def _create_initial_convergence_plots(
criterion_history,
params_history,
group_to_param_ids,
group_to_param_names,
):
"""Create the initial convergence plots.
Args:
criterion_history (bokeh ColumnDataSource)
params_history (bokeh ColumnDataSource)
group_to_param_ids (dict): Keys are the groups to be plotted. Values are the
ids of the parameters belonging to the respective group.
group_to_param_names (dict): Keys are the groups to be plotted. Values are the
names of the parameters belonging to the respective group.
Returns:
convergence_plots (list): List of bokeh Row elements, each containing one
convergence plot.
"""
param_plots = []
for group, param_ids in group_to_param_ids.items():
param_names = group_to_param_names[group]
param_group_plot = plot_time_series(
data=params_history,
y_keys=param_ids,
y_names=param_names,
x_name="iteration",
title=str(group),
)
param_plots.append(param_group_plot)
arranged_param_plots = [Row(plot) for plot in param_plots]
linear_criterion_plot = plot_time_series(
data=criterion_history,
x_name="iteration",
y_keys=["criterion"],
y_names=["criterion"],
title="Criterion",
name="linear_criterion_plot",
logscale=False,
)
log_criterion_plot = plot_time_series(
data=criterion_history,
x_name="iteration",
y_keys=["criterion"],
y_names=["criterion"],
title="Criterion",
name="log_criterion_plot",
logscale=True,
)
log_criterion_plot.visible = False
plot_list = [
Row(linear_criterion_plot),
Row(log_criterion_plot),
] + arranged_param_plots
return plot_list
def _setup_tabs(sec_to_elements):
"""Create tabs for each section in sec_to_elements with titles.
Args:
sec_to_elements (dict): A nested dictionary. The first level keys will be the
sections ("running", "succeeded", "failed", "scheduled"). The second level keys
are the database names and the second level values a list consisting of the
link to the dashboard and a button to activate tha dashboard.
Returns:
tabs (bokeh.models.Tabs): a tab for every section in sec_to_elements.
"""
tab_list = []
for section, name_to_row in sec_to_elements.items():
text = f"{len(name_to_row.column_names)} optimizations {section}"
table_rows = [
Row(Div(text=text, width=400), name=f"{section}_how_many")
]
for name, row in name_to_row.data.items():
table_rows.append(Row(*row, name=name))
panel = Panel(
child=Column(*table_rows, name=f"{section}_col"),
title=section.capitalize(),
name=f"{section}_panel",
)
tab_list.append(panel)
tabs = Tabs(tabs=tab_list, name="tabs")
return tabs
def create_disp():
##### 各画面要素作成
# テーブル作成
df_columns = target_data.columns.tolist()
datatable_source = ColumnDataSource(data=target_data)
table_columns = create_datatable_columns(df_columns)
data_table = DataTable(source=datatable_source,
selectable=True,
columns=table_columns,
sortable=True)
# カラムが変わった時に呼び出す関数リスト
column_change_functions = []
# 表示を切り替えるplotリスト
visible_plots = []
cm_operation_area, cm_plot_area = create_cm(column_change_functions,
visible_plots,
datatable_source)
# upload button がクリックされた時の処理
def upload_button_callback(event):
global target_data
global user
csv_str = base64.b64decode(csv_input.value)
target_data = pd.read_csv(io.BytesIO(csv_str))
df_columns = target_data.columns.tolist()
call_column_change(column_change_functions, df_columns)
table_columns = create_datatable_columns(df_columns)
data_table.columns = table_columns
datatable_source.data = target_data
data_table.update()
try:
user.csv_data = target_data.to_json()
db.session.commit()
except Exception as e:
warn = str(e)
# csvアップロード実行ボタン
upload_button = Button(label="Upload", button_type="success")
upload_button.on_event(ButtonClick, upload_button_callback)
# ファイル選択ボックス
csv_input = FileInput()
scatter_operation_area, scatter_plot_area = create_scatter(
column_change_functions, visible_plots, datatable_source)
# サブオペレーションエリア
sub_operation_area = Row(cm_operation_area, scatter_operation_area)
operation_area = Column(csv_input, upload_button, sub_operation_area,
data_table)
graph_area = Column(cm_plot_area, scatter_plot_area)
layout_disp = Row(graph_area, operation_area)
return layout_disp
def _create_initial_convergence_plots(criterion_history, params_history, start_params):
"""Create the initial convergence plots.
Args:
criterion_history (bokeh ColumnDataSource)
params_history (bokeh ColumnDataSource)
start_params (pd.DataFrame): params DataFrame that includes the "group" column.
Returns:
convergence_plots (list): List of bokeh Row elements, each containing one
convergence plot.
"""
criterion_plot = _plot_time_series(
data=criterion_history,
x_name="iteration",
y_keys=["value"],
y_names=["criterion"],
title="Criterion",
)
convergence_plots = [criterion_plot]
group_to_params = _map_groups_to_params(start_params)
for g, group_params in group_to_params.items():
param_group_plot = _plot_time_series(
data=params_history, y_keys=group_params, x_name="iteration", title=g,
)
convergence_plots.append(Row(param_group_plot))
return convergence_plots
def get_plots():
row = Row()
plots = [figure(plot_height=400, tools='') for i in range(2)]
[
plot.line(x='x', y='y', source=data_sources[index])
for index, plot in enumerate(plots)
]
[row.children.append(plot) for plot in plots]
return row
def make_tools(self):
#ボタンやスライダーを作成
end = self.make_button(self.exit_hander, "終了")
self.searchbox = self.make_search_input_textbox()
self.searchbox.on_change("value", self.celestial_bodies_search_handler)
self.celestial_text = Div(text="", width=300, height=220)
self.celestial_text.background = (10, 10, 10, 0.05)
self.plotting_mode_text = Div(text="投影図法：メルカトル", width=140, height=20)
self.plotting_mode_text.background = (10, 10, 10, 0.05)
self.plotting_linkage_button = self.make_button(
self.switch_plotting_linkage_handler,
"視等級とズームの連動",
default_size=100)
self.plotting_linkage_button.button_type = "primary"
self.angle_simu_enlarge_slider = self.make_slider(
self.update_angle_square_enlarge, "焦点距離", 35, 10, 200, 1)
self.angle_simu_rotate_slider = self.make_slider(
self.update_angle_square_rotate, "回転角度", 0, -360, 360, 1)
self.angle_simu_button = self.make_button(
self.switch_angle_simulator_handler, "撮影シミュレーション")
angle_simu = Column(self.angle_simu_button,
self.angle_simu_enlarge_slider,
self.angle_simu_rotate_slider)
angle_simu.background = (0, 100, 60, 0.2)
self.star_label_button = self.make_button(self.switch_starlabel_hander,
"恒星の名前：on/off",
button_type="primary")
self.galaxy_button = self.make_button(self.switch_galaxy_handler,
"銀河：off/点のみ/メシエ番号")
self.star_line_button = self.make_button(self.switch_star_line_handler,
"星座線：on/off")
self.constellation_button = self.make_button(
self.switch_constellation_handler, "星座の名前：on/off")
switches = Column(self.star_label_button, self.galaxy_button,
self.star_line_button, self.constellation_button)
switches.background = (0, 40, 100, 0.2)
tools = Column(
end, self.searchbox, self.celestial_text,
Row(self.plotting_mode_text, self.plotting_linkage_button),
angle_simu, switches)
return tools
def _make_doc_layout(self):
"""
Constructs the document layout object.
Note: Row and Column are experimental features. The positions of
the widgets could be hardcoded.
Returns:
--------
doc_layout : bokeh.layouts.layout object
Grid layout of bokeh models.
"""
top_row = Row(self.widgets['add'], Spacer(width=25),
self.widgets['clear'])
right_column = Column(self.widgets['method'],
self.widgets['num_train_inst'],
self.widgets['color'],
self.widgets['change_color'],
self.widgets['start'])
doc_layout = layout([top_row, [self.fig, right_column]])
return doc_layout
def update_reviews(attr, old, new):
data_set = pd.read_csv('Outputs/data_set.csv')
i = int(star_rating.value)
#output_review_list = extract_ngrams(str(data_set[(data_set['Star_count'] == i)]['Review'].values),2,3)
data_set_blob = data_set.copy()
data_set_blob['Noun_sentences'] = data_set_blob['Review'].apply(lambda x:get_nouns(x))
n_gram_blob = TextBlob(str(data_set_blob[(data_set_blob['Star_count'] == i)]['Noun_sentences'].values))
#Styling the paragraph element
text1 = Paragraph(style={'font-variant': 'small-caps','font-family': "Tahoma"})
text1.text=""
#review1 = text_cleaner(str(n_gram_blob.ngrams(1)[0]))
#review2 = text_cleaner(str(n_gram_blob.ngrams(1)[1]))
review1 = text_cleaner(n_gram_blob.ngrams(1)[1][0])
review2 = text_cleaner(n_gram_blob.ngrams(1)[2][0])
text1.text = "Top "+str(i)+" star reviews feel: "+review1+", followed by "+review2
curdoc().add_root(Row(text1))
plot_1.title.text = "Circular Layout (NodesAndLinkedEdges inspection policy)"
plot_1.add_tools(HoverTool(tooltips=None))
plot_2 = create_graph(nx.spring_layout,
selection_policy=NodesAndLinkedEdges(),
scale=2,
center=(0, 0))
plot_2.title.text = "Spring Layout (NodesAndLinkedEdges selection policy)"
plot_2.add_tools(TapTool(), BoxSelectTool())
plot_3 = create_graph(nx.random_layout,
inspection_policy=EdgesAndLinkedNodes(),
center=(0, 0))
plot_3.title.text = "Random Layout (EdgesAndLinkedNodes inspection policy)"
plot_3.add_tools(HoverTool(tooltips=None))
plot_4 = create_graph(nx.fruchterman_reingold_layout,
selection_policy=EdgesAndLinkedNodes(),
scale=2,
center=(0, 0),
dim=2)
plot_4.title.text = "FR Layout (EdgesAndLinkedNodes selection policy)"
plot_4.add_tools(TapTool())
layout = Column(Row(plot_1, plot_2), Row(plot_3, plot_4))
doc = curdoc()
doc.add_root(layout)
show(layout)
def monitoring_app(doc, database_name, session_data):
"""Create plots showing the development of the criterion and parameters.
Options are loaded from the database. Supported options are:
- rollover (int): How many iterations to keep before discarding.
Args:
doc (bokeh.Document): Argument required by bokeh.
database_name (str): Short and unique name of the database.
session_data (dict): Infos to be passed between and within apps.
Keys of this app's entry are:
- last_retrieved (int): last iteration currently in the ColumnDataSource.
- database_path (str or pathlib.Path)
- callbacks (dict): dictionary to be populated with callbacks.
"""
database = load_database(session_data["database_path"])
start_params = read_scalar_field(database, "start_params")
dash_options = read_scalar_field(database, "dash_options")
rollover = dash_options["rollover"]
tables = ["criterion_history", "params_history"]
criterion_history, params_history = _create_bokeh_data_sources(
database=database, tables=tables
)
session_data["last_retrieved"] = 1
# create initial bokeh elements without callbacks
initial_convergence_plots = _create_initial_convergence_plots(
criterion_history=criterion_history,
params_history=params_history,
start_params=start_params,
)
activation_button = Toggle(
active=False,
label="Start Updating from Database",
button_type="danger",
width=50,
height=30,
name="activation_button",
)
# add elements to bokeh Document
bokeh_convergence_elements = [Row(activation_button)] + initial_convergence_plots
convergence_tab = Panel(
child=Column(*bokeh_convergence_elements), title="Convergence Tab"
)
tabs = Tabs(tabs=[convergence_tab])
doc.add_root(tabs)
# add callbacks
activation_callback = partial(
_activation_callback,
button=activation_button,
doc=doc,
database=database,
session_data=session_data,
rollover=rollover,
tables=tables,
)
activation_button.on_change("active", activation_callback)
defaultBtn = Button(label="Default", button_type="default")
confirmedBtn = Button(label="Confirmed", button_type="warning")
deceasedBtn = Button(label="Deceased", button_type="danger")
recoveredBtn = Button(label="Recovered", button_type="primary")
menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None, ("Item 3", "item_3")]
dropdown = Dropdown(label="Dropdown button", button_type="success", menu=menu)
# Draw the figures
world_map = create_world_map(confirmed_df)
bar_chart = create_region_impact_bar(confirmed_df, death_df, recovered_df)
vstacked_chart = create_chart(confirmed_df,
death_df=death_df,
recovered_df=recovered_df)
pie_chart = create_pie_chart(confirmed_df, death_df, recovered_df)
#def confirmedBtn_onclick():
# create_chart(confirmed_df, "#FF5733")
#confirmedBtn.on_click(confirmedBtn_onclick)
#deceasedBtn.on_click(deceasedBtn_onclick)
#recoveredBtn.on_click(recoveredBtn_onclick)
# Create the Rows
#first_row = Row(defaultBtn, confirmedBtn, deceasedBtn, recoveredBtn)
second_row = Row(world_map, pie_chart)
third_row = Row(bar_chart, vstacked_chart)
# Add to current document
#curdoc().add_root(first_row)
curdoc().add_root(second_row)
curdoc().add_root(third_row)
matchup_hover_b = HoverTool(tooltips=[('Freq.', '@matchup{0.00%}')],
renderers=[matchup_b])
pitches_p.tools = [overall_hover_p, matchup_hover_p, SaveTool()]
pitches_b.tools = [overall_hover_b, matchup_hover_b, SaveTool()]
# tab pitch data
tab1 = Panel(child=pitches_p, title='Pitcher')
tab2 = Panel(child=pitches_b, title='Batter')
pitch_tabs = Tabs(tabs=[tab1, tab2])
# filters for count
balls = Select(title='Balls',
options=['All', '1', '2', '3'],
value='All',
width=60)
strikes = Select(title='Strikes',
options=['All', '1', '2'],
value='All',
width=60)
balls.on_change('value', lambda attr, old, new: filter_data())
strikes.on_change('value', lambda attr, old, new: filter_data())
l = layout([[Row(pfirstname, plastname, psearch)], [pitcherselect],
[Row(hfirstname, hlastname, hsearch)], [hitterselect],
[start_date, end_date], [run_button], [balls, strikes],
[plot, pitch_tabs]],
sizing_mode='fixed')
curdoc().add_root(l)
title='Which buildings use what fuel?',
tools=['pan', 'reset', 'tap'])
bar.xaxis.axis_label = 'Types of Buildings in NYC'
bar.yaxis.axis_label = 'Total number of Buildings'
bar.xaxis.major_label_orientation = pi / 3
barColor = factor_cmap('btype',
palette=Category20c[20],
factors=sorted(df['BuildingType'].unique()))
bar.vbar(x='btype', top='count', width=.3, color=barColor, source=cdsbar)
fuelSelect = Select(title='Select the fuel type',
options=list(df['PrimaryFuel'].unique()),
value='4 (Clean Oil)')
fuelSelect.on_change('value', updateBar)
barHover = HoverTool(
tooltips=[('Type of building',
'@btype'), ('Fuel used',
'@fuel'), ('Total # of buildings', '@count')])
bar.add_tools(barHover)
curdoc().add_root(
Column(Column(xSelect, line), Row(scatHigh, scatLow),
Column(fuelSelect, bar)))
def main() -> None:
"""メイン処理
Returns:
None: None
"""
# データソースの初期設定
source = ColumnDataSource(data=dict(length=[], width=[]))
source.data = {"0": [], "1": []}
df = pd.DataFrame()
# CSVファイル設定テキストボックス
csv_input = TextInput(value="default.csv", title="Input CSV")
# 可視化手法選択ラジオボタン
method_group = analysis_method()
method_radio_group = RadioGroup(labels=list(method_group.keys()), active=0)
# グラフ初期設定
p = figure(
tools=("pan,box_zoom,lasso_select,box_select,poly_select"
",tap,wheel_zoom,reset,save,zoom_in"),
title="Analyze Result",
plot_width=1000,
plot_height=800,
)
p.circle(x="0", y="1", source=source)
# データ表示データテーブル
data_table = DataTable(source=source,
columns=[],
width=600,
height=500,
fit_columns=False)
# 色設定項目用選択セレクトボックス
def color_select_callback(attr, old, new) -> None:
mapper = create_mapper(df, new)
p.circle(x="0", y="1", source=source, line_color=mapper, color=mapper)
color_select = Select(title="color:", value="0", options=[])
color_select.on_change("value", color_select_callback)
# 解析実行ボタン
def execute_button_callback_inner(evnet):
nonlocal df
df = pd.read_csv(csv_input.value, index_col=0)
result = method_group[method_radio_group.labels[
method_radio_group.active]](df)
source.data = to_source_data_from(df, result)
data_table.columns = to_datatable_columns_from(df)
mapper = create_mapper(df, df.columns.values[0])
p.circle(x="0", y="1", source=source, line_color=mapper, color=mapper)
p.add_tools(create_hover(["ID", df.columns.values[0]]))
color_select.options = list(df.columns)
execute_button = Button(label="Execute", button_type="success")
execute_button.on_event(ButtonClick, execute_button_callback_inner)
# レイアウト
operation_area = Column(csv_input, method_radio_group, execute_button,
color_select, data_table)
layout = Row(p, operation_area)
curdoc().add_root(layout)
import pandas as pd
from bokeh.io import curdoc
from scatter_plot import create_scatter_plot
from population_per_region import create_bar_chart
from child_mortality_plot import create_mortality_bar_plot
from bokeh.layouts import Row, Column
main_df = pd.read_csv('gapminder_tidy.csv', index_col='Year')
scatter_plot = create_scatter_plot(main_df)
pop_per_region_bar = create_bar_chart(main_df)
child_mortality_bar = create_mortality_bar_plot(main_df)
column = Column(pop_per_region_bar, child_mortality_bar)
first_row = Row(scatter_plot)
second_row = Row(child_mortality_bar, pop_per_region_bar)
curdoc().add_root(first_row)
curdoc().add_root(second_row)
def _create_button_row(
doc,
database,
session_data,
start_params,
updating_options,
):
"""Create a row with two buttons, one for (re)starting and one for scale switching.
Args:
doc (bokeh.Document)
database (sqlalchemy.MetaData): Bound metadata object.
session_data (dict): dictionary with the last retrieved rowid
start_params (pd.DataFrame): See :ref:`params`
updating_options (dict): Specification how to update the plotting data.
It contains rollover, update_frequency, update_chunk, jump and stride.
Returns:
bokeh.layouts.Row
"""
# (Re)start convergence plot button
activation_button = Toggle(
active=False,
label="Start Updating",
button_type="danger",
width=200,
height=30,
name="activation_button",
)
partialed_activation_callback = partial(
activation_callback,
button=activation_button,
doc=doc,
database=database,
session_data=session_data,
tables=["criterion_history", "params_history"],
start_params=start_params,
updating_options=updating_options,
)
activation_button.on_change("active", partialed_activation_callback)
# switch between linear and logscale button
logscale_button = Toggle(
active=False,
label="Show criterion plot on a logarithmic scale",
button_type="default",
width=200,
height=30,
name="logscale_button",
)
partialed_logscale_callback = partial(
logscale_callback,
button=logscale_button,
doc=doc,
)
logscale_button.on_change("active", partialed_logscale_callback)
button_row = Row(children=[activation_button, logscale_button],
name="button_row")
return button_row
def tab_visualize(csv):
csv_original = csv.copy()
csv_modified = csv.copy()
target_csv = {'csv': csv_original}
source_length = target_csv['csv'].shape[0]
table_source = ColumnDataSource(target_csv['csv'])
table_columns = [
TableColumn(field=col, title=col)
for col in list(target_csv['csv'].columns)
]
table_original = DataTable(source=table_source,
columns=table_columns,
width=1550,
height=250,
fit_columns=False,
name="Original")
g = target_csv['csv'].columns.to_series().groupby(
target_csv['csv'].dtypes).groups
g_list = list(g.keys())
float_list = []
rest_list = []
for l in g_list:
if ('float' or 'int' in str(l)):
float_list += list(g[l])
else:
rest_list += list(g[l])
print(float_list)
print(rest_list)
def make_dataset(col_list, range_start, range_end):
xs = []
ys = []
labels = []
colors = []
target_length = target_csv['csv'].shape[0]
end = range_end if (range_end < target_length) else target_length
target_data = target_csv['csv'][col_list +
rest_list].iloc[range_start:end]
target_x = list(np.arange(target_data.shape[0]) + range_start)
for col, i in zip(col_list, range(len(col_list))):
y = list(target_data[col].values)
xs.append(target_x)
ys.append(y)
labels.append(col)
colors.append(line_colors[i])
new_src = ColumnDataSource(data={
'x': xs,
'y': ys,
'label': labels,
'colors': colors
})
return new_src
def make_plot(src):
p = figure(plot_width=1300,
plot_height=400,
title='Raw data',
x_axis_label='Index',
y_axis_label='Sensor value')
p.multi_line('x',
'y',
legend='label',
color='colors',
line_width=1,
source=src)
tools = HoverTool()
TOOLTIPS = [("", ""), ("", "")]
return p
def update(attr, old, new):
column_to_plot = select_column.value
new_src = make_dataset(column_to_plot, range_select.value[0],
range_select.value[1])
src.data.update(new_src.data)
line_colors = Category20_16
line_colors.sort()
select_column = MultiSelect(title="Column visualization",
value=float_list,
options=float_list)
select_column.on_change("value", update)
range_select = RangeSlider(start=0,
end=source_length,
value=(0, int(source_length / 100)),
step=1,
title='Sensor range')
range_select.on_change('value', update)
src = make_dataset([float_list[0]], range_select.value[0],
range_select.value[1])
p = make_plot(src)
select_normalize = Select(title="Select normalize transformation",
options=["-", "Min-Max", "Z normalize", "Raw"])
button_get_normal = Button(label="Transform")
def normal_handler():
print("Normalize")
# cols_to_normalize = float_list
cols_to_normalize = select_transform.value
if (select_normalize.value == "Min-Max"):
scaler = preprocessing.MinMaxScaler()
csv_modified[cols_to_normalize] = scaler.fit_transform(
X=csv_original[cols_to_normalize].values)
target_csv['csv'] = csv_modified
table_source.data = ColumnDataSource(target_csv['csv']).data
csv[cols_to_normalize] = target_csv['csv'][cols_to_normalize]
print("Min Max Mormalize")
elif (select_normalize.value == "Z normalize"):
scaler = preprocessing.StandardScaler()
csv_modified[cols_to_normalize] = scaler.fit_transform(
X=csv_original[cols_to_normalize].values)
target_csv['csv'] = csv_modified
table_source.data = ColumnDataSource(target_csv['csv']).data
csv[cols_to_normalize] = target_csv['csv'][cols_to_normalize]
print("Z normalize")
elif (select_normalize.value == "Raw"):
csv_modified[cols_to_normalize] = csv_original[cols_to_normalize]
target_csv['csv'] = csv_modified
table_source.data = ColumnDataSource(target_csv['csv']).data
csv[cols_to_normalize] = target_csv['csv'][cols_to_normalize]
print("Raw")
button_get_normal.on_click(normal_handler)
select_transform = MultiSelect(title="Select columns for transformation",
value=list(target_csv['csv'].columns),
options=list(target_csv['csv'].columns))
controls = WidgetBox(select_normalize, select_transform, button_get_normal,
select_column, range_select)
layout = Column(Row(table_original), Row(controls, p))
tab = Panel(child=layout, title='Visualization')
return tab
# Use the filter in a view
long_filter = Filter([item == 'long' for item in data['type']])
long_view = CDSView(source=source, filters=[long_filter, js_filter])
short_filter = Filter([item == 'short' for item in data['type']])
short_view = CDSView(source=source, filters=[short_filter, js_filter])
fig = figure(toolbar_location=None)
fig.axis.visible = False
fig.xgrid.visible = False
fig.ygrid.visible = False
fig.outline_line_color = None
fig.rect(x='x',
y='y',
source=source,
view=long_view,
width=size - .05,
height=0.6,
color='red')
fig.rect(x='x',
y='y',
source=source,
view=short_view,
width=0.95,
height=0.6,
color='navy')
save(Row(slider, fig,text))
Figure(y_range=r1_ps[0].y_range, plot_height=ROW_HEIGHT, **pargs),
Figure(y_range=r2_ps[0].y_range,
plot_height=ROW_HEIGHT + axargs['plot_height'],
**pargs)
]
for i, y_ax in enumerate(y_axes):
y_ax.xgrid.visible = False
y_ax.ygrid.visible = False
y_ax.xaxis.visible = False
y_ax.min_border = 0
y_ax.toolbar.active_scroll = y_ax.tools[1]
# for some reason the following line totally breaks the profiler
y_ax.yaxis[0].formatter = PrintfTickFormatter(format="%4d")
y_ax.yaxis.axis_label = rnames[i]
y_ax.line(x='zeroes',
y='avg_wh_y' + str(i + 1),
line_width=0.5,
alpha=0.3,
line_cap='round',
source=source)
left_column = Column([Div(height=73, width=25)] + y_axes)
profile = Row([left_column] + [
Column(ti, inp, r1, r2, x_ax, width=COL_WIDTH)
for ti, inp, r1, r2, x_ax in zip(t0_ps, textrow, r1_ps, r2_ps, x_axes)
])
surftab = Tabs(tabs=[tab1, tab2], width=COL_WIDTH * 5)
curdoc().add_root(Column(profile, surftab))
active=True)
radius_slider = Slider(start=0,
end=0.2,
value=0.01,
step=0.0001,
title="Radius for visual classifier",
format="0[.]0000")
radius_source = ColumnDataSource({'x': [], 'y': [], 'rad': []})
color_from_dropdown = Select(
title="Color by:",
value='Categories',
options=['Categories', 'cats_ae_pred', 'original_pred'])
global_plot = Row(children=[])
def add_renderers():
color_attribute, label_mapping = {
'Categories': ('cat_str', categories),
'cats_ae_pred': ('cats_ae_pred_str', binary),
'original_pred': ('original_pred_str', binary),
}[color_from_dropdown.value]
plot = figure(x_range=(0, 1),
y_range=(0, 1),
width=800,
height=400,
tools=[hover_tool])
plot.add_tools(TapTool(behavior='select'))
print(old, new)
#update CDS
cdsAuto.data = dict(xVal=dfAuto[xSelect.value], yVal=dfAuto['City_MPG'])
scatChart.xaxis.axis_label = xSelect.value
dfAuto = pd.read_excel('data/Auto_Insu.xlsx')
cdsAuto = ColumnDataSource(
data=dict(xVal=dfAuto['HP'], yVal=dfAuto['City_MPG']))
xSelect = Select(title="Select the X variable from the list",
options=['HP', 'Length', 'Width'],
value='HP')
# Figure
scatChart = figure(height=500,
width=500,
title="Using Widgets",
x_axis_label="HP",
y_axis_label="City MPG")
scatChart.square(x='xVal', y='yVal', source=cdsAuto)
xSelect.on_change('value', updateScatter)
#output_file('Scat1.html')
#show(Row(xSelect,scatChart))
curdoc().add_root(Row(xSelect, scatChart))
# Glyph
s2.change.emit();
s3.change.emit();
s4.change.emit();
""".replace("{code}", sleep_scatter_code)))
#########################################################################################################
# Dashboard
#########################################################################################################
def space(width, height=0):
return Div(text=htmltext.div_space.format(width=width, height=height))
dash = Column(
Row(space('20'), Div(text=htmltext.div_social)),
Column(Row(space('40'), date_slider), Row(space('40'), pcal)),
Row(
space(2),
Div(text=htmltext.div_intro),
Div(text=htmltext.div_conclusion),
),
#Div(text=htmltext.div_squatchek),
Tabs(
tabs=[
Panel(child=Row(
Column(
Div(text=htmltext.div_sleep),
Row(
space('30'),
Column(
p.js_on_event(events.PanEnd, CustomJS.from_py_func(pan_end))
#def mouse_move(ld = tweet_data_controller.lod_dummy):
# print("Mouse Move:")
#p.js_on_event(events.MouseMove, CustomJS.from_py_func(mouse_move))
#def mouse_wheel(ld = tweet_data_controller.lod_dummy):
# print("Mouse Wheel:")
#p.js_on_event(events.MouseWheel, CustomJS.from_py_func(mouse_wheel))
lhs = Column(map_widgets.radio_button_data_type,
Row(map_widgets.toggle_data,
map_widgets.button_clear), map_widgets.text_selection_details,
map_widgets.toggle_sde_ellipse,
map_widgets.toggle_sibling_ellipses, map_widgets.toggle_dissolve,
map_widgets.toggle_blend, map_widgets.slider_blend,
map_widgets.text_input, map_widgets.button_find,
map_widgets.toggle_legend)
filter_sliders \
= Column( Row( Column(map_widgets.button_count_start_minus, map_widgets.button_count_start_plus, width=50),
map_widgets.range_slider_count,
Column(map_widgets.button_count_end_minus, map_widgets.button_count_end_plus)),
Row( Column(map_widgets.button_area_start_minus, map_widgets.button_area_start_plus, width=50),
map_widgets.range_slider_area,
Column(map_widgets.button_area_end_minus, map_widgets.button_area_end_plus)),
Row( Column(map_widgets.button_distance_start_minus, map_widgets.button_distance_start_plus, width=50),
map_widgets.range_slider_distance,
y_axis_label="City MPG")
# Adding the square glyph to the scatterplot
scatChart.square(x='xVal', y='yVal',source=cdsAuto)
# Getting dataset ready for viewing mean MPG values by Body_style.
# reset_index() converts the groupby object back to the dataframe.
meanCity = dfAuto.groupby(['Body_Style'])['City_MPG'].mean().reset_index()
# Preparing CDS object ready to populate the bar chart
cdsMean = ColumnDataSource(data=dict(xVal = meanCity['Body_Style'],
yVal = meanCity['City_MPG']))
# Bar chart and its vbar glyph
# Again remember that for bar charts, you need to specify x_range when you create
# the figure. List passed here should be unique values of type 'string'.
barChart = figure(height=400, width=700,
title="Summarizing City_MPG mean values",
x_range=meanCity['Body_Style'])
barChart.vbar(x='xVal', top='yVal', source=cdsMean, width=.3)
# Capturing the event of change of value
xSelect.on_change('value', updateScatter)
barSelect.on_change('value', updateBar)
weightSlider.on_change('value', updateScatter)
# Equivalent of show()... Curdoc() allows your charts to be rendered with server.
curdoc().add_root(Row(Column(xSelect, weightSlider, scatChart),
Column(barSelect, barChart)))
animate_start = animate_start + 2000
p_trade_feed.x_range.start = animate_start
p_trade_feed.x_range.end = animate_start + 600000
callback_id = None
def animate():
global callback_id, start, end
if button.label == '► Play':
button.label = '❚❚ Pause'
callback_id = curdoc().add_periodic_callback(animate_update, 1)
else:
button.label = '► Play'
start = datetime(2019, 3, 3, 14, 0, 0,
tzinfo=timezone.utc).timestamp() * 1000
end = datetime(2019, 3, 3, 15, 0, 0,
tzinfo=timezone.utc).timestamp() * 1000
curdoc().remove_periodic_callback(callback_id)
button = Button(label='► Play', width=60)
button.on_click(animate)
left = Column(p_trade_feed, p_candlestick, p_trade_feed_hist,
WidgetBox(button))
right = Column(p_order_book, p_trade_feed_distribution, p_buy_sell_pie)
curdoc().add_root(Row(left, right))
# mass_finder_range_text = Div(text= " Range mz:", width= 150, height=30 )
mass_finder_range_slider = RangeSlider(start=1.0, end=500.0, value=(1.0,50.0), title='Charge range:',name='mass_finder_range_slider', step=1, width= 250, height=30)
# mass_finder_mass_text = Div(text= " Mass of Complex (kDa):", width= 150, height=30 )
mass_finder_mass = Slider(value=100, start=0.0, end=1000.0, step=10.0, title='Mass of Complex (kDa)',name='gau_sigma', width=250, height=30)
mass_finder_exact_mass_text = Div(text= "Enter exact Mass (Da)", width= 150, height=30 )
mass_finder_exact_mass_sele = TextInput(value=str(mass_finder_mass.value*1000), disabled=False, width=100, height=30)
mass_finder_line_text = Div(text= "Show mz prediction", width= 150, height=30 )
mass_finder_line_sele = Toggle(label='off', active=False, width=100, height=30, callback=toggle_cb)
mass_finder_cb =CustomJS(args=dict(mass_finder_line_sele=mass_finder_line_sele, raw_mz=raw_mz, mass_finder_data=mass_finder_data, mass_finder_exact_mass_sele=mass_finder_exact_mass_sele, mass_finder_mass=mass_finder_mass, mass_finder_range_slider=mass_finder_range_slider, mfl=mfl), code=open(os.path.join(os.getcwd(), 'JS_Functions', "mass_finder_cb.js")).read())
mass_finder_exact_cb =CustomJS(args=dict(mass_finder_line_sele=mass_finder_line_sele, mass_finder_exact_mass_sele=mass_finder_exact_mass_sele, mass_finder_mass=mass_finder_mass), code=open(os.path.join(os.getcwd(), 'JS_Functions', "mass_finder_exact_cb.js")).read())
mass_finder_exact_mass_sele.js_on_change('value', mass_finder_exact_cb)
mass_finder_column=Column(mass_finder_header,mass_finder_mass, mass_finder_range_slider, Row(mass_finder_exact_mass_text,mass_finder_exact_mass_sele), Row(mass_finder_line_text, mass_finder_line_sele), visible=False)
mass_finder.js_link('active', mass_finder_column, 'visible')
mass_finder_line_sele.js_link('active', mfl, 'visible')
mass_finder_mass.js_on_change('value', mass_finder_cb)
mass_finder_line_sele.js_on_change('active', mass_finder_cb)
mass_finder_range_slider.js_on_change('value',mass_finder_cb)
### DATA PROCESSING ###
cropping = Div(text= " Range mz:", width= 150, height=30 )
# crop_max = Div(text= " ", width= 150, height=30 )
gau_name = Div(text= " Gaussian Smoothing:", width= 150, height=30 )
n_smooth_name = Div(text= " Repeats of Smoothing:", width= 150, height=30 )
# bin_name = Div(text= " Bin Every:", width= 150, height=30 )
int_name = Div(text= " Intensity Threshold (%)", width= 150, height=30 )
sub_name = Select(options=['Substract Minimum', 'Substract Line', 'Substract Curved'], name='sub_mode', value='Substract Minimum', width= 150, height=30 )
# add_name = Div(text= " Adduct Mass (Da)", width= 150, height=30 )
def dead_dropdown_callback(self, attr, old, new):
color_choice = dead_color_dropdown.value
dead_data_source.data['color'] = [color_choice] * len(dead_data_source.data['color'])
def speed_slider_callback(self, attr, old, new):
self.speed = speed_slider.value
def start_button_callback(self):
for generation_num in range(self.starting_gen_num, generations + 1):
plot.title.text = 'Generation ' + str(generation_num)
self.adjust_data(alive_data_source, dead_data_source, output_dict, generation_num)
sleep(1/(2*self.speed))
def adjust_data(self, alive_data_source, dead_data_source, output_dict, gen_num):
output_dict[gen_num][0].data['color'] = [alive_data_source.data['color'][0]] * len(output_dict[gen_num][0].data['x'])
output_dict[gen_num][1].data['color'] = [dead_data_source.data['color'][0]] * len(output_dict[gen_num][1].data['x'])
alive_data_source.data = output_dict[gen_num][0].data
dead_data_source.data = output_dict[gen_num][1].data
callbacks = Callbacks()
generations_slider.on_change('value', callbacks.generations_slider_callback)
speed_slider.on_change('value', callbacks.speed_slider_callback)
alive_color_dropdown.on_change('value', callbacks.alive_dropdown_callback)
dead_color_dropdown.on_change('value', callbacks.dead_dropdown_callback)
start_button.on_click(callbacks.start_button_callback)
layout = Row(Column(generations_slider, alive_color_dropdown, dead_color_dropdown, speed_slider, start_button), plot)
curdoc().add_root(layout)
def tab_testing():
data_list = glob.glob('./np/Regression/*.npy')
data_list = sorted(data_list)
select_data = Select(title="Data:", value="", options=data_list)
model_list = os.listdir('./model/Regression/')
model_list = sorted(model_list)
select_model = Select(title="Trained Model:", value="", options=model_list)
notifier = Paragraph(text=""" Notification """, width=200, height=100)
def refresh_handler():
data_list_new = glob.glob('./np/Regression/*.npy')
data_list_new = sorted(data_list_new)
select_data.options = data_list_new
model_list_new = os.listdir('./model/Regression/')
model_list_new = sorted(model_list_new)
select_model.options = model_list_new
button_refresh = Button(label="Refresh list")
button_refresh.on_click(refresh_handler)
button_test = Button(label="Test model")
select_result = MultiSelect(title="Key(result):")
src = ColumnDataSource()
df = pd.DataFrame(columns=['key', 'y', 'y_hat'])
table_src = ColumnDataSource(pd.DataFrame(columns=['Key', 'MSE', 'R^2']))
table_columns = [
TableColumn(field=col, title=col) for col in ['Key', 'MSE', 'R^2']
]
table_acc = DataTable(source=table_src,
columns=table_columns,
width=350,
height=400,
fit_columns=True,
name="Accuracy per Key")
def test_handler():
df.drop(df.index, inplace=True)
print("Start test")
tf.reset_default_graph()
K.clear_session()
notifier.text = """ Start testing """
if (select_data.value == ""):
data = np.load(select_data.options[0])
else:
data = np.load(select_data.value)
data = data.item()
notifier.text = """ Import data """
data_x = data.get('x')
if (data_x.shape[-1] == 1 and not 'cnn' in select_model.value):
data_x = np.squeeze(data_x, -1)
data_y = data.get('y')
data_key = data.get('key1')
print(data_x.shape)
print(data_y.shape)
df['key'] = data_key
df['y'] = data_y[:, 0]
op_list = []
for i in df['key'].unique():
op_list.append(str(i))
select_result.options = op_list
print(data_x.shape)
print(data_y.shape)
print(data.get('key1'))
if (select_model.value == ""):
model_name = select_model.options[0]
else:
model_name = select_model.value
model_save_dir = './model/Regression/' + model_name + '/'
model_dl = glob.glob(model_save_dir + '*.h5')
model_ml = glob.glob(model_save_dir + '*.sav')
print(model_save_dir + '*.h5')
print(model_dl)
print(model_ml)
if (len(model_dl) > len(model_ml)):
model = keras.models.load_model(model_save_dir + model_name +
'.h5')
target_hat = model.predict(data_x)
DL = True
elif (len(model_dl) < len(model_ml)):
model = pickle.load(
open(model_save_dir + model_name + '.sav', 'rb'))
data_x = data_x.reshape([data_x.shape[0], -1])
target_hat = model.predict(data_x)
target_hat = np.expand_dims(target_hat, -1)
DL = False
notifier.text = """ Model restored """
print("Model restored.")
xs = []
ys = []
keys = []
color = ['blue', 'red']
xs.append([i for i in range(data_y.shape[0])])
xs.append([i for i in range(data_y.shape[0])])
ys.append(data_y)
keys.append(data_key)
print(target_hat.shape)
K.clear_session()
ys.append(target_hat)
keys.append(data_key)
print(target_hat[:, 0])
df['y_hat'] = target_hat[:, 0]
src.data = ColumnDataSource(
data=dict(xs=xs, ys=ys, color=color, keys=keys)).data
figure_trend.multi_line('xs', 'ys', source=src, color='color')
line_mse = []
line_r_2 = []
for unit in df['key'].unique():
target = df[df['key'] == unit]
y = target['y'].values
y_hat = target['y_hat'].values
unit_mse = np.sum((y - y_hat)**2) / target.shape[0]
unit_r_2 = np.max([r2_score(y, y_hat), 0])
line_mse.append(unit_mse)
line_r_2.append(unit_r_2)
acc = pd.DataFrame(columns=['Key', 'MSE', 'R^2'])
acc['Key'] = df['key'].unique()
mse_mean = np.mean(line_mse)
r_2_mean = np.mean(line_r_2)
line_mse = list(map(lambda x: format(x, '.2f'), line_mse))
acc['MSE'] = line_mse
line_r_2 = list(map(lambda x: format(x, '.2f'), line_r_2))
acc['R^2'] = line_r_2
acc_append = pd.DataFrame(columns=acc.columns)
acc_append['Key'] = ['MSE average', 'R^2 average']
acc_append['MSE'] = [mse_mean, r_2_mean]
acc = pd.concat([acc, acc_append])
table_src.data = ColumnDataSource(acc).data
notifier.text = """ Drawing complete """
history.text = history.text + "\n\t" + model_name + "'s R^2 score: " + format(
np.mean(r_2_mean), '.2f')
def update(attr, old, new):
key_to_plot = select_result.value
xs = []
ys = []
keys = []
y = []
y_hat = []
key = []
key_type = type(df['key'].values[0])
for k in key_to_plot:
y += list(df[df['key'] == key_type(k)]['y'].values)
y_hat += list(df[df['key'] == key_type(k)]['y_hat'].values)
key += [k for _ in range(df[df['key'] == key_type(k)].shape[0])]
ys.append(y)
ys.append(y_hat)
xs.append([i for i in range(len(y))])
xs.append([i for i in range(len(y))])
keys.append(key)
keys.append(key)
color = ['blue', 'red']
src.data = ColumnDataSource(
data=dict(xs=xs, ys=ys, color=color, keys=keys)).data
select_result.on_change("value", update)
button_test.on_click(test_handler)
button_export = Button(label="Export result")
def handler_export():
df.to_csv('./Export/result.csv', index=False)
button_export.on_click(handler_export)
figure_trend = figure(title="Prediction result", width=800, height=460)
history = PreText(text="", width=300, height=460)
layout = Column(
Row(button_refresh),
Row(select_data, select_model, button_test, select_result, notifier),
Row(table_acc, figure_trend, history, button_export))
tab = Panel(child=layout, title='Regression Test')
return tab
def qso_updater(attr, old, new):
indexes = np.array(new['1d']['indices'],
dtype='int') # the indices of the selected QSOs.
mags = [qso_catalog.data['fuvmag'][i] for i in indexes]
print(mags)
hist_source.data['cos'] = [np.size(mags) * 20.]
hist_source.data['lumos'] = [np.size(mags) * 2.]
if (np.size(mags) * 20. > 250.): hist_source.data['coscolor'] = ['red']
if (np.size(mags) * 2. > 250.): hist_source.data['coscolor'] = ['red']
print("DONE WITH QSO_UPDATER")
aperture = Slider(title="Aperture (meters)",
value=15.,
start=3.,
end=15.0,
step=3.0,
callback_policy='mouseup',
width=550)
aperture.on_change('value', update_image)
#qso_syms.data_source.on_change('selected', qso_updater)
#plot1_tab = Panel(child=[p1], title='CenA', width=550, height=800)
#plot2_tab = Panel(child=[p2], title=galaxy['name'], width=550, height=800)
#info_tab = Panel(child=Div(text=h.help(),width=450, height=300), title='Info', width=550, height=300)
#tabs = Tabs(tabs=[plot2_tab], width=550)
#adf = gridplot([aperture],[tabs])
curdoc().add_root(Column(aperture, Row(p2, hist_plot)))
def fpe_fpa_plots(conn, start, end):
'''Combines plots to a tab
Parameters
----------
conn : DBobject
Connection object that represents database
start : time
Startlimit for x-axis and query (typ. datetime.now()- 4Months)
end : time
Endlimit for x-axis and query (typ. datetime.now())
Return
------
p : tab object
used by dashboard.py to set up dashboard
'''
descr = Div(text=
"""
<style>
table, th, td {
border: 1px solid black;
background-color: #efefef;
border-collapse: collapse;
padding: 5px
}
table {
border-spacing: 15px;
}
</style>
<body>
<table style="width:100%">
<tr>
<th><h6>Plotname</h6></th>
<th><h6>Mnemonic</h6></th>
<th><h6>Description</h6></th>
</tr>
<tr>
<td>ASIC (1,2) Voltages</td>
<td>IGDP_NRSD_ALG_A(1,2)_VDDA<br>
IGDP_NRSD_ALG_A(1,2)GND4VDA<br>
IGDP_NRSD_ALG_A(1,2)GND5VRF<br>
INRSD_ALG_A(1,2)_VDD3P3<br>
INRSD_ALG_A(1,2)_VDD<br>
INRSD_ALG_A(1,2)_REF<br>
INRSD_A(1,2)_DSUB_V<br>
INRSD_A(1,2)_VRESET_V<br>
INRSD_A(1,2)_CELLDRN_V<br>
INRSD_A(1,2)_DRAIN_V<br>
INRSD_A(1,2)_VBIASGATE_V<br>
INRSD_A(1,2)_VBIASPWR_V<br>
</td>
<td>
ASIC (1,2) VDDA Voltage<br>
ASIC (1,2) VDDA/Ground Voltage<br>
ASIC (1,2) Ref/Ground Voltage<br>
ASIC (1,2) VDD 3.3 Supply Voltage<br>
ASIC (1,2) VDD Voltage<br>
ASIC (1,2) Reference Voltage<br>
ASIC (1,2) Dsub Voltage<br>
ASIC (1,2) Reset Voltage<br>
ASIC (1,2) Cell Drain Voltage<br>
ASIC (1,2) Drain Voltage<br>
ASIC (1,2) Bias Gate Voltage<br>
ASIC (1,2) Bias Power Voltage<br>
</td>
</tr>
<tr>
<td>ASIC (1,2) Currents</td>
<td>IGDP_NRSD_ALG_A(1,2)_VDD_C<br>
IGDP_NRSD_ALG_A(1,2)VDAP12C<br>
IGDP_NRSD_ALG_A(1,2)VDAN12C<br>
INRSD_A(1,2)_VDDA_I<br>
</td>
<td>ASIC (1,2) VDD Current<br>
ASIC (1,2) VDDA +12V Current<br>
ASIC (1,2) VDDA -12V Current<br>
ASIC (1,2) VDDA Current<br>
</td>
</tr>
</table>
</body>
""", width=1100)
plot1 = asic_1_voltages(conn, start, end)
plot2 = asic_2_voltages(conn, start, end)
plot3 = asic_1_currents(conn, start, end)
plot4 = asic_2_currents(conn, start, end)
currents = Row(plot3, plot4)
layout = Column(descr, plot1, plot2, currents)
tab = Panel(child = layout, title = "FPE/FPA")
return tab
