def bin_extent(lat, lon, bins=100):
"""Bin random lon+lat points.
lat Nx1 array of latitude values
lon Nx1 array of longitude values
bins an integer or (bin_x, bin_y) describing how the extent
of the data in (lat, lon) is to be binned
If one integer is supplied the extent determined from the
point data (lat, lon) is binned that number of times in the
Y and X direction. If (M, N), the number of Y bins is M, etc.
Returns a 2D array (bin_x, bin_y) of lists containing indices of points
binned into each grid cell.
"""
# get bin width and number of cells
try:
(gnumy, gnumx) = bins
except TypeError:
gnumx = gnumy = bins
# convert two Nx1 arrays lat and lon to [[lon, lat], ...]
data = zip(lon, lat)
(ll_lat, ll_lon, ur_lat, ur_lon) = uge.get_extent(data, margin=0)
lon_width = ur_lon - ll_lon
lat_width = ur_lat - ll_lat
gwidx = lon_width / gnumx
gwidy = lat_width / gnumy
return bin_data(lat, lon, ll_lon, ll_lat, gwidx, gwidy, gnumx, gnumy)
def bin_extent(lat, lon, bins=100):
"""Bin random lon+lat points.
lat Nx1 array of latitude values
lon Nx1 array of longitude values
bins an integer or (bin_x, bin_y) describing how the extent
of the data in (lat, lon) is to be binned
If one integer is supplied the extent determined from the
point data (lat, lon) is binned that number of times in the
Y and X direction. If (M, N), the number of Y bins is M, etc.
Returns a 2D array (bin_x, bin_y) of lists containing indices of points
binned into each grid cell.
"""
# get bin width and number of cells
try:
(gnumy, gnumx) = bins
except TypeError:
gnumx = gnumy = bins
# convert two Nx1 arrays lat and lon to [[lon, lat], ...]
data = zip(lon, lat)
(ll_lat, ll_lon, ur_lat, ur_lon) = uge.get_extent(data, margin=0)
lon_width = ur_lon - ll_lon
lat_width = ur_lat - ll_lat
gwidx = lon_width / gnumx
gwidy = lat_width / gnumy
return bin_data(lat, lon, ll_lon, ll_lat, gwidx, gwidy, gnumx, gnumy)
def calc_sum_xyz(datafile, scale=None, bins=100, filter=None, invert=False,
clip=None):
"""A function to take a file of data and create an XYZ data object.
Take values at random x,y points and bin into summed values.
datafile a file containing (lat, lon, val) values
scale amount to scale the data (ie, divide)
bins number of bins in X and Y direction
if an int, # bins in X and Y direction
if [int, int] the X and Y bin counts may be different
filter a function to extract (lon, lat, val) from one datafile line
if not supplied an internal function is used
invert if 'filter' not supplied, an internal filter is used. if
'invert' is True, switch the order of lat/lon in data lines.
clip if defined is a dictionary defining clip limits. Recognised
keys in the dictionary are:
'high' sets high limit above which values are clipped
'low' sets low limit below which values are clipped
At least one of the above keys must exist.
The clipping is done before any scaling.
Returns a numpy array of XYZ data [[lon, lat, val], ...].
Values that are 0 replaced with Nan.
"""
# handle optional parameters
try:
bin_len = len(bins)
except TypeError:
bins_x = bins_y = bins
else:
try:
(bins_x, bins_y) = bins
except ValueError:
raise RuntimeError("Bad 'bins' value, expected int or [int, int]")
# if user didn't supply a data filter, use our own
if filter is None:
# define a default filter and use it
if invert:
def default_filter(line):
return (float(line[1]), float(line[0]), float(line[2]))
else:
def default_filter(line):
return (float(line[0]), float(line[1]), float(line[2]))
filter = default_filter
# get data into memory
data = []
for line in file(datafile):
# ignore blank or comment lines
line = line.strip()
if line == '' or line[0] in '%#':
continue
# get lon+lat+value from line
data.append(filter(SplitPattern.split(line)))
data = scipy.array(data)
# do clipping, if required
if clip:
low_clip = clip.get('low', None)
high_clip = clip.get('high', None)
if low_clip is None:
low_clip = scipy.min(data[:,2])
if high_clip is None:
high_clip = scipy.max(data[:,2])
data[:,2] = scipy.clip(data[:,2], low_clip, high_clip)
# handle any scaling
if scale:
scale = int(scale)
data[:,2] = data[:,2] / scale
# get extent of data (tight first, then with margin)
(tll_lat, tll_lon, tur_lat, tur_lon) = ge.get_extent(data, margin=0)
tr_opt = '-R%f/%f/%f/%f' % (tll_lon, tur_lon, tll_lat, tur_lat)
(ll_lat, ll_lon, ur_lat, ur_lon) = ge.get_extent(data)
r_opt = '-R%f/%f/%f/%f' % (ll_lon, ur_lon, ll_lat, ur_lat)
# now generate a binned dataset
(binned_data, xedges, yedges) = scipy.histogram2d(data[:,0], data[:,1],
bins=bins, normed=False,
weights=data[:,2])
# create XYZ object
# make sure X+Y is *centre* of each bin
xyz = []
xedges = scipy.array(xedges)
xedges = xedges[:-1] + (xedges[1] - xedges[0])/2
yedges = scipy.array(yedges)
yedges = yedges[:-1] + (yedges[1] - yedges[0])/2
for (xi, x) in enumerate(xedges):
for (yi, y) in enumerate(yedges):
xyz.append([x, y, binned_data[xi,yi]])
return scipy.array(xyz)
def calc_sum_xyz(datafile,
scale=None,
bins=100,
filter=None,
invert=False,
clip=None):
"""A function to take a file of data and create an XYZ data object.
Take values at random x,y points and bin into summed values.
datafile a file containing (lat, lon, val) values
scale amount to scale the data (ie, divide)
bins number of bins in X and Y direction
if an int, # bins in X and Y direction
if [int, int] the X and Y bin counts may be different
filter a function to extract (lon, lat, val) from one datafile line
if not supplied an internal function is used
invert if 'filter' not supplied, an internal filter is used. if
'invert' is True, switch the order of lat/lon in data lines.
clip if defined is a dictionary defining clip limits. Recognised
keys in the dictionary are:
'high' sets high limit above which values are clipped
'low' sets low limit below which values are clipped
At least one of the above keys must exist.
The clipping is done before any scaling.
Returns a numpy array of XYZ data [[lon, lat, val], ...].
Values that are 0 replaced with Nan.
"""
# handle optional parameters
try:
bin_len = len(bins)
except TypeError:
bins_x = bins_y = bins
else:
try:
(bins_x, bins_y) = bins
except ValueError:
raise RuntimeError("Bad 'bins' value, expected int or [int, int]")
# if user didn't supply a data filter, use our own
if filter is None:
# define a default filter and use it
if invert:
def default_filter(line):
return (float(line[1]), float(line[0]), float(line[2]))
else:
def default_filter(line):
return (float(line[0]), float(line[1]), float(line[2]))
filter = default_filter
# get data into memory
data = []
for line in file(datafile):
# ignore blank or comment lines
line = line.strip()
if line == '' or line[0] in '%#':
continue
# get lon+lat+value from line
data.append(filter(SplitPattern.split(line)))
data = scipy.array(data)
# do clipping, if required
if clip:
low_clip = clip.get('low', None)
high_clip = clip.get('high', None)
if low_clip is None:
low_clip = scipy.min(data[:, 2])
if high_clip is None:
high_clip = scipy.max(data[:, 2])
data[:, 2] = scipy.clip(data[:, 2], low_clip, high_clip)
# handle any scaling
if scale:
scale = int(scale)
data[:, 2] = data[:, 2] / scale
# get extent of data (tight first, then with margin)
(tll_lat, tll_lon, tur_lat, tur_lon) = ge.get_extent(data, margin=0)
tr_opt = '-R%f/%f/%f/%f' % (tll_lon, tur_lon, tll_lat, tur_lat)
(ll_lat, ll_lon, ur_lat, ur_lon) = ge.get_extent(data)
r_opt = '-R%f/%f/%f/%f' % (ll_lon, ur_lon, ll_lat, ur_lat)
# now generate a binned dataset
(binned_data, xedges, yedges) = scipy.histogram2d(data[:, 0],
data[:, 1],
bins=bins,
normed=False,
weights=data[:, 2])
# create XYZ object
# make sure X+Y is *centre* of each bin
xyz = []
xedges = scipy.array(xedges)
xedges = xedges[:-1] + (xedges[1] - xedges[0]) / 2
yedges = scipy.array(yedges)
yedges = yedges[:-1] + (yedges[1] - yedges[0]) / 2
for (xi, x) in enumerate(xedges):
for (yi, y) in enumerate(yedges):
xyz.append([x, y, binned_data[xi, yi]])
return scipy.array(xyz)
