def plot_series(self, out_widget, variable, loc_id):
"""
"""
dim_id = None
if dim_id is None:
dim_id = self.key
tts = self.x_data['q_obs_mm'].sel({dim_id: loc_id})
# if using bqplot down the track:
# points = gpx.get_points_data(distance_2d=True)
# px = [p.distance_from_start / 1000 for p in points]
# py = [p.point.elevation for p in points]
# x_scale, y_scale = LinearScale(), LinearScale()
# x_scale.allow_padding = False
# x_ax = Axis(label='Time', scale=x_scale)
# y_ax = Axis(label='Value', scale=y_scale, orientation='vertical')
# lines = Lines(x=px, y=py, scales={'x': x_scale, 'y': y_scale})
# elevation = Figure(title='Elevation Chart', axes=[x_ax, y_ax], marks=[lines])
# elevation.layout.width = 'auto'
# elevation.layout.height = 'auto'
# elevation.layout.min_height = '500px'
# elevation.interaction = IndexSelector(scale=x_scale)
# with(out_widget):
# plt.plot(tts)
out_widget.clear_output()
with(out_widget):
display(tts.plot(figsize=(16,8)))
show_inline_matplotlib_plots()
def on_button_clicked(b):
with output:
clear_output(True)
m_true,d_true,prob,TDsurvey = create_prob(rho_sliders,dep_cells)
plot_data(d_true,TDsurvey)
show_inline_matplotlib_plots()
return
def on_button_clicked(b):
with output:
clear_output(True)
m_true,d_true,prob,TDsurvey = create_prob(res_model_app)
plot_data(d_true,TDsurvey)
show_inline_matplotlib_plots()
return
def disp_plot(self, *args):
self.plot_out.clear_output()
if self.graph_type.value == None:
return
with self.plot_out:
if self.graph_type.value == "describe":
display(self.df.describe())
else:
try:
if self.graph_type.value == 'categorical':
g = sns.catplot(data=self.df,
kind=self.kind.value,
x=self.xsel.value,
y=self.ysel.value,
row=self.rsel.value,
col=self.csel.value,
hue=self.hsel.value)
g.set_xticklabels(rotation=45)
#)
else:
g = sns.displot(self.df,
x=self.xsel.value,
hue=self.hsel.value)
except:
pass
show_inline_matplotlib_plots()
def on_mpl_clicked(b):
with output_mpl:
clear_output(wait=True)
hdbdata_fil = filter_HDB(**{k: v.value for k,v in filterdict.items()})
fnc_out = resale_price_mean(hdbdata_fil)
plot_mpl(fnc_out)
show_inline_matplotlib_plots()
def plot_results(self, b):
self.out.clear_output()
show_inline_matplotlib_plots()
with self.out:
plot(self.cache)
def on_button_clicked(b):
print('Running inversion...')
with output:
clear_output(True)
mopt = prob_and_run(rho_sliders,dep_cells)
ax = plot_results(mopt,expmap,rho_sliders,dep_cells)
show_inline_matplotlib_plots()
return
def on_button_clicked(b):
print('Running inversion...')
with output:
clear_output(True)
mopt = prob_and_run(res_model_app)
f,ax = plot_results(mopt,expmap,res_model_app)
show_inline_matplotlib_plots()
return
def widget_change():
with self.out:
clear_output(wait=True)
arguments = dict()
for key, widget in additional_widgets.iteritems():
arguments[key] = widget.value
self.plotfunction(quantity, **arguments)
show_inline_matplotlib_plots()
def on_value_change(change):
with output:
clear_output(wait=True)
ideas_[idx] = change['new']
ideas_tensor = torch.from_numpy(ideas_)
res = model(ideas_tensor)
plt.plot(res.data.numpy())
show_inline_matplotlib_plots()
def on_plot_fwd_clicked(b):
try:
out_fwd_curve.clear_output()
curve = read_fwd_curve()
with out_fwd_curve:
curve.plot()
show_inline_matplotlib_plots()
except Exception as e:
logger.exception(e)
def display_plot(self, *_):
"""
Display the plot object associated with the UI state, unless the
user does not want interactive updates
"""
if not self.auto_update.value:
return
show_inline_matplotlib_plots()
with self.output:
clear_output(wait=True)
self.gen_plot()
show_inline_matplotlib_plots()
def on_clicked(b):
output_plot.clear_output()
with output_plot:
self.update_rm()
self.rm_original.fig_image_list = self.rm.get_images(
legend_list=w_legend.update(),
n_images=w_n_images.update(),
n_exps=w_n_exps.update(),
figsize=w_figsize.update(),
dirname=w_dirname.update())
show_inline_matplotlib_plots()
def on_clicked(b):
if d_style.value == 'True':
from IPython import get_ipython
ipython = get_ipython()
ipython.magic("matplotlib widget")
output_plot.clear_output()
with output_plot:
self.update_rm()
self.vars['y_metrics'] = list(t_y_metric.value)
self.vars['x_metric'] = d_x_metric_columns.value
w, h = 10, 5
if len(self.vars['y_metrics']) > 1:
figsize = (2 * int(w), int(h))
self.vars['figsize'] = figsize
else:
self.vars['figsize'] = (int(w), int(h))
self.vars['legend_list'] = list(llegend_list.value)
self.vars['legend_format'] = llegend_format.value
self.vars['log_metric_list'] = hu.get_list_from_str(
llog_metric_list.value)
self.vars['groupby_list'] = list(t_groupby_list.value)
self.vars['mode'] = t_mode.value
self.vars['title_list'] = list(t_title_list.value)
self.vars['bar_agg'] = t_bar_agg.value
self.vars['title_format'] = ltitle_format.value
self.vars['cmap'] = lcmap.value
self.vars['avg_across'] = d_avg_across_columns.value
avg_across_value = self.vars['avg_across']
if avg_across_value == "None":
avg_across_value = None
self.rm_original.fig_list = self.rm.get_plot_all(
y_metric_list=self.vars['y_metrics'],
x_metric=self.vars['x_metric'],
groupby_list=self.vars['groupby_list'],
legend_list=self.vars['legend_list'],
log_metric_list=self.vars['log_metric_list'],
mode=self.vars['mode'],
bar_agg=self.vars['bar_agg'],
figsize=self.vars['figsize'],
title_list=self.vars['title_list'],
legend_format=self.vars['legend_format'],
title_format=self.vars['title_format'],
cmap=self.vars['cmap'],
avg_across=avg_across_value)
show_inline_matplotlib_plots()
def plot_iteration(line: str):
"""call a registered plotter function for the given repetition and iteration"""
args = parse_argstring(plot_iteration, line)
items = []
from ipywidgets.widgets.interaction import show_inline_matplotlib_plots
global __instances, __instantiated_experiments
for exp_instance, exp_config in zip(__instances, __instantiated_experiments):
out = Output()
items.append(out)
with out:
# clear_output(wait=True)
figures = __iteration_plot_functions[args.plotter_name](exp_instance, args.args)
show_inline_matplotlib_plots()
if args.save_figures:
if args.format is None:
args.format = plt.rcParams['savefig.format']
os.makedirs('plots/{}'.format(exp_config['name']), exist_ok=True)
for i, f in enumerate(figures):
filename = 'plots/{}/{}figure_{}.{}'.format(exp_config['name'], args.prefix, i, args.format)
if args.format == 'tikz':
try:
from matplotlib2tikz import save as tikz_save
with Output():
tikz_save(filename, figureheight='\\figureheight', figurewidth='\\figurewidth')
except ModuleNotFoundError:
warnings.warn('Saving figure as tikz requires the module matplotlib2tikz.')
else:
f.savefig(filename, format=args.format)
if len(items) > 1:
tabs = Tab(children=items)
for i, exp in enumerate(__instantiated_experiments):
if args.tab_title:
if (args.tab_title[0] == args.tab_title[-1]) and args.tab_title.startswith(("'", '"')):
selectors = args.tab_title[1:-1]
else:
selectors = args.tab_title
selectors = selectors.split(' ')
values = [reduce(lambda a, b: a[b], [exp['params'], *selector.split('.')]) for selector in
selectors]
tabs.set_title(i, ' '.join(map(str, values)))
else:
tabs.set_title(i, '...' + exp['name'][-15:])
display(tabs)
elif len(items) == 1:
return items[0]
else:
warnings.warn('No plots available for {} with args {}'.format(args.plotter_name, args.args))
def display_plot(self, *_):
"""Close the current figure, get the widgets states to a kwargs dict
then call the .plot method"""
# close current figure
plt.close(self.fig)
show_inline_matplotlib_plots()
with self.output:
# clear_output de IPython.display
clear_output(wait=True)
# get all widgets names and values in a dict
kwargs = self.retrieve_enabled_kwargs()
# actual plot call
self.plot(kwargs)
show_inline_matplotlib_plots()
def plot_series(self,
out_widget: Output,
variable: str,
loc_id: str,
dim_id: str = None):
"""
"""
tts = self.get_data(variable=variable, loc_id=loc_id, dim_id=dim_id)
# if using bqplot down the track, see https://github.com/jtpio/voila-gpx-viewer
out_widget.clear_output()
with out_widget:
_ = tts.plot(figsize=(16, 8))
# do not use display(blah) which then displays the obnoxious matplotlib.lines.Line2D object at etc.>]
ax = plt.gca()
ax.set_title(loc_id)
show_inline_matplotlib_plots()
def on_clicked(b):
self.update_rm()
output_plot.clear_output()
with output_plot:
w, h = tfigsize.value.strip('(').strip(')').split(',')
self.vars['figsize_images'] = (int(w), int(h))
self.vars['legend_list'] = get_list_from_str(llegend_list.value)
self.vars['n_images'] = int(t_n_images.value)
self.vars['n_exps'] = int(t_n_exps.value)
self.vars['dirname'] = t_dirname.value
self.rm.get_images(legend_list=self.vars['legend_list'],
n_images=self.vars['n_images'],
n_exps=self.vars['n_exps'],
figsize=self.vars['figsize_images'],
dirname=self.vars['dirname'])
show_inline_matplotlib_plots()
def interactive_fwd(rho_sliders,dep_cells):
button = Button(description="Acquire AEM data!")
output = Output()
display(button, output)
def on_button_clicked(b):
with output:
clear_output(True)
m_true,d_true,prob,TDsurvey = create_prob(rho_sliders,dep_cells)
plot_data(d_true,TDsurvey)
show_inline_matplotlib_plots()
return
button.on_click(on_button_clicked)
show_inline_matplotlib_plots()
return button,output
def interactive_inversion(rho_sliders,dep_cells):
button = Button(description="Run inversion!")
output = Output()
display(button, output)
def on_button_clicked(b):
print('Running inversion...')
with output:
clear_output(True)
mopt = prob_and_run(rho_sliders,dep_cells)
ax = plot_results(mopt,expmap,rho_sliders,dep_cells)
show_inline_matplotlib_plots()
return
button.on_click(on_button_clicked)
show_inline_matplotlib_plots()
return button,output
def on_clicked(b):
# if d_style.value == 'True':
# from IPython import get_ipython
# ipython = get_ipython()
# ipython.magic("matplotlib widget")
output_plot.clear_output()
with output_plot:
self.update_rm()
w, h = 10, 5
if len(w_y_metrics.update()) == 0:
display('No results saved yet.')
return
elif len(w_y_metrics.update()) > 1:
figsize = (2*int(w), int(h))
self.vars['figsize'] = figsize
else:
self.vars['figsize'] = (int(w), int(h))
self.vars['legend_format'] = llegend_format.value
self.vars['log_metric_list'] = hu.get_list_from_str(
llog_metric_list.value)
self.vars['title_format'] = ltitle_format.value
self.vars['cmap'] = lcmap.value
self.rm_original.fig_list = self.rm.get_plot_all(y_metric_list=w_y_metrics.update(),
x_metric=w_x_metric.update(),
groupby_list=w_groupby.update(),
legend_list=w_legend.update(),
log_metric_list=self.vars['log_metric_list'],
mode=w_mode.update(),
bar_agg=w_bar_agg.update(),
figsize=self.vars['figsize'],
title_list=w_title.update(),
legend_format=self.vars['legend_format'],
title_format=self.vars['title_format'],
cmap=self.vars['cmap'],
avg_across=w_avg_across.update())
show_inline_matplotlib_plots()
def __call__(self, fetch_tuples):
""" render the window data
Args:
fetch_tuples: callback to get the next tuples from the ClassificationMetrics view
returns a list of tuples or None to terminate
"""
notDone = True
display(self.class_dashboard)
cnt = 1
while notDone:
try:
view_metrics = fetch_tuples()
if view_metrics is None:
notDone = False
return
for raw_metrics in view_metrics:
#metrics = json.loads(raw_metrics)
metrics = raw_metrics
for camera in self.cameras:
with self.class_widgets[camera]:
certain = metrics['camera_metrics'][camera][
'certain']
uncertain = metrics['camera_metrics'][camera][
'uncertain']
IPython.display.clear_output(wait=True)
df = pd.DataFrame(
{
'uncertain counts': uncertain,
'certain counts': certain
},
index=range(len(uncertain)))
ax = df.plot.bar(rot=0, title=camera)
ax.plot(figsize=[8, 1])
show_inline_matplotlib_plots()
self.class_status_widget.value = "{} of {} ts: {}".format(
cnt, len(view_metrics), metrics['timestamp'])
cnt += 1
time.sleep(2)
except KeyboardInterrupt:
notDone = False
raise
def btn_plot_vol_clicked(b):
out_vols.clear_output()
with out_vols:
if val_date_wgt.value is None or end_wgt.value is None:
print('Enter val date and storage end date.')
return
vol_model = MultiFactorModel.for_3_factor_seasonal(
freq, spot_mr_wgt.value, spot_vol_wgt.value, lt_vol_wgt.value,
seas_vol_wgt.value, val_date_wgt.value, end_wgt.value)
start_vol_period = pd.Period(val_date_wgt.value, freq=freq)
end_vol_period = start_vol_period + 1
periods = pd.period_range(start=end_vol_period,
end=end_wgt.value,
freq=freq)
fwd_vols = [
vol_model.integrated_vol(start_vol_period, end_vol_period, period)
for period in periods
]
fwd_vol_series = pd.Series(data=fwd_vols, index=periods)
fwd_vol_series.plot()
show_inline_matplotlib_plots()
def interactive_output(f, controls, process_controls=lambda x: x):
"""Connect widget controls to a function.
This function does not generate a user interface for the widgets (unlike `interact`).
This enables customisation of the widget user interface layout.
The user interface layout must be defined and displayed manually.
"""
out = Output()
def observer(change):
show_inline_matplotlib_plots()
with out:
clear_output(wait=True)
f(**unpack_controls(controls, process_controls))
show_inline_matplotlib_plots()
for k, w in controls.items():
w.observe(observer, 'value')
show_inline_matplotlib_plots()
observer(None)
return out
def on_clicked(b):
output_plot.clear_output()
with output_plot:
self.update_rm()
w, h = 10, 5
if len(w_y_metrics.update()) == 0:
display("No results saved yet.")
return
elif len(w_y_metrics.update()) > 1:
figsize = (2 * int(w), int(h))
self.vars["figsize"] = figsize
else:
self.vars["figsize"] = (int(w), int(h))
self.vars["legend_format"] = llegend_format.value
self.vars["log_metric_list"] = hu.get_list_from_str(
llog_metric_list.value)
self.vars["title_format"] = ltitle_format.value
self.vars["cmap"] = lcmap.value
self.rm_original.fig_list = self.rm.get_plot_all(
y_metric_list=w_y_metrics.update(),
x_metric=w_x_metric.update(),
groupby_list=w_groupby.update(),
legend_list=w_legend.update(),
log_metric_list=self.vars["log_metric_list"],
mode=w_mode.update(),
bar_agg=w_bar_agg.update(),
figsize=self.vars["figsize"],
title_list=w_title.update(),
legend_format=self.vars["legend_format"],
title_format=self.vars["title_format"],
cmap=self.vars["cmap"],
avg_across=w_avg_across.update(),
)
show_inline_matplotlib_plots()
def on_clicked(b):
self.update_rm()
output_plot.clear_output()
with output_plot:
self.vars['y_metrics'] = get_list_from_str(t_y_metric.value)
self.vars['x_metric'] = t_x_metric.value
w, h = 10, 5
if len(self.vars['y_metrics']) > 1:
figsize = (2 * int(w), int(h))
self.vars['figsize'] = figsize
else:
self.vars['figsize'] = (int(w), int(h))
self.vars['legend_list'] = get_list_from_str(
llegend_list.value)
self.vars['log_metric_list'] = get_list_from_str(
llog_metric_list.value)
self.vars['groupby_list'] = get_list_from_str(
t_groupby_list.value)
self.vars['mode'] = t_mode.value
self.vars['title_list'] = get_list_from_str(t_title_list.value)
self.vars['bar_agg'] = t_bar_agg.value
self.rm.get_plot_all(
y_metric_list=self.vars['y_metrics'],
x_metric=self.vars['x_metric'],
groupby_list=self.vars['groupby_list'],
legend_list=self.vars['legend_list'],
log_metric_list=self.vars['log_metric_list'],
mode=self.vars['mode'],
bar_agg=self.vars['bar_agg'],
figsize=self.vars['figsize'],
title_list=self.vars['title_list'])
show_inline_matplotlib_plots()
def _plot(self, field_name: str, output: widgets.Output):
dim_selection = {
name: slider.value
for name, slider in self.dim_sliders.items()
}
self.figure_display.clear_output()
with output:
try:
if field_name == 'Scalars':
plt.figure(figsize=(12, 5))
for name in self.app.curve_names:
plt.plot(*self.app.get_curve(name),
label=display_name(name))
plt.legend()
plt.tight_layout()
show_inline_matplotlib_plots()
else:
value = self.app.get_field(field_name, dim_selection)
if isinstance(value, SampledField):
try:
value = select_channel(value, {
'🡡': None,
'⬤': 'abs'
}.get(self.vector_select.value,
self.vector_select.value))
plot(value,
lib='matplotlib',
**self.config.get('plt_args', {}))
show_inline_matplotlib_plots()
except ValueError as err:
self.figure_display.append_stdout(f"{err}")
else:
self.figure_display.append_stdout(
f"{field_name} = {value}")
except Exception:
self.figure_display.append_stdout(traceback.format_exc())
self._last_plot_update_time = time.time()
def show_image(rb_inst):
with out:
clear_output()
if select_img.value == 'Notebook Dashboard':
if select_feat.value == 'Select an item':
w_img = mpimg.imread(
os.path.join('img', 'Jupyter_select.png'))
else:
w_img = mpimg.imread(
os.path.join('img', 'Jupyter_dashboard.png'))
if select_img.value == 'Notebook':
w_img = mpimg.imread(
os.path.join('img', 'Jupyter_notebook_head.png'))
if select_img.value == 'Notebook Cells':
w_img = mpimg.imread(os.path.join('img', 'Jupyter_cells.png'))
if select_img.value == 'Select':
feat_desc.value = ''
return
plt.figure(figsize=(20, 5))
plt.imshow(w_img)
plt.axis('off')
rect_patch = None
if select_feat.value == "'Files' tab":
rect_patch = plt.Rectangle((20, 60),
50,
30,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: This tab displays a window where you can view your files.'
if select_feat.value == "Files list":
rect_patch = plt.Rectangle((20, 170),
300,
75,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: All of your notebooks, files, and folders are displayed here.'
if select_feat.value == 'Select an item':
rect_patch = plt.Rectangle((0, 90),
350,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: This menu shows actions you can take with an item - these actions will change depending on what you have selected (notebook, file, or folder) and if the notebook is currently running. Actions include duplicating, renaming, moving, downloading, viewing, editing, or deleting an item, or shutting down a notebook.'
if select_feat.value == "'New' button":
rect_patch = plt.Rectangle((1080, 100),
50,
26,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: You can create a new notebook, text file, or folder using this button.'
if select_feat.value == 'Notebook title':
rect_patch = plt.Rectangle((135, 0),
400,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: The notebook title is displayed here. You can click to rename it.'
if select_feat.value == 'Save status':
rect_patch = plt.Rectangle((520, 0),
330,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: This indicates when the notebook was last saved. Save often!'
if select_feat.value == 'Menu bar':
rect_patch = plt.Rectangle((5, 43),
500,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: Various actions can be taken from the menu bar. A common action is restarting the kernel.'
if select_feat.value == 'Tool bar':
rect_patch = plt.Rectangle((5, 75),
540,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: Shortcuts for common actions are located here. Save using the left-most button often!'
if select_feat.value == 'Cell type selector':
rect_patch = plt.Rectangle((380, 74),
120,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = "<b>Feature Description</b>: You can use this dropdown to change the type of your selected cell(s). The two most common cell types are 'Code' and 'Markdown'."
if select_feat.value == 'Command palette':
rect_patch = plt.Rectangle((500, 74),
45,
35,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: Clicking here will bring up a useful list of keyboard shortcuts.'
if select_feat.value == 'Markdown cell':
rect_patch = plt.Rectangle((110, 24),
1000,
90,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: This is a markdown cell. Use it for descriptive text and headings. Both HTML and Jupyter Markdown formatting can be used to style text.'
if select_feat.value == 'Code cell':
rect_patch = plt.Rectangle((110, 120),
1000,
100,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = '<b>Feature Description</b>: This is a code cell. You can run script snippets here.'
if select_feat.value == 'Cell status':
rect_patch = plt.Rectangle((45, 120),
70,
30,
fill=None,
edgecolor='r',
linewidth=2)
feat_desc.value = "<b>Feature Description</b>: This number tells you the order that the cells have been run in. If it is blank, the cell hasn't been run. If it has an *, the cell is currently running."
if rect_patch:
plt.gca().add_patch(rect_patch)
show_inline_matplotlib_plots()
def runMorris(control_rows, output_dropdown, trajectories_text, levels_text,
grid_jump_text, run_morris_button, results_output):
inputs = [(control_row[0].value, control_row[1].value,
control_row[2].value) for control_row in control_rows
if control_row[0].value != no_selection]
target_parameters = dict([(input[0], (input[1], input[2]))
for input in inputs])
target_output_name = output_dropdown.value
n_trajectories = trajectories_text.value
num_levels = levels_text.value
grid_jump = grid_jump_text.value
run_morris_button.disabled = True
try:
problem, parameter_values, sis = hdm.run_salib_morris(
target_parameters,
target_output_name,
n_trajectories,
num_levels,
grid_jump,
1 # os.cpu_count()
)
except:
with results_output:
print("Invalid input", file=sys.stderr)
return
finally:
run_morris_button.disabled = False
from base64 import b64encode
header = ','.join([name for name in target_parameters.keys()])
lines = [','.join([str(d) for d in inputs]) for inputs in parameter_values]
csv = header + os.linesep + os.linesep.join(lines)
payload = b64encode(csv.encode())
payload = payload.decode()
fileName = "parameter_values.csv"
title = "Download design"
html = f'<a download="{fileName}" href="data:text/csv;charset=utf-8;base64,{payload}" target="_blank">{title}</a>'
link = widgets.HTML(html)
results_output.clear_output()
with results_output:
print("{0:<30} {1:>10} {2:>10} {3:>15} {4:>10}".format(
"Parameter", "Mu_Star", "Mu", "Mu_Star_Conf", "Sigma"))
num_vars = problem['num_vars']
for j in list(range(num_vars)):
print("{0:30} {1:10.3f} {2:10.3f} {3:15.3f} {4:10.3f}".format(
sis['names'][j], sis['mu_star'][j], sis['mu'][j],
sis['mu_star_conf'][j], sis['sigma'][j]))
display(link)
fig1, (ax1, ax2) = plt.subplots(1, 2)
horizontal_bar_plot(ax1, sis, {}, sortby='mu_star')
covariance_plot(ax2, sis, {})
fig2 = plt.figure()
sample_histograms(fig2, parameter_values, problem, {'color': 'y'})
fig1.tight_layout()
show_inline_matplotlib_plots()
def btn_clicked(b):
progress_wgt.value = 0.0
for vw in value_wgts:
vw.value = ''
btn_calculate.disabled = True
out_summary.clear_output()
out_triggers.clear_output()
try:
global fwd_curve
logger.debug('Reading forward curve.')
fwd_curve = read_fwd_curve()
logger.debug('Forward curve read successfully.')
global storage
global val_results_3f
if stor_type_wgt.value == 'Simple':
storage = CmdtyStorage(
freq,
storage_start=start_wgt.value,
storage_end=end_wgt.value,
injection_cost=inj_cost_wgt.value,
withdrawal_cost=with_cost_wgt.value,
min_inventory=invent_min_wgt.value,
max_inventory=invent_max_wgt.value,
max_injection_rate=inj_rate_wgt.value,
max_withdrawal_rate=with_rate_wgt.value,
cmdty_consumed_inject=inj_consumed_wgt.value,
cmdty_consumed_withdraw=with_consumed_wgt.value)
else:
ratchets = read_ratchets()
storage = CmdtyStorage(freq,
storage_start=start_wgt.value,
storage_end=end_wgt.value,
injection_cost=inj_cost_wgt.value,
withdrawal_cost=with_cost_wgt.value,
constraints=ratchets)
interest_rate_curve = pd.Series(index=pd.period_range(
val_date_wgt.value,
twentieth_of_next_month(pd.Period(end_wgt.value, freq='D')),
freq='D'),
dtype='float64')
interest_rate_curve[:] = ir_wgt.value
seed = None if seed_is_random_wgt.value else random_seed_wgt.value
fwd_sim_seed = fwd_sim_seed_wgt.value if fwd_sim_seed_set_wgt.value else None
logger.info('Valuation started.')
val_results_3f = three_factor_seasonal_value(
storage,
val_date_wgt.value,
inventory_wgt.value,
fwd_curve=fwd_curve,
interest_rates=interest_rate_curve,
settlement_rule=twentieth_of_next_month,
spot_mean_reversion=spot_mr_wgt.value,
spot_vol=spot_vol_wgt.value,
long_term_vol=lt_vol_wgt.value,
seasonal_vol=seas_vol_wgt.value,
num_sims=num_sims_wgt.value,
basis_funcs=basis_funcs_input_wgt.value,
discount_deltas=discount_deltas_wgt.value,
seed=seed,
fwd_sim_seed=fwd_sim_seed,
extra_decisions=extra_decisions_wgt.value,
num_inventory_grid_points=grid_points_wgt.value,
on_progress_update=on_progress,
numerical_tolerance=num_tol_wgt.value)
logger.info('Valuation completed without error.')
full_value_wgt.value = "{0:,.0f}".format(val_results_3f.npv)
intr_value_wgt.value = "{0:,.0f}".format(val_results_3f.intrinsic_npv)
extr_value_wgt.value = "{0:,.0f}".format(val_results_3f.extrinsic_npv)
intr_delta = val_results_3f.intrinsic_profile['net_volume']
active_fwd_curve = fwd_curve[storage.start:storage.end]
with out_summary:
ax_1 = val_results_3f.deltas.plot(legend=True)
ax_1.set_ylabel('Delta')
intr_delta.plot(legend=True, ax=ax_1)
ax_2 = active_fwd_curve.plot(secondary_y=True,
legend=True,
ax=ax_1)
ax_2.set_ylabel('Forward Price')
ax_1.legend(['Full Delta', 'Intrinsic Delta'])
ax_2.legend(['Forward Curve'])
show_inline_matplotlib_plots()
with out_triggers:
trigger_prices = val_results_3f.trigger_prices
ax_1 = trigger_prices['inject_trigger_price'].plot(legend=True)
ax_1.set_ylabel('Price')
trigger_prices['withdraw_trigger_price'].plot(legend=True, ax=ax_1)
active_fwd_curve.plot(legend=True, ax=ax_1)
ax_2 = val_results_3f.expected_profile['inventory'].plot(
secondary_y=True, legend=True, ax=ax_1)
ax_2.set_ylabel('Volume')
box = ax_1.get_position()
ax_1.set_position([
box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9
])
ax_1.legend([
'Inject Trigger Price', 'Withdraw Trigger Price',
'Forward Curve'
],
loc='upper center',
bbox_to_anchor=(0.2, -0.12))
ax_2.legend(['Expected Inventory'],
loc='upper center',
bbox_to_anchor=(0.7, -0.12))
show_inline_matplotlib_plots()
except Exception as e:
logger.error('Exception:')
logger.error(e)
finally:
btn_calculate.disabled = False
