def init_mention_plot():
cprint('%s: init @ mentios barcharts...' % TAG, 'yellow', attrs=['bold'])
global mention_count
global mention_barplot
mention_count = count_mentions()
y = []
mentions = []
for (mention, freq) in mention_count:
y.append(freq)
mentions.append(mention)
x = np.arange(len(mentions))
source = ColumnDataSource(dict(
x=x,
top=y,
))
glyph = VBar(x='x', top='top', bottom=0, width=0.85, fill_color='#ff7f0e')
mention_barplot.add_glyph(source, glyph)
xaxis = LinearAxis()
xaxis.ticker = x
xaxis.major_label_overrides = {
i: mention
for i, mention in enumerate(mentions)
}
mention_barplot.add_layout(xaxis, 'below')
mention_barplot.xaxis.major_label_orientation = +np.pi / 2
yaxis = LinearAxis()
yaxis.axis_label = 'Overall number of @ mentions'
yaxis.axis_label_text_font_size = '14pt'
yaxis.ticker = np.linspace(0, max(y), 11, dtype=np.int)[1:]
mention_barplot.add_layout(yaxis, 'left')
mention_barplot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
mention_barplot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
def init_user_plot():
cprint('%s: init @ users barcharts...' % TAG, 'yellow', attrs=['bold'])
global user_count
global user_barplot
user_count = count_users()
y = []
users = []
for (user, freq) in user_count.items():
y.append(freq)
users.append(user)
x = np.arange(len(users))
wlist = []
for user in users:
prefixes = ['@' + user]
for prefix, freq in sorted(user_tweet_freq[user].items(),
key=lambda kv: kv[1],
reverse=True)[:10]:
prefixes.append(' %s:%d' % (prefix, freq))
wlist.append(list(prefixes))
source = ColumnDataSource(dict(x=x, top=y, wlist=wlist))
glyph = VBar(x='x', top='top', bottom=0, width=0.85, fill_color='#1f77b4')
user_barplot.add_glyph(source, glyph)
xaxis = LinearAxis()
xaxis.ticker = x
xaxis.major_label_overrides = {
i: '@' + user
for i, user in enumerate(users)
}
#xaxis.major_label_standoff = -35
user_barplot.add_layout(xaxis, 'below')
user_barplot.xaxis.major_label_orientation = +np.pi / 2
yaxis = LinearAxis()
yaxis.axis_label = 'Overall number of tweets per @'
yaxis.axis_label_text_font_size = '14pt'
yaxis.ticker = np.linspace(0, max(y), 11, dtype=np.int)[1:]
user_barplot.add_layout(yaxis, 'left')
user_barplot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
user_barplot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
def init_plot():
cprint('%s: init barcharts per neighbourhood...' % TAG,
'yellow',
attrs=['bold'])
init_wordcount()
global prefix_count
global wword_count
global word_barplots
global word_sources
global mapper
global svg_div
init_user_plot()
init_mention_plot()
count_words(prefix_count, wword_count)
# update colorbar
min_freq, max_freq = get_freq_range(prefix_count)
mapper['transform'].low = min_freq
mapper['transform'].high = max_freq
# update colorbar tickers
steps = 13
while max_freq < steps:
steps = steps // 2
color_bar.ticker = FixedTicker(
ticks=np.linspace(min_freq, max_freq, steps, dtype=np.int))
y = np.arange(config.NUM_WORD_BARS)
for i, (neigh, wcount) in enumerate(prefix_count.items()):
wordfreqlist = sorted(wcount.items(),
key=lambda kv: kv[1],
reverse=True)
x = []
prefixes = []
wlist = []
for prefix, freq in wordfreqlist[:config.NUM_WORD_BARS]:
x.append(freq)
prefixes.append(prefix)
wlist.append([' %s:%d' % (k,v) \
for (k,v) in sorted(wword_count[neigh][prefix].items(),
key=lambda kv:kv[1], reverse=True)[:5]])
color_index = np.round(
minmax_scale([np.mean(x[:5]), min_freq, max_freq],
feature_range=(0, 5))[0])
map_fill_color = Spectral6[np.int(color_index)]
svg.change_fill_color(neigh.replace(' ', ''), map_fill_color)
plt = word_barplots[i]
src = word_sources[i]
src.data = dict(y=y, right=x, wlist=wlist)
t = Title()
t.text = neigh.title()[:13]
plt.title = t
glyph = HBar(y='y',
right='right',
left=0,
height=0.90,
fill_color=mapper)
word_hbarglyphs.append(glyph)
plt.add_glyph(src, glyph)
xaxis = LinearAxis()
xaxis.ticker = np.linspace(0, max(x), 5, dtype=np.int)[1:]
plt.add_layout(xaxis, 'below')
plt.xaxis.major_label_orientation = +np.pi / 2
yaxis = LinearAxis()
yaxis.ticker = y
yaxis.major_label_overrides = {
i: prefix
for i, prefix in enumerate(prefixes)
}
yaxis.major_label_standoff = -35
plt.add_layout(yaxis, 'left')
plt.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plt.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
svg_div.text = svg.to_string()
def parallel_plot(df, axes, color=None, palette=None):
"""From a dataframe create a parallel coordinate plot
"""
logger = logging.getLogger('cave.plot.parallel.plot')
npts = df.shape[0]
ndims = len(df.columns)
if color is None:
color = np.ones(npts)
if palette is None:
palette = ['#ff0000']
cmap = LinearColorMapper(high=color.min(),
low=color.max(),
palette=palette)
data_source = ColumnDataSource(
dict(xs=np.arange(ndims)[None, :].repeat(npts, axis=0).tolist(),
ys=np.array((df - df.min()) / (df.max() - df.min())).tolist(),
color=color))
p = figure(x_range=(-1, ndims),
y_range=(0, 1),
width=800,
tools="pan, box_zoom")
# Create x axis ticks from columns contained in dataframe
fixed_x_ticks = FixedTicker(ticks=np.arange(ndims), minor_ticks=[])
formatter_x_ticks = FuncTickFormatter(code="return columns[index]",
args={"columns": df.columns})
p.xaxis.ticker = fixed_x_ticks
p.xaxis.formatter = formatter_x_ticks
p.yaxis.visible = False
p.y_range.start = 0
p.y_range.end = 1
p.y_range.bounds = (-0.1, 1.1) # add a little padding around y axis
p.xgrid.visible = False
p.ygrid.visible = False
# Create extra y axis for each dataframe column
tickformatter = BasicTickFormatter(precision=1)
for index, col in enumerate(df.columns):
if col in axes:
start = axes[col]['lower']
end = axes[col]['upper']
else:
logger.warning(
"Parallel plot didn't receive information about the axes. "
"This will likely fail for categorical data")
start = df[col].min()
end = df[col].max()
if np.isnan(start) or np.isnan(end):
raise ValueError(
"NaN's not allowed in limits of axes! %s: (%s, %s)" %
(col, str(start), str(end)))
logger.debug('Limits for %s are (%s, %s)' % (col, start, end))
bound_min = start + abs(end - start) * (p.y_range.bounds[0] -
p.y_range.start)
bound_max = end + abs(end - start) * (p.y_range.bounds[1] -
p.y_range.end)
p.extra_y_ranges.update({
col:
Range1d(start=bound_min,
end=bound_max,
bounds=(bound_min, bound_max))
})
num_ticks = 8 if not 'choices' in axes[col] else len(
axes[col]['choices'])
fixedticks = FixedTicker(ticks=np.linspace(start, end, num_ticks),
minor_ticks=[])
axis = LinearAxis(fixed_location=index,
y_range_name=col,
ticker=fixedticks,
formatter=tickformatter)
if 'choices' in axes[col]:
# Note, override-dicts need to be created on assign (https://github.com/bokeh/bokeh/issues/8166)
axis.major_label_overrides = {
i: v
for i, v in enumerate(axes[col]['choices'])
}
p.add_layout(axis, 'right')
# create the data renderer ( MultiLine )
# specify selected and non selected stylew
non_selected_line_style = dict(line_color='grey',
line_width=0.1,
line_alpha=0.5)
selected_line_style = dict(line_color={
'field': 'color',
'transform': cmap
},
line_width=1)
parallel_renderer = p.multi_line(xs="xs",
ys="ys",
source=data_source,
**non_selected_line_style)
# Specify selection style
selected_lines = MultiLine(**selected_line_style)
# Specify non selection style
nonselected_lines = MultiLine(**non_selected_line_style)
parallel_renderer.selection_glyph = selected_lines
parallel_renderer.nonselection_glyph = nonselected_lines
p.y_range.start = p.y_range.bounds[0]
p.y_range.end = p.y_range.bounds[1]
rect_source = ColumnDataSource({
'x': [],
'y': [],
'width': [],
'height': []
})
# add rectange selections
selection_renderer = p.rect(x='x',
y='y',
width='width',
height='height',
source=rect_source,
fill_alpha=0.7,
fill_color='#009933')
selection_tool = ParallelSelectionTool(renderer_select=selection_renderer,
renderer_data=parallel_renderer,
box_width=10)
# custom resets (reset only axes not selections)
reset_axes = ParallelResetTool()
# add tools and activate selection ones
p.add_tools(selection_tool, reset_axes)
p.toolbar.active_drag = selection_tool
return p
def timeline_days_hours(interval_frequency, all_co2_dataframe,
sensors_with_anomalies, data, upper_bound,
destination_path):
times = pd.date_range(start='00:00:00',
end='23:55:00',
freq=str(interval_frequency) +
'Min').strftime('%H:%M:%S')
days = all_co2_dataframe['timestamp'].dt.strftime('%Y-%m-%d').to_list()
days = list(dict.fromkeys(days))
timestamp = list(times)
for sensor_name in sensors_with_anomalies:
all_durations = []
for k in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
k = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
k)
all_durations.append(
int(data['anomalies']['anomaly_co2_values'][sensor_name][0][k]
['duration']))
data['anomalies']['anomaly_co2_values'][sensor_name][0]
p = figure(plot_height=500,
plot_width=2000,
x_range=timestamp,
y_range=days,
title='Timeline of periods with CO2 levels higher than ' +
str(upper_bound) + ' ppm in ' + sensor_name +
'\n Based on data for last ' + str(len(days)) + ' days',
active_drag=None,
toolbar_location=None)
p.x_range.range_padding = 0
p.y_range.range_padding = 0
p.title.text_font_size = '15pt'
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.yaxis.major_label_text_font_size = '9pt'
p.xaxis.major_label_text_font_size = '5pt'
# set x axis to invisible
p.xaxis.visible = False
# Add custom axis with tickers labels only every 1 hour
labels = np.arange(0, 288, 12).tolist()
ticker = FixedTicker()
ticker.ticks = labels
xaxis = LinearAxis(ticker=ticker)
xaxis.major_label_orientation = math.pi / 3
p.add_layout(xaxis, 'below')
xaxis.major_label_overrides = {
0: '00:00',
12: '01:00',
24: '02:00',
36: '3:00',
48: '04:00',
60: '05:00',
72: '06:00',
84: '07:00',
96: '08:00',
108: '09:00',
120: '10:00',
132: '11:00',
144: '12:00',
156: '13:00',
168: '14:00',
180: '15:00',
192: '16:00',
204: '17:00',
216: '18:00',
228: '19:00',
240: '20:00',
252: '21:00',
264: '22:00',
276: '23:00'
}
#add anomalies recorded for each day in considered period
for i in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
i = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
i)
x = []
y = []
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][11:])
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][11:])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][0:10])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][0:10])
if data['anomalies']['anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][0][0][0:10] == data['anomalies'][
'anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][-1][0][0:10]:
# print('yes')
p.line(x,
y,
line_width=2,
color='blue',
legend_label='CO2 above critical value')
p.circle(x, y, fill_color="blue", line_color='blue', size=5)
else:
x1 = x.copy()
y1 = y.copy()
x2 = x.copy()
y2 = y.copy()
x1[-1] = times[-1]
y1[-1] = y1[0]
p.line(x1, y1, line_width=2, color='blue')
x2[0] = times[0]
y2[0] = y2[-1]
p.line(x2, y2, line_width=2, color='blue')
x3 = [x1[0], x2[-1]]
y3 = [y1[0], y2[-1]]
p.circle(x3, y3, fill_color="blue", line_color='blue', size=5)
#save graph in output location
output_file(destination_path + '/anomalies_timeline_' + sensor_name +
'.html')
save(p)
return p
