def plot_AGonia_boxes(data_path, Score, box_idx):
'''Function used as input for the dynamic map in function boxes_exploration():
holoviews based plot to see Agonia boxes on top of median projection and mean trace
for selected boxes.
Parameters
----------
data_path : string
path to folder containing the data
Score : float
threshold for detection confidence for each box
box_idx : int
index of box to plot trace
Returns
-------
Holoviews image to feed to dynamic map, selected cell is indicated with green square'''
data_name, median_projection, boxes_path = get_files_names(data_path)
#Yr, dims, T = cm.load_memmap(fname_new)
#images = np.reshape(Yr.T, [T] + list(dims), order='F')
images = io.imread(os.path.join(data_path, data_name[0]))
with open(boxes_path, 'rb') as f:
boxes = pickle.load(f)
f.close()
boxes = boxes[boxes[:, 4] > Score].astype('int')
roi_bounds = hv.Path([
hv.Bounds(
tuple([
roi[0], median_projection.shape[0] - roi[1], roi[2],
median_projection.shape[0] - roi[3]
])) for roi in boxes[:, :4]
]).options(color='red')
img = hv.Image(median_projection,
bounds=(0, 0, median_projection.shape[1],
median_projection.shape[0])).options(cmap='gray')
#box_trace = images[:,boxes[box_idx,1]:boxes[box_idx,3],boxes[box_idx,0]:boxes[box_idx,2]].mean(axis=(1,2))
ola = Extractor()
for frame in images:
ola.extract(frame, [boxes[box_idx]])
box_trace = ola.get_traces().squeeze()
box_square = hv.Path([
hv.Bounds(
tuple([
boxes[box_idx,
0], median_projection.shape[0] - boxes[box_idx, 1],
boxes[box_idx,
2], median_projection.shape[0] - boxes[box_idx, 3]
]))
]).options(color='lime')
return ((img * roi_bounds * box_square).opts(width=600, height=600) +
hv.Curve(
(np.linspace(0,
len(box_trace) - 1, len(box_trace)), box_trace),
'Frame', 'Mean box Fluorescence').opts(width=600,
framewise=True)).cols(1)
def hv_paths(a, b, c, d):
diag_1 = hv.Path([np.c_[loop.values[a, :],
loop.values[c, :]].T]).options(opts_path_1)
diag_2 = hv.Path([np.c_[loop.values[b, :],
loop.values[d, :]].T]).options(opts_path_2)
point_1 = ((loop.values[a, :] + loop.values[c, :]) / 2)
point_2 = ((loop.values[b, :] + loop.values[d, :]) / 2)
diag_1_point = hv.Points((point_1[0], point_1[1])).options(opts_point_1)
diag_2_point = hv.Points((point_2[0], point_2[1])).options(opts_point_2)
return diag_1 * diag_2 * diag_1_point * diag_2_point
def plot(self):
import holoviews as hv
if not self.data:
return hv.Scatter([]) * hv.Path([])
if not self.vdim > 1:
return hv.Scatter(self.data) * hv.Path([])
else:
xs = list(self.data.keys())
ys = list(self.data.values())
return hv.Path((xs, ys)) * hv.Scatter([])
def coastlines(resolution='110m', lon_360=False):
""" A custom method to plot in cylyndrical equi projection, most useful for
native projections, geoviews currently supports only Web Mercator in
bokeh mode.
Other resolutions can be 50m
lon_360 flag specifies if longitudes are from -180 to 180 (default) or 0 to 360
TODO: if hv.Polygons is used instead of overlay it is way faster but
something is wrong there.
"""
try:
import cartopy.io.shapereader as shapereader
from cartopy.io.shapereader import natural_earth
import shapefile
filename = natural_earth(resolution=resolution,
category='physical',
name='coastline')
sf = shapefile.Reader(filename)
fields = [x[0] for x in sf.fields][1:]
records = sf.records()
shps = [s.points for s in sf.shapes()]
pls = []
for shp in shps:
lons = [lo for lo, _ in shp]
lats = [la for _, la in shp]
if lon_360:
lats_patch1 = [lat for lon, lat in zip(lons, lats) if lon < 0]
lons_patch1 = [lon + 360.0 for lon in lons if lon < 0]
if any(lons_patch1):
pls.append(
hv.Path((lons_patch1, lats_patch1))(style={
'color': 'Black'
}))
lats_patch2 = [
lat if lon >= 0 else None for lon, lat in zip(lons, lats)
]
lons_patch2 = [lon if lon >= 0 else None for lon in lons]
if any(lons_patch2):
pls.append(
hv.Path((lons_patch2, lats_patch2))(style={
'color': 'Black'
}))
else:
pls.append(hv.Path((lons, lats))(style={'color': 'Black'}))
return hv.Overlay(pls)
except Exception as err:
print(
'Overlaying Coastlines not available from holoviews because: {0}'.
format(err))
def make_circle(radius=DEFAULT_RADIUS):
def circle(t):
return (radius * np.sin(t), radius * np.cos(t), t)
lin = np.linspace(-np.pi, np.pi, 200)
return hv.Path([circle(lin)]).opts(img_opts).opts(line_width=2,
color='red')
def histogram_plot(self, xtrack=None, atrack=None, x=None, y=None):
"""plot histogram at xtrack, atrack"""
if x is not None:
xtrack = x
if y is not None:
atrack = y
# atrack and xtrack are from mouse or user. get the nearest where histogram is defined
xtrack, atrack = self._get_xatrack(xtrack=xtrack, atrack=atrack)
# get histogram
hist_at = self.gradients_hist.sel(atrack=atrack,
xtrack=xtrack,
method='nearest',
tolerance=500)
hp_list = []
for sel_one2D in self.combine_all:
hist2D_at = hist_at.sel(sel_one2D)
hist2D360 = circ_hist(hist2D_at['weight'])
style = self._get_style(hist2D_at)
hp_list.append(
hv.Path(hist2D360, kdims=['xtrack_g',
'atrack_g']).opts(axiswise=False,
framewise=False,
aspect='equal',
**style))
return hv.Overlay(hp_list).opts(xlabel='xtrack %d' % xtrack,
ylabel='atrack %d' % atrack,
width=200,
height=200)
def U_plt(current_dataset,ssha_dataset,title,bounds=(-166,13.5,-154, 25)):
"""
plot current velocities magntiudes averaged from 2008 to 2016 and Jason-2 tracks from dataset
"""
# plot coastline
coast = gf.coastline.geoms('50m', bounds=bounds).opts(color='black',
xticks=5,
yticks=5,
yformatter='%f °N',
xformatter = '%f °E')
# plot surface velocity magnitudes
lats = current_dataset['lat'].reshape(len(current_dataset['lat']))
_lons = current_dataset['lon'].reshape(len(current_dataset['lon']))
lons = (_lons % 180) - (_lons >= 180) * 180
u = np.mean(current_dataset['u'],axis=0)
v = np.mean(current_dataset['v'],axis=0)
magnitude = np.sqrt(u ** 2 + v ** 2)
vel = hv.Image((lons,lats,magnitude)).opts(colorbar=True,
cmap ='viridis',
clabel='m/s',
projection=ccrs.PlateCarree(),
tools=['hover'])
# plot tracks
lon_range = (np.array(ssha_dataset['lon range']) % 180) - (np.array(ssha_dataset['lon range']) >= 180) * 180
lat_range = ssha_dataset['lat range']
track = hv.Path((lon_range ,lat_range)).opts(color = 'black')
return (coast * vel * track).opts(projection=ccrs.PlateCarree(),
title = title,
height=300,
width=500)
def ngon_ring(x=0,
y=0,
specs=[(1, 1), (.9, .9)],
arc=(0, 2 * np.pi),
orientation=0,
npoints=40,
line_color='k',
line_alpha=1,
fill_color='k',
fill_alpha=0.,
label=''):
''' an ngon outline with finite width
'''
l = ngon_data(x,
y,
specs=specs,
arc=arc,
orientation=orientation,
npoints=npoints)
d = (np.hstack([l[0][:, 0],
l[1][::-1, 0]]), np.hstack([l[0][:, 1], l[1][::-1, 1]]))
o_b = {
'Polygons': dict(color=fill_color, alpha=fill_alpha, line_alpha=0),
'Path': dict(line_color=line_color, line_alpha=line_alpha)
}
o_m = {
'Polygons': dict(color=fill_color, alpha=fill_alpha),
'Path': dict(color=line_color, alpha=line_alpha)
}
return (hv.Polygons([d], label=label) * hv.Path([l[0], l[1]])).opts(
o_b, backend='bokeh') #.opts( o_m, backend='matplotlib ')
def execute(csvfilepath):
#df = pd.read_csv('Dataset/desktop_tracks.csv')
df = pd.read_csv(csvfilepath)
print(max(df['Mean']))
tracks = [
df.loc[df['Mean'] == x, ['Slice', 'X', 'Y', 'Mean']]
for x in range(1, int(max(df['Mean'])))
]
print("Number of Tracks:", len(tracks))
tracklists = [
list(zip(tracks[x]['X'], tracks[x]['Y'], tracks[x]['Slice']))
for x in range(len(tracks))
]
tracklists[0:3]
trackplot = hv.Path(tracklists, vdims='time')
trackplot.opts(cmap='Inferno', color='time', line_width=2)
outfilename = "trackplot.html"
hv.save(trackplot, outfilename, fmt='html')
#file_name = "hello_world.txt"
#copyfile(input, file_name)
#f = open(file_name, "w")
#f.write("Hello, World!")
#f.close()
return {'Output': outfilename}
def tunes(bet_x, bet_y):
bet_x -= math.floor(bet_x)
bet_y -= math.floor(bet_y)
color = '#30d5c8'
path = hv.Path([[(bet_x - 0.05, bet_y), (bet_x + 0.05, bet_y)],
[(bet_x, bet_y - 0.05), (bet_x, bet_y + 0.05)]])
path.opts(color=color, line_width=4)
return path
def plot_surface(obj, **args):
region = args.get('region', None)
idx = obj.tag2idx(region)
tags = args.get('tags', False)
coord = args.get('coord', None)
locator = args.get('locator', False)
fill = args.get('fill', None)
amin = np.min(obj.bbox_min[idx], axis=0)
amax = np.max(obj.bbox_max[idx], axis=0)
wh = amax - amin
if np.min(wh) < 250:
wh = wh / np.min(wh) * 250
hvobj = hv.Path([obj.boundary[i]
for i in idx]).opts(style=dict(color='k'),
plot=dict(yaxis=None,
xaxis=None,
aspect='equal',
width=int(ceil(wh[0])),
height=int(ceil(wh[1]))))
if fill is not None:
vals = np.nan * np.zeros((obj.num, ))
for k in fill.keys():
_idx = obj.tag2idx(k)
for i in _idx:
vals[i] = fill[k]
imin = np.min(obj.bbox_min[idx], axis=0).astype(int)
imax = np.max(obj.bbox_max[idx], axis=0).astype(int)
im = np.nan * np.zeros(tuple((imax - imin + 1)[::-1].tolist()))
for i in idx:
im[obj._coords[i][:, 1] - imin[1],
obj._coords[i][:, 0] - imin[0]] = vals[i]
hvobj = hv.Image(np.flipud(im),
bounds=(imin[0], imin[1], imax[0], imax[1])).opts(
plot=dict(yaxis=None, xaxis=None))
if coord is not None:
hvobj *= hv.Curve([obj.hand2pixel((0,0)),obj.hand2pixel((coord,0))]) *\
hv.Curve([obj.hand2pixel((0,0)),obj.hand2pixel((0,coord))])
if tags:
hvobj *= hv.Labels({
'x': [obj._centers[i][0] for i in idx],
'y': [obj._centers[i][1] for i in idx],
'Label': [str(i) + ' ' + ''.join(obj.tags[i]) for i in idx]
})
# show cursor position in hand coordinates (works only in bokeh)
if locator:
pointer = hv.streams.PointerXY(x=0, y=0)
dm = hv.DynamicMap(lambda x, y: hvobj * hv.Text(
x, y + 5, '(%d,%d)' % tuple(obj.pixel2hand(np.array([x, y])))),
streams=[pointer])
return dm
return hvobj
def _update_insert_plot(self):
plot_data = DATA_A.loc[self.tool]
data = [(plot_data["Xo"], plot_data["Yo"], plot_data[self.variable])]
self.insert_plot_pane.object = hv.Path(data, vdims=self.variable).opts(
cmap=self.color_map,
color=self.variable,
line_width=4,
colorbar=True,
responsive=True)
def _boundary(self, data, index):
"Callback drawing Path element defining boundary"
self._selected_edge_index = index
xs, ys = data['x'], data['y']
lines = []
for i in range(len(xs)-1):
s, e = i, (i+1)
lines.append([(xs[s], ys[s]), (xs[e], ys[e])])
self.ready = self._ready()
return hv.Path(lines).opts(**self.edge_style)
def stem(data, curve=True, marker=True):
'''stem plot: data=(x,y,e)'''
x, y, e = data
vlines = [np.array([[x[i], y[i]], [x[i], e[i]]]) for i in range(len(x))]
hs = hv.Path(vlines).opts(show_legend=True, muted_alpha=0.)
if marker: hs = hs * hv.Scatter((x, e)).opts(size=4)
if curve: hs = hs * hv.Curve((x, y)).opts(line_width=0.8)
return hs
def plot_AGonia_boxes_interactive(data_path, Score, x, y):
'''Function used as input for the dynamic map in function boxes_exploration_interactive():
Same as plot_AGonia_boxes but now instead of having as input the index of the box the user
can click on a box and it will use the (x,y) coordinates of the click to find the related box.
The output now is only the boxes (all in red, selected in green)'''
data_name, median_projection, fnames, fname_new, results_caiman_path, boxes_path = get_files_names(
data_path)
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
with open(boxes_path, 'rb') as f:
boxes = pickle.load(f)
f.close()
boxes = boxes[boxes[:, 4] > Score].astype('int')
roi_bounds = hv.Path([
hv.Bounds(
tuple([
roi[0], median_projection.shape[0] - roi[3], roi[2],
median_projection.shape[0] - roi[1]
])) for roi in boxes[:, :4]
]).options(color='red')
if None not in [x, y]:
try:
box_idx = [
i for i, box in enumerate(boxes) if x < box[2] and x > box[0]
and y < (median_projection.shape[0] -
box[1]) and y > (median_projection.shape[0] - box[3])
][0]
except:
pass
else:
box_idx = 0
#box_trace = images[:,boxes[box_idx,1]:boxes[box_idx,3],boxes[box_idx,0]:boxes[box_idx,2]].mean(axis=(1,2))
box_square = hv.Path([
hv.Bounds(
tuple([
boxes[box_idx,
0], median_projection.shape[0] - boxes[box_idx, 3],
boxes[box_idx,
2], median_projection.shape[0] - boxes[box_idx, 1]
]))
]).options(color='lime')
return (roi_bounds * box_square).opts(width=600, height=600)
def plot_sample(sample: np.ndarray):
"""
Plots the gene counts for the given sample array as a bar-plot like single-path element.
:param np.ndarray sample: An array containing the count values.
:return:
"""
paths = _create_paths(sample)
plot = hv.Path(paths)
return plot.opts(width=400, height=400, padding=0.05)
def plot_count_sum_mass(counts: np.ndarray, lengths: np.ndarray):
"""
Plots the sum of the gene masses (along the sample axis) for the given count array.
:param counts:
:param lengths:
:return:
"""
sample_counts_sum = counts.sum(axis=0)
mass_sums = sample_counts_sum * lengths
paths = _create_paths(mass_sums)
plot = hv.Path(paths)
return plot.opts(width=400, height=400, padding=0.05)
def plot_sample_mass(sample: np.ndarray, lengths: np.ndarray):
"""
Plots gene masses (counts * lengths) for the given sample array.
:param np.ndarray sample: An array containing the count values.
:param np.ndarray lengths: An array containing the length values.
:return:
"""
masses = sample * lengths
paths = _create_paths(masses)
plot = hv.Path(paths)
return plot.opts(width=400, height=400, padding=0.05)
def plot_path(self, data):
# at this stage, plot is rendered and size known
if not self.zoom_initialized:
self.monitor_zoom_level()
self.zoom_initialized = True
path = hv.Path(data)
path.opts(
opts.Path(line_width=self.tool_width * self.zoom_level + 1,
color=self.cmap[self.dataset.drawing_label],
line_cap='round',
line_join='round'))
return path
def mle_path_plot(bs_reps, param_1 = 'α*', param_2 = 'b*', conf = 0.95):
# plot bootstrap samples, from BE/Bi103a lesson exercises
points = hv.Points(data=bs_reps, kdims=[param_1, param_2]).opts(
size=1, alpha=0.5, padding=0.1
)
# Get contour line
xs, ys = bebi103.viz.contour_lines_from_samples(
bs_reps[:, 0], bs_reps[:, 1], levels=conf
)
# Overlay with sample
out = points * hv.Path(data=(xs[0], ys[0])).opts(color="black")
return out
def track_plt(dataset,title, bounds=(-166,13.5,-154, 25)):
"""
plot Jason-2 tracks from dataset with coordinates: N = [-90, 90], E = [-180, 180]
"""
lon_range = (np.array(dataset['lon range']) % 180) - (np.array(dataset['lon range']) >= 180) * 180
lat_range = np.array(dataset['lat range'])
coast = gf.coastline.geoms('50m', bounds=bounds).opts(color='black',
xticks=5,
yticks=5,
yformatter='%f °N',
xformatter = '%f °E')
track = hv.Path((lon_range,lat_range)).opts(color = 'black')
return (coast * track ).opts(projection=ccrs.PlateCarree(),
title = title,
height=200,
width=200)
def spikes(data, y_base=0, dims=["Time", "x"], label="Signal", curve=True):
'''plot stems and a curve'''
if isinstance(data, tuple):
t, s = data
else:
t = np.arange(0, len(data), 1)
s = data
vlines = [np.array([[t[i], y_base], [t[i], s[i]]]) for i in range(len(s))]
hs = hv.Path(vlines, kdims=dims, label=label).opts(show_legend=True,
muted_alpha=0.,
color='black')
if curve:
hs = hs * hv.Curve(
(t, s), dims[0], dims[1], label=label).opts(line_width=0.8)
return hs
def _make_progress(self):
import holoviews as hv
import holoviews.plotting.bokeh # noqa
stages = len(self._stages)
line = hv.Path([[(0, 0), (stages - 1, 0)]]).options(line_width=10,
color='black',
backend='bokeh')
vals = np.arange(stages)
active = [1 if v == self._stage else 0 for v in vals]
points = hv.Points((vals, np.zeros(stages), active),
vdims=['active']).options(color_index='active',
line_color='black',
cmap={
0: 'white',
1: 'gray'
},
show_legend=False,
size=20,
default_tools=[],
tools=['tap'],
nonselection_alpha=1,
backend='bokeh')
point_labels = points.add_dimension('text',
0, [n for n, _ in self._stages],
vdim=True)
labels = hv.Labels(point_labels).options(yoffset=-2.5, backend='bokeh')
self._progress_sel.source = points
hv_plot = (line * points * labels).options(xaxis=None,
yaxis=None,
width=800,
show_frame=False,
toolbar=None,
height=80,
xlim=(-0.5, stages - 0.5),
ylim=(-4, 1.5),
clone=False,
backend='bokeh')
return HoloViews(hv_plot, backend='bokeh')
def _get_windows(self, xtrack=None, atrack=None, x=None, y=None):
if x is not None:
xtrack = x
if y is not None:
atrack = y
# atrack and xtrack are from mouse or user. get the nearest where histogram is defined
xtrack, atrack = self._get_xatrack(xtrack=xtrack, atrack=atrack)
windows_list = []
try:
ws_list = self.gradients_hist['window_size']
except KeyError:
# no 'window_size'. compute it from axtrack neighbors
ws_list = [
np.diff(
np.array([[
self.gradients_hist[ax].isel({
ax: i
}).item() for i in [0, 1]
] for ax in ['atrack', 'xtrack']])).mean()
]
for ws in ws_list:
# window as a hv.Path object corresponding to window_size
amin, amax, xmin, xmax = (atrack - ws / 2, atrack + ws / 2,
xtrack - ws / 2, xtrack + ws / 2)
try:
style = self._get_style(
self.gradients_hist.sel(window_size=ws))
except (IndexError, KeyError):
style = {}
windows_list.append(
hv.Path([[(xmin, amin), (xmin, amax), (xmax, amax),
(xmax, amin), (xmin, amin)]]).opts(**style))
return hv.Overlay(windows_list)
from random import randint
import holoviews as hv
import panel as pn
from holoviews import opts, streams
LINE = "red"
FILL1 = "green"
FILL2 = "fulvous"
FILL3 = "blue"
hv.extension("bokeh")
path = hv.Path([[(1, 5), (9, 5)]])
poly = hv.Polygons([[(2, 2), (5, 8), (8, 2)]])
path_stream = streams.PolyDraw(source=path, drag=True, show_vertices=True)
poly_stream = streams.PolyDraw(
source=poly,
drag=True,
num_objects=4,
show_vertices=True,
styles={"fill_color": [FILL1, FILL2, FILL3]},
)
def get_plot(path, poly):
return (path * poly).opts(
opts.Polygons(fill_alpha=0.3, active_tools=["poly_draw"]),
opts.Path(color=LINE, height=400, line_width=5, width=400),
)
def analysis_gate(self, df):
# global curr_savgol_plot, maxsavgol
gate_files.append(df) # append to a list containing all the gate diode files
# Remove initial kink from the data
start_value = np.mean(self.data[df]["data"]["Current"][10:20])
CurrentCopy = self.data[df]["data"]["Current"].copy()
for x in range(int(len(self.data[df]["data"]["Current"]) / 2)):
if CurrentCopy[x] < start_value:
CurrentCopy[x] = start_value
# Generate curve
plot_not_kink = self.add_single_plots(
self.data[df]["data"][self.xaxis], CurrentCopy, "Current"
)
# Try savgol filter
##try:
## i = 0
## while i < 2:
## curr_savgol = scipy.signal.savgol_filter(self.data[df]['data']['Current'], 31, 3) # window size 51, polynomial order 3
## i += 1
## maxsavgol = max(curr_savgol)
## curr_savgol_plot = self.add_single_plots(self.data[df]['data']['Voltage'], curr_savgol, "SavgolCurrent")
##except Exception:
## self.log.warning("No savgol plot possible... Error: {}")
# Interpolation current curve
xnew, ynew = self.interpolated_axis(
df, self.data[df]["data"][self.xaxis], CurrentCopy
)
curr_interp_plot = self.add_single_plots(xnew, ynew, "InterpolatedCurrent")
# Build the first derivatives
firstderi_interp = self.build_first_derivative(xnew, ynew)
dif_intep_plot = self.add_single_plots(
xnew, firstderi_interp, "FirstDerivativeCurrent"
)
# Second derivative
second_deriv_interp = self.build_second_derivative(xnew, ynew)
dif2_intep_plot = self.add_single_plots(
xnew, second_deriv_interp, "SecondDerivativeCurrent"
)
# Not interpolated first derivative
firstdev_not_interp = self.build_first_derivative(
self.data[df]["data"]["Current"], self.data[df]["data"]["Voltage"]
)
self.insert_in_df(df, 3, "firstderivative_gate", firstdev_not_interp)
# Try to find the start and ending indices of the points where you want to average, used to handle the problematic files
max1_index = list(firstderi_interp).index(max(firstderi_interp))
min1_index = list(firstderi_interp).index(min(firstderi_interp))
if min1_index < max1_index:
min1_index = max1_index + 1
max1_index = min1_index - 2
median_index = int(len(xnew) / 2)
if median_index < max1_index:
median_index = max1_index + 1
if min1_index < median_index:
min1_index = median_index + 1
max1_index = median_index - 1
# Find the segment where you want to average using the second derivative
interesting_section = sorted(
list(second_deriv_interp[max1_index:median_index]), reverse=True
)
firstminimum = interesting_section[0]
interesting_section2 = sorted(
list(second_deriv_interp[median_index:min1_index]), reverse=True
)
second_minimum = interesting_section2[0]
# Find average
start_average = list(second_deriv_interp).index(firstminimum)
end_average = list(second_deriv_interp).index(second_minimum)
I_surf_maxima_average = np.mean(list(ynew[start_average:end_average]))
# Compute the surface current with the average method
mxx = max(ynew) # find maximum value of the current-voltage curve
miny = np.mean(
list(ynew[-1000:])
) # find the minimum of the current-voltage curve by averaging 20 points values in the curve tail
I_surf_average = (
I_surf_maxima_average - miny
) # compute the surface current by computing the difference between the maximum and minimum value
I_surf_average_table = "{:.2e}".format(I_surf_average)
Surface_current_average.append(I_surf_average_table)
# Compute surface current with the maximum method
Isurf_max = (
mxx - miny
) # compute the surface current by computing the difference between the maximum and minimum value
Isurf_table = "{:.2e}".format(Isurf_max)
Surface_current.append(Isurf_table)
# Compute Surface_recombination_velocity with the maximum method
S_null_max = Isurf_max / (
self.config["IV_PQC_parameter"]["q"]
* self.config["IV_PQC_parameter"]["n_i_intrinsic_carrier_concentration"]
* (float(self.data[df]["header"][0].split(":")[1]) * (1e-8))
)
Surface_recombination_velocity.append(S_null_max)
# Compute Surface_recombination_velocity with the average method
S_null_average = I_surf_average / (
self.config["IV_PQC_parameter"]["q"]
* self.config["IV_PQC_parameter"]["n_i_intrinsic_carrier_concentration"]
* (float(self.data[df]["header"][0].split(":")[1]) * (1e-8))
)
Surface_recombination_velocity_average.append(S_null_average)
# Add text to the plot
text = hv.Text(
3,
9 * (1e-11),
"Isurf_max: {} A\n"
"Isurf_average: {} A\n"
"Surface recombination velocity_max: {} cm/s\n"
"Surface recombination velocity_average: {} cm/s".format(
np.round(Isurf_max, 15),
np.round(I_surf_average, 15),
np.round(S_null_max, 4),
np.round(S_null_average, 4),
),
).opts(style=dict(text_font_size="20pt"))
# Do this if the analysis is of just one file.
if len(gate_files) == 1:
# Add overlaid lines on the plot
Path_min = hv.Path([(-2, miny), (6, miny)]).opts(line_width=2.0)
Path_mxx = hv.Path([(-2, mxx), (6, mxx)]).opts(line_width=2.0)
##Path_savgolmax = hv.Path([(-2, maxsavgol), (6, maxsavgol)]).opts(line_width=2.0)
Path_average = hv.Path(
[(-2, I_surf_maxima_average), (6, I_surf_maxima_average)]
).opts(line_width=2.0)
Path_Isurf = hv.Arrow(-1, mxx, "max", "^")
Path_Isurf_average = hv.Arrow(0, I_surf_maxima_average, "average", "^")
# Plot all Measurements
self.donts_gatediode = [
"timestamp",
"voltage",
"Voltage",
"Current",
"Stepsize",
"Wait",
"Stepsize",
"Frequency",
"x",
"N",
] # don't plot these.
self.PlotDict["BasePlots_gate"] = plot_not_kink
self.PlotDict["BasePlots_gate"] += dif_intep_plot
self.PlotDict["BasePlots_gate"] += dif2_intep_plot
self.PlotDict["BasePlots_gate"] += curr_interp_plot
try:
self.PlotDict["BasePlots_gate"] += curr_savgol_plot
self.PlotDict["BasePlots_gate"] += curr_savgol_plot * plot_not_kink
except Exception:
self.log.warning("No savgol plot possible... Error: {}")
self.PlotDict["BasePlots_gate"] += (
text
* plot_not_kink
* Path_min
* Path_mxx
* Path_average
* Path_Isurf
* Path_Isurf_average
) # * Path_savgolmax
self.PlotDict["BasePlots_gate"] += (
curr_interp_plot * dif_intep_plot * dif2_intep_plot * plot_not_kink
)
# Add table that shows resulting parameters of the analysis
df4 = pd.DataFrame(
{
"Name": gate_files,
"Surface current_max (A)": Surface_current,
"Surface current_average (A)": Surface_current_average,
"Surface recombination velocity_max (cm/s)": Surface_recombination_velocity,
"Surface recombination velocity_average (cm/s)": Surface_recombination_velocity_average,
}
)
table3 = hv.Table((df4), label="Gate analysis")
table3.opts(width=1300, height=800)
self.PlotDict["BasePlots_gate"] += table3
# Do this if the analysis is of more than one file
elif len(gate_files) > 1:
self.donts = [
"timestamp",
"voltage",
"Voltage",
"Stepsize",
"Wait",
"Stepsize",
"Frequency",
"firstderivative_gate",
"x",
"N",
] # do not plot this data
self.basePlots3 = plot_all_measurements(
self.data,
self.config,
self.xaxis,
self.name,
do_not_plot=self.donts,
keys=gate_files,
)
self.PlotDict["BasePlots_gate"] = self.basePlots3
# Add table that shows resulting parameters of the analysis
df4 = pd.DataFrame(
{
"Name": gate_files,
"Surface current_max (A)": Surface_current,
"Surface current_average (A)": Surface_current_average,
"Surface recombination velocity_max (cm/s)": Surface_recombination_velocity,
"Surface recombination velocity_average (cm/s)": Surface_recombination_velocity_average,
}
)
table3 = hv.Table((df4), label="Gate analysis")
table3.opts(width=1300, height=800)
self.PlotDict["BasePlots_gate"] += table3
return self.PlotDict["BasePlots_gate"]
def create_plot(
data,
x,
y,
plot_type="scatter",
selected=None,
show_selected=True,
slow_render=False,
legend=True,
colours=True,
smaller_axes_limits=False,
bounds=None,
legend_position=None,
):
assert x in list(
data.columns), f"Column {x} is not a column in your dataframe."
assert y in list(
data.columns), f"Column {y} is not a column in your dataframe."
if bounds is not None:
data = data[data[x] >= bounds[0]]
data = data[data[y] <= bounds[1]]
data = data[data[x] <= bounds[2]]
data = data[data[y] >= bounds[3]]
if plot_type == "scatter":
p = hv.Points(
data,
[x, y],
).opts(active_tools=["pan", "wheel_zoom"])
elif plot_type == "line":
p = hv.Path(
data,
[x, y],
).opts(active_tools=["pan", "wheel_zoom"])
if show_selected:
if selected is not None:
cols = list(data.columns)
if len(selected.data[cols[0]]) == 1:
selected = pd.DataFrame(selected.data, columns=cols, index=[0])
if bounds is not None:
if ((selected[x][0] < bounds[0])
or (selected[y][0] > bounds[1])
or (selected[x][0] > bounds[2])
or (selected[y][0] < bounds[3])):
selected = pd.DataFrame(columns=cols)
else:
selected = pd.DataFrame(columns=cols)
selected_plot = hv.Scatter(
selected,
x,
y,
).opts(
fill_color="black",
marker="circle",
size=10,
active_tools=["pan", "wheel_zoom"],
)
if colours:
color_key = config.settings["label_colours"]
color_points = hv.NdOverlay({
config.settings["labels_to_strings"][f"{n}"]:
hv.Points([0, 0],
label=config.settings["labels_to_strings"][f"{n}"]).opts(
style=dict(color=color_key[n], size=0))
for n in color_key
})
if smaller_axes_limits:
max_x = np.max(data[x])
min_x = np.min(data[x])
max_y = np.max(data[y])
min_y = np.min(data[y])
x_sd = np.std(data[x])
x_mu = np.mean(data[x])
y_sd = np.std(data[y])
y_mu = np.mean(data[y])
max_x = np.min([x_mu + 4 * x_sd, max_x])
min_x = np.max([x_mu - 4 * x_sd, min_x])
max_y = np.min([y_mu + 4 * y_sd, max_y])
min_y = np.max([y_mu - 4 * y_sd, min_y])
if show_selected:
if selected is not None:
if selected.shape[0] > 0:
max_x = np.max([max_x, np.max(selected[x])])
min_x = np.min([min_x, np.min(selected[x])])
max_y = np.max([max_y, np.max(selected[y])])
min_y = np.min([min_y, np.min(selected[y])])
if colours:
if smaller_axes_limits:
plot = dynspread(
datashade(
p,
color_key=color_key,
aggregator=ds.by(config.settings["label_col"], ds.count()),
).opts(xlim=(min_x, max_x),
ylim=(min_y, max_y),
responsive=True),
threshold=0.75,
how="saturate",
)
else:
plot = dynspread(
datashade(
p,
color_key=color_key,
aggregator=ds.by(config.settings["label_col"], ds.count()),
).opts(responsive=True),
threshold=0.75,
how="saturate",
)
else:
if smaller_axes_limits:
plot = dynspread(
datashade(p, ).opts(xlim=(min_x, max_x),
ylim=(min_y, max_y),
responsive=True),
threshold=0.75,
how="saturate",
).redim.range(xdim=(min_x, max_x), ydim=(min_y, max_y))
else:
plot = dynspread(
datashade(p, ).opts(responsive=True),
threshold=0.75,
how="saturate",
)
if slow_render:
plot = p
if show_selected and (selected is not None):
plot = plot * selected_plot
if legend and colours:
plot = plot * color_points
if legend_position is not None:
plot = plot.opts(legend_position=legend_position)
return plot
def make_plot(t):
spring = hv.Scatter(([q(t)], [0])).opts(xlabel='q', size=6, show_grid=True, title="Spring")
phase_space = hv.Scatter(([q(t)], [p(t)])).opts(xlabel="q", ylabel='p', size=6, show_grid=True, title="Phase Space")
trajectory = hv.Path(([q(_t) for _t in np.linspace(0, 30, 200)], [p(_t) for _t in np.linspace(0, 30, 200)])).opts(xlabel="q", ylabel='p(x)', line_width=2, color="orange")
return (spring+ (phase_space * trajectory)).opts(shared_axes=False)
def make_rect(bounds=DEFAULT_BOUNDS, color='blue'):
minX, minY, maxX, maxY = bounds
return hv.Path([(minX, minY), (maxX, minY), (maxX, maxY), (minX, maxY),
(minX, minY)]).opts(img_opts).opts(line_width=2,
color='blue')
import numpy as np
import pandas as pd
import bokeh
import holoviews as hv
import datashader as ds
from holoviews.operation.datashader import aggregate, shade, datashade, dynspread
from holoviews.operation import decimate
SOURCE = '../data_preprocessed/TEs_CHR1.hdf'
HEIGHT = 350
WIDTH = 800
reads = pd.read_hdf(SOURCE, key='tes')
plot = dynspread(
datashade(hv.Path([reads]),
min_alpha=100,
aggregator=ds.count_cat('family'))).options(width=WIDTH,
height=HEIGHT,
bgcolor="black")
#renderer = hv.renderer('bokeh')
#renderer.save(plot, '/Users/cfltxm/Docs/talks/QRWbioinformatics2018/talk/fig/plot_4_te_reads')
doc = hv.renderer('bokeh').server_doc(plot)