docs: Disallow improper capitalization

.github/labeler.yml

@@ -33,6 +33,6 @@ LAMMPS:
 Gromacs:
   - changed-files:
       - any-glob-to-any-file: source/gmx/**/*
-i-Pi:
+i-PI:
   - changed-files:
       - any-glob-to-any-file: source/ipi/**/*

.pre-commit-config.yaml

@@ -136,5 +136,13 @@ repos:
           - --comment-style
           - <!--|  ~|  -->
           - --no-extra-eol
+  - repo: local
+    hooks:
+      - id: disallow-caps
+        name: Disallow improper capitalization
+        language: pygrep
+        entry: DeepMD|DeepMd|Pytorch|Tensorflow|Numpy|Github|Lammps|I-Pi|I-PI|i-Pi
+        # unclear why PairDeepMD is used instead of PairDeePMD
+        exclude: .pre-commit-config.yaml|source/lmp
 ci:
   autoupdate_branch: devel

README.md

@@ -91,7 +91,7 @@ The code is organized as follows:
 - `source/api_c`: source code of the C API.
 - `source/nodejs`: source code of the Node.js API.
 - `source/ipi`: source code of i-PI client.
-- `source/lmp`: source code of Lammps module.
+- `source/lmp`: source code of LAMMPS module.
 - `source/gmx`: source code of Gromacs plugin.
 
 # Contributing

codecov.yml

@@ -41,6 +41,6 @@ component_management:
       paths:
         - source/lmp/**
     - component_id: module_ipi
-      name: i-Pi
+      name: i-PI
       paths:
         - source/ipi/**

deepmd/infer/model_devi.py

@@ -378,7 +378,7 @@ def make_model_devi(
         The output file for model deviation results
     frequency : int
         The number of steps that elapse between writing coordinates
-        in a trajectory by a MD engine (such as Gromacs / Lammps).
+        in a trajectory by a MD engine (such as Gromacs / LAMMPS).
         This paramter is used to determine the index in the output file.
     real_error : bool, default: False
         If True, calculate the RMS real error instead of model deviation.

deepmd/utils/argcheck.py

@@ -2204,7 +2204,7 @@ def training_data_args():  # ! added by Ziyao: new specification style for data
 - int: all {link_sys} use the same batch size.\n\n\
 - string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\
 - string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.\n\n\
-- string "mixed:N": the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor for Tensorflow backend.\n\n\
+- string "mixed:N": the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor for TensorFlow backend.\n\n\
 If MPI is used, the value should be considered as the batch size per task.'
     doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\
 - "prob_uniform"  : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\

doc/data/data-conv.md

@@ -4,7 +4,7 @@ Two binary formats, NumPy and HDF5, are supported for training. The raw format i
 
 ## NumPy format
 
-In a system with the Numpy format, the system properties are stored as text files ending with `.raw`, such as `type.raw` and `type_map.raw`, under the system directory. If one needs to train a non-periodic system, an empty `nopbc` file should be put under the system directory. Both input and labeled frame properties are saved as the [NumPy binary data (NPY) files](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#npy-format) ending with `.npy` in each of the `set.*` directories. Take an example, a system may contain the following files:
+In a system with the NumPy format, the system properties are stored as text files ending with `.raw`, such as `type.raw` and `type_map.raw`, under the system directory. If one needs to train a non-periodic system, an empty `nopbc` file should be put under the system directory. Both input and labeled frame properties are saved as the [NumPy binary data (NPY) files](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#npy-format) ending with `.npy` in each of the `set.*` directories. Take an example, a system may contain the following files:
 
 ```
 type.raw
@@ -38,7 +38,7 @@ For training models with descriptor `se_atten`, a [new system format](../model/t
 
 ## HDF5 format
 
-A system with the HDF5 format has the same structure as the Numpy format, but in an HDF5 file, a system is organized as an [HDF5 group](https://docs.h5py.org/en/stable/high/group.html). The file name of a Numpy file is the key in an HDF5 file, and the data is the value of the key. One needs to use `#` in a DP path to divide the path to the HDF5 file and the HDF5 path:
+A system with the HDF5 format has the same structure as the NumPy format, but in an HDF5 file, a system is organized as an [HDF5 group](https://docs.h5py.org/en/stable/high/group.html). The file name of a NumPy file is the key in an HDF5 file, and the data is the value of the key. One needs to use `#` in a DP path to divide the path to the HDF5 file and the HDF5 path:
 
 ```
 /path/to/data.hdf5#/H2O
@@ -79,4 +79,4 @@ $ ls
 box.raw  coord.raw  energy.raw  force.raw  set.000  set.001  set.002  type.raw  virial.raw
 ```
 
-It generates three sets `set.000`, `set.001` and `set.002`, with each set containing 2000 frames in the Numpy format.
+It generates three sets `set.000`, `set.001` and `set.002`, with each set containing 2000 frames in the NumPy format.

doc/development/create-a-model-pt.md

@@ -199,6 +199,6 @@ When implementing an existing model in a new backend, directly apply the existin
 
 ### Consistent tests
 
-When transferring features from another backend to the PyTorch backend, it is essential to include a regression test in `/source/tests/consistent` to validate the consistency of the PyTorch backend with other backends. Presently, the regression tests cover self-consistency and cross-backend consistency between TensorFlow, PyTorch, and DP (Numpy) through the serialization/deserialization technique.
+When transferring features from another backend to the PyTorch backend, it is essential to include a regression test in `/source/tests/consistent` to validate the consistency of the PyTorch backend with other backends. Presently, the regression tests cover self-consistency and cross-backend consistency between TensorFlow, PyTorch, and DP (NumPy) through the serialization/deserialization technique.
 
-During the development of new components within the PyTorch backend, it is necessary to provide a DP (Numpy) implementation and incorporate corresponding regression tests. For PyTorch components, developers are also required to include a unit test using `torch.jit`.
+During the development of new components within the PyTorch backend, it is necessary to provide a DP (NumPy) implementation and incorporate corresponding regression tests. For PyTorch components, developers are also required to include a unit test using `torch.jit`.

doc/getting-started/quick_start.ipynb

@@ -58,7 +58,7 @@
     "\n",
     "* Prepare the formataive dataset and running scripts for training with DeePMD-kit;\n",
     "* Train, freeze, and test DeePMD-kit models;\n",
-    "* Use DeePMD-kit in Lammps for calculations;\n",
+    "* Use DeePMD-kit in LAMMPS for calculations;\n",
     "\n",
     "Work through this tutorial. It will take you 20 minutes, max!"
    ]
@@ -239,7 +239,7 @@
     "\n",
     "Detailed information about ABACUS can be found in its [documentation](https://abacus.deepmodeling.com/en/latest/). \n",
     "\n",
-    "DeePMD-kit uses a compressed data format. All training data should first be converted into this format before they can be used in DeePMD-kit. This data format is explained in detail in the DeePMD-kit manual, which can be found on [DeePMD-kit's Github](http://www.github.com/deepmodeling/deepmd-kit).\n",
+    "DeePMD-kit uses a compressed data format. All training data should first be converted into this format before they can be used in DeePMD-kit. This data format is explained in detail in the DeePMD-kit manual, which can be found on [DeePMD-kit's GitHub](http://www.github.com/deepmodeling/deepmd-kit).\n",
     "\n",
     "We provide a convenient tool **dpdata**, which can convert data generated by VASP, CP2K, Gaussian, Quantum Espresso, ABACUS, and LAMMPS into DeePMD-kit's compressed format.\n",
     "\n",
@@ -863,7 +863,7 @@
     "DEEPMD INFO    saved checkpoint model.ckpt\n",
     "```\n",
     "\n",
-    "They present the training and testing time counts. At the end of the 1000th batch, the model is saved in Tensorflow's checkpoint file `model.ckpt`. At the same time, the training and testing errors are presented in file `lcurve.out`. \n",
+    "They present the training and testing time counts. At the end of the 1000th batch, the model is saved in TensorFlow's checkpoint file `model.ckpt`. At the same time, the training and testing errors are presented in file `lcurve.out`. \n",
     "\n",
     "The file contains 8 columns, form left to right, are the training step, the validation loss, training loss, root mean square (RMS) validation error of energy, RMS training error of energy, RMS validation error of force, RMS training error of force and the learning rate. The RMS error (RMSE) of the energy is normalized by number of atoms in the system. \n",
     "```\n",

doc/install/easy-install.md

@@ -126,7 +126,7 @@ pip install torch --index-url https://download.pytorch.org/whl/cpu
 pip install deepmd-kit[cpu]
 ```
 
-[The LAMMPS module](../third-party/lammps-command.md) and [the i-Pi driver](../third-party/ipi.md) are only provided on Linux and macOS for the TensorFlow backend. To install LAMMPS and/or i-Pi, add `lmp` and/or `ipi` to extras:
+[The LAMMPS module](../third-party/lammps-command.md) and [the i-PI driver](../third-party/ipi.md) are only provided on Linux and macOS for the TensorFlow backend. To install LAMMPS and/or i-PI, add `lmp` and/or `ipi` to extras:
 
 ```bash
 pip install deepmd-kit[gpu,cu12,torch,lmp,ipi]

doc/install/install-from-source.md

@@ -224,7 +224,7 @@ If you don't install Horovod, DeePMD-kit will fall back to serial mode.
 
 ## Install the C++ interface
 
-If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python interface installed in the previous section does everything and he/she can safely skip this section.
+If one does not need to use DeePMD-kit with LAMMPS or i-PI, then the python interface installed in the previous section does everything and he/she can safely skip this section.
 
 ### Install Backends' C++ interface (optional)
 
@@ -234,9 +234,9 @@ If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python inte
 
 Since TensorFlow 2.12, TensorFlow C++ library (`libtensorflow_cc`) is packaged inside the Python library. Thus, you can skip building TensorFlow C++ library manually. If that does not work for you, you can still build it manually.
 
-The C++ interface of DeePMD-kit was tested with compiler GCC >= 4.8. It is noticed that the I-Pi support is only compiled with GCC >= 4.8. Note that TensorFlow may have specific requirements for the compiler version.
+The C++ interface of DeePMD-kit was tested with compiler GCC >= 4.8. It is noticed that the i-PI support is only compiled with GCC >= 4.8. Note that TensorFlow may have specific requirements for the compiler version.
 
-First, the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be consistent with the python interface. You may follow [the instruction](install-tf.2.12.md) or run the script `$deepmd_source_dir/source/install/build_tf.py` to install the corresponding C++ interface.
+First, the C++ interface of TensorFlow should be installed. It is noted that the version of TensorFlow should be consistent with the python interface. You may follow [the instruction](install-tf.2.12.md) or run the script `$deepmd_source_dir/source/install/build_tf.py` to install the corresponding C++ interface.
 
 :::
 

doc/install/install-gromacs.md

@@ -1,4 +1,4 @@
-# Install GROMACS with DeepMD
+# Install GROMACS with DeePMD-kit
 
 Before following this section, [DeePMD-kit C++ interface](install-from-source.md) should have be installed.
 

doc/third-party/gromacs.md

@@ -83,7 +83,7 @@ For comparison, the original topology file generated by `acpype` will be:
      4      1      5      1    1.0758e+02    3.2635e+02 ;     H3 - C1     - H4
 ```
 
-### DeepMD Settings
+### DeePMD-kit Settings
 
 Before running simulations, we need to tell GROMACS to use DeepPotential by setting the environment variable `GMX_DEEPMD_INPUT_JSON`:
 
@@ -119,7 +119,7 @@ Here is an explanation for these settings:
 ```
 
 - `lambda`: Optional, default 1.0. Used in alchemical calculations.
-- `pbc`: Optional, default true. If true, the GROMACS periodic condition is passed to DeepMD.
+- `pbc`: Optional, default true. If true, the GROMACS periodic condition is passed to DeePMD-kit.
 
 ### Run Simulation
 
@@ -136,7 +136,7 @@ HW           1       1.008   0.0000  A   0.00000e+00  0.00000e+00
 OW           8      16.00    0.0000  A   0.00000e+00  0.00000e+00
 ```
 
-As mentioned in the above section, `input.json` and relevant files (`index.raw`, `type.raw`) should also be created. Then, we can start the simulation under the NVT ensemble and plot the radial distribution function (RDF) by `gmx rdf` command. We can see that the RDF given by Gromacs+DP matches perfectly with Lammps+DP, which further provides an evidence on the validity of our simulation.
+As mentioned in the above section, `input.json` and relevant files (`index.raw`, `type.raw`) should also be created. Then, we can start the simulation under the NVT ensemble and plot the radial distribution function (RDF) by `gmx rdf` command. We can see that the RDF given by Gromacs+DP matches perfectly with LAMMPS+DP, which further provides an evidence on the validity of our simulation.
 ![rdf](../../examples/water/gmx/rdf.png)
 
 However, we still recommend you run an all-atom DP simulation using LAMMPS since it is more stable and efficient.

doc/train/multi-task-training-pt.md

@@ -16,7 +16,7 @@ For each dataset, a training task is defined as
     \min_{\boldsymbol \theta}   L^{(t)} (\boldsymbol x^{(t)}; \boldsymbol  \theta^{(t)}, \tau), \quad t=1, \dots, n_t.
 ```
 
-In the Pytorch implementation, during the multi-task training process, all tasks can share any portion of the model parameters.
+In the PyTorch implementation, during the multi-task training process, all tasks can share any portion of the model parameters.
 A typical scenario is that each task shares the same descriptor with trainable parameters $\boldsymbol{\theta}_ {d}$, while each has its own fitting network with trainable parameters $\boldsymbol{\theta}_ f^{(t)}$, thus
 $\boldsymbol{\theta}^{(t)} = \{ \boldsymbol{\theta}_ {d} , \boldsymbol{\theta}_ {f}^{(t)} \}$.
 At each training step, a task will be randomly selected from ${1, \dots, n_t}$ according to the user-specified probability,

doc/troubleshooting/installation.md

@@ -2,7 +2,7 @@
 
 ## Inadequate versions of gcc/g++
 
-Sometimes you may use a gcc/g++ of version < 4.8. In this way, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit, but i-Pi and GROMACS plugins will be disabled automatically. Or if you have a gcc/g++ of version > 4.8, say, 7.2.0, you may choose to use it by doing
+Sometimes you may use a gcc/g++ of version < 4.8. In this way, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit, but i-PI and GROMACS plugins will be disabled automatically. Or if you have a gcc/g++ of version > 4.8, say, 7.2.0, you may choose to use it by doing
 
 ```bash
 export CC=/path/to/gcc-7.2.0/bin/gcc

source/cmake/Findtensorflow.cmake

@@ -3,7 +3,7 @@
 # Output: TensorFlow_FOUND TensorFlow_INCLUDE_DIRS TensorFlow_LIBRARY
 # TensorFlow_LIBRARY_PATH TensorFlowFramework_LIBRARY
 # TensorFlowFramework_LIBRARY_PATH TENSORFLOW_LINK_LIBPYTHON : whether
-# Tensorflow::tensorflow_cc links libpython
+# TensorFlow::tensorflow_cc links libpython
 #
 # Target: TensorFlow::tensorflow_framework TensorFlow::tensorflow_cc
 

source/gmx/dp_gmx_patch

@@ -59,7 +59,7 @@ check_patched () {
 }
 
 dp_gmx_patch () {
-    echo "- Staring DeepMD patch program to GROMACS ${VERSION}"
+    echo "- Staring DeePMD-kit patch program to GROMACS ${VERSION}"
     echo "- Mode: patch"
     if [ ! -d $1 ]; then
         echo "- ERROR: invalid gromacs root: $1"
@@ -86,7 +86,7 @@ dp_gmx_patch () {
 }
 
 dp_gmx_revert () {
-    echo "- Staring DeepMD patch program to GROMACS ${VERSION}"
+    echo "- Staring DeePMD-kit patch program to GROMACS ${VERSION}"
     echo "- Mode: revert"
     check_patched $1
     if [ ! -d $1 ]; then

source/gmx/patches/2020.2/CMakeLists.txt.patch.in

@@ -4,8 +4,8 @@
  #    (i.e., something that is exposed in installed headers).
  set(GMX_PUBLIC_LIBRARIES "")
 
-+# DeepMD
-+message(STATUS "Compling with DeepMD...")
++# DeePMD-kit
++message(STATUS "Compiling with DeePMD-kit...")
 +add_definitions(-w) # close warning
 +# define deepmd and tensorflow root
 +if (NOT DEFINED GMX_DEEPMD_ROOT)
@@ -22,7 +22,7 @@
 +
 +# add link libraries
 +list (APPEND GMX_PUBLIC_LIBRARIES deepmd_gromacs)
-+# DeepMD
++# DeePMD-kit
 +
  ########################################################################
  # Check and warn if cache generated on a different host is being reused

source/gmx/patches/2020.2/src/gromacs/mdlib/sim_util.cpp.patch

@@ -22,14 +22,14 @@
                                 simulationWork.useGpuPmePpCommunication, false, wcycle);
      }
 
-+    /* DeepMD */
++    /* DeePMD-kit */
 +    double               dener;
 +    std::vector<double > dforce;
 +    if (useDeepmd)
 +    {
 +        if (DIM != 3)
 +        {
-+            gmx_fatal(FARGS, "DeepMD does not support DIM < 3.");
++            gmx_fatal(FARGS, "DeePMD-kit does not support DIM < 3.");
 +        }
 +        else
 +        {

source/install/build_tf.py

@@ -444,7 +444,7 @@ def built(self):
         return (PREFIX / "bin" / "bazelisk").exists()
 
 
-class BuildNumpy(Build):
+class BuildNumPy(Build):
     """Build NumPy."""
 
     @property
@@ -614,7 +614,7 @@ def dependencies(self) -> Dict[str, Build]:
             optional_dep["rocm"] = BuildROCM()
         return {
             "bazelisk": BuildBazelisk(),
-            "numpy": BuildNumpy(),
+            "numpy": BuildNumPy(),
             **optional_dep,
         }
 
@@ -865,7 +865,7 @@ def parse_args(args: Optional[List[str]] = None):
         takes arguments from sys.argv
     """
     parser = argparse.ArgumentParser(
-        description="Installer of Tensorflow C++ Library.\n\n" + pretty_print_env(),
+        description="Installer of TensorFlow C++ Library.\n\n" + pretty_print_env(),
         formatter_class=RawTextArgumentDefaultsHelpFormatter,
     )
     parser.add_argument(