def __init__(self):
self.StrCapabilityPlot = figure(plot_width=580, plot_height=595,
tools='', title="Storage capability curve")
self.StrCapabilityPlot.xaxis.axis_label = 'Q [p.u]'
self.StrCapabilityPlot.yaxis.axis_label = 'P [p.u]'
self.StrCapabilityPlot.toolbar.logo = None
self.StrCapabilityPlot.toolbar_location = None
self.StrCapabilityPlot.y_range = Range1d(*(-1.15, 1.15))
self.StrCapabilityPlot.x_range = Range1d(*(-1.15, 1.15))
self.StrCapabilityPlot.circle(x=[0], y=[0], radius=1.00, line_color ="navy", fill_color=None, size= 1)
self.StrCapabilityPlot.add_layout(Arrow(end=VeeHead(size=10), line_color="black",
x_start=0.0, y_start=-1.1, x_end=0, y_end=1.1))
self.StrCapabilityPlot.add_layout(Arrow(end=VeeHead(size=10), line_color="black",
x_start=-1.1, y_start=0, x_end=1.1, y_end=0))
self.StoragecurveDatasource = ColumnDataSource(data=dict(x0=[], x1=[], x2=[], y0=[], y1=[], y2=[]))
self.StrCapabilityPlot.line('x0', 'y0', source=self.StoragecurveDatasource, line_width=1, line_alpha=1.0,
line_color="red")
self.StrCapabilityPlot.line('x1', 'y1', source=self.StoragecurveDatasource, line_width=1, line_alpha=1.0,
line_color="red")
self.StrCapabilityPlot.line('x2', 'y2', source=self.StoragecurveDatasource, line_width=1, line_alpha=1.0,
line_color="red")
Storagewidgetbox = self.createStoragewidgetbox()
PVsettingsLayout = column(self.StrCapabilityPlot, Storagewidgetbox, width=800)
self.final_layout = PVsettingsLayout
for w in [self.kWratingSlider, self.PFslider]:
w.on_change('value', self.updateStoragecurves)
return
def generate(data, incoming_arrow_data, outgoing_arrow_data):
plot = figure(title="", x_axis_label='', y_axis_label='', sizing_mode="scale_height")
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.xaxis.major_tick_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.xaxis.major_label_text_color = '#ffffff'
plot.xaxis.major_label_text_font_size = '0px'
plot.xaxis.axis_line_color = "#ffffff"
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.yaxis.major_label_text_color = '#ffffff'
plot.yaxis.major_label_text_font_size = '0px'
plot.yaxis.axis_line_color = "#ffffff"
plot.toolbar.logo = None
plot.toolbar_location = None
source = ColumnDataSource(data)
mapper = LinearColorMapper(palette=colors_hex, low=0, high=len(colors_hex))
labels = LabelSet(x="x", y="y", text="value", text_align="center", text_font="helvetica", text_font_size="16pt", x_offset=0, y_offset=-12, source=source, render_mode='canvas')
plot.rect(x="x", y="y", width=1, height=1, source=source, fill_color={'field': 'value', 'transform': mapper}, line_color="#000000", line_alpha=0)
plot.add_layout(labels)
if incoming_arrow_data:
incoming_arrow_source = ColumnDataSource(incoming_arrow_data)
incoming_arrows = Arrow(end=VeeHead(fill_color="gray"), source=incoming_arrow_source, x_start='x_start', y_start='y_start', x_end='x_end', y_end='y_end', line_color="gray")
plot.add_layout(incoming_arrows)
if outgoing_arrow_data:
outgoing_arrow_source = ColumnDataSource(outgoing_arrow_data)
outgoing_arrows = Arrow(end=VeeHead(fill_color="black"), source=outgoing_arrow_source, x_start='x_start', y_start='y_start', x_end='x_end', y_end='y_end', line_color="black")
plot.add_layout(outgoing_arrows)
return plot
def _setup_graph_renderer(self, circle_size):
graph_renderer = GraphRenderer()
graph_renderer.node_renderer.data_source.add(
list(self.graph.vertices.keys()), "index")
graph_renderer.node_renderer.data_source.add(self._get_random_colors(),
"color")
graph_renderer.node_renderer.glyph = Circle(size=circle_size,
fill_color="color")
graph_renderer.edge_renderer.data_source.data = self._get_edge_indexes(
)
self.randomize()
for i in range(
len(graph_renderer.edge_renderer.data_source.data["start"])):
self.plot.add_layout(
Arrow(
end=VeeHead(size=20, fill_color="black"),
x_start=self.pos[graph_renderer.edge_renderer.data_source.
data["start"][i]][0],
y_start=self.pos[graph_renderer.edge_renderer.data_source.
data["start"][i]][1],
x_end=self.pos[graph_renderer.edge_renderer.data_source.
data["end"][i]][0],
y_end=self.pos[graph_renderer.edge_renderer.data_source.
data["end"][i]][1],
))
graph_renderer.layout_provider = StaticLayoutProvider(
graph_layout=self.pos)
self.plot.renderers.append(graph_renderer)
self._get_labels()
def test_arrow(output_file_url, selenium, screenshot):
# Have to specify x/y range as labels aren't included in the plot area solver
plot = figure(height=HEIGHT, width=WIDTH, x_range=(0,10), y_range=(0,10), tools='')
arrow1 = Arrow(x_start=1, y_start=3, x_end=6, y_end=8,
line_color='green', line_alpha=0.7,
line_dash='8 4', line_width=5, end=OpenHead()
)
arrow1.end.line_width=8
arrow2 = Arrow(x_start=2, y_start=2, x_end=7, y_end=7,
start=NormalHead(), end=VeeHead()
)
arrow2.start.fill_color = 'indigo'
arrow2.end.fill_color = 'orange'
arrow2.end.size = 50
plot.add_layout(arrow1)
plot.add_layout(arrow2)
# Save the plot and start the test
save(plot)
selenium.get(output_file_url)
# Take screenshot
assert screenshot.is_valid()
def arrows(pos_i=None, pos_f=None, p=None, names=None, color='black'):
if pos_i is None:
pos_i = np.zeros_like(pos_f)
for i, (x0, y0, xf, yf) in enumerate(np.hstack((pos_i, pos_f))):
p.add_layout(
Arrow(end=VeeHead(fill_color=color, line_color=color, size=10),
x_start=x0,
y_start=y0,
x_end=xf,
y_end=yf))
if names is not None:
vec_norm = np.sqrt((xf - x0)**2 + (yf - y0)**2)
labels = Label(x=xf,
y=yf,
text=names[i],
text_color=color,
x_offset=vec_norm * 0.2,
y_offset=vec_norm * 0.2)
p.add_layout(labels)
return p
def notebook_bokeh(stcs):
patch_xs = parse_s_region(stcs)['ra']
patch_ys = parse_s_region(stcs)['dec']
p = figure(plot_width=700,
x_axis_label="RA (deg)",
y_axis_label="Dec (deg)")
data = {'x': [patch_xs], 'y': [patch_ys]}
p.patches('x',
'y',
source=data,
fill_alpha=0.1,
line_color="black",
line_width=0.5)
p.add_layout(
Arrow(end=VeeHead(line_color="black", line_width=1),
line_width=2,
x_start=patch_xs[0],
y_start=patch_ys[0],
x_end=patch_xs[1],
y_end=patch_ys[1]))
p.y_range.flipped = True
output_notebook()
show(p)
def setup_timeline_backend_parts(plot: figure,
desc_label_source: ColumnDataSource) -> None:
"""
:param plot:
:param desc_label_source:
:return:
"""
start_date, end_date = plot.x_range.start, plot.x_range.end
arrow_x = start_date + datetime.timedelta(days=180)
# 補助線を引く
plot.line([start_date, end_date], [1, 1], line_width=3, line_color='pink')
plot.line([start_date, end_date], [0.5, 0.5],
line_width=3,
line_dash='dotted',
line_color='pink')
plot.line([start_date, end_date], [1.5, 1.5],
line_width=3,
line_dash='dotted',
line_color='pink')
# 矢印を表示する
plot.add_layout(
Arrow(end=VeeHead(size=15),
line_color='black',
x_start=arrow_x,
y_start=1.4,
x_end=arrow_x,
y_end=1.1))
plot.add_layout(
Arrow(end=VeeHead(size=15),
line_color='black',
x_start=arrow_x,
y_start=0.9,
x_end=arrow_x,
y_end=0.6))
plot.text(source=desc_label_source,
x='x',
y='y',
text='text',
text_font_size='size',
text_alpha=0.8)
def add_arrows(canvas, branch, color, pf_threshold=0, dist_threshold=0, n=1):
"""Add addorws for powerflow to figure.
:param bokeh.plotting.figure.Figure canvas: canvas to plot arrows onto.
:param pandas.DataFrame branch: data frame containing:
'pf', 'dist', 'arrow_size', 'from_x', 'from_y', 'to_x', 'to_y'.
x/y coordinates for to/from can be obtained from lat/lon coordinates
using :func:`postreise.plot.projection_helpers.project_branch`.
:param str color: arrow line color.
:param int/float pf_threshold: minimum power flow for a branch to get arrow(s).
:param int/float pf_threshold: minimum distance for a branch to get arrow(s).
:param int n: number of arrows to plot along each branch.
"""
positive_arrows = branch.loc[(branch.pf > pf_threshold)
& (branch.dist > dist_threshold)]
negative_arrows = branch.loc[(branch.pf < -1 * pf_threshold)
& (branch.dist > dist_threshold)]
# Swap direction of negative arrows
negative_arrows = negative_arrows.rename(columns={
"from_x": "to_x",
"to_x": "from_x",
"to_y": "from_y",
"from_y": "to_y"
})
# Finally, plot arrows
arrows = pd.concat([positive_arrows, negative_arrows])
for i in range(n):
start_fraction = i / n
end_fraction = (i + 1) / n
arrows.apply(
lambda a: canvas.add_layout(
Arrow(
end=VeeHead(
line_color="black",
fill_color="gray",
line_width=2,
fill_alpha=0.5,
line_alpha=0.5,
size=a["arrow_size"],
),
x_start=(a["from_x"] +
(a["to_x"] - a["from_x"]) * start_fraction),
y_start=(a["from_y"] +
(a["to_y"] - a["from_y"]) * start_fraction),
x_end=(a["from_x"] +
(a["to_x"] - a["from_x"]) * end_fraction),
y_end=(a["from_y"] +
(a["to_y"] - a["from_y"]) * end_fraction),
line_color=color,
line_alpha=0.7,
)),
axis=1,
)
def update() -> None:
if toggles['pause'].active:
return
ds_now = data_sources['now'].data
ds_history = data_sources['history'].data
if ds_now['epoch'][0] + 1 > sliders['max_epoch'].value:
return
batches_x, batches_y = create_batches()
for batch_x, batch_y in zip(batches_x, batches_y):
weight_previous = tensors['weight_model']
weight_updated = gradient_descent(batch_y, batch_x, weight_previous)
arrow_head = VeeHead(size=5,
fill_color='darkgrey',
line_color='darkgrey')
arrow = Arrow(end=arrow_head,
line_color='darkgrey',
x_start=ds_history['weight_0'][-1],
y_start=ds_history['weight_1'][-1],
x_end=weight_updated.numpy()[0, 0],
y_end=weight_updated.numpy()[1, 0])
fig.add_layout(arrow)
loss = loss_function(tensors['y_true'],
pass_neuron(tensors['X'],
weight_updated)).numpy()[0]
ds_history['weight_0'] += [weight_updated.numpy()[0, 0]]
ds_history['weight_1'] += [weight_updated.numpy()[1, 0]]
ds_now['loss'] = [loss]
ds_now['weight_0'] = [weight_updated.numpy()[0, 0]]
ds_now['weight_1'] = [weight_updated.numpy()[1, 0]]
tensors['weight_model'] = weight_updated
ds_now['epoch'] = [ds_now['epoch'][0] + 1]
for arrow in fig.center[2:-sliders['num_samples'].value]:
arrow.line_color = 'lightgrey'
arrow.end.fill_color = 'lightgrey'
arrow.end.line_color = 'lightgrey'
return
def create_plot(self):
super().create_plot()
self.plot.renderers[0].edge_renderer.data_source.data[
'edge_data'] = self.y
G = nx.convert_node_labels_to_integers(self.G)
layout_coords = pd.DataFrame(
[[
self.layout[x1][0], self.layout[x1][1], self.layout[x2][0],
self.layout[x2][1]
] for (x1, x2) in G.edges()],
columns=['x_start', 'y_start', 'x_end', 'y_end'])
layout_coords['x_end'] = (
layout_coords['x_end'] -
layout_coords['x_start']) / 2 + layout_coords['x_start']
layout_coords['y_end'] = (
layout_coords['y_end'] -
layout_coords['y_start']) / 2 + layout_coords['y_start']
self.plot.renderers[0].edge_renderer.data_source.data[
'x_start'] = layout_coords['x_start']
self.plot.renderers[0].edge_renderer.data_source.data[
'y_start'] = layout_coords['y_start']
self.plot.renderers[0].edge_renderer.data_source.data[
'x_end'] = layout_coords['x_end']
self.plot.renderers[0].edge_renderer.data_source.data[
'y_end'] = layout_coords['y_end']
self.plot.renderers[0].edge_renderer.glyph = MultiLine(
line_color=linear_cmap('edge_data', self.palette, self.lo,
self.hi),
line_width=5)
if self.show_bar:
cbar = ColorBar(color_mapper=LinearColorMapper(
palette=self.palette, low=self.lo, high=self.hi),
ticker=BasicTicker(),
title=self.desc)
self.plot.add_layout(cbar, 'right')
arrows = Arrow(end=VeeHead(size=8),
x_start='x_start',
y_start='y_start',
x_end='x_end',
y_end='y_end',
line_width=0,
source=self.plot.renderers[0].edge_renderer.data_source)
self.plot.add_layout(arrows)
# self.plot.tooltips.append((self.desc, '@edge_data'))
return self.plot
def _setup_graph_renderer(self, circle_size):
# define function to render graph
graph_renderer = GraphRenderer()
graph_renderer.node_renderer.data_source.add(
list(self.graph.vertices.keys()), "index"
)
# adds color randomizer
graph_renderer.node_renderer.data_source.add(self._get_random_colors(), "color")
# gives each node the shape of a circle with it's specified size and random color
graph_renderer.node_renderer.glyph = Circle(
size=circle_size, fill_color="color"
)
# adds function to get edge indexes
graph_renderer.edge_renderer.data_source.data = self._get_edge_indexes()
# randomize each new graph
self.randomize()
for i in range(len(graph_renderer.edge_renderer.data_source.data["start"])):
# add arrows from starting node to end node
self.plot.add_layout(
Arrow(
# Arrow head size and color
end=VeeHead(size=20, fill_color="black"),
# arrow start at x, y pos of vertex
x_start=self.pos[
graph_renderer.edge_renderer.data_source.data["start"][i]
][0],
y_start=self.pos[
graph_renderer.edge_renderer.data_source.data["start"][i]
][1],
x_end=self.pos[
graph_renderer.edge_renderer.data_source.data["end"][i]
][0],
y_end=self.pos[
graph_renderer.edge_renderer.data_source.data["end"][i]
][1],
)
)
graph_renderer.layout_provider = StaticLayoutProvider(graph_layout=self.pos)
self.plot.renderers.append(graph_renderer)
self._get_labels()
from bokeh.plotting import figure, output_file, show
from bokeh.models import Arrow, OpenHead, NormalHead, VeeHead
output_file("arrow.html", title="arrow.py example")
p = figure(plot_width=600,
plot_height=600,
x_range=(-0.1, 1.1),
y_range=(-0.1, 0.8))
p.circle(x=[0, 1, 0.5],
y=[0, 0, 0.7],
radius=0.1,
color=["navy", "yellow", "red"],
fill_alpha=0.1)
p.add_layout(Arrow(end=OpenHead(), x_start=0, y_start=0, x_end=1, y_end=0))
p.add_layout(
Arrow(end=NormalHead(), x_start=1, y_start=0, x_end=0.5, y_end=0.7))
p.add_layout(Arrow(end=VeeHead(), x_start=0.5, y_start=0.7, x_end=0, y_end=0))
show(p)
def plot(self):
lifemap = figure(plot_width=750,
plot_height=570,
toolbar_location='below')
lifemap.xgrid.visible = False
lifemap.ygrid.visible = False
lifemap.xaxis.visible = False
lifemap.yaxis.visible = False
self.network_reach.network.plot(lifemap)
reachids = list(np.array(self.reach_history).T[2])
pointx, pointy = list(
np.array([
self.network_reach.network.reach_with_id(id).midpoint
for id in reachids
]).T)
source = ColumnDataSource({
'xs': pointx,
'ys': pointy,
'reach_id': reachids
})
lifemap.scatter('xs',
'ys',
source=source,
name='scatterplot',
marker='circle',
size=10,
line_color='#cb7723',
fill_color='#fcb001',
alpha=1.0)
hover_tooltips = [('reach', '@reach_id')]
lifemap.add_tools(
HoverTool(tooltips=hover_tooltips, names=['scatterplot']))
lifemap.add_layout(
Label(x=30,
y=700,
x_units='screen',
y_units='screen',
text="Fish ID {0}".format(self.unique_id)))
title, lifetext, lifesource = self.activity_descriptors()
lifemap.title.text = title
lifemap.title.text_font_size = "10px"
lifemap.toolbar.logo = None
for i in range(1, len(pointx)):
lifemap.add_layout(
Arrow(end=VeeHead(size=10,
line_color='#cb7723',
fill_color='#fcb001'),
line_color='#fcb001',
x_start=pointx[i - 1],
y_start=pointy[i - 1],
x_end=pointx[i],
y_end=pointy[i]))
columns = [
TableColumn(field="age_weeks", title="Week", width=40),
TableColumn(field="age_years", title="Age", width=45),
TableColumn(field="life_event", title="Life Event", width=315)
]
data_table = DataTable(source=lifesource,
columns=columns,
row_headers=False,
width=400,
height=600)
lyt = row([
data_table, lifemap,
column([self.plot_growth(),
self.plot_temperature()])
],
sizing_mode='fixed')
return lyt
size=3 * s, angle=0, fill_alpha=0.9, line_color=None, color='firebrick')
# arrow
if parameters["windQuad"][case] == -3:
lw = 0
la = 0.1
elif parameters["windQuad"][case] == -2:
lw = 4
la = 1
else:
lw = 10
la = 1
# print 'le is',lw
if addArrow:
fig.add_layout(Arrow(end=VeeHead(size=20), line_color="red", line_alpha=la,
x_start=xs, y_start=ys, line_width=lw,
x_end=xs + r * np.cos(np.deg2rad(theta)), y_end=ys + r * np.sin(np.deg2rad(theta))))
return fig
def bokehQuadPlot(df=None, parameters=None, fig=None,
TOOLS="pan,crosshair,wheel_zoom\
,box_zoom,reset,box_select,lasso_select,undo,redo,save",
x_range=(506, 526), y_range=(506, 526),
output_backend="webgl", plot_width=500, plot_height=500,
s=4, title=None, addOdour=True,
addSmallTit=True, addStart=True, addArrow=True,
def makehtml_model(html_model_template, event, models, datasets, trange=None, crossrefs=None):
"""Create individual model of individual event html page"""
# set I/O shell display
tcol, tun, tend, tit = "\033[0m\033[35m", "\033[0m\033[1m\033[35m", "\033[0m", "\033[0m\033[3m"
# generate one page per individual model
mc = MagnificationCurve()
lc = LightCurve(datasets, trange=trange, dmag=None)
plt_width = 800
for i in range(len(models)):
model = models[i]
# html page name
printi(tcol + "Creating " + tit + "'" + event + '/' + event + '_' + str(i + 1) + '.html' + "'" + tcol + " web page" + tend)
# fit light curve using best-fit parameters
mc.create(model)
lc.fit(mc, 'central', '+', init=None)
# ligth curve plot template (bind x-axis on light curve plot, with automatic t-range)
plc = bplt.figure(width=plt_width, plot_height=500, title='Light curve', tools=["pan", "wheel_zoom", "box_zoom", "undo", "redo", "reset", "save"], active_drag="pan", active_scroll="wheel_zoom")
if trange:
tmin = lc.params['t0'] - 1.5 * lc.params['tE']
tmax = lc.params['t0'] + 1.5 * lc.params['tE']
plc.x_range = Range1d(tmin, tmax)
# residuals plot template
pres = bplt.figure(width=plt_width, plot_height=200, title='Residuals', x_range=plc.x_range, y_range=(-0.1, 0.1), tools=["pan", "wheel_zoom", "reset", "save"], active_drag="pan", active_scroll="wheel_zoom")
# caustics plot template
plt_height = 400
pcc = bplt.figure(width=plt_width, plot_height=plt_height, title='Source trajectory', tools=["pan", "wheel_zoom", "box_zoom", "undo", "redo", "reset", "save"], active_drag="pan", active_scroll="wheel_zoom", match_aspect=True, x_range=(- float(plt_width)/plt_height, float(plt_width)/plt_height), y_range=(-1, 1))
# plot caustics
cc = Caustics(mc.params['s'], mc.params['q'], N=256, cusp=True)
z = np.ravel(cc.zetac)
pcc.circle(np.real(z), np.imag(z), size=1, alpha=1., color='red')
# plot trajectory
traj = lambda t: (-lc.params['u0'] * np.sin(lc.params['alpha']) + np.cos(lc.params['alpha']) * t, lc.params['u0'] * np.cos(lc.params['alpha']) + np.sin(lc.params['alpha']) * t)
pcc.line(traj(np.array([-10., 10.]))[0], traj(np.array([-10., 10.]))[1], color='firebrick', line_width=1)
pcc.add_layout(Arrow(line_width=1, line_color='firebrick', end=VeeHead(size=10, line_color='firebrick'), x_start=traj((tmin - lc.params['t0'])/lc.params['tE'])[0], y_start=traj((tmin - lc.params['t0'])/lc.params['tE'])[1], x_end=traj((tmax - lc.params['t0'])/lc.params['tE'])[0], y_end=traj((tmax - lc.params['t0'])/lc.params['tE'])[1]))
# compute blended flux
flux = lc._mu * lc.params['Fs'][0] + lc.params['Fb'][0]
t = lc._t * lc.params['tE'] + lc.params['t0']
# remove points with negative flux and NaN
arg = np.logical_not(np.isnan(flux)) & (flux > 0.)
flux = flux[arg]
t = t[arg]
# invert light curve y-axis
plc.y_range = Range1d(1.02 * np.max(-2.5 * np.log10(flux)), 0.98 * np.min(-2.5 * np.log10(flux)))
# theoretical ligth curve
plc.line(t, -2.5 * np.log10(flux), color='firebrick')
# add data on plot
atel = iter(color_tel)
for k in range(len(lc._datalist)):
dat = lc._datalist[k]
# color and legend
_ , datai = split(dat[6])
tel = datai[0]
if tel not in color_tel:
tel = atel.next()
color = color_tel[tel]
# deblending
magnif = (np.power(10., - dat[2] / 2.5) - lc.params['Fb'][k]) / lc.params['Fs'][k]
flux = magnif * lc.params['Fs'][0] + lc.params['Fb'][0]
# remove points with negative flux and NaN, and compute mag
date = dat[1]
errbar = dat[3]
arg = np.logical_not(np.isnan(flux)) & (flux > 0.)
flux = flux[arg]
date = date[arg]
errbar = errbar[arg]
mag = - 2.5 * np.log10(flux)
mu = lc.content['mc']((date - lc.params['t0']) / lc.params['tE'])
res = - 2.5 * np.log10(mu * lc.params['Fs'][0] + lc.params['Fb'][0]) - mag
# plot light curve with error bars
plc.circle(date, mag, size=4, alpha=1., color=color, legend=tel)
y_err_x, y_err_y = [], []
for px, py, err in zip(date, mag, errbar):
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
plc.multi_line(y_err_x, y_err_y, color=color, alpha=0.4)
# plot residuals with error bars
pres.line((0., 1e6), (0., 0.), color='black', line_width=0.2)
pres.circle(date, res, size=4, alpha=1., color=color)
y_err_x, y_err_y = [], []
for px, py, err in zip(date, res, errbar):
y_err_x.append((px, px))
y_err_y.append((py - err, py + err))
pres.multi_line(y_err_x, y_err_y, color=color, alpha=0.4)
# color points at data dates on trajectory
pcc.circle(traj((date - lc.params['t0'])/lc.params['tE'])[0], traj((date - lc.params['t0'])/lc.params['tE'])[1], size=4, alpha=1., color=color)
# group plots in one column and generate best-fits html code
p = column([plc, pres, pcc])
script, divini = components(p)
div = '<div align="center">' + divini + '</div>'
# create search map html code
wsmap = '<img class="imageStyle" alt="' + event + '" src="./' + event + '.png' + '" width="800" />'
# label version
version = str(version_info).replace(', ', '.').replace('(', '(version ')
# list of best fit paramters to download
fitrank = event + '_rank.txt'
# date and time of event's update
if os.path.isfile(event + '/lastupdate.txt'):
with open(event + '/lastupdate.txt', 'r') as f:
fittime = f.read()
f.close()
else:
fittime = ''
# get multiple references of event
if crossrefs:
evname = getcrossrefs(crossrefs, event)
else:
evname = event
# write parameters
eventdet = 'Microlensing event ' + evname + ' : details of binary-lens model ' + str(i + 1)
lpar = '<i>Model paramaters</i>'
lpar += '<p align="left" style="color:#B22222">$s$={0:<.8f}<BR>$q$={1:<.8f}<BR>$u_0$={2:<.8f}<BR>$\\alpha$={3:<.5f}<BR>$t_E$={4:<.3f}<BR>$t_0$={5:<.6f}<BR>$\\rho$={6:<.8f}<BR>'.format(lc.params['s'], lc.params['q'], lc.params['u0'], lc.params['alpha'], lc.params['tE'], lc.params['t0'], lc.params['rho'])
if 'piEN' in lc.params.keys():
lpar += '$\\pi_{E, N}$={6:<.8f}<BR>'
if 'piEE' in lc.params.keys():
lpar += '$\\pi_{E, E}$={6:<.8f}<BR>'
if 'ds' in lc.params.keys():
lpar += '$ds/dt$={6:<.8f}<BR>'
if 'dalpha' in lc.params.keys():
lpar += '$d\\alpha/dt$={6:<.8f}<BR>'
lpar += '</p><BR>'
lpar += '<i>Light curve, residuals, source trajectory and caustics</i>'
# create model selection list
selmod = '<select name="display" onchange="location=this.value;">'
selmod += '<option value="/miiriads/MiSMap/Events/' + event + '.html">Overview</option>'
for j in range(len(models)):
if j == i:
selmod += '<option value="/miiriads/MiSMap/Events/' + event + '_' + str(j + 1) + '.html" selected>Model ' + str(j + 1) + '</option>'
else:
selmod += '<option value="/miiriads/MiSMap/Events/' + event + '_' + str(j + 1) + '.html">Model ' + str(j + 1) + '</option>'
selmod += '</select>'
# fill template html page
fill_webpage(html_model_template, event + '/' + event + '_' + str(i + 1) + '.html', [('_LASTUPDATE_', fittime), ('_VERSION_', version), ('_EVENT_', evname), ('_EVENTDET_', eventdet), ('_MODEL_', div), ('_PARAMS_', lpar), ('_SELECT_', selmod), ('_RANK_', fitrank), ('<!-- _SCRIPT_ -->', script)])
def drawGasPlot(combinedData, chemical, time, numToDateHashMap):
p = figure(title="Gas Measure of " + chemical,
x_axis_location=None,
y_axis_location=None,
x_axis_label='Map',
y_axis_label='Map',
tools="pan,wheel_zoom,reset,hover,save")
# p.circle(x = facs['x'], y = facs['y'], color = 'blue', size = 15)
sensor_measurements = findRanksBasedOnChemicalMeasurement(
combinedData, chemical, time)
rankSize = 10
print('wind direction is')
print(combinedData[time][-1][1])
angle = combinedData[time][-1][1]
print('sin shift {} and cos shift {}'.format(math.sin(math.radians(angle)),
math.cos(
math.radians(angle))))
print("date is ", numToDateHashMap[time])
for pair in sensor_measurements:
if pair[0] == 1.0:
p.circle(61,
21,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 2.0:
p.circle(66,
35,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 3.0:
p.circle(76,
41,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 4.0:
p.circle(88,
45,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 5.0:
p.circle(103,
43,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 6.0:
p.circle(102,
22,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 7.0:
p.circle(89,
3,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 8.0:
p.circle(74,
7,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
elif pair[0] == 9.0:
p.circle(119,
42,
size=pair[1] * 50,
line_color="green",
fill_color="green",
fill_alpha=0.5)
rankSize += 10
#show(p)
p.circle(x=50, y=0, fill_alpha=0, size=1)
p.circle(x=110, y=60, fill_alpha=0, size=1)
p.add_layout(
Arrow(end=VeeHead(size=35, fill_color='red'),
line_color="red",
x_start=65,
y_start=53,
x_end=65 - math.sin(math.radians(angle)),
y_end=53 - math.cos(math.radians(angle))))
speed = Label(x=65,
y=56,
background_fill_color='white',
text='wind speed: ' + str(combinedData[time][-1][3]))
p.add_layout(speed)
p.image_url(url=['https://i.imgur.com/Lz7D8KN.jpg'],
x=46,
y=65,
w=82,
h=70,
global_alpha=.5)
return p
# create a reference circle
p.circle(x=[0],
y=[0],
radius=circle_radius,
fill_alpha=0.0,
line_alpha=1.0,
color='black')
# create the arrows and hovertool layout ontop of multiline
cat10_palette = bokeh.palettes.Category10[10]
arrow_line_palette = [cat10_palette[3], cat10_palette[2]] # red and green
arrow_mapper = CategoricalColorMapper(palette=arrow_line_palette,
factors=['neg', 'pos'])
p.add_layout(
Arrow(end=VeeHead(size=10,
fill_alpha=0.50,
line_alpha=0.50,
fill_color=cat10_palette[7]),
source=source_arrows,
x_start='x_from',
y_start='y_from',
x_end='x_to',
y_end='y_to',
line_alpha=0.50,
line_color={
'field': 'Sentiment',
'transform': arrow_mapper
}))
p.multi_line(
source=source_arrows,
xs='xs',
ys='ys',
def figure_scatter_values(df_chisq):
df_chisq["casema07_diff07"] = df_chisq.case_ma07.diff(periods=1)
df_chisq["testsma07_diff07"] = df_chisq.tests_ma07.diff(periods=1)
df_chisq["casedet_diff07"] = df_chisq.case_detrended.diff(periods=1)
df_chisq["casedetpct_diff07"] = df_chisq.caseDet_pct.diff(periods=1)
df_chisq[
"angle"] = df_chisq.testsma07_diff07 / df_chisq.casema07_diff07 * 3.14
df_chisq["casema07_start"] = df_chisq.case_ma07 - df_chisq.casema07_diff07
df_chisq[
"testsma07_start"] = df_chisq.tests_ma07 - df_chisq.testsma07_diff07
df_chisq[
"casedet_start"] = df_chisq.case_detrended - df_chisq.casedet_diff07
df_chisq[
"casedetpct_start"] = df_chisq.caseDet_pct - df_chisq.casedetpct_diff07
df_chisq["dt_str"] = df_chisq.Date.dt.strftime("%Y-%m-%d")
# FIXME
# df_chisq.set_index(["CountryProv","Date"]).tail()[['case_ma07', 'tests_ma07', 'casema07_diff07', 'testsma07_diff07', 'casema07_start', 'testsma07_start']]
print("gathering moving 14-day windows")
#df_sub = df_chisq[df_chisq.Date >= "2020-04-28"]
df_sub = df_chisq
df_latest = []
dtmax_n = df_sub.Date.unique().max()
dtmin_n = df_sub.Date.unique().min()
import datetime as dt
#dt_range = df_sub.Date.unique()
dt_range = np.arange(dtmax_n, dtmin_n, dt.timedelta(days=-14))
#dtmax_s = str(dtmax_n)[:10] # http://stackoverflow.com/questions/28327101/ddg#28327650
for dt_i in dt_range:
dt_delta = (dt_i - dtmin_n).astype('timedelta64[D]').astype(int)
if dt_delta < 14: continue
print(dt_i, dt_delta)
df_i = df_sub[df_sub.Date <= dt_i]
df_i = df_i.groupby("CountryProv").apply(
lambda g: g.tail(14)).reset_index(drop=True)
df_i["color"] = "#73b2ff"
df_i["dtLast"] = dt_i
df_latest.append(df_i)
if len(df_latest) == 0: raise Exception("No data in moving window")
df_latest = pd.concat(df_latest, axis=0)
df_latest["display_cpcode"] = df_latest.apply(
lambda g: "" if g.dtLast != g.Date else g.cp_code, axis=1)
print("done")
#source_hist = ColumnDataSource(df_chisq)
#source_latest = ColumnDataSource(df_latest)
# since cannot use View iwth LabelSet, creating a different source per continent
# Couldn't figure out how to filter the datasource in add_layout or Arrow,
# so just grouping on both continent and dtLast
srcLatest_continent = df_latest.groupby(
["Continent", "dtLast"]).apply(lambda g: ColumnDataSource(g))
srcLatest_continent = srcLatest_continent.reset_index().rename(
columns={0: "src"})
plot_size_and_tools = {
'plot_height': 300,
'plot_width': 600,
'tools': ['box_select', 'reset', 'help', 'box_zoom'],
'x_axis_type': 'datetime'
}
# general-use lines
slope_y0 = Slope(gradient=0,
y_intercept=0,
line_color='orange',
line_width=50)
slope_x0 = Slope(gradient=np.Inf,
y_intercept=0,
line_color='orange',
line_width=50)
# scatter plot
TOOLTIPS = [
("Country/Region", "@CountryProv"),
("Date", "@dt_str"),
]
# first set for case vs tests, then second set for case diff vs test diff
params = (
#('values', 'tests_ma07', 'case_ma07', 'testsma07_start', 'casema07_start', 'ma07(Tests)', 'ma07(Cases)'),
#('diffs', 'casema07_diff07', 'testsma07_diff07', 'diff07(ma07(Cases))', 'diff07(ma07(Tests))'),
('values', 'case_detrended', 'case_ma07', 'casedet_start',
'casema07_start', 'detrended(cases)', 'ma07(Cases)'),
#('values', 'caseDet_pct', 'case_ma07', 'casedetpct_start', 'casema07_start', 'detrended(ma07(cases))/cases*100', 'ma07(Cases)'),
)
p_all = {'values': [], 'diffs': []}
from bokeh.models import Arrow, NormalHead, OpenHead, VeeHead
for k, fdxv, fdyv, fdxs, fdys, labx, laby in params:
p_cont = []
for srcCont_i in srcLatest_continent.iterrows():
srcCont_i = srcCont_i[1]
print("Adding plot for %s, %s" %
(srcCont_i.Continent, srcCont_i.dtLast))
#init_group=dtmax_s
#gf = GroupFilter(column_name='dtLast', group=init_group)
#view1 = CDSView(source=srcCont_i.src, filters=[gf])
p_d1 = figure(plot_width=600,
plot_height=400,
tooltips=TOOLTIPS,
title="%s %s" %
(srcCont_i.Continent, srcCont_i.dtLast))
#p_d1.triangle(fdxv, fdyv, source=srcCont_i.src, size=12, color='blue', angle="angle")
#p_d1.scatter(fdxs, fdys, source=srcCont_i.src, size=3, color='red') #, view=view1)
p_d1.scatter(fdxv, fdyv, source=srcCont_i.src, size=3, color='red')
p_d1.add_layout(
Arrow(end=VeeHead(size=6),
x_start=fdxs,
y_start=fdys,
x_end=fdxv,
y_end=fdyv,
line_color='blue',
source=srcCont_i.src
#view=view1 # srcCont_i.src
) #,
#view=view1 # not supported
)
p_d1.xaxis.axis_label = labx
p_d1.yaxis.axis_label = laby
from bokeh.models import LabelSet
labels = LabelSet(x=fdxv,
y=fdyv,
text='display_cpcode',
level='glyph',
x_offset=5,
y_offset=5,
source=srcCont_i.src,
render_mode='canvas')
p_d1.add_layout(labels)
p_d1.add_layout(slope_y0)
p_d1.add_layout(slope_x0)
p_cont.append(p_d1)
p_all[k] = p_cont
# group plots into 3 per row
# https://stackoverflow.com/a/1625013/4126114
from itertools import zip_longest
for k in ['values', 'diffs']:
p_cont = p_all[k]
p_cont = list(zip_longest(*(iter(p_cont), ) * 3))
p_cont = [[e for e in t if e != None] for t in p_cont]
p_all[k] = p_cont
g = gridplot(p_all['values'] + p_all['diffs'])
layout = column(g)
return layout
p.circle(x=[0, 1, 0.5],
y=[0, 0, 0.7],
radius=0.1,
color=["navy", "yellow", "red"],
fill_alpha=0.1)
p.add_layout(
Arrow(end=OpenHead(line_color="firebrick", line_width=4),
x_start=0,
y_start=0,
x_end=1,
y_end=0))
p.add_layout(
Arrow(end=NormalHead(fill_color="orange"),
x_start=1,
y_start=0,
x_end=0.5,
y_end=0.7))
p.add_layout(
Arrow(end=VeeHead(size=35),
line_color="red",
x_start=0.5,
y_start=0.7,
x_end=0,
y_end=0))
show(p)
def draw_network_graph_given(self, edges, nodes, probsa, vdata, eOnly):
# probs = dict key: nodename, value: dict(probname:probval)
probs = dict()
if not eOnly:
for nam in nodes:
if not nam in probsa: cond = "[]"
else: cond = probsa[nam]
probs[nam] = dict()
try:
cur_dist = vdata[nam]["cprob"][cond]
#print("No cprob given.")
except:
pass
for i in range(len(vdata[nam]["vals"])):
try:
probs[nam][vdata[nam]["vals"][i]] = cur_dist[i]
except:
pass
G = nx.DiGraph()
if not nodes:
nodes = []
for e in edges:
if e[0] not in nodes:
nodes.append(e[0])
if e[1] not in nodes:
nodes.append(e[1])
if eOnly:
edges_new = []
for e in edges:
if e[0] in nodes and e[1] in nodes:
edges_new.append(e)
edges = edges_new
G.add_edges_from(edges, weight="")
#edge_labels=dict([((u,v,),d['weight'])
# for u,v,d in G.edges(data=True)])
#edge_colors = len(G.edges())*['black']
#pos=nx.shell_layout(G) # circular_layout random_layout shell_layout spring_layout spectral_layout
#nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)
#nx.draw(G,pos, node_size=3000,edge_color=edge_colors)
#labels ={}
#for n in nodes:
# labels[n] = n
#nx.draw_networkx_labels(G, pos, labels, font_size=10)
# PLOT
plot = Plot(plot_width=1300,
plot_height=800,
x_range=Range1d(-1.1, 1.1),
y_range=Range1d(-1.1, 1.1))
plot.title.text = "Temporal Dependency Bayesian Network"
hover = HoverTool(tooltips=[
("desc", "@desc"),
])
plot.add_tools(hover, TapTool(), BoxSelectTool())
plot.add_tools(WheelZoomTool())
plot.add_tools(PanTool())
graph_renderer = from_networkx(G,
nx.spring_layout,
scale=1,
center=(0, 0))
desc_probs = []
for n in nodes:
try:
nodeCur = probs[n]
pr = ""
for k in nodeCur.keys():
pr += "\nP(" + str(k) + ")=" + str(nodeCur[k]) + ""
desc_probs += [pr]
except:
desc_probs += [""]
node_source = ColumnDataSource(data=dict(index=nodes, desc=desc_probs))
graph_renderer.node_renderer.data_source.data = node_source.data
graph_renderer.node_renderer.glyph = Circle(size=15,
fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(
size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(
size=15, fill_color=Spectral4[1])
graph_renderer.edge_renderer.glyph = MultiLine(line_color="firebrick",
line_alpha=0.8,
line_width=1.1)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
line_color=Spectral4[1], line_width=5)
plot.renderers.append(graph_renderer)
graph_renderer.selection_policy = NodesAndLinkedEdges()
#graph_renderer.inspection_policy = EdgesAndLinkedNodes()
# PLOT ARROWS FOR EDGES
pos_dict = graph_renderer.layout_provider.graph_layout
done = []
for e in edges:
plot.add_layout(
Arrow(end=VeeHead(line_width=2, size=4),
x_start=pos_dict[e[0]][0],
y_start=pos_dict[e[0]][1],
x_end=pos_dict[e[1]][0],
y_end=pos_dict[e[1]][1]))
font_size = "8pt"
if not str(e[0]) in done:
done += [str(e[0])]
plot.add_layout(
Label(x=pos_dict[e[0]][0],
y=pos_dict[e[0]][1] + 0.01,
text=str(e[0]),
text_font_size=font_size,
render_mode='css',
background_fill_alpha=1.0))
if not str(e[1]) in done:
done += [str(e[1])]
plot.add_layout(
Label(x=pos_dict[e[1]][0],
y=pos_dict[e[1]][1] + 0.01,
text=str(e[1]),
text_font_size=font_size,
render_mode='css',
background_fill_alpha=1.0))
graph_renderer.edge_renderer.glyph.line_join = 'round'
output_file("interactive_graphs.html")
show(plot)
return
def __init__(self):
self.InvCapabilityPlot = figure(plot_width=580,
plot_height=350,
tools='',
title="Inverter capability curve")
self.PVSettingsPlot = figure(plot_width=580,
plot_height=350,
tools='',
title="PV settings")
self.VoltVarDatasource = ColumnDataSource(data=dict(x=[], y=[]))
self.PVSettingsPlot.line('x',
'y',
source=self.VoltVarDatasource,
line_width=2,
line_alpha=0.6,
legend='volt/var',
line_color="blue")
self.VoltWattDatasource = ColumnDataSource(data=dict(x=[], y=[]))
self.PVSettingsPlot.line('x',
'y',
source=self.VoltWattDatasource,
line_width=2,
line_alpha=0.6,
legend='volt/watt',
line_color="red")
self.InvCapabilityPlot.xaxis.axis_label = 'Q [p.u]'
self.InvCapabilityPlot.yaxis.axis_label = 'P [p.u]'
self.InvCapabilityPlot.toolbar.logo = None
self.InvCapabilityPlot.toolbar_location = None
self.InvCapabilityPlot.y_range = Range1d(*(-0.05, 1.15))
self.InvCapabilityPlot.x_range = Range1d(*(-1.15, 1.15))
self.InvCapabilityPlot.circle(x=[0],
y=[0],
radius=0.001,
color=["black"])
self.InvCapabilityPlot.add_layout(
Arrow(end=VeeHead(size=10),
line_color="black",
x_start=0.0,
y_start=0,
x_end=0,
y_end=1.1))
self.InvCapabilityPlot.add_layout(
Arrow(end=VeeHead(size=10),
line_color="black",
x_start=-1.1,
y_start=0,
x_end=1.1,
y_end=0))
self.InvCapabilityPlot.arc(x=[0],
y=[0],
radius=1,
start_angle=0,
end_angle=3.142,
color="navy")
self.PVcurveDatasource = ColumnDataSource(data=dict(x0=[],
x1=[],
x2=[],
x3=[],
x4=[],
x5=[],
x6=[],
y0=[],
y1=[],
y2=[],
y3=[],
y4=[],
y5=[],
y6=[]))
self.InvCapabilityPlot.line('x0',
'y0',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x1',
'y1',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x2',
'y2',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x3',
'y3',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x4',
'y4',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x5',
'y5',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.InvCapabilityPlot.line('x6',
'y6',
source=self.PVcurveDatasource,
line_width=1,
line_alpha=1.0,
line_color="red")
self.PVSettingsPlot.xaxis.axis_label = 'Voltage [p.u]'
self.PVSettingsPlot.yaxis.axis_label = 'P,Q [p.u]'
self.PVSettingsPlot.toolbar.logo = None
self.PVSettingsPlot.toolbar_location = None
self.PVSettingsPlot.legend.location = "bottom_left"
self.PVSettingsPlot.legend.click_policy = "hide"
self.PVSettingsPlot.x_range = Range1d(*(0.9, 1.2))
self.PVSettingsPlot.y_range = Range1d(*(-1.15, 1.15))
self.PVSettingsPlot.circle(x=[1], y=[0], radius=0.001, color=["black"])
self.ArrowHead1 = Arrow(end=VeeHead(size=10),
line_color="black",
x_start=1.0,
y_start=-1.1,
x_end=1,
y_end=1.1)
self.ArrowHead2 = Arrow(end=VeeHead(size=10),
line_color="black",
x_start=0.9,
y_start=0,
x_end=1.2,
y_end=0)
self.PVSettingsPlot.add_layout(self.ArrowHead1)
self.PVSettingsPlot.add_layout(self.ArrowHead2)
PVwidgetbox = self.createPVwidgetbox()
PVsettingsLayout = column(self.InvCapabilityPlot,
self.PVSettingsPlot,
PVwidgetbox,
width=800)
self.final_layout = PVsettingsLayout
for w in [
self.PVvar, self.Vlb, self.Vub, self.Vdblb, self.Vdbub,
self.VWlb, self.VWub, self.VWPmin, self.VWumin,
self.PVRatingTB, self.PVcutinTB, self.PVcutoutTB, self.PVpf
]:
w.on_change('value', self.updatePVcurves)
self.updatePVcurves(None, None, None)
return
y_start=4,
x_end=6,
y_end=9,
line_color='green',
line_alpha=0.7,
line_dash='8 4',
line_width=5,
end=OpenHead())
arrow1.end.line_width = 8
arrow2 = Arrow(x_start=2,
y_start=3,
x_end=7,
y_end=8,
start=NormalHead(),
end=VeeHead())
arrow2.start.fill_color = 'indigo'
arrow2.end.fill_color = 'orange'
arrow2.end.size = 50
plot.add_layout(arrow1)
plot.add_layout(arrow2)
# test arrow body clipping
plot.add_layout(
Arrow(start=TeeHead(line_width=1),
x_start=6,
y_start=5,
x_end=8,
y_end=6,
line_width=10))
from bokeh.io import save
from bokeh.models import Arrow, NormalHead, OpenHead, TeeHead, VeeHead
from bokeh.plotting import figure
# Have to specify x/y range as labels aren't included in the plot area solver
plot = figure(width=600, height=600, x_range=(0,10), y_range=(0,10), toolbar_location=None)
arrow1 = Arrow(x_start=1, y_start=4, x_end=6, y_end=9,
line_color='green', line_alpha=0.7,
line_dash='8 4', line_width=5, end=OpenHead()
)
arrow1.end.line_width=8
arrow2 = Arrow(x_start=2, y_start=3, x_end=7, y_end=8,
start=NormalHead(), end=VeeHead()
)
arrow2.start.fill_color = 'indigo'
arrow2.end.fill_color = 'orange'
arrow2.end.size = 50
plot.add_layout(arrow1)
plot.add_layout(arrow2)
# test arrow body clipping
plot.add_layout(Arrow(start=TeeHead(line_width=1), x_start=6, y_start=5, x_end=8, y_end=6, line_width=10))
plot.add_layout(Arrow(start=VeeHead(line_width=1, fill_color="white"), x_start=6, y_start=4, x_end=8, y_end=5, line_width=10))
plot.add_layout(Arrow(start=NormalHead(line_width=1, fill_color="white"), x_start=6, y_start=3, x_end=8, y_end=4, line_width=10))
plot.add_layout(Arrow(start=OpenHead(line_width=1), x_start=6, y_start=2, x_end=8, y_end=3, line_width=10))
save(plot)
def create_choropleth(displaySet, displayParam, palette, ogCity):
"""
Fonction qui met à jour la coloration des communes affichées
Entrées :
- displaySet : dataFrame contenant les données affichées
- displayParam : paramètres que l'on souhaite aficher
- palette : liste de couleurs (identique à celle de la choroplèthe)
- ogCity : extract de la commune sélectionnée
Sorties :
- Figure contenant la carte.
"""
# On récupère les limites géographiques pour initialiser la carte
displayBounds = displaySet.total_bounds
# conversion du tracé des communes en json pour interpretation par Bokeh
geosource = GeoJSONDataSource(geojson=displaySet.to_json())
# Creation de la figure de la carte
choroPlot = figure(title='Taux ' + select_imp.value + " " +
str(slider_yr.value),
x_range=(displayBounds[0], displayBounds[2]),
y_range=(displayBounds[1], displayBounds[3]),
x_axis_type="mercator",
y_axis_type="mercator",
plot_height=500,
plot_width=850,
sizing_mode="scale_width",
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset",
x_axis_location=None,
y_axis_location=None)
choroPlot.xgrid.grid_line_color = None
choroPlot.ygrid.grid_line_color = None
# Ajout d'un évèmenent de type clic, pour sélectionnr la commune de référence
choroPlot.on_event(Tap, update_loc)
#outil de zoom molette activé par défaut
choroPlot.toolbar.active_scroll = choroPlot.select_one(WheelZoomTool)
# ajout du fond de carte
tile_provider = get_provider(Vendors.CARTODBPOSITRON)
choroPlot.add_tile(tile_provider)
# On détermine les vals min et max du jeu de test pour la gestion des couleurs
mini = displaySet[displayParam].min()
maxi = displaySet[displayParam].max()
# Création d'une échelle de couleur évoulant linéairement avec le paramètre à afficher
color_mapper = LinearColorMapper(palette=defaultPalette,
low=mini,
high=maxi,
nan_color='#808080')
# Ajout du tracé des communes sur la carte
citiesPatch = choroPlot.patches('xs',
'ys',
source=geosource,
line_color='gray',
line_width=0.25,
fill_alpha=0.5,
fill_color={
'field': displayParam,
'transform': color_mapper
})
# création de la legende #
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
location=(0, 0),
orientation='vertical')
choroPlot.add_layout(color_bar, 'right')
# ajout d'une flèche sur la commune de reférence
start = ogCity.geometry.centroid
pin_point = Arrow(end=VeeHead(size=15),
line_color="red",
x_start=start.x,
y_start=start.y,
x_end=start.x,
y_end=start.y - 0.001)
choroPlot.add_layout(pin_point)
# Ajout d'un tooltip au survol de la carte
choroHover = HoverTool(renderers=[citiesPatch],
tooltips=[('Commune', '@nom'),
(displayParam, '@' + displayParam)])
toolTip = choroPlot.add_tools(choroHover)
return choroPlot
plt_src = plot_tweet.circle(x='Long', y='Lat', alpha=0.2, size=2, color='Color', source=src)
plot_tweet.axis.visible = False
plot_tweet.toolbar.logo = None
plot_tweet.xgrid.grid_line_color = None
plot_tweet.ygrid.grid_line_color = None
plot_tweet.min_border = 0
plt_src.data_source.on_change('selected', on_selection_change)
#plot_wind()
###########################################################
source_wind = ColumnDataSource(data=dict(
x1=[], y1=[], x2=[], y2=[]))
arrows = Arrow(end=VeeHead(fill_color="black", size=10, fill_alpha=.8, line_alpha=.5),
x_start='x1', y_start='y1', x_end='x2', y_end='y2', source=source_wind)
plot_tweet.add_layout(arrows)
##################### slider, play buttons
start = relevant_tweet.Created_at.min() - timedelta(days=1)
end = relevant_tweet.Created_at.max() + timedelta(days=1)
curr_dt = start
end_dt = end
print(start, end, curr_dt)
plot_wind(curr_dt.date())
t1 = end - start
source=trade_balance_cds,
line_width=2,
mode="before",
color="green",
legend="Export")
p.xaxis.major_label_orientation = np.pi / 4 # Flip x-axis labels
hover = HoverTool(tooltips=[('Period', '@period'), (
'Import', '@import_EUR'), ('Export',
'@export_EUR'), ('Tradebalance',
'@balance_EUR')])
p.add_tools(hover)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.add_layout(
Arrow(
end=VeeHead(size=4),
line_color="black",
x_start=
1236552800000, # Dates converted to milliseconds using online converter
y_start=42000,
x_end=1218751200000,
y_end=37500))
p.add_layout(
Arrow(end=VeeHead(size=4),
line_color="black",
x_start=1083362400000,
y_start=40000,
x_end=1083362400000,
y_end=37500))
p.add_layout(
Label(x=1216552800000,
def gridded_plots(raw_data, TOOLS, gene_from_genome, upstreamBuf):
dataS = raw_data
'''
Stacked Plots (Left-Hand Side)
'''
array = []
for k in range(len(dataS)):
if (k+6 >= len(dataS)):
array.append("N/A")
else:
s = ""
list_1 = []
for l in range(k, k+6):
list_1.append(dataS.Base[l])
s = s.join(list_1)
array.append(s)
Seq = pd.DataFrame(array, columns = ['Seq'])
df_comp = pd.concat([dataS, Seq], axis = 1)
a = 1.5
b = 18.5
temp = []
for q in range(len(df_comp)):
temp.append(1 - 1/(1 + 2**(a*(df_comp.pASite[q] - b))))
df_comp['pASiteT'] = temp
temp2 = []
for s in range(len(df_comp)):
if (s == 0):
temp2.append(df_comp['pASiteT'][0])
else:
temp2.append(df_comp['pASiteT'][s]+temp2[s-1])
df_comp['cumulative'] = temp2
last = len(df_comp)
L_entry = df_comp.cumulative[last-1]
df_comp['cumulative'] = df_comp['cumulative']/L_entry
df_comp['pASiteT'] = df_comp['pASiteT']/L_entry
source = ColumnDataSource(df_comp)
''' Individual Line Graph for e1 '''
s1 = figure(title = 'e1', toolbar_location = "right", tools = TOOLS, tooltips = [('Position', '@Position'), ('Score', '@e1'), ('Sequence', '@Seq')])
s1.line('Position', 'e1', line_width = 2, color = Viridis5[0], source = source)
s1.xaxis.visible = False
''' Individual Line Graph for e2 '''
s2 = figure(title = 'e2', x_range = s1.x_range, y_range = s1.y_range, tools = TOOLS, tooltips = [('Position', '@Position'), ('Score', '@e2'), ('Sequence', '@Seq')])
s2.line('Position', 'e2', line_width = 2, color = Viridis5[1], source = source)
s2.xaxis.visible = False
''' Individual Line Graph for e3 '''
s3 = figure(title = 'e3',x_range = s1.x_range, y_range = s1.y_range, tools = TOOLS, tooltips = [('Position', '@Position'), ('Score', '@e3'), ('Sequence', '@Seq')])
s3.line('Position', 'e3', line_width = 2, color = Viridis5[2], source = source)
s3.xaxis.visible = False
''' Individual Line Graph for pA Site '''
s4 = figure(title = 'pA Site', x_range = s1.x_range, y_range = s1.y_range, tools = TOOLS,
tooltips = [('Position', '@Position'), ('Score', '@pASite'), ('Sequence', '@Seq')])
s4.line('Position', 'pASite', line_width = 2, color = Viridis5[3], source = source)
s4.xaxis.visible = False
''' Individual Line Graph for e4 '''
s5 = figure(title='e4',x_range = s1.x_range, y_range = s1.y_range, tools = TOOLS, tooltips = [('Position', '@Position'), ('Score', '@e4'), ('Sequence', '@Seq')])
s5.line('Position', 'e4', line_width = 2, color = Viridis5[4], source = source)
s5.yaxis.axis_label = "Score"
s5.xaxis.axis_label = "Position"
''' PCF11 '''
file = open('/var/kristoph_flask/data/pcf11_cumPa.txt', 'r')
for line in file:
if re.search(gene_from_genome, line):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneData.txt', 'a+') as f:
f.write(line)
file2 = open('/var/kristoph_flask/data/pcf11_paProb.txt', 'r')
for line2 in file2:
if re.search(gene_from_genome, line2):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneProb.txt', 'a+') as fa:
fa.write(line2)
data = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneData.txt', sep = '\s+', header = None, names = ['#gene', 'chromo', 'position', 'strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount','AGcount',
'Acount', 'Gcount', 'DHch01_20nt_Ttrim', 'DHch02_20nt_Ttrim', 'DHch03_20nt_Ttrim', 'DHch04_20nt_Ttrim', 'DHch05_20nt_Ttrim', 'DHch06_20nt_Ttrim', 'DHch07_20nt_Ttrim', 'DHch08_20nt_Ttrim', 'all'])
df = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneProb.txt', sep = '\s+', header = None, names = ['#gene', 'chromo', 'position', 'strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount','AGcount',
'Acount', 'Gcount', 'DHch01_20nt_Ttrim', 'DHch02_20nt_Ttrim', 'DHch03_20nt_Ttrim', 'DHch04_20nt_Ttrim', 'DHch05_20nt_Ttrim', 'DHch06_20nt_Ttrim', 'DHch07_20nt_Ttrim', 'DHch08_20nt_Ttrim', 'all'])
data = data.drop(['strand', 'avgGcount','AGcount', 'Acount', 'Gcount', 'DHch01_20nt_Ttrim', 'DHch02_20nt_Ttrim', 'DHch03_20nt_Ttrim', 'DHch04_20nt_Ttrim', 'DHch05_20nt_Ttrim', 'DHch06_20nt_Ttrim', 'DHch07_20nt_Ttrim', 'DHch08_20nt_Ttrim'], axis = 1)
df = df.drop(['strand', 'avgGcount','AGcount', 'Acount', 'Gcount', 'DHch01_20nt_Ttrim', 'DHch02_20nt_Ttrim', 'DHch03_20nt_Ttrim', 'DHch04_20nt_Ttrim', 'DHch05_20nt_Ttrim', 'DHch06_20nt_Ttrim', 'DHch07_20nt_Ttrim', 'DHch08_20nt_Ttrim'], axis = 1)
''' decay1 '''
file3 = open('/var/kristoph_flask/data/decay_cumPa.txt', 'r')
for line3 in file3:
if re.search(gene_from_genome, line3):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneData.txt', 'a+') as f:
f.write(line3)
file4 = open('/var/kristoph_flask/data/decay_paProb.txt', 'r')
for line4 in file4:
if re.search(gene_from_genome, line4):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneProb.txt', 'a+') as fa:
fa.write(line4)
data2 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneData.txt', sep = '\s+', header = None, names = ['#gene', 'chromo', 'position', 'strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e1_filtered_uniq30nt', 'UA05e1_filtered_uniq30nt', 'UA10e1_filtered_uniq30nt', 'UA20e1_filtered_uniq30nt', 'UA40e1_filtered_uniq30nt', 'UB00e1_filtered_uniq30nt', 'UB05e1_filtered_uniq30nt', 'UB10e1_filtered_uniq30nt', 'UB20e1_filtered_uniq30nt','UB40e1_filtered_uniq30nt','YA00e1_filtered_uniq30nt','YA05e1_filtered_uniq30nt','YA10e1_filtered_uniq30nt','YA20e1_filtered_uniq30nt','YA40e1_filtered_uniq30nt','YB00e1_filtered_uniq30nt','YB05e1_filtered_uniq30nt','YB10e1_filtered_uniq30nt','YB20e1_filtered_uniq30nt','YB40e1_filtered_uniq30nt','all'])
df2 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneProb.txt', sep = '\s+', header = None, names = ['#gene', 'chromo', 'position', 'strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e1_filtered_uniq30nt', 'UA05e1_filtered_uniq30nt', 'UA10e1_filtered_uniq30nt', 'UA20e1_filtered_uniq30nt', 'UA40e1_filtered_uniq30nt', 'UB00e1_filtered_uniq30nt', 'UB05e1_filtered_uniq30nt', 'UB10e1_filtered_uniq30nt', 'UB20e1_filtered_uniq30nt','UB40e1_filtered_uniq30nt','YA00e1_filtered_uniq30nt','YA05e1_filtered_uniq30nt','YA10e1_filtered_uniq30nt','YA20e1_filtered_uniq30nt','YA40e1_filtered_uniq30nt','YB00e1_filtered_uniq30nt','YB05e1_filtered_uniq30nt','YB10e1_filtered_uniq30nt','YB20e1_filtered_uniq30nt','YB40e1_filtered_uniq30nt','all'])
data2 = data2.drop(['strand', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e1_filtered_uniq30nt', 'UA05e1_filtered_uniq30nt', 'UA10e1_filtered_uniq30nt', 'UA20e1_filtered_uniq30nt', 'UA40e1_filtered_uniq30nt', 'UB00e1_filtered_uniq30nt', 'UB05e1_filtered_uniq30nt', 'UB10e1_filtered_uniq30nt', 'UB20e1_filtered_uniq30nt','UB40e1_filtered_uniq30nt','YA00e1_filtered_uniq30nt','YA05e1_filtered_uniq30nt','YA10e1_filtered_uniq30nt','YA20e1_filtered_uniq30nt','YA40e1_filtered_uniq30nt','YB00e1_filtered_uniq30nt','YB05e1_filtered_uniq30nt','YB10e1_filtered_uniq30nt','YB20e1_filtered_uniq30nt','YB40e1_filtered_uniq30nt'], axis=1)
df2 = df2.drop(['strand', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e1_filtered_uniq30nt', 'UA05e1_filtered_uniq30nt', 'UA10e1_filtered_uniq30nt', 'UA20e1_filtered_uniq30nt', 'UA40e1_filtered_uniq30nt', 'UB00e1_filtered_uniq30nt', 'UB05e1_filtered_uniq30nt', 'UB10e1_filtered_uniq30nt', 'UB20e1_filtered_uniq30nt','UB40e1_filtered_uniq30nt','YA00e1_filtered_uniq30nt','YA05e1_filtered_uniq30nt','YA10e1_filtered_uniq30nt','YA20e1_filtered_uniq30nt','YA40e1_filtered_uniq30nt','YB00e1_filtered_uniq30nt','YB05e1_filtered_uniq30nt','YB10e1_filtered_uniq30nt','YB20e1_filtered_uniq30nt','YB40e1_filtered_uniq30nt'], axis=1)
''' decay2 '''
file5 = open('/var/kristoph_flask/data/decay2_cumPa.txt', 'r')
for line5 in file5:
if re.search(gene_from_genome, line5):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneData.txt', 'a+') as f:
f.write(line5)
file6 = open('/var/kristoph_flask/data/decay2_paProb.txt', 'r')
for line6 in file6:
if re.search(gene_from_genome, line6):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneProb.txt', 'a+') as fa:
fa.write(line6)
data3 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneData.txt', sep = '\s+', header = None, names = ['#gene', 'chromo','position','strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e2_filtered_uniq30nt', 'UA05e2_filtered_uniq30nt', 'UA10e2_filtered_uniq30nt', 'UA20e2_filtered_uniq30nt', 'UA40e2_filtered_uniq30nt', 'UB00e2_filtered_uniq30nt', 'UB05e2_filtered_uniq30nt', 'UB10e2_filtered_uniq30nt', 'UB20e2_filtered_uniq30nt', 'UB40e2_filtered_uniq30nt', 'YA00e2_filtered_uniq30nt', 'YA05e2_filtered_uniq30nt','YA10e2_filtered_uniq30nt','YA20e2_filtered_uniq30nt','YA40e2_filtered_uniq30nt','YB00e2_filtered_uniq30nt','YB05e2_filtered_uniq30nt','YB10e2_filtered_uniq30nt','YB20e2_filtered_uniq30nt','YB40e2_filtered_uniq30nt','all'])
df3 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneProb.txt', sep = '\s+', header = None, names = ['#gene', 'chromo','position','strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e2_filtered_uniq30nt', 'UA05e2_filtered_uniq30nt', 'UA10e2_filtered_uniq30nt', 'UA20e2_filtered_uniq30nt', 'UA40e2_filtered_uniq30nt', 'UB00e2_filtered_uniq30nt', 'UB05e2_filtered_uniq30nt', 'UB10e2_filtered_uniq30nt', 'UB20e2_filtered_uniq30nt', 'UB40e2_filtered_uniq30nt', 'YA00e2_filtered_uniq30nt', 'YA05e2_filtered_uniq30nt','YA10e2_filtered_uniq30nt','YA20e2_filtered_uniq30nt','YA40e2_filtered_uniq30nt','YB00e2_filtered_uniq30nt','YB05e2_filtered_uniq30nt','YB10e2_filtered_uniq30nt','YB20e2_filtered_uniq30nt','YB40e2_filtered_uniq30nt','all'])
data3 = data3.drop(['strand','avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e2_filtered_uniq30nt', 'UA05e2_filtered_uniq30nt', 'UA10e2_filtered_uniq30nt', 'UA20e2_filtered_uniq30nt', 'UA40e2_filtered_uniq30nt', 'UB00e2_filtered_uniq30nt', 'UB05e2_filtered_uniq30nt', 'UB10e2_filtered_uniq30nt', 'UB20e2_filtered_uniq30nt', 'UB40e2_filtered_uniq30nt', 'YA00e2_filtered_uniq30nt', 'YA05e2_filtered_uniq30nt','YA10e2_filtered_uniq30nt','YA20e2_filtered_uniq30nt','YA40e2_filtered_uniq30nt','YB00e2_filtered_uniq30nt','YB05e2_filtered_uniq30nt','YB10e2_filtered_uniq30nt','YB20e2_filtered_uniq30nt','YB40e2_filtered_uniq30nt'], axis = 1)
df3 = df3.drop(['strand', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'UA00e2_filtered_uniq30nt', 'UA05e2_filtered_uniq30nt', 'UA10e2_filtered_uniq30nt', 'UA20e2_filtered_uniq30nt', 'UA40e2_filtered_uniq30nt', 'UB00e2_filtered_uniq30nt', 'UB05e2_filtered_uniq30nt', 'UB10e2_filtered_uniq30nt', 'UB20e2_filtered_uniq30nt', 'UB40e2_filtered_uniq30nt', 'YA00e2_filtered_uniq30nt', 'YA05e2_filtered_uniq30nt','YA10e2_filtered_uniq30nt','YA20e2_filtered_uniq30nt','YA40e2_filtered_uniq30nt','YB00e2_filtered_uniq30nt','YB05e2_filtered_uniq30nt','YB10e2_filtered_uniq30nt','YB20e2_filtered_uniq30nt','YB40e2_filtered_uniq30nt'], axis = 1)
''' Steinmetz '''
file7 = open('/var/kristoph_flask/data/steinmetz_cumPa.txt', 'r')
for line7 in file7:
if re.search(gene_from_genome, line7):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneData.txt', 'a+') as f:
f.write(line7)
file8 = open('/var/kristoph_flask/data/steinmetz_paProb.txt', 'r')
for line8 in file8:
if re.search(gene_from_genome, line8):
with open('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneProb.txt', 'a+') as fa:
fa.write(line8)
data4 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneData.txt', sep = '\s+', header = None, names = ['#gene', 'chromo','position', 'strand', 'distToCDSstop','distToCDSstart', 'avgGcount', 'AGcount', 'Acount','Gcount', 'Lane8ByIB_30nt_Ttrim', 'Lane8ByIIB_30nt_Ttrim', 'Lane8ByIIIB_30nt_Ttrim', 'Lane8TS1248IB_30nt_Ttrim', 'Lane8TS1248IIIB_30nt_Ttrim', 'Lane8TS685IB_30nt_Ttrim', 'Lane8TS685IIB_30nt_Ttrim', 'Lane8TS801IB_30nt_Ttrim', 'Lane8TS801IIB_30nt_Ttrim', 'Lane8TS801IIIB_30nt_Ttrim', 'lane3BY4741III_30nt_Ttrim', 'lane3TS1248II_30nt_Ttrim', 'lane3TS685III_30nt_Ttrim', 'lane3TS685II_30nt_Ttrim', 'lane3TS801III_30nt_Ttrim', 'lane3TS801I_30nt_Ttrim', 'all'])
df4 = pd.read_csv('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneProb.txt', sep = '\s+', header = None, names = ['#gene', 'chromo','position','strand', 'distToCDSstop', 'distToCDSstart', 'avgGcount', 'AGcount', 'Acount','Gcount', 'Lane8ByIB_30nt_Ttrim', 'Lane8ByIIB_30nt_Ttrim', 'Lane8ByIIIB_30nt_Ttrim', 'Lane8TS1248IB_30nt_Ttrim', 'Lane8TS1248IIIB_30nt_Ttrim', 'Lane8TS685IB_30nt_Ttrim', 'Lane8TS685IIB_30nt_Ttrim', 'Lane8TS801IB_30nt_Ttrim', 'Lane8TS801IIB_30nt_Ttrim', 'Lane8TS801IIIB_30nt_Ttrim', 'lane3BY4741III_30nt_Ttrim', 'lane3TS1248II_30nt_Ttrim', 'lane3TS685III_30nt_Ttrim', 'lane3TS685II_30nt_Ttrim', 'lane3TS801III_30nt_Ttrim', 'lane3TS801I_30nt_Ttrim', 'all'])
data4 = data4.drop(['strand', 'avgGcount', 'AGcount', 'Acount', 'Gcount', 'Lane8ByIB_30nt_Ttrim', 'Lane8ByIIB_30nt_Ttrim', 'Lane8ByIIIB_30nt_Ttrim', 'Lane8TS1248IB_30nt_Ttrim', 'Lane8TS1248IIIB_30nt_Ttrim', 'Lane8TS685IB_30nt_Ttrim', 'Lane8TS685IIB_30nt_Ttrim', 'Lane8TS801IB_30nt_Ttrim', 'Lane8TS801IIB_30nt_Ttrim', 'Lane8TS801IIIB_30nt_Ttrim', 'lane3BY4741III_30nt_Ttrim', 'lane3TS1248II_30nt_Ttrim', 'lane3TS685III_30nt_Ttrim', 'lane3TS685II_30nt_Ttrim', 'lane3TS801III_30nt_Ttrim', 'lane3TS801I_30nt_Ttrim'], axis = 1)
df4 = df4.drop(['strand', 'avgGcount', 'AGcount', 'Acount','Gcount', 'Lane8ByIB_30nt_Ttrim', 'Lane8ByIIB_30nt_Ttrim', 'Lane8ByIIIB_30nt_Ttrim', 'Lane8TS1248IB_30nt_Ttrim', 'Lane8TS1248IIIB_30nt_Ttrim', 'Lane8TS685IB_30nt_Ttrim', 'Lane8TS685IIB_30nt_Ttrim', 'Lane8TS801IB_30nt_Ttrim', 'Lane8TS801IIB_30nt_Ttrim', 'Lane8TS801IIIB_30nt_Ttrim', 'lane3BY4741III_30nt_Ttrim', 'lane3TS1248II_30nt_Ttrim', 'lane3TS685III_30nt_Ttrim', 'lane3TS685II_30nt_Ttrim', 'lane3TS801III_30nt_Ttrim', 'lane3TS801I_30nt_Ttrim'], axis = 1)
d1 = []
for m in range(len(data)):
if (m == 0):
d1.append((data['all'][m]))
else:
d1.append((data['all'][m]-data['all'][m-1]))
data['p_indep'] = d1
d2 = []
for h in range(len(data2)):
if (h == 0):
d2.append((data2['all'][h]))
else:
d2.append((data2['all'][h]-data2['all'][h-1]))
data2['p_indep'] = d2
d3 = []
for r in range(len(data3)):
if (r == 0):
d3.append((data3['all'][r]))
else:
d3.append((data3['all'][r]-data3['all'][r-1]))
data3['p_indep'] = d3
d4 = []
for w in range(len(data4)):
if (w == 0):
d4.append((data4['all'][w]))
else:
d4.append((data4['all'][w]-data4['all'][w-1]))
data4['p_indep'] = d4
if (data.position[0] > data.position[1]):
antisense = True
else:
antisense = False
print(type(upstreamBuf))
upstreamBuf = int(upstreamBuf)
print(type(upstreamBuf))
if (antisense==True):
CDS1 = data['position'][0] + data['distToCDSstart'][0]
gen_pos = CDS1 + upstreamBuf
df_comp['aligned_pos'] = gen_pos - df_comp.Position
elif (antisense==False):
CDS1 = data['position'][0] - data['distToCDSstart'][0]
gen_pos = CDS1 - upstreamBuf
df_comp['aligned_pos'] = df_comp.Position + gen_pos
print(antisense)
cumul2 = []
for y in range(len(df_comp)):
if (y == 0):
cumul2.append(df_comp.pASiteT[0])
else:
cumul2.append(cumul2[y-1]+(1 - cumul2[y-1])*df_comp.pASiteT[y])
df_comp['cumul2'] = cumul2
df_comp['cumul2'] = df_comp['cumul2']/df_comp.cumul2[len(df_comp)-1]
if (antisense == True):
cds1 = data.position[0]+data.distToCDSstart[0]
cds2 = data.position[0]+data.distToCDSstop[0]
elif (antisense == False):
cds1 = data.position[0]-data.distToCDSstart[0]
cds2 = data.position[0]-data.distToCDSstop[0]
if (antisense == True):
indep_c = figure(x_range = (df_comp['aligned_pos'][0], df_comp['aligned_pos'][len(df_comp)-1]))
c0 = indep_c.line(data['position'], data['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
c1 = indep_c.line(data2['position'], data2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
c2 = indep_c.line(data3['position'], data3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
c3 = indep_c.line(data4['position'], data4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
c4 = indep_c.line(df_comp['aligned_pos'], df_comp['cumulative'], color = '#000000', legend = '?', alpha = 1)
indep_c.add_layout(Arrow(end=VeeHead(size=25, fill_alpha=0.4, line_alpha=0), line_color="black", x_start=cds1, y_start=-0.05, x_end=cds2, y_end=-0.05, line_alpha = 0.4, line_width = 20))
indep_c.below[0].formatter.use_scientific = False
indep_c.legend.location = "top_left"
indep_c.legend.click_policy="hide"
indep_p = figure(x_range = indep_c.x_range)
p0 = indep_p.line(data['position'], data['p_indep'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
p1 = indep_p.line(data2['position'], data2['p_indep'], color = '#b8e186', legend = 'Decay1', alpha = 1)
p2 = indep_p.line(data3['position'], data3['p_indep'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
p3 = indep_p.line(data4['position'], data4['p_indep'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
p4 = indep_p.line(df_comp['aligned_pos'], df_comp['pASiteT'], color = '#000000', legend = '?', alpha = 1)
indep_p.below[0].formatter.use_scientific = False
indep_p.legend.location = "top_left"
indep_p.legend.click_policy="hide"
staged_c = figure(x_range = (df_comp['aligned_pos'][0], df_comp['aligned_pos'][len(df_comp)-1]))
sc0 = staged_c.line(data['position'], data['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
sc1 = staged_c.line(data2['position'], data2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
sc2 = staged_c.line(data3['position'], data3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
sc3 = staged_c.line(data4['position'], data4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
sc4 = staged_c.line(df_comp['aligned_pos'], df_comp['cumul2'], color = '#000000', legend = '?', alpha = 1)
staged_c.add_layout(Arrow(end=VeeHead(size=25, fill_alpha=0.4, line_alpha=0), line_color="black", x_start=cds1, y_start=-0.05, x_end=cds2, y_end=-0.05, line_alpha = 0.4, line_width = 20))
staged_c.below[0].formatter.use_scientific = False
staged_c.legend.location = "top_left"
staged_c.legend.click_policy="hide"
staged_p = figure(x_range = staged_c.x_range)
sp0 = staged_p.line(df['position'], df['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
sp1 = staged_p.line(df2['position'], df2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
sp2 = staged_p.line(df3['position'], df3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
sp3 = staged_p.line(df4['position'], df4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
sp4 = staged_p.line(df_comp['aligned_pos'], df_comp['pASiteT'], color = '#000000', legend = '?', alpha = 1)
staged_p.below[0].formatter.use_scientific = False
staged_p.legend.location = "top_left"
staged_p.legend.click_policy="hide"
elif (antisense == False):
indep_c = figure()
c0 = indep_c.line(data['position'], data['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
c1 = indep_c.line(data2['position'], data2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
c2 = indep_c.line(data3['position'], data3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
c3 = indep_c.line(data4['position'], data4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
c4 = indep_c.line(df_comp['aligned_pos'], df_comp['cumulative'], color = '#000000', legend = '?', alpha = 1)
indep_c.add_layout(Arrow(end=VeeHead(size=25, fill_alpha=0.4, line_alpha = 0), line_color="black", x_start=cds1, y_start=-0.05, x_end=cds2, y_end=-0.05, line_alpha = 0.4, line_width = 20))
indep_c.below[0].formatter.use_scientific = False
indep_c.legend.location = "top_left"
indep_c.legend.click_policy="hide"
indep_p = figure(x_range = indep_c.x_range)
p0 = indep_p.line(data['position'], data['p_indep'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
p1 = indep_p.line(data2['position'], data2['p_indep'], color = '#b8e186', legend = 'Decay1', alpha = 1)
p2 = indep_p.line(data3['position'], data3['p_indep'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
p3 = indep_p.line(data4['position'], data4['p_indep'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
p4 = indep_p.line(df_comp['aligned_pos'], df_comp['pASiteT'], color = '#000000', legend = '?', alpha = 1)
indep_p.below[0].formatter.use_scientific = False
indep_p.legend.location = "top_left"
indep_p.legend.click_policy="hide"
staged_c = figure()
sc0 = staged_c.line(data['position'], data['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
sc1 = staged_c.line(data2['position'], data2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
sc2 = staged_c.line(data3['position'], data3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
sc3 = staged_c.line(data4['position'], data4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
sc4 = staged_c.line(df_comp['aligned_pos'], df_comp['cumul2'], color = '#000000', legend = '?', alpha = 1)
staged_c.add_layout(Arrow(end=VeeHead(size=25, fill_alpha=0.4, line_alpha=0), line_color="black", x_start=cds1, y_start=-0.05, x_end=cds2, y_end=-0.05, line_alpha = 0.4, line_width = 20))
staged_c.below[0].formatter.use_scientific = False
staged_c.legend.location = "top_left"
staged_c.legend.click_policy="hide"
staged_p = figure()
sp0 = staged_p.line(df['position'], df['all'], color = '#4dac26', legend = 'pcf11_DRS', alpha = 1)
sp1 = staged_p.line(df2['position'], df2['all'], color = '#b8e186', legend = 'Decay1', alpha = 1)
sp2 = staged_p.line(df3['position'], df3['all'], color = '#f1b6da', legend = 'Decay2', alpha = 1)
sp3 = staged_p.line(df4['position'], df4['all'], color = '#d01c8b', legend = 'Steinmetz', alpha = 1)
sp4 = staged_p.line(df_comp['aligned_pos'], df_comp['pASiteT'], color = '#000000', legend = '?', alpha = 1)
staged_p.below[0].formatter.use_scientific = False
staged_p.legend.location = "top_left"
staged_p.legend.click_policy="hide"
col1 = column(children=[indep_c, indep_p], sizing_mode='stretch_both')
col2 = column(children=[s1, s2, s3, s4, s5], sizing_mode='stretch_both')
col3 = column(children=[staged_c, staged_p], sizing_mode='stretch_both')
col4 = column(children=[s1, s2, s3, s4, s5], sizing_mode='stretch_both')
l1 = gridplot([[col1, col2]])
l2 = gridplot([[col3, col4]])
tab1 = Panel(child=l1,title="Independent")
tab2 = Panel(child=l2,title="Staged")
tabs = Tabs(tabs=[ tab1, tab2 ])
print(os.listdir('/var/kristoph_flask/data/'))
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneData.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_pcf11_specGeneProb.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneData.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_decay_specGeneProb.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneData.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_decay2_specGeneProb.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneData.txt')
os.remove('/var/kristoph_flask/data/'+gene_from_genome+'_steinmetz_specGeneProb.txt')
return(tabs)
def protein_annotation(first):
protein_locs = []
protein_names = []
protein_lengths = []
# Shades in every other protein region. Provides warning if proteins overlap.
for i in range(0, proteins.shape[0]):
if (i == 0):
x1 = 0
elif (proteins.iloc[i, 1] < proteins.iloc[i - 1, 2]) and first:
print('WARNING: Protein-' + str(proteins.iloc[i, 0]) +
' is overlapping with Protein-' +
str(proteins.iloc[i - 1, 0]))
print(
'Analysis will continue but the visualization for these two proteins will look a little funny. Often the fix for this is simply deleting the small ancilary proteins that overlapping from the gff file and using the -f and -g flags. For more help see the readme.'
)
x1 = proteins.iloc[i, 1]
else:
x1 = proteins.iloc[i, 1]
if (i == proteins.shape[0] - 1):
x2 = proteins.iloc[i, 2]
else:
x2 = proteins.iloc[(i + 1), 1]
if (i % 2 == 0):
genome_plot.add_layout(
BoxAnnotation(left=x1,
right=x2,
fill_alpha=0.1,
fill_color='green'))
protein_locs.append((x1 + x2) / 2)
protein_lengths.append(x2 - x1)
protein_names.append(proteins.iloc[i, 0])
if (os.stat("mat_peptides_additions.txt").st_size != 0):
# Makes arrows for mature peptides.
for i in range(0, mat_peptides_list.shape[0]):
x1 = mat_peptides_list.iloc[i, 1]
x2 = mat_peptides_list.iloc[i, 2]
genome_plot.add_layout(
Arrow(end=VeeHead(size=20,
fill_color="cadetblue",
fill_alpha=0.3,
line_alpha=0),
line_color="cadetblue",
line_width=20,
x_start=x1,
x_end=x2,
y_start=5,
y_end=5,
line_alpha=0.3))
# Adds protein labels as tick marks.
genome_plot.xaxis.ticker = protein_locs
protein_locs2 = []
for protein_loc in protein_locs:
str_protein_loc = str(protein_loc)
## print("old str_protein_loc" + str_protein_loc)
if ".0" in str_protein_loc:
str_protein_loc = str_protein_loc.split('.')[0]
## print("split" + str_protein_loc)
protein_locs2.append(int(str_protein_loc))
else:
protein_locs2.append(float(str_protein_loc))
genome_plot.xaxis.major_label_overrides = dict(
zip(protein_locs2, protein_names))
return protein_names, protein_lengths
def _update_lines(self, line_size=0.25, line_scale=2.0):
for i, l in enumerate(self.lines):
shiftedWave = _airtovac(l['lambda']) * (1.0 + self.z)
visible = (self._line_in_range(shiftedWave)
and ((l['emission'] and self._emission) or
(self._absorption and not l['emission'])))
shiftedWave_y = 0.0
for channel in self.spectra.bands:
sign = -1.0
if l['emission']: sign = 1.0
y_envelope = self.xdata[channel].data[
'model'] + sign * line_scale / np.sqrt(
self.xdata[channel].data['ivar'])
if self.xdata[channel].data['wave'].min(
) < shiftedWave < self.xdata[channel].data['wave'].max():
shiftedWave_y = np.interp(shiftedWave,
self.xdata[channel].data['wave'],
y_envelope)
break
if l['emission']:
lc = 'blue'
y_start = shiftedWave_y + line_size
y_end = shiftedWave_y
else:
lc = 'red'
y_start = shiftedWave_y - line_size
y_end = shiftedWave_y
if 'span' in l:
l['source'].data = dict(x_start=[shiftedWave],
y_start=[y_start],
x_end=[shiftedWave],
y_end=[y_end])
l['span'].visible = visible
l['label'].x = shiftedWave
l['label'].y = y_start
l['label'].visible = visible
else:
l['source'] = ColumnDataSource(data=dict(x_start=[shiftedWave],
y_start=[y_start],
x_end=[shiftedWave],
y_end=[y_end]))
l['span'] = Arrow(end=VeeHead(size=2,
line_color=lc,
line_alpha=0.3,
fill_color=lc,
fill_alpha=0.3),
line_color=lc,
line_width=2,
line_alpha=0.3,
x_start='x_start',
y_start='y_start',
x_end='x_end',
y_end='y_end',
source=l['source'],
visible=visible)
l['label'] = Label(x=shiftedWave,
y=y_start,
text=l['name'],
text_color=lc,
text_alpha=0.5,
visible=visible)
self.specplot.add_layout(l['span'])
self.specplot.add_layout(l['label'])
def test_arrow(output_file_url, selenium, screenshot):
# Have to specify x/y range as labels aren't included in the plot area solver
plot = figure(height=HEIGHT,
width=WIDTH,
x_range=(0, 10),
y_range=(0, 10),
tools='',
toolbar_location="above")
arrow1 = Arrow(x_start=1,
y_start=3,
x_end=6,
y_end=8,
line_color='green',
line_alpha=0.7,
line_dash='8 4',
line_width=5,
end=OpenHead())
arrow1.end.line_width = 8
arrow2 = Arrow(x_start=2,
y_start=2,
x_end=7,
y_end=7,
start=NormalHead(),
end=VeeHead())
arrow2.start.fill_color = 'indigo'
arrow2.end.fill_color = 'orange'
arrow2.end.size = 50
plot.add_layout(arrow1)
plot.add_layout(arrow2)
# test arrow body clipping
plot.add_layout(
Arrow(start=VeeHead(line_width=1, fill_color="white"),
x_start=6,
y_start=4,
x_end=8,
y_end=5,
line_width=10))
plot.add_layout(
Arrow(start=NormalHead(line_width=1, fill_color="white"),
x_start=6,
y_start=3,
x_end=8,
y_end=4,
line_width=10))
plot.add_layout(
Arrow(start=OpenHead(line_width=1),
x_start=6,
y_start=2,
x_end=8,
y_end=3,
line_width=10))
# Save the plot and start the test
save(plot)
selenium.get(output_file_url)
assert has_no_console_errors(selenium)
# Take screenshot
screenshot.assert_is_valid()
