import numpy as np

import matplotlib.pyplot as pl

from language_processing import *

from extract_data import *

from loader import get_data

from extract_data import get_doc_rel_dates, get_ef_values, get_doc_keywords

import re

def plot_num_docs(patient_range = range(90)):

    rel_dates = dict()

    keyword_counts = dict()

    keywords = ['ef\w+(.+)%', 'ejection fraction:\w*(.+)%', 'ef of (.+)%','ejection fraction of (.+)%', 'ef is (.+)%', 'ef:\w*(.+)%','ejection fraction is (.+)%', 'ef:\w*(.+)%']

    overall_counts = dict()

    for i in patient_range:

        if i % 25 == 0:

            print i

        data = get_data([i])[0]

        rel_dates = get_doc_rel_dates(data, rel_dates, True)

        #keyword_counts = get_doc_keywords(data, keywords, keyword_counts, True)

        #ef_occurances = get_ef_values(data, car_only = True)

        if False and len(ef_occurances) > 2: #REMOVE FALSE TO SEE PLOTS

            dates, efs = zip(*ef_occurances)

            pl.figure()

            pl.scatter(dates, efs)

            pl.show()

        #for doc in keyword_counts:

        #    s = 0

        #    for key in keyword_counts[doc]:

        #        s += len(keyword_counts[doc][key])

        #    

        #    if not doc in overall_counts:

        #        overall_counts[doc] = [s]

        #    else:

        #        overall_counts[doc] += [s]

   # print overall_counts['Car']

   # pl.figure()

   # pl.hist(overall_counts['Car'])

   # pl.show()

    #for keyword in keyword_counts:

    #    print keyword, ": ", str(sum(keyword_counts[keyword]))

    #for doc in keyword_counts:

    #    print doc

    #    for keyword in keyword_counts[doc]:

    #        print "\t", keyword, ": ", str(sum(keyword_counts[doc][keyword]))

    note_deltas = []

    struct_deltas = []

    for doc_type in rel_dates:

        if is_note_doc(doc_type):

            note_deltas += [x.days for x in rel_dates[doc_type]]

        else:

            struct_deltas += [x.days for x in rel_dates[doc_type]]

    for word in keyword_counts:

        keyword_counts[word] = [x.days for x in rel_dates[doc_type]]

    bins = 100    

    print

    print "Notes: ", len(note_deltas)

    print "Structs: ", len(struct_deltas)

    pl.figure()

    h = pl.hist([note_deltas, struct_deltas], bins,stacked = True, color = ['blue', 'red'], label = ['Number of sentences in\nunstructured notes', 'Number of structured entries'])

    pl.legend(loc = 2)

    pl.title("Frequency of Occurances of New Data in Patient")

    pl.xlabel("Days Since Implant Procedure")

    pl.ylabel("Number of Pieces of Information")

    pl.show()

    for word in keyword_counts:

        pl.figure()

        pl.hist(keyword_counts[word], bins, color = ['blue'])

        pl.title("Occurances of " + word + " in corpus at time from procedure")

        pl.show()