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Functions1,068 in github.com/0todd0000/spm1d

⨍ Functions 1,068◇ Types & classes 451

↓ 1 callersMethodnUpcrossings

Number of upcrossings. :Parameters: *y* --- a 1D field *u* --- threshold height :Returns: *c* --- number of upcrossings

spm1d/rft1d/geom.py:435

↓ 1 callersMethodnumber_of_suprathreshold_resels

Number of resels expected in the entire excursion set at threshold *u*. These resels can come from multiple upcrossings. One resel contains (1

spm1d/rft1d/prob.py:424

↓ 1 callersMethodnumber_of_upcrossings

Number of upcrossings expected for threshold *u*. :Example: >>> calc = rft1d.prob.RFTCalculator('T', (1,8), 101, 15.0) >>> calc.expect

spm1d/rft1d/prob.py:400

↓ 1 callersFunctionp2string

spm1d/_plot.py:27

↓ 1 callersMethodplot

(self, color='k', lw=3, linestyle='-', marker=' ', facecolor='0.7', thresh_color='k', label=None)

spm1d/_plot.py:206

↓ 1 callersFunctionplot_ci_0d

Plot a one-sample confidence interval for 0D data.

spm1d/plot.py:43

↓ 1 callersFunctionplot_ci_multisample

Plot a multi-mean condfidence interval.

spm1d/plot.py:105

↓ 1 callersFunctionplot_ci_multisample_0d

Plot a paired- or two-sample confidence interval for 0D data.

spm1d/plot.py:57

↓ 1 callersMethodplot_cluster_patches

(self, facecolor='0.8')

spm1d/_plot.py:213

↓ 1 callersMethodplot_design

(self, factor_labels=None, fontsize=10)

spm1d/_plot.py:150

↓ 1 callersFunctionplot_other_thresholds

(ax, spms, colors)

spm1d/examples/nonparam/v0.4.50/roi/ex_ttest_paired.py:8

↓ 1 callersFunctionplot_other_thresholds

(ax, spms, colors)

spm1d/examples/nonparam/v0.4.50/roi/ex_ttest2.py:8

↓ 1 callersFunctionplot_other_thresholds

(ax, spms, colors)

spm1d/examples/nonparam/v0.4.50/roi/ex_regression.py:8

↓ 1 callersFunctionplot_other_thresholds

(ax, spms, colors)

spm1d/examples/nonparam/v0.4.50/roi/ex_ttest.py:8

↓ 1 callersFunctionplot_spm

Plot an **spm1d** SPM object as a line. :Parameters: - *spm* --- an **spm1d** SPM object (not needed if using the SPM.plot method) - *ax* ---

spm1d/plot.py:233

↓ 1 callersFunctionplot_spm_design

Plot the design matrix. :Returns: None

spm1d/plot.py:268

↓ 1 callersFunctionplot_spmi

Plot an **spm1d** SPM inference object as a line. :Parameters: - *spmi* --- an **spm1d** SPM object - *ax* --- optional matplotlib.axes objec

spm1d/plot.py:280

↓ 1 callersFunctionplot_spmi_p_values

Plot an **spm1d** SPM inference object's p values as text (if they exist). :Parameters: - *spmi* --- an **spm1d** SPM inference object - *ax*

spm1d/plot.py:319

↓ 1 callersFunctionplot_spmi_threshold_label

Plot an **spm1d** SPM inference object as a line. :Parameters: - *spmi* --- an **spm1d** SPM inference object - *ax* --- optional matplotlib.

spm1d/plot.py:357

↓ 1 callersFunctionpoisson_cdf

spm1d/rft1d/prob.py:151

↓ 1 callersMethodrandom_permutation

spm1d/stats/nonparam/factors.py:31

↓ 1 callersFunctionresels2fwhm

Get the FWHM from resel counts based on the number of field nodes. :Parameters: *resels* --- resel counts *nNodes* --- number of field no

spm1d/rft1d/geom.py:764

↓ 1 callersFunctionresels2nelements

Get the field size from resel counts based on the FWHM (element-based sampling). :Example: >>> resels = (1, 10.0) >>> nNodes = rft1d.geom.re

spm1d/rft1d/geom.py:824

↓ 1 callersFunctionresels2nnodes

Get the number of field nodes from resel counts based on the FWHM :Parameters: *resels* --- resel counts *fwhm* --- actual or estimated F

spm1d/rft1d/geom.py:840

↓ 1 callersMethodresels_per_upcrossing

Number of nodes expected for each uprcrossing at threshold *u*. One resel contains (1 x FWHM) nodes. Thus this is equivalent to: (FWHM x nodes

spm1d/rft1d/prob.py:438

↓ 1 callersMethodset_ax_prop

(self, *args, **kwdargs)

spm1d/_plot.py:125

↓ 1 callersMethodset_bonf

spm1d/rft1d/prob.py:646

↓ 1 callersMethodset_bonf

spm1d/rft1d/prob.py:747

↓ 1 callersMethodset_data

spm1d/_plot.py:189

↓ 1 callersMethodset_fwhm

spm1d/rft1d/prob.py:649

↓ 1 callersMethodset_fwhm

spm1d/rft1d/random.py:145

↓ 1 callersMethodteststat

spm1d/stats/nonparam/calculators.py:92

↓ 1 callersMethodteststat

spm1d/stats/nonparam/calculators.py:293

↓ 1 callersMethodtotal_excursion_set_extent

Total extent of the excursion set. :Parameters: *y* --- a 1D field *u* --- threshold height *fwhm* --- actual or estimated FW

spm1d/rft1d/geom.py:492

↓ 1 callersFunctionttest

spm1d/stats/normality/sw.py:87

↓ 1 callersFunctionttest2

Two-sample t test. :Parameters: - *YA* --- (J x Q) data array (J responses, Q nodes) - *YB* --- (J x Q) data array (J responses,

spm1d/stats/t.py:197

↓ 1 callersFunctionttest2

(y0, y1, roi=None)

spm1d/stats/nonparam/stats.py:110

↓ 1 callersFunctionttest2

(yA, yB, roi=None)

spm1d/stats/nonparam_old/stats.py:129

↓ 1 callersMethodupcrossing

Survival function (equivalent to **RFTCalculator.sf**) Probability that 1D Gaussian fields would produce a 1D statistic field whose maximum ex

spm1d/rft1d/prob.py:513

spm1d/stats/_spm.py:285

Method_00_parse

spm1d/stats/anova/factors.py:179

Method_00_parse

spm1d/stats/anova/factors.py:244

Method_00_parse

spm1d/stats/anova/factors.py:284

Method_01_check_unbalanced

spm1d/stats/anova/factors.py:184

Method_01_check_unbalanced

spm1d/stats/anova/factors.py:251

Method_01_check_unbalanced

spm1d/stats/anova/factors.py:291

Method__getitem__

spm1d/stats/_spmlist.py:34

(self, ax=None)

spm1d/_plot.py:34

(self, spmi, ax=None)

spm1d/_plot.py:203

spm1d/rft1d/prob.py:385

spm1d/rft1d/prob.py:456

(self, STAT='Z', df=None, nodes=101, FWHM=10.0, n=1, withBonf=False, version='spm12')

spm1d/rft1d/prob.py:582

(self, STAT='Z', df=None, resels=[1,10], n=1, withBonf=False, nNodes=None, version='spm12')

spm1d/rft1d/prob.py:722

(self, nResponses=1, nodes=101, nComponents=2, FWHM=10, W=None, pad=False)

spm1d/rft1d/random.py:186

(self, STAT, ndf)

spm1d/rft1d/distributions.py:142

spm1d/rft1d/distributions.py:446

spm1d/rft1d/distributions.py:464

spm1d/rft1d/distributions.py:500

spm1d/rft1d/distributions.py:518

(self, y, b, interp=True, wrap=True)

spm1d/rft1d/geom.py:38

(self, y, u, interp=True, wrap=False)

spm1d/rft1d/geom.py:531

spm1d/data/_base.py:117

spm1d/data/_base.py:144

spm1d/data/_base.py:153

spm1d/data/_base.py:163

spm1d/data/_base.py:168

spm1d/data/_base.py:176

spm1d/data/_base.py:184

spm1d/data/_base.py:192

spm1d/data/_base.py:197

spm1d/data/_base.py:207

spm1d/data/_base.py:213

spm1d/data/_base.py:223

spm1d/data/_base.py:233

spm1d/data/_base.py:240

spm1d/data/_base.py:247

spm1d/data/_base.py:255

spm1d/data/uv1d/regress.py:23

spm1d/data/uv1d/anova1.py:11

(self, name='A1')

spm1d/data/uv1d/normality.py:8

spm1d/stats/_reml.py:182

(self, FF, nFactors=2)

spm1d/stats/_spmlist.py:27

spm1d/stats/_datachecks.py:84

spm1d/stats/_datachecks.py:100

(self, Y, A, B)

spm1d/stats/_datachecks.py:117

spm1d/stats/_datachecks.py:135

spm1d/stats/_datachecks.py:148

spm1d/stats/_datachecks.py:158

(self, STAT, z, df, beta=None, residuals=None, sigma2=None)

spm1d/stats/_spm.py:94

(self, spm, alpha, zstar, p, two_tailed=False)

spm1d/stats/_spm.py:126

(self, z, df, ss=(0,0), ms=(0,0), eij=0, X0=None)

spm1d/stats/_spm.py:170

(self, z, df, beta=None, residuals=None, sigma2=None)

spm1d/stats/_spm.py:192

spm1d/stats/_spm.py:203

(self, z, df, residuals=None)

spm1d/stats/_spm.py:211

(self, z, df, fwhm, resels, X=None, beta=None, residuals=None, X0=None, sigma2=None, roi=None)

spm1d/stats/_spm.py:466

(self, z, df, fwhm, resels, X=None, beta=None, residuals=None, sigma2=None, roi=None)

spm1d/stats/_spm.py:517

(self, z, df, fwhm, resels, X=None, beta=None, residuals=None, sigma2=None, roi=None)

spm1d/stats/_spm.py:542

(self, z, df, fwhm, resels, X=None, beta=None, residuals=None, sigma2=None, roi=None)

spm1d/stats/_spm.py:563

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