def test_multiple_figures():
reset_dbplot()
for _ in xrange(2):
dbplot(np.random.randn(20, 20), 'a', fig='1')
dbplot(np.random.randn(20, 20), 'b', fig='1')
dbplot(np.random.randn(20, 20), 'c', fig='2')
dbplot(np.random.randn(20, 20), 'd', fig='2')
def test_particular_plot(n_steps=3):
reset_dbplot()
for i in xrange(n_steps):
r = np.random.randn(1)
dbplot(r,
plot_type=partial(HistogramPlot, edges=np.linspace(-5, 5, 20)))
def test_multiple_figures():
reset_dbplot()
for _ in xrange(2):
dbplot(np.random.randn(20, 20), 'a', fig='1')
dbplot(np.random.randn(20, 20), 'b', fig='1')
dbplot(np.random.randn(20, 20), 'c', fig='2')
dbplot(np.random.randn(20, 20), 'd', fig='2')
def test_list_of_images():
reset_dbplot()
for _ in xrange(2):
dbplot([
np.random.randn(12, 30),
np.random.randn(10, 10),
np.random.randn(15, 10)
])
def test_moving_point_multiple_points():
reset_dbplot()
for i in xrange(5):
dbplot(np.sin([i / 10., i / 15.]),
'unlim buffer',
plot_type=partial(MovingPointPlot))
dbplot(np.sin([i / 10., i / 15.]),
'lim buffer',
plot_type=partial(MovingPointPlot, buffer_len=20))
def test_two_plots_in_the_same_axis_version_2():
reset_dbplot()
# Option 2: Give both plots the same 'axis' argument
for i in xrange(5):
data = np.random.randn(200)
x = np.linspace(-5, 5, 100)
with hold_dbplots():
dbplot(data, 'histogram', plot_type='histogram', axis='hist')
dbplot((x, 1./np.sqrt(2*np.pi*np.var(data)) * np.exp(-(x-np.mean(data))**2/(2*np.var(data)))), 'density', axis='hist', plot_type='line')
def test_two_plots_in_the_same_axis_version_1():
reset_dbplot()
# Option 1: Name the 'axis' argument to the second plot after the name of the first
for i in xrange(5):
data = np.random.randn(200)
x = np.linspace(-5, 5, 100)
with hold_dbplots():
dbplot(data, 'histogram', plot_type='histogram')
dbplot((x, 1./np.sqrt(2*np.pi*np.var(data)) * np.exp(-(x-np.mean(data))**2/(2*np.var(data)))), 'density', axis='histogram', plot_type='line')
def test_freeze_dbplot():
reset_dbplot()
def random_walk():
data = 0
for i in xrange(10):
data += np.random.randn()
dbplot(data, 'walk')#, plot_type=lambda: MovingPointPlot(axes_update_mode='expand'))
random_walk()
freeze_all_dbplots()
random_walk()
def test_same_object():
"""
There was a bug where when you plotted two of the same array, you got "already seen object". This tests makes
sure it's gotten rid of. If it's gone, both matrices should plot. Otherwise you'll get "Already seen object" showing
up on one of the plots.
"""
reset_dbplot()
a = np.random.randn(20, 20)
for _ in xrange(5):
dbplot(a, 'a')
dbplot(a, 'b')
def test_same_object():
"""
There was a bug where when you plotted two of the same array, you got "already seen object". This tests makes
sure it's gotten rid of. If it's gone, both matrices should plot. Otherwise you'll get "Already seen object" showing
up on one of the plots.
"""
reset_dbplot()
a = np.random.randn(20, 20)
for _ in xrange(5):
dbplot(a, 'a')
dbplot(a, 'b')
def test_dbplot(n_steps = 3):
reset_dbplot()
arr = np.random.rand(10, 10)
for i in xrange(n_steps):
arr_sq=arr**2
arr = arr_sq/np.mean(arr_sq)
dbplot(arr, 'arr')
for j in xrange(3):
barr = np.random.randn(10, 2)
dbplot(barr, 'barr', plot_type=partial(LinePlot))
def test_dbplot(n_steps=3):
reset_dbplot()
arr = np.random.rand(10, 10)
for i in xrange(n_steps):
arr_sq = arr**2
arr = arr_sq / np.mean(arr_sq)
dbplot(arr, 'arr')
for j in xrange(3):
barr = np.random.randn(10, 2)
dbplot(barr, 'barr', plot_type=partial(LinePlot))
def test_history_plot_updating():
"""
This test checks that we've fixed the bug mentioned in issue 1: https://github.com/QUVA-Lab/artemis/issues/1
That was, when you are updating multiple plots with history in a loop, everytime any of the plots is updated, they
all get updated with the most recent data. You'll see this in plot 'c' - with the bug, it moves in steps, with 3
of the same sample in a row. If it works it should be spikey.
"""
reset_dbplot()
for i in xrange(10):
dbplot(np.random.randn(20, 20), 'a')
dbplot(np.random.randn(20, 20), 'b')
dbplot(np.random.randn(), 'c', plot_type=partial(MovingPointPlot))
def test_history_plot_updating():
"""
This test checks that we've fixed the bug mentioned in issue 1: https://github.com/QUVA-Lab/artemis/issues/1
That was, when you are updating multiple plots with history in a loop, everytime any of the plots is updated, they
all get updated with the most recent data. You'll see this in plot 'c' - with the bug, it moves in steps, with 3
of the same sample in a row. If it works it should be spikey.
"""
reset_dbplot()
for i in xrange(10):
dbplot(np.random.randn(20, 20), 'a')
dbplot(np.random.randn(20, 20), 'b')
dbplot(np.random.randn(), 'c', plot_type=partial(MovingPointPlot))
def test_dbplot_logscale(n_steps = 3):
reset_dbplot()
arr = np.random.rand(10, 10)
for i in xrange(n_steps):
arr_sq=arr**2
arr = arr_sq/np.mean(arr_sq)
dbplot(arr, 'arr')
for j in xrange(3):
barr = np.random.randn(10, 2)
kw = {"y_axis_type":"log"}
dbplot(barr, 'barr', plot_type=partial(LinePlot,y_axis_type='log'))
def test_moving_point_multiple_points():
reset_dbplot()
p1 = 5.
p2 = 8.
for i in xrange(50):
with hold_dbplots(draw_every=5):
dbplot(np.sin([i / p1, i / p2]),
'unlim buffer',
plot_type=partial(MovingPointPlot))
dbplot(np.sin([i / p1, i / p2]),
'lim buffer',
plot_type=partial(MovingPointPlot, buffer_len=20))
dbplot(
np.sin([i / p1, i / p2]),
'resampling buffer',
plot_type=partial(ResamplingLineHistory, buffer_len=20)
) # Only looks bad because of really small buffer length from testing.
def test_particular_plot(n_steps = 3):
reset_dbplot()
for i in xrange(n_steps):
r = np.random.randn(1)
dbplot(r, plot_type=partial(HistogramPlot,edges=np.linspace(-5, 5, 20)))
def test_list_of_images():
reset_dbplot()
for _ in xrange(2):
dbplot([np.random.randn(12, 30), np.random.randn(10, 10), np.random.randn(15, 10)])
def test_moving_point_multiple_points():
reset_dbplot()
for i in xrange(5):
dbplot(np.sin([i/10., i/15.]), 'unlim buffer', plot_type = partial(MovingPointPlot))
dbplot(np.sin([i/10., i/15.]), 'lim buffer', plot_type = partial(MovingPointPlot,buffer_len=20))
