Python scipy.any函数代码示例

    def _apply_percolation(self, inv_val):
        r"""
        Determine which pores and throats are invaded at a given applied
        capillary pressure.  This method is called by ``run``.
        """
        # Generate a list containing boolean values for throat state
        Tinvaded = self['throat.entry_pressure'] <= inv_val
        # Add residual throats, if any, to list of invaded throats
        Tinvaded = Tinvaded + self['throat.residual']
        # Find all pores that can be invaded at specified pressure
        [pclusters, tclusters] = self._net.find_clusters2(mask=Tinvaded,
                                                          t_labels=True)

        # Identify clusters connected to inlet sites
        inv_clusters = sp.unique(pclusters[self['pore.inlets']])
        inv_clusters = inv_clusters[inv_clusters >= 0]

        # Find pores on the invading clusters
        pmask = np.in1d(pclusters, inv_clusters)
        # Store current applied pressure in newly invaded pores
        pinds = (self['pore.inv_Pc'] == sp.inf) * (pmask)
        self['pore.inv_Pc'][pinds] = inv_val

        # Find throats on the invading clusters
        tmask = np.in1d(tclusters, inv_clusters)
        # Store current applied pressure in newly invaded throats
        tinds = (self['throat.inv_Pc'] == sp.inf) * (tmask)
        self['throat.inv_Pc'][tinds] = inv_val

        # Set residual pores and throats, if any, to invaded
        if sp.any(self['pore.residual']):
            self['pore.inv_Pc'][self['pore.residual']] = 0
        if sp.any(self['throat.residual']):
            self['throat.inv_Pc'][self['throat.residual']] = 0

    def _check_bounds(self,x_new):
        # If self.bounds_error = 1, we raise an error if any x_new values
        # fall outside the range of x.  Otherwise, we return an array indicating
        # which values are outside the boundary region.  
        # !! Needs some work for multi-dimensional x !!
        below_bounds = less(x_new,self.x[0])
        above_bounds = greater(x_new,self.x[-1])
        #  Note: sometrue has been redefined to handle length 0 arrays
        # !! Could provide more information about which values are out of bounds
        # RHC -- Changed these ValueErrors to PyDSTool_BoundsErrors
        if self.bounds_error and any(sometrue(below_bounds)):
##            print "Input:", x_new
##            print "Bound:", self.x[0]
##            print "Difference input - bound:", x_new-self.x[0]
            raise PyDSTool_BoundsError, " A value in x_new is below the"\
                              " interpolation range."
        if self.bounds_error and any(sometrue(above_bounds)):
##            print "Input:", x_new
##            print "Bound:", self.x[-1]
##            print "Difference input - bound:", x_new-self.x[-1]
            raise PyDSTool_BoundsError, " A value in x_new is above the"\
                              " interpolation range."
        # !! Should we emit a warning if some values are out of bounds.
        # !! matlab does not.
        out_of_bounds = logical_or(below_bounds,above_bounds)
        return out_of_bounds

def computePCsPython(out_dir,k,bfile,ffile):
    """ reading in """
    RV = plink_reader.readBED(bfile,useMAFencoding=True)
    X  = np.ascontiguousarray(RV['snps'])

    """ normalizing markers """
    print('Normalizing SNPs...')
    p_ref = X.mean(axis=0)/2.
    X -= 2*p_ref

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        X /= sp.sqrt(2*p_ref*(1-p_ref))

    hasNan = sp.any(sp.isnan(X),axis=0)
    if sp.any(hasNan):
        print(('%d SNPs have a nan entry. Exluding them for computing the covariance matrix.'%hasNan.sum()))
        X  = X[:,~hasNan]

    """ computing prinicipal components """
    U,S,Vt = ssl.svds(X,k=k)
    U -= U.mean(0)
    U /= U.std(0)
    U  = U[:,::-1]

    """ saving to output """
    np.savetxt(ffile, U, delimiter='\t',fmt='%.6f')

def crop_pts(coords, box):
    r'''
    Drop all points lying outside the box

    Parameters
    ----------
    coords : array_like
        An Np x ndims array off [x,y,z] coordinates

    box : array_like
        A 2 x ndims array of diametrically opposed corner coordintes

    Returns
    -------
    coords : array_like
        Inputs coordinates with outliers removed

    Notes
    -----
    This needs to be made more general so that an arbitray cuboid with any
    orientation can be supplied, using Np x 8 points
    '''
    coords = coords[_sp.any(coords < box[0], axis=1)]
    coords = coords[_sp.any(coords > box[1], axis=1)]
    return coords

 def process_file(self, file_ind) :
     params = self.params
     file_middle = params['file_middles'][file_ind]
     input_fname = (params['input_root'] + file_middle +
                    params['input_end'])
     sub_input_fname = (params['subtracted_input_root'] + file_middle
                        + params['input_end'])
     output_fname = (params['output_root']
                     + file_middle + params['output_end'])
     sub_output_fname = (params['subtracted_output_root']
                         + file_middle + params['output_end'])
     Writer = fitsGBT.Writer(feedback=self.feedback)
     SubWriter = fitsGBT.Writer(feedback=self.feedback)
     
     # Read in the data, and loop over data blocks.
     Reader = fitsGBT.Reader(input_fname, feedback=self.feedback)
     SubReader = fitsGBT.Reader(sub_input_fname, feedback=self.feedback)
     if (sp.any(Reader.scan_set != SubReader.scan_set)
         or sp.any(Reader.IF_set != SubReader.IF_set)) :
         raise ce.DataError("IFs and scans don't match signal subtracted"
                            " data.")
     # Get the number of scans if asked for all of them.
     scan_inds = params['scans']
     if len(scan_inds) == 0 or scan_inds is None :
         scan_inds = range(len(Reader.scan_set))
     if_inds = params['IFs']
     if len(if_inds) == 0 or scan_inds is None :
         if_inds = range(len(Reader.IF_set))
     if self.feedback > 1 :
         print "New flags each block:",
     # Loop over scans and IFs
     for thisscan in scan_inds :
         for thisIF in if_inds :
             Data = Reader.read(thisscan, thisIF)
             SubData = SubReader.read(thisscan, thisIF)
             n_flags = ma.count_masked(Data.data)
             # Now do the flagging.
             flag(Data, SubData, params['thres'])
             Data.add_history("Reflaged for outliers.", ("Used file: "
                 + utils.abbreviate_file_path(sub_input_fname),))
             SubData.add_history("Reflaged for outliers.")
             Writer.add_data(Data)
             SubWriter.add_data(SubData)
             # Report the numbe of new flags.
             n_flags = ma.count_masked(Data.data) - n_flags
             if self.feedback > 1 :
                 print n_flags,
     if self.feedback > 1 :
         print ''
     # Finally write the data back to file.
     utils.mkparents(output_fname)
     utils.mkparents(sub_output_fname)
     Writer.write(output_fname)
     SubWriter.write(sub_output_fname)

	def updateData(self, array = Array(sp.zeros((0,2)),scale=[0,0], Type = 0), action = 1, Type = None):
		""" Запис в тимчасовий файл даних з масиву
		action = {-1, 0, 1, 2}
			-1	:	undo
			0	:	reset
			1	:	add
		"""

		if Type is None:
			if sp.any(array):
				Type = array.Type
				#print(sp.shape(array),array.Type)
		
		emit = False
		#print(len(self.dataStack[Type]),action)
		# Запис в історію
		if action == 1:
			if sp.any(array) and sp.shape(array)[1] == 2 and sp.shape(array)[0] > 1:
				self.dataStack[Type].append(array)
				emit = True

			else: print('updateData: arrayError',sp.any(array) , sp.shape(array)[1] == 2 , sp.shape(array)[0] > 1)
		
		# Видалення останнього запису
		elif action == -1 and len(self.dataStack[Type])>=2:
			self.dataStack[Type].pop()
			emit = True
			#self.setActiveLogScale( Type)
		# Скидання історії, або запис першого елемента історії
		elif action == 0:
			print(0)
			if sp.any(array) and sp.shape(array)[1] == 2 and sp.shape(array)[0] > 1 and len(self.dataStack[Type])>=1:
				self.dataStack[Type][0:] = []
				self.dataStack[Type].append(array)
				emit = True
			if not sp.any(array) and len(self.dataStack[Type])>=2:
				self.dataStack[Type][1:] = []
				emit = True
			#self.setActiveLogScale( Type)
			
		else:
			print("updateData: Error0",len(self.dataStack[Type]))
			print(sp.shape(self.getData(Type)))
		try:
			for i in self.dataStack[Type]: print(i.scaleX, i.scaleY, i.shape)
		except:
			pass
		# Емітувати повідомлення про зміу даних
		if emit:
			self.data_signal.emit(Type, action)
			self.Plot(self.getData(Type) )

 def test_ransohoff_snapoff_verts(self):
     ws = op.Workspace()
     ws.clear()
     bp = sp.array([[0.25, 0.25, 0.25], [0.25, 0.75, 0.25],
                    [0.75, 0.25, 0.25], [0.75, 0.75, 0.25],
                    [0.25, 0.25, 0.75], [0.25, 0.75, 0.75],
                    [0.75, 0.25, 0.75], [0.75, 0.75, 0.75]])
     scale = 1e-4
     sp.random.seed(1)
     p = (sp.random.random([len(bp), 3])-0.5)/1000
     bp += p
     fiber_rad = 2e-6
     bp = op.topotools.reflect_base_points(bp, domain_size=[1, 1, 1])
     prj = op.materials.VoronoiFibers(fiber_rad=fiber_rad,
                                      resolution=1e-6,
                                      shape=[scale, scale, scale],
                                      points=bp*scale,
                                      name='test')
     net = prj.network
     del_geom = prj.geometries()['test_del']
     vor_geom = prj.geometries()['test_vor']
     f = op.models.physics.capillary_pressure.ransohoff_snap_off
     water = op.phases.GenericPhase(network=net)
     water['pore.surface_tension'] = 0.072
     water['pore.contact_angle'] = 45
     phys1 = op.physics.GenericPhysics(network=net,
                                       geometry=del_geom,
                                       phase=water)
     phys1.add_model(propname='throat.snap_off',
                     model=f,
                     wavelength=fiber_rad)
     phys1.add_model(propname='throat.snap_off_pair',
                     model=f,
                     wavelength=fiber_rad,
                     require_pair=True)
     phys2 = op.physics.GenericPhysics(network=net,
                                       geometry=vor_geom,
                                       phase=water)
     phys2.add_model(propname='throat.snap_off',
                     model=f,
                     wavelength=fiber_rad)
     phys2.add_model(propname='throat.snap_off_pair',
                     model=f,
                     wavelength=fiber_rad,
                     require_pair=True)
     ts = ~net['throat.interconnect']
     assert ~sp.any(sp.isnan(water['throat.snap_off'][ts]))
     assert sp.any(sp.isnan(water['throat.snap_off_pair'][ts]))
     assert sp.any(~sp.isnan(water['throat.snap_off_pair'][ts]))

 def execute(self):
     self.power_mat, self.thermal_expectation = self.full_calculation()
     n_chan = self.power_mat.shape[1]
     n_freq = self.power_mat.shape[0]
     # Calculate the the mean channel correlations at low frequencies.
     low_f_mat = sp.mean(self.power_mat[1:4 * n_chan + 1,:,:], 0).real
     # Factorize it into preinciple components.
     e, v = linalg.eigh(low_f_mat)
     self.low_f_mode_values = e
     # Make sure the eigenvalues are sorted.
     if sp.any(sp.diff(e) < 0):
         raise RuntimeError("Eigenvalues not sorted.")
     self.low_f_modes = v
     # Now subtract out the noisiest channel modes and see what is left.
     n_modes_subtract = 10
     mode_subtracted_power_mat = sp.copy(self.power_mat.real)
     mode_subtracted_auto_power = sp.empty((n_modes_subtract, n_freq))
     for ii in range(n_modes_subtract):
         mode = v[:,-ii]
         amp = sp.sum(mode[:,None] * mode_subtracted_power_mat, 1)
         amp = sp.sum(amp * mode, 1)
         to_subtract = amp[:,None,None] * mode[:,None] * mode
         mode_subtracted_power_mat -= to_subtract
         auto_power = mode_subtracted_power_mat.view()
         auto_power.shape = (n_freq, n_chan**2)
         auto_power = auto_power[:,::n_chan + 1]
         mode_subtracted_auto_power[ii,:] = sp.mean(auto_power, -1)
     self.subtracted_auto_power = mode_subtracted_auto_power

def isherm(Q):
    """Determines if given operator is Hermitian.
    
    Parameters
	----------
	Q : qobj
	    Quantum object
    
    Returns
    ------- 
    isherm : bool
        True if operator is Hermitian, False otherwise.
    
    Examples
    --------    
    >>> a=destroy(4)
    >>> isherm(a)
    False
    
    """
    if Q.dims[0]!=Q.dims[1]:
        return False
    else:
        dat=Q.data
        elems=(dat.transpose().conj()-dat).data
        if any(abs(elems)>1e-15):
            return False
        else:
            return True

def straight(network,
             geometry,
             pore_diameter='pore.diameter',
             L_negative = 1e-9,
             **kwargs):
    r"""
    Calculate throat length
    
    Parameters
    ----------
    L_negative : float
        The default throat length to use when negative lengths are found.  The
        default is 1 nm.  To accept negative throat lengths, set this value to 
        ``None``.
    """
    #Initialize throat_property['length']
    throats = network.throats(geometry.name)
    pore1 = network['throat.conns'][:,0]
    pore2 = network['throat.conns'][:,1]
    C1 = network['pore.coords'][pore1]
    C2 = network['pore.coords'][pore2]
    E = _sp.sqrt(_sp.sum((C1-C2)**2,axis=1))  #Euclidean distance between pores
    D1 = network[pore_diameter][pore1]
    D2 = network[pore_diameter][pore2]
    value = E-(D1+D2)/2.
    value = value[throats]
    if _sp.any(value<0) and (L_negative is not None):
        print('Negative throat lengths are calculated. Arbitrary positive length assigned: '+str(L_negative))
        Ts = _sp.where(value<0)[0]
        value[Ts] = L_negative
    return value

 def test_late_pore_and_throat_filling(self):
     mip = op.algorithms.Porosimetry(network=self.net)
     mip.setup(phase=self.hg)
     mip.set_inlets(pores=self.net.pores('left'))
     # Run without late pore filling
     mip.run()
     data_no_lpf = mip.get_intrusion_data()
     # Now run with late pore filling
     self.phys['pore.pc_star'] = 2/self.net['pore.diameter']
     self.phys.add_model(propname='pore.partial_filling',
                         pressure='pore.pressure',
                         Pc_star='pore.pc_star',
                         model=op.models.physics.multiphase.late_filling)
     mip.reset()
     mip.set_inlets(pores=self.net.pores('left'))
     mip.set_partial_filling(propname='pore.partial_filling')
     mip.run()
     self.phys.regenerate_models()
     data_w_lpf = mip.get_intrusion_data()
     assert sp.all(sp.array(data_w_lpf.Snwp) < sp.array(data_no_lpf.Snwp))
     # Now run with late throat filling
     self.phys['throat.pc_star'] = 2/self.net['throat.diameter']
     self.phys.add_model(propname='throat.partial_filling',
                         pressure='throat.pressure',
                         Pc_star='throat.pc_star',
                         model=op.models.physics.multiphase.late_filling)
     mip.reset()
     mip.set_inlets(pores=self.net.pores('left'))
     mip.set_partial_filling(propname='throat.partial_filling')
     mip.run()
     data_w_ltf = mip.get_intrusion_data()
     assert sp.any(sp.array(data_w_ltf.Snwp) < sp.array(data_w_lpf.Snwp))

def main():
    args = getArguments(getParser())

    # prepare logger
    logger = Logger.getInstance()
    if args.debug: logger.setLevel(logging.DEBUG)
    elif args.verbose: logger.setLevel(logging.INFO)
    
    # load input image
    data_input, header_input = load(args.input)
    
    # transform to uin8
    data_input = data_input.astype(scipy.uint8)
                                      
    # reduce to 3D, if larger dimensionality
    if data_input.ndim > 3:
        for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
        
    # iter over slices (2D) until first with content is detected
    for plane in data_input:
        if scipy.any(plane):
            # set pixel spacing
            spacing = list(header.get_pixel_spacing(header_input))
            spacing = spacing[1:3]
            __update_header_from_array_nibabel(header_input, plane)
            header.set_pixel_spacing(header_input, spacing)
            # save image
            save(plane, args.output, header_input, args.force)
            break
    
    logger.info("Successfully terminated.")    

def generate_matrices(model, col_map, c, r, m, t):
    noBrPointsPerEpoch = model.nbreakpoints
    nleaves = model.nleaves
    all_time_breakpoints, time_breakpoints = default_bps(model, c, r, t)

    M = []
    for e in xrange(len(noBrPointsPerEpoch)):
        newM = identity(nleaves)
        newM[:] = m[e]
        M.append(newM)

    pi, T, E = model.run(r, c, time_breakpoints, M, col_map=col_map)
    assert not any(isnan(pi))
    assert not any(isnan(T))
    assert not any(isnan(E))
    return pi, T, array(E)

def dispersion_relation_extraordinary(kx, ky, k, nO, nE, c):
    """Dispersion relation for the extraordinary wave.

    NOTE
    See eq. 16 in Glytsis, "Three-dimensional (vector) rigorous
    coupled-wave analysis of anisotropic grating diffraction",
    JOSA A, 7(8), 1990 Always give positive real or negative
    imaginary.
    """

    if kx.shape != ky.shape or c.size != 3:
        raise ValueError('kx and ky must have the same length and c must have 3 components')

    kz = S.empty_like(kx)

    for ii in xrange(0, kx.size):

        alpha = nE**2 - nO**2
        beta = kx[ii]/k * c[0] + ky[ii]/k * c[1]

        # coeffs
        C = S.array([nO**2 + c[2]**2 * alpha, \
                     2. * c[2] * beta * alpha, \
                     nO**2 * (kx[ii]**2 + ky[ii]**2) / k**2 + alpha * beta**2 - nO**2 * nE**2])

        # two solutions of type +x or -x, purely real or purely imag
        tmp_kz = k * S.roots(C)

        # get the negative imaginary part or the positive real one
        if S.any(S.isreal(tmp_kz)):
            kz[ii] = S.absolute(tmp_kz[0])
        else:
            kz[ii] = -1j * S.absolute(tmp_kz[0])

    return kz

def main(npulse=100, functlist=['spectrums', 'radardata', 'fitting', 'analysis'],radar='pfisr'):
    """ This function will call other functions to create the input data, config
        file and run the radar data sim. The path for the simulation will be
        created in the Testdata directory in the SimISR module. The new
        folder will be called BasicTest. The simulation is a long pulse simulation
        will the desired number of pulses from the user.
        Inputs
            npulse - Number of pulses for the integration period, default==100.
            functlist - The list of functions for the SimISR to do.
    """
    curloc = Path(__file__).resolve()
    testpath = curloc.parent.parent/'Testdata'/'BasicTest'
    if not testpath.is_dir():
        testpath.mkdir(parents=True)

    functlist_default = ['spectrums', 'radardata', 'fitting']
    check_list = sp.array([i in functlist for i in functlist_default])
    check_run = sp.any(check_list)
    functlist_red = sp.array(functlist_default)[check_list].tolist()

    config = testpath.joinpath('stats.yml')
    if not config.exists():
        configfilesetup(str(testpath), npulse, radar)

    (_, simparams) = readconfigfile(str(config))
    makedata(testpath, simparams['Tint'])
    if check_run:
        runsim(functlist_red, str(testpath), config, True)
    if 'analysis' in functlist:
        analysisdump(str(testpath), config)