def get_time_insights(df):
pop_cust_hour, pop_subs_hour, pop_hour = {}, {}, {}
for day in bs.DAYS:
dayframe = df[df['day_of_week'] == day]
# pop_hour[day] = dayframe['hour'].mode()[0]
cust_frame = dayframe[dayframe['User Type'] == 'Customer']
pop_cust_hour[day] = cust_frame['hour'].mode()[0]
subs_frame = dayframe[dayframe['User Type'] == 'Subscriber']
pop_subs_hour[day] = subs_frame['hour'].mode()[0]
traces = [
go.Line(x=list(pop_cust_hour.keys()),
y=list(pop_cust_hour.values()),
name='Customer',
marker=go.scatter.Marker(color='rgb(55, 83, 109)')),
go.Line(x=list(pop_subs_hour.keys()),
y=list(pop_subs_hour.values()),
name='Subscriber',
marker=go.scatter.Marker(color='rgb(26, 118, 255)'))
]
return traces
def GetTrace(datalist):
tracelist = []
for data in datalist:
trace = go.Bar(
x=data["x"],
y=data["y"],
width=0.03
#mode="lines",
# line=dict(
# color="#feb4d1"
# ),
)
tracelist.append(trace)
sinData = SinData()
trace = go.Scatter(x=sinData['x'],
y=sinData['y'],
line=go.Line(color='#eeeeee'))
tracelist.append(trace)
osinData = oSinData()
trace = go.Scatter(x=osinData['x'],
y=osinData['y'],
line=go.Line(color='#eeeeee'))
tracelist.append(trace)
return tracelist
def update_graph(input_value, c1, c2, yr, mon):
data = []
df_industry = df_eda[df_eda['Industry'] == input_value]
df_industry = df_industry[df_industry['Year'] == yr]
df_industry = df_industry[df_industry['Month_y.1'] == mon]
trace = go.Line(x=df_industry['Date_New_quarterly'].values,
y=df_industry['marketcap'].values,
text=df_industry['Industry'],
mode='line',
name="Market Cap",
line_color='rgb(231,107,243)',
marker=dict(color=c1))
data1 = go.Line(x=df_industry['Date_New_quarterly'],
y=df_industry.groupby(['Industry', 'Date_New_quarterly'
])['marketcap'].sum(),
mode="line",
text=df_industry['Industry'],
name="Total Market Cap",
line_color='rgb(0,176,246)',
marker=dict(color=c2))
data.append(trace)
data.append(data1)
layout = dict(xaxis={'title': "Date"},
yaxis={'title': "Market cap & Total Market cap"},
margin={
'l': 40,
'b': 40,
't': 10,
'r': 0
},
hovermode='closest')
return {"data": data, "layout": layout}
def populate_trace_for_extend(x=None,
y=None,
graph_legend_item_name='',
trace_unique_id='',
trace_unique_id_pattern=None,
shape_type=''):
#series list must be completely filled in using graph create order
#'text' param if added generates error
if not trace_unique_id_pattern:
trace_unique_id_pattern = []
if not x:
x = []
trace_pos = trace_unique_id_pattern.index(trace_unique_id)
trace_list = []
for i in range(len(trace_unique_id_pattern)):
if i == trace_pos:
trace_extend = graph_objs.Scatter(
x=x,
y=y,
name=graph_legend_item_name,
line=graph_objs.Line(shape=shape_type))
trace_list.append(trace_extend)
else:
trace_empty = graph_objs.Scatter(
x=[], y=[], line=graph_objs.Line(shape=shape_type))
trace_list.append(trace_empty)
L.l.debug('Extending graph serie {} {}'.format(graph_legend_item_name,
trace_unique_id))
return trace_list
def update_graph(type):
if type == 'All':
mask = data.groupby('Date')['Deaths'].max().sort_values(
ascending=True).reset_index()
return {
'data': [go.Line(x=mask['Date'], y=mask['Deaths'])],
'layout':
go.Layout(title='Number of Deaths Per Day',
xaxis={'title': 'Dates'},
yaxis={'title': 'Number of Deaths'},
paper_bgcolor='rgba(0,0,0,1)',
plot_bgcolor='rgba(0,0,0,1)')
}
else:
mask = data[data['Country/Region'] == type]
masks = mask.groupby('Date')['Deaths'].max().sort_values(
ascending=True).reset_index()
return {
'data': [go.Line(x=masks['Date'], y=masks['Deaths'])],
'layout':
go.Layout(title='Number of Deaths Per Day',
xaxis={'title': 'Dates'},
yaxis={'title': 'Number of Deaths'},
paper_bgcolor='rgba(0,0,0,1)',
plot_bgcolor='rgba(0,0,0,1)')
}
def draw_graph(G, stations):
node_trace = go.Scatter(
x=[],
y=[],
text=[],
mode='markers+text',
textposition='top center',
textfont=dict(
family='arial',
size=3,
color='rgb(0,0,0)'
),
hoverinfo='none',
marker=go.Marker(
showscale=False,
color='rgb(200,0,0)',
opacity=0.3,
size=8,
line=go.Line(width=1, color='rgb(0,0,0)')))
node_positions = {}
for id in stations.index:
lat, lng = stations.loc[id, ["Latitude", "Longitude"]].values
node_positions[id] = [lat, lng]
node_trace['x'].append(lat)
node_trace['y'].append(lng)
# node_trace['text'].append(id)
# The edges will be drawn as lines:
edge_trace = go.Scatter(
x=[],
y=[],
line=go.Line(width=2, color='rgb(10,10,10)'),
hoverinfo='none',
mode='lines')
for id in node_positions.keys():
for neigh_info in G.neighbors(id):
x0, y0 = node_positions[id]
x1, y1 = node_positions[neigh_info[0]]
edge_trace['x'].extend([x0, x1, None])
edge_trace['y'].extend([y0, y1, None])
# Create figure:
fig = go.Figure(data=go.Data([edge_trace, node_trace]),
layout=go.Layout(
title='Sample Network',
titlefont=dict(size=16),
showlegend=False,
hovermode='closest',
margin=dict(b=20, l=5, r=5, t=40),
xaxis=go.XAxis(showgrid=False, zeroline=False, showticklabels=False),
yaxis=go.YAxis(showgrid=False, zeroline=False, showticklabels=False)))
py.plot(fig)
def createFigure(filename, isAutoOpen=True, isUploadToServer=False):
# ------------Custom Candlestick Colors------------
# Make increasing ohlc sticks and customize their color and name
opens = [datarow.open for datarow in dataRows]
highs = [datarow.high for datarow in dataRows]
lows = [datarow.low for datarow in dataRows]
closes = [datarow.close for datarow in dataRows]
fig_increasing = FigureFactory.create_candlestick(
opens,
highs,
lows,
closes,
dates=datetime_xAxis,
direction='increasing',
marker=go.Marker(color='rgb(61, 153, 112)'),
line=go.Line(color='rgb(61, 153, 112)'))
# Make decreasing ohlc sticks and customize their color and name
fig_decreasing = FigureFactory.create_candlestick(
opens,
highs,
lows,
closes,
dates=datetime_xAxis,
direction='decreasing',
marker=go.Marker(color='rgb(255, 65, 54)'),
line=go.Line(color='rgb(255, 65, 54)'))
# Initialize the figure
fig = fig_increasing
fig['data'].extend(fig_decreasing['data'])
# ------------Add extensions------------
for index, extensionList in enumerate(extensionLists):
addLineExtension(fig, [data[0] for data in extensionList],
[data[1]
for data in extensionList], extensionNames[index],
extensionLineTypes[index], extensionColors[index])
# ------------Update layout And Draw the chart------------
filename = chartTitle
fig['layout'].update(
title=filename,
xaxis=dict(ticktext=[datarow.date for datarow in dataRows],
tickvals=datetime_xAxis,
showticklabels=False,
showgrid=True,
showline=True),
)
plotly.offline.plot(fig, filename=filename + '.html', auto_open=isAutoOpen)
print "----------The chart has been generated----------\n"
def build_data(digraph):
if digraph.nodes():
first_node = list(digraph.nodes)[0]
node_pos = nx.spring_layout(digraph, pos={first_node:(0,0)}, fixed=[first_node], seed=1)
maxX.append(max([pos[0] for pos in node_pos.values()]))
maxY.append(max([pos[1] for pos in node_pos.values()]))
minX.append(min([pos[1] for pos in node_pos.values()]))
minY.append(min([pos[1] for pos in node_pos.values()]))
else:
node_pos = nx.spring_layout(digraph, seed=1)
node_trace = go.Scatter(
x=[pos[0] for pos in node_pos.values()],
y=[pos[1] for pos in node_pos.values()],
text=name_list,
mode='markers+text',
textposition='top center',
textfont=dict(
family='arial',
size=18,
color='rgb(0,0,0)'
),
hoverinfo='none',
marker=go.Marker(
showscale=False,
color=[color_list[depth-1 % len(color_list)] for depth in depth_list],
opacity=1,
#size= [(start_size * 1/(depth)) for depth in depth_list],
size= size_list,
line=go.Line(width=1, color='rgb(0,0,0)')))
edgesX = []
edgesY = []
for edge in digraph.edges:
try:
x0, y0 = node_pos[edge[0]]
x1, y1 = node_pos[edge[1]]
edgesX.extend([x0, x1, None])
edgesY.extend([y0, y1, None])
except KeyError:
continue
edge_trace = go.Scatter(
x=edgesX,
y=edgesY,
line=go.Line(width=1, color='rgb(150,150,150)'),
hoverinfo='none',
mode='lines')
return go.Data([edge_trace, node_trace])
def gen_main_graph(child):
'''First trace'''
trace = go.Scatter(y=data.data.tail(n=10).Price,
x=data.data.tail(n=10).index,
line=go.Line(color='#009688'),
hoverinfo='XBT/EUR',
mode='lines+markers')
'''styking of the graph element'''
layout = go.Layout(
height=450,
xaxis=dict(range=[
data.data.tail(n=10).index.min(),
data.data.tail(n=10).index.max()
],
showgrid=True,
showline=False,
zeroline=False,
fixedrange=False,
showticklabels=False),
yaxis=dict(
range=[
min(list(data.data.tail(n=10).Price)) -
Config.ranj * min(list(data.data.tail(n=10).Price)),
max(list(data.data.tail(n=10).Price)) +
Config.ranj * max(list(data.data.tail(n=10).Price))
],
showline=False,
fixedrange=True,
zeroline=False,
),
margin=go.Margin(
t=45,
l=100,
r=100,
b=50,
))
basedata = [trace]
'''Checks for the Checkboxes to add a trace to the graph'''
if Config.input1 % 2 != 0:
move = data.moving_average('Price')
movave = go.Scatter(y=move.tail(n=10).Price,
x=move.tail(n=10).index,
line=go.Line(color='#00695c'),
hoverinfo='skip',
mode='lines+markers')
basedata.append(movave)
return go.Figure(data=basedata, layout=layout)
def update_graph(Manager):
if Manager == "All Managers":
df_plot = df.copy()
else:
df_plot = df.loc[Manager][
'Probability of Exceeding Threshold (%) '] #[df['Manager'] == Manager]
# pv = pd.pivot_table(
# df_plot,
# index=['Name'],
# columns=["Status"],
# values=['Quantity'],
# aggfunc=sum,
# fill_value=0)
trace1 = go.Line(x=df_plot.index,
y=df_plot.values.ravel(),
name='Probability of Exceeding Threshold (%) ')
# trace2 = go.Bar(x=pv.index, y=pv[('Quantity', 'pending')], name='Pending')
# trace3 = go.Bar(x=pv.index, y=pv[('Quantity', 'presented')], name='Presented')
# trace4 = go.Bar(x=pv.index, y=pv[('Quantity', 'won')], name='Won')
return {
'data': [trace1],
'layout':
go.Layout(
title='Probability of Rainfall Exceeding {} mm'.format(Manager),
barmode='stack')
}
def score_plot(matches, v):
import chart_studio.plotly as py
import plotly.graph_objs as go
import plotly.offline as offline
import plotly.express as px
match = [lis for lis in matches if lis[1] != 0.0]
total_recommendation = len(match)
print(total_recommendation)
#select top 60%
score = [lis[1] for lis in matches if lis[1]]
thres = round(max(score) * v, 2)
score_thres = [lis[1] for lis in matches if lis[1] > thres]
TP = len(score_thres)
TN = 615 - total_recommendation
FP = total_recommendation - TP
print(TP, TN, FP)
title = [lis[0] for lis in matches if lis[1] > thres]
# plt.plot(title,score, color='g')
# plt.xlabel('Title')
# plt.ylabel('Score')
# plt.title('Accuracy')
# plt.show()
data = [go.Line(x=title, y=score)]
layout = go.Layout(title='Accuracy',
width=700,
height=700,
xaxis_type='category')
fig = go.Figure(data=data, layout=layout)
#offline.plot(fig,filename='Accuracy.html')
precision = (TP / (TP + FP))
recall = (TP / TP)
F1 = 2 * ((precision * recall) / (precision + recall))
return print(precision, recall, F1)
def create_plot(data): df = pd.DataFrame(data) cities=df['city'].unique() graphs=[] for city in cities: cityData=df.loc[df['city']==city] fig=go.Figure(go.Line( x=cityData['date'], # assign x as the dataframe column 'x' y=cityData['num_trips'] )) fig.update_layout( title=city, xaxis_title="Journey Date", yaxis_title="No. of Trips", font=dict( family="Courier New, monospace", size=18, color="#7f7f7f" )) fig.layout.paper_bgcolor='rgba(0,0,0,0)' fig.layout.plot_bgcolor='rgba(0,0,0,0)' graphs.append(fig) ids = [cities[i] for i, _ in enumerate(graphs)] graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder) return [graphJSON,ids]
def line_trace_course_fr_diff(course_name,
colour,
semester=None,
dash_type=None,
showlegend=True):
qry = qry_location_diff_history(course_name)
df1 = db_extract_query_to_dataframe(qry,
postgres_cur,
print_messages=False)
if semester == 1:
df1 = df1.loc[df1['semester'] == 1]
elif semester == 2:
df1 = df1.loc[df1['semester'] == 2]
x = [i - 0.5 for i in range(1, 7)]
# Create Semester 1 trace (solid)
trace = go.Scatter(
x=x,
y=df1['fr_diff'].tolist(),
name='<span style="color: {1}"> {0} </span>'.format(
course_name, colour),
text=None,
textfont={
'size': 14,
'color': colour
},
line=go.Line(width=3, color=colour, dash=dash_type),
marker=go.Marker(color=colour, size=10, symbol='diamond'),
connectgaps=False,
mode='markers',
showlegend=showlegend,
textposition='bottom right',
)
return trace
def create_remainder_trace(
df_cdf,
key,
size_class,
area_values,
count_label,
is_first
):
"""
:param df_cdf:
:param key:
:param size_class:
:param area_values:
:param count_label:
:param is_first:
:return:
"""
area_values = np.add(area_values, 100.0 * df_cdf['y'][:-1])
scatter = go.Scatter(
name='%s (%s)' % (size_class['name'], count_label),
x=100.0 * df_cdf['x'][:-1],
y=area_values,
mode='lines',
fill='tozeroy' if is_first else 'tonexty',
line=go.Line(
width=1.0,
color=size_class['color']
)
)
return area_values, scatter
def plotly_single(ma, average_type, color, label, plot_type='line'):
"""A plotly version of plot_single. Returns a list of traces"""
summary = list(np.ma.__getattribute__(average_type)(ma, axis=0))
x = list(np.arange(len(summary)))
if isinstance(color, str):
color = list(matplotlib.colors.to_rgb(color))
traces = [go.Scatter(x=x, y=summary, name=label, line={'color': "rgba({},{},{},0.9)".format(color[0], color[1], color[2])}, showlegend=False)]
if plot_type == 'fill':
traces[0].update(fill='tozeroy', fillcolor=color)
if plot_type in ['se', 'std']:
if plot_type == 'se': # standard error
std = np.std(ma, axis=0) / np.sqrt(ma.shape[0])
else:
std = np.std(ma, axis=0)
x_rev = x[::-1]
lower = summary - std
trace = go.Scatter(x=x + x_rev,
y=np.concatenate([summary + std, lower[::-1]]),
fill='tozerox',
fillcolor="rgba({},{},{},0.2)".format(color[0], color[1], color[2]),
line=go.Line(color='transparent'),
showlegend=False,
name=label)
traces.append(trace)
return traces
def pcaWiki(self,file):
self.formDataPCA(file)
X_std = StandardScaler().fit_transform(self.X)
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
traces = []
for name in self.names:
trace = go.Scatter(
x=Y_sklearn[self.y==name,0],
y=Y_sklearn[self.y==name,1],
mode='markers',
name=name,
marker=go.Marker(
size=12,
line=go.Line(
color='rgba(217, 217, 217, 0.14)',
width=0.5),
opacity=0.8))
traces.append(trace)
data = go.Data(traces)
layout = go.Layout(xaxis=go.XAxis(title='PC1', showline=False),
yaxis=go.YAxis(title='PC2', showline=False))
fig = go.Figure(data=data, layout=layout)
if (self.outputType == 'file'):
print(py.plot(fig,filename='pca'))
else:
return (py.plot(fig,include_plotlyjs='False',output_type='div'))
def pca(self,winSize):
data = np.zeros((len(self.data),len(self.data['FUNC'][winSize])))
i=0
for dEl in sorted(self.data):
self.data[dEl][winSize] = normalizeMaxMin(self.data[dEl][winSize])
data[i] = self.data[dEl][winSize]
i+=1
X_std = StandardScaler().fit_transform(np.transpose(data))
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
traces = []
trace = go.Scatter(
x=Y_sklearn[:,0],
y=Y_sklearn[:,1],
mode='markers',
marker = go.Marker(
size=12,
line= go.Line(
color='rgba(217, 217, 217, 0.14)',
width=0.5),
opacity=0.8))
traces.append(trace)
data = go.Data(traces)
layout = go.Layout(xaxis = go.XAxis(title='PC1', showline=False),
yaxis = go.YAxis(title='PC2', showline=False))
fig = go.Figure(data=data, layout=layout)
if self.outputType=='file':
print(py.plot(fig, filename='pca.html'))
else:
return py.plot(fig, output_type='div')
def get_traces(trace,value,type,color=None,width=.2,opacity=.3): if not is_list(value): value=[value]*len(trace['y']) if values_minus: if is_list(values_minus): min_value=values_minus else: min_value=[values_minus]*len(trace['y']) else: min_value=value if 'percent' in type: y_up=[trace['y'][_]*(1+value[_]/100.00) for _ in range(len(value))] y_down=[trace['y'][_]*(1-min_value[_]/100.00) for _ in range(len(min_value))] else: y_up=[trace['y'][_]+value[_] for _ in range(len(value))] y_down=[trace['y'][_]-min_value[_] for _ in range(len(min_value))] y=trace['y'] upper=go.Scatter(y=y_up,mode='lines',showlegend=False, line=go.Line(width=width),x=trace['x']) if 'yaxis' in trace: upper['yaxis']=trace['yaxis'] if color: color=normalize(color) else: if 'color' in trace['line']: color=trace['line']['color'] else: color='charcoal' upper['line']['color']=color lower=upper.copy() name=trace['name']+'_' if 'name' in trace else '' upper.update(name=name+'upper') color=to_rgba(normalize(color),opacity) lower.update(fill='tonexty',fillcolor=color,name=name+'lower',y=y_down) return upper,lower
def create_plot(df):
grouped_user_ids = df.groupby(df.user_id)
# data = [
#
#
# go.Line(
# x=df['date_posted'], # assign x as the dataframe column 'x'
# y=df['reps']
# ),
# go.Line(
# x=df['date_posted'], # assign x as the dataframe column 'x'
# y=df['reps']
# )
#
# ]
data = []
unique_users = df['user_id'].unique()
for user in unique_users:
df_inloop = grouped_user_ids.get_group(user)
name = User.query.get(user)
data.append(
go.Line(name=f'user_id={user}, name ={name.username}',
x=df_inloop['date_posted'],
y=df_inloop['reps']))
graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
def make_ideo_shape(path, line_color, fill_color):
return dict(
line=go.Line(color=line_color, width=0.45),
path=path,
type="path",
fillcolor=fill_color,
layer="below",
)
def graph_draw(sensor_data):
data = [go.Line(x=list(range(len(sensor_data))), y=sensor_data)]
layout = plotly.graph_objs.Layout(title='sleeping_pattern_chart')
figure = plotly.graph_objs.Figure(data=data, layout=layout)
plotly.offline.plot(figure, filename='sleeping_pattern_chart.html')
def create_plot(json_response=False):
current_date = datetime.date.today()
starting_date = current_date - datetime.timedelta(days=60)
all_reals = Real.query.filter_by(
category="TOTAL CAUSAS SISTEMA RESPIRATORIO").filter(
and_(Real.date >= starting_date)).all()
result_real = reals_schema.dump(all_reals)
end_date = current_date + datetime.timedelta(days=60)
all_forecasteds = Forecasted.query.filter_by(
category="TOTAL CAUSAS SISTEMA RESPIRATORIO").filter(
and_(Forecasted.date >= current_date,
Forecasted.date <= end_date)).all()
result_forecasted = forecasteds_schema.dump(all_forecasteds)
df_real = pd.DataFrame.from_dict(result_real)
df_forecasted = pd.DataFrame.from_dict(result_forecasted)
data = [
go.Line(
x=df_real['date'], # assign x as the dataframe column 'x'
y=df_real['value'],
name="Real"),
go.Line(
x=df_forecasted['date'], # assign x as the dataframe column 'x'
y=df_forecasted['value'],
name="Estimado")
]
layout = {
"xaxis": {
"title": "Fecha"
},
"yaxis": {
"title": "Atenciones por Urgencia Respiratoria"
}
}
fig = dict(data=data, layout=layout)
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
if json_response:
graphJSON = json.loads(graphJSON)
return jsonify(graphJSON)
else:
return graphJSON
def graphRRline(df1):
df_w1 = df1.loc[df['intervention'] == 'week1']
df_nw1 = df1.loc[df['intervention'] != 'week1']
# all traces for plotly
traces = []
for i, r in df_w1.iterrows():
# check for valid colour value
j = i
while j > len(colourList)-1:
j = j-len(colourList)
edge_trace=go.Scatter(
x=[1, 2],
y=[r['w1_drr'], r['w2_drr']],
text="%s" % r['course_code'],
line=go.Line(width=2, color=colourList[j]),
hoverinfo=r['course_code'],
connectgaps=False,
mode='lines+markers',
showlegend=False)
traces.append(edge_trace)
title = 'CES course response rate'
title += '<br>'
title += '{} '.format('Intervention in Week 1')
fig = {'data': traces,
'layout': go.Layout(
title=title,
showlegend=True,
xaxis=dict(
title='Week',
range=[0.5, 2.5],
autotick=False,
ticks='outside',
tick0=1,
dtick=1,
ticklen=5,
zeroline=False,
),
yaxis=dict(
title='Change in response rate (2017-2018)',
ticklen=5,
zeroline=True,
zerolinewidth=4,
),
width=1200,
height=800,
hovermode='closest',
margin=dict(b=50, l=50, r=50, t=50),
hidesources=True,
)
}
plotly.offline.plot(fig, filename='C:\\Peter\\CoB\\CES Response Rates\\rr.html')
return fig
def make_ribbon(l, r, line_color, fill_color, radius=0.2):
poligon = ctrl_rib_chords(l, r, radius)
b, c = poligon
return dict(line=go.Line(color=line_color, width=0.5),
path=make_q_bezier(b) + make_ribbon_arc(r[0], r[1]) +
make_q_bezier(c[::-1]) + make_ribbon_arc(l[1], l[0]),
type='path',
fillcolor=fill_color,
layer='below')
def display_linegraphs(selected_values):
traces = []
[
traces.append(
go.Line(y=(features_log.loc[:, str(val)].values), name=str(val)))
for val in selected_values
]
layout = go.Layout(legend=dict(orientation="h"))
return go.Figure(data=traces, layout=layout)
def get_reference_trace_for_append(graph_unique_name='', shape_type=''):
global g_reference_trace_id
return graph_objs.Scatter(x=[utils.get_base_location_now_date()],
y=[0],
name=graph_unique_name,
text=g_reference_trace_id,
mode='none',
showlegend=False,
line=graph_objs.Line(shape=shape_type))
def plot_remainder(
data: dict,
key: str,
label: str,
is_log: bool = False,
normal_comparison = True
):
area_values = np.zeros(len(data['df_expected']['x']) - 1)
traces = []
for size_class in cd.shared.SIZE_CLASSES:
size_data = data['sizes'][size_class['id']]
track_count = int(size_data['count'])
area_values, area_trace = create_remainder_trace(
df_cdf=size_data['df_cdf'],
key=key,
area_values=area_values,
is_first=cd.shared.SIZE_CLASSES.index(size_class) < 1,
size_class=size_class,
count_label=locale.format('%d', track_count, grouping=True)
)
traces.append(area_trace)
title = 'Cumulative Remainder of {} Deviations ({} Tracks)'.format(
label,
locale.format('%d', data['count'], grouping=True)
)
y_axis = {'title': 'Population Remaining (%)'}
if is_log:
y_axis['title'] = 'Log {}'.format(y_axis['title'])
y_axis['type'] = 'log'
if normal_comparison:
traces.append(go.Scatter(
name='Normal Threshold',
x=100.0 * data['df_expected']['x'],
y=data['df_expected']['y'],
mode='lines',
line=go.Line(
color='rgba(0, 0, 0, 0.75)',
dash='dash',
width=1.0
)
))
cd.display.plotly(
data=traces,
layout=make_layout(
data,
title=title,
fixed=True,
y_axis=y_axis
)
)
def update_graph(selected_options):
data = []
for symbol in selected_options:
history = get_history(symbol, 180)
#log.debug(type(history))
data.append(go.Line(x=history.index, y=history['close'], text=symbol))
# log.debug(history)
return {'data': data, 'layout': go.Layout(yaxis={'title': 'Close Price'})}
def update_graph(type1):
if type1 == 'All':
masks = data1['detected_state'].value_counts().reset_index()
return {
'data': [go.Line(x=masks['index'], y=masks['detected_state'])],
'layout':
go.Layout(title='Number of Patients',
xaxis={'title': 'Number of Patients'},
yaxis={'title': 'states'})
}
else:
status = data1[data1['current_status'] == type1]
mask4 = status['detected_state'].value_counts().reset_index()
return {
'data': [go.Line(x=mask4['index'], y=mask4['detected_state'])],
'layout':
go.Layout(title='Number of Patients',
xaxis={'title': 'Number of patients'},
yaxis={'title': 'States'})
}
def history_plotly(hist):
init_notebook_mode(connected=True)
loss = []
val_loss = []
acc = []
val_acc = []
for history_temp in hist:
loss += history_temp['loss']
val_loss += history_temp['val_loss']
acc += history_temp['acc']
val_acc += history_temp['val_acc']
iplot([
go.Line(y=loss, name='loss'),
go.Line(y=val_loss, name='val_loss'),
go.Line(y=acc, name='acc'),
go.Line(y=val_acc, name='val_acc')
],
show_link=False)