Python numpy.where函数代码示例

    def test_float_modulus_exact(self):
        # test that float results are exact for small integers. This also
        # holds for the same integers scaled by powers of two.
        nlst = list(range(-127, 0))
        plst = list(range(1, 128))
        dividend = nlst + [0] + plst
        divisor = nlst + plst
        arg = list(itertools.product(dividend, divisor))
        tgt = list(divmod(*t) for t in arg)

        a, b = np.array(arg, dtype=int).T
        # convert exact integer results from Python to float so that
        # signed zero can be used, it is checked.
        tgtdiv, tgtrem = np.array(tgt, dtype=float).T
        tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
        tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)

        for dt in np.typecodes['Float']:
            msg = 'dtype: %s' % (dt,)
            fa = a.astype(dt)
            fb = b.astype(dt)
            # use list comprehension so a_ and b_ are scalars
            div = [self.floordiv(a_, b_) for  a_, b_ in zip(fa, fb)]
            rem = [self.mod(a_, b_) for a_, b_ in zip(fa, fb)]
            assert_equal(div, tgtdiv, err_msg=msg)
            assert_equal(rem, tgtrem, err_msg=msg)

def soft_threshold(lamda,b):

    th = float(lamda)/2.0
    print ("(lamda,Threshold)",lamda,th)
    print("The type of b is ..., its len is ",type(b),b.shape,len(b[0]))

    if(lamda == 0):
        return b
    m,n = b.shape

    x = np.zeros((m,n))

    k = np.where(b > th)
    # print("(b > th)",k)
    #print("Number of elements -->(b > th) ",type(k))
    x[k] = b[k] - th

    k = np.where(np.absolute(b) <= th)
    # print("abs(b) <= th",k)
    # print("Number of elements -->abs(b) <= th ",len(k))
    x[k] = 0

    k = np.where(b < -th )
    # print("(b < -th )",k)
    # print("Number of elements -->(b < -th ) <= th",len(k))
    x[k] = b[k] + th
    x = x[:]

    return x

    def plotResult(self, nn):
        cmask = np.where(self.y==1);
        plot(self.X[cmask,0], self.X[cmask,1], 'or', markersize=4)
        cmask = np.where(self.y==2);
        plot(self.X[cmask,0], self.X[cmask,1], 'ob', markersize=4)
        cmask = np.where(self.y==3);
        plot(self.X[cmask,0], self.X[cmask,1], 'og', markersize=4)

        minX = min(self.X[:,0])
        minY = min(self.X[:,1])
        maxX = max(self.X[:,0])
        maxY = max(self.X[:,1])

        grid_range = [minX, maxX, minY, maxY];
        delta = 0.05; levels = 100
        a = arange(grid_range[0],grid_range[1],delta)
        b = arange(grid_range[2],grid_range[3],delta)
        A, B = meshgrid(a, b)
        values = np.zeros(A.shape)

        for i in range(len(a)):
            for j in range(len(b)):
                values[j,i] = nn.getNetworkOutput( [ a[i], b[j] ] )
        contour(A, B, values, levels=[1], colors=['k'], linestyles='dashed')
        contourf(A, B, values, levels=linspace(values.min(),values.max(),levels), cmap=cm.RdBu)

def infos() :

    '''Some information about the neuronal populations for each structures'''
    print "Striatal populations: %d" % len(STR)
    print "Pallidal populations: %d" % len(GP)
    print

    C = (W_STR_STR > 0).sum(axis=1) + (W_STR_STR < 0).sum(axis=1)
    L = W_STR_STR[np.where(W_STR_STR != 0)]
    print "Collateral striatal connections"
    print "Mean number:  %g (+/- %g)" % (C.mean(), C.std())
    print "Mean length:  %g (+/- %g)" % (L.mean(), L.std())
    print

    C = (W_GP_GP > 0).sum(axis=1) + (W_GP_GP < 0).sum(axis=1)
    L = W_GP_GP[np.where(W_GP_GP != 0)]
    print "Collateral pallidal connections"
    print "Mean number:  %g (+/- %g)" % (C.mean(), C.std())
    print "Mean length:  %g (+/- %g)" % (L.mean(), L.std())
    print

    C = (W_STR_GP > 0).sum(axis=1) + (W_STR_GP < 0).sum(axis=1)
    L = W_STR_GP[np.where(W_STR_GP != 0)]
    print "Striato-pallidal connections"
    print "Mean number:  %g (+/- %g)" % (C.mean(), C.std())
    print "Mean length:  %g (+/- %g)" % (L.mean(), L.std())
    print

    print "Mean # collateral striato-pallidal connections:  %g (+/- %g)" % (C.mean(), C.std())
    C = (W_GP_STR > 0).sum(axis=1) + (W_GP_STR < 0).sum(axis=1) 
    L = W_GP_STR[np.where(W_GP_STR != 0)]
    print "Pallido-striatal connections"
    print "Mean number:  %g (+/- %g)" % (C.mean(), C.std())
    print "Mean length:  %g (+/- %g)" % (L.mean(), L.std())
    print

def compute_cost( X, y, theta, lam ):

    '''Compute cost for logistic regression.'''
    
    # Number of training examples
    m = y.shape[0]

    # Compute the prediction based on theta and X
    predictions = X.dot( theta )

    # Preprocessing values before sending to sigmoid function.
    # If the argument to sigmoid function >= 0, we know that the
    # sigmoid value is 1. Similarly for the negative values.
    predictions[ where( predictions >= 20 ) ] = 20
    predictions[ where( predictions <= -500 ) ] = -500
    hypothesis = sigmoid( predictions )

    hypothesis[ where( hypothesis == 1.0 ) ] = 0.99999

    # Part of the cost function without regularization
    J1 = ( -1.0 / m ) * sum( ( y * np.log( hypothesis ) ) + 
                            ( ( 1.0 - y ) * np.log( 1.0 - hypothesis ) ) ) 

    # Computing the regularization term
    J2 = lam / ( 2.0 * m ) * sum( theta[ 1:, ] * theta[ 1:, ] )
    error = hypothesis - y

    return J1 + J2

    def pixel_to_prime(self, x, y, color=0):
        # Secret decoder ring:
        #  http://www.sdss.org/dr7/products/general/astrometry.html
        # (color)0 is called riCut;
        # g0, g1, g2, and g3 are called
        #    dRow0, dRow1, dRow2, and dRow3, respectively;
        # h0, h1, h2, and h3 are called
        #    dCol0, dCol1, dCol2, and dCol3, respectively;
        # px and py are called csRow and csCol, respectively;
        # and qx and qy are called ccRow and ccCol, respectively.
        color0 = self._get_ricut()
        g0, g1, g2, g3 = self._get_drow()
        h0, h1, h2, h3 = self._get_dcol()
        px, py, qx, qy = self._get_cscc()

        # #$(%*&^(%$%*& bad documentation.
        (px,py) = (py,px)
        (qx,qy) = (qy,qx)

        yprime = y + g0 + g1 * x + g2 * x**2 + g3 * x**3
        xprime = x + h0 + h1 * x + h2 * x**2 + h3 * x**3

        # The code below implements this, vectorized:
        # if color < color0:
        #   xprime += px * color
        #   yprime += py * color
        # else:
        #   xprime += qx
        #   yprime += qy
        qx = qx * np.ones_like(x)
        qy = qy * np.ones_like(y)
        xprime += np.where(color < color0, px * color, qx)
        yprime += np.where(color < color0, py * color, qy)

        return (xprime, yprime)

    def processTrafficData(self):
        for index, row in self.traffic_data.iterrows():

            adjacent_list = []
            if index > 1 and index < 18467:
                if self.traffic_data.ix[index - 1]['inter1'] == row[0]:
                    key1 = self.traffic_data.ix[index - 1]['inter1'] + ', ' + self.traffic_data.ix[index - 1]['inter2']
                    adjacent_list.append(key1)
                    
                if self.traffic_data.ix[index + 1]['inter1'] == row[0]:
                    key2 = self.traffic_data.ix[index + 1]['inter1'] + ', ' + self.traffic_data.ix[index + 1]['inter2']
                    adjacent_list.append(key2)
                
                keylist = np.where(self.traffic_data['inter1'] == row[1])[0]
                keylist1 = np.where(self.traffic_data['inter2'] == row[0])[0]
                
                ind_list = np.intersect1d(keylist, keylist1)
                if len(ind_list) >= 1:
                    ind = ind_list[0]
                    
                    if ind > 1 and ind < 18467:
                        if self.traffic_data.ix[ind - 1]['inter1'] == row[1]:
                            key3 = self.traffic_data.ix[ind - 1]['inter1'] + ', ' + self.traffic_data.ix[ind - 1]['inter2']
                            adjacent_list.append(key3)
                        
                        if self.traffic_data.ix[ind + 1]['inter1'] == row[1]:
                            key4 = self.traffic_data.ix[ind + 1]['inter1'] + ', ' + self.traffic_data.ix[ind + 1]['inter2']
                            adjacent_list.append(key4)
                
            node = row['node']
            node.setAdjacents(adjacent_list)

def word2ind(word, vocab, utok_ind=None):
    ind = np.where(vocab == unicode(word))
    if len(ind[0]) == 0:
        if not utok_ind:
            utok_ind = np.where(vocab == UTOK)
        ind = utok_ind
    return ind[0][0]

def test_background_model(tmpdir):
    data_store = DataStore.from_dir('$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2/')
    bgmaker = OffDataBackgroundMaker(data_store, outdir=str(tmpdir))

    bgmaker.select_observations(selection='all')
    table = Table.read('run.lis', format='ascii.csv')
    assert table['OBS_ID'][1] == 23526

    bgmaker.group_observations()
    table = ObservationTable.read(str(tmpdir / 'obs.fits'))
    assert list(table['GROUP_ID']) == [0, 0, 0, 1]
    table = ObservationTable.read(str(tmpdir / 'group-def.fits'))
    assert list(table['ZEN_PNT_MAX']) == [49, 90]

    # TODO: Fix 3D code
    # bgmaker.make_model("3D")
    # bgmaker.save_models("3D")
    # model = CubeBackgroundModel.read(str(tmpdir / 'background_3D_group_001_table.fits.gz'))
    # assert model.counts_cube.data.sum() == 1527

    bgmaker.make_model("2D")
    bgmaker.save_models("2D")
    model = EnergyOffsetBackgroundModel.read(str(tmpdir / 'background_2D_group_001_table.fits.gz'))
    assert model.counts.data.value.sum() == 1398

    index_table_new = bgmaker.make_total_index_table(data_store, "2D", None, None)
    table_bkg = index_table_new[np.where(index_table_new["HDU_NAME"] == "bkg_2d")]
    name_bkg_run023523 = table_bkg[np.where(table_bkg["OBS_ID"] == 23523)]["FILE_NAME"]
    assert str(tmpdir) + "/" + name_bkg_run023523[0] == str(tmpdir) + '/background_2D_group_001_table.fits.gz'
    name_bkg_run023526 = table_bkg[np.where(table_bkg["OBS_ID"] == 23526)]["FILE_NAME"]
    assert str(tmpdir) + "/" + name_bkg_run023526[0] == str(tmpdir) + '/background_2D_group_000_table.fits.gz'

 def _lininterp(self,x,X,Y):
     if hasattr(x,'__len__'):
         xtype = 'array'
         xx=np.asarray(x).astype(np.float)
     else:
         xtype = 'scalar'
         xx=np.asarray([x]).astype(np.float)
     idx = X.searchsorted(xx)
     yy = xx*0
     yy[idx>len(X)-1] = Y[-1]  # over
     yy[idx<=0] = Y[0]          # under
     wok = np.where((idx>0) & (idx<len(X)))  # the good ones
     iok=idx[wok]
     yywok = Y[iok-1] + ( (Y[iok]-Y[iok-1])/(X[iok]-X[iok-1])
                          * (xx[wok]-X[iok-1]) )
     w = np.where( ((X[iok]-X[iok-1]) == 0) )   # where are the nan ?
     yywok[w] = Y[iok[w]-1]    # replace by previous value
     wl = np.where(xx[wok] == X[0])
     yywok[wl] = Y[0]
     wh = np.where(xx[wok] == X[-1])
     yywok[wh] = Y[-1]
     yy[wok] = yywok
     if xtype == 'scalar':
         yy = yy[0]
     return yy

def corrtag_image(in_data,xtype='XCORR',ytype='YCORR',pha=(2,30),bins=(1024,16384),times=None,ranges=((0,1023),(0,16384)),binning=(1,1),NUV=False):
    try: histogram2d
    except NameError: from numpy import histogram2d,where,zeros
    try: getdata
    except NameError: from pyfits import getdata
    try: events=getdata(in_data,1)
    except: events=in_data

    xlength = (ranges[1][1]+1)
    ylength = (ranges[0][1]+1)
    xbinning = binning[1]
    ybinning = binning[0]

    if NUV:
        bins = (1024,1024)
        pha = (-1,1)
        ranges = ( (0,1023), (0,1023) )

    if times != None:
        index = where( (events['TIME']>=times[0]) & (events['TIME'] <= times[1]) )
        events=  events[index]

    index = where((events['PHA']>=pha[0])&(events['PHA']<=pha[1]))

    if len(index[0]):
        image,y_r,x_r = histogram2d(events[ytype][index],events[xtype][index],bins=bins,range=ranges)
    else:
        image = zeros( (bins[0]//binning[0],bins[1]//binning[1]) )

    return image

 def __init__(self,turn,elem,single,name,s,x,xp,y,yp,pc,de,tau,**args):
     apc=float(pc[0])*1e9
     ade=float(de[0])
     self.m0=self.pmass
     en=np.sqrt(apc**2+self.pmass**2)
     self.e0=en-ade
     self.p0c=np.sqrt(self.e0**2-self.m0**2)
     # structure
     self.elem=np.array(elem,dtype=int)
     self.turn=np.array(turn,dtype=int)
     d0=np.where(np.diff(self.elem)!=0)[0][0]+1
     d1=(np.where(np.diff(self.turn)!=0)[0][0]+1)/d0
     d2=len(self.turn)/d1/d0
     self.single=np.array(single,dtype=int)
     self.name=np.array(name,dtype=str)
     self.s =np.array(s ,dtype=float)
     self.x =np.array(x ,dtype=float)
     self.y =np.array(y ,dtype=float)
     self.tau=-np.array(tau,dtype=float)*self.clight
     opd=np.array(pc,dtype=float)*(1e9/self.p0c)
     self.delta=opd-1
     self.pt=np.array(de,dtype=float)/self.p0c
     self.px=np.array(xp,dtype=float)*opd
     self.py=np.array(yp,dtype=float)*opd
     for nn,vv in self.__dict__.items():
         if hasattr(vv,'__len__') and len(vv)==d0*d1*d2:
             setattr(self,nn,vv.reshape(d2,d1,d0))

 def mutual_information(self, x_index, y_index, log_base, debug=False):
     """
     Calculate and return Mutual information between two random variables
     """
     # Check if index are into the bounds
     assert (0 <= x_index <= self.n_rows)
     assert (0 <= y_index <= self.n_rows)
     # Variable to return MI
     summation = 0.0
     # Get uniques values of random variables
     values_x = set(self.data[x_index])
     values_y = set(self.data[y_index])
     # Print debug info
     if debug:
         print 'MI between'
         print self.data[x_index]
         print self.data[y_index]
         # For each random
     for value_x in values_x:
         for value_y in values_y:
             px = shape(where(self.data[x_index] == value_x))[1] / self.n_cols
             py = shape(where(self.data[y_index] == value_y))[1] / self.n_cols
             pxy = len(where(in1d(where(self.data[x_index] == value_x)[0],
                                  where(self.data[y_index] == value_y)[0]) == True)[0]) / self.n_cols
             if pxy > 0.0:
                 summation += pxy * math.log((pxy / (px * py)), log_base)
             if debug:
                 print '(%d,%d) px:%f py:%f pxy:%f' % (value_x, value_y, px, py, pxy)
     return summation

 def entropy(self, x_index, y_index, log_base, debug=False):
     """
     Calculate the entropy between two random variable
     """
     assert (0 <= x_index <= self.n_rows)
     assert (0 <= y_index <= self.n_rows)
     # Variable to return MI
     summation = 0.0
     # Get uniques values of random variables
     values_x = set(self.data[x_index])
     values_y = set(self.data[y_index])
     # Print debug info
     if debug:
         print 'Entropy between'
         print self.data[x_index]
         print self.data[y_index]
         # For each random
     for value_x in values_x:
         for value_y in values_y:
             pxy = len(where(in1d(where(self.data[x_index] == value_x)[0],
                                  where(self.data[y_index] == value_y)[0]) == True)[0]) / self.n_cols
             if pxy > 0.0:
                 summation += pxy * math.log(pxy, log_base)
             if debug:
                 print '(%d,%d) pxy:%f' % (value_x, value_y, pxy)
     if summation == 0.0:
         return summation
     else:
         return - summation

def myTradingSystem(DATE, CLOSE, settings):
    ''' This system uses mean reversion techniques to allocate capital into the desired equities '''

    # This strategy evaluates two averages over time of the close over a long/short
    # scale and builds the ratio. For each day, "smaQuot" is an array of "nMarkets"
    # size.
    nMarkets = numpy.shape(CLOSE)[1]
    periodLong = 200
    periodShort = 40

    smaLong = numpy.sum(CLOSE[-periodLong:, :], axis=0)/periodLong
    smaRecent = numpy.sum(CLOSE[-periodShort:, :], axis=0)/periodShort
    smaQuot = smaRecent / smaLong

    # For each day, scan the ratio of moving averages over the markets and find the
    # market with the maximum ratio and the market with the minimum ratio:
    longEquity = numpy.where(smaQuot == numpy.nanmin(smaQuot))
    shortEquity = numpy.where(smaQuot == numpy.nanmax(smaQuot))

    # Take a contrarian view, going long the market with the minimum ratio and
    # going short the market with the maximum ratio. The array "pos" will contain
    # all zero entries except for those cases where we go long (1) and short (-1):
    pos = numpy.zeros((1, nMarkets))
    pos[0, longEquity[0][0]] = 1
    pos[0, shortEquity[0][0]] = -1

    # For the position sizing, we supply a vector of weights defining our
    # exposure to the markets in settings['markets']. This vector should be
    # normalized.
    pos = pos/numpy.nansum(abs(pos))

    return pos, settings