Class mxGraphHierarchyModel
- java.lang.Object
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- com.mxgraph.layout.hierarchical.model.mxGraphHierarchyModel
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public class mxGraphHierarchyModel extends java.lang.Object
Internal model of a hierarchical graph. This model stores nodes and edges equivalent to the real graph nodes and edges, but also stores the rank of the cells, the order within the ranks and the new candidate locations of cells. The internal model also reverses edge direction were appropriate , ignores self-loop and groups parallels together under one edge object.
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Nested Class Summary
Nested Classes Modifier and Type Class Description static interface
mxGraphHierarchyModel.CellVisitor
Defines the interface that visitors use to perform operations upon the graph information during depth first search (dfs) or other tree-traversal strategies implemented by subclassers.
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Field Summary
Fields Modifier and Type Field Description protected int
dfsCount
Count of the number of times the ancestor dfs has been usedprotected java.util.Map<java.lang.Object,mxGraphHierarchyEdge>
edgeMapper
Map from graph edges to internal model edgesint
maxRank
Stores the largest rank number allocatedjava.lang.Object
parent
The parent cell whose children are being laid outjava.util.Map<java.lang.Integer,mxGraphHierarchyRank>
ranks
Mapping from rank number to actual rankjava.util.List<java.lang.Object>
roots
Store of roots of this hierarchy model, these are real graph cells, not internal cellsprotected java.util.Map<java.lang.Object,mxGraphHierarchyNode>
vertexMapper
Map from graph vertices to internal model nodes
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Constructor Summary
Constructors Constructor Description mxGraphHierarchyModel(mxHierarchicalLayout layout, java.lang.Object[] vertices, java.util.List<java.lang.Object> roots, java.lang.Object parent)
Creates an internal ordered graph model using the vertices passed in.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected void
createInternalCells(mxHierarchicalLayout layout, java.lang.Object[] vertices, mxGraphHierarchyNode[] internalVertices)
Creates all edges in the internal modelvoid
dfs(mxGraphHierarchyNode parent, mxGraphHierarchyNode root, mxGraphHierarchyEdge connectingEdge, mxGraphHierarchyModel.CellVisitor visitor, java.util.Set<mxGraphHierarchyNode> seen, int layer)
Performs a depth first search on the internal hierarchy modelvoid
dfs(mxGraphHierarchyNode parent, mxGraphHierarchyNode root, mxGraphHierarchyEdge connectingEdge, mxGraphHierarchyModel.CellVisitor visitor, java.util.Set<mxGraphHierarchyNode> seen, int[] ancestors, int childHash, int layer)
Performs a depth first search on the internal hierarchy model.void
fixRanks()
Fixes the layer assignments to the values stored in the nodes.int
getDfsCount()
java.util.Map<java.lang.Object,mxGraphHierarchyEdge>
getEdgeMapper()
java.util.Map<java.lang.Object,mxGraphHierarchyNode>
getVertexMapper()
void
initialRank()
Basic determination of minimum layer ranking by working from from sources or sinks and working through each node in the relevant edge direction.void
setDfsCount(int dfsCount)
void
setEdgeMapper(java.util.Map<java.lang.Object,mxGraphHierarchyEdge> edgeMapper)
void
setVertexMapper(java.util.Map<java.lang.Object,mxGraphHierarchyNode> vertexMapping)
void
visit(mxGraphHierarchyModel.CellVisitor visitor, mxGraphHierarchyNode[] dfsRoots, boolean trackAncestors, java.util.Set<mxGraphHierarchyNode> seenNodes)
A depth first search through the internal hierarchy model
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Field Detail
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maxRank
public int maxRank
Stores the largest rank number allocated
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vertexMapper
protected java.util.Map<java.lang.Object,mxGraphHierarchyNode> vertexMapper
Map from graph vertices to internal model nodes
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edgeMapper
protected java.util.Map<java.lang.Object,mxGraphHierarchyEdge> edgeMapper
Map from graph edges to internal model edges
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ranks
public java.util.Map<java.lang.Integer,mxGraphHierarchyRank> ranks
Mapping from rank number to actual rank
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roots
public java.util.List<java.lang.Object> roots
Store of roots of this hierarchy model, these are real graph cells, not internal cells
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parent
public java.lang.Object parent
The parent cell whose children are being laid out
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dfsCount
protected int dfsCount
Count of the number of times the ancestor dfs has been used
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Constructor Detail
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mxGraphHierarchyModel
public mxGraphHierarchyModel(mxHierarchicalLayout layout, java.lang.Object[] vertices, java.util.List<java.lang.Object> roots, java.lang.Object parent)
Creates an internal ordered graph model using the vertices passed in. If there are any, leftward edge need to be inverted in the internal model- Parameters:
layout
- the enclosing layout objectvertices
- the vertices for this hierarchy
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Method Detail
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createInternalCells
protected void createInternalCells(mxHierarchicalLayout layout, java.lang.Object[] vertices, mxGraphHierarchyNode[] internalVertices)
Creates all edges in the internal model- Parameters:
layout
- reference to the layout algorithmvertices
- the vertices whom are to have an internal representation createdinternalVertices
- the blank internal vertices to have their information filled in using the real vertices
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initialRank
public void initialRank()
Basic determination of minimum layer ranking by working from from sources or sinks and working through each node in the relevant edge direction. Starting at the sinks is basically a longest path layering algorithm.
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fixRanks
public void fixRanks()
Fixes the layer assignments to the values stored in the nodes. Also needs to create dummy nodes for edges that cross layers.
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visit
public void visit(mxGraphHierarchyModel.CellVisitor visitor, mxGraphHierarchyNode[] dfsRoots, boolean trackAncestors, java.util.Set<mxGraphHierarchyNode> seenNodes)
A depth first search through the internal hierarchy model- Parameters:
visitor
- the visitor pattern to be called for each nodetrackAncestors
- whether or not the search is to keep track all nodes directly above this one in the search path
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dfs
public void dfs(mxGraphHierarchyNode parent, mxGraphHierarchyNode root, mxGraphHierarchyEdge connectingEdge, mxGraphHierarchyModel.CellVisitor visitor, java.util.Set<mxGraphHierarchyNode> seen, int layer)
Performs a depth first search on the internal hierarchy model- Parameters:
parent
- the parent internal node of the current internal noderoot
- the current internal nodeconnectingEdge
- the internal edge connecting the internal node and the parent internal node, if anyvisitor
- the visitor pattern to be called for each nodeseen
- a set of all nodes seen by this dfs a set of all of the ancestor node of the current nodelayer
- the layer on the dfs tree ( not the same as the model ranks )
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dfs
public void dfs(mxGraphHierarchyNode parent, mxGraphHierarchyNode root, mxGraphHierarchyEdge connectingEdge, mxGraphHierarchyModel.CellVisitor visitor, java.util.Set<mxGraphHierarchyNode> seen, int[] ancestors, int childHash, int layer)
Performs a depth first search on the internal hierarchy model. This dfs extends the default version by keeping track of cells ancestors, but it should be only used when necessary because of it can be computationally intensive for deep searches.- Parameters:
parent
- the parent internal node of the current internal noderoot
- the current internal nodeconnectingEdge
- the internal edge connecting the internal node and the parent internal node, if anyvisitor
- the visitor pattern to be called for each nodeseen
- a set of all nodes seen by this dfsancestors
- the parent hash codechildHash
- the new hash code for this nodelayer
- the layer on the dfs tree ( not the same as the model ranks )
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getVertexMapper
public java.util.Map<java.lang.Object,mxGraphHierarchyNode> getVertexMapper()
- Returns:
- Returns the vertexMapping.
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setVertexMapper
public void setVertexMapper(java.util.Map<java.lang.Object,mxGraphHierarchyNode> vertexMapping)
- Parameters:
vertexMapping
- The vertexMapping to set.
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getEdgeMapper
public java.util.Map<java.lang.Object,mxGraphHierarchyEdge> getEdgeMapper()
- Returns:
- Returns the edgeMapper.
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setEdgeMapper
public void setEdgeMapper(java.util.Map<java.lang.Object,mxGraphHierarchyEdge> edgeMapper)
- Parameters:
edgeMapper
- The edgeMapper to set.
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getDfsCount
public int getDfsCount()
- Returns:
- Returns the dfsCount.
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setDfsCount
public void setDfsCount(int dfsCount)
- Parameters:
dfsCount
- The dfsCount to set.
-
-


Amazon Glacier is a long-term persistent file-storage system for cold data storage.
As a GUI wrapper for the
Glacier command line tools, The Simple Amazon Glacier Uploader aims to be an
upload and download solution that is as durable as your data.
SAGU is a single .jar file Glacier interface
written in Java for
cross-platform accessibility. The use of Java assures that you will have access to your files regardless of your
operating system
when it is time to retrieve your data.
2022/11/01 - Version 0.77.1 - Download
- Bump dependency versions - mainly because of AWS SDK for Java version numbers from 1.9.5 to 1.10.31 are incompatible with TLS 1.3.
2020/09/21 - Version 0.77.0 - Download
- Add new region: South America (São Paulo)
Kudos to Eduardo Gomez Catalan
2018/12/09 - Version 0.76.0 - Download
- Add new regions: EU (Frankfurt, EU (London), EU (Paris), Asia Pacific (Seoul), Asia Pacific (Osaka-Local), Asia Pacific (Mumbai), Asia Pacific (Singapore), China (Beijing), China (Ningxia), Canada (Central), US East (Ohio), AWS GovCloud (US)
2016/02/07 - Version 0.75.0
- Upgrade to Java version 8 - Upgrade to Java version 8 (i.e. Java 8 is required).
- Redesign upload window. Add progress bar of current file.
- If properties not present in working dir, use '.sagu' dir in user's home.
- Add --properties-dir command line argument.
- Add escaping to YAML logs.
- Add enclosing of values to CSV log. Values in CSV log are now enclosed in double quotes. Double quotes in values
are being escaped by second double quote.
- Remove trailing comma from CSV logs. It's forbidden in CSV specification. See RFC 4180.
- One can now easily traverse between input fields and buttons using tab key.
2015/10/13 - Version 0.74.7
-Implemented logging to the well parsable YAML format
-Error Message is displayed if log file to open does not exist
-Code enhancements courtesy Fensterbank
-Additional endpoints and refactorings by Libor Rysavy
-Fixes by richardneish and philosophicles
-Dependency update
2013/08/02 - Version 0.74.6
-Update all dependencies
-Add something of a progressbar on multi-file uploads courtesy GitHubber greenwoodma
(https://github.com/greenwoodma)
-Code enhancements to swing functions courtesy greenwoodma
2012/12/14 - Version 0.74.5
-Regex clean file description for upload. This should reduce InvalidSignatureExceptions overall.
2012/12/04 - Version 0.74.4
-Fix issue with errors downloading from servers other than US East.
-Updated AWS SDK to 1.3.26
-Fixed bug listing only the first 10 vaults on a server.
2012/10/03 - Version 0.74.3
-Bug-patch: File descriptions under 0.74.2 were getting uploaded with a "?" character as a result of the Unicode
cleaning. Fixed. Check Glacier.txt and Glacier.csv for file descriptions of files uploaded under 0.74.2.
2012/10/02 - Version 0.74.2
-Increased length of time before socket timeout and doubled number of retries. Should reduce timeout errors for
large files on slower connections.
-Upload errors are now logged to GlacierErrors.txt.
-Clean non-unicode characters from upload file path description to prevent possible signature exception.
2012/09/24 - Version 0.74.1
-Inventory request is saved as format "[vaultname][requesteddatetime].txt" now.
-Minor misc. fixes.
2012/09/22 - Version 0.74
-Request a vault inventory from AWS. Current version is rough. Saves unparsed response to file.
(NB: This is not a replacement for maintaining a local log file. Amazon does not store named
files, only ArchiveID's. Archives uploaded with previous versions of SAGU have included the filename as a
descriptor, but archives uploaded with other programs may not. Use the inventory retrieval tool to acquire the
Archive ID's within a particular vault. It takes AWS approximately 4 hours to prepare your inventory for
download.)
-Various fixes.
-Updated AWS Java SDK to 1.3.21.
2012/09/20 - SAGU goes Git!
-Development stall for a couple of days while I learned Git.
-Check out the public repository here:
SAGU Source at Github
2012/09/15 - Version 0.73
-Masked secret key.
-Added a button to refresh vaults.
-Added logo.
2012/09/11 - Version 0.72
-Updated AWS SDK to 1.3.19.
-Removed 4GB file limit. The updated SDK should allow for files larger than 4GB. Please report errors.
2012/09/09 - Version 0.71
-Enhancements to upload bar. See what's uploading and what's done.
-Program now regenerates tree-hashes more frequently. Should result in fewer timeouts.
-Bug-fixes, code cleaning.
2012/09/06 - Version 0.7
-The threaded update. UI no longer sticks during upload/download.
-Indeterminate progressbar added. To be enhanced in upcoming releases.
-Batch multiple upload requests at once, even to different servers.
-Have several download requests processing at once.
-UI tweaks and graphical assets.
-SAGU now creates a Glacier.log file, a Glacier.txt file, and a Glacier.csv file by default.
-Glacier.log files are intended to be parsed by future versions of the program and for export. .txt files and .csv
files are for viewing convenience.
-OSX users should select the option to view .txt files to prevent logs from opening in the console.
-Known Bug: Progress bar disappears during multiple uploads if one completes first. This is
cosmetic. The process still continues in the background until completed or until an error is received. This will be
corrected in an update.
2012/09/04 - Version 0.62
-Select upload vault from existing. Selecting a server location populates a dropdown with existing vaults on that
server.
2012/09/02 - Version 0.61
-Ability to export logs.
-Improved file selection for download mode in OSX.
-Choose log output (.log, .txt, .csv)
-Save log output preferences and reload on restart.
2012/08/31 - Version 0.6
-UI redesign.
-Download support (Currently Experimental - Unthreaded)
-Create vault.
-Updated AWS libraries.
2012/08/29 - Version 0.52
-Bug-Fixes.
2012/08/28 - Version 0.51
-Better multi-file support.
-Better error handling.
-Files are now being hosted on dropbox to take some load off of my shared hosting.
2012/08/27 - Version 0.5.
-Added multiple file upload with drag and drop support. Be careful with this. Glacier is not a second hard drive, it
works by uploading single files and downloading or deleting by ArchiveID. Amazon does not provide a method to
retreive ArchiveID's right now, so your log files are your access to your files. I would recommend zipping large
numbers of files together and uploading them as a single file.
-Even better logging! Switched from DataOutput UTF to Writer. Logs should now have standardized layouts and no
machine characters. (I recommend saving your old log elsewhere and letting the program generate a new one. Your old
log is fine, but some text viewers get confused by dataoutput.)
-Added simple notification bar that appears when program is uploading. Progress bar and threading planned in near
future.
2012/08/26 - Version 0.4.
-Added delete archive function. Set AWS Keys and Location/Vault, then insert Archive ID to delete.
-Saves preferences to "SAGU.properties" in launch folder and reloads settings on launch.
-Built with Java 1.6 libraries. Java 7 no longer required.
-Set executable bit for Linux/OSX.
-Maintenance fix to logging. ArchiveID's logged under v0.2-0.3 contain an extra "Š" character at the beginning which
was a rogue Java artifact. (SAGU will automatically handle this if entered, but users should remove the antecedent Š
in early logs if using the archiveID with other utilities.)
2012/08/25 - Version 0.3.
-Direct links to AWS Key Page, Glacier Vault Creation, Log file, SAGU Update site.
-Enable right-click to copy/paste.
2012/08/24 - Version 0.2.
-Upload Logging!
-Logging is enabled by default. Saves to Glacier.log in program directory.
-Log contains Archive ID, Filename, Vault Name, Upload Date
-ArchiveID will be useful for deleting or retreiving individual archives.
2012/08/23 - Version 0.1.
-Ability to upload a single file at a time to the Glacier service.
-Requires Security Credentials from Amazon AWS Console.
https://portal.aws.amazon.com/gp/aws/securityCredentials
-Requires you to create a vault at the Glacier Console.
https://console.aws.amazon.com/glacier/home
-Select hosting location via dropdown.
-Select single file.
-Upload file to selected vault/server.
-(Amazon Glacier inventory is updated approximately once per day. It may take 24 hours for your files to
appear.)
SAGU Source Available at Github
GNU General Public License Version 3
Report errors or contact: Project issues
Simple Amazon Glacier Uploader is free and always will be.
Simple Amazon Glacier Uploader is not in any way affiliated with Amazon Web Services or Amazon.com.


String Monoid Elements#
AUTHORS:
David Kohel<EMAIL_ADDRESS>2007-01
Elements of free string monoids, internal representation subject to change.
These are special classes of free monoid elements with distinct printing.
The internal representation of elements does not use the exponential compression of FreeMonoid elements (a feature), and could be packed into words.
- class sage.monoids.string_monoid_element.StringMonoidElement(S, x, check=True)#
Bases:
FreeMonoidElement
Element of a free string monoid.
- character_count()#
Return the count of each unique character.
EXAMPLES:
Count the character frequency in an object comprised of capital letters of the English alphabet:
sage: M = AlphabeticStrings().encoding("abcabf") sage: sorted(M.character_count().items()) [(A, 2), (B, 2), (C, 1), (F, 1)]
In an object comprised of binary numbers:
sage: M = BinaryStrings().encoding("abcabf") sage: sorted(M.character_count().items()) [(0, 28), (1, 20)]
In an object comprised of octal numbers:
sage: A = OctalStrings() sage: M = A([1, 2, 3, 2, 5, 3]) sage: sorted(M.character_count().items()) [(1, 1), (2, 2), (3, 2), (5, 1)]
In an object comprised of hexadecimal numbers:
sage: A = HexadecimalStrings() sage: M = A([1, 2, 4, 6, 2, 4, 15]) sage: sorted(M.character_count().items()) [(1, 1), (2, 2), (4, 2), (6, 1), (f, 1)]
In an object comprised of radix-64 characters:
sage: A = Radix64Strings() sage: M = A([1, 2, 63, 45, 45, 10]); M BC/ttK sage: sorted(M.character_count().items()) [(B, 1), (C, 1), (K, 1), (t, 2), (/, 1)]
- coincidence_index(prec=0)#
Returns the probability of two randomly chosen characters being equal.
- decoding(padic=False)#
The byte string associated to a binary or hexadecimal string monoid element.
EXAMPLES:
sage: S = HexadecimalStrings() sage: s = S.encoding("A..Za..z"); s 412e2e5a612e2e7a sage: s.decoding() 'A..Za..z' sage: s = S.encoding("A..Za..z",padic=True); s 14e2e2a516e2e2a7 sage: s.decoding() '\x14\xe2\xe2\xa5\x16\xe2\xe2\xa7' sage: s.decoding(padic=True) 'A..Za..z' sage: S = BinaryStrings() sage: s = S.encoding("A..Za..z"); s<PHONE_NUMBER>101110001011100101101001100001001011100010111001111010 sage: s.decoding() 'A..Za..z' sage: s = S.encoding("A..Za..z",padic=True); s<PHONE_NUMBER>110100011101000101101010000110011101000111010001011110 sage: s.decoding() '\x82ttZ\x86tt^' sage: s.decoding(padic=True) 'A..Za..z'
- frequency_distribution(length=1, prec=0)#
Returns the probability space of character frequencies. The output of this method is different from that of the method
characteristic_frequency()
. One can think of the characteristic frequency probability of an element in an alphabet \(A\) as the expected probability of that element occurring. Let \(S\) be a string encoded using elements of \(A\). The frequency probability distribution corresponding to \(S\) provides us with the frequency probability of each element of \(A\) as observed occurring in \(S\). Thus one distribution provides expected probabilities, while the other provides observed probabilities.INPUT:
length
– (default1
) iflength=1
then consider the probability space of monogram frequency, i.e. probability distribution of single characters. Iflength=2
then consider the probability space of digram frequency, i.e. probability distribution of pairs of characters. This method currently supports the generation of probability spaces for monogram frequency (length=1
) and digram frequency (length=2
).prec
– (default0
) a non-negative integer representing the precision (in number of bits) of a floating-point number. The default valueprec=0
means that we use 53 bits to represent the mantissa of a floating-point number. For more information on the precision of floating-point numbers, see the functionRealField()
or refer to the modulereal_mpfr
.
EXAMPLES:
Capital letters of the English alphabet:
sage: M = AlphabeticStrings().encoding("abcd") sage: L = M.frequency_distribution().function() sage: sorted(L.items()) [(A, 0.250000000000000), (B, 0.250000000000000), (C, 0.250000000000000), (D, 0.250000000000000)]
The binary number system:
sage: M = BinaryStrings().encoding("abcd") sage: L = M.frequency_distribution().function() sage: sorted(L.items()) [(0, 0.593750000000000), (1, 0.406250000000000)]
The hexadecimal number system:
sage: M = HexadecimalStrings().encoding("abcd") sage: L = M.frequency_distribution().function() sage: sorted(L.items()) [(1, 0.125000000000000), (2, 0.125000000000000), (3, 0.125000000000000), (4, 0.125000000000000), (6, 0.500000000000000)]
Get the observed frequency probability distribution of digrams in the string “ABCD”. This string consists of the following digrams: “AB”, “BC”, and “CD”. Now find out the frequency probability of each of these digrams as they occur in the string “ABCD”:
sage: M = AlphabeticStrings().encoding("abcd") sage: D = M.frequency_distribution(length=2).function() sage: sorted(D.items()) [(AB, 0.333333333333333), (BC, 0.333333333333333), (CD, 0.333333333333333)]
- sage.monoids.string_monoid_element.is_AlphabeticStringMonoidElement(x)#
- sage.monoids.string_monoid_element.is_BinaryStringMonoidElement(x)#
- sage.monoids.string_monoid_element.is_HexadecimalStringMonoidElement(x)#
- sage.monoids.string_monoid_element.is_OctalStringMonoidElement(x)#
- sage.monoids.string_monoid_element.is_Radix64StringMonoidElement(x)#
- sage.monoids.string_monoid_element.is_StringMonoidElement(x)#


|
PMDK C++ bindings
1.2.0
This is the C++ bindings documentation for PMDK's libpmemobj.
|
Persistent memory aware allocator. More...
#include <libpmemobj++/detail/common.hpp>
#include <libpmemobj++/detail/life.hpp>
#include <libpmemobj++/persistent_ptr.hpp>
#include <libpmemobj++/pexceptions.hpp>
#include <libpmemobj++/pext.hpp>
#include <libpmemobj.h>
Go to the source code of this file.
Classes | |
| class | pmem::obj::object_traits< T > |
| Encapsulates object specific allocator functionality. More... | |
| struct | pmem::obj::object_traits< T >::rebind< U > |
| Rebind to a different type. More... | |
| class | pmem::obj::object_traits< void > |
| Object traits specialization for the void type. More... | |
| struct | pmem::obj::object_traits< void >::rebind< U > |
| Rebind to a different type. More... | |
| class | pmem::obj::standard_alloc_policy< T > |
| The allocation policy template for a given type. More... | |
| struct | pmem::obj::standard_alloc_policy< T >::rebind< U > |
| Rebind to a different type. More... | |
| class | pmem::obj::standard_alloc_policy< void > |
| Void specialization of the standard allocation policy. More... | |
| struct | pmem::obj::standard_alloc_policy< void >::rebind< U > |
| Rebind to a different type. More... | |
| class | pmem::obj::allocator< T, Policy, Traits > |
| (EXPERIMENTAL) Encapsulates the information about the persistent memory allocation model using PMDK's libpmemobj. More... | |
| struct | pmem::obj::allocator< T, Policy, Traits >::rebind< U > |
| Rebind to a different type. More... | |
Functions | |
| template<typename T , typename T2 > | |
| bool | pmem::obj::operator== (standard_alloc_policy< T > const &, standard_alloc_policy< T2 > const &) |
| Determines if memory from another allocator can be deallocated from this one. More... | |
| template<typename T , typename OtherAllocator > | |
| bool | pmem::obj::operator== (standard_alloc_policy< T > const &, OtherAllocator const &) |
| Determines if memory from another allocator can be deallocated from this one. More... | |
| template<typename T , typename P , typename Tr , typename T2 , typename P2 , typename Tr2 > | |
| bool | pmem::obj::operator== (const allocator< T, P, Tr > &lhs, const allocator< T2, P2, Tr2 > &rhs) |
| Determines if memory from another allocator can be deallocated from this one. More... | |
| template<typename T , typename P , typename Tr , typename OtherAllocator > | |
| bool | pmem::obj::operator!= (const allocator< T, P, Tr > &lhs, const OtherAllocator &rhs) |
| Determines if memory from another allocator can be deallocated from this one. More... | |
Persistent memory aware allocator.
(EXPERIMENTAL)
|
inline |
Determines if memory from another allocator can be deallocated from this one.
| [in] | lhs | left hand side allocator. |
| [in] | rhs | right hand side allocator. |
|
inline |
Determines if memory from another allocator can be deallocated from this one.
|
inline |
Determines if memory from another allocator can be deallocated from this one.
|
inline |
Determines if memory from another allocator can be deallocated from this one.
| [in] | lhs | left hand side allocator. |
| [in] | rhs | right hand side allocator. |


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German footballer given 3-year ban, his club a six-month suspension, after punching referee
Officials feel the ban could well end up being a lifetime one.
by AFPA REGIONAL LEAGUE footballer in Germany was today banned for three years, and his club suspended for six months, after he punched a referee who had just sent him off.
The player from FSV Muenster has been charged with assault and banned from playing by the governing body in Hesse for punching the referee in a regional league game against TV Semd last month.
The official was knocked out by the punch and had to be airlifted to hospital.
The Muenster club in west Germany has also been suspended from playing for six months and fined €500.
“Something like that does not belong on a sports pitch and the three-years are fully justified, it was the highest possible penalty,” Theodor Greiner of the Hessian Football Association (HFV), told AFP.
Greiner suspects the three-year ban handed to the player “could well be changed to life” when the HFV meets at the weekend.
FSV Muenster immediately suspended their team from playing after the incident and the club’s president has said the player responsible will be held liable for the fine.
Yesterday, there was a separate violent incident in the German lower leagues when a cup semi-final in south-west Germany was called off after an assistant referee was knocked to the ground during the match.


0.0.0¶ ↑
Feautures: - Partially working async, gotta tweak with it more, credit goes very much to github.com/freenet/lib-rubyFreenet/ in the design of ECommunicator sock_thrd and connect
0.0.0¶ ↑
I was feeling bad about using very near code, well to make mine function as it should I had to stray quite a bit, probably gonna need to create two clients along with the two communicators to keep code clean.
0.0.1¶ ↑
-
Gemspec format now, using proper paths and such, gem is this sources build
-
still going to be adding features, Rsite will be its own seperate project, fput and fget are the sample programs
0.0.2¶ ↑
-
added keep alive and a couple more message formats
-
have completed every thing except for plugin messages
-
also added auto keep alive sending of void messages, adjust the value in Communicator#heartbeat default is 300
0.0.3¶ ↑
-
huge change, removed a entire tool/ class, now with hashmaps and fcp_client method only, any one should now be able to mangle their own packets using just hashes
0.0.6¶ ↑
-
Gonna have to start playing by release rules of rubygems, added direct handling for get, should now work as expected and be able to handle multiple direct gets
0.0.9¶ ↑
-
Reduced complexity through inheirtence


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rails_best_practices output
Please go to
http://rails-bestpractices.com
to see more useful Rails Best Practices.
Found 77 errors.
Filename
Line Number
Error Message
./app/controllers/confirmations_controller.rb
18
move model logic into model (@confirmable use_count > 4)
./app/controllers/versions_controller.rb
12
move model logic into model (@version use_count > 4)
./app/views/authentications/show.html.haml
11
law of demeter
./app/views/educations/show.html.haml
32
law of demeter
./app/views/positions/index.html.haml
25
law of demeter
./app/views/positions/show.html.haml
34
law of demeter
./app/views/positions/show.html.haml
38
law of demeter
./app/views/queries/show.html.haml
18
law of demeter
./app/views/visits/show.html.haml
13
law of demeter
./db/schema.rb
16
always add db index (authentications => [user_id])
./db/schema.rb
37
always add db index (educations => [user_id])
./db/schema.rb
64
always add db index (positions => [company_id])
./db/schema.rb
64
always add db index (positions => [user_id])
./db/schema.rb
93
always add db index (queries => [user_id])
./db/schema.rb
145
always add db index (taggings => [tagger_id, tagger_type])
./db/schema.rb
162
always add db index (urls => [user_id])
./db/schema.rb
170
always add db index (users => [invited_by_id, invited_by_type])
./db/schema.rb
221
always add db index (users_friends => [user_id])
./db/schema.rb
221
always add db index (users_friends => [friend_id])
./db/schema.rb
244
always add db index (visits => [user_id])
./app/controllers/registrations_controller.rb
3,21
use before_filter for create,new
./app/controllers/versions_controller.rb
8,12
use before_filter for show,revert
./app/views/kaminari/_paginator.html.haml
14
move code into model (page use_count > 2)
./app/views/devise/registrations/_thankyou.html.erb
1
replace instance variable with local variable
./app/views/devise/shared/_links.html.erb
28
replace instance variable with local variable
./app/views/devise/shared/_links.html.erb
28
replace instance variable with local variable
./app/views/devise/shared/_links.html.erb
32
replace instance variable with local variable
./app/views/authentications/_form.html.haml
1
replace instance variable with local variable
./app/views/authentications/_form.html.haml
2
replace instance variable with local variable
./app/views/authentications/_form.html.haml
4
replace instance variable with local variable
./app/views/authentications/_form.html.haml
6
replace instance variable with local variable
./app/views/companies/_form.html.haml
1
replace instance variable with local variable
./app/views/educations/_form.html.haml
1
replace instance variable with local variable
./app/views/newsletters/_form.html.haml
1
replace instance variable with local variable
./app/views/queries/_form.html.haml
1
replace instance variable with local variable
./app/views/urls/_form.html.haml
1
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
4
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
4
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
6
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
7
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
7
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
9
replace instance variable with local variable
./app/views/users/_marker_template.html.haml
10
replace instance variable with local variable
./app/views/visits/_form.html.haml
1
replace instance variable with local variable
./app/views/authentications/edit.html.haml
10
simplify render in views
./app/views/companies/edit.html.haml
10
simplify render in views
./app/views/companies/new.html.haml
10
simplify render in views
./app/views/educations/edit.html.haml
10
simplify render in views
./app/views/educations/new.html.haml
10
simplify render in views
./app/views/newsletters/edit.html.haml
12
simplify render in views
./app/views/newsletters/new.html.haml
11
simplify render in views
./app/views/positions/edit.html.haml
10
simplify render in views
./app/views/positions/new.html.haml
10
simplify render in views
./app/views/queries/edit.html.haml
10
simplify render in views
./app/views/queries/new.html.haml
10
simplify render in views
./app/views/urls/edit.html.haml
10
simplify render in views
./app/views/urls/new.html.haml
10
simplify render in views
./app/views/users/new.html.haml
10
simplify render in views
./app/views/visits/edit.html.haml
10
simplify render in views
./app/views/visits/new.html.haml
10
simplify render in views
./app/controllers/registrations_controller.rb
18
simplify render in controllers
./app/helpers/authentications_helper.rb
1
remove empty helpers
./app/helpers/companies_helper.rb
1
remove empty helpers
./app/helpers/educations_helper.rb
1
remove empty helpers
./app/helpers/home_helper.rb
1
remove empty helpers
./app/helpers/import_helper.rb
1
remove empty helpers
./app/helpers/moderator_helper.rb
1
remove empty helpers
./app/helpers/newsletters_helper.rb
1
remove empty helpers
./app/helpers/positions_helper.rb
1
remove empty helpers
./app/helpers/queries_helper.rb
1
remove empty helpers
./app/helpers/registrations_helper.rb
1
remove empty helpers
./app/helpers/urls_helper.rb
1
remove empty helpers
./app/helpers/visits_helper.rb
1
remove empty helpers
./app/mailers/user_mailer.rb
3
remove trailing whitespace
./app/controllers/confirmations_controller.rb
16
remove trailing whitespace
./config/initializers/1_app.rb
6
remove trailing whitespace
./config/initializers/rolify.rb
4
remove trailing whitespace


Math
X Words Jumble And Guess
Score:
Please Click The Start Button To Play...
Gues the word
Developer: Erick A. Golo


@() @main("Welcome") { @defining(play.core.PlayVersion.current) { version =>
Test which is a new approach to have all solutions


--- layout: post title: no title date: '2015-07-14T20:04:15+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/124112607166 ---


Recently graduated geek
Engineer at BillPin
Organiser at GeekcampSG
Heads and contributes to a bunch of other user groups
3 years
Moved a company to Git
Still have tons to learn
What can Git do?
How do you fit it into your workflow
Practical Demonstration with Git
Before we answer that ...
(Angry birds not counted)
Git works like your checkpoint system in computer games
Have checkpoints along the way
And when you do something really stupid
Go back to your saved game and resume
No more trying to figure out what you edited
Copies an existing repository onto your machine
git clone git@github.com:laurenceputra/Orbit-Git-Workshop.git
Adds files you have changed to the changes that need to be staged
git add folder/*
OR
git add -A


You just got the best present or your birthday: A marble run!
A marble run kit consists of a number of pieces. Each piece goes slightly downhill so that the marble continues moving. Each piece has 1 entrance and 0 or more exits. If the piece has more than 1 exit, it randomly ends up in one of them. There are also fancier pieces that take longer than others, because they have a swirl, go back and forth, etc..
Example PiecesThere is going to be exactly one entrance to your marble coaster, and at least 1 exit. Your mom gets mad at you and says that it's dangerous to put more than one marble in the run at a time. So, you want to calculate the maximum time it will take for a marble to reach the end. An end of the run is defined as completely moving through a piece that has 0 exits.
The first line of input will contain a single integer R, the number of runs you want to make.
The first line of each run contains a single integer N, the number of pieces in the run. The first piece in the run is always the starting piece
N lines follow, representing the list of pieces in the run.
Each piece consists of a single line with at least 1 integer. The first integer is the time T it takes to move from the entrance to any of the exits of the piece. Each of the remaining integers on the line specifies an exit for the piece. The integer specifies which other piece's entrance that exit goes to, with the integer representing the 0-indexed piece in the run. It is guaranteed each piece except the start has exactly one piece going into it, and the start piece has 0 pieces going into it.
1 ≤ R ≤ 10
1 ≤ T ≤ 1000
1 ≤ N ≤ 1000
1 ≤ E < N
For each test case, print the maximum amount of time before the marble hits an exit.
2 3 4 2 6 3 1 4 5 1 2 2 3 7 3
13 12
In the first case, you start at piece 0. It takes the marble 4 seconds to reach the exit of piece 0. Then, it goes to the entrance of piece 2. In piece 2, the marble takes 3 seconds to reach the exit, then it goes to piece 1. Piece 1 takes 6 seconds, and has no exit, so the marble stops. This results in a total time of 4 + 3 + 6 = 13.
In the second case, you start at piece 0. It takes the marble 5 seconds to reach the exit. Then it either goes to piece 1 or piece 2. If it goes to piece 1, it takes 2 seconds to reach the exit, ad then goes to piece 3. It takes 3 seconds to reach the end of piece 3, at which point it stops. If it instead went to piece 2, it takes 7 seconds to reach the exit, and then stops. Thus, the total time is 5 + max(2 + 3, 7) = 5 + 7 = 12.


word LocalCustomParseDateTime( const char * str, DateTimeFormat format, TimerDateAndTime * dateTime);
This routine parses a date and time string by comparing it with the passed
DateTimeFormat
. It fills in the fields of the
TimerDateAndTime
structure. Any fields which are not specified in the format string will be filled with -1.
If the string parses correctly,
LocalCustomParseDateTime()
returns -1. Otherwise it reutrns the offset to the start of the text which did not parse correctly.
Include: localize.h
WWFixedAsDword LocalDistanceFromAscii( const char * buffer, DistanceUnit distanceUnits, MeasurementTypes measurementType);
This routine takes a function like "72 pt" and returns a number representing the distance. The returned answer represents the measure in points, inches, centimeters, or some other measure as specified by the passed unit.
Include: localize.h
word LocalDistanceToAscii( /* Length of string, including NULL */ char * buffer, /*Buffer to save formatted text in */ word value, DistanceUnit distanceUnits, MeasurementType measurementType);
This routine takes a distance and a set of units and returns a string containing a properly formatted distance.
Include: localize.h
Boolean LocalDosToGeos( char * str, word strSize, word defaultChar);
Convert a DOS string to GEOS text. Any characters for which there is no GEOS equivalent will be replaced by the passed default character.
Include: localize.h
word LocalDosToGeosChar( word ch, word defaultChar);
Convert a DOS character to GEOS text. Any characters for which there is no GEOS equivalent will be replaced by the passed default character.
Include: localize.h
word LocalDowncaseChar( word ch);
Return the lower case equivalent, if any, of the passed character.
Include: localize.h
void LocalDowncaseString( char * str, word size); /* Size of string, in bytes */
Convert the passed string to its all lower case equivalent.
Include: localize.h
void LocalFixedToAscii( char * buffer, WWFixedAsDWord value, word fracDigits);
This routine returns the ASCII expression of a fixed point number.
Include: localize.h
word LocalFormatDateTime( /* Length of returned string */ char * str, DateTimeFormat format, const TimerDateAndTime * dateTime);
This routine returns the string (e.g. "9:37") corresponding to the passed DateAndTime.
Include: localize.h
word LocalFormatFileDateTime( char *str, DateTimeFormat format, const FileDateAndTime *dateTime);
This utility routine provides a simple way to present a
FileDateAndTime
structure such as used with file-related operations.
Structures:
typedef struct { byte numberFormatFlags; /* NumberFormatFlags value */ byte decimalDigits; wchar thousandsSeparator; wchar decimalSeparator; wchar listSeparator; } LocalNumericFormat;
The first byte is a
NumberFormatFlags
field specifying whether or not to use a leading zero. The second byte is the number of digits to show after the decimal point.
Include: localize.h


About
Hi friends,
I graduated from VNU-UET in 2014.
I have been working in computer software engineering since 2014.
I am a passionate software developer also full-stack developer with my favorite languages are PHP, Rails and Javascript, VueJS and ReactJS... I am also a DevOps Engineer with using Docker, AWS, Kubernetes at work.
I am responsible and adaptive, humble to learn and try the technology at work.
With me, a dream developer where I want to build and scale a better system to serve customers and users across the world.
Hobbies: Reading book, Music, New Technology, Walking.
Fullstack Developer & Web Developer.
Software developer with passion and integrity hard work with more than 7 years of experiences and a variety of different domains in software projects.
- Birthday: 27 Sep 1992
- Website: www.zmerrychristmas.github.io
- Phone: +84 965 841 492
- City: Da Nang, VN
- Age: 30
- Degree: Bachelor
- PhEmailone<EMAIL_ADDRESS>- Freelance: Available
Have hands-on experience with the back-end side and database handling, web base integration with infrastructure such as CI/CD, Log, AWS services.
Have experience in building web-based systems such as booking, dashboard management, tourisment, visual view and evaluating projects. Developing the app with Japanese customers, integrating payment, building app extensions shopify, and developing server-side with well technology and highly user experience.
I can work independently and under pressure with high discipline and stability.
Good solution-oriented skills, problem-solving skills, teamwork spirit, humility, and friendly.
Facts
Problem Solver, Reponsible and Reliable at work. Passionate and Balancer between work and life. Avaible at Freelancer. I have work at Upwork.com for a year in 2021. Can found me at here
Happy Clients reponse
Projects positive response
Hours Of Support to clients
Hard Workers and collaborate
Skills
Overall skills on Web Development and Rest Api Development.
Resume
Highly experienced and skilled in PHP, Ruby on Rails and Laravel, RESTful API as well as Python, Golang, VueJS, DevOps, and other frameworks.
Sumary
Leo Nguyen
Innovative and deadline-driven Senior Software Developer with 7+ years of experience designing and developing web and rest api development from initial concept to completed build, highly accurate and rich features.
- (84) 965-841492
-<EMAIL_ADDRESS>Education
Bachelor of Computer Software & Networking
2010 - 2014
VNU - University of Engineering and Technology
Computer Software Engineer & Networking
- Computer software development - Algorithms and Datastructures - Security mechanism and role management
Certificates & Awards
Certificate of Center For Cooperating And Transferring Knowledge
2013
VNU - University of Engineering and Technology
Problem Solving (Basic) Certificate
2022
Hackkerank
Rest API (Intermediate) Certificate
2022
Hackkerank
Sales Pop
2017
Shopify
Top free app extension on shopify.Give a cutie notice but helpful content to help increase cross-sell and coverage of shop applications like buy, and advertisement.
Learning Math Android App
2013-2014
A consolation prize for a written Android app to help kids learn math
Pastimes & Activities
Passionate with programming and technologies. Listening to music and watching movies. Do Taichi and practical kendo to balance work time and lifetime. I like reading manga and anime. I volunteered at the library for students when I studied at university. Hard core gamer, upload video game on Youtube with have 200+ follower and thousands views.
Confidential Projects
PHP Developer - Rails Developer
- The projects are about developing web-based, features and maintaining websites for products such as e-commerce, Restful-Api, LMS, news, applications: http://izziedu.vn, Cenports Inc. Pentalog, http://ototreem.vn, property.starktech.jp … and other products as LMS education domain.
- Study and clarify requirements from customers.
- Build and develop project features, Write code and debug code, and code review.
Professional Experience
Senior Software Developer
11-2021 - Present
CMC
- Senior backdend developer specialist on E-commerce projects, Large and complex but enough of enthusiasm and passionate. Role for maintain and development with high features delivery with international team.
- Hands on task from backend in infrastures with effective code in e-commerce fashion industry. Team size from 15 to 50 members.
- Developed and presented findings and solutions to audiences including senior executives and stakeholders.
- Brought forth a passion and dedication to software development.Worked on internal and external projects with great care.
Fullstack Developer Specialist
2021
Upwork.com & zmerrychristmas.github.io
- Developed numerous website and backend rest api (Booking, Visual Admin Dashboard, Google SEO API, Youtube API, Agora.io, ...).
- More than 5+ projects or tasks at a given time while under pressure
-
makerspace-staging.vinova.sg, a website system to manage office bookings and office equipment such as tables and chairs, rooms, event organizers
- Tech:Docker , RoR, PostgressSQl
SugarOKR: Website to manage the progress of the company's team, with a set of tools to help managers have a visualization view and evaluate the project.
- Tech: Docker, RoR, PostgressSQl
- Tech: PHP, WordPress theme, Javascript, CSS
- Tech: PHP, Laravel 8, Agora.io, Javascript, CSS
Senior Backend Developer
Apr 2019 - Oct 2020
Rikkeisoft.Inc
- Coding platform challenger: Ricode is an open programming platform with a website interactive with students and those who love programming. URL: https://ricode.rikkei.org/. My role is to design architecture, build a compiler tool and track project progress, maintain and work with tasks.
- Position: Technical leader, Senior backend developer - Tech: Docker, RoR, Django, Mysql - Tools: Sublime text, Docker tools
Senior Backend Developer
Feb 2018 - Mar 2019
Wefit
- Wefit is a great startup for booking, reservation, and registration for gyms and spas. I have learned about expertise and soft skills
- Position: Fullstack developer.
- Projects: Wefit payment and maintenance.
- Responsible: Integrate payment with Napas payment. Blog website introduces and advertises news and fashion trends among young people.
- Tech:Ruby on Rails, Ruby, Laravel, Docker, Kubernetes, PostgreSQL - Tools: Sublime text, Docker, Kubernetes, google cloud, pgadmin
Backend Developer
Aug 2016 - Jan 2018
DCV JSC
- Building extensions, popular apps for e-commerce platforms: Shopify, woocommerce
- Technology: - Languages: PHP, JSON, Git Commit Framework: Symfony2, Vue.js
app: Sales Pop on Shopify. 100% Free but trusted by more than 658,000 online businesses all over the world - Department and a delivery partner of other companies like Panasonic, Innovatube, and Toshiba. The projects are about developing and maintaining websites for products from izziedu.vn, Cenports Inc, Pentalog, ototreem.vn, property.starktech.jp, and other products like learning management system domain.
- Write plugins and maintain source code for the project. Specifically redmine plugin at Panasonic inc.
- Write backend side on a project about construction engineers and management contractors at Innovatube inc.
- Position: Senior Dev PHP, Rails - Tech: Rails plugins, PHP, Laravel, MySQL, HTML, JS - Tools: Sublime Text, PHP Storm, Git, Mysql Workbench
Software Progamer
Jun 2014 - Jul 2016
Altplus.Inc
- Internship: During the internship, I familiarize myself with some PHP frameworks such as CodeIgniter, Magento, Bootstrap, familiarize with e-commerce, and write extensions for Magento. After Internship attend to a project with role of Software Develop I
-
Develop and Build the Server-side for the game Derby Road. Logic process in game about horse racing, help to respond requests API to client- Analyzed and designed new systems and applications.
- Technology:+ Languages: PHP, JSON, Git - Framework: Confidential Framework PHP, Laravel
- Develop and Build the server-side for Livmo app; Grab content, news from websites to building RSS index server.
- Impressive and design back to requirements - Technology:Ruby, SQL, Ruby On Rails, AWS Elastich Search. - Tools: Git, MySQL Workbench, PHPStorm, Sublime Text
Services
For the past time worked as a hark worker, software developer specialist; I understand software development process and how is hard from start something from scratch to development with an idea. If you like my work. Hire me!
Web developer
Building high quality and rick features, easy to understand and more suitable with client requirements.
Api Deverloper
RESTFull API design and architecture for web based, mobile, and microservices or small system but need a handon with highly accurate!
Fullstack Developer
I am a deadline driven to ensure my job done is necessary, I am keen on requirement and task and from my perspective completed client tickets is my success.
PHP Developer
Master in PHP skill, understand and have working on popular frameworks as Laravel, Symphony, CakePHP, Zend...
Ruby On Rails Developer
Rails is one of my favorite frameworks. Rails is power with performances, effective code save a lot of time from building and development.
Teach code
Mentor other people to learn code, algorithm and data structure, OOP and agile/scrum methodologies. This is my pleasure.
Contact
Please contact for futher discussion. Looking forward to hear from you!
Location:
CMC Tower, 11 Duy Tan, Cau Giay, Ha Noi
Email:
<EMAIL_ADDRESS>Call:
+84 965 841 492


Every TV General Election debate still to come in 2019 - BBC, Sky, Channel 4 and more
Their policies split opinion with neither leader coming out as the clear winner - but they have several more opportunities to try and gain support from the public
by James Rodger, Fionnula Hainey, https://www.facebook.com/jamesrodgerjournalistJeremy Corbyn and Boris Johnson are returning to the campaign trail after a tough TV grilling from a live audience on a BBC Question Time special.
The Labour leader told the audience he would take a "neutral stance" in an EU referendum while the Prime Minister was challenged over "racist rhetoric" and trustworthiness during the debate.
Their policies split opinion with neither leader coming out as the clear winner - but they have several more opportunities to try and gain support from the public.
The BBC will host another head-to-head debate, as well as one involving all the main party leaders, while Sky News as plans for a three-way battle and Channel 4 will focus solely on the environment.
Here's everything you need to know about the upcoming TV election debates, reports the M.E.N .
Sky News debate
What is it?
A three-way debate involving the leaders of the three main political parties in England. The Scottish National Party has argued that its leader should also be part of it - but Sky is yet to confirm if Nicola Sturgeon will be joining in.
Who's taking part?
The debate will see the prime minister Boris Johnson take on Labour leader Jeremy Corbyn and Liberal Democrats leader Jo Swinson.
When is it?
Thursday, November 28. Time to be announced.
What channel?
Sky News (Freeview channel 233)
BBC leaders debate
What is it?
A seven-way podium debate with representatives from all the major parties invited to take part. Nick Robinson will be hosting the show which will be broadcast live from Cardiff.
Who's taking part?
The Conservatives, Labour, Liberal Democrats, Plaid Cymru, Greens, Scottish National Party and the Brexit Party have all been invited to take part.
When is it?
Friday, November 29 at 7pm.
What channel?
BBC One and online on the BBC iPlayer
BBC prime ministerial debate
What is it?
A head-to-head debate between current prime minister Boris Johnson and opposition party leader Jeremy Corbyn. It will be chaired by Nick Robinson and broadcast live from Southampton.
Who's taking part?
Just the leaders of the two main parties.
When is it?
Friday, December 6 at 8.30pm.
What channel?
BBC One and online on the BBC iPlayer
Under 30s debate
What is it?
Emma Barnett will be the host of a Question Time special, focused on an audience made up of young voters. The show will be filmed in Hull and panelists will include representatives of all major parties.
Who's taking part?
The panelists have not yet been announced.
When is it?
Monday, December 9 at 8.30pm.
What channel?
BBC One and online on the BBC iPlayer
Channel 4 climate debate
What is it?
A leaders debate focusing only on the issue of climate change. All of the major party leaders have been invited to take part but Channel 4 has not yet received confirmation from the prime minister on whether he will be attending.
Who's taking part?
The leaders of the Labour Party, the Liberal Democrats, the Green Party and the Scottish National Party have so far all confirmed they will be there.
When is it?
Date to be announced. 7pm.
What channel?
Channel 4


The game was the result of a collaboration between Niantic and Nintendo, by way of The Pokémon Company, and was initially released in selected countries in July 2016. In the game, players use a mobile device's GPS ability to locate, capture, battle, and train virtual creatures, called Pokémon, who appear on the screen as if they were in the same real-world location as the player. The game supports in-app purchases for additional in-game items.


Squirrel GS API Index (801 functions)
Miscellaneous platform functions
Platform visibility functions
Licensing functions
In-app billing functions
Analytics functions
Host device functions
AI System Binding
AI Path functions
Animation System Binding
Provides control over an animation source.
Provides control over a group of animation sources.
Camera Binding
Generic functions
Aspect ratio functions.
Viewport functions
Clock Binding
General functions
Collision Binding
Collision Shape Type functions
Collision Shape functions
Item functions
Emitter Binding
Emitter functions
Engine Binding
Resource functions
Geometry Binding
Generic functions
Topology functions
Material functions
Group Binding
Group management functions
Group transformation functions
Group motion functions
Hash Binding
Hashing functions.
HTTP Binding
HTTP helper functions.
Instance
Generic functions
I/O Binding
File manipulation functions
Input device query functions
Input device state functions
Item Binding
Item registry functions.
ACE functions
Rendering functions
Motion functions
Transformation functions
Cast functions
State functions
General functions
Skinning related functions
Physic state functions
Physic functions
Physic functions
Physic hacking API functions
Script functions
Debugging functions
Light Binding
Light color functions
Spot functions
Generic functions
Material Binding
Render attributes functions
Generic functions
Rendering flag functions
Mixer Binding
Channel management functions
Mixer control functions
Channel control functions
Motion System Binding
Motion functions
Metafile Binding
File functions
Tag functions
Object Binding
Generic functions
Skin functions
Peer Network Binding
Handshaking functions
Networking functions
Physic Binding
Constraint management functions
Constraint configuration functions
Picture Binding
Management functions
Drawing functions
Manipulation functions
Blitting functions
Project Binding
General project functions
UI Font management functions
Layer management functions
Scene management functions
Raytracer Binding
Generic functions
Renderer Binding
Renderer registry functions.

Matrix 3x3
Quaternion Binding
Generic
UV Binding
Generic
Vector Binding
Generic


Transfer Learning using PyTorch
Today we learn how to perform transfer learning for image classification using PyTorch.
Introduction
What is PyTorch?
PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).
What is Transfer Learning?
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
For example, the knowledge gained while learning to recognize cars could apply when trying to recognize trucks.
Getting started
We use a Kaggle Notebook for this task since it provides free computation services which should be sufficient for the image classification task.
Import Libraries
We import various libraries along with the submodules required for the image classification.
%matplotlib inline
%reload_ext autoreload
%autoreload 2
# Import required libraries
import numpy as np
import pandas as pd
import time
import math
import tqdm as tqdm
import os
import csv
import torch
import torch.nn.functional as F
import torch.optim as optim
import torchvision.models as models
import matplotlib.pyplot as plt
from PIL import Image
from torch import nn
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
from torch.autograd import Variable
# Check if compute other than CPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Load Data
Dataset
We use the 102 Category Flower Dataset for our image classification task. This dataset contains images of flowers belonging to 102 different categories.
Set data folders
data_dir = '../input/oxford-flowers-data/flower_data'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
name_json = data_dir + '/cat_to_name.json'
Here we have three main directories train_dir
, valid_dir
, and test_dir
which contain training, validation, and testing data respectively.
We also have a cat_to_name.json
file which contains the mapping of flower category to the name of the flower.
Load test data
The dataset at hand contains training and validation images categorized inside their respective folders, however, the test images are not categorized and are merged in a single directory.
Thus, we need to define functions to load the test data as required by PyTorch during the training of the model.
def pil_loader(path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
class TestDataset(torch.utils.data.Dataset):
def __init__(self, path, transform=None):
self.path = path
self.files = []
for (dirpath, _, filenames) in os.walk(self.path):
for f in filenames:
if f.endswith('.jpg'):
p = {}
p['img_path'] = dirpath + '/' + f
self.files.append(p)
self.transform = transform
def __len__(self):
return len(self.files)
def __getitem__(self, idx):
img_path = self.files[idx]['img_path']
img_name = img_path.split('/')[-1]
image = pil_loader(img_path)
if self.transform:
image = self.transform(image)
return image, 0, img_name
Label mapping
We load the labels JSON file and define a function to get the class/category of a flower by passing in an index.
import json
with open(name_json, 'r') as f:
cat_to_name = json.load(f)
def get_cat_name(index):
return cat_to_name[idx_to_class[index]]
Transform
Now even though we might have enough data for training but performing various transforms or operations on the images helps the model learn better and perform efficiently on unseen data.
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize = transforms.Normalize(mean=mean, std=std)
data_transforms = transforms.Compose([
transforms.Pad(4, padding_mode='reflect'),
transforms.RandomRotation(10),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
test_transforms = transforms.Compose([
#transforms.Pad(4, padding_mode='reflect'),
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
normalize
])
Dataloader
Next, we provide the image dataset directories and the respective transforms to the dataloader function which will actually load images as needed.
train_datasets = datasets.ImageFolder(train_dir, data_transforms)
val_datasets = datasets.ImageFolder(valid_dir, test_transforms)
test_datasets = TestDataset(test_dir, test_transforms)
bs = 64
trainloader = torch.utils.data.DataLoader(train_datasets, batch_size=bs, shuffle=True)
validloader = torch.utils.data.DataLoader(val_datasets, batch_size=bs, shuffle=True)
testloader = torch.utils.data.DataLoader(test_datasets, batch_size=1, shuffle=False)
Finally, we create a dictionary to get the class/category of a flower from an index.
idx_to_class = {val:key for key, val in val_datasets.class_to_idx.items()}
Plot images
Its always a good idea to take a look at the dataset before training the model. Hence, we define a plot_img
function to plot a grid of random dataset images.
def plot_img(preds=None, is_pred=False):
fig = plt.figure(figsize=(8,8))
columns = 4
rows = 5
for i in range(1, columns*rows +1):
fig.add_subplot(rows, columns, i)
if is_pred:
img_xy = np.random.randint(len(test_datasets));
img = test_datasets[img_xy][0].numpy()
else:
img_xy = np.random.randint(len(train_datasets));
img = train_datasets[img_xy][0].numpy()
img = img.transpose((1, 2, 0))
img = std * img + mean
if is_pred:
plt.title(get_cat_name(preds[img_xy]) + "/" + get_cat_name(test_datasets[img_xy][1]))
else:
plt.title(str(get_cat_name(train_datasets[img_xy][1])))
plt.axis('off')
img = np.clip(img, 0, 1)
plt.imshow(img, interpolation='nearest')
plt.show()
plot_img()
Helper functions
Next, we define helper functions which will be required later while training and evaluating the model.
A function that returns accuracy
based on the output and target parameters.
def accuracy(output, target, is_test=False):
global total
global correct
batch_size = target.size(0)
total += batch_size
_, pred = output.max(dim=1)
if is_test:
preds.extend(pred)
correct += torch.sum(pred == target.data)
return (correct.float()/total) * 100
A reset
function to reset/clear evaluation metrics.
def reset():
global total, correct
global train_loss, test_loss, best_acc
global trn_losses, trn_accs, val_losses, val_accs
total, correct = 0, 0
train_loss, test_loss, best_acc = 0.0, 0.0, 0.0
trn_losses, trn_accs, val_losses, val_accs = [], [], [], []
A function to record the training and testing stats used to plot graphs.
class AvgStats(object):
def __init__(self):
self.reset()
def reset(self):
self.losses =[]
self.precs =[]
self.its = []
def append(self, loss, prec, it):
self.losses.append(loss)
self.precs.append(prec)
self.its.append(it)
A function to save checkpoint if a new best score is achieved while training
def save_checkpoint(model, is_best, filename='./checkpoint.pth.tar'):
if is_best:
torch.save(model.state_dict(), filename) # save checkpoint
else:
print ("=> Validation Accuracy did not improve")
A function to load a saved checkpoint.
def load_checkpoint(model, filename = './checkpoint.pth.tar'):
sd = torch.load(filename, map_location=lambda storage, loc: storage)
names = set(model.state_dict().keys())
for n in list(sd.keys()):
if n not in names and n+'_raw' in names:
if n+'_raw' not in sd: sd[n+'_raw'] = sd[n]
del sd[n]
model.load_state_dict(sd)
Cyclic Learning Rate
This method is described in the paper Cyclical Learning Rates for Training Neural Networks to find out the optimum learning rate.
The learning rate is increased from a lower value to a higher per iteration for some iterations till loss starts exploding. The learning rate one power lower than the one where loss is minimum is chosen as the optimum learning rate for training.
class CLR(object):
def __init__(self, optim, bn, base_lr=1e-7, max_lr=100):
self.base_lr = base_lr
self.max_lr = max_lr
self.optim = optim
self.bn = bn - 1
ratio = self.max_lr/self.base_lr
self.mult = ratio ** (1/self.bn)
self.best_loss = 1e9
self.iteration = 0
self.lrs = []
self.losses = []
def calc_lr(self, loss):
self.iteration +=1
if math.isnan(loss) or loss > 4 * self.best_loss:
return -1
if loss < self.best_loss and self.iteration > 1:
self.best_loss = loss
mult = self.mult ** self.iteration
lr = self.base_lr * mult
self.lrs.append(lr)
self.losses.append(loss)
return lr
def plot(self, start=10, end=-5):
plt.xlabel("Learning Rate")
plt.ylabel("Losses")
plt.plot(self.lrs[start:end], self.losses[start:end])
plt.xscale('log')
def plot_lr(self):
plt.xlabel("Iterations")
plt.ylabel("Learning Rate")
plt.plot(self.lrs)
plt.yscale('log')
Lookahead
Here we implement Lookahead optimizer. As proposed, the Lookahead optimizer goes k steps forward and 1 step backward.
from torch.optim import Optimizer
from collections import defaultdict
class Lookahead(Optimizer):
def __init__(self, optimizer, alpha=0.5, k=5):
assert(0.0 <= alpha <= 1.0)
assert(k >= 1)
self.optimizer = optimizer
self.alpha = alpha
self.k = k
self.param_groups = self.optimizer.param_groups
self.state = defaultdict(dict)
for group in self.param_groups:
group['k_counter'] = 0
self.slow_weights = [[param.clone().detach() for param in group['params']] for group in self.param_groups]
def step(self, closure=None):
loss = self.optimizer.step(closure)
for group, slow_Weight in zip(self.param_groups, self.slow_weights):
group['k_counter'] += 1
if group['k_counter'] == self.k:
for param, weight in zip(group['params'], slow_Weight):
weight.data.add_(self.alpha, (param.data - weight.data))
param.data.copy_(weight.data)
group['k_counter'] = 0
return loss
def state_dict(self):
fast_dict = self.optimizer.state_dict()
fast_state = fast_dict['state']
param_groups = fast_dict['param_groups']
slow_state = {(id(k) if isinstance(k, torch.Tensor) else k): v
for k, v in self.state.items()}
return {
'fast_state': fast_state,
'param_groups': param_groups,
'slow_state': slow_state
}
def load_state_dict(self, state_dict):
fast_dict = {
'state': state_dict['fast_state'],
'param_groups': state_dict['param_groups']
}
slow_dict = {
'state': state_dict['slow_state'],
'param_groups': state_dict['param_groups']
}
super(Lookahead, self).load_state_dict(slow_dict)
self.optimizer.load_state_dict(fast_dict)
LR Finder
Now, we define the logic to find Learning Rate (LR) using CLR.
def update_lr(optimizer, lr):
for g in optimizer.param_groups:
g['lr'] = lr
def lr_find(clr, model, optimizer=None):
t = tqdm.tqdm(trainloader, leave=False, total=len(trainloader))
running_loss = 0.
avg_beta = 0.98
model.train()
for i, (input, target) in enumerate(t):
input, target = input.to(device), target.to(device)
var_ip, var_tg = Variable(input), Variable(target)
output = model(var_ip)
loss = criterion(output, var_tg)
running_loss = avg_beta * running_loss + (1-avg_beta) *loss.item()
smoothed_loss = running_loss / (1 - avg_beta**(i+1))
t.set_postfix(loss=smoothed_loss)
lr = clr.calc_lr(smoothed_loss)
if lr == -1 :
break
update_lr(optimizer, lr)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
Train and Test
We implement the train and test logic along with the fit method which will be used in the next step for model training.
def train(epoch=0, model=None, optimizer=None):
model.train()
global best_acc
global trn_accs, trn_losses
is_improving = True
counter = 0
running_loss = 0.
avg_beta = 0.98
for i, (input, target) in enumerate(trainloader):
bt_start = time.time()
input, target = input.to(device), target.to(device)
var_ip, var_tg = Variable(input), Variable(target)
output = model(var_ip)
loss = criterion(output, var_tg)
running_loss = avg_beta * running_loss + (1-avg_beta) *loss.item()
smoothed_loss = running_loss / (1 - avg_beta**(i+1))
trn_losses.append(smoothed_loss)
# measure accuracy and record loss
prec = accuracy(output.data, target)
trn_accs.append(prec)
train_stats.append(smoothed_loss, prec, time.time()-bt_start)
if prec > best_acc :
best_acc = prec
save_checkpoint(model, True)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
def test(model=None):
with torch.no_grad():
model.eval()
global val_accs, val_losses
running_loss = 0.
avg_beta = 0.98
for i, (input, target) in enumerate(validloader):
bt_start = time.time()
input, target = input.to(device), target.to(device)
var_ip, var_tg = Variable(input), Variable(target)
output = model(var_ip)
loss = criterion(output, var_tg)
running_loss = avg_beta * running_loss + (1-avg_beta) *loss.item()
smoothed_loss = running_loss / (1 - avg_beta**(i+1))
# measure accuracy and record loss
prec = accuracy(output.data, target, is_test=True)
test_stats.append(loss.item(), prec, time.time()-bt_start)
val_losses.append(smoothed_loss)
val_accs.append(prec)
def fit(model=None, sched=None, optimizer=None):
print("Epoch\tTrn_loss\tVal_loss\tTrn_acc\t\tVal_acc")
for j in range(epoch):
train(epoch=j, model=model, optimizer=optimizer)
test(model)
if sched:
sched.step(j)
print("{}\t{:06.8f}\t{:06.8f}\t{:06.8f}\t{:06.8f}"
.format(j+1, trn_losses[-1], val_losses[-1], trn_accs[-1], val_accs[-1]))
Model and Training
Initialize variables
We initialize all the variables that will be required later while training and evaluating the model.
train_loss = 0.0
test_loss = 0.0
best_acc = 0.0
trn_losses = []
trn_accs = []
val_losses = []
val_accs = []
total = 0
correct = 0
Model
We use a pretrained ResNet50, a variant of the ResNet model which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer.
model = models.resnet50(pretrained=True)
Next, we configure the last fully connected layer of the model and save the model checkpoint.
model.fc = nn.Linear(in_features=model.fc.in_features, out_features=102)
for param in model.parameters():
param.require_grad = False
for param in model.fc.parameters():
param.require_grad = True
model = model.to(device)
save_checkpoint(model, True, 'before_start_resnet50.pth.tar')
Define loss and optimizer, find learning rate, and plot a graph.
criterion = nn.CrossEntropyLoss()
optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)
optimizer = Lookahead(optim)
clr = CLR(optim, len(trainloader))
lr_find(clr, model, optim)
clr.plot()
Training before unfreezing
Let’s load back the ResNet checkpoint that we saved earlier.
load_checkpoint(model, 'before_start_resnet50.pth.tar')
Initialize parameters for the model training.
preds = []
epoch = 30
train_stats = AvgStats()
test_stats = AvgStats()
We reset the parameters to makes sure that we record fresh results while training the model.
reset()
Define loss and optimizer just like before.
criterion = nn.CrossEntropyLoss()
optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)
optimizer = Lookahead(optim)
Finally, train the model for 30 epochs.
fit(model=model, optimizer=optimizer)
Save the model checkpoint after training.
save_checkpoint(model, True, 'before_unfreeze_resnet50.pth.tar')
We plot training and validation loss as well as training and validation accuracy graphs to check the model performance.
plt.xlabel("Iterations")
plt.ylabel("Accuracy")
plt.plot(train_stats.precs, 'r', label='Train')
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.plot(train_stats.losses, 'r', label='Train')
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Accuracy")
plt.plot(test_stats.precs, 'b', label='Valid')
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.plot(test_stats.losses, 'b', label='Valid')
plt.legend()
Training after unfreezing
Initialize parameters for the model training.
preds = []
train_stats = AvgStats()
test_stats = AvgStats()
We reset the parameters to makes sure that we record fresh results while training the model.
reset()
Enable differentiation tracking for each model parameter.
for param in model.parameters():
param.require_grad = True
Define the updated loss and optimizer and plot a losses vs learning rate graph.
optim = torch.optim.SGD(model.parameters(), lr=1e-6, momentum=0.9, weight_decay=1e-4)
clr = CLR(optim, len(trainloader), base_lr=1e-9, max_lr=10)
lr_find(clr, model, optim)
clr.plot(start=0)
Let’s load back the resnet checkpoint that we saved earlier.
load_checkpoint(model, 'before_unfreeze_resnet50.pth.tar')
Initialize parameters for the model training.
preds = []
epoch = 10
train_stats = AvgStats()
test_stats = AvgStats()
We reset the parameters to makes sure that we record fresh results while training the model.
reset()
Define the updated learning rate and optimizer for training.
optim = torch.optim.SGD(model.parameters(), lr=1e-9, momentum=0.9, weight_decay=1e-4)
At last, train the model for 10 epochs and save a checkpoint after training.
fit(model=model, optimizer=optimizer)
save_checkpoint(model, True, 'after_unfreeze_resnet50.pth.tar')
Next, we plot training and validation loss to compare the performance of the model.
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.plot(train_stats.losses, 'r', label='Train')
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.plot(test_stats.losses, 'b', label='Valid')
plt.legend()
Testing
We define a predict
method to predict labels for the test dataset.
def predict(img, model):
model.eval()
with torch.no_grad():
input = Variable(img)
input = input.to(device)
output = model(input)
_, pred = output.max(dim=1)
return pred[0].item()
Predict labels or classes for all the images in the test dataset.
result = dict()
for i, (input, _, path) in enumerate(testloader):
predicted = predict(input, model)
result[path[0]] = idx_to_class[predicted]
Save the result for future reference.
csv_file = open('dict.csv', 'w')
writer = csv.writer(csv_file)
writer.writerow(['file_name','id'])
for key, value in result.items():
writer.writerow([key, value])
csv_file.close()


Introduction
Saturnus is a Webhook-as-a-Service. You can schedule webhooks and also make them recurring. Once a webhook is configured, it will try POSTing to the specified URL as many times as configured in the retries parameter. Only HTTP Status 200 is considered a success. Requests should answer in less than 10 seconds.
Create a Hook
HTTP Request
curl https://api.saturnus.io/v1/hook \
-H `Content-Type: application/json` \
-d `{
"key": "MY_KEY",
"url": "https://endpoint.com/my_path",
"when": "2018-07-30T05:00:00Z",
"every": "2 hours",
"retries": 3,
"data": {
"foo": "bar"
},
"content_type": "application/json"
}`
Response (HTTP Status 200)
"Hook ID"
Query Parameters
| Name | Required | Type | Default | Description |
|---|---|---|---|---|
| key | yes | string | The API key | |
| url | yes | string | The POST endpoint | |
| when | yes | string | A ISO8601 date for when to fire the webhook | |
| every | no | string | "disabled" | A human readable interval. Example: "3 days and 4 hours" |
| retries | no | number | 2 | The number of retries. Maximum 5. |
| data | no | object | A object containing parameters to send to the endpoint. | |
| content_type | no | string | "application/json" | The content type of the endpoint. Example: "application/x-www-form-urlencoded" |
List Hooks
curl --request GET https://api.saturnus.io/v1/hooks \
-H `Content-Type: application/json` \
-d `{
"key":"MY_KEY",
"interval": ["2018-07-01T00:00:00Z","2018-07-30T23:59:59Z"],
"filter": [{
"comparator": "eq",
"field": "status",
"value": "pending"
}]
}`
Response
{ "data": [....], "next": "PAGINATION_URL" }
Query Parameters
| Name | Required | Type | Default | Description |
|---|---|---|---|---|
| key | yes | string | The API key | |
| interval | yes | array | An array of ISO8601 dates containing the interval to get hooks by creation date | |
| filter | no | array | A array of conditions to filter the hooks to be listed |
Get a Hook Information
curl --request GET https://api.saturnus.io/v1/hook/ID
Response
{
"id": "Hook id",
"created_at": "ISO8601 date",
"updated_at": "ISO8601 date of when it fired",
"url": "Endpoint URL",
"retries": "Number of retries",
"data": "Object containing data to be sent to the endpoint",
"when": "ISO8601 date",
"status": "Can be completed, pending or error",
"error": "An object containing the HTTP Status and the endpoint response if the status is 'error'",
"content_type": "Endpoint content type"
}
Delete a Hook
curl --request DELETE https://api.saturnus.io/v1/hook/ID
Response
"OK"
Errors
The Saturnus API uses the following error codes:
| Error Code | Meaning |
|---|---|
| 400 | Bad Request -- Your request is invalid. |
| 401 | Unauthorized -- Your API key is wrong. |
| 403 | Forbidden -- Something disallowed the request. |
| 404 | Not Found -- The specified endpoint could not be found. |
| 405 | Method Not Allowed -- You tried to access a endpoint with an invalid method. |
| 429 | Too Many Requests -- Rate limiter(5 reqs/s) |
| 500 | Internal Server Error -- We had a problem with our server. Try again later. |
| 503 | Service Unavailable -- We're temporarily offline for maintenance. Please try again later. |


{% extends "layout.html" %} {% block content %}
This is a sample polls application that demonstrates the use of Flask web framework.
The application can be configured to use one of the following repositories: Azure Table Storage, MongoDB or In-Memory.
Current repository: {{repository_name}}
{% endblock %}


AddressFree text
This is a GOV.UK pattern
Using a free text box
Use a free text box if you expect a broad range of address formats and you don’t need to use specific sub-parts of the address (for example, street or postcode).
Advantages of a free text box
A free text box:
- can handle any possible address format
- allows users to copy and paste addresses from their clipboard
- means users don’t have to work out which part of the address goes in which field
Disadvantages of a free text box
A free text box:
- makes it hard for you to separate an address into sub-parts, and impossible to do with 100% accuracy
- might not be compatible with legacy backend systems that use multi-field addresses
- can’t help users look up an address


The Halloween Store
For the little Goblin in all of us!
Home
Product List
Personal
Decorating Ideas
Join Email
Please enter the following information:
First name:
Last name:
Email:
Zip Code:


Not hearing anything? Sonic-Hash requires HTML5 <audio>,which your browser does not seem to support.Might I suggest Safari, Firefox, or Chrome?
Looking for something more glitzy? More razzamatazz? Check out Chroma-Hash, the more colorful sibling of Sonic-Hash


Welcome to Tiffany Velasquez Yoga!
We are a new studio located in Mendocino County, California.
There are four or five free sessions a week available for you
to come and learn our craft, interact with other students, and relax.
Drop us a line
if you are interested in learning more.
Om Namo Bagvate.
Be strong in body, clean in mind, lofty in ideals.
Self-confidence is the foundation of all great success and achievement.
You are never too old to set a new goal or dream a new dream.
The starting point of all achievement is desire.
Do something today that your future self will thank you for.
Strive not to be a success, but rather to be value. - Albert Einstein
No matter how cool, talented, educated or rich you think you are, how you treat people tells all. Integrity is everything.


Using Analyzer subclasses¶
This module defines several classes derived from enb.aanalysis.Analyzer
.
a minimal working example of enb.aanalysis
is provided in Basic workflow
,
which produce their results by invoking the enb.aanalysis.Analyzer.analyze_df()
method.
The main inputs to these subclasses’ methods are:
full_df: a
pandas.DataFrame
instance, typically produced byenb.atable.ATable
.target_columns: normally a list of column names (or pairs of column names) present in the DataFrame. These are the ones for which figures are produced. Syntax can vary between analyzer classes.
column_to_properties: although optional, this argument (a dict-like instance) provides information to the analyzer about how to display the results. Good news is, your
enb.atable.ATable
instance automatically contains this dict-like object in the .column_to_properties.For instance, the folowing column definition guarantees that the default plot range for this dimension starts in zero:
@atable.column_function("bpppc", label="Compressed data rate (bpppc)", plot_min=0) def set_bpppc(self, index, row): # _column_name equals "bpppc", or whatever label is passed to @atable.column_function row[_column_name] = ...In addition, subclasses of
enb.experiment.Experiment
also provide the .joined_column_to_properties property. This is convenient when cross-analyzing the properties of your dataset with the results you obtained (e.g., to measure relative efficiency). See Lossless Compression Experiment for a full working example of data analysis using an experiment.group_by: a column name is typically used by the analyzer to split all data in full_df into subgroups based on the value of one or more columns.


Often times, the diceroll module is found in other formats. You may already have a copy of diceroll that was distributed with another program you're using.
Besides its common .py format, diceroll can be found in a .pyd format as well. This format is packaged as a dynamic link library, and will work the same way as the .py format. The format is typically bundled with the software that was designed for using it.
Warning
The .pyd format can only be imported and will not execute at a CMD prompt.


Pypianoroll¶
A python package for handling multitrack pianorolls.
Features¶
Manipulate multitrack pianorolls intuitively
Visualize multitrack pianorolls in a modern DAW like style
Save and load multitrack pianorolls in space efficient formats
Parse MIDI files into multitrack pianorolls
Write multitrack pianorolls into MIDI files
Installation¶
To install Pypianoroll from PYPI:
$ pip install pypianoroll
To install Pypianoroll manually (please download the source code from either PYPI or Github first):
$ python setup.py install
Documentation¶
Documentation is provided as docstrings with the code. An online version is also available here.
Citing¶
Please cite the following paper if you use Pypianoroll in a published work:
Hao-Wen Dong, Wen-Yi Hsiao, and Yi-Hsuan Yang, “Pypianoroll: Open Source Python Package for Handling Multitrack Pianorolls,” in Late-Breaking Demos of the 18th International Society for Music Information Retrieval Conference (ISMIR), 2018. [paper] [poster]
Documentation¶
Why Pypianoroll¶
Our aim is to provide convenient classes for pianoroll matrix and MIDI-like track information (program number, track name, drum track indicator). Pypianoroll is also designed to provide efficient I/O for pianorolls, since pianorolls have long been considered an inefficient way to store music data due to the sparse nature.
Lakh Pianoroll Dataset¶
Lakh Pianoroll Dataset (LPD) is a new multitrack pianoroll dataset using Pypianoroll for efficient data I/O and to save space, which is used as the training dataset in our MuseGAN project.


| 1 | /* | |
| 2 | * Copyright 1999-2017 Alibaba Group. | |
| 3 | * | |
| 4 | * Licensed under the Apache License, Version 2.0 (the "License"); | |
| 5 | * you may not use this file except in compliance with the License. | |
| 6 | * You may obtain a copy of the License at | |
| 7 | * | |
| 8 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 9 | * | |
| 10 | * Unless required by applicable law or agreed to in writing, software | |
| 11 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 13 | * See the License for the specific language governing permissions and | |
| 14 | * limitations under the License. | |
| 15 | */ | |
| 16 | package com.alibaba.fastjson.parser; | |
| 17 | ||
| 18 | ||
| 19 | /** | |
| 20 | * @author<EMAIL_ADDRESS>| |
| 21 | */ | |
| 22 | @Deprecated | |
| 23 | public class DefaultExtJSONParser extends DefaultJSONParser { | |
| 24 | ||
| 25 | public DefaultExtJSONParser(String input){ | |
| 26 |
1
1. |
this(input, ParserConfig.getGlobalInstance()); |
| 27 | } | |
| 28 | ||
| 29 | public DefaultExtJSONParser(String input, ParserConfig mapping){ | |
| 30 | super(input, mapping); | |
| 31 | } | |
| 32 | ||
| 33 | public DefaultExtJSONParser(String input, ParserConfig mapping, int features){ | |
| 34 | super(input, mapping, features); | |
| 35 | } | |
| 36 | ||
| 37 | public DefaultExtJSONParser(char[] input, int length, ParserConfig mapping, int features){ | |
| 38 | super(input, length, mapping, features); | |
| 39 | } | |
| 40 | ||
| 41 | | |
| 42 | } | |
Mutations | ||
| 26 |
1.1 |


Budweiser Copper
Budweiser Copper Lager is the most American beer of 2019. A pair of huge multi-national conglomerates weaponizing America's collective self-regard to turn a profit is maybe too perfect a metaphor for the current State of the Union.
Billed as an "all-American" collaboration, Bud Copper is the child of Anheuser-Busch, a division of Belgian-owned AB InBev, and Jim Beam, owned by Japan-based Suntory Holdings. I wouldn't feel the need to mention the foreign ownership thing at all if brands like Budweiser weren't constantly wrapping themselves in Old Glory and harping on how "real" and "American" they are.
Before I savage Bud Copper further, Japanese ownership of a popular bourbon reminds me of the most mind-blowing thing I learned in Scotland. We spent a night in a castle that had a whisky bar downstairs. When the bartender recommended a Japanese whiskey, I said I'd never heard of such a thing. He then told me that Japan produces arguably the best whiskey in the world. He wasn't kidding. I had to travel to the birthplace of Scotch to learn about Japanese whiskey. Life is so weird and wonderful sometimes.
I remain flummoxed by AB InBev's decision to keep pumping out "reserve" versions of crappy beer. One can safely assume that AB InBev has an advanced analytics department. I want to see what piece of research tells them that it's more profitable to develop, produce, and market something like Bud Copper than it is to just buy up another craft brewery and keep cashing dem checks.
As for the beer itself Bud Copper, unlike whiskey, does not age well. I drank a six-pack of Bud Copper to review when it came out months ago and remember it being mildly enjoyable but obviously forgettable. Not wanting to invest in another six-pack, I made a Bud Copper part of one of those "make your own six-pack" deals at the grocery store.
This solo bottle did not even approach being mildly enjoyable, and the flavors listed on the bottle — "nutty taste with Caramel Rye and Vanilla notes" — were overpowering and impossible to forget. Time spent outside of the cooler apparently does more harm to Bud Copper than the "King of Beers", which always tastes like a Budweiser (for better or for worse) unless you bake it in the sun for a week.
If you love amber beers, Budweiser, and Jim Beam then Bud Copper is the beer for you. For the remaining 99.9% of the world, I'd recommend seeking out a truly great American beer and some Japanese whiskey. Good luck finding the latter. If you do obtain/have a bottle, give me a call. We should hang out.


January 8
And being not weak in faith, [Abraham] considered not his own body now dead, when he was about an hundred years old, neither yet the deadness of Sarah's womb: He staggered
not at the promise of God through unbelief; but was strong in faith, giving glory to God.
Romans 4:19-20
In the years I've spent living by faith, there's something I've learned to do that helps me receive from God in the toughest of situations. It's something I call stepping over the faith line.
 It's what you need when you want to be firm in your faith and yet you keep wavering back and forth between your circumstances and God's promises--believing first one, then the other.
It's what can make you like faithful Abraham. You know, Abraham had natural facts to deal with just like we do. He knew there was no natural way for God's promise to him to come true.
Yet the Word says Abraham considered not his own body. In other words, Abraham ignored the natural evidence around him and believed only God's promise.
Somewhere he stepped across the line of faith.
He made an irreversible decision to go with the Word of God. He made a final commitment. He chose to step past the point of no return. And if you and I are ever going to see God do the impossible in our lives, we're going to have to do the same thing!
How do you draw that faith line?
Begin with the Word. Search the promises of God and purposely believe what He has said, and is saying, about your need. Meditate on those promises until faith rises in your heart.
Then draw the line of faith. Draw it in your mind and heart. Draw it across the floor in your prayer room. Say, "In the presence of God, in the presence of all the angels in this room, and in the devils face, I am stepping across the line of faith. From this moment on, I consider this matter done. From this day forward, I give God the praise and the glory in the Name of Jesus.
From that moment on, speak only as if your miracle has already happened. Turn your back on the problems, on the doubts, and turn your face toward Jesus.
 Dare to step across the faith line! Scripture Reading: Romans 4:13-21


Kidozen - Storage Samples
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Home
Overview
Team
Publications
Project Team
Faculty
Jordan Boyd-Graber
Assistant Professor
the iSchool
Hal Daumé III
Assistant Professor
Computer Science
Jimmy Lin
Associate Professor
the iSchool
Philip Resnik
Professor
Linguistics
Students
Nima Asadi
Ph.D. student, Computer Science
Vlad Eidelman
Ph.D. student, Computer Science
Alan Du
High School student (summer intern)
He He
Ph.D. student, Computer Science
Yuening Hu
Ph.D. student, Computer Science
Viet-An Nguyen
Ph.D. student, Computer Science
Brianna Satinoff
M.S. student, Computer Science
(graduated Spring 2011)
Ke Zhai
Ph.D. student, Computer Science


List of all members.
Detailed Description
Definition at line 6 of file _coord.py.
Member Function Documentation
| def par_kinematics::srv::_coord::coordRequest::__init__ |
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Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
x,y,z
@param args: complete set of field values, in .msg order
@param kwds: use keyword arguments corresponding to message field names
to set specific fields.
Definition at line 18 of file _coord.py.
| def par_kinematics::srv::_coord::coordRequest::_get_types |
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| def par_kinematics::srv::_coord::coordRequest::deserialize |
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unpack serialized message in str into this message instance
@param str: byte array of serialized message
@type str: str
Definition at line 64 of file _coord.py.
| def par_kinematics::srv::_coord::coordRequest::deserialize_numpy |
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unpack serialized message in str into this message instance using numpy for array types
@param str: byte array of serialized message
@type str: str
@param numpy: numpy python module
@type numpy: module
Definition at line 95 of file _coord.py.
| def par_kinematics::srv::_coord::coordRequest::serialize |
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serialize message into buffer
@param buff: buffer
@type buff: StringIO
Definition at line 52 of file _coord.py.
| def par_kinematics::srv::_coord::coordRequest::serialize_numpy |
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serialize message with numpy array types into buffer
@param buff: buffer
@type buff: StringIO
@param numpy: numpy python module
@type numpy module
Definition at line 81 of file _coord.py.
Member Data Documentation
Initial value:"""float64 x
float64 y
float64 z
"""
Definition at line 10 of file _coord.py.
The documentation for this class was generated from the following file:
- /home/wouter/ros_packages/thp-pj2-08/par_kinematics/src/par_kinematics/srv/_coord.py


Airbnb looking for 100 of the best home chefs for a trip
NEW YORK: Airbnb is launching a global search for 100 top home cooks and treating them to a trip to Italy to learn how to refine their recipes with teachers including David Chang and his mom.
The lucky chosen will travel to the University of Gastronomic Sciences in Pollenzo, Italy, for a week’s worth of workshops and tastings to take their food "to the next level.” Their recipe will also feature in Airbnb’s first cookbook.
Applications open Monday. Airbnb users who are 18 and a resident of one of 30 or so eligible countries can nominate their favorite home cooks and complete an essay. Applications close Dec. 23.
Winners will be selected by judges from representatives from Airbnb, the Slow Food organization and the University of Gastronomic Sciences.
The contest is part of a larger push into food by Airbnb, which also is launching a new program, Cooking Experiences, which connects users with intimate workshops hosted by families, cooks and famers across more than 75 countries.
Local hosts will gather and describe how to use ingredients, welcome users to their home and show them how they put together traditional dishes, from making enchiladas in Ciudad de México, Mexico, to Uzbek dishes in New York City.
"Unlike typical cooking classes, which can feel intimidating or time-consuming, at the heart of every experience is human connection; people coming together to make and share a meal,” Airbnb said in a statement.
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Airbnb becomes a leading Olympic partner to 2028
Airbnb on Monday announced a nine-year deal to become a leading partner of the Olympics, promising safe and sustainable accommodation for visitors and athletes' families.


Maryland teacher charged after fight with student
by Jordan SmithPRINCE GEORGE’S COUNTY, Md. (Gray News) - Cellphone video captured a fight Friday morning between a teacher and student at a Maryland high school.
Thirty-six-year-old Vivian Noirie is charged with second-degree assault and physical child abuse.
The student is a minor, so police did not identify her. They did, however, describe her as a 17-year-old. Authorities are using the juvenile court system to pursue charges against her.
In an arrest report, investigators said the student confronted Noirie because the teacher had contacted her parents the night before.
Police said the student admitted to stepping on Noirie’s foot and bumping the teacher with her shoulder. When that happened, investigators said Noirie started throwing punches at the student.
Other employees momentarily broke up the brawl. But even after they’d been pulled apart, the student found her way back to Noirie to throw more punches, authorities said.
Administrators separated them again, this time for good, and called police.
Despite the fisticuffs, neither of them required medical attention.


Rapper Dark Lo Threatened Witness Under Alias Of Man Who Killed People In Kareem Abdul-Jabbar's House
by Mike WinslowThe Feds say rapper Dark Lo wrote a threatening letter to a witness testifying against Philly rapper AR-AB, who was just convicted of running a drug empire.
(AllHipHop News) More details have come forth surrounding the arrest of Philly rapper Dark Lo, who was accused of threatening a federal witness during AR-AB's drug trial.
The rapper and three of his associates are facing life in prison when they are sentenced for conspiracy to distribute drugs.
Reps for Dark Lo said the witness was not comfortable with him in the courtroom, but the Feds say the rapper's threat was much greater than his presence during a portion of the trial.
The Feds claim Dark Lo, real name Charles Salley, wrote a threatening letter to a witness under one of his rap aliases “Ron Harvey."
Dark Lo dropped an entire mixtape titled "Ron Harvey Jr.," which was named after the infamous co-founder of the Philadelphia Black Mafia.
The Feds say Dark Lo's letter under the "Ron Harvey" alias threatened to harm the witness for snitching on his O.B.H. associates.
On November 13, 2019, United States Magistrate Judge Lynne A. Sitarski ordered Dark Lo be held in custody until his trial.
If Dark Lo is convicted, he is facing up to 20 years in prison for witness tampering.