def test_vertical_ascii():
numpy.random.seed(123)
sample = numpy.random.normal(size=1000)
counts, bin_edges = numpy.histogram(sample, bins=40)
fig = apl.figure()
fig.hist(counts, bin_edges, force_ascii=True)
# fig.show()
string = fig.get_string()
assert (
string
== """\
**
****
******
******** *
* ***********
***************
******************
**********************
************************
* *********************************** *\
"""
)
return
def isLogged(self):
# TODO: Check for something else , This is just for the
# time beign.
try:
WebDriverWait(self._mDriver,
WEBDRIVER_SETTINGS['TimeToWait']).until(
EC.presence_of_element_located((By.ID, "page")))
el = self._mDriver.find_element_by_id("page")
except:
return False
try:
soup = BeautifulSoup(
self._mDriver.find_element_by_id("resulttable").get_attribute(
"outerHTML"), 'html.parser')
data = [[[
td.string.strip() for td in tr.find_all('td') if td.string
] for tr in table.find_all('tr')[1:]]
for table in soup.find_all('table')]
LogINFO("showing student information... ")
fig = apl.figure()
fig.table(data)
fig.show()
except:
return False
return True
def test_vertical():
numpy.random.seed(123)
sample = numpy.random.normal(size=1000)
counts, bin_edges = numpy.histogram(sample, bins=40)
fig = apl.figure()
fig.hist(counts, bin_edges)
fig.show()
string = fig.get_string()
assert (
string
== """\
▆█
▄▄██
▃█████
▁██████▃ ▅
▂ ████████▇▅█
▂█▅████████████
▂███████████████▃▂
▂▃██████████████████▃▁
▁▂██████████████████████
▂ ▃▂▄▄█████████████████████████▅▃▁▂▁▁ ▁\
"""
)
return
def test_horizontal():
numpy.random.seed(123)
sample = numpy.random.normal(size=1000)
counts, bin_edges = numpy.histogram(sample)
fig = apl.figure()
fig.hist(counts, bin_edges, orientation="horizontal")
# fig.show()
string = fig.get_string()
assert (
string
== """\
-3.23e+00 - -2.55e+00 [ 7] █
-2.55e+00 - -1.87e+00 [ 27] ███▊
-1.87e+00 - -1.19e+00 [ 95] █████████████▎
-1.19e+00 - -5.10e-01 [183] █████████████████████████▋
-5.10e-01 - +1.70e-01 [286] ████████████████████████████████████████
+1.70e-01 - +8.51e-01 [202] ████████████████████████████▎
+8.51e-01 - +1.53e+00 [142] ███████████████████▉
+1.53e+00 - +2.21e+00 [ 49] ██████▉
+2.21e+00 - +2.89e+00 [ 7] █
+2.89e+00 - +3.57e+00 [ 2] ▎\
"""
)
return
def iap(self):
'''
Inject and plot to terminal with ascii
This is useful for debugging new backends, in bash big/fast command line orientated optimization routines.
'''
model = self.dtc_to_model()
#new_tests['RheobaseTest']
if type(self.tests) is type({'1':1}):
if 'RheobaseTest' in self.tests.keys():
uset_t = self.tests['RheobaseTest']
else:
uset_t = self.tests['RheobaseTestP']
elif type(self.tests) is type(list):
for t in self.tests:
if t.name in str('RheobaseTest'):
uset_t = t
break
pms = uset_t.params
pms['injected_square_current']['amplitude'] = self.rheobase
model.inject_square_current(pms['injected_square_current'])
nspike = model.get_spike_train()
vm = model.get_membrane_potential()
t = [float(f) for f in vm.times]
v = [float(f) for f in vm.magnitude]
try:
fig = apl.figure()
fig.plot(t, v, label=str('observation waveform from inside dtc: ')+str(nspike), width=100, height=20)
fig.show()
except:
import warnings
print('ascii plot not installed')
return vm
def main():
cols, rows = shutil.get_terminal_size((80, 20))
y = [float(l) for l in sys.stdin.readlines()]
x = list((range(1, len(y) + 1)))
fig = apl.figure()
fig.plot(x, y, width=cols, height=rows)
fig.show()
def test_plot_lim():
x = numpy.linspace(0, 2 * numpy.pi, 10)
y = numpy.sin(x)
fig = apl.figure()
fig.plot(
x,
y,
label="data",
width=50,
height=15,
xlim=[-1, 1],
ylim=[-1, 1],
xlabel="x vals",
title="header",
)
string = fig.get_string()
ref = """ header
1 +---------------------------------------+
| ********|
0.5 | ************ |
| **** |
0 | *** |
| |
-0.5 | |
| |
-1 +---------------------------------------+
-1 -0.5 0 0.5 1
x vals"""
assert string == ref
return
def test_padding_2():
fig = apl.figure(padding=(1, 2))
fig.aprint("abc")
string = fig.get_string()
assert (string == """
abc
""")
return
def preview_plot(self):
x = []
y = []
for count, event in enumerate(self.last_values):
x.append(float(count))
y.append(float(event[1][0]))
fig = apl.figure()
fig.plot(x, y, label="ch0", width=25, height=6)
fig.show()
def test_padding_4():
fig = apl.figure(padding=(1, 2, 3, 4))
fig.aprint("abc")
string = fig.get_string()
assert (string == """
abc
""")
fig.show()
return
def plot_obs(self,ow):
'''
assuming a waveform exists (observed waved-form) plot to terminal with ascii
This is useful for debugging new backends, in bash big/fast command line orientated optimization routines.
'''
t = [float(f) for f in ow.times]
v = [float(f) for f in ow.magnitude]
fig = apl.figure()
fig.plot(t, v, label=str('observation waveform from inside dtc: '), width=100, height=20)
fig.show()
def createPlot(topW, numberWords):
labels = []
numbers = []
for l, n in topW:
labels.append(l)
numbers.append(n)
print('\n[TOP ' + str(numberWords) + ' WORDS IN BILL]\n')
fig = apl.figure()
fig.barh(numbers, labels, force_ascii=True)
fig.show()
def plot_to_notebook(time_sec, in_signal, n_samples, out_signal=None):
fig = apl.figure()
fig.plot(time_sec[:n_samples] * 1e6,
in_signal[:n_samples],
label='Input signal',
width=150,
height=45)
if out_signal is not None:
fig.plot(time_sec[:n_samples] * 1e6,
out_signal[:n_samples],
label='FIR output',
width=150,
height=45)
fig.show()
def hbar(h1, width=80, show_values=False):
if ENABLE_ASCIIPLOTLIB:
data = h1.frequencies
edges = h1.numpy_bins
fig = asciiplotlib.figure()
fig.hist(data, edges, orientation="horizontal")
fig.show()
else:
data = (h1.normalize().frequencies * width).round().astype(int)
for i in range(h1.bin_count):
if show_values:
print("#" * data[i], h1.frequencies[i])
else:
print("#" * data[i])
def test_noborder():
numpy.random.seed(0)
data = [
[["a", "bb", "ccc"]],
[[1, 2, 3], [613.23236243236, 613.23236243236, 613.23236243236]],
]
fig = apl.figure()
fig.table(data, border_style=None, padding=0)
string = fig.get_string()
assert (string == """a bb ccc
1 2 3
613.23236243236613.23236243236613.23236243236""")
return
def test_table_alignment():
numpy.random.seed(0)
data = [[1, 2, 3], [613.23236243236, 613.23236243236, 613.23236243236]]
fig = apl.figure()
fig.table(data, force_ascii=True, alignment="lcr")
string = fig.get_string()
assert (
string == """+-----------------+-----------------+-----------------+
| 1 | 2 | 3 |
+-----------------+-----------------+-----------------+
| 613.23236243236 | 613.23236243236 | 613.23236243236 |
+-----------------+-----------------+-----------------+""")
return
def test_table_mixed():
numpy.random.seed(0)
data = [[0, 0.123], [1, 2.13], [2, 613.2323]]
fig = apl.figure()
fig.table(data, border_style="thin", force_ascii=True)
string = fig.get_string()
assert (string == """+---+----------+
| 0 | 0.123 |
+---+----------+
| 1 | 2.13 |
+---+----------+
| 2 | 613.2323 |
+---+----------+""")
return
def test_barh_ascii():
fig = apl.figure()
fig.barh([3, 10, 5, 2], ["Cats", "Dogs", "Cows", "Geese"], force_ascii=True)
# fig.show()
string = fig.get_string()
assert (
string
== """\
Cats [ 3] ************
Dogs [10] ****************************************
Cows [ 5] ********************
Geese [ 2] ********\
"""
)
return
def plot_entropy(binary_file_path: str):
entropy_per_buffer, buffer_chunk_size = binary_entropy(binary_file_path)
average_entropy = sum(entropy_per_buffer) / len(entropy_per_buffer)
if average_entropy >= 0.8:
average_entropy = f_red(average_entropy)
else:
average_entropy = f_green(average_entropy)
print('Average entropy: {}'.format(average_entropy))
fig = apl.figure()
x = np.linspace(0,
len(entropy_per_buffer) * buffer_chunk_size,
len(entropy_per_buffer))
fig.plot(x, entropy_per_buffer, label="Entropy(E)", width=200, height=30)
fig.show()
def test_barh():
fig = apl.figure()
fig.barh([3, 10, 5, 2], ["Cats", "Dogs", "Cows", "Geese"])
# fig.show()
string = fig.get_string()
assert (
string
== """\
Cats [ 3] ████████████
Dogs [10] ████████████████████████████████████████
Cows [ 5] ████████████████████
Geese [ 2] ████████\
"""
)
return
def test_header():
data = [
[["a", "bb", "ccc"]],
[[1, 2, 3], [613.23236243236, 613.23236243236, 613.23236243236]],
]
fig = apl.figure()
fig.table(data, alignment="lcr")
string = fig.get_string()
assert (
string == """┌─────────────────┬─────────────────┬─────────────────┐
│ a │ bb │ ccc │
╞═════════════════╪═════════════════╪═════════════════╡
│ 1 │ 2 │ 3 │
├─────────────────┼─────────────────┼─────────────────┤
│ 613.23236243236 │ 613.23236243236 │ 613.23236243236 │
└─────────────────┴─────────────────┴─────────────────┘""")
return
def test_table_double():
numpy.random.seed(0)
data = numpy.random.rand(5, 2)
fig = apl.figure()
fig.table(data, border_style="double")
string = fig.get_string()
assert (string == """╔════════════════════╦════════════════════╗
║ 0.5488135039273248 ║ 0.7151893663724195 ║
╠════════════════════╬════════════════════╣
║ 0.6027633760716439 ║ 0.5448831829968969 ║
╠════════════════════╬════════════════════╣
║ 0.4236547993389047 ║ 0.6458941130666561 ║
╠════════════════════╬════════════════════╣
║ 0.4375872112626925 ║ 0.8917730007820798 ║
╠════════════════════╬════════════════════╣
║ 0.9636627605010293 ║ 0.3834415188257777 ║
╚════════════════════╩════════════════════╝""")
return
def test_table():
numpy.random.seed(0)
data = numpy.random.rand(5, 2)
fig = apl.figure()
fig.table(data)
string = fig.get_string()
assert (string == """┌────────────────────┬────────────────────┐
│ 0.5488135039273248 │ 0.7151893663724195 │
├────────────────────┼────────────────────┤
│ 0.6027633760716439 │ 0.5448831829968969 │
├────────────────────┼────────────────────┤
│ 0.4236547993389047 │ 0.6458941130666561 │
├────────────────────┼────────────────────┤
│ 0.4375872112626925 │ 0.8917730007820798 │
├────────────────────┼────────────────────┤
│ 0.9636627605010293 │ 0.3834415188257777 │
└────────────────────┴────────────────────┘""")
return
def test_header_ascii():
numpy.random.seed(0)
data = [
[["a", "bb", "ccc"]],
[[1, 2, 3], [613.23236243236, 613.23236243236, 613.23236243236]],
]
fig = apl.figure()
fig.table(data, force_ascii=True, alignment="lcr")
string = fig.get_string()
assert (
string == """+-----------------+-----------------+-----------------+
| a | bb | ccc |
+=================+=================+=================+
| 1 | 2 | 3 |
+-----------------+-----------------+-----------------+
| 613.23236243236 | 613.23236243236 | 613.23236243236 |
+-----------------+-----------------+-----------------+""")
return
def test_header_thick():
numpy.random.seed(0)
data = [
[["a", "bb", "ccc"]],
[[1, 2, 3], [613.23236243236, 613.23236243236, 613.23236243236]],
]
fig = apl.figure()
fig.table(data, border_style=("thin", "thick"), alignment="lcr")
string = fig.get_string()
assert (
string == """┌─────────────────┬─────────────────┬─────────────────┐
│ a │ bb │ ccc │
┝━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━┥
│ 1 │ 2 │ 3 │
├─────────────────┼─────────────────┼─────────────────┤
│ 613.23236243236 │ 613.23236243236 │ 613.23236243236 │
└─────────────────┴─────────────────┴─────────────────┘""")
return
def test_table_ascii():
numpy.random.seed(0)
data = numpy.random.rand(5, 2)
fig = apl.figure()
fig.table(data, border_style="thin", force_ascii=True)
string = fig.get_string()
assert (string == """+--------------------+--------------------+
| 0.5488135039273248 | 0.7151893663724195 |
+--------------------+--------------------+
| 0.6027633760716439 | 0.5448831829968969 |
+--------------------+--------------------+
| 0.4236547993389047 | 0.6458941130666561 |
+--------------------+--------------------+
| 0.4375872112626925 | 0.8917730007820798 |
+--------------------+--------------------+
| 0.9636627605010293 | 0.3834415188257777 |
+--------------------+--------------------+""")
return
def test_horizontal_ascii():
numpy.random.seed(123)
sample = numpy.random.normal(size=1000)
counts, bin_edges = numpy.histogram(sample)
fig = apl.figure()
fig.hist(counts, bin_edges, orientation="horizontal", force_ascii=True)
string = fig.get_string()
assert (string == """\
-3.23e+00 - -2.55e+00 [ 7] *
-2.55e+00 - -1.87e+00 [ 27] ****
-1.87e+00 - -1.19e+00 [ 95] **************
-1.19e+00 - -5.10e-01 [183] **************************
-5.10e-01 - +1.70e-01 [286] ****************************************
+1.70e-01 - +8.51e-01 [202] *****************************
+8.51e-01 - +1.53e+00 [142] ********************
+1.53e+00 - +2.21e+00 [ 49] *******
+2.21e+00 - +2.89e+00 [ 7] *
+2.89e+00 - +3.57e+00 [ 2] *\
""")
return
def test_vertical_strip():
numpy.random.seed(20)
sample = numpy.random.normal(size=10000)
counts, bin_edges = numpy.histogram(sample)
fig = apl.figure()
fig.hist(counts, bin_edges, grid=[5, 8], strip=True)
string = fig.get_string()
assert (string == """\
▉▆
▉█
▉█
▁▉█
█▉█
█▉██
█▉██
▁█▉██
██▉██▃
▃██▉██▉▂\
""")
return
def callback(stream_id, msg_line):
fh.write(msg_line + '\n')
if display == 'log':
print(msg_line)
outputlines_list.append(msg_line)
try:
msg = json.loads(msg_line)
except:
return
results_kernel_dict = results_dict[kernel][threads]
if msg['log'] == 'BEST_TIME':
if bodies not in results_kernel_dict.keys():
results_kernel_dict[bodies] = msg['value']
elif results_kernel_dict[bodies] != msg['value']:
results_kernel_dict[bodies] = msg['value']
else:
return
else:
return
if display != 'plot':
return
x = sorted(list(results_kernel_dict.keys()))
y = [results_kernel_dict[n] for n in x]
t = shutil.get_terminal_size((80, 20))
fig = apl.figure()
fig.plot(x,
y,
label='{kernel}@{threads}'.format(kernel=kernel,
threads=threads),
width=t.columns,
height=t.lines,
extra_gnuplot_arguments=[
'set logscale x 2',
'set format y "10^{%L}"',
'set logscale y 10',
])
fig.show()
def print_project_status(details: Dict) -> None:
formatters = {
'HEADER': '\033[95m',
'GREEN': '\033[92m',
'BOLD': '\033[1m',
'END': '\033[0m',
}
output = """
{BOLD}{HEADER}Status{END}
- {GREEN}phrases{END}: {phrases}
- {GREEN}words{END}: {words}
"""\
.format(
phrases=details['phrases'],
words=details['words'],
**formatters)
language_list = list(
map(
lambda language: [
language['code'], "{value} ({progress}%)".format(
value=language['translated']['phrases'],
progress=language['translated']['progress']),
"{value} ({progress}%)".format(
value=language['approved']['phrases'],
progress=language['approved']['progress'])
], details['languages']))
data = [[['languages', 'translated', 'approved']], language_list]
fig = apl.figure()
fig.table(data, border_style="thin", padding=(0, 1))
print(output)
fig.show()
