My name is Athul Raj and I am a passionate photographer, engineering student and designer who is also a web developer from Kerala, India. I tackle a broad variety of disciplines in the process of creating meaningful ideas that make a real difference. With carefully selected tools and skills I express my vision for making the product the finest one.


Welcome to Mr. Roboger's Neighborhood!
I hope you're ready for some non-sensical words & numbers!
Please enter a number:
Behold!


Defining a Monitoring Algorithm¶
Monitoring algorithms are the combination of three sub-algorithms :
- A Screening algorithm, that processes the available data and outputs description parameters of this data.
- A Triggering algorithm that analyses the description parameters and returns a binary decision, regarding the necessity to go verify the data in reported in a given reported.
- A Supervision algorithm, that describes the concrete implementation of the monitoring in the field.
The combination of these three elements uniquely define a monitoring strategy for a program. At each steps, the facility objects are updated to include the values of the supervision paraemters, and the result of the trigger algorithm.
The Screening algorithms input can be classified along two dimentsions :
- Longitudinal vs Transversal :
- Longitudinal data : Using only one facility, the algorithm considers the past validated values and infers the characteristics of the next expected values.
- Transversal data : Using a group of facilities, the algorithm compares the different facilities performances and their values.
- Simple reports vs Validation trail :
- Simple reports : The algorithm uses only the values previously validated in the facilities.
- Validation trails : The algorithm uses both the reported values and the validated values.
We need to specify these two dimensions when initiating the algorithm object, to orient the pre-processing of the data. The inputed data is then processed to form an appropriate training set that can be fed in the Screening algorithm.
-
class
monitoring_algorithms.algorithms_definition.
monitoring_algorithm
(algorithm_name, screening_method, alert_trigger, implementation_simulation=None, transversal=False, validation_trail=True, verbose=False)¶ Monitoring Algorithm objects
Parameters: algorithm_name : string
The name of the algorithm, to identify the output in the facility objects
screening_method : function
The Screening algorithm. More description on its characteristics in the function.
alert_trigger : function
The Triggering algorithm. More description on its characteristics in the function.
implementation_simulation : function
The Supervision algorithm. More description on its characteristics in the function.
transversal : boolean
True if the data is transveral, False if it is longitudinal. False by default.
validation_trail : boolean
True if the screening method uses a validation trail as input, False if it uses simple reports. False by default.
verbose : boolean
True if the user wants to have some monitoring prints in different parts of the functions.
Methods
make_training_set
make_transversal_TS
monitor
return_parameters
simulate_implementation
trigger_supervisions
-
make_training_set
(facility_data, mois)¶ Preparing the training set to be fed in the algorithm data processing routine. The processing varies depending if the algorithm uses longitudinal or transversal data. For the rest, it essentially takes all the data collected before the processing month and keeps the claimed or the verified data depending on the supervision status for the given month.
Parameters: facility_data : DataFrame
The data collected in the OpenRBF system. It does not need to be the subset of the data on which to fit algorithm for the observed month.
mois : The month for which the algorithm is fit.
-
monitor
(facility_data, mois, **kwargs)¶ Monitoring function
This function is the workhorse of the algorithm. It extracts the training set from the facility data, and performs the appropriate screening for the month indicated.
Parameters: facility_data : DataFrame
The data collected in the OpenRBF system. It does not need to be the subset of the data on which to fit algorithm for the observed month.
mois : The month for which the algorithm is fit.
**kwargs : Additional arguments necessary for the specific algorithm
-
simulate_implementation
(date_start, date_stop, data, **kwargs)¶ Simulates the implementation of the algorithm. Runs the data processing and triggering rules, and applies the supervision rule, then saves the supervision status of the reporting month for each facility.
Parameters: date_start : Start date of the simulation
date_stop : Finish date of the simulation
data : Full open rbf data formatted in facilities objects
**kwargs : Additional arguments necessary for the specific algorithm
-
trigger_supervisions
(mois, **kwargs)¶ Raising an alarm if the description parameters computed by the monitoring function are problematic according to the alert_trigger function.
Parameters: mois : The month for which the algorithm is fit.
**kwargs : Additional arguments necessary for the specific algorithm
-


Next: psetq, Previous: progv, Up: Data and Control Flow Dictionary
setq
{!pair}* ⇒ result
pair ::=var form
pronounced ’set ,ky\"u
var—a symbol naming a variable other than a constant variable.
form—a form.
result—the primary value of the last form, or nil if no pairs were supplied.
Assigns values to variables.
(setq var1 form1 var2 form2 ...) is the simple variable assignment statement of Lisp. First form1 is evaluated and the result is stored in the variable var1, then form2 is evaluated and the result stored in var2, and so forth. setq may be used for assignment of both lexical and dynamic variables.
If any var refers to a binding made by symbol-macrolet, then that var is treated as if setf (not setq) had been used.
;; A simple use of SETQ to establish values for variables. (setq a 1 b 2 c 3) ⇒ 3 a ⇒ 1 b ⇒ 2 c ⇒ 3 ;; Use of SETQ to update values by sequential assignment. (setq a (1+ b) b (1+ a) c (+ a b)) ⇒ 7 a ⇒ 3 b ⇒ 4 c ⇒ 7 ;; This illustrates the use of SETQ on a symbol macro. (let ((x (list 10 20 30))) (symbol-macrolet ((y (car x)) (z (cadr x))) (setq y (1+ z) z (1+ y)) (list x y z))) ⇒ ((21 22 30) 21 22)
The primary value of each form is assigned to the corresponding var.
Next: psetq, Previous: progv, Up: Data and Control Flow Dictionary


I knew I’d run into this when I released PandoraBoy 0.4.0, and someone ran into Issue 17. Moving from MacOS 10.4.10 to 10.4.11 included a massive change to WebKit, and the 0.4 line was meant to address this. Unfortunately I no longer had a 10.4.10 box to test against, and so I figured that something would probably break. Hope against hope, I figured everyone would have upgraded by now.
Anyway, I will now undertake a series of “did this fix it” patches and we’ll see if we can get it working again. Hopefully the entire problem was my removal of decidePolicyForNewWindowAction: which never seems to be called in the new WebKit under Pandora (but I should have kept for completeness anyway). But it’s possible that the problem is deeper.


| | |
- __builtin__.object
-
- NetworkInterface
- NetworkConfigurationStaticParameters(__builtin__.tuple)
-
- NetworkConfigurationStaticParameters
class NetworkConfigurationStaticParameters(NetworkConfigurationStaticParameters) |
| |
Static IP options for network configuration.
As implemented only supports IPv4. |
| |
- Method resolution order:
- NetworkConfigurationStaticParameters
- NetworkConfigurationStaticParameters
- __builtin__.tuple
- __builtin__.object
Class methods defined here:
- normalizeStaticIp(self, ipaddress, netmask='<IP_ADDRESS>', gateway=None, nameservers=None) from __builtin__.type
- Normalize static IP options for network configuration.
As implemented only supports IPv4.
ipaddress
IP address.
netmask
netmask.
Defaults to <IP_ADDRESS>.
gateway
gateway.
If None then default to ip.1.
nameservers
one nameserver or a list of nameservers.
If None then default to gateway.
If empty list then remove option.
return
a NetworkConfigurationStaticParameters instance.
Data descriptors defined here:
- localprefix
- routingprefixlength
- Return an integer.
Methods inherited from NetworkConfigurationStaticParameters:
- __getnewargs__(self)
- Return self as a plain tuple. Used by copy and pickle.
- __repr__(self)
- Return a nicely formatted representation string
- _asdict(self)
- Return a new OrderedDict which maps field names to their values
- _replace(_self, **kwds)
- Return a new NetworkConfigurationStaticParameters object replacing specified fields with new values
Class methods inherited from NetworkConfigurationStaticParameters:
- _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
- Make a new NetworkConfigurationStaticParameters object from a sequence or iterable
Static methods inherited from NetworkConfigurationStaticParameters:
- __new__(_cls, ipaddress, netmask, gateway, nameservers)
- Create new instance of NetworkConfigurationStaticParameters(ipaddress, netmask, gateway, nameservers)
Data descriptors inherited from NetworkConfigurationStaticParameters:
- gateway
- Alias for field number 2
- ipaddress
- Alias for field number 0
- nameservers
- Alias for field number 3
- netmask
- Alias for field number 1
Data and other attributes inherited from NetworkConfigurationStaticParameters:
- _fields = ('ipaddress', 'netmask', 'gateway', 'nameservers')
Methods inherited from __builtin__.tuple:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __iter__(...)
- x.__iter__() <==> iter(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- T.__sizeof__() -- size of T in memory, in bytes
- count(...)
- T.count(value) -> integer -- return number of occurrences of value
- index(...)
- T.index(value, [start, [stop]]) -> integer -- return first index of value.
Raises ValueError if the value is not present.
|
class NetworkInterface(__builtin__.object) |
| |
Utilities regarding network interfaces.
As implemented only supports IPv4. |
| |
Class methods defined here:
- commandsUsedInImplementation(cls) from __builtin__.type
- Return a list to be passed to SystemRequirements.commandsRequired().
This class can be passed to SystemRequirements.commandsRequiredByImplementations().
- ipAddressOf(cls, networkInterfaceName) from __builtin__.type
- For networkInterfaceName="lo" return "<IP_ADDRESS>".
networkInterfaceName
the name of the interface.
Return the IP address of the interface, or None.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
| |


Page 1 of 1
nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 10:15 am
by Richard. UK
Hey,
I am so annoyed right now
- Have been looking forward to playing again, was perfect last night with a few BSOD's here and there, but now I cannot log on all together because, when I click my character and the world starts to load, I just get a BSOD with the nv4_disp.dll error...
I really wish I could fix this somehow, I want to get going again!
Help! lol
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 10:54 am
by Karl G.
Was it the new client that did it? This is a possibility, and I can help you work through it.
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 10:57 am
by Richard. UK
I had been using the newer client for a little while last night, I did get more frequenet BSOD's, i think I had about 3 or so.
Now I came to this when i woke up afdter leaving the computer off for about 7 hours, and tried to connect into the world, and then it was throwing the blue screens up everytime I would click to enter the world with my character.
Is there any sort of debug that could help you ?
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 11:13 am
by Karl G.
Not really, we'll just have to take this step-by-step. I'll make a new version and upload it soon--gotta finish registering for classes.
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 11:20 am
by Karl G.
OK Richard
download this:
http://www.unseenstudios.com/evidyon/do ... vidyon.exeI have enabled the debugging flags 0-6. Set them all to "0" in config.ini, then log into the world. If you can successfully log in, switch another flag to "1". When it crashes, tell me which flag you switched.
Karl
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 11:51 am
by Richard. UK
Karl,
with that client with all of the flags (0-9) set = 1.
everything was running fine, do you want me to try each flag (0-9), set to 1 individually at a time.
so like,
0------1
1------0
2------0
3------0
4------0
5------0
6------0
7------0
8------0
9------0
0------0
1------1
2------0
3------0
4------0
5------0
6------0
7------0
8------0
9------0
0------0
1------0
2------1
3------0
4------0
5------0
6------0
7------0
8------0
9------0
And so on??
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 11:56 am
by Richard. UK
ok i was wrong...BSOD just happend again, after it started to work with all flags on, which I put all on, after going through them in this way.
0------1
1------0
2------0
3------0
4------0
5------0
6------0
7------1
8------1
9------1
0------1
1------1
2------0
3------0
4------0
5------0
6------0
7------1
8------1
9------1
0------1
1------1
2------1
3------0
4------0
5------0
6------0
7------1
8------1
9------1
and so on. till they were all flagged then it worked...then i came to post here, then went back on to the game and BSOd'd
Re: nv4_displ.dll - BSOD
Posted:
Wed Aug 27, 2008 1:25 pm
by Karl G.
Ok, it looks like it's the scenery renderer that's doing it.
Keep flag 0 disabled (set to 0) and see if that works.


{% extends "configure/configure.html" %} {% block configure %}
Fully UI-based editing will be implemented later


Contact Information
Any Questions? Contact us!
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Facebook: Maui Beach Resort
Home Page
Accomodations
Activities
Pictures
Travel Arragements


Touch Slider
This slider example shows how to use gestures on iOS where keyboard events are not present in order to implement an ARIA slider. The example also uses a simplistic version of an announcer to announce the updates using ARIA live.
To use this on iOS with VoiceOver turned on, use the gesture override (double-tap and hold) and then drag the slider left or right.
Note: This slider does not implement all the functionality of a slider. It does not implement all of the ARIA authoring guidelines keyboard interaction.


The goal with this app is to help families and individuals learn the responsibilities of pet care in a controlled environment. Many people don't realize just how much work and time it takes to take of a pet, especially shelter animals. The experience of Pet Pointers is grounded in the idea of making sure that pets are no longer sent back to the shelter, and this is done though building stronger connections with the user.
Month of research went into this project to make it fun and easy to use. Many groups were researched including; potential pet owners, current pet owners, trainers, vets, and shelters. All of this research was done to make the experience easy and effective. Every feature and page in this app is designed to make sure that you know what it takes to own a pet and what steps to take next. Through our Pet Points you will know exactly how to take care of your new friend when your bring them to their new home!
Having a pet is an extremely rewarding experience and we want you to have all the fun and love that a pet can have with less of the stress so that you can focus on what really matters, making a lasting bond. Shelter animals have been through a lot and they need you to be there for them because you are the key to their forever home. Although these animals come with baggage once you can make a connection they will be your best friend for the rest of their lives.


Master Selsia:
Then Ivory Tower may have planned this entire situation in order to keep Giran and Oren from acquiring this power.
In that case, either Gauen or Kaiena may be telling the truth, or...
some other group may have decided to snatch the information for itself.
"The final scenario is..."


SHONDA LYNN RHIMES
Shonda Lynn Rhimes (born January 13, 1970) is an American television
producer, television and film writer, and author. She is best known as
the showrunner-creator, head writer, and executive producer of the
television medical drama Grey's Anatomy, its spin-off Private Practice,
and the political thriller series Scandal. Rhimes has also served as the
executive producer of the ABC television series Off the Map, How to Get
Away with Murder, and The Catch.
Shonda Rhimes on the cover of Ms. in 2015
Here's a breakdown of some of Shonda Rhimes' achievements:
- 1970 - Born in Chicago Illinois.
- 1991 - At Dartmouth College, she majored in English and film studies and earned her bachelor's degree.
- 1995 - Rhimes worked as research director on the documentary, Hank Aaron: Chasing the Dream, which won the 1995 Peabody Award.
- 1998 - Rhimes made a short film, Blossoms and Veils, starring Jada Pinkett-Smith and Jeffrey Wright, which is her only credit as a film director.
- 2001 - Rhimes wrote Crossroads, the debut film of pop singer Britney Spears that grossed more than $60 million worldwide.
- 2005 - Grey's Anatomy, a series created by Shonda Rhimes, debuted as a mid-season replacement.
- 2007 - Rhimes was named one of TIME magazine's 100 People Who Help Shape the World.
- 2007 - Rhimes created and produced the Grey's Anatomy spin-off series Private Practice.
- 2015 - She published her first book, a memoir, Year of Yes: How to Dance It Out, Stand in the Sun, and Be Your Own Person.
To read more on Shonda Rhimes, visit
this wikipedia page.


Inherits netCokeIdsdk.OAuthClient.
Public Types | |
| enum | Method { GET, POST } |
| Public Types inherited from netCokeIdsdk.OAuthClient | |
| enum | Method { GET, POST } |
Public Member Functions | |
| OAuth (string client_id, string client_secret, int client_auth=OAuthClient.AUTH_TYPE_URI) | |
| string | http_build_query (Dictionary< string, string > dicParams, string separator) |
| build http query from dictionary | |
| string | buildLoginUrl (string endpoint_url, string redirect_url, string scope=null) |
| Builds the URL to login process. | |
| string | buildSignupUrl (string endpoint_url, string redirect_url, string scope=null) |
| Builds the URL to sign up process. | |
| StoredToken | getStoredToken (string name, int source=BOTH) |
| Returns a specific stored token. SESSION has more priority than COOKIE. | |
| string | buildEditAccountUrl (string endpoint_url, string next_url, string cancel_url, string scope=null) |
| Builds the URL to edit the user's data. | |
| string | buildCompleteAccountUrl (string endpoint_url, string next_url, string cancel_url, string scope=null) |
| Builds the URL to fill up data for a specific section. | |
| void | doLogout (string endpoint_url) |
| Performs revocation process. Removes all tokens from that user. | |
| void | purgeTokens (string redirect="") |
| Removes all tokens. | |
| void | deleteStoredToken (string name) |
| Removes a specific token. It will removed from SESSION and COOKIE. | |
| LoginStatus | doLoginStatus (string endpoint_url) |
| Checks if user is logged. | |
| AccessToken | doCheckLoginConnect (string endpoint_url, string cookie_value) |
| | |
| StoredToken | hasToken (string name, int source=BOTH) |
| Checks if we have a specific token. | |
| Boolean | tokenExpired (string name) |
| Checks if a token has expired. | |
| ClientToken | doGetClientToken (string endpoint_url) |
| Gets a "client_token" for the current web client. | |
| AccessToken | doGetAccessToken (string endpoint_url, string code, string redirect_url) |
| Gets an "access_token" for the current web client. | |
| bool | doRefreshToken (string endpoint_url) |
| Updates tokens. | |
| bool | storeToken (StoredToken token) |
| Stores a token. | |
| void | checkErrors (JObject response) |
| Checks if there are errors in the response. | |
| bool | doCheckUserCompleted (string endpoint_url, string scope="") |
| Checks if the user has completed all required data for the specified section (scope). | |
| Public Member Functions inherited from netCokeIdsdk.OAuthClient | |
| OAuthClient (string client_id, string client_secret, int client_auth=AUTH_TYPE_URI) | |
| Constructor | |
| void | setClientId (string p_client_id) |
| Sets web client identifier. | |
| string | getClientId () |
| Returns web client identifier. | |
| void | setClientSecret (string p_client_secret) |
| Sets web client password ("client_secret") | |
| string | getClientSecret () |
| Returns web client password ("client_secret") | |
| void | setClientAuthType (int p_client_auth) |
| Sets authentication method. | |
| JObject | executeRequest (string url, Dictionary< string, string > parameters, string http_method=HTTP_METHOD_GET, Boolean http_headers=false, string cookies="") |
| Sends a web request | |
| long | time () |
| long | time (DateTime time) |
| Dictionary< string, string > | deserializeSimpleDict (string strToken) |
Static Public Member Functions | |
| static string | WebRequest (Method method, string url, string postData, string useragent) |
| Web Request Wrapper | |
| static string | WebResponseGet (HttpWebRequest webRequest) |
| Process the web response. | |
| Static Public Member Functions inherited from netCokeIdsdk.OAuthClient | |
| static int | getClientAuthType () |
| Returns authentication method. | |
| static string | WebRequest (Method method, string url, string postData, string useragent, string strCookie="") |
| Web Request Wrapper | |
| static string | WebResponseGet (HttpWebRequest webRequest) |
| Process the web response. | |
Public Attributes | |
| const string | AUTHORIZE = "https://savvistest.cocacola.es/oauth2/authorize" |
| const string | ACCESS_TOKEN = "https://savvistest.cocacola.es/oauth2/token" |
| const string | CALLBACK_URL = "http://docs.cocacola.es/postlogin" |
| Public Attributes inherited from netCokeIdsdk.OAuthClient | |
| const int | AUTH_TYPE_URI = 0 |
| const int | AUTH_TYPE_AUTHORIZATION_BASIC = 1 |
| const int | AUTH_TYPE_FORM = 2 |
| const String | GRANT_TYPE_AUTH_CODE = "authorization_code" |
| const String | GRANT_TYPE_PASSWORD = "password" |
| const String | GRANT_TYPE_CLIENT_CREDENTIALS = "client_credentials" |
| const String | GRANT_TYPE_REFRESH_TOKEN = "refresh_token" |
| const String | HTTP_METHOD_GET = "GET" |
| const String | HTTP_METHOD_POST = "POST" |
| const String | HTTP_METHOD_PUT = "PUT" |
| const String | HTTP_METHOD_DELETE = "DELETE" |
| const String | HTTP_METHOD_HEAD = "HEAD" |
| const string | AUTHORIZE = "https://savvistest.cocacola.es/oauth2/authorize" |
| const string | ACCESS_TOKEN = "https://savvistest.cocacola.es/oauth2/token" |
| const string | CALLBACK_URL = "http://docs.cocacola.es/postlogin" |
Properties | |
| string | Token [get, set] |
| Properties inherited from netCokeIdsdk.OAuthClient | |
| string | Token [get, set] |
Additional Inherited Members | |
| Protected Attributes inherited from netCokeIdsdk.OAuthClient | |
| string | access_token = null |
| int | access_token_type = ACCESS_TOKEN_URI |
| string | access_token_secret = null |
| string | access_token_algorithm = null |
| string | access_token_param_name = "access_token" |
| Static Protected Attributes inherited from netCokeIdsdk.OAuthClient | |
| static string | client_id = null |
| static string | client_secret = null |
| static int | client_auth = AUTH_TYPE_URI |
| string netCokeIdsdk.OAuth.buildCompleteAccountUrl | ( | string | endpoint_url, |
| string | next_url, | ||
| string | cancel_url, | ||
| string | scope = null |
||
| ) |
Builds the URL to fill up data for a specific section.
| endpoint_url | The endpoint. Normally the 'edit_account_endpoint' of OAuth server. |
| next_url | Where the user will be redirected when finished fill up data. |
| cancel_url | Where the user will be redirected if the process is cancelled. |
| scope | Section-key identifier of the web client. The section-key is located in "oauthconf.xml" file. |
| string netCokeIdsdk.OAuth.buildEditAccountUrl | ( | string | endpoint_url, |
| string | next_url, | ||
| string | cancel_url, | ||
| string | scope = null |
||
| ) |
Builds the URL to edit the user's data.
| endpoint_url | The endpoint. Normally the 'edit_account_endpoint' of OAuth server. |
| next_url | Where the user will be redirected when finished editing data. |
| cancel_url | Where the user will be redirected if the process is cancelled. |
| scope | Section-key identifier of the web client. The section-key is located in "oauthconf.xml" file. |
| string netCokeIdsdk.OAuth.buildLoginUrl | ( | string | endpoint_url, |
| string | redirect_url, | ||
| string | scope = null |
||
| ) |
Builds the URL to login process.
| endpoint_url | The endpoint. Normally the 'authorization_endpoint' of OAuth server. |
| redirect_url | Where the user will be redirected, even on success or not. |
| scope | Section-key identifier of the web client. The section-key is located in "oauthconf.xml" file. |
| string netCokeIdsdk.OAuth.buildSignupUrl | ( | string | endpoint_url, |
| string | redirect_url, | ||
| string | scope = null |
||
| ) |
Builds the URL to sign up process.
| endpoint_url | The endpoint. Normally the 'signup_endpoint' of OAuth server. |
| redirect_url | Where the user will be redirected, even on success or not. |
| scope | Section-key identifier of the web client. The section-key is located in "oauthconf.xml" file. |
| void netCokeIdsdk.OAuth.checkErrors | ( | JObject | response | ) |
Checks if there are errors in the response.
| response | json objet where we will search errors. |
| void netCokeIdsdk.OAuth.deleteStoredToken | ( | string | name | ) |
Removes a specific token. It will removed from SESSION and COOKIE.
| name | The token we want to remove. Are defined in iTokenTypes |
| AccessToken netCokeIdsdk.OAuth.doCheckLoginConnect | ( | string | endpoint_url, |
| string | cookie_value | ||
| ) |
Checks if user is logged.
| endpoint_url | The endpoint where the request will be sent. |
| cookie_value | The content of the cookie that stores the SSO. |
| bool netCokeIdsdk.OAuth.doCheckUserCompleted | ( | string | endpoint_url, |
| string | scope = "" |
||
| ) |
Checks if the user has completed all required data for the specified section (scope).
| endpoint_url | The endpoint where the request will be sent. |
| scope | Section-key identifier of the web client. The section-key is located in "oauthconf.xml" file. |
| AccessToken netCokeIdsdk.OAuth.doGetAccessToken | ( | string | endpoint_url, |
| string | code, | ||
| string | redirect_url | ||
| ) |
Gets an "access_token" for the current web client.
| endpoint_url | The endpoint where "access_token" is requested. |
| code | The authorization code returned by Coke ID. |
| redirect_url | Where the user will be redirected. |
| ClientToken netCokeIdsdk.OAuth.doGetClientToken | ( | string | endpoint_url | ) |
Gets a "client_token" for the current web client.
| endpoint_url | The endpoint where "client_token" is requested. |
| LoginStatus netCokeIdsdk.OAuth.doLoginStatus | ( | string | endpoint_url | ) |
Checks if user is logged.
| endpoint_url | The endpoint where the request will be sent. |
| void netCokeIdsdk.OAuth.doLogout | ( | string | endpoint_url | ) |
Performs revocation process. Removes all tokens from that user.
| endpoint_url | The endpoint where the request will be sent. |
| bool netCokeIdsdk.OAuth.doRefreshToken | ( | string | endpoint_url | ) |
Updates tokens.
| endpoint_url | The endpoint where the request will be sent. |
| StoredToken netCokeIdsdk.OAuth.getStoredToken | ( | string | name, |
| int | source = BOTH |
||
| ) |
Returns a specific stored token. SESSION has more priority than COOKIE.
| name | The token we want to recover. Are defined in iTokenTypes |
| source | Where we will look for. One of them: OAuthJQC::SESSION OAuthJQC::COOKIE OAuthJQC::BOTH |
| StoredToken netCokeIdsdk.OAuth.hasToken | ( | string | name, |
| int | source = BOTH |
||
| ) |
Checks if we have a specific token.
| name | The token we want to check. Are defined in StoredToken |
| source | Where we will look for. One of them:
|
| string netCokeIdsdk.OAuth.http_build_query | ( | Dictionary< string, string > | dicParams, |
| string | separator | ||
| ) |
build http query from dictionary
| dicParams | dictionary contains keys par value |
| separator | key par value separator |
| void netCokeIdsdk.OAuth.purgeTokens | ( | string | redirect = "" | ) |
Removes all tokens.
| redirect | A string where user will be redirected after purge or "" with no redirect. Note: the user will be redirected only if no data sent to browser. |
| bool netCokeIdsdk.OAuth.storeToken | ( | StoredToken | token | ) |
Stores a token.
| token | An object with token data to be stored. |
| Boolean netCokeIdsdk.OAuth.tokenExpired | ( | string | name | ) |
Checks if a token has expired.
| name | stored token name |
|
static |
Web Request Wrapper
| method | Http Method |
| url | Full url to the web resource |
| postData | Data to post in querystring format |
|
static |
Process the web response.
| webRequest | The request object. |


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Select Option
Journey across the moon
Book a trip across moon's orbit for a travel time of 14 days
Payload delivery to orbit
Send payload such as satellites to orbit or cargo to the ISS


Dynamic Java
A key part of MDW's support for cloud deployments and "Platform as a Service" architecture
is the ability to execute dynamic Java transparently in both local and remote environments.
Dynamic Java workflow assets help provide this capability.
For a detailed walkthrough of MDW Cloud development setup,
see the Setup Guide for CloudFoundry.
Background
Dynamic Java takes advantage of the runtime compilation and execution framework built into JDK1.8.
Compiled class definitions are lazily cached in the CompiledJavaCache as specified in the application cache configuration.
An important aspect of the dynamic Java is that it enables you to transparently edit Java Code in a local
or remote environment without needing to redeploy or restart your server. You can also debug your code without
regard to where the workflow is hosted, setting breakpoints and viewing/changing variable values during process execution.
Dynamic Java is used to implement Custom Activities, Event Handlers and Variable Translators.
Other capabilities that can be implemented in dynamic Java include Activity/Adapter/Process/Service Monitors, Scheduled Jobs,
Cache Services, Startup Services, and custom JSON or XML based service implementations that can be exposed through any of the supported MDW protocols
(SOAP, REST, etc). For Manual Tasks dynamic Java customizations include Manual Task custom Routing Strategies, AutoAssign Strategies,
Prioritization Strategies, SubTask Strategies and Task Notifiers.
Usage
To create a custom dynamic Java Activity Implementor or Event Handler, use the appropriate Eclipse wizards and select dynamic Java for the code generation option.
For other types of custom code, you can create a dynamic Java asset by right-clicking on the desired workflow package location in Process Explorer
view and selecting New > Java Source. Then you can customize the generated skeleton code.
Note: If your workflow project is remote or cloud-based and it's the first time you've edited Java content in the project, you'll
be prompted to select a version of the MDW Framework Jars to download (see screenshot below). Your workflow project
will be converted to include the Java facet, and the framework libraries will be added to your project classpath.
This gives you access to all the standard Java editing capabilities built into Eclipse, with syntax highlighting
and autocomplete for the JDK classes as well as the classes provided by MDW itself.
Running and Debugging
When you're ready to test your dynamic Java, there's no need to compile or deploy; simply saving the artifact is sufficient.
As with all types of MDW assets, when you open the .java file you're actually opening a local
temporary copy, and this is persisted to the database once you save the file (you'll be promted whether to increment the version and so forth).
You can execute your workflow in the usual way and you'll have the ability to debug your dynamic Java.
To do this, simply set your breakpoint(s) in Eclipse, and make sure you're connected to your server process in the debugger.
Once launched, your breakpoint should be hit and you can proceed with stepping through code as you normally would in Eclipse.


com.xtremelabs.imageutils
Enum AbstractImageLoader.Options.ScalingPreference
java.lang.Object
java.lang.Enum<AbstractImageLoader.Options.ScalingPreference>
com.xtremelabs.imageutils.AbstractImageLoader.Options.ScalingPreference
- All Implemented Interfaces:
- java.io.Serializable, java.lang.Comparable<AbstractImageLoader.Options.ScalingPreference>
- Enclosing class:
- AbstractImageLoader.Options
public static enum AbstractImageLoader.Options.ScalingPreference
- extends java.lang.Enum<AbstractImageLoader.Options.ScalingPreference>
LARGER_THAN_VIEW_OR_FULL_SIZE
This option guarantees that the image being returned will be larger than the view's bounds, or it's maximum size. The image may be scaled down if it is possible to do so without becoming smaller than either of
the provided bounds. The image will not be scaled unless both a width and height bounds are specified.
MATCH_TO_LARGER_DIMENSION
This option is nearly identical to LARGER_THAN_VIEW_OR_FULL_SIZE
. The only difference is that if bounds are provided for only one dimension of the ImageView (ie. width OR height), the
image may be scaled according to that dimension.
MATCH_TO_SMALLER_DIMENSION
This option is nearly identical to LARGER_THAN_VIEW_OR_FULL_SIZE
. They differ in that if bounds are provided for only one dimension of the ImageView (ie. width OR height), the image
may be scaled according to that dimension, and if both width and height are provided, the image will scale to best fit within the bounds (as opposed to the other two options above, which will scale to the
larger of the two dimensions only).
ROUND_TO_CLOSEST_MATCH
The dimensions of the image returned will be as close to the dimension of the bounds as possible. The bitmap returned may be scaled down to be smaller than the view. This option may degrade image quality, but
often will consume less memory. This option will give preference to the smaller of the two bounds.
SMALLER_THAN_VIEW
The dimensions of the image being returned is guaranteed to be equal to or smaller than the size of the bounds provided. This guarantees memory savings in the event that images are larger than the ImageViews
they are being loaded into.
| Methods inherited from class java.lang.Enum |
compareTo, equals, getDeclaringClass, hashCode, name, ordinal, toString, valueOf |
| Methods inherited from class java.lang.Object |
getClass, notify, notifyAll, wait, wait, wait |
SMALLER_THAN_VIEW
public static final AbstractImageLoader.Options.ScalingPreference SMALLER_THAN_VIEW
ROUND_TO_CLOSEST_MATCH
public static final AbstractImageLoader.Options.ScalingPreference ROUND_TO_CLOSEST_MATCH
LARGER_THAN_VIEW_OR_FULL_SIZE
public static final AbstractImageLoader.Options.ScalingPreference LARGER_THAN_VIEW_OR_FULL_SIZE
MATCH_TO_LARGER_DIMENSION
public static final AbstractImageLoader.Options.ScalingPreference MATCH_TO_LARGER_DIMENSION
MATCH_TO_SMALLER_DIMENSION
public static final AbstractImageLoader.Options.ScalingPreference MATCH_TO_SMALLER_DIMENSION
values
public static AbstractImageLoader.Options.ScalingPreference[] values()
- Returns an array containing the constants of this enum type, in
the order they are declared. This method may be used to iterate
over the constants as follows:
for (AbstractImageLoader.Options.ScalingPreference c : AbstractImageLoader.Options.ScalingPreference.values())
System.out.println(c);
- Returns:
- an array containing the constants of this enum type, in
the order they are declared
valueOf
public static AbstractImageLoader.Options.ScalingPreference valueOf(java.lang.String name)
- Returns the enum constant of this type with the specified name.
The string must match exactly an identifier used to declare an
enum constant in this type. (Extraneous whitespace characters are
not permitted.)
- Parameters:
name
- the name of the enum constant to be returned.
- Returns:
- the enum constant with the specified name
- Throws:
java.lang.IllegalArgumentException
- if this enum type has no constant
with the specified name
java.lang.NullPointerException
- if the argument is null


User profiles
Loading profiles...
The following is a list of OSVR user profiles.
| Name | IPD | Standing Eye Height | Seated Eye Height | File Path | |
|---|---|---|---|---|---|
| {{userProfile.name}} | {{userProfile.ipd}} | {{userProfile.standingEyeHeight}} | {{userProfile.seatedEyeHeight}} | {{userProfile.filePath}} |
|
Edit Profile


This file contains Poly1305 definitions and functions.
More...
#include "config.h"
#include <stdint.h>
#include <stddef.h>
Go to the source code of this file.
This file contains Poly1305 definitions and functions.
Poly1305 is a one-time message authenticator that can be used to
authenticate messages. Poly1305-AES was created by Daniel
Bernstein https://cr.yp.to/mac/poly1305-20050329.pdf The generic
Poly1305 algorithm (not tied to AES) was also standardized in RFC
7539.
- Author
- Daniel King damak.nosp@m.i.gh.nosp@m.@gmai.nosp<EMAIL_ADDRESS>Definition in file poly1305.h.
◆ MBEDTLS_ERR_POLY1305_BAD_INPUT_DATA
| #define MBEDTLS_ERR_POLY1305_BAD_INPUT_DATA -0x0057 |
Invalid input parameter(s).
Definition at line 71 of file poly1305.h.
◆ MBEDTLS_ERR_POLY1305_FEATURE_UNAVAILABLE
| #define MBEDTLS_ERR_POLY1305_FEATURE_UNAVAILABLE -0x0059 |
Feature not available. For example, s part of the API is not implemented.
Definition at line 75 of file poly1305.h.
◆ MBEDTLS_ERR_POLY1305_HW_ACCEL_FAILED
| #define MBEDTLS_ERR_POLY1305_HW_ACCEL_FAILED -0x005B |
Poly1305 hardware accelerator failed.
Definition at line 79 of file poly1305.h.
◆ mbedtls_poly1305_context
◆ mbedtls_poly1305_finish()
This function generates the Poly1305 Message Authentication Code (MAC).
- Parameters
-
| ctx | The Poly1305 context to use for the Poly1305 operation. This must be initialized and bound to a key. |
| mac | The buffer to where the MAC is written. This must be a writable buffer of length 16 Bytes. |
- Returns
0
on success.
-
A negative error code on failure.
◆ mbedtls_poly1305_free()
This function releases and clears the specified Poly1305 context.
- Parameters
-
| ctx | The Poly1305 context to clear. This may be NULL , in which case this function is a no-op. If it is not NULL , it must point to an initialized Poly1305 context. |
◆ mbedtls_poly1305_init()
This function initializes the specified Poly1305 context.
It must be the first API called before using
the context.
It is usually followed by a call to
\c mbedtls_poly1305_starts(), then one or more calls to
\c mbedtls_poly1305_update(), then one call to
\c mbedtls_poly1305_finish(), then finally
\c mbedtls_poly1305_free().
- Parameters
-
| ctx | The Poly1305 context to initialize. This must not be NULL . |
◆ mbedtls_poly1305_mac()
| int mbedtls_poly1305_mac |
( |
const unsigned char |
key[32], |
|
|
const unsigned char * |
input, |
|
|
size_t |
ilen, |
|
|
unsigned char |
mac[16] |
|
) |
| |
This function calculates the Poly1305 MAC of the input buffer with the provided key.
- Warning
- The key must be unique and unpredictable for each invocation of Poly1305.
- Parameters
-
| key | The buffer containing the 32 Byte (256 Bit) key. |
| ilen | The length of the input data in Bytes. Any value is accepted. |
| input | The buffer holding the input data. This pointer can be NULL if ilen == 0 . |
| mac | The buffer to where the MAC is written. This must be a writable buffer of length 16 Bytes. |
- Returns
0
on success.
-
A negative error code on failure.
◆ mbedtls_poly1305_self_test()
| int mbedtls_poly1305_self_test |
( |
int |
verbose | ) |
|
The Poly1305 checkup routine.
- Returns
0
on success.
-
1
on failure.
◆ mbedtls_poly1305_starts()
This function sets the one-time authentication key.
- Warning
- The key must be unique and unpredictable for each invocation of Poly1305.
- Parameters
-
| ctx | The Poly1305 context to which the key should be bound. This must be initialized. |
| key | The buffer containing the 32 Byte (256 Bit) key. |
- Returns
0
on success.
-
A negative error code on failure.
◆ mbedtls_poly1305_update()
This functions feeds an input buffer into an ongoing Poly1305 computation.
It is called between mbedtls_cipher_poly1305_starts()
and mbedtls_cipher_poly1305_finish()
. It can be called repeatedly to process a stream of data.
- Parameters
-
| ctx | The Poly1305 context to use for the Poly1305 operation. This must be initialized and bound to a key. |
| ilen | The length of the input data in Bytes. Any value is accepted. |
| input | The buffer holding the input data. This pointer can be NULL if ilen == 0 . |
- Returns
0
on success.
-
A negative error code on failure.


---
layout: post
title: 'Aurora Seen from Northern Ontario: 1 October 2012'
date: '2012-10-01T14:48:00.000-04:00'
author: Bob Sica
tags:
- solar storm
- aurora
modified_time: '2013-07-30T14:50:58.654-04:00'
blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-8182657358002660038
blogger_orig_url: http://blog.purplecrowlidar.ca/2012/10/aurora-seen-from-northern-ontario-1.html
---
Spectacular photo of the aurora (Northern Lights) from last night’s large solar storm from Thunder Bay, Ontario (from The Weather Network).


|
| def | __init__ (self, control_rate_s, finger_type, enable_visualization, smoothing_params, use_real_robot=False, finger_config_suffix="0", synchronize=False) |
| | Intializes the constituents of the reaching environment. More...
|
| |
| def | step (self, action) |
| | The env step method. More...
|
| |
| def | reset (self) |
| | Episode reset.  More...
|
| |
| def | _get_state (self, observation, action, log_observation=False) |
| | Get the current observation from the env for the agent. More...
|
| |
◆ __init__()
| def trifinger_simulation.gym_wrapper.envs.trifinger_reach.TriFingerReach.__init__ |
( |
|
self, |
|
|
|
control_rate_s, |
|
|
|
finger_type, |
|
|
|
enable_visualization, |
|
|
|
smoothing_params, |
|
|
|
use_real_robot = False , |
|
|
|
finger_config_suffix = "0" , |
|
|
|
synchronize = False |
|
) |
| |
Intializes the constituents of the reaching environment.
Constructor sets up the finger robot depending on the finger type, and also whether an instance of the simulated or the real robot is to be created. Also sets up the observation and action spaces, smoothing for reducing jitter on the robot, and provides for a way to synchronize robots being trained independently.
- Parameters
-
| control_rate_s | the rate (in seconds) at which step method of the env will run. The actual robot controller may run at a higher rate, so this is used to compute the number of robot control updates per environment step. |
| finger_type | Name of the finger type. In order to get a dictionary of the valid finger types, call :meth:.finger_types_data.get_valid_finger_types |
| enable_visualization | if the simulation env is to be |
| visualized | smoothing_params (dict): |
| num_episodes | the total number of episodes for which the training is performed @param start_after the fraction of episodes after which the smoothing of applied actions to the motors should start |
| final_alpha | smoothing coeff that will be reached at the end of the smoothing |
| stop_after | the fraction of total episodes by which final alpha is to be reached, after which the same final alpha will be used for smoothing in the remainder of the episodes is_test (bool, optional): Include this for testing |
| use_real_robot | if training is to be performed on the real robot ([default] False) |
| finger_config_suffix | pass this if only one of the three fingers is to be trained. Valid choices include [0, 120, 240] ([default] 0) |
| synchronize | Set this to True if you want to train independently on three fingers in separate processes, but have them synchronized. ([default] False) |
◆ _compute_reward()
| def trifinger_simulation.gym_wrapper.envs.trifinger_reach.TriFingerReach._compute_reward |
( |
|
self, |
|
|
|
observation, |
|
|
|
goal |
|
) |
| |
|
private |
The reward function of the environment.
- Parameters
-
| observation | the observation at the current step |
| goal | the desired goal for the episode |
- Returns
- the reward, and the done signal
◆ _get_state()
| def trifinger_simulation.gym_wrapper.envs.trifinger_reach.TriFingerReach._get_state |
( |
|
self, |
|
|
|
observation, |
|
|
|
action, |
|
|
|
log_observation = False |
|
) |
| |
|
private |
Get the current observation from the env for the agent.
- Parameters
-
| log_observation | specify whether you want to log the observation |
- Returns
-
observation comprising of the observations corresponding to the key values in the observation_keys
◆ reset()
| def trifinger_simulation.gym_wrapper.envs.trifinger_reach.TriFingerReach.reset |
( |
|
self | ) |
|
Episode reset.
- Returns
- the scaled to [-1;1] observation from the env after the reset
◆ step()
| def trifinger_simulation.gym_wrapper.envs.trifinger_reach.TriFingerReach.step |
( |
|
self, |
|
|
|
action |
|
) |
| |
The env step method.
- Parameters
-
| action | the joint positions that have to be achieved |
- Returns
- the observation scaled to lie between [-1;1], the reward, the done signal, and info on if the agent was successful at the current step
The documentation for this class was generated from the following file:
- python/trifinger_simulation/gym_wrapper/envs/trifinger_reach.py


My Fabulous Garden
New Flower
Name
Image
Alstroemerias
Gardenias
Orchids
Roses
Sunflowers
Tulips
Peonies
Season
#
Image
Name
Season


Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation.
For the best experience please use the latest Chrome, Safari or Firefox browser.
AppEngine and Android
Archzilon Eshun-Davies(laudarch)
Software Engineer
- *App Engine
- -Custom Server
- -External Data Storage
- *Android
- *Ideas!
- *Joining forces(Demo Apps)
- *Questions?
App Engine
- -Custom Server(VPS, inHouse)
- -External Data Storage(Mysql, Postgres, Oracle)
The world is in your
PALM*
Android
visualize your big thoughts
Demo App
http://sikadroid.appspot.com
A small microfinance aka susu
system
the only limit is your
imagination


All functions
|
|
boolean_fill
|
Fill a nested logical list with TRUEs before or after the first TRUE |
|
compare_stage_keys
|
Compare two stage keys to see which one has a stage run first |
|
copy_env
|
Copy one environment into another recursively. |
|
is_pre_stagerunner
|
Whether or not an object can be transformed into a stageRunner. |
|
is.stagerunner is.stageRunner
|
Check whether an R object is a stageRunner object |
|
nested_names
|
Delimited names of a nested list. |
|
normalize_stage_keys
|
Normalize a reference to stage keys |
|
OOP_type_independent_method
|
Call a method on an object regardless of its OOP type. |
|
run
|
Run the stages in a stageRunner object. |
|
show_message
|
Show a progress message when executing a stagerunner. |
|
special_and_lists
|
AND two lists together with some regards for nesting |
|
special_or_lists
|
OR two lists together with some regards for nesting |
|
stageRunner_.before_env
|
Get an environment representing the context directly before executing a given stage. |
|
stageRunner_.clear_cache
|
Clear all caches in this stageRunner, and recursively. |
|
stageRunner_.mark_finished
|
Mark a given stage as being finished. |
|
stageRunner_.root
|
Determine the root of the stageRunner. |
|
stageRunner_.set_parents
|
Set all parents for this stageRunner, and recursively |
|
stageRunner_.set_prefixes
|
Set all prefixes for child stageRunners. |
|
stageRunner_append
|
Append one stageRunner to the end of another. |
|
stageRunner_around
|
Wrap a function around a stageRunner's terminal nodes |
|
stageRunner_coalesce
|
Coalescing a stageRunner object is taking another stageRunner object
with similar stage names and replacing the latter's cached environments
with the former's. |
|
stageRunner_current_stage
|
This allows us to get the furthest executed stage. |
|
stageRunner_has_key
|
Whether or not the stageRunner has a key matching this input. |
|
stagerunner_initialize
|
Initialize a stageRunner object. |
|
stageRunner_next_stage
|
For stageRunners with caching, find the next unexecuted stage. |
|
stageRunner_overlay
|
Overlaying a stageRunner object is taking another stageRunner object
with similar stage names and adding the latter's stages as terminal stages
to the former (for example, to support tests). |
|
stageRunner_show
|
Generic for printing stageRunner objects. |
|
stageRunner_stage_names
|
Retrieve a flattened list of canonical stage names for a stageRunner object |
|
stageRunner_transform
|
Transform the callable's of the terminal nodes of a stageRunner. |
|
stageRunner stagerunner
|
Stagerunners are parametrized sequences of linear execution. |
|
stageRunnerNode_around
|
Wrap a stageRunnerNode callable with another callable. |
|
stageRunnerNode_overlay
|
Append one stageRunnerNode around another. |
|
stageRunnerNode_run
|
Execute the callable of a stageRunnerNode. |
|
stageRunnerNode_transform
|
Transform a stageRunnerNode according to a functional. |
|
is.stageRunnerNode
|
Stagerunner nodes are environment wrappers around individual stages
(i.e. functions) in order to track meta-data (e.g., for caching). |
|
treeSkeleton__.parent_index
|
Find the index of the current object in the children of its parent. |
|
treeSkeleton__children
|
Find the children of the current object wrapped in treeSkeletons. |
|
treeSkeleton__find
|
Find the key with the given index using the names of the lists
that parametrize each node's children. |
|
treeSkeleton__first_leaf
|
Find the first leaf in a tree. |
|
treeSkeleton__initialize
|
Initialize a treeSkeleton object. |
|
treeSkeleton__last_leaf
|
Find the last leaf in a tree. |
|
treeSkeleton__parent
|
Find the parent of the current object wrapped in a treeSkeleton. |
|
treeSkeleton__predecessor
|
Attempt to find the predecessor of the current node. |
|
treeSkeleton__root
|
Find the root node of the tree (the only one with no parent). |
|
treeSkeleton__successor
|
Attempt to find the successor of the current node. |
|
treeSkeleton
|
This class implements iterators for a tree-based structure
without an actual underlying tree. |


Sourdough bread with seeds and grains
- ⏲ Prep time: 15 min
- 🍳 Cook time: 120 min
Ingredients
For the preparation
- 100g grain of choice (e.g. rye, spelt and wheat)
- 40g linseed
- 30g sesame
- 30g sunflower seeds
- 600g water
For the bread
- 400g sourdough
- 200g water (25-35°C)
- 200g rye flour
- 300g wheat flour
- 20g sea salt
- 2tsp bread spice
- soaked seeds and grains
Directions
Preparations (the day before)
- Feed your sourdough starter for the next day.
- Boil the grain in 500g water for 45 minutes.
- Put the seeds and 100g water into a bowl.
- Store everything in a locked bowl overnight (13-16h).
Bread
- Remove leftover water from the seeds and grains
- Stir all ingredients for the bread and knead them well.
- Let the dough rest for 5 hours in warm (25-30°C) conditions.
- Preheat the oven to 250°C.
- Bake for up to 20 minute with steam.
- Release the steam and turn the heat down to 150°C.
- Bake for another 50-60 minutes
Contribution
Recipe tags:
sourdough
bread


Reports files that do not have a type definition with the corresponding name.
The quick-fix offers to either rename the file to match the type definition's name or rename the type definition to match the file name.


--- layout: home block: home --- {{ site.title }} {% include svg-name.html %} Product Designer ✏ Tech Explorer 🚀 Projects {% for post in site.posts %} {{ post.title | escape }} {{ post.content | strip_html | truncatewords:24}} 👉 {% endfor %}
Product Designer ✏ Tech Explorer 🚀
{{ post.content | strip_html | truncatewords:24}} 👉


H = horizontal flip, V = vertical flip, 5 = linear shift to right by 5 spaces, -5 = linear shift to left by 5 spaces
Example: H, V, 5


Pre-authentication
Configuration of pre-authentication
One has to configure the pre-authentication request header inside yanel.xml
Java
- org.wyona.yanel.core.Resource.getYanel().isSignedIn()
- org.wyona.yanel.core.Resource.getYanel().isPreAuthenticationRequestHeaderName()
Testing pre-authentication
Set the configured pre-authentication request header inside your browser, for example with ModHeader when using Chrome


let us know where you've been
Title
Description
Continent Choose continent Africa Asia Europe North America South America
Country Choose country Canada Mexico United States
City Calgary Montreal Toronto Vancouver
Rate this photo:
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Date Taken:
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Matt Bahen’s Landscape Paintings Tap Into Climate Change
by Nadja Sayej“All gardening is landscape painting,” William Kent once said. But with climate change, how differently do we look at a landscape painting? Going into a museum today and looking at century old landscape paintings is one way of seeing how our planet has changed (or how we’ve destroyed our planet, depending on how you look at it).Canadian artist Matt Bahen recently had a solo show of new paintings at the Nicholas Metivier Gallery in Toronto, entitled The Promise, The Promise. These large-scale oil paintings look at how our planet has been destroyed. With references to literary masters like T.S. Eliot and Joseph Campbell, the paintings depict forest fires, muddy waters, floods and barbed wire. It is a sign of our times, or perhaps, one to come.
Bahen speaks from his Toronto studio about calamity, literary drama and environmental narratives.
So, this new series of paintings are inspired by literary works?
Matt Bahen: Most of the titles are taken from literary works, from TS Eliot to the Chinese science fiction writer, Liu Cixin. So it isn’t just old literature, as it’s nice to find new things, as well.
Some of the landscapes are based in British Columbia, when were you last there to visit?
When I was a musician and toured Canada with a rock band, we got to see the whole country. The last time I was in B.C. was five years ago. But I don’t tour anymore with the band, The Schomberg Fair. I was coming home with less money than I left with. So, since 2013, I’ve been focusing full time on painting.
Did you focus on just B.C. landscapes, or beyond?
They’re from all over. They’re all landscapes I’m familiar with, but their actual geographical locations are not that important. Because I’m familiar with the landscapes, I hope the viewer has an entrance to it, as well. You’ll notice there aren’t many tropical landscapes or desert scenes, or anything like that.
Canada has a long history of picturesque landscape art, what makes your work different or updated?
What I’ve tried to do is take a generic landscape and create tension within it, use a square canvas. I also haven’t used traditional blue skies, but grey skies or white. There’s one painting called ‘Quick said the bird, find them, find them,’ which depicts the Fraser River in B.C., which is quite muddy or turbulent. It’s just brown. Some of the paintings are from northern Ontario, which are similar to some European landscapes. But the paintings aren’t autobiographical or journalistic.
Why do some of the paintings have forest fires in them?
It’s a strong undercurent of what I’m thinking about - in today’s world, how can you look at a landscape painting and not think of climate change as a consideration? It’s about how the natural world is changing. Climate change is in a lot of literary works, nowadays, too.
How has climate change influenced landscape paintings, in your eyes?
It has changed it, for me. It’s a tricky thing. If you try to attack it head on, your work can become didactic, not as open. But if you have it as a tertiary aspect, that vibe can become a cross, a disturbance. Looking at the valley paintings, it’s hard not to notice the history of colonization, climate change, the possibility of forests being on fire.
There’s a painting called ‘What Might Have Been Or Could Been,’ why use barbed wire in this piece, and the few others that have them?
The barb wire paintings was a direct result of kids in cages. That isn’t right. I felt I needed to express that. It’s a sense of calamity. There’s also paintings where there’s a threshhold that you cross, a border. Others show a struggle, an aftermath and then a sense of home, or coming back home, where you are changed. That’s a feeling throughout many of the paintings. If you think about it, the meaningful events in our lives are generally processed through a narrative that we tell ourselves.


City Game API
Trails
GET /api/trails
Serves up all the trails
GET /api/trails/:trailId
Serves up a single trail to view (for Admin)
e.g: `/api/trails/manchester-city-trail`
POST /api/trails/:trailId
Serves up a trail for each player according to their index.
The request must be sent in the format:
{
"playerName": "name",
"index": 0
}
Game
POST /api/games
Creates a new game
The request must be sent in the format:
{
"gameName": "newGame",
"noOfPlayers": 4,
"trailId": "manchester-city-trail"
}
GET /api/games/:gamePin
Serves up a single game
DELETE /api/games/:gamePin
Deletes a single game
POST /api/games/:gamePin/players
Adds a new player to a game
Request should be in the format:
{
"playerName": "Name"
}
PATCH /api/games/:gamePin/players?progress=true
Increments the progress of the player by one. Requires a progress query of true
PATCH /api/games/:gamePin/players?end=true
Ends the game for a player and returns the 'totalTime' taken to complete the game. Requires an end query of true
PATCH /api/games/:gamePin/:playersName
Analyse an image uploaded by the player (using Google vision API)
Request should be in the format:
{
"URL": "/url/for/the/image"
}
Challenges
GET /api/challenges/:challengeId
Serves up the challenges for a single location
Players
GET /api/players
Serves up all the players in leader board
POST /api/players/:gamePin
Adds all the players from a game to the leaderboard once the game has finished


My Web Page
A Paragraph.
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myFunction is stored in an external file called "hello.js".


Hi everyone,
Hello again from TriDIYbio! I'm sending this email to remind all you wonderful people that we will be having our regularly scheduled monthly group meeting this weekend on Sunday.
Also, I wanted to highlight that this meeting will be a little different as it will feature a critical discussion for all of us bio-hackers in the Triangle Area and in our group. What's the big news you ask? From the very beginning the mission of this group has been focused on enabling our members to learn about, contribute, and be a part of the exciting and ever-growing scientific DIY community by giving all of us a common hands-on public space to do so, as well as a community to support it.
I want to share with you just one bit of news this week:
- We are VERY close to being able to fulfill that goal of having our own first public space! It will be a place to experiment, to explore, to learn, and develop--all at a very reasonable price--but the rest of the TriDIYbio board members and I would first like to discuss with you: 1.Whether or not this remains our group's unifying ultimate goal. and 2. What we can all do to help achieve it. Come on out to our meeting to learn more.
So for that reason I want to encourage everyone who is on this list to come out and participate in our meeting this week. Come share your projects, ideas, or just plain enthusiasm for bio projects, and also come discuss the future of DIYbio in the Triangle area.
Time: 6:30 pm. Sunday March 20th
Location: SplatSpace makerspace of Durham.
Check out our meetup for details.
Best regards,
Jim R.
**DIYbio Article of the Week:
Have any more ideas for what we should be doing as a group? Let us know!! Also be sure to let us know if you have any questions, are curious about how to find us, or want to know more about other ways to get involved with the group (such as our slack group online forum--where most group discussions take place) by replying to this email. Otherwise we hope to see you at our next meeting.As always if you would like to be removed from this mailing list just send a reply letting me know and I'll be happy to take you off the list, no questions asked!
by Jim Rose - 17 Mar 2016


PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Thanks to a growing catalog of disturbing short films, François Ozon has been dubbed `the bad boy of French cinema' and his latest, Criminal Lovers, will do nothing but bolster his claim to that crown. The film is a sick, twisted and, at times, terrifying blend of Natural Born Killers and Badlands, with a little Deliverance and Hansel & Gretel thrown in for good measure.
Lovers opens with a sexually aggressive student named Alice (Natacha Régnier, The Dreamlife of Angels) playing a nasty prank on her impotent boyfriend Luc (Jérémie Rénier). But this trick isn't the only one playful Alice has up her sleeve. She also convinces Luc to help her slaughter an Arab classmate named Saïd.  The teens hit the road, with Saïd's body in their trunk, stopping only to rob a jeweler and exercise the five-finger discount at a supermarket.
The murder occurs about ten minutes into the film, before we really learn anything about either Alice or Luc. You may begin to scratch your head and wonder who these kids are and what exactly led to this crime spree. Don't worry - Lovers is told via flashbacks in this uniquely structured film. What you might want to worry about is the frightening incident the teens experience upon stumbling on an isolated shack in the woods when they bury their classmate's body.
Ozon's cold direction (he also wrote the script) might leave some people shaking their heads and accusing him of attempting only to shock, but you can't deny that Ozon is a brilliant, stylish filmmaker. Lovers is only his second feature film, and the world should both anticipate and dread his next project.
1:30 – Not Rated but contains adult language, nudity, strong sexual content, rape, graphic violence, canibalism and a scene featuring a rabbit skinning
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Markov Decision Processes
In this lecture, we provide the models for the environment.
Markov Process
Markov Process is a tuple $<S,P>$, where
- $S$ is a finite set of states
- $P$ is the state transition probability matrix, namely
\[P=\left[\begin{array}P(S_{t+1}=s_1|S_t=s_1) & \dots & P(S_{t+1}=s_n|S_t=s_1) \\ \vdots & & \vdots \\ P(S_{t+1}=s_1|S_t=s_n) & \dots & P(S_{t+1}=s_n|S_t=s_n) \end{array}\right]\]
- The rows of transition matrix sum up to 1.
- A Markov Process can be visualized with a directed graph (nodes are states, there is also an absorbing state with infinite loop, and the edges are weightes according to the transition probability matrix).
- If $P$ changes over time, then you can convert back to stationary Markov process either by (i) adjusting over time or by (ii) adding more states.
Markov Reward Process
Markov Reward Process is a tuple $<S,P,R,\gamma>$, where
- $\dots$
- $R$ is a reward function, in particular $R_s=E\{R_{t+1}|S_t=s\}$ (how much reward I get for state $s$) -the reward for a state can be stochastic, that's why you have expectation in this definition
- $\gamma$ is a discount factor, namely $\gamma\in[0,1]$
- The discount factor is used to define the sample return, i.e. $G_t=R_{t+1} + \gamma R_{t+2} + \dots$
- $\gamma=0$ means that the return is focused on immediate return
- $\gamma=1$ requires that $\sum_{k=0}^{\infty}R_{t+k+1}<\infty$. For example, by ensuring that the absorbing node has reward 0. (Need to be careful for cyclic Markov processes).
- A Markov Reward Process can be visualized with a directed graph.
- To model aninmal/human behaviour you can use hyperbolic discounting.
- The state value function is the expected return starting from state $s$, viz. $v(s)=E\{G_t|S_t=s\}$.
How to compute the state value function?
Note that we always have a sequence of random variables, i.e. $\dots,S_t,R_{t+1},S_{t+1},R_{t+2},\dots$
- By sampling an infinite sequence of states/rewards
- By using the Bellman equation, namely
\[\begin{align}v(s) &= E\{G_t|S_t=s\} \\ &= E\{R_{t+1} + \gamma R_{t+2} + \dots|S_t=s\} \\ &= R_s + E\{\gamma R_{t+2} + \dots|S_t=s\} \text{ - by definition of conditional expectation} \\ &= R_s + \gamma E\{R_{t+2} + \gamma R_{t+3} + \dots|S_t=s\} \\ &= R_s + \gamma E\{G_{t+1}|S_t=s\} \\ &= R_s + \gamma \sum_{s'}P_{ss'}E\{G_{t+1}|S_{t+1}=s'\} \\ &= R_s + \gamma \sum_{s'}P_{ss'}v(s')\end{align}\]
Basically, we can rewrite it in matrix forms, namely $v=R+\gamma Pv$.
How to solve the Bellman equation?
- Direct computation by computing the inverse of $I-\gamma P$, but this is $O(n^3)$, where $n$ is the number of states. This is a solution for small MRPs.
- Using (i) dynamic programming, (ii) Monte Carlo evaluation (iii) temporal difference learning.
Markov Decision Process
Markov Decision Process is a tuple $<S,A,P,R,\gamma>$, where
- $\dots$
- $P$ is the state transition probability matrix, where each entry is defined as $P_{ss'}^a=P(S_{t+1}=s'|S_t=s, A_t=a)$
- $R$ is a reward function, in particular $R_s^a=E\{R_{t+1}|S_t=s, A_t=a\}$ (how much reward I get for state $s$)
- A Markov Decision Process can be visualized with a directed graph (i.e. you need to add a decision to each edge).
- A policy is a distribution over actions given states, namely $\pi(a|s)=P(A_t=a|S_t=s)$.
- There are two value functions:
- state-value function for a given policy $\pi$, which is the expected return starting from state $s$ and following policy $\pi$, namely $v_\pi(s)=E_\pi\{G_t|S_t=s\}$. Note that in this case, the expectation takes into account two sources of randomness, i.e. the randomness in the decision and the randomness in the state transition. (How good to be in particular state)
- action-value function is the expected return starting from state $s$, choosing action $a$ and following policy $\pi$, namely $q_\pi(a,s)=E_\pi\{G_t|S_t=s, A_t=a\}$. (How good to be in particular state and to choose a specific action)
How to compute the value functions?
Note that we always have a sequence of random variables, i.e. $\dots,S_t,A_t,R_{t+1},S_{t+1},A_{t+1},R_{t+2},\dots$
As before, we can compute the value functions
- by sampling infinite sequence
- using Bellman Expectation Equations, namely:
\[\begin{align}v_\pi(s) &= E_\pi\{G_t|S_t=s\} \\ &= \sum_{a}\pi(a|s)E_\pi\{G_t|S_t=s, A_t=a\} \text{ - by definition of conditional expectation}\\ &= \sum_{a}\pi(a|s)q_\pi(a,s)\end{align}\]
$$\begin{align}q_\pi(a,s) &= E_\pi\{G_t|S_t=s, A_t=a\} \\ &= E_\pi\{R_{t+1} + \gamma R_{t+2} + \dots|S_t=s, A_t=a\} \\ &= R_s^a + E_\pi\{\gamma R_{t+2} + \dots|S_t=s, A_t=a\} \text{ - by definition of conditional expectation}\\ &= R_s^a + \gamma E_\pi\{R_{t+2} + \gamma R_{t+3} + \dots|S_t=s, A_t=a\} \\ &= R_s^a + \gamma E_\pi\{G_{t+1}|S_t=s, A_t=a\} \\ &= R_s^a + \gamma \sum_{s'}P_{ss'}^aE_\pi\{G_{t+1}|S_{t+1}=s'\} \\ &= R_s^a + \gamma \sum_{s'}P_{ss'}^av_\pi(s')\end{align}$$
Now, you can basically obtain the Bellman equation for the state-value function, namely:
$$v_\pi(s)=\sum_{a}\pi(a|s)\big(R_s^a + \gamma \sum_{s'}P_{ss'}^av_\pi(s')\big)$$and also the Bellman equation for the action-value function, namely: $$q_\pi(a,s)=R_s^a + \gamma \sum_{s'}P_{ss'}^a\big(\sum_{a'}\pi(a'|s')q_\pi(a',s')\big)$$
The state-value function can be rewritten in the following way:
$$\begin{align}v_\pi(s)&=\sum_{a}\pi(a|s)\big(R_s^a + \gamma \sum_{s'}P_{ss'}^av_\pi(s')\big) \\ &= \sum_{a}\pi(a|s)R_s^a + \gamma\sum_{s'}\sum_{a}\pi(a|s)P_{ss'}^av_\pi(s') \\ &= \sum_{a}\pi(a|s)R_s^a + \gamma\sum_{s'}\big(\sum_{a}\pi(a|s)P_{ss'}^a\big)v_\pi(s') \\ &= R_s^\pi + \gamma\sum_{s'}P_{ss'}^\pi v_\pi(s')\end{align}$$Therefore, the matrix form is $v_\pi=R^\pi + \gamma P^\pi v_\pi$.
How to compute the optimal policy?
In order to compute the optimal policy, we need a criterion which allows us to rank the policies. Therefore, we can use the state-value (and action-value) function(s).
Maximum value functions. They are $$v^*(s)=\max_\pi v_\pi(s)$$ for every $s$. $$q^*(a,s)=\max_\pi q_\pi(a,s)$$ for every $s$. respectively
Now, we can define the following criterion
Policy $\pi$ is better than policy $\pi'$ iff for every $s$ we have that $v_\pi(s)\geq v_{\pi'}(s)$.
Therefore, the optimal policy is the one such that $\pi^*=arg\max_\pi v_\pi(s)$ for all $s$.
Does the optimal policy exist?
Puterman. There exists at least an optimal policy. And the optimal policy/ies achieve same values of the solution/s of the Bellman Optimal Equations.
Proof hints: define Bellman Optimality Equation $\tilde{v}(s)=\max_a R_s^a + \gamma\sum_{s'}P_{ss'}^a\tilde{v}(s')$. Show that this operator is a contraction mapping, then you can use the Banach fixed-point theorem and state that the solution is unique $\tilde{v}(s)$. Now, by using Theorem 6.2.2 in Puterman, you can prove that $\tilde{v}^*(s)=v^*(s)$.
Bellman Optimality Equations
Therefore, we have the Bellman Optimality Equation for the state-value function
$$v^*(s)=\max_a R_s^a + \gamma\sum_{s'}P_{ss'}^av^*(s')$$and note that
$$q^*(a,s)=\max_\pi q_\pi(a,s)=R_s^a + \gamma\sum_{s'}P_{ss'}^a\max_a v_\pi(s')=R_s^a + \gamma\sum_{s'}P_{ss'}^av^*(s')$$Therefore, substituting this last equation into the Bellman Optimality Equation, we get
$$v^*(s)=\max_a q^*(a,s)$$Finally, we obtain the Bellman Optimality Equation for the action-value function
$$q^*(a,s)=R_s^a + \gamma\sum_{s'}P_{ss'}^a\max_{a'} q^*(a',s')$$How to solve the Bellman equation?
- The Bellman Optimality equation is non-linear and it is therefore difficult to compute a closed-form solution.
- Using (i) dynamic programming, (ii) Q-learning (iii) Sarsa.
Extensions of MDP
- Infinite and continuous MDPs (continuous actions and/or continuous states)
- POMDPs
A POMDP is a tuple $<S,A,O,P,R,Z,\gamma>$, where
- $\dots$
- $O$ is a finite set of observations
- $Z$ is the observation probability, namely $Z_{s',o}^a=P(O_{t+1}=o|S_{t+1}=s',A_t=a)$
- Undiscounted, average reward MDP ($\gamma=1$)
Visualization Summary of Different Processes
Appendix: Probability Review
Given the sample space $\Omega$ of a random quantity.
Event. It is a subset of $\Omega$
Random variable. It is a valued-function defined on $\Omega$, namely $X: \Omega\rightarrow?$
Expectation. $E\{X\}=\sum_{\omega\in\Omega}X(\omega)P(\omega)$
Expectation conditioned on event (Conditional expectation). $E\{X|E\}=\sum_{\omega\in E}X(\omega)P(\omega)$


In this first example we set the applet to display 3 different messages in rotation with each message linking the JPowered site,
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--- layout: post title: Learning a soft skill tags: - Work Practices status: publish type: post published: true meta: {} ---
So if you're a developer who interacts with the business then you have a responsibility to respect the decisions of others, and you'll probably benefit from honing your influence skills as well as your technical skills.
Cogent Thoughts: Do unto others...
Quite right, however this begs the question "how does a young developer improve their influence skills?". I believe the answer lies in a)Some basic presentation skills training. These skills can also be applied to writing emails and reports. b)A lot of mentoring and specific guidance from senior team members when writing documents and emails. I encourage all team members to get their material read by others before sending it out. In some limited cases it may even justify a formal review (think code review). c) Short team workshops to improve active listening skills for the informal corridor conversations. Speaking for myself the skill of writing a lucid document or presentation for non technical audience took years to acquire and I think I could still often do better. Perhaps this could be a EAT course :-).Blogged with Flock