This app is a demo that shows a saner use of HTML5 History Management and Hashchange than the approach implied by the standard. In this case, the app is built to respond to the URL, not the other way around. In this way, the app can always enter the same, unique state, based only on the contents of the URL. This is essentially like making a RESTful client application. The app listens to changes to the URL through the "pathchange" event, which is provided by a jQuery plugin, jquery.pathchange.js. If your browser supports it, this app should be making use of HTML5 History Management and the HTML5 Hashchange event to provide the notifications. If your browser does not support it, some URLs will require a new page load, but the app will still enter the correct state. All of this means that browser back/forward, refresh, and bookmarking is supported throughout the app.
Hashchange is supported by Internet Explorer 8, Firefox 3, Safari, and Chrome. Support is created for Internet Explorer 6 and 7 by polling the hash on the window, 4 times per second. History Management is supported by Chrome, Safari, and the Firefox 4 beta, but suffers from a disappointing bug in Safari and Chrome.
This is put together by Ben Cherry. Ben is a front-end engineer at Twitter, and you can follow him at @bcherry. All of the code in this demo is MIT Licensed.


Download back-end code here and run as stated in readme. But please don't go through that code for the flag, because that's cheating, and that's not allowed :(
Here's my cool profile page. If I update info in my server, it will update on here, too. Pretty cool, huh? There's some information I don't want anyone to see on that server, though...


Actions, resources, and condition keys for Amazon S3 on Outposts
Amazon S3 on Outposts (service prefix:
s3-outposts
) provides the following service-specific resources, actions, and condition context keys for use in IAM permission policies.
References:
-
Learn how to configure this service .
-
View a list of the API operations available for this service .
-
Learn how to secure this service and its resources by using IAM permission policies.
Topics
Actions defined by Amazon S3 on Outposts
You can specify the following actions in the
Action
element of an IAM policy statement. Use policies to grant permissions to perform an operation in AWS. When you use an action in a policy, you usually allow or deny access to the API operation or CLI command with the same name. However, in some cases, a single action controls access to more than one operation. Alternatively, some operations require several different actions.
The
Resource types
column of the Actions table indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") to which the policy applies in the
Resource
element of your policy statement. If the column includes a resource type, then you can specify an ARN of that type in a statement with that action. If the action has one or more required resources, the caller must have permission to use the action with those resources. Required resources are indicated in the table with an asterisk (*). If you limit resource access with the
Resource
element in an IAM policy, you must include an ARN or pattern for each required resource type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one of the optional resource types.
The
Condition keys
column of the Actions table includes keys that you can specify in a policy statement's
Condition
element. For more information on the condition keys that are associated with resources for the service, see the
Condition keys
column of the Resource types table.
For details about the columns in the following table, see Actions table .
| Actions | Description | Access level | Resource types (*required) | Condition keys | Dependent actions |
|---|---|---|---|---|---|
| AbortMultipartUpload | Grants permission to abort a multipart upload | Write | |||
|
s3-outposts:DataAccessPointArn s3-outposts:DataAccessPointAccount |
|||||
| CreateAccessPoint | Grants permission to create a new access point | Write | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| CreateBucket | Grants permission to create a new bucket | Write | |||
| CreateEndpoint | Grants permission to create a new endpoint | Write | |||
| DeleteAccessPoint | Grants permission to delete the access point named in the URI | Write | |||
|
s3-outposts:DataAccessPointArn s3-outposts:DataAccessPointAccount |
|||||
| DeleteAccessPointPolicy | Grants permission to delete the policy on a specified access point | Permissions management | |||
|
s3-outposts:DataAccessPointArn s3-outposts:DataAccessPointAccount |
|||||
| DeleteBucket | Grants permission to delete the bucket named in the URI | Write | |||
| DeleteBucketPolicy | Grants permission to delete the policy on a specified bucket | Permissions management | |||
| DeleteEndpoint | Grants permission to delete the endpoint named in the URI | Write | |||
| DeleteObject | Grants permission to remove the null version of an object and insert a delete marker, which becomes the current version of the object | Write | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| DeleteObjectTagging | Grants permission to use the tagging subresource to remove the entire tag set from the specified object | Tagging | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| DeleteObjectVersion | Grants permission to remove a specific version of an object | Write | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| DeleteObjectVersionTagging | Grants permission to remove the entire tag set for a specific version of the object | Tagging | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| GetAccessPoint | Grants permission to return configuration information about the specified access point | Read |
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
||
| GetAccessPointPolicy | Grants permission to returns the access point policy associated with the specified access point | Read | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| GetBucket | Grants permission to return the bucket configuration associated with an Amazon S3 bucket | Read | |||
| GetBucketPolicy | Grants permission to return the policy of the specified bucket | Read | |||
| GetBucketTagging | Grants permission to return the tag set associated with an Amazon S3 bucket | Read | |||
| GetBucketVersioning | Grants permission to return the versioning state of an Amazon S3 bucket | Read | |||
| GetLifecycleConfiguration | Grants permission to return the lifecycle configuration information set on an Amazon S3 bucket | Read | |||
| GetObject | Grants permission to retrieve objects from Amazon S3 | Read | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| GetObjectTagging | Grants permission to return the tag set of an object | Read | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| GetObjectVersion | Grants permission to retrieve a specific version of an object | Read | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| GetObjectVersionForReplication | Grants permission to replicate both unencrypted objects and objects encrypted with SSE-KMS | Read | |||
| GetObjectVersionTagging | Grants permission to return the tag set for a specific version of the object | Read | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin |
|||||
| GetReplicationConfiguration | Grants permission to get the replication configuration information set on an Amazon S3 bucket | Read | |||
| ListAccessPoints | Grants permission to list access points | List | |||
| ListBucket | Grants permission to list some or all of the objects in an Amazon S3 bucket (up to 1000) | List | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| ListBucketMultipartUploads | Grants permission to list in-progress multipart uploads | List | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| ListBucketVersions | Grants permission to list metadata about all the versions of objects in an Amazon S3 bucket | List | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| ListEndpoints | Grants permission to list endpoints | List | |||
| ListMultipartUploadParts | Grants permission to list the parts that have been uploaded for a specific multipart upload | List | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| ListOutpostsWithS3 | Grants permission to list outposts with S3 capacity | List | |||
| ListRegionalBuckets | Grants permission to list all buckets owned by the authenticated sender of the request | List | |||
| ListSharedEndpoints | Grants permission to list shared endpoints | List | |||
| PutAccessPointPolicy | Grants permission to associate an access policy with a specified access point | Permissions management | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn |
|||||
| PutBucketPolicy | Grants permission to add or replace a bucket policy on a bucket | Permissions management | |||
| PutBucketTagging | Grants permission to add a set of tags to an existing Amazon S3 bucket | Tagging | |||
| PutBucketVersioning | Grants permission to set the versioning state of an existing Amazon S3 bucket | Write | |||
| PutLifecycleConfiguration | Grants permission to create a new lifecycle configuration for the bucket or replace an existing lifecycle configuration | Write | |||
| PutObject | Grants permission to add an object to a bucket | Write | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin s3-outposts:RequestObjectTag/<key> s3-outposts:RequestObjectTagKeys s3-outposts:x-amz-content-sha256 s3-outposts:x-amz-metadata-directive |
|||||
| PutObjectAcl | Grants permission to set the access control list (ACL) permissions for an object that already exists in a bucket | Permissions management | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin s3-outposts:ExistingObjectTag/<key> |
|||||
| PutObjectTagging | Grants permission to set the supplied tag-set to an object that already exists in a bucket | Tagging | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin s3-outposts:ExistingObjectTag/<key> s3-outposts:RequestObjectTag/<key> |
|||||
| PutObjectVersionTagging | Grants permission to set the supplied tag-set for a specific version of an object | Tagging | |||
|
s3-outposts:DataAccessPointAccount s3-outposts:DataAccessPointArn s3-outposts:AccessPointNetworkOrigin s3-outposts:ExistingObjectTag/<key> s3-outposts:RequestObjectTag/<key> |
|||||
| PutReplicationConfiguration | Grants permission to create a new replication configuration or replace an existing one | Write |
iam:PassRole |
||
| ReplicateDelete | Grants permission to replicate delete markers to the destination bucket | Write | |||
| ReplicateObject | Grants permission to replicate objects and object tags to the destination bucket | Write | |||
| ReplicateTags | Grants permission to replicate object tags to the destination bucket | Tagging | |||
Resource types defined by Amazon S3 on Outposts
The following resource types are defined by this service and can be used in the
Resource
element of IAM permission policy statements. Each action in the
Actions table
identifies the resource types that can be specified with that action. A resource type can also define which condition keys you can include in a policy. These keys are displayed in the last column of the Resource types table. For details about the columns in the following table, see
Resource types table
.
| Resource types | ARN | Condition keys |
|---|---|---|
| accesspoint |
arn:$
{
Partition}:s3-outposts:$
{
Region}:$
{
Account}:outpost/$
{
OutpostId}/accesspoint/$
{
AccessPointName}
|
|
| bucket |
arn:$
{
Partition}:s3-outposts:$
{
Region}:$
{
Account}:outpost/$
{
OutpostId}/bucket/$
{
BucketName}
|
|
| endpoint |
arn:$
{
Partition}:s3-outposts:$
{
Region}:$
{
Account}:outpost/$
{
OutpostId}/endpoint/$
{
EndpointId}
|
|
| object |
arn:$
{
Partition}:s3-outposts:$
{
Region}:$
{
Account}:outpost/$
{
OutpostId}/bucket/$
{
BucketName}/object/$
{
ObjectName}
|
Condition keys for Amazon S3 on Outposts
Amazon S3 on Outposts defines the following condition keys that can be used in the
Condition
element of an IAM policy. You can use these keys to further refine the conditions under which the policy statement applies. For details about the columns in the following table, see
Condition keys table
.
To view the global condition keys that are available to all services, see Available global condition keys .
| Condition keys | Description | Type |
|---|---|---|
| s3-outposts:AccessPointNetworkOrigin | Filters access by the network origin (Internet or VPC) | String |
| s3-outposts:DataAccessPointAccount | Filters access by the AWS Account ID that owns the access point | String |
| s3-outposts:DataAccessPointArn | Filters access by an access point Amazon Resource Name (ARN) | String |
| s3-outposts:ExistingObjectTag/<key> | Filters access by requiring that an existing object tag has a specific tag key and value | String |
| s3-outposts:RequestObjectTag/<key> | Filters access by restricting the tag keys and values allowed on objects | String |
| s3-outposts:RequestObjectTagKeys | Filters access by restricting the tag keys allowed on objects | String |
| s3-outposts:authType | Filters access by restricting incoming requests to a specific authentication method | String |
| s3-outposts:delimiter | Filters access by requiring the delimiter parameter | String |
| s3-outposts:max-keys | Filters access by limiting the maximum number of keys returned in a ListBucket request | Numeric |
| s3-outposts:prefix | Filters access by key name prefix | String |
| s3-outposts:signatureAge | Filters access by identifying the length of time, in milliseconds, that a signature is valid in an authenticated request | Numeric |
| s3-outposts:signatureversion | Filters access by identifying the version of AWS Signature that is supported for authenticated requests | String |
| s3-outposts:versionid | Filters access by a specific object version | String |
| s3-outposts:x-amz-acl | Filters access by requiring the x-amz-acl header with a specific canned ACL in a request | String |
| s3-outposts:x-amz-content-sha256 | Filters access by disallowing unsigned content in your bucket | String |
| s3-outposts:x-amz-copy-source | Filters access by restricting the copy source to a specific bucket, prefix, or object | String |
| s3-outposts:x-amz-metadata-directive | Filters access by enabling enforcement of object metadata behavior (COPY or REPLACE) when objects are copied | String |
| s3-outposts:x-amz-server-side-encryption | Filters access by requiring server-side encryption | String |
| s3-outposts:x-amz-storage-class | Filters access by storage class | String |


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Foreign Exchange Rates
The free subscription to the
Open Exchange Rates
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Watch: Bafana See Stars In Ghana
by Soccer LadumaIn a game that wasn’t televised in South Africa, Bafana Bafana were beaten 2-0 in their opening 2021 African Cup of Nations qualifier, and you can see the highlights here…
Read: Zongo’s Back On The Field… Again!
Molefi Ntseki’s side were confident going into the game for the head coach’s first competitive fixture since taking over from Stuart Baxter earlier in the year, but things didn’t go to plan as Thomas Partey and Mohammed Kudus scored one in each half to give them three points in Cape Coast on Thursday night.
The defeat was the first time that Bafana have lost an AFCON qualifier since the defeat to Mauritania back in 2015, with the defeat bringing to an end a 10-game unbeaten run in qualifying games for Africa’s showpiece international tournament.
A forced substitution for Bongani Zungu early in the game didn’t make things easier for Ntseki as he went up against his more experienced colleague, Kwesi Appiah, and things went from bad to worse when Partey opened the scoring not long afterwards.
Read: Major Blow For Chiefs Clash
With Bafana chasing an equaliser, Kudus robbed Thamsanqa Mkhize before bearing down on Ronwen Williams’ goal and smashing into the corner to put the game to bed, and the South Africans now find themselves desperately needing three points on Sunday against Sudan.
You can check out the highlights from Cape Sports Stadium in the video below:


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--- layout: "blogindex-bt" exclude_from_nav: true masthead: pat_path: /img/backgrounds/argyle.png date: 2015-01-01 12:00:01 author: chad ---


Contact: B. Wyman Reviewers: Change history: WebCVS Log for bgrid_core.f90
The basic driver module for the B-grid atmospheric dynamical core. This version can be run on distributed memory machines.
The B-grid dynamical core updates the tendencies of the prognostic variables, the variables themselves are not modified. The prognostic variables are surface pressure, the zonal and meridional momentum components, temperature, and optional tracers. Specific humidity is considered a tracer. This version of the B-grid dynamics use a two time level scheme. The gravity waves are integrated using a forward-backward scheme with a relatively short time step and the advection terms are integrated using a modified Euler backward scheme and longer time step. Horizontal diffusion is done on the advective time step. Derived-type variables for the horizontal and vertical constants must be initialized before initializing the B-grid dynamical core. The initialization routine of the B-grid dynamical core returns a derived-type variable that is needed when updating the tendencies of the prognostic dynamical variables. These prognostic variables are also contained as a derived-type variable. Here is a detailed write-up of the finite differencing used in the B-grid atmospheric dynamical core.
bgrid_prog_var_mod bgrid_horiz_mod bgrid_vert_mod bgrid_masks_mod bgrid_advection_mod bgrid_horiz_diff_mod bgrid_horiz_adjust_mod bgrid_vert_adjust_mod bgrid_polar_filter_mod bgrid_boundary_mod bgrid_sponge_mod fms_mod constants_mod
use bgrid_core_mod [ ,only: bgrid_dynam_type, bgrid_core_init, update_bgrid_core, bgrid_core_end ] bgrid_dynam_type A derived-type variable that contains grid constants, time step information, and other pre-computed terms that are needed by the dynamical core. This includes other derived-type variables and pointers to the physical grids of the model, but does not include the prognostic variables. bgrid_core_init Initializes the derived-type variable, bgrid_dynam_type, and initializes other modules used. There is no namelist for bgrid_core_mod, all runtime options are controlled with optional arguments to this subroutine. This subroutine must be called before update_bgrid_core. update_bgrid_core Called at every (atmospheric) time step to update the tendencies of the prognostic variables. No time differencing is done. bgrid_core_end Called at the end of the model run to terminate the module. Currently this call does nothing.
type bgrid_dynam_type -- derived data types contained in bgrid_dynam_type -- (see the appropriate module for details) Hgrid = horizontal grid constants, initialized by horiz_grid_mod [horiz_grid_type] Vgrid = vertical grid constants, initialized by vert_grid_mod [vert_grid_type] Masks = eta coordinate topography masks and indices, initialized by grid_masks_mod [grid_mask_type] Pfilt = polar filter constants, initialized by polar_filter_mod [pfilt_control_type] Advec = advection constants, initialized by advection_mod [advec_control_type] Hdiff = horizontal diffusion constants, initialized by horiz_diff_mod [hdiff_control_type] -- real, dimension(:,:) -- fis = geopotential height of the surface (m2/s2) fisl = geopotential height at eta=1. (for eta coord = 0.0, res = reciprocal of eta at the surface -- scalars -- nt_adj = no. of adjustment time steps per advection step [integer] nt_adv = no. of advection time steps per atmospheric step [integer] dt_adj = adjustment time step in seconds [real] dry_model = if true then water vapor will not be considered in the equation of state [logical] verbose = verbose flag [integer] wcorr = coefficient for the grid separation smoothing operator [real] fopt = filtering option [integer] NOTE Hgrid, Vgrid, fis, and res are pointers to the arguments passed via the initialization call (bgrid_core_init).
Dynam = bgrid_core_init ( Hgrid, Vgrid, fis, res, dt, ntadj, ntadv [, damp_order_vel, damp_order_tmp, damp_order_trs, damp_coeff_vel, damp_coeff_tmp, damp_coeff_trs, damp_scheme, damp_slope_coeff_vel, damp_slope_coeff_tmp, num_horiz_fill, num_vert_fill, advec_order_vel, advec_order_tmp, advec_order_trs, advec_coeff_vel, advec_coeff_tmp, advec_coeff_trs, grid_sep_coeff, filter_option, filter_weight, ref_lat_filter, num_sponge_levels, sponge_coeff_vel, sponge_coeff_tmp, sponge_coeff_trs, dry_model, verbose ] ) INPUT Hgrid Derived-type variable containing horizontal grid constants (see horiz_grid_mod). [type(horiz_grid_type)] Vgrid Derived-type variable containing vertical grid constants (see vert_grid_mod). [type(vert_grid_type)] fis Geopotential height of the surface (m2/s2). Should have horizontal indexing consistent with the B-grid core. [real, dimension(:,:)] res Reciprocal of eta at the surface. Used at a switch for sigma vs. eta. For the sigma coordinate, all res = 0. Should have horizontal indexing consistent with the B-grid core. [real, dimension(:,:)] dtadv Time step in seconds for each call to update_bgrid_core. This should be the atmospheric time step. [real] ntadj The number of adjustment time steps for each advective time step. [integer] ntadv The number of advection time steps for each call to update bgrid_core. [integer] RETURNS Dynam Derived-type variable that contains quantities needed by the dynamical core. [type(bgrid_dynam_type)] OPTIONAL INPUT (Note: These argument can be modified through a namelist interface in the bgrid_core_driver_mod) damp_order_vel The horizontal damping order for momentum, damp_order_tmp temperature, and default order for all damp_order_trs prognostic tracers. The damping order must be an even number; damp_order = 0 turns off damping. [integer, default: damp_order = 4] damp_coeff_vel The horizontal damping coefficients for damp_coeff_tmp momentum, temperature, and default value for damp_coeff_trs all prognostic tracers. The coefficients are expressed as non-dimensional values for the second-order diffusion operator (range = 0,1). [real, default: damp_coeff = 0.50] damp_scheme Determines how horizontal damping coefficients vary with latitude. = 1, constant = 2, varies as inverse of diagonal grid distance = 3, varies as inverse of x-axis grid distance Note: damp_scheme = 1 is recommended, damp_scheme = 2,3 is experimental. [integer, default: damp_scheme = 1] damp_slope_corr_vel The topography slope correction applied to horizontal damp_slope_corr_tmp damping of momentum and temperature (including all prognostic tracers). The coefficients (with range = 0,1) are expressed as arrays of size 4. The first 3 values are coefficients for the lowest 3 model layers, the last value represents the remaining uppermost layers. A NON-ZERO value turns the correction ON. Typical values might be (/ .25, .50, .75, .95 /). [real, dimension(4), default: damp_slope_corr = 0.,0.,0.,0.] advec_order_vel The advection order for momentum, temperature, advec_order_tmp and default order for all prognostic tracers. advec_order_trs The advection order must be an even number. [integer, default: advec_order = 2] advec_coeff_vel Coefficients for modified Euler-backward advection advec_coeff_tmp scheme for momentum, temperature, and all advec_coeff_trs prognostic tracers. NOTE: advec_coeff=0 is the Euler-forward scheme which is unstable, advec_coeff=1 is the Euler-backward scheme which is highly dissipative. [real, default: advec_coeff = 0.7] num_horiz_fill The number of successive horizontal and vertical passes num_vert_fill applied in the tracer borrowing/filling scheme. This conservative scheme is used to fill negative tracer values. It is applied in either the vertical and horizontal directions. Each successive pass should remove more negative values, however an optimum number of passes is probably between 1-3. This is applied after advection to all prognostic tracers. [integer, default: num_fill = 1] grid_sep_coeff Coefficient to suppress grid-separation problem associated with the B-grid. Currently, this option has been disabled within the model, so that this coefficient does nothing. [real, default: grid_sep_coeff = 0.00] filter_option Determines how polar filtering is performed. filter_option = 0, NO filtering = 1, not implemented = 2, filter horiz OMG/DIV, advec mass tendencies, and momentum [integer, default: filter_option = 2] filter_weight Weight applied to the polar filter that will increase (or decrease) the strength of the standard polar filter response function. SS(new) = SS(std)**filter_weight, where SS(std) is the Arakawa and Lamb response function. [integer, default: filter_weight = 1 ] ref_lat_filter The reference latitude at which polar filtering (in each hemisphere) will begin to be applied. Setting this argument >= 90. will turn off polar filtering. [real, default: ref_lat_filter = 60.] num_sponge_levels Number of uppermost model level where a band-pass filter is applied to damp undesirable waves. Currently num_sponge_levels > 1 is not allowed. If num_sponge_levels = 0, no damping is done. [integer, default: num_sponge_levels = 0 ] sponge_coeff_vel Damping coefficients for the sponge layer(s) in sponge_coeff_tmp the uppermost model levels. Coefficients have been sponge_coeff_trs normalized and must be in the range [0,1]. If num_sponge_levels = 0, the value of the coefficients is ignored. There is no option to specify coefficients that vary with level, although currently num_sponge_levels > 1 is not allowed. [real, default: sponge_coeff = 0.] dry_model Flag that determines whether or not water vapor effects are included in the hydrostatic equation. [logical, default: dry_model = true] verbose Flag that control additional printed output. Currently, this option is not being used. [integer, default: verbose = 0] NOTES fis and res should be dimension by the size of the global grid, the number of longitude points by number of latitude points. The declaration of fis and res must have the target attribute and the storage must be static. --------------------------------------------------------------- call update_bgrid_core ( Var, Var_dt, Dynam, omega ) INPUT Var A derived-type variable that contains the B-grid's prognostic variables. [prog_var_type, see prog_var_mod] INPUT/OUTPUT Var_dt A derived-type variable that contains the TENDENCIES for the B-grid's prognostic variables. [prog_var_type, see prog_var_mod] Dynam The derived-type variable returned by a previous call to bgrid_core_init (see above). [type(bgrid_dynam_type)] OUTPUT omega The omega diagnostic (from the thermodynamic equation) in pascals per second. The array should have horizontal dimensions that are consistent with the B-grid dynamical core. [real, dimension(ilb:,jlb:,:)] --------------------------------------------------------------- call bgrid_core_end ( Dynam ) INPUT Dynam Derived-type variable returned by a previous call to bgrid_core_init. [type(bgrid_dynam_type)]
FATAL ERRORS in bgrid_core_init input argument ntadj must be >= 1 If you were using the namelist interface &bgrid_core_driver_nml then check the namelist variable corresponding to this variable. input argument ntadv must be >= 1 If you were using the namelist interface &bgrid_core_driver_nml then check the namelist variable corresponding to this variable. input argument dtadv must be > 0. The model time step usually is set at the highest program level. Check the namelist for the main program.
Here is a detailed write-up of the finite differencing used in the B-grid atmospheric dynamical core.
There are no known bugs.
internal options: alpha_implicit determines how the coriolis and press grad force terms are solved = 0.5 trapezoidal implicit = 1.0 fully implicit [real, default: alpha_implicit = 0.5]
1) Addition polar filter options (e.g., filter prognostic variables or tendencies)


Disallows using the this
keyword outside of classes.
See the rule’s author’s rationale here.
One argument may be optionally provided:
check-function-in-method
disallows using the this
keyword in functions within class methods."no-invalid-this": true
"no-invalid-this": [true, "check-function-in-method"]
{
"type": "list",
"listType": {
"type": "enum",
"enumValues": [
"check-function-in-method"
]
}
}


Limit the maximum volume level of sources.
Limit the minimum level of the volume slider.
Activate adjustments. This will stop the currently playing source.


Conclusion
This is now the first "official" (i.e. post-beta) release version of adv3Lite. By now it should be settling down to be fairly stable, so it should be eminently safe to write a game in it, even though some bugs (hopefully relatively obscure ones by now) will doubtless remain.
At this stage it is unlikely that many (if any) features of the library will be substantially changed in future releases. As far as possible we'll try to ensure that everything described in this manual will continue to work as described, but we can't absolutely guarantee that nothing will change, since change may be unavoidable to implement some new feature or resolve some unanticipated difficulty. Any features of or code in the library not explicitly covered in this manual are liable to change without notice. That said, after so many beta releases it is unlikely that things will change very much.
At the moment adv3Lite is a young project and I have no idea how many people will be interested in it. So if you have read this far, thank you for your interest! I hope that the adv3Lite library may be able to serve some of your IF needs either now or in the future.
— Eric Eve
Oxford, England
November 2013


DRIVE A CROOKED ROAD (director/writer: Richard Quine; screenwriters: from the book by James Benson Nablo/Blake Edwards; cinematographer: Charles Lawton; editor: Jerome Thoms; cast: Mickey Rooney (Eddie Shannon), Dianne Foster (Barbara Mathews), Kevin McCarthy (Steve Norris), Jack Kelly (Harold Baker), Harry Landers (Ralph), Jerry Paris (Phil), Paul Picerni (Carl), Dick Crockett (Don), Mort Mills (Garage Foreman), Peggy Maley (Marge); Runtime: 82; Columbia; 1954)
Reviewed by Dennis Schwartz
A rather flat film noir that never gets that interesting. Eddie Shannon (Rooney) is a lonely grease monkey and amateur racing driver, who is cruelly ribbed by most of the men he works with and is insecure around women. Suddenly a very attractive lady, Barbara Mathews (Foster), lures him to be her steady date because her boyfriend, Steve Norris (Kevin McCarthy), whom he doesn't realize is her boyfriend, wants her to get him to drive the getaway car during the robbery of the Palm Springs bank. The gang needs an expert driver to go over a rough road and get to an exit in a short time so that the robbers will avoid the police roadblock.
Eddie only does it because he's afraid of losing Barbara, someone he has come to love, even though they have no physical contact, in the week he has known her. She cons him into doing it and he's so naive and hungry for her love that he's clueless as to what she's up to. Steve's strong-arm partner, the wisecracking Harold (Kelly), is ordered by the boss to bump off the poor sucker when he returns to Steve's place after the successful heist and the guilt-ridden Barbara tells him that he was setup as a chump.
Warning: spoiler to follow in the next paragraph.
But Eddie skillfully crashes the car headed for a secluded Santa Monica beach spot, where Steve is ordering him to go at gunpoint. This kills Harold, but the injured Eddie returns to Steve's house, where he shoots him when he sees him beating Barbara for disobeying his orders to not tell Eddie anything. The police arrive, as Eddie comforts Barbara.
The film is best at capturing how lonely and isolated Eddie is, and how his dreams of being a Grand Prix champion rider seem out of the question but are the only tangible things he has going for him until Foster enters his life. Eddie is portrayed in a sympathetic light, despite his poor judgment exercised. It's the simpleness of this tale and lack of any pretensions that makes it convincing but not necessarily sparkling.
REVIEWED ON 9/5/2001 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
========== X-RAMR-ID: 29406 X-Language: en X-RT-SourceID: 873 X-RT-AuthorID: 1315 X-RT-RatingText: C
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Frame can give informed answers to questions you ask it in context of its library, marking Frame as an advanced virtual assistant with instantaneous access to knowledge
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Yeah it's pretty fun.
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{# This template is only used by restify.core.email. #} {% block page_html %}
%(exception)s
%(function_name)s
%(current_line)s


Configuring and Running an Experiment¶
Aqua Chemistry supports two types of users:
- Chemistry practitioners, who are merely interested in executing Aqua Chemistry as a tool to compute chemistry properties. These users may not be interested in extending Aqua Chemistry with additional capabilities. In fact, they may not even be interested in learning the details of quantum computing, such as the notions of circuits, gates and qubits. What these users expect from quantum computing is the gains in performance and accuracy, and the reduction in computational complexity.
- Chemistry and quantum researchers, who are interested in extending Aqua Chemistry with new computational chemistry software drivers, new operators for classical-to-quantum input translation, and/or new quantum algorithms for more efficient and accurate computations.
In this section, we cover the first class of users — the chemistry practitioners. Specifically, this section describes how Aqua Chemistry can be accessed as a tool for quantum-based chemistry computations.
To see how you can extend Aqua Chemistry with new components, please refer to Section “Contributing to Aqua Chemistry”.
Execution Modes¶
Aqua Chemistry has both Graphical User Interface (GUI) and command line tools, which may be used when solving chemistry problems. Both can load and run an input file specifying a molecule configuration and the quantum algorithm to be used for the computation, along with the algorithm configuration and various other options to customize the experiment. If you are new to Aqua Chemistry, we highly recommend getting started with the GUI. Finally, Aqua Chemistry can also be accessed programmatically by users interested in customizing the experiments beyond what the command line and GUI can offer.
GUI¶
The GUI provides an easy means to create an input file from scratch, or to load an existing input file, and then run that input file to experiment with a chemistry problem on a quantum machine. An input file is created, edited and saved with validation of parameter values.
During the
Aqua Chemistry Code Installation via the pip install
command,
a script is created that allows you to start the GUI from the command line,
as follows:
qiskit_aqua_chemistry_ui
If you cloned Aqua Chemistry directly from the
GitHub repository instead of using pip
install
, then the script above will not be present and the launching command should be instead:
python qiskit_aqua_chemistry/ui
This command must be launched from the root folder of the aqua-chemistry
repository
clone.
When executing an Aqua Chemistry problem using the GUI, the user can choose to specify a JavaScript Object Notation (JSON) output file name by selecting the Generate Algorithm Input checkbox. When this is done, Aqua Chemistry will not attempt to bring the chemistry experiment to completion; rather, it will stop the execution of the experiment right after forming the input for the quantum algorithm, before invoking that algorithm, and will serialize the input to the quantum algorithm in a JSON Input File for Direct Algorithm Invocation.
Command Line¶
The Aqua Chemistry pip Code Installation process will automatically install the following command-line tool:
qiskit_aqua_chemistry_cmd
If you cloned Aqua Chemistry from its remote
GitHub repository
instead of using pip install
, then the command-line interface can be executed as follows:
python qiskit_aqua_chemistry
from the root folder of the aqua-chemistry
repository clone.
Here is a summary of the command-line options:
usage: qiskit_aqua_chemistry_cmd [-h] [-o output | -jo json output] input
Quantum Chemistry Program.
positional arguments:
input Aqua Chemistry input file
optional arguments:
-h, --help Show this help message and exit
-o output Output file name
-jo json output JSON output file name
As shown above, in addition to the mandatory input file name parameter, the user can specify an output file name where the output of the chemistry problem will be saved (otherwise it will just be printed on the command screen) or, alternatively, a JSON output file name. When the latter is specified, Aqua Chemistry will not attempt to bring the chemistry experiment to completion; rather, it will stop its execution right after forming the input for the quantum algorithm specified in the input file, before invoking that algorithm, and will serialize the quantum-algorithm to a JSON Input File for Direct Algorithm Invocation.
Programmable Interface¶
Aqua Chemistry also offers Application Programming Interfaces (APIs) to execute experiments programmatically. Numerous examples on how to do so can be found in the chemistry folder of the Aqua Tutorials GitHub repository.
Programming an Experiment Step by Step¶
It is very well possible to program an experiment step by step by invoking all the necessary APIs one by one to construct the flow that executes a classical computation software with a given molecular configuration, extracts from that execution the molecular structural data necessary to form the input to one of the Aqua quantum algorithms, and finally invokes that algorithm to build, compile and execute a circuit modeling the experiment on top of a quantum machine. An example of this is available in the PySCF_end2end tutorial.
Declarative Programming Interface¶
It should be noted, however, that Aqua Chemistry is
designed to be programmed in a declarative way as well. This was done in order
to simplify the programmatic access to Aqua Chemistry,
minimizing the chances for configuration errors, and addressing the needs of users
who might be experts in chemistry but not interested in writing a lot of code or
learning new Application Programming Interfaces (APIs). Even though there is
nothing preventing a user from accessing the Aqua Chemistry APIs and
programming an experiment step by step, Aqua Chemistry lets you
build a Python dictionary from an Input File. This can be achieved via the
GUI
by loading (or creating from scratch) the input file representing the
configuration of the desired experiment, and by then selecting Export Dictionary
from the File menu. Assuming that the programmer assigns the
exported dictionary to variable aqua_chemistry_dict
, then the
experiment can be executed with the following two lines of code:
solver = AQUAChemistry()
result = solver.run(aqua_chemistry_dict)
Executing the Python dictionary extracted from the Input File
via a call to the run
method of an AQUAChemistry
solver
is essentially what the Command Line and GUI
do too in order to execute an experiment.
The advantage of this approach is that users can now programmatically customize the Python dictionary extracted from the GUI according to their needs. Since a Python dictionary can be updated programmatically, the programmable interface of Aqua Chemistry makes it possible to carry out experiments that are more complicated than those that can be executed via the command line or the GUI.
The following example shows a simple programmatic use of two Python dictionaries extracted from the Aqua Chemistry GUI in order to compute the ground-state molecular energy of a hydrogen molecule computed via the Quantum Phase Estimation (QPE) algorithm and compare that result against the reference value computed via the Exact Eigensolver classical algorithm. A comparison with the Hartree-Fock energy is also offered.
distance = 0.735
molecule = 'H .0 .0 0; H .0 .0 {}'.format(distance)
# Input dictionaries to configure Aqua Chemistry using QPE and Exact Eigensolver
aqua_chemistry_qpe_dict = {
'driver': {'name': 'PYSCF'},
'PYSCF': {
'atom': molecule,
'basis': 'sto3g'
},
'operator': {'name': 'hamiltonian', 'transformation': 'full', 'qubit_mapping': 'parity'},
'algorithm': {
'name': 'QPE',
'num_ancillae': 9,
'num_time_slices': 50,
'expansion_mode': 'suzuki',
'expansion_order': 2,
},
'initial_state': {'name': 'HartreeFock'},
'backend': {
'name': 'local_qasm_simulator',
'shots': 100,
}
}
aqua_chemistry_ees_dict = {
'driver': {'name': 'PYSCF'},
'PYSCF': {'atom': molecule, 'basis': 'sto3g'},
'operator': {'name': 'hamiltonian', 'transformation': 'full', 'qubit_mapping': 'parity'},
'algorithm': {
'name': 'ExactEigensolver',
},
}
# Execute the experiments
result_qpe = AQUAChemistry().run(aqua_chemistry_qpe_dict)
result_ees = AQUAChemistry().run(aqua_chemistry_ees_dict)
# Extract the energy values
print('The ground-truth ground-state energy is {}.'.format(result_ees['energy']))
print('The ground-state energy as computed by QPE is {}.'.format(result_qpe['energy']))
print('The Hartree-Fock ground-state energy is {}.'.format(result_ees['hf_energy']))
More complex examples include plotting the dissociation curve or comparing results obtained via different algorithms.
Result Dictionary¶
As can be seen in the programmable-interface example above, the
AQUAChemistry
run
method returns a result dictionary.
The unit of measure for the energy values is
Hartree, while for the dipole-moment values it is atomic units (a.u.).
The dictionary contains the following fields of note:
energy
: the ground state energyenergies
: an array of energies comprising the ground-state molecular energy and any excited states if they were computednuclear_repulsion_energy
: the nuclear repulsion energyhf_energy
: the Hartree-Fock ground-state molecular energy as computed by the drivernuclear_dipole_moment
,electronic_dipole_moment
,dipole_moment
: nuclear, electronic, and combined dipole moments forx
,y
andz
total_dipole_moment
: total dipole momentalgorithm_retvals
: The result dictionary of the algorithm that produced the values in the experiment.
Input File¶
An input file is used to define a chemistry problem, and includes both chemistry and quantum configuration information. It contains at a minimum the definition of a molecule and its associated configuration, such as a basis set, in order to compute the electronic structure using one of the external ab-initio Drivers. Further configuration can also be supplied to explicitly control the processing and the quantum algorithm, used for the computation, instead of using defaulted values when none are supplied.
Several sample input files can be found in the chemistry folder of the Aqua Tutorials GitHub repository.
An input file comprises the following main sections, although not all are mandatory:
name
¶
This is an optional free-format text section. Here you can name and describe the problem solved by the input file. For example:
&name
H2 molecule experiment
Ground state energy computed via Variational Quantum Eigensolver
&end
driver
¶
This is a mandatory section, which defines the molecule and associated configuration for the electronic-structure computation by the chosen driver via its external computational chemistry program. The exact form of the configuration depends on the specific driver being used since Aqua Chemistry allows external drivers to be the system’s front-ends, without interposing any new programming language or API on top of existing drivers.
Here are a couple of examples.
Note that the driver
section names which specific chemistry driver will
be used, and a subsequent section in the input file, having the name of the driver, then
supplies the driver specific configuration. For example, if you
choose PSI4
as the driver, then a section called psi4
must
be defined, containing the molecular configuration written as a PSI4
input file. Users who have already collected input files for existing drivers
can simply paste those files’ contents into this section.
The following is an example showing how to use the PySCF driver
for the configuration of a Lithium Hydride (LiH) molecule. The
driver
section names PYSCF
as the driver and then a pyscf
section,
corresponding to the name of the chosen driver, must be provided in order to define,
at a minimum, the geometrical coordinates of the molecule’s atoms
and basis set (or sets) that will
be used by PySCF library to compute the
electronic structure.
&driver
name=PYSCF
&end
&pyscf
atom=Li 0.0 0.0 -0.8; H 0.0 0.0 0.8
unit=Angstrom
basis=sto3g
&end
Here is another example showing again how to configure the same LiH molecule as above,
this time using the PSI4 driver. Here, PSI4
is named as the driver to be used and the psi4
section contains the
molecule and basis set (or sets) directly in a form that PSI4 understands. The
language in which the molecular configuration is input is
the input-file language for PSI4, and thus should be familiar to
existing users of PSI4, who may have already collected such an input file
from previous experiments and whose only job at this point would be to copy and paste
its contents into the psi4
section of the input file.
&psi4
molecule LiH {
0 1
Li 0.0 0.0 -0.8
H 0.0 0.0 0.8
}
set {
basis sto-3g
scf_type pk
}
&end
The Aqua Chemistry documentation on Drivers explains how to install and configure the drivers currently interfaced by Aqua Chemistry.
As shown above, Aqua Chemistry allows input files from the classical driver libraries to be used directly, without any modification and without interposing any new programming language or API. This has a clear advantage, not only in terms of usability, but also in terms of functionality, because any capability of any chemistry library chosen by the user is automatically integrated into Aqua Chemistry, which would not have been possible if a new language or API had been interposed between the library and the user.
operator
¶
This is an optional section. This section can be configured to control the operator that converts the electronic structure information, obtained from the driver, to qubit-operator form, in order to be processed by the algorithm. The following parameters may be set:
The name of the operator:
name = hamiltonian
This parameter accepts a
str
value. However, currently,hamiltonian
is the only value allowed forname
since there is only one operator entity at present. The translation layer of Aqua Chemistry is extensible and new translation operators can be plugged in. Therefore, in the future, more operators may be supported.The transformation type of the operator:
transformation = full | particle_hole
The
transformation
parameter takes astr
value. The only two allowed values, currently, arefull
andparticle_hole
, withfull
, the default one, corresponding to the standard second quantized hamiltonian. Setting thetransformation
parameter toparticle_hole
yields a transformation of the electronic structure Hamiltonian in the second quantization framework into the particle-hole picture, which offers a better starting point for the expansion of the trial wave function from the Hartree Fock reference state. For trial wave functions in Aqua, such as Unitary Coupled Cluster Singles and Doubles (UCCSD), the p/h Hamiltonian can improve the speed of convergence of the Variational Quantum Eigensolver (VQE) algorithm in the calculation of the electronic ground state properties. More information on the particle-hole formalism can be found in arXiv:1805.04340.Note
For the reasons mentioned above, when the transformation type is set to be particle hole, then the configuration of the initial qubit state offsetting the computation of the final result should be set to be the Hartree-Fock energy of the molecule. This can be done by setting the
name
parameter in theinitial_state
section toHartree-Fock
, as explained in the documentation on Initial States.The desired Translators from fermions to qubits:
qubit_mapping = jordan_wigner | parity | bravyi_kitaev
This parameter takes a value of type
str
. Currently, only the three values above are supported, but new qubit mappings can easily be plugged in. Specifically:"jordan_wigner"
corresponds to the Jordan-Wigner transformation."parity"
, the default value for thequbit_mapping
parameter, corresponds to the Parity mapping transformation. When this mapping is selected, it is possible to reduce by 2 the number of qubits required by the computation without loss of precision by setting thetwo_qubit_reduction
parameter toTrue
, as explained next."bravyi_kitaev"
corresponds to the Bravyi-Kitaev transformation, also known as binary-tree-based qubit mapping.
A Boolean flag specifying whether or not to apply the precision-preserving two-qubit reduction optimization:
two_qubit_reduction : bool
The default value for this parameter is
True
. When the parity mapping is selected, andtwo_qubit_reduction
is set toTrue
, then the operator can be reduced by two qubits without loss of precision.Warning
If the mapping from fermionic to qubit is set to something other than the parity mapping, the value assigned to
two_qubit_reduction
is ignored.The maximum number of workers used when forming the input to the Aqua quantum algorithm:
max_workers = 1 | 2 | ...
Processing of the hamiltonian from fermionic to qubit can take advantage of multiple CPU cores to run parallel processes to carry out the transformation. The number of such worker processes used will not exceed the actual number of CPU cores or this
max_workers
positive integer, whichever is the smaller. The default value formax_worker
is4
.A Boolean value indicating whether or not to freeze the core orbitals in the computation:
freeze_core : bool
To reduce the number of qubits required to compute the molecular energy values, and improve computation efficiency, frozen core orbitals corresponding to the nearest noble gas can be removed from the subsequent computation performed by the Aqua algorithm, and a corresponding offset from this removal is added back into the final computed result. This approximation may be combined with
orbital_reduction
setting below. The default value for this parameter isFalse
.A list of molecular orbitals to remove from the computation:
orbital_reduction : [int, int, ... , int]
The orbitals from the electronic structure can be simplified for the subsequent computation through the use of this parameter, which allows the user to specify a set of orbitals to be removed from the computation as a list of
int
values, the default being an empty list. Each value in the list corresponds to an orbital to be removed from the subsequent computation. The list should be indices of the orbitals from0
ton - 1
, where the electronic structure hasn
orbitals.For ease of referring to the higher orbitals, the list also supports negative values with
-1
being the highest unoccupied orbital,-2
the next one down, and so on. Also note that, while orbitals may be listed to reduce the overall size of the problem, the final computation can be less accurate as a result of using this approximation.The following should be taken into account when assigning a value to the
orbital_reduction
parameter:- Any orbitals in the list that are occupied orbitals are frozen and an offset
is computed from their removal. These orbitals are not taken into account while performing the
molecular energy computation, except for the fact that the offset is added back at the end
into the final computed result.
This is the same procedure as that one that takes place
when
freeze_core
is set toTrue
, except that withorbital_reduction
you can specify exactly the orbitals you want to freeze. - Any orbitals in the list that are unoccupied orbitals are simply eliminated entirely from the subsequent computation. No offset is computed or added back into the final computed result for these orbitals.
- Any orbitals in the list that are occupied orbitals are frozen and an offset
is computed from their removal. These orbitals are not taken into account while performing the
molecular energy computation, except for the fact that the offset is added back at the end
into the final computed result.
This is the same procedure as that one that takes place
when
Note
When a list is specified along with freeze_core
set to True
, the effective
orbitals being removed from the computation are those in the frozen core combined with
those specified in the orbital_reduction
list.
Below is an example where, in addition to freezing the core orbitals, a couple of other orbitals are listed for removal. We assume that there are a total of ten orbitals, so the highest two unoccupied virtual orbitals will be eliminated from the subsequent computation, in addition to the frozen-core orbitals:
&operator
name=hamiltonian
qubit_mapping=jordan_wigner
freeze_core=True
orbital_reduction=[8, 9]
&end
Alternatively, the above code could be specified via the following, equivalent way, which simplifies expressing the higher orbitals using the fact that the numbering is relative to the highest orbital:
&operator
name=hamiltonian
qubit_mapping=jordan_wigner
freeze_core=True
orbital_reduction=[-2, -1]
&end
algorithm
¶
This is an optional section that allows you to specify which
algorithm will be used by the computation.
Quantum Algorithms are provided by
Aqua: A Library of Quantum Algorithms.
To compute reference values, Aqua also allows the use of
Classical Reference Algorithms.
In the algorithm
section, algorithms are disambiguated using the
declarative names
by which Aqua recognizes them, based on the JSON schema
each algorithm must provide according to the Aqua QuantumAlgorithm
API,
as explained in the documentation on both
quantum and classical reference algorithms.
The declarative name is specified as the name
parameter in the algorithm
section.
The default value for the name
parameter is VQE
, corresponding
to the Variational Quantum Eigensolver (VQE)
algorithm.
An algorithm typically comes with a set of configuration parameters.
For each of them, a default value is provided according to the
QuantumAlgorithm
API of Aqua.
Furthermore, according to each algorithm, additional sections may become relevant to optionally configure that algorithm’s components. For example, variational algorithms, such as Variational Quantum Eigensolver (VQE), allow the user to choose and configure an optimizer and a variational form from the Optimizers and Variational Forms libraries, respectively, whereas Quantum Phase Estimation (QPE) allows the user to configure which Inverse Quantum Fourier Transform (IQFT) from the Inverse Quantum Fourier Transforms library to use.
The documentation of Aqua: A Library of Quantum Algorithms explains how to configure Quantum Algorithms and any of the pluggable entities they may use, such as Optimizers, Variational Forms, Oracles, Inverse Quantum Fourier Transforms, and Initial States.
Here is an example in which the Variational Quantum Eigensolver (VQE) algorithm is selected along with the Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) optimizer and the RyRz variational form:
&algorithm
name=VQE
shots=1
operator_mode=matrix
&end
&optimizer
name=L_BFGS_B
factr=10
&end
&variational_form
name=RYRZ
entangler_map={0: [1]}
&end
backend
¶
Aqua allows for configuring the backend, which is the quantum machine
on which a quantum experiment will be run.
This configuration requires specifying
the Qiskit Terra quantum computational
backend to be used for computation, which is done by assigning a str
value to
the name
parameter of the backend
section:
name : string
The value of the name
parameter indicates either a real-hardware
quantum computer or a quantum simulator.
The underlying Qiskit core used by Aqua comes
with two predefined quantum device simulators: the local state vector simulator and
the local QASM simulator, corresponding to the following two
values for the name
parameter: "local_statevector_simulator"
(which
is the default value for the name
parameter) and "local_qasm_simulator"
, respectively.
However, any suitable quantum backend can be selected, including
a real quantum hardware device. The QConfig.py
file
needs to be setup for Qiskit to access remote devices. For this, it is sufficient to follow the
Qiskit Terra installation instructions.
The Aqua Chemistry GUI greatly simplifies the
configuration of QConfig.py
via a user friendly interface,
accessible through the Preferences… menu item.
Configuring the backend to use by an algorithm in the Quantum Algorithms library requires setting the following parameters too:
The number of repetitions of each circuit to be used for sampling:
shots : int
This parameter applies, in particular to the local QASM simulator and any real quantum device. The default value is
1024
.A
bool
value indicating whether or not the circuit should undergo optimization:skip_transpiler : bool
The default value is
False
. Ifskip_transpiler
is set toTrue
, then Qiskit will not perform circuit translation. If Aqua Chemistry has been configured to run an experiment with a quantum algorithm that uses only basis gates, then no translation of the circuit into basis gates is required. Only in such cases is it safe to skip circuit translation. Skipping the translation phase when only basis gates are used may improve overall performance, especially when many circuits are used repeatedly, as it is the case with the Variational Quantum Eigensolver (VQE) algorithm.Note
Use caution when setting
skip_transpiler
toTrue
as if the quantum algorithm does not restrict itself to the set of basis gates supported by the backend, then the circuit will fail to run.An optional dictionary can be supplied to control the backend’s noise model (see the Terra documentation on noise parameters for more details):
noise_params : dictionary
This is a Python dictionary consisting of key/value pairs. Configuring it is optional; the default value is
None
.The following is an example of such a dictionary that can be used:
noise_params: {"U": {"p_depol": 0.001, "p_pauli": [0, 0, 0.01], "gate_time": 1, "U_error": [ [[1, 0], [0, 0]] ] } }
problem
¶
In Aqua, a problem specifies the type of experiment being run. Configuring the problem is essential because it determines which algorithms are suitable for the specific experiment.
Problem Categories¶
Aqua comes with a set of predefined problems.
This set is extensible: new problems can be added,
just like new algorithms can be plugged in to solve existing problems in a different way,
or to solve new problems.
Currently, a problem can be configured by assigning a str
value to the name
parameter
of the problem
section of the input file:
name = energy | excited_states | ising | dynamics | search | svm_classification
As shown above, energy
, excited_states
, ising
, dynamics
,
search
, and svm_classification
are currently
the only values accepted for name
in Aqua, corresponding to the computation of
energy, excited states, Ising models, dynamics of evolution, search and
Support Vector Machine (SVM) classification, respectively.
New problems, disambiguated by their
name
parameter, can be programmatically
added to Aqua via the
AlgorithmInput
Application Programming Interface (API), and
both Quantum Algorithms and Classical Reference Algorithms library
should programmatically list the problems it is suitable for in its JSON schema, embedded into
the class implementing the QuantumAlgorithm
API. Typical choices of problems
in chemistry include energy and excited states.
Generating Repeatable Experiments¶
Aspects of the computation may include use of random numbers. For instance, Variational Quantum Eigensolver (VQE) is coded to use a random initial point if the variational form chosen from the Variational Forms library does not supply any preference based on the initial state and if the user does not explicitly supply an initial point. In this case, each run of VQE, for what would otherwise be a constant problem, can produce a different result, causing non-determinism and the inability to replicate the same result across different runs with identical configurations. Even though the final value might be numerically indistinguishable, the number of evaluations that led to the computation of that value may differ across runs. To enable repeatable experiments, with the exact same outcome, a random seed can be set, thereby forcing the same pseudo-random numbers to be generated every time the experiment is run:
random_seed : int
The default value for this parameter is None
.
Reconciling Chemistry and Quantum Configuration¶
The configuration of a chemistry problem directly affects the configuration
of the underlying quantum system. For example, the number of particles and
orbitals in a molecular system depends on the molecule being modeled and the
basis set chosen by the user, and that, in turn, directly affects the number of qubits
necessary to model the molecular system on a quantum machine. The number of
qubits directly derived from the molecular configuration can then be reduced
as indicated in the operator
section of the input file
via optimizations, such as the precision-preserving
two-qubit reduction based on the parity qubit mapping, or via approximations, obtained
by freezing the core or by virtually removing unoccupied orbitals. This is just an example
of how the chemistry
configuration can affect the quantum configuration. Letting the user set
the number of qubits would force the user to have to know the numbers of particles
and orbitals of the molecular system, and then precompute the number of
qubits based on the numbers of particles and
orbitals, as well as the qubit-reduction optimization
and approximation techniques. Any mistake in this manual computation
may lead to misconfiguring the whole experiment. For this reason,
Aqua Chemistry automatically computes the numbers of particles and orbitals,
infers the total number of qubits necessary to model the molecular system under analysis,
and subtracts from that total number of qubits the number of qubits that are
redundant based on the optimization and approximation techniques that the user
may have chosen to apply. In essence, Aqua Chemistry automatically
configures the quantum system.
Things become more subtle when configuring the
Initial States and Variational Forms
used by a quantum algorithm. These components are
configured in sections initial_state
and variational_form
, respectively,
which only become enabled when the algorithm
selected by the user supports them.
For example, the variational_form
section is enabled only
if the user has chosen to execute the experiment using a variational algorithm, such as
vqe.
The Aqua Chemistry :ref:`aqua-chemistry-gui disables the variational_form
section for non-variational algorithms.
The problem with the configuration of an initial state and a variational form is that
the values of parameters qubit_mapping
and two_qubit_reduction
may require matching
their settings across these two sections, as well as the settings applied to the
identically named parameters in the operator
section. This is the case, for example, for the Unitary Coupled Cluster Singles and Doubles (UCCSD) variational form
and the Hartree-Fock
initial state. Furthermore, some variational forms and initial states may require setting
the numbers of particles (num_particles
) and orbitals (num_orbitals
), which,
as discussed above, can be complicated to compute, especially for large and complex molecules.
Aqua Chemistry inherits the problem configuration from Aqua.
However, exclusive to Aqua Chemistry
is a Boolean field inside the problem
section which assists users with these
complicated settings:
auto_substitutions : bool
When this parameter is set to True
, which is the default, the values of parameters
num_particles
and num_orbitals
are automatically computed by Aqua Chemistry
for sections initial_state
and
variational_form
when UCCSD
and Hartree-Fock
are selected, respectively. As such,
the configuration of these two parameters is disabled; the user will not be required, or even allowed,
to assign values to
these two parameters. This is also reflected in the GUI, where
these two parameters will be grayed out and uneditable when auto_substitutions
is set to True
.
Furthermore, Aqua Chemistry automatically sets
parameters qubit_mapping
and two_qubit_reduction
in sections initial_state
and
variational_form
when UCCSD
and Hartree-Fock
are selected, respectively.
Specifically, Aqua Chemistry sets qubit_mapping
and two_qubit_reduction
to the values the user assigned to them in the operator
section
of the input file in order to enforce parameter-value matching across these three different
sections. As a result, the user will only have to configure qubit_mapping
and two_qubit_reduction
in the operator
section; the configuration of these two
parameters in sections initial_state
and variational_form
is disabled,
as reflected also in the GUI, where the values of these two parameters are only
editable in the operator
section, while the parameters themselves are grayed out in the
initial_state
and variational_form
sections.
On the other hand, if auto_substitutions
is set to False
,
then the end user has the full responsibility for the entire
configuration.
Warning
Setting auto_substitutions
to False
, while
made possible for experimental purposes, should only
be done with extreme care, since it could easily lead to misconfiguring
the entire experiment and producing imprecise results.
Input File for Direct Algorithm Invocation¶
Aqua allows for its Quantum Algorithms and Classical Reference Algorithms, to be invoked directly, without necessarily having to go through the execution of a domain-specific application. Aqua Chemistry supports accessing the Aqua algorithm-level entry point in the following way: after the translation process terminates with the creation of the input to a quantum algorithm, in the form of a qubit operator, Aqua Chemistry allows for that input to be serialized as a JavaScript Object Notation (JSON) file.
Serializing the input to the quantum algorithm at this point is useful in many scenarios because the contents of one of such JSON files are domain- and problem-independent:
- Users can share JSON files among each other in order to compare and contrast their experimental results at the algorithm level, for example to compare results obtained with the same input and different algorithms, or different implementations of the same algorithm, regardless of the domain in which those inputs were generated (chemistry, artificial intelligence, optimization, etc.) or the problem that the user was trying to solve.
- People performing research on quantum algorithms may be interested in having access to a number of such JSON files in order to test and refine their algorithm implementations, irrespective of the domain in which those JSON files were generated or the problem that the user was trying to solve.
- Repeating an experiment in which the domain-specific parameters remain the same, and the only difference is in the configuration of the quantum algorithm and its supporting components becomes much more efficient because the user can choose to restart any new experiment directly at the algorithm level, thereby bypassing the input extraction from the driver, and the input translation into a qubit operator.


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Objectives: Severity of illness scores have gained considerable interest for their use in predicting outcomes such as mortality and length of stay. The most sophisticated scoring systems require the collection of numerous physiologic measurements, making their use in real-time difficult. A severity of illness score based on a few parameters that can be captured electronically would be of great benefit. Using a machine-learning technique known as particle swarm optimization, we attempted to reduce the number of physiologic parameters collected in the Acute Physiology, Age, and Chronic Health Evaluation IV system without losing predictive accuracy. Design: Retrospective cohort study of ICU admissions from 2007 to 2011. Setting: Eighty-six ICUs at 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed. Patients: 81,087 admissions, of which 72,474 did not have any missing values. Interventions: None. Measurements and Main Results: Machine-learning algorithms were used to come up with the minimal set of variables that were capable of yielding an accurate severity of illness score: the Oxford Acute Severity of Illness Score. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score were developed on admissions during 2007–2009 and validated on admissions during 2010–2011. The most parsimonious Oxford Acute Severity of Illness Score consisted of seven physiologic measurements, elective surgery, age, and prior length of stay. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score achieved an area under the receiver operating characteristic curve of 0.88 and calibrated well. Conclusions: A reduced severity of illness score had discrimination and calibration equivalent to more complex existing models. This was accomplished in large part using machine-learning algorithms, which can effectively account for the nonlinear associations between physiologic parameters and outcome.
OASIS is a novel severity score which aims to simplify risk adjustment in critical care. While calculation of traditional severity scores can be laborious, in particular as some scores have over 20 variables and 100 diagnostic categories, OASIS only requires 10 variables. Furthermore, these variables are routinely collected for patients in the intensive care unit (ICU).


--- layout: page title: AvaTax REST v2 Error Codes ---
| Number | Message | Summary |
|---|---|---|
| 1 | ServerConfiguration | Indicates that there is a server configuration problem with one of the servers in Avalara's AvaTax cluster. |
| 2 | AccountInvalidException | You attempted to read information about an account that does not exist. |
| 3 | CompanyInvalidException | TBD |
| 4 | EntityNotFoundError | You attempted to act on, retrieve, update, or delete an object that does not exist. |
| 5 | ValueRequiredError | You submitted a request and did not provide a value in a required field. |
| 6 | RangeError | You attempted to set a value that must be within a range, but your value was outside of the range. |
| 7 | RangeCompareError | You specified an out-of-bounds field value. |
| 8 | RangeSetError | You attempted to set a value that was not permitted. |
| 9 | TaxpayerNumberRequired | Customers subscribing to Avalara Returns must identify each company by its United States Taxpayer ID Number (TIN). |
| 11 | CommonPassword | The password you have chosen is a commonly-guessed password and cannot be used. |
| 12 | WeakPassword | The password you specified does not meet minimum complexity requirements. |
| 13 | StringLengthError | One of the strings you uploaded to the server is too long and cannot be saved. |
| 15 | EmailValidationError | You attempted to provide an email address that does not conform to email address standards. |
| 16 | EmailMissingError | You must provide an email address with this request. |
| 17 | ParserFieldNameError | You attempted to search on a field that does not exist or cannot be searched. |
| 18 | ParserFieldValueError | You attempted to search for a value that is not a correct value type. |
| 19 | ParserSyntaxError | You provided an unrecognized string or token in the "$filter" parameter of your fetch request. |
| 20 | ParserTooManyParametersError | You attempted a fetch call that included too many parameters. |
| 21 | ParserUnterminatedValueError | You attempted to retrieve data with a "$filter" parameter that contained syntax errors. |
| 22 | DeleteUserSelfError | A user account may not call DELETE on the user itself. |
| 23 | OldPasswordInvalid | You attempted to reset a password but you did not provide a correct old password value. |
| 24 | CannotChangePassword | You attempted to change passwords for a user who is not permitted to change their password. |
| 25 | CannotChangeCompanyCode | The company code for a company is set when the company is created and may not be changed. |
| 30 | AuthenticationException | The credentials you provided to AvaTax could not be validated. |
| 31 | AuthorizationException | Your account is not authorized to call this API. |
| 32 | ValidationException | Your API call contained an incorrectly structured object. |
| 33 | InactiveUserError | This user account is currently inactive. |
| 34 | AuthenticationIncomplete | Your API call did not contain authentication information. |
| 35 | BasicAuthIncorrect | Your Basic authorization header was not encoded correctly. |
| 36 | IdentityServerError | A problem was detected with Avalara Identity. |
| 37 | BearerTokenInvalid | The Bearer Token that you used for authentication was not valid. |
| 38 | ModelRequiredException | You called an API that requires an object, but you did not provide an object. |
| 39 | AccountExpiredException | Your AvaTax account has expired, or is not yet enabled. You may need to contact your customer account manager for assistance. |
| 40 | VisibilityError | You attempted to request an object from AvaTax that you are not permitted to see. |
| 41 | BearerTokenNotSupported | Bearer Token authentication is not yet supported with this API. |
| 42 | InvalidSecurityRole | You do not have permission to create or update users with this security role. |
| 43 | InvalidRegistrarAction | The action you attempted is restricted. |
| 44 | RemoteServerError | A remote server AvaTax depends on is not working. |
| 45 | NoFilterCriteriaException | You provided a filter with your query, but did not specify any criteria. |
| 46 | OpenClauseException | Your $filter value has a mismatched open parenthesis / close parenthesis. |
| 47 | JsonFormatError | The JSON you sent with your request was invalid. |
| 50 | UnhandledException | The API you attempted to call resulted in an unhandled exception within Avalara AvaTax. |
| 60 | ReportingCompanyMustHaveContactsError | A company that is designated to report taxes must have at least one designated contact person. |
| 61 | CompanyProfileNotSet | This error occurs when you try to modify the tax profile of a company that inherits its tax profile from its parent. |
| 70 | ModelStateInvalid | You provided an incorrectly structured object to AvaTax. |
| 80 | DateRangeError | This error occurs when you create an object whose end date is before its effective date. |
| 81 | InvalidDateRangeError | You specified a date outside of the allowable range. |
| 100 | DeleteInformation | This message represents information provided about an object that was deleted. |
| 120 | CannotCreateDeletedObjects | You may not create an object with a "Deleted" flag. |
| 121 | CannotModifyDeletedObjects | If an object has been deleted, you may not modify it further after its deletion. |
| 122 | ReturnNameNotFound | You attempted to create a filing calendar for a return that is not recognized by AvaTax. |
| 123 | InvalidAddressTypeAndCategory | When creating a location, you must specify a compatible AddressType and AddressCategory value. |
| 124 | DefaultCompanyLocation | The default location for a company must be a physical-type location rather than a salesperson-type location. |
| 125 | InvalidCountry | The country code you provided is not recognized as a valid ISO 3166 country code. |
| 126 | InvalidCountryRegion | You specified a country/region that was not recognized by the ISO 3166 country/region code system. |
| 127 | BrazilValidationError | Reserved for future use. |
| 128 | BrazilExemptValidationError | Reserved for future use. |
| 129 | BrazilPisCofinsError | Reserved for future use. |
| 130 | JurisdictionNotFoundError | The specified jurisdiction could not be found. |
| 131 | MedicalExciseError | You attempted to create a tax rule that designated a device as medical excise tax for an incorrect jurisdiction. |
| 132 | RateDependsTaxabilityError | You created a tax rule with a RateDepends option, but that rule is not a TaxabilityRule. |
| 133 | RateDependsEuropeError | The RateDepends option is only valid for countries in the European Union. |
| 135 | RateTypeNotSupported | You attempted to choose a rate type that is not supported for the country you selected. |
| 136 | CannotUpdateNestedObjects | In AvaTax REST, you can create objects with nested children, but you cannot update objects with nested children. |
| 137 | UPCCodeInvalidChars | Your UPC code contains invalid characters. |
| 138 | UPCCodeInvalidLength | Your UPC code was too long to fit into the standard UPC object field. |
| 139 | IncorrectPathError | You attempted to modify an object but you provided an object that matches a different URL. |
| 140 | InvalidJurisdictionType | You specified a jurisdiction type that is not recognized. |
| 141 | MustConfirmResetLicenseKey | When resetting a license key for your account, you must provide a flag that indicates that you really want to reset your license key. |
| 142 | DuplicateCompanyCode | You cannot create two companies with the same company code. |
| 143 | TINFormatError | The U.S. Taxpayer Identification Number you provided is not in a recognized format. |
| 144 | DuplicateNexusError | Nexus is a concept used to declare that your business is subject to taxation by a particular jurisdiction; you may not declare any one jurisdiction more than once. |
| 145 | UnknownNexusError | You attempted to declare nexus in a jurisdiction that is not recognized by AvaTax. |
| 146 | ParentNexusNotFound | You attempted to create a nexus in a tax authority that is underneath a parent tax authority, but you have not yet declared nexus with the parent tax authority. |
| 147 | InvalidTaxCodeType | You specified a tax code type that is not recognized by Avalara. |
| 148 | CannotActivateCompany | A company can only be activated when it has a valid tax profile. |
| 149 | DuplicateEntityProperty | You attempted to create an object with a duplicate name or code. |
| 200 | BatchSalesAuditMustBeZippedError | Sales audit files must be uploaded in ZIP or RAR formats. |
| 201 | BatchZipMustContainOneFileError | Compressed files uploaded to the Batch service must contain exactly one file. |
| 202 | BatchInvalidFileTypeError | You uploaded a batch file with an incorrect file type. |
| 250 | PointOfSaleFileSize | The Point-Of-Sale API cannot build this file dynamically. |
| 251 | PointOfSaleSetup | Invalid parameter provided in the Point-Of-Sale file request. |
| 300 | GetTaxError | A problem occurred when you attempted to create a transaction through AvaTax. |
| 301 | AddressConflictException | You attempted to add multiple addresses to a transaction that was flagged as a single-address transaction. |
| 303 | DocumentCodeConflict | You attempted to create a document with a code that matches an existing transaction. |
| 304 | MissingAddress | When creating transactions, you must at a minimum provide an origin and destination address. |
| 305 | InvalidParameter | When adding parameters to your CreateTransactionModel, you must use a valid parameter name. |
| 306 | InvalidParameterValue | When adding parameters to your CreateTransactionModel, you must specify a parameter of the correct type. |
| 307 | CompanyCodeConflict | The company code in the URL of your API call did not match the company code of the transaction you uploaded. |
| 308 | DocumentFetchLimit | You attempted to fetch more than 1000 transaction documents at a time. |
| 400 | BadDocumentFetch | This error indicates that you have provided an incorrect "$include" value to the GET /api/v2/companies/{0}/transactions endpoint. |
| 500 | ServerUnreachable | One of the servers in the Avalara AvaTax API cluster is unreachable and your API call could not be completed. |
| 600 | SubscriptionRequired | This Avalara API call requires an active subscription to a specific service. |


[§431:2-301.5] Examiner defined. For purposes of this part, "examiner" means any individual or firm authorized by the commissioner to conduct an examination under the insurance code. [L 1993, c 321, pt of §2]