def heatpack(matrix,
palette=Magma256,
low=None,
high=None,
low_color='gray',
high_color='red',
start=None,
end=None,
step=None,
height=600,
width=600,
axes=None):
"""
input:
- width / height : width and height of the heatmap figure
- matrix: 2D array
- palette: list of colors
- low: low value of the ColorMapper
- high: high value of the ColorMapper
- low_color: values below "low" will have that color
- high_color: values above "high" will have that color
- start / end : range for the slider
- step: step of the range slider
output:
- the heatmap figure
- dummy figure with the colorbar
- range slider to change the low / high values of the color mapper
"""
# make sure the 2D array is a numpy array
if type(matrix) != np.ndarray:
matrix = np.array(matrix)
if start == None:
start = matrix.min()
if end == None:
end = matrix.max()
if step == None:
step = (end - start) / 100
if low == None:
low = matrix.min()
if high == None:
high = matrix.max()
mapper = LinearColorMapper(
palette=palette,
low=low,
high=high,
low_color=low_color,
high_color=high_color
) # linearly map the "magma" list to the [low,high] range
color_bar = ColorBar(
color_mapper=mapper, location=(0, 0),
label_standoff=15) # define the color bar with the linear color mapper
len_mat = len(matrix)
if axes is None:
IDs = list(
range(len_mat)
) * len_mat # list of column indices for the flattened matrix elements
else:
IDs = list(axes) * len_mat
source = ColumnDataSource(
data={
'row': np.array(sorted(IDs)),
'col': np.array(IDs),
'mat': matrix.flatten()
})
TOOLS = "box_zoom,hover,save,pan,reset,wheel_zoom" # interactive tools for the plot
# the heatmap plot
fig = figure(plot_width=width,
plot_height=height,
tools=TOOLS,
active_drag="box_zoom")
rect = fig.rect(x='col',
y='row',
width=1.1,
height=1.1,
source=source,
fill_color={
'field': 'mat',
'transform': mapper
},
line_alpha=0,
line_width=0,
dilate=True)
# plot cosmetics
fig.grid.grid_line_color = None
fig.axis.axis_line_color = None
fig.axis.major_tick_line_color = None
fig.axis.major_label_standoff = 0
fig.x_range.range_padding = 0 # prevents white bands on the inner sides of the plot
fig.y_range.range_padding = 0 # prevents white bands on the inner sides of the plot
# use a dummy figure to add the color bar (because add_layout will compress your plot)
dumx = range(10)
dumfig = figure(outline_line_alpha=0, plot_height=height, plot_width=120)
dumfig.line(x=dumx, y=dumx, visible=False)
for rend in dumfig.renderers:
rend.visible = False
dumfig.add_layout(color_bar, 'left')
# configure the Hover tool
fig.select_one(HoverTool).tooltips = [
('row, col', '@row, @col'),
('value', '@mat'),
]
# widgets
select = RangeSlider(start=start,
end=end,
step=step,
value=(low, high),
callback_policy='mouseup')
# javascript callback codes for the widgets
select_code = """
var maxval = select.value[1];
var minval = select.value[0]
map.high = maxval;
map.low = minval;
"""
# widget callbacks
select.callback = CustomJS(args=dict(map=mapper, src=source,
select=select),
code=select_code)
return fig, dumfig, select
def modify_doc(doc):
fid = polymer.getfid(ie) #dataframe
# TODO: more testing on get_x_axis
tau = get_x_axis(polymer.getparameter(ie))
#calculate magnetization:
startpoint = int(0.05 * polymer.getparvalue(ie, 'BS'))
endpoint = int(
0.1 * polymer.getparvalue(ie, 'BS')
) #TODO: make a range slider to get start- and endpoint interactively
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparvalue(ie, 'NBLK'),
polymer.getparvalue(ie, 'BS'))
#prepare magnetization decay curve for fit
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0]) / (
df['phi'].iloc[-1] - df['phi'].iloc[1])
polymer.addparameter(ie, 'df_magnetization', df)
fit_option = 2
p0 = [1, 2 * polymer.getparvalue(ie, 'T1MX')**-1, 0]
df, popt = magnetization_fit(df, p0, fit_option)
polymer.addparameter(ie, 'popt(mono_exp)', popt)
df['fit_phi'] = model_exp_dec(df.tau, *popt)
# convert data to handle in bokeh
source_fid = ColumnDataSource(data=ColumnDataSource.from_df(fid))
source_df = ColumnDataSource(data=ColumnDataSource.from_df(df))
# create and plot figures
p1 = figure(plot_width=300,
plot_height=300,
title='Free Induction Decay',
webgl=True)
p1.line('index', 'im', source=source_fid, color='blue')
p1.line('index', 'real', source=source_fid, color='green')
p1.line('index', 'magnitude', source=source_fid, color='red')
fid_slider = RangeSlider(start=1,
end=polymer.getparvalue(ie, 'BS'),
step=1,
callback_policy='mouseup')
p2 = figure(plot_width=300, plot_height=300, title='Magnetization Decay')
p2.circle_cross('tau', 'phi_normalized', source=source_df, color="navy")
p2.line('tau', 'fit_phi', source=source_df, color="teal")
# in the plot 4 use followingimpo
SIZES = list(range(6, 22, 3)) # for some sizes
COLORS = Spectral5 # for some colors (more colors would be nice somehow)
def plot_par():
xs = par_df[x.value].values
ys = par_df[y.value].values
x_title = x.value.title()
y_title = y.value.title()
kw = dict() #holds optional keyword arguments for figure()
if x.value in discrete:
kw['x_range'] = sorted(set(xs))
if y.value in discrete:
kw['y_range'] = sorted(set(ys))
if y.value in time:
kw['y_axis_type'] = 'datetime'
if x.value in time:
kw['x_axis_type'] = 'datetime'
kw['title'] = "%s vs %s" % (x_title, y_title)
p4 = figure(plot_height=300,
plot_width=600,
tools='pan,box_zoom,reset',
**kw)
p4.xaxis.axis_label = x_title
p4.yaxis.axis_label = y_title
if x.value in discrete:
p4.xaxis.major_label_orientation = pd.np.pi / 4 # rotates labels...
sz = 9
if size.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[size.value].values),
len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
c = "#31AADE"
if color.value != 'None':
groups = pd.qcut(
pd.to_numeric(par_df[color.value]).values, len(COLORS))
c = [COLORS[xx] for xx in groups.codes]
p4.circle(x=xs,
y=ys,
color=c,
size=sz,
line_color="white",
alpha=0.6,
hover_color='white',
hover_alpha=0.5)
return p4
def update(attr, old, new):
layout_p4.children[1] = plot_par()
def cb(attr, old, new):
## load experiment ie in plot p1 and p2
ie = new['value'][0]
fid = polymer.getfid(ie)
#print(fid)
#source_fid = ColumnDataSource.from_df(data=fid)
source_fid.data = ColumnDataSource.from_df(fid)
#print(source_fid)
try:
tau = get_x_axis(polymer.getparameter(ie))
#print(tau)
try:
startpoint = polymer.getparvalue(ie, 'fid_amp_start')
endpoint = polymer.getparvalue(ie, 'fid_amp_stop')
except:
startpoint = int(0.05 * polymer.getparvalue(ie, 'BS'))
endpoint = int(0.1 * polymer.getparvalue(ie, 'BS'))
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparvalue(ie, 'NBLK'),
polymer.getparvalue(ie, 'BS'))
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0]) / (
df['phi'].iloc[-1] - df['phi'].iloc[1])
polymer.addparameter(ie, 'df_magnetization', df)
fit_option = 2 #mono exponential, 3 parameter fit
p0 = [1.0, polymer.getparvalue(ie, 'T1MX')**-1 * 2, 0]
df, popt = magnetization_fit(df, p0, fit_option)
source_df.data = ColumnDataSource.from_df(df)
polymer.addparameter(ie, 'popt(mono_exp)', popt)
print(popt)
#print(df)
print(polymer.getparvalue(ie, 'df_magnetization'))
fid_slider = RangeSlider(start=1,
end=polymer.getparvalue(ie, 'BS'),
range=(startpoint, endpoint),
step=1,
callback_policy='mouseup')
layout_p1.children[2] = fid_slider
except KeyError:
print('no relaxation experiment found')
tau = np.zeros(1)
phi = np.zeros(1)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = np.zeros(1)
df['fit_phi'] = np.zeros(1)
source_df.data = ColumnDataSource.from_df(df)
#this source is only used to communicate to the actual callback (cb)
source = ColumnDataSource(data=dict(value=[]))
source.on_change('data', cb)
slider = Slider(start=1,
end=nr_experiments,
value=1,
step=1,
callback_policy='mouseup')
slider.callback = CustomJS(
args=dict(source=source),
code="""
source.data = { value: [cb_obj.value] }
"""
) #unfortunately this customjs is needed to throttle the callback in current version of bokeh
def calculate_mag_dec(attr, old, new):
ie = slider.value
polymer.addparameter(ie, 'fid_range', new['range'])
print(polymer.getparvalue(ie, 'fid_range')) #this works
start = new['range'][0]
stop = new['range'][1]
fid = polymer.getfid(ie)
tau = polymer.getparvalue(ie, 'df_magnetization').tau
phi = get_mag_amplitude(fid, start, stop,
polymer.getparvalue(ie, 'NBLK'),
polymer.getparvalue(ie, 'BS'))
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0]) / (
df['phi'].iloc[-1] - df['phi'].iloc[1])
fit_option = 2 #mono exponential, 3 parameter fit
p0 = polymer.getparvalue(ie, 'popt(mono_exp)')
df, popt = magnetization_fit(df, p0, fit_option)
source_df.data = ColumnDataSource.from_df(df)
polymer.addparameter(ie, 'df_magnetization', df)
polymer.addparameter(ie, 'popt(mono_exp)', popt)
pass
source2 = ColumnDataSource(data=dict(range=[], ie=[]))
source2.on_change('data', calculate_mag_dec)
fid_slider.callback = CustomJS(
args=dict(source=source2),
code="""
source.data = { range: cb_obj.range }
"""
) #unfortunately this customjs is needed to throttle the callback in current version of bokeh
# select boxes for p4
x = Select(title='X-Axis', value='ZONE', options=columns)
x.on_change('value', update)
y = Select(title='Y-Axis', value='TIME', options=columns)
y.on_change('value', update)
size = Select(title='Size', value='None', options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color',
value='None',
options=['None'] + quantileable)
color.on_change('value', update)
controls_p4 = widgetbox([x, y, color, size], width=150)
layout_p4 = row(controls_p4, plot_par())
#fitting on all experiments
p3 = figure(plot_width=300,
plot_height=300,
title='normalized phi vs normalized tau',
webgl=True,
y_axis_type='log',
x_axis_type='linear')
#fit magnetization decay for all experiments
r1 = np.zeros(nr_experiments)
MANY_COLORS = 0
p3_line_glyph = []
for i in range(nr_experiments):
try:
par = polymer.getparameter(i)
fid = polymer.getfid(i)
tau = get_x_axis(polymer.getparameter(i))
startpoint = int(0.05 * polymer.getparameter(i)['BS'])
endpoint = int(
0.1 * polymer.getparameter(i)['BS']
) #TODO: make a range slider to get start- and endpoint interactively
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparameter(i)['NBLK'],
polymer.getparameter(i)['BS'])
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0]) / (
df['phi'].iloc[-1] - df['phi'].iloc[1])
polymer.addparameter(i, 'df_magnetization', df)
p0 = [1, 2 * polymer.getparvalue(i, 'T1MX'), 0]
df, popt = magnetization_fit(df, p0, fit_option=2)
polymer.addparameter(i, 'amp', popt[0])
polymer.addparameter(i, 'r1', popt[1])
polymer.addparameter(i, 'noise', popt[2])
r1[i] = popt[1]
tau = popt[1] * df.tau
phi = popt[0]**-1 * (df.phi_normalized - popt[2])
p3_df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
source_p3 = ColumnDataSource(data=ColumnDataSource.from_df(p3_df))
p3_line_glyph.append(p3.line(
'tau', 'phi', source=source_p3)) #TODO add nice colors
MANY_COLORS += 1
except KeyError:
print('no relaxation experiment found')
COLORS = viridis(MANY_COLORS)
for ic in range(MANY_COLORS):
p3_line_glyph[ic].glyph.line_color = COLORS[ic]
par_df['r1'] = r1
layout_p1 = column(slider, p1, fid_slider, p2, p3)
doc.add_root(layout_p1)
doc.add_root(layout_p4)
doc.add_root(source) # i need to add source to detect changes
doc.add_root(source2)
