def knit_html(self,es):
#col1
fig1 = figure(width=250,height=250)
vis_bokeh.draw_1d_hist_from_es("dummy1",0,35,30,es,"run*",ax=fig1) #changes fig1, but also returns it
fig2=figure(width=250,height=250)
xmin,xmax = 0,65
xbins = 20
xname = "hcalEnergy"
ymin,ymax = 0,65
ybins = 20
yname = "muonHits"
vis_bokeh.draw_2d_hist_from_es(xname,xmin,xmax,xbins,yname,ymin,ymax,ybins,es,
index="run*",ax=fig2)
fig_column1 = vplot(fig1,fig2)
#col2
fig3 = figure(width=250,height=250)
fig3=vis_bokeh.draw_1d_hist_from_es("dummy23",0,100,30,es,"run*",ax=fig3,hist_drawer="classic")
fig4 = figure(width=250,height=250)
fig4=vis_bokeh.draw_1d_hist_from_es("dummy45",0,40,30,es,"run*",ax=fig4)
fig_column2 = vplot(fig3,fig4)
fig_grid = hplot(fig_column1,fig_column2)
return vis_bokeh.fig_to_html(fig_grid)
def mk_stat_plot(df):
p1 = Bar(df, label='TARGET_SITE', values='TOTAL_TIME', agg='mean', group='PIPELINE',
title="Mean Processing for each site grouped by Pipeline"+"(Data Size:"+str(len(df.index))+")",
legend='top_right',height=500,width=1000)
df_camp_firstcut = df[ (df.TARGET_SITE == "descampuscluster") & (df.PIPELINE == "firstcut") ]
df_fermi_firstcut = df[ (df.TARGET_SITE == "fermigrid") & (df.PIPELINE == "firstcut") ]
df_camp_multi = df[ (df.TARGET_SITE == "descampuscluster") & (df.PIPELINE == "multiepoch") ]
df_fermi_multi = df[ (df.TARGET_SITE == "fermigrid") & (df.PIPELINE == "multiepoch") ]
p2 = Bar(df_camp_firstcut, label='EXEC_HOST',
values='TOTAL_TIME', agg='mean', title="Mean Processing of firstcut in Campuscluster for each Exac_host"+"(Data Size:"+str(len(df_camp_firstcut.index))+")",
height=500, width=1000)
p3 = Bar(df_fermi_firstcut, label='EXEC_HOST', values='TOTAL_TIME',
agg='mean', title="Mean Processing of firstcut in Fermigrid for each Exac_host"+"(Data Size:"+str(len(df_fermi_firstcut.index))+")",
height=500,width=1000)
p4 = Bar(df_camp_multi, label='EXEC_HOST', values='TOTAL_TIME',
agg='mean', title="Mean Processing of multiepoch in Campuscluster for each Exac_host"+"(Data Size:"+str(len(df_camp_multi.index))+")",
height=500,width=1000)
p5 = Bar(df_fermi_multi, label='EXEC_HOST', values='TOTAL_TIME',
agg='mean', title="Mean Processing of multiepoch in Fermigrid for each Exac_host"+"(Data Size:"+str(len(df_fermi_multi.index))+")",
height=500,width=1000)
p6 = Bar(df, label='TARGET_SITE', values='TOTAL_TIME', stack='PIPELINE',
title="Total time used by each Target site", legend='top_right',
height=500,width=1000)
p = vplot(p1,p2,p3,p4,p5,p6)
return p
def print_graphs(scores):
output_file(s.plots + ".html")
properties_per_dataset = {
'train': {
'line_color': 'firebrick'
},
'test': {
'line_color': 'orange'
},
'valid': {
'line_color': 'olive'
}
}
plots = []
for metric in ConfusionMatrix._fields:
plot = figure(width=500, plot_height=500, title=metric)
for dataset_name in scores:
metric_scores = [
score.value for score in scores[dataset_name][metric]
]
plot.line(range(len(metric_scores)),
metric_scores,
legend=dataset_name,
**properties_per_dataset[dataset_name])
plots.append(plot)
p = vplot(*plots)
save(p)
def temporal_lda_plot(max_documents=50000000,
max_words_per_doc=50000000,
n_topics=100):
# load the model:
lda_model = pickle.load(open('../workspace/lda_model.m', 'rb'))
# get top words per topic for title above plots:
top_words = []
for topic in lda_model.show_topics(num_topics=100,
num_words=15,
formatted=False):
top_words.append(" ".join([w for _, w in topic]))
# extract years from in the file ids in the original file:
text_ids = list(
DirectoryIterator(
path_pattern='../workspace/wikified_periodicals/*.wikified',
max_documents=max_documents,
max_words_per_doc=max_words_per_doc,
get='filename'))
years = [id_.split('-')[-1].replace('.wikified', '') for id_ in text_ids]
year_scores = {y: list() for y in years}
# extract topic scores for each document:
for line, year in zip(
codecs.open('../workspace/mallet_output/doctopics.txt', 'r',
'utf8'), years):
line = line.strip()
if not line:
continue
comps = line.strip().split()
topic_scores = [float(t) for t in comps[2:]]
year_scores[year].append(topic_scores)
for year in year_scores:
m = np.asarray(year_scores[year], dtype='float32')
year_scores[year] = np.mean(m, axis=0)
year_scores = [(int(year), matrix) for year, matrix in year_scores.items()
if int(year) <= 2010]
year_scores = sorted(year_scores, key=itemgetter(0))
output_file("../figures/topics.html")
plots = []
year_labels = [year for year, _ in year_scores if int(year) <= 2010]
for topic_idx in range(n_topics):
scores = [m[topic_idx] for y, m in year_scores]
p = figure(title=top_words[topic_idx],
plot_width=1200,
plot_height=400,
title_text_font_size='12pt')
p.line(year_labels, scores, line_width=2)
plots.append(p)
p = vplot(*plots)
save(p)
def domains_dashboard(response, extra_plots=None):
"""
Domains dashboard plot function. Takes an arguments for extra plots which
will be added in a tab with the other plots.
"""
# Parsed Response Data
urls = [x[0][0] for x in response["pages"]]
parsed_urls = [urlparse(x).hostname for x in urls]
# Domain names Bar chart.
domains = Counter(parsed_urls).most_common(PLOT_ELEMENTS)
xdomains = [x[0] for x in domains]
ydomains = [y[1] for y in domains]
source_domains = ColumnDataSource(data=dict(x=xdomains, y=ydomains))
bar_domains = Bar(source_domains.data, values="y", label="x", title="Most Common Domains by Number",
bar_width=BAR_WIDTH, height=584, xlabel="Domains",
ylabel="Occurences")
panel_domains = Panel(child=bar_domains, title="Domains")
# Domain Information Table
columns_domain = [
TableColumn(field="x", title="Domain"),
TableColumn(field="y", title="Count"),
]
data_table_domain = DataTable(source=source_domains, columns=columns_domain, width=400,
height=280)
# Top level Domains Bar Chart
endings = Counter([x[x.rfind("."):] for x in parsed_urls]).most_common(PLOT_ELEMENTS)
xendings = [x[0] for x in endings]
yendings = [y[1] for y in endings]
source_top_level = ColumnDataSource(data=dict(x=xendings, y=yendings))
bar_top_level = Bar(source_top_level.data, values="y", label="x",
title="Most Common URL Endings by Number", bar_width=BAR_WIDTH, height=584)
panel_top_level = Panel(child=bar_top_level, title="Endings")
# Top level domains table
columns_top_level = [
TableColumn(field="x", title="Top Level Domain"),
TableColumn(field="y", title="Count"),
]
data_table_top_level = DataTable(source=source_top_level,
columns=columns_top_level, width=400, height=280)
# Add the plots and charts to a vform and organize them with VBox and HBox
plot_tabs = Tabs(tabs=[panel_domains, panel_top_level])
# Take the two tables and the graph, turn them into VBox, then organize them
# side by side in an HBox.
vbox_tables = VBox(children=[data_table_domain, data_table_top_level])
vbox_plots = VBox(children=[plot_tabs])
hbox_dashboard = HBox(children=[vbox_tables, vbox_plots])
return components(vplot(hbox_dashboard))
def start(self):
freemem_graph, freemem_table, cpu_graph, cpu_table, bat_graph, bat_table, temp_graph, temp_table, data_graph, events_table = self.last_report(
)
graph = vplot(events_table, freemem_graph, freemem_table, cpu_graph,
cpu_table, bat_graph, bat_table, temp_graph, temp_table,
data_graph)
s = self.folder[0] + '/' + 'report_%s.html' % self.build[0].replace(
'.', '_')
output_file(s,
title=self.build[0] + '-' + self.devicename[0] + 'Report')
return show(graph)
def plot_elements(self):
elements = (
'semi_major_axis',
'eccentricity',
'inclination',
'ascending_node',
'argument_of_periapsis',
'true_anomaly',
)
plots = [self.plot_element(element, show_plot=False) for element in elements]
show(vplot(*plots))
def endings_dashboard(response):
urls = [x[0][0] for x in response["pages"]]
parsed_urls = [urlparse(x).hostname for x in urls]
endings_counter = Counter([x[x.rfind("."):] for x in parsed_urls]).most_common(ENDING_PLOT_LIMIT)
xendings = [x[0] for x in endings_counter]
yendings = [y[1] for y in endings_counter]
source = ColumnDataSource(data=dict(x=xendings, y=yendings))
table = VBox(children=[endings_table(source)])
plot = VBox(children=[endings_plot(source)])
return components(vplot(HBox(children=[table, plot])))
def endings_dashboard(response):
urls = [x[0][0] for x in response["pages"]]
parsed_urls = [urlparse(x).hostname for x in urls]
endings_counter = Counter([x[x.rfind("."):] for x in parsed_urls
]).most_common(ENDING_PLOT_LIMIT)
xendings = [x[0] for x in endings_counter]
yendings = [y[1] for y in endings_counter]
source = ColumnDataSource(data=dict(x=xendings, y=yendings))
table = VBox(children=[endings_table(source)])
plot = VBox(children=[endings_plot(source)])
return components(vplot(HBox(children=[table, plot])))
def plot_elements(self):
elements = (
'semi_major_axis',
'eccentricity',
'inclination',
'ascending_node',
'argument_of_periapsis',
'true_anomaly',
)
plots = [
self.plot_element(element, show_plot=False) for element in elements
]
show(vplot(*plots))
def author_frequency_barplot(nb_top_ners=50, tf=True):
if tf:
output_file('../figures/tf_authors.html')
ner_freqs = pickle.load(open('../workspace/tf.m', "rb"))
else:
output_file('../figures/df_authors.html')
ner_freqs = pickle.load(open('../workspace/df.m', "rb"))
top_ners = [w for w, _ in ner_freqs.most_common(nb_top_ners)]
top_freqs = [c for _, c in ner_freqs.most_common(nb_top_ners)]
names = []
for name in top_ners:
name = name.replace('*', '')
if '(' in name:
name = name.split('(')[0].strip()
name = ' '.join([n.lower().capitalize() for n in name.split('_')])
name = ''.join([
c for c in unicodedata.normalize('NFKD', name)
if not unicodedata.combining(c)
])
names.append(name)
data = pd.DataFrame({'values': top_freqs[:25], 'labels': names[:25]})
bar1 = Bar(data,
label=CatAttr(columns=['labels'], sort=False),
values='values',
title='Author Frequency (1-25)',
width=800,
height=400)
xaxis = bar1.select(dict(type=Axis))[1]
xaxis.major_label_standoff = 0
xaxis.major_label_orientation = np.pi / 2
xaxis.major_label_standoff = 6
xaxis.major_tick_out = 0
data = pd.DataFrame({'values': top_freqs[25:50], 'labels': names[25:50]})
bar2 = Bar(data,
label=CatAttr(columns=['labels'], sort=False),
values='values',
title='Author Frequency (25-50)',
width=800,
height=400)
xaxis = bar2.select(dict(type=Axis))[1]
xaxis.major_label_standoff = 0
xaxis.major_label_orientation = np.pi / 2
xaxis.major_label_standoff = 6
xaxis.major_tick_out = 0
p = vplot(bar1, bar2)
save(p)
def domains_dashboard(response, extra_plots=None):
"""
Domains dashboard plot function. Takes an arguments for extra plots which
will be added in a tab with the other plots.
"""
# Parsed Response Data
urls = [x[0][0] for x in response["pages"]]
parsed_urls = [urlparse(x).hostname for x in urls]
# Domain names Bar chart.
domains_counter = Counter(parsed_urls).most_common(DOMAIN_PLOT_LIMIT)
xdomains = [x[0] for x in domains_counter]
ydomains = [y[1] for y in domains_counter]
source_domains = ColumnDataSource(data=dict(x=xdomains, y=ydomains))
bar_domains = Bar(source_domains.data,
values="y",
label="x",
title="Most Common Sites by Number",
bar_width=BAR_WIDTH,
height=584,
xlabel="Sites",
ylabel="Occurences")
panel_domains = Panel(child=bar_domains, title="Sites")
# Domain Information Table
table_domains_counter = Counter(parsed_urls).most_common(
DOMAIN_TABLE_LIMIT)
xdomains_table = [x[0] for x in table_domains_counter]
ydomains_table = [y[1] for y in table_domains_counter]
source_table_domains = ColumnDataSource(
data=dict(x=xdomains_table, y=ydomains_table))
columns_domain = [
TableColumn(field="x", title="Site Name"),
TableColumn(field="y", title="Count"),
]
data_table_domain = DataTable(source=source_table_domains,
columns=columns_domain,
width=400,
height=280)
# Add the plots and charts to a vform and organize them with VBox and HBox
plot_tabs = Tabs(tabs=[panel_domains, extra_plots])
# Take the plot and table and arrange them in a hbox.
vbox_tables = VBox(children=[data_table_domain])
vbox_plots = VBox(children=[plot_tabs])
hbox_dashboard = HBox(children=[vbox_tables, vbox_plots])
return components(vplot(hbox_dashboard))
def knit_html(self,es):
figs = []
for i in range(len(self.hists)):
figs.append(
[self.make_figure(hist,model,es,"run*") for hist,model in zip(self.hists[i],self.models[i])]
)
hrows = [hplot(*row) for row in figs]
grid = vplot(*hrows)
return vis_bokeh.fig_to_html(grid)
def main():
setup_logging()
logging.getLogger("requests").setLevel(logging.WARNING)
output_server("Temp Chart")
window = 200
width = 800
height = 200
now = datetime.now()
start = now - timedelta(hours=window)
r_start = time.mktime(start.timetuple()) * 1000 - 20000000
r_end = time.mktime(now.timetuple()) * 1000 - 20000000
ran = Range1d(r_start, r_end)
# line chart
line_fig = figure(plot_width=width, plot_height=height) #, x_range=ran)
line = line_fig.line(x=[], y=[])
# circle chart
c_fig = figure(plot_width=width, plot_height=height)
circle = c_fig.circle(x=[], y=[], size=1)
# format axes
line_fig.xaxis[0].formatter = DatetimeTickFormatter(formats=dict(days=["%T"]))
line_fig.xaxis.major_label_orientation = pi/4
c_fig.xaxis[0].formatter = DatetimeTickFormatter(formats=dict(days=["%T"]))
c_fig.xaxis.major_label_orientation = pi/4
p = vplot(line_fig, c_fig)
show(p)
line_ds = line.select(dict(type=GlyphRenderer))[0].data_source
circle_ds = circle.select(dict(type=GlyphRenderer))[0].data_source
lines = tail_generator(exit_after=0)
if True:
for line in lines:
line = decimate(lines, skip=300)
temp, date = parse_line(line)
if temp is None:
continue
update_series(line_ds, date, temp)
update_range(line_ds, line_fig, 60)
update_series(circle_ds, date, temp)
update_range(circle_ds, c_fig, 12)
time.sleep(0.01)
return c_fig, circle
def make_dts_plot():
plots = []
etime = datetime.combine(date.today(), datetime.min.time())
stime = etime - timedelta(14)
graphstime = datetime.strptime('01-01-15 00:00:00', '%m-%d-%y %H:%M:%S')
days = 1
### Fetch Data ###
accept_df = get_data.get_accept_time()
ingest_df = get_data.get_ingest_time()
sispi_df = pd.DataFrame(query.query_exptime(query.connect_to_db('db-sispi')[1], etime, datetime.now()), columns = ['sispi_time','filename'])
weekly_df = pd.DataFrame(query.query_dts_delay(query.connect_to_db('db-destest')[1], stime, etime), columns = ['total_time', 'ncsa_time', 'noao_time', 'xtime'])
alltime_df = pd.DataFrame(query.query_dts_delay(query.connect_to_db('db-destest')[1], graphstime, etime), columns = ['total_time', 'ncsa_time', 'noao_time', 'xtime'])
### Standardize file names for merge ###
trimed_fn = []
for i, line in sispi_df.iterrows():
try:
trimed_fn.append(os.path.basename(line['filename'].split(':')[1]))
except:
trimed_fn.append(os.path.basename(line['filename']))
sispi_df['filename']=trimed_fn
### Merge data ###
log_df = pd.merge(accept_df, ingest_df, how='inner', on=['filename'])
live_df = pd.merge(log_df, sispi_df, how='inner', on=['filename'])
live_df = get_data.convert_timezones(live_df)
### Smooth plot ###
sm_df = get_data.smooth_dts(weekly_df)
av_df = get_data.average_dts(alltime_df, graphstime, days)
### Plot Data ###
plots.append(plotter.plot_realtime_dts(sm_df, live_df))
plots.append(plotter.plot_monthly_dts(av_df, days))
plots.append(plotter.plot_average_dts(alltime_df, days))
### Writing plots to HTML ###
html = file_html(vplot(*plots),INLINE,'dts')
filename = 'dts_plot.html'
filepath = os.path.join(app.config["STATIC_PATH"],filename)
with open(filepath,'w') as h:
h.write('<h5> Last updated on: %s </h5>' % "{0}".format(datetime.now()))
h.write('<center>\n')
h.write(html)
h.write('</center>\n')
def init_controls(self):
btnStop = Button(label="Stop", type="danger")
btnStart = Button(label="Start", type="success")
btnStop.on_click(self.handle_btnStop_press)
btnStart.on_click(self.handle_btnStart_press)
curdoc().add_root(btnStop)
curdoc().add_root(btnStart)
sliderHPThreshold = Slider(start=0, end=500, value=100, step=1, title="High pass threshold")
sliderHPThreshold.on_change('value', self.onChangeHPThreshold)
curdoc().add_root(vplot(sliderHPThreshold))
def main():
data = [[1, 1]]
data += [[2, 3]]
data += [[4, 3]]
data += [[3, 2]]
data += [[5, 5]]
output_file("test.html")
lineGraph = figure(plot_width=800,
plot_height=800,
title=None,
toolbar_location='below',
toolbar_sticky=False)
# plotting data
xcord = [item[0] for item in data]
ycord = [item[1] for item in data]
lineGraph.square(xcord, ycord, size=10)
w = [0, 0]
learningRate = 0.001
errorArray = []
for test in range(100):
errorSum = 0
for point in data:
error = point[1] - evaluate(w, point[0])
w[0] = w[0] + (error * learningRate)
w[1] = w[1] + (error * learningRate * point[0])
errorSum += (error * error)
errorArray.append((1.0 / 5) * errorSum)
print(w)
lineGraph.line([0, 10], [w[0], evaluate(w, 10)], line_width=2)
errorGraph = figure(plot_width=800,
plot_height=800,
title=None,
toolbar_location='below',
toolbar_sticky=False)
errorGraph.line(range(len(errorArray)), errorArray, line_width=2)
graphs = vplot(lineGraph, errorGraph)
show(graphs)
def init_controls(self):
btnStop = Button(label="Stop", type="danger")
btnStart = Button(label="Start", type="success")
btnStop.on_click(self.handle_btnStop_press)
btnStart.on_click(self.handle_btnStart_press)
curdoc().add_root(btnStop)
curdoc().add_root(btnStart)
sliderHPThreshold = Slider(start=0,
end=500,
value=100,
step=1,
title="High pass threshold")
sliderHPThreshold.on_change('value', self.onChangeHPThreshold)
curdoc().add_root(vplot(sliderHPThreshold))
def make_system_plots(sys_df, res, des_df):
plots = []
try:
p_du = plotter.data_usage_plot(des_df)
plots.append(p_du)
except:
print "data_usage failed"
pass
try:
p_ttfts = plotter.plot_tape_tar_file_transfer_status(sys_df['run_time'],sys_df['number_transferred'],sys_df['number_not_transferred'])
plots.append(p_ttfts)
except:
print "tape_tar_file failed"
pass
try:
p_bs = plotter.plot_backup_size(sys_df['run_time'],sys_df['size_transferred'],sys_df['size_to_be_transferred'])
plots.append(p_bs)
except:
print "backup_size failed"
pass
try:
p_prp = plotter.plot_pipeline_run_progress(sys_df['run_time'],sys_df['pipe_processed'],sys_df['pipe_to_be_processed'])
plots.append(p_prp)
except:
print "pipeline_run failed"
pass
try:
p_dtss = plotter.plot_dts_status(sys_df['run_time'],sys_df['raw_processed'],sys_df['raw_to_be_processed'])
plots.append(p_dtss)
except:
print "dts_status failed"
pass
try:
p_ts = plotter.plot_system_transfer_rates(res['tdate'],res['tsize'],res['tav'])
plots.append(p_ts)
except:
print "system_transfer failed"
pass
html_vplots = vplot(*plots)
return html_vplots
def bokeh_plot(site, obs, obs_label, data_dict, outfile, title, resample_frequency, add_statistics=True):
'''
Plots a bokeh (interactive) daily or weekly series.
Inputs:
site: a string ID which is input to the obs and data_dict objects
obs: a pandas dataframe with columns labeled with the site IDs
data_dict: an ordered dictionary with keys each of the series you want to plot with structure:
{'series_name': {'data': pandas_dataframe, 'color': 'the color you want the timeseries to plot as'}}
outfile: path where you want to save the bokeh plot
title: title string for your plot
resample_frequency: either 'w' for weekly or 'd' for daily (this is used to decide the frequency of the plotting)
add_statistics: if True (default) will include the statistics of KGE and bias in the plot's legend
'''
reset_output()
resample_dict = {'W': 'Weekly',
'D': 'Daily'}
output_file(outfile, title=title)
p1 = figure(width=1000, height=400, x_axis_type = "datetime", title=title)
obs_resampled =obs.resample(resample_frequency, how='mean')
p1.line(obs_resampled.index.values,obs_resampled.values/100000, color='#000000', legend=obs_label, line_width=2)
for scenario in data_dict.keys():
sim = data_dict[scenario]['data'][site]
daily_kge = str(np.round(kge(sim, obs, 'D'), 2))
weekly_kge = str(np.round(kge(sim, obs, 'W'), 2))
scenario_bias = str(np.round(bias(sim, obs),2))
if add_statistics:
legend_label = scenario+', Daily KGE='+daily_kge+',\nWeekly KGE='+weekly_kge+' Bias='+scenario_bias+'%'
else:
legend_label = scenario
sim_resampled = data_dict[scenario]['data'][site].resample(resample_frequency, how='mean')
p1.line(sim_resampled.index.values, sim_resampled.values/100000,
color=data_dict[scenario]['color'], line_width=2,
legend=legend_label)
p1.grid.grid_line_alpha=0.3
p1.xaxis.axis_label = 'Date'
p1.yaxis.axis_label = 'Streamflow [CFS*100,000]'
p1.legend.label_text_font_size = '8'
window_size = 30
window = np.ones(window_size)/float(window_size)
g = vplot(p1)
save(g)
def execute(args, parser):
config = Config(args.config, verbose=False)
with config.trialscontext() as session:
q = (session.query(Trial)
.filter(Trial.status == 'SUCCEEDED')
.order_by(Trial.started))
data = [curr.to_dict() for curr in q.all()]
bk.output_file(args.filename, title='osprey')
p4 = plot_4(data)
p1 = plot_1(data)
p2 = plot_2(data)
p3 = plot_3(data, config.search_space())
p = vplot(p1, p2, p3, *p4)
if args.browser:
bk.show(p)
else:
bk.save(p)
def domains_dashboard(response, extra_plots=None):
"""
Domains dashboard plot function. Takes an arguments for extra plots which
will be added in a tab with the other plots.
"""
# Parsed Response Data
urls = [x[0][0] for x in response["pages"]]
parsed_urls = [urlparse(x).hostname for x in urls]
# Domain names Bar chart.
domains_counter = Counter(parsed_urls).most_common(DOMAIN_PLOT_LIMIT)
xdomains = [x[0] for x in domains_counter]
ydomains = [y[1] for y in domains_counter]
source_domains = ColumnDataSource(data=dict(x=xdomains, y=ydomains))
bar_domains = Bar(source_domains.data, values="y", label="x", title="Most Common Sites by Number",
bar_width=BAR_WIDTH, height=584, xlabel="Sites",
ylabel="Occurences")
panel_domains = Panel(child=bar_domains, title="Sites")
# Domain Information Table
table_domains_counter = Counter(parsed_urls).most_common(DOMAIN_TABLE_LIMIT)
xdomains_table = [x[0] for x in table_domains_counter]
ydomains_table = [y[1] for y in table_domains_counter]
source_table_domains = ColumnDataSource(data=dict(x=xdomains_table,
y=ydomains_table))
columns_domain = [
TableColumn(field="x", title="Site Name"),
TableColumn(field="y", title="Count"),
]
data_table_domain = DataTable(source=source_table_domains, columns=columns_domain, width=400,
height=280)
# Add the plots and charts to a vform and organize them with VBox and HBox
plot_tabs = Tabs(tabs=[panel_domains, extra_plots])
# Take the plot and table and arrange them in a hbox.
vbox_tables = VBox(children=[data_table_domain])
vbox_plots = VBox(children=[plot_tabs])
hbox_dashboard = HBox(children=[vbox_tables, vbox_plots])
return components(vplot(hbox_dashboard))
def print_graphs(scores):
output_file(s.plots + ".html")
properties_per_dataset = {
'train': {'line_color': 'firebrick'},
'test': {'line_color': 'orange'},
'valid': {'line_color': 'olive'}
}
plots = []
for metric in ConfusionMatrix._fields:
plot = figure(width=500, plot_height=500, title=metric)
for dataset_name in scores:
metric_scores = [score.value for score in scores[dataset_name][metric]]
plot.line(range(len(metric_scores)),
metric_scores,
legend=dataset_name,
**properties_per_dataset[dataset_name])
plots.append(plot)
p = vplot(*plots)
save(p)
def makeFeaturePlots(modelFolder, outputFolder, drugName):
clf = joblib.load(modelFolder + drugName)
output_file(outputFolder + drugName + ".html", title = drugName + ' Features')
featureDataFrame = joblib.load('../output/emptyDataFrame.pkl')
featureDataFrame['Feature Importance'] = clf.coef_
featureDataFrame.sort(columns='Feature Importance', inplace=True)
resistanceGenes= featureDataFrame.index.values[:25]
resistanceValues= featureDataFrame['Feature Importance'].values[:25]
featureDataFrame.sort(columns='Feature Importance', inplace=True, ascending= False)
sensitivityGenes = featureDataFrame.index.values[:25]
sensitivityValues = featureDataFrame['Feature Importance'].values[:25]
s1 = Bar(resistanceValues, cat=resistanceGenes.tolist(), title="Top 25 Resistance Genes for " + drugName, xlabel='Genes', ylabel = 'Coefficient', width=800, height=400, tools = False)
s2 = Bar(sensitivityValues, cat=sensitivityGenes.tolist(), title="Top 25 Senitivity Genes for " + drugName, xlabel='Genes', ylabel = 'Coefficient', width=800, height=400, tools = False, palette = ['#82CC9B'])
p = vplot(s1, s2)
show(p)
def _init_plot(self):
# get recoating data
query = "SELECT MAX(ReplacementDate) AS ReplacementDate, SegmentPosition" \
" FROM MirrorRecoating" \
" GROUP BY SegmentPosition ORDER BY ReplacementDate, SegmentPosition"
df = pd.read_sql(query, db.engine)
# add missing segment positions
missing_positions = [p for p in range(1, 92) if p not in df.SegmentPosition.values]
df_missing = pd.DataFrame(dict(SegmentPosition=missing_positions,
ReplacementDate=len(missing_positions) * [datetime.date(1970, 1, 1)]))
df = df.append(df_missing, ignore_index=True)
# sort by date (in descending order)
df.sort_values(by='ReplacementDate', ascending=False, inplace=True)
# add cumulative number of replacements since one recoating period ago
start = self.now - datetime.timedelta(self.RECOATING_PERIOD)
recoatings = len(df[df.ReplacementDate >= start])
df['RecoatingsSinceYearStart'] = [max(recoatings - i, 0) for i in range(0, 91)]
# add days since recoating
df['DaysSinceReplacement'] = [(self.now - d).days for d in df.ReplacementDate.values]
# add segment position coordinates
df['SegmentPositionX'] = [self.mirror_positions[m][0] for m in df.SegmentPosition]
df['SegmentPositionY'] = [self.mirror_positions[m][1] for m in df.SegmentPosition]
df['SegmentPositionCornerXs'] = [self._segment_corners(m)[0] for m in df.SegmentPosition]
df['SegmentPositionCornerYs'] = [self._segment_corners(m)[1] for m in df.SegmentPosition]
# create data source
source = ColumnDataSource(df)
table = self._table(source)
replacement_plot = self._replacement_plot(source)
segment_plot = self._segment_plot(source)
srp = vplot(replacement_plot, segment_plot)
self.plot = hplot(table, srp)
def getPlot(fileName):
source = ColumnDataSource(mpg)
manufacturers = sorted(mpg["manufacturer"].unique())
models = sorted(mpg["model"].unique())
columns = [
TableColumn(field="manufacturer", title="Manufacturer", editor=SelectEditor(options=manufacturers), formatter=StringFormatter(font_style="bold")),
TableColumn(field="model", title="Model", editor=StringEditor(completions=models)),
TableColumn(field="displ", title="Displacement", editor=NumberEditor(step=0.1), formatter=NumberFormatter(format="0.0")),
TableColumn(field="year", title="Year", editor=IntEditor()),
TableColumn(field="cyl", title="Cylinders", editor=IntEditor()),
TableColumn(field="cty", title="City MPG", editor=IntEditor()),
TableColumn(field="hwy", title="Highway MPG", editor=IntEditor()),
]
data_table = DataTable(source=source, columns=columns, editable=True)
plot = Plot(title=None, x_range= DataRange1d(), y_range=DataRange1d(), plot_width=1000, plot_height=300)
# Set up x & y axis
plot.add_layout(LinearAxis(), 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
# Add Glyphs
cty_glyph = Circle(x="index", y="cty", fill_color="#396285", size=8, fill_alpha=0.5, line_alpha=0.5)
hwy_glyph = Circle(x="index", y="hwy", fill_color="#CE603D", size=8, fill_alpha=0.5, line_alpha=0.5)
plot.add_glyph(source, cty_glyph)
plot.add_glyph(source, hwy_glyph)
layout = vplot(plot, data_table)
script, div = components(layout)
html = readHtmlFile(fileName)
html = gui.insertScriptIntoHeader(html, script)
html = gui.appendElementContent(html, div, "div", "bokehContent")
return html
def make_coadd_html():
try:
all_df, processed_df, band_df = get_data.create_coadd_map('db-desoper',"Y3A1_COADD")
p = plotter.plot_coadd(all_df, processed_df, band_df, "Y3A1_COADD")
except:
print 'Coadd plot not rendered!'
pass
# Creating output path
path = os.path.join(app.config["STATIC_PATH"],"reports/coadd/")
if not os.path.isdir(path): os.makedirs(path)
# Writing plots to HTML
html = file_html(vplot(p),INLINE,'coadd')
filename = 'coadd_map_save.html'
includepath = 'reports/coadd/coadd_map_save.html'
filepath = os.path.join(path,filename)
with open(filepath,'w') as h:
h.write('<h5> Last updated on: %s </h5>' % "{0}".format(datetime.now()))
h.write('<center>\n')
h.write(html)
h.write('</center>\n')
def plot_table_by_time(table):
plots = []
table = table.set_index('time')
for col in table:
s = table[col]
s.dropna(inplace=True)
if not np.isscalar(s.values[0]):
# replace with the sum
s = s.apply(np.sum)
x_range = plots[0].x_range if plots else None
fig = figure(title=col, x_axis_type='datetime', x_range=x_range)
fig.line(s.index, s, line_width=2)
fig.circle(s.index, s, fill_color='white', size=8)
plots.append(fig)
ncols = int(np.ceil(np.sqrt(len(plots))))
nrows = int(np.ceil(len(plots) / ncols))
rows = [hplot(*plots[row*ncols:(row+1)*ncols]) for row in range(nrows)]
plot = vplot(*rows)
print('plot = %s' % plot)
script, div = components(plot)
return {'plot_div': div, 'plot_resources': PLOT_RESOURCES,
'plot_script': script, 'bokeh_version': bokeh.__version__}
def execute(args, parser):
config = Config(args.config, verbose=False)
with config.trialscontext() as session:
q = (session.query(Trial)
.filter(Trial.status == 'SUCCEEDED')
.order_by(Trial.started))
data = [curr.to_dict() for curr in q.all()]
bk.output_file(args.filename, title='osprey')
plots = []
ss = config.search_space()
for plot in PLOTS:
plt = plot(data, ss)
if plt is not None:
plt = plt if isinstance(plt, list) else [plt]
plots.extend(plt)
p = vplot(*plots)
if args.browser:
bk.show(p)
else:
bk.save(p)
def create_objects(cls, symbol, df, securities):
descr_box = Paragraph(text='content loading...')
btn_close_loading = Button(label='Close Loading')
dialog_loading = Dialog(title='loading',
content=vplot(descr_box),
name='loading_dialog',
buttons=[btn_close_loading],
visible=False)
source_data = dict(df)
main_source = ColumnDataSource(dict(df))
source = ColumnDataSource(source_data)
# TODO: REMOVE THIS COMMENTED CODE! IT'S JUST THE PREVIOUS
# VERSION USED BEFORE NEW P&D Cached results and algorithm
# get the cached results of the P&D algorithm computed with the
# "default" configuration
# intervals = utils.cached_pumps.get(symbol, pumps.to_dicts(((),(),(),(),(),())))
# intervals['bottom'] = [0] * len(intervals['start'])
# intervals['values'] = [max(df['price'])] * len(intervals['start'])
#
# intervals = pd.DataFrame(intervals)
# new version
stats = utils.get_symbols_cached_stats()[symbol]
intervals = pd.DataFrame(stats)
intervals['bottom'] = [0] * len(intervals['start'])
intervals['values'] = [max(df['price'])] * len(intervals['start'])
conv = lambda x: utils.to_seconds(pd.to_datetime(x))
intervals = intervals[
(pd.to_datetime(intervals['start']) > conv(config.date_range[0])) &
(pd.to_datetime(intervals['start']) < conv(config.date_range[1]))]
# Create P&Ds intervals DataSource
intervals_source = ColumnDataSource(intervals)
source.tags = ['main_source']
trends = utils.load_trends_data(symbol, start_date=min(df['dt']))
trends_source = ColumnDataSource(trends)
trades = Slider(title="trades",
name='trades',
value=0,
start=0,
end=124,
step=1)
# Selectors
symbol = Select.create(options=securities,
value=symbol,
name='symbol',
title="")
window_selector = Select.create(options=['---'],
name='period_selector',
title="Search intervals with:")
symbol_filter = Select.create(
options=['All', 'Stocks with Spam', 'Stocks without Spam'],
name='symbol_filter',
title="Filter Symbols:",
value='Stocks with Spam')
callback = Callback(args={
'symbol_filter': symbol_filter,
'dialog_loading': dialog_loading
},
code=callbacks.symbol_filter)
symbol_filter.callback = callback
btn_detect_pumps = Button(label='Configure P&D Detection',
name='config_pumps')
main_tab = Panel(title="Main")
tabs = Tabs()
# Create STOCKS TABLE
ranks = utils.get_pumps_rank()
# quotient_metrics = utils.get_quotient_metrics()
# ranks['quotient'] = quotient_metrics['quotient']
foo = lambda x: utils.spams_count.get(x, 0)
ranks['spams'] = map(foo, ranks['symbol'])
ranks = ranks.sort(['spams', 'vol_quotient'], ascending=False)
cls._pre_filtered_ranks = {
'All': {k: ranks[k]
for k in ranks.columns},
'Stocks with Spam':
dict(ranks[ranks['spams'] > 0].sort('vol_quotient',
ascending=False)),
'Stocks without Spam':
dict(ranks[ranks['spams'] == 0].sort('vol_quotient',
ascending=False)),
}
source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['All'])
table_stocks_rank = DataTable(
source=source_stocks_rank,
width=560,
height=450,
selectable=True,
editable=True,
columns=[
TableColumn(field='symbol',
title='symbol',
width=130,
editor=StringEditor()),
TableColumn(field='vol_quotient',
title='volume ratio',
editor=StringEditor(),
default_sort='descending'),
TableColumn(field='risk_score',
title='risk',
width=100,
editor=StringEditor(),
default_sort='descending'),
TableColumn(field='spams',
title='spams',
width=130,
editor=StringEditor(),
default_sort='descending'),
])
callback = Callback(args={
'tr': table_stocks_rank,
'sr': source_stocks_rank,
'symb': symbol,
'dialog_loading': dialog_loading
},
code=callbacks.source_stocks_rank)
source_stocks_rank.callback = callback
return locals()
0.9,
source=source,
color='colors',
alpha='alphas',
line_color=None)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = np.pi / 3
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
('tgt/src', '@yname, @xname'),
('attn', '@count'),
]
return p
if __name__ == '__main__':
src = ["le", "est", "un"]
tgt = ["the", "one", "is", "about"]
alignment = np.array([[0, 1, 2, 5], [2, 4, 6, 7], [9, 1, 3, 3]]) / 10.0
print alignment
p1 = make_alignment_figure(src, tgt, alignment)
p2 = make_alignment_figure(src, tgt, alignment)
p_all = vplot(p1, p2)
show(p)
def plot(self):
plots = [
vis.plot_plane(self, plane=plane, show_plot=False)
for plane in ('XY', 'XZ', 'YZ')
]
show(vplot(*plots))
p = figure(title="diamond") p.scatter(x, y, marker="diamond", size=sizes, color="#1C9099", line_width=2) plots.append(p) p = figure(title="inverted_triangle") p.scatter(x, y, marker="inverted_triangle", size=sizes, color="#DE2D26") plots.append(p) p = figure(title="square_x") p.scatter(x, y, marker="square_x", size=sizes, color="#FDAE6B", fill_color=None, line_width=2) plots.append(p) p = figure(title="asterisk") p.scatter(x, y, marker="asterisk", size=sizes, color="#F0027F", line_width=2) plots.append(p) p = figure(title="square_cross") p.scatter(x, y, marker="square_cross", size=sizes, color="#7FC97F", fill_color=None, line_width=2) plots.append(p) p = figure(title="diamond_cross") p.scatter(x, y, marker="diamond_cross", size=sizes, color="#386CB0", fill_color=None, line_width=2) plots.append(p) p = figure(title="circle_cross") p.scatter(x, y, marker="circle_cross", size=sizes, color="#FB8072", fill_color=None, line_width=2) plots.append(p) show(vplot(*plots)) # open a browser
def queries_dashboard(response):
table = VBox(children=[queries_table(response)])
plot = VBox(children=[queries_plot(response)])
return components(vplot(HBox(children=[table, plot])))
def initialize_plot(self, ranges=None):
ranges = self.compute_ranges(self.layout, self.keys[-1], None)
plots = [[] for i in range(self.rows)]
passed_plots = []
tab_titles = {}
insert_rows, insert_cols = [], []
adjoined = False
for r, c in self.coords:
subplot = self.subplots.get((r, c), None)
if subplot is not None:
shared_plots = passed_plots if self.shared_axes else None
subplots = subplot.initialize_plot(ranges=ranges, plots=shared_plots)
# Computes plotting offsets depending on
# number of adjoined plots
offset = sum(r >= ir for ir in insert_rows)
if len(subplots) > 2:
adjoined = True
# Add pad column in this position
insert_cols.append(c)
if r not in insert_rows:
# Insert and pad marginal row if none exists
plots.insert(r+offset, [None for _ in range(len(plots[r]))])
# Pad previous rows
for ir in range(r):
plots[ir].insert(c+1, None)
# Add to row offset
insert_rows.append(r)
offset += 1
# Add top marginal
plots[r+offset-1] += [subplots.pop(-1), None]
elif len(subplots) > 1:
adjoined = True
# Add pad column in this position
insert_cols.append(c)
# Pad previous rows
for ir in range(r):
plots[r].insert(c+1, None)
# Pad top marginal if one exists
if r in insert_rows:
plots[r+offset-1] += 2*[None]
else:
# Pad top marginal if one exists
if r in insert_rows:
plots[r+offset-1] += [None] * (1+(c in insert_cols))
plots[r+offset] += subplots
if len(subplots) == 1 and c in insert_cols:
plots[r+offset].append(None)
passed_plots.append(subplots[0])
if self.tabs:
if isinstance(self.layout, Layout):
tab_titles[r, c] = ' '.join(self.paths[r,c])
else:
dim_vals = zip(self.layout.kdims, self.paths[r, c])
tab_titles[r, c] = ', '.join([d.pprint_value_string(k)
for d, k in dim_vals])
# Replace None types with empty plots
# to avoid bokeh bug
if adjoined:
plots = layout_padding(plots)
# Determine the most appropriate composite plot type
# If the object cannot be displayed in a single layout
# it will be split into Tabs, for 1-row or 1-column
# Layouts we use the vplot and hplots.
# If there is a table and multiple rows and columns
# everything will be forced to a vertical layout
if self.tabs:
panels = [Panel(child=child, title=tab_titles.get(r, c))
for r, row in enumerate(plots)
for c, child in enumerate(row)
if child is not None]
layout_plot = Tabs(tabs=panels)
elif len(plots) == 1 and not adjoined:
layout_plot = vplot(hplot(*plots[0]))
elif len(plots[0]) == 1:
layout_plot = vplot(*[p[0] for p in plots])
else:
layout_plot = gridplot(plots)
self.handles['plot'] = layout_plot
self.handles['plots'] = plots
self.drawn = True
return self.handles['plot']
def plot():
# FIGURES AND X-AXIS
fig1 = Figure(title = 'Dive Profile', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS)
fig2 = Figure(title = 'Dive Controls', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
fig3 = Figure(title = 'Attitude', plot_width = WIDTH, plot_height = HEIGHT, tools = TOOLS, x_range=fig1.x_range)
figs = gridplot([[fig1],[fig2],[fig3]])
# Formatting x-axis
timeticks = DatetimeTickFormatter(formats=dict(seconds =["%b%d %H:%M:%S"],
minutes =["%b%d %H:%M"],
hourmin =["%b%d %H:%M"],
hours =["%b%d %H:%M"],
days =["%b%d %H:%M"],
months=["%b%d %H:%M"],
years =["%b%d %H:%M %Y"]))
fig1.xaxis.formatter = timeticks
fig2.xaxis.formatter = timeticks
fig3.xaxis.formatter = timeticks
# removing gridlines
fig1.xgrid.grid_line_color = None
fig1.ygrid.grid_line_color = None
fig2.xgrid.grid_line_color = None
fig2.ygrid.grid_line_color = None
fig3.xgrid.grid_line_color = None
fig3.ygrid.grid_line_color = None
# INPUT WIDGETS
collection_list = CONN[DB].collection_names(include_system_collections=False)
gliders = sorted([platformID for platformID in collection_list if len(platformID)>2])
gliders = Select(title = 'PlatformID', value = gliders[0], options = gliders)
prev_glider = Button(label = '<')
next_glider = Button(label = '>')
glider_controlbox = HBox(children = [gliders, prev_glider, next_glider], height=80)
chunkations = Select(title = 'Chunkation', value = 'segment', options = ['segment', '24hr', '30days', '-ALL-'])
chunk_indicator = TextInput(title = 'index', value = '0')
prev_chunk = Button(label = '<')
next_chunk = Button(label = '>')
chunk_ID = PreText(height=80)
chunk_controlbox = HBox(chunkations,
HBox(chunk_indicator, width=25),
prev_chunk, next_chunk,
chunk_ID,
height = 80)
control_box = HBox(glider_controlbox,
chunk_controlbox)
# DATA VARS
deadby_date = ''
depth = ColumnDataSource(dict(x=[],y=[]))
vert_vel = ColumnDataSource(dict(x=[],y=[]))
mbpump = ColumnDataSource(dict(x=[],y=[]))
battpos = ColumnDataSource(dict(x=[],y=[]))
pitch = ColumnDataSource(dict(x=[],y=[]))
mfin = ColumnDataSource(dict(x=[],y=[]))
cfin = ColumnDataSource(dict(x=[],y=[]))
mroll = ColumnDataSource(dict(x=[],y=[]))
mheading = ColumnDataSource(dict(x=[],y=[]))
cheading = ColumnDataSource(dict(x=[],y=[]))
# AXIS setup
colors = COLORS[:]
fig1.y_range.flipped = True
fig1.yaxis.axis_label = 'm_depth (m)'
fig1.extra_y_ranges = {'vert_vel': Range1d(start=-50, end=50),
'dummy': Range1d(start=0, end=100)}
fig1.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'vert_vel',
axis_label = 'vertical velocity (cm/s)'))
fig1.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig1.yaxis[1].visible = False
fig1.yaxis[1].axis_line_alpha = 0
fig1.yaxis[1].major_label_text_alpha = 0
fig1.yaxis[1].major_tick_line_alpha = 0
fig1.yaxis[1].minor_tick_line_alpha = 0
fig2.yaxis.axis_label = 'pitch (deg)'
fig2.y_range.start, fig2.y_range.end = -40,40
fig2.extra_y_ranges = {'battpos': Range1d(start=-1, end = 1),
'bpump': Range1d(start=-275, end=275)}
fig2.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'battpos',
axis_label = 'battpos (in)'))
fig2.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'bpump',
axis_label = 'bpump (cc)'))
fig2.yaxis[1].visible = False # necessary for spacing. later gets set to true
fig3.yaxis.axis_label = 'fin/roll (deg)'
fig3.y_range.start, fig3.y_range.end = -30, 30
fig3.extra_y_ranges = {'heading': Range1d(start=0, end=360), #TODO dynamic avg centering
'dummy': Range1d(start=0, end=100)}
fig3.add_layout(place = 'right',
obj = LinearAxis(y_range_name = 'heading',
axis_label = 'headings (deg)'))
fig3.add_layout(place = 'left',
obj = LinearAxis(y_range_name = 'dummy',
axis_label = ' '))
fig3.yaxis[1].visible = False
fig3.yaxis[1].axis_line_alpha = 0
fig3.yaxis[1].major_label_text_alpha = 0
fig3.yaxis[1].major_tick_line_alpha = 0
fig3.yaxis[1].minor_tick_line_alpha = 0
# PLOT OBJECTS
fig1.line( 'x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.circle('x', 'y', source = depth, legend = 'm_depth', color = 'red')
fig1.line( 'x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.circle('x', 'y', source = vert_vel, legend = 'vert_vel', color = 'green', y_range_name = 'vert_vel')
fig1.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'vert_vel',
line_color= 'green', line_dash='dashed', line_width=1))
fig2.line( 'x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.circle('x', 'y', source = pitch, legend = "m_pitch", color = 'indigo')
fig2.line( 'x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.circle('x', 'y', source = battpos, legend = 'm_battpos', color = 'magenta', y_range_name = 'battpos')
fig2.line( 'x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.circle('x', 'y', source = mbpump, legend = "m_'bpump'", color = 'blue', y_range_name = 'bpump')
fig2.renderers.append(Span(location = 0, dimension = 'width',
line_color= 'black', line_dash='dashed', line_width=1))
fig3.line( 'x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.circle('x', 'y', source = mfin, legend = 'm_fin', color = 'cyan')
fig3.line( 'x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.circle('x', 'y', source = cfin, legend = 'c_fin', color = 'orange')
fig3.line( 'x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.circle('x', 'y', source = mroll, legend = 'm_roll', color = 'magenta')
fig3.line( 'x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.circle('x', 'y', source = mheading, legend = 'm_heading', color = 'blue', y_range_name = 'heading')
fig3.line( 'x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.circle('x', 'y', source = cheading, legend = 'c_heading', color = 'indigo', y_range_name = 'heading')
fig3.renderers.append(Span(location = 0, dimension = 'width', y_range_name = 'default',
line_color= 'black', line_dash='dashed', line_width=1))
# CALLBACK FUNCS
def update_data(attrib,old,new):
g = gliders.value
chnk = chunkations.value
chindex = abs(int(chunk_indicator.value))
depth.data = dict(x=[],y=[])
vert_vel.data = dict(x=[],y=[])
mbpump.data = dict(x=[],y=[])
battpos.data = dict(x=[],y=[])
pitch.data = dict(x=[],y=[])
mfin.data = dict(x=[],y=[])
cfin.data = dict(x=[],y=[])
mroll.data = dict(x=[],y=[])
mheading.data = dict(x=[],y=[])
cheading.data = dict(x=[],y=[])
depth.data,startend = load_sensor(g, 'm_depth', chnk, chindex)
if chnk == 'segment':
xbd = startend[2]
chunk_ID.text = '{} {} \n{} ({}) \nSTART: {} \nEND: {}'.format(g, xbd['mission'],
xbd['onboard_filename'], xbd['the8x3_filename'],
e2ts(xbd['start']), e2ts(xbd['end']))
if len(set(depth.data['x']))<=1 and attrib == 'chunk':
if old > new:
next_chunk.clicks += 1
else:
prev_chunk.clicks += 1
return
elif len(set(depth.data['x']))<=1 and chunk_indicator.value == 0:
chunk_indicator.value = 1
elif chnk in ['24hr', '30days']:
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g, e2ts(startend[0]), e2ts(startend[1]))
elif chnk == '-ALL-':
chunk_ID.text = '{} \nSTART: {} \nEND: {}'.format(g,e2ts(depth.data['x'][0] /1000),
e2ts(depth.data['x'][-1]/1000))
vert_vel.data = calc_vert_vel(depth.data)
mbpump.data,_ = load_sensor(g, 'm_de_oil_vol', chnk, chindex)
if len(mbpump.data['x']) > 1:
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_de_oil_vol'
pass
else:
mbpump.data,_ = load_sensor(g, 'm_ballast_pumped', chnk, chindex)
#for yax in fig2.select('mbpump'):
# yax.legend = 'm_ballast_pumped'
battpos.data,_ = load_sensor(g, 'm_battpos', chnk, chindex)
pitch.data,_ = load_sensor(g, 'm_pitch', chnk, chindex)
pitch.data['y'] = [math.degrees(y) for y in pitch.data['y']]
mfin.data,_ = load_sensor(g, 'm_fin', chnk, chindex)
cfin.data,_ = load_sensor(g, 'c_fin', chnk, chindex)
mroll.data,_ = load_sensor(g, 'm_roll', chnk, chindex)
mheading.data,_ = load_sensor(g, 'm_heading', chnk, chindex)
cheading.data,_ = load_sensor(g, 'c_heading', chnk, chindex)
mfin.data['y'] = [math.degrees(y) for y in mfin.data['y']]
cfin.data['y'] = [math.degrees(y) for y in cfin.data['y']]
mheading.data['y'] = [math.degrees(y) for y in mheading.data['y']]
cheading.data['y'] = [math.degrees(y) for y in cheading.data['y']]
mroll.data['y'] = [math.degrees(y) for y in mroll.data['y']]
fig1.yaxis[1].visible = True
fig2.yaxis[1].visible = True
fig3.yaxis[1].visible = True
#GLIDER SELECTS
def glider_buttons(increment):
ops = gliders.options
new_index = ops.index(gliders.value) + increment
if new_index >= len(ops):
new_index = 0
elif new_index < 0:
new_index = len(ops)-1
gliders.value = ops[new_index]
chunkation_update(None, None, None) #reset chunk indicator and clicks
def next_glider_func():
glider_buttons(1)
def prev_glider_func():
glider_buttons(-1)
def update_glider(attrib,old,new):
chunk_indicator.value = '0'
#update_data(None,None,None)
gliders.on_change('value', update_glider)
next_glider.on_click(next_glider_func)
prev_glider.on_click(prev_glider_func)
#CHUNK SELECTS
def chunkation_update(attrib,old,new):
chunk_indicator.value = '0'
prev_chunk.clicks = 0
next_chunk.clicks = 0
update_data(None,None,None)
if new == '-ALL-':
chunk_indicator.value = '-'
def chunk_func():
chunkdiff = prev_chunk.clicks - next_chunk.clicks
if chunkdiff < 0:
prev_chunk.clicks = 0
next_chunk.clicks = 0
chunkdiff = 0
print (chunkdiff)
chunk_indicator.value = str(chunkdiff)
def chunk_indicator_update(attrib,old,new):
try:
if abs(int(old)-int(new))>1: #manual update, triggers new non-manual indicator update, ie else clause below
prev_chunk.clicks = int(new)
next_chunk.clicks = 0
else:
update_data('chunk',int(old),int(new))
print("UPDATE", old, new)
except Exception as e:
print(type(e),e, old, new)
chunkations.on_change('value', chunkation_update)
chunk_indicator.on_change('value', chunk_indicator_update)
next_chunk.on_click(chunk_func)
prev_chunk.on_click(chunk_func)
update_data(None,None,None)
return vplot(control_box, figs)
p.scatter(x,
y,
marker="square_cross",
size=sizes,
color="#7FC97F",
fill_color=None,
line_width=2)
plots.append(p)
p = figure(title="diamond_cross")
p.scatter(x,
y,
marker="diamond_cross",
size=sizes,
color="#386CB0",
fill_color=None,
line_width=2)
plots.append(p)
p = figure(title="circle_cross")
p.scatter(x,
y,
marker="circle_cross",
size=sizes,
color="#FB8072",
fill_color=None,
line_width=2)
plots.append(p)
show(vplot(*plots)) # open a browser
def initialize_plot(self, ranges=None):
ranges = self.compute_ranges(self.layout, self.keys[-1], None)
plots = [[] for i in range(self.rows)]
passed_plots = []
tab_titles = {}
insert_rows, insert_cols = [], []
adjoined = False
for r, c in self.coords:
subplot = self.subplots.get((r, c), None)
if subplot is not None:
shared_plots = passed_plots if self.shared_axes else None
subplots = subplot.initialize_plot(ranges=ranges,
plots=shared_plots)
# Computes plotting offsets depending on
# number of adjoined plots
offset = sum(r >= ir for ir in insert_rows)
if len(subplots) > 2:
adjoined = True
# Add pad column in this position
insert_cols.append(c)
if r not in insert_rows:
# Insert and pad marginal row if none exists
plots.insert(r + offset,
[None for _ in range(len(plots[r]))])
# Pad previous rows
for ir in range(r):
plots[ir].insert(c + 1, None)
# Add to row offset
insert_rows.append(r)
offset += 1
# Add top marginal
plots[r + offset - 1] += [subplots.pop(-1), None]
elif len(subplots) > 1:
adjoined = True
# Add pad column in this position
insert_cols.append(c)
# Pad previous rows
for ir in range(r):
plots[r].insert(c + 1, None)
# Pad top marginal if one exists
if r in insert_rows:
plots[r + offset - 1] += 2 * [None]
else:
# Pad top marginal if one exists
if r in insert_rows:
plots[r + offset - 1] += [None] * (1 +
(c in insert_cols))
plots[r + offset] += subplots
if len(subplots) == 1 and c in insert_cols:
plots[r + offset].append(None)
passed_plots.append(subplots[0])
if self.tabs:
if isinstance(self.layout, Layout):
tab_titles[r, c] = ' '.join(self.paths[r, c])
else:
dim_vals = zip(self.layout.kdims, self.paths[r, c])
tab_titles[r, c] = ', '.join(
[d.pprint_value_string(k) for d, k in dim_vals])
# Replace None types with empty plots
# to avoid bokeh bug
if adjoined:
plots = layout_padding(plots)
# Determine the most appropriate composite plot type
# If the object cannot be displayed in a single layout
# it will be split into Tabs, for 1-row or 1-column
# Layouts we use the vplot and hplots.
# If there is a table and multiple rows and columns
# everything will be forced to a vertical layout
if self.tabs:
panels = [
Panel(child=child, title=tab_titles.get(r, c))
for r, row in enumerate(plots) for c, child in enumerate(row)
if child is not None
]
layout_plot = Tabs(tabs=panels)
elif len(plots) == 1 and not adjoined:
layout_plot = vplot(hplot(*plots[0]))
elif len(plots[0]) == 1:
layout_plot = vplot(*[p[0] for p in plots])
else:
layout_plot = gridplot(plots)
self.handles['plot'] = layout_plot
self.handles['plots'] = plots
self.drawn = True
return self.handles['plot']
global layout
global energy_per_capita
new_df = data_changed(energy_per_capita)
if new_df is not None:
energy_per_capita = new_df
plots_box.children[0] = create_line(energy_per_capita)
plots_box.children[1] = create_bar(energy_per_capita)
line = create_line(energy_per_capita)
bar = create_bar(energy_per_capita)
desc1 = Paragraph(text="""
This example shows live integration between bokeh server and Excel using
XLWings.""")
desc2 = Paragraph(text="""
*** YOU MUST HAVE EXCEL and XLWINGS INSTALLED ON YOUR MACHINE FOR IT TO WORK ***
""")
desc3 = Paragraph(text="""
It opens this plots window and an excel spreadsheet instance with the
values being plotted. When user changes the values on the excel spreadsheet
the plots will be updated accordingly. It's not required to save the spreadsheet for the plots to update.
""")
plots_box = hplot(line, bar)
layout = vplot(desc1, desc2, desc3, plots_box)
curdoc().add_root(layout)
curdoc().add_periodic_callback(update, 500)
session.show() # open the document in a browser
session.loop_until_closed() # run forever
def run_chart(exchange, asset, limit):
client = pymongo.MongoClient()
db = client['bitpredict_' + exchange]
collection = db[asset_ + '_predictions']
def get_data():
cursor = collection.find().limit(limit).sort('_id', pymongo.DESCENDING)
data = pd.DataFrame(list(cursor))
data = data.set_index('_id')
data = data.sort_index(ascending=True)
timestamps = pd.to_datetime(data.index, unit='s').to_series()
prices = data.price
predictions = data.prediction * 10000
returns = (data.position * data.change).cumsum() * 10000
return timestamps, prices, predictions, returns
timestamps, prices, predictions, returns = get_data()
output_server('bitpredict_' + exchange + '_extended')
background = '#f2f2f2'
ylabel_standoff = 0
xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%M"]))
yformatter = PrintfTickFormatter(format="%8.1f")
p1 = figure(title=None,
plot_width=750,
plot_height=300,
x_axis_type='datetime',
min_border_top=10,
min_border_bottom=33,
background_fill=background,
tools='',
toolbar_location=None)
p1.line(x=timestamps,
y=prices,
name='prices',
color='#4271ae',
line_width=1,
legend='Bitcoin Bid/Ask Midpoint',
line_cap='round',
line_join='round')
p1.legend.orientation = 'top_left'
p1.legend.border_line_color = background
p1.outline_line_color = None
p1.xgrid.grid_line_color = 'white'
p1.ygrid.grid_line_color = 'white'
p1.axis.axis_line_color = None
p1.axis.major_tick_line_color = None
p1.axis.minor_tick_line_color = None
p1.yaxis.axis_label = 'Price'
p1.yaxis.axis_label_standoff = ylabel_standoff
p1.xaxis.formatter = xformatter
p1.yaxis.formatter = PrintfTickFormatter(format='%8.2f')
p1.yaxis.major_label_text_font = 'courier'
p1.xaxis.major_label_text_font = 'courier'
p2 = figure(title=None,
plot_width=750,
plot_height=295,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=33,
background_fill=background,
tools='',
toolbar_location=None)
p2.line(x=timestamps,
y=predictions,
name='predictions',
color='#c82829',
line_width=1,
legend='30 Second Prediction',
line_cap='round',
line_join='round')
p2.legend.orientation = 'top_left'
p2.legend.border_line_color = background
p2.outline_line_color = None
p2.xgrid.grid_line_color = 'white'
p2.ygrid.grid_line_color = 'white'
p2.axis.axis_line_color = None
p2.axis.major_tick_line_color = None
p2.axis.minor_tick_line_color = None
p2.yaxis.axis_label = 'Basis Points'
p2.yaxis.axis_label_standoff = ylabel_standoff
p2.xaxis.formatter = xformatter
p2.yaxis.formatter = yformatter
p2.yaxis.major_label_text_font = 'courier'
p2.xaxis.major_label_text_font = 'courier'
p2.x_range = p1.x_range
p3 = figure(title=None,
plot_width=750,
plot_height=320,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=10,
background_fill=background,
x_axis_label='Greenwich Mean Time',
tools='',
toolbar_location=None)
p3.line(x=timestamps,
y=returns,
name='returns',
color='#8959a8',
line_width=1,
legend='Cumulative Return',
line_cap='round',
line_join='round')
p3.legend.orientation = 'top_left'
p3.legend.border_line_color = background
p3.outline_line_color = None
p3.xgrid.grid_line_color = 'white'
p3.ygrid.grid_line_color = 'white'
p3.axis.axis_line_color = None
p3.axis.major_tick_line_color = None
p3.axis.minor_tick_line_color = None
p3.yaxis.axis_label = 'Basis Points'
p3.yaxis.axis_label_standoff = ylabel_standoff
p3.xaxis.formatter = xformatter
p3.yaxis.formatter = yformatter
p3.xaxis.axis_label_standoff = 12
p3.yaxis.major_label_text_font = 'courier'
p3.xaxis.major_label_text_font = 'courier'
p3.x_range = p1.x_range
vp = vplot(p1, p2, p3)
push()
ip = load(urlopen('http://jsonip.com'))['ip']
ssn = cursession()
ssn.publish()
embed.autoload_server(vp, ssn, public=True)
renderer = p1.select(dict(name='prices'))
ds_prices = renderer[0].data_source
renderer = p2.select(dict(name='predictions'))
ds_predictions = renderer[0].data_source
renderer = p3.select(dict(name='returns'))
ds_returns = renderer[0].data_source
while True:
timestamps, prices, predictions, returns = get_data(exchange, limit)
ds_prices.data['x'] = timestamps
ds_predictions.data['x'] = timestamps
ds_returns.data['x'] = timestamps
ds_prices.data['y'] = prices
ds_predictions.data['y'] = predictions
ds_returns.data['y'] = returns
ssn.store_objects(ds_prices)
ssn.store_objects(ds_predictions)
ssn.store_objects(ds_returns)
time.sleep(60)
end=date(2004, 12, 31),
bounds=(date(2001, 1, 1), date(2006, 12, 31)))
y_range = Range1d(start=00, end=40, bounds=(10, 60))
y_range_extra = Range1d(start=300, end=700, bounds=(200, 1000))
###### -- end -- ########
plot_extra = figure(
x_axis_type="datetime",
x_range=x_range,
y_range=y_range,
title=
"Multiple ranges x:(2001/1/1, 2006/12/31), y1:(10, 60), y2:(200, 1000)")
plot_extra.line(x, apple_y, color='lightblue')
plot_extra.extra_y_ranges = {'goog': y_range_extra}
plot_extra.line(x, google_y, color='pink', y_range_name='goog')
plot_extra.add_layout(
LinearAxis(y_range_name="goog", major_label_text_color='pink'), 'left')
# Tweak the formats to make it all readable
plots = [
plot_datarange, plot_range, plot_range_un, plot_range_rev,
plot_cat_autobounded, plot_cat_unbounded, plot_cat_bounded, plot_extra
]
for plot in plots:
plot.title_text_font_size = '12pt'
show(
vplot(plot_datarange, hplot(plot_range, plot_range_un, plot_range_rev),
hplot(plot_cat_autobounded, plot_cat_unbounded, plot_cat_bounded),
plot_extra))
data['x'] = [];
data['y'] = [];
}
else {
if (mode == 'Linear') { interp = linear; }
else if (mode == 'Step (before)') { interp = step; step.set('mode', 'before'); }
else if (mode == 'Step (center)') { interp = step; step.set('mode', 'center'); }
else if (mode == 'Step (after)') { interp = step; step.set('mode', 'after'); }
for (i=0; i < %d; i++) {
data['x'][i] = i * dx
data['y'][i] = interp.compute(data['x'][i])
}
}
source.trigger('change')
""" % (N, N))
mode = Select(title='Interpolation Mode',
value='None',
options=[
'None', 'Linear', 'Step (before)', 'Step (center)',
'Step (after)'
],
callback=callback)
output_file("transform_interpolator.html", title="Example Transforms")
show(vplot(hplot(mode), p))
def create(self):
manufacturers = sorted(mpg["manufacturer"].unique())
models = sorted(mpg["model"].unique())
transmissions = sorted(mpg["trans"].unique())
drives = sorted(mpg["drv"].unique())
classes = sorted(mpg["class"].unique())
manufacturer_select = Select(title="Manufacturer:", value="All", options=["All"] + manufacturers)
manufacturer_select.on_change('value', self.on_manufacturer_change)
model_select = Select(title="Model:", value="All", options=["All"] + models)
model_select.on_change('value', self.on_model_change)
transmission_select = Select(title="Transmission:", value="All", options=["All"] + transmissions)
transmission_select.on_change('value', self.on_transmission_change)
drive_select = Select(title="Drive:", value="All", options=["All"] + drives)
drive_select.on_change('value', self.on_drive_change)
class_select = Select(title="Class:", value="All", options=["All"] + classes)
class_select.on_change('value', self.on_class_change)
columns = [
TableColumn(field="manufacturer", title="Manufacturer", editor=SelectEditor(options=manufacturers), formatter=StringFormatter(font_style="bold")),
TableColumn(field="model", title="Model", editor=StringEditor(completions=models)),
TableColumn(field="displ", title="Displacement", editor=NumberEditor(step=0.1), formatter=NumberFormatter(format="0.0")),
TableColumn(field="year", title="Year", editor=IntEditor()),
TableColumn(field="cyl", title="Cylinders", editor=IntEditor()),
TableColumn(field="trans", title="Transmission", editor=SelectEditor(options=transmissions)),
TableColumn(field="drv", title="Drive", editor=SelectEditor(options=drives)),
TableColumn(field="class", title="Class", editor=SelectEditor(options=classes)),
TableColumn(field="cty", title="City MPG", editor=IntEditor()),
TableColumn(field="hwy", title="Highway MPG", editor=IntEditor()),
]
data_table = DataTable(source=self.source, columns=columns, editable=True)
plot = Plot(title=None, x_range= DataRange1d(), y_range=DataRange1d(), plot_width=1000, plot_height=300)
# Set up x & y axis
plot.add_layout(LinearAxis(), 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
# Add Glyphs
cty_glyph = Circle(x="index", y="cty", fill_color="#396285", size=8, fill_alpha=0.5, line_alpha=0.5)
hwy_glyph = Circle(x="index", y="hwy", fill_color="#CE603D", size=8, fill_alpha=0.5, line_alpha=0.5)
cty = plot.add_glyph(self.source, cty_glyph)
hwy = plot.add_glyph(self.source, hwy_glyph)
# Add the tools
tooltips = [
("Manufacturer", "@manufacturer"),
("Model", "@model"),
("Displacement", "@displ"),
("Year", "@year"),
("Cylinders", "@cyl"),
("Transmission", "@trans"),
("Drive", "@drv"),
("Class", "@class"),
]
cty_hover_tool = HoverTool(renderers=[cty], tooltips=tooltips + [("City MPG", "@cty")])
hwy_hover_tool = HoverTool(renderers=[hwy], tooltips=tooltips + [("Highway MPG", "@hwy")])
select_tool = BoxSelectTool(renderers=[cty, hwy], dimensions=['width'])
plot.add_tools(cty_hover_tool, hwy_hover_tool, select_tool)
controls = vplot(manufacturer_select, model_select, transmission_select, drive_select, class_select)
top_panel = hplot(controls, plot)
layout = vplot(top_panel, data_table)
return layout
def update():
global layout
global energy_per_capita
new_df = data_changed(energy_per_capita)
if new_df is not None:
energy_per_capita = new_df
plots_box.children[0] = create_line(energy_per_capita)
plots_box.children[1] = create_bar(energy_per_capita)
line = create_line(energy_per_capita)
bar = create_bar(energy_per_capita)
desc1 = Paragraph(text="""
This example shows live integration between bokeh server and Excel using
XLWings.""")
desc2 = Paragraph(text="""
*** YOU MUST HAVE EXCEL and XLWINGS INSTALLED ON YOUR MACHINE FOR IT TO WORK ***
""")
desc3 = Paragraph(text="""
It opens this plots window and an excel spreadsheet instance with the
values being plotted. When user changes the values on the excel spreadsheet
the plots will be updated accordingly. It's not required to save the spreadsheet for the plots to update.
""")
plots_box = hplot(line, bar)
layout = vplot(desc1, desc2, desc3, plots_box)
curdoc().add_root(layout)
curdoc().add_periodic_callback(update, 500)
session.show() # open the document in a browser
session.loop_until_closed() # run forever
from bokeh.io import output_file, show, vplot
from bokeh.plotting import figure
output_file("layout.html")
x = list(range(11))
y0 = x
y1 = [10 - i for i in x]
y2 = [abs(i - 5) for i in x]
# create a new plot
s1 = figure(width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)
# create another one
s2 = figure(width=250, height=250, title=None)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)
# create and another
s3 = figure(width=250, height=250, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)
# put all the plots in a VBox
p = vplot(s1, s2, s3)
# show the results
show(p)
def make_patches_plot(x_range, y_range, source):
with make_plot(x_range, y_range, source) as plot:
glyph = Patches(xs='xs', ys='ys', fill_color='value', fill_alpha=0.8, line_color='#CCCCCC', line_alpha=0.8)
plot.add_glyph(source, glyph)
return plot
def make_multiline_plot(x_range, y_range, source):
with make_plot(x_range, y_range, source) as plot:
glyph = MultiLine(xs='xs', ys='ys', line_color='value', line_width=5, line_alpha=0.8)
plot.add_glyph(source, glyph)
return plot
patch = make_patch_plot(Range1d(2010, 2013), Range1d(0, 5), patch_data)
line = make_line_plot(Range1d(2010, 2013), Range1d(0, 5), patch_data)
patches = make_patches_plot(Range1d(-5, 10), Range1d(-5, 10), patches_data)
multiline = make_multiline_plot(Range1d(-5, 10), Range1d(-5, 10), patches_data)
patch_with_hole = make_patch_plot(Range1d(2010, 2017), Range1d(0, 5), patch_with_hole_data)
line_with_hole = make_line_plot(Range1d(2010, 2016), Range1d(0, 5), patch_with_hole_data)
patches_with_hole = make_patches_plot(Range1d(-5, 10), Range1d(-5, 10), patches_with_hole_data)
multiline_with_hole = make_multiline_plot(Range1d(-5, 10), Range1d(-5, 10), patches_with_hole_data)
show(vplot(
patch, line, patches, multiline,
patches_with_hole, line_with_hole, patches_with_hole, multiline_with_hole
))
def do_plot(self, inputDir, plotOutDir, plotOutFileName, simDataFile,
validationDataFile, metadata):
if metadata["variant"] != "tfActivity":
print "This plot only runs for the 'tfActivity' variant."
return
if not os.path.isdir(inputDir):
raise Exception, "inputDir does not currently exist as a directory"
ap = AnalysisPaths(inputDir, variant_plot=True)
variants = sorted(ap._path_data['variant'].tolist()
) # Sorry for accessing private data
if 0 in variants:
variants.remove(0)
if len(variants) == 0:
return
all_cells = sorted(
ap.get_cells(variant=variants, seed=[0], generation=[0]))
if not os.path.exists(plotOutDir):
os.mkdir(plotOutDir)
expectedProbBound = []
simulatedProbBound = []
expectedSynthProb = []
simulatedSynthProb = []
targetId = []
targetCondition = []
targetToTfType = {}
for variant, simDir in zip(variants, all_cells):
sim_data = cPickle.load(open(ap.get_variant_kb(variant), "rb"))
shape = sim_data.process.transcription_regulation.recruitmentData[
"shape"]
hI = sim_data.process.transcription_regulation.recruitmentData[
"hI"]
hJ = sim_data.process.transcription_regulation.recruitmentData[
"hJ"]
hV = sim_data.process.transcription_regulation.recruitmentData[
"hV"]
H = np.zeros(shape, np.float64)
H[hI, hJ] = hV
colNames = sim_data.process.transcription_regulation.recruitmentColNames
tfList = ["basal (no TF)"] + sorted(
sim_data.tfToActiveInactiveConds)
simOutDir = os.path.join(simDir, "simOut")
tf = tfList[(variant + 1) // 2]
tfStatus = None
if variant % 2 == 1:
tfStatus = "active"
else:
tfStatus = "inactive"
bulkMoleculesReader = TableReader(
os.path.join(simOutDir, "BulkMolecules"))
bulkMoleculeIds = bulkMoleculesReader.readAttribute("objectNames")
rnaSynthProbReader = TableReader(
os.path.join(simOutDir, "RnaSynthProb"))
rnaIds = rnaSynthProbReader.readAttribute("rnaIds")
tfTargetBoundIds = []
tfTargetBoundIndices = []
tfTargetSynthProbIds = []
tfTargetSynthProbIndices = []
for tfTarget in sorted(sim_data.tfToFC[tf]):
tfTargetBoundIds.append(tfTarget + "__" + tf)
tfTargetBoundIndices.append(
bulkMoleculeIds.index(tfTargetBoundIds[-1]))
tfTargetSynthProbIds.append(tfTarget + "[c]")
tfTargetSynthProbIndices.append(
rnaIds.index(tfTargetSynthProbIds[-1]))
tfTargetBoundCountsAll = bulkMoleculesReader.readColumn(
"counts")[:, tfTargetBoundIndices]
tfTargetSynthProbAll = rnaSynthProbReader.readColumn(
"rnaSynthProb")[:, tfTargetSynthProbIndices]
for targetIdx, tfTarget in enumerate(sorted(sim_data.tfToFC[tf])):
tfTargetBoundCounts = tfTargetBoundCountsAll[:,
targetIdx].reshape(
-1)
expectedProbBound.append(sim_data.pPromoterBound[tf + "__" +
tfStatus][tf])
simulatedProbBound.append(tfTargetBoundCounts[5:].mean())
tfTargetSynthProbId = [tfTarget + "[c]"]
tfTargetSynthProbIndex = np.array(
[rnaIds.index(x) for x in tfTargetSynthProbId])
tfTargetSynthProb = tfTargetSynthProbAll[:,
targetIdx].reshape(-1)
rnaIdx = np.where(
sim_data.process.transcription.rnaData["id"] == tfTarget +
"[c]")[0][0]
regulatingTfIdxs = np.where(H[rnaIdx, :])
for i in regulatingTfIdxs[0]:
if colNames[i].split("__")[1] != "alpha":
if tfTarget not in targetToTfType:
targetToTfType[tfTarget] = []
targetToTfType[tfTarget].append(
sim_data.process.transcription_regulation.
tfToTfType[colNames[i].split("__")[1]])
expectedSynthProb.append(
sim_data.process.transcription.rnaSynthProb[
tf + "__" + tfStatus][rnaIdx])
simulatedSynthProb.append(tfTargetSynthProb[5:].mean())
targetId.append(tfTarget)
targetCondition.append(tf + "__" + tfStatus)
bulkMoleculesReader.close()
rnaSynthProbReader.close()
expectedProbBound = np.array(expectedProbBound)
simulatedProbBound = np.array(simulatedProbBound)
expectedSynthProb = np.array(expectedSynthProb)
simulatedSynthProb = np.array(simulatedSynthProb)
regressionResult = scipy.stats.linregress(
np.log10(expectedProbBound[expectedProbBound > NUMERICAL_ZERO]),
np.log10(simulatedProbBound[expectedProbBound > NUMERICAL_ZERO]))
regressionResultLargeValues = scipy.stats.linregress(
np.log10(expectedProbBound[expectedProbBound > 1e-2]),
np.log10(simulatedProbBound[expectedProbBound > 1e-2]))
ax = plt.subplot(2, 1, 1)
ax.scatter(np.log10(expectedProbBound), np.log10(simulatedProbBound))
plt.xlabel("log10(Expected probability bound)", fontsize=6)
plt.ylabel("log10(Simulated probability bound)", fontsize=6)
plt.title(
"Slope: %0.3f Intercept: %0.3e (Without Small Values: Slope: %0.3f Intercept: %0.3e)"
% (regressionResult.slope, regressionResult.intercept,
regressionResultLargeValues.slope,
regressionResultLargeValues.intercept),
fontsize=6)
ax.tick_params(which='both', direction='out', labelsize=6)
regressionResult = scipy.stats.linregress(
np.log10(expectedSynthProb[expectedSynthProb > NUMERICAL_ZERO]),
np.log10(simulatedSynthProb[expectedSynthProb > NUMERICAL_ZERO]))
ax = plt.subplot(2, 1, 2)
ax.scatter(np.log10(expectedSynthProb), np.log10(simulatedSynthProb))
plt.xlabel("log10(Expected synthesis probability)", fontsize=6)
plt.ylabel("log10(Simulated synthesis probability)", fontsize=6)
plt.title("Slope: %0.3f Intercept: %0.3e" %
(regressionResult.slope, regressionResult.intercept),
fontsize=6)
ax.tick_params(which='both', direction='out', labelsize=6)
plt.tight_layout()
exportFigure(plt, plotOutDir, plotOutFileName, metadata)
plt.close("all")
# Probability bound - hover for ID
source1 = ColumnDataSource(data=dict(x=np.log10(expectedProbBound),
y=np.log10(simulatedProbBound),
ID=targetId,
condition=targetCondition))
hover1 = HoverTool(tooltips=[("ID", "@ID"), ("condition",
"@condition")])
tools1 = [
hover1,
BoxZoomTool(),
LassoSelectTool(),
PanTool(),
WheelZoomTool(),
ResizeTool(),
UndoTool(),
RedoTool(), "reset"
]
s1 = figure(x_axis_label="log10(Expected probability bound)",
y_axis_label="log10(Simulated probability bound)",
width=800,
height=500,
tools=tools1)
s1.scatter("x", "y", source=source1)
if not os.path.exists(os.path.join(plotOutDir, "html_plots")):
os.makedirs(os.path.join(plotOutDir, "html_plots"))
bokeh.io.output_file(os.path.join(
plotOutDir, "html_plots",
plotOutFileName + "__probBound" + ".html"),
title=plotOutFileName,
autosave=False)
bokeh.io.save(s1)
# Synthesis probability - hover for ID
source2 = ColumnDataSource(data=dict(x=np.log10(expectedSynthProb),
y=np.log10(simulatedSynthProb),
ID=targetId,
condition=targetCondition))
hover2 = HoverTool(tooltips=[("ID", "@ID"), ("condition",
"@condition")])
tools2 = [
hover2,
BoxZoomTool(),
LassoSelectTool(),
PanTool(),
WheelZoomTool(),
ResizeTool(),
UndoTool(),
RedoTool(), "reset"
]
s2 = figure(x_axis_label="log10(Expected synthesis probability)",
y_axis_label="log10(Simulated synthesis probability)",
width=800,
height=500,
tools=tools2)
s2.scatter("x", "y", source=source2)
bokeh.io.output_file(os.path.join(
plotOutDir, "html_plots",
plotOutFileName + "__synthProb" + ".html"),
title=plotOutFileName,
autosave=False)
bokeh.io.save(s2)
# Synthesis probability - filter targets by TF type
bokeh.io.output_file(os.path.join(
plotOutDir, "html_plots",
plotOutFileName + "__synthProb__interactive" + ".html"),
title=plotOutFileName,
autosave=False)
tfTypes = []
for i in targetId:
if i in targetToTfType:
uniqueSet = np.unique(targetToTfType[i])
if uniqueSet.shape[0] == 1:
tfTypes.append(uniqueSet[0])
elif uniqueSet.shape[0] == 3:
tfTypes.append("all")
else:
tfTypes.append(uniqueSet[0] + "_" + uniqueSet[1])
else:
tfTypes.append("none")
tfTypes = np.array(tfTypes)
x0 = np.copy(expectedSynthProb)
x0[np.where(tfTypes != "0CS")] = np.nan
x1 = np.copy(expectedSynthProb)
x1[np.where(tfTypes != "1CS")] = np.nan
x2 = np.copy(expectedSynthProb)
x2[np.where(tfTypes != "2CS")] = np.nan
x01 = np.copy(expectedSynthProb)
x01[np.where(tfTypes != "0CS_1CS")] = np.nan
x02 = np.copy(expectedSynthProb)
x02[np.where(tfTypes != "0CS_2CS")] = np.nan
x12 = np.copy(expectedSynthProb)
x12[np.where(tfTypes != "1CS_2CS")] = np.nan
y0 = np.copy(simulatedSynthProb)
y0[np.where(tfTypes != "0CS")] = np.nan
y1 = np.copy(simulatedSynthProb)
y1[np.where(tfTypes != "1CS")] = np.nan
y2 = np.copy(simulatedSynthProb)
y2[np.where(tfTypes != "2CS")] = np.nan
y01 = np.copy(simulatedSynthProb)
y01[np.where(tfTypes != "0CS_1CS")] = np.nan
y02 = np.copy(simulatedSynthProb)
y02[np.where(tfTypes != "0CS_2CS")] = np.nan
y12 = np.copy(simulatedSynthProb)
x12[np.where(tfTypes != "1CS_2CS")] = np.nan
source_all = ColumnDataSource(data=dict(x=np.log10(expectedSynthProb),
y=np.log10(simulatedSynthProb),
ID=targetId,
condition=targetCondition))
source_tf = ColumnDataSource(
data=dict(x0=np.log10(x0),
y0=np.log10(y0),
x1=np.log10(x1),
y1=np.log10(y1),
x2=np.log10(x2),
y2=np.log10(y2),
x01=np.log10(x01),
y01=np.log10(y01),
x02=np.log10(x02),
y02=np.log10(y02),
x12=np.log10(x12),
y12=np.log10(y12),
x123=np.log10(expectedSynthProb),
y123=np.log10(simulatedSynthProb),
ID=targetId,
condition=targetCondition))
hover3 = HoverTool(tooltips=[("ID", "@ID"), ("condition",
"@condition")])
tools3 = [
hover3,
BoxZoomTool(),
LassoSelectTool(),
PanTool(),
WheelZoomTool(),
ResizeTool(),
UndoTool(),
RedoTool(), "reset"
]
axis_max = np.ceil(np.log10(expectedSynthProb).max())
for i in np.sort(expectedSynthProb):
if i > 0:
break
axis_min = np.floor(np.log10(i))
s3 = figure(
x_axis_label="log10(Expected synthesis probability)",
y_axis_label="log10(Simulated synthesis probability)",
plot_width=800,
plot_height=500,
x_range=(axis_min, axis_max),
y_range=(axis_min, axis_max),
tools=tools3,
)
s3.scatter("x", "y", source=source_all)
callback = CustomJS(args=dict(source_all=source_all,
source_tf=source_tf),
code="""
var data_all = source_all.get('data');
var data_tf = source_tf.get('data');
data_all['x'] = data_tf['x' + cb_obj.get("name")];
data_all['y'] = data_tf['y' + cb_obj.get("name")];
source_all.trigger('change');
""")
toggle0 = Button(label="0CS", callback=callback, name="0")
toggle1 = Button(label="1CS", callback=callback, name="1")
toggle2 = Button(label="2CS", callback=callback, name="2")
toggle3 = Button(label="0CS and 1CS", callback=callback, name="01")
toggle4 = Button(label="0CS and 2CS", callback=callback, name="02")
toggle5 = Button(label="1CS and 2CS", callback=callback, name="12")
toggle6 = Button(label="All", callback=callback, name="123")
layout = vplot(toggle0, toggle1, toggle2, toggle3, toggle4, toggle5,
toggle6, s3)
bokeh.io.save(layout)
bokeh.io.curstate().reset()
p.circle(x='xu', y='y', color='navy', source=source, size=5, alpha=0.5)
label_data = ColumnDataSource(data=dict(
x=[1, 2, 3], y=[10, 10, 10], t=['Original', 'Normal', 'Uniform']))
labels = Label(x='x',
y='y',
text='t',
y_offset=2,
source=label_data,
render_mode='css',
text_align='center')
p.add_annotation(labels)
callback = CustomJS(args=dict(source=source, normal=normal, uniform=uniform),
code="""
data=source.get('data')
for (i=0; i < data['y'].length; i++) {
data['xn'][i] = normal.compute(data['x'][i]+1)
}
for (i=0; i < data['y'].length; i++) {
data['xu'][i] = uniform.compute(data['x'][i]+2)
}
source.trigger('change')
""")
button = Button(label='Press to apply Jitter!', callback=callback)
output_file("transform_jitter.html", title="Example Jitter Transform")
show(vplot(button, p))
s3.hbar(y = [1,2,3], height=0.5, left = 0, right = stats_deaths, color="#CAB2D6")"""
#vertical_plot = vplot(p,s3)
s3.xaxis.axis_label = 'Estimated Number of Cases (1000)'
#s3.yaxis.axis_label = 'Cancer Type'
s3.title.text_font_size = "25px"
label_opts = dict(
x=230, y=0,
x_units='screen', y_units='screen'
)
msg1 = '(Based on Developed and under developed Region)' #Subtitle addition hack as it is not supported adding through label for plot: mortality and new cases for breast cancer plot.
caption1 = Label(text =msg1, **label_opts)
s3.add_layout(caption1, "above")
p = hplot(s1,vplot(s2,s3))
"""p.legend.label_text_font = "times"
p.legend.label_text_font_style = "italic"
p.legend.label_text_color = "navy"""
#show(vertical_plot)
show(p)
#plots = {'Average': s1, "Stages": s2}
#script, div = components(plots)
#print script
#print div
def makeGraphs(self):
# create a new plot with a title and axis labels
heat = figure(title="#CapitalOne Caption Sentiment Heat Map",
y_axis_label='Sentiment Percentage (%)',
y_range=(-5,105), x_range=(-5,(MAX_COUNT/3)+5))
heat.axis[0].major_label_text_color = None
heat.axis[0].ticker.num_minor_ticks = 0
positiveColor = "#003A6F" # Capital One woodmark color
neutralColor = "#686868" # Grey neutral color
negativeColor = "#A12830" # Capital One logo color
# Create different colored circles based on sentiment
heat.circle(self.positiveX, self.positiveY, radius=self.positiveRadius, fill_color=positiveColor, fill_alpha=0.5, line_color=None)
heat.circle(self.neutralX, self.neutralY, radius=self.neutralRadius, fill_color=neutralColor, fill_alpha=0.5, line_color=None)
heat.circle(self.negativeX, self.negativeY, radius=self.negativeRadius, fill_color=negativeColor, fill_alpha=0.5, line_color=None)
# Adds sentiment n to n-1 to create a list from 0-1
captionAvgTemp = {
'positive': self.captionSentimentAverage['positive'],
'neutral': self.captionSentimentAverage['neutral']+self.captionSentimentAverage['positive'],
'negative': self.captionSentimentAverage['negative']+self.captionSentimentAverage['neutral']+self.captionSentimentAverage['positive']
}
# Creates slices for pie graphs
startsCaption = [captionAvgTemp[p]*2*pi for p in captionAvgTemp]
startsCaption.insert(0,0)
del startsCaption[-1]
endsCaption = [captionAvgTemp[p]*2*pi for p in captionAvgTemp]
colors1 = [positiveColor,neutralColor,negativeColor]
legend1 = ["Positive", "Neutral", "Negative"]
#create plot
captionPie = figure(title="#CapitalOne Caption Sentiment Pie Graph",x_range=(-1,1.5), y_range=(-1,1))
#create pie chart and legend
captionPie.wedge(x=.2, y=0, radius=1, start_angle=startsCaption, end_angle=endsCaption, color=colors1)
captionPie.circle(0,0,radius=0,legend=legend1[0],color=colors1[0])
captionPie.circle(0,0,radius=0,legend=legend1[1],color=colors1[1])
captionPie.circle(0,0,radius=0,legend=legend1[2],color=colors1[2])
# Turn off tick labels
captionPie.axis.major_label_text_color = None
# Turn off tick marks
captionPie.axis.major_tick_line_color = None
captionPie.axis[0].ticker.num_minor_ticks = 0
captionPie.axis[1].ticker.num_minor_ticks = 0
# Each average adds the average before it so the array will be a list containing floats from 0-1
# This is necessary to make the pie graph
imageAvgTemp = self.imageSentimentAverage
prevAvg = ""
for avg in imageAvgTemp:
if prevAvg != "":
imageAvgTemp[avg] += imageAvgTemp[prevAvg]
prevAvg = avg
# Create slices for pie graph
startsImage = [imageAvgTemp[p]*2*pi for p in imageAvgTemp]
startsImage.insert(0,0)
del startsImage[-1]
endsImage = [imageAvgTemp[p]*2*pi for p in imageAvgTemp]
# a color for each pie piece
colors2 = ["#A12830", "#FAA43A", "#5DA5DA", "#686868", "#DECF3F", "#003A6F"]
legend2 = ["Fear", "Angry", "Sad", "Neutral", "Surprise", "Happy"]
#create plot
imagePie = figure(title="#CapitalOne Image Sentiment Pie Graph",x_range=(-1,1.5), y_range=(-1,1))
#create pie chart and legend
imagePie.wedge(x=.2, y=0, radius=1, start_angle=startsImage, end_angle=endsImage, color=colors2)
imagePie.circle(0,0,radius=0,legend=legend2[0],color=colors2[0])
imagePie.circle(0,0,radius=0,legend=legend2[1],color=colors2[1])
imagePie.circle(0,0,radius=0,legend=legend2[2],color=colors2[2])
imagePie.circle(0,0,radius=0,legend=legend2[3],color=colors2[3])
imagePie.circle(0,0,radius=0,legend=legend2[4],color=colors2[4])
imagePie.circle(0,0,radius=0,legend=legend2[5],color=colors2[5])
# Turn off tick labels
imagePie.axis.major_label_text_color = None
# Turn off tick marks
imagePie.axis.major_tick_line_color = None
imagePie.axis[0].ticker.num_minor_ticks = 0
imagePie.axis[1].ticker.num_minor_ticks = 0
# output to static HTML file
output_file("sentimentgraphs.html", title="#CapitalOne Trend")
p = vplot(heat,captionPie,imagePie)
# show the results
show(p)
## Plot with multiple ranges that are bounded
x = np.array(AAPL['date'], dtype=np.datetime64)
x = x[0:1000]
apple_y = AAPL['adj_close'][0:1000]
google_y = GOOG['adj_close'][0:1000]
###### -- ranges set here -- ########
x_range = Range1d(
start=date(2000, 1, 1), end=date(2004, 12, 31),
bounds=(date(2000, 1, 1), date(2006, 12, 31)),
min_interval=timedelta(100),
)
y_range = Range1d(start=0, end=40, bounds=(0, 60))
y_range_extra = Range1d(start=300, end=700, bounds=(200, 1000))
###### -- end -- ########
plot_extra = figure(x_axis_type="datetime", x_range=x_range, y_range=y_range, title="Multiple ranges x:(2000/1/1, 2006/12/31), y1:(10, 60), y2:(200, 1000)")
plot_extra.line(x, apple_y, color='lightblue')
plot_extra.extra_y_ranges = {'goog': y_range_extra}
plot_extra.line(x, google_y, color='pink', y_range_name='goog')
plot_extra.add_layout(LinearAxis(y_range_name="goog", major_label_text_color='pink'), 'left')
# Tweak the formats to make it all readable
plots = [plot_default, plot_range, plot_range_un, plot_range_rev, plot_cat_autobounded, plot_cat_unbounded, plot_cat_bounded, plot_extra]
for plot in plots:
plot.title_text_font_size = '12pt'
show(vplot(plot_default, hplot(plot_range, plot_range_un, plot_range_rev), hplot(plot_cat_unbounded, plot_cat_autobounded, plot_cat_bounded), plot_extra))
def plot_time_series(self, moving_avg=False):
"""
Plots time series and moving average filter
when moving_avg set to true. This plot is currently done for
3 climate variables.
"""
t_fig_dict = dict()
# Parse individual time series
n_dict = parse_time_series(self.html_tags, self.txt)
series = list()
cds_dict = dict()
for k in list(sorted(n_dict.keys())):
v = n_dict[k]
# Creating fig inst in dict for plot k
t_fig_dict[k] = figure(title=k.split(",")[-1].replace(".txt", "").replace("_", " "),
height=290)
# Sort by x-axis(dates) in order to ensure a
# sound plot
tmp = list(v.items())
tmp.sort()
np.array(tmp)
date = np.array(tmp)[:, 0]
vals = np.array(tmp)[:, 1]
tooltips = OrderedDict([
("time", "@x"),
("value", "@y"),
])
cds_dict[k+'1'] = ColumnDataSource(
data=dict(
x=date,
y=vals,
)
)
if moving_avg:
# Moving averaged filter data
vals_a = self.causal_avg_filter(vals)
date_a = date[self.radius:]
cds_dict[k+'2'] = ColumnDataSource(
data=dict(
x=date_a,
y=vals_a,
)
)
# Plot moving averaged filtered data
# k + " avg data"
line_a = Line(x="x", y="y",
line_color='blue', line_width=6)
circle_renderer = t_fig_dict[k].add_glyph(cds_dict[k+'2'],
line_a)
_update_legend(plot=t_fig_dict[k],
legend_name=k .replace(".txt", "").replace(
"_", " ") + " avg data", glyph_renderer=circle_renderer)
t_fig_dict[k].add_tools(
HoverTool(tooltips=tooltips, renderers=[circle_renderer]))
# Plot raw data
line_r = Line(x="x", y="y",
line_color='red')
circle_renderer2 = t_fig_dict[k].add_glyph(cds_dict[k+'1'], line_r)
_update_legend(plot=t_fig_dict[k],
legend_name=k.replace(".txt", "").replace(
"_", " ") + " raw data", glyph_renderer=circle_renderer2)
t_fig_dict[k].add_tools(
HoverTool(tooltips=tooltips, renderers=[circle_renderer2]))
# Unpack the fig instances in dict plot as a vertical stack of
# horizontal plots
v = vplot(*list(t_fig_dict.values()))
return v
def update():
global layout
global energy_per_capita
new_df = data_changed(energy_per_capita)
if new_df is not None:
energy_per_capita = new_df
layout.children[0] = create_line(energy_per_capita)
layout.children[1] = create_bar(energy_per_capita)
line = create_line(energy_per_capita)
bar = create_bar(energy_per_capita)
desc1 = Paragraph(text="""
This example shows live integration between bokeh server and Excel using
XLWings.""")
desc2 = Paragraph(text="""
*** YOU MUST HAVE EXCEL and XLWINGS INSTALLED ON YOUR MACHINE FOR IT TO WORK ***
""")
desc3 = Paragraph(text="""
It opens this plots window and an excel spreadsheet instance with the
values being plotted. When user changes the values on the excel spreadsheet
the plots will be updated accordingly. It's not required to save the spreadsheet for the plots to update.
""")
layout = vplot(desc1, desc2, desc3, hplot(line, bar))
curdoc().add_root(layout)
curdoc().add_periodic_callback(update, 500)
session.show() # open the document in a browser
session.loop_until_closed() # run forever
ConfusionMatrix = namedtuple("confusion_matrix", "f1 precision recall")
def metrics():
return {metric: [random.random() for _ in range(10)]
for metric in ConfusionMatrix._fields}
scores = {dataset_name: metrics() for dataset_name in Datasets._fields}
properties_per_dataset = {
'train': {'line_color': 'firebrick'},
'test': {'line_color': 'orange'},
'valid': {'line_color': 'olive'}
}
plots = []
for metric in ConfusionMatrix._fields:
plot = figure(width=500, plot_height=500, title=metric)
for dataset_name in scores:
metric_scores = scores[dataset_name][metric]
plot.line(range(len(metric_scores)),
metric_scores,
legend=dataset_name,
**properties_per_dataset[dataset_name])
plots.append(plot)
p = vplot(*plots)
show(p)
p3.outline_line_color = None
p3.xgrid.grid_line_color = 'white'
p3.ygrid.grid_line_color = 'white'
p3.axis.axis_line_color = None
p3.axis.major_tick_line_color = None
p3.axis.minor_tick_line_color = None
p3.yaxis.axis_label = 'Basis Points'
p3.yaxis.axis_label_standoff = ylabel_standoff
p3.xaxis.formatter = xformatter
p3.yaxis.formatter = yformatter
p3.xaxis.axis_label_standoff = 12
p3.yaxis.major_label_text_font = 'courier'
p3.xaxis.major_label_text_font = 'courier'
p3.x_range = p1.x_range
vp = vplot(p1, p2, p3)
push()
ip = load(urlopen('http://jsonip.com'))['ip']
ssn = cursession()
ssn.publish()
tag = embed.autoload_server(vp, ssn, public=True).replace('localhost', ip)
html = """
{%% extends "layout.html" %%}
{%% block bokeh %%}
%s
{%% endblock %%}
""" % tag
with open('templates/index.html', 'w+') as f:
f.write(html)
'''
import pandas as pd
from bokeh.charts import Bar, output_file, show, defaults
import bokeh.plotting as bp
from bokeh.models import FixedTicker, LinearAxis
from bokeh.io import output_file, show, vplot
crapsArray(100, 5, 300)
csv=pd.read_csv('test.csv')
output_file("craps.html")
#Plot sizes for charts
defaults.width = 1200
defaults.height = 300
action = Bar(csv, 'roll', values = 'score', title = "Craps Outcomes", bar_width = 1, color = 'score')
money = Bar(csv, 'roll', values = 'bank', title = "Craps Bankroll", bar_width = 1, color = 'score')
winLose = Bar(csv, 'roll', values = 'value', title = "Craps Roll Win/Loss", bar_width = 1, color = 'score')
xaxis=action.select({'type' : LinearAxis})
xaxis.ticker=FixedTicker(ticks=[0,25,50,75,100])
yaxis=action.select({'type' : LinearAxis})
yaxis.ticker=FixedTicker(ticks=[2,3,4,5,6,7,8,9,10,11,12])
p = vplot(action, money,winLose)
show(p)
print("Cashing Out")
###### -- end -- ########
## Plot with multiple ranges that are bounded
x = np.array(AAPL['date'], dtype=np.datetime64)
x = x[0:1000]
apple_y = AAPL['adj_close'][0:1000]
google_y = GOOG['adj_close'][0:1000]
###### -- ranges set here -- ########
x_range = Range1d(
start=date(2000, 1, 1), end=date(2004, 12, 31),
bounds=(date(2001, 1, 1), date(2006, 12, 31))
)
y_range = Range1d(start=00, end=40, bounds=(10, 60))
y_range_extra = Range1d(start=300, end=700, bounds=(200, 1000))
###### -- end -- ########
plot_extra = figure(x_axis_type="datetime", x_range=x_range, y_range=y_range, title="Multiple ranges x:(2001/1/1, 2006/12/31), y1:(10, 60), y2:(200, 1000)")
plot_extra.line(x, apple_y, color='lightblue')
plot_extra.extra_y_ranges = {'goog': y_range_extra}
plot_extra.line(x, google_y, color='pink', y_range_name='goog')
plot_extra.add_layout(LinearAxis(y_range_name="goog", major_label_text_color='pink'), 'left')
# Tweak the formats to make it all readable
plots = [plot_default, plot_range, plot_range_un, plot_range_rev, plot_cat_autobounded, plot_cat_unbounded, plot_cat_bounded, plot_extra]
for plot in plots:
plot.title_text_font_size = '12pt'
show(vplot(plot_default, hplot(plot_range, plot_range_un, plot_range_rev), hplot(plot_cat_unbounded, plot_cat_autobounded, plot_cat_bounded), plot_extra))
for k, p in fourier.items():
p['plot'], p['sources'] = create_plot(
p['fs'],
'Fourier (Sum of the first 4 Harmonic Circles)',
r=p['f'](period),
cfoos=p['cfs'])
for k, p in fourier.items():
p['cplot'], p['csources'] = create_centric_plot(
p['fs'],
'Fourier First 4 Harmonics & Harmonic Circles',
r=p['f'](period),
cfoos=p['cfs'])
layout = vplot(*[f['plot'] for f in fourier.values()] +
[f['cplot'] for f in fourier.values()])
# open a session to keep our local document in sync with server
session = push_session(curdoc())
@repeat(range(N))
def cb(gind):
global newx
oldx = np.delete(newx, 0)
newx = np.hstack([oldx, [oldx[-1] + 2 * pi / N]])
for k, p in fourier.items():
update_sources(p['sources'], p['fs'], newx, gind, p['cfs'])
update_centric_sources(p['csources'], p['fs'], newx, gind, p['cfs'])
line_alpha=0.5)
cty = plot.add_glyph(source, cty_glyph)
hwy = plot.add_glyph(source, hwy_glyph)
# Add the tools
tooltips = [
("Manufacturer", "@manufacturer"),
("Model", "@model"),
("Displacement", "@displ"),
("Year", "@year"),
("Cylinders", "@cyl"),
("Transmission", "@trans"),
("Drive", "@drv"),
("Class", "@class"),
]
cty_hover_tool = HoverTool(renderers=[cty],
tooltips=tooltips + [("City MPG", "@cty")])
hwy_hover_tool = HoverTool(renderers=[hwy],
tooltips=tooltips + [("Highway MPG", "@hwy")])
select_tool = BoxSelectTool(renderers=[cty, hwy], dimensions=['width'])
plot.add_tools(cty_hover_tool, hwy_hover_tool, select_tool)
layout = vplot(plot, data_table)
if __name__ == "__main__":
filename = "data_tables.html"
with open(filename, "w") as f:
f.write(file_html(layout, INLINE, "Data Tables"))
print("Wrote %s" % filename)
view(filename)