def test_ONI_standard(self):
"Test the 'standard' option of load_oni"
if self.indices is None:
return
ONI = load_oni('standard')
ONI.set_indices(full_year=False, minimum_size=5, reference_season=None)
assert_equal(*ts.align_series(ONI.indices, self.indices))
def test_ONI_standard(self):
"Test the 'standard' option of load_oni"
if self.indices is None:
return
ONI = load_oni('standard')
ONI.set_indices(full_year=False, minimum_size=5, reference_season=None)
assert_equal(*ts.align_series(ONI.indices, self.indices))
def cvf(x,y,periodogram=True):
"""Computes the cross-covariance function of two series x and y.
The computations are performed on anomalies (deviations from average).
Gaps in the series are filled first, anomalies are then computed and missing
values filled with 0.
If x and y are valid TimeSeries object, they are aligned so that their starting
and ending point match.
The crosscovariance at lag k, $\hat{R_{x,y}}(k)$, of 2 series {x_1,...,x_n}
and {y_1,...,y_n} with mean 0 is defined as:
\hat{R_{x,y}(k) = \sum_{t=1}^{n-k}{x_t y_{t+k}} / \sum_{t=1}^{n-k}{a_t b_{t+k}}
where x_k (y_k) is set to 0 if x_k (y_k) is initially masked, where a_k = 1 if
x_k is not masked, a_k = 0 if x_k is masked, b_k = 1 if y_k is not masked and
b_k = 0 if y_k is masked.
If the optional parameter `periodogram` is True, the denominator of the previous
expression is $\sum_{t=1}^{n-k}{a_t a_{t+k}} + k$.
Parameters
----------
x : sequence
Input data.
y : sequence
Input data.
If y is longer than x, it is truncated to match the length of x.
If y is shorter than x, x is truncated.
periodogram : {True, False} optional
Whether to return a periodogram or a standard estimate of the autocovariance.
Returns
-------
cvf : ma.array
Cross-covariance at lags [0,1,...,n,n-1,...,-1]
"""
#
x = ma.array(x, copy=False, subok=True, dtype=float)
y = ma.array(y, copy=False, subok=True, dtype=float)
if (x.ndim > 1) or (y.ndim > 1):
raise ValueError("Input arrays should be 1D! (got %iD-%iD)" % \
(x.ndim, y.ndim))
# Make sure the series have the same size .............
if isinstance(x, TimeSeries):
if not isinstance(y, TimeSeries):
raise TypeError("The second input is NOT a valid TimeSeries")
(x,y) = ts.align_series(x,y)
elif isinstance(y, TimeSeries) and not isinstance(x, TimeSeries):
raise TypeError("The first input is NOT a valid TimeSeries")
else:
if len(y) > len(x):
y = y[:len(x)]
else:
x = x[:len(y)]
# Get the masks .......................................
mx = np.logical_not(ma.getmaskarray(x)).astype(int)
my = np.logical_not(ma.getmaskarray(y)).astype(int)
# Get the anomalies ...................................
x = x.anom().filled(0).view(ndarray)
y = y.anom().filled(0).view(ndarray)
n = len(x)
cvf_ = np.correlate(x, y, 'full')
dnm_ = np.correlate(mx, my, 'full')
if periodogram:
dnm_ += np.concatenate([np.arange(n-1,0,-1), np.arange(n)])
cvf_ /= dnm_
return ma.fix_invalid(np.concatenate([cvf_[n-1:],cvf_[:n-1]]))
def cvf(x, y, periodogram=True):
"""Computes the cross-covariance function of two series x and y.
The computations are performed on anomalies (deviations from average).
Gaps in the series are filled first, anomalies are then computed and missing
values filled with 0.
If x and y are valid TimeSeries object, they are aligned so that their starting
and ending point match.
The crosscovariance at lag k, $\hat{R_{x,y}}(k)$, of 2 series {x_1,...,x_n}
and {y_1,...,y_n} with mean 0 is defined as:
\hat{R_{x,y}(k) = \sum_{t=1}^{n-k}{x_t y_{t+k}} / \sum_{t=1}^{n-k}{a_t b_{t+k}}
where x_k (y_k) is set to 0 if x_k (y_k) is initially masked, where a_k = 1 if
x_k is not masked, a_k = 0 if x_k is masked, b_k = 1 if y_k is not masked and
b_k = 0 if y_k is masked.
If the optional parameter `periodogram` is True, the denominator of the previous
expression is $\sum_{t=1}^{n-k}{a_t a_{t+k}} + k$.
Parameters
----------
x : sequence
Input data.
y : sequence
Input data.
If y is longer than x, it is truncated to match the length of x.
If y is shorter than x, x is truncated.
periodogram : {True, False} optional
Whether to return a periodogram or a standard estimate of the autocovariance.
Returns
-------
cvf : ma.array
Cross-covariance at lags [0,1,...,n,n-1,...,-1]
"""
#
x = ma.array(x, copy=False, subok=True, dtype=float)
y = ma.array(y, copy=False, subok=True, dtype=float)
if (x.ndim > 1) or (y.ndim > 1):
raise ValueError("Input arrays should be 1D! (got %iD-%iD)" % \
(x.ndim, y.ndim))
# Make sure the series have the same size .............
if isinstance(x, TimeSeries):
if not isinstance(y, TimeSeries):
raise TypeError("The second input is NOT a valid TimeSeries")
(x, y) = ts.align_series(x, y)
elif isinstance(y, TimeSeries) and not isinstance(x, TimeSeries):
raise TypeError("The first input is NOT a valid TimeSeries")
else:
if len(y) > len(x):
y = y[:len(x)]
else:
x = x[:len(y)]
# Get the masks .......................................
mx = np.logical_not(ma.getmaskarray(x)).astype(int)
my = np.logical_not(ma.getmaskarray(y)).astype(int)
# Get the anomalies ...................................
x = x.anom().filled(0).view(ndarray)
y = y.anom().filled(0).view(ndarray)
n = len(x)
cvf_ = np.correlate(x, y, 'full')
dnm_ = np.correlate(mx, my, 'full')
if periodogram:
dnm_ += np.concatenate([np.arange(n - 1, 0, -1), np.arange(n)])
cvf_ /= dnm_
return ma.fix_invalid(np.concatenate([cvf_[n - 1:], cvf_[:n - 1]]))
def __call__(self, *tseries, **kwargs):
"""
generate a report. Parameter values are not saved to the Report instance.
Parameters
----------
Accepts same parameters as __init__ method of Report class
"""
option_dict = copy.copy(self.options)
option_dict.update(self.__make_dict(**kwargs))
if len(tseries) == 0:
tseries = self.tseries
def option(kw):
return option_dict.get(kw, _default_options[kw])
dates = option('dates')
header_row = option('header_row')
header_char = option('header_char')
header_justify = option('header_justify')
row_char = option('row_char')
footer_label = option('footer_label')
footer_char = option('footer_char')
footer_func = option('footer_func')
delim = option('delim')
justify = option('justify')
prefix = option('prefix')
postfix = option('postfix')
mask_rep = option('mask_rep')
datefmt = option('datefmt')
fmt_func = option('fmt_func')
wrap_func = option('wrap_func')
col_width = option('col_width')
nls=option('nls')
output=option('output')
fixed_width=option('fixed_width')
if header_row is not None:
has_header=True
if len(header_row) == len(tseries)+1:
# label for date column included
rows = [header_row]
elif len(header_row) == len(tseries):
# label for date column not included
rows = [['']+header_row]
else:
raise ValueError("mismatch with number of headers and series")
else:
has_header=False
rows=[]
if fixed_width:
def _standardize_justify(userspec):
if isinstance(userspec, str):
# justify all columns the the same way
return [userspec for x in range(len(tseries)+1)]
elif isinstance(userspec, list):
if len(userspec) == len(tseries):
# justification for date column not included, so set that
# to left by default
return ['left'] + userspec
else:
raise ValueError("invalid `justify` specification")
if justify is not None:
justify = _standardize_justify(justify)
else:
# default column justification
justify = ['left']
for ser in tseries:
if ser.dtype.char in 'SUO': justify.append('left')
else: justify.append('right')
if header_justify is not None:
header_justify = _standardize_justify(header_justify)
else:
# default column justification
header_justify = ['left' for x in range(len(tseries)+1)]
else:
justify = ['none' for x in range(len(tseries)+1)]
header_justify = justify
if datefmt is None:
def datefmt_func(date): return str(date)
else:
def datefmt_func(date): return date.strftime(datefmt)
if dates is None:
tseries = ts.align_series(*tseries)
dates = ts.date_array(start_date=tseries[0].start_date,
end_date=tseries[0].end_date)
else:
tseries = ts.align_series(start_date=dates[0], end_date=dates[-1], *tseries)
if isinstance(fmt_func, list):
fmt_func = [fmt_func_wrapper(f, mask_rep) for f in fmt_func]
else:
fmt_func = [fmt_func_wrapper(fmt_func, mask_rep)]*len(tseries)
def wrap_func_default(func):
if func is None: return lambda x:x
else: return func
if isinstance(wrap_func, list):
if len(wrap_func) == len(tseries):
wrap_func = [lambda x: x] + wrap_func
wrap_func = [wrap_func_default(func) for func in wrap_func]
else:
wrap_func = [wrap_func_default(wrap_func) for x in range(len(tseries)+1)]
if isinstance(col_width, list):
if len(col_width) == len(tseries):
col_width = [None] + col_width
else:
col_width = [col_width for x in range(len(tseries)+1)]
_sd = dates[0]
for d in dates:
rows.append(
[datefmt_func(d)] + \
[fmt_func[i](ser.series[d - _sd]) \
for i, ser in enumerate(tseries)]
)
if footer_func is not None:
has_footer=True
if not isinstance(footer_func, list):
footer_func = [footer_func]*len(tseries)
if footer_label is None: footer_label = ['']
else: footer_label = [footer_label]
footer_data = []
has_missing = dates.has_missing_dates()
for i, ser in enumerate(tseries):
if footer_func[i] is None:
footer_data.append('')
else:
if has_missing: _input = ser[dates]
else: _input = ser.series
footer_data.append(fmt_func[i](footer_func[i](_input)))
rows.append(footer_label + footer_data)
else:
has_footer=False
def rowWrapper(row):
newRows = [wrap_func[i](item).split('\n') for i, item in enumerate(row)]
return [[(substr or '') for substr in item] for item in map(None, *newRows)]
# break each logical row into one or more physical ones
logicalRows = [rowWrapper(row) for row in rows]
numLogicalRows = len(logicalRows)
# columns of physical rows
columns = map(None,*reduce(operator.add,logicalRows))
numCols = len(columns)
colNums = list(range(numCols))
# get the maximum of each column by the string length of its items
maxWidths = [max(col_width[i], *[len(str(item)) for item in column])
for i, column in enumerate(columns)]
def getSeparator(char, separate):
if char is not None and separate:
return char * (len(prefix) + len(postfix) + sum(maxWidths) + \
len(delim)*(len(maxWidths)-1))
else:
return None
header_separator = getSeparator(header_char, has_header)
footer_separator = getSeparator(footer_char, has_footer)
row_separator = getSeparator(row_char, True)
# select the appropriate justify method
justify_funcs = {'center':str.center, 'right':str.rjust, 'left':str.ljust,
'none':(lambda text, width: text)}
if has_header and has_footer:
data_start = 1
data_end = numLogicalRows-3
elif has_header:
data_start = 1
data_end = numLogicalRows-2
elif has_footer:
data_start = 0
data_end = numLogicalRows-3
else:
data_start = 0
data_end = numLogicalRows-2
for rowNum, physicalRows in enumerate(logicalRows):
if rowNum == 0 and header_separator:
_justify = header_justify
else:
_justify = justify
def apply_justify(colNum, item, width):
jfunc_key = str(_justify[colNum]).lower()
jfunc = justify_funcs[jfunc_key]
return jfunc(str(item), width)
for row in physicalRows:
output.write(
prefix + \
delim.join([
apply_justify(cn, item, width) \
for (cn, item, width) in zip(colNums, row, maxWidths)
]) + \
postfix + nls)
if row_separator and (data_start <= rowNum <= data_end):
output.write(row_separator + nls)
elif header_separator and rowNum < data_start:
output.write(header_separator + nls)
elif footer_separator and rowNum == data_end + 1:
output.write(footer_separator + nls)
for k,v in t.items():
write_to_xls_book(book,k,*v,names=n[k],metadata=m[k],cfg=cfg)
return
logger.debug('MAKE SHEET %s',sheetname)
sheet1 = book.add_sheet(sheetname)
datefmt=None
if kwargs.has_key('datefmt'): datefmt = kwargs['datefmt']
if datefmt is None:
def datefmt_func(date): return str(date)
else:
def datefmt_func(date): return date.strftime(datefmt)
try:
tseries = ts.align_series(*tseries)
except:
print tseries
raise ts.TimeSeriesError, "Cannot align time series!"
dates = ts.date_array(start_date=tseries[0].start_date,
end_date=tseries[0].end_date)
N = 1
panels,titles = [],[]
if _metadata:
_exists_one_panel_name = False
_exists_one_title = False
for i,n in enumerate(_metadata):
panel = n['panel']
_exists_one_panel_name = False if panel is None else True