def content():
return html.Div([
html.Div([
daq.NumericInput(
id='my-numeric-input',
label='Battery max. voltage',
value=0,
style={'padding': '20px 0px 20px 0px'}
),
html.Div(id='numeric-input-output'),
html.Div([
daq.BooleanSwitch(
id='my-daq-booleanswitch',
label='DCDC enabled',
on=True
)],
style={'padding': '20px 0px 20px 0px'}
),
html.Div(id='output-boolean', style={'align':'center'}),
], className="six columns"),
html.Div([
daq.NumericInput(
id='my-numeric-input',
label='Battery max. current',
value=0,
style={'padding': '20px 0px 20px 0px'}
),
html.Div(id='numeric-input-output'),
], className="six columns"),
], className="row")
def matrix_element(idx, value=0):
# TODO: maybe move element out of the name
mid = self.id(kwarg_label, is_kwarg=True, idx=idx)
if not is_int:
return dcc.Input(
id=mid,
inputMode="numeric",
className="input",
style={
"textAlign":
"center",
# shorter default width if matrix or vector
"width":
"2.5rem" if
(shape == (3, 3)) or (shape == (3, )) else "5rem",
"marginRight":
"0.2rem",
"marginBottom":
"0.2rem",
"height":
"1.5rem",
},
value=value,
persistence=True,
**kwargs,
)
else:
return daq.NumericInput(id=mid, value=value, **kwargs)
def __init__(self, monitor, room):
super().__init__(className=['text-center', 'm-2'])
self.monitor = monitor
self.room = room
self.title = html.H5(room.name, className='card-title')
self.tank = daq.Tank(id='tank-%s' % (room.id, ),
style=RoomView.TANK_STYLE,
scale={'interval': 1},
min=0,
value=room.occupancy_cur,
max=self.room.occupancy_max)
self.label = html.P(id='label-%s' % (room.id, ), children="0/0 people")
self.input = daq.NumericInput(id='input-%s' % (room.id, ),
min=1,
value=room.occupancy_max,
label='Max',
labelPosition='top')
self.update_color()
self.update_label()
self.children = [
dbc.CardBody([self.title, self.tank, self.label, self.input])
]
def create_settings(match_ids):
team_dict = {}
for match_id in match_ids:
team_list = get_team_list(match_id)
for team in team_list:
if team[0] in team_dict:
team_dict[team[0]] += ';' + team[1]
else:
team_dict[team[0]] = team[1]
return html.Div(children=[
html.Br(),
html.H6("Select Team"),
dcc.Dropdown(id="follow-team-select",
options=[{
"label": i,
"value": team_dict[i]
} for i in team_dict],
style=dict(width='20em')),
html.Br(),
html.H6(id='ex_secs',
children=['Exclude Seconds After Possession Change']),
daq.NumericInput(id='ex_secs-input-clustering', min=0, value=1,
max=10),
html.Br(),
html.H6(id='no_cluster', children=['Number of Cluster']),
daq.NumericInput(id='no_cluster-input', min=0, value=10, max=30),
html.Br(),
html.H6("Ball Possession Phase"),
dcc.RadioItems(id='possession-radio-clustering',
options=[{
'label': 'Ball possession',
'value': 'bp'
}, {
'label': 'No ball possession',
'value': 'nbp'
}, {
'label': 'Both',
'value': 'bo'
}],
value='bo'),
html.Br(),
html.Div(children=html.Button(id="start-btn-clustering",
children="Go"), )
])
def build_instructions():
return html.Div(
id="left-col",
children=[
html.P(className="instructions", children="Select candidates"),
html.Div(
id="choose-nclusters",
children=[
html.Label("Number of clusters:", className="eight columns", style={'color': 'orange'}),
html.Div(
daq.NumericInput(id="nclusters", className="setting-input", size=100, max=10000, value=50),
className="three columns", style={'color': 'green'}
),
],
className="row",
),
html.Div(
id="choose-ndcs",
children=[
html.Label("Number of DCs to open:", className="eight columns", style={'color': 'orange'}),
html.Div(
daq.NumericInput(id="ndcs", className="setting-input", size=100, max=10000, value=5),
className="three columns", style={'color': 'green'}
),
],
className="row",
),
# dcc.Dropdown(
# id="operator-select",
# options=[
# {"label": i, "value": i}
# for i in [1,2,3]
# ],
# multi=True,
# value=[1,2],
# ),
html.Div(
html.Button("Cluster and optimize", id="update-DCs", style={'color': 'white'}),
),
html.Label(id="products-summary-2"),
],
)
def turn_on_example_data(on):
if on:
return html.Div(children=[
html.Br(),
daq.NumericInput(id='timesteps',
min=1,
max=350,
value=10,
label='Choose number of timesteps',
size=120),
])
def gen_tab2(results_df):
return html.Div(
id='main_cols',
children=[
html.Div(
id='race',
children=[
daq.NumericInput(id='my-daq-numericinput',
max=60,
value=1,
label='Race Number',
labelPosition='top',
min=1),
html.Div(
id='lanes',
children=[
html.H3('Lanes'),
#html.H3(results_df.loc['race']),
html.Div(id='lane1', className="two columns"),
html.Div(id='lane2', className="two columns"),
html.Div(id='lane3', className="two columns"),
html.Div(id='lane4', className="two columns"),
],
className="twelve columns",
style={
'border': 'solid',
'text-align': 'center'
}),
html.Div(id='race-controls',
children=[
html.Button('Get more Results',
id='button-results'),
html.Button('Force Race End',
id='button-end'),
html.Button('Next Race', id='button-next'),
],
style={
'margin': '80px',
'text-align': 'center'
},
className="ten columns"),
],
className="nine columns"),
html.Div(
id='leader-board',
children=[
html.H3('Fastest Average Time'),
#html.Table([html.Tr(['Name ','Den ', 'Time '])]),
generate_table(results_df),
],
className="three columns",
style={'border': 'solid'}),
],
)
def get_numeric_input(id_name, value, title):
return html.Div(
id=f"{id_name}-menu",
# className='five columns',
children=[
html.Label(id=f"{id_name}-title", children=title),
daq.NumericInput(id=f"{id_name}-input",
className="setting-input",
value=value,
size=200,
max=9999999)
])
def Clustering_Options(options):
"""
Generate the layout of the dashboard.
Args:
options (list(dict)): Available datasets as options for `dcc.Dropdown`.
Returns:
A Dash element or list of elements.
"""
return html.Div(children=[
# Choose a dataset
html.Div(create_dropdown("Available datasets",
options,
multi=False,
id="dataset_choice_clustering"),
className="horizontal_dropdowns"),
# Choose an algorithm
html.Div(create_dropdown("Choose algorithm type",
options=[
{
'label': 'DBSCAN',
'value': 'dbscan'
},
{
'label': 'K-Means Clustering',
'value': 'kmc'
},
],
multi=False,
id="algo_choice_clustering"),
className="horizontal_dropdowns"),
## Two empty divs to be filled by callbacks
# Available choices for fitting
html.Div(id="variable_choices_clustering"),
#Available number of clusters
html.P("Number of clusters:"),
daq.NumericInput(id='clusters_input', min=0, value=3, max=10),
# The results
html.Div(id="training_results_clustering"),
# The graph
dcc.Graph(id="clustering_results"),
])
def html_numeric_input(name: str, value: float, min: float, max: float, id: str):
"""
:param name: The name of the input. Should be lowercase with spaces between words.
:param value: The default value of the input.
:param min: The minimum input value allowed.
:param max: The maximum input value allowed.
:param id: The id of the input field.
"""
return html.Div(
daq.NumericInput(
id=id,
size=100,
min=min,
max=max,
value=round(value, 3)),
style={'display': 'inline-block',
'margin-right': '1rem'})
def success_layout(self):
"""
Create a Dash layout after a successful login, and a menu \
to fetch data.
"""
return [
html.H4("Write the name of a subreddit:"),
dcc.Input(
id="reddit_subreddit",
type="text",
value="",
),
html.H4("How many topics to fetch?"),
daq.NumericInput(id="reddit_ntopics", value=5),
html.Button("Gimme dem reddits", id="get_reddit", n_clicks=0),
html.Br(),
html.Div(id="reddit_results"),
]
def _sub_layouts(self):
enable = daq.BooleanSwitch(
id=self.id("enable_transformation"),
style={
"display": "inline-block",
"vertical-align": "middle"
},
)
message = html.Div(id=self.id("message"))
description = dcc.Markdown(self.description)
options = html.Div(self.options_layouts(), id=self.id("options"))
preview = dcc.Loading(id=self.id("preview"))
if self.is_one_to_many:
ranked_list = daq.NumericInput(value=1,
min=1,
max=10,
id=self.id("ranked_list"))
else:
# if not 1-to-many, we don't need the control, we keep
# an empty container here to make the callbacks simpler
# since "ranked_list" will then always be present in layout
ranked_list = html.Div(id=self.id("ranked_list"))
return {
"options": options,
"description": description,
"enable": enable,
"message": message,
"preview": preview,
"ranked_list": ranked_list,
}
labelPosition="top",
on=True,
className="one columns",
style={
"width": "8%",
"paddingBottom": "1%"
},
color="",
),
daq.NumericInput(
id="led-select",
label="Led Select",
className="one columns",
style={
"width": "5%",
"textAlign": "center",
"marginLeft": "3%",
"paddingBottom": "1%",
},
value=0,
max=8,
disabled=False,
),
daq.BooleanSwitch(
id="all-switch",
on=False,
label="All",
vertical=True,
labelPosition="top",
className="one columns",
style={"width": "3%"},
disabled=False,
className='gauge')
on_button = daq.PowerButton(id='on_button',
size=60,
on=True,
label=' ',
labelPosition='top',
color='#55f4e2')
real_input = [
html.Div([
html.Div('Forecast Steps: ',
className='inputGroup__label d-inline-block'),
daq.NumericInput(id='real_step',
min=1,
value=6,
max=12,
className='inputGroup__input d-inline-block')
],
className='inputGroup inline d-inline-block'),
dbc.Button(id='show_button',
children='Forecast',
n_clicks=0,
className='button--primary d-inline-block'),
]
##################
# live plot
##################
live_plot = [
dcc.Graph(id='live_plot', style=dict(height='240px')),
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
import dash_daq as daq
from app import app
# define layout for the input menu (left hand side)
parameter_input = [
dbc.Row(children=[
dbc.Col(
className='md-6',
children=[
'offset',
daq.NumericInput(
id='offset', value=0, min=-1000, max=1000, disabled=True)
])
]),
dbc.Row(children=[
dbc.Col(
className='md-6',
children=[
'a',
html.Sub(children=['1']),
daq.NumericInput(
id='param1',
value=0, min=-1000, max=1000, disabled=True)
]),
dbc.Col(
className='md-6',
children=[
width={
"size": 6,
"offset": 1
})),
dbc.Row(
dbc.Col(html.H5(children='House hunter toolkit to fit their budget.'),
width={
"size": 6,
"offset": 1
})),
html.Br(),
dbc.Row([
dbc.Col([
dbc.Label("Budget"),
daq.NumericInput(
id='budget',
value=100000, min=12789, max=755000, size=200),
html.Br(),
dbc.Label("Gross Living Area (ft^2)"),
daq.NumericInput(
id='GrLivArea', value=1000, min=334, max=4676, size=200),
html.Br(),
dbc.Label("Number of cars in garage"),
daq.NumericInput(id='Garage_Cars', value=1, min=0, max=5,
size=200),
html.Br(),
dbc.Label("Overall condition"),
daq.NumericInput(
id='overall_cond', value=5, min=0, max=10, size=200),
html.Br(),
dbc.Label("Overall quality"),
def gen_tab2(results_df):
return html.Div(
id='main_cols',
children=[
html.Div(
id='race',
children=[
daq.NumericInput(id='my-daq-numericinput',
max=60,
value=1,
label='Race Number',
labelPosition='top',
min=1),
html.Div(
id='lanes',
children=[
html.H3('Lanes'),
#html.H3(results_df.loc['race']),
html.Div(
id='lane1',
children=[
html.H4('Lane 0'),
html.Div(id='lane1-name'),
html.H5('Master'),
daq.LEDDisplay(id='lane1-leddisplay',
value=2.140,
label='Time',
labelPosition='bottom',
size='20',
color="#FF5E5E",
backgroundColor="#000000"),
],
className="three columns",
style={
'background-color': '#ffd700',
'border': 'solid',
'border-color': '#ffd700',
'text-align': 'center'
},
),
html.Div(id='lane2',
children=[
html.H4('Lane 2'),
html.Div(id='lane2-name'),
html.H5('Joey Chestnut'),
daq.LEDDisplay(
id='lane1-leddisplay',
value='3.141',
label='Time',
labelPosition='bottom',
size='20',
color="#FF5E5E",
backgroundColor="#000000"),
],
className="three columns",
style={
'background-color': '#cd7f32',
'border': 'solid',
'text-align': 'center'
}),
html.Div(id='lane3', className="three columns"),
html.Div(id='lane4',
children=[
html.H4('Lane 4'),
html.H5('Joey Chestnut'),
daq.LEDDisplay(
id='lane1-leddisplay',
value='3.141',
label='Time',
labelPosition='bottom',
size='20',
color="#FF5E5E",
backgroundColor="#000000"),
],
className="two columns",
style={
'border': 'solid',
'text-align': 'center'
}),
html.Div(id='race-controls',
children=[
html.Button('Get Results',
id='button-results'),
html.Button('Force Race End',
id='button-end'),
html.Button('Next Race',
id='button-next'),
],
style={
'margin': '80px',
'text-align': 'center'
},
className="ten columns"),
]),
],
className="nine columns"),
html.Div(
id='leader-board',
children=[
html.H3('Fastest Average Time'),
#html.Table([html.Tr(['Name ','Den ', 'Time '])]),
generate_table(results_df),
],
className="three columns",
style={'border': 'solid'}),
],
style={'display': 'inline'})
html.Div(
className='five columns',
children=[
html.Div(
id='value-setter-panel'),
html.Br(),
html.Button('Update', id='value-setter-set-btn'),
html.Button('View current setup', id='value-setter-view-btn', n_clicks=0),
html.Div(id='value-setter-view-output', className='output-datatable')
]
)
]
ud_usl_input = daq.NumericInput(id='ud_usl_input', size=200, max=9999999, style={'width': '100%', 'height': '100%'})
ud_lsl_input = daq.NumericInput(id='ud_lsl_input', size=200, max=9999999, style={'width': '100%', 'height': '100%'})
ud_ucl_input = daq.NumericInput(id='ud_ucl_input', size=200, max=9999999, style={'width': '100%', 'height': '100%'})
ud_lcl_input = daq.NumericInput(id='ud_lcl_input', size=200, max=9999999, style={'width': '100%', 'height': '100%'})
def build_value_setter_line(line_num, label, value, col3):
return html.Div(
id=line_num,
children=[
html.Label(label, className='four columns'),
html.Label(value, className='four columns'),
html.Div(col3, className='four columns')],
className='row'
)
def generate_layout(graphs, configs):
if configs is None:
configs = {}
# Read-out graph view configs
elements = configs["elements"] if "elements" in configs else None
current_graph = configs[
"current_graph"] if "current_graph" in configs else ""
nodes_to_keep = configs["nodestokeep"] if "nodestokeep" in configs else []
top_n_slider_value = configs["top_n"] if "top_n" in configs else None
node_freq_type = configs[
"node_weight"] if "node_weight" in configs else "degree_frequency"
edge_freq_type = configs[
"edge_weight"] if "edge_weight" in configs else "npmi"
nodefreqslider = configs[
"nodefreqslider"] if "nodefreqslider" in configs else [0, 100000]
edgefreqslider = configs[
"edgefreqslider"] if "edgefreqslider" in configs else [0, 100000]
cluster_type = configs[
"cluster_type"] if "cluster_type" in configs else "entity_type"
clustersearch = configs[
"clustersearch"] if "clustersearch" in configs else []
current_layout = configs[
"current_layout"] if "current_layout" in configs else DEFAULT_LAYOUT
# Read-out old path search configs
searchpathfrom = configs[
"searchpathfrom"] if "searchpathfrom" in configs else None
searchpathto = configs[
"searchpathto"] if "searchpathto" in configs else None
searchnodetotraverse = configs[
"searchnodetotraverse"] if "searchnodetotraverse" in configs else None
searchpathlimit = configs[
"searchpathlimit"] if "searchpathlimit" in configs else 10
searchpathoverlap = configs[
"searchpathoverlap"] if "searchpathoverlap" in configs else [1]
nestedpaths = configs["nestedpaths"] if "nestedpaths" in configs else []
pathdepth = configs["pathdepth"] if "pathdepth" in configs else 2
global_button_group = dbc.FormGroup([
dbc.Col([
dbc.Button(html.Span(
[html.I(className="fas fa-redo"), " Reset view"]),
color="secondary",
className="mr-1",
id='bt-reset',
style={"margin": "2pt"}),
dbc.Tooltip(
"Reset the display to default values",
target="bt-reset",
placement="bottom",
)
],
width=6,
style={"padding-left": "0pt"}),
dbc.Col([
dbc.Label(html.Span("Recompute spanning tree", id="recomp-label"),
html_for="recompute-spanning-tree"),
dbc.Checklist(options=[{
"value": 1
}],
value=[1],
id="recompute-spanning-tree",
switch=True,
style={"margin-left": "5pt"}),
dbc.Tooltip(
"If enabled, the minimum spanning tree will be "
"recomputed on the nodes selected in the current "
"graph view (does not apply to filtering)",
target="recomp-label",
placement="bottom",
)
],
width=6)
],
row=True,
style={"margin-left": "5pt"})
editing_mode_radio = dbc.FormGroup([
dbc.Label("Choose the editing mode"),
dbc.RadioItems(
options=[
{
"label": "Editing (edit the graph object)",
"value": 1
},
{
"label": "Masking (edit the current view)",
"value": 2
},
],
value=1,
id="edit-mode",
),
])
edit_button_group = dbc.InputGroup([
dbc.Button(html.Span([html.I(className="fas fa-minus"), " Remove"]),
color="primary",
className="mr-1",
id='remove-button',
disabled=True,
style={"margin": "2pt"}),
dbc.Button(html.Span(
[html.I(className="fas fa-compress-alt"), " Merge"]),
color="primary",
className="mr-1",
id='merge-button',
style={"margin": "2pt"},
disabled=True),
dbc.Button(html.Span([html.I(className="fas fa-edit"), " Rename"]),
color="primary",
className="mr-1",
id='rename-button',
style={"margin": "2pt"},
disabled=True),
dbc.Button(html.Span([html.I(className="fas fa-redo"), " Reset"]),
color="secondary",
className="mr-1",
id='reset-elements-button',
style={
"float": "right",
"margin": "2pt"
},
disabled=False),
dbc.Modal(
[
dbc.ModalHeader("Merged label"),
dbc.ModalBody([
dbc.FormGroup([
dbc.Label("Would you like to"),
dbc.RadioItems(
options=[{
"label": "merge into one of the entities",
"value": 1
}, {
"label": "merge as a new entity",
"value": 2
}],
value=1,
id="merge-options",
),
]),
dbc.FormGroup([dbc.Input(id="merge-label-input")],
id="merge-input")
]),
dbc.ModalFooter([
dbc.Button("Apply",
id="merge-apply",
color="primary",
className="ml-auto"),
dbc.Button("Close",
id="merge-close",
color="default",
className="ml-auto")
]),
],
id="merge-modal",
),
dbc.Modal(
[
dbc.ModalHeader("Rename the selected node"),
dbc.ModalBody([
html.P(id="rename-error-message", style={"color": "red"}),
dbc.FormGroup([
dbc.Col(dbc.Label("New label"), width=3),
dbc.Col(dbc.Input(id="rename-input"), width=9)
],
row=True),
]),
dbc.ModalFooter([
dbc.Button("Apply",
id="rename-apply",
color="primary",
className="ml-auto"),
dbc.Button("Close",
id="rename-close",
color="default",
className="ml-auto")
]),
],
id="rename-modal",
),
])
dropdown_items = dcc.Dropdown(id="showgraph",
value=current_graph,
options=[{
'label': val.capitalize(),
'value': val
} for val in graphs],
style={"width": "100%"})
graph_type_dropdown = dbc.FormGroup([
dbc.Label(html.Span("Graph to display", id="showgraph-label"),
html_for="showgraph"),
dbc.Tooltip(
"Switch between different loaded graphs to display.",
target="showgraph-label",
placement="top",
), dropdown_items
])
search = dbc.FormGroup([
dbc.Label("Search node",
html_for="searchdropdown",
width=3,
style={
"text-align": "right",
"padding-right": "0pt"
}),
dbc.Col(dcc.Dropdown(id="searchdropdown", multi=False), width=6),
dbc.Col([
dbc.DropdownMenu([
dbc.DropdownMenuItem("png", id="png-menu"),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem("jpg", id="jpg-menu"),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem("svg", id="svg-menu"),
dbc.DropdownMenuItem(divider=True),
dbc.DropdownMenuItem(
"gml", id="gml-menu", href="/download/graph.gml")
] + [Download(id="download-gml")],
label="Download",
id='dropdown-download',
color="primary",
group=True,
className="mr-1",
in_navbar=True),
],
width=3)
],
row=True,
style={"margin": "3pt"})
freq_input_group = dbc.InputGroup([
dbc.Label(html.Span("Node Weight", id="node_freq_type-label"),
html_for="node_freq_type"),
dbc.Tooltip(
"Select a metric to use as the node weight "
"(node sizes are proportional to the selected weight)",
target="node_freq_type-label",
placement="top",
),
dcc.Dropdown(id="node_freq_type",
value=node_freq_type,
options=[{
'label': val[0],
'value': val[1]
} for val in node_frequency_type],
style={"width": "100%"}),
],
className="mb-1")
node_range_group = dbc.FormGroup([
dbc.Label(html.Span("Display Range", id="nodefreqslider-label"),
html_for="nodefreqslider"),
html.Div([
dcc.RangeSlider(id="nodefreqslider_content",
min=0,
max=100000,
value=nodefreqslider)
],
id="nodefreqslider"),
dbc.Tooltip(
"Adjust the node weight range (only the nodes having "
"the weight in the selected range will be displayed)",
target="nodefreqslider-label",
placement="bottom",
),
])
edge_input_group = dbc.InputGroup([
dbc.Label(html.Span("Edge Weight", id="edge_freq_type-label"),
html_for="edge_freq_type"),
dbc.Tooltip(
"Select a metric to use as the edge weight (edge thickness "
"is proportional to the selected weight)",
target="edge_freq_type-label",
placement="top",
),
dcc.Dropdown(id="edge_freq_type",
value=edge_freq_type,
options=[{
'label': val[0],
'value': val[1]
} for val in edge_frequency_type],
style={"width": "100%"})
],
className="mb-1")
edge_range_group = dbc.FormGroup([
dbc.Label(html.Span("Display Range", id="edgefreqslider-label"),
html_for="edgefreqslider"),
html.Div([
dcc.RangeSlider(id="edgefreqslider_content",
min=0,
max=100000,
value=edgefreqslider)
],
id="edgefreqslider"),
dbc.Tooltip(
"Adjust the edge weight range (only the edge having the "
"weight in the selected range will be displayed)",
target="edgefreqslider-label",
placement="bottom",
),
])
frequencies_form = dbc.FormGroup([
dbc.Col([freq_input_group, node_range_group], width=6),
dbc.Col([edge_input_group, edge_range_group], width=6)
],
style={"margin-bottom": "0pt"},
row=True)
display_message = html.P("Displaying top 100 most frequent entities",
id="display-message")
top_n_button = dbc.Button("Show N most frequent",
color="primary",
className="mr-1",
id='top-n-button',
style={"margin": "5pt"})
top_n_slider = daq.NumericInput(
id="top-n-slider",
min=1,
max=1000,
value=top_n_slider_value if top_n_slider_value else 100,
className="mr-1",
disabled=False,
style={"margin": "5pt"})
show_all_button = dbc.Button("Show all entities",
id="show-all-button",
color="primary",
className="mr-1",
style={
"float": "right",
"margin": "5pt"
})
top_n_groups = dbc.InputGroup([
top_n_button,
dbc.Tooltip(
"Display N nodes with the highest paper occurrence frequency, "
"where N is defined by the slider on the right.",
target="top-n-button",
placement="top",
), top_n_slider, show_all_button,
dbc.Tooltip(
"Display all the nodes of the current graph.",
target="show-all-button",
placement="top",
)
],
style={"margin-bottom": "10pt"})
item_details_card = dbc.Card(
dbc.CardBody([
html.H5("", className="card-title"),
html.H6("", className="card-subtitle"),
html.P("", className="card-text"),
dbc.Button("", color="primary", id="see-more-card")
],
id="item-card-body"))
view_selection_card = dbc.Card(dbc.CardBody([
html.H6("View selection", className="card-title"), graph_type_dropdown,
display_message, top_n_groups, frequencies_form
]),
id="view-selection-card",
style={"margin-bottom": "10pt"})
cluster_group = dbc.InputGroup([
dbc.Label(html.Span("Group by", id="group-by-label"),
html_for="cluster_type"),
dbc.Tooltip(
"Select a grouping factor for the nodes.",
target="group-by-label",
placement="top",
),
dcc.Dropdown(id="cluster_type",
value=cluster_type,
options=[{
'label': val[0],
'value': val[1]
} for val in cluster_types],
style={"width": "100%"})
],
className="mb-1")
cluster_filter = dcc.Dropdown(id="clustersearch",
multi=True,
options=[{
"label": el,
"value": el
} for el in clustersearch],
value=clustersearch)
filter_by_cluster = dbc.FormGroup([
dbc.Label(html.Span("Groups to display", id="clustersearch-label"),
html_for="clustersearch"),
dbc.Tooltip(
"Only the nodes beloning to the groups selected in the field "
"below will be displayed. You click or start typing to add new "
"groups to display, or click on the cross icon to remove a group.",
target="clustersearch-label",
placement="top",
), cluster_filter,
dbc.Button("Add all groups",
color="primary",
className="mr-1",
id='addAllClusters',
style={"margin-top": "10pt"}),
dbc.Tooltip(
"Add all available node groups to display.",
target="addAllClusters",
placement="bottom",
)
],
style={"margin-top": "10pt"})
cluster_layout_button = dbc.InputGroup([
dbc.Checklist(
options=[{
"value": 1,
"disabled": False
}],
value=[],
id="groupedLayout",
switch=True,
),
dbc.Label("Grouped Layout", html_for="groupedLayout"),
])
cluster_selection_card = dbc.Card(dbc.CardBody([
html.H6("Grouping", className="card-title"), cluster_group,
filter_by_cluster, cluster_layout_button
]),
style={"margin-bottom": "10pt"})
nodes_to_keep = dbc.FormGroup([
dbc.Label(html.Span("Nodes to keep", id="nodestokeep-label"),
html_for="nodestokeep"),
dcc.Dropdown(id="nodestokeep",
multi=True,
options=[{
"label": n,
"value": n
} for n in nodes_to_keep],
value=nodes_to_keep,
placeholder="Nodes to fix in the view..."),
dbc.Tooltip(
"The selected nodes will be fixed in the graph view "
"and will not be filtered by 'top N' or group filters "
"(start typing to obtain the nodes to select from).",
target="nodestokeep-label",
placement="top",
)
], )
form = dbc.Form([
global_button_group,
nodes_to_keep,
view_selection_card,
cluster_selection_card,
])
legend = dbc.FormGroup([html.Div(id="cluster_board", children=[])])
# ------ Path search form --------
path_from = dbc.FormGroup([
dbc.Label(html.Span("From", id="searchpathfrom-label"),
html_for="searchpathfrom",
width=3),
dbc.Tooltip(
"Select a node to use as the source in the path search",
target="searchpathfrom-label",
placement="top",
),
dbc.Col(dcc.Dropdown(id="searchpathfrom",
value=searchpathfrom,
options=([{
"label": searchpathfrom,
"value": searchpathfrom
}] if searchpathfrom else [])),
width=9)
],
row=True)
path_to = dbc.FormGroup([
dbc.Label(html.Span("To", id="searchpathto-label"),
html_for="searchpathto",
width=3),
dbc.Tooltip(
"Select a node to use as the target in the path search",
target="searchpathto-label",
placement="top",
),
dbc.Col(dcc.Dropdown(id="searchpathto",
options=([{
"label": searchpathto,
"value": searchpathto
}] if searchpathto else []),
value=searchpathto),
width=9)
],
row=True)
neighbor_control_group = html.Div([
dbc.FormGroup([
dbc.Col(dbc.Button("Find top N neighbors",
color="primary",
className="mr-1",
id='bt-neighbors',
style={"float": "right"}),
width=10),
dbc.Tooltip(
"Search for the neighbors with the highest mutual information "
"score.",
target="bt-neighbors",
placement="top",
),
dbc.Col(daq.NumericInput(
id="neighborlimit", min=1, max=100, value=10,
className="mr-1"),
width=2),
],
row=True)
])
neighbor_view_card = dbc.Card(dbc.CardBody([], id="neighbors-card-body"),
style={
"overflow-y": "scroll",
"height": "150pt"
})
top_n_paths = dbc.FormGroup([
dbc.Label(html.Span("Top N", id="searchpathlimit-label"),
html_for="searchpathlimit",
width=3),
dbc.Tooltip(
"Set a number of best paths to search for (the best paths are the "
"ones that maximize the mutual information)",
target="searchpathlimit-label",
placement="top",
),
dbc.Col(daq.NumericInput(id="searchpathlimit",
min=1,
max=50,
value=searchpathlimit,
className="mr-1"),
width=9)
],
row=True)
path_condition = dbc.FormGroup([
dbc.Label("Traversal conditions"),
dbc.FormGroup([
dbc.Col([
dbc.Label(html.Span("Entity to traverse",
id="searchnodetotraverse-label"),
html_for="searchnodetotraverse"),
dbc.Tooltip(
"Select an entity to traverse in the path search (if "
"selected, the search is performed in two steps: "
"from the source to the selected entity, and from "
"the selected entity to the target)",
target="searchnodetotraverse-label",
placement="top",
),
dcc.Dropdown(id="searchnodetotraverse",
value=searchnodetotraverse,
options=([{
"label": searchnodetotraverse,
"value": searchnodetotraverse
}] if searchnodetotraverse else []))
],
width=6),
dbc.Col([
dbc.Label(html.Span("Allow Overlap",
id="searchpathoverlap-label"),
html_for="searchpathoverlap"),
dbc.Tooltip(
"If the overlap is allowed, then the the paths "
"from the source to the intermediate entity can go "
"through the same entities as the paths from the "
"intermediary to the target. Otherwise the paths "
"should go through distinct entities",
target="searchpathoverlap-label",
placement="top",
),
dbc.Checklist(
options=[{
"value": 1
}],
value=searchpathoverlap,
id="searchpathoverlap",
switch=True,
)
],
width=6)
],
row=True)
])
nested_path = dbc.FormGroup([
dbc.Label("Nested path search"),
dbc.FormGroup([
dbc.Col(children=[
dbc.Label(html.Span("Nested", id="nestedpaths-label"),
html_for="nestedpaths"),
dbc.Tooltip(
"If enabled, the nested paths are found, i.e. for "
"every edge found in a path we search for other N "
"best paths from the source to the target of this "
"edge for <depth> times",
target="nestedpaths-label",
placement="top",
),
dbc.Checklist(
options=[{
"value": 1
}],
value=nestedpaths,
id="nestedpaths",
switch=True,
)
],
width=6),
dbc.Col([
dbc.Label(html.Span("Depth", id="pathdepth-label"),
html_for="pathdepth"),
dbc.Tooltip(
"Select the depth of nesting indicating for how many "
"iterations we expand the encountered edges into the "
"best paths.",
target="pathdepth-label",
placement="top",
),
daq.NumericInput(id="pathdepth",
min=1,
max=4,
value=pathdepth,
disabled=True,
className="mr-1")
],
width=6)
],
row=True)
])
search_path = dbc.InputGroup([
html.P("",
id="noPathMessage",
style={
"color": "red",
"margin-right": "10pt"
}),
dbc.Button(html.Span([html.I(className="fas fa-route"), " Find Paths"
]),
color="primary",
className="mr-1",
id='bt-path',
style={"float": "right"}),
dbc.Tooltip(
"Find paths between selected entities",
target="bt-path",
placement="bottom",
)
],
style={"float": "right"})
expand_edge = dbc.FormGroup([
dbc.Label("Edge expansion"),
dbc.FormGroup([
dbc.Col(dbc.Button("Expand edge",
color="primary",
className="mr-1",
id='bt-expand-edge',
disabled=True),
width=6),
dbc.Col(dbc.Label("N best paths",
html_for="expand-edge-n",
style={"margin-top": "5pt"}),
width=3),
dbc.Col(daq.NumericInput(
id="expand-edge-n", min=1, max=20, value=5, className="mr-1"),
width=3),
],
row=True)
])
form_path_finder = dbc.Form([
path_from, path_to, top_n_paths,
html.Hr(), path_condition,
html.Hr(), nested_path,
html.Hr(), expand_edge,
html.Hr(), search_path
])
graph_layout = dbc.FormGroup([
dbc.Label("Layout", html_for="searchdropdown", width=3),
dbc.Col(dcc.Dropdown(id='dropdown-layout',
options=[{
'label':
"{}{}".format(
val.capitalize(),
" ({})".format(graph_layout_options[val])
if graph_layout_options[val] else ""),
'value':
val
} for val in graph_layout_options.keys()],
value=current_layout,
clearable=False),
width=9)
],
row=True)
node_shape = dbc.FormGroup([
dbc.Label("Node Shape", html_for="dropdown-node-shape", width=3),
dbc.Col(dcc.Dropdown(id='dropdown-node-shape',
value='ellipse',
clearable=False,
options=([{
'label': val.capitalize(),
'value': val
} for val in node_shape_option_list])),
width=9)
],
row=True)
link_color_picker = dbc.FormGroup([
dbc.Col(
daq.ColorPicker(id='input-follower-color',
value=dict(rgb=dict(r=190, g=36, b=37, a=0)),
label="Highlight Color"))
],
row=True)
conf_form = dbc.Form([graph_layout, node_shape, link_color_picker])
# ---- Create a layout from components ----------------
cyto = cyto_module.Cytoscape(
id='cytoscape',
elements=elements,
stylesheet=CYTOSCAPE_STYLE_STYLESHEET,
style={
"width": "100%",
"height": "100%"
},
)
layout = html.Div([
dcc.Store(id='memory',
data={
"removed_nodes": [],
"removed_edges": [],
"added_nodes": [],
"added_edges": [],
"filtered_elements": [],
"removed_elements": {},
"renamed_elements": {},
"merging_backup": {
"added_elements": [],
"removed_elements": {}
},
"paper_backup": {}
}),
dbc.Row([]),
dbc.Row([
dbc.Col([
html.Div(style=VISUALIZATION_CONTENT_STYLE,
children=[cyto],
id="cyto-container"),
html.Div([
dcc.Loading(id="loading",
children=[html.Div(id="loading-output")],
type="default"),
],
id="loader-container",
className="fixed-top",
style={
"width": "60pt",
"height": "60pt",
"margin": "20pt"
}),
html.Div([
search,
],
id="search-container",
className="fixed-top",
style={
"width": "30%",
"margin-left": "80pt"
}),
html.Div([
dbc.Button(html.Span(
[html.I(className="fas fa-binoculars"), " Legend"]),
id="toggle-legend",
color="primary",
className="mr-1"),
dbc.Button(html.Span(
[html.I(className="fas fa-search-plus"), " Details"]),
id="toggle-details",
color="primary",
className="mr-1"),
dbc.Button(html.Span(
[html.I(className="fas fa-pen"), " Editing"]),
id="toggle-edit",
color="primary",
className="mr-1"),
dbc.Button(html.Span([
html.I(className="fas fa-star-of-life"), " Neighbors"
]),
id="toggle-neighbors",
color="primary",
className="mr-1"),
dbc.Button(html.Span(
[html.I(className="fas fa-angle-down"), ""]),
id="toggle-hide",
color="light",
className="mr-1"),
dbc.Collapse(dbc.Card([
dbc.CardHeader([
html.H6("Legend (colored by Entity Type)",
className="card-title",
style={"margin-bottom": "0pt"},
id="legend-title")
],
style={"margin-botton": "0pt"}),
dbc.CardBody([legend]),
],
style={"height": "100%"}),
style={"height": "150pt"},
id="collapse-legend"),
dbc.Collapse(dbc.Card([
dbc.CardHeader([
html.H6("Details",
className="card-title",
style={"margin-bottom": "0pt"})
],
style={"margin-botton": "0pt"}),
dbc.CardBody([item_details_card],
style={"overflow-y": "scroll"})
],
style={"height": "100%"}),
style={"height": "250pt"},
id="collapse-details"),
dbc.Collapse(dbc.Card([
dbc.CardHeader([
html.H6("Edit graph",
className="card-title",
style={"margin-bottom": "0pt"})
],
style={"margin-botton": "0pt"}),
dbc.CardBody([
dbc.Row([
dbc.Col([editing_mode_radio], width=5),
dbc.Col([edit_button_group], width=7)
])
])
],
style={"height": "100%"}),
style={"height": "150pt"},
id="collapse-edit"),
dbc.Collapse(dbc.Card([
dbc.CardHeader([
html.H6("Neighbors view",
className="card-title",
style={"margin-bottom": "0pt"})
],
style={"margin-botton": "0pt"}),
dbc.CardBody(
[neighbor_control_group, neighbor_view_card])
],
style={"height": "100%"}),
style={"height": "250pt"},
id="collapse-neighbors"),
],
className="fixed-bottom",
style={
"width": "55%",
})
],
width=8),
dbc.Col(html.Div(children=[
dbc.Button(html.Span(
[html.I(className="fas fa-cog"), " Controls"]),
id="collapse-button",
color="primary",
style={
"margin": "10pt",
"margin-left": "60%"
}),
dbc.Collapse(dbc.Tabs(
id='tabs',
children=[
dbc.Tab(id="graph-view-tab",
label='Graph view',
label_style={
"color": "#00AEF9",
"border-radius": "4px",
"background-color": "white"
},
children=[dbc.Card(dbc.CardBody([form]))]),
dbc.Tab(id="layout-tab",
label='Layout',
label_style={
"color": "#00AEF9",
"border-radius": "4px",
"background-color": "white"
},
children=[dbc.Card(dbc.CardBody([conf_form]))
]),
dbc.Tab(id="path-finder-tab",
label='Path finder',
label_style={
"color": "#00AEF9",
"border-radius": "4px",
"background-color": "white"
},
children=[
dbc.Card(dbc.CardBody([form_path_finder]))
])
]),
id="collapse"),
]),
width=4)
])
],
style={"overflow-x": "hidden"})
return cyto, layout, dropdown_items, cluster_filter
}],
value='Infected',
labelStyle={'display': 'inline-block'}),
dcc.RadioItems(id='variable6',
options=[{
'label': 'Optimisation',
'value': 'Optimisation'
}, {
'label': 'No optimisation',
'value': 'No optimisation'
}],
value='No optimisation',
labelStyle={'display': 'inline-block'}),
daq.NumericInput(id='variable3',
label='Population',
size=250,
max=3000000000,
value=1000),
daq.NumericInput(id='variable4',
label='Beta',
size=75,
max=1,
min=0.0001,
value=0.01),
daq.NumericInput(id='variable5',
label='Gamma',
size=75,
max=100,
min=0.001,
value=0.1)
]),
html.Div(
id="value-setter-view-output", className="output-datatable"
),
html.Div(
id="indicator-operation-output", className="operation-datatable"
),
],
),
],
),
]
ud_ul_input = daq.NumericInput(
id="ud_ul_input", className="setting-input", size=200, max=9999999, value=0
)
ud_ll_input = daq.NumericInput(
id="ud_ll_input", className="setting-input", size=200, max=9999999, value=0
)
ud_ml_input = daq.NumericInput(
id="ud_ml_input", className="setting-input", size=200, max=9999999, value=0
)
def build_value_setter_line(line_num, label, value, col3):
return html.Div(
[html.Label(label, className="four columns"),
html.Label(value, className="four columns"),
html.Div(col3, className="four columns"),
]
},
{
'label': 'KNER (Gudnason)',
'value': 'kner'
},
{
'label': 'KNDX (Nakka)',
'value': 'kndx'
},
],
value='knsb-nakka')
]),
width=4),
dbc.Col(dbc.FormGroup([
dbc.Label('Grain segment count'),
daq.NumericInput(id='N', value='4', min=1, max=8)
]),
width=4),
dbc.Col(dbc.FormGroup([
daq.BooleanSwitch(id='neutral_burn_profile',
label='Neutral burn profile',
on=True)
]),
width=4)
])
input_row_3 = dbc.Row([
dbc.Col(dbc.FormGroup([
dbc.Label('Grain diameter (mm)'),
dbc.Input(placeholder='Insert grain diameter...',
id='D_grain',
def __init__(self):
self.mcu = MCU_STM32F7()
self.start_button_clicks = 0
self.upload_button_clicks = 0
# self.app = dash.Dash(__name__, external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css'], title = 'Data Collector')
self.app = dash.Dash(__name__, title = 'WaveForm Analyzer')
self.app.css.append_css({"external_url": 'mycss.css'})
self.controll_limit_dict = SPECIFICATION.CONTROLL_LIMIT
self.data_table_fmt = SPECIFICATION.TABLE_FORMAT
self.fig_color_fmt = SPECIFICATION.FIGURE_FORMAT
self.LCL, self.UCL = self.controll_limit_dict['3'] #default 3.0 V
self.web_port = self.mcu.parameters.web_port
self.web_host = '127.0.0.1'
self.app.layout = html.Div([
html.H1(id = 'Program-Name',children='WaveForm Analyzer', style={'font-wmaxDataPts': 'bold'}), #style = {'text-align':'center', 'font-size':16, 'transform':"rotate(0deg)"}
html.Hr(),
html.Div(className = 'row',
style = {"margin-left": "10px", "margin-top": "10px"},
children = [
html.Div(className = 'three columns',
children = [dcc.RadioItems(id = 'mode-items',
options = [{'label':'Real Mode', 'value':'rm'},
{'label':'File Mode', 'value':'fm'}
],
value = 'rm',
labelStyle={'display': 'inline-block'}
)
], style={'float': 'left', "margin-left": "20px"}),
html.Div(className = 'three columns',
children = [
html.Div(className = 'five columns', children = "Voltage Selection: ",id='dropdown-label', style={'font-size':16}),
html.Div(className = 'four columns',
children = [
dcc.Dropdown(id='dropdown-voltage',
options=[
{'label': '3.0V', 'value': 3},
{'label': '5.0V', 'value': 5},
{'label': '12.0V', 'value': 12}
],
value=3,
clearable=False
)
],
style = {'width':'30%', 'height':'30px', 'display':'inline-block', 'float': 'left', "margin-left": "5px"},
)
]),
html.Div(className = 'three columns',
children = [
html.Div(className = 'four columns', children = "Time Scale: ",id='timescale-label', style={'font-size':16}),
html.Div(className = 'three columns',
children = [
daq.NumericInput(id='numeric-input',
value=1,
size = 60,
label = '(s)',
labelPosition = 'right',
min = 1,
max = 5)
], style = {'width':'30%', 'height':'30px', 'display':'inline-block', 'float': 'left', "margin-left": "5px"})
]),
]),
html.Div(className='row',
style = {"margin-top": "50px"},
children = [
html.Div(children = [
dbc.Button('Start', id = 'start-button', n_clicks = 0,style = {'float': 'left', "margin-left": "60px"}),
dbc.Spinner(html.Div(id="loading-output", children = ""),fullscreen=True),
# html.Button('Start', id = 'start-button', n_clicks = 0,style = {'float': 'left', "margin-left": "60px"}),
dcc.Upload(id = 'data-upload', children = [html.Button('Upload File', id = 'upload-button', n_clicks = 0, style = {'float': 'left', "margin-left": "20px"})]),
dcc.Input(id = "filename-input", type = "text", placeholder="Upload a file; Select File Mode at first", style = {'width':'20%', 'float': 'left', "margin-left": "20px"}, disabled = False)
])
]),
html.Div(className='row',
style = {"margin-top": "20px"},
children = [
dcc.Graph(id='live-graph', figure = {
'data' : [
go.Scatter(
x = [],
y = [],
mode = "lines",
name = "Data"
)
],
'layout' : go.Layout(
title = self.fig_color_fmt['title'],
xaxis = {'title': 'Time(ms)', 'gridcolor': 'rgb(255,255,255)'},
yaxis = {'title': 'Voltage (V)', 'gridcolor': 'rgb(255,255,255)'},
plot_bgcolor = 'rgb(200,200,200)'
)
}
),
]),
html.Div(className='row',
children = [
dash_table.DataTable(
id='data-table',
columns=[{"name": n, "id": n} for n in self.data_table_fmt['columns']],
data=self.data_table_fmt['data'],
style_cell = {'font_family': 'Arial','font_size': '14px', 'text_align': 'center'},
style_header={'backgroundColor': 'white','fontWeight': 'bold'}
)
], style={"margin-top": "50px", 'margin': 'auto', 'textAlign': 'center','width':'85%'}),
])
@self.app.callback(
[Output('live-graph', 'figure'),
Output('data-table', 'data'),
Output('loading-output', 'children')],
[Input('start-button', 'n_clicks'),
Input('dropdown-voltage','value'),
Input('mode-items','value'),
Input('numeric-input','value')])
def plot_diagram(n_clicks, dropdown_value, mode_value, numeric_value):
self.mcu.amplify_ratio = dropdown_value/3.0
self.LCL, self.UCL = self.controll_limit_dict[str(dropdown_value)]
self.mcu.change_itrs(target_time = numeric_value)
layout = go.Layout(
title = self.fig_color_fmt['title'],
xaxis = {'title': 'Time(ms)', 'gridcolor': 'rgb(255,255,255)'},
yaxis = {'title': 'Voltage (V)', 'gridcolor': 'rgb(255,255,255)'},
plot_bgcolor = 'rgb(200,200,200)'
)
fig_data_list = []
output_table_data = []
if self.start_button_clicks < n_clicks:
self.start_button_clicks = n_clicks
if mode_value == 'rm':
self.mcu.collect_long_data() ###
self.mcu.save_data()
x = self.mcu.time_list
for c_num, y_arr in enumerate([self.mcu.data_list_c1, self.mcu.data_list_c2, self.mcu.data_list_c3], 1):
approx_num = 4
if not np.isnan(y_arr[0]):
max_val = np.around(y_arr.max(), decimals = approx_num)
min_val = np.around(y_arr.min(), decimals = approx_num)
FD_val = np.around(y_arr.max()-y_arr.min(), decimals = approx_num)
ave_val = np.around(y_arr.mean(), decimals = approx_num)
rms_val = np.around(np.sqrt((y_arr**2).sum()/len(y_arr)), decimals = approx_num)
std_val = np.around(np.sqrt(((y_arr-ave_val)**2).sum()/len(y_arr)), decimals = approx_num)
cv_percentage = np.around(std_val/ave_val*100, decimals = approx_num)
outPercentage = np.around(np.sum((y_arr > self.UCL)|(y_arr < self.LCL))/len(y_arr)*100, decimals = approx_num)
else:
max_val = 'N.A.'
min_val = 'N.A.'
FD_val = 'N.A.'
ave_val = 'N.A.'
rms_val = 'N.A.'
std_val = 'N.A.'
cv_percentage = 'N.A.'
outPercentage = 'N.A.'
data = go.Scatter(
x=x,
y=y_arr,
name='Channel-%d'%c_num,
mode= 'lines',
marker = dict(color = self.fig_color_fmt['linecolor'][c_num-1])
)
fig_data_list += [data]
output_table_data += [{'Item': 'Channel-%d'%c_num,
'Maximum Voltage(V)': max_val,
'Minimum Voltage(V)':min_val,
'Full Deviation(V)':FD_val,
'Average Voltage(V)':ave_val,
'RMS':rms_val,
'STDEV(V)':std_val,
'CV(%)':cv_percentage,
'OutPercentage%(+ or - 5%)':outPercentage
}]
data_UCL = go.Scatter(
x = [0,np.max(x)],
y = [self.UCL, self.UCL],
mode = "lines",
name = "UCL({:.2f}V)".format(self.UCL),
marker = dict(color = 'rgb(255,0,0)'),
line = dict(dash = 'dash')
)
data_LCL = go.Scatter(
x = [0,np.max(x)],
y = [self.LCL, self.LCL],
mode = "lines",
name = "LCL({:.2f}V)".format(self.LCL),
marker = dict(color = 'rgb(0,255,0)'),
line = dict(dash = 'dash')
)
fig_data_list += [data_UCL , data_LCL]
output_fig = {'data': fig_data_list,'layout' : layout}
else:
data = go.Scatter(
x = [],
y = [],
mode = "lines",
name = "Data"
)
output_fig = {'data': [data],'layout' : layout}
output_table_data = self.data_table_fmt['data']
return output_fig, output_table_data, ""
@self.app.callback(
Output('filename-input', 'value'),
[Input('data-upload', 'filename'),
Input('data-upload', 'contents'),
Input('mode-items','value')])
def plot_diagram_by_upload(filename, contents, mode_value):
# columns = ['time(sec.)','C1_voltage(V)','C1_voltage(V)','C1_voltage(V)']
if contents != None and mode_value == 'fm':
try:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
if 'csv' in filename:
# Assume that the user uploaded a CSV or TXT file
df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
elif 'txt' or 'tsv' in filename:
# Assume that the user upl, delimiter = r'\s+'oaded an excel file
df = pd.read_csv(io.StringIO(decoded.decode('utf-8')), delimiter=r'\s+')
# self.mcu.reset_data()
self.mcu.time_list = df.iloc[:,0].values
self.mcu.data_list_c1 = df.iloc[:,1].values
self.mcu.data_list_c2 = df.iloc[:,2].values
self.mcu.data_list_c3 = df.iloc[:,3].values
return filename
except Exception as e:
print(e)
return 'There was an error processing this file.'
else:
return ""
id="select_independent",
options=[{'label':x, 'value':x} for x in obj_Data.df_train_dummies.columns],
value= list(obj_Data.df_train_dummies.columns),
multi=True,
className="dcc_control",
),
html.Div(
[
html.Div(
[
html.P("Select Number of KFOLD Splits", className="control_label"),
daq.NumericInput(
id='id-daq-splits',
min=0,
max=10,
size = 75,
value=2
),
],className="row flex-display",
),
]
),
html.P("Models", className="control_label"),
dcc.Dropdown(
id="select_models",
options = [{'label':x, 'value':x} for x in models],
value = models,
multi=True,
min=0,
max=10,
value=6,
id='darktheme-daq-knob',
className='dark-theme-control'
), html.Br(),
daq.LEDDisplay(
value="3.14159",
color=theme['primary'],
id='darktheme-daq-leddisplay',
className='dark-theme-control'
), html.Br(),
daq.NumericInput(
min=0,
max=10,
value=4,
id='darktheme-daq-numericinput',
className='dark-theme-control'
), html.Br(),
daq.PowerButton(
on=True,
color=theme['primary'],
id='darktheme-daq-powerbutton',
className='dark-theme-control'
), html.Br(),
daq.PrecisionInput(
precision=4,
value=299792458,
id='darktheme-daq-precisioninput',
className='dark-theme-control'
), html.Br(),
children=[
html.H5(
"Setpoint Mass Flow",
style={
"textAlign": "Center",
# "paddingTop": "2.5%",
"marginBottom": "5%",
},
)
], ),
html.Div(
[
daq.NumericInput(
id='flow-meter-setpoint-numeric-input',
value=0.0,
min=0,
max=5000,
size=100,
label='Setpoint (0 - 5000 mV)',
labelPosition='bottom'),
html.Button("Send",
id='flow-meter-setpoint-button',
style={
"marginLeft": "5%",
"marginRight": "5%",
"marginBottom": "5%",
}),
],
className="row",
style={
"display": "flex",
"justify-content": "center",
id="value-setter-view-btn",
n_clicks=0,
),
],
),
html.Div(id="value-setter-view-output",
className="output-datatable"),
],
),
],
),
]
ud_usl_input = daq.NumericInput(id="ud_usl_input",
className="setting-input",
size=200,
max=9999999)
ud_lsl_input = daq.NumericInput(id="ud_lsl_input",
className="setting-input",
size=200,
max=9999999)
ud_ucl_input = daq.NumericInput(id="ud_ucl_input",
className="setting-input",
size=200,
max=9999999)
ud_lcl_input = daq.NumericInput(id="ud_lcl_input",
className="setting-input",
size=200,
max=9999999)
def _getdropdowns(self):
''' dashboard dropdowns
'''
# PCA DROPDOWNS HERE
if self.method == 'PCA':
pca_dropdowns = []
if 'scatter' in self.plot_options:
pca_dropdowns.append(html.Div([
daq.NumericInput(
id='rem_vari',
disabled=True,
min=0,
max=100,
size=120,
label='Remained Variance %',
labelPosition='bottom',
value=self.stats.remained_variance)
], className='two columns'))
pca_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_pca',
min=1,
max=min([self.stats.d, 3]),
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2
),
], className='two columns'))
if 'box' in self.plot_options:
pca_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_pca',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=pca_dropdowns, className="row")
# TSNE OPTIONS HERE
if self.method == 'TSNE':
tsne_dropdowns = []
if 'scatter' in self.plot_options:
tsne_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_tsne',
min=1,
max=min([self.stats.d, 3]),
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2),
], className='two columns'))
tsne_dropdowns.append( html.Div([
daq.NumericInput(
id='perplexity_tsne',
min=1,
max=100,
size=120,
label='Perplexity',
labelPosition='bottom',
value=30),
], className='two columns'))
if 'box' in self.plot_options:
tsne_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_tsne',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=tsne_dropdowns, className="row")
# LLE DROPDOWNS HERE
if self.method == 'LLE':
lle_dropdowns = []
if 'scatter' in self.plot_options:
lle_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_lle',
min=1,
max=min([self.stats.d, 3]),
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2),
],className='two columns'))
lle_dropdowns.append(html.Div([
daq.NumericInput(
id='neighbours_lle',
min=1,
max=self.stats.n-1,
size=120,
label='K-Neighbours',
labelPosition='bottom',
value=5),
], className='two columns'))
if 'box' in self.plot_options:
lle_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_lle',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=lle_dropdowns, className="row")
# UMAP OPTIONS HERE
if self.method == 'UMAP':
umap_dropdowns = []
if 'scatter' in self.plot_options:
umap_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_umap',
min=1,
max=min([self.stats.d, 3]),
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2),
], className='two columns'))
umap_dropdowns.append( html.Div([
daq.NumericInput(
id='kneighbours_umap',
min=1,
max=100,
size=120,
label='k-Neighbours',
labelPosition='bottom',
value=5),
], className='two columns'))
if 'box' in self.plot_options:
umap_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_umap',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=umap_dropdowns, className="row")
# KMAP OPTIONS HERE
if self.method == 'KMAP':
kmap_dropdowns = []
if 'scatter' in self.plot_options:
kmap_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_kmap',
min=1,
max=self.stats.d_red,
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2),
], className='two columns'))
kmap_dropdowns.append(html.Div([
daq.NumericInput(
id='kneighbours_kmap',
min=1,
max=100,
size=120,
label='k-Neighbours',
labelPosition='bottom',
value=30),
], className='two columns'))
kmap_dropdowns.append(html.Div([
dcc.Dropdown(
id='algs_kmap',
options=[
{'label': 'PCA', 'value': 'PCA'},
{'label': 'UMAP', 'value': 'UMAP'},
{'label': 'LLE', 'value': 'LLE'},
{'label': 'TSNE', 'value': 'TSNE'},
{'label': 'ISOMAP', 'value': 'ISOMAP'},
{'label': 'MDS', 'value': 'MDS'}
],
value='PCA'),
], className='two columns'))
if 'box' in self.plot_options:
kmap_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_kmap',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=kmap_dropdowns, className="row")
# ISOMAP DROPDOWNS HERE
if self.method == 'ISOMAP':
isomap_dropdowns = []
if 'scatter' in self.plot_options:
isomap_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_isomap',
min=1,
max=self.stats.d_red,
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2),
], className='two columns'))
isomap_dropdowns.append( html.Div([
daq.NumericInput(
id='kneighbours_isomap',
min=1,
max=100,
size=120,
label='k-Neighbours',
labelPosition='bottom',
value=30),
], className='two columns'))
if 'box' in self.plot_options:
isomap_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_isomap',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=isomap_dropdowns, className="row")
# MDS DROPDOWNS HERE
if self.method == 'MDS':
mds_dropdowns = []
if 'scatter' in self.plot_options:
mds_dropdowns.append(html.Div([
daq.NumericInput(
id='red_dim_input_mds',
min=1,
max=min([self.stats.d, 3]),
size=120,
label='subspace dimension',
labelPosition='bottom',
value=2
),
], className='two columns'))
if 'box' in self.plot_options:
mds_dropdowns.append(html.Div([
daq.NumericInput(
id='box_red_dim_mds',
min=1,
max=self.stats.d_red,
size=120,
label='Boxplot dimension',
labelPosition='bottom',
value=2)
], className='two columns'))
if 'graph' in self.plot_options:
pass
dashboard = html.Div(children=mds_dropdowns, className="row")
return dashboard
#result = session.query(allweapons).filter(allweapons.name == "Face Smasher")
#for row in result:
# print(row.bid)
#----The following section plots historical trends for a given input item.----
external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
colors = {'background': '#111111', 'text': '#c7465d'}
app.layout = html.Div(children=[
html.H1(children='WoW Auction Intelligence',
style={
'textAlign': 'center',
'color': colors['text']
}),
html.Label('Select character level'),
daq.NumericInput(id='levelselection', min=0, max=110, value=20),
html.Label('Select Item Class'),
dcc.Dropdown(id='demodropdown', options=options_class, value=2),
html.Label('Select Your Realm'),
dcc.Dropdown(id='serverdropdown', options=options, value='Hellfire'),
html.Label('Select Your Class'),
dcc.Dropdown(id='classselection',
options=[{
'label': 'Mage',
'value': 'Mage'
}, {
'label': 'Priest',
'value': 'Priest'
}, {
'label': 'Warlock',
'value': 'Warlock'
min = 55,
max = 230,
step = 1,
value = 130,
marks = {'55': '55','75':'75','100': '100', '125': '125', '150': '150', '175': '175', '200':'200',
'230':'230'}),
html.Br(),
html.P('130', id='weight-selected-slider'),
html.Br(),
#year
html.P('Year of Olympics: ',style={'display':'inline-block'}),
daq.NumericInput(id='Year',
value = 2000,
min = 1896,
max = 2016,
style={'display':'inline-block'}),
#season
dcc.Markdown('Season'),
dcc.Dropdown(
id = 'Season',
options = [
{'label': 'Winter', 'value': 'Winter'},
{'label': 'Summer', 'value': 'Summer'},
],
className = 'mb-5',
value = 'Summer',
placeholder='Select a season'