# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import tensorflow as tf
import numpy as np
import scipy.misc
try:
    from StringIO import StringIO  # Python 2.7
except ImportError:
    from io import BytesIO         # Python 3.x


class TensorboardLogger(object):
    def __init__(self, log_dir):
        """Create a summary writer logging to log_dir."""
        self.writer = tf.summary.FileWriter(log_dir)

    def scalar_summary(self, tag, value, step):
        """Log a scalar variable."""
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
        self.writer.add_summary(summary, step)

    def image_summary(self, tag, images, step):
        """Log a list of images."""

        img_summaries = []
        for i, img in enumerate(images):
            # Write the image to a string
            try:
                s = StringIO()
            except:
                s = BytesIO()
            scipy.misc.toimage(img).save(s, format="png")

            # Create an Image object
            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                                       height=img.shape[0],
                                       width=img.shape[1])
            # Create a Summary value
            img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))

        # Create and write Summary
        summary = tf.Summary(value=img_summaries)
        self.writer.add_summary(summary, step)


    def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        # Original code:
        # counts, bin_edges = np.histogram(values, bins=bins)
        #
        # Now I use the following to get more detailed data
        # https://stackoverflow.com/questions/39418380/histogram-with-equal-number-of-points-in-each-bin
        counts, bin_edges = np.histogram(
            values,
            bins=histedges_equalN(values.flatten(), bins)
        )

        # Fill the fields of the histogram proto
        hist = tf.HistogramProto()
        hist.min = float(np.min(values))
        hist.max = float(np.max(values))
        hist.num = int(np.prod(values.shape))
        hist.sum = float(np.sum(values))
        hist.sum_squares = float(np.sum(values**2))

        # Drop the start of the first bin
        bin_edges = bin_edges[1:]

        # Add bin edges and counts
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()


def histedges_equalN(x, nbin):
    npt = len(x)
    return np.interp(np.linspace(0, npt, nbin + 1),
                     np.arange(npt),
                     np.sort(x))
