GBP/USD Technical Analysis: Cable advances to fresh weekly highs, trading above the 1.2900 handle
by Flavio Tosti- GBP/USD is ending the week near the weekly highs.
- Resistances to the upside are seen at the 1.2950 level and the 1.3000 handle.
GBP/USD daily chart
The sterling is currently correcting last month’s rally in the 1.2800-1.3000 price zone. GBP/USD, in October, hit levels not seen since mid-May 2019.
GBP/USD four-hour chart
GBP/USD is breaking above the 1.2874/1.2910 resistance zone. The spot is trading above its main SMAs, suggesting bullish momentum in the medium term. If the market breaks the 1.2910 resistance on a daily basis, the spot could reach the 1.2950 level and the 1.3000 handle on the way up, according to the Technical Confluences Indicator.
GBP/USD 30-minute chart
The spot is trading above its main SMAs, suggesting a bullish bias in the short term. Support is seen at the 1.2874 and the 1.2833 price levels.
Additional key levels
GBP/USD
| Overview | |
|---|---|
| Today last price | 1.2912 |
| Today Daily Change | 0.0033 |
| Today Daily Change % | 0.26 |
| Today daily open | 1.2879 |
| Trends | |
|---|---|
| Daily SMA20 | 1.2878 |
| Daily SMA50 | 1.2609 |
| Daily SMA100 | 1.2459 |
| Daily SMA200 | 1.2703 |
| Levels | |
|---|---|
| Previous Daily High | 1.2889 |
| Previous Daily Low | 1.2824 |
| Previous Weekly High | 1.2943 |
| Previous Weekly Low | 1.2769 |
| Previous Monthly High | 1.3013 |
| Previous Monthly Low | 1.2194 |
| Daily Fibonacci 38.2% | 1.2864 |
| Daily Fibonacci 61.8% | 1.2849 |
| Daily Pivot Point S1 | 1.2839 |
| Daily Pivot Point S2 | 1.2799 |
| Daily Pivot Point S3 | 1.2774 |
| Daily Pivot Point R1 | 1.2904 |
| Daily Pivot Point R2 | 1.2929 |
| Daily Pivot Point R3 | 1.2969 |


#set( $text = {
"subject": "Thank you for providing your consent",
"logo_alt": "Pancan Project logo",
"salutation_self": "Dear Participant,",
"salutation_parent": "Dear Parent,",
"thank_you": "Thank you for joining Count Me In and for giving us your consent for research.",
"instructions": "We will next mail you a kit with instructions on how to provide a saliva sample.",
"provide_samples": "In some cases, we may also contact you about providing a blood sample (1 tube or 2 teaspoons per sample),
which can be drawn at your physician's office, local clinic, or nearby lab facility. If applicable,
we will provide detailed instructions on how to do this. We may obtain copies
of {{\#if proxy_guid}}your child's{{else}}your{{/if}} medical records and,
after reviewing them, may also request a small portion of stored tumor sample from the hospital where it is kept.
Once we have analyzed {{\#if proxy_guid}}your child's{{else}}your{{/if}} requested samples,
we will return any unused tumor samples to the pathology department that sent them to us.",
"complete_process": "If you have not yet provided information about where {{\#if proxy_guid}}your child{{else}}you{{/if}}
has received care for your cancer(s), you can complete the process here:",
"button": "Contact Information Form",
"copy_of_consent": "For your convenience, we have attached a copy of your signed consent form.",
"contact": "If you have questions at any point and would like to talk to a member of our study staff, please reach out to us at
${contact.email}
or ${contact.phone}.",
"closing": "Sincerely,",
"study_team": "The Count Me In Team",
"footnote": "For additional updates, follow us on
Facebook,
Instagram or
Twitter."
})
#parse("sign_stool_collection_form.tmpl.html")


YANG is a data modeling language developed in the Internet Engineering Task Force. It is used to model configuration and state data manipulated by protocols such as NETCONF and RESTCONF. YANG enables easier management of network elements, such as routers, and is widely used and deployed by many network equipment vendors and network operators.
The YANG Catalog allows users to find models relevant to their use cases from the large and growing number of YANG modules being published. The YANG Catalog includes modules from various companies and organizations, including standards organizations such as the IETF, the Broadband Forum, the IEEE, the Metro Ethernet Forum, including some equipment vendors, as well as open source communities such as the Open Daylight Project.
Web Access
The YANG Catalog website provides links to:
- A NETCONF and REST (not RESTCONF-compliant yet) server loaded with the YANG module from draft-clacla-netmod-model-catalog. It currently only allows public read access to
the content. Feel free to reach out through the github forum if you are interested in write access. The username is
oper
and the password isoper
. - A YANG Validator: a web frontend that allows for validation of YANG modules and IETF Internet-Drafts
- A YANG Search: a web frontend that allows for searches of the content of the YANG Catalog
- A YANG Impact Analysis: a tool which graphs the dependencies across individual YANG modules
- YANG Module Detail Viewer: a web frontend which displays the detailed metadata of any particular module
- A YANG Regex Validator: a YANG regular expression validator which enables experimentation with W3C YANG "pattern" statements
API Accesss
The YANG Catalog includes a NETCONF and REST (not RESTCONF-compliant yet) server loaded with the YANG module from draft-clacla-netmod-model-catalog with public read access to the content. The username is oper and the password is oper. For write access, please submit a request via this form.
TFor access to the YANG Catalog’s REST interface using curl, use the following to receive the content of the organization’s subtree from the yang-catalog.yang module. (Note, the currently supported revision yang-catalog.yang is 2017-09-26)
$ curl https://yangcatalog.org/api/search/catalog
This will return JSON-formatted data. NOTE: the catalog is quite large and growing all the time. You may also use the following to pull the module metadata:
$ curl https://yangcatalog.org/api/search/modules
The following will provide vendor implementation metadata:
$ curl https://yangcatalog.org/api/search/vendors


Ploror688.github.io
A cover letter is a single-page letter that should be part of any job application. The only time a cover letter shouldn't be included is when a job ad clearly. Instant cover letter, covering letters and application letters for your job search and resume. Guaranteed job interviews.
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Void QuantConnect.Algorithm.QCAlgorithm.OnEndOfTimeStep ( )
Invoked at the end of every time step. This allows the algorithm to process events before advancing to the next time step.
This method requires no argument input.
Void - This method provides no return.
Void
Definition at line 65 of file Algorithm/QCAlgorithm.Universe.cs.


Webcharts
createTable()
Apply CSS?
Sortable?
Searchable?
# of items per page:
# of page links:
Pagination?
Exportable?
Randomize columns
Randomize headers


+++ title = "Veracruz" lat = 19.184800 lon = -96.129600 +++
I must say, Veracruz took me by surprise in many ways. I expected that, as a rich historical town and as the home of premier beach resorts, Veracruz would be much better than Xalapa, and would warrant a longer stay than Xalapa. But I was wrong.
I got the impression that Veracruz is mainly a family destination, for domestic and nearby-country tourists; and that it fails to attract fun-seeking backpackers such as myself. Except during Carnaval, which I missed by 3 days.
Veracruz is one of the oldest cities in Mexico, and since it's the port built by Hernán Cortés himself, it has immense historical value. There are numerous museums, monuments, and heritage buildings throughout the city's 16th-century centre.
{{< galleryphoto "2007-02-24-10-00-00--heritage-zocalo-veracruz.jpg" >}}However, the port of Veracruz has very little appeal. It's mainly an industrial area, and the container terminal has polluted the waters around the town.  The torterias, and the (numerous) street torta stands, are both great value, and full of friendly locals. However, apart from a few small bars and dance clubs, there isn't that much to do at night around here.


Compounds |
| class | TMenu |
| | A "wrapper" for the various other menu classes, such as TMenuItem, TSubMenu and TMenuView. More...
|
| class | TMenuBar |
| | The horizontal menu bar from which you make menu selections. More...
|
| class | TMenuBox |
| | These objects represent vertical menu boxes. More...
|
| class | TMenuItem |
| | Instances of TMenuItem serve as elements of a menu. More...
|
| class | TMenuPopup |
| | Part of the menu system. More...
|
| class | TMenuView |
| | An abstract base from which menu bar and menu box classes (either pull down or pop up) are derived. More...
|
| class | TStatusDef |
| | Represents a status line definition used by a TStatusLine view to display context-sensitive status lines. More...
|
| class | TStatusItem |
| | Represents a component of a linked list associated with a TStatusLine view. More...
|
| class | TStatusLine |
| | A specialized view, usually displayed at the bottom of the screen. More...
|
| class | TSubMenu |
| | Used to differentiate between different types of TMenuItem: individual menu items and submenus. More...
|
Functions |
| TSubMenu& | operator+ (TSubMenu &s, TMenuItem &i) |
| TSubMenu& | operator+ (TSubMenu &s1, TSubMenu &s2) |
| TStatusDef& | operator+ (TStatusDef &s1, TStatusItem &s2) |
| TStatusDef& | operator+ (TStatusDef &s1, TStatusDef &s2) |
| TMenuItem& | newLine () |
| ipstream& | operator>> (ipstream &is, TMenuView &cl) |
| ipstream& | operator>> (ipstream &is, TMenuView *&cl) |
| opstream& | operator<< (opstream &os, TMenuView &cl) |
| opstream& | operator<< (opstream &os, TMenuView *cl) |
| ipstream& | operator>> (ipstream &is, TMenuBar &cl) |
| ipstream& | operator>> (ipstream &is, TMenuBar *&cl) |
| opstream& | operator<< (opstream &os, TMenuBar &cl) |
| opstream& | operator<< (opstream &os, TMenuBar *cl) |
| ipstream& | operator>> (ipstream &is, TMenuBox &cl) |
| ipstream& | operator>> (ipstream &is, TMenuBox *&cl) |
| opstream& | operator<< (opstream &os, TMenuBox &cl) |
| opstream& | operator<< (opstream &os, TMenuBox *cl) |
| ipstream& | operator>> (ipstream &is, TStatusLine &cl) |
| ipstream& | operator>> (ipstream &is, TStatusLine *&cl) |
| opstream& | operator<< (opstream &os, TStatusLine &cl) |
| opstream& | operator<< (opstream &os, TStatusLine *cl) |


This example demonstrates a tool that can be built with an advanced-level knowledge of the MyGeotab system. It is suitable for those who have a well-versed understanding of the entire Geotab ecosystem and are confident with the MyGeotab SDK and leveraging the MyGeotab APIs within their projects.
| Vehicle Id | Id | Date | Latitude | Longitude | Speed | Diagnostic | Value |
|---|


OPEN CONSOLE TO SEE THE MAGIC
- * This demo introduces functions at a high level, saving a deeper explanation for the next class.
- * Clarify the difference between a function declaration and a function call/invocation
- * Clarify the difference between a function that has no parameters and a function that does take parameters, and demonstrate the result of calling a function without passing in the necessary arguments


This is an Angular based front-end app which allows the user to organize their favorite taco joints based on location with a Rails back-end.


Free Code Camp
Weather App
F
Feels Like:
Wind Speed:
Powered by
Dark Sky
. Weather icons created by
Erik Flowers


Clear
Filter by focus
Remote Testing
Tools
Filters
{{it.name}}
Add New Company
Company Name
Company Service
{{cl.selected.name}}
Desctiption
{{cl.selected.content}}
Services
{{cl.selected.services}}


Credits:
- DeathToStock - Demo stock photos. You guys are the best.
- OwlCarousel - Frontpage slideshow. Most creative one :)
- Foundation - we used Foundation for grid, topbar and menu. Foundation rocks. Always :)
- ColorPicker - ColorPicker in admin. Tiny but powerful :)
- Learn Grav Theme - this documentation theme.
Thanks !


Work Experience
Fareway
I have been working part time at my local Fareway's meat department since May of 2016. My job there is to provide customers with a wonderful experience by talking with them, packaging their requested items and getting them their items in a timely manner. Thanks to Fareway, I've been able to work on my communication skills with customers as well as work in an environment where there is a lot of customer interations.
Farmtek
I started working night shift at Farmtek in September of 2016 and stopped working there in May of 2018. While working at Farmtek, I learn how to work well with my coworkers due to the nature of the jobs we did. Farmtek heavily relies on teamwork to get our projects completed and shipped out to our customers. Thanks to our teamwork at farmtek, we were able to average about 15-20 orders a night with the sizes of the orders ranging from 20 feet by 20 feet to orders as big as 300 feet long by 70 feet wide.


Zero Robotics lets you touch space!
Zero Robotics seeks to inspire the next generation of great minds by allowing them unprecedented access to space at the high school and middle school level. By making the benefits and resources of the space program tangible to students, Zero Robotics hopes to cultivate an appreciation of science, technology, engineering and math through healthy, immersive, collaborative competition. Another major goal of Zero Robotics is to lead young minds to see the concept of working in space as "normal", allowing imaginations to go beyond the boundaries of rocket science.
Zero Robotics, following a mentor-driven model, brings students together with professionals while they complete a full engineering problem: designing, implementing, and operating their response to the Zero Robotics challenges.Zero Robotics seeks to be a permanent link between students and the industry leaders that work on the forefront of science and engineering. These students will in turn become the future leaders to push this frontier forward.
All Zero Robotics competitions are tied in some way to actual space research. By using crowdsourcing and other technologies, one day Zero Robotics aims to substantively involve students in the design and testing process for space software.
Zero Robotics hopes to provide a unique, challenging learning environment that inspires young minds to apply and sharpen their science, technology, engineering, and math skills. Through a series of competitions starting virtually online and aboard the International Space Station, students exercise their creative problem-solving skills and take on real world design problems. With a zero cost, zero setup, web-based environment that uses the latest web technologies, Zero Robotics provides students worldwide with a testbed to carry out space experiments.


From a pentesting standpoint: you cannot detect all installed/enabled modules, and you need to analyze headers manually, if you dont want to X-MISS something
investigate all server-headers. X- headers (X-Cache/X-Powered-By/etc) are usually very interesting.
and header-analysis is done manually; i dont know of any tool that might work like (if header X then alert Y); it's just experience
Installed modules might be displayed on default-error-pages (404) when ServerSignature On is set in the apache configuration.
Another setting is ServerTokens in the config.
Additionally, not all loaded modules are displayed with the server-header.
A bit of intuition and experience can also help.
Is the page loading php? mod_php
Is the page loading cgi? mod_cgi
Redirecting to ssl? mod_rewrite
if mod_php is enabled:

<?php print_r(apache_get_modules()); ?>

Local with no access to httpd, apachectl or processes:
Enabled modules: ls /etc/apache2/mods-enabled/
Available modules: ls /etc/apache2/mods-available/
Local:
apache2ctl -M
or
httpd -M
server-info using mod_info:
Adding to httpd.conf:
<Location /server-info>
</Location>
and browsing to http://host/server-info
Some parameter names http://host/server-info?<param>
?<module-name> Only information relevant to the named module
?config Just the configuration directives, not sorted by module
?hooks Only the list of Hooks each module is attached to
?list Only a simple list of enabled modules
?server Only the basic server information
Other methods


|
| file | jpc_bs.c |
| |
| file | jpc_bs.h [code] |
| |
| file | jpc_cod.h [code] |
| |
| file | jpc_cs.c |
| |
| file | jpc_cs.h [code] |
| |
| file | jpc_dec.c |
| |
| file | jpc_dec.h [code] |
| |
| file | jpc_enc.c |
| |
| file | jpc_enc.h [code] |
| |
| file | jpc_fix.h [code] |
| |
| file | jpc_flt.h [code] |
| |
| file | jpc_math.c |
| |
| file | jpc_math.h [code] |
| |
| file | jpc_mct.c |
| |
| file | jpc_mct.h [code] |
| |
| file | jpc_mqcod.c |
| |
| file | jpc_mqcod.h [code] |
| |
| file | jpc_mqdec.c |
| |
| file | jpc_mqdec.h [code] |
| |
| file | jpc_mqenc.c |
| |
| file | jpc_mqenc.h [code] |
| |
| file | jpc_qmfb.c |
| |
| file | jpc_qmfb.h [code] |
| |
| file | jpc_t1cod.c |
| |
| file | jpc_t1cod.h [code] |
| |
| file | jpc_t1dec.c |
| |
| file | jpc_t1dec.h [code] |
| |
| file | jpc_t1enc.c |
| |
| file | jpc_t1enc.h [code] |
| |
| file | jpc_t2cod.c |
| |
| file | jpc_t2cod.h [code] |
| |
| file | jpc_t2dec.c |
| |
| file | jpc_t2dec.h [code] |
| |
| file | jpc_t2enc.c |
| |
| file | jpc_t2enc.h [code] |
| |
| file | jpc_tagtree.c |
| |
| file | jpc_tagtree.h [code] |
| |
| file | jpc_tsfb.c |
| |
| file | jpc_tsfb.h [code] |
| |
| file | jpc_util.c |
| |
| file | jpc_util.h [code] |
| |


This is a simple financial simulation of Sal, a salaried person who works from age 20 to 60, then lives a retired life until age 85.
You are Sal's advisor and advise him a single consistent investment strategy for the funds coming from his savings and returns on investments. The allocation strategy will be applied at end of each year; this will also rebalance the portfolio.
The score is Sal's final inflation-adjusted net worth in mulitples of Sal's annual starting salary.
If Sal's investements do badly or expenses are more than revenue, Sal's net worth will decrease and may even go negative. Then he has to live off borrowings at 10% interest, and so, subsequently his net worth keeps going more negative every year. Therefore, with bad investement or insufficient savings rate, the final score may be negative.
There is a random element in returns from debt or equity, so the score will vary across runs. Extremely lucky runs can amass huge net worth.
For full details, see "dmrmoney" blog for post no. 7: 'Playing the Retirement Game'
| The Controls | |
|---|---|
| Expense Ratio | Expenditure as percentage of salary (not going into savings) |
| Allocate | Divide investments into classes |
| Fixed Deposit | Fixed income, low risk |
| Debt Fund | Debt instruments, medium risk, tax efficient |
| Equity | stock market, high risk |
| Chance of Loss | Probability that investment will go into loss |
| Number of Runs | repeat the game N times |
| Start | Click button to run the game N times |
| Display Results |
|---|
The output is a histogram that puts the individual scores of the N runs into buckets for different score ranges. The height of each bar is the number of runs that fall into that bucket. An ideal strategy would be
Note: In case of 100% investements into Fixed Deposits, all scores are identical and only a single fat bucket is created. |


transformer¶
attention_layer¶
Implementation of multiheaded attention and self-attention layers.
-
class
parts.transformer.attention_layer.
Attention
(hidden_size, num_heads, attention_dropout, train)[source]¶ Bases:
tensorflow.python.layers.base.Layer
Multi-headed attention layer.
-
call
(x, y, bias, cache=None)[source]¶ Apply attention mechanism to x and y.
Parameters: - x – a tensor with shape [batch_size, length_x, hidden_size]
- y – a tensor with shape [batch_size, length_y, hidden_size]
- bias – attention bias that will be added to the result of the dot product.
- cache –
(Used during prediction) dictionary with tensors containing results of previous attentions. The dictionary must have the items:
- {“k”: tensor with shape [batch_size, i, key_channels],
- ”v”: tensor with shape [batch_size, i, value_channels]}
where i is the current decoded length.
Returns: Attention layer output with shape [batch_size, length_x, hidden_size]
-
combine_heads
(x)[source]¶ Combine tensor that has been split.
Parameters: x – A tensor [batch_size, num_heads, length, hidden_size/num_heads] Returns: A tensor with shape [batch_size, length, hidden_size]
-
split_heads
(x)[source]¶ Split x into different heads, and transpose the resulting value.
The tensor is transposed to insure the inner dimensions hold the correct values during the matrix multiplication.
Parameters: x – A tensor with shape [batch_size, length, hidden_size] Returns: A tensor with shape [batch_size, num_heads, length, hidden_size/num_heads]
-
-
class
parts.transformer.attention_layer.
SelfAttention
(hidden_size, num_heads, attention_dropout, train)[source]¶ Bases:
parts.transformer.attention_layer.Attention
Multiheaded self-attention layer.
-
call
(x, bias, cache=None)[source]¶ Apply attention mechanism to x and y.
Parameters: - x – a tensor with shape [batch_size, length_x, hidden_size]
- y – a tensor with shape [batch_size, length_y, hidden_size]
- bias – attention bias that will be added to the result of the dot product.
- cache –
(Used during prediction) dictionary with tensors containing results of previous attentions. The dictionary must have the items:
- {“k”: tensor with shape [batch_size, i, key_channels],
- ”v”: tensor with shape [batch_size, i, value_channels]}
where i is the current decoded length.
Returns: Attention layer output with shape [batch_size, length_x, hidden_size]
-
beam_search¶
Beam search to find the translated sequence with the highest probability.
Source implementation from Tensor2Tensor: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/beam_search.py
-
class
parts.transformer.beam_search.
SequenceBeamSearch
(symbols_to_logits_fn, vocab_size, batch_size, beam_size, alpha, max_decode_length, eos_id)[source]¶ Bases:
object
Implementation of beam search loop.
-
_continue_search
(state)[source]¶ Return whether to continue the search loop.
- The loops should terminate when
- when decode length has been reached, or
- when the worst score in the finished sequences is better than the best score in the alive sequences (i.e. the finished sequences are provably unchanging)
Parameters: state – A dictionary with the current loop state. Returns: Bool tensor with value True if loop should continue, False if loop should terminate.
-
_create_initial_state
(initial_ids, initial_cache)[source]¶ Return initial state dictionary and its shape invariants.
Parameters: - initial_ids – initial ids to pass into the symbols_to_logits_fn. int tensor with shape [batch_size, 1]
- initial_cache – dictionary storing values to be passed into the symbols_to_logits_fn.
Returns: state and shape invariant dictionaries with keys from _StateKeys
-
_get_new_alive_state
(new_seq, new_log_probs, new_cache)[source]¶ Gather the top k sequences that are still alive.
Parameters: - new_seq – New sequences generated by growing the current alive sequences int32 tensor with shape [batch_size, 2 * beam_size, cur_index + 1]
- new_log_probs – Log probabilities of new sequences float32 tensor with shape [batch_size, beam_size]
- new_cache – Dict of cached values for each sequence.
Returns: - {Top beam_size sequences that are still alive (don’t end with eos_id)
Log probabilities of top alive sequences Dict cache storing decoder states for top alive sequences}
Return type: Dictionary with alive keys from _StateKeys
-
_get_new_finished_state
(state, new_seq, new_log_probs)[source]¶ Combine new and old finished sequences, and gather the top k sequences.
Parameters: - state – A dictionary with the current loop state.
- new_seq – New sequences generated by growing the current alive sequences int32 tensor with shape [batch_size, beam_size, i + 1]
- new_log_probs – Log probabilities of new sequences float32 tensor with shape [batch_size, beam_size]
Returns: - {Top beam_size finished sequences based on score,
Scores of finished sequences, Finished flags of finished sequences}
Return type: Dictionary with finished keys from _StateKeys
-
_grow_alive_seq
(state)[source]¶ Grow alive sequences by one token, and collect top 2*beam_size sequences.
2*beam_size sequences are collected because some sequences may have reached the EOS token. 2*beam_size ensures that at least beam_size sequences are still alive.
Parameters: state – A dictionary with the current loop state. Returns: Tuple of (Top 2*beam_size sequences [batch_size, 2 * beam_size, cur_index + 1], Scores of returned sequences [batch_size, 2 * beam_size], New alive cache, for each of the 2 * beam_size sequences)
-
_search_step
(state)[source]¶ Beam search loop body.
Grow alive sequences by a single ID. Sequences that have reached the EOS token are marked as finished. The alive and finished sequences with the highest log probabilities and scores are returned.
A sequence’s finished score is calculating by dividing the log probability by the length normalization factor. Without length normalization, the search is more likely to return shorter sequences.
Parameters: state – A dictionary with the current loop state. Returns: new state dictionary.
-
-
class
parts.transformer.beam_search.
_StateKeys
[source]¶ Bases:
object
Keys to dictionary storing the state of the beam search loop.
-
ALIVE_CACHE
= 'ALIVE_CACHE'¶
-
ALIVE_LOG_PROBS
= 'ALIVE_LOG_PROBS'¶
-
ALIVE_SEQ
= 'ALIVE_SEQ'¶
-
CUR_INDEX
= 'CUR_INDEX'¶
-
FINISHED_FLAGS
= 'FINISHED_FLAGS'¶
-
FINISHED_SCORES
= 'FINISHED_SCORES'¶
-
FINISHED_SEQ
= 'FINISHED_SEQ'¶
-
-
parts.transformer.beam_search.
_expand_to_beam_size
(tensor, beam_size)[source]¶ Tiles a given tensor by beam_size.
Parameters: - tensor – tensor to tile [batch_size, …]
- beam_size – How much to tile the tensor by.
Returns: Tiled tensor [batch_size, beam_size, …]
-
parts.transformer.beam_search.
_flatten_beam_dim
(tensor)[source]¶ Reshapes first two dimensions in to single dimension.
Parameters: tensor – Tensor to reshape of shape [A, B, …] Returns: Reshaped tensor of shape [A*B, …]
-
parts.transformer.beam_search.
_gather_beams
(nested, beam_indices, batch_size, new_beam_size)[source]¶ Gather beams from nested structure of tensors.
Each tensor in nested represents a batch of beams, where beam refers to a single search state (beam search involves searching through multiple states in parallel).
This function is used to gather the top beams, specified by beam_indices, from the nested tensors.
Parameters: - nested – Nested structure (tensor, list, tuple or dict) containing tensors with shape [batch_size, beam_size, …].
- beam_indices – int32 tensor with shape [batch_size, new_beam_size]. Each value in beam_indices must be between [0, beam_size), and are not necessarily unique.
- batch_size – int size of batch
- new_beam_size – int number of beams to be pulled from the nested tensors.
Returns: - Nested structure containing tensors with shape
[batch_size, new_beam_size, …]
-
parts.transformer.beam_search.
_gather_topk_beams
(nested, score_or_log_prob, batch_size, beam_size)[source]¶ Gather top beams from nested structure.
-
parts.transformer.beam_search.
_length_normalization
(alpha, length)[source]¶ Return length normalization factor.
-
parts.transformer.beam_search.
_shape_list
(tensor)[source]¶ Return a list of the tensor’s shape, and ensure no None values in list.
-
parts.transformer.beam_search.
_unflatten_beam_dim
(tensor, batch_size, beam_size)[source]¶ Reshapes first dimension back to [batch_size, beam_size].
Parameters: - tensor – Tensor to reshape of shape [batch_size*beam_size, …]
- batch_size – Tensor, original batch size.
- beam_size – int, original beam size.
Returns: Reshaped tensor of shape [batch_size, beam_size, …]
-
parts.transformer.beam_search.
sequence_beam_search
(symbols_to_logits_fn, initial_ids, initial_cache, vocab_size, beam_size, alpha, max_decode_length, eos_id)[source]¶ Search for sequence of subtoken ids with the largest probability.
Parameters: - symbols_to_logits_fn –
A function that takes in ids, index, and cache as arguments. The passed in arguments will have shape:
ids -> [batch_size * beam_size, index] index -> [] (scalar) cache -> nested dictionary of tensors [batch_size * beam_size, …]- The function must return logits and new cache.
- logits -> [batch * beam_size, vocab_size] new cache -> same shape/structure as inputted cache
- initial_ids – Starting ids for each batch item. int32 tensor with shape [batch_size]
- initial_cache – dict containing starting decoder variables information
- vocab_size – int size of tokens
- beam_size – int number of beams
- alpha – float defining the strength of length normalization
- max_decode_length – maximum length to decoded sequence
- eos_id – int id of eos token, used to determine when a sequence has finished
Returns: Top decoded sequences [batch_size, beam_size, max_decode_length] sequence scores [batch_size, beam_size]
- symbols_to_logits_fn –
common¶
embedding_layer¶
Implementation of embedding layer with shared weights.
Bases:
tensorflow.python.layers.base.Layer
Calculates input embeddings and pre-softmax linear with shared weights.
Creates the variables of the layer.
Get token embeddings of x.
Parameters: x – An int64 tensor with shape [batch_size, length] Returns: float32 tensor with shape [batch_size, length, embedding_size] padding: float32 tensor with shape [batch_size, length] indicating the locations of the padding tokens in x.Return type: embeddings
Computes logits by running x through a linear layer.
Parameters: x – A float32 tensor with shape [batch_size, length, hidden_size] Returns: float32 tensor with shape [batch_size, length, vocab_size].
ffn_layer¶
Implementation of fully connected network.
utils¶
Transformer model helper methods.
-
parts.transformer.utils.
get_decoder_self_attention_bias
(length)[source]¶ Calculate bias for decoder that maintains model’s autoregressive property.
Creates a tensor that masks out locations that correspond to illegal connections, so prediction at position i cannot draw information from future positions.
Parameters: length – int length of sequences in batch. Returns: float tensor of shape [1, 1, length, length]
-
parts.transformer.utils.
get_padding
(x, padding_value=0, dtype=tf.float32)[source]¶ Return float tensor representing the padding values in x.
Parameters: - x – int tensor with any shape
- padding_value – int value that
- dtype – type of the output
Returns: - flaot tensor with same shape as x containing values 0 or 1.
0 -> non-padding, 1 -> padding
-
parts.transformer.utils.
get_padding_bias
(x, res_rank=4, pad_sym=0)[source]¶ Calculate bias tensor from padding values in tensor.
Bias tensor that is added to the pre-softmax multi-headed attention logits, which has shape [batch_size, num_heads, length, length]. The tensor is zero at non-padding locations, and -1e9 (negative infinity) at padding locations.
Parameters: - x – int tensor with shape [batch_size, length]
- res_rank – int indicates the rank of attention_bias.
- dtype – type of the output attention_bias
- pad_sym – int the symbol used for padding
Returns: Attention bias tensor of shape [batch_size, 1, 1, length] if res_rank = 4 - for Transformer or [batch_size, 1, length] if res_rank = 3 - for ConvS2S
-
parts.transformer.utils.
get_position_encoding
(length, hidden_size, min_timescale=1.0, max_timescale=10000.0)[source]¶ Return positional encoding.
Calculates the position encoding as a mix of sine and cosine functions with geometrically increasing wavelengths. Defined and formulized in Attention is All You Need, section 3.5.
Parameters: - length – Sequence length.
- hidden_size – Size of the
- min_timescale – Minimum scale that will be applied at each position
- max_timescale – Maximum scale that will be applied at each position
Returns: Tensor with shape [length, hidden_size]


Preprocessing FEX data¶
How to preprocess and analyze facial expression data with Feat.¶
Written by Jin Hyun Cheong
Here we will be using a sample dataset by David Watson on “A Data-Driven Characterisation Of Natural Facial Expressions When Giving Good And Bad News” by Watson & Johnston 2020. The full dataset is available on OSF.
Let’s start by installing Py-FEAT if you have not already done so or using this on Google Colab
!pip install -q py-feat
First, we download the necessary files & videos.
import subprocess
files_to_download = ["4c5mb", "n6rt3", "3gh8v", "twqxs", "nc7d9", "nrwcm", "2rk9c", "mxkzq", "c2na7", "wj7zy", "mxywn",
"6bn3g", "jkwsp", "54gtv", "c3hpm", "utdqj", "hpw4a", "94swe", "qte5y", "aykvu", "3d5ry"]
for fid in files_to_download:
subprocess.run(f"wget --content-disposition https://osf.io/{fid}/download".split())
Check that videos have been downloaded and the attributes file, `clip_attrs.csv) explaining
import os, glob
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_context("talk")
clip_attrs = pd.read_csv("clip_attrs.csv")
videos = np.sort(glob.glob("*.mp4"))
print(videos)
['001.mp4' '002.mp4' '003.mp4' '004.mp4' '005.mp4' '006.mp4' '007.mp4'
'008.mp4' '009.mp4' '010.mp4' '011.mp4' '012.mp4' '013.mp4' '014.mp4'
'015.mp4' '016.mp4' '017.mp4' '018.mp4' '019.mp4' '020.mp4']
Process each video using our detector.
from feat import Detector
detector = Detector(au_model = "rf", emotion_model = "resmasknet")
for video in videos:
detector.detect_video(video, outputFname = video.replace(".mp4", ".csv"))
from feat.utils import read_feat
import pandas as pd
for ix ,video in enumerate(videos):
outputF = video.replace(".mp4", ".csv")
if ix == 0:
fex = read_feat(outputF)
else:
fex = pd.concat([fex, read_feat(outputF)])
fex = fex.dropna()
# Load in conditions
clip_attrs = pd.read_csv("clip_attrs.csv")
clip_attrs = clip_attrs.assign(input = clip_attrs.clipN.apply(lambda x: str(x).zfill(3)+".mp4"),
condition = clip_attrs['class'].replace({"gn":"goodNews", "ists":"badNews"}))
input_class_map = dict(zip(clip_attrs.input, clip_attrs['condition']))
clip_attrs.head()
| clipN | class | phraseN | phrase_txt | input | condition | |
|---|---|---|---|---|---|---|
| 0 | 1 | gn | 1 | your loan has been approved | 001.mp4 | goodNews |
| 1 | 2 | gn | 2 | you've got the job | 002.mp4 | goodNews |
| 2 | 3 | gn | 3 | the vendor has accepted your offer | 003.mp4 | goodNews |
| 3 | 4 | gn | 4 | your tests have come back clear | 004.mp4 | goodNews |
| 4 | 5 | gn | 5 | your application has been accepted | 005.mp4 | goodNews |
Extract features¶
You can set the sessions
attribute to provide a grouping of your experimental setup. This could be the name of each video if you want to extract features per video or it could be conditions to extract features per condition.
# Extract conditions between the two condtiiosn (gn: good news, ists: bad news)
conditions = dict(zip(clip_attrs.input, clip_attrs['condition']))
fex.sessions = fex.input().map(conditions)
average_au_intensity_per_video = fex.extract_mean()
display(average_au_intensity_per_video.head())
| mean_AU01 | mean_AU02 | mean_AU04 | mean_AU05 | mean_AU06 | mean_AU07 | mean_AU09 | mean_AU10 | mean_AU11 | mean_AU12 | ... | mean_y_61 | mean_y_62 | mean_y_63 | mean_y_64 | mean_y_65 | mean_y_66 | mean_y_67 | mean_y_7 | mean_y_8 | mean_y_9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| badNews | 0.370603 | 0.347909 | 0.274446 | 0.470608 | 0.144680 | 0.350956 | 0.057206 | 0.367228 | 0.414330 | 0.245751 | ... | 714.571030 | 715.994335 | 711.230906 | 703.585629 | 715.312303 | 720.341735 | 718.979703 | 823.783636 | 827.212203 | 813.770216 |
| goodNews | 0.437070 | 0.399585 | 0.209061 | 0.219561 | 0.641241 | 0.642395 | 0.161864 | 0.775689 | 0.412116 | 0.814605 | ... | 684.355943 | 686.797688 | 683.110011 | 676.147267 | 693.425396 | 697.551264 | 695.402943 | 810.933791 | 817.027471 | 806.257672 |
2 rows × 169 columns
Or simply extract features per video
# Extract features per video
fex.sessions = fex.input()
average_au_intensity_per_video = fex.extract_mean()
display(average_au_intensity_per_video.head())
| mean_AU01 | mean_AU02 | mean_AU04 | mean_AU05 | mean_AU06 | mean_AU07 | mean_AU09 | mean_AU10 | mean_AU11 | mean_AU12 | ... | mean_y_61 | mean_y_62 | mean_y_63 | mean_y_64 | mean_y_65 | mean_y_66 | mean_y_67 | mean_y_7 | mean_y_8 | mean_y_9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 001.mp4 | 0.367902 | 0.304953 | 0.217278 | 0.247205 | 0.529641 | 0.546710 | 0.127434 | 0.689482 | 0.405150 | 0.757060 | ... | 694.305659 | 696.236139 | 692.150659 | 682.592831 | 696.408174 | 700.882806 | 699.187324 | 814.856982 | 819.742603 | 808.427911 |
| 002.mp4 | 0.384290 | 0.372414 | 0.218073 | 0.238233 | 0.466245 | 0.568260 | 0.139705 | 0.645774 | 0.406103 | 0.665162 | ... | 686.464658 | 688.592334 | 685.344299 | 677.572549 | 688.540245 | 692.234408 | 690.349490 | 806.882534 | 812.669819 | 802.669351 |
| 003.mp4 | 0.475926 | 0.435061 | 0.193346 | 0.235034 | 0.628517 | 0.614398 | 0.130048 | 0.758156 | 0.406965 | 0.822223 | ... | 678.527397 | 681.056783 | 677.828855 | 672.959971 | 688.190226 | 691.792301 | 689.539840 | 803.369594 | 810.371285 | 800.508759 |
| 004.mp4 | 0.465396 | 0.431265 | 0.215378 | 0.136780 | 0.719656 | 0.712953 | 0.195271 | 0.852897 | 0.413281 | 0.888342 | ... | 682.179613 | 684.565805 | 680.513723 | 674.068777 | 694.367983 | 698.833575 | 696.740101 | 811.564575 | 817.511093 | 806.248735 |
| 005.mp4 | 0.358873 | 0.346983 | 0.196898 | 0.132950 | 0.729992 | 0.724506 | 0.230352 | 0.844839 | 0.421897 | 0.883427 | ... | 701.603967 | 703.849507 | 699.122215 | 687.917109 | 711.293560 | 716.390717 | 714.357210 | 827.466633 | 833.453529 | 821.740933 |
5 rows × 169 columns
Analyzing FEX data¶
Simple t-test¶
You can use a simple t-test to test if the average activation of a certain AU is significantly higher than .5 (chance). The results suggests that AU10 (upper lip raiser), 12 (lip corner puller), and 14 (dimpler) is significantly activitated when providing good news.
average_au_intensity_per_video.sessions = average_au_intensity_per_video.index.map(input_class_map)
t, p = average_au_intensity_per_video[average_au_intensity_per_video.sessions=="goodNews"].aus().ttest_1samp(.5)
pd.DataFrame({"t": t, "p": p}, index= average_au_intensity_per_video.au_columns)
| t | p | |
|---|---|---|
| mean_AU01 | -2.927201 | 1.683346e-02 |
| mean_AU02 | -5.838201 | 2.473720e-04 |
| mean_AU04 | -49.805912 | 2.660164e-12 |
| mean_AU05 | -11.942339 | 8.022300e-07 |
| mean_AU06 | 3.905366 | 3.589972e-03 |
| mean_AU07 | 5.487334 | 3.864728e-04 |
| mean_AU09 | -22.647961 | 3.024835e-09 |
| mean_AU10 | 10.361594 | 2.660301e-06 |
| mean_AU11 | -42.691265 | 1.059418e-11 |
| mean_AU12 | 11.949046 | 7.984040e-07 |
| mean_AU14 | 23.155621 | 2.485336e-09 |
| mean_AU15 | -12.587565 | 5.119202e-07 |
| mean_AU17 | -0.460219 | 6.562759e-01 |
| mean_AU20 | -45.470892 | 6.019391e-12 |
| mean_AU23 | -0.300423 | 7.706780e-01 |
| mean_AU24 | -4.533345 | 1.419377e-03 |
| mean_AU25 | 3.119502 | 1.232852e-02 |
| mean_AU26 | -5.015769 | 7.232461e-04 |
| mean_AU28 | -7.169935 | 5.251451e-05 |
| mean_AU43 | -78.139016 | 4.660607e-14 |
Two sample independent t-test¶
You can also perform an independent two sample ttest between two sessions which in this case is goodNews vs badNews.
columns2compare = "mean_AU12"
sessions = ("goodNews", "badNews")
t, p = average_au_intensity_per_video.ttest_ind(col = columns2compare, sessions=sessions)
print(f"T-test between {sessions[0]} vs {sessions[1]}: t={t:.2g}, p={p:.3g}")
sns.barplot(x = average_au_intensity_per_video.sessions,
y = columns2compare,
data = average_au_intensity_per_video);
T-test between goodNews vs badNews: t=17, p=2.32e-12
Prediction¶
If you want to know what combination of features predic the good news or bad news conditions. To investigate this problem, we can train a Logistc Regression model using emotion labels to predict the conditions. Results suggest that detections of happy expressions predict the delivery of good news.
fex.sessions = fex.input().map(input_class_map)
from sklearn.linear_model import LogisticRegression
clf = fex.predict(X=fex.emotions(), y = fex.sessions, model = LogisticRegression, solver="liblinear")
print(f"score: {clf.score(fex.emotions(), fex.sessions):.3g}")
print(f"coefficients for predicting class: {clf.classes_[1]}")
display(pd.DataFrame(clf.coef_, columns = fex.emotions().columns))
score: 0.933
coefficients for predicting class: goodNews
| anger | disgust | fear | happiness | sadness | surprise | neutral | |
|---|---|---|---|---|---|---|---|
| 0 | -0.722285 | -0.566583 | -2.868222 | 8.24894 | -2.434085 | -0.096825 | -3.030539 |
Regression¶
We can also run an fMRI style regression to predict the Action Unit activities from a contrast of conditions. This analysis can be conducted through the regress
method. In this example, we identify the action units that are significantly more active in the good news versus the bad news conditions.
fex.sessions = fex.input().map(input_class_map).replace({"goodNews":.5, "badNews":-.5})
X = pd.DataFrame(fex.sessions)
X['intercept'] = 1
b, t, p, df, residuals = fex.regress(X = X, y = fex.aus())
print("Betas predicting good news estimated for each emotion.")
results = pd.concat([b.round(3).loc[[0]].rename(index={0:"betas"}),
t.round(3).loc[[0]].rename(index={0:"t-stats"}),
p.round(3).loc[[0]].rename(index={0:"p-values"})])
display(results)
Betas predicting good news estimated for each emotion.
/home/jcheong/anaconda3/lib/python3.8/site-packages/nltools/stats.py:970: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
stderr = np.sqrt(np.diag(np.linalg.pinv(np.dot(X.T, X))))[:, np.newaxis] * sigma[np.newaxis, :]
| AU01 | AU02 | AU04 | AU05 | AU06 | AU07 | AU09 | AU10 | AU11 | AU12 | AU14 | AU15 | AU17 | AU20 | AU23 | AU24 | AU25 | AU26 | AU28 | AU43 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| betas | 0.066 | 0.052 | -0.065 | -0.251 | 0.497 | 0.291 | 0.105 | 0.408 | -0.002 | 0.569 | 0.022 | 0.111 | 0.101 | 0.038 | -0.029 | -0.284 | 0.305 | 0.140 | -0.016 | 0.013 |
| t-stats | 8.221 | 8.219 | -17.153 | -31.413 | 43.783 | 33.364 | 21.079 | 35.625 | -2.671 | 46.713 | 4.088 | 17.515 | 15.673 | 7.927 | -5.063 | -22.483 | 19.415 | 16.822 | -2.293 | 2.422 |
| p-values | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.008 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.022 | 0.016 |
Intersubject (or intervideo) correlations¶
To compare the similarity of signals over time between subjects or videos, you can use the isc
method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time.
In this example, we are calculating the ISC over videos. We want to check how similar AU01 activations are across videos so our session is set to the input
which is the video name. Executing the isc
method shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions.
fex.sessions = fex.input()
isc = fex.isc(col = "AU01")
sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap="RdBu_r");


Environmental Health & Safety
Environmental Health &Safety (EHS) provides and coordinates services for the University's Makerspace, Shop and Studio Managers. You can find safety and health resources at our EHS website including: the University Policy for students working safely in these environments; the User Agreement to use tools and equipment in University Maker Lab Spaces, Shops, and Studios; and Online Safety Training Modules. Please give us a call,we love "a day in the shop"!
Location: |
One Morton Drive Suite 320 Charlottesville, VA 22903 |
Access: |
|
Hours: |
Available for consultation M-F, 8am-5pm |
Contact: |
434-982-4836 |
People
Kate Smith
Title: Research Safety Specialist
Department: Environmental Health & Safety
Clarissa Lynch
Title: Chemical Safety Officer
Department: Environmental Health and Safety


The Virupaksha temple is the oldest shrine, the principal destination for pilgrims and tourists, and remains an active Hindu worship site.Parts of the Shiva, Pampa and Durga temples existed in the 11th-century; it was extended during the Vijayanagara era.
The temple is a collection of smaller temples, a regularly repainted, 50-metre (160 ft) high gopuram, a Hindu monastery dedicated to Vidyaranya of Advaita Vedanta tradition, a water tank (Manmatha), a community kitchen, other monuments and a 750 metres (2,460 ft)-long ruined stone market with a monolithic Nandi shrine on the east end. It is 0.35 kilometers from hampi.
Visiting Timings:-9:00 AM - 1:00 PM, 5:00 PM - 9:00 PM
The Krishna temple, also called Balakrishna temple, on the other side of Hemakuta hill, is about 1 kilometre (0.62 mi) south of Virupaksha temple. It is dated to 1515 CE; this part of the Hampi complex is called Krishnapura in inscriptions.
In front of the ruined temple is a long market street, also referred to locally as the bazaar. Between the colonnaded stone shop ruins is a broad road that allowed chariots to transport goods to and from the market, and hosted ceremonial functions and festive celebrations.
To the north of this road and middle of the market is a large Pushkarani—a public utility-stepped water tank with an artistic pavilion in its centre. Next to the tank is a public hall (mandapa) for people to sit. It is 0.8 kilometers from hampi.
Visiting Timings:-4:15 a.m. to 5:00 a.m., 7:15 a.m. to 1:00 p.m. and 4:00 p.m. to 8.20 p.m
The Achyutaraya temple, also called the Tiruvengalanatha temple, is about 1 kilometre (0.62 mi) east of Virupaksha temple and a part of its sacred centre is close to the Tungabhadra River. It is referred to be in Achyutapura in inscriptions and is dated to 1534 CE. It is one of the four largest complexes in Hampi.
The temple is unusual because it faced north. It is dedicated to Vishnu. In Vijayanagara times, the temple was traditionally approached from the river, first past a ceremonial tank then along the market street with a broad road. The temple had an outer gopuram leading into a courtyard with a 100-column hall and an inner gopuram leading to the Vishnu temple.
On each side of each pillar in the 100-column hall are reliefs of avatars of Vishnu; other deities such as Shiva, Surya, Durga; scenes of daily life—rishi, amorous couples, jokers; people in yoga asanas; people in namaste poses; and Vijayanagara emblems.The temple gateway shows the Vijayanagara dynastic emblems; a boar from Varaha, a sword, the sun and the moon. The temple and the market street are ruined but their layout suggests it was a major market with streets provided for chariot traffic. It is 0.9 kilometers from hampi.
Visiting Timings:-5:30 AM - 1:00 PM,5:30 PM - 9:00 PM
The Vitthala temple and market complex is over 3 kilometres (1.9 mi) north-east of the Virupaksha temple near the banks of the Tungabhadra River. It is the most artistically sophisticated Hindu temple in Hampi, and is part of the sacred centre of Vijayanagara.
It is unclear when the temple complex was built, and who built it; most scholars date it to a period of construction in the early-to-mid-16th century.The inscriptions include male and female names, suggesting that the complex was built by multiple sponsors.
The temple was dedicated to Vitthala, a form of Krishna also called Vithoba.The temple opens to the east, has a square plan and features an entrance gopuram with two side gopurams. The main temple stands in the middle of a paved courtyard and several subsidiary shrines, all aligned to the east. It is 9.6 kilometers from hampi.
Visiting Timings:-8:30AM–6PM
The hill has more than thirty small-to-moderate-sized temples, together with water cisterns, gateways and secular pavilions.They latest examples are dated to the early 14th century.Some of the structures are differently-sized prototypes of temples or mandapas, assembled from blocks of stones. Others are completed monuments of different designs, such as the Phamsana style.
Two temple groups in this style look similar; each has a triple vimana consisting of square sanctums with each set connected to its own shared square mandapa.The towers (shikaras) on these are pyramidal granite structures consisting of eleven stacked, shrinking squares and a top in the Deccan-style square kalasha finial.
Both sets are Shiva temples with triple linga; early sources misidentified these as Jain temples because of their simple exterior and interior walls. One of these groups has a historically important inscription that records that Kampila built the monument in the early 14th century. This inscription links Hampi with the Kampili kingdom and suggests an association of the Kampili history with that of Vijayanagara Empire that followed it.
The style of temples on the Hemakuta hill suggest it may have been a study centre for experimenting with different types of Hindu temples. The styles present include those of the Chalukya period, the Rashtrakuta period and later periods. It may also have been the template for the original Virupaksha temple, which was later greatly expanded with gopuram, mandala and other additions. A similar monument dedicated to Narasimha, the man-lion avatar of Vishnu, is located east of Hampi; an inscription near it states that it was operating in 1379 CE. It is 1.7 kilometers from hampi.
Visiting Timings:-8am to 5:30pm
The Hazara Rama temple, referred to as the Ramachandra temple in inscriptions, occupied the western part of the urban core in the royal centre section of Hampi. This temple was dedicated to Rama of the Ramayana fame, and an avatar of Vishnu.
It was the ceremonial temple for the royal family. The temple is dated to the early 15th century and is attributed to Devaraya I.The temple's outer walls portray the Hindu Mahanavami (Dasara) and the spring Holi festival procession and celebrations in parallel bands of artwork.
The lowest band shows marching elephants, above it are horses led by horsemen, then soldiers celebrated by the public, then dancers and musicians, with a top layer depicting a boisterous procession of the general public. The depiction mirrors the description of festivals and processions in surviving memoirs of Persians and Portuguese who visited the Vijayanagara capital. It is 3.2 kilometers from hampi.
Visiting Timings:-6am to 6pm
The Kodandarama temple complex lies near the Tungabhadra River, and is north of Achyutaraya temple. The temple overlooks Chakratirtha, where the Tungabhadra turns northwards towards the Himalayas. The river banks, considered holy, accommodate a Vijayanagara-era ghat and mandapa facilities for bathing. In front of the temple is a dipa stambha(lighting pillar) under a Pipal tree, and inside is a sanctum dedicated to Rama, Sita, Lakshmana and Hanuman.
Nearby, and continuing until Kotitirtha to its north, are a number of smaller shrines, dedicated to Vitthala, Anjaneya, Shiva linga and other deities. On the rock face are reliefs of Anantashayana Vishnu (reclining Vishnu creating the cosmic cycle, Ranganatha), friezes narrating the legends of Narasimha and Prahlada, and the twenty-four avatars of Vishnu according to the Puranic tradition of Vaishnavism. Near the river is a rock carved with Shaivism's 1,008 lingas. It is 0.8 kilometers from hampi.
Visiting Timings:-6:00 AM - 9:30 PM
The Pattabhirama temple complex is in the southern suburban centre outside the sacred centre and the urban core, about 500 metres (550 yd) from the ASI Hampi museum.It was at the nucleus of economic and cultural activity of this suburb, now located north-east of Kamalapura.
The complex, also known as Varadevi Ammana Pattana, was likely built in the early 16th century and dedicated to Rama (Vishnu avatar).The complex has a main temple, a colonnaded courtyard inside an enclosure and a 64 (8x8 square)-pillared and roofed mandapa in front of the sanctum. The complex and the sanctum face east; the normal entrance was through the eastern gopura.
The ruins suggest the gopuram had six tiers. The Pattabhirama temple included a 100-pillared hall—likely a feeding hall—attached to the southern wall of the enclosed compound. The pillars have reliefs depicting Hindu themes which include gods, goddesses, a scene from a Hindu text, yoga and namaste. It is 5.5 kilometers from hampi.
Visiting Timings:-7am to 6pm
The Mahanavami platform, also called the "Great Platform", "Audience Hall", "Dasara" or "Mahanavami Dibba" monument, is within a 7.5-hectare (19-acre) enclosure at one of the highest points inside the royal centre (urban core). It has ceremonial structures.
It is mentioned in the memoirs of foreigners who visited Vijayanagara, some calling it the "House of Victory".The largest monument in this complex has three ascending square stages leading to a large, square platform that likely had a wooden mandapa above it. This was burnt down during the destruction of Hampi. It is 3.5 kilometers from hampi bus stand.
Visiting Timings:-6:00 AM - 6:00 PM
The Square Water Pavilion, also called the Queen's Bath, is in the south-east of the royal centre. It has a pavilion, a water basin and a method of moving fresh water to it and taking away wash water and overflows. The basin is enclosed within an ornate, pillared, vaulted bay.
Nearby are ruins of the aqueduct.The modern name of this building, the Queen's bath, is probably a misnomer because this was a public bath for men and travellers.The building's interior arches show influence of the Indo-Islamic style, reflecting an era in which Hindu and Muslim arts influenced each other in India. It is 10 kilometers from hampi.
Visiting Timings:-11am to 7pm
Several major temples in Hampi have an embedded kitchen and 100-or-more-pillared feeding halls. Hampi also had a dedicated public Bhojana shala (house of food) where numerous thali (dish) were carved in series in a rock on both sides of a water channel.
One example is found near an octagonal fountain in the south of the royal centre; according to epigraphical sources, this Hampi bhojan shala was a utada kaluve or "canal connected with eating".
Visiting Timings:-All days of the week except Monday,7:00 PM - 8:30 PM
In the east of the royal centre lies the Gajashala, or elephant stables, which consist of eleven square chambers aligned north-south. The openings to the stables are arched; above ten chambers are alternating fluted and plain domes. In the middle of the stables are stairs to reach the roof.The Zenana enclosure is close to the elephant stables; it was thus named in a Persian memoir whose 19th-century translation was an early introduction to Hampi ruins for many.
The name "Zenana" is a misnomer, states George Michell, because it gives the impression that the women of Vijayanagar royalty lived here; its design and location makes that highly unlikely.The Zenana enclosure contains the Lotus Mahal, the latter being a two-storeyed pavilion in the royal centre.The Lotus Mahal combines a symmetrical, square, Hindu mandala design with lobed arches, vaults and domes of the Indo-Islamic style.
Its basement and pyramidal towers are based on Hindu temple architecture.Like almost all of the structures in Hampi's royal centre, this monument has no inscriptions nor epigraphs mentioning it and therefore dating it and establishing its function with evidence has been difficult. The Lotus Mahal and other structures in the Hampi urban core, however, were not built with Muslim patronage, unlike the tombs in the various Muslim quarters of the city.
These buildings reflect the assimilative approach of the Vijayanagara Hindu rulers. Lotus Mahal looks like a syncretic, congested space and its purpose is unclear. Speculations include it being a council hall. It is 3.7 kilometers from hampi.
Visiting Timings:-8:30 AM to 5:30 PM
The Ganigitti Jain temple is near Bhima's gate in the south-east of the urban core section of Hampi. In front of it is a monolithic lamp pillar.The temple faced north; it is dated to 1385 CE, during the rule of Hindu king Harihara II, based on an inscription in the temple.
It is dedicated to Tirthankara Kunthunatha and has plain walls, a pillared mandapa and a square sanctum from which the Jina's statue is missing. There are capitals on the pillars and the doorways have decoration. Over the sanctum is a Dravidian-style, narrowing square, pyramidal tower. Other monuments in the temple compound are in ruins. It is 9.6 kilometers from hampi.
Visiting Timings:-8:30am to 6pm
There is a Muslim monument in the south-east of the urban core on the road from Kamalapura to Anegondi, before Turuttu canal in the irrigated valley. This monument was first built in 1439 by Ahmad Khan, a Muslim officer in the army of Hindu king Devaraya II.
The monuments include a mosque, an octagonal well and a tomb. The mosque lacks a dome and is a pillared pavilion, while the tomb has a dome and arches. Other Muslim monuments and a graveyard were added later near the Ahmad Khan's legacy. It is 1.5 kilometers from hampi.
Visiting Timings:-7:00 AM -7:00 PM


--- layout: default title: Home description: CoScale documentation, tutorials and examples. ---
CoScale provides full stack performance monitoring, optimized for container and microservices environments. The CoScale platform continuously monitors your containerized infrastructure and in-container applications, as well as the impact on user experience and key business metrics.
On our docs you will find information on basic functionality and setup of the CoScale platform, as well as in depth tutorials and advanced configuration. If in doubt about a CoScale feature, please don't hesitate to contact our support.
Integrating CoScale into your environment is an easy straightforward process, just follow the steps below:
The CoScale lightweight agent is used to monitor server infrastructure and services, it uses log parsing and global status pages to gather its data and pushes the data to the CoScale platform through HTTPS calls.
The CoScale Real User Monitoring service, or RUM for short, is a Javascript snippet that provides insight into the performance on the users computer and devices.
Events are used to annotate data inside the CoScale platform, they provide you with an overview of what happened with your application and can speed up debugging.
The CoScale custom metrics options allow you to monitor aspects of your application that are not monitored by default with CoScale. They provide an easy way to extend the CoScale monitoring platform with metrics that are specific to your environment.
If you're looking for something specific, try out our search below. It is also always available in the sidebar to quickly search from any page in the documentation.


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x.__init__(...) initializes x; see x.__class__.__doc__ for signature
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Read null.
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Reads and returns an integer from the stream.
See Also: Parsing integers on OSFlash for the AMF3 integer data format. |
Reads and returns a string from the stream.
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Read date from the stream. The timezone is ignored as the date is always in UTC. |
Reads an array from the stream. Warning:
There is a very specific problem with AMF3 where the first three
bytes of an encoded empty See Also: Docuverse blog (external) |
Reads an object from the stream.
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Return the source object of a proxied object. Since: 0.4 |
Reads an object from the stream.
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Reads a string from the data stream and converts it into an XML Tree.
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Read a legacy XML Document from the stream.
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Reads a string of data from the stream. Detects if the ByteArray was compressed using See Also: ByteArray Note: This is not supported in ActionScript 1.0 and 2.0. |
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| Generated by Epydoc 3.0.1 on Mon Sep 7 23:17:45 2009 | http://epydoc.sourceforge.net |


Class CameraInfoMessage
This message defines meta information for a camera. It should be in a camera namespace on topic "camera_info" and accompanied by up to five image topics named:
image_raw - raw data from the camera driver, possibly Bayer encoded image - monochrome, distorted image_color - color, distorted image_rect - monochrome, rectified image_rect_color - color, rectified
The image_pipeline contains packages (image_proc, stereo_image_proc) for producing the four processed image topics from image_raw and camera_info. The meaning of the camera parameters are described in detail at http://www.ros.org/wiki/image_pipeline/CameraInfo.
The image_geometry package provides a user-friendly interface to common operations using this meta information. If you want to, e.g., project a 3d point into image coordinates, we strongly recommend using image_geometry.
/// If the camera is uncalibrated, the matrices D, K, R, P should be left
zeroed out. In particular, clients may assume that K[0] == 0.0
indicates an uncalibrated camera.
Inheritance
Implements
Inherited Members
Namespace: ZO.ROS.MessageTypes.Sensor
Assembly: Unity.ZeroSim.dll
Syntax
public class CameraInfoMessage : ZOROSMessageInterface
Constructors
| Improve this Doc View SourceCameraInfoMessage()
Declaration
public CameraInfoMessage()
CameraInfoMessage(HeaderMessage, UInt32, UInt32, String, Double[], Double[], Double[], Double[], UInt32, UInt32, RegionOfInterestMessage)
Declaration
public CameraInfoMessage(HeaderMessage header, uint height, uint width, string distortion_model, double[] D, double[] K, double[] R, double[] P, uint binning_x, uint binning_y, RegionOfInterestMessage roi)
Parameters
| Type | Name | Description |
|---|---|---|
| HeaderMessage | header | |
| System.UInt32 | height | |
| System.UInt32 | width | |
| System.String | distortion_model | |
| System.Double[] | D | |
| System.Double[] | K | |
| System.Double[] | R | |
| System.Double[] | P | |
| System.UInt32 | binning_x | |
| System.UInt32 | binning_y | |
| RegionOfInterestMessage | roi |
Properties
| Improve this Doc View Sourcebinning_x
Binning refers here to any camera setting which combines rectangular neighborhoods of pixels into larger "super-pixels." It reduces the resolution of the output image to (width / binning_x) x (height / binning_y). The default values binning_x = binning_y = 0 is considered the same as binning_x = binning_y = 1 (no subsampling).
Declaration
public uint binning_x { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
binning_y
Declaration
public uint binning_y { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
D
The distortion parameters, size depending on the distortion model. For "plumb_bob", the 5 parameters are: (k1, k2, t1, t2, k3).
Declaration
public double[] D { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double[] |
distortion_model
The distortion model used. Supported models are listed in sensor_msgs/distortion_models.h. For most cameras, "plumb_bob" - a simple model of radial and tangential distortion - is sufficient.
Declaration
public string distortion_model { get; set; }
Property Value
| Type | Description |
|---|---|
| System.String |
header
Time of image acquisition, camera coordinate frame ID Header timestamp should be acquisition time of image Header frame_id should be optical frame of camera origin of frame should be optical center of camera +x should point to the right in the image +y should point down in the image +z should point into the plane of the image
Declaration
public HeaderMessage header { get; set; }
Property Value
| Type | Description |
|---|---|
| HeaderMessage |
height
The image dimensions with which the camera was calibrated. Normally this will be the full camera resolution in pixels.
Declaration
public uint height { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
K
Intrinsic camera matrix for the raw (distorted) images. [fx 0 cx] K = [ 0 fy cy] [ 0 0 1] Projects 3D points in the camera coordinate frame to 2D pixel coordinates using the focal lengths (fx, fy) and principal point (cx, cy).
Declaration
public double[] K { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double[] |
MessageType
Declaration
[JsonIgnore]
public string MessageType { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
P
Projection/camera matrix [fx' 0 cx' Tx] P = [ 0 fy' cy' Ty] [ 0 0 1 0] By convention, this matrix specifies the intrinsic (camera) matrix of the processed (rectified) image. That is, the left 3x3 portion is the normal camera intrinsic matrix for the rectified image. It projects 3D points in the camera coordinate frame to 2D pixel coordinates using the focal lengths (fx', fy') and principal point (cx', cy') - these may differ from the values in K. For monocular cameras, Tx = Ty = 0. Normally, monocular cameras will also have R = the identity and P[1:3,1:3] = K. For a stereo pair, the fourth column [Tx Ty 0]' is related to the position of the optical center of the second camera in the first camera's frame. We assume Tz = 0 so both cameras are in the same stereo image plane. The first camera always has Tx = Ty = 0. For the right (second) camera of a horizontal stereo pair, Ty = 0 and Tx = -fx' * B, where B is the baseline between the cameras. Given a 3D point [X Y Z]', the projection (x, y) of the point onto the rectified image is given by: [u v w]' = P * [X Y Z 1]' x = u / w y = v / w This holds for both images of a stereo pair.
3x4 row-major matrix
Declaration
public double[] P { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double[] |
R
Rectification matrix (stereo cameras only) A rotation matrix aligning the camera coordinate system to the ideal stereo image plane so that epipolar lines in both stereo images are parallel.
Declaration
public double[] R { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double[] |
roi
Declaration
public RegionOfInterestMessage roi { get; set; }
Property Value
| Type | Description |
|---|---|
| RegionOfInterestMessage |
Type
Declaration
[JsonIgnore]
public static string Type { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
width
Declaration
public uint width { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
Methods
| Improve this Doc View SourceBuildCameraInfo(UInt32, UInt32, Double)
Declaration
public void BuildCameraInfo(uint width, uint height, double fovRadians)
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt32 | width | |
| System.UInt32 | height | |
| System.Double | fovRadians |
BuildCameraInfo(UInt32, UInt32, Double, Double, Double)
Builds up the camera info given width, height, focal length and sensor size
Declaration
public void BuildCameraInfo(uint width, uint height, double focalLengthMM, double sensorSizeXMM, double sensorSizeYMM)
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt32 | width | Width of camera in pixels. |
| System.UInt32 | height | Height of camera in pixels. |
| System.Double | focalLengthMM | Focal length in millimeters. |
| System.Double | sensorSizeXMM | Sensor width in millimeters. |
| System.Double | sensorSizeYMM | Sensor height in millimeters. |
Update()
Declaration
public void Update()


jQuery.Colorimazer, is an jQuery Plugin that allows basic color manipulations such as grayscale, inverse, polarize, opacity, hue, colorize...
It copies the image in an canvas and allows modification before resourcing the image element. jQuery.Colorimazer can be chained and applied on many elements.
Easy as possible, .grayscale()
is enough!
$("#gray").grayscale();
Another grayscale?  You can use .hue(180)
to rotate by 180 degrees!
$("#hue").hue({ mode: "add", hue: 180 });
{ mode: "add", saturation: -55 }
- Default mode: add{ mode: "add", value: 40 }
{ mode: "add", hue: 180, saturation: -55, value: 40 }
You need to colorize your picture? You can use .colorize()
$("#colorize").colorize({ mode: "add", r: 80 });
{ mode: "add", r: -55 }
{ mode: "add", grayscale: "average", g: 40 }
{ mode: "add", b: 10, r: -55, g: 40 }
You can .save()
and .restore()
an image. (one save deep)
And also create .custom()
function
$("img").save().custom(function(pixel, informations) { // pixel for the current pixel, informations for the image informations // pixel.x or .y for coordonates // informations.height or .width for dimension // informations.pixel(x, y) for one pixel // informations.pixel(x, y, newpixel) for apply one pixel like when you flip // apply 0 value on red by pixel.r = 0; // Like this: pixel.r = 255 * Math.random(); pixel.g = 255 * Math.random(); pixel.b = 255 * Math.random(); }); $(".btn").on("click", function() { $("img").restore(); });


[gecode-users] Adding constraints between branchings
Christian Schulte
cschulte at kth.se
Thu Jan 22 12:52:31 CET 2009
Please check the following thread:
http://thread.gmane.org/gmane.comp.lib.gecode.user/1861/focus=1872
Cheers
Christian
--
Christian Schulte, www.ict.kth.se/~cschulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Alberto Delgado
Sent: Thursday, January 22, 2009 11:20 AM
To: users at gecode.org
Subject: [gecode-users] Adding constraints between branchings
Hi all,
I'm branching over two different set of variables in my model and i'd
like to add some extra constraints in between the two branchings. I
can think about a way to do this implementing my own search engine but
i thought it would be better to ask first for any other alternative.
I implemented both branchings myself so a modification there is easy
to make.
Thanks in advance,
Alberto
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
More information about the gecode-users
mailing list


1)
Mom is cooking dinner right now.
2)
Tony is writing in his diary about his achievements.
3)
Bill and Mel are cycling in the park.
4)
The children are swimming in the pool at the club.
5)
Mr. Anderson is travelling to Australia next Monday.
6)
The baby is crying because of a stomach ache.
7)
Those carpenters are making new furniture for our house.
8)
Shelley is drawing a picture of the sunset at the beach.
9)
I am meeting my friends at 6 o’clock today.
10)
The cheerleaders are dancing with pompoms.


To acquire valuable knowledge and skills to complement those that I have learned for school in an actual job environment.
Junior Developer, Software Company (2010 - Present)
HTML and photoshop training
Designed and implemented HTML
Wrote third-party JavaScript and c++ libraries
Languages: C#, JavaScript, Python, c++,HTML
*Oriented in Microsoft Office Application such as:
Microsoft Office Word
Microsoft Office Excel
Microsoft Powerpoint
*Capable of editing pictures,
Bachelor of Science in Information Technology,University of the Cordilleras
Leonard Prim Francis Reyes
University of the Cordilleras
09**********


Chocolate Pizza
POSTED ON 15 DEC 2013/DESSERTS
Contrary to popular belief, Lorem Ipsum is not simply random text.  Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable.
If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text. All the Lorem Ipsum generators on the Internet tend to repeat predefined chunks as necessary, making this the first true generator on the Internet.


19th June 2020: The new Apache Archiva release version 2.2.5 is ready for download . This is a bugfix release. Please have a look at the release notes for further information. As this release contains security fixes, we recommend to update to the new version immediately.
Apache Archiva is an extensible repository management software that helps taking care of your own personal or enterprise-wide build artifact repository. It is the perfect companion for build tools such as Maven, Continuum, and ANT.
Archiva offers several capabilities, amongst which remote repository proxying, security access management, build artifact storage, delivery, browsing, indexing and usage reporting, extensible scanning functionality and many more! Get the latest updates, follow us on twitter @archiva.
And in case you were wondering, it is pronounced ahr-kahy-vuh, like archive but with an a!
Simple out of the box configuration and quick start guide
Read the users guide and administration documentation
For security issues, read the security vulnerabilities document
Documentation is also available for previous versions
Get support from the mailing lists and wiki, file issues, and even get involved in the development.
Archiva is distributed under the Apache License, version 2.0
Archiva is part of the Apache software Foundation. We would like to thank all the sponsors that provide financial assistance to the Apache Software Foundation. For more information on how you can support the foundation, see the sponsorship page.
You can also attend Apache Events: don't hesitate to ask on the Archiva User mailing list if Archiva team members will be there, it can be a great opportunity to meet them.
Need more? Try some of these resources.
Apache Maven 2: Effective Implementation (Maria Odea Ching, Brett Porter; September 2009)
Build and manage applications with Maven, Continuum and Archiva
Java Power Tools (John Smart; April 2008)
Contains a section on Archiva and repository management


Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes
Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes
Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes
Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes
Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes
Address: 1 High Street, farnborough, Hampshire, GU14 6SE
Status: In Production
Date Created: 05-May-2016
Completed: Yes


Brandon Raphael
Software Developer
<EMAIL_ADDRESS>Github: https://github.com/brandonraphaelAllstate's CompoZed Labs, Tempe, AZ
Graduated from the University of Arizona, class of 2015 Majored in Physics/Astronomy, minored in Math/CS Started working at CompoZed in mid-2016
First stop: The coffee maker... Usually leave around 6:50.
CompoZed provides its employees with a delectable assortment of breakfasts
Community meeting through appear.in, usually to discuss cool new tech, or ask for help to difficult problems.
Short, sweet, simple discussion meant as a review for the team on today's activities.
Usually, 20-30 minutes spent checking emails/Pivotal Tracker/Slack.
Engineers at CompoZed work strictly in pairs
Agile Development cycle
TDD all day every day
Depending on the day, I may spend time working on open-source projects, or developing my own skillset.
I may take smaller breaks throughout the day, but usually I take two larger breaks, each lasting ~30min.
Lunch is spent away from the computers. Work/life spearation is a key philosophy of CompoZed. Weekly lunch n' learns and lightning talks
Similar to the first, although it may be shorter depending on how in-depth I am in a problem.
May head to a tech meetup
Occasional Allstate-sponsored (or unsponsored) happy hour


Variables | |
| nav_msgs::OccupancyGrid | area |
| The grid map representing the environment to be covered. More... | |
| ServiceClient | area_getter |
| Service client to get coordinates of the area to cover. More... | |
| bool | divide_area |
| Whether to divide the area among the CPSs before generating the path or to generate the path on the complete map. More... | |
| Subscriber | map_subscriber |
| Service client to get the assigned area. More... | |
| bool | map_valid |
| Whether a valid grid map has been received. More... | |
| Publisher | mst_publisher |
| Publisher to visualize the minimum spanning tree. More... | |
| mst_path | path |
| The coverage path. More... | |
| Publisher | path_publisher |
| Publisher to visualize the coverage path. More... | |
| bool | reconfigure |
| Whether the swarm configuration has changed which requires a replanning of the path. More... | |
| double | resolution |
| The grid map underlying the path planning will be downsampled to this resolution in meter / cell. More... | |
| spanning_tree | tree |
| The minimum-spanning-tree (MST) that defines the coverage path. More... | |
| bool | turning_points |
| Whether there are only waypoints at turning points of the path or also waypoints regularly spaced on straight line segments of the path. More... | |
| bool | vertical |
| Whether the sweeping pattern is vertical or horizontal. More... | |
| bool | visualize |
| Whether to publish the coverage path on a topic for visualization. More... | |
| Publisher | wp_publisher |
| Publisher to visualize the current waypoint. More... | |
| nav_msgs::OccupancyGrid area |
The grid map representing the environment to be covered.
Definition at line 49 of file coverage_path.h.
| ServiceClient area_getter |
Service client to get coordinates of the area to cover.
Definition at line 44 of file coverage_path.h.
| bool divide_area |
Whether to divide the area among the CPSs before generating the path or to generate the path on the complete map.
Definition at line 79 of file coverage_path.h.
| Subscriber map_subscriber |
Service client to get the assigned area.
Definition at line 39 of file coverage_path.h.
| bool map_valid |
Whether a valid grid map has been received.
Definition at line 54 of file coverage_path.h.
| Publisher mst_publisher |
Publisher to visualize the minimum spanning tree.
Definition at line 34 of file coverage_path.h.
| mst_path path |
The coverage path.
Definition at line 64 of file coverage_path.h.
| Publisher path_publisher |
Publisher to visualize the coverage path.
Definition at line 24 of file coverage_path.h.
| bool reconfigure |
Whether the swarm configuration has changed which requires a replanning of the path.
Definition at line 94 of file coverage_path.h.
| double resolution |
The grid map underlying the path planning will be downsampled to this resolution in meter / cell.
Definition at line 69 of file coverage_path.h.
| spanning_tree tree |
The minimum-spanning-tree (MST) that defines the coverage path.
Definition at line 59 of file coverage_path.h.
| bool turning_points |
Whether there are only waypoints at turning points of the path or also waypoints regularly spaced on straight line segments of the path.
Definition at line 89 of file coverage_path.h.
| bool vertical |
Whether the sweeping pattern is vertical or horizontal.
Definition at line 84 of file coverage_path.h.
| bool visualize |
Whether to publish the coverage path on a topic for visualization.
Definition at line 74 of file coverage_path.h.
| Publisher wp_publisher |
Publisher to visualize the current waypoint.
Definition at line 29 of file coverage_path.h.


- Drag empty space to pan.
- Use mouse wheel to scroll vertically.
- Hold "Ctrl" and use mouse wheel to zoom.
- Click a node to select it.
- Double click a node to edit it's text.
- Drag & drop a node to change it's position
- "Enter" to add a sibling node of the selected node.
- "Ctrl + Enter" to add a child node of the selected node.
- "Ctrl + C" to copy the selected node.
- "Ctrl + X" to cut the selected node.
- "Ctrl + V" to paste copied node to the selected node.


Contains a series of classes that implement lifecycle strategy. Currently supported options are:
Implement
Startable
and/or
Disposable
Use configuration to wire methods that are start/stop/dispose equiv.
Todo:
Annotation-Based lifecycles.


An option of type Flag
takes no argument, but if present the argument is true.
| Option | Type | Description and notes |
|---|---|---|
| Input query options | ||
-query_loc |
String | Location on the query sequence in 1-based offsets (Format: start-stop) |
-strand |
String | Query strand(s) to search against database. Permissible values: both , minus , plus . Default: both |
-query_gencode |
Integer, values between: 1-6, 9-16, 21-31, 33 | Genetic code to use to translate query (see here for details). Default = 1 |
| General search options | ||
-word_size |
Integer, >= 2 | Word size for wordfinder algorithm |
-max_intron_length |
Integer, >= 0 | Length of the largest intron allowed in a translated nucleotide sequence when linking multiple distinct alignments. Default = 0 |
-matrix |
String | Scoring matrix name (normally BLOSUM62 ) |
-threshold |
Real, >= 0 | Minimum word score such that the word is added to the BLAST lookup table |
-db_gencode |
Integer, values between: 1-6, 9-16, 21-31, 33 | Genetic code to use to translate subject sequences (see here for details). Default = 1 |
| Query filtering options | ||
-seg |
String | Filter query sequence with SEG (Format: yes , window locut hicut , or no to disable). Default = 12 2.2 2.5 |
-soft_masking |
Boolean | Apply filtering locations as soft masks. Default = false |
-lcase_masking |
Flag | Use lower case filtering in query and subject sequence(s)? |
| Restrict search or results | ||
-db_soft_mask |
String | Filtering algorithm ID to apply to the BLAST database as soft masking. Incompatible with: -db_hard_mask |
-db_hard_mask |
String | Filtering algorithm ID to apply to the BLAST database as hard masking. Incompatible with: -db_soft_mask |
-qcov_hsp_perc |
Real, 0..100 | Percent query coverage per hsp |
-max_hsps |
Integer, >= 1 | Set maximum number of HSPs per subject sequence to save for each query |
-culling_limit |
Integer, >= 0 | If the query range of a hit is enveloped by that of at least this many higher-scoring hits, delete the hit. Incompatible with: -best_hit_overhang , -best_hit_score_edge |
-best_hit_overhang |
Real, (> 0 and < 0.5) | Best Hit algorithm overhang value (recommended value: 0.1 ). Incompatible with: -culling_limit |
-best_hit_score_edge |
Real, (> 0 and < 0.5) | Best Hit algorithm score edge value (recommended value: 0.1 ). Incompatible with: -culling_limit |
-subject_besthit |
Flag | Turn on best hit per subject sequence |
| Statistical options | ||
-dbsize |
Integer | Effective length of the database |
-searchsp |
Integer, >= 0 | Effective length of the search space |
-sum_stats |
Boolean | Use sum statistics |
| Extension options | ||
-xdrop_ungap |
Real | X-dropoff value (in bits) for ungapped extensions |
-window_size |
Integer, >= 0 | Multiple hits window size, use 0 to specify 1-hit algorithm |


apache_beam.metrics package¶
Submodules¶
apache_beam.metrics.cells module¶
This file contains metric cell classes. A metric cell is used to accumulate in-memory changes to a metric. It represents a specific metric in a single context.
Cells depend on a ‘dirty-bit’ in the CellCommitState class that tracks whether a cell’s updates have been committed.
apache_beam.metrics.execution module¶
This module is for internal use only; no backwards-compatibility guarantees.
The classes in this file keep shared state, and organize metrics information.
Available classes:
- MetricKey - Internal key for a metric.
- MetricResult - Current status of a metric’s updates/commits.
- _MetricsEnvironment - Keeps track of MetricsContainer and other metrics
- information for every single execution working thread.
- MetricsContainer - Holds the metrics of a single step and a single
- unit-of-commit (bundle).
-
class
apache_beam.metrics.execution.
MetricKey
(step, metric)[source]¶ Bases:
object
Key used to identify instance of metric cell.
Metrics are internally keyed by the step name they associated with and the name of the metric.
-
class
apache_beam.metrics.execution.
MetricResult
(key, committed, attempted)[source]¶ Bases:
object
Keeps track of the status of a metric within a single bundle.
It contains the physical and logical updates to the metric. Physical updates are updates that have not necessarily been committed, but that have been made during pipeline execution. Logical updates are updates that have been committed.
-
key
¶ A
MetricKey
that identifies the metric and bundle of this result.
-
committed
¶ The committed updates of the metric. This attribute’s type is that of the underlying cell data (e.g. int, DistributionData).
-
attempted
¶ The logical updates of the metric. This attribute’s type is that of the underlying cell data (e.g. int, DistributionData).
-
-
class
apache_beam.metrics.execution.
MetricUpdates
(counters=None, distributions=None)[source]¶ Bases:
object
Contains updates for several metrics.
A metric update is an object containing information to update a metric. For Distribution metrics, it is DistributionData, and for Counter metrics, it’s an int.
-
class
apache_beam.metrics.execution.
MetricsContainer
(step_name)[source]¶ Bases:
object
Holds the metrics of a single step and a single bundle.
apache_beam.metrics.metric module¶
User-facing classes for Metrics API.
The classes in this file allow users to define and use metrics to be collected and displayed as part of their pipeline execution.
- Metrics - This class lets pipeline and transform writers create and access
- metric objects such as counters, distributions, etc.
-
class
apache_beam.metrics.metric.
Metrics
[source]¶ Bases:
object
Lets users create/access metric objects during pipeline execution.
-
static
counter
(namespace, name)[source]¶ Obtains or creates a Counter metric.
Parameters: - namespace – A class or string that gives the namespace to a metric
- name – A string that gives a unique name to a metric
Returns: A Counter object.
-
static
distribution
(namespace, name)[source]¶ Obtains or creates a Distribution metric.
Distribution metrics are restricted to integer-only distributions.
Parameters: - namespace – A class or string that gives the namespace to a metric
- name – A string that gives a unique name to a metric
Returns: A Distribution object.
-
static
-
class
apache_beam.metrics.metric.
MetricsFilter
[source]¶ Bases:
object
Simple object to filter metrics results.
This class is experimental. No backwards-compatibility guarantees.
If filters by matching a result’s step-namespace-name with three internal sets. No execution/matching logic is added to this object, so that it may be used to construct arguments as an RPC request. It is left for runners to implement matching logic by themselves.
-
names
¶
-
namespaces
¶
-
steps
¶
-
apache_beam.metrics.metricbase module¶
The classes in this file are interfaces for metrics. They are not intended to be subclassed or created directly by users. To work with and access metrics, users should use the classes and methods exposed in metric.py.
Available classes:
- Metric - Base interface of a metrics object.
- Counter - Counter metric interface. Allows a count to be incremented or
- decremented during pipeline execution.
- Distribution - Distribution Metric interface. Allows statistics about the
- distribution of a variable to be collected during pipeline execution.
- MetricName - Namespace and name used to refer to a Metric.
-
class
apache_beam.metrics.metricbase.
Metric
[source]¶ Bases:
object
Base interface of a metric object.
-
class
apache_beam.metrics.metricbase.
Counter
[source]¶ Bases:
apache_beam.metrics.metricbase.Metric
Counter metric interface. Allows a count to be incremented/decremented during pipeline execution.
-
class
apache_beam.metrics.metricbase.
Distribution
[source]¶ Bases:
apache_beam.metrics.metricbase.Metric
Distribution Metric interface. Allows statistics about the distribution of a variable to be collected during pipeline execution.


Here are several ways to retrieve the data from a query. For lots of data, it's best to use the iterator. For single-row results, use fetchone().
#-- simple one-row fetch: good when you know only one row is returned #-- returns None when there is no more data curs.execute('select 2+2 from dual') print curs.fetchone() #-- iterating over a cursor #-- best way to loop over a results set curs.execute('select a,b from cxdemo_t1') for row in curs: print row #-- fetch all rows in one call curs.execute('select a,b from cxdemo_t1') print curs.fetchall()


Quick Start¶
Once the sdk is installed you should be ready to connect to services. Please see the sections below for details.
Usage¶
In order to use this SDK you will need to start by getting an API KEY from a WuXi Nextcode server. These API Keys are JWT token that you must use to initialize the SDK.
You can then get started with the following:
import nextcode
client = nextcode.Client(api_key="xxx")
qry = client.service("query")
qry.status()
This example allows you to verify that the API Key is correct and that the query service is responding. Please refer to the included documents for actual use cases.
API Key¶
You will need an API Key for all actions in the nextcode sdk. If you have a local keycloak user (non-saml) you can get the key with the following code:
from nextcode.client import get_api_key
api_key = get_api_key("www.myserver.com", "username", "password")
This will work for service users that have been added to keycloak by an administrator but if you are using your own account through OAuth you will need to log in via a webpage. The SDK has a helper method for this:
from nextcode.keycloak import auth_popup
api_key = auth_popup("www.myserver.com")
Alternatively you can navigate your brower directy to https://myserver.com/api-key-service
The API Key lifetime varies between keycloak installations but typically it is a month and the time will reset each time it is used. Therefore you should be able to save your api key locally for use in the sdk.
The SDK can cache the api key for you via profiles. To create a new profile you can use the following:
from nextcode.config import create_profile
create_profile("myprofile", api_key)
After this your profile is saved into ~/.nextcode.
Therefore, for a complete example you would do the following the first time you use the SDK using a service user:
import nextcode
nextcode.keycloak.auth_popup("www.myserver.com")
# ... retrieve the api key
api_key = "..."
config.create_profile("myprofile", api_key)
nextcode.config.set_default_profile("myprofile")
Now you can start a new python session and use:
import nextcode
svc = nextcode.get_service("query")
svc.status()
This will use your saved API key from the previous session.
Also note that if you use the nextcode-cli this process is simplified.


--- layout: default --- Yiqiu Shen PhD Candidate in Data Science, New York University I study deep learning models for medical image analysis, under the supervision of Professor Kyunghyun Cho and Professor Krzysztof J. Geras. Google Scholar Code Linkedin Academic Background
I study deep learning models for medical image analysis, under the supervision of Professor Kyunghyun Cho and Professor Krzysztof J. Geras.
Google Scholar Code Linkedin