| Beyonwiz::Recording::Header |
use Beyonwiz::Recording::Header;
Provides access to the Beyonwiz recording file header.
TVHDR
The name of the header file for a digital TV recording (header.tvwiz
).
RADHDR
The name of the header file for a digital radio recording (header.radwiz
).
DAY
Number of seconds in a day. Used internally for time conversion.
MAX_TS_POINT
Maximum number of offsets in offsets (8640).
HDR_SIZE
Total size of the header (256kiB).
HDR_MAIN_OFF
HDR_MAIN_SIZE
HDR_OFFSETS_OFF
HDR_OFFSETS_SIZE
HDR_BOOKMARKS_OFF
HDR_BOOKMARKS_SZ
HDR_EPISODE_OFF
HDR_EPISODE_SZ
HDR_EXTINFO_OFF
HDR_EXTINFO_SZ
Offsets in the header and sizes for the sections of the header file.
Beyonwiz::Recording::Header->new($accessor, $ie)
Create a new Beyonwiz recording header object.
$accessor
is a reference to a
Beyonwiz::Recording::Accessor
used to carry out the media file access functions in
Beyonwiz::Recording::Header
.
<$ie> is a reference to a
Beyonwiz::Recording::IndexEntry
used to retrieve name and path information for the recording.
$h->accessor([$val]);
Returns (sets) the media file accessor object reference.
$h->ie([$val]);
Returns (sets) the associated
Beyonwiz::Recording::IndexEntry
.
$h->path;
Returns folder name part of the path in the associated
Beyonwiz::Recording::IndexEntry
.
$h->name;
Returns index name in the associated
Beyonwiz::Recording::IndexEntry
.
$h->headerName([$val]);
Returns (sets) the name of the header document (file name part only).
$h->magic([$val]);
Returns (sets) the header magic number (0x1062). Returns undef
if the header is for a media file.
$h->version([$val]);
Returns (sets) the header version number (0x1062). Returns undef
if the header is for a media file.
$h->pids([$val]);
Returns (sets) a reference to a list of Packet IDs (PIDs) for the recording.
$h->pids->[0]
is the video PID,
$h->pids->[1]
is the main audio pid,
$h->pids->[2]
is the Program Clock Reference (PCR) PID and
$h->pids->[3]
is the Program Map table PID for the recorded program.
Returns [0, 0, 0, 0]
if the header is for a media file.
The lowest-order 13 bits of the values contain the PID
$h->pids->[$n] & 0x1fff
.
The remaining bits are reserved for flags.
The only known flag is 0x8000
in the main audio PID,
which indicates that the audio AC3 (rather than MPEG-2).
$h->pid($n);
=item $h->pidFlags($n);
Two utility that return, respectively,
$h->pids->[$n] & 0x1fff
and
$h->pids->[$n] & ~0x1fff
,
the corresponding true PID value from $h->pids->[$n]
and the flags component.
Both methods return 0 if the header is not for a Beyonwiz recording.
$h->isTV; $h->isRadio; $h->isMediaFolder $h->isMediaFile
Returns true if $h->validMain;
is true and the recording
is respectively digital TV, digital radio,
a Beyonwiz folder format for large media files,
or a single media file.
All can return false if
$h->headerName
has not been set.
$h->lock([$val]);
Returns (sets) the flag implementing the Beyonwiz File Lock on the recording.
$h->full([$val]);
Returns (sets) the full flag. Purpose unknown. Unused in WizFX.
$h->inRec([$val]);
Returns (sets) the "currently recording" flag.
$h->service([$val]);
Returns (sets) the recording service (LCN) name.
$h->title([$val]);
Returns (sets) the recording title (event name). Returns the non-folder part of recording's index name if it has no title set. Any leading ASCII control characters (0x00-0x1f) in the header value are stripped off.
$h->episode([$val]);
Returns (sets) the recording episode name (subtitle). Any leading ASCII control characters (0x00-0x1f) in the header value are stripped off.
In free-to-air EPGs, this field is sometimes used as the program
synopsis (see $h->extInfo
), rather than the episode name.
$h->extInfo([$val]);
Returns (sets) the recording extended information (program synopsis). Any leading ASCII control characters (0x00-0x1f) in the header value are stripped off.
$h->longTitle([$addEpisode[, $sep]];
Returns $h->title . '/' . $h->episode
if the episode name
can be loaded and is non-empty, otherwise returns
$h->title
.
If $addEpisode
is specified and false, the episode name is not
added in any case.
If $sep
is specified, it is used instead of '/'
as the separator
between title and episode name.
$h->mjd([$val]);
Returns (sets) the recording start date. The name suggests that it is the Modified Julian Date, but it isn't.
$h->mjd == int(true_MJD + time_zone_offset_in_minutes/(24*60))
where time_zone_offset_in_minutes
is the time zone setting in
minutes current at the start of the recording.
The Beyonwiz appears to keep local time rather than UTC as its internal time.
$h->start([$val]);
Returns (sets) number of seconds into the day indicated by $h->mjd
when the recording started.
$h->last([$val]); $h->sec([$val]);
Return (set) two parameters describing the recording duration.
The recording duration in seconds is: $self->last*10 + $self->sec
.
$h->playtime
is a convenience method that calculates the playtime
from last and sec.
$h->endOffset([$val]);
Return the offset of the logical end of the recording.
Returns a bignum
.
$h->offsets([$val]);
Return (set) the table of offsets (possibly at even time intervals?)
of logical file offsets (as described in $h->endOffset([$val]);
).
Offsets are returned as bignum
s.
If the offsets are at even intervals, it's probably 10 seconds.
For efficiency reasons, only populated for $h->load(1)
.
$h->noffsets;
Returns the number of offsets.
$h->bookmarks([$val]);
Return (set) the table of bookmarks (possibly at even time intervals?)
of logical file offsets (as described in $h->endOffset([$val]);
).
Offsets are returned as bignum
s.
For efficiency reasons, only populated for $h->load(1)
.
$h->nbookmarks;
Returns the number of bookmarks.
$h->validMain;
$h->validEpisode;
$h->validExtInfo;
$h->validBookmarks;
$h->validOffsets;
Returns true if the last $h->loadMain;
(resp. $h->loadEpisode
,
$h->loadExtInfo
,
$h->loadBookmarks
,
or $h->loadOffsets
)
was successful.
$s->reconstructed([$val]);
Returns (sets) a flag marking that the object represents a reconstructed file, and the file should be encoded from the object rather than being copied from the source.
There is no general reconstruction method for
Beyonwiz::Recording::Header
,
but reconstruction of other headers may need to modify the header object,
and they should set this flag when the header is modified.
Reset by any call of $h->loadMain
,
$h->loadEpisode
,
$h->loadExtInfo
,
$h->loadBookmarks
,
or $h->loadOffsets
.
$h->size;
Returns the size of the header file (256kB).
$h->playtime
A convenience method that returns the duration of the recording in seconds.
$h->fileLenTime([$file])
Return the tuple ($len, $modifyTime) for the trunc file.
The modify time is a Unix timestamp (seconds since 00:00:00) Jan 1 1970 UTC).
If $file
is specified, use that as the name of the trunc file,
otherwise use $t-
headerName> for the name of the file.
Returns undef
if the data can't be found
(access denied or file not found).
$h->starttime
Returns a Unix-like timestamp for the start time of the recording in local time (rather than in UTC, like a true Unix timestamp). More precisely, returns the number of seconds since midnight at the start of 1 Jan 1970, plus the number of seconds in the timezone offset at the time the recording was started.
This can be converted into the calender/clock fields for the
local time at the start of the recording using gmtime
(not localtime
).
The local time fields can then be converted into a genuine Unix timestamp
using Time::Local::timelocal
.
$h->offsetTime($offset)
Convert an offset into a time. $h->loadOffsets
must have been called,
otherwise -1 is returned. Interpolates between values in the offset table.
Returns 0 if $offset <= $self->offsets->[0]
and
$self->playtime
if $offset >= $self->endOffset
.
$h->updateOffsets($newStart, $newEnd)
Update the offsets so that $h->endOffset;
is set to $newEnd
and the offset table is adjusted to start at $newStart
.
Intended to be used when the trunc header has been reconstructed.
$h->loadMain;
$h->loadHdrWmmeta;
$h->loadHdrFile;
$h->loadEpisode;
$h->loadExtInfo;
$h->loadBookmarks;
$h->loadOffsets;
Load parts of the header object from the header on the Beyonwiz.
$h->loadMain
loads the basics,
$h->loadEpisode
loads the episode name/subtitle informtion,
$h->loadExtInfo
loads the extended event informtion,
$h->loadBookmarks
loads the bookmark information
and $h->loadOffsets
loads the 10-second offset data.
$h->loadHdrWmmeta;
and $h->loadHdrFile;
load as
much of the header as possible with information about media
content either in the Beyonwiz folder format for large
files ($h->loadHdrWmmeta;
),
or in single files ($h->loadHdrFile;
).
$h->decodeMain($hdr_data)
$h->decodeEpisode($hdr_data)
$h->decodeExtInfo($hdr_data)
$h->decodeBookmarks($hdr_data)
$h->decodeOffsets($hdr_data)
Decodes parts of the header object from $hdr_data
on the Beyonwiz.
The data for each part is assumed to satart at the beginning
of the respective $hdr_data
.
$h->encodeMain
$h->encodeEpisode
$h->encodeExtInfo
$h->encodeBookmarks
$h->encodeOffsets
Encodes parts of the header object ready for writing back to a header file. The methods encode the corresponding data to the decode functions above.
$h->encodeMain
Encodes the header object ready for writing back to a header file.
$h->_setUnixTime($time);
Set $h->mjd([$val]);
and $h->start([$val]);
from the given Unix time (seconds since 00:00:00 Jan 1 1097 UTC).
$h->_setMainMediaFile($size, $time);
Set as many fields as possible to reasonable values given the size and timestamp of a media file.
$h->_readHdrChunk($offset, $size);
Read a chunk of the header file.
Reads from $h->headerName([$val]);
if it
is defined, otherwise tries reading from
/TVHDR
then /RADHDR
and sets
the header name from the first to succeed.
Reads $size
bytes at byte offst $offset
from the start of the header file.
Uses packages:
Beyonwiz::Utils
,
Beyonwiz::Recording::Trunc
;
LWP::Simple
,
URI
,
URI::Escape
,
Time::Local
,
File::Basename
.
Uses bignum
for 64-bit integers, even when the underlying
Perl integers are 64 bits.
The bugs to do with time are in the Beyonwiz.
| Beyonwiz::Recording::Header |


Match Play. Double Match Point. How should Red play 31?
pips Red=93 White=111
(CorrectAnswer= 0/0, ErrorScore= 0)
This is actually a pay-me-now, pay-me-later problem.
Over the board I played 6/2 but this wrong. Red is very unlikely to get home safely from his two outer-board stripped points and he will leave multiple indirect shots as he tries to do so.
Better is 4/3*/1* paying now. If White doesn’t roll a 1 next turn Red will very likely have no further problems. It takes courage to leave that ace shot, but it is the right play. 6/2 is an error but not a blunder.
Would you have the courage to make the play in the final of a major tournament?
Over the board I played 6/2 but this wrong. Red is very unlikely to get home safely from his two outer-board stripped points and he will leave multiple indirect shots as he tries to do so.
Better is 4/3*/1* paying now. If White doesn’t roll a 1 next turn Red will very likely have no further problems. It takes courage to leave that ace shot, but it is the right play. 6/2 is an error but not a blunder.
Would you have the courage to make the play in the final of a major tournament?
1. Cubeful 2-ply 5/4*/1* Eq.: +0.317 0.659 0.267 0.008 - 0.341 0.119 0.003 2. Cubeful 2-ply 6/2 Eq.: +0.272 (-0.045) 0.636 0.068 0.002 - 0.364 0.059 0.001 3. Cubeful 2-ply 11/8 5/4* Eq.: +0.190 (-0.127) 0.595 0.207 0.008 - 0.405 0.216 0.013 4. Cubeful 2-ply 13/10 5/4* Eq.: +0.188 (-0.129) 0.594 0.208 0.008 - 0.406 0.216 0.013 5. Cubeful 2-ply 11/8 2/1* Eq.: +0.164 (-0.153) 0.582 0.184 0.005 - 0.418 0.206 0.011


This page is using the production build of Piral. ✅
Open the developer tools, and a tab called "Piral" will appear to the right.


register_top_value_methods.Rd
Register user-defined top-value methods
register_top_value_methods(...)
| ... | A named list of functions. |
|---|
The user-defined function should accept one argument which is the data matrix where the scores are calculated by rows. Rows with top scores are treated as "top rows" in cola analysis. Following is how we register "SD" (standard deviation) top-value method:
register_top_value_methods(SD = function(mat) apply(mat, 1, sd))
Of course, you can use rowSds
to give a faster calculation of row SD:
register_top_value_methods(SD = rowSds)
The registered top-value method will be used as defaults in run_all_consensus_partition_methods
.
To remove a top-value method, use remove_top_value_methods
.
There are four default top-value methods:
standard deviation, by rowSds
.
coefficient variance, calculated as sd/(mean+s)
where s
is the 10^th percentile of all row means.
median absolute deviation, by rowMads
.
the ATC
method.
No value is returned.
all_top_value_methods
lists all registered top-value methods.
#> [1] "SD" "CV" "MAD" "ATC"register_top_value_methods( ATC_spearman = function(mat) ATC(mat, method = "spearman") ) all_top_value_methods()#> [1] "SD" "CV" "MAD" "ATC" "ATC_spearman"remove_top_value_methods("ATC_spearman")


Track
Indianapolis Motor Speedway
80 laps, Rolling start
July 22, 2014 - New Discount Program
Interested in a New Car or Track?
Take advantage of these great volume discount options!
10% Bulk Purchase Discount: Purchase/License any three to five pieces of content (cars/tracks) at once and receive 10% off the total.
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25% Loyalty Discount: Purchase/License any 40 pieces of paid content (cars or tracks) and receive a 25% discount from that point forward on future content purchases. In other words, once you own any 40 pieces of paid content, every content purchase after that is automatically discounted by 25%.
30% Loyalty Discount: Purchase/License all content or all remaining content and receive 30% off the total. Affectionately known as the 100% club by our members.
All discounts are automatically calculated at checkout. This offer may not be combined with any other discount or promotion. All discounts are calculated either including or excluding preorders. All of our volume discount programs exclude Tech Tracks.
July 15, 2014 - Week 13 is Upon Us!
Week 13 is upon us. Again. As always, it’s a final chance for iRacers from Rookie to Class B to ascend to the next rung on the license ladder in time for Season 3 . . . and time for iRacers of all stripes and licenses to have oodles of fun.
Road and oval racers alike have an opportunity to advance to the next license level in two official series. On the road side there’s the Mazda Cup featuring the Mazda MX-5 Cup with 20 lap races at Okayama (short and long), Charlotte (road), Mazda Laguna Seca Raceway, Summit Point (full course and Jefferson reverse) and Lime Rock. Oval racers can tackle the Legends Cup with 30 lappers in the ’34 Legends Ford at South Boston, Thompson, Oxford Plains, Charlotte (Legends) and Lanier National Speedway.
Safety Ratings and iRatings are in effect in these two official series, offering competitors an opportunity to boost their license ratings before promotions for Season 3 are finalized at week’s end. Likewise, rookies who have yet to complete the category-specific minimum participation requirements for graduation to a D license (two official races or four time trials in each category) can do so in Week 13.
Week 13’s unofficial competition sees the return of the Carburetor Cup at Talladega Superspeedway featuring the Gen6 Chevy SS, Ford Fusion and Toyota Camry.
Want more? How about 144 hours of fun? For the arithmetically-challenged, that breaks down to six (very) different 24 Hours of Fun series.
When the build is released, 24 Hours of Fun I will see the Nationwide Ford Mustang and the Chevrolet Camaro in 50 lap races at the updated Phoenix International Raceway.
Likewise, when the new build is available, 24 Hours of Fun II will pit the GT3 cars (BMW, McLaren and RUF-Track) for 22 lap events on our newest road course, historic Donington Park Racing Circuit.
The 24 Hours of Fun III has our fastest open wheel cars – the Williams-Toyota FW31 and both the IndyCar Dallara and Dallara DW12 – tackling the Daytona road course in 25 lap races.
The 24 Hours of Fun IV brings the Star Mazda to Lucas Oil Raceway for 50 lappers in an online version of the famed Night Before the 500.
24 Hours of Fun V? That would be something completely different, as in the SpecRacer Ford for 15 laps around Irwindale Speedway’s figure 8 layout.
And last but by no means least, 24 Hours of Fun VI boasts 46 car fields of Mazda MX-5 Cups and Roadsters, Pontiac Solstices, ’34 Legend-Fords and SpecRacer Fords at Laguna Seca in 25 lap events guaranteed to put the “mixed” in mixed class sim racing.


Before Change
Returns:
numpy.ndarray: the list of integer numbers.
number_list = np.random.randint(low=1, high=(weight_range+1), size=n)
if savefile:
with open(savefile, "w") as outfile:
for i in range(n):
outfile.write("{}\n".format(number_list[i]))
return number_list
def get_exact_cover_qubitops(list_of_subsets):
Construct the Hamiltonian for the exact solver problem.
After Change
"qiskit.aqua.translators.ising.common, "
"the method here will be removed after Aqua 0.7+",
DeprecationWarning)
return redirect_func(n=n, weight_range=weight_range, savefile=savefile)
def read_numbers_from_file(filename):
from .common import read_numbers_from_file as redirect_func


##Background
I’ve been for the better part of the last decade working elbow deep in the trenches of single paged browser applications. Throughout my experience I have usually faced the following major issues.
###Issue 1
After analysing many frameworks and libraries, is that most of the big frameworks have one or two cool features but come packed with dozens of undesired stuff and sometimes they mess with each other (if you try to mix them to use the best of all of them). Mixing them all together also means the amount of javascript code loaded into the browser is astronomical and at the end of the day you’re only using a small percentage of the huge amount of code you have loaded.
###Issue 2
One of the problems with these big frameworks is that they’re very invasive of your code. When you want to use them properly you are filling the code you write with idiosyncrasies from the library you’re using. Of course there are some small libraries and frameworks where the same happens and I dislike them for the same reason. This will tie your code very deeply to the framework in question and sometimes goes as far as deeply tying your code to a specific version of that framework.
###Issue 3
Due to issue #1 and #2 I became a big fan of micro-frameworks where I could mix and match the best suited and least invasive. This “liberates” your code making it less dependant on some specific usage usually forced by the big libraries. This of course comes at the price of having the window object “polluted” by all kinds of things.
##Arenite
In a continuous effort for most of my time as a javascript developer I have tried by myself and with others to overcome these issues throughout time.
###Iteration 1 - “Genesis V1”
This first iteration was developed in a start-up I co-founded with a few friends. This iteration was mostly about how we actually organised our code and how to break it down into smaller pieces while still exposing that wonderful functionality of all the micro libraries to all the code. This approach was heavily based on a application-wide event bus, principle I still favour to this day. The services, like the bus, templating, etc. were exposed through an object that was shared by all the pieces of the application. This was accomplished using the Sandbox pattern, thus reducing the pollution the window object.
Many of the services provided were third party and we used very small wrappers to expose the functionality giving us the freedom to change the library with another quite easily by just wrapping said other with a thin wrapper specific for that library. This gave us an initial good solution.
The problems we then faced were related to the difficulty in trying to debug something that only happened in production when the code was minified for performance. Also we had a bit of duplication of instances (models for example) because they were used in many different parts of the application. Everything was pretty much passed as a reference. Even though the pieces of the application were small they were still highly coupled.
###Iteration 2 - “Genesis v2”
Hence came the second version of “Genesis”. In this version we tried solving the issues around the difficulty of analysing an app in production and the over instantiation of objects like models.
The issue around the production code was solved with a script loader that was able to understand if you wanted to use the human-readable versions of the files instead of the minified version so that debugging was easier but the page was still very fast when not in debug mode. We also added a sort of service registry in the Sandbox that allowed you to register instances that you wanted to re-use somewhere else.
This version still had quite a bit of boilerplate code and the pieces of the applications were still as tightly bound as before.
###Iteration 3 - No Name
For this third iteration I had changed jobs and had to re-design the system achieved by both previous iterations from scratch. This did however provide the opportunity to improve on the weak points of it’s previous iterations. This iteration had no formal name as I just named it after the company I was working in.
For this iteration I decided to be get rid of all required dependencies for the Sandbox (jQuery was a dependecy of both previous iterations).
Since it was designed from scratch, I was able to achieve a much better modularization of the Sandbox. Instead of having the Sandbox straight away populated with all the services developed for it, the Sandbox itself was just a small set of features required for it to understand a configuration and load the resources depending on the environment. All other services required were then specified on the configuration to be loaded and extend the Sandbox before the app was loaded. This created the concept of a Sandbox extension.
Unlike before I could have several different wrappers for the same service using different underlying third party libraries that exposed the same api. It was then only a matter of selecting the desired one in the configuration.
In this version, the concept of the registry was also extended and most of the instances used in the application were declared in the configuration. The instances with variable numbers throughout the execution of the app were still bound to the code.
This approached allowed for something that hadn’t been required in both previous iterations which was to use the exact same code and application appear and behave differently to different clients just by changing a configuration file. Also most of the code tight coupling was gone.
One of the “issues” with this version was that the configuration of each intance was very tied to the instance itself which meant the configuration was very different, in structure, between components.
###Iteration 4 - “Arenite”
In an effort to not have to redesign the Sandbox over and over again I decided to use my personal time to create an implementation a could just reuse even if I changed jobs.
In this version I made the footprint of the core sandbox much smaller, around 4KB where the smallest of all three previous iterations was around 20KB. At the same time I increased it’s base capabilities, especially in helper methods for manipulating arrays and objects, which were essential to achieve the semi dependency injection of iteration 3 and in arenite.
This iteration finally solves all three issues very well. All the parts of the app are very small. The code is all completely loosely coupled and has no idiosyncrasies since the required “glue” is provided by the configurations.
I called it arenite in reference to the way it is structured and the way I believe we should structure our web apps — Lots of little pieces together forming something big — Allowing to easily create complex web apps made of small simple parts.


|
BrawlCrate v0.40a
Wii File Editor
|
Implements a fully editable byte provider for file data of any size. More...
Public Member Functions | |
| DynamicFileByteProvider (string fileName) | |
| Constructs a new DynamicFileByteProvider instance. More... | |
| DynamicFileByteProvider (string fileName, bool readOnly) | |
| Constructs a new DynamicFileByteProvider instance. More... | |
| DynamicFileByteProvider (Stream stream) | |
| Constructs a new DynamicFileByteProvider instance. More... | |
| byte | ReadByte (long index) |
| See IByteProvider.ReadByte for more information. More... | |
| void | WriteByte (long index, byte value) |
| See IByteProvider.WriteByte for more information. More... | |
| void | InsertBytes (long index, byte[] bs) |
| See IByteProvider.InsertBytes for more information. More... | |
| void | DeleteBytes (long index, long length) |
| See IByteProvider.DeleteBytes for more information. More... | |
| bool | HasChanges () |
| See IByteProvider.HasChanges for more information. More... | |
| void | ApplyChanges () |
| See IByteProvider.ApplyChanges for more information. More... | |
| bool | SupportsWriteByte () |
| See IByteProvider.SupportsWriteByte for more information. More... | |
| bool | SupportsInsertBytes () |
| See IByteProvider.SupportsInsertBytes for more information. More... | |
| bool | SupportsDeleteBytes () |
| See IByteProvider.SupportsDeleteBytes for more information. More... | |
| void | Dispose () |
| See IDisposable.Dispose for more information. More... | |
| byte | ReadByte (long index) |
| Reads a byte from the provider More... | |
| void | WriteByte (long index, byte value) |
| Writes a byte into the provider More... | |
| void | InsertBytes (long index, byte[] bs) |
| Inserts bytes into the provider More... | |
| void | DeleteBytes (long index, long length) |
| Deletes bytes from the provider More... | |
| bool | HasChanges () |
| True, when changes are done. More... | |
| void | ApplyChanges () |
| Applies changes. More... | |
| bool | SupportsWriteByte () |
| Returns a value if the WriteByte methods is supported by the provider. More... | |
| bool | SupportsInsertBytes () |
| Returns a value if the InsertBytes methods is supported by the provider. More... | |
| bool | SupportsDeleteBytes () |
| Returns a value if the DeleteBytes methods is supported by the provider. More... | |
Public Attributes | |
| Stream | _stream |
| bool | _supportsInsDel = true |
Properties | |
| long | Length [get] |
| See IByteProvider.Length for more information. More... | |
| bool | ReadOnly [get] |
| Gets a value, if the file is opened in read-only mode. More... | |
| Properties inherited from BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider | |
| long | Length [get] |
| Returns the total length of bytes the byte provider is providing. More... | |
Events | |
| EventHandler | LengthChanged |
| See IByteProvider.LengthChanged for more information. More... | |
| EventHandler | Changed |
| See IByteProvider.Changed for more information. More... | |
| Events inherited from BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider | |
| EventHandler | LengthChanged |
| Occurs, when the Length property changed. More... | |
| EventHandler | Changed |
| Occurs, when bytes are changed. More... | |
Implements a fully editable byte provider for file data of any size.
Only changes to the file are stored in memory with reads from the original data occurring as required.
|
inline |
Constructs a new DynamicFileByteProvider instance.
| fileName | The name of the file from which bytes should be provided. |
|
inline |
Constructs a new DynamicFileByteProvider instance.
| fileName | The name of the file from which bytes should be provided. |
| readOnly | True, opens the file in read-only mode. |
|
inline |
Constructs a new DynamicFileByteProvider instance.
| stream | the stream containing the data. |
The stream must supported seek operations.
|
inline |
See IByteProvider.ApplyChanges for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.DeleteBytes for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
|
inline |
See IByteProvider.HasChanges for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.InsertBytes for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.ReadByte for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.SupportsDeleteBytes for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.SupportsInsertBytes for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.SupportsWriteByte for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
inline |
See IByteProvider.WriteByte for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
| Stream BrawlLib.Internal.Windows.Controls.Hex_Editor.DynamicFileByteProvider._stream |
| bool BrawlLib.Internal.Windows.Controls.Hex_Editor.DynamicFileByteProvider._supportsInsDel = true |
|
get |
See IByteProvider.Length for more information.
Implements BrawlLib.Internal.Windows.Controls.Hex_Editor.IByteProvider.
|
get |
Gets a value, if the file is opened in read-only mode.
| EventHandler BrawlLib.Internal.Windows.Controls.Hex_Editor.DynamicFileByteProvider.Changed |
See IByteProvider.Changed for more information.
| EventHandler BrawlLib.Internal.Windows.Controls.Hex_Editor.DynamicFileByteProvider.LengthChanged |
See IByteProvider.LengthChanged for more information.


Hey, my name is Konstantin
and I'm a front-end developer
Technical Skills: JavaScript(ES6), Python 3, Java, HTML5, CSS3, PostgreSQL, SQLite, Agile
Soft Skills: Bilingual Communicator (English, Russian), Creative, Responsible, Team Worker
Educational Background: Bachelor of Arts in Business Administration with minor in Economics (Brandon University, Canada) [GPA: 3.71 / 4.30]


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About Renews
ReNews v0.1(Alpha)
ReNews is open source application for reading latest news and content from all across the region.
This application is not meant to be for sale and for any commercial purpose.
Author
Nimitt Ajmera
Software Developer
<EMAIL_ADDRESS><EMAIL_ADDRESS>Indore , India
Socials
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github.com/nimitt12
References
Ionic v5
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News Api
ReNews v0.1(Alpha)


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result in an object having its state modified unexpectedly. While occasionally useful for performance
reasons, this construct is inherently bug-prone.
Use the checkbox below to ignore private methods returning a java.lang.Date or
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Object Oriented Programming
Unit-1
Software Evolution
The software evolution occurred in several phases. Since the beginning of the first computer, programming for the computer started to develop software. The earlier electronic computer ENIAC was programmed in machine language by using switches to enter 1 and 0.
Procedure-Oriented Programming:
In procedure oriented programming a large program is broken down into smaller manageable parts called procedures or functions. In procedure oriented programming priority is given on function rather than data. In procedure oriented programming language, a program basically consists of sequence of instructions each of which tells the computer to do something such as reading inputs from the user, doing necessary calculation, displaying output. High Level Programming Languages like COBOL, FORTRAN, Pascal, C are common procedure oriented programming languages.
Object-oriented Programming:
The fundamental idea behind object-oriented programming is to combine or encapsulate both data (or instance variables) and functions (or methods) that operate on that data into a single unit. This unit is called an object. The data is hidden, so it is safe from accidental alteration. An object’s functions typically provide the only way to access its data. In order to access the data in an object, we should know exactly what functions interact with it. No other functions can access the data. Hence OOP focuses on data portion rather than the process of solving the problem.Characteristics of OOPs
Some noticeable characteristics of OOP are as follows:• Emphasis is on data rather than procedures.
• Programs are divided into objects.
• Function and data are tied together in a single unit.
• Data can be hidden to prevent from accidental alteration from other function or objects.
• Data access is done through the visible functions so that communication between objects is possible.
• Data structures are modelled as objects.
• Follows Bottom up approach of program design methodology.
Characteristics of Object-Oriented Language
• Objects:
Objects are the basic run-time entities in an object-oriented language. They may represent a person, a place, a bank account, a table of data or any item that the program has to handle. They may also represent user-defined data such as vectors, time and lists. Program objects should be chosen such that they match closely with the real-world objects. Objects take up space in the memory.
Classes:
A class is a collection of objects of similar types. Classes are user-defined data types and behaves like the built-in types of a programming language. A class also consists method (i.e. function). So, both data and functions are wrapped into a single class.
Inheritance:
• It is the process by which objects of one class acquire the properties of another class.
• It supports the concept of hierarchical classification. In OOL, the concept of inheritance provides idea of reusability.
• Reusability:
Object-oriented programs are built from reusable software components. Once a class is completed and tested, it can be distributed to other programmers for use in their own programs. This is called reusability. If those programmers want to add new features or change the existing ones, new classes can be derived from existing one. Reusability reduces the time of software development.
• Creating new data types: There are other problems with procedural language. One is the difficulty of creating new data types. Computer languages typically have several built-in data types: integers, floating point number, characters and so on. If you want to invent your own data types, you can. You want to work with complex number, two dimensional co-ordinates or dates, you can create data type complex, date, co-ordinate, etc.
• Polymorphism and Overloading:
• Polymorphism is another important characteristics of OOL. Polymorphism, a Greek term, means the ability to take more than one form. The process of making an operator to exhibit different behaviours in different instances is known as operator overloading. Using a single function name to perform different types of tasks is known as function overloading.
• For example, consider the operation of addition. For two numbers, the operation will generate a sum. If the operands are strings, then the operation would produce a third string by concatenation.
• All operator overloading and function overloading are examples of polymorphism.
• Polymorphism is extensively used in implementing inheritance.
Benefits of using OOP:
Object orientation programming promises greater programmer productivity, better quality of software and lesser maintenance cost. The principle advantages are:
• Through inheritance, we can eliminate redundant code & extend the use of existing classes.
• Reusability saves the development time and helps in higher productivity.
• It is possible to map objects in the problem domain to those in the program.
• It is easy to partition the work in a project based on objects.
• Object oriented systems can be easily upgraded from small to large system.
• Software complexity can be easily managed.
Application of using OOP:
• Real time system
• Simulation and modeling
• Object oriented databases
• Hypertext, hypermedia n/w containing interlinked information and expertext units.
• AI and expert system
• Neural networks and parallel programming.
• Decision support and office automation systems.


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--- title: "Introducing squashinformr" author: "Hayden MacDonald" date: "2020-05-04" output: blogdown::html_page: toc: false slug: introducing-squashinformr image_preview: "img/squashinformr_ghcard.png" tags: - web-scraping - R - r-package - data analysis - squash description: "Politely web scrape data from SquashInfo in R" ---
Earlier this year, I enrolled in a Data Management course as a part of my MSc in Business Analytics. The final project in this course involved presenting a business case for a database and building a MySQL database as a solution. As a squash enthusiast, I chose to base my project on the Professional Squash Association (PSA).
Long story short, I did not complete this project because it was cancelled due the spread of COVID-19. Unfortunately, I had already put in approximately 20 hours of coding into the project. So being as stubborn as I am, I committed to reusing the code by building an R package. The result is squashinformr
: a package for scraping player, tournament, and ranking data from SquashInfo.
squashinformr
is built on the polite
package and, therefore, inherits its principles of “being nice on the web”. In short, web scraping is a data collection method that can be harmful to websites and their users, if done carelessly. Therefore, it is important to use our manners:
Following polite
principles incurs mandatory delays (set by SquashInfo’s robots.txt file) on the scraping process. Therefore, it is important that users are patient when using squashinformr
. SquashInfo currently offers full access to their data and extra features through a premium membership. Please consider signing up and/or subscribing to SquashInfo to support their work.
Install squashinformr
via CRAN:
install.packages("squashinformr")
Install the development version of squashinformr
from its GitHub repository via:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("HaydenMacDonald/squashinformr")
Generally, squashinformr
functions fall into one of three families:
get_players()
get_player_recent_results()
get_player_recent_matches()
get_player_recent_games()
get_player_rankings_history()
get_matchup()
get_rankings()
get_historical_rankings()
get_tournaments()
get_tournament_players()
get_tournament_matches()
get_tournament_games()
Let’s take a closer look at some of these functions in action…
Use get_players()
to extract biographical information on players currently ranked within the top 500. Here, we’ll just scrape the current top 25 men’s players.
library(squashinformr)
(top_25_men <- get_players(top = 25, category = "mens"))
## # A tibble: 25 x 15
## rank first last age gender birthplace nationality residence height weight
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 Moha~ Elsh~ 29 Male Alexandria Egypt Bristol,~ 185 82
## 2 2 Ali Farag 28 Male Cairo Egypt Cairo 183 70
## 3 3 Karim Abde~ 28 Male Alexandria Egypt Giza 173 72
## 4 4 Tarek Momen 32 Male Cairo Egypt Cairo 174 66
## 5 5 Paul Coll 27 Male Greymouth New Zealand Amsterda~ 179 83
## 6 6 Diego Elias 23 Male Lima Peru Lima 188 82
## 7 7 Marw~ Elsh~ 26 Male Alexandria Egypt Bristol,~ 183 73
## 8 8 Simon Rösn~ 32 Male Wurzburg Germany Paderborn 190 89
## 9 9 Migu~ Rodr~ 34 Male Bogota Colombia Bogota 170 72
## 10 10 Joel Makin 25 Male Haverford~ Wales Birmingh~ 180 80
## # ... with 15 more rows, and 5 more variables: plays <chr>, racket <chr>,
## # joined_psa <dbl>, university <chr>, club <chr>
If you’re familiar with squash, you might be under the impression that players that are younger, taller, or lighter on their feet might have competitive advantages.
The data returned from SquashInfo seem to imply that this is not the case! Age, height, weight, and BMI do not seem to have any clear relationship with PSA rankings. There are many factors that contribute to a player’s success, including endurance, positioning, accuracy, and play-style.
Use get_player_recent_matches()
to retrieve a player’s recent match data (past January 1, 2019). Let’s get Raneem El Welily’s recent match data.
(welily <- get_player_recent_matches(player = "Raneem El Welily", category = "womens"))
## # A tibble: 71 x 12
## rank player opponent result games_won games_lost match_time round date
## <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <date>
## 1 1 Ranee~ Nour El~ L 2 3 72 QF 2020-03-01
## 2 1 Ranee~ Yathreb~ W NA NA NA R3 2020-03-01
## 3 1 Ranee~ Alexand~ W 3 1 36 R2 2020-03-01
## 4 1 Ranee~ Nour El~ L 2 3 59 F 2020-03-01
## 5 1 Ranee~ Nouran ~ W 3 0 31 SF 2020-03-01
## 6 1 Ranee~ Olivia ~ W 3 0 29 QF 2020-03-01
## 7 1 Ranee~ Salma H~ W 3 0 22 R3 2020-03-01
## 8 1 Ranee~ Juliann~ W 3 0 26 R2 2020-03-01
## 9 1 Ranee~ Nouran ~ W 1 0 15 F 2020-02-01
## 10 1 Ranee~ Hania E~ W 3 0 40 SF 2020-02-01
## # ... with 61 more rows, and 3 more variables: event <chr>, country <chr>,
## # psa <chr>
El Welily’s 2019-2020 match data exemplify her general dominance of women’s competitons. She has a 5.45 win/loss ratio across the tournaments she participated in and 65% of her match wins are 3-0s. Even when she does lose a match, she does not make it easy for her opponents. Her median match times in losses are longer regardless of the number of games played. Although, the sample size is low for El Welily’s losses.
Given El Welily’s general dominance, how does she play versus the current #2, Nouran Gohar? We can use get_matchup()
to get a quantitative take on their head-to-head performance.
welily_v_gohar <- get_matchup("Raneem El Welily", "Nouran Gohar", category = "womens")
El Welily’s currently holds a lead on Gohar by five matches since Janary 1, 2019 and a majority of her wins over Gohar have been decisive (3-1 or 3-0).
| Raneem El Welily v Nouran Gohar | |
|---|---|
| Head-to-Head Game Statistics, 2019-2020 | |
| Metric | Value |
| Games Played | 32 |
| Player 1 Games Won | 20 |
| Player 2 Games Won | 11 |
| Player 1 Avg Advantage | 4.75 |
| Player 2 Avg Advantage | 4.73 |
| Avg Point Diff | 4.74 |
| Player 1 Tiebreak Wins | 2 |
| Player 2 Tiebreak Wins | 1 |
| Pct Games Tiebreak | 6.25 |
PSA Rankings are the definitive international ranking that decide which players are the best in the world. They have a huge influence on how tournament organizers seed individual players. Using get_rankings()
, let’s scrape the top 15 players from the most recent rankings tables (April 2020 at time of writing) for both competition categories.
apr <- get_rankings(top = 15, category = "both")
| Men's PSA Rankings (April 2020) | ||||||
|---|---|---|---|---|---|---|
| Rank | Previous Rank | Change | Name | Highest Ranking | Highest Ranking Date | Country |
| 1 | 1 | — | Mohamed Elshorbagy | 1 | 2014-11-01 | EGY |
| 2 | 2 | — | Ali Farag | 1 | 2019-03-01 | EGY |
| 3 | 4 | 1 | Karim Abdel Gawad | 1 | 2017-05-01 | EGY |
| 4 | 3 | −1 | Tarek Momen | 3 | 2019-02-01 | EGY |
| 5 | 5 | — | Paul Coll | 5 | 2019-04-01 | NZL |
| 6 | 6 | — | Diego Elias | 6 | 2020-01-01 | PER |
| 7 | 8 | 1 | Marwan Elshorbagy | 3 | 2018-05-01 | EGY |
| 8 | 7 | −1 | Simon Rösner | 3 | 2018-12-01 | GER |
| 9 | 9 | — | Miguel Rodriguez | 4 | 2015-06-01 | COL |
| 10 | 10 | — | Joel Makin | 10 | 2020-03-01 | WAL |
| 11 | 11 | — | Mohamed Abouelghar | 7 | 2019-06-01 | EGY |
| 12 | 12 | — | Fares Dessouky | 8 | 2017-11-01 | EGY |
| 13 | 13 | — | Saurav Ghosal | 10 | 2019-04-01 | IND |
| 14 | 14 | — | Mazen Hesham | 13 | 2015-12-01 | EGY |
| 15 | 17 | 2 | Omar Mosaad | 3 | 2016-06-01 | EGY |
| Women's PSA Rankings (April 2020) | ||||||
|---|---|---|---|---|---|---|
| Rank | Previous Rank | Change | Name | Highest Ranking | Highest Ranking Date | Country |
| 1 | 1 | — | Raneem El Welily | 1 | 2015-09-01 | EGY |
| 2 | 2 | — | Nouran Gohar | 2 | 2017-01-01 | EGY |
| 3 | 4 | 1 | Nour El Sherbini | 1 | 2016-05-01 | EGY |
| 4 | 3 | −1 | Camille Serme | 2 | 2017-02-01 | FRA |
| 5 | 5 | — | Nour El Tayeb | 3 | 2018-02-01 | EGY |
| 6 | 7 | 1 | Sarah-Jane Perry | 6 | 2017-09-01 | ENG |
| 7 | 10 | 3 | Hania El Hammamy | 7 | 2020-04-01 | EGY |
| 8 | 8 | — | Amanda Sobhy | 6 | 2016-10-01 | USA |
| 9 | 6 | −3 | Joelle King | 3 | 2019-02-01 | NZL |
| 10 | 9 | −1 | Tesni Evans | 9 | 2018-11-01 | WAL |
| 11 | 12 | 1 | Joshna Chinappa | 10 | 2016-07-01 | IND |
| 12 | 13 | 1 | Salma Hany | 12 | 2019-03-01 | EGY |
| 13 | 16 | 3 | Olivia Blatchford Clyne | 12 | 2017-12-01 | USA |
| 14 | 15 | 1 | Yathreb Adel | 14 | 2020-04-01 | EGY |
| 15 | 14 | −1 | Alison Waters | 3 | 2010-10-01 | ENG |
get_rankings()
is useful for analyzing how player rankings have changed recently. Alternatively, we can look back on an individual player’s ranking history with get_player_rankings_history()
.ph <- get_player_rankings_history(rank = 1:4, category = "womens")
This is excellent data to use when discussing the story-lines of the sport. In this example, the current top 4 women’s players all began their careers before 2012. Since the end of 2014, they have occupied the top 20 rankings. We can see that Nouran Gohar had a meteoric rise through the rankings after her first PSA ranking in January 2011. The following graphic shows the last year of these players’ ranking histories.
How will these rankings shift over the next couple of years?
squashinformr
provides useful functions for pulling tournament data at many levels of detail. This includes tournament metadata, players, matches, and games.
The lowest level of detail is the tournament games, accessed via get_tournament_games()
. Let’s use this function to look at the JP Morgan Tournament of Champions, one of the premier tournaments in squash.
## Return game data for 2020's Tournament of Champions.
(toc <- get_tournament_games("tournament of champions", year = 2020))
## # A tibble: 388 x 15
## tournament_name category tournament_date round match game player_1 player_2
## <chr> <chr> <date> <ord> <int> <int> <chr> <chr>
## 1 JP Morgan Tour~ Men's 2020-01-17 F 64 4 Mohamed~ Tarek M~
## 2 JP Morgan Tour~ Men's 2020-01-17 F 64 3 Mohamed~ Tarek M~
## 3 JP Morgan Tour~ Men's 2020-01-17 F 64 2 Mohamed~ Tarek M~
## 4 JP Morgan Tour~ Men's 2020-01-17 F 64 1 Mohamed~ Tarek M~
## 5 JP Morgan Tour~ Women's 2020-01-17 F 62 3 Camille~ Nour El~
## 6 JP Morgan Tour~ Women's 2020-01-17 F 62 2 Camille~ Nour El~
## 7 JP Morgan Tour~ Women's 2020-01-17 F 62 1 Camille~ Nour El~
## 8 JP Morgan Tour~ Men's 2020-01-17 SF 63 5 Tarek M~ Ali Far~
## 9 JP Morgan Tour~ Men's 2020-01-17 SF 63 4 Tarek M~ Ali Far~
## 10 JP Morgan Tour~ Men's 2020-01-17 SF 63 3 Tarek M~ Ali Far~
## # ... with 378 more rows, and 7 more variables: game_winner <chr>,
## # player_1_score <dbl>, player_2_score <dbl>, player_1_seed <dbl>,
## # player_2_seed <dbl>, player_1_nationality <chr>, player_2_nationality <chr>
Let’s compare the consistency of a player’s performance in the men’s tournament. We can ask, by what point margin did players win or lose on average throughout this tournament?
If we only look at the quarter-finalists, we can see that most were able to defeat their opponents with a comfortable lead of 4 or 5 points.
The averages for Karim Abdel Gawad, Simon Rösner, and Paul Coll are particularly high. This reflects how they handedly defeated many of their opponents up to the third round of the tournament.
Conversely, two players stand out from a “defensive” perspective. Simon Rösner only lost 3 games, all by a 2 or 3 point margin, during a game 5 defeat to Karim Abdel Gawad. Meanwhile, Mohamed Elshorbagy, the eventual champion, lost only one game. Tarek Momen took a game off of Elshorbagy in the final match, but only by a margin of 2 points. Elshorbagy certainly came to play in this tournament!
In this post, I’ve shown only a small selection of the functions provided in squashinformr
. Install the package and try it out yourself! There is a huge amount of data on this excellent sport that has yet to be explored. Cheers!
The full code for this post is available here.
If you find a bug, please submit an issue on GitHub.
If you are interested in helping me extend the functionality of this package, fork the repository, make changes, and submit them as a pull request. The squashinformr
project is released with a Contributor Code of Conduct. By contributing to this project, you agree to its terms.
SquashInfo is a valuable resource for the international squash community. By creating and sharing this package, I do not intend to compete with SquashInfo or any of its stakeholders. The squashinformr
package was created to allow individuals to access data from SquashInfo in an efficient and responsible way, using polite
principles. SquashInfo currently offers full access to their data and extra features through a premium membership. Please consider signing up and subscribing to SquashInfo to support their work.
The squashinformr
package is released under a GPL-3 license.


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Web Developer | Triathlete | #Trini
Download ResumeI am a web developer that just made a career transition from structural engineering. In my last position, I designed bridges—primarily in New England, but some across the United States. Many of my most exciting bridge design experiences involved creating models using proprietary software or visual basic to optimize the designs. These experiences reminded me of my experience in graduate school, where I used software to model the behavior of buildings during earthquakes. As I considered my career change, I reflected on my enjoyment solving problems using code.
At the same time, I have been a lifelong leader in athletic endeavors, mostly as a captain of various swim teams from childhood through the collegiate level, then as the Athletic Director of Boston Triathlon Team today. One of my greatest athletic passions has always been understanding training/performance data, and how it translates to athletic improvements. I have always enjoyed “looking at the numbers” with respect to sports that I participate in. The combination of my passions for solving problems with code and analysis during my athletic endeavors makes the role of a software developer in a team environment a natural fit for me.
Meet duckCubeJavaScript
jQuery
Rails
React.js
Ruby
Express.js
Node.js
mongoDB
PostgreSQL
HTML5
CSS3
Bootstrap
Sass
Handlebars
Git
Github
Heroku
AWS
I completed a 12-week, full-time, immersive, full-stack web development bootcamp. The bootcamp was divided into four modules, each with its own project. Each module/project focused on various skills in front- and back-end web development. Three projects were completed individually, and one was completed in a development team.
I am an elected member of the board of directors, responsible for the operation and strategic direction of the Boston Triathlon Team, a 501(c)3 athletic organization. I created an automated platform for team members to enter results from their races and display them on the team website. I also manage the annual Indoor Time Trial, which generates approx. 20% of the team's income.
I contributed to cross-disciplinary teams that designed and maintained highway bridges across the U.S. I regularly wrote custom functions in Excel and bridge design software packages, to optimize designs of irregularly-shaped bridges and to increase the efficiency of analyses. Design teams produced construction plans, specifications, cost estimates and calculations. Project values ranged from $30,000 to $40,000,000.
As a graduate student, my interests brought me to study buildings and their resistance to earthquake loads. At the culmination of my thesis research, I presented a method for modeling the behavior of buildings during major seismic events.
While I was a Civil Engineering student, I placed emphasis on having a rounded college experience. I intentionally crossed borders by studying Japanese, Economics and Psychology, and by joining the varsity swim team, ATO of Massachusetts and the International Club. I grasped every opportunity to spread my wings.
I would love to hear from you. Please don't hesitate to reach out at any link below!


Register Access
Introduction
To make the processor registers directly available to developers, We added the Register class.
Register PCONP = new Register(0xE01FC0C4);
// Enable timer2 and timer3
PCONP.SetBits((1 << 22) | (1 << 23));
Do you understand why 1 << 22 was used? It's easy to get the first bit (0x01), second bit (0x02) and so on but what about the higher bits such as 0x00400000? Which bit is this one? Instead of complicating things, let the compiler do the work for you. Simply use 1 << 22 for the 22nd bit.
Pulse Counter
NETMF doesn't have a way to count pulses. What if the processor supports it?
Here is an example that sets timer 3 and P0.4 on the LPC2478 processor to count pulses. While this code will compile on any platform, it will only work on LPC2478. Other processors have different registers and different features.
Tip
You will need the LPC2478 datasheet and manuall to fully understand the example.
using System;
using System.Threading;
using Microsoft.SPOT;
using GHI.Processor;
namespace MFConsoleApplication1
{
public class Program
{
public static void Main()
{
Register PCONP = new Register(0xE01FC0C4);
PCONP.SetBits(1 << 22);//enable timer2
// Select IO0 on EMX CAP2.0
Register PINSEL0 = new Register(0xE002C000);
PINSEL0.SetBits((3 << 8));//set bits 8 and 9
// To enable timer/counter
Register T2TCR = new Register(0xE0070004);
T2TCR.Value = 1;
// set prescale to 0
Register T2PR = new Register(0xE007000C);
T2PR.Value = 0;
Register T2CTCR = new Register(0xE0070070);
T2CTCR.Value = (2 << 0 | 0 << 2);//count on falling edge and use CAPn.0
// should be 0 for a counter
Register T2CCR = new Register(0xE0070028);
T2CCR.ClearBits(0x07);
// Don't do anything on match
Register T2MCR = new Register(0xE0070014);
T2MCR.Value = 0;
// To reset the counter
T2TCR.SetBits((1 << 1));
T2TCR.ClearBits((1 << 1));
// To read
Register T2TC = new Register(0xE0070008);
while (true)
{
uint count = T2TC.Value;
Debug.Print("Total count: " + count);
Thread.Sleep(1000);
}
}
}
}


SUPERNOVA
Review by John Beachem
* *
Directed by: Walter Hill Written by: William Malone (story), Daniel Chuba
It's never a good sign when a director won't allow his name to be shown in a film's credits. Director Walter Hill has used his pseudonym, Thomas Lee, here, which set warning bells off for me before ever reaching the movie. The fact that the script has been worked on by numerous different writers and that MGM didn't allow for an advance screening to critics, further went to show what a dog they had on their hands here. As a result, I went in expecting a godawful film. Sure enough, what I got was a piece of garbage. A rather nice looking one, but still trash through and through.
The deep space medical ship, Nightingale, has just picked up a distress signal from an abandoned mining colony. The crew is led by Nick Vanzant (James Spader) after the former captain, A. J. Marley (Robert Forster), is killed in the first fifteen minutes. The medical staff is comprised of Kaela Evers (Angela Bassett), Yerzy Peneloza (Lou Diamond Phillips), Danika Lund (Robin Tunney) and Benj Stotomajor (Wilson Cruz). Upon reaching the colony, the crew finds one man, Troy Larson (Peter Facinelli), who is in posession of an alien object he found at the site and whose father had a fling with Kaela. It soon appears that Troy has been influenced in some way by the artifact, and he is now after the crew. Meanwhile, an accident has caused the ship to run out of fuel, and they are slowly being sucked into a nearby star.
If my plot summary sounds rather insipid, that's because the film itself is so utterly ludicrous.  After a dull, plodding start, which I suppose was present to develop the characters (it failed miserably), the film leaps into overdrive and doesn't let up; this includes the camera bouncing all over the place (i.e. "The Blair Witch Project"). As for the acting, very little is required. Besides Spader and Bassett, the crew members are required to scream a lot and run around. Spader is rather humorous at times, particularly when speaking with the ship's computer in a few surprisingly amusing scenes.
The film is certainly rubbish, but it's refuse that is pleasing to the eye. The ship itself is an interesting design, looking something like the Nostromo from "Alien" crossed with a modern space shuttle. The moon on which the mining camp is located looks wonderful during several scenes; especially those in which Spader walks along a massive tunnel and we are granted a view of the entire camp with the blue star in the background. The alien artifact is oddly designed, with waves of energy constantly pulsating from it and distorting all objects nearby. As for the film's ending (which I normally wouldn't mention but I feel obliged to here), it never exactly ends. The film simply stops, leaving us with several small unanswered questions and one huge one. The nice thing is, you won't care in the slightest after leaving the theatre.
Robin Tunney (End of Days) is quickly approaching being one of my least favorite actors. The girl may be attractive, but she obviously cannot act her way out of a paper bag. Her part here requires a slight acting job, and she still fails to deliver an adequate performance (not that her dialogue was any help). "Supernova" may be rated PG-13, but it sure pushes that rating to extremes. The film is full of nudity, violence and sex. It just screams for an R rating, but all the scenes are cut just around it so you seldom fully see anything. The movie runs 125 minutes which could easily have been hacked down to 95 or so. I'd recommend missing this one and grant it two stars.
Feel free to e-mail with comments at<EMAIL_ADDRESS>* * * * * - One of the greatest movies ever made, see it now. * * * * - Great flick. Try and catch this one. * * * - Okay movie, hits and misses. * * - Pretty bad. See it if you've got nothing better to do. * - One of the worst movies ever. See it only if you enjoy pain.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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The port authority should be contacted on VHF Channel 16 for instructions on arrival.
Customs & Immigration offices are open 08:00-12:30, 14:00-17:00 Monday to Friday. Customs will require a copy of the Ship's papers, as well as a payment of 5,000 CFA (US$12).
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Last updated October 2014.
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| Name | Azimuthal Equidistant |
| EPSG Code | |
| GeoTIFF Code | CT_AzimuthalEquidistant (12) |
| OGC WKT Name | Azimuthal_Equidistant |
| Supported By | GeoTIFF, PROJ.4, OGC WKT |
| Name | EPSG # | GeoTIFF ID | OGC WKT | Units | Notes |
|---|---|---|---|---|---|
| Latitude of projection center | 1 | ProjCenterLat | latitude_of_center | Angular | |
| Longitude of projection center | 2 | ProjCenterLong | longitude_of_center | Angular | |
| False Easting | 6 | FalseEasting | false_easting | Linear | |
| False Northing | 7 | FalseNorthing | false_northing | Linear |
I also can't get projection results to match between PROJ.4 and PCI (GCTP) as mentioned in Random Issues.
+proj=aeqd +lat_0=Latitude at projection center +lon_0=Longitude at projection center +x_0=False Easting +y_0=False Northing


Scream 2 *** (out of ****)
DIRECTED BY Wes Cravens
Starring Neve Campbell, Courteney Cox, David Arquette, Sarah Michelle Gellar, Jamie Kennedy, Laurie Metcalf, Jerry O'Connell
We're not breaking new ground here, folks.
If you liked the original Scream, you'll enjoy the sequel. But it is still a sequel. The joy of discovery (as with the original Scream, which was the first good slasher pic in more than 10 years) is gone.
Everyone who survived the first Scream (yes, I'm going to spoil the original's plot; if you haven't seen it by now, tough noogies) is back -- plucky Sydney Prescott (Neve Campbell) is now a college freshman, majoring in theater. Randy (Jamie Kennedy), the kid who knew all the horror cliches, is at the same college, majoring in film. Deputy Dewey (David Arquette), crippled by his injuries, is no longer a cop; Gale Weathers (Courteney Cox) is still a sleazy journalist, and has written a book about the Woodsboro murders of the first film.
As Scream 2 opens, the movie of Weathers' book on the murders from the first film (stay with me here) is opening. Jada Pinkett and Omar Epps play an unlucky young couple who attend the premiere of Stab, as the movie-in-a-movie is called.
The movie-in-a-movie stuff is a real hoot. Sydney's worst fear is realized -- in Stab, she's portrayed by Tori Spelling. If you've seen Scream, the movie-in-a-movie will seem very strange ... I kept thinking "No, that's not how it really happened! I was there, I know!" This must be the way Joey Buttafuoco felt when he watched the three TV movies about him and Amy Fisher.
After this very promising beginning, Scream 2 becomes a pretty damned good slasher-pic sequel. The characters have fun with the sequel cliches (though not as much as in the original). Many people are killed. There are some neat, nightmarish sequences, such as Sydney's rehearsal of "Agamemnon," in which she plays Cassandra.
The movie cheats at the end; any good whodunit (which is, in essence, what both Scream flicks are) should give subtle clues to the killer's identity. When he or she is finally revealed, the audience should think, "Of course! I should've known!"
Not here. The killer's identity came totally out of left field. All right, as one character in Scream said, "It's the millennium, motives are incidental." But I don't think it's too much to ask that the perp have some reason for doing what he or she does.
All criticism aside, Scream 2 is an entertaining movie. Fans of the first are unlikely to be disappointed.
Scream 3, apparently, is in the works. Wes Craven says he planned the movies as a trilogy all along. I'm skeptical, but maybe he'll break new ground with the third installment. We'll see.
-- Julie M. Prince
____________ http://www.peak.org/~jprince *<EMAIL_ADDRESS>________________
"The opposite of war isn't peace -- | Jonathan Larson it's CREATION." | R E N T
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


{% trans "Hi" %} {{ party.name }}! {% trans "Thanks for your response!" %}
{% if invitation.any_guests_attending %}{% trans "We can't wait to celebrate with you!" %}
{% trans "If you have any other questions, don't hesitate to email us at" %} {{ support_email }}.
{% else %}{% trans "We're sorry you won't be able to join us." %}
{% trans "We look forward to catching up with you some other time!" %}
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Lecture 2:
Image Classification pipeline
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 1 6 Jan 2016
First assignment will come out tonight (or tomorrow at worst)
It is due January 20 (i.e. in two weeks). Handed in through CourseWork It includes:
Write/train/evaluate a kNN classifier
Write/train/evaluate a Linear Classifier (SVM and Softmax)
Write/train/evaluate a 2-layer Neural Network (backpropagation!)
Requires writing numpy/Python code
Warning: don’t work on assignments from last year!
Compute: Can use your own laptops, or Terminal.com
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 2 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 3 6 Jan 2016
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: a core task in Computer Vision
(assume given set of discrete labels)
{dog, cat, truck, plane, ...}
cat
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 6 6 Jan 2016
semantic gap
Images are represented as 3D arrays of numbers, with integers between [0, 255].
E.g.
300 x 100 x 3
(3 for 3 color channels RGB)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 7 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 8 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 9 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 10 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 11 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 12 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 13 6 Jan 2016
Unlike e.g. sorting a list of numbers,
no obvious way to hard-code the algorithm for recognizing a cat, or other classes.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 14 6 Jan 2016
???
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 15 6 Jan 2016
Data-driven approach:
Collect a dataset of images and labels
Use Machine Learning to train an image classifier
Evaluate the classifier on a withheld set of test images
Example training set
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 16 6 Jan 2016
Nearest Neighbor Classifier
Remember all training images and their labels
Predict the label of the most similar training image
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 17 6 Jan 2016
CIFAR-10
labels
50,000 training images, each image is tiny: 32x32
10,000 test images.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 18 6 Jan 2016
CIFAR-10
labels
50,000 training images
examples of nearest neighbors in rows
10,000 test images.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 19 6 Jan 2016
distance metric?
L1 distance:
add
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 20 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 21 6 Jan 2016
remember the training data
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 22 6 Jan 2016
Nearest Neighbor classifier
for every test image:
- find nearest train image with L1 distance
- predict the label of nearest training image
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how does the classification speed depend on the size of the training data?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 24 6 Jan 2016
Nearest Neighbor classifier
Q: how does the classification speed depend on the size of the training data? linearly :(
This is backwards:
- test time performance is usually much more important in practice.
- CNNs flip this: expensive training, cheap test evaluation
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find approximate nearest neighbors quickly
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 26 6 Jan 2016
hyperparameter common choices:
L1 (Manhattan) distance L2 (Euclidean) distance
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 27 6 Jan 2016
find the k nearest images, have them vote on the label
the data NN classifier 5-NN classifier
http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 28 6 Jan 2016
CIFAR-10
labels
50,000 training images
examples of nearest neighbors in rows
10,000 test images.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 29 6 Jan 2016
NN classifier 5-NN classifier
what is the accuracy of the nearest neighbor classifier on the training data, when using the Euclidean distance?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 30 6 Jan 2016
NN classifier 5-NN classifier
Q2: what is the accuracy of the k-nearest neighbor classifier on the training data?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 31 6 Jan 2016
distance to use?
What is the best value of k to use?
i.e. how do we set the hyperparameters?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 32 6 Jan 2016
distance to use?
What is the best value of k to use?
i.e. how do we set the hyperparameters?
Very problem-dependent.
Must try them all out and see what works best.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 33 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 34 6 Jan 2016
Very bad idea. The test set is a proxy for the generalization performance! Use only VERY SPARINGLY, at the end.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 35 6 Jan 2016
Validation data
use to tune hyperparameters
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 36 6 Jan 2016
Cross-validation
cycle through the choice of which fold
is the validation fold, average results.
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k.
Each point: single outcome.
The line goes
through the mean, bars indicated standard deviation
(Seems that k ~= 7 works best for this data)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 38 6 Jan 2016
k-Nearest Neighbor on images never used.
terrible performance at test time
distance metrics on level of whole images can be very unintuitive
(all 3 images have same L2 distance to the one on the left)
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Image Classification: We are given a Training Set of labeled images, asked to predict labels on Test Set. Common to report the Accuracy of predictions (fraction of correctly predicted images)
We introduced the k-Nearest Neighbor Classifier, which predicts the labels based on nearest images in the training set
We saw that the choice of distance and the value of k are hyperparameters that are tuned using a validation set, or through cross-validation if the size of the data is small.
Once the best set of hyperparameters is chosen, the classifier is evaluated once on the test set, and reported as the performance of kNN on that data.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 40 6 Jan 2016
Linear Classification
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 41 6 Jan 2016
language control
see
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 42 6 Jan 2016
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 43 6 Jan 2016
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CNN
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 45 6 Jan 2016
CIFAR-10
labels
50,000 training images each image is 32x32x3
10,000 test images.
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 46 6 Jan 2016
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[32x32x3]
array of numbers 0...1
(3072 numbers total)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 47 6 Jan 2016
Linear classifier
10 numbers,
indicating class
scores
[32x32x3]
array of numbers 0...1
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 48 6 Jan 2016
Linear classifier
3072x1
10x1 10x3072
10 numbers,
indicating class
scores
[32x32x3]
array of numbers 0...1
parameters, or “weights”
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 49 6 Jan 2016
Linear classifier
3072x1
10x1
10x1 10x3072
10 numbers,
indicating class
scores
[32x32x3]
array of numbers 0...1
parameters, or “weights”
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 50 6 Jan 2016
cat/dog/ship)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 51 6 Jan 2016
Q: what does the linear classifier do, in English?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 52 6 Jan 2016
Example trained weights of a linear classifier trained on CIFAR-10:
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 53 6 Jan 2016
[32x32x3]
array of numbers 0...1 (3072 numbers total)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 54 6 Jan 2016
Q2: what would be a very hard set of classes for a linear classifier to distinguish?
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 55 6 Jan 2016
We defined a (linear) score function:
Example class scores for 3 images, with a random W:
-3.45 -0.51 3.42
-8.87 6.04 4.64
0.09 5.31 2.65
2.9 -4.22 5.1
4.48 -4.19 2.64
8.02 3.58 5.55
3.78 4.49 -4.34
1.06 -4.37 -1.5
-0.36 -2.09 -4.79
-0.72 -2.93 6.14
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 56 6 Jan 2016
Coming up:
Loss function
Optimization
ConvNets!
(quantifying what it means to have a “good” W)
(start with random W and find a W that minimizes the loss)
(tweak the functional form of f)
Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 2 57 6 Jan 2016


Read Met Office Unified Model (TM) (hereafter MetUM) netCDF files and extact the variables in varlist. MetUM = read_MetUM_forcing(files, varlist) DESCRIPTION: Given a cell array of file names, extract the variables in varlist into a struct. Field names are the variable names gives. INPUT: files - cell array of file names (full paths to the files) varlist [optional] - list of variable names to extract. If omitted, all variables are extracted. OUTPUT: MetUM - MATLAB struct with the data from the variables specified in varlist. EXAMPLE USAGE: varlist = {'x', 'y', 't_1', 'sh', 'x-wind', 'y-wind', 'rh', 'sh', ... 'lh', 'solar', 'longwave'}; files = {'/tmp/sn_2011010100_s00.nc', '/tmp/sn_201101016_s00.nc'}; MetUM = read_MetUM_forcing(files, varlist); NOTE: The last 4 times are dropped from each file because the Met Office Unified Model is a forecast model with four hours of forecast in these PP files. Author(s): Pierre Cazenave (Plymouth Marine Laboratory) Revision history: 2013-08-29 First version. 2013-09-02 Amend the way the 3 and 4D variables are appended to one another. The assumption now is time is the last dimension and arrays are appended with time. 2013-09-06 Trim the last 4 time samples from all variables (these are the forecast results which we don't want/need for forcing the model. I suppose at some point, given the patchy temporal coverage of the data (i.e. the Met Office FTP server doesn't have all files in a usable state), it might be better to use the forecast data to partially fill in gaps from missing files. However, given this forcing is hourly and I was previously using four times daily forcing, I'm not that fussed. 2013-09-12 Add support for extracting the surface pressure level from the 4D temperature variable (temp_2). 2013-10-23 Fix the way time is handled. Previously a time variable had to be specified in varlist. Now, each data variable's time is returned as an array within the MetUM.(variable) struct, giving MetUM.(variable).time and MetUM.(variable).data. This means if each data variable uses a different time sampling, that can be accounted for later (by interpolating to a common time series with interp3, for example). Currently the code extracts the first 6 hour's worth of data. The assumption there is that the Met Office do 4 runs per day, so 6 hours of data from each run gives you a day's worth. ==========================================================================
0001 function MetUM = read_MetUM_forcing(files, varlist) 0002 % Read Met Office Unified Model (TM) (hereafter MetUM) netCDF files and 0003 % extact the variables in varlist. 0004 % 0005 % MetUM = read_MetUM_forcing(files, varlist) 0006 % 0007 % DESCRIPTION: 0008 % Given a cell array of file names, extract the variables in varlist into 0009 % a struct. Field names are the variable names gives. 0010 % 0011 % INPUT: 0012 % files - cell array of file names (full paths to the files) 0013 % varlist [optional] - list of variable names to extract. If omitted, all 0014 % variables are extracted. 0015 % 0016 % OUTPUT: 0017 % MetUM - MATLAB struct with the data from the variables specified in 0018 % varlist. 0019 % 0020 % EXAMPLE USAGE: 0021 % varlist = {'x', 'y', 't_1', 'sh', 'x-wind', 'y-wind', 'rh', 'sh', ... 0022 % 'lh', 'solar', 'longwave'}; 0023 % files = {'/tmp/sn_2011010100_s00.nc', '/tmp/sn_201101016_s00.nc'}; 0024 % MetUM = read_MetUM_forcing(files, varlist); 0025 % 0026 % NOTE: 0027 % The last 4 times are dropped from each file because the Met Office 0028 % Unified Model is a forecast model with four hours of forecast in these 0029 % PP files. 0030 % 0031 % Author(s): 0032 % Pierre Cazenave (Plymouth Marine Laboratory) 0033 % 0034 % Revision history: 0035 % 2013-08-29 First version. 0036 % 2013-09-02 Amend the way the 3 and 4D variables are appended to one 0037 % another. The assumption now is time is the last dimension and arrays 0038 % are appended with time. 0039 % 2013-09-06 Trim the last 4 time samples from all variables (these are 0040 % the forecast results which we don't want/need for forcing the model. I 0041 % suppose at some point, given the patchy temporal coverage of the data 0042 % (i.e. the Met Office FTP server doesn't have all files in a usable 0043 % state), it might be better to use the forecast data to partially fill 0044 % in gaps from missing files. However, given this forcing is hourly and I 0045 % was previously using four times daily forcing, I'm not that fussed. 0046 % 2013-09-12 Add support for extracting the surface pressure level from 0047 % the 4D temperature variable (temp_2). 0048 % 2013-10-23 Fix the way time is handled. Previously a time variable had 0049 % to be specified in varlist. Now, each data variable's time is returned 0050 % as an array within the MetUM.(variable) struct, giving 0051 % MetUM.(variable).time and MetUM.(variable).data. This means if each 0052 % data variable uses a different time sampling, that can be accounted for 0053 % later (by interpolating to a common time series with interp3, for 0054 % example). Currently the code extracts the first 6 hour's worth of data. 0055 % The assumption there is that the Met Office do 4 runs per day, so 6 0056 % hours of data from each run gives you a day's worth. 0057 % 0058 %========================================================================== 0059 0060 subname = 'read_MetUM_forcing'; 0061 0062 global ftbverbose 0063 if ftbverbose 0064 fprintf('\nbegin : %s \n', subname) 0065 end 0066 0067 assert(iscell(files), 'List of files provided must be a cell array.') 0068 0069 % Find the approximate surface pressure level (1013.25mbar) for the 4D 0070 % temperature data. 0071 nc = netcdf.open(files{1}, 'NOWRITE'); 0072 [~, numvars, ~, ~] = netcdf.inq(nc); 0073 levelidx = []; 0074 for f = 1:numvars 0075 [varname, ~, ~, ~] = netcdf.inqVar(nc, f - 1); 0076 if strcmp(varname, 'p') % p = pressure levels in the temp_2 variable. 0077 varid = netcdf.inqVarID(nc, varname); 0078 tmpdata = netcdf.getVar(nc, varid, 'double'); 0079 % Find the index for the level closest to the surface. The 0080 % documentation at: 0081 % http://badc.nerc.ac.uk/data/um/Met_Office_NAE_Output.pdf 0082 % suggests 980hPa is the surface. 0083 [~, levelidx] = min(abs(tmpdata - 980)); 0084 end 0085 end 0086 0087 % If that failed, use a best guess of the 5th index (based on my checking a 0088 % bunch of random files where the 1000mbar value falls in the p index). 0089 if isempty(levelidx) 0090 levelidx = 5; 0091 end 0092 0093 MetUM = struct(); 0094 0095 for f = 1:length(files) 0096 0097 % Set the number of time steps to extract to default to 6. It should be 0098 % checked for each file anyway (assuming there's a time variable being 0099 % requested). 0100 nh = 6; 0101 0102 if ftbverbose 0103 % Don't display the full path if it's really long. 0104 if length(files{f}) > 80 0105 [~, fname, fext] = fileparts(files{f}); 0106 dispname = [fname, fext]; 0107 else 0108 dispname = files{f}; 0109 end 0110 fprintf('File %i of %i (%s)... ', f, length(files), dispname) 0111 end 0112 0113 nc = netcdf.open(files{f}, 'NOWRITE'); 0114 0115 % Query the netCDF file to file the variable names. If the name matches 0116 % one in the list we've been given (or if we haven't been given any 0117 % particular variables), save it in the output struct. 0118 [~, numvars, ~, ~] = netcdf.inq(nc); 0119 0120 for ii = 1:numvars 0121 % Find the name of the current variable 0122 [varname, ~, ~, varAtts] = netcdf.inqVar(nc, ii - 1); 0123 0124 if ismember(varname, varlist) || nargin == 1 0125 varid = netcdf.inqVarID(nc, varname); 0126 0127 % Some variables contain illegal (in MATLAB) characters. Remove 0128 % them here. 0129 safename = regexprep(varname, '-', ''); 0130 0131 % Append the data on the assumption the last dimension is time. 0132 % Don't append data with only 2 dimensions as it's probably 0133 % longitude or latitude data. The time variable ('t') is 0134 % turned into a list of time stamps. 0135 tmpdata = squeeze(netcdf.getVar(nc, varid, 'double')); 0136 nn = ndims(tmpdata); 0137 0138 if isfield(MetUM, safename) 0139 switch varname 0140 case {'x', 'y', 'x_1', 'y_1', 'longitude', 'latitude', 'lsm'} 0141 continue 0142 case {'t', 't_1', 't_2', 't_3', 't_4', 't_5', 't_6', 't_7', 't_8'} 0143 % Ignore time variables. 0144 continue 0145 otherwise 0146 try 0147 % Extract the time for this variable. 0148 temptime = fix_time(nc, varid); 0149 0150 % Find how many indices to extract to at least 0151 % 6 hours of data. 0152 interval = mean(roundn(diff(datenum(temptime)) * 24 * 60, 0)); 0153 if abs(60 - interval) < abs(30 - interval) 0154 % Hourly 0155 nh = 6; 0156 elseif abs(30 - interval) < abs(60 - interval) 0157 % Half-hourly 0158 nh = 12; 0159 else 0160 error('Unsupported time sampling interval (support hourly and half-hourly sampling).') 0161 end 0162 % Check we don't try and get more data than we 0163 % have. 0164 if nh > size(temptime, 1); 0165 nh = size(temptime, 1); 0166 end 0167 0168 MetUM.(safename).time = [MetUM.(safename).time; temptime(1:nh, :)]; 0169 0170 % Append along last dimension. 0171 if nn == 3 0172 MetUM.(safename).data = cat(nn, MetUM.(safename).data, tmpdata(:, :, 1:nh)); 0173 else 0174 % We're flattening from 4D to 3D here, so 0175 % nn - 1. 0176 MetUM.(safename).data = cat(nn - 1, MetUM.(safename).data, squeeze(tmpdata(:, :, levelidx, 1:nh))); 0177 end 0178 catch err 0179 fprintf('\n') 0180 warning('Couldn''t append %s to the existing field from file %s.', safename, files{f}) 0181 warning('%s\n', err.message) 0182 end 0183 0184 end 0185 else % first time around 0186 switch varname 0187 case {'x', 'y', 'x_1', 'y_1', 'longitude', 'latitude', 'lsm'} 0188 MetUM.(safename).data = tmpdata; 0189 case {'t', 't_1', 't_2', 't_3', 't_4', 't_5', 't_6', 't_7', 't_8'} 0190 % Ignore time variables. 0191 continue 0192 otherwise 0193 % This is data. 0194 0195 % Extract the time for this variable. 0196 temptime = fix_time(nc, varid); 0197 0198 % Find how many indices to extract to at least 0199 % 6 hours of data. 0200 interval = mean(roundn(diff(datenum(temptime)) * 24 * 60, 0)); 0201 if abs(60 - interval) < abs(30 - interval) 0202 % Hourly 0203 nh = 6; 0204 elseif abs(30 - interval) < abs(60 - interval) 0205 % Half-hourly 0206 nh = 12; 0207 else 0208 error('Unsupported time sampling interval (support hourly and half-hourly sampling).') 0209 end 0210 % Check we don't try and get more data than we 0211 % have. 0212 if nh > size(temptime, 1); 0213 nh = size(temptime, 1); 0214 end 0215 0216 MetUM.(safename).time = temptime(1:nh, :); 0217 0218 if nn == 3 0219 MetUM.(safename).data = tmpdata(:, :, 1:nh); 0220 else 0221 % Assume temperature at pressure levels. 0222 % Extract the 1000mb pressure level 0223 % (approximately the surface). 0224 MetUM.(safename).data = squeeze(tmpdata(:, :, levelidx, 1:nh)); 0225 end 0226 end 0227 end 0228 end 0229 end 0230 0231 if ftbverbose 0232 fprintf('done.\n') 0233 end 0234 0235 end 0236 0237 % Squeeze out singleton dimensions. 0238 fields = fieldnames(MetUM); 0239 for i = 1:length(MetUM) 0240 MetUM.(fields{i}) = squeeze(MetUM.(fields{i})); 0241 end 0242 0243 if ftbverbose 0244 fprintf('end : %s \n', subname) 0245 end 0246 0247 function fixedtime = fix_time(nc, varid) 0248 % Little helper function to get the time data for the current variable. 0249 % 0250 % INPUT: 0251 % nc : netCDF file handle 0252 % varid : current variable ID 0253 % 0254 % OUTPUT: 0255 % tt : date string for the current file (Gregorian date) 0256 0257 % Extract the time array for this variable's time dimension. 0258 [numdims, ~, ~, ~] = netcdf.inq(nc); 0259 dimnames = cell(numdims, 1); 0260 for jj = 1:numdims 0261 [dimname, ~] = netcdf.inqDim(nc, jj - 1); 0262 dimnames{jj} = dimname; 0263 end 0264 0265 % Find the dimensions of this variable. 0266 [~, ~, dimids, ~] = netcdf.inqVar(nc, varid); 0267 % We presume the time variable starts with a t. 0268 ttidx = strncmpi(dimnames(dimids + 1), 't', length('t')); 0269 ttvarid = netcdf.inqVarID(nc, dimnames{dimids(ttidx) + 1}); 0270 % There are issues around precision here, so 0271 % convert tt to minutes and then back to fractions 0272 % of a day. 0273 tt = netcdf.getVar(nc, ttvarid, 'double'); 0274 tt = roundn(tt * 24 * 60, -1) / 24 / 60; 0275 0276 [~, ~, ~, tVarAtts] = netcdf.inqVar(nc, ttvarid); 0277 0278 for j = 1:tVarAtts 0279 timeatt = netcdf.inqAttName(nc, ttvarid, j - 1); 0280 if strcmpi(timeatt, 'time_origin') 0281 timeval = netcdf.getAtt(nc, ttvarid, timeatt); 0282 end 0283 end 0284 mt = datenum(timeval, 'dd-mmm-yyyy:HH:MM:SS'); 0285 0286 fixedtime = datestr(mt + tt, 'yyyy-mm-dd HH:MM:SS');


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Program started at Thu-10-Apr-2008 10:07.
Analysed requests from Sun-01-Apr-2007 00:00 to Mon-31-Mar-2008 23:59 (366.00 days).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report contains overall statistics.
Successful requests: 204,094,482
Average successful requests per day: 557,636
Successful requests for pages: 59,680,716
Average successful requests for pages per day: 163,062
Failed requests: 82,682
Redirected requests: 4,623,199
Distinct files requested: 2,722
Distinct hosts served: 2,070,280
Corrupt logfile lines: 5,495
Unwanted logfile entries: 114,472,033
Data transferred: 3.24 terabytes
Average data transferred per day: 9.07 gigabytes
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the activity in each month.
Each unit () represents 200,000 requests for pages or part thereof.
| month | reqs | pages | |
|---|---|---|---|
| Apr 2007 | 19667809 | 6026154 | |
| May 2007 | 9442819 | 2956232 | |
| Jun 2007 | 3412184 | 1110491 | |
| Jul 2007 | 4934016 | 1658499 | |
| Aug 2007 | 10854020 | 3474448 | |
| Sep 2007 | 21636558 | 6496285 | |
| Oct 2007 | 24608574 | 7046519 | |
| Nov 2007 | 23437843 | 6821276 | |
| Dec 2007 | 16767018 | 4706740 | |
| Jan 2008 | 21865443 | 6235687 | |
| Feb 2008 | 22292058 | 6327946 | |
| Mar 2008 | 25176141 | 6820439 |
Busiest month: Oct 2007 (7,046,519 requests for pages).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each day of the week, summed over all the weeks in the report.
Each unit () represents 400,000 requests for pages or part thereof.
| day | reqs | pages | |
|---|---|---|---|
| Sun | 38045268 | 10851287 | |
| Mon | 41283898 | 12245302 | |
| Tue | 27658712 | 8249362 | |
| Wed | 25589466 | 7577141 | |
| Thu | 23018552 | 6931234 | |
| Fri | 20979503 | 6287707 | |
| Sat | 27519084 | 7538683 |
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each hour of the day, summed over all the days in the report.
Each unit () represents 150,000 requests for pages or part thereof.
| hour | reqs | pages | |
|---|---|---|---|
| 0 | 3006567 | 939050 | |
| 1 | 1409087 | 490104 | |
| 2 | 798905 | 311830 | |
| 3 | 552746 | 243458 | |
| 4 | 482607 | 217063 | |
| 5 | 601295 | 252492 | |
| 6 | 1348686 | 445883 | |
| 7 | 3704382 | 1113609 | |
| 8 | 8016077 | 2323574 | |
| 9 | 11278042 | 3284349 | |
| 10 | 11650588 | 3418973 | |
| 11 | 11724548 | 3462560 | |
| 12 | 12988302 | 3856851 | |
| 13 | 12886031 | 3808724 | |
| 14 | 10753754 | 3182261 | |
| 15 | 10960577 | 3222923 | |
| 16 | 13126706 | 3790628 | |
| 17 | 15106214 | 4229066 | |
| 18 | 15331744 | 4324176 | |
| 19 | 15183645 | 4329295 | |
| 20 | 14136292 | 4074538 | |
| 21 | 12724038 | 3649705 | |
| 22 | 10280706 | 2934335 | |
| 23 | 6042944 | 1775269 |


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About Me
My name is Kabita LimbuI am from Nepal and currently living in Japan
Education Background
Year
Degree
School/University
Major
2014
+2
Public Higher Secondary School
Management
2018
Bachelor's Degree
Dharan Model College
Bachelor in Business Studies