def test_function_selector_could_not_match():
# should return False
obj = go.layout.Annotation(x=1, y=2, text="pat metheny")
def _sel(d):
return d["x"] == 1 and d["y"] == 3 and d["text"] == "pat metheny"
assert BaseFigure._selector_matches(obj, _sel) == False
def test_baseplotlytypes_cannot_match_subset():
# should return False because "undefined" keys in sel return None, and are
# compared (because "key in sel" returned True, it's value was None)
obj = go.layout.Annotation(x=1, y=2, text="pat metheny")
sel = go.layout.Annotation(
x=1,
y=2,
)
assert BaseFigure._selector_matches(obj, sel) == False
def test_selector_has_nonmatching_value():
# should return False
assert (
BaseFigure._selector_matches(
dict(hello="everybody", today="cloudy", myiq=55),
dict(myiq=55, today="sunny"),
)
== False
)
def test_selector_has_nonmatching_key():
# should return False
assert (
BaseFigure._selector_matches(
dict(hello="everybody", today="cloudy", myiq=55),
dict(myiq=55, cronenberg="scanners"),
)
== False
)
def test_selector_matches_subset_of_obj():
# should return True
assert (
BaseFigure._selector_matches(
dict(hello="everybody", today="cloudy", myiq=55),
dict(myiq=55, today="cloudy"),
)
== True
)
def test_string_selector_matches_type_key():
assert BaseFigure._selector_matches(dict(type="bar"), "bar")
assert BaseFigure._selector_matches(dict(type="scatter"), "bar") == False
def test_selector_none():
# should return True
assert BaseFigure._selector_matches({}, None) == True # arbitrary,
def test_baseplotlytypes_could_not_match():
# should return False
obj = go.layout.Annotation(x=1, y=3, text="pat metheny")
sel = go.layout.Annotation(x=1, y=2, text="pat metheny")
assert BaseFigure._selector_matches(obj, sel) == False
def test_selector_empty_dict():
# should return True
assert (
BaseFigure._selector_matches(dict(hello="everybody"), {}) == True # arbitrary,
)
def index():
global colors
# extract data needed for visuals
data = df.iloc[:, 4:]
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
cat_count = (df.iloc[:, 4:] != 0).sum()
cat_names = list(cat_count.index)
graphs = [
{
'data': [
bar(
x=genre_names,
y=genre_counts,
color=genre_counts
)
],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
}
}
},
#top 5 most popular categories
{
'data': [
bar(
x=cat_names,
y=cat_count,
color=cat_count, color_continuous_scale=px.colors.diverging.Tealrose, color_continuous_midpoint=2
)
],
'layout': {
'title': 'Counts of All Message Categories',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Categories"
}
}
}
]
#corr_list = []
#for row in data.corr().values:
# corr_list.append(list(row))
# create visuals
#strength
#Default graph: genre distribution graph
'''
graphs = [
{
'data': [
Bar(
x=genre_names,
y=genre_counts,
marker = dict(color=color)
)
],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
}
}
},
#top 5 most popular categories
{
'data': [
Bar(
x=cat_names,
y=cat_count,
marker = dict(color=color)
)
],
'layout': {
'title': 'Counts of All Message Categories',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Categories"
}
}
}
]
'''
#assign unique id to each graph
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graphJSON = BaseFigure.write_json('graph-1')
# encode plotly graphs in JSON
#graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graphJSON)