def construct_best_odds_figure(self, x, y, z, t, trade_id, sticker):
# workaround to format date in the hover tool at the moment bokeh does not supported in the tool tips
source_back_odds = ColumnDataSource(data=dict(x=x, y=y, time=[e.strftime('%d-%m-%Y %H:%M:%S') for e in x]))
hover = HoverTool(
tooltips=[
("index", "$index"),
("x", "@time"),
("y", "@y"),
],
active=False
)
# create a new plot with a a datetime axis type
p2 = Figure(plot_width=1000, plot_height=600, title=sticker, toolbar_location="above", x_axis_type="datetime",
tools=[hover, 'box_zoom, box_select, crosshair, resize, reset, save, wheel_zoom'])
orders = self.map_trade_id_to_orders[trade_id, sticker]
for order in orders:
a = [order.placed_dt]
b = [order.price]
source_placed = ColumnDataSource(data=dict(a=a, b=b, time=[a[0].strftime('%d-%m-%Y %H:%M:%S')]))
p2.square(a, b, legend="placed", fill_color="red", line_color="red", size=8, source=source_placed)
p2.circle(x, y, size=8, color='navy', alpha=0.2, legend="best back odds", source=source_back_odds)
p2.line(x, y, color='navy', legend="best back odds", source=source_back_odds)
# lay odds
source_lay_odds = ColumnDataSource(data=dict(z=z, t=t, time=[e.strftime('%d-%m-%Y %H:%M:%S') for e in z]))
p2.triangle(z, t, size=8, color='green', alpha=0.2, legend="best lay odds", source=source_lay_odds)
p2.line(z, t, color='green', legend="best lay odds", source=source_lay_odds)
# NEW: customize by setting attributes
# p2.title = sticker
p2.legend.location = "top_left"
p2.grid.grid_line_alpha = 0
p2.xaxis.axis_label = 'Date'
p2.yaxis.axis_label = "Best odds"
p2.ygrid.band_fill_color = "olive"
p2.ygrid.band_fill_alpha = 0.1
return p2
def map():
x_range = (-10000000, -11000000)
y_range = (3500000, 5200000)
plot = Figure(
tools=TOOLS, title="Power Plant Locations", plot_width=1000, plot_height=500, x_range=x_range, y_range=y_range
)
plot.add_tile(STAMEN_TONER)
plot.axis.visible = False
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
m1 = plot.circle(x="lat", y="lon", source=source_map_bwr, size="size", fill_alpha=0, line_width=2, color="red")
m2 = plot.square(x="lat", y="lon", source=source_map_pwr, size="size", fill_alpha=0, line_width=2, color="red")
plot.select(dict(type=HoverTool)).tooltips = [("Plant Name", "@name"), ("Reactor and Containment", "@type")]
return plot
size=20)
plot.triangle(x='x',
y='y',
source=col2.tri2,
color='black',
angle=np.pi / 2,
fill_alpha=0,
size=20)
plot.triangle(x='x',
y='y',
source=col3.tri2,
color='black',
angle=np.pi / 2,
fill_alpha=0,
size=20)
plot.square(x='x', y='y', source=col4.square, color='black', size=20)
# Plot buckling lenghts
plot.line(x='x', y='y', source=col1.sk, color='#A2AD00', line_width=2)
plot.line(x='x', y='y', source=col2.sk, color='#A2AD00', line_width=2)
plot.line(x='x', y='y', source=col3.sk, color='#A2AD00', line_width=2)
plot.line(x='x', y='y', source=col4.sk, color='#A2AD00', line_width=2)
#Main plot properties:
plot.axis.visible = False
plot.grid.visible = False
plot.toolbar.logo = None
plot.outline_line_width = 1
plot.outline_line_alpha = 0.5
plot.outline_line_color = "Black"
plot.title.text_font_size = "18pt"
plot.width = 900
names=['name', 'msini', 'semi', 'mstar'])
a_rad = 2.7 * ulens['mstar']
ulens_syms = p1.asterisk(ulens['semi'] / a_rad,
ulens['msini'] * mjup,
color='green',
size=8)
# imaging
ulens = Table.read(
'/Users/tumlinson/Dropbox/LUVOIR_STDT/luvoir_simtools/planetspace/imaging.dat',
format='ascii',
names=['name', 'msini', 'semi', 'mstar'])
a_rad = 2.7 * ulens['mstar']
ulens_syms = p1.square(ulens['semi'] / a_rad,
ulens['msini'] * mjup,
fill_alpha=0.4,
line_alpha=0.9,
color='purple',
size=8)
p1.text([5.2 / 2.7], [317.8], ['J'], text_color="red", text_align="center")
#xyouts, 5.2/2.7, 317.8, 'J', align=0.5
#xyouts, 9.6/2.7, 95.2, 'S', align=0.5
#xyouts, 19.2/2.7, 14.5, 'U', align=0.5
#xyouts, 30.1/2.7, 17.1, 'N', align=0.5
#xyouts, 1.0/2.7, 1.0, 'E', align=0.5
#xyouts, 0.72/2.7, 0.8, 'V', align=0.5
#xyouts, 1.5/2.7, 0.1, 'M', align=0.5
#xyouts, 0.39/2.7, 0.055, 'M', align=0.5
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
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)
hover = HoverTool(renderers=[shutters],
point_policy="snap_to_data",
tooltips="""
<div>
# Set defaults
fill_color=c_ztfg, line_color=c_ztfg,
fill_alpha=a_ztfg, line_alpha=a_ztfg,
# set visual properties for selected glyphs
selection_color=c_ztfg,
selection_alpha=a_ztfg,
# set visual properties for non-selected glyphs
nonselection_color=c_ztfg,
nonselection_alpha=a_ztfg,
legend = 'ZTF-g')
f_ztfr = fig_lc.square('x', 'y', source=source_ztf_r, size=6,
# Set defaults
fill_color=c_ztfr, line_color=c_ztfr,
fill_alpha=a_ztfr, line_alpha=a_ztfr,
# set visual properties for selected glyphs
selection_color=c_ztfr,
selection_alpha=a_ztfr,
# set visual properties for non-selected glyphs
nonselection_color=c_ztfr,
nonselection_alpha=a_ztfr,
legend = 'ZTF-r')
# Plot LCO g and r light curve data
f_lcog = fig_lc.triangle('x', 'y', source=source_lco_g, size=7,
# Set defaults
fill_color=c_lcog, line_color=c_lcog,
fill_alpha=a_lcog, line_alpha=a_lcog,
# set visual properties for selected glyphs
selection_color=c_lcog,
selection_alpha=a_lcog,
print('Plotting NWS warn points...')
read_nws = open(
os.getcwd() + os.sep + 'nws_warns' + os.sep +
'JamesCreek_NWS_alerts.csv', 'r')
parse_nws = pd.read_csv(read_nws,
sep=',',
header=0,
usecols=[4, 5],
parse_dates=['Datetime_issued'])
read_nws.close()
dt_issue = parse_nws['Datetime_issued'].tolist()
plot_pos = parse_nws['plot_pos'].tolist()
p1.square(dt_issue,
6,
legend='NWS Alert',
line_color="orange",
fill_color="orange",
alpha=0.9,
size=9)
### axis font size
p1.title.text_font_size = "15pt"
p1.xaxis.axis_label_text_font_size = "15pt"
p1.xaxis.major_label_text_font_size = "10pt"
p1.yaxis.axis_label_text_font_size = "15pt"
p1.yaxis.major_label_text_font_size = "12pt"
p1.legend.location = "top_right"
p1.xaxis.formatter = DatetimeTickFormatter(
minsec=["%Y-%m-%d %H:%M:%S"],
def generate_genetic_plot():
my_path = os.path.abspath(os.path.dirname(__file__))
im_dir = Path(my_path, "..", "static", 'images', 'genetic', '2d_data.csv')
data = pd.read_csv(im_dir)
dict_data = {}
avg_fit_dict = {}
max_fit_dict = {}
for i in range(100):
temp = data[(data['generation'] == i + 1)
& (data['type'] == 'cfacts')].sort_values('fitness',
axis=0,
ascending=False)
dict_data[str(i) + "x_best"] = [temp['X2'].iloc[0]] * 100
dict_data[str(i) + "y_best"] = [temp['X1'].iloc[0]] * 100
dict_data[str(i) + "x_good"] = temp['X2'].iloc[1:int(len(temp) / 2)]
dict_data[str(i) + "y_good"] = temp['X1'].iloc[1:int(len(temp) / 2)]
dict_data[str(i) + "x_okay"] = temp['X2'].iloc[int(len(temp) / 2):]
dict_data[str(i) + "y_okay"] = temp['X1'].iloc[int(len(temp) / 2):]
dict_data[str(i) + "x_bad"] = data[(data['generation'] == i + 1)
& (data['type'] == 'bads')]['X2']
dict_data[str(i) + "y_bad"] = data[(data['generation'] == i + 1)
& (data['type'] == 'bads')]['X1']
avg_fit_dict[str(i)] = [
data[(data['generation'] == i + 1)
& (data['type'] == 'cfacts')].iloc[:int(len(temp) /
2)].fitness.mean()
]
max_fit_dict[str(i)] = [
data[(data['generation'] == i + 1)
& (data['type'] == 'cfacts')].fitness.max()
]
dict_data['dispx_best'] = dict_data['0x_best']
dict_data['dispy_best'] = dict_data['0y_best']
dict_data['dispx_good'] = dict_data['0x_good']
dict_data['dispy_good'] = dict_data['0y_good']
dict_data['dispx_okay'] = dict_data['0x_okay']
dict_data['dispy_okay'] = dict_data['0y_okay']
dict_data['dispx_bad'] = dict_data['0x_bad']
dict_data['dispy_bad'] = dict_data['0y_bad']
src = ColumnDataSource(dict_data)
avg_fit_src = ColumnDataSource(avg_fit_dict)
max_fit_src = ColumnDataSource(max_fit_dict)
div_avg_text = Div(text="Avergage Fitness: ")
div_avg = Div(text=("%3.2f" % avg_fit_dict['0'][0]))
div_max_text = Div(text="Maximum Fitness: ")
div_max = Div(text=("%3.2f" % max_fit_dict['0'][0]))
slider = Slider(start=0, end=99, value=0, step=1, title="Generation")
callback = CustomJS(args=dict(source=src,
offset=slider,
avg_fits=avg_fit_src,
max_fits=max_fit_src,
avg_div=div_avg,
max_div=div_max),
code="""
const data = source.data;
const B = offset.value;
const avg_data = avg_fits.data[String(B)][0]
const max_data = max_fits.data[String(B)][0]
max_div.text = String(max_data.toFixed(2))
avg_div.text = String(avg_data.toFixed(2))
console.log(avg_data[String(B)])
console.log(max_data[String(B)])
data['dispx_best'] = data[String(B)+"x_best"];
data['dispy_best'] = data[String(B)+"y_best"];
data['dispx_good'] = data[String(B)+"x_good"];
data['dispy_good'] = data[String(B)+"y_good"];
data['dispx_okay'] = data[String(B)+"x_okay"];
data['dispy_okay'] = data[String(B)+"y_okay"];
data['dispx_bad'] = data[String(B)+"x_bad"];
data['dispy_bad'] = data[String(B)+"y_bad"];
source.change.emit();
""")
slider.js_on_change('value', callback)
x = np.arange(0, .866, .001)[::2]
y = x**2 + .25
p = Figure(x_range=(0, 1), y_range=(0, 1), width=500, height=500)
p.line(x=x, y=y)
p.line(x=[0] * 2, y=[.25, 1])
p.line(x=[0, .866] * 2, y=[1, 1])
p.scatter(x='dispx_good',
y='dispy_good',
source=src,
color='blue',
size=5,
legend_label="Top 50% C-Fact")
p.scatter(x='dispx_okay',
y='dispy_okay',
source=src,
color='green',
size=5,
legend_label="Bottom 50% C-Fact")
p.scatter(x='dispx_bad',
y='dispy_bad',
source=src,
color='red',
size=5,
legend_label="Same Class")
p.scatter(x='dispx_best',
y='dispy_best',
source=src,
color='purple',
size=5,
legend_label="Best C-Fact")
p.square(x=.25, y=.25, color='black', size=5, legend_label="Individual")
p.legend.location = "top_right"
p.legend.click_policy = "hide"
return column(
row(column(slider, width=100), p),
row(column(width=100), div_avg_text, div_avg, div_max_text, div_max))