import os
import glob
import numpy as np
import numpy.testing as npt
import warnings
import sofar as sf
[docs]
def version():
"""Return version of sofar and SOFA conventions"""
sofa_conventions = os.path.join(
os.path.dirname(__file__), "sofa_conventions", 'VERSION')
with open(sofa_conventions) as file:
sofa_conventions = file.readline().strip()
return (f"sofar v{sf.__version__} implementing "
f"SOFA standard {sofa_conventions}")
def _verify_convention_and_version(version, convention):
"""
Verify if convention and version exist. Raise a Value error if it does not.
Parameters
----------
version : str
The version to be checked
convention : str
The name of the convention to be checked
"""
# check if the convention exists
if convention not in _get_conventions("name"):
raise ValueError(
f"Convention '{convention}' does not exist")
name_version = _get_conventions("name_version")
# check which version is wanted
version_exists = False
for versions in name_version:
# check if convention and version match
if versions[0] == convention \
and str(float(versions[1])) == version:
version_exists = True
if not version_exists:
raise ValueError((
f"{convention} v{version} is not a valid SOFA Convention."
"If you are trying to read the data use "
"sofar.read_sofa_as_netcdf(). Call sofar.list_conventions() for a "
"list of valid Conventions"))
[docs]
def list_conventions():
"""
List available SOFA conventions by printing to the console.
"""
print(_get_conventions("string"))
def _get_conventions(return_type, conventions_path=None):
"""
Get available SOFA conventions.
Parameters
----------
return_type : string, optional
``'path'``
Return a list with the full paths and filenames of the convention
files (json files)
``'path_source'``
Return a list with the full paths and filenames of the source
convention files from API_MO (csv files)
``'name'``
Return a list of the convention names without version
``'name_version'``
Return a list of tuples containing the convention name and version.
``'string'``
Returns a string that lists the names and versions of all
conventions.
conventions_path : str, optional
The path to the the `conventions` folder containing the csv and json
files.
Returns
-------
See parameter `return_type`.
"""
# directory containing the SOFA conventions
if conventions_path is None:
conventions_path = os.path.join(
os.path.dirname(__file__), "sofa_conventions", 'conventions')
reg_str = "*.csv" if return_type == "path_source" else "*.json"
# SOFA convention files
standardized = list(glob.glob(os.path.join(conventions_path, reg_str)))
deprecated = list(
glob.glob(os.path.join(conventions_path, "deprecated", reg_str)))
paths = standardized + deprecated
conventions_str = "Available SOFA conventions:\n"
conventions = []
versions = []
for path in paths:
fileparts = os.path.basename(path).split(sep="_")
conventions += [fileparts[0]]
versions += [fileparts[1][:-5]]
conventions_str += f"{conventions[-1]} (Version {versions[-1]})\n"
if return_type is None:
return
elif return_type.startswith("path"):
return paths
elif return_type == "name":
return conventions
elif return_type == "name_version":
return list(zip(conventions, versions))
elif return_type == "string":
return conventions_str
else:
raise ValueError(f"return_type {return_type} is invalid")
[docs]
def equals(sofa_a, sofa_b, verbose=True, exclude=None):
"""
Compare two SOFA objects against each other.
Parameters
----------
sofa_a : Sofa
SOFA object
sofa_b : Sofa
SOFA object
verbose : bool, optional
Print differences to the console. The default is True.
exclude : str, optional
Specify what fields should be excluded from the comparison
``'GLOBAL'``
Exclude all global attributes, i.e., fields starting with 'GLOBAL:'
``'DATE'``
Exclude date attributs, i.e., fields that contain 'Date'
``'ATTR'``
Exclude all attributes, i.e., fields that contain ':'
The default is None, which does not exclude anything.
Returns
-------
is_identical : bool
``True`` if sofa_a and sofa_b are identical, ``False`` otherwise.
"""
is_identical = True
# get and filter keys
# ('_*' are SOFA object private variables, '__' are netCDF attributes)
keys_a = [k for k in sofa_a.__dict__.keys() if not k.startswith("_")]
keys_b = [k for k in sofa_b.__dict__.keys() if not k.startswith("_")]
if exclude is not None:
if exclude.upper() == "GLOBAL":
keys_a = [k for k in keys_a if not k.startswith("GLOBAL_")]
keys_b = [k for k in keys_b if not k.startswith("GLOBAL_")]
elif exclude.upper() == "ATTR":
keys_a = [k for k in keys_a if
sofa_a._convention[k]["type"] != "attribute"]
keys_b = [k for k in keys_b if
sofa_b._convention[k]["type"] != "attribute"]
elif exclude.upper() == "DATE":
keys_a = [k for k in keys_a if "Date" not in k]
keys_b = [k for k in keys_b if "Date" not in k]
else:
raise ValueError(
f"exclude is {exclude} but must be GLOBAL, DATE, or ATTR")
# check for equal length
if len(keys_a) != len(keys_b):
return _equals_raise_warning((
f"not identical: sofa_a has {len(keys_a)} attributes for "
f"comparison and sofa_b has {len(keys_b)}."), verbose)
# check if the keys match
if set(keys_a) != set(keys_b):
return _equals_raise_warning(
"not identical: sofa_a and sofa_b do not have the ame attributes",
verbose)
# compare the data inside the SOFA object
for key in keys_a:
# get data and types
a = getattr(sofa_a, key)
b = getattr(sofa_b, key)
type_a = sofa_a._convention[key]["type"]
type_b = sofa_b._convention[key]["type"]
# compare attributes
if type_a == "attribute" and type_b == "attribute":
# compare
if a != b:
is_identical = _equals_raise_warning(
f"not identical: different values for {key}", verbose)
# compare double variables
elif type_a == "double" and type_b == "double":
try:
npt.assert_allclose(np.squeeze(a), np.squeeze(b))
except AssertionError:
is_identical = _equals_raise_warning(
"not identical: different values for {key}", verbose)
# compare string variables
elif type_a == "string" and type_b == "string":
try:
assert np.all(
np.squeeze(a).astype("S") == np.squeeze(b).astype("S"))
except AssertionError:
is_identical = _equals_raise_warning(
"not identical: different values for {key}", verbose)
else:
is_identical = _equals_raise_warning(
(f"not identical: {key} has different data types "
f"({type_a}, {type_b})"), verbose)
return is_identical
def _equals_raise_warning(message, verbose):
if verbose:
warnings.warn(message)
return False
def _atleast_nd(array, ndim):
"""
Get numpy array with specified number of dimensions. Dimensions are
appended at the end if ndim > 3.
"""
try:
array = array.copy()
except AttributeError:
array = array
if ndim == 1:
array = np.atleast_1d(array)
if ndim == 2:
array = np.atleast_2d(array)
if ndim >= 3:
array = np.atleast_3d(array)
for _ in range(ndim - array.ndim):
array = array[..., np.newaxis]
return array
def _nd_newaxis(array, ndim):
"""Append dimensions to the end of an array until array.ndim == ndim"""
array = np.array(array)
for _ in range(ndim - array.ndim):
array = array[..., np.newaxis]
return array
def _complete_sofa(convention="GeneralTF"):
"""
Generate SOFA file with all required data for testing verification rules.
"""
sofa = sf.Sofa(convention)
# Listener meta data
sofa.add_variable("ListenerView", [1, 0, 0], "double", "IC")
sofa.add_attribute("ListenerView_Type", "cartesian")
sofa.add_attribute("ListenerView_Units", "metre")
sofa.add_variable("ListenerUp", [0, 0, 1], "double", "IC")
# Receiver meta data
sofa.add_variable("ReceiverView", [1, 0, 0], "double", "IC")
sofa.add_attribute("ReceiverView_Type", "cartesian")
sofa.add_attribute("ReceiverView_Units", "metre")
sofa.add_variable("ReceiverUp", [0, 0, 1], "double", "IC")
# Source meta data
sofa.add_variable("SourceView", [1, 0, 0], "double", "IC")
sofa.add_attribute("SourceView_Type", "cartesian")
sofa.add_attribute("SourceView_Units", "metre")
sofa.add_variable("SourceUp", [0, 0, 1], "double", "IC")
# Emitter meta data
sofa.add_variable("EmitterView", [1, 0, 0], "double", "IC")
sofa.add_attribute("EmitterView_Type", "cartesian")
sofa.add_attribute("EmitterView_Units", "metre")
sofa.add_variable("EmitterUp", [0, 0, 1], "double", "IC")
sofa.add_attribute("GLOBAL_EmitterDescription", "what an emitter")
sofa.add_variable("EmitterDescriptions", ["emitter array"], "string", "MS")
# Room meta data
sofa.add_attribute("GLOBAL_RoomShortName", "Hall")
sofa.add_attribute("GLOBAL_RoomDescription", "Wooden floor")
sofa.add_attribute("GLOBAL_RoomLocation", "some where nice")
sofa.add_variable("RoomTemperature", 0, "double", "I")
sofa.add_attribute("RoomTemperature_Units", "kelvin")
sofa.add_attribute("GLOBAL_RoomGeometry", "some/file")
sofa.add_variable("RoomVolume", 200, "double", "I")
sofa.add_attribute("RoomVolume_Units", "cubic metre")
sofa.add_variable("RoomCornerA", [0, 0, 0], "double", "IC")
sofa.add_variable("RoomCornerB", [1, 1, 1], "double", "IC")
sofa.add_variable("RoomCorners", 0, "double", "I")
sofa.add_attribute("RoomCorners_Type", "cartesian")
sofa.add_attribute("RoomCorners_Units", "metre")
sofa.verify()
return sofa
