def make_amount_plot(infile):
purchases = infra.pd.read_parquet(infile)
purchases = purchases.assign(count=1)
purchases = purchases[["amount_bytes", "count"
]].groupby(["amount_bytes"]).sum().reset_index()
purchases["amount_mb"] = purchases["amount_bytes"] / 1000**2
alt.Chart(purchases).mark_point().encode(
x=alt.X(
"amount_mb",
title="Session Purchase Amount (MB) (Log Scale)",
scale=alt.Scale(type="log", domain=(10, 2000)),
),
y=alt.Y(
"count",
title="Occurrences (Count) (Log Scale)",
scale=alt.Scale(type="log", ),
),
color=alt.condition(
'datum.count>1000',
alt.ColorValue('red'),
alt.ColorValue('steelblue'),
),
shape=alt.condition(
'datum.count>1000',
alt.ShapeValue('diamond'),
alt.ShapeValue('triangle'),
),
).properties(
width=500,
height=300,
).save("renders/purchase_timing_per_user_clumped_amounts.png",
scale_factor=2.0)
print(purchases)
def available_parking():
data = available_parking_data()
person = (
"m 0 0 l 14 -15 a 5 5 0 0 1 9 8 l -14 15 l 9 26 l -4 5 l -12 -22 l -12 12 l 1 5 l -2 3 l -12 -11 l 3 -2 l 5 0 l 9 -14 l -19 -11 l 4 -3 l 22 4"
)
bars = alt.Chart(data).transform_calculate(
row="ceil(datum.id/5)").transform_calculate(
col="datum.id - datum.row*5").mark_point(
filled=True,
size=30,
).encode(
x=alt.X("col:O", axis=None),
y=alt.Y("row:O", axis=None),
shape=alt.ShapeValue(person),
color=alt.condition(
alt.datum.occupied == 1,
alt.value('orange'),
alt.value('steelblue'),
),
).properties(width=700, height=700, title="Available Parking")
text = bars.mark_text(
align='center',
baseline='middle',
fontSize=25,
dx=-3,
dy=-16 # Nudges text to right so it doesn't appear on top of the bar
).encode(text='id:Q')
return (bars + text).configure_title(
fontSize=25,
font='Calibri', #This can be editted
anchor='start',
color='gray').configure_view(strokeWidth=0)
def labeling_buttons(title):
colors = ('#33cc33', '#ace600', '#e6e600', '#ff9900', '#ff3300')
data = pd.DataFrame([{'id': i, 'color': c} for i, c in enumerate(colors)])
brush = alt.selection_single(nearest=True, empty='none', fields=['id'])
return alt.Chart(data).mark_point(filled=True, size=100).encode(
x=alt.X("id:O", axis=None),
shape=alt.ShapeValue(PERSON),
color=alt.condition(
alt.datum.id <= brush.id, alt.Color('color:N', scale=None),
alt.value('skyblue'))).properties(
width=400, height=100, title=title).configure_view(
strokeWidth=0).add_selection(brush).configure_title(
fontSize=16)
def predictresults():
h1 = House.query.order_by(House.id.desc()).first()
# Creating feature array
new_instance = np.array([[h1.livearea, h1.bdrms, h1.baths, h1.one_story,
h1.att_garage, h1.basement]])
# Predicting new_instance target
prediction = rf.predict(new_instance)
# Principal Components
lle_new_instance = lle_pipe.transform(new_instance)
lle_new_data = pd.DataFrame(lle_new_instance, columns=['pca1', 'pca2'])
# Returning text depending on the prediction
pred = "not" if prediction[0] == 0 else ""
# Creating a sweet altair chart
lle_chart = alt.Chart(lle_data).mark_circle(opacity=.4).encode(
alt.X("pca1", title="principal component 1"),
alt.Y("pca2", title="principal component 2"),
alt.Color("target:N", title='Built before 1980', scale=alt.Scale(range=["gray", "orange"]))
)
lle_chart = lle_chart + alt.Chart(lle_new_data).mark_point(
opacity=1, color="#CA03FF", filled=True, size=150).encode(
alt.X("pca1"),
alt.Y("pca2"),
alt.ShapeValue('cross')).properties(**props).interactive()
# Saving chart to file -- probably will be building this inside the app though...
lle_chart.save("b41980/static/lle_spec.json")
# Creating dictionary to pass into the html
posts = {
"livearea": h1.livearea,
"bdrms": h1.bdrms,
"baths": h1.baths,
"one_story": h1.one_story,
"att_garage": h1.att_garage,
"basement": h1.basement,
"prediction": pred
}
return render_template("predictresults.html", title="Prediction Results",
posts=posts)
data = pd.DataFrame([dict(id=i) for i in range(1, 101)])
person = ("M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 "
"-0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 "
"0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 "
"0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 "
"0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 "
"-0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 "
"-0.6 -0.4 -0.6z")
alt.Chart(data).transform_calculate(
row="ceil(datum.id/10)").transform_calculate(
col="datum.id - datum.row*10").mark_point(filled=True, size=50).encode(
x=alt.X("col:O", axis=None),
y=alt.Y("row:O", axis=None),
shape=alt.ShapeValue(person)).properties(
width=400, height=400).configure_view(strokeWidth=0)
##
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).mark_line(point=True).encode(x='date:T',
y='price:Q',
color='symbol:N')
##
import altair as alt
from vega_datasets import data
def _plot_isotype_array(baseline_ef,
exposed_ef,
population_size=100,
title="",
configure_chart=True,
icon_shape=PERSON_SHAPE,
icon_size=75,
stroke_color="black",
stroke_width=1.3,
cross_shape=CROSS_SHAPE,
cross_width=None,
chart_width=350,
chart_height=400):
if isinstance(baseline_ef, float) or isinstance(exposed_ef, float):
warnings.warn(
"Can't currently plot (color) fractional icons. Rounding to nearest integer."
)
baseline_ef = round(baseline_ef)
exposed_ef = round(exposed_ef)
data = __generate_chart_source_data(baseline_ef, exposed_ef,
population_size)
root = round(math.sqrt(
population_size)) # Create a square grid of total `population_size`
# https://altair-viz.github.io/gallery/isotype_grid.html
base_chart = alt.Chart(data).transform_calculate(
row=f"ceil(datum.id/{root})",
col=f"datum.id - datum.row*{root}",
).encode(
x=alt.X("col:O", axis=None),
y=alt.Y("row:O", axis=None),
).properties(width=chart_width,
height=chart_height,
title=title if title else "")
icons = base_chart.mark_point(
filled=True,
stroke=stroke_color,
strokeWidth=stroke_width, # 2,
size=icon_size,
).encode(
color=alt.Color(
'hue:N',
scale=alt.Scale(
domain=[
0, 1, 2
], # Explicitly specify `hue` values or coloring will fail if <3 levels exist in data
range=[
"#FFFFFF", # Population (0)
"#4A5568", # Baseline (1)
"#FA5765", # Exposed (2) "#4078EF"
]),
# TODO: add uncertainty using shade: lighter color fill of icons in the 95% CI.
legend=None),
shape=alt.ShapeValue(icon_shape),
)
chart = icons
if exposed_ef < baseline_ef:
stroke_out = base_chart.mark_point(
# shape="cross",
filled=True,
stroke="#4078EF", # "black"
# strokeWidth=cross_width,
strokeWidth=math.sqrt(icon_size) /
1.7 if cross_width is None else cross_width,
strokeCap="round",
size=icon_size,
).encode(shape=alt.ShapeValue(cross_shape),
opacity=alt.Opacity(
'reduced:N',
legend=None,
scale=alt.Scale(domain=[False, True], range=[0, 1]),
))
chart += stroke_out
if configure_chart: # Configured charts cannot be later concatenated.
chart = chart.configure_title(
align="left",
anchor="start",
offset=-10,
).configure_view(strokeWidth=0, )
return chart