Python numpy.arange() Examples

def roll_zeropad(a, shift, axis=None):
    a = np.asanyarray(a)
    if shift == 0: return a
    if axis is None:
        n = a.size
        reshape = True
    else:
        n = a.shape[axis]
        reshape = False
    if np.abs(shift) > n:
        res = np.zeros_like(a)
    elif shift < 0:
        shift += n
        zeros = np.zeros_like(a.take(np.arange(n-shift), axis))
        res = np.concatenate((a.take(np.arange(n-shift,n), axis), zeros), axis)
    else:
        zeros = np.zeros_like(a.take(np.arange(n-shift,n), axis))
        res = np.concatenate((zeros, a.take(np.arange(n-shift), axis)), axis)
    if reshape:
        return res.reshape(a.shape)
    else:
        return res 

def rhoA(self):
        # rhoA
        rhoA = pd.DataFrame(0, index=np.arange(1), columns=self.latent)

        for i in range(self.lenlatent):
            weights = pd.DataFrame(self.outer_weights[self.latent[i]])
            weights = weights[(weights.T != 0).any()]
            result = pd.DataFrame.dot(weights.T, weights)
            result_ = pd.DataFrame.dot(weights, weights.T)

            S = self.data_[self.Variables['measurement'][
                self.Variables['latent'] == self.latent[i]]]
            S = pd.DataFrame.dot(S.T, S) / S.shape[0]
            numerador = (
                np.dot(np.dot(weights.T, (S - np.diag(np.diag(S)))), weights))
            denominador = (
                (np.dot(np.dot(weights.T, (result_ - np.diag(np.diag(result_)))), weights)))
            rhoA_ = ((result)**2) * (numerador / denominador)
            if(np.isnan(rhoA_.values)):
                rhoA[self.latent[i]] = 1
            else:
                rhoA[self.latent[i]] = rhoA_.values

        return rhoA.T 

def plot_sent_trajectories(sents, decode_plot):
   
    font = {'family' : 'normal',
            'size'   : 14}

    matplotlib.rc('font', **font) 
    i = 0    
    l = ["Portuguese","Catalan"]
    
    axes = plt.gca()
    #axes.set_xlim([xmin,xmax])
    axes.set_ylim([-1,1])

    for sent, enc in zip(sents, decode_plot):
	if i==2: continue
        i += 1
        #times = np.arange(len(enc))
        times = np.linspace(0,1,len(enc))
    	plt.plot(times, enc, label=l[i-1])
    plt.title("Hidden Node Trajectories")
    plt.xlabel('timestep')
    plt.ylabel('trajectories')
    plt.legend(loc='best')
    plt.savefig("final_tests/cr_por_cat_hidden_cell_trajectories", bbox_inches="tight")
    plt.close() 

def _generate_data():
    """
    ?????
    ????u(k-1) ? y(k-1)?????y(k)
    """
    # u = np.random.uniform(-1,1,200)
    # y=[]
    # former_y_value = 0
    # for i in np.arange(0,200):
    #     y.append(former_y_value)
    #     next_y_value = (29.0 / 40) * np.sin(
    #         (16.0 * u[i] + 8 * former_y_value) / (3.0 + 4.0 * (u[i] ** 2) + 4 * (former_y_value ** 2))) \
    #                    + (2.0 / 10) * u[i] + (2.0 / 10) * former_y_value
    #     former_y_value = next_y_value
    # return u,y
    u1 = np.random.uniform(-np.pi,np.pi,200)
    u2 = np.random.uniform(-1,1,200)
    y = np.zeros(200)
    for i in range(200):
        value = np.sin(u1[i]) + u2[i]
        y[i] =  value
    return u1, u2, y 

def plot_counts(counts, gene_type):
	"""Plot expression counts. Return a Figure object"""
	import matplotlib
	matplotlib.use('agg')
	import matplotlib.pyplot as plt
	import seaborn as sns
	import numpy as np

	fig = plt.figure(figsize=((50 + len(counts) * 5) / 25.4, 210/25.4))
	matplotlib.rcParams.update({'font.size': 14})
	ax = fig.gca()
	ax.set_title('{} gene usage'.format(gene_type))
	ax.set_xlabel('{} gene'.format(gene_type))
	ax.set_ylabel('Count')
	ax.set_xticks(np.arange(len(counts)) + 0.5)
	ax.set_xticklabels(counts.index, rotation='vertical')
	ax.grid(axis='x')
	ax.set_xlim((-0.25, len(counts)))
	ax.bar(np.arange(len(counts)), counts['count'])
	fig.set_tight_layout(True)
	return fig 

def _create_figure(predictions_dict):
  """Creates and returns a new figure that visualizes
  attention scores for for a single model predictions.
  """

  # Find out how long the predicted sequence is
  target_words = list(predictions_dict["predicted_tokens"])

  prediction_len = _get_prediction_length(predictions_dict)

  # Get source words
  source_len = predictions_dict["features.source_len"]
  source_words = predictions_dict["features.source_tokens"][:source_len]

  # Plot
  fig = plt.figure(figsize=(8, 8))
  plt.imshow(
      X=predictions_dict["attention_scores"][:prediction_len, :source_len],
      interpolation="nearest",
      cmap=plt.cm.Blues)
  plt.xticks(np.arange(source_len), source_words, rotation=45)
  plt.yticks(np.arange(prediction_len), target_words, rotation=-45)
  fig.tight_layout()

  return fig 

def mypsd(Rates,time_range,bin_w = 5., nmax = 4000):

      bins = np.arange(0,len(time_range),1)
      #print bins
      a,b = np.histogram(Rates, bins)
      ff = (1./len(bins))*abs(np.fft.fft(Rates- np.mean(Rates)))**2
      Fs = 1./(1*0.001)
      freq2 = np.fft.fftfreq(len(bins))[0:len(bins/2)+1] # d= dt
      freq = np.fft.fftfreq(len(bins))[:len(ff)/2+1]
      px = ff[0:len(ff)/2+1]
      max_px = np.max(px[1:])
      idx = px == max_px
      corr_freq = freq[pl.find(idx)]
      new_px = px
      max_pow = new_px[pl.find(idx)]
      return new_px,freq,corr_freq[0],freq2, max_pow 

def remove_artifacts(self, image):
        """
        Remove the connected components that are not within the parameters
        Operates in place
        :param image: sudoku's thresholded image w/o grid
        :return: None
        """
        labeled, features = label(image, structure=CROSS)
        lbls = np.arange(1, features + 1)
        areas = extract_feature(image, labeled, lbls, np.sum,
                                np.uint32, 0)
        sides = extract_feature(image, labeled, lbls, min_side,
                                np.float32, 0, True)
        diags = extract_feature(image, labeled, lbls, diagonal,
                                np.float32, 0, True)

        for index in lbls:
            area = areas[index - 1] / 255
            side = sides[index - 1]
            diag = diags[index - 1]
            if side < 5 or side > 20 \
                    or diag < 15 or diag > 25 \
                    or area < 40:
                image[labeled == index] = 0
        return None 

def remove_artifacts(self, image):
        """
        Remove the connected components that are not within the parameters
        Operates in place
        :param image: sudoku's thresholded image w/o grid
        :return: None
        """
        labeled, features = label(image, structure=CROSS)
        lbls = np.arange(1, features + 1)
        areas = extract_feature(image, labeled, lbls, np.sum,
                                np.uint32, 0)
        sides = extract_feature(image, labeled, lbls, min_side,
                                np.float32, 0, True)
        diags = extract_feature(image, labeled, lbls, diagonal,
                                np.float32, 0, True)

        for index in lbls:
            area = areas[index - 1] / 255
            side = sides[index - 1]
            diag = diags[index - 1]
            if side < 5 or side > 20 \
                    or diag < 15 or diag > 25 \
                    or area < 40:
                image[labeled == index] = 0
        return None 

def next_batch(self, batch_size, fake_data=False):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1.0 for _ in xrange(784)]
      fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)]
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Shuffle the data
      perm = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size
      assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end] 

def next_batch(self, batch_size, fake_data=False):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1.0 for _ in xrange(784)]
      fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)]
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Shuffle the data
      perm = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size
      assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end] 

def split_dataset(dataset, split_ratio, mode):
    if mode=='SPLIT_CLASSES':
        nrof_classes = len(dataset)
        class_indices = np.arange(nrof_classes)
        np.random.shuffle(class_indices)
        split = int(round(nrof_classes*split_ratio))
        train_set = [dataset[i] for i in class_indices[0:split]]
        test_set = [dataset[i] for i in class_indices[split:-1]]
    elif mode=='SPLIT_IMAGES':
        train_set = []
        test_set = []
        min_nrof_images = 2
        for cls in dataset:
            paths = cls.image_paths
            np.random.shuffle(paths)
            split = int(round(len(paths)*split_ratio))
            if split<min_nrof_images:
                continue  # Not enough images for test set. Skip class...
            train_set.append(ImageClass(cls.name, paths[0:split]))
            test_set.append(ImageClass(cls.name, paths[split:-1]))
    else:
        raise ValueError('Invalid train/test split mode "%s"' % mode)
    return train_set, test_set 

def quantize_from_probs2(probs, resolution):
    """Quantize multiple non-normalized probs to given resolution.

    Args:
        probs: An [N, M]-shaped numpy array of non-normalized probabilities.

    Returns:
        An [N, M]-shaped array of quantized probabilities such that
        np.all(result.sum(axis=1) == resolution).
    """
    assert len(probs.shape) == 2
    N, M = probs.shape
    probs = probs / probs.sum(axis=1, keepdims=True)
    result = np.zeros(probs.shape, np.int8)
    range_N = np.arange(N, dtype=np.int32)
    for _ in range(resolution):
        sample = probs.argmax(axis=1)
        result[range_N, sample] += 1
        probs[range_N, sample] -= 1.0 / resolution
    return result 

def get_train_data():
    # definite the dataset as two input , one output
    DS = SupervisedDataSet(2, 1)

    u1, u2, y = _generate_data()
    # add data element to the dataset
    for i in np.arange(199):
        DS.addSample([u1[i], u2[i]], [y[i + 1]])

    # you can get your input/output this way
    # X = DS['input']
    # Y = DS['target']

    # split the dataset into train dataset and test dataset
    dataTrain, dataTest = DS.splitWithProportion(0.8)

    return dataTrain, dataTest 

def read_chunk(self, idx, chunk_size, padding=(0, 0), nodes=None):
        
        self._open()

        t_start, t_stop = self._get_t_start_t_stop(idx, chunk_size, padding)

        if self.time_axis == 0:
            local_chunk  = self.data[t_start:t_stop, :]
        elif self.time_axis == 1:
            local_chunk  = self.data[:, t_start:t_stop].T
        self._close()

        if nodes is not None:
            if not numpy.all(nodes == numpy.arange(self.nb_channels)):
                local_chunk = numpy.take(local_chunk, nodes, axis=1)

        return self._scale_data_to_float32(local_chunk) 

def _get_slice_(self, t_start, t_stop):

        x_beg = numpy.int64(t_start // self.SAMPLES_PER_RECORD)
        r_beg = numpy.mod(t_start, self.SAMPLES_PER_RECORD)
        x_end = numpy.int64(t_stop // self.SAMPLES_PER_RECORD)
        r_end = numpy.mod(t_stop, self.SAMPLES_PER_RECORD)

        if x_beg == x_end:
            g_offset = x_beg * self.bytes_per_block_div + self.block_offset_div
            data_slice = numpy.arange(g_offset + r_beg * self.nb_channels, g_offset + r_end * self.nb_channels, dtype=numpy.int64)
            yield data_slice
        else:
            for count, nb_blocks in enumerate(numpy.arange(x_beg, x_end + 1, dtype=numpy.int64)):
                g_offset = nb_blocks * self.bytes_per_block_div + self.block_offset_div
                if count == 0:
                    data_slice = numpy.arange(g_offset + r_beg * self.nb_channels, g_offset + self.block_size_div, dtype=numpy.int64)
                elif (count == (x_end - x_beg)):
                    data_slice = numpy.arange(g_offset, g_offset + r_end * self.nb_channels, dtype=numpy.int64)
                else:
                    data_slice = numpy.arange(g_offset, g_offset + self.block_size_div, dtype=numpy.int64)

                yield data_slice 

def _get_slice_(self, t_start, t_stop):
        x_beg = numpy.int64(t_start // self.SAMPLES_PER_RECORD)
        r_beg = numpy.mod(t_start, self.SAMPLES_PER_RECORD)
        x_end = numpy.int64(t_stop // self.SAMPLES_PER_RECORD)
        r_end = numpy.mod(t_stop, self.SAMPLES_PER_RECORD)

        data_slice  = []

        if x_beg == x_end:
            g_offset = x_beg * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(x_beg + 1) + self.OFFSET_PER_BLOCK[1]*x_beg
            data_slice = numpy.arange(g_offset + r_beg, g_offset + r_end, dtype=numpy.int64)
        else:
            for count, nb_blocks in enumerate(numpy.arange(x_beg, x_end + 1, dtype=numpy.int64)):
                g_offset = nb_blocks * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(nb_blocks + 1) + self.OFFSET_PER_BLOCK[1]*nb_blocks
                if count == 0:
                    data_slice += numpy.arange(g_offset + r_beg, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
                elif (count == (x_end - x_beg)):
                    data_slice += numpy.arange(g_offset, g_offset + r_end, dtype=numpy.int64).tolist()
                else:
                    data_slice += numpy.arange(g_offset, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
        return data_slice 

def read_chunk(self, idx, chunk_size, padding=(0, 0), nodes=None):
        
        t_start, t_stop = self._get_t_start_t_stop(idx, chunk_size, padding)
        local_shape     = t_stop - t_start

        if nodes is None:
            nodes = numpy.arange(self.nb_channels)

        local_chunk = numpy.zeros((local_shape, len(nodes)), dtype=self.data_dtype)
        data_slice  = self._get_slice_(t_start, t_stop) 

        self._open()
        for count, i in enumerate(nodes):
            local_chunk[:, count] = self.data[i][data_slice]
        self._close()

        return self._scale_data_to_float32(local_chunk) 

def _get_slice_(self, t_start, t_stop):

        x_beg = numpy.int64(t_start // self.SAMPLES_PER_RECORD)
        r_beg = numpy.mod(t_start, self.SAMPLES_PER_RECORD)
        x_end = numpy.int64(t_stop // self.SAMPLES_PER_RECORD)
        r_end = numpy.mod(t_stop, self.SAMPLES_PER_RECORD)

        data_slice  = []

        if x_beg == x_end:
            g_offset = x_beg * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(x_beg + 1) + self.OFFSET_PER_BLOCK[1]*x_beg
            data_slice = numpy.arange(g_offset + r_beg, g_offset + r_end, dtype=numpy.int64)
        else:
            for count, nb_blocks in enumerate(numpy.arange(x_beg, x_end + 1, dtype=numpy.int64)):
                g_offset = nb_blocks * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(nb_blocks + 1) + self.OFFSET_PER_BLOCK[1]*nb_blocks
                if count == 0:
                    data_slice += numpy.arange(g_offset + r_beg, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
                elif (count == (x_end - x_beg)):
                    data_slice += numpy.arange(g_offset, g_offset + r_end, dtype=numpy.int64).tolist()
                else:
                    data_slice += numpy.arange(g_offset, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
        return data_slice 

def read_chunk(self, idx, chunk_size, padding=(0, 0), nodes=None):
        
        t_start, t_stop = self._get_t_start_t_stop(idx, chunk_size, padding)
        local_shape     = t_stop - t_start

        if nodes is None:
            nodes = numpy.arange(self.nb_channels)

        local_chunk = numpy.zeros((local_shape, len(nodes)), dtype=self.data_dtype)
        data_slice  = self._get_slice_(t_start, t_stop) 

        self._open()
        for count, i in enumerate(nodes):
            local_chunk[:, count] = self.data[i][data_slice]
        self._close()

        return self._scale_data_to_float32(local_chunk) 

def view_trigger_snippets_bis(trigger_snippets, elec_index, save=None):
    fig = pylab.figure()
    ax = fig.add_subplot(1, 1, 1)
    for n in xrange(0, trigger_snippets.shape[2]):
        y = trigger_snippets[:, elec_index, n]
        x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
        b = 0.5 + 0.5 * numpy.random.rand()
        ax.plot(x, y, color=(0.0, 0.0, b), linestyle='solid')
    ax.grid(True)
    ax.set_xlim([numpy.amin(x), numpy.amax(x)])
    ax.set_xlabel("time")
    ax.set_ylabel("amplitude")
    if save is None:
        pylab.show()
    else:
        pylab.savefig(save)
        pylab.close(fig)
    return 

def cost(self, x):
        Rdx = dl.Vector()
        self.Prior.init_vector(Rdx,0)
        dx = x[PARAMETER] - self.Prior.mean
        self.Prior.R.mult(dx, Rdx)
        reg = .5*Rdx.inner(dx)
        
        u  = dl.Vector()
        ud = dl.Vector()
        self.Q.init_vector(u,0)
        self.Q.init_vector(ud,0)
    
        misfit = 0
        for t in np.arange(self.t_1, self.t_final+(.5*self.dt), self.dt):
            x[STATE].retrieve(u,t)
            self.ud.retrieve(ud,t)
            diff = u - ud
            Qdiff = self.Q * diff
            misfit += .5/self.noise_variance*Qdiff.inner(diff)
            
        c = misfit + reg
                
        return [c, reg, misfit] 

def _flow_index(self, n, batch_size=32, shuffle=False, seed=None):
        # ensure self.batch_index is 0
        self.reset()
        while 1:
            if seed is not None:
                np.random.seed(seed + self.total_batches_seen)
            if self.batch_index == 0:
                index_array = np.arange(n)
                if shuffle:
                    index_array = np.random.permutation(n)

            current_index = (self.batch_index * batch_size) % n
            if n >= current_index + batch_size:
                current_batch_size = batch_size
                self.batch_index += 1
            else:
                current_batch_size = n - current_index
                self.batch_index = 0
            self.total_batches_seen += 1
            yield (index_array[current_index: current_index + current_batch_size],
                   current_index, current_batch_size) 

def make_split(X_full, Y_full, split):
    N = X_full.shape[0]
    n = int(N * PROPORTION_TRAIN)
    ind = np.arange(N)    
    
    np.random.seed(split + SEED) 
    np.random.shuffle(ind)
    train_ind = ind[:n]
    test_ind= ind[n:]
    
    X = X_full[train_ind]
    Xs = X_full[test_ind]
    Y = Y_full[train_ind]
    Ys = Y_full[test_ind]
    
    return X, Y, Xs, Ys 

def plot_difference_histogram(group, gene_name, bins=np.arange(20.1)):
	"""
	Plot a histogram of percentage differences for a specific gene.
	"""
	exact_matches = group[group.V_SHM == 0]
	CDR3s_exact = len(set(s for s in exact_matches.CDR3_nt if s))
	Js_exact = len(set(exact_matches.J_gene))

	fig = Figure(figsize=(100/25.4, 60/25.4))
	ax = fig.gca()
	ax.set_xlabel('Percentage difference')
	ax.set_ylabel('Frequency')
	fig.suptitle('Gene ' + gene_name, y=1.08, fontsize=16)
	ax.set_title('{:,} sequences assigned'.format(len(group)))

	ax.text(0.25, 0.95,
		'{:,} ({:.1%}) exact matches\n  {} unique CDR3\n  {} unique J'.format(
			len(exact_matches), len(exact_matches) / len(group),
			CDR3s_exact, Js_exact),
		transform=ax.transAxes, fontsize=10,
		bbox=dict(boxstyle='round', facecolor='white', alpha=0.5),
		horizontalalignment='left', verticalalignment='top')
	_ = ax.hist(list(group.V_SHM), bins=bins)
	return fig 

def create_decoder(self, helper, mode):
    attention_fn = AttentionLayerDot(
        params={"num_units": self.attention_dim},
        mode=tf.contrib.learn.ModeKeys.TRAIN)
    attention_values = tf.convert_to_tensor(
        np.random.randn(self.batch_size, self.input_seq_len, 32),
        dtype=tf.float32)
    attention_keys = tf.convert_to_tensor(
        np.random.randn(self.batch_size, self.input_seq_len, 32),
        dtype=tf.float32)
    params = AttentionDecoder.default_params()
    params["max_decode_length"] = self.max_decode_length
    return AttentionDecoder(
        params=params,
        mode=mode,
        vocab_size=self.vocab_size,
        attention_keys=attention_keys,
        attention_values=attention_values,
        attention_values_length=np.arange(self.batch_size) + 1,
        attention_fn=attention_fn) 

def make_copy(num_examples, min_len, max_len):
  """
  Generates a dataset where the target is equal to the source.
  Sequence lengths are chosen randomly from [min_len, max_len].

  Args:
    num_examples: Number of examples to generate
    min_len: Minimum sequence length
    max_len: Maximum sequence length

  Returns:
    An iterator of (source, target) string tuples.
  """
  for _ in range(num_examples):
    turn_length = np.random.choice(np.arange(min_len, max_len + 1))
    source_tokens = np.random.choice(
        list(VOCABULARY), size=turn_length, replace=True)
    target_tokens = source_tokens
    yield " ".join(source_tokens), " ".join(target_tokens) 

def make_reverse(num_examples, min_len, max_len):
  """
  Generates a dataset where the target is equal to the source reversed.
  Sequence lengths are chosen randomly from [min_len, max_len].

  Args:
    num_examples: Number of examples to generate
    min_len: Minimum sequence length
    max_len: Maximum sequence length

  Returns:
    An iterator of (source, target) string tuples.
  """
  for _ in range(num_examples):
    turn_length = np.random.choice(np.arange(min_len, max_len + 1))
    source_tokens = np.random.choice(
        list(VOCABULARY), size=turn_length, replace=True)
    target_tokens = source_tokens[::-1]
    yield " ".join(source_tokens), " ".join(target_tokens) 

def update_dividends(self, new_dividends):
        """
        Update our dividend frame with new dividends.  @new_dividends should be
        a DataFrame with columns containing at least the entries in
        zipline.protocol.DIVIDEND_FIELDS.
        """

        # Mark each new dividend with a unique integer id.  This ensures that
        # we can differentiate dividends whose date/sid fields are otherwise
        # identical.
        new_dividends['id'] = np.arange(
            self._dividend_count,
            self._dividend_count + len(new_dividends),
        )
        self._dividend_count += len(new_dividends)

        self.dividend_frame = sort_values(pd.concat(
            [self.dividend_frame, new_dividends]
        ), ['pay_date', 'ex_date']).set_index('id', drop=False) 

def create_test_panel_ohlc_source(sim_params, env):
    start = sim_params.first_open \
        if sim_params else pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)

    end = sim_params.last_close \
        if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)

    index = env.days_in_range(start, end)
    price = np.arange(0, len(index)) + 100
    high = price * 1.05
    low = price * 0.95
    open_ = price + .1 * (price % 2 - .5)
    volume = np.ones(len(index)) * 1000
    arbitrary = np.ones(len(index))

    df = pd.DataFrame({'price': price,
                       'high': high,
                       'low': low,
                       'open': open_,
                       'volume': volume,
                       'arbitrary': arbitrary},
                      index=index)
    panel = pd.Panel.from_dict({0: df})

    return DataPanelSource(panel), panel 

def test_expect_dtypes_with_tuple(self):

        allowed_dtypes = (dtype('datetime64[ns]'), dtype('float'))

        @expect_dtypes(a=allowed_dtypes)
        def foo(a, b):
            return a, b

        for d in allowed_dtypes:
            good_a = arange(3).astype(d)
            good_b = object()
            ret_a, ret_b = foo(good_a, good_b)
            self.assertIs(good_a, ret_a)
            self.assertIs(good_b, ret_b)

        with self.assertRaises(TypeError) as e:
            foo(arange(3, dtype='uint32'), object())

        expected_message = (
            "{qualname}() expected a value with dtype 'datetime64[ns]' "
            "or 'float64' for argument 'a', but got 'uint32' instead."
        ).format(qualname=qualname(foo))
        self.assertEqual(e.exception.args[0], expected_message) 

def test_bad_input(self):
        data = arange(100).reshape(self.ndates, self.nsids)
        baseline = DataFrame(data, index=self.dates, columns=self.sids)
        loader = DataFrameLoader(
            USEquityPricing.close,
            baseline,
        )

        with self.assertRaises(ValueError):
            # Wrong column.
            loader.load_adjusted_array(
                [USEquityPricing.open], self.dates, self.sids, self.mask
            )

        with self.assertRaises(ValueError):
            # Too many columns.
            loader.load_adjusted_array(
                [USEquityPricing.open, USEquityPricing.close],
                self.dates,
                self.sids,
                self.mask,
            ) 

def test_baseline(self):
        data = arange(100).reshape(self.ndates, self.nsids)
        baseline = DataFrame(data, index=self.dates, columns=self.sids)
        loader = DataFrameLoader(USEquityPricing.close, baseline)

        dates_slice = slice(None, 10, None)
        sids_slice = slice(1, 3, None)
        [adj_array] = loader.load_adjusted_array(
            [USEquityPricing.close],
            self.dates[dates_slice],
            self.sids[sids_slice],
            self.mask[dates_slice, sids_slice],
        ).values()

        for idx, window in enumerate(adj_array.traverse(window_length=3)):
            expected = baseline.values[dates_slice, sids_slice][idx:idx + 3]
            assert_array_equal(window, expected) 

def get_normalized_dispersion(mat_mean, mat_var, nbins=20):
    mat_disp = (mat_var - mat_mean) / np.square(mat_mean)

    quantiles = np.percentile(mat_mean, np.arange(0, 100, 100 / nbins))
    quantiles = np.append(quantiles, mat_mean.max())

    # merge bins with no difference in value
    quantiles = np.unique(quantiles)

    if len(quantiles) <= 1:
        # pathological case: the means are all identical. just return raw dispersion.
        return mat_disp

    # calc median dispersion per bin
    (disp_meds, _, disp_bins) = scipy.stats.binned_statistic(mat_mean, mat_disp, statistic='median', bins=quantiles)

    # calc median absolute deviation of dispersion per bin
    disp_meds_arr = disp_meds[disp_bins-1] # 0th bin is empty since our quantiles start from 0
    disp_abs_dev = abs(mat_disp - disp_meds_arr)
    (disp_mads, _, disp_bins) = scipy.stats.binned_statistic(mat_mean, disp_abs_dev, statistic='median', bins=quantiles)

    # calculate normalized dispersion
    disp_mads_arr = disp_mads[disp_bins-1]
    disp_norm = (mat_disp - disp_meds_arr) / disp_mads_arr
    return disp_norm 

def compute_nearest_neighbors(submatrix, balltree, k, row_start):
    """ Compute k nearest neighbors on a submatrix
    Args: submatrix (np.ndarray): Data submatrix
          balltree: Nearest neighbor index (from sklearn)
          k: number of nearest neigbors to compute
          row_start: row offset into larger matrix
    Returns a COO sparse adjacency matrix of nearest neighbor relations as (i,j,x)"""

    nn_dist, nn_idx = balltree.query(submatrix, k=k+1)

    # Remove the self-as-neighbors
    nn_idx = nn_idx[:,1:]
    nn_dist = nn_dist[:,1:]

    # Construct a COO sparse matrix of edges and distances
    i = np.repeat(row_start + np.arange(nn_idx.shape[0]), k)
    j = nn_idx.ravel().astype(int)
    return (i, j, nn_dist.ravel()) 

def preprocess_matrix(matrix, num_bcs=None, use_bcs=None, use_genes=None, force_cells=None):
        if force_cells is not None:
            bc_counts = matrix.get_reads_per_bc()
            bc_indices, _, _ = cr_stats.filter_cellular_barcodes_fixed_cutoff(bc_counts, force_cells)
            matrix = matrix.select_barcodes(bc_indices)
        elif use_bcs is not None:
            bc_seqs = cr_utils.load_csv_rownames(use_bcs)
            bc_indices = matrix.bcs_to_ints(bc_seqs)
            matrix = matrix.select_barcodes(bc_indices)
        elif num_bcs is not None and num_bcs < matrix.bcs_dim:
            bc_indices = np.sort(np.random.choice(np.arange(matrix.bcs_dim), size=num_bcs, replace=False))
            matrix = matrix.select_barcodes(bc_indices)

        if use_genes is not None:
            gene_ids = cr_utils.load_csv_rownames(use_genes)
            gene_indices = matrix.gene_ids_to_ints(gene_ids)
            matrix = matrix.select_genes(gene_indices)

        matrix, _, _ = matrix.select_nonzero_axes()
        return matrix 

def get_depth_info(read_iter, chrom, cstart, cend):

    depths = np.zeros(cend-cstart, np.int32)

    for read in read_iter:
        pos = read.pos
        rstart = max(pos, cstart)

        # Increment to the end of the window or the end of the
        # alignment, whichever comes first
        rend = min(read.aend, cend)
        depths[(rstart-cstart):(rend-cstart)] += 1

    positions = np.arange(cstart, cend, dtype=np.int32)

    depth_df = pd.DataFrame({"chrom": chrom, "pos": positions, "coverage": depths})
    return depth_df 

def getDataRecorderConfiguration(self):
        nRecorders= self.getNumberOfRecorderTables()
        sourceBufSize= 256
        source= ctypes.create_string_buffer('\000', sourceBufSize)
        option= CIntArray(np.zeros(nRecorders, dtype=np.int32))
        table=CIntArray(np.arange(1, nRecorders + 1))

        self._lib.PI_qDRC.argtypes= [c_int, CIntArray, c_char_p,
                                     CIntArray, c_int, c_int]

        self._convertErrorToException(
            self._lib.PI_qDRC(self._id, table, source,
                              option, sourceBufSize, nRecorders))

        sources= [x.strip() for x in source.value.split('\n')]
        cfg= DataRecorderConfiguration()
        for i in range(nRecorders):
            cfg.setTable(table.toNumpyArray()[i],
                         sources[i],
                         option.toNumpyArray()[i])
        return cfg 

def loadLogoSet(path, rows,cols,test_data_rate=0.15):
    random.seed(612)
    _, imgID = readItems('data.txt')
    y, _ = modelDict(path)
    nPics =  len(y)
    faceassset = np.zeros((nPics,rows,cols), dtype = np.uint8) ### gray images
    noImg = []
    for i in range(nPics):
        temp = cv2.imread(path +'logo/'+imgID[i]+'.jpg', 0)
        if temp == None:
            noImg.append(i)
        elif temp.size < 1000:
            noImg.append(i)
        else:
            temp = cv2.resize(temp,(cols, rows), interpolation = cv2.INTER_CUBIC)
            faceassset[i,:,:] = temp
    y = np.delete(y, noImg,0); faceassset = np.delete(faceassset, noImg, 0)
    nPics = len(y)
    index = random.sample(np.arange(nPics), int(nPics*test_data_rate))
    x_test = faceassset[index,:,:]; x_train = np.delete(faceassset, index, 0)
    y_test = y[index]; y_train = np.delete(y, index, 0)
    return (x_train, y_train), (x_test, y_test) 

def batch_iter(data, batch_size, num_epochs, shuffle=True):
    """
    Generates a batch iterator for a dataset.
    """
    data = np.array(data)
    data_size = len(data)
    num_batches_per_epoch = int(len(data)/batch_size) + 1
    for epoch in range(num_epochs):
        # Shuffle the data at each epoch
        if shuffle:
            shuffle_indices = np.random.permutation(np.arange(data_size))
            shuffled_data = data[shuffle_indices]
        else:
            shuffled_data = data
        for batch_num in range(num_batches_per_epoch):
            start_index = batch_num * batch_size
            end_index = min((batch_num + 1) * batch_size, data_size)
            yield shuffled_data[start_index:end_index] 

def _gen_centroids():
    a = np.arange(SSIZE/18, SSIZE, SSIZE/9)
    x, y = np.meshgrid(a, a)
    return np.dstack((y, x)).reshape((81, 2)) 

def classify(self, image):
        """
        Given a 28x28 image, returns an array representing the 2 highest
        probable prediction
        :param image:
        :return: array of 2 highest prob-digit tuples
        """
        if cv2.__version__[0] == '2':
            res = self.model.find_nearest(np.array([self.feature(image)]), k=11)
        else:
            res = self.model.findNearest(np.array([self.feature(image)]), k=11)
        hist = np.histogram(res[2], bins=9, range=(1, 10), normed=True)[0]
        zipped = sorted(zip(hist, np.arange(1, 10)), reverse=True)
        return np.array(zipped[:2]) 

def blend2(x1,x2,y, metric, task, x1valid, x2valid, x1test, x2test):
	try:
		mm = no_transform()
		mbest_score = -2
		for w1 in np.arange(0.2, 1, 0.1):
			w2 = 1- w1
			x = mm.fit_transform(x1)*w1  +  mm.fit_transform(x2)*w2
			exec('score = libscores.'+ metric  + '(y, x, "' + task + '")')
			try:
				if score <= 0:
					exec('CVscore_auc = libscores.auc_metric(y, x, "' + task + '")')
					score += CVscore_auc/10
			except:
				pass
			
			if score > mbest_score:
				mbest_score = score
				mbest_w1 = w1
				mbest_x  = x
		mbest_w2 = 1- mbest_w1
		xvalid = mm.fit_transform(x1valid) * mbest_w1 +  mm.fit_transform(x2valid)* mbest_w2
		xtest =  mm.fit_transform(x1test) * mbest_w1 +  mm.fit_transform(x2test) * mbest_w2

		return mbest_score, xvalid, xtest
	except:
		return 0.01, x1valid, x1test 

def blend3(x1,x2, x3, y, metric, task, x1valid, x2valid, x3valid, x1test, x2test, x3test):
	try:
		mm = no_transform()
		mbest_score = -2
		for w1 in np.arange(0.2, 1, 0.2):
			for w2 in np.arange(0.1, 0.6, 0.2):
				w3 = 1- w1 - w2
				if w3 > 0:
					x = mm.fit_transform(x1)*w1  +  mm.fit_transform(x2)*w2 +  mm.fit_transform(x3)*w3
					exec('score = libscores.'+ metric  + '(y, x, "' + task + '")')
					try:
						if score <= 0:
							exec('CVscore_auc = libscores.auc_metric(y, x, "' + task + '")')
							score += CVscore_auc/10
					except:
						pass
					if score > mbest_score:
						mbest_score = score
						mbest_w1 = w1
						mbest_w2 = w2
		
		mbest_w3 = 1- mbest_w1- mbest_w2
		xvalid = mm.fit_transform(x1valid) * mbest_w1 +  mm.fit_transform(x2valid)* mbest_w2 +  mm.fit_transform(x3valid)* mbest_w3
		xtest =  mm.fit_transform(x1test) * mbest_w1 +  mm.fit_transform(x2test) * mbest_w2 +  mm.fit_transform(x3test) * mbest_w3

		return mbest_score, xvalid, xtest
	except:
		return 0.01, x1valid, x1test 

def tiedrank(a):
    ''' Return the ranks (with base 1) of a list resolving ties by averaging.
     This works for numpy arrays.'''    
    m=len(a)
    # Sort a in ascending order (sa=sorted vals, i=indices)
    i=a.argsort()
    sa=a[i]
    # Find unique values
    uval=np.unique(a)     
    # Test whether there are ties 
    R=np.arange(m, dtype=float)+1 # Ranks with base 1
    if len(uval)!=m:
        # Average the ranks for the ties 
        oldval=sa[0]
        newval=sa[0]
        k0=0
        for k in range(1,m):
            newval=sa[k]
            if newval==oldval:
                # moving average
                R[k0:k+1]=R[k-1]*(k-k0)/(k-k0+1)+R[k]/(k-k0+1)
            else:
                k0=k;
                oldval=newval
    # Invert the index
    S=np.empty(m)
    S[i]=R
    return S 

def plot_trajectories(src_sent, src_encoding, idx):
    
    # encoding is (time_steps, hidden_dim)
    #pca = PCA(n_components=1)
    
    #pca_result = pca.fit_transform(src_encoding)
    times = np.arange(src_encoding.shape[0])
    plt.plot(times, src_encoding)
    plt.title(" ".join(src_sent))
    plt.xlabel('timestep')
    plt.ylabel('trajectories')
    plt.savefig("misc_hidden_cell_trajectories_"+str(idx), bbox_inches="tight")
    plt.close() 

def dense_to_one_hot(labels_dense, num_classes=10):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot 

def dense_to_one_hot(labels_dense, num_classes=10):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot 

def iterate_minibatches(inputs, targets, batchsize, shuffle=False, augment=False):
    assert len(inputs) == len(targets)
    if shuffle:
        indices = np.arange(len(inputs))
        np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batchsize]
        else:
            excerpt = slice(start_idx, start_idx + batchsize)
        if augment:
            # as in paper :
            # pad feature arrays with 4 pixels on each side
            # and do random cropping of 32x32
            padded = np.pad(inputs[excerpt],((0,0),(0,0),(4,4),(4,4)),mode='constant')
            random_cropped = np.zeros(inputs[excerpt].shape, dtype=np.float32)
            crops = np.random.random_integers(0,high=8,size=(batchsize,2))
            for r in range(batchsize):
                random_cropped[r,:,:,:] = padded[r,:,crops[r,0]:(crops[r,0]+32),crops[r,1]:(crops[r,1]+32)]
            inp_exc = random_cropped
        else:
            inp_exc = inputs[excerpt]

        yield inp_exc, targets[excerpt]

# ############################## Main program ################################ 

def __init__(self, env):
        self.env = env

        if isinstance(env.observation_space, Discrete):
            self.state_size = 1
        else:
            self.state_size = numel(env.observation_space.shape)

        if isinstance(self.env.action_space, Discrete):
            self.is_discrete = True
            self.action_size = env.action_space.n
            self.actions = np.arange(self.action_size)
        else:
            self.is_discrete = False
            self.action_size = numel(env.action_space.sample())