PlotShape: beginline · color · color_all · color_list · colormap · erase · erase_all · exec_menu · fastflush · flush · hinton · label · len_scale · line · mark · menu_action · menu_tool · nearest · observe · plot · printfile · push_selected · rotate · save_name · scale · show · size · unmap · variable · view · view_count

PlotShape

class PlotShape: Class for making a Shape window useful for coloring a shape according to a variable value and creating time and space graphs of a variable. The default variable is v. The first arg may be a SectionList.

PlotShape.plot()

Syntax:

ps.plot(graphics_object)

Description:

In NEURON 7.7+, PlotShape.plot works both with and without Interviews support. Variables, sectionlists, and scale are supported. Clicking on a segment displays the value and the segment id.

Extra arguments and keyword arguments are passed to the underlying graphics library (currently only matplotlib Figure objects, pyplot, and plotly are supported, with plotly support added in 7.8).

Note

If Interviews is enabled, the flag False must be passed to the h.PlotShape constructor to avoid additionally displaying a PlotShape using Interviews graphics. See the example:

Example:

If no seclist argument is provided, PlotShape.plot will plot all sections

from neuron import h
from matplotlib import pyplot
h.load_file('c91662.ses')  # a morphology file
for sec in h.allsec():
    if 'apic' in str(sec):
        sec.v = 0
ps = h.PlotShape(False)  # False tells h.PlotShape not to use NEURON's gui
ps.plot(pyplot)
pyplot.show()

Note

In Jupyter, you can use %matplotlib notebook to get interactive PlotShape or use plotly instead.

Example:

You can also pass in a SectionList argument to only plot specific sections

from neuron import h
from matplotlib import pyplot, cm
h.load_file('c91662.ses')
sl = h.SectionList([sec for sec in h.allsec() if 'apic' in str(sec)])
for sec in sl:
    sec.v = 0
ps = h.PlotShape(sl, False)
ps.scale(-80, 40)
ps.variable('v')
ax = ps.plot(pyplot, cmap=cm.jet)
pyplot.show()

Example:

Line width across the neuron morphology is able to be altered depending on different modes. ps.show(0) allows for visualizing diameters for each segment across the cell. Additionally, when mode = 1 or mode = 2 , line_width argument can be passed in to specify fixed width across cell.

For plotting on matplotlib:

from neuron import h, gui
from neuron.units import mV, ms
from matplotlib.pyplot import cm
from matplotlib import pyplot

h.load_file("c91662.ses")

for sec in h.allsec():
    sec.nseg = int(1 + 2 * (sec.L // 40))
    sec.insert(h.hh)

ic = h.IClamp(h.soma(0.5))
ic.delay = 1 * ms
ic.dur = 1 * ms
ic.amp = 10

h.finitialize(-65 * mV)
h.continuerun(2 * ms)

ps = h.PlotShape(False)
ps.variable("v")
ps.show(1)
ps.plot(pyplot, cmap=cm.magma, line_width=10, color="red")
pyplot.show()

For plotting on plotly:

import plotly
import matplotlib
from neuron import h
from neuron.units import mV, ms

h.load_file("c91662.ses")
for sec in h.allsec():
    sec.nseg = int(1 + 2 * (sec.L // 40))
    sec.insert(h.hh)

ic = h.IClamp(h.soma(0.5))
ic.delay = 1 * ms
ic.dur = 1 * ms
ic.amp = 10

h.finitialize(-65 * mV)
h.continuerun(2 * ms)

ps = h.PlotShape(False)
ps.variable("v")
ps.show(1)
ps.plot(plotly, width=7, cmap=matplotlib.colormaps["viridis"]).show()

Example:

Color argument can also be passed in when consistent color across cell is preferred. When not specified, the morphology will be plotted in color gradient passed as cmap in accordance with voltage values of each segment after simulation is initiated. To specifiy cmap,

from neuron import h
from matplotlib import pyplot, cm

h.load_file("c91662.ses")
sl = h.SectionList([sec for sec in h.allsec() if "apic" in str(sec)])
for sec in sl:
    sec.v = 0
ps = h.PlotShape(False)
ps.scale(-80, 40)
ps.variable("v")
ax = ps.plot(pyplot, line_width=3, color="red")
pyplot.show()

PlotShape.scale()

Syntax:: .scale(low, high)
Description:: Sets blue and red values for the color scale and default axes for time and space plots.

PlotShape.view(): See also

Shape.view()

PlotShape.size(): See also

Shape.size()

PlotShape.view_count(): See also

Shape.view_count()

PlotShape.show(): See also

Shape.show()

PlotShape.flush(): See also

Shape.flush()

PlotShape.fastflush()

Syntax:: shapeplot.fastflush()
Description:: Speeds up drawing of PlotShape.hinton() elements.

PlotShape.variable()

Syntax:: .variable("rangevar")

Description: Range variable (v, m_hh, etc.) to be used for time, space, and shape plots.

Additionally, the variable can also be identified by species or specific region to show the corresponding voltage across.

Example:

from neuron import h, rxd
from neuron.units import mM, µm, ms, mV
import plotly
h.load_file("stdrun.hoc")

dend1 = h.Section('dend1')
dend2 = h.Section('dend2')
dend2.connect(dend1(1))

dend1.nseg = dend1.L = dend2.nseg = dend2.L = 11
dend1.diam = dend2.diam = 2 * µm

cyt = rxd.Region(dend1.wholetree(), nrn_region="i")
cyt2 = rxd.Region(dend2.wholetree(), nrn_region="i")

ca = rxd.Species([cyt,cyt2], name="ca", charge=2, initial=0 * mM, d=1 * µm ** 2 / ms)

ca.nodes(dend1(0.5))[0].include_flux(1e-13, units="mmol/ms")

h.finitialize(-65 * mV)
h.continuerun(50 * ms)

ps = h.PlotShape(False)

ps.variable(ca[cyt])

ps.plot(plotly).show()

PlotShape.save_name(): See also

Shape.save_name()

PlotShape.unmap(): See also

Shape.unmap()

PlotShape.printfile(): See also

Shape.printfile()

PlotShape.menu_action(): See also

Graph.menu_action()

PlotShape.menu_tool(): See also

Shape.menu_tool()

PlotShape.observe(): See also

Shape.observe()

PlotShape.nearest(): See also

Shape.nearest()

PlotShape.push_selected(): See also

Shape.push_selected()

PlotShape.exec_menu(): See also

Graph.exec_menu()

PlotShape.erase(): See also

Graph.erase()

PlotShape.erase_all()

Description:: Erases everything in the PlotShape, including all Sections and hinton plots

See also

Graph.mark()

PlotShape.label(): See also

Graph.label()

PlotShape.color()

Syntax:: shape.color(i, sec=sec)
Description:: colors the specified section according to color index (index same as specified in Graph class). If there are several sections to color it is more efficient to make a SectionList and use .color_list

PlotShape.color_all()

Syntax:: .color_all(i)
Description:: colors all the sections

PlotShape.color_list()

Syntax:: .color_list(SectionList, i)
Description:: colors the sections in the list

PlotShape.colormap()

Syntax:

s.colormap(size, [global = 0])

s.colormap(index, red, green, blue)

Description:

If the optional global argument is 1 then these functions refer to the global (default) Colormap and a change will affect all PlotShape instances that use it. Otherwise these function create a colormap that is local to this PlotShape.

With a single argument, destroys the old and creates a new colormap for shape plots with space for size colors. All colors are initialized to gray.

The four argument syntax, specifies the color of the index element of the colormap. the red, green, and blue must be integers within the range 0-255 and specify the intensity of these colors.

If an existing colormap is displayed in the view, it will be redrawn with the proper colors when PlotShape.scale() is called.

PlotShape.hinton()

Syntax:

s.hinton(_ref_varname, x, y, size)

s.hinton(_ref_varname, x, y, xsize, ysize)

Description:

A filled square or rectangle is drawn with center at (x, y) and edge length given by size. The color depends on the PlotShape.colormap() and PlotShape.scale() and is redrawn on PlotShape.flush().

If there are many of these elements then PlotShape.fastflush() can speed plotting by up to a factor of 4 if not too many elements change color between fastflush calls.

Example:

            from neuron import h, gui
            import time

            soma = h.Section(name="soma")

            sl = h.SectionList()

            s = h.PlotShape(sl)
            s.colormap(3)
            s.colormap(0, 255, 0, 0)
            s.colormap(1, 255, 255, 0)
            s.colormap(2, 200, 200, 200)
            s.scale(0, 2)

            nx = 30
            ny = 30
            vec = h.Vector(nx*ny)
            vec.fill(0)

            for i in range(nx):
                    for j in range(ny):
                            s.hinton(vec._ref_x[i*ny + j], float(i)/nx, float(j)/ny, 1./nx)

            s.size(-.5, 1, 0, 1)
            s.exec_menu("Shape Plot")

            r = h.Random()
            r.poisson(.01)

            h.doNotify()

            def p():
                    for i in range(1,1001):
                            vec.setrand(r)
                            s.fastflush() # faster by up to a factor of 4
                            h.doNotify()

            start = time.perf_counter()
            p()
            print(time.perf_counter() - start)

PlotShape.len_scale()

Syntax:: shape.len_scale(scl, sec=sec)
Description:: See Shape.len_scale()

PlotShape.rotate()

Syntax:

shape.rotate()

shape.rotate(xorg, yorg, zorg, xrad, yrad, zrad)

Description:

See Shape.rotate()