def testplot(self):
pic = r'c:\temp\plot.png'
x = xrange(20)
ap.plot(x, out_file=pic, openit=False)
y = xrange(50,70)
ap.plot(x, y, pic, 'Main', 'X [m]', 'Y [m]', 'o', 'k', openit=False)
os.remove(pic)
with self.assertRaises(ap.ArcapiError):
ap.plot(x, [1,2,3], pic, 'Main', 'X [m]', 'Y [m]', 'o', 'k', openit=False)
pass
def testplot(self):
pic = r'c:\temp\plot.png'
x = xrange(20)
ap.plot(x, out_file=pic, openit=False)
y = xrange(50,70)
ap.plot(x, y, pic, 'Main', 'X [m]', 'Y [m]', 'o', 'k', openit=False)
os.remove(pic)
with self.assertRaises(ap.ArcapiError):
ap.plot(x, [1,2,3], pic, 'Main', 'X [m]', 'Y [m]', 'o', 'k', openit=False)
pass
# 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':
# http://cran.r-project.org/web/packages/sp/sp.pdf
landuse2luse = {
'Aa': 'Agriculture/unspecified', 'Ab': 'Agr/sugar beetsm',
'Ag': 'Agr/small grains', 'Ah': 'Agr/??', 'Am': 'Agr/maize', 'B': 'woods',
# 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',
'Ab': 'Agr/sugar beetsm',
'Ag': 'Agr/small grains',
'Ah': 'Agr/??',
'Am': 'Agr/maize',