python类floor()的实例源码

def stft(sig, frameSize, overlapFac=0.75, window=np.hanning):
    """ short time fourier transform of audio signal """
    win = window(frameSize)
    hopSize = int(frameSize - np.floor(overlapFac * frameSize))
    # zeros at beginning (thus center of 1st window should be for sample nr. 0)
    # samples = np.append(np.zeros(np.floor(frameSize / 2.0)), sig)
    samples = np.array(sig, dtype='float64')
    # cols for windowing
    cols = np.ceil((len(samples) - frameSize) / float(hopSize)) + 1
    # zeros at end (thus samples can be fully covered by frames)
    # samples = np.append(samples, np.zeros(frameSize))
    frames = stride_tricks.as_strided(
        samples,
        shape=(cols, frameSize),
        strides=(samples.strides[0] * hopSize, samples.strides[0])).copy()
    frames *= win
    return np.fft.rfft(frames)


# all the definition of the flowing variable can be found
# train_net.py

def fftfilt(b, x, *n):
    N_x = len(x)
    N_b = len(b)
    N = 2**np.arange(np.ceil(np.log2(N_b)),np.floor(np.log2(N_x)))
    cost = np.ceil(N_x / (N - N_b + 1)) * N * (np.log2(N) + 1)
    N_fft = int(N[np.argmin(cost)])
    N_fft = int(N_fft)    
    # Compute the block length:
    L = int(N_fft - N_b + 1)
    # Compute the transform of the filter:
    H = np.fft.fft(b,N_fft)
    y = np.zeros(N_x, x.dtype)
    i = 0
    while i <= N_x:
        il = np.min([i+L,N_x])
        k = np.min([i+N_fft,N_x])
        yt = np.fft.ifft(np.fft.fft(x[i:il],N_fft)*H,N_fft) # Overlap..
        y[i:k] = y[i:k] + yt[:k-i]                          # and add
        i += L
    return y

def __call__(self, batch):
        images, labels = zip(*batch)

        imgH = self.imgH
        imgW = self.imgW
        if self.keep_ratio:
            ratios = []
            for image in images:
                w, h = image.size
                ratios.append(w / float(h))
            ratios.sort()
            max_ratio = ratios[-1]
            imgW = int(np.floor(max_ratio * imgH))
            imgW = max(imgH * self.min_ratio, imgW)  # assure imgH >= imgW

        transform = resizeNormalize((imgW, imgH))
        images = [transform(image) for image in images]
        images = torch.cat([t.unsqueeze(0) for t in images], 0)

        return images, labels

def split_episodes(self, episode_paths, n_train, n_valid, n_test, seed=None, use_all=True):
        """Split episodes between training, validation and test sets.

        seed: random seed (have split performed consistently every time)"""
        if seed is not None:
            random_state = np.random.get_state()
            np.random.seed(seed)
            np.random.shuffle(episode_paths)
            np.random.set_state(random_state)
        else:
            np.random.shuffle(episode_paths)
        if use_all:
            multiplier = float(len(episode_paths)) / float(n_train + n_valid + n_test)
            n_train = int(math.floor(multiplier * n_train))
            n_valid = int(math.floor(multiplier * n_valid))
            n_test = int(math.floor(multiplier * n_test))

        assert n_train + n_valid + n_test <= len(episode_paths)
        return (episode_paths[:n_train], episode_paths[n_train:n_train + n_valid],
                episode_paths[n_train + n_test:n_train + n_test + n_test])

def split_episodes(self, episode_paths, n_train, n_valid, n_test, seed=None, use_all=True):
        """Split episodes between training, validation and test sets.

        seed: random seed (have split performed consistently every time)"""
        if seed is not None:
            random_state = np.random.get_state()
            np.random.seed(seed)
            np.random.shuffle(episode_paths)
            np.random.set_state(random_state)
        else:
            np.random.shuffle(episode_paths)
        if use_all:
            multiplier = float(len(episode_paths)) / float(n_train + n_valid + n_test)
            n_train = int(math.floor(multiplier * n_train))
            n_valid = int(math.floor(multiplier * n_valid))
            n_test = int(math.floor(multiplier * n_test))

        assert n_train + n_valid + n_test <= len(episode_paths)
        return (episode_paths[:n_train], episode_paths[n_train:n_train + n_valid],
                episode_paths[n_train + n_test:n_train + n_test + n_test])

def split_episodes(self, episode_paths, n_train, n_valid, n_test, seed=None, use_all=True):
        """Split episodes between training, validation and test sets.

        seed: random seed (have split performed consistently every time)"""
        if seed is not None:
            random_state = np.random.get_state()
            np.random.seed(seed)
            np.random.shuffle(episode_paths)
            np.random.set_state(random_state)
        else:
            np.random.shuffle(episode_paths)
        if use_all:
            multiplier = float(len(episode_paths)) / float(n_train + n_valid + n_test)
            n_train = int(math.floor(multiplier * n_train))
            n_valid = int(math.floor(multiplier * n_valid))
            n_test = int(math.floor(multiplier * n_test))

        assert n_train + n_valid + n_test <= len(episode_paths)
        return (episode_paths[:n_train], episode_paths[n_train:n_train + n_valid],
                episode_paths[n_train + n_test:n_train + n_test + n_test])

def split_episodes(self, episode_paths, n_train, n_valid, n_test, seed=None, use_all=True):
        """Split episodes between training, validation and test sets.

        seed: random seed (have split performed consistently every time)"""
        if seed is not None:
            random_state = np.random.get_state()
            np.random.seed(seed)
            np.random.shuffle(episode_paths)
            np.random.set_state(random_state)
        else:
            np.random.shuffle(episode_paths)
        if use_all:
            multiplier = float(len(episode_paths)) / float(n_train + n_valid + n_test)
            n_train = int(math.floor(multiplier * n_train))
            n_valid = int(math.floor(multiplier * n_valid))
            n_test = int(math.floor(multiplier * n_test))

        assert n_train + n_valid + n_test <= len(episode_paths)
        return (episode_paths[:n_train], episode_paths[n_train:n_train + n_valid],
                episode_paths[n_train + n_test:n_train + n_test + n_test])

def earn_dividend(self, dividend):
        """
        Register the number of shares we held at this dividend's ex date so
        that we can pay out the correct amount on the dividend's pay date.
        """
        assert dividend['sid'] == self.sid
        out = {'id': dividend['id']}

        # stock dividend
        if dividend['payment_sid']:
            out['payment_sid'] = dividend['payment_sid']
            out['share_count'] = np.floor(self.amount
                                          * float(dividend['ratio']))

        # cash dividend
        if dividend['net_amount']:
            out['cash_amount'] = self.amount * dividend['net_amount']
        elif dividend['gross_amount']:
            out['cash_amount'] = self.amount * dividend['gross_amount']

        payment_owed = zp.dividend_payment(out)
        return payment_owed

def handle_data(self, data):
        if self.target_shares == 0:
            assert 0 not in self.portfolio.positions
            self.order(self.sid(0), 10)
            self.target_shares = 10
            return
        else:
            assert self.portfolio.positions[0]['amount'] == \
                self.target_shares, "Orders not filled immediately."
            assert self.portfolio.positions[0]['last_sale_price'] == \
                data[0].price, "Orders not filled at current price."

        self.order_percent(self.sid(0), .001)

        if isinstance(self.sid(0), Equity):
            self.target_shares += np.floor(
                (.001 * self.portfolio.portfolio_value) / data[0].price
            )
        if isinstance(self.sid(0), Future):
            self.target_shares += np.floor(
                (.001 * self.portfolio.portfolio_value) /
                (data[0].price * self.sid(0).multiplier)
            )

def int_bilin_MT(f, x, y):
    # assume x, y are in pixel
    fint = np.zeros(len(x))

    for i in range(len(x)):
        t = y[i] - np.floor(y[i])
        u = x[i] - np.floor(x[i])
        y0 = f[np.int(np.floor(y[i])), np.int(np.floor(x[i]))]
        y1 = f[np.int(np.floor(y[i])) + 1, np.int(np.floor(x[i]))]
        y2 = f[np.int(np.floor(y[i])) + 1, np.int(np.floor(x[i])) + 1]
        y3 = f[np.int(np.floor(y[i])), np.int(np.floor(x[i])) + 1]

        fint[i] = t * u * (y0 - y1 + y2 - y3)
        fint[i] += t * (y1 - y0)
        fint[i] += u * (y3 - y0)
        fint[i] += y0

    return fint

def expand_to_chunk_size(self, chunk_size, offset=Vec(0,0,0, dtype=int)):
    """
    Align a potentially non-axis aligned bbox to the grid by growing it
    to the nearest grid lines.

    Required:
      chunk_size: arraylike (x,y,z), the size of chunks in the 
                    dataset e.g. (64,64,64)
    Optional:
      offset: arraylike (x,y,z), the starting coordinate of the dataset
    """
    chunk_size = np.array(chunk_size, dtype=np.float32)
    result = self.clone()
    result = result - offset
    result.minpt = np.floor(result.minpt / chunk_size) * chunk_size
    result.maxpt = np.ceil(result.maxpt / chunk_size) * chunk_size 
    return result + offset

def shrink_to_chunk_size(self, chunk_size, offset=Vec(0,0,0, dtype=int)):
    """
    Align a potentially non-axis aligned bbox to the grid by shrinking it
    to the nearest grid lines.

    Required:
      chunk_size: arraylike (x,y,z), the size of chunks in the 
                    dataset e.g. (64,64,64)
    Optional:
      offset: arraylike (x,y,z), the starting coordinate of the dataset
    """
    chunk_size = np.array(chunk_size, dtype=np.float32)
    result = self.clone()
    result = result - offset
    result.minpt = np.ceil(result.minpt / chunk_size) * chunk_size
    result.maxpt = np.floor(result.maxpt / chunk_size) * chunk_size 
    return result + offset

def resize_image(image,target_shape, pad_value = 0):
    assert isinstance(target_shape, list) or isinstance(target_shape, tuple)
    add_shape, subs_shape = [], []

    image_shape = image.shape
    shape_difference = np.asarray(target_shape, dtype=int) - np.asarray(image_shape,dtype=int)
    for diff in shape_difference:
        if diff < 0:
            subs_shape.append(np.s_[int(np.abs(np.ceil(diff/2))):int(np.floor(diff/2))])
            add_shape.append((0, 0))
        else:
            subs_shape.append(np.s_[:])
            add_shape.append((int(np.ceil(1.0*diff/2)),int(np.floor(1.0*diff/2))))
    output = np.pad(image, tuple(add_shape), 'constant', constant_values=(pad_value, pad_value))
    output = output[subs_shape]
    return output

def get_mask_boundaries(self,image_shape,mask_shape,ROI_mask):
        half_segment_dimensions = np.zeros((len(image_shape), 2), dtype='int32')
        for index, dim in enumerate(image_shape):
            if dim % 2 == 0:
                half_segment_dimensions[index, :] = [dim / 2 - 1, dim / 2]
            else:
                half_segment_dimensions[index, :] = [np.floor(dim / 2)] * 2

        mask_boundaries = np.zeros(mask_shape, dtype='int32')
        mask_boundaries[half_segment_dimensions[0][0]:-half_segment_dimensions[0][1],
        half_segment_dimensions[1][0]:-half_segment_dimensions[1][1],
        half_segment_dimensions[2][0]:-half_segment_dimensions[2][1]] = 1

        if ROI_mask is None:
            return  mask_boundaries
        else:
            return mask_boundaries * ROI_mask

def logTickValues(self, minVal, maxVal, size, stdTicks):

        ## start with the tick spacing given by tickValues().
        ## Any level whose spacing is < 1 needs to be converted to log scale

        ticks = []
        for (spacing, t) in stdTicks:
            if spacing >= 1.0:
                ticks.append((spacing, t))

        if len(ticks) < 3:
            v1 = int(np.floor(minVal))
            v2 = int(np.ceil(maxVal))
            #major = list(range(v1+1, v2))

            minor = []
            for v in range(v1, v2):
                minor.extend(v + np.log10(np.arange(1, 10)))
            minor = [x for x in minor if x>minVal and x<maxVal]
            ticks.append((None, minor))
        return ticks

def logTickValues(self, minVal, maxVal, size, stdTicks):

        ## start with the tick spacing given by tickValues().
        ## Any level whose spacing is < 1 needs to be converted to log scale

        ticks = []
        for (spacing, t) in stdTicks:
            if spacing >= 1.0:
                ticks.append((spacing, t))

        if len(ticks) < 3:
            v1 = int(np.floor(minVal))
            v2 = int(np.ceil(maxVal))
            #major = list(range(v1+1, v2))

            minor = []
            for v in range(v1, v2):
                minor.extend(v + np.log10(np.arange(1, 10)))
            minor = [x for x in minor if x>minVal and x<maxVal]
            ticks.append((None, minor))
        return ticks

def get_batch_idx(self, idx, **kwargs):
        if self.mode == 'train':
            new_idx = []
            # self.log.info('Label IDX: {}'.format(idx))
            if self.stats_provider is None:
                label_ids = [ii % self._real_size for ii in idx]
            else:
                # print idx, self.stats_provider.get_size()
                stats_batch = self.stats_provider.get_batch_idx(idx)
                label_ids = []
                for ii in xrange(len(idx)):
                    label_ids.append(np.argmax(stats_batch['y_gt'][ii]))

            for ii in label_ids:
                data_group = self.data_provider.label_idx[ii]
                num_ids = len(data_group)
                kk = int(np.floor(self.rnd.uniform(0, num_ids)))
                new_idx.append(data_group[kk])
        else:
            new_idx = idx
        return self.data_provider.get_batch_idx(new_idx)

def transform(self, images):
        if self._aug_flag:
            transformed_images =\
                np.zeros([images.shape[0], self._imsize, self._imsize, 3])
            ori_size = images.shape[1]
            for i in range(images.shape[0]):
                h1 = np.floor((ori_size - self._imsize) * np.random.random())
                w1 = np.floor((ori_size - self._imsize) * np.random.random())
                cropped_image =\
                    images[i][w1: w1 + self._imsize, h1: h1 + self._imsize, :]
                if random.random() > 0.5:
                    transformed_images[i] = np.fliplr(cropped_image)
                else:
                    transformed_images[i] = cropped_image
            return transformed_images
        else:
            return images

def gaussian_kernel(kernel_shape, sigma=None):
    """
    Get 2D Gaussian kernel
    :param kernel_shape: kernel size
    :param sigma: sigma of Gaussian distribution
    :return: 2D Gaussian kernel
    """
    kern = numpy.zeros((kernel_shape, kernel_shape), dtype='float32')

    # get sigma from kernel size
    if sigma is None:
        sigma = 0.3*((kernel_shape-1.)*0.5 - 1.) + 0.8

    def gauss(x, y, s):
        Z = 2. * numpy.pi * s ** 2.
        return 1. / Z * numpy.exp(-(x ** 2. + y ** 2.) / (2. * s ** 2.))

    mid = numpy.floor(kernel_shape / 2.)
    for i in xrange(0, kernel_shape):
        for j in xrange(0, kernel_shape):
            kern[i, j] = gauss(i - mid, j - mid, sigma)

    return kern / kern.sum()

def _hpd_interval(self, x, width):
        """
        Code adapted from pymc3.stats.calc_min_interval:
        https://github.com/pymc-devs/pymc3/blob/master/pymc3/stats.py
        """
        x = np.sort(x)
        n = len(x)

        interval_idx_inc = int(np.floor(width * n))
        n_intervals = n - interval_idx_inc
        interval_width = x[interval_idx_inc:] - x[:n_intervals]

        if len(interval_width) == 0:
            raise ValueError('Too few elements for interval calculation')

        min_idx = np.argmin(interval_width)
        hdi_min = x[min_idx]
        hdi_max = x[min_idx + interval_idx_inc]

        index = ['hpd{}_{}'.format(width, x) for x in ['lower', 'upper']]
        return pd.Series([hdi_min, hdi_max], index=index)

def SLdshear(inputArray, k, axis):
    """
    Computes the discretized shearing operator for a given inputArray, shear
    number k and axis.

    This version is adapted such that the MATLAB indexing can be used here in the
    Python version.
    """
    axis = axis - 1
    if k==0:
        return inputArray
    rows = np.asarray(inputArray.shape)[0]
    cols = np.asarray(inputArray.shape)[1]

    shearedArray = np.zeros((rows, cols), dtype=inputArray.dtype)

    if axis == 0:
        for col in range(cols):
            shearedArray[:,col] = np.roll(inputArray[:,col], int(k * np.floor(cols/2-col)))
    else:
        for row in range(rows):
            shearedArray[row,:] = np.roll(inputArray[row,:], int(k * np.floor(rows/2-row)))
    return shearedArray

def value_to_bin_index(val, **kwargs):
    """Convert value to bin index

    Convert a numeric or timestamp column to an integer bin index.

    :param bin_width: bin_width value needed to convert column to an integer bin index
    :param bin_offset: bin_offset value needed to convert column to an integer bin index
    """

    try:
        # NOTE this notation also works for timestamps
        bin_width = kwargs.get('bin_width', 1)
        bin_offset = kwargs.get('bin_offset', 0)
        bin_index = int(np.floor((val - bin_offset) / bin_width))
        return bin_index
    except BaseException:
        pass
    return val

def value_to_bin_center(val, **kwargs):
    """Convert value to bin center

    Convert a numeric or timestamp column to a common bin center value.

    :param bin_width: bin_width value needed to convert column to a common bin center value
    :param bin_offset: bin_offset value needed to convert column to a common bin center value
    """

    try:
        # NOTE this notation also works for timestamps, and does not change the
        # unit
        bin_width = kwargs.get('bin_width', 1)
        bin_offset = kwargs.get('bin_offset', 0)
        bin_index = int(np.floor((val - bin_offset) / bin_width))
        obj_type = type(bin_width)
        return bin_offset + obj_type((bin_index + 0.5) * bin_width)
    except BaseException:
        pass
    return val

def save_fft(fil,audio_in):
    samples = len(audio_in)
    fft_size = 2**int(floor(log(samples)/log(2.0)))
    freq = fft(audio_in[0:fft_size])
    s_data = numpy.zeros(fft_size/2)
    x_data = numpy.zeros(fft_size/2)
    peak = 0;
    for j in xrange(fft_size/2):
        if (abs(freq[j]) > peak):
            peak = abs(freq[j])

    for j in xrange(fft_size/2):
        x_data[j] = log(2.0*(j+1.0)/fft_size);
        if (x_data[j] < -10):
            x_data[j] = -10
        s_data[j] = 10.0*log(abs(freq[j])/peak)/log(10.0)
    plt.ylim([-50,0])
    plt.plot(x_data,s_data)
    plt.title('fft log power')
    plt.grid()

    fields = fil.split('.')
    plt.savefig(fields[0]+'_fft.png', bbox_inches="tight")
    plt.clf()
    plt.close()

def _gene_embed_space(self,vec):
        shape = vec.shape
        vec = vec.flatten()
        combo_neg_idx = np.array([1 if vec[i]<0  else 0 for i in range(len(vec))])

        vec_pos = np.abs(vec)
        int_part = np.floor(vec_pos)
        frac_part = np.round(vec_pos - int_part,2)

        bi_int_part=[] #?????????????signature???????
        for i in range(len(int_part)):
            bi=list(bin(int(int_part[i]))[2:])
            bie = [0] * (16 - len(bi))
            bie.extend(bi)
            bi_int_part.append(np.array(bie,dtype=np.uint16))
        bi_int_part = np.array(bi_int_part)

        sig = []
        for i in range(len(bi_int_part)):
            sig.append(bi_int_part[i][10])
        sig = np.array(sig).reshape(shape)
        return np.array(bi_int_part),frac_part.reshape(shape),combo_neg_idx.reshape(shape),sig

def _gene_embed_space(self,vec):
        shape = vec.shape
        vec = vec.flatten()
        combo_neg_idx = np.array([1 if vec[i]<0  else 0 for i in range(len(vec))])

        vec_pos = np.abs(vec)
        int_part = np.floor(vec_pos)
        frac_part = np.round(vec_pos - int_part,2)

        bi_int_part=[] #?????????????signature???????
        for i in range(len(int_part)):
            bi=list(bin(int(int_part[i]))[2:])
            bie = [0] * (16 - len(bi))
            bie.extend(bi)
            bi_int_part.append(np.array(bie,dtype=np.uint16))
        bi_int_part = np.array(bi_int_part)

        sig = []
        for i in range(len(bi_int_part)):
            sig.append(bi_int_part[i][10])
        sig = np.array(sig).reshape(shape)
        return np.array(bi_int_part),frac_part.reshape(shape),combo_neg_idx.reshape(shape),sig

def M(self):
        """Returns the :math:`M` matrix of integers that determine points at which the
        functions are sampled in the unit cell.

        Examples:
            For `S = [2, 2, 1]`, the returned matrix is:

        .. code-block:: python

            np.ndarray([[0,0,0],
                        [1,0,0],
                        [0,1,0],
                        [1,1,0]], dtype=int)
        """
        if self._M is None:
            ms = np.arange(np.prod(self.S, dtype=int))
            m1 = np.fmod(ms, self.S[0])
            m2 = np.fmod(np.floor(ms/self.S[0]), self.S[1])
            m3 = np.fmod(np.floor(ms/(self.S[0]*self.S[1])), self.S[2])
            #Make sure we explicitly use an integer array; it's faster.
            self._M = np.asarray(np.vstack((m1, m2, m3)).T, dtype=int)
        return self._M

def _latvec_plot(self, R=True, withpts=False, legend=False):
        """Plots the lattice vectors (for real or reciprocal space).
        """
        import matplotlib.pyplot as plt
        from mpl_toolkits.mplot3d import Axes3D
        fig = plt.figure()
        ax = fig.gca(projection='3d')

        vecs = self.R if R else self.K
        for i in range(3):
            steps = np.linspace(0, 1, np.floor(10*np.linalg.norm(vecs[:,i])))
            Ri = vecs[:,i]
            Ri.shape = (1, 3)
            steps.shape = (len(steps), 1)
            line = np.dot(steps, Ri)
            ax.plot(line[:,0], line[:,1], line[:,2], label="R{0:d}".format(i+1))

        if withpts:
            pts = self.r if R else self.G
            ax.scatter(pts[:,0], pts[:,1], pts[:,2], color='k')

        if legend:
            ax.legend()

        return (fig, ax)

def cumultativesumstest(binin):
    ''' The focus of this test is the maximal excursion (from zero) of the random walk defined by the cumulative sum of adjusted (-1, +1) digits in the sequence. The purpose of the test is to determine whether the cumulative sum of the partial sequences occurring in the tested sequence is too large or too small relative to the expected behavior of that cumulative sum for random sequences.  This cumulative sum may be considered as a random walk. For a random sequence, the random walk should be near zero. For non-random sequences, the excursions of this random walk away from zero will be too large.'''
    n = len(binin)
    ss = [int(el) for el in binin]
    sc = map(sumi, ss)
    cs = np.cumsum(sc)
    z = max(abs(cs))
    ra = 0
    start = int(np.floor(0.25 * np.floor(-n / z) + 1))
    stop = int(np.floor(0.25 * np.floor(n / z) - 1))
    pv1 = []
    for k in xrange(start, stop + 1):
        pv1.append(sst.norm.cdf((4 * k + 1) * z / np.sqrt(n)) - sst.norm.cdf((4 * k - 1) * z / np.sqrt(n)))
    start = int(np.floor(0.25 * np.floor(-n / z - 3)))
    stop = int(np.floor(0.25 * np.floor(n / z) - 1))
    pv2 = []
    for k in xrange(start, stop + 1):
        pv2.append(sst.norm.cdf((4 * k + 3) * z / np.sqrt(n)) - sst.norm.cdf((4 * k + 1) * z / np.sqrt(n)))
    pval = 1
    pval -= reduce(su, pv1)
    pval += reduce(su, pv2)

    return pval

def rel_crop(im, rel_cx, rel_cy, crop_size):

    map_size = im.shape[1]
    r = crop_size / 2
    abs_cx = rel_cx * map_size
    abs_cy = rel_cy * map_size
    na = np.floor([abs_cy-r, abs_cy+r, abs_cx-r, abs_cx+r]).astype(np.int32)
    a = np.clip(na, 0, map_size)
    px0 = a[2] - na[2]
    px1 = na[3] - a[3]
    py0 = a[0] - na[0]
    py1 = na[1] - a[1]
    crop = im[a[0]:a[1], a[2]:a[3]]
    crop = np.pad(crop, ((py0, py1), (px0, px1), (0, 0)),
                  mode='reflect')

    assert crop.shape == (crop_size, crop_size, im.shape[2])
    return crop