def dist_interact(P, S, d_0, n=50):
# Interactively plot the unconditional distribution of X_n where {X_n, n >= 0} is a DTMC
xlabels = list(map(str, S))
p = bplt.figure(x_range=xlabels) # Create a Bokeh figure
source = ColumnDataSource(data=dict(x=xlabels, top=d_0)) # Start with d_0
glyph = VBar(x="x", top="top", bottom=0, width=0.8,
fill_color='blue') # Specify a vertical bar plot
labels = LabelSet(x='x',
y='top',
text='top',
text_align='center',
source=source) # Label each bar with the value
p.add_glyph(source, glyph)
p.add_layout(labels)
p.yaxis.axis_label = 'd_n'
p.y_range = Range1d(0, 1)
handle = None
def update(n):
# Function for updating the data and pushing to the figure handle
source.data['top'] = np.round(
np.dot(d_0, np.linalg.matrix_power(P, n)), 4)
if handle is not None:
push_notebook(handle=handle)
display(Math(r'$d_0 = ' + pmatrix([d_0], frac=True) +
'$')) # Display initial distribution
# Interactive slider and plot
widgets.interact(update,
n=widgets.IntSlider(value=0,
min=0,
max=n,
description='n'))
handle = show(p, notebook_handle=True)
def create_bar_chart(data, title, x_name, y_name, hover_tool=None):
"""Creates a bar chart plot with the exact styling for the centcom
dashboard. Pass in data as a dictionary, desired plot title,
name of x axis, y axis and the hover tool HTML.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0,end=1)
tools = []
if hover_tool:
tools = [hover_tool,]
plot = figure(title=title, x_range=xdr, y_range=ydr, h_symmetry=True, v_symmetry=True,
min_border=0, toolbar_location="above", tools=tools,
outline_line_color="#666666")
glyph = VBar(x=x_name, top=y_name, bottom=0, width=.8)
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Prediction Probability"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Recommend Prediction"
plot.xaxis.major_label_orientation = 1
return plot
def create_bar_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=1200,
height=300):
"""
Creates a bar chart plot with the exact styling for the centcom
dashboard. Pass in data as a dictionary, desired plot title,
name of x axis, y axis and the hover tool HTML.
"""
source = ColumnDataSource(data)
xdr = FactorRange(
factors=data[x_name]) # xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]))
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
plot_width=width,
plot_height=height,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location="above",
tools=tools,
responsive=True,
outline_line_color="#666666")
glyph = VBar(x=x_name,
top=y_name,
bottom=0,
width=.8,
fill_color="#e12127")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Count"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Bins"
plot.xaxis.major_label_orientation = 1
plot.xaxis.minor_tick_line_color = "orange"
return plot
def sort(idx, metric):
if metric == 'Layer':
metric_idx = -2
else:
metric_idx = metric_names.index(metric)
tmp_metrics = metrics.copy()
reverse = True
if metric == 'Average distance ratio' or metric == 'Layer':
reverse = False
tmp_metrics = sorted(tmp_metrics,
key=lambda a: a[1 + metric_idx],
reverse=reverse)
xs_tmp = [metric[-1] + 1 for metric in tmp_metrics]
for jdx, i in enumerate(xs_tmp):
xs[idx][i - 1] = jdx + 1
sources[idx] = ColumnDataSource(dict(x=xs[idx], top=ys[idx]))
plots[idx].renderers.remove(renderers[idx])
plots[idx].xaxis.ticker = [i + 1 for i in range(len(xs[idx]))]
override = {}
for i in range(len(metrics)):
override[xs[idx][i]] = str(i + 1)
plots[idx].xaxis.major_label_overrides = override
renderers[idx] = plots[idx].add_glyph(
sources[idx], VBar(x='x', top='top', bottom=0, width=0.2))
def create_bar_chart(data, title, x_name, y_name, hover_tool = None, width = 1200, height = 300):
source = ColumnDataSource(data)
xdr = FactorRange("0 < i < 0.01", "0.01 <= i < 0.1", "0.1 <= i < 1", "1 <= i < 5", "5 <= i < 25", "25 <= i < 50", "50 <= i < 250", "250 <= i < 1000", "i => 1000")
ydr = Range1d(start = 0, end = max(data[y_name])*1.1)
tools = []
if hover_tool:
tools = [hover_tool]
plot = figure(title=title, x_range=xdr, y_range=ydr, plot_width=width,
plot_height=height, h_symmetry=False, v_symmetry=False,
min_border=0, toolbar_location="above", tools=tools,
responsive=True, outline_line_color="#666666")
glyph = VBar(x = x_name, top = y_name, bottom = 0, width = 0.8, fill_color = "#e12127")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Number of transactions"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Amount of BTC transacted (i = transaction value)"
plot.xaxis.major_label_orientation = 1
return plot
def init_mention_plot():
cprint('%s: init @ mentios barcharts...' % TAG, 'yellow', attrs=['bold'])
global mention_count
global mention_barplot
mention_count = count_mentions()
y = []
mentions = []
for (mention, freq) in mention_count:
y.append(freq)
mentions.append(mention)
x = np.arange(len(mentions))
source = ColumnDataSource(dict(
x=x,
top=y,
))
glyph = VBar(x='x', top='top', bottom=0, width=0.85, fill_color='#ff7f0e')
mention_barplot.add_glyph(source, glyph)
xaxis = LinearAxis()
xaxis.ticker = x
xaxis.major_label_overrides = {
i: mention
for i, mention in enumerate(mentions)
}
mention_barplot.add_layout(xaxis, 'below')
mention_barplot.xaxis.major_label_orientation = +np.pi / 2
yaxis = LinearAxis()
yaxis.axis_label = 'Overall number of @ mentions'
yaxis.axis_label_text_font_size = '14pt'
yaxis.ticker = np.linspace(0, max(y), 11, dtype=np.int)[1:]
mention_barplot.add_layout(yaxis, 'left')
mention_barplot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
mention_barplot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
def create_bar_chart(data, title, x_name, y_name, hover_tool = None, width = 1200, height = 300):
source = ColumnDataSource(data)
xdr = FactorRange(factors = data[x_name])
ydr = Range1d(start = 250000, end = 450000)
tools = []
if hover_tool:
tools = [hover_tool]
plot = figure(title=title, x_range=xdr, y_range=ydr, plot_width=width,
plot_height=height, h_symmetry=False, v_symmetry=False,
min_border=0, toolbar_location="above", tools=tools,
responsive=True, outline_line_color="#666666")
glyph = VBar(x = x_name, top = y_name, bottom = 0, width = 0.8, fill_color = "#e12127")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Value of transaction (BTC)"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Transaction Block Number"
plot.xaxis.major_label_orientation = 1
return plot
def draw_vol():
g1 = VBar(x='T',
top='V',
bottom=0,
width=timeframe * 1000 - 2,
fill_color='cyan',
line_color='black')
return g1
def draw_stand_candles():
global timeframe
g1 = VBar(x='T',
top='O',
bottom='C',
width=timeframe * 1000 - 2,
fill_color='black',
line_color='black')
return g1
def create_bar_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=1100,
height=400):
"""Creates a bar chart plot with the exact styling for the centcom
dashboard. Pass in data as a dictionary, desired plot title,
name of x axis, y axis and the hover tool HTML.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=list(data[x_name]))
ydr = Range1d(start=0, end=max(data[y_name]) * 1.05)
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
plot_width=width,
plot_height=height,
min_border=0,
toolbar_location="above",
tools=tools,
outline_line_color="#666666")
glyph = VBar(x=x_name,
top=y_name,
bottom=0,
width=.8,
fill_color="#ffdf00")
plot.add_glyph(source, glyph)
plot.add_tools(WheelZoomTool())
plot.add_tools(BoxZoomTool())
plot.add_tools(ZoomOutTool())
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Weight"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Grouping"
plot.xaxis.major_label_orientation = 1
return plot
def create_bar_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=1200,
height=300):
"""Создаёт столбчатую диаграмму.
Принимает данные в виде словаря, подпись для графика,
названия осей и шаблон подсказки при наведении.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]) * 1.5)
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
plot_width=width,
plot_height=height,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location="above",
tools=tools,
responsive=True,
outline_line_color="#666666")
glyph = VBar(x=x_name,
top=y_name,
bottom=0,
width=.8,
fill_color="#e12127")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Bugs found"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Days after app deployment"
plot.xaxis.major_label_orientation = 1
return plot
def create_bar_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=1200,
height=300):
"""
Création d'un bargraph qui compte l'occurence du nombre d'album dans le top 200 (seules les 20 premieres valeurs sont affichées)
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]) * 1.5)
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
plot_width=width,
plot_height=height,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location="above",
tools=tools,
outline_line_color="#666666")
glyph = VBar(x=x_name,
top=y_name,
bottom=0,
width=.8,
fill_color="#e8ddb5")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Number of albums"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.major_label_orientation = 1
return plot
def test_VBar():
glyph = VBar()
assert glyph.x is None
assert glyph.width is None
assert glyph.top is None
assert glyph.bottom == 0
check_fill_properties(glyph)
check_line_properties(glyph)
check_properties_existence(glyph, [
"x",
"width",
"top",
"bottom",
], FILL, LINE, GLYPH)
def test_VBar():
glyph = VBar()
assert glyph.x is None
assert glyph.width is None
assert glyph.top is None
assert glyph.bottom == 0
yield check_fill, glyph
yield check_line, glyph
yield (check_props, glyph, [
"x",
"width",
"top",
"bottom",
], FILL, LINE)
def bar_glyphs(data, xlabels=None):
fields = data.columns.values
sources = dict(zip(fields, np.zeros(len(fields))))
glyphs = dict(zip(fields, np.zeros(len(fields))))
for field in fields:
if xlabels is None:
x = data[field].index
width = 1
else:
x = xlabels
width = 1
sources[field] = ColumnDataSource(data=dict(x=x, top=data[field].values))
glyphs[field] = VBar(x="x", top="top", bottom=0, width=width, fill_color='blue')
return sources, glyphs
def test_VBar() -> None:
glyph = VBar()
assert glyph.x == field("x")
assert glyph.width is None
assert glyph.top is None
assert glyph.bottom == 0
check_fill_properties(glyph)
check_hatch_properties(glyph)
check_line_properties(glyph)
check_properties_existence(glyph, [
"x",
"width",
"top",
"bottom",
], FILL, HATCH, LINE, GLYPH)
def create_bar_chart(data, title, x_name, y_name, width=1200, height=300):
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]) * 1.5)
plot = figure(title=title, x_range=xdr, y_range=ydr)
glyph = VBar(x=x_name, top=y_name, bottom=0, width=.8)
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
return plot
def hist_glyphs(data, bins=None):
fields = data.columns.values
sources = dict(zip(fields, np.zeros(len(fields))))
glyphs = dict(zip(fields, np.zeros(len(fields))))
for field in fields:
if bins is None:
counts = pd.Series(data[field].value_counts(sort=False, ascending=True).sort_index())
x = counts.index
width = 1
else:
counts = pd.Series(data[field].value_counts(sort=False, ascending=True, bins=bins).sort_index())
x = counts.index.values.mid
width = np.mean(counts.index.length)
sources[field] = ColumnDataSource(data=dict(x=x, top=counts.values))
glyphs[field] = VBar(x="x", top="top", bottom=0, width=width, fill_color='blue')
return sources, glyphs
def create_bar_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=1200,
height=300):
"""Creates a barchart plot with the exact styling for the centcom
dashboard, Pass in data as a dictionary, desired plot title,
name of x axis, y axis.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]) * 1.5)
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
tools=tools,
plot_width=width,
plot_height=height,
toolbar_location="above")
glyph = VBar(x=x_name, width=.8, top=y_name, fill_color="#e12127")
plot.add_glyph(source, glyph)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.yaxis.axis_label = "Bugs found"
plot.xaxis.axis_label = "Days after app deployment"
plot.toolbar.logo = None
plot.xgrid.grid_line_color = None
plot.xaxis.major_label_orientation = 1
return plot
def init_user_plot():
cprint('%s: init @ users barcharts...' % TAG, 'yellow', attrs=['bold'])
global user_count
global user_barplot
user_count = count_users()
y = []
users = []
for (user, freq) in user_count.items():
y.append(freq)
users.append(user)
x = np.arange(len(users))
wlist = []
for user in users:
prefixes = ['@' + user]
for prefix, freq in sorted(user_tweet_freq[user].items(),
key=lambda kv: kv[1],
reverse=True)[:10]:
prefixes.append(' %s:%d' % (prefix, freq))
wlist.append(list(prefixes))
source = ColumnDataSource(dict(x=x, top=y, wlist=wlist))
glyph = VBar(x='x', top='top', bottom=0, width=0.85, fill_color='#1f77b4')
user_barplot.add_glyph(source, glyph)
xaxis = LinearAxis()
xaxis.ticker = x
xaxis.major_label_overrides = {
i: '@' + user
for i, user in enumerate(users)
}
#xaxis.major_label_standoff = -35
user_barplot.add_layout(xaxis, 'below')
user_barplot.xaxis.major_label_orientation = +np.pi / 2
yaxis = LinearAxis()
yaxis.axis_label = 'Overall number of tweets per @'
yaxis.axis_label_text_font_size = '14pt'
yaxis.ticker = np.linspace(0, max(y), 11, dtype=np.int)[1:]
user_barplot.add_layout(yaxis, 'left')
user_barplot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
user_barplot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
def create_bar_chart(data, title, x_name, y_name, hover_tool=None,
width=1200, height=300):
"""
Creates a bar chart plot with the exact styling for the centcom
dashboard. Pass in data as a dictionary, desired plot title,
name of x axis, y axis and the hover tool HTML.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0,end=max(data[y_name])*1.5)
tools = []
if hover_tool:
tools = [hover_tool,]
plot = figure(title=title, x_range=xdr, y_range=ydr, plot_width=width, x_axis_type="datetime",
plot_height=height,
min_border=0, toolbar_location="above", tools=tools, sizing_mode='fixed',
outline_line_color="#666666")
glyph = VBar(x=x_name, top=y_name, bottom=0, width=.8,
fill_color="#e12127")
plot.add_glyph(source, glyph)
plot.xaxis.ticker = DaysTicker(days=np.arange(1,32))
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "Bugs found"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "Days after app deployment"
plot.xaxis.major_label_orientation = 1
return plot
def drawGraph(self, **options):
p = []
source = []
iterator = 0
for hisTitle, projectionList in zip(*[iter(self.selectionList)] * 2):
dimList = list(map(int, projectionList))
nDim = len(dimList)
if nDim > 1:
raise NotImplementedError(
"Sorry!!.. Multidimensional projections have not been implemented, yet"
)
histogram = self.histArray.FindObject(hisTitle).Projection(
dimList[0])
cds = makeCDS(histogram)
histLabel = histogram.GetTitle()
xLabel = histogram.GetXaxis().GetTitle()
yLabel = histogram.GetYaxis().GetTitle()
localHist = figure(title=histLabel,
tools=options['tooltips'],
background_fill_color=options['bg_color'],
y_axis_type=options['y_axis_type'],
x_axis_type=options['x_axis_type'])
glyph = VBar(top='value',
x='bin',
width=abs(cds.data['bin'][2] - cds.data['bin'][1]) /
1.5,
fill_color=options['color'],
line_color=options['line_color'])
localHist.add_glyph(cds, glyph)
localHist.y_range.start = 0
localHist.xaxis.axis_label = xLabel
localHist.yaxis.axis_label = yLabel
source.append(cds)
p.append(localHist)
iterator = iterator + 1
pAll = gridplot(p,
ncols=options['nCols'],
plot_width=options['plot_width'],
plot_height=options['plot_height'])
handle = show(pAll, notebook_handle=True)
return pAll, handle, source
vbar_renderer = p2.vbar( # creates bars
x='x',
top='y',
width=0.5,
source=sourcep2, # defines source
color='blue', # default color of the bars
# properties of selected / unselected bars
#selection_fill_alpha = 1.0, # opacity of selected bar
#nonselection_fill_alpha=0.2, # opacity of non-selected bar
)
from bokeh.models.glyphs import VBar
vbar_selected = VBar(fill_alpha=0.2,
fill_color='blue',
line_color='blue',
hatch_pattern='right_diagonal_line',
hatch_color='blue',
hatch_alpha=1.0,
hatch_weight=0.5)
vbar_nonselected = VBar(fill_alpha=0.2, fill_color='blue', line_color='blue')
vbar_renderer.selection_glyph = vbar_selected
vbar_renderer.nonselection_glyph = vbar_nonselected
p2.xgrid.grid_line_color = None # removes gridlines
picker = ColorPicker(
title="Line Color") # allows to change the color of the bars
picker.js_link(
'color', vbar_renderer.selection_glyph,
'fill_color') # link the selected bar color properties to the selector
picker.js_link('color', vbar_renderer.selection_glyph, 'line_color')
redo_1exp_btn = Button(label="Redo experiment")
# tracks if one of the parameter changed to know if we start the stats from
# 0 again
has_param_changed = True
source_1exp = ColumnDataSource(data=dict(n=[], error=[]))
p_1exp = figure(plot_height=350,
title="1 time error",
x_axis_type="log",
y_range=[0, 100],
tools="ypan, ywheel_pan, ywheel_zoom",
y_axis_label="error",
x_axis_label="nb participants")
glyph = VBar(x='n', top='error', bottom=0.1, width="n", hatch_scale=None)
p_1exp.add_glyph(source_1exp, glyph)
p_1exp.y_range.start = 0
p_1exp.x_range.range_padding = 0.1
p_1exp.xaxis.major_label_orientation = 1
p_1exp.xgrid.grid_line_color = None
def modular_abs(x, y, p):
return (min((x - y) % p, (y - x) % p))
def update_1exp():
sanitize_params()
eps_val = float(eps.value)
small_delta_val = float(small_delta.value)
def meditation_figs(data, height=500, width=1300):
#Timeseries Plot
bz_daily = BoxZoomTool()
wz_cumu = WheelZoomTool(dimensions='width')
plot_daily_tools = [
PanTool(dimensions='width'), bz_daily,
ResetTool(),
SaveTool()
]
plot_cumu_tools = [
PanTool(dimensions='width'), wz_cumu,
ResetTool(),
SaveTool()
]
data['date_str'] = data['date'].map(str)
cds_w = ColumnDataSource(
dict(date=data['date'],
date_str=data['date_str'],
meditation_time=data['meditation_time']))
cds_s = ColumnDataSource(
dict(date=data['date'],
date_str=data['date_str'],
meditation_time=data['meditation_time']))
d = cds_w.data
d_s = cds_w.data
#Calculate weeklong MA value, and cumulative value
m_t_ma, m_t_c = [], []
# #MA (inital values: 10;0;0;10;10;10;10)
m_t_ma.append(10.0)
m_t_ma.append(5.0)
m_t_ma.append(3.33)
m_t_ma.append(5.0)
m_t_ma.append(6.0)
m_t_ma.append(6.67)
m_t_ma.append(7.14)
for j in range(7, len(d['date'])):
m_t_ma.append(
(d['meditation_time'][j] + d['meditation_time'][j - 1] +
d['meditation_time'][j - 2] + d['meditation_time'][j - 3] +
d['meditation_time'][j - 4] + d['meditation_time'][j - 5] +
d['meditation_time'][j - 6]) / 7.0)
# print(d['date'].count())
# print(d.get(datetime.date(2018,12,30)))
cds_w.add(m_t_ma, name='m_t_ma')
#Cumu (inital value: 10)
m_t_c.append(10.0)
for k in range(1, d['date'].count()):
m_t_c.append(d['meditation_time'][k] + m_t_c[-1])
cds_w.add(m_t_c, name='m_t_c')
#PLOT DAILY
plot_daily = figure(x_axis_type="datetime",
title="Daily Meditation",
h_symmetry=False,
v_symmetry=False,
min_border=0,
plot_height=height,
plot_width=width,
toolbar_location="above",
outline_line_color="#666666",
tools=plot_daily_tools)
glyph = VBar(x="date",
top="meditation_time",
bottom=0,
width=.8,
fill_color="#41A2E8",
line_color="#41A2E8")
plot_daily.add_glyph(cds_w, glyph)
plot_daily.line('date',
'm_t_ma',
name='m_t_ma',
source=cds_w,
line_color="#8B0A50",
line_width=3,
line_alpha=0.6,
legend="Moving Average (7 days)")
plot_daily.legend.location = "top_left"
plot_daily.legend.click_policy = "hide"
plot_daily.yaxis.axis_label = "Minutes"
#plot_daily.Bar('date', 'meditation_time', source=cds_w, name='meditation_time')
plot_cumu = figure(x_axis_type="datetime",
title="Cumulative Meditation",
h_symmetry=False,
v_symmetry=False,
min_border=0,
plot_height=height,
plot_width=width,
toolbar_location="above",
outline_line_color="#666666",
active_scroll=wz_cumu,
tools=plot_cumu_tools)
glyph = VBar(x="date",
top="m_t_c",
bottom=0,
width=.8,
fill_color="#41A2E8",
line_color="#41A2E8")
plot_cumu.add_glyph(cds_w, glyph)
plot_cumu.yaxis.visible = False
#plot_daily = Histogram(cds_w, 'meditation_time', title="Daily Meditation", plot_height=height, plot_width=width, active_scroll=wz)
#plot_cumu = Histogram(cds_w, 'meditation_time', title="Daily Meditation", plot_height=height, plot_width=width, active_scroll=wz)
#plot_max = figure(x_axis_type="datetime", title="MAXes", h_symmetry=False, v_symmetry=False, min_border=0, plot_height=height, plot_width=width, toolbar_location="above", outline_line_color="#666666", active_scroll=wz_max,tools=plot_max_tools_d)
script, (plot_daily_div, plot_cumu_div) = components(
(plot_daily, plot_cumu))
return script, plot_daily_div, plot_cumu_div
def create_line_chart(data,
title,
x_name,
y_name,
hover_tool=None,
width=800,
height=300,
fill_color="#fcb900"):
"""Creates a bar chart plot with the exact styling for the centcom
dashboard. Pass in data as a dictionary, desired plot title,
name of x axis, y axis and the hover tool HTML.
"""
source = ColumnDataSource(data)
xdr = FactorRange(factors=data[x_name])
ydr = Range1d(start=0, end=max(data[y_name]) * 1.5)
tools = []
if hover_tool:
tools = [
hover_tool,
]
plot = figure(title=title,
x_range=xdr,
y_range=ydr,
plot_width=width,
plot_height=height,
h_symmetry=False,
v_symmetry=False,
min_border=0,
toolbar_location="above",
tools=tools,
responsive=True,
outline_line_color="#666666",
background_fill_alpha=0.0,
border_fill_alpha=0.0)
#glyph = Line(x=x_name, y=y_name, line_color="#F46D43", line_width=6, line_alpha=0.6)
glyph = VBar(x=x_name,
top=y_name,
bottom=0,
width=1,
fill_color=fill_color)
#glyph2 = VBar(x=x_name, top='costs', bottom=0, width=1, fill_color='#e12127')
#e12127
plot.add_glyph(source, glyph)
#plot.add_glyph(source, glyph2)
xaxis = LinearAxis()
yaxis = LinearAxis()
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.toolbar.logo = None
plot.min_border_top = 0
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = "#999999"
plot.yaxis.axis_label = "usage"
plot.ygrid.grid_line_alpha = 0.1
plot.xaxis.axis_label = "days"
plot.xaxis.major_label_orientation = 1
return plot
))
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 = VBar(x="x", top="top", bottom=0, width=0.5, fill_color="#b3de69")
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 generate_plot(self):
"""Calculates mean and standard deviation for measure(s) by model,
then generates a bar plot of model vs mean performance.
TODO: Finish this doc.
"""
# Use this for a built-in bar plot instead,
# but doesn't work with custom hover tool
#p = Bar(self.data,label='modelname',values=self.measure,agg='mean',\
# title='Mean %s Performance By Model'%self.measure,legend=None,\
# tools=tools, color='modelname')
#self.script,self.div = components(p)
#return
# TODO: hardcoded self.measure[0] for now, but should incorporate
# a for loop somewhere to subplot for each selected measure
# TODO: add significance information (see plot_bar_pretty
# and randttest in narf_analysis)
# build new pandas series of stdev values to be added to dataframe
# if want to show more info on tooltip in the future, just need
# to build an appropriate series to add and then build its tooltip
#in the create_hover function
modelnames = self.data.index.levels[0].tolist()
stdev_col = pd.Series(index=modelnames)
mean_col = pd.Series(index=modelnames)
median_col = pd.Series(index=modelnames)
n_cells_col = pd.Series(index=modelnames)
#for each model, find the stdev and mean over the measure values, then
#assign those values to new Series objects to use for the plot
for model in modelnames:
values = self.data[self.measure[0]].loc[model]
stdev = values.std(skipna=True)
mean = values.mean(skipna=True)
median = values.median(skipna=True)
if (math.isnan(stdev)) or (math.isnan(mean)) or (math.isnan(median)):
# If either statistic comes out as NaN, entire column was NaN,
# so model doesn't have the necessary data.
continue
stdev_col.at[model] = stdev
mean_col.at[model] = mean
median_col.at[model] = median
n_cells_col.at[model] = values.count()
newData = pd.DataFrame.from_dict({
'stdev':stdev_col, 'mean':mean_col, 'median':median_col,
'n_cells':n_cells_col,
})
# Drop any models with NaN values, since that means they had no
# performance data for one or more columns.
newData.dropna(axis=0, how='any', inplace=True)
if newData.size == 0:
self.script,self.div = (
"Error, no plot to display.",
"None of the models contained valid performance data."
)
return
dat_source = ColumnDataSource(newData)
tools = [
PanTool(), SaveTool(), WheelZoomTool(),
ResetTool(), self.create_hover()
]
xrange = FactorRange(factors=modelnames)
yrange = Range1d(
start=0,
end=(max(newData['mean'])*1.5)
)
p = figure(
x_range=xrange, x_axis_label=(
"Modelname, prefix: {0}, suffix: {1}"
.format(self.pre, self.suf)
),
y_range=yrange, y_axis_label='Mean %s'%self.measure[0],
title="Mean %s Performance By Model"%self.measure[0],
tools=tools, responsive=True, toolbar_location=TOOL_LOC,
toolbar_sticky=TOOL_STICK,
output_backend="svg"
)
p.xaxis.major_label_orientation=-(np.pi/4)
glyph = VBar(
x='index', top='mean', bottom=0, width=VBAR_WIDTH,
fill_color=VBAR_FILL, line_color='black'
)
p.add_glyph(dat_source,glyph)
# workaround to prevent title and toolbar from overlapping
grid = gridplot(
[p], ncols=GRID_COLS, responsive=True,
)
self.script, self.div = components(grid)
for colRef,bar in enumerate(ylist,1):
y.append(calcEnergy(condf,bar))
colorList.append(Category10[(len(ylist)+2)][colRef])
barDict = dict(x=ylist, top=y, col = colorList)
barDS = ColumnDataSource((barDict))
for colRef,line in enumerate(ylist,1):
lines = fig.line(x='DateTime', y=line, source=dataSource,
color = Category10[(len(ylist)+2)][colRef],
line_width=2)
bars = VBar(x='x', top='top',width=0.5,
line_color='col',
fill_color='col')
barfig.add_glyph(barDS, bars)
fig.outline_line_color = None
fig.background_fill_color = "#efefef"
fig.xaxis.formatter = DatetimeTickFormatter(days = [ '%a%e-%b'])
fig.xaxis.minor_tick_line_color = 'black'
#fig.axis.major_tick_line_color = None
fig.axis.axis_line_color = 'black'
fig.y_range.start=0
barfig.y_range.start=0
#fig.yaxis.axis_label = 'Total electricity supplied in Quarter (TWh)'
def plot_va(filename, tag, kind, color=DEFAULT_COLOR, orient='horizontal'):
bp.curdoc().clear()
title = verbose_kinds[kind] + ' variability analysis for {} iJO1366 model'\
.format(tag)
data = pd.read_csv(os.path.join(outputs_folder, filename), index_col=0)
if not data.columns[0] in ['minimum', 'maximum']:
data.columns = ['minimum', 'maximum']
data += 1e-9 # Resolution of the solver
f = lambda x: np.sqrt(x[0] * x[1])
# data['score'] = data.mean(axis=1)
data['score'] = data.apply(f, axis=1)
data.sort_values(by='score', ascending=False, inplace=True)
data['y'] = range(len(data))
data['name'] = data.index
source = ColumnDataSource(data)
xdr = DataRange1d()
ydr = DataRange1d()
_tools_to_show = 'box_zoom,pan,save,hover,reset,tap,wheel_zoom'
if orient == 'vertical':
p1 = bp.figure(
title=title,
x_range=xdr,
y_range=ydr,
x_axis_type='log',
plot_width=600,
plot_height=1000,
tools=_tools_to_show,
# h_symmetry=False, v_symmetry=False,
min_border=0)
glyph = HBar(y="y",
right="maximum",
left="minimum",
height=0.9,
fill_color=color,
fill_alpha=0.5,
line_color=None)
p1.add_glyph(source, glyph)
p1.circle(x='score',
y='y',
fill_color=color,
line_color=None,
source=source)
axis1 = p1.xaxis
axis2 = p1.yaxis
elif orient == 'horizontal':
p1 = bp.figure(
title=title,
x_range=ydr,
y_range=xdr,
y_axis_type='log',
plot_width=1000,
plot_height=600,
tools=_tools_to_show,
# h_symmetry=False, v_symmetry=False,
min_border=0)
glyph = VBar(x="y",
top="maximum",
bottom="minimum",
width=0.9,
fill_color=color,
fill_alpha=0.5,
line_color=None)
p1.add_glyph(source, glyph)
p1.circle(y='score',
x='y',
fill_color=color,
line_color=None,
source=source)
axis1 = p1.yaxis
axis2 = p1.xaxis
else:
raise ValueError("orient should be 'vertical' or 'horizontal'")
# Fix ticks
label_dict = {}
for i, s in enumerate(data.index):
label_dict[i] = s
axis2.formatter = FuncTickFormatter(code="""
var labels = %s;
return labels[tick];
""" % label_dict)
axis1.axis_label = '[{}]'.format(verbose_kinds[kind])
# p1.yaxis.ticker = [x for x in range(len(data))]
hover = p1.select(dict(type=HoverTool))
hover.tooltips = [
(verbose_kinds[kind], "@name"),
("min", "@minimum"),
("max", "@maximum"),
]
hover.mode = 'mouse'
path = os.path.join(plots_folder, 'va_' + filename)
bp.output_file(path + '.html')
bp.show(p1)
p1.output_backend = 'svg'
export_svgs(p1, filename=path + '.svg')
bp.curdoc().clear()
return data
