Commit c6589c0f authored by William Clarke's avatar William Clarke
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Merge branch 'doc/update_install_info' into 'master'

DOC: Update documentation for new release process.

Closes #21

See merge request !39
parents 57bea33f 40f0aad4
Pipeline #12095 passed with stages
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......@@ -83,7 +83,7 @@ pytest:
- source ~/.bashrc
- conda create -n fsl_mrs -y python=3.7
- conda activate fsl_mrs
- conda install -y -c conda-forge -c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/channel/ --file requirements.txt
- conda install -y -c conda-forge -c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/public/ --file requirements.txt
- conda install -y -c conda-forge pytest
- pip install .
script:
......
......@@ -13,6 +13,8 @@ This document contains the FSL-MRS release history in reverse chronological orde
- Allow tissue fractions with larger errors, but normalise. Error thrown if sum < 0.9.
- Specialist phase & frequency alignment via dynamic fitting added.
- Added fsl_mrs_preproc_edit as a script for preprocessing edited data.
- Updated documentation with new install instructions.
- Updated interactive fitting documentation
1.1.8 (Tuesday 5th October 2021)
--------------------------------
......
......@@ -21,7 +21,7 @@ Then activate the environment:
Finally install FSL-MRS and its dependencies from the FSL conda channel.
conda install -c conda-forge -c defaults \
-c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/channel/ \
-c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/public/ \
fsl_mrs
### Source code
......
......@@ -17,7 +17,7 @@ A basic call to :code:`fsl_mrs`, the SVS wrapper script, is as follows:
--basis my_basis_spectra \
--output example_fit
This will run nonlinear optimisation using the Truncated Newton algorithm, as implemented in Scipy, and will produce a simple PNG file summarising the fit, and several CSV files containing concentrations, uncertainties, and QC parameters for further analysis.
This will run non-linear optimisation using the Truncated Newton algorithm, as implemented in Scipy, and will produce a simple PNG file summarising the fit, and several CSV files containing concentrations, uncertainties, and QC parameters for further analysis.
A more complete call to :code:`fsl_mrs` may be as follows.
......@@ -67,31 +67,31 @@ Output
Results from :code:`fsl_mrsi` are stored in a single folder containing the following output:
- An interactive HTML report showing the fit to the average FID across all voxels in the mask.
- NIFTI files summarising parameters, concentrations, and QC measures (one such file per metabolite)
- Model prediction in the time domain (NIFTI)
- Residuals (NIFTI)
- Fitted Baseline (NIFTI)
- NIfTI files summarising parameters, concentrations, and QC measures (one such file per metabolite)
- Model prediction in the time domain (NIfTI)
- Residuals (NIfTI)
- Fitted Baseline (NIfTI)
The above NIFTI output can all be visualied in FSLeyes alongside the original data.
The above NIfTI output can all be visualised in FSLeyes alongside the original data.
Python & Interactive Interface
------------------------------
FSL-MRS can also be used in an interactive Python environment. The following is an example fitting and visualisation of data that has already been processed (e.g. with :code:`fsl_mrs_proc`).
In an IPython or Jupyter Notebook environment, run the follwing (the example data resides in the main :code:`fsl_mrs` package folder):
In an IPython or Jupyter Notebook environment, run the following (the example data resides in the main :code:`fsl_mrs` package folder):
Loading and preparing the data:
::
from fsl_mrs import MRS
from fsl_mrs.utils import mrs_io
FID_file = 'example_usage/example_data/metab.nii'
basis_folder = 'example_usage/example_data/steam_11ms'
mrs = MRS()
mrs.from_files(FID_file,basis_folder)
data = mrs_io.read_FID(FID_file)
mrs = data.mrs(basis_file=basis_folder)
mrs.processForFitting()
Fitting the model to the data:
......
......@@ -24,7 +24,7 @@ The primary installation method is via *conda*. After installing conda and creat
::
conda install -c conda-forge -c defaults \
-c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/channel/ \
-c https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/public/ \
fsl_mrs
Example data with conda
......
......@@ -9,14 +9,19 @@ Instructions for releasing a new version of FSL-MRS
5. Check all tests pass.
6. Merge to fsl/fsl_mrs:master.
7. Repeat with other features.
Each time the fsl/fsl_mrs master branch is updated and the CI pipeline is run, the `trigger-staging-package` job triggers a pipeline on the conda recipe repository to build a package from the current state of master. This can be turned into a development release by running the `deploy-conda-package` on the latest job of `fsl/conda/fsl_mrs`. This will be published to the same channel and have a format `<last_tag>.YYMMDD`.
8. For new version create new tag -tag name should be "x.x.x" with the message "Vx.x.x". New changelog contents are copied into release notes.
9. This will trigger doc build (published [here](https://open.win.ox.ac.uk/pages/fsl/fsl_mrs/)) and package build (currently broken).
10. Update local master.
9. This will trigger doc build (published [here](https://open.win.ox.ac.uk/pages/fsl/fsl_mrs/)) and package build.
10. Trigger the package build by accepting the relevant merge request on [fsl/conda/fsl_mrs](https://git.fmrib.ox.ac.uk/fsl/conda/fsl_mrs) branch. This will trigger a pipeline from which the conda package be released. Do this by manually triggering the `deploy-conda-package` job after the build has run.
11. New packages will be published to the [FSL conda channel](https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/public/).
12. To request a version to be included in the next FSL release manually trigger the `update-fsl-manifest` job. This opens a MR for the development team on the [manifest repository](https://git.fmrib.ox.ac.uk/fsl/conda/manifest/).
13. Optionally update local and fork master.
```git fetch upstream --tags```
```git checkout master```
```git merge upstream/master```
```git push origin --tags```
11. Trigger manual package build using the [fsl/conda/fsl-mrs:pre-conda](https://git.fmrib.ox.ac.uk/fsl/conda/fsl-mrs:pre-conda) branch. Do this by updating the version number [here](https://git.fmrib.ox.ac.uk/fsl/conda/fsl-mrs/-/blob/mnt/pre-conda/meta.yaml#L6). This will trigger a pipeline from which the conda package can be downloaded (*Download build-noarch-conda-package:archive artifact*).
12. Send to MW for manual upload to the FSL conda channel.
For local development installation run: ```pip install --no-deps -e .```
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