def test_get_single_plot():
expected = Figure(data=Data(),
layout=Layout(xaxis1=XAxis(domain=[0.0, 1.0],
anchor='y1'),
yaxis1=YAxis(domain=[0.0, 1.0],
anchor='x1')))
assert tls.get_subplots() == expected
def test_spacing():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 0.3],
anchor='y1'
),
xaxis2=XAxis(
domain=[0.35, 0.6499999999999999],
anchor='y2'
),
xaxis3=XAxis(
domain=[0.7, 1.0],
anchor='y3'
),
xaxis4=XAxis(
domain=[0.0, 0.3],
anchor='y4'
),
xaxis5=XAxis(
domain=[0.35, 0.6499999999999999],
anchor='y5'
),
xaxis6=XAxis(
domain=[0.7, 1.0],
anchor='y6'
),
yaxis1=YAxis(
domain=[0.0, 0.45],
anchor='x1'
),
yaxis2=YAxis(
domain=[0.0, 0.45],
anchor='x2'
),
yaxis3=YAxis(
domain=[0.0, 0.45],
anchor='x3'
),
yaxis4=YAxis(
domain=[0.55, 1.0],
anchor='x4'
),
yaxis5=YAxis(
domain=[0.55, 1.0],
anchor='x5'
),
yaxis6=YAxis(
domain=[0.55, 1.0],
anchor='x6'
)
)
)
fig = tls.get_subplots(2, 3,
horizontal_spacing=.05,
vertical_spacing=.1)
assert fig == expected
def test_spacing():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 0.3],
anchor='y1'
),
xaxis2=XAxis(
domain=[0.35, 0.6499999999999999],
anchor='y2'
),
xaxis3=XAxis(
domain=[0.7, 1.0],
anchor='y3'
),
xaxis4=XAxis(
domain=[0.0, 0.3],
anchor='y4'
),
xaxis5=XAxis(
domain=[0.35, 0.6499999999999999],
anchor='y5'
),
xaxis6=XAxis(
domain=[0.7, 1.0],
anchor='y6'
),
yaxis1=YAxis(
domain=[0.0, 0.45],
anchor='x1'
),
yaxis2=YAxis(
domain=[0.0, 0.45],
anchor='x2'
),
yaxis3=YAxis(
domain=[0.0, 0.45],
anchor='x3'
),
yaxis4=YAxis(
domain=[0.55, 1.0],
anchor='x4'
),
yaxis5=YAxis(
domain=[0.55, 1.0],
anchor='x5'
),
yaxis6=YAxis(
domain=[0.55, 1.0],
anchor='x6'
)
)
)
assert expected == tls.get_subplots(2, 3, .05, .1)
def test_two_row():
expected = Figure(data=Data(),
layout=Layout(xaxis1=XAxis(domain=[0.0, 1.0],
anchor='y1'),
xaxis2=XAxis(domain=[0.0, 1.0],
anchor='y2'),
yaxis1=YAxis(domain=[0.0, 0.425],
anchor='x1'),
yaxis2=YAxis(domain=[0.575, 1.0],
anchor='x2')))
assert tls.get_subplots(2) == expected
def test_get_single_plot():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 1.0],
anchor='y1'
),
yaxis1=YAxis(
domain=[0.0, 1.0],
anchor='x1'
)
)
)
assert tls.get_subplots() == expected
def _plot_plotly(self, layout):
import plotly.tools as tls
from copy import deepcopy
axis_style_empty = dict(title='',
showline=False,
showgrid=False,
zeroline=False,
showticklabels=False)
fig = tls.get_subplots(rows=self.rows,
columns=self.columns,
horizontal_spacing=0.3 / self.columns,
vertical_spacing=0.3 / self.rows)
splts, splts_empty = self._get_splts(self.rows, self.columns,
len(self.plots))
for index, plotter in zip(splts, self.plots):
fig_one = plotter._plot_plotly(deepcopy(layout))
for data in fig_one['data']:
data['xaxis'] = 'x%d' % index
data['yaxis'] = 'y%d' % index
fig_one['layout']['xaxis%d' % index] = fig_one['layout']['xaxis']
fig_one['layout']['yaxis%d' % index] = fig_one['layout']['yaxis']
fig_one['layout']['xaxis%d' % index].update(
anchor='y%d' % index,
title=plotter.xlabel + '<br>%s' % plotter.title)
fig_one['layout']['yaxis%d' % index].update(title=plotter.ylabel)
# fig_one['layout']['title%d' % index] = plotter.title
fig_one['layout'].pop('xaxis')
fig_one['layout'].pop('yaxis')
fig_one['layout'].pop('title')
fig['data'] += fig_one['data']
fig['layout'].update(fig_one['layout'])
for index in splts_empty:
fig['layout']['xaxis{}'.format(index)].update(axis_style_empty)
fig['layout']['yaxis{}'.format(index)].update(axis_style_empty)
fig['layout'].update(autosize=False)
return fig
def _plot_plotly(self, layout):
import plotly.tools as tls
from copy import deepcopy
axis_style_empty = dict(
title='',
showline=False,
showgrid=False,
zeroline=False,
showticklabels=False
)
fig = tls.get_subplots(rows=self.rows, columns=self.columns, horizontal_spacing=0.3 / self.columns,
vertical_spacing=0.3 / self.rows)
splts, splts_empty = self._get_splts(self.rows, self.columns, len(self.plots))
for index, plotter in zip(splts, self.plots):
fig_one = plotter._plot_plotly(deepcopy(layout))
for data in fig_one['data']:
data['xaxis'] = 'x%d' % index
data['yaxis'] = 'y%d' % index
fig_one['layout']['xaxis%d' % index] = fig_one['layout']['xaxis']
fig_one['layout']['yaxis%d' % index] = fig_one['layout']['yaxis']
fig_one['layout']['xaxis%d' % index].update(anchor='y%d' % index,
title=plotter.xlabel + '<br>%s' % plotter.title)
fig_one['layout']['yaxis%d' % index].update(title=plotter.ylabel)
# fig_one['layout']['title%d' % index] = plotter.title
fig_one['layout'].pop('xaxis')
fig_one['layout'].pop('yaxis')
fig_one['layout'].pop('title')
fig['data'] += fig_one['data']
fig['layout'].update(fig_one['layout'])
for index in splts_empty:
fig['layout']['xaxis{}'.format(index)].update(axis_style_empty)
fig['layout']['yaxis{}'.format(index)].update(axis_style_empty)
fig['layout'].update(autosize=False)
return fig
def test_two_row():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 1.0],
anchor='y1'
),
xaxis2=XAxis(
domain=[0.0, 1.0],
anchor='y2'
),
yaxis1=YAxis(
domain=[0.0, 0.425],
anchor='x1'
),
yaxis2=YAxis(
domain=[0.575, 1.0],
anchor='x2'
)
)
)
assert tls.get_subplots(2) == expected
def test_non_integer_rows():
tls.get_subplots(rows=2.1)
)
# Set title
layout = Layout(
title='Raspberry Pi Streaming Temperature Boxes'
)
#Plotting one figure
#fig = Figure(data=[measured_temp, sample_est], layout=layout)
# Plotting subplots
data = [target, measured_temp, heater_est, air_est, sample_est, heaterPWM, target2, measured_temp2, heater_est2, air_est2, sample_est2, heaterPWM2]
fig = tls.get_subplots(rows=2, columns=2)
fig['data'] += data
fig['layout'].update(title='Raspberry Pi Streaming Temperature Boxes')
fig['layout'].update(showlegend=True)
print py.plot(fig, filename='Raspberry Pi Streaming Temperature Boxes')
# Open a stream for each data structure
stream1 = py.Stream(stream_tokens[0])
stream1.open()
stream2 = py.Stream(stream_tokens[1])
stream2.open()
stream3 = py.Stream(stream_tokens[2])
stream3.open()
stream4 = py.Stream(stream_tokens[3])
xanchor='center',
align='center',
font=Font(size=14),
showarrow=False,
xref='x{}'.format(sbplt_in),
yref='y{}'.format(sbplt_in))
# Generate figure object with subplot layout:
# In[9]:
#
figure = tls.get_subplots(rows=len(datasets),
columns=len(classifiers) + 1,
horizontal_spacing=0.01,
vertical_spacing=0.05,
print_grid=True)
# Add a few style options:
# In[10]:
#
figure['layout'].update(showlegend=False,
hovermode='closest',
autosize=False,
width=1472,
height=490)
#
def test_a_lot():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(domain=[0.0, 0.05714285714285713], anchor='y1'),
xaxis10=XAxis(domain=[0.3142857142857143, 0.3714285714285714],
anchor='y10'),
xaxis11=XAxis(domain=[0.4714285714285714, 0.5285714285714286],
anchor='y11'),
xaxis12=XAxis(domain=[0.6285714285714286, 0.6857142857142857],
anchor='y12'),
xaxis13=XAxis(domain=[0.7857142857142857, 0.8428571428571429],
anchor='y13'),
xaxis14=XAxis(domain=[0.9428571428571428, 1.0], anchor='y14'),
xaxis15=XAxis(domain=[0.0, 0.05714285714285713], anchor='y15'),
xaxis16=XAxis(domain=[0.15714285714285714, 0.21428571428571427],
anchor='y16'),
xaxis17=XAxis(domain=[0.3142857142857143, 0.3714285714285714],
anchor='y17'),
xaxis18=XAxis(domain=[0.4714285714285714, 0.5285714285714286],
anchor='y18'),
xaxis19=XAxis(domain=[0.6285714285714286, 0.6857142857142857],
anchor='y19'),
xaxis2=XAxis(domain=[0.15714285714285714, 0.21428571428571427],
anchor='y2'),
xaxis20=XAxis(domain=[0.7857142857142857, 0.8428571428571429],
anchor='y20'),
xaxis21=XAxis(domain=[0.9428571428571428, 1.0], anchor='y21'),
xaxis22=XAxis(domain=[0.0, 0.05714285714285713], anchor='y22'),
xaxis23=XAxis(domain=[0.15714285714285714, 0.21428571428571427],
anchor='y23'),
xaxis24=XAxis(domain=[0.3142857142857143, 0.3714285714285714],
anchor='y24'),
xaxis25=XAxis(domain=[0.4714285714285714, 0.5285714285714286],
anchor='y25'),
xaxis26=XAxis(domain=[0.6285714285714286, 0.6857142857142857],
anchor='y26'),
xaxis27=XAxis(domain=[0.7857142857142857, 0.8428571428571429],
anchor='y27'),
xaxis28=XAxis(domain=[0.9428571428571428, 1.0], anchor='y28'),
xaxis3=XAxis(domain=[0.3142857142857143, 0.3714285714285714],
anchor='y3'),
xaxis4=XAxis(domain=[0.4714285714285714, 0.5285714285714286],
anchor='y4'),
xaxis5=XAxis(domain=[0.6285714285714286, 0.6857142857142857],
anchor='y5'),
xaxis6=XAxis(domain=[0.7857142857142857, 0.8428571428571429],
anchor='y6'),
xaxis7=XAxis(domain=[0.9428571428571428, 1.0], anchor='y7'),
xaxis8=XAxis(domain=[0.0, 0.05714285714285713], anchor='y8'),
xaxis9=XAxis(domain=[0.15714285714285714, 0.21428571428571427],
anchor='y9'),
yaxis1=YAxis(domain=[0.0, 0.1375], anchor='x1'),
yaxis10=YAxis(domain=[0.2875, 0.425], anchor='x10'),
yaxis11=YAxis(domain=[0.2875, 0.425], anchor='x11'),
yaxis12=YAxis(domain=[0.2875, 0.425], anchor='x12'),
yaxis13=YAxis(domain=[0.2875, 0.425], anchor='x13'),
yaxis14=YAxis(domain=[0.2875, 0.425], anchor='x14'),
yaxis15=YAxis(domain=[0.575, 0.7124999999999999], anchor='x15'),
yaxis16=YAxis(domain=[0.575, 0.7124999999999999], anchor='x16'),
yaxis17=YAxis(domain=[0.575, 0.7124999999999999], anchor='x17'),
yaxis18=YAxis(domain=[0.575, 0.7124999999999999], anchor='x18'),
yaxis19=YAxis(domain=[0.575, 0.7124999999999999], anchor='x19'),
yaxis2=YAxis(domain=[0.0, 0.1375], anchor='x2'),
yaxis20=YAxis(domain=[0.575, 0.7124999999999999], anchor='x20'),
yaxis21=YAxis(domain=[0.575, 0.7124999999999999], anchor='x21'),
yaxis22=YAxis(domain=[0.8624999999999999, 1.0], anchor='x22'),
yaxis23=YAxis(domain=[0.8624999999999999, 1.0], anchor='x23'),
yaxis24=YAxis(domain=[0.8624999999999999, 1.0], anchor='x24'),
yaxis25=YAxis(domain=[0.8624999999999999, 1.0], anchor='x25'),
yaxis26=YAxis(domain=[0.8624999999999999, 1.0], anchor='x26'),
yaxis27=YAxis(domain=[0.8624999999999999, 1.0], anchor='x27'),
yaxis28=YAxis(domain=[0.8624999999999999, 1.0], anchor='x28'),
yaxis3=YAxis(domain=[0.0, 0.1375], anchor='x3'),
yaxis4=YAxis(domain=[0.0, 0.1375], anchor='x4'),
yaxis5=YAxis(domain=[0.0, 0.1375], anchor='x5'),
yaxis6=YAxis(domain=[0.0, 0.1375], anchor='x6'),
yaxis7=YAxis(domain=[0.0, 0.1375], anchor='x7'),
yaxis8=YAxis(domain=[0.2875, 0.425], anchor='x8'),
yaxis9=YAxis(domain=[0.2875, 0.425], anchor='x9')))
fig = tls.get_subplots(4, 7, horizontal_spacing=0.1, vertical_spacing=0.15)
assert fig == expected
def test_wrong_kwarg():
tls.get_subplots(stuff='no gonna work')
xanchor='center',
align='center',
font= Font(size=14),
showarrow=False,
xref= 'x{}'.format(sbplt_in),
yref= 'y{}'.format(sbplt_in))
# Generate figure object with subplot layout:
# In[9]:
#
figure = tls.get_subplots(
rows=len(datasets),
columns=len(classifiers)+1,
horizontal_spacing=0.01,
vertical_spacing=0.05,
print_grid=True)
# Add a few style options:
# In[10]:
#
figure['layout'].update(showlegend=False,
hovermode='closest',
autosize=False,
width=1472,
height=490)
def test_default_spacing():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(domain=[0.0, 0.16799999999999998], anchor='y1'),
xaxis10=XAxis(domain=[0.832, 1.0], anchor='y10'),
xaxis11=XAxis(domain=[0.0, 0.16799999999999998], anchor='y11'),
xaxis12=XAxis(domain=[0.208, 0.376], anchor='y12'),
xaxis13=XAxis(domain=[0.416, 0.584], anchor='y13'),
xaxis14=XAxis(domain=[0.624, 0.792], anchor='y14'),
xaxis15=XAxis(domain=[0.832, 1.0], anchor='y15'),
xaxis16=XAxis(domain=[0.0, 0.16799999999999998], anchor='y16'),
xaxis17=XAxis(domain=[0.208, 0.376], anchor='y17'),
xaxis18=XAxis(domain=[0.416, 0.584], anchor='y18'),
xaxis19=XAxis(domain=[0.624, 0.792], anchor='y19'),
xaxis2=XAxis(domain=[0.208, 0.376], anchor='y2'),
xaxis20=XAxis(domain=[0.832, 1.0], anchor='y20'),
xaxis21=XAxis(domain=[0.0, 0.16799999999999998], anchor='y21'),
xaxis22=XAxis(domain=[0.208, 0.376], anchor='y22'),
xaxis23=XAxis(domain=[0.416, 0.584], anchor='y23'),
xaxis24=XAxis(domain=[0.624, 0.792], anchor='y24'),
xaxis25=XAxis(domain=[0.832, 1.0], anchor='y25'),
xaxis26=XAxis(domain=[0.0, 0.16799999999999998], anchor='y26'),
xaxis27=XAxis(domain=[0.208, 0.376], anchor='y27'),
xaxis28=XAxis(domain=[0.416, 0.584], anchor='y28'),
xaxis29=XAxis(domain=[0.624, 0.792], anchor='y29'),
xaxis3=XAxis(domain=[0.416, 0.584], anchor='y3'),
xaxis30=XAxis(domain=[0.832, 1.0], anchor='y30'),
xaxis4=XAxis(domain=[0.624, 0.792], anchor='y4'),
xaxis5=XAxis(domain=[0.832, 1.0], anchor='y5'),
xaxis6=XAxis(domain=[0.0, 0.16799999999999998], anchor='y6'),
xaxis7=XAxis(domain=[0.208, 0.376], anchor='y7'),
xaxis8=XAxis(domain=[0.416, 0.584], anchor='y8'),
xaxis9=XAxis(domain=[0.624, 0.792], anchor='y9'),
yaxis1=YAxis(domain=[0.0, 0.125], anchor='x1'),
yaxis10=YAxis(domain=[0.175, 0.3], anchor='x10'),
yaxis11=YAxis(domain=[0.35, 0.475], anchor='x11'),
yaxis12=YAxis(domain=[0.35, 0.475], anchor='x12'),
yaxis13=YAxis(domain=[0.35, 0.475], anchor='x13'),
yaxis14=YAxis(domain=[0.35, 0.475], anchor='x14'),
yaxis15=YAxis(domain=[0.35, 0.475], anchor='x15'),
yaxis16=YAxis(domain=[0.5249999999999999, 0.6499999999999999],
anchor='x16'),
yaxis17=YAxis(domain=[0.5249999999999999, 0.6499999999999999],
anchor='x17'),
yaxis18=YAxis(domain=[0.5249999999999999, 0.6499999999999999],
anchor='x18'),
yaxis19=YAxis(domain=[0.5249999999999999, 0.6499999999999999],
anchor='x19'),
yaxis2=YAxis(domain=[0.0, 0.125], anchor='x2'),
yaxis20=YAxis(domain=[0.5249999999999999, 0.6499999999999999],
anchor='x20'),
yaxis21=YAxis(domain=[0.7, 0.825], anchor='x21'),
yaxis22=YAxis(domain=[0.7, 0.825], anchor='x22'),
yaxis23=YAxis(domain=[0.7, 0.825], anchor='x23'),
yaxis24=YAxis(domain=[0.7, 0.825], anchor='x24'),
yaxis25=YAxis(domain=[0.7, 0.825], anchor='x25'),
yaxis26=YAxis(domain=[0.875, 1.0], anchor='x26'),
yaxis27=YAxis(domain=[0.875, 1.0], anchor='x27'),
yaxis28=YAxis(domain=[0.875, 1.0], anchor='x28'),
yaxis29=YAxis(domain=[0.875, 1.0], anchor='x29'),
yaxis3=YAxis(domain=[0.0, 0.125], anchor='x3'),
yaxis30=YAxis(domain=[0.875, 1.0], anchor='x30'),
yaxis4=YAxis(domain=[0.0, 0.125], anchor='x4'),
yaxis5=YAxis(domain=[0.0, 0.125], anchor='x5'),
yaxis6=YAxis(domain=[0.175, 0.3], anchor='x6'),
yaxis7=YAxis(domain=[0.175, 0.3], anchor='x7'),
yaxis8=YAxis(domain=[0.175, 0.3], anchor='x8'),
yaxis9=YAxis(domain=[0.175, 0.3], anchor='x9')))
fig = tls.get_subplots(rows=6, columns=5)
assert fig == expected
def test_non_integer_columns():
tls.get_subplots(columns=2/3)
def test_non_integer_rows():
tls.get_subplots(rows=2.1)
py.iplot(data, filename='apple stock moving average')
first_plot_url = py.plot(data, filename='apple stock moving average', auto_open=False,)
print first_plot_url
tickers = ['AAPL', 'GE', 'IBM', 'KO', 'MSFT', 'PEP']
prices = []
for ticker in tickers:
quotes = quotes_historical_yahoo(ticker, date1, date2)
prices.append( [q[1] for q in quotes] )
df = pd.DataFrame( prices ).transpose()
df.columns = tickers
df.head()
fig = plotly_tools.get_subplots(rows=6, columns=6, print_grid=True, horizontal_spacing= 0.05, vertical_spacing= 0.05)
"""Kernel Density Estimation with Scipy"""
# From https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/
# Note that scipy weights its bandwidth by the covariance of the
# input data. To make the results comparable to the other methods,
# we divide the bandwidth by the sample standard deviation here.
kde = gaussian_kde(x, bw_method=bandwidth / x.std(ddof=1), **kwargs)
return kde.evaluate(x_grid)
subplots = range(1,37)
sp_index = 0
data = []
for i in range(1,7):
x_ticker = df.columns[i-1]
for j in range(1,7):
def scatter_matrix(df, marker_color=None,
marker_text=None,
figsize=(1024,1024),
title='',
outfile=None,
cmap='deep', ):
"""
Plot pairwise relationships between all columns in the dataframe
<df>
Paramters
---------
df: pandas dataframe
Data to visualize
marker_color: can be a) a string indicating a column in <df>; b) an
array-like of values, each corresponding to a row in <df>,
or c) None, for which each variable gets its own color
title: str
The figure title
figsize: tuple
The (height, width) of the figure
cmap: str
The name of the colormap used (i.e. colorbrewer)
outfile: filepath str
If provided, output to an HTML file at provided location
Example
-------
from sklearn.datasets import make_classification
N_FEATURES = 4
X, y = make_classification(n_samples=100, n_clusters_per_class=1, n_classes=4, n_features=N_FEATURES)
df = pd.DataFrame(X, columns=['feature_%d' % f for f in range(N_FEATURES)])
df['class'] = y
scatter_matrix(df, colors='class', title='Scatter Matrix')
"""
if hasattr(marker_text, '__call__'):
texts = marker_text(df)
elif isinstance(marker_text, str):
texts = df[marker_text].values.tolist()
elif hasattr(marker_text, '__iter__'):
texts = marker_text
if isinstance(marker_color, str):
tmp = marker_color
marker_color = df[marker_color].values.tolist()
del df[tmp]
columns = df.columns
n_columns = len(columns)
alpha = .5
if hasattr(marker_color, '__call__'): # function provided
colors = marker_color(df)
elif hasattr(marker_color, '__iter__'):
if isinstance(marker_color[0], str): # array-like of strings (i.e. categories)
unique_vals = np.unique(marker_color).tolist()
n_colors = len(unique_vals)
# set up colors
palette = sns.color_palette(cmap, n_colors)
rgb = [[int(p*256) for p in color] for color in palette]
colors = []
for c in marker_color:
colors.append(rgb[unique_vals.index(c)])
colors = ['rgba(' + ','.join([str(c) for c in color])+',%1.2f)'% alpha for color in colors]
else:
colors = marker_color
if marker_color is None:
# set up colors
palette = sns.color_palette(cmap, n_columns + 1)
# rescale to 0 - 256
rgb = [[int(p*256) for p in color] for color in palette]
marker_rgba = [r+[alpha] for r in rgb]
subplots = range(1,n_columns**2 + 1)
subplot_idx = 0
data = []
# setup subplots
fig = get_subplots(rows=n_columns, columns=n_columns,
horizontal_spacing=0.05,
vertical_spacing=0.05)
for i in range(1,n_columns + 1):
if marker_color is None:
row_color ='rgba('+ ','.join([str(v) for v in marker_rgba[i]]) +')'
scatter_color = row_color
else:
row_color = 'gray'
scatter_color = colors
x_column = df.columns[i-1]
for j in range(1, n_columns + 1):
y_column = df.columns[j-1]
if i==j: # plot histogram and kde along diagonal
x = df[x_column]
x_grid = np.linspace(x.min(), x.max(), 100)
sub_plot = [go.Histogram(x=x, histnorm='probability density',
marker=go.Marker(color=row_color)), \
go.Scatter(x=x_grid, y=kde(x.as_matrix(), x_grid), \
line=go.Line(width=2, color='black'))]
else: # scatter plot
sub_plot = [go.Scatter(y=df[x_column], x=df[y_column],
mode='markers',
marker=go.Marker(size=6,
color=scatter_color,
colorscale=cmap))] # colorscale gets ignore if rgba() provided
# set text for each datapoint
for pt in sub_plot:
pt.update(xaxis='x{}'.format(subplots[subplot_idx]),\
yaxis='y{}'.format(subplots[subplot_idx]), \
name='{0}'.format(x_column))
if i!=j:
if texts:
pt.update(text=texts)
else:
pt.update(text='{0}<br>vs<br>{1}'.format(y_column,x_column))
subplot_idx += 1
data += sub_plot
# add x and y labels
left_index = 1
bottom_index = 1
for col in df.columns:
fig['layout']['xaxis{}'.format(left_index)].update(title=col)
fig['layout']['yaxis{}'.format(bottom_index)].update(title=col)
left_index=left_index + 1
bottom_index=bottom_index + n_columns
# Remove legend by updating 'layout' key
fig['layout'].update(showlegend=False, height=figsize[1],width=figsize[0],title=title)
fig['data'] = go.Data(data)
ol.iplot(fig, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(fig, auto_open=False, filename=outfile)
def test_default_spacing():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y1'
),
xaxis10=XAxis(
domain=[0.832, 1.0],
anchor='y10'
),
xaxis11=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y11'
),
xaxis12=XAxis(
domain=[0.208, 0.376],
anchor='y12'
),
xaxis13=XAxis(
domain=[0.416, 0.584],
anchor='y13'
),
xaxis14=XAxis(
domain=[0.624, 0.792],
anchor='y14'
),
xaxis15=XAxis(
domain=[0.832, 1.0],
anchor='y15'
),
xaxis16=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y16'
),
xaxis17=XAxis(
domain=[0.208, 0.376],
anchor='y17'
),
xaxis18=XAxis(
domain=[0.416, 0.584],
anchor='y18'
),
xaxis19=XAxis(
domain=[0.624, 0.792],
anchor='y19'
),
xaxis2=XAxis(
domain=[0.208, 0.376],
anchor='y2'
),
xaxis20=XAxis(
domain=[0.832, 1.0],
anchor='y20'
),
xaxis21=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y21'
),
xaxis22=XAxis(
domain=[0.208, 0.376],
anchor='y22'
),
xaxis23=XAxis(
domain=[0.416, 0.584],
anchor='y23'
),
xaxis24=XAxis(
domain=[0.624, 0.792],
anchor='y24'
),
xaxis25=XAxis(
domain=[0.832, 1.0],
anchor='y25'
),
xaxis26=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y26'
),
xaxis27=XAxis(
domain=[0.208, 0.376],
anchor='y27'
),
xaxis28=XAxis(
domain=[0.416, 0.584],
anchor='y28'
),
xaxis29=XAxis(
domain=[0.624, 0.792],
anchor='y29'
),
xaxis3=XAxis(
domain=[0.416, 0.584],
anchor='y3'
),
xaxis30=XAxis(
domain=[0.832, 1.0],
anchor='y30'
),
xaxis4=XAxis(
domain=[0.624, 0.792],
anchor='y4'
),
xaxis5=XAxis(
domain=[0.832, 1.0],
anchor='y5'
),
xaxis6=XAxis(
domain=[0.0, 0.16799999999999998],
anchor='y6'
),
xaxis7=XAxis(
domain=[0.208, 0.376],
anchor='y7'
),
xaxis8=XAxis(
domain=[0.416, 0.584],
anchor='y8'
),
xaxis9=XAxis(
domain=[0.624, 0.792],
anchor='y9'
),
yaxis1=YAxis(
domain=[0.0, 0.125],
anchor='x1'
),
yaxis10=YAxis(
domain=[0.175, 0.3],
anchor='x10'
),
yaxis11=YAxis(
domain=[0.35, 0.475],
anchor='x11'
),
yaxis12=YAxis(
domain=[0.35, 0.475],
anchor='x12'
),
yaxis13=YAxis(
domain=[0.35, 0.475],
anchor='x13'
),
yaxis14=YAxis(
domain=[0.35, 0.475],
anchor='x14'
),
yaxis15=YAxis(
domain=[0.35, 0.475],
anchor='x15'
),
yaxis16=YAxis(
domain=[0.5249999999999999, 0.6499999999999999],
anchor='x16'
),
yaxis17=YAxis(
domain=[0.5249999999999999, 0.6499999999999999],
anchor='x17'
),
yaxis18=YAxis(
domain=[0.5249999999999999, 0.6499999999999999],
anchor='x18'
),
yaxis19=YAxis(
domain=[0.5249999999999999, 0.6499999999999999],
anchor='x19'
),
yaxis2=YAxis(
domain=[0.0, 0.125],
anchor='x2'
),
yaxis20=YAxis(
domain=[0.5249999999999999, 0.6499999999999999],
anchor='x20'
),
yaxis21=YAxis(
domain=[0.7, 0.825],
anchor='x21'
),
yaxis22=YAxis(
domain=[0.7, 0.825],
anchor='x22'
),
yaxis23=YAxis(
domain=[0.7, 0.825],
anchor='x23'
),
yaxis24=YAxis(
domain=[0.7, 0.825],
anchor='x24'
),
yaxis25=YAxis(
domain=[0.7, 0.825],
anchor='x25'
),
yaxis26=YAxis(
domain=[0.875, 1.0],
anchor='x26'
),
yaxis27=YAxis(
domain=[0.875, 1.0],
anchor='x27'
),
yaxis28=YAxis(
domain=[0.875, 1.0],
anchor='x28'
),
yaxis29=YAxis(
domain=[0.875, 1.0],
anchor='x29'
),
yaxis3=YAxis(
domain=[0.0, 0.125],
anchor='x3'
),
yaxis30=YAxis(
domain=[0.875, 1.0],
anchor='x30'
),
yaxis4=YAxis(
domain=[0.0, 0.125],
anchor='x4'
),
yaxis5=YAxis(
domain=[0.0, 0.125],
anchor='x5'
),
yaxis6=YAxis(
domain=[0.175, 0.3],
anchor='x6'
),
yaxis7=YAxis(
domain=[0.175, 0.3],
anchor='x7'
),
yaxis8=YAxis(
domain=[0.175, 0.3],
anchor='x8'
),
yaxis9=YAxis(
domain=[0.175, 0.3],
anchor='x9'
)
)
)
fig = tls.get_subplots(rows=6, columns=5)
assert fig == expected
def test_a_lot():
expected = Figure(
data=Data(),
layout=Layout(
xaxis1=XAxis(
domain=[0.0, 0.05714285714285713],
anchor='y1'
),
xaxis10=XAxis(
domain=[0.3142857142857143, 0.3714285714285714],
anchor='y10'
),
xaxis11=XAxis(
domain=[0.4714285714285714, 0.5285714285714286],
anchor='y11'
),
xaxis12=XAxis(
domain=[0.6285714285714286, 0.6857142857142857],
anchor='y12'
),
xaxis13=XAxis(
domain=[0.7857142857142857, 0.8428571428571429],
anchor='y13'
),
xaxis14=XAxis(
domain=[0.9428571428571428, 1.0],
anchor='y14'
),
xaxis15=XAxis(
domain=[0.0, 0.05714285714285713],
anchor='y15'
),
xaxis16=XAxis(
domain=[0.15714285714285714, 0.21428571428571427],
anchor='y16'
),
xaxis17=XAxis(
domain=[0.3142857142857143, 0.3714285714285714],
anchor='y17'
),
xaxis18=XAxis(
domain=[0.4714285714285714, 0.5285714285714286],
anchor='y18'
),
xaxis19=XAxis(
domain=[0.6285714285714286, 0.6857142857142857],
anchor='y19'
),
xaxis2=XAxis(
domain=[0.15714285714285714, 0.21428571428571427],
anchor='y2'
),
xaxis20=XAxis(
domain=[0.7857142857142857, 0.8428571428571429],
anchor='y20'
),
xaxis21=XAxis(
domain=[0.9428571428571428, 1.0],
anchor='y21'
),
xaxis22=XAxis(
domain=[0.0, 0.05714285714285713],
anchor='y22'
),
xaxis23=XAxis(
domain=[0.15714285714285714, 0.21428571428571427],
anchor='y23'
),
xaxis24=XAxis(
domain=[0.3142857142857143, 0.3714285714285714],
anchor='y24'
),
xaxis25=XAxis(
domain=[0.4714285714285714, 0.5285714285714286],
anchor='y25'
),
xaxis26=XAxis(
domain=[0.6285714285714286, 0.6857142857142857],
anchor='y26'
),
xaxis27=XAxis(
domain=[0.7857142857142857, 0.8428571428571429],
anchor='y27'
),
xaxis28=XAxis(
domain=[0.9428571428571428, 1.0],
anchor='y28'
),
xaxis3=XAxis(
domain=[0.3142857142857143, 0.3714285714285714],
anchor='y3'
),
xaxis4=XAxis(
domain=[0.4714285714285714, 0.5285714285714286],
anchor='y4'
),
xaxis5=XAxis(
domain=[0.6285714285714286, 0.6857142857142857],
anchor='y5'
),
xaxis6=XAxis(
domain=[0.7857142857142857, 0.8428571428571429],
anchor='y6'
),
xaxis7=XAxis(
domain=[0.9428571428571428, 1.0],
anchor='y7'
),
xaxis8=XAxis(
domain=[0.0, 0.05714285714285713],
anchor='y8'
),
xaxis9=XAxis(
domain=[0.15714285714285714, 0.21428571428571427],
anchor='y9'
),
yaxis1=YAxis(
domain=[0.0, 0.1375],
anchor='x1'
),
yaxis10=YAxis(
domain=[0.2875, 0.425],
anchor='x10'
),
yaxis11=YAxis(
domain=[0.2875, 0.425],
anchor='x11'
),
yaxis12=YAxis(
domain=[0.2875, 0.425],
anchor='x12'
),
yaxis13=YAxis(
domain=[0.2875, 0.425],
anchor='x13'
),
yaxis14=YAxis(
domain=[0.2875, 0.425],
anchor='x14'
),
yaxis15=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x15'
),
yaxis16=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x16'
),
yaxis17=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x17'
),
yaxis18=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x18'
),
yaxis19=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x19'
),
yaxis2=YAxis(
domain=[0.0, 0.1375],
anchor='x2'
),
yaxis20=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x20'
),
yaxis21=YAxis(
domain=[0.575, 0.7124999999999999],
anchor='x21'
),
yaxis22=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x22'
),
yaxis23=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x23'
),
yaxis24=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x24'
),
yaxis25=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x25'
),
yaxis26=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x26'
),
yaxis27=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x27'
),
yaxis28=YAxis(
domain=[0.8624999999999999, 1.0],
anchor='x28'
),
yaxis3=YAxis(
domain=[0.0, 0.1375],
anchor='x3'
),
yaxis4=YAxis(
domain=[0.0, 0.1375],
anchor='x4'
),
yaxis5=YAxis(
domain=[0.0, 0.1375],
anchor='x5'
),
yaxis6=YAxis(
domain=[0.0, 0.1375],
anchor='x6'
),
yaxis7=YAxis(
domain=[0.0, 0.1375],
anchor='x7'
),
yaxis8=YAxis(
domain=[0.2875, 0.425],
anchor='x8'
),
yaxis9=YAxis(
domain=[0.2875, 0.425],
anchor='x9'
)
)
)
fig = tls.get_subplots(4, 7, horizontal_spacing=0.1, vertical_spacing=0.15)
assert fig == expected
def scatter_matrix(
df,
marker_color=None,
marker_text=None,
figsize=(1024, 1024),
title='',
outfile=None,
cmap='deep',
):
"""
Plot pairwise relationships between all columns in the dataframe
<df>
Paramters
---------
df: pandas dataframe
Data to visualize
marker_color: can be a) a string indicating a column in <df>; b) an
array-like of values, each corresponding to a row in <df>,
or c) None, for which each variable gets its own color
title: str
The figure title
figsize: tuple
The (height, width) of the figure
cmap: str
The name of the colormap used (i.e. colorbrewer)
outfile: filepath str
If provided, output to an HTML file at provided location
Example
-------
from sklearn.datasets import make_classification
N_FEATURES = 4
X, y = make_classification(n_samples=100, n_clusters_per_class=1, n_classes=4, n_features=N_FEATURES)
df = pd.DataFrame(X, columns=['feature_%d' % f for f in range(N_FEATURES)])
df['class'] = y
scatter_matrix(df, colors='class', title='Scatter Matrix')
"""
if hasattr(marker_text, '__call__'):
texts = marker_text(df)
elif isinstance(marker_text, str):
texts = df[marker_text].values.tolist()
elif hasattr(marker_text, '__iter__'):
texts = marker_text
if isinstance(marker_color, str):
tmp = marker_color
marker_color = df[marker_color].values.tolist()
del df[tmp]
columns = df.columns
n_columns = len(columns)
alpha = .5
if hasattr(marker_color, '__call__'): # function provided
colors = marker_color(df)
elif hasattr(marker_color, '__iter__'):
if isinstance(marker_color[0],
str): # array-like of strings (i.e. categories)
unique_vals = np.unique(marker_color).tolist()
n_colors = len(unique_vals)
# set up colors
palette = sns.color_palette(cmap, n_colors)
rgb = [[int(p * 256) for p in color] for color in palette]
colors = []
for c in marker_color:
colors.append(rgb[unique_vals.index(c)])
colors = [
'rgba(' + ','.join([str(c) for c in color]) + ',%1.2f)' % alpha
for color in colors
]
else:
colors = marker_color
if marker_color is None:
# set up colors
palette = sns.color_palette(cmap, n_columns + 1)
# rescale to 0 - 256
rgb = [[int(p * 256) for p in color] for color in palette]
marker_rgba = [r + [alpha] for r in rgb]
subplots = range(1, n_columns**2 + 1)
subplot_idx = 0
data = []
# setup subplots
fig = get_subplots(rows=n_columns,
columns=n_columns,
horizontal_spacing=0.05,
vertical_spacing=0.05)
for i in range(1, n_columns + 1):
if marker_color is None:
row_color = 'rgba(' + ','.join([str(v)
for v in marker_rgba[i]]) + ')'
scatter_color = row_color
else:
row_color = 'gray'
scatter_color = colors
x_column = df.columns[i - 1]
for j in range(1, n_columns + 1):
y_column = df.columns[j - 1]
if i == j: # plot histogram and kde along diagonal
x = df[x_column]
x_grid = np.linspace(x.min(), x.max(), 100)
sub_plot = [go.Histogram(x=x, histnorm='probability density',
marker=go.Marker(color=row_color)), \
go.Scatter(x=x_grid, y=kde(x.as_matrix(), x_grid), \
line=go.Line(width=2, color='black'))]
else: # scatter plot
sub_plot = [
go.Scatter(y=df[x_column],
x=df[y_column],
mode='markers',
marker=go.Marker(size=6,
color=scatter_color,
colorscale=cmap))
] # colorscale gets ignore if rgba() provided
# set text for each datapoint
for pt in sub_plot:
pt.update(xaxis='x{}'.format(subplots[subplot_idx]),\
yaxis='y{}'.format(subplots[subplot_idx]), \
name='{0}'.format(x_column))
if i != j:
if texts:
pt.update(text=texts)
else:
pt.update(
text='{0}<br>vs<br>{1}'.format(y_column, x_column))
subplot_idx += 1
data += sub_plot
# add x and y labels
left_index = 1
bottom_index = 1
for col in df.columns:
fig['layout']['xaxis{}'.format(left_index)].update(title=col)
fig['layout']['yaxis{}'.format(bottom_index)].update(title=col)
left_index = left_index + 1
bottom_index = bottom_index + n_columns
# Remove legend by updating 'layout' key
fig['layout'].update(showlegend=False,
height=figsize[1],
width=figsize[0],
title=title)
fig['data'] = go.Data(data)
ol.iplot(fig, show_link=False)
# write figure to HTML file
if outfile:
print('Exporting copy of figure to %s...' % outfile)
ol.plot(fig, auto_open=False, filename=outfile)
def test_non_integer_columns():
tls.get_subplots(columns=2 / 3)
import plotly.plotly as py
from plotly.graph_objs import *
py.sign_in('>>>username<<<', '>>>api_key<<<')
import plotly.tools as tls
trace1 = Bar(y=[1, 2, 3], xaxis='x1', yaxis='y1')
trace2 = Bar(y=[1, 2, 3], xaxis='x2', yaxis='y2')
trace3 = Bar(y=[1, 2, 3], xaxis='x3', yaxis='y3')
trace4 = Bar(y=[1, 2, 3], xaxis='x4', yaxis='y4')
data = Data([trace1, trace2, trace3, trace4])
fig = tls.get_subplots(rows=2, columns=2)
fig['data'] += data
fig['layout'].update(title='i <3 subplots')
plot_url = py.plot(fig, filename='>>>filename<<<')
def test_wrong_kwarg():
tls.get_subplots(stuff='no gonna work')