def data_bar_jail():
county_data = read_county_from_db(session.get(
'current_state'), session.get('current_county'))
# Create a label for the jail population to be included in the chart.
# Result of lambda is a float, thus the slice notation is used
county_data['total_jail_pop_label'] = county_data['total_jail_pop'].apply(lambda x: "{:,}".format(x)[:-2])
county_data['total_jail_pretrial_label'] = county_data['total_jail_pretrial'].apply(lambda x: "{:,}".format(x)[:-2])
# Create the chart
jail = Chart(data=county_data, height=HEIGHT, width=WIDTH).mark_bar(color='#444760').encode(
X('year:O', axis=Axis(title='Year')),
Y('total_jail_pop', axis=Axis(title='Total Jail Population')),
tooltip=[alt.Tooltip('year', title='Year'), alt.Tooltip(
'total_jail_pop_label', title='Total jail population')]
).properties(
title='Jail population in {}'.format(session.get('current_county'))
).interactive()
# Create pre-trial chart to overlay on top
pre_trial = Chart(data=county_data, height=HEIGHT, width=WIDTH).mark_bar(
color="#d66241", interpolate='step-after', line=True,
).encode(
X('year:O', axis=Axis(title='Year')),
Y('total_jail_pretrial', axis=Axis(title='Number of inmates')),
tooltip=[alt.Tooltip('year', title='Year'), alt.Tooltip(
'total_jail_pretrial_label', title='Pre-trial jail population')]
).properties(
title='Pre-trial jail population in {}'.format(
session.get('current_county'))
).interactive()
chart = alt.layer(jail + pre_trial)
return chart.to_json()
def data_bar_jail():
county_data = read_county_from_db(session.get('current_state'),
session.get('current_county'))
# Create the chart
jail = Chart(data=county_data, height=HEIGHT,
width=WIDTH).mark_bar(color='#444760').encode(
X('year:O', axis=Axis(title='Year')),
Y('total_jail_pop',
axis=Axis(title='Total Jail Population')),
tooltip=[
alt.Tooltip('year', title='Year'),
alt.Tooltip('total_jail_pop',
title='Total jail population')
]).properties(title='Jail population in {}'.format(
session.get('current_county'))).interactive()
# Create pre-trial chart to overlay on top
pre_trial = Chart(data=county_data, height=HEIGHT, width=WIDTH).mark_bar(
color="#d66241",
interpolate='step-after',
line=True,
).encode(X('year:O', axis=Axis(title='Year')),
Y('total_jail_pretrial', axis=Axis(title='Number of inmates')),
tooltip=[
alt.Tooltip('year', title='Year'),
alt.Tooltip('total_jail_pretrial',
title='Pre-trial jail population')
]).properties(title='Pre-trial jail population in {}'.format(
session.get('current_county'))).interactive()
chart = alt.layer(jail + pre_trial)
return chart.to_json()
def county_scatter():
state_name = session.get('current_state')
county_name = session.get('current_county')
# Connect to the database
conn = sqlite3.connect('./db/incarceration.db')
# Determine whether 2015 or 2016 has more data
year_2016_nulls = test_nulls_for_year(2016, state_name, conn)
year_2015_nulls = test_nulls_for_year(2015, state_name, conn)
year = 2016 # default year
# Test to see if 2015 has more non-null values
if year_2016_nulls.iloc[0]['PercentNotNull'] < year_2015_nulls.iloc[0]['PercentNotNull']:
year = 2015
# Select prison population data for the entire state for the selected year
all_counties_prison_pop = pd.read_sql_query(f"""SELECT county_name, total_pop, total_prison_pop, urbanicity
FROM
incarceration
WHERE state = '{state_name}'
AND year = {year};
""", conn)
# Select prison population data for the specific county for the selected year
county_prison_pop = pd.read_sql_query(f"""SELECT county_name, total_pop, total_prison_pop, urbanicity
FROM
incarceration
WHERE state = '{state_name}'
AND county_name = '{county_name}'
AND year = {year};
""", conn)
# Close connection
conn.close()
state_chart = Chart(data=all_counties_prison_pop, height=HEIGHT, width=WIDTH).mark_circle(size=70).encode(
X('total_pop', axis=Axis(title='County population')),
Y('total_prison_pop', axis=Axis(title='Total prison population')),
color=alt.Color('urbanicity', legend=alt.Legend(title='Urbanicity')),
size=alt.Color('total_pop', legend=alt.Legend(
title='Total population')),
tooltip=[alt.Tooltip('county_name', title='County'), alt.Tooltip(
'total_pop', title='Total county population'), alt.Tooltip('total_prison_pop', title='Total prison population')],
).properties(
title='Statewide prison population {}, {}'.format(year, state_name)).interactive()
county_chart=Chart(data=county_prison_pop, height=HEIGHT, width=WIDTH).mark_square(
size=250, fillOpacity=0.5, stroke='black', color='black').encode(
X('total_pop', axis=Axis(title='County population')),
Y('total_prison_pop', axis=Axis(title='Total prison population')),
tooltip=['county_name', 'total_pop', 'total_prison_pop']
).interactive()
chart=alt.layer(county_chart, state_chart)
return chart.to_json()
def create_bins(data):
columns = ['Wind Speed (kts)', 'Rain (mm)', 'Temp (◦C)', 'Humidity (%)']
filenumber = 1
for items in columns:
means = data.groupby(['Location'])[items].mean()
means = means.to_frame()
bins = pd.cut(means[items], 3, labels=['Low', 'Moderate', 'High'])
bins = bins.to_frame()
bracket = items.index('(')
legend = items[:bracket]
bins.columns.values[0] = 'Average ' + legend + 'Category '
means.columns.values[0] = 'Average ' + items + ' Per Station'
new_df = pd.concat([means, bins], axis=1, join_axes=[means.index])
new_df.reset_index(level=0, inplace=True)
new_df
graph = alt.Chart(new_df).mark_bar().encode(
x='Location',
y=Y(means.columns.values[0], axis=Axis(format='f')),
#https://github.com/altair-viz/altair/issues/191
color=bins.columns.values[0])
filename = 'templates/plot' + str(filenumber) + '.html'
graph.savechart(filename)
filenumber += 1
def data_line():
chart = (Chart(data=sample_data.df_list, height=HEIGHT,
width=WIDTH).mark_line(color="green").encode(
X("name", axis=Axis(title="Sample")),
Y("data", axis=Axis(title="Value")),
).interactive())
return chart.to_json()
def make_charts(df, color, x_axis_title, y_axis_title, title):
chart = Chart(data=df, height=height,
width=width).mark_bar(color=color).encode(
X('name', axis=Axis(title=x_axis_title), sort=None),
Y('data',
axis=Axis(title=y_axis_title))).properties(title=title)
return chart.to_json()
def plot_sig_recon(sigs, recons, time=None):
"""
sigs: channels x samples
"""
long_dfs = []
for sig_name, sig_array in zip(('sig', 'recon'), (sigs, recons)):
df = pd.DataFrame(data=sig_array.T)
if time is not None:
sdf['time'] = time
else:
df['time'] = df.index
df_long = pd.melt(df,
id_vars=('time', ),
var_name='signal',
value_name='magnitude')
df_long['src'] = sig_name
long_dfs.append(df_long)
combined_dfs = pd.concat(long_dfs, ignore_index=True)
combined_dfs.src = pd.Categorical(combined_dfs.src,
categories=('sig', 'recon'),
ordered=True)
sig_chart = (Chart(combined_dfs).encode(
X('time'), Y('magnitude'), Row('signal'),
Column('src:O', scale=Scale(domain=('sig', 'recon'),
type='ordinal'))).mark_point())
return sig_chart
def data_waterfall():
chart = (Chart(
data=sample_data.df_water,
width=WIDTH,
).mark_bar(color="gray").encode(
X("Name", axis=Axis(title="Sample")),
Y("Value", axis=Axis(title="Value")),
).interactive())
return chart.to_json()
def gen_var_barcharts_by_geo(data, var, agg_type, geo):
data = aggregate_data(data, agg_type, geo)
titlex = agg_type + ' of ' + var.split('_', 1)[1].replace('_', ' ')
titley = geo.replace('_', ' ')
bar_chart = alt.Chart(data).mark_bar().encode(x=X(var,
axis=Axis(title=titlex)),
y=Y((geo + ':O'),
axis=Axis(title=titley)))
with open('./runs/%s_by_%s.json' % (var, geo), 'w') as outfile:
json.dump(bar_chart.to_json(), outfile)
def gen_custom_barchart(table, var):
df = orca.get_table(table).to_frame(['parcel_id', var]).\
groupby(var).count().reset_index()
df.rename(columns={'parcel_id': 'count_' + table}, inplace=True)
chart = alt.Chart(df).mark_bar().encode(x=X('count_' + table,
axis=Axis(title='count_' +
table)),
y=Y(var + ':O',
axis=Axis(title=var)))
with open('./runs/%s_by_%s.json' % (table, var), 'w') as outfile:
json.dump(chart.to_json(), outfile)
def gen_barcharts_n_largest(data, var, agg_type, geo, n):
data = aggregate_data(data, agg_type, geo)
max_data = data.nlargest(n, var).reset_index()
titlex = agg_type + ' of ' + var.split('_', 1)[1].replace('_', ' ')
titley = geo.replace('_', ' ')
bar_chart = alt.Chart(max_data).mark_bar().encode(
x=X(var, axis=Axis(title=titlex)),
y=Y(geo + ':O', axis=Axis(title=titley)))
with open('./runs/%s_%ss_with_max_%s.json' % (n, geo, var),
'w') as outfile:
json.dump(bar_chart.to_json(), outfile)
def data_bar_prison():
county_data = read_county_from_db(session.get('current_state'),
session.get('current_county'))
# Create the chart
chart = Chart(
data=county_data, height=HEIGHT,
width=WIDTH).mark_bar(color='#2f3142').encode(
X('year:O', axis=Axis(title='Year')),
Y('total_prison_pop',
axis=Axis(title='Total Prison Population'))).properties(
title='Prison population in {}'.format(
session.get('current_county')))
return chart.to_json()
def visualize(result, fileName):
from altair import Chart, Color, Y, Scale
#chart = LayeredChart(result)
#chart += Chart().mark_line().encode(x='Search:O', y='Elapsed Time:Q')
chart = Chart(result).mark_point().encode(x='Search:O',
color='Problem:O',
y=Y('Elapsed Time:Q',
scale=Scale(type='log')))
#x='Search:O', color='Problem:O', y='Elapsed Time:Q')
# with open('out.html', 'w') as f:
# f.write(html)
chart.savechart(fileName + ".l.svg")
def _encode_fields(self,
xfield,
yfield,
time_unit=None,
scale=Scale(zero=False)):
"""
Encode the fields in Altair format
"""
if scale is None:
scale = Scale()
xfieldtype = xfield[1]
yfieldtype = yfield[1]
x_options = None
if len(xfield) > 2:
x_options = xfield[2]
y_options = None
if len(yfield) > 2:
y_options = yfield[2]
if time_unit is not None:
if x_options is None:
xencode = X(xfieldtype, timeUnit=time_unit)
else:
xencode = X(xfieldtype,
axis=Axis(**x_options),
timeUnit=time_unit,
scale=scale)
else:
if x_options is None:
xencode = X(xfieldtype)
else:
xencode = X(xfieldtype, axis=Axis(**x_options), scale=scale)
if y_options is None:
yencode = Y(yfieldtype, scale=scale)
else:
yencode = Y(yfieldtype, axis=Axis(**y_options), scale=scale)
return xencode, yencode
def pretrial_jail_chart():
county_data = read_county_from_db(session.get('current_state'),
session.get('current_county'))
chart = Chart(data=county_data, height=HEIGHT, width=WIDTH).mark_line(
color="#08080B",
interpolate='step-after',
line=True,
).encode(X('year:O', axis=Axis(title='Year')),
Y('total_jail_pretrial', axis=Axis(title='Number of inmates')),
tooltip=[
'year', 'total_jail_pretrial'
]).properties(title='Pre-trial jail population in {}'.format(
session.get('current_county'))).interactive()
return chart.to_json()
def plot_signals(sigs, time=None):
"""
sigs: channels x samples
"""
df = pd.DataFrame(data=sigs.T)
if time is not None:
df['time'] = time
else:
df['time'] = df.index
df_long = pd.melt(df,
id_vars=('time', ),
var_name='signal',
value_name='magnitude')
sig_chart = (Chart(df_long).encode(X('time'), Y('magnitude'),
Row('signal')).mark_line())
return sig_chart
def gen_var_scatters(data, var1, var2, agg1, agg2, geo_points, geo_large):
colors = data.groupby(geo_points).min().reset_index()
colors = colors[[geo_points, geo_large]]
data_1 = aggregate_data(data, agg1, geo_points)[[var1, geo_points]]
data_2 = aggregate_data(data, agg2, geo_points)[[var2, geo_points]]
data = pd.merge(data_1, data_2, on=geo_points, how='left')
data = pd.merge(data, colors, on=geo_points, how='left')
titlex = agg1 + ' of ' + var1.split('_', 1)[1].replace('_',
' ') + ' by zone'
titley = agg2 + ' of ' + var2.split('_', 1)[1].replace('_',
' ') + ' by zone'
scatter = alt.Chart(data).mark_point().encode(
x=X(var1, axis=Axis(title=titlex)),
y=Y(var2, axis=Axis(title=titley)),
color=geo_large + ':N',
)
with open('./runs/%s_vs_%s.json' % (var2, var1), 'w') as outfile:
json.dump(scatter.to_json(), outfile)
def plotter(name, x, label):
df = pandas.DataFrame({
"x": [el[0] for el in x],
"y": [el[1] for el in x],
"z": [el[2] for el in x]
})
chart = alt.Chart(
df, title=label[2], width=60, height=600).mark_bar().encode(
column=Column('x', title=label[3], spacing=75),
x=X('z', title=""),
y=Y('y', title=label[4]),
color=Color(
'z', title=label[5],
scale=Scale(range=['#6fbf4a', '#5f249f']))).configure_view(
strokeWidth=0.0, width=300)
chart = chart.configure_title(fontSize=25, offset=15)
chart.save(f"{name}.html")
def data_bar_prison():
county_data = read_county_from_db(session.get(
'current_state'), session.get('current_county'))
# Create a label for the prison population to be included in the chart.
# Result of lambda is a float, thus the slice notation is used
county_data['total_prison_pop_label'] = county_data['total_prison_pop'].apply(lambda x: "{:,}".format(x)[:-2])
# Create the chart
chart = Chart(data=county_data, height=HEIGHT, width=WIDTH).mark_bar(color='#2f3142').encode(
X('year:O', axis=Axis(title='Year')),
Y('total_prison_pop', axis=Axis(title='Total Prison Population')),
tooltip=[alt.Tooltip('year', title='Year'), alt.Tooltip(
'total_prison_pop_label', title='Total prison population')]
).properties(
title='Prison population in {}'.format(session.get('current_county'))
).interactive()
return chart.to_json()
def chartLog():
"Display chart for selected log"
db_folder = app.config['UPLOAD_FOLDER']
logFiles = glob.glob('%s/*.db' % db_folder)
form = ChartLog()
form.logFile.choices = [(f, f) for f in logFiles]
form.chartId.choices = [(q['id'], q['id']) for q in queries.graphs]
try:
dbname = app.dbname
if os.path.exists(dbname):
form.logFile.data = dbname
except:
pass
if not form.validate_on_submit():
return render_template('chartLog.html',
chart={},
dbName=None,
form=form)
dbname = os.path.join(form.logFile.data)
if not os.path.exists(dbname):
flash('Database does not exist', 'error')
return render_template('error.html', title='Database error')
try:
conn = sqlite3.connect(dbname)
except Exception as e:
app.logger.error(traceback.format_exc())
flash('Error: %s' % (str(e)), 'error')
return render_template('error.html',
title='Error in database reporting')
chartId = form.chartId.data
charts = [q for q in queries.graphs if q['id'] == chartId]
if not charts:
flash("Error: logic error couldn't find chartId", 'error')
return render_template(
'error.html', title='Error in in configuration of chart reports')
q = charts[0]
app.logger.debug("running chart query: %s - %s" % (q['title'], q['sql']))
start = datetime.now()
try:
df = pd.read_sql_query(q['sql'], conn)
except Exception as e:
flash('Error: %s' % (str(e)), 'error')
return render_template('error.html',
title='Error in database reporting')
end = datetime.now()
delta = end - start
if q['graph_type'] == 'line':
chart = Chart(data=df, height=HEIGHT, width=WIDTH).mark_line().encode(
X(q['x']['field'],
axis=Axis(title=q['x']['title'], labelOverlap='greedy')),
Y(q['y']['field'], axis=Axis(title=q['y']['title'])))
else:
chart = Chart(data=df, height=HEIGHT, width=WIDTH).mark_bar().encode(
X(q['x']['field'],
axis=Axis(title=q['x']['title'], labelOverlap='greedy')),
Y(q['y']['field'], axis=Axis(title=q['y']['title'])))
data = {
'id': "chart",
'data': chart.to_json(),
'title': q['title'],
'explanation': q['explanation'],
'sql': q['sql'],
'time_taken': str(delta)
}
return render_template('chartLog.html',
chart=data,
dbName=dbname,
form=form)
def data_waterfall():
chart = Chart(data.df_water).mark_bar(color='lightgreen').encode(
X('Name', axis=Axis(title='Sample')),
Y('Value', axis=Axis(title='Value')))
return chart.to_json()
def data_line():
chart = Chart(data=data.df_list, height=HEIGHT,
width=WIDTH).mark_line().encode(
X('name', axis=Axis(title='Sample')),
Y('data', axis=Axis(title='Value')))
return chart.to_json()
# coding=utf-8
from dataviz import Dataviz
from altair import Chart, load_dataset, X, Y
df = load_dataset('seattle-weather')
dataviz = Dataviz("Seattle Weather")
overview_chart = Chart(df).mark_bar(stacked='normalize').encode(
X('date:T', timeUnit='month'),
Y('count(*):Q'),
color='weather',
)
dataviz.add("commented", title="Overview", charts=[overview_chart],
comment= "Lorem ipsum dolor sit amet, cum pertinacia definitionem an. His ne oratio facilis voluptatum, nam lorem putant qualisque ad. Mea in affert nostrum. Mea cu ignota adipiscing. Omnis mnesarchum vix cu, omnes impedit democritum nec te. Malorum urbanitas consectetuer ei eam, no sea paulo tollit detracto."
)
chart_a = Chart(df).mark_bar().encode(
X('precipitation', bin=True),
Y('count(*):Q')
)
chart_b = Chart(df).mark_line().encode(
X('date:T', timeUnit='month'),
Y('average(precipitation)')
)
chart_c = Chart(df).mark_line().encode(
X('date:T', timeUnit='month'),
Y('average(temp_max)'),
df_dados = result.loc[:][['Rodada','Temporada','Time','Resultado']].copy()
df_dados.sort_values(by=['Rodada'] ,ascending=True , inplace=True)
df_dados['Acumulado'] = df_dados.groupby(['Temporada','Time'])[['Time','Resultado']].cumsum()
x = st.slider('Selecione o ano',2012, 2019, (2012))
df_dados = df_dados[df_dados['Temporada'] == x].reset_index(drop=True)
st.dataframe(df_dados)
bars = alt.Chart(df_dados).mark_bar().encode(
x=X('2:Q',axis=Axis(title='Brasileirao')),
y=Y('0:Q',axis=Axis(title='Times'))
).properties(
width=650,
height=400
)
bar_plot = st.altair_chart(bars)
def plot_bar_animated_altair(df,week):
bars = alt.Chart(df, title="Ranking por Rodada :"+week)
for week in range(1,39):
teste = str(week)
bars = plot_bar_animated_altair(df_dados[df_dados['Rodada']== teste],teste)
time.sleep(0.01)
def course_grade():
session_id = os.environ.get("SESSION_ID")
selected_course = session.get('selected_course', None)
# show all course grades if haven't selected course from dropdown
if selected_course == None:
source = pd.read_sql(grade_calculations.Co_grade_query(user_id=session_id).statement, db.session.bind)
# make chart here
bar = alt.Chart(
data=source).mark_bar().encode(
Y('cograde:Q',
scale=Scale(domain=(0, 5))
),
alt.X('course:N',
sort=alt.EncodingSortField(field= "cograde", op="sum", order = "descending")),
#color=alt.Color('course:N', legend=None))#.interactive()
color=alt.Color('major:N'))#.interactive()
line = alt.Chart(source).mark_rule(color='red').encode(
y='mean(cograde):Q'
)
chart = alt.layer(
bar, line
).properties(
width=480, height=336
)
# always return to json.
return chart.to_json()
else:
# show individual course trend for the selected course from dropdown list
source = grade_calculations.co_grade_over_time(user_id=session_id, course=selected_course)
# make interactive chart
# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection(type='single', nearest=True, on='mouseover',
fields=['Date'], empty='none')
# The basic line
line = alt.Chart(source).mark_line().encode(
x='Date:N',
y='Course Grade:Q',
)
# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = alt.Chart(source).mark_point().encode(
x='Date:N',
opacity=alt.value(0),
).add_selection(
nearest
)
# Draw points on the line, and highlight based on selection
points = line.mark_point().encode(
opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)
# Draw text labels near the points, and highlight based on selection
text = line.mark_text(align='left', dx=5, dy=-5).encode(
text=alt.condition(nearest, 'Course Grade:Q', alt.value(' '))
)
# Draw a rule at the location of the selection
rules = alt.Chart(source).mark_rule(color='gray').encode(
x='Date:N',
).transform_filter(
nearest
)
# Put the five layers into a chart and bind the data
chart = alt.layer(
line, selectors, points, rules, text
).properties(
width=500, height=336
)
return chart.to_json()
import pandas as pd
import numpy as np
from altair import Chart, X, Y, SortField, Detail, Axis
csv_path = "../data/dropped-frames.csv"
df = pd.read_csv(csv_path, parse_dates=["Dropped Frame Start", "Dropped Frame End"], low_memory=False)
data = df[['Officer ID', 'Dropped Frame Start', 'Duration', 'FPS', 'Dropped Frames', 'Resolution', 'File Size', 'File Name', 'Frame Range', 'Player Time Range']]
data = data.rename(columns={'Dropped Frame Start': 'Timestamp'})
## Overview
Chart(data.sample(100)).configure_axis(gridColor='#ccc').mark_line(interpolate='linear').encode(
X(field='Timestamp', type='temporal', timeUnit='yearmonth', axis=Axis(title=' ', ticks=6, labelAngle=0, tickSizeEnd=0, tickSize=0, tickPadding=10)),
Y('sum(Duration)', axis=Axis(title='Seconds lost'))
).savechart('test.svg')
def data_bar():
chart = Chart(data=sample_data.df_list, height=HEIGHT,
width=WIDTH).mark_bar(color='yellow').encode(
X('name', axis=Axis(title='Sample')),
Y('data', axis=Axis(title='Value'))).interactive()
return chart.to_json()
def data_waterfall():
chart = Chart(sample_data.df_water).mark_bar(color='gray').encode(
X('Name', axis=Axis(title='Sample')),
Y('Value', axis=Axis(title='Value'))).interactive()
return chart.to_json()