def test_figure(self):
plt.figure()
self.assertEqual(plt.curplot(), None)
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), p)
plt.figure()
self.assertEqual(plt.curplot(), None)
def plot_circle_density(nodes, degrees, plot_width=800, plot_height=800):
print("Plotting circle density graph")
TOOLS="hover,pan,wheel_zoom,box_zoom,reset,click,previewsave"
plt.figure(plot_width=plot_width, plot_height=plot_height, tools=TOOLS)
theta = np.random.uniform(0, 2*np.pi, size=len(nodes))
max_d, min_d = np.max(degrees), np.min(degrees)
scale = 1.0/np.log(degrees) - 1.0/np.log(max_d)
xs = np.cos(theta)*scale
ys = np.sin(theta)*scale
source_dict = dict(
xs = xs,
ys = ys,
degrees = degrees,
nodes = nodes,
alphas = np.log(degrees)/np.log(max(degrees)),
)
source = ColumnDataSource(source_dict)
plt.hold(True)
plt.circle('xs', 'ys', source=source,
radius=0.0025,
fill_alpha='alphas',
x_axis_type=None, y_axis_type=None,
title="Density Distribution of Degrees")
plt.text([max(xs), max(xs)],
[.95*max(ys), .85*max(ys)],
["distance from center = 1 / log(deg)",
"angle = random"],
angle=0,
text_baseline="bottom", text_align="right")
hover = [t for t in plt.curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
('node', '@nodes'), ('degree', '@degrees')
])
plt.hold(False)
return plt.curplot()
def get_plottag(script_path):
""" Saves js in script_path and returns tag for embedding in html
"""
import numpy as np
import pandas as pd
import os
y_data = np.random.randn(100)
x_data = pd.date_range('31-Aug-2014', periods=len(y_data))
figure(x_axis_type='datetime',
tools='pan,wheel_zoom,box_zoom,reset,previewsave,crosshair',
name='test plot')
hold()
line(x_data, y_data,
line_color="#D95B43", line_width=4, alpha=0.7,
legend='random',
background_fill= '#cccccc')
circle(x_data, y_data,
color='red', fill_color=None, size=6,
legend='random')
curplot().title = 'Test Plot'
xaxis().axis_label='date'
yaxis().axis_label='some random numbers'
grid().grid_line_color='white'
grid().grid_line_alpha = 0.5
plotid = curplot()._id
script_path = os.path.join(script_path, plotid+'.js')
js, tag = autoload_static(curplot(), CDN, script_path=script_path)
with open(script_path, 'w') as f:
f.write(js)
return tag
def build_punchcard(datamap_list, concept_list,
radii_list, fields_in_concept_list,
datamaps, concepts,
plot_width=1200, plot_height=800):
source = ColumnDataSource(
data=dict(
datamap=datamap_list, # x
concept=concept_list, # y
radii=radii_list,
fields_in_concept=fields_in_concept_list,
)
)
output_file('')
hold()
figure()
plot_properties = {
'title': None,
'tools': "hover,resize,previewsave",
'y_range': [get_datamap_label(datamap) for datamap in datamaps],
'x_range': concepts,
'plot_width': plot_width,
'plot_height': plot_height,
}
rect('concept', 'datamap', # x, y
1, 1, # height, width
source=source,
color='white', # put in background
**plot_properties)
circle('concept', 'datamap', # x, y
size='radii',
source=source,
color='black',
**plot_properties)
grid().grid_line_color = None
x = xaxis()
x.major_label_orientation = pi / 4
hover = [t for t in curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
("Datamap", "@datamap"),
("Concept", "@concept"),
("Fields", "@fields_in_concept"),
])
return curplot().create_html_snippet(
static_path=settings.STATIC_URL,
embed_save_loc=settings.BOKEH_EMBED_JS_DIR,
embed_base_url=reverse('bokeh'),
)
def test_figure(self):
p = plt.figure()
self.assertEqual(plt.curplot(), p)
q = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), q)
self.assertNotEqual(p, q)
r = plt.figure()
self.assertEqual(plt.curplot(), r)
self.assertNotEqual(p, r)
self.assertNotEqual(q, r)
plt.hold()
s = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), s)
self.assertEqual(r, s)
def test_figure(self):
p = plt.figure()
self.assertEqual(plt.curplot(), p)
q = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), q)
self.assertNotEqual(p, q)
r = plt.figure()
self.assertEqual(plt.curplot(), r)
self.assertNotEqual(p, r)
self.assertNotEqual(q, r)
plt.hold()
s = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), s)
self.assertEqual(r, s)
def bokeh_lsa(year, df):
plt.output_file('output/lsa/' + str(year) + '.html')
topics = [str(a) for a in df.columns]
words = list(to_str(df.index))
p = plt.figure(x_range=topics,
y_range=words,
plot_width=800,
plot_height=600,
title=year,
tools='resize, save')
plot_sizes = []
plot_topics = []
plot_words = []
print df
for word, coeff in df.iterrows():
for n, c in enumerate(coeff):
if c > 0:
plot_sizes.append(c)
plot_topics.append(str(n))
plot_words.append(word)
if len(plot_sizes) == 0:
return
max_size = np.max(plot_sizes)
plot_sizes = [int(a / max_size * 75) + 25 for a in plot_sizes]
print plot_sizes, plot_words, plot_topics
p.circle(x=plot_topics, y=plot_words, size=plot_sizes, fill_alpha=0.6)
plt.show(p)
return plt.curplot()
def bokeh_lsa(year, df):
plt.output_file('output/lsa/'+str(year)+'.html')
topics = [str(a) for a in df.columns]
words = list(to_str(df.index))
p = plt.figure(x_range=topics, y_range=words,
plot_width=800, plot_height=600,
title=year, tools='resize, save')
plot_sizes = []
plot_topics = []
plot_words = []
print df
for word, coeff in df.iterrows():
for n, c in enumerate(coeff):
if c > 0:
plot_sizes.append(c)
plot_topics.append(str(n))
plot_words.append(word)
if len(plot_sizes) == 0:
return
max_size = np.max(plot_sizes)
plot_sizes = [int(a/max_size * 75) + 25 for a in plot_sizes]
print plot_sizes, plot_words, plot_topics
p.circle(x=plot_topics, y=plot_words, size=plot_sizes, fill_alpha=0.6)
plt.show(p)
return plt.curplot()
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma ** 2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist, bottom=np.zeros(len(hist)), left=edges[:-1], right=edges[1:],
fill_color="#333333", line_color="#E5E5E5", line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
def animated():
from numpy import pi, cos, sin, linspace, zeros_like
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = ["FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8",
"#253494", "#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"] * 5
cx = cy = zeros_like(rmin)
output_server("animated_reveal")
figure(title="Animations")
hold()
annular_wedge(
cx, cy, rmin, rmax, theta[:-1], theta[1:],
x_range=[-11, 11],
y_range=[-11, 11],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
tools="pan,wheel_zoom,box_zoom,reset,previewsave"
)
return curplot(), cursession()
def plot_3(data, ss):
"""t-SNE embedding of the parameters, colored by score
"""
scores = np.array([d['mean_test_score'] for d in data])
# maps each parameters to a vector of floats
warped = np.array([ss.point_to_moe(d['parameters']) for d in data])
# Embed into 2 dimensions with t-SNE
X = TSNE(n_components=2).fit_transform(warped)
e_scores = np.exp(scores)
mine, maxe = np.min(e_scores), np.max(e_scores)
color = (e_scores - mine) / (maxe - mine)
mapped_colors = map(rgb2hex, cm.get_cmap('RdBu_r')(color))
bk.figure(title='t-SNE (unsupervised)')
bk.hold()
df_params = nonconstant_parameters(data)
df_params['score'] = scores
bk.circle(
X[:, 0], X[:, 1], color=mapped_colors, radius=1,
source=ColumnDataSource(df_params), fill_alpha=0.6,
line_color=None, tools=TOOLS)
cp = bk.curplot()
hover = cp.select(dict(type=HoverTool))
format_tt = [(s, '@%s' % s) for s in df_params.columns]
hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
xax, yax = bk.axis()
xax.axis_label = 't-SNE coord 1'
yax.axis_label = 't-SNE coord 2'
def tweetsGraph():
logger.info("Drawing graphs to %s" % path_to_graphs+"Stats.html")
stat_db_cursor.execute('SELECT * FROM tweets')
tweets = stat_db_cursor.fetchall()
date, volume, cumulative, volumePast24h = zip(*[(datetime.datetime.strptime(t['date'], "%Y-%m-%dT%H"), t['current_hour'], t['cumulative'], t['past_24h']) for t in tweets])
hourly =zip([datetime.datetime(year=d.year, month=d.month, day=d.day) for d in date],volume)
hourly.sort()
days, dailyVolume = zip(*[(d, sum([v[1] for v in vol])) for d,vol in itertools.groupby(hourly, lambda i:i[0])])
bokeh_plt.output_file(path_to_graphs+"Stats.html")
bokeh_plt.hold()
bokeh_plt.quad(days, [d+datetime.timedelta(days=1) for d in days], dailyVolume, [0]*len(dailyVolume), x_axis_type="datetime", color='gray', legend="Daily volume")
bokeh_plt.line(date, volume, x_axis_type="datetime", color='red', legend="Hourly volume")
bokeh_plt.line(date, volumePast24h, x_axis_type="datetime", color='green', legend="Volume in the past 24 hours")
bokeh_plt.curplot().title = "Volume"
bokeh_plt.figure()
bokeh_plt.line(date, cumulative, x_axis_type="datetime")
bokeh_plt.curplot().title = "Cumulative volume"
fig, ax = matplotlib_plt.subplots()
f=DateFormatter("%Y-%m-%d")
ax.xaxis.set_major_formatter(f)
matplotlib_plt.plot(date, volume)
matplotlib_plt.plot(date, volumePast24h)
matplotlib_plt.plot(days, dailyVolume)
matplotlib_plt.xticks(np.concatenate((np.array(date)[range(0,len(date),24*7)],[date[-1]])), rotation=70)
matplotlib_plt.savefig(path_to_graphs+"volume.png", bbox_inches="tight")
stat_db_cursor.execute('SELECT * FROM users')
users = stat_db_cursor.fetchall()
date, nUsers, nUsersWithFriends = zip(*[(datetime.datetime.strptime(u['date'], "%Y-%m-%dT%H:%M:%S.%f"), u['total'], u['with_friends']) for u in users])
bokeh_plt.figure()
bokeh_plt.line(date, nUsers, x_axis_type="datetime", legend="Total")
bokeh_plt.line(date, nUsersWithFriends, x_axis_type="datetime", legend="Friendship collected")
bokeh_plt.legend().orientation = "top_left"
bokeh_plt.curplot().title = "Number of users"
bokeh_plt.save()
matplotlib_plt.figure()
fig, ax = matplotlib_plt.subplots()
f=DateFormatter("%Y-%m-%d")
ax.xaxis.set_major_formatter(f)
matplotlib_plt.plot(date, nUsers)
matplotlib_plt.plot(date, nUsersWithFriends)
matplotlib_plt.xticks(np.concatenate((np.array(date)[range(0,len(date),24*7)],[date[-1]])), rotation=70)
matplotlib_plt.savefig(path_to_graphs+"users.png", bbox_inches="tight")
def plot_adj(adj, sparcity):
N, _ = adj.shape
TOOLS="pan,wheel_zoom,box_zoom,reset,click,previewsave"
plt.figure(plot_width=N+100, plot_height=N+100, tools=TOOLS)
plt.image(image=[adj], x =[0], y=[0], dw=[N], dh=[N],
x_range=[0, N], y_range=[0, N],
palette=["Blues-3"],
title="Adjacency of top %d nodes (%.2f%% sparcity)" % (N, sparcity),
x_axis_type=None, y_axis_type=None)
return plt.curplot()
def build_timeplot(data):
output_file('distribution.html', title='distribution')#, js="relative", css="relative")
x,y=[],[]
if len(data)>0:
x,y=zip(*[(k, data[k]) for k in sorted(data)])
line([pd.DatetimeIndex([dt])[0] for dt in x] , [int(yt) for yt in y],x_axis_type='datetime', tools="pan,zoom,resize", width=400,height=300, title = 'voorkomens')
xaxis()[0].axis_label = "Datum"
yaxis()[0].axis_label = "# artikels"
# Create an HTML snippet of our plot.
snippet = curplot().create_html_snippet(embed_base_url='/static/plots/', embed_save_loc=plotdir)
# Return the snippet we want to place in our page.
return snippet
def merge(*args, **kwargs):
import bokeh.plotting as bk
bk.figure()
bk.hold()
y = kwargs.get('y', None)
x = kwargs.get('x', 'Pos')
try:
kwargs.pop('y')
kwargs.pop('x')
except:
pass
y = (y if type(y) == list else [y] * len(args)) #FIXME do the same for x
for yi, p in zip(y, args):
p.show(x=x, y=yi, color=next(kwargs["colors"]), **kwargs)
return bk.curplot()
def merge(*args, **kwargs):
import bokeh.plotting as bk
bk.figure()
bk.hold()
y = kwargs.get('y',None)
x = kwargs.get('x','Pos')
try:
kwargs.pop('y')
kwargs.pop('x')
except:
pass
y = (y if type(y) == list else [y]*len(args)) #FIXME do the same for x
for yi,p in zip(y,args):
p.show(x=x, y=yi, color=next(kwargs["colors"]), **kwargs)
return bk.curplot()
def graph():
print("Graphing")
with open(DATA_FILE, "rb") as fin:
issues = [Issue(x) for x in pickle.load(fin)]
plotting.output_file("{}.{}.html".format(GH_USER, GH_REPO),
title="ghantt.py")
numbers = [iss.number for iss in issues]
source = ColumnDataSource(
data=dict(
number = [iss.number for iss in issues],
ago = [iss.ago + iss.length/2 for iss in issues],
length = [iss.length for iss in issues],
title = [iss.title for iss in issues],
pull_request = [iss.pull_request for iss in issues],
color = [assign_color(iss) for iss in issues],
since = ["{} days".format(int(abs(iss.ago))) for iss in issues],
),
)
plotting.hold()
plotting.rect("ago", "number", "length", 1, source=source,
color="color", title="{}/{}".format(GH_USER, GH_REPO),
y_range=(min(numbers), max(numbers)),
tools="resize,hover,previewsave,pan,wheel_zoom",
fill_alpha=0.8)
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_align": "left",
"text_baseline": "middle"
}
plotting.grid().grid_line_color = None
hover = [t for t in plotting.curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
("number", "@number"),
("title", "@title"),
("since", "@since"),
("pull_request", "@pull_request"),
])
plotting.show()
def draw(self, x, y, z, title, func, **kwargs):
import bokeh.plotting as bk
#TODO: change colors if y is of list type
y = (y if type(y) == list else [y]) # wrap y to a list so that we can iterate
kwargs.update({ "outline_line_color":"black", #FIXME refactor
"plot_width":300,
"plot_height":300,
})
bk.hold(True)
for yi in y:
x_data, y_data = self[x], self[yi]
func(x=x_data,
y=y_data,
title=title,
**kwargs)
bk.hold(False)
ret = bk.curplot()
def _label(axis, field):
label = kwargs.get(axis + '_label', False)
if label:
self.properties.plot_properties.insert(field, {axis + '_label':label})
else:
label = self.properties.plot_properties.select(field, axis + '_label', "None")
return label
def _range(axis, field):
from bokeh.objects import Range1d
p_range_args = kwargs.get(axis + '_range', False)
if p_range_args:
self.properties.plot_properties.insert(field, {axis + '_range': p_range})
else:
p_range = self.properties.plot_properties.select(field, axis + '_range')
if not p_range:
return False
else:
return Range1d(start=p_range[0], end=p_range[1])
bk.xaxis().axis_label = _label('x', x)
if _range('x', x):
ret.x_range = _range('x', x)
bk.yaxis().axis_label = _label('y', y[0]) #TODO can this make sense for multiplots?
if _range('y', y[0]):
ret.y_range = _range('y', y[0])
return ret
def _draw(self, x, y, z, overlay, inst_func, **kwargs):
import bokeh.plotting as bk
import numpy as np
def greatest_divisor(number):
if number == 1:
return 1
for i in reversed(range(number)):
if number % i == 0:
return i
else:
return 1
if not overlay:
rows = []
for name, instance in self.cases.iteritems():
bk.figure()
rows.append(
getattr(instance, inst_func)(x=x,
y=y,
title=str(name),
**kwargs) #FIXME num cars
)
rows = np.array(rows).reshape(greatest_divisor(len(rows)),
-1).tolist()
return bk.GridPlot(children=rows, title="Scatter")
else:
bk.hold()
colors = plot.next_color()
exp_legend = kwargs.get("legend", None)
if exp_legend != None:
kwargs.pop("legend")
exp_title = kwargs.get("title", None)
if exp_title != None:
kwargs.pop("title")
for name, instance in self.cases.iteritems():
color = next(colors)
legend = (exp_legend if exp_legend != None else name)
title = (exp_title if exp_title != None else "")
getattr(instance, inst_func)(x=x,
y=y,
title=title,
color=color,
legend=legend,
**kwargs)
bk.hold(False)
return bk.curplot()
def count_by_day_graph(collection_name):
# TODO: выделить на графике выходные дни
import numpy as np
from bokeh.plotting import output_file, hold, figure, line, curplot, grid, show
a = count_by_day(collection_name)
output_file("output/count_by_day_graph.html", title="count_by_day_graph")
hold()
figure(x_axis_type="datetime", tools="pan,wheel_zoom,box_zoom,reset,previewsave", plot_width=1800)
line(np.array(a["date"], 'M64'), a['count'], color='#A6CEE3', legend='Количество статей')
line(np.array(a["date"], 'M64'), [averange_count(collection_name)] * len(a["date"]), color='#ff0000', legend='Среднее количество статей')
curplot().title = "Количество статей по дням"
grid().grid_line_alpha=0.3
show()
def build_scatter_tooltip(x, y, tt, add_line=True, radius=3, title='My Plot',
xlabel='Iteration number', ylabel='Score'):
bk.figure(title=title)
bk.hold()
bk.circle(
x, y, radius=radius, source=ColumnDataSource(tt),
fill_alpha=0.6, line_color=None, tools=TOOLS)
if add_line:
bk.line(x, y, line_width=2)
xax, yax = bk.axis()
xax.axis_label = xlabel
yax.axis_label = ylabel
cp = bk.curplot()
hover = cp.select(dict(type=HoverTool))
format_tt = [(s, '@%s' % s) for s in tt.columns]
hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
def build_plot(datalist,logx=True):
# Set the output for our plot.
output_file('plot.html', title='Plot')#, js="relative", css="relative")
# Create some data for our plot.
colors=['red','green','blue','yellow','black']
# colors=['tomato','navy']
hold()
cnt=0
for name,data in datalist:
cnt+=1
x,y=zip(*data)
if logx:
scatter([np.log(x_el) for x_el in x] ,list(y) , color=colors[cnt], legend=name)
else:
scatter(list(x) ,list(y) , color=colors[cnt], legend=name)
# Create an HTML snippet of our plot.
snippet = curplot().create_html_snippet(embed_base_url='/static/plots/', embed_save_loc=plotdir)
# Return the snippet we want to place in our page.
return snippet
def animated():
from numpy import pi, cos, sin, linspace, zeros_like
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = [
"FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8", "#253494",
"#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"
] * 5
cx = cy = zeros_like(rmin)
output_server("animated_reveal")
figure(title="Animations",
x_range=[-11, 11],
y_range=[-11, 11],
tools="pan,wheel_zoom,box_zoom,reset,previewsave")
hold()
annular_wedge(
cx,
cy,
rmin,
rmax,
theta[:-1],
theta[1:],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
)
return curplot(), cursession()
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu)**2 /
(2 * sigma**2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma**2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist,
bottom=np.zeros(len(hist)),
left=edges[:-1],
right=edges[1:],
fill_color="#333333",
line_color="#E5E5E5",
line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
def _draw(self, x, y, z, overlay, inst_func, **kwargs):
import bokeh.plotting as bk
import numpy as np
def greatest_divisor(number):
if number == 1:
return 1
for i in reversed(range(number)):
if number % i == 0:
return i
else:
return 1
if not overlay:
rows=[]
for name, instance in self.cases.iteritems():
bk.figure()
rows.append(
getattr(instance, inst_func)
(x=x, y=y, title=str(name), **kwargs) #FIXME num cars
)
rows = np.array(rows).reshape(greatest_divisor(len(rows)),-1).tolist()
return bk.GridPlot(children=rows, title="Scatter")
else:
bk.hold()
colors = plot.next_color()
exp_legend = kwargs.get("legend", None)
if exp_legend != None:
kwargs.pop("legend")
exp_title = kwargs.get("title", None)
if exp_title != None:
kwargs.pop("title")
for name, instance in self.cases.iteritems():
color = next(colors)
legend = (exp_legend if exp_legend != None else name)
title = (exp_title if exp_title != None else "")
getattr(instance, inst_func)(x=x, y=y, title=title, color=color, legend=legend, **kwargs)
bk.hold(False)
return bk.curplot()
def get(self, request, *args, **kwargs):
return self.render_to_json_response({'plot': u''})
# measurement_type_pk = kwargs.get('measurement_type_pk')
# measurement_type = MeasurementType.objects.get(pk=measurement_type_pk)
collection = Collection.objects.get(pk=kwargs.get('pk'))
units = Unit.objects.filter(measurements__match={'measurement_type': measurement_type.pk, 'active': True},
pk__in=[unit.pk for unit in collection.units], active=True)
if units:
bk.hold()
bk.figure(x_axis_type="datetime", tools="pan,wheel_zoom,box_zoom,reset,previewsave")
colors = self.get_colors(len(units))
for i, unit in enumerate(units):
measurements = [(measurement.created_at, measurement.value) for measurement in unit.measurements
if measurement.active and measurement.measurement_type == measurement_type]
measurements.sort(key=lambda measurement: measurement[0])
data = {
'date': [measurement[0] for measurement in measurements],
'value': [measurement[1] for measurement in measurements]
}
bk.line(np.array(data['date']), data['value'], color=colors[i], line_width=2, legend=unit.__unicode__())
bk.grid().grid_line_alpha = 0.3
xax, yax = bk.axis()
xax.axis_label = ugettext('Date')
yax.axis_label = ugettext('Values')
plot = bk.curplot()
bk.get_default_color()
plot.title = ugettext('Measurements type')
# plot.title = ugettext('Measurements type {}'.format(measurement_type.__unicode__()))
js, tag = autoload_static(plot, Resources(mode='server', root_url=settings.STATIC_URL), "")
return self.render_to_json_response({'plot': u'{}<script>{}</script>'.format(tag, js)})
return self.render_to_json_response(ugettext('Not found'), status=404)
def drawPlot(shapeFilename, zipBorough, zipMaxAgency):
# Read the ShapeFile
dat = shapefile.Reader(shapeFilename)
# Creates a dictionary for zip: {lat_list: [], lng_list: []}.
zipCodes = []
hoverZip = []
hoverAgency = []
hoverComplaints = []
polygons = {'lat_list': [], 'lng_list': [], 'centerLat_list': [], 'centerLon_list': []}
record_index = 0
for r in dat.iterRecords():
currentZip = r[0]
# Keeps only zip codes in NY area.
if currentZip in zipMaxAgency: # was in zipBorough:
# zipCodes.append(currentZip) # moving this line into the parts loop
# Gets shape for this zip.
shape = dat.shapeRecord(record_index).shape
for part in range(len(shape.parts)):
zipCodes.append(currentZip)
hoverZip.append(currentZip)
hoverAgency.append(zipMaxAgency[currentZip][0])
hoverComplaints.append(zipMaxAgency[currentZip][1])
start = shape.parts[part]
if part == len(shape.parts) - 1:
end = len(shape.points)
else:
end = shape.parts[part + 1]
points = shape.points[start : end]
# Breaks into lists for lat/lng.
lngs = [p[0] for p in points]
lats = [p[1] for p in points]
# Calculate centers
center_lngs = min(lngs) + (max(lngs) - min(lngs))/2
center_lats = min(lats) + (max(lats) - min(lats))/2
# Store centroids for current part shape
polygons['centerLat_list'].append(center_lats)
polygons['centerLon_list'].append(center_lngs)
# Stores lat/lng for current zip shape.
polygons['lng_list'].append(lngs)
polygons['lat_list'].append(lats)
record_index += 1
# Palette
##d9d9d9
brewer11 = ['#8dd3c7', '#ffffb3', '#bebada','#fb8072','#80b1d3','#fdb462','#b3de69','#fccde5','#d9d9d9','#bc80bd','#ccebc5']
#brewer11 = ['#a6cee3', '#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99']
#agencies = sorted(list({zipMaxAgency[zipCode] for zipCode in zipMaxAgency}))
biggestComplaints = {}
for zz, aa in zipMaxAgency.iteritems():
if aa[0] in biggestComplaints:
biggestComplaints[aa[0]] += 1
else:
biggestComplaints[aa[0]] = 1
# sorting agencies by number of zip codes (to try to get better colors)
agencies = list(biggestComplaints.iteritems())
agencies = sorted(agencies, key = lambda x: x[1], reverse = True)
agencies = [agency[0] for agency in agencies]
# Assign colors to agencies
agencyColor = {agencies[i] : brewer11[i] for i in range(len(brewer11))}
polygons['colors'] = [agencyColor[zipMaxAgency[zipCode][0]] for zipCode in zipCodes]
# Prepare hover
#source = bk.ColumnDataSource(data=dict(hoverAgency=hoverAgency, hoverZip=hoverZip, hoverComplaintCount=hoverComplaintCount,))
source = bk.ColumnDataSource(data=dict(hoverZip = hoverZip, hoverAgency = hoverAgency, hoverComplaints = hoverComplaints),)
# Creates the Plot
bk.output_file("problem1.html")
bk.hold()
TOOLS="pan,wheel_zoom,box_zoom,reset,previewsave,hover"
fig = bk.figure(title="311 Complaints by Zip Code", \
tools=TOOLS, plot_width=800, plot_height=650)
# Creates the polygons.
bk.patches(polygons['lng_list'], polygons['lat_list'], fill_color=polygons['colors'], line_color="gray", source = source)
# RP: add hover
hover = bk.curplot().select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Zip", "@hoverZip"),
("Agency", "@hoverAgency"),
("Number of complaints", "@hoverComplaints"),
])
### Zip codes as text on polygons
#for i in range(len(polygons['centerLat_list'])):
# y = polygons['centerLat_list'][i]
# x = polygons['centerLon_list'][i]
# zipCode = zipCodes[i]
# bk.text([x], [y], text=zipCode, angle=0, text_font_size="8pt", text_align="center", text_baseline="middle")
fonts = ["Comic sans MS", "Papyrus", "Curlz", "Impact", "Zapf dingbats", "Comic sans MS", "Papyrus", "Curlz", "Impact", "Zapf Dingbats", "Comic sans MS"]
### Legend
x = -73.66
y = 40.50
#x = -74.25
#y = 40.9
for agency, color in agencyColor.iteritems():
#print "Color: ", a
#print "x:", x
#print "y:", y
bk.rect([x], [y], color = color, width=0.03, height=0.015)
bk.text([x], [y], text = agency, angle=0, text_font_size="7pt", text_align="center", text_baseline="middle")
y = y + 0.015
#bokeh.embed.components(fig, bokeh.resources.CDN)
bk.show()
deploys = deploydata[:, 1]
lwrs = []
for v in deploydata[:, 1]:
lwrs.append(100 - int(v / 3))
bp.output_file("sfr.html", title="bokeh_sfr.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time,
deploys,
x_axis_label="Time (m)",
y_axis_label="Reactors (#)",
color='#1F78B4',
legend='SFRs')
bp.hold()
bp.line(time,
lwrs,
x_axis_label="Time (m)",
y_axis_label="Reactors (#)",
color="green",
legend='LWRs')
bp.curplot().title = "SFRs"
bp.grid().grid_line_alpha = 0.3
bp.show() # open a browser
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(
time_x,
pres_y,
color='#0000FF',
tools=
'pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,
height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
time.sleep(1)
t = t + 1
time_x = np.append(time_x, time_x[t - 1] + 1)
pres_y = np.append(
pres_y, pres_y[t - 1] + np.random.random() * pres_y[t - 1] * 0.01)
from bokeh.objects import Glyph
import time
t = 0
time_x = np.array([0])
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(time_x, pres_y, color='#0000FF',
tools='pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
time.sleep(1)
t = t + 1
time_x = np.append(time_x,
time_x[t-1]+1)
pres_y = np.append(pres_y,
pres_y[t-1] + np.random.random()*pres_y[t-1]*0.01)
# Make a text glyph for each rect glyph to show the cabinet names and locations
bk.text(x=dict(field="posx", units="data"),
y=dict(field="text_locy", units="data"),
text=dict(field="loc", units="data"),
text_font_style="bold", text_font_size="12pt", **text_props)
bk.text(x=dict(field="posx", units="data"),
y=dict(field="text_namey", units="data"),
text=dict(field="name", units="data"),
text_font_style="bold", text_font_size="9pt", **text_props)
bk.text(x=dict(field="posx", units="data"),
y=dict(field="text_suby", units="data"),
text=dict(field="sub", units="data"),
text_font_style="bold", text_font_size="9pt", **text_props)
# turn off grid lines
bk.grid().grid_line_color = None
# Add hovertool to show equipment in tooltips
hover = bk.curplot().select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("name", "@name"),
("subsystem", "@sub"),
("equipment","@equip"),
("phone","@phone")
])
# Show the graph
bk.show()
def drawPlot(shapeFilename, zipBorough, zipMaxAgency):
# Read the ShapeFile
dat = shapefile.Reader(shapeFilename)
# Creates a dictionary for zip: {lat_list: [], lng_list: []}.
zipCodes = []
hoverZip = []
hoverAgency = []
hoverComplaints = []
polygons = {
'lat_list': [],
'lng_list': [],
'centerLat_list': [],
'centerLon_list': []
}
record_index = 0
for r in dat.iterRecords():
currentZip = r[0]
# Keeps only zip codes in NY area.
if currentZip in zipMaxAgency: # was in zipBorough:
# zipCodes.append(currentZip) # moving this line into the parts loop
# Gets shape for this zip.
shape = dat.shapeRecord(record_index).shape
for part in range(len(shape.parts)):
zipCodes.append(currentZip)
hoverZip.append(currentZip)
hoverAgency.append(zipMaxAgency[currentZip][0])
hoverComplaints.append(zipMaxAgency[currentZip][1])
start = shape.parts[part]
if part == len(shape.parts) - 1:
end = len(shape.points)
else:
end = shape.parts[part + 1]
points = shape.points[start:end]
# Breaks into lists for lat/lng.
lngs = [p[0] for p in points]
lats = [p[1] for p in points]
# Calculate centers
center_lngs = min(lngs) + (max(lngs) - min(lngs)) / 2
center_lats = min(lats) + (max(lats) - min(lats)) / 2
# Store centroids for current part shape
polygons['centerLat_list'].append(center_lats)
polygons['centerLon_list'].append(center_lngs)
# Stores lat/lng for current zip shape.
polygons['lng_list'].append(lngs)
polygons['lat_list'].append(lats)
record_index += 1
# Palette
##d9d9d9
brewer11 = [
'#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3', '#fdb462',
'#b3de69', '#fccde5', '#d9d9d9', '#bc80bd', '#ccebc5'
]
#brewer11 = ['#a6cee3', '#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99']
#agencies = sorted(list({zipMaxAgency[zipCode] for zipCode in zipMaxAgency}))
biggestComplaints = {}
for zz, aa in zipMaxAgency.iteritems():
if aa[0] in biggestComplaints:
biggestComplaints[aa[0]] += 1
else:
biggestComplaints[aa[0]] = 1
# sorting agencies by number of zip codes (to try to get better colors)
agencies = list(biggestComplaints.iteritems())
agencies = sorted(agencies, key=lambda x: x[1], reverse=True)
agencies = [agency[0] for agency in agencies]
# Assign colors to agencies
agencyColor = {agencies[i]: brewer11[i] for i in range(len(brewer11))}
polygons['colors'] = [
agencyColor[zipMaxAgency[zipCode][0]] for zipCode in zipCodes
]
# Prepare hover
#source = bk.ColumnDataSource(data=dict(hoverAgency=hoverAgency, hoverZip=hoverZip, hoverComplaintCount=hoverComplaintCount,))
source = bk.ColumnDataSource(data=dict(hoverZip=hoverZip,
hoverAgency=hoverAgency,
hoverComplaints=hoverComplaints), )
# Creates the Plot
bk.output_file("problem1.html")
bk.hold()
TOOLS = "pan,wheel_zoom,box_zoom,reset,previewsave,hover"
fig = bk.figure(title="311 Complaints by Zip Code", \
tools=TOOLS, plot_width=800, plot_height=650)
# Creates the polygons.
bk.patches(polygons['lng_list'],
polygons['lat_list'],
fill_color=polygons['colors'],
line_color="gray",
source=source)
# RP: add hover
hover = bk.curplot().select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Zip", "@hoverZip"),
("Agency", "@hoverAgency"),
("Number of complaints", "@hoverComplaints"),
])
### Zip codes as text on polygons
#for i in range(len(polygons['centerLat_list'])):
# y = polygons['centerLat_list'][i]
# x = polygons['centerLon_list'][i]
# zipCode = zipCodes[i]
# bk.text([x], [y], text=zipCode, angle=0, text_font_size="8pt", text_align="center", text_baseline="middle")
fonts = [
"Comic sans MS", "Papyrus", "Curlz", "Impact", "Zapf dingbats",
"Comic sans MS", "Papyrus", "Curlz", "Impact", "Zapf Dingbats",
"Comic sans MS"
]
### Legend
x = -73.66
y = 40.50
#x = -74.25
#y = 40.9
for agency, color in agencyColor.iteritems():
#print "Color: ", a
#print "x:", x
#print "y:", y
bk.rect([x], [y], color=color, width=0.03, height=0.015)
bk.text([x], [y],
text=agency,
angle=0,
text_font_size="7pt",
text_align="center",
text_baseline="middle")
y = y + 0.015
#bokeh.embed.components(fig, bokeh.resources.CDN)
bk.show()
def make_plot(xr):
yr = Range1d(start=-10, end=10)
figure(plot_width=800,
plot_height=350,
y_range=yr,
x_range=xr,
tools="xpan,xwheel_zoom,hover,box_zoom,reset")
hold()
genes = pd.read_csv('/home/hugoshi/data/lab7/genes.refseq.hg19.bed',
sep='\t',
skiprows=[0],
header=None)
genes.rename(columns={0: "chromosome", 1: "start", 2: "end"}, inplace=True)
genes = genes[genes.chromosome == 'chr5']
g_len = len(genes)
quad(
genes.start - 0.5, # left edge
genes.end - 0.5, # right edge
[2.3] * g_len, # top edge
[1.7] * g_len,
['blue'] * g_len) # bottom edge
exons = pd.read_csv('/home/hugoshi/data/lab7/exons.refseq.hg19.bed',
sep='\t',
skiprows=[0],
header=None)
exons.columns = ["chromosome", "start", "end", "meta1", "meta2", "meta3"]
exons = exons[exons.chromosome == 'chr5']
e_len = len(exons)
quad(
exons.start - 0.5, # left edge
exons.end - 0.5, # right edge
[1.3] * e_len, # top edge
[0.7] * e_len,
['blue'] * e_len) # bottom edge
df = pd.read_csv('/home/hugoshi/data/lab7/CHP2.20131001.hotspots.bed',
sep='\t',
skiprows=[0],
header=None)
df.columns = ["chromosome", "start", "end", "meta1", "meta2", "meta3"]
df = df[df.chromosome == 'chr5']
singles = df[df.start + 1 == df.end]
widers = df[df.start + 1 != df.end]
slen = len(singles)
wlen = len(widers)
s_source = ColumnDataSource(data=dict(start=singles.start,
end=singles.end,
meta1=singles.meta1,
meta2=singles.meta2,
meta3=singles.meta3))
rect(
'start', # x center
[1] * slen, # y center
[0.9] * slen,
[1] * slen,
color=['red'] * slen,
source=s_source) # height
hover = [t for t in curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
# add to this
("position", "@start"),
("meta 1", "@meta1"),
("meta 2", "@meta2"),
("meta 3", "@meta3")
])
quad(
widers.start - 0.5, # left edge
widers.end - 0.5, # right edge
[0.3] * wlen, # top edge
[-0.3] * wlen) # bottom edge
hold()
return curplot()
def plot_points(cells,
style="circle",
size=2,
color=None,
figure=None,
**kwargs):
"""
Plot molecule locations.
If no colour is explicitly defined, colouring is based on the cell index.
Arguments:
cells (Partition):
full partition
style (str):
point marker style
size (float):
point marker size (in screen units)
color (str or numpy.ndarray):
cell colours
figure (bokeh.plotting.figure.Figure):
figure handle
returns:
list: handles of the various clouds of points
"""
# for compatibility with tramway.plot.mesh.plot_points
try:
kwargs.pop("min_count")
except KeyError:
pass
if isinstance(cells, np.ndarray):
points = cells
label = None
elif isinstance(cells, pd.DataFrame):
points = cells[["x", "y"]].values
label = None
elif isinstance(cells, Partition):
points = cells.descriptors(cells.points, asarray=True)
label = cells.cell_index
npts = points.shape[0]
ncells = cells.location_count.size
# if label is not a single index vector, convert it following
# tessellation.base.Delaunay.cell_index with `preferred`='force index'.
merge = mplt.nearest_cell(points, cells.tessellation.cell_centers)
label = format_cell_index(cells.cell_index,
format="array",
select=merge,
shape=(npts, ncells))
if isstructured(points):
x = points["x"]
y = points["y"]
else:
x = points[:, 0]
y = points[:, 1]
if figure is None:
assert False
try:
figure = plt.curplot() # does not work
except:
figure = plt.figure()
handles = []
if label is None:
if color is None:
color = "k"
elif isinstance(color, (pd.Series, pd.DataFrame)):
raise NotImplementedError
color = np.asarray(color)
if isinstance(color, np.ndarray):
raise NotImplementedError
cmin, cmax = np.min(color), np.max(color)
color = (color - cmin) / (cmax - cmin)
cmap = plt.get_cmap()
color = [cmap(c) for c in color]
else:
color = long_colour_name(color)
h = figure.scatter(x,
y,
marker=style,
color=color,
size=size,
**kwargs)
handles.append(h)
else:
L = np.unique(label)
if color in [None, "light"]:
if color == "light" and "alpha" not in kwargs:
kwargs["alpha"] = 0.2
if 2 < len(L):
color = __colors__
color = ["gray"] + list(
itertools.islice(itertools.cycle(color), len(L)))
elif len(L) == 2:
color = ["gray", "k"]
else:
color = "k"
elif isinstance(color, str) and L.size == 1:
color = [color]
for i, l in enumerate(L):
clr_i = long_colour_name(color[i])
h = figure.scatter(x[label == l],
y[label == l],
marker=style,
color=clr_i,
size=size,
**kwargs)
handles.append(h)
## resize window
# try:
# figure.x_range = Range1d(*cells.bounding_box['x'].values)
# figure.y_range = Range1d(*cells.bounding_box['y'].values)
# except AttributeError:
# pass
# except ValueError:
# traceback.print_exc()
return handles
def plot_trajectories(trajs,
color=None,
loc_style="circle",
figure=None,
**kwargs):
"""
Plot trajectories.
If no colour is explicitly defined, colouring is based on the trajectory index.
Arguments:
trajs (pandas.DataFrame):
full partition
loc_style (str):
location marker style
loc_size (float):
location marker size (in screen units)
color (str or numpy.ndarray):
trajectory colours
figure (bokeh.plotting.figure.Figure):
figure handle
returns:
list: handles of the glyphs
"""
loc_kwargs = {}
line_kwargs = {}
for attr in dict(kwargs):
if attr.startswith("loc_"):
loc_kwargs[attr[4:]] = kwargs.pop(attr)
if attr.startswith("line_"):
if attr == "line_width":
# compatibility for old bokeh versions
line_kwargs[attr] = kwargs.pop(attr)
continue
line_kwargs[attr[5:]] = kwargs.pop(attr)
loc_kwargs.update(kwargs)
line_kwargs.update(kwargs)
lines_x, lines_y = [], []
for _, df in trajs.groupby([trajs["n"]]):
lines_x.append(df["x"].values)
lines_y.append(df["y"].values)
if figure is None:
assert False
try:
figure = plt.curplot() # does not work
except:
figure = plt.figure()
handles = []
if color is None:
ntrajs = len(lines_x)
if color in [None, "light"]:
if color == "light" and "alpha" not in kwargs:
kwargs["alpha"] = 0.2
if 2 < ntrajs:
color = __colors__
color = ["gray"] + list(
itertools.islice(itertools.cycle(color), ntrajs))
elif ntrajs == 2:
color = ["gray", "k"]
else:
color = "k"
elif isinstance(color, str) and ntrajs == 1:
color = [color]
for i, line in enumerate(zip(lines_x, lines_y)):
line_x, line_y = line
clr_i = long_colour_name(color[i])
h = figure.line(line_x, line_y, color=clr_i, **line_kwargs)
handles.append(h)
h = figure.scatter(line_x,
line_y,
color=clr_i,
marker=loc_style,
**loc_kwargs)
handles.append(h)
else:
color = long_colour_name(color)
h = figure.multi_line(lines_x, lines_y, color=color, **line_kwargs)
handles.append(h)
h = figure.scatter(list(itertools.chain(*lines_x)),
list(itertools.chain(*lines_y)),
color=color,
marker=loc_style,
**loc_kwargs)
handles.append(h)
return handles
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load deploys.csv into a numpy array
deploydata = np.loadtxt('sfr_dep.dat',delimiter=" ")
# provide handles for the x and y columns
time = deploydata[:,0]
deploys = deploydata[:,1]
lwrs = []
for v in deploydata[:,1] :
lwrs.append(100-int(v/3))
bp.output_file("sfr.html", title="bokeh_sfr.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time, deploys, x_axis_label="Time (m)", y_axis_label="Reactors (#)",
color='#1F78B4', legend='SFRs')
bp.hold()
bp.line(time, lwrs, x_axis_label="Time (m)", y_axis_label="Reactors (#)",
color="green", legend='LWRs')
bp.curplot().title = "SFRs"
bp.grid().grid_line_alpha=0.3
bp.show() # open a browser
fig = bplt.figure()
bplt.hold()
### build data source for hover tool
source = bplt.ColumnDataSource(data=dict(x=all_x, y=all_y, stefilename=all_stefilename, Nmodes=all_Nmodes, Ngens=all_Ngens, Ntriples=all_Ntriples))
### plot circle glyphs
bplt.circle(all_x, all_y, source=source, tools=TOOLS, fill_color=None, fill_alpha=0.6, line_color=all_color, Title="%s vs %s"%(xlabel, ylabel), plot_width=plot_width, plot_height=plot_height)
# bplt.circle(all_x, all_y, radius=radii, source=source, tools=TOOLS, fill_color=None, fill_alpha=0.6, line_color=all_color, Title="%s vs %s"%(xlabel, ylabel))
### annotate circle glyphs
# text(x, y, text=inds, alpha=0.5, text_font_size="5pt", text_baseline="middle", text_align="center", angle=0)
### find hover tool, and tell it what to look for
hover = [t for t in bplt.curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
#("index", "$index"),
("(x,y)", "($x, $y)"),
("stefilename", "@stefilename"),
("Nmodes","@Nmodes"),
("Ntriples","@Ntriples"),
("Ngens","@Ngens"),
])
### label axes
bplt.xaxis().axis_label=xlabel
bplt.yaxis().axis_label=ylabel
# bplt.show()
import numpy as np
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load decays.csv into a numpy array
decaydata = np.loadtxt("decays2.csv", delimiter=",", skiprows=1)
# provide handles for the x and y columns
time = decaydata[:, 0]
decays = decaydata[:, 1]
bp.output_file("decays.html", title="bokeh_decay.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time, decays, x_axis_label="Time (s)", y_axis_label="Decays (#)", color="#1F78B4", legend="Decays per second")
bp.curplot().title = "Decays"
bp.grid().grid_line_alpha = 0.3
bp.show() # open a browser
def make_plot(xr):
yr = Range1d(start=-10, end=10)
figure(plot_width=800, plot_height=350,
y_range=yr,
x_range=xr,
tools="xpan,xwheel_zoom,hover,box_zoom,reset")
hold()
genes = pd.read_csv('/home/hugoshi/data/lab7/genes.refseq.hg19.bed', sep='\t',
skiprows=[0], header=None)
genes.rename(columns={
0: "chromosome",
1: "start",
2: "end"},
inplace=True
)
genes = genes[genes.chromosome == 'chr5']
g_len = len(genes)
quad(genes.start - 0.5, # left edge
genes.end - 0.5, # right edge
[2.3] * g_len, # top edge
[1.7] * g_len,
['blue'] * g_len) # bottom edge
exons = pd.read_csv('/home/hugoshi/data/lab7/exons.refseq.hg19.bed', sep='\t', skiprows=[0], header=None)
exons.columns = ["chromosome", "start", "end", "meta1", "meta2", "meta3"]
exons = exons[exons.chromosome == 'chr5']
e_len = len(exons)
quad(exons.start - 0.5, # left edge
exons.end - 0.5, # right edge
[1.3] * e_len, # top edge
[0.7] * e_len,
['blue'] * e_len) # bottom edge
df = pd.read_csv('/home/hugoshi/data/lab7/CHP2.20131001.hotspots.bed', sep='\t', skiprows=[0], header=None)
df.columns = ["chromosome", "start", "end", "meta1", "meta2", "meta3"]
df = df[df.chromosome == 'chr5']
singles = df[df.start+1 == df.end]
widers = df[df.start+1 != df.end]
slen = len(singles)
wlen = len(widers)
s_source = ColumnDataSource(
data = dict(
start=singles.start,
end=singles.end,
meta1=singles.meta1,
meta2=singles.meta2,
meta3=singles.meta3))
rect('start', # x center
[1]*slen, # y center
[0.9]*slen,
[1]*slen,
color=['red']*slen,
source=s_source) # height
hover = [t for t in curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
# add to this
("position", "@start"),
("meta 1", "@meta1"),
("meta 2", "@meta2"),
("meta 3", "@meta3")
])
quad(widers.start - 0.5, # left edge
widers.end - 0.5, # right edge
[0.3] * wlen, # top edge
[-0.3] * wlen) # bottom edge
hold()
return curplot()
import numpy as np
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load decays.csv into a numpy array
decaydata = np.loadtxt('decays.csv', delimiter=",", skiprows=1)
# provide handles for the x and y columns
time = decaydata[:, 0]
decays = decaydata[:, 1]
bp.output_file("decays.html", title="bokeh_decay.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time,
decays,
x_axis_label="Time (s)",
y_axis_label="Decays (#)",
color='#1F78B4',
legend='Decays per second')
bp.curplot().title = "Decays"
bp.grid().grid_line_alpha = 0.3
bp.show() # open a browser