Python matplotlib.pylab.annotate() Examples

The following are 5 code examples of matplotlib.pylab.annotate(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module matplotlib.pylab , or try the search function .
Example #1
Source File: models.py    From philo2vec with MIT License 7 votes vote down vote up 
def plot(self, words, num_points=None):
        if not num_points:
            num_points = len(words)

        embeddings = self.get_words_embeddings(words)
        tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
        two_d_embeddings = tsne.fit_transform(embeddings[:num_points, :])

        assert two_d_embeddings.shape[0] >= len(words), 'More labels than embeddings'
        pylab.figure(figsize=(15, 15))  # in inches
        for i, label in enumerate(words[:num_points]):
            x, y = two_d_embeddings[i, :]
            pylab.scatter(x, y)
            pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
                           ha='right', va='bottom')
        pylab.show()
Example #2
Source File: portfolio.py    From FinQuant with MIT License 6 votes vote down vote up 
def plot_stocks(self, freq=252):
        """Plots the Expected annual Returns over annual Volatility of
        the stocks of the portfolio.

        :Input:
         :freq: ``int`` (default: ``252``), number of trading days, default
             value corresponds to trading days in a year.
        """
        # annual mean returns of all stocks
        stock_returns = self.comp_mean_returns(freq=freq)
        stock_volatility = self.comp_stock_volatility(freq=freq)
        # adding stocks of the portfolio to the plot
        # plot stocks individually:
        plt.scatter(stock_volatility, stock_returns, marker="o", s=100, label="Stocks")
        # adding text to stocks in plot:
        for i, txt in enumerate(stock_returns.index):
            plt.annotate(
                txt,
                (stock_volatility[i], stock_returns[i]),
                xytext=(10, 0),
                textcoords="offset points",
                label=i,
            )
            plt.legend()
Example #3
Source File: assign5_word2vec.py    From deep-learning-samples with The Unlicense 5 votes vote down vote up 
def plot(embeddings, labels):
  assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
  pylab.figure(figsize=(15,15))  # in inches
  for i, label in enumerate(labels):
    x, y = embeddings[i,:]
    pylab.scatter(x, y)
    pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
                   ha='right', va='bottom')
  pylab.show()
Example #4
Source File: 5_word2vec.py    From udacity-deep-learning with GNU General Public License v3.0 5 votes vote down vote up 
def plot(embeddings, labels):
    assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
    pylab.figure(figsize=(15, 15))  # in inches
    for i, label in enumerate(labels):
        x, y = embeddings[i, :]
        pylab.scatter(x, y)
        pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
                       ha='right', va='bottom')
    pylab.show()
Example #5
Source File: 5_word2vec.py    From udacity-deep-learning with GNU General Public License v3.0 5 votes vote down vote up 
def plot(embeddings, labels):
    assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
    pylab.figure(figsize=(15, 15))  # in inches
    for i, label in enumerate(labels):
        x, y = embeddings[i, :]
        pylab.scatter(x, y)
        pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
                       ha='right', va='bottom')
    pylab.show()