"""
Input/Output tools for working with binary data.
The Stata input tools were originally written by Joe Presbrey as part of PyDTA.
You can find more information here http://presbrey.mit.edu/PyDTA
See Also
--------
numpy.lib.io
"""
import warnings
from statsmodels.compat.python import (lzip, lmap, lrange,
lfilter, asbytes, asstr)
from struct import unpack, calcsize, pack
from struct import error as struct_error
import datetime
import sys
import numpy as np
import statsmodels.tools.data as data_util
from pandas import isnull
from pandas.io.stata import StataMissingValue
from statsmodels.iolib.openfile import get_file_obj
_date_formats = ["%tc", "%tC", "%td", "%tw", "%tm", "%tq", "%th", "%ty"]
def _datetime_to_stata_elapsed(date, fmt):
"""
Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime
Parameters
----------
date : datetime.datetime
The date to convert to the Stata Internal Format given by fmt
fmt : str
The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
"""
if not isinstance(date, datetime.datetime):
raise ValueError("date should be datetime.datetime format")
stata_epoch = datetime.datetime(1960, 1, 1)
if fmt in ["%tc", "tc"]:
delta = date - stata_epoch
return (delta.days * 86400000 + delta.seconds*1000 +
delta.microseconds/1000)
elif fmt in ["%tC", "tC"]:
from warnings import warn
warn("Stata Internal Format tC not supported.", UserWarning)
return date
elif fmt in ["%td", "td"]:
return (date- stata_epoch).days
elif fmt in ["%tw", "tw"]:
return (52*(date.year-stata_epoch.year) +
(date - datetime.datetime(date.year, 1, 1)).days / 7)
elif fmt in ["%tm", "tm"]:
return (12 * (date.year - stata_epoch.year) + date.month - 1)
elif fmt in ["%tq", "tq"]:
return 4*(date.year-stata_epoch.year) + int((date.month - 1)/3)
elif fmt in ["%th", "th"]:
return 2 * (date.year - stata_epoch.year) + int(date.month > 6)
elif fmt in ["%ty", "ty"]:
return date.year
else:
raise ValueError("fmt %s not understood" % fmt)
def _stata_elapsed_date_to_datetime(date, fmt):
"""
Convert from SIF to datetime. http://www.stata.com/help.cgi?datetime
Parameters
----------
date : int
The Stata Internal Format date to convert to datetime according to fmt
fmt : str
The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
Examples
--------
>>> _stata_elapsed_date_to_datetime(52, "%tw") datetime.datetime(1961, 1, 1, 0, 0)
Notes
-----
datetime/c - tc
milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day
datetime/C - tC - NOT IMPLEMENTED
milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds
date - td
days since 01jan1960 (01jan1960 = 0)
weekly date - tw
weeks since 1960w1
This assumes 52 weeks in a year, then adds 7 * remainder of the weeks.
The datetime value is the start of the week in terms of days in the
year, not ISO calendar weeks.
monthly date - tm
months since 1960m1
quarterly date - tq
quarters since 1960q1
half-yearly date - th
half-years since 1960h1 yearly
date - ty
years since 0000
If you do not have pandas with datetime support, then you cannot do
milliseconds accurately.
"""
#NOTE: we could run into overflow / loss of precision situations here
# casting to int, but I'm not sure what to do. datetime will not deal with
# numpy types and numpy datetime is not mature enough / we cannot rely on
# pandas version > 0.7.1
#TODO: IIRC relative delta does not play well with np.datetime?
date = int(date)
stata_epoch = datetime.datetime(1960, 1, 1)
if fmt in ["%tc", "tc"]:
from dateutil.relativedelta import relativedelta
return stata_epoch + relativedelta(microseconds=date*1000)
elif fmt in ["%tC", "tC"]:
from warnings import warn
warn("Encountered %tC format. Leaving in Stata Internal Format.",
UserWarning)
return date
elif fmt in ["%td", "td"]:
return stata_epoch + datetime.timedelta(int(date))
elif fmt in ["%tw", "tw"]: # does not count leap days - 7 days is a week
year = datetime.datetime(stata_epoch.year + date // 52, 1, 1)
day_delta = (date % 52 ) * 7
return year + datetime.timedelta(int(day_delta))
elif fmt in ["%tm", "tm"]:
year = stata_epoch.year + date // 12
month_delta = (date % 12 ) + 1
return datetime.datetime(year, month_delta, 1)
elif fmt in ["%tq", "tq"]:
year = stata_epoch.year + date // 4
month_delta = (date % 4) * 3 + 1
return datetime.datetime(year, month_delta, 1)
elif fmt in ["%th", "th"]:
year = stata_epoch.year + date // 2
month_delta = (date % 2) * 6 + 1
return datetime.datetime(year, month_delta, 1)
elif fmt in ["%ty", "ty"]:
if date > 0:
return datetime.datetime(date, 1, 1)
else: # do not do negative years bc cannot mix dtypes in column
raise ValueError("Year 0 and before not implemented")
else:
raise ValueError("Date fmt %s not understood" % fmt)
### Helper classes for StataReader ###
class _StataVariable(object):
"""
A dataset variable. Not intended for public use.
Parameters
----------
variable_data
Attributes
----------
format : str
Stata variable format. See notes for more information.
index : int
Zero-index column index of variable.
label : str
Data Label
name : str
Variable name
type : str
Stata data type. See notes for more information.
value_format : str
Value format.
Notes
-----
More information: http://www.stata.com/help.cgi?format
"""
def __init__(self, variable_data):
self._data = variable_data
def __int__(self):
"""the variable's index within an observation"""
return self.index
def __str__(self):
"""the name of the variable"""
return self.name
@property
def index(self):
"""the variable's index within an observation"""
return self._data[0]
@property
def type(self):
"""
The data type of variable
Possible types are:
{1..244:string, b:byte, h:int, l:long, f:float, d:double)
"""
return self._data[1]
@property
def name(self):
"""the name of the variable"""
return self._data[2]
@property
def format(self):
"""the variable's Stata format"""
return self._data[4]
@property
def value_format(self):
"""the variable's value format"""
return self._data[5]
@property
def label(self):
"""The variable's label"""
return self._data[6]
[docs]class StataReader(object):
"""
Stata .dta file reader.
.. deprecated:: 0.11
Use pandas.read_stata or pandas.io.stata.StataReader
Provides methods to return the metadata of a Stata .dta file and
a generator for the data itself.
Parameters
----------
file : file-like
A file-like object representing a Stata .dta file.
missing_values : bool
If missing_values is True, parse missing_values and return a
Missing Values object instead of None.
encoding : str, optional
Used for Python 3 only. Encoding to use when reading the .dta file.
Defaults to `locale.getpreferredencoding`
See Also
--------
statsmodels.iolib.foreign.genfromdta
pandas.read_stata
pandas.io.stata.StataReader
Notes
-----
This is known only to work on file formats 113 (Stata 8/9), 114
(Stata 10/11), and 115 (Stata 12). Needs to be tested on older versions.
Known not to work on format 104, 108. If you have the documentation for
older formats, please contact the developers.
For more information about the .dta format see
http://www.stata.com/help.cgi?dta
http://www.stata.com/help.cgi?dta_113
"""
_header = {}
_data_location = 0
_col_sizes = ()
_has_string_data = False
_missing_values = False
#type code
#--------------------
#str1 1 = 0x01
#str2 2 = 0x02
#...
#str244 244 = 0xf4
#byte 251 = 0xfb (sic)
#int 252 = 0xfc
#long 253 = 0xfd
#float 254 = 0xfe
#double 255 = 0xff
#--------------------
#NOTE: the byte type seems to be reserved for categorical variables
# with a label, but the underlying variable is -127 to 100
# we're going to drop the label and cast to int
DTYPE_MAP = dict(lzip(lrange(1,245), ['a' + str(i) for i in range(1,245)]) + \
[(251, np.int16),(252, np.int32),(253, int),
(254, np.float32), (255, np.float64)])
TYPE_MAP = lrange(251)+list('bhlfd')
#NOTE: technically, some of these are wrong. there are more numbers
# that can be represented. it's the 27 ABOVE and BELOW the max listed
# numeric data type in [U] 12.2.2 of the 11.2 manual
MISSING_VALUES = { 'b': (-127,100), 'h': (-32767, 32740), 'l':
(-2147483647, 2147483620), 'f': (-1.701e+38, +1.701e+38), 'd':
(-1.798e+308, +8.988e+307) }
def __init__(self, fname, missing_values=False, encoding=None):
warnings.warn(
"StataReader is deprecated as of 0.10.0 and will be removed after"
" the 0.12 release. Use pandas.read_stata or "
"pandas.io.stata.StataReader instead.",
FutureWarning)
if encoding is None:
import locale
self._encoding = locale.getpreferredencoding()
else:
self._encoding = encoding
self._missing_values = missing_values
self._parse_header(fname)
[docs] def file_label(self):
"""
Returns the dataset's label.
Returns
-------
out: str
"""
return self._header['data_label']
[docs] def file_timestamp(self):
"""
Returns the date and time Stata recorded on last file save.
Returns
-------
out : str
"""
return self._header['time_stamp']
[docs] def variables(self):
"""
Returns a list of the dataset's StataVariables objects.
"""
return lmap(_StataVariable, zip(lrange(self._header['nvar']),
self._header['typlist'], self._header['varlist'],
self._header['srtlist'],
self._header['fmtlist'], self._header['lbllist'],
self._header['vlblist']))
[docs] def dataset(self, as_dict=False):
"""
Returns a Python generator object for iterating over the dataset.
Parameters
----------
as_dict : bool, optional
If as_dict is True, yield each row of observations as a dict.
If False, yields each row of observations as a list.
Returns
-------
Generator object for iterating over the dataset. Yields each row of
observations as a list by default.
Notes
-----
If missing_values is True during instantiation of StataReader then
observations with StataMissingValue(s) are not filtered and should
be handled by your application.
"""
try:
self._file.seek(self._data_location)
except Exception:
pass
if as_dict:
vars = lmap(str, self.variables())
for i in range(len(self)):
yield dict(zip(vars, self._next()))
else:
for i in range(self._header['nobs']):
yield self._next()
### Python special methods
def __len__(self):
"""
Return the number of observations in the dataset.
This value is taken directly from the header and includes observations
with missing values.
"""
return self._header['nobs']
def __getitem__(self, k):
"""
Seek to an observation indexed k in the file and return it, ordered
by Stata's output to the .dta file.
k is zero-indexed. Prefer using R.data() for performance.
"""
if not (isinstance(k, int)) or k < 0 or k > len(self)-1:
raise IndexError(k)
loc = self._data_location + sum(self._col_size()) * k
if self._file.tell() != loc:
self._file.seek(loc)
return self._next()
# Private methods
def _null_terminate(self, s, encoding):
null_byte = asbytes('\x00')
try:
s = s.lstrip(null_byte)[:s.index(null_byte)]
except Exception:
pass
return s.decode(encoding)
def _parse_header(self, file_object):
self._file = file_object
encoding = self._encoding
# parse headers
self._header['ds_format'] = unpack('b', self._file.read(1))[0]
if self._header['ds_format'] not in [113, 114, 115]:
raise ValueError("Only file formats >= 113 (Stata >= 9)"
" are supported. Got format %s. Please report "
"if you think this error is incorrect." %
self._header['ds_format'])
byteorder = self._header['byteorder'] = unpack('b',
self._file.read(1))[0]==0x1 and '>' or '<'
self._header['filetype'] = unpack('b', self._file.read(1))[0]
self._file.read(1)
nvar = self._header['nvar'] = unpack(byteorder+'h',
self._file.read(2))[0]
self._header['nobs'] = unpack(byteorder+'i', self._file.read(4))[0]
self._header['data_label'] = self._null_terminate(self._file.read(81),
encoding)
self._header['time_stamp'] = self._null_terminate(self._file.read(18),
encoding)
# parse descriptors
typlist =[ord(self._file.read(1)) for i in range(nvar)]
self._header['typlist'] = [self.TYPE_MAP[typ] for typ in typlist]
self._header['dtyplist'] = [self.DTYPE_MAP[typ] for typ in typlist]
self._header['varlist'] = [self._null_terminate(self._file.read(33),
encoding) for i in range(nvar)]
self._header['srtlist'] = unpack(byteorder+('h'*(nvar+1)),
self._file.read(2*(nvar+1)))[:-1]
if self._header['ds_format'] <= 113:
self._header['fmtlist'] = \
[self._null_terminate(self._file.read(12), encoding) \
for i in range(nvar)]
else:
self._header['fmtlist'] = \
[self._null_terminate(self._file.read(49), encoding) \
for i in range(nvar)]
self._header['lbllist'] = [self._null_terminate(self._file.read(33),
encoding) for i in range(nvar)]
self._header['vlblist'] = [self._null_terminate(self._file.read(81),
encoding) for i in range(nvar)]
# ignore expansion fields
# When reading, read five bytes; the last four bytes now tell you the
# size of the next read, which you discard. You then continue like
# this until you read 5 bytes of zeros.
while True:
data_type = unpack(byteorder+'b', self._file.read(1))[0]
data_len = unpack(byteorder+'i', self._file.read(4))[0]
if data_type == 0:
break
self._file.read(data_len)
# other state vars
self._data_location = self._file.tell()
self._has_string_data = len(lfilter(lambda x: isinstance(x, int),
self._header['typlist'])) > 0
self._col_size()
def _calcsize(self, fmt):
return isinstance(fmt, int) and fmt or \
calcsize(self._header['byteorder']+fmt)
def _col_size(self, k = None):
"""Calculate size of a data record."""
if len(self._col_sizes) == 0:
self._col_sizes = lmap(lambda x: self._calcsize(x),
self._header['typlist'])
if k is None:
return self._col_sizes
else:
return self._col_sizes[k]
def _unpack(self, fmt, byt):
d = unpack(self._header['byteorder']+fmt, byt)[0]
if fmt[-1] in self.MISSING_VALUES:
nmin, nmax = self.MISSING_VALUES[fmt[-1]]
if d < nmin or d > nmax:
if self._missing_values:
return StataMissingValue(nmax, d)
else:
return None
return d
def _next(self):
typlist = self._header['typlist']
if self._has_string_data:
data = [None]*self._header['nvar']
for i in range(len(data)):
if isinstance(typlist[i], int):
data[i] = self._null_terminate(self._file.read(typlist[i]),
self._encoding)
else:
data[i] = self._unpack(typlist[i],
self._file.read(self._col_size(i)))
return data
else:
return lmap(lambda i: self._unpack(typlist[i],
self._file.read(self._col_size(i))),
lrange(self._header['nvar']))
def _set_endianness(endianness):
if endianness.lower() in ["<", "little"]:
return "<"
elif endianness.lower() in [">", "big"]:
return ">"
else: # pragma : no cover
raise ValueError("Endianness %s not understood" % endianness)
def _dtype_to_stata_type(dtype):
"""
Converts dtype types to stata types. Returns the byte of the given ordinal.
See TYPE_MAP and comments for an explanation. This is also explained in
the dta spec.
1 - 244 are strings of this length
251 - chr(251) - for int8 and int16, byte
252 - chr(252) - for int32, int
253 - chr(253) - for int64, long
254 - chr(254) - for float32, float
255 - chr(255) - double, double
If there are dates to convert, then dtype will already have the correct
type inserted.
"""
#TODO: expand to handle datetime to integer conversion
if dtype.type == np.string_:
return chr(dtype.itemsize)
elif dtype.type == np.object_:
# try to coerce it to the biggest string
# not memory efficient, what else could we do?
return chr(244)
elif dtype == np.float64:
return chr(255)
elif dtype == np.float32:
return chr(254)
elif dtype == np.int64:
return chr(253)
elif dtype == np.int32:
return chr(252)
elif dtype == np.int8 or dtype == np.int16: # ok to assume bytes?
return chr(251)
else: # pragma : no cover
raise ValueError("Data type %s not currently understood. "
"Please report an error to the developers." % dtype)
def _dtype_to_default_stata_fmt(dtype):
"""
Maps numpy dtype to stata's default format for this type. Not terribly
important since users can change this in Stata. Semantics are
string -> "%DDs" where DD is the length of the string
float64 -> "%10.0g"
float32 -> "%9.0g"
int64 -> "%9.0g"
int32 -> "%9.0g"
int16 -> "%9.0g"
int8 -> "%8.0g"
"""
#TODO: expand this to handle a default datetime format?
if dtype.type == np.string_:
return "%" + str(dtype.itemsize) + "s"
elif dtype.type == np.object_:
return "%244s"
elif dtype == np.float64:
return "%10.0g"
elif dtype == np.float32:
return "%9.0g"
elif dtype == np.int64:
return "%9.0g"
elif dtype == np.int32:
return "%8.0g"
elif dtype == np.int8 or dtype == np.int16: # ok to assume bytes?
return "%8.0g"
else: # pragma : no cover
raise ValueError("Data type %s not currently understood. "
"Please report an error to the developers." % dtype)
def _pad_bytes(name, length):
"""
Takes a char string and pads it wih null bytes until it's length chars
"""
return name + "\x00" * (length - len(name))
def _default_names(nvar):
"""
Returns default Stata names v1, v2, ... vnvar
"""
return ["v%d" % i for i in range(1,nvar+1)]
def _convert_datetime_to_stata_type(fmt):
"""
Converts from one of the stata date formats to a type in TYPE_MAP
"""
if fmt in ["tc", "%tc", "td", "%td", "tw", "%tw", "tm", "%tm", "tq",
"%tq", "th", "%th", "ty", "%ty"]:
return np.float64 # Stata expects doubles for SIFs
else:
raise ValueError("fmt %s not understood" % fmt)
def _maybe_convert_to_int_keys(convert_dates, varlist):
new_dict = {}
for key in convert_dates:
if not convert_dates[key].startswith("%"): # make sure proper fmts
convert_dates[key] = "%" + convert_dates[key]
if key in varlist:
new_dict.update({varlist.index(key) : convert_dates[key]})
else:
if not isinstance(key, int):
raise ValueError("convery_dates key is not in varlist "
"and is not an int")
new_dict.update({key : convert_dates[key]})
return new_dict
_type_converters = {253 : int, 252 : int}
[docs]class StataWriter(object):
"""
A class for writing Stata binary dta files from array-like objects
.. deprecated:: 0.11
Use pandas.read_stata or pandas.io.stata.StataReader
Parameters
----------
fname : file path or buffer
Where to save the dta file.
data : array_like
Array-like input to save. Pandas objects are also accepted.
convert_dates : dict
Dictionary mapping column of datetime types to the stata internal
format that you want to use for the dates. Options are
'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a
number or a name.
encoding : str
Default is latin-1. Note that Stata does not support unicode.
byteorder : str
Can be ">", "<", "little", or "big". The default is None which uses
`sys.byteorder`
Returns
-------
writer : StataWriter instance
The StataWriter instance has a write_file method, which will
write the file to the given `fname`.
Examples
--------
>>> writer = StataWriter('./data_file.dta', data)
>>> writer.write_file()
Or with dates
>>> writer = StataWriter('./date_data_file.dta', date, {2 : 'tw'})
>>> writer.write_file()
"""
#type code
#--------------------
#str1 1 = 0x01
#str2 2 = 0x02
#...
#str244 244 = 0xf4
#byte 251 = 0xfb (sic)
#int 252 = 0xfc
#long 253 = 0xfd
#float 254 = 0xfe
#double 255 = 0xff
#--------------------
#NOTE: the byte type seems to be reserved for categorical variables
# with a label, but the underlying variable is -127 to 100
# we're going to drop the label and cast to int
DTYPE_MAP = dict(lzip(lrange(1,245), ['a' + str(i) for i in range(1,245)]) + \
[(251, np.int16),(252, np.int32),(253, int),
(254, np.float32), (255, np.float64)])
TYPE_MAP = lrange(251)+list('bhlfd')
MISSING_VALUES = { 'b': 101,
'h': 32741,
'l' : 2147483621,
'f': 1.7014118346046923e+38,
'd': 8.98846567431158e+307}
def __init__(self, fname, data, convert_dates=None, encoding="latin-1",
byteorder=None):
warnings.warn(
"StataWriter is deprecated as of 0.10.0 and will be removed after"
" the 0.12 release. Use pandas.DataFrame.to_stata or "
"pandas.io.stata.StatWriter instead.",
FutureWarning)
self._convert_dates = convert_dates
# attach nobs, nvars, data, varlist, typlist
if data_util._is_using_pandas(data, None):
self._prepare_pandas(data)
elif data_util._is_array_like(data, None):
data = np.asarray(data)
if data_util._is_structured_ndarray(data):
self._prepare_structured_array(data)
else:
if convert_dates is not None:
raise ValueError("Not able to convert dates in a plain"
" ndarray.")
self._prepare_ndarray(data)
else: # pragma : no cover
raise ValueError("Type %s for data not understood" % type(data))
if byteorder is None:
byteorder = sys.byteorder
self._byteorder = _set_endianness(byteorder)
self._encoding = encoding
self._file = get_file_obj(fname, 'wb', encoding)
def _write(self, to_write):
"""
Helper to call asbytes before writing to file for Python 3 compat.
"""
self._file.write(asbytes(to_write))
def _prepare_structured_array(self, data):
self.nobs = len(data)
self.nvar = len(data.dtype)
self.data = data
self.datarows = iter(data)
dtype = data.dtype
descr = dtype.descr
if dtype.names is None:
varlist = _default_names(self.nvar)
else:
varlist = dtype.names
# check for datetime and change the type
convert_dates = self._convert_dates
if convert_dates is not None:
convert_dates = _maybe_convert_to_int_keys(convert_dates,
varlist)
self._convert_dates = convert_dates
for key in convert_dates:
descr[key] = (
descr[key][0],
_convert_datetime_to_stata_type(convert_dates[key])
)
dtype = np.dtype(descr)
self.varlist = varlist
self.typlist = [_dtype_to_stata_type(dtype[i])
for i in range(self.nvar)]
self.fmtlist = [_dtype_to_default_stata_fmt(dtype[i])
for i in range(self.nvar)]
# set the given format for the datetime cols
if convert_dates is not None:
for key in convert_dates:
self.fmtlist[key] = convert_dates[key]
def _prepare_ndarray(self, data):
if data.ndim == 1:
data = data[:,None]
self.nobs, self.nvar = data.shape
self.data = data
self.datarows = iter(data)
#TODO: this should be user settable
dtype = data.dtype
self.varlist = _default_names(self.nvar)
self.typlist = [_dtype_to_stata_type(dtype) for i in range(self.nvar)]
self.fmtlist = [_dtype_to_default_stata_fmt(dtype)
for i in range(self.nvar)]
def _prepare_pandas(self, data):
#NOTE: we might need a different API / class for pandas objects so
# we can set different semantics - handle this with a PR to pandas.io
class DataFrameRowIter(object):
def __init__(self, data):
self.data = data
def __iter__(self):
for i, row in data.iterrows():
yield row
data = data.reset_index()
self.datarows = DataFrameRowIter(data)
self.nobs, self.nvar = data.shape
self.data = data
self.varlist = data.columns.tolist()
dtypes = data.dtypes
convert_dates = self._convert_dates
if convert_dates is not None:
convert_dates = _maybe_convert_to_int_keys(convert_dates,
self.varlist)
self._convert_dates = convert_dates
for key in convert_dates:
new_type = _convert_datetime_to_stata_type(convert_dates[key])
dtypes[key] = np.dtype(new_type)
self.typlist = [_dtype_to_stata_type(dt) for dt in dtypes]
self.fmtlist = [_dtype_to_default_stata_fmt(dt) for dt in dtypes]
# set the given format for the datetime cols
if convert_dates is not None:
for key in convert_dates:
self.fmtlist[key] = convert_dates[key]
[docs] def write_file(self):
self._write_header()
self._write_descriptors()
self._write_variable_labels()
# write 5 zeros for expansion fields
self._write(_pad_bytes("", 5))
if self._convert_dates is None:
self._write_data_nodates()
else:
self._write_data_dates()
#self._write_value_labels()
def _write_header(self, data_label=None, time_stamp=None):
byteorder = self._byteorder
# ds_format - just use 114
self._write(pack("b", 114))
# byteorder
self._write(byteorder == ">" and "\x01" or "\x02")
# filetype
self._write("\x01")
# unused
self._write("\x00")
# number of vars, 2 bytes
self._write(pack(byteorder+"h", self.nvar)[:2])
# number of obs, 4 bytes
self._write(pack(byteorder+"i", self.nobs)[:4])
# data label 81 bytes, char, null terminated
if data_label is None:
self._write(self._null_terminate(_pad_bytes("", 80),
self._encoding))
else:
self._write(self._null_terminate(_pad_bytes(data_label[:80],
80), self._encoding))
# time stamp, 18 bytes, char, null terminated
# format dd Mon yyyy hh:mm
if time_stamp is None:
time_stamp = datetime.datetime.now()
elif not isinstance(time_stamp, datetime):
raise ValueError("time_stamp should be datetime type")
self._write(self._null_terminate(
time_stamp.strftime("%d %b %Y %H:%M"),
self._encoding))
def _write_descriptors(self, typlist=None, varlist=None, srtlist=None,
fmtlist=None, lbllist=None):
nvar = self.nvar
# typlist, length nvar, format byte array
for typ in self.typlist:
self._write(typ)
# varlist, length 33*nvar, char array, null terminated
for name in self.varlist:
name = self._null_terminate(name, self._encoding)
name = _pad_bytes(asstr(name[:32]), 33)
self._write(name)
# srtlist, 2*(nvar+1), int array, encoded by byteorder
srtlist = _pad_bytes("", (2*(nvar+1)))
self._write(srtlist)
# fmtlist, 49*nvar, char array
for fmt in self.fmtlist:
self._write(_pad_bytes(fmt, 49))
# lbllist, 33*nvar, char array
#NOTE: this is where you could get fancy with pandas categorical type
for i in range(nvar):
self._write(_pad_bytes("", 33))
def _write_variable_labels(self, labels=None):
nvar = self.nvar
if labels is None:
for i in range(nvar):
self._write(_pad_bytes("", 81))
def _write_data_nodates(self):
data = self.datarows
byteorder = self._byteorder
TYPE_MAP = self.TYPE_MAP
typlist = self.typlist
for row in data:
#row = row.squeeze().tolist() # needed for structured arrays
for i,var in enumerate(row):
typ = ord(typlist[i])
if typ <= 244: # we've got a string
if len(var) < typ:
var = _pad_bytes(asstr(var), len(var) + 1)
self._write(var)
else:
try:
if typ in _type_converters:
var = _type_converters[typ](var)
self._write(pack(byteorder+TYPE_MAP[typ], var))
except struct_error:
# have to be strict about type pack will not do any
# kind of casting
self._write(pack(byteorder+TYPE_MAP[typ],
_type_converters[typ](var)))
def _write_data_dates(self):
convert_dates = self._convert_dates
data = self.datarows
byteorder = self._byteorder
TYPE_MAP = self.TYPE_MAP
MISSING_VALUES = self.MISSING_VALUES
typlist = self.typlist
for row in data:
#row = row.squeeze().tolist() # needed for structured arrays
for i,var in enumerate(row):
typ = ord(typlist[i])
#NOTE: If anyone finds this terribly slow, there is
# a vectorized way to convert dates, see genfromdta for going
# from int to datetime and reverse it. will copy data though
if i in convert_dates:
var = _datetime_to_stata_elapsed(var, self.fmtlist[i])
if typ <= 244: # we've got a string
if isnull(var):
var = "" # missing string
if len(var) < typ:
var = _pad_bytes(var, len(var) + 1)
self._write(var)
else:
if isnull(var): # this only matters for floats
var = MISSING_VALUES[typ]
self._write(pack(byteorder+TYPE_MAP[typ], var))
def _null_terminate(self, s, encoding):
null_byte = '\x00'
s += null_byte
return s.encode(encoding)
[docs]def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
convert_dates=True):
"""
Returns an ndarray or DataFrame from a Stata .dta file.
.. deprecated:: 0.11
Use pandas.read_stata or pandas.io.stata.StataReader
Parameters
----------
fname : str or filehandle
Stata .dta file.
missing_flt : numeric
The numeric value to replace missing values with. Will be used for
any numeric value.
encoding : str, optional
Used for Python 3 only. Encoding to use when reading the .dta file.
Defaults to `locale.getpreferredencoding`
pandas : bool
Optionally return a DataFrame instead of an ndarray
convert_dates : bool
If convert_dates is True, then Stata formatted dates will be converted
to datetime types according to the variable's format.
"""
warnings.warn(
"genfromdta is deprecated as of 0.10.0 and will be removed after the "
"0.12 release future version. Use pandas.read_stata instead.",
FutureWarning)
if isinstance(fname, str):
fhd = StataReader(open(fname, 'rb'), missing_values=False,
encoding=encoding)
elif not hasattr(fname, 'read'):
raise TypeError("The input should be a string or a filehandle. "\
"(got %s instead)" % type(fname))
else:
fhd = StataReader(fname, missing_values=False, encoding=encoding)
# validate_names = np.lib._iotools.NameValidator(excludelist=excludelist,
# deletechars=deletechars,
# case_sensitive=case_sensitive)
#TODO: This needs to handle the byteorder?
header = fhd.file_headers()
types = header['dtyplist']
nobs = header['nobs']
numvars = header['nvar']
varnames = header['varlist']
fmtlist = header['fmtlist']
dataname = header['data_label']
labels = header['vlblist'] # labels are thrown away unless DataArray
# type is used
data = np.zeros((nobs,numvars))
stata_dta = fhd.dataset()
dt = np.dtype(lzip(varnames, types))
data = np.zeros((nobs), dtype=dt) # init final array
for rownum,line in enumerate(stata_dta):
# does not handle missing value objects, just casts
# None will only work without missing value object.
if None in line:
for i,val in enumerate(line):
#NOTE: This will only be scalar types because missing strings
# are empty not None in Stata
if val is None:
line[i] = missing_flt
data[rownum] = tuple(line)
if pandas:
from pandas import DataFrame
data = DataFrame.from_records(data)
if convert_dates:
cols = np.where(lmap(lambda x : x in _date_formats, fmtlist))[0]
for col in cols:
i = col
col = data.columns[col]
data[col] = data[col].apply(_stata_elapsed_date_to_datetime,
args=(fmtlist[i],))
elif convert_dates:
# date_cols = np.where(map(lambda x : x in _date_formats,
# fmtlist))[0]
# make the dtype for the datetime types
cols = np.where(lmap(lambda x: x in _date_formats, fmtlist))[0]
dtype = data.dtype.descr
dtype = [(sub_dtype[0], object) if i in cols else sub_dtype
for i, sub_dtype in enumerate(dtype)]
data = data.astype(dtype) # have to copy
for col in cols:
def convert(x):
return _stata_elapsed_date_to_datetime(x, fmtlist[col])
data[data.dtype.names[col]] = lmap(convert,
data[data.dtype.names[col]])
return data
[docs]def savetxt(fname, X, names=None, fmt='%.18e', delimiter=' '):
"""
Save an array to a text file.
This is just a copy of numpy.savetxt patched to support structured arrays
or a header of names. Does not include py3 support now in savetxt.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : array_like
Data to be saved to a text file.
names : list, optional
If given names will be the column header in the text file. If None and
X is a structured or recarray then the names are taken from
X.dtype.names.
fmt : str or sequence of strs
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored.
delimiter : str
Character separating columns.
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into a ``.npz`` compressed archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to preceed result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : str of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<http://docs.python.org/library/string.html#
format-specification-mini-language>`_, Python Documentation.
Examples
--------
>>> savetxt('test.out', x, delimiter=',') # x is an array
>>> savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
with get_file_obj(fname, 'w') as fh:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.descr)
else:
ncol = X.shape[1]
# `fmt` can be a string with multiple insertion points or a list of formats.
# E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if isinstance(fmt, (list, tuple)):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
format = delimiter.join(fmt)
elif isinstance(fmt, str):
if fmt.count('%') == 1:
fmt = [fmt, ]*ncol
format = delimiter.join(fmt)
elif fmt.count('%') != ncol:
raise AttributeError('fmt has wrong number of %% formats. %s'
% fmt)
else:
format = fmt
# handle names
if names is None and X.dtype.names:
names = X.dtype.names
if names is not None:
fh.write(delimiter.join(names) + '\n')
for row in X:
fh.write(format % tuple(row) + '\n')