def main(method_num):
# Method 1: Quickly create a web visualization with Python back-end
# support.
if method_num == 1:
# NOTE: Documentation states, "Initializes the pyviz notebook extension
# to allow plotting with bokeh and enable comms." It seems that
# `panel.extension` is not required when generating web
# visualizations with the `.show()` method.
pn.extension()
pn.interact(panel_wrapper).show()
# Method 2: Adjust the layout of the quick creation in Method 1.
if method_num == 2:
i = pn.interact(panel_wrapper)
text = "<br>\n# Random Walk as a Function of Standard Deviation"
p = pn.Row(i[1][0], pn.Column(text, i[0][0]))
p.show()
# Method 3, 4, & 5: Explicitly build the panel objects.
if method_num == 3 or method_num == 4 or method_num == 5:
c_opts = ["default", "optimized", "calibrated", "variable"]
dummy_catagory = pn.widgets.RadioButtonGroup(name="dummy_catagory",
value="default",
options=c_opts)
stdev = pn.widgets.FloatSlider(name="stdev",
value=0.03,
start=0,
end=0.1,
step=0.01)
@pn.depends(stdev, dummy_catagory)
def reactive_panel(stdev, dummy_catagory):
return panel_wrapper(stdev, dummy_catagory)
text = "<br>\n# Random Walk as a Function of Standard Deviation"
widgets = pn.Column(text, stdev, dummy_catagory)
p_rw = pn.Row(reactive_panel, widgets)
# Method 3: Deploy a web visualization with a Python server.
if method_num == 3:
p_rw.show()
# Method 4: Deploy a web visualization with data embedded in HTML.
# NOTE: This stand alone version works with the following web browsers:
# - Chrome Version 80.0.3987.122
# - Microsoft Edge 41.16299.1480.0
# This stand alone version does not work with the following web
# browsers:
# - Internet Explorer Version 11.1685.16299.0.
if method_num == 4:
f_out = "test.html"
p_rw.save(f_out, title="This is a test", embed=True, max_opts=10)
# Method 5: Deploy a web visualization referencing json data files.
if method_num == 5:
f_out = "test_json.html"
p_rw.save(f_out,
title="This is a test",
embed=True,
max_opts=10,
embed_json=True)
return 0
def interactive_logistic_regression(X,
y,
target_names=None,
feature_names=None,
stacked: bool = False):
from panel import widgets
import panel as pn
X_train, X_test, y_train, y_test = train_test_split(X,
y,
stratify=y,
random_state=42)
embedding = umap.UMAP(n_components=2, random_state=160290).fit_transform(
np.concatenate([X_train, X_test]))
def interactive_model(C, penalty, fit_intercept, intercept_scaling,
l1_ratio, class_weight):
model = LogisticRegression(
C=C,
penalty=penalty,
solver="saga",
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling,
l1_ratio=l1_ratio,
class_weight=class_weight,
random_state=42,
).fit(X, y)
return plot_model_evaluation(
model,
X_train,
y_train,
X_test,
y_test,
target_names=target_names,
feature_names=feature_names,
stacked=stacked,
embedding=embedding,
)
c_slider = widgets.LiteralInput(value=1, name="C")
penalty_tog = widgets.RadioButtonGroup(
name="penalty", options=["none", "l1", "l2", "elasticnet"])
fit_intercept = widgets.Toggle(name="fit_intercept")
intercept_scaling = widgets.LiteralInput(value=1, name="intercept_scaling")
l1_ratio = widgets.FloatSlider(end=1.0,
start=0.0,
value=0.5,
name="l1_ratio")
class_weight = widgets.LiteralInput(value=None, name="class_weight")
return pn.interact(
interactive_model,
C=c_slider,
penalty=penalty_tog,
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling,
l1_ratio=l1_ratio,
class_weight=class_weight,
)
def plot(self):
def interact_frame(time_stamp=0):
plot_title = 'Time: ' + str(self.time_stamps[time_stamp]) + ' min'
return hv.Points(data=self.dfs[time_stamp],
kdims=[('Potential', 'Potential [V]'),
("Diff", "Current [A]")],
vdims=['Time Stamp']).opts(tools=['hover'],
title=plot_title)
plot = pn.interact(interact_frame,
time_stamp=(0, len(self.time_stamps) - 1))
return plot
def create_dashboard():
mgrDir_list = ['Manager', 'Director']
mgrDirWidget = pn.interact(mgrDir, ManagerDirector=mgrDir_list)
assignmentsWidget = pn.interact(
chooseAssignmentLevel,
Quarter_Detail=['With_Quarters', 'Without_Quarters'],
ManagerDirector=mgrDir_list)
managerJobsWidget = pn.interact(displayManagerJobs,
Manager=df['Manager'].unique(),
ManagerDirector=mgrDir_list)
gspec = pn.GridSpec(sizing_mode='stretch_both', max_height=800)
gspec[0, :2] = pn.Row("# Novak Francella Manager/Partner Hours")
gspec[1:4, 0] = mgrDirWidget
gspec[1:6, 1] = pn.Column('')
gspec[1:3, 2] = assignmentsWidget
gspec[1:6, 3] = pn.Column('')
gspec[1:6, 4] = managerJobsWidget
return gspec
def main():
dir_name = setup()
print('\n', 'Start of main', '\n')
p = Path('extract_dir')
p.mkdir(exist_ok=True)
demux = dir_name + '/demux.qzv'
print(demux)
data_zip = zipfile.ZipFile(demux, 'r')
data_zip.extractall(path='extract_dir')
# potential to add a RegEx condition to create extract_dir based
# on input files or directory
for file in glob.iglob('extract_dir/*/data/per-*.csv'):
print(file)
data = pd.read_csv(file)
data.head()
fig = px.bar(data, x='Sample name', y='Sequence count', title='test graph')
# HIDE FOR NOW fig.show()
fig1 = go.Figure(
go.Scatter(x=data['Sample name'],
y=data['Sequence count'],
name='test graph object'))
fig1.update_layout(title='Demux data',
plot_bgcolor='rgb(230,200,170)',
showlegend=True)
# HIDE FOR NOW fig1.show()
print(type(data)) #produces <class 'pandas.core.frame.DataFrame'>
df_widget = pn.widgets.DataFrame(data, name='DataFrame')
df_widget.show()
###PANEL MODULE
def select_row(row=0):
return data.loc[row]
slide = pn.interact(select_row, row=(0, len(data) - 1))
# THIS STOPS EXECUTION --> slide.show()
# slide1 = pn.interact(select_row, row=(0, 25))
# slide1.show()
#slide.servable() # FOR USE WITH 'panel serve' command on notebook file .ipynb
# this above call should also work for .py files
tutorial(dir_name)
def plot_DEM_difference(data,
histogram='ground_track',
scatter='longitude',
bins=100,
subplots=False):
"""
Panel to explore pointwise differences with ArcticDEM
Includes a histogram, a scatter plot, and a summary table
"""
# Static plot
def difference(data=data, histogram=histogram, scatter=scatter):
plot_hist = data.hvplot.hist('height_diff',
bins=bins,
by=histogram,
subplots=subplots,
alpha=0.5)
plot_scat = data.hvplot.scatter(scatter,
'height_diff',
size=1,
alpha=0.1)
return (pn.Column(plot_scat, plot_hist))
# Widget values
kw = dict(histogram=[
'ground_track', 'atl06_quality_summary', 'bsnow_conf', 'bsnow_h',
'bsnow_od', 'cloud_flg_asr', 'cloud_flg_atm', 'msw_flag', 'layer_flag'
],
scatter=[
'segment_id', 'latitude', 'longitude', 'h_li',
'n_fit_photons', 'w_surface_window_final',
'n_fit_photons_ratio_w', 'bckgrd', 'dem_h', 'geoid_h'
])
# Interactive plot
i = pn.interact(difference, **kw)
# Layout
df = pd.DataFrame(data.height_diff.describe())
text = "<br>\n# Difference in estimated height with ArcticDEM"
p = pn.Row(pn.Column(text, i[0][1], i[0][0], df), pn.Column(i[1][0]))
return (p)
def plot_daily(data, day=None, col='ground_track'):
"""
Panel with daily plots: lat vs lon (map view), and height vs lat (profile view)
Interactivity on day and color
"""
# Static plot
def daily_scatter(data=data, day=day, col='ground_track'):
mapp = data[data.loc[:, 'time'].dt.date == day].hvplot.scatter(
x='longitude',
y='latitude',
s=0.2,
c='h_li',
alpha=0.2,
cmap='viridis')
scatter = data[data.loc[:, 'time'].dt.date == day].hvplot.scatter(
x='latitude', y='h_li', s=0.2, c=col)
global lat_bounds
lat_bounds = BoundsX(
source=scatter) # select box to define area for external data
return (pn.Column(mapp, scatter))
# Widget values
kw = dict(col=sorted(list(data.columns)),
day=data['time'].dt.date.unique())
global im # used to extract selected day and retrieve external data
# Interactive plot
im = pn.interact(daily_scatter, **kw)
# Layout
text = "<br>\n# Daily ATL06 data for the selected area\nSelect a day and a color variable"
p = pn.Row(pn.Column(text, im[0][1], im[0][0]), im[1][0])
return (p)
rus = RandomUnderSampler(random_state=2019)
X, y = rus.fit_resample(train.drop("validation", axis=1), train.validation)
train_clas = pd.DataFrame(X)
train_clas["4"] = y
train_clas.columns = df.columns
pn.extension()
pn.extension('katex')
def select_row(row=0):
return df[df.index == row]
pn.interact(select_row, row=(0, len(df) - 1))
# Language model data
data_lm = TextLMDataBunch.from_df(".",
train,
val,
text_cols="direction_list",
mark_fields=False)
# Classifier model data
data_clas = TextClasDataBunch.from_df(
".",
train_clas,
val,
text_cols="direction_list",
label_cols="validation",
vocab=data_lm.train_ds.vocab,
return x, y
#=================================================================================================
def Clifford_plot(x0=0,
y0=0,
n=100000,
a=1.6,
b=-0.7,
c=-1.8,
d=-1.9,
cmap=["pink", "purple"]):
cvs = ds.Canvas(plot_width=700, plot_height=700)
x, y = Clifford_trajectory(x0, y0, n, a, b, c, d)
agg = cvs.points(pd.DataFrame({'x': x, 'y': y}), 'x', 'y')
return tf.shade(agg, cmap)
#=================================================================================================
pn.extension()
pn.interact(Clifford_plot, n=(1, 1000000))
#=================================================================================================
# The value of this attractor can be changed freely. Try it in the jupyter notebook.
)
# %%
# Interactive holoviews scatter plot to find referencegroundtrack needed
# Tip: Hover over the points, and find those with high 'dhdt_slope' values
layout: pn.layout.Column = pn.interact(
dhdt_plot,
cycle=pn.widgets.IntSlider(name="Cycle Number",
start=2,
end=7,
step=1,
value=7),
dhdt_variable=pn.widgets.RadioButtonGroup(
name="dhdt_variables",
value="dhdt_slope",
options=["referencegroundtrack", "dhdt_slope", "h_corr"],
),
dhdt_range=pn.widgets.RangeSlider(name="dhdt range ±",
start=0,
end=20,
value=(1, 10),
step=0.5),
rasterize=pn.widgets.Checkbox(name="Rasterize"),
datashade=pn.widgets.Checkbox(name="Datashade"),
)
dashboard: pn.layout.Column = pn.Column(
pn.Row(
pn.Column(layout[0][1], align="center"),
pn.Column(layout[0][0], layout[0][2], align="center"),
pn.Column(layout[0][3], layout[0][4], align="center"),
),
x, y = np.zeros(n), np.zeros(n)
x[0], y[0] = x0, y0
for i in np.arange(n - 1):
x[i + 1] = y[i] - np.sqrt(abs(b * x[i] - c)) * np.sign(x[i])
y[i + 1] = a - x[i]
return x, y
#---------------------------------------------------------------------------------
def hopalong1_plot(a=1.000, b=2.000, c=6.600, n=4000000, colormap=ps['fire']):
cvs = ds.Canvas(plot_width=500, plot_height=500)
x, y = hopalong1_trajectory(a, b, c, 0, 0, n)
agg = cvs.points(pd.DataFrame({'x': x, 'y': y}), 'x', 'y')
return tf.shade(agg, cmap=colormap)
#---------------------------------------------------------------------------------
pn.interact(hopalong1_plot, n=(1, 10000000), colormap=ps)
#---------------------------------------------------------------------------------
# The value of this attractor can be changed freely.
# Try it in the jupyter notebook.
return view_fn(avg, avg[outliers])
kw = dict(window=(1, 60), variable=sorted(list(data.columns)), sigma=(1, 20))
tap = hv.streams.PointerX(x=data.index.min())
def hvplot2(avg, highlight):
line = avg.hvplot(height=300, width=500)
outliers = highlight.hvplot.scatter(color="orange", padding=0.1)
tap.source = line
return (line * outliers).opts(legend_position="top_right")
@pn.depends(tap.param.x)
def table(x):
index = np.abs((data.index - x).astype(int)).argmin()
return data.iloc[index]
app = pn.interact(find_outliers, view_fn=hvplot2, **kw)
row = pn.Row(
pn.Column("## Room Occupancy\nHover over the plot for more information.",
app[0]),
pn.Row(app[1], table),
)
row.servable()
select_gene = pn.widgets.Select(name='Select gene',
options=list(outdict.keys()))
select_growth = pn.widgets.Select(name='Select growth',
options=list(
outdict[select_gene.value].keys()))
def plot_infoshift(gene='aphA', growth='arabinose'):
plt.clf()
plt.cla()
s = outdict[gene][growth]
colorinputs = np.zeros((160))
for i in range(160):
if s[i] < 0:
colorinputs[i] = 0.0
else:
colorinputs[i] = 1.0
fig, ax = plt.subplots(figsize=(10, 2))
shiftcolors = plt.cm.bwr(colorinputs)
s_abs = np.abs(s)
ax.bar(range(160), np.abs(s), color=shiftcolors)
#fig = plt.bar(range(160),np.abs(s),color=shiftcolors)
plt.close(fig)
return fig
m = pn.interact(plot_infoshift, gene=select_gene, growth=select_growth)
mout = pn.Row(pn.Column(m[0]), pn.Column(m[1])).servable()
mout.save('/home/bill/test.html')
def dd_interactive(J1, J2, w):
return n_coupling(J1, J2, w=w)
def ddd_interactive(J1, J2, J3):
return n_coupling(J1, J2, J3)
def dddd_interactive(J1, J2, J3, J4):
return n_coupling(J1, J2, J3, J4)
dd_app = pn.interact(dd_interactive,
J1=(0.0, 10.0, 0.1, 3.0),
J2=(0.0, 10.0, 0.1, 7.0),
w=(0.0, 5.0, 0.1, 0.5)
)
ddd_app = pn.interact(ddd_interactive,
J1=(0.0, 15.0, 0.1, 4.0),
J2=(0.0, 15.0, 0.1, 10.0),
J3=(0.0, 15.0, 0.1, 12.0))
dddd_app = pn.interact(dddd_interactive,
J1=(0, 20, 0.1, 3.8),
J2=(0, 20, 0.1, 6.6),
J3=(0, 20, 0.1, 6.6),
J4=(0, 20, 0.1, 6.6))
x, y = np.zeros(n), np.zeros(n)
x[0], y[0] = x0, y0
for i in np.arange(n - 1):
x[i + 1] = np.sin(x[i] * y[i] / b) * y[i] + np.cos(a * x[i] - y[i])
y[i + 1] = np.sin(y[i]) / b + x[i]
return x, y
#---------------------------------------------------------------------------------
def bedhead_plot(a=1.40, b=1.14, n=1000000, colormap=ps['blues']):
cvs = ds.Canvas(plot_width=500, plot_height=500)
x, y = bedhead_trajectory(a, b, 1, 1, n)
agg = cvs.points(pd.DataFrame({'x': x, 'y': y}), 'x', 'y')
return tf.shade(agg, cmap=colormap)
#---------------------------------------------------------------------------------
pn.interact(bedhead_plot, n=(1, 10000000), colormap=ps)
#---------------------------------------------------------------------------------
# The value of this attractor can be changed freely.
# Try it in the jupyter notebook.
}
def stock_price(stock):
chosen = stocks[stock]
return (hv.Curve((chosen['Open'].index, chosen['Open'])) * hv.Curve(
(chosen['Close'].index, chosen['Close']))).options(
width=700, height=400, legend_position='top_left')
# In[ ]
stock_logos = {
'goog': Image.open('./data/google_logo.png'),
'aapl': Image.open('./data/apple_logo.png')
}
def stock_price_with_logo(stock):
return pn.Row(pn.panel(stock_logos[stock], height=200), stock_price(stock))
stock_explorer = pn.interact(stock_price_with_logo, stock=['goog', 'aapl'])
stock_explorer
title = """<span style="font-weight:bold;font-size:x-large">Stock price dashboard</span>"""
pn.Column(pn.panel(title, height=10), stock_explorer)
dashboard = pn.Column(pn.Row(pn.Spacer(width=350), pn.panel(title, height=10)),
stock_explorer)
dashboard
for i in np.arange(n - 1):
x[i + 1] = a * y[i] * (1 - b * y[i] * y[i]) + y[i] + Gumowski(x[i], mu)
y[i + 1] = -x[i] + Gumowski(x[i + 1], mu)
return x, y
#--------------------------------------------------------------------------------------
def GumowskiMira_plot(a=0.01, b=0.05, mu=-0.8, n=100000, colormap=ps['bgyw']):
cvs = ds.Canvas(plot_width=800, plot_height=800)
x, y = GumowskiMira_trajectory(a, b, mu, 0, 0.5, n)
agg = cvs.points(pd.DataFrame({'x': x, 'y': y}), 'x', 'y')
return tf.shade(agg, cmap=colormap)
#--------------------------------------------------------------------------------------
pn.interact(GumowskiMira_plot, n=(1, 1000000))
#--------------------------------------------------------------------------------------
# The value of this attractor can be changed freely.
# Try it in the jupyter notebook.
return x, y
#===================================================================================================
def JasonRampe1_plot(x0=0,
y0=0,
n=100000,
a=2.6,
b=-2.6,
c=-0.6,
d=0.7,
cmap=["yellow", "orange"]):
cvs = ds.Canvas(plot_width=700, plot_height=700)
x, y = JasonRampe1_trajectory(x0, y0, n, a, b, c, d)
agg = cvs.points(pd.DataFrame({'x': x, 'y': y}), 'x', 'y')
return tf.shade(agg, cmap)
#===================================================================================================
pn.extension()
pn.interact(JasonRampe1_plot, n=(1, 1000000))
#===================================================================================================
# The value of this attractor can be changed freely. Try it in the jupyter notebook.
return fig
def find_outliers(variable='Temperature',
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)