The python-awips package provides access to the entire AWIPS Maps Database for use in Python GIS applications. Map objects are returned as Shapely geometries (Polygon, Point, MultiLineString, etc.) and can be easily plotted by Matplotlib, Cartopy, MetPy, and other packages.
Notes
- This notebook requires: python-awips, numpy, matplotplib, cartopy, shapely
- Use datatype maps and addIdentifier('table', <postgres maps schema>) to define the map table.
Use request.setLocationNames() and request.addIdentifier() to spatially filter a map resource. In the example below, WFO ID BOU (Boulder, Colorado) is used to query counties within the BOU county watch area (CWA)
request.addIdentifier('geomField', 'the_geom') request.addIdentifier('inLocation', 'true') request.addIdentifier('locationField', 'cwa') request.setLocationNames('BOU') request.addIdentifier('cwa', 'BOU')From an EDEX server list the available table schemas with the command
psql maps -c "\dt mapdata.*;"psql maps -c "\dt mapdata.*;" Schema | Name | Type | Owner ---------+-----------------+-------+------- mapdata | airport | table | awips mapdata | allrivers | table | awips mapdata | artcc | table | awips ...To describe a single table schema use the command
psql maps -c "\d+ mapdata.county;"psql maps -c "\d+ mapdata.county;" Column | Type ----------------+----------------------------- gid | integer state | character varying(2) cwa | character varying(9) countyname | character varying(24) fips | character varying(5) the_geom | geometry(MultiPolygon,4326) ...note the MultiPolygon geometry definition for the_geom
Setup
from __future__ import print_function
from awips.dataaccess import DataAccessLayer
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.feature import ShapelyFeature,NaturalEarthFeature
from shapely.geometry import Polygon
from shapely.ops import cascaded_union
def make_map(bbox, projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(12,12),
subplot_kw=dict(projection=projection))
ax.set_extent(bbox)
ax.coastlines(resolution='50m')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return fig, ax
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
request = DataAccessLayer.newDataRequest('maps')
request.addIdentifier('table', 'mapdata.county')
Request County Boundaries for a WFO
Use request.setParameters() to define fields to be returned by the request.
# Define a WFO ID for location
# tie this ID to the mapdata.county column "cwa" for filtering
request.setLocationNames('BOU')
request.addIdentifier('cwa', 'BOU')
# enable location filtering (inLocation)
# locationField is tied to the above cwa definition (BOU)
request.addIdentifier('geomField', 'the_geom')
request.addIdentifier('inLocation', 'true')
request.addIdentifier('locationField', 'cwa')
# Get response and create dict of county geometries
response = DataAccessLayer.getGeometryData(request, [])
counties = np.array([])
for ob in response:
counties = np.append(counties,ob.getGeometry())
print("Using " + str(len(counties)) + " county MultiPolygons")
%matplotlib inline
fig, ax = make_map(bbox=bbox)
# Plot political/state boundaries handled by Cartopy
political_boundaries = NaturalEarthFeature(category='cultural',
name='admin_0_boundary_lines_land',
scale='50m', facecolor='none')
states = NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lines',
scale='50m', facecolor='none')
ax.add_feature(political_boundaries, linestyle='-', edgecolor='black')
ax.add_feature(states, linestyle='-', edgecolor='black',linewidth=2)
# Plot CWA counties
for i, geom in enumerate(counties):
cbounds = Polygon(geom)
intersection = cbounds.intersection
geoms = (intersection(geom)
for geom in counties
if cbounds.intersects(geom))
shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(),
facecolor='none', linestyle="-",edgecolor='#86989B')
ax.add_feature(shape_feature)
Using 25 county MultiPolygons
Create a merged CWA with cascaded_union
# All WFO counties merged to a single Polygon
merged_counties = cascaded_union(counties)
envelope = merged_counties.buffer(2)
boundaries=[merged_counties]
# Get bounds of this merged Polygon to use as buffered map extent
bounds = merged_counties.bounds
bbox=[bounds[0]-1,bounds[2]+1,bounds[1]-1.5,bounds[3]+1.5]
# Plot CWA envelope
for i, geom in enumerate(boundaries):
gbounds = Polygon(geom)
intersection = gbounds.intersection
geoms = (intersection(geom)
for geom in boundaries
if gbounds.intersects(geom))
shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(),
facecolor='none', linestyle="-",linewidth=3,edgecolor='#4070a0')
ax.add_feature(shape_feature)
fig
WFO boundary spatial filter for interstates, cities, topo
Using the previously-defined envelope=merged_counties.buffer(2) in newDataRequest() to request geometries which fall inside the buffered boundary.
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
request.addIdentifier('table', 'mapdata.interstate')
request.addIdentifier('geomField', 'the_geom')
request.addIdentifier('locationField', 'pretype')
request.addIdentifier('pretype', 'I') # see below
request.setParameters('name')
interstates = DataAccessLayer.getGeometryData(request, [])
print("Using " + str(len(interstates)) + " interstate MultiLineStrings")
# Plot interstates
for ob in interstates:
shape_feature = ShapelyFeature(ob.getGeometry(),ccrs.PlateCarree(),
facecolor='none', linestyle="-",edgecolor='orange')
ax.add_feature(shape_feature)
fig
Using 148 interstate MultiLineStrings
Road type from
select distinct(pretype) from mapdata.interstate;Ushy Hwy Ave Cord Rt Loop I Sthy
Nearby cities
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
request.addIdentifier('table', 'mapdata.city')
request.addIdentifier('geomField', 'the_geom')
request.setParameters('name','population','prog_disc')
cities = DataAccessLayer.getGeometryData(request, [])
print("Found " + str(len(cities)) + " city Points")
Found 1201 city Points
Filter cities by population and progressive disclosure level
Warning: the prog_disc field is not entirely understood and values appear to change significantly depending on WFO site.
citylist = []
cityname = []
# For BOU, progressive disclosure values above 50 and pop above 5000 looks good
for ob in cities:
if ((ob.getNumber("prog_disc")>50) and int(ob.getString("population")) > 5000):
citylist.append(ob.getGeometry())
cityname.append(ob.getString("name"))
print("Using " + str(len(cityname)) + " city Points")
# Plot city markers
ax.scatter([point.x for point in citylist],
[point.y for point in citylist],
transform=ccrs.Geodetic(),marker="+",facecolor='black')
# Plot city names
for i, txt in enumerate(cityname):
ax.annotate(txt, (citylist[i].x,citylist[i].y),
xytext=(3,3), textcoords="offset points")
fig
Using 57 city Points
Topography
Spatial envelopes are required for topo requests.
import numpy.ma as ma
request = DataAccessLayer.newDataRequest()
request.setDatatype("topo")
request.addIdentifier("group", "/")
request.addIdentifier("dataset", "full")
request.setEnvelope(envelope)
gridData = DataAccessLayer.getGridData(request)
print(gridData)
print("Number of grid records: " + str(len(gridData)))
print("Sample grid data shape:\n" + str(gridData[0].getRawData().shape) + "\n")
print("Sample grid data:\n" + str(gridData[0].getRawData()) + "\n")
[<awips.dataaccess.PyGridData.PyGridData object at 0x107113810>]
Number of grid records: 1
Sample grid data shape:
(778, 1058)
Sample grid data:
[[ 1694. 1693. 1688. ..., 757. 761. 762.]
[ 1701. 1701. 1701. ..., 758. 760. 762.]
[ 1703. 1703. 1703. ..., 760. 761. 762.]
...,
[ 1767. 1741. 1706. ..., 769. 762. 768.]
[ 1767. 1746. 1716. ..., 775. 765. 761.]
[ 1781. 1753. 1730. ..., 766. 762. 759.]]
grid=gridData[0]
topo=ma.masked_invalid(grid.getRawData())
lons, lats = grid.getLatLonCoords()
# Plot topography
cs = ax.contourf(lons, lats, topo, 80, cmap=plt.get_cmap('terrain'),alpha=0.1)
cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')
cbar.set_label("topography height in meters")
fig
