| Package | flare.vis |
| Class | public class Visualization |
| Inheritance | Visualization flash.display.Sprite |
Data
instance containing DataSprite
objects that visually represent individual data elementsOperatorList
of visualization operators that
determine visual encodings for position, color, size and other
properties.ControlList
of interactive controls that enable
interaction with the visualized data.Axes
instance for presenting axes for metric
data visualizations. Axes are often configuring automatically by
the visualization's operators.Visual objects are added to the display list within the
marks
property of the visualization, as the
Data
object is not a DisplayObjectContainer
.
All visual elements are contained within layers
Sprite.
This includes the axes
, marks
, and
(optionally) labels
layers. Clients who wish to add
additional layers to a visualization should add them directly to the
layers
sprite. Just take care to maintain the desired order
of elements to avoid occlusion.
To create a new Visualization, load in a data set, construct
a Data
instance, and instantiate a new
Visualization
with the input data. Then add the series
of desired operators to the operators
property to
define the visual encodings.
See also
| Property | Defined by | ||
|---|---|---|---|
| axes : Axes
The axes for this visualization.
| Visualization | ||
| bounds : Rectangle The layout bounds of the visualization.
| Visualization | ||
| continuousUpdates : Boolean Flag indicating if the visualization should update with every
frame.
| Visualization | ||
| controls : ControlList
[read-only] The control list containing interactive controls.
| Visualization | ||
| data : Data
The visual data elements in this visualization.
| Visualization | ||
| labels : Sprite Sprite containing a separate layer for labels.
| Visualization | ||
| layers : Sprite [read-only] Container sprite holding each layer in the visualization.
| Visualization | ||
| marks : Sprite [read-only] Sprite containing the
DataSprite instances. | Visualization | ||
| operators : OperatorList
[read-only] The operator list for defining the visual encodings.
| Visualization | ||
| props : Object An object storing extra properties for the visualziation.
| Visualization | ||
| tree : Tree
[read-only] Tree structure of visual data elements in this visualization.
| Visualization | ||
| xyAxes : CartesianAxes
[read-only] The axes as an x-y
CartesianAxes instance. | Visualization |
| Method | Defined by | ||
|---|---|---|---|
|
Creates a new Visualization with the given data and axes.
| Visualization | ||
|
Retrieves the operator with the given name.
| Visualization | ||
|
removeOperator(name:String):IOperator
Removes a named operator.
| Visualization | ||
|
setAspectRatio(ar:Number, width:Number = -1, height:Number = -1):void
Updates the data display bounds for a visualization based on a
given aspect ratio and provided width and height values.
| Visualization | ||
|
setHitArea(evt:Event = null):void
Creates a sprite covering the bounds for this visualization and
sets it to be this visualization's hit area.
| Visualization | ||
|
Sets a new named operator.
| Visualization | ||
|
update(t:* = null, ... operators):Transitioner
Update this visualization, re-calculating axis layout and running
the operator chain.
| Visualization | ||
|
updateLater(... operators):Function
A function generator that can be used to invoke a visualization
update at a later time.
| Visualization |
| Method | Defined by | ||
|---|---|---|---|
|
Data listener invoked when new items are added to this
Visualization's
data instance. | Visualization | ||
|
dataRemoved(evt:DataEvent):void
Data listener invoked when new items are removed from this
Visualization's
data instance. | Visualization | ||
|
fireEvent(type:String, t:Transitioner, params:Array):void
Fires a visualization event of the given type.
| Visualization |
| Event | Summary | Defined by | ||
|---|---|---|---|---|
| Visualization |
| axes | property |
axes:Axes
[read-write]The axes for this visualization. May be null if no axes are needed.
Implementation public function get axes():Axes
public function set axes(value:Axes):void
| bounds | property |
bounds:Rectangle
[read-write]The layout bounds of the visualization. This determines the layout region for data elements. For example, with an axis layout, the bounds determined the data layout region--this does not include space used by axis labels.
Implementation public function get bounds():Rectangle
public function set bounds(value:Rectangle):void
| continuousUpdates | property |
continuousUpdates:Boolean
[read-write]Flag indicating if the visualization should update with every frame. False by default.
Implementation public function get continuousUpdates():Boolean
public function set continuousUpdates(value:Boolean):void
| controls | property |
controls:ControlList
[read-only]The control list containing interactive controls.
Implementation public function get controls():ControlList
| data | property |
data:Data
[read-write]The visual data elements in this visualization.
Implementation public function get data():Data
public function set data(value:Data):void
| labels | property |
labels:Sprite
[read-write]Sprite containing a separate layer for labels. Null by default.
Implementation public function get labels():Sprite
public function set labels(value:Sprite):void
| layers | property |
layers:Sprite
[read-only]Container sprite holding each layer in the visualization.
Implementation public function get layers():Sprite
| marks | property |
marks:Sprite
[read-only] Sprite containing the DataSprite
instances.
public function get marks():Sprite
| operators | property |
operators:OperatorList
[read-only]The operator list for defining the visual encodings.
Implementation public function get operators():OperatorList
| props | property |
public var props:Object
An object storing extra properties for the visualziation.
| tree | property |
tree:Tree
[read-only]Tree structure of visual data elements in this visualization. Generates a spanning tree over a graph structure, if necessary.
Implementation public function get tree():Tree
| xyAxes | property |
xyAxes:CartesianAxes
[read-only] The axes as an x-y CartesianAxes
instance. Returns
null if axes
is null or not a cartesian axes instance.
public function get xyAxes():CartesianAxes
| Visualization | () | constructor |
public function Visualization(data:Data = null, axes:Axes = null)
Creates a new Visualization with the given data and axes.
Parametersdata:Data (default = null ) — the Data instance containing the
DataSprite elements in this visualization.
|
|
axes:Axes (default = null ) — the Axes to use with this visualization.
Null by default; layout operators may re-configure the axes.
|
| dataAdded | () | method |
protected function dataAdded(evt:DataEvent):void
Data listener invoked when new items are added to this
Visualization's data
instance.
evt:DataEvent — the data event
|
| dataRemoved | () | method |
protected function dataRemoved(evt:DataEvent):void
Data listener invoked when new items are removed from this
Visualization's data
instance.
evt:DataEvent — the data event
|
| fireEvent | () | method |
protected function fireEvent(type:String, t:Transitioner, params:Array):void
Fires a visualization event of the given type.
Parameterstype:String — the type of the event
|
|
t:Transitioner — a transitioner that listeners should use for any value
updates performed in response to this event
|
|
params:Array |
| operator | () | method |
public function operator(name:String):IOperator
Retrieves the operator with the given name. The name "main" will
return the operator list stored in the operators
property.
name:String — the name of the operator
|
IOperator —
the operator
|
| removeOperator | () | method |
public function removeOperator(name:String):IOperator
Removes a named operator. An error will be thrown if the caller attempts to remove the operator "main".
Parametersname:String — the name of the operator to remove
|
IOperator —
the removed operator
|
| setAspectRatio | () | method |
public function setAspectRatio(ar:Number, width:Number = -1, height:Number = -1):void
Updates the data display bounds for a visualization based on a
given aspect ratio and provided width and height values. If both
width and height values are provided, they will be treated as the
maximum bounds. If only one of the width or height is provided, then
the width or height will match that value, and the other will be
determined by the aspect ratio. Finally, if neither width nor height
is provided, then the current width and height of the display bounds
will be used as the maximum bounds. After calling this method, a
call to update
is necessary to reflect the change.
ar:Number — the desired aspect ratio for the data display
|
|
width:Number (default = -1 ) — the desired width. If a height value is also provided,
this width value will be treated as the maximum possible width
(the actual width may be lower).
|
|
height:Number (default = -1 ) — the desired height. If a width value is also provided,
this height value will be treated as the maximum possible height
(the actual height may be lower).
|
| setHitArea | () | method |
public function setHitArea(evt:Event = null):void
Creates a sprite covering the bounds for this visualization and
sets it to be this visualization's hit area. Typically, this
method is triggered in response to a RENDER
event.
To disable automatic hit area calculation, use
stage.removeEventListener(Event.RENDER, vis.setHitArea)
after the visualization has been added to the stage.
evt:Event (default = null ) — an event that triggered the hit area update
|
| setOperator | () | method |
public function setOperator(name:String, op:IOperator):IOperator
Sets a new named operator. This method can be used to add extra
operators to a visualization, in addition to those in the main
operators
property. These operators can be invoked by
passing the operator name as an additional parameter of the
update
method. If an operator of the same name
already exists, it will be replaced. Note that the name "main"
refers to the same operator list as the operators
property and can not be replaced.
name:String — the name of the operator to add
|
|
op:IOperator — the operator to add
|
IOperator —
the added operator
|
| update | () | method |
public function update(t:* = null, ... operators):Transitioner
Update this visualization, re-calculating axis layout and running
the operator chain. The input transitioner is used to actually
perform value updates, enabling animated transitions. This method
also issues a VisualizationEvent.UPDATE
event to any
registered listeners.
t:* (default = null ) — a transitioner or time span for updating object values. If
the input is a transitioner, it will be used to store the updated
values. If the input is a number, a new Transitioner with duration
set to the input value will be used. The input is null by default,
in which case object values are updated immediately.
|
|
... operators — an optional list of named operators to run in the
update.
|
Transitioner —
the transitioner used to store updated values.
|
| updateLater | () | method |
public function updateLater(... operators):Function
A function generator that can be used to invoke a visualization
update at a later time. This method returns a function that
accepts a Transitioner
as its sole argument and then
executes a visualization update using the specified named
operators.
... operators — an optional array of named operators to run
|
Function — a function that takes a Transitioner argument
and invokes an update.
|
| update | event |


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@Repeatable(value=JCompositeIndexes.class) @Retention(value=RUNTIME) @Target(value={ANNOTATION_TYPE,TYPE}) @Documented public @interface JCompositeIndex
PermazenType
-annotated sub-types.
A composite index is an index on two or more fields (to define a single-field index,
just set JField.indexed()
to true). All fields indexed in a composite index
must be (a) simple and (b) not a sub-field of a complex field.
@JCompositeIndex
is a repeatable annotation. Instances
may annotate any @PermazenType
-annotated class or supertype thereof. Put another way,
instances of a @PermazenType
-annotated class that is a subtype of a
@JCompositeIndex
-annotated type will be included in the corresponding composite index.
Uniqueness Constraints
Uniqueness constraints are supported, and their enforcement is handled in the same way as for uniqueness constraints on simply indexed fields (see@JField.unique()
). A uniqueness constriant on a composite
index means each object's combination of field values must be unique. Specific field value combinations may be
excluded from the uniqueness constraint by specifying the corresponding comma-separated String
encoded tuples
in uniqueExclude()
.PermazenType
,
JField.indexed()
| Modifier and Type | Required Element and Description |
|---|---|
String[] |
fields
The names of the indexed fields, in the desired order.
|
String |
name
The name of this composite index.
|
| Modifier and Type | Optional Element and Description |
|---|---|
int |
storageId
The storage ID for this composite index.
|
boolean |
unique
Require each object's field combination to be unique among all types to which this annotation applies.
|
String[] |
uniqueExclude
Specify field value combination(s) to be excluded from the uniqueness constraint.
|
public abstract String name
public abstract String[] fields
public abstract int storageId
StorageIdGenerator
will be consulted to auto-generate a value.StorageIdGenerator.generateCompositeIndexStorageId()
public abstract boolean unique
This property creates an implicit uniqueness validation constraint.
The constraint will be checked any time normal validation is performed on an object.
 Therefore, uniqueness constraints
are included in default validation, but you can also validate only uniqueness constraints via
myobj.revalidate(UniquenessConstraints.class)
.
uniqueExclude()
,
UniquenessConstraints
public abstract String[] uniqueExclude
The specified values must String
s containing comma-separated encodings of the associated fields.
Unlike the field values used with @JField.uniqueExclude()
, the field values
contained here are the self-delimiting string forms.
For some field types this makes a difference: e.g., String
values must be surrounded by double quotes.
For example:
@PermazenType(compositeIndexes = @JCompositeIndex(name = "firstAndLast", fields = { "lastName", firstName" }, unique = true, uniqueExclude = "\"Unknown\", \"Unknown\"")) public abstract class Person { public abstract String getLastName(); public abstract void setLastName(String x); public abstract String getFirstName(); public abstract void setFirstName(String x); }
To specify a null value in the list, specify the self-delimiting string form of the null value (this is almost always the string "null"
):
@PermazenType(compositeIndexes = @JCompositeIndex(name = "firstAndLast", fields = { "lastName", firstName" }, unique = true, uniqueExclude = "null, null")) public abstract class Person { public abstract String getLastName(); public abstract void setLastName(String x); public abstract String getFirstName(); public abstract void setFirstName(String x); }
This property must be left empty when unique()
is false.
unique()
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IBC for NBFCs a credit positive for Indian banks: Moody’s
Enables orderly resolution: Moody’s
by Special CorrespondentThe government’s recent move to amend the Insolvency and Bankruptcy Code to enable resolution of non-banking finance companies (NBFCs) is a credit positive for Indian banks, rating agency Moody’s said on Monday.
“Inclusion of the NBFIs [non-banking finance institutions] in the country’s bankruptcy code is credit positive for India’s banks that are NBFIs’ largest lenders because the IBC rule provides for the orderly resolution of a stressed company. Until now, the only resolution framework for NBFIs was through liquidation,” Moody’s said.
Following the amendment, RBI said bankruptcy proceedings would be initiated for Dewan Housing Finance Corporation Ltd. Moody’s noted that since July 2019, banks and other creditors to Dewan had been unsuccessful in trying to restructure its liabilities.
“We expect the RBI to selectively approach the IBC to resolve NBFIs with severe liquidity or solvency issues, or to resolve companies whose weak corporate governance is deterring potential buyers,” it added.


Welcome to PyTplot’s documentation!¶
Introduction¶
Pytplot is a python package which aims to mimic the functionality of the IDL “tplot” libraries. The primary routine (tplot) generates HTML files for the specified plots, and automatically opens the files in a Qt interface.
These plots have several user interaction tools built in, such as zooming and panning. The can be exported as standalone HTML files (to retain their interactivity) or as static PNG files.
Pytplot can be used in python scripts, or interactively through IPython and the Jupyter notebook.
How It Works¶
Data is read into pytplot by using the “store_data” command. Each dataset is assigned a unique name by the user.
The data is stored in a “tplot variable” class. The tplot variables contain all the information required to create a plot of the dataset. The details of the plot, such as axis titles, types, line colors, etc, can be changed through other functions in pytplot.
When you are ready to create a graph of your dataset(s), supply the dataset names you wish to plot to the “tplot” function, and a graph will be generated.
Install Python¶
You will need the Anaconda distribution of Python 3 in order to run pytplot.
Anaconda comes with a suite of packages that are useful for data science.
Running PyTplot¶
To start using pytplot in a similar manner to IDL tplot, start up an interactive environment through the terminal command:
ipython
or, if you prefer the jupyter interactive notebook:
jupyter notebook
then, just import the package by typing the command:
import pytplot
Storing Data in Memory¶
store_data¶
-
store_data
(name, data=None, delete=False)¶ This function creates a “Tplot Variable” based on the inputs, and stores this data in memory. Tplot Variables store all of the information needed to generate a plot.
- Parameters:
- name : str
- Name of the tplot variable that will be created
- data : dict
A python dictionary object.
‘x’ should be a 1-dimensional array that represents the data’s x axis. Typically this data is time, represented in seconds since epoch (January 1st 1970)
‘y’ should be the data values. This can be 2 dimensions if multiple lines or a spectrogram are desired.
‘v’ is optional, and is only used for spectrogram plots. This will be a list of bins to be used. If this is provided, then ‘y’ should have dimensions of x by z.
‘x’ and ‘y’ can be any data format that can be read in by the pandas module. Python lists, numpy arrays, or any pandas data type will all work.
- delete : bool, optional
- Deletes the tplot variable matching the “name” parameter
Note
If you want to combine multiple tplot variables into one, simply supply the list of tplot variables to the “data” parameter. This will cause the data to overlay when plotted.
- Returns:
- None
- Examples:
>>> # Store a single line >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data})
>>> # Store a two lines >>> x_data = [1,2,3,4,5] >>> y_data = [[1,5],[2,4],[3,3],[4,2],[5,1]] >>> pytplot.store_data("Variable2", data={'x':x_data, 'y':y_data})
>>> # Store a specrogram >>> x_data = [1,2,3] >>> y_data = [ [1,2,3] , [4,5,6], [7,8,9] ] >>> v_data = [1,2,3] >>> pytplot.store_data("Variable3", data={'x':x_data, 'y':y_data, 'v':v_data})
>>> # Combine two different line plots >>> pytplot.store_data("Variable1and2", data=['Variable1', 'Variable2'])
tplot_rename¶
-
tplot_rename
(old_name, new_name)¶ This function will rename tplot variables that are already stored in memory.
- Parameters:
- old_name : str
- Old name of the Tplot Variable
- new_name : str
- New name of the Tplot Variable
- Returns:
- None
- Examples:
>>> # Rename Variable 1 to Variable 2 >>> import pytplot >>> pytplot.tplot_rename("Variable1", "Variable2")
Retrieveing Data¶
get_data¶
-
get_data
(name)¶ This function will get extract the data from the Tplot Variables stored in memory.
- Parameters:
- name : str
- Name of the tplot variable
- Returns:
- time_val : pandas dataframe index data_val : list
- Examples:
>>> # Retrieve the data from Variable 1 >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> time, data = pytplot.get_data("Variable1")
get_timespan¶
-
get_timespan
(name)¶ This function will get extract the time span from the Tplot Variables stored in memory.
- Parameters:
- name : str
- Name of the tplot variable
- Returns:
- time_begin : float
- The beginning of the time series
- time_end : float
- The end of the time series
- Examples:
>>> # Retrieve the time span from Variable 1 >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> time1, time2 = pytplot.get_timespan("Variable1")
get_ylimits¶
-
get_ylimits
(name, trg=None)¶ This function will get extract the y limites from the Tplot Variables stored in memory.
- Parameters:
- name : str
- Name of the tplot variable
- trg : list, optional
- The time range that you would like to look in
- Returns:
- ymin : float
- The minimum value of y
- ymax : float
- The maximum value of y
- Examples:
>>> # Retrieve the y limits from Variable 1 >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> y1, y2 = pytplot.get_ylimits("Variable1")
tplot_names¶
-
tplot_names
()¶ This function will print out and return a list of all current Tplot Variables stored in the memory.
- Parameters:
- None
- Returns:
- list : list of str
- A list of all Tplot Variables stored in the memory
- Examples:
>>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> tnames = pyplot.tplot_names() 0 : Variable 1
Setting Plot Options¶
options¶
-
options
(name, option, value)¶ This function allows the user to set a large variety of options for individual plots.
- Parameters:
- name : str
- Name of the tplot variable
- option : str
- The name of the option. See section below
- value : str/int/float/list
- The value of the option. See section below.
- Options:
Options Value type Notes Color str/list Red, Orange, Yellow, Green, Blue, etc Colormap str/list https://matplotlib.org/examples/color/colormaps_reference.html Spec int 1 sets the Tplot Variable to spectrogram mode, 0 reverts Alt int 1 sets the Tplot Variable to altitude plot mode, 0 reverts Map int 1 sets the Tplot Variable to latitude/longitude mode, 0 reverts ylog int 1 sets the y axis to log scale, 0 reverts zlog int 1 sets the z axis to log scale, 0 reverts (spectrograms only) legend_names list A list of strings that will be used to identify the lines line_style str solid_line, dot, dash, dash_dot, dash_dot_dot_dot, long_dash name str The title of the plot panel_size flt Number between (0,1], representing the percent size of the plot basemap str Full path and name of a background image for “Map” plots alpha flt Number between [0,1], gives the transparancy of the plot lines yrange flt list Two numbers that give the y axis range of the plot zrange flt list Two numbers that give the z axis range of the plot ytitle str Title shown on the y axis ztitle str Title shown on the z axis. Spec plots only. - Returns:
- None
- Examples:
>>> # Change the y range of Variable1 >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> pytplot.options('Variable1', 'yrange', [2,4])
>>> # Change Variable1 to use a log scale >>> pytplot.options('Variable1', 'ylog', 1)
>>> # Change the line color of Variable1 >>> pytplot.options('Variable1', 'ylog', 1)
tplot_options¶
-
tplot_options
(option, value)¶ This function allows the user to set several global options for the generated plots.
- Parameters:
- option : str
- The name of the option. See section below
- value : str/int/float/list
- The value of the option. See section below.
- Options:
Options Value type Notes title str Title of the the entire output title_size int Font size of the output wsize [int, int] [height, width], pixel size of the plot window title_align int Offset position in pixels of the title var_label srt Name of the tplot variable to be used as another x axis alt_range [flt, flt] The min and max altitude to be plotted on all alt plots - Returns:
- None
- Examples:
>>> # Set the plot title >>> import pytplot >>> pytplot.tplot_options('title', 'SWEA Data for Orbit 1563')
>>> # Set the window size >>> pytplot.tplot_options('wsize', [1000,500])
timebar¶
-
timebar
(t, varname=None, databar=False, delete=False, color='black', thick=1, dash=False)¶ This function will add a vertical bar to all time series plots. This is useful if you want to bring attention to a specific time.
- Parameters:
- t : flt/list
- The time in seconds since Jan 01 1970 to place the vertical bar. If a list of numbers are supplied, multiple bars will be created. If “databar” is set, then “t” becomes the point on the y axis to place a horizontal bar.
- varname : str/list, optional
- The variable(s) to add the vertical bar to. If not set, the default is to add it to all current plots.
- databar : bool, optional
- This will turn the timebar into a horizontal data bar. If this is set True, then variable “t” becomes the point on the y axis to place a horizontal bar.
- delete : bool, optional
- If set to True, at lease one varname must be supplied. The timebar at point “t” for variable “varname” will be removed.
- color : str
- The color of the bar
- thick : int
- The thickness of the bar
- dash : bool
- If set to True, the bar is dashed rather than solid
- Returns:
- None
- Examples:
>>> # Place a green time bar at 2017-07-17 00:00:00 >>> import pytplot >>> pytplot.timebar(1500249600, color='grean)
>>> # Place a dashed data bar at 5500 on the y axis >>> pytplot.timebar(5500, dashed=True, databar=True)
timespan¶
-
timespan
(t1, dt, keyword='days')¶ This function will set the time range for all time series plots. This is a wrapper for the function “xlim” to better handle time axes.
- Parameters:
- t1 : flt/str
- The time to start all time series plots. Can be given in seconds since epoch, or as a string in the format “YYYY-MM-DD HH:MM:SS”
- dt : flt
- The time duration of the plots. Default is number of days.
- keyword : str
- Sets the units of the “dt” variable. Days, hours, minutes, and seconds are all accepted.
- Returns:
- None
- Examples:
>>> # Set the timespan to be 2017-07-17 00:00:00 plus 1 day >>> import pytplot >>> pytplot.timespan(1500249600, 1)
>>> # The same as above, but using different inputs >>> pytplot.timespan("2017-07-17 00:00:00", 24, keyword='hours')
timestamp¶
-
timestamp
(val)¶ This function will turn on a time stamp that shows up at the bottom of every generated plot.
- Parameters:
- val : str
- A string that can either be ‘on’ or ‘off’.
- Returns:
- None
- Examples:
>>> # Turn on the timestamp >>> import pytplot >>> pytplot.timestamp('on')
xlim¶
-
xlim
(min, max)¶ This function will set the x axis range for all time series plots
- Parameters:
- min : flt
- The time to start all time series plots. Can be given in seconds since epoch, or as a string in the format “YYYY-MM-DD HH:MM:SS”
- max : flt
- The time to end all time series plots. Can be given in seconds since epoch, or as a string in the format “YYYY-MM-DD HH:MM:SS”
- Returns:
- None
- Examples:
>>> # Set the timespan to be 2017-07-17 00:00:00 plus 1 day >>> import pytplot >>> pytplot.xlim(1500249600,<PHONE_NUMBER> + 86400)
>>> # The same as above, but using different inputs >>> pytplot.xlim("2017-07-17 00:00:00", "2017-07-18 00:00:00")
ylim¶
-
ylim
(name, min, max)¶ This function will set the y axis range displayed for a specific tplot variable.
- Parameters:
- name : str
- The name of the tplot variable that you wish to set y limits for.
- min : flt
- The start of the y axis.
- max : flt
- The end of the y axis.
- Returns:
- None
- Examples:
>>> # Change the y range of Variable1 >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> pytplot.ylim('Variable1', 2, 4)
zlim¶
-
zlim
(name, min, max)¶ This function will set the z axis range displayed for a specific tplot variable. This is only used for spec plots, where the z axis represents the magnitude of the values in each bin.
- Parameters:
- name : str
- The name of the tplot variable that you wish to set z limits for.
- min : flt
- The start of the z axis.
- max : flt
- The end of the z axis.
- Returns:
- None
- Examples:
>>> # Change the z range of Variable1 >>> import pytplot >>> x_data = [1,2,3] >>> y_data = [ [1,2,3] , [4,5,6], [7,8,9] ] >>> v_data = [1,2,3] >>> pytplot.store_data("Variable3", data={'x':x_data, 'y':y_data, 'v':v_data}) >>> pytplot.zlim('Variable1', 2, 3)
Plotting Data¶
tplot¶
-
tplot
(name, var_label=None, auto_color=True, interactive=False, combine_axes=True, nb=False, save_file=None, gui=False, qt=True)¶ This is the function used to display the tplot variables stored in memory. The default output is to show the plots stacked on top of one another inside a GUI window. The GUI window has the option to export the plots in either PNG or HTML formats.
Note
This plotting routine uses the python Bokeh library, which creates plots using HTML and Javascript. Bokeh is technically still in beta, so future patches to Bokeh may require updates to this function.
- Parameters:
- name : str / list
- List of tplot variables that will be plotted
- var_label : str, optional
- The name of the tplot variable you would like as a second x axis.
- auto_color : bool, optional
- Automatically color the plot lines.
- interactive : bool, optional
- If True, a secondary interactive plot will be generated next to spectrogram plots. Mousing over the spectrogram will display a slice of data from that time on the interactive chart.
- combine_axis : bool, optional
- If True, the axes are combined so that they all display the same x range. This also enables scrolling/zooming/panning on one plot to affect all of the other plots simultaneously.
- nb : bool, optional
- If True, the plot will be displayed inside of a current Jupyter notebook session.
- save_file : str, optional
- A full file name and path. If this option is set, the plot will be automatically saved to the file name provided in an HTML format. The plots can then be opened and viewed on any browser without any requirements.
- gui : bool, optional
- If True, then this function will output the 2 HTML components of the generated plots as string variables. This is useful if you are embedded the plots in your own GUI. For more information, see http://bokeh.pydata.org/en/latest/docs/user_guide/embed.html
- qt : bool, optional
- If True, then this function will display the plot inside of the Qt window. From this window, you can choose to export the plots as either an HTML file, or as a PNG.
- Returns:
- None
- Examples:
>>> #Plot a single line >>> import pytplot >>> x_data = [2,3,4,5,6] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> pytplot.tplot("Variable1")
>>> #Display two plots >>> x_data = [1,2,3,4,5] >>> y_data = [[1,5],[2,4],[3,3],[4,2],[5,1]] >>> pytplot.store_data("Variable2", data={'x':x_data, 'y':y_data}) >>> pytplot.tplot(["Variable1", "Variable2"])
>>> #Display 2 plots, using Variable1 as another x axis >>> x_data = [1,2,3] >>> y_data = [ [1,2,3] , [4,5,6], [7,8,9] ] >>> v_data = [1,2,3] >>> pytplot.store_data("Variable3", data={'x':x_data, 'y':y_data, 'v':v_data}) >>> pytplot.options("Variable3", 'spec', 1) >>> pytplot.tplot(["Variable2", "Variable3"], var_label='Variable1')
>>> #Plot all 3 tplot variables, sending the output to an HTML file >>> pytplot.tplot(["Variable1", "Variable2", "Variable3"], save_file='C:/temp/pytplot_example.html')
>>> #Plot all 3 tplot variables, sending the HTML output to a pair of strings >>> div, component = pytplot.tplot(["Variable1", "Variable2", "Variable3"], gui=True)
Saving and Restoring Sessions¶
tplot_save¶
-
tplot_save
(names, filename=None)¶ This function will save tplot variables into a single file by using the python “pickle” function. This file can then be “restored” using tplot_restore. This is useful if you want to end the pytplot session, but save all of your data/options. All variables and plot options can be read back into tplot with the “tplot_restore” command.
- Parameters:
- names : str/list
- A string or a list of strings of the tplot variables you would like saved.
- filename : str, optional
- The filename where you want to save the file.
- Returns:
- None
- Examples:
>>> # Save a single tplot variable >>> import pytplot >>> x_data = [1,2,3,4,5] >>> y_data = [1,2,3,4,5] >>> pytplot.store_data("Variable1", data={'x':x_data, 'y':y_data}) >>> pytplot.ylim('Variable1', 2, 4) >>> pytplot.save('Variable1', filename='C:/temp/variable1.pytplot')
tplot_restore¶
-
tplot_restore
(filename)¶ This function will restore tplot variables that have been saved with the “tplot_save” command.
Note
This function is compatible with the IDL tplot_save routine. If you have a ”.tplot” file generated from IDL, this procedure will restore the data contained in the file. Not all plot options will transfer over at this time.
- Parameters:
- filename : str
- The file name and full path generated by the “tplot_save” command.
- Returns:
- None
- Examples:
>>> # Restore the saved data from the tplot_save example >>> import pytplot >>> pytplot.restore('C:/temp/variable1.pytplot')


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Four Consecutive Pucks is interesting strategy game for kids and
adults. It has an educational value as it inspires the players
to strategize and plan ahead. It improves the challenger minds
and exercises their brain. It has different synonym names such
as Connect Four, Captain's Mistress, Four Up, Plot Four, Find
Four, Fourplay, Four in a Row and Four in a Line. It is a game
for two players at the age range 7 and up.
The game consist of
a grid of seven columns and six rows.
Each player has his won color to identify his discs. The main idea of the game is to make four consecutive discs even vertical, horizontal or diagonal.


While Select2 is designed to be used with a <select>
tag, the data that is used to search through and display the results can be loaded from a JavaScript array using the data
option. This option should be passed in during the initialization of Select2.
$('select').select2({ data: [ { id: 'value', text: 'Text to display' }, // ... more data objects ... ] });
The id
and text
properties are required on each object, and these are the properties that Select2 uses for the internal data objects. Any additional paramters passed in with data objects will be included on the data objects that Select2 exposes.
Nested results should be specified using the children
property on the data objects that are passed in. This children
property should be an array of data objects that are grouped under this option, and the label for the group should be specified as the text
property on the root data object.
{ text: 'Group label', children: [ { id: 'nested-1', text: 'First nested option' }, // ... more data objects ... ] }
Because Select2 falls back to an <optgroup>
when creating nested options, only a single level of nesting is supported. Any additional levels of nesting is not guarenteed to be displayed properly across all browsers and devices.
<option>
tags being created?
The data
option is a shortcut that Select2 provides which allows you to load options into your select
from a data array.
id
for their unique identifiers, what can I do?
You can re-map your identifier before passing it to Select2.
{% include options/not-written.html %}text
for the text that needs to be displayed
These can also be re-mapped.
{% include options/not-written.html %}


TABLE OF INTERACTIONS WITH
OLAPARIB
From the French ANSM drug interactions document of
September 2016, pp. 166-167
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
AMIODARONE RxNorm: 703 ATC: C01BD01 |
Increase of the plasma concentrations of olaparib by the amiodarone |
Not recommended If administering these medications together cannot be avoided, limit the dose of olaparib to 200 mg twice a day with the amiodarone |
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
DILTIAZEM RxNorm: 3443 ATC: C08DB01 |
Increase of the plasma concentrations of olaparib by the diltiazam |
Not recommended If administering these medications together cannot be avoided, limit the dose of olaparib to 200 mg twice a day. |
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
FLUCONAZOLE RxNorm: 4450 ATC: D01AC15 J02AC01 |
Increase of the plasma concentrations of olaparib by the fluconazole |
Not recommended If administering these medications together cannot be avoided, limit the dose of olaparib to 200 mg twice a day. |
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
ENZYME INDUCERS CLASS CODE: N03-J05-001 |
Decrease, possibly very large depending on the inducer, of the plasma concentrations of the olaparib due to increase of its hepatique metabolism by the inducer |
Not recommended |
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
STRONG INHIBITORS OF CYP3A4 CLASS CODE: J02-J05 |
Increase of the plasma concentrations of olaparib by the inhibitor |
Not recommended If administering these medications together cannot be avoided, limit the dose of olaparib to 150 mg twice a day. |
OLAPARIB RxNorm: 1597582 ATC: L01XX46 |
VERAPAMIL RxNorm: 11170 ATC: C08DA01 |
Increase of the plasma concentrations of olaparib by the verapamil. |
Not recommended If administering these medications together cannot be avoided, limit the dose of olaparid to 200 mg twice a day. |


UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>Getting Started Guide for UKCloud for OpenStack
Overview
This Getting Started Guide covers basic topics like where to find your instances (virtual machines, or VMs, in VMware terminology), as well as more in-depth topics such as instance storage choice (ephemeral or volume storage) and its implications.
Intended audience
This guide is intended for users who want to learn more about UKCloud for OpenStack.
Logging in to the OpenStack Horizon dashboard
To manage your OpenStack projects, you can log in to the OpenStack Horizon dashboard via our Single Sign-On (SSO) service, using your UKCloud Portal credentials. Additionally, if you've set up two-factor authentication (2FA) in the Portal, when logging in using your Portal credentials, you'll be prompted for a 2FA code.
Go to the URL provided in your welcome email, for example,
https://cor00005-2.cni.ukcloud.com/
.From the Authenticate using list, select:
Keystone Credentials if your account has not been migrated to SSO
UKCloud SSO if your account has been migrated to SSO
Note
If you're using UKCloud SSO, you'll require the v3 version of the OpenStack RC file. You can find more details about the OpenStack RC file in How to use the OpenStack API using an SSO enabled user.
If you selected Keystone Credentials, enter your UKCloud Portal login credentials and click Connect.
If you selected UKCloud SSO click Connect.
After logging in, the first screen you'll see is the Overview page.
From here you can get a summary of your project and view your resource usage and the quotas configured on the project.
For more information about the Horizon dashboard, see the Introduction to the OpenStack dashboard.
Creating your network infrastructure
Before you can begin creating instances, you must first build the required network components. In a freshly provisioned project, the only resource that will exist initially is a shared external network called internet
.
You need to create the following minimum set of resources:
A router using the internet external network
A network and associated subnet, specifying the network address using CIDR notation (for example, <IP_ADDRESS>/24), and DHCP settings for DNS, any static routes, and so on.
A router interface to connect the router to your new subnet
When you have created these elements, you can view them in the Network Topology page in the Horizon dashboard.
You can create your network infrastructure in one of the following ways:
Using Horizon to manually create your infrastructure (see Using Horizon to manually create your infrastructure)
Using the OpenStack Heat orchestration tool to create stacks (see Using the OpenStack Heat orchestration tool to create stacks)
Using the OpenStack API and automation (see Using the OpenStack API and automation)
Using the OpenStack CLI clients (see Command-line clients for UKCloud for OpenStack)
Using Horizon to manually create your infrastructure
The Network Topology page is a useful page to get started with. This page provides a resizable, moveable and interactive view of your OpenStack project, along with easily accessible buttons for manually creating routers and networks and for launching instances.
To create your network infrastructure:
In the Horizon dashboard, expand the Network menu on the left side of the screen and select Network Topology.
On the Network Topology page, click the Create Router button.
In the Create Router dialog box, enter a Router Name.
From the Admin State list, ensure that UP is selected so that the router is online at the point of creation.
From the External Network list, select the external network to which you want to attach your router.
Click Create Router.
Back on the Network Topology page, click the Create Network button.
On the Network page of the Create Network wizard, enter a Network Name and click Next.
On the Subnet page, enter a Subnet Name.
In the Network Address field, allocate your network a suitable address space.
In the Gateway IP field, enter an IP address from the address space for the router you created earlier to use to access the network.
Click Next.
On the Subnet Details page, the only field you'll probably want to change is DNS Name Servers.
UKCloud does not provide DNS as a service, so to start with you could make use of Google's public <IP_ADDRESS> service. You can update this field later if you intend to run your own DNS caching service.
If you leave the Allocation Pools field blank, then by default the whole address space is used for the pool.
Click Create.
To add the router interface to link your new network to the router, on the Network Topology page, select the router and click the Add Interface button.
In the Add Interface dialog box, from the Subnet list, select your subnet and then click Add Interface.
Understanding access and security
Security groups
In OpenStack, rather than deploying a perimeter firewall to protect your network from the outside world, every instance is protected by its own firewall. This instance firewall is applied at the network level, independent of any additional firewall the guest operating system may be using. The instance firewall is controlled by applying one or more security groups to the instance, either at instance creation or real-time at any point during the lifecycle of the instance. Where multiple security groups are specified, all the firewall rules in those security groups are applied.
A security group consists of one or more rules. Each rule is composed of five elements:
Role --- Custom or predefined protocol type (for example, TCP, UDP or ICMP)
Direction --- Ingress or Egress
Open Port or Port Range --- Open either a single port or a range of ports
Remote --- The source of the traffic to be allowed via this rule (source address for an inbound rule, or destination address for an outbound rule)
The ether type (that is IPv4 or IPv6) --- Not currently available
When adding a new rule to a security group, you can choose from a pre-populated list of common rules, for example, SSH, DNS, HTTP or SMTP, or you can specify your own custom rule.
SSH key pairs
Another change if you're used to using VMware Cloud Director, is the authenticated access to your instances. OpenStack, like most other cloud providers, doesn't use templates that allow password controlled access. The standard cloud method of authenticating access to a new instance is to use SSH key pair authentication - your SSH client uses the private key and OpenStack injects the public key into your new instance using the cloud-init package pre-installed in the instance image.
Before you launch any instances on OpenStack, you need to set up at least one SSH key pair. You can use OpenStack to create a new key pair for you, or if you have an existing SSH key pair, you can import the public key so that OpenStack can use it to inject into new instances.
To see what public keys have already been set up for your use:
In the Horizon dashboard, expand the Compute menu on the left side of the screen and select Access & Security.
Select the Key Pairs tab.
If you want OpenStack to create a key pair for you:
Click the Create Key Pair button.
In the Create Key Pair dialog box, enter a Key Pair Name and then click Create Key Pair.
OpenStack creates the key pair and downloads it in your web browser. Save the file somewhere safe as you will need it when connecting your SSH client to your instances.
To import an existing public key:
On the Key Pairs tab of the Access & Security page, click the Import Key Pair button.
In the Import Key Pair dialog box, enter a Key Pair Name and paste the contents of your existing public key file into the Public Key field.
Click Import Key Pair.
Note for Windows users
If you're using a Windows desktop environment, the chances are that you're using the PuTTY program for your SSH client. The PuTTY SSH client doesn't use OpenSSH format files for the key pair files. It does however provide a PuTTYgen program that can import and export in the correct format as outlined below.
If you want OpenStack to create your key pair:
Open PuTTYgen.
From the Conversions menu select Import key.
Select the downloaded private key file generated by OpenStack.
Click the Save private key button to save the imported key as a
ppk
file that PuTTY can use for authentication.
If you want to import an existing key:
Open PuTTYgen.
Click the Generate button.
Copy the public key from the top of the dialog box.
Paste the public key into the Public Key field in Horizon's Import Key Pair dialog box.
Launching your instances
Now that you've considered security, created a network and a subnet, and you've connected them to your external network with a router, you can start to think about launching some instances attached to the network. There are a few choices you need to make when launching your instances. The following sections outline how to achieve this task via the Horizon dashboard, however you can also use the OpenStack CLI, HEAT templates or the OpenStack API.
Choosing your image
When you create an instance in OpenStack, its disk is cloned from an image managed by OpenStack. The image may be a custom image that has been developed for a specific purpose and uploaded to your project. It may also be a custom image that has been specifically shared between two projects. More likely, you'll start by using one of the public images that we provide and manage on our platform.
You can see the images that are available for you to use by expanding the Compute menu on the left side of the Horizon dashboard and clicking Images.
Choosing your instance size
If you're already familiar with our UKCloud for VMware service, then you may be used to being able to choose exactly how many vCPUs and how much memory a VM is allocated.
In OpenStack, you choices are restricted to a number of pre-defined flavors that define how many vCPUs, how much memory and how much storage an instance may use. Although full details for each flavor are available through the Horizon UI, there's no overall list available showing all the different flavors and sizes of instances. However, if you've installed the OpenStack CLI tools (see http://docs.openstack.org/user-guide/common/cli-install-openstack-command-line-clients.html), you can run the following command to retrieve a list of the flavors and their makeup:
\> openstack flavor list
Choosing your storage type
There are two storage options available for use with your instances:
Ephemeral storage (default)
Persistent volume storage
Ephemeral storage
When you create an instance, the default behaviour is to clone your chosen disk image onto the physical disk attached to the host that will be running the instance. This is not dedicated locally attached storage to your instance, but rather a RAID array of fast SSD disks that is shared by all the instances running on the host hypervisor.
The performance of the ephemeral storage makes for very fast disk access. However, as the disk image is stored locally on the host hypervisor, you're at risk of data loss in the event of hardware failure on the host. For scheduled maintenance on the host, you can live‑migrate instances onto other hosts with minimal interruption. If the host were to suffer a hardware failure, it's likely that you'd lose the data stored on the disk image, so you should architect your application to expect and be able to recover from failure.
Persistent volume storage
As an alternative to ephemeral storage you can clone your chosen disk image to an OpenStack volume, which is a persistent storage option backed by a Ceph storage array. The volume is then network mounted onto the host hypervisor and the instance booted from the network attached storage. For scheduled maintenance on the host, these instances are more easily managed onto other hosts. In the event of a hardware failure on the host, the volume can just be mounted to another host and the instance restarted without any data loss.
For more information about volumes, see Working with volumes.
Creating an instance
You can create a new instance in the Horizon UI.
Click or select Launch Instance on one of the following pages:
Images page (under the Compute menu)
Instances page (under the Compute menu)
Network Topology page (under the Networks menu)
On the Details tab of the Launch Instance dialog box, enter an Instance Name and select a Flavor.
As you select a flavor from the list, the dialog box updates to reflect the VCPUs, Disk and RAM that the selected flavour gives you and the green bars under Project Limits update to show what your usage would be after launching the instance. If you select a flavour that would take you over your project's quota, the bar is red rather than green.
From the Instance Boot Source list, select:
Boot from image to create an instance that boots from the local ephemeral storage.
Boot from image (creates a new volume) to create an instance that boots from a volume
From the Image Name list, select your preferred image.
If you're creating an image that boots from a volume, you can choose what size to create the boot disk without being restricted to the disk allocation specified by the selected flavour.
On the Access & Security tab, from the Key Pair list, select the SSH key pair you want to inject into your new instance.
If there is only one SSH key pair configured in your project, it will be automatically selected.
Select one or more Security Groups to apply to the instance.
On the Networking tab, from the Available networks list, select the network that you want to create the new instance on.
If your project has only a single network defined, that network will already be listed under Selected networks.
Click Launch and OpenStack reports that your new instance is launching.
Accessing your instance
Having successfully launched your new instance, you'll want to log in and start installing your applications. To access your new instance from the outside world, you have to associate a floating IP address to the instance.
A floating IP address is a public routable address that is allocated to your project. Having allocated an address to your project, you can then choose to associate it to a particular instance. When you've done this, a DNAT rule is automatically created on the router you created earlier to forward all incoming traffic to your chosen instance.
To associate a floating IP address to your new instance:
On the Instances page, from the Actions list for your instance, select Associate Floating IP.
In the Manage Floating IP Associations dialog box, from the IP Address list, select the floating IP address that you want to allocate to the instance.
If you don't have any spare floating IP addresses allocated to your project, you can click the + button next to the IP Address list to allocate a new address.
In the Allocate Floating IP dialog box, from the Pool list, select the pool from which to allocate the floating IP address.
Currently the only option is internet, but the list will expand as additional networks come online.
Click Allocate IP to allocate the new floating IP address to your project.
Back in the Manage Floating IP Associations dialog box, associate the new address with your instance.
Click Allocate IP.
You can use your SSH client to connect to the floating IP address, authenticating the connection with the private key that matches the key pair you selected when you launched the instance.
You can find more information about using and controlling instances and images on OpenStack in the Red Hat documentation at https://access.redhat.com/documentation/en/red-hat-openstack-platform/8/instances-and-images-guide/instances-and-images-guide.
Working with volumes
Creating a volume
Volumes are block devices that you can attach to instances. Because volumes are replicated across OpenStack's storage array, effectively providing three copies of data from a resilience perspective, they provide the basis of our persistent storage offering.
In the Horizon dashboard, expand the Compute menu on the left side of the screen and select Volumes.
On the Volumes page, click the Create Volume button.
In the Create Volume dialog box, enter a Volume Name and Description.
From the Volume Source list, select:
No source, empty volume to create a totally empty volume for customer to populate with their data (this is the default option)
Image to enable customer to create a prepopulated volume from a previously created image
From the Volume Type list, select:
Tier 1 for our most performant block storage for workloads requiring consistently higher disk throughput
Tier 2 for block storage with typical performance characteristics for use by production applications or storage (this is the default option)
From the Size list, select the size of your volume in GiB.
From the Availability Zone list, select the availability zone to which you want to target your volume.
Create Create Volume.
Retyping a volume
You can retype a volume to take an existing volume of a particular volume type and converting it to another (that is, from Tier 1 to Tier 2 or vice versa).
Retyping a volume attached to an instance
Due to a dependency on the underlying hypervisor, it is not currently possible to retype a volume that is attached to an instance. If you want to retype a volume that is attached to an instance, you must first detach the volume from the instance.
To detach the volume from the instance, in the Horizon dashboard, expand the Compute menu on the left side of the screen and select Volumes.
On the Volumes page, for the volume that you want to detach, from the list on the far right of the page, select Manage Attachments.
In the Manage Volume Attachments dialog box, click the Detach Volume button.
Retype the volume by following the steps in Retyping an independent volume.
On the Volumes page, for the volume that you want to reattach, from the list on the far right, select Manage Attachments.
In the Manage Volume Attachments dialog box, from the Attach to Instance list, select the instance to which you want to reattach the volume and click the Attach Volume button.
Note
If you attempt to retype a volume that is still attached to an instance, the process will attempt to run but will fail without notification.
Retyping an independent volume
If the volume that you want to retype is not attached to an instance, you can retype it.
Ensure that the volume is detached from any instances.
For information about how to detach an volume from an instance, see Retyping a volume attached to an instance.
In the Horizon dashboard, expand the Compute menu on the left side of the screen and select Volumes.
On the Volumes page, for the volume that you want to retype, from the list on the far right of the page, select Change Volume Type.
In the Change Volume Type dialog box, from the Type drop down list, select the new type for the volume.
From the Migration Policy list, ensure that you select On Demand.
Note
If any other option is selected, the retyping process will not work.
Click Change Volume Type.
Note
You may notice duplicate volumes while the retyping process is running. These duplicate volumes will disappear when the process has completed.
Important
Do not attach a retyped volume to an instance until the retyping process has completed successfully.
Retyping an independent volume using the OpenStack CLI
To retype a volume using the OpenStack CLI, use the following command:
\> cinder retype *volume* *volume-type*
Where:
volume
is the name or ID of the volume that you want to retypevolume-type
is the new volume type
Working with the API and automation
Using the OpenStack Heat orchestration tool to create stacks
OpenStack provides an orchestration tool out of the box. The Heat tool enables you to describe your infrastructure as a text file, referred to as a Heat template, defining one or more resources using a YAML format. Having defined your resources in your Heat template, you then use the template to create a stack. The stack contains the instantiation of the resources defined in the YAML file, along with a history of events and operations that have happened to the resources in the stack.
For an example of what a basic Heat template looks like, take a look at https://github.com/UKCloud/automation_examples/blob/master/heat/example.yaml
The example.yaml template creates the same router, network, subnet and router interface that you created manually above. In addition, the template defines an SSH key pair, security groups and launch instances. We'll look at these other resource elements in more detail later.
To use a template to create a stack in your project:
In the Horizon dashboard, expand the Orchestration menu on the left side of the screen and select Stacks.
On the Stacks page, click the Launch Stack button.
On the Select Template page, from the Template Source list, select URL.
In the Template URL field, enter the URL of the Raw version of the template that you want to use (for example, the example.yaml template).
Click Next.
On the Launch Stack page, enter a Stack Name and select the flavour and image as required.
Click Launch to start creating the stack resources in the background.
You can go back to the Stacks page to view progress, drill down into the resources defined in the stack and see the events as they happen.
You can also use an updated Heat template to apply an update to resources in a stack. You can also destroy an entire stack, cleaning up all the resources that you previously created. This makes Heat a very powerful tool for creating short‑lived test and preproduction environments on‑demand that may form part of a continuous delivery pipeline.
Using the OpenStack API and automation
OpenStack also provides a fully functional API to enable programmatic control over your project. The Horizon UI you used earlier is itself a client that makes calls to this underlying API.
Although you can write your own code to interact with the API, there are a number of different libraries already available covering most popular programming languages.
Alternatively, there is also a growing number of DevOps automation tools available (for example, Terraform and Ansible) that interact with the OpenStack API to orchestrate the creation and ongoing management of your infrastructure.
For an example Terraform configuration to create a similar network infrastructure to the one you created in the previous examples, take a look at https://github.com/UKCloud/automation_examples/tree/master/terraform-os.
Whatever library or automation tools you choose to use, you'll need to know a few important details to make a connection to and authenticate against the API.
In the Horizon dashboard, expand the Compute menu on the left side of the screen and select Access & Security.
On the Access and Security page, select the API Access tab.
On the API Access tab, you can see all the relevant API endpoints for making connections to the various OpenStack subsystems, including the Identify endpoint, which provides the initial authentication process that you'll typically need when configuring connections.
You can also download your user's OpenStack RC file for use in the following section.
Note
If your account is SSO-enabled, download the v3 RC file and make the modifications stated here.
Using the OpenStack command-line client
The OpenStack project provides a command‑line client that enables you to access the project API through easy‑to‑use commands. For example, the compute service provides a NOVA command‑line client.
You can run the commands from the command line, or include the commands in scripts to automate tasks. If you provide OpenStack credentials, such as your user name and password, you can run these commands on any computer.
The following file is a bash shell script format file that sets up all the relevant environment variables in the Linux shell session so that you can use the OpenStack CLI tools. The OpenStack CLI tools, just like the Horizon UI, also communicate with the OpenStack API. This means you can examine the contents of the RC file to find the settings that you may need to pass into, for example, a Terraform provider definition when configuring it to manage your OpenStack project.
The key values to note here are the OS_AUTH_URL
, OS_TENANT_NAME
and OS_USERNAME
environment variables, as these will be common to any API authentication request.
In addition, creating the line export OS_INTERFACE=public
in the RC file should address any issues you may have accessing the Admin API endpoints.
Next steps
In this Getting Started Guide, you've learned how to use the OpenStack Horizon UI to set up UKCloud for OpenStack and create new instances. You've also learned about the other tools available including the OpenStack API and command-line client.
You can find additional information in the following places:
OpenStack API --- For more information about using the API, see the OpenStack documentation at https://developer.openstack.org/api-guide/quick-start/
OpenStack Client --- For more information about using the OpenStack Client, see the OpenStack documentation at http://docs.openstack.org/user-guide/cli.html
HEAT templates --- You can find more information about the Heat orchestration tool and the resources you can use in the YAML format templates in the OpenStack documentation at https://wiki.openstack.org/wiki/Heat and https://docs.openstack.org/developer/heat/template_guide/index.html
Terraform --- For information about using Terraform and the OpenStack provider, see the Terraform documentation at https://www.terraform.io/intro/ and https://www.terraform.io/docs/providers/openstack/index.html
Ansible --- For information on Ansible's support for working with OpenStack resources, see the Ansible documentation at https://docs.ansible.com/ansible/list_of_cloud_modules.html#openstack
Glossary
This section provides a glossary of terms specific to UKCloud for OpenStack.
UKCloud for OpenStack A UKCloud IaaS service that provides a cloud platform specifically designed around the needs of digital communities, and engineered to facilitate true cloud-native applications.
ephemeral storage A UKCloud for OpenStack storage option that exists only as long as the associated OpenStack instance exists.
flavor An OpenStack construct that defines how many vCPUs, how much memory and how much storage an instance may use.
floating IP address A public routable address that is allocated to your project and can then be associated to an instance to route external traffic to the instance.
Heat template A YAML‑formatted text file that defines your infrastructure.
image A file containing a virtual disk with a bootable operating system, used to create virtual machine instances within UKCloud for OpenStack.
instance A customer provisioned compute resource within OpenStack, similar to the virtual machine concept within VMware.
persistent volume storage A UKCloud for OpenStack block storage option that is always available, regardless of the state of the VM.
private key The part of the SSH key pair that your SSH client uses to authenticate with an instance.
public key The part of the SSH key pair that is stored in the instance.
security group An OpenStack construct that consists of one or more rules that control access to an instance.
SSH key pair A method of authentication used by OpenStack to authenticate access to instances that includes a public key and a private key. The SSH client uses a private key that is paired to a public key in the instance.
stack An OpenStack construct that contains the resources created from a Heat template.
volume A persistent storage option backed by a Ceph storage array that is network mounted onto the host hypervisor.
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


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Documentation generated on Sun, 07 Feb 2010 17:54:01 +0000 by phpDocumentor 1.4.0


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