From d1ef01e13e67bc13537be7feadbe05d27ae94810 Mon Sep 17 00:00:00 2001
From: Russell Jarvis <rjjarvis@asu.edu>
Date: Mon, 22 Jun 2020 12:27:52 +1000
Subject: [PATCH] Delete plotting_author_versus_distribution.py
---
plotting_author_versus_distribution.py | 215 -------------------------
1 file changed, 215 deletions(-)
delete mode 100644 plotting_author_versus_distribution.py
diff --git a/plotting_author_versus_distribution.py b/plotting_author_versus_distribution.py
deleted file mode 100644
index c758cd6..0000000
--- a/plotting_author_versus_distribution.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import pickle
-import copy
-import matplotlib as mpl
-import numpy as np
-import pandas as pd
-mpl.use("Agg")
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-
-def find_nearest(array, value):
- array = np.asarray(array)
- idx = (np.abs(array - value)).argmin()
- return idx
-
-bmark = pickle.load(open('benchmarks.p','rb'))
-NAME,ar = pickle.load(open('more_authors_results.p','rb'))
-#print(ar)
-print(bmark)
-import pdb; pdb.set_trace()
-import os
-
-if os.path.exists('traingDats.p'):
- with open('traingDats.p','rb') as f:
- trainingDats = pickle.load(f)
-else:
- from ..Examples import use_training
- with open('traingDats.p','rb') as f:
- trainingDats = pickle.load(f)
-
-def other_files():
- NAME = str('J. Bryan Henderson')
-
- brian = pickle.load(open('ben_results.p','rb'))
- ar1 = brian.T.to_dict().values()
-
- arp = pickle.load(open('author_results_processed.p','rb'))
- arp.pop('rgerkin',None)
-
- #import pdb; pdb.set_trace()
- for NAME in arp.keys():
- ar = []
- for i in range(0,len(arp[NAME].values())):
- ar.append(list(arp[NAME].values())[i])
- traingDats.extend(ar)
-
-standard_sci = [ t['standard'] for t in trainingDats ]
-ar = [ t for t in ar if type(t) is type({})]
-ar = [ t for t in ar if 'standard' in t.keys()]
-#print(ar)
-xys = [ (h.get_x(),h.get_height()) for h in sns.distplot(standard_sci).patches ]
-# this plot not used yet.
-
-fig = plt.figure()
-ax1 = fig.add_subplot(111)
-#print(ar)
-mean_ = np.mean([a['standard'] for a in ar])
-min_ = np.min([a['standard'] for a in ar])
-max_ = np.max([a['standard'] for a in ar])
-std_ = np.std([a['standard'] for a in ar])
-stats_items = [mean_,min_,max_]
-
-#import pdb
-#pdb.set_trace()
-g = sns.distplot(standard_sci, label="Readability Index")
-
-
-histogram_content = [x[0] for x in xys]
-height_content = np.array([x[1] for x in xys])
-
-hc = np.array(histogram_content)
-
-# code for plotting std deviation.
-sub_set = np.where((histogram_content>=mean_-std_) & (histogram_content<=mean_+std_))
-x_sub_set = np.array(histogram_content)[sub_set]
-
-std_plot_ind = height_content[sub_set]
-sub_set = sub_set[0].tolist()
-
-assert len(sub_set) < len(histogram_content)
-
-#vertical_postions = map()
-def get_heights(stats_items,histogram_content,x_sub_set):
- vertical_postions_indexs = []
- for i in stats_items:
- vertical_postions_indexs.append(find_nearest(histogram_content, i))
- bin_width_offset = (xys[1][0] - xys[0][0])/2.0
- x_sub_set = [ i+bin_width_offset for i in x_sub_set ]
-
-
- heights = []
- for i in vertical_postions_indexs:
- heights.append(xys[i][1])
- return heights, bin_width_offset
-
-bmark_stats_items = [ b['standard'] for b in bmark ]
-categories = [ "upgoer 5", "Readibility Declining Over Time","Science of Writing","Post Modern Essay Generator","G Nicholas"]
-#categories = [b['link'] for b in bmark]
-bmark_heights, bwo = get_heights(bmark_stats_items,histogram_content,x_sub_set)
-heights, bwo = get_heights(stats_items,histogram_content,x_sub_set)
-bmark_stats_items = [i+bwo for i in bmark_stats_items]
-sub_set = [i+bwo for i in sub_set]
-print(heights)
-mean_a = mean_# + bin_width_offset
-min_a = min_ #+ bin_width_offset
-max_a = max_ #+ bin_width_offset
-#std_a = mean_ + bin_width_offset
-index = find_nearest(histogram_content, mean_)
-#mean_link = histogram_content[index]['link']
-index = find_nearest(histogram_content, min_)
-#min_link = histogram_content[index]['link']
-index = find_nearest(histogram_content, max_)
-#max_link = histogram_content[index]['link']
-
-#print(bmarks)
-#import pdb; pdb.set_trace()
-
-#bmark_stats_items = [ b['standard'] for b in bmark ]
-#bmark_heights = get_heights(bmark_stats_items,histogram_content,x_sub_set)
-
-
-benchmarks = pd.DataFrame({
-'benchmarks': bmark_stats_items,
- 'CDF': bmark_heights
- })
-
-author_stats =[i+bwo for i in [mean_,min_,max_]]
-data0 = pd.DataFrame({
-'mean, min, maximum': author_stats,
- 'CDF': heights
- })
-
-
-data2 = pd.DataFrame({
-'Standard Reading Level': [mean_a+bwo],
- 'CDF': [heights[0]]
- })
-
-
-data1 = pd.DataFrame({
-'Standard Reading Level': x_sub_set,
- 'CDF': std_plot_ind
- })
-
-legend_properties = {'weight':'bold','size':8}
-
-ax = sns.regplot(data=benchmarks, x="benchmarks", y="CDF", fit_reg=False, marker="o", color="green")
-
-
-#bbox_props = dict(boxstyle="rarrow", fc=(0.8,0.9,0.9), ec="b", lw=2)
-
-#t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=90,
-# size=15,
-# bbox=bbox_props)
-#import pdb; pdb.set_trace()
-#bmark_heights.reverse()
-for i in bmark_heights:
- print(i)
-#import pdb
-#pdb.set_trace()
-cnt=0
-for i,j,k in zip(bmark_stats_items[0:-1],bmark_heights[0:-1],categories):
- print(i,j,k)
- if cnt==1:
- j=j+0.02
- ax.text(i+bwo,j,k, rotation=0)
-
- else:
- #j=j+0.1
- print(j)
- ax.text(i,j,k, rotation=0)
- cnt +=1
-cnt = 0
-for i,j,k in zip(author_stats,heights,[str(NAME)+' mean',str(NAME)+' min',str(NAME)+' max']):
- print(i,j,k)
- #if cnt==3:
- # ax.text(i+bwo,j,k)
-
- #else:
- #j=j+0.15
- print(j)
- ax.text(i,j,k, rotation=0)
- cnt +=1
-
-ax = sns.regplot(data=data0, x="mean, min, maximum", y="CDF", fit_reg=False, marker="o", color="blue")
-#ax = sns.regplot(x='Standard Reading Level', y='CDF',data=data1, fit_reg=False, marker="o", color="green")#, data=fmri)
-ax = sns.regplot(data=data2, x="Standard Reading Level", y="CDF", fit_reg=False, marker="o", color="red")
-
-legendMain=ax.legend(labels=[str("std deviation")], prop=legend_properties,loc='upper right')
-
-legendSide0=ax.legend(labels=[NAME],prop=legend_properties,loc='center right')
-legendSide1=ax.legend(labels=[str('Number of Documents: '+str(len(ar)))],prop=legend_properties,loc='upper left')
-#print(len(ar))
-
-
-legendMain=ax.legend(labels=[str("ART Corpus+ other scholar authors")], prop=legend_properties,loc='upper right')
-ax.add_artist(legendMain)
-ax.add_artist(legendSide0)
-ax.add_artist(legendSide1)
-#g=sns.clustermap(corrmat, vmax=.8, square=True)
-print(ax.get_xticklabels())
-rotation = 90
-#for i, ax in enumerate(legendMain.fig.axes): ## getting all axes of the fig object
-# ax.set_xticklabels([a['link'] for a in ar], rotation = rotation)
-
-
-#g.fig.show()
-
-
-locs, labels = plt.xticks()
-plt.setp(labels, rotation=45)
-#print(NAME)
-#import pdb; pdb.set_trace()
-plt.savefig(str(NAME)+'_author_readability.png')
-plt.show()
--
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