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Python progressbar.ProgressBar类代码示例

本文整理汇总了Python中amico.progressbar.ProgressBar的典型用法代码示例。如果您正苦于以下问题:Python ProgressBar类的具体用法?Python ProgressBar怎么用?Python ProgressBar使用的例子?那么, 这里精选的类代码示例或许可以为您提供帮助。


在下文中一共展示了ProgressBar类的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: generate

    def generate( self, out_path, aux, idx_in, idx_out ):
        scheme_high = amico.lut.create_high_resolution_scheme( self.scheme, b_scale = 1 )
        protocolHR = self.scheme2noddi( scheme_high )

        nATOMS = len(self.IC_ODs)*len(self.IC_VFs) + 1
        progress = ProgressBar( n=nATOMS, prefix="   ", erase=True )

        # Coupled contributions
        IC_KAPPAs = 1 / np.tan(self.IC_ODs*np.pi/2)
        for kappa in IC_KAPPAs:
            signal_ic = self.synth_meas_watson_SH_cyl_neuman_PGSE( np.array([self.dPar*1E-6, 0, kappa]), protocolHR['grad_dirs'], np.squeeze(protocolHR['gradient_strength']), np.squeeze(protocolHR['delta']), np.squeeze(protocolHR['smalldel']), np.array([0,0,1]), 0 )

            for v_ic in self.IC_VFs:
                dPerp = self.dPar*1E-6 * (1 - v_ic)
                signal_ec = self.synth_meas_watson_hindered_diffusion_PGSE( np.array([self.dPar*1E-6, dPerp, kappa]), protocolHR['grad_dirs'], np.squeeze(protocolHR['gradient_strength']), np.squeeze(protocolHR['delta']), np.squeeze(protocolHR['smalldel']), np.array([0,0,1]) )

                signal = v_ic*signal_ic + (1-v_ic)*signal_ec
                lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, False )
                np.save( pjoin( out_path, 'A_%03d.npy'%progress.i) , lm )
                progress.update()

        # Isotropic
        signal = self.synth_meas_iso_GPD( self.dIso*1E-6, protocolHR)
        lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, True )
        np.save( pjoin( out_path, 'A_%03d.npy'%progress.i) , lm )
        progress.update()
开发者ID:davidrs06,项目名称:AMICO,代码行数:26,代码来源:models.py

示例2: resample

    def resample( self, in_path, idx_out, Ylm_out, doMergeB0 ) :
        if doMergeB0:
            nS = 1+self.scheme.dwi_count
            merge_idx = np.hstack((self.scheme.b0_idx[0],self.scheme.dwi_idx))
        else:
            nS = self.scheme.nS
            merge_idx = np.arange(nS)
        KERNELS = {}
        KERNELS['model'] = self.id
        KERNELS['D']     = np.zeros( (len(self.d_perps),181,181,nS), dtype=np.float32 )
        KERNELS['CSF']   = np.zeros( (len(self.d_isos),nS), dtype=np.float32 )

        nATOMS = len(self.d_perps) + len(self.d_isos)
        progress = ProgressBar( n=nATOMS, prefix="   ", erase=True )

        # Tensor compartment(s)
        for i in xrange(len(self.d_perps)) :
            lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
            KERNELS['D'][i,...] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, False )[:,:,merge_idx]
            progress.update()

        # Isotropic compartment(s)
        for i in xrange(len(self.d_isos)) :
            lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
            KERNELS['CSF'][i,...] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, True )[merge_idx]
            progress.update()

        return KERNELS
开发者ID:davidrs06,项目名称:AMICO,代码行数:28,代码来源:models.py

示例3: resample

    def resample( self, in_path, idx_out, Ylm_out ):
        nATOMS = len(self.IC_ODs)*len(self.IC_VFs) + 1

        KERNELS = {}
        KERNELS['model'] = self.id
        KERNELS['wm']    = np.zeros( (nATOMS-1,181,181,self.scheme.nS), dtype=np.float32 )
        KERNELS['iso']   = np.zeros( self.scheme.nS, dtype=np.float32 )
        KERNELS['kappa'] = np.zeros( nATOMS-1, dtype=np.float32 )
        KERNELS['icvf']  = np.zeros( nATOMS-1, dtype=np.float32 )
        KERNELS['norms'] = np.zeros( (self.scheme.dwi_count, nATOMS-1) )

        progress = ProgressBar( n=nATOMS, prefix="   ", erase=True )

        # Coupled contributions
        for i in xrange( len(self.IC_ODs) ):
            for j in xrange( len(self.IC_VFs) ):
                lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
                idx = progress.i - 1
                KERNELS['wm'][idx,:,:,:] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, False )
                KERNELS['kappa'][idx] = 1.0 / np.tan( self.IC_ODs[i]*np.pi/2.0 )
                KERNELS['icvf'][idx]  = self.IC_VFs[j]
                KERNELS['norms'][:,idx] = 1 / np.linalg.norm( KERNELS['wm'][idx,0,0,self.scheme.dwi_idx] ) # norm of coupled atoms (for l1 minimization)
                progress.update()

        # Isotropic
        lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
        KERNELS['iso'] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, True )
        progress.update()

        return KERNELS
开发者ID:steelec,项目名称:AMICO,代码行数:30,代码来源:models.py

示例4: debiasRician

def debiasRician(DWI,SNR,mask,scheme):
    debiased_DWI = np.zeros(DWI.shape)
    t = time.time()
    progress = ProgressBar( n=mask.sum(), prefix="   ", erase=True )
    for ix in range(DWI.shape[0]):
        for iy in range(DWI.shape[1]):
            for iz in range(DWI.shape[2]):
                if mask[ix,iy,iz]:
                    b0 = DWI[ix,iy,iz,scheme.b0_idx].mean()
                    sigma_diff = b0/SNR
                    init_guess = DWI[ix,iy,iz,:].copy()
                    tmp = minimize(F_norm_Diff_K, init_guess, args=(init_guess,sigma_diff), method = 'L-BFGS-B', jac=der_Diff)
                    debiased_DWI[ix,iy,iz] = tmp.x
                    progress.update()
    print('   [ %s ]' % ( time.strftime("%Hh %Mm %Ss", time.gmtime(time.time()-t) ) ))
    return debiased_DWI
开发者ID:daducci,项目名称:AMICO,代码行数:16,代码来源:preproc.py

示例5: fit

    def fit(self):
        """Fit the model to the data iterating over all voxels (in the mask) one after the other.
        Call the appropriate fit() method of the actual model used.
        """
        if self.niiDWI is None:
            raise RuntimeError('Data not loaded; call "load_data()" first.')
        if self.model is None:
            raise RuntimeError('Model not set; call "set_model()" first.')
        if self.KERNELS is None:
            raise RuntimeError(
                'Response functions not generated; call "generate_kernels()" and "load_kernels()" first.'
            )
        if self.KERNELS["model"] != self.model.id:
            raise RuntimeError("Response functions were not created with the same model.")

        self.set_config("fit_time", None)
        totVoxels = np.count_nonzero(self.niiMASK_img)
        print '\n-> Fitting "%s" model to %d voxels:' % (self.model.name, totVoxels)

        # setup fitting directions
        peaks_filename = self.get_config("peaks_filename")
        if peaks_filename is None:
            DIRs = np.zeros(
                [self.get_config("dim")[0], self.get_config("dim")[1], self.get_config("dim")[2], 3], dtype=np.float32
            )
            nDIR = 1
            gtab = gradient_table(self.scheme.b, self.scheme.raw[:, :3])
            DTI = dti.TensorModel(gtab)
        else:
            niiPEAKS = nibabel.load(pjoin(self.get_config("DATA_path"), peaks_filename))
            DIRs = niiPEAKS.get_data().astype(np.float32)
            nDIR = np.floor(DIRs.shape[3] / 3)
            print "\t* peaks dim = %d x %d x %d x %d" % DIRs.shape[:4]
            if DIRs.shape[:3] != self.niiMASK_img.shape[:3]:
                raise ValueError("PEAKS geometry does not match with DWI data")

        # setup other output files
        MAPs = np.zeros(
            [
                self.get_config("dim")[0],
                self.get_config("dim")[1],
                self.get_config("dim")[2],
                len(self.model.maps_name),
            ],
            dtype=np.float32,
        )

        if self.get_config("doComputeNRMSE"):
            NRMSE = np.zeros(
                [self.get_config("dim")[0], self.get_config("dim")[1], self.get_config("dim")[2]], dtype=np.float32
            )

        if self.get_config("doSaveCorrectedDWI"):
            DWI_corrected = np.zeros(self.niiDWI.shape, dtype=np.float32)

        # fit the model to the data
        # =========================
        t = time.time()
        progress = ProgressBar(n=totVoxels, prefix="   ", erase=True)
        for iz in xrange(self.niiMASK_img.shape[2]):
            for iy in xrange(self.niiMASK_img.shape[1]):
                for ix in xrange(self.niiMASK_img.shape[0]):
                    if self.niiMASK_img[ix, iy, iz] == 0:
                        continue

                    # prepare the signal
                    y = self.niiDWI_img[ix, iy, iz, :].astype(np.float64)
                    y[y < 0] = 0  # [NOTE] this should not happen!

                    if self.scheme.b0_count > 0:
                        b0 = np.mean(y[self.scheme.b0_idx])

                    if self.get_config("doNormalizeSignal") and self.scheme.b0_count > 0:
                        if b0 > 1e-3:
                            y = y / b0

                    # fitting directions
                    if peaks_filename is None:
                        dirs = DTI.fit(y).directions[0]
                    else:
                        dirs = DIRs[ix, iy, iz, :]

                    # dispatch to the right handler for each model
                    MAPs[ix, iy, iz, :], DIRs[ix, iy, iz, :], x, A = self.model.fit(
                        y, dirs.reshape(-1, 3), self.KERNELS, self.get_config("solver_params")
                    )

                    # compute fitting error
                    if self.get_config("doComputeNRMSE"):
                        y_est = np.dot(A, x)
                        den = np.sum(y ** 2)
                        NRMSE[ix, iy, iz] = np.sqrt(np.sum((y - y_est) ** 2) / den) if den > 1e-16 else 0

                    if self.get_config("doSaveCorrectedDWI"):

                        if self.model.name == "Free-Water":
                            n_iso = len(self.model.d_isos)
                            x[-1 * n_iso :] = 0

                            # print(y, x, b0, A.shape)
#.........这里部分代码省略.........
开发者ID:yzhizai,项目名称:AMICO,代码行数:101,代码来源:core.py

示例6: fit

    def fit( self ) :
        """Fit the model to the data iterating over all voxels (in the mask) one after the other.
        Call the appropriate fit() method of the actual model used.
        """
        if self.niiDWI is None :
            raise RuntimeError( 'Data not loaded; call "load_data()" first.' )
        if self.model is None :
            raise RuntimeError( 'Model not set; call "set_model()" first.' )
        if self.KERNELS is None :
            raise RuntimeError( 'Response functions not generated; call "generate_kernels()" and "load_kernels()" first.' )
        if self.KERNELS['model'] != self.model.id :
            raise RuntimeError( 'Response functions were not created with the same model.' )

        self.set_config('fit_time', None)
        totVoxels = np.count_nonzero(self.niiMASK_img)
        print '\n-> Fitting "%s" model to %d voxels:' % ( self.model.name, totVoxels )

        # setup fitting directions
        peaks_filename = self.get_config('peaks_filename')
        if peaks_filename is None :
            DIRs = np.zeros( [self.get_config('dim')[0], self.get_config('dim')[1], self.get_config('dim')[2], 3], dtype=np.float32 )
            nDIR = 1
            gtab = gradient_table( self.scheme.b, self.scheme.raw[:,:3] )
            DTI = dti.TensorModel( gtab )
        else :
            niiPEAKS = nibabel.load( pjoin( self.get_config('DATA_path'), peaks_filename) )
            DIRs = niiPEAKS.get_data().astype(np.float32)
            nDIR = np.floor( DIRs.shape[3]/3 )
            print '\t* peaks dim = %d x %d x %d x %d' % DIRs.shape[:4]
            if DIRs.shape[:3] != self.niiMASK_img.shape[:3] :
                raise ValueError( 'PEAKS geometry does not match with DWI data' )

        # setup other output files
        MAPs = np.zeros( [self.get_config('dim')[0], self.get_config('dim')[1], self.get_config('dim')[2], len(self.model.maps_name)], dtype=np.float32 )
        if self.get_config('doComputeNRMSE') :
            NRMSE = np.zeros( [self.get_config('dim')[0], self.get_config('dim')[1], self.get_config('dim')[2]], dtype=np.float32 )

        # fit the model to the data
        # =========================
        t = time.time()
        progress = ProgressBar( n=totVoxels, prefix="   ", erase=True )
        for iz in xrange(self.niiMASK_img.shape[2]) :
            for iy in xrange(self.niiMASK_img.shape[1]) :
                for ix in xrange(self.niiMASK_img.shape[0]) :
                    if self.niiMASK_img[ix,iy,iz]==0 :
                        continue

                    # prepare the signal
                    y = self.niiDWI_img[ix,iy,iz,:].astype(np.float64)
                    y[ y < 0 ] = 0 # [NOTE] this should not happen!

                    if self.get_config('doNormalizeSignal') and self.scheme.b0_count > 0 :
                        b0 = np.mean( y[self.scheme.b0_idx] )
                        if b0 > 1e-3 :
                            y = y / b0

                    # fitting directions
                    if peaks_filename is None :
                        dirs = DTI.fit( y ).directions[0]
                    else :
                        dirs = DIRs[ix,iy,iz,:]

                    # dispatch to the right handler for each model
                    MAPs[ix,iy,iz,:], DIRs[ix,iy,iz,:], x, A = self.model.fit( y, dirs.reshape(-1,3), self.KERNELS, self.get_config('solver_params') )

                    # compute fitting error
                    if self.get_config('doComputeNRMSE') :
                        y_est = np.dot( A, x )
                        den = np.sum(y**2)
                        NRMSE[ix,iy,iz] = np.sqrt( np.sum((y-y_est)**2) / den ) if den > 1e-16 else 0

                    progress.update()

        self.set_config('fit_time', time.time()-t)
        print '   [ %s ]' % ( time.strftime("%Hh %Mm %Ss", time.gmtime(self.get_config('fit_time')) ) )

        # store results
        self.RESULTS = {}
        self.RESULTS['DIRs']  = DIRs
        self.RESULTS['MAPs']  = MAPs
        if self.get_config('doComputeNRMSE') :
            self.RESULTS['NRMSE'] = NRMSE
开发者ID:kylerhodgson,项目名称:AMICO,代码行数:82,代码来源:core.py


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