update readme

Author: jayghoshter

README.md

@@ -1,8 +1,6 @@
-# ChroMOO (or chromoo)
+# chromoo
 
-Chromatography optimization with cadet and pymoo. I built this because CADET-Match didn't offer optimization with certain parameters and objectives. For example, optimizing SOLUTION_BULK is not possible currently. Input of vector parameters is also currently not possible.
-
-While I haven't yet fully looked through the source code of CADET-Match, I would like to take inspiration from it while building chromoo. Eventually, when I get a better understanding of pymoo, CADET-Match and the problem, I believe it should be possible to merge the code into CADET-Match.
+`chromoo` a chromatography multi-objective optimization tool built on `Cadet-Core` and `pymoo==0.5`.
 
 # Installation
 
@@ -17,14 +15,12 @@ conda install cadet
 pip install -r requirements.txt
 
 # Install this package. Use -e for an editable install.
-pip install . 
+pip install [-e] . 
 ```
 
 # Usage
 
-Chromoo requires a YAML config file. I use ruamel.yaml, which allows using YAML v1.2, meaning comments are allowed, and exponential notation is better parsed.
-
-A template of the config follows:
+Chromoo requires a YAML config file of the following form.
 
 ```yaml
 filename: 10k-mono.mono1d.h5
@@ -65,36 +61,14 @@ termination:
 - parameters and objectives are **lists**
 - Objective targets can be provided as an (times,values) csv file in `objectives.filename` or with the times separately specified in `objectives.times`
     - chromatograms already contain times, so it's easier to just provide the filename
-    - solution_bulk data obtained from 3D sims are multidimensional, and we would like to try fitting the whole thing as a flat vector first
 - The `solution_times` section of the provided cadet simulation will be changed to match those of `objectives[0]` exactly.
 - Recommended population sizes for n-dimensional problems is 100*n
-- [CRIT] Don't fit porosity and velocity together. You can fit porosity and flowrate instead
-- Provided examples, while valid, are *NOT* guaranteed to be correct. Though I will try to keep them correct. 
-- Checkpoints are saved at every generation by default. Checkpoints help avoid the pain of libpthread (or other) crashes from having to completely restart the fitting.
-- Use `force_checkpoint_continue` to force the algorithm to continue from a terminated checkpoint. Helpful if you made the termination criteria stricter.
+- Don't fit porosity and velocity together. You can fit porosity and flowrate instead
+- Provided examples, while valid, are *NOT* guaranteed to be correct as the software is not guaranteed to be stable in terms of development and backwards compatibility.
+- Checkpoints are saved at every generation by default.
+- Use `force_checkpoint_continue` to force the algorithm to continue from a _terminated_ checkpoint. Helpful if you made the termination criteria stricter than required.
 - Be careful when resuming from a checkpoint. Any changes to problem parameters might not be reflected because the algorithm/problem is fully restored from the checkpoint
-- `transforms.parameters` (choices = [`lognorm`, `norm`, `none`]) applies to all inputs individually
-- `transforms.objectives` (choices = [`none`, `mean`, `geometric`]) will be made to convert multiple objectives into a single objective.
 
 # Known Issues
-- [CRIT] Got simulation failure due to `error 4 in libpthread` (see dmesg when it happens) on IBT012. Simulation runs manually.
 - Reading inputs from YAML loads strings as `str` and from h5 files we get `numpy.bytes_`. CADET-Python run_load() uses load_results() instead of full load(). So if we check for input string values after simulation, the type of it depends on whether we use full load() or load_results() since we deal with YAML files as well. So we have to consider whether we deal with strings or bytestrings. Simple solution: Don't use run_load in scripts.
 - Loading checkpoints also loads the previous values for all/most parameters. So if nproc is updated before loading, the new value isn't used.
-
-# TODO
-- [ONGO] Move to numpy arrays
-- [TASK] Hypervolume indicator as a convergence criterion
-- [DONE] Replace readArray, readChromatogram with np.genfromtxt or loadtxt
-- [DONE] Implement Objective vs Objective 2D plots
-- [DONE] Pickle pop xs and fs for use with chromoo-post
-- [TASK] Write out opts per generation
-- [TASK] Write a configHandler method to output a dummy config.
-- [TASK] Unified interface/method for deep getting and setting from/to a Dict or dict
-- [TASK] Consider weighting objectives: Look at weighted least squares.
-    - [NOTE] Normalizing equalizes weights, treating relative error on each curve.
-- [TASK] Consider multi-started/restarted systems
-- [TASK] Check out numpickle: https://gwang-jin-kim.medium.com/faster-loading-and-saving-of-pandas-data-frames-using-numpickle-numpy-and-pickle-d15870519529
-- [TASK] Performance of np.take() with numbajit vs boolean indexing: https://stackoverflow.com/questions/46041811/performance-of-various-numpy-fancy-indexing-methods-also-with-numba
-- [TASK] Improved plotting for large number of subplots in post: https://stackoverflow.com/questions/13046127/matplotlib-very-slow-is-it-normal/13060980#13060980
-- [CRIT] Check for behavior when take=[2,[0,1,2...]]. i.e., when indices is a list. Does verify fail? What happens?
-- [TASK] Use tabplot for plots