def __init__(self, kind="scatter", **kwargs):
"""Useful Chart kwargs:
Parameters:
xlabel (str): override the automatic x_axis_label. Default is None.
ylabel (str): override the automatic y_axis_label. Default is None.
callback (str): clicking on a point will link to the given HTML address. `@<IdProperty>` can be used as placeholder for the point id (e.g. Compound_Id). Default is None."""
self.data = {}
self.kwargs = kwargs
self.kind = kind
self.height = kwargs.get("height", 450)
self.title = kwargs.get("title", "Scatter Plot")
self.position = kwargs.get("position", kwargs.get("pos", "top_left"))
self.series_counter = 0
self.tools_added = False
tools = ["pan", "wheel_zoom", "box_zoom", "reset", "resize", "save"]
self.callback = kwargs.get("callback", None)
if self.callback is not None:
tools.append("tap")
self.plot = figure(plot_height=self.height, title=self.title, tools=tools)
self.plot.axis.axis_label_text_font_size = "14pt"
self.plot.axis.major_label_text_font_size = "14pt"
self.plot.title.text_font_size = "18pt"
if self.callback is not None:
taptool = self.plot.select(type=TapTool)
taptool.callback = OpenURL(url=self.callback)
def make_plot_bio_overview(df, x_val, label_bio_overview,
tooltips_bio_overview):
hov_bio_overview = HoverTool(tooltips=tooltips_bio_overview, mode='mouse')
p_bio_overview = figure(y_range=sorted(df.name, reverse=True),
title=label_bio_overview + ' by Bioregion',
width=fig_width,
height=fig_height,
x_axis_label=label_bio_overview,
y_axis_label="Bioregion",
tools=[
hov_bio_overview,
TapTool(),
BoxZoomTool(),
WheelZoomTool(),
SaveTool(),
ResetTool()
])
p_bio_overview.hbar(y='name',
height=0.9,
left=0,
right=x_val,
color=primary_color,
source=df)
p_bio_overview.xaxis.major_label_orientation = x_angle
p_bio_overview.xgrid.grid_line_color = None
p_bio_overview.ygrid.grid_line_color = '#BFBFBF'
p_bio_overview.x_range.start = 0
p_bio_overview.yaxis.minor_tick_line_color = None
p_bio_overview.xaxis[0].formatter = NumeralTickFormatter(format="0")
p_bio_overview_url = "https://www.antweb.org/bioregion.do?name=@name"
taptoo1__bio_overview = p_bio_overview.select(type=TapTool)
taptoo1__bio_overview.callback = OpenURL(url=p_bio_overview_url)
return p_bio_overview
def chart_remaining_assignees(self, stats_data):
self.log.info('Generating graph about remaining effort per assignee')
plot_values, total_metric = self.get_points_per_assignee(stats_data)
dat = pd.DataFrame(plot_values, columns=['entity', 'value', 'jira_url'])
source = self.get_remaining_source(dat)
# Declare tools
hover = HoverTool(
tooltips=[
('Assignee:', '@x_values'),
('Points:', '@y_values')
]
)
plot = figure(
plot_width=600
, plot_height=300
, x_axis_label='Assignee'
, y_axis_label=self.config.get_config_value('stats_metric')
, title='Remaining ' + self.config.get_config_value('stats_metric') + ' per Jira Assignee (Total: ' + str(
total_metric) + ')'
, x_range=FactorRange(factors=list(dat.entity))
, tools=['save', 'pan', 'box_zoom', 'reset', hover, 'tap']
)
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url='@jira_url')
plot.vbar(source=source, x='x_values', top='y_values', bottom=0, width=0.3, color='blue')
return plot
def __init__(self, scheduler, width=600, **kwargs):
with log_errors():
self.last = 0
self.scheduler = scheduler
self.source = ColumnDataSource({'nprocessing': [1, 2],
'nprocessing-half': [0.5, 1],
'nprocessing-color': ['red', 'blue'],
'nbytes': [1, 2],
'nbytes-half': [0.5, 1],
'worker': ['a', 'b'],
'y': [1, 2],
'nbytes-color': ['blue', 'blue'],
'bokeh_address': ['', '']})
processing = figure(title='Tasks Processing', tools='resize', id='bk-nprocessing-plot',
width=int(width / 2), **kwargs)
processing.rect(source=self.source,
x='nprocessing-half', y='y',
width='nprocessing', height=1,
color='nprocessing-color')
processing.x_range.start = 0
nbytes = figure(title='Bytes stored', tools='resize',
id='bk-nbytes-worker-plot', width=int(width / 2),
**kwargs)
nbytes.rect(source=self.source,
x='nbytes-half', y='y',
width='nbytes', height=1,
color='nbytes-color')
nbytes.xaxis[0].formatter = NumeralTickFormatter(format='0.0 b')
nbytes.xaxis.major_label_orientation = -math.pi / 12
nbytes.x_range.start = 0
for fig in [processing, nbytes]:
fig.xaxis.minor_tick_line_alpha = 0
fig.yaxis.visible = False
fig.ygrid.visible = False
tap = TapTool(callback=OpenURL(url='http://@bokeh_address/'))
fig.add_tools(tap)
fig.toolbar.logo = None
fig.toolbar_location = None
fig.yaxis.visible = False
hover = HoverTool()
hover.tooltips = "@worker : @nprocessing tasks. Click for worker page"
hover.point_policy = 'follow_mouse'
processing.add_tools(hover)
hover = HoverTool()
hover.tooltips = "@worker : @nbytes bytes. Click for worker page"
hover.point_policy = 'follow_mouse'
nbytes.add_tools(hover)
self.processing_figure = processing
self.nbytes_figure = nbytes
processing.y_range = nbytes.y_range
self.root = row(nbytes, processing, sizing_mode='scale_width')
def plot_map(self):
today = self.df.groupby('location').last()
today['size']=today.C/today.C.max()*50+5
wgs84_to_web_mercator(today)
tile_provider = get_provider(CARTODBPOSITRON)
p = figure(plot_width=900, plot_height=600,
#x_range=x_range, y_range=y_range,
#x_range=(today.long.min(), today.long.max()), y_range=(today.lat.min(), today.long.max()),
x_axis_type="mercator", y_axis_type="mercator", tools='tap')
p.add_tile(tile_provider)
p.circle(x="x", y="y", size="size", fill_color="blue", fill_alpha=0.5, source=today)
htool = HoverTool(
tooltips=[
("Location", "@location"),
("Confirmed", "@C"),
("Recovered", "@R"),
("Deaths", "@D")
]
)
wzt = WheelZoomTool()
p.add_tools(htool)
p.add_tools(wzt)
p.toolbar.active_scroll = wzt
url = "/plot/@location"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url, same_tab=True)
return components(p)
def __init__(self, scheduler, **kwargs):
with log_errors():
self.scheduler = scheduler
self.source = ColumnDataSource({'occupancy': [0, 0],
'worker': ['a', 'b'],
'x': [0.0, 0.1],
'y': [1, 2],
'ms': [1, 2],
'color': ['red', 'blue'],
'bokeh_address': ['', '']})
fig = figure(title='Occupancy', tools='', id='bk-occupancy-plot',
x_axis_type='datetime', **kwargs)
rect = fig.rect(source=self.source, x='x', width='ms', y='y', height=1,
color='color')
rect.nonselection_glyph = None
fig.xaxis.minor_tick_line_alpha = 0
fig.yaxis.visible = False
fig.ygrid.visible = False
# fig.xaxis[0].formatter = NumeralTickFormatter(format='0.0s')
fig.x_range.start = 0
tap = TapTool(callback=OpenURL(url='http://@bokeh_address/'))
hover = HoverTool()
hover.tooltips = "@worker : @occupancy s."
hover.point_policy = 'follow_mouse'
fig.add_tools(hover, tap)
self.root = fig
def graph_abs_magnitude(abs_mag=None, velocity=None):
"""Create Bokeh Scatter Plot."""
if len(abs_mag) != len(velocity):
raise UnknownAxisException
if abs_mag == None and velocity == None:
abs_mag = []
velocity = []
here = os.path.abspath(__file__)
graph_file_path = os.path.join(os.path.dirname(os.path.dirname(here)),
"static/abs_magnitude.html")
output_file(graph_file_path)
p = figure(title="Brightness and Velocity",
tools="tap",
x_axis_label='Absolute Magnitude',
y_axis_label='Velocity km/s')
source = ColumnDataSource(data=dict(
x=abs_mag, y=velocity, color=["navy" for i in range(len(abs_mag))]))
p.circle('x', 'y', color='color', size=20, source=source)
url = "static/details_neo1.html"
#url = "http://www.colors.commutercreative.com/@color/" <---- save this for now, it is an example for later
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
show(p)
def build_plot(tweet_feature):
tweet_source = ColumnDataSource(data=dict(x=tweet_feature['x'],
y=tweet_feature['y'],
text=tweet_feature['text'],
url=tweet_feature['url'],
color=tweet_feature['color']))
p = figure(plot_width=600, plot_height=600,
tools=[HoverTool(tooltips="""<div style="width:300px">@text</div>"""),
TapTool()],
toolbar_location=None,
title='hover to view tweet text, click to view tweet in a pop-up window')
tap = p.select(type=TapTool)
tap.callback = OpenURL(url='@url')
p.axis.visible = False
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
color_mapper=LogColorMapper(palette='Viridis256',
low=1, high=max(tweet_feature['popularity'])+1)
color_bar=ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=6, border_line_color=None, location=(0,0),
title="likes")
p.circle(x='x', y='y', source=tweet_source, size=8,
fill_color='color', line_color='color')
p.add_layout(color_bar, 'right')
return components(p)
def create_submit_chart_html(submits: List[Submit],
assignments: List[AssignedTask]) -> str:
main_assignment = assignments[0] if assignments else None
def format_points(submit: Submit):
if not main_assignment or not main_assignment.max_points:
return "not graded"
points = submit.points or submit.assigned_points
if points is None:
return "no points assigned"
return f"{points}/{main_assignment.max_points}"
frame = pd.DataFrame({
"date": [submit.created_at for submit in submits],
"student": [submit.student.username for submit in submits],
"submit_num": [submit.submit_num for submit in submits],
"submit_url": [
reverse("task_detail",
kwargs=dict(assignment_id=submit.assignment.id,
login=submit.student.username,
submit_num=submit.submit_num))
for submit in submits
],
"points": [format_points(submit) for submit in submits],
})
frame["count"] = 1
frame["cumsum"] = frame["count"].cumsum()
source = ColumnDataSource(data=frame)
plot = figure(plot_width=1200,
plot_height=400,
x_axis_type="datetime",
tools="pan,wheel_zoom,box_zoom,save,reset,tap")
plot.line("date", "cumsum", source=source)
students = sorted(set(frame["student"]))
mapper = factor_cmap(field_name="student",
palette=CategoricalPalette,
factors=students)
points = plot.circle("date", "cumsum", color=mapper, source=source, size=8)
plot.yaxis.axis_label = "# submits"
url = "@submit_url#src"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
hover = HoverTool(tooltips=[("submit", "@student #@submit_num"),
("points", "@points"),
("date", "@date{%d. %m. %Y %H:%M:%S}")],
formatters={'@date': 'datetime'},
renderers=[points])
plot.add_tools(hover)
for assignment in assignments:
if assignment.deadline is not None:
draw_deadline_line(plot, assignment.deadline)
return file_html(plot, CDN, "Submits over time")
def patches(plot, div, patch_data):
color_mapper = LinearColorMapper(palette=palette)
patches = MultiPolygons(xs='xs',
ys='ys',
fill_color={
'field': 'rank',
'transform': color_mapper
},
fill_alpha=0.5,
line_color="lightblue",
line_alpha=0.3,
line_width=3.0)
hover_patches = MultiPolygons(xs='xs',
ys='ys',
fill_color={
'field': 'rank',
'transform': color_mapper
},
fill_alpha=0.5,
line_color="purple",
line_alpha=0.8,
line_width=3.0)
patch_source = geodf_patches_to_geods(patch_data)
render = plot.add_glyph(patch_source,
patches,
hover_glyph=hover_patches,
selection_glyph=patches,
nonselection_glyph=patches)
parsed_geojson = json.loads(patch_source.geojson)
# str.source.selected.indices gives you a list of things that you
# immediately clicked on
code = """
var features = json_source['features'];
var properties = features[cb_data.index.indices[0]];
if (properties != undefined) {
var rank = properties['properties']['rank'] + 1;
var name = properties['properties']['name'];
var protestcount = properties['properties']['protestcount'];
div.text = name +
'<br>' + 'Protest Count: ' + protestcount
}
"""
callback = CustomJS(args=dict(json_source=parsed_geojson, div=div),
code=code)
hover = HoverTool(tooltips=None,
renderers=[render],
point_policy="follow_mouse",
callback=callback)
plot.add_tools(hover)
plot.toolbar.active_inspect = hover
tap = TapTool(renderers=[render],
callback=OpenURL(url='/spa/@perma{safe}'))
plot.add_tools(tap)
return plot
def plot_t_sne(read_counting_table, tsne_result,
output_file_colorized_by_clusters):
read_counting_table["t-SNE-component_1"] = [pos[0] for pos in tsne_result]
read_counting_table["t-SNE-component_2"] = [pos[1] for pos in tsne_result]
color_palette = bokeh.palettes.Colorblind[(len(
read_counting_table["Cluster_label"].unique()))]
color = read_counting_table["Cluster_label"].apply(
lambda lable: color_palette[lable])
label = read_counting_table.apply(_label_clustering, axis=1)
hower_data = dict(x=read_counting_table["t-SNE-component_1"],
y=read_counting_table["t-SNE-component_2"],
feature=read_counting_table["Protein.names"],
id=read_counting_table["Protein.IDs"],
cluster_label=read_counting_table["Cluster_label"],
color=color,
label=label)
hower_data_df = pd.DataFrame.from_dict(hower_data)
hower_data_df.to_csv("hower_data_df.csv", sep='\t', index=None)
source = ColumnDataSource(hower_data)
hover = HoverTool(tooltips=[("Protein.names",
"@feature"), ("Protein.IDs", "@id")])
plot = figure(plot_width=900,
plot_height=900,
tools=[
hover,
BoxZoomTool(),
ResetTool(),
PanTool(),
WheelZoomTool(), "tap"
],
title="Grad-Seq t-SNE proteins")
plot.circle("x",
"y",
source=source,
size=7,
alpha=3,
color='color',
legend="label",
line_color="black")
plot.yaxis.axis_label_text_font_size = "15pt"
plot.xaxis.axis_label_text_font_size = "15pt"
plot.title.text_font_size = '15pt'
url = "https://www.uniprot.org/uniprot/?query=@feature"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
plot.xaxis.axis_label = "Dimension 1"
plot.yaxis.axis_label = "Dimension 2"
output_file(output_file_colorized_by_clusters)
save(plot)
def generateNavigatorFigure(dataframe, i, title):
global pixelsForTitle;
global pixelsPerHeightUnit;
global plotWidth;
# Generate the colors, such that the current interval is shown in a
# different color than the rest.
#
numIntervals = dataframe['intervalnumber'].size;
color = ["white" for x in range(numIntervals)];
color[i] = "salmon";
dataframe['color'] = color;
cds = ColumnDataSource(dataframe);
title = title + " CLICK TO NAVIGATE";
hover = HoverTool(tooltips = [
("interval #", "@intervalnumber"),
("interval start", "@intervalbegin{0,0}"),
("interval end", "@intervalend{0,0}")]);
TOOLS = [hover, "tap"];
p = figure(title = title, plot_width = plotWidth,
x_range = (0, numIntervals),
plot_height = 2 * pixelsPerHeightUnit + pixelsForTitle,
x_axis_label = "",
y_axis_label = "", tools = TOOLS,
toolbar_location="above");
# No minor ticks or labels on the y-axis
p.yaxis.major_tick_line_color = None;
p.yaxis.minor_tick_line_color = None;
p.yaxis.major_label_text_font_size = '0pt';
p.yaxis.ticker = FixedTicker(ticks = range(0, 1));
p.ygrid.ticker = FixedTicker(ticks = range(0, 1));
p.xaxis.formatter = NumeralTickFormatter(format="0,");
p.title.align = "center";
p.title.text_font_style = "normal";
p.quad(left = 'intervalnumber', right = 'intervalnumbernext',
bottom = 0, top = 2, color = 'color', source = cds,
nonselection_fill_color='color',
nonselection_fill_alpha = 1.0,
line_color = "aliceblue",
selection_fill_color = "white",
selection_line_color="lightgrey"
);
url = "@bucketfiles";
taptool = p.select(type=TapTool);
taptool.callback = OpenURL(url=url);
return p;
def bokeh_scatter(df,
colourDimension='EmployerSize',
title="Mean (x) vs Median (y) Scatter"):
'''
Make a scatter plot from a dataframe
:param df:
:param colors:
:return:
'''
from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource, HoverTool, TapTool, OpenURL
from bokeh.transform import factor_cmap
#bokeh data
srce = ColumnDataSource(df[[
'DiffMeanHourlyPercent', 'DiffMedianHourlyPercent', 'CurrentName',
'EmployerSize', 'DiffMeanHourlyPercent', 'DiffMedianHourlyPercent',
'CompanyLinkToGPGInfo', 'Sector'
]])
# output to static HTML file
output_file("{}.html".format(title))
# create a new plot
p = figure(tools="pan,box_zoom,reset,save, tap",
title=title,
x_axis_label='mean gap %',
y_axis_label='median gap %')
p.circle(source=srce,
x='DiffMeanHourlyPercent',
y='DiffMedianHourlyPercent',
legend="y=x",
color=colourDimension,
fill_color=colourDimension,
size=8)
##attempts to get colour grouping workings
# color_map = CategoricalColorMapper(factors=df[colourDimension].unique())
# p.circle(source=srce, x='DiffMeanHourlyPercent', y='DiffMedianHourlyPercent', legend="y=x", color={'field': colourDimension, 'transform': color_map}, size=8)
url = '@CompanyLinkToGPGInfo/'
#make the axes stand out -- got rid of this as couldn't work out how to
# p.xaxis.axis_line_width = 3
# p.yaxis.axis_line_width = 3
# p.xaxis.axis_line_color = "Black"
# p.xaxis.axis_line_join()
# add some renderers
# index_cmap = factor_cmap('DiffMeanHourlyPercent', palette = Spectral6, factors=sorted(df[colourDimension].unique()), end=1)
p.add_tools(
HoverTool(tooltips=[(
"Name", "@CurrentName"), (
"EmployerSize",
"@EmployerSize"), ("MedianDiff", "@DiffMedianHourlyPercent"
), ("MeanDiff", "@DiffMeanHourlyPercent")]))
# p.add_tools(TapTool())
# p.add_tools(TapTool(behaviour = '', tooltips=[("Name", "@CurrentName"), ("EmployerSize", "@EmployerSize"), ("MedianDiff", "@DiffMedianHourlyPercent"), ("MeanDiff", "@DiffMeanHourlyPercent")]))
t = p.select(type=TapTool)
t.callback = OpenURL(url=url)
return p, t
def make_plot(source, title):
plot = figure(
tools="pan,wheel_zoom,box_zoom,reset,tap,lasso_select,save,hover",
toolbar_location="above",
plot_width=1100,
plot_height=600)
#plot2=figure()
plot.title.text = title
legends = [
"January", "february", "March", "April", "May", "June", "July",
"August", "September", "October", "November", "December"
]
color_list = [
"Red", "orange", "yellow", "maroon", "black", "blue", "blue", "black",
"green", "blue", "blue", "Red"
]
for i, j, k in zip(All_record, legends, color_list):
plot.line(x='YEAR',
y=i,
source=source,
color=k,
legend=str(j),
line_cap='butt',
line_width=2)
# plot.line(x='YEAR',y='JAN',source=source,color="black", legend="january",line_cap='butt',line_width=4)
# plot.line(x='YEAR',y='FEB',source=source,color="yellow", legend="February")
# plot.line(x='YEAR',y='MAR',source=source, legend="March",color="green")
# plot.line(x='YEAR',y='APR',source=source, legend="April",color="red")
# plot.line(x='YEAR',y='JUN',source=source, legend="june",color="orange")
# plot.line(x='YEAR',y='JUL',source=source, legend="july")
# plot.line(x='YEAR',y='AUG',source=source, legend="august")
# plot.line(x='YEAR',y='SEP',source=source, legend="september")
# plot.line(x='YEAR',y='OCT',source=source, legend="october")
# plot.line(x='YEAR',y='NOV',source=source, legend="november")
# plot.line(x='YEAR',y='DEC',source=source, legend="december")
url = "https://data.gov.in/keywords/annual-rainfall"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#chup=source._df_index_name
plot.xaxis.axis_label = "Year"
plot.yaxis.axis_label = "Rain (mm)"
plot.legend.click_policy = "hide"
hover = plot.select(dict(type=HoverTool))
hover.tooltips = [
("Year", "@YEAR"),
]
hover.mode = 'mouse'
return plot
def generate_plot(title, cds, x, y, tooltip):
plot = figure(x_axis_type="datetime", title=title, tooltips=tooltip)
plot.circle(x=x, y=y, source=cds, size=8)
taptool = TapTool()
# taptool.callback = OpenURL(url='http://www.dilbert.com/strip/@{AAPL_p}')
# craft URL by hand, as flask escapes all the symbols - so bokeh doesnt interpolate
taptool.callback = OpenURL(url="/display/@{AAPL_p}")
plot.add_tools(taptool)
script, div = components(plot)
return {'script': script, 'div': div}
def __init__(self, scheduler, **kwargs):
self.scheduler = scheduler
self.layout = GraphLayout(scheduler)
self.invisible_count = 0 # number of invisible nodes
self.node_source = ColumnDataSource({
'x': [],
'y': [],
'name': [],
'state': [],
'visible': [],
'key': []
})
self.edge_source = ColumnDataSource({'x': [], 'y': [], 'visible': []})
node_view = CDSView(
source=self.node_source,
filters=[GroupFilter(column_name='visible', group='True')])
edge_view = CDSView(
source=self.edge_source,
filters=[GroupFilter(column_name='visible', group='True')])
node_colors = factor_cmap(
'state',
factors=['waiting', 'processing', 'memory', 'released', 'erred'],
palette=['gray', 'green', 'red', 'blue', 'black'])
self.root = figure(title='Task Graph', **kwargs)
self.root.multi_line(xs='x',
ys='y',
source=self.edge_source,
line_width=1,
view=edge_view,
color='black',
alpha=0.3)
rect = self.root.square(x='x',
y='y',
size=10,
color=node_colors,
source=self.node_source,
view=node_view,
legend='state')
self.root.xgrid.grid_line_color = None
self.root.ygrid.grid_line_color = None
hover = HoverTool(point_policy="follow_mouse",
tooltips="<b>@name</b>: @state",
renderers=[rect])
tap = TapTool(callback=OpenURL(url='info/task/@key.html'),
renderers=[rect])
rect.nonselection_glyph = None
self.root.add_tools(hover, tap)
def onc_skyplot(t):
"""
Create a sky plot of the database objects
"""
# Convert to Pandas data frame
data = t.to_pandas()
data.index = data['id']
script, div, warning_message = '', '', ''
if 'ra' in data and 'dec' in data:
# Remove objects without RA/Dec
num_missing = np.sum(pd.isnull(data.get('ra')))
if num_missing > 0:
warning_message = 'Note: {} objects had missing coordinate information and were removed.'.format(
num_missing)
data = data[pd.notnull(data.get('ra'))]
else:
warning_message = ''
# Coerce to numeric
data['ra'] = pd.to_numeric(data['ra'])
data['dec'] = pd.to_numeric(data['dec'])
source = ColumnDataSource(data=data)
tools = "resize,tap,pan,wheel_zoom,box_zoom,reset"
p = figure(tools=tools,
title='',
plot_width=500,
plot_height=300,
min_border=0,
min_border_bottom=0)
# Add the data
p.scatter('ra', 'dec', source=source, size=8, alpha=0.6)
tooltip = [("Source ID", "@id"), ("Name", "@shortname"),
("(RA, Dec)", "(@ra, @dec)")]
p.add_tools(HoverTool(tooltips=tooltip))
# When clicked, go to the Summary page
url = "inventory/@id"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
# Axis labels
p.yaxis.axis_label = 'Decl. (deg)'
p.xaxis.axis_label = 'R.A. (deg)'
script, div = components(p)
return script, div, warning_message
def index():
cz = drought >> mask(X.country_name == 'Czech Republic')
plotdf = utils.drought_add_facecolor(cz)
f_drought = utils.drought_rate_plot(plotdf)
url = "/station_detail/station_id=@station"
# taptool = f_drought.select(type=TapTool)
# taptool.callback = OpenURL(url=url)
f_drought.add_tools(TapTool(callback=OpenURL(url=url)))
bokeh_map = utils.drought_map(plotdf)
bokeh_map.add_tools(TapTool(callback=OpenURL(url=url)))
script_drought, div_drought = components(f_drought)
script_map, div_map = components(bokeh_map)
return render_template(
'main_page.html',
stations=cz,
js_resources=js_resources,
css_resources=css_resources,
script_drought=script_drought,
script_map=script_map,
div_drought=div_drought,
div_map=div_map,
)
def plot_errors(data, device="PTL_DEFAULT", **kwargs):
dates = list(data.date)
if 'x_range' in kwargs:
x_range = kwargs['x_range']
x_range = x_range[0] - timedelta(days=1), x_range[1] + timedelta(days=1)
else:
x_range = (min(dates) - timedelta(days=1), max(dates) + timedelta(days=1))
y_range = kwargs.get('y_range', None)
if y_range and y_range[1] >= 1000:
bottom = 0.1
y_range = bottom, y_range[1]
y_axis_type = "log"
else:
bottom = 0
y_axis_type = "linear"
data_source = ColumnDataSource(data)
vbar_width = timedelta(days=1) / 2
fig = figure(x_axis_type="datetime", x_range=x_range,
y_axis_type=y_axis_type, y_range=y_range,
plot_height=200, plot_width=800,
title="Errors per day", tools="tap")
fig.output_backend = "svg"
fig.toolbar.logo = None
error_hover_tool = HoverTool(names=['errors'],
tooltips=[('date', '@date{%F}'),
('errors', '@error_count{%d}')],
formatters={'date': 'datetime',
'error_count': 'printf'},
mode='vline')
fig.add_tools(error_hover_tool)
fig.add_tools(SaveTool())
fig.vbar(x='date',
width=vbar_width,
bottom=bottom,
top='error_count',
color='#c61803',
source=data_source,
name="errors")
url = url_for("show_logs", device=device, timestamp="TIMESTAMP", duration=24*60, log_level="ERROR")
url = url.replace("TIMESTAMP", "@end_of_day")
taptool = fig.select(type=TapTool)[0]
taptool.callback = OpenURL(url=url, same_tab=False)
return fig
def lifecycles(data):
"Returns (script, div) tuple of Bokeh chart of Issue lifecycles for a repo"
source = ColumnDataSource(data=dict(
left=[s['span']['start'] for s in data['spans']],
date=[s['span']['start'].strftime('%d %B %Y') for s in data['spans']],
right=[s['span']['end'] for s in data['spans']],
bottom=[s['index'] * (THICKNESS + 2 * MARGIN) for s in data['spans']],
top=[
s['index'] * (THICKNESS + 2 * MARGIN) + THICKNESS
for s in data['spans']
],
color=data['colors'],
issue=[s['issue'].title for s in data['spans']],
status=[', '.join(s['span']['milestones']) for s in data['spans']],
assignee=[s['issue'].assignee_login for s in data['spans']],
url=[s['issue'].html_url for s in data['spans']],
final=[s['final'] for s in data['spans']],
))
fig = figure(
x_axis_type='datetime',
title='Issue progress',
)
fig.yaxis.major_label_text_color = None
fig.quad(
left='left',
bottom='bottom',
right='right',
top='top',
source=source,
color='color',
)
hover = HoverTool(tooltips=[
("issue", "@issue"),
("date", "@date"),
("status", "@status"),
("final", "@final"),
("assignee", "@assignee"),
("url", "@url"),
])
fig.add_tools(hover)
taptool = TapTool(callback=OpenURL(url="@url"))
fig.add_tools(taptool)
result = components(fig)
result = {'script': result[0], 'div': result[1]}
return result
def render_grid(source, x_range, y_range, TOOLS="hover,tap"):
"""Function to render a bokeh figure with a rectangular grid
representing the SOM nodes, colored by cluster id and showing
the number of articles that they match. By hovering on the nodes
one display the top topics.
Parameters
----------
source: ColumnDataSource
Data to be displayed, as generated by get_grid method
x_range, yrange: lists
List used to scale the nodes coordinates
"""
p = figure(title="Self-organizing map features and cluster ID",
tools=TOOLS)
p.plot_width = 800
p.toolbar_location = "left"
# Draw the rectangular color grid
p.rect("x", "y", 1, 1, source=source, fill_alpha=0.6, color="color")
# Write some content in the boxes
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_align": "left",
"text_baseline": "middle"
}
p.text(x="x",
y="y",
text="hits",
text_font_style="bold",
text_font_size="8pts",
**text_props)
p.grid.grid_line_color = None
# Create a hover tool for representing the node data
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Node_id", "@node")] + [("Top Words no. %d\t" %
(i + 1), "@word_%d" % (i + 1))
for i in range(3)]
# Create a link to the node data
url = "/som/node/@node"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return p
def get_plot_stuff(plot):
plot.xaxis.formatter = DatetimeTickFormatter(seconds=["%d %B %Y"],
minutes=["%d %B %Y"],
hours=["%d %b %Y"],
days=["%d %b %Y"],
months=["%d %b %Y"],
years=["%d %b %Y"])
hover = plot.select(dict(type=HoverTool))
hover.tooltips = [("pic", "<img src='@pic' alt='' />"),
("id", "@object_id"), ("Address", "@address"),
("Info", "@desctext"), ("Description", "@desc"),
("Price", "@price"), ("Last scraped", "@lastdl")]
hover.mode = 'mouse'
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url="@link")
def price_partnum(self, dist):
hover = HoverHandler('price_partnum')
# formatters={'unit_price' : 'printf'})
'''
y_range = [
-50, dist.dist_found['nunit_price'].max()*1.07
if dist.dist_found['nunit_price'].max() > 1
or dist.dist_found['nunit_price'].max() < -1 else 0.5
]
'''
p = self.config_figure(plot_height=400,
hover=[hover.make_hover_tool],
x_range=self.x_range_range_partnum,
yaxis_label='Unit ptice',
xaxis_orient=pi / 4)
p.add_tools(TapTool())
url = '@dist_partlink'
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
p.yaxis[0].formatter = NumeralTickFormatter(format='$0.00')
p.segment('part_num',
0,
'part_num',
'nunit_price',
line_width=3,
line_color='clr',
source=self.source)
p.circle('part_num',
'nunit_price',
size=10,
fill_color='dist_clr',
line_color='dist_clr',
line_width=3,
hover_fill_color="white",
hover_line_color='dist_clr',
fill_alpha=1,
muted_color='dist_clr',
muted_alpha=0.2,
legend='dist',
source=self.source)
p.legend.location = "top_left"
p.legend.click_policy = "mute"
return p
def dashboard_instrument_pie_chart(self):
"""Create piechart showing number of files per instrument
Returns
-------
plot : bokeh.plotting.figure
Pie chart figure
"""
# Replace with jwql.website.apps.jwql.data_containers.build_table
data = build_table('filesystem_instrument')
if not pd.isnull(self.delta_t):
data = data[(data['date'] >= self.date - self.delta_t) & (data['date'] <= self.date)]
try:
file_counts = {'nircam': data.instrument.str.count('nircam').sum(),
'nirspec': data.instrument.str.count('nirspec').sum(),
'niriss': data.instrument.str.count('niriss').sum(),
'miri': data.instrument.str.count('miri').sum(),
'fgs': data.instrument.str.count('fgs').sum()}
except AttributeError:
file_counts = {'nircam': 0,
'nirspec': 0,
'niriss': 0,
'miri': 0,
'fgs': 0}
data = pd.Series(file_counts).reset_index(name='value').rename(columns={'index': 'instrument'})
data['angle'] = data['value'] / data['value'].sum() * 2 * pi
data['color'] = ['#F8B195', '#F67280', '#C06C84', '#6C5B7B', '#355C7D']
plot = figure(title="Number of Files Per Instruments", toolbar_location=None,
tools="hover,tap", tooltips="@instrument: @value", x_range=(-0.5, 1.0))
plot.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", color='color', legend='instrument', source=data)
url = "{}/@instrument".format(get_base_url())
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
plot.axis.axis_label = None
plot.axis.visible = False
plot.grid.grid_line_color = None
return plot
def histogram_plot(data, width=600, height=300):
'''
'''
if len(data) > 5:
data = data[:5]
ids = [d['id'] for d in data]
topics = ['Topic {}'.format(d['id']) for d in data]
dist = [d['dist'] for d in data]
labels = [', '.join(d['words']) for d in data]
source = ColumnDataSource(
data=dict(topics=topics, distribution=dist, ids=ids, labels=labels))
hover = HoverTool(tooltips="""
<div>
<div>
<span style="font-size: 20px;">Topics: <strong>@ids</strong></span><br>
<span style="font-size: 12px;">(Click to see more about the topic)</span><br>
<span style="font-size: 20px;">Words: @labels</span>
</div>
</div>
""")
hover.attachment = 'right'
plot = figure(x_range=topics,
width=width,
height=height,
tools=[hover, 'tap', 'save'],
title='Relevant topics')
plot.vbar(x='topics',
top='distribution',
width=0.8,
source=source,
line_color='white',
fill_color=factor_cmap('topics', palette=COLORS, factors=topics))
plot.y_range.start = 0
plot.xgrid.grid_line_color = None
plot.xaxis.axis_label = 'Topics'
plot.yaxis.axis_label = 'Proportion'
plot.title.text_font_size = '1.5em'
plot.xaxis.axis_label_text_font_size = "1.5em"
plot.yaxis.axis_label_text_font_size = "1.5em"
plot.xaxis.major_label_text_font_size = "1em"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url="/topic/@ids")
plot.toolbar.active_inspect = [hover]
return components(plot)
def plot_error_heatmap(data, device="PTL_DEFAULT", **kwargs):
if 'x_range' in kwargs:
x_range = kwargs['x_range']
x_range = x_range[0] - timedelta(days=1), x_range[1] + timedelta(days=1)
else:
dates = list(data.date)
x_range = (min(dates) - timedelta(days=1), max(dates) + timedelta(days=1))
fig = figure(plot_height=200, plot_width=800,
title=f"Error heatmap",
x_axis_type='datetime',
x_range=x_range,
y_range=(0, 1),
tools="tap")
fig.output_backend = "svg"
fig.toolbar.logo = None
fig.yaxis.formatter = NumeralTickFormatter(format='0 %')
error_hover_tool = HoverTool(tooltips=[('date', '@date_label{%F}'),
('location', '@location'),
('errors', '@error_count{%d}')],
formatters={'date_label': 'datetime',
'location': 'printf',
'error_count': 'printf'})
fig.add_tools(error_hover_tool)
fig.add_tools(SaveTool())
for date in data.index.get_level_values(0).unique():
data_source = ColumnDataSource(data.loc[date])
fig.vbar(bottom=cumsum('error_count_normalized', include_zero=True),
top=cumsum('error_count_normalized'),
x=date, width=timedelta(days=1)/2, source=data_source, fill_color='colors')
url = url_for("show_logs", device=device, timestamp="TIMESTAMP", duration=24*60, log_level="ERROR",
filename='FILENAME',
line_number='LINENUMBER')
url = url.replace("TIMESTAMP", "@end_of_day") \
.replace("FILENAME", "@filename") \
.replace("LINENUMBER", "@line_number")
taptool = fig.select(type=TapTool)[0]
taptool.callback = OpenURL(url=url, same_tab=False)
#tooltips="@location: @error_count Errors",
return fig
def get_plot(data):
countries = data.country_name.unique().tolist()
countries = [country for country in countries if str(country) != 'nan']
countries.append(' All')
countries.sort()
p = figure(tools='pan, wheel_zoom, tap, reset',
x_range=x_range,
y_range=y_range,
plot_width=950,
toolbar_location="right",
active_scroll='wheel_zoom')
p.axis.visible = False
url = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{Z}/{Y}/{X}.jpg'
attribution = "Tiles by Carto, under CC BY 3.0. Data by OSM, under ODbL"
p.add_tile(WMTSTileSource(url=url, attribution=attribution))
source = ColumnDataSource(
dict(latitude=latlon.latitude,
longitude=latlon.longitude,
country_name=latlon.country_name,
latitude_t=latlon.latitude,
longitude_t=latlon.longitude,
country_name_t=latlon.country_name,
landslide_size=latlon.landslide_size,
source_link=latlon.source_link,
location_description=latlon.location_description))
cmap = factor_cmap('landslide_size',
palette=[
'#ffffff', '#f7a8e2', '#fcdc12', '#fa774c',
'#FC3232', '#040100'
],
factors=latlon.landslide_size.unique().tolist())
p.add_tools(HoverTool(tooltips='@location_description', mode='mouse'))
url = "@source_link"
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
p.circle(x='latitude',
y='longitude',
fill_color=cmap,
size=8,
source=source,
legend='landslide_size')
return (p)
def get_states_plot():
source = AjaxDataSource(data={
'STATE': [],
'STNAME': [],
'STUSAB': [],
'TOT_POP': [],
'TOT_MALE': [],
'TOT_FEMALE': []
},
data_url='/api/states/',
mode='replace',
method='GET')
hover = HoverTool(tooltips=[
("State", "@STNAME"),
("Population", "@TOT_POP"),
("Female Population", "@TOT_FEMALE"),
("Male Population", "@TOT_MALE"),
])
plot = figure(
title='Population by State',
plot_width=1200,
plot_height=500,
x_range=FactorRange(factors=get_state_abbreviations()),
y_range=(0, 40000000),
tools=[hover, 'tap', 'box_zoom', 'wheel_zoom', 'save', 'reset'])
plot.toolbar.active_tap = 'auto'
plot.xaxis.axis_label = 'State'
plot.yaxis.axis_label = 'Population'
plot.yaxis.formatter = NumeralTickFormatter(format="0a")
plot.sizing_mode = 'scale_width'
plot.vbar(bottom=0,
top='TOT_POP',
x='STUSAB',
legend=None,
width=0.5,
source=source)
url = "/counties/@STATE/"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return plot
def scatter_any(df,
x,
y,
xlabel=None,
ylabel=None,
color_field=None,
xformatter=NumeralTickFormatter(),
yformatter=NumeralTickFormatter(),
**kwargs):
f = figure(tools="wheel_zoom,pan,tap,save",
webgl=True,
width=defaults.plot_width,
height=defaults.plot_height,
**kwargs)
f.xaxis[0].formatter = xformatter
f.yaxis[0].formatter = yformatter
f.xaxis.axis_label = xlabel
f.yaxis.axis_label = ylabel
taptool = f.select(type=TapTool)
taptool.callback = OpenURL(url='@url')
if color_field:
uniques = sorted(df[color_field].unique())
# colors = brewer["PiYG"][len(uniques)]
dark2 = [
"#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02",
"#A6761D", "#666666"
]
for i, u in enumerate(uniques):
sdf = df[df[color_field] == u]
f.scatter(x,
y,
source=ColumnDataSource(sdf),
color=dark2[i],
legend=str(u))
else:
f.scatter(x, y, source=ColumnDataSource(df))
return f
def do_plot_graph(nodes, edges, colors, sizes, description, output_plot):
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
hover = HoverTool(tooltips=[
("name", "@index")])
plot = figure(plot_width=900, plot_height=900, x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1),
tools=[hover, BoxZoomTool(), ResetTool(), PanTool(),
WheelZoomTool(), "tap"],
title=output_plot)
plot.toolbar.logo = None
plot.title.text = description
url_protein = "https://www.ncbi.nlm.nih.gov/gene/?term=@index"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url_protein)
graph_renderer = from_networkx(G, nx.fruchterman_reingold_layout, scale=1)
source = ColumnDataSource({'index': nodes, 'fill_color': colors, 'size': sizes})
graph_renderer.node_renderer.data_source = source
graph_renderer.node_renderer.glyph = Circle(size="size", fill_color="fill_color", line_width=0,
line_color="fill_color")
graph_renderer.node_renderer.selection_glyph = Circle(size="size", fill_color=Spectral4[2], line_width=0,
line_color=Spectral4[1])
graph_renderer.node_renderer.hover_glyph = Circle(size="size", fill_color=Spectral4[1], line_width=0,
line_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=1, line_width=0.2)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=1)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=1)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = NodesAndLinkedEdges()
plot.renderers.append(graph_renderer)
output_file(output_plot)
