def plot_surface(gr, ax, keys, imshow=False):
# from https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
xx = np.array([p.dtc.attrs[str(keys[0])] for p in gr])
yy = np.array([p.dtc.attrs[str(keys[1])] for p in gr])
zz = np.array([np.sum(list(p.dtc.scores.values())) for p in gr])
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == False:
ax.pcolormesh(X, Y, Z, edgecolors='black')
else:
import seaborn as sns
sns.set()
ax = sns.heatmap(Z)
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax
def plot_surface(gr, ax, keys, imshow=False):
# from
# https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered
# https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
#gr = [ g
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
z = [np.sum(list(p.dtc.scores.values())) for p in gr]
x = [p.dtc.attrs[str(keys[0])] for p in gr]
y = [p.dtc.attrs[str(keys[1])] for p in gr]
# impute missings
if len(x) != 100:
delta = 100 - len(x)
for i in range(0, delta):
x.append(np.mean(x))
y.append(np.mean(y))
z.append(np.mean(z))
xx = np.array(x)
yy = np.array(y)
zz = np.array(z)
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == False:
ax.pcolormesh(X, Y, Z, edgecolors='black')
else:
import seaborn as sns
sns.set()
ax = sns.heatmap(Z)
#ax.imshow(Z)
#ax.pcolormesh(xi, yi, zi, edgecolors='black')
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax
def plot_surface(gr, ax, keys, constant, imshow=True):
# from https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
xx = np.array([p.dtc.attrs[str(keys[0])] for p in gr])
yy = np.array([p.dtc.attrs[str(keys[1])] for p in gr])
zz = np.array([p.dtc.get_ss() for p in gr])
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == True:
img = ax.pcolormesh(X, Y, Z, edgecolors='black')
#ax.colorbar()
else:
import seaborn as sns
sns.set()
current_palette = sns.color_palette()
sns.palplot(current_palette)
#df = pd.DataFrame(Z, columns=xx)
img = sns.heatmap(Z) #,cm=current_palette)
#ax.colorbar()
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax, img