Python scipy.zeros_like函数代码示例

 def fitKronApprox(a):
     Sbg = SP.zeros_like(S[0])
     Kbg = SP.zeros_like(K[0])
     for i in range(len(S)):  Sbg+= a[i]*S[i]
     for i in range(len(K)):  Kbg+= a[i+len(S)]*K[i]
     Gamma1 = SP.kron(Sbg,Kbg)
     return ((Gamma-Gamma1)**2).sum()

def estCumPos(position,offset=0,chrom_len=None):
    '''
    compute the cumulative position of each variant given the position and the chromosome
    Also return the starting cumulativeposition of each chromosome

    Args:
        position:   pandas DataFrame of basepair positions (key='pos') and chromosome values (key='chrom')
                    The DataFrame will be updated with field 'pos_cum'
        chrom_len:  vector with predefined chromosome length
        offset:     offset between chromosomes for cumulative position (default 0 bp)
    
    Returns:
        chrom_pos,position:
        chrom_pos:  numpy.array of starting cumulative positions for each chromosome
        position:   augmented position object where cumulative positions are defined
    '''
    RV = position.copy()
    chromvals =  sp.unique(position['chrom'])# sp.unique is always sorted
    chrom_pos_cum= sp.zeros_like(chromvals)#get the starting position of each Chrom
    pos_cum= sp.zeros_like(position.shape[0])
    if not 'pos_cum' in position:
        RV["pos_cum"]= sp.zeros_like(position['pos'])#get the cum_pos of each variant.
    pos_cum=RV['pos_cum'].values
    maxpos_cum=0
    for i,mychrom in enumerate(chromvals):
        chrom_pos_cum[i] = maxpos_cum
        i_chr=position['chrom']==mychrom
        if chrom_len is None:
            maxpos = position['pos'][i_chr].max()+offset
        else:
            maxpos = chrom_len[i]+offset
        pos_cum[i_chr.values]=maxpos_cum+position.loc[i_chr,'pos']
        maxpos_cum+=maxpos      
    
    return RV,chrom_pos_cum

def makeinputh5(Iono,basedir):
    basedir = Path(basedir).expanduser()

    Param_List = Iono.Param_List
    dataloc = Iono.Cart_Coords
    times = Iono.Time_Vector
    velocity = Iono.Velocity
    zlist,idx = sp.unique(dataloc[:,2],return_inverse=True)
    siz = list(Param_List.shape[1:])
    vsiz = list(velocity.shape[1:])

    datalocsave = sp.column_stack((sp.zeros_like(zlist),sp.zeros_like(zlist),zlist))
    outdata = sp.zeros([len(zlist)]+siz)
    outvel = sp.zeros([len(zlist)]+vsiz)

    for izn,iz in enumerate(zlist):
        arr = sp.argwhere(idx==izn)
        outdata[izn]=sp.mean(Param_List[arr],axis=0)
        outvel[izn]=sp.mean(velocity[arr],axis=0)

    Ionoout = IonoContainer(datalocsave,outdata,times,Iono.Sensor_loc,ver=0,
                            paramnames=Iono.Param_Names, species=Iono.Species,velocity=outvel)


    ofn = basedir/'startdata.h5'
    print('writing {}'.format(ofn))
    Ionoout.saveh5(str(ofn))

def estCumPos(pos,chrom,offset = 20000000):
    '''
    compute the cumulative position of each variant given the position and the chromosome
    Also return the starting cumulativeposition of each chromosome

    Args:
        pos:        scipy.array of basepair positions (on the chromosome)
        chrom:      scipy.array of chromosomes
        offset:     offset between chromosomes for cumulative position (default 20000000 bp)
    
        Returns:
        cum_pos:    scipy.array of cumulative positions
        chrom_pos:  scipy.array of starting cumulative positions for each chromosme
    '''
    chromvals = SP.unique(chrom)#SP.unique is always sorted
    chrom_pos=SP.zeros_like(chromvals)#get the starting position of each Chrom
    cum_pos = SP.zeros_like(pos)#get the cum_pos of each variant.
    maxpos_cum=0
    for i,mychrom in enumerate(chromvals):
        chrom_pos[i] = maxpos_cum
        i_chr=chrom==mychrom
        maxpos = pos[i_chr].max()+offset
        maxpos_cum+=maxpos
        cum_pos[i_chr]=chrom_pos[i]+pos[i_chr]
    return cum_pos,chrom_pos

def makeinputh5(Iono,basedir):
    """This will make a h5 file for the IonoContainer that can be used as starting
    points for the fitter. The ionocontainer taken will be average over the x and y dimensions
    of space to make an average value of the parameters for each altitude.
    Inputs
    Iono - An instance of the Ionocontainer class that will be averaged over so it can
    be used for fitter starting points.
    basdir - A string that holds the directory that the file will be saved to.
    """
    # Get the parameters from the original data
    Param_List = Iono.Param_List
    dataloc = Iono.Cart_Coords
    times = Iono.Time_Vector
    velocity = Iono.Velocity
    zlist,idx = sp.unique(dataloc[:,2],return_inverse=True)
    siz = list(Param_List.shape[1:])
    vsiz = list(velocity.shape[1:])

    datalocsave = sp.column_stack((sp.zeros_like(zlist),sp.zeros_like(zlist),zlist))
    outdata = sp.zeros([len(zlist)]+siz)
    outvel = sp.zeros([len(zlist)]+vsiz)
    #  Do the averaging across space
    for izn,iz in enumerate(zlist):
        arr = sp.argwhere(idx==izn)
        outdata[izn] = sp.mean(Param_List[arr],axis=0)
        outvel[izn] = sp.mean(velocity[arr],axis=0)

    Ionoout = IonoContainer(datalocsave,outdata,times,Iono.Sensor_loc,ver=0,
                            paramnames=Iono.Param_Names, species=Iono.Species,velocity=outvel)
    Ionoout.saveh5(basedir/'startdata.h5')

 def _get_indices(self,element,labels,return_indices,mode):
     r'''
     This is the actual method for getting indices, but should not be called
     directly.  
     '''
     if mode == 'union':
         union = sp.zeros_like(self._get_info(element=element,label='all'),dtype=bool)
         for item in labels: #iterate over labels list and collect all indices
                 union = union + self._get_info(element=element,label=item)
         ind = union
     elif mode == 'intersection':
         intersect = sp.ones_like(self._get_info(element=element,label='all'),dtype=bool)
         for item in labels: #iterate over labels list and collect all indices
                 intersect = intersect*self._get_info(element=element,label=item)
         ind = intersect
     elif mode == 'not_intersection':
         not_intersect = sp.zeros_like(self._get_info(element=element,label='all'),dtype=int)
         for item in labels: #iterate over labels list and collect all indices
             info = self._get_info(element=element,label=item)
             not_intersect = not_intersect + sp.int8(info)
         ind = (not_intersect == 1)
     elif mode == 'none':
         none = sp.zeros_like(self._get_info(element=element,label='all'),dtype=int)
         for item in labels: #iterate over labels list and collect all indices
             info = self._get_info(element=element,label=item)
             none = none - sp.int8(info)
         ind = (none == 0)
     if return_indices: ind = sp.where(ind==True)[0]
     return ind

def rankStandardizeNormal(X):
	"""
	Gaussianize X: [samples x phenotypes]
	- each phentoype is converted to ranks and transformed back to normal using the inverse CDF
	"""
	Is = X.argsort(axis=0)
	RV = SP.zeros_like(X)
	rank = SP.zeros_like(X)
	for i in xrange(X.shape[1]):
		x =  X[:,i]
		i_nan = SP.isnan(x)
		if 0:
			Is = x.argsort()
			rank = SP.zeros_like(x)
			rank[Is] = SP.arange(X.shape[0])
			#add one to ensure nothing = 0
			rank +=1
		else:
			rank = st.rankdata(x[~i_nan])
		#devide by (N+1) which yields uniform [0,1]
		rank /= ((~i_nan).sum()+1)
		#apply inverse gaussian cdf
		RV[~i_nan,i] = SP.sqrt(2) * special.erfinv(2*rank-1)
		RV[i_nan,i] = x[i_nan]
	return RV

 def plot_drainage_curve(self,
                         pore_volume='volume',
                         throat_volume='volume',pore_label='all',throat_label='all'):
       r"""
       Plot drainage capillary pressure curve
       """
       try:
         PcPoints = sp.unique(self['pore.inv_Pc'])
       except:
         raise Exception('Cannot print drainage curve: ordinary percolation simulation has not been run')
       pores=self._net.pores(labels=pore_label)
       throats = self._net.throats(labels=throat_label)
       Snwp_t = sp.zeros_like(PcPoints)
       Snwp_p = sp.zeros_like(PcPoints)
       Pvol = self._net['pore.'+pore_volume]
       Tvol = self._net['throat.'+throat_volume]
       Pvol_tot = sum(Pvol)
       Tvol_tot = sum(Tvol)
       for i in range(0,sp.size(PcPoints)):
           Pc = PcPoints[i]
           Snwp_p[i] = sum(Pvol[self._p_inv[pores]<=Pc])/Pvol_tot
           Snwp_t[i] = sum(Tvol[self._t_inv[throats]<=Pc])/Tvol_tot
       if sp.mean(self._phase_inv["pore.contact_angle"]) < 90:
           Snwp_p = 1 - Snwp_p
           Snwp_t = 1 - Snwp_t
           PcPoints *= -1
       plt.plot(PcPoints,Snwp_p,'r.-')
       plt.plot(PcPoints,Snwp_t,'b.-')
       r'''
       TODO: Add legend to distinguish the pore and throat curves
       '''
       #plt.xlim(xmin=0)
       plt.show()

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()

 def cut_to_stump(self):
     self.max_depth = 0
     self.node_ind = 0
     self.nodes[self.node_ind] = 0
     self.start_index[self.node_ind] = 0
     self.end_index[self.node_ind] = self.subsample.size
     self.num_nodes = 1
     self.num_leafs = 0
     self.left_child = SP.zeros_like(self.left_child)
     self.right_child = SP.zeros_like(self.right_child)

def predict(data, coeffs):
    """
    Calculate the an autoregressive linear prediction given the signal
    and the prediction coefficients.

    Parameters
    ----------
    data : numpy array
        The signal.
    coeffs : numpy array
        The prediction coefficients.

    Returns
    -------
    data : numpy array
        The predicted signal

    Notes
    -----

    * The first coefficient, 1, is assumed to be left out.

    Prediction works as follows:

          P = a1+ a2+ a3+ a4

          #   _   _   _   _
          #   #   _   _   _
          #   #   #   _   _
          # = # + # + # + _
          _   #   #   #   #
          _   _   #   #   #
          _   _   _   #   #
          _   _   _   _   #

    Where # is a number and _ is a "dont care"

    This means

     1. Create empty pred vector, padded by the number of coefficients
        at the end
     2. Pad original values by number of coefficients at both ends
     3. Crop data in each step accordingly
     4. Crop prediction

    """
    coeffs *= -1
    pred = scipy.zeros_like(data)
    tmp = numpy.hstack((scipy.zeros_like(coeffs), data))

    for j in range(0, coeffs.size):
        offset = coeffs.size - j - 1
        pred = pred + coeffs[j] * tmp[offset:offset + len(pred)]

    return pred[:len(data)]

def par_fixed_effect(tc, X, oob, depth):
    import scipy as SP
    dview = tc[:]
    dview.block = True
    results = dview.apply(fixed_effect, *[X, oob, depth])
    fixed_sum = SP.zeros_like(results[0][0])
    count = SP.zeros_like(results[0][1])
    for res in results:
        fixed_sum += res[0]
        count += res[1]
    return fixed_sum, count

def par_get_variable_scores(tc):
    import scipy as SP
    dview = tc[:]
    dview.block = True
    results = dview.apply(get_variable_scores)
    var_used = SP.zeros_like((results[0])[0])
    log_importance = SP.zeros_like(var_used)
    for result in results:
        var_used +=  result[0]
        log_importance += result[1]
    return var_used, log_importance

def ranktrafo(data):
    X = data.values[:, None]
    Is = X.argsort(axis=0)
    RV = sp.zeros_like(X)
    rank = sp.zeros_like(X)
    for i in xrange(X.shape[1]):
        x =  X[:,i]
        rank = sp.stats.rankdata(x)
        rank /= (X.shape[0]+1)
        RV[:,i] = sp.sqrt(2) * sp.special.erfinv(2*rank-1)

    return RV.flatten()

def exp_gauss_warp(X, n, l0, *msb):
    """Length scale function which is an exponential of a sum of Gaussians.

    The centers and widths of the Gaussians are free parameters.

    The length scale function is given by

    .. math::

        l = l_0 \exp\left ( \sum_{i=1}^{N}\beta_i\exp\left ( -\frac{(x-\mu_i)^2}{2\sigma_i^2} \right ) \right )

    The number of parameters is equal to the three times the number of Gaussians
    plus 1 (for :math:`l_0`). This function is inspired by what Gibbs used in
    his PhD thesis.

    Parameters
    ----------
    X : 1d or 2d array of float
        The points to evaluate the function at. If 2d, it should only have
        one column (but this is not checked to save time).
    n : int
        The derivative order to compute. Used for all `X`.
    l0 : float
        The covariance length scale at the edges of the domain.
    *msb : floats
        Means, standard deviations and weights for each Gaussian, in that order.
    """
    X = scipy.asarray(X, dtype=float)
    msb = scipy.asarray(msb, dtype=float)
    mm = msb[:len(msb) / 3]
    ss = msb[len(msb) / 3:2 * len(msb) / 3]
    bb = msb[2 * len(msb) / 3:]

    # This is done with for-loops, because trying to get fancy with
    # broadcasting was being too memory-intensive for some reason.
    if n == 0:
        l = scipy.zeros_like(X)
        for m, s, b in zip(mm, ss, bb):
            l += b * scipy.exp(-(X - m)**2.0 / (2.0 * s**2.0))
        l = l0 * scipy.exp(l)
        return l
    elif n == 1:
        l1 = scipy.zeros_like(X)
        l2 = scipy.zeros_like(X)
        for m, s, b in zip(mm, ss, bb):
            term = b * scipy.exp(-(X - m)**2.0 / (2.0 * s**2.0))
            l1 += term
            l2 += term * (X - m) / s**2.0
        l = -l0 * scipy.exp(l1) * l2
        return l
    else:
        raise NotImplementedError("Only n <= 1 is supported!")