def __call__(self,
labels=(2, 3),
filename='trajectory_plots.pdf',
exclude_labels=(0, )):
cmap = unsupervised_cmap(self._nclusters)
try:
pdf = PdfPages(join(self._odir, filename))
for title_, tracks in self._data.iteritems():
title = '%s (%s)' % (self._position, title_.lower())
# checking for binary matrix
if np.unique(tracks).size == 2:
is_binary = True
labels_ = (1, )
else:
is_binary = False
labels_ = labels
# trajectory plots
fig = plots.trajectories(tracks,
labels_,
title=title,
cmap=cmap)
pdf.savefig(fig)
delchars = NameValidator.defaultdeletechars.copy()
delchars.remove('-')
validator = NameValidator(case_sensitive='lower',
deletechars=delchars)
fname = validator.validate(title)[0] + '.csv'
np.savetxt(join(self._odir, fname),
tracks,
fmt='%d',
delimiter=',')
# boxplots
if not is_binary:
ylabel = "dwell time (%s)" % self.timeunit
xlabel = "class labels"
dwell_times = self.dwell_times(tracks)
fig1 = plots.dwell_boxplot(dwell_times,
title,
ylabel=ylabel,
xlabel=xlabel,
exclude_labels=exclude_labels,
cmap=cmap)
fig2 = plots.barplot(dwell_times,
title,
xlabel=xlabel,
ylabel=ylabel,
exclude_labels=exclude_labels,
cmap=cmap)
pdf.savefig(fig1)
pdf.savefig(fig2)
finally:
pdf.close()
def test_case_sensitivity(self):
"Test case sensitivity"
names = ['A', 'a', 'b', 'c']
test = NameValidator().validate(names)
assert_equal(test, ['A', 'a', 'b', 'c'])
test = NameValidator(case_sensitive=False).validate(names)
assert_equal(test, ['A', 'A_1', 'B', 'C'])
test = NameValidator(case_sensitive='upper').validate(names)
assert_equal(test, ['A', 'A_1', 'B', 'C'])
test = NameValidator(case_sensitive='lower').validate(names)
assert_equal(test, ['a', 'a_1', 'b', 'c'])
def test_case_sensitivity(self):
"Test case sensitivity"
names = ["A", "a", "b", "c"]
test = NameValidator().validate(names)
assert_equal(test, ["A", "a", "b", "c"])
test = NameValidator(case_sensitive=False).validate(names)
assert_equal(test, ["A", "A_1", "B", "C"])
test = NameValidator(case_sensitive="upper").validate(names)
assert_equal(test, ["A", "A_1", "B", "C"])
test = NameValidator(case_sensitive="lower").validate(names)
assert_equal(test, ["a", "a_1", "b", "c"])
# check exceptions
assert_raises(ValueError, NameValidator, case_sensitive="foobar")
def test_case_sensitivity(self):
"Test case sensitivity"
names = ['A', 'a', 'b', 'c']
test = NameValidator().validate(names)
assert_equal(test, ['A', 'a', 'b', 'c'])
test = NameValidator(case_sensitive=False).validate(names)
assert_equal(test, ['A', 'A_1', 'B', 'C'])
test = NameValidator(case_sensitive='upper').validate(names)
assert_equal(test, ['A', 'A_1', 'B', 'C'])
test = NameValidator(case_sensitive='lower').validate(names)
assert_equal(test, ['a', 'a_1', 'b', 'c'])
# check exceptions
assert_raises(ValueError, NameValidator, case_sensitive='foobar')
def test_validate_nb_names(self):
"Test validate nb names"
namelist = ('a', 'b', 'c')
validator = NameValidator()
assert_equal(validator(namelist, nbfields=1), ('a',))
assert_equal(validator(namelist, nbfields=5, defaultfmt="g%i"),
['a', 'b', 'c', 'g0', 'g1'])
def __call__(self, labels=(2,3), filename='trajectory_plots.pdf',
exclude_labels=(0, )):
cmap = unsupervised_cmap(self._nclusters)
try:
pdf = PdfPages(join(self._odir, filename))
for title_, tracks in self._data.iteritems():
title = '%s (%s)' %(self._position, title_.lower())
# checking for binary matrix
if np.unique(tracks).size == 2:
is_binary = True
labels_ = (1, )
else:
is_binary = False
labels_ = labels
# trajectory plots
fig = plots.trajectories(tracks, labels_, title=title, cmap=cmap)
pdf.savefig(fig)
delchars = NameValidator.defaultdeletechars.copy()
delchars.remove('-')
validator = NameValidator(case_sensitive='lower',
deletechars=delchars)
fname = validator.validate(title)[0]+'.csv'
np.savetxt(join(self._odir, fname), tracks, fmt='%d',
delimiter=',')
# boxplots
if not is_binary:
ylabel = "dwell time (%s)" %self.timeunit
xlabel = "class labels"
dwell_times = self.dwell_times(tracks)
fig1 = plots.dwell_boxplot(dwell_times, title,
ylabel=ylabel, xlabel=xlabel,
exclude_labels=exclude_labels,
cmap=cmap)
fig2 = plots.barplot(dwell_times, title, xlabel=xlabel,
ylabel=ylabel,
exclude_labels=exclude_labels,
cmap=cmap)
pdf.savefig(fig1)
pdf.savefig(fig2)
finally:
pdf.close()
def test_validate_nb_names(self):
"Test validate nb names"
namelist = ("a", "b", "c")
validator = NameValidator()
assert_equal(validator(namelist, nbfields=1), ("a", ))
assert_equal(
validator(namelist, nbfields=5, defaultfmt="g%i"),
["a", "b", "c", "g0", "g1"],
)
def test_missing_names(self):
"Test validate missing names"
namelist = ("a", "b", "c")
validator = NameValidator()
assert_equal(validator(namelist), ["a", "b", "c"])
namelist = ("", "b", "c")
assert_equal(validator(namelist), ["f0", "b", "c"])
namelist = ("a", "b", "")
assert_equal(validator(namelist), ["a", "b", "f0"])
namelist = ("", "f0", "")
assert_equal(validator(namelist), ["f1", "f0", "f2"])
def test_missing_names(self):
"Test validate missing names"
namelist = ('a', 'b', 'c')
validator = NameValidator()
assert_equal(validator(namelist), ['a', 'b', 'c'])
namelist = ('', 'b', 'c')
assert_equal(validator(namelist), ['f0', 'b', 'c'])
namelist = ('a', 'b', '')
assert_equal(validator(namelist), ['a', 'b', 'f0'])
namelist = ('', 'f0', '')
assert_equal(validator(namelist), ['f1', 'f0', 'f2'])
def validate_names(s: Optional[str] = None, d: Optional[dict] = None,
replace_space: Optional[str] = "_",
deletechars: Optional[str] = None, **kwargs) \
-> Union[str, Dict]:
"""Remove invalid characters from string or dictionary keys.
Parameters
----------
s : str, optional
remove invalid characters from string
d : dict, optional
remove invalid characters from keys from dictionary
replace_space : str, optional
replace spaces by this character, by default "_"
deletechars : str, optional
a string combining invalid characters, by default None
Returns
-------
str, dict
string or dict with invalid characters removed
"""
validator = NameValidator(replace_space=replace_space,
deletechars=deletechars,
**kwargs)
if s is not None:
new_str = validator(s) # tuple
if len(new_str) == 1:
return new_str[0]
else:
return new_str
elif d is not None:
new_dict = {}
for k, v in d.items():
new_dict[validator(k)[0]] = v
return new_dict
else:
raise ValueError("Provide one of 's' (string) or 'd' (dict)!")
def test_excludelist(self):
"Test excludelist"
names = ['dates', 'data', 'Other Data', 'mask']
validator = NameValidator(excludelist=['dates', 'data', 'mask'])
test = validator.validate(names)
assert_equal(test, ['dates_', 'data_', 'Other_Data', 'mask_'])
def genfromtxt(fname,
dtype=float,
comments='#',
delimiter=None,
skiprows=0,
skip_header=0,
skip_footer=0,
converters=None,
missing='',
missing_values=None,
filling_values=None,
usecols=None,
names=None,
excludelist=None,
deletechars=None,
replace_space='_',
autostrip=False,
case_sensitive=True,
defaultfmt="f%i",
unpack=None,
usemask=False,
loose=True,
invalid_raise=True):
"""
Load data from a text file, with missing values handled as specified.
Each line past the first `skip_header` lines is split at the `delimiter`
character, and characters following the `comments` character are discarded.
Parameters
----------
fname : file or str
File, filename, or generator to read. If the filename extension is
`.gz` or `.bz2`, the file is first decompressed. Note that
generators must return byte strings in Python 3k.
dtype : dtype, optional
Data type of the resulting array.
If None, the dtypes will be determined by the contents of each
column, individually.
comments : str, optional
The character used to indicate the start of a comment.
All the characters occurring on a line after a comment are discarded
delimiter : str, int, or sequence, optional
The string used to separate values. By default, any consecutive
whitespaces act as delimiter. An integer or sequence of integers
can also be provided as width(s) of each field.
skip_header : int, optional
The numbers of lines to skip at the beginning of the file.
skip_footer : int, optional
The numbers of lines to skip at the end of the file
converters : variable, optional
The set of functions that convert the data of a column to a value.
The converters can also be used to provide a default value
for missing data: ``converters = {3: lambda s: float(s or 0)}``.
missing_values : variable, optional
The set of strings corresponding to missing data.
filling_values : variable, optional
The set of values to be used as default when the data are missing.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
names : {None, True, str, sequence}, optional
If `names` is True, the field names are read from the first valid line
after the first `skip_header` lines.
If `names` is a sequence or a single-string of comma-separated names,
the names will be used to define the field names in a structured dtype.
If `names` is None, the names of the dtype fields will be used, if any.
excludelist : sequence, optional
A list of names to exclude. This list is appended to the default list
['return','file','print']. Excluded names are appended an underscore:
for example, `file` would become `file_`.
deletechars : str, optional
A string combining invalid characters that must be deleted from the
names.
defaultfmt : str, optional
A format used to define default field names, such as "f%i" or "f_%02i".
autostrip : bool, optional
Whether to automatically strip white spaces from the variables.
replace_space : char, optional
Character(s) used in replacement of white spaces in the variables
names. By default, use a '_'.
case_sensitive : {True, False, 'upper', 'lower'}, optional
If True, field names are case sensitive.
If False or 'upper', field names are converted to upper case.
If 'lower', field names are converted to lower case.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``
usemask : bool, optional
If True, return a masked array.
If False, return a regular array.
invalid_raise : bool, optional
If True, an exception is raised if an inconsistency is detected in the
number of columns.
If False, a warning is emitted and the offending lines are skipped.
Returns
-------
out : ndarray
Data read from the text file. If `usemask` is True, this is a
masked array.
See Also
--------
numpy.loadtxt : equivalent function when no data is missing.
Notes
-----
* When spaces are used as delimiters, or when no delimiter has been given
as input, there should not be any missing data between two fields.
* When the variables are named (either by a flexible dtype or with `names`,
there must not be any header in the file (else a ValueError
exception is raised).
* Individual values are not stripped of spaces by default.
When using a custom converter, make sure the function does remove spaces.
References
----------
.. [1] Numpy User Guide, section `I/O with Numpy
<http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
Examples
---------
>>> from StringIO import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO("1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Using dtype = None
>>> s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Specifying dtype and names
>>> s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
An example with fixed-width columns
>>> s = StringIO("11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
array((1, 1.3, 'abcde'),
dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
"""
# Py3 data conversions to bytes, for convenience
#comments = asbytes(comments)
#if isinstance(delimiter, unicode):
# delimiter = asbytes(delimiter)
#if isinstance(missing, unicode):
# missing = asbytes(missing)
#if isinstance(missing_values, (unicode, list, tuple)):
# missing_values = asbytes_nested(missing_values)
if usemask:
from numpy.ma import MaskedArray, make_mask_descr
# Check the input dictionary of converters
user_converters = converters or {}
if not isinstance(user_converters, dict):
errmsg = "The input argument 'converter' should be a valid dictionary "\
"(got '%s' instead)"
raise TypeError(errmsg % type(user_converters))
# Initialize the filehandle, the LineSplitter and the NameValidator
own_fhd = False
try:
if isinstance(fname, basestring):
fhd = iter(np.lib._datasource.open(fname, 'rbU'))
own_fhd = True
else:
fhd = iter(fname)
except TypeError:
raise TypeError("fname mustbe a string, filehandle, or generator. "\
"(got %s instead)" % type(fname))
validate_names = NameValidator(excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Get the first valid lines after the first skiprows ones ..
if skiprows:
warnings.warn(\
"The use of `skiprows` is deprecated, it will be removed in numpy 2.0.\n" \
"Please use `skip_header` instead.",
DeprecationWarning)
skip_header = skiprows
set_names = False
if names is True:
set_names = True
infer_types = False
if dtype is None:
infer_types = True
whitespace_delims = False
if delimiter is None:
delimiter = ' '
whitespace_delims = True
compression = None
if isinstance(fname, basestring) and fname[-3:] == '.gz':
compression = 'gzip'
try:
if isinstance(delimiter, basestring):
adapter = textadapter.text_adapter(
fname,
parser='csv',
delimiter=delimiter,
comment=comments,
header=skip_header,
footer=skip_footer,
compression=compression,
field_names=set_names,
infer_types=True,
whitespace_delims=whitespace_delims)
elif isinstance(delimiter, int) or isinstance(delimiter,
(list, tuple)):
adapter = textadapter.text_adapter(fname,
parser='fixed_width',
field_widths=delimiter,
comment=comments,
header=skip_header,
footer=skip_footer,
field_names=set_names,
infer_types=True)
except EOFError:
return np.array([])
field_names = None
if isinstance(names, basestring):
field_names = [name.strip() for name in names.split(',')]
elif isinstance(names, tuple):
field_names = list(names)
elif isinstance(names, list):
field_names = names
elif set_names is True:
field_names = adapter.field_names
if usecols is None:
usecols = [x for x in range(0, adapter.field_count)]
elif isinstance(usecols, basestring) and field_names is None:
raise ValueError('usecols contains unknown field names')
elif isinstance(usecols, basestring):
if field_names is None:
raise ValueError('usecols contains unknown field names')
else:
usecols = [
field_names.index(name.strip()) for name in usecols.split(',')
]
elif isinstance(usecols, (list, tuple)):
if len(usecols) == 0:
raise ValueError('usecols must contain at least one col')
tempCols = []
for col in usecols:
if isinstance(col, basestring) and field_names is None:
raise ValueError('usecols contains unknown field names')
elif isinstance(col, basestring):
tempCols.append(field_names.index(col))
elif isinstance(col, int):
tempCols.append(col)
else:
raise ValueError(
'usecols must contain either field numbers or field names')
usecols = tempCols
elif isinstance(usecols, int_types):
usecols = [usecols]
elif isinstance(usecols, numpy.ndarray):
usecols = usecols.tolist()
else:
usecols = list(usecols)
# adjust negative indices
for i, col in enumerate(usecols):
if col < 0:
usecols[i] = adapter.field_count + col
if isinstance(field_names,
(list, dict)) and len(field_names) < len(usecols):
field_names.extend([''] * (len(usecols) - len(field_names)))
# Process the deprecated `missing`
if missing != asbytes(''):
warnings.warn(\
"The use of `missing` is deprecated, it will be removed in Numpy 2.0.\n" \
"Please use `missing_values` instead.",
DeprecationWarning)
missing_values = missing
# Initialize the output lists ...
# ... rows
rows = []
append_to_rows = rows.append
# ... masks
if usemask:
masks = []
append_to_masks = masks.append
# ... invalid
invalid = []
append_to_invalid = invalid.append
# create valid dtype object
if isinstance(dtype, (list, tuple)):
dtype = [dt if isinstance(dt, tuple) else ('', dt) for dt in dtype]
dtype = np.dtype(dtype)
# create list of dtypes to send to TextAdapter
if dtype.names is None:
# create list of homogenous scalar dtypes from single scalar dtype
numFields = len(usecols)
dtypes = [dtype] * numFields
else:
# create list of scalar dtypes from struct dtype
dtypes, dtype_field_names = unpack_dtype(dtype)
if field_names is None:
field_names = dtype_field_names
else:
s = set(field_names)
# if all entries in field_names are empty, use dtype field names
if len(s) == 1 and s.pop() == '':
field_names = dtype_field_names
# use field names from dtype if field names were not specified by user
# and not read from first line in file
#if names is None and dtype.names is not None:
# field_names = dtype.names
if field_names is not None:
adapter.field_names = field_names
if infer_types is False:
adapter.set_field_types(types=dict(zip(usecols, dtypes)))
if converters is not None:
for field, converter in converters.items():
adapter.set_converter(field, converter)
if isinstance(missing_values, basestring):
values = missing_values.split(',')
missing_values = {}
for i in range(adapter.field_count):
missing_values[i] = values
if missing_values is not None:
adapter.set_missing_values(missing_values)
if isinstance(filling_values, basestring):
filling_values = filling_values.split(',')
if filling_values is not None:
filling_values = dict([(key, value)
for key, value in filling_values.items()
if key in usecols])
adapter.set_fill_values(filling_values, loose)
try:
array = adapter[usecols][:]
except DataTypeError:
raise ValueError
if own_fhd:
fhd.close()
# Adapter returns an array with struct dtype.
# If no field names were specified or read from file,
# and specified dtype is scalar, reset array to scalar dtype.
if dtype.fields is None and field_names is None and set_names is False:
# Can't set final dtype to scalar if struct dtype includes objects
if array.dtype.fields is not None \
and np.object_ not in [x[0] for x in array.dtype.fields.values()]:
array.dtype = dtype
# If no fields were read, we want to keep this a 1-d empty array.
# Otherwise, set the proper shape.
if adapter.field_count > 0:
array.shape = (adapter.size, len(usecols))
#elif dtype.names is None and isinstance(names, list):
# array.dtype = zip([names[i] for i in usecols], [dtype]*len(usecols))
# Construct the final array
#if usemask:
# array = array.view(MaskedArray)
# array._mask = outputmask
if unpack:
return array.squeeze().T
return array.squeeze()
def test_validate_wo_names(self):
"Test validate no names"
namelist = None
validator = NameValidator()
assert_(validator(namelist) is None)
assert_equal(validator(namelist, nbfields=3), ['f0', 'f1', 'f2'])
def test_excludelist(self):
"Test excludelist"
names = ['dates', 'data', 'Other Data', 'mask']
validator = NameValidator(excludelist=['dates', 'data', 'mask'])
test = validator.validate(names)
assert_equal(test, ['dates_', 'data_', 'Other_Data', 'mask_'])
def test_validate_wo_names(self):
"Test validate no names"
namelist = None
validator = NameValidator()
assert_(validator(namelist) is None)
assert_equal(validator(namelist, nbfields=3), ["f0", "f1", "f2"])
def test_excludelist(self):
"Test excludelist"
names = ["dates", "data", "Other Data", "mask"]
validator = NameValidator(excludelist=["dates", "data", "mask"])
test = validator.validate(names)
assert_equal(test, ["dates_", "data_", "Other_Data", "mask_"])
def test_excludelist(self):
"Test excludelist"
names = ["dates", "data", "Other Data", "mask"]
validator = NameValidator(excludelist=["dates", "data", "mask"])
test = validator.validate(names)
assert_equal(test, ["dates_", "data_", "Other_Data", "mask_"])