def kanban_list(
_value, _status
): # _value is(urgent,imp,normal) and _status is (to do,progress,done)
return dbc.ListGroup([
dbc.ListGroupItem(children=[
task_card(i, _value, _status) for i in range(len(df['to_do_task']))
])
])
def uploaded_dataset_list(self, *datasets):
#
# TODO: active can be set to True for highligthing selected File (however for the moment we limit to one
# component)
# TODO: write a callback for each entry
#
all_datasets = [dbc.ListGroup(self.dataset_list(*datasets))]
return all_datasets
def twitter_feed(state=None) -> List[dbc.Card]:
"""Displays twitter feed on the left hand side of the display.
TODO: Add callbacks based on state
:params state: display twitter feed for a particular state. If None, display twitter feed
for the whole US.
:return cards: A list of dash boostrap Card components, where each card contains tweets for twitter feed.
:rtype: list
"""
response = requests.get(NCOV19_API + "twitter").json()
if response["success"] == True:
data = response["message"]
username = data["username"]
full_name = data["full_name"]
tweets = data["tweets"]
else:
username = "******"
full_name = "John Cena"
tweets = [{
"tweet_id": "0",
"full_text": "John Cena to Corona Virus : You Can't See Me !",
"created_at": "2020-03-25T22:05:24",
}]
# 2020-03-19 triage. lots of empty list at the end of tweets, filtering them out
# tweet["full_text"][:100]
cards = [
dbc.ListGroupItem(
[
html.A(
html.P(
tweet["full_text"][:100] + "...",
className="tweet-text",
),
href=
f"https://twitter.com/{username}/status/{tweet['tweet_id']}",
target="_blank",
),
html.P(
[
# html.Strong(f"- {full_name} (@{username})"),
html.P(
f"- @{username} {parse(tweet['created_at']).strftime('%a %d, %Y at %I: %M %p')}",
className="tweet-dt",
),
],
className="tweets-txt-by-dt",
),
],
className="tweet-item",
) for tweet in tweets
]
list_group = dbc.ListGroup(cards, flush=True)
return list_group
def states_deaths_stats(state="US") -> dbc.ListGroup:
"""
:params state: display news feed for a particular state. If None, display news feed
for the whole US
:return list_group: A bootstramp ListGroup containing ListGroupItem returns news feeds.
:rtype: dbc.ListGroup
"""
try:
URL = config.NCOV19_API + config.COUNTY
response = requests.get(URL).json()
data = response["message"]
data = pd.DataFrame.from_records(data)
data["state_name"] = data["state_name"].str.title()
if state == "US":
deaths = data.groupby(["state_name"])["deaths"].sum()
deaths = confirmed.sort_values(ascending=False).to_dict()
else:
deaths = data[data["state_name"] == state]
confirmed = df[["County Name", "Confirmed"]]
confirmed = dict(
confirmed.sort_values(by="Confirmed",
ascending=False).to_records(index=False))
del response, data
except:
print("[ERROR] states_confirmed_stats error accessing ncov19.us API")
list_group = dbc.ListGroup(
[
dbc.ListGroupItem(
[
html.P(
[
html.Span(
f"{value:,d}",
className="states-stats-deaths-list-num",
),
html.Span(
f" {key}",
className="states-stats-deaths-list-state",
),
],
className="states-stats-deaths-list-txt",
),
],
id=f"states-death-{key}",
className="states-stats-deaths-list-item",
) for key, value in death.items()
],
flush=True,
className="states-stats-death-listgroup",
)
return list_group
def news_feed(state="US") -> dbc.ListGroup:
"""Displays news feed on the right hand side of the display. Adjust the NewsAPI time
time to Eastern Time (w/ DST).
:params state: display news feed for a particular state. If None, display news feed
for the whole US
:return list_group: A bootstramp ListGroup containing ListGroupItem returns news feeds.
:rtype: dbc.ListGroup
"""
URL = config.NCOV19_API + config.NEWS
if state == "US":
response = requests.get(URL)
else:
payload = {"state": state, "topic": "coronavirus"}
payload = json.dumps(payload)
response = requests.post(config.NCOV19_API + config.NEWS, data=payload)
if response.status_code == 200:
json_data = response.json()
json_data = json_data["message"]
df = pd.DataFrame.from_records(json_data)
df = pd.DataFrame(df[["title", "url", "published"]])
max_rows = 50
list_group = dbc.ListGroup(
[
dbc.ListGroupItem(
[
html.Div(
[
html.H6(
f"{df.iloc[i]['title'].split(' - ')[0]}.",
className="news-txt-headline",
),
html.P(
f"{df.iloc[i]['title'].split(' - ')[1]} {df.iloc[i]['published']}",
className="news-txt-by-dt",
),
],
className="news-item-container",
)
],
className="news-item",
href=df.iloc[i]["url"],
target="_blank",
) for i in range(min(len(df), max_rows))
],
flush=True,
)
del response, json_data, df
gc.collect()
else:
list_group = []
return list_group
def twitter_feed(state=None) -> List[dbc.Card]:
"""Displays twitter feed on the left hand side of the display.
TODO:
TODO: Add callbacks based on state
:params state: display twitter feed for a particular state. If None, display twitter feed
for the whole US.
:return cards: A list of dash boostrap Card components, where each cahrd contains tweets for twitter feed.
:rtype: list
"""
if state is None:
doc = tm.get_tweet_by_state("US")
cards = [
# dbc.Card(
# dbc.CardHeader([html.I(className="fab fa-twitter mr-1"), "Twitter Feed"])
# )
]
username = doc["username"]
full_name = doc["full_name"]
tweets = doc["tweets"]
# 2020-03-19 triage. lots of empty list at the end of tweets, filtering them out
tweets = [*filter(None, tweets)]
tweets = sorted(tweets, key=lambda i: i["created_at"], reverse=True)
# print(tweets)
cards += [
# dbc.Card(
# dbc.CardBody(
dbc.ListGroupItem(
[
html.A(
html.P(tweet["full_text"][:100] + "...", ),
href=
f"https://twitter.com/{username}/status/{tweet['tweet_id']}",
target="_blank",
),
html.P(
[
html.Strong(f"- {full_name} (@{username})"),
html.P(
f"{tweet['created_at'].strftime('%a %d, %Y at %I: %M %p')}",
className="tweet-dt",
),
],
className="tweets-txt-by-dt",
),
],
className="tweet-item",
# ),
) for tweet in tweets
]
list_group = dbc.ListGroup(cards, flush=True)
# return cards
return list_group
def news_feed(state=None) -> dbc.ListGroup:
"""Displays news feed on the right hand side of the display. Adjust the NewsAPI time
time to Eastern Time (w/ DST).
TODO: Add callbacks to fetch local state news, if none get entire US news
:params state: display news feed for a particular state. If None, display news feed
for the whole US
:return list_group: A bootstramp ListGroup containing ListGroupItem returns news feeds.
:rtype: dbc.ListGroup
"""
news_requests = requests.get(NEWS_API_URL)
json_data = news_requests.json()["articles"]
df = pd.DataFrame(json_data)
df = pd.DataFrame(df[["title", "url", "publishedAt"]])
# Infer datetime
df["publishedAt"] = pd.to_datetime(df["publishedAt"],
infer_datetime_format=True)
# Assuming timedelta of 5 hours based on what i compared from CNN articles from API.
df["publishedAt"] = df["publishedAt"] - pd.Timedelta("5 hours")
"""
# Format date time way you want to display, https://strftime.org/
"""
def dt_fmt(val):
return val.strftime("%a %d, %Y, %I: %M %p ET")
# Apply pandas function to format news published date
df["publishedAt"] = df["publishedAt"].apply(dt_fmt)
max_rows = 50
list_group = dbc.ListGroup(
[
# dbc.Card(
# dbc.CardHeader([html.I(className="fas fa-newspaper mr-1"), "News Feed"])
# )
] + [
dbc.ListGroupItem(
[
html.H6(
f"{df.iloc[i]['title'].split(' - ')[0]}.",
className="news-txt-headline",
),
html.P(
f"by {df.iloc[i]['title'].split(' - ')[1]} {df.iloc[i]['publishedAt']}",
className="news-txt-by-dt",
),
],
className="news-item",
href=df.iloc[i]["url"],
target="_blank",
) for i in range(min(len(df), max_rows))
],
flush=True,
)
return list_group
def init_dashboard(server):
"""Create a Plotly Dash dashboard."""
dash_app = dash.Dash(
server=server,
routes_pathname_prefix="/dashapp/",
external_stylesheets=[dbc.themes.MATERIA],
)
dash_app.layout = dbc.Container(
dbc.Row(
dbc.Col(
[
html.
H4("Change the value in the text box to see callbacks in action!"
),
html.Div([
dbc.Input(
id="my-input",
value="initial value",
type="text",
autoFocus=True,
),
]),
html.Br(),
dbc.Card(id="my-output", body=True),
dbc.ListGroup(
[
dbc.ListGroupItem(
dbc.NavLink(
"Home", href="/", external_link=True)),
dbc.ListGroupItem(
dbc.NavLink("About",
href="/about",
external_link=True)),
dbc.ListGroupItem(
dbc.NavLink("Internal Link — Dash App 2",
href="/dashapp2")),
dbc.ListGroupItem(
dbc.NavLink(
"External Link — Dash App 2",
href="/dashapp2",
external_link=True,
)),
],
className="mt-4",
),
],
className="mt-5",
)))
@dash_app.callback(
Output(component_id="my-output", component_property="children"),
Input(component_id="my-input", component_property="value"),
)
def update_output_div(input_value):
return f"Output: {input_value}"
return dash_app.server
def render(title, courses: CourseList):
element = dbc.Card([
dbc.CardBody([
html.H4(title, className="card-title text-white mb-0"),
]),
dbc.ListGroup([CourseInput.render(course[0], course[1])
for course in courses], flush=True)
], color="primary text-primary mt-3")
return element
def macro_component():
"""
The main macro panel
:return:
"""
title = "Macro Data"
output_row = dbc.Row(
dbc.Tabs(
id="macro-tab-selector",
active_tab="ust-curve",
children=[
dbc.Tab(label="US Treasury Curve",
tab_id="ust-curve",
children=[subpanel.usd_treasury_curve()]),
dbc.Tab(
label="USD Swap Curve",
tab_id="usd-swap-curve",
children=[
subpanel.usd_swap_curve(),
dbc.Col([
html.A(
dbc.Button("Data Citations",
className="mr-1",
id="usd-swap-citations-button"))
],
width=4),
dbc.Popover(
[
dbc.PopoverHeader("USD Swap Citations"),
dbc.PopoverBody([
dbc.ListGroup([
dbc.ListGroupItem([
dbc.ListGroupItemHeading(
f"ICE USD Swap {yr} Yr"),
dbc.ListGroupItemText(
dl.macro.get_usd_swap_citation(
yr))
]) for yr in dl.macro.maturities
])
])
],
id="usd-swap-citations",
is_open=False,
target="usd-swap-citations-button",
)
]),
dbc.Tab(label="Coming Soon...",
tab_id="coming-soon",
disabled=True)
]))
obj = panel_template(title, output_row)
return obj
def make_activity_info_header(activity: Activity):
if activity.type == ('Run' or 'Walk'):
return html.Div([])
metrics = ActivityMetrics(activity=activity, config={'ftp': 290})
line1 = dbc.ListGroup(
[
dbc.ListGroupItem([
dbc.ListGroupItem(activity.name,
style={'font-size': '0.7rem'}),
dbc.ListGroupItem(activity.date),
dbc.ListGroupItem(""),
],
style={
'font-size': '0.6rem',
'line-height': '0.1em'
}),
dbc.ListGroupItem([
dbc.ListGroupItem("Power", style={'font-size': '0.7rem'}),
dbc.ListGroupItem(f"{metrics.average_power}"),
dbc.ListGroupItem(f"{metrics.normalized}"),
dbc.ListGroupItem(f"{metrics.work}"),
],
style={
'font-size': '0.6rem',
'line-height': '0.1em'
}),
dbc.ListGroupItem([
dbc.ListGroupItem("HR", style={'font-size': '0.7rem'}),
dbc.ListGroupItem(f"{metrics.average_hr}"),
dbc.ListGroupItem(f"{metrics.max_hr}"),
],
style={
'font-size': '0.6rem',
'line-height': '0.1em'
}),
],
flush=True,
horizontal=True,
className="mb-1",
style={'font-size': '0.8rem'},
)
# line2 = dbc.ListGroup(
# [
#
# ],
# flush=True,
# )
list_group = html.Div(
[
line1,
# line2,
], )
return list_group
def daily_stats_mobile(state="US") -> List[dbc.Row]:
"""Returns a top bar as a list of Plotly dash components displaying tested, confirmed , and death cases for the top row.
TODO: move to internal API.
:param none: none
:return cols: A list of plotly dash boostrap components Card objects displaying tested, confirmed, deaths.
:rtype: list of plotly dash bootstrap coomponent Col objects.
"""
# 1. Fetch Stats
# print(f"daily_stats_mobile for state {STATES_COORD[state]['stateAbbr']}")
stats = get_daily_stats_mobile(STATES_COORD[state]['stateAbbr'])
# print("Mobile Site ---> ", stats)
# 2. Dynamically generate list of dbc Cols. Each Col contains a single Card. Each card displays
# items and values of the stats pulled from the API.
cards = []
for key, value in stats.items():
if key == "Tested":
card = dbc.ListGroupItem(
[
html.P(" .", className=f"mobile-top-bar-perc-change-{key.lower()}"),
html.H1(value, className=f"mobile-top-bar-value-{key.lower()}"),
html.P(f"{key}", className="mobile-card-text"),
],
className=f"mobile-top-bar-card-{key.lower()}",
)
elif key == "Death Rate":
card = dbc.ListGroupItem(
[
html.P(
f" {float(value[1]):+0.2f}% change",
className=f"mobile-top-bar-perc-change-{key.lower()}",
),
html.H1(f"{value[0]}%", className=f"mobile-top-bar-value-{key.lower()}"),
html.P(f"{key}", className="mobile-card-text"),
],
className=f"mobile-top-bar-card-{key.lower()}",
)
else:
card = dbc.ListGroupItem(
[
html.P(
f"+ {value[1]} new",
className=f"mobile-top-bar-perc-change-{key.lower()}",
),
html.H1(value[0], className=f"mobile-top-bar-value-{key.lower()}"),
html.P(f"{key}", className="mobile-card-text"),
],
className=f"mobile-top-bar-card-{key.lower()}",
)
cards.append(card)
cards = dbc.ListGroup(cards)
return cards
def state_info(state, data):
if not state:
return dbc.Col("")
state_info, state_current = data.get_state_data(state)
state_grade = data.get_state_grade(state)
last_update = data.state_last_update(state)
return [
dbc.Col([graph_tabs(id="state-graph")]),
dbc.Col(
dbc.Card([
dbc.CardHeader(
dbc.Row([
dbc.Col(html.H3(state_info['name'])),
dbc.Col(grade_card(state_grade), align='center')
],
justify='between')),
dbc.CardBody([
dbc.Row(
dbc.Col([
html.H5("Current Totals"),
html.Small(f"Last updated - {last_update}"),
build_table(state_current, id='state-data')
])),
dbc.Row([
dbc.Col([
html.H5("State Links"),
dbc.ListGroup([
dbc.ListGroupItem(
"Covid Site",
href=state_info['covid19Site'],
target="_blank"),
dbc.ListGroupItem(
"Secondary Covid Site",
href=state_info['covid19SiteSecondary'],
target="_blank"),
dbc.ListGroupItem(
"Twitter",
href=
f"https://twitter.com/{state_info['twitter']}",
target="_blank")
])
],
width=4),
dbc.Col([
html.H5("Data notes"),
dcc.Markdown(state_info.get('notes', ""))
])
])
])
]))
]
def news_feed(state="US") -> dbc.ListGroup:
"""Displays news feed on the right hand side of the display. Adjust the NewsAPI time
time to Eastern Time (w/ DST).
TODO: Add callbacks to fetch local state news, if none get entire US news
:params state: display news feed for a particular state. If None, display news feed
for the whole US
:return list_group: A bootstramp ListGroup containing ListGroupItem returns news feeds.
:rtype: dbc.ListGroup
"""
if state == "US":
json_data = requests.get(NCOV19_API + "news").json()
else:
payload = {
"state": STATES_COORD[state]["stateAbbr"],
"topic": "coronavirus"
}
payload = json.dumps(payload)
json_data = requests.post(NCOV19_API + "news", data=payload).json()
if json_data["success"] == True:
json_data = json_data["message"]
df = pd.read_json(json_data)
df = pd.DataFrame(df[["title", "url", "published"]])
max_rows = 50
list_group = dbc.ListGroup(
[
dbc.ListGroupItem(
[
html.H6(
f"{df.iloc[i]['title'].split(' - ')[0]}.",
className="news-txt-headline",
),
html.P(
f"{df.iloc[i]['title'].split(' - ')[1]} {df.iloc[i]['published']}",
className="news-txt-by-dt",
),
],
className="news-item",
href=df.iloc[i]["url"],
target="_blank",
) for i in range(min(len(df), max_rows))
],
flush=True,
)
else:
list_group = []
return list_group
def display_click_data(clickData):
#print(1)
url_text = ""
url_link = ""
jobs_text = ""
jobs_link = ""
contact_text = ""
contact_link = ""
if not clickData:
return ""
item_id = clickData["points"][0]["id"]
item_name = clickData["points"][0]["label"]
item_desc = clickData["points"][0]["customdata"]
listGroup = ""
#print(companies_dict[item_name])
if len(companies_dict[item_name]) > 2:
url_text = companies_dict[item_name]
url_link = companies_dict[item_name]
jobs_text = "Jobs at " + item_name
jobs_link = "https://jobs.urban.us/?q=" + item_name
contact_text = "Get in touch with us about " + item_name
contact_link = "https://share.hsforms.com/1SNKBwhDaTjGHU5BAO2cFZQ10due?what_would_you_like_to_discuss_=" "" + item_name
listGroup = dbc.ListGroup([
dbc.ListGroupItem(html.A(url_text, target='_blank',
href=url_link)),
dbc.ListGroupItem(
html.A(jobs_text, target='_blank', href=jobs_link)),
dbc.ListGroupItem(
html.A(contact_text, target='_blank', href=contact_link)),
])
return_card = dbc.Card(
[
dbc.CardBody([
html.H4(item_name, className="card-title"),
html.P(
item_desc,
className="card-text",
),
html.Br(),
listGroup,
]),
],
body=True,
)
#print(item_name)
#print(clickData)
return return_card
def display_rebalance_output(n_clicks, text_area: str, results):
if not n_clicks:
raise PreventUpdate
if not text_area:
raise PreventUpdate
if not results:
raise PreventUpdate
try:
portfolio = json.loads(text_area)
except (Exception):
return dbc.Alert("Invalid JSON format", color="danger"),
min_vol = results[MIN_VOL]['Expected Weights']
max_sharpe = results[MAX_SHARPE]['Expected Weights']
symbols = portfolio.keys()
with open('config.json', 'r') as file:
config = json.load(file)['crypto']
api_key = config['crypto_compare_api_key']
url = 'https://min-api.cryptocompare.com/data/pricemulti'
params = {"fsyms": ','.join(symbols), "tsyms": "USD", "api_key": api_key}
exchange_rate = requests.get(url, params=params).json()
usd_price = {key: value['USD'] for key, value in exchange_rate.items()}
display_result = {}
results = pd.DataFrame({"a": portfolio, 'usd_price': usd_price})
results['holding'] = results['a'] * results['usd_price']
results['w'] = results['holding'] / results['holding'].sum()
total_asset_value = results['holding'].sum()
for entry in [('Maximmum Sharpe Ratio', max_sharpe),
('Minimmum Volatitly', min_vol)]:
results['ew'] = pd.Series(entry[1])
results['ew_r'] = results['ew'] - results['w']
results['ew_usd'] = results['ew_r'] * \
results['holding'].sum()
results['ew_a'] = results['ew_usd'] / results['usd_price']
display_result[entry[0]] = results['ew_a'].to_dict()
return [html.H4(f'Total Asset Value: ${total_asset_value:.4f}'),
html.Hr()] +\
[dbc.Row(dbc.Col([
html.H5(name),
dbc.ListGroup([
dbc.ListGroupItem([
dbc.ListGroupItemHeading(f"{value:.5f} {key}"),
dbc.ListGroupItemText(f"{value*usd_price[key]:=.2f} USD"),
]) for key, value in ew.items()
], horizontal=True),
html.Hr()
]),
align='center', justify='center'
) for name, ew in display_result.items()] +\
[dbc.Label("* positive value is for buy" +
" and negative value is for sell")]
def make_emission_nav(current_page):
df = predict_emissions()
target_year = get_variable('target_year')
ts = df.sort_index().drop(columns='Forecast', level=0).loc[target_year]
items = []
current_sector = current_page.emission_sector if current_page and current_page.emission_sector else None
# Sort sectors based on the target year emissions
sector_emissions = ts.sum(level=0).sort_values(ascending=False)
def render_sector(s, sector_path, level):
sector_emissions = s.sum(level=0).sort_values(ascending=False)
for subsector_name, emissions in sector_emissions.iteritems():
if not subsector_name:
continue
subsector_path = tuple([*sector_path, subsector_name])
next_metadata = SECTORS
for sp in subsector_path:
metadata = next_metadata[sp]
next_metadata = metadata.get('subsectors', {})
if current_sector == subsector_path:
active = True
else:
active = False
page = get_page_for_emission_sector(*subsector_path)
item = _make_nav_item(metadata['name'],
emissions,
level,
page,
active=active)
items.append(item)
ss = s[subsector_name]
if isinstance(ss, pd.Series):
render_sector(ss, subsector_path, level + 1)
render_sector(ts, tuple(), 0)
items.append(
_make_nav_item('Yhteensä', sector_emissions.sum(), 0, None, bold=True))
return html.Div([
html.H6('Päästöt vuonna %s' % target_year),
dbc.ListGroup(children=items)
])
def PaletteList():
return dbc.Row(dbc.ListGroup([
PaletteItem(idx=1, name='Contact Density'),
PaletteItem(idx=2, name='Conservation'),
PaletteItem(idx=3, name='Custom files'),
PaletteItem(idx=4, name='Disorder'),
PaletteItem(idx=5, name='Membrane Topology'),
PaletteItem(idx=6, name='MSA Coverage'),
PaletteItem(idx=7, name='Secondary Structure'),
PaletteItem(idx=8, name='Hydrophobicity'),
PaletteItem(idx=9, name='Heatmap'),
],
style={'width': '75%'}),
justify='center',
align='center')
def update_userid(value):
reviews = get_reviewed_restaurants(value)
n = len(reviews)
recs = get_n_preds(value, n)
htmls = [
dbc.ListGroupItem(reviews[i][0] + ': ' + reviews[i][1])
for i in range(n)
]
urls = [
'http://www.google.com/maps/place/' + str(recs[i][2]) + ',' +
str(recs[i][3]) for i in range(n)
]
preds = [
dbc.ListGroupItem([
recs[i][0] + ': ' + str(round(recs[i][1], 2)) + ' ',
html.A('(map)', href=urls[i])
]) for i in range(n)
]
return [
html.Div(children=[
dbc.Card(
dbc.CardBody([
html.H4("{0} User Reviews".format(value)),
dbc.ListGroup(htmls)
]), )
],
className="col-md-6"),
html.Div(children=[
dbc.Card(
dbc.CardBody([
html.H4('Recommendations {0} User'.format(value)),
dbc.ListGroup(preds)
]), )
],
className="col-md-6")
]
def make_transaction_group(df: pd.DataFrame):
"""Create a group of transactions"""
list_items = []
for idx, transaction in df.iterrows():
list_items.append(
dbc.ListGroupItem(
[
dbc.ListGroupItemHeading(html.H6(transaction.payee)),
dbc.ListGroupItemText([
html.P(transaction.display_name),
html.P(f"$ {transaction.total}")
]),
],
action=True,
))
return dbc.ListGroup(list_items)
def _update_project_options(n_clicks):
"""Use the Rubicon client to load the available projects."""
app._rubicon_model.update_projects()
return dbc.ListGroup(children=[
dbc.ListGroupItem(
id={
"type": "project-selection--project",
"index": project["value"]
},
className="project-selection--project",
children=project["value"],
n_clicks=0,
action=True,
) for project in app._rubicon_model.project_options
])
def daily_stats_mobile() -> List[dbc.Row]:
"""Returns a top bar as a list of Plotly dash components displaying tested, confirmed , and death cases for the top row.
TODO: move to internal API.
:param none: none
:return cols: A list of plotly dash boostrap components Card objects displaying tested, confirmed, deaths.
:rtype: list of plotly dash bootstrap coomponent Col objects.
"""
# 1. Fetch Stats
stats = get_daily_stats()
# print("Mobile Site ---> ", stats)
# 2. Dynamically generate list of dbc Cols. Each Col contains a single Card. Each card displays
# items and values of the stats pulled from the API.
cards = []
for key, value in stats.items():
if key not in ["Tested", "Recovered"]:
card = dbc.ListGroupItem(
[
html.P(
f"+ {value[1]} in past 24h",
className=f"mobile-top-bar-perc-change-{key.lower()}",
),
html.H1(value[0],
className=f"mobile-top-bar-value-{key.lower()}"),
html.P(f"{key}", className="mobile-card-text"),
],
className=f"mobile-top-bar-card-{key.lower()}",
)
else:
# card = dbc.Row(
card = dbc.ListGroupItem(
[
html.P(
" .",
className=f"mobile-top-bar-perc-change-{key.lower()}"),
html.H1(value,
className=f"mobile-top-bar-value-{key.lower()}"),
html.P(f"{key}", className="mobile-card-text"),
],
className=f"mobile-top-bar-card-{key.lower()}",
)
cards.append(card)
cards = dbc.ListGroup(cards)
return cards
def TutorialList():
return dbc.Row(
dbc.ListGroup(
[
TutorialItem(idx=1, name='Creating your first plot'),
TutorialItem(
idx=2,
name='Compare a contact prediction with a PDB file'),
TutorialItem(idx=3,
name='Storing, loading and sharing a session'),
TutorialItem(idx=4,
name='Residue-Residue distance predictions'),
# TutorialItem(idx=5, name='Video tutorial')
],
style={'width': '75%'}),
justify='center',
align='center')
def news_feed(state=None) -> dbc.ListGroup:
"""Displays news feed on the right hand side of the display. Adjust the NewsAPI time
time to Eastern Time (w/ DST).
TODO: Add callbacks to fetch local state news, if none get entire US news
:params state: display news feed for a particular state. If None, display news feed
for the whole US
:return list_group: A bootstramp ListGroup containing ListGroupItem returns news feeds.
:rtype: dbc.ListGroup
"""
json_data = requests.get(NCOV19_API + "news").json()
if json_data["success"] == True:
json_data = json_data["message"]
df = pd.read_json(json_data)
df = pd.DataFrame(df[["title", "url", "publishedAt"]])
max_rows = 50
list_group = dbc.ListGroup(
[
dbc.ListGroupItem(
[
html.H6(
f"{df.iloc[i]['title'].split(' - ')[0]}.",
className="news-txt-headline",
),
html.P(
f"by {df.iloc[i]['title'].split(' - ')[1]} {df.iloc[i]['publishedAt']}",
className="news-txt-by-dt",
),
],
className="news-item",
href=df.iloc[i]["url"],
target="_blank",
) for i in range(min(len(df), max_rows))
],
flush=True,
)
else:
print("getting executed for no reason")
list_group = []
return list_group
def news_feed(country):
newsapi = NewsApiClient(api_key='6c7bb4a85ec049b3b9459379a35a36fa')
q = 'corona'
json_data = newsapi.get_top_headlines(q=q, language='en', country='us')
# country_name_code_dict.get(str(country)
data = pd.DataFrame(json_data['articles'])
source = data['source'][0].get('name')
source = []
for i in range(data.shape[0]):
news = data['source'][i].get('name')
source.append(news)
news_data = pd.DataFrame(data[["title", "url"]])
news_data['source'] = source
max_rows = 50
list_group = dbc.ListGroup(
[
dbc.ListGroupItem(
[
html.Div(
[
html.H6(
f"{news_data.iloc[i]['title'].split(' - ')[0]}.",
className="news-txt-headline",
),
html.P(
f"{news_data.iloc[i]['title'].split(' - ')[1]}"
f" {news_data.iloc[i]['source']}",
className="news-txt-by-dt",
),
],
className="news-item-container",
)
],
className="news-item",
href=news_data.iloc[i]["url"],
target="_blank",
) for i in range(min(len(news_data), max_rows))
],
flush=True,
)
return list_group
def poledir(lat, lon, dec, inc, a95):
if lat and lon and dec and inc and a95:
PLat, PLon, dp, dm, PaleoLat = paleo_pole(lat, lon, dec, inc, a95)
return (dbc.ListGroup([
dbc.Row([
dbc.Col([
dbc.ListGroupItemText("PLat",
style={
"margin-bottom": "-2px",
}),
dbc.ListGroupItem(PLat),
],
width=3),
dbc.Col([
dbc.ListGroupItemText("PLon",
style={
"margin-bottom": "-2px",
}),
dbc.ListGroupItem(PLon),
],
width=3),
dbc.Col([
dbc.ListGroupItemText("dp/dm",
style={
"margin-bottom": "-2px",
}),
dbc.ListGroupItem(str(dp) + "/" + str(dm)),
],
width=3),
dbc.Col([
dbc.ListGroupItemText("PaleoLat",
style={
"margin-bottom": "-2px",
}),
dbc.ListGroupItem(PaleoLat),
],
width=3)
])
])
# html.P(
# "PLat: " + str(PLat) + " PLon: " + str(PLon) +
# " dp/dm: " + str(dp)+"/"+str(dm) + " PaleoLat: " + str(PaleoLat),
# className="text-center"
# ),
)
def generate_list(tellers):
teller_list = []
for i in tellers:
credit = get_teller_credits(i)
debit = get_teller_debits(i)
teller_list.append(
dbc.Row(dbc.ListGroup([
dbc.ListGroupItem(i),
dbc.ListGroupItem("Credit: {}".format(credit)),
dbc.ListGroupItem("Debit: {}".format(debit)),
]))
)
return teller_list
def get_link_list(link_list, name):
list_group = dbc.ListGroup(children=[])
if len(link_list)==0:
list_group.children.append(
dbc.ListGroupItem(
"No " + name + " available" )
)
return list_group
else:
for i in range(len(link_list)):
list_group.children.append(
dbc.ListGroupItem(
name + " #" + str(i+1), href=link_list[i], target='_blank'
)
)
return list_group
def _show_metadata_from_graphs(passed_data: dict,
duration_data: dict) -> tuple:
"""
"""
if not (passed_data or duration_data):
raise PreventUpdate
metadata = no_update
open_canvas = False
title = "Job Statistics"
trigger_id = callback_context.triggered[0]["prop_id"].split(".")[0]
if trigger_id == "graph-passed":
graph_data = passed_data["points"][0].get("hovertext", "")
elif trigger_id == "graph-duration":
graph_data = duration_data["points"][0].get("text", "")
if graph_data:
metadata = [
dbc.Card(
class_name="gy-2 p-0",
children=[
dbc.CardHeader(children=[
dcc.Clipboard(
target_id="metadata",
title="Copy",
style={"display": "inline-block"}), title
]),
dbc.CardBody(
id="metadata",
class_name="p-0",
children=[
dbc.ListGroup(children=[
dbc.ListGroupItem([
dbc.Badge(x.split(":")[0]),
x.split(": ")[1]
]) for x in graph_data.split("<br>")
],
flush=True),
])
])
]
open_canvas = True
return metadata, open_canvas
def update_meta_data_list(series_data_dict, **kwargs):
model_name = kwargs["model_selector"]
model_description = series_data_dict["all_forecasts"][model_name][
"model_description"]
if model_description == model_name:
model_description = ""
model_cv_score = series_data_dict["all_forecasts"][model_name][
"cv_score"]
return dbc.ListGroup([
dbc.ListGroupItem([
dbc.ListGroupItemHeading("Model Details"),
dbc.ListGroupItemText([
html.P(model_name),
html.P(model_description),
html.P("CV score: %f" % model_cv_score),
]),
]),
dbc.ListGroupItem([
dbc.ListGroupItemHeading("Forecast Updated At"),
dbc.ListGroupItemText(
series_data_dict["forecasted_at"].strftime(
"%Y-%m-%d %H:%M:%S")),
]),
dbc.ListGroupItem([
dbc.ListGroupItemHeading("Data Collected At"),
dbc.ListGroupItemText(
series_data_dict["downloaded_dict"]
["downloaded_at"].strftime("%Y-%m-%d %H:%M:%S")),
]),
dbc.ListGroupItem([
dbc.ListGroupItemHeading("Data Source"),
dbc.ListGroupItemText([
html.A(
series_data_dict["data_source_dict"]["url"],
href=series_data_dict["data_source_dict"]["url"],
)
]),
]),
])
