def prep (var, iaxis, weight, out):
from pygeode.timeaxis import Time
from pygeode.var import Var
from pygeode.axis import Axis
from pygeode.view import View
from warnings import warn
from pygeode import MAX_ARRAY_SIZE
assert isinstance(var,Var)
assert var.naxes >= 2, "need at least 2 axes"
# Check the outputs
out = whichout(out)
del out # not actually used here
# Keep the name
name = var.name
# Normalize by area weight?
var = apply_weights(var, weight=weight)
del weight
timeaxes = iaxis
del iaxis
if timeaxes is None:
if var.hasaxis(Time): timeaxes = Time
else:
warn ("No explicit record axis provided. Using the first axis.", stacklevel=2)
timeaxes = 0
# Keep the record axis/axes as a tuple
# (in case we have more than one axis, i.e. time and ensemble)
if not isinstance(timeaxes,(list,tuple)):
assert isinstance(timeaxes,int) or issubclass(timeaxes,Axis), 'unknown iaxis type %s'%type(timeaxes)
timeaxes = [timeaxes]
# Convert the axes to integer ids
timeaxes = [var.whichaxis(a) for a in timeaxes]
spaceaxes = [i for i in range(var.naxes) if i not in timeaxes]
# Convert to axis objects
timeaxes = [var.axes[i] for i in timeaxes]
spaceaxes = [var.axes[i] for i in spaceaxes]
# Create a view, to hold the axes together
# (provides us with other useful stuff, like a notion of 'shape' and 'size')
time = View(axes=timeaxes)
space = View(axes=spaceaxes)
# var = SquishedVar(var, timeaxes, spaceaxes)
# Preload the data, if possible
if var.size <= MAX_ARRAY_SIZE: var = var.load()
return var, time, space
def getview (self, view, pbar):
from pygeode.view import View
import numpy as np
# Indices of the full axes
fullaxis_ind = [self.whichaxis(a) for a in iaxes]
# Prepend the other axes
ind = [i for i in range(self.naxes) if i not in fullaxis_ind] + fullaxis_ind
# print "ind:", ind
# Reverse order
rind = [-1] * len(ind)
for i,I in enumerate(ind):
rind[I] = i
assert len(ind) == self.naxes and len(set(ind)) == self.naxes
# Construct a view with this new order of axes, and with the specified axes unsliced.
axes = tuple([view.axes[i] for i in ind])
slices = tuple([view.slices[i] for i in ind])
bigview = View(axes, slices = slices)
bigview = bigview.unslice(*fullaxis_ind)
viewloop = list(bigview.loop_mem())
out = np.empty(view.shape, self.dtype)
for i,smallview in enumerate(viewloop):
# print '??', i
for I in fullaxis_ind:
assert smallview.shape[I] == bigview.shape[I], "can't get all of axis '%s' at once"%view.axes[I].name
# Slicing relative to the original view
outsl = tuple(smallview.map_to(bigview.clip()).slices)
# Reorder the axes to the original order
axes = tuple([smallview.axes[I] for I in rind])
assert axes == self.axes
slices = tuple([smallview.slices[I] for I in rind])
smallview = View (axes, slices = slices)
# fudge outsl for this new order
outsl = tuple([outsl[I] for I in rind])
# Slicing the 'full' axes to get what we originally needed
insl = [slice(None)] * self.naxes
for I in fullaxis_ind: insl[I] = view.slices[I]
# Get the data
tmp = old_getview (self, smallview, pbar = pbar.part(i,len(viewloop)) )
# print '??', out.shape, '[', outsl, ']', ' = ', tmp.shape, '[', insl, ']'
out[outsl] = tmp[insl]
return out
def getview(self, view, pbar):
import numpy as np
from pygeode.view import View, simplify
out = np.empty(view.shape, dtype=self.dtype)
out[()] = float('nan')
out_axes = view.clip().axes
# Loop over all available files.
N = 0 # Number of points covered so far
for filename, opener, axes in self._table:
subaxes = [
self._axis_manager._get_axis_intersection([a1, a2])
for a1, a2 in zip(out_axes, axes)
]
reorder = []
mask = []
if any(len(a) == 0 for a in subaxes): continue
for a1, a2 in zip(out_axes, subaxes):
# Figure out where the input chunk fits into the output
re = np.searchsorted(a2.values, a1.values)
# Mask out elements that we don't actually have in the chunk
m = [
r < len(a2.values) and a2.values[r] == v
for r, v in zip(re, a1.values)
]
m = np.array(m)
# Convert mask to integer indices
m = np.arange(len(m))[m]
# and then to a slice (where possible)
m = simplify(m)
re = re[m]
# Try to simplify the re-ordering array
if np.all(re == np.sort(re)):
re = simplify(re)
reorder.append(re)
mask.append(m)
var = [v for v in opener(filename) if v.name == self._varname][0]
v = View(subaxes)
chunk = v.get(var)
# Note: this may break if there is more than one axis with integer indices.
assert len([
r for r in reorder if isinstance(r, (tuple, np.ndarray))
]) <= 1, "Unhandled advanced indexing case."
assert len([m for m in mask if isinstance(m, (tuple, np.ndarray))
]) <= 1, "Unhandled advanced indexing case."
out[mask] = chunk[reorder]
N = N + chunk.size
pbar.update(100. * N / out.size)
return out
def getview (self, view, pbar):
from pygeode.view import View
import numpy as np
# Do a brute-force mapping of the indices to the internal axes
# (should work if the axes are in 1:1 correspondence)
data = View(self.var.axes, force_slices=view.slices,
force_integer_indices=view.integer_indices).get(self.var, pbar=pbar)
return data
def write_xdr(var, wfile):
import struct
import numpy as np
from pygeode.view import View
lenstr = struct.pack('!2l', var.size, var.size)
wfile.write(lenstr)
# Break the values into memory-friendly chunks
if hasattr (var, 'values'):
values_iter = [var.values]
else:
view = View(var.axes)
# Trap and handle any I/O errors
viewloop = view.loop_mem()
#TODO: make this more general - should we be futzing around with the axes at this level
# Break it up even further along the time axis? (so we don't start a long process through the whole dataset)
if var.naxes > 2:
new_viewloop = []
for v in viewloop:
for s in v.integer_indices[0]:
new_viewloop.append(v.modify_slice(0,[s]))
viewloop = new_viewloop
values_iter = (get_data_trap_io(v,var) for v in viewloop)
for values in values_iter:
daptype = np2dap[values.dtype.name]
if daptype in ('Byte','String'):
# # Do byte encoding here
# raise Exception
values = np.ascontiguousarray(values, 'uint8');
s = lib.int8toStr(values)
elif daptype in ('UInt16', 'Int16', 'UInt32', 'Int32'):
values = np.ascontiguousarray(values, 'int32')
s = lib.int32toStr(values)
elif daptype == 'Float32':
values = np.ascontiguousarray(values, 'float32')
s = lib.float32toStr(values)
elif daptype == 'Float64':
values = np.ascontiguousarray(values, 'float64')
s = lib.float64toStr(values)
wfile.write(s)
def write_xdr(var, wfile):
import struct
import numpy as np
from pygeode.view import View
lenstr = struct.pack('!2', var.size, var.size)
wfile.write(lenstr)
# Break the values into memory-friendly chunks
if hasattr(var, 'values'):
values_iter = [var.values]
else:
view = View(var.axes)
# Trap and handle any I/O errors
viewloop = view.loop_mem()
#TODO: make this more general - should we be futzing around with the axes at this level
# Break it up even further along the time axis? (so we don't start a long process through the whole dataset)
if var.naxes > 2:
new_viewloop = []
for v in viewloop:
for s in v.integer_indices[0]:
new_viewloop.append(v.modify_slice(0, [s]))
viewloop = new_viewloop
values_iter = (get_data_trap_io(v, var) for v in viewloop)
for values in values_iter:
daptype = np2dap[values.dtype.name]
if daptype in ('Byte', 'String'):
# # Do byte encoding here
# raise Exception
values = np.ascontiguousarray(values, 'uint8')
s = lib.int8toStr(values)
elif daptype in ('UInt16', 'Int16', 'UInt32', 'Int32'):
values = np.ascontiguousarray(values, 'int32')
s = lib.int32toStr(values)
elif daptype == 'Float32':
values = np.ascontiguousarray(values, 'float32')
s = lib.float32toStr(values)
elif daptype == 'Float64':
values = np.ascontiguousarray(values, 'float64')
s = lib.float64toStr(values)
wfile.write(s)
def getview (self, view, pbar):
import numpy as np
from pygeode.view import View, simplify
out = np.empty(view.shape, dtype=self.dtype)
out[()] = float('nan')
out_axes = view.clip().axes
# Loop over all available files.
N = 0 # Number of points covered so far
for filename, opener, axes in self._table:
subaxes = [self._axis_manager._get_axis_intersection([a1,a2]) for a1,a2 in zip(out_axes,axes)]
reorder = []
mask = []
if any(len(a)==0 for a in subaxes): continue
for a1,a2 in zip(out_axes,subaxes):
# Figure out where the input chunk fits into the output
re = np.searchsorted(a2.values, a1.values)
# Mask out elements that we don't actually have in the chunk
m = [r<len(a2.values) and a2.values[r]==v for r,v in zip(re,a1.values)]
m = np.array(m)
# Convert mask to integer indices
m = np.arange(len(m))[m]
# and then to a slice (where possible)
m = simplify(m)
re = re[m]
# Try to simplify the re-ordering array
if np.all(re == np.sort(re)):
re = simplify(re)
reorder.append(re)
mask.append(m)
var = [v for v in opener(filename) if v.name == self._varname][0]
v = View(subaxes)
chunk = v.get(var)
# Note: this may break if there is more than one axis with integer indices.
assert len([r for r in reorder if isinstance(r,(tuple,np.ndarray))]) <= 1, "Unhandled advanced indexing case."
assert len([m for m in mask if isinstance(m,(tuple,np.ndarray))]) <= 1, "Unhandled advanced indexing case."
out[mask] = chunk[reorder]
N = N + chunk.size
pbar.update(100.*N/out.size)
return out
def EOF_cov (x, num=1, iaxis=None, weight=True, out=None):
import numpy as np
from pygeode.view import View
x, time, space = prep (x, iaxis, weight=weight, out=out)
del iaxis
# Initialize space for accumulating the covariance matrix
cov = np.zeros ([space.size, space.size], dtype='d')
# Accumulate the covariance
for inview in View(x.axes).loop_mem():
X = inview.get(x)
assert X.size >= space.size, "Spatial pattern is too large"
X = X.reshape(-1,space.size)
cov += np.dot(X.transpose(),X)
# Decompose the eigenvectors & eigenvalues
w, v = np.linalg.eigh(cov/(time.size-1))
variance = w.sum()
eig = np.sqrt(w[::-1][:num])
eof = v.transpose()[::-1,:][:num,:]
# Compute the timeseries
pc = []
for inview in View(x.axes).loop_mem():
X = inview.get(x).reshape(-1,space.size)
pc.append(np.dot(eof, X.transpose()))
pc = np.concatenate(pc, axis=1)
# Normalize
pc /= eig.reshape(num,1)
return finalize (x, time, space, eof, eig, pc, variance, weight=weight, out=out)
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 getview(self, view, pbar):
from pygeode.view import View
import numpy as np
# Indices of the full axes
fullaxis_ind = [self.whichaxis(a) for a in iaxes]
# Prepend the other axes
ind = [i for i in range(self.naxes) if i not in fullaxis_ind
] + fullaxis_ind
# print "ind:", ind
# Reverse order
rind = [-1] * len(ind)
for i, I in enumerate(ind):
rind[I] = i
assert len(ind) == self.naxes and len(set(ind)) == self.naxes
# Construct a view with this new order of axes, and with the specified axes unsliced.
axes = tuple([view.axes[i] for i in ind])
slices = tuple([view.slices[i] for i in ind])
bigview = View(axes, slices=slices)
bigview = bigview.unslice(*fullaxis_ind)
viewloop = list(bigview.loop_mem())
out = np.empty(view.shape, self.dtype)
for i, smallview in enumerate(viewloop):
# print '??', i
for I in fullaxis_ind:
assert smallview.shape[I] == bigview.shape[
I], "can't get all of axis '%s' at once" % view.axes[
I].name
# Slicing relative to the original view
outsl = tuple(smallview.map_to(bigview.clip()).slices)
# Reorder the axes to the original order
axes = tuple([smallview.axes[I] for I in rind])
assert axes == self.axes
slices = tuple([smallview.slices[I] for I in rind])
smallview = View(axes, slices=slices)
# fudge outsl for this new order
outsl = tuple([outsl[I] for I in rind])
# Slicing the 'full' axes to get what we originally needed
insl = [slice(None)] * self.naxes
for I in fullaxis_ind:
insl[I] = view.slices[I]
# Get the data
tmp = old_getview(self,
smallview,
pbar=pbar.part(i, len(viewloop)))
# print '??', out.shape, '[', outsl, ']', ' = ', tmp.shape, '[', insl, ']'
out[outsl] = tmp[insl]
return out
def getview (self, view, pbar):
from pygeode.view import View
import numpy as np
fillvalue = self.fillvalue
scale = self.scale
offset = self.offset
# Do a brute-force mapping of the indices to the internal axes
# (should work if the axes are in 1:1 correspondence)
data = View(self.var.axes, force_slices=view.slices,
force_integer_indices=view.integer_indices).get(self.var, pbar=pbar)
if fillvalue is not None or scale is not None or offset is not None: data = np.copy(data)
if fillvalue is not None: w = np.where(data==fillvalue)
data = np.asarray(data, self.dtype)
if scale is not None: data *= scale
if offset is not None: data += offset
if fillvalue is not None: data[w] = float('nan')
return data
def EOF_guess (x, num=1, iaxis=None, weight=True, out=None):
import numpy as np
from pygeode.var import Var
from pygeode.view import View
from pygeode import eofcore as lib
x, time, space = prep (x, iaxis, weight=weight, out=out)
del iaxis
print("working on array shape %s"%(x.shape,))
# Initialize workspace
work = lib.start (num, space.size)
eof = np.empty((num,)+space.shape, dtype='d')
eig = np.empty([num], dtype='d')
pc = np.empty((num,)+time.shape, dtype='d')
# Variance accumulation
variance = 0.0
# Loop over chunks of the data
for inview in View(x.axes).loop_mem():
X = np.ascontiguousarray(inview.get(x), dtype='d')
assert X.size >= space.size, "Spatial pattern is too large"
nrec = X.size // space.size
lib.process (work, nrec, X)
# Accumulate variance
variance += (X**2).sum()
# Get result
lib.endloop (work, eof, eig, pc)
# Free workspace
lib.finish (work)
# Wrap the stuff
return finalize (x, time, space, eof, eig, pc, variance, weight=weight, out=out)
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)
def EOF_iter (x, num=1, iaxis=None, subspace = -1, max_iter=1000, weight=True, out=None):
"""
(See svd.SVD for documentation on a similar function, but replace each xxx1 and xxx2 parameter with a single xxx parameter.)
"""
import numpy as np
from pygeode import libpath
from pygeode.view import View
from math import sqrt
from pygeode.varoperations import fill
from pygeode import svdcore as lib
# Need vector subspace to be at least as large as the number of EOFs extracted.
if subspace < num: subspace = num
# Run the single-pass guess to seed the first iteration
guess_eof, guess_eig, guess_pc = EOF_guess (x, subspace, iaxis, weight=weight, out=None)
# Convert NaNs to zeros so they don't screw up the matrix operations
guess_eof = fill (guess_eof, 0)
x, time, space = prep(var=x, iaxis=iaxis, weight=weight, out=out)
del iaxis
eofshape = (subspace,) + space.shape
pcshape = time.shape + (subspace,)
pcs = np.empty(pcshape,dtype='d')
oldeofs = np.empty(eofshape,dtype='d')
# Seed with initial guess (in the weighted space)
neweofs = apply_weights (guess_eof, weight=weight).get()
neweofs = np.array(neweofs, dtype='d') # so we can write
# neweofs = np.random.rand(*eofshape)
# Workspace for smaller representative matrix
work1 = np.empty([subspace,subspace], dtype='d')
work2 = np.empty([subspace,subspace], dtype='d')
NX = space.size
# Variance accumulation (on first iteration only)
variance = 0.0
for iter_num in range(1,max_iter+1):
print('iter_num: %d'%iter_num)
neweofs, oldeofs = oldeofs, neweofs
# Reset the accumulation arrays for the next approximations
neweofs[()] = 0
# Apply the covariance matrix
for inview in View(x.axes).loop_mem():
X = np.ascontiguousarray(inview.get(x), dtype='d')
assert X.size >= space.size, "spatial pattern is too large"
nt = inview.shape[0]
time_offset = inview.slices[0].start
ier = lib.build_eofs (subspace, nt, NX, X, oldeofs,
neweofs, pcs[time_offset,...])
assert ier == 0
# Compute variance?
if iter_num == 1:
variance += (X**2).sum()
# Useful dot products
lib.dot(subspace, NX, oldeofs, neweofs, work1)
lib.dot(subspace, NX, neweofs, neweofs, work2)
# Compute surrogate matrix (using all available information from this iteration)
A, residues, rank, s = np.linalg.lstsq(work1,work2,rcond=1e-30)
# Eigendecomposition on surrogate matrix
w, P = np.linalg.eig(A)
# Sort by eigenvalue
S = np.argsort(w)[::-1]
w = w[S]
print(w)
# assert P.dtype.name == 'float64', P.dtype.name
P = np.ascontiguousarray(P[:,S], dtype='d')
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX, P, neweofs)
# Normalize
lib.normalize (subspace, NX, neweofs)
# # verify orthogonality
# for i in range(num):
# print [np.dot(neweofs[i,...].flatten(), neweofs[j,...].flatten()) for j in range(num)]
if np.allclose(oldeofs[:num,...],neweofs[:num,...], atol=0):
print('converged after %d iterations'%iter_num)
break
assert iter_num != max_iter, "no convergence"
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
eof = neweofs[:num]
pc = pcs[...,:num].transpose()
# Extract the eigenvalues
# (compute magnitude of pc arrays)
#TODO: keep eigenvalues as a separate variable in the iteration loop
eig = np.array([sqrt( (pc[i,...]**2).sum() ) for i in range(pc.shape[0]) ])
pc = np.dot(np.diag(1/eig), pc)
return finalize (x, time, space, eof, eig, pc, variance, weight=weight, out=out)
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,
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=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 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 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 get(self, pbar=None, **kwargs):
# {{{
"""
Gets a raw numpy array containing the values of the variable.
Parameters
----------
pbar : boolean (optional)
If ``True``, will display a progress bar while the data is being
retrieved. This requires the *python-progressbar* package (not included
with PyGeode).
**kwargs : keyword arguments (optional)
One or more keyword arguments may be included to subset the variable
before grabbing the data. See :func:`Var.__call__` for a similar
method which uses this keyword subsetting.
Returns
-------
out : numpy.ndarray
The requested values, as a numpy array.
Notes
-----
Once you grab the data as a numpy array, you can no longer use the PyGeode
functions to do further work on it directly. You can, however, use
:func:`Var.__init__` to re-wrap your numpy array as a PyGeode Var. This
may be useful if you want to do some very complicated operations on the
data using the numpy interface as an intermediate step.
PyGeode variables can be huge! They can be larger than the available RAM
in your computer, or even larger than your hard disk. Numpy arrays, on
the other hand, need to fit in memory, so make sure you are only getting
a reasonable piece of data at a time.
Examples
--------
>>> from pygeode.tutorial import t1
>>> print t1.Temp
<Var 'Temp'>:
Shape: (lat,lon) (32,64)
Axes:
lat <Lat> : 85 S to 85 N (32 values)
lon <Lon> : 0 E to 354 E (64 values)
Attributes:
{'units': 'K'}
Type: Var (dtype="float64")
>>> x = t1.Temp.get()
>>> print x
[[ 261.05848727 259.81373805 258.6761858 ..., 264.37317879
263.44078874 262.30323649]
[ 261.66049058 260.49545075 259.43074336 ..., 264.76292084
263.89023779 262.82553041]
[ 262.53448988 261.44963014 260.45819779 ..., 265.42340543
264.61078196 263.61934962]
...,
[ 262.53448988 263.61934962 264.61078196 ..., 259.64557433
260.45819779 261.44963014]
[ 261.66049058 262.82553041 263.89023779 ..., 258.55806031
259.43074336 260.49545075]
[ 261.05848727 262.30323649 263.44078874 ..., 257.74379575 258.6761858
259.81373805]]
"""
from pygeode.view import View
import numpy as np
var = self.__call__(**kwargs)
data = View(var.axes).get(var, pbar=pbar)
if isinstance(data, np.ndarray):
data = np.array(data, copy=True)
return data
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 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 get(self, pbar=None, **kwargs):
# {{{
"""
Gets a raw numpy array containing the values of the variable.
Parameters
----------
pbar : boolean (optional)
If ``True``, will display a progress bar while the data is being
retrieved. This requires the *python-progressbar* package (not included
with PyGeode).
**kwargs : keyword arguments (optional)
One or more keyword arguments may be included to subset the variable
before grabbing the data. See :func:`Var.__call__` for a similar
method which uses this keyword subsetting.
Returns
-------
out : numpy.ndarray
The requested values, as a numpy array.
Notes
-----
Once you grab the data as a numpy array, you can no longer use the PyGeode
functions to do further work on it directly. You can, however, use
:func:`Var.__init__` to re-wrap your numpy array as a PyGeode Var. This
may be useful if you want to do some very complicated operations on the
data using the numpy interface as an intermediate step.
PyGeode variables can be huge! They can be larger than the available RAM
in your computer, or even larger than your hard disk. Numpy arrays, on
the other hand, need to fit in memory, so make sure you are only getting
a reasonable piece of data at a time.
Examples
--------
>>> from pygeode.tutorial import t1
>>> print(t1.Temp)
<Var 'Temp'>:
Units: K Shape: (lat,lon) (31,60)
Axes:
lat <Lat> : 90 S to 90 N (31 values)
lon <Lon> : 0 E to 354 E (60 values)
Attributes:
{}
Type: Add_Var (dtype="float64")
>>> x = t1.Temp.get()
>>> print(x)
[[260.73262556 258.08759192 256.45287123 ... 265.01237988 265.01237988
263.37765919]
[261.22683172 258.75813366 257.23239435 ... 265.22126909 265.22126909
263.69552978]
[261.98265134 259.69028886 258.27353093 ... 265.69177175 265.69177175
264.27501382]
...
[261.98265134 264.27501382 265.69177175 ... 258.27353093 258.27353093
259.69028886]
[261.22683172 263.69552978 265.22126909 ... 257.23239435 257.23239435
258.75813366]
[260.73262556 263.37765919 265.01237988 ... 256.45287123 256.45287123
258.08759192]]
"""
from pygeode.view import View
import numpy as np
var = self.__call__(**kwargs)
data = View(var.axes).get(var, pbar=pbar)
if isinstance(data, np.ndarray):
data = np.array(data, copy=True)
return data
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 SVD (var1, var2, num=1, subspace=-1, iaxis=Time, weight1=True, weight2=True, matrix='cov'):
"""
Finds coupled EOFs of two fields. Note that the mean/trend/etc. is NOT
removed in this routine.
Parameters
----------
var1, var2 : :class:`Var`
The variables to analyse.
num : integer
The number of EOFs to compute (default is ``1``).
weight1, weight2 : optional
Weights to use for defining orthogonality in the var1, var2 domains,
respectively. Patterns X and Y in the var1 domain are orthogonal if the
sum over X*Y*weights1 is 0. Patterns Z and W in the var2 domain are
orthogonal if the sum over Z*W*weights2 is 0. Default is to use internal
weights defined for var1 accessed by :meth:`Var.getweights()`. If set to
``False`` no weighting is used.
matrix : string, optional ['cov']
Which matrix we are diagonalizing (default is 'cov').
* 'cov': covariance matrix of var1 & var2
* 'cov': correlation matrix of var1 & var2
iaxis : Axis identifier
The principal component / expansion coefficient axis, i.e., the 'time'
axis. Can be an integer (the axis number, leftmost = 0), the axis name
(string), or a Pygeode axis class. If not specified, will try to use
pygeode.timeaxis.Time, and if that fails, the leftmost axis.
Returns
-------
(eof1, pc1, eof2, pc2): tuple
* eof1: The coupled eof patterns for var1.
* pc1: The principal component / expansion coefficients for var1.
* eof2: The coupled eof patterns for var2.
* pc2: The principal component / expansion coefficients for var2.
Notes
-----
Multiple orders of EOFs are concatenated along an 'order' axis.
"""
import numpy as np
from pygeode.timeaxis import Time
from pygeode.var import Var
from pygeode.view import View
from pygeode import MAX_ARRAY_SIZE
from warnings import warn
from pygeode import svdcore as lib
if matrix in ('cov', 'covariance'): matrix = 'cov'
elif matrix in ('cor', 'corr', 'correlation'): matrix = 'cor'
else:
warn ("invalid matrix type '%'. Defaulting to covariance."%matrix, stacklevel=2)
matrix = 'cov'
MAX_ITER = 1000
# Iterate over more EOFs than we need
# (this helps with convergence)
# TODO: a more rigorous formula for the optimum number of EOFs to use
if subspace <= 0: subspace = 2*num + 8
if subspace < num: subspace = num # Just in case
# Remember the names
prefix1 = var1.name+'_' if var1.name != '' else ''
prefix2 = var2.name+'_' if var2.name != '' else ''
# Apply weights?
# if weight1 is not None: var1 *= weight1.sqrt()
# if weight2 is not None: var2 *= weight2.sqrt()
if weight1 is True: weight1 = var1.getweights()
if weight1 is not False:
assert not weight1.hasaxis(iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight1.sum() / weight1.size
weight1 /= W
# Apply the weights
var1 *= weight1.sqrt()
if weight2 is True: weight2 = var2.getweights()
if weight2 is not False:
assert not weight2.hasaxis(iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight2.sum() / weight2.size
weight2 /= W
# Apply the weights
var2 *= weight2.sqrt()
#TODO: allow multiple iteration axes (i.e., time and ensemble)
# if iaxis is None:
# if var1.hasaxis(Time) and var2.hasaxis(Time):
# iaxis1 = var1.whichaxis(Time)
# iaxis2 = var2.whichaxis(Time)
# else:
# iaxis1 = 0
# iaxis2 = 0
# else:
iaxis1 = var1.whichaxis(iaxis)
iaxis2 = var2.whichaxis(iaxis)
assert var1.axes[iaxis1] == var2.axes[iaxis2], "incompatible iteration axes"
del iaxis # so we don't use this by accident
# Special case: can load entire variable in memory
# This will save some time, especially if the field is stored on disk, or is heavily derived
if var1.size <= MAX_ARRAY_SIZE:
print('preloading '+repr(var1))
var1 = var1.load()
if var2.size <= MAX_ARRAY_SIZE:
print('preloading '+repr(var2))
var2 = var2.load()
# Use correlation instead of covariance?
# (normalize by standard deviation)
if matrix == 'cor':
print('computing standard deviations')
std1 = var1.stdev(iaxis1).load()
std2 = var2.stdev(iaxis2).load()
# account for grid points with zero standard deviation?
std1.values = std1.values + (std1.values == 0)
std2.values = std2.values + (std2.values == 0)
var1 /= std1
var2 /= std2
eofshape1 = (subspace,) + var1.shape[:iaxis1] + var1.shape[iaxis1+1:]
eofshape2 = (subspace,) + var2.shape[:iaxis2] + var2.shape[iaxis2+1:]
pcshape1 = (var1.shape[iaxis1], subspace)
pcshape2 = (var2.shape[iaxis2], subspace)
# number of spatial grid points
NX1 = var1.size // var1.shape[iaxis1]
assert NX1 <= MAX_ARRAY_SIZE, 'field is too large!'
NX2 = var2.size // var2.shape[iaxis2]
assert NX2 <= MAX_ARRAY_SIZE, 'field is too large!'
# Total number of timesteps
NT = var1.shape[iaxis1]
# Number of timesteps we can do in one fetch
dt = MAX_ARRAY_SIZE // max(NX1,NX2)
pcs1 = np.empty(pcshape1,dtype='d')
pcs2 = np.empty(pcshape2,dtype='d')
X = np.empty(eofshape2,dtype='d')
U = np.empty(eofshape1,dtype='d')
# Seed with sinusoids superimposed on random values
Y = np.random.rand(*eofshape1)
V = np.random.rand(*eofshape2)
from math import pi
for i in range(subspace):
Y[i,...].reshape(NX1)[:] += np.cos( np.arange(NX1,dtype='d') / NX1 * 2 * pi * (i+1))
V[i,...].reshape(NX2)[:] += np.cos( np.arange(NX2,dtype='d') / NX2 * 2 * pi * (i+1))
# raise Exception
# Workspace for C code
UtAX = np.empty([subspace,subspace], dtype='d')
XtAtU = np.empty([subspace,subspace], dtype='d')
VtV = np.empty([subspace,subspace], dtype='d')
YtY = np.empty([subspace,subspace], dtype='d')
# Views over whole variables
# (rearranged to be compatible with our output eof arrays)
view1 = View( (var1.axes[iaxis1],) + var1.axes[:iaxis1] + var1.axes[iaxis1+1:] )
view2 = View( (var2.axes[iaxis2],) + var2.axes[:iaxis2] + var2.axes[iaxis2+1:] )
for iter_num in range(1,MAX_ITER+1):
print('iter_num: %d'%iter_num)
assert Y.shape == U.shape
assert X.shape == V.shape
U, Y = Y, U
X, V = V, X
# Reset the accumulation arrays for the next approximations
Y[()] = 0
V[()] = 0
# Apply the covariance/correlation matrix
for t in range(0,NT,dt):
# number of timesteps we actually have
nt = min(dt,NT-t)
# Read the data
chunk1 = view1.modify_slice(0, slice(t,t+nt)).get(var1)
chunk1 = np.ascontiguousarray(chunk1, dtype='d')
chunk2 = view2.modify_slice(0, slice(t,t+nt)).get(var2)
chunk2 = np.ascontiguousarray(chunk2, dtype='d')
ier = lib.build_svds (subspace, nt, NX1, NX2, chunk1, chunk2,
X, Y, pcs2[t,...])
assert ier == 0
ier = lib.build_svds (subspace, nt, NX2, NX1, chunk2, chunk1,
U, V, pcs1[t,...])
assert ier == 0
# Useful dot products
lib.dot(subspace, NX1, U, Y, UtAX)
lib.dot(subspace, NX2, V, V, VtV)
lib.dot(subspace, NX1, Y, U, XtAtU)
lib.dot(subspace, NX1, Y, Y, YtY)
# Compute surrogate matrices (using all available information from this iteration)
A1, residues, rank, s = np.linalg.lstsq(UtAX,VtV,rcond=1e-30)
A2, residues, rank, s = np.linalg.lstsq(XtAtU,YtY,rcond=1e-30)
# Eigendecomposition on surrogate matrices
Dy, Qy = np.linalg.eig(np.dot(A1,A2))
Dv, Qv = np.linalg.eig(np.dot(A2,A1))
# Sort by eigenvalue (largest first)
S = np.argsort(np.real(Dy))[::-1]
Dy = Dy[S]
Qy = np.ascontiguousarray(Qy[:,S], dtype='d')
S = np.argsort(np.real(Dv))[::-1]
Dv = Dv[S]
Qv = np.ascontiguousarray(Qv[:,S], dtype='d')
# get estimate of true eigenvalues
D = np.sqrt(Dy) # should also = np.sqrt(Dv) in theory
print(D)
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX1, Qy, Y)
lib.transform(subspace, NX2, Qv, V)
# Normalize
lib.normalize (subspace, NX1, Y)
lib.normalize (subspace, NX2, V)
if not np.allclose(U[:num,...],Y[:num,...], atol=0): continue
if not np.allclose(X[:num,...],V[:num,...], atol=0): continue
print('converged after %d iterations'%iter_num)
break
assert iter_num != MAX_ITER, "no convergence"
# Flip the sign of the var2 EOFs and PCs so that the covariance is positive
lib.fixcov (subspace, NT, NX2, pcs1, pcs2, V)
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
orderaxis = order(num)
eof1 = np.array(Y[:num])
pc1 = np.array(pcs1[...,:num]).transpose()
eof1 = Var((orderaxis,)+var1.axes[:iaxis1]+var1.axes[iaxis1+1:], values=eof1)
pc1 = Var((orderaxis,var1.axes[iaxis1]), values = pc1)
eof2 = np.array(V[:num])
pc2 = np.array(pcs2[...,:num]).transpose()
eof2 = Var((orderaxis,)+var2.axes[:iaxis2]+var2.axes[iaxis2+1:], values=eof2)
pc2 = Var((orderaxis,var2.axes[iaxis2]), values = pc2)
# Apply weights?
if weight1 is not False: eof1 /= weight1.sqrt()
if weight2 is not False: eof2 /= weight2.sqrt()
# Use correlation instead of covariance?
# Re-scale the fields by standard deviation
if matrix == 'cor':
eof1 *= std1
eof2 *= std2
# Give it a name
eof1.name = prefix1 + "EOF"
pc1.name = prefix1 + "PC"
eof2.name = prefix2 + "EOF"
pc2.name = prefix2 + "PC"
return eof1, pc1, eof2, pc2
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 to_xarray(dataset):
"""
Converts a PyGeode Dataset into an xarray Dataset.
Parameters
----------
dataset : pygeode.Dataset
The dataset to be converted.
Returns
-------
out : xarray.Dataset
An object which can be used with the xarray package.
"""
from pygeode.dataset import asdataset
from pygeode.formats.cfmeta import encode_cf
from pygeode.view import View
from dask.base import tokenize
import dask.array as da
import xarray as xr
dataset = asdataset(dataset)
# Encode the axes/variables with CF metadata.
dataset = encode_cf(dataset)
out = dict()
# Loop over each axis and variable.
for var in list(dataset.axes) + list(dataset.vars):
# Generate a unique name to identify it with dask.
name = var.name + "-" + tokenize(var)
dsk = dict()
dims = [a.name for a in var.axes]
# Special case: already have the values in memory.
if hasattr(var, 'values'):
out[var.name] = xr.DataArray(var.values,
dims=dims,
attrs=var.atts,
name=var.name)
continue
# Keep track of all the slices that were made over each dimension.
# This information will be used to determine the "chunking" that was done
# on the variable from inview.loop_mem().
slice_order = [[] for a in var.axes]
chunks = []
# Break up the variable into into portions that are small enough to fit
# in memory. These will become the "chunks" for dask.
inview = View(var.axes)
for outview in inview.loop_mem():
integer_indices = list(map(tuple, outview.integer_indices))
# Determine *how* loop_mem is splitting the axes, and define the chunk
# sizes accordingly.
# A little indirect, but loop_mem doesn't make its chunking choices
# available to the caller.
for o, sl in zip(slice_order, integer_indices):
if sl not in o:
o.append(sl)
ind = [o.index(sl) for o, sl in zip(slice_order, integer_indices)]
# Add this chunk to the dask array.
key = tuple([name] + ind)
dsk[key] = (var.getview, outview, False)
# Construct the dask array.
chunks = [list(map(len, sl)) for sl in slice_order]
arr = da.Array(dsk, name, chunks, dtype=var.dtype)
# Wrap this into an xarray.DataArray (with metadata and named axes).
out[var.name] = xr.DataArray(arr,
dims=dims,
attrs=var.atts,
name=var.name)
# Build the final xarray.Dataset.
out = xr.Dataset(out, attrs=dataset.atts)
# Re-decode the CF metadata on the xarray side.
out = xr.conventions.decode_cf(out)
return out
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 SVD(var1,
var2,
num=1,
subspace=-1,
iaxis=Time,
weight1=True,
weight2=True,
matrix='cov'):
"""
Finds coupled EOFs of two fields. Note that the mean/trend/etc. is NOT
removed in this routine.
Parameters
----------
var1, var2 : :class:`Var`
The variables to analyse.
num : integer
The number of EOFs to compute (default is ``1``).
weight1, weight2 : optional
Weights to use for defining orthogonality in the var1, var2 domains,
respectively. Patterns X and Y in the var1 domain are orthogonal if the
sum over X*Y*weights1 is 0. Patterns Z and W in the var2 domain are
orthogonal if the sum over Z*W*weights2 is 0. Default is to use internal
weights defined for var1 accessed by :meth:`Var.getweights()`. If set to
``False`` no weighting is used.
matrix : string, optional ['cov']
Which matrix we are diagonalizing (default is 'cov').
* 'cov': covariance matrix of var1 & var2
* 'cov': correlation matrix of var1 & var2
iaxis : Axis identifier
The principal component / expansion coefficient axis, i.e., the 'time'
axis. Can be an integer (the axis number, leftmost = 0), the axis name
(string), or a Pygeode axis class. If not specified, will try to use
pygeode.timeaxis.Time, and if that fails, the leftmost axis.
Returns
-------
(eof1, pc1, eof2, pc2): tuple
* eof1: The coupled eof patterns for var1.
* pc1: The principal component / expansion coefficients for var1.
* eof2: The coupled eof patterns for var2.
* pc2: The principal component / expansion coefficients for var2.
Notes
-----
Multiple orders of EOFs are concatenated along an 'order' axis.
"""
import numpy as np
from pygeode.timeaxis import Time
from pygeode.var import Var
from pygeode.view import View
from pygeode import MAX_ARRAY_SIZE
from warnings import warn
from pygeode import svdcore as lib
if matrix in ('cov', 'covariance'): matrix = 'cov'
elif matrix in ('cor', 'corr', 'correlation'): matrix = 'cor'
else:
warn("invalid matrix type '%'. Defaulting to covariance." % matrix,
stacklevel=2)
matrix = 'cov'
MAX_ITER = 1000
# Iterate over more EOFs than we need
# (this helps with convergence)
# TODO: a more rigorous formula for the optimum number of EOFs to use
if subspace <= 0: subspace = 2 * num + 8
if subspace < num: subspace = num # Just in case
# Remember the names
prefix1 = var1.name + '_' if var1.name != '' else ''
prefix2 = var2.name + '_' if var2.name != '' else ''
# Apply weights?
# if weight1 is not None: var1 *= weight1.sqrt()
# if weight2 is not None: var2 *= weight2.sqrt()
if weight1 is True: weight1 = var1.getweights()
if weight1 is not False:
assert not weight1.hasaxis(
iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight1.sum() / weight1.size
weight1 /= W
# Apply the weights
var1 *= weight1.sqrt()
if weight2 is True: weight2 = var2.getweights()
if weight2 is not False:
assert not weight2.hasaxis(
iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight2.sum() / weight2.size
weight2 /= W
# Apply the weights
var2 *= weight2.sqrt()
#TODO: allow multiple iteration axes (i.e., time and ensemble)
# if iaxis is None:
# if var1.hasaxis(Time) and var2.hasaxis(Time):
# iaxis1 = var1.whichaxis(Time)
# iaxis2 = var2.whichaxis(Time)
# else:
# iaxis1 = 0
# iaxis2 = 0
# else:
iaxis1 = var1.whichaxis(iaxis)
iaxis2 = var2.whichaxis(iaxis)
assert var1.axes[iaxis1] == var2.axes[
iaxis2], "incompatible iteration axes"
del iaxis # so we don't use this by accident
# Special case: can load entire variable in memory
# This will save some time, especially if the field is stored on disk, or is heavily derived
if var1.size <= MAX_ARRAY_SIZE:
print('preloading ' + repr(var1))
var1 = var1.load()
if var2.size <= MAX_ARRAY_SIZE:
print('preloading ' + repr(var2))
var2 = var2.load()
# Use correlation instead of covariance?
# (normalize by standard deviation)
if matrix == 'cor':
print('computing standard deviations')
std1 = var1.stdev(iaxis1).load()
std2 = var2.stdev(iaxis2).load()
# account for grid points with zero standard deviation?
std1.values = std1.values + (std1.values == 0)
std2.values = std2.values + (std2.values == 0)
var1 /= std1
var2 /= std2
eofshape1 = (subspace, ) + var1.shape[:iaxis1] + var1.shape[iaxis1 + 1:]
eofshape2 = (subspace, ) + var2.shape[:iaxis2] + var2.shape[iaxis2 + 1:]
pcshape1 = (var1.shape[iaxis1], subspace)
pcshape2 = (var2.shape[iaxis2], subspace)
# number of spatial grid points
NX1 = var1.size // var1.shape[iaxis1]
assert NX1 <= MAX_ARRAY_SIZE, 'field is too large!'
NX2 = var2.size // var2.shape[iaxis2]
assert NX2 <= MAX_ARRAY_SIZE, 'field is too large!'
# Total number of timesteps
NT = var1.shape[iaxis1]
# Number of timesteps we can do in one fetch
dt = MAX_ARRAY_SIZE // max(NX1, NX2)
pcs1 = np.empty(pcshape1, dtype='d')
pcs2 = np.empty(pcshape2, dtype='d')
X = np.empty(eofshape2, dtype='d')
U = np.empty(eofshape1, dtype='d')
# Seed with sinusoids superimposed on random values
Y = np.random.rand(*eofshape1)
V = np.random.rand(*eofshape2)
from math import pi
for i in range(subspace):
Y[i, ...].reshape(NX1)[:] += np.cos(
np.arange(NX1, dtype='d') / NX1 * 2 * pi * (i + 1))
V[i, ...].reshape(NX2)[:] += np.cos(
np.arange(NX2, dtype='d') / NX2 * 2 * pi * (i + 1))
# raise Exception
# Workspace for C code
UtAX = np.empty([subspace, subspace], dtype='d')
XtAtU = np.empty([subspace, subspace], dtype='d')
VtV = np.empty([subspace, subspace], dtype='d')
YtY = np.empty([subspace, subspace], dtype='d')
# Views over whole variables
# (rearranged to be compatible with our output eof arrays)
view1 = View((var1.axes[iaxis1], ) + var1.axes[:iaxis1] +
var1.axes[iaxis1 + 1:])
view2 = View((var2.axes[iaxis2], ) + var2.axes[:iaxis2] +
var2.axes[iaxis2 + 1:])
for iter_num in range(1, MAX_ITER + 1):
print('iter_num: %d' % iter_num)
assert Y.shape == U.shape
assert X.shape == V.shape
U, Y = Y, U
X, V = V, X
# Reset the accumulation arrays for the next approximations
Y[()] = 0
V[()] = 0
# Apply the covariance/correlation matrix
for t in range(0, NT, dt):
# number of timesteps we actually have
nt = min(dt, NT - t)
# Read the data
chunk1 = view1.modify_slice(0, slice(t, t + nt)).get(var1)
chunk1 = np.ascontiguousarray(chunk1, dtype='d')
chunk2 = view2.modify_slice(0, slice(t, t + nt)).get(var2)
chunk2 = np.ascontiguousarray(chunk2, dtype='d')
ier = lib.build_svds(subspace, nt, NX1, NX2, chunk1, chunk2, X, Y,
pcs2[t, ...])
assert ier == 0
ier = lib.build_svds(subspace, nt, NX2, NX1, chunk2, chunk1, U, V,
pcs1[t, ...])
assert ier == 0
# Useful dot products
lib.dot(subspace, NX1, U, Y, UtAX)
lib.dot(subspace, NX2, V, V, VtV)
lib.dot(subspace, NX1, Y, U, XtAtU)
lib.dot(subspace, NX1, Y, Y, YtY)
# Compute surrogate matrices (using all available information from this iteration)
A1, residues, rank, s = np.linalg.lstsq(UtAX, VtV, rcond=1e-30)
A2, residues, rank, s = np.linalg.lstsq(XtAtU, YtY, rcond=1e-30)
# Eigendecomposition on surrogate matrices
Dy, Qy = np.linalg.eig(np.dot(A1, A2))
Dv, Qv = np.linalg.eig(np.dot(A2, A1))
# Sort by eigenvalue (largest first)
S = np.argsort(np.real(Dy))[::-1]
Dy = Dy[S]
Qy = np.ascontiguousarray(Qy[:, S], dtype='d')
S = np.argsort(np.real(Dv))[::-1]
Dv = Dv[S]
Qv = np.ascontiguousarray(Qv[:, S], dtype='d')
# get estimate of true eigenvalues
D = np.sqrt(Dy) # should also = np.sqrt(Dv) in theory
print(D)
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX1, Qy, Y)
lib.transform(subspace, NX2, Qv, V)
# Normalize
lib.normalize(subspace, NX1, Y)
lib.normalize(subspace, NX2, V)
if not np.allclose(U[:num, ...], Y[:num, ...], atol=0): continue
if not np.allclose(X[:num, ...], V[:num, ...], atol=0): continue
print('converged after %d iterations' % iter_num)
break
assert iter_num != MAX_ITER, "no convergence"
# Flip the sign of the var2 EOFs and PCs so that the covariance is positive
lib.fixcov(subspace, NT, NX2, pcs1, pcs2, V)
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
orderaxis = order(num)
eof1 = np.array(Y[:num])
pc1 = np.array(pcs1[..., :num]).transpose()
eof1 = Var((orderaxis, ) + var1.axes[:iaxis1] + var1.axes[iaxis1 + 1:],
values=eof1)
pc1 = Var((orderaxis, var1.axes[iaxis1]), values=pc1)
eof2 = np.array(V[:num])
pc2 = np.array(pcs2[..., :num]).transpose()
eof2 = Var((orderaxis, ) + var2.axes[:iaxis2] + var2.axes[iaxis2 + 1:],
values=eof2)
pc2 = Var((orderaxis, var2.axes[iaxis2]), values=pc2)
# Apply weights?
if weight1 is not False: eof1 /= weight1.sqrt()
if weight2 is not False: eof2 /= weight2.sqrt()
# Use correlation instead of covariance?
# Re-scale the fields by standard deviation
if matrix == 'cor':
eof1 *= std1
eof2 *= std2
# Give it a name
eof1.name = prefix1 + "EOF"
pc1.name = prefix1 + "PC"
eof2.name = prefix2 + "EOF"
pc2.name = prefix2 + "PC"
return eof1, pc1, eof2, pc2
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 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 to_xarray(dataset):
"""
Converts a PyGeode Dataset into an xarray Dataset.
Parameters
----------
dataset : pygeode.Dataset
The dataset to be converted.
Returns
-------
out : xarray.Dataset
An object which can be used with the xarray package.
"""
from pygeode.dataset import asdataset
from pygeode.formats.cfmeta import encode_cf
from pygeode.view import View
from dask.base import tokenize
import dask.array as da
import xarray as xr
dataset = asdataset(dataset)
# Encode the axes/variables with CF metadata.
dataset = encode_cf(dataset)
out = dict()
# Loop over each axis and variable.
for var in list(dataset.axes) + list(dataset.vars):
# Generate a unique name to identify it with dask.
name = var.name + "-" + tokenize(var)
dsk = dict()
dims = [a.name for a in var.axes]
# Special case: already have the values in memory.
if hasattr(var,'values'):
out[var.name] = xr.DataArray(var.values, dims=dims, attrs=var.atts, name=var.name)
continue
# Keep track of all the slices that were made over each dimension.
# This information will be used to determine the "chunking" that was done
# on the variable from inview.loop_mem().
slice_order = [[] for a in var.axes]
chunks = []
# Break up the variable into into portions that are small enough to fit
# in memory. These will become the "chunks" for dask.
inview = View(var.axes)
for outview in inview.loop_mem():
integer_indices = map(tuple,outview.integer_indices)
# Determine *how* loop_mem is splitting the axes, and define the chunk
# sizes accordingly.
# A little indirect, but loop_mem doesn't make its chunking choices
# available to the caller.
for o, sl in zip(slice_order, integer_indices):
if sl not in o:
o.append(sl)
ind = [o.index(sl) for o, sl in zip(slice_order, integer_indices)]
# Add this chunk to the dask array.
key = tuple([name] + ind)
dsk[key] = (var.getview, outview, False)
# Construct the dask array.
chunks = [map(len,sl) for sl in slice_order]
arr = da.Array(dsk, name, chunks, dtype=var.dtype)
# Wrap this into an xarray.DataArray (with metadata and named axes).
out[var.name] = xr.DataArray(arr, dims = dims, attrs = var.atts, name=var.name)
# Build the final xarray.Dataset.
out = xr.Dataset(out, attrs=dataset.atts)
# Re-decode the CF metadata on the xarray side.
out = xr.conventions.decode_cf(out)
return out
