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vis.py
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vis.py
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import argparse
import math
import pandas as pd
import numpy
from scipy.stats import gmean
from numpy import mean
from bokeh.plotting import figure
from bokeh.io import curdoc, output_file, save
from bokeh.resources import CDN
from bokeh.embed import file_html, components
from bokeh.layouts import row, column
from bokeh.models import HoverTool, CustomJS, ColumnDataSource, FactorRange, Panel, Tabs
from bokeh.models.widgets import RadioButtonGroup, CheckboxButtonGroup, Select
from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
from bokeh.events import ButtonClick
_FIELDS = ['Frequency of Reuse (Exact Matches)',
'Frequency of Reuse (0-0.1)',
'Frequency of Reuse (0-0.25)',
'None',
'ANGER',
'ANTICIPATION',
'DISGUST',
'FEAR',
'JOY',
'SADNESS',
'SURPRISE',
'TRUST',
'NEGATIVE',
'POSITIVE',]
# 'None']
_AGG_FUNCS = [lambda x: gmean(x + 1) - 1] * 3
_AGG_FUNCS += [mean] * 11
# Possibly dead code now. TODO: Check and if so, remove.
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--static', action='store_true',
default=False,
help="save a full html file")
# parser.add_argument('-o', '--output', action='store',
# default='reuse.html',
# help="output filename")
args = parser.parse_args()
args.words_per_chunk = 140
args.data_path = 'fandom-data.csv'
title = 'Average Quantity of Text Reuse by {}-word Section'
title = title.format(args.words_per_chunk)
args.title = title
args.out_filename = 'star-wars-reuse.html'
return args
def unnan(val):
# Pandas annoyingly converts the string 'nan' into a floating
# point nan value, even in an all-string column.
if isinstance(val, float) and math.isnan(val):
return 'nan'
else:
return val
def word_formatter(names=None):
if names is None:
names = []
punctuation = [',', '.', '!', '?', '\'', '"', ':', '-', '--']
endpunctuation = ['.', '!', '?', '"', '...', '....', '--']
contractions = ['\'ve', '\'m', '\'ll', '\'re', '\'s', '\'t', 'n\'t', 'na']
capitals = ['i']
def span(content, highlight=None):
if highlight is None:
return '<span>{}</span>'.format(content)
else:
style = 'background-color: rgba(16, 96, 255, {:04.3f})'.format(highlight)
return '<span style="{}">{}</span>'.format(style, content)
def format_word(word, prev_word, character, new_char, new_scene, highlight=None):
character = unnan(character).upper()
word = unnan(word)
parts = []
if new_scene:
parts.append(span('-- next scene--<br \>'))
if new_char:
parts.append('\n')
parts.append(span(' ' + character.upper() + ': '))
if word in punctuation or word in contractions:
# no space before punctuation
parts.append(span(word, highlight))
elif not prev_word or prev_word in endpunctuation:
# capitalize first word of sentence
parts.append(span(' ' + word.capitalize(), highlight))
elif word in capitals:
# format things like 'i'
parts.append(span(' ' + word.upper(), highlight))
elif word.capitalize() in names:
# format names
parts.append(span(' ' + word.capitalize(), highlight))
else:
# all other words
parts.append(span(' ' + word, highlight))
return ''.join(parts)
return format_word
def chart_cols(fandom_data, words_per_chunk):
words = fandom_data['LOWERCASE'].tolist()
prevwords = [None] + words[:-1]
chars = fandom_data['CHARACTER'].tolist()
newchar = fandom_data['CHARACTER'][:-1].values != fandom_data['CHARACTER'][1:].values
newchar = [True] + list(newchar)
newscene = fandom_data['SCENE'].values
newscene[numpy.isnan(newscene)] = 0
newscene = fandom_data['SCENE'][:-1].values != fandom_data['SCENE'][1:].values
newscene = [False] + list(newscene)
highlights = fandom_data['Frequency of Reuse (Exact Matches)'].tolist()
chunks = (fandom_data.index // words_per_chunk).tolist()
chunkmax = {}
global_max = max(highlights)
for h, c in zip(highlights, chunks):
if c not in chunkmax or chunkmax[c] < h:
#chunkmax[c] = h
chunkmax[c] = global_max
# highlights = [math.log(1 + h, 1.25) / (1.6 * math.log(1 + chunkmax[c], 1.25)) if chunkmax[c] > 0 else 0
# for h, c in zip(highlights, chunks)]
highlights = [(1 + h) ** 0.33 / (1.6 * (1 + chunkmax[c]) ** 0.33) if chunkmax[c] > 0 else 0
for h, c in zip(highlights, chunks)]
wform = word_formatter()
spans = list(map(wform, words, prevwords, chars, newchar, newscene, highlights))
fandom_data = fandom_data.assign(
**{'None': fandom_data[_FIELDS[0]].values * 0}
)
# fandom_data = fandom_data.assign(
# **{'None': fandom_data[_FIELDS[0]].values * 0}
# )
character_cols = [x for x in fandom_data.columns if x.startswith("CHARACTER_")]
chart_cols = fandom_data[_FIELDS + character_cols]
chart_cols = chart_cols.assign(chunk=chunks)
chart_cols = chart_cols.assign(span=spans)
return chart_cols
def join_wrap(seq):
lines = []
line = []
last_br = 0
for span in seq:
if '\n' in span or last_br > 7 and '> ' in span:
# Convert newlines to div breaks. Also insert breaks
# whenever we've seen 7 words and there is some
# leading whitespace in the current span.
lines.append(''.join(line))
line = []
last_br = 0
else:
last_br += 1
line.append(span)
tail = ''.join(line)
if tail.strip():
lines.append(tail)
return '\n'.join('<div>{}</div>'.format(l) for l in lines)
def chart_pivot(chart_cols):
character_cols = [x for x in chart_cols.columns if x.startswith("CHARACTER_")]
fields = _FIELDS + character_cols + ['span']
aggfuncs = _AGG_FUNCS + [mean] * len(character_cols) + [join_wrap]
table = pd.pivot_table(
chart_cols,
values=fields,
index=chart_cols.chunk,
aggfunc=dict(zip(fields, aggfuncs))
)
# apparently when you create a pandas pivot table, it will automatically
# sort your columns alphabetically (which is dumb). This is their work
# around, where you literally give the table the fields you already gave
# them, so that they "reindex" it.
return table.reindex(fields, axis=1)
def build_bar_plot(data_path, words_per_chunk, title='Reuse'):
#Read in from csv
flat_data = pd.read_csv(data_path)
flat_data = chart_cols(flat_data, words_per_chunk)
flat_data = chart_pivot(flat_data)
# Scale so that both maxima have the same height
reuse_y = flat_data['Frequency of Reuse (Exact Matches)']
emo_y = flat_data['None']
reuse_max = reuse_y.values.max()
emo_max = emo_y.values.max()
#Make ratio work
ratio_denom = min(reuse_max, emo_max)
ratio_num = max(reuse_max, emo_max)
ratio = ratio_num / ratio_denom if ratio_denom > 0 else 1
to_scale = reuse_y if reuse_max < emo_max else emo_y
to_scale *= ratio
# Create data columns
grouped_x = [(str(x), key)
for x in flat_data.index
for key in ('Reuse', 'Emotion')]
y = [re for re_pair in zip(reuse_y, emo_y) for re in re_pair]
span = zip(flat_data.span, flat_data.span)
span = [s for s_pair in span for s in s_pair]
flat_data_source = ColumnDataSource(flat_data)
source = ColumnDataSource(dict(x=grouped_x,
y=y,
span=span))
plot = figure(x_range=FactorRange(*grouped_x),
plot_width=800, plot_height=600,
title=title, tools="hover")
# Turn off ticks, major labels, and x grid lines, etc.
# Axis settings:
plot.xaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_tick_line_color = None
plot.xaxis.minor_tick_line_color = None
# CategoricalAxis settings:
plot.xaxis.group_text_font_size = '0pt'
plot.xaxis.separator_line_color = None
# Grid settings:
plot.xgrid.grid_line_color = None
plot.ygrid.minor_grid_line_color = 'black'
plot.ygrid.minor_grid_line_alpha = 0.03
hover = plot.select(dict(type=HoverTool))
hover.tooltips = "<div>@span{safe}</div>"
plot.vbar(x='x',
width=1.0,
bottom=0,
source=source,
top='y',
line_color='white',
fill_color=factor_cmap('x', palette=Spectral6,
factors=['Reuse', 'Emotion'],
start=1, end=2))
reuse_button_group = RadioButtonGroup(
labels= [_FIELDS[0]] + ["Frequency of Reuse (Fuzzy Matches)"], button_type='primary',
active=0
)
emotion_button_group = RadioButtonGroup(
labels=_FIELDS[3:], button_type = "success",
active=0
)
callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group
),
code="""
var reuse = reuse_button_group.labels[reuse_button_group.active];
if (reuse == "Frequency of Reuse (Fuzzy Matches)") {
reuse = "Frequency of Reuse (0-0.25)";
}
var emo = emotion_button_group.labels[emotion_button_group.active];
var reuse_data = flat_data_source.data[reuse].slice(); // Copy
var emo_data = flat_data_source.data[emo].slice(); // Copy
var reuse_max = Math.max.apply(Math, reuse_data);
var emo_max = Math.max.apply(Math, emo_data);
var ratio = 0;
var to_scale = null;
if (emo_max > reuse_max) {
to_scale = reuse_data;
ratio = emo_max / reuse_max;
} else {
to_scale = emo_data;
if (emo_max > 0) {
ratio = reuse_max / emo_max;
} else {
ratio = 1;
}
}
for (var i = 0; i < to_scale.length; i++) {
to_scale[i] *= ratio;
}
var x = source.data['x'];
var y = source.data['y'];
for (var i = 0; i < x.length; i++) {
if (i % 2 === 0) {
// This is a reuse bar
y[i] = reuse_data[i / 2];
} else {
// This is an emotion bar
y[i] = emo_data[(i - 1) / 2];
}
}
source.change.emit();
"""
)
reuse_button_group.js_on_change('active', callback)
emotion_button_group.js_on_change('active', callback)
layout = column(reuse_button_group, emotion_button_group, plot)
tab1 = Panel(child=layout, title='Bar')
return tab1
def build_line_plot(data_path, words_per_chunk, title='Reuse'):
#Read in from csv
flat_data = pd.read_csv(data_path)
flat_data = chart_cols(flat_data, words_per_chunk)
flat_data = chart_pivot(flat_data)
# Scale so that both maxima have the same height
reuse_y = flat_data['Frequency of Reuse (Exact Matches)']
emo_y = flat_data['None']
char_y = flat_data['None']
reuse_max = reuse_y.values.max()
emo_max = emo_y.values.max()
char_max = char_y.values.max()
#Make ratio work
ratio_denom = min(char_max, min(reuse_max, emo_max))
ratio_num = max(char_max, max(reuse_max, emo_max))
ratio = ratio_num / ratio_denom if ratio_denom > 0 else 1
if reuse_max < emo_max and reuse_max < char_max:
to_scale = reuse_y
elif emo_max < char_max and emo_max < reuse_max:
to_scale = emo_y
else:
to_scale = char_y
to_scale *= ratio
# Create data columns
x = [str(i) for i in flat_data.index]
reuse_y=reuse_y
reuse_zero = len(reuse_y) * [0]
span = flat_data.span
flat_data_source = ColumnDataSource(flat_data)
source = ColumnDataSource(dict(x=x,
reuse_y=reuse_y,
emo_y=emo_y,
char_y=char_y,
reuse_zero=reuse_zero,
span=span))
plot = figure(x_range=FactorRange(*x),
plot_width=800, plot_height=600,
title=title, tools="hover")
# Turn off ticks, major labels, and x grid lines, etc.
# Axis settings:
plot.xaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_tick_line_color = None
plot.xaxis.minor_tick_line_color = None
# CategoricalAxis settings:
plot.xaxis.group_text_font_size = '0pt'
plot.xaxis.separator_line_color = None
# Grid settings:
plot.xgrid.grid_line_color = None
plot.ygrid.minor_grid_line_color = 'black'
plot.ygrid.minor_grid_line_alpha = 0.03
hover = plot.select(dict(type=HoverTool))
hover.tooltips = "<div>@span{safe}</div>"
plot.varea(x='x', source = source, y1 = 'reuse_y', y2 = 'reuse_zero', fill_color = Spectral6[0], fill_alpha = 0.6)
plot.line(x='x', source = source, y = 'reuse_y', line_color = Spectral6[0], line_alpha = 0.0)
plot.line(x='x', line_width=2.0, source=source, y='emo_y', line_color = Spectral6[1])
plot.line(x='x', line_width=2.0, source=source, y='char_y', line_color = 'red')
reuse_button_group = RadioButtonGroup(
labels=[_FIELDS[0]] + ["Frequency of Reuse (Fuzzy Matches)"],
button_type='primary',
active=0
)
emotion_button_group = RadioButtonGroup(
labels=_FIELDS[3:],
button_type='success',
active=0
)
char_button_group = RadioButtonGroup(
labels= ['None'] + [x.replace("CHARACTER_", "") for x in flat_data.columns if x.startswith("CHARACTER_")],
button_type='danger',
active=0
)
callback_code="""
var reuse = reuse_button_group.labels[reuse_button_group.active];
if (reuse == "Frequency of Reuse (Fuzzy Matches)") {
reuse = "Frequency of Reuse (0-0.25)";
}
var emo = emotion_button_group.labels[emotion_button_group.active];
var char = char_button_group.labels[char_button_group.active];
var reuse_data = flat_data_source.data[reuse].slice(); // Copy
var emo_data = flat_data_source.data[emo].slice(); // Copy
if (char == "None") {
var char_data = flat_data_source.data["None"].slice();
} else {
var char_data = flat_data_source.data["CHARACTER_" + char].slice();
} // Copy
var reuse_max = Math.max.apply(Math, reuse_data);
var emo_max = Math.max.apply(Math, emo_data);
var char_max = Math.max.apply(Math, char_data);
var ratio = 0;
var to_scale = null;
var to_scale_other = null;
if (emo_max > reuse_max && emo_max > char_max) {
to_scale = reuse_data;
to_scale_also = char_data;
ratio_one = emo_max / reuse_max;
ratio_two = emo_max / char_max;
} else if (char_max > emo_max && char_max > reuse_max) {
to_scale = reuse_data;
to_scale_also = emo_data;
ratio_one = char_max / reuse_max;
ratio_two = char_max / emo_max;
} else {
to_scale = emo_data;
to_scale_also = char_data;
ratio_one = reuse_max / emo_max;
ratio_two = reuse_max / char_max;
}
for (var i = 0; i < to_scale.length; i++) {
to_scale[i] *= ratio_one;
to_scale_also[i] *= ratio_two;
}
var x = source.data['x'];
var reuse_y = source.data['reuse_y'];
var emo_y = source.data['emo_y'];
var char_y = source.data['char_y']
for (var i = 0; i < x.length; i++) {
reuse_y[i] = reuse_data[i];
emo_y[i] = emo_data[i];
char_y[i] = char_data[i];
}
source.change.emit();
if (char_button_group.active == 0 || emotion_button_group.active == 0) {
return;
}
if (other_button_group) {
other_button_group.active = 0;
}
"""
reuse_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
char_button_group=char_button_group,
other_button_group=None
), code = callback_code)
emo_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
char_button_group=char_button_group,
other_button_group=char_button_group
), code = callback_code)
char_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
char_button_group=char_button_group,
other_button_group=emotion_button_group
), code = callback_code)
reuse_button_group.js_on_change('active', reuse_callback)
emotion_button_group.js_on_change('active', emo_callback)
char_button_group.js_on_change('active', char_callback)
layout = column(reuse_button_group, emotion_button_group, char_button_group, plot)
tab1 = Panel(child=layout, title='Line')
return tab1
def build_line_plot_compare(data_path, words_per_chunk, title='Degree of Reuse'):
#Read in from csv
flat_data = pd.read_csv(data_path)
flat_data = chart_cols(flat_data, words_per_chunk)
flat_data = chart_pivot(flat_data)
# Scale so that both maxima have the same height
reuse_y = flat_data['Frequency of Reuse (Exact Matches)']
emo_y = flat_data['None']
first_char_y = flat_data['None']
second_char_y = flat_data['None']
# reuse_max = reuse_y.values.max()
# emo_max = emo_y.values.max()
# char_max = char_y.values.max()
# mult_char_max = mult_char_y.values.max()
#
# #Make ratio work
# ratio_denom = min(mult_char_max, min(reuse_max, emo_max))
# ratio_num = max(mult_char_max, max(reuse_max, emo_max))
# ratio = ratio_num / ratio_denom if ratio_denom > 0 else 1
# if reuse_max < emo_max and reuse_max < mult_char_max:
# to_scale = reuse_y
# elif emo_max < mult_char_max and emo_max < reuse_max:
# to_scale = emo_y
# else:
# to_scale = mult_char_y
# to_scale *= ratio
# Create data columns
x = [str(i) for i in flat_data.index]
reuse_y=reuse_y
reuse_zero = len(reuse_y) * [0]
span = flat_data.span
flat_data_source = ColumnDataSource(flat_data)
source = ColumnDataSource(dict(x=x,
reuse_y=reuse_y,
emo_y=emo_y,
first_char_y=first_char_y,
second_char_y=second_char_y,
reuse_zero=reuse_zero,
span=span))
plot = figure(x_range=FactorRange(*x),
plot_width=800, plot_height=600,
title=title, tools="hover")
# Turn off ticks, major labels, and x grid lines, etc.
# Axis settings:
plot.xaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_tick_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.major_label_text_font_size = '0pt'
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
# CategoricalAxis settings:
plot.xaxis.group_text_font_size = '0pt'
plot.xaxis.separator_line_color = None
# Grid settings:
plot.xgrid.grid_line_color = None
# plot.ygrid.minor_grid_line_color = 'black'
# plot.ygrid.minor_grid_line_alpha = 0.03
plot.xaxis.axis_label = 'Beginning of Script ← → End of Script'
plot.yaxis.axis_label = 'Low Reuse Medium Reuse High Reuse'
hover = plot.select(dict(type=HoverTool))
hover.tooltips = "<div>@span{safe}</div>"
plot.varea(x='x', source = source, y1 = 'reuse_y', y2 = 'reuse_zero', fill_color = Spectral6[0], fill_alpha = 0.6)
plot.line(x='x', source = source, y = 'reuse_y', line_color = Spectral6[0], line_alpha = 0.0)
plot.line(x='x', line_width=2.0, source=source, y='emo_y', line_color = 'red')
plot.line(x='x', line_width=2.0, source=source, y='first_char_y', line_color = Spectral6[1])
plot.line(x='x', line_width=2.0, source=source, y='second_char_y', line_color = '#F0AD4E')
reuse_button_group = RadioButtonGroup(
labels=[_FIELDS[0]] + ["Frequency of Reuse (Fuzzy Matches)"],
button_type='primary',
active=0
)
emotion_button_group = CheckboxButtonGroup(
labels= _FIELDS[4:],
button_type='danger',
active=[],
)
first_char_button_group = CheckboxButtonGroup(
labels= [x.replace("CHARACTER_", "") for x in flat_data.columns if x.startswith("CHARACTER_")],
button_type='success',
active=[]
)
second_char_button_group = CheckboxButtonGroup(
labels= [x.replace("CHARACTER_", "") for x in flat_data.columns if x.startswith("CHARACTER_")],
button_type='warning',
active=[]
)
callback_code="""
var reuse = reuse_button_group.labels[reuse_button_group.active];
if (reuse == "Frequency of Reuse (Fuzzy Matches)") {
reuse = "Frequency of Reuse (0-0.25)";
}
var second_char = [];
for (i = 0; i < second_char_button_group.active.length; i++) {
second_char.push(second_char_button_group.labels[second_char_button_group.active[i]]);
}
var first_char = [];
for (i = 0; i < first_char_button_group.active.length; i++) {
first_char.push(first_char_button_group.labels[first_char_button_group.active[i]]);
}
var emo_arr = [];
for (i = 0; i < emotion_button_group.active.length; i++) {
emo_arr.push(emotion_button_group.labels[emotion_button_group.active[i]]);
}
var reuse_data = flat_data_source.data[reuse].slice(); // Copy
if (second_char_button_group.active.length > 1) {
if (second_char_button_group.active[0] == window.secondprev) {
second_char_button_group.active.splice(0,1);
second_char.splice(0,1);
window.secondprev = second_char_button_group.active[0];
} else {
second_char_button_group.active.splice(1,1);
second_char.splice(1,1);
window.secondprev = second_char_button_group.active[0];
}
} else {
window.secondprev = second_char_button_group.active[0];
}
if (first_char_button_group.active.length > 1) {
if (first_char_button_group.active[0] == window.firstprev) {
first_char_button_group.active.splice(0,1);
first_char.splice(0,1);
window.firstprev = first_char_button_group.active[0];
} else {
first_char_button_group.active.splice(1,1);
first_char.splice(1,1);
window.firstprev = first_char_button_group.active[0];
}
} else {
window.firstprev = first_char_button_group.active[0];
}
if (emotion_button_group.active.length > 1) {
if (emotion_button_group.active[0] == window.thirdprev) {
emotion_button_group.active.splice(0,1);
emo_arr.splice(0,1);
window.thirdprev = emotion_button_group.active[0];
} else {
emotion_button_group.active.splice(1,1);
emo_arr.splice(1,1);
window.thirdprev = emotion_button_group.active[0];
}
} else {
window.thirdprev = emotion_button_group.active[0];
}
//for (i = 0; i < mult_char.length; i++) {
// if (mult_char[i] == "Clear") {
// listOfLists.push(flat_data_source.data["None"].slice());
// } else {
// listOfLists.push(flat_data_source.data["CHARACTER_" + mult_char[i]].slice());
// }
//}
//
//for (i = 0; i < char.length; i++) {
// if (char[i] == "Clear") {
// charListOfLists.push(flat_data_source.data["None"].slice());
// } else {
// charListOfLists.push(flat_data_source.data["CHARACTER_" + char[i]].slice());
// }
//}
//
//var emoListOfLists = [];
//for (i = 0; i < emo_arr.length; i++) {
// if (emo_arr[i] == "Clear") {
// emoListOfLists.push(flat_data_source.data["None"].slice());
// } else {
// emoListOfLists.push(flat_data_source.data[emo_arr[i]].slice());
// }
//}
function fill(a, b) {
for (i = 0; i < b.length; i++) {
a.push(flat_data_source.data[b[i]].slice());
}
return a;
}
function fillChar(a, b) {
for (i = 0; i < b.length; i++) {
a.push(flat_data_source.data["CHARACTER_" + b[i]].slice());
}
return a;
}
function zip(a) {
if (a.length == 0) {
return [];
}
var output = [];
var length = a[0].length;
for (i = 0; i < length; i++) {
var newRow = [];
for (j = 0; j < a.length; j++) {
newRow.push(a[j][i]);
}
output.push(newRow);
}
return output;
}
function gMean(a) {
var starter = 1;
for (i = 0; i < a.length; i++) {
starter = starter * a[i];
}
if (starter == 0) {
return 0;
} else {
return Math.pow(starter, 1/a.length);
}
}
var firstCharListOfLists = [];
var secondCharListOfLists = [];
var emoListOfLists = [];
var first_char_data = zip(fillChar(firstCharListOfLists, first_char)).map(gMean);
var emo_data = zip(fill(emoListOfLists, emo_arr)).map(gMean);
var second_char_data = zip(fillChar(secondCharListOfLists, second_char)).map(gMean);
var reuse_max = Math.max.apply(Math, reuse_data);
var emo_max = Math.max.apply(Math, emo_data);
var first_char_max = Math.max.apply(Math, first_char_data);
var second_char_max = Math.max.apply(Math, second_char_data);
for (var i = 0; i < second_char_data.length; i++) {
second_char_data[i] /= second_char_max;
}
for (var i = 0; i < first_char_data.length; i++) {
first_char_data[i] /= first_char_max;
}
for (var i = 0; i < emo_data.length; i++) {
emo_data[i] /= emo_max;
}
for (var i = 0; i < reuse_data.length; i++) {
reuse_data[i] /= reuse_max;
}
var x = source.data['x'];
var reuse_y = source.data['reuse_y'];
var emo_y = source.data['emo_y'];
var first_char_y = source.data['first_char_y'];
var second_char_y = source.data['second_char_y']
for (var i = 0; i < x.length; i++) {
reuse_y[i] = reuse_data[i];
emo_y[i] = emo_data[i];
first_char_y[i] = first_char_data[i];
second_char_y[i] = second_char_data[i];
}
source.change.emit();
"""
reuse_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
first_char_button_group=first_char_button_group,
second_char_button_group=second_char_button_group,
other_button_group=None
), code = callback_code)
emo_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
first_char_button_group=first_char_button_group,
second_char_button_group=second_char_button_group,
other_button_group=first_char_button_group
), code = callback_code)
char_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
first_char_button_group=first_char_button_group,
second_char_button_group=second_char_button_group,
other_button_group=emotion_button_group
), code = callback_code)
mult_char_callback = CustomJS(
args=dict(
source=source,
flat_data_source=flat_data_source,
reuse_button_group=reuse_button_group,
emotion_button_group=emotion_button_group,
first_char_button_group=first_char_button_group,
second_char_button_group=second_char_button_group,
other_button_group=emotion_button_group
), code = callback_code)
reuse_button_group.js_on_change('active', reuse_callback)
emotion_button_group.js_on_change('active', emo_callback)
first_char_button_group.js_on_change('active', char_callback)
second_char_button_group.js_on_change('active', mult_char_callback)
layout = column(reuse_button_group, first_char_button_group, second_char_button_group, emotion_button_group, plot)
tab1 = Panel(child=layout, title='Both')
#return tab1
return layout
def build_line_plot_affect(data_path, words_per_chunk, title='Degree of Reuse'):
#Read in from csv
flat_data = pd.read_csv(data_path)
flat_data = chart_cols(flat_data, words_per_chunk)
flat_data = chart_pivot(flat_data)
# Scale so that both maxima have the same height
reuse_y = flat_data['Frequency of Reuse (Exact Matches)']
emo_y = flat_data['None']
char_y = flat_data['None']
mult_char_y = flat_data['None']
reuse_max = reuse_y.values.max()
emo_max = emo_y.values.max()
char_max = char_y.values.max()
mult_char_max = mult_char_y.values.max()
#Make ratio work
ratio_denom = min(mult_char_max, min(reuse_max, emo_max))
ratio_num = max(mult_char_max, max(reuse_max, emo_max))
ratio = ratio_num / ratio_denom if ratio_denom > 0 else 1
if reuse_max < emo_max and reuse_max < mult_char_max:
to_scale = reuse_y
elif emo_max < mult_char_max and emo_max < reuse_max:
to_scale = emo_y
else:
to_scale = mult_char_y
to_scale *= ratio
# Create data columns
x = [str(i) for i in flat_data.index]
reuse_y=reuse_y
reuse_zero = len(reuse_y) * [0]
span = flat_data.span
flat_data_source = ColumnDataSource(flat_data)
source = ColumnDataSource(dict(x=x,
reuse_y=reuse_y,
emo_y=emo_y,
char_y=char_y,
mult_char_y=mult_char_y,
reuse_zero=reuse_zero,
span=span,))
plot = figure(x_range=FactorRange(*x),
plot_width=800, plot_height=600,
title=title, tools="hover")
# Turn off ticks, major labels, and x grid lines, etc.
# Axis settings:
plot.xaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_tick_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.major_label_text_font_size = '0pt'
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
# CategoricalAxis settings:
plot.xaxis.group_text_font_size = '0pt'
plot.xaxis.separator_line_color = None
# Grid settings:
plot.xgrid.grid_line_color = None
# plot.ygrid.minor_grid_line_color = 'black'
# plot.ygrid.minor_grid_line_alpha = 0.03
plot.xaxis.axis_label = 'Beginning of Script ← → End of Script'
plot.yaxis.axis_label = 'Low Reuse Medium Reuse High Reuse'
hover = plot.select(dict(type=HoverTool))
hover.tooltips = "<div>@span{safe}</div>"
plot.varea(x='x', source = source, y1 = 'reuse_y', y2 = 'reuse_zero', fill_color = Spectral6[0], fill_alpha = 0.6)
plot.line(x='x', source = source, y = 'reuse_y', line_color = Spectral6[0], line_alpha = 0.0)
plot.line(x='x', line_width=2.0, source=source, y='emo_y', line_color = Spectral6[1])
plot.line(x='x', line_width=2.0, source=source, y='char_y', line_color = 'red')
plot.line(x='x', line_width=2.0, source=source, y='mult_char_y', line_color = 'red')
reuse_button_group = RadioButtonGroup(
labels=[_FIELDS[0]] + ["Frequency of Reuse (Fuzzy Matches)"],
button_type='primary',
active=0
)
emotion_button_group = CheckboxButtonGroup(
labels= ['Clear'] + _FIELDS[4:],
button_type='success',
active=[],
)
char_button_group = RadioButtonGroup(
labels= ['None'] + [x.replace("CHARACTER_", "") for x in flat_data.columns if x.startswith("CHARACTER_")],
button_type='danger',
active=0
)
mult_char_button_group = CheckboxButtonGroup(
labels= ['Clear'] + [x.replace("CHARACTER_", "") for x in flat_data.columns if x.startswith("CHARACTER_")],
button_type='danger',
active=[]
)
callback_code="""
var reuse = reuse_button_group.labels[reuse_button_group.active];
if (reuse == "Frequency of Reuse (Fuzzy Matches)") {
reuse = "Frequency of Reuse (0-0.25)";
}
//var emo = emotion_button_group.labels[emotion_button_group.active];
var char = char_button_group.labels[char_button_group.active];
var mult_char = [];
for (i = 0; i < mult_char_button_group.active.length; i++) {
mult_char.push(mult_char_button_group.labels[mult_char_button_group.active[i]]);
}
if (mult_char.includes("Clear")) {
mult_char = [];
mult_char_button_group.active = []
}
var emo_arr = [];
for (i = 0; i < emotion_button_group.active.length; i++) {
emo_arr.push(emotion_button_group.labels[emotion_button_group.active[i]]);
}
console.log(emo_arr);
if (emo_arr.includes("Clear")) {
emo_arr = [];
emotion_button_group.active = []
}