def atacseq_summary(config, input, output, Cutadapt, Qualimap,
MarkDuplicates, InsertMetrics, AlignmentMetrics):
"""Make atacseq summary"""
# Dictionary of figures
d = {}
Cutadapt.read_aggregate_data()
d.update({'cutadapt': {'fig': Cutadapt.plot('cutadapt')[0]}})
Qualimap.read_aggregate_data()
d.update({'qualimap': {'fig': {'globals': Qualimap.plot("Globals")[0],
'coverage_per_contig': Qualimap.plot("Coverage_per_contig")[0]}}})
InsertMetrics.read_aggregate_data()
plist = [InsertMetrics.plot('metrics')[0]] + [x for sublist in InsertMetrics.plot("hist")[0].children for x in sublist]
gp = gridplot([plist[i:i+3] for i in range(0, len(plist), 3)])
d.update({'picard': {'InsertMetrics': {'atacseq' : {'fig': gp}}}})
MarkDuplicates.read_aggregate_data()
d['picard'].update({'DuplicationMetrics': {'atacseq':
{'fig':
gridplot([[MarkDuplicates.plot('metrics')[0],
MarkDuplicates.plot('hist')[0]]])}}})
d.update({'rulegraph' : {'fig' : input.rulegraph, 'uri': data_uri(input.rulegraph),
'target' : 'atacseq_all'}})
# Write the resulting html
tp = Env.get_template('workflow_atacseq_qc.html')
with open(output.html, "w") as fh:
fh.write(static_html(tp, template_variables=d, css_raw=css_files))
def plot_LP_YP(file, outfile='Lx-P.html',
title='L-P Relation for Radio Halo Clusters',
categories = ["Limit+SZ", "Limit-SZ", "RHclusters"],
colors=['#462066', '#00AAA0', '#FF7A5A'],
label_font_size='10pt', title_font_size='12pt',
x_axis_type="log", y_axis_type="log",
legend=["Upper limits on RH power, with SZ",
"Upper limits on RH power, no SZ", "RH detections"],
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,hover,hover,hover,previewsave",
withSZ=False, withLP=True, withLineEq=False):
clusters = filter_clusters(file)
output_file(outfile, title=title)
if withSZ == True and withLP == True:
p1 = plot_LP(clusters, withLineEq=withLineEq, ysize=375)
p2 = plot_YP(clusters, withLineEq=withLineEq, ysize=375)
p2.y_range = p1.y_range
p = gridplot([[p1],[p2]], toolbar_location='above')
show(p)
elif withLP != True and withSZ == True:
p2 = plot_YP(clusters, withLineEq=withLineEq)
show(p2)
elif withLP == True and withSZ != True:
p1 = plot_LP(clusters, withLineEq=withLineEq)
show(p1)
def render_basic(
itmdt: Intermediate,
yscale: str,
plot_width: int,
plot_height: int,
) -> Box:
"""
Render plots from plot(df)
"""
figs = list()
for col, dtype, data in itmdt["data"]:
if dtype == DType.Categorical:
df, total_grps, miss_pct = data
fig = bar_viz(
df[:-1],
total_grps,
miss_pct,
col,
yscale,
plot_width,
plot_height,
False,
)
figs.append(fig)
elif dtype == DType.Numerical:
df, miss_pct = data
fig = hist_viz(df, miss_pct, col, yscale, plot_width, plot_height,
False)
figs.append(fig)
return gridplot(
children=figs,
sizing_mode=None,
toolbar_location=None,
ncols=3,
)
def plot_LP_YP(
file,
outfile='Lx-P.html',
title='L-P Relation for Radio Halo Clusters',
categories=["Limit+SZ", "Limit-SZ", "RHclusters"],
colors=['#462066', '#00AAA0', '#FF7A5A'],
label_font_size='10pt',
title_font_size='12pt',
x_axis_type="log",
y_axis_type="log",
legend=[
"Upper limits on RH power, with SZ",
"Upper limits on RH power, no SZ", "RH detections"
],
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,hover,hover,hover,previewsave",
withSZ=False,
withLP=True,
withLineEq=False):
clusters = filter_clusters(file)
output_file(outfile, title=title)
if withSZ == True and withLP == True:
p1 = plot_LP(clusters, withLineEq=withLineEq, ysize=375)
p2 = plot_YP(clusters, withLineEq=withLineEq, ysize=375)
p2.y_range = p1.y_range
p = gridplot([[p1], [p2]], toolbar_location='above')
show(p)
elif withLP != True and withSZ == True:
p2 = plot_YP(clusters, withLineEq=withLineEq)
show(p2)
elif withLP == True and withSZ != True:
p1 = plot_LP(clusters, withLineEq=withLineEq)
show(p1)
def fig1(xs, g, P_sb, P_sc, I, R=8.4/2):
"""Compute y-values, and lay out Fig. 1 plots."""
kwargs = {}
ys_1a = [g(x, 8.4) for x in xs]
ys_1a_dashed = [g(x, R) for x in xs]
fig1a, data_1a = curve(xs, ys_1a, ys_1a_dashed, y_range=[-0.5, 15],
title='Child growth curve', **kwargs)
fig1a.xaxis.axis_label = "Age (month)"
fig1a.yaxis.axis_label = "Head radius R (cm)"
ys_1b = [P_sb(x, 8.4) for x in xs]
ys_1b_dashed = [P_sb(x, R) for x in xs]
fig1b, data_1b = curve(xs, ys_1b, ys_1b_dashed, y_range=[-0.05, 1.05],
title='Birth survival curve', **kwargs)
fig1b.xaxis.axis_label = "Birth Age T (month)"
fig1b.yaxis.axis_label = "P(survive birth)"
ys_1c = [P_sc(x, I(8.4)) for x in xs]
ys_1c_dashed = [P_sc(x, I(R)) for x in xs]
fig1c, data_1c = curve(xs, ys_1c, ys_1c_dashed, y_range=[-0.05, 1.05],
title='Childhood survival curve', **kwargs)
fig1c.xaxis.axis_label = "Time until maturity M (month)"
fig1c.yaxis.axis_label = "P(survive childhood)"
fig = bkp.gridplot([[fig1a, fig1b, fig1c]])
# fig.toolbar.location = None
handle = bkp.show(fig, notebook_handle=True)
return handle, (data_1a, data_1b, data_1c)
def plot_bokeh_correlations(filepath, nonan=1):
from bokeh.plotting import figure, gridplot
head, data, nresp = read_mml_evaldb_nd(filepath, nonan=nonan)
# create xy scatter plots
y = data[:, -nresp]
sort = np.argsort(y)
y = y[sort]
keys = head[:-nresp]
colors = ('blue', 'green', 'red', 'cyan',
'maroon', 'yellow', 'black', 'white')
ylabel = r"{0}".format(head[-1])
plots = []
for i, key in enumerate(keys):
x = data[:, i][sort]
m2, m, b = np.polyfit(x, y, 2)
m2, (m, b) = 0, np.polyfit(x, y, 1)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save,resize"
y_axis_label = ylabel if not i else None
p = figure(tools=TOOLS, x_axis_label=r'{0}'.format(key),
y_axis_label=y_axis_label)
p.scatter(x, y, color=colors[i])
p.line(x, m2 * x * x + m * x + b, color='black')
plots.append(p)
return gridplot([plots])
def bokeh_linked():
"""these are taken EXACTLY from
http://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html
"""
# prepare some data
N = 100
x = np.linspace(0, 4*np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
y2 = np.sin(x) + np.cos(x)
# create a new plot
s1 = bkp.figure(width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)
# NEW: create a new plot and share both ranges
s2 = bkp.figure(
x_range=s1.x_range, y_range=s1.y_range, width=250, plot_height=250,
title=None
)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)
# NEW: create a new plot and share only one range
s3 = bkp.figure(x_range=s1.x_range, width=250, plot_height=250, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)
# NEW: put the subplots in a gridplot
p = bkp.gridplot([[s1, s2, s3]], toolbar_location=None)
return bke.components(p)
def bokeh_brushed():
"""these are taken EXACTLY from
http://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html
"""
# prepare some date
N = 300
x = np.linspace(0, 4*np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
# NEW: create a column data source for the plots to share
source = bkm.ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))
# create a new plot and add a renderer
left = bkp.figure(tools=BOKEH_TOOLS, width=350, height=350, title=None)
left.circle('x', 'y0', source=source)
# create another new plot and add a renderer
right = bkp.figure(tools=BOKEH_TOOLS, width=350, height=350, title=None)
right.circle('x', 'y1', source=source)
# put the subplots in a gridplot
p = bkp.gridplot([[left, right]])
return bke.components(p)
def bokeh_plot(df):
tooltip = """
<div>
<div>
<img
src="@image_files" height="60" alt="image"
style="float: left; margin: 0px 15px 15px 0px; image-rendering: pixelated;"
border="2"
></img>
</div>
<div>
<span style="font-size: 17px;">@source_filenames</span>
</div>
</div>
"""
filenames = b64_image_files(df['images'])
df['image_files'] = filenames
colors_raw = cm.viridis((df['time'] - df['time'].min()) /
(df['time'].max() - df['time'].min()), bytes=True)
colors_str = ['#%02x%02x%02x' % tuple(c[:3]) for c in colors_raw]
df['color'] = colors_str
source = ColumnDataSource(df)
bplot.output_file('plot.html')
hover0 = HoverTool(tooltips=tooltip)
hover1 = HoverTool(tooltips=tooltip)
tools0 = [t() for t in TOOLS] + [hover0]
tools1 = [t() for t in TOOLS] + [hover1]
pca = bplot.figure(tools=tools0)
pca.circle('PC1', 'PC2', color='color', source=source)
tsne = bplot.figure(tools=tools1)
tsne.circle('tSNE-0', 'tSNE-1', color='color', source=source)
p = bplot.gridplot([[pca, tsne]])
bplot.show(p)
def plot_bias_variance(N=8, random_seed=42, err=0.5):
np.random.seed(random_seed)
x = 10 ** np.linspace(-2, 0, N)
y = test_func(x)
xfit = np.linspace(-0.2, 1.2, 1000)
titles = ['d = 1 (under-fit; high bias)',
'd = 2',
'd = 6 (over-fit; high variance)']
degrees = [1, 2, 6]
row = []
for i, d in enumerate(degrees):
fig = bk.figure(plot_width=240, plot_height=240,
title=titles[i], x_range=(-0.2, 1.2), y_range=(0, 12))
fig.title_text_font_size = '11pt'
fig.xaxis.axis_label_text_font_size = '9pt'
fig.yaxis.axis_label_text_font_size = '9pt'
fig.x(x, y, color='black', size=12)
p = np.polyfit(x, y, d)
yfit = np.polyval(p, xfit)
fig.line(xfit, yfit, line_color='blue')
fig.xaxis.axis_label = 'house size'
fig.xaxis.axis_label_text_font_size = '9pt'
if i == 0:
fig.yaxis.axis_label = 'price'
row.append(fig)
gp = bk.gridplot([row], border_space=0)
bk.show(gp)
def bokeh_brushed():
"""these are taken EXACTLY from
http://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html
"""
# prepare some date
N = 300
x = np.linspace(0, 4 * np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
# NEW: create a column data source for the plots to share
source = bkm.ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))
# create a new plot and add a renderer
left = bkp.figure(tools=BOKEH_TOOLS, width=350, height=350, title=None)
left.circle('x', 'y0', source=source)
# create another new plot and add a renderer
right = bkp.figure(tools=BOKEH_TOOLS, width=350, height=350, title=None)
right.circle('x', 'y1', source=source)
# put the subplots in a gridplot
p = bkp.gridplot([[left, right]])
return bke.components(p)
def bokeh_linked():
"""these are taken EXACTLY from
http://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html
"""
# prepare some data
N = 100
x = np.linspace(0, 4 * np.pi, N)
y0 = np.sin(x)
y1 = np.cos(x)
y2 = np.sin(x) + np.cos(x)
# create a new plot
s1 = bkp.figure(width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)
# NEW: create a new plot and share both ranges
s2 = bkp.figure(x_range=s1.x_range,
y_range=s1.y_range,
width=250,
plot_height=250,
title=None)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)
# NEW: create a new plot and share only one range
s3 = bkp.figure(x_range=s1.x_range, width=250, plot_height=250, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)
# NEW: put the subplots in a gridplot
p = bkp.gridplot([[s1, s2, s3]], toolbar_location=None)
return bke.components(p)
def plot_cohort_employment(dataf):
dataf = dataf.copy()
plts = []
subset_dict = {
'male_west': [0, 0],
'male_east': [0, 1],
'female_west': [1, 0],
'female_east': [1, 1],
}
for name, vals in subset_dict.items():
data = data_cohort_employment(dataf, vals)
cols = data.columns.tolist()
cols.remove("age")
p = figure(title=name)
for ind, var in enumerate(cols):
p.line(y=data[var],
x=data['age'],
legend_label=var,
color=Spectral6[ind],
line_width=2)
plts.append(p)
p = gridplot([[plts[0], plts[1]], [plts[2], plts[3]]])
return p
def plot_stepdb(df) -> None:
"""plot step distribution
Parameters
----------
df : DataFrame
outcome of cal_stepinfo with duration, ap_ptp, ml_ptp, balance, LR
"""
f_amp = figure(width=300,
height=300,
title='Step Duration vs Amplitude',
x_axis_label='Duration [s]',
y_axis_label='ptp Magnitude [g]')
f_amp.circle(df.duration, df.ap_ptp, color=df.LR)
f_bal = figure(width=300,
height=300,
title='Balance Check',
x_axis_label='Duration Right [s]',
y_axis_label='Duration Left [s]')
f_bal.circle(df.duration.loc[df.balance > 0],
df.duration.loc[df.balance <= 0],
color='black')
f_bal.line([df.duration.min(), df.duration.max()],
[df.duration.min(), df.duration.max()])
fig = gridplot([f_amp, f_bal], ncols=2)
show(fig)
export_png(fig, filename='steps_info.png')
def plot_bias_variance(N=8, random_seed=42, err=0.5):
np.random.seed(random_seed)
x = 10**np.linspace(-2, 0, N)
y = test_func(x)
xfit = np.linspace(-0.2, 1.2, 1000)
titles = [
'd = 1 (under-fit; high bias)', 'd = 2',
'd = 6 (over-fit; high variance)'
]
degrees = [1, 2, 6]
row = []
for i, d in enumerate(degrees):
fig = bk.figure(plot_width=240,
plot_height=240,
title=titles[i],
x_range=(-0.2, 1.2),
y_range=(0, 12))
fig.title_text_font_size = '11pt'
fig.xaxis.axis_label_text_font_size = '9pt'
fig.yaxis.axis_label_text_font_size = '9pt'
fig.x(x, y, color='black', size=12)
p = np.polyfit(x, y, d)
yfit = np.polyval(p, xfit)
fig.line(xfit, yfit, line_color='blue')
fig.xaxis.axis_label = 'house size'
fig.xaxis.axis_label_text_font_size = '9pt'
if i == 0:
fig.yaxis.axis_label = 'price'
row.append(fig)
gp = bk.gridplot([row], border_space=0)
bk.show(gp)
def bokeh_plot(df):
tooltip = """
<div>
<div>
<img
src="@image_files" height="60" alt="image"
style="float: left; margin: 0px 15px 15px 0px; image-rendering: pixelated;"
border="2"
></img>
</div>
<div>
<span style="font-size: 17px;">@source_filenames</span>
</div>
</div>
"""
filenames = b64_image_files(df['images'])
df['image_files'] = filenames
colors_raw = cm.viridis((df['time'] - df['time'].min()) /
(df['time'].max() - df['time'].min()),
bytes=True)
colors_str = ['#%02x%02x%02x' % tuple(c[:3]) for c in colors_raw]
df['color'] = colors_str
source = ColumnDataSource(df)
bplot.output_file('plot.html')
hover0 = HoverTool(tooltips=tooltip)
hover1 = HoverTool(tooltips=tooltip)
tools0 = [t() for t in TOOLS] + [hover0]
tools1 = [t() for t in TOOLS] + [hover1]
pca = bplot.figure(tools=tools0)
pca.circle('PC1', 'PC2', color='color', source=source)
tsne = bplot.figure(tools=tools1)
tsne.circle('tSNE-0', 'tSNE-1', color='color', source=source)
p = bplot.gridplot([[pca, tsne]])
bplot.show(p)
def plot_bokeh_correlations(filepath, nonan=1):
from bokeh.plotting import figure, gridplot
head, data, nresp = read_mml_evaldb_nd(filepath, nonan=nonan)
# create xy scatter plots
y = data[:, -nresp]
sort = np.argsort(y)
y = y[sort]
keys = head[:-nresp]
colors = ('blue', 'green', 'red', 'cyan', 'maroon', 'yellow', 'black',
'white')
ylabel = r"{0}".format(head[-1])
plots = []
for i, key in enumerate(keys):
x = data[:, i][sort]
m2, m, b = np.polyfit(x, y, 2)
m2, (m, b) = 0, np.polyfit(x, y, 1)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save,resize"
y_axis_label = ylabel if not i else None
p = figure(tools=TOOLS,
x_axis_label=r'{0}'.format(key),
y_axis_label=y_axis_label)
p.scatter(x, y, color=colors[i])
p.line(x, m2 * x * x + m * x + b, color='black')
plots.append(p)
return gridplot([plots])
def plot_comparaison_bokeh(df_probs, rep_visualisation):
"""
courbe pour comparer les dc_0, dc_0.5
dh_0, dh_0.5
correl_dc_dh_0, correl_dc_dh_0.5
"""
for k_erreur in df_probs["k_erreur"].unique():
df_k = df_probs[df_probs["k_erreur"] == k_erreur]
df_k.fillna(-5, inplace=True)
df_k['graph_number'] = np.arange(1, df_k.shape[0] + 1)
output_file(rep_visualisation + "/" +
"comparaison_dashboard_p0_p05_k_" + str(k_erreur) +
".html")
#dc
dcs = ["dc_0", "dc_0.5"]
p_dc = plot_compa_cols_k(df_k, dcs, k_erreur)
#dh
dhs = ["dh_0", "dh_0.5"]
p_dh = plot_compa_cols_k(df_k, dhs, k_erreur)
#correl
correls = ["correl_dc_dh_0", "correl_dc_dh_0.5"]
p_correl = plot_compa_cols_k(df_k, correls, k_erreur)
#runtime
runtimes = ["runtime_0", "runtime_0.5"]
p_runtime = plot_compa_cols_k(df_k, runtimes, k_erreur)
p = gridplot([[p_dc, p_dh, p_correl, p_runtime]],
toolbar_location='above')
show(p)
def make_plot(title1, plot1, title2, plot2, title3, plot3, title4, plot4,
title5, plot5, sample_df):
plot1.title = title1
plot2.title = title2
plot3.title = title3
plot4.title = title4
plot5.title = title5
plot1.yaxis.axis_label = data_dict['DO']['title']
plot1.yaxis.axis_label_text_font_style = "italic"
plot2.yaxis.axis_label = data_dict['Ammonium']['title']
plot2.yaxis.axis_label_text_font_style = "italic"
plot3.yaxis.axis_label = data_dict['pH']['name']
plot3.yaxis.axis_label_text_font_style = "italic"
plot4.yaxis.axis_label = data_dict['N2 Mass Flow Controller']['title']
plot4.yaxis.axis_label_text_font_style = "italic"
plot5.yaxis.axis_label = data_dict['Air Mass Flow Controller']['title']
plot5.yaxis.axis_label_text_font_style = "italic"
plot1.line(x=sample_df.index, y=sample_df[title1], color="navy")
plot2.line(x=sample_df.index, y=sample_df[title2], color="firebrick")
plot3.line(x=sample_df.index,
y=sample_df[title3],
color="#28D0B4",
line_width=2)
plot4.line(x=sample_df.index, y=sample_df[title4], color="orange")
plot5.line(x=sample_df.index, y=sample_df[title5], color="black")
p = gridplot([[plot1, plot2, plot3], [plot4, plot5, None]])
return p
def create_plot(team="LAA", year=2012):
expr = bz.by(db.Salaries.teamID,
avg=db.Salaries.salary.mean(),
max=db.Salaries.salary.max(),
ratio=db.Salaries.salary.max() / db.Salaries.salary.min())
expr = expr.sort('ratio', ascending=False)
df_salary_gb = into(pd.DataFrame, expr)
source1 = into(ColumnDataSource, df_salary_gb[["teamID", "avg"]])
plot1 = plt.figure(title="Salary ratio by team", x_range=list(df_salary_gb["teamID"]))
plot1.scatter(x="teamID", y="avg", source=source1, size=20)
plot1.xaxis.major_label_orientation = np.pi/3
df = into(pd.DataFrame, db.Salaries)
df = df[df["teamID"] == team]
df = df[df["yearID"] == year]
df = df[["playerID","salary"]].sort('salary')
source_team = into(ColumnDataSource, df)
p_team = plt.figure(title="Salary of players for %s during %s" % (team, year),
x_range=list(df["playerID"]))#, tools=TOOLS)
p_team.scatter(x="playerID", y="salary", source=source_team, size=20)
p_team.xaxis.major_label_orientation = np.pi/3
p = plt.gridplot([[plot1, p_team]])
return p
def make_rseqc_summary_plots(rd_file, gc_file, do_qc=True, min_exonmap=60.0, max_three_prime_map=10.0):
"""Make rseqc summary plots"""
df_rd = pd.read_csv(rd_file, index_col=0)
df_gc = pd.read_csv(gc_file, index_col=0)
samples = list(df_gc.index)
# Use tags for formula
df = df_rd.pivot_table(columns=["Group"], values=["Tag_count"], index="sample")
df['Tag_count', "ExonMap"] = 100.0 * (df['Tag_count', "CDS_Exons"] + df['Tag_count', "3'UTR_Exons"] + df['Tag_count', "5'UTR_Exons"]) / df['Tag_count', "Total_Assigned_Tags"]
df.columns = df.columns.droplevel()
df['i'] = list(range(0, len(df.index)))
df['samples'] = samples
df_gc["three_prime_map"] = 100.0 * df_gc.loc[:, "91":"100"].sum(axis=1) / df_gc.loc[:, "1":"100"].sum(axis=1)
df = pd.concat([df, df_gc], axis=1)
colors = brewer["PiYG"][3]
colormap = {'False' : colors[0], 'True' : colors[2]}
columns = [
TableColumn(field="samples", title="Sample"),
TableColumn(field="ExonMap", title="Tags mapping to exons (%)"),
TableColumn(field="3' Map", title="Tags mapping to 3' end (%)"),
]
source = ColumnDataSource(df)
# Default tools, plot_config and tooltips
TOOLS="pan,box_zoom,box_select,lasso_select,reset,save,hover"
plot_config=dict(plot_width=300, plot_height=300,
tools=TOOLS, title_text_font_size='12pt',
x_range=[0, len(samples)], y_range=[0, 105],
x_axis_type=None, y_axis_type="linear",
xaxis={'axis_label' : "sample", 'major_label_orientation' : np.pi/3, 'axis_label_text_font_size' : '10pt'},
yaxis={'axis_label' : "percent (%)", 'major_label_orientation' : 1, 'axis_label_text_font_size' : '10pt'})
# Exonmap plot
qc = QCArgs(x=[0,len(samples)],
y=[min_exonmap, min_exonmap],
line_dash=[2,4]) if do_qc else None
c1 = list(map(lambda x: colormap[str(x)],
df['ExonMap'] < min_exonmap)) if do_qc else colors[0]
p1 = scatterplot(x='i', y='ExonMap',
source=source, color=c1, qc=qc,
tooltips = [{'type':HoverTool, 'tips' : [
('Sample', '@samples'),('ExonMap', '@ExonMap'),]}],
title="Tags mapping to exons", **plot_config)
# Fraction reads mapping to the 10% right-most end
qc = QCArgs(x=[0,len(samples)],
y=[max_three_prime_map, max_three_prime_map],
line_dash=[2,4]) if do_qc else None
c2 = list(map(lambda x: colormap[str(x)],
df['three_prime_map'] > max_three_prime_map)) if do_qc else colors[0]
p2 = scatterplot(x = 'i', y = 'three_prime_map',
color = c2, source = source,
qc=qc,
tooltips = [{'type':HoverTool, 'tips' : [
('Sample', '@samples'),('ExonMap', '@ExonMap'),]}],
title="Reads mapping to 3' end", **plot_config)
return {'fig' : gridplot([[p1, p2]]),
'uri' : [data_uri(rd_file), data_uri(gc_file)],
'file' : [rd_file, gc_file]}
def simple_chart(request):
plot = figure()
import numpy as np
# x = np.arange(-10,10,0.1)
# y = np.arange(-10,10,0.1)
# plot.circle([1,2], [3,4])
script, div = components(plot, CDN)
tp, hp = np.loadtxt('/Users/sebastian/phd/data/phenEOB-data/phenP/hp.dat').T
tc, hc = np.loadtxt('/Users/sebastian/phd/data/phenEOB-data/phenP/hc.dat').T
# select the tools we want
# TOOLS="reset,pan,wheel_zoom,box_zoom,save"
# p1 = figure(tools=TOOLS, plot_width=300*2, plot_height=300)
p1 = figure(plot_width=300*2, plot_height=300)
p1.line(tp, hp, color="red", alpha=0.5)
# p2 = figure(tools=TOOLS, plot_width=300*2, plot_height=300, x_range=p1.x_range, y_range=p1.y_range,)
p2 = figure(plot_width=300*2, plot_height=300, x_range=p1.x_range, y_range=p1.y_range,)
p2.line(tc, hc, color="blue", alpha=0.5)
from bokeh.plotting import gridplot
p = gridplot([[p1],[p2]])
# plots = {'Red': p1, 'Blue': p2}
# script, div = components(plots)
script, div = components(p)
return render(request, "bokplot/simple_chart.html", {"the_script": script, "the_div": div})
def plot_LP_YP(file, outfile='Lx-P.html',
title='L-P Relation for Radio Halo Clusters',
categories = ["Limit+SZ", "Limit-SZ", "RHclusters"],
colors=['#462066', '#00AAA0', '#FF7A5A'],
xaxis_label='0.1-2.4 keV X-ray Luminosity (x 1E+44 erg/s)',
yaxis_label='1.4 GHz Radio Halo Power (x 1E+24 W/Hz)',
label_font_size='14pt', title_font_size='16pt',
x_range=[1, 50], y_range=[0.1,100], xsize=600, ysize=600,
x_axis_type="log", y_axis_type="log",
legend=["Upper limits on RH power, with SZ",
"Upper limits on RH power, no SZ", "RH detections"],
TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,hover,hover,hover,previewsave",
withSZ=False, withLP=True):
clusters = filter_clusters(file)
output_file(outfile, title=title)
if withSZ == True and withLP == True:
p1 = plot_LP(clusters)
p2 = plot_YP(clusters)
p2.y_range = p1.y_range
p = gridplot([[p1,p2]], toolbar_location='above')
show(p)
elif withLP != True and withSZ == True:
p2 = plot_YP(clusters)
show(p2)
elif withLP == True and withSZ != True:
p1 = plot_LP(clusters)
show(p1)
def apply(self, func, **kwargs):
# create a figure with specified layout
d = [[] for _ in range(len(self.layout))]
for i, row in enumerate(self.layout):
is_bottom = ((i + 1) == len(self.layout))
for j, seqid in enumerate(row):
is_left = (j == 0)
if seqid is not None:
contig_size = len(self.genome[seqid])
px = int(contig_size * 1e-6 * self.plot_width_per_mb)
px += self.min_border_left
px += self.min_border_right
try:
yrange = s1.y_range
except NameError:
yrange = None
s1 = figure(width=px,
plot_height=self.figheight,
min_border_left=self.min_border_left,
min_border_right=self.min_border_right,
tools=self.tools,
title=self.chrom_label_func(seqid),
y_range=yrange,
x_range=(1, contig_size))
s1.xaxis.ticker = FixedTicker(
ticks=np.arange(0, contig_size, self.major_tick_dist))
s1.xaxis[0].formatter = NumeralTickFormatter(format="0a.0")
# handle general plot things specific to genome not data
if self.pfunc is not None:
self.pfunc(seqid, s1)
# function lives here
func(self.genome[seqid],
s1,
is_left=is_left,
is_bottom=is_bottom,
**kwargs)
d[i].append(s1)
else:
d[i].append(None)
# put the subplots in a grid plot
p = gridplot(d,
toolbar_location="left",
sizing_mode='fixed',
plot_width=None)
show(p)
def plot_candlestick(inputDf: pd.DataFrame, dateStart: str = "19000101", ma1Period: int = 0, ma2Period: int = 0,
stochPeriod: int = 10,
close: str = "<CLOSE>", open: str = "<OPEN>", high: str = "<HIGH>", low: str = "<LOW>",
ticker: str = "<TICKER>", vol: str = "<VOL>") -> None:
df = inputDf.copy()
w = 12*60*60*1000 # half day in ms
if ma1Period > 0:
df['MA_'+str(ma1Period)] = df[close].rolling(ma1Period).mean()
if ma2Period > 0:
df['MA_'+str(ma2Period)] = df[close].rolling(ma2Period).mean()
df = df.loc[df.index >= dateStart]
inc = df[close] > df[open]
dec = df[open] > df[close]
TOOLS = "crosshair,pan,wheel_zoom,box_zoom,reset,save"
df["%K"] = (100
* (df[close] - df[close].rolling(stochPeriod).min())
/ (df[close].rolling(stochPeriod).max() - df[close].rolling(stochPeriod).min()))
df["%D"] = df["%K"].rolling(3).mean()
df["%D-slow"] = df["%D"].rolling(2).mean()
pp = figure(x_axis_type="datetime", tools=TOOLS, plot_width=1000, title = df[ticker].values[0])
ppv = figure(x_axis_type="datetime", x_range=pp.x_range, tools=TOOLS,
plot_width=1000, plot_height=200, title="VOLUME")
ppstoch = figure(x_axis_type="datetime", x_range=pp.x_range, tools=TOOLS,
plot_width=1000, plot_height=200, title="STOCH")
pp.xaxis.major_label_orientation = pi/4
pp.xaxis.ticker.desired_num_ticks = 60
pp.yaxis.ticker.desired_num_ticks = 20
pp.grid.grid_line_alpha=0.3
ppv.xaxis.major_label_orientation = pi/4
ppv.xaxis.ticker.desired_num_ticks = 60
ppv.yaxis.ticker.desired_num_ticks = 5
ppv.grid.grid_line_alpha=0.3
ppstoch.xaxis.major_label_orientation = pi/4
ppstoch.xaxis.ticker.desired_num_ticks = 60
ppstoch.yaxis.ticker.desired_num_ticks = 5
ppstoch.grid.grid_line_alpha=0.3
pp.segment(df.index, df[high], df.index, df[low], color="black")
pp.vbar(df.index[inc], w, df[open][inc], df[close][inc], fill_color="#D5E1DD", line_color="black")
pp.vbar(df.index[dec], w, df[open][dec], df[close][dec], fill_color="#F2583E", line_color="black")
pp.add_tools(HoverTool(tooltips=[( 'price', '$y')]))
if ma1Period > 0:
pp.line(df.index, df['MA_'+str(ma1Period)], color="blue")
if ma2Period > 0:
pp.line(df.index, df['MA_'+str(ma2Period)], color="red")
ppv.vbar(df.index, w, 0, df[vol], fill_color="#F2583E", line_color="black")
ppstoch.line(df.index, df['%D'], color="red")
ppstoch.line(df.index, df['%D-slow'], color="green")
gg = gridplot([[pp], [ppv], [ppstoch]])
show(gg) # open a browser
def plot_errors(filepath, pred_errors, baseline_errors, show_plot=True):
"""Create a plot to evaluate the model against the baseline.
This function uses the Bokeh plotting library to create an html document
comparing model performance to baseline for each cohort.
Args:
filepath (string): The file and path of the html document.
pred_errors (pandas.DataFrame): The model outputs from `estimate_cohort_rates`.
baseline_errors (pandas.Series): The absolute difference between the actual
conversion rates for each cohort and a global conversionrate (across all
cohorts).
show_plot (bool): If True, open the html document in the browser. If False,
just output the Bokeh plot object.
Returns:
bokeh.plotting.figure.Figure: If show_plot is False, return the plot object.
Otherwise, return nothing.
"""
if show_plot:
output_file(filepath)
# create one plot for each cohort group (defined by trial date)
grid_list = []
for trial_date in pred_errors.index:
baseline = baseline_errors[trial_date]
days = pred_errors['fold_1'].columns.tolist()
baselines = [baseline for d in days]
plot = figure(plot_width=250,
plot_height=200,
x_axis_label='day from ' +
trial_date.strftime('%Y-%m-%d'),
y_axis_label='error',
y_range=[0.0, 0.075],
toolbar_location=None)
# plot one gray line for each fold of the model validation
for fold in [
l for l in pred_errors.columns.get_level_values(0).unique()
if 'fold' in l
]:
plot.line(days,
pred_errors.loc[trial_date, fold].tolist(),
line_width=1,
line_color='grey')
# add a black line for the baseline
plot.line(days, baselines, line_width=1, line_color='black')
grid_list.append(plot)
gp = gridplot(grid_list, ncols=3, toolbar_location=None)
if show_plot:
show(gp)
else:
return gp
def renderPlot(s1, s2, s3):
p = gridplot(([[s1, s2, s3]]))
output_file(
"C:\\Users\\SROY\\Desktop\\Courses\\CS513\\Project\\DTM_Scatter.html",
title="Digital Terrain Model")
print("Opening Browser")
show(p) # open a browser
return 0
def show(grid):
"""Display graphs, accepts a list of list of figures.
The postures(), goals() and effects() functions display their
graphs automatically unless the keyword argument `show` is set
to False.
"""
bkp.show(bkp.gridplot(grid))
def show(grid):
"""Display graphs, accepts a list of list of figures.
The postures(), goals() and effects() functions display their
graphs automatically unless the keyword argument `show` is set
to False.
"""
bkp.show(bkp.gridplot(grid))
def plot_layer(data, title, dim, layer_plot_directory):
bplt.output_file(os.path.join(layer_plot_directory, title + ".html"))
data = data.detach().cpu().numpy()
if dim == 2:
data1 = data[:, 0]
data2 = data[:, 1]
p1 = bplt.figure(title=title + "_1")
p1.line(np.arange(len(data1)), data1)
p1.xaxis.axis_label = "frames"
p1.yaxis.axis_label = "features range and mean: " + str(np.min(data1)) + ", " + str(np.max(data1)) + \
", " + str(np.mean(data1))
p2 = bplt.figure(title=title + "_2")
p2.line(np.arange(len(data2)), data2)
p2.xaxis.axis_label = "frames"
p2.yaxis.axis_label = "features range and mean: " + str(np.min(data2)) + ", " + str(np.max(data2)) + \
", " + str(np.mean(data2))
bplt.save(bplt.gridplot([p1], [p2]))
if dim == 3:
data1 = data[:, 0, :]
data2 = data[:, 1, :]
p1 = bplt.figure(x_range=(0, data1.shape[0]),
y_range=(0, data1.shape[1]),
title=title + "_1")
p1.image(image=[np.log(data1 + np.abs(np.min(data1)) + 1e-10).T],
x=[0],
y=[0],
dw=[data1.shape[0]],
dh=[data1.shape[1]],
palette='Greys256')
p1.xaxis.axis_label = "frames"
p1.yaxis.axis_label = "features range and mean: " + str(np.min(data1)) + ", " + str(np.max(data1)) + \
", " + str(np.mean(data1))
p2 = bplt.figure(x_range=(0, data2.shape[0]),
y_range=(0, data2.shape[1]),
title=title + "_2")
p2.image(image=[np.log(data2 + np.abs(np.min(data2)) + 1e-10).T],
x=[0],
y=[0],
dw=[data2.shape[0]],
dh=[data2.shape[1]],
palette='Greys256')
p2.xaxis.axis_label = "frames"
p2.yaxis.axis_label = "features range and mean: " + str(np.min(data2)) + ", " + str(np.max(data2)) + \
", " + str(np.mean(data2))
bplt.save(bplt.gridplot([p1], [p2]))
def bokeh_plot(u, t, legends, u_e, t_e, I, w, t_range, filename):
"""
Make plots for u vs t using the Bokeh library.
u and t are lists (several experiments can be compared).
legends contain legend strings for the various u,t pairs.
Each plot has u vs t and the exact solution u_e vs t_e.
"""
import numpy as np
import bokeh.plotting as plt
plt.output_file(filename, mode='cdn', title='Comparison')
# Assume that all t arrays have the same range
t_fine = np.linspace(0, t[0][-1], 1001) # fine mesh for u_e
tools = 'pan,wheel_zoom,box_zoom,reset,'\
'save,box_select,lasso_select'
u_range = [-1.2 * I, 1.2 * I]
font_size = '8pt'
p = [] # list of all individual plots
p_ = plt.figure(width=300,
plot_height=250,
title=legends[0],
x_axis_label='t',
y_axis_label='u',
x_range=t_range,
y_range=u_range,
tools=tools,
title_text_font_size=font_size)
p_.xaxis.axis_label_text_font_size = font_size
p_.yaxis.axis_label_text_font_size = font_size
p_.line(t[0], u[0], line_color='blue')
p_.line(t_e, u_e, line_color='red', line_dash='4 4')
p.append(p_)
for i in range(1, len(t)):
p_ = plt.figure(width=300,
plot_height=250,
title=legends[i],
x_axis_label='t',
y_axis_label='u',
x_range=p[0].x_range,
y_range=p[0].y_range,
tools=tools,
title_text_font_size=font_size)
p_.xaxis.axis_label_text_font_size = font_size
p_.yaxis.axis_label_text_font_size = font_size
p_.line(t[i], u[i], line_color='blue')
p_.line(t_e, u_e, line_color='red', line_dash='4 4')
p.append(p_)
# Arrange in grid with 3 plots per row
grid = [[]]
for i, p_ in enumerate(p):
grid[-1].append(p_)
if (i + 1) % 3 == 0:
# New row
grid.append([])
plot = plt.gridplot(grid, toolbar_location='left')
plt.save(plot)
plt.show(plot)
def _make_document(self, doc: Document) -> None:
doc.title = "Train Tracker"
# for _, plot in self._plots.items():
# doc.add_root(plot.fig)
figs = [plot.fig for _, plot in self._plots.items()]
grid = [figs[i:i + 3] for i in range(0, len(figs), 3)]
doc.add_root(gridplot(grid))
doc.add_periodic_callback(lambda: self._update_plots(doc), TIMEOUT)
def facet_grid(fig, x, y, df=None, source=None, groups=None, ncol=3,
share_x_range=False, share_y_range=False, **kwargs):
"""
facet_grid - generate a simple gridplot from a figure
Args:
fig (:py:class:`~bokeh.plotting.Plot`): bokeh Plot object
x (str): string for x component
y (str, list): string or list of strings for y component
df (:py:class:`~pandas.DataFrame`): pandas DataFram
groups (str, list(str)): groups to group by
ncol (int): number of columns to use in gridplot
share_x_range (bool): share x range across plots
share_y_range (bool): share y range across plots
kwargs: keyword arguments to pass to figure
Returns:
:class:~bokeh.models.GridPlot` object
"""
if not groups:
smllogger.warning("no groups defined; returning without modifying figure")
return
try:
grouped = df.groupby(groups)
except:
raise
flist = []
gr = fig.select(GlyphRenderer)
lgd = fig.select(Legend)
if len(gr) == 0:
smllogger.warning("no glyph renderer defined for plot; aborting")
return
j = 0
for name, group in grouped:
subfig = figure(title=str(name), **kwargs)
for glyph, yy in zip(gr, y):
plotfn = str(glyph.glyph).split(", ")[0].lower()
kw = glyph.glyph.properties_with_values()
kw.pop('x', None)
kw.pop('y', None)
kw.pop('xs', None)
kw.pop('ys', None)
source = ColumnDataSource(group)
kw['legend'] = str(yy) if len(lgd) > 0 else None
if plotfn == "multiline":
plotfn = "line"
getattr(subfig, plotfn)(x=x, y=yy, source=source, **kw)
else:
getattr(subfig, plotfn)(x=x, y=yy, source=source, **kw)
if j > 0:
if share_x_range:
subfig.x_range = flist[0].x_range
if share_y_range:
subfig.y_range = flist[0].y_range
j = j + 1
flist.append(subfig)
return gridplot([flist[i:i+ncol] for i in range(0, len(flist), ncol)])
def readTempData():
attacks = ['190C', '200C', '210C', '220C', '230C']
# Create a dictionary to store each figure in
p = {}
legendColors = ['navy', 'olive', 'firebrick', 'orange', 'purple']
titles = [
'Lowered to 190C', 'Lowered to 200C', 'Control - 210C',
'Raised to 220C', 'Raised to 230C'
]
i = int(1)
# Iterate through all 5 temperature attacks (190C-230C)
for x in attacks:
# Format the Bokeh plots for the temperature graphs
p[x] = figure(width=900,
plot_height=600,
title=titles[i - 1],
x_axis_label='Strain',
y_axis_label='Stress (MPa)',
x_range=(0, 0.0225),
y_range=(0, 34))
output_file("Attack_5_" + x + ".html")
p[x].title.text_font = 'Segoe UI'
p[x].title.text_font_size = '26pt'
p[x].title.align = 'center'
p[x].xaxis.axis_label_text_font_size = '26pt'
p[x].xaxis.major_label_text_font_size = '24pt'
p[x].yaxis.axis_label_text_font_size = '26pt'
p[x].yaxis.major_label_text_font_size = '24pt'
p[x].min_border = 35
# Load the mat file for each temperature
mat = sio.loadmat('Attack_5_' + x + '.mat')
# Iterate through all five specimens
for specimen in range(5):
# print("Specmimen:",specimen+1)
# Assign values for stress and strain from the MAT file
stress = mat['Temp_Test_Batch_' + str(i) + '_' +
x]['stress'][0][0][:, specimen]
strain = mat['Temp_Test_Batch_' + str(i) + '_' +
x]['strain'][0][0][:, specimen]
p[x].line(strain,
stress,
legend=None,
line_width=1,
line_color=legendColors[specimen])
i = i + 1
# Write output files
outputWrite = 'Plot_' + x + '.png'
# export_png(p,filename=outputWrite)
print("Finished Writing File: " + outputWrite)
reset_output()
# Debugging
# l=gridplot([[p['190C']],[p['200C']],[p['210C']],[p['220C']],[p['230C']]]) # Vertical
l = gridplot([[p['190C'], p['200C']], [p['210C'], None],
[p['220C'], p['230C']]]) # Horizontal
# export_png(l,filename='Gridplot.png')
show(l)
def generate_grid_scatter_plot(data_frame):
y = data_frame['Diabetes_Pct'].values
x1 = data_frame['Black_Pct'].values
x2 = data_frame['Hispanic_Pct'].values
x3 = data_frame['LowAccess_Pct'].values
x4 = data_frame['HouseholdIncome'].values
source = ColumnDataSource(data=dict(y=y, x1=x1, x2=x2, x3=x3, x4=x4))
TOOLS = "crosshair,wheel_zoom,reset,tap,pan,box_select"
p1 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p1.scatter('x1', 'y', source=source, fill_alpha=0.8, line_color=None)
p1.xaxis.axis_label = 'Percent Black'
p1.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p1.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p2 = figure(tools=TOOLS,
toolbar_location="right",
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p2.scatter('x2', 'y', source=source, fill_alpha=0.8, line_color=None)
p2.xaxis.axis_label = 'Percent Hispanic'
p2.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p2.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p3 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p3.scatter('x3', 'y', source=source, fill_alpha=0.8, line_color=None)
p3.xaxis.axis_label = 'Low Access Population'
p3.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p3.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p4 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(9.8, 11.8),
y_range=(0, 0.21))
p4.scatter('x4', 'y', source=source, fill_alpha=0.8, line_color=None)
p4.xaxis.axis_label = 'Median Household Income (log scale)'
p4.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
plot_quad = gridplot([[p1, p2], [p3, p4]])
script, dev = components(plot_quad)
return script, dev
def stop(self):
pdb.set_trace()
x_range = [self.datetime[0], self.datetime[-1]]
# equity/cash diagram
y_range1 = [0.95 * min(min(self.equity), min(self.cash)),
1.05 * max(max(self.equity), max(self.cash))]
equity_figure = bp.figure(x_range=x_range, y_range=y_range1, x_axis_type="datetime",
plot_width=self.p.plot_w, plot_height=self.p.plot_h,
title = 'Equity Curve, $')
# equity filled area
equity_figure.patch(self.datetime+[self.datetime[-1], self.datetime[0]],
self.equity+[0.0, 0.0], alpha=0.75, line_width=1.0, color='green')
# margin/position size
equity_figure.patch(self.datetime+self.datetime[::-1], self.equity+self.cash[::-1],
alpha=0.75, line_width=1.0, color='lightblue')
# equity curve
equity_figure.line(self.datetime, self.equity, color='darkgreen', line_width=2.0)
# money drawdown diagram
y_range2 = [1.05 * self.maxddd[-1], 0]
ddd_figure = bp.figure(x_range=equity_figure.x_range, y_range=y_range2,
plot_width=self.p.plot_w, plot_height=int(self.p.plot_h/2.5),
x_axis_type="datetime", title = 'Drawdown, $')
# money drawdown filled area
ddd_figure.patch(self.datetime+[self.datetime[-1], self.datetime[0]],
self.ddd+[0.0, 0.0], alpha=0.25, line_width=1.0, color='red')
# money drawdown curve
ddd_figure.line(self.datetime, self.ddd, color='red', line_width=2.0)
# max money drawdown curve
ddd_figure.line(self.datetime, self.maxddd,
color='black', line_width=1.0, line_dash='dashed')
# percent drawdown diagram
y_range3 = [1.05 * self.maxddp[-1], 0]
ddp_figure = bp.figure(x_range=equity_figure.x_range, y_range=y_range3,
plot_width=self.p.plot_w, plot_height=int(self.p.plot_h/2.5),
x_axis_type="datetime", title = 'Drawdown, %')
# percent drawdown filled area
ddp_figure.patch(self.datetime+[self.datetime[-1], self.datetime[0]],
self.ddp+[0.0, 0.0], alpha=0.25, line_width=1.0, color='red')
# percent drawdown curve
ddp_figure.line(self.datetime, self.ddp, color='red', line_width=2.0)
# max percent drawdown curve
ddp_figure.line(self.datetime, self.maxddp,
color='black', line_width=1.0, line_dash='dashed')
bp.output_file(self.p.html_name, title=self.p.html_title)
bp.show(bp.gridplot([equity_figure, ddd_figure, ddp_figure], ncols=1))
def generate_seir_charts(options, results):
output_file("all-series.html")
all_plots = []
plot = generate_seir_chart(options, results.global_series)
all_plots.append([plot])
for s in results.series:
plot = generate_seir_chart(options, s)
all_plots.append(([plot]))
p = gridplot(all_plots)
show(p)
def diagnostics(resid_, date_, lag_ ):
resid_, date_ = resid_[lag_:], date_[lag_:]
source = ColumnDataSource(data=dict(
resid_ = resid_,
qqplot_x = norm.ppf((np.arange(1,len(resid_) + 1) - 0.5)/len(resid_)),
qqplot_y = np.sort(resid_),
date_ = date_,
date_label = [str(d)[:7] for d in date_[np.argsort(resid_)]],
ind_sort = np.arange(0, len(resid_))[np.argsort(resid_)],
ind = np.arange(0, len(resid_))
))
s1 = figure(x_axis_type="datetime", plot_width = 400, plot_height = 400, title = "TS plot for residuals",
tooltips = [("index","@ind")])
s1.line(x = "date_", y = "resid_",source = source)
s1.xaxis.axis_label = "Date"
s1.yaxis.axis_label = "Residuals"
hist, edges = np.histogram(resid_, density=True)
s2 = figure(plot_width = 400, plot_height = 400, title = "Histagram for residuals")
s2.quad(top = hist, bottom = 0,left=edges[:-1], right=edges[1:],
fill_color="navy", line_color="white", alpha=0.5)
s2.xaxis.axis_label = "Residuals"
s2.yaxis.axis_label = "Frequency"
acf, ci = ts_tool.acf(resid_, alpha = 0.05)
s3 = figure(plot_width = 400, plot_height = 400, title = "ACF plot of residuals")
s3.vbar(x = list(range(1,41)), width = 0.5, bottom = 0,
top = acf[1:], color="firebrick")
s3.line(x = list(range(1,41)), y = 2/(len(resid_))**0.5)
s3.line(x = list(range(1,41)), y = -2/(len(resid_))**0.5)
s3.xaxis.axis_label = "Lag"
s3.yaxis.axis_label = "ACF"
s4 = figure(plot_width = 400, plot_height = 400, title = "Q-Q plot of residuals",tooltips = [("date", "@date_label"),("index","@ind_sort")])
s4.circle(x = "qqplot_x", y = "qqplot_y", source = source)
y_,x_ = np.quantile(resid_, [0.25, 0.75]), norm.ppf([0.25, 0.75])
slope = (y_[0] - y_[1])/(x_[0] - x_[1])
intercept = y_[0] - slope*x_[0]
line_ = norm.ppf((np.arange(1,len(resid_) + 1) - 0.5)/len(resid_))*slope + intercept
s4.line(x = norm.ppf((np.arange(1,len(resid_) + 1) - 0.5)/len(resid_)), y = line_)
s4.xaxis.axis_label = "Theoretical Quantile"
s4.yaxis.axis_label = "Sample Quantile"
p = gridplot([[s1, s2], [s3, s4]])
# print("Shapiro Test for normality has p-value:",shapiro(resid_).pvalue)
# output_file("%s.html"%("diagnostics"))
# show(p)
# reset_output()
# output_notebook()
return p, shapiro(resid_).pvalue
def visualize(profilers, file_path=None, show=True, save=True, **kwargs):
"""Visualize the results of profiling in a bokeh plot.
If multiple profilers are passed in, the plots are stacked vertically.
Parameters
----------
profilers : profiler or list
Profiler or list of profilers.
file_path : string, optional
Name of the plot output file.
show : boolean, optional
If True (default), the plot is opened in a browser.
save : boolean, optional
If True (default), the plot is saved to disk.
**kwargs
Other keyword arguments, passed to bokeh.figure. These will override
all defaults set by visualize.
Returns
-------
The completed bokeh plot object.
"""
if not _state._notebook:
file_path = file_path or "profile.html"
bp.output_file(file_path)
if not isinstance(profilers, list):
profilers = [profilers]
figs = [prof._plot(**kwargs) for prof in profilers]
# Stack the plots
if len(figs) == 1:
p = figs[0]
else:
top = figs[0]
for f in figs[1:]:
f.x_range = top.x_range
f.title = None
f.min_border_top -= 30
f.plot_height -= 30
for f in figs[:-1]:
f.xaxis.axis_label = None
f.min_border_bottom -= 30
f.plot_height -= 30
for f in figs:
f.min_border_left = 75
f.min_border_right = 75
p = bp.gridplot([[f] for f in figs])
if show:
bp.show(p)
if file_path and save:
bp.save(p)
return p
def visualize(profilers, file_path=None, show=True, save=True, **kwargs):
"""Visualize the results of profiling in a bokeh plot.
If multiple profilers are passed in, the plots are stacked vertically.
Parameters
----------
profilers : profiler or list
Profiler or list of profilers.
file_path : string, optional
Name of the plot output file.
show : boolean, optional
If True (default), the plot is opened in a browser.
save : boolean, optional
If True (default), the plot is saved to disk.
**kwargs
Other keyword arguments, passed to bokeh.figure. These will override
all defaults set by visualize.
Returns
-------
The completed bokeh plot object.
"""
if not _state._notebook:
file_path = file_path or "profile.html"
bp.output_file(file_path)
if not isinstance(profilers, list):
profilers = [profilers]
figs = [prof._plot(**kwargs) for prof in profilers]
# Stack the plots
if len(figs) == 1:
p = figs[0]
else:
top = figs[0]
for f in figs[1:]:
f.x_range = top.x_range
f.title = None
f.min_border_top = 20
f.plot_height -= 30
for f in figs[:-1]:
f.xaxis.axis_label = None
f.min_border_bottom = 20
f.plot_height -= 30
for f in figs:
f.min_border_left = 75
f.min_border_right = 75
p = bp.gridplot([[f] for f in figs])
if show:
bp.show(p)
if file_path and save:
bp.save(p)
return p
def _make_multiplot(self):
self._make_datasource()
self._make_hovertool_string()
figs = [self._make_patch_plot(title) for title in self.plot_params['plot_titles']]
for fig_i in figs[1:]:
fig_i.x_range = figs[0].x_range
fig_i.y_range = figs[0].y_range
for i, fig in enumerate(figs):
fig.title_text_color = 'black'
fig.axis.axis_label_text_color = 'black'
fig.axis.major_label_text_color = '#B3B3B3'
fig.title_text_font_size = '18pt'
fig.axis.axis_label_text_font_size = '12pt'
fig.axis.major_label_text_font_size= '9pt'
fig.axis.minor_tick_line_color = None
fig.axis.major_tick_in = -2
fig.axis.major_tick_out = 8
fig.axis.major_tick_line_color = '#B3B3B3'
fig.axis.major_tick_line_width = 2
fig.axis.major_tick_line_cap = 'butt'
fig.xaxis.axis_label = self.plot_params['x_label']
fig.yaxis.axis_label = self.plot_params['y_label']
fig.outline_line_width = 0.5
fig.outline_line_color = 'black'
if not self.logvals:
fig.xaxis[0].formatter = PrintfTickFormatter(format="%0.1e")
fig.yaxis[0].formatter = PrintfTickFormatter(format="%0.1e")
hover = fig.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = self._hovertool_html[i]
n_cols = self.plot_params['n_cols']
n_figs = len(figs)
figs = [figs[i*n_cols:(i+1)*n_cols] for i in range((n_figs//n_cols)+1)]
if n_figs % n_cols == 0:
figs=figs[:-1]
fig = gridplot(figs)
save_string = self.plot_params['save_string']
if not save_string:
output_notebook()
show(fig)
elif save_string == 'return':
return(fig)
elif save_string.endswith('.html'):
output_file(save_string)
save(fig)
def _facet_grid(df, return_source, glyph, colormap, facet, **kwargs):
groups = df.groupby(facet)
flist = []
ncol = kwargs.pop('ncol')
x = kwargs.pop('x')
y = kwargs.pop('y')
plot_height = kwargs.pop('plot_height')
plot_width = kwargs.pop('plot_width')
j = 0
source_dict = {}
for name, group in groups:
name = str(name)
kwfig = utils.fig_args(kwargs)
subfig = utils.create_bokeh_fig(plot_height=plot_height,
plot_width=plot_width,
name = name,
**kwfig)
fig_props = set(subfig.properties())
kwfig = utils.fig_args(kwargs, fig_props)
subfig.set(**kwfig)
source = ColumnDataSource(group)
kw = {'legend': name,
'x': x,
'y': y}
getattr(subfig, glyph)(source=source, **kw)
source_dict[name] = source
if j > 0:
if kwargs.get('share_x_range', None):
subfig.x_range = flist[0].x_range
if kwargs.get('share_y_range', None):
subfig.y_range = flist[0].y_range
j = j + 1
flist.append(subfig)
if return_source:
return gridplot([flist[i:i+ncol] for i in range(0, len(flist), ncol)]), source_dict
else:
return gridplot([flist[i:i+ncol] for i in range(0, len(flist), ncol)]), None
def create_grid(graphs, columns=6, **kwargs):
grid = []
current_row = []
i = 1
for graph in graphs:
current_row.append(graph)
if i % columns == 0:
grid.append(current_row)
current_row = []
i += 1
if len(current_row) > 0:
grid.append(current_row)
return gridplot(grid, **kwargs)
def __exit__(self, *args):
global _figures, _figsize
if len(_figures) > 1 or len(_figures[0]) > 0:
f = _bplt.gridplot(_figures, merge_tools=False)
if _static_images:
_show_static_images(f)
else:
_process_canvas([])
_bplt.show(f)
_process_canvas(
[item for sublist in _figures for item in sublist])
_figures = None
_figsize = self.ofigsize
def plot_map(perf_map, res, title='performance map', colors=BLUE_COLORS, show=True, scale='default'):
ps = list(perf_map.values())
p_min, p_max = np.min(ps), np.max(ps)
if scale == 'log':
c_min, c_max = -math.log(-p_min+EPSILON), -math.log(-p_max+EPSILON)
else:
c_min, c_max = p_min, p_max
img = np.zeros((res, res), dtype=np.uint32)
view = img.view(dtype=np.uint8).reshape(img.shape + (4,))
for (i, j), p in perf_map.items():
if scale == 'log':
p = -math.log(-p+EPSILON)
c_idx = int(np.floor((len(colors)-1)*(p - c_min)/(c_max-c_min)))
r, g, b = colors[c_idx]
view[j, i, 0] = r
view[j, i, 1] = g
view[j, i, 2] = b
view[j, i, 3] = 255
plot = bkp.figure(width=SIZE, height=SIZE, x_range=(-0.7, 0.7), y_range=(-0.7, 0.7),
title=title, title_text_font_size='12pt',
tools = "pan,box_zoom,reset,save")
plot.image_rgba([img], x=[-0.7], y=[-0.7], dh=[1.4], dw=[1.4])
if scale == 'log':
cbar = colorbar(colors, inversed=True)
cb_plot = bkp.figure(width=100, height=SIZE, x_range=(0, 1.0), y_axis_type="log", y_range=(-p_max, -p_min))
cb_plot.image_rgba([cbar], x=[0.0], y=[-p_max], dw=[1.0], dh=[p_max-p_min])
else:
cbar = colorbar(colors)
cb_plot = bkp.figure(width=100, height=SIZE, x_range=(0, 1.0), y_range=(p_min, p_max))
cb_plot.image_rgba([cbar], x=[0.0], y=[p_min], dw=[1.0], dh=[p_max-p_min])
cb_plot.min_border_right = 25
cb_plot.xgrid.grid_line_color = None
cb_plot.xaxis.minor_tick_line_color = None
cb_plot.xaxis.major_tick_line_color = None
cb_plot.xaxis.axis_line_color = None
cb_plot.xaxis[0].formatter = PrintfTickFormatter(format="")
if scale == 'log':
cb_plot.yaxis.formatter = PrintfTickFormatter(format="-%1.0e")
if show:
bkp.show(bkp.gridplot([[plot, cb_plot]]))
return [plot, cb_plot]
def bokeh_plot(u, t, legends, u_e, t_e, I, w, t_range, filename):
"""
Make plots for u vs t using the Bokeh library.
u and t are lists (several experiments can be compared).
legends contain legend strings for the various u,t pairs.
Each plot has u vs t and the exact solution u_e vs t_e.
"""
import numpy as np
import bokeh.plotting as plt
plt.output_file(filename, mode='cdn', title='Comparison')
# Assume that all t arrays have the same range
t_fine = np.linspace(0, t[0][-1], 1001) # fine mesh for u_e
tools = 'pan,wheel_zoom,box_zoom,reset,'\
'save,box_select,lasso_select'
u_range = [-1.2*I, 1.2*I]
font_size = '8pt'
p = []
p_ = plt.figure(
width=300, plot_height=250, title=legends[0],
x_axis_label='t', y_axis_label='u',
x_range=t_range, y_range=u_range, tools=tools,
title_text_font_size=font_size)
p_.xaxis.axis_label_text_font_size=font_size
p_.yaxis.axis_label_text_font_size=font_size
p_.line(t[0], u[0], line_color='blue')
p_.line(t_e, u_e, line_color='red', line_dash='4 4')
p.append(p_)
for i in range(1, len(t)):
p_ = plt.figure(
width=300, plot_height=250, title=legends[i],
x_axis_label='t', y_axis_label='u',
x_range=p[0].x_range, y_range=p[0].y_range, tools=tools,
title_text_font_size=font_size)
p_.xaxis.axis_label_text_font_size=font_size
p_.yaxis.axis_label_text_font_size=font_size
p_.line(t[i], u[i], line_color='blue')
p_.line(t_e, u_e, line_color='red', line_dash='4 4')
p.append(p_)
# Arrange in grid with 3 plots per row
grid = [[]]
for i, p_ in enumerate(p):
grid[-1].append(p_)
if (i+1) % 3 == 0:
# New row
grid.append([])
plot = plt.gridplot(grid, toolbar_location='left')
plt.save(plot)
plt.show(plot)
def atacseq_qualimap_plot_globals(df, **kwargs):
"""Plot qualimap global statistics.
Args:
df(:class:`~pandas.DataFrame`): data frame holding summary of qualimap results. The summary is provided by :meth:`snakemakelib.odo.qualimap.resource_genome_results`.
kwargs(dict): keyword arguments
Returns:
(:class:`bokeh.plotting.gridplot`): bokeh gridplot
Examples:
.. bokeh-plot::
:source-position: above
import pandas as pd
from bokeh.plotting import show
from snakemakelib_workflows.atacseq.app import atacseq_qualimap_plot_globals
df = pd.DataFrame([['S1', 'number of reads', 80, 100],
['S1', 'number of mapped reads', 20, 25],
['S1', 'number of duplicated reads', 10, 25],
['S1', 'number of unique reads', 10, 12.5]],
columns=['SM','statistic','value', 'percent'])
p = atacseq_qualimap_plot_globals(df, height=200, width=200)
show(p)
"""
READ_ROWS = ["number of reads",
"number of mapped reads",
"number of duplicated reads",
"number of unique reads"]
df.set_index(['statistic', 'SM'], inplace=True)
df.sortlevel(inplace=True)
df = df.loc[pd.IndexSlice[READ_ROWS, :]].reset_index()
df["value"] = [log10(x) for x in df["value"]]
df["SM"] = df["SM"].astype(str)
p1 = Scatter(df, x="SM", y="value", color="statistic",
legend="top_right", ylabel="log10(count)",
title="Qualimap read summary",
height=kwargs.get("height", 400),
width=kwargs.get("width", 400))
p2 = Scatter(df, x="SM", y="percent",
color="statistic", legend="top_right",
title="Qualimap read summary, percent",
height=kwargs.get("height", 400),
width=kwargs.get("width", 400))
return gridplot([[p1, p2]])
def get_figure(self):
canvases = list(self._collect_unique_canvas())
n_cols = 3
n_rows = (len(canvases)-1)//3 + 1
# set correct size of figure
for c in canvases:
c.plot_width = self.fig_width//n_cols
c.plot_height = self.fig_height//n_rows
# get figures
grid = []
for row in range(n_rows):
grid.append(canvases[n_cols*row:n_cols*(row+1)])
return gridplot(grid)
def basic_bokeh(out_fn="estuary_out.html"):
""" Basic 3-panel plot of output using Bokeh """
if os.path.exists(out_fn):
os.remove(out_fn)
output_file(out_fn)
# Construct Bokeh plot
source = ColumnDataSource(result)
day_range = Range1d(0, result.iloc[-1]["day"])
# colors = sns.cycle([rgb_to_hex(c) for c in sns.color_palette('Dark2', 3)])
colors = ["MediumSeaGreen", "OrangeRed", "DarkViolet"]
TOOLS = "box_zoom,reset"
# Create a new plot and add a renderer
top = figure(tools=TOOLS, width=1000, height=300, title=None, x_range=day_range)
top.line("day", "S", source=source, line_color=colors[0], line_width=3, line_cap="round")
top.y_range = Range1d(0, 1.05 * result["S"].max())
top.yaxis.axis_label = "Salinity (g/kg)"
mid = figure(tools=TOOLS, width=1000, height=300, title=None, x_range=top.x_range)
mid.line("day", "N", source=source, line_color=colors[1], line_width=3, line_cap="round")
mid.y_range = Range1d(0, 1.05 * result["N"].max())
mid.yaxis.axis_label = "Nitrate (mu mol/L)"
bot = figure(tools=TOOLS, width=1000, height=300, title=None, x_range=top.x_range)
bot.line("day", "O", source=source, line_color=colors[2], line_width=3, line_cap="round")
bot.y_range = Range1d(0, 1.05 * result["O"].max())
bot.yaxis.axis_label = "Oxygen (mu mol/L)"
# Set plot aesthetics
for p in [top, mid, bot]:
p.xgrid.grid_line_color = None
p.ygrid.grid_line_alpha = 0.5
p.xaxis.axis_label = "Day"
# Add spin-up annotation box
spin_up_box = BoxAnnotation(plot=p, left=0, right=2, fill_alpha=0.75, fill_color="grey")
p.renderers.extend([spin_up_box])
# Generate multi-panel plot and display
p = gridplot([[top], [mid], [bot]], toolbar_location="above")
show(p)
def atacseq_qualimap_plot_globals(df, **kwargs):
READ_ROWS = ["number of reads", "number of mapped reads", "number of duplicated reads", "number of unique reads"]
df.set_index(["statistic", "SM"], inplace=True)
df.sortlevel(inplace=True)
df = df.loc[pd.IndexSlice[READ_ROWS, :]].reset_index()
df["value"] = [log10(x) for x in df["value"]]
df["SM"] = df["SM"].astype(str)
p1 = Scatter(
df,
x="SM",
y="value",
color="statistic",
legend="top_right",
ylabel="log10(count)",
title="Qualimap read summary",
)
p2 = Scatter(df, x="SM", y="percent", color="statistic", legend="top_right", title="Qualimap read summary, percent")
return gridplot([[p1, p2]])
def make_figure():
top = figure(tools=TOOLS, width=600, height=400,
x_axis_label='x',
y_axis_label='f(x)')
top.line('x', 'y1', source=source, line_width=2)
top.scatter('x', 'y1', source=source, size=0)
bottom = figure(tools=TOOLS, width=600, height=400,
x_axis_label='x',
y_axis_label="f(x)'")
bottom.line('x', 'y2', source=source, alpha=1, line_width=2)
bottom.scatter('x', 'y2', source=source, size=0)
plot = gridplot([[top], [bottom]])
show(plot)
return plot
def plot_data(self): output_file(self.name+'_'+self.choice+'.html',title=self.name) tools = "pan,wheel_zoom,box_zoom,reset,save" fig1=figure(title=self.name+'_'+self.choice,title_text_align='center',y_axis_label='I',x_range=self.plot_limits[0],y_range=self.plot_limits[1],min_border_left=50,min_border_bottom=10,tools=tools,width=800,plot_height=380) T=np.arange(self.models[0][1][0]-3*self.models[0][1][1],self.models[0][1][0]+3*self.models[0][1][1],0.01) fig1.line(T,18-2.5*np.log10(self.lightcurve_model(T)),line_width=0.7,color='red') for i in xrange(len(self.telescopes)): #fig1.scatter(self.aligned_lightcurves[i][:,2],self.aligned_lightcurves[i][:,0],fill_color='#'+self.colors[self.telescopes[i][0]],line_color=None,legend=self.telescopes[i][1],size=5/10**np.abs(self.aligned_lightcurves[i][:,1])) fig1.segment(self.aligned_lightcurves[i][:,2],self.aligned_lightcurves[i][:,0]+np.abs(self.aligned_lightcurves[i][:,1]),self.aligned_lightcurves[i][:,2],self.aligned_lightcurves[i][:,0]-np.abs(self.aligned_lightcurves[i][:,1]),color='#'+self.colors[self.telescopes[i][0]],line_alpha=0.3) fig1.scatter(self.aligned_lightcurves[i][:,2],self.aligned_lightcurves[i][:,0],fill_color='#'+self.colors[self.telescopes[i][0]],line_color=None,legend=self.telescopes[i][1],size=4) fig1.xaxis.minor_tick_line_color=None fig1.xaxis.major_tick_line_color=None fig1.xaxis.major_label_text_font_size='0pt' fig1.legend.label_width=190 fig1.legend.label_width=50 fig1.legend.background_fill_alpha=0.5 fig1.legend.label_text_alpha=0.5 fig2=figure(width=800,plot_height=120,x_range=fig1.x_range,y_range=(-0.5,0.5),x_axis_label='HJD-2450000',y_axis_label='Delta_I',min_border_left=70,min_border_top=10) for i in xrange(len(self.telescopes)): residuals=self.residuals(i) fig2.segment(self.lightcurves[i][:,2],residuals+np.abs(self.lightcurves[i][:,1]),self.lightcurves[i][:,2],residuals-np.abs(self.lightcurves[i][:,1]),color='#'+self.colors[self.telescopes[i][0]],line_alpha=0.3) fig2.scatter(self.lightcurves[i][:,2],residuals,fill_color='#'+self.colors[self.telescopes[i][0]],line_color=None,size=4) fig2.xaxis.minor_tick_line_color=None p=gridplot([[fig1],[fig2]],toolbar_location="right") show(p)
def create_grid_images(self, img_list, name, n_cols=3):
# Create a list with the image figures and image data sources
p_images = []
data_sources = []
for i in xrange(len(img_list)):
p_img, data_img = self.create_figure_image(img_list[i], name=name + str(i))
p_images.append(p_img)
data_sources.append(data_img)
# Add the needed Nones so we can transform in a square matrix
for i in xrange(n_cols - len(img_list) % n_cols):
p_images.append(None)
# Reshape as a matrix (List format)
p_images = np.asarray(p_images).reshape(-1, 3).tolist()
# Plot the images
p = gridplot(p_images)
# return the grid
return p, data_sources
def create_plot(N, func1, func2, color):
N = 300
x = np.linspace(0, 4*np.pi, N)
y1 = np.sin(x)
y2 = np.cos(x)
source = ColumnDataSource()
source.add(data=x, name='x')
source.add(data=y1, name='y1')
source.add(data=y2, name='y2')
TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select,lasso_select"
s1 = plt.figure(tools=TOOLS, plot_width=350, plot_height=350)
s1.scatter('x', 'y1', source=source, fill_color=color)
s2 = plt.figure(tools=TOOLS, plot_width=350, plot_height=350)
s2.scatter('x', 'y2', source=source, fill_color=color)
p = plt.gridplot([[s1,s2]])
return p
def make_picard_summary_plots(inputfiles, ncol=4):
d = {}
TOOLS = "pan,box_zoom,wheel_zoom,box_select,lasso_select,resize,reset,save,hover"
for (metrics_file, hist_file) in zip(inputfiles[0::2], inputfiles[1::2]):
df_met = _read_metrics(metrics_file)
df_hist = _read_metrics(hist_file)
p1 = df_met.plot_metrics(tools=TOOLS)
key = os.path.splitext(metrics_file)[0]
if df_met.label not in d:
d[df_met.label] = {}
d[df_met.label][key] = {}
d[df_met.label][key]['uri'] = [data_uri(metrics_file)]
d[df_met.label][key]['file'] = [metrics_file]
if df_hist is not None:
p2 = df_hist.plot_hist(tools=TOOLS)
d[df_met.label][key]['uri'].append(data_uri(hist_file))
d[df_met.label][key]['file'].append(hist_file)
else:
p2 = []
plist = p1 + p2
gp = gridplot([plist[i:i+ncol] for i in range(0, len(plist), ncol)])
d[df_met.label][key]['fig'] = gp
return d
def list(s1):
s=int(s1)
print("Exercise n: ", s)
#output_file("home.html")
exercise = list_exercises[s]
T = exercise.X.shape[1]
t = np.arange(T)/100
plot = figure(width=350, plot_height=250, title="Droit Acceleration X")
plot.line(t,exercise.get_signal("DAX")[0])
plot2=figure(width=350, plot_height=250, title="Droit Acceleration Y")
plot2.line(t,exercise.get_signal("DAY")[0])
plot3=figure(width=350, plot_height=250, title="Droit Acceleration Z")
plot3.line(t,exercise.get_signal("DAZ")[0])
plot4=figure(width=350, plot_height=250, title="Droit Rotation X")
plot4.line(t,exercise.get_signal("DRX")[0])
plot5=figure(width=350, plot_height=250, title="Droit Rotation X")
plot5.line(t,exercise.get_signal("DRY")[0])
plot6=figure(width=350, plot_height=250, title="Droit Rotation X")
plot6.line(t,exercise.get_signal("DRZ")[0])
#p = hplot(plot, plot2)
p = gridplot([[plot, plot2, plot3], [plot4, plot5, plot6]])
html = file_html(p, CDN, "home")
return html
def make_plot(title1, plot1, title2, plot2, title3, plot3, title4, plot4, title5, plot5, sample_df):
plot1.title = title1
plot2.title = title2
plot3.title = title3
plot4.title = title4
plot5.title = title5
plot1.yaxis.axis_label = data_dict['DO']['title']
plot1.yaxis.axis_label_text_font_style = "italic"
plot2.yaxis.axis_label = data_dict['Ammonium']['title']
plot2.yaxis.axis_label_text_font_style = "italic"
plot3.yaxis.axis_label = data_dict['pH']['name']
plot3.yaxis.axis_label_text_font_style = "italic"
plot4.yaxis.axis_label = data_dict['N2 Mass Flow Controller']['title']
plot4.yaxis.axis_label_text_font_style = "italic"
plot5.yaxis.axis_label = data_dict['Air Mass Flow Controller']['title']
plot5.yaxis.axis_label_text_font_style = "italic"
plot1.line(x=sample_df.index, y=sample_df[title1], color="navy")
plot2.line(x=sample_df.index, y=sample_df[title2], color="firebrick")
plot3.line(x=sample_df.index, y=sample_df[title3], color="#28D0B4", line_width=2)
plot4.line(x=sample_df.index, y=sample_df[title4], color="orange")
plot5.line(x=sample_df.index, y=sample_df[title5], color="black")
p = gridplot([[plot1, plot2, plot3], [plot4, plot5, None]])
return p
def make_figure():
#%% Create Time Series Graph
#Create Time Series plot area
time_plot = figure(plot_height= 400, plot_width= 800, title="", x_axis_label ='Time',
tools='', y_axis_label = 'l1013aspv', toolbar_location="left",
x_axis_type="datetime",
y_range=(min(data_source.data["y1"]) -min(data_source.data["y1"]*0.1 ),
max(data_source.data["y1"]) + max(data_source.data["y1"]*0.1)))
#modify the BoxSelectTool
#dimensions = specify the dimension in which the box selection is free in
#select_every_mousemove = select points as box moves over
time_plot.add_tools(BoxSelectTool(dimensions = ["width"], select_every_mousemove = True))
#add anther axis
time_plot.extra_y_ranges = {"foo": Range1d(start = min(data_source.data["y2"])
- min(data_source.data["y1"]*0.1),
end = max(data_source.data["y2"]) + max(data_source.data["y1"]*0.1))}
#add data to scatter plot (data points on time plot)
time_scat = time_plot.scatter("x", "y1", source = data_source,size = 1, color = "green")
time_scat2 = time_plot.scatter("x", "y2", source = data_source,size= 1, color = "blue", y_range_name = "foo")
#add time series line
time_plot.line("x","y1",source=data_source,color = time_scat.glyph.fill_color,
alpha=0.5)
time_plot.line("x","y2",source=data_source,color= time_scat2.glyph.fill_color,
alpha=0.5,y_range_name="foo")
#Customize time_plot grid lines
time_plot.xgrid.grid_line_color = None
time_plot.ygrid.grid_line_alpha = 0.2
#First axes styling
time_plot.yaxis.axis_line_color = time_scat.glyph.fill_color
time_plot.yaxis.minor_tick_line_color = time_scat.glyph.fill_color
time_plot.yaxis.major_tick_line_color = time_scat.glyph.fill_color
time_plot.yaxis.axis_label_text_color = time_scat.glyph.fill_color
time_plot.yaxis.major_label_text_color = time_scat.glyph.fill_color
#add second axis to time_plot and styling
time_plot.add_layout(LinearAxis(y_range_name = "foo",
axis_line_color = str(time_scat2.glyph.fill_color),
major_label_text_color = str(time_scat2.glyph.fill_color),
axis_label_text_color = str(time_scat2.glyph.fill_color),
major_tick_line_color = str(time_scat2.glyph.fill_color),
minor_tick_line_color = str(time_scat2.glyph.fill_color),
axis_label= "l1015asop"), "left")
#%% Create Marginal Histogram and KDE
#Create marginal histogram for y-axis data density
#set up figure
hist_plot = figure(plot_height = 400, plot_width = 200, y_range = time_plot.y_range)
#add second axis to histogram
hist_plot.extra_y_ranges = {"foo":
Range1d(start = min(data_source.data["y2"]) - min(data_source.data["y1"]*0.1),
end = max(data_source.data["y2"]) + max(data_source.data["y1"]*0.1))}
#Customize hist_plot grid lines
hist_plot.xgrid.grid_line_alpha = 0.2
hist_plot.ygrid.grid_line_alpha = 0.5
#get histogram data
hist, edges = histogram(data_source.data["y1"], density = True, bins = 20)
hist2, edges2 = histogram(data_source.data["y2"], density = True, bins = 20)
#styleing histograms axises
hist_plot.xaxis.axis_label = ""
hist_plot.yaxis.axis_label = ""
hist_plot.xaxis.visible = None
#add gaussian kernel density estomator
y_span = linspace(min(data_source.data["y1"]),
max(data_source.data["y1"]), size(data_source.data["y1"]))
kde = gkde(data_source.data["y1"]).evaluate(y_span)
y_span2 = linspace(min(data_source.data["y2"]),
max(data_source.data["y2"]), size(data_source.data["y2"]))
kde2 = gkde(data_source.data["y2"]).evaluate(y_span2)
#Histogram First axes styling
hist_plot.yaxis.axis_line_color = time_scat.glyph.fill_color
hist_plot.yaxis.minor_tick_line_color = time_scat.glyph.fill_color
hist_plot.yaxis.major_tick_line_color = time_scat.glyph.fill_color
hist_plot.yaxis.axis_label_text_color = time_scat.glyph.fill_color
hist_plot.yaxis.major_label_text_color = time_scat.glyph.fill_color
#Histogram second axes styling
hist_plot.add_layout(LinearAxis(y_range_name = "foo",
axis_line_color = str(time_scat2.glyph.fill_color),
major_label_text_color = str(time_scat2.glyph.fill_color),
axis_label_text_color = str(time_scat2.glyph.fill_color),
major_tick_line_color = str(time_scat2.glyph.fill_color),
minor_tick_line_color = str(time_scat2.glyph.fill_color)), "left")
#%% Create Scatter Graph
scat_plot = figure(plot_height = 400, plot_width = 400, title = "", x_axis_label = 'l1015asop',
y_axis_label = 'l1013aspv')
#scatter plot axis cutomization
scat_plot.yaxis.axis_line_color = time_scat.glyph.fill_color
scat_plot.yaxis.minor_tick_line_color = time_scat.glyph.fill_color
scat_plot.yaxis.major_tick_line_color = time_scat.glyph.fill_color
scat_plot.yaxis.axis_label_text_color = time_scat.glyph.fill_color
scat_plot.yaxis.major_label_text_color = time_scat.glyph.fill_color
scat_plot.xaxis.axis_line_color = time_scat2.glyph.fill_color
scat_plot.xaxis.minor_tick_line_color = time_scat2.glyph.fill_color
scat_plot.xaxis.major_tick_line_color = time_scat2.glyph.fill_color
scat_plot.xaxis.axis_label_text_color = time_scat2.glyph.fill_color
scat_plot.xaxis.major_label_text_color = time_scat2.glyph.fill_color
#%% Add data to Histogram and scatter plot (this data is updated in callback fuction)
#Create updateable plots
u_hist_source = ColumnDataSource(data=dict(top=edges[1:],bottom=edges[:-1],left=zeros_like(edges),right=hist))
u_hist_source2 = ColumnDataSource(data=dict(top=edges2[1:],bottom=edges2[:-1],left=zeros_like(edges2),right=hist2))
u_kde_source = ColumnDataSource(data=dict(x = kde, y = y_span))
u_kde_source2 = ColumnDataSource(data=dict(x = kde2, y = y_span2))
scat_data = ColumnDataSource(data=dict(x=[0],y=[0]))
#Updateble histogram
hist_plot.quad(top = 'top', bottom = 'bottom', left = 'left', right = 'right', source = u_hist_source,
fill_color = time_scat.glyph.fill_color, alpha = 0.5)
hist_plot.quad(top = 'top', bottom = 'bottom', left = 'left', right = 'right', source = u_hist_source2,
fill_color = time_scat2.glyph.fill_color, alpha = 0.3, y_range_name = "foo")
#Updateble kde line
hist_plot.line('x', 'y', source=u_kde_source ,line_color = "#008000")
hist_plot.line('x', 'y', source=u_kde_source2 ,line_color = "#000099", y_range_name = "foo")
#Updateble scatter plot
scat_plot.scatter('x', 'y', source=scat_data,size=2, alpha=0.3)
#%% Updating fuction
data_source.callback = CustomJS(args=dict(hist_data=u_hist_source, hist_data2=u_hist_source2,
kde_d = u_kde_source, kde_d2 = u_kde_source2, sc=scat_data),
code="""
Update_ALL_Figures(cb_obj, hist_data, hist_data2, kde_d, kde_d2, sc)
""")
#%% create plot layout
layout = gridplot([[time_plot, hist_plot], [scat_plot, None]])
return layout #need to return the layout