Exemple #1
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    def set_input_selector(self):
        # print ("set_input_selector")
        """Creates and configures each selector (drop-down menu)."""
        col_names = [x['name'] for x in self.columns]
        col_names.append('None')

        self.plot_selector = Select.create(
            title="PlotType",
            name="plot_type",
            value=self.plot_type,
            options=["line", "scatter", "bar"],
        )

        self.x_selector = Select.create(
            name="x",
            value=self.x,
            options=col_names,
        )

        self.y_selector = Select.create(
            name="y",
            value=self.y,
            options=col_names,
        )

        self.agg_selector = Select.create(
            name='agg',
            value=self.agg,
            options=['sum', 'mean', 'last', 'count', 'percent'],
        )
Exemple #2
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    def set_input_selector(self):
        # print ("set_input_selector")
        """Creates and configures each selector (drop-down menu)."""
        col_names = [x['name'] for x in self.columns]
        col_names.append('None')

        self.plot_selector = Select.create(
            title="PlotType",
            name="plot_type",
            value=self.plot_type,
            options=["line", "scatter", "bar"],
        )

        self.x_selector = Select.create(
            name="x",
            value=self.x,
            options=col_names,
        )

        self.y_selector = Select.create(
            name="y",
            value=self.y,
            options=col_names,
        )

        self.agg_selector = Select.create(
            name='agg',
            value=self.agg,
            options=['sum', 'mean', 'last', 'count', 'percent'],
        )
Exemple #3
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    def create_layout(self):

        # create figure
        self.x_range = Range1d(start=self.model.map_extent[0],
                               end=self.model.map_extent[2], bounds=None)
        self.y_range = Range1d(start=self.model.map_extent[1],
                               end=self.model.map_extent[3], bounds=None)

        self.fig = Figure(tools='wheel_zoom,pan', x_range=self.x_range,
                          y_range=self.y_range)
        self.fig.plot_height = 660
        self.fig.plot_width = 990
        self.fig.axis.visible = False

        # add tiled basemap
        self.tile_source = WMTSTileSource(url=self.model.basemap)
        self.tile_renderer = TileRenderer(tile_source=self.tile_source)
        self.fig.renderers.append(self.tile_renderer)

        # add datashader layer
        self.image_source = ImageSource(url=self.model.service_url,
                                        extra_url_vars=self.model.shader_url_vars)
        self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
        self.fig.renderers.append(self.image_renderer)

        # add ui components
        axes_select = Select.create(name='Axes',
                                        options=self.model.axes)
        axes_select.on_change('value', self.on_axes_change)

        field_select = Select.create(name='Field', options=self.model.fields)
        field_select.on_change('value', self.on_field_change)

        aggregate_select = Select.create(name='Aggregate',
                                         options=self.model.aggregate_functions)
        aggregate_select.on_change('value', self.on_aggregate_change)

        transfer_select = Select.create(name='Transfer Function',
                                        options=self.model.transfer_functions)
        transfer_select.on_change('value', self.on_transfer_function_change)

        basemap_select = Select.create(name='Basemap', value='Toner',
                                       options=self.model.basemaps)
        basemap_select.on_change('value', self.on_basemap_change)

        opacity_slider = Slider(title="Opacity", value=100, start=0,
                                end=100, step=1)
        opacity_slider.on_change('value', self.on_opacity_slider_change)

        controls = [axes_select, field_select, aggregate_select,
                    transfer_select, basemap_select, opacity_slider]
        self.controls = HBox(width=self.fig.plot_width, children=controls)
        self.layout = VBox(width=self.fig.plot_width,
                           height=self.fig.plot_height,
                           children=[self.controls, self.fig])
def line_config(app, df):
    options = CheckboxGroup(
        labels=['Show peaks', 'Show Std. Dev.', 'Bolinger Bands'],
        name='new_chart_options')

    title = TextInput(title="Chart Title",
                      name='new_chart_title',
                      value="Line")

    new_chart_index = Select.create(options=['---'] + list(df.columns),
                                    name='"new_chart_index"',
                                    title="Chart index")
    chart_values_select = MultiSelect.create(options=df.columns,
                                             name="chart_values",
                                             title="Values")

    chart_data = AppHBox(
        app=app, children=[new_chart_index, chart_values_select, options])
    main_tab = AppVBox(app=app, children=[title, chart_data, 'select_palette'])
    confirm_chart_button = Button(label="Confirm",
                                  type="success",
                                  name='confirm_add_chart')

    return {
        'dlg':
        Dialog(buttons=[confirm_chart_button], content=main_tab, visible=True),
        "new_chart_index":
        new_chart_index,
        'new_chart_title':
        title,
        'chart_values':
        chart_values_select,
        'new_chart_options':
        options
    }
Exemple #5
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    def create_layout(self):
        self.plot_selector = Select.create(
            title="PlotType",
            name="plot_type",
            value='area',
            options=['area', "line", "scatter", "bar"],
        )

        self.btn_confirm = Button(label='add plot')
        self.layout = vplot(hplot(self.plot_selector, self.btn_confirm), self.plot)
Exemple #6
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 def make_inst_date_input(self):
     observations = self._ccf_interface.get_observations(self.star)
     observations = ['/'.join(obs).ljust(20, ' ') for obs in observations]
     self.inst_date = observations[0]
     if isinstance(self.inst_date_select, Select):
         self.inst_date_select.update(value=observations[0], options=observations)
     else:
         self.inst_date_select = Select.create(
             name='Instrument/Date',
             value=observations[0],
             options=observations,
         )
Exemple #7
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def line_config(app, df):
    options = CheckboxGroup(
        labels=['Show peaks', 'Show Std. Dev.', 'Bolinger Bands'],
        name='new_chart_options'
    )

    title = TextInput(title="Chart Title", name='new_chart_title', value="Line")
    new_chart_index = Select.create(options=['---']+list(df.columns), name='"new_chart_index"', title="Chart index")
    chart_values_select = MultiSelect.create(options=df.columns, name="chart_values", title="Values")
    chart_data = AppHBox(app=app, children=[new_chart_index, chart_values_select, options])
    main_tab = AppVBox(app=app, children=[title, chart_data, 'select_palette'])
    confirm_chart_button = Button(label="Confirm", type="success", name='confirm_add_chart')

    return {
        'dlg': Dialog(buttons=[confirm_chart_button], content=main_tab, visible=True),
        "new_chart_index": new_chart_index,
        'new_chart_title': title,
        'chart_values': chart_values_select,
        'new_chart_options': options
    }
Exemple #8
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def dashboard():
    """
    Creates the main app objects
    """
    # TODO: Should find a better design and remove those global objs...
    global source
    global csource

    source = ColumnDataSource(df)
    source.tags = ['main_source']

    csource = ColumnDataSource(cdf)
    csource.tags = ['crash_source']

    select_sec = Select.create(options=SECURITIES, name='select_sec', title="")

    asel = Select.create(options=df.columns, name='field_to_add', title="")
    chart_sel = Select.create(options=chart_types, name='chart_to_add', title="")

    msel = MultiSelect.create(options=[], name='ms')
    abutton = Button(label="add", type="success", name='add_button')
    rbutton = Button(label="remove", type="danger", name='remove_button')
    add_chart_button = Button(label="add chart", type="success", name='add_chart')


    data_table = DataTable(source=source, editable=True, width=500, height=400)
    ccolumns = [TableColumn(field=x, title=x, editor=NumberEditor()) for x in cdf.columns]
    crashes_table = DataTable(source=csource, editable=True, width=1200, height=400, columns=ccolumns)

    charts_box = AppVBox(children=[], name='charts_box')

    counts = cdf['crashes']
    crashes_hist = charts.Histogram({'crash_counts': counts}, bins=20, height=300, title="# Crashes per Symbol")
    crashes_scatter = create_crashes_scatter()

    main_tab = VBox(children=[], name='d_box')
    dlg = Dialog(buttons=[], content=main_tab , visible=False)

    options = CheckboxGroup(
        labels=['Show peaks', 'Show Std. Dev.', 'Bolinger Bands'],
        name='new_chart_options'
    )

    select_palette = Select.create(options=brewer.keys(), name='select_palette', title="Palette")
    return {
        # security tab
        'select_sec': select_sec,
        'field_to_add': asel,
        'chart_to_add': chart_sel,
        'ms': msel,
        'dt': data_table,
        'add_button': abutton,
        'remove_button': rbutton,
        'add_chart': add_chart_button,
        'charts_box': charts_box,
        'dlg': dlg,

        # Crashes tab objs
        'crashes_hist': crashes_hist,
        'crashes_scatter': crashes_scatter,
        'crashes_table': crashes_table,
        'crash_stats': PreText(text="NO CRASHES FOR SYMBOL %s" % SYMBOL, width=300),

        #
        'chart_values': MultiSelect.create(options=df.columns, name="chart_values", title="Values"),
        'new_chart_index': Select.create(options=['---']+list(df.columns), title="Chart index", name="new_chart_index"),
        'new_chart_title': TextInput(title="Chart Title", name='new_chart_title', value=''),
        'new_chart_options': options,
        'select_palette': select_palette,

        # ANNOTATIONS
        "annotations": MultiSelect.create(options=[], name="annotations", title="Annotations"),
        "add_annotation": Button(label="add", type="success", name='add_annotation'),
        "delete_annotation": Button(label="remove", type="danger", name='delete_annotation'),
        "show_annotation": Button(label="Show", type="link", name='show_annotation'),
        'new_annotation_dlg': Dialog(buttons=[], content=main_tab , visible=False),
        'annotation_txt': TextInput(title="New Annotation", name='annotation_txt', value=''),
        'new_annotation_options': CheckboxGroup(labels=[], name='new_annotation_options'),
    }
Exemple #9
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    def create_objects(cls, symbol, df, securities):
        descr_box = Paragraph(text='content loading...')

        btn_close_loading = Button(label='Close Loading')
        dialog_loading = Dialog(
            title='loading', content=vplot(descr_box), name='loading_dialog',
            buttons=[btn_close_loading], visible=False)

        source_data = dict(df)
        main_source = ColumnDataSource(dict(df))
        source = ColumnDataSource(source_data)

        # TODO: REMOVE THIS COMMENTED CODE! IT'S JUST THE PREVIOUS
        # VERSION USED BEFORE NEW P&D Cached results and algorithm
        # get the cached results of the P&D algorithm computed with the
        # "default" configuration
        # intervals = utils.cached_pumps.get(symbol, pumps.to_dicts(((),(),(),(),(),())))
        # intervals['bottom'] = [0] * len(intervals['start'])
        # intervals['values'] = [max(df['price'])] * len(intervals['start'])
        #
        # intervals = pd.DataFrame(intervals)

        # new version
        stats = utils.get_symbols_cached_stats()[symbol]
        intervals = pd.DataFrame(stats)
        intervals['bottom'] = [0] * len(intervals['start'])
        intervals['values'] = [max(df['price'])] * len(intervals['start'])

        conv = lambda x: utils.to_seconds(pd.to_datetime(x))

        intervals = intervals[
            (pd.to_datetime(intervals['start']) > conv(config.date_range[0])) &
            (pd.to_datetime(intervals['start']) < conv(config.date_range[1]))
        ]

        # Create P&Ds intervals DataSource
        intervals_source = ColumnDataSource(intervals)
        source.tags = ['main_source']

        trends = utils.load_trends_data(symbol, start_date=min(df['dt']))
        trends_source = ColumnDataSource(trends)

        trades = Slider(
            title="trades", name='trades',
            value=0, start=0, end=124, step=1
        )

        # Selectors
        symbol = Select.create(
            options=securities, value=symbol, name='symbol', title=""
        )
        window_selector = Select.create(
            options=['---'], name='period_selector', title="Search intervals with:"
        )
        symbol_filter = Select.create(
            options=['All', 'Stocks with Spam', 'Stocks without Spam'],
            name='symbol_filter', title="Filter Symbols:",
            value='Stocks with Spam'
        )
        callback = Callback(
            args={'symbol_filter': symbol_filter,
                  'dialog_loading': dialog_loading},
            code=callbacks.symbol_filter
        )
        symbol_filter.callback = callback


        btn_detect_pumps = Button(label='Configure P&D Detection', name='config_pumps')

        main_tab = Panel(title="Main")
        tabs = Tabs()

        # Create STOCKS TABLE
        ranks = utils.get_pumps_rank()
        # quotient_metrics = utils.get_quotient_metrics()
        # ranks['quotient'] = quotient_metrics['quotient']

        foo = lambda x: utils.spams_count.get(x, 0)
        ranks['spams'] = map(foo, ranks['symbol'])
        ranks = ranks.sort(['spams', 'vol_quotient'], ascending=False)

        cls._pre_filtered_ranks = {
            'All': {k: ranks[k] for k in ranks.columns},
            'Stocks with Spam': dict(ranks[ranks['spams'] > 0].
                                     sort('vol_quotient', ascending=False)),
            'Stocks without Spam': dict(ranks[ranks['spams'] == 0].
                                        sort('vol_quotient', ascending=False)),
        }

        source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['All'])


        table_stocks_rank = DataTable(
            source=source_stocks_rank, width=560, height=450,
            selectable=True, editable=True,
            columns=[
                TableColumn(field='symbol', title='symbol', width=130, editor=StringEditor()),
                TableColumn(field='vol_quotient', title='volume ratio', editor=StringEditor(),
                            default_sort='descending'),
                TableColumn(field='risk_score', title='risk', width=100, editor=StringEditor(),
                            default_sort='descending'),
                TableColumn(field='spams', title='spams', width=130, editor=StringEditor(),
                            default_sort='descending'),
            ])

        callback = Callback(args={'tr': table_stocks_rank, 'sr': source_stocks_rank, 'symb': symbol,
                                  'dialog_loading': dialog_loading},
                            code=callbacks.source_stocks_rank)
        source_stocks_rank.callback = callback

        return locals()
Exemple #10
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    def create_objects(cls, symbol, df, securities):
        descr_box = Paragraph(text='content loading...')

        btn_close_loading = Button(label='Close Loading')
        dialog_loading = Dialog(title='loading',
                                content=vplot(descr_box),
                                name='loading_dialog',
                                buttons=[btn_close_loading],
                                visible=False)

        source_data = dict(df)
        main_source = ColumnDataSource(dict(df))
        source = ColumnDataSource(source_data)

        # TODO: REMOVE THIS COMMENTED CODE! IT'S JUST THE PREVIOUS
        # VERSION USED BEFORE NEW P&D Cached results and algorithm
        # get the cached results of the P&D algorithm computed with the
        # "default" configuration
        # intervals = utils.cached_pumps.get(symbol, pumps.to_dicts(((),(),(),(),(),())))
        # intervals['bottom'] = [0] * len(intervals['start'])
        # intervals['values'] = [max(df['price'])] * len(intervals['start'])
        #
        # intervals = pd.DataFrame(intervals)

        # new version
        stats = utils.get_symbols_cached_stats()[symbol]
        intervals = pd.DataFrame(stats)
        intervals['bottom'] = [0] * len(intervals['start'])
        intervals['values'] = [max(df['price'])] * len(intervals['start'])

        conv = lambda x: utils.to_seconds(pd.to_datetime(x))

        intervals = intervals[
            (pd.to_datetime(intervals['start']) > conv(config.date_range[0])) &
            (pd.to_datetime(intervals['start']) < conv(config.date_range[1]))]

        # Create P&Ds intervals DataSource
        intervals_source = ColumnDataSource(intervals)
        source.tags = ['main_source']

        trends = utils.load_trends_data(symbol, start_date=min(df['dt']))
        trends_source = ColumnDataSource(trends)

        trades = Slider(title="trades",
                        name='trades',
                        value=0,
                        start=0,
                        end=124,
                        step=1)

        # Selectors
        symbol = Select.create(options=securities,
                               value=symbol,
                               name='symbol',
                               title="")
        window_selector = Select.create(options=['---'],
                                        name='period_selector',
                                        title="Search intervals with:")
        symbol_filter = Select.create(
            options=['All', 'Stocks with Spam', 'Stocks without Spam'],
            name='symbol_filter',
            title="Filter Symbols:",
            value='Stocks with Spam')
        callback = Callback(args={
            'symbol_filter': symbol_filter,
            'dialog_loading': dialog_loading
        },
                            code=callbacks.symbol_filter)
        symbol_filter.callback = callback

        btn_detect_pumps = Button(label='Configure P&D Detection',
                                  name='config_pumps')

        main_tab = Panel(title="Main")
        tabs = Tabs()

        # Create STOCKS TABLE
        ranks = utils.get_pumps_rank()
        # quotient_metrics = utils.get_quotient_metrics()
        # ranks['quotient'] = quotient_metrics['quotient']

        foo = lambda x: utils.spams_count.get(x, 0)
        ranks['spams'] = map(foo, ranks['symbol'])
        ranks = ranks.sort(['spams', 'vol_quotient'], ascending=False)

        cls._pre_filtered_ranks = {
            'All': {k: ranks[k]
                    for k in ranks.columns},
            'Stocks with Spam':
            dict(ranks[ranks['spams'] > 0].sort('vol_quotient',
                                                ascending=False)),
            'Stocks without Spam':
            dict(ranks[ranks['spams'] == 0].sort('vol_quotient',
                                                 ascending=False)),
        }

        source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['All'])

        table_stocks_rank = DataTable(
            source=source_stocks_rank,
            width=560,
            height=450,
            selectable=True,
            editable=True,
            columns=[
                TableColumn(field='symbol',
                            title='symbol',
                            width=130,
                            editor=StringEditor()),
                TableColumn(field='vol_quotient',
                            title='volume ratio',
                            editor=StringEditor(),
                            default_sort='descending'),
                TableColumn(field='risk_score',
                            title='risk',
                            width=100,
                            editor=StringEditor(),
                            default_sort='descending'),
                TableColumn(field='spams',
                            title='spams',
                            width=130,
                            editor=StringEditor(),
                            default_sort='descending'),
            ])

        callback = Callback(args={
            'tr': table_stocks_rank,
            'sr': source_stocks_rank,
            'symb': symbol,
            'dialog_loading': dialog_loading
        },
                            code=callbacks.source_stocks_rank)
        source_stocks_rank.callback = callback

        return locals()