def test_setting_options_from_list_tuples(self):
d = Dropdown()
assert d.options == ()
d.options = [('One', 1), ('Two', 2), ('Three', 3)]
assert d.get_state('_options_labels') == {'_options_labels': ('One', 'Two', 'Three')}
d.value = 2
assert d.get_state('index') == {'index': 1}
##################################### OpenFoam Input tab ######################################
ofCaseName = Dropdown()
ofOwnCaseName = Dropdown(options=["Select Input Case of Own"])
ofCbox = Checkbox(value = False, description = "Use Own Input")
casesTutorials = ["Select Input Case of Tutorials"]
casesOwn = ["Select Input Case of Own"]
with logStash:
cmd("tar -xvf input_openfoam.tgz")
with open("input_openfoam/tutorials/cases.txt", 'r') as fr:
lines = fr.readlines()
for line in lines:
casesTutorials.append(line)
ofCaseName.options = casesTutorials
def ofCbox_change(change):
if change['type'] == 'change' and change['name'] == 'value':
if(change['new'] == True):
ownfiles = os.listdir("input_openfoam/foam_run/")
ofOwnCaseName.options = ownfiles
if(change['new'] == False):
ofOwnCaseName.options = ["Select Input Case of Own"]
ofCbox.observe(ofCbox_change)
def ofUpInput_btn_clicked(a):
if (ofCbox.value == True):
with logOp:
cmd("rm -f input.tgz")
def test_setting_options_from_dict(self):
d = Dropdown()
assert d.options == ()
with pytest.raises(TypeError):
d.options = {'One': 1}
def test_setting_options_from_list(self):
d = Dropdown()
assert d.options == ()
d.options = ['One', 'Two', 'Three']
assert d.get_state('_options_labels') == {'_options_labels': ('One', 'Two', 'Three')}
def feature_vector_distribution(features,
label_column,
bins=25,
group_columns=None,
f_lim=None,
colors=None):
"""
features (dataframe): a data frame of feature vectors along with a label column and other metadata
label_column (str): the name of the column in the features dataframe that refers to the label infomration
bins (int): the number of bins in the histograms
group_columns (list): if you want other metadata in the tooltip, these columns will be added
f_lim (dict): this sets the limits for max and min of the plots to a constant
{'max':10, 'min':10}. otherwise defaults to the values of the current features
which can be missleading.
colors (list): list of colors to use. Internally has a list of 10. If the labels
are longer you will need to pass your own
"""
dist = unicode("640px")
third_dist = unicode("213px")
if f_lim:
sc_x = LinearScale(min=f_lim['min'], max=f_lim['max'])
sc_y = LinearScale(min=f_lim['min'], max=f_lim['max'])
else:
sc_x = LinearScale()
sc_y = LinearScale()
scale_y = LinearScale(min=0)
x_ord_legend = OrdinalScale()
y_lin_legend = LinearScale()
if group_columns is None:
count_column = features.columns[1]
group_columns = []
else:
count_column = group_columns[0]
if colors is None:
colors = [
"#E6B0AA", "#C39BD3", "#73C6B6", "#F7DC6F", "#F0B27A", "#D0D3D4",
"#85929E", "#6E2C00", "#1A5276", "#17202A"
]
box_color = 'black'
feature_x = Dropdown(description='Feature 1')
feature_y = Dropdown(description='Feature 2')
feature_x.options = [
x for x in features.columns if x not in [label_column] + group_columns
]
feature_y.options = [
x for x in features.columns if x not in [label_column] + group_columns
]
feature1 = feature_x.options[0]
feature2 = feature_y.options[1]
tt = Tooltip(fields=['name'],
labels=[', '.join(['index', label_column] + group_columns)])
scatters = []
hists_y = []
hists_x = []
h_bins_x = get_h_bins(features[[feature1]], bins, f_lim)
h_bins_y = get_h_bins(features[[feature2]], bins, f_lim)
for index, group in enumerate(features.groupby([label_column])):
# put the label column and any group column data in the tooltip
names = []
for row in range(group[1].shape[0]):
names.append('{},'.format(row) + ','.join([
str(x) for x in group[1][[label_column] +
group_columns].iloc[row].values
]))
# create a scatter plot for each group
scatters.append(
Scatter(
x=group[1][feature1].values,
y=group[1][feature2].values,
names=names,
display_names=False,
default_opacities=[0.5],
default_size=30,
scales={
'x': sc_x,
'y': sc_y
},
colors=[colors[index]],
tooltip=tt,
))
# create a histograms using a bar chart for each group
# histogram plot for bqplot does not have enough options (no setting range, no setting orientation)
h_y, h_x = np.histogram(group[1][feature1].values, bins=h_bins_x)
hists_x.append(
Bars(x=h_x,
y=h_y,
opacities=[0.3] * bins,
scales={
'x': sc_x,
'y': scale_y
},
colors=[colors[index]],
orientation='vertical'))
h_y, h_x = np.histogram(group[1][feature2].values, bins=h_bins_y)
hists_y.append(
Bars(x=h_x,
y=h_y,
opacities=[0.3] * bins,
scales={
'x': sc_x,
'y': scale_y
},
colors=[colors[index]],
orientation='horizontal'))
# legend will show the names of the labels as well as a total count of each
legend_bar = Bars(
x=features.groupby(label_column).count()[count_column].index,
y=features.groupby(label_column).count()[count_column].values,
colors=colors,
opacities=[0.3] * 6,
scales={
'x': x_ord_legend,
'y': y_lin_legend
},
orientation='horizontal')
ax_x_legend = Axis(
scale=x_ord_legend,
tick_style={'font-size': 24},
label='',
orientation='vertical',
tick_values=features.groupby(label_column).count()[count_column].index)
ax_y_legend = Axis(scale=y_lin_legend,
orientation='horizontal',
label='Total',
color=box_color,
num_ticks=4)
#these are blank blank axis that are used to fill in the boarder for the top and right of the figures
ax_top = Axis(scale=sc_x,
color=box_color,
side='top',
tick_style={'font-size': 0})
ax_right = Axis(scale=sc_x,
color=box_color,
side='right',
tick_style={'font-size': 0})
ax_left = Axis(scale=sc_x,
color=box_color,
side='left',
tick_style={'font-size': 0})
ax_bottom = Axis(scale=sc_x,
color=box_color,
side='bottom',
tick_style={'font-size': 0})
#scatter plot axis
ax_x = Axis(label=feature1, scale=sc_x, color=box_color)
ax_y = Axis(label=feature2,
scale=sc_y,
orientation='vertical',
color=box_color)
#count column of histogram
ax_count_vert = Axis(label='',
scale=scale_y,
orientation='vertical',
color=box_color,
num_ticks=5)
ax_count_horiz = Axis(label='',
scale=scale_y,
orientation='horizontal',
color=box_color,
num_ticks=5)
#histogram bin axis
ax_hist_x = Axis(label='',
scale=sc_x,
orientation='vertical',
color=box_color)
ax_hist_y = Axis(label='',
scale=sc_x,
orientation='horizontal',
color=box_color)
#create figures for each plot
f_scatter = Figure(
axes=[ax_x, ax_y, ax_top, ax_right],
background_style={'fill': 'white'}, #css is inserted directly
marks=scatters,
min_aspect_ratio=1,
max_aspect_ratio=1,
fig_margin={
"top": 0,
"bottom": 60,
"left": 60,
"right": 0
},
)
f_hists_y = Figure(
axes=[ax_left, ax_count_horiz, ax_top, ax_right],
background_style={'fill': 'white'},
marks=hists_y,
min_aspect_ratio=.33,
max_aspect_ratio=.33,
fig_margin={
"top": 0,
"bottom": 60,
"left": 10,
"right": 0
},
)
f_hists_x = Figure(
axes=[ax_count_vert, ax_bottom, ax_top, ax_right],
background_style={'fill': 'white'},
marks=hists_x,
min_aspect_ratio=3,
max_aspect_ratio=3,
fig_margin={
"top": 20,
"bottom": 10,
"left": 60,
"right": 0
},
)
f_legend = Figure(marks=[legend_bar],
axes=[ax_x_legend, ax_y_legend],
title='',
legend_location='bottom-right',
background_style={'fill': 'white'},
min_aspect_ratio=1,
max_aspect_ratio=1,
fig_margin={
"top": 10,
"bottom": 30,
"left": 20,
"right": 20
})
# we already set the ratios, but it is necessary to set the size explicitly anyway
# this is kind of cool, inserts this into the style in html
f_legend.layout.height = third_dist
f_legend.layout.width = third_dist
f_hists_x.layout.height = third_dist
f_hists_x.layout.width = dist
f_hists_y.layout.height = dist
f_hists_y.layout.width = third_dist
f_scatter.layout.height = dist
f_scatter.layout.width = dist
# we create some functions that allow changes when the widgets notice an event
def change_x_feature(b):
h_bins_x = get_h_bins(features[[feature_x.value]], bins, f_lim)
for index, group in enumerate(features.groupby([label_column])):
scatters[index].x = group[1][feature_x.value]
h_y, h_x = np.histogram(group[1][feature_x.value].values,
bins=h_bins_x)
hists_x[index].y = h_y
ax_x.label = feature_x.value
def change_y_feature(b):
h_bins_y = get_h_bins(features[[feature_y.value]], bins, f_lim)
for index, group in enumerate(features.groupby([label_column])):
scatters[index].y = group[1][feature_y.value]
h_y, h_x = np.histogram(group[1][feature_y.value].values,
bins=h_bins_y)
hists_y[index].y = h_y
ax_y.label = feature_y.value
# when the user selects a different feature, switch the data plotted
feature_x.observe(change_x_feature, 'value')
feature_y.observe(change_y_feature, 'value')
#return the stacked figures to be plotted
return VBox([
HBox([feature_x, feature_y]),
HBox([f_hists_x, f_legend]),
HBox([f_scatter, f_hists_y])
])
