def class_area_histogram_with_select(record_stats_list, class_map=None, width=500, height=500):
"""Creates histograms for areas from a list of record_stats."""
# gui
# remove the first entry from the class_map, because the background class is not explicit part of the annotaitons
unique_labels = sorted(pd.unique(record_stats_list[0]["label"]))
for record_stats in record_stats_list[1:]:
if not all(pd.unique(sorted(record_stats["label"])) == unique_labels):
raise ValueError("All dataframes in the records_stats_list need to have the same set of unique values.")
options = pd.unique(record_stats_list[0]["label"])
options.sort()
if class_map is not None:
options = np.vectorize(class_map.get_id)(options)
class_dropdown = pnw.Select(options=options.tolist())
bins_slider = pnw.IntSlider(name="Bins", start=10, end=100, step=5)
checkbox_normalized = pnw.Checkbox(name="Normalized", value=False)
@pn.depends(class_dropdown.param.value, bins_slider.param.value, checkbox_normalized.param.value)
def _draw_histogram(class_label, bins, normalized):
nonlocal class_map
nonlocal width
nonlocal height
nonlocal record_stats_list
class_label = class_label if class_map is None else class_map.get_name(class_label)
return pn.Row(*[draw_area_histogram(record_stats, class_label, class_map, bins, normalized, width, height) for record_stats in record_stats_list])
return pn.Column(class_dropdown, pn.Row(bins_slider, checkbox_normalized), _draw_histogram)
def test_app():
"""Test dashboard"""
def refresh(event):
plot1.object = plotters.test1(cols=col_sl.value,rows=row_sl.value, plot_width=600)
plot2.object = plotters.test2(rows=row_sl.value, plot_width=600)
return
from . import __version__
title = pn.pane.Markdown('# pybioviz v%s test plots' %__version__)
plot1 = pn.pane.Bokeh()
plot2 = pn.pane.Bokeh()
col_sl = pnw.IntSlider(name='cols',value=30,start=5,end=200,step=1)
col_sl.param.watch(refresh, 'value')
row_sl = pnw.IntSlider(name='rows',value=10,start=5,end=100,step=1)
row_sl.param.watch(refresh, 'value')
col_sl.param.trigger('value')
app = pn.Column(title,col_sl,row_sl,plot1,plot2)
return app
def sequence_alignment_viewer(filename=None):
"""Sequence alignment viewer"""
title = pn.pane.Markdown()
aln_btn = pnw.Button(name='align',width=100,button_type='primary')
file_input = pnw.FileInput(name='load file',width=100,accept='.fa,.fasta,.faa')
aligner_sel = pnw.Select(name='aligner',value='muscle',options=['muscle','clustal','maaft'],width=100)
highlight_sel = pnw.Select(name='highlight mode',value='default',options=['default',''],width=100)
rowheight_sl = pnw.IntSlider(name='row height',value=10,start=5,end=20,width=120)
seq_pane = pn.pane.HTML(name='sequences',height=200,css_classes=['scrollingArea'])
bokeh_pane = pn.pane.Bokeh()
def update_title(filename):
title.object = '### Sequence aligner: %s' %filename
def update_file(event):
nonlocal seqtext
seqtext = file_input.value.decode('utf-8')
title.object = file_input.filename
update_title(file_input.filename)
return
def align(event):
#this function does the alignment
nonlocal seqtext
if seqtext is not None:
sequences = SeqIO.parse(io.StringIO(seqtext),format='fasta')
elif filename is not None:
sequences = SeqIO.parse(filename, format='fasta')
else:
return
sequences = list(sequences)
#print (sequences)
aligner = aligner_sel.value
if aligner == 'muscle':
aln = utils.muscle_alignment(sequences)
elif aligner == 'clustal':
aln = utils.clustal_alignment(sequences)
elif aligner == 'mafft':
aln = utils.mafft_alignment(sequences)
if aln is None:
bokeh_pane.object = plotters.plot_empty('%s not installed?' %aligner,900)
else:
#the bokeh pane is then updated with the new figure
bokeh_pane.object = plotters.plot_sequence_alignment(aln, row_height=rowheight_sl.value)
return
seqtext = None
file_input.param.watch(update_file,'value')
rowheight_sl.param.watch(align,'value')
aln_btn.param.watch(align, 'clicks')
aln_btn.param.trigger('clicks')
update_title(filename)
side = pn.Column(aln_btn,file_input,aligner_sel,highlight_sel,rowheight_sl,seq_pane,css_classes=['form'],width=200,margin=20)
app = pn.Column(title,pn.Row(side, bokeh_pane), sizing_mode='stretch_width',width_policy='max',margin=20)
return app
if self.p.normalize:
dr = data.ravel()
data = (data - dr.mean()) / dr.std() * 2**16
vmin, vmax = zscale(data.ravel())
new_data = data.clip(vmin, vmax)
label = element.label
# make an exact copy of the element with all settings, just with different data and label:
element = element.clone((xs, ys, new_data), label=label)
return element
channel = pnw.IntSlider(name='Channel', value=1, start=1, end=15)
zslice = pnw.Select(name='Slice', options=[])
class CubeViewer(param.Parameterized):
channel = param.Integer()
zslice = param.ObjectSelector()
def __init__(self,
data_dir,
imgtype='cubes',
xaxis=None,
yaxis=None,
colorbar=True,
toolbar='below',
import panel.widgets as pnw
from matplotlib.backends.backend_agg import FigureCanvas
from matplotlib.figure import Figure
DATA_URL = (
"https://raw.githubusercontent.com/LuisM78/Occupancy-detection-data/master/datatraining.txt"
)
data = pd.read_csv(DATA_URL)
data["date"] = data.date.astype("datetime64[ns]")
data = data.set_index("date")
variable = pnw.RadioButtonGroup(name="variable",
value="Temperature",
options=list(data.columns))
window = pnw.IntSlider(name="window", value=10, start=1, end=60)
def mpl_plot(avg, highlight):
fig = Figure()
FigureCanvas(fig) # not needed in mpl >= 3.1
ax = fig.add_subplot()
avg.plot(ax=ax)
if len(highlight):
highlight.plot(style="o", ax=ax)
return fig
def find_outliers(variable="Temperature",
window=30,
sigma=10,
files = os.listdir('data')
AAFMG = []
for f in files:
data = pd.read_csv('data/' + f)
data['Symbol'] = f.replace('.csv', '')
AAFMG.append(data)
AAFMG = pd.concat(AAFMG)
AAFMG['Date'] = pd.to_datetime(AAFMG['Date'])
# Create Widget Components
symbol = pnw.Select(name='symbol',
options=['AAPL', 'AMZN', 'FB', 'GOOGL', 'MSFT'])
value = pnw.RadioButtonGroup(
name='value',
options=['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'])
window = pnw.IntSlider(name='window', start=7, end=365, value=30)
date_range = pnw.DateRangeSlider(name='date range',
start=datetime.datetime(1980, 12, 12),
end=datetime.datetime(2020, 4, 1),
value=(datetime.datetime(2017, 4, 1),
datetime.datetime(2020, 4, 1)))
# Define Reactive Plot Function
@pn.depends(symbol, value, window, date_range)
def reactive_plot(symbol, value, window, date_range):
df = AAFMG.loc[AAFMG['Symbol'] == symbol]
df = df.sort_values('Date')
df['MA'] = df[value].rolling(window=window).mean()
df = df.loc[(df['Date'] >= date_range[0]) & (df['Date'] <= date_range[1])]
def bam_viewer(bam_file, ref_file, gff_file=None, width=1000, height=200, color='gray'):
"""Bam viewer widget.
Args:
bam_file: sorted bam file
ref_file: reference sequence in fasta format
gff_file: optional genomic features file
"""
slider = pnw.IntSlider(name='location',start=1,end=10000,value=500,step=500,width=300)
main_pane = pn.pane.Bokeh(height=100)
cov_pane = pn.pane.Bokeh(height=60)
loc_pane = pn.pane.Str(50,width=250,style={'margin': '4pt'})
feat_pane = pn.pane.Bokeh(height=60)
ref_pane = pn.pane.Bokeh(height=60)
xzoom_slider = pnw.IntSlider(name='x zoom',start=50,end=8000,value=1000,step=10,width=100)
yzoom_slider = pnw.IntSlider(name='y zoom',start=10,end=100,value=20,step=5,width=100)#,orientation='vertical')
panleft_btn = pnw.Button(name='<',width=50,button_type='primary')
panright_btn = pnw.Button(name='>',width=50,button_type='primary')
chroms_select = pnw.Select(name='Chromosome', options=[], width=250)
colorby = pnw.Select(name='Color by', options=['quality','pair orientation','read strand'], width=180)
search_pane = pnw.TextInput(name='search',width=200)
trans_option = pnw.Checkbox(name='show translation')
debug_pane = pn.pane.Markdown()
def pan_right(event):
plot = main_pane.object
plot.x_range
start = slider.value
loc = slider.value+100
slider.value=loc
update(event)
return
def pan_left(event):
loc = slider.value-100
if loc<1:
return
slider.value=loc
update(event)
return
def update_features():
"""Load features"""
if gff_file is None:
return
ext = os.path.splitext(gff_file)[1]
if ext in ['.gff','.gff3']:
feats = utils.gff_to_features(gff_file)
elif ext in ['.gb','.gbff']:
feats = utils.genbank_to_features(gff_file)
p = feat_pane.object = plotters.plot_features(feats,
plot_width=width, plot_height=100)
return p
def search_features(event):
"""Find a feature"""
term = search_pane.value
feats = utils.gff_to_features(gff_file)
df = utils.features_to_dataframe(feats)
df['gene'] = df.gene.fillna('')
f = df[df.gene.str.contains(term)].iloc[0]
debug_pane.object = str(f.start)
slider.value = int(f.start)
update(event)
return
def update_ref(filename, start, end):
"""Update reference sequence"""
if filename == None:
return
seqlen = utils.get_fasta_length(filename)
slider.end = seqlen
refseq = Fasta(filename)
chroms = list(refseq.keys())
chroms_select.options = chroms
key = chroms[0]
seq = refseq[key][int(start):int(end)].seq
ref_pane.object = plotters.plot_sequence(seq, plot_height=50,fontsize='9pt',xaxis=False)
return
def update(event):
"""Update viewers on widget change"""
xzoom = xzoom_slider.value
yzoom = yzoom_slider.value
start = slider.value
N = xzoom/2
end = start+N
chrom = utils.get_chrom(bam_file)
loc_pane.object = '%s:%s-%s' %(chrom,start,int(end))
cov = utils.get_coverage(bam_file,chrom,start,end)
cov_pane.object = plotters.plot_coverage(cov,plot_width=width)
main_pane.object = plotters.plot_bam_alignment(bam_file,chrom,start,end,height=yzoom,plot_width=width,plot_height=height)
update_ref(ref_file, start, end)
if feature_plot:
feature_plot.x_range.start = start
feature_plot.x_range.end = end
debug_pane.object = ''
return
slider.param.watch(update, 'value')
xzoom_slider.param.watch(update, 'value')
yzoom_slider.param.watch(update, 'value')
panright_btn.param.watch(pan_right, 'clicks')
panleft_btn.param.watch(pan_left, 'clicks')
search_pane.param.watch(search_features, 'value')
feature_plot = update_features()
#initialise slider
slider.param.trigger('value')
#menus = pn.Row(bam_input, ref_input, gff_input)
top = pn.Row(slider,xzoom_slider,yzoom_slider,panleft_btn,panright_btn,loc_pane)
bottom = pn.Row(chroms_select, search_pane,colorby,trans_option)
app = pn.Column(top,cov_pane,main_pane,ref_pane,feat_pane,bottom,debug_pane)
return app
window=30,
sigma=10,
view_fn=mpl_plot):
avg = data[variable].rolling(window=window).mean()
residual = data[variable] - avg
std = residual.rolling(window=window).std()
outliers = (np.abs(residual) > std * sigma)
return view_fn(avg, avg[outliers])
pn.extension()
pn.interact(find_outliers)
text = "<br>\n# Room Occupancy\nSelect the variable, and the time window for smoothing"
kw = dict(window=(1, 60), variable=sorted(list(data.columns)), sigma=(1, 20))
i = pn.interact(find_outliers, **kw)
p = pn.Row(i[1][0], pn.Column(text, i[0][0], i[0][1]))
variable = pnw.RadioButtonGroup(name='variable',
value='Temperature',
options=list(data.columns))
window = pnw.IntSlider(name='window', value=10, start=1, end=60)
reactive_outliers = pn.bind(find_outliers, variable, window, 10)
widgets = pn.Column("<br>\n# Room occupancy", variable, window)
occupancy = pn.Row(reactive_outliers, widgets)
occupancy.servable()
def genome_features_viewer(gff_file, ref_file=None, plot_width=900):
"""Gene feature viewer app"""
if gff_file is None:
return
features = utils.gff_to_features(gff_file)
df = utils.features_to_dataframe(features)
loc_pane = pnw.TextInput(name='location',value='',width=150)
search_pane = pnw.TextInput(name='find_gene',value='',width=220)
slider = pnw.IntSlider(name='start',start=0,end=10000,step=500,value=1,width=plot_width)
xzoom_slider = pnw.IntSlider(name='zoom',start=1,end=500,value=100,step=5,width=100)
chrom_select = pnw.Select(name='chrom',width=220)
left_button = pnw.Button(name='<',width=40)
right_button = pnw.Button(name='>',width=40)
feature_pane = pn.pane.Bokeh(height=100,margin=10)
seq_pane = pn.pane.Bokeh(height=50,margin=10)
debug_pane = pn.pane.Str('debug',width=200,style={'background':'yellow','margin': '4pt'})
if ref_file is not None:
seqlen = utils.get_fasta_length(ref_file)
slider.end = seqlen
else:
slider.end = int(df.end.max())
def search_features(event):
"""Find a feature"""
term = search_pane.value
feats = utils.gff_to_features(gff_file)
df = utils.features_to_dataframe(feats)
df['gene'] = df.gene.fillna('')
f = df[df.gene.str.contains(term)].iloc[0]
#debug_pane.object = str(f.start)
slider.value = int(f.start)-100
update(event)
return
def pan(event):
p = feature_pane.object
rng = p.x_range.end-p.x_range.start
inc = int(rng/10)
print (event.obj.name)
if event.obj.name == '<':
slider.value = int(slider.value) - inc
else:
slider.value = int(slider.value) + inc
update(event)
return
def update(event):
print (event.obj.name)
if event.obj.name in ['start', 'zoom']:
xzoom = xzoom_slider.value*200
start = int(slider.value)
N = xzoom/2
end = int(start+N)
loc_pane.value = str(start)+':'+str(end)
elif event.obj.name == 'location':
vals = loc_pane.value.split(':')
start = int(vals[0])
end = int(vals[1])
slider.value = start
#debug_pane.object=type(start)
p = feature_pane.object
p.x_range.start = start
p.x_range.end = end
if ref_file:
sequence = utils.get_fasta_sequence(ref_file, start, end)
seq_pane.object = plotters.plot_sequence(sequence, plot_width, plot_height=50,fontsize='9pt',xaxis=False)
return
slider.param.watch(update,'value',onlychanged=True)
#slider.param.trigger('value')
xzoom_slider.param.watch(update,'value')
search_pane.param.watch(search_features,'value')
loc_pane.param.watch(update,'value',onlychanged=True)
left_button.param.watch(pan,'clicks')
right_button.param.watch(pan,'clicks')
#debug_pane.object = utils.get_fasta_names(ref_file)[0]
if ref_file != None:
chrom_select.options = utils.get_fasta_names(ref_file)
#plot
p = feature_pane.object = plotters.plot_features(features, 0, 10000, plot_width=plot_width, tools="", rows=4)
#side = pn.Column(file_input,css_classes=['form'],width=200,margin=20)
top = pn.Row(loc_pane,xzoom_slider,search_pane,chrom_select,left_button,right_button)
main = pn.Column(feature_pane, seq_pane, sizing_mode='stretch_width')
app = pn.Column(top,slider,main,debug_pane, sizing_mode='stretch_width',width_policy='max',margin=20)
return app
def _update_widgets_panel(self):
self._default_component[self.component_type] = self.component
component = None
controls = None
if self.component is pn.pane.HoloViews:
component = pn.pane.HoloViews(_create_hvplot())
if self.component is pn.pane.ECharts:
# Issue https://github.com/holoviz/panel/issues/1817
component = pn.pane.ECharts(_create_echarts_plot(),
min_height=400,
min_width=200,
sizing_mode="stretch_both")
if self.component is pnw.Ace:
py_code = inspect.getsource(_create_hvplot)
component = pnw.Ace(
value=py_code,
sizing_mode="stretch_width",
language="python",
height=400,
theme=self._ace_theme,
)
elif self.component is pnw.AutocompleteInput:
component = pnw.AutocompleteInput(
name="Autocomplete Input",
options=["Biology", "Chemistry", "Physics"],
placeholder="Write something here",
)
elif self.component is pnw.Button:
component = pnw.Button(name="Click me", button_type="primary")
elif self.component is pnw.CheckBoxGroup:
component = pnw.CheckBoxGroup(
name="Checkbox Group",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
inline=True,
)
elif self.component is pnw.CheckButtonGroup:
component = pnw.CheckButtonGroup(
name="Check Button Group",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
button_type="success",
)
elif self.component is pnw.Checkbox:
component = pnw.Checkbox(name="Checkbox")
elif self.component is pnw.ColorPicker:
component = pnw.ColorPicker(name="Color Picker", value="#DF3874")
elif self.component is pnw.CrossSelector:
component = pnw.CrossSelector(
name="Fruits",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
height=300,
)
elif self.component is pnw.DataFrame:
component = self.component(name="Hello")
component.value = get_dataframe()
component.formatters = get_default_formatters(component.value)
controls = pn.Spacer()
elif self.component is pnw.DatePicker:
component = pnw.DatePicker(name="Date Picker")
# Issue: https://github.com/holoviz/panel/issues/1810
# component.start = date(2020, 1, 20)
# component.end = date(2020, 2, 20)
# component.value = date(2020, 2, 18)
elif self.component is pnw.DateRangeSlider:
component = self.component(name="Hello")
component.start = date(2020, 1, 20)
component.end = date(2020, 2, 20)
component.value = (date(2020, 2, 18), date(2020, 2, 20))
elif self.component is pnw.DateSlider:
component = self.component(name="Hello")
component.start = date(2020, 1, 20)
component.end = date(2020, 2, 20)
component.value = date(2020, 2, 18)
elif self.component is pnw.DatetimeInput:
component = self.component(name="Hello")
component.value = datetime(2020, 2, 18, 1, 2, 3)
elif self.component is pnw.DatetimeRangeInput:
component = self.component(
name="Hello",
start=datetime(2020, 1, 20),
end=datetime(2020, 2, 20),
value=(datetime(2020, 2, 18), datetime(2020, 2, 20)),
)
elif self.component is pnw.DiscretePlayer:
component = pnw.DiscretePlayer(
name="Discrete Player",
options=[2, 4, 8, 16, 32, 64, 128],
value=32,
loop_policy="loop",
)
elif self.component is pnw.DiscreteSlider:
component = pnw.DiscreteSlider(name="Discrete Slider",
options=[2, 4, 8, 16, 32, 64, 128],
value=32)
elif self.component is pnw.FileDownload:
component = pnw.FileDownload(file="README.md",
filename="README.md")
elif self.component is pnw.FileInput:
component = pnw.FileInput(accept=".csv,.json")
elif self.component is pnw.FileSelector:
component = pnw.FileSelector(name="Hello", max_height=400)
elif self.component is pnw.FloatInput:
component = pnw.FloatInput(name="FloatInput",
value=5.0,
step=1e-1,
start=0,
end=1000)
elif self.component is pnw.FloatSlider:
component = pnw.FloatSlider(name="Float Slider",
start=0,
end=3.141,
step=0.01,
value=1.57)
elif self.component is pnw.IntInput:
component = pnw.IntInput(name="IntInput",
value=5,
step=2,
start=0,
end=1000)
elif self.component is pnw.IntRangeSlider:
component = pnw.IntRangeSlider(name="Integer Range Slider",
start=0,
end=100,
value=(8, 40),
step=2)
elif self.component is pnw.IntSlider:
component = pnw.IntSlider(name="Integer Slider",
start=0,
end=20,
step=2,
value=4)
elif self.component is pnw.LiteralInput:
component = pnw.LiteralInput(name="Literal Input (dict)",
value={"key": [1, 2, 3]},
type=dict)
elif self.component is pnw.MenuButton:
menu_items = [
("Option A", "a"),
("Option B", "b"),
("Option C", "c"),
None,
("Help", "help"),
]
component = pnw.MenuButton(name="Dropdown",
items=menu_items,
button_type="primary")
elif self.component is pnw.MultiChoice:
component = pnw.MultiChoice(
name="MultiSelect",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
)
elif self.component is pnw.MultiSelect:
component = pnw.MultiSelect(
name="MultiSelect",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
size=8,
)
elif self.component is pnw.PasswordInput:
component = pnw.PasswordInput(name="Password Input",
placeholder="Enter a string here...")
elif self.component is pnw.Player:
component = pnw.Player(name="Player",
start=0,
end=100,
value=32,
loop_policy="loop")
elif self.component is pnw.Progress:
component = pnw.Progress(name="Progress", value=20, width=200)
elif self.component is pnw.RadioBoxGroup:
component = pnw.RadioBoxGroup(
name="RadioBoxGroup",
options=["Biology", "Chemistry", "Physics"],
inline=True)
elif self.component is pnw.RadioButtonGroup:
component = pnw.RadioButtonGroup(
name="Radio Button Group",
options=["Biology", "Chemistry", "Physics"],
button_type="success",
)
elif self.component is pnw.RangeSlider:
component = pnw.RangeSlider(
name="Range Slider",
start=0,
end=math.pi,
value=(math.pi / 4.0, math.pi / 2.0),
step=0.01,
)
elif self.component is pnw.Select:
component = pnw.Select(name="Select",
options=["Biology", "Chemistry", "Physics"])
elif self.component is pnw.StaticText:
component = pnw.StaticText(name="Static Text", value="A string")
elif self.component is pnw.TextAreaInput:
component = pnw.input.TextAreaInput(
name="Text Area Input", placeholder="Enter a string here...")
elif self.component is pnw.TextInput:
component = pnw.TextInput(name="Text Input",
placeholder="Enter a string here...")
elif self.component == pnw.Toggle:
component = pnw.Toggle(name="Toggle", button_type="success")
elif self.component == pnw.VideoStream:
component = pnw.VideoStream(name="Video Stream",
sizing_mode="stretch_width",
height=300)
if not component:
component = self.component(name="Hello")
if not controls:
controls = component.controls()
controls.margin = 0
self._component_panel[:] = [
pn.pane.Markdown("## " + component.__class__.name + " " +
self.component_type),
component,
pn.layout.Divider(),
pn.pane.Markdown("## Parameters"),
controls,
]
import hvplot.pandas
import pandas as pd
import panel as pn
import panel.widgets as pnw
pn.extension()
from bokeh.sampledata.autompg import autompg
autompgi = autompg.interactive()
year = pnw.IntSlider(start=70, end=82, value=70, name="Year")
out = (
autompgi[autompgi["yr"] == year]
.groupby("origin")
.mean()
.hvplot("origin", "mpg", kind="bar", ylim=(0, 50))
)
pn.pane.HoloViews(out).servable()
w_tab = pn.Column(rb, ppp, css_classes=['panel-widget-box'], margin=0)
#Histogram and descriptive statistics
variable = pnw.Select(name='Select',
options=['rating', 'desc_length', 'price', 'vintage'],
width=75)
limits = pnw.RangeSlider(name='Limits',
start=80,
end=100,
value=(80, 100),
step=1,
orientation='horizontal')
bins = pnw.IntSlider(name='bins',
value=20,
start=10,
end=100,
orientation='vertical',
width=50)
def cb(event):
if event.new == 'price':
limits.value = (int(wine['price'].min()), int(wine['price'].max()))
limits.start = int(wine['price'].min())
limits.end = int(wine['price'].max())
limits.step = 50
elif event.new == 'rating':
limits.value = (int(wine['rating'].min()), int(wine['rating'].max()))
limits.start = int(wine['rating'].min())
limits.end = int(wine['rating'].max())
pn.extension()
def f(x):
return x
interact(f, x=10)
interact(f, x=True)
interact(f, x='Hello there!')
@interact(x=True, y=1.0)
def g(x, y):
return (x, y)
g
layout = interact(f, x=10)
pn.Column('**A custom interact layout**', pn.Row(layout[0], layout[1]))
def h(p, q):
return (p, q)
interact(h, p=5, q=fixed(20))
interact(f, x=widgets.IntSlider(start=-10,end=30,step=1,value=10))
def baseline(self):
self.peak_currents = np.zeros(len(self.dfs))
#eak_left = -0.34
#eak_right = -0.28
#eft_limit = 30
steppp = self.dfs[0]["Potential"][0] - self.dfs[0]["Potential"][1]
first_point = self.dfs[0]["Potential"].iloc[0]
last_point = self.dfs[0]["Potential"].iloc[-1]
peak_left_slider = pnw.FloatSlider(name='Peak Left',
value=-0.35,
start=last_point,
end=first_point,
step=0.01)
peak_right_slider = pnw.FloatSlider(name='Peak Right',
value=-0.25,
start=last_point,
end=first_point,
step=0.01)
limit_left_slider = pnw.IntSlider(name='Limit Left',
value=10,
start=0,
end=len(self.dfs[0]["Potential"]) -
int(0.1 / steppp))
limit_right_slider = pnw.IntSlider(name='Limit Right',
value=10,
start=0,
end=len(self.dfs[0]["Potential"]) -
int(0.1 / steppp))
time_step_slider = pnw.IntSlider(name='Time Step',
value=0,
start=0,
end=len(self.dfs) - 1)
@pn.depends(time_step_slider, peak_left_slider, peak_right_slider,
limit_left_slider, limit_right_slider)
def max_current(time_stamp=time_step_slider.param.value,
a=peak_left_slider,
b=peak_right_slider,
c=limit_left_slider,
d=limit_right_slider):
return pn.pane.Markdown(
"peak current [A]: " +
str("{:.3e}".format(self.peak_currents[0])))
def update_sliders(time_stamp):
peak_left_slider.start = float(
decimal.Decimal(limit_left_slider.value * steppp +
self.dfs[time_stamp]["Potential"].iloc[-1] +
steppp).quantize(decimal.Decimal('.01'),
rounding=decimal.ROUND_UP))
#print(limit_left_slider.value*steppp+dfs[time_stamp]["Potential"].iloc[-1])
#print(float(decimal.Decimal(limit_left_slider.value*steppp+dfs[time_stamp]["Potential"].iloc[-1]+steppp).quantize(decimal.Decimal('.01'), rounding=decimal.ROUND_DOWN)))
#print(dfs[time_stamp]["Potential"].iloc[-(limit_left_slider.value+1)])
peak_left_slider.end = peak_right_slider.value
if (peak_left_slider.value > peak_left_slider.end):
peak_left_slider.value = peak_left_slider.end
if (peak_left_slider.value < peak_left_slider.start):
peak_left_slider.value = peak_left_slider.start
peak_right_slider.end = round(
+self.dfs[time_stamp]["Potential"].iloc[0] -
limit_right_slider.value * steppp, 2)
if (peak_right_slider.value > peak_right_slider.end):
peak_right_slider.value = peak_right_slider.end
if (peak_right_slider.value < peak_right_slider.start):
peak_right_slider.value = peak_right_slider.start
max_current.value = self.peak_currents[time_stamp]
def interact_frame(time_stamp=0,
peak_left=peak_left_slider.value,
peak_right=peak_right_slider.value,
left_limit=limit_left_slider.value,
right_limit=limit_right_slider.value):
peak_removed = [None] * len(self.dfs)
removed = [None] * len(self.dfs)
fit = [None] * len(self.dfs)
baseline = [None] * len(self.dfs)
for i, meas in enumerate(self.dfs):
peak_removed[i] = self.dfs[i].loc[((self.dfs[i].Potential <= peak_left)) | (self.dfs[i].Potential >= peak_right ),\
['Potential', 'Diff']]
removed[i] = pd.concat([
self.dfs[i].iloc[0:right_limit],
self.dfs[i].iloc[-left_limit:]
]) #['Potential', 'Diff'])
baseline[i] = pd.concat([
peak_removed[i].iloc[right_limit:-left_limit],
peak_removed[i].iloc[right_limit:-left_limit]
])
fit[i] = np.polynomial.polynomial.polyfit(
peak_removed[i]['Potential'][right_limit:-left_limit],
peak_removed[i]['Diff'][right_limit:-left_limit], 1)
for j in self.dfs[i].index:
self.dfs[i].at[j,
'Fit'] = np.polynomial.polynomial.polyval(
self.dfs[i].loc[j]['Potential'], fit[i])
if (self.dfs[i].at[j, 'Potential'] > peak_left) and (
self.dfs[i].at[j, 'Potential'] < peak_right):
self.dfs[i].at[
j, 'Peak'] = abs(self.dfs[i].loc[j]['Diff'] -
self.dfs[i].loc[j]['Fit'])
self.peak_currents[i] = self.dfs[i]['Peak'].max()
plot_title = 'Time' + str(self.time_stamps[time_stamp])
measurement_list = [
hv.Points(data=self.dfs[time_stamp],
kdims=[('Potential', 'Potential [V]'),
('Diff', 'Current [A]')],
vdims=[])
]
fit_list = [
hv.Points(data=self.dfs[time_stamp],
kdims=[('Potential', 'Potential [V]'),
('Fit', 'Current [A]')],
vdims=[])
]
fit_list2 = [
hv.Points(data=peak_removed[time_stamp],
kdims=[('Potential', 'Potential [V]'),
('Diff', 'Current [A]')],
vdims=[]).opts(color="r")
]
fit_list3 = [
hv.Points(data=removed[time_stamp],
kdims=[('Potential', 'Potential [V]'),
('Diff', 'Current [A]')],
vdims=[]).opts(color="y")
]
fit_list4 = [
hv.Points(data=baseline[time_stamp],
kdims=[('Potential', 'Potential [V]'),
('Diff', 'Current [A]')],
vdims=[]).opts(color="purple")
]
overlay = hv.Overlay(measurement_list + fit_list + fit_list2 +
fit_list3 + fit_list4)
overlay = overlay.redim.label(x='Potential [V]', y='Current [A]')
update_sliders(time_stamp)
return overlay
reactive_baseline = pn.bind(interact_frame, time_step_slider,
peak_left_slider, peak_right_slider,
limit_left_slider, limit_right_slider)
widgets = pn.Column("<br>\n## Baseline set:", peak_left_slider,
peak_right_slider, limit_left_slider,
limit_right_slider, max_current)
occupancy = pn.Row(reactive_baseline, widgets)
time_column = pn.Column("<br>\n## Time stamp:", time_step_slider)
pn_overlay = pn.Column(time_column, occupancy)
interact_frame()
return pn_overlay
