def interp2d(obj, newaxes, dims=None, order=1):
""" bilinear interpolation: wrapper around mpl_toolkits.basemap.interp
input:
obj : DimArray
newaxes: list of Axis object, or list of 1d arrays
dims, optional: list of str (axis names), required if newaxes is a list of arrays
output:
interpolated data (n-d)
"""
# make sure input axes have the valid format
newaxes = Axes._init(newaxes, dims) # valid format
newaxes.sort(obj.dims) # re-order according to object's dimensions
x, y = newaxes # 2-d interpolation
# make new grid 2-D
x1, x1 = np.meshgrid(x.values, y.values, indexing='ij')
def interp2d(obj, order=1):
""" 2-D interpolation function appled recursively on the object
"""
x0, y0 = obj.axes[x.name].values, obj.axes[y.name].values
res = interp(obj.values, x0, y0, x1, y1, order=order)
return obj._constructor(res, newaxes, **obj._metadata)
result = apply_recursive(obj, (x.name, y.name), interp2d)
return result.transpose(obj.dims) # transpose back to original dimensions
def interp2d(obj, newaxes, dims=None, order=1):
""" bilinear interpolation: wrapper around mpl_toolkits.basemap.interp
input:
obj : DimArray
newaxes: list of Axis object, or list of 1d arrays
dims, optional: list of str (axis names), required if newaxes is a list of arrays
output:
interpolated data (n-d)
"""
# make sure input axes have the valid format
newaxes = Axes._init(newaxes, dims) # valid format
newaxes.sort(obj.dims) # re-order according to object's dimensions
x, y = newaxes # 2-d interpolation
# make new grid 2-D
x1, x1 = np.meshgrid(x.values, y.values, indexing='ij')
def interp2d(obj, order=1):
""" 2-D interpolation function appled recursively on the object
"""
x0, y0 = obj.axes[x.name].values, obj.axes[y.name].values
res = interp(obj.values, x0, y0, x1, y1, order=order)
return obj._constructor(res, newaxes, **obj._metadata)
result = apply_recursive(obj, (x.name, y.name), interp2d)
return result.transpose(obj.dims) # transpose back to original dimensions
def read_dimensions(f, name=None, ix=slice(None), verbose=False):
""" return an Axes object
name, optional: variable name
return: Axes object
"""
f, close = check_file(f, mode='r', verbose=verbose)
# dimensions associated to the variable to load
if name is None:
dims = f.dimensions.keys()
else:
try:
dims = f.variables[name].dimensions
except KeyError:
print "Available variable names:", f.variables.keys()
raise
dims = [str(d) for d in dims] # conversion to string
# load axes
axes = Axes()
for k in dims:
try:
values = f.variables[k][ix]
except KeyError:
msg = "'{}' dimension not found, define integer range".format(k)
warnings.warn(msg)
values = np.arange(len(f.dimensions[k]))
# replace unicode by str as a temporary bug fix (any string seems otherwise to be treated as unicode in netCDF4)
if values.size > 0 and type(values[0]) is unicode:
for i, val in enumerate(values):
if type(val) is unicode:
values[i] = str(val)
axes.append(Axis(values, k))
if close: f.close()
return axes
def read_dimensions(f, name=None, ix=slice(None), verbose=False):
""" return an Axes object
name, optional: variable name
return: Axes object
"""
f, close = check_file(f, mode='r', verbose=verbose)
# dimensions associated to the variable to load
if name is None:
dims = f.dimensions.keys()
else:
try:
dims = f.variables[name].dimensions
except KeyError:
print "Available variable names:",f.variables.keys()
raise
dims = [str(d) for d in dims] # conversion to string
# load axes
axes = Axes()
for k in dims:
try:
values = f.variables[k][ix]
except KeyError:
msg = "'{}' dimension not found, define integer range".format(k)
warnings.warn(msg)
values = np.arange(len(f.dimensions[k]))
# replace unicode by str as a temporary bug fix (any string seems otherwise to be treated as unicode in netCDF4)
if values.size > 0 and type(values[0]) is unicode:
for i, val in enumerate(values):
if type(val) is unicode:
values[i] = str(val)
axes.append(Axis(values, k))
if close: f.close()
return axes
def _createVariable(f, name, axes, dims=None, dtype=float, verbose=False, mode='a+', format=FORMAT, **kwargs):
""" Create empty netCDF4 variable from axes
Parameters
----------
f: string or netCDF4.Dataset file handle
name : variable name
axes : Axes's instance or list of numpy arrays (axis values)
dims: sequence, optional
dimension names (to be used in combination with axes to create Axes instance)
dtype : optional, variable type
mode : `str`, optional
{write_modes}
default is 'a+'
{netCDF4}
Examples
--------
>>> tmpdir = getfixture('tmpdir').strpath # some temporary directory (py.test)
>>> outfile = os.path.join(tmpdir, 'test.nc')
>>> da.write_nc(outfile,'myvar', axes=[[1,2,3,4],['a','b','c']], dims=['dim1', 'dim2'], mode='w')
>>> a = DimArray([11, 22, 33, 44], axes=[[1, 2, 3, 4]], dims=('dim1',)) # some array slice
>>> da.write_nc(outfile, a, 'myvar', indices='b', axis='dim2')
>>> da.write_nc(outfile, [111,222,333,444], 'myvar', indices='a', axis='dim2')
>>> da.read_nc(outfile,'myvar')
dimarray: 8 non-null elements (4 null)
0 / dim1 (4): 1 to 4
1 / dim2 (3): a to c
array([[ 111., 11., nan],
[ 222., 22., nan],
[ 333., 33., nan],
[ 444., 44., nan]])
"""
f, close = _check_file(f, mode=mode, verbose=verbose, format=format)
# make sure axes is an Axes instance
if not isinstance(axes, Axes):
axes = Axes._init(axes, dims=dims)
_check_dimensions(f, axes)
# Create Variable
v = f.createVariable(name, dtype, [ax.name for ax in axes], **kwargs)
if close:
f.close()
return v
def read_dimensions(f, name=None, ix=slice(None), dimensions_mapping=None, verbose=False):
""" return an Axes object
Parameters
----------
name : str, optional
variable name
ix : integer (position) index
dimensions_mapping : dict, optional
mapping between dimension and variable names
Returns
-------
Axes object
"""
f, close = _check_file(f, mode='r', verbose=verbose)
# dimensions associated to the variable to load
if name is None:
dims = f.dimensions.keys()
else:
try:
dims = f.variables[name].dimensions
except KeyError:
print "Available variable names:",f.variables.keys()
raise
dims = [str(d) for d in dims] # conversion to string
# load axes
axes = Axes()
for dim in dims:
if dimensions_mapping is not None:
# user-provided mapping between dimensions and variable name
dim_name = dimensions_mapping[dim]
values = f.variables[dim_name][ix]
elif dim in f.variables.keys():
# assume that the variable and dimension have the same name
values = f.variables[dim][ix]
else:
# default, dummy dimension axis
msg = "'{}' dimension not found, define integer range".format(dim)
warnings.warn(msg)
values = np.arange(len(f.dimensions[dim]))
# replace unicode by str as a temporary bug fix (any string seems otherwise to be treated as unicode in netCDF4)
if values.size > 0 and type(values[0]) is unicode:
for i, val in enumerate(values):
if type(val) is unicode:
values[i] = str(val)
axis = Axis(values, dim)
# add metadata
if dim in f.variables.keys():
meta = _read_attributes(f, dim)
axis._metadata(meta)
axes.append(axis)
if close: f.close()
return axes
