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@property
def n(self):
'Returns the number of elements in the set covered by this cover.'
return self._n | 2,698,383,659,266,666,500 | Returns the number of elements in the set covered by this cover. | igraph/clustering.py | n | tuandnvn/ecat_learning | python | @property
def n(self):
return self._n |
def size(self, idx):
'Returns the size of a given cluster.\n\n @param idx: the cluster in which we are interested.\n '
return len(self[idx]) | -2,611,264,052,909,075,500 | Returns the size of a given cluster.
@param idx: the cluster in which we are interested. | igraph/clustering.py | size | tuandnvn/ecat_learning | python | def size(self, idx):
'Returns the size of a given cluster.\n\n @param idx: the cluster in which we are interested.\n '
return len(self[idx]) |
def sizes(self, *args):
'Returns the size of given clusters.\n\n The indices are given as positional arguments. If there are no\n positional arguments, the function will return the sizes of all clusters.\n '
if args:
return [len(self._clusters[idx]) for idx in args]
return [len(cluster) for cluster in self] | -449,967,433,735,330,750 | Returns the size of given clusters.
The indices are given as positional arguments. If there are no
positional arguments, the function will return the sizes of all clusters. | igraph/clustering.py | sizes | tuandnvn/ecat_learning | python | def sizes(self, *args):
'Returns the size of given clusters.\n\n The indices are given as positional arguments. If there are no\n positional arguments, the function will return the sizes of all clusters.\n '
if args:
return [len(self._clusters[idx]) for idx in args]
return [len(cluster) for cluster in self] |
def size_histogram(self, bin_width=1):
'Returns the histogram of cluster sizes.\n\n @param bin_width: the bin width of the histogram\n @return: a L{Histogram} object\n '
return Histogram(bin_width, self.sizes()) | -2,461,763,455,575,568,000 | Returns the histogram of cluster sizes.
@param bin_width: the bin width of the histogram
@return: a L{Histogram} object | igraph/clustering.py | size_histogram | tuandnvn/ecat_learning | python | def size_histogram(self, bin_width=1):
'Returns the histogram of cluster sizes.\n\n @param bin_width: the bin width of the histogram\n @return: a L{Histogram} object\n '
return Histogram(bin_width, self.sizes()) |
def summary(self, verbosity=0, width=None):
'Returns the summary of the cover.\n\n The summary includes the number of items and clusters, and also the\n list of members for each of the clusters if the verbosity is nonzero.\n\n @param verbosity: determines whether the cluster members should be\n printed. Zero verbosity prints the number of items and clusters only.\n @return: the summary of the cover as a string.\n '
out = StringIO()
((print >> out), ('Cover with %d clusters' % len(self)))
if (verbosity < 1):
return out.getvalue().strip()
ndigits = len(str(len(self)))
wrapper = _get_wrapper_for_width(width, subsequent_indent=(' ' * (ndigits + 3)))
for (idx, cluster) in enumerate(self._formatted_cluster_iterator()):
wrapper.initial_indent = ('[%*d] ' % (ndigits, idx))
((print >> out), '\n'.join(wrapper.wrap(cluster)))
return out.getvalue().strip() | -6,739,493,703,869,613,000 | Returns the summary of the cover.
The summary includes the number of items and clusters, and also the
list of members for each of the clusters if the verbosity is nonzero.
@param verbosity: determines whether the cluster members should be
printed. Zero verbosity prints the number of items and clusters only.
@return: the summary of the cover as a string. | igraph/clustering.py | summary | tuandnvn/ecat_learning | python | def summary(self, verbosity=0, width=None):
'Returns the summary of the cover.\n\n The summary includes the number of items and clusters, and also the\n list of members for each of the clusters if the verbosity is nonzero.\n\n @param verbosity: determines whether the cluster members should be\n printed. Zero verbosity prints the number of items and clusters only.\n @return: the summary of the cover as a string.\n '
out = StringIO()
((print >> out), ('Cover with %d clusters' % len(self)))
if (verbosity < 1):
return out.getvalue().strip()
ndigits = len(str(len(self)))
wrapper = _get_wrapper_for_width(width, subsequent_indent=(' ' * (ndigits + 3)))
for (idx, cluster) in enumerate(self._formatted_cluster_iterator()):
wrapper.initial_indent = ('[%*d] ' % (ndigits, idx))
((print >> out), '\n'.join(wrapper.wrap(cluster)))
return out.getvalue().strip() |
def _formatted_cluster_iterator(self):
'Iterates over the clusters and formats them into a string to be\n presented in the summary.'
for cluster in self:
(yield ', '.join((str(member) for member in cluster))) | 810,895,616,325,758,500 | Iterates over the clusters and formats them into a string to be
presented in the summary. | igraph/clustering.py | _formatted_cluster_iterator | tuandnvn/ecat_learning | python | def _formatted_cluster_iterator(self):
'Iterates over the clusters and formats them into a string to be\n presented in the summary.'
for cluster in self:
(yield ', '.join((str(member) for member in cluster))) |
def __init__(self, graph, clusters=None):
'Creates a cover object for a given graph.\n\n @param graph: the graph that will be associated to the cover\n @param clusters: the list of clusters. If C{None}, it is assumed\n that there is only a single cluster that covers the whole graph.\n '
if (clusters is None):
clusters = [range(graph.vcount())]
Cover.__init__(self, clusters, n=graph.vcount())
if (self._n > graph.vcount()):
raise ValueError('cluster list contains vertex ID larger than the number of vertices in the graph')
self._graph = graph | 3,603,294,436,514,720,300 | Creates a cover object for a given graph.
@param graph: the graph that will be associated to the cover
@param clusters: the list of clusters. If C{None}, it is assumed
that there is only a single cluster that covers the whole graph. | igraph/clustering.py | __init__ | tuandnvn/ecat_learning | python | def __init__(self, graph, clusters=None):
'Creates a cover object for a given graph.\n\n @param graph: the graph that will be associated to the cover\n @param clusters: the list of clusters. If C{None}, it is assumed\n that there is only a single cluster that covers the whole graph.\n '
if (clusters is None):
clusters = [range(graph.vcount())]
Cover.__init__(self, clusters, n=graph.vcount())
if (self._n > graph.vcount()):
raise ValueError('cluster list contains vertex ID larger than the number of vertices in the graph')
self._graph = graph |
def crossing(self):
'Returns a boolean vector where element M{i} is C{True} iff edge\n M{i} lies between clusters, C{False} otherwise.'
membership = [frozenset(cluster) for cluster in self.membership]
return [membership[v1].isdisjoint(membership[v2]) for (v1, v2) in self.graph.get_edgelist()] | 3,467,809,426,251,922,400 | Returns a boolean vector where element M{i} is C{True} iff edge
M{i} lies between clusters, C{False} otherwise. | igraph/clustering.py | crossing | tuandnvn/ecat_learning | python | def crossing(self):
'Returns a boolean vector where element M{i} is C{True} iff edge\n M{i} lies between clusters, C{False} otherwise.'
membership = [frozenset(cluster) for cluster in self.membership]
return [membership[v1].isdisjoint(membership[v2]) for (v1, v2) in self.graph.get_edgelist()] |
@property
def graph(self):
'Returns the graph belonging to this object'
return self._graph | -6,013,293,917,706,169,000 | Returns the graph belonging to this object | igraph/clustering.py | graph | tuandnvn/ecat_learning | python | @property
def graph(self):
return self._graph |
def subgraph(self, idx):
"Get the subgraph belonging to a given cluster.\n\n @param idx: the cluster index\n @return: a copy of the subgraph\n @precondition: the vertex set of the graph hasn't been modified since\n the moment the cover was constructed.\n "
return self._graph.subgraph(self[idx]) | -3,335,315,451,606,916,600 | Get the subgraph belonging to a given cluster.
@param idx: the cluster index
@return: a copy of the subgraph
@precondition: the vertex set of the graph hasn't been modified since
the moment the cover was constructed. | igraph/clustering.py | subgraph | tuandnvn/ecat_learning | python | def subgraph(self, idx):
"Get the subgraph belonging to a given cluster.\n\n @param idx: the cluster index\n @return: a copy of the subgraph\n @precondition: the vertex set of the graph hasn't been modified since\n the moment the cover was constructed.\n "
return self._graph.subgraph(self[idx]) |
def subgraphs(self):
"Gets all the subgraphs belonging to each of the clusters.\n\n @return: a list containing copies of the subgraphs\n @precondition: the vertex set of the graph hasn't been modified since\n the moment the cover was constructed.\n "
return [self._graph.subgraph(cl) for cl in self] | -6,612,431,575,192,130,000 | Gets all the subgraphs belonging to each of the clusters.
@return: a list containing copies of the subgraphs
@precondition: the vertex set of the graph hasn't been modified since
the moment the cover was constructed. | igraph/clustering.py | subgraphs | tuandnvn/ecat_learning | python | def subgraphs(self):
"Gets all the subgraphs belonging to each of the clusters.\n\n @return: a list containing copies of the subgraphs\n @precondition: the vertex set of the graph hasn't been modified since\n the moment the cover was constructed.\n "
return [self._graph.subgraph(cl) for cl in self] |
def __plot__(self, context, bbox, palette, *args, **kwds):
'Plots the cover to the given Cairo context in the given\n bounding box.\n\n This is done by calling L{Graph.__plot__()} with the same arguments, but\n drawing nice colored blobs around the vertex groups.\n\n This method understands all the positional and keyword arguments that\n are understood by L{Graph.__plot__()}, only the differences will be\n highlighted here:\n\n - C{mark_groups}: whether to highlight the vertex clusters by\n colored polygons. Besides the values accepted by L{Graph.__plot__}\n (i.e., a dict mapping colors to vertex indices, a list containing\n lists of vertex indices, or C{False}), the following are also\n accepted:\n\n - C{True}: all the clusters will be highlighted, the colors matching\n the corresponding color indices from the current palette\n (see the C{palette} keyword argument of L{Graph.__plot__}.\n\n - A dict mapping cluster indices or tuples of vertex indices to\n color names. The given clusters or vertex groups will be\n highlighted by the given colors.\n\n - A list of cluster indices. This is equivalent to passing a\n dict mapping numeric color indices from the current palette\n to cluster indices; therefore, the cluster referred to by element\n I{i} of the list will be highlighted by color I{i} from the\n palette.\n\n The value of the C{plotting.mark_groups} configuration key is also\n taken into account here; if that configuration key is C{True} and\n C{mark_groups} is not given explicitly, it will automatically be set\n to C{True}.\n\n In place of lists of vertex indices, you may also use L{VertexSeq}\n instances.\n\n In place of color names, you may also use color indices into the\n current palette. C{None} as a color name will mean that the\n corresponding group is ignored.\n\n - C{palette}: the palette used to resolve numeric color indices to RGBA\n values. By default, this is an instance of L{ClusterColoringPalette}.\n\n @see: L{Graph.__plot__()} for more supported keyword arguments.\n '
if (('edge_color' not in kwds) and ('color' not in self.graph.edge_attributes())):
colors = ['grey20', 'grey80']
kwds['edge_color'] = [colors[is_crossing] for is_crossing in self.crossing()]
if ('palette' in kwds):
palette = kwds['palette']
else:
palette = ClusterColoringPalette(len(self))
if ('mark_groups' not in kwds):
if Configuration.instance()['plotting.mark_groups']:
kwds['mark_groups'] = enumerate(self)
else:
kwds['mark_groups'] = _handle_mark_groups_arg_for_clustering(kwds['mark_groups'], self)
return self._graph.__plot__(context, bbox, palette, *args, **kwds) | -8,213,005,111,044,524,000 | Plots the cover to the given Cairo context in the given
bounding box.
This is done by calling L{Graph.__plot__()} with the same arguments, but
drawing nice colored blobs around the vertex groups.
This method understands all the positional and keyword arguments that
are understood by L{Graph.__plot__()}, only the differences will be
highlighted here:
- C{mark_groups}: whether to highlight the vertex clusters by
colored polygons. Besides the values accepted by L{Graph.__plot__}
(i.e., a dict mapping colors to vertex indices, a list containing
lists of vertex indices, or C{False}), the following are also
accepted:
- C{True}: all the clusters will be highlighted, the colors matching
the corresponding color indices from the current palette
(see the C{palette} keyword argument of L{Graph.__plot__}.
- A dict mapping cluster indices or tuples of vertex indices to
color names. The given clusters or vertex groups will be
highlighted by the given colors.
- A list of cluster indices. This is equivalent to passing a
dict mapping numeric color indices from the current palette
to cluster indices; therefore, the cluster referred to by element
I{i} of the list will be highlighted by color I{i} from the
palette.
The value of the C{plotting.mark_groups} configuration key is also
taken into account here; if that configuration key is C{True} and
C{mark_groups} is not given explicitly, it will automatically be set
to C{True}.
In place of lists of vertex indices, you may also use L{VertexSeq}
instances.
In place of color names, you may also use color indices into the
current palette. C{None} as a color name will mean that the
corresponding group is ignored.
- C{palette}: the palette used to resolve numeric color indices to RGBA
values. By default, this is an instance of L{ClusterColoringPalette}.
@see: L{Graph.__plot__()} for more supported keyword arguments. | igraph/clustering.py | __plot__ | tuandnvn/ecat_learning | python | def __plot__(self, context, bbox, palette, *args, **kwds):
'Plots the cover to the given Cairo context in the given\n bounding box.\n\n This is done by calling L{Graph.__plot__()} with the same arguments, but\n drawing nice colored blobs around the vertex groups.\n\n This method understands all the positional and keyword arguments that\n are understood by L{Graph.__plot__()}, only the differences will be\n highlighted here:\n\n - C{mark_groups}: whether to highlight the vertex clusters by\n colored polygons. Besides the values accepted by L{Graph.__plot__}\n (i.e., a dict mapping colors to vertex indices, a list containing\n lists of vertex indices, or C{False}), the following are also\n accepted:\n\n - C{True}: all the clusters will be highlighted, the colors matching\n the corresponding color indices from the current palette\n (see the C{palette} keyword argument of L{Graph.__plot__}.\n\n - A dict mapping cluster indices or tuples of vertex indices to\n color names. The given clusters or vertex groups will be\n highlighted by the given colors.\n\n - A list of cluster indices. This is equivalent to passing a\n dict mapping numeric color indices from the current palette\n to cluster indices; therefore, the cluster referred to by element\n I{i} of the list will be highlighted by color I{i} from the\n palette.\n\n The value of the C{plotting.mark_groups} configuration key is also\n taken into account here; if that configuration key is C{True} and\n C{mark_groups} is not given explicitly, it will automatically be set\n to C{True}.\n\n In place of lists of vertex indices, you may also use L{VertexSeq}\n instances.\n\n In place of color names, you may also use color indices into the\n current palette. C{None} as a color name will mean that the\n corresponding group is ignored.\n\n - C{palette}: the palette used to resolve numeric color indices to RGBA\n values. By default, this is an instance of L{ClusterColoringPalette}.\n\n @see: L{Graph.__plot__()} for more supported keyword arguments.\n '
if (('edge_color' not in kwds) and ('color' not in self.graph.edge_attributes())):
colors = ['grey20', 'grey80']
kwds['edge_color'] = [colors[is_crossing] for is_crossing in self.crossing()]
if ('palette' in kwds):
palette = kwds['palette']
else:
palette = ClusterColoringPalette(len(self))
if ('mark_groups' not in kwds):
if Configuration.instance()['plotting.mark_groups']:
kwds['mark_groups'] = enumerate(self)
else:
kwds['mark_groups'] = _handle_mark_groups_arg_for_clustering(kwds['mark_groups'], self)
return self._graph.__plot__(context, bbox, palette, *args, **kwds) |
def _formatted_cluster_iterator(self):
'Iterates over the clusters and formats them into a string to be\n presented in the summary.'
if self._graph.is_named():
names = self._graph.vs['name']
for cluster in self:
(yield ', '.join((str(names[member]) for member in cluster)))
else:
for cluster in self:
(yield ', '.join((str(member) for member in cluster))) | 6,838,424,363,819,696,000 | Iterates over the clusters and formats them into a string to be
presented in the summary. | igraph/clustering.py | _formatted_cluster_iterator | tuandnvn/ecat_learning | python | def _formatted_cluster_iterator(self):
'Iterates over the clusters and formats them into a string to be\n presented in the summary.'
if self._graph.is_named():
names = self._graph.vs['name']
for cluster in self:
(yield ', '.join((str(names[member]) for member in cluster)))
else:
for cluster in self:
(yield ', '.join((str(member) for member in cluster))) |
def __init__(self, graph, blocks=None, cohesion=None, parent=None):
'Constructs a new cohesive block structure for the given graph.\n\n If any of I{blocks}, I{cohesion} or I{parent} is C{None}, all the\n arguments will be ignored and L{Graph.cohesive_blocks()} will be\n called to calculate the cohesive blocks. Otherwise, these three\n variables should describe the *result* of a cohesive block structure\n calculation. Chances are that you never have to construct L{CohesiveBlocks}\n instances directly, just use L{Graph.cohesive_blocks()}.\n\n @param graph: the graph itself\n @param blocks: a list containing the blocks; each block is described\n as a list containing vertex IDs.\n @param cohesion: the cohesion of each block. The length of this list\n must be equal to the length of I{blocks}.\n @param parent: the parent block of each block. Negative values or\n C{None} mean that there is no parent block for that block. There\n should be only one parent block, which covers the entire graph.\n @see: Graph.cohesive_blocks()\n '
if ((blocks is None) or (cohesion is None) or (parent is None)):
(blocks, cohesion, parent) = graph.cohesive_blocks()
VertexCover.__init__(self, graph, blocks)
self._cohesion = cohesion
self._parent = parent
for (idx, p) in enumerate(self._parent):
if (p < 0):
self._parent[idx] = None | -2,177,125,745,029,691,600 | Constructs a new cohesive block structure for the given graph.
If any of I{blocks}, I{cohesion} or I{parent} is C{None}, all the
arguments will be ignored and L{Graph.cohesive_blocks()} will be
called to calculate the cohesive blocks. Otherwise, these three
variables should describe the *result* of a cohesive block structure
calculation. Chances are that you never have to construct L{CohesiveBlocks}
instances directly, just use L{Graph.cohesive_blocks()}.
@param graph: the graph itself
@param blocks: a list containing the blocks; each block is described
as a list containing vertex IDs.
@param cohesion: the cohesion of each block. The length of this list
must be equal to the length of I{blocks}.
@param parent: the parent block of each block. Negative values or
C{None} mean that there is no parent block for that block. There
should be only one parent block, which covers the entire graph.
@see: Graph.cohesive_blocks() | igraph/clustering.py | __init__ | tuandnvn/ecat_learning | python | def __init__(self, graph, blocks=None, cohesion=None, parent=None):
'Constructs a new cohesive block structure for the given graph.\n\n If any of I{blocks}, I{cohesion} or I{parent} is C{None}, all the\n arguments will be ignored and L{Graph.cohesive_blocks()} will be\n called to calculate the cohesive blocks. Otherwise, these three\n variables should describe the *result* of a cohesive block structure\n calculation. Chances are that you never have to construct L{CohesiveBlocks}\n instances directly, just use L{Graph.cohesive_blocks()}.\n\n @param graph: the graph itself\n @param blocks: a list containing the blocks; each block is described\n as a list containing vertex IDs.\n @param cohesion: the cohesion of each block. The length of this list\n must be equal to the length of I{blocks}.\n @param parent: the parent block of each block. Negative values or\n C{None} mean that there is no parent block for that block. There\n should be only one parent block, which covers the entire graph.\n @see: Graph.cohesive_blocks()\n '
if ((blocks is None) or (cohesion is None) or (parent is None)):
(blocks, cohesion, parent) = graph.cohesive_blocks()
VertexCover.__init__(self, graph, blocks)
self._cohesion = cohesion
self._parent = parent
for (idx, p) in enumerate(self._parent):
if (p < 0):
self._parent[idx] = None |
def cohesion(self, idx):
'Returns the cohesion of the group with the given index.'
return self._cohesion[idx] | -3,294,174,005,412,983,000 | Returns the cohesion of the group with the given index. | igraph/clustering.py | cohesion | tuandnvn/ecat_learning | python | def cohesion(self, idx):
return self._cohesion[idx] |
def cohesions(self):
'Returns the list of cohesion values for each group.'
return self._cohesion[:] | -9,078,904,226,652,061,000 | Returns the list of cohesion values for each group. | igraph/clustering.py | cohesions | tuandnvn/ecat_learning | python | def cohesions(self):
return self._cohesion[:] |
def hierarchy(self):
'Returns a new graph that describes the hierarchical relationships\n between the groups.\n\n The new graph will be a directed tree; an edge will point from\n vertex M{i} to vertex M{j} if group M{i} is a superset of group M{j}.\n In other words, the edges point downwards.\n '
from igraph import Graph
edges = [pair for pair in izip(self._parent, xrange(len(self))) if (pair[0] is not None)]
return Graph(edges, directed=True) | 5,892,941,643,755,646,000 | Returns a new graph that describes the hierarchical relationships
between the groups.
The new graph will be a directed tree; an edge will point from
vertex M{i} to vertex M{j} if group M{i} is a superset of group M{j}.
In other words, the edges point downwards. | igraph/clustering.py | hierarchy | tuandnvn/ecat_learning | python | def hierarchy(self):
'Returns a new graph that describes the hierarchical relationships\n between the groups.\n\n The new graph will be a directed tree; an edge will point from\n vertex M{i} to vertex M{j} if group M{i} is a superset of group M{j}.\n In other words, the edges point downwards.\n '
from igraph import Graph
edges = [pair for pair in izip(self._parent, xrange(len(self))) if (pair[0] is not None)]
return Graph(edges, directed=True) |
def max_cohesion(self, idx):
'Finds the maximum cohesion score among all the groups that contain\n the given vertex.'
result = 0
for (cohesion, cluster) in izip(self._cohesion, self._clusters):
if (idx in cluster):
result = max(result, cohesion)
return result | 4,111,417,056,152,360,000 | Finds the maximum cohesion score among all the groups that contain
the given vertex. | igraph/clustering.py | max_cohesion | tuandnvn/ecat_learning | python | def max_cohesion(self, idx):
'Finds the maximum cohesion score among all the groups that contain\n the given vertex.'
result = 0
for (cohesion, cluster) in izip(self._cohesion, self._clusters):
if (idx in cluster):
result = max(result, cohesion)
return result |
def max_cohesions(self):
'For each vertex in the graph, returns the maximum cohesion score\n among all the groups that contain the vertex.'
result = ([0] * self._graph.vcount())
for (cohesion, cluster) in izip(self._cohesion, self._clusters):
for idx in cluster:
result[idx] = max(result[idx], cohesion)
return result | -9,051,087,033,062,930,000 | For each vertex in the graph, returns the maximum cohesion score
among all the groups that contain the vertex. | igraph/clustering.py | max_cohesions | tuandnvn/ecat_learning | python | def max_cohesions(self):
'For each vertex in the graph, returns the maximum cohesion score\n among all the groups that contain the vertex.'
result = ([0] * self._graph.vcount())
for (cohesion, cluster) in izip(self._cohesion, self._clusters):
for idx in cluster:
result[idx] = max(result[idx], cohesion)
return result |
def parent(self, idx):
'Returns the parent group index of the group with the given index\n or C{None} if the given group is the root.'
return self._parent[idx] | -2,531,504,258,032,719,000 | Returns the parent group index of the group with the given index
or C{None} if the given group is the root. | igraph/clustering.py | parent | tuandnvn/ecat_learning | python | def parent(self, idx):
'Returns the parent group index of the group with the given index\n or C{None} if the given group is the root.'
return self._parent[idx] |
def parents(self):
'Returns the list of parent group indices for each group or C{None}\n if the given group is the root.'
return self._parent[:] | 4,112,353,736,681,686,500 | Returns the list of parent group indices for each group or C{None}
if the given group is the root. | igraph/clustering.py | parents | tuandnvn/ecat_learning | python | def parents(self):
'Returns the list of parent group indices for each group or C{None}\n if the given group is the root.'
return self._parent[:] |
def __plot__(self, context, bbox, palette, *args, **kwds):
'Plots the cohesive block structure to the given Cairo context in\n the given bounding box.\n\n Since a L{CohesiveBlocks} instance is also a L{VertexCover}, keyword\n arguments accepted by L{VertexCover.__plot__()} are also accepted here.\n The only difference is that the vertices are colored according to their\n maximal cohesions by default, and groups are marked by colored blobs\n except the last group which encapsulates the whole graph.\n\n See the documentation of L{VertexCover.__plot__()} for more details.\n '
prepare_groups = False
if ('mark_groups' not in kwds):
if Configuration.instance()['plotting.mark_groups']:
prepare_groups = True
elif (kwds['mark_groups'] == True):
prepare_groups = True
if prepare_groups:
colors = [pair for pair in enumerate(self.cohesions()) if (pair[1] > 1)]
kwds['mark_groups'] = colors
if ('vertex_color' not in kwds):
kwds['vertex_color'] = self.max_cohesions()
return VertexCover.__plot__(self, context, bbox, palette, *args, **kwds) | 3,229,657,450,994,385,400 | Plots the cohesive block structure to the given Cairo context in
the given bounding box.
Since a L{CohesiveBlocks} instance is also a L{VertexCover}, keyword
arguments accepted by L{VertexCover.__plot__()} are also accepted here.
The only difference is that the vertices are colored according to their
maximal cohesions by default, and groups are marked by colored blobs
except the last group which encapsulates the whole graph.
See the documentation of L{VertexCover.__plot__()} for more details. | igraph/clustering.py | __plot__ | tuandnvn/ecat_learning | python | def __plot__(self, context, bbox, palette, *args, **kwds):
'Plots the cohesive block structure to the given Cairo context in\n the given bounding box.\n\n Since a L{CohesiveBlocks} instance is also a L{VertexCover}, keyword\n arguments accepted by L{VertexCover.__plot__()} are also accepted here.\n The only difference is that the vertices are colored according to their\n maximal cohesions by default, and groups are marked by colored blobs\n except the last group which encapsulates the whole graph.\n\n See the documentation of L{VertexCover.__plot__()} for more details.\n '
prepare_groups = False
if ('mark_groups' not in kwds):
if Configuration.instance()['plotting.mark_groups']:
prepare_groups = True
elif (kwds['mark_groups'] == True):
prepare_groups = True
if prepare_groups:
colors = [pair for pair in enumerate(self.cohesions()) if (pair[1] > 1)]
kwds['mark_groups'] = colors
if ('vertex_color' not in kwds):
kwds['vertex_color'] = self.max_cohesions()
return VertexCover.__plot__(self, context, bbox, palette, *args, **kwds) |
def safeintdiv(x, y):
'Safe integer division that handles None gracefully'
if (x is None):
return None
return int((x / y)) | 5,398,877,169,241,703,000 | Safe integer division that handles None gracefully | igraph/clustering.py | safeintdiv | tuandnvn/ecat_learning | python | def safeintdiv(x, y):
if (x is None):
return None
return int((x / y)) |
def safebisect(intervals, x):
'Safe list bisection that handles None gracefully'
if (x is None):
return None
return bisect(intervals, x) | 8,481,049,750,746,316,000 | Safe list bisection that handles None gracefully | igraph/clustering.py | safebisect | tuandnvn/ecat_learning | python | def safebisect(intervals, x):
if (x is None):
return None
return bisect(intervals, x) |
@pytest.fixture
def pressure_sensor() -> mmr920C04.PressureSensor:
'Fixture for pressure sensor driver.'
return mmr920C04.PressureSensor() | 876,683,139,357,676,000 | Fixture for pressure sensor driver. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | pressure_sensor | Opentrons/protocol_framework | python | @pytest.fixture
def pressure_sensor() -> mmr920C04.PressureSensor:
return mmr920C04.PressureSensor() |
@pytest.fixture
def capacitive_sensor() -> fdc1004.CapacitiveSensor:
'Fixture for capacitive sensor driver.'
return fdc1004.CapacitiveSensor() | -5,125,160,098,761,263,000 | Fixture for capacitive sensor driver. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | capacitive_sensor | Opentrons/protocol_framework | python | @pytest.fixture
def capacitive_sensor() -> fdc1004.CapacitiveSensor:
return fdc1004.CapacitiveSensor() |
@pytest.fixture
def temperature_sensor() -> hdc2080.EnvironmentSensor:
'Fixture for temperature sensor driver.'
return hdc2080.EnvironmentSensor(SensorType.temperature) | -6,278,661,759,028,029,000 | Fixture for temperature sensor driver. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | temperature_sensor | Opentrons/protocol_framework | python | @pytest.fixture
def temperature_sensor() -> hdc2080.EnvironmentSensor:
return hdc2080.EnvironmentSensor(SensorType.temperature) |
@pytest.fixture
def humidity_sensor() -> hdc2080.EnvironmentSensor:
'Fixture for humidity sensor driver.'
return hdc2080.EnvironmentSensor(SensorType.humidity) | -2,967,201,093,913,927,000 | Fixture for humidity sensor driver. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | humidity_sensor | Opentrons/protocol_framework | python | @pytest.fixture
def humidity_sensor() -> hdc2080.EnvironmentSensor:
return hdc2080.EnvironmentSensor(SensorType.humidity) |
@pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, BaselineSensorRequest(payload=BaselineSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), sample_rate=UInt16Field(10)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, BaselineSensorRequest(payload=BaselineSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), sample_rate=UInt16Field(10)))]])
async def test_polling(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
'Test that a polling function sends the expected message.'
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.get_baseline(messenger, node, 10, 10))
messenger.send.assert_called_once_with(node_id=node, message=message) | -4,447,089,151,653,519,400 | Test that a polling function sends the expected message. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_polling | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, BaselineSensorRequest(payload=BaselineSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), sample_rate=UInt16Field(10)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, BaselineSensorRequest(payload=BaselineSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), sample_rate=UInt16Field(10)))]])
async def test_polling(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.get_baseline(messenger, node, 10, 10))
messenger.send.assert_called_once_with(node_id=node, message=message) |
@pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')]])
async def test_receive_data_polling(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
'Test that data is received from the polling function.'
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
return_data = (await sensor.get_baseline(mock_messenger, NodeId.pipette_left, 10, 10))
assert (return_data == SensorDataType.build([0, 1, 0])) | 2,886,839,213,322,297,000 | Test that data is received from the polling function. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_receive_data_polling | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')]])
async def test_receive_data_polling(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
return_data = (await sensor.get_baseline(mock_messenger, NodeId.pipette_left, 10, 10))
assert (return_data == SensorDataType.build([0, 1, 0])) |
@pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.temperature), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('humidity_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.humidity), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))]])
async def test_write(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
'Check that writing sensor data is successful.'
data = SensorDataType.build([2, 2, 0, 0])
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.write(messenger, NodeId.pipette_left, data))
messenger.send.assert_called_once_with(node_id=node, message=message) | 9,097,184,948,181,648,000 | Check that writing sensor data is successful. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_write | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.temperature), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))], [lazy_fixture('humidity_sensor'), NodeId.pipette_left, WriteToSensorRequest(payload=WriteToSensorRequestPayload(sensor=SensorTypeField(SensorType.humidity), sensor_id=SensorIdField(SensorId.S0), data=UInt32Field(SensorDataType.build([2, 2, 0, 0]).to_int), reg_address=UInt8Field(0)))]])
async def test_write(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
data = SensorDataType.build([2, 2, 0, 0])
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.write(messenger, NodeId.pipette_left, data))
messenger.send.assert_called_once_with(node_id=node, message=message) |
@pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.temperature), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('humidity_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.humidity), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))]])
async def test_read(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
'Test that a read function sends the expected message.'
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.read(messenger, node, False))
messenger.send.assert_called_once_with(node_id=node, message=message) | -3,859,175,399,935,780,000 | Test that a read function sends the expected message. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_read | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node', 'message'], argvalues=[[lazy_fixture('pressure_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.pressure), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('capacitive_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.capacitive), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.temperature), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))], [lazy_fixture('humidity_sensor'), NodeId.pipette_left, ReadFromSensorRequest(payload=ReadFromSensorRequestPayload(sensor=SensorTypeField(SensorType.humidity), sensor_id=SensorIdField(SensorId.S0), offset_reading=UInt8Field(False)))]])
async def test_read(sensor: sensor_abc.AbstractAdvancedSensor, node: NodeId, message: MessageDefinition) -> None:
messenger = mock.AsyncMock(spec=CanMessenger)
(await sensor.read(messenger, node, False))
messenger.send.assert_called_once_with(node_id=node, message=message) |
@pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')], [lazy_fixture('temperature_sensor')], [lazy_fixture('humidity_sensor')]])
async def test_receive_data_read(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
'Test that data is received from the read function.'
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
return_data = (await sensor.read(mock_messenger, NodeId.pipette_left, False, 10))
assert (return_data == SensorDataType.build([0, 1, 0])) | -7,847,721,743,718,292,000 | Test that data is received from the read function. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_receive_data_read | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')], [lazy_fixture('temperature_sensor')], [lazy_fixture('humidity_sensor')]])
async def test_receive_data_read(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
return_data = (await sensor.read(mock_messenger, NodeId.pipette_left, False, 10))
assert (return_data == SensorDataType.build([0, 1, 0])) |
@pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')]])
async def test_threshold(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
'Test that data is received from the threshold function.'
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, SetSensorThresholdRequest):
can_message_notifier.notify(SensorThresholdResponse(payload=SensorThresholdResponsePayload(threshold=message.payload.threshold, sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), mode=message.payload.mode)), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
threshold = SensorDataType.build([0, 5])
mock_messenger.send.side_effect = responder
return_data = (await sensor.send_zero_threshold(mock_messenger, NodeId.pipette_left, threshold, 10))
assert (return_data == threshold) | -666,913,451,662,296,800 | Test that data is received from the threshold function. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_threshold | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor'], argvalues=[[lazy_fixture('pressure_sensor')], [lazy_fixture('capacitive_sensor')]])
async def test_threshold(sensor: sensor_abc.AbstractAdvancedSensor, mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier) -> None:
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, SetSensorThresholdRequest):
can_message_notifier.notify(SensorThresholdResponse(payload=SensorThresholdResponsePayload(threshold=message.payload.threshold, sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), mode=message.payload.mode)), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id)))
threshold = SensorDataType.build([0, 5])
mock_messenger.send.side_effect = responder
return_data = (await sensor.send_zero_threshold(mock_messenger, NodeId.pipette_left, threshold, 10))
assert (return_data == threshold) |
@pytest.mark.parametrize(argnames=['node_id', 'timeout', 'sensor'], argvalues=[[NodeId.pipette_left, 1, lazy_fixture('pressure_sensor')], [NodeId.pipette_left, 5, lazy_fixture('capacitive_sensor')]])
async def test_bind_to_sync(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for bind_to_sync.\n\n Tests that bind_to_sync does in fact\n send out a BindSensorOutputRequest.\n '
async with sensor.bind_output(mock_messenger, node_id, SensorOutputBinding.sync):
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.sync))))
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.none)))) | -859,439,439,960,516,100 | Test for bind_to_sync.
Tests that bind_to_sync does in fact
send out a BindSensorOutputRequest. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_bind_to_sync | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['node_id', 'timeout', 'sensor'], argvalues=[[NodeId.pipette_left, 1, lazy_fixture('pressure_sensor')], [NodeId.pipette_left, 5, lazy_fixture('capacitive_sensor')]])
async def test_bind_to_sync(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for bind_to_sync.\n\n Tests that bind_to_sync does in fact\n send out a BindSensorOutputRequest.\n '
async with sensor.bind_output(mock_messenger, node_id, SensorOutputBinding.sync):
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.sync))))
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.none)))) |
@pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_right, 10], [lazy_fixture('pressure_sensor'), NodeId.pipette_left, 2]])
async def test_get_baseline(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for get_baseline.\n\n Tests that a BaselineSensorRequest gets sent,\n and reads ReadFromSensorResponse message containing the\n correct information.\n '
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, BaselineSensorRequest):
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), sensor_data=Int32Field(50))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
baseline = (await sensor.get_baseline(mock_messenger, node_id, 100, timeout))
assert (baseline == SensorDataType.build(Int32Field(50))) | -1,316,188,757,608,001,800 | Test for get_baseline.
Tests that a BaselineSensorRequest gets sent,
and reads ReadFromSensorResponse message containing the
correct information. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_get_baseline | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_right, 10], [lazy_fixture('pressure_sensor'), NodeId.pipette_left, 2]])
async def test_get_baseline(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for get_baseline.\n\n Tests that a BaselineSensorRequest gets sent,\n and reads ReadFromSensorResponse message containing the\n correct information.\n '
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, BaselineSensorRequest):
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), sensor_data=Int32Field(50))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
baseline = (await sensor.get_baseline(mock_messenger, node_id, 100, timeout))
assert (baseline == SensorDataType.build(Int32Field(50))) |
@pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_left, 2], [lazy_fixture('pressure_sensor'), NodeId.pipette_right, 3]])
async def test_debug_poll(mock_messenger: mock.AsyncMock, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for debug poll.'
async with sensor.bind_output(mock_messenger, node_id, SensorOutputBinding.report):
for i in range(2):
with patch.object(sensor._scheduler, '_wait_for_response', new=AsyncMock(return_value=SensorDataType.build(50))):
data = (await sensor.get_report(node_id, mock_messenger, timeout))
assert (data == SensorDataType.build(Int32Field(50)))
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.none)))) | -8,902,417,442,430,636,000 | Test for debug poll. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_debug_poll | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_left, 2], [lazy_fixture('pressure_sensor'), NodeId.pipette_right, 3]])
async def test_debug_poll(mock_messenger: mock.AsyncMock, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
async with sensor.bind_output(mock_messenger, node_id, SensorOutputBinding.report):
for i in range(2):
with patch.object(sensor._scheduler, '_wait_for_response', new=AsyncMock(return_value=SensorDataType.build(50))):
data = (await sensor.get_report(node_id, mock_messenger, timeout))
assert (data == SensorDataType.build(Int32Field(50)))
mock_messenger.send.assert_called_with(node_id=node_id, message=BindSensorOutputRequest(payload=BindSensorOutputRequestPayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), binding=SensorOutputBindingField(SensorOutputBinding.none)))) |
@pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_left, 2], [lazy_fixture('pressure_sensor'), NodeId.pipette_right, 3], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, 2], [lazy_fixture('humidity_sensor'), NodeId.pipette_right, 2]])
async def test_peripheral_status(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
'Test for getting peripheral device status.'
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, PeripheralStatusRequest):
can_message_notifier.notify(PeripheralStatusResponse(payload=PeripheralStatusResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), status=UInt8Field(1))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
status = (await sensor.get_device_status(mock_messenger, node_id, timeout))
assert status | 3,318,708,624,848,140,300 | Test for getting peripheral device status. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | test_peripheral_status | Opentrons/protocol_framework | python | @pytest.mark.parametrize(argnames=['sensor', 'node_id', 'timeout'], argvalues=[[lazy_fixture('capacitive_sensor'), NodeId.pipette_left, 2], [lazy_fixture('pressure_sensor'), NodeId.pipette_right, 3], [lazy_fixture('temperature_sensor'), NodeId.pipette_left, 2], [lazy_fixture('humidity_sensor'), NodeId.pipette_right, 2]])
async def test_peripheral_status(mock_messenger: mock.AsyncMock, can_message_notifier: MockCanMessageNotifier, sensor: sensor_abc.AbstractAdvancedSensor, node_id: NodeId, timeout: int) -> None:
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, PeripheralStatusRequest):
can_message_notifier.notify(PeripheralStatusResponse(payload=PeripheralStatusResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), status=UInt8Field(1))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id)))
mock_messenger.send.side_effect = responder
status = (await sensor.get_device_status(mock_messenger, node_id, timeout))
assert status |
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) | -330,064,312,598,272,060 | Message responder. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | responder | Opentrons/protocol_framework | python | def responder(node_id: NodeId, message: MessageDefinition) -> None:
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) |
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) | -330,064,312,598,272,060 | Message responder. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | responder | Opentrons/protocol_framework | python | def responder(node_id: NodeId, message: MessageDefinition) -> None:
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor_data=Int32Field(256), sensor_id=SensorIdField(SensorId.S0), sensor=SensorTypeField(sensor._sensor_type))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) |
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, SetSensorThresholdRequest):
can_message_notifier.notify(SensorThresholdResponse(payload=SensorThresholdResponsePayload(threshold=message.payload.threshold, sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), mode=message.payload.mode)), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) | 7,150,935,569,391,111,000 | Message responder. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | responder | Opentrons/protocol_framework | python | def responder(node_id: NodeId, message: MessageDefinition) -> None:
if isinstance(message, SetSensorThresholdRequest):
can_message_notifier.notify(SensorThresholdResponse(payload=SensorThresholdResponsePayload(threshold=message.payload.threshold, sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), mode=message.payload.mode)), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=NodeId.host, function_code=0, originating_node_id=node_id))) |
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, BaselineSensorRequest):
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), sensor_data=Int32Field(50))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id))) | -778,312,438,194,596,100 | Message responder. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | responder | Opentrons/protocol_framework | python | def responder(node_id: NodeId, message: MessageDefinition) -> None:
if isinstance(message, BaselineSensorRequest):
can_message_notifier.notify(ReadFromSensorResponse(payload=ReadFromSensorResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), sensor_data=Int32Field(50))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id))) |
def responder(node_id: NodeId, message: MessageDefinition) -> None:
'Message responder.'
if isinstance(message, PeripheralStatusRequest):
can_message_notifier.notify(PeripheralStatusResponse(payload=PeripheralStatusResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), status=UInt8Field(1))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id))) | -2,441,369,327,342,985,000 | Message responder. | hardware/tests/opentrons_hardware/sensors/test_sensor_drivers.py | responder | Opentrons/protocol_framework | python | def responder(node_id: NodeId, message: MessageDefinition) -> None:
if isinstance(message, PeripheralStatusRequest):
can_message_notifier.notify(PeripheralStatusResponse(payload=PeripheralStatusResponsePayload(sensor=SensorTypeField(sensor._sensor_type), sensor_id=SensorIdField(SensorId.S0), status=UInt8Field(1))), ArbitrationId(parts=ArbitrationIdParts(message_id=ReadFromSensorResponse.message_id, node_id=node_id, function_code=0, originating_node_id=node_id))) |
def has_mongo():
'Determine if MongoDB is up and usable'
if os.environ.get('MP_FAKEMONGO'):
mongo = False
else:
try:
pymongo.MongoClient()
mongo = True
except:
mongo = False
return mongo | 2,335,455,765,548,238,300 | Determine if MongoDB is up and usable | pymatgen/db/tests/common.py | has_mongo | chc273/pymatgen-db | python | def has_mongo():
if os.environ.get('MP_FAKEMONGO'):
mongo = False
else:
try:
pymongo.MongoClient()
mongo = True
except:
mongo = False
return mongo |
def connect(self, clear=False):
'Connect to Mongo DB\n\n :return: pymongo Database\n '
c = pymongo.MongoClient()
db = c[self.DB]
if clear:
for coll in (self.SRC, self.DST):
db[coll].remove()
tcoll = ((coll + '.') + CollectionTracker.TRACKING_NAME)
db[tcoll].remove()
return db | 8,094,381,745,248,001,000 | Connect to Mongo DB
:return: pymongo Database | pymatgen/db/tests/common.py | connect | chc273/pymatgen-db | python | def connect(self, clear=False):
'Connect to Mongo DB\n\n :return: pymongo Database\n '
c = pymongo.MongoClient()
db = c[self.DB]
if clear:
for coll in (self.SRC, self.DST):
db[coll].remove()
tcoll = ((coll + '.') + CollectionTracker.TRACKING_NAME)
db[tcoll].remove()
return db |
def run_command(self, args, options):
'Run the command-line given by the list\n in `args`, adding the dictionary given by\n options as long-form --{key}=value pairs.\n '
for (key, value) in options:
args.append('--{}'.format(key))
if value:
args.append(value)
return subprocess.call(args) | 6,931,213,289,327,645,000 | Run the command-line given by the list
in `args`, adding the dictionary given by
options as long-form --{key}=value pairs. | pymatgen/db/tests/common.py | run_command | chc273/pymatgen-db | python | def run_command(self, args, options):
'Run the command-line given by the list\n in `args`, adding the dictionary given by\n options as long-form --{key}=value pairs.\n '
for (key, value) in options:
args.append('--{}'.format(key))
if value:
args.append(value)
return subprocess.call(args) |
def myfunc(self, x, slope, intercept):
'\n input, slope, intercept\n '
return ((slope * x) + intercept) | -5,121,193,954,077,837,000 | input, slope, intercept | helloword/ml.py | myfunc | badpaybad/mypython | python | def myfunc(self, x, slope, intercept):
'\n \n '
return ((slope * x) + intercept) |
async def begin_create_or_update(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image: '_models.GalleryImage', **kwargs) -> AsyncLROPoller['_models.GalleryImage']:
"Create or update a gallery Image Definition.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to\n be created.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be created or updated.\n The allowed characters are alphabets and numbers with dots, dashes, and periods allowed in the\n middle. The maximum length is 80 characters.\n :type gallery_image_name: str\n :param gallery_image: Parameters supplied to the create or update gallery image operation.\n :type gallery_image: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,\n False for no polling, or your own initialized polling object for a personal polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either GalleryImage or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2019_03_01.models.GalleryImage]\n :raises ~azure.core.exceptions.HttpResponseError:\n "
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_initial(resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, gallery_image=gallery_image, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('GalleryImage', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | -4,321,324,683,486,622,700 | Create or update a gallery Image Definition.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to
be created.
:type gallery_name: str
:param gallery_image_name: The name of the gallery Image Definition to be created or updated.
The allowed characters are alphabets and numbers with dots, dashes, and periods allowed in the
middle. The maximum length is 80 characters.
:type gallery_image_name: str
:param gallery_image: Parameters supplied to the create or update gallery image operation.
:type gallery_image: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,
False for no polling, or your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either GalleryImage or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2019_03_01.models.GalleryImage]
:raises ~azure.core.exceptions.HttpResponseError: | sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations/_gallery_images_operations.py | begin_create_or_update | Darkstar1t/azure-sdk-for-python | python | async def begin_create_or_update(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image: '_models.GalleryImage', **kwargs) -> AsyncLROPoller['_models.GalleryImage']:
"Create or update a gallery Image Definition.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to\n be created.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be created or updated.\n The allowed characters are alphabets and numbers with dots, dashes, and periods allowed in the\n middle. The maximum length is 80 characters.\n :type gallery_image_name: str\n :param gallery_image: Parameters supplied to the create or update gallery image operation.\n :type gallery_image: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,\n False for no polling, or your own initialized polling object for a personal polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either GalleryImage or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2019_03_01.models.GalleryImage]\n :raises ~azure.core.exceptions.HttpResponseError:\n "
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_initial(resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, gallery_image=gallery_image, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('GalleryImage', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
async def get(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, **kwargs) -> '_models.GalleryImage':
'Retrieves information about a gallery Image Definition.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery from which the Image Definitions are\n to be retrieved.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be retrieved.\n :type gallery_image_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: GalleryImage, or the result of cls(response)\n :rtype: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2019-03-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('GalleryImage', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | 5,591,217,699,664,159,000 | Retrieves information about a gallery Image Definition.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param gallery_name: The name of the Shared Image Gallery from which the Image Definitions are
to be retrieved.
:type gallery_name: str
:param gallery_image_name: The name of the gallery Image Definition to be retrieved.
:type gallery_image_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GalleryImage, or the result of cls(response)
:rtype: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage
:raises: ~azure.core.exceptions.HttpResponseError | sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations/_gallery_images_operations.py | get | Darkstar1t/azure-sdk-for-python | python | async def get(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, **kwargs) -> '_models.GalleryImage':
'Retrieves information about a gallery Image Definition.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery from which the Image Definitions are\n to be retrieved.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be retrieved.\n :type gallery_image_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: GalleryImage, or the result of cls(response)\n :rtype: ~azure.mgmt.compute.v2019_03_01.models.GalleryImage\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2019-03-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('GalleryImage', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized |
async def begin_delete(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, **kwargs) -> AsyncLROPoller[None]:
"Delete a gallery image.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to\n be deleted.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be deleted.\n :type gallery_image_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,\n False for no polling, or your own initialized polling object for a personal polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n "
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_initial(resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | 7,484,213,028,850,654,000 | Delete a gallery image.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to
be deleted.
:type gallery_name: str
:param gallery_image_name: The name of the gallery Image Definition to be deleted.
:type gallery_image_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,
False for no polling, or your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError: | sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations/_gallery_images_operations.py | begin_delete | Darkstar1t/azure-sdk-for-python | python | async def begin_delete(self, resource_group_name: str, gallery_name: str, gallery_image_name: str, **kwargs) -> AsyncLROPoller[None]:
"Delete a gallery image.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery in which the Image Definition is to\n be deleted.\n :type gallery_name: str\n :param gallery_image_name: The name of the gallery Image Definition to be deleted.\n :type gallery_image_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,\n False for no polling, or your own initialized polling object for a personal polling strategy.\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n "
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_initial(resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str'), 'galleryImageName': self._serialize.url('gallery_image_name', gallery_image_name, 'str')}
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
def list_by_gallery(self, resource_group_name: str, gallery_name: str, **kwargs) -> AsyncIterable['_models.GalleryImageList']:
'List gallery Image Definitions in a gallery.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery from which Image Definitions are to\n be listed.\n :type gallery_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either GalleryImageList or the result of cls(response)\n :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.compute.v2019_03_01.models.GalleryImageList]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2019-03-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list_by_gallery.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('GalleryImageList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) | -6,598,339,211,277,231,000 | List gallery Image Definitions in a gallery.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param gallery_name: The name of the Shared Image Gallery from which Image Definitions are to
be listed.
:type gallery_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either GalleryImageList or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.compute.v2019_03_01.models.GalleryImageList]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations/_gallery_images_operations.py | list_by_gallery | Darkstar1t/azure-sdk-for-python | python | def list_by_gallery(self, resource_group_name: str, gallery_name: str, **kwargs) -> AsyncIterable['_models.GalleryImageList']:
'List gallery Image Definitions in a gallery.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param gallery_name: The name of the Shared Image Gallery from which Image Definitions are to\n be listed.\n :type gallery_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either GalleryImageList or the result of cls(response)\n :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.compute.v2019_03_01.models.GalleryImageList]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2019-03-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list_by_gallery.metadata['url']
path_format_arguments = {'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'galleryName': self._serialize.url('gallery_name', gallery_name, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('GalleryImageList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) |
def _InternalConstructMessage(full_name):
'Constructs a nested message.'
from google.protobuf import symbol_database
return symbol_database.Default().GetSymbol(full_name)() | -4,080,538,055,633,935,000 | Constructs a nested message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | _InternalConstructMessage | JustinACoder/H22-GR3-UnrealAI | python | def _InternalConstructMessage(full_name):
from google.protobuf import symbol_database
return symbol_database.Default().GetSymbol(full_name)() |
def __eq__(self, other_msg):
'Recursively compares two messages by value and structure.'
raise NotImplementedError | 3,488,240,450,900,297,000 | Recursively compares two messages by value and structure. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | __eq__ | JustinACoder/H22-GR3-UnrealAI | python | def __eq__(self, other_msg):
raise NotImplementedError |
def __str__(self):
'Outputs a human-readable representation of the message.'
raise NotImplementedError | -275,227,890,260,641,820 | Outputs a human-readable representation of the message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | __str__ | JustinACoder/H22-GR3-UnrealAI | python | def __str__(self):
raise NotImplementedError |
def __unicode__(self):
'Outputs a human-readable representation of the message.'
raise NotImplementedError | -7,489,656,935,393,816,000 | Outputs a human-readable representation of the message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | __unicode__ | JustinACoder/H22-GR3-UnrealAI | python | def __unicode__(self):
raise NotImplementedError |
def MergeFrom(self, other_msg):
'Merges the contents of the specified message into current message.\n\n This method merges the contents of the specified message into the current\n message. Singular fields that are set in the specified message overwrite\n the corresponding fields in the current message. Repeated fields are\n appended. Singular sub-messages and groups are recursively merged.\n\n Args:\n other_msg (Message): A message to merge into the current message.\n '
raise NotImplementedError | -5,259,389,814,106,578,000 | Merges the contents of the specified message into current message.
This method merges the contents of the specified message into the current
message. Singular fields that are set in the specified message overwrite
the corresponding fields in the current message. Repeated fields are
appended. Singular sub-messages and groups are recursively merged.
Args:
other_msg (Message): A message to merge into the current message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | MergeFrom | JustinACoder/H22-GR3-UnrealAI | python | def MergeFrom(self, other_msg):
'Merges the contents of the specified message into current message.\n\n This method merges the contents of the specified message into the current\n message. Singular fields that are set in the specified message overwrite\n the corresponding fields in the current message. Repeated fields are\n appended. Singular sub-messages and groups are recursively merged.\n\n Args:\n other_msg (Message): A message to merge into the current message.\n '
raise NotImplementedError |
def CopyFrom(self, other_msg):
'Copies the content of the specified message into the current message.\n\n The method clears the current message and then merges the specified\n message using MergeFrom.\n\n Args:\n other_msg (Message): A message to copy into the current one.\n '
if (self is other_msg):
return
self.Clear()
self.MergeFrom(other_msg) | 3,342,220,506,585,992,000 | Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
Args:
other_msg (Message): A message to copy into the current one. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | CopyFrom | JustinACoder/H22-GR3-UnrealAI | python | def CopyFrom(self, other_msg):
'Copies the content of the specified message into the current message.\n\n The method clears the current message and then merges the specified\n message using MergeFrom.\n\n Args:\n other_msg (Message): A message to copy into the current one.\n '
if (self is other_msg):
return
self.Clear()
self.MergeFrom(other_msg) |
def Clear(self):
'Clears all data that was set in the message.'
raise NotImplementedError | -7,482,799,018,333,739,000 | Clears all data that was set in the message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | Clear | JustinACoder/H22-GR3-UnrealAI | python | def Clear(self):
raise NotImplementedError |
def SetInParent(self):
'Mark this as present in the parent.\n\n This normally happens automatically when you assign a field of a\n sub-message, but sometimes you want to make the sub-message\n present while keeping it empty. If you find yourself using this,\n you may want to reconsider your design.\n '
raise NotImplementedError | 1,214,768,362,767,601,200 | Mark this as present in the parent.
This normally happens automatically when you assign a field of a
sub-message, but sometimes you want to make the sub-message
present while keeping it empty. If you find yourself using this,
you may want to reconsider your design. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | SetInParent | JustinACoder/H22-GR3-UnrealAI | python | def SetInParent(self):
'Mark this as present in the parent.\n\n This normally happens automatically when you assign a field of a\n sub-message, but sometimes you want to make the sub-message\n present while keeping it empty. If you find yourself using this,\n you may want to reconsider your design.\n '
raise NotImplementedError |
def IsInitialized(self):
'Checks if the message is initialized.\n\n Returns:\n bool: The method returns True if the message is initialized (i.e. all of\n its required fields are set).\n '
raise NotImplementedError | -6,005,976,788,694,109,000 | Checks if the message is initialized.
Returns:
bool: The method returns True if the message is initialized (i.e. all of
its required fields are set). | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | IsInitialized | JustinACoder/H22-GR3-UnrealAI | python | def IsInitialized(self):
'Checks if the message is initialized.\n\n Returns:\n bool: The method returns True if the message is initialized (i.e. all of\n its required fields are set).\n '
raise NotImplementedError |
def MergeFromString(self, serialized):
'Merges serialized protocol buffer data into this message.\n\n When we find a field in `serialized` that is already present\n in this message:\n\n - If it\'s a "repeated" field, we append to the end of our list.\n - Else, if it\'s a scalar, we overwrite our field.\n - Else, (it\'s a nonrepeated composite), we recursively merge\n into the existing composite.\n\n Args:\n serialized (bytes): Any object that allows us to call\n ``memoryview(serialized)`` to access a string of bytes using the\n buffer interface.\n\n Returns:\n int: The number of bytes read from `serialized`.\n For non-group messages, this will always be `len(serialized)`,\n but for messages which are actually groups, this will\n generally be less than `len(serialized)`, since we must\n stop when we reach an ``END_GROUP`` tag. Note that if\n we *do* stop because of an ``END_GROUP`` tag, the number\n of bytes returned does not include the bytes\n for the ``END_GROUP`` tag information.\n\n Raises:\n DecodeError: if the input cannot be parsed.\n '
raise NotImplementedError | 185,471,447,270,742,600 | Merges serialized protocol buffer data into this message.
When we find a field in `serialized` that is already present
in this message:
- If it's a "repeated" field, we append to the end of our list.
- Else, if it's a scalar, we overwrite our field.
- Else, (it's a nonrepeated composite), we recursively merge
into the existing composite.
Args:
serialized (bytes): Any object that allows us to call
``memoryview(serialized)`` to access a string of bytes using the
buffer interface.
Returns:
int: The number of bytes read from `serialized`.
For non-group messages, this will always be `len(serialized)`,
but for messages which are actually groups, this will
generally be less than `len(serialized)`, since we must
stop when we reach an ``END_GROUP`` tag. Note that if
we *do* stop because of an ``END_GROUP`` tag, the number
of bytes returned does not include the bytes
for the ``END_GROUP`` tag information.
Raises:
DecodeError: if the input cannot be parsed. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | MergeFromString | JustinACoder/H22-GR3-UnrealAI | python | def MergeFromString(self, serialized):
'Merges serialized protocol buffer data into this message.\n\n When we find a field in `serialized` that is already present\n in this message:\n\n - If it\'s a "repeated" field, we append to the end of our list.\n - Else, if it\'s a scalar, we overwrite our field.\n - Else, (it\'s a nonrepeated composite), we recursively merge\n into the existing composite.\n\n Args:\n serialized (bytes): Any object that allows us to call\n ``memoryview(serialized)`` to access a string of bytes using the\n buffer interface.\n\n Returns:\n int: The number of bytes read from `serialized`.\n For non-group messages, this will always be `len(serialized)`,\n but for messages which are actually groups, this will\n generally be less than `len(serialized)`, since we must\n stop when we reach an ``END_GROUP`` tag. Note that if\n we *do* stop because of an ``END_GROUP`` tag, the number\n of bytes returned does not include the bytes\n for the ``END_GROUP`` tag information.\n\n Raises:\n DecodeError: if the input cannot be parsed.\n '
raise NotImplementedError |
def ParseFromString(self, serialized):
'Parse serialized protocol buffer data into this message.\n\n Like :func:`MergeFromString()`, except we clear the object first.\n '
self.Clear()
return self.MergeFromString(serialized) | -5,109,456,638,149,453,000 | Parse serialized protocol buffer data into this message.
Like :func:`MergeFromString()`, except we clear the object first. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | ParseFromString | JustinACoder/H22-GR3-UnrealAI | python | def ParseFromString(self, serialized):
'Parse serialized protocol buffer data into this message.\n\n Like :func:`MergeFromString()`, except we clear the object first.\n '
self.Clear()
return self.MergeFromString(serialized) |
def SerializeToString(self, **kwargs):
"Serializes the protocol message to a binary string.\n\n Keyword Args:\n deterministic (bool): If true, requests deterministic serialization\n of the protobuf, with predictable ordering of map keys.\n\n Returns:\n A binary string representation of the message if all of the required\n fields in the message are set (i.e. the message is initialized).\n\n Raises:\n EncodeError: if the message isn't initialized (see :func:`IsInitialized`).\n "
raise NotImplementedError | 7,587,470,124,439,645,000 | Serializes the protocol message to a binary string.
Keyword Args:
deterministic (bool): If true, requests deterministic serialization
of the protobuf, with predictable ordering of map keys.
Returns:
A binary string representation of the message if all of the required
fields in the message are set (i.e. the message is initialized).
Raises:
EncodeError: if the message isn't initialized (see :func:`IsInitialized`). | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | SerializeToString | JustinACoder/H22-GR3-UnrealAI | python | def SerializeToString(self, **kwargs):
"Serializes the protocol message to a binary string.\n\n Keyword Args:\n deterministic (bool): If true, requests deterministic serialization\n of the protobuf, with predictable ordering of map keys.\n\n Returns:\n A binary string representation of the message if all of the required\n fields in the message are set (i.e. the message is initialized).\n\n Raises:\n EncodeError: if the message isn't initialized (see :func:`IsInitialized`).\n "
raise NotImplementedError |
def SerializePartialToString(self, **kwargs):
"Serializes the protocol message to a binary string.\n\n This method is similar to SerializeToString but doesn't check if the\n message is initialized.\n\n Keyword Args:\n deterministic (bool): If true, requests deterministic serialization\n of the protobuf, with predictable ordering of map keys.\n\n Returns:\n bytes: A serialized representation of the partial message.\n "
raise NotImplementedError | -5,496,072,132,301,760,000 | Serializes the protocol message to a binary string.
This method is similar to SerializeToString but doesn't check if the
message is initialized.
Keyword Args:
deterministic (bool): If true, requests deterministic serialization
of the protobuf, with predictable ordering of map keys.
Returns:
bytes: A serialized representation of the partial message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | SerializePartialToString | JustinACoder/H22-GR3-UnrealAI | python | def SerializePartialToString(self, **kwargs):
"Serializes the protocol message to a binary string.\n\n This method is similar to SerializeToString but doesn't check if the\n message is initialized.\n\n Keyword Args:\n deterministic (bool): If true, requests deterministic serialization\n of the protobuf, with predictable ordering of map keys.\n\n Returns:\n bytes: A serialized representation of the partial message.\n "
raise NotImplementedError |
def ListFields(self):
'Returns a list of (FieldDescriptor, value) tuples for present fields.\n\n A message field is non-empty if HasField() would return true. A singular\n primitive field is non-empty if HasField() would return true in proto2 or it\n is non zero in proto3. A repeated field is non-empty if it contains at least\n one element. The fields are ordered by field number.\n\n Returns:\n list[tuple(FieldDescriptor, value)]: field descriptors and values\n for all fields in the message which are not empty. The values vary by\n field type.\n '
raise NotImplementedError | 7,323,349,097,671,602,000 | Returns a list of (FieldDescriptor, value) tuples for present fields.
A message field is non-empty if HasField() would return true. A singular
primitive field is non-empty if HasField() would return true in proto2 or it
is non zero in proto3. A repeated field is non-empty if it contains at least
one element. The fields are ordered by field number.
Returns:
list[tuple(FieldDescriptor, value)]: field descriptors and values
for all fields in the message which are not empty. The values vary by
field type. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | ListFields | JustinACoder/H22-GR3-UnrealAI | python | def ListFields(self):
'Returns a list of (FieldDescriptor, value) tuples for present fields.\n\n A message field is non-empty if HasField() would return true. A singular\n primitive field is non-empty if HasField() would return true in proto2 or it\n is non zero in proto3. A repeated field is non-empty if it contains at least\n one element. The fields are ordered by field number.\n\n Returns:\n list[tuple(FieldDescriptor, value)]: field descriptors and values\n for all fields in the message which are not empty. The values vary by\n field type.\n '
raise NotImplementedError |
def HasField(self, field_name):
'Checks if a certain field is set for the message.\n\n For a oneof group, checks if any field inside is set. Note that if the\n field_name is not defined in the message descriptor, :exc:`ValueError` will\n be raised.\n\n Args:\n field_name (str): The name of the field to check for presence.\n\n Returns:\n bool: Whether a value has been set for the named field.\n\n Raises:\n ValueError: if the `field_name` is not a member of this message.\n '
raise NotImplementedError | 221,908,891,018,159,100 | Checks if a certain field is set for the message.
For a oneof group, checks if any field inside is set. Note that if the
field_name is not defined in the message descriptor, :exc:`ValueError` will
be raised.
Args:
field_name (str): The name of the field to check for presence.
Returns:
bool: Whether a value has been set for the named field.
Raises:
ValueError: if the `field_name` is not a member of this message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | HasField | JustinACoder/H22-GR3-UnrealAI | python | def HasField(self, field_name):
'Checks if a certain field is set for the message.\n\n For a oneof group, checks if any field inside is set. Note that if the\n field_name is not defined in the message descriptor, :exc:`ValueError` will\n be raised.\n\n Args:\n field_name (str): The name of the field to check for presence.\n\n Returns:\n bool: Whether a value has been set for the named field.\n\n Raises:\n ValueError: if the `field_name` is not a member of this message.\n '
raise NotImplementedError |
def ClearField(self, field_name):
'Clears the contents of a given field.\n\n Inside a oneof group, clears the field set. If the name neither refers to a\n defined field or oneof group, :exc:`ValueError` is raised.\n\n Args:\n field_name (str): The name of the field to check for presence.\n\n Raises:\n ValueError: if the `field_name` is not a member of this message.\n '
raise NotImplementedError | -4,180,667,636,435,284,500 | Clears the contents of a given field.
Inside a oneof group, clears the field set. If the name neither refers to a
defined field or oneof group, :exc:`ValueError` is raised.
Args:
field_name (str): The name of the field to check for presence.
Raises:
ValueError: if the `field_name` is not a member of this message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | ClearField | JustinACoder/H22-GR3-UnrealAI | python | def ClearField(self, field_name):
'Clears the contents of a given field.\n\n Inside a oneof group, clears the field set. If the name neither refers to a\n defined field or oneof group, :exc:`ValueError` is raised.\n\n Args:\n field_name (str): The name of the field to check for presence.\n\n Raises:\n ValueError: if the `field_name` is not a member of this message.\n '
raise NotImplementedError |
def WhichOneof(self, oneof_group):
'Returns the name of the field that is set inside a oneof group.\n\n If no field is set, returns None.\n\n Args:\n oneof_group (str): the name of the oneof group to check.\n\n Returns:\n str or None: The name of the group that is set, or None.\n\n Raises:\n ValueError: no group with the given name exists\n '
raise NotImplementedError | 7,706,930,664,240,627,000 | Returns the name of the field that is set inside a oneof group.
If no field is set, returns None.
Args:
oneof_group (str): the name of the oneof group to check.
Returns:
str or None: The name of the group that is set, or None.
Raises:
ValueError: no group with the given name exists | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | WhichOneof | JustinACoder/H22-GR3-UnrealAI | python | def WhichOneof(self, oneof_group):
'Returns the name of the field that is set inside a oneof group.\n\n If no field is set, returns None.\n\n Args:\n oneof_group (str): the name of the oneof group to check.\n\n Returns:\n str or None: The name of the group that is set, or None.\n\n Raises:\n ValueError: no group with the given name exists\n '
raise NotImplementedError |
def HasExtension(self, extension_handle):
'Checks if a certain extension is present for this message.\n\n Extensions are retrieved using the :attr:`Extensions` mapping (if present).\n\n Args:\n extension_handle: The handle for the extension to check.\n\n Returns:\n bool: Whether the extension is present for this message.\n\n Raises:\n KeyError: if the extension is repeated. Similar to repeated fields,\n there is no separate notion of presence: a "not present" repeated\n extension is an empty list.\n '
raise NotImplementedError | 4,907,608,339,312,964,000 | Checks if a certain extension is present for this message.
Extensions are retrieved using the :attr:`Extensions` mapping (if present).
Args:
extension_handle: The handle for the extension to check.
Returns:
bool: Whether the extension is present for this message.
Raises:
KeyError: if the extension is repeated. Similar to repeated fields,
there is no separate notion of presence: a "not present" repeated
extension is an empty list. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | HasExtension | JustinACoder/H22-GR3-UnrealAI | python | def HasExtension(self, extension_handle):
'Checks if a certain extension is present for this message.\n\n Extensions are retrieved using the :attr:`Extensions` mapping (if present).\n\n Args:\n extension_handle: The handle for the extension to check.\n\n Returns:\n bool: Whether the extension is present for this message.\n\n Raises:\n KeyError: if the extension is repeated. Similar to repeated fields,\n there is no separate notion of presence: a "not present" repeated\n extension is an empty list.\n '
raise NotImplementedError |
def ClearExtension(self, extension_handle):
'Clears the contents of a given extension.\n\n Args:\n extension_handle: The handle for the extension to clear.\n '
raise NotImplementedError | -8,967,653,825,512,342,000 | Clears the contents of a given extension.
Args:
extension_handle: The handle for the extension to clear. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | ClearExtension | JustinACoder/H22-GR3-UnrealAI | python | def ClearExtension(self, extension_handle):
'Clears the contents of a given extension.\n\n Args:\n extension_handle: The handle for the extension to clear.\n '
raise NotImplementedError |
def UnknownFields(self):
'Returns the UnknownFieldSet.\n\n Returns:\n UnknownFieldSet: The unknown fields stored in this message.\n '
raise NotImplementedError | 5,739,973,485,065,129,000 | Returns the UnknownFieldSet.
Returns:
UnknownFieldSet: The unknown fields stored in this message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | UnknownFields | JustinACoder/H22-GR3-UnrealAI | python | def UnknownFields(self):
'Returns the UnknownFieldSet.\n\n Returns:\n UnknownFieldSet: The unknown fields stored in this message.\n '
raise NotImplementedError |
def DiscardUnknownFields(self):
'Clears all fields in the :class:`UnknownFieldSet`.\n\n This operation is recursive for nested message.\n '
raise NotImplementedError | 2,413,369,931,560,882,700 | Clears all fields in the :class:`UnknownFieldSet`.
This operation is recursive for nested message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | DiscardUnknownFields | JustinACoder/H22-GR3-UnrealAI | python | def DiscardUnknownFields(self):
'Clears all fields in the :class:`UnknownFieldSet`.\n\n This operation is recursive for nested message.\n '
raise NotImplementedError |
def ByteSize(self):
'Returns the serialized size of this message.\n\n Recursively calls ByteSize() on all contained messages.\n\n Returns:\n int: The number of bytes required to serialize this message.\n '
raise NotImplementedError | -7,189,590,014,356,224,000 | Returns the serialized size of this message.
Recursively calls ByteSize() on all contained messages.
Returns:
int: The number of bytes required to serialize this message. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | ByteSize | JustinACoder/H22-GR3-UnrealAI | python | def ByteSize(self):
'Returns the serialized size of this message.\n\n Recursively calls ByteSize() on all contained messages.\n\n Returns:\n int: The number of bytes required to serialize this message.\n '
raise NotImplementedError |
def _SetListener(self, message_listener):
'Internal method used by the protocol message implementation.\n Clients should not call this directly.\n\n Sets a listener that this message will call on certain state transitions.\n\n The purpose of this method is to register back-edges from children to\n parents at runtime, for the purpose of setting "has" bits and\n byte-size-dirty bits in the parent and ancestor objects whenever a child or\n descendant object is modified.\n\n If the client wants to disconnect this Message from the object tree, she\n explicitly sets callback to None.\n\n If message_listener is None, unregisters any existing listener. Otherwise,\n message_listener must implement the MessageListener interface in\n internal/message_listener.py, and we discard any listener registered\n via a previous _SetListener() call.\n '
raise NotImplementedError | 2,148,599,048,241,910,500 | Internal method used by the protocol message implementation.
Clients should not call this directly.
Sets a listener that this message will call on certain state transitions.
The purpose of this method is to register back-edges from children to
parents at runtime, for the purpose of setting "has" bits and
byte-size-dirty bits in the parent and ancestor objects whenever a child or
descendant object is modified.
If the client wants to disconnect this Message from the object tree, she
explicitly sets callback to None.
If message_listener is None, unregisters any existing listener. Otherwise,
message_listener must implement the MessageListener interface in
internal/message_listener.py, and we discard any listener registered
via a previous _SetListener() call. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | _SetListener | JustinACoder/H22-GR3-UnrealAI | python | def _SetListener(self, message_listener):
'Internal method used by the protocol message implementation.\n Clients should not call this directly.\n\n Sets a listener that this message will call on certain state transitions.\n\n The purpose of this method is to register back-edges from children to\n parents at runtime, for the purpose of setting "has" bits and\n byte-size-dirty bits in the parent and ancestor objects whenever a child or\n descendant object is modified.\n\n If the client wants to disconnect this Message from the object tree, she\n explicitly sets callback to None.\n\n If message_listener is None, unregisters any existing listener. Otherwise,\n message_listener must implement the MessageListener interface in\n internal/message_listener.py, and we discard any listener registered\n via a previous _SetListener() call.\n '
raise NotImplementedError |
def __getstate__(self):
'Support the pickle protocol.'
return dict(serialized=self.SerializePartialToString()) | 5,659,051,950,600,202,000 | Support the pickle protocol. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | __getstate__ | JustinACoder/H22-GR3-UnrealAI | python | def __getstate__(self):
return dict(serialized=self.SerializePartialToString()) |
def __setstate__(self, state):
'Support the pickle protocol.'
self.__init__()
serialized = state['serialized']
if (not isinstance(serialized, bytes)):
serialized = serialized.encode('latin1')
self.ParseFromString(serialized) | -2,837,338,939,880,771,000 | Support the pickle protocol. | Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/google/protobuf/message.py | __setstate__ | JustinACoder/H22-GR3-UnrealAI | python | def __setstate__(self, state):
self.__init__()
serialized = state['serialized']
if (not isinstance(serialized, bytes)):
serialized = serialized.encode('latin1')
self.ParseFromString(serialized) |
def train(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((MSE + Reg))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((MSE_test + Reg_test))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) | -7,086,159,953,085,371,000 | [summary]
Args:
model (DeepMoD): [description]
data (torch.Tensor): [description]
target (torch.Tensor): [description]
optimizer ([type]): [description]
sparsity_scheduler ([type]): [description]
log_dir (Optional[str], optional): [description]. Defaults to None.
max_iterations (int, optional): [description]. Defaults to 10000. | src/multitaskpinn/training/training.py | train | GJBoth/MultiTaskPINN | python | def train(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((MSE + Reg))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((MSE_test + Reg_test))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) |
def train_multitask(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((((torch.exp((- model.s[:, 0])) * MSE) + (torch.exp((- model.s[:, 1])) * Reg)) + torch.sum(model.s)))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((((torch.exp((- model.s[:, 0])) * MSE_test) + (torch.exp((- model.s[:, 1])) * Reg_test)) + torch.sum(model.s)))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) | -735,518,995,854,886,700 | [summary]
Args:
model (DeepMoD): [description]
data (torch.Tensor): [description]
target (torch.Tensor): [description]
optimizer ([type]): [description]
sparsity_scheduler ([type]): [description]
log_dir (Optional[str], optional): [description]. Defaults to None.
max_iterations (int, optional): [description]. Defaults to 10000. | src/multitaskpinn/training/training.py | train_multitask | GJBoth/MultiTaskPINN | python | def train_multitask(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((((torch.exp((- model.s[:, 0])) * MSE) + (torch.exp((- model.s[:, 1])) * Reg)) + torch.sum(model.s)))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((((torch.exp((- model.s[:, 0])) * MSE_test) + (torch.exp((- model.s[:, 1])) * Reg_test)) + torch.sum(model.s)))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) |
def train_multitask_capped(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
cutoff = torch.tensor(15.0).to(target.device)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2), dim=0) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
s_capped = torch.min(torch.max(model.s, (- cutoff)), cutoff)
loss = torch.sum((((torch.exp((- s_capped[:, 0])) * MSE) + (torch.exp((- s_capped[:, 1])) * Reg)) + torch.sum(s_capped)))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((((torch.exp((- s_capped[:, 0])) * MSE_test) + (torch.exp((- s_capped[:, 1])) * Reg_test)) + torch.sum(s_capped)))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) | 3,808,070,609,708,569,000 | [summary]
Args:
model (DeepMoD): [description]
data (torch.Tensor): [description]
target (torch.Tensor): [description]
optimizer ([type]): [description]
sparsity_scheduler ([type]): [description]
log_dir (Optional[str], optional): [description]. Defaults to None.
max_iterations (int, optional): [description]. Defaults to 10000. | src/multitaskpinn/training/training.py | train_multitask_capped | GJBoth/MultiTaskPINN | python | def train_multitask_capped(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
cutoff = torch.tensor(15.0).to(target.device)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2), dim=0) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
s_capped = torch.min(torch.max(model.s, (- cutoff)), cutoff)
loss = torch.sum((((torch.exp((- s_capped[:, 0])) * MSE) + (torch.exp((- s_capped[:, 1])) * Reg)) + torch.sum(s_capped)))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = torch.sum((((torch.exp((- s_capped[:, 0])) * MSE_test) + (torch.exp((- s_capped[:, 1])) * Reg_test)) + torch.sum(s_capped)))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) |
def train_gradnorm(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, alpha, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.cat([torch.mean(((dt - (theta @ coeff_vector)) ** 2), dim=0) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
task_loss = (torch.exp(model.weights) * torch.stack((MSE, Reg), axis=1)).flatten()
loss = torch.sum(task_loss)
if (iteration == 0):
ini_loss = task_loss.data
if torch.any((task_loss.data > ini_loss)):
ini_loss[(task_loss.data > ini_loss)] = task_loss.data[(task_loss.data > ini_loss)]
optimizer.zero_grad()
loss.backward(retain_graph=True)
model.weights.grad.data = (model.weights.grad.data * 0.0)
G = torch.tensor([torch.norm(torch.autograd.grad(loss_i, list(model.parameters())[(- 2)], retain_graph=True, create_graph=True)[0], 2) for loss_i in task_loss]).to(data.device)
G_mean = torch.mean(G)
rel_loss = (task_loss / ini_loss)
inv_train_rate = (rel_loss / torch.mean(rel_loss))
grad_norm_loss = torch.sum(torch.abs((G - (G_mean * (inv_train_rate ** alpha)))))
model.weights.grad = torch.autograd.grad(grad_norm_loss, model.weights)[0]
optimizer.step()
normalize_coeff = (task_loss.shape[0] / torch.sum(model.weights))
model.weights.data = torch.log((torch.exp(model.weights.data) * normalize_coeff))
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = (model.weights @ torch.stack((MSE, Reg), axis=0))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, w=model.weights)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) | 6,851,036,863,452,085,000 | [summary]
Args:
model (DeepMoD): [description]
data (torch.Tensor): [description]
target (torch.Tensor): [description]
optimizer ([type]): [description]
sparsity_scheduler ([type]): [description]
log_dir (Optional[str], optional): [description]. Defaults to None.
max_iterations (int, optional): [description]. Defaults to 10000. | src/multitaskpinn/training/training.py | train_gradnorm | GJBoth/MultiTaskPINN | python | def train_gradnorm(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, alpha, test='mse', split: float=0.8, log_dir: Optional[str]=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'[summary]\n\n Args:\n model (DeepMoD): [description]\n data (torch.Tensor): [description]\n target (torch.Tensor): [description]\n optimizer ([type]): [description]\n sparsity_scheduler ([type]): [description]\n log_dir (Optional[str], optional): [description]. Defaults to None.\n max_iterations (int, optional): [description]. Defaults to 10000.\n '
start_time = time.time()
board = Tensorboard(log_dir)
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
convergence = Convergence(**convergence_kwargs)
print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |')
for iteration in np.arange(0, (max_iterations + 1)):
(prediction, time_derivs, thetas) = model(data_train)
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.cat([torch.mean(((dt - (theta @ coeff_vector)) ** 2), dim=0) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
task_loss = (torch.exp(model.weights) * torch.stack((MSE, Reg), axis=1)).flatten()
loss = torch.sum(task_loss)
if (iteration == 0):
ini_loss = task_loss.data
if torch.any((task_loss.data > ini_loss)):
ini_loss[(task_loss.data > ini_loss)] = task_loss.data[(task_loss.data > ini_loss)]
optimizer.zero_grad()
loss.backward(retain_graph=True)
model.weights.grad.data = (model.weights.grad.data * 0.0)
G = torch.tensor([torch.norm(torch.autograd.grad(loss_i, list(model.parameters())[(- 2)], retain_graph=True, create_graph=True)[0], 2) for loss_i in task_loss]).to(data.device)
G_mean = torch.mean(G)
rel_loss = (task_loss / ini_loss)
inv_train_rate = (rel_loss / torch.mean(rel_loss))
grad_norm_loss = torch.sum(torch.abs((G - (G_mean * (inv_train_rate ** alpha)))))
model.weights.grad = torch.autograd.grad(grad_norm_loss, model.weights)[0]
optimizer.step()
normalize_coeff = (task_loss.shape[0] / torch.sum(model.weights))
model.weights.data = torch.log((torch.exp(model.weights.data) * normalize_coeff))
if ((iteration % write_iterations) == 0):
(prediction_test, coordinates) = model.func_approx(data_test)
(time_derivs_test, thetas_test) = model.library((prediction_test, coordinates))
with torch.no_grad():
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
Reg_test = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))])
loss_test = (model.weights @ torch.stack((MSE, Reg), axis=0))
_ = model.sparse_estimator(thetas, time_derivs)
estimator_coeff_vectors = model.estimator_coeffs()
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0)
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item())
board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, w=model.weights)
if ((iteration % write_iterations) == 0):
if (test == 'mse'):
sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
else:
sparsity_scheduler(iteration, loss_test, model, optimizer)
if (sparsity_scheduler.apply_sparsity is True):
with torch.no_grad():
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
sparsity_scheduler.reset()
convergence(iteration, torch.sum(l1_norm))
if (convergence.converged is True):
print('Algorithm converged. Stopping training.')
break
board.close()
if (log_dir is None):
path = 'model.pt'
else:
path = join(log_dir, 'model.pt')
torch.save(model.state_dict(), path) |
def train_SBL(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, extra_params, sparsity_scheduler, split=0.8, exp_ID: str=None, log_dir: str=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'Trains the DeepMoD model. This function automatically splits the data set in a train and test set. \n\n Args:\n model (DeepMoD): A DeepMoD object.\n data (torch.Tensor): Tensor of shape (n_samples x (n_spatial + 1)) containing the coordinates, first column should be the time coordinate.\n target (torch.Tensor): Tensor of shape (n_samples x n_features) containing the target data.\n optimizer ([type]): Pytorch optimizer.\n sparsity_scheduler ([type]): Decides when to update the sparsity mask.\n split (float, optional): Fraction of the train set, by default 0.8.\n exp_ID (str, optional): Unique ID to identify tensorboard file. Not used if log_dir is given, see pytorch documentation.\n log_dir (str, optional): Directory where tensorboard file is written, by default None.\n max_iterations (int, optional): [description]. Max number of epochs , by default 10000.\n write_iterations (int, optional): [description]. Sets how often data is written to tensorboard and checks train loss , by default 25.\n '
logger = Logger(exp_ID, log_dir)
sparsity_scheduler.path = logger.log_dir
(t, a, l) = extra_params
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
M = 12
N = data_train.shape[0]
threshold = 10000.0
convergence = Convergence(**convergence_kwargs)
for iteration in torch.arange(0, max_iterations):
(prediction, time_derivs, thetas) = model(data_train)
tau_ = torch.exp(t)
alpha_ = torch.min(torch.exp(a), torch.tensor(100000000.0, dtype=torch.float32))
lambda_ = torch.min(torch.exp(l), torch.tensor(20000.0, dtype=torch.float32))
y = time_derivs[0]
X = (thetas[0] / torch.norm(thetas[0], dim=0, keepdim=True))
p_MSE = ((N / 2) * (((tau_ * torch.mean(((prediction - target_train) ** 2), dim=0)) - t) + np.log((2 * np.pi))))
A = (torch.diag(lambda_) + ((alpha_ * X.T) @ X))
mn = ((lambda_ < threshold)[:, None] * (((alpha_ * torch.inverse(A)) @ X.T) @ y))
E = ((alpha_ * torch.sum(((y - (X @ mn)) ** 2))) + ((mn.T @ torch.diag(lambda_)) @ mn))
p_reg = ((1 / 2) * (((E + torch.sum(torch.log(torch.diag(A)[(lambda_ < threshold)]))) - (torch.sum(l[(lambda_ < threshold)]) + (N * a))) - (N * np.log((2 * np.pi)))))
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((p_MSE + p_reg))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
with torch.no_grad():
prediction_test = model.func_approx(data_test)[0]
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
_ = model.sparse_estimator(thetas, time_derivs)
logger(iteration, loss, MSE, Reg, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), model.estimator_coeffs(), MSE_test=MSE_test, p_MSE=p_MSE, p_reg=p_reg, tau=tau_, alpha=alpha_, lambda_=lambda_, mn=mn)
update_sparsity = sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
if update_sparsity:
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)))
converged = convergence(iteration, l1_norm)
if converged:
break
logger.close(model) | -1,715,964,040,550,835,700 | Trains the DeepMoD model. This function automatically splits the data set in a train and test set.
Args:
model (DeepMoD): A DeepMoD object.
data (torch.Tensor): Tensor of shape (n_samples x (n_spatial + 1)) containing the coordinates, first column should be the time coordinate.
target (torch.Tensor): Tensor of shape (n_samples x n_features) containing the target data.
optimizer ([type]): Pytorch optimizer.
sparsity_scheduler ([type]): Decides when to update the sparsity mask.
split (float, optional): Fraction of the train set, by default 0.8.
exp_ID (str, optional): Unique ID to identify tensorboard file. Not used if log_dir is given, see pytorch documentation.
log_dir (str, optional): Directory where tensorboard file is written, by default None.
max_iterations (int, optional): [description]. Max number of epochs , by default 10000.
write_iterations (int, optional): [description]. Sets how often data is written to tensorboard and checks train loss , by default 25. | src/multitaskpinn/training/training.py | train_SBL | GJBoth/MultiTaskPINN | python | def train_SBL(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, extra_params, sparsity_scheduler, split=0.8, exp_ID: str=None, log_dir: str=None, max_iterations: int=10000, write_iterations: int=25, **convergence_kwargs) -> None:
'Trains the DeepMoD model. This function automatically splits the data set in a train and test set. \n\n Args:\n model (DeepMoD): A DeepMoD object.\n data (torch.Tensor): Tensor of shape (n_samples x (n_spatial + 1)) containing the coordinates, first column should be the time coordinate.\n target (torch.Tensor): Tensor of shape (n_samples x n_features) containing the target data.\n optimizer ([type]): Pytorch optimizer.\n sparsity_scheduler ([type]): Decides when to update the sparsity mask.\n split (float, optional): Fraction of the train set, by default 0.8.\n exp_ID (str, optional): Unique ID to identify tensorboard file. Not used if log_dir is given, see pytorch documentation.\n log_dir (str, optional): Directory where tensorboard file is written, by default None.\n max_iterations (int, optional): [description]. Max number of epochs , by default 10000.\n write_iterations (int, optional): [description]. Sets how often data is written to tensorboard and checks train loss , by default 25.\n '
logger = Logger(exp_ID, log_dir)
sparsity_scheduler.path = logger.log_dir
(t, a, l) = extra_params
n_train = int((split * data.shape[0]))
n_test = (data.shape[0] - n_train)
(data_train, data_test) = torch.split(data, [n_train, n_test], dim=0)
(target_train, target_test) = torch.split(target, [n_train, n_test], dim=0)
M = 12
N = data_train.shape[0]
threshold = 10000.0
convergence = Convergence(**convergence_kwargs)
for iteration in torch.arange(0, max_iterations):
(prediction, time_derivs, thetas) = model(data_train)
tau_ = torch.exp(t)
alpha_ = torch.min(torch.exp(a), torch.tensor(100000000.0, dtype=torch.float32))
lambda_ = torch.min(torch.exp(l), torch.tensor(20000.0, dtype=torch.float32))
y = time_derivs[0]
X = (thetas[0] / torch.norm(thetas[0], dim=0, keepdim=True))
p_MSE = ((N / 2) * (((tau_ * torch.mean(((prediction - target_train) ** 2), dim=0)) - t) + np.log((2 * np.pi))))
A = (torch.diag(lambda_) + ((alpha_ * X.T) @ X))
mn = ((lambda_ < threshold)[:, None] * (((alpha_ * torch.inverse(A)) @ X.T) @ y))
E = ((alpha_ * torch.sum(((y - (X @ mn)) ** 2))) + ((mn.T @ torch.diag(lambda_)) @ mn))
p_reg = ((1 / 2) * (((E + torch.sum(torch.log(torch.diag(A)[(lambda_ < threshold)]))) - (torch.sum(l[(lambda_ < threshold)]) + (N * a))) - (N * np.log((2 * np.pi)))))
MSE = torch.mean(((prediction - target_train) ** 2), dim=0)
Reg = torch.stack([torch.mean(((dt - (theta @ coeff_vector)) ** 2)) for (dt, theta, coeff_vector) in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))])
loss = torch.sum((p_MSE + p_reg))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((iteration % write_iterations) == 0):
with torch.no_grad():
prediction_test = model.func_approx(data_test)[0]
MSE_test = torch.mean(((prediction_test - target_test) ** 2), dim=0)
_ = model.sparse_estimator(thetas, time_derivs)
logger(iteration, loss, MSE, Reg, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), model.estimator_coeffs(), MSE_test=MSE_test, p_MSE=p_MSE, p_reg=p_reg, tau=tau_, alpha=alpha_, lambda_=lambda_, mn=mn)
update_sparsity = sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer)
if update_sparsity:
model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs)
l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)))
converged = convergence(iteration, l1_norm)
if converged:
break
logger.close(model) |
def decoderawtransaction_asm_sighashtype(self):
'Test decoding scripts via RPC command "decoderawtransaction".\n\n This test is in with the "decodescript" tests because they are testing the same "asm" script decodes.\n '
tx = '0100000001696a20784a2c70143f634e95227dbdfdf0ecd51647052e70854512235f5986ca010000008a47304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb014104d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536ffffffff0100e1f505000000001976a914eb6c6e0cdb2d256a32d97b8df1fc75d1920d9bca88ac00000000'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb[ALL] 04d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536', rpc_result['vin'][0]['scriptSig']['asm'])
tx = '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'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('8e3730608c3b0bb5df54f09076e196bc292a8e39a78e73b44b6ba08c78f5cbb0', rpc_result['txid'])
assert_equal('0 3045022100ae3b4e589dfc9d48cb82d41008dc5fa6a86f94d5c54f9935531924602730ab8002202f88cf464414c4ed9fa11b773c5ee944f66e9b05cc1e51d97abc22ce098937ea[ALL] 3045022100b44883be035600e9328a01b66c7d8439b74db64187e76b99a68f7893b701d5380220225bf286493e4c4adcf928c40f785422572eb232f84a0b83b0dea823c3a19c75[ALL] 5221020743d44be989540d27b1b4bbbcfd17721c337cb6bc9af20eb8a32520b393532f2102c0120a1dda9e51a938d39ddd9fe0ebc45ea97e1d27a7cbd671d5431416d3dd87210213820eb3d5f509d7438c9eeecb4157b2f595105e7cd564b3cdbb9ead3da41eed53ae', rpc_result['vin'][0]['scriptSig']['asm'])
assert_equal('OP_DUP OP_HASH160 dc863734a218bfe83ef770ee9d41a27f824a6e56 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm'])
assert_equal('OP_HASH160 2a5edea39971049a540474c6a99edf0aa4074c58 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm'])
txSave = CTransaction()
txSave.deserialize(BytesIO(hex_str_to_bytes(tx)))
tx = '01000000015ded05872fdbda629c7d3d02b194763ce3b9b1535ea884e3c8e765d42e316724020000006b48304502204c10d4064885c42638cbff3585915b322de33762598321145ba033fc796971e2022100bb153ad3baa8b757e30a2175bd32852d2e1cb9080f84d7e32fcdfd667934ef1b012103163c0ff73511ea1743fb5b98384a2ff09dd06949488028fd819f4d83f56264efffffffff0200000000000000000b6a0930060201000201000180380100000000001976a9141cabd296e753837c086da7a45a6c2fe0d49d7b7b88ac00000000'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('OP_RETURN 300602010002010001', rpc_result['vout'][0]['scriptPubKey']['asm'])
tx = '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'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('OP_DUP OP_HASH160 3011020701010101010101020601010101010101 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm'])
assert_equal('OP_HASH160 3011020701010101010101020601010101010101 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm'])
push_signature = bytes_to_hex_str(txSave.vin[0].scriptSig)[2:((72 * 2) + 4)]
signature = push_signature[2:]
der_signature = signature[:(- 2)]
signature_sighash_decoded = (der_signature + '[ALL]')
signature_2 = (der_signature + '82')
push_signature_2 = ('48' + signature_2)
signature_2_sighash_decoded = (der_signature + '[NONE|ANYONECANPAY]')
txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature)
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(signature_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature_2)
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes((('00' + push_signature) + push_signature_2))
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(((('0 ' + signature_sighash_decoded) + ' ') + signature_2_sighash_decoded), rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes('6a143011020701010101010101020601010101010101')
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal('OP_RETURN 3011020701010101010101020601010101010101', rpc_result['vin'][0]['scriptSig']['asm']) | 3,089,507,438,065,855,000 | Test decoding scripts via RPC command "decoderawtransaction".
This test is in with the "decodescript" tests because they are testing the same "asm" script decodes. | test/functional/rpc_decodescript.py | decoderawtransaction_asm_sighashtype | Black-NET/erexcoin-source | python | def decoderawtransaction_asm_sighashtype(self):
'Test decoding scripts via RPC command "decoderawtransaction".\n\n This test is in with the "decodescript" tests because they are testing the same "asm" script decodes.\n '
tx = '0100000001696a20784a2c70143f634e95227dbdfdf0ecd51647052e70854512235f5986ca010000008a47304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb014104d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536ffffffff0100e1f505000000001976a914eb6c6e0cdb2d256a32d97b8df1fc75d1920d9bca88ac00000000'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb[ALL] 04d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536', rpc_result['vin'][0]['scriptSig']['asm'])
tx = '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'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('8e3730608c3b0bb5df54f09076e196bc292a8e39a78e73b44b6ba08c78f5cbb0', rpc_result['txid'])
assert_equal('0 3045022100ae3b4e589dfc9d48cb82d41008dc5fa6a86f94d5c54f9935531924602730ab8002202f88cf464414c4ed9fa11b773c5ee944f66e9b05cc1e51d97abc22ce098937ea[ALL] 3045022100b44883be035600e9328a01b66c7d8439b74db64187e76b99a68f7893b701d5380220225bf286493e4c4adcf928c40f785422572eb232f84a0b83b0dea823c3a19c75[ALL] 5221020743d44be989540d27b1b4bbbcfd17721c337cb6bc9af20eb8a32520b393532f2102c0120a1dda9e51a938d39ddd9fe0ebc45ea97e1d27a7cbd671d5431416d3dd87210213820eb3d5f509d7438c9eeecb4157b2f595105e7cd564b3cdbb9ead3da41eed53ae', rpc_result['vin'][0]['scriptSig']['asm'])
assert_equal('OP_DUP OP_HASH160 dc863734a218bfe83ef770ee9d41a27f824a6e56 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm'])
assert_equal('OP_HASH160 2a5edea39971049a540474c6a99edf0aa4074c58 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm'])
txSave = CTransaction()
txSave.deserialize(BytesIO(hex_str_to_bytes(tx)))
tx = '01000000015ded05872fdbda629c7d3d02b194763ce3b9b1535ea884e3c8e765d42e316724020000006b48304502204c10d4064885c42638cbff3585915b322de33762598321145ba033fc796971e2022100bb153ad3baa8b757e30a2175bd32852d2e1cb9080f84d7e32fcdfd667934ef1b012103163c0ff73511ea1743fb5b98384a2ff09dd06949488028fd819f4d83f56264efffffffff0200000000000000000b6a0930060201000201000180380100000000001976a9141cabd296e753837c086da7a45a6c2fe0d49d7b7b88ac00000000'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('OP_RETURN 300602010002010001', rpc_result['vout'][0]['scriptPubKey']['asm'])
tx = '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'
rpc_result = self.nodes[0].decoderawtransaction(tx)
assert_equal('OP_DUP OP_HASH160 3011020701010101010101020601010101010101 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm'])
assert_equal('OP_HASH160 3011020701010101010101020601010101010101 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm'])
push_signature = bytes_to_hex_str(txSave.vin[0].scriptSig)[2:((72 * 2) + 4)]
signature = push_signature[2:]
der_signature = signature[:(- 2)]
signature_sighash_decoded = (der_signature + '[ALL]')
signature_2 = (der_signature + '82')
push_signature_2 = ('48' + signature_2)
signature_2_sighash_decoded = (der_signature + '[NONE|ANYONECANPAY]')
txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature)
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(signature_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature_2)
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes((('00' + push_signature) + push_signature_2))
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal(((('0 ' + signature_sighash_decoded) + ' ') + signature_2_sighash_decoded), rpc_result['vin'][0]['scriptSig']['asm'])
txSave.vin[0].scriptSig = hex_str_to_bytes('6a143011020701010101010101020601010101010101')
rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize()))
assert_equal('OP_RETURN 3011020701010101010101020601010101010101', rpc_result['vin'][0]['scriptSig']['asm']) |
def create_dynamic_ordering_constraint(index: int) -> str:
'\n Creates a valid AMPL constraint of the form:\n [LaTex]: $start\\_time_j+1 >= start\\_time_j + duration_j$, $\x0corall j \\in BATCH$\n :param index: j index where the current constraint should start\n :return: single AMPL JIT constraint as a string\n '
i = str(index)
i_next = str((index + 1))
constraint_name = f'ordering_{i_next}_{i}'
return f's.t. {constraint_name}: start_time[{i_next}] >= start_time[{i}] + duration[{i}];' | 6,747,510,227,925,415,000 | Creates a valid AMPL constraint of the form:
[LaTex]: $start\_time_j+1 >= start\_time_j + duration_j$, $ orall j \in BATCH$
:param index: j index where the current constraint should start
:return: single AMPL JIT constraint as a string | ampljit/utils.py | create_dynamic_ordering_constraint | jkomyno/amplrestapi | python | def create_dynamic_ordering_constraint(index: int) -> str:
'\n Creates a valid AMPL constraint of the form:\n [LaTex]: $start\\_time_j+1 >= start\\_time_j + duration_j$, $\x0corall j \\in BATCH$\n :param index: j index where the current constraint should start\n :return: single AMPL JIT constraint as a string\n '
i = str(index)
i_next = str((index + 1))
constraint_name = f'ordering_{i_next}_{i}'
return f's.t. {constraint_name}: start_time[{i_next}] >= start_time[{i}] + duration[{i}];' |
def dict_to_list(obj: dict) -> list:
"\n Converts a dictionary to a list, extracting the values of the dictionary.\n The list is sorted according to the dict's keys ascendant order.\n The given dictionary should always have the same numeric keys as the result of create_batch_dictionary().\n :param obj: the dictionary to convert which should have numeric keys\n :return: the list of values in the dictionary\n "
return list(obj.values()) | 9,012,645,828,889,174,000 | Converts a dictionary to a list, extracting the values of the dictionary.
The list is sorted according to the dict's keys ascendant order.
The given dictionary should always have the same numeric keys as the result of create_batch_dictionary().
:param obj: the dictionary to convert which should have numeric keys
:return: the list of values in the dictionary | ampljit/utils.py | dict_to_list | jkomyno/amplrestapi | python | def dict_to_list(obj: dict) -> list:
"\n Converts a dictionary to a list, extracting the values of the dictionary.\n The list is sorted according to the dict's keys ascendant order.\n The given dictionary should always have the same numeric keys as the result of create_batch_dictionary().\n :param obj: the dictionary to convert which should have numeric keys\n :return: the list of values in the dictionary\n "
return list(obj.values()) |
def strings_to_datetimes(str_date_lst: list, datetime_format: str) -> list:
'\n Converts a list of strings into a list of datetime objects\n :param str_date_lst: list of string objects compatible with the ISO8601 format\n :param datetime_format: format of the datetime\n :return: list of datetime objects equivalent to the given str_date_lst\n '
return [datetime.strptime(d, datetime_format) for d in str_date_lst] | -3,893,893,176,523,418,600 | Converts a list of strings into a list of datetime objects
:param str_date_lst: list of string objects compatible with the ISO8601 format
:param datetime_format: format of the datetime
:return: list of datetime objects equivalent to the given str_date_lst | ampljit/utils.py | strings_to_datetimes | jkomyno/amplrestapi | python | def strings_to_datetimes(str_date_lst: list, datetime_format: str) -> list:
'\n Converts a list of strings into a list of datetime objects\n :param str_date_lst: list of string objects compatible with the ISO8601 format\n :param datetime_format: format of the datetime\n :return: list of datetime objects equivalent to the given str_date_lst\n '
return [datetime.strptime(d, datetime_format) for d in str_date_lst] |
def minute_timedelta(first: datetime, second: datetime) -> int:
'\n Returns the difference expressed in minutes between 2 datetime objects\n :param first: datetime object that comes before second\n :param second: datetime object that comes after first\n :return: difference in minutes between second and first\n '
delta: timedelta = (second - first)
return divmod(delta.total_seconds(), 60)[0] | 5,728,973,006,739,140,000 | Returns the difference expressed in minutes between 2 datetime objects
:param first: datetime object that comes before second
:param second: datetime object that comes after first
:return: difference in minutes between second and first | ampljit/utils.py | minute_timedelta | jkomyno/amplrestapi | python | def minute_timedelta(first: datetime, second: datetime) -> int:
'\n Returns the difference expressed in minutes between 2 datetime objects\n :param first: datetime object that comes before second\n :param second: datetime object that comes after first\n :return: difference in minutes between second and first\n '
delta: timedelta = (second - first)
return divmod(delta.total_seconds(), 60)[0] |
def minute_timedeltas_wrt_first(datetime_lst: list) -> list:
"\n Converts a list of datetime objects into a list of minute time deltas with respect to the first item.\n For example, given the input datetime_lst:\n [\n '2019-08-22 14:32',\n '2019-08-22 14:38',\n '2019-08-22 14:42',\n '2019-08-22 14:52',\n '2019-08-22 14:57'\n ],\n the result would be:\n [32, 38, 42, 52, 57]\n\n :param datetime_lst: list of datetime objects\n :return: minute time deltas with respect to the first item of datetime_lst\n "
first_datetime: datetime = datetime_lst[0]
partial_deltas = [minute_timedelta(first=first_datetime, second=v) for v in datetime_lst[1:]]
first_minutes = first_datetime.minute
return ([first_minutes] + list(map((lambda x: (x + first_minutes)), partial_deltas))) | 4,508,611,415,876,424,000 | Converts a list of datetime objects into a list of minute time deltas with respect to the first item.
For example, given the input datetime_lst:
[
'2019-08-22 14:32',
'2019-08-22 14:38',
'2019-08-22 14:42',
'2019-08-22 14:52',
'2019-08-22 14:57'
],
the result would be:
[32, 38, 42, 52, 57]
:param datetime_lst: list of datetime objects
:return: minute time deltas with respect to the first item of datetime_lst | ampljit/utils.py | minute_timedeltas_wrt_first | jkomyno/amplrestapi | python | def minute_timedeltas_wrt_first(datetime_lst: list) -> list:
"\n Converts a list of datetime objects into a list of minute time deltas with respect to the first item.\n For example, given the input datetime_lst:\n [\n '2019-08-22 14:32',\n '2019-08-22 14:38',\n '2019-08-22 14:42',\n '2019-08-22 14:52',\n '2019-08-22 14:57'\n ],\n the result would be:\n [32, 38, 42, 52, 57]\n\n :param datetime_lst: list of datetime objects\n :return: minute time deltas with respect to the first item of datetime_lst\n "
first_datetime: datetime = datetime_lst[0]
partial_deltas = [minute_timedelta(first=first_datetime, second=v) for v in datetime_lst[1:]]
first_minutes = first_datetime.minute
return ([first_minutes] + list(map((lambda x: (x + first_minutes)), partial_deltas))) |
def set_minutes_to_datetimes(datetime_lst: list, minutes_lst: list) -> list:
'\n Given a list of minutes and datetime objects, sets each amount of minutes to each datetime object with respect\n to the list index. The two lists must have the same size.\n :param datetime_lst: list of datetime objects\n :param minutes_lst: list of minutes to set to a list of datetime objects\n :return: list of datetime objects similar to datetime_lst but shifted according to minutes_lst\n '
return [(d.replace(minute=0) + timedelta(minutes=m)) for (d, m) in zip(datetime_lst, minutes_lst)] | -1,701,486,862,418,520,600 | Given a list of minutes and datetime objects, sets each amount of minutes to each datetime object with respect
to the list index. The two lists must have the same size.
:param datetime_lst: list of datetime objects
:param minutes_lst: list of minutes to set to a list of datetime objects
:return: list of datetime objects similar to datetime_lst but shifted according to minutes_lst | ampljit/utils.py | set_minutes_to_datetimes | jkomyno/amplrestapi | python | def set_minutes_to_datetimes(datetime_lst: list, minutes_lst: list) -> list:
'\n Given a list of minutes and datetime objects, sets each amount of minutes to each datetime object with respect\n to the list index. The two lists must have the same size.\n :param datetime_lst: list of datetime objects\n :param minutes_lst: list of minutes to set to a list of datetime objects\n :return: list of datetime objects similar to datetime_lst but shifted according to minutes_lst\n '
return [(d.replace(minute=0) + timedelta(minutes=m)) for (d, m) in zip(datetime_lst, minutes_lst)] |
def datetimes_to_strings(datetime_lst: list, datetime_format: str) -> list:
'\n Converts a list of datetime objects to strings, according to a certain datetime format.\n :param datetime_lst: list of datetime objects to convert to string\n :param datetime_format: format of the datetime\n :return: the list of datetime objects converted to strings in the given datetime format\n '
return [d.strftime(datetime_format) for d in datetime_lst] | 213,313,511,449,219,460 | Converts a list of datetime objects to strings, according to a certain datetime format.
:param datetime_lst: list of datetime objects to convert to string
:param datetime_format: format of the datetime
:return: the list of datetime objects converted to strings in the given datetime format | ampljit/utils.py | datetimes_to_strings | jkomyno/amplrestapi | python | def datetimes_to_strings(datetime_lst: list, datetime_format: str) -> list:
'\n Converts a list of datetime objects to strings, according to a certain datetime format.\n :param datetime_lst: list of datetime objects to convert to string\n :param datetime_format: format of the datetime\n :return: the list of datetime objects converted to strings in the given datetime format\n '
return [d.strftime(datetime_format) for d in datetime_lst] |
def test_len(self):
' test for boostrap helper'
for i in range(len(test_cases)):
self.assertEqual(len(results[i]), test_cases[i]['len'])
self.assertEqual(len(resultsInt64[i]), test_cases[i]['len']) | 7,617,248,818,454,541,000 | test for boostrap helper | neaps-api/neaps_lib/bootstrap_test.py | test_len | ExpediaGroup/neaps | python | def test_len(self):
' '
for i in range(len(test_cases)):
self.assertEqual(len(results[i]), test_cases[i]['len'])
self.assertEqual(len(resultsInt64[i]), test_cases[i]['len']) |
def test_value(self):
' docstring '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertEqual(results[i][j], test_cases[i]['mean'])
self.assertEqual(resultsInt64[i][j], test_cases[i]['meanInt64'])
self.assertIsInstance(results[i][j], np.float64)
self.assertIsInstance(resultsInt64[i][j], np.int64) | -7,203,681,843,411,713,000 | docstring | neaps-api/neaps_lib/bootstrap_test.py | test_value | ExpediaGroup/neaps | python | def test_value(self):
' '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertEqual(results[i][j], test_cases[i]['mean'])
self.assertEqual(resultsInt64[i][j], test_cases[i]['meanInt64'])
self.assertIsInstance(results[i][j], np.float64)
self.assertIsInstance(resultsInt64[i][j], np.int64) |
def test_less(self):
' docstring '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertLessEqual(results[i][j], max(test_cases[i]['sample'])) | -198,653,698,443,766,720 | docstring | neaps-api/neaps_lib/bootstrap_test.py | test_less | ExpediaGroup/neaps | python | def test_less(self):
' '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertLessEqual(results[i][j], max(test_cases[i]['sample'])) |
def test_greater(self):
' docstring '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertGreaterEqual(results[i][j], min(test_cases[i]['sample'])) | 9,088,840,391,431,958,000 | docstring | neaps-api/neaps_lib/bootstrap_test.py | test_greater | ExpediaGroup/neaps | python | def test_greater(self):
' '
for i in range(len(test_cases)):
for j in range(len(results[i])):
self.assertGreaterEqual(results[i][j], min(test_cases[i]['sample'])) |
def __init__(self, authority=None, content=None, default_port=None, file=None, host=None, path=None, port=None, protocol=None, query=None, ref=None, user_info=None):
'URL - a model defined in Swagger'
self._authority = None
self._content = None
self._default_port = None
self._file = None
self._host = None
self._path = None
self._port = None
self._protocol = None
self._query = None
self._ref = None
self._user_info = None
self.discriminator = None
if (authority is not None):
self.authority = authority
if (content is not None):
self.content = content
if (default_port is not None):
self.default_port = default_port
if (file is not None):
self.file = file
if (host is not None):
self.host = host
if (path is not None):
self.path = path
if (port is not None):
self.port = port
if (protocol is not None):
self.protocol = protocol
if (query is not None):
self.query = query
if (ref is not None):
self.ref = ref
if (user_info is not None):
self.user_info = user_info | -4,837,812,409,801,342,000 | URL - a model defined in Swagger | tb_rest_client/models/models_pe/url.py | __init__ | CSTC-WTCB-BBRI/python_tb_rest_client | python | def __init__(self, authority=None, content=None, default_port=None, file=None, host=None, path=None, port=None, protocol=None, query=None, ref=None, user_info=None):
self._authority = None
self._content = None
self._default_port = None
self._file = None
self._host = None
self._path = None
self._port = None
self._protocol = None
self._query = None
self._ref = None
self._user_info = None
self.discriminator = None
if (authority is not None):
self.authority = authority
if (content is not None):
self.content = content
if (default_port is not None):
self.default_port = default_port
if (file is not None):
self.file = file
if (host is not None):
self.host = host
if (path is not None):
self.path = path
if (port is not None):
self.port = port
if (protocol is not None):
self.protocol = protocol
if (query is not None):
self.query = query
if (ref is not None):
self.ref = ref
if (user_info is not None):
self.user_info = user_info |
@property
def authority(self):
'Gets the authority of this URL. # noqa: E501\n\n\n :return: The authority of this URL. # noqa: E501\n :rtype: str\n '
return self._authority | 6,008,489,197,909,201,000 | Gets the authority of this URL. # noqa: E501
:return: The authority of this URL. # noqa: E501
:rtype: str | tb_rest_client/models/models_pe/url.py | authority | CSTC-WTCB-BBRI/python_tb_rest_client | python | @property
def authority(self):
'Gets the authority of this URL. # noqa: E501\n\n\n :return: The authority of this URL. # noqa: E501\n :rtype: str\n '
return self._authority |
@authority.setter
def authority(self, authority):
'Sets the authority of this URL.\n\n\n :param authority: The authority of this URL. # noqa: E501\n :type: str\n '
self._authority = authority | 5,373,869,373,672,434,000 | Sets the authority of this URL.
:param authority: The authority of this URL. # noqa: E501
:type: str | tb_rest_client/models/models_pe/url.py | authority | CSTC-WTCB-BBRI/python_tb_rest_client | python | @authority.setter
def authority(self, authority):
'Sets the authority of this URL.\n\n\n :param authority: The authority of this URL. # noqa: E501\n :type: str\n '
self._authority = authority |
@property
def content(self):
'Gets the content of this URL. # noqa: E501\n\n\n :return: The content of this URL. # noqa: E501\n :rtype: object\n '
return self._content | -5,395,410,704,641,924,000 | Gets the content of this URL. # noqa: E501
:return: The content of this URL. # noqa: E501
:rtype: object | tb_rest_client/models/models_pe/url.py | content | CSTC-WTCB-BBRI/python_tb_rest_client | python | @property
def content(self):
'Gets the content of this URL. # noqa: E501\n\n\n :return: The content of this URL. # noqa: E501\n :rtype: object\n '
return self._content |
@content.setter
def content(self, content):
'Sets the content of this URL.\n\n\n :param content: The content of this URL. # noqa: E501\n :type: object\n '
self._content = content | -3,132,387,909,026,987,500 | Sets the content of this URL.
:param content: The content of this URL. # noqa: E501
:type: object | tb_rest_client/models/models_pe/url.py | content | CSTC-WTCB-BBRI/python_tb_rest_client | python | @content.setter
def content(self, content):
'Sets the content of this URL.\n\n\n :param content: The content of this URL. # noqa: E501\n :type: object\n '
self._content = content |
@property
def default_port(self):
'Gets the default_port of this URL. # noqa: E501\n\n\n :return: The default_port of this URL. # noqa: E501\n :rtype: int\n '
return self._default_port | -2,337,253,307,847,136,000 | Gets the default_port of this URL. # noqa: E501
:return: The default_port of this URL. # noqa: E501
:rtype: int | tb_rest_client/models/models_pe/url.py | default_port | CSTC-WTCB-BBRI/python_tb_rest_client | python | @property
def default_port(self):
'Gets the default_port of this URL. # noqa: E501\n\n\n :return: The default_port of this URL. # noqa: E501\n :rtype: int\n '
return self._default_port |
@default_port.setter
def default_port(self, default_port):
'Sets the default_port of this URL.\n\n\n :param default_port: The default_port of this URL. # noqa: E501\n :type: int\n '
self._default_port = default_port | 1,449,772,015,788,586,500 | Sets the default_port of this URL.
:param default_port: The default_port of this URL. # noqa: E501
:type: int | tb_rest_client/models/models_pe/url.py | default_port | CSTC-WTCB-BBRI/python_tb_rest_client | python | @default_port.setter
def default_port(self, default_port):
'Sets the default_port of this URL.\n\n\n :param default_port: The default_port of this URL. # noqa: E501\n :type: int\n '
self._default_port = default_port |
Subsets and Splits