From 502459e3cf8e13fed14663c7089511e23d93f040 Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Thu, 13 Jun 2019 23:15:29 +0100 Subject: update readme --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3c360f0..e28bdfe 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ Some concepts are so much overlapping with the CCPPETMR project that we have cho This package consists of the following Python modules: 1. `ccpi.framework` 2. `ccpi.optimisation` +3. `ccpi.io` ### `ccpi.framework` @@ -25,6 +26,7 @@ In `ccpi.framework` we define a number of common classes normally used in tomogr * `DataSetProcessor` * `ImageData` * `AcquisitionData` + * `BlockDataContainer` #### `DataContainer` Generic class to hold data. Currently the data is currently held in a numpy arrays, but we are currently planning to create a `GPUDataContainer` and `BoostDataContainer` which will hold the data in an array on GPU or in a boost multidimensional array respectively. @@ -79,9 +81,9 @@ In `ccpi.framework` we define a number of common classes normally used in tomogr Fixed parameters can be passed in during the creation of the `function` object. The methods of the `function` reflect the properties of it, for example, if the function represented is differentiable - the `function` should contain a method `grad` which should return the gradient of the function evaluated at + the `function` should contain a method `gradient` which should return the gradient of the function evaluated at an input point. If the function is not differentiable but allows a simple proximal operator, the method - `prox` should return the proxial operator evaluated at an input point. The function value + `proximal` should return the proxial operator evaluated at an input point. The function value is evaluated by calling the function itself, e.g. `f(x)` for a `function` `f` and input point `x`. @@ -91,7 +93,7 @@ In `ccpi.framework` we define a number of common classes normally used in tomogr is designed for a particular generic optimisation problem accepts and number of `Function`s and/or `Operator`s as input to define a specific instance of the generic optimisation problem to be solved. - They are iterable objects which can be run in a `for` loop. The user can provide a stopping cryterion different than the default max_iteration. + They are iterable objects which can be run in a `for` loop. The user can provide a stopping criterion different than the default max_iteration. `Algorithm`s provide a courtesy method `run(number_of_iterations, verbose)` which allows the user to easily run a `number_of_iterations` and receive a print to screen. New algorithms can be easily created by extending the `Algorithm` class. The user is required to implement only 4 methods: `set_up`, `__init__`, `update` and `update_objective`. -- cgit v1.2.3