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@property
def response_code(self):
'\n Gets the response_code of this UpdateHttpRedirectDetails.\n The response code returned for the redirect to the client. For more information, see `RFC 7231`__.\n\n __ https://tools.ietf.org/html/rfc7231#section-6.4\n\n\n :return: The response_code of this UpdateHttpRedirectDetails.\n :rtype: int\n '
return self._response_code | -2,241,557,004,388,330,200 | Gets the response_code of this UpdateHttpRedirectDetails.
The response code returned for the redirect to the client. For more information, see `RFC 7231`__.
__ https://tools.ietf.org/html/rfc7231#section-6.4
:return: The response_code of this UpdateHttpRedirectDetails.
:rtype: int | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | response_code | revnav/sandbox | python | @property
def response_code(self):
'\n Gets the response_code of this UpdateHttpRedirectDetails.\n The response code returned for the redirect to the client. For more information, see `RFC 7231`__.\n\n __ https://tools.ietf.org/html/rfc7231#section-6.4\n\n\n :return: The response_code of this UpdateHttpRedirectDetails.\n :rtype: int\n '
return self._response_code |
@response_code.setter
def response_code(self, response_code):
'\n Sets the response_code of this UpdateHttpRedirectDetails.\n The response code returned for the redirect to the client. For more information, see `RFC 7231`__.\n\n __ https://tools.ietf.org/html/rfc7231#section-6.4\n\n\n :param response_code: The response_code of this UpdateHttpRedirectDetails.\n :type: int\n '
self._response_code = response_code | 1,383,919,902,411,671,000 | Sets the response_code of this UpdateHttpRedirectDetails.
The response code returned for the redirect to the client. For more information, see `RFC 7231`__.
__ https://tools.ietf.org/html/rfc7231#section-6.4
:param response_code: The response_code of this UpdateHttpRedirectDetails.
:type: int | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | response_code | revnav/sandbox | python | @response_code.setter
def response_code(self, response_code):
'\n Sets the response_code of this UpdateHttpRedirectDetails.\n The response code returned for the redirect to the client. For more information, see `RFC 7231`__.\n\n __ https://tools.ietf.org/html/rfc7231#section-6.4\n\n\n :param response_code: The response_code of this UpdateHttpRedirectDetails.\n :type: int\n '
self._response_code = response_code |
@property
def freeform_tags(self):
'\n Gets the freeform_tags of this UpdateHttpRedirectDetails.\n Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Department": "Finance"}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :return: The freeform_tags of this UpdateHttpRedirectDetails.\n :rtype: dict(str, str)\n '
return self._freeform_tags | 8,730,621,104,488,083,000 | Gets the freeform_tags of this UpdateHttpRedirectDetails.
Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.
For more information, see `Resource Tags`__.
Example: `{"Department": "Finance"}`
__ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm
:return: The freeform_tags of this UpdateHttpRedirectDetails.
:rtype: dict(str, str) | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | freeform_tags | revnav/sandbox | python | @property
def freeform_tags(self):
'\n Gets the freeform_tags of this UpdateHttpRedirectDetails.\n Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Department": "Finance"}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :return: The freeform_tags of this UpdateHttpRedirectDetails.\n :rtype: dict(str, str)\n '
return self._freeform_tags |
@freeform_tags.setter
def freeform_tags(self, freeform_tags):
'\n Sets the freeform_tags of this UpdateHttpRedirectDetails.\n Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Department": "Finance"}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :param freeform_tags: The freeform_tags of this UpdateHttpRedirectDetails.\n :type: dict(str, str)\n '
self._freeform_tags = freeform_tags | -6,774,238,780,399,861,000 | Sets the freeform_tags of this UpdateHttpRedirectDetails.
Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.
For more information, see `Resource Tags`__.
Example: `{"Department": "Finance"}`
__ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm
:param freeform_tags: The freeform_tags of this UpdateHttpRedirectDetails.
:type: dict(str, str) | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | freeform_tags | revnav/sandbox | python | @freeform_tags.setter
def freeform_tags(self, freeform_tags):
'\n Sets the freeform_tags of this UpdateHttpRedirectDetails.\n Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Department": "Finance"}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :param freeform_tags: The freeform_tags of this UpdateHttpRedirectDetails.\n :type: dict(str, str)\n '
self._freeform_tags = freeform_tags |
@property
def defined_tags(self):
'\n Gets the defined_tags of this UpdateHttpRedirectDetails.\n Defined tags for this resource. Each key is predefined and scoped to a namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Operations": {"CostCenter": "42"}}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :return: The defined_tags of this UpdateHttpRedirectDetails.\n :rtype: dict(str, dict(str, object))\n '
return self._defined_tags | -2,485,616,187,404,974,600 | Gets the defined_tags of this UpdateHttpRedirectDetails.
Defined tags for this resource. Each key is predefined and scoped to a namespace.
For more information, see `Resource Tags`__.
Example: `{"Operations": {"CostCenter": "42"}}`
__ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm
:return: The defined_tags of this UpdateHttpRedirectDetails.
:rtype: dict(str, dict(str, object)) | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | defined_tags | revnav/sandbox | python | @property
def defined_tags(self):
'\n Gets the defined_tags of this UpdateHttpRedirectDetails.\n Defined tags for this resource. Each key is predefined and scoped to a namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Operations": {"CostCenter": "42"}}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :return: The defined_tags of this UpdateHttpRedirectDetails.\n :rtype: dict(str, dict(str, object))\n '
return self._defined_tags |
@defined_tags.setter
def defined_tags(self, defined_tags):
'\n Sets the defined_tags of this UpdateHttpRedirectDetails.\n Defined tags for this resource. Each key is predefined and scoped to a namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Operations": {"CostCenter": "42"}}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :param defined_tags: The defined_tags of this UpdateHttpRedirectDetails.\n :type: dict(str, dict(str, object))\n '
self._defined_tags = defined_tags | 651,850,713,821,746,700 | Sets the defined_tags of this UpdateHttpRedirectDetails.
Defined tags for this resource. Each key is predefined and scoped to a namespace.
For more information, see `Resource Tags`__.
Example: `{"Operations": {"CostCenter": "42"}}`
__ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm
:param defined_tags: The defined_tags of this UpdateHttpRedirectDetails.
:type: dict(str, dict(str, object)) | darling_ansible/python_venv/lib/python3.7/site-packages/oci/waas/models/update_http_redirect_details.py | defined_tags | revnav/sandbox | python | @defined_tags.setter
def defined_tags(self, defined_tags):
'\n Sets the defined_tags of this UpdateHttpRedirectDetails.\n Defined tags for this resource. Each key is predefined and scoped to a namespace.\n For more information, see `Resource Tags`__.\n\n Example: `{"Operations": {"CostCenter": "42"}}`\n\n __ https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm\n\n\n :param defined_tags: The defined_tags of this UpdateHttpRedirectDetails.\n :type: dict(str, dict(str, object))\n '
self._defined_tags = defined_tags |
def __call__(self, image, label):
'Call function of RandomMirrow_pair.\n\n :param image: usually the feature image, for example, the LR image for super solution dataset,\n the initial image for the segmentation dataset, and etc\n :type image: PIL image\n :param label: usually the label image, for example, the HR image for super solution dataset,\n the mask image for the segmentation dataset, and etc\n :type lebel: PIL image\n :return: the image after transform\n :rtype: list, erery item is a PIL image, the first one is feature image, the second is label image\n '
flip = ((np.random.choice(2) * 2) - 1)
channels_image = image.shape[(- 1)]
channels_label = label.shape[(- 1)]
if (channels_image == 3):
image = image[:, :, ::flip]
else:
image = image[:, ::flip]
if (channels_label == 3):
label = label[:, :, ::flip]
else:
label = label[:, ::flip]
return (image, label) | 1,972,414,202,479,548,700 | Call function of RandomMirrow_pair.
:param image: usually the feature image, for example, the LR image for super solution dataset,
the initial image for the segmentation dataset, and etc
:type image: PIL image
:param label: usually the label image, for example, the HR image for super solution dataset,
the mask image for the segmentation dataset, and etc
:type lebel: PIL image
:return: the image after transform
:rtype: list, erery item is a PIL image, the first one is feature image, the second is label image | vega/datasets/transforms/RandomMirrow_pair.py | __call__ | NiuRc/vega | python | def __call__(self, image, label):
'Call function of RandomMirrow_pair.\n\n :param image: usually the feature image, for example, the LR image for super solution dataset,\n the initial image for the segmentation dataset, and etc\n :type image: PIL image\n :param label: usually the label image, for example, the HR image for super solution dataset,\n the mask image for the segmentation dataset, and etc\n :type lebel: PIL image\n :return: the image after transform\n :rtype: list, erery item is a PIL image, the first one is feature image, the second is label image\n '
flip = ((np.random.choice(2) * 2) - 1)
channels_image = image.shape[(- 1)]
channels_label = label.shape[(- 1)]
if (channels_image == 3):
image = image[:, :, ::flip]
else:
image = image[:, ::flip]
if (channels_label == 3):
label = label[:, :, ::flip]
else:
label = label[:, ::flip]
return (image, label) |
@classmethod
def setup_class(cls):
'Setup the test class.'
super().setup_class()
cls._patch_logger()
doc_path = os.path.join(ROOT_DIR, MD_FILE)
cls.code_blocks = extract_code_blocks(filepath=doc_path, filter_='python')
test_code_path = os.path.join(CUR_PATH, PY_FILE)
cls.python_file = extract_python_code(test_code_path) | -5,111,679,204,263,988,000 | Setup the test class. | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | setup_class | valory-xyz/agents-aea | python | @classmethod
def setup_class(cls):
super().setup_class()
cls._patch_logger()
doc_path = os.path.join(ROOT_DIR, MD_FILE)
cls.code_blocks = extract_code_blocks(filepath=doc_path, filter_='python')
test_code_path = os.path.join(CUR_PATH, PY_FILE)
cls.python_file = extract_python_code(test_code_path) |
def test_read_md_file(self):
'Test the last code block, that is the full listing of the demo from the Markdown.'
assert (self.code_blocks[(- 1)] == self.python_file), 'Files must be exactly the same.' | -1,915,740,591,947,311,900 | Test the last code block, that is the full listing of the demo from the Markdown. | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | test_read_md_file | valory-xyz/agents-aea | python | def test_read_md_file(self):
assert (self.code_blocks[(- 1)] == self.python_file), 'Files must be exactly the same.' |
@pytest.mark.integration(reruns=MAX_FLAKY_RERUNS_INTEGRATION)
def test_run_end_to_end(self):
'Run the transaction from the file.'
try:
run()
self.mocked_logger_info.assert_any_call('Transaction complete.')
except RuntimeError:
test_logger.info('RuntimeError: Some transactions have failed') | 6,095,263,727,091,250,000 | Run the transaction from the file. | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | test_run_end_to_end | valory-xyz/agents-aea | python | @pytest.mark.integration(reruns=MAX_FLAKY_RERUNS_INTEGRATION)
def test_run_end_to_end(self):
try:
run()
self.mocked_logger_info.assert_any_call('Transaction complete.')
except RuntimeError:
test_logger.info('RuntimeError: Some transactions have failed') |
def test_code_blocks_exist(self):
'Test that all the code-blocks exist in the python file.'
for blocks in self.code_blocks:
assert (blocks in self.python_file), "Code-block doesn't exist in the python file." | 5,125,484,754,777,977,000 | Test that all the code-blocks exist in the python file. | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | test_code_blocks_exist | valory-xyz/agents-aea | python | def test_code_blocks_exist(self):
for blocks in self.code_blocks:
assert (blocks in self.python_file), "Code-block doesn't exist in the python file." |
@tf_export('PeriodicResample')
def periodic_resample(values, shape, name=None):
'Periodically resample elements of a tensor to conform to `shape`.\n\n This function implements a slightly more generic version of the subpixel\n convolutions found in this [paper](https://arxiv.org/abs/1609.05158).\n\n The formula for computing the elements in the `output` tensor is as follows:\n `T` = `values` tensor of rank `R`\n `S` = desired `shape` of output tensor (vector of length `R`)\n `P` = `output` tensor of rank `R`\n \\((T_1,\\ldots,T_R)\\) = shape(`T`)\n \\([S_1,\\ldots,S_q,\\ldots,S_R]\\) = elements of vector `S`\n\n A single element in `S` is left unspecified (denoted \\(S_q=-1\\)).\n Let \\(f_i\\) denote the (possibly non-integer) factor that relates the original\n dimension to the desired dimensions, \\(S_i=f_i T_i\\), for \\(i\\neq q\\) where\n \\(f_i>0\\).\n Define the following:\n \\(g_i=\\lceil f_i\\rceil\\)\n \\(t=\\prod_i T_i\\)\n \\(s=\\prod_{i\\neq q} S_i\\)\n \\(S_q\\) can then be defined as by \\(S_q=\\lfloor t/s\\rfloor\\).\n The elements of the resulting tensor are defined as\n \\(P_{s_1,\\ldots,s_R}=T_{h_1,\\ldots,h_q,\\ldots,h_R}\\).\n The \\(h_i\\) (\\(i\\neq q\\)) are defined by \\(h_i=\\lfloor s_i/g_i\\rfloor\\).\n \\(h_q=S_q\\sum_{j\\neq q}^{q-1}G_j \\mathrm{mod}(s_j,g_j) + s_q\\), where\n \\(G_j=\\prod_{i}^{j-1}g_i\\) (\\(G_0=1\\)).\n\n One drawback of this method is that whenever the output dimensions are slightly\n less than integer multiples of the input dimensions, many of the tensor elements\n are repeated in an inefficient way. This is resolved by specifying that all\n desired dimensions are integer multiples of the input tensor.\n\n For example:\n\n ```prettyprint\n `input` is [[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]]\n\n tf.periodic_resample(input, [6, None]) ==> [[ 0 1]\n [ 2 3]\n [ 4 5]\n [ 6 7]\n [ 8 9]\n [10 11]]\n ```\n\n Args:\n values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`, `uint32`, `uint64`, `bfloat16`.\n The tensor of rank `R` to periodic_resample\n shape: A `tf.TensorShape` or list of `ints`.\n A 1-D tensor representing the desired shape of the output tensor.\n Exactly one element of this tensor must have the value `None` which represents\n that this dimension of `values` can be adjusted downward in order to\n accommodate increases in other dimensions. The specified sizes of the\n non-adjustable dimensions must by at least as large as in the `values` tensor.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `values`.\n Periodically resampled tensor that has dimensions specified as in\n `shape` except that the dimension specified as `None` will be minimally\n decreased as necessary.\n '
shape = _execute.make_shape(shape, 'shape')
_ctx = _context.context()
if _ctx.in_graph_mode():
(_, _, _op) = _op_def_lib._apply_op_helper('PeriodicResample', values=values, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ('T', _op.get_attr('T'), 'shape', _op.get_attr('shape'))
else:
(_attr_T, (values,)) = _execute.args_to_matching_eager([values], _ctx)
_inputs_flat = [values]
_attrs = ('T', _attr_T, 'shape', shape)
_result = _execute.execute(b'PeriodicResample', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient('PeriodicResample', _inputs_flat, _attrs, _result, name)
(_result,) = _result
return _result | 377,029,510,498,402,050 | Periodically resample elements of a tensor to conform to `shape`.
This function implements a slightly more generic version of the subpixel
convolutions found in this [paper](https://arxiv.org/abs/1609.05158).
The formula for computing the elements in the `output` tensor is as follows:
`T` = `values` tensor of rank `R`
`S` = desired `shape` of output tensor (vector of length `R`)
`P` = `output` tensor of rank `R`
\((T_1,\ldots,T_R)\) = shape(`T`)
\([S_1,\ldots,S_q,\ldots,S_R]\) = elements of vector `S`
A single element in `S` is left unspecified (denoted \(S_q=-1\)).
Let \(f_i\) denote the (possibly non-integer) factor that relates the original
dimension to the desired dimensions, \(S_i=f_i T_i\), for \(i\neq q\) where
\(f_i>0\).
Define the following:
\(g_i=\lceil f_i\rceil\)
\(t=\prod_i T_i\)
\(s=\prod_{i\neq q} S_i\)
\(S_q\) can then be defined as by \(S_q=\lfloor t/s\rfloor\).
The elements of the resulting tensor are defined as
\(P_{s_1,\ldots,s_R}=T_{h_1,\ldots,h_q,\ldots,h_R}\).
The \(h_i\) (\(i\neq q\)) are defined by \(h_i=\lfloor s_i/g_i\rfloor\).
\(h_q=S_q\sum_{j\neq q}^{q-1}G_j \mathrm{mod}(s_j,g_j) + s_q\), where
\(G_j=\prod_{i}^{j-1}g_i\) (\(G_0=1\)).
One drawback of this method is that whenever the output dimensions are slightly
less than integer multiples of the input dimensions, many of the tensor elements
are repeated in an inefficient way. This is resolved by specifying that all
desired dimensions are integer multiples of the input tensor.
For example:
```prettyprint
`input` is [[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
tf.periodic_resample(input, [6, None]) ==> [[ 0 1]
[ 2 3]
[ 4 5]
[ 6 7]
[ 8 9]
[10 11]]
```
Args:
values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`, `uint32`, `uint64`, `bfloat16`.
The tensor of rank `R` to periodic_resample
shape: A `tf.TensorShape` or list of `ints`.
A 1-D tensor representing the desired shape of the output tensor.
Exactly one element of this tensor must have the value `None` which represents
that this dimension of `values` can be adjusted downward in order to
accommodate increases in other dimensions. The specified sizes of the
non-adjustable dimensions must by at least as large as in the `values` tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `values`.
Periodically resampled tensor that has dimensions specified as in
`shape` except that the dimension specified as `None` will be minimally
decreased as necessary. | tensorflow/contrib/periodic_resample/python/ops/gen_periodic_resample_op.py | periodic_resample | gian1312/suchen | python | @tf_export('PeriodicResample')
def periodic_resample(values, shape, name=None):
'Periodically resample elements of a tensor to conform to `shape`.\n\n This function implements a slightly more generic version of the subpixel\n convolutions found in this [paper](https://arxiv.org/abs/1609.05158).\n\n The formula for computing the elements in the `output` tensor is as follows:\n `T` = `values` tensor of rank `R`\n `S` = desired `shape` of output tensor (vector of length `R`)\n `P` = `output` tensor of rank `R`\n \\((T_1,\\ldots,T_R)\\) = shape(`T`)\n \\([S_1,\\ldots,S_q,\\ldots,S_R]\\) = elements of vector `S`\n\n A single element in `S` is left unspecified (denoted \\(S_q=-1\\)).\n Let \\(f_i\\) denote the (possibly non-integer) factor that relates the original\n dimension to the desired dimensions, \\(S_i=f_i T_i\\), for \\(i\\neq q\\) where\n \\(f_i>0\\).\n Define the following:\n \\(g_i=\\lceil f_i\\rceil\\)\n \\(t=\\prod_i T_i\\)\n \\(s=\\prod_{i\\neq q} S_i\\)\n \\(S_q\\) can then be defined as by \\(S_q=\\lfloor t/s\\rfloor\\).\n The elements of the resulting tensor are defined as\n \\(P_{s_1,\\ldots,s_R}=T_{h_1,\\ldots,h_q,\\ldots,h_R}\\).\n The \\(h_i\\) (\\(i\\neq q\\)) are defined by \\(h_i=\\lfloor s_i/g_i\\rfloor\\).\n \\(h_q=S_q\\sum_{j\\neq q}^{q-1}G_j \\mathrm{mod}(s_j,g_j) + s_q\\), where\n \\(G_j=\\prod_{i}^{j-1}g_i\\) (\\(G_0=1\\)).\n\n One drawback of this method is that whenever the output dimensions are slightly\n less than integer multiples of the input dimensions, many of the tensor elements\n are repeated in an inefficient way. This is resolved by specifying that all\n desired dimensions are integer multiples of the input tensor.\n\n For example:\n\n ```prettyprint\n `input` is [[ 0 1 2 3]\n [ 4 5 6 7]\n [ 8 9 10 11]]\n\n tf.periodic_resample(input, [6, None]) ==> [[ 0 1]\n [ 2 3]\n [ 4 5]\n [ 6 7]\n [ 8 9]\n [10 11]]\n ```\n\n Args:\n values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`, `uint32`, `uint64`, `bfloat16`.\n The tensor of rank `R` to periodic_resample\n shape: A `tf.TensorShape` or list of `ints`.\n A 1-D tensor representing the desired shape of the output tensor.\n Exactly one element of this tensor must have the value `None` which represents\n that this dimension of `values` can be adjusted downward in order to\n accommodate increases in other dimensions. The specified sizes of the\n non-adjustable dimensions must by at least as large as in the `values` tensor.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `values`.\n Periodically resampled tensor that has dimensions specified as in\n `shape` except that the dimension specified as `None` will be minimally\n decreased as necessary.\n '
shape = _execute.make_shape(shape, 'shape')
_ctx = _context.context()
if _ctx.in_graph_mode():
(_, _, _op) = _op_def_lib._apply_op_helper('PeriodicResample', values=values, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ('T', _op.get_attr('T'), 'shape', _op.get_attr('shape'))
else:
(_attr_T, (values,)) = _execute.args_to_matching_eager([values], _ctx)
_inputs_flat = [values]
_attrs = ('T', _attr_T, 'shape', shape)
_result = _execute.execute(b'PeriodicResample', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient('PeriodicResample', _inputs_flat, _attrs, _result, name)
(_result,) = _result
return _result |
def encrypt(password):
'\n Take a password string, encrypt it with Fernet symmetric encryption,\n and return the result (bytes), with the decryption key (bytes)\n '
encryption_key = Fernet.generate_key()
fernet = Fernet(encryption_key)
encrypted_password = fernet.encrypt(password.encode('utf-8'))
return (encrypted_password, encryption_key) | 1,055,429,369,133,855,200 | Take a password string, encrypt it with Fernet symmetric encryption,
and return the result (bytes), with the decryption key (bytes) | snappass/main.py | encrypt | 47Billion/snappass | python | def encrypt(password):
'\n Take a password string, encrypt it with Fernet symmetric encryption,\n and return the result (bytes), with the decryption key (bytes)\n '
encryption_key = Fernet.generate_key()
fernet = Fernet(encryption_key)
encrypted_password = fernet.encrypt(password.encode('utf-8'))
return (encrypted_password, encryption_key) |
def decrypt(password, decryption_key):
'\n Decrypt a password (bytes) using the provided key (bytes),\n and return the plain-text password (bytes).\n '
fernet = Fernet(decryption_key)
return fernet.decrypt(password) | 4,633,125,073,615,073,000 | Decrypt a password (bytes) using the provided key (bytes),
and return the plain-text password (bytes). | snappass/main.py | decrypt | 47Billion/snappass | python | def decrypt(password, decryption_key):
'\n Decrypt a password (bytes) using the provided key (bytes),\n and return the plain-text password (bytes).\n '
fernet = Fernet(decryption_key)
return fernet.decrypt(password) |
@check_redis_alive
def set_password(password, ttl):
'\n Encrypt and store the password for the specified lifetime.\n\n Returns a token comprised of the key where the encrypted password\n is stored, and the decryption key.\n '
storage_key = (REDIS_PREFIX + uuid.uuid4().hex)
(encrypted_password, encryption_key) = encrypt(password)
redis_client.setex(storage_key, ttl, encrypted_password)
encryption_key = encryption_key.decode('utf-8')
token = TOKEN_SEPARATOR.join([storage_key, encryption_key])
return token | 7,699,990,961,423,685,000 | Encrypt and store the password for the specified lifetime.
Returns a token comprised of the key where the encrypted password
is stored, and the decryption key. | snappass/main.py | set_password | 47Billion/snappass | python | @check_redis_alive
def set_password(password, ttl):
'\n Encrypt and store the password for the specified lifetime.\n\n Returns a token comprised of the key where the encrypted password\n is stored, and the decryption key.\n '
storage_key = (REDIS_PREFIX + uuid.uuid4().hex)
(encrypted_password, encryption_key) = encrypt(password)
redis_client.setex(storage_key, ttl, encrypted_password)
encryption_key = encryption_key.decode('utf-8')
token = TOKEN_SEPARATOR.join([storage_key, encryption_key])
return token |
@check_redis_alive
def get_password(token):
'\n From a given token, return the initial password.\n\n If the token is tilde-separated, we decrypt the password fetched from Redis.\n If not, the password is simply returned as is.\n '
(storage_key, decryption_key) = parse_token(token)
password = redis_client.get(storage_key)
redis_client.delete(storage_key)
if (password is not None):
if (decryption_key is not None):
password = decrypt(password, decryption_key)
return password.decode('utf-8') | 7,419,285,449,449,767,000 | From a given token, return the initial password.
If the token is tilde-separated, we decrypt the password fetched from Redis.
If not, the password is simply returned as is. | snappass/main.py | get_password | 47Billion/snappass | python | @check_redis_alive
def get_password(token):
'\n From a given token, return the initial password.\n\n If the token is tilde-separated, we decrypt the password fetched from Redis.\n If not, the password is simply returned as is.\n '
(storage_key, decryption_key) = parse_token(token)
password = redis_client.get(storage_key)
redis_client.delete(storage_key)
if (password is not None):
if (decryption_key is not None):
password = decrypt(password, decryption_key)
return password.decode('utf-8') |
def clean_input():
"\n Make sure we're not getting bad data from the front end,\n format data to be machine readable\n "
if empty(request.form.get('password', '')):
abort(400)
if empty(request.form.get('ttl', '')):
abort(400)
time_period = request.form['ttl'].lower()
if (time_period not in TIME_CONVERSION):
abort(400)
return (TIME_CONVERSION[time_period], request.form['password']) | 4,107,296,819,267,789,000 | Make sure we're not getting bad data from the front end,
format data to be machine readable | snappass/main.py | clean_input | 47Billion/snappass | python | def clean_input():
"\n Make sure we're not getting bad data from the front end,\n format data to be machine readable\n "
if empty(request.form.get('password', )):
abort(400)
if empty(request.form.get('ttl', )):
abort(400)
time_period = request.form['ttl'].lower()
if (time_period not in TIME_CONVERSION):
abort(400)
return (TIME_CONVERSION[time_period], request.form['password']) |
def prepare_to_disconnect(self) -> None:
'Called when we will disconnect with the peer.'
pass | 4,461,026,119,724,351,500 | Called when we will disconnect with the peer. | hathor/p2p/states/base.py | prepare_to_disconnect | HathorNetwork/hathor-core | python | def prepare_to_disconnect(self) -> None:
pass |
def get_classes(dataset):
'Get class names of a dataset.'
alias2name = {}
for (name, aliases) in dataset_aliases.items():
for alias in aliases:
alias2name[alias] = name
if mmcv.is_str(dataset):
if (dataset in alias2name):
labels = eval((alias2name[dataset] + '_classes()'))
else:
raise ValueError('Unrecognized dataset: {}'.format(dataset))
else:
raise TypeError('dataset must a str, but got {}'.format(type(dataset)))
return labels | 1,408,958,000,846,626,000 | Get class names of a dataset. | my_configs/new/mmdet/core/evaluation/class_names.py | get_classes | UESTC-Liuxin/TianChi | python | def get_classes(dataset):
alias2name = {}
for (name, aliases) in dataset_aliases.items():
for alias in aliases:
alias2name[alias] = name
if mmcv.is_str(dataset):
if (dataset in alias2name):
labels = eval((alias2name[dataset] + '_classes()'))
else:
raise ValueError('Unrecognized dataset: {}'.format(dataset))
else:
raise TypeError('dataset must a str, but got {}'.format(type(dataset)))
return labels |
def add_entities(self, devices, action):
'Mock add devices.'
for device in devices:
self.DEVICES.append(device) | 436,858,714,708,463,360 | Mock add devices. | tests/components/sensor/test_canary.py | add_entities | 27tech/home-assistant | python | def add_entities(self, devices, action):
for device in devices:
self.DEVICES.append(device) |
def setUp(self):
'Initialize values for this testcase class.'
self.hass = get_test_home_assistant()
self.config = copy.deepcopy(VALID_CONFIG) | 1,489,049,775,505,830,400 | Initialize values for this testcase class. | tests/components/sensor/test_canary.py | setUp | 27tech/home-assistant | python | def setUp(self):
self.hass = get_test_home_assistant()
self.config = copy.deepcopy(VALID_CONFIG) |
def tearDown(self):
'Stop everything that was started.'
self.hass.stop() | 8,876,712,114,682,698,000 | Stop everything that was started. | tests/components/sensor/test_canary.py | tearDown | 27tech/home-assistant | python | def tearDown(self):
self.hass.stop() |
def test_setup_sensors(self):
'Test the sensor setup.'
online_device_at_home = mock_device(20, 'Dining Room', True, 'Canary')
offline_device_at_home = mock_device(21, 'Front Yard', False, 'Canary')
online_device_at_work = mock_device(22, 'Office', True, 'Canary')
self.hass.data[DATA_CANARY] = Mock()
self.hass.data[DATA_CANARY].locations = [mock_location('Home', True, devices=[online_device_at_home, offline_device_at_home]), mock_location('Work', True, devices=[online_device_at_work])]
canary.setup_platform(self.hass, self.config, self.add_entities, None)
assert (6 == len(self.DEVICES)) | 5,771,754,367,131,772,000 | Test the sensor setup. | tests/components/sensor/test_canary.py | test_setup_sensors | 27tech/home-assistant | python | def test_setup_sensors(self):
online_device_at_home = mock_device(20, 'Dining Room', True, 'Canary')
offline_device_at_home = mock_device(21, 'Front Yard', False, 'Canary')
online_device_at_work = mock_device(22, 'Office', True, 'Canary')
self.hass.data[DATA_CANARY] = Mock()
self.hass.data[DATA_CANARY].locations = [mock_location('Home', True, devices=[online_device_at_home, offline_device_at_home]), mock_location('Work', True, devices=[online_device_at_work])]
canary.setup_platform(self.hass, self.config, self.add_entities, None)
assert (6 == len(self.DEVICES)) |
def test_temperature_sensor(self):
'Test temperature sensor with fahrenheit.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home', False)
data = Mock()
data.get_reading.return_value = 21.1234
sensor = CanarySensor(data, SENSOR_TYPES[0], location, device)
sensor.update()
assert ('Home Family Room Temperature' == sensor.name)
assert ('°C' == sensor.unit_of_measurement)
assert (21.12 == sensor.state)
assert ('mdi:thermometer' == sensor.icon) | 2,299,848,690,376,426,500 | Test temperature sensor with fahrenheit. | tests/components/sensor/test_canary.py | test_temperature_sensor | 27tech/home-assistant | python | def test_temperature_sensor(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home', False)
data = Mock()
data.get_reading.return_value = 21.1234
sensor = CanarySensor(data, SENSOR_TYPES[0], location, device)
sensor.update()
assert ('Home Family Room Temperature' == sensor.name)
assert ('°C' == sensor.unit_of_measurement)
assert (21.12 == sensor.state)
assert ('mdi:thermometer' == sensor.icon) |
def test_temperature_sensor_with_none_sensor_value(self):
'Test temperature sensor with fahrenheit.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home', False)
data = Mock()
data.get_reading.return_value = None
sensor = CanarySensor(data, SENSOR_TYPES[0], location, device)
sensor.update()
assert (sensor.state is None) | 4,995,280,553,888,205,000 | Test temperature sensor with fahrenheit. | tests/components/sensor/test_canary.py | test_temperature_sensor_with_none_sensor_value | 27tech/home-assistant | python | def test_temperature_sensor_with_none_sensor_value(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home', False)
data = Mock()
data.get_reading.return_value = None
sensor = CanarySensor(data, SENSOR_TYPES[0], location, device)
sensor.update()
assert (sensor.state is None) |
def test_humidity_sensor(self):
'Test humidity sensor.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 50.4567
sensor = CanarySensor(data, SENSOR_TYPES[1], location, device)
sensor.update()
assert ('Home Family Room Humidity' == sensor.name)
assert ('%' == sensor.unit_of_measurement)
assert (50.46 == sensor.state)
assert ('mdi:water-percent' == sensor.icon) | 845,884,128,519,718,300 | Test humidity sensor. | tests/components/sensor/test_canary.py | test_humidity_sensor | 27tech/home-assistant | python | def test_humidity_sensor(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 50.4567
sensor = CanarySensor(data, SENSOR_TYPES[1], location, device)
sensor.update()
assert ('Home Family Room Humidity' == sensor.name)
assert ('%' == sensor.unit_of_measurement)
assert (50.46 == sensor.state)
assert ('mdi:water-percent' == sensor.icon) |
def test_air_quality_sensor_with_very_abnormal_reading(self):
'Test air quality sensor.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 0.4
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (0.4 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_VERY_ABNORMAL == air_quality) | 1,296,633,011,677,636,000 | Test air quality sensor. | tests/components/sensor/test_canary.py | test_air_quality_sensor_with_very_abnormal_reading | 27tech/home-assistant | python | def test_air_quality_sensor_with_very_abnormal_reading(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 0.4
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (0.4 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_VERY_ABNORMAL == air_quality) |
def test_air_quality_sensor_with_abnormal_reading(self):
'Test air quality sensor.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 0.59
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (0.59 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_ABNORMAL == air_quality) | -5,240,649,530,815,184,000 | Test air quality sensor. | tests/components/sensor/test_canary.py | test_air_quality_sensor_with_abnormal_reading | 27tech/home-assistant | python | def test_air_quality_sensor_with_abnormal_reading(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 0.59
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (0.59 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_ABNORMAL == air_quality) |
def test_air_quality_sensor_with_normal_reading(self):
'Test air quality sensor.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 1.0
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (1.0 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_NORMAL == air_quality) | -8,526,312,091,169,359,000 | Test air quality sensor. | tests/components/sensor/test_canary.py | test_air_quality_sensor_with_normal_reading | 27tech/home-assistant | python | def test_air_quality_sensor_with_normal_reading(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 1.0
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert ('Home Family Room Air Quality' == sensor.name)
assert (sensor.unit_of_measurement is None)
assert (1.0 == sensor.state)
assert ('mdi:weather-windy' == sensor.icon)
air_quality = sensor.device_state_attributes[ATTR_AIR_QUALITY]
assert (STATE_AIR_QUALITY_NORMAL == air_quality) |
def test_air_quality_sensor_with_none_sensor_value(self):
'Test air quality sensor.'
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = None
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert (sensor.state is None)
assert (sensor.device_state_attributes is None) | 2,885,458,907,634,710,000 | Test air quality sensor. | tests/components/sensor/test_canary.py | test_air_quality_sensor_with_none_sensor_value | 27tech/home-assistant | python | def test_air_quality_sensor_with_none_sensor_value(self):
device = mock_device(10, 'Family Room', 'Canary')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = None
sensor = CanarySensor(data, SENSOR_TYPES[2], location, device)
sensor.update()
assert (sensor.state is None)
assert (sensor.device_state_attributes is None) |
def test_battery_sensor(self):
'Test battery sensor.'
device = mock_device(10, 'Family Room', 'Canary Flex')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 70.4567
sensor = CanarySensor(data, SENSOR_TYPES[4], location, device)
sensor.update()
assert ('Home Family Room Battery' == sensor.name)
assert ('%' == sensor.unit_of_measurement)
assert (70.46 == sensor.state)
assert ('mdi:battery-70' == sensor.icon) | -7,136,044,291,612,272,000 | Test battery sensor. | tests/components/sensor/test_canary.py | test_battery_sensor | 27tech/home-assistant | python | def test_battery_sensor(self):
device = mock_device(10, 'Family Room', 'Canary Flex')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = 70.4567
sensor = CanarySensor(data, SENSOR_TYPES[4], location, device)
sensor.update()
assert ('Home Family Room Battery' == sensor.name)
assert ('%' == sensor.unit_of_measurement)
assert (70.46 == sensor.state)
assert ('mdi:battery-70' == sensor.icon) |
def test_wifi_sensor(self):
'Test battery sensor.'
device = mock_device(10, 'Family Room', 'Canary Flex')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = (- 57)
sensor = CanarySensor(data, SENSOR_TYPES[3], location, device)
sensor.update()
assert ('Home Family Room Wifi' == sensor.name)
assert ('dBm' == sensor.unit_of_measurement)
assert ((- 57) == sensor.state)
assert ('mdi:wifi' == sensor.icon) | 6,827,206,662,562,437,000 | Test battery sensor. | tests/components/sensor/test_canary.py | test_wifi_sensor | 27tech/home-assistant | python | def test_wifi_sensor(self):
device = mock_device(10, 'Family Room', 'Canary Flex')
location = mock_location('Home')
data = Mock()
data.get_reading.return_value = (- 57)
sensor = CanarySensor(data, SENSOR_TYPES[3], location, device)
sensor.update()
assert ('Home Family Room Wifi' == sensor.name)
assert ('dBm' == sensor.unit_of_measurement)
assert ((- 57) == sensor.state)
assert ('mdi:wifi' == sensor.icon) |
def lock_change_receiver():
'\n A decorator for connecting receivers to signals that a lock has change.\n\n @receiver(post_save, sender=MyModel)\n def signal_receiver(sender, **kwargs):\n ...\n\n '
def _decorator(func):
LockCache.lock_change_receivers.append(func)
return func
return _decorator | 6,489,382,657,107,884,000 | A decorator for connecting receivers to signals that a lock has change.
@receiver(post_save, sender=MyModel)
def signal_receiver(sender, **kwargs):
... | chroma_core/services/job_scheduler/lock_cache.py | lock_change_receiver | beevans/integrated-manager-for-lustre | python | def lock_change_receiver():
'\n A decorator for connecting receivers to signals that a lock has change.\n\n @receiver(post_save, sender=MyModel)\n def signal_receiver(sender, **kwargs):\n ...\n\n '
def _decorator(func):
LockCache.lock_change_receivers.append(func)
return func
return _decorator |
@asyncio.coroutine
def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
'Setup the Kodi platform.'
host = config.get(CONF_HOST)
port = config.get(CONF_PORT)
if (host.startswith('http://') or host.startswith('https://')):
host = host.lstrip('http://').lstrip('https://')
_LOGGER.warning('Kodi host name should no longer conatin http:// See updated definitions here: https://home-assistant.io/components/media_player.kodi/')
entity = KodiDevice(hass, name=config.get(CONF_NAME), host=host, port=port, username=config.get(CONF_USERNAME), password=config.get(CONF_PASSWORD), turn_off_action=config.get(CONF_TURN_OFF_ACTION))
(yield from async_add_entities([entity], update_before_add=True)) | -8,385,398,847,880,551,000 | Setup the Kodi platform. | homeassistant/components/media_player/kodi.py | async_setup_platform | sbidoul/home-assistant | python | @asyncio.coroutine
def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
host = config.get(CONF_HOST)
port = config.get(CONF_PORT)
if (host.startswith('http://') or host.startswith('https://')):
host = host.lstrip('http://').lstrip('https://')
_LOGGER.warning('Kodi host name should no longer conatin http:// See updated definitions here: https://home-assistant.io/components/media_player.kodi/')
entity = KodiDevice(hass, name=config.get(CONF_NAME), host=host, port=port, username=config.get(CONF_USERNAME), password=config.get(CONF_PASSWORD), turn_off_action=config.get(CONF_TURN_OFF_ACTION))
(yield from async_add_entities([entity], update_before_add=True)) |
def __init__(self, hass, name, host, port, username=None, password=None, turn_off_action=None):
'Initialize the Kodi device.'
import jsonrpc_async
self.hass = hass
self._name = name
kwargs = {'timeout': DEFAULT_TIMEOUT, 'session': async_get_clientsession(hass)}
if (username is not None):
kwargs['auth'] = aiohttp.BasicAuth(username, password)
image_auth_string = '{}:{}@'.format(username, password)
else:
image_auth_string = ''
self._http_url = 'http://{}:{}/jsonrpc'.format(host, port)
self._image_url = 'http://{}{}:{}/image'.format(image_auth_string, host, port)
self._server = jsonrpc_async.Server(self._http_url, **kwargs)
self._turn_off_action = turn_off_action
self._players = list()
self._properties = None
self._item = None
self._app_properties = None | 52,345,360,531,689,720 | Initialize the Kodi device. | homeassistant/components/media_player/kodi.py | __init__ | sbidoul/home-assistant | python | def __init__(self, hass, name, host, port, username=None, password=None, turn_off_action=None):
import jsonrpc_async
self.hass = hass
self._name = name
kwargs = {'timeout': DEFAULT_TIMEOUT, 'session': async_get_clientsession(hass)}
if (username is not None):
kwargs['auth'] = aiohttp.BasicAuth(username, password)
image_auth_string = '{}:{}@'.format(username, password)
else:
image_auth_string =
self._http_url = 'http://{}:{}/jsonrpc'.format(host, port)
self._image_url = 'http://{}{}:{}/image'.format(image_auth_string, host, port)
self._server = jsonrpc_async.Server(self._http_url, **kwargs)
self._turn_off_action = turn_off_action
self._players = list()
self._properties = None
self._item = None
self._app_properties = None |
@property
def name(self):
'Return the name of the device.'
return self._name | -4,231,536,673,663,769,600 | Return the name of the device. | homeassistant/components/media_player/kodi.py | name | sbidoul/home-assistant | python | @property
def name(self):
return self._name |
@asyncio.coroutine
def _get_players(self):
'Return the active player objects or None.'
import jsonrpc_async
try:
return (yield from self._server.Player.GetActivePlayers())
except jsonrpc_async.jsonrpc.TransportError:
if (self._players is not None):
_LOGGER.info('Unable to fetch kodi data')
_LOGGER.debug('Unable to fetch kodi data', exc_info=True)
return None | 2,476,486,676,554,726,000 | Return the active player objects or None. | homeassistant/components/media_player/kodi.py | _get_players | sbidoul/home-assistant | python | @asyncio.coroutine
def _get_players(self):
import jsonrpc_async
try:
return (yield from self._server.Player.GetActivePlayers())
except jsonrpc_async.jsonrpc.TransportError:
if (self._players is not None):
_LOGGER.info('Unable to fetch kodi data')
_LOGGER.debug('Unable to fetch kodi data', exc_info=True)
return None |
@property
def state(self):
'Return the state of the device.'
if (self._players is None):
return STATE_OFF
if (len(self._players) == 0):
return STATE_IDLE
if ((self._properties['speed'] == 0) and (not self._properties['live'])):
return STATE_PAUSED
else:
return STATE_PLAYING | 2,635,478,583,098,425,300 | Return the state of the device. | homeassistant/components/media_player/kodi.py | state | sbidoul/home-assistant | python | @property
def state(self):
if (self._players is None):
return STATE_OFF
if (len(self._players) == 0):
return STATE_IDLE
if ((self._properties['speed'] == 0) and (not self._properties['live'])):
return STATE_PAUSED
else:
return STATE_PLAYING |
@asyncio.coroutine
def async_update(self):
'Retrieve latest state.'
self._players = (yield from self._get_players())
if ((self._players is not None) and (len(self._players) > 0)):
player_id = self._players[0]['playerid']
assert isinstance(player_id, int)
self._properties = (yield from self._server.Player.GetProperties(player_id, ['time', 'totaltime', 'speed', 'live']))
self._item = (yield from self._server.Player.GetItem(player_id, ['title', 'file', 'uniqueid', 'thumbnail', 'artist']))['item']
self._app_properties = (yield from self._server.Application.GetProperties(['volume', 'muted']))
else:
self._properties = None
self._item = None
self._app_properties = None | 3,957,076,217,456,253,400 | Retrieve latest state. | homeassistant/components/media_player/kodi.py | async_update | sbidoul/home-assistant | python | @asyncio.coroutine
def async_update(self):
self._players = (yield from self._get_players())
if ((self._players is not None) and (len(self._players) > 0)):
player_id = self._players[0]['playerid']
assert isinstance(player_id, int)
self._properties = (yield from self._server.Player.GetProperties(player_id, ['time', 'totaltime', 'speed', 'live']))
self._item = (yield from self._server.Player.GetItem(player_id, ['title', 'file', 'uniqueid', 'thumbnail', 'artist']))['item']
self._app_properties = (yield from self._server.Application.GetProperties(['volume', 'muted']))
else:
self._properties = None
self._item = None
self._app_properties = None |
@property
def volume_level(self):
'Volume level of the media player (0..1).'
if (self._app_properties is not None):
return (self._app_properties['volume'] / 100.0) | -3,302,400,317,399,943,700 | Volume level of the media player (0..1). | homeassistant/components/media_player/kodi.py | volume_level | sbidoul/home-assistant | python | @property
def volume_level(self):
if (self._app_properties is not None):
return (self._app_properties['volume'] / 100.0) |
@property
def is_volume_muted(self):
'Boolean if volume is currently muted.'
if (self._app_properties is not None):
return self._app_properties['muted'] | -8,554,226,172,136,375,000 | Boolean if volume is currently muted. | homeassistant/components/media_player/kodi.py | is_volume_muted | sbidoul/home-assistant | python | @property
def is_volume_muted(self):
if (self._app_properties is not None):
return self._app_properties['muted'] |
@property
def media_content_id(self):
'Content ID of current playing media.'
if (self._item is not None):
return self._item.get('uniqueid', None) | 595,466,316,143,477,800 | Content ID of current playing media. | homeassistant/components/media_player/kodi.py | media_content_id | sbidoul/home-assistant | python | @property
def media_content_id(self):
if (self._item is not None):
return self._item.get('uniqueid', None) |
@property
def media_content_type(self):
'Content type of current playing media.'
if ((self._players is not None) and (len(self._players) > 0)):
return self._players[0]['type'] | 5,483,545,840,153,864,000 | Content type of current playing media. | homeassistant/components/media_player/kodi.py | media_content_type | sbidoul/home-assistant | python | @property
def media_content_type(self):
if ((self._players is not None) and (len(self._players) > 0)):
return self._players[0]['type'] |
@property
def media_duration(self):
'Duration of current playing media in seconds.'
if ((self._properties is not None) and (not self._properties['live'])):
total_time = self._properties['totaltime']
return (((total_time['hours'] * 3600) + (total_time['minutes'] * 60)) + total_time['seconds']) | 5,925,154,130,673,134,000 | Duration of current playing media in seconds. | homeassistant/components/media_player/kodi.py | media_duration | sbidoul/home-assistant | python | @property
def media_duration(self):
if ((self._properties is not None) and (not self._properties['live'])):
total_time = self._properties['totaltime']
return (((total_time['hours'] * 3600) + (total_time['minutes'] * 60)) + total_time['seconds']) |
@property
def media_image_url(self):
'Image url of current playing media.'
if (self._item is None):
return None
url_components = urllib.parse.urlparse(self._item['thumbnail'])
if (url_components.scheme == 'image'):
return '{}/{}'.format(self._image_url, urllib.parse.quote_plus(self._item['thumbnail'])) | -1,714,821,105,424,071,200 | Image url of current playing media. | homeassistant/components/media_player/kodi.py | media_image_url | sbidoul/home-assistant | python | @property
def media_image_url(self):
if (self._item is None):
return None
url_components = urllib.parse.urlparse(self._item['thumbnail'])
if (url_components.scheme == 'image'):
return '{}/{}'.format(self._image_url, urllib.parse.quote_plus(self._item['thumbnail'])) |
@property
def media_title(self):
'Title of current playing media.'
if (self._item is not None):
return self._item.get('title', self._item.get('label', self._item.get('file', 'unknown'))) | -5,467,127,369,238,786,000 | Title of current playing media. | homeassistant/components/media_player/kodi.py | media_title | sbidoul/home-assistant | python | @property
def media_title(self):
if (self._item is not None):
return self._item.get('title', self._item.get('label', self._item.get('file', 'unknown'))) |
@property
def supported_media_commands(self):
'Flag of media commands that are supported.'
supported_media_commands = SUPPORT_KODI
if (self._turn_off_action in TURN_OFF_ACTION):
supported_media_commands |= SUPPORT_TURN_OFF
return supported_media_commands | -2,403,220,702,515,875,300 | Flag of media commands that are supported. | homeassistant/components/media_player/kodi.py | supported_media_commands | sbidoul/home-assistant | python | @property
def supported_media_commands(self):
supported_media_commands = SUPPORT_KODI
if (self._turn_off_action in TURN_OFF_ACTION):
supported_media_commands |= SUPPORT_TURN_OFF
return supported_media_commands |
@asyncio.coroutine
def async_turn_off(self):
'Execute turn_off_action to turn off media player.'
if (self._turn_off_action == 'quit'):
(yield from self._server.Application.Quit())
elif (self._turn_off_action == 'hibernate'):
(yield from self._server.System.Hibernate())
elif (self._turn_off_action == 'suspend'):
(yield from self._server.System.Suspend())
elif (self._turn_off_action == 'reboot'):
(yield from self._server.System.Reboot())
elif (self._turn_off_action == 'shutdown'):
(yield from self._server.System.Shutdown())
else:
_LOGGER.warning('turn_off requested but turn_off_action is none') | 4,009,115,081,220,730,400 | Execute turn_off_action to turn off media player. | homeassistant/components/media_player/kodi.py | async_turn_off | sbidoul/home-assistant | python | @asyncio.coroutine
def async_turn_off(self):
if (self._turn_off_action == 'quit'):
(yield from self._server.Application.Quit())
elif (self._turn_off_action == 'hibernate'):
(yield from self._server.System.Hibernate())
elif (self._turn_off_action == 'suspend'):
(yield from self._server.System.Suspend())
elif (self._turn_off_action == 'reboot'):
(yield from self._server.System.Reboot())
elif (self._turn_off_action == 'shutdown'):
(yield from self._server.System.Shutdown())
else:
_LOGGER.warning('turn_off requested but turn_off_action is none') |
@asyncio.coroutine
def async_volume_up(self):
'Volume up the media player.'
assert ((yield from self._server.Input.ExecuteAction('volumeup')) == 'OK') | 3,553,895,284,560,768,500 | Volume up the media player. | homeassistant/components/media_player/kodi.py | async_volume_up | sbidoul/home-assistant | python | @asyncio.coroutine
def async_volume_up(self):
assert ((yield from self._server.Input.ExecuteAction('volumeup')) == 'OK') |
@asyncio.coroutine
def async_volume_down(self):
'Volume down the media player.'
assert ((yield from self._server.Input.ExecuteAction('volumedown')) == 'OK') | -4,431,140,225,079,229,400 | Volume down the media player. | homeassistant/components/media_player/kodi.py | async_volume_down | sbidoul/home-assistant | python | @asyncio.coroutine
def async_volume_down(self):
assert ((yield from self._server.Input.ExecuteAction('volumedown')) == 'OK') |
def async_set_volume_level(self, volume):
'Set volume level, range 0..1.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._server.Application.SetVolume(int((volume * 100))) | 8,457,646,432,285,553,000 | Set volume level, range 0..1.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_set_volume_level | sbidoul/home-assistant | python | def async_set_volume_level(self, volume):
'Set volume level, range 0..1.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._server.Application.SetVolume(int((volume * 100))) |
def async_mute_volume(self, mute):
'Mute (true) or unmute (false) media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._server.Application.SetMute(mute) | 97,019,771,065,311,620 | Mute (true) or unmute (false) media player.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_mute_volume | sbidoul/home-assistant | python | def async_mute_volume(self, mute):
'Mute (true) or unmute (false) media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._server.Application.SetMute(mute) |
@asyncio.coroutine
def async_set_play_state(self, state):
'Helper method for play/pause/toggle.'
players = (yield from self._get_players())
if (len(players) != 0):
(yield from self._server.Player.PlayPause(players[0]['playerid'], state)) | 6,011,301,021,335,337,000 | Helper method for play/pause/toggle. | homeassistant/components/media_player/kodi.py | async_set_play_state | sbidoul/home-assistant | python | @asyncio.coroutine
def async_set_play_state(self, state):
players = (yield from self._get_players())
if (len(players) != 0):
(yield from self._server.Player.PlayPause(players[0]['playerid'], state)) |
def async_media_play_pause(self):
'Pause media on media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state('toggle') | -7,341,939,771,844,527,000 | Pause media on media player.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_media_play_pause | sbidoul/home-assistant | python | def async_media_play_pause(self):
'Pause media on media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state('toggle') |
def async_media_play(self):
'Play media.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state(True) | -2,520,068,111,156,763,600 | Play media.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_media_play | sbidoul/home-assistant | python | def async_media_play(self):
'Play media.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state(True) |
def async_media_pause(self):
'Pause the media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state(False) | 6,682,939,788,905,580,000 | Pause the media player.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_media_pause | sbidoul/home-assistant | python | def async_media_pause(self):
'Pause the media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self.async_set_play_state(False) |
@asyncio.coroutine
def async_media_stop(self):
'Stop the media player.'
players = (yield from self._get_players())
if (len(players) != 0):
(yield from self._server.Player.Stop(players[0]['playerid'])) | -6,347,300,543,494,530,000 | Stop the media player. | homeassistant/components/media_player/kodi.py | async_media_stop | sbidoul/home-assistant | python | @asyncio.coroutine
def async_media_stop(self):
players = (yield from self._get_players())
if (len(players) != 0):
(yield from self._server.Player.Stop(players[0]['playerid'])) |
@asyncio.coroutine
def _goto(self, direction):
'Helper method used for previous/next track.'
players = (yield from self._get_players())
if (len(players) != 0):
if (direction == 'previous'):
(yield from self._server.Player.Seek(players[0]['playerid'], 0))
(yield from self._server.Player.GoTo(players[0]['playerid'], direction)) | -3,141,966,149,428,018,700 | Helper method used for previous/next track. | homeassistant/components/media_player/kodi.py | _goto | sbidoul/home-assistant | python | @asyncio.coroutine
def _goto(self, direction):
players = (yield from self._get_players())
if (len(players) != 0):
if (direction == 'previous'):
(yield from self._server.Player.Seek(players[0]['playerid'], 0))
(yield from self._server.Player.GoTo(players[0]['playerid'], direction)) |
def async_media_next_track(self):
'Send next track command.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._goto('next') | 4,079,830,510,610,262,000 | Send next track command.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_media_next_track | sbidoul/home-assistant | python | def async_media_next_track(self):
'Send next track command.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._goto('next') |
def async_media_previous_track(self):
'Send next track command.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._goto('previous') | -763,769,962,323,606,400 | Send next track command.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_media_previous_track | sbidoul/home-assistant | python | def async_media_previous_track(self):
'Send next track command.\n\n This method must be run in the event loop and returns a coroutine.\n '
return self._goto('previous') |
@asyncio.coroutine
def async_media_seek(self, position):
'Send seek command.'
players = (yield from self._get_players())
time = {}
time['milliseconds'] = int(((position % 1) * 1000))
position = int(position)
time['seconds'] = int((position % 60))
position /= 60
time['minutes'] = int((position % 60))
position /= 60
time['hours'] = int(position)
if (len(players) != 0):
(yield from self._server.Player.Seek(players[0]['playerid'], time)) | -2,504,506,522,176,259,600 | Send seek command. | homeassistant/components/media_player/kodi.py | async_media_seek | sbidoul/home-assistant | python | @asyncio.coroutine
def async_media_seek(self, position):
players = (yield from self._get_players())
time = {}
time['milliseconds'] = int(((position % 1) * 1000))
position = int(position)
time['seconds'] = int((position % 60))
position /= 60
time['minutes'] = int((position % 60))
position /= 60
time['hours'] = int(position)
if (len(players) != 0):
(yield from self._server.Player.Seek(players[0]['playerid'], time)) |
def async_play_media(self, media_type, media_id, **kwargs):
'Send the play_media command to the media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
if (media_type == 'CHANNEL'):
return self._server.Player.Open({'item': {'channelid': int(media_id)}})
else:
return self._server.Player.Open({'item': {'file': str(media_id)}}) | 1,485,067,972,021,763,000 | Send the play_media command to the media player.
This method must be run in the event loop and returns a coroutine. | homeassistant/components/media_player/kodi.py | async_play_media | sbidoul/home-assistant | python | def async_play_media(self, media_type, media_id, **kwargs):
'Send the play_media command to the media player.\n\n This method must be run in the event loop and returns a coroutine.\n '
if (media_type == 'CHANNEL'):
return self._server.Player.Open({'item': {'channelid': int(media_id)}})
else:
return self._server.Player.Open({'item': {'file': str(media_id)}}) |
def gradients(output_node, node_list, scheduler_policy=None):
'Take gradient of output node with respect to each node in node_list.\n\n Parameters\n ----------\n output_node: output node that we are taking derivative of.\n node_list: list of nodes that we are taking derivative wrt.\n\n Returns\n -------\n A list of gradient values, one for each node in node_list respectively.\n\n '
from . import OnesLike
node_to_output_grads_list = {}
node_to_output_grads_list[output_node] = [OnesLike.oneslike_op(output_node)]
node_to_output_grad = {}
reverse_topo_order = reversed(find_topo_sort([output_node]))
for node in reverse_topo_order:
output_grad = sum_node_list(node_to_output_grads_list[node])
node_to_output_grad[node] = output_grad
input_grads_list = node.op.gradient(node, output_grad)
for i in range(len(node.inputs)):
if (node.inputs[i] not in node_to_output_grads_list):
node_to_output_grads_list[node.inputs[i]] = []
node_to_output_grads_list[node.inputs[i]].append(input_grads_list[i])
if (scheduler_policy == 'swap'):
for node in node_list:
if node.swap:
node_to_output_grad[node].swap = True
grad_node_list = [node_to_output_grad[node] for node in node_list]
return grad_node_list | -7,271,513,188,193,601,000 | Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns
-------
A list of gradient values, one for each node in node_list respectively. | python/athena/gpu_ops/StreamExecutor.py | gradients | DMALab/TSplit | python | def gradients(output_node, node_list, scheduler_policy=None):
'Take gradient of output node with respect to each node in node_list.\n\n Parameters\n ----------\n output_node: output node that we are taking derivative of.\n node_list: list of nodes that we are taking derivative wrt.\n\n Returns\n -------\n A list of gradient values, one for each node in node_list respectively.\n\n '
from . import OnesLike
node_to_output_grads_list = {}
node_to_output_grads_list[output_node] = [OnesLike.oneslike_op(output_node)]
node_to_output_grad = {}
reverse_topo_order = reversed(find_topo_sort([output_node]))
for node in reverse_topo_order:
output_grad = sum_node_list(node_to_output_grads_list[node])
node_to_output_grad[node] = output_grad
input_grads_list = node.op.gradient(node, output_grad)
for i in range(len(node.inputs)):
if (node.inputs[i] not in node_to_output_grads_list):
node_to_output_grads_list[node.inputs[i]] = []
node_to_output_grads_list[node.inputs[i]].append(input_grads_list[i])
if (scheduler_policy == 'swap'):
for node in node_list:
if node.swap:
node_to_output_grad[node].swap = True
grad_node_list = [node_to_output_grad[node] for node in node_list]
return grad_node_list |
def distributed_gradients(output_node, node_list, scheduler_policy=None):
'Take gradient of output node with respect to each node in node_list.\n\n Parameters\n ----------\n output_node: output node that we are taking derivative of.\n node_list: list of nodes that we are taking derivative wrt.\n\n Returns\n -------\n A list of gradient values, one for each node in node_list respectively.\n\n '
from .OnesLike import oneslike_op
node_to_output_grads_list = {}
node_to_output_grads_list[output_node] = [oneslike_op(output_node)]
node_to_output_grad = {}
reverse_topo_order = reversed(find_topo_sort([output_node]))
for node in reverse_topo_order:
output_grad = sum_node_list(node_to_output_grads_list[node])
node_to_output_grad[node] = output_grad
input_grads_list = node.op.gradient(node, output_grad)
for i in range(len(node.inputs)):
if (node.inputs[i] not in node_to_output_grads_list):
node_to_output_grads_list[node.inputs[i]] = []
node_to_output_grads_list[node.inputs[i]].append(input_grads_list[i])
if (scheduler_policy == 'swap'):
for node in node_list:
if node.swap:
node_to_output_grad[node].swap = True
grad_node_list = [distributed_communicate_op(node_to_output_grad[node]) for node in node_list]
return grad_node_list | -8,072,602,044,061,928,000 | Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns
-------
A list of gradient values, one for each node in node_list respectively. | python/athena/gpu_ops/StreamExecutor.py | distributed_gradients | DMALab/TSplit | python | def distributed_gradients(output_node, node_list, scheduler_policy=None):
'Take gradient of output node with respect to each node in node_list.\n\n Parameters\n ----------\n output_node: output node that we are taking derivative of.\n node_list: list of nodes that we are taking derivative wrt.\n\n Returns\n -------\n A list of gradient values, one for each node in node_list respectively.\n\n '
from .OnesLike import oneslike_op
node_to_output_grads_list = {}
node_to_output_grads_list[output_node] = [oneslike_op(output_node)]
node_to_output_grad = {}
reverse_topo_order = reversed(find_topo_sort([output_node]))
for node in reverse_topo_order:
output_grad = sum_node_list(node_to_output_grads_list[node])
node_to_output_grad[node] = output_grad
input_grads_list = node.op.gradient(node, output_grad)
for i in range(len(node.inputs)):
if (node.inputs[i] not in node_to_output_grads_list):
node_to_output_grads_list[node.inputs[i]] = []
node_to_output_grads_list[node.inputs[i]].append(input_grads_list[i])
if (scheduler_policy == 'swap'):
for node in node_list:
if node.swap:
node_to_output_grad[node].swap = True
grad_node_list = [distributed_communicate_op(node_to_output_grad[node]) for node in node_list]
return grad_node_list |
def find_topo_sort(node_list):
'Given a list of nodes, return a topo ordering of nodes ending in them.\n\n A simple algorithm is to do a post-order DFS traversal on the given nodes,\n going backwards based on input edges. Since a node is added to the ordering\n after all its predecessors are traversed due to post-order DFS, we get a\n topological sort.\n\n '
visited = set()
topo_order = []
for node in node_list:
topo_sort_dfs(node, visited, topo_order)
return topo_order | -6,221,163,888,668,146,000 | Given a list of nodes, return a topo ordering of nodes ending in them.
A simple algorithm is to do a post-order DFS traversal on the given nodes,
going backwards based on input edges. Since a node is added to the ordering
after all its predecessors are traversed due to post-order DFS, we get a
topological sort. | python/athena/gpu_ops/StreamExecutor.py | find_topo_sort | DMALab/TSplit | python | def find_topo_sort(node_list):
'Given a list of nodes, return a topo ordering of nodes ending in them.\n\n A simple algorithm is to do a post-order DFS traversal on the given nodes,\n going backwards based on input edges. Since a node is added to the ordering\n after all its predecessors are traversed due to post-order DFS, we get a\n topological sort.\n\n '
visited = set()
topo_order = []
for node in node_list:
topo_sort_dfs(node, visited, topo_order)
return topo_order |
def topo_sort_dfs(node, visited, topo_order):
'Post-order DFS'
if (node in visited):
return
visited.add(node)
for n in node.inputs:
topo_sort_dfs(n, visited, topo_order)
topo_order.append(node) | -1,676,523,686,901,708,000 | Post-order DFS | python/athena/gpu_ops/StreamExecutor.py | topo_sort_dfs | DMALab/TSplit | python | def topo_sort_dfs(node, visited, topo_order):
if (node in visited):
return
visited.add(node)
for n in node.inputs:
topo_sort_dfs(n, visited, topo_order)
topo_order.append(node) |
def sum_node_list(node_list):
'Custom sum func to avoid creating redundant nodes in Python sum func.'
from operator import add
from functools import reduce
return reduce(add, node_list) | -2,496,900,066,984,713,700 | Custom sum func to avoid creating redundant nodes in Python sum func. | python/athena/gpu_ops/StreamExecutor.py | sum_node_list | DMALab/TSplit | python | def sum_node_list(node_list):
from operator import add
from functools import reduce
return reduce(add, node_list) |
def broadcast_rule(shape_a, shape_b):
'Return output shape of broadcast shape_a, shape_b.\n e.g. broadcast_rule((3,2), (4,3,2))\n returns output_shape = (4,3,2)\n\n Check out explanations and more examples at\n https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html\n http://eli.thegreenplace.net/2015/broadcasting-arrays-in-numpy/\n '
assert isinstance(shape_a, tuple)
assert isinstance(shape_b, tuple)
if (len(shape_a) > len(shape_b)):
(longer_shape, shorter_shape) = (shape_a, shape_b)
else:
(longer_shape, shorter_shape) = (shape_b, shape_a)
len_diff = (len(longer_shape) - len(shorter_shape))
for i in range(len_diff):
shorter_shape = ((1,) + shorter_shape)
assert (len(shorter_shape) == len(longer_shape))
output_shape = list(longer_shape)
for i in range(len(output_shape)):
assert ((shorter_shape[i] == longer_shape[i]) or (shorter_shape[i] == 1) or (longer_shape[i] == 1))
output_shape[i] = max(shorter_shape[i], longer_shape[i])
return tuple(output_shape) | -5,697,759,041,333,517,000 | Return output shape of broadcast shape_a, shape_b.
e.g. broadcast_rule((3,2), (4,3,2))
returns output_shape = (4,3,2)
Check out explanations and more examples at
https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html
http://eli.thegreenplace.net/2015/broadcasting-arrays-in-numpy/ | python/athena/gpu_ops/StreamExecutor.py | broadcast_rule | DMALab/TSplit | python | def broadcast_rule(shape_a, shape_b):
'Return output shape of broadcast shape_a, shape_b.\n e.g. broadcast_rule((3,2), (4,3,2))\n returns output_shape = (4,3,2)\n\n Check out explanations and more examples at\n https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html\n http://eli.thegreenplace.net/2015/broadcasting-arrays-in-numpy/\n '
assert isinstance(shape_a, tuple)
assert isinstance(shape_b, tuple)
if (len(shape_a) > len(shape_b)):
(longer_shape, shorter_shape) = (shape_a, shape_b)
else:
(longer_shape, shorter_shape) = (shape_b, shape_a)
len_diff = (len(longer_shape) - len(shorter_shape))
for i in range(len_diff):
shorter_shape = ((1,) + shorter_shape)
assert (len(shorter_shape) == len(longer_shape))
output_shape = list(longer_shape)
for i in range(len(output_shape)):
assert ((shorter_shape[i] == longer_shape[i]) or (shorter_shape[i] == 1) or (longer_shape[i] == 1))
output_shape[i] = max(shorter_shape[i], longer_shape[i])
return tuple(output_shape) |
def __init__(self, eval_node_list, ctx=None, stream=None, policy=None):
'\n Parameters\n ----------\n eval_node_list: list of nodes whose values need to be computed.\n ctx: runtime DLContext, default is None which means np.ndarray on cpu\n topo_order: list of nodes in topological order\n node_to_shape_map: dict from node to shape of the node\n node_to_arr_map: dict from node to ndarray.NDArray allocated for node\n feed_shapes: shapes of feed_dict from last run(...)\n '
self.eval_node_list = eval_node_list
self.ctx = ctx
if (stream is None):
self.stream = create_stream_handle(ctx)
else:
self.stream = stream
self.stream.sync()
self.topo_order = find_topo_sort(self.eval_node_list)
self.node_to_shape_map = None
self.node_to_arr_map = None
self.feed_shapes = None
self.policy = policy
if (self.policy == 'swap'):
self.swap_queue = [] | 6,018,590,634,589,362,000 | Parameters
----------
eval_node_list: list of nodes whose values need to be computed.
ctx: runtime DLContext, default is None which means np.ndarray on cpu
topo_order: list of nodes in topological order
node_to_shape_map: dict from node to shape of the node
node_to_arr_map: dict from node to ndarray.NDArray allocated for node
feed_shapes: shapes of feed_dict from last run(...) | python/athena/gpu_ops/StreamExecutor.py | __init__ | DMALab/TSplit | python | def __init__(self, eval_node_list, ctx=None, stream=None, policy=None):
'\n Parameters\n ----------\n eval_node_list: list of nodes whose values need to be computed.\n ctx: runtime DLContext, default is None which means np.ndarray on cpu\n topo_order: list of nodes in topological order\n node_to_shape_map: dict from node to shape of the node\n node_to_arr_map: dict from node to ndarray.NDArray allocated for node\n feed_shapes: shapes of feed_dict from last run(...)\n '
self.eval_node_list = eval_node_list
self.ctx = ctx
if (stream is None):
self.stream = create_stream_handle(ctx)
else:
self.stream = stream
self.stream.sync()
self.topo_order = find_topo_sort(self.eval_node_list)
self.node_to_shape_map = None
self.node_to_arr_map = None
self.feed_shapes = None
self.policy = policy
if (self.policy == 'swap'):
self.swap_queue = [] |
def infer_shape(self, feed_shapes):
'Given shapes of feed_dict nodes, infer shape for all nodes in graph.\n\n Implementation note:\n Iteratively calls node.op.infer_shape to infer shapes.\n Node shapes stored in self.node_to_shape_map.\n\n Parameters\n ----------\n feed_shapes: node->shapes mapping for feed_dict nodes.\n '
'TODO: Your code here'
self.node_to_shape_map = {}
for node in self.topo_order:
if (node in feed_shapes):
self.node_to_shape_map[node] = feed_shapes[node]
else:
input_shapes = [self.node_to_shape_map[n] for n in node.inputs]
self.node_to_shape_map[node] = node.op.infer_shape(node, input_shapes) | 7,062,469,075,092,815,000 | Given shapes of feed_dict nodes, infer shape for all nodes in graph.
Implementation note:
Iteratively calls node.op.infer_shape to infer shapes.
Node shapes stored in self.node_to_shape_map.
Parameters
----------
feed_shapes: node->shapes mapping for feed_dict nodes. | python/athena/gpu_ops/StreamExecutor.py | infer_shape | DMALab/TSplit | python | def infer_shape(self, feed_shapes):
'Given shapes of feed_dict nodes, infer shape for all nodes in graph.\n\n Implementation note:\n Iteratively calls node.op.infer_shape to infer shapes.\n Node shapes stored in self.node_to_shape_map.\n\n Parameters\n ----------\n feed_shapes: node->shapes mapping for feed_dict nodes.\n '
'TODO: Your code here'
self.node_to_shape_map = {}
for node in self.topo_order:
if (node in feed_shapes):
self.node_to_shape_map[node] = feed_shapes[node]
else:
input_shapes = [self.node_to_shape_map[n] for n in node.inputs]
self.node_to_shape_map[node] = node.op.infer_shape(node, input_shapes) |
def memory_plan(self, feed_shapes):
'Allocates ndarray.NDArray for every node except feed_dict nodes.\n\n Implementation note:\n Option 1: Alloc a ndarray.NDArray per node that persists across run()\n Option 2: Implement a memory pool to reuse memory for nodes of same\n shapes. More details see Lecture 7.\n\n For both options, self.node_to_arr_map stores node->NDArray mapping to\n allow mapping to persist across multiple executor.run().\n\n Hint: use ndarray.empty(shape, ctx=self.ctx) to allocate NDArray.\n\n Parameters\n ----------\n feed_shapes: node->shapes mapping for feed_dict nodes.\n '
'TODO: Your code here'
assert (self.ctx is not None)
self.node_to_arr_map = {}
for (node, shape) in self.node_to_shape_map.items():
if (self.policy == 'swap'):
if (not node.swap):
self.node_to_arr_map[node] = ndarray.empty(shape, ctx=self.ctx)
elif (self.policy == 'vdnn'):
self.node_to_arr_map[node] = np.empty(shape)
else:
self.node_to_arr_map[node] = ndarray.empty(shape, ctx=self.ctx) | 4,789,048,243,198,603,000 | Allocates ndarray.NDArray for every node except feed_dict nodes.
Implementation note:
Option 1: Alloc a ndarray.NDArray per node that persists across run()
Option 2: Implement a memory pool to reuse memory for nodes of same
shapes. More details see Lecture 7.
For both options, self.node_to_arr_map stores node->NDArray mapping to
allow mapping to persist across multiple executor.run().
Hint: use ndarray.empty(shape, ctx=self.ctx) to allocate NDArray.
Parameters
----------
feed_shapes: node->shapes mapping for feed_dict nodes. | python/athena/gpu_ops/StreamExecutor.py | memory_plan | DMALab/TSplit | python | def memory_plan(self, feed_shapes):
'Allocates ndarray.NDArray for every node except feed_dict nodes.\n\n Implementation note:\n Option 1: Alloc a ndarray.NDArray per node that persists across run()\n Option 2: Implement a memory pool to reuse memory for nodes of same\n shapes. More details see Lecture 7.\n\n For both options, self.node_to_arr_map stores node->NDArray mapping to\n allow mapping to persist across multiple executor.run().\n\n Hint: use ndarray.empty(shape, ctx=self.ctx) to allocate NDArray.\n\n Parameters\n ----------\n feed_shapes: node->shapes mapping for feed_dict nodes.\n '
'TODO: Your code here'
assert (self.ctx is not None)
self.node_to_arr_map = {}
for (node, shape) in self.node_to_shape_map.items():
if (self.policy == 'swap'):
if (not node.swap):
self.node_to_arr_map[node] = ndarray.empty(shape, ctx=self.ctx)
elif (self.policy == 'vdnn'):
self.node_to_arr_map[node] = np.empty(shape)
else:
self.node_to_arr_map[node] = ndarray.empty(shape, ctx=self.ctx) |
def run(self, feed_dict, convert_to_numpy_ret_vals=False):
'\n Parameters\n ----------\n feed_dict: a dictionary of node->np.ndarray supplied by user.\n convert_to_numpy_ret_vals: whether to convert ret vals to np.array\n\n Returns\n -------\n A list of values for nodes in eval_node_list. NDArray or np.ndarray.\n '
def are_feed_shapes_equal(sa, sb):
if ((not isinstance(sa, dict)) or (not isinstance(sb, dict))):
return False
unmatched_item = (set(sa.items()) ^ set(sb.items()))
return (len(unmatched_item) == 0)
use_numpy = (self.ctx is None)
node_to_val_map = {}
for (node, value) in feed_dict.items():
if use_numpy:
assert isinstance(value, np.ndarray)
node_to_val_map[node] = value
elif isinstance(value, np.ndarray):
node_to_val_map[node] = ndarray.array(value, ctx=self.ctx)
elif isinstance(value, ndarray.NDArray):
node_to_val_map[node] = value
else:
assert False, 'feed_dict value type not supported'
feed_shapes = {}
for node in node_to_val_map:
feed_shapes[node] = node_to_val_map[node].shape
if (not are_feed_shapes_equal(feed_shapes, self.feed_shapes)):
self.infer_shape(feed_shapes)
self.feed_shapes = feed_shapes
if (not use_numpy):
self.memory_plan(self.feed_shapes)
for node in self.topo_order:
if (node in node_to_val_map):
continue
input_vals = [node_to_val_map[n] for n in node.inputs]
if use_numpy:
node_val = np.empty(shape=self.node_to_shape_map[node])
else:
node_val = self.node_to_arr_map[node]
node.op.compute(node, input_vals, node_val, use_numpy, self.stream)
node_to_val_map[node] = node_val
self.stream.sync()
if ((not use_numpy) and convert_to_numpy_ret_vals):
return [node_to_val_map[n].asnumpy() for n in self.eval_node_list]
return [node_to_val_map[n] for n in self.eval_node_list] | 2,019,878,336,962,382,000 | Parameters
----------
feed_dict: a dictionary of node->np.ndarray supplied by user.
convert_to_numpy_ret_vals: whether to convert ret vals to np.array
Returns
-------
A list of values for nodes in eval_node_list. NDArray or np.ndarray. | python/athena/gpu_ops/StreamExecutor.py | run | DMALab/TSplit | python | def run(self, feed_dict, convert_to_numpy_ret_vals=False):
'\n Parameters\n ----------\n feed_dict: a dictionary of node->np.ndarray supplied by user.\n convert_to_numpy_ret_vals: whether to convert ret vals to np.array\n\n Returns\n -------\n A list of values for nodes in eval_node_list. NDArray or np.ndarray.\n '
def are_feed_shapes_equal(sa, sb):
if ((not isinstance(sa, dict)) or (not isinstance(sb, dict))):
return False
unmatched_item = (set(sa.items()) ^ set(sb.items()))
return (len(unmatched_item) == 0)
use_numpy = (self.ctx is None)
node_to_val_map = {}
for (node, value) in feed_dict.items():
if use_numpy:
assert isinstance(value, np.ndarray)
node_to_val_map[node] = value
elif isinstance(value, np.ndarray):
node_to_val_map[node] = ndarray.array(value, ctx=self.ctx)
elif isinstance(value, ndarray.NDArray):
node_to_val_map[node] = value
else:
assert False, 'feed_dict value type not supported'
feed_shapes = {}
for node in node_to_val_map:
feed_shapes[node] = node_to_val_map[node].shape
if (not are_feed_shapes_equal(feed_shapes, self.feed_shapes)):
self.infer_shape(feed_shapes)
self.feed_shapes = feed_shapes
if (not use_numpy):
self.memory_plan(self.feed_shapes)
for node in self.topo_order:
if (node in node_to_val_map):
continue
input_vals = [node_to_val_map[n] for n in node.inputs]
if use_numpy:
node_val = np.empty(shape=self.node_to_shape_map[node])
else:
node_val = self.node_to_arr_map[node]
node.op.compute(node, input_vals, node_val, use_numpy, self.stream)
node_to_val_map[node] = node_val
self.stream.sync()
if ((not use_numpy) and convert_to_numpy_ret_vals):
return [node_to_val_map[n].asnumpy() for n in self.eval_node_list]
return [node_to_val_map[n] for n in self.eval_node_list] |
@inlineCallbacks
def _callback(self):
'\n This will be called repeatedly every `self.interval` seconds.\n `self.subscriptions` contain tuples of (obj, args, kwargs) for\n each subscribing object.\n\n If overloading, this callback is expected to handle all\n subscriptions when it is triggered. It should not return\n anything and should not traceback on poorly designed hooks.\n The callback should ideally work under @inlineCallbacks so it\n can yield appropriately.\n\n The _hook_key, which is passed down through the handler via\n kwargs is used here to identify which hook method to call.\n\n '
self._to_add = []
self._to_remove = []
self._is_ticking = True
for (store_key, (args, kwargs)) in self.subscriptions.iteritems():
callback = (yield kwargs.pop('_callback', 'at_tick'))
obj = (yield kwargs.pop('_obj', None))
try:
if callable(callback):
(yield callback(*args, **kwargs))
continue
if ((not obj) or (not obj.pk)):
self._to_remove.append(store_key)
continue
else:
(yield _GA(obj, callback)(*args, **kwargs))
except ObjectDoesNotExist:
log_trace('Removing ticker.')
self._to_remove.append(store_key)
except Exception:
log_trace()
finally:
kwargs['_callback'] = callback
kwargs['_obj'] = obj
self._is_ticking = False
for store_key in self._to_remove:
self.remove(store_key)
for (store_key, (args, kwargs)) in self._to_add:
self.add(store_key, *args, **kwargs)
self._to_remove = []
self._to_add = [] | -6,995,095,253,856,614,000 | This will be called repeatedly every `self.interval` seconds.
`self.subscriptions` contain tuples of (obj, args, kwargs) for
each subscribing object.
If overloading, this callback is expected to handle all
subscriptions when it is triggered. It should not return
anything and should not traceback on poorly designed hooks.
The callback should ideally work under @inlineCallbacks so it
can yield appropriately.
The _hook_key, which is passed down through the handler via
kwargs is used here to identify which hook method to call. | evennia/scripts/tickerhandler.py | _callback | orkim/evennia | python | @inlineCallbacks
def _callback(self):
'\n This will be called repeatedly every `self.interval` seconds.\n `self.subscriptions` contain tuples of (obj, args, kwargs) for\n each subscribing object.\n\n If overloading, this callback is expected to handle all\n subscriptions when it is triggered. It should not return\n anything and should not traceback on poorly designed hooks.\n The callback should ideally work under @inlineCallbacks so it\n can yield appropriately.\n\n The _hook_key, which is passed down through the handler via\n kwargs is used here to identify which hook method to call.\n\n '
self._to_add = []
self._to_remove = []
self._is_ticking = True
for (store_key, (args, kwargs)) in self.subscriptions.iteritems():
callback = (yield kwargs.pop('_callback', 'at_tick'))
obj = (yield kwargs.pop('_obj', None))
try:
if callable(callback):
(yield callback(*args, **kwargs))
continue
if ((not obj) or (not obj.pk)):
self._to_remove.append(store_key)
continue
else:
(yield _GA(obj, callback)(*args, **kwargs))
except ObjectDoesNotExist:
log_trace('Removing ticker.')
self._to_remove.append(store_key)
except Exception:
log_trace()
finally:
kwargs['_callback'] = callback
kwargs['_obj'] = obj
self._is_ticking = False
for store_key in self._to_remove:
self.remove(store_key)
for (store_key, (args, kwargs)) in self._to_add:
self.add(store_key, *args, **kwargs)
self._to_remove = []
self._to_add = [] |
def __init__(self, interval):
'\n Set up the ticker\n\n Args:\n interval (int): The stepping interval.\n\n '
self.interval = interval
self.subscriptions = {}
self._is_ticking = False
self._to_remove = []
self._to_add = []
self.task = ExtendedLoopingCall(self._callback) | -8,686,783,412,515,952,000 | Set up the ticker
Args:
interval (int): The stepping interval. | evennia/scripts/tickerhandler.py | __init__ | orkim/evennia | python | def __init__(self, interval):
'\n Set up the ticker\n\n Args:\n interval (int): The stepping interval.\n\n '
self.interval = interval
self.subscriptions = {}
self._is_ticking = False
self._to_remove = []
self._to_add = []
self.task = ExtendedLoopingCall(self._callback) |
def validate(self, start_delay=None):
'\n Start/stop the task depending on how many subscribers we have\n using it.\n\n Args:\n start_delay (int): Time to way before starting.\n\n '
subs = self.subscriptions
if self.task.running:
if (not subs):
self.task.stop()
elif subs:
self.task.start(self.interval, now=False, start_delay=start_delay) | -7,546,078,601,667,794,000 | Start/stop the task depending on how many subscribers we have
using it.
Args:
start_delay (int): Time to way before starting. | evennia/scripts/tickerhandler.py | validate | orkim/evennia | python | def validate(self, start_delay=None):
'\n Start/stop the task depending on how many subscribers we have\n using it.\n\n Args:\n start_delay (int): Time to way before starting.\n\n '
subs = self.subscriptions
if self.task.running:
if (not subs):
self.task.stop()
elif subs:
self.task.start(self.interval, now=False, start_delay=start_delay) |
def add(self, store_key, *args, **kwargs):
'\n Sign up a subscriber to this ticker.\n Args:\n store_key (str): Unique storage hash for this ticker subscription.\n args (any, optional): Arguments to call the hook method with.\n\n Kwargs:\n _start_delay (int): If set, this will be\n used to delay the start of the trigger instead of\n `interval`.\n\n '
if self._is_ticking:
self._to_start.append((store_key, (args, kwargs)))
else:
start_delay = kwargs.pop('_start_delay', None)
self.subscriptions[store_key] = (args, kwargs)
self.validate(start_delay=start_delay) | -7,771,333,856,930,565,000 | Sign up a subscriber to this ticker.
Args:
store_key (str): Unique storage hash for this ticker subscription.
args (any, optional): Arguments to call the hook method with.
Kwargs:
_start_delay (int): If set, this will be
used to delay the start of the trigger instead of
`interval`. | evennia/scripts/tickerhandler.py | add | orkim/evennia | python | def add(self, store_key, *args, **kwargs):
'\n Sign up a subscriber to this ticker.\n Args:\n store_key (str): Unique storage hash for this ticker subscription.\n args (any, optional): Arguments to call the hook method with.\n\n Kwargs:\n _start_delay (int): If set, this will be\n used to delay the start of the trigger instead of\n `interval`.\n\n '
if self._is_ticking:
self._to_start.append((store_key, (args, kwargs)))
else:
start_delay = kwargs.pop('_start_delay', None)
self.subscriptions[store_key] = (args, kwargs)
self.validate(start_delay=start_delay) |
def remove(self, store_key):
'\n Unsubscribe object from this ticker\n\n Args:\n store_key (str): Unique store key.\n\n '
if self._is_ticking:
self._to_remove.append(store_key)
else:
self.subscriptions.pop(store_key, False)
self.validate() | 5,622,023,714,555,823,000 | Unsubscribe object from this ticker
Args:
store_key (str): Unique store key. | evennia/scripts/tickerhandler.py | remove | orkim/evennia | python | def remove(self, store_key):
'\n Unsubscribe object from this ticker\n\n Args:\n store_key (str): Unique store key.\n\n '
if self._is_ticking:
self._to_remove.append(store_key)
else:
self.subscriptions.pop(store_key, False)
self.validate() |
def stop(self):
'\n Kill the Task, regardless of subscriptions.\n\n '
self.subscriptions = {}
self.validate() | -9,085,155,564,099,422,000 | Kill the Task, regardless of subscriptions. | evennia/scripts/tickerhandler.py | stop | orkim/evennia | python | def stop(self):
'\n \n\n '
self.subscriptions = {}
self.validate() |
def __init__(self):
'\n Initialize the pool.\n\n '
self.tickers = {} | 3,492,824,550,037,405,000 | Initialize the pool. | evennia/scripts/tickerhandler.py | __init__ | orkim/evennia | python | def __init__(self):
'\n \n\n '
self.tickers = {} |
def add(self, store_key, *args, **kwargs):
'\n Add new ticker subscriber.\n\n Args:\n store_key (str): Unique storage hash.\n args (any, optional): Arguments to send to the hook method.\n\n '
(_, _, _, interval, _, _) = store_key
if (not interval):
log_err(_ERROR_ADD_TICKER.format(store_key=store_key))
return
if (interval not in self.tickers):
self.tickers[interval] = self.ticker_class(interval)
self.tickers[interval].add(store_key, *args, **kwargs) | -1,308,441,702,683,874,800 | Add new ticker subscriber.
Args:
store_key (str): Unique storage hash.
args (any, optional): Arguments to send to the hook method. | evennia/scripts/tickerhandler.py | add | orkim/evennia | python | def add(self, store_key, *args, **kwargs):
'\n Add new ticker subscriber.\n\n Args:\n store_key (str): Unique storage hash.\n args (any, optional): Arguments to send to the hook method.\n\n '
(_, _, _, interval, _, _) = store_key
if (not interval):
log_err(_ERROR_ADD_TICKER.format(store_key=store_key))
return
if (interval not in self.tickers):
self.tickers[interval] = self.ticker_class(interval)
self.tickers[interval].add(store_key, *args, **kwargs) |
def remove(self, store_key):
'\n Remove subscription from pool.\n\n Args:\n store_key (str): Unique storage hash to remove\n\n '
(_, _, _, interval, _, _) = store_key
if (interval in self.tickers):
self.tickers[interval].remove(store_key)
if (not self.tickers[interval]):
del self.tickers[interval] | 5,483,067,318,597,050,000 | Remove subscription from pool.
Args:
store_key (str): Unique storage hash to remove | evennia/scripts/tickerhandler.py | remove | orkim/evennia | python | def remove(self, store_key):
'\n Remove subscription from pool.\n\n Args:\n store_key (str): Unique storage hash to remove\n\n '
(_, _, _, interval, _, _) = store_key
if (interval in self.tickers):
self.tickers[interval].remove(store_key)
if (not self.tickers[interval]):
del self.tickers[interval] |
def stop(self, interval=None):
'\n Stop all scripts in pool. This is done at server reload since\n restoring the pool will automatically re-populate the pool.\n\n Args:\n interval (int, optional): Only stop tickers with this\n interval.\n\n '
if (interval and (interval in self.tickers)):
self.tickers[interval].stop()
else:
for ticker in self.tickers.values():
ticker.stop() | -8,835,157,636,026,703,000 | Stop all scripts in pool. This is done at server reload since
restoring the pool will automatically re-populate the pool.
Args:
interval (int, optional): Only stop tickers with this
interval. | evennia/scripts/tickerhandler.py | stop | orkim/evennia | python | def stop(self, interval=None):
'\n Stop all scripts in pool. This is done at server reload since\n restoring the pool will automatically re-populate the pool.\n\n Args:\n interval (int, optional): Only stop tickers with this\n interval.\n\n '
if (interval and (interval in self.tickers)):
self.tickers[interval].stop()
else:
for ticker in self.tickers.values():
ticker.stop() |
def __init__(self, save_name='ticker_storage'):
'\n Initialize handler\n\n save_name (str, optional): The name of the ServerConfig\n instance to store the handler state persistently.\n\n '
self.ticker_storage = {}
self.save_name = save_name
self.ticker_pool = self.ticker_pool_class() | 8,811,394,493,094,823,000 | Initialize handler
save_name (str, optional): The name of the ServerConfig
instance to store the handler state persistently. | evennia/scripts/tickerhandler.py | __init__ | orkim/evennia | python | def __init__(self, save_name='ticker_storage'):
'\n Initialize handler\n\n save_name (str, optional): The name of the ServerConfig\n instance to store the handler state persistently.\n\n '
self.ticker_storage = {}
self.save_name = save_name
self.ticker_pool = self.ticker_pool_class() |
def _get_callback(self, callback):
"\n Analyze callback and determine its consituents\n\n Args:\n callback (function or method): This is either a stand-alone\n function or class method on a typeclassed entitye (that is,\n an entity that can be saved to the database).\n\n Returns:\n ret (tuple): This is a tuple of the form `(obj, path, callfunc)`,\n where `obj` is the database object the callback is defined on\n if it's a method (otherwise `None`) and vice-versa, `path` is\n the python-path to the stand-alone function (`None` if a method).\n The `callfunc` is either the name of the method to call or the\n callable function object itself.\n\n "
(outobj, outpath, outcallfunc) = (None, None, None)
if callable(callback):
if inspect.ismethod(callback):
outobj = callback.im_self
outcallfunc = callback.im_func.func_name
elif inspect.isfunction(callback):
outpath = ('%s.%s' % (callback.__module__, callback.func_name))
outcallfunc = callback
else:
raise TypeError(('%s is not a callable function or method.' % callback))
return (outobj, outpath, outcallfunc) | -7,416,390,526,428,229,000 | Analyze callback and determine its consituents
Args:
callback (function or method): This is either a stand-alone
function or class method on a typeclassed entitye (that is,
an entity that can be saved to the database).
Returns:
ret (tuple): This is a tuple of the form `(obj, path, callfunc)`,
where `obj` is the database object the callback is defined on
if it's a method (otherwise `None`) and vice-versa, `path` is
the python-path to the stand-alone function (`None` if a method).
The `callfunc` is either the name of the method to call or the
callable function object itself. | evennia/scripts/tickerhandler.py | _get_callback | orkim/evennia | python | def _get_callback(self, callback):
"\n Analyze callback and determine its consituents\n\n Args:\n callback (function or method): This is either a stand-alone\n function or class method on a typeclassed entitye (that is,\n an entity that can be saved to the database).\n\n Returns:\n ret (tuple): This is a tuple of the form `(obj, path, callfunc)`,\n where `obj` is the database object the callback is defined on\n if it's a method (otherwise `None`) and vice-versa, `path` is\n the python-path to the stand-alone function (`None` if a method).\n The `callfunc` is either the name of the method to call or the\n callable function object itself.\n\n "
(outobj, outpath, outcallfunc) = (None, None, None)
if callable(callback):
if inspect.ismethod(callback):
outobj = callback.im_self
outcallfunc = callback.im_func.func_name
elif inspect.isfunction(callback):
outpath = ('%s.%s' % (callback.__module__, callback.func_name))
outcallfunc = callback
else:
raise TypeError(('%s is not a callable function or method.' % callback))
return (outobj, outpath, outcallfunc) |
def _store_key(self, obj, path, interval, callfunc, idstring='', persistent=True):
'\n Tries to create a store_key for the object.\n\n Args:\n obj (Object, tuple or None): Subscribing object if any. If a tuple, this is\n a packed_obj tuple from dbserialize.\n path (str or None): Python-path to callable, if any.\n interval (int): Ticker interval.\n callfunc (callable or str): This is either the callable function or\n the name of the method to call. Note that the callable is never\n stored in the key; that is uniquely identified with the python-path.\n idstring (str, optional): Additional separator between\n different subscription types.\n persistent (bool, optional): If this ticker should survive a system\n shutdown or not.\n\n Returns:\n store_key (tuple): A tuple `(packed_obj, methodname, outpath, interval,\n idstring, persistent)` that uniquely identifies the\n ticker. Here, `packed_obj` is the unique string representation of the\n object or `None`. The `methodname` is the string name of the method on\n `packed_obj` to call, or `None` if `packed_obj` is unset. `path` is\n the Python-path to a non-method callable, or `None`. Finally, `interval`\n `idstring` and `persistent` are integers, strings and bools respectively.\n\n '
interval = int(interval)
persistent = bool(persistent)
packed_obj = pack_dbobj(obj)
methodname = (callfunc if (callfunc and isinstance(callfunc, basestring)) else None)
outpath = (path if (path and isinstance(path, basestring)) else None)
return (packed_obj, methodname, outpath, interval, idstring, persistent) | 6,292,473,907,682,195,000 | Tries to create a store_key for the object.
Args:
obj (Object, tuple or None): Subscribing object if any. If a tuple, this is
a packed_obj tuple from dbserialize.
path (str or None): Python-path to callable, if any.
interval (int): Ticker interval.
callfunc (callable or str): This is either the callable function or
the name of the method to call. Note that the callable is never
stored in the key; that is uniquely identified with the python-path.
idstring (str, optional): Additional separator between
different subscription types.
persistent (bool, optional): If this ticker should survive a system
shutdown or not.
Returns:
store_key (tuple): A tuple `(packed_obj, methodname, outpath, interval,
idstring, persistent)` that uniquely identifies the
ticker. Here, `packed_obj` is the unique string representation of the
object or `None`. The `methodname` is the string name of the method on
`packed_obj` to call, or `None` if `packed_obj` is unset. `path` is
the Python-path to a non-method callable, or `None`. Finally, `interval`
`idstring` and `persistent` are integers, strings and bools respectively. | evennia/scripts/tickerhandler.py | _store_key | orkim/evennia | python | def _store_key(self, obj, path, interval, callfunc, idstring=, persistent=True):
'\n Tries to create a store_key for the object.\n\n Args:\n obj (Object, tuple or None): Subscribing object if any. If a tuple, this is\n a packed_obj tuple from dbserialize.\n path (str or None): Python-path to callable, if any.\n interval (int): Ticker interval.\n callfunc (callable or str): This is either the callable function or\n the name of the method to call. Note that the callable is never\n stored in the key; that is uniquely identified with the python-path.\n idstring (str, optional): Additional separator between\n different subscription types.\n persistent (bool, optional): If this ticker should survive a system\n shutdown or not.\n\n Returns:\n store_key (tuple): A tuple `(packed_obj, methodname, outpath, interval,\n idstring, persistent)` that uniquely identifies the\n ticker. Here, `packed_obj` is the unique string representation of the\n object or `None`. The `methodname` is the string name of the method on\n `packed_obj` to call, or `None` if `packed_obj` is unset. `path` is\n the Python-path to a non-method callable, or `None`. Finally, `interval`\n `idstring` and `persistent` are integers, strings and bools respectively.\n\n '
interval = int(interval)
persistent = bool(persistent)
packed_obj = pack_dbobj(obj)
methodname = (callfunc if (callfunc and isinstance(callfunc, basestring)) else None)
outpath = (path if (path and isinstance(path, basestring)) else None)
return (packed_obj, methodname, outpath, interval, idstring, persistent) |
def save(self):
'\n Save ticker_storage as a serialized string into a temporary\n ServerConf field. Whereas saving is done on the fly, if called\n by server when it shuts down, the current timer of each ticker\n will be saved so it can start over from that point.\n\n '
if self.ticker_storage:
start_delays = dict(((interval, ticker.task.next_call_time()) for (interval, ticker) in self.ticker_pool.tickers.items()))
to_save = {store_key: (args, kwargs) for (store_key, (args, kwargs)) in self.ticker_storage.items() if ((store_key[1] and (('_obj' in kwargs) and kwargs['_obj'].pk) and hasattr(kwargs['_obj'], store_key[1])) or store_key[2])}
for (store_key, (args, kwargs)) in to_save.items():
interval = store_key[1]
kwargs['_start_delay'] = start_delays.get(interval, None)
ServerConfig.objects.conf(key=self.save_name, value=dbserialize(to_save))
else:
ServerConfig.objects.conf(key=self.save_name, delete=True) | -2,642,650,010,556,636,700 | Save ticker_storage as a serialized string into a temporary
ServerConf field. Whereas saving is done on the fly, if called
by server when it shuts down, the current timer of each ticker
will be saved so it can start over from that point. | evennia/scripts/tickerhandler.py | save | orkim/evennia | python | def save(self):
'\n Save ticker_storage as a serialized string into a temporary\n ServerConf field. Whereas saving is done on the fly, if called\n by server when it shuts down, the current timer of each ticker\n will be saved so it can start over from that point.\n\n '
if self.ticker_storage:
start_delays = dict(((interval, ticker.task.next_call_time()) for (interval, ticker) in self.ticker_pool.tickers.items()))
to_save = {store_key: (args, kwargs) for (store_key, (args, kwargs)) in self.ticker_storage.items() if ((store_key[1] and (('_obj' in kwargs) and kwargs['_obj'].pk) and hasattr(kwargs['_obj'], store_key[1])) or store_key[2])}
for (store_key, (args, kwargs)) in to_save.items():
interval = store_key[1]
kwargs['_start_delay'] = start_delays.get(interval, None)
ServerConfig.objects.conf(key=self.save_name, value=dbserialize(to_save))
else:
ServerConfig.objects.conf(key=self.save_name, delete=True) |
def restore(self, server_reload=True):
'\n Restore ticker_storage from database and re-initialize the\n handler from storage. This is triggered by the server at\n restart.\n\n Args:\n server_reload (bool, optional): If this is False, it means\n the server went through a cold reboot and all\n non-persistent tickers must be killed.\n\n '
restored_tickers = ServerConfig.objects.conf(key=self.save_name)
if restored_tickers:
restored_tickers = dbunserialize(restored_tickers)
self.ticker_storage = {}
for (store_key, (args, kwargs)) in restored_tickers.iteritems():
try:
(obj, callfunc, path, interval, idstring, persistent) = store_key
if ((not persistent) and (not server_reload)):
continue
if (isinstance(callfunc, basestring) and (not obj)):
continue
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
if (obj and callfunc):
kwargs['_callback'] = callfunc
kwargs['_obj'] = obj
elif path:
(modname, varname) = path.rsplit('.', 1)
callback = variable_from_module(modname, varname)
kwargs['_callback'] = callback
kwargs['_obj'] = None
else:
log_err(('Tickerhandler: Removing malformed ticker: %s' % str(store_key)))
continue
except Exception:
log_trace(('Tickerhandler: Removing malformed ticker: %s' % str(store_key)))
continue
self.ticker_storage[store_key] = (args, kwargs)
self.ticker_pool.add(store_key, *args, **kwargs) | 4,263,887,369,786,780,700 | Restore ticker_storage from database and re-initialize the
handler from storage. This is triggered by the server at
restart.
Args:
server_reload (bool, optional): If this is False, it means
the server went through a cold reboot and all
non-persistent tickers must be killed. | evennia/scripts/tickerhandler.py | restore | orkim/evennia | python | def restore(self, server_reload=True):
'\n Restore ticker_storage from database and re-initialize the\n handler from storage. This is triggered by the server at\n restart.\n\n Args:\n server_reload (bool, optional): If this is False, it means\n the server went through a cold reboot and all\n non-persistent tickers must be killed.\n\n '
restored_tickers = ServerConfig.objects.conf(key=self.save_name)
if restored_tickers:
restored_tickers = dbunserialize(restored_tickers)
self.ticker_storage = {}
for (store_key, (args, kwargs)) in restored_tickers.iteritems():
try:
(obj, callfunc, path, interval, idstring, persistent) = store_key
if ((not persistent) and (not server_reload)):
continue
if (isinstance(callfunc, basestring) and (not obj)):
continue
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
if (obj and callfunc):
kwargs['_callback'] = callfunc
kwargs['_obj'] = obj
elif path:
(modname, varname) = path.rsplit('.', 1)
callback = variable_from_module(modname, varname)
kwargs['_callback'] = callback
kwargs['_obj'] = None
else:
log_err(('Tickerhandler: Removing malformed ticker: %s' % str(store_key)))
continue
except Exception:
log_trace(('Tickerhandler: Removing malformed ticker: %s' % str(store_key)))
continue
self.ticker_storage[store_key] = (args, kwargs)
self.ticker_pool.add(store_key, *args, **kwargs) |
def add(self, interval=60, callback=None, idstring='', persistent=True, *args, **kwargs):
'\n Add subscription to tickerhandler\n\n Args:\n interval (int, optional): Interval in seconds between calling\n `callable(*args, **kwargs)`\n callable (callable function or method, optional): This\n should either be a stand-alone function or a method on a\n typeclassed entity (that is, one that can be saved to the\n database).\n idstring (str, optional): Identifier for separating\n this ticker-subscription from others with the same\n interval. Allows for managing multiple calls with\n the same time interval and callback.\n persistent (bool, optional): A ticker will always survive\n a server reload. If this is unset, the ticker will be\n deleted by a server shutdown.\n args, kwargs (optional): These will be passed into the\n callback every time it is called.\n\n Notes:\n The callback will be identified by type and stored either as\n as combination of serialized database object + methodname or\n as a python-path to the module + funcname. These strings will\n be combined iwth `interval` and `idstring` to define a\n unique storage key for saving. These must thus all be supplied\n when wanting to modify/remove the ticker later.\n\n '
if isinstance(callback, int):
raise RuntimeError('TICKER_HANDLER.add has changed: the interval is now the first argument, callback the second.')
(obj, path, callfunc) = self._get_callback(callback)
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
kwargs['_obj'] = obj
kwargs['_callback'] = callfunc
self.ticker_storage[store_key] = (args, kwargs)
self.ticker_pool.add(store_key, *args, **kwargs)
self.save() | -9,201,289,920,555,109,000 | Add subscription to tickerhandler
Args:
interval (int, optional): Interval in seconds between calling
`callable(*args, **kwargs)`
callable (callable function or method, optional): This
should either be a stand-alone function or a method on a
typeclassed entity (that is, one that can be saved to the
database).
idstring (str, optional): Identifier for separating
this ticker-subscription from others with the same
interval. Allows for managing multiple calls with
the same time interval and callback.
persistent (bool, optional): A ticker will always survive
a server reload. If this is unset, the ticker will be
deleted by a server shutdown.
args, kwargs (optional): These will be passed into the
callback every time it is called.
Notes:
The callback will be identified by type and stored either as
as combination of serialized database object + methodname or
as a python-path to the module + funcname. These strings will
be combined iwth `interval` and `idstring` to define a
unique storage key for saving. These must thus all be supplied
when wanting to modify/remove the ticker later. | evennia/scripts/tickerhandler.py | add | orkim/evennia | python | def add(self, interval=60, callback=None, idstring=, persistent=True, *args, **kwargs):
'\n Add subscription to tickerhandler\n\n Args:\n interval (int, optional): Interval in seconds between calling\n `callable(*args, **kwargs)`\n callable (callable function or method, optional): This\n should either be a stand-alone function or a method on a\n typeclassed entity (that is, one that can be saved to the\n database).\n idstring (str, optional): Identifier for separating\n this ticker-subscription from others with the same\n interval. Allows for managing multiple calls with\n the same time interval and callback.\n persistent (bool, optional): A ticker will always survive\n a server reload. If this is unset, the ticker will be\n deleted by a server shutdown.\n args, kwargs (optional): These will be passed into the\n callback every time it is called.\n\n Notes:\n The callback will be identified by type and stored either as\n as combination of serialized database object + methodname or\n as a python-path to the module + funcname. These strings will\n be combined iwth `interval` and `idstring` to define a\n unique storage key for saving. These must thus all be supplied\n when wanting to modify/remove the ticker later.\n\n '
if isinstance(callback, int):
raise RuntimeError('TICKER_HANDLER.add has changed: the interval is now the first argument, callback the second.')
(obj, path, callfunc) = self._get_callback(callback)
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
kwargs['_obj'] = obj
kwargs['_callback'] = callfunc
self.ticker_storage[store_key] = (args, kwargs)
self.ticker_pool.add(store_key, *args, **kwargs)
self.save() |
def remove(self, interval=60, callback=None, idstring='', persistent=True):
'\n Remove object from ticker or only remove it from tickers with\n a given interval.\n\n Args:\n interval (int, optional): Interval of ticker to remove.\n callback (callable function or method): Either a function or\n the method of a typeclassed object.\n idstring (str, optional): Identifier id of ticker to remove.\n\n '
if isinstance(callback, int):
raise RuntimeError('TICKER_HANDLER.remove has changed: the interval is now the first argument, callback the second.')
(obj, path, callfunc) = self._get_callback(callback)
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
to_remove = self.ticker_storage.pop(store_key, None)
if to_remove:
self.ticker_pool.remove(store_key)
self.save() | 7,312,347,190,382,608,000 | Remove object from ticker or only remove it from tickers with
a given interval.
Args:
interval (int, optional): Interval of ticker to remove.
callback (callable function or method): Either a function or
the method of a typeclassed object.
idstring (str, optional): Identifier id of ticker to remove. | evennia/scripts/tickerhandler.py | remove | orkim/evennia | python | def remove(self, interval=60, callback=None, idstring=, persistent=True):
'\n Remove object from ticker or only remove it from tickers with\n a given interval.\n\n Args:\n interval (int, optional): Interval of ticker to remove.\n callback (callable function or method): Either a function or\n the method of a typeclassed object.\n idstring (str, optional): Identifier id of ticker to remove.\n\n '
if isinstance(callback, int):
raise RuntimeError('TICKER_HANDLER.remove has changed: the interval is now the first argument, callback the second.')
(obj, path, callfunc) = self._get_callback(callback)
store_key = self._store_key(obj, path, interval, callfunc, idstring, persistent)
to_remove = self.ticker_storage.pop(store_key, None)
if to_remove:
self.ticker_pool.remove(store_key)
self.save() |
def clear(self, interval=None):
'\n Stop/remove tickers from handler.\n\n Args:\n interval (int): Only stop tickers with this interval.\n\n Notes:\n This is the only supported way to kill tickers related to\n non-db objects.\n\n '
self.ticker_pool.stop(interval)
if interval:
self.ticker_storage = dict(((store_key, store_key) for store_key in self.ticker_storage if (store_key[1] != interval)))
else:
self.ticker_storage = {}
self.save() | 952,231,011,741,671,700 | Stop/remove tickers from handler.
Args:
interval (int): Only stop tickers with this interval.
Notes:
This is the only supported way to kill tickers related to
non-db objects. | evennia/scripts/tickerhandler.py | clear | orkim/evennia | python | def clear(self, interval=None):
'\n Stop/remove tickers from handler.\n\n Args:\n interval (int): Only stop tickers with this interval.\n\n Notes:\n This is the only supported way to kill tickers related to\n non-db objects.\n\n '
self.ticker_pool.stop(interval)
if interval:
self.ticker_storage = dict(((store_key, store_key) for store_key in self.ticker_storage if (store_key[1] != interval)))
else:
self.ticker_storage = {}
self.save() |
def all(self, interval=None):
'\n Get all subscriptions.\n\n Args:\n interval (int): Limit match to tickers with this interval.\n\n Returns:\n tickers (list): If `interval` was given, this is a list of\n tickers using that interval.\n tickerpool_layout (dict): If `interval` was *not* given,\n this is a dict {interval1: [ticker1, ticker2, ...], ...}\n\n '
if (interval is None):
return dict(((interval, ticker.subscriptions) for (interval, ticker) in self.ticker_pool.tickers.iteritems()))
else:
ticker = self.ticker_pool.tickers.get(interval, None)
if ticker:
return {interval: ticker.subscriptions} | 4,919,551,878,231,397,000 | Get all subscriptions.
Args:
interval (int): Limit match to tickers with this interval.
Returns:
tickers (list): If `interval` was given, this is a list of
tickers using that interval.
tickerpool_layout (dict): If `interval` was *not* given,
this is a dict {interval1: [ticker1, ticker2, ...], ...} | evennia/scripts/tickerhandler.py | all | orkim/evennia | python | def all(self, interval=None):
'\n Get all subscriptions.\n\n Args:\n interval (int): Limit match to tickers with this interval.\n\n Returns:\n tickers (list): If `interval` was given, this is a list of\n tickers using that interval.\n tickerpool_layout (dict): If `interval` was *not* given,\n this is a dict {interval1: [ticker1, ticker2, ...], ...}\n\n '
if (interval is None):
return dict(((interval, ticker.subscriptions) for (interval, ticker) in self.ticker_pool.tickers.iteritems()))
else:
ticker = self.ticker_pool.tickers.get(interval, None)
if ticker:
return {interval: ticker.subscriptions} |
def all_display(self):
'\n Get all tickers on an easily displayable form.\n\n Returns:\n tickers (dict): A list of all storekeys\n\n '
store_keys = []
for ticker in self.ticker_pool.tickers.itervalues():
for ((objtup, callfunc, path, interval, idstring, persistent), (args, kwargs)) in ticker.subscriptions.iteritems():
store_keys.append((kwargs.get('_obj', None), callfunc, path, interval, idstring, persistent))
return store_keys | -7,547,110,222,363,837,000 | Get all tickers on an easily displayable form.
Returns:
tickers (dict): A list of all storekeys | evennia/scripts/tickerhandler.py | all_display | orkim/evennia | python | def all_display(self):
'\n Get all tickers on an easily displayable form.\n\n Returns:\n tickers (dict): A list of all storekeys\n\n '
store_keys = []
for ticker in self.ticker_pool.tickers.itervalues():
for ((objtup, callfunc, path, interval, idstring, persistent), (args, kwargs)) in ticker.subscriptions.iteritems():
store_keys.append((kwargs.get('_obj', None), callfunc, path, interval, idstring, persistent))
return store_keys |
def parse_command_line():
'Parse the command line options'
desc = 'The Stars Align - Day 10 of Advent of Code 2018'
sample = 'sample: python aoc_10.py input.txt'
parser = argparse.ArgumentParser(description=desc, epilog=sample)
parser.add_argument('-v', '--verbose', action='store_true', default=False, dest='verbose', help='Print status messages to stdout')
parser.add_argument('-p', '--part', action='store', default=1, type=int, dest='part', help='Puzzle Part (1 or 2)')
parser.add_argument('-l', '--limit', action='store', default=0, type=int, dest='limit', help='Maximum limit (e.g., time, size, recursion) before stopping')
parser.add_argument('filepath', metavar='FILENAME', action='store', type=str, help='Location of puzzle input')
return parser.parse_args() | -1,921,321,716,781,806,600 | Parse the command line options | 2018/10_TheStarsAlign/aoc_10.py | parse_command_line | deanearlwright/AdventOfCode | python | def parse_command_line():
desc = 'The Stars Align - Day 10 of Advent of Code 2018'
sample = 'sample: python aoc_10.py input.txt'
parser = argparse.ArgumentParser(description=desc, epilog=sample)
parser.add_argument('-v', '--verbose', action='store_true', default=False, dest='verbose', help='Print status messages to stdout')
parser.add_argument('-p', '--part', action='store', default=1, type=int, dest='part', help='Puzzle Part (1 or 2)')
parser.add_argument('-l', '--limit', action='store', default=0, type=int, dest='limit', help='Maximum limit (e.g., time, size, recursion) before stopping')
parser.add_argument('filepath', metavar='FILENAME', action='store', type=str, help='Location of puzzle input')
return parser.parse_args() |
def part_one(args, input_lines):
'Process part one of the puzzle'
solver = lights.Lights(part2=False, text=input_lines)
solution = solver.part_one(verbose=args.verbose, limit=args.limit)
if (solution is None):
print('There is no solution')
else:
print(('The solution for part one is %s' % solution))
return (solution is not None) | 3,090,454,062,781,860,000 | Process part one of the puzzle | 2018/10_TheStarsAlign/aoc_10.py | part_one | deanearlwright/AdventOfCode | python | def part_one(args, input_lines):
solver = lights.Lights(part2=False, text=input_lines)
solution = solver.part_one(verbose=args.verbose, limit=args.limit)
if (solution is None):
print('There is no solution')
else:
print(('The solution for part one is %s' % solution))
return (solution is not None) |
def part_two(args, input_lines):
'Process part two of the puzzle'
solver = lights.Lights(part2=True, text=input_lines)
solution = solver.part_two(verbose=args.verbose, limit=args.limit)
if (solution is None):
print('There is no solution')
else:
print(('The solution for part two is %s' % solution))
return (solution is not None) | -7,008,301,873,587,400,000 | Process part two of the puzzle | 2018/10_TheStarsAlign/aoc_10.py | part_two | deanearlwright/AdventOfCode | python | def part_two(args, input_lines):
solver = lights.Lights(part2=True, text=input_lines)
solution = solver.part_two(verbose=args.verbose, limit=args.limit)
if (solution is None):
print('There is no solution')
else:
print(('The solution for part two is %s' % solution))
return (solution is not None) |
def from_file(filepath):
'Read the file'
return from_text(open(filepath).read()) | 5,800,071,608,339,332,000 | Read the file | 2018/10_TheStarsAlign/aoc_10.py | from_file | deanearlwright/AdventOfCode | python | def from_file(filepath):
return from_text(open(filepath).read()) |
def from_text(text):
'Break the text into trimed, non-comment lines'
lines = []
for line in text.split('\n'):
line = line.rstrip(' \r')
if (not line):
continue
if line.startswith('!'):
continue
lines.append(line)
return lines | -6,028,014,871,540,136,000 | Break the text into trimed, non-comment lines | 2018/10_TheStarsAlign/aoc_10.py | from_text | deanearlwright/AdventOfCode | python | def from_text(text):
lines = []
for line in text.split('\n'):
line = line.rstrip(' \r')
if (not line):
continue
if line.startswith('!'):
continue
lines.append(line)
return lines |
def main():
'Read the Advent of Code problem and solve it'
args = parse_command_line()
input_text = from_file(args.filepath)
if (args.part == 1):
result = part_one(args, input_text)
else:
result = part_two(args, input_text)
if result:
sys.exit(0)
sys.exit(2) | 3,592,193,595,729,941,500 | Read the Advent of Code problem and solve it | 2018/10_TheStarsAlign/aoc_10.py | main | deanearlwright/AdventOfCode | python | def main():
args = parse_command_line()
input_text = from_file(args.filepath)
if (args.part == 1):
result = part_one(args, input_text)
else:
result = part_two(args, input_text)
if result:
sys.exit(0)
sys.exit(2) |
def read_country_code():
'\n 获取国家中英文字典\n :return:\n '
country_dict = {}
for (key, val) in namemap.nameMap.items():
country_dict[val] = key
return country_dict | -7,936,220,243,036,575,000 | 获取国家中英文字典
:return: | python-data-analysis/2019-nCoV-global/global_map.py | read_country_code | DearCasper/python-learning | python | def read_country_code():
'\n 获取国家中英文字典\n :return:\n '
country_dict = {}
for (key, val) in namemap.nameMap.items():
country_dict[val] = key
return country_dict |
def read_csv():
'\n 读取数据，返回国家英文名称列表和累计确诊数列表\n :return:\n '
country_dict = read_country_code()
data = pd.read_csv('2019-nCoV.csv', index_col=False)
countrys_names = list()
confirmed_count = list()
for x in range(len(data.index)):
if (data['name'].iloc[x] in country_dict.keys()):
countrys_names.append(country_dict[data['name'].iloc[x]])
confirmed_count.append(data['confirm'].iloc[x])
else:
print(data['name'].iloc[x])
return (countrys_names, confirmed_count) | -7,097,631,232,518,895,000 | 读取数据，返回国家英文名称列表和累计确诊数列表
:return: | python-data-analysis/2019-nCoV-global/global_map.py | read_csv | DearCasper/python-learning | python | def read_csv():
'\n 读取数据，返回国家英文名称列表和累计确诊数列表\n :return:\n '
country_dict = read_country_code()
data = pd.read_csv('2019-nCoV.csv', index_col=False)
countrys_names = list()
confirmed_count = list()
for x in range(len(data.index)):
if (data['name'].iloc[x] in country_dict.keys()):
countrys_names.append(country_dict[data['name'].iloc[x]])
confirmed_count.append(data['confirm'].iloc[x])
else:
print(data['name'].iloc[x])
return (countrys_names, confirmed_count) |
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