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Sven Riwoldt
2024-04-01 20:30:24 +02:00
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# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2009- Spyder Kernels Contributors
#
# Licensed under the terms of the MIT License
# (see spyder_kernels/__init__.py for details)
# -----------------------------------------------------------------------------
"""
Utilities to build a namespace view.
"""
from __future__ import print_function
from itertools import islice
import inspect
import re
# Local imports
from spyder_kernels.py3compat import (NUMERIC_TYPES, INT_TYPES, TEXT_TYPES,
to_text_string, is_text_string,
is_type_text_string,
is_binary_string, PY2,
to_binary_string, iteritems)
from spyder_kernels.utils.lazymodules import (
bs4, FakeObject, numpy as np, pandas as pd, PIL)
#==============================================================================
# Numpy support
#==============================================================================
def get_numeric_numpy_types():
return (np.int64, np.int32, np.int16, np.int8, np.uint64, np.uint32,
np.uint16, np.uint8, np.float64, np.float32, np.float16,
np.complex64, np.complex128, np.bool_)
def get_numpy_dtype(obj):
"""
Return Numpy data type associated to `obj`.
Return None if Numpy is not available, if we get errors or if `obj` is not
a Numpy array or scalar.
"""
# Check if NumPy is available
if np.ndarray is not FakeObject:
# All Numpy scalars inherit from np.generic and all Numpy arrays
# inherit from np.ndarray. If we check that we are certain we have one
# of these types then we are less likely to generate an exception
# below.
# Note: The try/except is necessary to fix spyder-ide/spyder#19516.
try:
scalar_or_array = (
isinstance(obj, np.generic) or isinstance(obj, np.ndarray)
)
except Exception:
return
if scalar_or_array:
try:
return obj.dtype.type
except (AttributeError, RuntimeError):
# AttributeError: some NumPy objects have no dtype attribute
# RuntimeError: happens with NetCDF objects (Issue 998)
return
def get_numpy_type_string(value):
"""Get the type of a Numpy object as a string."""
np_dtype = get_numpy_dtype(value)
if np_dtype is None or not hasattr(value, 'size'):
return 'Unknown'
elif value.size == 1:
return 'Scalar'
else:
return 'Array'
#==============================================================================
# Misc.
#==============================================================================
def address(obj):
"""Return object address as a string: '<classname @ address>'"""
return "<%s @ %s>" % (obj.__class__.__name__,
hex(id(obj)).upper().replace('X', 'x'))
def try_to_eval(value):
"""Try to eval value"""
try:
return eval(value)
except (NameError, SyntaxError, ImportError):
return value
def get_size(item):
"""Return shape/size/len of an item of arbitrary type"""
try:
if (
hasattr(item, 'size') and hasattr(item.size, 'compute') or
hasattr(item, 'shape') and hasattr(item.shape, 'compute')
):
# This is necessary to avoid an error when trying to
# get the size/shape of dask objects. We don't compute the
# size/shape since such operation could be expensive.
# Fixes spyder-ide/spyder#16844
return 1
elif (
hasattr(item, 'shape') and
isinstance(item.shape, (tuple, np.integer))
):
try:
if item.shape:
# This is needed since values could return as
# `shape` an instance of a `tuple` subclass.
# See spyder-ide/spyder#16348
if isinstance(item.shape, tuple):
return tuple(item.shape)
return item.shape
else:
# Scalar value
return 1
except RecursionError:
# This is necessary to avoid an error when trying to
# get the shape of these objects.
# Fixes spyder-ide/spyder-kernels#217
return (-1, -1)
elif (hasattr(item, 'size') and
isinstance(item.size, (tuple, np.integer))):
try:
return item.size
except RecursionError:
return (-1, -1)
elif hasattr(item, '__len__'):
return len(item)
else:
return 1
except Exception:
# There is one item
return 1
def get_object_attrs(obj):
"""
Get the attributes of an object using dir.
This filters protected attributes
"""
attrs = [k for k in dir(obj) if not k.startswith('__')]
if not attrs:
attrs = dir(obj)
return attrs
#==============================================================================
# Date and datetime objects support
#==============================================================================
import datetime
try:
from dateutil.parser import parse as dateparse
except:
def dateparse(datestr): # analysis:ignore
"""Just for 'year, month, day' strings"""
return datetime.datetime( *list(map(int, datestr.split(','))) )
def datestr_to_datetime(value):
rp = value.rfind('(')+1
v = dateparse(value[rp:-1])
print(value, "-->", v) # spyder: test-skip
return v
def str_to_timedelta(value):
"""Convert a string to a datetime.timedelta value.
The following strings are accepted:
- 'datetime.timedelta(1, 5, 12345)'
- 'timedelta(1, 5, 12345)'
- '(1, 5, 12345)'
- '1, 5, 12345'
- '1'
if there are less then three parameters, the missing parameters are
assumed to be 0. Variations in the spacing of the parameters are allowed.
Raises:
ValueError for strings not matching the above criterion.
"""
m = re.match(r'^(?:(?:datetime\.)?timedelta)?'
r'\(?'
r'([^)]*)'
r'\)?$', value)
if not m:
raise ValueError('Invalid string for datetime.timedelta')
args = [int(a.strip()) for a in m.group(1).split(',')]
return datetime.timedelta(*args)
#==============================================================================
# Supported types
#==============================================================================
def is_editable_type(value):
"""
Return True if data type is editable with a standard GUI-based editor,
like CollectionsEditor, ArrayEditor, QDateEdit or a simple QLineEdit.
"""
if not is_known_type(value):
return False
else:
supported_types = [
'bool', 'int', 'long', 'float', 'complex', 'list', 'set', 'dict',
'tuple', 'str', 'unicode', 'NDArray', 'MaskedArray', 'Matrix',
'DataFrame', 'Series', 'PIL.Image.Image', 'datetime.date',
'datetime.timedelta'
]
if (get_type_string(value) not in supported_types and
not isinstance(value, pd.Index)):
np_dtype = get_numpy_dtype(value)
if np_dtype is None or not hasattr(value, 'size'):
return False
return True
#==============================================================================
# Sorting
#==============================================================================
def sort_against(list1, list2, reverse=False, sort_key=None):
"""
Arrange items of list1 in the same order as sorted(list2).
In other words, apply to list1 the permutation which takes list2
to sorted(list2, reverse).
"""
if sort_key is None:
key = lambda x: x[0]
else:
key = lambda x: sort_key(x[0])
try:
return [item for _, item in
sorted(zip(list2, list1), key=key, reverse=reverse)]
except:
return list1
def unsorted_unique(lista):
"""Removes duplicates from lista neglecting its initial ordering"""
return list(set(lista))
#==============================================================================
# Display <--> Value
#==============================================================================
def default_display(value, with_module=True):
"""Default display for unknown objects."""
object_type = type(value)
try:
name = object_type.__name__
module = object_type.__module__
# Classes correspond to new types
if name == 'type':
name = 'class'
if with_module:
if name == 'module':
return value.__name__ + ' module'
if module == 'builtins':
return name + ' object'
return name + ' object of ' + module + ' module'
return name
except Exception:
type_str = to_text_string(object_type)
return type_str[1:-1]
def collections_display(value, level):
"""Display for collections (i.e. list, set, tuple and dict)."""
is_dict = isinstance(value, dict)
is_set = isinstance(value, set)
# Get elements
if is_dict:
elements = iteritems(value)
else:
elements = value
# Truncate values
truncate = False
if level == 1 and len(value) > 10:
elements = islice(elements, 10) if is_dict or is_set else value[:10]
truncate = True
elif level == 2 and len(value) > 5:
elements = islice(elements, 5) if is_dict or is_set else value[:5]
truncate = True
# Get display of each element
if level <= 2:
if is_dict:
displays = [value_to_display(k, level=level) + ':' +
value_to_display(v, level=level)
for (k, v) in list(elements)]
else:
displays = [value_to_display(e, level=level)
for e in elements]
if truncate:
displays.append('...')
display = ', '.join(displays)
else:
display = '...'
# Return display
if is_dict:
display = '{' + display + '}'
elif isinstance(value, list):
display = '[' + display + ']'
elif isinstance(value, set):
display = '{' + display + '}'
else:
display = '(' + display + ')'
return display
def value_to_display(value, minmax=False, level=0):
"""Convert value for display purpose"""
# To save current Numpy printoptions
np_printoptions = FakeObject
numeric_numpy_types = get_numeric_numpy_types()
try:
if np.ndarray is not FakeObject:
# Save printoptions
np_printoptions = np.get_printoptions()
# Set max number of elements to show for Numpy arrays
# in our display
np.set_printoptions(threshold=10)
if isinstance(value, np.recarray):
if level == 0:
fields = value.names
display = 'Field names: ' + ', '.join(fields)
else:
display = 'Recarray'
elif isinstance(value, np.ma.MaskedArray):
display = 'Masked array'
elif isinstance(value, np.ndarray):
if level == 0:
if minmax:
try:
display = 'Min: %r\nMax: %r' % (value.min(), value.max())
except (TypeError, ValueError):
if value.dtype.type in numeric_numpy_types:
display = str(value)
else:
display = default_display(value)
elif value.dtype.type in numeric_numpy_types:
display = str(value)
else:
display = default_display(value)
else:
display = 'Numpy array'
elif any([type(value) == t for t in [list, set, tuple, dict]]):
display = collections_display(value, level+1)
elif isinstance(value, PIL.Image.Image):
if level == 0:
display = '%s Mode: %s' % (address(value), value.mode)
else:
display = 'Image'
elif isinstance(value, pd.DataFrame):
if level == 0:
cols = value.columns
if PY2 and len(cols) > 0:
# Get rid of possible BOM utf-8 data present at the
# beginning of a file, which gets attached to the first
# column header when headers are present in the first
# row.
# Fixes Issue 2514
try:
ini_col = to_text_string(cols[0], encoding='utf-8-sig')
except:
ini_col = to_text_string(cols[0])
cols = [ini_col] + [to_text_string(c) for c in cols[1:]]
else:
cols = [to_text_string(c) for c in cols]
display = 'Column names: ' + ', '.join(list(cols))
else:
display = 'Dataframe'
elif isinstance(value, bs4.element.NavigableString):
# Fixes Issue 2448
display = to_text_string(value)
if level > 0:
display = u"'" + display + u"'"
elif isinstance(value, pd.Index):
if level == 0:
try:
display = value._summary()
except AttributeError:
display = value.summary()
else:
display = 'Index'
elif is_binary_string(value):
# We don't apply this to classes that extend string types
# See issue 5636
if is_type_text_string(value):
try:
display = to_text_string(value, 'utf8')
if level > 0:
display = u"'" + display + u"'"
except:
display = value
if level > 0:
display = b"'" + display + b"'"
else:
display = default_display(value)
elif is_text_string(value):
# We don't apply this to classes that extend string types
# See issue 5636
if is_type_text_string(value):
display = value
if level > 0:
display = u"'" + display + u"'"
else:
display = default_display(value)
elif (isinstance(value, datetime.date) or
isinstance(value, datetime.timedelta)):
display = str(value)
elif (isinstance(value, NUMERIC_TYPES) or
isinstance(value, bool) or
isinstance(value, numeric_numpy_types)):
display = repr(value)
else:
if level == 0:
display = default_display(value)
else:
display = default_display(value, with_module=False)
except Exception:
display = default_display(value)
# Truncate display at 70 chars to avoid freezing Spyder
# because of large displays
if len(display) > 70:
if is_binary_string(display):
ellipses = b' ...'
else:
ellipses = u' ...'
display = display[:70].rstrip() + ellipses
# Restore Numpy printoptions
if np_printoptions is not FakeObject:
np.set_printoptions(**np_printoptions)
return display
def display_to_value(value, default_value, ignore_errors=True):
"""Convert back to value"""
from qtpy.compat import from_qvariant
value = from_qvariant(value, to_text_string)
try:
np_dtype = get_numpy_dtype(default_value)
if isinstance(default_value, bool):
# We must test for boolean before NumPy data types
# because `bool` class derives from `int` class
try:
value = bool(float(value))
except ValueError:
value = value.lower() == "true"
elif np_dtype is not None:
if 'complex' in str(type(default_value)):
value = np_dtype(complex(value))
else:
value = np_dtype(value)
elif is_binary_string(default_value):
value = to_binary_string(value, 'utf8')
elif is_text_string(default_value):
value = to_text_string(value)
elif isinstance(default_value, complex):
value = complex(value)
elif isinstance(default_value, float):
value = float(value)
elif isinstance(default_value, int):
try:
value = int(value)
except ValueError:
value = float(value)
elif isinstance(default_value, datetime.datetime):
value = datestr_to_datetime(value)
elif isinstance(default_value, datetime.date):
value = datestr_to_datetime(value).date()
elif isinstance(default_value, datetime.timedelta):
value = str_to_timedelta(value)
elif ignore_errors:
value = try_to_eval(value)
else:
value = eval(value)
except (ValueError, SyntaxError):
if ignore_errors:
value = try_to_eval(value)
else:
return default_value
return value
# =============================================================================
# Types
# =============================================================================
def get_type_string(item):
"""Return type string of an object."""
# The try/except is necessary to fix spyder-ide/spyder#19516.
try:
# Numpy objects (don't change the order!)
if isinstance(item, np.ma.MaskedArray):
return "MaskedArray"
if isinstance(item, np.matrix):
return "Matrix"
if isinstance(item, np.ndarray):
return "NDArray"
# Pandas objects
if isinstance(item, pd.DataFrame):
return "DataFrame"
if isinstance(item, pd.Index):
return type(item).__name__
if isinstance(item, pd.Series):
return "Series"
except Exception:
pass
found = re.findall(r"<(?:type|class) '(\S*)'>",
to_text_string(type(item)))
if found:
if found[0] == 'type':
return 'class'
return found[0]
else:
return 'Unknown'
def is_known_type(item):
"""Return True if object has a known type"""
# Unfortunately, the masked array case is specific
return (isinstance(item, np.ma.MaskedArray) or
get_type_string(item) != 'Unknown')
def get_human_readable_type(item):
"""Return human-readable type string of an item"""
# The try/except is necessary to fix spyder-ide/spyder#19516.
try:
if isinstance(item, (np.ndarray, np.ma.MaskedArray)):
return u'Array of ' + item.dtype.name
elif isinstance(item, PIL.Image.Image):
return "Image"
else:
text = get_type_string(item)
return text[text.find('.')+1:]
except Exception:
return 'Unknown'
#==============================================================================
# Globals filter: filter namespace dictionaries (to be edited in
# CollectionsEditor)
#==============================================================================
def is_supported(value, check_all=False, filters=None, iterate=False):
"""Return True if value is supported, False otherwise."""
assert filters is not None
if value is None:
return True
if is_callable_or_module(value):
return True
elif not is_editable_type(value):
return False
elif not isinstance(value, filters):
return False
elif iterate:
if isinstance(value, (list, tuple, set)):
valid_count = 0
for val in value:
if is_supported(val, filters=filters, iterate=check_all):
valid_count += 1
if not check_all:
break
return valid_count > 0
elif isinstance(value, dict):
for key, val in list(value.items()):
if not is_supported(key, filters=filters, iterate=check_all) \
or not is_supported(val, filters=filters,
iterate=check_all):
return False
if not check_all:
break
return True
def is_callable_or_module(value):
"""Return True if value is a callable or module, False otherwise."""
try:
callable_or_module = callable(value) or inspect.ismodule(value)
except Exception:
callable_or_module = False
return callable_or_module
def globalsfilter(input_dict, check_all=False, filters=None,
exclude_private=None, exclude_capitalized=None,
exclude_uppercase=None, exclude_unsupported=None,
excluded_names=None, exclude_callables_and_modules=None):
"""Keep objects in namespace view according to different criteria."""
output_dict = {}
_is_string = is_type_text_string
for key, value in list(input_dict.items()):
excluded = (
(exclude_private and _is_string(key) and key.startswith('_')) or
(exclude_capitalized and _is_string(key) and key[0].isupper()) or
(exclude_uppercase and _is_string(key) and key.isupper() and
len(key) > 1 and not key[1:].isdigit()) or
(key in excluded_names) or
(exclude_callables_and_modules and is_callable_or_module(value)) or
(exclude_unsupported and
not is_supported(value, check_all=check_all, filters=filters))
)
if not excluded:
output_dict[key] = value
return output_dict
#==============================================================================
# Create view to be displayed by NamespaceBrowser
#==============================================================================
REMOTE_SETTINGS = ('check_all', 'exclude_private', 'exclude_uppercase',
'exclude_capitalized', 'exclude_unsupported',
'excluded_names', 'minmax', 'show_callable_attributes',
'show_special_attributes', 'exclude_callables_and_modules')
def get_supported_types():
"""
Return a dictionnary containing types lists supported by the
namespace browser.
Note:
If you update this list, don't forget to update variablexplorer.rst
in spyder-docs
"""
from datetime import date, timedelta
editable_types = [int, float, complex, list, set, dict, tuple, date,
timedelta] + list(TEXT_TYPES) + list(INT_TYPES)
try:
from numpy import ndarray, matrix, generic
editable_types += [ndarray, matrix, generic]
except:
pass
try:
from pandas import DataFrame, Series, Index
editable_types += [DataFrame, Series, Index]
except:
pass
picklable_types = editable_types[:]
try:
from PIL import Image
editable_types.append(Image.Image)
except:
pass
return dict(picklable=picklable_types, editable=editable_types)
def get_remote_data(data, settings, mode, more_excluded_names=None):
"""
Return globals according to filter described in *settings*:
* data: data to be filtered (dictionary)
* settings: variable explorer settings (dictionary)
* mode (string): 'editable' or 'picklable'
* more_excluded_names: additional excluded names (list)
"""
supported_types = get_supported_types()
assert mode in list(supported_types.keys())
excluded_names = list(settings['excluded_names'])
if more_excluded_names is not None:
excluded_names += more_excluded_names
return globalsfilter(
data,
check_all=settings['check_all'],
filters=tuple(supported_types[mode]),
exclude_private=settings['exclude_private'],
exclude_uppercase=settings['exclude_uppercase'],
exclude_capitalized=settings['exclude_capitalized'],
exclude_unsupported=settings['exclude_unsupported'],
exclude_callables_and_modules=settings['exclude_callables_and_modules'],
excluded_names=excluded_names)
def make_remote_view(data, settings, more_excluded_names=None):
"""
Make a remote view of dictionary *data*
-> globals explorer
"""
data = get_remote_data(data, settings, mode='editable',
more_excluded_names=more_excluded_names)
remote = {}
for key, value in list(data.items()):
view = value_to_display(value, minmax=settings['minmax'])
remote[key] = {
'type': get_human_readable_type(value),
'size': get_size(value),
'view': view,
'python_type': get_type_string(value),
'numpy_type': get_numpy_type_string(value)
}
return remote