Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
from_unix_timestamp64_milli | (self) | Check the fromUnixTimestamp64Milli functions with DateTime64 extended range.
| Check the fromUnixTimestamp64Milli functions with DateTime64 extended range.
| def from_unix_timestamp64_milli(self):
"""Check the fromUnixTimestamp64Milli functions with DateTime64 extended range.
"""
from_unix_timestamp64_milli_micro_nano(scale=3) | [
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438,
0
] | [
441,
51
] | python | en | ['en', 'en', 'en'] | True |
from_unix_timestamp64_micro | (self) | Check the fromUnixTimestamp64Micro functions with DateTime64 extended range.
| Check the fromUnixTimestamp64Micro functions with DateTime64 extended range.
| def from_unix_timestamp64_micro(self):
"""Check the fromUnixTimestamp64Micro functions with DateTime64 extended range.
"""
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448,
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] | [
451,
51
] | python | en | ['en', 'en', 'en'] | True |
from_unix_timestamp64_nano | (self) | Check the fromUnixTimestamp64Nano functions with DateTime64 extended range.
| Check the fromUnixTimestamp64Nano functions with DateTime64 extended range.
| def from_unix_timestamp64_nano(self):
"""Check the fromUnixTimestamp64Nano functions with DateTime64 extended range.
"""
from_unix_timestamp64_milli_micro_nano(scale=9) | [
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] | [
458,
0
] | [
461,
51
] | python | en | ['en', 'en', 'en'] | True |
type_conversion | (self, node="clickhouse1") | Check the type conversion operations with DateTime64.
Cast can be set as Requirement thereby as the module
tests exactly what CAST does.
| Check the type conversion operations with DateTime64.
Cast can be set as Requirement thereby as the module
tests exactly what CAST does.
| def type_conversion(self, node="clickhouse1"):
"""Check the type conversion operations with DateTime64.
Cast can be set as Requirement thereby as the module
tests exactly what CAST does.
"""
self.context.node = self.context.cluster.node(node)
for scenario in loads(current_module(), Scenario):
... | [
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get_dataset_2 | (d, nt, nb, nq) | A dataset that is not completely random but still challenging to
index
| A dataset that is not completely random but still challenging to
index
| def get_dataset_2(d, nt, nb, nq):
"""A dataset that is not completely random but still challenging to
index
"""
d1 = 10 # intrinsic dimension (more or less)
n = nb + nt + nq
rs = np.random.RandomState(1338)
x = rs.normal(size=(n, d1))
x = np.dot(x, rs.rand(d1, d))
# now we have a... | [
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"... | [
84,
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] | [
98,
45
] | python | en | ['en', 'en', 'en'] | True |
compare_binary_result_lists | (D1, I1, D2, I2) | comparing result lists is difficult because there are many
ties. Here we sort by (distance, index) pairs and ignore the largest
distance of each result. Compatible result lists should pass this. | comparing result lists is difficult because there are many
ties. Here we sort by (distance, index) pairs and ignore the largest
distance of each result. Compatible result lists should pass this. | def compare_binary_result_lists(D1, I1, D2, I2):
"""comparing result lists is difficult because there are many
ties. Here we sort by (distance, index) pairs and ignore the largest
distance of each result. Compatible result lists should pass this."""
assert D1.shape == I1.shape == D2.shape == I2.shape
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"ndiff... | [
108,
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] | [
127,
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] | python | en | ['en', 'en', 'en'] | True |
feature | (self, node="clickhouse1") | Check drop role query syntax.
```sql
DROP ROLE [IF EXISTS] name [,...] [ON CLUSTER cluster_name]
```
| Check drop role query syntax. | def feature(self, node="clickhouse1"):
"""Check drop role query syntax.
```sql
DROP ROLE [IF EXISTS] name [,...] [ON CLUSTER cluster_name]
```
"""
node = self.context.cluster.node(node)
@contextmanager
def setup(role):
try:
with Given("I have a role"):
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] | [
83,
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] | python | en | ['fr', 'en', 'en'] | True |
feature | (self, node="clickhouse1") | Check drop quota query syntax.
```sql
DROP QUOTA [IF EXISTS] name [,...] [ON CLUSTER cluster_name]
```
| Check drop quota query syntax. | def feature(self, node="clickhouse1"):
"""Check drop quota query syntax.
```sql
DROP QUOTA [IF EXISTS] name [,...] [ON CLUSTER cluster_name]
```
"""
node = self.context.cluster.node(node)
@contextmanager
def cleanup(quota):
try:
with Given("I have a quota"):
... | [
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"Gi... | [
9,
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] | [
86,
103
] | python | en | ['es', 'en', 'en'] | True |
simple_get | (url) |
with requests.Session() as s:
try:
s.mount("http://",
requests.adapters.HTTPAdapter(max_retries=MAX_HTTP_RETRIES)) # set max retries to: MAX_HTTP_RETRIES
s.mount("https://",
requests.adapters.HTTPAdapter(max_retries=MAX_HTTP_RETRIES)) # set ... |
with requests.Session() as s:
try:
s.mount("http://",
requests.adapters.HTTPAdapter(max_retries=MAX_HTTP_RETRIES)) # set max retries to: MAX_HTTP_RETRIES
s.mount("https://",
requests.adapters.HTTPAdapter(max_retries=MAX_HTTP_RETRIES)) # set ... | def simple_get(url):
'''
with requests.Session() as s:
try:
s.mount("http://",
requests.adapters.HTTPAdapter(max_retries=MAX_HTTP_RETRIES)) # set max retries to: MAX_HTTP_RETRIES
s.mount("https://",
requests.adapters.HTTPAdapter(max_retri... | [
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28,
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] | [
54,
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] | python | en | ['en', 'error', 'th'] | False |
is_good_response | (resp) |
Returns True if the response seems to be HTML, False otherwise.
|
Returns True if the response seems to be HTML, False otherwise.
| def is_good_response(resp):
"""
Returns True if the response seems to be HTML, False otherwise.
"""
content_type = resp.headers['Content-Type'].lower()
return (resp.status_code == 200
and content_type is not None
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] | [
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] | python | en | ['en', 'error', 'th'] | False |
empty_server_name | (self, timeout=300) | Check that empty string as a server name is not allowed.
| Check that empty string as a server name is not allowed.
| def empty_server_name(self, timeout=300):
"""Check that empty string as a server name is not allowed.
"""
servers = {"": {"host": "foo", "port": "389", "enable_tls": "no",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
}}
invalid_server_config(servers, timeout=t... | [
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"\"cn=\... | [
10,
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] | [
16,
51
] | python | en | ['en', 'en', 'en'] | True |
invalid_host | (self) | Check that server returns an error when LDAP server
host name is invalid.
| Check that server returns an error when LDAP server
host name is invalid.
| def invalid_host(self):
"""Check that server returns an error when LDAP server
host name is invalid.
"""
servers = {"foo": {"host": "foo", "port": "389", "enable_tls": "no"}}
users = [{
"server": "foo", "username": "user1", "password": "user1", "login": True,
"exitcode": 4,
"... | [
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"... | [
23,
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] | [
33,
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] | python | en | ['en', 'lb', 'en'] | True |
empty_host | (self) | Check that server returns an error when LDAP server
host value is empty.
| Check that server returns an error when LDAP server
host value is empty.
| def empty_host(self):
"""Check that server returns an error when LDAP server
host value is empty.
"""
servers = {"foo": {"host": "", "port": "389", "enable_tls": "no"}}
users = [{
"server": "foo", "username": "user1", "password": "user1", "login": True,
"exitcode": 4,
"messag... | [
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50,
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] | python | en | ['en', 'lb', 'en'] | True |
missing_host | (self) | Check that server returns an error when LDAP server
host is missing.
| Check that server returns an error when LDAP server
host is missing.
| def missing_host(self):
"""Check that server returns an error when LDAP server
host is missing.
"""
servers = {"foo": {"port": "389", "enable_tls": "no"}}
users = [{
"server": "foo", "username": "user1", "password": "user1", "login": True,
"exitcode": 4,
"message": "DB::Excep... | [
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"\"user1\"... | [
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67,
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] | python | en | ['en', 'lb', 'en'] | True |
invalid_port | (self) | Check that server returns an error when LDAP server
port is not valid.
| Check that server returns an error when LDAP server
port is not valid.
| def invalid_port(self):
"""Check that server returns an error when LDAP server
port is not valid.
"""
servers = {"openldap1": {"host": "openldap1", "port": "3890", "enable_tls": "no"}}
users = [{
"server": "openldap1", "username": "user1", "password": "user1", "login": True,
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"... | [
73,
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] | [
83,
26
] | python | en | ['en', 'lb', 'en'] | True |
invalid_auth_dn_prefix | (self) | Check that server returns an error when LDAP server
port is not valid.
| Check that server returns an error when LDAP server
port is not valid.
| def invalid_auth_dn_prefix(self):
"""Check that server returns an error when LDAP server
port is not valid.
"""
servers = {"openldap1": {"host": "openldap1", "port": "389", "enable_tls": "no",
"auth_dn_prefix": "foo=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
}}
users = [{
... | [
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"\... | [
90,
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] | [
102,
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] | python | en | ['en', 'lb', 'en'] | True |
invalid_auth_dn_suffix | (self) | Check that server returns an error when LDAP server
port is not valid.
| Check that server returns an error when LDAP server
port is not valid.
| def invalid_auth_dn_suffix(self):
"""Check that server returns an error when LDAP server
port is not valid.
"""
servers = {"openldap1": {"host": "openldap1", "port": "389", "enable_tls": "no",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",foo=users,dc=company,dc=com"
}}
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108,
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] | [
120,
26
] | python | en | ['en', 'lb', 'en'] | True |
invalid_enable_tls_value | (self) | Check that server returns an error when enable_tls
option has invalid value.
| Check that server returns an error when enable_tls
option has invalid value.
| def invalid_enable_tls_value(self):
"""Check that server returns an error when enable_tls
option has invalid value.
"""
servers = {"openldap1": {"host": "openldap1", "port": "389", "enable_tls": "foo",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
}}
users = [{... | [
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126,
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] | [
138,
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] | python | en | ['en', 'en', 'en'] | True |
invalid_tls_require_cert_value | (self) | Check that server returns an error when tls_require_cert
option has invalid value.
| Check that server returns an error when tls_require_cert
option has invalid value.
| def invalid_tls_require_cert_value(self):
"""Check that server returns an error when tls_require_cert
option has invalid value.
"""
servers = {"openldap2": {
"host": "openldap2", "port": "636", "enable_tls": "yes",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com",... | [
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144,
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] | [
160,
26
] | python | en | ['en', 'lb', 'en'] | True |
empty_ca_cert_dir | (self) | Check that server returns an error when ca_cert_dir is empty.
| Check that server returns an error when ca_cert_dir is empty.
| def empty_ca_cert_dir(self):
"""Check that server returns an error when ca_cert_dir is empty.
"""
servers = {"openldap2": {"host": "openldap2", "port": "636", "enable_tls": "yes",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com",
"tls_require_cert": "demand",
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166,
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180,
26
] | python | en | ['en', 'lb', 'en'] | True |
empty_ca_cert_file | (self) | Check that server returns an error when ca_cert_file is empty.
| Check that server returns an error when ca_cert_file is empty.
| def empty_ca_cert_file(self):
"""Check that server returns an error when ca_cert_file is empty.
"""
servers = {"openldap2": {"host": "openldap2", "port": "636", "enable_tls": "yes",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com",
"tls_require_cert": "demand",
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186,
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] | python | en | ['en', 'en', 'en'] | True |
auth_dn_value | (self) | Check that server configuration can properly define the `dn` value of the user. | Check that server configuration can properly define the `dn` value of the user. | def auth_dn_value(self):
"""Check that server configuration can properly define the `dn` value of the user."""
servers = {
"openldap1": {
"host": "openldap1", "port": "389", "enable_tls": "no",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
}}
... | [
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"\"auth_dn_s... | [
208,
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] | [
217,
24
] | python | en | ['en', 'en', 'en'] | True |
invalid_verification_cooldown_value | (self, invalid_value, timeout=300) | Check that server returns an error when LDAP server
verification cooldown parameter is invalid.
| Check that server returns an error when LDAP server
verification cooldown parameter is invalid.
| def invalid_verification_cooldown_value(self, invalid_value, timeout=300):
"""Check that server returns an error when LDAP server
verification cooldown parameter is invalid.
"""
error_message = ("<Error> Access(user directories): Could not parse LDAP server"
" \\`openldap1\\`: Poco::Exception. ... | [
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syntax | (self) | Check that server configuration with valid syntax can be loaded.
```xml
<yandex>
<ldap_server>
<host>localhost</host>
<port>636</port>
<auth_dn_prefix>cn=</auth_dn_prefix>
<auth_dn_suffix>, ou=users, dc=example, dc=com</auth_dn_suffix>
<verific... | Check that server configuration with valid syntax can be loaded.
```xml
<yandex>
<ldap_server>
<host>localhost</host>
<port>636</port>
<auth_dn_prefix>cn=</auth_dn_prefix>
<auth_dn_suffix>, ou=users, dc=example, dc=com</auth_dn_suffix>
<verific... | def syntax(self):
"""Check that server configuration with valid syntax can be loaded.
```xml
<yandex>
<ldap_server>
<host>localhost</host>
<port>636</port>
<auth_dn_prefix>cn=</auth_dn_prefix>
<auth_dn_suffix>, ou=users, dc=example, dc=com</auth_dn_suf... | [
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feature | (self, node="clickhouse1") | Check that LDAP server configuration.
| Check that LDAP server configuration.
| def feature(self, node="clickhouse1"):
"""Check that LDAP server configuration.
"""
self.context.node = self.context.cluster.node(node)
for scenario in loads(current_module(), Scenario):
scenario() | [
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add_cutout_box | (axes, window_size=400, periphery_offset=(-800, -1000),
colors='r', linestyles='--', plot_fovea=True,
plot_periphery=True, **kwargs) | Add square to axes to show where the cutout comes from.
Parameters
----------
axes : array_like
arrays to add square to (different images should be indexed
along first dimension)
window_size : int
The size of the cut-out to plot, in pixels (this is the length
of one side... | Add square to axes to show where the cutout comes from. | def add_cutout_box(axes, window_size=400, periphery_offset=(-800, -1000),
colors='r', linestyles='--', plot_fovea=True,
plot_periphery=True, **kwargs):
"""Add square to axes to show where the cutout comes from.
Parameters
----------
axes : array_like
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add_fixation_cross | (axes, cross_size=50, colors='r', linestyles='-', **kwargs) | add fixation cross to center of axes
Parameters
----------
axes : array_like
arrays to add square to (different images should be indexed
along first dimension)
cross_size : int, optional
total size of the lines in the cross, in pixels
colors, linestyle : str, optional
... | add fixation cross to center of axes | def add_fixation_cross(axes, cross_size=50, colors='r', linestyles='-', **kwargs):
"""add fixation cross to center of axes
Parameters
----------
axes : array_like
arrays to add square to (different images should be indexed
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cross_size : int, optional
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get_image_cutout | (images, window_size=400, periphery_offset=(-800, -1000)) | get foveal and peripheral cutouts from images
Parameters
----------
images : array_like
images to plot (different images should be indexed along first
dimension)
window_size : int
The size of the cut-out to plot, in pixels (this is the length
of one side of the square).
... | get foveal and peripheral cutouts from images | def get_image_cutout(images, window_size=400, periphery_offset=(-800, -1000)):
"""get foveal and peripheral cutouts from images
Parameters
----------
images : array_like
images to plot (different images should be indexed along first
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window_size : int
The size of t... | [
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cutout_figure | (images, window_size=400, periphery_offset=(-800, -1000), max_ecc=26.8,
plot_fovea=True, plot_periphery=True, label=True) | create figure showing cutout views of different images
if both `plot_fovea` and `plot_periphery` are False, this just
returns None
Parameters
----------
images : array_like
images to plot (different images should be indexed along first
dimension)
window_size : int
The s... | create figure showing cutout views of different images | def cutout_figure(images, window_size=400, periphery_offset=(-800, -1000), max_ecc=26.8,
plot_fovea=True, plot_periphery=True, label=True):
"""create figure showing cutout views of different images
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scaling_comparison_figure | (model_name, image_name, scaling_vals, seed, window_size=400,
periphery_offset=(-800, -1000), max_ecc=26.8, **kwargs) | r"""Create a figure showing cut-out views of all scaling values
We want to be able to easily visually compare metamers across
scaling values (and with the reference image), but they're very
large. In order to facilitate this, we create this figure with
'cut-out' views, where we compare the reference im... | r"""Create a figure showing cut-out views of all scaling values | def scaling_comparison_figure(model_name, image_name, scaling_vals, seed, window_size=400,
periphery_offset=(-800, -1000), max_ecc=26.8, **kwargs):
r"""Create a figure showing cut-out views of all scaling values
We want to be able to easily visually compare metamers across
sca... | [
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pooling_window_example | (windows, image, target_eccentricity=24,
windows_scale=0, linewidths=5, **kwargs) | Plot example window on image.
This plots a single window, as close to the target_eccentricity as
possible, at half-max amplitude, to visualize the size of the pooling
windows
Parameters
----------
windows : pooling.PoolingWindows
The PoolingWindows object to plot.
image : np.ndarra... | Plot example window on image. | def pooling_window_example(windows, image, target_eccentricity=24,
windows_scale=0, linewidths=5, **kwargs):
"""Plot example window on image.
This plots a single window, as close to the target_eccentricity as
possible, at half-max amplitude, to visualize the size of the pooling
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synthesis_schematic | (metamer, iteration=0, plot_synthesized_image=True,
plot_rep_comparison=True, plot_signal_comparison=True,
**kwargs) | Create schematic of synthesis, for animating.
WARNING: Currently, only works with images of size (256, 256), will need a
small amount of tweaking to work with differently sized images. (And may
never look quite as good with them)
Parameters
----------
metamer : pop.Metamer
The Metamer ... | Create schematic of synthesis, for animating. | def synthesis_schematic(metamer, iteration=0, plot_synthesized_image=True,
plot_rep_comparison=True, plot_signal_comparison=True,
**kwargs):
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synthesis_video | (metamer_save_path, model_name=None) | Create video showing synthesis progress, for presentations.
Creates videos showing the metamer, representation, and pixels over time.
Works best if synthesis was run with store_progress=1, or some other low
value. Will create three videos, so can be used for a build. Will be saved
in the same directory... | Create video showing synthesis progress, for presentations. | def synthesis_video(metamer_save_path, model_name=None):
"""Create video showing synthesis progress, for presentations.
Creates videos showing the metamer, representation, and pixels over time.
Works best if synthesis was run with store_progress=1, or some other low
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pooling_window_area | (windows, windows_scale=0, units='degrees') | Plot window area as function of eccentricity.
Plots the area of the window bands as function of eccentricity, with a
horizontal line corresponding to a single pixel.
Parameters
----------
windows : pooling.PoolingWindows
The PoolingWindows object to plot.
windows_scale : int, optional
... | Plot window area as function of eccentricity. | def pooling_window_area(windows, windows_scale=0, units='degrees'):
"""Plot window area as function of eccentricity.
Plots the area of the window bands as function of eccentricity, with a
horizontal line corresponding to a single pixel.
Parameters
----------
windows : pooling.PoolingWindows
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synthesis_pixel_diff | (stim, stim_df, scaling) | Show average pixel-wise squared error for a given scaling value.
WARNING: This is reasonably memory-intensive.
stim has dtype np.uint8. We convert back to np.float32 (and rescale to [0,
1] interval) for these calculations.
Parameters
----------
stim : np.ndarray
The array of metamers ... | Show average pixel-wise squared error for a given scaling value. | def synthesis_pixel_diff(stim, stim_df, scaling):
"""Show average pixel-wise squared error for a given scaling value.
WARNING: This is reasonably memory-intensive.
stim has dtype np.uint8. We convert back to np.float32 (and rescale to [0,
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Parameters
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simulate_num_trials | (params, row='critical_scaling_true', col='variable') | Create figure summarizing num_trials simulations.
Assumes only one true value of proportionality_factor (will still work if
not true, just might not be as good-looking).
Parameters
----------
params : pd.DataFrame
DataFrame containing results from several num_trials simulations
Return... | Create figure summarizing num_trials simulations. | def simulate_num_trials(params, row='critical_scaling_true', col='variable'):
"""Create figure summarizing num_trials simulations.
Assumes only one true value of proportionality_factor (will still work if
not true, just might not be as good-looking).
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----------
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performance_plot | (expt_df, col='image_name', row=None, hue=None, style=None,
col_wrap=5, ci=95, curve_fit=False, logscale_xaxis=False,
tabular_trial_type_legend=False, **kwargs) | Plot performance as function of scaling.
With default arguments, this is meant to show the results for all sessions
and a single subject, showing the different images on each column. It
should be flexible enough to handle other variants.
Parameters
----------
expt_df : pd.DataFrame
Dat... | Plot performance as function of scaling. | def performance_plot(expt_df, col='image_name', row=None, hue=None, style=None,
col_wrap=5, ci=95, curve_fit=False, logscale_xaxis=False,
tabular_trial_type_legend=False, **kwargs):
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run_length_plot | (expt_df, col=None, row=None, hue=None, col_wrap=None,
comparison='ref') | Plot run length.
With default arguments, this is meant to show the results for all sessions
and a single subject. It should be flexible enough to handle other
variants.
Parameters
----------
expt_df : pd.DataFrame
DataFrame containing the results of at least one session for at least
... | Plot run length. | def run_length_plot(expt_df, col=None, row=None, hue=None, col_wrap=None,
comparison='ref'):
"""Plot run length.
With default arguments, this is meant to show the results for all sessions
and a single subject. It should be flexible enough to handle other
variants.
Parameters
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compare_loss_and_performance_plot | (expt_df, stim_df, x='loss', col='scaling',
row=None, hue='image_name', col_wrap=4,
plot_kind='scatter', height=3,
logscale_xaxis=True) | Compare an image metric with behavioral performance.
By default, this plots synthesis loss on the x-axis and proportion correct
on the y-axis, to see if there's any relationship there. Hopefully, there's
not (that is, synthesis progressed to the point where there's no real
difference in image from more... | Compare an image metric with behavioral performance. | def compare_loss_and_performance_plot(expt_df, stim_df, x='loss', col='scaling',
row=None, hue='image_name', col_wrap=4,
plot_kind='scatter', height=3,
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posterior_predictive_check | (inf_data, col=None, row=None, hue=None,
style=None, col_wrap=5, comparison='ref',
logscale_xaxis=False, hdi=.95, query_str=None,
tabular_trial_type_legend=False, **kwargs) | Plot posterior predictive check.
In order to make sure that our MCMC gave us a reasonable fit, we plot the
posterior predictive probability correct curve against the observed
responses.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (xarray-like) create... | Plot posterior predictive check. | def posterior_predictive_check(inf_data, col=None, row=None, hue=None,
style=None, col_wrap=5, comparison='ref',
logscale_xaxis=False, hdi=.95, query_str=None,
tabular_trial_type_legend=False, **kwargs):
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mcmc_diagnostics_plot | (inf_data) | Plot MCMC diagnostics.
This plot contains the posterior distributions and sampling trace for all
parameters (each chain showne), with r-hat and effective sample size (both
diagnostic stats) on the plots.
r-hat: ratio of average variance of samples within each chain to the
variance of pooled sample... | Plot MCMC diagnostics. | def mcmc_diagnostics_plot(inf_data):
"""Plot MCMC diagnostics.
This plot contains the posterior distributions and sampling trace for all
parameters (each chain showne), with r-hat and effective sample size (both
diagnostic stats) on the plots.
r-hat: ratio of average variance of samples within eac... | [
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parameter_pairplot | (inf_data, vars=None,
query_str="distribution=='posterior'", **kwargs) | Joint distributions of posterior parameter values.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (xarray-like) created by `run_inference`.
vars : list or None, optional
List of strs giving the parameters to plot here. If None, will plot all.
query_s... | Joint distributions of posterior parameter values. | def parameter_pairplot(inf_data, vars=None,
query_str="distribution=='posterior'", **kwargs):
"""Joint distributions of posterior parameter values.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (xarray-like) created by `run_inference`.
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1082,
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1129,
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psychophysical_curve_parameters | (inf_data, x='image_name', y='value',
hue='subject_name', col='parameter',
row='trial_type', style=None,
query_str="distribution=='posterior'",
height=2.5, x_dodge=.15, hdi=.95... | Show psychophysical curve parameters, with HDI error bars.
This plots the psychophysical curve parameters for all full curves we can
draw. That is, we combine the effects of our model and show the values for
each trial type, image, and subject.
Parameters
----------
inf_data : arviz.InferenceD... | Show psychophysical curve parameters, with HDI error bars. | def psychophysical_curve_parameters(inf_data, x='image_name', y='value',
hue='subject_name', col='parameter',
row='trial_type', style=None,
query_str="distribution=='posterior'",
... | [
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ref_image_summary | (stim, stim_df, zoom=.125) | Display grid of reference images used for metamer synthesis.
We gamma-correct the reference images before display and title each with
the simple name (e.g., "llama", "troop")
Parameters
----------
stim : np.ndarray
The array of metamers we want to check, should correspond to stim_df
st... | Display grid of reference images used for metamer synthesis. | def ref_image_summary(stim, stim_df, zoom=.125):
"""Display grid of reference images used for metamer synthesis.
We gamma-correct the reference images before display and title each with
the simple name (e.g., "llama", "troop")
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stim : np.ndarray
The array of metame... | [
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synthesis_distance_plot | (distances, xy='trial_type', hue='ref_image',
col='distance_scaling',
row='image_1_init_supertype', x=None, y=None,
**kwargs) | Plot distances between metamers and their reference images.
With default arguments, scatterplot comparing the metamer_vs_metamer
distance to the metamer_vs_reference distance (where distance and synthesis
were computed using the same model). We can then see that metamers
initialized with uniform noise ... | Plot distances between metamers and their reference images. | def synthesis_distance_plot(distances, xy='trial_type', hue='ref_image',
col='distance_scaling',
row='image_1_init_supertype', x=None, y=None,
**kwargs):
"""Plot distances between metamers and their reference images.
With defau... | [
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partially_pooled_metaparameters | (inf_data, hue='model', style='trial_type',
distribution='posterior', hdi=.95,
height=5, aspect=1,
rotate_xticklabels=False,
x_dodge=False, **kwargs) | Plot the metaparameters of the partially pooled mcmc model.
The metaparametesr are the lapse rate and those that control the
distribution across images/subjects of a0 and s0.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (xarray-like) created by `run_infer... | Plot the metaparameters of the partially pooled mcmc model. | def partially_pooled_metaparameters(inf_data, hue='model', style='trial_type',
distribution='posterior', hdi=.95,
height=5, aspect=1,
rotate_xticklabels=False,
x_dodge=False, *... | [
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partially_pooled_parameters | (inf_data, hue='model', style='trial_type',
distribution='posterior', hdi=.95, height=4,
aspect=2, rotate_xticklabels=True,
x_dodge=False, **kwargs) | Plot the subject/image level parameters of partially pooled mcmc model.
These are log_a0/s0_image/subject.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (xarray-like) created by `run_inference`
hue, style : str, optional
variables to facet along.
... | Plot the subject/image level parameters of partially pooled mcmc model. | def partially_pooled_parameters(inf_data, hue='model', style='trial_type',
distribution='posterior', hdi=.95, height=4,
aspect=2, rotate_xticklabels=True,
x_dodge=False, **kwargs):
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psychophysical_grouplevel_means | (inf_data, x='dependent_var', y='value',
hue='model', col='level', row='parameter',
style='trial_type',
query_str="distribution=='posterior'",
height=4, aspect=2, x_dodge=.15, ... | Show psychophysical group-level means, with HDI error bars.
This plots the psychophysical group-level means for all full curves we can
draw. That is, we compute the psychophysical curve parameters and then
average over the images and subjects (separately), plotting their
distributions.
Parameters
... | Show psychophysical group-level means, with HDI error bars. | def psychophysical_grouplevel_means(inf_data, x='dependent_var', y='value',
hue='model', col='level', row='parameter',
style='trial_type',
query_str="distribution=='posterior'",
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amplitude_spectra | (spectra, hue='scaling', style=None, col='image_name',
row=None, col_wrap=5, kind='line', estimator=None,
height=2.5, aspect=1, **kwargs) | Compare amplitude spectra of natural and synthesized images.
Parameters
----------
spectra : xarray.Dataset
Dataset containing the spectra for synthesized metamers and our natural
reference images.
hue, style, col, row : str or None, optional
The dimensions in spectra to fac... | Compare amplitude spectra of natural and synthesized images.
Parameters
----------
spectra : xarray.Dataset
Dataset containing the spectra for synthesized metamers and our natural
reference images.
hue, style, col, row : str or None, optional
The dimensions in spectra to fac... | def amplitude_spectra(spectra, hue='scaling', style=None, col='image_name',
row=None, col_wrap=5, kind='line', estimator=None,
height=2.5, aspect=1, **kwargs):
"""Compare amplitude spectra of natural and synthesized images.
Parameters
----------
spectra :... | [
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amplitude_orientation | (spectra, hue='scaling', style=None, col='image_name',
row=None, col_wrap=5, kind='point',
estimator=np.median, height=2.5, aspect=2,
demean=False, **kwargs) | Compare orientation distributions of natural and synthesized images.
Note this is fairly memory intensive. Setting `n_boot` to a lower number
(defaults to 1000) seems to help with this.
Parameters
----------
spectra : xarray.Dataset
Dataset containing the spectra for synthesized metamers a... | Compare orientation distributions of natural and synthesized images. | def amplitude_orientation(spectra, hue='scaling', style=None, col='image_name',
row=None, col_wrap=5, kind='point',
estimator=np.median, height=2.5, aspect=2,
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dacey_mcmc_plot | (inf_data, df, aspect=1, logscale_axes=True, hdi=.95) | Plot data and predicted lines from Dacey 1992 data and our MCMC fit.
We use MCMC to fit hinged line (with intercept) to Dacey 1992's dendritic
field diameter data. This plots that data with the line with HDI.
Parameters
----------
inf_data : arviz.InferenceData
arviz InferenceData object (... | Plot data and predicted lines from Dacey 1992 data and our MCMC fit. | def dacey_mcmc_plot(inf_data, df, aspect=1, logscale_axes=True, hdi=.95):
"""Plot data and predicted lines from Dacey 1992 data and our MCMC fit.
We use MCMC to fit hinged line (with intercept) to Dacey 1992's dendritic
field diameter data. This plots that data with the line with HDI.
Parameters
-... | [
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compare_distance_and_performance | (expt_df, dist_df, col='image_name',
row=None, hue='scaling',
style='trial_type', col_wrap=5, height=3,
logscale_xaxis=False) | Compare model distance and human performance.
This is intended for use with a single distance model (e.g., the
ObserverModel), to visualize the relationship between model distance and
human behavioral performance. We just plot them against each other, not
doing anything to try and use distance to predi... | Compare model distance and human performance. | def compare_distance_and_performance(expt_df, dist_df, col='image_name',
row=None, hue='scaling',
style='trial_type', col_wrap=5, height=3,
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feature | (self, node="clickhouse1") | Check create settings profile query syntax.
```sql
CREATE [SETTINGS] PROFILE [IF NOT EXISTS | OR REPLACE] name [ON CLUSTER cluster_name]
[SETTINGS variable [= value] [MIN [=] min_value] [MAX [=] max_value]
[READONLY] | [INHERIT|PROFILE 'profile_name']] [,...]
[TO {role [,...] | ALL | ALL EXCEPT... | Check create settings profile query syntax. | def feature(self, node="clickhouse1"):
"""Check create settings profile query syntax.
```sql
CREATE [SETTINGS] PROFILE [IF NOT EXISTS | OR REPLACE] name [ON CLUSTER cluster_name]
[SETTINGS variable [= value] [MIN [=] min_value] [MAX [=] max_value]
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snr | (s, n) | Compute the signal-to-noise ratio in dB where X=SNR(signal,noise) (it does not subtract the means).
Parameters
----------
s: `torch.Tensor`
Signal tensor
n: `torch.Tensor`
Noise tensor
| Compute the signal-to-noise ratio in dB where X=SNR(signal,noise) (it does not subtract the means). | def snr(s, n):
"""Compute the signal-to-noise ratio in dB where X=SNR(signal,noise) (it does not subtract the means).
Parameters
----------
s: `torch.Tensor`
Signal tensor
n: `torch.Tensor`
Noise tensor
"""
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modskew | (ch, sk, p=1) | Adjust the sample skewness of a vector/matrix using gradient projection.
This adjusts the skewness without affecting its sample mean and
variance.
This operation is not an orthogonal projection, but the projection angle is
near pi/2 when sk is close to the original skewness, which is a realistic
a... | Adjust the sample skewness of a vector/matrix using gradient projection. | def modskew(ch, sk, p=1):
"""Adjust the sample skewness of a vector/matrix using gradient projection.
This adjusts the skewness without affecting its sample mean and
variance.
This operation is not an orthogonal projection, but the projection angle is
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Clamper.clamp | (self) | Clamp the image
| Clamp the image
| def clamp(self):
"""Clamp the image
"""
pass | [
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RangeClamper.clamp | (self, im) | Clamp ``im`` so its range lies within ``range``
We use ``torch.clamp``, so that all values below
``self.range[0]`` are set to ``self.range[0]`` and all values
above ``self.range[1]`` are set to ``self.range[1]``.
Parameters
----------
im : torch.Tensor
The i... | Clamp ``im`` so its range lies within ``range`` | def clamp(self, im):
"""Clamp ``im`` so its range lies within ``range``
We use ``torch.clamp``, so that all values below
``self.range[0]`` are set to ``self.range[0]`` and all values
above ``self.range[1]`` are set to ``self.range[1]``.
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----------
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TwoMomentsClamper.clamp | (self, im) | Clamp ``im`` so its range and first two moments match ``targ``
We first match the mean and variance, then clamp the range (so
that all values below ``self.target.min()`` are set to
``self.targ.min()`` and all values above ``self.targ.max()`` are
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FourMomentsClamper.clamp | (self, im) | Clamp ``im`` so its range and first four moments match ``targ``
We first match the kurtosis, then the skew, then mean and
variance (together), then clamp the range (so that all values
below ``self.target.min()`` are set to ``self.targ.min()`` and
all values above ``self.targ.max()`` are... | Clamp ``im`` so its range and first four moments match ``targ`` | def clamp(self, im):
"""Clamp ``im`` so its range and first four moments match ``targ``
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RangeRemapper.clamp | (self, im) | Remap the range of ``im`` to ``self.range``
We remap as follows:
```
im = im + im.min() + range[0]
im = (im / im.max()) * range[1]
```
Note that we first check whether this is necessary: we don't do
the first line if im.min() > range[0], and we don't do the
... | Remap the range of ``im`` to ``self.range`` | def clamp(self, im):
"""Remap the range of ``im`` to ``self.range``
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```
im = im + im.min() + range[0]
im = (im / im.max()) * range[1]
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summarize_trials | (raw_behavioral_path) | r"""Summarize trials in order to determine whether subject was correct or not.
With this, we create a n_trials by 4 array with the following
structure: [trial number, time of trial end, button pressed, time
button press was recorded, time when image flashed off].
If you're using pyglet, as assumed her... | r"""Summarize trials in order to determine whether subject was correct or not. | def summarize_trials(raw_behavioral_path):
r"""Summarize trials in order to determine whether subject was correct or not.
With this, we create a n_trials by 4 array with the following
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] | python | en | ['en', 'en', 'en'] | True |
plot_timing_info | (trials, subject_name, session_number, run_number,
figsize=(5,5)) | Create scatter plot of timing info.
This takes the trials array created to summarize subject responses and
plots the response time vs the time before trial end, with color determined
by which button was pressed. This is mainly done to make sure everything's
working as expected: time before trial end sh... | Create scatter plot of timing info. | def plot_timing_info(trials, subject_name, session_number, run_number,
figsize=(5,5)):
"""Create scatter plot of timing info.
This takes the trials array created to summarize subject responses and
plots the response time vs the time before trial end, with color determined
by which ... | [
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_get_values | (df, trial_num, correct_answers, idx=[], labels=[],
dep_variables=['scaling']) | Grab appropriate values out of df.
Helper function for assembling the experiment_df based on a set of
presentation indices
| Grab appropriate values out of df. | def _get_values(df, trial_num, correct_answers, idx=[], labels=[],
dep_variables=['scaling']):
"""Grab appropriate values out of df.
Helper function for assembling the experiment_df based on a set of
presentation indices
"""
sub_df = df[['image_name']]
tmp = sub_df.loc[idx[0]].... | [
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create_experiment_df_split | (df, presentation_idx, dep_variables=['scaling']) | r"""Create a dataframe summarizing the trials of the split-screen experiment.
This function takes in the dataframe summarizing the stimuli, the
presentation indices, and some dependent variables, and creates a
dataframe summarizing each trial in the experiment. We have the
following columns:
- 'ima... | r"""Create a dataframe summarizing the trials of the split-screen experiment. | def create_experiment_df_split(df, presentation_idx, dep_variables=['scaling']):
r"""Create a dataframe summarizing the trials of the split-screen experiment.
This function takes in the dataframe summarizing the stimuli, the
presentation indices, and some dependent variables, and creates a
dataframe su... | [
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add_response_info | (expt_df, trials, subject_name, session_number, run_number) | r"""Add information about subject's response and correctness to expt_df.
This function takes the expt_df, which summarizes the trials of the
experiment, and adds several additional columns:
- 'subject_response', which gives the number (1 or 2) the subject pressed
on this trial
- 'response_time',... | r"""Add information about subject's response and correctness to expt_df. | def add_response_info(expt_df, trials, subject_name, session_number, run_number):
r"""Add information about subject's response and correctness to expt_df.
This function takes the expt_df, which summarizes the trials of the
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- 'subject_response', which gi... | [
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summarize_expt | (expt_df, dep_variables=['subject_name', 'scaling', 'trial_type'], bootstrap_num=0) | r"""Summarize expt_df to get proportion correct
Here, we take the ``expt_df`` summarizing the experiment's trials and the
subject's responses, and we compute the proportion correct on each trial
type. We end up with a DataFrame that has the columns ``['subject_name',
'model', 'trial_type'] + dep_variab... | r"""Summarize expt_df to get proportion correct | def summarize_expt(expt_df, dep_variables=['subject_name', 'scaling', 'trial_type'], bootstrap_num=0):
r"""Summarize expt_df to get proportion correct
Here, we take the ``expt_df`` summarizing the experiment's trials and the
subject's responses, and we compute the proportion correct on each trial
type.... | [
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feature | (self, node="clickhouse1") | Check create row policy query syntax.
```sql
CREATE [ROW] POLICY [IF NOT EXISTS | OR REPLACE] policy_name [ON CLUSTER cluster_name] ON [db.]table
[AS {PERMISSIVE | RESTRICTIVE}]
[FOR SELECT]
[USING condition]
[TO {role [,...] | ALL | ALL EXCEPT role [,...]}]
```
| Check create row policy query syntax. | def feature(self, node="clickhouse1"):
"""Check create row policy query syntax.
```sql
CREATE [ROW] POLICY [IF NOT EXISTS | OR REPLACE] policy_name [ON CLUSTER cluster_name] ON [db.]table
[AS {PERMISSIVE | RESTRICTIVE}]
[FOR SELECT]
[USING condition]
[TO {role [,...] | ALL | ALL EXCEPT role... | [
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day_name | (obj) | Backwards compatible name of day getting function.
Parameters
----------
obj : Union[Series, pd.Timestamp]
Returns
-------
str
The name of the day corresponding to `obj`
| Backwards compatible name of day getting function. | def day_name(obj):
"""Backwards compatible name of day getting function.
Parameters
----------
obj : Union[Series, pd.Timestamp]
Returns
-------
str
The name of the day corresponding to `obj`
"""
try:
return obj.day_name()
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load_data | (database_filepath) |
Read the data to be modeled from sqlite database
:param database_filepath: Path of database, including database name
:return:
Messages to be used as features for modeling
Binary values for target features
Unique target classes in the data
|
Read the data to be modeled from sqlite database
:param database_filepath: Path of database, including database name
:return:
Messages to be used as features for modeling
Binary values for target features
Unique target classes in the data
| def load_data(database_filepath):
"""
Read the data to be modeled from sqlite database
:param database_filepath: Path of database, including database name
:return:
Messages to be used as features for modeling
Binary values for target features
Unique target classes in the data
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tokenize | (text) |
Clean, tokenize and lemmatize text
:param text: Text to tokenize
:return: list of tokens
|
Clean, tokenize and lemmatize text
:param text: Text to tokenize
:return: list of tokens
| def tokenize(text):
"""
Clean, tokenize and lemmatize text
:param text: Text to tokenize
:return: list of tokens
"""
tokenizer = RegexpTokenizer(r'\w+')
tokens = tokenizer.tokenize(text)
# tagged = nltk.pos_tag(tokens)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
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get_wordnet_pos | (treebank_tag) |
Convert Treebank tags to Wordnet tags
:param treebank_tag: Treebank tag
:return: Wordnet tag or None
|
Convert Treebank tags to Wordnet tags
:param treebank_tag: Treebank tag
:return: Wordnet tag or None
| def get_wordnet_pos(treebank_tag):
"""
Convert Treebank tags to Wordnet tags
:param treebank_tag: Treebank tag
:return: Wordnet tag or None
"""
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
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build_model | () |
Pipeline model with grid search to tune parameters
:return: Model
|
Pipeline model with grid search to tune parameters
:return: Model
| def build_model():
"""
Pipeline model with grid search to tune parameters
:return: Model
"""
pipeline = Pipeline([
('vec', CountVectorizer(strip_accents='unicode', tokenizer=tokenize,
max_features=5000, stop_words=stopwords.words('english'))),
('tfidf'... | [
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evaluate_model | (model, X_test, Y_test, category_names) |
Predict targets for test data and evaluate model performance through classification report
:param model: Trained model
:param X_test: Features for test data
:param Y_test: Actual targets for test data
:param category_names: Categories in data
:return: None
|
Predict targets for test data and evaluate model performance through classification report
:param model: Trained model
:param X_test: Features for test data
:param Y_test: Actual targets for test data
:param category_names: Categories in data
:return: None
| def evaluate_model(model, X_test, Y_test, category_names):
"""
Predict targets for test data and evaluate model performance through classification report
:param model: Trained model
:param X_test: Features for test data
:param Y_test: Actual targets for test data
:param category_names: Categorie... | [
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save_model | (model, model_filepath) |
Pickle trained model
:param model: Trained model
:param model_filepath: Location to save pickled model
:return: None
|
Pickle trained model
:param model: Trained model
:param model_filepath: Location to save pickled model
:return: None
| def save_model(model, model_filepath):
"""
Pickle trained model
:param model: Trained model
:param model_filepath: Location to save pickled model
:return: None
"""
pickle.dump(model, open(model_filepath, 'wb')) | [
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privileges_granted_directly | (self, node=None) | Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted directly.
| Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted directly.
| def privileges_granted_directly(self, node=None):
"""Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted directly.
"""
user_name = f"user_{getuid()}"
if node is None:
node = self.context.node
with user(node, f"{user_name}"):
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privileges_granted_via_role | (self, node=None) | Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted via role.
| Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted via role.
| def privileges_granted_via_role(self, node=None):
"""Check that a user is able to execute `SYSTEM FLUSH LOGS` commands if and only if
the privilege has been granted via role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
if node is None:
node = self.context.node
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flush_logs | (self, privilege, on, grant_target_name, user_name, node=None) | Check that user is only able to execute `SYSTEM START REPLICATED FLUSH` when they have privilege.
| Check that user is only able to execute `SYSTEM START REPLICATED FLUSH` when they have privilege.
| def flush_logs(self, privilege, on, grant_target_name, user_name, node=None):
"""Check that user is only able to execute `SYSTEM START REPLICATED FLUSH` when they have privilege.
"""
exitcode, message = errors.not_enough_privileges(name=user_name)
if node is None:
node = self.context.node
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distributed_privileges_granted_directly | (self, node=None) | Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted directly.
| Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted directly.
| def distributed_privileges_granted_directly(self, node=None):
"""Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted directly.
"""
user_name = f"user_{getuid()}"
if node is None:
node = self.context.node
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distributed_privileges_granted_via_role | (self, node=None) | Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted via role.
| Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted via role.
| def distributed_privileges_granted_via_role(self, node=None):
"""Check that a user is able to execute `SYSTEM FLUSH DISTRIBUTED` commands if and only if
the privilege has been granted via role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
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flush_distributed | (self, privilege, on, grant_target_name, user_name, table_name, node=None) | Check that user is only able to execute `SYSTEM FLUSH DISTRIBUTED` when they have privilege.
| Check that user is only able to execute `SYSTEM FLUSH DISTRIBUTED` when they have privilege.
| def flush_distributed(self, privilege, on, grant_target_name, user_name, table_name, node=None):
"""Check that user is only able to execute `SYSTEM FLUSH DISTRIBUTED` when they have privilege.
"""
exitcode, message = errors.not_enough_privileges(name=user_name)
table0_name = f"table0_{getuid()}"
if... | [
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feature | (self, node="clickhouse1") | Check the RBAC functionality of SYSTEM FLUSH.
| Check the RBAC functionality of SYSTEM FLUSH.
| def feature(self, node="clickhouse1"):
"""Check the RBAC functionality of SYSTEM FLUSH.
"""
self.context.node = self.context.cluster.node(node)
Suite(run=privileges_granted_directly, setup=instrument_clickhouse_server_log)
Suite(run=privileges_granted_via_role, setup=instrument_clickhouse_server_lo... | [
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normal_range_start | (self) | Check DateTime64 can accept a dates around the start of the normal range that begins at 1970-01-01 00:00:00.000.
| Check DateTime64 can accept a dates around the start of the normal range that begins at 1970-01-01 00:00:00.000.
| def normal_range_start(self):
"""Check DateTime64 can accept a dates around the start of the normal range that begins at 1970-01-01 00:00:00.000.
"""
with When("I do incrementing time sweep", description="check different time points in the first 24 hours at given date"):
walk_datetime_in_incrementin... | [
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normal_range_end | (self) | Check DateTime64 can accept a dates around the end of the normal range that ends at 2105-12-31 23:59:59.99999.
| Check DateTime64 can accept a dates around the end of the normal range that ends at 2105-12-31 23:59:59.99999.
| def normal_range_end(self):
"""Check DateTime64 can accept a dates around the end of the normal range that ends at 2105-12-31 23:59:59.99999.
"""
with When("I do decrementing time sweep",
description="check different time points in the last 24 hours at given date"):
walk_datetime_in_decr... | [
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"\"2105... | [
25,
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] | [
30,
94
] | python | en | ['en', 'en', 'en'] | True |
extended_range_start | (self) | Check DateTime64 supports dates around the beginning of the extended range that begins at 1698-01-01 00:00:00.000000.
| Check DateTime64 supports dates around the beginning of the extended range that begins at 1698-01-01 00:00:00.000000.
| def extended_range_start(self):
"""Check DateTime64 supports dates around the beginning of the extended range that begins at 1698-01-01 00:00:00.000000.
"""
with When("I do incrementing time sweep",
description="check different time points in the first 24 hours at given date"):
walk_date... | [
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"\... | [
37,
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] | [
42,
94
] | python | en | ['en', 'en', 'en'] | True |
extended_range_end | (self, precision=3) | Check DateTime64 supports dates around the beginning of the extended range that ends at 2377-12-31T23:59:59.999999.
| Check DateTime64 supports dates around the beginning of the extended range that ends at 2377-12-31T23:59:59.999999.
| def extended_range_end(self, precision=3):
"""Check DateTime64 supports dates around the beginning of the extended range that ends at 2377-12-31T23:59:59.999999.
"""
with When("I do decrementing time sweep",
description="check different time points in the last 24 hours at given date"):
w... | [
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49,
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] | [
54,
94
] | python | en | ['en', 'en', 'en'] | True |
timezone_local_below_normal_range | (self) | Check how UTC normal range time value treated
when current timezone time value is out of normal range.
| Check how UTC normal range time value treated
when current timezone time value is out of normal range.
| def timezone_local_below_normal_range(self):
"""Check how UTC normal range time value treated
when current timezone time value is out of normal range.
"""
with When("I do incrementing time sweep",
description="check different time points when UTC datetime fits normal range but below it for l... | [
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"walk_datetime_in_incr... | [
61,
0
] | [
67,
110
] | python | en | ['en', 'en', 'en'] | True |
timezone_local_above_normal_range | (self) | Check how UTC normal range time value treated
when current timezone time value is out of normal range.
| Check how UTC normal range time value treated
when current timezone time value is out of normal range.
| def timezone_local_above_normal_range(self):
"""Check how UTC normal range time value treated
when current timezone time value is out of normal range.
"""
with When("I do decrementing time sweep",
description="check different time points when UTC datetime fits normal range but above it for... | [
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")",
":",
"walk_datetime_in_decr... | [
74,
0
] | [
80,
109
] | python | en | ['en', 'en', 'en'] | True |
comparison_check | (self) | Check how comparison works with DateTime64 extended range.
| Check how comparison works with DateTime64 extended range.
| def comparison_check(self):
"""Check how comparison works with DateTime64 extended range.
"""
stress = self.context.stress
comparators = (">", "<", "==", "<=", ">=", "!=")
timezones = timezones_range(stress=stress)
datetimes = []
for year in years_range(stress=stress):
datetimes += ... | [
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... | [
87,
0
] | [
111,
68
] | python | en | ['en', 'en', 'en'] | True |
timezones_support | (self) | Check how timezones work with DateTime64 extended range.
| Check how timezones work with DateTime64 extended range.
| def timezones_support(self):
"""Check how timezones work with DateTime64 extended range.
"""
stress = self.context.stress
timezones = timezones_range(stress=stress)
for year in years_range(stress):
with Given("I select datetimes in a year"):
datetimes = select_dates_in_year(year... | [
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... | [
118,
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] | [
136,
71
] | python | en | ['en', 'en', 'en'] | True |
generic | (self, node="clickhouse1") | Check the basic operations with DateTime64
| Check the basic operations with DateTime64
| def generic(self, node="clickhouse1"):
"""Check the basic operations with DateTime64
"""
self.context.node = self.context.cluster.node(node)
for scenario in loads(current_module(), Scenario, Suite):
Scenario(run=scenario) | [
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"(",... | [
140,
0
] | [
146,
30
] | python | en | ['en', 'en', 'en'] | True |
compile | (expr, params=None) |
Force compilation of expression as though it were an expression depending
on MapD. Note you can also call expr.compile()
Returns
-------
compiled : string
|
Force compilation of expression as though it were an expression depending
on MapD. Note you can also call expr.compile() | def compile(expr, params=None):
"""
Force compilation of expression as though it were an expression depending
on MapD. Note you can also call expr.compile()
Returns
-------
compiled : string
"""
from ibis.mapd.compiler import to_sql
return to_sql(expr, dialect.make_context(params=pa... | [
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7,
0
] | [
17,
60
] | python | en | ['en', 'error', 'th'] | False |
verify | (expr, params=None) |
Determine if expression can be successfully translated to execute on
MapD
|
Determine if expression can be successfully translated to execute on
MapD
| def verify(expr, params=None):
"""
Determine if expression can be successfully translated to execute on
MapD
"""
try:
compile(expr, params=params)
return True
except com.TranslationError:
return False | [
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] | python | en | ['en', 'error', 'th'] | False |
connect | (
uri=None, user=None, password=None, host=None,
port=9091, database=None, protocol='binary',
execution_type=EXECUTION_TYPE_CURSOR
) | Create a MapDClient for use with Ibis
Parameters could be
:param uri: str
:param user: str
:param password: str
:param host: str
:param port: int
:param database: str
:param protocol: str
:param execution_type: int
Returns
-------
MapDClient
| Create a MapDClient for use with Ibis | def connect(
uri=None, user=None, password=None, host=None,
port=9091, database=None, protocol='binary',
execution_type=EXECUTION_TYPE_CURSOR
):
"""Create a MapDClient for use with Ibis
Parameters could be
:param uri: str
:param user: str
:param password: str
:param host: str
:... | [
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privileges_granted_directly | (self, node=None) | Check that a user is able to grant role with `ROLE ADMIN` privilege granted directly.
| Check that a user is able to grant role with `ROLE ADMIN` privilege granted directly.
| def privileges_granted_directly(self, node=None):
"""Check that a user is able to grant role with `ROLE ADMIN` privilege granted directly.
"""
user_name = f"user_{getuid()}"
if node is None:
node = self.context.node
with user(node, f"{user_name}"):
Suite(test=role_admin)(grant_ta... | [
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8,
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] | [
19,
80
] | python | en | ['en', 'en', 'en'] | True |
privileges_granted_via_role | (self, node=None) | Check that a user is able to grant role with `ROLE ADMIN` privilege granted through a role.
| Check that a user is able to grant role with `ROLE ADMIN` privilege granted through a role.
| def privileges_granted_via_role(self, node=None):
"""Check that a user is able to grant role with `ROLE ADMIN` privilege granted through a role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
if node is None:
node = self.context.node
with user(node, f"{user_name}"),... | [
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80
] | python | en | ['en', 'en', 'en'] | True |
role_admin | (self, grant_target_name, user_name, node=None) | Check that user is able to execute to grant roles if and only if they have `ROLE ADMIN`.
| Check that user is able to execute to grant roles if and only if they have `ROLE ADMIN`.
| def role_admin(self, grant_target_name, user_name, node=None):
"""Check that user is able to execute to grant roles if and only if they have `ROLE ADMIN`.
"""
exitcode, message = errors.not_enough_privileges(name=user_name)
if node is None:
node = self.context.node
with Scenario("Grant rol... | [
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139,
115
] | python | en | ['en', 'en', 'en'] | True |
feature | (self, node="clickhouse1") | Check the RBAC functionality of ROLE ADMIN.
| Check the RBAC functionality of ROLE ADMIN.
| def feature(self, node="clickhouse1"):
"""Check the RBAC functionality of ROLE ADMIN.
"""
self.context.node = self.context.cluster.node(node)
Suite(run=privileges_granted_directly, setup=instrument_clickhouse_server_log)
Suite(run=privileges_granted_via_role, setup=instrument_clickhouse_server_log) | [
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... | [
148,
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] | [
154,
82
] | python | en | ['en', 'en', 'en'] | True |
GroupedTableExpr.having | (self, expr) |
Add a post-aggregation result filter (like the having argument in
`aggregate`), for composability with the group_by API
Parameters
----------
expr : ibis.expr.types.Expr
Returns
-------
grouped : GroupedTableExpr
|
Add a post-aggregation result filter (like the having argument in
`aggregate`), for composability with the group_by API | def having(self, expr):
"""
Add a post-aggregation result filter (like the having argument in
`aggregate`), for composability with the group_by API
Parameters
----------
expr : ibis.expr.types.Expr
Returns
-------
grouped : GroupedTableExpr
... | [
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115,
9
] | python | en | ['en', 'error', 'th'] | False |
GroupedTableExpr.order_by | (self, expr) |
Expressions to use for ordering data for a window function
computation. Ignored in aggregations.
Parameters
----------
expr : value expression or list of value expressions
Returns
-------
grouped : GroupedTableExpr
|
Expressions to use for ordering data for a window function
computation. Ignored in aggregations. | def order_by(self, expr):
"""
Expressions to use for ordering data for a window function
computation. Ignored in aggregations.
Parameters
----------
expr : value expression or list of value expressions
Returns
-------
grouped : GroupedTableExpr
... | [
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135,
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] | python | en | ['en', 'error', 'th'] | False |
GroupedTableExpr.mutate | (self, exprs=None, **kwds) |
Returns a table projection with analytic / window functions
applied. Any arguments can be functions.
Parameters
----------
exprs : list, default None
kwds : key=value pairs
Examples
--------
>>> import ibis
>>> t = ibis.table([
.... |
Returns a table projection with analytic / window functions
applied. Any arguments can be functions. | def mutate(self, exprs=None, **kwds):
"""
Returns a table projection with analytic / window functions
applied. Any arguments can be functions.
Parameters
----------
exprs : list, default None
kwds : key=value pairs
Examples
--------
>>> i... | [
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