def test_Patch():
glyph = Patch()
assert glyph.x == "x"
assert glyph.y == "y"
yield check_fill, glyph
yield check_line, glyph
yield check_props, glyph, ["x", "y"], FILL, LINE
def test_Patch():
glyph = Patch()
assert glyph.x is None
assert glyph.y is None
check_fill_properties(glyph)
check_line_properties(glyph)
check_properties_existence(glyph, [
"x",
"y",
], FILL, LINE, GLYPH)
def test_Patch():
glyph = Patch()
assert glyph.x is None
assert glyph.y is None
yield check_fill, glyph
yield check_line, glyph
yield (check_props, glyph, [
"x",
"y",
], FILL, LINE)
def test_Patch():
glyph = Patch()
assert glyph.x is None
assert glyph.y is None
yield check_fill_properties, glyph
yield check_line_properties, glyph
yield (check_properties_existence, glyph, [
"x",
"y",
], FILL, LINE, GLYPH)
def test_Patch() -> None:
glyph = Patch()
assert glyph.x == field("x")
assert glyph.y == field("y")
check_fill_properties(glyph)
check_hatch_properties(glyph)
check_line_properties(glyph)
check_properties_existence(glyph, [
"x",
"y",
], FILL, HATCH, LINE, GLYPH)
h=0.4,
url=dict(
value="http://bokeh.pydata.org/en/latest/_static/images/logo.png"
),
anchor="center")),
("line", Line(x="x", y="y", line_color="#F46D43")),
("multi_line",
MultiLine(xs="xs", ys="ys", line_color="#8073AC", line_width=2)),
("oval",
Oval(x="x",
y="y",
width=screen(15),
height=screen(25),
angle=-0.7,
fill_color="#1D91C0")),
("patch", Patch(x="x", y="y", fill_color="#A6CEE3")),
("patches", Patches(xs="xs", ys="ys", fill_color="#FB9A99")),
("quad",
Quad(left="x", right="xp01", top="y", bottom="ym01",
fill_color="#B3DE69")),
("quadratic",
Quadratic(x0="x",
y0="y",
x1="xp02",
y1="y",
cx="xp01",
cy="yp01",
line_color="#4DAF4A",
line_width=3)),
("ray",
Ray(x="x",
text_source = ColumnDataSource(
dict(
dates=[d1, d2, d3],
times=[dt.time(11, 30)] * 3,
texts=["CST (UTC+1)", "CEST (UTC+2)", "CST (UTC+1)"],
))
xdr = DataRange1d()
ydr = DataRange1d()
plot = Plot(x_range=xdr, y_range=ydr, plot_width=800, plot_height=400)
plot.title.text = "Daylight Hours - Warsaw, Poland"
plot.toolbar_location = None
patch1 = Patch(x="dates", y="times", fill_color="skyblue", fill_alpha=0.8)
plot.add_glyph(patch1_source, patch1)
patch2 = Patch(x="dates", y="times", fill_color="orange", fill_alpha=0.8)
plot.add_glyph(patch2_source, patch2)
sunrise_line = Line(x="dates",
y="sunrises",
line_color="yellow",
line_width=2,
label=value("sunrise"))
sunrise_line_renderer = plot.add_glyph(source, sunrise_line)
sunset_line = Line(x="dates",
y="sunsets",
line_color="red",
# # data = models['305']['val_accuracy'] # p1.line([i for i in range(0, len(data) + 1)], [0] + data, legend="CNN2: Kernel Size 8x8", line_width=2, color='orangered') # # p1.xaxis.axis_label_text_font_size = '12pt' # p1.yaxis.axis_label_text_font_size = '12pt' # p1.legend.location = 'bottom_right' data1 = [0] + models['304']['train_accuracy'] data2 = [0] + models['304']['val_accuracy'] x = np.hstack(([i for i in range(0, len(data2))], [i for i in range(0, len(data1))][::-1])) y = np.hstack((data2, data1[::-1])) source = ColumnDataSource(dict(x=x, y=y)) p1.line([i for i in range(0, len(data1) + 1)], data1, legend="Training kernel size 3x3", line_width=2, color='forestgreen', line_dash='dashed') p1.line([i for i in range(0, len(data2) + 1)], data2, legend="Validation kernel size 3x3", line_width=2, color='forestgreen') glyph = Patch(x='x', y='y', fill_color="forestgreen", fill_alpha=0.1, line_alpha=0) p1.add_glyph(source, glyph) data3 = [0] + models['305']['train_accuracy'] data4 = [0] + models['305']['val_accuracy'] x = np.hstack(([i for i in range(0, len(data4))], [i for i in range(0, len(data3))][::-1])) y = np.hstack((data4, data3[::-1])) source = ColumnDataSource(dict(x=x, y=y)) p1.line([i for i in range(0, len(data3) + 1)], data3, legend="Training kernel size 8x8", line_width=2, color='salmon', line_dash='dashed') p1.line([i for i in range(0, len(data4) + 1)], data4, legend="Validation kernel size 8x8", line_width=2, color='salmon') glyph = Patch(x='x', y='y', fill_color="salmon", fill_alpha=0.3, line_alpha=0) p1.add_glyph(source, glyph) p1.xaxis.axis_label_text_font_size = '12pt' p1.yaxis.axis_label_text_font_size = '12pt' p1.legend.location = 'bottom_right'
source = ColumnDataSource(dict(x=x, y=y))
xdr = DataRange1d()
ydr = DataRange1d()
plot = Plot(title=None,
x_range=xdr,
y_range=ydr,
plot_width=300,
plot_height=300,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location=None)
glyph = Patch(x="x", y="y", fill_color="#a6cee3")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis()
plot.add_layout(yaxis, 'left')
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
curdoc().add_root(plot)
show(plot)
def plot(self,
x,
y,
marker=None,
color=None,
scale=['linear', 'linear'],
xlabel=None,
ylabel=None,
fig=None,
order=None,
data=None,
identify=None,
id_color='red',
label_points=False,
exclude=None,
**kwargs):
"""Plot parameter x versus parameter y
Parameters
----------
x: str
The name of the x axis parameter, e.g. 'SpT'
y: str
The name of the y axis parameter, e.g. 'Teff'
marker: str (optional)
The name of the method for the desired marker
color: str (optional)
The color to use for the points
scale: sequence
The (x,y) scale for the plot
xlabel: str
The label for the x-axis
ylable : str
The label for the y-axis
fig: bokeh.plotting.figure (optional)
The figure to plot on
order: int
The polynomial order to fit
data: dict
Additional data to add to the plot
identify: idx, str, sequence
Names of sources to highlight in the plot
id_color: str
The color of the identified points
label_points: bool
Print the name of the object next to the point
Returns
-------
bokeh.plotting.figure.Figure
The figure object
"""
# Grab the source and valid params
source = copy(self.source)
params = [k for k in source.column_names if not k.endswith('_unc')]
# Check if the x parameter is a color
if '-' in x and all([i in params for i in x.split('-')]):
colordata = self.get_data(x)[0]
if len(colordata) == 3:
_ = source.add(colordata[0], x)
_ = source.add(colordata[1], '{}_unc'.format(x))
params.append(x)
# Check if the y parameter is a color
if '-' in y and all([i in params for i in y.split('-')]):
colordata = self.get_data(y)[0]
if len(colordata) == 3:
_ = source.add(colordata[0], y)
_ = source.add(colordata[1], '{}_unc'.format(y))
params.append(y)
# Check the params are in the table
if x not in params:
raise Exception(
"'{}' is not a valid x parameter. Please choose from {}".
format(x, params))
if y not in params:
raise Exception(
"'{}' is not a valid y parameter. Please choose from {}".
format(y, params))
# Make the figure
if fig is None:
# Tooltip names can't have '.' or '-'
xname = source.add(source.data[x],
x.replace('.', '_').replace('-', '_'))
yname = source.add(source.data[y],
y.replace('.', '_').replace('-', '_'))
# Set up hover tool
tips = [('Name', '@name'), (x, '@{}'.format(xname)),
(y, '@{}'.format(yname))]
hover = HoverTool(tooltips=tips, names=['points'])
# Make the plot
TOOLS = ['pan', 'reset', 'box_zoom', 'save', hover]
title = '{} v {}'.format(x, y)
fig = figure(plot_width=800,
plot_height=500,
title=title,
y_axis_type=scale[1],
x_axis_type=scale[0],
tools=TOOLS)
# # Exclude sources
# if exclude is not None:
# exc_idx = [i for i, v in enumerate(source.data['name']) if v in exclude]
# patches = {x : [(i, np.nan) for i in exc_idx],
# y : [(i, np.nan) for i in exc_idx]}
#
# source.patch(patches)
# Get marker class
size = kwargs.get('size', 8)
kwargs['size'] = size
marker = getattr(fig, marker or self.marker)
color = color or self.color
marker(x,
y,
source=source,
color=color,
fill_alpha=0.7,
name='points',
**kwargs)
# Plot y errorbars
yval, yerr = source.data[y], source.data['{}_unc'.format(y)]
yval[yval == None] = np.nan
yerr[yerr == None] = np.nan
y_err_x = [(i, i) for i in source.data[x]]
y_err_y = [(i, j) for i, j in zip(yval - yerr, yval + yerr)]
fig.multi_line(y_err_x, y_err_y, color=color)
# Plot x errorbars
xval, xerr = source.data[x], source.data['{}_unc'.format(x)]
xval[xval == None] = np.nan
xerr[xerr == None] = np.nan
x_err_y = [(i, i) for i in source.data[y]]
x_err_x = [(i, j) for i, j in zip(xval - xerr, xval + xerr)]
fig.multi_line(x_err_x, x_err_y, color=color)
# Label points
if label_points:
labels = LabelSet(x=x,
y=y,
text='name',
level='glyph',
x_offset=5,
y_offset=5,
source=source,
render_mode='canvas')
fig.add_layout(labels)
# Fit polynomial
if isinstance(order, int):
# Only fit valid values
idx = [
n for n, (i, j) in enumerate(zip(xval, yval))
if not hasattr(i, 'mask') and not np.isnan(i)
and not hasattr(j, 'mask') and not np.isnan(j)
]
xd = np.array(xval, dtype=float)[idx]
yd = np.array(yval, dtype=float)[idx]
ye = np.array(yerr, dtype=float)[idx]
# Plot data
label = 'Order {} fit'.format(order)
xaxis = np.linspace(min(xd), max(xd), 100)
coeffs = None
# Fit the polynomial
try:
coeffs, cov = np.polyfit(x=xd,
y=yd,
deg=order,
w=1. / ye,
cov=True)
except Exception:
try:
coeffs, cov = np.polyfit(x=xd, y=yd, deg=order, cov=True)
except:
pass
# Plot the line
if coeffs is None or any([np.isnan(i) for i in coeffs]):
if self.verbose:
print(
"Could not fit that data with an order {} polynomial".
format(order))
else:
# Calculate values and 1-sigma
TT = np.vstack([xaxis**(order - i)
for i in range(order + 1)]).T
yaxis = np.dot(TT, coeffs)
C_yi = np.dot(TT, np.dot(cov, TT.T))
sig = np.sqrt(np.diag(C_yi))
# Plot the line and shaded error
fig.line(xaxis,
yaxis,
legend=label,
color=color,
line_alpha=0.3)
xpat = np.hstack((xaxis, xaxis[::-1]))
ypat = np.hstack((yaxis + sig, (yaxis - sig)[::-1]))
err_source = ColumnDataSource(dict(xaxis=xpat, yaxis=ypat))
glyph = Patch(x='xaxis',
y='yaxis',
fill_color=color,
line_color=None,
fill_alpha=0.1)
fig.add_glyph(err_source, glyph)
# Set axis labels
xunit = source.data[x].unit
yunit = source.data[y].unit
fig.xaxis.axis_label = '{}{}'.format(
x, ' [{}]'.format(xunit) if xunit else '')
fig.yaxis.axis_label = '{}{}'.format(
y, ' [{}]'.format(yunit) if yunit else '')
# Formatting
fig.legend.location = "top_right"
# Identify sources
if isinstance(identify, list):
id_cat = Catalog('Identified')
for obj_id in identify:
id_cat.add_SED(self.get_SED(obj_id))
fig = id_cat.plot(x,
y,
fig=fig,
size=size + 5,
marker='circle',
line_color=id_color,
fill_color=None,
line_width=2,
label_points=True)
del id_cat
return fig
# Add patches to figure
plot = figure( #title="Retaining Wall and Backfill Properties",
#title_location="above",
x_axis_label='x-axis',
y_axis_label='Height (m)',
plot_width=350,
plot_height=400,
y_range=(0, 1.3 * H),
x_range=(0, 0.829 * 1.3 * H),
toolbar_location=None,
background_fill_alpha=0.1)
plot.xaxis.visible = False
wall = Patch(x='x',
y='y',
fill_color='#DCDDDE',
line_color='black',
line_width=1.5)
plot.add_glyph(source_wall, wall)
earth = Patch(x='x',
y='y',
fill_color='#FFFF99',
line_color='black',
line_width=1.5)
plot.add_glyph(source_earth, earth)
plot.min_border_left = 50
water = Patch(x='x',
y='y',
fill_color='LightSeaGreen',
def get_map(zipcode, zdf):
import matplotlib
matplotlib.use('Agg')
# load required Python libraries
from uszipcode import SearchEngine
from bokeh.io import export_png, output_file, show
from bokeh.models import ColumnDataSource, GMapOptions, LinearColorMapper, ColorBar, NumeralTickFormatter
from bokeh.models.glyphs import Patch
from bokeh.plotting import gmap
from bokeh.embed import components
from bokeh.transform import linear_cmap
from bokeh.palettes import inferno
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from babel.numbers import format_currency
#WRITE NOTIFICATION TO LOG
print("Beginning Map...")
# ns/dsi8 - July 2019
# Project 5 Team: N.Scott, J.Huessy, E.Stokes
# Problem 4: Extracting Building Values from Zillow
# this function accepts input arguments for zipcode and a dataframe (Zillow Zestimate data)
# invoke SearchEngine for zipcode search
search = SearchEngine(simple_zipcode=False)
# pass a zipcode into search
zipcode_data = search.by_zipcode(zipcode)
# create a dictionary with retrieved details
zip_dict = zipcode_data.to_dict()
# get all longitudes, latitudes for specified zip
zip_poly = zip_dict['polygon']
# isolate all latitudes for specified zip
zip_poly_lat = [poly[1] for poly in zip_poly]
# isolate all longitudes for specified zip
zip_poly_lng = [poly[0] for poly in zip_poly]
# define zipcode border (latitude & longitude)
zip_dict_gmap_lat = zip_dict['lat']
zip_dict_gmap_lng = zip_dict['lng']
zip_dict_zipcode = zip_dict['zipcode']
zest_total = zdf['price'].sum()
zest_dollar = format_currency(zest_total, 'USD', locale='en_US')
# output filename includes zipcode
#filename2 = f'''ZIP_{zip_dict_zipcode}_gmap.html'''
# mapping options based on Bokeh documentation
# https://bokeh.pydata.org/en/latest/docs/reference/models/map_plots.html#bokeh.models.map_plots.GMapOptions
map_options = GMapOptions(lat=zip_dict_gmap_lat,
lng=zip_dict_gmap_lng,
map_type="roadmap",
zoom=11)
# For GMaps to function, Google requires you obtain and enable an API key:
# https://developers.google.com/maps/documentation/javascript/get-api-key
# Replace the value below with your personal API key:
# p = gmap("GOOGLE_API_KEY", map_options, title="Austin")
# map title includes zipcode and TOTAL Zillow "Zestimate" value for specified zipcode
p = gmap("", map_options)
#, title=f'''ZIPCODE: {zip_dict_zipcode} ----> Zestimate TOTAL: {zest_dollar}''')
# based on code from Boston DSI cohort - Jan 2019
source = ColumnDataSource(data=dict(lat=zip_poly_lat, lon=zip_poly_lng))
patch = Patch(x='lon', y='lat', fill_color="blue", fill_alpha=0.08)
p.add_glyph(source, patch)
#ADDING SCATTERPLOT TO MAP
#CREATE COLUMN DATA SOURCE FROM ZIPCODE DATAFRAME
x = zdf['long']
y = zdf['lat']
z = zdf['price']
source = ColumnDataSource(dict(x=x, y=y, z=z))
#CREATE COLOR MAPPER FROM PRICE COLUMN
mapper = linear_cmap(field_name='z',
palette=inferno(256),
low=min(z),
high=max(z))
#ADD CIRCLES AT EACH LAT/LONG PAIR COLOR-CODED BY PRICE
p.circle(x='x',
y='y',
line_color='black',
color=mapper,
fill_alpha=1,
size=5,
source=source)
#CURRENCY FORMAT FOR TICKS
formatter = NumeralTickFormatter(format='$0,0.00')
#ADD COLORBAR LEGEND
color_bar = ColorBar(color_mapper=mapper['transform'],
width=8,
formatter=formatter,
location=(0, 0))
p.add_layout(color_bar, 'right')
# CREATE HTML/JAVASCRIPT COMPONENTS OF BOKEH PLOT
script, div = components(p)
return script, div
d2 = summer_start + (summer_end - summer_start) / 2
d3 = summer_end + (calendar_end - summer_end) / 2
text_source = ColumnDataSource(
dict(
dates=[d1, d2, d3],
times=[dt.time(11, 30)] * 3,
texts=["CST (UTC+1)", "CEST (UTC+2)", "CST (UTC+1)"],
))
plot = Plot(plot_width=800, plot_height=400)
plot.title.text = "Daylight Hours 2013 - Warsaw, Poland"
plot.toolbar_location = None
plot.x_range.range_padding = 0
patch1 = Patch(x="dates", y="times", fill_color="#282e54")
plot.add_glyph(patch1_source, patch1)
patch2 = Patch(x="dates", y="times", fill_color="#ffdd91")
plot.add_glyph(patch2_source, patch2)
sunrise_line = Line(x="dates", y="sunrises", line_color="orange", line_width=4)
sunrise_line_renderer = plot.add_glyph(source, sunrise_line)
sunset_line = Line(x="dates", y="sunsets", line_color="crimson", line_width=4)
sunset_line_renderer = plot.add_glyph(source, sunset_line)
text = Text(x="dates",
y="times",
text="texts",
text_align="center",
def update_plot(self):
today = Timestamp.today().strftime('%Y-%m-%d')
today_date = to_datetime(today)
df = self.counts_data
weights = self.similarities_data.xs(self.school, level='school_name', axis=1)
df_all = df[df['school_name'].eq(self.school)]
present_academic_year = df['academic_year'].unique()[0]
tooltips = '''
<div style="font-size: 12px;">
<span style="font-weight: bold;">@school_name</span> (@school_type, @school_age years old)<br>
Grade @grade enrollment, as of @reference_date_string<br>
<br>
<span style="font-weight: bold;">Attrition</span>: @exit_count_present_cumm (@exit_percent_present_cumm{'0.0%'} change from October 1)<br>
<span style="font-weight: bold;">Backfill</span>: @enter_count_present_cumm (@enter_percent_present_cumm{'0.0%'} change from October 1)<br>
<span style="font-weight: bold;">Current total enrollment</span>: @total_enrollment
</div>
'''
ticker_ref = DataFrame({'month': date_range('2014-10-01', '2015-06-01', freq='MS')})
ticker_ref['marker'] = ticker_ref['month']
ticker_ref['month'] = ticker_ref['month'].apply(lambda x: x.strftime('%B'))
ticker_ref = ticker_ref.set_index('marker')['month'].to_dict()
x_range1d = Range1d(-0.05, 290.0)
y_index = max(
-df_all['exit_percent_present_cumm'].min(),
df_all['enter_percent_present_cumm'].max(),
-df_all['exit_percent_past_cumm'].min(),
df_all['enter_percent_past_cumm'].max()
)
y_range1d = Range1d(-y_index - 0.005, y_index + 0.005)
plots = []
focus_cols = ['enter_percent_present_cumm', 'exit_percent_present_cumm']
unique_grades = sorted(df_all['grade'].unique().astype('int'))
for grade in unique_grades:
grade_weight = weights[grade]
benchmark_df = DataFrame(columns=focus_cols)
df_copy = df[df['academic_year'].eq(present_academic_year)].copy().set_index(['school_name', 'grade'])
for rid, label in sorted(ticker_ref.items(), reverse=True):
df_copy = df_copy[df_copy['reference_date'].le(rid)]
df_ave = df_copy.groupby(level=['school_name', 'grade'])[focus_cols].tail(1).\
mul(grade_weight, axis=0).sum() / grade_weight.sum()
df_ave.name = label
benchmark_df = benchmark_df.append(df_ave)
benchmark_df = benchmark_df.reset_index()
benchmark_df['reference_date'] = benchmark_df['index'].map({y: x for x, y in ticker_ref.items()})
benchmark_df['reference_id'] = benchmark_df['reference_date'].apply(lambda x: x.toordinal()) - \
df_all['reference_date'].min().toordinal()
source_df = df_all[df_all['grade'].eq(grade)]
source_df_rev = source_df.sort_values('reference_id', ascending=False)
source_df_trunc = source_df.loc[source_df['reference_date'].le(today_date), :]
source = ColumnDataSource(source_df)
source_trunc = ColumnDataSource(source_df_trunc)
patch_source = ColumnDataSource(dict(
x_past=source_df['reference_id'].tolist() + source_df_rev['reference_id'].tolist(),
y_past=source_df['enter_percent_past_cumm'].tolist() + source_df_rev['exit_percent_past_cumm'].tolist()
))
plot1 = Plot(
x_range=x_range1d,
y_range=y_range1d,
min_border_bottom=5,
min_border_top=10,
min_border_right=10,
plot_width=700,
plot_height=150,
title=None,
title_text_font_size='0pt',
title_text_color='grey',
outline_line_alpha=0.0)
plot1.add_layout(
LinearAxis(
axis_label='Grade ' + str(grade), axis_label_text_font_size='9pt', minor_tick_line_alpha=0.0,
axis_label_text_color='grey',
axis_line_alpha=0.1, major_tick_line_alpha=0.1, major_label_text_color='grey',
major_label_text_font_size='7pt', formatter=NumeralTickFormatter(format='0%')
), 'left')
patch = Patch(x='x_past', y='y_past', fill_color='#AFAFAD', fill_alpha=0.25, line_alpha=0.0)
plot1.add_glyph(patch_source, patch)
line1 = Line(
x='reference_id', y='enter_percent_present_cumm', line_width=2, line_color='#f7910b', line_alpha=1.0)
plot1.add_glyph(source_trunc, line1)
line2 = Line(
x='reference_id', y='exit_percent_present_cumm', line_width=2, line_color='#f7910b', line_alpha=1.0)
plot1.add_glyph(source_trunc, line2)
line_h = Line(x='reference_id', y=0, line_width=1, line_color='black', line_alpha=0.1)
line_renderer = GlyphRenderer(data_source=source, glyph=line_h, name='line')
plot1.add_glyph(source, line_h)
for ind, series in benchmark_df.iterrows():
x = series['reference_id']
y_enter = series['enter_percent_present_cumm']
y_exit = series['exit_percent_present_cumm']
label = series['index']
line = Segment(x0=x, x1=x, y0=-y_index, y1=y_index, line_width=1, line_color='#165788', line_alpha=0.1)
plot1.add_glyph(line)
linec1 = Segment(
x0=x - 3, x1=x + 3, y0=y_enter, y1=y_enter, line_width=1, line_color='#ed2939', line_alpha=1.0)
plot1.add_glyph(linec1)
linec2 = Segment(
x0=x - 3, x1=x + 3, y0=y_exit, y1=y_exit, line_width=1, line_color='#ed2939', line_alpha=1.0)
plot1.add_glyph(linec2)
text = Text(x=x+3, y=-y_index, text=[label], text_font_size='8pt', text_color='grey', text_alpha=0.5)
plot1.add_glyph(text)
hover_tool = HoverTool(
plot=plot1, renderers=[line_renderer], tooltips=tooltips, always_active=False, mode='vline',
point_policy='follow_mouse', line_policy='prev')
crosshair_tool = CrosshairTool(plot=plot1, dimensions=['height'])
zoom_tool = BoxZoomTool(plot=plot1, dimensions=['width'])
reset_tool = ResetTool(plot=plot1)
save_tool = PreviewSaveTool(plot=plot1)
pan_tool = PanTool(plot=plot1, dimensions=['width'])
help_tool = HelpTool(plot=plot1, help_tooltip='App help page', redirect='http://data.successacademies.org/blog/')
plot1.tools.extend([hover_tool, zoom_tool, pan_tool, reset_tool, save_tool, help_tool, crosshair_tool])
plot1.renderers.extend([line_renderer])
plots.append([plot1])
self.plot.children = plots
