def geoview_flat_plot(region_da, time_sel=0, level_sel=False, wide_chile=True):
if type(level_sel) is int:
da = region_da.isel(level=level_sel)
else:
da = region_da
gv_ds = gv.Dataset(da.isel(time=time_sel))
plot = gv_ds.to(gv.Image, ['lon', 'lat'], 't', 'time').opts(cmap='viridis',
colorbar=True)
extras = []
if wide_chile:
easter_island_text = gv.Text(-104.360481, -24.104671, 'Easter Island').opts(color='white')
easter_island_point = gv.Points([(-109.360481, -27.104671)]).opts(color='red')
easter = easter_island_point * easter_island_text
falkland_islands_text = gv.Text(-49.563412, -56.820557, 'Falklands').opts(color='white')
falkland_islands_point = gv.Points([(-51.563412, -59.820557)]).opts(color='red')
falkland = falkland_islands_point * falkland_islands_text
extras.append(easter * falkland)
plot = contours(plot, filled=True, overlaid=True)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
final_plot = plot * gf.coastline
for extra in extras:
final_plot *= extra
gv.output(final_plot)
def view_time_series(filename, cmap='jet'):
"""
Visualize and interact with time series netcdf files.
"""
# Open geoviews interactive session.
gv.extension('bokeh', 'matplotlib')
with xr.open_dataset(filename, decode_times=False) as da:
dataset = gv.Dataset(da)
ensemble = dataset.to(gv.Image, ['x', 'y'])
gv.output(ensemble.opts(cmap=cmap,
colorbar=True,
fig_size=200,
backend='matplotlib'),
backend='matplotlib')
return
def shapeplot(dframe,
var,
coords,
raxis,
cm='Jet_r',
extralayers=[],
lcolor='black'):
lcoords = len(coords)
xly = len(extralayers)
if lcoords == 2 and xly > 0:
gvdata = gv.Dataset(dframe,
kdims=[coords[0], coords[1], raxis],
vdims=var)
back = gvdata.to(gv.Image, [coords[0], coords[1]], var)
back = back.opts(colorbar=True,
backend='bokeh',
cmap=cm,
width=600,
height=400)
for layer in extralayers:
ss = gv.Shape.from_shapefile(layer).opts(line_color=lcolor)
back *= ss
return gv.output(back.opts(width=600, height=400), backend='bokeh')
else:
print("Incomplete coordinates")
def fastplot(dframe, var, coords, raxis, cm='Jet'):
lcoords = len(coords)
if lcoords == 2:
gvdata = gv.Dataset(dframe,
kdims=[coords[0], coords[1], raxis],
vdims=var)
back = gvdata.to(gv.Image, [coords[0], coords[1]], var)
return gv.output(back.opts(colorbar=True,
backend='bokeh',
cmap=cm,
width=600,
height=400),
backend='bokeh')
else:
print("Incomplete coordinates")
def quick_map(da, dims=["lon", "lat"], redim_range=None, **user_options):
options = dict(
cmap="viridis",
colorbar=True,
fig_size=300,
projection=crs.Robinson(central_longitude=180),
)
options.update(user_options)
name = da.name
dataset = gv.Dataset(da)
image = dataset.to(gv.Image, dims, dynamic=True).options(**options)
if redim_range is not None:
image = image.redim.range(**{name: redim_range})
return gv.output(image * gf.coastline(), backend="matplotlib")
# Evalúa qué puntos del grid del CRU corresponden a
# la Cuenca del Valle de México.
import os
import pandas as pd
import numpy as np
import geoviews as gv
import geopandas as gpd
gv.extension("matplotlib")
gv.output(size = 150)
fdir_d = os.getcwd() + "/data/Cuencas/Regiones_Hidrologicas_Administrativas/"
fdir_r = os.getcwd() + "/results/sequia/"
fname = "rha250kgw.shp"
# Si no existe la carpeta, la crea.
if not os.path.exists(fdir_r):
os.mkdir(fdir_r)
# Se cargan las regiones hidrológico administrativas.
gdf = gpd.read_file(fdir_d + fname)
# Se obtiene el contorno de las cuencas-
gdf["boundary"] = gdf.boundary
# Se selecciona la Cuenca del Valle de México.
cuenca = gv.Path(
import warnings
from pathlib import Path
import shutil
import xarray as xr
# Plotting Libraries
import matplotlib.pyplot as plt
from holoviews.operation import contours
import geoviews as gv
import geoviews.feature as gf
# Plot Options
gv.extension('matplotlib')
gv.output(size=200)
from IPython.core.display import HTML
# Center matplotlib's figures
HTML("""
<style>
.output_png {
display: table-cell;
text-align: center;
vertical-align: middle;
}
</style>
""")
import os
import h5py
import pandas as pd
import geopandas as gp
import holoviews as hv
import geoviews as gv
from shapely.geometry import Point
from geoviews import opts
from geoviews import tile_sources as gvts
gv.extension('bokeh', 'matplotlib', display_formats=['svg', 'html'])
gv.output(fig='png')
def point_visual(features, vdims):
"""
:param features:
:param vdims:
:return:
"""
return (gvts.EsriImagery *
gv.Points(features, vdims=vdims).options(tools=['hover'],
height=500,
width=900,
size=5,
color='yellow',
fontsize={
'xticks': 10,
'yticks': 10,
'xlabel': 16,