def testfrequency(self):
est = ap.frequency([1,1,2,3,4,4,4])
obs = {1: 2, 2: 1, 3: 1, 4: 3}
samekeys = set(est.keys()) == set(obs.keys())
good = all([samekeys] + [est[i] == obs[i] for i in est])
self.assertTrue(good)
pass
def testfrequency(self):
est = ap.frequency([1,1,2,3,4,4,4])
obs = {1: 2, 2: 1, 3: 1, 4: 3}
samekeys = set(est.keys()) == set(obs.keys())
good = all([samekeys] + [est[i] == obs[i] for i in est])
self.assertTrue(good)
pass
# Make a quick map # If you are in ArcMap's Python window, add fc as a feature layer. # This will add the layer if your ArcMap's Geoprocessing options # allow to 'Add results of geoprocessing operations to the display'. ap.flyr(fc) # If you are not in ArcMap, you can still plot the feature class: ap.chart(fc) # You can plot values in a column once you read its values into Python. # Let's first find out what unique landuse categories there are in fc: ap.distinct(fc, 'landuse') # How many records of each species are there? x = ap.values(fc, 'landuse') ap.frequency(x) # Now plot zinc concentration for landuse 'W' z = ap.values(fc, 'zinc', '"landuse" = \'W\'', '"OBJECTID" ASC') ap.plot(z) # Arcapi now plots histograms too! ap.hist(z) # Show scatter plot of zinc against distance from the river # The 'order by' clause ensures values come in the same order d = ap.values(fc, 'dist_m', '"landuse" = \'W\'', '"OBJECTID" ASC') ap.plot(d, z, main='Zinc', xlab='Ditance', ylab='Zn', pch='o', color='k') # Suppose we want to add full labels indicating landuse at points. # This can come as a table, json, or other forms. Anyhow, we would # convert it to a Python dictionary. I simply re-typed help of 'sp':
tmp = ap.summary(fc, ['lime', 'soil', 'ffreq'], ['CAT', 'CAT', 'CAT'])
# Make a quick map
# If you are in ArcMap's Python window, add fc as a feature layer.
# This will add the layer if your ArcMap's Geoprocessing options
# allow to 'Add results of geoprocessing operations to the display'.
ap.flyr(fc)
# If you are not in ArcMap, you can still plot the feature class:
ap.chart(fc)
# You can plot values in a column once you read its values into Python.
# Let's first find out what unique landuse categories there are in fc:
ap.distinct(fc, 'landuse')
# How many records of each species are there?
x = ap.values(fc, 'landuse')
ap.frequency(x)
# Now plot zinc concentration for landuse 'W'
z = ap.values(fc, 'zinc', '"landuse" = \'W\'', '"OBJECTID" ASC')
ap.plot(z)
# Show scatter plot of zinc against distance from the river
# The 'order by' clause ensures values come in the same order
d = ap.values(fc, 'dist_m', '"landuse" = \'W\'', '"OBJECTID" ASC')
ap.plot(d, z, main='Zinc', xlab='Ditance', ylab='Zn', pch='o', color='k')
# Suppose we want to add full labels indicating landuse at points.
# This can come as a table, json, or other forms. Anyhow, we would
# convert it to a Python dictionary. I simply re-typed help of 'sp':
# http://cran.r-project.org/web/packages/sp/sp.pdf
landuse2luse = {
'Aa': 'Agriculture/unspecified',
