def get_average(file, points, radius=40):
'''Like get_values, but computes the average value within a circle of the
specified radius (in km).
Missing values are ignored. Returns None if there were no values within the
circle.
>>> lat_lons = [(10,10), (20,20), (0,0)]
>>> [type(x) for x in get_average('bio1', lat_lons, 0)]
[<type 'float'>, <type 'float'>, <type 'NoneType'>]
>>> get_average('bio1', lat_lons, 100) != get_average('bio1', lat_lons, 50) != get_average('bio1', lat_lons, 0)
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for point in points:
within = points_within_distance(*(point + ul + dims), radius=radius)
raster_positions = [
xy_coords(lat, lon, *(ul + dims)) for (lat, lon) in within
]
values = [
get_point(data, pos[0], pos[1], no_value)
for pos in raster_positions
]
values = [v for v in values if not v is None]
if len(values) == 0: result.append(None)
else:
result.append(sum(values) / float(len(values)))
return result
def get_values(file, points):
'''Given a .bil file (or other file readable by GDAL) and a set of (lat,lon)
points, return a list of values for those points. -9999 will be converted to
None.
>>> lat_lons = [(15,15), (20,20), (-60,0)]
# Two points on land, one in the water:
>>> [type(x) for x in get_values('bio1', lat_lons)]
[<type 'float'>, <type 'float'>, <type 'NoneType'>]
# Sahara hotter than Siberia (max temp. of hottest quarter):
>>> get_values('bio10', [(0, 20)])[0] > get_values('bio10', [(140, 65)])[0]
True
# Albuquerque hotter than Anchorage (mean annual temperature):
>>> get_values('bio1', [(35, -107)])[0] > get_values('bio1', [(61, -150)])[0]
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for (lat, lon) in points:
x, y = xy_coords(lat, lon, *(ul + dims))
value = get_point(data, x, y, no_value)
if value == no_value: value = None
result.append(value)
return result
def get_average(file, points, radius=40):
'''Like get_values, but computes the average value within a circle of the
specified radius (in km).
Missing values are ignored. Returns None if there were no values within the
circle.
>>> lat_lons = [(10,10), (20,20), (0,0)]
>>> [type(x) for x in get_average('bio1', lat_lons, 0)]
[<type 'float'>, <type 'float'>, <type 'NoneType'>]
>>> get_average('bio1', lat_lons, 100) != get_average('bio1', lat_lons, 50) != get_average('bio1', lat_lons, 0)
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for point in points:
within = points_within_distance(*(point + ul + dims), radius=radius)
raster_positions = [xy_coords(lat, lon, *(ul + dims)) for (lat, lon) in within]
values = [get_point(data, pos[0], pos[1], no_value)
for pos in raster_positions]
values = [v for v in values if not v is None]
if len(values) == 0: result.append(None)
else:
result.append(sum(values)/float(len(values)))
return result
def get_values(file, points):
'''Given a .bil file (or other file readable by GDAL) and a set of (lat,lon)
points, return a list of values for those points. -9999 will be converted to
None.
>>> lat_lons = [(15,15), (20,20), (-60,0)]
# Two points on land, one in the water:
>>> [type(x) for x in get_values('bio1', lat_lons)]
[<type 'float'>, <type 'float'>, <type 'NoneType'>]
# Sahara hotter than Siberia (max temp. of hottest quarter):
>>> get_values('bio10', [(0, 20)])[0] > get_values('bio10', [(140, 65)])[0]
True
# Albuquerque hotter than Anchorage (mean annual temperature):
>>> get_values('bio1', [(35, -107)])[0] > get_values('bio1', [(61, -150)])[0]
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for (lat, lon) in points:
x,y = xy_coords(lat, lon, *(ul+dims))
value = get_point(data, x, y, no_value)
if value == no_value: value = None
result.append(value)
return result
def draw_map(file, map=None, show=True, title=None, log=False, map_type=None):
'''Use Matplotlib's basemap to generate a map of a given BIOCLIM data
file.
You can supply a Basemap object (in any projection) as the optional
keyword argument "map." If none is provided, the default Miller
projection will be used.'''
data, no_value, ul, dims, size = extract_attributes(file)
data = get_dataset(file)
lats = np.linspace(ul[0], ul[0]-dims[0]*size[0], size[0], endpoint=False)
lons = np.linspace(ul[1], ul[1]+dims[1]*size[1], size[1], endpoint=False)
if map_type == 'variance':
x, y = np.meshgrid(lons, lats)
raster = np.zeros(x.shape)
values = get_spatial_variance(file,
[(lat, lon)
for lat in lats
for lon in lons])
for a in range(data.shape[0]):
for b in range(data.shape[1]):
data[a,b] = values.pop()
else:
raster = data.ReadAsArray()
# because missing data is entered as -9999, created a masked array so that
# these points will not be plotted
values = np.ma.masked_where(raster==no_value, raster)
# log transform data, if requested
if log:
if (values < 0).any():
values -= min(values)
values = np.log1p(values)
plt.figure()
if title is None:
title = '%s' % file
if file in variable_names:
title += ': %s' % variable_names[file]
plt.title(title)
if map is None:
map = Basemap(projection='mill',lon_0=0)
map.drawcoastlines(linewidth=1)
map.drawcountries(linewidth=1)
map.drawstates(linewidth=0.5)
parallels = np.arange(-90.,90,10.)
map.drawparallels(parallels,labels=[False,True,True,False])
meridians = np.arange(-180.,180.,20.)
map.drawmeridians(meridians,labels=[True,False,False,True])
x, y = np.meshgrid(lons, lats)
map.pcolormesh(x, y, data=values, latlon=True, cmap=plt.cm.Spectral_r)
cbar = plt.colorbar()
if show: plt.show()
def get_spatial_variance(file, points, radius=40):
'''Like get_values, but computes the spatial variance within a circle of the
specified radius (in km).
Missing values are ignored. Returns None if there were no values within the
circle.
>>> lat_lons = [(10,10), (20,20), (0,0)]
>>> get_spatial_variance('bio1', lat_lons, 0)
[0.0, 0.0, None]
>>> (get_spatial_variance('bio1', lat_lons[0:1], 100) >=
... get_spatial_variance('bio1', lat_lons[0:1], 50) >=
... get_spatial_variance('bio1', lat_lons[0:1], 0))
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for point in points:
# because the distance between longitude points approaches 0 at the
# poles, only compute variance between 60 N and 60 S
if abs(point[0]) > 60:
result.append(None)
continue
within = points_within_distance(*(point + ul + dims), radius=radius)
raster_positions = [
xy_coords(lat, lon, *(ul + dims)) for (lat, lon) in within
]
values = [
get_point(data, pos[0], pos[1], no_value)
for pos in raster_positions
]
values = [v for v in values if not v is None]
if len(values) == 0: result.append(None)
else:
result.append(float(np.var(values)))
return result
def get_spatial_variance(file, points, radius=40):
'''Like get_values, but computes the spatial variance within a circle of the
specified radius (in km).
Missing values are ignored. Returns None if there were no values within the
circle.
>>> lat_lons = [(10,10), (20,20), (0,0)]
>>> get_spatial_variance('bio1', lat_lons, 0)
[0.0, 0.0, None]
>>> (get_spatial_variance('bio1', lat_lons[0:1], 100) >=
... get_spatial_variance('bio1', lat_lons[0:1], 50) >=
... get_spatial_variance('bio1', lat_lons[0:1], 0))
True
'''
data, no_value, ul, dims, size = extract_attributes(file)
result = []
for point in points:
# because the distance between longitude points approaches 0 at the
# poles, only compute variance between 60 N and 60 S
if abs(point[0]) > 60:
result.append(None)
continue
within = points_within_distance(*(point + ul + dims), radius=radius)
raster_positions = [xy_coords(lat, lon, *(ul + dims)) for (lat, lon) in within]
values = [get_point(data, pos[0], pos[1], no_value)
for pos in raster_positions]
values = [v for v in values if not v is None]
if len(values) == 0: result.append(None)
else:
result.append(float(np.var(values)))
return result
