# Path: tupa/scripts/export.py
# def load_model(filename):
#     model = Model(filename=filename)
#     model.load()
#     return model


from collections import OrderedDict
from configargparse import ArgParser
from scipy.misc import imresize
from matplotlib.ticker import MaxNLocator
from tupa.scripts.export import load_model
from tqdm import tqdm
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import re
    for key, value in values.items():
        if isinstance(value, np.ndarray):  # TODO group by prefix, showing W and b side by side
            cols.setdefault(key[:-2] + key[-1] if len(key) > 1 and key[-2] in "bW" else key, []).append((key, value))
    _, axes = plt.subplots(2, len(cols), figsize=(5*len(cols), 10))  # TODO https://stackoverflow.com/a/13784887/223267
    plt.tight_layout()
    for j, col in enumerate(tqdm(cols.values(), unit="param", desc=filename)):
        for i in range(2):
            axis = axes[i, j] if len(values) > 1 else axes
            if len(col) <= i:
                plt.delaxes(axis)
            else:
                plt.sca(axis)
                key, value = col[i]
                plt.colorbar(plt.pcolormesh(smooth(value)))
                for pattern, repl in REPLACEMENTS:  # TODO map 0123->ifoc
                    key = re.sub(pattern, repl, key)
                plt.title(key)
                for axis in (plt.gca().xaxis, plt.gca().yaxis):
                    axis.set_major_locator(MaxNLocator(integer=True))
    output_file = filename + ".png"
    plt.savefig(output_file)
    plt.clf()
    print("Saved '%s'." % output_file)


def main():
    argparser = ArgParser(description="Load TUPA model and visualize, saving to .png file.")
    argparser.add_argument("models", nargs="+", help="model file basename(s) to load")
    args = argparser.parse_args()
    for filename in args.models:

# Path: tupa/scripts/export.py
# def load_model(filename):
#     model = Model(filename=filename)
#     model.load()
#     return model


# Path: tupa/scripts/viz.py
from collections import OrderedDict
from configargparse import ArgParser
from scipy.misc import imresize
from matplotlib.ticker import MaxNLocator
from tupa.scripts.export import load_model
from tqdm import tqdm
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import re

    for key, value in values.items():
        if isinstance(value, np.ndarray):  # TODO group by prefix, showing W and b side by side
            cols.setdefault(key[:-2] + key[-1] if len(key) > 1 and key[-2] in "bW" else key, []).append((key, value))
    _, axes = plt.subplots(2, len(cols), figsize=(5*len(cols), 10))  # TODO https://stackoverflow.com/a/13784887/223267
    plt.tight_layout()
    for j, col in enumerate(tqdm(cols.values(), unit="param", desc=filename)):
        for i in range(2):
            axis = axes[i, j] if len(values) > 1 else axes
            if len(col) <= i:
                plt.delaxes(axis)
            else:
                plt.sca(axis)
                key, value = col[i]
                plt.colorbar(plt.pcolormesh(smooth(value)))
                for pattern, repl in REPLACEMENTS:  # TODO map 0123->ifoc
                    key = re.sub(pattern, repl, key)
                plt.title(key)
                for axis in (plt.gca().xaxis, plt.gca().yaxis):
                    axis.set_major_locator(MaxNLocator(integer=True))
    output_file = filename + ".png"
    plt.savefig(output_file)
    plt.clf()
    print("Saved '%s'." % output_file)


def main():
    argparser = ArgParser(description="Load TUPA model and visualize, saving to .png file.")
    argparser.add_argument("models", nargs="+", help="model file basename(s) to load")
    args = argparser.parse_args()
    for filename in args.models:

        model = load_model(filename)