import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


class Logger(object):

    def __init__(self, log_dir):
        """Create a summary writer logging to log_dir."""
        self.writer = tf.compat.v1.summary.FileWriter(log_dir)
        # self.writer = tf.summary.create_file_writer('log_dir')

    def scalar_summary(self, tag, value, step):
        """Log a scalar variable."""
        summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)])
        self.writer.add_summary(summary, step)

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

        values = np.array(values)
        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the histogram proto
        hist = tf.compat.v1.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.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
