def panel(self, debug=False):
title = pn.pane.Markdown('# Search MAST for Moving Targets')
row1 = pn.Row(self.obj_name, self.id_type)
row2 = pn.Row(self.start_time, self.stop_time, self.time_step)
button_row = pn.Row(self.param['ephem_button'],
self.param['tap_button'])
output_tabs = pn.Tabs(
('Ephemerides', pn.Column(self.eph_col_choice, self.get_ephem)),
('MAST Results', pn.Column(self.mast_col_choice, self.get_mast)),
('MAST Plot', self.mast_figure))
if debug:
output_tabs.append(('Debug', self.fetch_stcs))
gspec = pn.GridSpec(width=800, height=400)
gspec[:1, :3] = title
gspec[2, :3] = row1
gspec[3, :3] = row2
gspec[4, :3] = button_row
gspec[5, :3] = output_tabs
'''
mypanel = pn.Column(title, pn.layout.Divider(),
row1, row2, button_row,
output_tabs)'''
return gspec
def dashboard_column(plots, shared_slider=False):
# Generate a Panel dashboard from a list of interactive plots
# Create a Panel object to host our plots
app = pn.GridSpec(sizing_mode='stretch_both')
# Arrange plots in a column
column = pn.Column(*plots)
# Add plots and sliders to Panel app
if shared_slider:
# Link all the sliders to one slider
# TODO: Add check for whether sliders can be linked
slider1 = column[0][1][0]
for plot in column[1:]:
slider = plot[1][0]
slider1.link(slider, value='value')
# Append all the plots to the app (using 3/4 of the horizontal space)
for i, plot in enumerate(column):
app[i, 0:4] = plot[0]
# Add the linked slider (using the last 1/4 of the horizontal space)
app[0, 4] = slider1
else:
# Append whole column (with individual sliders) to the app
app[0, 0] = column
return app
def plot_without_asr_dipdip(self):
if self.plot_check_asr_dipdip_btn.clicks == 0: return
# Insert results in grid.
gspec = pn.GridSpec(sizing_mode='scale_width')
asr_plotter = self.ddb.anacompare_asr(
asr_list=(0, 2),
chneut_list=(1, ),
dipdip=1,
lo_to_splitting=self.lo_to_splitting,
nqsmall=self.nqsmall,
ndivsm=self.ndivsm,
dos_method=self.dos_method,
ngqpt=None,
verbose=self.verbose,
mpi_procs=self.mpi_procs)
gspec[0, :1] = asr_plotter.plot(**self.fig_kwargs)
dipdip_plotter = self.ddb.anacompare_dipdip(
chneut_list=(1, ),
asr=2,
lo_to_splitting=self.lo_to_splitting,
nqsmall=self.nqsmall,
ndivsm=self.ndivsm,
dos_method=self.dos_method,
ngqpt=None,
verbose=self.verbose,
mpi_procs=self.mpi_procs)
gspec[1, :1] = dipdip_plotter.plot(**self.fig_kwargs)
return gspec
def visualize(self):
daily_plot_cols_labels = {"INFECTED": "Infected", "RECOVERED": "Recovered", "DEAD": "dead"}
change_plot_cols_labels = {
"num_recovered_cases": "# newly recovered",
"num_deceased_cases": "# of new deaths",
"num_new_cases": "# of new cases"
}
mobility_plot_cols_labels = {
"num_state_changed": "# of residents moved" # NOTE: Only people moving is considered a state change as of today
}
gspec = pn.GridSpec(width=975, margin=0, sizing_mode="stretch_both")
mobility_plot = self.curve.visualize_time_series(mobility_plot_cols_labels.keys(),
rename_cols=mobility_plot_cols_labels,
options=opts(title="∆ in number of residents moved", ylabel="Periodic change", height=300, show_legend=False))
daily_plot = self.curve.visualize_time_series(daily_plot_cols_labels.keys(),
rename_cols=daily_plot_cols_labels,
options=opts(ylabel="Total as of date"))
change_plot = self.curve.visualize_time_series(change_plot_cols_labels.keys(),
rename_cols=change_plot_cols_labels,
options=opts(ylabel="Periodic change in metric"))
gspec[0, 0:2] = self.city.visualize()
gspec[0, 2:5] = mobility_plot
gspec[0, 5] = self.curve.visualize_recent_residents_moved(opts(width=100, title=""))
gspec[1, :3] = daily_plot
gspec[1, 3:] = change_plot
return gspec
def _create_app():
player_select_widget = pn.widgets.Select(
options=list(_get_epl_strikers_df()["player_name"].unique()),
value="Bruno Fernandes",
min_width=1600,
max_width=1800,
)
# assign css class to achieve better design granularity
player_select_row = pn.Row(
pn.pane.Str("Choose A Player for Analysis:", sizing_mode="fixed"),
player_select_widget,
align="center",
css_classes=["player_select_row"],
)
# Panel HTML elements can take a 'style' parameter directly.
# You could alternatively assign a css class as above and include the css code in raw_css
title_html_pane = pn.pane.HTML(
"""
<h1>Panel Soccer Analytics Dashboard</h1>
""",
style={
"color": "white",
"width": "90%",
"text-align": "center"
},
)
# bind the 'player_select_widget' to our functions above
bound_player_png_image_pane = pn.bind(_get_and_display_player_image,
player=player_select_widget)
plot_horizontal_bar_pane = pn.bind(_plot_horizontal_bar,
player=player_select_widget)
bound_plotly_line_plot_pane = pn.bind(_plot_line_plot,
player=player_select_widget)
bound_plotly_scatter_plot_pane = pn.bind(_plot_scatter_plot,
player=player_select_widget)
# create a new gridspec with 14 columns and 12 rows
gspec = pn.GridSpec(ncols=14,
nrows=12,
sizing_mode="stretch_both",
css_classes=["gspec_container"])
# place application elements in the grid, using pn.Spacer() for improved layout spacing and
# control
gspec[0, :14] = title_html_pane
gspec[1, :14] = player_select_row
gspec[3:6, 1:3] = bound_player_png_image_pane
gspec[2:7, 3] = pn.Spacer()
gspec[2:7, 4:14] = plot_horizontal_bar_pane
gspec[7:10, 0:7] = bound_plotly_line_plot_pane
gspec[7:10, 7:14] = bound_plotly_scatter_plot_pane
gspec[11, 0:14] = pn.Row(pn.Spacer(), CSS_PANE)
return gspec
def panel(self):
"""The image viewer as a panel to display.
"""
self._set_css()
# Widget to show image informations.
info_widget = self._get_image_info
info_container = pn.Column(pn.pane.Markdown("## Informations"), info_widget)
# Widget with parameter widgets to control image viewer.
parameters_widget = pn.Param(
self.param,
show_name=False,
parameters=self.active_param_widgets,
widgets=self.param_widgets,
sizing_mode="scale_both",
)
parameters_widget = pn.WidgetBox(parameters_widget)
parameters_container = pn.Column(
pn.pane.Markdown("## Viewer Configurations"), parameters_widget
)
# Assemble informations and parameters widgets.
tools_widget = pn.Column(info_container, parameters_container)
# Image container
image_container = pn.Row(self._get_fig)
# Log container
log_container = self.log.panel(height=200)
# Viewer layout
main_pane_args = {}
main_pane_args["css_classes"] = [f"viewer-{self.viewer_id}"]
main_pane_args["margin"] = 0
if self.width and self.height:
main_pane_args["width"] = self.width
main_pane_args["height"] = self.height
elif self.width:
main_pane_args["width"] = self.width
main_pane_args["sizing_mode"] = "stretch_height"
elif self.height:
main_pane_args["height"] = self.height
main_pane_args["sizing_mode"] = "stretch_width"
else:
main_pane_args["sizing_mode"] = "scale_both"
gspec = pn.GridSpec(**main_pane_args)
gspec[0, :1] = tools_widget
gspec[0, 1:20] = image_container
# gspec[1, :20] = viewer.drawer.panel()
gspec[1, :20] = log_container
return gspec
def view(self):
spatial = self._get_spatial_distribution(self.gene, self.exact)
fpkm = self._get_fpkm(self.gene, self.exact)
expression = self._get_expression(self.gene, self.exact)
gspec = pn.GridSpec(width=1000, height=800)
gspec[0, 0:6] = spatial
gspec[1, 0:2] = fpkm
gspec[1, 2:6] = expression
return gspec
def plots_as_matrix(plots, ncols, nrows, width=500, height=500):
"""Takes a list of plots and puts them into a matrix"""
plot_matrix = pn.GridSpec(ncols=ncols, nrows=nrows, width=width, height=height)
for index, plot in enumerate(plots):
row_pos = index//ncols
col_pos = index%ncols
if row_pos >= nrows:
break
else:
plot_matrix[row_pos, col_pos] = plot
return plot_matrix
def build_gui(self):
"""All filters used below need to have two functions get_selection and update_with_mask."""
data_selection = getattr(self.dataset, self.DESCRIPTOR_DATA)[self.columns]
self.generate_filters(data_selection)
# put the images in the grid
self.gui = pn.GridSpec(ncols=self.n_cols, nrows=self.n_rows, width=self.width, height=self.height)
for index, gui_filter in enumerate(self.filters):
self.gui[index//self.n_cols, index%self.n_cols] = gui_filter.show()
# hook all control elements to the update functions
for single_filter in self.filters:
single_filter.register_callback(self.update_plots)
def getApp(doc):
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"
baseVizElement = getBaseVizElement(doc.session_context.request.headers["viz-element-id"])
init = True
viz = baseVizElement._vizApp
gspec = pn.GridSpec(sizing_mode='stretch_both', max_height=800)
gspec[0, :3] = pn.Row(pn.Row(viz.param, parameters=['update']),viz.view)
gspec[1, :3] = pn.Row(viz.viewDataFrame)
gspec.server_doc(doc)
def report_dboard():
input_data = pd.DataFrame.from_dict({
"longitude": [-0.155, 0.2795, -0.12344],
"latitude": [51.5229, 51.5560, 51.5046313],
})
interactive_map = maps.ExampleMap(input_data)
gspec = pn.GridSpec(sizing_mode="stretch_both")
gspec[0, 0] = interactive_map.param
gspec[1, 0] = interactive_map.view
return gspec
def layout(server_name):
global server, e, valid_stdnames
global progressbar
global wds_menu, wds_date, wstdname_menu, wstdname_date
global dsnames, dsmap, df_dsmap
wstdname_date = pn.widgets.DateRangeSlider(
start=datetime.datetime(2011, 1, 1),
end=utcnow,
value=(utcnow - datetime.timedelta(days=14), utcnow),
)
wds_date = pn.widgets.DateRangeSlider(
name="Limits for the timeseries plot",
start=datetime.datetime(2011, 1, 1),
end=utcnow,
value=(wstdname_date.value_start, wstdname_date.value_end),
)
progressbar = pn.indicators.Progress(
name="Checking the variables available for this server",
bar_color="info",
value=0,
width=200,
)
valid_stdnames, server, e = get_valid_stdnames(server_name)
wstdname_menu = f_wstdname_menu(valid_stdnames)
wds_menu = f_wds_menu()
wstdname_menu.param.watch(update_wds_menu, "value")
wstdname_date.param.watch(update_wds_date, "value")
dyndsmap = create_dyndsmap()
dyntseries = create_dyntseries()
gspec = pn.GridSpec(sizing_mode="stretch_both", max_height=800)
gspec[0, 0] = OSM() * dyndsmap
gspec[0, 1] = pn.WidgetBox(
"## ERDDAP timeseries explorer",
wstdname_menu,
wstdname_date,
wds_menu,
wds_date,
)
gspec[1, :] = dyntseries
display(gspec)
def make_view(self):
state = fetch_all_models_metrics_for_feature_on_dataset(self.protected_feature, self.ad_category)
total_records_count = reduce(lambda acc, model_stats: model_stats["total_count"] + acc, state.values(), 0)
plotted_records_count = reduce(lambda acc, model_stats: model_stats["plot_count"] + acc, state.values(), 0)
num_models = len(state.values())
html_stats_div = lambda s: f"""<p style="text-align: center; margin-top: 3rem;">{s}</p>"""
stats_text = html_stats_div(f"{self.ad_category} ad category has {plotted_records_count / num_models:.2f} records of {total_records_count / num_models:.0f} records in total on an average")
gspec = pn.GridSpec(sizing_mode="stretch_both", align="center") # background="gray"
gspec[:15, :2] = chart_for_metric(state, FPR, fpr_opts)
gspec[:15, 2:4] = chart_for_metric(state, EOD, gfm_opts)
gspec[:15, 4:6] = chart_for_metric(state, AOD, gfm_opts)
gspec[15, :] = stats_text
return gspec
def plot_model_evaluation(
model,
X_train,
y_train,
X_test,
y_test,
target_names=None,
feature_names=None,
normalize: bool = False,
resolution: int = 100,
stacked: bool = False,
embedding: np.ndarray = None,
):
import panel as pn
y_pred_test = model.predict(X_test)
metrics = plot_classification_report(y=y_test,
y_pred=y_pred_test,
target_names=target_names)
conf_mat = plot_confussion_matrix(y_test=y_test,
y_pred=y_pred_test,
target_names=target_names,
normalize=normalize)
bounds = plot_decision_boundaries(
X_train=X_train,
y_train=y_train,
y_pred_train=model.predict(X_train),
X_test=X_test,
y_test=y_test,
y_pred_test=model.predict(X_test),
resolution=resolution,
embedding=embedding,
)
# features = plot_feature_importances(
# model=model, target_names=target_names, feature_names=feature_names, stacked=stacked
# )
gspec = pn.GridSpec(min_height=700,
height=700,
max_height=1200,
min_width=750,
max_width=1980,
width=750)
gspec[0, 0] = bounds
gspec[1, 1] = metrics
gspec[1, 0] = pn.pane.HTML(str(model), margin=0)
gspec[0, 1] = conf_mat
# gspec[3:5, :] = features
return gspec
def _create_grids(self):
"""
Creates a 2x2 grid of the prior and posterior predictive checks
for min, max, mean and std function.
Sets:
--------
_grids A Dict of pn.GridSpec objects:
{<var_name>:{<space>:pn.GridSpec}}
"""
for var in self._pred_checks:
if self._data.is_observed_variable(var):
c_min = PredictiveChecksCell(var, self._mode, self._ic, "min")
c_max = PredictiveChecksCell(var, self._mode, self._ic, "max")
c_mean = PredictiveChecksCell(var, self._mode, self._ic,
"mean")
c_std = PredictiveChecksCell(var, self._mode, self._ic, "std")
self._cells['min'] = c_min
self._cells['max'] = c_max
self._cells['mean'] = c_mean
self._cells['std'] = c_std
##Add to grid
cell_spaces = c_min.get_spaces()
self._grids[var] = {}
for space in cell_spaces:
if space not in self._grids[var]:
self._grids[var][space] = pn.GridSpec(
sizing_mode='stretch_both')
for row in [0, 1]:
for i in [0, 1]:
col = int(
(MAX_NUM_OF_COLS_PER_ROW - 2. * COLS_PER_VAR) /
2.)
start_point = (row, int(col + i * COLS_PER_VAR))
end_point = (row + 1,
int(col + (i + 1) * COLS_PER_VAR))
if row == 0 and i == 0:
self._grids[var][space][ start_point[0]:end_point[0], start_point[1]:end_point[1] ] = \
pn.Column(c_min.get_plot(space,add_info=False), width=220, height=220)
elif row == 0 and i == 1:
self._grids[var][space][ start_point[0]:end_point[0], start_point[1]:end_point[1] ] = \
pn.Column(c_max.get_plot(space,add_info=False), width=220, height=220)
elif row == 1 and i == 0:
self._grids[var][space][ start_point[0]:end_point[0], start_point[1]:end_point[1] ] = \
pn.Column(c_mean.get_plot(space,add_info=False), width=220, height=220)
elif row == 1 and i == 1:
self._grids[var][space][ start_point[0]:end_point[0], start_point[1]:end_point[1] ] = \
pn.Column(c_std.get_plot(space,add_info=False), width=220, height=220)
def _generate_annotations_tab(self):
plot_width, plot_height = floor(self.width * 0.45), floor(self.height *
0.45)
# mixing of classes
mixing_matrix_classes_in_images = utils.calculate_mixing_matrix(
getattr(self.dataset, self.DESCRIPTOR_DATA),
self.IMAGE_IDENTIFIER_COL, self.ANNOTATON_LABEL_COL)
self.class_mixing_matrix_plot = pn.Column(
"<b>Class mixing</b>",
heatmap(mixing_matrix_classes_in_images,
"row_name",
"col_name",
"values",
width=plot_width,
height=plot_height),
height=self.height)
# number of object per image, stacked hist
self.classes_for_objects_per_image_stacked_hist = pn.Column(
"<b>Objects per Image</b>",
stacked_hist(getattr(self.dataset, self.DESCRIPTOR_DATA),
self.OBJECTS_PER_IMAGE_COL,
self.ANNOTATON_LABEL_COL,
"Objects per Image",
width=plot_width,
height=plot_height))
# categorical overview
self.categorical_2d_histogram = categorical_2d_histogram_with_gui(
getattr(self.dataset, self.DESCRIPTOR_DATA),
category_cols=["label", "num_annotations", "width", "height"],
hist_cols=[
"num_annotations", "area", "area_normalized",
"area_square_root", "area_square_root_normalized",
"bbox_ratio", "bbox_xmin", "bbox_xmax", "bbox_ymin",
"bbox_ymax", "width", "height"
],
height=self.height // 2,
width=self.width // 2)
# ratio distribution
grid = pn.GridSpec(ncols=2,
nrows=2,
width=self.width,
height=self.height,
align="center")
grid[0, 0] = self.class_mixing_matrix_plot
grid[1, 0] = self.classes_for_objects_per_image_stacked_hist
grid[:, 1] = pn.Column(self.categorical_2d_histogram, align="center")
return grid
def combine_scatterplot_with_hists(select_x, select_y, select_color, xhist,
yhist, scatterplot):
select_buttons = column(select_x,
select_y,
select_color,
sizing_mode='scale_width')
gspec = pn.GridSpec(sizing_mode='stretch_both',
max_width=2000,
max_height=1500)
gspec[0, 0:3] = xhist
gspec[0, 3] = select_buttons
gspec[1:3, 0:3] = scatterplot
gspec[1:3, 3] = yhist
return gspec
def plot_ifc(self):
if self.plot_ifc_btn.clicks == 0: return
ifc = self.ddb.anaget_ifc(asr=self.asr,
chneut=self.chneut,
dipdip=self.dipdip)
# Insert results in grid.
gspec = pn.GridSpec(sizing_mode='scale_width')
gspec[0, :1] = ifc.plot_longitudinal_ifc(title="Longitudinal IFCs",
show=False)
gspec[1, :1] = ifc.plot_longitudinal_ifc_short_range(
title="Longitudinal IFCs short range", show=False)
gspec[2, :1] = ifc.plot_longitudinal_ifc_ewald(
title="Longitudinal IFCs Ewald", show=False)
return gspec
def __init__(self, parent):
"""Initialize single adsorbent row.
:param parent: Adsorbates instance
"""
super().__init__()
self.parent = parent
self.btn_add = pw.Button(name='+', button_type='primary')
self.btn_add.on_click(self.on_click_add)
self.btn_remove = pw.Button(name='-', button_type='primary')
self.btn_remove.on_click(self.on_click_remove)
#self.inp_refcode = pw.TextInput(name='Refcode')
#self.row = pn.Row(self.inp_name, self.btn_add, self.btn_remove)
self.row = pn.GridSpec(height=50)
self.row[0, 0:8] = self.inp_name
self.row[0, 8] = self.btn_add
self.row[0, 9] = self.btn_remove
def plot_phbands_and_phdos(self, event=None):
"""Compute phonon bands and ph-DOSes from DDB and plot the results."""
if self.plot_phbands_btn.clicks == 0: return
#self.plot_phbands_btn.button_type = "warning"
print("Computing phbands")
with self.ddb.anaget_phbst_and_phdos_files(
nqsmall=self.nqsmall,
qppa=None,
ndivsm=self.ndivsm,
line_density=None,
asr=self.asr,
chneut=self.chneut,
dipdip=self.dipdip,
dos_method=self.dos_method,
lo_to_splitting=self.lo_to_splitting,
verbose=self.verbose,
mpi_procs=self.mpi_procs,
return_input=True) as g:
phbst_file, phdos_file = g
phbands, phdos = phbst_file.phbands, phdos_file.phdos
print("Computing phbands completed")
# Build grid
gspec = pn.GridSpec(sizing_mode='scale_width')
gspec[0, 0] = phbands.plot_with_phdos(phdos,
units=self.units,
**self.fig_kwargs)
gspec[0, 1] = phdos_file.plot_pjdos_type(units=self.units,
exchange_xy=True,
**self.fig_kwargs)
gspec[1, 0] = phdos_file.msqd_dos.plot(units=self.units,
**self.fig_kwargs)
temps = self.temp_range.value
gspec[1, 1] = phdos.plot_harmonic_thermo(tstart=temps[0],
tstop=temps[1],
num=50,
**self.fig_kwargs)
#msqd_dos.plot_tensor(**self.fig_kwargs)
#self.plot_phbands_btn.button_type = "primary"
# Add HTML pane with input
gspec[2, :] = pn.pane.HTML(g.input._repr_html_())
return gspec
def get_panel(self):
"""Return tabs with widgets to interact with the DDB file."""
tabs = pn.Tabs()
app = tabs.append
mocc = self.mocc
gspec = pn.GridSpec(sizing_mode='scale_width')
gspec[0, 0] = mocc.plot_memory_usage(show=False, title="Memory usage")
gspec[0, 1] = mocc.plot_hist(show=False, title="Allocation histogram")
gspec[1, :] = mocc.plot_peaks(maxlen=10,
title="Memory peaks",
show=False)
maxlen = 50
df = mocc.get_peaks(maxlen=maxlen, as_dataframe=True)
df.drop(columns=["locus", "line", "action", "ptr"], inplace=True)
col = pn.Column(gspec,
f"## DataFrame with the first {maxlen} peaks",
_df(df),
sizing_mode="scale_width")
app(("Plots", col))
#app(("DataFrame", _df(mocc.dataframe)))
hotdf = mocc.get_hotspots_dataframe()
ax = hotdf.plot.pie(y='malloc_mb')
import matplotlib
fig = matplotlib.pyplot.gcf()
app(("Hotspots",
pn.Column(
"### DataFrame with total memory allocated per Fortran file.",
fig,
_df(hotdf),
sizing_mode='scale_width',
)))
app((
"Intense",
pn.Column(
"### DataFrame with variables that are allocated/freed many times.",
_df(mocc.get_intense_dataframe()),
sizing_mode='scale_width')))
#retcode = memfile.find_memleaks()
#app(("Memleaks", _df(mocc.find_memleaks())))
return tabs
def _init_view(self):
info = """## Candle Stick Plot
We can use a [*candlestick chart*](https://en.wikipedia.org/wiki/Candlestick_chart) to visualize the
Open, High, Low, Close price data and histograms to visualize the Volume.
Technically we use HoloViews [`Segments`]\
(https://holoviews.org/reference/elements/bokeh/Segments.html), [`Rectangles`]\
(https://holoviews.org/reference/elements/bokeh/Rectangles.html) and [`Histogram`]\
(https://holoviews.org/reference/elements/bokeh/Histogram.html) to create the plots and Panel
[`GridSpec`](https://panel.holoviz.org/reference/layouts/GridSpec.html) to lay them
out.
"""
self.view = pn.Column(
pn.pane.Markdown(info),
pn.GridSpec(sizing_mode="stretch_both", margin=0),
sizing_mode="stretch_both",
)
def generate_interface():
app = Application()
handlers = pn.Param(app.param)
# Analysis Page
analysis_page = pn.GridSpec(height=850,
width=1850,
max_height=850,
max_width=1850)
analysis_page[0:8, 0:2] = handlers[1]
analysis_page[0:10, 8:10] = handlers[2]
analysis_page[8:9, 0:2] = handlers[3]
analysis_page[9:10, 0:2] = handlers[4]
analysis_page[0:5, 2:8] = app.performance_graph
analysis_page[5:10, 2:8] = app.parameter_graph
interface = pn.Tabs(('Analysis Page', analysis_page), )
return interface
def _create_grids(self):
"""
Creates one Cell object per variable. Cell object is the smallest
visualization unit in the grid. Moreover, it creates one Panel GridSpec
object per space.
Sets:
--------
_cells A Dict {<var_name>:Cell object}.
_grids A Dict of pn.GridSpec objects:
{<space>:pn.GridSpec}
"""
graph_grid_map = self._create_graph_grid_mapping()
for row, map_data in graph_grid_map.items():
level = map_data[0]
vars_list = map_data[1]
level_previous = -1
if (row - 1) in graph_grid_map:
level_previous = graph_grid_map[row - 1][0]
if level != level_previous:
col = int(
(MAX_NUM_OF_COLS_PER_ROW - len(vars_list) * COLS_PER_VAR) /
2.)
else:
col = int((MAX_NUM_OF_COLS_PER_ROW -
MAX_NUM_OF_VARS_PER_ROW * COLS_PER_VAR) / 2.)
for i, var_name in enumerate(vars_list):
start_point = (row, int(col + i * COLS_PER_VAR))
end_point = (row + 1, int(col + (i + 1) * COLS_PER_VAR))
#col_l = int(col_f + (i+1)*COLS_PER_VAR)
grid_bgrd_col = level
c = VariableCell(var_name, self._mode, self._ic)
self._cells[var_name] = c
##Add to grid
cell_spaces = c.get_spaces()
for space in cell_spaces:
if space not in self._spaces:
self._spaces.append(space)
if space not in self._grids:
self._grids[space] = pn.GridSpec(
sizing_mode='stretch_both')
self._grids[space][ start_point[0]:end_point[0], start_point[1]:end_point[1] ] = pn.Column(c.get_plot(space), \
width=220, height=220)
self._ic._num_cells = len(self._cells)
def create_candle_stick_with_histograms(data: pd.DataFrame) -> pn.GridSpec:
"""Returns a candle stick plot with volume distributions on the sides
Args:
data (pd.DataFrame): A dataframe with columns time, open, high, low, close and volume
Returns:
pn.GridSpec: A GridSpec containing the plots
"""
gridspec = pn.GridSpec(sizing_mode="stretch_both",
min_height=600,
margin=0)
if not data is None:
volume_plot = _create_time_and_volume_histogram(data).opts(
responsive=True)
candle_stick_plot = create_candle_stick(data).opts(responsive=True)
pav_plot = _create_price_and_volume_histogram(data).opts(
responsive=True)
gridspec[0:2, 0:8] = volume_plot
gridspec[2:10, 0:8] = candle_stick_plot
gridspec[2:10, 8:10] = pav_plot
return gridspec
def create_dashboard():
mgrDir_list = ['Manager', 'Director']
mgrDirWidget = pn.interact(mgrDir, ManagerDirector=mgrDir_list)
assignmentsWidget = pn.interact(
chooseAssignmentLevel,
Quarter_Detail=['With_Quarters', 'Without_Quarters'],
ManagerDirector=mgrDir_list)
managerJobsWidget = pn.interact(displayManagerJobs,
Manager=df['Manager'].unique(),
ManagerDirector=mgrDir_list)
gspec = pn.GridSpec(sizing_mode='stretch_both', max_height=800)
gspec[0, :2] = pn.Row("# Novak Francella Manager/Partner Hours")
gspec[1:4, 0] = mgrDirWidget
gspec[1:6, 1] = pn.Column('')
gspec[1:3, 2] = assignmentsWidget
gspec[1:6, 3] = pn.Column('')
gspec[1:6, 4] = managerJobsWidget
return gspec
def plot_vsound(self):
"""
Compute the speed of sound by fitting phonon frequencies
along selected directions by linear least-squares fit.
"""
if self.plot_vsound_btn.clicks == 0: return
from abipy.dfpt.vsound import SoundVelocity
sv = SoundVelocity.from_ddb(self.ddb.filepath,
num_points=20,
qpt_norm=0.1,
ignore_neg_freqs=True,
asr=self.asr,
chneut=self.chneut,
dipdip=self.dipdip,
verbose=self.verbose,
mpi_procs=self.mpi_procs)
# Insert results in grid.
gspec = pn.GridSpec(sizing_mode='scale_width')
gspec[0, :1] = sv.get_dataframe()
gspec[1, :1] = sv.plot(**self.fig_kwargs)
return gspec
def plot_eps0w(self):
"""Compute eps0(omega) from DDB and plot the results."""
if self.plot_eps0w_btn.clicks == 0: return
gen, inp = self.ddb.anaget_dielectric_tensor_generator(
asr=self.asr,
chneut=self.chneut,
dipdip=self.dipdip,
mpi_procs=self.mpi_procs,
verbose=self.verbose,
return_input=True)
ws = self.w_range.value
w_max = ws[1]
if w_max == 1.0:
w_max = None # Will compute w_max in plot routine from ph freqs.
def p(component, reim):
return gen.plot(w_min=ws[0],
w_max=w_max,
gamma_ev=self.gamma_ev,
num=500,
component=component,
reim=reim,
units=self.units,
**self.fig_kwargs)
# Build grid
gspec = pn.GridSpec(sizing_mode='scale_width')
gspec[0, 0] = p("diag", "re")
gspec[0, 1] = p("diag", "im")
gspec[1, 0] = p("offdiag", "re")
gspec[1, 1] = p("offdiag", "im")
gspec[2, :] = gen.get_oscillator_dataframe(reim="all", tol=1e-6)
# Add HTML pane with input.
gspec[3, 0] = pn.pane.HTML(inp._repr_html_())
return gspec
def panel(self):
appbar = pn.Row(
pn.pane.HTML(
'''<div>Videogames sales dashboard</div>''',
style={
"font-size":
"4rem",
"text-align":
"left",
"height":
"10vh",
"line-height":
"10vh",
"color":
"#fcedd8",
"background":
"#d52e3f",
"font-family":
"Niconne, cursive",
"font-weight":
"200",
"text-shadow":
"2px 2px 0px #eb452b, 4px 4px 0px #efa032, 6px 6px 0px #46b59b, 8px 8px 0px #017e7f, 10px 10px 0px #052939, 12px 12px 0px #c11a2b, 14px 14px 0px #c11a2b, 16px 16px 0px #c11a2b, 18px 18px 0px #c11a2b"
},
width=1000),
pn.layout.HSpacer(height=0),
pn.pane.PNG(
"./scripts/cont.png",
width=200,
align="center",
sizing_mode="fixed",
margin=(10, 50, 10, 5),
),
sizing_mode="stretch_width",
css_classes=["app-bar"],
)
gspec = pn.GridSpec(sizing_mode='stretch_both', background="#f2f2f2")
gspec[0, :3] = pn.Column(appbar, pn.layout.HSpacer(height=10))
gspec[1, 0] = pn.Column(pn.Param(self.param,
parameters=["piattaforma", "valore"],
widgets={
"piattaforma": {
"type": pn.widgets.Select,
"inline": True,
"align": "end",
"color": "#fcba03"
}
}),
css_classes=['app-container'])
gspec[1, 1] = pn.Row(
pn.layout.VSpacer(width=10),
pn.Column(self.plot_1, css_classes=["app-container"]))
gspec[1, 2] = pn.Row(
pn.layout.VSpacer(width=10),
pn.Column(self.plot_2,
css_classes=["app-container"],
sizing_mode="stretch_width"))
gspec[2, 0] = pn.Row(pn.layout.VSpacer(width=10), self.Html_ret)
gspec[2, 1] = pn.Row(self.Html_ret_2, width=100)
return gspec
hvplot_plot = dataframe.hvplot(x="x", y="y")
hvplot_pane = pn.pane.HoloViews(hvplot_plot, name="Holoviews Plot")
altair_plot = (
alt.Chart(dataframe).mark_line().encode(x="x", y="y").properties(width="container", height=400)
)
altair_pane = pn.pane.Vega(altair_plot, name="Altair Plot")
plotly_plot = px.line(dataframe, x="x", y="y")
plotly_pane = pn.pane.Plotly(plotly_plot, name="Plotly Plot")
fig = figure()
fig.scatter([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 2, 1, 0, -1, -2, -3])
gspec = pn.GridSpec(sizing_mode="stretch_both", max_height=800, name="GridSpec")
gspec[0, :3] = pn.Spacer(background="#FF0000")
gspec[1:3, 0] = pn.Spacer(background="#0000FF")
gspec[1:3, 1:3] = fig
gspec[3:5, 0] = hv.Curve([1, 2, 3])
gspec[
3:5, 1
] = "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png"
gspec[4:5, 2] = pn.Column(
pn.widgets.FloatSlider(), pn.widgets.ColorPicker(), pn.widgets.Toggle(name="Toggle Me!")
)
if st.checkbox("Only hv_plot?"):
tabs = pn.Tabs(hvplot_pane)
