def plot_downstream(clwe, table, output, ylim):
df = pd.read_csv(data_file(table))
df = df[df.clwe == clwe]
df = df.assign(
refine=pd.Categorical(df['refine'], ['Original', '+retrofit', '+synthetic']),
language=pd.Categorical(df['language'], ['DE', 'ES', 'FR', 'IT', 'JA', 'RU', 'ZH', 'AVG'])
)
g = p9.ggplot(df, p9.aes(x='language', y='accuracy', fill='refine'))
g += p9.geom_bar(position='dodge', stat='identity', width=.8)
g += p9.coord_cartesian(ylim=ylim)
g += p9.scale_fill_manual(['#999999', '#EA5F94', '#FFB14E'])
g += p9.theme_void(base_size=FONT_SIZE, base_family='Arial')
g += p9.theme(
plot_background=p9.element_rect(fill='white'),
panel_grid_major_y=p9.element_line(),
axis_text_x=p9.element_text(margin={'t': 10}),
axis_text_y=p9.element_text(margin={'r': 8}),
legend_position=(.7, .9),
legend_direction='horizontal',
legend_title=p9.element_blank(),
legend_text=p9.element_text(size=FONT_SIZE),
legend_box_margin=0,
figure_size=(12, 3)
)
g.save(filename=output_file(output))
def plot_fusion(self):
"""
plot fusion count
"""
p9.theme_set(p9.theme_void())
for ref in self.pos_dict:
if ref in self.df_tsne.columns:
out_plot_file = f'{self.out_prefix}_{ref}_fusion.pdf'
plot = p9.ggplot(self.df_tsne, p9.aes(x="tSNE_1", y="tSNE_2", color=ref)) + \
p9.geom_point(size=0.2) + \
p9.theme_bw() + \
p9.scale_color_gradient(low="lightgrey",high="blue")
plot.save(out_plot_file)
def plot_patch(patch, vmin=None, vmax=None, round_digits=1, threshold=None):
if len(patch.shape) == 1:
patch = patch.reshape(1, -1)
if threshold is None:
threshold = patch.mean()
vmin = vmin or patch.min()
vmax = vmax or patch.max()
tile_height = tile_width = 0.95
hshift = 0
vshift = 0.5 * tile_height
plotr = pd.DataFrame({
'x': (
np.tile(np.arange(patch.shape[1]), patch.shape[0]).flatten()
+ hshift
),
'y': (
- np.repeat(np.arange(patch.shape[0]), patch.shape[1]).flatten()
+ vshift
),
'value': np.round(patch.flatten(), round_digits),
'color_text': patch.flatten() < threshold
})
return (
p9.ggplot(p9.aes('x', 'y'))
+ p9.geom_tile(plotr, p9.aes(width=tile_width, height=tile_height))
+ p9.geom_text(plotr, p9.aes(label='value', color='color_text'))
+ p9.aes(fill='value')
+ p9.coord_equal(expand=False)
+ p9.theme_void()
+ p9.scales.scale_fill_gradient(
high='#f0f0f0', low='#252525', guide=False)
+ p9.scales.scale_color_gray(breaks=[False, True], guide=False)
)
def pictures(self, mode='bw', subset=None, n_random=10):
"""Returns a picture of the selected images.
Creates either a colored or a black-white picture of the selected
images.
Args:
mode: Should the picture be black-white ('bw') or in color
('color')?
subset: Optional list of picture indices that should be included in
the dataframe. If specified, n_random will be ignored.
n_random: Optional number of randomly selected images. If neither
subset nor n_random are specified, all images will be included.
Returns:
A plotnine object including all pictures with their label.
Raises:
NotImplementedError: mode must be either 'bw' or 'color'."""
dataframe = self.rgb_dataframe(subset=subset, n_random=n_random)
if mode == 'bw':
fill_key = 'rgb_bw'
elif mode == 'color':
fill_key = 'rgb'
else:
raise NotImplementedError("Pictures are either in black-white"
"('bw') or in color ('color').")
picture = (
gg.ggplot(dataframe, gg.aes(x='x', y='y', fill=fill_key)) +
gg.geom_tile() + gg.theme_void() +
gg.theme(legend_position='none') + gg.scale_fill_manual(
values={key: key
for key in dataframe[fill_key].unique()}) +
gg.facet_wrap('image_id', labeller=self.labeller) +
gg.scale_y_reverse() + gg.coord_fixed())
return picture
def test_theme_void(self):
p = self.g + labs(title='Theme Void') + theme_void()
assert p + _theme == 'theme_void'
def test_theme_void(self):
p = self.g + labs(title='Theme Void') + theme_void()
assert p + _theme == 'theme_void'
gradient = (
(0.99, 0.88, 0.87),
(0.98, 0.62, 0.71),
(0.86, 0.20, 0.59),
bcolor, bcolor,
bcolor_darker, bcolor_darker)
df1 = df[:n//3:9]
df2 = df[n//3:2*n//3]
df3 = df[2*n//3::12]
p = (ggplot(aes('x', 'y', color='y', fill='y'))
+ annotate(geom='label', x=0.295, y=0.495, label='pl tnine',
label_size=1.5, label_padding=.1, size=24,
fill=bcolor_lighter, color=bcolor)
+ geom_point(df1, size=8, stroke=0, show_legend=False)
+ geom_line(df2, size=2, color=bcolor_darker, show_legend=False)
+ geom_bar(df3, aes('x+.06'), stat='identity', size=0, show_legend=False)
+ scale_color_gradientn(colors=gradient)
+ scale_fill_gradientn(colors=gradient)
+ theme_void()
+ theme(figure_size=(3.6, 3.6)))
p.save('logo.pdf', pad_inches=-0.04)
# Remove the project name
p.layers = p.layers.__class__(p.layers[1:])
p.save('logo-small.pdf', pad_inches=-0.04)
bcolor_darker = '#631d41'
gradient = ((0.99, 0.88, 0.87), (0.98, 0.62, 0.71), (0.86, 0.20, 0.59), bcolor,
bcolor, bcolor_darker, bcolor_darker)
df1 = df[:n // 3:9]
df2 = df[n // 3:2 * n // 3]
df3 = df[2 * n // 3::12]
p = (ggplot(aes('x', 'y', color='y', fill='y')) + annotate(geom='label',
x=0.295,
y=0.495,
label='pl tnine',
label_size=1.5,
label_padding=.1,
size=24,
fill=bcolor_lighter,
color=bcolor) +
geom_point(df1, size=8, stroke=0, show_legend=False) +
geom_line(df2, size=2, color=bcolor_darker, show_legend=False) +
geom_bar(df3, aes('x+.06'), stat='identity', size=0, show_legend=False) +
scale_color_gradientn(colors=gradient) +
scale_fill_gradientn(colors=gradient) + theme_void() +
theme(figure_size=(3.6, 3.6)))
p.save('logo.pdf', pad_inches=-0.04)
# Remove the project name
p.layers = p.layers.__class__(p.layers[1:])
p.save('logo-small.pdf', pad_inches=-0.04)
def plot_sinew_inputs(df, var=None, sweep_ind="sweep_ind"):
r"""Inspect a sinew design
Create a scatterplot matrix with hues. Often used to visualize a sinew
design before evaluating the model functions.
Usually called as a dispatch from plot_auto().
Args:
df (Pandas DataFrame): Input design data
var (list of strings): Variables to plot
sweep_ind (string): Sweep index column in df
Returns:
Seaborn scatterplot matrix
Examples:
>>> import grama as gr
>>> import matplotlib.pyplot as plt
>>> from grama.models import make_cantilever_beam
>>> md = make_cantilever_beam()
>>> ## Dispatch from autoplotter
>>> (
>>> md
>>> >> gr.ev_sinews(df_det="swp", skip=True)
>>> >> gr.pt_auto()
>>> )
>>> ## Re-create without metadata
>>> (
>>> md
>>> >> gr.ev_sinews(df_det="swp")
>>> >> gr.pt_sinew_inputs(var=md.var)
>>> )
"""
if var is None:
raise ValueError("Must provide input columns list as keyword var")
## Define helpers
labels_blank = lambda v: [""] * len(v)
breaks_min = lambda lims: (lims[0], 0.5 * (lims[0] + lims[1]), lims[1])
## Make blank figure
fig = (
df
>> ggplot()
+ geom_blank()
+ theme_void()
).draw(show=False)
gs = gridspec.GridSpec(len(var), len(var))
for i, v1 in enumerate(var):
for j, v2 in enumerate(var):
ax = fig.add_subplot(gs[i, j])
## Switch labels
if j == 0:
labels_y = _sci_format
else:
labels_y = labels_blank
if i == len(var) - 1:
labels_x = _sci_format
else:
labels_x = labels_blank
## Label
if i == j:
p = (
df
>> ggplot()
+ annotate(
"label",
x=0,
y=0,
label=v1,
)
+ theme_void()
+ guides(color=None)
)
## Scatterplot
else:
p = (
df
>> ggplot(aes(v2, v1, color="factor("+sweep_ind+")"))
+ geom_point(size=0.1)
+ scale_x_continuous(
breaks=breaks_min,
labels=labels_x,
)
+ scale_y_continuous(
breaks=breaks_min,
labels=labels_y,
)
+ guides(color=None)
+ theme_minimal()
+ theme(
axis_title=element_text(va="top", size=12),
)
)
_ = p._draw_using_figure(fig, [ax])
## Plot
# NB Returning the figure causes a "double plot" in Jupyter....
fig.show()
def plot_scattermat(df, var=None):
r"""Create a scatterplot matrix
Create a scatterplot matrix. Often used to visualize a design (set of inputs
points) before evaluating the functions.
Usually called as a dispatch from plot_auto().
Args:
var (list of strings): Variables to plot
Returns:
ggplot: Scatterplot matrix
Examples:
>>> import grama as gr
>>> import matplotlib.pyplot as plt
>>> from grama.models import make_cantilever_beam
>>> md = make_cantilever_beam()
>>> ## Dispatch from autoplotter
>>> (
>>> md
>>> >> gr.ev_sample(n=100, df_det="nom", skip=True)
>>> >> gr.pt_auto()
>>> )
>>> ## Re-create plot without metadata
>>> (
>>> md
>>> >> gr.ev_sample(n=100, df_det="nom")
>>> >> gr.pt_scattermat(var=md.var)
>>> )
"""
if var is None:
raise ValueError("Must provide input columns list as keyword var")
## Define helpers
labels_blank = lambda v: [""] * len(v)
breaks_min = lambda lims: (lims[0], 0.5 * (lims[0] + lims[1]), lims[1])
## Make blank figure
fig = (
df
>> ggplot()
+ geom_blank()
+ theme_void()
).draw(show=False)
gs = gridspec.GridSpec(len(var), len(var))
for i, v1 in enumerate(var):
for j, v2 in enumerate(var):
ax = fig.add_subplot(gs[i, j])
## Switch labels
if j == 0:
labels_y = _sci_format
else:
labels_y = labels_blank
if i == len(var) - 1:
labels_x = _sci_format
else:
labels_x = labels_blank
## Density
if i == j:
xmid = 0.5 * (
df[v1].min() + df[v1].max()
)
p = (
df
>> ggplot(aes(v1))
+ geom_density()
+ scale_x_continuous(
breaks=breaks_min,
labels=labels_x,
)
+ scale_y_continuous(
breaks=breaks_min,
labels=labels_y,
)
+ annotate(
"label",
x=xmid,
y=0,
label=v1,
va="bottom",
)
+ theme_minimal()
+ labs(title=v1)
)
## Scatterplot
else:
p = (
df
>> ggplot(aes(v2, v1))
+ geom_point()
+ scale_x_continuous(
breaks=breaks_min,
labels=labels_x,
)
+ scale_y_continuous(
breaks=breaks_min,
labels=labels_y,
)
+ theme_minimal()
+ theme(
axis_title=element_text(va="top", size=12),
)
)
_ = p._draw_using_figure(fig, [ax])
## Plot
# NB Returning the figure causes a "double plot" in Jupyter....
fig.show()