def make_plot(xname, yname, xax=False, yax=False):
mbl = 40 if yax else 0
mbb = 40 if xax else 0
plot = Plot(
x_range=xdr, y_range=ydr, background_fill_color="#efe8e2",
border_fill_color='white', plot_width=200 + mbl, plot_height=200 + mbb,
min_border_left=2+mbl, min_border_right=2, min_border_top=2, min_border_bottom=2+mbb)
circle = Circle(x=xname, y=yname, fill_color="color", fill_alpha=0.2, size=4, line_color="color")
r = plot.add_glyph(source, circle)
xdr.renderers.append(r)
ydr.renderers.append(r)
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
plot.add_layout(Grid(dimension=0, ticker=xticker))
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = yname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.add_layout(Grid(dimension=1, ticker=yticker))
plot.add_tools(PanTool(), WheelZoomTool())
return plot
def make_plot(xname, yname, xax=False, yax=False):
mbl = 40 if yax else 0
mbb = 40 if xax else 0
plot = Plot(
x_range=xdr, y_range=ydr, background_fill_color="#efe8e2",
border_fill_color='white', plot_width=200 + mbl, plot_height=200 + mbb,
min_border_left=2+mbl, min_border_right=2, min_border_top=2, min_border_bottom=2+mbb)
circle = Circle(x=xname, y=yname, fill_color="color", fill_alpha=0.2, size=4, line_color="color")
r = plot.add_glyph(source, circle)
xdr.renderers.append(r)
ydr.renderers.append(r)
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
plot.add_layout(Grid(dimension=0, ticker=xticker))
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = yname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.add_layout(Grid(dimension=1, ticker=yticker))
plot.add_tools(PanTool(), WheelZoomTool())
return plot
def make_scatter(source, data, xname, yname, xax=False, yax=False):
# sets range and borders for plot
xdr = DataRange1d(bounds=None)
ydr = DataRange1d(bounds=None)
mbl = 40 if yax else 0
mbb = 40 if xax else 0
plot = figure(x_range=xdr,
y_range=ydr,
background_fill_color="#efe8e2",
border_fill_color='white',
plot_width=200 + mbl,
plot_height=200 + mbb,
min_border_left=2 + mbl,
min_border_right=2,
min_border_top=2,
min_border_bottom=2 + mbb)
# plots points
circle = Circle(x=xname,
y=yname,
fill_color="blue",
fill_alpha=0.2,
size=4,
line_color="blue")
plot.add_glyph(source, circle)
# calculates and plots regression line
a, b, mse = make_regression_line(data, xname, yname)
mse_mean = mse.mean()
print(xname, yname, mse_mean)
x = data[xname]
plot.line(x, a * x + b, color='red')
plot.axis.visible = False
# makes axis according to place in matrix
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
plot.add_layout(Grid(dimension=0, ticker=xticker))
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = yname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.add_layout(Grid(dimension=1, ticker=yticker))
return plot
def make_dist(data, xname, xax=False, yax=False):
# sets ranges and borders for plot
xdr = DataRange1d(bounds=None)
ydr = DataRange1d(bounds=None)
mbl = 40 if yax else 0
mbb = 40 if xax else 0
TOOLS = "hover, save, reset"
plot = figure(x_range=xdr,
y_range=ydr,
tools=TOOLS,
x_axis_label="",
y_axis_label="",
background_fill_color="#E8DDCB",
plot_width=200 + mbl,
plot_height=200 + mbb,
min_border_left=2 + mbl,
min_border_right=2,
min_border_top=2,
min_border_bottom=2 + mbb)
# makes histogram
measured = data[xname]
hist, edges = np.histogram(measured, density=True, bins=50)
# plots histogram
plot.quad(top=hist,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color="#036564",
line_color="#033649")
# makes axis according to place in matrix
plot.axis.visible = False
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = xname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.legend.location = "center_right"
plot.legend.background_fill_color = "darkgrey"
return plot
def metrix_plot(xname,
yname,
xax=False,
yax=False,
xdr=None,
ydr=None,
source=None):
mbl = 40 if yax else 0
mbb = 40 if xax else 0
plot = Plot(x_range=xdr,
y_range=ydr,
plot_width=200 + mbl,
plot_height=200 + mbb,
min_border_left=1 + mbl,
min_border_right=1,
min_border_top=1,
min_border_bottom=1 + mbb)
circle = Circle(x=xname,
y=yname,
fill_color="color",
size=4,
line_color="color")
r = plot.add_glyph(source, circle)
xdr.renderers.append(r)
ydr.renderers.append(r)
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
plot.add_layout(Grid(dimension=0, ticker=xticker))
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = yname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.add_layout(Grid(dimension=1, ticker=yticker))
plot.add_tools(PanTool(), WheelZoomTool(), BoxZoomTool(), SaveTool(),
ResetTool())
return plot
def show_coeff(data, xname, yname, xax=False, yax=False):
# sets ranges and borders of plot
xdr = DataRange1d(bounds=None)
ydr = DataRange1d(bounds=None)
mbl = 40 if yax else 0
mbb = 40 if xax else 0
plot = figure(x_range=xdr,
y_range=ydr,
background_fill_color="#efe8e2",
border_fill_color='white',
plot_width=200 + mbl,
plot_height=200 + mbb,
min_border_left=2 + mbl,
min_border_right=2,
min_border_top=2,
min_border_bottom=2 + mbb)
# calculates and plots correlation coefficient
measured1 = data[xname]
measured2 = data[yname]
coeff = np.corrcoef(measured1, measured2)
coeff = round(coeff[0][1], 3)
plot.text(text_align='center',
text_baseline='middle',
text=[coeff],
text_font_size='35pt',
x=0,
y=-5)
# adjust axes according to place in matrix
plot.axis.visible = False
xticker = BasicTicker()
if xax:
xaxis = LinearAxis()
xaxis.axis_label = xname
plot.add_layout(xaxis, 'below')
xticker = xaxis.ticker
yticker = BasicTicker()
if yax:
yaxis = LinearAxis()
yaxis.axis_label = yname
yaxis.major_label_orientation = 'vertical'
plot.add_layout(yaxis, 'left')
yticker = yaxis.ticker
plot.add_layout(Grid(dimension=0, ticker=xticker))
plot.add_layout(Grid(dimension=1, ticker=yticker))
return plot
def timeline_days_hours(interval_frequency, all_co2_dataframe,
sensors_with_anomalies, data, upper_bound,
destination_path):
times = pd.date_range(start='00:00:00',
end='23:55:00',
freq=str(interval_frequency) +
'Min').strftime('%H:%M:%S')
days = all_co2_dataframe['timestamp'].dt.strftime('%Y-%m-%d').to_list()
days = list(dict.fromkeys(days))
timestamp = list(times)
for sensor_name in sensors_with_anomalies:
all_durations = []
for k in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
k = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
k)
all_durations.append(
int(data['anomalies']['anomaly_co2_values'][sensor_name][0][k]
['duration']))
data['anomalies']['anomaly_co2_values'][sensor_name][0]
p = figure(plot_height=500,
plot_width=2000,
x_range=timestamp,
y_range=days,
title='Timeline of periods with CO2 levels higher than ' +
str(upper_bound) + ' ppm in ' + sensor_name +
'\n Based on data for last ' + str(len(days)) + ' days',
active_drag=None,
toolbar_location=None)
p.x_range.range_padding = 0
p.y_range.range_padding = 0
p.title.text_font_size = '15pt'
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.yaxis.major_label_text_font_size = '9pt'
p.xaxis.major_label_text_font_size = '5pt'
# set x axis to invisible
p.xaxis.visible = False
# Add custom axis with tickers labels only every 1 hour
labels = np.arange(0, 288, 12).tolist()
ticker = FixedTicker()
ticker.ticks = labels
xaxis = LinearAxis(ticker=ticker)
xaxis.major_label_orientation = math.pi / 3
p.add_layout(xaxis, 'below')
xaxis.major_label_overrides = {
0: '00:00',
12: '01:00',
24: '02:00',
36: '3:00',
48: '04:00',
60: '05:00',
72: '06:00',
84: '07:00',
96: '08:00',
108: '09:00',
120: '10:00',
132: '11:00',
144: '12:00',
156: '13:00',
168: '14:00',
180: '15:00',
192: '16:00',
204: '17:00',
216: '18:00',
228: '19:00',
240: '20:00',
252: '21:00',
264: '22:00',
276: '23:00'
}
#add anomalies recorded for each day in considered period
for i in data['anomalies']['anomaly_co2_values'][sensor_name][0]:
i = data['anomalies']['anomaly_co2_values'][sensor_name][0].index(
i)
x = []
y = []
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][11:])
x.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][11:])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][0][0][0:10])
y.append(data['anomalies']['anomaly_co2_values'][sensor_name][0][i]
['anomalies_details'][-1][0][0:10])
if data['anomalies']['anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][0][0][0:10] == data['anomalies'][
'anomaly_co2_values'][sensor_name][0][i][
'anomalies_details'][-1][0][0:10]:
# print('yes')
p.line(x,
y,
line_width=2,
color='blue',
legend_label='CO2 above critical value')
p.circle(x, y, fill_color="blue", line_color='blue', size=5)
else:
x1 = x.copy()
y1 = y.copy()
x2 = x.copy()
y2 = y.copy()
x1[-1] = times[-1]
y1[-1] = y1[0]
p.line(x1, y1, line_width=2, color='blue')
x2[0] = times[0]
y2[0] = y2[-1]
p.line(x2, y2, line_width=2, color='blue')
x3 = [x1[0], x2[-1]]
y3 = [y1[0], y2[-1]]
p.circle(x3, y3, fill_color="blue", line_color='blue', size=5)
#save graph in output location
output_file(destination_path + '/anomalies_timeline_' + sensor_name +
'.html')
save(p)
return p
def generate_performance_plot(hds, hcols):
aux_cols = ['Model', "Number_of_Drives", "Percent_of_Drives", "Color"]
cols = ['Failure_Rate', 'Capacity', 'Interface', 'Cache', 'RPM', 'Price_GB']
data = {}
for c in cols:
#print('col: ', c)
data[c] = hds[c]
for c in aux_cols:
#print('col: ', c)
data[c] = hds[c]
max_scale = 1.0
min_scale = 0.0
max_cache = np.max(hds["Cache"])
min_cache = np.min(hds["Cache"])
cache = ( (max_scale - min_scale) /(max_cache - min_cache))*(hds["Cache"] - max_cache) + max_scale
max_rpm = np.max(hds["RPM"])
min_rpm = np.min(hds["RPM"])
rpm = ( (max_scale - min_scale) /(max_rpm - min_rpm))*(hds["RPM"] - max_rpm) + max_scale
max_interface = np.max(hds["Interface"])
min_interface = np.min(hds["Interface"])
interface = ( (max_scale - min_scale) /(max_interface - min_interface))*(hds["Interface"] - max_interface) + max_scale
performance = interface + rpm + cache
max_performance = np.max(performance)
min_performance = np.min(performance)
performance = ( (max_scale - min_scale) /(max_performance - min_performance))*(performance - max_performance) + max_scale
max_failure = np.max(hds["Failure_Rate"])
min_failure = np.min(hds["Failure_Rate"])
reliability = ( (max_scale - min_scale) /(max_failure - min_failure))*(hds["Failure_Rate"] - max_failure) + max_scale
reliability = 1.0 - reliability
max_cost = np.max(hds["Price_GB"])
min_cost = np.min(hds["Price_GB"])
cost = ( (max_scale - min_scale) /(max_cost-min_cost))*(hds["Price_GB"]-max_cost) + max_scale
cost = 1.0 - cost
slider_start = .5
data["x"] = np.arange(0,len(hds['Model']),1)
data["y"] = slider_start * cost + slider_start * performance + slider_start * reliability
data["Cost"] = slider_start * cost
data["Performance"] = slider_start * performance
data["Reliability"] = slider_start * reliability
sizes = list(range(6, 24, 4))
groups = pd.cut(hds["Capacity"].values, len(sizes))
sz = [sizes[i] for i in groups.codes]
data["Size"] = sz
static_data = {}
static_data["Cost"] = cost
static_data["Performance"] = performance
static_data["Reliability"] = reliability
_source = ColumnDataSource(data=data)
_static_source = ColumnDataSource(data=static_data)
title = "Relative Hard Drive Value"
plot = figure(title=title, x_axis_location='below', y_axis_location='left', tools=['hover','save'])
hover = plot.select(dict(type=HoverTool))
hover.tooltips = [
("Model ", "@Model"),
("Failure Rate ", "@Failure_Rate"),
(hcols["Capacity"], "@Capacity"),
(hcols["Interface"], "@Interface"),
(hcols["RPM"], "@RPM"),
(hcols["Cache"], "@Cache"),
(hcols["Price_GB"], "@Price_GB{1.11}")
]
p1 = plot.circle('x', 'y', source = _source, size='Size', color="Color")#, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
from bokeh.models import FuncTickFormatter#, FixedTickFormatter
label_dict = {}
for i, s in enumerate(hds["Model"]):
label_dict[i] = s
plot.y_range = Range1d(-.1, 3.1)
plot.toolbar.logo = None
plot.xaxis.visible = False
plot.yaxis.visible = False
from bokeh.models import SingleIntervalTicker
ticker = SingleIntervalTicker(interval=1, num_minor_ticks=0)
xaxis = LinearAxis(axis_label="Model", ticker=ticker)
#yaxis = LinearAxis()#axis_label="Relative Merit")
xaxis.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % label_dict)
xaxis.major_label_orientation = -np.pi/2.7
plot.add_layout(xaxis, 'below')
#plot.add_layout(yaxis, 'left')
callback1 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
var static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
perf = data['Performance']
cost = data['Cost']
static_cost = static_data['Cost']
for (i = 0; i < y.length; i++) {
cost[i] = f * static_cost[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
callback2 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
var static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
static_reli = static_data['Reliability']
perf = data['Performance']
cost = data['Cost']
for (i = 0; i < y.length; i++) {
reli[i] = f * static_reli[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
callback3 = CustomJS(args=dict(source=_source, static_source=_static_source), code="""
var data = source.get("data");
static_data = static_source.get("data");
var f = cb_obj.value
y = data['y']
reli = data['Reliability']
perf = data['Performance']
static_perf = static_data['Performance']
cost = data['Cost']
for (i = 0; i < y.length; i++) {
perf[i] = f*static_perf[i]
y[i] = reli[i] + cost[i] + perf[i]
}
source.trigger('change');
""")
plot.min_border_left = 0
plot.xaxis.axis_line_width = 2
plot.yaxis.axis_line_width = 2
plot.title.text_font_size = '16pt'
plot.xaxis.axis_label_text_font_size = "14pt"
plot.xaxis.major_label_text_font_size = "14pt"
plot.yaxis.axis_label_text_font_size = "14pt"
plot.yaxis.major_label_text_font_size = "14pt"
plot.ygrid.grid_line_color = None
plot.xgrid.grid_line_color = None
plot.toolbar.logo = None
plot.outline_line_width = 0
plot.outline_line_color = "white"
slider1 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Price")
slider2 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Reliability")
slider3 = Slider(start=0.0, end=1.0, value=slider_start, step=.05, title="Performance")
slider1.js_on_change('value', callback1)
slider2.js_on_change('value', callback2)
slider3.js_on_change('value', callback3)
controls = widgetbox([slider1, slider2,slider3], width=200)
layout = row(controls, plot)
return layout
