def result(self, boxkwargs=None, figsize=(12, 10), maxpoints=1000):
"""
:return:
"""
if boxkwargs is None:
boxkwargs = {}
filtered = random.sample(list(self.df.index), maxpoints)
sampled_df = self.df.ix[filtered]
fig = mpl.figure.Figure(figsize=figsize, tight_layout=True)
canvas = mbb.FigureCanvasAgg(fig)
grid = gs.GridSpec(4, 3, width_ratios=[4, 4, 1], height_ratios=[2, 1, 2, 1])
axm = fig.add_subplot(grid[0], label="axm")
axmd = fig.add_subplot(grid[1], label="axmd")
axtm = fig.add_subplot(grid[3], label="axtm", frameon=False)
axtmd = fig.add_subplot(grid[4], label="axtmd", frameon=False)
axs = fig.add_subplot(grid[6], label="axs")
axsd = fig.add_subplot(grid[7], label="axsd")
axts = fig.add_subplot(grid[9], label="axts", frameon=False)
axtsd = fig.add_subplot(grid[10], label="axtsd", frameon=False)
jp.boxplot("Group", "Mean", sampled_df, axes=axm, legend="Group", **boxkwargs)
jp.boxplot("Group", "Mean Delta", sampled_df, axes=axmd, legend="Group", **boxkwargs)
jp.boxplot("Group", "Sigma", sampled_df, axes=axs, legend="Group", **boxkwargs)
jp.boxplot("Group", "Sigma Delta", sampled_df, axes=axsd, legend="Group", **boxkwargs)
jp.components.datatable("Mean", self.df, axtm, by="Group", probs="bayes")
jp.components.datatable("Mean Delta", self.df, axtmd, by="Group", probs="bayes")
jp.components.datatable("Sigma", self.df, axts, by="Group", probs="bayes")
jp.components.datatable("Sigma Delta", self.df, axtsd, by="Group", probs="bayes")
for ax in axm, axmd, axtm, axtmd, axs, axsd, axts, axtsd:
if not ax:
continue
ax.tick_params(top="off")
ax.tick_params(bottom="off")
ax.tick_params(right="off")
ax.tick_params(left="off")
fig.suptitle("")
return canvas.figure
def result(self, boxkwargs=None, figsize=(12, 10), maxpoints=1000):
"""
:return:
"""
if boxkwargs is None:
boxkwargs = {}
filtered = random.sample(list(self.df.index), maxpoints)
sampled_df = self.df.ix[filtered]
fig = mpl.figure.Figure(figsize=figsize, tight_layout=True)
canvas = mbb.FigureCanvasAgg(fig)
grid = gs.GridSpec(4,
3,
width_ratios=[4, 4, 1],
height_ratios=[2, 1, 2, 1])
axm = fig.add_subplot(grid[0], label='axm')
axmd = fig.add_subplot(grid[1], label='axmd')
axtm = fig.add_subplot(
grid[3],
label='axtm',
frameon=False,
)
axtmd = fig.add_subplot(
grid[4],
label='axtmd',
frameon=False,
)
axs = fig.add_subplot(grid[6], label='axs')
axsd = fig.add_subplot(grid[7], label='axsd')
axts = fig.add_subplot(
grid[9],
label='axts',
frameon=False,
)
axtsd = fig.add_subplot(
grid[10],
label='axtsd',
frameon=False,
)
jp.boxplot('Group',
'Mean',
sampled_df,
axes=axm,
legend='Group',
**boxkwargs)
jp.boxplot('Group',
'Mean Delta',
sampled_df,
axes=axmd,
legend='Group',
**boxkwargs)
jp.boxplot('Group',
'Sigma',
sampled_df,
axes=axs,
legend='Group',
**boxkwargs)
jp.boxplot('Group',
'Sigma Delta',
sampled_df,
axes=axsd,
legend='Group',
**boxkwargs)
jp.components.datatable('Mean',
self.df,
axtm,
by='Group',
probs='bayes')
jp.components.datatable('Mean Delta',
self.df,
axtmd,
by='Group',
probs='bayes')
jp.components.datatable('Sigma',
self.df,
axts,
by='Group',
probs='bayes')
jp.components.datatable('Sigma Delta',
self.df,
axtsd,
by='Group',
probs='bayes')
for ax in axm, axmd, axtm, axtmd, axs, axsd, axts, axtsd:
if not ax:
continue
ax.tick_params(top="off")
ax.tick_params(bottom="off")
ax.tick_params(right="off")
ax.tick_params(left="off")
fig.suptitle('')
return canvas.figure
def varchart(x: list,
y: str,
data: pd.DataFrame,
legend=None,
cumprob: bool=False,
fig=None,
**kwargs):
"""
varchart function
:param x: list of strings
:param y: str
:param data: pd.Dataframe, source of data
:param legend: str, color code by this column
:param cumprob, turn on or off the cumprob plots
:param table: turn on or off the datatable
**kwargs: other parameters to pass into the jumpy.boxplot function
"""
local_data = data.copy()
local_data = local_data.reset_index()
strx = str(x)
strl = None
# ensure blox plot x axis is a string and y data is all float
for var in x:
local_data[var] = local_data[var].astype('str')
local_data[y] = local_data[y].astype('float').dropna()
# join all x's into a single column
for i, part in enumerate(x):
if i == 0:
local_data[strx] = local_data[part].map(str)
else:
local_data[strx] += local_data[part].map(str)
if i < len(x) - 1:
local_data[strx] += ', '
# create a new legend column if legend is an array
if legend and isinstance(legend, str):
strl = legend
elif str(legend) == strx:
strl = strx
elif legend:
# make a column that has the concatenated legend label
strl = str(legend)
for i, part in enumerate(legend):
if i == 0:
local_data[strl] = local_data[part].map(str)
else:
local_data[strl] += local_data[part].map(str)
if i < len(legend) - 1:
local_data[strl] += ', '
if fig:
fig = boxplot(x=strx, y=y, data=local_data, orderby=strx,
legend=strl, cumprob=cumprob, fig=fig, **kwargs)
else:
fig = boxplot(x=strx, y=y, data=local_data, orderby=strx,
legend=strl, cumprob=cumprob, **kwargs)
axm, axc, axl, axt = components.get_axes(fig, clear=False)
yvals = axm.get_ylim()
colors = ['w', 'b', 'r', 'g', 'c']
# this array holds all the multple numbers for when to draw a line.
# % operator is called on each of these
nummods = [len(set(local_data[x[k]])) for k in range(len(x)) if k >= 1]
for j, k in enumerate(reversed(range(len(nummods)))):
if j == 0:
nummods[k] = nummods[k]
else:
nummods[k] *= nummods[k-1]
# generate every possible permutation of the incoming arrays
for i, combo in enumerate(sorted(set(local_data[strx])), start=1):
# draw in vertical lines
for j, mod in enumerate(reversed(nummods)):
if not i % mod:
axm.vlines(i-.5, *yvals, color='{}'.format(colors[j+1]),
alpha=.5)
axm.set_ylim(*yvals)
fig.suptitle('')
return fig
def varchart(x: list,
y: str,
data: pd.DataFrame,
legend=None,
cumprob: bool = False,
fig=None,
**kwargs):
"""
varchart function
:param x: list of strings
:param y: str
:param data: pd.Dataframe, source of data
:param legend: str, color code by this column
:param cumprob, turn on or off the cumprob plots
:param table: turn on or off the datatable
**kwargs: other parameters to pass into the jumpy.boxplot function
"""
local_data = data.copy()
local_data = local_data.reset_index()
strx = str(x)
strl = None
# ensure blox plot x axis is a string and y data is all float
for var in x:
local_data[var] = local_data[var].astype('str')
local_data[y] = local_data[y].astype('float').dropna()
# join all x's into a single column
for i, part in enumerate(x):
if i == 0:
local_data[strx] = local_data[part].map(str)
else:
local_data[strx] += local_data[part].map(str)
if i < len(x) - 1:
local_data[strx] += ', '
# create a new legend column if legend is an array
if legend and isinstance(legend, str):
strl = legend
elif str(legend) == strx:
strl = strx
elif legend:
# make a column that has the concatenated legend label
strl = str(legend)
for i, part in enumerate(legend):
if i == 0:
local_data[strl] = local_data[part].map(str)
else:
local_data[strl] += local_data[part].map(str)
if i < len(legend) - 1:
local_data[strl] += ', '
if fig:
fig = boxplot(x=strx,
y=y,
data=local_data,
orderby=strx,
legend=strl,
cumprob=cumprob,
fig=fig,
**kwargs)
else:
fig = boxplot(x=strx,
y=y,
data=local_data,
orderby=strx,
legend=strl,
cumprob=cumprob,
**kwargs)
axm, axc, axl, axt = components.get_axes(fig, clear=False)
yvals = axm.get_ylim()
colors = ['w', 'b', 'r', 'g', 'c']
# this array holds all the multple numbers for when to draw a line.
# % operator is called on each of these
nummods = [len(set(local_data[x[k]])) for k in range(len(x)) if k >= 1]
for j, k in enumerate(reversed(range(len(nummods)))):
if j == 0:
nummods[k] = nummods[k]
else:
nummods[k] *= nummods[k - 1]
# generate every possible permutation of the incoming arrays
for i, combo in enumerate(sorted(set(local_data[strx])), start=1):
# draw in vertical lines
for j, mod in enumerate(reversed(nummods)):
if not i % mod:
axm.vlines(i - .5,
*yvals,
color='{}'.format(colors[j + 1]),
alpha=.5)
axm.set_ylim(*yvals)
fig.suptitle('')
return fig
