def test_two():
"""Second charting test."""
expect = {
'layout': {'xaxis': {'title': 'x axis'}, 'yaxis': {'title': 'y label'}},
'data': [
{
'y': np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'x': np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'line': {'color': 'red', 'width': 5, 'dash': 'dashdot'},
'marker': dict(size=6),
'type': 'scatter',
'text': "",
'yaxis': 'y',
'mode': 'lines+markers',
'name': 'hello',
}
],
}
x = np.arange(10)
line0 = pw.line(y=x, label='hello', color='red', dash='dashdot', width=5)
line0.xlabel = 'x axis'
line0.ylabel = 'y label'
line0.show(auto_open=False)
line1 = pw.line(x, label='hello', color='red', dash='dashdot', width=5)
line1.xlabel = 'x axis'
line1.ylabel = 'y label'
line1.show(auto_open=False)
compare_figs(line0.dict, line1.dict)
compare_figs(line0.dict, expect)
def upgraph():
data = cache.load('data')
value = float(sigma.get())
data.pop(0)
data.append(value * rng.randn() + data[-1])
mainplot.do_all(pw.line(data).to_json())
cache.save('data', data)
def mainregress(selection, alpha):
if len(selection) < 2:
return
x = xdown.get()['value']
y = ydown.get()['value']
tabdata = []
mldatax = []
mldatay = []
species = iris.Species.unique()
for i, p in enumerate(selection['points']):
mldatax.append(p['x'])
mldatay.append(p['y'])
tabdata.append({
x: p['x'],
y: p['y'],
'species': species[p['curve']]
})
X = np.c_[mldatax, np.array(mldatax) ** 2]
ridge = KernelRidge(alpha=alpha).fit(X, mldatay)
xspace = np.linspace(min(mldatax)-1, max(mldatax)+1, 100)
plot = pw.scatter(mldatax, mldatay, label='train', markersize=15)
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot += pw.line(xspace, ridge.predict(np.c_[xspace, xspace**2]), label='model', mode='lines')
plot.xlabel = x
plot.ylabel = y
linear.do_all(plot.dict)
table1.do_data(pd.DataFrame(tabdata))
def upgraph():
data = cache.load('data')
value = float(sigma.get())
data.pop(0)
data.append(value * rng.randn() + data[-1])
mainplot.do_all(pw.line(data).to_json())
cache.save('data', data)
def test_two():
"""Second charting test."""
expect = {
'layout': {
'xaxis': {
'title': 'x axis'
},
'yaxis': {
'title': 'y label'
}
},
'data': [{
'y': np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'x': np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'line': {
'color': 'red',
'width': 5,
'dash': 'dashdot'
},
'marker': dict(size=6),
'type': 'scatter',
'text': "",
'yaxis': 'y',
'mode': 'lines+markers',
'name': 'hello',
}],
}
x = np.arange(10)
line0 = pw.line(y=x, label='hello', color='red', dash='dashdot', width=5)
line0.xlabel = 'x axis'
line0.ylabel = 'y label'
line0.show(auto_open=False)
line1 = pw.line(x, label='hello', color='red', dash='dashdot', width=5)
line1.xlabel = 'x axis'
line1.ylabel = 'y label'
line1.show(auto_open=False)
compare_figs(line0.dict, line1.dict)
compare_figs(line0.dict, expect)
def test_dataframe_lines():
"""Test dataframe lines chart."""
columns = list('abc')
x = np.arange(10)
y = rng.randn(10, 3)
df = pd.DataFrame(y, x, columns)
p1 = df.plotly.line()
p1.show(auto_open=False)
p2 = pw.line(x, y, columns)
p2.show(auto_open=False)
compare_figs(p1.dict, p2.dict)
def test_dataframe_lines():
"""Test dataframe lines chart."""
columns = list('abc')
x = np.arange(10)
y = rng.randn(10, 3)
df = pd.DataFrame(y, x, columns)
p1 = df.plotly.line()
p1.show(auto_open=False)
p2 = pw.line(x, y, columns)
p2.show(auto_open=False)
compare_figs(p1.dict, p2.dict)
def test_dict():
"""Test dict accessor works."""
js = pw.line(range(3)).dict
expected = {
'layout': {},
'data': [{
'mode': 'lines+markers',
'marker': dict(size=6),
'text': "",
'y': [0, 1, 2],
'x': [0, 1, 2],
'yaxis': 'y',
'type': 'scatter',
}],
}
compare_figs(js, expected)
def test_dict():
"""Test dict accessor works."""
js = pw.line(range(3)).dict
expected = {
'layout': {},
'data': [
{
'mode': 'lines+markers',
'marker': dict(size=6),
'text': "",
'y': [0, 1, 2],
'x': [0, 1, 2],
'yaxis': 'y',
'type': 'scatter',
}
],
}
compare_figs(js, expected)
def test_one():
"""First charting test."""
x = np.arange(3)
bars = pw.bar(x=x, y=[20, 14, 23], label='new york')
bars2 = pw.bar(x=x, y=[12, 18, 29]) # , label='la')
line = pw.line(x=x, y=[3, 8, 9], label='hello', color='red', dash='dashdot', width=5)
plot = bars + bars2 + line
# print(bars.data)
plot.xlabel = 'x axis'
plot.ylabel = 'y label'
plot.stack()
plot.show(auto_open=False)
expect = {
'layout': {'barmode': 'stack', 'xaxis': {'title': 'x axis'}, 'yaxis': {'title': 'y label'}},
'data': [
{
'y': np.array([20, 14, 23]),
'x': np.array([0, 1, 2]),
'type': 'bar',
'yaxis': 'y',
'name': 'new york',
},
{'y': np.array([12, 18, 29]), 'x': np.array([0, 1, 2]), 'type': 'bar', 'yaxis': 'y'},
{
'y': np.array([3, 8, 9]),
'x': np.array([0, 1, 2]),
'line': {'color': 'red', 'width': 5, 'dash': 'dashdot'},
'type': 'scatter',
'marker': dict(size=6),
'yaxis': 'y',
'text': "",
'mode': 'lines+markers',
'name': 'hello',
},
],
}
compare_figs(plot.dict, expect)
def mainregress(selection, alpha):
if len(selection) < 2:
return
x = xdown.get()['value']
y = ydown.get()['value']
tabdata = []
mldatax = []
mldatay = []
species = iris.Species.unique()
for i, p in enumerate(selection['points']):
mldatax.append(p['x'])
mldatay.append(p['y'])
tabdata.append({
x: p['x'],
y: p['y'],
'species': species[p['curveNumber']]
})
X = np.c_[mldatax, np.array(mldatax)**2]
ridge = KernelRidge(alpha=alpha).fit(X, mldatay)
xspace = np.linspace(min(mldatax) - 1, max(mldatax) + 1, 100)
plot = pw.scatter(mldatax, mldatay, label='train', markersize=15)
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot += pw.line(xspace,
ridge.predict(np.c_[xspace, xspace**2]),
label='model',
mode='lines')
plot.xlabel(x)
plot.ylabel(y)
linear.do_all(plot.to_json())
table1.do_update(tabdata)
def listener(freq):
freq = float(freq[0])
t = np.linspace(0, 10, 100)
sine_plot.do_all(pw.line(t, np.sin(freq * t)).to_json())
def walk():
value = float(sigma.get())
data.pop(0)
data.append(value * rng.randn() + data[-1])
mainplot.do_all(pw.line(data).to_json())
def twin_axes():
chart = pw.bar(range(20, 15, -1))
chart += pw.line(range(5), yaxis=2)
chart.yaxis_right(2)
chart.save('fig_twinx.html', **options)
def test_no_args():
"""Test no args raises an error."""
with pytest.raises(AssertionError):
pw.line()
with pytest.raises(AssertionError):
pw.bar()
def line():
pw.line(data).save('fig_line.html', **options)
def line():
pw.line(data).save('fig_line.html', **options)
def test_one():
"""First charting test."""
x = np.arange(3)
bars = pw.bar(x=x, y=[20, 14, 23], label='new york')
bars2 = pw.bar(x=x, y=[12, 18, 29]) # , label='la')
line = pw.line(x=x,
y=[3, 8, 9],
label='hello',
color='red',
dash='dashdot',
width=5)
plot = bars + bars2 + line
# print(bars.data)
plot.xlabel = 'x axis'
plot.ylabel = 'y label'
plot.stack()
plot.show(auto_open=False)
expect = {
'layout': {
'barmode': 'stack',
'xaxis': {
'title': 'x axis'
},
'yaxis': {
'title': 'y label'
}
},
'data': [
{
'y': np.array([20, 14, 23]),
'x': np.array([0, 1, 2]),
'type': 'bar',
'yaxis': 'y',
'name': 'new york',
},
{
'y': np.array([12, 18, 29]),
'x': np.array([0, 1, 2]),
'type': 'bar',
'yaxis': 'y'
},
{
'y': np.array([3, 8, 9]),
'x': np.array([0, 1, 2]),
'line': {
'color': 'red',
'width': 5,
'dash': 'dashdot'
},
'type': 'scatter',
'marker': dict(size=6),
'yaxis': 'y',
'text': "",
'mode': 'lines+markers',
'name': 'hello',
},
],
}
compare_figs(plot.dict, expect)
def twin_axes():
chart = pw.bar(range(20, 15, -1))
chart += pw.line(range(5), yaxis=2)
chart.yaxis_right(2)
chart.save('fig_twinx.html', **options)
def test_no_args():
"""Test no args raises an error."""
with pytest.raises(AssertionError):
pw.line()
with pytest.raises(AssertionError):
pw.bar()