function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def test_safe_search_detection(self, annotator_client_mock):
# Given
safe_search_detection_method = annotator_client_mock.safe_search_detection
safe_search_detection_method.return_value = AnnotateImageResponse(
safe_search_annotation=SafeSearchAnnotation(
adult="VERY_... | apache/incubator-airflow | [
29418,
12032,
29418,
869,
1428948298
] |
def test_safe_search_detection_with_additional_properties(self, annotator_client_mock):
# Given
safe_search_detection_method = annotator_client_mock.safe_search_detection
safe_search_detection_method.return_value = AnnotateImageResponse(
safe_search_annotation=SafeSearchAnnotation(
... | apache/incubator-airflow | [
29418,
12032,
29418,
869,
1428948298
] |
def score_dt(model_name, model, X, y, y_actual, output_folder):
"""
Score a decision tree model.
:param string model_name: title for the model used on the output filename
:param dataframe model: model reference
:param dataframe X: examples
:param dataframe y: targets
:param dataframe y_actu... | priscillaboyd/SPaT_Prediction | [
11,
2,
11,
2,
1496250432
] |
def save_dt_model(model_name, model, folder):
"""
Save model using Pickle binary format.
:param dataframe model: model reference
:param string model_name: title for the model used on the output filename
:param string folder: location of model output
"""
print("Saving model...")
model_fi... | priscillaboyd/SPaT_Prediction | [
11,
2,
11,
2,
1496250432
] |
def _get_ovn_controller(self, install_method="sandbox"):
ovn_nbctl = self.controller_client("ovn-nbctl")
ovn_nbctl.set_sandbox("controller-sandbox", install_method,
self.context['controller']['host_container'])
ovn_nbctl.set_daemon_socket(self.context.get("daemon_so... | openvswitch/ovn-scale-test | [
17,
21,
17,
22,
1460439683
] |
def _stop_daemon(self):
ovn_nbctl = self._get_ovn_controller(self.install_method)
ovn_nbctl.stop_daemon() | openvswitch/ovn-scale-test | [
17,
21,
17,
22,
1460439683
] |
def _create_lswitches(self, lswitch_create_args, num_switches=-1):
self.RESOURCE_NAME_FORMAT = "lswitch_XXXXXX_XXXXXX"
if (num_switches == -1):
num_switches = lswitch_create_args.get("amount", 1)
batch = lswitch_create_args.get("batch", num_switches)
start_cidr = lswitch_cr... | openvswitch/ovn-scale-test | [
17,
21,
17,
22,
1460439683
] |
def _connect_network_to_router(self, router, network):
LOG.info("Connect network %s to router %s" % (network["name"], router["name"]))
ovn_nbctl = self.controller_client("ovn-nbctl")
ovn_nbctl.set_sandbox("controller-sandbox", self.install_method,
self.context['con... | openvswitch/ovn-scale-test | [
17,
21,
17,
22,
1460439683
] |
def __init__(self, conf, topic_name):
self.topic_name = topic_name
self.producer = Producer(conf)
self.counter = 0
self.running = True | ibm-messaging/message-hub-samples | [
92,
163,
92,
5,
1444758625
] |
def on_delivery(self, err, msg):
if err:
print('Delivery report: Failed sending message {0}'.format(msg.value()))
print(err)
# We could retry sending the message
else:
print('Message produced, offset: {0}'.format(msg.offset())) | ibm-messaging/message-hub-samples | [
92,
163,
92,
5,
1444758625
] |
def __init__(self):
super(TestAppModelSimple, self).__init__()
self.my_key = ""
self.stringField = "" | icoxfog417/pykintone | [
21,
11,
21,
8,
1435900514
] |
def __init__(self, target=None, listeners=None, name=None, service=None, cleanup_method=None,
heartbeat_secs=10, **kwargs):
"""
Constructs the control part of an ION process.
Used by the container's IonProcessThreadManager, as part of spawn_process.
@param target ... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def time_stats(self):
"""
Returns a 5-tuple of (total time, idle time, processing time, time since prior interval start,
busy since prior interval start), all in ms (int).
"""
now = get_ion_ts_millis()
running_time = now - self._start_time
idle_time = running_time... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def add_endpoint(self, listener, activate=True):
"""
Adds a listening endpoint to be managed by this ION process.
Spawns the listen loop and sets the routing call to synchronize incoming messages
here. If this process hasn't been started yet, adds it to the list of listeners
to ... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def target(self, *args, **kwargs):
"""
Entry point for the main process greenlet.
Setup the base properties for this process (mainly the control thread).
"""
if self.name:
threading.current_thread().name = "%s-target" % self.name
# start time
self._st... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def has_pending_call(self, ar):
"""
Returns true if the call (keyed by the AsyncResult returned by _routing_call) is still pending.
"""
for _, qar, _, _, _, _ in self._ctrl_queue.queue:
if qar == ar:
return True
return False | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def _interrupt_control_thread(self):
"""
Signal the control flow thread that it needs to abort processing, likely due to a timeout.
"""
self._ctrl_thread.proc.kill(exception=OperationInterruptedException, block=False) | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def _control_flow(self):
"""
Entry point for process control thread of execution.
This method is run by the control greenlet for each ION process. Listeners attached
to the process, either RPC Servers or Subscribers, synchronize calls to the process
by placing call requests into... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def _compute_proc_stats(self, start_proc_time):
cur_time = get_ion_ts_millis()
self._record_proc_time(cur_time)
proc_time = cur_time - start_proc_time
self._proc_time += proc_time | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def _notify_stop(self):
"""
Called when the process is about to be shut down.
Instructs all listeners to close, puts a StopIteration into the synchronized queue,
and waits for the listeners to close and for the control queue to exit.
"""
for listener in self.listeners:
... | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def _create_thread(self, target=None, **kwargs):
return IonProcessThread(target=target, heartbeat_secs=self.heartbeat_secs, **kwargs) | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def call_process(self, message, stream_route, stream_id):
"""
Handles pre-processing of packet and process work
"""
self.process(message) | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def get_ion_actor_id(process):
"""Given an ION process, return the ion-actor-id from the context, if set and present"""
ion_actor_id = None
if process:
ctx = process.get_context()
ion_actor_id = ctx.get(MSG_HEADER_ACTOR, None) if ctx else None
return ion_actor_id | scionrep/scioncc | [
3,
10,
3,
1,
1435685091
] |
def cmd_list(self, argument):
"""List commands"""
arg = argument.lower()
index = self.bot.help_index
public = "public commands -- %s" % " ".join(index['public'])
private = "private commands -- %s" % " ".join(index['private'])
if 'all' in arg or 'both' in arg:
... | nickraptis/fidibot | [
1,
2,
1,
2,
1367162213
] |
def cmd_help(self, argument):
"""Get help on a command or module"""
arg = argument.lower()
index = self.bot.help_index
target = self.target
args = arg.split()
if not args:
s = "usage: help <command> [public|private] / help module <module>"
self.sen... | nickraptis/fidibot | [
1,
2,
1,
2,
1367162213
] |
def test_column_list_select2(self):
# make sure SDC copies the columns like Pandas does
def test_impl(df):
df2 = df[['A']]
df2['A'] += 10
return df2.A, df.A
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame(
{'A': np.arange(... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_pd_DataFrame_from_series_par(self):
def test_impl(n):
S1 = pd.Series(np.ones(n))
S2 = pd.Series(np.random.ranf(n))
df = pd.DataFrame({'A': S1, 'B': S2})
return df.A.sum()
hpat_func = self.jit(test_impl)
n = 11
self.assertEqual(hpa... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_getitem_bool_series(self):
def test_impl(df):
return df['A'][df['B']].values
hpat_func = self.jit(test_impl)
df = pd.DataFrame({'A': [1, 2, 3], 'B': [True, False, True]})
np.testing.assert_array_equal(test_impl(df), hpat_func(df)) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_fillna(self):
def test_impl():
A = np.array([1., 2., 3.])
A[0] = np.nan
df = pd.DataFrame({'A': A})
B = df.A.fillna(5.0)
return B.sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl()) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_fillna_inplace(self):
def test_impl():
A = np.array([1., 2., 3.])
A[0] = np.nan
df = pd.DataFrame({'A': A})
df.A.fillna(5.0, inplace=True)
return df.A.sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_im... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_mean(self):
def test_impl():
A = np.array([1., 2., 3.])
A[0] = np.nan
df = pd.DataFrame({'A': A})
return df.A.mean()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl()) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_var(self):
def test_impl():
A = np.array([1., 2., 3.])
A[0] = 4.0
df = pd.DataFrame({'A': A})
return df.A.var()
hpat_func = self.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(), test_impl()) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_std(self):
def test_impl():
A = np.array([1., 2., 3.])
A[0] = 4.0
df = pd.DataFrame({'A': A})
return df.A.std()
hpat_func = self.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(), test_impl()) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_map(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n)})
df['B'] = df.A.map(lambda a: 2 * a)
return df.B.sum()
n = 121
hpat_func = self.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(n), test_impl(n)) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_map_arg(self):
def test_impl(df):
df['B'] = df.A.map(lambda a: 2 * a)
return
n = 121
df1 = pd.DataFrame({'A': np.arange(n)})
df2 = pd.DataFrame({'A': np.arange(n)})
hpat_func = self.jit(test_impl)
hpat_func(df1)
self.assert... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_cumsum(self):
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
Ac = df.A.cumsum()
return Ac.sum()
hpat_func = self.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqual(count... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_column_distribution(self):
# make sure all column calls are distributed
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
df.A.fillna(5.0, inplace=True)
DF = df.A.fillna(5.0)
s = DF.sum()
m = df.A.mean()
... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_quantile_parallel(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(0, n, 1, np.float64)})
return df.A.quantile(.25)
hpat_func = self.jit(test_impl)
n = 1001
np.testing.assert_almost_equal(hpat_func(n), test_impl(n))
self.assertEqual(cou... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_quantile_parallel_float_nan(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(0, n, 1, np.float32)})
df.A[0:100] = np.nan
df.A[200:331] = np.nan
return df.A.quantile(.25)
hpat_func = self.jit(test_impl)
n = 1001
np.testin... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_quantile_parallel_int(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(0, n, 1, np.int32)})
return df.A.quantile(.25)
hpat_func = self.jit(test_impl)
n = 1001
np.testing.assert_almost_equal(hpat_func(n), test_impl(n))
self.assertEqual(c... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_quantile_sequential(self):
def test_impl(A):
df = pd.DataFrame({'A': A})
return df.A.quantile(.25)
hpat_func = self.jit(test_impl)
n = 1001
A = np.arange(0, n, 1, np.float64)
np.testing.assert_almost_equal(hpat_func(A), test_impl(A)) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_nunique(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n)})
df.A[2] = 0
return df.A.nunique()
hpat_func = self.jit(test_impl)
n = 1001
np.testing.assert_almost_equal(hpat_func(n), test_impl(n))
# test compile again for ove... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_nunique_parallel(self):
# TODO: test without file
def test_impl():
df = pq.read_table('example.parquet').to_pandas()
return df.four.nunique()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
self.assertEqual(count_array_... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_nunique_str(self):
def test_impl(n):
df = pd.DataFrame({'A': ['aa', 'bb', 'aa', 'cc', 'cc']})
return df.A.nunique()
hpat_func = self.jit(test_impl)
n = 1001
np.testing.assert_almost_equal(hpat_func(n), test_impl(n))
# test compile again for overl... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_nunique_str_parallel(self):
# TODO: test without file
def test_impl():
df = pq.read_table('example.parquet').to_pandas()
return df.two.nunique()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
self.assertEqual(count_arr... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_unique_parallel(self):
# TODO: test without file
def test_impl():
df = pq.read_table('example.parquet').to_pandas()
return (df.four.unique() == 3.0).sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
self.assertEqual... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_unique_str_parallel(self):
# TODO: test without file
def test_impl():
df = pq.read_table('example.parquet').to_pandas()
return (df.two.unique() == 'foo').sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
self.assert... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_describe(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(0, n, 1, np.float64)})
return df.A.describe()
hpat_func = self.jit(test_impl)
n = 1001
hpat_func(n)
# XXX: test actual output
self.assertEqual(count_array_REPs(), 0)
... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_contains_regex(self):
def test_impl():
A = StringArray(['ABC', 'BB', 'ADEF'])
df = pd.DataFrame({'A': A})
B = df.A.str.contains('AB*', regex=True)
return B.sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), 2) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_contains_noregex(self):
def test_impl():
A = StringArray(['ABC', 'BB', 'ADEF'])
df = pd.DataFrame({'A': A})
B = df.A.str.contains('BB', regex=False)
return B.sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), 1) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_replace_regex(self):
def test_impl(df):
return df.A.str.replace('AB*', 'EE', regex=True)
df = pd.DataFrame({'A': ['ABCC', 'CABBD']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_replace_noregex(self):
def test_impl(df):
return df.A.str.replace('AB', 'EE', regex=False)
df = pd.DataFrame({'A': ['ABCC', 'CABBD']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_replace_regex_parallel(self):
def test_impl(df):
B = df.A.str.replace('AB*', 'EE', regex=True)
return B
n = 5
A = ['ABCC', 'CABBD', 'CCD', 'CCDAABB', 'ED']
start, end = get_start_end(n)
df = pd.DataFrame({'A': A[start:end]})
hpat_func... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split(self):
def test_impl(df):
return df.A.str.split(',')
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D', 'G', '', 'g,f']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_default(self):
def test_impl(df):
return df.A.str.split()
df = pd.DataFrame({'A': ['AB CC', 'C ABB D', 'G ', ' ', 'g\t f']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_filter(self):
def test_impl(df):
B = df.A.str.split(',')
df2 = pd.DataFrame({'B': B})
return df2[df2.B.str.len() > 1]
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D', 'G', '', 'g,f']})
hpat_func = self.jit(test_impl)
pd.testing.assert_f... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_box_df(self):
def test_impl(df):
return pd.DataFrame({'B': df.A.str.split(',')})
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df).B, test_impl(df).B, check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_unbox_df(self):
def test_impl(df):
return df.A.iloc[0]
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D']})
df2 = pd.DataFrame({'A': df.A.str.split(',')})
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(df2), test_impl(df2)) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_bool_index(self):
def test_impl(df):
C = df.A.str.split(',')
return C[df.B == 'aa']
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D'], 'B': ['aa', 'bb']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), tes... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split_parallel(self):
def test_impl(df):
B = df.A.str.split(',')
return B
n = 5
start, end = get_start_end(n)
A = ['AB,CC', 'C,ABB,D', 'CAD', 'CA,D', 'AA,,D']
df = pd.DataFrame({'A': A[start:end]})
hpat_func = self.jit(distributed={'d... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_get(self):
def test_impl(df):
B = df.A.str.split(',')
return B.str.get(1)
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_split(self):
def test_impl(df):
return df.A.str.split(',')
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(df), test_impl(df), check_names=False) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_get_parallel(self):
def test_impl(df):
A = df.A.str.split(',')
B = A.str.get(1)
return B
n = 5
start, end = get_start_end(n)
A = ['AB,CC', 'C,ABB,D', 'CAD,F', 'CA,D', 'AA,,D']
df = pd.DataFrame({'A': A[start:end]})
hpat_fu... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_get_to_numeric(self):
def test_impl(df):
B = df.A.str.split(',')
C = pd.to_numeric(B.str.get(1), errors='coerce')
return C
df = pd.DataFrame({'A': ['AB,12', 'C,321,D']})
hpat_func = self.jit(locals={'C': types.int64[:]})(test_impl)
pd.tes... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_flatten(self):
def test_impl(df):
A = df.A.str.split(',')
return pd.Series(list(itertools.chain(*A)))
df = pd.DataFrame({'A': ['AB,CC', 'C,ABB,D']})
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(
hpat_func(df), test_impl(... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_str_flatten_parallel(self):
def test_impl(df):
A = df.A.str.split(',')
B = pd.Series(list(itertools.chain(*A)))
return B
n = 5
start, end = get_start_end(n)
A = ['AB,CC', 'C,ABB,D', 'CAD', 'CA,D', 'AA,,D']
df = pd.DataFrame({'A': A[st... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_to_numeric(self):
def test_impl(df):
B = pd.to_numeric(df.A, errors='coerce')
return B
df = pd.DataFrame({'A': ['123.1', '331.2']})
hpat_func = self.jit(locals={'B': types.float64[:]})(test_impl)
pd.testing.assert_series_equal(
hpat_func(df),... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_1D_Var_len(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n), 'B': np.arange(n) + 1.0})
df1 = df[df.A > 5]
return len(df1.B)
hpat_func = self.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEq... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_rolling1(self):
# size 3 without unroll
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n), 'B': np.random.ranf(n)})
Ac = df.A.rolling(3).sum()
return Ac.sum()
hpat_func = self.jit(test_impl)
n = 121
self.assertEqual(hpat_func(n),... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_rolling2(self):
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
df['moving average'] = df.A.rolling(window=5, center=True).mean()
return df['moving average'].sum()
hpat_func = self.jit(test_impl)
n = 121
self.as... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_rolling3(self):
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
Ac = df.A.rolling(3, center=True).apply(lambda a: a[0] + 2 * a[1] + a[2])
return Ac.sum()
hpat_func = self.jit(test_impl)
n = 121
self.assertEqual(... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_shift1(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n) + 1.0, 'B': np.random.ranf(n)})
Ac = df.A.shift(1)
return Ac.sum()
hpat_func = self.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqu... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_shift2(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n) + 1.0, 'B': np.random.ranf(n)})
Ac = df.A.pct_change(1)
return Ac.sum()
hpat_func = self.jit(test_impl)
n = 11
np.testing.assert_almost_equal(hpat_func(n), test_impl(n))
... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_df_input(self):
def test_impl(df):
return df.B.sum()
n = 121
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
hpat_func = self.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(df), test_impl(df)) | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_df_input2(self):
def test_impl(df):
C = df.B == 'two'
return C.sum()
n = 11
df = pd.DataFrame({'A': np.random.ranf(3 * n), 'B': ['one', 'two', 'three'] * n})
hpat_func = self.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(df), test_impl(... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_df_input_dist1(self):
def test_impl(df):
return df.B.sum()
n = 121
A = [3, 4, 5, 6, 1]
B = [5, 6, 2, 1, 3]
n = 5
start, end = get_start_end(n)
df = pd.DataFrame({'A': A, 'B': B})
df_h = pd.DataFrame({'A': A[start:end], 'B': B[start:en... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_concat(self):
def test_impl(n):
df1 = pd.DataFrame({'key1': np.arange(n), 'A': np.arange(n) + 1.0})
df2 = pd.DataFrame({'key2': n - np.arange(n), 'A': n + np.arange(n) + 1.0})
df3 = pd.concat([df1, df2])
return df3.A.sum() + df3.key2.sum()
hpat_f... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_concat_str(self):
def test_impl():
df1 = pq.read_table('example.parquet').to_pandas()
df2 = pq.read_table('example.parquet').to_pandas()
A3 = pd.concat([df1, df2])
return (A3.two == 'foo').sum()
hpat_func = self.jit(test_impl)
self.assert... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_concat_series(self):
def test_impl(n):
df1 = pd.DataFrame({'key1': np.arange(n), 'A': np.arange(n) + 1.0})
df2 = pd.DataFrame({'key2': n - np.arange(n), 'A': n + np.arange(n) + 1.0})
A3 = pd.concat([df1.A, df2.A])
return A3.sum()
hpat_func = self... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_concat_series_str(self):
def test_impl():
df1 = pq.read_table('example.parquet').to_pandas()
df2 = pq.read_table('example.parquet').to_pandas()
A3 = pd.concat([df1.two, df2.two])
return (A3 == 'foo').sum()
hpat_func = self.jit(test_impl)
... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_intraday(self):
def test_impl(nsyms):
max_num_days = 100
all_res = 0.0
for i in sdc.prange(nsyms):
s_open = 20 * np.ones(max_num_days)
s_low = 28 * np.ones(max_num_days)
s_close = 19 * np.ones(max_num_days)
... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def test_var_dist1(self):
def test_impl(A, B):
df = pd.DataFrame({'A': A, 'B': B})
df2 = df.groupby('A', as_index=False)['B'].sum()
# TODO: fix handling of df setitem to force match of array dists
# probably with a new node that is appended to the end of basic blo... | IntelLabs/hpat | [
645,
65,
645,
54,
1496336381
] |
def _create_3d_axis():
"""creates a subplot with 3d projection if one does not already exist"""
from matplotlib.projections import get_projection_class
from matplotlib import _pylab_helpers
create_axis = True
if _pylab_helpers.Gcf.get_active() is not None:
if isinstance(plt.gca(), get_proje... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def __phasor_plot(ax, up, idq, uxdq):
uref = max(up, uxdq[0])
uxd = uxdq[0]/uref
uxq = uxdq[1]/uref
u1d, u1q = (uxd, 1+uxq)
u1 = np.sqrt(u1d**2 + u1q**2)*uref
i1 = np.linalg.norm(idq)
i1d, i1q = (idq[0]/i1, idq[1]/i1)
qhw = 6 # width arrow head
qhl = 15 # length arrow head
ql... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def iqd_phasor(up, iqd, uqd, ax=0):
"""creates a phasor plot
up: internal voltage
iqd: current
uqd: terminal voltage"""
uxdq = (uqd[1]/np.sqrt(2), (uqd[0]/np.sqrt(2)-up))
__phasor_plot(ax, up, (iqd[1]/np.sqrt(2), iqd[0]/np.sqrt(2)), uxdq) | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def airgap(airgap, ax=0):
"""creates plot of flux density in airgap"""
if ax == 0:
ax = plt.gca()
ax.set_title('Airgap Flux Density [T]')
ax.plot(airgap['pos'], airgap['B'],
label='Max {:4.2f} T'.format(max(airgap['B'])))
ax.plot(airgap['pos'], airgap['B_fft'],
label=... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def torque(pos, torque, ax=0):
"""creates plot from torque vs position"""
k = 20
alpha = np.linspace(pos[0], pos[-1],
k*len(torque))
f = ip.interp1d(pos, torque, kind='quadratic')
unit = 'Nm'
scale = 1
if np.min(torque) < -9.9e3 or np.max(torque) > 9.9e3:
scal... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def force(title, pos, force, xlabel='', ax=0):
"""plot force vs position"""
unit = 'N'
scale = 1
if min(force) < -9.9e3 or max(force) > 9.9e3:
scale = 1e-3
unit = 'kN'
if ax == 0:
ax = plt.gca()
ax.set_title('{} / {}'.format(title, unit))
ax.grid(True)
ax.plot(pos... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def forcedens(title, pos, fdens, ax=0):
"""plot force densities"""
if ax == 0:
ax = plt.gca()
ax.set_title(title)
ax.grid(True)
ax.plot(pos, [1e-3*ft for ft in fdens[0]], label='F tang')
ax.plot(pos, [1e-3*fn for fn in fdens[1]], label='F norm')
ax.legend()
ax.set_xlabel('Pos / ... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def forcedens_fft(title, fdens, ax=0):
"""plot force densities FFT
Args:
title: plot title
fdens: force density object
"""
if ax == 0:
ax = plt.axes(projection="3d")
F = 1e-3*fdens.fft()
fmin = 0.2
num_bars = F.shape[0] + 1
_xx, _yy = np.meshgrid(np.arange(1, num_bar... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def winding_current(pos, current, ax=0):
"""plot winding currents"""
if ax == 0:
ax = plt.gca()
ax.set_title('Winding Currents / A')
ax.grid(True)
for p, i in zip(pos, current):
ax.plot(p, i) | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def voltage_fft(title, order, voltage, ax=0):
"""plot FFT harmonics of voltage"""
if ax == 0:
ax = plt.gca()
ax.set_title('{} / V'.format(title))
ax.grid(True)
if max(order) < 5:
order += [5]
voltage += [0]
try:
bw = 2.5E-2*max(order)
ax.bar(order, voltage... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def mcv_muer(mcv, ax=0):
"""plot rel. permeability vs. B of mcv dict"""
MUE0 = 4e-7*np.pi
bi, ur = zip(*[(bx, bx/hx/MUE0)
for bx, hx in zip(mcv['curve'][0]['bi'],
mcv['curve'][0]['hi']) if not hx == 0])
if ax == 0:
ax = plt.gca()
ax.plo... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def mtpv(pmrel, u1max, i1max, title='', projection='', ax=0):
"""create a line or surface plot with voltage and mtpv curve"""
w1 = pmrel.w2_imax_umax(i1max, u1max)
nsamples = 20
if projection == '3d':
nsamples = 50
iqmax, idmax = pmrel.iqdmax(i1max)
iqmin, idmin = pmrel.iqdmin(i1max)
... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def pmrelsim(bch, title=''):
"""creates a plot of a PM/Rel motor simulation"""
cols = 2
rows = 4
if len(bch.flux['1']) > 1:
rows += 1
htitle = 1.5 if title else 0
fig, ax = plt.subplots(nrows=rows, ncols=cols,
figsize=(10, 3*rows + htitle))
if title:
... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def fasttorque(bch, title=''):
"""creates a plot of a Fast Torque simulation"""
cols = 2
rows = 4
if len(bch.flux['1']) > 1:
rows += 1
htitle = 1.5 if title else 0
fig, ax = plt.subplots(nrows=rows, ncols=cols,
figsize=(10, 3*rows + htitle))
if title:
... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def transientsc(bch, title=''):
"""creates a transient short circuit plot"""
cols = 1
rows = 2
htitle = 1.5 if title else 0
fig, ax = plt.subplots(nrows=rows, ncols=cols,
figsize=(10, 3*rows + htitle))
if title:
fig.suptitle(title, fontsize=16)
row = 1
... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def i1beta_ld(i1, beta, ld, ax=0):
"""creates a surface plot of ld vs i1, beta"""
if ax == 0:
_create_3d_axis()
ax = plt.gca()
_plot_surface(ax, i1, beta, np.asarray(ld)*1e3,
(u'I1/A', u'Beta/°', u'Ld/mH'),
azim=60) | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def i1beta_psim(i1, beta, psim, ax=0):
"""creates a surface plot of psim vs i1, beta"""
if ax == 0:
_create_3d_axis()
ax = plt.gca()
_plot_surface(ax, i1, beta, psim,
(u'I1/A', u'Beta/°', u'Psi m/Vs'),
azim=60) | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
def i1beta_psid(i1, beta, psid, ax=0):
"""creates a surface plot of psid vs i1, beta"""
if ax == 0:
_create_3d_axis()
ax = plt.gca()
azim = -60
if 0 < np.mean(beta) or -90 > np.mean(beta):
azim = 60
_plot_surface(ax, i1, beta, psid,
(u'I1/A', u'Beta/°', u'Ps... | SEMAFORInformatik/femagtools | [
17,
10,
17,
4,
1471511034
] |
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