def generate_tooltip(self, format: str = "") -> alt.Tooltip:
"""Wrapper arount alt.Tooltip creation.
Args:
format (str): Desired format within tooltip
Returns:
An instance of alt.Tooltip containing relevant information from the PlotComponent class.
"""
return alt.Tooltip(
field=self.name,
type=self.alt_type,
title=self.title,
format=format,
)
def display_main_chart(metrics_df,
metric_id,
metric_name,
asset_names,
container,
scale="linear"):
# the selection brush oriented on the x-axis
# important not here had to comment out the interactive function below
# to convert the graph to static
brush = alt.selection(type='interval', encodings=['x'])
base = alt.Chart(metrics_df).properties(width=800)
chart = base.mark_line().encode(x=alt.X("time",
type="temporal",
title="Time"),
y=alt.Y(metric_id,
type="quantitative",
title=metric_name,
scale=alt.Scale(type=scale),
stack=True),
opacity=alt.condition(
brush, alt.OpacityValue(1),
alt.OpacityValue(0.7)),
tooltip=[
alt.Tooltip("time",
type="temporal",
title="Time"),
alt.Tooltip(metric_id,
type="quantitative",
title=metric_name)
],
color="Name").add_selection(brush)
line = base.mark_rule().encode(y=alt.Y(f"average({metric_id}):Q"),
size=alt.SizeValue(3),
color="Name").transform_filter(brush)
container.write(chart + line)
def question_senti_barplot(senti_df):
"""Barplot for individual question's sentiment."""
senti_plot = (alt.Chart(senti_df).mark_bar().encode(
alt.Y("questions", title="Questions", sort="-x"),
alt.X(cts.SENTI, title="Sentiment"),
tooltip=[alt.Tooltip(cts.SENTI, title="Sentiment")],
opacity=alt.value(0.7),
color=alt.condition(
alt.datum[cts.SENTI] > 0,
alt.value("steelblue"),
alt.value("red"),
),
).properties(width=700, height=450)).interactive()
return senti_plot
def pair_plot(df, x_str, y_str, scale_str, tooltip_cols=[]):
scale_params = {
'zero': False
} if scale_str == 'linear' else {
'type': 'log',
'domain': [1, 10000000]
}
return alt.Chart(df).mark_point().encode(
x=alt.X(x_str, scale=alt.Scale(type='log')),
y=alt.Y(y_str, scale=alt.Scale(**scale_params)),
tooltip=[
alt.Tooltip(col, title=col.split(':')[0]) for col in tooltip_cols
],
color=alt.Color('statename:N')).properties(width=600, height=400)
def bar_chart(data, column):
''' create bar charts for displaying categorical data
:param data: data from which to display
:param column: column on which to create histogram
:return: altair bar plot '''
return alt.Chart(data).mark_area().encode(
x=alt.X(column, sort='-y', axis=alt.Axis(labels=False, grid=False)),
y=alt.Y('count():Q', axis=alt.Axis(grid=False),
title='Clusters (ordered by size)'),
tooltip=[alt.Tooltip('count()', title='Number of ads in cluster')]
).properties(
width=600,
height=400
)
def html_demande_type_presta():
df = create_df_service_amount()
df.loc[df.ttc_amount.isnull(), 'payment_status'] = 'Refused'
df.loc[~df.ttc_amount.isnull(), 'payment_status'] = 'Accepted'
df.label_services = df.label_services.str.title()
show_comp = False
plot = alt.Chart(
df, title='Nombre de vente/refus par prestation').mark_bar().encode(
y=alt.Y('label_services:N',
title='Types prestations',
sort=alt.EncodingSortField(field="ht_amount",
op="count",
order='descending')),
x=alt.X('count()', title='Nombre de ventes'),
color=alt.Color('compagnie' if show_comp else 'payment_status',
title='Status paiement'),
tooltip=[
alt.Tooltip('distinct(compagnie)', title='Nbr entreprise'),
alt.Tooltip('count()', title='Nbr devis')
],
)
plot.save('app/templates/plot/analyse_vente_type_presta.html')
return plot
def _transform_encoding(self, encoding, value):
import altair as alt
if isinstance(value, dict):
value = dict(value)
for kw, val in value.items():
if kw == 'scale':
if isinstance(val, list):
val = alt.Scale(range=val)
else:
val = alt.Scale(**val)
if kw == 'tooltip':
val = [alt.Tooltip(**v) for v in val]
value[kw] = val
value = getattr(alt, encoding.capitalize())(**value)
return value
def make_customer_satisfaction(store_id='A'):
# Create customer satisfaction heat map (unfiltered by store)
customer_satisfaction = (alt.Chart(df).mark_rect().encode(
alt.X('Day_of_week:N', title=None, sort=days),
alt.Y('Time_of_day:N', title=None, sort=times),
alt.Color('mean(Rating):Q',
title=None,
scale=alt.Scale(scheme='greens')),
tooltip=[
alt.Tooltip('Day_of_week', title='Day of the week'),
alt.Tooltip('Time_of_day', title="Time of the day"),
alt.Tooltip('mean(Rating):Q',
title='Average satisfaction',
format='.2f')
]).transform_filter(
alt.FieldEqualPredicate(
field='Branch', equal=store_id)).configure_axis(
labelFontSize=13,
titleFontSize=13).configure_title(fontSize=14).properties(
width=180, height=130, title='Customer satisfaction'))
return customer_satisfaction
def create_top_n_barplot(world_source: pd.DataFrame) -> alt.Chart:
"""Return alt.Chart stacked barplot of top `n` most affected countries.
Parameters
----------
world_source : pd.DataFrame
Returns
-------
top_n_bar : alt.Chart
"""
most_affected = get_most_affected(world_source, n=10)
top_n_bar = (alt.Chart(most_affected).mark_bar().encode(
y=alt.Y("country_region:N", title="", sort="-x"),
x=alt.X("sum(count):Q", title="Count"),
color=alt.Color("status:N", scale=alt.Scale(scheme="tableau20")),
tooltip=[
alt.Tooltip("country_region:N", title="Country"),
alt.Tooltip("sum(count):Q", title="Count"),
alt.Tooltip("status:N", title="Status"),
],
).properties(title="10 most affected nations", width=600,
height=300).configure_legend(orient="top"))
return top_n_bar
def make_module_coverage_heatmap(module_coverage, mag_order=None):
num_mags_in_frame = len(set(module_coverage['genome']))
c = alt.Chart(module_coverage, title='Module').encode(
x=alt.X('module_name',
title=None,
sort=None,
axis=alt.Axis(labelLimit=0, labelAngle=90)),
y=alt.Y('genome',
title=None,
sort=mag_order,
axis=alt.Axis(labelLimit=0)),
tooltip=[
alt.Tooltip('genome', title='Genome'),
alt.Tooltip('module_name', title='Module Name'),
alt.Tooltip('steps', title='Module steps'),
alt.Tooltip('steps_present', title='Steps present')
]).mark_rect().encode(
color=alt.Color('step_coverage',
legend=alt.Legend(title='% Complete'),
scale=alt.Scale(domain=(0, 1)))).properties(
width=HEATMAP_CELL_WIDTH *
len(HEATMAP_MODULES),
height=HEATMAP_CELL_HEIGHT * num_mags_in_frame)
return c
def plot_pathway_by_virus():
pathways = wikidata_from_file("queries/covidpathways.rq")
pathways_transf = (pathways.groupby(
["virusLabel", "pathwayLabel",
"wikiPathID"]).count().reset_index().drop(
["componentLabel", "virus", "pathway"], axis=1).replace(
{
"severe acute respiratory syndrome-related coronavirus":
"SARS-related CoV"
},
regex=True,
))
pathways_transf["url"] = (
"https://www.wikipathways.org/index.php/Pathway:" +
pathways_transf["wikiPathID"])
plot = (
alt.Chart(pathways_transf).mark_circle().encode(
# x=alt.X("virusLabel:N", title="# of components"),
y=alt.Y("wikiPathID:N", title=None),
x=alt.X("virusLabel:N", title=None),
color=alt.Color("virusLabel:N", legend=None),
size=alt.Size("component:Q",
legend=None,
scale=alt.Scale(range=[100, 300])),
href="url:N",
tooltip=[
alt.Tooltip("component:Q", title="# of components"),
alt.Tooltip("pathwayLabel:N", title="Pathway"),
],
))
return plot
def plot_immunity(data):
df = data.data[[
'Date', 'Immunity (Lower Bound)', 'Immunity (Upper Bound)'
]].copy()
df = df.set_index('Date').rolling(window='14d').mean().reset_index()
chart = alt.Chart(df).mark_area(opacity=0.5).encode(
x='Date:T',
y=alt.Y('Immunity (Lower Bound):Q',
title='Immunity %',
axis=alt.Axis(format='%')),
y2=alt.Y2('Immunity (Upper Bound):Q', title=''),
tooltip=alt.Tooltip('Date'))
return chart
def plot_bar_behavior(selection, y_axis=y_axis):
"""
Plot user selected behavior by park area.
Parameters
----------
selection : alt.selection_multi() object
selection paramter for setting up interactive multi-selection feature of the plots
y_axis: character
Drop-down menu selection value of behavior for making the plot accordingly
Returns
-------
b_chart
The plot of the selected behavior.
"""
b_chart = (alt.Chart(squirrel_b_json).mark_bar(
color='gray').add_selection(selection).encode(
alt.X('properties.' + y_axis + ':Q',
title="Squirrel Count",
axis=alt.Axis(labelFontSize=18, titleFontSize=20)),
alt.Y('properties.sitename_short:N',
title="Park Region",
axis=alt.Axis(labelFontSize=14, titleFontSize=20),
sort=sort_order),
opacity=alt.condition(brush, alt.value(1.0), alt.value(0.2)),
tooltip=[
alt.Tooltip('properties.sitename:N', title="Park Region"),
alt.Tooltip('properties.' + y_axis + ':Q',
title="Count " + y_axis.replace('_', ' '))
]).properties(title="Squirrel Behavior by Park Region: " +
y_axis.replace('_', ' '),
width=pw,
height=ph))
return b_chart
def plot(selection, transformed_df):
""""""
color = alt.condition(selection, alt.Color("country:N", legend=None),
alt.value("lightgray"))
base = alt.Chart(transformed_df, title="Flu cases per week").encode(
x=alt.X("week:Q", title="Week"),
y=alt.Y("flu_cases:Q", title="Flu Cases"),
color=color,
tooltip=([alt.Tooltip("flu_cases:Q", title="Total Cases")]),
)
line = base.mark_line().add_selection(selection).interactive(bind_y=False)
return line, color
def show_world_scatter(df, label):
'''
Creates scatter plot with LOESS lines
'''
world_data = get_world_data(df, label)
cases_scale = max(world_data[label])
date_scale = max(world_data['date'])
oldest = min(world_data['date'])
scale_y = alt.Scale(domain=(0, cases_scale + 10000))
scale_x = alt.Scale(domain=(oldest, (date_scale + pd.DateOffset(days=3))))
base = alt.Chart(world_data).mark_circle(
size=150,
opacity=0.5).transform_fold(fold=[label], as_=['cases', 'y']).encode(
x=alt.X('date:T', scale=scale_x, title='Date'),
y=alt.Y('y:Q',
scale=scale_y,
title=label.replace('_', ' ').title()),
fill=alt.Color(label + ':Q',
legend=alt.Legend(title=label.replace('_', ' '))),
tooltip=[
alt.Tooltip('date'),
alt.Tooltip(label,
format=",.0f",
title=label.replace('_', ' '))
])
loess = base + base.transform_loess('date', 'y').mark_line(size=4)
chart = (base + loess).configure_axis(
labelFontSize=11, titleFontSize=13,
titleColor='grey').configure_legend(
titleFontSize=13,
labelFontSize=12,
).configure_axisX(labelAngle=-30).properties(height=350).interactive()
st.altair_chart(chart, use_container_width=True)
def generate_chart(chart_type, data, width=0, height=0):
if data is None:
# Use an empty-ish dict because if we use None the x axis labels rotate
# 90 degrees. No idea why. Need to debug.
data = {"": []}
if not isinstance(data, pd.DataFrame):
data = type_util.convert_anything_to_df(data)
index_name = data.index.name
if index_name is None:
index_name = "index"
data = pd.melt(data.reset_index(), id_vars=[index_name])
if chart_type == "area":
opacity = {"value": 0.7}
else:
opacity = {"value": 1.0}
# Set the X and Y axes' scale to "utc" if they contain date values.
# This causes time data to be displayed in UTC, rather the user's local
# time zone. (By default, vega-lite displays time data in the browser's
# local time zone, regardless of which time zone the data specifies:
# https://vega.github.io/vega-lite/docs/timeunit.html#output).
x_scale = (alt.Scale(
type="utc") if _is_date_column(data, index_name) else alt.Undefined)
y_scale = alt.Scale(
type="utc") if _is_date_column(data, "value") else alt.Undefined
x_type = alt.Undefined
# Bar charts should have a discrete (ordinal) x-axis, UNLESS type is date/time
# https://github.com/streamlit/streamlit/pull/2097#issuecomment-714802475
if chart_type == "bar" and not _is_date_column(data, index_name):
x_type = "ordinal"
chart = (getattr(alt.Chart(data, width=width, height=height),
"mark_" + chart_type)().encode(
alt.X(index_name,
title="",
scale=x_scale,
type=x_type),
alt.Y("value", title="", scale=y_scale),
alt.Color("variable", title="", type="nominal"),
alt.Tooltip([index_name, "value", "variable"]),
opacity=opacity,
).interactive())
return chart
def map_state(curr_day_num, state_txt, state_counties, confirmed,
confirmed_min, confirmed_max, deaths, deaths_min, deaths_max):
# Washington State
base_state = alt.Chart(state_counties).mark_geoshape(
fill='white',
stroke='lightgray',
).properties(
width=1000,
height=800,
).project(type='mercator')
# counties
base_state_counties = alt.Chart(
us_counties).mark_geoshape().transform_lookup(
lookup='id',
from_=alt.LookupData(
confirmed[(confirmed['confirmed'] > 0)
& (confirmed['day_num'] == curr_day_num)], 'fips',
['confirmed', 'county'])).encode(
color=alt.Color('confirmed:Q',
scale=alt.Scale(
type='log',
domain=[confirmed_min, confirmed_max]),
title='Confirmed'),
tooltip=[
alt.Tooltip('confirmed:Q'),
alt.Tooltip('county:O'),
],
)
# deaths by long, latitude
points = alt.Chart(
deaths[(deaths['deaths'] > 0) & (deaths['day_num'] == curr_day_num)]
).mark_point(opacity=0.75, filled=True).encode(
longitude='long_:Q',
latitude='lat:Q',
size=alt.Size('sum(deaths):Q',
scale=alt.Scale(type='symlog',
domain=[deaths_min, deaths_max]),
title='deaths'),
color=alt.value('#BD595D'),
stroke=alt.value('brown'),
tooltip=[
alt.Tooltip('lat'),
alt.Tooltip('long_'),
alt.Tooltip('deaths'),
alt.Tooltip('county:O'),
],
).properties(
# update figure title
title=
f'COVID-19 {state_txt} Confirmed Cases and Deaths per 100K by County [{curr_day_num}]'
)
return (base_state + base_state_counties + points)
def monthly_returns_heatmap(report):
monthly_returns = report.resample(
"M")["Total Portfolio"].last().pct_change().reset_index()
monthly_returns.columns = ["Date", "Monthly Returns"]
chart = alt.Chart(monthly_returns).mark_rect().encode(
alt.X("year(Date):O", title="Year"),
alt.Y("month(Date):O", title="Month"),
alt.Color(
"mean(Monthly Returns)",
title="Return",
scale=alt.Scale(scheme="redyellowgreen")),
alt.Tooltip("mean(Monthly Returns)",
format=".2f")).properties(title="Average Monthly Returns")
return chart
def chart_regression_gd(df, ws):
df = df.copy(deep=True)
df["Yhat"] = ws[0] + ws[1]*df["Werbeausgaben"]
min_x, max_x = df["Werbeausgaben"].min(), df["Werbeausgaben"].max()
min_y, max_y = -19,20#df.min().min(), df.max().max()
points = alt.Chart(df).mark_circle(color="#434A56",size=100).encode(
alt.X("Werbeausgaben",scale=alt.Scale(domain=(0.9*min_x,1.1*max_x))),
alt.Y("Absatz",scale=alt.Scale(domain=(0.9*min_y,1.1*max_y))
, title="Absatz"),
tooltip= [alt.Tooltip(field="Yhat", title="geschätzter Absatz", type="quantitative", format=".2f")]
)
line = alt.Chart(df).mark_line(color="#F63366", size=2).encode(
alt.X("Werbeausgaben"),#,scale=alt.Scale(domain=(0.9*min_x,1.1*max_x))),
alt.Y("Yhat")#alt.Y("Absatz_Prognose")
)
return (points + line).properties(width=700)
def make_functional_heatmap(functional_df, mag_order=None):
# build heatmaps
charts = list()
for i, (group,
frame) in enumerate(functional_df.groupby('category', sort=False)):
# set variables for chart
function_order = get_ordered_uniques(list(frame.function_name))
num_mags_in_frame = len(set(frame['genome']))
chart_width = HEATMAP_CELL_WIDTH * len(function_order)
chart_height = HEATMAP_CELL_HEIGHT * num_mags_in_frame
# if this is the first chart then make y-ticks otherwise none
if i == 0:
y = alt.Y('genome',
title=None,
sort=mag_order,
axis=alt.Axis(
labelLimit=0,
labelExpr="replace(datum.label, /_\d*$/gi, '')"))
else:
y = alt.Y('genome',
axis=alt.Axis(title=None, labels=False, ticks=False),
sort=mag_order)
# set up colors for chart
rect_colors = alt.Color('present',
legend=alt.Legend(title="Function is Present",
symbolType='square',
values=[True, False]),
sort=[True, False],
scale=alt.Scale(range=['#2ca25f', '#e5f5f9']))
# define chart
# TODO: Figure out how to angle title to take up less space
c = alt.Chart(frame, title=alt.TitleParams(group)).encode(
x=alt.X('function_name',
title=None,
axis=alt.Axis(labelLimit=0, labelAngle=90),
sort=function_order),
tooltip=[
alt.Tooltip('genome', title='Genome'),
alt.Tooltip('category', title='Category'),
alt.Tooltip('subcategory', title='Subcategory'),
alt.Tooltip('function_ids', title='Function IDs'),
alt.Tooltip('function_name', title='Function'),
alt.Tooltip('long_function_name', title='Description'),
alt.Tooltip('gene_symbol', title='Gene Symbol')
]).mark_rect().encode(y=y, color=rect_colors).properties(
width=chart_width, height=chart_height)
charts.append(c)
# merge and return
function_heatmap = alt.hconcat(*charts, spacing=5)
return function_heatmap
def set_options(self, **kwargs):
self.options.update(kwargs)
if isinstance(self.options["x"], str):
self.options["x"] = alt.X(self.options["x"])
if isinstance(self.options["y"], str):
self.options["y"] = alt.X(self.options["y"])
if "tooltip" in self.options and isinstance(self.options["tooltip"], list):
nl = []
for t in self.options["tooltip"]:
if isinstance(t, str):
nl.append(alt.Tooltip(t.replace(".", ""), title=t))
else:
t.title = t.shorthand
t.shorthand = t.shorthand.replace(".", "")
nl.append(t)
self.options["tooltip"] = nl
def create_genetic_chart(data):
scales = alt.selection_interval(bind='scales')
chart = alt.Chart(data, height=500, width=850).mark_line(point=True).encode(
x=alt.X('IterationNumber:N', title='Кількість ітерацій'),
y=alt.Y('Result',
scale=alt.Scale(domain=[1,15]),
title='Середня кількість кандидатів в комітеті',
sort=sorted(set(data['Result']), reverse=True)
),
color=alt.Color('Algorithm:N',title='Алгоритм', scale=alt.Scale(scheme='category10')),
tooltip=alt.Tooltip('Result', title='Значення ЦФ')
).add_selection(
scales
).configure_point(
size=80
)
return chart
def create_greedy_chart(data):
scales = alt.selection_interval(bind='scales')
chart = alt.Chart(data, height=500, width=850).mark_line(point=True).encode(
x=alt.X('CandidatesNum:N', title='Кількість кандидатів'),
y=alt.Y('Result',
axis=alt.Axis(tickMinStep=0.2),
title='Середня кількість кандидатів в комітеті',
sort=sorted(set(data['Result']), reverse=True)
),
color=alt.Color('Algorithm:N',title='Алгоритм', scale=alt.Scale(scheme='category10')),
tooltip=alt.Tooltip('Result', title='Значення ЦФ')
).add_selection(
scales
).configure_point(
size=80
)
return chart
def close_for_how_long(yelp_join):
st.markdown(
"From now on, we retrive the original information in **Temporary Closed Until**, **Covid Banner**, and **highlights**."
)
st.write(
"First, let's see when did these temporarly closed businesses plan to reopen in June 10. There are {} businesses uploading closure notification."
.format(sum(yelp_covid_df['Temporary Closed Until'] != 'FALSE')))
st.write(
"You may select certain category you are interested in from the bottom box, and brush certain time sub-interval from the upper figure."
)
close_time = yelp_join[yelp_join['Temporary Closed Until'] !=
'FALSE']['Temporary Closed Until']
close_time = list(close_time)
close_time = [ele[:-5] for ele in close_time]
category = yelp_join[yelp_join['Temporary Closed Until'] !=
'FALSE']['categories'].fillna('').apply(find_category)
category = list(category)
df = pd.DataFrame()
df['Close Until'] = close_time
df['Category'] = category
brush = alt.selection_interval()
input_dropdown = alt.binding_select(options=cate_list, name="Category of ")
picked = alt.selection_single(encodings=["color"], bind=input_dropdown)
base = alt.Chart(df[df['Close Until'] < '2021-01-01']).mark_area().encode(
alt.X("Close Until:T"),
alt.Y("count()")).properties(height=50, width=500).add_selection(brush)
chart = base & alt.Chart(df[
df['Close Until'] < '2021-01-01T00:00:00']).mark_bar(size=20).encode(
alt.X("Close Until:T", scale=alt.Scale(domain=brush)),
alt.Y("count()", title='Business number'),
alt.Tooltip(["Close Until:T", "Category:N", "count()"]),
color=alt.condition(picked, "Category:N", alt.value("lightgray")),
).add_selection(picked).properties(height=300, width=500)
st.write(chart)
st.write(
"It is interesting that the planned reopen time is quite concentrated, most during June and July, and on the start or end of a certain month."
)
def chart2(x_val = 'gini_index'):
"""
This function creates a chart for avg hate crime rate across a specified factor given as input.
-------------------
Arguments:
x_val - The factor for which we want to compare the avg hate crime rate
-------------------
Returns:
A altair chart with the scatter plot for avg. hate crime rate and specified factor.
"""
df = pd.read_csv('data/hate_crimes.csv').loc[:,[x_val,'avg_hatecrimes_per_100k_fbi','state']]
df = df.dropna()
df = pd.DataFrame({'x': df.iloc[:,0], 'y': df.iloc[:,1], 'state':df.iloc[:,2] })
# Define the degree of the polynomial fit
degree = 1
# Build a dataframe with the fitted data
poly_data = pd.DataFrame({'xfit': np.linspace(df['x'].min(), df['x'].max(), 500)})
poly_data[str(degree)] = np.poly1d(np.polyfit(df['x'], df['y'], degree))(poly_data['xfit'])
type_dict = {'gini_index': 'Income Disparity',
'share_unemployed_seasonal': 'Unemployment rate seasonal',
'share_white_poverty': 'White people poverty rate',
'share_non_citizen': 'Percentage of Non-citizens',
'share_population_in_metro_areas': 'Percentage of people in metro cities'}
# Plot the data points on an interactive axis
points = alt.Chart(df).mark_circle(color='black', size = 40, opacity = 0.6).encode(
x=alt.X('x:Q', title=type_dict[x_val], scale = alt.Scale(domain = [min(df['x']),max(df['x'])])),
y=alt.Y('y:Q', title='Average hate crime per 100K people'),
tooltip = [alt.Tooltip('state:O', title = "State")]
)
# Plot the best fit polynomials
polynomial_fit = alt.Chart(poly_data).mark_line().encode(
x='xfit:Q',
y='1:Q'
)
return (points + polynomial_fit).properties(title = 'Hate crime rate across socio-economic factors', width = 400, height = 300)
def plot_aq(date=0):
"""
Returns map of interpolated AQ values for single date
"""
plot_df = interp_buffer.query('date_int ==' +
str(date)).reset_index(drop=True)
interpolated_plot = (alt.Chart(plot_df).mark_geoshape().encode(
fill=alt.Color(param,
scale=alt.Scale(domain=[
min(interp_buffer[param].dropna()),
max(interp_buffer[param].dropna())
],
type='linear',
scheme='yelloworangebrown'),
title=units_dict[param]),
tooltip=[
alt.Tooltip(param,
title=param_title[param] + " \
" + units_dict[param],
format='0.5')
]))
if date < mar1_offset + 16:
return ((interpolated_plot + base_map).properties(
title="Bay Area Avg.{} ({}): {}".format(
param_title[param], units_dict[param],
plot_df.loc[0, 'date']),
height=450,
width=500).configure_title(fontSize=20).configure_legend(
titleFontSize=16, labelFontSize=14))
else:
annotation = alt.Chart(plot_df).mark_text(
align='left',
baseline='middle',
fontSize=16,
dx=65,
dy=-210,
text='Shelter-in-place enforced',
color='red').encode()
return ((interpolated_plot + base_map + annotation).properties(
title="Bay Area Avg.{} ({}): {}".format(
param_title[param], units_dict[param],
plot_df.loc[0, 'date']),
height=450,
width=500).configure_title(fontSize=20).configure_legend(
titleFontSize=16, labelFontSize=14))
def newcomer():
#TOP 10 der Entitäten, die in den letzten 365 Tagen erstellt wurden
if satzart == 'alle':
st.subheader(f'TOP 10 GND-Newcomer')
st.write(
'TOP 10 der GND-Entitäten, die in den letzten 365 Tagen angelegt wurden.'
)
newcomer_daten = pd.read_csv(
f'{path}/../stats/title_gnd_newcomer_top10.csv', index_col=None)
newcomer = alt.Chart(newcomer_daten).mark_bar().encode(
alt.X('gnd_id', title='Entitäten', sort='-y'),
alt.Y('count', title='Anzahl'),
alt.Color('name', sort='-y', title='Entität'),
tooltip=[
alt.Tooltip('name:N', title='Entität'),
alt.Tooltip('bbg:N', title='Satzart'),
alt.Tooltip('gnd_id:N', title='IDN'),
alt.Tooltip('count:Q', title='Anzahl')
])
else:
st.subheader(f'TOP 10 {satzart} GND-Newcomer')
st.write(
f'TOP 10 der {satzart} Sätze, die in den letzten 365 Tagen angelegt wurden.'
)
newcomer_daten = load_gnd_top_daten('newcomer_top10')
newcomer = alt.Chart(
newcomer_daten.loc[newcomer_daten['bbg'].str.startswith(
satzart[:2],
na=False)]).mark_bar().encode(alt.X('gnd_id:O',
title='Entitäten',
sort='-y'),
alt.Y('count', title='Anzahl'),
alt.Color('name',
sort='-y',
title='Entität'),
tooltip=[
alt.Tooltip('name:N',
title='Entität'),
alt.Tooltip('gnd_id:N',
title='IDN'),
alt.Tooltip('count:Q',
title='Anzahl')
])
st.altair_chart(newcomer, use_container_width=True)
def plot_immunity(data):
df = data.data[[
"Date", "Immunity (Lower Bound)", "Immunity (Upper Bound)"
]].copy()
df = df.set_index("Date").rolling(window="14d").mean().reset_index()
chart = (alt.Chart(df).mark_area(opacity=0.5).encode(
x="Date:T",
y=alt.Y(
"Immunity (Lower Bound):Q",
title="Immunity %",
axis=alt.Axis(format="%"),
),
y2=alt.Y2("Immunity (Upper Bound):Q", title=""),
tooltip=alt.Tooltip("Date"),
))
return chart
def title_plot(region_filter):
sorted_genre = list(sales_data[sales_data.Region == region_filter].groupby(
"Genre").sum().sort_values("Sales", ascending=False).index)
chart = alt.Chart(
sales_data[sales_data.Region == region_filter]).mark_circle(
size=50).encode(
alt.X("Genre", sort=sorted_genre, title=None),
alt.Y("Sales:Q", stack=None, title="Sales (in millions)"),
alt.Color("Genre", scale=alt.Scale(scheme='category20')),
alt.Tooltip("Name"))
chart = chart + alt.Chart(
sales_data[sales_data.Region == region_filter].sort_values(
"Sales", ascending=False).iloc[:5, ]).mark_text(
align="left", dx=10).encode(
alt.X("Genre", sort=sorted_genre),
alt.Y("Sales:Q"),
text="Name").properties(title=region_filter)
return chart.to_html()
def show_publisher_region_bar(df):
st.write('## Popular Publishers')
st.write(
"**It might also be interesting to compare different publishers.🤔**")
st.write("💡 *You can select specific year range with slider*")
st.write(
"💡 *Hover over publishers to see the number of games they released during the time*"
)
min_year, max_year = get_year_range(df)
selected_year = st.slider("View Popular Publisher in Year Range:",
min_year, max_year, (min_year, max_year), 1)
df_selected = df[(df["Year"] >= selected_year[0])
& (df["Year"] <= selected_year[1])]
regions = [
'Global_Sales', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales'
]
new_df = get_publisher_agg_df(df_selected, regions, 10)
brush = alt.selection_single(encodings=['color'])
titles = {
'Global_Sales': 'Global',
'NA_Sales': 'North America',
'EU_Sales': 'Europe',
'JP_Sales': 'Japan',
'Other_Sales': 'Other Regions'
}
for region in regions:
st.write("**%s**" % (titles[region]))
st.write(
alt.Chart(new_df[region]).mark_bar().encode(
x=alt.X("Publisher:N", sort='-y'),
y=alt.Y(region, title='Sale (in millions)'),
color=alt.Color("Publisher:N",
scale=alt.Scale(scheme='tableau20')),
tooltip=[
alt.Tooltip(field="Name",
title="Total Released Games",
type="quantitative")
]).properties(width=600, height=350))
