quick documentation update

Makefile

@@ -61,7 +61,7 @@ clean:
 
 condaenv:
 	@echo "Creating $(D)PHD$(E) conda environment according to '$(C)environment.yml$(E)' file."
-	@conda env create -f environment.yml
+	@conda env create -f docker/environment.yml
 	@echo "Adding $(D)PHD$(E) environment to base ipython kernel."
 	@source activate PHD
 	@ipython kernel install --user --name=PHD

README.md

@@ -1,5 +1,5 @@
 <h1 align="center">
-  <b>pyhdtoolkit</b>
+  <b>PyhDToolkit</b>
 </h1>
 
 <p align="center">

docs/About_PyhDToolkit.md

@@ -2,31 +2,31 @@
 
 ## Purpose
 
-This package is meant to be an all-in-one collection of utilities and scripts I use in my PhD work.
+This package is an all-in-one collection of baseline utilities I use in my PhD work.
 Most of the codes here have their use in my day-to-day work, but not necessarily in our team's softwares.
 
 ## Functionality
 
 For now, `PyhDToolkit` provides some of the following features:
 
-- Useful tools to integrate with [`cpymad`][cpymad], a Python bindings library for the [`MAD-X`][madx] code, including generators, matching routines and plotting utilities.
+- A `cpymadtools` module with tools to integrate with [`cpymad`][cpymad], a Python bindings library for the [`MAD-X`][madx] code; including generators, matching routines, plotting utilities etc.
 - A `maths` module to incorporate useful methods used in analysis.
-- A `plotting` module for my favorite defaults and helpers.
 - An `optics` module for particle accelerator physics related calculations and analysis.
-- A `scripts` module to handle different simulations setups.
+- A `plotting` module for my favorite defaults and helpers.
 - A `tfstools` module similar to `cpymadtools`, with functionality revolving around handling `tfs` files and plotting their contents.
-- A `utils` module for various utilities.
+- A `utils` module for various Python and UNIX utilities.
 
 ## Roadmap
 
 In addition to developping current modules, more will be added to better incorporate with the workhorse softwares of the OMC team, notably [`tfs-pandas`][tfs] and [`omc3`][omc3].
 Foreseen development includes:
 
-- Expansion of existing modules, particularly the `optics` module to include most simple calculations on beam properties.
-- An `omcwrapper` module to handle different usecases of the `omc3` package.
-- A `sixtracklibtools` module for utility functions surrounding the use of `sixtracklib`.
+- Expansion of the `optics` module to include most simple calculations on beam properties.
+- Expansion of the `cpymadtools` with utilities as their needs arise in my studies.
+- A `sixtracklibtools` module for utility functions surrounding the use of [`sixtracklib`][sixtracklib], eventually.
 
 [cpymad]: https://github.com/hibtc/cpymad
 [madx]: https://mad.web.cern.ch/mad/
 [omc3]: https://github.com/pylhc/omc3
 [tfs]: https://github.com/pylhc/tfs
+[sixtracklib]: https://github.com/SixTrack/sixtracklib

docs/Getting_Started.md

@@ -40,6 +40,7 @@ You can now install this simply in a virtual environment with:
 
 ### With Docker
 
+Docker provides an easy way to get access to a fully-fledged environment identical to the one I use, for reproducibility of my results.
 You can directly pull a pre-built image from `Dockerhub` (with tag `latest` being an automated build) with:
 ```bash
 > docker pull fsoubelet/simenv
@@ -51,6 +52,8 @@ Assuming you pulled the provided image from Dockerhub, run a jupyterlab server o
 > docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v <host_dir_to_mount>:/home/jovyan/work fsoubelet/simenv
 ```
 
+Any jupyter notebook or Python files in the mounted directory can then be used / ran with an environment identical to mine.
+
 [virtual_env_primer]: https://realpython.com/python-virtual-environments-a-primer/
 [black_formatter]: https://github.com/psf/black
 [Docker]: https://www.docker.com/

pyhdtoolkit/cpymadtools/optics.py

@@ -0,0 +1,16 @@
+"""
+Module cpymadtools.optics
+-------------------------
+
+Created on 2020.02.19
+:author: Felix Soubelet (felix.soubelet@cern.ch)
+
+A module with functions to access `optics_functions` functionality directly onto `cpymad.madx.Madx` objects.
+"""
+from typing import Sequence
+
+import tfs
+import optics_functions
+
+from cpymad.madx import Madx
+from loguru import logger

pyhdtoolkit/cpymadtools/special.py

@@ -8,7 +8,7 @@
 A module with functions to perform MAD-X actions with a cpymad.madx.Madx object, that are very specific to
 LHC and HLLHC use cases.
 """
-from typing import Dict, List, Sequence, Tuple
+from typing import List, Sequence, Tuple
 
 import numpy as np
 import tfs
@@ -262,8 +262,8 @@ def install_ac_dipole(
 
     if not geometric_emit:
         logger.debug(
-            f"No value provided for the geometric emittance used when creating the beam, the value will be "
-            f"queried from MAD-X's global 'geometric_emit'"
+            "No value provided for the geometric emittance used when creating the beam, the value will be "
+            "queried from MAD-X's global 'geometric_emit'"
         )
         geometric_emit = madx.globals["geometric_emit"]
 

pyhdtoolkit/cpymadtools/track.py

@@ -7,7 +7,7 @@
 
 A module with functions to manipulate MAD-X TRACK functionality through a cpymad.madx.Madx object.
 """
-from typing import Dict, Tuple
+from typing import Tuple
 
 import pandas as pd
 

pyproject.toml

@@ -40,6 +40,7 @@ tfs-pandas = "^2.0"
 loguru = "^0"
 numba = ">=0.51,<1.0"
 cpymad = "^1.6"
+optics-functions = "^0"
 
 # Defining optional dependencies for extras
 portray = { version = "^1.4", optional = true }

tests/test_cpymadtools.py

@@ -9,9 +9,9 @@
 import tfs
 
 from cpymad.madx import Madx
+from pandas import DataFrame
 from pandas._testing import assert_dict_equal
 from pandas.testing import assert_frame_equal
-from pandas import DataFrame
 
 from pyhdtoolkit.cpymadtools.constants import (  # coverage
     CORRECTOR_LIMITS,