def show_mpl():
cli = tw.WatcherClient()
st_isum = cli.open_stream('isums')
st_rsum = cli.open_stream('rsums')
line_plot = tw.Visualizer(st_isum, vis_type='line', xtitle='i', ytitle='isum')
line_plot.show()
line_plot2 = tw.Visualizer(st_rsum, vis_type='line', host=line_plot, ytitle='rsum')
tw.plt_loop()
def reader3():
print('---------------------------reader3---------------------------')
watcher = tw.Watcher(filename=r'c:\temp\test.log', port=None)
stream1 = watcher.open_stream('metric1')
stream2 = watcher.open_stream('metric2')
vis1 = tw.Visualizer(stream1, vis_type='line')
vis2 = tw.Visualizer(stream2, vis_type='line', host=vis1)
vis1.show()
tw.plt_loop()
def text_stats():
train_cli = tw.WatcherClient()
stream = train_cli.create_stream(
event_name="batch", expr='lambda d:(d.x, d.metrics.batch_loss)')
trl = tw.Visualizer(stream, vis_type=None)
trl.show()
input('Paused...')
def dynamic_hist():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='histogram', bins=6, clear_after_each=True)
v.show()
for _ in range(100):
s.write([random.random() * 10 for _ in range(100)])
tw.plt_loop(count=3)
def dynamic_bar():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='bar', clear_after_each=True)
v.show()
for i in range(100):
s.write([('a' + str(i), random.random() * 10) for i in range(10)])
tw.plt_loop(count=3)
def static_hist():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='histogram', bins=6)
v.show()
for _ in range(100):
s.write(random.random() * 10)
tw.plt_loop()
def static_bar():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='bar')
v.show()
for i in range(10):
s.write(int(random.random() * 10))
tw.plt_loop()
def dynamic_line3d():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='line3d', clear_after_each=True)
v.show()
for i in range(100):
s.write([(i, random.random() * 10, z) for i in range(10)
for z in range(10)])
tw.plt_loop(count=3)
def dynamic_pie():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='pie', bins=6, clear_after_each=True)
v.show()
for _ in range(100):
s.write([('label' + str(i), random.random() * 10, None, i * 0.01)
for i in range(12)])
tw.plt_loop(count=3)
def static_pie():
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='pie', bins=6)
v.show()
for i in range(6):
s.write(('label' + str(i), random.random() * 10, None,
0.5 if i == 3 else 0))
tw.plt_loop()
def batch_stats():
train_cli = tw.WatcherClient()
stream = train_cli.create_stream(
event_name="batch",
expr='lambda v:(v.metrics.epochf, v.metrics.batch_loss)',
throttle=0.75)
train_loss = tw.Visualizer(stream,
clear_after_end=False,
vis_type='mpl-line',
xtitle='Epoch',
ytitle='Train Loss')
#train_acc = tw.Visualizer('lambda v:(v.metrics.epochf, v.metrics.epoch_loss)', event_name="batch",
# xtitle='Epoch', ytitle='Train Accuracy', clear_after_end=False, yrange=(0,1),
# vis=train_loss, vis_type='mpl-line')
train_loss.show()
tw.image_utils.plt_loop()
import tensorwatch as tw stream = tw.ArrayStream([(i, i*i) for i in range(50)]) img_plot = tw.Visualizer(stream, vis_type='mpl-line', viz_img_scale=3, xtitle='Epochs', ytitle='Gain') # img_plot.show() # tw.plt_loop() img_plot.save(r'c:\temp\fig1.png')
import tensorwatch as tw
from regim import *
ds = DataUtils.mnist_datasets(linearize=True, train_test=False)
ds = DataUtils.sample_by_class(ds, k=5, shuffle=True, as_np=True, no_test=True)
comps = tw.get_tsne_components(ds)
print(comps)
plot = tw.Visualizer(comps,
hover_images=ds[0],
hover_image_reshape=(28, 28),
vis_type='tsne')
plot.show()
def show_text():
cli = tw.WatcherClient()
s1 = cli.create_stream(expr='lambda v:(v.i, v.sum)')
text = tw.Visualizer(s1)
text.show()
input('Waiting')
import tensorwatch as tw
import numpy as np
import time
import torchvision.datasets as datasets
fruits_ds = datasets.ImageFolder(r'D:\datasets\fruits-360\Training')
mnist_ds = datasets.MNIST('../data', train=True, download=True)
images = [tw.ImageData(fruits_ds[i][0], title=str(i)) for i in range(5)] + \
[tw.ImageData(mnist_ds[i][0], title=str(i)) for i in range(5)]
stream = tw.ArrayStream(images)
img_plot = tw.Visualizer(stream, vis_type='image', viz_img_scale=3)
img_plot.show()
tw.image_utils.plt_loop()
import tensorwatch as tw
w = tw.Watcher()
s = w.create_stream()
v = tw.Visualizer(s, vis_type='line')
v.show()
for i in range(10):
i = float(i)
s.write(tw.PointData(i, i * i, low=i * i - i, high=i * i + i))
tw.plt_loop()
def show_text():
cli = tw.WatcherClient(r'c:\temp\sum.log')
s1 = cli.open_stream('sum_2')
text = tw.Visualizer(s1)
text.show()
input('Waiting')
def show_text():
cli = tw.WatcherClient()
text_vis = tw.Visualizer(st_isum, vis_type='text')
text_vis.show()
input('Waiting')
def file_read():
watcher = WatcherBase()
stream = watcher.open_stream(devices=[r'c:\temp\obs.txt'])
vis = tw.Visualizer(stream, vis_type='mpl-line')
vis.show()
plt_loop()
