torch_geometric.datasets¶
-
class
KarateClub
(transform=None)[source]¶ Zachary’s karate club network from the “An Information Flow Model for Conflict and Fission in Small Groups” paper, containing 34 nodes, connected by 154 (undirected and unweighted) edges. Every node is labeled by one of two classes.
Parameters: transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)
-
class
TUDataset
(root, name, transform=None, pre_transform=None, pre_filter=None, use_node_attr=False)[source]¶ A variety of graph kernel benchmark datasets, .e.g. “IMDB-BINARY”, “REDDIT-BINARY” or “PROTEINS”, collected from the TU Dortmund University.
Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string) – The name of the dataset.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
) - use_node_attr (bool, optional) – If
True
, the dataset will contain additional continuous node features (if present). (default:False
)
-
class
Planetoid
(root, name, transform=None, pre_transform=None)[source]¶ The citation network datasets “Cora”, “CiteSeer” and “PubMed” from the “Revisiting Semi-Supervised Learning with Graph Embeddings” paper. Nodes represent documents and edges represent citation links. Training, validation and test splits are given by binary masks.
Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string) – The name of the dataset (
"Cora"
,"CiteSeer"
,"PubMed"
). - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
-
class
CoraFull
(root, transform=None, pre_transform=None)[source]¶ The full Cora citation network dataset from the “Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking” paper. Nodes represent documents and edges represent citation links.
Parameters: - root (string) – Root directory where the dataset should be saved.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
The Coauthor CS and Coauthor Physics networks from the “Pitfalls of Graph Neural Network Evaluation” paper. Nodes represent authors that are connected by an edge if they co-authored a paper. Given paper keywords for each author’s papers, the task is to map authors to their respective field of study.
Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string) – The name of the dataset (
"CS"
,"Physics"
). - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
-
class
Amazon
(root, name, transform=None, pre_transform=None)[source]¶ The Amazon Computers and Amazon Photo networks from the “Pitfalls of Graph Neural Network Evaluation” paper. Nodes represent goods and edges represent that two goods are frequently bought together. Given product reviews as bag-of-words node features, the task is to map goods to their respective product category.
Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string) – The name of the dataset (
"Computers"
,"Photo"
). - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
-
class
PPI
(root, split='train', transform=None, pre_transform=None, pre_filter=None)[source]¶ Protein-protein interaction networks from the “Predicting Multicellular Function through Multi-layer Tissue Networks” paper, containing positional gene sets, motif gene sets and immunological signatures as features (50 in total) and gene ontology sets as labels (121 in total).
Parameters: - root (string) – Root directory where the dataset should be saved.
- split (string) – If
"train"
, loads the training dataset. If"val"
, loads the validation dataset. If"test"
, loads the test dataset. (default:"train"
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
QM7b
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ The QM7b dataset from the “MoleculeNet: A Benchmark for Molecular Machine Learning” paper, consisting of 7,211 molecules with 14 regression targets.
Parameters: - root (string) – Root directory where the dataset should be saved.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
QM9
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ The QM9 dataset from the “MoleculeNet: A Benchmark for Molecular Machine Learning” paper, consisting of about 130,000 molecules with 13 regression targets. Each molecule includes complete spatial information for the single low energy conformation of the atoms in the molecule. In addition, we provide the atom features from the “Neural Message Passing for Quantum Chemistry” paper.
Parameters: - root (string) – Root directory where the dataset should be saved.
- transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
Entities
(root, name, transform=None, pre_transform=None)[source]¶ The relational entities networks “AIFB”, “MUTAG”, “BGS” and “AM” from the “Modeling Relational Data with Graph Convolutional Networks” paper. Training and test splits are given by node indices.
Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string) – The name of the dataset (
"AIFB"
,"MUTAG"
,"BGS"
,"AM"
). - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
-
class
MNISTSuperpixels
(root, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ MNIST superpixels dataset from the “Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs” paper, containing 70,000 graphs with 75 nodes each. Every graph is labeled by one of 10 classes.
Parameters: - root (string) – Root directory where the dataset should be saved.
- train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
FAUST
(root, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ The FAUST humans dataset from the “FAUST: Dataset and Evaluation for 3D Mesh Registration” paper, containing 100 watertight meshes representing 10 different poses for 10 different subjects.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
DynamicFAUST
(root, subjects=None, categories=None, transform=None, pre_transform=None, pre_filter=None)[source]¶ The dynamic FAUST humans dataset from the “Dynamic FAUST: Registering Human Bodies in Motion” paper.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- subjects (list, optional) – List of subjects to include in the
dataset. Can include the subjects
"50002"
,"50004"
,"50007"
,"50009"
,"50020"
,"50021"
,"50022"
,"50025"
,"50026"
,"50027"
. If set toNone
, the dataset will contain all subjects. (default:None
) - categories (list, optional) – List of categories to include in the
dataset. Can include the categories
"chicken_wings"
,"hips"
,"jiggle_on_toes"
,"jumping_jacks"
,"knees"
,"light_hopping_loose"
,"light_hopping_stiff"
,"one_leg_jump"
,"one_leg_loose"
,"personal_move"
,"punching"
,"running_on_spot"
,"running_on_spot_bugfix"
,"shake_arms"
,"shake_hips"
,"shoulders"
. If set toNone
, the dataset will contain all categories. (default:None
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
ShapeNet
(root, category, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ The ShapeNet part level segmentation dataset from the “A Scalable Active Framework for Region Annotation in 3D Shape Collections” paper, containing about 17,000 3D shape point clouds from 16 shape categories. Each category is annotated with 2 to 6 parts.
Parameters: - root (string) – Root directory where the dataset should be saved.
- category (string) – The category of the CAD models (one of
"Airplane"
,"Bag"
,"Cap"
,"Car"
,"Chair"
,"Earphone"
,"Guitar"
,"Knife"
,"Lamp"
,"Laptop"
,"Motorbike"
,"Mug"
,"Pistol"
,"Rocket"
,"Skateboard"
,"Table"
). - train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
ModelNet
(root, name='10', train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ The ModelNet10/40 datasets from the “3D ShapeNets: A Deep Representation for Volumetric Shapes” paper, containing CAD models of 10 and 40 categories, respectively.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- name (string, optional) – The name of the dataset (
"10"
for ModelNet10,"40"
for ModelNet40). (default:"10"
) - train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
CoMA
(root, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ The CoMA 3D faces dataset from the “Generating 3D faces using Convolutional Mesh Autoencoders” paper, containing 20,466 meshes of extreme expressions captured over 12 different subjects.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
SHREC2016
(root, partiality, category, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ The SHREC 2016 partial matching dataset from the “SHREC‘16: Partial Matching of Deformable Shapes” paper. The reference shape can be referenced via
dataset.ref
.Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- partiality (string) – The partiality of the dataset (one of
"Holes"
,"Cuts"
) - category (string) – The category of the dataset (one of
"Cat"
,"Centaur"
,"David"
,"Dog"
,"Horse"
,"Michael"
,"Victoria"
,"Wolf"
). - train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
TOSCA
(root, categories=None, transform=None, pre_transform=None, pre_filter=None)[source]¶ The TOSCA dataset from the “Numerical Geometry of Non-Ridig Shapes” book, containing 80 meshes. Meshes within the same category have the same triangulation and an equal number of vertices numbered in a compatible way.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- categories (list, optional) – List of categories to include in the
dataset. Can include the categories
"Cat"
,"Centaur"
,"David"
,"Dog"
,"Gorilla"
,"Horse"
,"Michael"
,"Victoria"
,"Wolf"
. If set toNone
, the dataset will contain all categories. (default:None
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
PCPNetDataset
(root, category, split='train', transform=None, pre_transform=None, pre_filter=None)[source]¶ The PCPNet dataset from the “PCPNet: Learning Local Shape Properties from Raw Point Clouds” paper, consisting of 30 shapes, each given as a point cloud, densely sampled with 100k points. For each shape, surface normals and local curvatures are given as node features.
Parameters: - root (string) – Root directory where the dataset should be saved.
- category (string) – The training set category (one of
"NoNoise"
,"Noisy"
,"VarDensity"
,"NoisyAndVarDensity"
forsplit="train"
orsplit="val"
, or one of"All"
,"LowNoise"
,"MedNoise"
,"HighNoise", :obj:
”VarDensityStriped”,"VarDensityGradient"
forsplit="test"
). - split (string) – If
"train"
, loads the training dataset. If"val"
, loads the validation dataset. If"test"
, loads the test dataset. (default:"train"
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
GeometricShapes
(root, train=True, transform=None, pre_transform=None, pre_filter=None)[source]¶ Synthetic dataset of various geometric shapes like cubes, spheres or pyramids.
Note
Data objects hold mesh faces instead of edge indices. To convert the mesh to a graph, use the
torch_geometric.transforms.FaceToEdge
aspre_transform
. To convert the mesh to a point cloud, use thetorch_geometric.transforms.SamplePoints
astransform
to sample a fixed number of points on the mesh faces according to their face area.Parameters: - root (string) – Root directory where the dataset should be saved.
- train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
BitcoinOTC
(root, edge_window_size=10, transform=None, pre_transform=None)[source]¶ The Bitcoin-OTC dataset from the “EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs” paper, consisting of 138 who-trusts-whom networks of sequential time steps.
Parameters: - root (string) – Root directory where the dataset should be saved.
- edge_window_size (int, optional) – The window size for the existence of
an edge in the graph sequence since its initial creation.
(default:
10
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)
-
class
ICEWS18
(root, split='train', transform=None, pre_transform=None, pre_filter=None)[source]¶ The Integrated Crisis Early Warning System (ICEWS) dataset used in the, e.g., “Recurrent Event Network for Reasoning over Temporal Knowledge Graphs” paper, consisting of events collected from 1/1/2018 to 10/31/2018 (24 hours time granularity).
Parameters: - root (string) – Root directory where the dataset should be saved.
- split (string) – If
"train"
, loads the training dataset. If"val"
, loads the validation dataset. If"test"
, loads the test dataset. (default:"train"
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
class
GDELT
(root, split='train', transform=None, pre_transform=None, pre_filter=None)[source]¶ The Global Database of Events, Language, and Tone (GDELT) dataset used in the, e.g., “Recurrent Event Network for Reasoning over Temporal Knowledge Graphs” paper, consisting of events collected from 1/1/2018 to 1/31/2018 (15 minutes time granularity).
Parameters: - root (string) – Root directory where the dataset should be saved.
- split (string) – If
"train"
, loads the training dataset. If"val"
, loads the validation dataset. If"test"
, loads the test dataset. (default:"train"
) - transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
) - pre_transform (callable, optional) – A function/transform that takes in
an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)


Aliquot Sum s(n) of a positive integer n is the sum of all proper divisors of n, that is, all divisors of n other than n itself.
Primitive Abundant Number
Check if a number can be expressed as sum two abundant numbers
Hosoya’s Triangle
Cassini’s Identity
Primorial of a number


|
MOTS Finder
Find non-star-shaped MOTSs
|
Base class for truncated series expressions. More...
Public Member Functions | |
| def | __init__ (self, a_n, domain, kw) |
| Parent init for series expressions. More... | |
| def | get_dof (self) |
| Return the degrees of freedom, i.e. the current number of coefficients. More... | |
| def | N (self) |
| Degrees of freedom, i.e. current number of coefficients. More... | |
| def | copy (self) |
| Create a copy of the series expansion expression. More... | |
| def | resample (self, num, use_mp=False, dps=None) |
| Change resolution of the expansion. More... | |
| def | interpolate (self, y, x=None, kind='cubic', num=None, lobatto=True, use_mp=False, dps=None) |
| Interpolate data and model the result as a series. More... | |
| def | approximate (self, f, num=None, lobatto=True, use_mp=False, dps=None) |
| Approximate a function. More... | |
| def | set_coefficients (self, a_n, physical_space=False, lobatto=True, use_mp=False, dps=None) |
| Store a new list of coefficients after optional transformation. More... | |
| def | collocation_points (self, num=None, lobatto=True, internal_domain=False, use_mp=False, dps=None, kw) |
| Return the Gauss-Lobatto or Gauss points for this series. More... | |
| def | internal_domain (self) |
| def | create_collocation_points (cls, num, lobatto=True, use_mp=False, dps=None, kw) |
| Subclasses should implement this as . More... | |
| def | is_orthogonal (self) |
| Whether this series consists of orthogonal functions. More... | |
| def | from_function (cls, f, num, domain=None, lobatto=True, use_mp=False, dps=None, kw) |
| Public Member Functions inherited from NumericExpression | |
| def | __init__ (self, domain=None, name=None, verbosity=1, sub_exprs) |
| Base class init for numeric expressions. More... | |
| def | domain (self) |
| Domain on which this expression is defined. More... | |
| def | domain (self, domain) |
| def | name (self) |
| Name given to this instance of the expression. More... | |
| def | name (self, name) |
| def | verbosity (self) |
| Verbosity setting, which may be used during complex computations. More... | |
| def | verbosity (self, verbosity) |
| def | nice_name (self) |
| More descriptive name, which may be overridden by sub classes. More... | |
| def | traverse_tree (self, include_root=False, skip_zeros=False, parents=None) |
| Generator that walks through a complete expression tree. More... | |
| def | print_tree (self, root_name='root', nice_names=True, skip_zeros=False) |
| Print the whole expression tree. More... | |
| def | save (self, filename, overwrite=False, verbose=True) |
| Save the expression object to disk. More... | |
| def | load (cls, filename) |
| Static function to load an expression object from disk. More... | |
| def | __getstate__ (self) |
| Return a picklable state object representing the whole expression. More... | |
| def | __setstate__ (self, state) |
| Restore a complete expression from the given unpickled state. More... | |
| def | __repr__ (self) |
| Return a string representing the whole expression tree. More... | |
| def | force_evaluation_mode (self, use_mp) |
| Override the evaluation mode of future evaluators. More... | |
| def | evaluator (self, use_mp=False) |
| Create an evaluator for the expression in the current state. More... | |
| def | store_domain (self, obj) |
| Store the domain of this expression on the given object. More... | |
| def | str (self) |
| Return the expression and any values of local parameters as a string. More... | |
| def | is_zero_expression (self) |
| Return whether this expression is zero and constant. More... | |
| def | mpmath_context (cls, use_mp) |
Return the mpmath.mp or mpmath.fp contexts. More... | |
| def | context (cls, use_mp, dps) |
| Convenience function to be used as context manager. More... | |
| def | set_sub_exprs (self, sub_exprs) |
| Set/replace sub expressions under public attributes of this object. More... | |
Public Attributes | |
| a_n | |
| a | |
| b | |
| Public Attributes inherited from NumericExpression | |
| domain | |
Domain getter/setter automatically updating attributes such as domainX , domainY for 2D expressions. More... | |
| zero | |
| Pointer to the common._zero_function() function. More... | |
Private Member Functions | |
| def | _check_a_n (self, a_n) |
| Make sure the coefficients are iterable. | |
| def | _approximate (self, f, num, lobatto, use_mp, dps) |
| Perform the approximation of a function. More... | |
| def | _approximate_constant (self, value, num, lobatto, use_mp, dps) |
| Approximate a constant value. More... | |
| def | _from_physical_space (self, a_n, lobatto, use_mp, dps) |
| Convert values in physical space to spectral space. | |
Base class for truncated series expressions.
This base class is responsible for managing the coefficient list, domain transformations and many of the sampling and resampling tasks. To perform its task, sub-classes need to implement the following functions:
_from_physical_space() # compute coefficients from function values internal_domain() # the domain of the basis create_collocation_points() # collocation points of the basis
... plus the following for numexpr.NumericExpression:
_expr_str() # represent the expression _evaluator() # create an evaluator object
| def __init__ | ( | self, | |
| a_n, | |||
| domain, | |||
| kw | |||
| ) |
Parent init for series expressions.
| a_n | Iterable of coefficient values. May also be empty. |
| domain | Domain on which this expansion should be defined. This is independent of the domain the basis is defined on. |
Further keyword arguments are passed to the parent init, that is to numexpr.NumericExpression.__init__().
|
private |
Perform the approximation of a function.
This samples the function at the desired number of collocation points and then triggers these samples to be converted to coefficients. The actual conversion will be handled by set_coefficients().
|
private |
Approximate a constant value.
Subclasses may implement a more efficient algorithm here. This default implementation simply creates a constant function and approximates this function.
| def approximate | ( | self, | |
| f, | |||
num = None , |
|||
lobatto = True , |
|||
use_mp = False , |
|||
dps = None |
|||
| ) |
Approximate a function.
This samples the function f
at num
points in the current interval [a,b]
to create a series approximation using num
terms.
After calling this, the series represented by this object is guaranteed to have num
elements, corresponding to N = num-1
in most textbooks for most bases.
| f | The function to approximate. May also be a constant, in which case we still create num elements (most of which will be zero for most bases). |
| num | Number of terms to use for approximation. This is also the number of times f is evaluated. Default is to use the current length of the coefficient list. |
| lobatto | Whether to use the Gauss-Lobatto (default) points for sampling or just the Gauss points. May not be used by all child classes. |
| use_mp | Whether to use mpmath computations. Default is False . |
| dps | Number of decimal places to use when use_mp==True . Default is to use the current global setting. |
| def collocation_points | ( | self, | |
num = None , |
|||
lobatto = True , |
|||
internal_domain = False , |
|||
use_mp = False , |
|||
dps = None , |
|||
| kw | |||
| ) |
Return the Gauss-Lobatto or Gauss points for this series.
The returned list will have num
elements. If you know the value of N
, you can find the corresponding value of num
by checking the textbooks for the number of degrees of freedom. The exact use of N
seems to be inconsistent across different books for many bases.
The **kw
argument is passed to the subclass method for creating the collocation points. These may be used to further specify options such as symmetry, etc.
| num | (int, optional) Number of collocation points to create. Note that in most printed formulas, the points have indices 0, 1, ..., N , i.e. there are N+1 points. This function, however, returns num points. To get the points that belong to a certain value of N in these formulas, call this function with num=N+1 . The default is the current number of coefficients. |
| lobatto | (boolean, optional) If True (default), return the Gauss-Lobatto points, which include the endpoints of the interval. Otherwise, return the interior (Gauss) points. |
| internal_domain | (boolean, optional) If False (the default), return the collocation points mapped to the range [a,b] . If True , use the internal domain (depends on subclass). |
| use_mp | (boolean, optional) Whether to compute the points using mpmath arbitrary precision calculations. The result will then be a list of mpmath.mpf floats. Default is False . |
| dps | (int, optional) Number of decimal places to use when use_mp==True . |
internal_domain==True
) or the physical domain (if internal_domain==False
). | def copy | ( | self | ) |
Create a copy of the series expansion expression.
The coefficients will be copied, i.e. the new expression can be modified independently.
Sub classes are responsible for transferring additional state over to the copy and should override this method in case they need to.
| def create_collocation_points | ( | cls, | |
| num, | |||
lobatto = True , |
|||
use_mp = False , |
|||
dps = None , |
|||
| kw | |||
| ) |
Subclasses should implement this as .
| def get_dof | ( | self | ) |
Return the degrees of freedom, i.e. the current number of coefficients.
| def interpolate | ( | self, | |
| y, | |||
x = None , |
|||
kind = 'cubic' , |
|||
num = None , |
|||
lobatto = True , |
|||
use_mp = False , |
|||
dps = None |
|||
| ) |
Interpolate data and model the result as a series.
This is a convenience function that interpolates the given data using scipy.interpolate.interp1d
via cubic splines (by default) and then approximates the result as a series with num
terms.
If no x-values are present, the values y
are assumed to lie equidistant in the domain [a,b]
(borders included).
Since the resulting interpolation function is approximated as a series, there is no guarantee that
holds. In general, the higher the value of num
, the better the above equation is satisfied.
| y | Data y-values. |
| x | Positions of the data values on the x-axis. By default, an equidistant grid on the domain [a,b] is used. |
| kind | (string or int, optional) kind parameter for the interp1d call. Default is 'cubic' . |
| num | Number of series terms to use for approximation. Default is to use the current length of the coefficient list. |
| use_mp | Whether to use mpmath computations. Default is False . Only affects the approximation step as the interpolation is done by SciPy. |
| dps | Number of decimal places to use when use_mp==True . Default is to use the current global setting. |
| def is_orthogonal | ( | self | ) |
Whether this series consists of orthogonal functions.
| def N | ( | self | ) |
Degrees of freedom, i.e. current number of coefficients.
| def resample | ( | self, | |
| num, | |||
use_mp = False , |
|||
dps = None |
|||
| ) |
Change resolution of the expansion.
For orthogonal bases, upsampling is lossless. Also in that case, increasing and then reducing the resolution to its original value is lossless and introduces no numerical noise.
For non-orthogonal bases, the function currently represented by the expansion will be sampled at the new requested number of collocation points to perform a lossy resampling.
| def set_coefficients | ( | self, | |
| a_n, | |||
physical_space = False , |
|||
lobatto = True , |
|||
use_mp = False , |
|||
dps = None |
|||
| ) |
Store a new list of coefficients after optional transformation.
Takes a new coefficient list and either stores it directly. If the list actually represents function values at the collocation points (i.e. in physical space), they are transformed to spectral space before being stored.
The actual transformation must be implemented by sub classes in the _from_physical_space() method.


August 2, 2019 North Carolina is First Southern State to Take Statewide Action to Protect LGBTQ Young People from ‘Conversion Therapy,’ as Governor Signs Executive Order Today, North Carolina Governor Roy Cooper signed an Executive Order directing the North Carolina Department of Health & Human Services to ensure that no taxpayer dollars are used for conversion therapy for minors, including North Carolina Medicaid and North Carolina Health Choice. →
April 15, 2019 U.S. Supreme Court Leaves in Place Lower Court Ruling that Upholds Law Protecting Minors from ‘Conversion Therapy’ The United States Supreme Court denied review of a cert. petition in King v. Murphy, a case challenging New Jersey’s law that protects young people from anti-LGBTQ ‘conversion therapy.’ This is the latest indication from the Supreme Court that the U.S. Constitution does not prohibit states from protecting minors from this discredited practice. →
April 1, 2019 Supermajority of NC Republicans and Democrats Support Protecting LGBTQ Youth From “Conversion Therapy” A February 2019 poll conducted by Public Policy Polling and commissioned by Equality NC and the Campaign for Southern Equality, found that a supermajority of Republicans (87%), Democrats (75%) and Independents (78%) support protecting North Carolina’s young people from conversion therapy. →
March 28, 2019 Launching Today: The Campaign to Protect North Carolina's Young People from Conversion Therapy Today marked the campaign launch of Born Perfect NC, announced on a historic day in North Carolina, when lawmakers in Raleigh introduced three separate bills that would advance equality, dignity, and respect for LGBTQ people across the Tarheel State.
Raleigh News & Observer: "Anti-gay ‘conversion therapy’ won’t get any state money, Gov. Roy Cooper orders" (8/2/19) →
The Winston-Salem Journal: "Our view: Ending LGBTQ discrimination" (4/2/19) →
WSPA 7: "Bills Introduced to Repeal HB2, Ban Conversion Therapy in NC" (4/2/19) →
The Progressive Pulse: "Poll: Overwhelming Bipartisan Support for Outlawing 'Conversion' Therapy" (4/1/19) →
Indy Week: "Op-Ed: LGBTQ Young People in North Carolina, You’re #BornPerfect As You Are" (3/29/19) →
The Fayetteville Observer: "Our View: Good Move to Halt Discrimination" (3/29/19) →
The Asheville Citizen-Times: "Asheville Area Lawmakers Propose North Carolina Ban on LGBT 'Conversion Therapy'" (3/28/19) →
WLOS 13: "Asheville activists hold news conference in response to new LGBTQ legislation" (3/28/19) →
Spectrum News: Capital Tonight: "Legislation Aims To Add LGTBQ Protections" (3/28/19) →
NC Policy Watch: "NC lawmakers introduce package of LGBTQ-friendly bills" (3/28/19) →
The Daily Tarheel: "N.C. Legislators Propose New Protections for LGBTQ+ Residents" (3/28/19) →
WRAL.com: "Democratic lawmakers, LGBT advocates want NC to ban 'conversion therapy'" (3/28/19) →
U.S. News & World Report: "Bills Opposing 'Conversion Therapy,' HB2 Remnants Filed" (3/28/19) →
The Post and Courier: "Taught to Hate Myself’: How Gay Conversion Therapy in SC is Thriving" →
The Daily Beast: "Meet the Conversion Therapists Who Turned Against Conversion Therapy" →
The Washington Post: "Why We Still Haven’t Banished Conversion Therapy in 2018" →
USA Today: "I Survived Conversion Therapy as a Child. Now, I'm Part of the Movement to Ban it for Good. →


{% extends "layout.html" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Pick a date Day Month Year Yesterday Tomorrow Two weeks from now {% endblock %}
Yesterday Tomorrow Two weeks from now


Chicago's.Pro
-
Maple & Ash Matches Carry Out Dollar for Dollar Giftcard for Use After Pandemic
Although not advertised on their website, if you place any carry out order via phone every dollar spent will go toward a giftcard of equal value to use at the Gold Coast stable after resturant restrictions end.
Read More -
Chicago Resturants Selling DIY Meal Kits during Covid Crisis
Happy Camper and Summer House Santa Moncia are amount the several resturants who decided to get creative during this time by offering meal kits for delivery or pick-up!
-
Order Takeout or Delivery from a Michelin-Starred Restaurant in Chicago
None of Chicago’s 25 Michelin restaurants offered takeout or delivery options before the novel coronavirus outbreak. Now that the city is in the second week of the state’s stay-at-home order that allows for takeout and delivery (restaurants are able to operate as they’re considered essential), more restaurants have unveiled options. Currently, eight restaurants offer to-go or delivery in one form other the other.
-
How to Help Chicago Bar and Restaurant Workers During the COVID-19 Outbreak
A running log of merch, prizes, and donation opportunities to help bridge the gap for employees during the mandated closure
-
Chicago-Born Directory of Restaurants That Are Serving Food for Off-site Consumption
Two people who have never met or communicated until Sunday have collaborated on the biggest and most comprehensive list of Chicago restaurants which are offering carryout and delivery to continue business as dining rooms across Illinois remain closed due to the COVID-19 pandemic.
-
Six Chicago Hospitality Groups Sue Their Insurance Company Over COVID-19 Denials
The owners of Happy Camper, Whale, and Headquarters are among the plaintiffs


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Elixir v1.3.4 Protocol
Functions for working with protocols.
Summary
Functions
Checks if the given module is loaded and is an implementation of the given protocol
Checks if the given module is loaded and is protocol
Receives a protocol and a list of implementations and consolidates the given protocol
Returns true
if the protocol was consolidated
Extracts all types implemented for the given protocol from the given paths
Extracts all protocols from the given paths
Functions
Checks if the given module is loaded and is an implementation of the given protocol.
Returns :ok
if so, otherwise raises ArgumentError
.
Checks if the given module is loaded and is protocol.
Returns :ok
if so, otherwise raises ArgumentError
.
consolidate(module, [module]) :: {:ok, binary} | {:error, :not_a_protocol} | {:error, :no_beam_info}
Receives a protocol and a list of implementations and consolidates the given protocol.
Consolidation happens by changing the protocol impl_for
in the abstract format to have fast lookup rules. Usually
the list of implementations to use during consolidation
are retrieved with the help of extract_impls/2
.
It returns the updated version of the protocol bytecode. A given bytecode or protocol implementation can be checked to be consolidated or not by analyzing the protocol attribute:
Protocol.consolidated?(Enumerable)
If the first element of the tuple is true
, it means
the protocol was consolidated.
This function does not load the protocol at any point nor loads the new bytecode for the compiled module. However each implementation must be available and it will be loaded.
Returns true
if the protocol was consolidated.
Extracts all types implemented for the given protocol from the given paths.
The paths can be either a charlist or a string. Internally they are worked on as charlists, so passing them as lists avoid extra conversion.
Does not load any of the implementations.
Examples
# Get Elixir's ebin and retrieve all protocols
iex> path = :code.lib_dir(:elixir, :ebin)
iex> mods = Protocol.extract_impls(Enumerable, [path])
iex> List in mods
true
Extracts all protocols from the given paths.
The paths can be either a charlist or a string. Internally they are worked on as charlists, so passing them as lists avoid extra conversion.
Does not load any of the protocols.
Examples
# Get Elixir's ebin and retrieve all protocols
iex> path = :code.lib_dir(:elixir, :ebin)
iex> mods = Protocol.extract_protocols([path])
iex> Enumerable in mods
true
Macros
Defines a new protocol function.
Protocols do not allow functions to be defined directly, instead, the
regular Kernel.def/*
macros are replaced by this macro which
defines the protocol functions with the appropriate callbacks.


Create a new class called EmailList. The constructor function should take in one parameter, "name", and return an instance which contains the following:
Name, the name of the mailing list, such as "BSSA Students" or "BSSA Teachers".
list, an array of all the emails in that list. Note that initially, the array will be empty.
Add a function to the EmailList's prototype. This function should be called "addEmail", and it should take one parameter, an email address and add it to the "list" property of the instance.
Add another function to the EmailList's prototype, called "sendEmailToAll". This function should take in one parameter: the text of the email, and then "send an email to each address". Since that lecture will be sometime in week 6, for now, simply print out the following:
"Email content:" - list the text of the email to be sent "Sending email to:" - list all email addresses


toe(1) toe(1) NAME toe - table of (terminfo) entries SYNOPSIS toe [-v[n]] [-ahuUV] file... DESCRIPTION With no options, toe lists all available terminal types by primary name with descriptions. File arguments specify the directories to be scanned; if no such arguments are given, your default terminfo directory is scanned. If you also specify the -h option, a directory header will be issued as each directory is entered. There are other options intended for use by terminfo file maintainers: -a report on all of the terminal databases which ncurses would search, rather than only the first one that it finds. -u file says to write a report to the standard output, listing dependen‐ cies in the given terminfo/termcap source file. The report con‐ denses the `use' relation: each line consists of the primary name of a terminal that has use capabilities, followed by a colon, fol‐ lowed by the whitespace-separated primary names of all terminals which occur in those use capabilities, followed by a newline -U file says to write a report to the standard output, listing reverse dependencies in the given terminfo/termcap source file. The report reverses the `use' relation: each line consists of the pri‐ mary name of a terminal that occurs in use capabilities, followed by a colon, followed by the whitespace-separated primary names of all terminals which depend on it, followed by a newline. -vn specifies that (verbose) output be written to standard error, showing toe's progress. The optional parameter n is a number from 1 to 10, interpreted as for tic(1). -V reports the version of ncurses which was used in this program, and exits. FILES /etc/terminfo/?/* Compiled terminal description database. SEE ALSO tic(1), infocmp(1), captoinfo(1), infotocap(1), ncurses(3NCURSES), ter‐ minfo(5). This describes ncurses version 5.9 (patch 20110404). toe(1)


The main museum of the Northern Territory, MAGNT presents both local and national exhibitions.
The museum hosts internationally renowned scientific and cultural collections and is home to sweetheart the crocodile.More information here.
Territory Child Care Group offers a range of high quality education and care services.
Darwin Family Day Care is a home based option where children attend in a small group setting in the Educators home.More information here.


github.com/jengopockets
github.com/theJOhnwatt
github.com/Chards79
github.com/toddmurphy
github.com/KParrish193
github.com/MicheSi
"The greatness of a nation and its moral progress can be judged by the way its animals are treated."


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--- company: Net Nanny subject: Are Smart Phones Fueling Your Teen's Insomnia? date-month: Jan. date-year: 2017 date-vol: II tracking-ace: "&ace=----ACE_ID----" tracking-campaign: "?pid=1-249&utm_source=newsletter&utm_medium=email&utm_campaign=2017newsletter&utm_content=" url-webroot: "https://www.netnanny.com/" url-blog: "https://www.netnanny.com/blog/" url-blogasset: "https://www.netnanny.com/assets/blogimages/" url-asset: "https://www.netnanny.com/assets/email/newsletter/" blog: [{ "title": "5 Tech Toys from the 2017 CES Show", "url": "https://www.netnanny.com/blog/5-ces-tech-toys-parents-will-be-talking-about/", "image": "https://www.netnanny.com/assets/blogimages/5-ces-tech-toys-parents-will-be-talking-about.jpg", "tracking": "?pid=1-249&utm_source=newsletter&utm_medium=email&utm_campaign=2017newsletter&utm_content=5-ces-tech-toys-parents-will-be-talking-about&ace=----ACE_ID----" }, { "title": "Help Your Teen With Their Tech Addition", "url": "https://www.netnanny.com/blog/how-to-help-your-teen-with-their-technology-addiction/", "image": "https://www.netnanny.com/assets/blogimages/how-to-help-your-teen-with-their-technology-addiction.jpg", "tracking": "?pid=1-249&utm_source=newsletter&utm_medium=email&utm_campaign=2017newsletter&utm_content=how-to-help-your-teen-with-their-technology-addiction&ace=----ACE_ID----" }, { "title": "What Smartphones do to Children’s eyes", "url": "https://www.netnanny.com/blog/what-smart-phones-do-to-your-childrens-eyes/", "image": "https://www.netnanny.com/assets/blogimages/what-smart-phones-do-to-your-childrens-eyes.jpg", "tracking": "?pid=1-249&utm_source=newsletter&utm_medium=email&utm_campaign=2017newsletter&utm_content=what-smart-phones-do-to-your-childrens-eyes&ace=----ACE_ID----" } ] ---
{{ date-month }} Vol.{{ date-vol }} {{ date-year }}
Tech addiction is not just an idea. It’s here, it’s real and it’s taking over our lives. Try the Forbes 30 Day Digital Detox Challenge! Day 19“
Here are 3 of our readers' top favorite blog posts.
{{ this.title }}


Antz (1998) 4 stars out of 4. Featuring voices of Woody Allen, Sharon Stone, Sylvester Stallone, Gene Hackman and Chirstopher Walken.
Antz is a witty, entertaining story about conformity vs. individuality, the worth of the individual vs. the welfare of the whole and, of course, true love.
Oh, it's also about ants. And after seeing this computer-animated feature, you'll hesitate before trying to crush another one again.
Featuring an all-star cast of vocal talent, including Woody Allen, Sharon Stone, Anne Bancroft, Gene Hackman, Sylvester Stallone, Dan Aykroyd, Danny Glover and Christopher Walken, to name just a few, the story will capture the interest of young and old viewers.
And the story is strong enough that you quickly accept that these are computer-created creatures and get totally involved in their insect lives and dreams.
Allen is Z, a worker ant who is tired of being like all the other workers. He tells his shrink that he "feels insignificant," to which the psychologist (voiced by director-actor Paul Mazursky) congratulates him on making a breakthrough. "You are insignificant," he explains to Z.
At a workers' bar, Z meets Princess Bala, who is slumming away from the palace. They dance, creating their own steps, thus breaking away from the others who are marching in step.
To see her again, the smitten Z trades places with Weaver (Stallone), a soldier ant. After a dress parade, the soldiers are sent by the mad Gen. Mandible (Hackman) to make war on the termites.
Z is the only survivor and is declared a hero.
Later, Z and Bala share many adventures and thwart Mandible's wild scheme to begin a new colony.
It's all fairy tale-like, but in a sense rather adult.
The script, by Todd Alcott and Chris and Paul Weitz, is droll and quirky, filled with many zingers and one-liners. The writing, especially that of Allen's Z, feels as if the writers watched most of Allen's films to get his vocal cadence and rhythms. In a sense, when Allen speaks, it almost sounds as if he wrote his own lines.
What makes Antz successful is that the performers take their roles seriously. Hackman sounds like a demented Patton, while Walken brings his usual creepy persona to his role as the general's aide.
Even Stallone has some fun as the soldier ant, who trades places with Z so he can meet some female workers.
Antz is a rewarding experience. It will enchant the children and rivet their attention. Adults will appreciate the movie's technique and humor.
It is truly a film for the entire family and a portend of the road the new Dreamworks studio is creating.
With this, Steven Spielberg's Saving Private Ryan, and the upcoming Prince of Egypt, Dreamworks will be a Hollywood innovator.
Bob Bloom is the film critic at the Journal and Courier in Lafayette, Ind. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Ping-Pong
Enter the number of balls you would like to start your game with. not that each number divisible by three will read "ping" and each divisible by five will read "pong", and each number divisible by either (ie, divisible by 15) will read both ping and "pong"


This page describes how to contribute changes to Esprima.
Please do not create a pull request without reading this guide first. Failure to do so may result in the rejection of the pull request.
1. Create a ticket in the issue tracker. This serves as a placeholder for important feedback, review, or any future updates.
2. Run all the tests. This requires Node.js: npm install
to set up, npm test
to run the tests.
3. Work on a feature branch. If the change still needs some tweaks, it will not clutter the master branch.
4. Write a reasonable commit message:
For more information, please check the detailed contribution guide.


In fact, I'm so excited about the prospect of joining your UX team that I put this HTML+CSS page together to show the commitment, interest and energy that I'd bring with me.
14 Years of Creative Design Experience in both print and digital media.
Experienced Front-End Web Developer HTML, CSS, JS, AngularJS and more.
Team player with a curious mind. If I don't know how to do something, I will learn how. That's the fun part.
Solid understanding of backend development which will help in collaborating with the dev team. I speak their language.
Eager to jump into new challenges such as: Large scale projects with many moving pieces and voices preaching for their own ideas, and to defend my ideas and decisions.
I believe that as an addition to the team, collectively we will achieve sharper creative thought-provoking solutions.
I look forward to hearing from you, and hope to have the opportunity to help your team continue to excel.


Coming together is a beginning. Keeping together is progress. Working together is success.
Henry Ford
CP Hub is a programming club focused primarily in the domain of Competitive Coding . Aim of CP Hub is to foster Competitive Programming culture in NIT Calicut and help students to tackle challenges involved in Problem Solving through computer programming and in turn advance their career development.
"Groom NITCians to stand out from crowd and compete in ICPC World Finals."
The ICPC, the “International Collegiate Programming Contest”, is competitive programming sport of the universities of the world. ICPC competitions provide students with opportunities to demonstrate, and improve their teamwork, programming, and problem-solving skill.
Witness daily improvement in each one of us by conducting programming contests at college or Inter-NIT level. Overcome our weakness by conducting ICPC
mirrors and competing among ourselves. Organizing Competitive Programming Workshop for spreading awareness and
knowledge about Competitive Programming. In addition to these we strongly believe in
"All work and no play makes Jack a dull boy"
to comply with it we even organize non-conventional workshops based on interest of club members as part of recreational activities.


MTA Space
Account
Scan
Logout
How many Passengers are in the car you're on?
Passengers: 3
Manual: Enter number of passengers below
Value:
Time of the snapshot


The Linked Modernisms Project is an open-source project that takes into consideration, expert opinions and verifications on our data. If you feel you are qualified and would like to contribute to the integrity of our data, please get in contact with Project Lead Stephen Ross.


Regular expression for getting the root of a posix or windows filepath.
You might also be interested in path-root.
The module exposes a function that must be called to get the regex (modified from the split device regex in the node.js path module);
var pathRootRegex = require('path-root-regex');
console.log(pathRootRegex() instanceof RegExp);
//=> true
See the path-root module for examples.
You might also be interested in these projects:
path.isAbolute
. Returns true if a file path is absolute. | homepagepath.parse
method if… more | homepagePull requests and stars are always welcome. For bugs and feature requests, please create an issue.
Install dev dependencies:
$ npm install -d && npm test
Jon Schlinkert
Copyright © 2016, Jon Schlinkert. Released under the MIT license.
This file was generated by verb, v, on March 29, 2016.


|
ARK2D
1.0
Game development framework for Windows, Mac OS X and Android.
|
Load assets in to game. Images, Sounds, TiledMaps, LocalHighscores, Text files, Fonts, etc. More...
#include <Resource.h>
Public Member Functions | |
| Resource () | |
| Image * | asImage () |
| Sound * | asSound () |
| ARK::Font::Font * | asFont () |
| LocalHighscores * | asLocalHighscores () |
| TiledMap * | asTiledMap () |
| ARK::Core::String * | asString () |
| PathGroup * | asPathGroup () |
| SpriteSheetDescription * | asSpriteSheetDescription () |
| KeyPairFile * | asKeyPairFile () |
| ARK::Spine::Skeleton * | asSkeleton () |
| virtual | ~Resource () |
Static Public Member Functions | |
| static Resource * | get (string ref) |
| static bool | exists (string ref) |
| static string | latestName () |
| static void | setLatestName (string s) |
| static void | copy (string ref, string ref2) |
Static Protected Member Functions | |
| static Resource * | get (string ref, bool appendPath) |
| static bool | exists (string ref, bool appendPath) |
| static RawDataReturns * | getRawData (string ref) |
| static unsigned int | getResourceTypeByExtension (string extension) |
| static void | init () |
Static Protected Attributes | |
| static string | s_latestName |
Friends | |
| class | ARK::Graphics::TextureStore |
| class | ARK::Tiled::TiledMapParser_TinyXml |
| class | ARK::Tiled::TiledMapParser_RapidXml |
| class | ARK::Tiled::TiledMapParser_JSON |
Load assets in to game. Images, Sounds, TiledMaps, LocalHighscores, Text files, Fonts, etc.
Definition at line 45 of file Resource.h.
| ARK::Core::Resource::Resource | ( | ) |
Definition at line 678 of file Resource.cpp.
|
virtual |
Definition at line 713 of file Resource.cpp.
| ARK::Font::Font * ARK::Core::Resource::asFont | ( | ) |
Definition at line 688 of file Resource.cpp.
| Image * ARK::Core::Resource::asImage | ( | ) |
Definition at line 682 of file Resource.cpp.
| KeyPairFile * ARK::Core::Resource::asKeyPairFile | ( | ) |
Definition at line 703 of file Resource.cpp.
| LocalHighscores * ARK::Core::Resource::asLocalHighscores | ( | ) |
Definition at line 691 of file Resource.cpp.
| PathGroup * ARK::Core::Resource::asPathGroup | ( | ) |
Definition at line 694 of file Resource.cpp.
| ARK::Spine::Skeleton * ARK::Core::Resource::asSkeleton | ( | ) |
Definition at line 706 of file Resource.cpp.
| Sound * ARK::Core::Resource::asSound | ( | ) |
Definition at line 685 of file Resource.cpp.
| SpriteSheetDescription * ARK::Core::Resource::asSpriteSheetDescription | ( | ) |
Definition at line 700 of file Resource.cpp.
| String * ARK::Core::Resource::asString | ( | ) |
Definition at line 709 of file Resource.cpp.
| TiledMap * ARK::Core::Resource::asTiledMap | ( | ) |
Definition at line 697 of file Resource.cpp.
|
static |
Definition at line 388 of file Resource.cpp.
|
static |
Definition at line 410 of file Resource.cpp.
|
staticprotected |
Definition at line 413 of file Resource.cpp.
|
static |
Definition at line 65 of file Resource.cpp.
|
staticprotected |
Definition at line 69 of file Resource.cpp.
|
staticprotected |
Definition at line 532 of file Resource.cpp.
|
staticprotected |
Definition at line 653 of file Resource.cpp.
|
staticprotected |
Definition at line 495 of file Resource.cpp.
|
static |
Definition at line 57 of file Resource.cpp.
|
static |
Definition at line 61 of file Resource.cpp.
|
friend |
Definition at line 46 of file Resource.h.
|
friend |
Definition at line 49 of file Resource.h.
|
friend |
Definition at line 48 of file Resource.h.
|
friend |
Definition at line 47 of file Resource.h.
|
staticprotected |
Definition at line 67 of file Resource.h.


{{projects[0].Title}}
{{projects[0].Type}}
{{projects[0].Desc}}
Something about this, or that, and then the other thing. Much love. Something about this, or that, and then the other thing. Much love.
{{projects[0].Desc}}
{{projects[1].Desc}}
{{projects[2].Desc}}
{{projects[3].Desc}}


| Routes + Queries |
What they do |
GET /api/v1/characters
|
get all characters (default 20 per page)
|
GET /api/v1/characters?perPage=NUMBER&page=NUMBER
|
get all characters pagination
|
|
GET /api/v1/characters/CHARACTER_ID
|
get a specific character by id |
|
GET /api/v1/characters/random
|
get one random character |
|
GET /api/v1/characters/random?count=NUMBER
|
get a number of random characters |
|
GET /api/v1/characters/avatar
|
get all avatars |
|
GET /api/v1/characters?affiliation=NATION+NAME
|
get characters with a specific affiliation
Choices: Fire Nation, Water Tribe, Earth Kingdom, and more! |
|
GET /api/v1/characters?name=CHARACTER+NAME
|
get characters who's name matches a string |
|
GET /api/v1/characters?enemies=CHARACTER+NAME
|
get characters that are enemies of a specific character |
|
GET /api/v1/characters?allies=CHARACTER+NAME
|
get characters who are allies of a specific character |


About Me Quiz Questions
What is your name?
1. Is rock my favorite music genre?
2. Do I want to get a pet rabbit/bunny someday?
3. Do I play Video Games?
4.
5. Was my first console the Playstation 2?
- Work Experience
- I haven't had a job yet before and not exactly looking for a job yet because of Covid19. But after that is over which is hopefully soon I will look for a job. I do want to get a job in coding after the DeltaV courses though.
- Education Summary
- I only graduated from highschool. Once I graduated in 2020 I went straight to DeltaV code school. I really am enjoying these courses so far. Even though there are some times that are stressful but we getting through it.
My top 10 favorite bands/singers!
- Hollywood Undead
- Motionless in White
- New Medicine
- Theory of a Deadman
- Marilyn Manson
- Get Scared
- Linkin Park
- Korn
- Falling in Reverse
- My Darkest Days


Streaming is a hobby of mine. I love playing video games and editing videos so why not combine both and do some streaming! I started streaming a couple of years ago. I have streamed a variety of different games.
The most recent games I have streamed are:
- Crash Bandicoot
- Fortnite
- Resident Evil Revelations 2
Sometimes I stream with my two friends and one has his own youtube channel as well called JayGamerShow! Together we are going to be creating a gaming podcast talking about the latest games and our opinions on certain topics.
My favourite games to play are stealth, shooters, or platform games. For example Splinter Cell and Hitman for Stealth, Overwatch and Fortnite for shooting games and Crash Bandicoot and Mario for platforms.
I grew up on the super ninetendo when I was four years old. Now I am 26 years old and I still love it just as much as I did when I was younger.


vignettes/introduction-to-vistributions.Rmd
introduction-to-vistributions.Rmd
In exploring statistical distributions, we focus on the following:
To explore the above 3 concepts, we have defined functions for the following distributions:
Visualize how changes in mean and standard deviation affect the shape of the normal distribution.
Suppose X, the grade on a exam, is normally distributed with mean 60 and standard deviation 3. The teacher wants to give 10% of the class an A. What should be the cutoff to determine who gets an A?
The teacher wants to give lower 15% of the class a D. What cutoff should the teacher use to determine who gets an D?
The teacher wants to give middle 50% of the class a B. What cutoff should the teacher use to determine who gets an B?
Let X be the IQ of a randomly selected student of a school. Assume X ~ N(90, 4). What is the probability that a randomly selected student has an IQ below 80?
What is the probability that a randomly selected student has an IQ above 100?
What is the probability that a randomly selected student has an IQ between 85 and 100?
Visualize how changes in number of trials and the probability of success affect the shape of the binomial distribution.
Assume twenty-percent (20%) of Magemill have no health insurance. Randomly sample n = 12 Magemillians. Let X denote the number in the sample with no health insurance. What is the probability that exactly 4 of the 15 sampled have no health insurance?
What is the probability that at most one of those sampled has no health insurance?
What is the probability that more than seven have no health insurance?
What is the probability that fewer than 5 have no health insurance?
Visualize how changes in degrees of freedom affect the shape of the chi square distribution.
Visualize how changes in degrees of freedom affect the shape of the F distribution.
Let X be an F random variable with 4 numerator degrees of freedom and 5 denominator degrees of freedom. What is the upper twenth percentile?
What is the 35th percentile?
What is the probability that an F random variable with 4 numerator degrees of freedom and 5 denominator degrees of freedom is greater than 3.89?
What is the probability that an F random variable with 4 numerator degrees of freedom and 5 denominator degrees of freedom is less than 2.63?
Let T follow a t-distribution with r = 6 df.
What is the probability that the value of T is less than 2?
What is the probability that the value of T is greater than 2?
What is the probability that the value of T is between -2 and 2?
What is the probability that the absolute value of T is greater than 2?


GitFetch - Profile Finder
for GitHub
Check out the repos, followers and more,
just by entering a username!
Search


Read. Write. Review.
Novelius is a place to write without pressure, get instant feedback, and build a following of loyal readers. If you're not ready to start writing, that's ok! You can read to your hearts content. Subsrcribe to local authors and follow along witht their stories as they write them!
Follow along with people as they write their stories! If you don't have time every day to sit down and open up an entire novel, that's ok. At Novelius, authors can publish individual chapters and you will be able to follow along as they publish each chapter, be a part of a developing narrative, and even provide encouragement. Unless you contribute to the community yourself, your suggestions and review of the story are limited. And, as always at Novelius, this is a place for positive feedback and encouragement.
Write to your hearts content! Novelius provides a free, simple, and distraction free environment for you to tell your story to the world. If you're feeling brave enough, publish your chapters as you write them, build a loyal following, and continue to be motivated and encouraged to write. At Novelius, your ideas are yours to do with as you please: contribute to the community, or just use it as a place to find new ideas and jot down some of your own. The goal is to allow you a space to write, read others work, be inspired, and push your boundaries, all in a simple and easy to use environment.
As a writer, you have the opportunity to help others with their writing, whether that is just basic editing, spell-checking, or stylistic suggestions. Writing groups are a great way for young (and old!) writers to develop their passion, find new ideas, and be a part of an encouraging community. However, based on where you live or your schedule, you may find it unrealistic to spend your Saturday mornings at the local Y with a writing group. Novelius is the same thing, but without the physical limitations and the time requirements. Be a part of an ongoing discussion, join in writing exercises, and connect with other people who share your passion.


Software developer in moringa school kigali.
Am a woman who live in kigali and is so proud to be in wecode studing programing i feel good when am coding because it make me that i can solve and am willing to learn more about coding because am just a begginer in programming. For now i have experince in html, javascript and css language and i want to finish this program and be a good programer.
This is the project that i did when i was starting to learn git hubGithubLink
This was my secong project that i did when i was starting to learning git hubGithubLink 2
In this link is a project that i did when i have finished learning html and cssrepl.it


| Product description | User's email address | Recommended retail price | Estimated sales/year |
| Dummy description 1 | Dummy email address 1 | Dummy retail price 1 | Dummy estimated sales/year 1 |
| Dummy description 2 | Dummy email address 2 | Dummy retail price 2 | Dummy estimated sales/year 2 |
| Dummy description 3 | Dummy email address 3 | Dummy retail price 3 | Dummy estimated sales/year 3 |


Module: ActiveRecord::Integration
| Relationships & Source Files | |
| Namespace Children | |
|
Modules:
| |
| Extension / Inclusion / Inheritance Descendants | |
|
Included In:
| |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
::ActiveSupport::Concern
|
|
| Defined in: | activerecord/lib/active_record/integration.rb |
Class Method Summary
::ActiveSupport::Concern
- Extended
| class_methods | Define class methods from given block. |
| included | Evaluate given block in context of base class, so that you can write class macros here. |
| prepended | Evaluate given block in context of base class, so that you can write class macros here. |
| append_features, prepend_features |
Instance Method Summary
-
#cache_key
Returns a stable cache key that can be used to identify this record.
-
#cache_key_with_version
Returns a cache key along with the version.
-
#cache_version
Returns a cache version that can be used together with the cache key to form a recyclable caching scheme.
-
#to_param
Returns a
::String
, which Action Pack uses for constructing a URL to this object. -
#can_use_fast_cache_version?(timestamp) ⇒ Boolean
private
Detects if the value before type cast can be used to generate a cache_version.
-
#raw_timestamp_to_cache_version(timestamp)
private
Converts a raw database string to
:usec
format.
DSL Calls
included
[ GitHub ]9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
# File 'activerecord/lib/active_record/integration.rb', line 9
included do ## # :singleton-method: # Indicates the format used to generate the timestamp in the cache key, if # versioning is off. Accepts any of the symbols in Time::DATE_FORMATS. # # This is :usec, by default. class_attribute :, instance_writer: false, default: :usec ## # :singleton-method: # Indicates whether to use a stable #cache_key method that is accompanied # by a changing version in the #cache_version method. # # This is true, by default on \Rails 5.2 and above. class_attribute :cache_versioning, instance_writer: false, default: false ## # :singleton-method: # Indicates whether to use a stable #cache_key method that is accompanied # by a changing version in the #cache_version method on collections. # # This is false, by default until \Rails 6.1. class_attribute :collection_cache_versioning, instance_writer: false, default: false end
Instance Method Details
#cache_key
Returns a stable cache key that can be used to identify this record.
Product.new.cache_key # => "products/new"
Product.find(5).cache_key # => "products/5"
If Base.cache_versioning is turned off, as it was in Rails 5.1 and earlier, the cache key will also include a version.
Product.cache_versioning = false
Product.find(5).cache_key # => "products/5-20071224150000" (updated_at available)
# File 'activerecord/lib/active_record/integration.rb', line 72
def cache_key if new_record? "#{model_name.cache_key}/new" else if cache_version "#{model_name.cache_key}/#{id}" else = if = .utc.to_fs( ) "#{model_name.cache_key}/#{id}-#{}" else "#{model_name.cache_key}/#{id}" end end end end
#cache_key_with_version
Returns a cache key along with the version.
# File 'activerecord/lib/active_record/integration.rb', line 114
def cache_key_with_version if version = cache_version "#{cache_key}-#{version}" else cache_key end end
#cache_version
Returns a cache version that can be used together with the cache key to form a recyclable caching scheme. By default, the #updated_at
column is used for the cache_version, but this method can be overwritten to return something else.
Note, this method will return nil if Base.cache_versioning is set to false
.
# File 'activerecord/lib/active_record/integration.rb', line 97
def cache_version return unless cache_versioning if has_attribute?("updated_at") = updated_at_before_type_cast if can_use_fast_cache_version?( ) ( ) elsif = updated_at .utc.to_fs( ) end elsif self.class.has_attribute?("updated_at") raise ActiveModel::MissingAttributeError, "missing attribute 'updated_at' for #{self.class}" end end
#can_use_fast_cache_version?(timestamp) ⇒ Boolean
(private)
Detects if the value before type cast can be used to generate a cache_version.
The fast cache version only works with a string value directly from the database.
We also must check if the timestamp format has been changed or if the timezone is not set to UTC then we cannot apply our transformations correctly.
# File 'activerecord/lib/active_record/integration.rb', line 178
def can_use_fast_cache_version?( ) .is_a?(String) && == :usec && self.class.connection.default_timezone == :utc && !updated_at_came_from_user? end
#raw_timestamp_to_cache_version(timestamp) (private)
Converts a raw database string to :usec
format.
Example:
= "2018-10-15 20:02:15.266505"
( )
# => "20181015200215266505"
PostgreSQL truncates trailing zeros, github.com/postgres/postgres/commit/3e1beda2cde3495f41290e1ece5d544525810214 to account for this we pad the output with zeros
# File 'activerecord/lib/active_record/integration.rb', line 197
def ( ) key = .delete("- :.") if key.length < 20 key.ljust(20, "0") else key end end
#to_param
Returns a ::String
, which Action Pack uses for constructing a URL to this object. The default implementation returns this record’s id as a ::String
, or nil
if this record’s unsaved.
For example, suppose that you have a User model, and that you have a resources :users
route. Normally, user_path
will construct a path with the user object’s ‘id’ in it:
user = User.find_by(name: 'Phusion')
user_path(user) # => "/users/1"
You can override to_param
in your model to make user_path
construct a path using the user’s name instead of the user’s id:
class User < ActiveRecord::Base
def to_param # overridden
name
end
end
user = User.find_by(name: 'Phusion')
user_path(user) # => "/users/Phusion"
# File 'activerecord/lib/active_record/integration.rb', line 57
def to_param # We can't use alias_method here, because method 'id' optimizes itself on the fly. id && id.to_s # Be sure to stringify the id for routes end


Queuing System for the Fresh Counter
Authors: Asif and Diman
Products
Add cheese
Add meat
Add something else
Your status in the queue:
..
..
Welcome
template by w3schools.com
Friday, 11 April 2019


Winning EuroMillions numbers land UK ticket-holder huge £105m jackpot
The winning EuroMillions numbers for Tuesday, November 19 scooped one lucky person £105m
by Agency StaffThe £105m EuroMillions jackpot on the 25th anniversary of the first National Lottery draw has been won by a UK ticket-holder.
The winning EuroMillions numbers were 08, 10, 15, 30 and 42 and the Lucky Star numbers were 04 and 06.
Andy Carter, senior winners' adviser at the National Lottery, said: "On our 25th birthday, one very lucky ticket-holder has scooped tonight's entire £105m EuroMillions jackpot.
"Players across the country are now urged to check their tickets to see if they have won big.
"Tonight's big winner has scooped the sixth EuroMillions jackpot banked in the UK this year."
Someone also became a millionaire by matching the Millionaire Maker code ZMSF62402.
A UK ticket-holder won a £170m EuroMillions jackpot in October, making them the UK's biggest ever winner.
The previous biggest UK winners were Colin and Chris Weir, from Largs in North Ayrshire, Scotland, who won £161m in July 2011.
A £123m jackpot was won by a UK ticket-holder in June and a UK winner scooped £38m in the EuroMillions in April.
Ade Goodchild, from Hereford, bagged £71m on March 15 and Patrick and Frances Connolly, from Northern Ireland, won £114.9m on New Year's Day.


|
This is your data view. Below you find a list of visualizations and the networks currently in your browser. |
Visualizations |
|
VistorianLab
Your NetworksYour Tables |


Coordinate Reference Systems Intro
Overview
In QGIS the default projection, or CRS, is WGS84 Geographic Coordinate System.
The WGS84 CRS has become a global standard for latitude and longitude positions, such as those captured with GPS devices. Note that "unprojected" data, or those with projections that cannot be interpreted will be projected by default in WGS-84 when using QGIS.
It is possible to load layers with different CRS into a QGIS project and view them seamlessly in the same coordinate space.
This exercise will introduce the basics of managing CRS in QGIS.
See also Peter H. Dana's Map Projection Overview.
1. From the top Menu go to PROJECT | PROJECT PROPERTIES
Then go the CRS tab
1. Start by ticking the checkbox to "Enable "on the fly" CRS transformations (OTF)"
2. The "Recently used" CRS Panel can be used to reset the Project CRS to any options listed
3. The "CRS of the World" panel lists all the pre-configured CRS that QGIS has to choose from
4. Below this is the SELECTED CRS which lists the Name of the currently set CRS
5. Next is a proj4 text panel, where CUSTOMIZED projections can be set by pasting in proj4 format CRS statements. You can look up proj4 statements by EPSG number at SpatialReference.org
6. At the top of the CRS tab is a FILTER box, where you can Search by CRS Name or by EPSG number
---
Note: in the main QGIS User Interface, the current PROJECT CRS is always indicated in the lower right corner of the STATUS BAR as EPSG #.
The following exercise uses data from:
- Workshop Data 2015-03 PART TWO Egypt
- Workshop Data 2014-11 PART TWO Spain
- Workshop Data 2014-11 PART TWO China
1. For this exercise go to PROJECT | PROPERTIES and UNCHECK the "on the fly" transformations setting and click APPLY then OK.
2. Now we will Add Vector Data
3. Set the Encoding to ISO-8859-1 [Latin alphabet with European standard diacritic marks]
4. Add the Egypt layer: egypt_admi1_iso-8850=9-1
5. Also using Encoding: ISO-8859-1, add the Spain layer: ESP_adm4
1. Add another layer, changing Encoding = UTF8, the egypt_gns_pts_utf-8
2. click on the ZOOM FULL button (Zoom to Full Extent of active layers)
3. Now you should see the Egypt and Spain data on the Map View.
1. Right click on the ESP_adm4 layer, and then PROPERTIES | GENERAL
2. You should see that the CRS is set to EPSG:4326 - WGS 84
3. Close the Layer Properties window and note that the PROJECT CRS is also set to EPSG:4326 on the lower right of the status bar
4. Right click on one the Egypt layers and you will find that the same CRS settings are found, EPSG:4326
5. Because ALL layers in our project are in the same CRS and the Project itself is the same CRS, they are shown correctly, even though we have UNCHECKED the setting for ENABLE ON-THE-FLY CRS TRANSFORMATIONS !
1. Now let us test this by adding a layer with a very different CRS definition
2. First make sure that in PROJECT | PROJECT PROPERTIES the ON-THE-FLY Transformation is still UNCHECKED (ie. disabled)
3. Now Add Vector Data in a DIFFERENT CRS
4. Set the Encoding to GBK
5. Browse to the China Provinces sample data in the workshop folder 6. workshop_data_2014-11/part_2/v4_1820_prov_pgn_gbk then OPEN.
1. When adding incompatible CRS layers in the same map view, and with the ON-THE-FLY Transformation unchecked, the Map View will either remain unchanged, or may go blank
2. Now click on the ZOOM FULL button
3. QGIS 2.0 will automatically RESET the ON-THE-FLY Transform option, and your layers should now show up in correct relative positions
1. Now make sure the v4_1820_prov_pgn_gbk layer is the ACTIVE layer, by clicking on it in the Layers Panel
2. With the China Provinces layer ACTIVE, go to PROJECT | PROJECT PROPERTIES and UNCHECK the ON-THE-FLY Transform option
3. At this point the projection should transform to the CRS of the active layer
4. Click ZOOM FULL on the navigation toolbar
5. The Map View will attempt to view all the layers, but they will no longer be in compatible projection space!
6. The China data, being the only layer that matches the Project Layer (see the EPSG # 2333 for the Project in the STATUS BAR on the lower right), will be visible.
7. What this means is that the Project CRS has been set to the active layer, which will look proportionally correct, while the other data layers will be so tiny that they will be visible as a single dot, or not shown on the screen at all
1. Now go to PROJECT | PROJECT PROPERTIES and CHECK the ON-THE-FLY Transform option again
2. The data layers will be transformed to the CRS of the active layer (EPSG 2333)
3. Click ZOOM FULL on the navigation toolbar
4. The Map View will attempt to view all the layers, but in this example, the Egypt & Spain layers will be very distorted, because the WGS-84 CRS is not appropriate to be transformed to the China CRS
5. As shown in the STATUS BAR, the Project is set to EPSG:2333 a specialized Gauss Kruger Xian 1980 projection for China. That is why the other layers get distorted.
1. Finally, let us go to PROJECT | PROJECT PROPERTIES and reset the PROJECT PROJECTION to WGS-84
2. You should be able to see WGS-84 EPSG:4326 in the RECENTLY USED projections list
3. Click on the EPSG:4326 line and APPLY
4. The selected CRS should now read WGS 84
5. Click OK and then ZOOM FULL
6. The Map View should now seem more or less as we would expect it to, with the layers for Egypt, China, and Spain showing up in their relative positions.
7. That is one advantage of using the Global Geographic CRS, WGS-84, because it should work for a map containing elements from anywhere on the standard global grid, unlike a custom projection that will only work for a specific area of the world.
Proceed to Batch Reproject Instructions


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TOP was a perfect match for my learning style. Not walking me through an entire project but actually forcing me to figure it out on my own, by pointing me...
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TOP was a perfect match for my learning style. Not walking me through an entire project but actually forcing me to figure it out on my own, by pointing me...


LE FRANC A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): ** 1/2
If you've never seen films made by African filmmakers before, LE FRANC provides an interesting introduction. Written and directed by Senegal's Djibril Diop Mambéty, LE FRANC presents a Capraesque, good-spirited view of humanity.
As the story opens, Marigo (Dieye Ma Dieye) wakes up in his tiny room. Marigo is a dirt-poor musician with a big hangover. He uses his one pitcher of water to serve multiple purposes as he washes up. As he is trying to get ready for the day, his landlady starts yelling at him through the wall. He's six months behind in his rent, and she wants her money.
After sneaking out, his fortune finally turns. Picking up money that a guy drops on the ground, he uses it to buy a lottery ticket since today is the day of the big lottery. The rest of the story concerns his troubles in attempting to collect the big prize that he wins. Full of visual humor, the best is the door to which he has stuck the ticket. He carries the door all over town, including on the top of a bus.
The movie works on the level of a simple, short story as well as a travelogue to a poor area that few tourists will ever visit. You leave feeling that you know the people a little better even if the tale is a fairly universal one.
The most enjoyable part of the movie is unique. An instrument called the congoma provides the film's lovely Senegalese rhythms. Looking like an extra large shoebox, the brightly painted instrument has a dual purpose -- it also provides sanctuary for the local birds. Now, what other country would have such a charming instrument, serving as both birdcage and musical instrument?
LE FRANC runs 45 minutes. The film, which has almost no dialog, is in Wolof with English subtitles. It is not rated but would be a G since it has nothing to offend anyone.
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