Functions¶
- Example function:
- Combination Functions:
- TransferMechanism Functions:
- Integrator Functions:
- Distribution Functions:
NormalDist
ExponentialDist
UniformDist
GammaDist
WaldDist
- Learning Functions:
Overview¶
A Function is a component
that “packages” a function (in its function
method)
for use by PsyNeuLink components. Every component
in PsyNeuLink is assigned a Function, and when that
component is executed, that Function’s function <Function_Base.function
is executed. The
function <Function_Base.function
can be any callable operation, although most commonly it is a mathematical operation
(and, for those, almost always uses calls to numpy function).
There are two reasons PsyNeuLink packages functions in a Function component. The first is to manage parameters.
Parameters are attributes of a function that either remain stable over multiple calls to the
function (e.g., the gain or bias of a logistic function, or the learning rate of a learning function);
or, if they change, they do less frequently or under the control of different factors than the function’s variable
(i.e., its input). As a consequence, it is useful to manage these separately from the function’s variable,
and not have to provide them every time the function is called. To address this, every PsyNeuLink Function has a
set of attributes corresponding to the parameters of the function, that can be specified at the time the Function is
created (in arguments to its constructor), and can be modified independently of a call to its function
.
Modifications can be directly (e.g., in a script), or by the operation of other PsyNeuLink components (e.g.,
AdaptiveMechanisms
). The second to reason PsyNeuLink uses Functions is for modularity. By providing a standard
interface, any Function assigned to a components in PsyNeuLink can be replaced with other PsyNeuLink Functions, or with
user-written custom functions (so long as they adhere to certain standards (the PsyNeuLink Function API
).
Creating a Function¶
A Function can be created directly by calling its constructor. Functions are also created automatically whenever
any other type of PsyNeuLink component is created (and its function
is not otherwise specified). The
constructor for a Function has an argument for its variable
and each of the parameters of
its function
. The variable
argument is used both to format the
input to the function
, and assign its default value. The arguments for each parameter can
be used to specify the default value for that parameter; the values can later be modified in various ways as described
below.
Structure¶
Every Function has a variable
that provides the input to its
function
method. It also has an attribute for each of the parameters of its function
. If a Function has been assigned to another component, then it also has an owner
attribute that refers to that component. Each of the Function’s attributes is also assigned
as an attribute of the owner
, and those are each associated with with a
parameterState of the owner
. Projections to those parameterStates can be
used to modify the Function’s parameters.
Execution¶
Functions are not executable objects, but their function
can be called. This can be done
directly. More commonly, however, they are called when their owner
is executed. The parameters
of the function
can be modified when it is executed, by assigning a
parameter specification dictionary to the Function.params
argument in the call to the
function
. For mechanisms, this can also be done by specifying runtime_params for the mechanism when it is executed
.
Class Reference¶
-
class
Function.
Function_Base
(variable_default, params=None, owner=None, name=None, prefs=None)¶ Implement abstract class for Function category of Component class
Parameters: - variable (value : default variableClassDefault) – specifies the format and a default value for the input to function.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ value – format and default value can be specified by the
variable
argument of the constructor; otherwise, they are specified by the Function’svariableClassDefault
.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
class
Function.
ArgumentTherapy
(variable, propensity=Manner.CONTRARIAN, pertinacity=10.0 params=None, owner=None, name=None, prefs=None)¶ Return
True
orFalse
according to the manner of the therapist.Parameters: - variable (boolean or statement that resolves to one : default variableClassDefault) – assertion for which a therapeutic response will be offered.
- propensity (Manner value : default Manner.CONTRARIAN) – specifies preferred therapeutic manner
- pertinacity (float : default 10.0) – specifies therapeutic consistency
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ boolean – assertion to which a therapeutic response is made.
-
propensity
¶ Manner value : default Manner.CONTRARIAN – determines therapeutic manner: tendency to agree or disagree.
-
pertinacity
¶ float : default 10.0 – determines consistency with which the manner complies with the propensity.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
class
Manner
¶ An enumeration.
-
ArgumentTherapy.
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Returns a boolean that is (or tends to be) the same as or opposite the one passed in.
Parameters: - variable (boolean : default variableClassDefault) – an assertion to which a therapeutic response is made.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: therapeutic response
Return type: boolean
-
class
Function.
UserDefinedFunction
(function, variable=None, params=None, owner=None, name=None, prefs=None)¶ Implement user-defined Function.
This is used to “wrap” custom functions in the PsyNeuLink
Function API
. It is automatically invoked and applied to any function that is assigned to thefunction
attribute of a PsyNeuLink component (other than a Function itself). The function can take any arguments and return any values. However, if UserDefinedFunction is used to create a custom version of another PsyNeuLink Function, then it must conform to the requirements of that Function’s type.Note
Currently the arguments for the
function
of a UserDefinedFunction are NOT assigned as attributes of the UserDefinedFunction object or its owner, nor to itsuser_params
dict.Parameters: - function (function) – specifies function to “wrap.” It can be any function, take any arguments (including standard ones,
such as
params
andcontext
) and return any value(s), so long as these are consistent with the context in which the UserDefinedFunction will be used. - variable (value : default variableClassDefault) – specifies the format and a default value for the input to function.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ value – format and default value can be specified by the
variable
argument of the constructor; otherwise, they are specified by the Function’svariableClassDefault
.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
- function (function) – specifies function to “wrap.” It can be any function, take any arguments (including standard ones,
such as
-
class
Function.
Reduce
(variable_default=variableClassDefault, operation=SUM, params=None, owner=None, prefs=None)¶ Combine values in each of one or more arrays into a single value for each array.
Parameters: - variable_default (list or np.array : default variableClassDefault) – specifies a template for the value to be transformed and its default value; all entries must be numeric.
- operation (SUM or PRODUCT : default SUM) – specifies whether to sum or multiply the elements in
variable
offunction
. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable_default
¶ list or np.array – contains array(s) to be reduced.
-
operation
¶ SUM or PRODUCT – determines whether elements of each array in
variable
offunction
are summmed or multiplied.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Returns a scalar value for each array in
variable
that is either the sum or product of the elements in that array.Parameters: - variable (list or np.array : default variableClassDefault) – a list or np.array of numeric values.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: Sum or product of arrays in variable – in an array that is one dimension less than
variable
.Return type: np.array
-
class
Function.
LinearCombination
(variable_default, weights=None, exponents=None, scale=1.0, offset=0.0, operation=SUM, params=None, owner=None, name=None, prefs=None)¶ Linearly combine arrays of values with optional weighting, exponentiation, scaling and/or offset.
Combines the arrays in the items of the
variable
argument. Each array can be individually weighted and/or exponentiated; they can combined additively or multiplicatively; and the resulting array can be multiplicatively transformed and/or additively offset.Parameters: - variable (1d or 2d np.array : default variableClassDefault) – specifies a template for the arrays to be combined. If it is 2d, all items must have the same length.
- weights (1d or 2d np.array) – specifies values used to multiply the elements of each array in
variable
. If it is 1d, its length must equal the number of items invariable
; if it is 2d, the length of each item must be the same as those invariable
, and there must be the same number of items as there are invariable
(seeweights
for details) - exponents (1d or 2d np.array) – specifies values used to exponentiate the elements of each array in
variable
. If it is 1d, its length must equal the number of items invariable
; if it is 2d, the length of each item must be the same as those invariable
, and there must be the same number of items as there are invariable
(seeexponents
for details) - scale (float) – specifies a value by which to multiply each element of the output of
function
(seescale
for details) - offset (float) – specifies a value to add to each element of the output of
function
(seeoffset
for details) - operation (SUM or PRODUCT) – specifies whether the
function
takes the elementwise (Hadamarad) sum or product of the arrays invariable
. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ 1d or 2d np.array – contains the arrays to be combined by function. If it is 1d, the array is simply linearly transformed by and
scale
andoffset
. If it is 2d, the arrays (all of which must be of equal length) are weighted and/or exponentiated as specified byweights
and/orexponents
and then combined as specified byoperation
.
-
weights
¶ 1d or 2d np.array – if it is 1d, each element is used to multiply all elements in the corresponding array of
variable
; if it is 2d, then each array is multiplied elementwise (i.e., the Hadamard Product is taken) with the corresponding array ofvariable
. Allweights
are applied before any exponentiation (if it is specified).
-
exponents
¶ 1d or 2d np.array – if it is 1d, each element is used to exponentiate the elements of the corresponding array of
variable
; if it is 2d, the element of each array is used to exponentiate the correspnding element of the corresponding array ofvariable
. In either case, exponentiating is applied after application of theweights
(if any are specified).
-
scale
¶ float – value is multiplied by each element of the array after applying the
operation
(seescale
for details); this done before applying theoffset
(if it is specified).
-
offset
¶ float – value is added to each element of the array after applying the
operation
andscale
(if it is specified).
-
operation
¶ SUM or PRODUCT – determines whether the
function
takes the elementwise (Hadamard) sum or product of the arrays invariable
.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Apply
weights
and/orexponents
to the arrays invariable
, then take their sum or product (as specified byoperation
), applyscale
and/oroffset
, and return the resulting array.Parameters: - variable (1d or 2d np.array : default variableClassDefault) – a single numeric array, or multiple arrays to be combined; if it is 2d, all arrays must have the same length.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: combined array – the result of linearly combining the arrays in
variable
.Return type: 1d np.array
-
class
Function.
Linear
(variable_default, slope=1.0, intercept=0.0, params=None, owner=None, name=None, prefs=None)¶ Linearly transform variable.
Note: default values for
slope
andintercept
implement the IDENTITY_FUNCTIONParameters: - variable (number or np.array : default variableClassDefault) – specifies a template for the value to be transformed.
- slope (float : default 1.0) – specifies a value by which to multiply
variable
. - intercept (float : default 0.0) – specifies a value to add to each element of
variable
after applyingslope
. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ number or np.array – contains value to be transformed.
-
slope
¶ float – value by which each element of
variable
is multiplied before applying theintercept
(if it is specified).
-
intercept
¶ float – value added to each element of
variable
after applying theslope
(if it is specified).
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return:
slope
*variable
+intercept
.Parameters: - variable (number or np.array : default variableClassDefault) – a single value or array to be transformed.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: linear transformation of variable
Return type: number or np.array
-
class
Function.
Exponential
(variable_default, scale=1.0, rate=1.0, params=None, owner=None, name=None, prefs=None)¶ Exponentially transform variable.
Parameters: - variable (number or np.array : default variableClassDefault) – specifies a template for the value to be transformed.
- rate (float : default 1.0) – specifies a value by which to multiply
variable
before exponentiation. - scale (float : default 1.0) – specifies a value by which to multiply the exponentiated value of
variable
. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ number or np.array – contains value to be transformed.
-
scale
¶ float – value by which the exponentiated value is multipled.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return:
scale
* e**(rate
*variable
).Parameters: - variable (number or np.array : default variableClassDefault) – a single value or array to be exponentiated.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: exponential transformation of variable
Return type: number or np.array
-
class
Function.
Logistic
(variable_default, gain=1.0, bias=0.0, params=None, owner=None, name=None, prefs=None)¶ Logistically transform variable.
Parameters: - variable (number or np.array : default variableClassDefault) – specifies a template for the value to be transformed.
- gain (float : default 1.0) – specifies a value by which to multiply
variable
before logistic transformation - bias (float : default 0.0) – specifies a value to add to each element of
variable
after applyinggain
but before logistic transformation. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ number or np.array – contains value to be transformed.
-
gain
¶ float – value by which each element of
variable
is multiplied before applying thebias
(if it is specified).
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return: 1 / (1 + e**( (
gain
*variable
) +bias
))Parameters: - variable (number or np.array : default variableClassDefault) – a single value or array to be transformed.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: logistic transformation of variable
Return type: number or np.array
-
class
Function.
SoftMax
(variable_default, gain=1.0, output=ALL, params=None, owner=None, name=None, prefs=None)¶ SoftMax transform of variable.
Parameters: - variable (1d np.array : default variableClassDefault) – specifies a template for the value to be transformed.
- gain (float : default 1.0) – specifies a value by which to multiply
variable
before softmax transformation. - output (ALL, MAX_VAL, MAX_INDICATOR, or PROB : default ALL) – specifies the format of array returned by
function
(seeoutput
for details). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ 1d np.array – contains value to be transformed.
-
gain
¶ float – value by which
variable
is multiplied before the softmax transformation; determines the “sharpness” of the distribution.
-
output
¶ ALL, MAX_VAL, MAX_INDICATOR, or PROB – determines how the softmax-transformed values of the elements in
variable
are reported in the array returned byfunction
:- ALL: array of all softmax-transformed values;
- MAX_VAL: softmax-transformed value for the element with the maximum such value, 0 for all others;
- MAX_INDICATOR: 1 for the element with the maximum softmax-transformed value, 0 for all others;
- PROB: probabilistically chosen element based on softmax-transformed values after normalizing sum of values to 1, 0 for all others.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return: e**(
gain
*variable
) / sum(e**(gain
*variable
)), filtered byouptput
specification.Parameters: - variable (1d np.array : default variableClassDefault) – an array to be transformed.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: softmax transformation of variable
Return type: number or np.array
-
class
Function.
LinearMatrix
(variable_default, matrix=None, params=None, owner=None, name=None, prefs=None)¶ Matrix transform of variable:
Parameters: - variable (list or 1d np.array : default variableClassDefault) – specifies a template for the value to be transformed; length must equal the number of rows of
matrix
. - matrix (number, list, 1d or 2d np.ndarray, np.matrix, function, or matrix keyword : default IDENTITY_MATRIX) – specifies matrix used to transform
variable
(seematrix
for specification details). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ 1d np.array – contains value to be transformed.
-
matrix
¶ 2d np.array – matrix used to transform
variable
. Can be specified as any of the following:- number - used as the filler value for all elements of the
matrix
(call to np.fill); - list of arrays, 2d np.array or np.matrix - assigned as the value of
matrix
; - matrix keyword - see
MatrixKeywords
for list of options.
Rows correspond to elements of the input array (outer index), and columns correspond to elements of the output array (inner index).
- number - used as the filler value for all elements of the
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
instantiate_matrix
(specification, context=None)¶ Implements matrix indicated by specification
Specification is derived from MATRIX param (passed to self.__init__ or self.function)
- Specification (validated in _validate_params):
- single number (used to fill self.matrix)
- matrix keyword (see get_matrix)
- 2D list or np.ndarray of numbers
Return matrix: (2D list)
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ -
Parameters: - variable (list or 1d np.array) – array to be transformed; length must equal the number of rows of ‘matrix <LinearMatrix.matrix>`.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: dot product of variable and matrix – length of the array returned equals the number of columns of
matrix
.Return type: 1d np.array
- variable (list or 1d np.array : default variableClassDefault) – specifies a template for the value to be transformed; length must equal the number of rows of
-
class
Function.
Integrator
(variable_default=None, rate=1.0, weighting=CONSTANT, noise=0.0, time_step_size=1.0, params=None, owner=None, prefs=None)¶ Integrate current value of
variable
with its prior value.Parameters: - variable_default (number, list or np.array : default variableClassDefault) – specifies a template for the value to be integrated; if it is a list or array, each element is independently integrated.
- rate (float, list or 1d np.array : default 1.0) – specifies the rate of integration. If it is a list or array, it must be the same length as
variable
and all elements must be floats between 0 and 1 (seerate
for details). - weighting (CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION : default CONSTANT) – specifies type of integration (see
weighting
for details). - noise (float, list or 1d np.array : default 0.0) – specifies random value to be added in each call to
function
. If it is a list or array, it must be the same length asvariable
and all elements must be floats between 0 and 1. - time_step_size (float : default 0.0) – determines the timing precision of the integration process when
weighting
is set to DIFFUSION (seetime_step_size
for details. - float, list or 1d np.array (initializer) – specifies starting value for integration. If it is a list or array, it must be the same length as
variable_default
(seeinitializer
for details). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ number or np.array – current input value some portion of which (determined by
rate
) that will be added to the prior value; if it is an array, each element is independently integrated.
-
rate
¶ 1d np.array – determines the rate of integration based on current and prior values. All elements are between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it applies to all elements of
variable
; if it has more than one element, each element applies to the corresponding element ofvariable
.
-
weighting
¶ CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION –
- specifies type of integration:
- CONSTANT:
old_value
+rate
+noise
(ignoresvariable
); - SIMPLE:
old_value
+rate
*variable
+noise
; - ADAPTIVE: (1-
rate
) *variable
+ (rate
*old_value
) +noise
(Weiner filter or Delta rule); - DIFFUSION:
old_value
+ (rate
*old_value
*time_step_size
) + √(time_step_size
*noise
* Gaussian(0,1)) (Drift Diffusion Model).
- CONSTANT:
-
time_step_size
¶ float – determines the timing precision of the integration process when
weighting
is set to DIFFUSION (and used to scale thenoise
parameter appropriately).
-
initializer
¶ float or 1d np.array – determines the starting value for integration (i.e., the value to which
old_value
is set. If it is assigned as aruntime_param
it resetsold_value
to the specified value (seeinitializer
for details).
-
old_value
¶ 1d np.array : default variableClassDefault – stores previous value with which
variable
is integrated.
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return: some fraction of
variable
combined with some fraction ofold_value
(seeweighting
).Parameters: - variable (number, list or np.array : default variableClassDefault) – a single value or array of values to be integrated.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: updated value of integral
Return type: 2d np.array
-
class
Function.
BogaczEtAl
(variable_default=variableCLassDefault, drift_rate=1.0, threshold=1.0, starting_point=0.0, t0=0.2 noise=0.5, params=None, owner=None, prefs=None)¶ Return terminal value of decision variable, mean accuracy, and mean response time computed analytically for the drift diffusion process as described in Bogacz et al (2006).
Parameters: - variable_default (number, list or np.array : default variableClassDefault) – specifies a template for decision variable(s); if it is list or array, a separate solution is computed independently for each element.
- drift_rate (float, list or 1d np.array : default 1.0) – specifies the drift_rate of the drift diffusion process. If it is a list or array,
it must be the same length as
variable_default
. - threshold (float, list or 1d np.array : default 1.0) – specifies the threshold (boundary) of the drift diffusion process. If it is a list or array,
it must be the same length as
variable_default
. - starting_point (float, list or 1d np.array : default 1.0) – specifies the initial value of the decision variable for the drift diffusion process. If it is a list or
array, it must be the same length as
variable_default
. - noise (float, list or 1d np.array : default 0.0) – specifies the noise term (corresponding to the diffusion component) of the drift diffusion process.
If it is a float, it must be a number from 0 to 1. If it is a list or array, it must be the same length as
variable_default
and all elements must be floats from 0 to 1. - t0 (float, list or 1d np.array : default 0.2) – specifies the non-decision time for solution. If it is a float, it must be a number from 0 to 1. If it is a
list or array, it must be the same length as
variable_default
and all elements must be floats from 0 to 1. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ number or 1d np.array – holds initial value assigned to
variable_default
argument; ignored byfunction
.
-
drift_rate
¶ float or 1d np.array – determines the drift component of the drift diffusion process.
-
threshold
¶ float or 1d np.array – determines the threshold (boundary) of the drift diffusion process (i.e., at which the integration process is assumed to terminate).
-
starting_point
¶ float or 1d np.array – determines the initial value of the decision variable for the drift diffusion process.
-
noise
¶ float or 1d np.array – determines the diffusion component of the drift diffusion process (used to specify the variance of a Gaussian random process).
-
t0
¶ float or 1d np.array – determines the assumed non-decision time to determine the response time returned by the solution.
-
bias
¶ float or 1d np.array – normalized starting point: (
starting_point
+threshold
) / (2 *threshold
)
-
owner
¶ Mechanism –
component
to which the Function has been assigned.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None)¶ Return: terminal value of decision variable, mean accuracy (error rate; ER) and mean response time (RT)
Parameters: - variable (2d np.array) – ignored.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: Decision variable, mean ER, mean RT
Return type: (float, float, float)
Note
Use of this Function requires that the MatLab engine is installed.
Parameters: - variable_default (number, list or np.array : default variableClassDefault) – specifies a template for decision variable(s); if it is list or array, a separate solution is computed independently for each element.
- drift_rate (float, list or 1d np.array : default 1.0) – specifies the drift_rate of the drift diffusion process. If it is a list or array,
it must be the same length as
variable_default
. - threshold (float, list or 1d np.array : default 1.0) – specifies the threshold (boundary) of the drift diffusion process. If it is a list or array,
it must be the same length as
variable_default
. - starting_point (float, list or 1d np.array : default 1.0) – specifies the initial value of the decision variable for the drift diffusion process. If it is a list or
array, it must be the same length as
variable_default
. - noise (float, list or 1d np.array : default 0.0) – specifies the noise term (corresponding to the diffusion component) of the drift diffusion process.
If it is a float, it must be a number from 0 to 1. If it is a list or array, it must be the same length as
variable_default
and all elements must be floats from 0 to 1. - t0 (float, list or 1d np.array : default 0.2) – specifies the non-decision time for solution. If it is a float, it must be a number from 0 to 1. If it is a
list or array, it must be the same length as
variable_default
and all elements must be floats from 0 to 1. - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
number or 1d np.array – holds initial value assigned to
variable_default
argument; ignored byfunction
.
float or 1d np.array – determines the drift component of the drift diffusion process.
float or 1d np.array – determines the threshold (bound) of the drift diffusion process (i.e., at which the integration process is assumed to terminate).
float or 1d np.array – determines the initial value of the decision variable for the drift diffusion process.
float or 1d np.array – determines the diffusion component of the drift diffusion process (used to specify the variance of a Gaussian random process).
float or 1d np.array – determines the non-decision time used to compute the solution.
float or 1d np.array – normalized starting point: (
starting_point
+threshold
) / (2 *threshold
)
Mechanism –
component
to which the Function has been assigned.
PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
Return: terminal value of decision variable, mean accuracy (error rate; ER), mean response time (RT), correct RT mean, correct RT variance and correct RT skew. Requires that the MatLab engine is installed.
Parameters: - variable (2d np.array) – ignored.
- params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: Decision variable, mean ER, mean RT, correct RT mean, correct RT variance, correct RT skew
Return type: (float, float, float, float, float, float)
-
class
Function.
LearningFunction
(variable_default, params, owner=None, name=None, prefs=None, context='Function_Base Init')¶ Abstract class of Functions used for learning.
All LearningFunctions take three input values (specified in each of the three required items of the
variable
argument), and return two output values.- Input values:
- input to the parameter being modified (variable[0]);
- output of the parameter being modified (variable[1]);
- error associated with the output (variable[2]).
- Output values:
- learning_signal: modifications calculated by the function that attempt to reduce the error;
- error_signal: the error received, possibly modified by the function.
-
class
Function.
Reinforcement
(variable_default=variableClassDefault, activation_function=SoftMax, learning_rate=None, params=None, name=None, prefs=None)¶ Implements a function that calculates a diagonal matrix of weight changes using the reinforcement (delta) learning rule.
Parameters: - variable (List or 2d np.array [length 3] : default variableClassDefault) – template for the three items provided as the variable in the call to the
function
(in order):activation_input
(1d np.array),activation_output
(1d np.array),error_signal
(1d np.array). - activation_function (Function or function : SoftMax) – specifies the function of the mechanism that generates
activation_output
. - learning_rate (float : default default_learning_rate) – supercedes any specification for the process and/or system to which the function’s
owner
belongs (seelearning_rate
for details). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ 2d np.array – specifies three values used as input to the
function
:activation_input
,activation_output
, anderror_signal
.
-
activation_input
¶ 1d np.array – first item of
variable
; this is not used (it is implemented for consistency with otherLearningFunctions
).
-
activation_output
¶ 1d np.array – the output of the function for which the matrix being modified provides the input; must have a single non-zero value (corresponding to the selected “action”).
-
error_signal
¶ 1d np.array – the error signal associated with the
activation_output
; must be the same length asactivation_output
and must have a single non-zero value in the same position as the one inactivation_output
.
-
activation_function
¶ Function or function : SoftMax – the function of the mechanism that generates
activation_output
; must return and array with a single non-zero value.
-
learning_rate
¶ float – the learning rate used by the function. If specified, it supercedes any learning_rate specified for the
process
and/orsystem
to which the function’sowner
belongs. If it isNone
, then the learning_rate specified for the process to which theowner
belongs is used; and, if that isNone
, then the learning_rate for the system to which it belongs is used. If all areNone
, then thedefault_learning_rate
is used.
-
default_learning_rate
¶ float – the value used for the
learning_rate
if it is not otherwise specified.
-
function
¶ function – the function that computes the weight change matrix, and returns that along with the
error_signal
received.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None) Calculate a matrix of weight changes from a single (scalar) error term
Parameters: - variable (List or 2d np.array [length 3] : default variableClassDefault) – must have three items that are the values for (in order):
activation_input
(not used),activation_output
(1d np.array with a single non-zero value),error_signal
(1d np.array). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: - diagonal weight change matrix (2d np.array) – has a single non-zero entry in the same row and column as the one in
activation_output
anderror_signal
. - error signal (1d np.array) – same as value received in
error_signal
argument.
- variable (List or 2d np.array [length 3] : default variableClassDefault) – must have three items that are the values for (in order):
- variable (List or 2d np.array [length 3] : default variableClassDefault) – template for the three items provided as the variable in the call to the
-
class
Function.
BackPropagation
(variable_default=variableClassDefault, activation_derivative_fct=Logistic().derivative, error_derivative_fct=Logistic().derivative, error_matrix=None, learning_rate=None, params=None, name=None, prefs=None)¶ Implements a function that calculate a matrix of weight changes using the backpropagation (Generalized Delta Rule) learning algorithm.
Parameters: - variable (List or 2d np.array [length 3] : default variableClassDefault) – specifies a template for the three items provided as the variable in the call to the
function
(in order):activation_input
(1d np.array),activation_output
(1d np.array),error_signal
(1d np.array). - activation_derivative (Function or function) – specifies the derivative for the function of the mechanism that generates
activation_output
. - error_derivative (Function or function) – specifies the derivative for the function of the mechanism that is the receiver of the
error_matrix
. - error_matrix (List, 2d np.array, np.matrix, ParameterState, or MappingProjection) – matrix, the output of which is used to calculate the
error_signal
. If it is specified as a ParameterState it must be one for thematrix
parameter of a MappingProjection; if it is a MappingProjection, it must be one with a MATRIX parameterState. - learning_rate (float : default default_learning_rate) – supercedes any specification for the process and/or system to which the function’s
owner
belongs (seelearning_rate
for details). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- owner (Component) –
component
to which to assign the Function. - prefs (Optional[PreferenceSet or specification dict : Function.classPreferences]) – the
PreferenceSet
for the Function. If it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
variable
¶ 2d np.array – contains the three values used as input to the
function
:activation_input
,activation_output
, anderror_signal
.
-
activation_input
¶ 1d np.array – the input to the matrix being modified; same as 1st item of `variable <BackPropagation.variable>.
-
activation_output
¶ 1d np.array – the output of the function for which the matrix being modified provides the input; same as 2nd item of `variable <BackPropagation.variable>.
-
error_signal
¶ 1d np.array – the error signal for the next matrix (layer above) in the learning sequence, or the error computed from the target (training signal) and the output of the last mechanism in the sequence; same as 3rd item of `variable <BackPropagation.variable>.
-
error_matrix
¶ 2d np.array or ParameterState – matrix, the output of which is used to calculate the
error_signal
; if it is a ParameterState, it refers to the MATRIX parameterState of the MappingProjection being learned.
-
learning_rate
¶ float – the learning rate used by the function. If specified, it supercedes any learning_rate specified for the
process
and/orsystem
to which the function’sowner
belongs. If it isNone
, then the learning_rate specified for the process to which theowner
belongs is used; and, if that isNone
, then the learning_rate for the system to which it belongs is used. If all areNone
, then thedefault_learning_rate
is used.
-
default_learning_rate
¶ float – the value used for the
learning_rate
if it is not otherwise specified.
-
function
¶ function – the function that computes the weight change matrix, and returns that along with the
error_signal
received, weighted by the contribution made by each element ofactivation_output
as a function of theerror_matrix
.
-
prefs
¶ PreferenceSet or specification dict : Projection.classPreferences – the
PreferenceSet
for function. Specified in theprefs
argument of the constructor for the function; if it is not specified, a default is assigned usingclassPreferences
defined in __init__.py (see PreferenceSet for details).
-
function
(variable=None, params=None, time_scale=<TimeScale.TRIAL: 1>, context=None) Calculate and return a matrix of weight changes from arrays of inputs, outputs and error terms
Parameters: - variable (List or 2d np.array [length 3] : default variableClassDefault) – must have three items that are the values for (in order):
activation_input
(1d np.array),activation_output
(1d np.array),error_signal
(1d np.array). - params (Optional[Dict[param keyword, param value]]) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
- time_scale (TimeScale : default TimeScale.TRIAL) – specifies whether the function is executed on the time_step or trial time scale.
Returns: - weight change matrix (2d np.array) – the modifications to make to the matrix.
- weighted error signal (1d np.array) –
error_signal
, weighted by the contribution made by each element ofactivation_output
as a function oferror_matrix
.
- variable (List or 2d np.array [length 3] : default variableClassDefault) – must have three items that are the values for (in order):
- variable (List or 2d np.array [length 3] : default variableClassDefault) – specifies a template for the three items provided as the variable in the call to the


HYDROSKIN- hydrocortisone lotion
Rugby Laboratories
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Hydrocortisone 1%
Uses
- for temporary relief of itching associated with minor skin irritations, inflammation, and rashes due to:
- eczema
- psoriasis
- poison ivy, oak, sumac
- cosmetics
- insect bites
- detergents
- jewelry
- seborrheic dermatitis
- soaps
- external genital and anal itching
- other uses of this product should be only under the advice and supervision of a doctor
Warnings
For external use only
Do not use
- for the treatment of diaper rash. Ask a doctor.
- for external genital itching if you have a vaginal discharge. Ask a doctor.
When using this product
- avoid contact with the eyes
- do not begin use of any other hydrocortisone product unless you have asked a doctor
- for external anal itching do not exceed the recommended daily dosage unless directed by a doctor. In case of bleeding, consult a doctor promptly.
- do not put this product into the rectum by using fingers or any mechanical device or applicator
Stop use and ask a doctor if
- condition worsens
- symptoms persist for more than 7 days or clear up and occur again within a few days. Do not begin use of any other hydrocortisone product unless you have consulted a doctor.
Keep out of reach of children.
If swallowed, get medical help or contact a Poison Control Center right away.
Directions
shake well before use
adults and children 2 years of age and older:
- apply to affected area not more than 3 to 4 times daily
children under 2 years of age:
for external anal itching:
- adults: when practical, cleanse the affected area with mild soap and warm water and rinse thoroughly. Gently dry by patting or blotting with toilet tissue or a soft cloth before application of this product.
- children under 12 years of age with external anal itching: ask a doctor
Inactive ingredients
benzyl alcohol, cetyl alcohol, glycerin, glyceryl monostearate, isopropyl palmitate, lactic acid, polysorbate 60, propylene glycol, purified water, sorbitan monostearate, sorbitol solution 70%, stearyl alcohol, white wax.
Questions or comments?
1-800-645-2158
Package/Label Principal Display Panel
Rugby Laboratories
Maximum Strength
HydroSKIN
hydrocortisone 1% Lotion


With a whole cow you would get approximately 440 pounds of beef. It will be approximately 200 pounds of ground beef, and the other 220 pounds are in cuts like steaks, roasts, ribs, brisket, tenderloin, etc
In theory, you can harvest honey as soon as you have one or more frames in your super that hold only capped honey. In practice, most beekeepers find it more practical to harvest once a year in the fall or late summer.


Welcome to DJ Dan. We are entertainment that focuses on pushing you to your absolute limit. With over 10 years expierence, we provide a enterainting spectical at all kinds of events. From a wedding to a Quinceañera, a corpearate event to a Bar Mitzvah. No event is off the table.
Download our complete brochure to get started today!
When you book DJ Dan, you can rest assured that you and your guests are going to have a GREAT time. Here are a few of the things that make us stand out from the rest.
Contact us to ask any questions, schedule an event, inquire about prices, talk to our DJs or anything else


Left panel: Temperature evolutions of potential energies of bcc Fe (001) nanoplates with different diameters. Right panel: Snapshots of the nanoplate with d = 12 a at a 23 K, b 52 K, c 62 K, and d 120 K; Snapshots of the nanoplate with e d = 40 a at 190 K and (f) d = 50 a at 190 K


Classic. Useful.
The classic top bar is always useful to organize the pages of a website.
Tutorial
The code is original from w3schools with couple of adjustments. It's a combination of HTML and CSS, no need for Javascript.
HTML
1. Use
 or  to hold your navigation bar. Add a class to that element which we will use later to style.
Code Example:

Home
News
Contact
About

CSS
1. Time to style the code. Use the class from the HTML code to add styling. In this case we used the class "topnav".
Code Example:
.topnav {
background-color: black;
overflow: hidden;}
.topnav a {
float: left;
color: white;
text-align: center;
padding: 1em 1em;
text-decoration: none;
font-size: 1em;}
.topnav a:hover {
background-color: #ddd;
color: black;}


Open Source software has always been associated with voluntary work by individuals across the world who don’t necessarily get paid for all their hard work. This software runs on every device, website and application you can think of. It is often created by volunteers, hobbyists and enthusiasts who selflessly dedicate their time and effort to the cause.
Utopian is building an open source economy, in which the people who make valuable contributions to open source software projects get rewarded for their hard work. Built ontop of the Steem blockchain, Utopian uses the wisdom of the crowd to reward valuable contributions. Project owners and other contributors all get to decide how rewards are distributed through voting or liking individual contributions, and they also have the option to support contributors directly by paying them in a cryptocurrency called STEEM, which can easily be traded for other cryptocurrencies such as Bitcoin and Ethereum.
Steem is a next-generation blockchain that supports social media applications and their content. It uses a unique Proof-of-Brain mechanism that utilizes the wisdom of the crowd to reward people who make valuable contributions to their respective communities. Its native currency is called STEEM and it is the currency that Utopian contributors are rewarded in.
Steem’s economic model allows for the creation of new tokens (or units of currency) at a predefined inflation rate of around 9.5% per year, going down by approximately 0.5% every year over the next twenty and a half years, until it reaches a 0.95% yearly inflation rate . These new tokens are distributed continuously to people performing actions deemed valuable by their communities through community voting. In the case of the Utopian community, as project owners, fellow contributors and other people with vested interests in open source software development “upvote” or “like” contributions, they have a say in how these new tokens are distributed, in direct relation to their stake in the Steem ecosystem (i.e. how much Steem Power they have). The more Steem Power they have, the more rewards they can give to contributors.
Contributors are able to redeem their rewards seven days after the post or contribution has been published.
Everyone is welcome to join the Utopian community. Open Source is central to Utopian’s existence and as such, it is used by people who care about Open Source projects.
Contributors use Utopian to:
Project Owners use Utopian to:
You can post a message in the #help channel on Discord and one of Utopian’s moderators will get in touch.
If another Utopian user is using this platform in an unlawful or illegal manner, harassing others or violating your rights to use the Utopian platform in any way, you can report it on Discord, in the #report-abuse channel.
Utopian’s aim is to create an environment of collaborative harmony that leads to the progress of Open Source projects. Utopian encourages all of its users to respect each other and maintain good working relationships. The following Utopian documentation gives tips and guidance on how one can interact with other users to that end.


Class Stack
A standard Stack data structure.
Info:
- License: MIT
- Author: Richard Voelker
Methods
| Stack.new (self[, stack={}]) | Create a new Stack. |
| Stack.Push (self, el) | Push an element onto the top of the Stack. |
| Stack.Pop (self) | Remove an element from the top of the Stack. |
| Stack.Peek (self) | Returns the top element from the Stack without removing it. |
| Stack.IsEmpty (self) | Returns whether or not the Stack is empty (length of the Stack is 0). |
| Stack.Clone (self) | Performs a shallow clone (new Stack is created, but individual elements of the Stack are the same) of this Stack. |
Methods
- Stack.new (self[, stack={}])
-
Create a new Stack.
Parameters:
- Stack.Push (self, el)
-
Push an element onto the top of the Stack.
Parameters:
- self Stack
- el The element to add.
- Stack.Pop (self)
-
Remove an element from the top of the Stack.
Parameters:
- self Stack
Returns:
The top element of the Stack, or nil if the Stack is
empty.
- Stack.Peek (self)
-
Returns the top element from the Stack without removing it.
Parameters:
- self Stack
Returns:
The top element of the Stack, or nil if the Stack is
empty.
- Stack.IsEmpty (self)
-
Returns whether or not the Stack is empty (length of the
Stack is 0).
Parameters:
- self Stack
Returns:
-
bool
True if the Stack is empty, false otherwise.
- Stack.Clone (self)
-
Performs a shallow clone (new Stack is created, but
individual elements of the Stack are the same) of this Stack.
Parameters:
- self Stack
Returns:
-
Stack
The cloned Stack.


SpeedBumpPHP's Speed is Ridiculous!
Launch a new project in under 27 steps!
Don't fall for the 2, 3 or 4-step setup processes of other frameworks. You deserve to enjoy bootstrapping your application by checking off each of 27 steps.
Get to market in months, not days.
When every moment counts, SpeedBumpPHP is there to take up each one of them. As they say, "speed kills." Download SpeedBumpPHP and let it protect you.
Code Like No Other
Drab Code with Complexity
Create lifeless, drab code that will astound no one. Ugly code, done the hard way: that's how you know its a SpeedBumpPHP framework project.
Requires Significant Configuration
The true sign of quality is the amount of configuration a project requires: all SpeedBumpPHP require an extraordinary amount!
Lethargic Performance
Compare SpeedBumpPHP to other frameworks: you'll never have to consider performance optimization again!
Who Uses SpeedBumpPHP?
What SpeedBumpPHP Looks Like?
SpeedBumpPHP's strength comes from its versatility and verbosity. Hello World has never looked like this before!
Ready to Get Started?
Ready to spend the next 10,000 hours not achieving mastery of SpeedBumpPHP?


Welcome to Bethany's Pie Shop!
From pecan to plum, our pies are made in with the warmth and traditional techniques. Take a moment to Brouse our pies and see which of our delectable deserts are soon to arrive on your doorstep.
Our History
For many years, Bethany has been baking the most delicious pies at
her home. In 2013, we opened our first store in Brussels (Belgium)
in a cosy little street near the Grand Market. People from all over the
world fell in love with the soft cakes, heavenly cheese cakes, spicy
apple pies...
The store became a success in no time.
In 2015, no less than 100 pies were sold every day and Bethany's Pie Shop
moved into a larger building. After receiving many requests, it became
clear that our next step was making it possible to order pies from
the comfort of your own home and from anywhere in the world.
You are now looking at the result of this: our very own webstore,
making it possible to order Bethany's delicious pies whenever you
feel like it!
Bethany is famous for using only the best ingredients. This way, we can make sure that our pies are absolutely the best.
Download our full price list


San Antonio's place to build, share, and learn about civic tech.
Join us Saturday May 31!Come join a group of passionate folks working at the intersection of open government, cities, and technology. This is an evening of civic tech hacking, learning, and hanging.
The San Antonio Brigade is for those who want to:
There's always food, too!
That's ok! We encourage non-technical folks to pair up and learn from our community's designers and developers. Often times, the best civic projects come from teams with a diverse background.
Remember, there's more to making a great app than just coding.
6pm Thursday May 22nd
Old Geekdom space on the 11th floor of
the Weston Center
110 E Houston St, San Antonio, TX 78205
Join our Google Group to discuss new and ongoing projects
Join our Facebook Group or Fan Page and post cool stuff
Follow us on Twitter @openSATX and keep up with hack night events and projects with the #openSATX hashtag
New to civic hacking? Don't even know what that means? Every week, Christopher Whitaker leads quick workshop to get you up to speed. Or you can watch it online.
Many of our attendees are new to programming and want to learn. Every week Juan-Pablo Velez leads an informal session on how to use the command line and some basic principals of programming.
Hack nights anchor and grow volunteer civic hacking communities while avoiding 'hackathon fatigue'. To learn how to organize one in your city, see the Code for America blog.
Friends have forked the hack night in many cities:
The San Antonio Brigade is organized and run by Allen Buck, Pegy Brimhall, Ian Warshak, and Wayne Hartman.
Got questions about the event or have a project to present? Email us.


gopm
Programs
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State
Description
Action
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Confirmation
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${field(form.grain_size)}
${field(form.mash_thickness)}
${field(form.boil_time)}
${field(form.batch_volume)}
${field(form.target_mash_temp)}
${field(form.grain_absorption)}
${field(form.mash_tun_loss)}
${field(form.trub_loss)}
${field(form.boil_evaporation_rate)}
${field(form.grain_temp)}


# rza2mbarcodetypedetectiondemo
Barcode Type Detection Demo with RZ/A2M
This demo can quickly identify 5 kinds of barcode encoding formats including QR, Micro QR, DataMatrix, AZTec and HanXin code in 8~10ms when data is received from a 1280x720 (1 million) camera
This demo also integrated ZXing as backend of barcode decoding. When barcode type detection module successfully detected a barcode image, it will output barcode type and cropped barcode image to ZXing module. It will reduce ZXing decoding time. In this sample, it can support 3 kinds of barcode decoding: QR, DataMatrix and AZTec
Finally, the result is shown on a display (HDMI monitor).
RZ/A2M Evaluation Board Kit
e²studio project name: “rza2mbarcodetypefreertosgcc_evk”
(e²studio User’s Manual: Getting Started Guide / RZ Family)
Gadget Renesas board “GR-MANGO”
e²studio project name: “rza2mbarcodetypefreertosgcc_grmango”
(e²studio for GR-MANGO; GR-MANGO 'hands-on')
Barcode Type Detection format support: QR, MicroQR, AZTEC, DataMatrix, HanXin.
Barcode Decoding format support: QR, AZTEC, DataMatrix.
Barcode Type Detection performance: 8 to 12ms.
Total duration(detection+decoding): 12 to 16ms.
(DRP).
Please check the Application Note for more details.
Please contact your local Renesas Sales representative in case you like to get more information about RZ/A2M, DRP
Please contact us below if you have any questions. Q&A Forum


30 Beauty Gifts for Every Type of Friend In Your Life
by Maya AllenWe do a whole lot for our friends: literally bend over backwards to catch their best angles, stay on the phone forever for girl talk, embarrass ourselves playing wing woman, the list goes on. Every holiday season, we're also tasked with gifting our friends thoughtful things, which can be really hard, especially if they've been your friend for a really long time.
Look no further, though, because I've rounded up the best new beauty gifts for every type of friend in your life. Literally *every type*, That one extra friend, frugal friend, makeup-loving, or fragrance-obsessed friend, she's covered. Ahead are 30 gifts you'll probably want to keep for yourself, but should definitely treat your girls to.
The Studio Box
Olive & June
oliveandjune.com
$50.00
Save your girl a trip to the salon and encourage her to indulge in some self-care with this ultimate DIY manicure kit that'll make her feel so profesh in the comfort of her own home. My favorite part: This brilliantly crafted box that allows you to use your phone as a prop for whatever your heart desires.
1 For You, 2 For Me Mascara Set
Tarte
ulta.com
$35.00
Lashes deserve all the love in the world, no? Supply your bestie with three-month's worth of full-size mascaras and she'll love you forever (not that she doesn't already, but she'll definitely love you a lil' more).
Stars Aligned Glitter & Glow Liquid Eyeshadow Set
Stila
ulta.com
$12.00
Sometimes, our girlfriends need a pick-me-up after a rough day, and these sparkly liquid eyeshadows will make her shine bright like a diamond. These champagne and silver shades will serve as the best reminder: you're a star!
Wet Brush Frozen II Detangler
Wet Brush
ulta.com
$14.99
Everyone needs a Wet Brush in their life to make detangling a breeze. Your friend really needs this Frozen-themed brush because it's cheeky and too cute not to smile at.
The Mila
Fancii
fancii.com
$47.00
Fact: a good friend knows how crucial good lighting is. This compact, LED-lit, magnified glass mirror will win you Friend of the Year. To top it off, the lighting adjusts from dim to bright.
Dry Styling Set
ORIBE
nordstrom.com
$75.00
Everyone appreciates a helping hand during bad hair days. The hype around Oribe's dry shampoo is real, okay, so you should spread the love to your friend with this dry styling set that'll come through when she really needs it.
PÜR Crystal Clear Ultimate Blockbuster Palette
PÜR
ulta.com
$38.00
Treat your makeup-obsessed friend to a palette that'll feel like her playground. From neutral and vivid shades to matte and shimmer textures, this palette truly has it all.
Mini Amuse Bouche Supercharged Lipstick Set
Bite Beauty
sephora.com
$10.50
Not only are these baby lipsticks adorable and named after the most appetizing holiday treats, the colors are actually gorgeous. This insanely cheap gift will allow your friend to try before she buys the full-size shade she likes the best.
Platinum and Rose Gold Gift Set
ghd
ghdhair.com
$249.00
We all have that one friend who's addicted to straight hair. Equipped with high-powered technology, a rose gold finish fit for a queen, and a chic leather case, this will definitely be the flat-iron of her boujee dreams.
Dosist Dose Dial
calm
dosist.com
$30.00
In a perfect world, we'd be able to call our friends and vent at any given time. The way life is set up, sometimes you just need something right away to calm the jitters. This dose-controlled cannabis dissolves and instantly zens you out.
Glossy Posse Mini Gloss Bomb Set
FENTY BEAUTY by Rihanna
sephora.com
$42.00
Your Rihanna-obsessed friend will fan-girl tf out of these baby gloss bombs. Why? Well, because Fenty Beauty is a direct reflection of RiRi and everyone wants to look like the ultimate bad gal.
Bon Voyage Travel Kit
Glamsquad
glamsquad.com
$26.00
Unless your friend is Kim Kardashian, a stylist isn't always at her service. It's why she needs this convenient set of on-the-go stylers to keep her hair on-point at all times.
Jetsetter Brow Kit
Arches & Halos
target.com
$29.99
The gift of good brows is always a good idea. This brow starter kit is equipped with everything your brows could ever need to live their best lives.
Holiday Shave Gift Set
Flamingo
shopflamingo.com
$16.00
There's no doubt your friend will love a fancy razor and shaving cream to make her shower experience that much more enjoyable. Plus, there's a nice lotion in this set made with willow bark to provide soft exfoliation and long-lasting hydration post-shave.
Too Faced Christmas Party Essentials
Too Faced
ulta.com
$26.00
We all want our friends to have good sex.  This is one of those no-brainer gifts that'd make any beauty lover happy.
Miss Dior Eau de Parfum Gift Set
Dior
macys.com
$107.00
Gifting your girl a designer fragrance is like gifting her a designer bag, but on a smaller and more budget-friendly scale. Made with roses picked straight from the fields of Grasse, this fancy floral scent is opulence in a bottle.
Flora Fortune, Pine Protector, and Amber Bliss Candle Set
Diptyque
nordstrom.com
$114.00
Encourage your friend to burn the stress of the day away with these luxurious candles. Known to deliver the most delicious-smelling scents, these decked out candles are a top-tier gift.
Eau de Parfum Premium Gift Set
Acqua di Parma
bloomingdales.com
$319.00
Here's another iconic fragrance to add to your friends' collection. Made with top notes of bergamot, cedarwood, musk, and more this intoxicating scent will put an extra pep in anyone's step. Plus, I'm speaking from experience, this shower gel will transform a shower experience from regular to incredibly lavish.
Discover Clean Makeup Set
ILIA
sephora.com
$38.00
Spoil your clean beauty-obsessed friend with this travel-friendly bag of color cosmetics that will actually show up and show out on her face. It's hard to get "clean" makeup right, but Ilia uses thoughtful formulations that'll make you wish you made the switch to an all-natural makeup routine sooner.
Kalan Eau de Parfum
PARFUMS DE MARLY
nordstrom.com
$205.00
This is a fragrance fit for your badass friend who likes to own the room. The blend of black pepper, spices, blood orange, lavender, demands attention. So, you should gift it to your single friend who's on a mission to meet a man.
Jewel Pop Glass Glow Mini Lip Collection
Kevyn Aucoin
sephora.com
$25.00
True makeup lovers are aware of how legendary Kevyn Aucoin's products are. Not one but four shine-blinding lip glosses is like asking for a single scoop of ice cream but getting two: a blessing.
The Gift of Pillow Talk Lips
Charlotte Tilbury
net-a-porter.com
$35.00
Every celebrity and their mama talks about the iconic Pillow Talk shade created by the inimitable Charlotte Tilbury. Let your friend experience the magic with this lipstick set.
Le Monochromatique Face Color Mini Palette
Lancôme
nordstrom.com
$28.90
When it comes to fire holiday drops, Lancôme never dissapoints. Gift your friend this glistening, cream-to-powder palette, which is meant to glam up every area of the face and makes blending a breeze.
Extra Concentrated Eau de Parfum
Eau de Juice
ulta.com
$55.00
You know that one girlfriend of yours who likes to go out all night? She needs this *extra* fragrance that will match her party girl ways. Packed with apple, champagne accord, praline, white patchouli, and golden amber, this was designed especially for your over-the-top friend.
Best Brows Ever Set
Anastasia Beverly Hills
nordstrom.com
$25.50
The Queen of Brows is here to bless your friend with a life-saving kit for the best-kept brows, like ever. Warning: Anastasia Beverly Hills products are addictive.
Mini Nail Polish Holiday Set
Essie
macys.com
$12.99
It's not like your friend needs any more nail polishes, but four more won't hurt, right? Especially these winter hues that feel so festive and fun.
Beauty Vault
Moroccanoil
sephora.com
$45.00
Winter is here, which means staying moisturized isn't an option anymore—it's a priority. Only bad friends allow their friends to be ashy. This argan oil-infused collection of the brand's bestsellers is the secret to staying hydrated.
F*king Fabulous Eau de Parfum & Lip Color Set
Tom Ford
nordstrom.com
$208.00
Does gifting your girlfriend Tom Ford need any explanation at all? It's Tom Ford. It's f***ing fabulous. Period.
Edge Styler
Baby Tress
babytress.com
$15.00
If you have a curly-haired friend, she'll thank you later for this edge slayer. Using soft boar bristles, this styler will lay and smooth down frizzy edges. They're lifesavers and you'll probably be tempted to keep it for yourself.
For more stories like this, including celebrity news, beauty and fashion advice, savvy political commentary, and fascinating features, sign up for the Marie Claire newsletter (subscribe here).


Using Open Data Sets with Kabanero
Service Integration Demo
Videos
Demo
U.N. Data
This application
GitHub repo
Kabanero Building Blocks
Kabanero on the Web
Additional Content
Kabanero World Tour
Introduction to Appsody
Customizing Appsody Stacks
Use Codewind with Eclipse and VS Code
Continuous delivery with Tekton Dashboards


Baseboard
Schema Definition
This is the schema definition for the Baseboard resource.Properties
| Property | Type | Nullable | Required | Permission | Description |
|---|---|---|---|---|---|
| @odata.context | String | read-only | The OData description of a payload. Format: uri | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| @odata.type | String | read-only | The type of a resource. | ||
| Actions { | Object | read-write | The available actions for this resource. | ||
| #Baseboard.Reset { | Object | read-write | This action is used to reset all systems hosted on this Baseboard. | ||
| target | String | read-write | Link to invoke action Format: uri | ||
| title | String | read-write | Friendly action name | ||
| } | |||||
| Oem { } | Object | read-write | |||
| } | |||||
| BaseboardType | String | Yes | read-only | The type of the Baseboard. Possible Values:
| |
| BladeSlot | Int64 | read-only | The one-based position of the blade slot containing this Baseboard within the Chassis. Minimum: 1 | ||
| Chassis { | Reference(Chassis) | read-only | The Chassis hosting this Baseboard | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| } | |||||
| ComputerSystems { | Reference(ComputerSystemCollection) | read-only | This is a link to a collection of ComputerSystems hosted on this Baseboard. | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| } | |||||
| Description | String | Yes | read-only | Provides a description of this resource and is used for commonality in the schema definitions. | |
| ExpansionBoardInserted | Boolean | read-only | Indicates, if an expansion board is inserted into this baseboard. | ||
| Id | String | GET | read-only | Uniquely identifies the resource within the collection of like resources. | |
| Links { | Object | read-only | The links object contains the links to other resources that are related to this resource. | ||
| Oem { } | Object | read-write | Oem extension object. | ||
| } | |||||
| Name | String | GET | read-only | The name of the resource or array element. | |
| Oem { } | Object | read-write | This is the manufacturer/provider specific extension moniker used to divide the Oem object into sections. | ||
| PCIeDevices { | Reference(PCIeDeviceCollection) | read-only | This is a link to a collection of PCIeDevices hosted on this Baseboard. | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| } | |||||
| Power { | Reference(Power) | read-only | A reference to the power properties (power supplies, power policies, sensors) of this baseboard. | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| } | |||||
| Status { | Object | Yes | read-write | This type describes the status and health of a resource and its children. | |
| Health | String | Yes | read-only | This represents the health state of this resource in the absence of its dependent resources. Possible Values:
| |
| HealthRollup | String | Yes | read-only | This represents the overall health state from the view of this resource. Possible Values:
| |
| Oem { } | Object | read-write | Oem extension object. | ||
| State | String | Yes | read-only | This indicates the known state of the resource, such as if it is enabled. Possible Values:
| |
| } | |||||
| Thermal { | Reference(Thermal) | read-only | A reference to the thermal properties (fans, cooling, sensors) of this baseboard. | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| } |
Actions
#Baseboard.Reset
Parameters
| Property | Type | Nullable | Required | Permission | Description |
|---|---|---|---|---|---|
| ResetType | String | Yes | read-write | The type of reset to be performed. Possible Values:
|
Request
POST /redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Actions/Baseboard.Reset
Content-Type: application/json
{
"ResetType": "On"
}
Content-Type: application/json
{
"ResetType": "On"
}
Response
HTTP/1.1 204
Location: http://{IP}:{PORT}/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1
Location: http://{IP}:{PORT}/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1
Operations
GET
Request
GET /redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1
Content-Type: application/json
Content-Type: application/json
Response
{
<EMAIL_ADDRESS>"uri",
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1",
<EMAIL_ADDRESS>"String",
"Actions": {
"#Baseboard.Reset": {
"target": "/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Actions/Baseboard.Reset",
<EMAIL_ADDRESS>["On", "ForceOff", "GracefulShutdown", "GracefulRestart", "ForceRestart", "ForceOn", "PushPowerButton"],
"title": "String"
}
"Oem": { }
}
"BaseboardType": "COMExpress",
"BladeSlot": 1,
"Chassis": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1"
},
"ComputerSystems": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/ComputerSystems"
},
"Description": "String",
"ExpansionBoardInserted": false,
"Id": "String",
"Links": {
"Oem": { }
}
"Name": "String",
"Oem": { },
"PCIeDevices": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/PCIeDevices"
},
"Power": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Power"
},
"Status": {
"Health": "OK",
"HealthRollup": "OK",
"Oem": { },
"State": "Enabled"
}
"Thermal": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Thermal"
}
}
<EMAIL_ADDRESS>"uri",
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1",
<EMAIL_ADDRESS>"String",
"Actions": {
"#Baseboard.Reset": {
"target": "/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Actions/Baseboard.Reset",
<EMAIL_ADDRESS>["On", "ForceOff", "GracefulShutdown", "GracefulRestart", "ForceRestart", "ForceOn", "PushPowerButton"],
"title": "String"
}
"Oem": { }
}
"BaseboardType": "COMExpress",
"BladeSlot": 1,
"Chassis": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1"
},
"ComputerSystems": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/ComputerSystems"
},
"Description": "String",
"ExpansionBoardInserted": false,
"Id": "String",
"Links": {
"Oem": { }
}
"Name": "String",
"Oem": { },
"PCIeDevices": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/PCIeDevices"
},
"Power": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Power"
},
"Status": {
"Health": "OK",
"HealthRollup": "OK",
"Oem": { },
"State": "Enabled"
}
"Thermal": {
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Baseboards/Baseboard1/Thermal"
}
}
POST
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed
PATCH
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed
DELETE
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed


{% extends 'assessment-rooted.html' %} {% load add_class %} {% load crispy_forms_tags %} {% block content %} {% crispy form %}
Loading this approach will delete all {{assessment.get_rob_name_display|lower}} information currently saved in this assessment. This includes all domains and metrics which currently exist, as well as any completed evaluations for studies already assessed.
Adapted from the US National Toxicology Program Risk of Bias Tool.
Adapted from the US EPA ORD Staff Handbook for Developing IRIS Assessments.


| Full Trust Approach | App Approach |
|---|---|
|
XSLT
XSLT is a language designed to transform structured data from one layout to another. In SharePoint this is often used to change the way a WebPart displays information from a list or a search result. |
Client Side Rendering
The way the field type is altered from a basic field type in the new model is through Client Side Rendering (CSR). Including a JSLink file and adding it to the view allows the developer to modify the view in any way they desire. |
| Impact of Full Trust Approach | Benefit of App Approach |
|
SharePoint 2013 has moved to Client Side Rendering
XSLT should not be used from SharePoint 2013 onwards as it is replaced by JSLink in CSR. |
Client Side Rendering Benefits
CSR moved processing from the server to the client further reducing server load when displaying a page, and making pages load faster in general. For developers, JSLink came from the need to simplify the way client side rendering was done, in order to reduce complication. |
Efforts and Benefits
| Migration Impact | Critical |
| Re-Design Effort | Low |
| Re-Development Effort | Medium |
| Long Term Benefit | Critical |
Effort Drivers


RT @tricycle_champ: [please create a password]
abc123
[your password must be at least eight characters]
atleasteightcharacters
3/16/2015, 7:36:52 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @tricycle_champ: [please create a password]
abc123
[your password must be at least eight characters]
atleasteightcharacters
3/16/2015, 7:36:52 PM
Favs: 0
Retweets: 0
link


# Episode 027
## Up and running with Docker Compose
ASP.NET Core: From 0 to overkill
## In the previous episode
- We containerized all the applications that make up the PlayBall application as a whole
## In this episode
- Use Docker Compose to get everything up and running with one command
- New `appsettings.json` files for the Docker environment
- Create a reverse proxy container to front the interactions with our applications
- Create a Docker Compose file, setting up our PlayBall application as a whole
## The usual plugs...
- If these videos are being useful
- Like
- Share
- Leave feedback
- More stuff at:
- Blog: [blog.codingmilitia.com](https://blog.codingmilitia.com)
- Twitter: [@joaofbantunes](https://twitter.com/joaofbantunes)


| 008:016 Khan | : |
And whoever turns his back to them on such a day - unless it be a stratagem of war, or to retreat to a troop (of his own), - he indeed has drawn upon himself wrath from Allah. And his abode is Hell, and worst indeed is that destination! |
| 008:016 Maulana | : |
And whoso turns his back to them on that day -- unless manoeuvring for battle turning to join a company -- he, indeed, incurs Allah's wrath and his refuge is hell. And an evil destination it is. |
| 008:016 Pickthal | : |
Whoso on that day turneth his back to them, unless manoeuvring for battle or intent to join a company, he truly hath incurred wrath from Allah, and his habitation will be hell, a hapless journey's end. |
| 008:016 Rashad | : |
Anyone who turns back on that day, except to carry out a battle plan, or to join his group, has incurred wrath from GOD, and his abode is Hell; what a miserable destiny! |
| 008:016 Sarwar | : |
(Whoever deserts the believers) will incur the wrath of God and will dwell in hell, a terrible dwelling. |
| 008:016 Shakir | : |
And whoever shall turn his back to them on that day-- unless he turn aside for the sake of fighting or withdraws to a company-- then he, indeed, becomes deserving of Allah's wrath, and his abode is hell; and an evil destination shall it be. |
| 008:016 Sherali | : |
And whoso turns his back to them on such a day, unless manoeuvring for battle or turning to join another company, he indeed draws upon himself the wrath of ALLAH, and Hell shall be his abode. And an evil resort it is. |
| 008:016 Yusufali | : |
If any do turn his back to them on such a day - unless it be in a stratagem of war, or to retreat to a troop (of his own)- he draws on himself the wrath of Allah, and his abode is Hell,- an evil refuge (indeed)! |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > غضبه Pillars of Islam> Islamic> Oneness of Allah> His Anger الإيمان > الغيب > النار > أسماء النار > بئس المصير Faith> The Unseen World> Hell-Fire>Hell Names> Bad End الإيمان > الغيب > النار > أصحاب النار Faith> The Unseen World> Hell-Fire>Hell Fellows الجهاد > (تعليمات حربية (أحكام خاصة > (تعليمات حربية (أحكام خاصة > نظام الجهاد وقانونه Jihad> Warlike Instructions\Special Rules> Warlike Instructions\Special Rules> The System of the Holy War and its Law الجهاد > الجهاد في سبيل الإسلام > الدعوة إلى الجهاد Jihad> The Call to Jihad>The Call for the Holy War الجهاد > الجهاد في سبيل الإسلام > مدح الجهاد Jihad> The Call to Jihad>Praising the Holy War الجهاد > الغزوات > غزوة بدر Jihad> The Holy Battles> Bader Battle الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > الجبن Man and The Moral Relations>The Dispraised Morals>Cowardness
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


What are the class sizes of the clubs?
Class sizes for after-school coding clubs at our hack space are limited to six students per class. This ensures that every student gets the help they need regardless of ability. At school coding clubs we teach a maximum of 10 pupils per class to ensure that every student gets an opportuity to join the weekly clubs. To reflect the larger class sizes at schools prices are often reduced.
I can't afford the classes - is there help available?
Scholarships are available on all our school coding clubs and places on our weekly clubs are available at a discounted rate for parents who are on income support and who's children receive pupil premium payments at school. Please get in touch for more details.
Can my children keep what they make?
Anything your children 3D print or build they are welcome to take home - however some additional costs may apply for projects containing microcontrollers such as the MakeyMakey, Crumble, and Arduino. We will let you know before the club begins the additional cost to take projects home (if there is one).
Are my children assessed?
At Rocket Coders we don't believe that digital creativity can be measured through an examination - we look to see how coders use their imagination, logical and critical thinking skills to produce projects and takle problems. Following camps and clubs we'll give you a brief assesment form that will outline in what areas your child triumphed and where they may needa helping hand in the future.
Do I need to bring a laptop?
We ask that all children come with a working laptop for the classes with Google Chrome or FireFox installed. We are hoping to purchase laptops in the future but currently we only have a few available for hire. To check availability please contact the team before you attend your club.
Can my children bring snacks?
We provide healthy organic snacks at all our coding clubs but feel free to send your children with something extra. We ask that you don't send your children with anything containing nuts please.
Are you insured and DBS checked?
Yes we are fully insured and everyone in the building has a valid DBS certificate. All our staff are also qualified in first aid. For more information please see our child safeguarding policy here.
Can I see my childrens work?
We will upload anonymous projects to the Rocket Coders website and may show off student projects on our social media pages (with permission). For projects that can't be shared online we can email you images and videos at your request.
Can you start a club at my child's school?
We'd love to! Email us today to find out more about our parent ambassador program where you can receive free classes for your children in return for an introduction to their school.


| DOSE: |
2 flowers |
oral |
Datura |
(tea) |
| |
2 leaves |
oral |
Datura |
(tea) |
T - 0:00 - Consumed tea consisting of 2 flowers and 2 leaves (Datura inoxa and Datura metel).
T - 0:10 - Feel anxious.
T - 0:20 - Feel slightly stoned. Similar to small dose of cannabis.
T - 0:25 - Quite dizzy and tired. Focusing is difficult. Movements are exaggerated. A 5 degree rotation of my head feels like 45 degrees. The cannabis like buzz is transforming into something unfamiliar.
T - 0:30 - Mild sedation with an intense drawn out rush. Struggling to keep eyes open. Feel peaceful enough to just be.
T - 0:40 - Sat in meditation for 10 minutes. Reality doesnít feel right.
T - 0:42 - Balance slightly off. Floating and sinking at the same time.
T - 0:45 - Mouth very dry. Strong heartbeat.
T - 0:55 - Body feels extremely drunk. No euphoria or disassociation.
T - 1:00 - Eyes bloodshot and pupils dilated.
T - 1:05 - Desperately need to lay down.
T- 1:08 - Get in bed. I get up every 30 minutes to use the toilet.
T - 3:00 - Iím walking in the countryside with my dog. I try to call her name but canĎt speak. Within a split second Iím home watching television with the remote control in my hand. I keep trying to change channel but canít focus on the buttons.
T- 3:01 - Remote control vanishes and I realise Iím sat up in bed arm outstretched pressing my empty palm. Then I realize Iím not even watching television. I donĎt have a TV in my room.
T- 3:15 - I must have been sleeping for a while. My dreams are more real than reality.
T- 3:16 - Eyes shaking from side to side. Feel too ill to relax. Exhausted but canít keep still. I wander from bedroom to bathroom 7 or 8 times then sit on the edge of my bed.
T- 3:17- Mouth ridiculously dry. I have water but swallowing is too painful.
T- 3:18 - A girl is standing in my doorway waving at me. A small child appears next to her. I approach to see if they are real but they disappear when I get close.
T- 3:19 - I step back and the girls reappear. I notice a snake behind my stereo, worms in my bed and spiders on my door. The floor is covered with food. I reach for some but it disappears. My dog multiplies. I can see 3 of her.
T- 3:20- Turn lights on to make some notes. All the hallucinations disappear when the lights come on but everything now has rainbow trails like HPPD.
T- 6:00 - Iíve been watching people gather in my hallway for 3 hours.
T-6:15- Fall asleep (3:30am). Very lucid dreaming. This was the most enjoyable part of the experience because the unpleasant physical effects were left behind. The dreaming state was clearer than the waking state. I occasionally wake up next to a porcelain head then realize itís actually my pillow.
T- 13:15- Panic attack (10:30am). I calm down when I realize where I am.
T- 17:00- Motor control back to normal.
T- 21:00- Vision back to normal. Eyes no longer dilated. Overall an interesting but rather unpleasant experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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{% endblock sidebar_active %} {% block current_active %}For analyzing Istio performance, please refer to Analyzing Istio Performance Wiki Page.
Please go to Github istio/istio issues section to file an Istio Performance Issue.