def pointsApp(doc):
radii = [str(x) for x in df.Radius.unique()]
# Create Input controls
number = Slider(title="Value Cutoff",
value=0,
start=0,
end=4,
step=0.1,
width=150)
residue = TextInput(title="Residue name (3 letter code):", width=200)
gpcr = Select(title="Proteins",
value=list(df.Protein.unique())[0],
options=list(df.Protein.unique()),
width=100)
lipid = Select(title="Lipids",
value=list(df.Lipids.unique())[0],
options=list(df.Lipids.unique()),
width=100)
radius = Select(title="Radius",
value=radii[-1],
options=radii,
width=100)
options = list(df.columns)[:-5] + ['ResID']
x_axis = Select(title="X Axis",
options=options,
value="ResID",
width=150)
y_axis = Select(title="Y Axis",
options=options,
value=options[0],
width=150)
cc_colors = [
x for x in cc.all_original_names()
if x.startswith('linear') or x.startswith('rainbow')
]
cmap = Select(title="Colormap",
options=cc_colors,
value='linear_kryw_0_100_c71',
width=150)
# Create Column Data Source that will be used by the plot
source = ColumnDataSource(
data=dict(x=[], y=[], ResName=[], ResID=[], Protein=[]))
TOOLTIPS = [("ResName", "@ResName"), ("ResID", "@ResID"),
("Value", "@y")]
mapper = linear_cmap(
field_name='y',
palette=cc.CET_L19,
low=df[df.Protein == gpcr.value][y_axis.value].min(),
high=df[df.Protein == gpcr.value][y_axis.value].max())
# p = figure(plot_height=400, plot_width=800, title="", toolbar_location="right", tooltips=TOOLTIPS)
p = figure(tooltips=TOOLTIPS, )
global c
c = p.circle(x="x",
y="y",
source=source,
line_color='black',
fill_color=mapper,
**kwargs)
p.toolbar.autohide = True
p.axis.axis_label_text_font_size = "12pt"
p.axis.axis_label_text_font_style = "bold"
p.title.align = 'center'
def update(df):
y_value = y_axis.value
x_value = x_axis.value
df = df[(df[y_value] >= number.value)
& (df['Protein'] == gpcr.value) &
(df['Lipids'] == lipid.value) &
(df['Radius'] == float(radius.value))]
if (residue.value != ""):
df = df[df.ResName.str.contains(residue.value.upper()) == True]
# x_value = x_value
# y_value = y_value
mapper = linear_cmap(
field_name='y',
palette=cc.palette[cmap.value],
low=df[df.Protein == gpcr.value][y_value].min(),
high=df[df.Protein == gpcr.value][y_value].max())
c.glyph.fill_color = mapper
p.xaxis.axis_label = x_value
p.yaxis.axis_label = y_value
p.title.text = "Showing %d Data Points " % len(df)
source.data = dict(
x=df[x_value],
y=df[y_value],
ResName=df["ResName"],
ResID=df["ResID"],
Protein=df["Protein"],
)
controls = [number, gpcr, lipid, radius, y_axis, x_axis, residue, cmap]
for control in controls:
control.on_change('value', lambda attr, old, new: update(df))
sizing_mode = 'scale_width'
inputs = row(*controls, sizing_mode=sizing_mode)
inputs2 = row([gpcr, lipid, radius, residue], sizing_mode=sizing_mode)
inputs3 = row([number, x_axis, y_axis, cmap], sizing_mode=sizing_mode)
layout1 = layout([[inputs2]], sizing_mode=sizing_mode)
layout2 = layout([p], sizing_mode=sizing_mode)
layout3 = layout([inputs3], sizing_mode="scale_width")
update(df)
doc.add_root(layout1)
doc.add_root(layout2)
doc.add_root(layout3)
doc.title = "Scatter Application"
doc.theme = Theme(json=yaml.load("""
attrs:
Figure:
toolbar_location: above
height: 500
width: 800
Grid:
grid_line_dash: [6, 4]
grid_line_color: black
""",
Loader=yaml.FullLoader))
def scatter_handler(doc: Document) -> None:
"""
Handler function for the scatter application.
"""
# Load data
task_id = doc.session_context.request.arguments.get('task_id')
username = doc.session_context.request.arguments.get('username')
user_dir = os.path.join(settings.USER_DATA, username)
path_to_db = os.path.join(user_dir, task_id)
conn = os.path.join(path_to_db, task_id + '.csv')
df = pd.read_csv(conn)
for col in df.columns:
if col.endswith('Error'):
if df[col].max() == 0:
del df[col]
# widgets
radii = [str(x) for x in df.Radius.unique()]
number = Slider(title="Value Cutoff", value=0, start=0, end=4, step=0.1, width=150)
residue = TextInput(title="Residue name (3 letter code):", width=200)
gpcr = Select(title="Proteins", value=list(df.Protein.unique())[0],
options=list(df.Protein.unique()), width=100)
lipid = Select(title="Lipids", value=list(df.Lipids.unique())[0],
options=list(df.Lipids.unique()), width=100)
radius = Select(title="Radius", value=radii[-1], options=radii, width=100)
options = list(df.columns)[:-5] + ['ResID']
x_axis = Select(title="X Axis", options=options, value="ResID", width=150)
y_axis = Select(title="Y Axis", options=options, value=options[0], width=150)
# colors and plotting
cc_colors = [x for x in cc.all_original_names() if x.startswith('linear') or x.startswith('rainbow')]
cmap = Select(title="Colormap", options=cc_colors, value='linear_kryw_0_100_c71', width=150)
# Create Column Data Source that will be used by the plot
source = ColumnDataSource(data=dict(x=[], y=[], ResName=[], ResID=[], Protein=[]))
TOOLTIPS=[
("ResName", "@ResName"),
("ResID", "@ResID"),
("Value", "@y")
]
point_color_mapper = linear_cmap(field_name='y', palette=cc.CET_L19,
low=df[df.Protein == gpcr.value][y_axis.value].min(),
high=df[df.Protein == gpcr.value][y_axis.value].max())
p = figure(plot_height=400, plot_width=800, tooltips=TOOLTIPS,)
circle_plot = p.circle(x="x", y="y", source=source, line_color='black', fill_color=point_color_mapper, size=7)
circle_object = {'circle': circle_plot}
# make plot pretty
p.toolbar.autohide = True
p.axis.axis_label_text_font_size = "12pt"
p.axis.axis_label_text_font_style = "bold"
p.title.align = 'center'
def update(df):
y_value = y_axis.value
x_value = x_axis.value
df = df[
(df[y_value] >= number.value) &
(df['Protein'] == gpcr.value) &
(df['Lipids'] == lipid.value) &
(df['Radius'] == float(radius.value))
]
if (residue.value != ""):
df = df[df.ResName.str.contains(residue.value.upper())==True]
point_color_mapper = linear_cmap(field_name='y', palette=cc.palette[cmap.value],
low=df[df.Protein == gpcr.value][y_value].min(),
high=df[df.Protein == gpcr.value][y_value].max())
circle_object['circle'].glyph.fill_color = point_color_mapper
p.xaxis.axis_label = x_value
p.yaxis.axis_label = y_value
p.title.text = "Showing %d Data Points " % len(df)
source.data = dict(
x=df[x_value],
y=df[y_value],
ResName=df["ResName"],
ResID=df["ResID"],
Protein=df["Protein"],
)
# add controls
controls = [number, gpcr, lipid, radius, y_axis, x_axis, residue, cmap]
for control in controls:
control.on_change('value', lambda attr, old, new: update(df))
# build layout
# TODO: this should be made easier to read
sizing_mode = 'scale_width'
inputs2 = row([gpcr, lipid, radius, residue], sizing_mode=sizing_mode)
inputs3 = row([number, x_axis, y_axis, cmap], sizing_mode=sizing_mode)
layout1 = layout([[inputs2]], sizing_mode=sizing_mode)
layout2 = layout([p], sizing_mode=sizing_mode)
layout3 = layout([inputs3], sizing_mode="scale_width")
# render
update(df)
doc.add_root(layout1)
doc.add_root(layout2)
doc.add_root(layout3)
doc.title = "Point Application"
import colorcet as cc
from colorcet.plotting import swatches, sine_combs
import holoviews as hv
import panel as pn
hv.extension('bokeh')
diverging_n = cc.all_original_names(group='diverging', only_aliased=True)
linear_n = cc.all_original_names(group='linear',
not_group='diverging',
only_aliased=True)
cat_n = cc.all_original_names(group='glasbey', only_aliased=True)
misc_n = sorted(
[k for k in cc.aliases if k not in cat_n + diverging_n + linear_n])
diverging_col = pn.Column(
'#Diverging',
sine_combs(*diverging_n, width=400, height=150).opts(toolbar=None))
linear_col = pn.Column(
'#Linear',
sine_combs(*linear_n, width=400, height=150).opts(toolbar=None))
cat_col = pn.Column(
'#Categorical',
swatches(*cat_n, width=400, height=150, cols=1).opts(toolbar=None))
misc_col = pn.Column(
'#Misc',
sine_combs(*misc_n, width=400, height=150).opts(toolbar=None))
all_named = pn.Row(
linear_col, pn.Spacer(width=100),
def test_bokeh_palette_glasbey_do_not_start_with_bw():
for name in cc.all_original_names(group='glasbey'):
cmap = cc.palette[name]
assert isinstance(cmap, list)
assert len(cmap) == 256
assert {cmap[0], cmap[1]} != {'#00000', '#ffffff'}
def test_all_original_names_nopic_and_only_aliased():
assert len(cc.all_original_names(group='nopic', only_aliased=True)) == 2
def test_all_original_names_not_glasbey():
assert len(cc.all_original_names(not_group='glasbey')) == 71
def test_all_original_names_nopic():
assert len(cc.all_original_names(group='nopic')) == 10
def test_all_original_names_only_aliased():
assert len(cc.all_original_names(only_aliased=True)) == 28
def test_all_original_names():
assert len(cc.all_original_names()) == 79