#!/usr/bin/env python3
"""
histogram: plot a histogram of a file of numbers. Numbers can be floats, one per
line. Lines with two numbers are interpreted as pre-counted, with the number of
repeats of the first being given by the second.

Multiple instances of the same value in a category will be merged by adding
weights.

Re-uses sample code and documentation from 
<http://users.soe.ucsc.edu/~karplus/bme205/f12/Scaffold.html>
"""

import argparse, sys, os, itertools, math, numpy, collections
import matplotlib, matplotlib.ticker

def intify(x):
    """
    Turn an integral float into an int, if applicable.
    """
    
    if isinstance(x, float) and x.is_integer():
        return int(x)
    return x
    
def draw_labels(bin_counts, bar_patches, size=None):
    """
    Put the given count labels on the given bar patches, on the current axes.
    Takes an optional font size.
    
    """
    
    from matplotlib import pyplot
    
    # Grab the axes
    axes = pyplot.gca()

    for bin_count, bar_patch in zip(bin_counts, bar_patches):
        
        if(bin_count.is_integer()):
            # Intify if applicable
            bin_count = int(bin_count)
        
        # Label each bar
        if bin_count == 0:
            # Except those for empty bins
            continue
            
        # Find the center of the bar
        bar_center_x = bar_patch.get_x() + bar_patch.get_width() / float(2)
        # And its height
        bar_height = bar_patch.get_height()
        
        # Label the bar
        axes.annotate("{:,}".format(bin_count), (bar_center_x, bar_height), 
            ha="center", va="bottom", rotation=45, xytext=(0, 5), 
            textcoords="offset points", size=size)

def parse_args(args):
    """
    Takes in the command-line arguments list (args), and returns a nice argparse
    result with fields for all the options.
    Borrows heavily from the argparse documentation examples:
    <http://docs.python.org/library/argparse.html>
    """
    
    # The command line arguments start with the program name, which we don't
    # want to treat as an argument for argparse. So we remove it.
    args = args[1:]
    
    # Construct the parser (which is stored in parser)
    # Module docstring lives in __doc__
    # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847
    # And a formatter class so our examples in the docstring look good. Isn't it
    # convenient how we already wrapped it to 80 characters?
    # See http://docs.python.org/library/argparse.html#formatter-class
    parser = argparse.ArgumentParser(description=__doc__, 
        formatter_class=argparse.RawDescriptionHelpFormatter)
    
    # Now add all the options to it
    parser.add_argument("data", nargs="+",
        help="the file to read")
    parser.add_argument("--redPortion", type=float, action="append", default=[],
        help="portion of each bin to color red")
    parser.add_argument("--redWeight", type=float, action="append", default=[],
        help="value to plot in red in each bin")
    parser.add_argument("--title", default="Histogram",
        help="the plot title")
    parser.add_argument("--x_label", default="Value",
        help="the plot title")
    parser.add_argument("--y_label", default="Number of Items (count)",
        help="the plot title")
    parser.add_argument("--bins", type=int, default=10,
        help="the number of histogram bins")
    parser.add_argument("--x_min", "--min", type=float, default=None,
        help="minimum value allowed")
    parser.add_argument("--x_max", "--max", type=float, default=None,
        help="maximum value allowed")
    parser.add_argument("--y_min", type=float, default=None,
        help="minimum count on plot")
    parser.add_argument("--y_max", type=float, default=None,
        help="maximum count on plot")
    parser.add_argument("--cutoff", type=float, default=None,
        help="note portion above and below a value, and draw a vertical line")
    parser.add_argument("--font_size", type=int, default=12,
        help="the font size for text")
    parser.add_argument("--categories", nargs="+", default=None,
        help="categories to plot, in order")
    parser.add_argument("--category_labels", "--labels", nargs="+",
        default=[],
        help="labels for all categories or data files, in order")
    parser.add_argument("--colors", nargs="+", default=[],
        help="use the specified Matplotlib colors per category or file")
    parser.add_argument("--styles", nargs="+", default=[],
        help="use the specified line styles per category or file")
    parser.add_argument("--cumulative", action="store_true",
        help="plot cumulatively")
    parser.add_argument("--log", action="store_true",
        help="take the base-10 logarithm of values before plotting histogram")
    parser.add_argument("--log_counts", "--logCounts", action="store_true",
        help="take the logarithm of counts before plotting histogram")
    parser.add_argument("--fake_zero", action="store_true",
        help="split lines where points would be 0")
    parser.add_argument("--split_at_zero", action="store_true",
        help="split lines between positive and negative")
    parser.add_argument("--stats", action="store_true",
        help="print data stats")
    parser.add_argument("--save",
        help="save figure to the given filename instead of showing it")
    parser.add_argument("--dpi", type=int, default=300,
        help="save the figure with the specified DPI, if applicable")
    parser.add_argument("--sparse_ticks", action="store_true",
        help="use sparse tick marks on both axes")
    parser.add_argument("--sparse_x", action="store_true",
        help="use sparse tick marks on X axis")
    parser.add_argument("--sparse_y", action="store_true",
        help="use sparse tick marks on Y axis")
    parser.add_argument("--ticks", nargs="+", default=None,
        help="use particular X tick locations")
    parser.add_argument("--scientific_x", action="store_true",
        help="use scientific notation on the X axis")
    parser.add_argument("--scientific_y", action="store_true",
        help="use scientific notation on the Y axis")
    parser.add_argument("--label", action="store_true",
        help="label bins with counts")
    parser.add_argument("--label_size", type=float,
        help="bin count label font size")
    parser.add_argument("--no_n", dest="show_n", action="store_false",
        help="don't add n value to title")
    parser.add_argument("--normalize", action="store_true",
        help="normalize to total weight of 1")
    parser.add_argument("--line", action="store_true",
        help="draw a line instead of a barchart")
    parser.add_argument("--no_zero_ends", dest="zero_ends", default=True,
        action="store_false",
        help="don't force line ends to zero")
    parser.add_argument("--legend_overlay", default=None,
        help="display the legend overlayed on the graph at this location")
    parser.add_argument("--no_legend", action="store_true",
        help="don't display a legend when one would otherwise be dispalyed")
    parser.add_argument("--points", action="store_true",
        help="draw points instead of a barchart")
    parser.add_argument("--width", type=float, default=8,
        help="plot width in inches")
    parser.add_argument("--height", type=float, default=6,
        help="plot height in inches")
    
        
    return parser.parse_args(args)
    
def filter2(criterion, key_list, other_list):
    """
    Filter two lists of corresponding items based on some function of the first
    list.
    
    """
    
    # Make the output lists
    out1 = []
    out2 = []
    
    for key_val, other_val in zip(key_list, other_list):
        # Pair up the items
        if criterion(key_val):
            # The key passed the filter, so take both.
            out1.append(key_val)
            out2.append(other_val)
            
    return out1, out2
    
def filter_n(*args):
    """
    Filter any number of lists of corresponding items based on some function of
    the first list.
    
    """
    
    filter_function = args[0]
    to_filter = args[1:]
    
    to_return = [list() for _ in to_filter]
    
    for i in range(len(to_filter[0])):
        # For each run of entries
        if filter_function(to_filter[0][i]):
            # If the key passes the filter
            for j in range(len(to_filter)):
                # Keep the whole row
                if i < len(to_filter[j]):
                    to_return[j].append(to_filter[j][i])
            
            
    # return all the lists as a tuple, which unpacks as multiple return values
    return tuple(to_return)

def main(args):
    """
    Parses command line arguments, and plots a histogram.
    "args" specifies the program arguments, with args[0] being the executable
    name. The return value should be used as the program's exit code.
    """
    
    options = parse_args(args) # This holds the nicely-parsed options object
    
    if options.save is not None:
        # Set up plot for use in headless mode if we just want to save. See
        # <http://stackoverflow.com/a/2766194/402891>. We need to do this before
        # we grab pyplot.
        matplotlib.use('Agg')
        
    from matplotlib import pyplot
    
    # Make the figure with the appropriate size and DPI.
    pyplot.figure(figsize=(options.width, options.height), dpi=options.dpi)
    
    # This will hold a dict of dicts from data value to weight, by category or
    # file name. Later gets converted to a dict of lists of (value, weight)
    # pairs, aggregated by value.
    all_data = collections.defaultdict(lambda: collections.defaultdict(float))
    
    for data_filename in options.data:
        
        for line_number, line in enumerate(open(data_filename)):
            # Split each line
            parts = line.split()
            
            if len(parts) == 1:
                # This is one instance of a value
                
                all_data[data_filename][float(parts[0])] += 1.0
            elif len(parts) == 2:
                if len(options.data) > 1:
                    # This is multiple instances of a value, and we are doing
                    # categories by filename.
                    all_data[data_filename][float(parts[0])] += float(parts[1])
                else:
                    try:
                        value = float(parts[0])
                        # If the first column is a number, this is value, weight
                        # data.
                        all_data[data_filename][value] += float(parts[1])
                    except ValueError:
                        # This is category, instance data, since first column
                        # isn't a number.
                        all_data[parts[0]][float(parts[1])] += 1.0
            elif len(parts) == 3:
                # This is category, instance, weight data
                all_data[parts[0]][float(parts[1])] += float(parts[2])
            else:
                raise Exception("Wrong number of fields on {} line {}".format(
                    data_filename, line_number + 1))
        
    for category in all_data.keys():
        # Strip NaNs and Infs and weight-0 entries, and convert to a dict of
        # lists of tuples.
        all_data[category] = [(value, weight) for (value, weight)
            in all_data[category].items() if 
            value < float("+inf") and value > float("-inf") and weight > 0]
        
        
    
    # Calculate our own bins, over all the data. First we need the largest and
    # smallest observed values. The fors in the comprehension have to be in
    # normal for loop order and not the other order.
    # Make sure to filter out 0s from bounds determination if using log space.
    bin_min = options.x_min if options.x_min is not None else min((pair[0]
        for pair_list in all_data.values() for pair in pair_list if (not options.log or pair[0] != 0)))
    bin_max = options.x_max if options.x_max is not None else max((pair[0]
        for pair_list in all_data.values() for pair in pair_list if (not options.log or pair[0] != 0)))
    
    if options.log:
        # Do our bins in log space, so they look evenly spaced on the plot.
        bin_max = math.log10(bin_max)
        bin_min = math.log10(bin_min)
    
    # Work out what step we should use between bin edges
    bin_step = (bin_max - bin_min) / float(options.bins)
    # Work out where the bin edges should be
    bins = [bin_min + bin_step * i for i in range(options.bins + 1)]
    # Work out where the bin centers should be
    bin_centers = [left_edge + bin_step / 2.0 for left_edge in bins[:-1]]    
    
    if options.log:
        # Bring bins back into data space
        bins = [math.pow(10, x) for x in bins]
        bin_centers = [math.pow(10, x) for x in bin_centers]
    
    if options.categories is not None:
        # Order data by category order
        ordered_data = [(category, all_data[category]) for category in
            options.categories]
    elif len(options.data) > 1:
        # Order data by file order
        ordered_data = [(filename, all_data[filename]) for filename in
            options.data]
    else:
        # Order arbitrarily
        ordered_data = list(all_data.items())
        
    if options.categories is not None and options.category_labels == []:
        # Label categories with their internal names
        category_labels = options.categories 
    else:
        # Label categories exactly as asked
        category_labels = options.category_labels
    
    for (category, data_and_weights), label, color, line_style, marker in \
        zip(ordered_data,
        itertools.chain(category_labels, itertools.repeat(None)),
        itertools.chain(options.colors, itertools.cycle(
        ['b', 'g', 'r', 'c', 'm', 'y', 'k'])),
        itertools.chain(options.styles, itertools.cycle(
        ['-', '--', ':', '-.'])),
        itertools.cycle(
        ['o', 'v', '^', '<', '>', 's', '+', 'x', 'D', '|', '_'])):
        # For every category and its display properties...
        
        if len(data_and_weights) == 0:
            # Skip categories with no data
            continue

        # Split out the data and the weights for this category/file
        data = [pair[0] for pair in data_and_weights]
        weights = [pair[1] for pair in data_and_weights] 
        
        # For each set of data and weights that we want to plot, and the label
        # it needs (or None)...
            
        # We may want to normalize by total weight
        # We need a float here so we don't get int division later.
        total_weight_overall = float(0)
        
        for value, weight in zip(data, weights):
            # Sum up the weights overall
            total_weight_overall += weight
        
        if options.normalize and total_weight_overall > 0:
            # Normalize all the weight to 1.0 total weight.
            weights = [w / total_weight_overall for w in weights]
            
        # Apply the limits after normalization
        if options.x_min is not None:
            data, weights = filter2(lambda x: x >= options.x_min, data, weights)
        if options.x_max is not None:
            data, weights = filter2(lambda x: x <= options.x_max, data, weights)
           
        # Work out how many samples there are left within the chart area
        samples = intify(sum(weights))
            
        if options.stats:
            # Compute and report some stats
            data_min = numpy.min(data)
            data_min_count = weights[numpy.argmin(data)]
            data_max = numpy.max(data)
            data_max_count = weights[numpy.argmax(data)]
            # The mode is the data item with maximal count
            data_mode = data[numpy.argmax(weights)]
            data_mode_count = numpy.max(weights)
            
            # Intify floats pretending to be ints
            data_min = intify(data_min)
            data_min_count = intify(data_min_count)
            data_max = intify(data_max)
            data_max_count = intify(data_max_count)
            data_mode = intify(data_mode)
            data_mode_count = intify(data_mode_count)
        
            # TODO: median, mean
            
            print("Min: {} occurs {} times".format(data_min, data_min_count))
            print("Mode: {} occurs {} times".format(data_mode, data_mode_count))
            print("Max: {} occurs {} times".format(data_max, data_max_count))
            
            if options.cutoff is not None:
                # Work out how much weight is above and below the cutoff
                above = 0
                below = 0
                
                for value, weight in zip(data, weights):
                    if value > options.cutoff:
                        above += weight
                    else:
                        below += weight
                
                # Report the results wrt the cutoff.
                print("{} above {}, {} below".format(
                    above / total_weight_overall, options.cutoff, 
                    below / total_weight_overall))
            
        if options.line or options.points:
            # Do histogram binning manually
            
            # Do the binning
            bin_values, _ = numpy.histogram(data, bins=bins, weights=weights)
            
            if options.cumulative:
                # Calculate cumulative weights for each data point
                bin_values = numpy.cumsum(bin_values)
                
            if options.zero_ends:
                if options.cumulative:
                    # Pin things to 0 on the low end and max on the high
                    all_bin_centers = [bins[0]] + list(bin_centers) + [bins[-1]]
                    all_bin_values = [0] + list(bin_values) + [sum(weights)]
                else:
                    # Pin things to 0 on the end
                    all_bin_centers = [bins[0]] + list(bin_centers) + [bins[-1]]
                    all_bin_values = [0] + list(bin_values) + [0]
            else:
                all_bin_centers = bin_centers
                all_bin_values = bin_values
                
            # Now we make a bunch of deries for each line, potentially. This
            # holds pairs of (centers, values) lists.
            series = []
            
            if options.fake_zero or options.split_at_zero:
                # We need to split into multiple series, potentially.
                # This holds the series we are working on.
                this_series = ([], [])
                
                # What was the last bin we saw?
                last_bin = 0
                
                for center, value in zip(all_bin_centers,
                    all_bin_values):
                    # For every point on the line, see if we need to break here
                    # because it's zero.
                    
                    # This logic gets complicated so we do some flags.
                    # Do we keep this point?
                    includeSample = True
                    # Do we split the line?
                    breakSeries = False
                    
                    if options.fake_zero and value == 0:
                        # We don't want this sample, and we need to break the
                        # series
                        includeSample = False
                        breakSeries = True
                        
                    if options.split_at_zero and last_bin < 0 and center > 0:
                        # We crossed the y axis, or we went down to the x axis.
                        # We can maybe keep the sample, and we need to break the
                        # series
                        breakSeries = True
                        
                    if breakSeries and len(this_series[0]) > 0:
                        # Finish the series and start another
                        series.append(this_series)
                        this_series = ([], [])
                        
                    if includeSample:
                        # Stick this point in the series
                        this_series[0].append(center)
                        this_series[1].append(value)
                        
                    last_bin = center
                        
                if len(this_series[0]) > 0:
                    # Finish the last series
                    series.append(this_series)
                
            else:
                # Just do one series
                series.append((all_bin_centers, all_bin_values))
                
            # We only want to label the first series in the legend, so we'll
            # none this out after we use it.
            label_to_use = label
                
            for series_centers, series_values in series: 
                # Plot every series
                
                if options.line and options.points:
                    # Do the plots as lines with points
                    pyplot.plot(series_centers, series_values,
                        label=label_to_use, linestyle=line_style, color=color,
                        marker=marker)
                    label_to_use = None
                elif options.line:
                    # Do the plots as lines only
                    pyplot.plot(series_centers, series_values,
                        label=label_to_use, linestyle=line_style, color=color)
                    label_to_use= None
                elif options.points:
                    # Do the plot as points.   
                    pyplot.scatter(series_centers, series_values,
                        label=label_to_use, color=color, marker=marker)
                    label_to_use = None
            
            if options.log_counts:
                # Log the Y axis
                pyplot.yscale('log')
                
            if options.split_at_zero:
                # Put a big vertical line.
                pyplot.axvline(linewidth=2, color="k")
        
        else:
            # Do the plot. Do cumulative, or logarithmic Y axis, optionally.
            # Keep the bin total counts and the bar patches.
            bin_counts, _, bar_patches = pyplot.hist(data, bins,
                cumulative=options.cumulative, log=options.log_counts,
                weights=weights, alpha=0.5 if len(ordered_data) > 1 else 1.0,
                label=label)
                
        if options.cutoff is not None:
            # Put a vertical line at the cutoff.
            pyplot.axvline(x=options.cutoff, color="r")
                
            
    if len(options.redPortion) > 0:
        # Plot a red histogram over that one, modified by redPortion.
        
        red_data = []
        red_weights = []
        
        for item, weight in zip(data, weights):
            # For each item, what bin is it in?
            bin_number = int(item / bin_step)
            
            if bin_number < len(options.redPortion):
                # We have a potentially nonzero scaling factor. Apply that.
                weight *= options.redPortion[bin_number]
                
                # Keep this item.
                red_data.append(item)
                red_weights.append(weight)
        
        # Plot the re-weighted data with the same bins, in red
        red_counts, _, red_patches = pyplot.hist(red_data, bins,
            cumulative=options.cumulative, log=options.log_counts,
            weights=red_weights, color='#FF9696', hatch='/'*6)
            
        if options.label:
            # Label all the red portion-based bars
            draw_labels(red_counts, red_patches, size=options.label_size)
            
        
            
    if len(options.redWeight) > 0:
        # Plot a red histogram over that one, modified by redPortion.
        
        # Grab an item in each bin
        items = bins[0:len(options.redWeight)]
        
        # Plot the re-weighted data with the same bins, in red
        red_counts, _, red_patches = pyplot.hist(items, bins,
            cumulative=options.cumulative, log=options.log_counts,
            weights=options.redWeight, color='#FF9696', hatch='/'*6)
            
        if options.label:
            # Label all the red weight-based bars
            draw_labels(red_counts, red_patches, size=options.label_size)
            
        
        
    # StackOverflow provides us with font sizing. See
    # <http://stackoverflow.com/q/3899980/402891>
    matplotlib.rcParams.update({"font.size": options.font_size})
    if options.show_n:
        # Add an n value to the title
        options.title += " (n = {:,})".format(samples)
    pyplot.title(options.title)
    pyplot.xlabel(options.x_label)
    pyplot.ylabel(options.y_label)
    
    if options.log:
        # Set the X axis to log mode
        pyplot.xscale('log')
    
    if options.x_min is not None:
        # Set only the lower x limit
        pyplot.xlim((options.x_min, pyplot.xlim()[1]))
    if options.x_max is not None:
        # Set only the upper x limit
        pyplot.xlim((pyplot.xlim()[0], options.x_max))
        
    if options.y_min is not None:
        # Set only the lower y limit
        pyplot.ylim((options.y_min, pyplot.ylim()[1]))
    elif options.log_counts:
        # Make sure the default lower Y limit is 1 on log plots.
        pyplot.ylim((1, pyplot.ylim()[1]))
    if options.y_max is not None:
        # Set only the upper y limit
        pyplot.ylim((pyplot.ylim()[0], options.y_max))
        
    if options.sparse_ticks or options.sparse_x:
        # Set up X tickmarks to have only 2 per axis, at the ends
        pyplot.gca().xaxis.set_major_locator(
            matplotlib.ticker.FixedLocator(pyplot.xlim()))
    if options.sparse_ticks or options.sparse_y:
        # Same for the Y axis
        pyplot.gca().yaxis.set_major_locator(
            matplotlib.ticker.FixedLocator(pyplot.ylim()))
            
    if options.ticks is not None:
        # Use these particular X ticks instead
         pyplot.gca().xaxis.set_major_locator(
            matplotlib.ticker.FixedLocator(
            [float(pos) for pos in options.ticks]))
            
    # Make sure tick labels don't overlap. See
    # <http://stackoverflow.com/a/20599129/402891>
    pyplot.gca().tick_params(axis="x", pad=0.5 * options.font_size)
    
    # Make our own scientific notation formatter since set_scientific is not
    # working
    sci_formatter = matplotlib.ticker.FormatStrFormatter("%1.2e")
    if options.scientific_x:
        # Force scientific notation on X axis
        pyplot.gca().xaxis.set_major_formatter(sci_formatter)
    if options.scientific_y:
        # Force scientific notation on Y axis
        pyplot.gca().yaxis.set_major_formatter(sci_formatter)
        
    if options.label:
        # Label all the normal bars
        draw_labels(bin_counts, bar_patches, size=options.label_size)
    
    # Make everything fit
    pyplot.tight_layout()
    
    if len(category_labels) > 0 and not options.no_legend:
        # We need a legend
        
        if options.legend_overlay is None:
            # We want the default legend, off to the right of the plot.
        
            # First shrink the plot to make room for it.
            # TODO: automatically actually work out how big it will be.
            bounds = pyplot.gca().get_position()
            pyplot.gca().set_position([bounds.x0, bounds.y0,
                bounds.width * 0.5, bounds.height])
                
            # Make the legend
            pyplot.legend(loc="center left", bbox_to_anchor=(1.05, 0.5))
            
        else:
            # We want the legend on top of the plot at the user-specified
            # location, and we want the plot to be full width.
            pyplot.legend(loc=options.legend_overlay)
    
    if options.save is not None:
        # Save the figure to a file
        pyplot.savefig(options.save, dpi=options.dpi)
    else:
        # Show the figure to the user
        pyplot.show()
        
    return 0

if __name__ == "__main__" :
    sys.exit(main(sys.argv))

