# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2009- Spyder Kernels Contributors
#
# Licensed under the terms of the MIT License
# (see spyder_kernels/__init__.py for details)
# -----------------------------------------------------------------------------
"""
Input/Output Utilities
Note: 'load' functions has to return a dictionary from which a globals()
namespace may be updated
"""
from __future__ import print_function
# Standard library imports
import sys
import os
import os.path as osp
import tarfile
import tempfile
import shutil
import types
import warnings
import json
import inspect
import dis
import copy
import glob
# Third party imports
# - If pandas fails to import here (for any reason), Spyder
# will crash at startup (e.g. see Issue 2300)
# - This also prevents Spyder to start IPython kernels
# (see Issue 2456)
try:
import pandas as pd
except:
pd = None #analysis:ignore
# Local imports
from spyder_kernels.py3compat import getcwd, pickle, PY2, to_text_string
class MatlabStruct(dict):
"""
Matlab style struct, enhanced.
Supports dictionary and attribute style access. Can be pickled,
and supports code completion in a REPL.
Examples
========
>>> from spyder.utils.iofuncs import MatlabStruct
>>> a = MatlabStruct()
>>> a.b = 'spam' # a["b"] == 'spam'
>>> a.c["d"] = 'eggs' # a.c.d == 'eggs'
>>> print(a)
{'c': {'d': 'eggs'}, 'b': 'spam'}
"""
def __getattr__(self, attr):
"""Access the dictionary keys for unknown attributes."""
try:
return self[attr]
except KeyError:
msg = "'MatlabStruct' object has no attribute %s" % attr
raise AttributeError(msg)
def __getitem__(self, attr):
"""
Get a dict value; create a MatlabStruct if requesting a submember.
Do not create a key if the attribute starts with an underscore.
"""
if attr in self.keys() or attr.startswith('_'):
return dict.__getitem__(self, attr)
frame = inspect.currentframe()
# step into the function that called us
if frame.f_back.f_back and self._is_allowed(frame.f_back.f_back):
dict.__setitem__(self, attr, MatlabStruct())
elif self._is_allowed(frame.f_back):
dict.__setitem__(self, attr, MatlabStruct())
return dict.__getitem__(self, attr)
def _is_allowed(self, frame):
"""Check for allowed op code in the calling frame"""
allowed = [dis.opmap['STORE_ATTR'], dis.opmap['LOAD_CONST'],
dis.opmap.get('STOP_CODE', 0)]
bytecode = frame.f_code.co_code
instruction = bytecode[frame.f_lasti + 3]
instruction = ord(instruction) if PY2 else instruction
return instruction in allowed
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
@property
def __dict__(self):
"""Allow for code completion in a REPL"""
return self.copy()
def get_matlab_value(val):
"""
Extract a value from a Matlab file
From the oct2py project, see
https://pythonhosted.org/oct2py/conversions.html
"""
import numpy as np
# Extract each item of a list.
if isinstance(val, list):
return [get_matlab_value(v) for v in val]
# Ignore leaf objects.
if not isinstance(val, np.ndarray):
return val
# Convert user defined classes.
if hasattr(val, 'classname'):
out = dict()
for name in val.dtype.names:
out[name] = get_matlab_value(val[name].squeeze().tolist())
cls = type(val.classname, (object,), out)
return cls()
# Extract struct data.
elif val.dtype.names:
out = MatlabStruct()
for name in val.dtype.names:
out[name] = get_matlab_value(val[name].squeeze().tolist())
val = out
# Extract cells.
elif val.dtype.kind == 'O':
val = val.squeeze().tolist()
if not isinstance(val, list):
val = [val]
val = get_matlab_value(val)
# Compress singleton values.
elif val.size == 1:
val = val.item()
# Compress empty values.
elif val.size == 0:
if val.dtype.kind in 'US':
val = ''
else:
val = []
return val
try:
import numpy as np
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import scipy.io as spio
except AttributeError:
# Python 2.5: warnings.catch_warnings was introduced in Python 2.6
import scipy.io as spio # analysis:ignore
except:
spio = None
if spio is None:
load_matlab = None
save_matlab = None
else:
def load_matlab(filename):
try:
out = spio.loadmat(filename, struct_as_record=True)
data = dict()
for (key, value) in out.items():
data[key] = get_matlab_value(value)
return data, None
except Exception as error:
return None, str(error)
def save_matlab(data, filename):
try:
spio.savemat(filename, data, oned_as='row')
except Exception as error:
return str(error)
except:
load_matlab = None
save_matlab = None
try:
import numpy as np # analysis:ignore
def load_array(filename):
try:
name = osp.splitext(osp.basename(filename))[0]
data = np.load(filename)
if hasattr(data, 'keys'):
return data, None
else:
return {name: data}, None
except Exception as error:
return None, str(error)
def __save_array(data, basename, index):
"""Save numpy array"""
fname = basename + '_%04d.npy' % index
np.save(fname, data)
return fname
except:
load_array = None
try:
from spyder.pil_patch import Image
if sys.byteorder == 'little':
_ENDIAN = '<'
else:
_ENDIAN = '>'
DTYPES = {
"1": ('|b1', None),
"L": ('|u1', None),
"I": ('%si4' % _ENDIAN, None),
"F": ('%sf4' % _ENDIAN, None),
"I;16": ('|u2', None),
"I;16S": ('%si2' % _ENDIAN, None),
"P": ('|u1', None),
"RGB": ('|u1', 3),
"RGBX": ('|u1', 4),
"RGBA": ('|u1', 4),
"CMYK": ('|u1', 4),
"YCbCr": ('|u1', 4),
}
def __image_to_array(filename):
img = Image.open(filename)
try:
dtype, extra = DTYPES[img.mode]
except KeyError:
raise RuntimeError("%s mode is not supported" % img.mode)
shape = (img.size[1], img.size[0])
if extra is not None:
shape += (extra,)
return np.array(img.getdata(), dtype=np.dtype(dtype)).reshape(shape)
def load_image(filename):
try:
name = osp.splitext(osp.basename(filename))[0]
return {name: __image_to_array(filename)}, None
except Exception as error:
return None, str(error)
except:
load_image = None
def load_pickle(filename):
"""Load a pickle file as a dictionary"""
try:
if pd:
return pd.read_pickle(filename), None
else:
with open(filename, 'rb') as fid:
data = pickle.load(fid)
return data, None
except Exception as err:
return None, str(err)
def load_json(filename):
"""Load a json file as a dictionary"""
try:
if PY2:
args = 'rb'
else:
args = 'r'
with open(filename, args) as fid:
data = json.load(fid)
return data, None
except Exception as err:
return None, str(err)
def save_dictionary(data, filename):
"""Save dictionary in a single file .spydata file"""
filename = osp.abspath(filename)
old_cwd = getcwd()
os.chdir(osp.dirname(filename))
error_message = None
skipped_keys = []
data_copy = {}
try:
# Copy dictionary before modifying it to fix #6689
for obj_name, obj_value in data.items():
# Skip modules, since they can't be pickled, users virtually never
# would want them to be and so they don't show up in the skip list.
# Skip callables, since they are only pickled by reference and thus
# must already be present in the user's environment anyway.
if not (callable(obj_value) or isinstance(obj_value,
types.ModuleType)):
# If an object cannot be deepcopied, then it cannot be pickled.
# Ergo, we skip it and list it later.
try:
data_copy[obj_name] = copy.deepcopy(obj_value)
except Exception:
skipped_keys.append(obj_name)
data = data_copy
if not data:
raise RuntimeError('No supported objects to save')
saved_arrays = {}
if load_array is not None:
# Saving numpy arrays with np.save
arr_fname = osp.splitext(filename)[0]
for name in list(data.keys()):
try:
if isinstance(data[name],
np.ndarray) and data[name].size > 0:
# Save arrays at data root
fname = __save_array(data[name], arr_fname,
len(saved_arrays))
saved_arrays[(name, None)] = osp.basename(fname)
data.pop(name)
elif isinstance(data[name], (list, dict)):
# Save arrays nested in lists or dictionaries
if isinstance(data[name], list):
iterator = enumerate(data[name])
else:
iterator = iter(list(data[name].items()))
to_remove = []
for index, value in iterator:
if isinstance(value,
np.ndarray) and value.size > 0:
fname = __save_array(value, arr_fname,
len(saved_arrays))
saved_arrays[(name, index)] = (
osp.basename(fname))
to_remove.append(index)
for index in sorted(to_remove, reverse=True):
data[name].pop(index)
except (RuntimeError, pickle.PicklingError, TypeError,
AttributeError, IndexError):
# If an array can't be saved with numpy for some reason,
# leave the object intact and try to save it normally.
pass
if saved_arrays:
data['__saved_arrays__'] = saved_arrays
pickle_filename = osp.splitext(filename)[0] + '.pickle'
# Attempt to pickle everything.
# If pickling fails, iterate through to eliminate problem objs & retry.
with open(pickle_filename, 'w+b') as fdesc:
try:
pickle.dump(data, fdesc, protocol=2)
except (pickle.PicklingError, AttributeError, TypeError,
ImportError, IndexError, RuntimeError):
data_filtered = {}
for obj_name, obj_value in data.items():
try:
pickle.dumps(obj_value, protocol=2)
except Exception:
skipped_keys.append(obj_name)
else:
data_filtered[obj_name] = obj_value
if not data_filtered:
raise RuntimeError('No supported objects to save')
pickle.dump(data_filtered, fdesc, protocol=2)
# Use PAX (POSIX.1-2001) format instead of default GNU.
# This improves interoperability and UTF-8/long variable name support.
with tarfile.open(filename, "w", format=tarfile.PAX_FORMAT) as tar:
for fname in ([pickle_filename]
+ [fn for fn in list(saved_arrays.values())]):
tar.add(osp.basename(fname))
os.remove(fname)
except (RuntimeError, pickle.PicklingError, TypeError) as error:
error_message = to_text_string(error)
else:
if skipped_keys:
skipped_keys.sort()
error_message = ('Some objects could not be saved: '
+ ', '.join(skipped_keys))
finally:
os.chdir(old_cwd)
return error_message
def load_dictionary(filename):
"""Load dictionary from .spydata file"""
filename = osp.abspath(filename)
old_cwd = getcwd()
tmp_folder = tempfile.mkdtemp()
os.chdir(tmp_folder)
data = None
error_message = None
try:
with tarfile.open(filename, "r") as tar:
tar.extractall()
pickle_filename = glob.glob('*.pickle')[0]
# 'New' format (Spyder >=2.2 for Python 2 and Python 3)
with open(pickle_filename, 'rb') as fdesc:
data = pickle.loads(fdesc.read())
saved_arrays = {}
if load_array is not None:
# Loading numpy arrays saved with np.save
try:
saved_arrays = data.pop('__saved_arrays__')
for (name, index), fname in list(saved_arrays.items()):
arr = np.load( osp.join(tmp_folder, fname) )
if index is None:
data[name] = arr
elif isinstance(data[name], dict):
data[name][index] = arr
else:
data[name].insert(index, arr)
except KeyError:
pass
# Except AttributeError from e.g. trying to load function no longer present
except (AttributeError, EOFError, ValueError) as error:
error_message = to_text_string(error)
# To ensure working dir gets changed back and temp dir wiped no matter what
finally:
os.chdir(old_cwd)
try:
shutil.rmtree(tmp_folder)
except OSError as error:
error_message = to_text_string(error)
return data, error_message
class IOFunctions(object):
def __init__(self):
self.load_extensions = None
self.save_extensions = None
self.load_filters = None
self.save_filters = None
self.load_funcs = None
self.save_funcs = None
def setup(self):
iofuncs = self.get_internal_funcs()+self.get_3rd_party_funcs()
load_extensions = {}
save_extensions = {}
load_funcs = {}
save_funcs = {}
load_filters = []
save_filters = []
load_ext = []
for ext, name, loadfunc, savefunc in iofuncs:
filter_str = to_text_string(name + " (*%s)" % ext)
if loadfunc is not None:
load_filters.append(filter_str)
load_extensions[filter_str] = ext
load_funcs[ext] = loadfunc
load_ext.append(ext)
if savefunc is not None:
save_extensions[filter_str] = ext
save_filters.append(filter_str)
save_funcs[ext] = savefunc
load_filters.insert(0, to_text_string("Supported files"+" (*"+\
" *".join(load_ext)+")"))
load_filters.append(to_text_string("All files (*.*)"))
self.load_filters = "\n".join(load_filters)
self.save_filters = "\n".join(save_filters)
self.load_funcs = load_funcs
self.save_funcs = save_funcs
self.load_extensions = load_extensions
self.save_extensions = save_extensions
def get_internal_funcs(self):
return [
('.spydata', "Spyder data files",
load_dictionary, save_dictionary),
('.npy', "NumPy arrays", load_array, None),
('.npz', "NumPy zip arrays", load_array, None),
('.mat', "Matlab files", load_matlab, save_matlab),
('.csv', "CSV text files", 'import_wizard', None),
('.txt', "Text files", 'import_wizard', None),
('.jpg', "JPEG images", load_image, None),
('.png', "PNG images", load_image, None),
('.gif', "GIF images", load_image, None),
('.tif', "TIFF images", load_image, None),
('.pkl', "Pickle files", load_pickle, None),
('.pickle', "Pickle files", load_pickle, None),
('.json', "JSON files", load_json, None),
]
def get_3rd_party_funcs(self):
other_funcs = []
try:
from spyder.otherplugins import get_spyderplugins_mods
for mod in get_spyderplugins_mods(io=True):
try:
other_funcs.append((mod.FORMAT_EXT, mod.FORMAT_NAME,
mod.FORMAT_LOAD, mod.FORMAT_SAVE))
except AttributeError as error:
print("%s: %s" % (mod, str(error)), file=sys.stderr)
except ImportError:
pass
return other_funcs
def save(self, data, filename):
ext = osp.splitext(filename)[1].lower()
if ext in self.save_funcs:
return self.save_funcs[ext](data, filename)
else:
return "Unsupported file type '%s'" % ext
def load(self, filename):
ext = osp.splitext(filename)[1].lower()
if ext in self.load_funcs:
return self.load_funcs[ext](filename)
else:
return None, "Unsupported file type '%s'" % ext
iofunctions = IOFunctions()
iofunctions.setup()
def save_auto(data, filename):
"""Save data into filename, depending on file extension"""
pass
if __name__ == "__main__":
import datetime
testdict = {'d': 1, 'a': np.random.rand(10, 10), 'b': [1, 2]}
testdate = datetime.date(1945, 5, 8)
example = {'str': 'kjkj kj k j j kj k jkj',
'unicode': u'éù',
'list': [1, 3, [4, 5, 6], 'kjkj', None],
'tuple': ([1, testdate, testdict], 'kjkj', None),
'dict': testdict,
'float': 1.2233,
'array': np.random.rand(4000, 400),
'empty_array': np.array([]),
'date': testdate,
'datetime': datetime.datetime(1945, 5, 8),
}
import time
t0 = time.time()
save_dictionary(example, "test.spydata")
print(" Data saved in %.3f seconds" % (time.time()-t0)) # spyder: test-skip
t0 = time.time()
example2, ok = load_dictionary("test.spydata")
os.remove("test.spydata")
print("Data loaded in %.3f seconds" % (time.time()-t0)) # spyder: test-skip