def testPlotKline(ts_code, days=1000):
"""
绘制K线,pe走势图
:param ts_code: string, 股票代码, 600619
:param days: int, 走势图显示的总天数
:return:
"""
df = readKlineDf(ts_code, days)
source = ColumnDataSource(df)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width = 1000
klineHeight = int(width / 16 * 6)
peHeight = int(width / 16 * 3)
selectHeight = int(width / 16 * 1)
# 绘制K线图
dataLen = df.shape[0]
tooltips = [('date', '@date'), ('close', '@close')]
pkline = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=klineHeight,
plot_width=width,
x_axis_location="above",
title="kline: %s" % ts_code,
tooltips=tooltips,
x_range=(dataLen - 200, dataLen - 1))
pkline.xaxis.major_label_overrides = df['date'].to_dict()
plotCandlestick(pkline, df)
print(type(pkline.y_range))
print(pkline.y_range)
tooltips = [('pe', '@pe')]
ppe = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=peHeight, plot_width=width,
tooltips=tooltips,
# x_axis_location=None,
# x_axis_location="bottom",
x_range=pkline.x_range)
ppe.xaxis.major_label_overrides = df['date'].to_dict()
plotPE(ppe, source)
select = figure(
# title="Drag the middle and edges of the selection box to change the range above",
plot_height=selectHeight,
plot_width=width,
# y_range=ppe.y_range,
# x_axis_type="datetime",
y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#efefef")
select.xaxis.major_label_overrides = df['date'].to_dict()
plotPE(select, source)
range_tool = RangeTool(x_range=pkline.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
column_layout = column([pkline, ppe, select])
output_file("../kline.html", title="kline plot test")
show(column_layout) # open a browser
def make_plot():
# prepare the data
dates = np.array(AAPL['date'], dtype=np.datetime64)
source = ColumnDataSource(data=dict(date=dates, close=AAPL['adj_close']))
# prepare the chart
p = figure(plot_height=300, plot_width=800, tools='xpan', toolbar_location=None,
x_axis_type='datetime', x_axis_location='above',
background_fill_color="#efefef", x_range=(dates[1500], dates[2500]))
p.line('date', 'close', source=source)
p.yaxis.axis_label = 'Price'
# get the selection/dragger
select = figure(title="Drag the middle and edges of the selection box to change the range above",
plot_height=130, plot_width=800, y_range=p.y_range,
x_axis_type="datetime", y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#efefef")
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
return column(p, select)
def init_componets(self):
'''
init of components, must be done in a seperate function.
On load of page, a standard document is offerd to the renderer function,
the components created in this function are
'''
dates = [datetime(2017, 1, 1), datetime(2018, 1, 1)]
self.main_vw = figure(name='main_view',
sizing_mode='scale_width',
plot_height=100,
plot_width=500,
x_axis_type="datetime",
x_range=(dates[0], dates[1]))
self.select_vw = figure(name='select_view',
sizing_mode='scale_width',
plot_height=100,
plot_width=500,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None)
range_rool = RangeTool(x_range=self.main_vw.x_range)
range_rool.overlay.fill_color = "navy"
range_rool.overlay.fill_alpha = 0.2
self.select_vw.ygrid.grid_line_color = None
self.select_vw.add_tools(range_rool)
self.select_vw.toolbar.active_multi = range_rool
def make_plot(name, wave_list, spec_list, color_list, label_list, image,
sx, sy):
plot = figure(plot_height=300, plot_width=800,
toolbar_location=None, x_axis_location="above",
background_fill_color="#efefef", x_range=(3470., 5540.),
y_axis_type="linear")
imageplot = figure(plot_height=540, plot_width=430,
tools="crosshair, pan, reset, save, wheel_zoom",
y_axis_location="right",
tooltips=[("x", "$x"), ("y", "$y"),
("value", "@image")],
x_range=(image[1].min(), image[1].max()),
y_range=(image[2].min(), image[2].max()))
select = figure(title=("Drag the selection "
"box to change the range above"),
plot_height=240, plot_width=800, y_range=plot.y_range,
y_axis_type="linear",
tools="", toolbar_location=None,
background_fill_color="#efefef")
for wavelength, spectrum, color, label in zip(wave_list, spec_list,
color_list, label_list):
source = ColumnDataSource(data=dict(wavelength=wavelength,
spectrum=spectrum))
plot.line('wavelength', 'spectrum', source=source, line_width=3,
line_alpha=0.6, line_color=color,
legend=label)
select.line('wavelength', 'spectrum', source=source,
line_color=color)
plot.yaxis.axis_label = '1e-17 ergs/s/cm^2/A'
for p in [plot, select]:
p.xaxis.major_label_text_font_size = "14pt"
p.yaxis.major_label_text_font_size = "14pt"
p.xaxis.axis_label = 'Wavelength'
p.xaxis.axis_label_text_font_size = "16pt"
p.yaxis.axis_label_text_font_size = "16pt"
p.xaxis.major_tick_line_color = "firebrick"
p.xaxis.major_tick_line_width = 3
p.xaxis.minor_tick_line_color = "orange"
p.yaxis.major_tick_line_color = "firebrick"
p.yaxis.major_tick_line_width = 3
p.yaxis.minor_tick_line_color = "orange"
p.yaxis[0].formatter = PrintfTickFormatter(format="%3.1f")
select.ygrid.grid_line_color = None
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
imageplot.image(image=[image[0]], x=image[1].min(), y=image[2].min(),
dw=image[1].max()-image[1].min(),
dh=image[2].max()-image[2].min())
imageplot.scatter(sx, sy, marker='circle_x', size=15,
line_color="orange", fill_color="red", alpha=0.75)
output_file(name+".html", title=name)
save(row(column(plot, select), imageplot))
def plotIndexPE():
"""
待删除
:return:
"""
sql = 'select date, pe from pehistory where name="all"'
df = pd.read_sql(sql, engine)
print(df)
source = ColumnDataSource(df)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width = 1000
peHeight = int(width / 16 * 3)
selectHeight = int(width / 16 * 1)
# # 绘制K线图
# tooltips = [('date', '@date'), ('close', '@close')]
# pkline = figure(x_axis_type="datetime", tools=TOOLS,
# plot_height=klineHeight,
# plot_width=width,
# x_axis_location="above",
# title="kline: %s" % ts_code,
# tooltips=tooltips,
# pkline.xaxis.major_label_overrides = df['date'].to_dict()
# plotCandlestick(pkline, df)
# print(type(pkline.y_range))
# print(pkline.y_range)
dataLen = df.shape[0]
tooltips = [('pe', '@pe')]
ppe = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=peHeight, plot_width=width,
# x_axis_location=None,
# x_axis_location="bottom",
x_range=(dataLen - 200, dataLen - 1),
tooltips=tooltips)
# ppe.xaxis.major_label_overrides = df['date'].to_dict()
plotPE(ppe, source)
select = figure(plot_height=selectHeight,
plot_width=width,
# y_range=ppe.y_range,
# x_axis_type="datetime",
y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#efefef")
# select.xaxis.major_label_overrides = df['date'].to_dict()
plotPE(select, source)
range_tool = RangeTool(x_range=ppe.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
column_layout = column([ppe, select])
output_file("pe.html", title="index pe plot test")
show(column_layout) # open a browser
def uploaded_file(filename):
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
def search_string_in_file(file_name, string_to_search):
line_number = 0
list_of_results = []
with open(file_name, 'r') as read_obj:
for line in read_obj:
line_number += 1
if string_to_search in line:
list_of_results.append((line_number, line.rstrip()))
return list_of_results
matched_lines = search_string_in_file(filepath, '[Data]')
elem = ()
for elem in matched_lines:
break
df = pd.read_csv(filepath, skipinitialspace=True, skiprows=elem[0])
df.dropna(axis=1, inplace=True)
source = ColumnDataSource(df)
p = figure(plot_height=700, plot_width=1000, toolbar_location="right", tools="save,xwheel_zoom,"
",reset,crosshair",
x_axis_type="linear", x_axis_location="below",
background_fill_color=None, x_range=(df['Angle'].iloc[0], df['Angle'].iloc[-1]))
p.add_tools(BoxSelectTool(dimensions='width'))
p.add_tools(CustomAction(icon="/home/cleber/Documentos/GitHub/fitdrx/site2/static/icon.png",
callback=CustomJS(code='alert("foo")')))
legend = filename[0:-4]
p.scatter('Angle', 'Det1Disc1', source=source, legend_label=legend, color='red', size=0.2, alpha=0)
p.line('Angle', 'Det1Disc1', source=source, legend_label=legend, line_color='#0020C2')
p.yaxis.axis_label = 'I (U.A.)'
p.xaxis.axis_label = '2-theta (degree)'
# button = Button(label='FIT', aspect_ratio=10, background='red',
# align='center', button_type='primary',
# width_policy='max')
select = figure(title='Selecionar pico',
plot_height=110, plot_width=1030, y_range=p.y_range,
x_axis_type="linear", y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#efefef")
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('Angle', 'Det1Disc1', source=df)
select.ygrid.grid_line_color = None
select.xgrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
script, divs = components((p, select))
return render_template('page2.html', script=script, div0=divs[0], div1=divs[1])
def get_select_RangeTool(p,
x_col,
y_cols,
source,
palette=viridis(1),
output_backend='canvas',
x_axis_type='datetime'):
"""Return a new figure that uses the RangeTool to control the figure p.
Args:
p (figure): Bokeh figure to control with RangeTool.
x_col (str): Name of column for x axis.
y_cols (list): Names of columns for y axis.
source (ColumnDataSource): Bokeh's data.
palette (list, optional): Color palette. Defaults to viridis(1).
output_backend (str, optional): Bokeh's rendering backend. Defaults
to 'canvas'.
x_axis_type (str, optional): Type of x axis, 'datetime' or 'linear'.
Returns:
select (TYPE): DESCRIPTION.
"""
select = figure(
plot_height=45,
y_range=p.y_range,
tools="",
x_axis_type=x_axis_type,
y_axis_type=None,
toolbar_location=None,
background_fill_color="#efefef",
output_backend=output_backend,
height_policy="fixed",
width_policy="fit",
)
for y_col in y_cols: # Show all lines of primary figure in "select", too
select.line(x_col, y_col, source=source)
# Create a RangeTool, that will be applied to the "select" figure
range_tool = RangeTool(x_range=p.x_range) # Link figure and RangeTool
range_tool.overlay.fill_color = palette[0]
range_tool.overlay.fill_alpha = 0.25
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
return select
def _make_plot():
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1]))
r = Range1d(start=0.4, end=0.6)
plot = Plot(height=400, width=1100, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Rect(x='x', y='y', width=0.9, height=0.9))
tool = RangeTool(x_range=r)
plot.add_tools(tool)
plot.min_border_right = 100
code = RECORD("start", "t.x_range.start", final=False) + RECORD("end", "t.x_range.end")
plot.tags.append(CustomJS(name="custom-action", args=dict(t=tool), code=code))
plot.toolbar_sticky = False
return plot
def _create_range_tool(
data,
min_time,
max_time,
plot_range,
width,
height,
time_column: str = None,
y: str = "y_index",
):
"""Create plot bar to act as as range selector."""
ext_min = min_time - ((max_time - min_time) * 0.15)
ext_max = max_time + ((max_time - min_time) * 0.15)
plot_height = max(120, int(height * 0.20))
rng_select = figure(
x_range=(ext_min, ext_max),
title="Range Selector",
plot_height=plot_height,
plot_width=width,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
)
help_str = (
"Drag the middle or edges of the selection box to change "
+ "the range in the main chart"
)
rng_select.add_layout(
Title(text=help_str, align="right", text_font_size="10px"), "below"
)
rng_select.xaxis[0].formatter = _get_tick_formatter()
if isinstance(data, dict):
for _, series_def in data.items():
rng_select.circle(
x=series_def["time_column"],
y=y,
color=series_def["color"],
source=series_def["source"],
)
elif isinstance(data, pd.DataFrame):
rng_select.circle(
x=time_column, y=y, color="blue", source=ColumnDataSource(data)
)
range_tool = RangeTool(x_range=plot_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
rng_select.ygrid.grid_line_color = None
rng_select.add_tools(range_tool)
rng_select.toolbar.active_multi = range_tool
return rng_select
def build_normalized_price_fig(data):
# ========== themes & appearance ============= #
STK_1_LINE_COLOR = "#053061"
STK_2_LINE_COLOR = "#67001f"
STK_1_LINE_WIDTH = 1.5
STK_2_LINE_WIDTH = 1.5
WINDOW_SIZE = 10
TITLE = "PRICE OF X vs Y"
HEIGHT = 250
SLIDER_HEIGHT = 150
WIDTH = 600
# ========== data ============= #
# use sample data from ib-data folder
dates = np.array(data['date'], dtype=np.datetime64)
STK_1_source = ColumnDataSource(data=dict(date=dates, close=data['data0']))
STK_2_source = ColumnDataSource(data=dict(date=dates, close=data['data1']))
# ========== plot data points ============= #
# x_range is the zoom in slider setup. Pls ensure both STK_1 and STK_2 have same length, else some issue
normp = figure(plot_height=HEIGHT,
plot_width=WIDTH,
x_range=(dates[-WINDOW_SIZE], dates[-1]),
title=TITLE,
toolbar_location=None)
normp.line('date', 'close', source=STK_1_source, line_color = STK_1_LINE_COLOR, line_width = STK_1_LINE_WIDTH)
normp.line('date', 'close', source=STK_2_source, line_color = STK_2_LINE_COLOR, line_width = STK_2_LINE_WIDTH)
normp.yaxis.axis_label = 'Price'
normp.xaxis[0].formatter = DatetimeTickFormatter()
# ========== RANGE SELECT TOOL ============= #
select = figure(title="Drag the middle and edges of the selection box to change the range above",
plot_height=SLIDER_HEIGHT, plot_width=WIDTH, y_range=normp.y_range,
x_axis_type="datetime", y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#efefef")
range_tool = RangeTool(x_range=normp.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=STK_1_source, line_color = STK_1_LINE_COLOR, line_width = STK_1_LINE_WIDTH)
select.line('date', 'close', source=STK_2_source, line_color = STK_2_LINE_COLOR, line_width = STK_2_LINE_WIDTH)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
return column(normp, select)
def plot1():
sim_charges_df = pd.read_csv(
"/Users/brendanpolidori/Desktop/project_repos/inter_webapp.nosync/e3f2s/e3f2s/webapp/apis/api_test_bokeh/sim_booking_requests_test_bre.csv"
)
dates = np.array(sim_charges_df["start_time"], dtype=np.datetime64)
source = ColumnDataSource(data=dict(
date=np.array(sim_charges_df["start_time"], dtype=np.datetime64),
close=list(sim_charges_df["n_vehicles_available"])))
p = figure(plot_height=300,
plot_width=800,
tools="xpan",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[1500], dates[2500]))
p.line('date', 'close', source=source)
p.yaxis.axis_label = 'Number Vehicles Available'
select = figure(
title=
"Drag the middle and edges of the selection box to change the range above",
plot_height=130,
plot_width=800,
y_range=p.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef")
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
show(column(p, select))
return json.dumps(json_item(p, "myplot"))
def plot(source):
fig = setup_figure()
# plot data
rect = Rect(x='dates',
y='y',
width='w',
height='h',
line_width=0,
fill_color='colors',
fill_alpha=1)
fig.add_glyph(source, rect)
line = Line(x='dates', y='avg_to_bed_times')
fig.add_glyph(source, line)
# circ = Circle(x='dates', y='avg_to_bed_times', size=3)
# fig.add_glyph(source, circ)
line = Line(x='dates', y='avg_get_up_times')
fig.add_glyph(source, line)
# circ = Circle(x='dates', y='avg_get_up_times', size=3)
# fig.add_glyph(source, circ)
# plot select
select = figure(
# background_fill_color='#333333'
title="",
plot_width=PLOT_WIDTH,
plot_height=80,
toolbar_location=None, # tools="xwheel_pan",
x_axis_type="datetime", # y_axis_type=None,
x_axis_location=None,
y_axis_location=None,
y_range=fig.y_range,
)
range_tool = RangeTool(x_range=fig.x_range)
select.add_tools(range_tool)
select.line(x=[dt(2013, 5, 1), dt.now()], y=[0, 0], color=None)
select.add_glyph(source, rect)
select.ygrid.grid_line_color = None
select.toolbar.active_multi = range_tool
select.yaxis.ticker = []
return fig, select
def bohek_slider(df, col, title=None):
titledwd = 'Official Outdoor Observations Braunschweig (City Center) Germany'
dates = df.index.values
source = ColumnDataSource(data=dict(date=dates, close=df[col]))
p = figure(title=col + ":" + title,
plot_height=300,
plot_width=800,
tools="",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[0], dates[df[col].shape[0] - 1]))
p.line('date', 'close', source=source)
p.yaxis.axis_label = 'Celcius'
select = figure(
title=
"Drag the middle and edges of the selection box to change the range above",
plot_height=130,
plot_width=800,
y_range=p.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef")
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
return column(p, select)
def _create_vert_range_tool(data,
min_y,
max_y,
plot_range,
width,
height,
x_col,
y_col,
fill_map="navy"):
"""Return vertical range too for plot."""
rng_select = figure(
plot_width=width,
plot_height=height,
y_range=(min_y - 1, max_y + 1),
toolbar_location=None,
)
x_dodge = dodge(x_col, -0.5)
rng_select.rect(
x=x_dodge,
y=y_col,
width=1.2,
height=0.8,
source=data,
fill_alpha=0.6,
fill_color=fill_map,
)
rng_select.xaxis.visible = False
rng_select.yaxis.visible = False
range_tool = RangeTool(y_range=plot_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
rng_select.ygrid.grid_line_color = None
rng_select.xgrid.grid_line_color = None
rng_select.add_tools(range_tool)
rng_select.toolbar.active_multi = range_tool
return rng_select
def build_range_tool_graph(stock_data):
source = ColumnDataSource(data=dict(date=stock_data.time, close=stock_data.close))
plot = figure(
plot_height=300,
plot_width=950,
sizing_mode='scale_width',
tools="xpan",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="white", # color here
x_range=(stock_data.time[280], stock_data.time[355])
)
plot.sizing_mode = "scale_both"
plot.line('date', 'close', source=source)
plot.yaxis.axis_label = 'Price'
select = figure(
# title="Drag the middle and edges of the selection box to change the range above",
plot_height=100,
# plot_width=1000,
sizing_mode='scale_width',
y_range=plot.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="white" # color here
)
select.sizing_mode = "scale_both"
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "white" # color here
range_tool.overlay.fill_alpha = 0.1 # transparent
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
return components(column(plot, select))
def bokeh_plot(df):
dates = df['Date']
source = ColumnDataSource(
data={
'date': dates,
'close': df['Adj_Close'],
'high': df['High'],
'low': df['Low'],
'volume': df['Adj_Volume']
})
p = figure(title="Drag to change range",
plot_height=300,
plot_width=700,
tools="xpan",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="below",
sizing_mode="scale_width",
background_fill_color="#f5f5f5",
x_range=(dates[len(dates) - 300], dates[len(dates) - 1]))
hover_tool = HoverTool(
tooltips=[
('Date', '@date{%F}'), # use @{ } for field names with spaces
('Close', '$@close{%0.2f}'),
('High', '$@high{%0.2f}'),
('Low', '$@low{%0.2f}'),
('Volume', '@volume{0.00 a}'),
],
formatters={
'date': 'datetime', # use 'datetime' formatter for 'date' field
'close': 'printf', # use 'printf' formatter for 'adj close' field
'high': 'printf',
'low': 'printf'
# use default 'numeral' formatter for other fields
},
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline')
# cross_tool = CrosshairTool()
p.add_tools(hover_tool)
p.line('date', 'close', source=source)
p.yaxis.axis_label = 'Price'
select = figure(title="Drag to change the range",
plot_height=130,
plot_width=930,
y_range=p.y_range,
x_axis_type="datetime",
y_axis_type=None,
sizing_mode="scale_width",
tools="",
toolbar_location=None,
background_fill_color="#f5f5f5")
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = "white"
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
c = column(p, select)
script, div = components(p)
return script, div
def __init__(self, days=1000):
sql = 'select date, pe from pehistory where name="all"'
self.df = pd.read_sql(sql, engine)
self.source = ColumnDataSource(self.df)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width = 1000
peHeight = int(width / 16 * 3)
selectHeight = int(width / 16 * 1)
# 绘制K线图
dataLen = self.df.shape[0]
# tooltips = [('index', '@index'), ('date', '@date'), ('close', '@close')]
# ymin = self.df.low[-200:].min()
# ymax = self.df.high[-200:].max()
# start = ymin - (ymax - ymin) * 0.05
# end = ymax + (ymax - ymin) * 0.05
# self.pkline = figure(x_axis_type="datetime", tools=TOOLS,
# plot_height=klineHeight,
# plot_width=width,
# x_axis_location="above",
# x_range=(dataLen - 200, dataLen - 1),
# y_range=(start, end),
# tooltips=tooltips)
# self.pkline.xaxis.major_label_overrides = self.df['date'].to_dict()
# self.plotCandlestick()
tooltips = [('pe', '@pe')]
ymin = self.df.pe[-200:].min()
ymax = self.df.pe[-200:].max()
start = ymin - (ymax - ymin) * 0.05
end = ymax + (ymax - ymin) * 0.05
self.ppe = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=peHeight, plot_width=width,
tooltips=tooltips,
x_range=(dataLen - 200, dataLen - 1),
# x_range=self.pkline.x_range,
y_range=(start, end))
self.ppe.xaxis.major_label_overrides = self.df['date'].to_dict()
self.plotPE(self.ppe)
self.select = figure(plot_height=selectHeight,
plot_width=width,
x_range=(0, days - 1),
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef")
self.select.xaxis.major_label_overrides = self.df['date'].to_dict()
self.plotPE(self.select)
range_tool = RangeTool(x_range=self.ppe.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
self.select.add_tools(range_tool)
self.select.toolbar.active_multi = range_tool
# kline和pe显示范围变动时自动更新y轴范围
code = """
var xstart = parseInt(ppe.x_range.start);
if(xstart<0){xstart=0;}
var xend = parseInt(ppe.x_range.end);
if(xend>maxdays - 1){xend=maxdays - 1;}
console.log('xstart: ', xstart);
console.log('xend: ', xend);
console.log('maxdays: ', maxdays);
var data = source.data;
var highdata = data['high'];
var lowdata = data['low'];
var pedata = data['pe'];
var peymax = pedata[xstart];
var peymin = pedata[xstart];
for (var i = xstart + 1; i < xend; i++) {
peymax = Math.max(peymax, pedata[i]);
peymin = Math.min(peymin, pedata[i]);
// console.log('pedata[i]: ', pedata[i]);
// console.log('i:', i);
}
ppe.y_range.start = peymin - (peymax - peymin) * 0.05;
ppe.y_range.end = peymax + (peymax - peymin) * 0.05;
console.log('peymax: ', peymax);
console.log('peymin: ', peymin);
"""
# code = """
# ppe.y_range.end = 30
# """
callback = CustomJS(args=dict(ppe=self.ppe,
source=self.source,
maxdays=days),
code=code)
self.ppe.x_range.js_on_change('start', callback)
self.ppe.x_range.js_on_change('end', callback)
self.column_layout = column([self.ppe, self.select])
def __init__(self, ID, df):
df['date'] = [i.strftime('%Y%m%d') for i in df.date]
self.df = df
days = df.shape[0]
self.source = ColumnDataSource(self.df)
TOOLS = 'pan,wheel_zoom,box_zoom,reset,save,crosshair'
width = 1000
klineHeight = int(width / 16 * 6)
peHeight = int(width / 16 * 3)
selectHeight = int(width / 16 * 1)
# 绘制K线图
dataLen = self.df.shape[0]
tooltips = [('date', '@date'), ('close', '@close')]
ymin = self.df.low[-200:].min()
ymax = self.df.high[-200:].max()
start = ymin - (ymax - ymin) * 0.05
end = ymax + (ymax - ymin) * 0.05
self.pkline = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=klineHeight,
plot_width=width,
x_axis_location="above",
x_range=(dataLen - 200, dataLen),
y_range=(start, end),
tooltips=tooltips)
self.pkline.xaxis.major_label_overrides = self.df['date'].to_dict()
self.plotCandlestick()
tooltips = [('date', '@date'), ('pe', '@pe')]
ymin = self.df.pe[-200:].min()
ymax = self.df.pe[-200:].max()
start = ymin - (ymax - ymin) * 0.05
end = ymax + (ymax - ymin) * 0.05
self.ppe = figure(x_axis_type="datetime", tools=TOOLS,
plot_height=peHeight, plot_width=width,
tooltips=tooltips,
x_range=self.pkline.x_range,
y_range=(start, end))
self.ppe.xaxis.major_label_overrides = self.df['date'].to_dict()
self.plotPE(self.ppe)
self.select = figure(plot_height=selectHeight,
plot_width=width,
x_range=(-2, days + 2),
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef")
self.select.xaxis.major_label_overrides = self.df['date'].to_dict()
self.plotPE(self.select)
range_tool = RangeTool(x_range=self.pkline.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
self.select.add_tools(range_tool)
self.select.toolbar.active_multi = range_tool
# kline和pe显示范围变动时自动更新y轴范围
code = """
var xstart = parseInt(ppe.x_range.start);
if(xstart<0){xstart=0;}
var xend = parseInt(ppe.x_range.end);
if(xend>maxdays){xend=maxdays;}
console.log('xstart: ', xstart);
console.log('xend: ', xend);
console.log('maxdays: ', maxdays);
var data = source.data;
var highdata = data['high'];
var lowdata = data['low'];
var klineymax = highdata[xstart];
var klineymin = lowdata[xstart];
var pedata = data['pe'];
var peymax = pedata[xstart];
var peymin = pedata[xstart];
for (var i = xstart + 1; i <= xend; i++) {
klineymax = Math.max(klineymax, highdata[i]);
klineymin = Math.min(klineymin, lowdata[i]);
peymax = Math.max(peymax, pedata[i]);
peymin = Math.min(peymin, pedata[i]);
// console.log('pedata[i]: ', pedata[i]);
// console.log('i:', i);
}
pkline.y_range.start = klineymin - (klineymax - klineymin) * 0.05;
pkline.y_range.end = klineymax + (klineymax - klineymin) * 0.05;
ppe.y_range.start = peymin - (peymax - peymin) * 0.05;
ppe.y_range.end = peymax + (peymax - peymin) * 0.05;
console.log('klineymax: ', klineymax);
console.log('klineymin: ', klineymin);
console.log('peymax: ', peymax);
console.log('peymin: ', peymin);
"""
# code = """
# ppe.y_range.end = 30
# """
callback = CustomJS(args=dict(ppe=self.ppe,
pkline=self.pkline,
source=self.source,
maxdays=days - 1),
code=code)
self.ppe.x_range.js_on_change('start', callback)
self.ppe.x_range.js_on_change('end', callback)
self.column_layout = column([self.pkline, self.ppe, self.select])
# plot the globally averaged AOD
# ------------------------------------
p2 = figure(plot_height=300,
plot_width=900,
x_axis_type='datetime',
y_axis_type='log',
y_range=(1e-4, 1e-1),
x_range=p.x_range)
a0 = p2.line(x='time', y='aod', source=aod_df, color='gray')
a1 = p2.line(x='time', y='aod_diff', source=aod_diff_df, line_width=1)
p2.yaxis.axis_label = 'Global AOD [1020nm]'
# ------------------------------------
# Add the range tool for selection
# ------------------------------------
range_tool = RangeTool(
x_range=Range1d(np.datetime64('1991-05-01'), np.datetime64('1996-06-01')))
range_tool.overlay.fill_color = "gray"
range_tool.overlay.fill_alpha = 0.1
range_tool.x_range.on_change('start', vci_callback)
range_tool.x_range.on_change('end', vci_callback)
p2.ygrid.grid_line_color = None
p2.add_tools(range_tool)
p.add_tools(range_tool)
p2.toolbar.active_multi = range_tool
aod_band_diff2 = calc_aod_in_period()
glyph = Patch(x="time",
y="aod_fill",
fill_color="#f46842",
def make_plot(alarm, warning_msg, label_Tx=None):
output_file("static/bokeh.html", title="Seizure Alarm")
# define what to plot
alarm_x = np.arange(alarm.shape[0])
source = ColumnDataSource(data=dict(alarm_x=alarm_x, alarm=alarm))
if label_Tx is not None:
shift = label_Tx.shape[0] - alarm.shape[0]
alarm = np.pad(alarm, (shift, 0), mode="constant", constant_values=0)
label_Tx_x = np.arange(label_Tx.shape[0])
source = ColumnDataSource(data=dict(
alarm_x=label_Tx_x,
alarm=alarm,
label_Tx_x=label_Tx_x,
label_Tx=label_Tx,
))
# make an upper figure in the column object
start = 1000
end = 1060
if alarm_x.shape[0] < 1060:
start = 0
end = 60
p = figure(
title=warning_msg,
plot_height=400,
plot_width=800,
tools="xpan",
toolbar_location=None,
x_axis_location="above",
background_fill_color="#efefef",
x_range=(alarm_x[start], alarm_x[end]),
)
# draw alarm in upper figure
p.line(
"alarm_x",
"alarm",
legend_label="Alarm",
line_width=3,
color="red",
source=source,
)
# make a lower figure in the column object
select = figure(
plot_height=200,
plot_width=800,
y_range=p.y_range,
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef",
)
# plot alarm in lower figure
select.line("alarm_x", "alarm", color="red", source=source)
if label_Tx is not None:
# draw label in the upper figure
p.line(
"label_Tx_x",
"label_Tx",
legend_label="clinician label",
line_width=3,
color="orange",
line_dash="4 4",
source=source,
)
# draw label in the lower figure
select.line("label_Tx_x",
"label_Tx",
color="orange",
line_dash="4 4",
source=source)
# cosmetic changes
for panel in [p, select]:
panel.yaxis.ticker = [0, 1]
panel.yaxis.major_label_overrides = {0: "OFF", 1: "ON"}
p.yaxis.axis_label = "Seizure"
p.xaxis.axis_label = "Time (seconds)"
p.title.text_font_size = "20pt"
select.ygrid.grid_line_color = None
# create a range tool
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
# add range tool to lower figure
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
# save column object to html defined by output_file
save(column(p, select))
##########################################
select = figure(
title=
"Drag the middle and edges of the selection box to change the range of plots",
plot_height=130,
plot_width=920,
y_range=temp_plot.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef",
height_policy="fixed",
min_width=920)
range_tool = RangeTool(x_range=temp_plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'kitchen_temp', source=source_static)
select.line('date', 'outside_temp', source=source_static, line_color="tomato")
select.line('date', 'bathroom_temp', source=source_static, line_color="green")
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
select.sizing_mode = 'stretch_width'
####################################################################################################
battery_plot = figure(plot_width=200,
formatters={'date': 'datetime'}))
add_plot_lines(plot_queue, source, plot_sources[3:], Spectral3)
plot_queue.yaxis.axis_label = 'r'
plot_queue.background_fill_color = "#f5f5f5"
plot_queue.grid.grid_line_color = "white"
# plot seletor
select = figure(plot_height=320,
y_range=plot_queue.y_range,
sizing_mode="scale_width",
x_axis_type="datetime",
y_axis_type=None,
toolbar_location="right",
tools=tools)
select
range_rool = RangeTool(x_range=plot_sys.x_range)
range_rool.overlay.fill_color = "navy"
range_rool.overlay.fill_alpha = 0.2
#select.line('date', 'sy_max', source=source)
add_plot_lines(select, source, plot_sources, Spectral6)
select.ygrid.grid_line_color = None
select.add_tools(range_rool)
select.toolbar.active_multi = range_rool
select.background_fill_color = "#f5f5f5"
select.grid.grid_line_color = "white"
select.x_range.range_padding = 0.01
# define a tabela
columns = [
TableColumn(field="date", title="Data",
def generate_plot(plot_df):
window_start = plot_df.query(f"{AGE_COL} >= {DEFAULT_WINDOW_SIZE}").index.max()
# Creating Main Plot
window_total_max = plot_df.loc[plot_df.index > window_start,
[STILL_OPEN_COL]].max().max() * 1.1
logging.debug(f"window_start={window_start}, window_total_max={window_total_max}")
plot = figure(
plot_height=None, plot_width=None,
sizing_mode="scale_both",
x_range=(-5, DEFAULT_WINDOW_SIZE),
y_range=(0, window_total_max),
toolbar_location=None,
x_axis_label="Request Age (days)",
y_axis_label="Open SR Count (#)",
background_fill_color="#eaeaf2",
)
plot.extra_y_ranges = {"proportion": Range1d(start=0, end=plot_df[CUMULATIVE_PROPORTION_COL].max() * 1.1)}
secondary_axis = LinearAxis(y_range_name="proportion", axis_label="Request Proportion (%)")
plot.add_layout(secondary_axis, 'right')
# Open and less than 180 days
vbar_open = plot.vbar(
top=STILL_OPEN_COL, x=AGE_COL, width=0.75, source=plot_df.query(f"{AGE_COL} < @MARKER_LOCATION"),
fill_color="#4c72b0", line_color="#4c72b0", alpha=0.8, line_alpha=0.8
)
# Open and more than 180 days
vbar_still_open = plot.vbar(
top=STILL_OPEN_COL, x=AGE_COL, width=0.75, source=plot_df.query(f"{AGE_COL} >= @MARKER_LOCATION"),
fill_color="#c44e52", line_color="#c44e52", alpha=0.8, line_alpha=0.8
)
# 180 day Marker line
marker_span = Span(
location=MARKER_LOCATION,
dimension='height', line_color="#dd8452",
line_dash='dashed', line_width=4
)
plot.add_layout(marker_span)
# Cumulative Proportion Line
cum_prop_line = plot.line(
y=CUMULATIVE_PROPORTION_COL, x=AGE_COL, width=2, source=plot_df, y_range_name="proportion",
line_color="#55a868", alpha=0.8, line_alpha=0.8
)
# Plot grid and axis
service_delivery_volume_code_metric_plot_widgets_to_minio.stlye_axes(plot)
plot.yaxis.formatter = NumeralTickFormatter(format="0.[0] a")
secondary_axis.formatter = NumeralTickFormatter(format="0.[00] %")
# Plot legend
legend_items = [("Open < 180 days", [vbar_open]),
("Open > 180 days", [vbar_still_open]),
("Open Requests", [cum_prop_line])]
service_delivery_volume_code_metric_plot_widgets_to_minio.add_legend(plot, legend_items)
# Plot tooltip
hover_tool = HoverTool(tooltips=HOVER_COLS, mode="vline",
formatters={f'@{DATE_COL}': 'datetime'},
renderers=[cum_prop_line])
plot.add_tools(hover_tool)
# Adding select figure below main plot
select = figure(plot_height=75, plot_width=None,
y_range=plot.y_range,
sizing_mode="scale_width",
y_axis_type=None,
tools="", toolbar_location=None, background_fill_color="#eaeaf2")
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select_open = select.line(
y=STILL_OPEN_COL, x=AGE_COL, line_width=1, source=plot_df.query(f"{AGE_COL} < @MARKER_LOCATION"),
line_color="#4c72b0", alpha=0.6, line_alpha=0.6
)
select_still_open = select.line(
y=STILL_OPEN_COL, x=AGE_COL, line_width=1, source=plot_df.query(f"{AGE_COL} >= @MARKER_LOCATION"),
line_color="#c44e52", alpha=0.6, line_alpha=0.6
)
select.xgrid.grid_line_color = "White"
service_delivery_volume_code_metric_plot_widgets_to_minio.stlye_axes(select)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
combined_plot = column(plot, select, height_policy="max", width_policy="max")
plot_html = file_html(combined_plot, CDN, "Business Continuity Service Delivery Request Age Distribution")
return plot_html
# Add an interactive plot tool to select a date range
# First, build the plot
select = figure(
title=
"Drag the middle and edges of the selection box to change the range above",
plot_height=200,
plot_width=1000,
y_range=plot.y_range,
y_axis_type=None,
tools=["xpan", "xwheel_zoom", "pan", "reset"],
background_fill_color="#efefef")
select.toolbar.active_scroll = select.select_one(WheelZoomTool)
select.toolbar.logo = None
# Build the range tool we will overlay on the plot
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
# Add all the data lines to the plot, and store them (for setting visible later)
lines2 = []
for index, source in enumerate(sources):
lines2.append(
select.line('Time', 'GM_AOD', source=source, color=colours[index]))
# Overlay the range tool on the plot
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
# Build a checkbox group to select the visible datasets
# First, write the javascript callback that will set lines visible or not
def generate_plot(plot_df):
window_start = pandas.to_datetime(WINDOW_START, format=ISO601_DATE_FORMAT)
window_end = plot_df.index.max() + pandas.Timedelta(days=1)
y_range_end = plot_df.loc[WINDOW_START:, DURATION_DAYS_COL].max() * 1.1
logging.debug(f"x axis range: {window_start} - {window_end}")
plot = figure(
plot_height=None,
plot_width=None,
sizing_mode="scale_both",
x_range=(window_start, window_end),
y_range=(0, y_range_end),
x_axis_type='datetime',
toolbar_location=None,
y_axis_label="SR Duration (Days)",
background_fill_color="#eaeaf2",
)
# Previous year lines
for i in range(1, PREVIOUS_YEARS + 1):
prefix = PREVIOUS_YEAR_TEMPLATE.format(i)
for col in COLS_SET:
previous_year_line = plot.line(x=DATE_COL,
y=f"{prefix}_{col}",
source=plot_df,
color="Grey",
line_width=2,
alpha=0.5)
line = plot.line(x=DATE_COL,
y=DURATION_DAYS_COL,
source=plot_df,
color="#c44e52",
line_width=4,
alpha=0.8,
line_alpha=0.8)
circle = plot.circle(x=DATE_COL,
y=DURATION_DAYS_COL,
source=plot_df,
color="#c44e52",
size=6,
alpha=0.8,
line_alpha=0.8)
# Marker lines
marker_span = Span(location=pandas.to_datetime(REFERENCE_DATE),
dimension='height',
line_color="#4c72b0",
line_dash='dashed',
line_width=4)
plot.add_layout(marker_span)
second_marker_span = Span(
location=pandas.to_datetime(SECOND_REFERENCE_DATE),
dimension='height',
line_color="#4c72b0",
line_dash='dashed',
line_width=4)
plot.add_layout(second_marker_span)
# Plot grid and axis
plot.grid.grid_line_color = "white"
plot.xaxis.major_label_orientation = math.pi / 4
plot.xaxis.formatter = DatetimeTickFormatter(days=ISO601_DATE_FORMAT)
plot.y_range.start = 0
plot.axis.axis_label_text_font_size = "12pt"
plot.axis.major_label_text_font_size = "12pt"
legend_items = [("Duration (days)", [line, circle]),
("Previous Years", [previous_year_line])]
legend = Legend(items=legend_items,
location="center",
orientation="horizontal",
margin=2,
padding=2)
plot.add_layout(legend, 'below')
hover_tool = HoverTool(tooltips=TOOL_TIPS,
renderers=[line],
mode="vline",
formatters={f'@{DATE_COL}': 'datetime'})
plot.add_tools(hover_tool)
# Adding select figure below main plot
select = figure(plot_height=75,
plot_width=None,
y_range=plot.y_range,
sizing_mode="scale_width",
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#eaeaf2")
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select_line = select.line(y=DURATION_DAYS_COL,
x=DATE_COL,
line_width=1,
source=plot_df,
line_color="#c44e52",
alpha=0.6,
line_alpha=0.6)
select_span = Span(location=pandas.to_datetime(REFERENCE_DATE),
dimension='height',
line_color="#4c72b0",
line_dash='dashed',
line_width=2)
select.add_layout(select_span)
second_select_span = Span(
location=pandas.to_datetime(SECOND_REFERENCE_DATE),
dimension='height',
line_color="#4c72b0",
line_dash='dashed',
line_width=2)
select.add_layout(second_select_span)
select.xgrid.grid_line_color = "White"
select.ygrid.grid_line_color = None
select.xaxis.formatter = DatetimeTickFormatter(days=ISO601_DATE_FORMAT)
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
select.axis.axis_label_text_font_size = "12pt"
select.axis.major_label_text_font_size = "12pt"
combined_plot = column(plot,
select,
height_policy="max",
width_policy="max")
plot_html = file_html(
combined_plot, CDN,
"Business Continuity Service Request Duration Time Series")
return plot_html
def sentiment_tab():
# Load "sentiment from headlines", creadted by a separate code
df_head = pd.read_csv('./dashboard/data/headsentiment.csv',
parse_dates=True)
df_head.reset_index()
df_body = pd.read_csv('./dashboard/data/bodysentiment.csv',
parse_dates=True)
df_body.reset_index()
# Join two dataframes into one dataframe using the key, named 'time'
df = df_head.join(df_body, lsuffix='_head', rsuffix='_body')
dates = np.array(df['time_head'], dtype=np.datetime64)
# Get a column of pandas df, return a moving average of that column with window size <- window_size
def set_moving_avg(df, window_size):
window = np.ones(window_size) / float(window_size)
return np.convolve(df.copy(), window, 'same')
# Set initial window sizes to 10 to both of the head and body.
mv_subj_head = set_moving_avg(df['subj_head'], 5)
mv_subj_body = set_moving_avg(df['subj_body'], 5)
# Create a cds object for headlines
cds_head = ColumnDataSource(data=dict(
date=dates, polar_head=df['polar_head'], subj_head=mv_subj_head))
# Create a cds object for body
cds_body = ColumnDataSource(data=dict(
date=dates, polar_body=df['polar_body'], subj_body=mv_subj_body))
p_polar_head = figure(plot_height=300,
plot_width=500,
tools="",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[-10000], dates[-1]),
y_range=(-1, 1))
p_subj_head = figure(plot_height=300,
plot_width=500,
tools="",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[-10000], dates[-1]),
y_range=(0, 1))
p_polar_body = figure(plot_height=300,
plot_width=500,
tools="",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[-10000], dates[-1]),
y_range=(-1, 1))
p_subj_body = figure(plot_height=300,
plot_width=500,
tools="",
toolbar_location=None,
x_axis_type="datetime",
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[-10000], dates[-1]),
y_range=(0, 1))
# Setting: Plot a line chart for polar - News headlines
p_polar_head.line('date',
'polar_head',
source=cds_head,
line_color=Greys5[1])
p_polar_head.title.text = 'Polar in news headlines'
p_polar_head.yaxis.axis_label = 'Measurements'
# Setting: Plot a line chart for subjectivity - News headlines
p_subj_head.line('date',
'subj_head',
source=cds_head,
line_color=Blues5[0])
p_subj_head.title.text = 'Subjectivity in news headlines'
p_subj_head.yaxis.axis_label = 'Measurements'
# Setting: Plot a line chart for polar - News bodies
p_polar_body.line('date',
'polar_body',
source=cds_body,
line_color=Greys5[1])
p_polar_body.title.text = 'Polar in news bodies'
p_polar_body.yaxis.axis_label = 'Measurements'
# Setting: Plot a line chart for subjectivity - News bodies
p_subj_body.line('date',
'subj_body',
source=cds_body,
line_color=Blues5[0])
p_subj_body.title.text = 'Subjectivity in news bodies'
p_subj_body.yaxis.axis_label = 'Measurements'
select_polar_head = figure(
title=
"Drag the middle and edges of the box below to change the range above",
plot_height=110,
plot_width=500,
y_range=(-1, 1),
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="white")
select_subj_head = figure(
title=
"Drag the middle and edges of the box below to change the range above",
plot_height=110,
plot_width=500,
y_range=p_subj_head.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="white")
select_polar_body = figure(title="",
plot_height=110,
plot_width=500,
y_range=(-1, 1),
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="white")
select_subj_body = figure(title="",
plot_height=110,
plot_width=500,
y_range=p_subj_head.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="white")
# Setting: Plot a range tool for polar - News headlines
range_tool_polar_head = RangeTool(x_range=p_polar_head.x_range)
range_tool_polar_head.overlay.fill_color = Blues5[2]
range_tool_polar_head.overlay.fill_alpha = 0.4
# Setting: Plot a range tool for subjectivity - News headlines
range_tool_subj_head = RangeTool(x_range=p_subj_head.x_range)
range_tool_subj_head.overlay.fill_color = Blues5[2]
range_tool_subj_head.overlay.fill_alpha = 0.4
# Setting: Plot a range tool for polar - News headlines
range_tool_polar_body = RangeTool(x_range=p_polar_body.x_range)
range_tool_polar_body.overlay.fill_color = Blues5[2]
range_tool_polar_body.overlay.fill_alpha = 0.4
# Setting: Plot a range tool for subjectivity - News headlines
range_tool_subj_body = RangeTool(x_range=p_subj_body.x_range)
range_tool_subj_body.overlay.fill_color = Blues5[2]
range_tool_subj_body.overlay.fill_alpha = 0.4
# Plot a range tool for polar - News headlines
select_polar_head.line('date',
'polar_head',
source=cds_head,
line_color=Greys5[3])
select_polar_head.ygrid.grid_line_color = None
select_polar_head.add_tools(range_tool_polar_head)
select_polar_head.toolbar.active_multi = range_tool_polar_head
# Plot a range tool for subjectivity - News headlines
select_subj_head.line('date',
'subj_head',
source=cds_head,
line_color=Blues5[1])
select_subj_head.ygrid.grid_line_color = None
select_subj_head.add_tools(range_tool_subj_head)
select_subj_head.toolbar.active_multi = range_tool_subj_head
# Plot a range tool for polar - News bodies
select_polar_body.line('date',
'polar_body',
source=cds_body,
line_color=Greys5[3])
select_polar_body.ygrid.grid_line_color = None
select_polar_body.add_tools(range_tool_polar_body)
select_polar_body.toolbar.active_multi = range_tool_polar_body
# Plot a range tool for subjectivity - News bodies
select_subj_body.line('date',
'subj_body',
source=cds_body,
line_color=Blues5[1])
select_subj_body.ygrid.grid_line_color = None
select_subj_body.add_tools(range_tool_subj_body)
select_subj_body.toolbar.active_multi = range_tool_subj_body
# Plot sliders
slider_mv = Slider(title="Smoothing by N days (moving average)",
value=5,
start=1,
end=20,
step=1,
width=300)
#s_subj_body = Slider(title="", value=5, start=1, end=20, step=1)
# Register event handlers for sliders to get a window size of moving avergaes
def update_mv(attrname, old, new):
cds_head.data['subj_head'] = set_moving_avg(df['subj_head'], new)
cds_body.data['subj_body'] = set_moving_avg(df['subj_body'], new)
slider_mv.on_change('value', update_mv)
hover_polar_head = p_polar_head.select(dict(type=HoverTool))
hover_polar_head.tooltips = [("value", "@polar_head"), ("date", "@date")]
hover_polar_head.mode = 'mouse'
l = gridplot(
children=[[p_polar_head, p_polar_body],
[select_polar_head, select_polar_body],
[p_subj_head, p_subj_body],
[select_subj_head, select_subj_body], [None, slider_mv]])
tab = Panel(child=l, title='Sentiment')
return tab
def plot_actual_predictions_series(
data,
results,
model_errors,
joe=None,
par=False,
model='arima',
target='cumStatpoints',
metric='mase',
player_name='Leon Draisaitl',
deepar_model_name='deepar_truncated_results_unit_s_ne300_lr1e-3_bs64_nl3_cl3.csv'
):
""" plots the real and predicted time series along with confidence intervals for a player"""
if results is None:
model = 'deepar'
if model == 'arima':
player_df = data.loc[data.loc[:, 'name'] == player_name]
player_errors = model_errors.loc[
model_errors.loc[:, 'name.arima_results_m3'] == player_name]
try:
player_error = player_errors['arima_results_m3'].values.astype(
float).item()
except ValueError:
st.write(
'Unfortunately, Auto-ARIMA was unable to find a suitable model for this player.'
)
return
series_dataframe = calculate_predictions(data,
results,
player_name=player_name,
target=target)
intervals = return_intervals(results, player_name)
dates = series_dataframe.index.values.astype(np.datetime64)
joe = joe.loc[joe.loc[:, 'date'].isin(player_df.date)]
joe = joe.set_index('date')
compare_dataframe = joe.loc[:, 'cumStatpoints']
compare_dataframe = compare_dataframe.sort_index()
elif model == 'deepar':
player_df = data.loc[data.loc[:, 'name'] == player_name]
if model_errors is not None:
player_errors = model_errors.loc[
model_errors.loc[:, 'name.{}'.format(deepar_model_name)] ==
player_name]
try:
player_error = player_errors[deepar_model_name].values.astype(
float).item()
except ValueError:
st.write('This player was not able to be evaluated.')
else:
player_df = player_df.sort_values('gameNumber', ascending=True)
player_df = player_df.set_index('gameNumber')
# player_df.dropna(axis=1, subset=['predictions'])
# _, _, rmse = calculate_errors(calculate_residuals(player_df))
if model_errors is not None:
series_dataframe = player_df.loc[:, [target, 'predictions']]
else:
series_dataframe = player_df.loc[:, 'predictions']
series_dataframe = series_dataframe.sort_index()
intervals = player_df.loc[:, ['high', 'low']].dropna()
intervals = intervals.sort_index()
if model_errors is not None:
dates = series_dataframe.index.values.astype(np.datetime64)
else:
gameNumbers = series_dataframe.index.values.astype(int)
# st.write(dates)
if model_errors is not None:
joe = joe.loc[joe.loc[:, 'date'].isin(series_dataframe.index)]
joe = joe.set_index('date')
compare_dataframe = joe.loc[:, 'cumStatpoints']
compare_dataframe = compare_dataframe.sort_index()
if par:
calculate_par(series_dataframe, compare_dataframe)
elif data is None and results is None:
player_name = 'Joe Schmo'
joe = joe.set_index('date')
series_dataframe = joe.loc[:, 'cumStatpoints']
series_dataframe = series_dataframe.sort_index()
dates = series_dataframe.index.values.astype(np.datetime64)
start_date = dt.strptime('2018-10-03', '%Y-%m-%d')
if model_errors is not None:
# st.dataframe(model_errors)
real_source = ColumnDataSource(
data=dict(date=dates, points=series_dataframe[target]))
compare_source = ColumnDataSource(
data=dict(date=dates, points=compare_dataframe))
pred_source = ColumnDataSource(
data=dict(date=dates, points=series_dataframe['predictions']))
interval_dates = dates[-intervals.shape[0]:].reshape(-1, 1)
interval_dates = np.hstack((interval_dates, interval_dates))
else:
pred_source = ColumnDataSource(
data=dict(gameNumber=gameNumbers, points=series_dataframe))
interval_games = gameNumbers[-intervals.shape[0]:].reshape(-1, 1)
interval_games = np.hstack((interval_games, interval_games))
if model_errors is not None:
y_max = max([
series_dataframe.loc[:, target].max(), joe.loc[:, target].max()
]) + 50
else:
try:
y_min = series_dataframe.values[0]
except IndexError:
st.write('This player was not able to be predicted.')
return
y_max = series_dataframe.max() + 50
if model == 'arima':
player_line = figure(title=('{0}({1},{2},{3})({4},{5},{6},{7}) ({8}: {9:.3f})') \
.format(player_name,
results.loc[player_name, 'p'],
results.loc[player_name, 'd'],
results.loc[player_name, 'q'],
results.loc[player_name, 'P'],
results.loc[player_name, 'D'],
results.loc[player_name, 'Q'],
3, # TODO: undo hardcoding
metric.upper(),
player_error), # TODO: change to MASE
plot_height=300,
plot_width=800,
tools="xpan",
toolbar_location='above',
x_axis_type="datetime",
x_axis_location="below",
x_range=(start_date, dates[-1]),
y_range=(0, y_max),
x_axis_label='Game Date',
y_axis_label='Cumulative Points',
background_fill_color="#efefef"
)
else:
if model_errors is not None:
player_line = figure(
title=('{0} ({1}: {2:.3f})'.format(
player_name, metric.upper(),
player_error)), # TODO: add error
plot_height=300,
plot_width=800,
tools="xpan",
toolbar_location='above',
x_axis_type="datetime",
x_axis_location="below",
x_range=(start_date, dates[-1]),
y_range=(0, y_max),
x_axis_label='Game Date',
y_axis_label='Cumulative Points',
background_fill_color="#efefef")
else:
player_line = figure(
title=('{0}'.format(player_name)), # TODO: add error
plot_height=300,
plot_width=800,
tools="xpan",
toolbar_location='above',
x_axis_location="below",
x_range=(0, 82),
y_range=(y_min, y_max),
x_axis_label='Game Date',
y_axis_label='Cumulative Points',
background_fill_color="#efefef")
# elif data is None and results is None:
# player_line = figure(title=('{0}'.format(player_name)), # TODO: add error
# plot_height=300,
# plot_width=800,
# tools="xpan",
# toolbar_location='above',
# x_axis_type="datetime",
# x_axis_location="below",
# x_range=(dates[0], dates[-1]),
# background_fill_color="#efefef"
# )
# hover_tool = HoverTool(tooltips=[("date", "@date"),
# ("points", "@points")
# ],
# mode='vline'
# )
if model_errors is not None:
player_line.line('date',
'points',
source=real_source,
line_color='blue',
legend='actual')
player_line.line('date',
'points',
source=compare_source,
line_color='green',
legend='average')
player_line.circle('date',
'points',
source=pred_source,
line_color='red',
fill_color='red',
legend='predicted')
player_line.varea(x=interval_dates[:, 0],
y1=intervals.loc[:, 'high'],
y2=intervals.loc[:, 'low'],
fill_alpha=0.4,
color='red',
legend='predicted')
else:
player_line.circle('gameNumber',
'points',
source=pred_source,
line_color='red',
fill_color='red',
legend='predicted')
player_line.varea(x=interval_games[:, 0],
y1=intervals.loc[:, 'high'],
y2=intervals.loc[:, 'low'],
fill_alpha=0.4,
color='red',
legend='predicted')
player_line.legend.location = 'bottom_right'
player_line.legend.click_policy = 'hide'
# player_line.add_tools(hover_tool)
# player_line.toolbar.active_multi = hover_tool
# player_line.yaxis.axis_label('Cumulative Points')
test_start = Span(location=start_date,
dimension='height',
line_color='green',
line_dash='dashed',
line_width=3)
player_line.add_layout(test_start)
if model_errors is not None:
select = figure(title=("Drag the middle and edges of the"
"selection box to change the range above"),
plot_height=130,
plot_width=800,
y_range=player_line.y_range,
x_axis_type="datetime",
y_axis_type=None,
x_axis_label='Game Date',
y_axis_label='Cumulative Points',
tools="",
toolbar_location=None,
background_fill_color="#efefef",
x_range=(dates[0], dates[-1]))
else:
select = figure(title=("Drag the middle and edges of the"
"selection box to change the range above"),
plot_height=130,
plot_width=800,
y_range=player_line.y_range,
x_axis_type="datetime",
y_axis_type=None,
x_axis_label='Game Date',
y_axis_label='Cumulative Points',
tools="",
toolbar_location=None,
background_fill_color="#efefef",
x_range=(0, 82))
# range_tool = RangeTool(x_range=Range1d(start_date, dates[-1]))
range_tool = RangeTool(x_range=player_line.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
if model_errors is not None:
select.line('date', 'points', source=real_source, line_color='blue')
select.line('date',
'points',
source=compare_source,
line_color='green')
select.circle('date',
'points',
source=pred_source,
line_color='red',
fill_color='red')
else:
select.circle('gameNumber',
'points',
source=pred_source,
line_color='red',
fill_color='red')
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
select.add_layout(test_start)
chart = column(player_line, select)
st.bokeh_chart(chart)
# st.write("{}'s dataframe:".format(player_name))
# st.dataframe(series_dataframe)
return chart
p.line('date', 'close', source=source, line_width=2, alpha=0.7)
p.yaxis.axis_label = 'Traffic'
p.background_fill_color = "#f5f5f5"
p.grid.grid_line_color = "white"
select = figure(plot_height=50,
plot_width=800,
y_range=p.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
sizing_mode="scale_width")
range_rool = RangeTool(x_range=p.x_range)
range_rool.overlay.fill_color = "navy"
range_rool.overlay.fill_alpha = 0.2
select.line('date', 'close', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_rool)
select.toolbar.active_multi = range_rool
select.background_fill_color = "#f5f5f5"
select.grid.grid_line_color = "white"
select.x_range.range_padding = 0.01
layout = column(p, select, sizing_mode="scale_width", name="line")
curdoc().add_root(layout)
# create the select plot below
select = figure(title='Drag the selection box to change the range above',
plot_height=130,
plot_width=800,
y_range=p.y_range,
x_axis_type='datetime',
y_axis_type='linear',
tools='',
toolbar_location=None,
background_fill_color='#efefef')
# set hovertool
p.select_one(HoverTool).tooltips = [
('Date', '@year/@month/@day'),
('AQI', '@value'),
]
# set range_tool
range_tool = RangeTool(x_range=p.x_range)
range_tool.overlay.fill_color = 'navy'
range_tool.overlay.fill_alpha = 0.2
select.line('date', 'value', source=source)
select.ygrid.grid_line_color = None
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
output_file("aqi_sh.html")
show(column(p, select))
'image': [shaded.data],
'x': [x_range.start],
'y': [y_range.start],
'dw': [x_range.end],
'dh': [y_range.end]
}
# for (i, source) in enumerate(sub_data_sources):
# source.data['image'] = [datashade(data_frames[i], x_range, y_range, main_plot.plot_width, sub_plot.plot_height)]
# source.data['x'] = [x_range.start]
# source.data['y'] = [y_range.start]
# source.data['dw'] = [x_range.end]
# source.data['dh'] = [y_range.end]
range_tool = RangeTool(x_range=main_plot.x_range, y_range=main_plot.y_range)
sub_plot.add_tools(range_tool)
sub_plot.toolbar.active_multi = range_tool
def xrange_change_callback(attr, old, new):
for i in checkbox_group.active:
resample_data_source(i, main_data_sources, x_range)
x_range.on_change('start', xrange_change_callback)
x_range.on_change('end', xrange_change_callback)
def handle_reset(_):
print("Trigger reset")