def get_along(self, *args, unit="SI", is_norm=False, axis_data=[]):
"""Returns the ndarray of the field, using conversions and symmetries if needed.
Parameters
----------
self: Data
a Data object
*args: list of strings
List of axes requested by the user, their units and values (optional)
unit: str
Unit requested by the user ("SI" by default)
is_norm: bool
Boolean indicating if the field must be normalized (False by default)
axis_data: list
list of ndarray corresponding to user-input data
Returns
-------
list of 1Darray of axes values, ndarray of field values
"""
# Read the axes input in args
if len(args) == 1 and type(args[0]) == tuple:
args = args[0] # if called from another script with *args
axes_list = read_input_strings(args, axis_data)
# Extract the requested axes (symmetries + unit)
axes_list, transforms = self.comp_axes(axes_list)
# Get the field
values = self.get_field(axes_list)
# Inverse fft
if "ifft" in transforms:
values = comp_ifftn(values, axes_list)
# Slices along time/space axes
values, axes_dict_other = self.extract_slices(values, axes_list)
# fft
if "fft" in transforms:
values = comp_fftn(values, axes_list)
# Slices along fft axes
values = self.extract_slices_fft(values, axes_list)
# Rebuild symmetries
values = self.rebuild_symmetries(values, axes_list)
# Interpolate over axis values
values = self.interpolate(values, axes_list)
# Conversions
values = self.convert(values, unit, is_norm)
# Return axes and values
return_dict = {}
for axis_requested in axes_list:
return_dict[axis_requested.name] = axis_requested.values
return_dict[self.symbol] = values
return_dict["axes_list"] = axes_list
return_dict["axes_dict_other"] = axes_dict_other
return return_dict
def get_harmonics(self,
N_harm,
*args,
unit="SI",
is_norm=False,
is_flat=False):
"""Returns the complex Fourier Transform of the field, using conversions and symmetries if needed.
Parameters
----------
self: Data
a Data object
N_harm: int
Number of largest harmonics to be extracted
args: list
Axes names, ranges and units
unit: str
Unit demanded by the user ("SI" by default)
is_norm: bool
Boolean indicating if the field must be normalized (False by default)
is_flat: bool
Boolean if the output data remains flattened (for 2D cases)
Returns
-------
list of 1Darray of axes values, ndarray of magnitude of FT
"""
# Read the axes input in args
if len(args) == 1 and type(args[0]) == tuple:
args = args[0] # if called from another script with *args
axes_list = read_input_strings(args, [])
# Extract the requested axes (symmetries + unit)
for axis_requested in axes_list:
if axis_requested[3] == "values":
# Get original values of the axis
axis_requested.append(
self.get_FT_axis(axis_requested[0] + axis_requested[1]))
# Interpolate axis with input data
if str(axis_requested[4]) == "whole":
axis_requested[4] = axis_requested[5]
axis_requested[5] = "whole"
else:
axis_requested[4] = get_common_base(axis_requested[5],
axis_requested[4])
# Change fft name for the slices of the field
if axis_requested[0] == "freqs":
axis_requested[0] = "time"
elif axis_requested[0] == "wavenumber":
axis_requested[0] = "angle"
# Check if the requested axis is defined in the Data object
for axis_requested in axes_list:
axis_name = axis_requested[0]
is_match = False
for index, axis in enumerate(self.axes):
if axis.name == axis_name:
is_match = True
if not is_match:
axes_list.remove(axis_requested)
# Rebuild symmetries of field if axis is extracted
values = self.values
for index, axis in enumerate(self.axes):
for axis_requested in axes_list:
if axis.name in self.symmetries.keys(
) and axis.name == axis_requested[0]:
values = rebuild_symmetries(values, index,
self.symmetries.get(axis.name))
# Extract the slices of the field (single values)
for index, axis in enumerate(self.axes):
is_match = False
for axis_requested in axes_list:
if axis.name == axis_requested[0]:
is_match = True
if axis_requested[3] == "indices" and axis_requested[
2] == "single":
values = take(values, axis_requested[4], axis=index)
if not is_match: # Axis was not specified -> take slice at the first value
values = take(values, [0], axis=index)
# Interpolate over axis values (single values)
for index, axis in enumerate(self.axes):
for axis_requested in axes_list:
if (axis.name == axis_requested[0]
and axis_requested[3] == "values"
and axis_requested[2] == "single"):
values = apply_along_axis(
get_interpolation,
index,
values,
axis_requested[5],
axis_requested[4],
)
# Perform Fourier Transform
values = np_abs(comp_fft(values))
# Extract slices again (intervals)
for index, axis in enumerate(self.axes):
for axis_requested in axes_list:
if axis.name == axis_requested[0]:
if axis_requested[2] == "indices" and axis_requested[
2] == "interval":
values = take(values, axis_requested[4], axis=index)
# Interpolate over axis values again (intervals)
for index, axis in enumerate(self.axes):
for axis_requested in axes_list:
if axis.name == axis_requested[0]:
if axis_requested[3] == "values" and axis_requested[
2] == "interval":
values = apply_along_axis(
get_interpolation,
index,
values,
axis_requested[5],
axis_requested[4],
)
# Eliminate dimensions=1
values = squeeze(values)
# Test if data is 1D or 2D
if len(values.shape) > 2:
raise AxisError("ERROR: only 1D or 2D implemented")
else:
# Convert into right unit
if unit == self.unit or unit == "SI":
if is_norm:
try:
values = values / self.normalizations.get("ref")
except:
raise NormError(
"ERROR: Reference value not specified for normalization"
)
elif unit == "dB":
ref_value = 1.0
if "ref" in self.normalizations.keys():
ref_value *= self.normalizations.get("ref")
values = to_dB(values, self.unit, ref_value)
elif unit == "dBA":
ref_value = 1.0
is_match = False
if "ref" in self.normalizations.keys():
ref_value *= self.normalizations.get("ref")
for axis_requested in axes_list:
if axis_requested[0] == "time":
is_match = True
values = to_dBA(values, axis_requested[4], self.unit,
ref_value)
if not is_match:
raise UnitError(
"ERROR: dBA conversion only available for fft with frequencies"
)
elif unit in self.normalizations:
values = values / self.normalizations.get(unit)
else:
values = convert(values, self.unit, unit)
# 1D case
if len(values.shape) == 1:
for axis_requested in axes_list:
if axis_requested[2] == "interval":
axis_values = axis_requested[4]
indices = argsort(negative(values))
indices = indices[:N_harm]
axis_values = axis_values[indices]
values = values[indices]
return [axis_values, values]
# 2D case
else:
for axis_requested in axes_list:
if axis_requested[0] == "angle":
r = axis_requested[4]
elif axis_requested[0] == "time":
f = axis_requested[4]
# Flatten the data
values_flat = values.flatten()
R, F = meshgrid(r, f)
f = F.flatten()
r = R.flatten()
# Get the N_harm largest peaks
indices = argsort(negative(values_flat))
indices = indices[:N_harm]
values = values_flat[indices]
f = f[indices]
r = r[indices]
if len(values.shape) == 2 and not is_flat:
f.reshape((N_harm, N_harm))
r.reshape((N_harm, N_harm))
values.reshape((N_harm, N_harm))
return [f, r, values]
def get_along(self,
*args,
unit="SI",
is_norm=False,
axis_data=[],
is_squeeze=True):
"""Returns the ndarray of the field, using conversions and symmetries if needed.
Parameters
----------
self: Data
a Data object
*args: list of strings
List of axes requested by the user, their units and values (optional)
unit: str
Unit requested by the user ("SI" by default)
is_norm: bool
Boolean indicating if the field must be normalized (False by default)
axis_data: list
list of ndarray corresponding to user-input data
Returns
-------
list of 1Darray of axes values, ndarray of field values
"""
# Read the axes input in args
if len(args) == 1 and type(args[0]) == tuple:
args = args[0] # if called from another script with *args
axes_list = read_input_strings(args, axis_data)
# Extract the requested axes (symmetries + unit)
axes_list, transforms = self.comp_axes(axes_list)
# Get the field
values = self.get_field(axes_list)
# If DataFreq + 1 ifft axis + 1 fft axis: perform ifft on all axes then fft
save_transforms = None
save_names = None
if "ifft" in transforms and "fft" in transforms:
save_transforms = [axis.transform for axis in axes_list]
save_names = [axis.name for axis in axes_list]
for axis in axes_list:
if axis.name == "freqs":
axis.transform = "ifft"
axis.name = "time"
elif axis.name == "wavenumber":
axis.transform = "ifft"
axis.name = "angle"
# Inverse fft
if "ifft" in transforms:
values = comp_ifftn(values, axes_list, is_real=self.is_real)
# Prepare fft in ifft/fft case
if save_transforms is not None:
for i, transform in enumerate(save_transforms):
axes_list[i].name = save_names[i]
if transform == "fft_axis":
axes_list[i].transform = "fft"
save_transforms[i] = "fft"
else:
axes_list[i].transform = transform
# Slices along time/space axes
values, axes_dict_other = self.extract_slices(values, axes_list)
# fft
if "fft" in transforms:
values = comp_fftn(values, axes_list, is_real=self.is_real)
# Slices along fft axes
values = self.extract_slices_fft(values, axes_list)
# Rebuild symmetries
values = self.rebuild_symmetries(values, axes_list)
# Interpolate over axis values
values = self.interpolate(values, axes_list, is_squeeze=is_squeeze)
# Conversions
values = self.convert(values, unit, is_norm, axes_list)
# Return axes and values
return_dict = {}
for axis_requested in axes_list:
if axis_requested.extension in [
"sum", "rss", "mean", "rms", "integrate"
]:
return_dict[axis_requested.name] = axis_requested.extension
else:
return_dict[axis_requested.name] = axis_requested.values
return_dict[self.symbol] = values
return_dict["axes_list"] = axes_list
return_dict["axes_dict_other"] = axes_dict_other
return return_dict