def save(config: dict,
file: FileOrPathLike,
flat: OptBool = None,
comment: OptStr = None) -> None:
"""Save configuration dictionary to INI-file.
Args:
config: Configuration dictionary
file: String or :term:`path-like object` that represents to a writeable
file in the directory structure of the system, or a :term:`file
object` in write mode.
flat: Determines if the desired INI format structure contains sections.
By default sections are used, if the dictionary contains
subdictionaries.
comment: String containing comment lines, which are stored as initial
'#' lines in the INI-file. By default no comment is written.
"""
# Convert configuration dictionary to INI formated text
try:
text = encode(config, flat=flat, comment=comment)
except Exception as err:
raise ValueError("dictionary is not valid") from err
# Write text to file
with textfile.openx(file, mode='w') as fh:
fh.write(text)
def load(file: FileOrPathLike,
structure: OptStrucDict = None,
flat: OptBool = None) -> StrucDict:
"""Import configuration dictionary from INI file.
Args:
file: String or :term:`path-like object` that points to a readable file
in the directory structure of the system, or a :term:`file object`
in read mode.
structure: Dictionary of dictionaries, which determines the structure of
the configuration dictionary. If structure is None, the INI-file is
completely imported and all values are interpreted as strings. If
the structure is a dictionary of dictionaries, the keys of the outer
dictionary describe valid section names by strings, that are
interpreted as regular expressions. Therupon, the keys of the
respective inner dictionaries describe valid parameter names as
strings, that are also interpreted as regular expressions. Finally
the values of the inner dictionaries define the type of the
parameters by their own type, e.g. str, int, float etc. Accepted
types can be found in the documentation of the function
`literal.decode`_.
flat: Determines if the desired INI format structure contains sections
or not. By default sections are used, if the first non empty, non
comment line in the string identifies a section.
Return:
Structured configuration dictionary
"""
# Read configuration from file-like or path-like object
with textfile.openx(file, mode='r') as fh:
return decode(fh.read(), structure=structure, flat=flat)
def save(
config: dict, file: FileOrPathLike, flat: OptBool = None,
comment: OptStr = None) -> None:
"""Save configuration dictionary to INI-file.
Args:
config: Configuration dictionary
file: String or :term:`path-like object` that represents to a writeable
file in the directory structure of the system, or a :term:`file
object` in write mode.
flat: Determines if the desired INI format structure contains sections.
By default sections are used, if the dictionary contains
subdictionaries.
comment: String containing comment lines, which are stored as initial
'#' lines in the INI-file. By default no comment is written.
"""
# Convert configuration dictionary to INI formated text
try:
text = encode(config, flat=flat, comment=comment)
except Exception as err:
raise ValueError("dictionary is not valid") from err
# Write text to file
with textfile.openx(file, mode='w') as fh:
fh.write(text)
def load(
file: FileOrPathLike, structure: OptStrucDict = None,
flat: OptBool = None) -> StrucDict:
"""Import configuration dictionary from INI file.
Args:
file: String or :term:`path-like object` that points to a readable file
in the directory structure of the system, or a :term:`file object`
in read mode.
structure: Dictionary of dictionaries, which determines the structure of
the configuration dictionary. If structure is None, the INI-file is
completely imported and all values are interpreted as strings. If
the structure is a dictionary of dictionaries, the keys of the outer
dictionary describe valid section names by strings, that are
interpreted as regular expressions. Therupon, the keys of the
respective inner dictionaries describe valid parameter names as
strings, that are also interpreted as regular expressions. Finally
the values of the inner dictionaries define the type of the
parameters by their own type, e.g. str, int, float etc. Accepted
types can be found in the documentation of the function
`literal.decode`_.
flat: Determines if the desired INI format structure contains sections
or not. By default sections are used, if the first non empty, non
comment line in the string identifies a section.
Return:
Structured configuration dictionary
"""
# Read configuration from file-like or path-like object
with textfile.openx(file, mode='r') as fh:
return decode(fh.read(), structure=structure, flat=flat)
def save(
file: FileOrPathLike, data: NpArray, labels: OptStrList = None,
comment: OptStr = None, delim: str = ',') -> None:
"""Save NumPy array to CSV-file.
Args:
file: String, :term:`path-like object` or :term:`file object` that
points to a valid CSV-file in the directory structure of the system.
data: :class:`numpy.ndarray` containing the data which is to be
exported to a CSV-file.
comment: String, which is included in the CSV-file whithin initial
'#' lines. By default no initial lines are created.
labels: List of strings with column names.
delim: String containing CSV-delimiter. The default value is ','
Returns:
True if no error occured.
"""
# Check and prepare arguments
if isinstance(comment, str):
comment = '# ' + comment.replace('\n', '\n# ') + '\n\n'
if isinstance(labels, list):
comment += delim.join(labels)
elif isinstance(labels, list):
comment = delim.join(labels)
# Get number of columns from last entry in data.shape
cols = list(getattr(data, 'shape'))[-1]
# Get column format
fmt = delim.join(['%s'] + ['%10.10f'] * (cols - 1))
with textfile.openx(file, mode='w') as fh:
np.savetxt(fh, data, fmt=fmt, header=comment, comments='')
def _get_delim(self) -> OptStr:
# Return delimiter if set manually
if self._delim is not None:
return self._delim
# Initialize CSV-Sniffer with default values
sniffer = csv.Sniffer()
sniffer.preferred = self._delim_candidates
delim: OptStr = None
# Detect delimiter
with textfile.openx(self._file, mode='r') as fd:
size, probe = 0, ''
for line in fd:
# Check termination criteria
if size > self._delim_maxcount:
break
# Check exclusion criteria
strip = line.strip()
if not strip or strip.startswith('#'):
continue
# Increase probe size
probe += line
size += 1
if size <= self._delim_mincount:
continue
# Try to detect delimiter from probe using csv.Sniffer
try:
dialect = sniffer.sniff(probe)
except csv.Error:
continue
delim = dialect.delimiter
break
return delim
def _get_delim(self) -> OptStr:
# Return delimiter if set manually
if self._delim is not None:
return self._delim
# Initialize CSV-Sniffer with default values
sniffer = csv.Sniffer()
sniffer.preferred = self._delim_candidates
delim: OptStr = None
# Detect delimiter
with textfile.openx(self._file, mode='r') as fd:
size, probe = 0, ''
for line in fd:
# Check termination criteria
if size > self._delim_maxcount:
break
# Check exclusion criteria
strip = line.strip()
if not strip or strip.startswith('#'):
continue
# Increase probe size
probe += line
size += 1
if size <= self._delim_mincount:
continue
# Try to detect delimiter from probe using csv.Sniffer
try:
dialect = sniffer.sniff(probe)
except csv.Error:
continue
delim = dialect.delimiter
break
return delim
def _get_skiprows(self) -> int:
# Count how many 'comment' and 'blank' rows are to be skipped
skiprows = 1
with textfile.openx(self._file, mode='r') as fd:
for line in fd:
strip = line.strip()
if not strip or strip.startswith('#'):
skiprows += 1
continue
break
return skiprows
def _get_skiprows(self) -> int:
# Count how many 'comment' and 'blank' rows are to be skipped
skiprows = 1
with textfile.openx(self._file, mode='r') as fd:
for line in fd:
strip = line.strip()
if not strip or strip.startswith('#'):
skiprows += 1
continue
break
return skiprows
def test_openx(self) -> None:
filepath = Path(tempfile.NamedTemporaryFile().name + '.txt')
with self.subTest(file=filepath):
with textfile.openx(filepath, mode='w') as fh:
fh.write(self.text)
if filepath.is_file():
with textfile.openx(filepath, mode='r') as fh:
text = fh.read()
filepath.unlink()
self.assertTrue(text == self.text)
file = filepath.open(mode='w')
with self.subTest(file=file):
with textfile.openx(file, mode='w') as fh:
fh.write(self.text)
if not file.closed:
file.close()
file = filepath.open(mode='r')
with textfile.openx(file, mode='r') as fh:
text = fh.read()
if not file.closed:
file.close()
self.assertTrue(text == self.text)
def _get_rownames(self) -> OptList:
# Check type of 'cols'
lblcol = self._get_namecol()
if lblcol is None:
return None
lbllbl = self.colnames[lblcol]
# Import CSV-file to NumPy ndarray
with textfile.openx(self._file, mode='r') as fh:
rownames = np.loadtxt(fh,
skiprows=self._get_skiprows(),
delimiter=self._get_delim(),
usecols=(lblcol, ),
dtype={'names': (lbllbl, ), 'formats': ('<U12', )})
return [name[0] for name in rownames.flat]
def _get_rownames(self) -> OptList:
# Check type of 'cols'
lblcol = self._get_namecol()
if lblcol is None:
return None
lbllbl = self.colnames[lblcol]
# Import CSV-file to NumPy ndarray
with textfile.openx(self._file, mode='r') as fh:
rownames = np.loadtxt(fh,
skiprows=self._get_skiprows(),
delimiter=self._get_delim(),
usecols=(lblcol, ),
dtype={
'names': (lbllbl, ),
'formats': ('<U12', )
})
return [name[0] for name in rownames.flat]
def select(self, columns: OptStrTuple = None) -> OptNpArray:
"""Load numpy ndarray from CSV-file.
Args:
columns: List of column labels in CSV-file. By default the list of
column labels is taken from the first content line in the
CSV-file.
Returns:
:class:`numpy.ndarray` containing data from CSV-file, or None if
the data could not be imported.
"""
# Check type of 'cols'
check.has_opt_type("'columns'", columns, tuple)
# Get column names and formats
usecols = self._get_usecols(columns)
colnames = self._get_colnames()
names = tuple(colnames[colid] for colid in usecols)
lblcol = self._get_namecol()
if lblcol is None:
formats = tuple(['<f8'] * len(usecols))
elif lblcol not in usecols:
formats = tuple(['<U12'] + ['<f8'] * len(usecols))
names = ('label', ) + names
usecols = (lblcol, ) + usecols
else:
lbllbl = colnames[lblcol]
formats = tuple(['<U12'] + ['<f8'] * (len(usecols) - 1))
names = tuple(['label'] + [l for l in names if l != lbllbl])
usecols = tuple([lblcol] + [c for c in usecols if c != lblcol])
# Import data from CSV-file as numpy array
with textfile.openx(self._file, mode='r') as fh:
return np.loadtxt(fh,
skiprows=self._get_skiprows(),
delimiter=self._get_delim(),
usecols=usecols,
dtype={
'names': names,
'formats': formats
})
def select(self, columns: OptStrTuple = None) -> OptNpArray:
"""Load numpy ndarray from CSV-file.
Args:
columns: List of column labels in CSV-file. By default the list of
column labels is taken from the first content line in the
CSV-file.
Returns:
:class:`numpy.ndarray` containing data from CSV-file, or None if
the data could not be imported.
"""
# Check type of 'cols'
check.has_opt_type("'columns'", columns, tuple)
# Get column names and formats
usecols = self._get_usecols(columns)
colnames = self._get_colnames()
names = tuple(colnames[colid] for colid in usecols)
lblcol = self._get_namecol()
if lblcol is None:
formats = tuple(['<f8'] * len(usecols))
elif lblcol not in usecols:
formats = tuple(['<U12'] + ['<f8'] * len(usecols))
names = ('label', ) + names
usecols = (lblcol, ) + usecols
else:
lbllbl = colnames[lblcol]
formats = tuple(['<U12'] + ['<f8'] * (len(usecols) - 1))
names = tuple(['label'] + [l for l in names if l != lbllbl])
usecols = tuple([lblcol] + [c for c in usecols if c != lblcol])
# Import data from CSV-file as numpy array
with textfile.openx(self._file, mode='r') as fh:
return np.loadtxt(fh,
skiprows=self._get_skiprows(),
delimiter=self._get_delim(),
usecols=usecols,
dtype={'names': names, 'formats': formats})
def save(file: FileOrPathLike,
data: NpArray,
labels: OptStrList = None,
comment: OptStr = None,
delim: str = ',') -> None:
"""Save NumPy array to CSV-file.
Args:
file: String, :term:`path-like object` or :term:`file object` that
points to a valid CSV-file in the directory structure of the system.
data: :class:`numpy.ndarray` containing the data which is to be
exported to a CSV-file.
comment: String, which is included in the CSV-file whithin initial
'#' lines. By default no initial lines are created.
labels: List of strings with column names.
delim: String containing CSV-delimiter. The default value is ','
Returns:
True if no error occured.
"""
# Check and prepare arguments
if isinstance(comment, str):
comment = '# ' + comment.replace('\n', '\n# ') + '\n\n'
if isinstance(labels, list):
comment += delim.join(labels)
elif isinstance(labels, list):
comment = delim.join(labels)
# Get number of columns from last entry in data.shape
cols = list(getattr(data, 'shape'))[-1]
# Get column format
fmt = delim.join(['%s'] + ['%10.10f'] * (cols - 1))
with textfile.openx(file, mode='w') as fh:
np.savetxt(fh, data, fmt=fmt, header=comment, comments='')