 When David first reported the crime, the police had insufficient evidence to prosecute but now there is a chance that Josh could pay for his crime.
David last came into contact with Josh when he was in prison. During a riot, Josh was stabbed and David took the blame as Josh framed him for the attempted murder. However, after some pressure from Paul Foreman (Peter Ash), Josh told the truth.
Now, Coronation Street cast have been filming scenes at court which will see David take the stand against Josh, who was in prison after committing a further sexual assault. Will David be believed as he relives the horrendous ordeal and hopefully seizes closure?
Jack and Ryan were spotted taking a break from filming and having some lunch outside of the court set on location. It has already been confirmed that Julia Goulding has filmed her final scenes as David’s wife Shona so it seems likely that she won’t be present to support him in court.
These could well be the final scenes for Josh as his storyline reaches a close. He was first introduced as a mate for David but he soon took advantage by drugging him and sexually assaulting him.
David felt too ashamed to open up but it was the suicide of Aidan Connor which prompted David to speak out as he feared he could meet a similar fate. Charges against Josh were dropped but David drove him out of Weatherfield, only coming face to face with him again when he was jailed for Nick’s (Ben Price) theft of Audrey’s (Sue Nicholls) money.
The scenes will air early next year.
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The main entry point to Oak security is the SecurityProvider, which is registered to the Oak repository upon creation. The provider is in charge of collecting and exposing all security related modules present in a given Oak repository.
Each security module comes with one or multiple SecurityConfiguration(s) that are registered with the provider, identified (and possibly aggregated) by their name.
Currently Oak comes with the following built-in security modules, each defined by a dedicated sub-interfaces of SecurityConfiguration:
The package org.apache.jackrabbit.oak.spi.security defines the following interfaces and base implementations:
The SecurityProvider is the key to Oak security by providing access to the individual security modules and the configurations associated. Since version 1.3.7 Oak provides two implementations of the SecurityProvider suited for OSGi and non-OSGi setup, respectively.
Since Oak 1.3.7 the core bundle will install a dedicated OSGi component (SecurityProviderRegistration, labeled “Apache Jackrabbit Oak SecurityProvider”), which registers the SecurityProvider once all mandatory references have successfully been resolved. This new approach addresses issues present with the initial security provider implementation and has been backported to existing branches (see OAK-3201 and OAK-3441).
While optional configuration setting can be changed or extended at runtime, modules and extensions considered required for a functional security setup, need to be listed in the “Required Service PIDs” property. This asserts both reliable security setup and proper initialization of the individual modules. See also sections Configuration and Pluggability below.
In a non-OSGi setup the SecurityProvider (be it the default or a custom implementation) gets passed to the repository constructor. See section pluggability for details wrt module initialization.
The following example has been extracted from the basic test setup:
NodeStore nodeStore = ... ConfigurationParameters params = ... // TODO: provide config options SecurityProvider sp = new SecurityProviderImpl(params); // Optional: bind additional/custom implementations of the supported `SecurityConfiguration`s Repository repository = new Jcr(nodeStore).with(sp).createRepository();
The SecurityConfiguration interface defines functionality common to all security related modules. Apart from simple configuration parameters the basic interface defines the means to properly initialize a given security module and provide Oak internal mechanism to asserts proper validation and data consistency for all security relevant operations.
Please note, that RepositoryInitializer and WorkspaceInitializer as exposed by the base interface are only invoked upon repository|workspace initialization and consequently impact the pluggability of a given security module. Examples includes modules that require modifications to the global repository content such as node types, namespaces and privileges or require the installation of initial content or index definitions for proper execution.
The following subinterfaces of SecurityConfiguration are currently defined by Oak:
While Oak ships default implementation for all security configurations listed above, only authentication and authorization are mandatory for a functional Oak repository.
This is compliant with the security requirements defined by JSR 283 which defines API to login into the repository and mandates minimal permission evaluation, be it implementation specific of imposed by the optional access control management.
The minimal security setup may consequently be reduced to a setup as defined by the following imaginary, custom SecurityProvider (see also OpenSecurityProvider)
public class MySecurityProvider implements SecurityProvider { [...] public <T> T getConfiguration(Class<T> configClass) { if (AuthenticationConfiguration.class == configClass) { return (T) new MyAuthentication(); } else if (AuthorizationConfiguration.class == configClass) { return (T) new MyAuthorization(); } else { throw new IllegalArgumentException(); } } private final class MyAuthentication extends SecurityConfiguration.Default implements AuthenticationConfiguration { [...] } private final class MyAuthorization extends SecurityConfiguration.Default implements AuthorizationConfiguration { public AccessControlManager getAccessControlManager(Root root, NamePathMapper namePathMapper) { throw new UnsupportedOperationException(); } public RestrictionProvider getRestrictionProvider() { throw new UnsupportedOperationException(); } public PermissionProvider getPermissionProvider(Root root, String workspaceName, Set<Principal> principals) { return MyPermissionProvider.getInstance(principals); } } }
All other security modules can be considered optional from an Oak repository point of view. Please note the following dependencies and special cases:
The configuration parameters of individual security modules are described in the corresponding sections. The following paragraphs describe the configuration of SecurityProvider and CompositeConfiguration in an OSGi-base setup.
| Parameter | Type | Default | Description |
|---|---|---|---|
| Required Services | String[] | see below | Service references mandatory for the SecurityProvider registration. |
The value of the individual configuration entries can be one of:
By default the SecurityProviderRegistration defines the following mandatory services. As long as these required references are not resolved the SecurityProviderRegistration will not register the SecurityProvider service and ultimately prevent premature initialization of the Oak repository instance.
The value of this configuration parameter needs to be adjusted for any additional module or functionality that is considered required for a successful security setup. See section pluggability below.
| Parameter | Type | Default | Description | |
|---|---|---|---|---|
| Authorization Composition Type | String (AND | OR) | AND | The Composite Authorization model uses this flag to determine what type of logic to apply to the existing providers |
Given a set of permission providers, the composite model can aggregate the results by applying an AND logic (for example all providers must allow a specific privilege in order to be granted), or an OR (for example any provider can allow a privilege). By default the AND version is used.
| Parameter | Type | Default | Description |
|---|---|---|---|
| PARAM_RANKING | int | NO_RANKING (Integer.MIN_VALUE) | Optional configuration parameter to define the ranking within the aggregation. |
Note: Security modules that allow for multiple configurations may choose to expose the PARAM_RANKING option in order to allow for explicit ordering of the individual implementations. If the ranking parameter is omitted the CompositeConfiguration will try to use the SERVICE_RANKING to define the order. If neither is available (or set to NO_RANKING) the new entry will be appended to the list.
In a default setup Oak allows to plug custom or additional implementations of the various SecurityConfiguration described before. Similarly it would be possible to provide a custom SecurityProvider.
Please note: this is only recommended for experts having in-depth understanding of Oak internals and which understand the security risk associated with custom replacements or extensions.
The default SecurityProvider service could be replaced by deploying a custom service to the OSGi container. In a non-OSGi setup the JCR|Oak repository needs to be created with the custom implementation:
SecurityProvider sp = new MySecurityProvider(); Repository repository = new Jcr().with(sp).createRepository();
The default Oak security setup distinguishes between the following types of modules:
Plugging an implementation of an unary module will replace the default provided by Oak. As far as the multiple modules are concerned a custom implementation plugged into the repository will result in the creation of a CompositeConfiguration. The aggregated modules are kept in a list while the insertion order is defined by the PARAM_RANKING or by the OSGi service ranking in case the explicit ranking parameter is missing.
The following steps are required to replace an existing unary security component or add an additional implementation (for multiple configurations only):
In this case the individual security modules get “manually” bound/unbound to the SecurityProvider instance. The provider itself might be the default or a custom implementation of the interface. If and how security modules can be added to the provider remains an implementation detail and is not part of the SecurityProvider interface definition.
Extend the default SecurityProvider with a custom PrincipalConfiguration. See also oak-exercise module for an example.
SecurityProvider sp = new SecurityProviderImpl(); sp.bindPrincipalConfiguration(new MyPrincipalConfiguration()); Repository repository = new Jcr().with(sp).createRepository();
If a given security modules mandates repository and|or workspace initialization steps such as e.g. node type registration or persisting new index definitions, the deployment of the module requires a reload of the SecurityProvider.
In the default OSGi-based setup this is achieved by adding the PID of corresponding service to the Required Service PIDs property mentioned above ultimately forcing the re-registration of the SecurityProvider service with the bundle context.
Other setup scenarios would need to recreate the ContentRepository object or adjust the repository ‘initial’ content before binding the new configuration to the SecurityProvider in order to avoid inconsistencies.


maccorcyclingdata package¶
Submodules¶
maccorcyclingdata.testdata module¶
-
maccorcyclingdata.testdata.
clean_maccor_df
(df)[source]¶ Given the testdata dataframe, this function will rename the headers and drop unnecessary columns. It will also change some of the units to match the column name and will remove all commas.
- Parameters
df (pandas dataframe) – The testdata dataframe
- Returns
df – The cleaned pandas df of the testdata
- Return type
pandas dataframe
Notes
If the following columns exist, the function will delete these:
ACR
,DCIR
,Watt-hr
, andnnnamed
.Examples
>>> import maccorcyclingdata.testdata as testdata >>> df = testdata.clean_maccor_df(df) >>> df.head(5)
-
maccorcyclingdata.testdata.
delete_cycle_steps
(df, steps_to_delete, decrement=False)[source]¶ Given the testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions) and a list of integers (step numbers that you want to delete), this function will delete all rows from the dataframe that have a cycle step index that matches any in the list of integers
- Parameters
df (pandas dataframe) – The testdata dataframe
steps_to_delete (array) – An array that has the step numbers you want to delete
decrement (boolean) – If set to True, would shift cycle steps to adjust for the deleted steps
- Returns
df – The dataframe with the corresponding steps deleted
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.testdata as testdata >>> del_df = testdata.delete_cycle_steps(df, [1], True) >>> del_df.head(5)
-
maccorcyclingdata.testdata.
get_cycle_data
(df, Headings, cyc_range, cycle_step_idx=[])[source]¶ Given the testdata df (from the import_maccor_data or import_multiple_csv_data functions), this function gets the data specified in the “Headings” for each sample within the specified cyc_range.
- Parameters
df (pandas dataframe) – The testdata dataframe
Headings (array) – An array with the headers you want the data for
cyc_range (array) – An array of the cycle numbers you want data for
cycle_step_idx (array) – The step numbers within each cycle that you want the data for. Default value is all steps within each cycle.
- Returns
data_df – A pandas dataframe that has the data for the specified headers at the specified cycles and steps.
- Return type
pandas dataframe
Examples
>>> from maccorcyclingdata.testdata import get_cycle_data >>> data = testdata.get_cycle_data(df, ['current_ma', 'voltage_v'], [1, 3, 5], [12]) >>> print(data[:6])
-
maccorcyclingdata.testdata.
get_index_range
(df, cyc_range, cycle_step_idx=[])[source]¶ Given the testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions), this function returns the index range for the specified cycle range, or if a cycle step index is passed, as subset of each cyle for only that specific cycle step.
- Parameters
df (pandas dataframe) – The testdata dataframe
cyc_range (array) – An array of the cycles you want the indices for
cycle_step_idx (array) – The step numbers that you want the indices of. Default value is all steps within each cycle.
- Returns
index_range – A vector of the range of df indices for the specified cycle range
- Return type
vector
Examples
>>> from maccorcyclingdata.testdata import get_index_range >>> ind = testdata.get_cycle_data(df, [1, 3, 5], [12]) >>> print(ind[:6])
-
maccorcyclingdata.testdata.
get_num_cycles
(df)[source]¶ Given the testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions), this function will return the number of cycles.
- Parameters
df (pandas dataframe) – The testdata dataframe
- Returns
number_of_cycles – An integer of the number of cycles in the dataframe
- Return type
integer
Notes
This function assumes that the first cycle is cycle 0.
Examples
>>> from maccorcyclingdata.testdata import get_num_cycles >>> get_num_cycles(df)
-
maccorcyclingdata.testdata.
import_maccor_data
(file_path, file_name, header=0)[source]¶ Given the file path and file name of the testdata file, this function will import the csv file as a pandas df and clean it.
- Parameters
file_path (string) – File path
file_name (string) – Filename
header (integer) – Optional input that sets the header to a line number (default=2)
- Returns
df – The cleaned testdata file as a pandas df
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.testdata as testdata >>> df = testdata.import_maccor_data('example_data/', 'testdata.csv') >>> df.head(5)
-
maccorcyclingdata.testdata.
import_multiple_csv_data
(file_path)[source]¶ Given the file path that holds multiple csv files (testdata files), this function will import and append all of the csv files to one another as one dataframe. Returns a cleaned version of that dataframe.
- Parameters
file_path (string) – File path
- Returns
df – All of the cleaned csv files appended to one another as a pandas df
- Return type
pandas dataframe
Notes
This function will append the csv files to one another depending on the order they appear in the directory.
Examples
>>> import maccorcyclingdata.testdata as testdata >>> mult_df = testdata.import_multiple_csv_data('example_data/multiple_csv/') >>> mult_df.head(5)
maccorcyclingdata.schedules module¶
-
maccorcyclingdata.schedules.
import_schedules
(file_path, file_name)[source]¶ Given the file path and file name (of the schedule file that is inputted into the Maccor Cycler), this function will import and clean the schedule file and return it as a df.
- Parameters
file_path (string) – File path
file_name (string) – Filename
- Returns
df – A cleaned schedule df
- Return type
pandas dataframe
Notes
The schedule file should be input as a csv.
Examples
>>> import maccorcyclingdata.schedules as schedules >>> schedule_df = schedules.import_schedules('example_data/','schedule.csv') >>> schedule_df.head(5)
-
maccorcyclingdata.schedules.
sort_scheduler_steps
(schedule_df)[source]¶ Given the schedule_df (the df that was output by import_schedules function), this function will sort rest, charge, discharge, advance cycle, and end step numbers.
- Parameters
schedule_df (pandas dataframe) – The cleaned dataframe of the schedule file
- Returns
rest_steps (array) – An array of the steps from the schedule file that correlate to rest steps
charge_steps (array) – An array of the steps from the schedule file that correlate to charging steps
advance_steps (array) – An array of the steps from the schedule file that correlate to steps when the cycle is advanced
discharge_steps (array) – An array of the steps from the schedule file that correlate to discharging steps
end_steps (array) – An array of the steps from the schedule file that correlate to ending steps
max_step (integer) – The last step from the schedule file
Examples
>>> import maccorcyclingdata.schedules as schedules >>> rest_steps, charge_steps, advance_steps, discharge_steps, end_steps, max_step = sort_scheduler_steps(schedule_df)
maccorcyclingdata.validate module¶
-
maccorcyclingdata.validate.
validate_test_data
(schedule_df, df, cell_id, time_interval, temp_interval, max_temp, discharge_neg, temp_tol=3, char_tol=2)[source]¶ This is a wrapper function that validates the testdata against the schedule file. The sub-modules that are validated are:
validation_check_rest(validation_df, df, i, cell_id)
validation_check_charging(validation_df, df, schedule_df, i, cell_id)
validation_check_discharging(validation_df, df, schedule_df, i, cell_id, discharge_neg)
validation_check_advanced_cycle(validation_df, df, i, cell_id)
validation_check_max_step_num(validation_df, df, max_step, i, cell_id)
validation_check_max_temp(validation_df, df, max_temp, i, cell_id, tol=3)
validation_check_time_interval(validation_df, df, time_interval, i, cell_id)
validation_check_temp_interval(validation_df, df, temp_interval, i, cell_id)
- Parameters
schedule_df (pandas dataframe) – The dataframe of the cleaned schedule file (from import_schedules function)
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
cell_id (integer) – The cell id of the testdata
time_interval (integer) – The maximum interval of how often the cycler should be recording data
temp_interval (integer) – The maximum interval of a temperature change
max_temp (integer) – The threshold for the highest temperature allowed
discharge_neg (boolean) – Set to True if the current was exported as negative during discharge steps.
temp_tol (integer) – Sets the tolerance between warning, error, and ABORT messages. Default is 3 degrees.
char_tol (integer) – Sets the tolerance between the current/discharging current values and the set value in the schedule file. Default is 2.
- Returns
validation_df – The validation dataframe with any errors (if any) listed Headers of the validation_df:
time (the current time of when the validation occurs)
run (tells whether the validation function is in progress or complete)
cell_num (the cell number of the testdata)
row_number (the row number where the error occurs)
error (what the error is)
- Return type
pandas dataframe
Notes
Depending on the size of your testdata and schedules, this function may take much longer to run.
There are 3 possibilities of error messages:
warning - temperature approaching the max! (current temperature + temp_tol > max)
error - temperature has surpassed the max! (current temperature >= max)
ABORT - temperature is way too hot! (current temperature > max + temp_tol)
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validate_test_data(schedule_df, df, 1, 10, 30, True, 5) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_advanced_cycle
(validation_df, df, i, cell_id)[source]¶ This function will validate the testdata against the advance cycle steps by making sure the cycle advances
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_advanced_cycle(validation_df, df, i, 1) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_charging
(validation_df, df, schedule_df, i, cell_id, char_tol=2)[source]¶ This function will validate the testdata against the charging steps by making sure the value of either the voltage or current matches with the value specfied in the scheduler.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
schedule_df (pandas dataframe) – The dataframe of the cleaned schedule file (from import_schedules function)
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
char_tol (integer) – Sets the tolerance between the current/discharging current values and the set value in the schedule file. Default is 2.
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_charging(validation_df, df, schedule_df, i, 1) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_discharging
(validation_df, df, schedule_df, i, cell_id, discharge_neg, char_tol=2)[source]¶ This function will validate the testdata against the discharging steps by making sure the value of either the voltage or current matches with the value specfied in the scheduler.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
schedule_df (pandas dataframe) – The dataframe of the cleaned schedule file (from the import_schedules function)
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
discharge_neg (boolean) – Set to True if the current is exported as negative during discharge steps.
char_tol (integer) – Sets the tolerance between the current/discharging current values and the set value in the schedule file. Default is 2.
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_discharging(validation_df, df, schedule_df, i, 1, True) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_max_step_num
(validation_df, df, max_step, i, cell_id)[source]¶ This function will validate the testdata against the max step by making sure no steps surpass the max.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
max_step (integer) – The last step from the schedule file
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_max_step_num(validation_df, df, max_step, i, 1) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_max_temp
(validation_df, df, max_temp, i, cell_id, temp_tol=3)[source]¶ This function will validate the testdata against the max temperature by making sure no steps surpass the max.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
max_temp (integer) – The threshold for the highest temperature allowed
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Notes
There are 3 possibilities of error messages:
warning - temperature approaching the max! (current temperature + tol > max)
error - temperature has surpassed the max! (current temperature >= max)
ABORT - temperature is way too hot! (current temperature > max + tol)
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_max_temp(validation_df, df, 30, i, 1, 3) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_rest
(validation_df, df, i, cell_id)[source]¶ This function will validate the testdata against the rest steps by making sure the current is at 0 when resting.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_rest_steps(validation_df, df, i, 1) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_temp_interval
(validation_df, df, temp_interval, i, cell_id)[source]¶ This function will validate the testdata to make sure the temperature does not fluctuate suddenly.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
temp_interval (integer) – The maximum temperature change allowed between two data points.
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_temp_interval(validation_df, df, 10, i, 1) >>> validation_df
-
maccorcyclingdata.validate.
validation_check_time_interval
(validation_df, df, time_interval, i, cell_id)[source]¶ This function will validate the testdata to make sure data was collected regularly at the correct time interval.
- Parameters
validation_df (pandas dataframe) – The validation dataframe where any errors will be recorded
df (pandas dataframe) – The testdata dataframe (from the import_maccor_data or import_multiple_csv_data functions)
time_interval (integer) – The time interval between data point. How often data should be collected.
i (integer) – An integer of the index where you want to validate
cell_id (integer) – The cell id of the testdata
- Returns
validation_df – The validation dataframe with any errors listed
- Return type
pandas dataframe
Examples
>>> import maccorcyclingdata.validate as validate >>> validation_df = validate.validation_check_time_interval(validation_df, df, 10, i, 1) >>> validation_df


{% extends "layout.html" %}
{% block pageTitle %}
GOV.UK prototyping kit
{% endblock %}
{% block content %}
PIP Prototypes
- PIP-23 - Design sprint
-
- Moved declaration to after personal details page
- Added registed page to tell users to check their emails
- Added tell us about your most important HCP before end of Pip1
- Updated content on pip2start page, included making completion date standout more to users
- Also exit page to make completion date standout more to users
- Added Do you want to take a break heading above save and exit link
- Added Answer later link just after heading on each page
- PIP-22
-
- Split up Bank details question to 2 pages
- Added filter questions for SRTI, date of birth, postcode
- Added additional support question
- Removed email field from Contact Details
- Adjusted size of account number field
- PIP-21
-
- Updated How far can you walk without stopping question
- Added Typeahead for country select
- Added Completion date and submit your application pages
- Added Keep a copy link on Thank you page
- Added Answer later button and unanswered questions page.
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Ben's prototype
{% endblock %}


def create_scaladoc_command(classpath, gendir, *targets):
sources = []
for target in targets:
sources.extend(os.path.join(target.target_base, source) for source in target.sources)
if not sources:
return None
def create_scaladoc_command(classpath, gendir, *targets):
sources = []
for target in targets:
sources.extend(target.sources_relative_to_buildroot())
if not sources:
return None


- getBytes(String) - Method in class provided.rmiUtils.classServer.ClassFileServer
-
Returns an array of bytes containing the bytecodes for
the class represented by the argument path.
- getBytes(String) - Method in class provided.rmiUtils.classServer.ClassServer
-
Returns an array of bytes containing the bytecodes for
the class represented by the argument path.
- getDisplayMethod() - Method in interface engine.engineModel.IEngineModel2ViewAdapter
-
- getFormatter() - Method in class client.clientModel.task.MultiTask
-
- getFormatter() - Method in class client.clientModel.task.PalinNum
-
- getFormatter() - Method in class client.clientModel.task.PerfectSqr
-
- getFormatter() - Method in class client.clientModel.task.Pi2
-
Return a a formatter that makes a string of the form:
"Pi = [result] ([digits] places)"
Notice how the "digits" value is not part of the result, but that the
formatter can use it anyway because the formatter is closing over
the whole task.
- getFormatter() - Method in class client.clientModel.task.ReverseVowels
-
- getFormatter() - Method in class client.clientModel.task.ReverseWordsInString
-
- getFormatter() - Method in class client.clientModel.task.Sqroot
-
- getFormatter() - Method in class provided.client.model.task.GetInfo
-
Returns an formatter that creates a string of the form:
"GetInfo: System properties = [result] (input = [input])"
Notice how the "input" value is not part of the result, but that the
formatter can use it anyway because the formatter is closing over
the whole task.
- getFormatter() - Method in class provided.client.model.task.Pi2
-
Return a a formatter that makes a string of the form:
"Pi = [result] ([digits] places)"
Notice how the "digits" value is not part of the result, but that the
formatter can use it anyway because the formatter is closing over
the whole task.
- getFormatter() - Method in interface provided.compute.ITask
-
Get an instance of the matched ITaskResultFormatter for this task.
- GetInfo - Class in provided.client.model.task
-
Task that gets the server's system properties plus demonstrates that it can carry
internal data to a remote system when the entire object is serialized and sent
over.
- GetInfo(String) - Constructor for class provided.client.model.task.GetInfo
-
Constructor for the class.
- getLocalAddress() - Method in interface provided.rmiUtils.IRMIUtils
-
This method is designed to reliably return the actual local IP address
across multiple platforms, particularly Linux.
- getLocalAddress() - Method in class provided.rmiUtils.RMIUtils
-
This method is designed to reliably return the actual local IP address
across multiple platforms, particularly Linux.
- getLocalRegistry() - Method in interface provided.rmiUtils.IRMIUtils
-
Returns the RMI Registry on the local machine on port
IRMI_Defs.REGISTRY_PORT, as a server would require.
- getLocalRegistry() - Method in class provided.rmiUtils.RMIUtils
-
Returns the Registry on the local machine on port
IRMI_Defs.REGISTRY_PORT, as a server would require.
- getRemoteRegistry(String) - Method in interface provided.rmiUtils.IRMIUtils
-
Returns the RMI Registry on a remote machine on port
IRMI_Defs.REGISTRY_PORT, as a client would require.
- getRemoteRegistry(String) - Method in class provided.rmiUtils.RMIUtils
-
Returns the Registry on the given machine on port
IRMI_Defs.REGISTRY_PORT, as a client would require.