def time_comp(unwound_data):
df = pd.DataFrame(pd.io.json.json_normalize(unwound_data))
p = figure()
x = df['TEST.DATETIME']
p.circle(x, df['TEST.VALUE'])
p.xaxis.formatter = formatters.DatetimeTickFormatter(days=["%Y-%m-%d"])
#add limits
if 'LOWER LIMIT' in df.columns:
p.line(x, df['LOWER LIMIT'], line_color='red')
if 'NOMINAL' in df.columns:
p.line(x, df['NOMINAL'], line_color='green')
if 'UPPER LIMIT' in df.columns:
p.line(x, df['UPPER LIMIT'], line_color='red')
script, div = components(p)
return script, div
def box_whisker(data):
"""
data: mongo list result with fields DATE,LL,NOM,UL,Q1,Q3,MEAN,MAX,MIN
"""
#https://bokeh.pydata.org/en/latest/docs/gallery/boxplot.html
#%%
df = pd.DataFrame(pd.io.json.json_normalize(data))
df2 = pd.DataFrame(df['STATS.QUARTILES'].tolist(),
columns=['Q1', 'Q2', 'Q3'])
df.pop('STATS.QUARTILES')
df = pd.concat([df, df2], axis=1)
figs = []
for name, group in df.groupby('TESTER_NAME'):
#somehow combine data for dates
top = float(
pd.to_numeric(group.UPPER_LIMIT).mode() +
group['STATS.STDEV'].min())
bot = float(
pd.to_numeric(group.LOWER_LIMIT).mode() -
group['STATS.STDEV'].min())
p = figure(title=name, y_range=(bot, top))
x = group['DATE']
#stems
p.segment(x, group['STATS.MAX'], x, group['Q3'])
p.segment(x, group['STATS.MIN'], x, group['Q1'])
#boxes
w = datetime.timedelta(days=0.75) #lucky guess
p.vbar(x,
w,
group['Q1'],
group['Q2'],
fill_color="#E08E79",
line_color="black")
p.vbar(x,
w,
group['Q2'],
group['Q3'],
fill_color="#3B8686",
line_color="black")
# whiskers (squares are easier)
p.square(x, group['STATS.MIN'])
p.square(x, group['STATS.MAX'])
#limits
p.line(x, group['UPPER_LIMIT'], line_color="red")
p.line(x, group['NOMINAL'], line_color="green")
p.line(x, group['LOWER_LIMIT'], line_color="red")
p.xaxis.formatter = formatters.DatetimeTickFormatter(days=["%Y-%m-%d"])
figs.append(p)
#%%
'''
#show table
td = {'TESTER_NAME' : list(df.TESTER_NAME),
'DATE' : list(df.DATE.astype(str)),
'LOG_FILE' : list(df.LOG_FILE)}
source = ColumnDataSource(td)
cols = [TableColumn(field='TESTER_NAME', title='Tester'),
TableColumn(field='DATE', title='Date'),
TableColumn(field='LOG_FILE', title='Log File Link')]
w = sum([fig.plot_width for fig in figs]) #spans all plots
table = DataTable(source=source, columns=cols, width=w)
grid = gridplot([figs, [widgetbox(table)]])
'''
grid = gridplot([figs])
script, div = components(grid)
return script, div
def index():
"""Get datapoints and attributes and display in a graph.
The arguments are provided through the GET request.
Args:
attribute: string with the attribute to filter by.
timeIntervallType: string with the type of time intervall to filter by.
timeArgument: list of string(s) containing the time ranges.
Returns:
A rendered HTML site with the graph included.
"""
attributes = backend.get_attributes()
current_attribute = None
filters = {}
if 'attribute' in request.args:
filters['Argument'] = request.args['attribute']
for attribute in attributes:
if request.args['attribute'] == attribute['name']:
current_attribute = attribute
break
if 'timeIntervallType' in request.args and 'timeArgument' in request.args:
intervallType = request.args['timeIntervallType']
time_argument = json.loads(request.args['timeArgument'])
if intervallType == 'months' and len(time_argument[0]) == 2:
filters['timeIntervallType'] = 'MONTH'
filters['timeArgument'] = [time_argument[0]]
else:
filters['timeIntervallType'] = 'TIME_INTERVALL'
if intervallType == 'year':
date_start = datetime.datetime(int(time_argument[0]), 1, 1)
date_end = datetime.datetime(int(time_argument[0]), 12, 31)
elif intervallType == 'months':
date = datetime.datetime.strptime(time_argument[0], '%Y-%m')
date_start = datetime.datetime(date.year, date.month, 1)
days_in_month = calendar.monthrange(date.year, date.month)[1]
date_end = datetime.datetime(date.year, date.month,
days_in_month)
else:
date_start = datetime.datetime.strptime(
time_argument[0], '%Y-%m-%d')
date_end = datetime.datetime.strptime(time_argument[1],
'%Y-%m-%d')
date_start = date_start.strftime('%Y-%m-%d')
date_end = date_end.strftime('%Y-%m-%d')
filters['timeArgument'] = [date_start, date_end]
if filters:
data_points = backend.get_data(filters)
else:
data_points = []
if not current_attribute:
current_attribute = attributes[0]
for dp in data_points:
dp['date'] = datetime.datetime(dp['year'], dp['month'], dp['day'],
dp['time'])
res = {}
for dp in data_points:
for attr in dp:
if attr in res:
res[attr].append(dp[attr])
else:
res[attr] = [dp[attr]]
data_points = ColumnDataSource(res)
if data_points.data:
p = figure(x_axis_label='Time',
y_axis_label=current_attribute['displayName'],
x_axis_type='datetime',
y_range=[
min(0,
min(data_points.data['value']) - 1),
max(data_points.data['value']) + 1
])
p.xaxis.ticker = DaysTicker(days=list(range(1, 32)))
p.xaxis.formatter = formatters.DatetimeTickFormatter(days="%Y-%m-%d")
p.line(x='date', y='value', line_width=2, source=data_points)
p.circle(x='date', y='value', size=10, source=data_points)
p.add_tools(
HoverTool(tooltips=[('Datetime', '@date{%Y-%m-%d %H:%M:%S}'),
('Value', '@value{0.0} @unit')],
formatters={'@date': 'datetime'},
mode='vline'))
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
script, div = components(p)
else:
js_resources, css_resources, script, div = '', '', '', ''
return render_template('index.html',
attributes=attributes,
selected_attribute=current_attribute,
js=js_resources,
css=css_resources,
script=script,
div=div)
def build_ohlc_components(self):
plot_dict = {}
for table in self.ohlc_tables:
tname = table.__tablename__
cp = tname[:6]
df = pd.read_sql_table(
tname,
self.db.engine,
columns=['timestamp','open', 'high','low','close']
)
df.drop_duplicates(inplace=True)
r = pd.date_range(
start=df.timestamp.min(), end=df.timestamp.max()
)
df = df.set_index('timestamp').reindex(r).fillna(np.NaN)
df.index = df.index.rename('timestamp')
df['open_inc'],df['high_inc'],df['low_inc'],df['close_inc']=\
df['open'], df['high'], df['low'], df['close']
df['open_dec'],df['high_dec'],df['low_dec'],df['close_dec']=\
df['open'], df['high'], df['low'], df['close']
inc = df.close > df.open
dec = df.open > df.close
df['open_dec'][inc]=None
df['high_dec'][inc]=None
df['low_dec'][inc]=None
df['close_dec'][inc]=None
df['open_inc'][dec]=None
df['high_inc'][dec]=None
df['low_inc'][dec]=None
df['close_inc'][dec]=None
source = ColumnDataSource(data=df)
reference = ColumnDataSource(data=df)
plot = figure(
x_axis_type="datetime",
plot_height=250,
plot_width=1400,
toolbar_location="above",
tools=self.TOOLS,
# these didn't do much
# sizing_mode='stretch_width',
# sizing_mode='stretch_height',
# sizing_mode='stretch_both',
# sizing_mode='scale_width',
# sizing_mode='scale_height',
# sizing_mode='scale_both',
# sizing_mode='fixed',
title=title_dict[cp]
)
seg = plot.segment(
x0='timestamp',
y0='high',
x1='timestamp',
y1='low',
color='black',
source=source
)
inc_bar = plot.vbar(
x='timestamp',
width=20*60*60*1000,
top='close_inc',
bottom='open_inc',
fill_color="#D5E1DD",
line_color="black",
source=source
)
dec_bar = plot.vbar(
x='timestamp',
width=20*60*60*1000,
top='open_dec',
bottom='close_dec',
fill_color="#F2583E",
line_color="black",
source=source
)
date_range_slider = DateRangeSlider(
# width=400,
align='center',
margin=(25,50,5,50),
# format="%x",
format="%B %e, %Y",
value=(
df.index.min(),
df.index.max()
),
start=df.index.min(),
end=df.index.max(),
background='#343a40',
bar_color='#f8f9fa',
step=86400000
)
args = dict(
source=source,
reference=reference
)
code =\
"""
const data = source.data;
const data_ref = reference.data;
const from_date = this.value[0];
const to_date = this.value[1];
var from_pos = data_ref['timestamp'].indexOf(to_date);
var to_pos = data_ref['timestamp'].indexOf(from_date);
data['timestamp'] = data_ref['timestamp'].slice(to_pos,from_pos);
data['high'] = data_ref['high'].slice(to_pos,from_pos);
data['low'] = data_ref['low'].slice(to_pos,from_pos);
data['open_inc'] = data_ref['open_inc'].slice(to_pos,from_pos);
data['high_inc'] = data_ref['high_inc'].slice(to_pos,from_pos);
data['low_inc'] = data_ref['low_inc'].slice(to_pos,from_pos);
data['close_inc'] = data_ref['close_inc'].slice(to_pos,from_pos);
data['open_dec'] = data_ref['open_dec'].slice(to_pos,from_pos);
data['high_dec'] = data_ref['high_dec'].slice(to_pos,from_pos);
data['low_dec'] = data_ref['low_dec'].slice(to_pos,from_pos);
data['close_dec'] = data_ref['close_dec'].slice(to_pos,from_pos);
source.change.emit();
"""
callback = CustomJS(args=args, code=code)
date_range_slider.js_on_change("value", callback)
# NaN's sometimes show up in hover tool
hover_tool = HoverTool(
tooltips=[
# https://docs.bokeh.org/en/latest/docs/reference/models/formatters.html#bokeh.models.formatters.DatetimeTickFormatter
('timestamp', '@{timestamp}{%x}'),
('open', '@{open}{%0.4f}'),
('high', '@{high}{%0.4f}'),
('low', '@{low}{%0.4f}'),
('close', '@{close}{%0.4f}')
],
formatters={
'@{timestamp}': 'datetime',
'@{open}': 'printf',
'@{high}': 'printf',
'@{low}': 'printf',
'@{close}': 'printf'
},
renderers=[inc_bar,dec_bar]
)
plot.add_tools(hover_tool)
plot.background_fill_color = '#f8f9fa'
plot.border_fill_color = "#343a40"
plot.title.text_color = "#f8f9fa"
plot.xaxis.axis_label_text_color = "#f8f9fa"
plot.yaxis.axis_label_text_color = "#f8f9fa"
plot.xaxis.major_label_text_color = "#f8f9fa"
plot.yaxis.major_label_text_color = "#f8f9fa"
plot.xgrid.visible = False
plot.xaxis.formatter = formatters.DatetimeTickFormatter(
days="%m/%d/%Y",
months = "%m/%d/%Y"
)
# plot.background_fill_color = '#868e96'
plot_dict[cp] = column(plot, date_range_slider)
return components(plot_dict)
def build_components(self, is_closed):
plot_dict = {}
for table in self.tables:
cp = table.__tablename__
url =f"http://*****:*****@{timestamp}{%d %b %Y %l:%M:%S:%N %P}'),
('rate', '@{rate}{%0.4f}'),
],
formatters={
'@{timestamp}': 'datetime',
'@{rate}': 'printf'
}
)
plot.add_tools(hover_tool)
callback = CustomJS(
args={'line':line, 'source':source, 'cp':cp}, code="""
var rates = source.data.rate;
var first_val = rates[0];
var last_val = rates[rates.length-1];
var delta = Number.parseFloat(Math.abs(last_val-first_val)).toFixed(5);
var increasing = first_val < last_val;
if (increasing) {
line.glyph.line_color = 'green';
} else {
line.glyph.line_color = 'red';
}
var card_class_dict = {
true: {
"card_class":"card-text text-center font-weight-lighter text-success",
"new_color": "green",
"arrow": "▲"
},
false: {
"card_class":"card-text text-center font-weight-lighter text-danger",
"new_color": "red",
"arrow": "▼"
}
}
var formats = card_class_dict[increasing];
$('#delta_'+cp)
.removeClass()
.addClass(
formats['card_class']
)
.html(
formats["arrow"].concat(delta)
);
$('#current_'+cp).html(last_val);
"""
)
source.js_on_change('change:data', callback)
plot_dict[cp] = plot
return components(plot_dict)
class Graph_Create():
colors = [
"#c6e3cb",
"#9ed5cd",
"#83cacf",
"#47aed0",
"#3984b6",
"#2c5a9c",
"#1e3082",
]
name_o_days = [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday",
]
dem = Dem()
dem.create_week_pop()
dem.create_week_volume()
dem.create_week_density()
days = [1]
time_format = formatters.DatetimeTickFormatter()
time_format.hours = ["%H:%M"]
datetime = dt.datetime(2018,6,1,6)
one_hour = dt.timedelta(hours=1)
one_day = dt.timedelta(days=1)
time_var =[datetime]
def week_graphs(self):
p = figure( width = 1200,height=650,title="Week",x_axis_type="datetime", logo = None,tools="pan, wheel_zoom,reset", active_drag="pan")#fill week
for day in range(7):
self.time_var = [self.datetime]
hours_open = len(self.dem.week_pop[day])-1
for x in range(hours_open):
self.datetime = self.datetime+self.one_hour
self.time_var.append(self.datetime)
# datetime= datetime+one_day
self.datetime = self.datetime - hours_open*self.one_hour
source = ColumnDataSource(dict(
x=self.time_var,
y=self.dem.week_pop[day],
volume=self.dem.week_volume[day],
density=self.dem.week_density[day],
))
hover = HoverTool()
hover.tooltips = [
("(x,y)","($x,$y)"),
("volume","@volume"),
("density","@density")
]
p.xaxis.formatter= self.time_format
p.yaxis.axis_label = "Population"
p.xaxis.axis_label = "Time"
p.circle("x","y",source=source,legend="{0}".format(self.name_o_days[day]),color = self.colors[day])
p.line("x","y",source=source,color = self.colors[day])
p.tools.append(hover)
show(p)
def week_single_graphs(self):
for day in range(7):
p = figure( width = 1200,height=650,title="{0}".format(self.name_o_days[day]),x_axis_type="datetime", logo = None,tools="pan, wheel_zoom,reset", active_drag="pan")#fill week
self.time_var = [self.datetime]
hours_open = len(self.dem.week_pop[day])-1
for x in range(hours_open):
self.datetime = self.datetime+self.one_hour
self.time_var.append(self.datetime)
# datetime= datetime+one_day
self.datetime = self.datetime - hours_open*self.one_hour
source = ColumnDataSource(dict(
x=self.time_var,
y=self.dem.week_pop[day],
volume=self.dem.week_volume[day],
density=self.dem.week_density[day],
))
hover = HoverTool()
hover.tooltips = [
("(x,y)","($x,$y)"),
("volume","@volume"),
("density","@density")
]
p.xaxis.formatter= self.time_format
p.yaxis.axis_label = "Population"
p.xaxis.axis_label = "Time"
p.circle("x","y",source=source,legend="{0}".format(self.name_o_days[day]),color = self.colors[day])
p.line("x","y",source=source,color = self.colors[day])
p.tools.append(hover)
show(p)