def write_var(ncfile, dataset, unlimited=None, compress=False):
# {{{
from pygeode.view import View
from pygeode.axis import Axis
import numpy as np
from pygeode.progress import PBar, FakePBar
from pygeode.tools import combine_axes
vars = list(dataset.vars)
axes = combine_axes(v.axes for v in vars)
# Define the dimensions
for a in axes:
ncfile.createDimension(a.name,
size=(None if a.name == unlimited else len(a)))
# Define the variables (including axes)
for var in vars:
dimensions = [a.name for a in var.axes]
v = ncfile.createVariable(var.name,
datatype=var.dtype,
dimensions=dimensions,
zlib=compress,
fill_value=var.atts.get('_FillValue', None))
v.setncatts(var.atts)
# global attributes
ncfile.setncatts(dataset.atts)
# Relative progress of each variable
sizes = [v.size for v in vars]
prog = np.cumsum([0.] + sizes) / np.sum(sizes) * 100
pbar = PBar(message="Saving '%s':" % ncfile.filepath())
# number of actual variables (non-axes) for determining our progress
N = len([v for v in vars if not isinstance(v, Axis)])
# Write the data
for i, var in enumerate(vars):
ncvar = ncfile.variables[var.name]
varpbar = pbar.subset(prog[i], prog[i + 1])
views = list(View(var.axes).loop_mem())
for j, v in enumerate(views):
vpbar = varpbar.part(j, len(views))
ncvar[v.slices] = v.get(var, pbar=vpbar)
def clim_detrend(var, yrlen, itime=-1, sig=False):
# {{{
''' clim_detrend() - returns detrended time series with a daily trend.'''
from pygeode.timeaxis import Time
from . import stats
from numpy import arange
if itime == -1: itime = var.whichaxis(Time)
tlen = var.shape[itime]
vary = composite(var, itime, list(range(0, tlen, yrlen)), yrlen)
yrs = vary.axes[itime]
yrs.values = arange(len(yrs)).astype(yrs.dtype)
print('Computing regression')
from pygeode.progress import PBar
m, b, p = stats.regress(yrs, vary, pbar=PBar())
varz = flatten(vary - (m * yrs + b), itime + 1)
varz.axes = var.axes
# Since the axes have been modified after initialization, redo the init to get
# shortcuts to the axes names
Var.__init__(varz, varz.axes, varz.dtype)
if var.name != '':
varz.name = var.name + "'"
if sig:
return m, b, varz, p
else:
return m, b, varz
def write_var (ncfile, dataset, unlimited=None, compress=False):
# {{{
from pygeode.view import View
from pygeode.axis import Axis
import numpy as np
from pygeode.progress import PBar, FakePBar
from pygeode.tools import combine_axes
vars = list(dataset.vars)
axes = combine_axes(v.axes for v in vars)
# Define the dimensions
for a in axes:
ncfile.createDimension(a.name, size=(None if a.name == unlimited else len(a)))
# Define the variables (including axes)
for var in vars:
dimensions = [a.name for a in var.axes]
v = ncfile.createVariable(var.name, datatype=var.dtype, dimensions=dimensions, zlib=compress, fill_value=var.atts.get('_FillValue',None))
v.setncatts(var.atts)
# global attributes
ncfile.setncatts(dataset.atts)
# Relative progress of each variable
sizes = [v.size for v in vars]
prog = np.cumsum([0.]+sizes) / np.sum(sizes) * 100
pbar = PBar(message="Saving '%s':"%ncfile.filepath())
# number of actual variables (non-axes) for determining our progress
N = len([v for v in vars if not isinstance(v,Axis)])
# Write the data
for i,var in enumerate(vars):
ncvar = ncfile.variables[var.name]
varpbar = pbar.subset(prog[i], prog[i+1])
views = list(View(var.axes).loop_mem())
for j,v in enumerate(views):
vpbar = varpbar.part(j, len(views))
ncvar[v.slices] = v.get(var, pbar=vpbar)
def scan_files(self, files, opener):
from os.path import getmtime, normpath
from pygeode.progress import PBar
table = self.table
# Special case: no files given
if len(files) == 0: return
self.selected_files.extend(files)
# Strip out extra separators, etc. from the filenames.
# Otherwise, if the files are scanned a second time with different
# separators, it may cause the same file to be included more than once.
files = [normpath(f) for f in files]
if self.filename is not None:
pbar = PBar(message="Scanning files for %s" % self.filename)
else:
pbar = PBar(message="Scanning files")
# Construct / add to the table
for i, f in enumerate(files):
pbar.update(i * 100. / len(files))
if f in table:
# File has changed since last time?
if int(getmtime(f)) > self.mtime:
# Remove existing info
del table[f]
else:
# Otherwise, we've already dealt with the file, so skip it.
continue
# Always use the latest modification time to represent the valid time of
# the whole table.
self.mtime = max(self.mtime, int(getmtime(f)))
# Record all variables from the file.
entries = []
table[f] = entries
for var in opener(f):
axes = self.axis_manager.lookup_axes(var.axes)
entries.append((var.name, axes, var.atts))
self.modified_table = True
pbar.update(100)
def load(self, pbar=True):
# {{{
''' Returns a version of this variable with all data loaded into memory.
Parameters
----------
pbar : boolean
If True, display a progress bar while loading data.
'''
from pygeode.progress import PBar
if hasattr(self, 'values'): return self
if pbar is True:
pbar = PBar(message="Loading %s:" % repr(self))
var = Var(self.axes, values=self.get(pbar=pbar))
copy_meta(self, var)
return var
def scan_files (self, files, opener):
from os.path import getmtime, normpath
from pygeode.progress import PBar
table = self.table
# Special case: no files given
if len(files) == 0: return
self.selected_files.extend(files)
# Strip out extra separators, etc. from the filenames.
# Otherwise, if the files are scanned a second time with different
# separators, it may cause the same file to be included more than once.
files = [normpath(f) for f in files]
if self.filename is not None:
pbar = PBar (message = "Scanning files for %s"%self.filename)
else:
pbar = PBar (message = "Scanning files")
# Construct / add to the table
for i,f in enumerate(files):
pbar.update(i*100./len(files))
if f in table:
# File has changed since last time?
if int(getmtime(f)) > self.mtime:
# Remove existing info
del table[f]
else:
# Otherwise, we've already dealt with the file, so skip it.
continue
# Always use the latest modification time to represent the valid time of
# the whole table.
self.mtime = max(self.mtime,int(getmtime(f)))
# Record all variables from the file.
entries = []
table[f] = entries
for var in opener(f):
axes = self.axis_manager.lookup_axes(var.axes)
entries.append((var.name, axes, var.atts))
self.modified_table = True
pbar.update(100)
def correlate(X, Y, axes=None, pbar=None):
# {{{
r'''Computes correlation between variables X and Y.
Parameters
==========
X, Y : :class:`Var`
Variables to correlate. Must have at least one axis in common.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to shared by X and Y.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
rho, p : :class:`Var`
The correlation coefficient :math:`\rho_{XY}` and p-value, respectively.
Notes
=====
The coefficient :math:`\rho_{XY}` is computed following von Storch and Zwiers
1999, section 8.2.2. The p-value is the probability of finding the given
result under the hypothesis that the true correlation coefficient between X
and Y is zero. It is computed from the t-statistic given in eq (8.7), in
section 8.2.3, and assumes normally distributed quantities.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npnansum
from pygeode.view import View
# Put all the axes being reduced over at the end
# so that we can reshape
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' % (a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [srcaxes[i] for i in oiaxes]
inaxes = oaxes + [srcaxes[i] for i in riaxes]
oview = View(oaxes)
iview = View(inaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
# Construct work arrays
x = np.zeros(oview.shape, 'd')*np.nan
y = np.zeros(oview.shape, 'd')*np.nan
xx = np.zeros(oview.shape, 'd')*np.nan
yy = np.zeros(oview.shape, 'd')*np.nan
xy = np.zeros(oview.shape, 'd')*np.nan
Na = np.zeros(oview.shape, 'd')*np.nan
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
for outsl, (xdata, ydata) in loopover([X, Y], oview, inaxes, pbar=pbar):
xdata = xdata.astype('d')
ydata = ydata.astype('d')
xydata = xdata*ydata
xbc = [s1 // s2 for s1, s2 in zip(xydata.shape, xdata.shape)]
ybc = [s1 // s2 for s1, s2 in zip(xydata.shape, ydata.shape)]
xdata = np.tile(xdata, xbc)
ydata = np.tile(ydata, ybc)
xdata[np.isnan(xydata)] = np.nan
ydata[np.isnan(xydata)] = np.nan
# It seems np.nansum does not broadcast its arguments automatically
# so there must be a better way of doing this...
x[outsl] = np.nansum([x[outsl], npnansum(xdata, siaxes)], 0)
y[outsl] = np.nansum([y[outsl], npnansum(ydata, siaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, siaxes)], 0)
yy[outsl] = np.nansum([yy[outsl], npnansum(ydata**2, siaxes)], 0)
xy[outsl] = np.nansum([xy[outsl], npnansum(xydata, siaxes)], 0)
# Sum of weights
Na[outsl] = np.nansum([Na[outsl], npnansum(~np.isnan(xydata), siaxes)], 0)
eps = 1e-14
imsk = ~(Na < eps)
xx[imsk] -= (x*x)[imsk]/Na[imsk]
yy[imsk] -= (y*y)[imsk]/Na[imsk]
xy[imsk] -= (x*y)[imsk]/Na[imsk]
# Compute correlation coefficient, t-statistic, p-value
den = np.zeros(oview.shape, 'd')
rho = np.zeros(oview.shape, 'd')
den[imsk] = np.sqrt((xx*yy)[imsk])
rho[den > 0.] = xy[den > 0.] / np.sqrt(xx*yy)[den > 0.]
den = 1 - rho**2
# Saturate the denominator (when correlation is perfect) to avoid div by zero warnings
den[den < eps] = eps
t = np.zeros(oview.shape, 'd')
p = np.zeros(oview.shape, 'd')
t[imsk] = np.abs(rho)[imsk] * np.sqrt((Na[imsk] - 2.)/den[imsk])
p[imsk] = tdist.cdf(t[imsk], Na[imsk]-2) * np.sign(rho[imsk])
p[~imsk] = np.nan
rho[~imsk] = np.nan
# Construct and return variables
xn = X.name if X.name != '' else 'X' # Note: could write: xn = X.name or 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
Rho = Var(oaxes, values=rho, name='C(%s, %s)' % (xn, yn))
P = Var(oaxes, values=p, name='P(C(%s,%s) != 0)' % (xn, yn))
return Rho, P
def correlate(X, Y, axes=None, output='r2,p', pbar=None):
# {{{
r'''Computes correlation between variables X and Y.
Parameters
==========
X, Y : :class:`Var`
Variables to correlate. Must have at least one axis in common.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to shared by X and Y.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'r2,p'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : :class:`Dataset`
The names of the variables match the output request string (i.e. if ``ds``
is the returned dataset, the correlation coefficient can be obtained
through ``ds.r2``).
* 'r2': The correlation coefficient :math:`\rho_{XY}`
* 'p': The p-value; see notes.
Notes
=====
The coefficient :math:`\rho_{XY}` is computed following von Storch and Zwiers
1999, section 8.2.2. The p-value is the probability of finding a correlation
coeefficient of equal or greater magnitude (two-sided) to the given result
under the hypothesis that the true correlation coefficient between X and Y is
zero. It is computed from the t-statistic given in eq (8.7), in section
8.2.3, and assumes normally distributed quantities.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npnansum
from pygeode.view import View
# Split output request now
ovars = ['r2', 'p']
output = [o for o in output.split(',') if o in ovars]
if len(output) < 1:
raise ValueError(
'No valid outputs are requested from correlation. Possible outputs are %s.'
% str(ovars))
# Put all the axes being reduced over at the end
# so that we can reshape
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' %
(a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [srcaxes[i] for i in oiaxes]
inaxes = oaxes + [srcaxes[i] for i in riaxes]
oview = View(oaxes)
iview = View(inaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
# Construct work arrays
x = np.full(oview.shape, np.nan, 'd')
y = np.full(oview.shape, np.nan, 'd')
xx = np.full(oview.shape, np.nan, 'd')
yy = np.full(oview.shape, np.nan, 'd')
xy = np.full(oview.shape, np.nan, 'd')
Na = np.full(oview.shape, np.nan, 'd')
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
for outsl, (xdata, ydata) in loopover([X, Y], oview, inaxes, pbar=pbar):
xdata = xdata.astype('d')
ydata = ydata.astype('d')
xydata = xdata * ydata
xbc = [s1 // s2 for s1, s2 in zip(xydata.shape, xdata.shape)]
ybc = [s1 // s2 for s1, s2 in zip(xydata.shape, ydata.shape)]
xdata = np.tile(xdata, xbc)
ydata = np.tile(ydata, ybc)
xdata[np.isnan(xydata)] = np.nan
ydata[np.isnan(xydata)] = np.nan
# It seems np.nansum does not broadcast its arguments automatically
# so there must be a better way of doing this...
x[outsl] = np.nansum([x[outsl], npnansum(xdata, siaxes)], 0)
y[outsl] = np.nansum([y[outsl], npnansum(ydata, siaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, siaxes)], 0)
yy[outsl] = np.nansum([yy[outsl], npnansum(ydata**2, siaxes)], 0)
xy[outsl] = np.nansum([xy[outsl], npnansum(xydata, siaxes)], 0)
# Count of non-NaN data points
Na[outsl] = np.nansum(
[Na[outsl], npnansum(~np.isnan(xydata), siaxes)], 0)
imsk = (Na > 0)
xx[imsk] -= (x * x)[imsk] / Na[imsk]
yy[imsk] -= (y * y)[imsk] / Na[imsk]
xy[imsk] -= (x * y)[imsk] / Na[imsk]
# Ensure variances are non-negative
xx[xx <= 0.] = 0.
yy[yy <= 0.] = 0.
# Compute correlation coefficient, t-statistic, p-value
den = np.zeros(oview.shape, 'd')
rho = np.zeros(oview.shape, 'd')
den[imsk] = np.sqrt((xx * yy)[imsk])
dmsk = (den > 0.)
rho[dmsk] = xy[dmsk] / np.sqrt(xx * yy)[dmsk]
den = 1 - rho**2
# Saturate the denominator (when correlation is perfect) to avoid div by zero warnings
den[den < eps] = eps
t = np.zeros(oview.shape, 'd')
p = np.zeros(oview.shape, 'd')
t[imsk] = np.abs(rho)[imsk] * np.sqrt((Na[imsk] - 2.) / den[imsk])
p[imsk] = 2. * (1. - tdist.cdf(t[imsk], Na[imsk] - 2))
p[~imsk] = np.nan
rho[~imsk] = np.nan
p[~dmsk] = np.nan
rho[~dmsk] = np.nan
# Construct and return variables
xn = X.name if X.name != '' else 'X' # Note: could write: xn = X.name or 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
from pygeode.dataset import asdataset
rvs = []
if 'r2' in output:
r2 = Var(oaxes, values=rho, name='r2')
r2.atts['longname'] = 'Correlation coefficient between %s and %s' % (
xn, yn)
rvs.append(r2)
if 'p' in output:
p = Var(oaxes, values=p, name='p')
p.atts[
'longname'] = 'p-value for correlation coefficient between %s and %s' % (
xn, yn)
rvs.append(p)
ds = asdataset(rvs)
ds.atts['description'] = 'correlation analysis %s against %s' % (yn, xn)
return ds
def paired_difference(X,
Y,
axes=None,
alpha=0.05,
N_fac=None,
output='d,p,ci',
pbar=None):
# {{{
r'''Computes the mean value and statistics of X - Y, assuming that individual elements
of X and Y can be directly paired. In contrast to :func:`difference`, X and Y must have the same
shape.
Parameters
==========
X, Y : :class:`Var`
Variables to difference. Must share all axes over which the means are being computed.
axes : list, optional
Axes over which to compute means; if nothing is specified, the mean
is computed over all axes common to X and Y.
alpha : float
Confidence level for which to compute confidence interval.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom of
X and Y; the effective number will be given by the number estimated from the
dataset divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'd,p,ci'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : :class:`Dataset`
The returned variables are specified by the ``output`` argument. The names
of the variables match the output request string (i.e. if ``ds`` is the
returned dataset, the average of the difference can be obtained by
``ds.d``). The following four quantities can be computed:
* 'd': The difference in the means, X - Y
* 'df': The effective number of degrees of freedom, :math:`df`
* 'p': The p-value; see notes.
* 'ci': The confidence interval of the difference at the level specified by ``alpha``
See Also
========
isnonzero
difference
Notes
=====
Following section 6.6.6 of von Storch and Zwiers 1999, a one-sample t test is used to test the
hypothesis. The number of degrees of freedom is the sample size scaled by N_fac, less one. This
provides a means of taking into account serial correlation in the data (see sections 6.6.7-9), but
the appropriate number of effective degrees of freedom are not calculated explicitly by this
routine. The p-value and confidence interval are computed based on the t-statistic in eq
(6.21).'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npnansum
from pygeode.view import View
# Split output request now
ovars = ['d', 'df', 'p', 'ci']
output = [o for o in output.split(',') if o in ovars]
if len(output) < 1:
raise ValueError(
'No valid outputs are requested from correlation. Possible outputs are %s.'
% str(ovars))
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' %
(a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [a for i, a in enumerate(srcaxes) if i not in riaxes]
oview = View(oaxes)
ixaxes = [X.whichaxis(n) for n in axes if X.hasaxis(n)]
Nx = np.product([len(X.axes[i]) for i in ixaxes])
iyaxes = [Y.whichaxis(n) for n in axes if Y.hasaxis(n)]
Ny = np.product([len(Y.axes[i]) for i in iyaxes])
assert ixaxes == iyaxes and Nx == Ny, 'For the paired difference test, X and Y must have the same size along the reduction axes.'
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert Nx > 1, '%s has only one element along the reduction axes' % X.name
assert Ny > 1, '%s has only one element along the reduction axes' % Y.name
# Construct work arrays
d = np.full(oview.shape, np.nan, 'd')
dd = np.full(oview.shape, np.nan, 'd')
N = np.full(oview.shape, np.nan, 'd')
# Accumulate data
for outsl, (xdata, ydata) in loopover([X, Y],
oview,
inaxes=srcaxes,
pbar=pbar):
ddata = xdata.astype('d') - ydata.astype('d')
d[outsl] = np.nansum([d[outsl], npnansum(ddata, ixaxes)], 0)
dd[outsl] = np.nansum([dd[outsl], npnansum(ddata**2, ixaxes)], 0)
# Count of non-NaN data points
N[outsl] = np.nansum([N[outsl], npnansum(~np.isnan(ddata), ixaxes)], 0)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
imsk = (N > 1)
dd[imsk] -= (d * d)[imsk] / N[imsk]
dd[imsk] /= (N[imsk] - 1)
d[imsk] /= N[imsk]
# Ensure variance is non-negative
dd[dd <= 0.] = 0.
if N_fac is not None: eN = N // N_fac
else: eN = N
emsk = (eN > 1)
den = np.zeros(oview.shape, 'd')
p = np.zeros(oview.shape, 'd')
ci = np.zeros(oview.shape, 'd')
den = np.zeros(oview.shape, 'd')
den[emsk] = np.sqrt(dd[emsk] / (eN[emsk] - 1))
dmsk = (den > 0.)
p[dmsk] = np.abs(d[dmsk] / den[dmsk])
p[dmsk] = 2. * (1. - tdist.cdf(p[dmsk], eN[dmsk] - 1))
ci[dmsk] = tdist.ppf(1. - alpha / 2, eN[dmsk] - 1) * den[dmsk]
# Construct dataset to return
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
from pygeode.dataset import asdataset
rvs = []
if 'd' in output:
d = Var(oaxes, values=d, name='d')
d.atts['longname'] = 'Difference (%s - %s)' % (xn, yn)
rvs.append(d)
if 'df' in output:
df = Var(oaxes, values=eN - 1, name='df')
df.atts['longname'] = 'Degrees of freedom used for t-test'
rvs.append(df)
if 'p' in output:
p = Var(oaxes, values=p, name='p')
p.atts[
'longname'] = 'p-value for t-test of paired difference (%s - %s)' % (
xn, yn)
rvs.append(p)
if 'ci' in output:
ci = Var(oaxes, values=ci, name='ci')
ci.atts[
'longname'] = 'Confidence Interval (alpha = %.2f) of paired difference (%s - %s)' % (
alpha, xn, yn)
rvs.append(ci)
ds = asdataset(rvs)
ds.atts['alpha'] = alpha
ds.atts['N_fac'] = N_fac
ds.atts['description'] = 't-test of paired difference (%s - %s)' % (yn, xn)
return ds
def difference(X,
Y,
axes=None,
alpha=0.05,
Nx_fac=None,
Ny_fac=None,
output='d,p,ci',
pbar=None):
# {{{
r'''Computes the mean value and statistics of X - Y.
Parameters
==========
X, Y : :class:`Var`
Variables to difference. Must have at least one axis in common.
axes : list, optional, defaults to None
Axes over which to compute means; if othing is specified, the mean
is computed over all axes common to X and Y.
alpha : float, optional; defaults to 0.05
Confidence level for which to compute confidence interval.
Nx_fac : integer, optional: defaults to None
A factor by which to rescale the estimated number of degrees of freedom of
X; the effective number will be given by the number estimated from the
dataset divided by ``Nx_fac``.
Ny_fac : integer, optional: defaults to None
A factor by which to rescale the estimated number of degrees of freedom of
Y; the effective number will be given by the number estimated from the
dataset divided by ``Ny_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'd,p,ci'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : :class:`Dataset`
The returned variables are specified by the ``output`` argument. The names
of the variables match the output request string (i.e. if ``ds`` is the
returned dataset, the average of the difference can be obtained by
``ds.d``). The following four quantities can be computed:
* 'd': The difference in the means, X - Y
* 'df': The effective number of degrees of freedom, :math:`df`
* 'p': The p-value; see notes.
* 'ci': The confidence interval of the difference at the level specified by ``alpha``
See Also
========
isnonzero
paired_difference
Notes
=====
The effective number of degrees of freedom is estimated using eq (6.20) of
von Storch and Zwiers 1999, in which :math:`n_X` and :math:`n_Y` are scaled by
Nx_fac and Ny_fac, respectively. This provides a means of taking into account
serial correlation in the data (see sections 6.6.7-9), but the number of effective
degrees of freedom are not calculated explicitly by this routine. The p-value and
confidence interval are computed based on the t-statistic in eq (6.19).'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npnansum
from pygeode.view import View
# Split output request now
ovars = ['d', 'df', 'p', 'ci']
output = [o for o in output.split(',') if o in ovars]
if len(output) < 1:
raise ValueError(
'No valid outputs are requested from correlation. Possible outputs are %s.'
% str(ovars))
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' %
(a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [a for i, a in enumerate(srcaxes) if i not in riaxes]
oview = View(oaxes)
ixaxes = [X.whichaxis(n) for n in axes if X.hasaxis(n)]
iyaxes = [Y.whichaxis(n) for n in axes if Y.hasaxis(n)]
Nx = np.product([len(X.axes[i]) for i in ixaxes])
Ny = np.product([len(Y.axes[i]) for i in iyaxes])
assert Nx > 1, '%s has only one element along the reduction axes' % X.name
assert Ny > 1, '%s has only one element along the reduction axes' % Y.name
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
# Construct work arrays
x = np.full(oview.shape, np.nan, 'd')
y = np.full(oview.shape, np.nan, 'd')
xx = np.full(oview.shape, np.nan, 'd')
yy = np.full(oview.shape, np.nan, 'd')
Nx = np.full(oview.shape, np.nan, 'd')
Ny = np.full(oview.shape, np.nan, 'd')
# Accumulate data
for outsl, (xdata, ) in loopover([X], oview, pbar=pbar):
xdata = xdata.astype('d')
x[outsl] = np.nansum([x[outsl], npnansum(xdata, ixaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, ixaxes)], 0)
# Count of non-NaN data points
Nx[outsl] = np.nansum(
[Nx[outsl], npnansum(~np.isnan(xdata), ixaxes)], 0)
for outsl, (ydata, ) in loopover([Y], oview, pbar=pbar):
ydata = ydata.astype('d')
y[outsl] = np.nansum([y[outsl], npnansum(ydata, iyaxes)], 0)
yy[outsl] = np.nansum([yy[outsl], npnansum(ydata**2, iyaxes)], 0)
# Count of non-NaN data points
Ny[outsl] = np.nansum(
[Ny[outsl], npnansum(~np.isnan(ydata), iyaxes)], 0)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
imsk = (Nx > 1) & (Ny > 1)
xx[imsk] -= (x * x)[imsk] / Nx[imsk]
xx[imsk] /= (Nx[imsk] - 1)
x[imsk] /= Nx[imsk]
yy[imsk] -= (y * y)[imsk] / Ny[imsk]
yy[imsk] /= (Ny[imsk] - 1)
y[imsk] /= Ny[imsk]
# Ensure variances are non-negative
xx[xx <= 0.] = 0.
yy[yy <= 0.] = 0.
if Nx_fac is not None: eNx = Nx // Nx_fac
else: eNx = Nx
if Ny_fac is not None: eNy = Ny // Ny_fac
else: eNy = Ny
emsk = (eNx > 1) & (eNy > 1)
# Compute difference
d = x - y
den = np.zeros(oview.shape, 'd')
df = np.zeros(oview.shape, 'd')
p = np.zeros(oview.shape, 'd')
ci = np.zeros(oview.shape, 'd')
# Convert to variance of the mean of each sample
xx[emsk] /= eNx[emsk]
yy[emsk] /= eNy[emsk]
den[emsk] = xx[emsk]**2 / (eNx[emsk] - 1) + yy[emsk]**2 / (eNy[emsk] - 1)
dmsk = (den > 0.)
df[dmsk] = (xx[dmsk] + yy[dmsk])**2 / den[dmsk]
den[emsk] = np.sqrt(xx[emsk] + yy[emsk])
dmsk &= (den > 0.)
p[dmsk] = np.abs(d[dmsk] / den[dmsk])
p[dmsk] = 2. * (1. - tdist.cdf(p[dmsk], df[dmsk]))
ci[dmsk] = tdist.ppf(1. - alpha / 2, df[dmsk]) * den[dmsk]
df[~dmsk] = np.nan
p[~dmsk] = np.nan
ci[~dmsk] = np.nan
# Construct dataset to return
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
from pygeode.dataset import asdataset
rvs = []
if 'd' in output:
d = Var(oaxes, values=d, name='d')
d.atts['longname'] = 'Difference (%s - %s)' % (xn, yn)
rvs.append(d)
if 'df' in output:
df = Var(oaxes, values=df, name='df')
df.atts['longname'] = 'Degrees of freedom used for t-test'
rvs.append(df)
if 'p' in output:
p = Var(oaxes, values=p, name='p')
p.atts['longname'] = 'p-value for t-test of difference (%s - %s)' % (
xn, yn)
rvs.append(p)
if 'ci' in output:
ci = Var(oaxes, values=ci, name='ci')
ci.atts[
'longname'] = 'Confidence Interval (alpha = %.2f) of difference (%s - %s)' % (
alpha, xn, yn)
rvs.append(ci)
ds = asdataset(rvs)
ds.atts['alpha'] = alpha
ds.atts['Nx_fac'] = Nx_fac
ds.atts['Ny_fac'] = Ny_fac
ds.atts['description'] = 't-test of difference (%s - %s)' % (yn, xn)
return ds
def multiple_regress(Xs, Y, axes=None, N_fac=None, output='B,p', pbar=None):
# {{{
r'''Computes least-squares multiple regression of Y against variables Xs.
Parameters
==========
Xs : list of :class:`Var` instances
Variables to treat as independent regressors. Must have at least one axis
in common with each other and with Y.
Y : :class:`Var`
The dependent variable. Must have at least one axis in common with the Xs.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to the Xs and Y.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom; the effective
number will be given by the number estimated from the dataset divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'B,p'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : tuple of floats or :class:`Var` instances.
The return values are specified by the ``output`` argument. The names of the
variables match the output request string (i.e. if ``ds`` is the returned dataset, the
linear coefficient of the regression can be obtained by ``ds.m``).
A fit of the form :math:`Y = \sum_i \beta_i X_i + \epsilon` is assumed.
Note that a constant term is not included by default. The following
parameters can be returned:
* 'B': Linear coefficients :math:`\beta_i` of each regressor
* 'r2': Fraction of the variance in Y explained by all Xs (:math:`R^2`)
* 'p': p-value of regession; see notes.
* 'sb': Standard deviation of each linear coefficient
* 'covb': Covariance matrix of the linear coefficients
* 'se': Standard deviation of residuals
The outputs 'B', 'p', and 'sb' will produce as many outputs as there are
regressors.
Notes
=====
The statistics described are computed following von Storch and Zwiers 1999,
section 8.4. The p-value 'p' is computed using the t-statistic appropriate
for the multi-variate normal estimator :math:`\hat{\vec{a}}` given in section
8.4.2; it corresponds to the probability of obtaining the regression
coefficient under the null hypothesis that there is no linear relationship.
Note this may not be the best way to determine if a given parameter is
contributing a significant fraction to the explained variance of Y. The
variances 'se' and 'sb' are :math:`\hat{\sigma}_E` and the square root of the
diagonal elements of :math:`\hat{\sigma}^2_E (\chi^T\chi)` in von Storch and
Zwiers, respectively. The data is assumed to be normally distributed.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npsum
from pygeode.view import View
# Split output request now
ovars = ['beta', 'r2', 'p', 'sb', 'covb', 'se']
output = [o for o in output.split(',') if o in ovars]
if len(output) < 1:
raise ValueError(
'No valid outputs are requested from correlation. Possible outputs are %s.'
% str(ovars))
Nr = len(Xs)
Xaxes = combine_axes(Xs)
srcaxes = combine_axes([Xaxes, Y])
oiaxes, riaxes = shared_axes(srcaxes, [Xaxes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
ia = whichaxis(srcaxes, a)
if ia in riaxes: ri_new.append(ia)
else:
raise KeyError(
'One of the Xs or Y does not have the axis %s.' % a)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = tuple([srcaxes[i] for i in oiaxes])
inaxes = oaxes + tuple([srcaxes[i] for i in riaxes])
oview = View(oaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert len(
riaxes) > 0, 'Regressors and %s share no axes to be regressed over' % (
Y.name)
# Construct work arrays
os = oview.shape
os1 = os + (Nr, )
os2 = os + (Nr, Nr)
y = np.zeros(os, 'd')
yy = np.zeros(os, 'd')
xy = np.zeros(os1, 'd')
xx = np.zeros(os2, 'd')
xxinv = np.zeros(os2, 'd')
N = np.prod([len(srcaxes[i]) for i in riaxes])
# Accumulate data
for outsl, datatuple in loopover(Xs + [Y], oview, inaxes, pbar=pbar):
ydata = datatuple[-1].astype('d')
xdata = [datatuple[i].astype('d') for i in range(Nr)]
y[outsl] += npsum(ydata, siaxes)
yy[outsl] += npsum(ydata**2, siaxes)
for i in range(Nr):
xy[outsl + (i, )] += npsum(xdata[i] * ydata, siaxes)
for j in range(i + 1):
xx[outsl + (i, j)] += npsum(xdata[i] * xdata[j], siaxes)
# Fill in opposite side of xTx
for i in range(Nr):
for j in range(i):
xx[..., j, i] = xx[..., i, j]
# Compute inverse of covariance matrix (could be done more intellegently? certainly the python
# loop over oview does not help)
xx = xx.reshape(-1, Nr, Nr)
xxinv = xxinv.reshape(-1, Nr, Nr)
for i in range(xx.shape[0]):
xxinv[i, :, :] = np.linalg.inv(xx[i, :, :])
xx = xx.reshape(os2)
xxinv = xxinv.reshape(os2)
beta = np.sum(xy.reshape(os + (1, Nr)) * xxinv, -1)
vare = np.sum(xy * beta, -1)
if N_fac is None: N_eff = N
else: N_eff = N // N_fac
sigbeta = [
np.sqrt((yy - vare) * xxinv[..., i, i] / N_eff) for i in range(Nr)
]
xns = [X.name if X.name != '' else 'X%d' % i for i, X in enumerate(Xs)]
yn = Y.name if Y.name != '' else 'Y'
from .var import Var
from .dataset import asdataset
from .axis import NonCoordinateAxis
ra = NonCoordinateAxis(values=np.arange(Nr),
regressor=xns,
name='regressor')
ra2 = NonCoordinateAxis(values=np.arange(Nr),
regressor=xns,
name='regressor2')
Nd = len(oaxes)
rvs = []
if 'beta' in output:
B = Var(oaxes + (ra, ), values=beta, name='beta')
B.atts['longname'] = 'regression coefficient'
rvs.append(B)
if 'r2' in output:
vary = (yy - y**2 / N)
R2 = 1 - (yy - vare) / vary
R2 = Var(oaxes, values=R2, name='R2')
R2.atts['longname'] = 'fraction of variance explained'
rvs.append(R2)
if 'p' in output:
p = [
2. *
(1. - tdist.cdf(np.abs(beta[..., i] / sigbeta[i]), N_eff - Nr))
for i in range(Nr)
]
p = np.transpose(np.array(p), [Nd] + list(range(Nd)))
p = Var(oaxes + (ra, ), values=p, name='p')
p.atts['longname'] = 'p-values'
rvs.append(p)
if 'sb' in output:
sigbeta = np.transpose(np.array(sigbeta), [Nd] + list(range(Nd)))
sb = Var(oaxes + (ra, ), values=sigbeta, name='sb')
sb.atts['longname'] = 'standard deviation of linear coefficients'
rvs.append(sb)
if 'covb' in output:
sigmat = np.zeros(os2, 'd')
for i in range(Nr):
for j in range(Nr):
#sigmat[..., i, j] = np.sqrt((yy - vare) * xxinv[..., i, j] / N_eff)
sigmat[..., i, j] = (yy - vare) * xxinv[..., i, j] / N_eff
covb = Var(oaxes + (ra, ra2), values=sigmat, name='covb')
covb.atts['longname'] = 'Covariance matrix of the linear coefficients'
rvs.append(covb)
if 'se' in output:
se = np.sqrt((yy - vare) / N_eff)
se = Var(oaxes, values=se, name='se')
se.atts['longname'] = 'standard deviation of residual'
rvs.append(se)
ds = asdataset(rvs)
ds.atts[
'description'] = 'multiple linear regression parameters for %s regressed against %s' % (
yn, xns)
return ds
def regress(X, Y, axes=None, N_fac=None, output='m,b,p', pbar=None):
# {{{
r'''Computes least-squares linear regression of Y against X.
Parameters
==========
X, Y : :class:`Var`
Variables to regress. Must have at least one axis in common.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to X and Y.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom; the effective
number will be given by the number estimated from the dataset divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'm,b,p'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : :class:`Dataset`
The returned variables are specified by the ``output`` argument. The names of the
variables match the output request string (i.e. if ``ds`` is the returned dataset, the
linear coefficient of the regression can be obtained by ``ds.m``).
A fit of the form :math:`Y = m X + b + \epsilon` is assumed, and the
following parameters can be returned:
* 'm': Linear coefficient of the regression
* 'b': Constant coefficient of the regression
* 'r2': Fraction of the variance in Y explained by X (:math:`R^2`)
* 'p': p-value of regression; see notes.
* 'sm': Standard deviation of linear coefficient estimate
* 'se': Standard deviation of residuals
Notes
=====
The statistics described are computed following von Storch and Zwiers 1999,
section 8.3. The p-value 'p' is computed using the t-statistic given in
section 8.3.8, and confidence intervals for the slope and intercept can be
computed from 'se' and 'se' (:math:`\hat{\sigma}_E` and
:math:`\hat{\sigma}_E/\sqrt{S_{XX}}` in von Storch and Zwiers, respectively).
The data is assumed to be normally distributed.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npnansum
from pygeode.view import View
# Split output request now
ovars = ['m', 'b', 'r2', 'p', 'sm', 'se']
output = [o for o in output.split(',') if o in ovars]
if len(output) < 1:
raise ValueError(
'No valid outputs are requested from regression. Possible outputs are %s.'
% str(ovars))
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' %
(a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [srcaxes[i] for i in oiaxes]
inaxes = oaxes + [srcaxes[i] for i in riaxes]
oview = View(oaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert len(riaxes) > 0, '%s and %s share no axes to be regressed over' % (
X.name, Y.name)
# Construct work arrays
x = np.full(oview.shape, np.nan, 'd')
y = np.full(oview.shape, np.nan, 'd')
xx = np.full(oview.shape, np.nan, 'd')
yy = np.full(oview.shape, np.nan, 'd')
xy = np.full(oview.shape, np.nan, 'd')
Na = np.full(oview.shape, np.nan, 'd')
# Accumulate data
for outsl, (xdata, ydata) in loopover([X, Y], oview, inaxes, pbar=pbar):
xdata = xdata.astype('d')
ydata = ydata.astype('d')
xydata = xdata * ydata
xbc = [s1 // s2 for s1, s2 in zip(xydata.shape, xdata.shape)]
ybc = [s1 // s2 for s1, s2 in zip(xydata.shape, ydata.shape)]
xdata = np.tile(xdata, xbc)
ydata = np.tile(ydata, ybc)
xdata[np.isnan(xydata)] = np.nan
ydata[np.isnan(xydata)] = np.nan
# It seems np.nansum does not broadcast its arguments automatically
# so there must be a better way of doing this...
x[outsl] = np.nansum([x[outsl], npnansum(xdata, siaxes)], 0)
y[outsl] = np.nansum([y[outsl], npnansum(ydata, siaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, siaxes)], 0)
yy[outsl] = np.nansum([yy[outsl], npnansum(ydata**2, siaxes)], 0)
xy[outsl] = np.nansum([xy[outsl], npnansum(xydata, siaxes)], 0)
# Sum of weights
Na[outsl] = np.nansum(
[Na[outsl], npnansum(~np.isnan(xydata), siaxes)], 0)
if N_fac is None:
N_eff = Na - 2.
else:
N_eff = Na / N_fac - 2.
nmsk = (N_eff > 0.)
xx[nmsk] -= (x * x)[nmsk] / Na[nmsk]
yy[nmsk] -= (y * y)[nmsk] / Na[nmsk]
xy[nmsk] -= (x * y)[nmsk] / Na[nmsk]
dmsk = (xx > 0.)
m = np.zeros(oview.shape, 'd')
b = np.zeros(oview.shape, 'd')
r2 = np.zeros(oview.shape, 'd')
m[dmsk] = xy[dmsk] / xx[dmsk]
b[nmsk] = (y[nmsk] - m[nmsk] * x[nmsk]) / Na[nmsk]
r2den = xx * yy
d2msk = (r2den > 0.)
r2[d2msk] = xy[d2msk]**2 / r2den[d2msk]
sige = np.zeros(oview.shape, 'd')
sigm = np.zeros(oview.shape, 'd')
t = np.zeros(oview.shape, 'd')
p = np.zeros(oview.shape, 'd')
sige[nmsk] = (yy[nmsk] - m[nmsk] * xy[nmsk]) / N_eff[nmsk]
sigm[dmsk] = np.sqrt(sige[dmsk] / xx[dmsk])
sige[nmsk] = np.sqrt(sige[dmsk])
t[dmsk] = np.abs(m[dmsk]) / sigm[dmsk]
p[nmsk] = 2. * (1. - tdist.cdf(t[nmsk], N_eff[nmsk]))
msk = nmsk & dmsk
m[~msk] = np.nan
b[~msk] = np.nan
sige[~msk] = np.nan
sigm[~msk] = np.nan
p[~msk] = np.nan
msk = nmsk & d2msk
r2[~msk] = np.nan
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
from pygeode.dataset import asdataset
rvs = []
if 'm' in output:
M = Var(oaxes, values=m, name='m')
M.atts['longname'] = 'slope'
rvs.append(M)
if 'b' in output:
B = Var(oaxes, values=b, name='b')
B.atts['longname'] = 'intercept'
rvs.append(B)
if 'r2' in output:
R2 = Var(oaxes, values=r2, name='r2')
R2.atts['longname'] = 'fraction of variance explained'
rvs.append(R2)
if 'p' in output:
P = Var(oaxes, values=p, name='p')
P.atts['longname'] = 'p-value'
rvs.append(P)
if 'sm' in output:
SM = Var(oaxes, values=sigm, name='sm')
SM.atts['longname'] = 'standard deviation of slope parameter'
rvs.append(SM)
if 'se' in output:
SE = Var(oaxes, values=sige, name='se')
SE.atts['longname'] = 'standard deviation of residual'
rvs.append(SE)
ds = asdataset(rvs)
ds.atts[
'description'] = 'linear regression parameters for %s regressed against %s' % (
yn, xn)
return ds
def save (filename, var, iaxis=None, fps=15, palette='bw', minmax=None):
from pygeode.axis import TAxis
from pygeode.var import Var
from pygeode.progress import PBar
import tempfile, shutil
import Image
import numpy as np
import os
assert isinstance(var, Var)
# Remove any degenerate dimensions, make sure the axes are in a consistent order
var = var.squeeze().sorted()
assert var.naxes == 3, "can only work with 3D data"
if iaxis is None: iaxis = var.whichaxis(TAxis)
assert iaxis >= 0, "no time axis found"
tmpdir = tempfile.mkdtemp (prefix='pygeode_mpeg')
sl = [slice(None)] * 3
# Get max & min values of the whole dataset
if minmax is None:
#TODO: calculate both of these at once, with a progress bar to help the process
min = float(var.min())
max = float(var.max())
else:
assert len(minmax) == 2, "invalid minmax argument"
min, max = minmax
print "Saving %s:"%filename
pbar = PBar()
# Loop over each timestep, generate a temporary image file
for i in range(len(var.axes[iaxis])):
fpbar = pbar.part(i,len(var.axes[iaxis]))
sl[iaxis] = i
# Get data, flip y axis, add an 'RGB' axis
data = var[sl].squeeze()[::-1,:,np.newaxis]
data = (data-min)/(max-min) * 255
if palette == 'bw':
# Same data for R, G, and B channels
data = np.concatenate([data,data,data], axis=2)
elif palette == 'rainbow':
# Piecewise linear palette
part1 = data <= 85
part2 = (85 < data) & (data <= 170)
part3 = 170 < data
b = np.zeros(data.shape)
b[part1] = 255
b[part2] = 255 - (data[part2] - 85)*3
g = np.zeros(data.shape)
g[part1] = data[part1] * 3
g[part2] = 255
g[part3] = 255 - (data[part3] - 170) * 3
r = np.zeros(data.shape)
r[part2] = (data[part2] - 85) * 3
r[part3] = 255
data = np.concatenate([r,g,b], axis=2)
# Encode as an 8-bit array
data = np.asarray(np.round(data), 'uint8')
# Save
framefile = tmpdir+"/frame%04d.jpg"%i
Image.fromarray(data,"RGB").save(framefile, quality=95)
# os.system("display "+framefile)
# break
fpbar.update(100)
shape = list(var.shape)
shape = shape[:iaxis] + shape[iaxis+1:]
h, w = shape
# """
# Make the movie file
os.system("mencoder mf://%s/*.jpg -mf w=%s:h=%s:type=jpg:fps=%s \
-ovc lavc -lavcopts vcodec=mpeg4:vbitrate=8000 -oac copy \
-o %s" % (tmpdir, w, h, fps, filename) )
# """
# Clean up files
shutil.rmtree (tmpdir)
def paired_difference(X, Y, axes, alpha=0.05, N_fac = None, pbar=None):
# {{{
r'''Computes the mean value and statistics of X - Y, assuming that individual elements
of X and Y can be directly paired. In contrast to :func:`difference`, X and Y must have the same
shape.
Parameters
==========
X, Y : :class:`Var`
Variables to difference. Must have at least one axis in common.
axes : list, optional
Axes over which to compute means; if nothing is specified, the mean
is computed over all axes common to X and Y.
alpha : float
Confidence level for which to compute confidence interval.
Nx_fac : integer
A factor by which to rescale the estimated number of degrees of freedom of
X; the effective number will be given by the number estimated from the
dataset divided by ``Nx_fac``.
Ny_fac : integer
A factor by which to rescale the estimated number of degrees of freedom of
Y; the effective number will be given by the number estimated from the
dataset divided by ``Ny_fac``.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : tuple or :class:`Dataset` instance.
Four quantities are computed:
* The difference in the means, X - Y
* The effective number of degrees of freedom, :math:`df`
* The probability of the computed difference if the population difference was zero
* The confidence interval of the difference at the level specified by alpha
If the average is taken over all axes of X and Y resulting in a scalar,
the above values are returned as a tuple in the order given. If not, the
results are provided as :class:`Var` objects in a dataset.
See Also
========
isnonzero
difference
Notes
=====
Following section 6.6.6 of von Storch and Zwiers 1999, a one-sample t test is used to test the
hypothesis. The number of degrees of freedom is the sample size scaled by N_fac, less one. This
provides a means of taking into account serial correlation in the data (see sections 6.6.7-9), but
the appropriate number of effective degrees of freedom are not calculated explicitly by this
routine. The p-value and confidence interval are computed based on the t-statistic in eq
(6.21).'''
from pygeode.tools import combine_axes, whichaxis, loopover, npsum, npnansum
from pygeode.view import View
srcaxes = combine_axes([X, Y])
riaxes = [whichaxis(srcaxes, n) for n in axes]
raxes = [a for i, a in enumerate(srcaxes) if i in riaxes]
oaxes = [a for i, a in enumerate(srcaxes) if i not in riaxes]
oview = View(oaxes)
ixaxes = [X.whichaxis(n) for n in axes if X.hasaxis(n)]
Nx = np.product([len(X.axes[i]) for i in ixaxes])
iyaxes = [Y.whichaxis(n) for n in axes if Y.hasaxis(n)]
Ny = np.product([len(Y.axes[i]) for i in iyaxes])
assert ixaxes == iyaxes and Nx == Ny, 'For the paired difference test, X and Y must have the same size along the reduction axes.'
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert Nx > 1, '%s has only one element along the reduction axes' % X.name
assert Ny > 1, '%s has only one element along the reduction axes' % Y.name
# Construct work arrays
d = np.zeros(oview.shape, 'd')
dd = np.zeros(oview.shape, 'd')
N = np.zeros(oview.shape, 'd')
d[()] = np.nan
dd[()] = np.nan
N[()] = np.nan
# Accumulate data
for outsl, (xdata, ydata) in loopover([X, Y], oview, inaxes=srcaxes, pbar=pbar):
ddata = xdata.astype('d') - ydata.astype('d')
d[outsl] = np.nansum([d[outsl], npnansum(ddata, ixaxes)], 0)
dd[outsl] = np.nansum([dd[outsl], npnansum(ddata**2, ixaxes)], 0)
# Sum of weights (kludge to get masking right)
N[outsl] = np.nansum([N[outsl], npnansum(1. + ddata*0., ixaxes)], 0)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
dd = (dd - d**2/N) / (N - 1)
d /= Nx
if N_fac is not None: eN = N//N_fac
else: eN = N
#print 'average eff. Nx = %.1f, average eff. Ny = %.1f' % (eNx.mean(), eNy.mean())
den = np.sqrt(dd/(eN - 1))
p = tdist.cdf(abs(d/den), eN - 1)*np.sign(d)
ci = tdist.ppf(1. - alpha/2, eN - 1) * den
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
if xn == yn: name = xn
else: name = '%s-%s'%(xn, yn)
if len(oaxes) > 0:
from pygeode import Var, Dataset
D = Var(oaxes, values=d, name=name)
DF = Var(oaxes, values=eN-1, name='df_%s' % name)
P = Var(oaxes, values=p, name='p_%s' % name)
CI = Var(oaxes, values=ci, name='CI_%s' % name)
return Dataset([D, DF, P, CI])
else: # Degenerate case
return d, eN-1, p, ci
def save(filename,
in_dataset,
version=3,
pack=None,
compress=False,
cfmeta=True,
unlimited=None):
# {{{
from ctypes import c_int, c_long, byref
from pygeode.view import View
from pygeode.tools import combine_axes, point
from pygeode.axis import Axis, DummyAxis
import numpy as np
from pygeode.progress import PBar, FakePBar
from pygeode.formats import finalize_save
from pygeode.dataset import asdataset
assert isinstance(filename, str)
in_dataset = asdataset(in_dataset)
dataset = finalize_save(in_dataset, cfmeta, pack)
# Version?
if compress: version = 4
assert version in (3, 4)
fileid = c_int()
vars = list(dataset.vars)
# The output axes
axes = combine_axes(v.axes for v in vars)
# Include axes in the list of vars (for writing to netcdf).
# Exclude axes which don't have any intrinsic values.
vars = vars + [a for a in axes if not isinstance(a, DummyAxis)]
#vars.extend(axes)
# Variables (and axes) must all have unique names
assert len(set([v.name for v in vars])) == len(
vars), "vars must have unique names: %s" % [v.name for v in vars]
if unlimited is not None:
assert unlimited in [a.name for a in axes]
# Functions for writing entire array
allf = {
1: lib.nc_put_var_schar,
2: lib.nc_put_var_text,
3: lib.nc_put_var_short,
4: lib.nc_put_var_int,
5: lib.nc_put_var_float,
6: lib.nc_put_var_double,
7: lib.nc_put_var_uchar,
8: lib.nc_put_var_ushort,
9: lib.nc_put_var_uint,
10: lib.nc_put_var_longlong,
11: lib.nc_put_var_ulonglong
}
# Functions for writing chunks
chunkf = {
1: lib.nc_put_vara_schar,
2: lib.nc_put_vara_text,
3: lib.nc_put_vara_short,
4: lib.nc_put_vara_int,
5: lib.nc_put_vara_float,
6: lib.nc_put_vara_double,
7: lib.nc_put_vara_uchar,
8: lib.nc_put_vara_ushort,
9: lib.nc_put_vara_uint,
10: lib.nc_put_vara_longlong,
11: lib.nc_put_vara_ulonglong
}
# Create the file
if version == 3:
ret = lib.nc_create(filename.encode('ascii'), 0, byref(fileid))
if ret != 0: raise IOError(lib.nc_strerror(ret))
elif version == 4:
ret = lib.nc_create(filename.encode('ascii'), 0x1000,
byref(fileid)) # 0x1000 = NC_NETCDF4
if ret != 0: raise IOError(lib.nc_strerror(ret))
else: raise Exception
try:
# Define the dimensions
dimids = [None] * len(axes)
for i, a in enumerate(axes):
dimids[i] = c_int()
if unlimited == a.name:
ret = lib.nc_def_dim(fileid, a.name.encode('ascii'), c_long(0),
byref(dimids[i]))
else:
ret = lib.nc_def_dim(fileid, a.name.encode('ascii'),
c_long(len(a)), byref(dimids[i]))
assert ret == 0, lib.nc_strerror(ret)
# Define the variables (including axes)
chunks = [None] * len(vars)
varids = [None] * len(vars)
for i, var in enumerate(vars):
t = nc_type[version][var.dtype.name]
# Generate the array of dimension ids for this var
d = [dimids[list(axes).index(a)] for a in var.axes]
# Make it C-compatible
d = (c_int * var.naxes)(*d)
varids[i] = c_int()
ret = lib.nc_def_var(fileid, var.name.encode('ascii'), t,
var.naxes, d, byref(varids[i]))
assert ret == 0, lib.nc_strerror(ret)
# Compress the data? (only works for netcdf4 or (higher?))
if compress:
ret = lib.nc_def_var_deflate(fileid, varids[i], 1, 1, 2)
assert ret == 0, lib.nc_strerror(ret)
# Write the attributes
# global attributes
put_attributes(fileid, -1, dataset.atts, version)
# variable attributes
for i, var in enumerate(vars):
# modify axes to be netcdf friendly (CF-compliant, etc.)
put_attributes(fileid, varids[i], var.atts, version)
# Don't pre-fill the file
oldmode = c_int()
ret = lib.nc_set_fill(fileid, 256, byref(oldmode))
assert ret == 0, "Can't set fill mode: %s (error %d)" % (
lib.nc_strerror(ret), ret)
# Finished defining the variables, about to start writing the values
ret = lib.nc_enddef(fileid)
assert ret == 0, "Error leaving define mode: %s (error %d)" % (
lib.nc_strerror(ret), ret)
# Relative progress of each variable
sizes = [v.size for v in vars]
prog = np.cumsum([0.] + sizes) / np.sum(sizes) * 100
# print "Saving '%s':"%filename
pbar = PBar(message="Saving '%s':" % filename)
# pbar = FakePBar()
# Write the data
for i, var in enumerate(vars):
t = nc_type[version][var.dtype.name]
dtype = numpy_type[t]
# print 'writing', var.name
# number of actual variables (non-axes) for determining our progress
N = len([v for v in vars if not isinstance(v, Axis)])
varpbar = pbar.subset(prog[i], prog[i + 1])
views = list(View(var.axes).loop_mem())
for j, v in enumerate(views):
vpbar = varpbar.part(j, len(views))
# print '???', repr(str(v))
# Should always be slices (since we're looping over whole thing contiguously?)
for sl in v.slices:
assert isinstance(sl, slice)
for sl in v.slices:
assert sl.step in (1, None)
start = [sl.start for sl in v.slices]
count = [sl.stop - sl.start for sl in v.slices]
start = (c_long * var.naxes)(*start)
count = (c_long * var.naxes)(*count)
if isinstance(var, Axis):
assert len(start) == len(count) == 1
data = var.values
data = data[
start[0]:start[0] +
count[0]] # the above gives us the *whole* axis,
# but under extreme conditions we may be looping over smaller pieces
vpbar.update(100)
else:
data = v.get(var, pbar=vpbar)
# Ensure the data is stored contiguously in memory
data = np.ascontiguousarray(data, dtype=dtype)
ret = chunkf[t](fileid, varids[i], start, count, point(data))
assert ret == 0, "Error writing var '%s' to netcdf: %s (error %d)" % (
var.name, lib.nc_strerror(ret), ret)
finally:
# Finished
lib.nc_close(fileid)
def difference(X, Y, axes, alpha=0.05, Nx_fac = None, Ny_fac = None, pbar=None):
# {{{
r'''Computes the mean value and statistics of X - Y.
Parameters
==========
X, Y : :class:`Var`
Variables to difference. Must have at least one axis in common.
axes : list, optional
Axes over which to compute means; if nothing is specified, the mean
is computed over all axes common to X and Y.
alpha : float
Confidence level for which to compute confidence interval.
Nx_fac : integer
A factor by which to rescale the estimated number of degrees of freedom of
X; the effective number will be given by the number estimated from the
dataset divided by ``Nx_fac``.
Ny_fac : integer
A factor by which to rescale the estimated number of degrees of freedom of
Y; the effective number will be given by the number estimated from the
dataset divided by ``Ny_fac``.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : tuple or :class:`Dataset` instance.
Four quantities are computed:
* The difference in the means, X - Y
* The effective number of degrees of freedom, :math:`df`
* The probability of the computed difference if the population difference was zero
* The confidence interval of the difference at the level specified by alpha
If the average is taken over all axes of X and Y resulting in a scalar,
the above values are returned as a tuple in the order given. If not, the
results are provided as :class:`Var` objects in a dataset.
See Also
========
isnonzero
paired_difference
Notes
=====
The effective number of degrees of freedom is estimated using eq (6.20) of
von Storch and Zwiers 1999, in which :math:`n_X` and :math:`n_Y` are scaled by
Nx_fac and Ny_fac, respectively. This provides a means of taking into account
serial correlation in the data (see sections 6.6.7-9), but the number of effective
degrees of freedom are not calculated explicitly by this routine. The p-value and
confidence interval are computed based on the t-statistic in eq (6.19).'''
from pygeode.tools import combine_axes, whichaxis, loopover, npsum, npnansum
from pygeode.view import View
srcaxes = combine_axes([X, Y])
riaxes = [whichaxis(srcaxes, n) for n in axes]
raxes = [a for i, a in enumerate(srcaxes) if i in riaxes]
oaxes = [a for i, a in enumerate(srcaxes) if i not in riaxes]
oview = View(oaxes)
ixaxes = [X.whichaxis(n) for n in axes if X.hasaxis(n)]
Nx = np.product([len(X.axes[i]) for i in ixaxes])
iyaxes = [Y.whichaxis(n) for n in axes if Y.hasaxis(n)]
Ny = np.product([len(Y.axes[i]) for i in iyaxes])
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert Nx > 1, '%s has only one element along the reduction axes' % X.name
assert Ny > 1, '%s has only one element along the reduction axes' % Y.name
# Construct work arrays
x = np.zeros(oview.shape, 'd')
y = np.zeros(oview.shape, 'd')
xx = np.zeros(oview.shape, 'd')
yy = np.zeros(oview.shape, 'd')
Nx = np.zeros(oview.shape, 'd')
Ny = np.zeros(oview.shape, 'd')
x[()] = np.nan
y[()] = np.nan
xx[()] = np.nan
yy[()] = np.nan
Nx[()] = np.nan
Ny[()] = np.nan
# Accumulate data
for outsl, (xdata,) in loopover([X], oview, pbar=pbar):
xdata = xdata.astype('d')
x[outsl] = np.nansum([x[outsl], npnansum(xdata, ixaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, ixaxes)], 0)
# Sum of weights (kludge to get masking right)
Nx[outsl] = np.nansum([Nx[outsl], npnansum(1. + xdata*0., ixaxes)], 0)
for outsl, (ydata,) in loopover([Y], oview, pbar=pbar):
ydata = ydata.astype('d')
y[outsl] = np.nansum([y[outsl], npnansum(ydata, iyaxes)], 0)
yy[outsl] = np.nansum([yy[outsl], npnansum(ydata**2, iyaxes)], 0)
# Sum of weights (kludge to get masking right)
Ny[outsl] = np.nansum([Ny[outsl], npnansum(1. + ydata*0., iyaxes)], 0)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
xx = (xx - x**2/Nx) / (Nx - 1)
yy = (yy - y**2/Ny) / (Ny - 1)
x /= Nx
y /= Ny
if Nx_fac is not None: eNx = Nx//Nx_fac
else: eNx = Nx
if Ny_fac is not None: eNy = Ny//Ny_fac
else: eNy = Ny
#print 'average eff. Nx = %.1f, average eff. Ny = %.1f' % (eNx.mean(), eNy.mean())
d = x - y
den = np.sqrt(xx/eNx + yy/eNy)
df = (xx/eNx + yy/eNy)**2 / ((xx/eNx)**2/(eNx - 1) + (yy/eNy)**2/(eNy - 1))
p = tdist.cdf(abs(d/den), df)*np.sign(d)
ci = tdist.ppf(1. - alpha/2, df) * den
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
if xn == yn: name = xn
else: name = '%s-%s'%(xn, yn)
if len(oaxes) > 0:
from pygeode import Var, Dataset
D = Var(oaxes, values=d, name=name)
DF = Var(oaxes, values=df, name='df_%s' % name)
P = Var(oaxes, values=p, name='p_%s' % name)
CI = Var(oaxes, values=ci, name='CI_%s' % name)
return Dataset([D, DF, P, CI])
else: # Degenerate case
return d, df, p, ci
def multiple_regress(Xs, Y, axes=None, pbar=None, N_fac=None, output='B,p'):
# {{{
r'''Computes least-squares multiple regression of Y against variables Xs.
Parameters
==========
Xs : list of :class:`Var` instances
Variables to treat as independent regressors. Must have at least one axis
in common with each other and with Y.
Y : :class:`Var`
The dependent variable. Must have at least one axis in common with the Xs.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to the Xs and Y.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom; the effective
number will be given by the number estimated from the dataset divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'B,p'.
Returns
=======
results : tuple of floats or :class:`Var` instances.
The return values are specified by the ``output`` argument. A fit of the form
:math:`Y = \sum_i \beta_i X_i + \epsilon` is assumed. Note that a constant term
is not included by default. The following parameters can be returned:
* 'B': Linear coefficients :math:`\beta_i` of each regressor
* 'r': Fraction of the variance in Y explained by all Xs (:math:`R^2`)
* 'p': Probability of this fit if the true linear coefficient was zero for each regressor
* 'sb': Standard deviation of each linear coefficient
* 'covb': Covariance matrix of the linear coefficients
* 'se': Standard deviation of residuals
If the regression is computed over all axes so that the result is a scalar,
the above are returned as a tuple of floats in the order specified by
``output``. Otherwise they are returned as :class:`Var` instances. The outputs
'B', 'p', and 'sb' will produce as many outputs as there are regressors.
Notes
=====
The statistics described are computed following von Storch and Zwiers 1999,
section 8.4. The p-value 'p' is computed using the t-statistic appropriate
for the multi-variate normal estimator :math:`\hat{\vec{a}}` given in section
8.4.2; note this may not be the best way to determine if a given parameter is
contributing a significant fraction to the explained variance of Y. The
variances 'se' and 'sb' are :math:`\hat{\sigma}_E` and the square root of the
diagonal elements of :math:`\hat{\sigma}^2_E (\chi^T\chi)` in von Storch and
Zwiers, respectively. The data is assumed to be normally distributed.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npsum
from pygeode.view import View
Nr = len(Xs)
Xaxes = combine_axes(Xs)
srcaxes = combine_axes([Xaxes, Y])
oiaxes, riaxes = shared_axes(srcaxes, [Xaxes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
ia = whichaxis(srcaxes, a)
if ia in riaxes: ri_new.append(ia)
else: raise KeyError('One of the Xs or Y does not have the axis %s.' % a)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [srcaxes[i] for i in oiaxes]
inaxes = oaxes + [srcaxes[i] for i in riaxes]
oview = View(oaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert len(riaxes) > 0, 'Regressors and %s share no axes to be regressed over' % (Y.name)
# Construct work arrays
os = oview.shape
os1 = os + (Nr,)
os2 = os + (Nr,Nr)
y = np.zeros(os, 'd')
yy = np.zeros(os, 'd')
xy = np.zeros(os1, 'd')
xx = np.zeros(os2, 'd')
xxinv = np.zeros(os2, 'd')
N = np.prod([len(srcaxes[i]) for i in riaxes])
# Accumulate data
for outsl, datatuple in loopover(Xs + [Y], oview, inaxes, pbar=pbar):
ydata = datatuple[-1].astype('d')
xdata = [datatuple[i].astype('d') for i in range(Nr)]
y[outsl] += npsum(ydata, siaxes)
yy[outsl] += npsum(ydata**2, siaxes)
for i in range(Nr):
xy[outsl+(i,)] += npsum(xdata[i]*ydata, siaxes)
for j in range(i+1):
xx[outsl+(i,j)] += npsum(xdata[i]*xdata[j], siaxes)
# Fill in opposite side of xTx
for i in range(Nr):
for j in range(i):
xx[..., j, i] = xx[..., i, j]
# Compute inverse of covariance matrix (could be done more intellegently? certainly the python
# loop over oview does not help)
xx = xx.reshape(-1, Nr, Nr)
xxinv = xxinv.reshape(-1, Nr, Nr)
for i in range(xx.shape[0]):
xxinv[i,:,:] = np.linalg.inv(xx[i,:,:])
xx = xx.reshape(os2)
xxinv = xxinv.reshape(os2)
beta = np.sum(xy.reshape(os + (1, Nr)) * xxinv, -1)
vare = np.sum(xy * beta, -1)
if N_fac is None: N_eff = N
else: N_eff = N // N_fac
sigbeta = [np.sqrt((yy - vare) * xxinv[..., i, i] / N_eff) for i in range(Nr)]
xns = [X.name if X.name != '' else 'X%d' % i for i, X in enumerate(Xs)]
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
output = output.split(',')
ret = []
for o in output:
if o == 'B':
if len(oaxes) == 0:
ret.append(beta)
else:
ret.append([Var(oaxes, values=beta[...,i], name='beta_%s' % xns[i]) for i in range(Nr)])
elif o == 'r':
vary = (yy - y**2/N)
R2 = 1 - (yy - vare) / vary
if len(oaxes) == 0:
ret.append(R2)
else:
ret.append(Var(oaxes, values=R2, name='R2'))
elif o == 'p':
ps = [tdist.cdf(np.abs(beta[...,i]/sigbeta[i]), N_eff-Nr) * np.sign(beta[...,i]) for i in range(Nr)]
if len(oaxes) == 0:
ret.append(ps)
else:
ret.append([Var(oaxes, values=ps[i], name='p_%s' % xns[i]) for i in range(Nr)])
elif o == 'sb':
if len(oaxes) == 0:
ret.append(sigbeta)
else:
ret.append([Var(oaxes, values=sigbeta[i], name='sig_%s' % xns[i]) for i in range(Nr)])
elif o == 'covb':
from .axis import NonCoordinateAxis as nca
cr1 = nca(values=list(range(Nr)), regressor1=[X.name for X in Xs], name='regressor1')
cr2 = nca(values=list(range(Nr)), regressor2=[X.name for X in Xs], name='regressor2')
sigmat = np.zeros(os2, 'd')
for i in range(Nr):
for j in range(Nr):
#sigmat[..., i, j] = np.sqrt((yy - vare) * xxinv[..., i, j] / N_eff)
sigmat[..., i, j] = (yy - vare) * xxinv[..., i, j] / N_eff
ret.append(Var(oaxes + [cr1, cr2], values=sigmat, name='smat'))
elif o == 'se':
se = np.sqrt((yy - vare) / N_eff)
if len(oaxes) == 0:
ret.append(se)
else:
ret.append(Var(oaxes, values=se, name='sig_resid'))
else:
print('multiple_regress: unrecognized output "%s"' % o)
return ret
def regress(X, Y, axes=None, pbar=None, N_fac=None, output='m,b,p'):
# {{{
r'''Computes least-squares linear regression of Y against X.
Parameters
==========
X, Y : :class:`Var`
Variables to regress. Must have at least one axis in common.
axes : list, optional
Axes over which to compute correlation; if nothing is specified, the correlation
is computed over all axes common to X and Y.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom; the effective
number will be given by the number estimated from the dataset divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'm,b,p'.
Returns
=======
results : list of :class:`Var` instances.
The return values are specified by the ``output`` argument. A fit of the form
:math:`Y = m X + b + \epsilon` is assumed, and the following parameters
can be returned:
* 'm': Linear coefficient of the regression
* 'b': Constant coefficient of the regression
* 'r': Fraction of the variance in Y explained by X (:math:`R^2`)
* 'p': Probability of this fit if the true linear coefficient was zero
* 'sm': Variance in linear coefficient
* 'se': Variance of residuals
Notes
=====
The statistics described are computed following von Storch and Zwiers 1999,
section 8.3. The p-value 'p' is computed using the t-statistic given in
section 8.3.8, and confidence intervals for the slope and intercept can be
computed from 'se' and 'se' (:math:`\hat{\sigma}_E` and
:math:`\hat{\sigma}_E/\sqrt{S_{XX}}` in von Storch and Zwiers, respectively).
The data is assumed to be normally distributed.'''
from pygeode.tools import loopover, whichaxis, combine_axes, shared_axes, npsum
from pygeode.view import View
srcaxes = combine_axes([X, Y])
oiaxes, riaxes = shared_axes(srcaxes, [X.axes, Y.axes])
if axes is not None:
ri_new = []
for a in axes:
i = whichaxis(srcaxes, a)
if i not in riaxes:
raise KeyError('%s axis not shared by X ("%s") and Y ("%s")' % (a, X.name, Y.name))
ri_new.append(i)
oiaxes.extend([r for r in riaxes if r not in ri_new])
riaxes = ri_new
oaxes = [srcaxes[i] for i in oiaxes]
inaxes = oaxes + [srcaxes[i] for i in riaxes]
oview = View(oaxes)
siaxes = list(range(len(oaxes), len(srcaxes)))
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert len(riaxes) > 0, '%s and %s share no axes to be regressed over' % (X.name, Y.name)
# Construct work arrays
x = np.zeros(oview.shape, 'd')
y = np.zeros(oview.shape, 'd')
xx = np.zeros(oview.shape, 'd')
xy = np.zeros(oview.shape, 'd')
yy = np.zeros(oview.shape, 'd')
# Accumulate data
for outsl, (xdata, ydata) in loopover([X, Y], oview, inaxes, pbar=pbar):
xdata = xdata.astype('d')
ydata = ydata.astype('d')
x[outsl] += npsum(xdata, siaxes)
y[outsl] += npsum(ydata, siaxes)
xx[outsl] += npsum(xdata**2, siaxes)
yy[outsl] += npsum(ydata**2, siaxes)
xy[outsl] += npsum(xdata*ydata, siaxes)
N = np.prod([len(srcaxes[i]) for i in riaxes])
# remove the mean (NOTE: numerically unstable if mean >> stdev)
xx -= x**2/N
yy -= y**2/N
xy -= (x*y)/N
m = xy/xx
b = (y - m*x)/float(N)
if N_fac is None: N_eff = N
else: N_eff = N // N_fac
sige = (yy - m * xy) / (N_eff - 2.)
sigm = np.sqrt(sige / xx)
t = np.abs(m) / sigm
p = tdist.cdf(t, N-2) * np.sign(m)
xn = X.name if X.name != '' else 'X'
yn = Y.name if Y.name != '' else 'Y'
from pygeode.var import Var
output = output.split(',')
ret = []
if 'm' in output:
M = Var(oaxes, values=m, name='%s vs. %s' % (yn, xn))
ret.append(M)
if 'b' in output:
B = Var(oaxes, values=b, name='Intercept (%s vs. %s)' % (yn, xn))
ret.append(B)
if 'r' in output:
ret.append(Var(oaxes, values=xy**2/(xx*yy), name='R2(%s vs. %s)' % (yn, xn)))
if 'p' in output:
P = Var(oaxes, values=p, name='P(%s vs. %s != 0)' % (yn, xn))
ret.append(P)
if 'sm' in output:
ret.append(Var(oaxes, values=sigm, name='Sig. Intercept (%s vs. %s != 0)' % (yn, xn)))
if 'se' in output:
ret.append(Var(oaxes, values=np.sqrt(sige), name='Sig. Resid. (%s vs. %s != 0)' % (yn, xn)))
return ret
def save (filename, in_dataset, version=3, pack=None, compress=False, cfmeta = True, unlimited=None):
# {{{
from ctypes import c_int, c_long, byref
from pygeode.view import View
from pygeode.tools import combine_axes, point
from pygeode.axis import Axis, DummyAxis
import numpy as np
from pygeode.progress import PBar, FakePBar
from pygeode.formats import finalize_save
from pygeode.dataset import asdataset
assert isinstance(filename,str)
in_dataset = asdataset(in_dataset)
dataset = finalize_save(in_dataset, cfmeta, pack)
# Version?
if compress: version = 4
assert version in (3,4)
fileid = c_int()
vars = list(dataset.vars)
# The output axes
axes = combine_axes(v.axes for v in vars)
# Include axes in the list of vars (for writing to netcdf).
# Exclude axes which don't have any intrinsic values.
vars = vars + [a for a in axes if not isinstance(a,DummyAxis)]
#vars.extend(axes)
# Variables (and axes) must all have unique names
assert len(set([v.name for v in vars])) == len(vars), "vars must have unique names: %s"% [v.name for v in vars]
if unlimited is not None:
assert unlimited in [a.name for a in axes]
# Functions for writing entire array
allf = {1:lib.nc_put_var_schar, 2:lib.nc_put_var_text, 3:lib.nc_put_var_short,
4:lib.nc_put_var_int, 5:lib.nc_put_var_float,
6:lib.nc_put_var_double, 7:lib.nc_put_var_uchar,
8:lib.nc_put_var_ushort, 9:lib.nc_put_var_uint,
10:lib.nc_put_var_longlong, 11:lib.nc_put_var_ulonglong}
# Functions for writing chunks
chunkf = {1:lib.nc_put_vara_schar, 2:lib.nc_put_vara_text, 3:lib.nc_put_vara_short,
4:lib.nc_put_vara_int, 5:lib.nc_put_vara_float,
6:lib.nc_put_vara_double, 7:lib.nc_put_vara_uchar,
8:lib.nc_put_vara_ushort, 9:lib.nc_put_vara_uint,
10:lib.nc_put_vara_longlong, 11:lib.nc_put_vara_ulonglong}
# Create the file
if version == 3:
ret = lib.nc_create (filename.encode('ascii'), 0, byref(fileid))
if ret != 0: raise IOError(lib.nc_strerror(ret))
elif version == 4:
ret = lib.nc_create (filename.encode('ascii'), 0x1000, byref(fileid)) # 0x1000 = NC_NETCDF4
if ret != 0: raise IOError(lib.nc_strerror(ret))
else: raise Exception
try:
# Define the dimensions
dimids = [None] * len(axes)
for i,a in enumerate(axes):
dimids[i] = c_int()
if unlimited == a.name:
ret = lib.nc_def_dim (fileid, a.name.encode('ascii'), c_long(0), byref(dimids[i]))
else:
ret = lib.nc_def_dim (fileid, a.name.encode('ascii'), c_long(len(a)), byref(dimids[i]))
assert ret == 0, lib.nc_strerror(ret)
# Define the variables (including axes)
chunks = [None] * len(vars)
varids = [None] * len(vars)
for i, var in enumerate(vars):
t = nc_type[version][var.dtype.name]
# Generate the array of dimension ids for this var
d = [dimids[list(axes).index(a)] for a in var.axes]
# Make it C-compatible
d = (c_int * var.naxes)(*d)
varids[i] = c_int()
ret = lib.nc_def_var (fileid, var.name.encode('ascii'), t, var.naxes, d, byref(varids[i]))
assert ret == 0, lib.nc_strerror(ret)
# Compress the data? (only works for netcdf4 or (higher?))
if compress:
ret = lib.nc_def_var_deflate (fileid, varids[i], 1, 1, 2)
assert ret == 0, lib.nc_strerror(ret)
# Write the attributes
# global attributes
put_attributes (fileid, -1, dataset.atts, version)
# variable attributes
for i, var in enumerate(vars):
# modify axes to be netcdf friendly (CF-compliant, etc.)
put_attributes (fileid, varids[i], var.atts, version)
# Don't pre-fill the file
oldmode = c_int()
ret = lib.nc_set_fill (fileid, 256, byref(oldmode))
assert ret == 0, "Can't set fill mode: %s (error %d)" % (lib.nc_strerror(ret), ret)
# Finished defining the variables, about to start writing the values
ret = lib.nc_enddef (fileid)
assert ret == 0, "Error leaving define mode: %s (error %d)" % (lib.nc_strerror(ret), ret)
# Relative progress of each variable
sizes = [v.size for v in vars]
prog = np.cumsum([0.]+sizes) / np.sum(sizes) * 100
# print "Saving '%s':"%filename
pbar = PBar(message="Saving '%s':"%filename)
# pbar = FakePBar()
# Write the data
for i, var in enumerate(vars):
t = nc_type[version][var.dtype.name]
dtype = numpy_type[t]
# print 'writing', var.name
# number of actual variables (non-axes) for determining our progress
N = len([v for v in vars if not isinstance(v,Axis)])
varpbar = pbar.subset(prog[i], prog[i+1])
views = list(View(var.axes).loop_mem())
for j,v in enumerate(views):
vpbar = varpbar.part(j, len(views))
# print '???', repr(str(v))
# Should always be slices (since we're looping over whole thing contiguously?)
for sl in v.slices: assert isinstance(sl, slice)
for sl in v.slices: assert sl.step in (1,None)
start = [sl.start for sl in v.slices]
count = [sl.stop - sl.start for sl in v.slices]
start = (c_long*var.naxes)(*start)
count = (c_long*var.naxes)(*count)
if isinstance(var, Axis):
assert len(start) == len(count) == 1
data = var.values
data = data[start[0]:start[0]+count[0]] # the above gives us the *whole* axis,
# but under extreme conditions we may be looping over smaller pieces
vpbar.update(100)
else: data = v.get(var, pbar=vpbar)
# Ensure the data is stored contiguously in memory
data = np.ascontiguousarray(data, dtype=dtype)
ret = chunkf[t](fileid, varids[i], start, count, point(data))
assert ret == 0, "Error writing var '%s' to netcdf: %s (error %d)" % (var.name, lib.nc_strerror(ret), ret)
finally:
# Finished
lib.nc_close(fileid)
def plotvar (var, **kwargs):
# {{{
''' plotvar(var, title, clevs, cmap, ax, ifig, hold)
Produces a plot of the pygeode variable var. The routine can plot
1d or 2d data; degenerate axes (of length 1) are ignored; their value is
displayed in the title of the plot.
If the axes are longitude and latitude, the Basemap package is used to plot
variable on a map of the world.
If one of the axes is a ZAxis, it is plotted on the y-axes, logarithmically if
appropriate.
keyword arguments:
title: Title of the plot
ax: A matplotlib axes object on which to produce the plot
lblx: Show xaxis titles and labels
lbly: Show yaxis titles and labels
scaleAx: Scale values with coordinate value (for logarithmic axes only)
colorbar: Show colorbar
clevs: Filled contour levels, if None, no filled contours are plotted
cmap: A colormap passed on to the contour pylab function
clines: Outlined levels, if None, no contour lines are plotted
perx: Roll values in x axis (appropriate for periodic axes)
ifig: Index of the matplotlib figure on which to produce the plot
hold: If True, don't clear the contents of the axis
wait: if True, don't invoke the show() command
(the plotting main loop is not called, so subsequent pygeode commands
can be invoked)
'''
from matplotlib.pyplot import figure, show, ion, ioff, draw, cm, clf, isinteractive
### from matplotlib.numerix import ma
from numpy import ma
from numpy import isnan, isinf, where
from pygeode.progress import PBar
from copy import copy
# Get # of dimensions - can only do 1D or 2D
nd = len([s for s in var.shape if s > 1])
assert nd > 0, "the specified data has no dimensions. Nothing to plot!"
assert nd == 1 or nd == 2, "can only plot 1D or 2D arrays. Try slicing along some dimensions."
axes = var.axes
ret = None
# Create title if none has been specified
title = kwargs.pop('title', None)
if title is None:
title = _buildvartitle(axes, var.name, **var.plotatts)
pbar = kwargs.pop('pbar', True)
if pbar is True:
pbar = PBar(message='Loading plot values from %s:'%repr(var))
values = var.get(pbar=pbar).squeeze()
else:
values = var.get().squeeze()
# Mask out missing values (NaN)
values = ma.masked_where(isnan(values), values)
# Apply linear rescaling for plotting
values = _scalevalues(values, **var.plotatts)
# Scaling by coordinate value preserves integral for log-scaling
scaleAx = kwargs.pop('scaleAx',False) # for line plots
scaleX = kwargs.pop('scaleX',False) # for surface plots
scaleY = kwargs.pop('scaleY',False) # for surface plots
# Log scale for values (not axis)
logVal = kwargs.pop('logVal',False)
wasint = isinteractive()
ioff()
ax = kwargs.pop('ax', None)
ifig = kwargs.pop('ifig', None)
hold = kwargs.pop('hold', False)
wait = kwargs.pop('wait', False)
if ax is None:
if ifig is None:
fig = figure()
else:
fig=figure(ifig)
if not hold: clf()
ax = fig.add_subplot(111)
else:
fig = ax.figure
if not hold and title: ax.set_title(title)
# 1D case:
if nd == 1:
from pygeode.axis import ZAxis, Pres, Hybrid
xaxis = [copy(a) for a in axes if len(a)>1][0]
# adjust axis scaling
#if xaxis.atts['units'] != xaxis.plotatts['plotunits']:
xaxis.values = xaxis.values*xaxis.plotatts.get('scalefactor',1) + xaxis.plotatts.get('offset',0)
# Scaling by coordinate value preserves integral for log-scaling
if (scaleAx and xaxis.plotatts.get('plotscale', 'linear')=='log' and
var.plotatts.get('preserve', 'value')=='area'):
values = values * xaxis.values
# Vertical?
if isinstance(xaxis,ZAxis):
lblx = kwargs.pop('lblx', False) # preserve previous behaviour
lbly = kwargs.pop('lbly', True)
ax.plot(values, xaxis.values, **kwargs)
if logVal or var.plotatts.get('plotscale', 'linear')=='log': ax.set_xscale('log') # value axis
else: ax.set_xscale('linear') # value axis
# ax.set_xscale(var.plotatts.get('plotscale', 'linear')) # value axis
ax.set_yscale(xaxis.plotatts.get('plotscale', 'linear')) # coordiante
ylims = min(xaxis.values),max(xaxis.values)
ax.set_ylim(ylims[::xaxis.plotatts['plotorder']])
# coordinate axis
ax.yaxis.set_major_formatter(xaxis.formatter())
if lbly:
loc = xaxis.locator()
if loc is not None: ax.yaxis.set_major_locator(loc)
ax.set_ylabel(_buildaxistitle(**xaxis.plotatts))
# value axis
if lblx:
ax.set_xlabel(_buildaxistitle(name = var.name, **var.plotatts))
else:
lblx = kwargs.pop('lblx', True)
lbly = kwargs.pop('lbly', False) # preserve previous behaviour
ax.plot(xaxis.values, values, **kwargs)
if logVal or var.plotatts.get('plotscale', 'linear')=='log': ax.set_yscale('log') # value axis
else: ax.set_yscale('linear') # value axis
# ax.set_yscale(var.plotatts.get('plotscale', 'linear')) # value axis
ax.set_xscale(xaxis.plotatts['plotscale']) # coordinate
xlims = min(xaxis.values),max(xaxis.values)
ax.set_xlim(xlims[::xaxis.plotatts['plotorder']])
ax.xaxis.set_major_formatter(xaxis.formatter())
# coordinate axis
if lblx:
loc = xaxis.locator()
if loc is not None: ax.xaxis.set_major_locator(loc)
ax.set_xlabel(_buildaxistitle(**xaxis.plotatts))
# value axis
if lbly:
ax.set_ylabel(_buildaxistitle(name = var.name, **var.plotatts))
# 2D case:
elif nd == 2:
from numpy import meshgrid, concatenate, log10
from matplotlib.pyplot import contourf, colorbar, xlim, ylim, xlabel, ylabel, gca
from pygeode.axis import Lat, Lon, ZAxis, Pres, Hybrid, SpectralM, SpectralN
# Patch for some versions of matplotlib, which leave gaps between polygons
kwargs.setdefault('antialiased',False)
yaxis, xaxis = [copy(a) for a in axes if len(a) > 1]
# adjust x-axis scaling
#if xaxis.atts['units'] != xaxis.plotatts['plotunits']:
xaxis.values = xaxis.values*xaxis.plotatts.get('scalefactor',1) + xaxis.plotatts.get('offset',0)
# adjust y-axis scaling
#if yaxis.atts['units'] != yaxis.plotatts['plotunits']:
yaxis.values = yaxis.values*yaxis.plotatts.get('scalefactor',1) + yaxis.plotatts.get('offset',0)
# Transpose vertical axis?
if isinstance(xaxis, ZAxis):
values = values.transpose()
xaxis, yaxis = yaxis, xaxis
if isinstance(xaxis, SpectralN) and isinstance(yaxis, SpectralM):
values = values.transpose()
xaxis, yaxis = yaxis, xaxis
if isinstance(xaxis, Lat) and isinstance(yaxis, Lon):
values = values.transpose()
xaxis, yaxis = yaxis, xaxis
perx = kwargs.pop('perx', False)
if perx:
xvals = concatenate([xaxis.values, [xaxis.values[-1] + (xaxis.values[1] - xaxis.values[0])]])
yvals = yaxis.values
meshx, meshy = meshgrid (xvals, yvals)
else:
xvals = xaxis.values
yvals = yaxis.values
meshx, meshy = meshgrid (xvals, yvals)
# Scaling by coordinate value preserves integral for log-scaling
if (scaleX and xaxis.plotatts.get('plotscale', 'linear')=='log' and
var.plotatts.get('preserve', 'value')=='area'):
values = values * meshx
if (scaleY and yaxis.plotatts.get('plotscale', 'linear')=='log' and
var.plotatts.get('preserve', 'value')=='area'):
values = values * meshy
# scaling of field values
if logVal: values = log10(values)
#cmap = kwargs.pop('cmap', cm.gist_rainbow_r)
clevs = kwargs.pop('clevs', 21)
clines = kwargs.pop('clines', None)
cbar = kwargs.pop('colorbar', {'orientation':'vertical'})
pcolor = kwargs.pop('pcolor', False)
mask = kwargs.pop('mask', None)
if mask is not None:
values = ma.masked_where(mask(values), values)
if perx:
concatenate([values, values[0:1, :]], axis=0)
#
# Map?
Basemap = None
if kwargs.pop('map', True):
# New toolkit path
try:
from mpl_toolkits.basemap import Basemap
except ImportError: pass
# Old toolkit path
try:
from matplotlib.toolkits.basemap import Basemap
except ImportError: pass
if isinstance(xaxis,Lon) and isinstance(yaxis,Lat) and Basemap is not None:
from numpy import arange
# pop some arguments related to projection grid labelling
projargs = dict(kwargs.pop('projection', {}))
# meridians setup (latitude / y)
meridians = projargs.pop('meridians',[-180,-90,0,90,180,270,360])
# parallels setup (longitude / x)
parallels = projargs.pop('parallels',[-90,-60,-30,0,30,60,90])
# show labels for meridians and parallels in given location
# labels[0]: left, labels[1]: right, labels[2]: top, labels[3]: bottom
labels = projargs.pop('labels',[1,0,0,1])
# default axes boundaries
bnds = {'llcrnrlat':yvals.min(),
'urcrnrlat':yvals.max(),
'llcrnrlon':xvals.min(),
'urcrnrlon':xvals.max()}
# default projection
proj = {'projection':'cyl', 'resolution':'l'}
# read projection arguments
proj.update(projargs)
if proj['projection'] in ['cyl', 'merc', 'mill', 'gall']:
bnds.update(proj)
proj.update(bnds)
# construct projection axis
m = Basemap(ax=ax, **proj)
m.drawcoastlines(ax=ax)
# draw meridians and parallels (using arguments from above)
m.drawmeridians(meridians,labels=labels,ax=ax)
m.drawparallels(parallels,labels=labels,ax=ax)
m.drawmapboundary()
# Transform mesh
px, py = m(meshx, meshy)
cont = None
# Colour individual grid boxes? (no contours)
if pcolor:
clevs = None # can't have both
cont = m.pcolor(px, py, values, **kwargs)
ret = cont
# Filled contours?
if clevs is not None:
cont = m.contourf(px, py, values, clevs, **kwargs)
ret = cont
# Colour bar?
if cbar and cont is not None:
fig.colorbar(cont, ax=ax, **cbar)
# Contour lines?
if clines is not None:
ret = m.contour(px, py, values, clines, colors='k')
else:
cont = None
# Colour individual grid boxes? (no contours)
if pcolor:
clevs = None # can't have both
cont = ax.pcolor(meshx, meshy, values, **kwargs)
ret = cont
# Filled contours?
if clevs is not None:
cont = ax.contourf(meshx, meshy, values, clevs, **kwargs)
ret = cont
# Colour bar?
if cbar and cont is not None:
fig.colorbar(cont, ax=ax, **cbar)
# Contour lines?
if clines is not None:
ret = ax.contour(meshx, meshy, values, clines, colors='k')
# Disable autoscale. Otherwise, if we set a log scale below, then
# the range of our axes will get screwed up.
# (This is a 'feature' of matplotlib!)
# http://www.mail-archive.com/[email protected]/msg10527.html
gca().set_autoscale_on(False)
# Set the axis limits
ax.set_xscale(xaxis.plotatts['plotscale'])
xlims = min(xvals),max(xvals)
ax.set_xlim(xlims[::xaxis.plotatts['plotorder']])
ax.set_yscale(yaxis.plotatts['plotscale'])
ylims = min(yaxis.values),max(yaxis.values)
ax.set_ylim(ylims[::yaxis.plotatts['plotorder']])
# Set x and y labels and formatters
if kwargs.pop('lblx', True):
ax.set_xlabel(_buildaxistitle(**xaxis.plotatts))
ax.xaxis.set_major_formatter(xaxis.formatter())
loc = xaxis.locator()
if loc is not None: ax.xaxis.set_major_locator(loc)
else:
ax.set_xticklabels('')
if kwargs.pop('lbly', True):
ax.set_ylabel(_buildaxistitle(**yaxis.plotatts))
ax.yaxis.set_major_formatter(yaxis.formatter())
loc = yaxis.locator()
if loc is not None: ax.yaxis.set_major_locator(loc)
else:
ax.set_yticklabels('')
if wasint:
ion()
draw()
if not wait: show()
if ret is not None: return ret
def save(filename, var, iaxis=None, fps=15, palette='bw', minmax=None):
from pygeode.axis import TAxis
from pygeode.var import Var
from pygeode.progress import PBar
import tempfile, shutil
import Image
import numpy as np
import os
assert isinstance(var, Var)
# Remove any degenerate dimensions, make sure the axes are in a consistent order
var = var.squeeze().sorted()
assert var.naxes == 3, "can only work with 3D data"
if iaxis is None: iaxis = var.whichaxis(TAxis)
assert iaxis >= 0, "no time axis found"
tmpdir = tempfile.mkdtemp(prefix='pygeode_mpeg')
sl = [slice(None)] * 3
# Get max & min values of the whole dataset
if minmax is None:
#TODO: calculate both of these at once, with a progress bar to help the process
min = float(var.min())
max = float(var.max())
else:
assert len(minmax) == 2, "invalid minmax argument"
min, max = minmax
print("Saving %s:" % filename)
pbar = PBar()
# Loop over each timestep, generate a temporary image file
for i in range(len(var.axes[iaxis])):
fpbar = pbar.part(i, len(var.axes[iaxis]))
sl[iaxis] = i
# Get data, flip y axis, add an 'RGB' axis
data = var[sl].squeeze()[::-1, :, np.newaxis]
data = (data - min) / (max - min) * 255
if palette == 'bw':
# Same data for R, G, and B channels
data = np.concatenate([data, data, data], axis=2)
elif palette == 'rainbow':
# Piecewise linear palette
part1 = data <= 85
part2 = (85 < data) & (data <= 170)
part3 = 170 < data
b = np.zeros(data.shape)
b[part1] = 255
b[part2] = 255 - (data[part2] - 85) * 3
g = np.zeros(data.shape)
g[part1] = data[part1] * 3
g[part2] = 255
g[part3] = 255 - (data[part3] - 170) * 3
r = np.zeros(data.shape)
r[part2] = (data[part2] - 85) * 3
r[part3] = 255
data = np.concatenate([r, g, b], axis=2)
# Encode as an 8-bit array
data = np.asarray(np.round(data), 'uint8')
# Save
framefile = tmpdir + "/frame%04d.jpg" % i
Image.fromarray(data, "RGB").save(framefile, quality=95)
# os.system("display "+framefile)
# break
fpbar.update(100)
shape = list(var.shape)
shape = shape[:iaxis] + shape[iaxis + 1:]
h, w = shape
# """
# Make the movie file
os.system("mencoder mf://%s/*.jpg -mf w=%s:h=%s:type=jpg:fps=%s \
-ovc lavc -lavcopts vcodec=mpeg4:vbitrate=8000 -oac copy \
-o %s" % (tmpdir, w, h, fps, filename))
# """
# Clean up files
shutil.rmtree(tmpdir)
def check_dataset (dataset):
from pygeode.view import View
from pygeode.tools import combine_axes
from pygeode.progress import PBar
from pygeode.dataset import asdataset
import numpy as np
# Make sure we have a dataset (in case we're sent a simple list of vars)
dataset = asdataset(dataset)
vars = list(dataset.vars)
# Include axes in the list of vars (to check these values too)
axes = combine_axes(v.axes for v in vars)
vars.extend(axes)
# Relative progress of each variable
sizes = [v.size for v in vars]
prog = np.cumsum([0.]+sizes) / np.sum(sizes) * 100
pbar = PBar(message="Checking %s for I/O errors:"%repr(dataset))
failed_indices = {}
error_messages = {}
# Loop over the data
for i,var in enumerate(vars):
varpbar = pbar.subset(prog[i], prog[i+1])
# Scan the outer axis (record axis?) for failures.
N = var.shape[0]
failed_indices[var.name] = []
error_messages[var.name] = []
for j in range(N):
vpbar = varpbar.part(j, N)
try:
# Try fetching the data, see if something fails
var[j] if var.naxes == 1 else var[j,...]
except Exception as e:
failed_indices[var.name].append(j)
error_messages[var.name].append(str(e))
vpbar.update(100)
# Print summary information for each variable
everything_ok = True
for var in vars:
indices = failed_indices[var.name]
messages = error_messages[var.name]
if len(indices) == 0: continue
everything_ok = False
print "\nFailures encountered with variable '%s':"%var.name
# Group together record indices that give the same error message
unique_messages = []
aggregated_indices = []
for ind,msg in zip(indices,messages):
if len(unique_messages) == 0 or msg != unique_messages[-1]:
unique_messages.append(msg)
aggregated_indices.append([ind])
else:
aggregated_indices[-1].append(ind)
# Print each error message encountered (and the record indices that give the error)
for ind,msg in zip(aggregated_indices,unique_messages):
# Group records together that have are consecutive (instead of printing each record separately)
groups = []
for i in ind:
if len(groups) == 0 or i-1 not in groups[-1]:
groups.append([i])
else:
groups[-1].append(i)
for g in groups:
print "=> at %s:\n %s"% (var.axes[0].slice[g[0]:g[-1]+1], msg)
if not everything_ok: raise Exception("Problem encountered with the dataset.")
def isnonzero(X, axes, alpha=0.05, N_fac = None, pbar=None):
# {{{
r'''Computes the mean value and statistics of X, against the hypothesis that it is 0.
Parameters
==========
X : :class:`Var`
Variable to average.
axes : list, optional
Axes over which to compute the mean; if nothing is specified, the mean is
computed over all axes.
alpha : float
Confidence level for which to compute confidence interval.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom;
the effective number will be given by the number estimated from the dataset
divided by ``N_fac``.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : tuple or :class:`Dataset` instance.
Three quantities are computed:
* The mean value of X
* The probability of the computed value if the population mean was zero
* The confidence interval of the mean at the level specified by alpha
If the average is taken over all axes of X resulting in a scalar,
the above values are returned as a tuple in the order given. If not, the
results are provided as :class:`Var` objects in a dataset.
See Also
========
difference
Notes
=====
The number of effective degrees of freedom can be scaled as in :meth:`difference`.
The p-value and confidence interval are computed for the t-statistic defined in
eq (6.61) of von Storch and Zwiers 1999.'''
from pygeode.tools import combine_axes, whichaxis, loopover, npsum, npnansum
from pygeode.view import View
riaxes = [X.whichaxis(n) for n in axes]
raxes = [a for i, a in enumerate(X.axes) if i in riaxes]
oaxes = [a for i, a in enumerate(X.axes) if i not in riaxes]
oview = View(oaxes)
N = np.product([len(X.axes[i]) for i in riaxes])
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert N > 1, '%s has only one element along the reduction axes' % X.name
# Construct work arrays
x = np.zeros(oview.shape, 'd')
xx = np.zeros(oview.shape, 'd')
Na = np.zeros(oview.shape, 'd')
x[()] = np.nan
xx[()] = np.nan
Na[()] = np.nan
# Accumulate data
for outsl, (xdata,) in loopover([X], oview, pbar=pbar):
xdata = xdata.astype('d')
x[outsl] = np.nansum([x[outsl], npnansum(xdata, riaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, riaxes)], 0)
# Sum of weights (kludge to get masking right)
Na[outsl] = np.nansum([Na[outsl], npnansum(1. + xdata*0., riaxes)], 0)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
xx = (xx - x**2/Na) / (Na - 1)
x /= Na
if N_fac is not None:
eN = N//N_fac
eNa = Na//N_fac
else:
eN = N
eNa = Na
#print 'eff. N = %.1f' % eN
sdom = np.sqrt(xx/eNa)
p = tdist.cdf(abs(x/sdom), eNa - 1)*np.sign(x)
ci = tdist.ppf(1. - alpha/2, eNa - 1) * sdom
name = X.name if X.name != '' else 'X'
if len(oaxes) > 0:
from pygeode import Var, Dataset
X = Var(oaxes, values=x, name=name)
P = Var(oaxes, values=p, name='p_%s' % name)
CI = Var(oaxes, values=ci, name='CI_%s' % name)
return Dataset([X, P, CI])
else: # Degenerate case
return x, p, ci
def isnonzero(X, axes=None, alpha=0.05, N_fac=None, output='m,p', pbar=None):
# {{{
r'''Computes the mean value of X and statistics relevant for a test against
the hypothesis that it is 0.
Parameters
==========
X : :class:`Var`
Variable to average.
axes : list, optional
Axes over which to compute the mean; if nothing is specified, the mean is
computed over all axes.
alpha : float
Confidence level for which to compute confidence interval.
N_fac : integer
A factor by which to rescale the estimated number of degrees of freedom;
the effective number will be given by the number estimated from the dataset
divided by ``N_fac``.
output : string, optional
A string determining which parameters are returned; see list of possible outputs
in the Returns section. The specifications must be separated by a comma. Defaults
to 'm,p'.
pbar : progress bar, optional
A progress bar object. If nothing is provided, a progress bar will be displayed
if the calculation takes sufficiently long.
Returns
=======
results : :class:`Dataset`
The names of the variables match the output request string (i.e. if ``ds``
is the returned dataset, the mean value can be obtained through ``ds.m``).
The following quantities can be calculated.
* 'm': The mean value of X
* 'p': The probability of the computed value if the population mean was zero
* 'ci': The confidence interval of the mean at the level specified by alpha
If the average is taken over all axes of X resulting in a scalar,
the above values are returned as a tuple in the order given. If not, the
results are provided as :class:`Var` objects in a dataset.
See Also
========
difference
Notes
=====
The number of effective degrees of freedom can be scaled as in :meth:`difference`.
The p-value and confidence interval are computed for the t-statistic defined in
eq (6.61) of von Storch and Zwiers 1999.'''
from pygeode.tools import combine_axes, whichaxis, loopover, npsum, npnansum
from pygeode.view import View
riaxes = [X.whichaxis(n) for n in axes]
raxes = [a for i, a in enumerate(X.axes) if i in riaxes]
oaxes = [a for i, a in enumerate(X.axes) if i not in riaxes]
oview = View(oaxes)
N = np.product([len(X.axes[i]) for i in riaxes])
if pbar is None:
from pygeode.progress import PBar
pbar = PBar()
assert N > 1, '%s has only one element along the reduction axes' % X.name
# Construct work arrays
x = np.zeros(oview.shape, 'd')
xx = np.zeros(oview.shape, 'd')
Na = np.zeros(oview.shape, 'd')
x[()] = np.nan
xx[()] = np.nan
Na[()] = np.nan
# Accumulate data
for outsl, (xdata, ) in loopover([X], oview, pbar=pbar):
xdata = xdata.astype('d')
x[outsl] = np.nansum([x[outsl], npnansum(xdata, riaxes)], 0)
xx[outsl] = np.nansum([xx[outsl], npnansum(xdata**2, riaxes)], 0)
# Sum of weights (kludge to get masking right)
Na[outsl] = np.nansum(
[Na[outsl], npnansum(~np.isnan(xdata), riaxes)], 0)
imsk = (Na > 0.)
# remove the mean (NOTE: numerically unstable if mean >> stdev)
xx[imsk] -= x[imsk]**2 / Na[imsk]
xx[imsk] = xx[imsk] / (Na[imsk] - 1)
x[imsk] /= Na[imsk]
if N_fac is not None:
eN = N // N_fac
eNa = Na // N_fac
else:
eN = N
eNa = Na
sdom = np.zeros((oview.shape), 'd')
p = np.zeros((oview.shape), 'd')
t = np.zeros((oview.shape), 'd')
ci = np.zeros((oview.shape), 'd')
sdom[imsk] = np.sqrt(xx[imsk] / eNa[imsk])
dmsk = (sdom > 0.)
t[dmsk] = np.abs(x[dmsk]) / sdom[dmsk]
p[imsk] = 2. * (1. - tdist.cdf(t[imsk], eNa[imsk] - 1))
ci[imsk] = tdist.ppf(1. - alpha / 2, eNa[imsk] - 1) * sdom[imsk]
name = X.name if X.name != '' else 'X'
from pygeode.var import Var
from pygeode.dataset import asdataset
rvs = []
if 'm' in output:
m = Var(oaxes, values=x, name='m')
m.atts['longname'] = 'Mean value of %s' % (name, )
rvs.append(m)
if 'p' in output:
p = Var(oaxes, values=p, name='p')
p.atts['longname'] = 'p-value of test %s is 0' % (name, )
rvs.append(p)
if 'ci' in output:
ci = Var(oaxes, values=ci, name='ci')
ci.atts[
'longname'] = 'Confidence intervale of the mean value of %s' % (
name, )
rvs.append(ci)
return asdataset(rvs)
