def volplot_2(df1):
#Data for volcano plot 2:
df1=df1.dropna(how='any')
df1 = df1[df1.Fold_change != 0] #remove rows where fold change is 0
df1["Log_Fold_change"]=np.log2(df1["Fold_change"])
df1["Log_p_value"]=-np.log10(df1["p_value"])
#Volcano plot 2:
source=ColumnDataSource(df1)
vol_hover=HoverTool(tooltips=[('Kinase','@Kinase'), ('Substrate', '@Substrate'),
('Modified Residue','@Phosphosite'), ('Fold Change','@Fold_change'), ('p-value', '@p_value')])
p2 = figure(plot_width=700, plot_height=500)
p2.title.text="Volcano Plot of the Log Fold Change and Log p-value for All Identified Kinases"
p2.title.text_font_size = "15px"
p2.xaxis.axis_label ="Log Fold Change"
p2.yaxis.axis_label ="-Log p-value"
p2.scatter(x='Log_Fold_change', y='Log_p_value', source=source)
p2.add_tools(vol_hover)
#Significance thresholds:
sig5=Span(location=1.3, dimension='width', line_color='#800000', line_width=1.75, line_dash='dashed') #5%
sig1=Span(location=2, dimension='width', line_color='#2F4F4F', line_width=1.75, line_dash='dashed') #1%
toggle1=Toggle(label='1% Significance', button_type="success", active=True)
toggle1.js_link('active', sig1, 'visible')
toggle2=Toggle(label='5% Significance', button_type="success", active=True)
toggle2.js_link('active', sig5, 'visible')
p2.add_layout(sig1) #adds horizontal line where points below line are non-sig fold changes(-log(0.05)=1.3)
p2.add_layout(sig5)
plot3=layout([p2], [toggle1, toggle2])
return plot3
def toggle(filter_col, filter):
title = re.sub(r'\s*[(]F[0-9]+[)]\s*', '', filter_col)
class_select = title.replace(" ", "")
title = title.upper()
select_toggle = Toggle(
label=title,
width=175,
name=class_select,
active=True
)
select_toggle.js_link('active', filter, 'visible')
return select_toggle
hover = HoverTool(tooltips=[("Ice Temp (C)","@temp_list2"),("Bacterial Population", "$x"),("Viral Population", "$y"), ("Lytic %", "@lytic")])
mapper = LinearColorMapper(palette=viridis(256), low=min(temp_list2), high=max(temp_list2))
p = figure(plot_height=500, plot_width=500, x_axis_type = 'log', y_axis_type = 'log',x_range = (1e4, 1e8), y_range = (1e5,1e10), tools = [hover])
p.xaxis.axis_label = 'Bacteria per mL'
p.yaxis.axis_label = 'Virus per mL'
# plot the ratio lines
Ratiolines = np.linspace(1e4, 1e8,1000)
Lines = p.line(Ratiolines, 1*Ratiolines)
Lines2 = p.line(Ratiolines, 10*Ratiolines)
Lines3 = p.line(Ratiolines, 100*Ratiolines)
Lines4 =p.line(Ratiolines, 1000*Ratiolines)
Lines5= p.line(Ratiolines, 10000*Ratiolines)
toggle2 = Toggle(label="Ratio Lines", button_type="success", active=True)
toggle2.js_link('active', Lines , 'visible')
toggle2.js_link('active', Lines2 , 'visible')
toggle2.js_link('active', Lines3 , 'visible')
toggle2.js_link('active', Lines4 , 'visible')
toggle2.js_link('active', Lines5 , 'visible')
#plot the data
p.circle('x', 'y', fill_color= transform('temp_list2', mapper), size = 'size_list', fill_alpha=0.6, line_color=None, source = source)
layout = row(
p, toggle2
)
show(layout)
right=box_right,
line_width=1,
line_color="black",
line_dash="dashed",
fill_alpha=0.2,
fill_color="orange",
name="Lockdown Introduction",
)
fig_london_mob.add_layout(box_london_mob)
fig_london_mob.xaxis.major_tick_line_color = "firebrick"
fig_london_mob.xaxis.major_tick_line_width = 1
fig_london_mob.xaxis.minor_tick_line_color = "black"
toggle_london_mob = Toggle(label="Show National Lockdown",
button_type="success",
active=True)
toggle_london_mob.js_link("active", box_london_mob, "visible")
## Set up the figure for UK mobility
fig_uk_mob = figure(
title="COVID-19 Mobility Variables for UK",
plot_height=550,
plot_width=900,
x_axis_label="Date",
y_axis_label="Change from the baseline in %",
x_minor_ticks=2,
y_range=(-150, 170),
toolbar_location=None,
x_axis_type="datetime",
)
# Connect to and draw the data
for c in df_uk_mobility.iloc[:, 1:]:
from bokeh.io import output_file, show
from bokeh.layouts import layout
from bokeh.models import BoxAnnotation, Toggle
from bokeh.plotting import figure
output_file("styling_visible_annotation_with_interaction.html")
p = figure(width=600, height=200, tools='')
p.line([1, 2, 3], [1, 2, 1], line_color="#0072B2")
pink_line = p.line([1, 2, 3], [2, 1, 2], line_color="#CC79A7")
green_box = BoxAnnotation(left=1.5,
right=2.5,
fill_color='#009E73',
fill_alpha=0.1)
p.add_layout(green_box)
# Use js_link to connect button active property to glyph visible property
toggle1 = Toggle(label="Green Box", button_type="success", active=True)
toggle1.js_link('active', green_box, 'visible')
toggle2 = Toggle(label="Pink Line", button_type="success", active=True)
toggle2.js_link('active', pink_line, 'visible')
show(layout([p], [toggle1, toggle2]))
def showGUI(self, pth_to_img, y_form, pred):
'''
Method builds the bokeh GUI
Parameters
----------
pth_to_img: path to ultrasound image
y_form: true form of the lesion
pred: predicted form the lesion
'''
##############
#Set up a figure
##############
p = figure(x_range=(0, self.DIMS[0]),
y_range=(0, self.DIMS[1]),
tools=self._tools_to_show,
plot_width=self.DIMS[0],
plot_height=self.DIMS[1],
toolbar_location="above")
#Add image as background
p.image_url(url=[self.root_pth + pth_to_img],
x=431,
y=302,
w=862,
h=604,
anchor="center")
#Nicier plot
self._makeShiny(plot=p)
##############
#Add lines and plot them
##############
src_true, src_pred = self._getData()
self._plotLines(plot=p, src_true=src_true, src_pred=src_pred)
##############
#Add table
##############
table = self._addTable(src_pred=src_pred)
##############
#Add polygons
##############
true_pol, c_t = self._addLesionForm(form=y_form, color='red', plot=p)
pred_pol, c_p = self._addLesionForm(form=pred, color='blue', plot=p)
#Add toggles for polygons
toggle_true = Toggle(label="Show true form",
button_type="primary",
active=True)
toggle_true.js_link('active', true_pol, 'visible')
toggle_true.js_link('active', c_t, 'visible')
toggle_pred = Toggle(label="Show predicted form",
button_type="primary",
active=True)
toggle_pred.js_link('active', pred_pol, 'visible')
toggle_true.js_link('active', c_p, 'visible')
##############
#Add download button
##############
button_csv = Button(label="Download", button_type="primary")
button_csv.callback = CustomJS(args=dict(source=src_pred),
code=open(self.root_pth +
"download.js").read())
##############
#Add title div
##############
div_title = Div(text="""<div> <b>LESION ADJUSTER</b> </div>""",
align='center',
style={
'font-size': '150%',
'color': '#1f77b4'
})
##############
#Add description to the buttons
##############
div_desc = Div(text="""<div> <b>CONTROLS</b> </div>""",
align='center',
style={
'font-size': '110%',
'color': '#1f77b4'
})
##############
#Add Div to show euclidean distance and button to recalculate it
##############
div_euclid = Div(text="""
<b>Diameter of predicted form is:</b> 334.80 <br>
<b>Diameter of true form is:</b> 368.64 <br>
<b>RMSE is:</b> 34.13
""",
align='center',
style={'font-size': '100%'})
p.js_on_event(
events.MouseMove,
CustomJS(args=dict(div=div_euclid,
source_data_pred=src_pred,
source_data_true=src_true),
code="""
var data_p = source_data_pred.data;
var data_t = source_data_true.data;
var x_p = data_p['x']
var y_p = data_p['y']
var x_t = data_t['x']
var y_t = data_t['y']
var diam_p = 0
var diam_t = 0
var rmse = 0
//Diameter of pred form
diam_p = Math.sqrt(Math.pow((x_p[0]-x_p[1]),2) + Math.pow((y_p[0]-y_p[1]),2))
//Diameter of true form
diam_t = Math.sqrt(Math.pow((x_t[0]-x_t[1]),2) + Math.pow((y_t[0]-y_t[1]),2))
//RMSE
rmse = Math.sqrt(Math.pow(diam_p - diam_t,2)/1)
//Result
div.text = "<b>Diameter of predicted form is: </b>" + diam_p.toFixed(2) + "<br> <b>Diameter of true form is: </b>" + diam_t.toFixed(2) + " <br> <b>RMSE is: </b>" + rmse.toFixed(2);
"""))
##############
#Show
##############
show(
Column(
div_title,
Row(
Column(p, table),
Column(div_desc, toggle_true, toggle_pred, button_csv,
div_euclid))))
class Carte_tronçons(Fond_de_carte):
"""Carte de tronçons de lignes.
Arguments:
tronçons: GeoDataFrame, table des tronçons
Attributs:
tron: table des tronçons avec habillage
tron_idx_name: le nom de l'index des tronçons transformé en colonne dans `tron`
"""
titre_ref = "carte_tronçons"
titre_tron = "Tronçons"
# coordonnées arbitraires de la carte pour créer des objets de la légende
x_legende, y_legende = [277409, 277409], [6248780, 6248780]
tab_width = 500
def __init__(self, tronçons: DataFrame, **kwargs) -> None:
self.tron = tronçons
self.tron["line_width"] = self.line_width
self.tron["line_color"] = self.line_color
self.tron.reset_index(inplace=True)
self.tron_idx_name = self.tron.columns[0]
self.cols = self.tron.columns.drop(
["geometry", "line_width", "line_color"], errors="ignore")
super().__init__(**kwargs)
@property
def cols_tron(self) -> list[TableColumn]:
return [TableColumn(field=c, title=c) for c in self.cols]
@property
def line_width(self) -> Series:
"""Épaisseur de l'affichage des tronçons.
Doit renvoyer une série alignable avec l'argument tronçons
et peut y faire référence via self.tron.
"""
return 2
@property
def line_color(self) -> Series:
"""Couleur de l'affichage des tronçons.
Doit renvoyer une série alignable avec l'argument tronçons
et peut y faire référence via self.tron.
"""
return "blue"
@property
def tooltips(self) -> list[tuple[str, str]]:
return [(c, f"@{c}") for c in self.cols]
@property
def texte_aide(self) -> str:
return """<b>Mode d'emploi</b>
<p>La sélection d'une ou plusieurs lignes est possible directement sur la carte (shift + clic) ou dans la table (shift/ctrl + clic).</p>
<p>On peut aussi sélectionner une ligne en indiquant son numéro de ligne et de rang dans l'entrée texte située en haut à droite.
Le bouton "Affiche les extrémités" permet de rendre visibles ou non les extrémités des lignes sélectionnées.</p>
<p>En cas de problème, utiliser l'outil reset sur la droite de la carte.</p>
"""
def ajoute_table_tron(self) -> None:
view = CDSView(source=self.source_lines, filters=[self.filter])
self.table_tron = DataTable(
source=self.source_lines,
view=view,
columns=self.cols_tron,
autosize_mode="none",
sizing_mode="stretch_height",
width=self.tab_width,
height=200,
)
def ajoute_toggle_extrémités(self) -> None:
size = 4
fill_color = "DarkSlateGray"
self.g = (self.tron.set_index(
self.tron_idx_name).geometry.boundary.dropna().explode().droplevel(
1).rename("geometry").reset_index().reset_index())
idx_g = self.g.columns[0] # colonne qui contient le numéro de ligne
self.src_extr = GeoJSONDataSource(geojson=self.g.to_json())
self.filter_extr = IndexFilter(list(range(self.g.shape[0])))
self.index_extrémités_par_tron = (
self.tron.reset_index() # numéro de ligne dans la colonne idx_g
.merge(
self.g, on=self.tron_idx_name
) # inner join donc tous les tronçons non localisés n'y sont pas
.groupby(f"{idx_g}_x").apply(
lambda s: list(s[f"{idx_g}_y"])).to_dict())
view = CDSView(source=self.src_extr, filters=[self.filter_extr])
self.extr_renderer = self.p.circle(
x="x",
y="y",
size=size,
fill_color=fill_color,
line_color=fill_color,
source=self.src_extr,
visible=False,
view=view,
)
self.toggle_extr = Toggle(label="Affiche les extrémités",
button_type="success",
width=100)
self.toggle_extr.js_link("active", self.extr_renderer, "visible")
def ajoute_lignes(self) -> None:
self.p.multi_line(
xs="xs",
ys="ys",
line_color="line_color",
line_width="line_width",
source=self.source_lines,
name="tronçons",
)
@property
def callback_selected(self) -> CustomJS:
return CustomJS(
args=dict(
src_lines=self.source_lines,
src_extr=self.src_extr,
filter=self.filter,
filter_extr=self.filter_extr,
index_extr_dict=self.index_extrémités_par_tron,
),
code="""var sel = src_lines.selected.indices;
if (sel.length == 0) {
sel = [...Array(src_lines.length).keys()];
var sel2 = [...Array(src_extr.length).keys()];
} else {
var sel2 = sel.flatMap(el => index_extr_dict[el]);
}
filter.indices = sel;
filter_extr.indices = sel2;
src_lines.change.emit();
src_extr.change.emit();
""",
)
def ajoute_input_num(self,
title: str = None,
groupby: str = None,
max_width=80) -> None:
"""Sélection du tronçon par une colonne de la table."""
if groupby is None:
groupby = self.tron_idx_name
if title is None:
title = self.tron_idx_name
index_par_tron = DataFrame(self.tron).groupby(groupby).apply(
lambda s: list(s.index)).to_dict()
self.input_num = TextInput(value="", title=title, max_width=max_width)
callback_text = CustomJS(
args=dict(
text=self.input_num,
src_lines=self.source_lines,
index_par_tron=index_par_tron,
),
code="""if (text.value in index_par_tron) {
src_lines.selected.indices = index_par_tron[text.value]
src_lines.change.emit();
}
""",
)
self.input_num.js_on_change("value", callback_text)
def ajoute_légende(self) -> None:
pass
@property
def titre_tron_div(self) -> Div:
return Div(text=f"<b>{self.titre_tron}</b>")
def init_layout(self) -> None:
super().init_layout()
geojson = self.tron.to_json().replace(
"null", '{"type":"Point","coordinates":[]}')
self.source_lines = GeoJSONDataSource(geojson=geojson)
self.filter = IndexFilter(list(range(self.tron.shape[0])))
self.ajoute_lignes()
self.ajoute_toggle_extrémités()
self.source_lines.selected.js_on_change("indices",
self.callback_selected)
self.ajoute_table_tron()
self.ajoute_input_num()
self.hover_tool = self.p.select(type=HoverTool)
self.hover_tool.names = ["tronçons"]
self.ajoute_légende()
self.première_ligne = row(self.input_num, self.toggle_extr)
def cstr_layout(self) -> Box:
return row(
self.p,
column(
self.première_ligne,
self.titre_tron_div,
self.table_tron,
self.questions,
self.aide,
),
)
#Redimensionnement des tableaux
Xi = np.array(xi).reshape(-1, 1)
Yi = np.array(yi).reshape(-1, 1)
# Création du model de regression linéaire
regression = linear_model.LinearRegression()
#Entrainement du model sur les données Xi et Yi
regression.fit(Xi, Yi)
#Utilisation du model pour une prédiction sur les données Xi
pred = regression.predict(Xi)
#Nuage de points et droite de regression linéaire
nuage = figure(
plot_width=500,
plot_height=300,
title="Droite de regression linéaire et corrélation entre Xi et Yi")
nuage.xaxis.axis_label = "Xi"
nuage.yaxis.axis_label = "Yi"
nuage.scatter(xi, yi, size=10, color="blue")
droite = nuage.line(xi, pred.flatten(), line_color="#FC0A05")
#Bouton
bouton = Toggle(label="Masquer / Afficher la droite de regression linéaire",
button_type="success",
active=True)
bouton.js_link("active", droite, "visible")
show(layout([nuage], [bouton]))
def JSandDivDoublePlotWithToggle(x_axis,
y_axisRaw,
y_axisBin,
change_points=[],
title="",
TOOLTIPS=[],
transList=[],
alarmList=[],
abnormalList=[]):
if TOOLTIPS:
plot = figure(tooltips=TOOLTIPS,
title=title,
x_axis_label='Timestamp',
y_axis_label='Flux',
sizing_mode="stretch_both")
else:
TOOLTIPS = [
("index", "$index"),
("(x,y)", "(@x{0,0}, @y{0,0.000}"),
]
plot = figure(tooltips=TOOLTIPS,
title=title,
x_axis_label='Timestamp',
y_axis_label='Flux',
sizing_mode="stretch_both")
rawSource = ColumnDataSource(data=dict(x=x_axis, y=y_axisRaw))
rawLine = plot.line('x',
'y',
source=rawSource,
line_alpha=0.4,
color='black',
line_width=2,
legend_label="Raw")
rawCircle = plot.circle(x_axis,
y_axisRaw,
color="black",
fill_alpha=0.4,
legend_label="Raw",
size=2)
toggle1 = Toggle(label="Raw Data", button_type="success", active=True)
toggle1.js_link('active', rawLine, 'visible')
toggle1.js_link('active', rawCircle, 'visible')
binSource = ColumnDataSource(data=dict(x=x_axis, y=y_axisBin))
binLine = plot.line('x',
'y',
source=binSource,
color='red',
line_width=4,
legend_label="DyBin")
toggle2 = Toggle(label="Dynamic bin", button_type="success", active=True)
toggle2.js_link('active', binLine, 'visible')
if change_points:
for change_point in change_points:
change_point_span = Span(location=float(change_point),
dimension='height',
line_color='#009E73',
line_dash='dashed',
line_width=1)
plot.add_layout(change_point_span)
toggle3 = Toggle(label="AnswerTran", button_type="success", active=True)
if transList:
for trans in transList:
start_point = int(trans[0])
end_point = int(trans[1])
tran_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
L_span = Span(location=x_axis[start_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
R_span = Span(location=x_axis[end_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(tran_box)
plot.add_layout(L_span)
plot.add_layout(R_span)
toggle3.js_link('active', tran_box, 'visible')
toggle3.js_link('active', L_span, 'visible')
toggle3.js_link('active', R_span, 'visible')
toggle4 = Toggle(label="Alarm", button_type="success", active=True)
if alarmList:
for alarm in alarmList:
# alarm : start,end,alarm
start_point = int(alarm['start'])
end_point = int(alarm['end'])
alarm_point = int(alarm['alarm'])
alarm_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
alarm_span = Span(location=x_axis[alarm_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(alarm_box)
plot.add_layout(alarm_span)
toggle4.js_link('active', alarm_box, 'visible')
toggle4.js_link('active', alarm_span, 'visible')
toggle5 = Toggle(label="F-test", button_type="success", active=True)
if abnormalList:
for abnormal in abnormalList:
start_point = int(abnormal[0])
end_point = int(abnormal[1])
abnormal_box = BoxAnnotation(left=x_axis[start_point],
right=x_axis[end_point],
fill_alpha=0.2,
fill_color="#F0E442")
L_abnormal_span = Span(location=x_axis[start_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
R_abnormal_span = Span(location=x_axis[end_point],
dimension='height',
line_color='#a2ff00',
line_dash='4 4',
line_width=2)
plot.add_layout(abnormal_box)
plot.add_layout(L_abnormal_span)
plot.add_layout(R_abnormal_span)
toggle5.js_link('active', abnormal_box, 'visible')
toggle5.js_link('active', L_abnormal_span, 'visible')
toggle5.js_link('active', R_abnormal_span, 'visible')
plot.legend.border_line_width = 3
plot.legend.click_policy = "hide"
plot.legend.border_line_color = "navy"
plot.legend.border_line_alpha = 0.
plot.xaxis.axis_label_text_font_size = "16pt"
plot.xaxis.major_label_text_font_size = "13pt"
plot.yaxis.axis_label_text_font_size = "16pt"
plot.yaxis.major_label_text_font_size = "13pt"
drawSource = ColumnDataSource(data=dict(x=[], y=[]))
r = plot.multi_line('xs', 'ys', source=drawSource)
tool = FreehandDrawTool(renderers=[r])
plot.add_tools(tool)
js, div = components(
layout([plot],
row(toggle1,
toggle2,
toggle3,
toggle4,
toggle5,
sizing_mode='scale_width'),
sizing_mode="stretch_both"))
return js, div
# linear regression object
regr = linear_model.LinearRegression()
# fit linear model
regr.fit(X, Y)
# make predictions
pred = regr.predict(X)
# plot with regression line
regr_plot = figure(plot_width=500, plot_height=300)
regr_plot.scatter(x, y, size=10)
regr_line = regr_plot.line(x, pred.flatten(), line_color='red')
toggle_button = Toggle(label='line of best fit', button_type='success', active=True)
toggle_button.js_link('active', regr_line, 'visible')
show(layout([regr_plot], [toggle_button]))
Loading BokehJS ...
In [87]:
# slider.js_link?
Interactive Widgets with ipywidgets
In [88]:
seattle_weather['year'] = pd.DatetimeIndex(seattle_weather['date']).year
seattle_weather.tail()
Out[88]:
date precipitation temp_max temp_min wind weather year
1456 2015-12-27 8.6 4.4 1.7 2.9 fog 2015
1457 2015-12-28 1.5 5.0 1.7 1.3 fog 2015
1458 2015-12-29 0.0 7.2 0.6 2.6 fog 2015
1459 2015-12-30 0.0 5.6 -1.0 3.4 sun 2015
line_color=mapper_WFH,
color=mapper_WFH,
alpha=0.5,
source=source
)
Graph_COVIDlog = p_WFH.line(
x='conf1_date',
y='conf1_num',
color='#FC4F4F',
alpha=0.5,
source=source1
)
toggle1 = Toggle(label="코로나 상황", button_type="warning", active=True)
toggle1.js_link('active', Graph_COVIDlog, 'visible')
# ================== Work From Home finish ==========================
# ==================== Restaurant ===================================
Restaurant_month = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
Restaurant_sales = [13.2, 12.3, 11.3, 13.4, 16.2, 14.2]
p_Restaurant = figure(
x_range=Restaurant_month,
max_width=500,
plot_height=250,
toolbar_location=None,
tools=''
)
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.layouts import layout
from bokeh.models import Toggle, BoxAnnotation
output_file("styling_visible_annotation_with_interaction.html")
p = figure(plot_width=600, plot_height=200, tools='')
p.line([1, 2, 3], [1, 2, 1], line_color="blue")
pink_line = p.line([1, 2, 3], [2, 1, 2], line_color="pink")
green_box = BoxAnnotation(left=1.5, right=2.5, fill_color='green', fill_alpha=0.1)
p.add_layout(green_box)
# Use js_link to connect button active property to glyph visble property
toggle1 = Toggle(label="Green Box", button_type="success", active=True)
toggle1.js_link('active', green_box, 'visible')
toggle2 = Toggle(label="Pink Line", button_type="success", active=True)
toggle2.js_link('active', pink_line, 'visible')
show(layout([p], [toggle1, toggle2]))
