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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
feature | (self, node="clickhouse1", stress=None, parallel=None) | Check the RBAC functionality of TRUNCATE.
| Check the RBAC functionality of TRUNCATE.
| def feature(self, node="clickhouse1", stress=None, parallel=None):
"""Check the RBAC functionality of TRUNCATE.
"""
self.context.node = self.context.cluster.node(node)
if parallel is not None:
self.context.parallel = parallel
if stress is not None:
self.context.stress = stress
... | [
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... | [
125,
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] | [
143,
77
] | python | en | ['en', 'en', 'en'] | True |
TestGetVectorIdsBase.test_list_id_in_segment_collection_name_None | (self, connect, collection) |
target: get vector ids where collection name is None
method: call list_id_in_segment with the collection_name: None
expected: exception raised
|
target: get vector ids where collection name is None
method: call list_id_in_segment with the collection_name: None
expected: exception raised
| def test_list_id_in_segment_collection_name_None(self, connect, collection):
'''
target: get vector ids where collection name is None
method: call list_id_in_segment with the collection_name: None
expected: exception raised
'''
collection_name = None
name = self.g... | [
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... | [
37,
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] | [
46,
82
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_collection_name_not_existed | (self, connect, collection) |
target: get vector ids where collection name does not exist
method: call list_id_in_segment with a random collection_name, which is not in db
expected: status not ok
|
target: get vector ids where collection name does not exist
method: call list_id_in_segment with a random collection_name, which is not in db
expected: status not ok
| def test_list_id_in_segment_collection_name_not_existed(self, connect, collection):
'''
target: get vector ids where collection name does not exist
method: call list_id_in_segment with a random collection_name, which is not in db
expected: status not ok
'''
collection_nam... | [
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"test_list_id_in_segment_collection_name_not_existed",
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"... | [
49,
4
] | [
58,
30
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_collection_name_invalid | (self, connect, collection, get_collection_name) |
target: get vector ids where collection name is invalid
method: call list_id_in_segment with invalid collection_name
expected: status not ok
|
target: get vector ids where collection name is invalid
method: call list_id_in_segment with invalid collection_name
expected: status not ok
| def test_list_id_in_segment_collection_name_invalid(self, connect, collection, get_collection_name):
'''
target: get vector ids where collection name is invalid
method: call list_id_in_segment with invalid collection_name
expected: status not ok
'''
collection_name = get_... | [
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"test_list_id_in_segment_collection_name_invalid",
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... | [
68,
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] | [
77,
30
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_name_None | (self, connect, collection) |
target: get vector ids where segment name is None
method: call list_id_in_segment with the name: None
expected: exception raised
|
target: get vector ids where segment name is None
method: call list_id_in_segment with the name: None
expected: exception raised
| def test_list_id_in_segment_name_None(self, connect, collection):
'''
target: get vector ids where segment name is None
method: call list_id_in_segment with the name: None
expected: exception raised
'''
valid_name = self.get_valid_name(connect, collection)
segment... | [
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"Excep... | [
80,
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] | [
89,
80
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_name_not_existed | (self, connect, collection) |
target: get vector ids where segment name does not exist
method: call list_id_in_segment with a random segment name
expected: status not ok
|
target: get vector ids where segment name does not exist
method: call list_id_in_segment with a random segment name
expected: status not ok
| def test_list_id_in_segment_name_not_existed(self, connect, collection):
'''
target: get vector ids where segment name does not exist
method: call list_id_in_segment with a random segment name
expected: status not ok
'''
valid_name = self.get_valid_name(connect, collectio... | [
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"... | [
92,
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] | [
102,
30
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_without_index_A | (self, connect, collection) |
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_A(self, connect, collection):
'''
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
vectors = gen_vector(10, dim)
status, ids = ... | [
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105,
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] | [
122,
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] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_without_index_B | (self, connect, collection) |
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_B(self, connect, collection):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
'''
... | [
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"gen_vec... | [
126,
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146,
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TestGetVectorIdsBase.test_list_id_in_segment_with_index_A | (self, connect, collection, get_simple_index) |
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_A(self, connect, collection, get_simple_index):
'''
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
index_param = get_simple_index["index_pa... | [
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"test_list_id_in_segment_with_index_A",
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"s... | [
161,
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] | [
182,
42
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_with_index_B | (self, connect, collection, get_simple_index) |
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_B(self, connect, collection, get_simple_index):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
... | [
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"i... | [
185,
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] | [
209,
42
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsBase.test_list_id_in_segment_after_delete_vectors | (self, connect, collection) |
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
|
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
| def test_list_id_in_segment_after_delete_vectors(self, connect, collection):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
'''
vectors = ge... | [
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"test_list_id_in_segment_after_delete_vectors",
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... | [
212,
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] | [
229,
38
] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsIP.test_list_id_in_segment_without_index_A | (self, connect, ip_collection) |
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_A(self, connect, ip_collection):
'''
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
vectors = gen_vector(10, dim)
status, ids... | [
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... | [
239,
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] | [
256,
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] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsIP.test_list_id_in_segment_without_index_B | (self, connect, ip_collection) |
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_B(self, connect, ip_collection):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
'''
... | [
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280,
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] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsIP.test_list_id_in_segment_with_index_A | (self, connect, ip_collection, get_simple_index) |
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_A(self, connect, ip_collection, get_simple_index):
'''
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
index_param = get_simple_index["index... | [
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... | [
295,
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316,
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] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsIP.test_list_id_in_segment_with_index_B | (self, connect, ip_collection, get_simple_index) |
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_B(self, connect, ip_collection, get_simple_index):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
... | [
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TestGetVectorIdsIP.test_list_id_in_segment_after_delete_vectors | (self, connect, ip_collection) |
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
|
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
| def test_list_id_in_segment_after_delete_vectors(self, connect, ip_collection):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
'''
vectors =... | [
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TestGetVectorIdsJAC.test_list_id_in_segment_without_index_A | (self, connect, jac_collection) |
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_A(self, connect, jac_collection):
'''
target: get vector ids when there is no index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
tmp, vectors = gen_binary_vectors(10, dim)
... | [
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TestGetVectorIdsJAC.test_list_id_in_segment_without_index_B | (self, connect, jac_collection) |
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_without_index_B(self, connect, jac_collection):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
'''
... | [
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413,
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TestGetVectorIdsJAC.test_list_id_in_segment_with_index_A | (self, connect, jac_collection, get_jaccard_index) |
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_A(self, connect, jac_collection, get_jaccard_index):
'''
target: get vector ids when there is index
method: call list_id_in_segment and check if the segment contains vectors
expected: status ok
'''
index_param = get_jaccard_index["in... | [
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427,
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] | [
448,
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] | python | en | ['en', 'error', 'th'] | False |
TestGetVectorIdsJAC.test_list_id_in_segment_with_index_B | (self, connect, jac_collection, get_jaccard_index) |
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
|
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok
| def test_list_id_in_segment_with_index_B(self, connect, jac_collection, get_jaccard_index):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call list_id_in_segment, check if the segment contains vectors
expected: status ok... | [
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... | [
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475,
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TestGetVectorIdsJAC.test_list_id_in_segment_after_delete_vectors | (self, connect, jac_collection) |
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
|
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
| def test_list_id_in_segment_after_delete_vectors(self, connect, jac_collection):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call list_id_in_segment
expected: status ok, vector_ids decreased after vectors deleted
'''
tmp, vec... | [
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plain_text | (self) | Check that we can perform LDAP user authentication using `plain text` connection protocol.
| Check that we can perform LDAP user authentication using `plain text` connection protocol.
| def plain_text(self):
"""Check that we can perform LDAP user authentication using `plain text` connection protocol.
"""
servers = {
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plain_text_with_custom_port | (self) | Check that we can perform LDAP user authentication using `plain text` connection protocol
with the server that uses custom port.
| Check that we can perform LDAP user authentication using `plain text` connection protocol
with the server that uses custom port.
| def plain_text_with_custom_port(self):
"""Check that we can perform LDAP user authentication using `plain text` connection protocol
with the server that uses custom port.
"""
servers = {
"openldap3": {
"host": "openldap3",
"port": "3089",
"enable_tls": "no",
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tls_with_custom_port | (self) | Check that we can perform LDAP user authentication using `TLS` connection protocol
with the server that uses custom port.
| Check that we can perform LDAP user authentication using `TLS` connection protocol
with the server that uses custom port.
| def tls_with_custom_port(self):
"""Check that we can perform LDAP user authentication using `TLS` connection protocol
with the server that uses custom port.
"""
servers = {
"openldap4": {
"host": "openldap4",
"port": "6036",
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starttls_with_custom_port | (self) | Check that we can perform LDAP user authentication using `StartTLS` connection protocol
with the server that uses custom port.
| Check that we can perform LDAP user authentication using `StartTLS` connection protocol
with the server that uses custom port.
| def starttls_with_custom_port(self):
"""Check that we can perform LDAP user authentication using `StartTLS` connection protocol
with the server that uses custom port.
"""
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tls_connection | (enable_tls, tls_require_cert) | Try to login using LDAP user authentication over a TLS connection.
| Try to login using LDAP user authentication over a TLS connection.
| def tls_connection(enable_tls, tls_require_cert):
"""Try to login using LDAP user authentication over a TLS connection.
"""
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tls | (self) | Check that we can perform LDAP user authentication using `TLS` connection protocol.
| Check that we can perform LDAP user authentication using `TLS` connection protocol.
| def tls(self):
"""Check that we can perform LDAP user authentication using `TLS` connection protocol.
"""
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tls_enable_tls_default_yes | (self) | Check that the default value for the `enable_tls` is set to `yes`.
| Check that the default value for the `enable_tls` is set to `yes`.
| def tls_enable_tls_default_yes(self):
"""Check that the default value for the `enable_tls` is set to `yes`.
"""
servers = {
"openldap2": {
"host": "openldap2",
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tls_require_cert_default_demand | (self) | Check that the default value for the `tls_require_cert` is set to `demand`.
| Check that the default value for the `tls_require_cert` is set to `demand`.
| def tls_require_cert_default_demand(self):
"""Check that the default value for the `tls_require_cert` is set to `demand`.
"""
servers = {
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starttls | (self) | Check that we can perform LDAP user authentication using legacy `StartTLS` connection protocol.
| Check that we can perform LDAP user authentication using legacy `StartTLS` connection protocol.
| def starttls(self):
"""Check that we can perform LDAP user authentication using legacy `StartTLS` connection protocol.
"""
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tls_cipher_suite | (self) | Check that `tls_cipher_suite` parameter can be used specify allowed cipher suites.
| Check that `tls_cipher_suite` parameter can be used specify allowed cipher suites.
| def tls_cipher_suite(self):
"""Check that `tls_cipher_suite` parameter can be used specify allowed cipher suites.
"""
servers = {
"openldap4": {
"host": "openldap4",
"port": "6036",
"tls_require_cert": "never",
"tls_cipher_suite": "SECURE256:+SECURE128... | [
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tls_minimum_protocol_version | (self, version, exitcode, message) | Check that `tls_minimum_protocol_version` parameter can be used specify
to specify the minimum protocol version of SSL/TLS.
| Check that `tls_minimum_protocol_version` parameter can be used specify
to specify the minimum protocol version of SSL/TLS.
| def tls_minimum_protocol_version(self, version, exitcode, message):
"""Check that `tls_minimum_protocol_version` parameter can be used specify
to specify the minimum protocol version of SSL/TLS.
"""
servers = {
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feature | (self, node="clickhouse1") | Check different LDAP connection protocols.
| Check different LDAP connection protocols.
| def feature(self, node="clickhouse1"):
"""Check different LDAP connection protocols.
"""
self.context.node = self.context.cluster.node(node)
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KanikoImage.build | (self) | build docker image from target
| build docker image from target
| def build(self) -> None:
''' build docker image from target
'''
logger.info(f'Building docker image for target: {self._target.info}')
logger.info(f"Docker image: {self._image_uri}")
# Build docker image with kaniko executor
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KanikoImage.update_config | (settings:dict) | Update kaniko configuration
| Update kaniko configuration
| def update_config(settings:dict) -> None:
''' Update kaniko configuration
'''
CI_REGISTRY = os.environ.get("CI_REGISTRY")
CI_REGISTRY_USER = os.environ.get("CI_REGISTRY_USER")
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_reduce_geom_precision | (geom, precision=2) | Grom solaris.utils.geo | Grom solaris.utils.geo | def _reduce_geom_precision(geom, precision=2):
"Grom solaris.utils.geo"
geojson = mapping(geom)
geojson['coordinates'] = np.round(np.array(geojson['coordinates']),
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convert_poly_coords | (geom:shape, raster_src:str=None, affine_obj:affine.Affine=None,
inverse:bool=False, precision=None) | Adapted from solaris. Converts georeferenced coordinates to pixel coordinates and vice versa | Adapted from solaris. Converts georeferenced coordinates to pixel coordinates and vice versa | def convert_poly_coords(geom:shape, raster_src:str=None, affine_obj:affine.Affine=None,
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affine_transform_gdf | (gdf:gpd.GeoDataFrame, affine_obj:affine.Affine, inverse:bool=False,
geom_col:str='geometry', precision:int=None) | Adapted from solaris, transforms all geometries in GeoDataFrame to pixel coordinates from
Georeferced coordinates and vice versa | Adapted from solaris, transforms all geometries in GeoDataFrame to pixel coordinates from
Georeferced coordinates and vice versa | def affine_transform_gdf(gdf:gpd.GeoDataFrame, affine_obj:affine.Affine, inverse:bool=False,
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gdf_to_px | (gdf:gpd.GeoDataFrame, im_path, geom_col:str='geometry', precision:int=None,
outpath=None, override_crs=False) | Adapted from https://solaris.readthedocs.io/en/latest/_modules/solaris/vector/polygon.html#geojson_to_px_gdf | Adapted from https://solaris.readthedocs.io/en/latest/_modules/solaris/vector/polygon.html#geojson_to_px_gdf | def gdf_to_px(gdf:gpd.GeoDataFrame, im_path, geom_col:str='geometry', precision:int=None,
outpath=None, override_crs=False) -> gpd.GeoDataFrame:
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ObserverModel.forward | (self, image, scales=[]) | r"""Generate the V1 representation of an image.
Parameters
----------
image : torch.Tensor
A tensor containing the image to analyze. We want to operate
on this in the pytorch-y way, so we want it to be 4d (batch,
channel, height, width).
scales : list... | r"""Generate the V1 representation of an image. | def forward(self, image, scales=[]):
r"""Generate the V1 representation of an image.
Parameters
----------
image : torch.Tensor
A tensor containing the image to analyze. We want to operate
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ObserverModel._sf_weighting | (self) | Get weights based on sf_weighting.
This should then be passed to PoolingWindows, so it can appropriately
weight the different scales at each location.
| Get weights based on sf_weighting. | def _sf_weighting(self):
"""Get weights based on sf_weighting.
This should then be passed to PoolingWindows, so it can appropriately
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ObserverModel._modal_frequencies | (self) | Compute modal frequencies (in cpd) of steerable pyramid scales.
This sets self.modal_freqs_cpd.
In order to weight the spatial frequency representation at each
location, need to know the modal frequency of each scale of the
pyramid. This computes that by finding the argmax of the radia... | Compute modal frequencies (in cpd) of steerable pyramid scales. | def _modal_frequencies(self):
"""Compute modal frequencies (in cpd) of steerable pyramid scales.
This sets self.modal_freqs_cpd.
In order to weight the spatial frequency representation at each
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ObserverModel._gen_spatial_masks | (self, n_angles=4) | r"""Generate spatial masks.
Create and return masks that allow us to specifically select values
from model's representation that correspond to different regions of the
image. See ``summarize_representation()`` for an example of how to use
them
Parameters
----------
... | r"""Generate spatial masks. | def _gen_spatial_masks(self, n_angles=4):
r"""Generate spatial masks.
Create and return masks that allow us to specifically select values
from model's representation that correspond to different regions of the
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ObserverModel.to | (self, *args, do_windows=True, **kwargs) | r"""Moves and/or casts the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
.. function:: to(dtype, non_blocking=False)
.. function:: to(tensor, non_blocking=False)
Its signature is similar to :meth:`torch.Tensor.to`... | r"""Moves and/or casts the parameters and buffers. | def to(self, *args, do_windows=True, **kwargs):
r"""Moves and/or casts the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None, non_blocking=False)
.. function:: to(dtype, non_blocking=False)
.. function:: to(tensor, non_blocking=False)
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ObserverModel.plot_windows | (self, ax=None, contour_levels=None, colors='r',
subset=True, **kwargs) | r"""Plot the pooling windows on an image.
This is just a simple little helper to plot the pooling windows
on an existing axis. The use case is overlaying this on top of
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and so we require an axis to be passed
Any addi... | r"""Plot the pooling windows on an image. | def plot_windows(self, ax=None, contour_levels=None, colors='r',
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ObserverModel.summarize_window_sizes | (self) | r"""Summarize window sizes.
This function returns a dictionary summarizing the window sizes
at the minimum and maximum eccentricity. Let ``min_window`` be
the window whose center is closest to ``self.min_eccentricity``
and ``max_window`` the one whose center is closest to
``self... | r"""Summarize window sizes. | def summarize_window_sizes(self):
r"""Summarize window sizes.
This function returns a dictionary summarizing the window sizes
at the minimum and maximum eccentricity. Let ``min_window`` be
the window whose center is closest to ``self.min_eccentricity``
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ObserverModel.plot_window_widths | (self, units='degrees', figsize=(5, 5), jitter=.25) | r"""Plot the widths of the windows, in degrees or pixels.
We plot the width of the window in both angular and radial direction,
as well as showing the 'half' and 'full' widths (full is the width of
the entire window; half is the width at the half-max value, which is
what corresponds to ... | r"""Plot the widths of the windows, in degrees or pixels. | def plot_window_widths(self, units='degrees', figsize=(5, 5), jitter=.25):
r"""Plot the widths of the windows, in degrees or pixels.
We plot the width of the window in both angular and radial direction,
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ObserverModel.plot_window_areas | (self, units='degrees', figsize=(5, 5)) | r"""Plot the approximate areas of the windows, in degrees or pixels.
We plot the approximate area of the window, calculated using 'half' and
'full' widths (full is the width of the entire window; half is the
width at the half-max value, which is what corresponds to the scaling
value). T... | r"""Plot the approximate areas of the windows, in degrees or pixels. | def plot_window_areas(self, units='degrees', figsize=(5, 5)):
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ObserverModel.save_reduced | (self, file_path) | r"""Save the relevant parameters to make saving/loading more efficient.
This saves self.state_dict_reduced, which contains the
attributes necessary to initialize the model plus a 'model_name'
key, which the ``load_reduced`` method uses to determine which
model constructor to call
... | r"""Save the relevant parameters to make saving/loading more efficient. | def save_reduced(self, file_path):
r"""Save the relevant parameters to make saving/loading more efficient.
This saves self.state_dict_reduced, which contains the
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ObserverModel.load_reduced | (cls, file_path) | r"""Load from the dictionary saved by ``save_reduced``.
Parameters
----------
file_path : str
The path to load the model object from
| r"""Load from the dictionary saved by ``save_reduced``. | def load_reduced(cls, file_path):
r"""Load from the dictionary saved by ``save_reduced``.
Parameters
----------
file_path : str
The path to load the model object from
"""
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ObserverModel.from_state_dict_reduced | (cls, state_dict_reduced) | r"""Initialize model from ``state_dict_reduced``.
Parameters
----------
state_dict_reduced : dict
The reduced state dict to load
| r"""Initialize model from ``state_dict_reduced``. | def from_state_dict_reduced(cls, state_dict_reduced):
r"""Initialize model from ``state_dict_reduced``.
Parameters
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state_dict_reduced : dict
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ObserverModel.output_to_representation | (self, data, scales=[]) | r"""Convert output to representation.
This takes data that looks like the output from the ``forward()`` call
(a big 3d tensor) and returns a dictionary whose keys are
``"mean_luminance"`` and ``(scale, orientation)`` tuples).
Parameters
----------
data : torch.Tensor
... | r"""Convert output to representation. | def output_to_representation(self, data, scales=[]):
r"""Convert output to representation.
This takes data that looks like the output from the ``forward()`` call
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ObserverModel.summarize_representation | (self, data, summary_func='mse', by_angle=False) | r"""Summarize representation by key and (optionally) quadrant.
This takes data and summarizes it within each key of the
dictionary. The intended use case is to get the mean-squared
error (by passing ``data=metamer.representation_error()``)
within each orientation and scale. With ``by_an... | r"""Summarize representation by key and (optionally) quadrant. | def summarize_representation(self, data, summary_func='mse', by_angle=False):
r"""Summarize representation by key and (optionally) quadrant.
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ObserverModel._get_title | (cls, title_list, idx, default_title) | r"""Helper function for dealing with the default way we handle title.
We have a couple possible ways of handling title in these
plotting functions, so this helper function consolidates
that.
When picking a title, we know we'll either have a list of titles
or a single None.
... | r"""Helper function for dealing with the default way we handle title. | def _get_title(cls, title_list, idx, default_title):
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We have a couple possible ways of handling title in these
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ObserverModel._representation_for_plotting | (self, data, batch_idx=0) | Transform representation for plotting.
Convert the 3d tensor output to a dictionary of 1d arrays containing
responses for a single batch_idx. Keys are ``'mean_luminance'`` and
``(scale, orientation)`` tuples.
Parameters
----------
data : torch.Tensor
The dat... | Transform representation for plotting. | def _representation_for_plotting(self, data, batch_idx=0):
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ObserverModel.update_plot | (self, axes, data, batch_idx=0) | r"""Update the information in our representation plot.
This is used for creating an animation of the representation
over time. In order to create the animation, we need to know how
to update the matplotlib Artists, and this provides a simple way
of doing that. It relies on the fact that... | r"""Update the information in our representation plot. | def update_plot(self, axes, data, batch_idx=0):
r"""Update the information in our representation plot.
This is used for creating an animation of the representation
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ObserverModel._plot_helper | (self, n_rows, n_cols, figsize=(25, 15),
ax=None, title=None, batch_idx=0) | r"""Helper function for plotting that takes care of a lot of the standard stuff.
Parameters
----------
n_rows : int
The number of rows in the (sub-)figure we're creating
n_cols : int
The number oc columns in the (sub-)figure we're creating
figsize : tuple... | r"""Helper function for plotting that takes care of a lot of the standard stuff. | def _plot_helper(self, n_rows, n_cols, figsize=(25, 15),
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ObserverModel.plot_representation | (self, data, figsize=(25, 15), ylim=None, ax=None,
title=None, batch_idx=0) | r"""Plot the representation of the V1 model.
Since our PooledV1 model has more statistics than the
PooledRGC model, this is a much more complicated
plot. We end up creating a grid, showing each band and scale
separately, and then a separate plot, off to the side, for the
mean pi... | r"""Plot the representation of the V1 model. | def plot_representation(self, data, figsize=(25, 15), ylim=None, ax=None,
title=None, batch_idx=0):
r"""Plot the representation of the V1 model.
Since our PooledV1 model has more statistics than the
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ObserverModel.plot_representation_image | (self, data, figsize=(22, 11), ax=None,
title=None, batch_idx=0, vrange='auto1',
zoom=1) | r"""Plot representation as an image, using the weights from PoolingWindows.
Our representation is composed of pooled energy at several different
scales and orientations, plus the pooled mean pixel intensity. In order
to visualize these as images, we take each statistic, multiply it by
t... | r"""Plot representation as an image, using the weights from PoolingWindows. | def plot_representation_image(self, data, figsize=(22, 11), ax=None,
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add | (x, y) | Simple addition function | Simple addition function | def add(x, y):
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subtract | (x, y) | Simple subtraction function | Simple subtraction function | def subtract(x, y):
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multiply | (x, y) | Simple multiplication function | Simple multiplication function | def multiply(x, y):
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divide | (x, y) | Simple division function | Simple division function | def divide(x, y):
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BaseEnv.__init__ | (self,
cost_function,
model,
dim_action,
simulation_horizon,
discrete=True,
seed=np.random.randint(1e6)
) |
Base class for epidemic-based environments.
Parameters
----------
cost_function: BaseCostFunction
A cost function.
model: BaseModel
An epidemiological model.
dim_action: int
Dimension of the action space.
simulation_horizon: i... |
Base class for epidemic-based environments. | def __init__(self,
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Base class for epidemic-based environments.
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BaseEnv.reset | (self) |
Reset the environment and the tracking of data.
Returns
-------
nd.array
The initial environment state.
|
Reset the environment and the tracking of data. | def reset(self):
"""
Reset the environment and the tracking of data.
Returns
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nd.array
The initial environment state.
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BaseEnv._update_env_state | (self) |
Update the environment state.
|
Update the environment state. | def _update_env_state(self):
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Update the environment state.
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BaseEnv.run_model | (self) |
Run model for one step.
Returns
-------
nd.array
New environment state.
|
Run model for one step. | def run_model(self):
"""
Run model for one step.
Returns
-------
nd.array
New environment state.
"""
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BaseEnv.step | (self, action) |
Traditional step function from OpenAI Gym envs. Uses the action to update the environment.
Parameters
----------
action: int if discrete, nd.array if continuous.
Returns
-------
state: nd.array
New environment state.
cost_aggregated: float
... |
Traditional step function from OpenAI Gym envs. Uses the action to update the environment. | def step(self, action):
"""
Traditional step function from OpenAI Gym envs. Uses the action to update the environment.
Parameters
----------
action: int if discrete, nd.array if continuous.
Returns
-------
state: nd.array
New environment stat... | [
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buildRapidNJ | (rapidnj, refList, coreMat, outPrefix, threads = 1) | Use rapidNJ for more rapid tree building
Creates a phylip of core distances, system call to rapidnj executable, loads tree as
dendropy object (cleaning quotes in node names), removes temporary files.
Args:
rapidnj (str)
Location of rapidnj executable
refList (list)
... | Use rapidNJ for more rapid tree building | def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, threads = 1):
"""Use rapidNJ for more rapid tree building
Creates a phylip of core distances, system call to rapidnj executable, loads tree as
dendropy object (cleaning quotes in node names), removes temporary files.
Args:
rapidnj (str)
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write_tree | (tree, prefix, suffix, overwrite) | Prints a Newick-formatted string to an output file
Args:
tree (str)
Newick-formatted string representation of tree
prefix (str)
Prefix for output files
suffix (str)
Suffix for output files
overwrite (bool)
Whether to overwrite existing... | Prints a Newick-formatted string to an output file | def write_tree(tree, prefix, suffix, overwrite):
"""Prints a Newick-formatted string to an output file
Args:
tree (str)
Newick-formatted string representation of tree
prefix (str)
Prefix for output files
suffix (str)
Suffix for output files
ov... | [
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load_tree | (prefix, type, distances = 'core') | Checks for existing trees from previous runs.
Args:
prefix (str)
Output prefix used for search
type (str)
Type of tree (NJ or MST)
Returns:
tree_string (str)
Newick-formatted string of NJ tree
| Checks for existing trees from previous runs. | def load_tree(prefix, type, distances = 'core'):
"""Checks for existing trees from previous runs.
Args:
prefix (str)
Output prefix used for search
type (str)
Type of tree (NJ or MST)
Returns:
tree_string (str)
Newick-formatted string of NJ tree
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generate_nj_tree | (coreMat, seqLabels, outPrefix, rapidnj, threads) | Generate phylogeny using dendropy or RapidNJ
Writes a neighbour joining tree (.nwk) from core distances.
Args:
coreMat (numpy.array)
n x n array of core distances for n samples.
seqLabels (list)
Processed names of sequences being analysed.
outPrefix (str)
... | Generate phylogeny using dendropy or RapidNJ | def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads):
"""Generate phylogeny using dendropy or RapidNJ
Writes a neighbour joining tree (.nwk) from core distances.
Args:
coreMat (numpy.array)
n x n array of core distances for n samples.
seqLabels (list)
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mst_to_phylogeny | (mst_network, names, use_gpu = False) | Convert a MST graph to a phylogeny
Args:
mst_network (network)
Minimum spanning tree from
:func:`~PopPUNK.network.generate_minimum_spanning_tree`
names (list)
Names of sequences in network
use_gpu (bool)
Whether to use GPU-specific libraries for pr... | Convert a MST graph to a phylogeny | def mst_to_phylogeny(mst_network, names, use_gpu = False):
"""Convert a MST graph to a phylogeny
Args:
mst_network (network)
Minimum spanning tree from
:func:`~PopPUNK.network.generate_minimum_spanning_tree`
names (list)
Names of sequences in network
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round_int_inline | (self, func, expected_result, supported, int_type, min, max, node=None) | Check rounding functions with Int128, UInt128, Int256, and UInt256 using inline tests.
| Check rounding functions with Int128, UInt128, Int256, and UInt256 using inline tests.
| def round_int_inline(self, func, expected_result, supported, int_type, min, max, node=None):
"""Check rounding functions with Int128, UInt128, Int256, and UInt256 using inline tests.
"""
if node is None:
node = self.context.node
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round_int_table | (self, func, expected_result, supported, int_type, min, max, node=None) | Check rounding functions with Int128, UInt128, Int256, and UInt256 using table tests.
| Check rounding functions with Int128, UInt128, Int256, and UInt256 using table tests.
| def round_int_table(self, func, expected_result, supported, int_type, min, max, node=None):
"""Check rounding functions with Int128, UInt128, Int256, and UInt256 using table tests.
"""
table_name = f"table_{getuid()}"
if node is None:
node = self.context.node
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round_dec_inline | (self, func, expected_result, supported, node=None) | Check rounding functions with Decimal256 using inline tests.
| Check rounding functions with Decimal256 using inline tests.
| def round_dec_inline(self, func, expected_result, supported, node=None):
"""Check rounding functions with Decimal256 using inline tests.
"""
min = Decimal256_min_max[0]
max = Decimal256_min_max[1]
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round_dec_table | (self, func, expected_result, supported, node=None) | Check rounding functions with Decimal256 using table tests.
| Check rounding functions with Decimal256 using table tests.
| def round_dec_table(self, func, expected_result, supported, node=None):
"""Check rounding functions with Decimal256 using table tests.
"""
min = Decimal256_min_max[0]
max = Decimal256_min_max[1]
table_name = f"table_{getuid()}"
if node is None:
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with Given(... | [
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feature | (self, node="clickhouse1", mysql_node="mysql1", stress=None, parallel=None) | Check that rounding functions work with extended precision data types.
| Check that rounding functions work with extended precision data types.
| def feature(self, node="clickhouse1", mysql_node="mysql1", stress=None, parallel=None):
"""Check that rounding functions work with extended precision data types.
"""
self.context.node = self.context.cluster.node(node)
self.context.mysql_node = self.context.cluster.node(mysql_node)
with allow_experi... | [
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row_policy | (self, name, table, node=None) | Create a row policy with a given name on a given table.
| Create a row policy with a given name on a given table.
| def row_policy(self, name, table, node=None):
"""Create a row policy with a given name on a given table.
"""
if node is None:
node = self.context.node
try:
with Given(f"I create row policy {name}"):
node.query(f"CREATE ROW POLICY {name} ON {table}")
yield
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convert_to_database_compatible_value | (value) | Pandas 0.23 broke DataFrame.to_sql, so we workaround it by rolling our
own extremely low-tech conversion routine
| Pandas 0.23 broke DataFrame.to_sql, so we workaround it by rolling our
own extremely low-tech conversion routine
| def convert_to_database_compatible_value(value):
"""Pandas 0.23 broke DataFrame.to_sql, so we workaround it by rolling our
own extremely low-tech conversion routine
"""
if pd.isnull(value):
return None
if isinstance(value, pd.Timestamp):
return value.to_pydatetime()
try:
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read_submission | (directory, expected_sequence_names, start_index=0, end_index=-1) |
Read all the submissions for all the sequences outlined in the given folder.
Each sequence's detections are provided in a file ending with 'detections.json'.
Each detections.json file contains a dictionary which has a key 'detections' containing a list of list of
detections for each image.
Individu... |
Read all the submissions for all the sequences outlined in the given folder.
Each sequence's detections are provided in a file ending with 'detections.json'.
Each detections.json file contains a dictionary which has a key 'detections' containing a list of list of
detections for each image.
Individu... | def read_submission(directory, expected_sequence_names, start_index=0, end_index=-1):
"""
Read all the submissions for all the sequences outlined in the given folder.
Each sequence's detections are provided in a file ending with 'detections.json'.
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read_sequence | (sequence_json, start_index=0, end_index=-1) |
Read a sequence's detection json file.
json file contains a dictionary which has a key 'detections' containing a list of list of
detections for each image.
Individual detections are given as dictionaries which have the keys:
'img_size': (height x width)
'img_num': int identifying which ... |
Read a sequence's detection json file.
json file contains a dictionary which has a key 'detections' containing a list of list of
detections for each image.
Individual detections are given as dictionaries which have the keys:
'img_size': (height x width)
'img_num': int identifying which ... | def read_sequence(sequence_json, start_index=0, end_index=-1):
"""
Read a sequence's detection json file.
json file contains a dictionary which has a key 'detections' containing a list of list of
detections for each image.
Individual detections are given as dictionaries which have the keys:
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gen_img_pboxes | (img_dets, class_mapping, num_classes=len(class_list.CLASSES), img_idx=-1, sequence_name='unknown') |
Generate DetectionInstances for a given image.
DetectionInstances will be of sub-class BBoxDetInst or PBoxDetInst.
BBoxDetInst is DetectionInstance for standard bounding box detection.
PBoxDetInst is DetectionInstance for probabilistic bounding box detection.
:param img_dets: list of detections giv... |
Generate DetectionInstances for a given image.
DetectionInstances will be of sub-class BBoxDetInst or PBoxDetInst.
BBoxDetInst is DetectionInstance for standard bounding box detection.
PBoxDetInst is DetectionInstance for probabilistic bounding box detection.
:param img_dets: list of detections giv... | def gen_img_pboxes(img_dets, class_mapping, num_classes=len(class_list.CLASSES), img_idx=-1, sequence_name='unknown'):
"""
Generate DetectionInstances for a given image.
DetectionInstances will be of sub-class BBoxDetInst or PBoxDetInst.
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is_positive_semi_definite | (mat) |
Check if a matrix is positive semi-definite, that is, all it's eigenvalues are positive.
All covariance matrices must be positive semi-definite.
Only works on symmetric matricies (due to eigh), so check that first
:param mat:
:return:
|
Check if a matrix is positive semi-definite, that is, all it's eigenvalues are positive.
All covariance matrices must be positive semi-definite.
Only works on symmetric matricies (due to eigh), so check that first
:param mat:
:return:
| def is_positive_semi_definite(mat):
"""
Check if a matrix is positive semi-definite, that is, all it's eigenvalues are positive.
All covariance matrices must be positive semi-definite.
Only works on symmetric matricies (due to eigh), so check that first
:param mat:
:return:
"""
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get_palette | (col, col_unique=None, as_dict=True) | Get palette for column.
Parameters
----------
col : {'subject_name', 'model', 'scaling', 'cell_type', str}
The column to return the palette for. If we don't have a particular
palette picked out, the palette will contain the strings 'C0', 'C1',
etc, which use the default palette.
... | Get palette for column. | def get_palette(col, col_unique=None, as_dict=True):
"""Get palette for column.
Parameters
----------
col : {'subject_name', 'model', 'scaling', 'cell_type', str}
The column to return the palette for. If we don't have a particular
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get_style | (col, col_unique, as_dict=True) | Get style for plotting.
Parameters
----------
col : {'trial_type', 'mcmc_model_type', list containing both elements}
The column to return the palette for. If we don't have a particular
style picked out, we raise an exception. If it's a list containing both
elements, we will combine ... | Get style for plotting. | def get_style(col, col_unique, as_dict=True):
"""Get style for plotting.
Parameters
----------
col : {'trial_type', 'mcmc_model_type', list containing both elements}
The column to return the palette for. If we don't have a particular
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get_order | (col, col_unique=[]) | Get order to plot something in.
For rows, cols, or x.
Parameters
----------
col : {'image_name', str}
The column to return the palette for. If we don't have a particular
order picked out, will just sort col_unique.
col_unique : list, optional
List of unique values in col.
... | Get order to plot something in. | def get_order(col, col_unique=[]):
"""Get order to plot something in.
For rows, cols, or x.
Parameters
----------
col : {'image_name', str}
The column to return the palette for. If we don't have a particular
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myLogFormat | (y, pos) | formatter that only shows the required number of decimal points
this is for use with log-scaled axes, and so assumes that everything greater than 1 is an
integer and so has no decimal points
to use (or equivalently, with `axis.xaxis`):
```
from matplotlib import ticker
axis.yaxis.set_major_for... | formatter that only shows the required number of decimal points | def myLogFormat(y, pos):
"""formatter that only shows the required number of decimal points
this is for use with log-scaled axes, and so assumes that everything greater than 1 is an
integer and so has no decimal points
to use (or equivalently, with `axis.xaxis`):
```
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_marker_adjust | (axes, marker_adjust, label_map) | Helper function to adjust markers after the fact. Pretty hacky.
Parameters
----------
axes : list
List of axes containing the markers to adjust
marker_adjust : dict
Dictionary with keys identifying the style level and values describing
how to adjust the markers. Can contain the ... | Helper function to adjust markers after the fact. Pretty hacky. | def _marker_adjust(axes, marker_adjust, label_map):
"""Helper function to adjust markers after the fact. Pretty hacky.
Parameters
----------
axes : list
List of axes containing the markers to adjust
marker_adjust : dict
Dictionary with keys identifying the style level and values des... | [
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_remap_image_names | (df, **extra_cols) | Prepare image names for plotting.
This function remaps the image names in df to drop the parts that are
duplicated across images ('symmetric_' and
'_range_-.05,.95_size-2048,2600'), as well as grabbing the correct order
Parameters
----------
df : pd.DataFrame
DataFrame containing the b... | Prepare image names for plotting. | def _remap_image_names(df, **extra_cols):
"""Prepare image names for plotting.
This function remaps the image names in df to drop the parts that are
duplicated across images ('symmetric_' and
'_range_-.05,.95_size-2048,2600'), as well as grabbing the correct order
Parameters
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_psychophysical_curve_ticks | (df, axes, logscale_xaxis=False, height=5,
col=None) | Set ticks appropriately for psychophysical curve plots.
This assumes scaling is on the x-axis (named exactly that) and proportion
correct is on the y-axis (name unconstrained). This sets major yticks at .5
and 1, as well as minor ones at .4, .6, .7, .8, .9. For xticks, we try to
determine a reasonable ... | Set ticks appropriately for psychophysical curve plots. | def _psychophysical_curve_ticks(df, axes, logscale_xaxis=False, height=5,
col=None):
"""Set ticks appropriately for psychophysical curve plots.
This assumes scaling is on the x-axis (named exactly that) and proportion
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_add_legend | (df, fig=None, hue=None, style=None, palette={},
final_markers={}, dashes_dict={}, legend_content='full',
tabular_trial_type_legend=False) | Add legend, making use of custom hue and style.
Since we modify the markers after the fact, we can't rely on seaborn's
built-in legend-generator. Instead, we create our own, based on how seaborn
does it, showing both hue and style in separable manner.
Parameters
----------
df : pd.DataFrame
... | Add legend, making use of custom hue and style. | def _add_legend(df, fig=None, hue=None, style=None, palette={},
final_markers={}, dashes_dict={}, legend_content='full',
tabular_trial_type_legend=False):
"""Add legend, making use of custom hue and style.
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_jitter_data | (data, jitter) | Optionally jitter data some amount.
jitter can be None / False (in which case no jittering is done), a number
(in which case we add uniform noise with a min of -jitter, max of jitter),
or True (in which case we do the uniform thing with min/max of -.1/.1)
based on seaborn.linearmodels._RegressionPlott... | Optionally jitter data some amount. | def _jitter_data(data, jitter):
"""Optionally jitter data some amount.
jitter can be None / False (in which case no jittering is done), a number
(in which case we add uniform noise with a min of -jitter, max of jitter),
or True (in which case we do the uniform thing with min/max of -.1/.1)
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637,
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is_numeric | (s) | Check whether data s is numeric.
s should be something that can be converted to an array: list, Series,
array, column from a DataFrame, etc
this is based on the function
seaborn.categorical._CategoricalPlotter.infer_orient.is_not_numeric
Parameters
----------
s :
data to check
... | Check whether data s is numeric. | def is_numeric(s):
"""Check whether data s is numeric.
s should be something that can be converted to an array: list, Series,
array, column from a DataFrame, etc
this is based on the function
seaborn.categorical._CategoricalPlotter.infer_orient.is_not_numeric
Parameters
----------
s :... | [
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_map_dataframe_prep | (data, x, y, estimator, x_jitter, x_dodge, x_order,
ci=68) | Prepare dataframe for plotting.
Several of the plotting functions are called by map_dataframe and
need a bit of prep work before plotting. These include:
- computing the central trend
- computing the CIs
- jittering, dodging, or ordering the x values
Parameters
----------
data : pd.Dat... | Prepare dataframe for plotting. | def _map_dataframe_prep(data, x, y, estimator, x_jitter, x_dodge, x_order,
ci=68):
"""Prepare dataframe for plotting.
Several of the plotting functions are called by map_dataframe and
need a bit of prep work before plotting. These include:
- computing the central trend
- com... | [
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752,
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scatter_ci_dist | (x, y, ci=68, x_jitter=None, join=False,
estimator=np.median, draw_ctr_pts=True, ci_mode='lines',
ci_alpha=.2, size=5, x_dodge=None, all_labels=None,
like_pointplot=False, style=None, dashes_dict={},
markers={}, **kwargs) | Plot center points and specified CIs, for use with map_dataframe.
based on seaborn.linearmodels.scatterplot. CIs are taken from a
distribution in this function. Therefore, it's assumed that the values
being passed to it are values from a bootstrap distribution or a Bayesian
posterior. seaborn.pointplot... | Plot center points and specified CIs, for use with map_dataframe. | def scatter_ci_dist(x, y, ci=68, x_jitter=None, join=False,
estimator=np.median, draw_ctr_pts=True, ci_mode='lines',
ci_alpha=.2, size=5, x_dodge=None, all_labels=None,
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map_flat_line | (x, y, data, linestyles='--', colors='k', ax=None, **kwargs) | Plot a flat line across every axis in a FacetGrid.
For use with seaborn's map_dataframe, this will plot a horizontal or
vertical line across all axes in a FacetGrid.
Parameters
----------
x, y : str, float, or list of floats
One of these must be a float (or list of floats), one a str. The ... | Plot a flat line across every axis in a FacetGrid. | def map_flat_line(x, y, data, linestyles='--', colors='k', ax=None, **kwargs):
"""Plot a flat line across every axis in a FacetGrid.
For use with seaborn's map_dataframe, this will plot a horizontal or
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Parameters
----------
x, y : str, float, or li... | [
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1028,
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get_log_ax_lims | (vals, base=10) | Get good limits for log-scale axis.
Since several plotting functions have trouble automatically doing this.
Parameters
----------
vals : array_like
The values plotted on the axis.
base : float, optional
The base of the axis
Returns
-------
lims : tuple
tuple of... | Get good limits for log-scale axis. | def get_log_ax_lims(vals, base=10):
"""Get good limits for log-scale axis.
Since several plotting functions have trouble automatically doing this.
Parameters
----------
vals : array_like
The values plotted on the axis.
base : float, optional
The base of the axis
Returns
... | [
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title_experiment_summary_plots | (g, expt_df, summary_text, post_text='') | Handle suptitle for FacetGrids summarizing experiment.
We want to handle the suptitle for these FacetGrids in a standard way:
- Add suptitle that describes contents (what subjects, sessions,
comparison)
- Make sure it's visible
Currently, used by: run_length_plot, compare_loss_and_performance_... | Handle suptitle for FacetGrids summarizing experiment. | def title_experiment_summary_plots(g, expt_df, summary_text, post_text=''):
"""Handle suptitle for FacetGrids summarizing experiment.
We want to handle the suptitle for these FacetGrids in a standard way:
- Add suptitle that describes contents (what subjects, sessions,
comparison)
- Make sure i... | [
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_label_and_title_psychophysical_curve_plot | (g, df, summary_text, ci=None, hdi=None) | Label and title plot.
Does some changes to titles and labels to make the plots pretty.
Parameters
----------
g : sns.FacetGrid
FacetGrid containing the plot
df : pd.DataFrame
Dataframe contanining the plotted data
summary_text : str
String summarizing what's shown in th... | Label and title plot. | def _label_and_title_psychophysical_curve_plot(g, df, summary_text, ci=None, hdi=None):
"""Label and title plot.
Does some changes to titles and labels to make the plots pretty.
Parameters
----------
g : sns.FacetGrid
FacetGrid containing the plot
df : pd.DataFrame
Dataframe co... | [
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