def _plot_output(self, type, latpositions, longpositions, data, media_file):
fig = plt.figure(figsize=plt.figaspect(0.3))
image = self.input_config.image
impositions = self.input_config.positions
imfactors = self.input_config.factors
intensities = self.input_config.intensities
writer = FFMpegFileWriter(fps=5, codec='mpeg4')
writer.setup(
fig, media_file, dpi=80,
frame_prefix=os.path.splitext(media_file)[0]+'_')
writer.frame_format = 'png'
step = 100
plt.hold(False)
#TODO keep DRY
U, V = np.mgrid[0:np.pi/2:complex(0, self.PARALLELS),
0:2*np.pi:complex(0, self.MERIDIANS)]
X = np.cos(V)*np.sin(U)
Y = np.sin(V)*np.sin(U)
Z = np.cos(U)
for i, d in enumerate(data):
if i % step == 0:
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_title('Image')
if type == 'image':
ax1.imshow(image, cmap=cm.Greys_r)
elif type == 'video':
# TODO show red rectangle
ax1 = fig.add_subplot(1, 3, 1)
xind = impositions[i, 0:2]
yind = impositions[i, 2:4]
ax1.imshow(image[int(xind[0]):int(xind[1]),
int(yind[0]):int(yind[1])],
cmap=cm.Greys_r)
else:
raise ValueError('Invalid value for media type: {}'
', valid values image, video'.format(type))
colors = self.compute_colors(U, V, latpositions,
longpositions, intensities[i])
ax2 = fig.add_subplot(1, 3, 2, projection='3d')
ax2.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.gray(colors),
antialiased=False, shade=False)
ax2.set_title('Intensities')
colors = self.compute_colors(U, V, latpositions,
longpositions, data[i])
ax3 = fig.add_subplot(1, 3, 3, projection='3d')
ax3.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.gray(colors),
antialiased=False, shade=False)
ax3.set_title('Photoreceptor outputs')
fig.canvas.draw()
writer.grab_frame()
writer.finish()
def savemp4(images, videofile, step=10):
'''
Generates a frame every 10(default) images and saves all
of them to a video file
parameters:
images: a numpy array where each row corresponds to an image
videofile: file to store video
step: every that number of images will be stored in the file
the rest will be ignored (e.g if step is 10
and images are 50, only images 1,11,21,31,41 will be stored)
'''
from matplotlib import cm
from matplotlib.animation import FFMpegFileWriter, AVConvFileWriter
import matplotlib.pyplot as plt
fig = plt.figure(figsize=plt.figaspect(1.0))
writer = FFMpegFileWriter(fps=5, codec='mpeg4')
writer.setup(fig,
videofile,
dpi=80,
frame_prefix=os.path.splitext(videofile)[0] + '_')
writer.frame_format = 'png'
plt.hold(False)
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0, right=1.0)
for i in range(0, len(images), step):
ax.imshow(images[i],
cmap=cm.Greys_r,
vmin=images.min(),
vmax=images.max())
fig.canvas.draw()
writer.grab_frame()
writer.finish()
def savemp4(images, videofile, step=10):
'''
Generates a frame every 10(default) images and saves all
of them to a video file
parameters:
images: a numpy array where each row corresponds to an image
videofile: file to store video
step: every that number of images will be stored in the file
the rest will be ignored (e.g if step is 10
and images are 50, only images 1,11,21,31,41 will be stored)
'''
from matplotlib import cm
from matplotlib.animation import FFMpegFileWriter, AVConvFileWriter
import matplotlib.pyplot as plt
fig = plt.figure(figsize=plt.figaspect(1.0))
writer = FFMpegFileWriter(fps=5, codec='mpeg4')
writer.setup(
fig, videofile, dpi=80,
frame_prefix=os.path.splitext(videofile)[0]+'_')
writer.frame_format = 'png'
plt.hold(False)
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0, right=1.0)
for i in range(0, len(images), step):
ax.imshow(images[i], cmap=cm.Greys_r,
vmin=images.min(), vmax=images.max())
fig.canvas.draw()
writer.grab_frame()
writer.finish()
class visualizer(object):
"""
Visualize the output produced by LPU models.
Example
-------
import neurokernel.LPU.utils.visualizer as vis
V = vis.visualizer()
config1 = {}
config1['type'] = 'image'
config1['shape'] = [32,24]
config1['clim'] = [-0.6,0.5]
config2 = config1.copy()
config2['clim'] = [-0.55,-0.45]
V.add_LPU('lamina_output.h5', 'lamina.gexf.gz','lamina')
V.add_plot(config1, 'lamina', 'R1')
V.add_plot(config1, 'lamina', 'L1')
V._update_interval = 50
V.out_filename = 'test.avi'
V.run()
"""
def __init__(self):
self._xlim = [0,1]
self._ylim = [-1,1]
self._imlim = [-1, 1]
self._update_interval = 50
self._out_file = None
self._fps = 5
self._codec = 'libtheora'
self._config = OrderedDict()
self._rows = 0
self._cols = 0
self._figsize = (16,9)
self._fontsize = 18
self._t = 1
self._dt = 1
self._data = {}
self._graph = {}
self._maxt = None
self._title = None
def add_LPU(self, data_file, gexf_file=None, LPU=None, win=None):
'''
Add data associated with a specific LPU to a visualization.
To add a plot containing neurons from a particular LPU,
the LPU needs to be added to the visualization using this
function. Not that outputs from multiple neurons can
be visualized using the same visualizer object.
Parameters
----------
data_file: str
Location of the h5 file generated by neurokernel
containing the output of the LPU
gexf_file: str
Location of the gexf file describing the LPU.
If not specified, it will be assumed that the h5 file
contains input.
LPU: str
Name of the LPU. Will be used as identifier to add plots.
'''
if gexf_file:
self._graph[LPU] = nx.read_gexf(gexf_file)
else:
if LPU:
LPU = 'input_' + str(LPU)
else:
LPU = 'input_' + str(len(self._data))
if not LPU:
LPU = len(self._data)
self._data[LPU] = np.transpose(sio.read_array(data_file))
if win is not None:
self._data[LPU] = self._data[LPU][:,win]
if self._maxt:
self._maxt = min(self._maxt, self._data[LPU].shape[1])
else:
self._maxt = self._data[LPU].shape[1]
def run(self, final_frame_name=None, dpi=300):
'''
Starts the visualization process The final frame is saved to the specified
file name; otherwise, the visualization is displayed in a window without being saved.
'''
self._initialize()
self._t = self._update_interval+1
for i in range(self._update_interval,self._maxt, self._update_interval):
self.update()
if final_frame_name is not None:
self.f.savefig(final_frame_name, dpi=dpi)
if self.out_filename:
self.close()
def _set_wrapper(self, obj, name, value):
name = name.lower()
func = getattr(obj, 'set_'+name, None)
if func:
try:
func(value, fontsize=self._fontsize, weight='bold')
except:
try:
func(value)
except:
pass
def _initialize(self):
# Count number of plots to create:
num_plots = 0
for config in self._config.itervalues():
num_plots += len(config)
# Set default grid of plot positions:
if not self._rows*self._cols == num_plots:
self._cols = int(np.ceil(np.sqrt(num_plots)))
self._rows = int(np.ceil(num_plots/float(self._cols)))
self.f, self.axarr = plt.subplots(self._rows, self._cols,
figsize=self._figsize)
# Remove unused subplots:
for i in xrange(num_plots, self._rows*self._cols):
plt.delaxes(self.axarr[np.unravel_index(i, (self._rows, self._cols))])
cnt = 0
self.handles = []
self.types = []
keywds = ['handle', 'ydata', 'fmt', 'type', 'ids', 'shape']
if not isinstance(self.axarr, np.ndarray):
self.axarr = np.asarray([self.axarr])
for LPU, configs in self._config.iteritems():
for plt_id, config in enumerate(configs):
ind = np.unravel_index(cnt, self.axarr.shape)
cnt+=1
# Some plot types require specific numbers of
# neuron ID arrays:
if 'type' in config:
if config['type'] == 'quiver':
assert len(config['ids'])==2
config['type'] = 0
elif config['type'] == 'hsv':
assert len(config['ids'])==2
config['type'] = 1
elif config['type'] == 'image':
assert len(config['ids'])==1
config['type'] = 2
elif config['type'] == 'waveform':
config['type'] = 3
elif config['type'] == 'raster':
config['type'] = 4
elif config['type'] == 'rate':
config['type'] = 5
else:
raise ValueError('Plot type not supported')
else:
if str(LPU).startswith('input') or not self._graph[LPU][str(config['ids'][0])]['spiking']:
config['type'] = 2
else:
config['type'] = 4
if config['type'] < 3:
if not 'shape' in config:
# XXX This can cause problems when the number
# of neurons is not equal to
# np.prod(config['shape'])
num_neurons = len(config['ids'][0])
config['shape'] = [int(np.ceil(np.sqrt(num_neurons)))]
config['shape'].append(int(np.ceil(num_neurons/float(config['shape'][0]))))
if config['type'] == 0:
config['handle'] = self.axarr[ind].quiver(\
np.reshape(self._data[LPU][config['ids'][0],0],config['shape']),\
np.reshape(self._data[LPU][config['ids'][1],0],config['shape']))
elif config['type'] == 1:
X = np.reshape(self._data[LPU][config['ids'][0],0],config['shape'])
Y = np.reshape(self._data[LPU][config['ids'][1],0],config['shape'])
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X,Y)+np.pi)/(2*np.pi)
S = np.ones_like(V)
HSV = np.dstack((H,S,V))
RGB = hsv_to_rgb(HSV)
config['handle'] = self.axarr[ind].imshow(RGB)
elif config['type'] == 2:
if 'trans' in config:
if config['trans'] is True:
to_transpose = True
else:
to_transpose = False
else:
to_transpose = False
config['trans'] = False
if to_transpose:
temp = self.axarr[ind].imshow(np.transpose(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape'])))
else:
temp = self.axarr[ind].imshow(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape']))
temp.set_clim(self._imlim)
temp.set_cmap(plt.cm.gist_gray)
config['handle'] = temp
elif config['type'] == 3:
fmt = config['fmt'] if 'fmt' in config else ''
self.axarr[ind].set_xlim(self._xlim)
self.axarr[ind].set_ylim(self._ylim)
if len(config['ids'][0])==1:
config['handle'] = self.axarr[ind].plot([0], \
[self._data[LPU][config['ids'][0][0],0]], fmt)[0]
config['ydata'] = [self._data[LPU][config['ids'][0][0],0]]
else:
config['handle'] = self.axarr[ind].plot(self._data[LPU][config['ids'][0],0])[0]
elif config['type'] == 4:
config['handle'] = self.axarr[ind]
config['handle'].vlines(0, 0, 0.01)
config['handle'].set_ylim([.5, len(config['ids'][0]) + .5])
config['handle'].set_ylabel('Neurons',
fontsize=self._fontsize-1, weight='bold')
config['handle'].set_xlabel('Time (s)',fontsize=self._fontsize-1, weight='bold')
config['handle'].set_xlim([0,len(self._data[LPU][config['ids'][0][0],:])*self._dt])
config['handle'].axes.set_yticks([])
config['handle'].axes.set_xticks([])
for key in config.iterkeys():
if key not in keywds:
try:
self._set_wrapper(self.axarr[ind],key, config[key])
except:
pass
try:
self._set_wrapper(config['handle'],key, config[key])
except:
pass
if config['type']<3:
config['handle'].axes.set_xticks([])
config['handle'].axes.set_yticks([])
if self.suptitle is not None:
self.f.suptitle(self._title, fontsize=self._fontsize+1, x=0.5,y=0.03, weight='bold')
plt.tight_layout()
if self.out_filename:
self.writer = FFMpegFileWriter(fps=self.fps, codec=self.codec)
# Use the output file to determine the name of the temporary frame
# files so that two concurrently run visualizations don't clobber
# each other's frames:
self.writer.setup(self.f, self.out_filename, dpi=80,
frame_prefix=os.path.splitext(self.out_filename)[0]+'_')
self.writer.frame_format = 'png'
self.writer.grab_frame()
else:
self.f.show()
def update(self):
dt = self._dt
t = self._t
for key, configs in self._config.iteritems():
data = self._data[key]
for config in configs:
if config['type'] == 3:
if len(config['ids'][0])==1:
config['ydata'].extend(np.reshape(np.double(\
data[config['ids'][0], \
max(0,t-self._update_interval):t]),(-1,)))
config['handle'].set_xdata(dt*np.arange(0, t))
config['handle'].set_ydata(np.asarray(config['ydata']))
else:
config['handle'].set_ydata(\
data[config['ids'][0], t])
elif config['type']==4:
for j,id in enumerate(config['ids'][0]):
for time in np.where(data[id,max(0,t-self._update_interval):t])[0]:
config['handle'].vlines(float(t-time)*self._dt,j+0.75, j+1.25)
else:
if config['type'] == 0:
shape = config['shape']
ids = config['ids']
config['handle'].U = np.reshape(data[ids[0], t],shape)
config['handle'].V = np.reshape(data[ids[1], t],shape)
elif config['type']==1:
shape = config['shape']
ids = config['ids']
X = np.reshape(data[ids[0], t],shape)
Y = np.reshape(data[ids[1], t],shape)
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X,Y)+np.pi)/(2*np.pi)
S = np.ones_like(V)
HSV = np.dstack((H,S,V))
RGB = hsv_to_rgb(HSV)
config['handle'].set_data(RGB)
elif config['type'] == 2:
ids = config['ids']
if config['trans']:
config['handle'].set_data(
np.transpose(np.reshape(data[ids[0], t], config['shape'
])))
else:
config['handle'].set_data(
np.reshape(data[ids[0], t], config['shape']))
self.f.canvas.draw()
if self.out_filename:
self.writer.grab_frame()
self._t+=self._update_interval
def add_plot(self, config_dict, LPU=0, names=[''], shift=0):
config = config_dict.copy()
if not isinstance(names, list):
names = [names]
if not LPU in self._config:
self._config[LPU] = []
if 'ids' in config:
# XXX should check whether the specified ids are within range
self._config[LPU].append(config)
elif str(LPU).startswith('input'):
config['ids'] = [range(0, self._data[LPU].shape[0])]
self._config[LPU].append(config)
else:
config['ids'] = {}
for i,name in enumerate(names):
config['ids'][i]=[]
for id in range(len(self._graph[LPU].node)):
if self._graph[LPU].node[str(id)]['name'] == name:
config['ids'][i].append(id-shift)
self._config[LPU].append(config)
if not 'title' in config:
if names[0]:
config['title'] = "{0} - {1}".format(str(LPU),str(names[0]))
else:
if str(LPU).startswith('input_'):
config['title'] = LPU.split('_',1)[1] + ' - ' + 'Input'
else:
config['title'] = str(LPU)
def close(self):
self.writer.finish()
@property
def xlim(self): return self._xlim
@xlim.setter
def xlim(self, value):
self._xlim = value
@property
def ylim(self): return self._ylim
@ylim.setter
def ylim(self, value):
self._ylim = value
@property
def imlim(self): return self._imlim
@imlim.setter
def imlim(self, value):
self._imlim = value
@property
def out_filename(self): return self._out_file
@out_filename.setter
def out_filename(self, value):
assert(isinstance(value, str))
self._out_file = value
@property
def fps(self): return self._fps
@fps.setter
def fps(self, value):
assert(isinstance(value, int))
self._fps = value
@property
def codec(self): return self._codec
@codec.setter
def codec(self, value):
assert(isinstance(value, str))
self._codec = value
@property
def rows(self): return self._rows
@rows.setter
def rows(self, value):
self._rows = value
@property
def cols(self): return self._cols
@cols.setter
def cols(self, value):
self._cols = value
@property
def dt(self): return self._dt
@dt.setter
def dt(self, value):
self._dt = value
@property
def figsize(self): return self._figsize
@figsize.setter
def figsize(self, value):
assert(isinstance(value, tuple) and len(value)==2)
self._figsize = value
@property
def fontsize(self): return self._fontsize
@fontsize.setter
def fontsize(self, value):
self._fontsize = value
@property
def suptitle(self): return self._title
@suptitle.setter
def suptitle(self, value):
self._title = value
@property
def update_interval(self): return self._update_interval
@update_interval.setter
def update_interval(self, value):
self._update_interval = value
class visualizer(object):
"""
Visualize the output produced by LPU models.
Examples
--------
import neurokernel.LPU.utils.visualizer as vis
V = vis.visualizer()
config1 = {}
config1['type'] = 'image'
config1['shape'] = [32,24]
config1['clim'] = [-0.6,0.5]
config2 = config1.copy()
config2['clim'] = [-0.55,-0.45]
V.add_LPU('lamina_output.h5', 'lamina.gexf.gz','lamina')
V.add_plot(config1, 'lamina', 'R1')
V.add_plot(config2, 'lamina', 'L1')
V.update_interval = 50
V.out_filename = 'test.avi'
V.run()
"""
def __init__(self):
self._xlim = [0, 1]
self._ylim = [-1, 1]
self._imlim = [-1, 1]
self._update_interval = 50
self._out_file = None
self._fps = 5
self._codec = 'libtheora'
self._config = OrderedDict()
self._rows = 0
self._cols = 0
self._figsize = (16, 9)
self._fontsize = 18
self._t = 1
self._dt = 1
self._data = {}
self._graph = {}
self._id_to_data_idx = {}
self._maxt = None
self._title = None
def add_LPU(self, data_file, gexf_file=None, LPU=None, win=None):
'''
Add data associated with a specific LPU to a visualization.
To add a plot containing neurons from a particular LPU,
the LPU needs to be added to the visualization using this
function. Note that outputs from multiple neurons can
be visualized using the same visualizer object.
Parameters
----------
data_file: str
Location of the h5 file generated by neurokernel
containing the output of the LPU
gexf_file: str
Location of the gexf file describing the LPU.
If not specified, it will be assumed that the h5 file
contains input.
LPU: str
Name of the LPU. Will be used as identifier to add plots.
For input signals, the name of the LPU will be prepended
with 'input_'. For example::
V.add_LPU('vision_in.h5', LPU='vision')
will create the LPU identifier 'input_vision'.
Therefore, adding a plot depicting this input can be done by::
V.add_plot({''type':'image',imlim':[-0.5,0.5]},LPU='input_vision)
win: slice/list
Can be used to limit the visualization to a specific time window.
'''
if gexf_file:
self._graph[LPU] = nx.read_gexf(gexf_file)
# Map neuron ids to index into output data array:
self._id_to_data_idx[LPU] = {m:i for i, m in \
enumerate(sorted([int(n) for n, k in \
self._graph[LPU].nodes_iter(True) if k['spiking']]))}
else:
if LPU:
LPU = 'input_' + str(LPU)
else:
LPU = 'input_' + str(len(self._data))
if not LPU:
LPU = len(self._data)
self._data[LPU] = np.transpose(sio.read_array(data_file))
if win is not None:
self._data[LPU] = self._data[LPU][:, win]
if self._maxt:
self._maxt = min(self._maxt, self._data[LPU].shape[1])
else:
self._maxt = self._data[LPU].shape[1]
def run(self, final_frame_name=None, dpi=300):
'''
Starts the visualization process. If the property out_filename is set,
the visualization is saved as a video to the disk. If it is not
specified, the animation will be displayed on screen.
Please refer to documentation of add_LPU, add_plot and
the properties of this class on how to configure the visualizer before call this
method. An example can be found in the class doc string.
Paramters
----------
final_frame_name: str
Optional. If specified, the final frame of the animation will be saved
to disk.
dpi: int
Default(300). If final_frame_name is specified, this parameter will control
the resolution at which the final frame is saved to disk.
Note:
-----
If update_interval is set to 0 or None, it will be replaced by the
index of the final time step. As a result, the visualizer will only
generate the final frame.
'''
self._initialize()
if not self._update_interval:
self._update_interval = self._maxt - 1
self._t = self._update_interval + 1
for _ in range(self._update_interval, self._maxt,
self._update_interval):
self._update()
if final_frame_name is not None:
self.f.savefig(final_frame_name, dpi=dpi)
if self.out_filename:
self._close()
def _set_wrapper(self, obj, name, value):
name = name.lower()
func = getattr(obj, 'set_' + name, None)
if func:
try:
func(value, fontsize=self._fontsize, weight='bold')
except:
try:
func(value)
except:
pass
def _initialize(self):
# Count number of plots to create:
num_plots = 0
for config in self._config.itervalues():
num_plots += len(config)
# Set default grid of plot positions:
if not self._rows * self._cols == num_plots:
self._cols = int(np.ceil(np.sqrt(num_plots)))
self._rows = int(np.ceil(num_plots / float(self._cols)))
self.f, self.axarr = plt.subplots(self._rows,
self._cols,
figsize=self._figsize)
# Remove unused subplots:
for i in xrange(num_plots, self._rows * self._cols):
plt.delaxes(self.axarr[np.unravel_index(i,
(self._rows, self._cols))])
cnt = 0
self.handles = []
self.types = []
keywds = ['handle', 'ydata', 'fmt', 'type', 'ids', 'shape']
if not isinstance(self.axarr, np.ndarray):
self.axarr = np.asarray([self.axarr])
for LPU, configs in self._config.iteritems():
for plt_id, config in enumerate(configs):
ind = np.unravel_index(cnt, self.axarr.shape)
cnt += 1
# Some plot types require specific numbers of
# neuron ID arrays:
if 'type' in config:
if config['type'] == 'quiver':
assert len(config['ids']) == 2
config['type'] = 0
elif config['type'] == 'hsv':
assert len(config['ids']) == 2
config['type'] = 1
elif config['type'] == 'image':
assert len(config['ids']) == 1
config['type'] = 2
elif config['type'] == 'waveform':
config['type'] = 3
elif config['type'] == 'raster':
config['type'] = 4
elif config['type'] == 'rate':
config['type'] = 5
else:
raise ValueError('Plot type not supported')
else:
if str(LPU).startswith('input') or not self._graph[LPU][
str(config['ids'][0])]['spiking']:
config['type'] = 2
else:
config['type'] = 4
if config['type'] < 3:
if not 'shape' in config:
# XXX This can cause problems when the number
# of neurons is not equal to
# np.prod(config['shape'])
num_neurons = len(config['ids'][0])
config['shape'] = [int(np.ceil(np.sqrt(num_neurons)))]
config['shape'].append(
int(
np.ceil(num_neurons /
float(config['shape'][0]))))
if config['type'] == 0:
config['handle'] = self.axarr[ind].quiver(\
np.reshape(self._data[LPU][config['ids'][0],0],config['shape']),\
np.reshape(self._data[LPU][config['ids'][1],0],config['shape']))
elif config['type'] == 1:
X = np.reshape(self._data[LPU][config['ids'][0], 0],
config['shape'])
Y = np.reshape(self._data[LPU][config['ids'][1], 0],
config['shape'])
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X, Y) + np.pi) / (2 * np.pi)
S = np.ones_like(V)
HSV = np.dstack((H, S, V))
RGB = hsv_to_rgb(HSV)
config['handle'] = self.axarr[ind].imshow(RGB)
elif config['type'] == 2:
if 'trans' in config:
if config['trans'] is True:
to_transpose = True
else:
to_transpose = False
else:
to_transpose = False
config['trans'] = False
if to_transpose:
temp = self.axarr[ind].imshow(np.transpose(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape'])))
else:
temp = self.axarr[ind].imshow(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape']))
temp.set_clim(self._imlim)
temp.set_cmap(plt.cm.gist_gray)
config['handle'] = temp
elif config['type'] == 3:
fmt = config['fmt'] if 'fmt' in config else ''
self.axarr[ind].set_xlim(self._xlim)
self.axarr[ind].set_ylim(self._ylim)
if len(config['ids'][0]) == 1:
config['handle'] = self.axarr[ind].plot([0], \
[self._data[LPU][config['ids'][0][0],0]], fmt)[0]
config['ydata'] = [
self._data[LPU][config['ids'][0][0], 0]
]
else:
config['handle'] = self.axarr[ind].plot(
self._data[LPU][config['ids'][0], 0])[0]
elif config['type'] == 4:
config['handle'] = self.axarr[ind]
config['handle'].vlines(0, 0, 0.01)
config['handle'].set_ylim([.5, len(config['ids'][0]) + .5])
config['handle'].set_ylabel('Neurons',
fontsize=self._fontsize - 1,
weight='bold')
config['handle'].set_xlabel('Time (s)',
fontsize=self._fontsize - 1,
weight='bold')
min_id = min(self._id_to_data_idx[LPU].keys())
min_idx = self._id_to_data_idx[LPU][min_id]
config['handle'].set_xlim(
[0, len(self._data[LPU][min_idx, :]) * self._dt])
config['handle'].axes.set_yticks([])
config['handle'].axes.set_xticks([])
for key in config.iterkeys():
if key not in keywds:
try:
self._set_wrapper(self.axarr[ind], key,
config[key])
except:
pass
try:
self._set_wrapper(config['handle'], key,
config[key])
except:
pass
if config['type'] < 3:
config['handle'].axes.set_xticks([])
config['handle'].axes.set_yticks([])
if self.suptitle is not None:
self.f.suptitle(self._title,
fontsize=self._fontsize + 1,
x=0.5,
y=0.03,
weight='bold')
plt.tight_layout()
if self.out_filename:
self.writer = FFMpegFileWriter(fps=self.fps, codec=self.codec)
# Use the output file to determine the name of the temporary frame
# files so that two concurrently run visualizations don't clobber
# each other's frames:
self.writer.setup(
self.f,
self.out_filename,
dpi=80,
frame_prefix=os.path.splitext(self.out_filename)[0] + '_')
self.writer.frame_format = 'png'
self.writer.grab_frame()
else:
self.f.show()
def _update(self):
dt = self._dt
t = self._t
for key, configs in self._config.iteritems():
data = self._data[key]
for config in configs:
if config['type'] == 3:
if len(config['ids'][0]) == 1:
config['ydata'].extend(np.reshape(np.double(\
data[config['ids'][0], \
max(0,t-self._update_interval):t]),(-1,)))
config['handle'].set_xdata(dt * np.arange(0, t))
config['handle'].set_ydata(np.asarray(config['ydata']))
else:
config['handle'].set_ydata(\
data[config['ids'][0], t])
elif config['type'] == 4:
for j, id in enumerate(config['ids'][0]):
# Convert neuron id to index into array of generated outputs:
try:
idx = self._id_to_data_idx[key][id]
except:
continue
else:
for time in np.where(
data[idx,
max(0, t -
self._update_interval):t])[0]:
config['handle'].vlines(
float(t - time) * self._dt, j + 0.75,
j + 1.25)
else:
if config['type'] == 0:
shape = config['shape']
ids = config['ids']
config['handle'].U = np.reshape(data[ids[0], t], shape)
config['handle'].V = np.reshape(data[ids[1], t], shape)
elif config['type'] == 1:
shape = config['shape']
ids = config['ids']
X = np.reshape(data[ids[0], t], shape)
Y = np.reshape(data[ids[1], t], shape)
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X, Y) + np.pi) / (2 * np.pi)
S = np.ones_like(V)
HSV = np.dstack((H, S, V))
RGB = hsv_to_rgb(HSV)
config['handle'].set_data(RGB)
elif config['type'] == 2:
ids = config['ids']
if config['trans']:
config['handle'].set_data(
np.transpose(
np.reshape(data[ids[0], t],
config['shape'])))
else:
config['handle'].set_data(
np.reshape(data[ids[0], t], config['shape']))
self.f.canvas.draw()
if self.out_filename:
self.writer.grab_frame()
self._t += self._update_interval
def add_plot(self, config_dict, LPU, names=[''], shift=0):
'''
Add a plot to the visualizer
Parameters
----------
config_dict: dict
A dictionary specifying the plot attributes. The attribute
names should be the keys.
The following are the plot attributes that can be specfied using
this dict.
type - str
This specifies the type of the plot. Has to be one of
['waveform', 'raster', 'image','hsv','quiver']
ids - dict with either 1 or 2 entries
Specifies the neuron ids from the associated LPU.
The keys should be in [0,1] and the values
should be a list of ids.
For example::
{'ids':{0:[1,2]}}
will plot neurons with ids 1 and 2.
Two entries in the dictionary are needed if the plot is
of type 'hsv' or 'quiver'
For example::
{'ids':{0:[:768],1:[768:1536]},'type':'HSV'}
can be used to generate a HSV plot where the hue channel is
controlled by the angle of the vector defined by the membrane
potentials of the neurons with ids [:768] and [768:1536] and
the value will be the magnitude of the same vector.
This parameter is optional for the following cases::
1) The plot is associated with input signals.
2) The names parameter is specified.
If the above doesn't hold, this attribute needs to be specified.
shape - list or tuple with two entries
This attribute specifies the dimensions for plots of type image,
hsv or quiver.
title - str
Optional. Can be used to control the title of the plot.
In addition to the above, any parameter supported by matlpotlib
for the particular type of plot can be specified.
For example - 'imlim','clim','xlim','ylim' etc.
LPU: str
The name of the LPU associated to this plot.
names: list
Optional. A list of str specifying the neurons
to plot. Can be used instead of specifying ids in the
config_dict. The gexf file of the LPU needs to have
the name attribute in order for this to be used.
'''
config = config_dict.copy()
if not isinstance(names, list):
names = [names]
if not LPU in self._config:
self._config[LPU] = []
if 'ids' in config:
# XXX should check whether the specified ids are within range
self._config[LPU].append(config)
elif str(LPU).startswith('input'):
config['ids'] = [range(0, self._data[LPU].shape[0])]
self._config[LPU].append(config)
else:
config['ids'] = {}
for i, name in enumerate(names):
config['ids'][i] = []
for id in range(len(self._graph[LPU].node)):
if self._graph[LPU].node[str(id)]['name'] == name:
config['ids'][i].append(id - shift)
self._config[LPU].append(config)
if not 'title' in config:
if names[0]:
config['title'] = "{0} - {1}".format(str(LPU), str(names[0]))
else:
if str(LPU).startswith('input_'):
config['title'] = LPU.split('_', 1)[1] + ' - ' + 'Input'
else:
config['title'] = str(LPU)
def _close(self):
self.writer.finish()
@property
def xlim(self):
'''
Get or set the limits of the x-axis for all the raster and waveform plots.
Can be superseded for individual plots by specifying xlim in the confid_dict
for that plot.
See also
--------
add_plot
'''
return self._xlim
@xlim.setter
def xlim(self, value):
self._xlim = value
@property
def ylim(self):
'''
Get or set the limits of the y-axis for all the raster and waveform plots.
Can be superseded for individual plots by specifying xlim in the confid_dict
for that plot.
See also
--------
add_plot
'''
return self._ylim
@ylim.setter
def ylim(self, value):
self._ylim = value
@property
def imlim(self):
return self._imlim
@imlim.setter
def imlim(self, value):
self._imlim = value
@property
def out_filename(self):
return self._out_file
@out_filename.setter
def out_filename(self, value):
assert (isinstance(value, str))
self._out_file = value
@property
def fps(self):
return self._fps
@fps.setter
def fps(self, value):
assert (isinstance(value, int))
self._fps = value
@property
def codec(self):
return self._codec
@codec.setter
def codec(self, value):
assert (isinstance(value, str))
self._codec = value
@property
def rows(self):
return self._rows
@rows.setter
def rows(self, value):
self._rows = value
@property
def cols(self):
return self._cols
@cols.setter
def cols(self, value):
self._cols = value
@property
def dt(self):
return self._dt
@dt.setter
def dt(self, value):
self._dt = value
@property
def figsize(self):
return self._figsize
@figsize.setter
def figsize(self, value):
assert (isinstance(value, tuple) and len(value) == 2)
self._figsize = value
@property
def fontsize(self):
return self._fontsize
@fontsize.setter
def fontsize(self, value):
self._fontsize = value
@property
def suptitle(self):
return self._title
@suptitle.setter
def suptitle(self, value):
self._title = value
@property
def update_interval(self):
"""
Gets or sets the update interval(in terms of time steps) for the animation.
If value is 0 or None, update_interval will be set to the index of the
final step. As a consequence, only the final frame will be generated.
"""
return self._update_interval
@update_interval.setter
def update_interval(self, value):
self._update_interval = value
def run_simulation(adj_list, node_mappings, verbose=0, visualize=False):
def draw_legend(results):
key_patches = []
key_patches.append(
mpatches.Patch(color='lightgray',
label='Unclaimed: %d' % results[None]))
for k in node_mappings.keys():
key_patches.append(
mpatches.Patch(color=m.to_rgba(key_colors[k]),
label="%s: %d" % (str(k), results[k])))
ax.legend(loc='upper left',
bbox_to_anchor=(-0.1, 1.1),
fancybox=True,
handles=key_patches)
"""
Function: run_simulation
------------------------
Runs the simulation. Returns a dictionary with the key as the "color"/name,
and the value as the number of nodes that "color"/name got.
adj_list: A dictionary representation of the graph adjacencies.
node_mappings: A dictionary where the key is a name and the value is a list
of seed nodes associated with that name.
"""
# Stores a mapping of nodes to their color.
node_color = dict([(node, None) for node in adj_list.keys()])
# print('Initializing test graph...')
init(node_mappings, node_color, verbose)
# print('Done')
if visualize:
print('Preparing graph for visualization...', end='', flush=True)
# Load and build graph
G = load_graph(adj_list)
pos = nx.drawing.layout.spring_layout(G,
k=0.1,
random_state=0,
scale=10)
# pos = nx.drawing.layout.kamada_kawai_layout(G, scale=10)
# pos = nx.nx_pydot.pydot_layout(G)
# Set up animation writers
from matplotlib.animation import FFMpegFileWriter
writer = FFMpegFileWriter(fps=1)
import time
filename = str(len(node_mappings.keys(
))) + "_Players " + time.strftime("%Y%m%d %H%M%S") + ".mp4"
# Set up pyplot
fig, ax = plt.subplots(figsize=(16, 9))
fig.subplots_adjust(bottom=0.2)
key_colors = dict(
map(lambda x: (x[1], x[0]), enumerate(node_mappings.keys())))
key_colors[None] = -1
import matplotlib.patches as mpatches
key_patches = []
colormap = cm.tab10
colormap.set_bad('lightgray')
m = cm.ScalarMappable(cmap=colormap,
norm=colors.Normalize(0,
len(node_mappings.keys())))
key_patches.append(mpatches.Patch(color='lightgray',
label='Unclaimed'))
for k in node_mappings.keys():
key_patches.append(
mpatches.Patch(color=m.to_rgba(key_colors[k]), label=str(k)))
# fig.legend(loc='upper left', bbox_to_anchor=(0, 1), fancybox=True, handles=key_patches)
# plt.tight_layout()
writer.setup(fig, filename, dpi=200)
print('DONE')
if verbose:
print('Initial nodes counts minus overlaps:')
print(get_result(node_mappings.keys(), node_color))
generation = 1
# Keep calculating the epidemic until it stops changing. Randomly choose
# number between 100 and 200 as the stopping point if the epidemic does not
# converge.
prev = None
nodes = adj_list.keys()
last_iter = randint(100, 200)
while not is_stable(generation, last_iter, prev, node_color):
legends = list(node_mappings.keys())
legends.append(None)
results = get_result(legends, node_color)
if verbose:
print(results)
if visualize:
ax.clear()
values = np.array(
[key_colors[node_color.get(node, None)] for node in G.nodes()])
values = np.ma.masked_where(values < 0, values)
draw_frame(G, pos, ax, m.to_rgba(values), generation)
draw_legend(results)
plt.axis('off')
writer.grab_frame()
prev = deepcopy(node_color)
for node in nodes:
(changed, color) = update(adj_list, prev, node)
# Store the node's new color only if it changed.
if changed: node_color[node] = color
# NOTE: prev contains the state of the graph of the previous generation,
# node_colros contains the state of the graph at the current generation.
# You could check these two dicts if you want to see the intermediate steps
# of the epidemic.
generation += 1
if visualize:
writer.finish()
# writer.cleanup()
return get_result(node_mappings.keys(), node_color)
class visualizer(object):
"""
Visualize the output produced by LPU models.
Examples
--------
import neurokernel.LPU.utils.visualizer as vis
V = vis.visualizer()
config1 = {}
config1['type'] = 'image'
config1['shape'] = [32,24]
config1['clim'] = [-0.6,0.5]
config2 = config1.copy()
config2['clim'] = [-0.55,-0.45]
V.add_LPU('lamina_output.h5', 'lamina.gexf.gz','lamina')
V.add_plot(config1, 'lamina', 'R1')
V.add_plot(config2, 'lamina', 'L1')
V.update_interval = 50
V.out_filename = 'test.avi'
V.run()
"""
def __init__(self):
self._xlim = [0,1]
self._ylim = [-1,1]
self._imlim = [-1, 1]
self._update_interval = 50
self._out_file = None
self._fps = 5
self._codec = 'libtheora'
self._config = OrderedDict()
self._rows = 0
self._cols = 0
self._figsize = (16,9)
self._fontsize = 18
self._t = 1
self._dt = 1
self._data = {}
self._graph = {}
self._id_to_data_idx = {}
self._maxt = None
self._title = None
def add_LPU(self, data_file, gexf_file=None, LPU=None, win=None):
'''
Add data associated with a specific LPU to a visualization.
To add a plot containing neurons from a particular LPU,
the LPU needs to be added to the visualization using this
function. Note that outputs from multiple neurons can
be visualized using the same visualizer object.
Parameters
----------
data_file: str
Location of the h5 file generated by neurokernel
containing the output of the LPU
gexf_file: str
Location of the gexf file describing the LPU.
If not specified, it will be assumed that the h5 file
contains input.
LPU: str
Name of the LPU. Will be used as identifier to add plots.
For input signals, the name of the LPU will be prepended
with 'input_'. For example::
V.add_LPU('vision_in.h5', LPU='vision')
will create the LPU identifier 'input_vision'.
Therefore, adding a plot depicting this input can be done by::
V.add_plot({''type':'image',imlim':[-0.5,0.5]},LPU='input_vision)
win: slice/list
Can be used to limit the visualization to a specific time window.
'''
if gexf_file:
self._graph[LPU] = nx.read_gexf(gexf_file)
# Map neuron ids to index into output data array:
self._id_to_data_idx[LPU] = {m:i for i, m in \
enumerate(sorted([int(n) for n, k in \
self._graph[LPU].nodes_iter(True) if k['spiking']]))}
else:
if LPU:
LPU = 'input_' + str(LPU)
else:
LPU = 'input_' + str(len(self._data))
if not LPU:
LPU = len(self._data)
self._data[LPU] = np.transpose(sio.read_array(data_file))
if win is not None:
self._data[LPU] = self._data[LPU][:,win]
if self._maxt:
self._maxt = min(self._maxt, self._data[LPU].shape[1])
else:
self._maxt = self._data[LPU].shape[1]
def run(self, final_frame_name=None, dpi=300):
'''
Starts the visualization process. If the property out_filename is set,
the visualization is saved as a video to the disk. If it is not
specified, the animation will be displayed on screen.
Please refer to documentation of add_LPU, add_plot and
the properties of this class on how to configure the visualizer before call this
method. An example can be found in the class doc string.
Paramters
----------
final_frame_name: str
Optional. If specified, the final frame of the animation will be saved
to disk.
dpi: int
Default(300). If final_frame_name is specified, this parameter will control
the resolution at which the final frame is saved to disk.
Note:
-----
If update_interval is set to 0 or None, it will be replaced by the
index of the final time step. As a result, the visualizer will only
generate the final frame.
'''
self._initialize()
if not self._update_interval:
self._update_interval = self._maxt - 1
self._t = self._update_interval + 1
for _ in range(self._update_interval,
self._maxt, self._update_interval):
self._update()
if final_frame_name is not None:
self.f.savefig(final_frame_name, dpi=dpi)
if self.out_filename:
self._close()
def _set_wrapper(self, obj, name, value):
name = name.lower()
func = getattr(obj, 'set_'+name, None)
if func:
try:
func(value, fontsize=self._fontsize, weight='bold')
except:
try:
func(value)
except:
pass
def _initialize(self):
# Count number of plots to create:
num_plots = 0
for config in self._config.itervalues():
num_plots += len(config)
# Set default grid of plot positions:
if not self._rows*self._cols == num_plots:
self._cols = int(np.ceil(np.sqrt(num_plots)))
self._rows = int(np.ceil(num_plots/float(self._cols)))
self.f, self.axarr = plt.subplots(self._rows, self._cols,
figsize=self._figsize)
# Remove unused subplots:
for i in xrange(num_plots, self._rows*self._cols):
plt.delaxes(self.axarr[np.unravel_index(i, (self._rows, self._cols))])
cnt = 0
self.handles = []
self.types = []
keywds = ['handle', 'ydata', 'fmt', 'type', 'ids', 'shape']
if not isinstance(self.axarr, np.ndarray):
self.axarr = np.asarray([self.axarr])
for LPU, configs in self._config.iteritems():
for plt_id, config in enumerate(configs):
ind = np.unravel_index(cnt, self.axarr.shape)
cnt+=1
# Some plot types require specific numbers of
# neuron ID arrays:
if 'type' in config:
if config['type'] == 'quiver':
assert len(config['ids'])==2
config['type'] = 0
elif config['type'] == 'hsv':
assert len(config['ids'])==2
config['type'] = 1
elif config['type'] == 'image':
assert len(config['ids'])==1
config['type'] = 2
elif config['type'] == 'waveform':
config['type'] = 3
elif config['type'] == 'raster':
config['type'] = 4
elif config['type'] == 'rate':
config['type'] = 5
else:
raise ValueError('Plot type not supported')
else:
if str(LPU).startswith('input') or not self._graph[LPU][str(config['ids'][0])]['spiking']:
config['type'] = 2
else:
config['type'] = 4
if config['type'] < 3:
if not 'shape' in config:
# XXX This can cause problems when the number
# of neurons is not equal to
# np.prod(config['shape'])
num_neurons = len(config['ids'][0])
config['shape'] = [int(np.ceil(np.sqrt(num_neurons)))]
config['shape'].append(int(np.ceil(num_neurons/float(config['shape'][0]))))
if config['type'] == 0:
config['handle'] = self.axarr[ind].quiver(\
np.reshape(self._data[LPU][config['ids'][0],0],config['shape']),\
np.reshape(self._data[LPU][config['ids'][1],0],config['shape']))
elif config['type'] == 1:
X = np.reshape(self._data[LPU][config['ids'][0],0],config['shape'])
Y = np.reshape(self._data[LPU][config['ids'][1],0],config['shape'])
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X,Y)+np.pi)/(2*np.pi)
S = np.ones_like(V)
HSV = np.dstack((H,S,V))
RGB = hsv_to_rgb(HSV)
config['handle'] = self.axarr[ind].imshow(RGB)
elif config['type'] == 2:
if 'trans' in config:
if config['trans'] is True:
to_transpose = True
else:
to_transpose = False
else:
to_transpose = False
config['trans'] = False
if to_transpose:
temp = self.axarr[ind].imshow(np.transpose(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape'])))
else:
temp = self.axarr[ind].imshow(np.reshape(\
self._data[LPU][config['ids'][0],0], config['shape']))
temp.set_clim(self._imlim)
temp.set_cmap(plt.cm.gist_gray)
config['handle'] = temp
elif config['type'] == 3:
fmt = config['fmt'] if 'fmt' in config else ''
self.axarr[ind].set_xlim(self._xlim)
self.axarr[ind].set_ylim(self._ylim)
if len(config['ids'][0])==1:
config['handle'] = self.axarr[ind].plot([0], \
[self._data[LPU][config['ids'][0][0],0]], fmt)[0]
config['ydata'] = [self._data[LPU][config['ids'][0][0],0]]
else:
config['handle'] = self.axarr[ind].plot(self._data[LPU][config['ids'][0],0])[0]
elif config['type'] == 4:
config['handle'] = self.axarr[ind]
config['handle'].vlines(0, 0, 0.01)
config['handle'].set_ylim([.5, len(config['ids'][0]) + .5])
config['handle'].set_ylabel('Neurons',
fontsize=self._fontsize-1, weight='bold')
config['handle'].set_xlabel('Time (s)',fontsize=self._fontsize-1, weight='bold')
min_id = min(self._id_to_data_idx[LPU].keys())
min_idx = self._id_to_data_idx[LPU][min_id]
config['handle'].set_xlim([0,len(self._data[LPU][min_idx,:])*self._dt])
config['handle'].axes.set_yticks([])
config['handle'].axes.set_xticks([])
for key in config.iterkeys():
if key not in keywds:
try:
self._set_wrapper(self.axarr[ind],key, config[key])
except:
pass
try:
self._set_wrapper(config['handle'],key, config[key])
except:
pass
if config['type']<3:
config['handle'].axes.set_xticks([])
config['handle'].axes.set_yticks([])
if self.suptitle is not None:
self.f.suptitle(self._title, fontsize=self._fontsize+1, x=0.5,y=0.03, weight='bold')
plt.tight_layout()
if self.out_filename:
self.writer = FFMpegFileWriter(fps=self.fps, codec=self.codec)
# Use the output file to determine the name of the temporary frame
# files so that two concurrently run visualizations don't clobber
# each other's frames:
self.writer.setup(self.f, self.out_filename, dpi=80,
frame_prefix=os.path.splitext(self.out_filename)[0]+'_')
self.writer.frame_format = 'png'
self.writer.grab_frame()
else:
self.f.show()
def _update(self):
dt = self._dt
t = self._t
for key, configs in self._config.iteritems():
data = self._data[key]
for config in configs:
if config['type'] == 3:
if len(config['ids'][0])==1:
config['ydata'].extend(np.reshape(np.double(\
data[config['ids'][0], \
max(0,t-self._update_interval):t]),(-1,)))
config['handle'].set_xdata(dt*np.arange(0, t))
config['handle'].set_ydata(np.asarray(config['ydata']))
else:
config['handle'].set_ydata(\
data[config['ids'][0], t])
elif config['type']==4:
for j, id in enumerate(config['ids'][0]):
# Convert neuron id to index into array of generated outputs:
try:
idx = self._id_to_data_idx[key][id]
except:
continue
else:
for time in np.where(data[idx, max(0,t-self._update_interval):t])[0]:
config['handle'].vlines(float(t-time)*self._dt,j+0.75, j+1.25)
else:
if config['type'] == 0:
shape = config['shape']
ids = config['ids']
config['handle'].U = np.reshape(data[ids[0], t],shape)
config['handle'].V = np.reshape(data[ids[1], t],shape)
elif config['type']==1:
shape = config['shape']
ids = config['ids']
X = np.reshape(data[ids[0], t],shape)
Y = np.reshape(data[ids[1], t],shape)
V = (X**2 + Y**2)**0.5
H = (np.arctan2(X,Y)+np.pi)/(2*np.pi)
S = np.ones_like(V)
HSV = np.dstack((H,S,V))
RGB = hsv_to_rgb(HSV)
config['handle'].set_data(RGB)
elif config['type'] == 2:
ids = config['ids']
if config['trans']:
config['handle'].set_data(
np.transpose(np.reshape(data[ids[0], t], config['shape'
])))
else:
config['handle'].set_data(
np.reshape(data[ids[0], t], config['shape']))
self.f.canvas.draw()
if self.out_filename:
self.writer.grab_frame()
self._t+=self._update_interval
def add_plot(self, config_dict, LPU, names=[''], shift=0):
'''
Add a plot to the visualizer
Parameters
----------
config_dict: dict
A dictionary specifying the plot attributes. The attribute
names should be the keys.
The following are the plot attributes that can be specfied using
this dict.
type - str
This specifies the type of the plot. Has to be one of
['waveform', 'raster', 'image','hsv','quiver']
ids - dict with either 1 or 2 entries
Specifies the neuron ids from the associated LPU.
The keys should be in [0,1] and the values
should be a list of ids.
For example::
{'ids':{0:[1,2]}}
will plot neurons with ids 1 and 2.
Two entries in the dictionary are needed if the plot is
of type 'hsv' or 'quiver'
For example::
{'ids':{0:[:768],1:[768:1536]},'type':'HSV'}
can be used to generate a HSV plot where the hue channel is
controlled by the angle of the vector defined by the membrane
potentials of the neurons with ids [:768] and [768:1536] and
the value will be the magnitude of the same vector.
This parameter is optional for the following cases::
1) The plot is associated with input signals.
2) The names parameter is specified.
If the above doesn't hold, this attribute needs to be specified.
shape - list or tuple with two entries
This attribute specifies the dimensions for plots of type image,
hsv or quiver.
title - str
Optional. Can be used to control the title of the plot.
In addition to the above, any parameter supported by matlpotlib
for the particular type of plot can be specified.
For example - 'imlim','clim','xlim','ylim' etc.
LPU: str
The name of the LPU associated to this plot.
names: list
Optional. A list of str specifying the neurons
to plot. Can be used instead of specifying ids in the
config_dict. The gexf file of the LPU needs to have
the name attribute in order for this to be used.
'''
config = config_dict.copy()
if not isinstance(names, list):
names = [names]
if not LPU in self._config:
self._config[LPU] = []
if 'ids' in config:
# XXX should check whether the specified ids are within range
self._config[LPU].append(config)
elif str(LPU).startswith('input'):
config['ids'] = [range(0, self._data[LPU].shape[0])]
self._config[LPU].append(config)
else:
config['ids'] = {}
for i,name in enumerate(names):
config['ids'][i]=[]
for id in range(len(self._graph[LPU].node)):
if self._graph[LPU].node[str(id)]['name'] == name:
config['ids'][i].append(id-shift)
self._config[LPU].append(config)
if not 'title' in config:
if names[0]:
config['title'] = "{0} - {1}".format(str(LPU),str(names[0]))
else:
if str(LPU).startswith('input_'):
config['title'] = LPU.split('_',1)[1] + ' - ' + 'Input'
else:
config['title'] = str(LPU)
def _close(self):
self.writer.finish()
@property
def xlim(self):
'''
Get or set the limits of the x-axis for all the raster and waveform plots.
Can be superseded for individual plots by specifying xlim in the confid_dict
for that plot.
See also
--------
add_plot
'''
return self._xlim
@xlim.setter
def xlim(self, value):
self._xlim = value
@property
def ylim(self):
'''
Get or set the limits of the y-axis for all the raster and waveform plots.
Can be superseded for individual plots by specifying xlim in the confid_dict
for that plot.
See also
--------
add_plot
'''
return self._ylim
@ylim.setter
def ylim(self, value):
self._ylim = value
@property
def imlim(self): return self._imlim
@imlim.setter
def imlim(self, value):
self._imlim = value
@property
def out_filename(self): return self._out_file
@out_filename.setter
def out_filename(self, value):
assert(isinstance(value, str))
self._out_file = value
@property
def fps(self): return self._fps
@fps.setter
def fps(self, value):
assert(isinstance(value, int))
self._fps = value
@property
def codec(self): return self._codec
@codec.setter
def codec(self, value):
assert(isinstance(value, str))
self._codec = value
@property
def rows(self): return self._rows
@rows.setter
def rows(self, value):
self._rows = value
@property
def cols(self): return self._cols
@cols.setter
def cols(self, value):
self._cols = value
@property
def dt(self): return self._dt
@dt.setter
def dt(self, value):
self._dt = value
@property
def figsize(self): return self._figsize
@figsize.setter
def figsize(self, value):
assert(isinstance(value, tuple) and len(value)==2)
self._figsize = value
@property
def fontsize(self): return self._fontsize
@fontsize.setter
def fontsize(self, value):
self._fontsize = value
@property
def suptitle(self): return self._title
@suptitle.setter
def suptitle(self, value):
self._title = value
@property
def update_interval(self):
"""
Gets or sets the update interval(in terms of time steps) for the animation.
If value is 0 or None, update_interval will be set to the index of the
final step. As a consequence, only the final frame will be generated.
"""
return self._update_interval
@update_interval.setter
def update_interval(self, value):
self._update_interval = value
from matplotlib.animation import FuncAnimation, FFMpegFileWriter
rawdata = pd.read_csv(
'D:/code/python/workspace/LTEDataVis/src/data/bbkyields1968.csv',
dtype={
'year': int,
'plot': str,
'grain': float,
'colour': str
})
years = rawdata['year'].unique()
plt.rcdefaults()
fig, ax = plt.subplots()
writer = FFMpegFileWriter()
writer.setup(fig, "test6.mp4", 100)
def init():
pass
def update(i):
year = years[i]
yeardata = rawdata[rawdata['year'] == year].sort_values(by='grain',
ascending=False)
print(yeardata)
ypos = np.arange(len(yeardata['treatment']))
ax.barh(ypos,
def setup(self, fig, outfile, dpi):
return FFMpegFileWriter.setup(self, fig, outfile, dpi,
frame_prefix=self.temp_prefix,
clear_temp=self.clear_temp)