示例#1
0
 def initTau(self, pa, pb, qa, qb, qE):
     # Method to initialise the precision of the noise
     # Inputs:
     #  pa (float): 'a' parameter of the prior distribution
     #  pb (float): 'b' parameter of the prior distribution
     #  qb (float): initialisation of the 'b' parameter of the variational distribution
     #  qE (float): initial expectation of the variational distribution
     tau_list = [None] * self.M
     for m in range(self.M):
         if self.lik[m] == "poisson":
             tmp = 0.25 + 0.17 * s.amax(self.data[m], axis=0)
             tau_list[m] = Constant_Node(dim=(self.D[m], ), value=tmp)
         elif self.lik[m] == "bernoulli":
             # seeger
             # tau_list[m] = Constant_Node(dim=(self.D[m],), value=0.25)
             # Jaakkola
             tau_list[m] = Tau_Jaakkola(dim=((self.N, self.D[m])), value=1.)
         elif self.lik[m] == "binomial":
             tmp = 0.25 * s.amax(self.data["tot"][m], axis=0)
             tau_list[m] = Constant_Node(dim=(self.D[m], ), value=tmp)
         elif self.lik[m] == "gaussian":
             tau_list[m] = Tau_Node(dim=(self.D[m], ),
                                    pa=pa[m],
                                    pb=pb[m],
                                    qa=qa[m],
                                    qb=qb[m],
                                    qE=qE[m])
     self.Tau = Multiview_Mixed_Node(self.M, *tau_list)
     self.nodes["Tau"] = self.Tau
	def get_xs(self):
		""" Retrieve xs information to populate self.xscurves. 
		Also create and populate self.max_q_query with maximum q 
		value for querying interpolated rating curves."""
		prof = []
		disch = []
		stage = []
		self.max_q_query = 0
		self.max_disch = 0
		self.max_h_query = 0
		self.max_stage = 0

		# Retrieve xs information and populate self.xscurves
		stations = self.xs['RiverStation'].unique()
		# a = self.xs[self.xs['RiverStation'].isin(stations)]
		for i, rs in enumerate( stations ): 
			# stage-height values for RiverStation rs
			h = self.xs[self.xs['RiverStation'] == rs]['Stage_Height_ft_'].values
# ************
# If multiple zeros, ignore this RiverStation and proceed to next
# ************
			# Test if repeated zeroes (ie. multiple xs datasets for this RiverStation)
			repeats = [item for item, count in Counter(h).iteritems() if count > 1]
			if repeats: continue
			# Process xs data
			current = self.xs[ self.xs['RiverStation'] == rs ]
			prof.append(current['ProfileM'].unique()[0]) # xs location along reach
			disch.append(map(float,current['Discharge_cfs_'].values)) # disch vals
			stage.append(map(float,current['Stage_Height_ft_'].values)) # stage vals

			# Find max q value for querying interpolations
			# Find max disch value for plotting x_axis
			max_disch = int( scipy.amax(disch[-1]) )
			if self.max_q_query == 0:
				self.max_q_query = max_disch
				self.max_disch = max_disch	
			elif max_disch < self.max_q_query: 
				self.max_q_query = max_disch
			elif max_disch > self.max_disch: 
				self.max_disch = max_disch

			# Find max h value for querying interpolations
			# Find max stage value for plotting y_axis
			max_stage = int( scipy.amax(stage[-1]) )
			if self.max_h_query == 0:
				self.max_h_query = max_stage
				self.max_stage = max_stage	
			elif max_stage < self.max_h_query: 
				self.max_h_query = max_stage
			elif max_stage > self.max_stage: 
				self.max_stage = max_stage
		if len(disch) != 0:
			xs_profs = scipy.array(prof).astype(float)
			self.xs_profs = scipy.unique(xs_profs) # remove repeats
			self.xs_disch = scipy.array(disch)
			self.xs_stage = scipy.array(stage)
			# print '\n------------------------\n'
			# for s,p in zip(stations,self.xs_profs): print s,p
			# print '\n------------------------\n'
			return 1
示例#3
0
    def plot(self):
        ex = self.ex
        hy = self.hy
        ngridx = self.ngridx
        nSteps = self.numSteps

        x = np.linspace(0, ngridx, ngridx)
        ymin1 = S.amin(ex)
        ymin2 = S.amin(hy)
        ymax1 = S.amax(ex)
        ymax2 = S.amax(hy)
        yminimum = min(ymin1, ymin2)
        ymaximum = max(ymax1, ymax2)

        title1 = 'EX and Hy field in FDTD 1D simulation.'

        fig = plt.figure()

        ax1 = fig.add_subplot(121)
        ax1.set_xlabel('FDTD Cells', fontsize=12)
        ax1.plot(x, ex, 'tab:blue', label='Ex (Normalized)')
        ax1.set_xlim([0, ngridx])
        ax1.legend(loc='best', shadow=True, ncol=2)
        #  ax1.legend(loc = 'upper center', bbox_to_anchor=(0.5, 0.1),  shadow=True, ncol=2)

        ax2 = fig.add_subplot(122)
        ax2.set_xlabel('FDTD Cells', fontsize=12)
        ax2.plot(x, hy, 'tab:red', label='Hy')
        ax2.set_xlim([0, ngridx])
        ax2.legend(loc='best', shadow=True, ncol=2)
        # ax2.legend(loc = 'upper center', bbox_to_anchor=(0.5, 0.1),  shadow=True, ncol=2)

        plt.suptitle(title1, fontsize=20)
        plt.savefig('Figure.png')
        plt.show()
示例#4
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    def integrate(self,t,u,v):
        # integrate only one step in time.
        # assume same delta in x and y
        maxu = amax(u);
        maxv = amax(v);
        maxVel = amax((maxu,maxv));

        dt = self.cflConstant*self.dx/maxVel;
        print 'Time step selected: ', dt;

        k1 = self.dudt(t,u,v);
        l1 = self.dvdt(t,u,v);

        k2 = self.dudt(t+dt/2, u+(dt*k1/2), v+(dt*l1/2));
        l2 = self.dvdt(t+dt/2, u+(dt*k1/2), v+(dt*l1/2));

        k3 = self.dudt(t+dt/2, u+(dt*k2/2), v+(dt*l2/2));
        l3 = self.dvdt(t+dt/2, u+(dt*k2/2), v+(dt*l2/2));

        k4 = self.dudt(t+dt, u+(dt*k3), v+(dt*l3));
        l4 = self.dvdt(t+dt, u+(dt*k3), v+(dt*l3));

        k = (k1 + 2*k2 + 2*k3 + k4)/6;
        l = (l1 + 2*l2 + 2*l3 + l4)/6;

        un = u + dt*k;
        vn = v + dt*k;
        tn = t + dt;

        return (tn,un,vn);
示例#5
0
def line_segment(X0, X1):
    r"""
    Calculate the voxel coordinates of a straight line between the two given
    end points

    Parameters
    ----------
    X0 and X1 : array_like
        The [x, y] or [x, y, z] coordinates of the start and end points of
        the line.

    Returns
    -------
    coords : list of lists
        A list of lists containing the X, Y, and Z coordinates of all voxels
        that should be drawn between the start and end points to create a solid
        line.
    """
    X0 = sp.around(X0).astype(int)
    X1 = sp.around(X1).astype(int)
    if len(X0) == 3:
        L = sp.amax(sp.absolute([[X1[0]-X0[0]], [X1[1]-X0[1]], [X1[2]-X0[2]]])) + 1
        x = sp.rint(sp.linspace(X0[0], X1[0], L)).astype(int)
        y = sp.rint(sp.linspace(X0[1], X1[1], L)).astype(int)
        z = sp.rint(sp.linspace(X0[2], X1[2], L)).astype(int)
        return [x, y, z]
    else:
        L = sp.amax(sp.absolute([[X1[0]-X0[0]], [X1[1]-X0[1]]])) + 1
        x = sp.rint(sp.linspace(X0[0], X1[0], L)).astype(int)
        y = sp.rint(sp.linspace(X0[1], X1[1], L)).astype(int)
        return [x, y]
示例#6
0
    def plot_drainage_curve(self,
                            data=None,
                            x_values='capillary_pressure',
                            y_values='invading_phase_saturation'):
        r"""
        Plot the drainage curve as the non-wetting phase saturation vs the
        applied capillary pressure.

        Parameters
        ----------
        data : dictionary of arrays
            This dictionary should be obtained from the ``get_drainage_data``
            method.

        x_values and y_values : string
            The dictionary keys of the arrays containing the x-values and
            y-values

        """
        # Begin creating nicely formatted plot
        if data is None:
            data = self.get_drainage_data()
        xdata = data[x_values]
        ydata = data[y_values]
        fig = plt.figure()
        plt.plot(xdata, ydata, 'ko-')
        plt.ylabel(y_values)
        plt.xlabel(x_values)
        plt.grid(True)
        if sp.amax(xdata) <= 1:
            plt.xlim(xmin=0, xmax=1)
        if sp.amax(ydata) <= 1:
            plt.ylim(ymin=0, ymax=1)
        return fig
示例#7
0
def merged_event_breakpoint_stats(mev):
    bp1d, bp2d = [], []
    bend1 = bend2 = None
    reads = []
    quals = []
    for ev in mev.events:
        bp1d.append(ev.bp1.pos)
        bp2d.append(ev.bp2.pos)
        reads.append(ev.reads)
        quals.append(ev.qual)
        bend1 = ev.bp1.breakend
        bend2 = ev.bp2.breakend
    bp1d = np.array(bp1d)
    bp2d = np.array(bp2d)
    if bend1 == "+":
        bp1limit = scipy.amin(bp1d)
    else:
        bp1limit = scipy.amax(bp1d)
    if bend2 == "+":
        bp2limit = scipy.amin(bp2d)
    else:
        bp2limit = scipy.amax(bp2d)
    reads_median = int(scipy.median(reads))
    qual_median = int(scipy.median(quals))
    return int(bp1limit), int(bp2limit), int(bp2limit - bp1limit), scipy.mean(
        bp1d), scipy.amax(bp1d) - scipy.amin(bp1d), scipy.std(
            bp1d), scipy.mean(bp2d), scipy.amax(bp2d) - scipy.amin(
                bp2d), scipy.std(bp2d), reads_median, qual_median
示例#8
0
    def plot_drainage_curve(self,
                            data=None,
                            x_values='capillary_pressure',
                            y_values='invading_phase_saturation'):
        r"""
        Plot the drainage curve as the non-wetting phase saturation vs the
        applied capillary pressure.

        Parameters
        ----------
        data : dictionary of arrays
            This dictionary should be obtained from the ``get_drainage_data``
            method.

        x_values and y_values : string
            The dictionary keys of the arrays containing the x-values and
            y-values

        """
        # Begin creating nicely formatted plot
        if data is None:
            data = self.get_drainage_data()
        xdata = data[x_values]
        ydata = data[y_values]
        fig = plt.figure()
        plt.plot(xdata, ydata, 'ko-')
        plt.ylabel(y_values)
        plt.xlabel(x_values)
        plt.grid(True)
        if sp.amax(xdata) <= 1:
            plt.xlim(xmin=0, xmax=1)
        if sp.amax(ydata) <= 1:
            plt.ylim(ymin=0, ymax=1)
        return fig
    def get_xs(self):
        """ Retrieve xs information to populate self.xscurves. 
		Also create and populate self.max_q_query with maximum q 
		value for querying interpolated rating curves."""
        xscurves = []
        self.max_q_query = 0
        self.max_disch = 0
        self.max_h_query = 0
        self.max_stage = 0

        # Retrieve xs information and populate self.xscurves
        stations = self.xs['RiverStation'].unique()
        # a = self.xs[self.xs['RiverStation'].isin(stations)]
        for i, rs in enumerate(stations):
            # stage-height values for RiverStation rs
            a = self.xs[self.xs['RiverStation'] ==
                        rs]['Stage_Height_ft_'].values
            # Test if repeated zeroes (meaning multiple xs datasets for this RiverStation)
            # ************
            # If multiple zeros, ignore this RiverStation and proceed to next
            # ************
            s = [item for item, count in Counter(a).iteritems() if count > 1]
            if s: continue
            # Process xs data
            current = self.xs[self.xs['RiverStation'] == rs]
            prof = current['ProfileM'].unique()[
                0]  # location of xs relative to river reach
            disch = map(float,
                        current['Discharge_cfs_'].values)  # xs disch vals
            stage = map(float,
                        current['Stage_Height_ft_'].values)  # xs stage vals

            # Find max q value for querying interpolations
            # Find max disch value for plotting x_axis
            max_disch = int(scipy.amax(disch))
            if self.max_q_query == 0:
                self.max_q_query = max_disch
                self.max_disch = max_disch
            elif max_disch < self.max_q_query:
                self.max_q_query = max_disch
            elif max_disch > self.max_disch:
                self.max_disch = max_disch

            # Find max q value for querying interpolations
            # Find max disch value for plotting x_axis
            max_stage = int(scipy.amax(stage))
            if self.max_h_query == 0:
                self.max_h_query = max_stage
                self.max_stage = max_stage
            elif max_stage < self.max_h_query:
                self.max_h_query = max_stage
            elif max_stage > self.max_stage:
                self.max_stage = max_stage

            pack = (prof, zip(disch, stage)
                    )  # pack xs profile name w/ disch & stage vals
            xscurves.append(pack)
        if len(xscurves) != 0:
            self.xscurves = xscurves
            return 1
示例#10
0
 def amalgamate_throat_data(self,fluids='all'):
     r"""
     Returns a dictionary containing ALL throat data from all fluids, physics and geometry objects
     """
     self._throat_data_amalgamate = {}
     if type(fluids)!= sp.ndarray and fluids=='all':
         fluids = self._fluids
     elif type(fluids)!= sp.ndarray: 
         fluids = sp.array(fluids,ndmin=1)
     #Add fluid data
     for item in fluids:
         if type(item)==sp.str_: item =  self.find_object_by_name(item)
         for key in item._throat_data.keys():
             if sp.amax(item._throat_data[key]) < sp.inf:
                 dict_name = item.name+'_throat_'+key
                 self._throat_data_amalgamate.update({dict_name : item._throat_data[key]})
         for key in item._throat_info.keys():
             if sp.amax(item._throat_info[key]) < sp.inf:
                 dict_name = item.name+'_throat_label_'+key
                 self._throat_data_amalgamate.update({dict_name : item._throat_info[key]})
     #Add geometry data
     for key in self._throat_data.keys():
         if sp.amax(self._throat_data[key]) < sp.inf:
             dict_name = 'throat'+'_'+key
             self._throat_data_amalgamate.update({dict_name : self._throat_data[key]})
     for key in self._throat_info.keys():
         if sp.amax(self._throat_info[key]) < sp.inf:
             dict_name = 'throat'+'_label_'+key
             self._throat_data_amalgamate.update({dict_name : self._throat_info[key]})
     return self._throat_data_amalgamate
示例#11
0
def test_distance_center():
    shape = sp.array([7, 5, 9])
    spacing = sp.array([2, 1, 0.5])
    pn = OpenPNM.Network.Cubic(shape=shape, spacing=spacing)
    sx, sy, sz = spacing
    center_coord = sp.around(topology.find_centroid(pn['pore.coords']), 7)
    cx, cy, cz = center_coord
    coords = pn['pore.coords']
    x, y, z = coords.T
    coords = sp.concatenate((coords, center_coord.reshape((1, 3))))
    pn['pore.center'] = False
    mask1 = (x <= (cx + sx / 2)) * (y <= (cy + sy / 2)) * (z <= (cz + sz / 2))
    mask2 = (x >= (cx - sx / 2)) * (y >= (cy - sy / 2)) * (z >= (cz - sz / 2))
    center_pores_mask = pn.Ps[mask1 * mask2]
    pn['pore.center'][center_pores_mask] = True
    center = pn.Ps[pn['pore.center']]
    L1 = sp.amax(
        topology.find_pores_distance(network=pn, pores1=center, pores2=pn.Ps))
    L2 = sp.amax(
        topology.find_pores_distance(network=pn, pores1=pn.Ps, pores2=pn.Ps))
    l1 = ((shape[0] - 1) * sx)**2
    l2 = ((shape[1] - 1) * sy)**2
    l3 = ((shape[2] - 1) * sz)**2
    L3 = sp.sqrt(l1 + l2 + l3)
    assert sp.around(L1 * 2, 7) == sp.around(L2, 7)
    assert sp.around(L2, 7) == sp.around(L3, 7)
def test_distance_center():
    shape = sp.array([7, 5, 9])
    spacing = sp.array([2, 1, 0.5])
    pn = OpenPNM.Network.Cubic(shape=shape, spacing=spacing)
    sx, sy, sz = spacing
    center_coord = sp.around(topology.find_centroid(pn['pore.coords']), 7)
    cx, cy, cz = center_coord
    coords = pn['pore.coords']
    x, y, z = coords.T
    coords = sp.concatenate((coords, center_coord.reshape((1, 3))))
    pn['pore.center'] = False
    mask1 = (x <= (cx + sx/2)) * (y <= (cy + sy/2)) * (z <= (cz + sz/2))
    mask2 = (x >= (cx - sx/2)) * (y >= (cy - sy/2)) * (z >= (cz - sz/2))
    center_pores_mask = pn.Ps[mask1 * mask2]
    pn['pore.center'][center_pores_mask] = True
    center = pn.Ps[pn['pore.center']]
    L1 = sp.amax(topology.find_pores_distance(network=pn,
                                              pores1=center,
                                              pores2=pn.Ps))
    L2 = sp.amax(topology.find_pores_distance(network=pn,
                                              pores1=pn.Ps,
                                              pores2=pn.Ps))
    l1 = ((shape[0] - 1) * sx) ** 2
    l2 = ((shape[1] - 1) * sy) ** 2
    l3 = ((shape[2] - 1) * sz) ** 2
    L3 = sp.sqrt(l1 + l2 + l3)
    assert sp.around(L1 * 2, 7) == sp.around(L2, 7)
    assert sp.around(L2, 7) == sp.around(L3, 7)
示例#13
0
    def loadfile(self, skiprows=2):
        self.d.efld = np.loadtxt(self.efile, skiprows=skiprows)
        self.d.hfld = np.loadtxt(self.hfile, skiprows=skiprows)
        erows, ecols = np.shape(self.d.efld)
        hrows, hcols = np.shape(self.d.hfld)
        if (erows != hrows) or (ecols != hcols):
            raise TypeError('Input file size of E and H is inconsistent.')

        exl = np.unique(self.d.efld[:, cx])
        eyl = np.unique(self.d.efld[:, cy])
        ezl = np.unique(self.d.efld[:, cz])
        hxl = np.unique(self.d.hfld[:, cx])
        hyl = np.unique(self.d.hfld[:, cy])
        hzl = np.unique(self.d.hfld[:, cz])

        if any(exl != hxl) or any(eyl != hyl) or any(eyl != hyl):
            raise TypeError('Input data grid of E and H is inonsisitent.')

        self.d.xmin = np.amin(exl)
        self.d.xmax = np.amax(exl)
        self.d.ymin = np.amin(eyl)
        self.d.ymax = np.amax(eyl)
        self.d.zmin = np.amin(ezl)
        self.d.zmax = np.amax(ezl)

        self.d.dsize = erows
        self.d.xsize = len(exl) - 1
        self.d.ysize = len(eyl) - 1
        self.d.zsize = len(ezl) - 1

        self.d.dx = (self.d.xmax - self.d.xmin) / float(self.d.xsize)
        self.d.dy = (self.d.ymax - self.d.ymin) / float(self.d.ysize)
        self.d.dz = (self.d.zmax - self.d.zmin) / float(self.d.zsize)

        self.zyxsort()
示例#14
0
	def __or__(self,other):
		priority_normalize = 'first'
		import copy
		new = self.deepcopy()
		new.params.update(copy.deepcopy(other.params))
		for key in ['los','ndim']: assert getattr(self,key) == getattr(other,key)
		if scipy.amax(self.s) <= scipy.amax(other.s):
			first = self
			second = other
		else:
			first = other
			second = self
		#assert (first.index(first.zero) == 0) and (second.index(second.zero) == 0)
		if self.ndim == 1:
			firsts = [first.s]
			seconds = [second.s]
		else:
			firsts = first.s
			seconds = second.s
		firstshape = scipy.asarray(first.window.shape[1:])
		firstipoles = [ipole for ipole,pole in enumerate(first.poles) if pole in second.poles]
		secondipoles = [second.poles.index(first.poles[ipole]) for ipole in firstipoles]
		new.poles = [first.poles[ipole] for ipole in firstipoles]
		secondmask = [s2 >= s1[-1] for s1,s2 in zip(firsts,seconds)]
		new.s = [scipy.concatenate([s1,s2[mask2]],axis=-1) for s1,s2,mask2 in zip(firsts,seconds,secondmask)]
		if self.ndim == 1: new.s = new.s[0]
		overlaps = [s2[~mask2] for s2,mask2 in zip(seconds,secondmask)]
		ratio = first(overlaps,first.zero,kind_interpol='linear')/second(overlaps,second.zero,kind_interpol='linear')
		#norm = scipy.mean(ratio)
		slices = (slice(1,None),)*self.ndim
		norm = scipy.mean(ratio[slices]) #do not take (imprecise or padded) first point
	
		def normalize_first_second(first,second,norm,priority_normalize):
			firstnorm = secondnorm = 1.
			if priority_normalize == 'second': norm = 1./norm
			if getattr(first,'norm',None) is not None: new.norm = first.norm			
			if getattr(second,'norm',None) is None:
				secondnorm = norm
				self.logger.info('Rescaling {} part of the window function by {:.3f}.'.format(priority_normalize,secondnorm))
			elif getattr(first,'norm',None) is None:
				firstnorm = 1/norm
				new.norm = second.norm
				self.logger.info('Rescaling {} part of the window function by {:.3f}.'.format('second' if priority_normalize=='first' else 'first',firstnorm))
			else:
				self.logger.info('No rescaling, as both window functions are normalized (first over second ratio found: {:.3f}).'.format(norm))
			return firstnorm,secondnorm
		
		if priority_normalize == 'first':
			firstnorm,secondnorm = normalize_first_second(first,second,norm,priority_normalize)
		else:
			secondnorm,firstnorm = normalize_first_second(second,first,norm,priority_normalize)

		new.window = secondnorm*second(new.s,new.poles,kind_interpol='linear')
		slices = (slice(None),) + tuple(slice(0,end) for end in firstshape)
		new.window[slices] = firstnorm*first.window[...]
		if hasattr(second,'error'):
			new.error = secondnorm*second.poisson_error(new.s,kind_interpol='linear')
			if hasattr(first,'error'): new.error[slices[1:]] = firstnorm*first.error[...]
		return new
示例#15
0
 def test_get_coords(self):
     f = OpenPNM.Network.models.pore_topology.adjust_spacing
     self.net.models.add(propname='pore.coords2',
                         model=f,
                         new_spacing=2)
     assert 'pore.coords2' in self.net.keys()
     a = sp.amax(self.net['pore.coords'])
     assert sp.amax(self.net['pore.coords2']) == 2*a
示例#16
0
def porosity_profile(network,
                      fig=None, axis=2):

    r'''
    Compute and plot the porosity profile in all three dimensions

    Parameters
    ----------
    network : OpenPNM Network object
    axis : integer type 0 for x-axis, 1 for y-axis, 2 for z-axis

    Notes
    -----
    the area of the porous medium at any position is calculated from the
    maximum pore coordinates in each direction

    '''
    if fig is None:
        fig = _plt.figure()
    L_x = _sp.amax(network['pore.coords'][:,0]) + _sp.mean(((21/88.0)*network['pore.volume'])**(1/3.0))
    L_y = _sp.amax(network['pore.coords'][:,1]) + _sp.mean(((21/88.0)*network['pore.volume'])**(1/3.0))
    L_z = _sp.amax(network['pore.coords'][:,2]) + _sp.mean(((21/88.0)*network['pore.volume'])**(1/3.0))
    if axis is 0:
        xlab = 'x-direction'
        area = L_y*L_z
    elif axis is 1:
        xlab = 'y-direction'
        area = L_x*L_z
    else:
        axis = 2
        xlab = 'z-direction'
        area = L_x*L_y
    n_max = _sp.amax(network['pore.coords'][:,axis]) + _sp.mean(((21/88.0)*network['pore.volume'])**(1/3.0))
    steps = _sp.linspace(0,n_max,100,endpoint=True)
    vals = _sp.zeros_like(steps)
    p_area = _sp.zeros_like(steps)
    t_area = _sp.zeros_like(steps)

    rp = ((21/88.0)*network['pore.volume'])**(1/3.0)
    p_upper = network['pore.coords'][:,axis] + rp
    p_lower = network['pore.coords'][:,axis] - rp
    TC1 = network['throat.conns'][:,0]
    TC2 = network['throat.conns'][:,1]
    t_upper = network['pore.coords'][:,axis][TC1]
    t_lower = network['pore.coords'][:,axis][TC2]

    for i in range(0,len(steps)):
        p_temp = (p_upper > steps[i])*(p_lower < steps[i])
        t_temp = (t_upper > steps[i])*(t_lower < steps[i])
        p_area[i] = sum((22/7.0)*(rp[p_temp]**2 - (network['pore.coords'][:,axis][p_temp]-steps[i])**2))
        t_area[i] = sum(network['throat.area'][t_temp])
        vals[i] = (p_area[i]+t_area[i])/area
    yaxis = vals
    xaxis = steps/n_max
    _plt.plot(xaxis,yaxis,'bo-')
    _plt.xlabel(xlab)
    _plt.ylabel('Porosity')
    fig.show()
示例#17
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def standardizeImage(im): #Scales image down to 640x480 or whatever the correct aspect ratio is with conf.imSize as the height
	im = array(im, 'float32') 
	if im.shape[0] > conf.imSize:
		resize_factor = float(conf.imSize) / im.shape[0]	 # don't remove trailing .0 to avoid integer devision
		im = imresize(im, resize_factor)
	if amax(im) > 1.1:
		im = im / 255.0
	assert((amax(im) > 0.01) & (amax(im) <= 1))
	assert((amin(im) >= 0.00))
	return im
示例#18
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def standarizeImage(im):
    im = array(im, 'float32')
    if np.shape(im)[0] > 480:
        resize_factor = 480.0 / np.shape(im)[0]
        im = imresize(im, resize_factor)
    if amax(im) > 1.1:
        im = im / 255.0
    assert ((amax(im) > 0.01) & (amax(im) <= 1))
    assert ((amin(im) >= 0.00))
    return im
示例#19
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def standarizeImage(im):
    im = array(im, 'float32') 
    if im.shape[0] > 480:
        resize_factor = 480.0 / im.shape[0]  # don't remove trailing .0 to avoid integer devision
        im = imresize(im, resize_factor)
    if amax(im) > 1.1:
        im = im / 255.0
    assert((amax(im) > 0.01) & (amax(im) <= 1))
    assert((amin(im) >= 0.00))
    return im
def standarizeImage(im):
    im = array(im, 'float32') 
    if im.shape[0] > 480:
        resize_factor = 480.0 / im.shape[0]  # don't remove trailing .0 to avoid integer devision
        im = imresize(im, resize_factor)
    if amax(im) > 1.1:
        im = im / 255.0
    assert((amax(im) > 0.01) & (amax(im) <= 1))
    assert((amin(im) >= 0.00))
    return im
示例#21
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 def interpolate(self, canvas, status=None):
     # Clear the interpolated canvas
     canvas.interpolated = sp.zeros_like(canvas.fringes_image) - 1024.0
     if status is not None:
         status.set("Performing the interpolation", 70)
     else:
         print("Performing the interpolation")
     # Iterate over all the triangles in the triangulation
     for triangle in self.triangles:
         # Create a shortcut to the triangle's vertices
         co = triangle.vert_coordinates
         # Calculate a few constants for the Barycentric Coordinates
         # More info: https://codeplea.com/triangular-interpolation
         div = (co[1, 0] - co[2, 0]) * (co[0, 1] - co[2, 1]) + (
             co[2, 1] - co[1, 1]) * (co[0, 0] - co[2, 0])
         a0 = (co[1, 0] - co[2, 0])
         a1 = (co[2, 1] - co[1, 1])
         a2 = (co[2, 0] - co[0, 0])
         a3 = (co[0, 1] - co[2, 1])
         # Calculate the bounds of a rectangle that fully encloses
         # the current triangle
         xmin = int(sp.amin(triangle.vert_coordinates[:, 1]))
         xmax = int(sp.amax(triangle.vert_coordinates[:, 1])) + 1
         ymin = int(sp.amin(triangle.vert_coordinates[:, 0]))
         ymax = int(sp.amax(triangle.vert_coordinates[:, 0])) + 1
         # Take out slices of the x and y arrays,
         # containing the points' coordinates
         x_slice = canvas.x[ymin:ymax, xmin:xmax]
         y_slice = canvas.y[ymin:ymax, xmin:xmax]
         # Use Barycentric Coordinates and the magic of numpy (scipy in this
         # case) to perform the calculations with the C backend, instead
         # of iterating on pixels with Python loops.
         # If you have not worked with numpy arrays befor dear reader,
         # the idea is that if x = [[0 1]
         #                          [2 3]],
         # then x*3+1 is a completely valid operation, returning
         # x = [[1 4]
         #      [7 10]]
         # Basically, we can do maths on arrays as if they were variables.
         # Convenient, and really fast!
         w0 = (a0 * (x_slice - co[2, 1]) + a1 * (y_slice - co[2, 0])) / div
         w1 = (a2 * (x_slice - co[2, 1]) + a3 * (y_slice - co[2, 0])) / div
         w2 = sp.round_(1 - w0 - w1, 10)
         # Calculate the values for a rectangle enclosing our triangle
         slice = (self.values[triangle.vertices[0]] * w0 +
                  self.values[triangle.vertices[1]] * w1 +
                  self.values[triangle.vertices[2]] * w2)
         # Make a mask (so that we only touch the points
         # inside of the triangle).
         # In Barycentric Coordinates the points outside of the triangle
         # have at least one of the coefficients negative, so we use that
         mask = sp.logical_and(sp.logical_and(w0 >= 0, w1 >= 0), w2 >= 0)
         # Change the points in the actual canvas
         canvas.interpolated[ymin:ymax, xmin:xmax][mask] = slice[mask]
     canvas.interpolation_done = True
 def __init__(
         self, spike_count_range, train_count_range, num_units_range,
         firing_rate=50 * pq.Hz):
     self.spike_count_range = spike_count_range
     self.train_count_range = train_count_range
     self.num_units_range = num_units_range
     self.num_trains_per_spike_count = \
         sp.amax(num_units_range) * sp.amax(train_count_range)
     self.trains = [
         [stg.gen_homogeneous_poisson(firing_rate, max_spikes=num_spikes)
          for i in xrange(self.num_trains_per_spike_count)]
         for num_spikes in spike_count_range]
 def test_respects_refractory_period(self):
     refractory = 100 * pq.ms
     st = self.invoke_gen_func(
         self.highRate, max_spikes=1000, refractory=refractory)
     self.assertGreater(
         sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
         refractory.rescale(pq.s).magnitude)
     st = self.invoke_gen_func(
         self.highRate, t_stop=10 * pq.s, refractory=refractory)
     self.assertGreater(
         sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
         refractory.rescale(pq.s).magnitude)
示例#24
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 def test_from_neighbor_throats_min(self):
     self.geo.pop('pore.seed', None)
     self.geo.models.pop('pore.seed', None)
     self.geo.models.pop('throat.seed', None)
     self.geo['throat.seed'] = sp.rand(self.net.Nt, )
     self.geo.add_model(model=mods.from_neighbor_throats,
                        propname='pore.seed',
                        throat_prop='throat.seed',
                        mode='min')
     assert sp.all(sp.in1d(self.geo['pore.seed'], self.geo['throat.seed']))
     pmax = sp.amax(self.geo['pore.seed'])
     tmax = sp.amax(self.geo['throat.seed'])
     assert pmax <= tmax
示例#25
0
def expectation_prop_inner(m0, V0, Y, Z, F, z, needed):
    #expectation propagation on multivariate gaussian for soft inequality constraint
    #m0,v0 are mean vector , covariance before EP
    #Y is inequality value, Z is sign, 1 for geq, -1 for leq, F is softness variance
    #z is number of ep rounds to run
    #returns mt, Vt the value and variance for observations created by ep
    m0 = sp.array(m0).flatten()
    V0 = sp.array(V0)
    n = V0.shape[0]
    print "expectation prpagation running on " + str(
        n) + " dimensions for " + str(z) + " loops:"
    mt = sp.zeros(n)
    Vt = sp.eye(n) * float(1e10)
    m = sp.empty(n)
    V = sp.empty([n, n])
    conv = sp.empty(z)
    for i in xrange(z):

        #compute the m V give ep obs
        m, V = gaussian_fusion(m0, mt, V0, Vt)
        mtprev = mt.copy()
        Vtprev = Vt.copy()
        for j in [k for k in xrange(n) if needed[k]]:
            print[i, j]
            #the cavity dist at index j
            tmp = 1. / (Vt[j, j] - V[j, j])
            v_ = (V[j, j] * Vt[j, j]) * tmp
            m_ = tmp * (m[j] * Vt[j, j] - mt[j] * V[j, j])
            alpha = sp.sign(Z[j]) * (m_ - Y[j]) / (sp.sqrt(v_ + F[j]))
            pr = PhiR(alpha)

            if sp.isnan(pr):

                pr = -alpha
            beta = pr * (pr + alpha) / (v_ + F[j])
            kappa = sp.sign(Z[j]) * (pr + alpha) / (sp.sqrt(v_ + F[j]))

            #print [alpha,beta,kappa,pr]
            mt[j] = m_ + 1. / kappa
            #mt[j] = min(abs(mt[j]),1e5)*sp.sign(mt[j])
            Vt[j, j] = min(1e10, 1. / beta - v_)
        #print sp.amax(mtprev-mt)
        #print sp.amax(sp.diagonal(Vtprev)-sp.diagonal(Vt))
        #TODO make this a ratio instead of absolute
        delta = max(sp.amax(mtprev - mt),
                    sp.amax(sp.diagonal(Vtprev) - sp.diagonal(Vt)))
        conv[i] = delta
    print "EP finished with final max deltas " + str(conv[-3:])
    V = V0.dot(spl.solve(V0 + Vt, Vt))
    m = V.dot((spl.solve(V0, m0) + spl.solve(Vt, mt)).T)
    return mt, Vt
示例#26
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def standardizeImage(im): #Scales image down to 640x480
	im = array(im, 'float32') 
	if im.shape[0] > conf.imSize:
		resize_factor = float(conf.imSize) / im.shape[0]	 # don't remove trailing .0 to avoid integer devision
		im = imresize(im, resize_factor)
	if amax(im) > 1.1:
		im = im / 255.0
	assert((amax(im) > 0.01) & (amax(im) <= 1))
	assert((amin(im) >= 0.00))
	"""r = 480.0 / im.shape[1]
	dim = (480, int(im.shape[0] * r))
	im = cv2.resize(im, dim, interpolation = cv2.INTER_AREA)"""

	return im
示例#27
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 def test_neighbor_min(self):
     catch = self.geo.pop('pore.seed', None)
     catch = self.geo.models.pop('pore.seed', None)
     catch = self.geo.models.pop('throat.seed', None)
     mod = gm.pore_misc.neighbor
     self.geo['throat.seed'] = sp.rand(self.net.Nt,)
     self.geo.models.add(model=mod,
                         propname='pore.seed',
                         throat_prop='throat.seed',
                         mode='min')
     assert sp.all(sp.in1d(self.geo['pore.seed'], self.geo['throat.seed']))
     pmax = sp.amax(self.geo['pore.seed'])
     tmax = sp.amax(self.geo['throat.seed'])
     assert pmax <= tmax
示例#28
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def SetTimeStep(CFL, space, fluid):
    if (space.u.any() != 0):
        dt_hyper = CFL / max(
            sci.amax(space.u) / space.dx,
            sci.amax(space.v) / space.dy)
    else:
        dt_hyper = CFL * space.dx

    dt_para = min(space.dx**2 / (2 * fluid.mu), space.dy**2 / (2 * fluid.mu))
    dt_temp = min(space.dx**2 / (2 * fluid.alpha),
                  space.dy**2 / (2 * fluid.alpha))
    dt_conc = min(space.dx**2 / (2 * fluid.D), space.dy**2 / (2 * fluid.D))
    dt_min = min(dt_hyper, dt_para, dt_temp, dt_conc)
    space.dt = dt_min
示例#29
0
文件: eprop.py 项目: markm541374/GPc
def expectation_prop_inner(m0,V0,Y,Z,F,z,needed):
    #expectation propagation on multivariate gaussian for soft inequality constraint
    #m0,v0 are mean vector , covariance before EP
    #Y is inequality value, Z is sign, 1 for geq, -1 for leq, F is softness variance
    #z is number of ep rounds to run
    #returns mt, Vt the value and variance for observations created by ep
    m0=sp.array(m0).flatten()
    V0=sp.array(V0)
    n = V0.shape[0]
    print "expectation prpagation running on "+str(n)+" dimensions for "+str(z)+" loops:"
    mt =sp.zeros(n)
    Vt= sp.eye(n)*float(1e10)
    m = sp.empty(n)
    V = sp.empty([n,n])
    conv = sp.empty(z)
    for i in xrange(z):
        
        #compute the m V give ep obs
        m,V = gaussian_fusion(m0,mt,V0,Vt)
        mtprev=mt.copy()
        Vtprev=Vt.copy()
        for j in [k for k in xrange(n) if needed[k]]:
            print [i,j]
            #the cavity dist at index j
            tmp = 1./(Vt[j,j]-V[j,j])
            v_ = (V[j,j]*Vt[j,j])*tmp
            m_ = tmp*(m[j]*Vt[j, j]-mt[j]*V[j, j])
            alpha = sp.sign(Z[j])*(m_-Y[j]) / (sp.sqrt(v_+F[j]))
            pr = PhiR(alpha)
            
            
            if sp.isnan(pr):
                
                pr = -alpha
            beta = pr*(pr+alpha)/(v_+F[j])
            kappa = sp.sign(Z[j])*(pr+alpha) / (sp.sqrt(v_+F[j]))
            
            #print [alpha,beta,kappa,pr]
            mt[j] = m_+1./kappa
            #mt[j] = min(abs(mt[j]),1e5)*sp.sign(mt[j])
            Vt[j,j] = min(1e10,1./beta - v_)
        #print sp.amax(mtprev-mt)
        #print sp.amax(sp.diagonal(Vtprev)-sp.diagonal(Vt))
        #TODO make this a ratio instead of absolute
        delta = max(sp.amax(mtprev-mt),sp.amax(sp.diagonal(Vtprev)-sp.diagonal(Vt)))
        conv[i]=delta
    print "EP finished with final max deltas "+str(conv[-3:])
    V = V0.dot(spl.solve(V0+Vt,Vt))
    m = V.dot((spl.solve(V0,m0)+spl.solve(Vt,mt)).T)
    return mt, Vt
示例#30
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def patch_color_labels(s, freq=[1], cmap='Paired', shuffle=True):
    ''' color by freq of labels '''
    s.vColor = sp.zeros(s.vertices.shape)
    _, labels = sp.unique(s.labels, return_inverse=True)
    labels += 1
    colr = get_cmap(sp.amax(labels) + 1, cmap=cmap)
    s.vColor = s.vColor + 1
    perm1 = sp.mod(3511 * sp.arange(sp.amax(labels) + 1), sp.amax(labels) + 1)
    freq = sp.reshape(freq, (len(freq), 1))
    if shuffle == True:
        s.vColor = (1 - freq) + freq * sp.array(colr(perm1[labels])[:, :3])
    else:
        s.vColor = (1 - freq) + freq * sp.array(colr(labels)[:, :3])
    return s
 def test_respects_refractory_period(self):
     refractory = 100 * pq.ms
     st = self.invoke_gen_func(self.highRate,
                               max_spikes=1000,
                               refractory=refractory)
     self.assertGreater(
         sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
         refractory.rescale(pq.s).magnitude)
     st = self.invoke_gen_func(self.highRate,
                               t_stop=10 * pq.s,
                               refractory=refractory)
     self.assertGreater(
         sp.amax(sp.absolute(sp.diff(st.rescale(pq.s).magnitude))),
         refractory.rescale(pq.s).magnitude)
示例#32
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def array_factor(number_of_elements, scan_angle, element_spacing, frequency, theta, window_type, side_lobe_level):
    """
    Calculate the array factor for a linear binomial excited array.
    :param window_type: The string name of the window.
    :param side_lobe_level: The sidelobe level for Tschebyscheff window (dB).
    :param number_of_elements: The number of elements in the array.
    :param scan_angle: The angle to which the main beam is scanned (rad).
    :param element_spacing: The distance between elements.
    :param frequency: The operating frequency (Hz).
    :param theta: The angle at which to evaluate the array factor (rad).
    :return: The array factor as a function of angle.
    """
    # Calculate the wavenumber
    k = 2.0 * pi * frequency / c

    # Calculate the phase
    psi = k * element_spacing * (cos(theta) - cos(scan_angle))

    # Calculate the coefficients
    if window_type == 'Uniform':
        coefficients = ones(number_of_elements)
    elif window_type == 'Binomial':
        coefficients = binom(number_of_elements-1, range(0, number_of_elements))
    elif window_type == 'Tschebyscheff':
        warnings.simplefilter("ignore", UserWarning)
        coefficients = chebwin(number_of_elements, at=side_lobe_level, sym=True)
    elif window_type == 'Kaiser':
        coefficients = kaiser(number_of_elements, 6, True)
    elif window_type == 'Blackman-Harris':
        coefficients = blackmanharris(number_of_elements, True)
    elif window_type == 'Hanning':
        coefficients = hanning(number_of_elements, True)
    elif window_type == 'Hamming':
        coefficients = hamming(number_of_elements, True)

    # Calculate the offset for even/odd
    offset = int(floor(number_of_elements / 2))

    # Odd case
    if number_of_elements & 1:
        coefficients = roll(coefficients, offset + 1)
        coefficients[0] *= 0.5
        af = sum(coefficients[i] * cos(i * psi) for i in range(offset + 1))
        return af / amax(abs(af))
    # Even case
    else:
        coefficients = roll(coefficients, offset)
        af = sum(coefficients[i] * cos((i + 0.5) * psi) for i in range(offset))
        return af / amax(abs(af))
示例#33
0
    def genParams(self, df):
        data = []
        ys = sp.array(df.filter(like='Ids')).T
        fits = sp.array(df.filter(like='tcfit')).T

        cols = [i.replace('_Ids', '') for i in df.filter(like='Ids').columns]

        for y, f in zip(ys, fits):
            on = sp.amax(y)
            off = sp.amin(y)
            data.append([off, on, on / off, sp.amax(f)])
        datadf = pd.DataFrame(sp.array(data).T,
                              index=['off', 'on', 'onoff', 'maxtc'],
                              columns=cols)
        return datadf
示例#34
0
    def newtonRaphson(sys):
        eps = 1e-9  # Abbruchkriterium
        relDif = np.amax(np.absolute(sys.b)) * eps
        imax = CircuitAnalysis.MAX_NEWTON_ITERATIONS
        i = 0  # Iterationsnummer
        ungenau = True
        wenig_iterationen = True
        d = 10
        movelen = 10
        sys.curNewtonIteration = 0
        x_backup = np.copy(sys.x)

        Vmax = np.amax(np.absolute(sys.b))

        while (ungenau and wenig_iterationen):

            xvorher = sys.x
            i += 1
            sys.curNewtonIteration = i

            CircuitAnalysis.nonlin(sys)

            if (sys.n > 1000):
                A = sys.A + sys.J
                b = sys.J.dot(sys.x) - sys.g + sys.b
                sys.x = spsolve(A, b, permc_spec="NATURAL")
            else:
                sys.x = np.linalg.solve(sys.A + sys.J,
                                        np.dot(sys.J, sys.x) - sys.g + sys.b)

            movelen, d = CircuitAnalysis.subNewtonRaphson(
                xvorher, sys, d, movelen, Vmax)

            dif = np.amax(np.absolute(sys.x - xvorher))
            wenig_iterationen = (i < imax)
            ungenau = d > relDif or (dif > relDif)  #eps)# and (d > eps)

            #print("NR: %i"%(i))
            if (i > 50):
                print("Newton: Iteration: " + str(i) + "     ", end='\r')
            if i == imax:
                sys.x = np.copy(x_backup)
                print("Newton-Raphson convergence failure")
                #raise NRConvergenceException()

        print("                                                              ",
              end='\r')
        return [sys.A, sys.x, sys.J, i]
def output_percentile_set(data_field, args):
    r"""
    Does three sets of percentiles and stacks them as columns: raw data,
    absolute value data, normalized+absolute value
    """
    data = {}
    #
    # outputting percentiles of initial subtraction to screen
    field = data_field.clone()
    pctle = Percentiles(field, percentiles=args.perc)
    pctle.process()
    data['raw'] = pctle.processed_data
    #
    # normalizing data
    field = data_field.clone()
    field.data_map = field.data_map/sp.amax(sp.absolute(field.data_map))
    field.data_vector = sp.ravel(field.data_map)
    pctle = Percentiles(field, percentiles=args.perc)
    pctle.process()
    data['norm'] = pctle.processed_data
    #
    # taking absolute value of data
    field = data_field.clone()
    field.data_map = sp.absolute(field.data_map)
    field.data_vector = sp.absolute(field.data_vector)
    pctle = Percentiles(field, percentiles=args.perc)
    pctle.process()
    data['abs'] = pctle.processed_data
    #
    # absolute value + normed
    field.data_map = field.data_map/sp.amax(field.data_map)
    field.data_vector = sp.ravel(field.data_map)
    pctle = Percentiles(field, percentiles=args.perc)
    pctle.process()
    data['abs+norm'] = pctle.processed_data
    #
    # outputting stacked percentiles
    fmt = '    {:>6.2f}\t{: 0.6e}\t{: 0.6e}\t{: 0.6e}\t{: 0.6e}\n'
    content = 'Percentile\tRaw Data\tAbsolute\tNormalized\tNorm+abs\n'
    data = zip(args.perc, data['raw'].values(),
               data['abs'].values(),
               data['norm'].values(),
               data['abs+norm'].values())
    #
    for row in data:
        content += fmt.format(*row)
    content += '\n'
    print(content)
示例#36
0
def sem(im, direction='X'):
    r"""
    Simulates an SEM photograph looking into the porous material in the
    specified direction.  Features are colored according to their depth into
    the image, so darker features are further away.

    Parameters
    ----------
    im : array_like
        ND-image of the porous material with the solid phase marked as 1 or
        True

    direction : string
        Specify the axis along which the camera will point.  Options are
        'X', 'Y', and 'Z'.

    Returns
    -------
    A 2D greyscale image suitable for use in matplotlib\'s ```imshow```
    function.
    """
    im = sp.array(~im, dtype=int)
    if direction in ['Y', 'y']:
        im = sp.transpose(im, axes=[1, 0, 2])
    if direction in ['Z', 'z']:
        im = sp.transpose(im, axes=[2, 1, 0])
    t = im.shape[0]
    depth = sp.reshape(sp.arange(0, t), [t, 1, 1])
    im = im * depth
    im = sp.amax(im, axis=0)
    return im
示例#37
0
文件: vmc.py 项目: EPFL-LQM/gpvmc
def GetStat(filename,Nsamp=1):
    hfile=[]
    if type(filename)==list:
        for i,f in enumerate(filename):
            hfile.append(h5py.File(f,'r'))
    elif type(filename)==str:
        hfile.append(h5py.File(filename,'r'))
        filename=[filename]
    stats=[]
    datapath=[]
    for ih,h in enumerate(hfile):
        for r in h:
            for d in h[r]:
                try:
                    stats.append(int(h[r][d].attrs['statistics'][0]))
                except KeyError as err:
                    stats.append(1)
                datapath.append((filename[ih],"/{0}/{1}".format(r,d)))
    bunches,args=vln.bunch(stats,Nsamp,indices=True)
    addstat=sc.array([sum(bunches[i]) for i in range(Nsamp)])
    print("Average statistics of {0} (min: {1}, max: {2})"\
            .format(sc.mean(addstat),sc.amin(addstat),sc.amax(addstat)))
    for f in hfile:
        f.close()
    return datapath,args
示例#38
0
 def test_random(self):
     self.geo.models.add(propname='throat.seed',
                         model=OpenPNM.Geometry.models.throat_seed.random,
                         seed=0,
                         num_range=[0.1, 2])
     assert sp.amax(self.geo['throat.seed']) > 1.9
     assert sp.amin(self.geo['throat.seed']) > 0.1
示例#39
0
def neighbor(geometry, network, pore_prop='pore.seed', mode='min', **kwargs):
    r"""
    Adopt a value based on the values in the neighboring pores

    Parameters
    ----------
    mode : string
        Indicates how to select the values from the neighboring pores.  The
        options are:

        - min : (Default) Uses the minimum of the value found in the neighbors
        - max : Uses the maximum of the values found in the neighbors
        - mean : Uses an average of the neighbor values

    pore_prop : string
        The dictionary key containing the pore property to be used.
    """
    throats = network.throats(geometry.name)
    P12 = network.find_connected_pores(throats)
    pvalues = network[pore_prop][P12]
    if mode == 'min':
        value = _sp.amin(pvalues, axis=1)
    if mode == 'max':
        value = _sp.amax(pvalues, axis=1)
    if mode == 'mean':
        value = _sp.mean(pvalues, axis=1)
    return value
示例#40
0
 def test_random_with_range(self):
     mod = gm.throat_misc.random
     self.geo.models.add(model=mod,
                         propname='throat.seed',
                         num_range=[0.1, 0.9])
     assert sp.amax(self.geo['throat.seed']) <= 0.9
     assert sp.amin(self.geo['throat.seed']) >= 0.1
示例#41
0
def plot_delta():     
    beta = 0.99
    N = 1000
    u = lambda c: sp.sqrt(c)
    W = sp.linspace(0,1,N)
    X, Y = sp.meshgrid(W,W)
    Wdiff = sp.transpose(X-Y)
    index = Wdiff <0
    Wdiff[index] = 0
    util_grid = u(Wdiff)
    util_grid[index] = -10**10
    
    Vprime = sp.zeros((N,1))
    delta = sp.ones(1)
    tol = 10**-9
    it = 0
    max_iter = 500
    
    while (delta[-1] >= tol) and (it < max_iter):
        V = Vprime
        it += 1;
        print(it)
        val = util_grid + beta*sp.transpose(V)
        Vprime = sp.amax(val, axis = 1)
        Vprime = Vprime.reshape((N,1))
        delta = sp.append(delta,sp.dot(sp.transpose(Vprime - V),Vprime-V))
        
    plt.figure()
    plt.plot(delta[1:])
    plt.ylabel(r'$\delta_k$')
    plt.xlabel('iteration')
    plt.savefig('convergence.pdf')
示例#42
0
def Problem3Real():
    beta = 0.9
    N = 1000
    u = lambda c: sp.sqrt(c)
    W = sp.linspace(0,1,N)
    X, Y = sp.meshgrid(W,W)
    Wdiff = sp.transpose(X-Y)
    index = Wdiff <0
    Wdiff[index] = 0
    util_grid = u(Wdiff)
    util_grid[index] = -10**10
    
    Vprime = sp.zeros((N,1))
    psi = sp.zeros((N,1))
    delta = 1.0
    tol = 10**-9
    it = 0
    max_iter = 500
    
    while (delta >= tol) and (it < max_iter):
        V = Vprime
        it += 1;
        #print(it)
        val = util_grid + beta*sp.transpose(V)
        Vprime = sp.amax(val, axis = 1)
        Vprime = Vprime.reshape((N,1))
        psi_ind = sp.argmax(val,axis = 1)
        psi    = W[psi_ind]
        delta = sp.dot(sp.transpose(Vprime - V),Vprime-V)
    
    return psi
示例#43
0
def Problem1Real():
    beta = 0.9;
    T = 10;
    N = 100;
    u = lambda c: sp.sqrt(c);
    W = sp.linspace(0,1,N);
    X, Y = sp.meshgrid(W,W);
    Wdiff = Y-X
    index = Wdiff <0;
    Wdiff[index] = 0;
    util_grid = u(Wdiff);
    util_grid[index] = -10**10;
    V = sp.zeros((N,T+2));
    psi = sp.zeros((N,T+1));


    for k in xrange(T,-1,-1):
        val = util_grid + beta*sp.tile(sp.transpose(V[:,k+1]),(N,1));
        vt = sp.amax(val, axis = 1);
        psi_ind = sp.argmax(val,axis = 1)
        V[:,k]    = vt;
        psi[:,k]    = W[psi_ind];

    
    return V,psi
def test_late_pore_and_throat_filling():
    phys.models.add(propname='pore.fractional_filling',
                    model=OpenPNM.Physics.models.multiphase.late_pore_filling,
                    Pc=0,
                    Swp_star=0.2,
                    eta=1)
    mod = OpenPNM.Physics.models.multiphase.late_throat_filling
    phys.models.add(propname='throat.fractional_filling',
                    model=mod,
                    Pc=0,
                    Swp_star=0.2,
                    eta=1)
    phys.regenerate()
    drainage.setup(invading_phase=water, defending_phase=air,
                   pore_filling='pore.fractional_filling',
                   throat_filling='throat.fractional_filling')
    drainage.set_inlets(pores=pn.pores('boundary_top'))
    drainage.run()
    data = drainage.get_drainage_data()
    assert sp.amin(data['invading_phase_saturation']) == 0.0
    assert sp.amax(data['invading_phase_saturation']) < 1.0

    drainage.return_results(Pc=5000)
    assert 'pore.occupancy' in water.keys()
    assert 'throat.occupancy' in water.keys()
    assert 'pore.partial_occupancy' in water.keys()
    assert 'throat.partial_occupancy' in water.keys()
示例#45
0
    def __MR_get_adj_loop(self, labels):
        s = sp.amax(labels) + 1
        adj = np.ones((s, s), np.bool)

        for i in range(labels.shape[0] - 1):
            for j in range(labels.shape[1] - 1):
                if labels[i, j]<>labels[i+1, j]:
                    adj[labels[i, j],       labels[i+1, j]]              = False
                    adj[labels[i+1, j],   labels[i, j]]                  = False
                if labels[i, j]<>labels[i, j + 1]:
                    adj[labels[i, j],       labels[i, j+1]]              = False
                    adj[labels[i, j+1],   labels[i, j]]                  = False
                if labels[i, j]<>labels[i + 1, j + 1]:
                    adj[labels[i, j]        ,  labels[i+1, j+1]]       = False
                    adj[labels[i+1, j+1],  labels[i, j]]               = False
                if labels[i + 1, j]<>labels[i, j + 1]:
                    adj[labels[i+1, j],   labels[i, j+1]]              = False
                    adj[labels[i, j+1],   labels[i+1, j]]              = False
        
        upper_ids = sp.unique(labels[0,:]).astype(int)
        right_ids = sp.unique(labels[:,labels.shape[1]-1]).astype(int)
        low_ids = sp.unique(labels[labels.shape[0]-1,:]).astype(int)
        left_ids = sp.unique(labels[:,0]).astype(int)
        
        bd = np.append(upper_ids, right_ids)
        bd = np.append(bd, low_ids)
        bd = sp.unique(np.append(bd, left_ids))
        
        for i in range(len(bd)):
            for j in range(i + 1, len(bd)):
                adj[bd[i], bd[j]] = False
                adj[bd[j], bd[i]] = False

        return adj
def plot_optimal_uncertainty_reduction(results_for_exp, results_for_exp_inftau):
    """ Plot the percentage of uncertainty reduction of the optimal classifiers.

    :param results_for_exp: The results of one experiment as 4-D array of the
        shape (metrics, z-values, tau-values, experimental repetitions).
    :type results_for_exp: 4-D array
    :param result_list_inftau: The results of one experiment for `tau = inf` as
        3-D array of the shape (metrics, z-values, experimental repetitions).
    :type results_for_exp_inftau: 3-D array.
    """
    plt.ylim(0, 1)

    plot_param_per_metric_and_z(
        sp.mean(sp.amax(results_for_exp, axis=2), axis=2),
        sp.std(sp.amax(results_for_exp, axis=2), axis=2))
    plot_param_per_metric_and_z(sp.mean(results_for_exp_inftau, axis=2), c='g')
示例#47
0
 def test_random(self):
     self.geo.models.add(propname='throat.seed',
                         model=OpenPNM.Geometry.models.throat_seed.random,
                         seed=0,
                         num_range=[0.1, 2])
     assert sp.amax(self.geo['throat.seed']) > 1.9
     assert sp.amin(self.geo['throat.seed']) > 0.1
示例#48
0
def print_all_stats(ctx, series):
    ftime = get_ftime(series)
    start = 0 
    end = ctx.interval
    print('start-time, samples, min, avg, median, 90%, 95%, 99%, max')
    while (start < ftime):  # for each time interval
        end = ftime if ftime < end else end
        sample_arrays = [ s.get_samples(start, end) for s in series ]
        samplevalue_arrays = []
        for sample_array in sample_arrays:
            samplevalue_arrays.append( 
                [ sample.value for sample in sample_array ] )
        #print('samplevalue_arrays len: %d' % len(samplevalue_arrays))
        #print('samplevalue_arrays elements len: ' + \
               #str(map( lambda l: len(l), samplevalue_arrays)))
        # collapse list of lists of sample values into list of sample values
        samplevalues = reduce( array_collapser, samplevalue_arrays, [] )
        #print('samplevalues: ' + str(sorted(samplevalues)))
        # compute all stats and print them
        myarray = scipy.fromiter(samplevalues, float)
        mymin = scipy.amin(myarray)
        myavg = scipy.average(myarray)
        mymedian = scipy.median(myarray)
        my90th = scipy.percentile(myarray, 90)
        my95th = scipy.percentile(myarray, 95)
        my99th = scipy.percentile(myarray, 99)
        mymax = scipy.amax(myarray)
        print( '%f, %d, %f, %f, %f, %f, %f, %f, %f' % (
            start, len(samplevalues), 
            mymin, myavg, mymedian, my90th, my95th, my99th, mymax))

        # advance to next interval
        start += ctx.interval
        end += ctx.interval
    def run(self, npts=25, inv_points=None, access_limited=True, **kwargs):
        r"""
        Parameters
        ----------
        npts : int (default = 25)
            The number of pressure points to apply.  The list of pressures
            is logarithmically spaced between the lowest and highest throat
            entry pressures in the network.

        inv_points : array_like, optional
            A list of specific pressure point(s) to apply.

        """
        if 'inlets' in kwargs.keys():
            logger.info('Inlets recieved, passing to set_inlets')
            self.set_inlets(pores=kwargs['inlets'])
        if 'outlets' in kwargs.keys():
            logger.info('Outlets recieved, passing to set_outlets')
            self.set_outlets(pores=kwargs['outlets'])
        self._AL = access_limited
        if inv_points is None:
            logger.info('Generating list of invasion pressures')
            min_p = sp.amin(self['throat.entry_pressure']) * 0.98  # nudge down
            max_p = sp.amax(self['throat.entry_pressure']) * 1.02  # bump up
            inv_points = sp.logspace(sp.log10(min_p),
                                     sp.log10(max_p),
                                     npts)

        self._npts = sp.size(inv_points)
        # Execute calculation
        self._do_outer_iteration_stage(inv_points)
示例#50
0
文件: pgpda.py 项目: lennepkade/PGPDA
def scale(x, M=None, m=None, REVERSE=None):
    """ Function that standardize the data
        Input:
            x: the data
            M: the Max vector
            m: the Min vector
        Output:
            x: the standardize data
            M: the Max vector
            m: the Min vector
    """
    if not sp.issubdtype(x.dtype, float):
        do_convert = 1
    else:
        do_convert = 0
    if REVERSE is None:
        if M is None:
            M = sp.amax(x, axis=0)
            m = sp.amin(x, axis=0)
            if do_convert:
                xs = 2 * (x.astype("float") - m) / (M - m) - 1
            else:
                xs = 2 * (x - m) / (M - m) - 1
            return xs, M, m
        else:
            if do_convert:
                xs = 2 * (x.astype("float") - m) / (M - m) - 1
            else:
                xs = 2 * (x - m) / (M - m) - 1
            return xs
    else:
        return (1 + x) / 2 * (M - m) + m
示例#51
0
    def add_boundaries(self):
        r'''
        This method uses ``clone`` to clone the surface pores (labeled 'left',
        'right', etc), then shifts them to the periphery of the domain, and
        gives them the label 'right_face', 'left_face', etc.
        '''
        x, y, z = self['pore.coords'].T

        Lc = sp.amax(sp.diff(x))  #this currently works but is very fragile

        offset = {}
        offset['front'] = offset['left'] = offset['bottom'] = [0, 0, 0]
        offset['back'] = [x.max() + Lc / 2, 0, 0]
        offset['right'] = [0, y.max() + Lc / 2, 0]
        offset['top'] = [0, 0, z.max() + Lc / 2]

        scale = {}
        scale['front'] = scale['back'] = [0, 1, 1]
        scale['left'] = scale['right'] = [1, 0, 1]
        scale['bottom'] = scale['top'] = [1, 1, 0]

        for label in ['front', 'back', 'left', 'right', 'bottom', 'top']:
            ps = self.pores(label)
            self.clone(pores=ps, apply_label=[label + '_boundary', 'boundary'])
            #Translate cloned pores
            ind = self.pores(label + '_boundary')
            coords = self['pore.coords'][ind]
            coords = coords * scale[label] + offset[label]
            self['pore.coords'][ind] = coords
示例#52
0
def RREFscaled(mymat):
#    Pdb().set_trace()
    scalevect=scipy.amax(abs(mymat),1)
    scaledrows=[]
    for sf,row in zip(scalevect,mymat):
        row=row/sf
        scaledrows.append(row)
    scaledmat=scipy.vstack(scaledrows)
#    scaledmat=mymat
    nc=scipy.shape(scaledmat)[1]
    nr=scipy.shape(scaledmat)[0]
    for j in range(nr-1):
#        print('=====================')
#        print('j='+str(j))
        pivrow=scipy.argmax(abs(scaledmat[j:-1,j]))
        pivrow=pivrow+j
#        print('pivrow='+str(pivrow))
        if pivrow!=j:
            temprow=copy.copy(scaledmat[j,:])
            scaledmat[j,:]=scaledmat[pivrow,:]
            scaledmat[pivrow,:]=temprow
#        Pdb().set_trace()
        for i in range(j+1,nr):
#            print('i='+str(i))
            scaledmat[i,:]-=scaledmat[j,:]*(scaledmat[i,j]/scaledmat[j,j])
    return scaledmat, scalevect
示例#53
0
def neighbor(geometry, network, pore_prop='pore.seed', mode='min', **kwargs):
    r"""
    Adopt a value based on the values in the neighboring pores

    Parameters
    ----------
    mode : string
        Indicates how to select the values from the neighboring pores.  The
        options are:

        - min : (Default) Uses the minimum of the value found in the neighbors
        - max : Uses the maximum of the values found in the neighbors
        - mean : Uses an average of the neighbor values

    pore_prop : string
        The dictionary key containing the pore property to be used.
    """
    throats = network.throats(geometry.name)
    P12 = network.find_connected_pores(throats)
    pvalues = network[pore_prop][P12]
    if mode == 'min':
        value = _sp.amin(pvalues, axis=1)
    if mode == 'max':
        value = _sp.amax(pvalues, axis=1)
    if mode == 'mean':
        value = _sp.mean(pvalues, axis=1)
    return value
def test_residual_and_lpf():
    phys.models.add(propname='pore.fractional_filling',
                    model=OpenPNM.Physics.models.multiphase.late_pore_filling,
                    Pc=0,
                    Swp_star=0.2,
                    eta=1)
    phys.models.add(propname='throat.fractional_filling',
                    model=OpenPNM.Physics.models.multiphase.late_throat_filling,
                    Pc=0,
                    Swp_star=0.2,
                    eta=1)
    phys.regenerate()
    drainage.setup(invading_phase=water, defending_phase=air,
                   pore_filling='pore.fractional_filling',
                   throat_filling='throat.fractional_filling')
    drainage.set_inlets(pores=pn.pores('boundary_top'))
    resPs = pn.pores('internal')[sp.random.random(len(pn.pores('internal')))<0.1]
    resTs = pn.throats('internal')[sp.random.random(len(pn.throats('internal')))<0.1]
    drainage.set_residual(pores=resPs, throats=resTs)
    drainage.run()
    drainage.return_results(Pc=5000)
    data = drainage.get_drainage_data()
    assert sp.all(water["pore.partial_occupancy"][resPs] == 1.0)
    assert sp.all(water["throat.partial_occupancy"][resTs] == 1.0)
    assert sp.amin(data['invading_phase_saturation']) > 0.0
    assert sp.amax(data['invading_phase_saturation']) < 1.0
    assert sp.all(water["pore.occupancy"]+air["pore.occupancy"] == 1.0)
    total_pp = water["pore.partial_occupancy"]+air["pore.partial_occupancy"]
    assert sp.all(total_pp == 1.0)
    assert sp.all(water["throat.occupancy"]+air["throat.occupancy"] == 1.0)
    total_pt = water["throat.partial_occupancy"]+air["throat.partial_occupancy"] 
    assert sp.all(total_pt == 1.0)
def Problem3Real():
    beta = 0.9
    N = 1000
    u = lambda c: sp.sqrt(c)
    W = sp.linspace(0, 1, N)
    X, Y = sp.meshgrid(W, W)
    Wdiff = sp.transpose(X - Y)
    index = Wdiff < 0
    Wdiff[index] = 0
    util_grid = u(Wdiff)
    util_grid[index] = -10**10

    Vprime = sp.zeros((N, 1))
    psi = sp.zeros((N, 1))
    delta = 1.0
    tol = 10**-9
    it = 0
    max_iter = 500

    while (delta >= tol) and (it < max_iter):
        V = Vprime
        it += 1
        #print(it)
        val = util_grid + beta * sp.transpose(V)
        Vprime = sp.amax(val, axis=1)
        Vprime = Vprime.reshape((N, 1))
        psi_ind = sp.argmax(val, axis=1)
        psi = W[psi_ind]
        delta = sp.dot(sp.transpose(Vprime - V), Vprime - V)

    return psi
示例#56
0
def main(database):

    #Commits per committer limited to the 30 first with the highest accumulated activity
    query = "select count(*) from scmlog group by committer_id order by count(*) desc limit 40"

    #Connecting to the data base and retrieving data
    connector = connect(database)
    results = int(connector.execute(query))
    if results > 0:
        results_aux = connector.fetchall()
    else:
        print("Error when retrieving data")
        return

    #Moving data to a list
    commits = []
    for commit in results_aux[5:]:
        #   for commits in results_aux:
        commits.append(int(commit[0]))

    #Calculating basic statistics
    print "max: " + str(sp.amax(commits))
    print "min: " + str(sp.amin(commits))
    print "mean: " + str(sp.mean(commits))
    print "median: " + str(sp.median(commits))
    print "std: " + str(sp.std(commits))
    print ".25 quartile: " + str(sp.percentile(commits, 25))
    print ".50 quartile: " + str(sp.percentile(commits, 50))
    print ".75 quartile: " + str(sp.percentile(commits, 75))
def process_maps(aper_map, data_map1, data_map2, args):
    r"""
    subtracts the data maps and then calculates percentiles of the result
    before outputting a final map to file.
    """
    #
    # creating resultant map from clone of aperture map
    result = aper_map.clone()
    result.data_map = data_map1 - data_map2
    result.data_vector = sp.ravel(result.data_map)
    result.infile = args.out_name
    result.outfile = args.out_name
    #
    print('Percentiles of data_map1 - data_map2')
    output_percentile_set(result, args)
    #
    # checking if data is to be normalized and/or absolute
    if args.post_abs:
        result.data_map = sp.absolute(result.data_map)
        result.data_vector = sp.absolute(result.data_vector)
    #
    if args.post_normalize:
        result.data_map = result.data_map/sp.amax(sp.absolute(result.data_map))
        result.data_vector = sp.ravel(result.data_map)
    #
    return result
 def _do_one_outer_iteration(self, **kwargs):
     r"""
     One iteration of an outer iteration loop for an algorithm
     (e.g. time or parametric study)
     """
     # Checking for the necessary values in Picard algorithm
     nan_tol = sp.isnan(self['pore.source_tol'])
     nan_max = sp.isnan(self['pore.source_maxiter'])
     self._tol_for_all = sp.amin(self['pore.source_tol'][~nan_tol])
     self._maxiter_for_all = sp.amax(self['pore.source_maxiter'][~nan_max])
     if self._guess is None:
         self._guess = sp.zeros(self._coeff_dimension)
     t = 1
     step = 0
     # The main Picard loop
     while t > self._tol_for_all and step <= self._maxiter_for_all:
         X, t, A, b = self._do_inner_iteration_stage(guess=self._guess,
                                                     **kwargs)
         logger.info('tol for Picard source_algorithm in step ' +
                     str(step) + ' : ' + str(t))
         self._guess = X
         step += 1
     # Check for divergence
     self._steps = step
     if t >= self._tol_for_all and step > self._maxiter_for_all:
         raise Exception('Iterative algorithm for the source term reached '
                         'to the maxiter: ' + str(self._maxiter_for_all) +
                         ' without achieving tol: ' +
                         str(self._tol_for_all))
     logger.info('Picard algorithm for source term converged!')
     self.A = A
     self.b = b
     self._tol_reached = t
     return X