'WA_particles.nc',t,marker='+',markersize=8) plt.show()
import os
from os.path import join
from scipy.io import loadmat
from datetime import datetime, timedelta
from calendar import monthrange
import numpy as np
import scipy.io as sio
from netCDF4 import Dataset, num2date
import matplotlib.pyplot as plt
from pylab import figure, cm
from matplotlib.colors import LogNorm
from post_gnome.plotting import geo_plots
reload(geo_plots)
from post_gnome import nc_particles
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
data_path = '/DATA/forecastData/'
output_test='/DATA/forecastData/Output/2019-07-19/P1/output.nc'
for i in range(1,72):
t0 = datetime(2019,06,11,1)+timedelta(hours=i)
ax = geo_plots.add_map(bbox=(-98, -80,18.1, 30), bna=data_path+'BaseMaps/gulf.bna')
geo_plots.plot_particles(ax,output_test,t0,marker='+',markersize=1)
#ax = geo_plots.add_vectors(ax,data_path+'Currents/hycom_forecast_20190611.nc',t0,2000,bbox=(-98, -88,18.1, 30), lonvar='lon',latvar='lat',uvar='u',vvar='v')
plt.savefig('test'+str(i)+'.png')
from post_gnome.plotting import geo_plots
reload(geo_plots)
import matplotlib.pyplot as plt
import datetime
import cartopy.crs as ccrs
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
import cartopy.feature as cfeature
import os
plt.clf()
#ax = geo_plots.add_map(bbox=(-89,-85,27.5,31))
ax = geo_plots.add_map(bbox=(-90, -84, 27.5, 31))
#land_10m = cfeature.NaturalEarthFeature('physical','land','50m',\
# edgecolor='face',facecolor='0.75')
##ax.add_feature(cfeature.LAND, facecolor='0.75')
#ax.add_feature(land_10m)
#if bbox not specified, this will use map bounds from bna
bathy_file = os.path.join('gom_bathy', 'Bathymetry.shp')
bathy = Reader(bathy_file)
for rec, geo in zip(bathy.records(), bathy.geometries()):
if rec.attributes['DEPTH_METR'] in ['100m', '500m', '1000m']:
shape_feature = ShapelyFeature(geo,
ccrs.PlateCarree(),
facecolor='none')
ax.add_feature(shape_feature)
#add particles at one time
reload(geo_plots)
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from post_gnome import nc_particles
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.pyplot as plt
from post_gnome import nc_particles
from netCDF4 import Dataset, num2date
import numpy as np
plt.clf()
lat = [18.1, 30]
lon = [-98, -88]
ax = geo_plots.add_map(bbox=(-98, -88,18.1, 30), bna='/DATA/forecastData/BaseMaps/gulf.bna')
#if bbox not specified, this will use map bounds from bna
def contour_particles(ax, filename, t, depth=0, varname=None, criteria=None, levels=[0.1, 0.4, 0.8, 1]):
'''
contour all LEs at one time step
ax: (matplotlib.axes object) the map on which the LEs will be plotted
filename: (str) complete path filename of particle file
t: (datetime obj) closest time to this will be plottted
depth: (float) depth of particles to include (all if None)
'''
import scipy.stats as st
particles = nc_particles.Reader(filename)
times = particles.times
dt = [np.abs(((output_t - t).total_seconds()) / 3600) for output_t in times]
tidx = dt.index(min(dt))
