import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
# external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__) # , external_stylesheets=external_stylesheets
app.layout = html.Div([
dcc.Input(id='num', type='number', value=5),
html.Table([
html.Tr([html.Td(['x', html.Sup(2)]),
html.Td(id='square')]),
html.Tr([html.Td(['x', html.Sup(3)]),
html.Td(id='cube')]),
html.Tr([html.Td([2, html.Sup('x')]),
html.Td(id='twos')]),
html.Tr([html.Td([3, html.Sup('x')]),
html.Td(id='threes')]),
html.Tr([html.Td(['x', html.Sup('x')]),
html.Td(id='x^x')]),
]),
])
@app.callback([
Output('square', 'children'),
Output('cube', 'children'),
Output('twos', 'children'),
Output('threes', 'children'),
def render_app_info(self, app_id, environ, seq_job_data):
if environ is None:
raise PreventUpdate
app_data = kvstore.client.get(app_id)
if not app_data:
raise PreventUpdate
app_info = ApplicationMetrics.from_json(kvstore.client.get(app_id))
active_executors_count = len([e for e in app_info.executor_metrics if e.isActive])
containers = f"{active_executors_count} / {len(app_info.executor_metrics)}"
jobs_running = f"{len([x for x in app_info.jobs_stages.values() if x.job.status == 'RUNNING'])}"
job_start_times = np.array([x.job.submissionTime for x in seq_job_data])
job_endtimes = np.array([x.job.completionTime for x in seq_job_data])
job_durations = job_endtimes - job_start_times
levels = (5, 75, 95)
perc_values = [np.percentile(job_durations, level) for level in levels]
perc_table = html.Table(
[
html.Tr(
[html.Th(str(level)) for level in levels]
),
html.Tr(
[html.Td(str(v)) for v in perc_values]
)
]
)
fig = Figure(
data={
"x": list(range(len(job_durations))),
"y": [x.seconds for x in sorted(job_durations)],
}
)
perc_graph = dcc.Graph(figure=fig)
p_data = html.Div([perc_table, perc_graph])
common_style = {"width": "180px", "margin": "6px"}
wide_style = common_style.copy()
wide_style["width"] = "500px"
job_id = seq_job_data[-1].job.jobId
scores = graphitestore.client.load(
[f"aliasByNode(hybrid_metrics.app.{app_id}.job_group.*.test.skewness_score, 4)"],
since="now-1h",
until="now",
lastrow=True,
)
return dbc.Col([
dbc.Row(
children=[
dbc.Card(
[html.H5("Containers"), html.P(containers)],
body=True,
style=common_style
),
dbc.Card(
[html.H5("Jobs Running"), html.P(jobs_running)],
body=True,
style=common_style
),
]),
dbc.Row(
[
dbc.Card([
html.H6(f"S.T. for {name}"),
html.P(f"{score:.2f}", style={"font-size": "12pt"}),
], style={**common_style, "padding": "3px"})
for name, (score, *_) in scores[0].items() if name.startswith("job_group")
]
),
dbc.Row(dbc.Card(
[html.H5("Jobs run time stats"), p_data],
body=True,
style=wide_style
))
])
],className = 'row'),
#Note here the slider value of 1 should correspond to days_recorded days displayed on the graph due to labelling weirdness
dcc.Slider(
id='time-slider',
min=1,
max=5,
value=1,
step=None,
marks={5:'1 day', 4:'3 days', 3:'1 week', 2:'2 weeks', 1:'1 month'}
),
dcc.Markdown(children=markdown_text_middle ),
html.Table(children = [html.Tr([html.Th(string) for string in ['Score','Range','Low','High']]) ] +
[html.Tr([html.Th(string) for string in ['Polarity','-1 to 1','Negative emotion','Positive Emotion']]),
html.Tr([html.Th(string) for string in ['Subjectivity', '0 to 1', 'Objective', 'Subjective']])]
, className = 'u-full-width'),
dcc.Markdown(children=markdown_text_below),
],className = 'container')
@app.callback(
dash.dependencies.Output('test-top-1-polarity', 'figure'),
[dash.dependencies.Input('1-or-10', 'value'), dash.dependencies.Input('pol-or-sub', 'value'),
dash.dependencies.Input('time-slider','value'),
dash.dependencies.Input('series-plot', 'values')])
def update_figure(oneORten,polORsub,timevalue,plottedseries):
#dictionary to tranlate the value of the time slider to a number of days
time_dict = { 5:1, 4:3, 3:7, 2:14, 1:30}
html.Table([html.Tr([html.Td(
dcc.Graph(
id='network-graph',
figure={
'data': graph_traces(random_graph),
'layout': go.Layout(
title = 'Network Graph',
titlefont_size=16,
showlegend=False,
hovermode='closest',
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
)
}
)
),
html.Td(#properties and update button
html.Table([html.Tr([html.Td(
html.Div([html.H5('Selected Node'),html.P(id='selected-node', children = '')])
)]),
html.Tr([html.Td(
html.Div([html.H5('x'),dcc.Input(id='node-x', type='number', placeholder='x', value='')])
)]),
html.Tr([html.Td(
html.Div([html.H5('y'),dcc.Input(id='node-y', type='number', placeholder='y', value='')])
)]),
html.Tr([html.Td(
html.Div([html.H5('Outgoing edges'),
dcc.Dropdown(id='node-property',
options=[{'label': str(n), 'value': n} for n in random_graph],
value=[],
multi=True)
])
)]),
html.Tr([html.Td(
html.Button('Update', id='update-button')
)])
])
)])])
html.Div(
style={'margin-top': '100px', 'margin-bottom': '100px'},
children=[
html.H6("OLS Regression Results"),
html.Table([
html.Tr([
html.Td(
style={'font-weight':'bold'},
children=['Dep. Variable']),
html.Td(id='dep-var'),
html.Td(
style={'font-weight':'bold'},
children=['No. Observations']),
html.Td(id='num-obs'),
html.Td(
style={'font-weight':'bold'},
children=['R-squared']),
html.Td(id='r-sq'),
html.Td(
style={'font-weight':'bold'},
children=['F-statistic']),
html.Td(id='f-stat'),
html.Td(
style={'font-weight':'bold'},
children=['Log-Likelihood']),
html.Td(id='log-like'),
]),
html.Tr([
html.Td(
style={'font-weight':'bold'},
children=['Model']),
html.Td(id='model'),
},
{
'label': 'ETH',
'value': 'df_eth'
},
],
value='df_btc'),
html.P(),
html.Button(id='botton-update-market-data',
children='Update'),
html.H6(style=style_section_header,
children='Market Indicators'),
html.Table(children=[
html.Tr(children=[
html.Th('Asset'),
html.Th('Index 0'),
html.Th('Index 1'),
]),
html.Tr(children=[
html.Td('ETH'),
html.Td('Number 0'),
html.Td('Number 1'),
]),
html.Tr(children=[
html.Td('BTC'),
html.Td('Number 1'),
html.Td('Number 2'),
]),
]),
]),
html.Div(
dbc.Col([
dropdown2,
]),
]),
],
fluid=True,
style={'textAlign': 'left'}),
])
#ANOVA table
table_header = [
html.Thead(
html.Tr([
html.Th(""),
html.Th("df"),
html.Th("sum_sq"),
html.Th("mean_sq"),
html.Th("F"),
html.Th("PR(>F)")
]))
]
row1 = html.Tr([
html.Td("C(type)"),
html.Td("2.0"),
html.Td("632.84"),
html.Td("316.42"),
html.Td("262.28"),
html.Td("8.391599e-71"),
])
row2 = html.Tr([
html.Td("Residual"),
html.Td("357.0"),
row = html.Tr([
html.Td(
dbc.Checkbox(id='standalone-checkbox-'+elem.replace(' ', '-'))
),
html.Td(
elem
),
html.Td(
dbc.Badge(
'__',#elem,
color=elements[elem]['color']
)
),
html.Td(
dbc.Input(
id='z-'+elem.replace(' ', '-'),
value=0,
type='number',
min=0,
max=10,
step=0.0000001,
placeholder='z'
)
),
html.Td(
dbc.Input(
id='v-'+elem.replace(' ', '-'),
type='number',
placeholder='Velocity (km/s)',
value=0
)
)
])
def change_redshift(z, *args, **kwargs):
elem_input_array = []
for elem in list(elements.keys())[:9]:
row = html.Tr([
html.Td(
dbc.Checkbox(id='standalone-checkbox-'+elem.replace(' ', '-'))
),
html.Td(
elem
),
html.Td(
dbc.Badge(
'__',#elem,
color=elements[elem]['color']
)
),
html.Td(
dbc.Input(
id='z-'+elem.replace(' ', '-'),
value=z,
type='number',
min=0,
max=10,
step=0.0000001,
placeholder='z'
)
),
html.Td(
dbc.Input(
id='v-'+elem.replace(' ', '-'),
type='number',
placeholder='Velocity (km/s)',
value=0
)
)
])
elem_input_array.append(row)
table_body_one = [html.Tbody(elem_input_array)]
elem_input_array = []
for elem in list(elements.keys())[9:]:
row = html.Tr([
html.Td(
dbc.Checkbox(id='standalone-checkbox-'+elem.replace(' ', '-'))
),
html.Td(
elem
),
html.Td(
dbc.Badge(
'__',#elem,
color=elements[elem]['color']
)
),
html.Td(
dbc.Input(
id='z-'+elem.replace(' ', '-'),
value=z,
type='number',
min=0,
max=10,
step=0.0000001,
placeholder='z'
)
),
html.Td(
dbc.Input(
id='v-'+elem.replace(' ', '-'),
type='number',
placeholder='Velocity (km/s)',
value=0
)
)
])
elem_input_array.append(row)
table_body_two = [html.Tbody(elem_input_array)]
return [dbc.Row([
dbc.Table(
html.Tbody([
html.Tr([
html.Td(
dbc.Table(table_body_one, bordered=True),
),
html.Td(
dbc.Table(table_body_two, bordered=True),
)
]),
])
)
])
]
def generate_table(dataframe):
"""Generate dash table for pandas dataframe.
Parameters
----------
dataframe: pandas.DataFrame
Returns
-------
table: html.Table
"""
rows = []
cols_to_retain = [
"experiment_accession",
"experiment_title",
"run_accession",
"ribotricer_orfs",
"ribotricer_metagene_5p",
"ribotricer_metagene_3p",
"ribotricer_metagene_plot",
"ribotricer_protocol",
"ribotricer_bam_summary",
]
dataframe = dataframe.loc[:, cols_to_retain]
for i in range(len(dataframe)):
row = []
for col in dataframe.columns:
value = dataframe.iloc[i][col]
if (col in [
"ribotricer_orfs",
"ribotricer_metagene_5p",
"ribotricer_metagene_3p",
"ribotricer_metagene_plot",
"ribotricer_protocol",
"ribotricer_bam_summary",
] and str(value) != "nan"):
try:
cell = html.Td(
html.A(
href="/download?value=" + value,
children=[html.I(className="fa fa-download")],
),
style={"text-align": "center"},
)
except:
print(value)
else:
cell = html.Td(children=value)
row.append(cell)
rows.append(html.Tr(row))
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] + rows,
style={
"width": "100%",
"margin-right": "auto",
"margin-left": "10%",
"align": "center",
"display": "inline-block",
},
)
4: 'Life and earth sciences',
5: 'Mathematics and computer sciense'
}
## load papers
df_tiab = pd.read_excel(f'{data_dir}/待判别62clusters.xlsx',
sheet_name='主要论文与关键词')
target_clusters = list(df_tiab['micro_level_field_id'].unique())
df_ins = pd.read_excel(f'{data_dir}/待判别62clusters.xlsx', sheet_name='主要机构')
# formulate marker text
df_map['journals'] = df_map['journals'].apply(lambda x: x.split(';'))
df_map['keywords'] = df_map['keywords'].apply(lambda x: x.split(';'))
df_map['journals_tab'] = df_map['journals'].apply(
lambda x: [html.Tr(f"{x[i].strip()}") for i in range(len(x))])
df_map['keywords_tab'] = df_map['keywords'].apply(
lambda x: [html.Tr(f"{x[i].strip()}") for i in range(len(x))])
df_map['idx'] = df_map['id']
df_map.set_index('idx', inplace=True)
df_map = df_map[df_map['id'].isin(target_clusters)]
def text(row):
journals = ["代表性期刊："] + [f"{jour}" for jour in row['journals']] + ['', '']
terms = ["代表性关键词："] + [f"{term}" for term in row['keywords']]
#y = [f'N:{row["N"]}',f'CSI:{row["CSI"]}',f'IF:{row["IF"]}',y]
y = [f'主题编号：{row["label"]}', ''] + journals + terms
y = '<br>'.join(y)
return y
def generate_table():
global MH01_rules
global MH02_rules
global MH03_rules
global rule_broken
global rule_broken_index
global count, time, maxval
global groupm
global machlist
global groupl
global DispRules
MH01_rules = list(MH01.columns[MH01.columns.str.contains('rule')].values)
MH02_rules = list(MH02.columns[MH02.columns.str.contains('rule')].values)
MH03_rules = list(MH03.columns[MH03.columns.str.contains('rule')].values)
rule_broken = breakdetect()
rule_broken_index = pd.to_numeric(prov_index(rule_broken))
count, time, maxval = Timegen()
DispRules = pd.DataFrame(Rules.iloc[rule_broken_index, :])
DispRules.drop(labels=['Value'], axis=1, inplace=True)
DispRules = DispRules.assign(Count=count, Last_Occurence=time, Max=maxval)
DispRules.sort_values(['Important', 'Count'],
ascending=False,
kind='mergesort',
inplace=True)
groupm = list(DispRules.groupby('Machine'))
machlist = list()
groupl = list()
for i in range(len(groupm)):
machlist.append(groupm[i][0])
groupl.append(len(groupm[i][1]))
return html.Table(
# Header
[
html.Tr([
html.Th(col,
style={
'textAlign': 'Center',
'fontFamily': 'Arial',
'fontSize': '16',
'color': 'rgb(88, 88, 101)'
}) for col in
DispRules.columns[~DispRules.columns.str.contains('Important')]
])
] +
# Body
[
html.Tr([
html.Td(DispRules.iloc[i][col],
style={
'textAlign': 'Center',
'fontFamily': 'Arial',
'fontSize': '12',
'color': coldec(i),
'fontWeight': '800'
}) for col in
DispRules.columns[~DispRules.columns.str.contains('Important')]
]) for i in range(len(DispRules))
],
style={
'marginLeft': '10',
'backgroundColor': 'rgb(230,230,210)',
'paddingTop': '15',
'paddingRight': '10',
'paddingLeft': '3',
'border-radius': '15px',
'border': 'solid',
'borderColor': 'rgb(220,220,200)'
})
def df_to_table(df):
return html.Table([html.Tr([html.Th(col) for col in df.columns])] + [
html.Tr([html.Td(df.iloc[i][col]) for col in df.columns])
for i in range(len(df))
])
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
# The memory store reverts to the default on every page refresh
dcc.Store(id='memory'),
# The local store will take the initial data
# only the first time the page is loaded
# and keep it until it is cleared.
dcc.Store(id='local', storage_type='local'),
# Same as the local store but will lose the data
# when the browser/tab closes.
dcc.Store(id='session', storage_type='session'),
html.Table([
html.Thead([
html.Tr(html.Th('Click to store in:', colSpan="3")),
html.Tr([
html.Th(html.Button('memory', id='memory-button')),
html.Th(html.Button('localStorage', id='local-button')),
html.Th(html.Button('sessionStorage', id='session-button'))
]),
html.Tr([
html.Th('Memory clicks'),
html.Th('Local clicks'),
html.Th('Session clicks')
])
]),
html.Tbody([
html.Tr([
html.Td(0, id='memory-clicks'),
html.Td(0, id='local-clicks'),
import dash
import dash_html_components as html
import dash_bootstrap_components as dbc
app = dash.Dash(__name__)
app.layout = html.Div(
dbc.Container(
dbc.Table([
html.Thead(html.Tr([html.Th('字段1'), html.Th('字段2')])),
html.Tbody([
html.Tr([html.Th('1'), html.Td('test')]),
html.Tr([html.Th('2'), html.Td('test')]),
html.Tr([html.Td('3'), html.Td('test')])
])
],
striped=True),
style={
'margin-top': '50px' # 设置顶部留白区域高度
}))
if __name__ == '__main__':
app.run_server(debug=True)
def get_data_list(data):
contents = []
for title, value in data.items():
contents.append(html.Tr([html.Td(Label(title)), html.Td(value)]))
return html.Table([html.Tbody(contents)], className="table")
# url_base_pathname='/stdhd/',
# requests_pathname_prefix='/stdcheck/',
external_stylesheets=external_stylesheets)
# auth = dash_auth.BasicAuth(
# app,
# VALID_USERNAME_PASSWORD_PAIRS
# )
app.layout = html.Div([
html.H2('Welcome to DUSKASH'),
html.H6('This site provides toolkits to plot charts. You only need to upload specific format excel/csv. And you will get 2d/3d chart after several mouse clicking.'),
html.Div([
html.Table([
html.Tr([
html.Th('Tools'),
html.Th('Description'),
html.Th('Version')]
),
html.Tr([ # row of s2d
html.Td(
html.A('S2d', href='/s2d/'),
),
html.Td('After upload the dataset, it returns a 2d scatter chart. No classification fuction, but you can plot multiple features on the screen'
),
html.Td(
'1.2'
)], className='active'
),
html.Tr([ # row of m2d
html.Td(
html.A('M2d', href='/m2d/'),
def get_table(rows):
contents = []
for row in rows:
contents.append(html.Tr([html.Td(item) for item in row]))
return html.Table([html.Tbody(contents)], className="table")
def fubukiSolver(n_clicks, position, posValue, r1, r2, r3, c1, c2, c3):
'''
Function to solve fubuki puzzle by checking prefilled values with the
combined values of each permutations. Brute-force approach.
@param n_clicks the click amount for Button that when user hits the button, @callback is fired.
@param position the position on the puzzle user want to set value
@param posValu the value for the defined position
@param r1-r3 R1 through R3 values defined by user
@param c1-c3 C1 through C3 values defined by user
'''
resOut = []
n = 0 #to count number of solutions
myList = np.arange(1, 10, dtype=int) #Create a list from 1 - 9 of type int
newList = np.delete(myList, np.where(
myList ==
posValue)) #delete the prefilled value and its position given by user
permList = permutations(
newList) #to calculate all permutations for 9 numbers
myPrefilledVals = np.array(
[r1, r2, r3, c1, c2,
c3]) #put all the user-filled values into an array
#iterate through all permutations to make comparisons
for orderList in permList:
orderList = np.insert(orderList, position, posValue)
row1 = sum(orderList[0:3])
row2 = sum(orderList[3:6])
row3 = sum(orderList[6:9])
col1 = orderList[0] + orderList[3] + orderList[6]
col2 = orderList[1] + orderList[4] + orderList[7]
col3 = orderList[2] + orderList[5] + orderList[8]
#put all combinations into an array
solution = np.array([row1, row2, row3, col1, col2, col3])
'''Condition to check if solution matches the prefilledVals
If it does, n increments and resOut append its solution'''
if (solution == myPrefilledVals).all():
n = n + 1
resOut.append(orderList[0:3])
resOut.append(orderList[3:6])
resOut.append(orderList[6:9])
myResults = np.array(resOut)
##Create a pandas DataFrame to be input into html.Table() later on
dataframe = pd.DataFrame(
myResults,
columns=['C1', 'C2', 'C3'],
index=['R{0}'.format(i) for i in range(1,
len(resOut) + 1)])
'''To check: if there are solutions, then table will be generated,
Otherwise, return there is no result'''
if n > 0:
for i in range(n):
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] +
# Body
[
html.Tr([
html.Td(dataframe.iloc[i][col])
for col in dataframe.columns
]) for i in range(min(len(dataframe), len(dataframe)))
])
else:
return 'There is no solution'
def test_multi_output(self):
app = dash.Dash(__name__)
app.scripts.config.serve_locally = True
app.layout = html.Div([
html.Button('OUTPUT', id='output-btn'),
html.Table([
html.Thead(
[html.Tr([html.Th('Output 1'),
html.Th('Output 2')])]),
html.Tbody([
html.Tr([html.Td(id='output1'),
html.Td(id='output2')]),
])
]),
html.Div(id='output3'),
html.Div(id='output4'),
html.Div(id='output5')
])
@app.callback(
[Output('output1', 'children'),
Output('output2', 'children')], [Input('output-btn', 'n_clicks')],
[State('output-btn', 'n_clicks_timestamp')])
def on_click(n_clicks, n_clicks_timestamp):
if n_clicks is None:
raise PreventUpdate
return n_clicks, n_clicks_timestamp
# Dummy callback for DuplicateCallbackOutput test.
@app.callback(Output('output3', 'children'),
[Input('output-btn', 'n_clicks')])
def dummy_callback(n_clicks):
if n_clicks is None:
raise PreventUpdate
return 'Output 3: {}'.format(n_clicks)
# Test that a multi output can't be included in a single output
with self.assertRaises(DuplicateCallbackOutput) as context:
@app.callback(Output('output1', 'children'),
[Input('output-btn', 'n_clicks')])
def on_click_duplicate(n_clicks):
if n_clicks is None:
raise PreventUpdate
return 'something else'
self.assertTrue('output1' in context.exception.args[0])
# Test a multi output cannot contain a used single output
with self.assertRaises(DuplicateCallbackOutput) as context:
@app.callback(
[Output('output3', 'children'),
Output('output4', 'children')],
[Input('output-btn', 'n_clicks')])
def on_click_duplicate_multi(n_clicks):
if n_clicks is None:
raise PreventUpdate
return 'something else'
self.assertTrue('output3' in context.exception.args[0])
with self.assertRaises(DuplicateCallbackOutput) as context:
@app.callback(
[Output('output5', 'children'),
Output('output5', 'children')],
[Input('output-btn', 'n_clicks')])
def on_click_same_output(n_clicks):
return n_clicks
self.assertTrue('output5' in context.exception.args[0])
with self.assertRaises(DuplicateCallbackOutput) as context:
@app.callback(
[Output('output1', 'children'),
Output('output5', 'children')],
[Input('output-btn', 'n_clicks')])
def overlapping_multi_output(n_clicks):
return n_clicks
self.assertTrue(
'{\'output1.children\'}' in context.exception.args[0]
or "set(['output1.children'])" in context.exception.args[0])
self.startServer(app)
t = time.time()
btn = self.wait_for_element_by_id('output-btn')
btn.click()
time.sleep(1)
self.wait_for_text_to_equal('#output1', '1')
output2 = self.wait_for_element_by_css_selector('#output2')
self.assertGreater(int(output2.text), t)
def update_modal(data, year):
# if black frame of treemap, don't update
if data is None:
return dash.no_update
# if no currentpath or country is chosen, don't update
elif data['points'][0].get('currentPath') is None:
return dash.no_update
elif data['points'][0]['currentPath'] == '/':
if data['points'][0].get('label') is None:
return dash.no_update
else:
label_slct = data['points'][0]['label']
if year == '2018':
dff = df[df["Country"] == label_slct]
if year == '2019':
dff = df19[df19["Country"] == label_slct]
extract_country_index = dff[dff['Country'] ==
label_slct].index.values.tolist()
borders = [
1 if i in extract_country_index else 4 for i in range(len(dff))
]
# build map
fig = px.choropleth(dff,
locations="iso_alpha",
hover_name="Country",
scope='africa',
projection='natural earth').update_traces(
marker_line_width=borders,
showlegend=False).update_layout(
margin=dict(t=25, r=0, l=5, b=20))
# create the table
table_header = [html.Thead(html.Tr([html.Th(label_slct)]))]
rows = []
for x in dff["notes"]:
if pd.isnull(x):
continue
rows.append(html.Tr([html.Td(x)]))
table_body = [html.Tbody(rows)]
return fig, dbc.Table(table_header + table_body, bordered=True)
# if axis of intervention is chosen (country is parent), don't update
elif data['points'][0]['parent'] in df.Country.unique():
label_slct = data['points'][0]['label']
parent_slct = data['points'][0]['parent']
if year == '2018':
dff = df[(df["Axis of intervention"] == label_slct)
& (df["Country"] == parent_slct)]
if year == '2019':
dff = df19[(df19["Axis of intervention"] == label_slct)
& (df19["Country"] == parent_slct)]
# make selected countries' borders on map thicker
extract_country_index = dff[dff['Country'] ==
parent_slct].index.values.tolist()
borders = [
1 if i in extract_country_index else 4 for i in range(len(dff))
]
# build map
fig = px.choropleth(dff,
locations="iso_alpha",
hover_name="Country",
scope='africa',
projection='natural earth').update_traces(
marker_line_width=borders,
showlegend=False).update_layout(
margin=dict(t=25, r=0, l=5, b=20))
# create the table
table_header = [html.Thead(html.Tr([html.Th(label_slct)]))]
rows = []
for x in dff["notes"]:
if pd.isnull(x):
continue
rows.append(html.Tr([html.Td(x)]))
table_body = [html.Tbody(rows)]
return fig, dbc.Table(table_header + table_body, bordered=True)
# if Region is chosen, build table
else:
label_slct = data['points'][0]['label']
parent_slct = data['points'][0]['parent']
print(label_slct), print(parent_slct), print(data)
if year == '2018':
dff = df[(df.Region == label_slct)
& (df["Axis of intervention"] == parent_slct)]
if year == '2019':
dff = df19[(df19.Region == label_slct)
& (df19["Axis of intervention"] == parent_slct)]
# make selected countries' borders on map thicker
extract_country = data['points'][0]['currentPath']
extract_country = extract_country.split('/')[1]
extract_country_index = dff[dff['Country'] ==
extract_country].index.values.tolist()
borders = [
1 if i in extract_country_index else 4 for i in range(len(dff))
]
# build map
fig = px.choropleth(dff,
locations="iso_alpha",
hover_name="Country",
scope='africa',
projection='natural earth').update_traces(
marker_line_width=borders,
showlegend=False).update_layout(
margin=dict(t=25, r=0, l=5, b=20))
# create the table
table_header = [html.Thead(html.Tr([html.Th(label_slct)]))]
rows = []
for x in dff["notes"]:
if pd.isnull(x):
continue
rows.append(html.Tr([html.Td(x)]))
table_body = [html.Tbody(rows)]
return fig, dbc.Table(table_header + table_body, bordered=True)
style=tab_style,
selected_style=tab_selected_style),
dcc.Tab(id='cat2',
label='Financial Markets',
value='cat-2',
style=tab_style,
selected_style=tab_selected_style),
]),
## displayed below category tabs ##
html.Div(id='categories-content',
children=[
html.Table(
style={'border-spacing': 7},
children=[
html.Tr(id='radio-label',
children='Radio:'),
dcc.RadioItems(id='radio'),
html.Tr([
html.Div(
id='dropdown1_label',
children="Dropdown1:"),
dcc.Dropdown(id='dropdown1'),
]),
html.Tr([
html.Div(
id='dropdown2_label',
children="Dropdown2:"),
dcc.Dropdown(id='dropdown2'),
]),
]),
dcc.Markdown(markdown_paragraph),
def html_status_tables():
# Process scanning
ps_config = CONFIG["ps"]
# Add this process into the
ptable_header = [
html.Tr([
html.Th(label, className=table_header_style) for label in [
"Process Group", "Running?", "PIDs", "Description", "Ports",
"CPU"
]
])
]
ptable_rows = []
for pname, pinfo in ps_config.items():
pq = pinfo["query"]
plist = ps_query(pq)
if plist:
running = True
pids_formatted = ",".join(str(p.pid) for p in plist)
plist_cpu = sum([p.cpu_percent(interval=interval)
for p in plist]) / psutil.cpu_count()
plist_cpu = f"{plist_cpu}%"
else:
if pname == "dsmt_app":
running = True
pids_formatted = os.getpid()
plist_cpu = psutil.Process(pids_formatted).cpu_percent(
interval=interval) / psutil.cpu_count()
plist_cpu = f"{plist_cpu}%"
else:
running = False
pids_formatted = "null"
plist_cpu = "null"
ptable_rows.append(
html.Tr([
html.Td(pname, className=monospace_style),
html.Td(check_img if running else x_img),
html.Td(pids_formatted),
html.Td(pinfo['description']),
html.Td(",".join([str(port) for port in pinfo["ports"]])),
html.Td(plist_cpu)
]))
proc_table = html.Table(ptable_header + ptable_rows, className=table_style)
proc_div = html.Div(
[html.Div("Processes", className=header_style), proc_table],
className=box_style)
# Docker with default configuration running locally
if CONFIG["docker"]:
dtable_header = [
html.Tr([
html.Th(label, className=table_header_style) for label in
["Container Name", "Running?", "Status", "Image", "Ports"]
])
]
dtable_rows = []
client = docker.from_env()
for c in client.containers.list(all=True):
running = True if c.status == "running" else False
if c.ports:
ports = []
for cport, hportset in c.ports.items():
hport = hportset[0]["HostPort"] if hportset else "null"
ports.append(f"{cport} (container) -> {hport}")
ports_html = html.Div([html.Div(port) for port in ports])
else:
ports_html = "null"
dtable_rows.append(
html.Tr([
html.Td(c.name, className=monospace_style),
html.Td(check_img if running else x_img),
html.Td(c.status),
html.Td(c.image.attrs["RepoTags"][0]),
html.Td(ports_html),
]))
docker_table = html.Table(dtable_header + dtable_rows,
className=table_style)
docker_div = html.Div([
html.Div("Docker containers", className=header_style), docker_table
],
className=box_style)
else:
docker_div = html.Div(children='')
# SystemD services
stable_header = [
html.Tr([
html.Th(label, className=table_header_style) for label in [
"Service name", "Running?", "Main PID", "Description", "Ports",
"CPU"
]
])
]
stable_rows = []
for sname, sinfo in CONFIG["systemd"].items():
squery = sinfo["query"]
info = os.popen(f'systemctl status {squery}').read()
infolist = [i.strip() for i in info.split("\n")]
desc_dirty = infolist[0]
status_dirty = [i for i in infolist if "Active: " in i][0]
running = True if "(running) since" in status_dirty else False
if running:
pid_dirty = [i for i in infolist
if "Main PID:" in i][0].replace("Main PID: ", "")
pid_clean = int(pid_dirty.split(" ")[0].strip())
else:
pid_dirty = "null"
pid_clean = "null"
try:
cpu_usage = psutil.Process(pid_clean).cpu_percent(
interval=interval)
cpu_usage = f"{cpu_usage}%"
except:
cpu_usage = "N/A"
stable_rows.append(
html.Tr([
html.Td(sname, className=monospace_style),
html.Td(check_img if running else x_img),
html.Td(pid_dirty),
html.Td(f"{sinfo['description']} ({desc_dirty})"),
html.Td(", ".join([str(port) for port in sinfo["ports"]])),
html.Td(cpu_usage)
]))
systemd_table = html.Table(stable_header + stable_rows,
className=table_style)
systemd_div = html.Div(
[html.Div("SystemD services", className=header_style), systemd_table],
className=box_style)
return html.Div(
id="display",
children=[proc_div, divider, docker_div, divider, systemd_div])
className='color-text'),
dcc.Tab(label='Compare the models',
value='compare-models',
className='color-text')
]),
html.Div(id='regression-layout')
]
about_the_author_layout = html.Table([
html.Tbody([
html.Tr([
html.Td(html.Div(html.Img(src=app.get_asset_url('photo.jpeg'),
className='photo'),
className='photo-div'),
rowSpan=3),
html.Td(
html.Table([
html.Tr(html.Td(html.H3('Inês Andrade Rainho'))),
html.Tr(html.Td(html.H4('IST Student Number: 90396'))),
html.Tr(html.Td(html.H4('*****@*****.**')))
]))
])
])
])
# -- END
# callbacks
# callback for the button with dropdown menu
@app.callback(Output('dropdown_menu', 'children'),
Input('button_dropdown', 'n_clicks'))
def toggle_dropdown(n_clicks):
def createTierMappingMenus(eafFilename):
print("=== createTierMappingMenus: %s [exists: %s]" %
(eafFilename, os.path.exists(eafFilename)))
dropDownMenus = html.H5("☠️ failure in extracting tierIDs from %s" %
eafFilename)
if (os.path.exists(eafFilename)):
tmpDoc = etree.parse(eafFilename)
userProvidedTierNamesToAssignToStandardTiers = [
tier.attrib["TIER_ID"] for tier in tmpDoc.findall("TIER")
]
print(userProvidedTierNamesToAssignToStandardTiers)
tierChoices = userProvidedTierNamesToAssignToStandardTiers
dropDownMenus = html.Table(
id="tierMappingMenus",
children=[
html.Tr([
html.Th("Standard interlinear tiers", className="first"),
html.Th("(e.g., from Totonac)", className="second"),
html.Th("Select ELAN tier", className="third")
]),
html.Tr([
html.Td(children=[
html.Div("line", style={'display': 'inline-block'}),
html.Div("*",
style={
'display': 'inline-block',
'color': 'red'
})
]),
html.Td("tanhe:x’a'ha:ma:lhtzá'"),
html.Td(createPulldownMenu("speech", tierChoices))
]),
html.Tr([
html.Td("alternate transcription"),
html.Td("taŋʔeːš’a̰ʔaːmaːɬtsá̰"),
html.Td(createPulldownMenu("transcription2", tierChoices))
]),
html.Tr([
html.Td("morphological analysis"),
html.Td("taŋʔeː–š’a̰ʔáː–maːɬ=tsá̰"),
html.Td(createPulldownMenu("morpheme", tierChoices))
]),
html.Tr([
html.Td("morpheme glosses"),
html.Td(children=[
html.Div("basin–", style={'display': 'inline-block'}),
html.Div("shine", style={'display': 'inline-block'}),
html.Div("–prog",
style={
'font-variant': 'small-caps',
'display': 'inline-block'
}),
html.Div("=now",
style={
'font-variant': 'small-caps',
'display': 'inline-block'
})
]),
html.Td(createPulldownMenu("morphemeGloss", tierChoices))
]),
html.Tr([
html.Td(children=[
html.Div("translation",
style={'display': 'inline-block'}),
html.Div("*",
style={
'display': 'inline-block',
'color': 'red'
})
]),
html.Td("‘The horizon is growing light.’"),
html.Td(createPulldownMenu("translation", tierChoices))
]),
html.Tr([
html.Td("second translation"),
html.Td("‘Está aclarando donde sale el sol.’"),
html.Td(createPulldownMenu("translation2", tierChoices))
])
],
className="tiermap")
saveTierMappingChoicesButton = html.Button(
'save', id='saveTierMappingSelectionsButton', className="button")
tierMappingChoicesResultDisplay = html.Span(
id="tierMappingChoicesResultDisplay",
children="",
style={
"border": 1,
"margin-left": 10
})
requiredTiersFootnote = html.Span("*Required",
id='requiredTiersFootnote',
className="warningfootnote")
children = [
dropDownMenus,
html.Br(), saveTierMappingChoicesButton,
tierMappingChoicesResultDisplay, requiredTiersFootnote
]
enclosingDiv = html.Div(children=children)
return (enclosingDiv)
# Begin of First Tr
html.Tr(
#Begin Tr children
children=[
# Begin Th
html.Th(style={'width':'30%'},
# Begin Th children
children=[
html.H3('the graph')
# End of Th children
]
# End of Th - Notice a comma is placed here to separate the next Th
),
# Begin of Th
html.Th(style={'width':'70%'},
# Begin of Th children
children=[
html.H3('correlation')
# End of Th children
]
# End of Th
)
# End of Tr children
]
# End of First Tr - Notice a comma is placed here to separate the next Tr
),
#########################################################################
player_df = player_df.sort_values(by=['last'])
weeks = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
players = player_df['name'].values.tolist()
style = {'padding': '1em'}
layout = html.Div([
html.Table(
[
html.Col(style={'width': '400px'}),
html.Col(style={'width': '100px'}),
html.Col(style={'width': '400px'}),
html.Thead(),
# Row 1 - Header
html.Tr(),
html.Th(dcc.Markdown("""
#### Select a Week
"""),
colSpan='3'),
html.Tbody(),
# Row 2 - Week
html.Tr(),
html.Td(),
html.Td(
dcc.Dropdown(id='week_no',
options=[{
'label': week,
'value': week
} for week in weeks],
value=1,
html.Tr([
html.Td([
html.Div(
id='intro',
className="section",
children=[
html.Table([
html.Tr(
[
html.Td([
html.Label("Search"),
]),
html.Td(
[html.Label("Gene:")],
style={'text-align': 'right'}),
html.Td([
dcc.Input(
type='text',
placeholder="E.g.: DDR1",
spellCheck='false',
size='20',
id='sel-gene',
),
]),
#
html.Td(
[
html.Label("Dataset:"),
],
style={'text-align': 'right'}),
html.Td(
[
selector('Dataset',
id='sel-dataset'),
# Riccardo I need to change the width of dataset selector
],
style={'width': '60px'}),
#
html.Td(
[
html.Label('Keyword'),
],
style={'text-align': 'right'}),
html.Td([
dcc.Input(
type='text',
placeholder=
"E.g.: cell cycle",
spellCheck='false',
size='20',
id='sel-functions',
),
])
#html.Button('Search', id='searchBtn', n_clicks_timestamp=0),
], ),
#]
]),
]),
html.Tr([
# html.Div(
# id='report',
# className="container section",
# children=[
# html.P("""
# Click on the small arrows besides column names to sort by that column.
# """),
html.Div(
id='table-container',
children=[table()],
),
# ]
# ),
]),
])
])
# dictionary comprehension to shorten state abbreviation
shorthand = {x: y for x, y in zip(states, usa)}
shorthand[''] = ''
# Dash Web Layout Creator
app = dash.Dash(__name__)
# Browser tab title
app.title = "TESTING"
# Start of Dash HTML Loading
app.layout = html.Div([
# Description of template table
# HTML Table tag
html.Table(
# TR = Table Row, creates title that spans two columns
[html.Tr(html.Th("HELP", colSpan='2'))] +
# TR for each variable
[
html.Tr([
html.Td("Increment"),
html.Td(("Count of sizes to buy. MUST BE INCLUDED."))
])
] + [
html.Tr([
html.Td("Size"),
html.Td(("Dollar Amount of bonds to buy (min $10k)."
" MUST BE INCLUDED."))
])
] + [
html.Tr([
html.Td("General Market"),
Enter a host to view its open ports
'''),
html.Br(),
dcc.Input(
placeholder='Enter a host...',
type='text',
value='',
id='port_host_input'
),
html.Button(id='port_search_button_state', n_clicks=0, children='Search'),
#define the port table
html.Table(id='port_data', children =[
html.Tr(),
html.Th(children='Port Number'),
html.Th(children='Service')
]
),
html.Div(children=' Geolocation', style ={'textAlign': 'center',
'color': '#3A2BDA'}),
html.Div([
dcc.Graph(figure=fig1)
]),
# send ip address to the map
html.Div([
