def show_results(x: TensorSeq,
y,
samples,
outs,
ctxs=None,
max_n=9,
nrows=None,
ncols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(len(samples), max_n),
nrows=nrows,
ncols=ncols,
add_vert=1,
figsize=figsize)
for i in range(len(outs[0])):
ctxs = [
TSTensorSeqy(b, m='*r', label='pred').show(ctx=c, **kwargs)
for b, c, _ in zip(outs.itemgot(i), ctxs, range(max_n))
]
for i in range(len(samples[0])):
ctxs = [
b.show(ctx=c, **kwargs)
for b, c, _ in zip(samples.itemgot(i), ctxs, range(max_n))
]
return ctxs
def show_results(x: TensorTS,
y,
samples,
outs,
ctxs=None,
max_n=9,
nrows=None,
ncols=None,
figsize=(14, 12),
**kwargs):
if ctxs is None:
ctxs = get_grid(min(len(samples), max_n),
nrows=nrows,
ncols=ncols,
add_vert=1,
figsize=figsize)
# outs = [('6.0',),('2.0',)]
outs = [detuplify(o) for o in outs]
# outs = ['6.0', '2.0']
ctxs = default_show_results(x,
y,
samples,
outs,
ctxs=ctxs,
max_n=max_n,
figsize=figsize,
**kwargs)
return ctxs
def show_batch(x:TensorTS, y, samples, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, title=None, **kwargs):
if ctxs is None: ctxs = get_grid(max_n, rows=rows, cols=cols, figsize=figsize)
ctxs = default_show_batch(x, y, samples, ctxs=ctxs, max_n=max_n, **kwargs)
if title:
plt.suptitle(title, fontsize=16)
plt.subplots_adjust()
plt.subplots_adjust(left=0.0, wspace=0.4, top=0.9, bottom=0.5)
return ctxs
def show_batch(x: TensorTS,
y,
samples,
ctxs=None,
max_n=9,
nrows=None,
ncols=None,
figsize=(14, 12),
**kwargs):
if ctxs is None:
ctxs = get_grid(min(len(samples), max_n),
nrows=nrows,
ncols=ncols,
figsize=figsize)
ctxs = default_show_batch(x, y, samples, ctxs=ctxs, max_n=max_n, **kwargs)
return ctxs
def show_graphs(arrays,
rows=None,
cols=None,
figsize=None,
titles=None,
**kwargs):
"Show all images `arrays` as subplots with `rows` using `titles`"
if titles is None: titles = [None] * len(arrays)
axs = get_grid(len(arrays),
rows=rows,
cols=cols,
add_vert=1,
figsize=figsize)
for a, t, ax in zip(arrays, titles, axs):
ctx = show_graph(a[0], ax=ax, title=t)
for y in a[1:]:
ctx = y.show(ctx=ctx)
return axs
def show_batch(x: TSMulti,
y: TensorSeq,
its,
*args,
ctxs=None,
max_n=10,
rows=None,
cols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(x[0].shape[0], max_n),
add_vert=1,
figsize=figsize,
**kwargs)
for i, ctx in enumerate(ctxs):
o = TSMulti([type(o)(o, **o._meta) for o in its[i] if o.shape[-1] > 0])
ctx = o.show(ctx=ctx)
return ctxs
def show_batch(x: TSMulti,
none_1,
none_2,
*args,
ctxs=None,
max_n=10,
rows=None,
cols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(x[0].shape[0], max_n),
add_vert=1,
figsize=figsize,
**kwargs)
for i, ctx in enumerate(ctxs):
o = TSMulti([type(o)(o[i], **o._meta) for o in x])
ctx = o.show(ctx=ctx)
return ctxs
def show_results(x:TensorTS, y, samples, outs, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, **kwargs):
# if ctxs is None: ctxs = get_grid(min(len(samples), max_n), rows=rows, cols=cols, add_vert=1, figsize=figsize)
s = len(samples) # min(len(samples), max_n)
# max_n = min(s, max_n)
if ctxs is None: ctxs = get_grid(max_n, rows=rows, cols=cols, add_vert=1, figsize=figsize)
# print(len(samples), max_n)
# print(samples)
# print(type(y))
# outs = [('6.0',),('2.0',)]
# print(f'outs - before detuplify : {outs}')
outs = [detuplify(o) for o in outs]
# outs = ['6.0', '2.0']
# print(f'outs : {outs})
ctxs = [b[0].show(ctx=c, title=f'{o} / {b[1]}', **kwargs) for b,o,c,_ in zip(samples,outs,ctxs,range(max_n))]
# if title:
# plt.suptitle(title, fontsize=16)
# plt.subplots_adjust(left=0.0, wspace=0.4, top=0.9)
plt.tight_layout()
return ctxs
def show_results(x: TSMulti,
y,
its,
outs,
ctxs=None,
max_n=9,
rows=None,
cols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(x[0].shape[0], max_n),
add_vert=1,
figsize=figsize,
**kwargs)
for i, ctx in enumerate(ctxs):
r = [type(o)(o, **o._meta) for o in its[i] if o.shape[-1] > 0]
r.append(type(its[i][-1])(outs[i][0], label=['pred_y'], m=['r']))
o = TSMulti(r)
ctx = o.show(ctx=ctx)
def show_results(x: TSMulti,
x1,
none_1,
none_2,
ctxs=None,
max_n=9,
rows=None,
cols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(x[0].shape[0], max_n),
add_vert=1,
figsize=figsize,
**kwargs)
for i, ctx in enumerate(ctxs):
r = [type(o)(o[i], **o._meta) for o in x]
r.append(type(x1[-1])(x1[-1][i], label=['pred'], m=['r']))
o = TSMulti(r)
ctx = o.show(ctx=ctx)
def show_batch(x: TensorSeq,
y,
samples,
ctxs=None,
max_n=10,
nrows=None,
ncols=None,
figsize=None,
**kwargs):
if ctxs is None:
ctxs = get_grid(min(len(samples), max_n),
nrows=nrows,
ncols=ncols,
add_vert=1,
figsize=figsize)
ctxs = show_batch[object](x,
y,
samples=samples,
ctxs=ctxs,
max_n=max_n,
**kwargs)
return ctxs
def show_batch(x: TSeries, y, samples, ctxs=None, max_n=10,rows=None, cols=None, figsize=None, **kwargs):
"Show batch for TSeries objects"
if ctxs is None: ctxs = get_grid(min(len(samples), max_n), rows=rows, cols=cols, add_vert=1, figsize=figsize)
ctxs = show_batch[object](x, y, samples=samples, ctxs=ctxs, max_n=max_n, **kwargs)
return ctxs
