def data_for_single_day():
df = pd.read_csv('data.csv')
g1 = df.sort_values(by='Report_datetime', ascending=False).head(1)
data = g1.to_dict(orient='list')
dates = g1['Report_datetime'].tolist()
output_file("dodged_bars1.html")
source = ColumnDataSource(data=data)
p1 = figure(x_range=dates,
y_range=(0, g1[['fail', 'Pass']].values.max() + 3),
plot_height=250,
title="Latest execution data",
toolbar_location=None,
tools="")
p1.vbar(x=dodge('Report_datetime', -0.25, range=p1.x_range),
top='fail',
width=0.4,
source=source,
color="#d53e4f",
legend=value("fail"))
p1.vbar(x=dodge('Report_datetime', 0.25, range=p1.x_range),
top='Pass',
width=0.4,
source=source,
color="#99d594",
legend=value("Pass"))
p1.x_range.range_padding = 0.1
p1.xgrid.grid_line_color = None
p1.legend.location = "top_left"
p1.legend.orientation = "horizontal"
show(p1)
def plot_literacy_rate(source, districts):
lit_rate = figure(x_range=districts,
y_range=(0, 100),
plot_height=250,
title="Literacy rate",
tools='save, box_zoom, wheel_zoom,reset')
m = lit_rate.vbar(x=dodge('District', -0.25, range=lit_rate.x_range),
top='Literacy_Rate_Male',
width=0.2,
source=source,
color="#718dbf")
f = lit_rate.vbar(x=dodge('District', 0.0, range=lit_rate.x_range),
top='Literacy_Rate_Female',
width=0.2,
source=source,
color="#e84d60")
#p.x_range.range_padding = 0.1
lit_rate.xgrid.grid_line_color = None
lit_rate.legend.location = "top_left"
lit_rate.xaxis.major_label_orientation = 1.2
lit_rate.legend.orientation = "horizontal"
legend = Legend(items=[('Male', [m]), ('Female', [f])],
location=(0, 30))
legend.click_policy = "hide"
lit_rate.add_layout(legend, 'right')
return lit_rate
def _init_imp_plot(self):
p = figure(plot_width=600, plot_height=200, x_range=CHAN_LIST[0:self.n_chan],
y_range=[str(1)], toolbar_location=None)
p.circle(x='channel', y="row", radius=.3, source=self.imp_source, fill_alpha=0.6, color="color",
line_color='color', line_width=2)
text_props = {"source": self.imp_source, "text_align": "center",
"text_color": "black", "text_baseline": "middle", "text_font": "helvetica",
"text_font_style": "bold"}
x = dodge("channel", -0.1, range=p.x_range)
r = p.text(x=x, y=dodge('row', -.4, range=p.y_range), text="impedance", **text_props)
r.glyph.text_font_size = "10pt"
r = p.text(x=x, y=dodge('row', -.3, range=p.y_range), text="channel", **text_props)
r.glyph.text_font_size = "12pt"
p.outline_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_standoff = 0
p.axis.visible = False
return p
def histogram_weekdays(filename, metrics):
bkh.reset_output()
bkh.output_file(filename, title=filename)
weekdays = [
'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
'Sunday'
]
fig = bkh.figure(x_range=weekdays,
title='Message distribution over weekdays',
width=720,
height=480)
fig.vbar(x=dodge('index', 0.35, range=fig.x_range),
top='frequency',
width=0.3,
source=metrics['A']['frame_weekdays'],
color=colors[0],
legend=metrics['A']['name'])
fig.vbar(x=dodge('index', 0.65, range=fig.x_range),
top='frequency',
width=0.3,
source=metrics['B']['frame_weekdays'],
color=colors[1],
legend=metrics['B']['name'])
fig.xaxis.axis_label = 'Weekday'
fig.yaxis.axis_label = 'Message count'
bkh.show(fig)
return
def make_plot():
"""Bokeh bar chart pulling data from database, grouped by ethnicity and course """
ethnicity = ['British', 'French', 'Medit', 'Indian', 'M East', 'Asian', 'Afr',
'Mex', 'Other']
course = ['Starter', 'Main', 'Desert']
data = {'ethnicity' : ethnicity,
'Starter' : count_course('Starter'),
'Main' : count_course('Main'),
'Desert' : count_course('Desert')}
source = ColumnDataSource(data=data)
p = figure(x_range=ethnicity, y_range=(0, 10), plot_height=350, title="Recipe counts by nationality",
toolbar_location=None, tools="", background_fill_alpha=0.6, sizing_mode="scale_both")
p.vbar(x=dodge('ethnicity', -0.25, range=p.x_range), top='Starter', width=0.3, source=source,
color="#84f02e", legend=value("Starter"))
p.vbar(x=dodge('ethnicity', 0.0, range=p.x_range), top='Main', width=0.3, source=source,
color="#37d26b", legend=value("Main"))
p.vbar(x=dodge('ethnicity', 0.25, range=p.x_range), top='Desert', width=0.3, source=source,
color="#e2f50c", legend=value("Desert"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
p.border_fill_color = "#d7e0d6"
script, div = components(p)
return script, div
def make_race_plot(source):
p = figure(x_range=races, y_range=(0, 1), plot_height=250, title="Coronavirus cases and deaths percentage of each races in California",
toolbar_location=None) #, tools="hover", tooltips="$name: @$name")
p.vbar(x=dodge('races', -0.25, range=p.x_range), top='confirmed', width=0.2, source=source,
color="blue", legend_label="confirmed percentage")
p.vbar(x=dodge('races', 0.0, range=p.x_range), top='death', width=0.2, source=source,
color="red", legend_label="death percentage")
p.vbar(x=dodge('races', 0.25, range=p.x_range), top='population', width=0.2, source=source,
color="orange", legend_label="population percentage")
p.add_tools(HoverTool(
tooltips=[
("race", "@races"),
("confirmed", "@confirmed{0,0.000}"+"%"),
("death", "@death{0,0.000}"+"%"),
("population", "@population{0,0.000}"+"%"),
]
))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_right"
p.legend.orientation = "horizontal"
return p
def get_data(self, element, ranges, style):
style = self.style[self.cyclic_index]
if 'angle' in style and isinstance(style['angle'], (int, float)):
style['angle'] = np.deg2rad(style.get('angle', 0))
dims = element.dimensions()
coords = (1, 0) if self.invert_axes else (0, 1)
xdim, ydim, tdim = (dimension_sanitizer(dims[i].name) for i in coords+(2,))
mapping = dict(x=xdim, y=ydim, text=tdim)
data = {d: element.dimension_values(d) for d in (xdim, ydim)}
if self.xoffset is not None:
mapping['x'] = dodge(xdim, self.xoffset)
if self.yoffset is not None:
mapping['y'] = dodge(ydim, self.yoffset)
data[tdim] = [dims[2].pprint_value(v) for v in element.dimension_values(2)]
self._categorize_data(data, (xdim, ydim), element.dimensions())
cdim = element.get_dimension(self.color_index)
if cdim is None:
return data, mapping, style
cdata, cmapping = self._get_color_data(element, ranges, style, name='text_color')
if dims[2] is cdim and cdata:
# If color dim is same as text dim, rename color column
data['text_color'] = cdata[tdim]
mapping['text_color'] = dict(cmapping['text_color'], field='text_color')
else:
data.update(cdata)
mapping.update(cmapping)
return data, mapping, style
def data_group_by_date():
df = pd.read_csv('data.csv')
g1 = df.groupby(['Report_datetime']).sum()
g1 = df.groupby('Report_datetime', as_index=False).sum()
data = g1.to_dict(orient='list')
dates = g1['Report_datetime'].tolist()
output_file("dodged_bars.html")
source = ColumnDataSource(data=data)
p = figure(x_range=dates,
y_range=(0, g1[['fail', 'Pass']].values.max() + 3),
plot_height=250,
title="Day wise execution data",
toolbar_location=None,
tools="")
p.vbar(x=dodge('Report_datetime', -0.25, range=p.x_range),
top='fail',
width=0.4,
source=source,
color="#fc8d59",
legend=value("fail"))
p.vbar(x=dodge('Report_datetime', 0.25, range=p.x_range),
top='Pass',
width=0.4,
source=source,
color="#718dbf",
legend=value("Pass"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
show(p)
def total_6t(self, match):
self.cds_r = bk_models.ColumnDataSource(self.df_new_6t("red", match))
all_teams = list(pd.unique(self.data.schedule.team.sort_values()))
self.cds_b = bk_models.ColumnDataSource(self.df_new_6t("blue", match))
r_teams = self.cds_r.column_names[1:4]
b_teams = self.cds_b.column_names[1:4]
tasks = self.cds_r.data['task']
plt_name = "Six Team Power Cells Placed: Match " + match
self.plt_pc = bk_plotting.figure(title=plt_name,
x_range=tasks,
plot_width=700,
plot_height=300,
toolbar_location=None,
tools="hover",
tooltips="$name- @task: @$name")
self.plt_pc.vbar_stack(r_teams,
x=bk_transform.dodge('task',
-0.17,
range=self.plt_pc.x_range),
width=0.3,
source=self.cds_r,
legend_label=[" " + x for x in r_teams],
color=bk_palettes.Reds3)
self.plt_pc.vbar_stack(b_teams,
x=bk_transform.dodge('task',
0.17,
range=self.plt_pc.x_range),
width=0.3,
source=self.cds_b,
color=bk_palettes.Blues3,
legend_label=[" " + x for x in b_teams])
return self.plt_pc
def draw_year_AQI(df):
df['date_ym'] = df['date'].apply(lambda x: x.strftime('%Y-%m'))
df_month = df.groupby(by='date_ym').mean().reset_index()
df_month['month'] = df_month['date_ym'].apply(lambda x: x.split('-')[-1])
df_month['year'] = df_month['date_ym'].apply(lambda x: x.split('-')[0])
df_2017 = df_month[df_month['year']=='2017'][['month', '北京']]
df_2018 = df_month[df_month['year']=='2018'][['month', '北京']]
df_2019 = df_month[df_month['year']=='2019'][['month', '北京']]
source_2017 = ColumnDataSource(df_2017)
source_2018 = ColumnDataSource(df_2018)
source_2019 = ColumnDataSource(df_2019)
p = figure(x_range=list(df_2017['month']), title="2017-2019年北京AQI对比", plot_width=1150, plot_height=400)
p.vbar(x=dodge('month', -0.25, range=p.x_range), top='北京', width=0.2, color="#c9d9d3", legend_label="2017", source=source_2017)
p.vbar(x=dodge('month', 0, range=p.x_range), top='北京', width=0.2, color="#718dbf", legend_label="2018", source=source_2018)
p.vbar(x=dodge('month', 0.25, range=p.x_range), top='北京', width=0.2, color="#e84d60", legend_label="2019", source=source_2019)
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.add_tools(HoverTool(tooltips=[("时间", "@month"), ("AQI", "@{北京}")]))
return p
def _create_plot() -> (Figure, ColumnDataSource):
"""Utility function for creating and styling the bar plot."""
global source, aspects, stats
pos_counts, neg_counts = ([
stats.loc[(asp, pol, False), "Quantity"] for asp in aspects
] for pol in POLARITIES)
np.seterr(divide="ignore")
source = ColumnDataSource(
data={
"aspects": aspects,
"POS": pos_counts,
"NEG": neg_counts,
"log-POS": np.log2(pos_counts),
"log-NEG": np.log2(neg_counts),
})
np.seterr(divide="warn")
p = figure(
plot_height=145,
sizing_mode="scale_width",
x_range=aspects,
toolbar_location="right",
tools="save, tap",
)
rs = [
p.vbar(
x=dodge("aspects", -0.207, range=p.x_range),
top="log-POS",
width=0.4,
source=source,
color="limegreen",
legend=value("POS"),
name="POS",
),
p.vbar(
x=dodge("aspects", 0.207, range=p.x_range),
top="log-NEG",
width=0.4,
source=source,
color="orangered",
legend=value("NEG"),
name="NEG",
),
]
for r in rs:
p.add_tools(
HoverTool(tooltips=[("Aspect", "@aspects"),
(r.name, "@" + r.name)],
renderers=[r]))
p.add_layout(
Title(text=" " * 7 + "Sentiment Count (log scale)",
align="left",
text_font_size="23px"),
"left",
)
p.yaxis.ticker = []
p.y_range.start = 0
p.xgrid.grid_line_color = None
p.xaxis.major_label_text_font_size = "20pt"
p.legend.label_text_font_size = "20pt"
return p, source
def makeGroupBar(src):
groups = trips_df.trip_purpose.drop_duplicates().values.tolist()
p = figure(x_range=FactorRange(*groups),
plot_width=column_width,
plot_height=bar_height,
tools="hover",
toolbar_location=None)
p.vbar(x=dodge('Group', -0.25, range=p.x_range),
top='Observed',
width=0.25,
source=src,
color=survey_color,
legend=value("Observed"))
p.vbar(x=dodge('Group', 0, range=p.x_range),
top='Model',
width=0.25,
source=src,
color=cmap_color,
legend=value("Model"))
p.select_one(HoverTool).tooltips = [('Observed - Trip',
"@Observed{0,}"),
('Model - Trip', "@Model{0,}")]
# Styling
p = style(p)
p.legend.click_policy = "hide"
return p
def Hbarchart(src):
source = src
#Plot figure
p = figure(y_range=source.data['town'], x_range=(0, getmaxprice+50000), plot_height=800, plot_width=800,
title="Barchart of Average Median Resale Price for Selected Year 1 vs Selected Year 2",
toolbar_location=None, tools="")
p.hbar(y=dodge('town', -0.15, range=p.y_range), right=src.column_names[2] , height=0.2, source=source,
color="#00008b", legend='name_year1')
p.hbar(y=dodge('town', 0.15, range=p.y_range), right=src.column_names[3] , height=0.2, source=source,
color="#87cefa", legend='name_year2')
# Hover tool with hline mode
hover = HoverTool(tooltips=[('Town', '@town'),
('Median1', '@year_1'),
('Median2', '@year_2'),
('% Change', '@percent_change')],
mode='hline')
# display a tooltip whenever the cursor is horizontally in line with a glyph
p.add_tools(hover)
p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.yaxis.axis_label = 'Towns'
p.xaxis.axis_label = 'Average Median Prices'
p.legend.location = "top_right"
p.legend.orientation = "vertical"
return (p)
def make_transit_chart(src, groups):
p = figure(x_range=FactorRange(*groups),
plot_width=column_width,
plot_height=bar_height,
tools="hover",
toolbar_location=None)
p.vbar(x=dodge('Group', -0.25, range=p.x_range),
top='Observed',
width=0.25,
source=src,
color=survey_color,
legend=value("Observed"))
p.vbar(x=dodge('Group', 0, range=p.x_range),
top='Model',
width=0.25,
source=src,
color=cmap_color,
legend=value("Model"))
p.select_one(HoverTool).tooltips = [
('Observed - Transit Trip', "@Observed{0.0}%"),
('Model - Transit Trip', "@Model{0.0}%")
]
# Styling
p.yaxis.formatter = NumeralTickFormatter(format='0%')
p.legend.click_policy = "hide"
return p
def draw_bar(confirm, healRate, name):
print('-- -- ' * 10, 'drawing picture of bar v1', '-- -- ' * 10)
'''解决中文显示的问题'''
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
'''输出文件'''
output_file('2019-nCoV各国确诊和治愈率对比1.html')
yr = ['confirm', 'healRate']
data = {'country': name, 'confirm': confirm, 'healRate': healRate}
source = ColumnDataSource(data=data)
p = figure(x_range=name, y_range=(0, 90), plot_height=500,
title='2019-nCoV各国确诊和治愈率对比1',
toolbar_location=None, tools='')
p.vbar(x=dodge('country', 0.0, range=p.x_range), top='confirm', width=0.2,
source=source, color='#c9d9d3', legend_label='confirm')
p.vbar(x=dodge('country', 0.25, range=p.x_range), top='healRate', width=0.2,
source=source, color='#718dbf', legend_label='healRate')
p.x_range.range_padding =0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
show(p)
def make_plot(src):
p = figure(x_range=list_of_params, plot_height=450, toolbar_location="right",
title="Comparison of the Performance of Websites",
x_axis_label = 'Performance Metric name', y_axis_label = 'Performance Metrics Value')
p.vbar(x=dodge('params',-0.15,range=p.x_range),
top = 'Standard', width=0.65, source=src, color = "#41b6c4", muted_color="#41b6c4",
muted_alpha=0.2, fill_alpha = 0.7, hover_fill_color = 'navy', hover_fill_alpha = 1.0, legend=value("Standard"))
p.vbar(x=dodge('params',0.15,range=p.x_range),
top = 'input', width=0.5, source=src, color = "#084594", muted_color="#084594",
muted_alpha=0.2,fill_alpha = 0.7, hover_fill_color = 'navy', hover_fill_alpha = 1.0, legend=value("input site"))
# hover = HoverTool(tooltips=[('Parameter','@name'),
# # ('Website','@w_name'),
# # ('Proportion','@p_proportion')
# ],
# mode='vline')
p.legend.click_policy="mute"
hover = HoverTool(tooltips=[('Percentage','@y_axis_label'),
#('Distribution', '@src'),
#('Density', '@ys'),
],
line_policy = 'next')
p.add_tools(hover)
p = style(p)
return p
def _init_imp_plot(self):
plot = figure(plot_width=600, plot_height=200, x_range=self.chan_key_list[0:self.n_chan],
y_range=[str(1)], toolbar_location=None, sizing_mode="scale_width")
plot.circle(x='channel', y="row", size=50, source=self.imp_source, fill_alpha=0.6, color="color",
line_color='color', line_width=2)
text_props = {"source": self.imp_source, "text_align": "center",
"text_color": "white", "text_baseline": "middle", "text_font": "helvetica",
"text_font_style": "bold"}
x = dodge("channel", -0.1, range=plot.x_range)
plot.text(x=x, y=dodge('row', -.35, range=plot.y_range),
text="impedance", **text_props).glyph.text_font_size = "10pt"
plot.text(x=x, y=dodge('row', -.25, range=plot.y_range), text="channel",
**text_props).glyph.text_font_size = "12pt"
plot.outline_line_color = None
plot.grid.grid_line_color = None
plot.axis.axis_line_color = None
plot.axis.major_tick_line_color = None
plot.axis.major_label_standoff = 0
plot.axis.visible = False
return plot
def histogram_hourofday(filename, metrics, key, title_str, ylabel):
bkh.reset_output()
bkh.output_file(filename, title=filename)
hours = [
'00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00',
'08:00', '09:00', '10:00', '11:00', '12:00', '13:00', '14:00', '15:00',
'16:00', '17:00', '18:00', '19:00', '20:00', '21:00', '22:00', '23:00'
]
fig = bkh.figure(x_range=hours, title=title_str, width=1280, height=480)
fig.vbar(x=dodge('index', 0.35, range=fig.x_range),
top='frequency',
width=0.3,
source=metrics['A'][key],
color=colors[0],
legend=metrics['A']['name'])
fig.vbar(x=dodge('index', 0.65, range=fig.x_range),
top='frequency',
width=0.3,
source=metrics['B'][key],
color=colors[1],
legend=metrics['B']['name'])
fig.xaxis.axis_label = 'Time'
fig.yaxis.axis_label = ylabel
bkh.show(fig)
return
def plot_figure(res, male_res, female_res):
"""
Plot a line plot and a box plot in each tab
:param res: a dictionary with mean gender coefficient for each bin
:param male_res: a dictionary with mean male subject score for each bin
:param female_res: a dictionary with mean female subject score for each bin
:return: a grid plot
"""
x_list = list(res.keys())
y_list = list(res.values())
data = {'x': x_list,
'overall_avg': y_list,
'female_avg': list(female_res.values()),
'male_avg': list(male_res.values())
}
source1 = ColumnDataSource(data=data)
tooltips1 = [('Gender test coeff', '@overall_avg')]
p1_line = figure(x_range=x_list,
plot_height=500,
tooltips=tooltips1,
title='Performance between male and female' +
'students for each country')
p1_line.line(x_list, y_list, line_width=2)
p1_line.circle(x='x', y='overall_avg',
fill_color="blue", size=8, source=source1)
tooltips2 = [('Female average score', '@female_avg'),
('Male average score', '@male_avg')]
p1_bar = figure(x_range=x_list,
plot_height=500,
tooltips=tooltips2,
title='Performance male and female students' +
'for each country')
p1_bar.vbar(x=dodge('x', -0.15, range=p1_line.x_range),
top='female_avg',
source=source1,
color='#718dbf',
legend='female average score',
width=0.3)
p1_bar.vbar(x=dodge('x', 0.15, range=p1_line.x_range),
top='male_avg',
source=source1,
color='#e84d60',
legend='male average score',
width=0.3)
# change just some things about the y-axes
p1_line.yaxis.axis_label = "Percentage (%)"
p1_line.yaxis.major_label_text_color = "black"
p1_line.yaxis.major_label_orientation = "vertical"
p1_bar.yaxis.axis_label = "Score"
p1_bar.yaxis.major_label_text_color = "black"
p1_bar.yaxis.major_label_orientation = "vertical"
return gridplot([[p1_bar, p1_line]])
def plot_bokeh(L1, L2, name1, name2):
bokeh.plotting.reset_output #clear the plot from last times
name1 = name1.lower() #location 1
name2 = name2.lower() #location 2
#compute the chart range
m = max(L1 + L2)
mm = min(L1 + L2 + [0])
m = math.ceil(m) + 1
mm = math.floor(mm) - 3
#labels for bars and legend
env_factors = ['commute time', 'population density', 'safety']
locations = [name1, name2]
data = {
'env_factors': env_factors,
name1: L1,
name2: L2
} #data dictionary for bokeh plot
#make the plot Future: Use seaborn. This is more complicated than I like.
source = ColumnDataSource(data=data)
p = figure(x_range=env_factors,
y_range=(mm, m),
plot_height=350,
plot_width=800,
toolbar_location=None,
tools="")
p.vbar(x=dodge('env_factors', -0.25, range=p.x_range),
top=name1,
width=0.2,
source=source,
color="#469e34",
legend=value(name1))
p.vbar(x=dodge('env_factors', 0.0, range=p.x_range),
top=name2,
width=0.2,
source=source,
color="#e37d59",
legend=value(name2))
hline = Span(location=0,
dimension='width',
line_color='black',
line_width=1)
p.renderers.extend([hline])
p.x_range.range_padding = 0.1
p.legend.location = "bottom_right"
p.legend.orientation = "vertical"
p.legend.label_text_font_size = "20pt"
p.xaxis.major_label_text_font_size = "15pt"
p.background_fill_color = None
p.border_fill_color = None
return p
def cases_gender_and_age():
covid_age_gender = 'https://www.skane.se/globalassets/lagesbild-covid-19-i-skane/sjukdomsfall-per-alder-kon.xlsx'
cag = pd.read_excel(covid_age_gender,
sheet_name='Antal',
usecols=['Unnamed: 0', 'Män', 'Kvinnor'])
cag.rename(columns={'Unnamed: 0': 'Ålder'}, inplace=True)
cag = cag.dropna(subset=['Ålder'])
cag['Ålder'] = cag['Ålder'].replace({'90 år eller äldre': '90 >'})
cag = cag.replace({',': '.'})
ages = cag['Ålder'].tolist()
men = cag['Män'].tolist()
men = [int(i) for i in men]
women = cag['Kvinnor'].tolist()
women = [int(i) for i in women]
data = {
'ages': ages,
'men': men,
'women': women,
}
source = ColumnDataSource(data=data)
tooltips = [
("Ålder", "@ages"),
("Antal Män", "@men"),
("Antal Kvinnor", "@women"),
]
p = figure(x_range=ages,
plot_height=250,
plot_width=670,
title="Sjukdomsfall per ålder och kön. Uppdateras varje onsdag",
toolbar_location=None,
tooltips=tooltips,
tools="")
p.vbar(x=dodge('ages', -0.15, range=p.x_range),
top='men',
width=0.3,
source=source,
color="#abb7ed",
legend_label="Antal Män")
p.vbar(x=dodge('ages', 0.15, range=p.x_range),
top='women',
width=0.3,
source=source,
color="#5069c8",
legend_label="Antal Kvinnor")
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "vertical"
return p
def dataChart():
global data
listData = data
ho = list(map(str, listData['roomNo']))
Legacy_1 = list(map(str, listData['firstMethod']))
Legacy_2 = list(map(str, listData['secondMethod']))
Proposed = list(map(str, listData['proposed']))
ElectroFee = list(map(str, listData['electroFee']))
chartData = {
'ho': ho,
'Legacy_1': Legacy_1,
'Legacy_2': Legacy_2,
'Proposed': Proposed,
'ElectroFee': ElectroFee,
}
source = ColumnDataSource(chartData)
p = figure(x_range=ho,
y_range=(0, 4000),
plot_height=350,
title="Crossroad Apartment",
toolbar_location="below",
tools=tools)
p.vbar(x=dodge('ho', -0.25, range=p.x_range),
top='Legacy_1',
width=0.2,
source=source,
color="#c9d9d3",
legend=value("Legacy_1"))
p.vbar(x=dodge('ho', 0.0, range=p.x_range),
top='Legacy_2',
width=0.2,
source=source,
color="#718dbf",
legend=value("Legacy_2"))
p.vbar(x=dodge('ho', 0.25, range=p.x_range),
top='Proposed',
width=0.2,
source=source,
color="#e84d60",
legend=value("Proposed"))
p.line(x=ho, y=ElectroFee, color="red", line_width=2.5)
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
p.xaxis.axis_label = 'House'
p.yaxis.axis_label = 'Fee Charged($)'
return p
def make_group_bar(df):
groups = df.index.values.tolist()
census = df['Census'].values.tolist()
survey = df['Survey'].values.tolist()
model = df['Model'].values.tolist()
data = {
'Group': groups,
'Census': census,
'Survey': survey,
'Model': model
}
src = ColumnDataSource(data=data)
TOOLS = "hover"
p = figure(x_range=FactorRange(*groups),
title="Figure 1 - Auto Ownership Distribution",
plot_width=column_width,
plot_height=bar_height,
tools="hover")
p.vbar(x=dodge('Group', -0.25, range=p.x_range),
top='Census',
width=0.25,
source=src,
color=census_color,
legend=value("Census"))
p.vbar(x=dodge('Group', 0, range=p.x_range),
top='Survey',
width=0.25,
source=src,
color=survey_color,
legend=value("Survey"))
p.vbar(x=dodge('Group', 0.25, range=p.x_range),
top='Model',
width=0.25,
source=src,
color=cmap_color,
legend=value("Model"))
p.select_one(HoverTool).tooltips = [
('Census - Household Total', "@Census{0,}"),
('Survey - Household Total', "@Survey{0,}"),
('Model - Household Total', "@Model{0,}")
]
# Styling
p = bar_style(p)
p.legend.click_policy = "hide"
return p
def oneteam_plot(team, num_matches=12):
measures = oneteam_df(team)
matches = measures.match.unique()
t1plot = plt.figure(x_range=matches,
title="One Team Graph: " + team,
plot_height=350,
plot_width=800,
toolbar_location='left',
tools="hover",
tooltips="$name: @$name")
# Hatch panels
hatch_tasks = [
'getHatch', 'csHatch', 'rocketHatch1', 'rocketHatch2', 'rocketHatch3'
]
hcds_all = get_cds_averages(measures, hatch_tasks)
hatch_r = t1plot.vbar_stack(hatch_tasks,
x=btransform.dodge('match',
-.14,
range=t1plot.x_range),
width=0.25,
source=hcds_all,
color=bpalettes.BuPu5)
# Cargo
cargo_tasks = [
'getCargo', 'csCargo', 'rocketCargo1', 'rocketCargo2', 'rocketCargo3'
]
ccds_all = get_cds_averages(measures, cargo_tasks)
cargo_r = t1plot.vbar_stack(cargo_tasks,
x=btransform.dodge('match',
.14,
range=t1plot.x_range),
width=0.25,
source=ccds_all,
color=bpalettes.Oranges5)
# Climb
climb_tasks = ['climb1', 'climb2', 'climb3']
cl_cds = shape_climb_data(team)
climb_r = t1plot.vbar_stack(climb_tasks,
x=btransform.dodge('match',
.41,
range=t1plot.x_range),
width=0.25,
source=cl_cds,
color=bpalettes.Viridis3)
hatch_items = [(x, [hatch_r[hatch_tasks.index(x)]]) for x in hatch_tasks]
cargo_items = [(x, [cargo_r[cargo_tasks.index(x)]]) for x in cargo_tasks]
climb_items = [(x, [climb_r[climb_tasks.index(x)]]) for x in climb_tasks]
legend = bmodels.Legend(items=hatch_items + cargo_items + climb_items,
location=(0, 0))
t1plot.add_layout(legend, 'right')
return t1plot
def plt_bar(hist_tabel, lbl_list, ylabel, xlabel, filename, wigth, xl, yl):
w = 0.2
output_file(filename)
palette = ["#c9d9d3", "#718dbf", "#e84d60"]
od = cl.OrderedDict(sorted(hist_tabel['m'].items()))
el = cl.OrderedDict(sorted(hist_tabel['m_el'].items()))
ll = cl.OrderedDict(sorted(hist_tabel['m_ll'].items()))
odl = [od[l] if l in od else 0 for l in sorted(lbl_list)]
ell = [el[l] if l in el else 0 for l in sorted(lbl_list)]
lll = [ll[l] if l in ll else 0 for l in sorted(lbl_list)]
num = [str(s) for s in sorted(lbl_list)]
d = {'num': num, 'Entity + Literal': odl, 'Entity': ell, 'Literal': lll}
l = max(odl + ell + lll)
p = figure(x_range=num,
y_range=(0, l + (l / 4)),
plot_height=350,
plot_width=wigth,
toolbar_location=None,
tools="",
x_axis_label=xl,
y_axis_label=yl)
p.vbar(x=dodge('num', -0.25, range=p.x_range),
top='Entity + Literal',
width=0.1,
source=d,
color="#c9d9d3",
legend=value("Entity + Literal"))
p.vbar(x=dodge('num', 0.0, range=p.x_range),
top='Entity',
width=0.1,
source=d,
color="#718dbf",
legend=value("Entity"))
p.vbar(x=dodge('num', 0.25, range=p.x_range),
top='Literal',
width=0.1,
source=d,
color="#e84d60",
legend=value("Literal"))
#p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.orientation = "horizontal"
p.legend.location = "top_left"
save(p)
def create_plot(mode):
"""
create glyphs, text and arrows and insert them into the figures
:param is_previous: is the customized or ideal tree being created?
:return: plot p and the arrow data source
"""
title = "Karar Ağacı " + ("(Algoritmanın Seçtiği Kök Nitelikli Hali)"
if mode == "customized" else
"(Seçtiğiniz Kök Nitelikli Hali)") + ("\t\t\t\tTahmin Başarısı (%): "
+ str(round(acc * 100, 1)) if acc else "")
hover = HoverTool(names=["circles", "rectangles"])
wheel = WheelZoomTool()
p = figure(title=title, toolbar_location="below", tools=[hover, wheel, ResetTool(), PanTool()],
x_range=groups, y_range=list(periods),
tooltips=TOOLTIPS)
p.axis.visible = False
arrow_data_source, label = draw_arrow(data_source.data, p)
p.toolbar.active_scroll = wheel
p.add_layout(label)
circles = p.circle("y", "x", radius=circle_radius, radius_units='screen', source=data_source,
name="circles", legend="attribute_type",
color=factor_cmap('attribute_type',
palette=list(get_all_colors()), factors=Instance().attr_list))
rectangles = p.rect("y", "x", rect_width, rect_height, source=data_source, name="rectangles",
legend="attribute_type", color=factor_cmap('attribute_type', palette=list(get_all_colors()),
factors=Instance().attr_list))
p.select(name="rectangles").visible = False
# Drawing on the rectangles
p.text(x="nonLeafNodes_y", y=dodge("nonLeafNodes_x", 0, range=p.x_range),
name="detailed_text", text="nonLeafNodes_stat", **text_props, text_font_size="7pt")
p.text(x="y", y=dodge("x", 0.3, range=p.x_range), name="detailed_text", text="attribute_type", **text_props,
text_font_size="8pt", text_font_style="bold")
p.text(x="y", y=dodge("x", -0.3, range=p.x_range), name="detailed_text", text="instances", **text_props,
text_font_size="8pt")
p.text(x="leafNodes_y", y="leafNodes_x", name="detailed_text", text="decision", **text_props,
text_font_size="8pt")
p.select(name="detailed_text").visible = False
p.text(x="leafNodes_y", text_color="orange", y=dodge("leafNodes_x", -0.4),
name="decision_text", text="decision", **text_props, text_font_size="8pt")
# Final settings
p.outline_line_color = "white"
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_standoff = 0
p.legend.orientation = "vertical"
p.legend.location = "top_right"
return p, arrow_data_source, circles, rectangles
def create_chart():
Color_Palette = YlOrRd[8]
hour = list(range(0, 24))
hour_str = []
for x in hour:
hour_str.append(str(x) + ":00")
avg_type = ['Overall', 'Selection']
day = mapping_day(days_options.active)
overall = average_checkins(day)
selection = selection_avg_checkins(day, df['flag_5'])
hour_str.reverse()
overall.reverse()
selection.reverse()
data = {'hour': hour_str, 'Overall': overall, 'Selection': selection}
source = ColumnDataSource(data=data)
b = figure(y_range=hour_str,
x_range=(0, 62),
title="Checkin Counts Comparison",
y_axis_label='Hour of Day',
x_axis_label="Average Checkins",
toolbar_location=None,
tools="",
plot_width=290,
plot_height=550)
b.hbar(y=dodge('hour', -0.22, range=b.y_range),
right='Overall',
height=0.35,
source=source,
color=Color_Palette[4],
legend=value("Overall"))
b.hbar(y=dodge('hour', +0.22, range=b.y_range),
right='Selection',
height=0.35,
source=source,
color=Color_Palette[1],
legend=value("Selection"))
b.outline_line_alpha = 0
b.legend.location = "center_right"
b.legend.orientation = "vertical"
b.yaxis.axis_line_color = None
b.yaxis.major_tick_line_color = None
b.yaxis.major_tick_out = 0
b.ygrid.grid_line_color = None
b.xaxis.axis_line_color = None
b.xaxis.minor_tick_line_color = None
b.xaxis.major_tick_line_color = "#a7a7a7"
return b
def job_num_salary_plot():
category = request.form['category']
app.vars['category'] = category
job_salary_num = dill.load(open('job_salary_num_1120.pkl', 'rb'))
job_df = job_salary_num[job_salary_num['title'] == category]
df = job_df[job_salary_num['title'] == category]
df['sal'] = df['salary'].apply(
lambda x: int(x.replace('$', '').replace(',', '')))
df2 = df.sort_values(by='job num', ascending=False)[:10]
cities = df2.Loc.values
output_file('./templates/job_top10.html', title="Top10 cities")
ymin = int(df2['sal'].min()) // 1000 * 1000
ymax = int(df2['sal'].max()) // 1000 * 1050
p = figure(x_range=cities,
y_range=(60000, ymax),
plot_width=1200,
plot_height=350,
title=('Top 10 cities with most job opportunities for ' +
category),
toolbar_location=None,
tools='')
p.yaxis[0].formatter = NumeralTickFormatter(format="$0,0")
p.vbar(x=dodge('Loc', -0.1, range=p.x_range),
top='sal',
width=0.2,
source=df2,
line_color='white',
legend_label="Salary",
color='green')
y2max = (int(df2['job num'].max()) // 100) * 150
p.extra_y_ranges = {'Job Number': Range1d(start=0, end=y2max)}
p.add_layout(LinearAxis(y_range_name="Job Number"), 'right')
p.vbar(x=dodge('Loc', 0.1, range=p.x_range),
top='job num',
width=0.2,
source=df2,
line_color='white',
legend_label="Job number",
color='orange',
y_range_name='Job Number')
p.xgrid.grid_line_color = None
p.legend.location = "top_right"
p.legend.orientation = "horizontal"
hover = HoverTool()
hover.tooltips = [('Title', '@title'), ('Average salary', '@salary'),
('Job number', '@{job num}')]
hover.mode = 'vline'
p.add_tools(hover)
save(p)
return render_template('job_num_salary_plot.html', title=category)
def plt_bar2(hist_tabel, used, ylabel, xlabel, filename, wigth, xl, yl):
w = 0.2
output_file(filename)
gold = hist_tabel['gold']
pre = hist_tabel['pre']
up = [u.replace('http://dbpedia.org/ontology/', '') for u in used]
goldl = [
gold['http://dbpedia.org/ontology/' +
l] if 'http://dbpedia.org/ontology/' + l in gold.keys() else 0
for l in up
]
prel = [
pre['http://dbpedia.org/ontology/' +
l] if 'http://dbpedia.org/ontology/' + l in pre.keys() else 0
for l in up
]
data = {'up': up, 'Gold': goldl, 'Preprocessing': prel}
source = ColumnDataSource(data=data)
p = figure(x_range=up,
plot_height=800,
plot_width=wigth,
toolbar_location=None,
tools="",
x_axis_label=xl,
y_axis_label=yl)
p.vbar(x=dodge('up', -0.25, range=p.x_range),
top='Gold',
width=0.2,
source=data,
color="#c9d9d3",
legend=value("Gold"))
p.vbar(x=dodge('up', 0.0, range=p.x_range),
top='Preprocessing',
width=0.2,
source=data,
color="#718dbf",
legend=value("Preprocessing"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.xaxis.major_label_orientation = 1
p.legend.orientation = "horizontal"
p.legend.location = "top_left"
save(p)
def _create_plot() -> (Figure, ColumnDataSource):
"""Utility function for creating and styling the bar plot."""
global source, aspects, stats
pos_counts, neg_counts = \
([stats.loc[(asp, pol, False), 'Quantity'] for asp in aspects] for pol in POLARITIES)
np.seterr(divide='ignore')
source = ColumnDataSource(
data={
'aspects': aspects,
'POS': pos_counts,
'NEG': neg_counts,
'log-POS': np.log2(pos_counts),
'log-NEG': np.log2(neg_counts)
})
np.seterr(divide='warn')
p = figure(plot_height=145,
sizing_mode="scale_width",
x_range=aspects,
toolbar_location='right',
tools='save, tap')
rs = [
p.vbar(x=dodge('aspects', -0.207, range=p.x_range),
top='log-POS',
width=0.4,
source=source,
color="limegreen",
legend=value('POS'),
name='POS'),
p.vbar(x=dodge('aspects', 0.207, range=p.x_range),
top='log-NEG',
width=0.4,
source=source,
color="orangered",
legend=value('NEG'),
name='NEG')
]
for r in rs:
p.add_tools(
HoverTool(tooltips=[('Aspect', '@aspects'),
(r.name, '@' + r.name)],
renderers=[r]))
p.add_layout(
Title(text=' ' * 7 + 'Sentiment Count (log scale)',
align='left',
text_font_size='23px'), 'left')
p.yaxis.ticker = []
p.y_range.start = 0
p.xgrid.grid_line_color = None
p.xaxis.major_label_text_font_size = "20pt"
p.legend.label_text_font_size = '20pt'
return p, source
def test_basic(self):
t = bt.dodge("foo", 0.5)
assert isinstance(t, dict)
assert set(t) == {"field", "transform"}
assert t['field'] == "foo"
assert isinstance(t['transform'], Dodge)
assert t['transform'].value == 0.5
assert t['transform'].range is None
def test_with_range(self):
r = FactorRange("a")
t = bt.dodge("foo", 0.5, range=r)
assert isinstance(t, dict)
assert set(t) == {"field", "transform"}
assert t['field'] == "foo"
assert isinstance(t['transform'], Dodge)
assert t['transform'].value == 0.5
assert t['transform'].range is r
assert t['transform'].range.factors == ["a"]
output_file("bar_dodged.html")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ['2015', '2016', '2017']
data = {'fruits' : fruits,
'2015' : [2, 1, 4, 3, 2, 4],
'2016' : [5, 3, 3, 2, 4, 6],
'2017' : [3, 2, 4, 4, 5, 3]}
source = ColumnDataSource(data=data)
p = figure(x_range=fruits, y_range=(0, 10), plot_height=350, title="Fruit Counts by Year",
toolbar_location=None, tools="")
p.vbar(x=dodge('fruits', -0.25, range=p.x_range), top='2015', width=0.2, source=source,
color="#c9d9d3", legend=value("2015"))
p.vbar(x=dodge('fruits', 0.0, range=p.x_range), top='2016', width=0.2, source=source,
color="#718dbf", legend=value("2016"))
p.vbar(x=dodge('fruits', 0.25, range=p.x_range), top='2017', width=0.2, source=source,
color="#e84d60", legend=value("2017"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
show(p)
"noble gas" : "#eaeaea",
"nonmetal" : "#f1d4Af",
"transition metal" : "#599d7A",
}
source = ColumnDataSource(df)
p = figure(plot_width=900, plot_height=500, title="Periodic Table (omitting LA and AC Series)",
x_range=groups, y_range=list(reversed(periods)), toolbar_location=None, tools="hover")
p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6, legend="metal",
color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))
text_props = {"source": source, "text_align": "left", "text_baseline": "middle"}
x = dodge("group", -0.4, range=p.x_range)
r = p.text(x=x, y="period", text="symbol", **text_props)
r.glyph.text_font_style="bold"
r = p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number", **text_props)
r.glyph.text_font_size="8pt"
r = p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name", **text_props)
r.glyph.text_font_size="5pt"
r = p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass", **text_props)
r.glyph.text_font_size="5pt"
p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")
