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/nipyPublic

Commit3437e20

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np.float64 for npy.float
1 parent526f697 commit3437e20

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49 files changed

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‎examples/ds105/ds105_example.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -243,13 +243,13 @@ def run_model(subj, run):
243243
tempdict= {}
244244
forvin ['sd','t','effect']:
245245
tempdict[v]=np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
246-
% (n,v)),dtype=np.float,
246+
% (n,v)),dtype=np.float64,
247247
shape=volshape,mode='w+')
248248
output[n]=tempdict
249249

250250
forninfcons:
251251
output[n]=np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
252-
% (n,v)),dtype=np.float,
252+
% (n,v)),dtype=np.float64,
253253
shape=volshape,mode='w+')
254254

255255
# Loop over the unique values of ar1

‎examples/fiac/fiac_example.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -231,13 +231,13 @@ def run_model(subj, run):
231231
tempdict= {}
232232
forvin ['sd','t','effect']:
233233
tempdict[v]=np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
234-
% (n,v)),dtype=np.float,
234+
% (n,v)),dtype=np.float64,
235235
shape=volshape,mode='w+')
236236
output[n]=tempdict
237237

238238
forninfcons:
239239
output[n]=np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
240-
% (n,v)),dtype=np.float,
240+
% (n,v)),dtype=np.float64,
241241
shape=volshape,mode='w+')
242242

243243
# Loop over the unique values of ar1

‎examples/fiac/fiac_util.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -336,12 +336,12 @@ def rewrite_spec(subj, run, root = "/home/jtaylo/FIAC-HBM2009"):
336336
# This is due to the FIAC design
337337

338338
ifdesigntype=='evt':
339-
b=np.array([(d[0]['time'],1)],np.dtype([('time',np.float),
339+
b=np.array([(d[0]['time'],1)],np.dtype([('time',np.float64),
340340
('initial',np.int)]))
341341
d=d[1:]
342342
else:
343343
k=np.equal(np.arange(d.shape[0])%6,0)
344-
b=np.array([(tt,1)forttind[k]['time']],np.dtype([('time',np.float),
344+
b=np.array([(tt,1)forttind[k]['time']],np.dtype([('time',np.float64),
345345
('initial',np.int)]))
346346
d=d[~k]
347347

‎examples/formula/fir.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -44,7 +44,7 @@ def anon(x,k1=k1,k2=k2,k3=k3):
4444

4545
# We're going to evaluate at these specific values of time
4646
tt=np.linspace(0,50,101)
47-
tvals=tt.view(np.dtype([('t',np.float)]))
47+
tvals=tt.view(np.dtype([('t',np.float64)]))
4848

4949
# Some inter-stimulus intervals
5050
isis=np.random.uniform(low=0,high=3,size=(4,))+10.

‎examples/formula/multi_session_contrast.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -84,8 +84,8 @@
8484
# our case the terms are t1, t2, and the (indicator coding) terms from the run
8585
# factor.
8686
rec=np.array([(tv1,tv2,s)fortv1,tv2,sinzip(ttval1,ttval2,run_no)],
87-
np.dtype([('t1',np.float),
88-
('t2',np.float),
87+
np.dtype([('t1',np.float64),
88+
('t2',np.float64),
8989
('run',np.int)]))
9090

9191
# The contrast we care about

‎examples/formula/parametric_design.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -41,7 +41,7 @@
4141
X3=f3.design(tval,params,return_float=True)
4242

4343
# the columns or d/d_b0 and d/dl
44-
tt=tval.view(np.float)
44+
tt=tval.view(np.float64)
4545
v1=np.sum([hrf.glovert(tt-s)*np.exp(-4.5*a)fors,ainzip(t,dt)],0)
4646
v2=np.sum([-3.5*a*hrf.glovert(tt-s)*np.exp(-4.5*a)fors,ainzip(t,dt)],0)
4747

‎examples/interfaces/process_ds105.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -274,7 +274,7 @@ def smooth(self, in_prefix='', out_prefix='s'):
274274
len(fwhm)
275275
exceptTypeError:
276276
fwhm= [fwhm]*3
277-
fwhm=np.asarray(fwhm,dtype=np.float).reshape(1,3)
277+
fwhm=np.asarray(fwhm,dtype=np.float64).reshape(1,3)
278278
sess_scans=scans_for_fnames(
279279
fnames_presuffix(self.data_def['functionals'],in_prefix))
280280
sinfo=make_job('spatial','smooth',

‎examples/interfaces/process_fiac.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -166,7 +166,7 @@ def smooth(data_def, fwhm=8.0):
166166
len(fwhm)
167167
exceptTypeError:
168168
fwhm= [fwhm]*3
169-
fwhm=np.asarray(fwhm,dtype=np.float).reshape(1,3)
169+
fwhm=np.asarray(fwhm,dtype=np.float64).reshape(1,3)
170170
sess_scans=scans_for_fnames(fnames_presuffix(data_def['functionals'],'wa'))
171171
sinfo=make_job('spatial','smooth',
172172
{'data':np.vstack(sess_scans.flat),

‎examples/labs/need_data/glm_beta_and_variance.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -105,15 +105,15 @@
105105
mask=fmri_glm.mask.get_data()>0
106106

107107
# output beta images
108-
beta_map=np.tile(mask.astype(np.float)[...,np.newaxis],dim)
108+
beta_map=np.tile(mask.astype(np.float64)[...,np.newaxis],dim)
109109
beta_map[mask]=beta_hat.T
110110
beta_image=Nifti1Image(beta_map,fmri_glm.affine)
111111
beta_image.get_header()['descrip']= (
112112
'Parameter estimates of the localizer dataset')
113113
save(beta_image,path.join(write_dir,'beta.nii'))
114114
print("Beta image witten in %s"%write_dir)
115115

116-
variance_map=mask.astype(np.float)
116+
variance_map=mask.astype(np.float64)
117117
variance_map[mask]=variance_hat
118118

119119
# Create a snapshots of the variance image contrasts

‎nipy/algorithms/clustering/bgmm.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -146,7 +146,7 @@ def wishart_eval(n, V, W, dV=None, dW=None, piV=None):
146146
ltr=-np.trace(np.dot(piV,W))/2
147147
la= (n*p*math.log(2)+math.log(dV)*n)/2
148148
lg=math.log(math.pi)*p* (p-1)/4
149-
lg+=gammaln(np.arange(n-p+1,n+1).astype(np.float)/2).sum()
149+
lg+=gammaln(np.arange(n-p+1,n+1).astype(np.float64)/2).sum()
150150
lt=ldW+ltr-la-lg
151151
returnmath.exp(lt)
152152

@@ -757,7 +757,7 @@ def conditional_posterior_proba(self, x, z, perm=None):
757757
By default, no permutation is performed
758758
"""
759759
pop=self.pop(z)
760-
rpop= (pop+ (pop==0)).astype(np.float)
760+
rpop= (pop+ (pop==0)).astype(np.float64)
761761
dof=self.prior_dof+pop+1
762762
shrinkage=self.prior_shrinkage+pop
763763
weights=pop+self.prior_weights

‎nipy/algorithms/clustering/ggmixture.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -290,7 +290,7 @@ def show(self, x):
290290
step=3.5*np.std(x)/np.exp(np.log(np.size(x))/3)
291291
bins=max(10,int((x.max()-x.min())/step))
292292
h,c=np.histogram(x,bins)
293-
h=h.astype(np.float)/np.size(x)
293+
h=h.astype(np.float64)/np.size(x)
294294
p=self.mixt
295295

296296
dc=c[1]-c[0]
@@ -646,7 +646,7 @@ def show(self, x, mpaxes=None):
646646
step=3.5*np.std(x)/np.exp(np.log(np.size(x))/3)
647647
bins=max(10,int((x.max()-x.min())/step))
648648
h,c=np.histogram(x,bins)
649-
h=h.astype(np.float)/np.size(x)
649+
h=h.astype(np.float64)/np.size(x)
650650
dc=c[1]-c[0]
651651

652652
ng=self.mixt[0]*_gam_dens(self.shape_n,self.scale_n,-c)

‎nipy/algorithms/clustering/tests/test_clustering.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ def testkmeans2(self):
2525
X=X+3*A
2626
L=np.concatenate([np.ones(5000),np.zeros(5000)]).astype(np.int)
2727
C,L,J=kmeans(X,2,L)
28-
l=L[:7000].astype(np.float)
28+
l=L[:7000].astype(np.float64)
2929
self.assert_(np.mean(l)>0.9)
3030

3131

‎nipy/algorithms/clustering/von_mises_fisher_mixture.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -360,7 +360,7 @@ def select_vmm_cv(krange, precision, x, null_class, cv_index,
360360
forkinkrange:
361361
mll.append(-np.inf)
362362
forjinrange(1):
363-
ll=np.zeros_like(cv_index).astype(np.float)
363+
ll=np.zeros_like(cv_index).astype(np.float64)
364364
foriinnp.unique(cv_index):
365365
xl=x[cv_index!=i]
366366
xt=x[cv_index==i]

‎nipy/algorithms/graph/tests/test_field.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -13,14 +13,14 @@
1313

1414
defbasic_field(nx=10,ny=10,nz=10):
1515
xyz=np.reshape(np.indices((nx,ny,nz)), (3,nx*ny*nz)).T
16-
data=np.sum(xyz,1).astype(np.float)
16+
data=np.sum(xyz,1).astype(np.float64)
1717
myfield=field_from_graph_and_data(wgraph_from_3d_grid(xyz,26),data)
1818
returnmyfield
1919

2020

2121
defbasic_field_random(nx=10,ny=10,nz=1):
2222
xyz=np.reshape(np.indices((nx,ny,nz)), (3,nx*ny*nz)).T
23-
data=0.5*nr.randn(nx*ny*nz,1)+np.sum(xyz,1).astype(np.float)
23+
data=0.5*nr.randn(nx*ny*nz,1)+np.sum(xyz,1).astype(np.float64)
2424
myfield=field_from_graph_and_data(wgraph_from_3d_grid(xyz,26),data)
2525
returnmyfield
2626

@@ -252,7 +252,7 @@ def test_constrained_voronoi_2(nbseeds=3):
252252
# Test the geodisc k-means algorithm
253253
xyz,x=np.zeros((30,3)),np.arange(30)
254254
xyz[:,0]=x
255-
y=np.array((x//10),np.float)
255+
y=np.array((x//10),np.float64)
256256
myfield=field_from_graph_and_data(wgraph_from_3d_grid(xyz,6),y)
257257
seeds=np.array([1,18,25])
258258
label=myfield.constrained_voronoi(seeds)

‎nipy/algorithms/graph/tests/test_graph.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -396,7 +396,7 @@ def test_concatenate2(n=10):
396396
deftest_anti_symmeterize():
397397
n=10
398398
eps=1.e-7
399-
M= (nr.rand(n,n)>0.7).astype(np.float)
399+
M= (nr.rand(n,n)>0.7).astype(np.float64)
400400
C=M-M.T
401401
G=wgraph_from_adjacency(M)
402402
G.anti_symmeterize()
@@ -432,7 +432,7 @@ def test_graph_create_from_coo_matrix():
432432
"""Test the creation of a graph from a sparse coo_matrix
433433
"""
434434
importscipy.sparseasspp
435-
a= (np.random.randn(5,5)>.8).astype(np.float)
435+
a= (np.random.randn(5,5)>.8).astype(np.float64)
436436
s=spp.coo_matrix(a)
437437
wg=wgraph_from_coo_matrix(s)
438438
b=wg.to_coo_matrix()
@@ -442,7 +442,7 @@ def test_graph_create_from_coo_matrix():
442442
deftest_to_coo_matrix():
443443
""" Test the generation of a sparse matrix as output
444444
"""
445-
a= (np.random.randn(5,5)>.8).astype(np.float)
445+
a= (np.random.randn(5,5)>.8).astype(np.float64)
446446
wg=wgraph_from_adjacency(a)
447447
b=wg.to_coo_matrix().todense()
448448
assert_array_equal(a,b)

‎nipy/algorithms/statistics/bench/bench_intvol.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -20,7 +20,7 @@ def bench_lips3d():
2020
repeat=4
2121
bx_sz=60
2222
box1,box2,edge1,edge2=nonintersecting_boxes((bx_sz,)*3)
23-
c=np.indices(box1.shape).astype(np.float)
23+
c=np.indices(box1.shape).astype(np.float64)
2424
sys.stdout.flush()
2525
print("\nIntrinsic volumes 3D")
2626
print("--------------------")
@@ -44,7 +44,7 @@ def bench_lips2d():
4444
repeat=4
4545
bx_sz=500
4646
box1,box2,edge1,edge2=nonintersecting_boxes((bx_sz,)*2)
47-
c=np.indices(box1.shape).astype(np.float)
47+
c=np.indices(box1.shape).astype(np.float64)
4848
sys.stdout.flush()
4949
print("\nIntrinsic volumes 2D")
5050
print("--------------------")
@@ -68,7 +68,7 @@ def bench_lips1d():
6868
repeat=4
6969
bx_sz=100000
7070
box1,box2,edge1,edge2=nonintersecting_boxes((bx_sz,))
71-
c=np.indices(box1.shape).astype(np.float)
71+
c=np.indices(box1.shape).astype(np.float64)
7272
sys.stdout.flush()
7373
print("\nIntrinsic volumes 1D")
7474
print("--------------------")

‎nipy/algorithms/statistics/empirical_pvalue.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -208,7 +208,7 @@ def learn(self, left=0.2, right=0.8):
208208
medge=ledge+0.5*step
209209

210210
# remove null bins
211-
hist=hist[hist>0].astype(np.float)
211+
hist=hist[hist>0].astype(np.float64)
212212
medge=medge[:-1][hist>0]# edges include rightmost outer
213213

214214
# fit the histogram

‎nipy/algorithms/statistics/formula/formulae.py

Lines changed: 15 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -175,7 +175,7 @@ class Term(sympy.Symbol):
175175
recarray used to create a design matrix.
176176
177177
>>> t = Term('x')
178-
>>> xval = np.array([(3,),(4,),(5,)], np.dtype([('x', np.float)]))
178+
>>> xval = np.array([(3,),(4,),(5,)], np.dtype([('x', np.float64)]))
179179
>>> f = t.formula
180180
>>> d = f.design(xval)
181181
>>> print(d.dtype.descr)
@@ -377,7 +377,7 @@ def make_recarray(rows, names, dtypes=None, drop_name_dim=_NoValue):
377377
Create a recarray with named columns from a list or ndarray of `rows` and
378378
sequence of `names` for the columns. If `rows` is an ndarray, `dtypes` must
379379
be None, otherwise we raise a ValueError. Otherwise, if `dtypes` is None,
380-
we cast the data in all columns in `rows` as np.float. If `dtypes` is not
380+
we cast the data in all columns in `rows` as np.float64. If `dtypes` is not
381381
None, the routine uses `dtypes` as a dtype specifier for the output
382382
structured array.
383383
@@ -422,7 +422,7 @@ def make_recarray(rows, names, dtypes=None, drop_name_dim=_NoValue):
422422
array([(3, 4), (4, 6), (6, 8)],
423423
dtype=[('x', '<i8'), ('y', '<i8')])
424424
>>> make_recarray([[3, 4], [4, 6], [7, 9]], 'wv',
425-
... [np.float, np.int]) #doctest: +STRUCTARR_EQUAL
425+
... [np.float64, np.int_]) #doctest: +STRUCTARR_EQUAL
426426
array([(3.0, 4), (4.0, 6), (7.0, 9)],
427427
dtype=[('w', '<f8'), ('v', '<i8')])
428428
@@ -440,7 +440,7 @@ def make_recarray(rows, names, dtypes=None, drop_name_dim=_NoValue):
440440
return_recarray_from_array(rows,names,drop_name_dim)
441441
# Structured array from list
442442
ifdtypesisNone:
443-
dtype=np.dtype([(n,np.float)forninnames])
443+
dtype=np.dtype([(n,np.float64)forninnames])
444444
else:
445445
dtype=np.dtype([(n,d)forn,dinzip(names,dtypes)])
446446
# Peek at first value in iterable
@@ -519,7 +519,7 @@ def _getdiff(self):
519519

520520
def_getdtype(self):
521521
vnames= [str(s)forsinself.design_expr]
522-
returnnp.dtype([(n,np.float)forninvnames])
522+
returnnp.dtype([(n,np.float64)forninvnames])
523523
dtype=property(_getdtype,doc='The dtype of the design matrix of the Formula.')
524524

525525
def__repr__(self):
@@ -797,9 +797,9 @@ def _setup_design(self):
797797
# There is also an argument for parameters that are not
798798
# Terms.
799799

800-
self._dtypes= {'param':np.dtype([(str(p),np.float)forpinparams]),
801-
'term':np.dtype([(str(t),np.float)fortinterms]),
802-
'preterm':np.dtype([(n,np.float)forninpreterm])}
800+
self._dtypes= {'param':np.dtype([(str(p),np.float64)forpinparams]),
801+
'term':np.dtype([(str(t),np.float64)fortinterms]),
802+
'preterm':np.dtype([(n,np.float64)forninpreterm])}
803803

804804
self.__terms=terms
805805

@@ -819,7 +819,7 @@ def design(self,
819819
Recarray including fields that are not Terms in
820820
getparams(self.design_expr)
821821
return_float : bool, optional
822-
If True, return a np.float array rather than a np.recarray
822+
If True, return a np.float64 array rather than a np.recarray
823823
contrasts : None or dict, optional
824824
Contrasts. The items in this dictionary should be (str,
825825
Formula) pairs where a contrast matrix is constructed for
@@ -854,9 +854,9 @@ def design(self,
854854
# If the only term is an intercept,
855855
# the return value is a matrix of 1's.
856856
iflist(self.terms)== [sympy.Number(1)]:
857-
a=np.ones(preterm_recarray.shape[0],np.float)
857+
a=np.ones(preterm_recarray.shape[0],np.float64)
858858
ifnotreturn_float:
859-
a=a.view(np.dtype([('intercept',np.float)]))
859+
a=a.view(np.dtype([('intercept',np.float64)]))
860860
returna
861861
elifnotself._dtypes['term']:
862862
raiseValueError("none of the expresssions are self.terms "
@@ -882,7 +882,7 @@ def design(self,
882882
# The lambda created in self._setup_design needs to take a tuple of
883883
# columns as argument, not an ndarray, so each column
884884
# is extracted and put into float_tuple.
885-
float_array=term_recarray.view(np.float)
885+
float_array=term_recarray.view(np.float64)
886886
float_array.shape= (term_recarray.shape[0],-1)
887887
float_array=float_array.T
888888
float_tuple=tuple(float_array)
@@ -924,7 +924,7 @@ def design(self,
924924
ifreturn_floatorcontrasts:
925925
# If the design matrix is just a column of 1s
926926
# return a 1-dimensional array.
927-
D=np.squeeze(D.astype(np.float))
927+
D=np.squeeze(D.astype(np.float64))
928928
# If there are contrasts, the pseudo-inverse of D
929929
# must be computed.
930930
ifcontrasts:
@@ -980,7 +980,7 @@ def natural_spline(t, knots=None, order=3, intercept=False):
980980
--------
981981
>>> x = Term('x')
982982
>>> n = natural_spline(x, knots=[1,3,4], order=3)
983-
>>> xval = np.array([3,5,7.]).view(np.dtype([('x', np.float)]))
983+
>>> xval = np.array([3,5,7.]).view(np.dtype([('x', np.float64)]))
984984
>>> n.design(xval, return_float=True)
985985
array([[ 3., 9., 27., 8., 0., -0.],
986986
[ 5., 25., 125., 64., 8., 1.],
@@ -1120,7 +1120,7 @@ def fromcol(col, name):
11201120
11211121
Examples
11221122
--------
1123-
>>> data = np.array([(3,'a'),(4,'a'),(5,'b'),(3,'b')], np.dtype([('x', np.float), ('y', 'S1')]))
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>>> data = np.array([(3,'a'),(4,'a'),(5,'b'),(3,'b')], np.dtype([('x', np.float64), ('y', 'S1')]))
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>>> f1 = Factor.fromcol(data['y'], 'y')
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>>> f2 = Factor.fromcol(data['x'], 'x')
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>>> d = f1.design(data)

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