From 66b7e938d5372fae86f5879a5c800499a0f807ac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Sat, 18 Dec 2021 15:07:48 +0000
Subject: [PATCH] update readme with new aliby structure
---
README.md | 80 +++++++++++++++++--------------------------------------
1 file changed, 25 insertions(+), 55 deletions(-)
diff --git a/README.md b/README.md
index 5a82b17a..a2165d6e 100644
--- a/README.md
+++ b/README.md
@@ -36,74 +36,44 @@ docker-compose stop
```
### Raw data access
-Raw data access can be found in `core.experiment` and `core.timelapse`, and
-is organised inspired by the Swain Lab MATLAB pipeline.
-
-The `Experiment` classes are basically the only ones that really need to be
-accessed by a user. The `ExperimentOMERO` and `ExperimentLocal` classes
-implement the different possible sources of the data.
-If the experiment is saved locally, we expect the organisation to be as in
-[this repository](https://github.com/SynthSys/omero_connect_demo/tree/master/test_data)
-`Experiment` cannot be instantiated as it is an abstract class, but calling
-`Experiment.from_source()` will instantiate either an `ExperimentOMERO` or an
-`ExperimentLocal` class depending on the arguments, and the differences between
-the two are invisible to the user from then on.
-
-```python
-from core.experiment import Experiment
-local_expt = Experiment.from_source('path/to/data/directory/')
-omero_expt = Experiment.from_source(10421, #Experiment ID on OMERO
- 'user', #OMERO Username
- 'password', #OMERO Password
- 'host.omero.ed.ac.uk', #OMERO host
- port=4064 #This is default
- )
-```
-
-Data is organised in each experiment as `Timelapse` classes. These are also
-separated into `TimelapseOMERO` and `TimelapseLocal`.
-The main function of these objects is to give a direct interface to the raw
-data, whatever form it is saved in.
-These objects are sliceable, meaning that data can be accessed like a numpy
-array (with some reservations). This can be done directly through the
- `Experiment` object.
```python
-bf_1 = expt[0, 0, :, :, :] # First channel, first timepoint, all x,y,z
+from aliby.io.omero import Dataset, Image
+
+server_info= {
+ "host": "host_address",
+ "username": "user",
+ "password": "xxxxxx"}
+expt_id = XXXX
+tps = [0, 1] # Subset of positions to get.
+
+with Dataset(expt_id, **server_info) as conn:
+ image_ids = conn.get_images()
+
+#To get the first position
+with Image(list(image_ids.values())[0], **server_info) as image:
+ dimg = image.data
+ imgs = dimg[tps, image.metadata["channels"].index("Brightfield"), 2, ...].compute()
+ # tps timepoints, Brightfield channel, z=2, all x,y
```
-Aside from the argument parsing, this is implemented through the
-`get_hypercube()` function, which can be called directly from the `Experiment`
-object.
-
-```python
-x, y, width, height, z_positions, channels, timepoints = [None]*7 #Get full pos
-expt.get_hypercube(x, y, width, height, z_positions, channels,
- timepoints)
-```
-To change position (`Timelapse`), one simply needs to set `Experiment
-.curent_position` to the desired position name.
-
-```python
-position = expt.positions[0] #This is the default position when expt initalized
-expt.current_position = positions
-```
-
### Tiling the raw data
-The tiling of raw data is done through a `Tiler` object.
-It takes a raw `Experiment` object as an argument.
+A `Tiler` object performs trap registration. It is built in different ways, the easiest one is using an image and a the default parameters set.
```python
-from core.segment import Tiler
-seg_expt = Tiler(expt)
+from aliby.segment import Tiler, TilerParameters
+with Image(list(image_ids.values())[0], **server_info) as image:
+ tiler = Tiler.from_image(image, TilerParameters.default())
```
-The initialization should take a few seconds, as it needs to align the images
+The initialisation should take a few seconds, as it needs to align the images
in time.
+It fetches the metadata from the Image object, and uses the TilerParameters values (all Processes in aliby depend on an associated Parameters class, which is in essence a dictionary turned into a class.)
+
#### Get a timelapse for a given trap
-From there, you can obtain a timelapse for a single trap as follows:
+TODO: Update this
```python
channels = [0] #Get only the first channel, this is also the default
z = [0, 1, 2, 3, 4] #Get all z-positions
--
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