2017-08-14 11:14:48 -06:00
2017-09-15 13:17:54 -06:00
The < a href = "http://python-awips.readthedocs.io" >< b > python-awips</ b ></ a > package provides access to the entire AWIPS Maps Database for use in Python GIS applications. Map objects are returned as < a href = "http://toblerity.org/shapely/manual.html" >< b > Shapely</ b ></ a > geometries (*Polygon*, *Point* , *MultiLineString* , etc.) and can be plotted by Matplotlib, Cartopy, MetPy, and other packages.
2017-08-14 11:14:48 -06:00
2017-09-15 13:17:54 -06:00
Each map database table has a geometry field called `the_geom` , which can be used to spatially select map resources for any column of type geometry.
> See the <a href="http://unidata.github.io/awips2/python/maps-database/#mapdatacwa"><b>Maps Database Reference Page</b></a> for available database tables, column names, and types.
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## Notes
* This notebook requires: **python-awips, numpy, matplotplib, cartopy, shapely**
* Use datatype **maps** and **addIdentifier('table', <postgres maps schema>)** to define the map table:
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
request = DataAccessLayer.newDataRequest('maps')
request.addIdentifier('table', 'mapdata.county')
* 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')
> Note the geometry definition of `the_geom` for each data type, which can be **Point**, **MultiPolygon**, or **MultiLineString**.
## Setup
```python
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
# Standard map plot
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
# Server, Data Request Type, and Database Table
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.
```python
# 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')
# This is essentially the same as "'"select count(*) from mapdata.cwa where cwa='BOU';" (=1)
# 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
# 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]
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
```python
# 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='#cc5000 ')
ax.add_feature(shape_feature)
fig
```
## WFO boundary spatial filter for interstates
Using the previously-defined **envelope=merged_counties.buffer(2)** in **newDataRequest()** to request geometries which fall inside the buffered boundary.
```python
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
request.addIdentifier('table', 'mapdata.interstate')
request.addIdentifier('geomField', 'the_geom')
request.addIdentifier('locationField', 'hwy_type')
request.addIdentifier('hwy_type', 'I') # I (interstate), U (US highway), or S (state highway)
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 223 interstate MultiLineStrings
> Road type from `select distinct(hwy_type) from mapdata.interstate;`
>
> I - Interstates
> U - US Highways
> S - State Highways
## Nearby cities
Request the city table and filter 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.
```python
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
```python
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
## Lakes
```python
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
request.addIdentifier('table', 'mapdata.lake')
request.addIdentifier('geomField', 'the_geom')
request.setParameters('name')
# Get lake geometries
response = DataAccessLayer.getGeometryData(request, [])
lakes = np.array([])
for ob in response:
lakes = np.append(lakes,ob.getGeometry())
print("Using " + str(len(lakes)) + " lake MultiPolygons")
# Plot lakes
for i, geom in enumerate(lakes):
cbounds = Polygon(geom)
intersection = cbounds.intersection
geoms = (intersection(geom)
for geom in lakes
if cbounds.intersects(geom))
shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(),
facecolor='blue', linestyle="-",edgecolor='#20B2AA ')
ax.add_feature(shape_feature)
fig
```
Using 208 lake MultiPolygons
## Major Rivers
```python
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
request.addIdentifier('table', 'mapdata.majorrivers')
request.addIdentifier('geomField', 'the_geom')
request.setParameters('pname')
rivers = DataAccessLayer.getGeometryData(request, [])
print("Using " + str(len(rivers)) + " river MultiLineStrings")
# Plot rivers
for ob in rivers:
shape_feature = ShapelyFeature(ob.getGeometry(),ccrs.PlateCarree(),
facecolor='none', linestyle=":",edgecolor='#20B2AA ')
ax.add_feature(shape_feature)
fig
```
Using 758 river MultiLineStrings
## Topography
Spatial envelopes are required for topo requests, which can become slow to download and render for large (CONUS) maps.
```python
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 0x1174adf50 > ]
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.]]
```python
grid=gridData[0]
topo=ma.masked_invalid(grid.getRawData())
lons, lats = grid.getLatLonCoords()
print(topo.min())
print(topo.max())
# 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
```
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