def createHistogram(key):
data = json.load(open('FinishedCourseData/pausePlayBins.json'))[key]
x = sorted([float(num) for num in data.keys()])
strX = [str(i) for i in x]
y = np.array([data[i] for i in strX], dtype=np.float)
output_file('pausePlay.html')
p2 = figure(title="PausePlay for " + key,
tools="",
x_range=strX,
y_range=[0, int(max(y))],
background_fill='#59636C',
plot_width=1600,
plot_height=800)
xSize = [c + ":0.5" for c in strX]
p2.rect(x=xSize, y=y / 2, width=0.2, height=y, color="gold", alpha=0.8)
p2.xgrid.grid_line_color = None
p2.axis.major_label_text_font_size = "8pt"
p2.axis.major_label_standoff = 0
p2.xaxis.major_label_orientation = np.pi / 3
p2.xaxis.major_label_standoff = 5
p2.xaxis.major_tick_out = 0
# show the plots arrayed in a VBox
show(VBox(p2))
def uploaded_file(filename):
#plik pickle
try:
plots_data = pickle.load(
open(os.path.join(app.config['UPLOAD_FOLDER'], filename), "rb"))
except:
return render_template('error.html')
stops1_tag = None
stops2_tag = None
stops3_tag = None
#wykres stops1
if 'stops1' in plots_data:
stops1_plot, stops1_session = plot_bokeh(plots_data['stops1'],
normalized_types=False,
uniq_id=filename)
stops1_tag = autoload_server(stops1_plot, stops1_session).replace(
"localhost", "biquad.mimuw.edu.pl")
#wykres stops2
if 'stops2' in plots_data:
start_pop = plots_data['stops2'][1][0]
grid_shape = plots_data['stops2'][0]
all_pop_mat = plots_data['stops2'][1][1:]
color_dict = plots_data['stops2'][2]
stops2_anim_plot, stops2_anim_session = plot_hexagonal_grid.draw_bokeh_plot(
start_pop, grid_shape, color_dict, uniq_id=filename)
stops2_tag = autoload_server(stops2_anim_plot,
stops2_anim_session).replace(
"localhost", "biquad.mimuw.edu.pl")
thread = Thread(target=update_animation_stops2,
args=(stops2_anim_plot, stops2_anim_session,
all_pop_mat, color_dict))
thread.start()
if 'stops3' in plots_data:
all_freqs_after_reception = plots_data['stops3'][0]
all_segm_dict = plots_data['stops3'][1]
stops3_anim_bar, stops3_anim_plot, stops3_anim_session, geny = plot_segments.draw_plot(
all_freqs_after_reception, all_segm_dict, uniq_id=filename)
stops3_tag = autoload_server(VBox(stops3_anim_bar, stops3_anim_plot),
stops3_anim_session).replace(
"localhost", "biquad.mimuw.edu.pl")
thread = Thread(target=update_animation_stops3,
args=(stops3_anim_bar, stops3_anim_plot,
stops3_anim_session, all_freqs_after_reception,
all_segm_dict, geny))
thread.start()
return render_template('plot.html',
tag1=stops1_tag,
tag2=stops2_tag,
tag3=stops3_tag,
filename=filename)
def plot_to_html(self):
output_file('scatter_plots.html', title='scatter plots')
tools = 'pan,wheel_zoom,box_zoom,reset,save,box_select'
width = 800
height = 400
vbox = VBox()
for netloc in self.unique_url_netlocs:
x, y = self._get_axis(netloc)
p = figure(
tools=tools,
title=netloc,
plot_width=width,
plot_height=height,
y_range=[0.0, 4000.0],
x_axis_label='elapsed time in test (seconds)',
y_axis_label='ttfb latency (milliseconds)',
)
p.scatter(x, y, color='blue')
vbox.children.append(p)
show(vbox)
y2 = [sin(x) for x in x2]
x3 = list(range(11))
y3 = [x**2 for x in x3]
# specify and output static HTML file
output_file("scatter.html")
# EXERCISE: Plot all the sets of points on different plots p1, p2, p3.
# Try setting `color` (or `line_color`) and `alpha` (or `line_alpha`).
# You can also set `line_dash` and `line_width`. One example is given.
p1 = figure(plot_width=300, plot_height=300)
p1.line(x1, y1, size=12, color="red", alpha=0.5)
p2 = figure(plot_width=300, plot_height=300)
p2.line(x2, y2, size=12, color="blue", line_dash=[2, 4])
p3 = figure(plot_width=300, plot_height=300)
p3.line(x3, y3, size=12, line_color="orange", line_width=3)
# create a figure
p4 = figure()
# EXERCISE: add all the same renderers above, on this one plot
p4.line(x1, y1, size=12, color="red", alpha=0.5)
p4.line(x2, y2, size=12, color="blue", line_dash=[2, 4])
p4.line(x3, y3, size=12, line_color="orange", line_width=2)
# show the plots arrayed in a VBox
show(VBox(HBox(p1, p2, p3), p4))
p1.line(IBM['Date'], IBM['Adj Close'], color='#33A02C', legend='IBM') p1.line(MSFT['Date'], MSFT['Adj Close'], color='#FB9A99', legend='MSFT') # EXERCISE: style the plot, set a title, lighten the gridlines, etc. p1.title = "Stock Closing Prices" p1.grid.grid_line_alpha = 0.3 # EXERCISE: start a new figure p2 = figure(title="AAPL average") # Here is some code to compute the 30-day moving average for AAPL aapl = AAPL['Adj Close'] aapl_dates = AAPL['Date'] window_size = 30 window = np.ones(window_size) / float(window_size) aapl_avg = np.convolve(aapl, window, 'same') # EXERCISE: plot a scatter of circles for the individual AAPL prices with legend # 'close'. Remember to set the x axis type and tools on the first renderer p2.scatter(aapl_dates, aapl, size=4, color='#A6CEE3', legend='close') # EXERCISE: plot a line of the AAPL moving average data with the legeng 'avg' p2.line(aapl_dates, aapl_avg, color='red', legend='avg') # EXERCISE: style the plot, set a title, lighten the gridlines, etc. p2.title = "AAPL One-Month Average" p2.grid.grid_line_alpha = 0.3 show(VBox(p1, p2)) # open a browser
palette=palette,
title="categorical heatmap, pd_input",
height=400,
width=1000)
hm_data = df3.values.T
hm2 = HeatMap(hm_data,
palette=palette,
title="Unemployment (Array)",
xlabel='Years since 1948',
ylabel='Months',
height=400,
width=1000)
simple_df = pd.DataFrame(
{
'apples': [4, 5, 8, 12, 4],
'pears': [6, 5, 4, 8, 7],
'bananas': [1, 2, 4, 8, 12]
},
index=['2009', '2010', '2011', '2012', '2013'])
hm3 = HeatMap(simple_df,
palette=palette,
title="Fruit comsumption per year",
height=400,
width=1000)
output_file("heatmap.html")
show(VBox(hm1, hm2, hm3))
p4.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")
p4.legend.orientation = "top_left"
p5 = figure(title="Weibull Distribution (λ=1, k=1.25)",
background_fill="#E8DDCB",
tools="")
lam, k = 1, 1.25
# sample the distribution
measured = lam * (-np.log(np.random.uniform(0, 1, 1000)))**(1 / k)
hist, edges = np.histogram(measured, density=True, bins=50)
# compute ideal values
x = np.linspace(0, 8, 1000)
pdf = (k / lam) * (x / lam)**(k - 1) * np.exp(-(x / lam)**k)
cdf = 1 - np.exp(-(x / lam)**k)
p5.quad(top=hist,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color="#036564",
line_color="#033649")
p5.line(x, pdf, line_color="#D95B43", line_width=8, alpha=0.7, legend="PDF")
p5.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")
p5.legend.orientation = "top_left"
# show all the plots arrayed in a VBox
show(VBox(p1, p2, p3, p4, p5))
title="stacked, dict_input",
xlabel="countries",
ylabel="medals",
legend=True,
width=800,
height=600,
stacked=True)
df = pd.DataFrame(medals, index=countries)
bar3 = Bar(df,
title="stacked, df_input",
xlabel="countries",
ylabel="medals",
legend=True,
width=800,
height=600,
stacked=True)
medals = np.array([bronze, silver, gold])
bar4 = Bar(df,
title="grouped, df_input",
xlabel="countries",
ylabel="medals",
legend=True,
width=800,
height=600)
output_file("bar.html")
show(VBox(bar1, bar2, bar3, bar4))
y6_temps = temps_by_tank[6]
y7_temps = temps_by_tank[7]
y8_temps = temps_by_tank[8]
Document = Document()
output_file("/var/www/index.html")
attrib_dict = dict(width=300, height=300, x_axis_type="datetime")
line_dict = dict(size=12, alpha=0.5)
plot0 = figure(title="Tank 1", **attrib_dict)
plot1 = figure(title="Tank 2", **attrib_dict)
plot2 = figure(title="Tank 3", **attrib_dict)
plot3 = figure(title="Tank 4", **attrib_dict)
plot4 = figure(title="Tank 5", **attrib_dict)
plot5 = figure(title="Tank 6", **attrib_dict)
plot6 = figure(title="Tank 7", **attrib_dict)
plot7 = figure(title="Tank 8", **attrib_dict)
plot0.line(x_axis_time_stamps, y0_temps, color="black", **line_dict)
plot1.line(x_axis_time_stamps, y1_temps, color="red", **line_dict)
plot2.line(x_axis_time_stamps, y2_temps, color="green", **line_dict)
plot3.line(x_axis_time_stamps, y3_temps, color="blue", **line_dict)
plot4.line(x_axis_time_stamps, y4_temps, color="violet", **line_dict)
plot5.line(x_axis_time_stamps, y5_temps, color="blue", **line_dict)
plot6.line(x_axis_time_stamps, y6_temps, color="red", **line_dict)
plot7.line(x_axis_time_stamps, y7_temps, color="purple", **line_dict)
show(
VBox(HBox(plot0, plot1, plot2, plot3), HBox(plot4, plot5, plot6,
plot7)))
cat=categories,
title="All negative input | Grouped",
ylabel="Random Number",
width=width,
height=height)
np_custom = Bar(mixed,
cat=categories,
title="Custom range (start=-3, end=0.4)",
ylabel="Random Number",
width=width,
height=height,
continuous_range=Range1d(start=-3, end=0.4))
np_mixed_grouped = Bar(mixed,
cat=categories,
title="Mixed-sign input | Grouped",
ylabel="Random Number",
width=width,
height=height)
# Collect and display
output_file("bar.html")
show(
VBox(
HBox(dict_stacked, df_grouped),
HBox(np_stacked, np_negative_grouped),
HBox(np_mixed_grouped, np_custom),
))
# EXERCISE: add a `rect` renderer to stack the gold medal results
# EXERCISE: use grid(), axis(), etc. to style the plot. Some suggestions:
# - remove the grid lines
# - change the major label standoff, and major_tick_out values
# - make the tick labels smaller
# - set the x-axis orientation to vertical, or angled
# create a new figure
p2 = figure(title="Olympic Medals by Country (grouped)", tools="",
x_range=countries, y_range=[0, max([gold.max(), silver.max(), bronze.max()])],
background_fill='#59636C', plot_width=1000, plot_height=300)
# Categorical percentage coordinates can be used for positioning/grouping
countries_bronze = [c+":0.3" for c in countries]
countries_silver = [c+":0.5" for c in countries]
countries_gold = [c+":0.7" for c in countries]
# EXERCISE: re create the medal plot, but this time:
# - do not stack the bars on the y coordinate
# - use countries_gold, etc. to positions the bars on the x coordinate
# EXERCISE: use grid(), axis(), etc. to style the plot. Some suggestions:
# - remove the axis and grid lines
# - remove the major ticks
# - make the tick labels smaller
# - set the x-axis orientation to vertical, or angled
# show the plots arrayed in a VBox
show(VBox(p1, p2))
def plot_bokeh(symulacja, normalized_types = False, types_to_plot = None, type_labels = None, ligand_labels = None,uniq_id="no-id"):
from bokeh.plotting import figure, VBox, output_server, cursession, push, Session
from bokeh.models import Range1d
from bokeh.document import Document
pops = symulacja[0]
ligands = symulacja[1]
types = symulacja[2]
if types_to_plot == None:
types_to_plot = types.keys()
if type_labels == None:
type_labels = dict([])
for k in types.keys():
type_labels[k] = k
if ligand_labels == None:
ligand_labels = dict([])
for k in ligands.keys():
ligand_labels[k] = k
output_server("stops1"+uniq_id)
len_pops = len(pops)
step_axis = range(len_pops)
TOOLS="resize,pan,wheel_zoom,box_zoom,reset,previewsave,poly_select"
xrange1 = Range1d(start=0, end=len_pops-1)
if normalized_types:
#dwa wykresy - rozmiar populacji i procent komorek
fig1 = figure(title="Population size",width=800, height=332,tools=TOOLS,x_range=xrange1)
fig1.line(step_axis,pops,line_color = "blue",line_width=2,line_alpha=0.9)
fig2 = figure(title="% of cells of specific types in the population",width=724,height=300,tools=TOOLS,x_range=xrange1)
for k, v in types.items():
if k in types_to_plot:
norm_sizes = [(float(v[i]) / pops[i] * 100) for i in range(len(pops))]
color = lambda: random.randint(0,255)
rcolor = ('#%02X%02X%02X' % (color(),color(),color()))
fig2.line(step_axis, norm_sizes, line_width=2, line_color = rcolor, legend=type_labels[k])
fig2.legend.orientation = "top_right"
else:
fig1 = figure(title="Population / type size",width=724,height=300,tools=TOOLS,x_range=xrange1)
for k, v in types.items():
if k in types_to_plot:
color = lambda: random.randint(0,255)
rcolor = ('#%02X%02X%02X' % (color(),color(),color()))
fig1.line(step_axis, v, line_width = 2, line_color=rcolor, legend = type_labels[k])
fig1.legend.orientation = "top_right"
fig1.line(step_axis, pops, line_width = 2.0, line_color = "blue", legend = "population size")
fig3 = figure(title="Ligands",width=724,height=300,tools=TOOLS,x_range=xrange1)
for k, v in ligands.items():
color = lambda: random.randint(0,255)
rcolor = ('#%02X%02X%02X' % (color(),color(),color()))
fig3.line(step_axis, v, line_width = 3, line_color = rcolor, legend = ligand_labels[k])
fig3.legend.orientation = "top_right"
push()
if normalized_types:
p = VBox(fig1,fig2,fig3)
else:
p = VBox(fig1,fig3)
session=cursession()
return p,session
scatter1 = Scatter(xyvalues, title="iris dataset, dict_input", xlabel="petal_length",
ylabel="petal_width", legend='top_left', marker="triangle")
groupped_df = flowers[["petal_length", "petal_width", "species"]].groupby("species")
scatter2 = Scatter(groupped_df, title="iris dataset, dict_input", xlabel="petal_length",
ylabel="petal_width", legend='top_left')
pdict = OrderedDict()
for i in groupped_df.groups.keys():
labels = groupped_df.get_group(i).columns
xname = labels[0]
yname = labels[1]
x = getattr(groupped_df.get_group(i), xname)
y = getattr(groupped_df.get_group(i), yname)
pdict[i] = list(zip(x, y))
df = pd.DataFrame(pdict)
scatter3 = Scatter(df, title="iris dataset, dict_input", xlabel="petal_length",
ylabel="petal_width", legend='top_left')
scatter4 = Scatter(list(xyvalues.values()), title="iris dataset, dict_input", xlabel="petal_length",
ylabel="petal_width", legend='top_left')
output_file("scatter.html")
show(VBox(scatter1, scatter2, scatter3, scatter4))