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dump2module.py
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dump2module.py
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'''Save a set of numpy arrays to a python module file that can be imported
Author : Josef Perktold
'''
from __future__ import print_function
from statsmodels.compat.python import iterkeys
import numpy as np
class HoldIt(object):
'''Class to write numpy arrays into a python module
Calling save on the instance of this class write all attributes of the
instance into a module file. For details see the save method.
'''
def __init__(self, name):
self.name = name
def save(self, what=None, filename=None, header=True, useinstance=True,
comment=None, print_options=None):
'''write attributes of this instance to python module given by filename
Parameters
----------
what : list or None
list of attributes that are added to the module. If None (default)
then all attributes in __dict__ that do not start with an underline
will be saved.
filename : string
specifies filename with path. If the file does not exist, it will be
created. If the file is already exists, then the new data will be
appended to the file.
header : bool
If true, then the imports of the module and the class definition are
written before writing the data.
useinstance : bool
If true, then the data in the module are attached to an instance of a
holder class. If false, then each array will be saved as separate
variable.
comment : string
If comment is not empty then this string will be attached as a
description comment to the data instance in the saved module.
print_options : dict or None
The print_options for the numpy arrays will be updated with this.
see notes
Notes
-----
The content of an numpy array are written using repr, which can be
controlled with the np.set_printoptions. The numpy default is updated
with: precision=20, linewidth=100, nanstr='nan', infstr='inf'
This should provide enough precision for double floating point numbers.
If one array has more than 1000 elements, then threshold should be
overwritten by the user, see keyword argument print_options.
'''
print_opt_old = np.get_printoptions()
print_opt = dict(precision=20, linewidth=100, nanstr='nan',
infstr='inf')
if print_options:
print_opt.update(print_options)
np.set_printoptions(**print_opt)
#precision corrects for non-scientific notation
if what is None:
what = (i for i in self.__dict__ if i[0] != '_')
if header:
txt = ['import numpy as np\n'
'from numpy import array, rec, inf, nan\n\n']
if useinstance:
txt.append('class Holder(object):\n pass\n\n')
else:
txt = []
if useinstance:
txt.append('%s = Holder()' % self.name)
prefix = '%s.' % self.name
else:
prefix = ''
if not comment is None:
txt.append("%scomment = '%s'" % (prefix, comment))
for x in what:
txt.append('%s%s = %s' % (prefix, x, repr(getattr(self,x))))
txt.extend(['','']) #add empty lines at end
if not filename is None:
file(filename, 'a+').write('\n'.join(txt))
np.set_printoptions(**print_opt_old)
self._filename = filename
self._useinstance = useinstance
self._what = what
return txt
def verify(self):
'''load the saved module and verify the data
This tries several ways of comparing the saved and the attached data,
but might not work for all possible data structures.
Returns
-------
all_correct : bool
true if no differences are found, for floating point numbers
rtol=1e-16, atol=1e-16 is used to determine equality (allclose)
correctli : list
list of attribute names that compare as equal
incorrectli : list
list of attribute names that did not compare as equal, either
because they differ or because the comparison does not handle the
data structure correctly
'''
module = __import__(self._filename.replace('.py',''))
if not self._useinstance:
raise NotImplementedError('currently only implemented when'
'useinstance is true')
data = getattr(module, self.name)
correctli = []
incorrectli = []
for d in self._what:
self_item = getattr(data, d)
saved_item = getattr(data, d)
#print(d)
#try simple equality
correct = np.all(self.item == saved_item)
#try allclose
if not correct and not self.item.dtype == np.dtype('object'):
correct = np.allclose(self_item, saved_item,
rtol=1e-16, atol=1e-16)
if not correct:
import warnings
warnings.warn("inexact precision in "+d, RuntimeWarning)
#try iterating, if object array
if not correct:
correlem =[np.all(data[d].item()[k] ==
getattr(testsave.var_results, d).item()[k])
for k in iterkeys(data[d].item())]
if not correlem:
#print(d, "wrong")
incorrectli.append(d)
correctli.append(d)
return len(incorrectli)==0, correctli, incorrectli
if __name__ == '__main__':
data = np.load(r"E:\Josef\eclipsegworkspace\statsmodels-josef-experimental-030\dist\statsmodels-0.3.0dev_with_Winhelp_a2\statsmodels-0.3.0dev\scikits\statsmodels\tsa\vector_ar\tests\results\vars_results.npz")
res_var = HoldIt('var_results')
for d in data:
setattr(res_var, d, data[d])
np.set_printoptions(precision=120, linewidth=100)
res_var.save(filename='testsave.py', header=True,
comment='VAR test data converted from vars_results.npz')
import testsave
for d in data:
print(d)
correct = np.all(data[d] == getattr(testsave.var_results, d))
if not correct and not data[d].dtype == np.dtype('object'):
correct = np.allclose(data[d], getattr(testsave.var_results, d),
rtol=1e-16, atol=1e-16)
if not correct: print("inexact precision")
if not correct:
correlem =[np.all(data[d].item()[k] ==
getattr(testsave.var_results, d).item()[k])
for k in iterkeys(data[d].item())]
if not correlem:
print(d, "wrong")
print(res_var.verify())