def fromtextfile(
fname, delimitor=None, commentchar="#", missingchar="", dates_column=None, varnames=None, vartypes=None, dates=None
):
"""Creates a multitimeseries from data stored in the file `filename`.
:Parameters:
- `filename` : file name/handle
Handle of an opened file.
- `delimitor` : Character *None*
Alphanumeric character used to separate columns in the file.
If None, any (group of) white spacestring(s) will be used.
- `commentchar` : String *['#']*
Alphanumeric character used to mark the start of a comment.
- `missingchar` : String *['']*
String indicating missing data, and used to create the masks.
- `datescol` : Integer *[None]*
Position of the columns storing dates. If None, a position will be
estimated from the variable names.
- `varnames` : Sequence *[None]*
Sequence of the variable names. If None, a list will be created from
the first non empty line of the file.
- `vartypes` : Sequence *[None]*
Sequence of the variables dtypes. If None, the sequence will be estimated
from the first non-commented line.
Ultra simple: the varnames are in the header, one line"""
# Try to open the file ......................
f = openfile(fname)
# Get the first non-empty line as the varnames
while True:
line = f.readline()
firstline = line[: line.find(commentchar)].strip()
_varnames = firstline.split(delimitor)
if len(_varnames) > 1:
break
if varnames is None:
varnames = _varnames
# Get the data ..............................
_variables = MA.asarray([line.strip().split(delimitor) for line in f if line[0] != commentchar and len(line) > 1])
(nvars, nfields) = _variables.shape
# Check if we need to get the dates..........
if dates_column is None:
dates_column = [i for (i, n) in enumerate(list(varnames)) if n.lower() in ["_dates", "dates"]]
elif isinstance(dates_column, (int, float)):
if dates_column > nfields:
raise ValueError, "Invalid column number: %i > %i" % (dates_column, nfields)
dates_column = [dates_column]
if len(dates_column) > 0:
cols = range(nfields)
[cols.remove(i) for i in dates_column]
newdates = date_array(_variables[:, dates_column[-1]])
_variables = _variables[:, cols]
varnames = [varnames[i] for i in cols]
if vartypes is not None:
vartypes = [vartypes[i] for i in cols]
nfields -= len(dates_column)
else:
newdates = None
# Try to guess the dtype ....................
if vartypes is None:
vartypes = _guessvartypes(_variables[0])
else:
vartypes = [numeric.dtype(v) for v in vartypes]
if len(vartypes) != nfields:
msg = "Attempting to %i dtypes for %i fields!"
msg += " Reverting to default."
warnings.warn(msg % (len(vartypes), nfields))
vartypes = _guessvartypes(_variables[0])
# Construct the descriptor ..................
mdescr = [(n, f) for (n, f) in zip(varnames, vartypes)]
# Get the data and the mask .................
# We just need a list of masked_arrays. It's easier to create it like that:
_mask = _variables.T == missingchar
_datalist = [masked_array(a, mask=m, dtype=t) for (a, m, t) in zip(_variables.T, _mask, vartypes)]
#
newdates = __getdates(dates=dates, newdates=newdates, length=nvars, freq=None, start_date=None)
return MultiTimeSeries(_datalist, dates=newdates, dtype=mdescr)
def fromarrays(
arraylist, dates=None, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None
):
"""Creates a mrecarray from a (flat) list of masked arrays.
:Parameters:
- `arraylist` : Sequence
A list of (masked) arrays. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
- `dtype` : numeric.dtype
Data type descriptor.
- `shape` : Integer *[None]*
Number of records. If None, `shape` is defined from the shape of the first
array in the list.
- `formats` :
(Description to write)
- `names` :
(description to write)
- `titles`:
(Description to write)
- `aligned`: Boolen *[False]*
(Description to write, not used anyway)
- `byteorder`: Boolen *[None]*
(Description to write, not used anyway)
"""
arraylist = [MA.asarray(x) for x in arraylist]
# Define/check the shape.....................
if shape is None or shape == 0:
shape = arraylist[0].shape
if isinstance(shape, int):
shape = (shape,)
# Define formats from scratch ...............
if formats is None and dtype is None:
formats = _getformats(arraylist)
# Define the dtype ..........................
if dtype is not None:
descr = numeric.dtype(dtype)
_names = descr.names
else:
parsed = format_parser(formats, names, titles, aligned, byteorder)
_names = parsed._names
descr = parsed._descr
# Determine shape from data-type.............
if len(descr) != len(arraylist):
msg = "Mismatch between the number of fields (%i) and the number of " "arrays (%i)"
raise ValueError, msg % (len(descr), len(arraylist))
d0 = descr[0].shape
nn = len(d0)
if nn > 0:
shape = shape[:-nn]
# Make sure the shape is the correct one ....
for k, obj in enumerate(arraylist):
nn = len(descr[k].shape)
testshape = obj.shape[: len(obj.shape) - nn]
if testshape != shape:
raise ValueError, "Array-shape mismatch in array %d" % k
# Reconstruct the descriptor, by creating a _data and _mask version
return MultiTimeSeries(arraylist, dtype=descr)
