def histogram_plot(states,
height,
width,
data_top,
data_source,
n_ticks,
n_minor_ticks=0,
fill_color=None):
'''Create a bokeh histogram using quad shapes'''
from bokeh.plotting import Figure
# Create histogram object
hist_obj = Figure(plot_height=height,
plot_width=width,
title="",
toolbar_location=None)
# Data top is string of var name from data_source
fill_color = '#f0f5f5' if fill_color == None else fill_color
hist_obj.quad(top=data_top,
bottom=0,
source=data_source,
left="left",
right="right",
fill_color=fill_color,
line_color='black')
# set number of major and minor ticks
hist_obj.xaxis[0].ticker.desired_num_ticks = n_ticks
hist_obj.xaxis[0].ticker.num_minor_ticks = n_minor_ticks
hist_obj.y_range = Range1d(0, 1)
hist_obj.x_range = Range1d(states[0] - 0.5, states[-1] + 0.5)
return hist_obj
def render_global_percent_resource_usage(
figure: Figure,
source: ColumnDataSource,
report: ClusterReport,
hostnames: List[str],
):
node_count = len(hostnames)
figure.y_range = Range1d(0, node_count + 1)
figure.yaxis.axis_label = "Usage (%)"
# Draw resource usage
colors = select_colors(hostnames)
figure.vbar_stack(
hostnames,
x="x_start",
width=1000.0,
color=colors,
line_color="black",
legend_label=[get_node_description(report, hostname) for hostname in hostnames],
source=source,
)
# Draw ceiling
figure.line(
source.data["x"],
[node_count] * len(source.data["x"]),
color="red",
line_width=2,
)
def render_bytes_sent_received(
figure: Figure, df: pd.DataFrame, label: str, read_col: str, write_col: str
):
time = df[DATETIME_KEY]
def accumulate(column):
values = df[column]
values = values - values.min()
return resample(values, time)
read = accumulate(read_col)
write = accumulate(write_col)
data = ColumnDataSource(
dict(rx=read, rx_kb=read / 1024, tx=write, tx_kb=write / 1024, x=read.index)
)
tooltips = [("RX", "@rx_kb KiB"), ("TX", "@tx_kb KiB")]
figure.add_tools(HoverTool(tooltips=tooltips))
figure.yaxis[0].formatter = NumeralTickFormatter(format="0.0b")
y_max = max(max(data.data["rx"]), max(data.data["tx"]))
figure.y_range = Range1d(0, y_max + 0.01)
figure.line(
x="x", y="rx", color="blue", legend_label="{} RX".format(label), source=data
)
figure.line(
x="x", y="tx", color="red", legend_label="{} TX".format(label), source=data
)
def make_precipitation_plot(source):
plot = Figure(x_axis_type="datetime",
plot_width=1000,
plot_height=125,
min_border_left=50,
min_border_right=50,
min_border_top=0,
min_border_bottom=0,
toolbar_location=None)
plot.title = None
plot.quad(top='actual_precipitation',
bottom=0,
left='left',
right='right',
color=Greens4[1],
source=source)
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.yaxis.axis_label = "Precipitation (in)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.y_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def render_node_network_connections(figure: Figure, df: pd.DataFrame):
connections_series = df[NETWORK_CONNECTIONS_KEY]
connections = resample(connections_series, df[DATETIME_KEY])
figure.y_range = Range1d(0, connections_series.max() + 10)
figure.add_tools(HoverTool(tooltips=[("Connection count", "@count")]))
data = ColumnDataSource(dict(count=connections, x=connections.index))
figure.line(x="x", y="count", legend_label="Network connections", source=data)
def render_node_mem_pct_utilization(figure: Figure, df: pd.DataFrame):
mem = resample(df[MEM_KEY], df[DATETIME_KEY])
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
figure.add_tools(HoverTool(tooltips=[("Memory", "@mem")]))
data = ColumnDataSource(dict(mem=mem / 100.0, x=mem.index))
figure.line(x="x", y="mem", color="red", legend_label="Memory", source=data)
def createPlots():
######################################## BS Pressure Plot ########################################
sourceBuySell, sourceVolume = requestHoseData()
pBuySell = Figure(plot_width=PLOT_WIDTH, plot_height=BUYSELL_PLOT_HEIGHT, name="pltBuySell")
pBuySell.xaxis.ticker = [8.75, 9, 9.5, 10, 10.5, 11, 11.5, 13, 13.5, 14, 14.5, 14.75]
pBuySell.xaxis.major_label_overrides = {
8.5: "8:30", 8.75: "8:45", 9: "9:00", 9.5: "9:30", 10: "10:00",
10.5: "10:30", 11: "11:00", 11.5: "11:30", 13: "13:00",
13.5: "13:30", 14: "14:00", 14.5: "14:30", 14.75: "14:45"}
pBuySell.line(x='index', y='buyPressure', source=sourceBuySell, color='green',
legend_label="Tổng đặt mua", name="glyphSellPressure")
pBuySell.line(x='index', y='sellPressure', source=sourceBuySell, color='red',
legend_label="Tổng đặt bán", name="glyphBuyPressure")
wz = WheelZoomTool(dimensions="width"); pBuySell.add_tools(wz); pBuySell.toolbar.active_scroll = wz
pBuySell.toolbar.logo = None
pBuySell.axis[0].visible = False
pBuySell.legend.location = "top_left"
pBuySell.legend.click_policy = "hide"
pBuySell.legend.background_fill_alpha = 0.0
######################################## Volume Plot ########################################
pVolume = Figure(width=PLOT_WIDTH, height=VOLUME_PLOT_HEIGHT, tools="pan, reset",
name="pltVolume")
pVolume.toolbar.logo = None
wz = WheelZoomTool(dimensions="height"); pVolume.add_tools(wz); pVolume.toolbar.active_scroll = wz
pVolume.vbar(x='index', top='totalValue', width=1 /60, color='blue',
source=sourceVolume, fill_alpha=LIQUIDITY_ALPHA, line_alpha=LIQUIDITY_ALPHA,
name="glyphTotalValue", legend_label="Thanh khoản toàn tt")
pVolume.vbar(x='index', top='nnBuy', width=1/1.2/60, color='green', source=sourceVolume
,name="glyphNNBuy", legend_label="NN mua",)
pVolume.vbar(x='index', top='nnSell', width=1/1.2/60, color='red', source=sourceVolume
,name="glyphNNSell", legend_label="NN bán",)
pVolume.x_range = pBuySell.x_range
pVolume.y_range=Range1d(-10, 45)
pVolume.legend.location = "top_left"
pVolume.legend.click_policy = "hide"
pVolume.legend.background_fill_alpha = 0.0
######################################### OHLC plot #########################################
orders, source, pressure = requestPSData()
plotOhlc = createOhlcPlot(source)
pCandle, divCandle = hookupFigure(plotOhlc) # "divCustomJS" "pltOHLC" "glyphOHLCSegment"
pDebug = Paragraph(text=f"""["num_ps_orders": "{len(orders['index'])}"]\n"""
"""""",
width=DIV_TEXT_WIDTH, height=100, name="pDebug")
################################# Putting all plots together ################################
def activation_function():
pBuySell._document.add_periodic_callback(lambda: updateDoc(pBuySell._document), 500)
print("Document activated !")
return pCandle, pBuySell, pVolume, divCandle , activation_function, sourceBuySell, sourceVolume, pDebug
def make_plot():
plot = Figure(
plot_height=400,
plot_width=800,
responsive=True,
tools="xpan,xwheel_zoom,xbox_zoom,reset",
x_axis_type='datetime'
)
plot.x_range.follow = "end"
plot.x_range.follow_interval = 120 * 24 * 60 * 60 * 1000
plot.x_range.range_padding = 0
plot.y_range = Range1d(0, 12)
return plot
def render_node_per_cpu_pct_utilization(figure: Figure, df: pd.DataFrame):
time = df[DATETIME_KEY]
cpu_series = df[CPU_KEY]
cpu_count = len(cpu_series.iloc[0])
cpus = [
resample(cpu_series.apply(lambda res: res[i]), time) for i in range(cpu_count)
]
cpu_mean = resample(cpu_series.apply(lambda res: average(res)), time)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
data = {}
tooltips = []
for (i, cpu) in enumerate(cpus):
key = f"cpu_{i}"
data[key] = cpu / 100.0
data[f"{key}_x"] = cpu.index
tooltips.append((f"CPU #{i}", f"@{key}"))
data["cpu_mean"] = cpu_mean / 100.0
data["cpu_mean_x"] = cpu_mean.index
tooltips.append(("CPU avg.", "@cpu_mean"))
figure.add_tools(HoverTool(tooltips=tooltips))
data = ColumnDataSource(data)
colors = select_colors(cpus)
for (i, color) in enumerate(colors):
figure.line(
x=f"cpu_{i}_x",
y=f"cpu_{i}",
color=color,
legend_label=f"CPU #{i}",
source=data,
)
figure.line(
x="cpu_mean_x",
y="cpu_mean",
color="red",
legend_label="Average CPU",
line_dash="dashed",
line_width=5,
source=data,
)
def utilization_bar(max_y):
plot = Figure(
plot_width=
150, # this is more for the ratio, because we have auto-width scaling
plot_height=150,
tools=[], # no tools needed for this one
title='Utilization')
plot.toolbar.logo = None # hides logo
plot.x_range = Range1d(0, 1)
plot.y_range = Range1d(
0,
max_y) # sometimes you want it to be way less than 1, to see it move
plot.xaxis.visible = False # hide x axis
# Add input buffer
manager = Manager()
plot._input_buffer = manager.dict()
return plot
def make_precipitation_plot(source):
plot = Figure(x_axis_type="datetime", plot_width=1000, plot_height=125, min_border_left=50, min_border_right=50, min_border_top=0, min_border_bottom=0, toolbar_location=None)
plot.title = None
plot.quad(top='actual_precipitation', bottom=0, left='left', right='right', color=Greens4[1], source=source)
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.yaxis.axis_label = "Precipitation (in)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.y_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def histogram_plot(states, height, width, data_top, data_source, n_ticks,
n_minor_ticks=0, fill_color=None):
'''Create a bokeh histogram using quad shapes'''
from bokeh.plotting import Figure
# Create histogram object
hist_obj = Figure(plot_height=height, plot_width=width, title="",
toolbar_location=None)
# Data top is string of var name from data_source
fill_color = '#f0f5f5' if fill_color==None else fill_color
hist_obj.quad(top=data_top, bottom=0, source=data_source, left="left",
right="right", fill_color=fill_color, line_color='black')
# set number of major and minor ticks
hist_obj.xaxis[0].ticker.desired_num_ticks = n_ticks
hist_obj.xaxis[0].ticker.num_minor_ticks = n_minor_ticks
hist_obj.y_range = Range1d(0, 1)
hist_obj.x_range = Range1d(states[0]-0.5, states[-1]+0.5)
return hist_obj
def make_plot():
plot = Figure(
plot_height=400,
plot_width=800,
responsive=True,
tools="xpan,xwheel_zoom,xbox_zoom,reset",
x_axis_type='datetime',
min_border_top=10,
min_border_right=0,
min_border_bottom=0,
min_border_left=30,
outline_line_color=None,
)
plot.x_range.follow = "end"
plot.x_range.follow_interval = 120 * 24 * 60 * 60 * 1000
plot.x_range.range_padding = 0
plot.y_range = Range1d(0, 12)
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.yaxis.bounds = (0, 9)
plot.yaxis.minor_tick_line_color = None
plot.yaxis.ticker = FixedTicker(ticks=[0, 3, 6, 9])
return plot
grate5=ColumnDataSource(dict(x=londf.values[:,0]-kgx,y=londf.values[:,2]))
grate6=ColumnDataSource(dict(x=londf.values[:,0]+kgx,y=londf.values[:,2]))
grate7=ColumnDataSource(dict(x=londf.values[:,0],y=londf.values[:,2]+kgy))
grate8=ColumnDataSource(dict(x=londf.values[:,0],y=londf.values[:,2]-kgy))
p.line(x='x',y='y',source=grate1, color="red",line_width=5)
p.line(x='x',y='y',source=grate2, color="red",line_width=5)
p.line(x='x',y='y',source=grate3, color="red",line_width=5)
p.line(x='x',y='y',source=grate4, color="red",line_width=5)
p.line(x='x',y='y',source=grate5, color="red",line_width=5)
p.line(x='x',y='y',source=grate6, color="red",line_width=5)
p.line(x='x',y='y',source=grate7, color="red",line_width=5)
p.line(x='x',y='y',source=grate8, color="red",line_width=5)
p.x_range=Range1d(-1 , 1)
p.y_range=Range1d(-1 , 1)
def update():
tilt=float(tiltin.value)*np.pi/180
wl=2.997e4/float(freqin.value)
wlout.text='Wavelength = '+str(wl)+' cm'
kgy=wl/float(dyin.value)
if lattice.value=='Triangular':
kgx=2*wl/float(dxin.value)
kmult=0.5
else:
kgx=wl/float(dxin.value)
kmult=1
kgxout.text='kgx = '+str(kgx)
kgyout.text='kgy = '+str(kgy)
line_width=0)
p1.patch([0.003, 0.04, 0.04, 0.003, 0.003], [100, 100, 30000., 30000., 10000.],
alpha=0.6,
fill_color='lightblue',
line_width=0)
p1.patch([3, 3000, 3000, 30, 3], [300, 300, 30000., 30000., 300.],
alpha=0.6,
fill_color='purple',
line_width=0)
p1.yaxis.axis_label = 'Planet Mass / Earth'
p1.xaxis.axis_label = 'Semi-major Axis / Snow Line'
p1.xaxis.axis_label_text_font_style = 'bold'
p1.yaxis.axis_label_text_font_style = 'bold'
p1.xaxis[0].formatter = NumeralTickFormatter(format="0.00")
p1.x_range = Range1d(0.003, 3000, bounds=(0.003, 3000))
p1.y_range = Range1d(0.01, 30000, bounds=(0.01, 30000))
techniques = [
'RV', 'Space Transits', 'Ground Transits', 'Microlensing', 'Direct Imaging'
]
checkbox_button_group = CheckboxGroup(
labels=["RV", "Transits", "Microlensing", "Direct Imaging"],
active=[0, 1, 2, 3])
# transits - ground
tground = Table.read(
'/Users/tumlinson/Dropbox/LUVOIR_STDT/luvoir_simtools/planetspace/transit_ground.dat',
format='ascii',
names=['name', 'msini', 'semi', 'mstar'])
a_rad = 2.7 * tground['mstar']
orig_ztf_r = ColumnDataSource(data=dict(x=ztf_rmjd, y=ztf_rmag,
lower=ztf_rlow, upper=ztf_rupp))
orig_lco_g = ColumnDataSource(data=dict(x=lco_gmjd, y=lco_gmag,
lower=lco_glow, upper=lco_gupp))
orig_lco_r = ColumnDataSource(data=dict(x=lco_rmjd, y=lco_rmag,
lower=lco_rlow, upper=lco_rupp))
# Initialize Light Curve Plot
fig_lc = Figure(plot_height=320, plot_width=750,sizing_mode='scale_both',
tools="pan,wheel_zoom,box_zoom,tap,reset",
toolbar_location="above", border_fill_color="whitesmoke")
fig_lc.toolbar.logo = None
fig_lc.yaxis.axis_label = 'Mag'
fig_lc.xaxis.axis_label = 'MJD'
fig_lc.y_range = Range1d(start=yupp, end=ylow)
# Choose plotting colors
c_ztfg = "cornflowerblue"
c_ztfr = "firebrick"
c_lcog = "forestgreen"
c_lcor = "darkorange"
# Choose plotting alphas
a_ztfg = 1.0
a_ztfr = 1.0
a_lcog = 1.0
a_lcor = 1.0
return results
########################################################################
# Construct basic plot architecture
results = DataFrame({'day': 1, 'S': 0, 'N': 0, 'O': 0, 'V': 1e9, 'Z': 5.},
dtype=float, index=get_timestep_index())
source = ColumnDataSource(results)
# Create a new plot and add a renderer
top = Figure(tools=tools, title=None, x_range=day_range,
**figure_style_kws)
top.line('day', 'S', source=source,
line_color=colors[0], line_width=3, line_cap='round')
top.y_range = Range1d(0., 40.)
top.yaxis.axis_label = "Salinity (g/kg)"
top.xaxis.axis_label_text_font_size = label_fontsize
top.yaxis.axis_label_text_font_size = label_fontsize
# overlay volume level chart to salinity
tc = "MediumBlue" # tide color
tide_range = Range1d(start=0, end=15)
tide_axis = LinearAxis(y_range_name="Z")
tide_axis.axis_label = "Tidal Height (m)"
tide_axis.axis_label_text_color = tc
tide_axis.axis_label_text_font_size = label_fontsize
tide_axis.major_tick_line_color = tc
tide_axis.major_label_text_color = tc
tide_axis.minor_tick_line_alpha = 0.
box_select = BoxSelectTool()
plot1 = Figure(plot_height=500,
plot_width=770,
x_axis_type=None,
y_axis_type=None,
tools=["pan,reset,tap,wheel_zoom,save", box_select],
x_range=[-75, 75],
y_range=[-50, 50],
toolbar_location='left')
plot1.image_url(url=["http://luvoir.stsci.edu/M83.jpeg"],
x=[-75],
y=[50],
w=150,
h=100)
plot1.x_range = Range1d(-75, 75, bounds=(-75, 75))
plot1.y_range = Range1d(-50, 50, bounds=(-50, 50))
shutters = plot1.square('x',
'y',
source=shutter_positions,
fill_color='yellow',
fill_alpha=0.2,
line_color=None,
size=20,
name="my_shutters")
shutters.selection_glyph = Square(fill_alpha=0.5,
fill_color="green",
line_color='green',
line_width=3)
shutters.nonselection_glyph = Square(fill_alpha=0.2,
fill_color="yellow",
line_color=None)
# initialize the plot data
update_source_data(True)
# make the plot responsive to slider changes
standard_deviation_slider.on_change(
'value', lambda attr, old_value, new_value: update_source_data(True))
cutoff_slider.on_change(
'value', lambda attr, old_value, new_value: update_source_data(False))
# create the figure
p = Figure(title='Normal Distribution')
p.scatter(source=source,
x='x',
y='y',
color='green',
alpha='alpha',
radius=0.1)
p.x_range = Range1d(start=-8, end=8)
p.y_range = Range1d(start=-8, end=8)
content = column(standard_deviation_slider, cutoff_slider, p)
# register the figure
curdoc().add_root(content)
curdoc().title = 'Normal Distribution'
alpha_p0=alpha_P0,
alpha_p1=alpha_P1,
alpha_p2=alpha_P2,
beta_p0=beta_P0,
beta_p1=beta_P1,
beta_p2=beta_P2,
sarah_p0=sarah_P0,
sarah_p1=sarah_P1,
sarah_p2=sarah_P2))
p = Figure(tools='', toolbar_location=None, height=800, width=850)
p.grid.grid_line_color = None
p.axis.visible = False
p.grid.grid_line_color = None
p.axis.visible = False
p.y_range = Range1d(-.25, 1.35)
p.x_range = Range1d(-.15, 1.55)
# plot simplex
left_corner = [0.0, 0.0]
right_corner = [np.sqrt(2), 0.0]
top_corner = [np.sqrt(2) / 2.0, np.sqrt(6) / 2.0]
p.line([left_corner[0], top_corner[0]], [left_corner[1], top_corner[1]],
color='black',
line_width=2)
p.line([right_corner[0], top_corner[0]], [right_corner[1], top_corner[1]],
color='black',
line_width=2)
p.line([left_corner[0], right_corner[0]], [left_corner[1], right_corner[1]],
color='black',
def construct_total_pnl_figure(self, x, y, t):
str_total_pnl = "Total Pnl " + POUND_SYMBOL
# workaround to format date in the hover tool at the moment bokeh does not supported in the tool tips
time = [e.strftime('%d %b %Y') for e in x]
source_total_pnl = ColumnDataSource(data=dict(x=x, y=y, time=time))
tooltips_total_pnl = [
("Date", "@time"),
("Total Pnl", "@y{0.00}"),
]
tooltips_capital = [
("Date", "@time"),
("Capital", "@y{0.00}"),
]
# create a new pnl plot
p2 = Figure(x_axis_type="datetime", title="Total Pnl/Capital Allocated " + POUND_SYMBOL,
toolbar_location="above", tools=['box_zoom, box_select, crosshair, resize, reset, save, wheel_zoom'])
# add renderers
r1 = p2.circle(x, y, size=8, color='black', alpha=0.2, legend=str_total_pnl, source=source_total_pnl)
r11 = p2.line(x, y, color='navy', legend=str_total_pnl, source=source_total_pnl)
# add renderers to the HoverTool instead of to the figure so we can have different tooltips for each glyph
p2.add_tools(HoverTool(renderers=[r1, r11], tooltips=tooltips_total_pnl))
max_total_pnl = max(y)
min_total_pnl = min(y)
# offset to adjust the plot so the max and min ranges are visible
offset = (max(abs(max_total_pnl), abs(min_total_pnl))) * 0.10
p2.y_range = Range1d(min_total_pnl - offset, max_total_pnl + offset)
# NEW: customize by setting attributes
# p2.title = "Total Pnl/Capital Allocated " + POUND_SYMBOL
p2.legend.location = "top_left"
p2.grid.grid_line_alpha = 0
p2.xaxis.axis_label = 'Date'
p2.yaxis.axis_label = str_total_pnl
p2.ygrid.band_fill_color = "olive"
p2.ygrid.band_fill_alpha = 0.1
p2.xaxis.formatter = DatetimeTickFormatter(formats={'days': ['%d %b'], 'months': ['%b %Y']})
# formatter without exponential notation
p2.yaxis.formatter = PrintfTickFormatter(format="%.0f")
# secondary axis
max_capital = max(t)
min_capital = min(t)
# offset to adjust the plot so the max and min ranges are visible
offset = (max(abs(max_capital), abs(min_capital))) * 0.10
p2.extra_y_ranges = {"capital": Range1d(start=min_capital - offset, end=max_capital + offset)}
# formatter without exponential notation
formatter = PrintfTickFormatter()
formatter.format = "%.0f"
# formatter=NumeralTickFormatter(format="0,0"))
p2.add_layout(LinearAxis(y_range_name="capital", axis_label="Capital allocated " + POUND_SYMBOL,
formatter=formatter), 'right')
# create plot for capital series
source_capital = ColumnDataSource(data=dict(x=x, t=t, time=time))
r2 = p2.square(x, t, size=8, color='green', alpha=0.2, legend="Capital " + POUND_SYMBOL, y_range_name="capital",
source=source_capital)
r22 = p2.line(x, t, color='green', legend="Capital " + POUND_SYMBOL, y_range_name="capital", source=source_capital)
# add renderers to the HoverTool instead of to the figure so we can have different tooltips for each glyph
p2.add_tools(HoverTool(renderers=[r2, r22], tooltips=tooltips_capital))
return p2
signal_to_noise = simulate_exposure(luvoir, lumos, spec_dict[template_to_start_with].wave, spec_dict[template_to_start_with].flux, 1.0)
flux_cut = spec_dict[template_to_start_with].flux
flux_cut[spec_dict[template_to_start_with].wave < lumos.lambda_range[0]] = -999.
flux_cut[spec_dict[template_to_start_with].wave > lumos.lambda_range[0]] = -999.
spectrum_template = ColumnDataSource(data=dict(w=spec_dict[template_to_start_with].wave, f=spec_dict[template_to_start_with].flux, \
w0=spec_dict[template_to_start_with].wave, f0=spec_dict[template_to_start_with].flux, \
flux_cut=flux_cut, sn=signal_to_noise))
# set up the flux plot
flux_plot = Figure(plot_height=400, plot_width=800,
tools="crosshair,hover,pan,reset,resize,save,box_zoom,wheel_zoom", outline_line_color='black',
x_range=[900, 2000], y_range=[0, 4e-16], toolbar_location='right')
flux_plot.x_range=Range1d(900,2000,bounds=(900,2000))
flux_plot.y_range=Range1d(0,4e-16,bounds=(0,None))
flux_plot.background_fill_color = "beige"
flux_plot.background_fill_alpha = 0.5
flux_plot.yaxis.axis_label = 'Flux'
flux_plot.xaxis.axis_label = 'Wavelength'
flux_plot.line('w', 'f', source=spectrum_template, line_width=3, line_color='firebrick', line_alpha=0.7, legend='Source Flux')
flux_plot.line(lumos.wave, lumos.bef, line_width=3, line_color='darksalmon', line_alpha=0.7, legend='Background')
# set up the flux plot
sn_plot = Figure(plot_height=400, plot_width=800,
tools="crosshair,hover,pan,reset,resize,save,box_zoom,wheel_zoom", outline_line_color='black',
x_range=[900, 2000], y_range=[0, 40], toolbar_location='right')
sn_plot.x_range=Range1d(900,2000,bounds=(900,2000))
# Calculate 1-sigma errors
sig= Cratio/SNR
# Add gaussian noise to flux ratio
spec = Cratio + np.random.randn(len(Cratio))*sig
planet = ColumnDataSource(data=dict(lam=lam, cratio=Cratio*1e9, spec=spec*1e9, downerr=(spec-sig)*1e9, uperr=(spec+sig)*1e9))
################################
# BOKEH PLOTTING
################################
snr_plot = Figure(plot_height=400, plot_width=750,
tools="crosshair,pan,reset,resize,save,box_zoom,wheel_zoom",
x_range=[0.3, 2.7], y_range=[0, 1], toolbar_location='right')
snr_plot.x_range = Range1d(0.3, 2.7, bounds=(0.3, 2.7))
snr_plot.y_range = Range1d(0.0, 1.2, bounds=(0.3, 5.0))
snr_plot.background_fill_color = "beige"
snr_plot.background_fill_alpha = 0.5
snr_plot.yaxis.axis_label='F_p/F_s (x10^9)'
snr_plot.xaxis.axis_label='Wavelength [micron]'
snr_plot.line('lam','cratio',source=planet,line_width=2.0, color="green", alpha=0.7)
snr_plot.circle('lam', 'spec', source=planet, fill_color='red', line_color='black', size=8)
snr_plot.segment('lam', 'downerr', 'lam', 'uperr', source=planet, line_width=1, line_color='grey', line_alpha=0.5)
def change_filename(attrname, old, new):
format_button_group.active = None
instruction0 = Div(text="""Choose a file rootname here
(no special characters):""", width=300, height=15)
# tags [debut, fin, pas, timer (en microseconde), type = "add, mul"]
anim=[1, 40, 1, 300, "add", 1]
# dessin
p = Figure(x_range = especes, title="Evolution", plot_width=800, plot_height=500,toolbar_location = None)
p.title.text_font_size = "25px"
p.xaxis[0].axis_label = 'Espèces'
if data['unite'] == 1:
p.yaxis[0].axis_label = 'Quantités (mol)'
else:
p.yaxis[0].axis_label = 'Masses (g)'
# Calcul longueur axe
max = max(max(quantites), max(reste))*1.2
p.y_range = Range1d(start=0, end=max)
# définition de la source
source = [ColumnDataSource(data=dict(x=data_x[i], top=data_top[i], grow = data_grow[i])) for i in range(len(especes))]
for i in range(len(especes)):
p.vbar(x='x', top='top', width = width, bottom = bottom, color = color[i],
source = source[i], legend_label = labels[i])
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
p.legend.click_policy = "hide"
p.legend.glyph_width = 60
# création du bouton
bt = Button(label = "Lance", id = '1', button_type = "success")
# définition de la source
source = ColumnDataSource(
data=dict(x=data_x, top=data_top, color=color, grow=data_grow))
# dessin
p = Figure(title="graph bar",
x_range=data_x,
y_range=(0, 100),
plot_height=400,
toolbar_location=None)
p.title.text_font_size = "25px"
p.yaxis[0].axis_label = 'Quantité'
p.xaxis[0].axis_label = 'Espèces'
p.y_range = Range1d(start=0, end=100)
p.vbar(x='x',
top='top',
width=width,
bottom=bottom,
color='color',
source=source,
legend_field='x')
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
# création du bouton
bt = Button(label="Lance", button_type="success")
# fonction de callback
spec=spec * 1e9,
downerr=(spec - sig) * 1e9,
uperr=(spec + sig) * 1e9))
################################
# BOKEH PLOTTING
################################
snr_plot = Figure(plot_height=400,
plot_width=750,
tools="crosshair,pan,reset,resize,save,box_zoom,wheel_zoom",
x_range=[0.3, 2.7],
y_range=[0, 1],
toolbar_location='right')
snr_plot.x_range = Range1d(0.3, 2.7, bounds=(0.3, 2.7))
snr_plot.y_range = Range1d(0.0, 1.2, bounds=(0.3, 5.0))
snr_plot.background_fill_color = "beige"
snr_plot.background_fill_alpha = 0.5
snr_plot.yaxis.axis_label = 'F_p/F_s (x10^9)'
snr_plot.xaxis.axis_label = 'Wavelength [micron]'
snr_plot.line('lam',
'cratio',
source=planet,
line_width=2.0,
color="green",
alpha=0.7)
snr_plot.circle('lam',
'spec',
source=planet,
fill_color='red',
