def generate_plot(data):
# try:
# data["Time:"] = [datetime.datetime.fromtimestamp(x) for x in data["Time:"]]
# data['Time:'] = [x.strftime("%H:%M:%S.{} %p".format(x.microsecond % 100)) for x in data["Time:"]]
# except TypeError:
# pass
print(data)
source = ColumnDataSource(data)
print(source.column_names)
p = figure(x_axis_type='datetime')
print(p.line(x='Time:', y='Temp Value:', source=source))
# p.line(x='Time:', y='Strain Value:', source=source)
p.plot_width = 800
p.plot_height = 800
p.title.text = 'Device Readings'
p.xaxis.axis_label = 'Time of Reading'
p.yaxis.axis_label = 'Readings'
hover = HoverTool()
hover.tooltips = [
('Time of Reading', '@{Time:}{%M:%S.%3N}'),
]
hover.formatters = {"@{Time:}": "datetime"}
p.add_tools(hover)
# show(p)
return p, source
months=dtformat,
hours=dtformat,
minutes=dtformat)
# Add legend
p_world_covid.legend.location = "top_left"
# Add a hover tool
hover = HoverTool()
hover.tooltips = [
#('Type', "$name"),
('', '$name: @$name{0,0.} on @date{%a-%b-%d}'),
]
# hover.mode = 'vline'
hover.formatters = {
'@date': 'datetime', # use 'datetime' formatter for '@date' field
'$name': 'printf' # use 'printf' formatter for the name of the column
}
hover.renderers = glyphs
p_world_covid.add_tools(hover)
# # Extra formatting
# for ax in p_world_covid.yaxis:
# ax.axis_label_text_font_style = 'bold'
# ax.axis_label_text_font_size = '16pt'
# p_world_covid.title.text_font_size = '20pt'
# p_world_covid.title.text_font_style = 'italic'
glyphs_covid_world = glyphs
# %% Make Buttons for state graph
"""
def create_bk_fig(x=None, xlab=None, x_min=None, x_max=None,
ylab=None, fh=None, fw=None,
title=None, pw=None, ph=None, x_axis_type="linear",
y_axis_type="linear", x_name=None, y_name=None, **kwargs):
""" Generates a bokeh figure
Parameters
----------
x :obj:`DataArray`
Contains x-axis data
xlab : :obj:`str`
X-axis label
x_min : :obj:`float`
Min x value
x_max : :obj:`float`
Max x value
ylab : :obj:`str`
Y-axis label
fh: :obj:`int`
True height of figure without legends, axes titles etc
fw: :obj:`int`
True width of figure without legends, axes etc
title: :obj:`str`
Title of plot
pw: :obj:`int`
Plot width including legends, axes etc
ph: :obj:`int`
Plot height including legends, axes etc
x_axis_type: :obj:`str`
Type of x-axis can be linear, log, or datetime
y_axis_type: :obj:`str`
Can be linear, log or datetime
x_name: :obj:`str`
Name of the column used for the x-axis. Mostly used to form tooltips
y_name: :obj:`str`
Name of the column used for the y-axis. Also used for tooltips
add_grid: :obj:`bool`
Whether or not to add grid
add_title: :obj:`bool`
Whether or not to add title to plot
add_xaxis: :obj:`bool`
Whether or not to add x-axis and tick marks
add_yaxis: :obj:`bool`
Add y-axis or not
fix_plotsize: :obj:`bool`
Enforce certain dimensions on plot. This is useful for ensuring a plot
is not obscure by axes and other things. If activated, plot's
dimensions will not be responsive. It utilises fw and fh.
Returns
-------
p : :obj:`Plot`
A bokeh Plot object
"""
add_grid = kwargs.pop("add_grid", False)
add_title = kwargs.pop("add_title", True)
add_xaxis = kwargs.pop("add_xaxis", False)
add_yaxis = kwargs.pop("add_yaxis", False)
fix_plotsize = kwargs.pop("fix_plotsize", True)
# addition plot specs
pl_specs = kwargs.pop("pl_specs", {})
# additional axis specs
ax_specs = kwargs.pop("ax_specs", {})
# ticker specs
ti_specs = kwargs.pop("ti_specs", {})
plot_specs = dict(background="white", border_fill_alpha=0.1,
border_fill_color="white", min_border=3,
name="plot", outline_line_dash="solid",
outline_line_width=2, outline_line_color="#017afe",
outline_line_alpha=0.4, output_backend="canvas",
sizing_mode="stretch_width", title_location="above",
toolbar_location="above")
plot_specs.update(pl_specs)
axis_specs = dict(minor_tick_line_alpha=0, axis_label_text_align="center",
axis_label_text_font="monospace",
axis_label_text_font_size="10px",
axis_label_text_font_style="normal",
major_label_orientation="horizontal")
axis_specs.update(ax_specs)
tick_specs = dict(desired_num_ticks=5)
tick_specs.update(ti_specs)
# Define frame width and height
# This is the actual size of the plot without the titles et al
if fix_plotsize and not(fh or fw):
fw = int(0.98 * pw)
fh = int(0.93 * ph)
# define the axes ranges
x_range = DataRange1d(name="p_x_range", only_visible=True)
y_range = DataRange1d(name="p_y_range", only_visible=True)
if x_min is not None and x_max is not None and x_name.lower() in ["channel", "frequency"]:
x_range = Range1d(name="p_x_range", start=x_min, end=x_max)
y_range.only_visible = False
# define items to add on the plot
p_htool = HoverTool(tooltips=[(x_name, "$x"),
(y_name, "$y")],
name="p_htool", point_policy="snap_to_data")
if x_name.lower() == "time":
p_htool.tooltips[0] = (x_name, "$x{%d-%m-%Y %H:%M}")
p_htool.formatters = {"$x": "datetime"}
p_toolbar = Toolbar(name="p_toolbar",
tools=[p_htool, BoxSelectTool(), BoxZoomTool(),
# EditTool(), # BoxEditTool(), # RangeTool(),
LassoSelectTool(), PanTool(), ResetTool(),
SaveTool(), UndoTool(), WheelZoomTool()])
p_ticker = BasicTicker(name="p_ticker", **tick_specs)
# select the axis scales for x and y
if x_axis_type == "linear":
x_scale = LinearScale(name="p_x_scale")
# define the axes and tickers
p_x_axis = LinearAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "datetime":
x_scale = LinearScale(name="p_x_scale")
# define the axes and tickers
p_x_axis = DatetimeAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "log":
x_scale = LogScale(name="p_x_scale")
p_x_axis = LogAxis(axis_label=xlab, name="p_x_axis",
ticker=p_ticker, **axis_specs)
if y_axis_type == "linear":
y_scale = LinearScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = LinearAxis(axis_label=ylab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
elif x_axis_type == "datetime":
y_scale = LinearScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = DatetimeAxis(axis_label=xlab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
elif y_axis_type == "log":
y_scale = LogScale(name="p_y_scale")
# define the axes and tickers
p_y_axis = LogAxis(axis_label=ylab, name="p_y_axis",
ticker=p_ticker, **axis_specs)
# Create the plot object
p = Plot(plot_width=pw, plot_height=ph, frame_height=fh, frame_width=fw,
toolbar=p_toolbar, x_range=x_range, x_scale=x_scale,
y_range=y_range, y_scale=y_scale, **plot_specs)
if add_title:
p_title = Title(align="center", name="p_title", text=title,
text_font_size="24px",
text_font="monospace", text_font_style="bold",)
p.add_layout(p_title, "above")
if add_xaxis:
p.add_layout(p_x_axis, "below")
if add_yaxis:
p.add_layout(p_y_axis, "left")
if add_grid:
p_x_grid = Grid(dimension=0, ticker=p_ticker)
p_y_grid = Grid(dimension=1, ticker=p_ticker)
p.add_layout(p_x_grid)
p.add_layout(p_y_grid)
return p
click_policy='hide',
title="Click on States to Switch ON/OFF",
title_text_font_style="bold")
p.add_layout(legend1, 'right')
p.add_layout(legend2, 'right')
cases_summary['day'] = cases_summary['day'].astype('str')
source = ColumnDataSource(cases_summary)
hover = HoverTool(line_policy='next')
hover.tooltips = [
('Date', '@x{%F}'),
('Cases', '@y{0000}') # @$name gives the value corresponding to the legend
]
hover.formatters = {'@x': 'datetime'}
p.add_tools(hover)
citation = Label(x=0,
y=0,
x_units='screen',
y_units='screen',
text='Last Updated : {}'.format(latest_date),
render_mode='css',
text_font_size='12px')
p.add_layout(citation, 'above')
div = Div(text="""<b>Source:</b>
COVID-19 REST API for India: <a href='https://api.rootnet.in/covid19-in/stats/history' target="_blank"> The Ministry of Health and Family Welfare</a> """,
width=300,
height=50,
line_color="orange",
muted_alpha=0.2,
line_dash="1 8",
legend_label="Chania Province")
p.circle(x='dates',
y='Chania',
source=source,
fill_color="orange",
muted_alpha=0.2,
size=4,
legend_label="Chania Province")
p.legend.location = "top_left"
p.y_range.start = 0
p.x_range.start = df_infections.index[0] - timedelta(1)
p.xaxis.formatter.days = '%d/%m/%Y'
p.legend.click_policy = 'mute'
from bokeh.models.tools import HoverTool
hover = HoverTool()
hover.tooltips = [
('Date', '@dates{%d %b %Y}'),
('Chania', '@Chania{0,0}'),
('Crete', '@Crete{0,0}'),
]
hover.formatters = {"@dates": "datetime"}
p.add_tools(hover)
show(p)
colour=['#ff7f0e','#2ca02c','#d62728','#1f77b4']
#output_file("test1.html")
p = figure(x_axis_label='Time', y_axis_label=selected_features,x_axis_type='datetime',title=selected_node, plot_width=700, plot_height=400)
p.line('time','count',source=source1,line_width=2, color=str(colour[0]),legend_label=str(df5.iloc[1,0]))
p.line('time','count',source=source2,line_width=2, color=str(colour[1]),legend_label=str(df6.iloc[1,0]))
p.line('time','count',source=source3,line_width=4, color=str(colour[2]),legend_label=str(df7.iloc[1,0]))
else:
pass
p.legend.click_policy="hide"
# add a hover tool and show the date in date time format
hover = HoverTool(mode='mouse')
hover.tooltips=[
('Date', '@date{%F}'),
('Time', '@time{%H:%M}'),
('Count', '@count'),
#('(x,y)', '($x, $y)')
]
hover.formatters = {'@date': 'datetime','@time': 'datetime'}
p.add_tools(hover)
##show the chart
st.write('Chart:')
st.write(selected_features, 'of', selected_node)
st.bokeh_chart(p)
st.button("Re-run")
def scatter(status, furthest):
#Check if furthest points is selected and get correct data
if furthest == 'True':
with open('status_times_furthest_condensed.csv', 'wb') as f:
s3.download_fileobj(s3_bucket,
'status_times_furthest_condensed.csv', f)
df = pd.read_csv('status_times_furthest_condensed.csv', sep="|")
else:
with open('status_times_condensed.csv', 'wb') as f:
s3.download_fileobj(s3_bucket, 'status_times_condensed.csv', f)
df = pd.read_csv('status_times_condensed.csv', sep="|")
df['Ship Date'] = pd.to_datetime(df['Ship Date'])
df[status] = pd.to_numeric(df[status], downcast="float")
source = ColumnDataSource(df)
p = figure(x_axis_type='datetime',
plot_width=1200,
plot_height=400,
title=f'{status}',
y_axis_label='Hours',
tools="pan,box_select,zoom_in,zoom_out,save,reset")
p.scatter(
y=status,
x='Ship Date',
# fill_color = factor_cmap(
# 'Car',
# palette=Blues8,
# factors=car_list
# ),
fill_alpha=0.5,
source=source,
)
#Custom html hover box for individual points on scatter plot
hover = HoverTool()
html = """
<div>
<h4>@Story</h4>
<div><strong>Ship Date: </strong>@{Ship Date}{%F}</div>
<div><strong>Hours: </strong>@{$status}{%0.2f}</div>
</div>
"""
replaced_html = Template(html).safe_substitute(status=status)
hover.tooltips = replaced_html
hover.formatters = {
"@{Ship Date}": "datetime",
f"@\u007b{status}\u007d": "printf",
}
p.add_tools(hover)
save(p)
#Send back js and html
script, div = components(p)
return script, div
