From b268bf834eb51a1f57772af3469ee6eabad8fdf8 Mon Sep 17 00:00:00 2001 From: robinzyb <38876805+robinzyb@users.noreply.github.com> Date: Fri, 30 Aug 2024 14:54:20 +0200 Subject: [PATCH 1/5] fix typo in readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index fe6a45d..f285120 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ ![PyPI - pip install](https://img.shields.io/pypi/dm/cp2kdata?logo=pypi&label=pip%20install) ![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/cp2kdata?label=conda-forge) -Python Package to postprocess cp2k data, including cube, pdos, output files +A Python Package to postprocess cp2k data, including cube, pdos, output files # Installation @@ -35,7 +35,7 @@ pip install . - [Manipulate CP2K Pdos Files](./docs/pdos/README.md) # Additional Features -- [Plug in for dpdata](./docs/dpdata_plugin.md) +- [The plugin for dpdata](./docs/dpdata_plugin.md) # Feature Request Any advice is welcome. If you would like to request a new feature, please open an issue in github and upload example input and output files. From d0ed6ad0e687e73c37e8fe79fab78b23bc0b8b24 Mon Sep 17 00:00:00 2001 From: robinzyb <38876805+robinzyb@users.noreply.github.com> Date: Sat, 14 Sep 2024 21:17:48 +0200 Subject: [PATCH 2/5] update pyproject.toml add maintainer entry --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 04113b9..7184fcd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,9 @@ description = "A Small Package to Postprocess Cp2k Output" authors = [ {name = "Yong-Bin Zhuang", email = "robinzhuang@outlook.com"} ] +maintainers = [ + {name = "Yong-Bin Zhuang", email = "robinzhuang@outlook.com"} + ] license = {file = "LICENSE"} readme = "README.md" classifiers = [ From 6298486d34783c7af18df7ddfe2bd955b71725d2 Mon Sep 17 00:00:00 2001 From: robinzyb <38876805+robinzyb@users.noreply.github.com> Date: Sat, 2 Nov 2024 20:07:12 +0100 Subject: [PATCH 3/5] bug fixed double energy parse info When -pos-.xyz file is not present in the md folder. md energy will be parsed from the -1.ener file. then parsed again from output file. This is fixed by checking the energies_list attribut. --- cp2kdata/output.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/cp2kdata/output.py b/cp2kdata/output.py index e5038d4..b46e6d1 100644 --- a/cp2kdata/output.py +++ b/cp2kdata/output.py @@ -78,7 +78,7 @@ def __init__( raise ValueError( "please provide cp2k output file with MEDIUM print level. Print Level Low doesn't provide necessary information for initialize the cp2kdata class.") - # -- set some basic attribute -- + # -- set some basic attributes -- self.num_frames = None self.init_atomic_coordinates = None self.atomic_kind = None @@ -373,12 +373,23 @@ def parse_md(self): self.md_info = parse_md_info(self.filename) self.check_md_type(md_type=self.md_info.ensemble_type) + # parse md energies ener_file_list = glob.glob(os.path.join(self.path_prefix, "*.ener")) if ener_file_list: self.energies_list = parse_md_ener(ener_file_list[0]) + # parse md poses pos_xyz_file_list = glob.glob( - os.path.join(self.path_prefix, "*pos*.xyz")) + os.path.join(self.path_prefix, "*-pos-*.xyz")) + + n_pos_xyz_files = len(pos_xyz_file_list) + if n_pos_xyz_files > 1: + raise ValueError( + f"Cp2kData found {n_pos_xyz_files} pos files.\n" + f"{pos_xyz_file_list}.\n" + f"Please remove extra pos files and keep only one pos file in the folder." + ) + if pos_xyz_file_list: # TODO: Is it possible to have no pos file? self.atomic_frames_list, energies_list_from_pos, self.chemical_symbols = parse_pos_xyz( @@ -388,10 +399,11 @@ def parse_md(self): self.energies_list = energies_list_from_pos else: # if no pos file and ener file, parse energies from the output file - format_logger(info="Energies", filename=self.filename) - self.energies_list = parse_energies_list(self.output_file) - self.energies_list = self.drop_last_info( - self.cp2k_info, self.energies_list) + if not hasattr(self, "energies_list"): + format_logger(info="Energies", filename=self.filename) + self.energies_list = parse_energies_list(self.output_file) + self.energies_list = self.drop_last_info( + self.cp2k_info, self.energies_list) self.atomic_frames_list = None frc_xyz_file_list = glob.glob( From 110f6d5dbd481246431d67473819fa47a1aa80d8 Mon Sep 17 00:00:00 2001 From: robinzyb <38876805+robinzyb@users.noreply.github.com> Date: Wed, 4 Dec 2024 16:57:48 +0100 Subject: [PATCH 4/5] add method to get homo lumo gap --- cp2kdata/pdos/pdos.py | 55 ++++++++++++++++++++++++------------------- 1 file changed, 31 insertions(+), 24 deletions(-) diff --git a/cp2kdata/pdos/pdos.py b/cp2kdata/pdos/pdos.py index f671659..162598f 100644 --- a/cp2kdata/pdos/pdos.py +++ b/cp2kdata/pdos/pdos.py @@ -243,15 +243,15 @@ def read_dos_fermi(self): return fermi def read_dos_energies(self): - """ - Reads the DOS energies from the file and converts them to electron volts (eV). + """ + Reads the DOS energies from the file and converts them to electron volts (eV). - Returns: - numpy.ndarray: An array of DOS energies in eV. - """ - energies = np.loadtxt(self.file, usecols=1) - energies = energies * au2eV - return energies + Returns: + numpy.ndarray: An array of DOS energies in eV. + """ + energies = np.loadtxt(self.file, usecols=1) + energies = energies * au2eV + return energies @property def occupation(self): @@ -260,26 +260,33 @@ def occupation(self): return occupation def get_homo_ener(self): - """ - Get the energy of the highest occupied molecular orbital (HOMO). + """ + Get the energy of the highest occupied molecular orbital (HOMO). - Returns: - float: The energy of the HOMO. - """ - homo_idx = np.where(self.occupation == 0)[0][0]-1 - homo_ener = self.energies[homo_idx] + Returns: + float: The energy of the HOMO. + """ + homo_idx = np.where(self.occupation == 0)[0][0]-1 + homo_ener = self.energies[homo_idx] - return homo_ener + return homo_ener def get_lumo_ener(self): - """ - Get the energy of the lowest unoccupied molecular orbital (LUMO). - - Returns: - lumo_ener (float): The energy of the LUMO. - """ - lumo_ener = self.energies[self.occupation == 0][0] - return lumo_ener + """ + Get the energy of the lowest unoccupied molecular orbital (LUMO). + + Returns: + lumo_ener (float): The energy of the LUMO. + """ + lumo_ener = self.energies[self.occupation == 0][0] + return lumo_ener + + def get_homo_lumo_gap(self): + """ + Get the lumo - homo gap from energy + """ + gap_ener = self.get_lumo_ener() - self.get_homo_ener() + return gap_ener def get_raw_dos(self, dos_type="total", steplen=0.1, usecols=None): From f929bedb9c6ca4888f2e5b4960decf2ee7a6e984 Mon Sep 17 00:00:00 2001 From: robinzyb <38876805+robinzyb@users.noreply.github.com> Date: Wed, 4 Dec 2024 23:09:59 +0100 Subject: [PATCH 5/5] add color blind friendly colormaps --- cp2kdata/plots/colormaps.py | 27 ++++++++ docs/backlog.md | 9 ++- docs/figures/cb_lcmap_plot.png | Bin 0 -> 73386 bytes docs/figures/cb_lscmap_plot.png | Bin 0 -> 110572 bytes docs/figures/cp2kdata_cb_lcmap.png | Bin 0 -> 7004 bytes docs/figures/cp2kdata_cb_lscmap.png | Bin 0 -> 7759 bytes docs/plots.md | 104 ++++++++++++++++++++++++++++ docs/references.md | 3 + jupyter-book/_toc.yml | 3 + jupyter-book/references.bib | 64 +++-------------- 10 files changed, 151 insertions(+), 59 deletions(-) create mode 100644 cp2kdata/plots/colormaps.py create mode 100644 docs/figures/cb_lcmap_plot.png create mode 100644 docs/figures/cb_lscmap_plot.png create mode 100644 docs/figures/cp2kdata_cb_lcmap.png create mode 100644 docs/figures/cp2kdata_cb_lscmap.png create mode 100644 docs/plots.md create mode 100644 docs/references.md diff --git a/cp2kdata/plots/colormaps.py b/cp2kdata/plots/colormaps.py new file mode 100644 index 0000000..756416a --- /dev/null +++ b/cp2kdata/plots/colormaps.py @@ -0,0 +1,27 @@ +# create color blind friendly colormaps + +from matplotlib.colors import LinearSegmentedColormap, ListedColormap +import matplotlib as mpl + +# the colormap was taken from the following source: +# [1] Wong, Bang. "Points of view: Color coding." nature methods 7.8 (2010): 573. +color_blind_map = [ + #[0.0/256, 0.0/256, 0.0/256, 1], # Black + [230.0/256, 159.0/256, 0.0/256, 1], # Orange + [86.0/256, 180.0/256, 233.0/256, 1], # Sky Blue + [0.0/256, 158.0/256, 115.0/256, 1], # Bluish Green + [240.0/256, 228.0/256, 66.0/256, 1], # Yellow + [0.0/256, 114.0/256, 178.0/256, 1], # Blue + [213.0/256, 94.0/256, 0.0/256, 1], # Vermilion + [204.0/256, 121.0/256, 167.0/256, 1], # Reddish Purple +] + +cb_lcmap = ListedColormap(color_blind_map, name='cp2kdata_cb_lcmap') +cb_lscmap = LinearSegmentedColormap.from_list(name='cp2kdata_cb_lscmap', colors=color_blind_map) + +mpl.colormaps.register(cmap=cb_lcmap) +print("color blind friendly colormap registered as cp2kdata_cb_lcmap") +mpl.colormaps.register(cmap=cb_lscmap) +print("color blind friendly colormap registered as cp2kdata_cb_lscmap") + + diff --git a/docs/backlog.md b/docs/backlog.md index dbea104..aa140bd 100644 --- a/docs/backlog.md +++ b/docs/backlog.md 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recommended color blind friendly colormaps in the CP2KData package. +The usage is summarized in the following, + +1. Register the colormaps using cp2kdata + + ```python + import matplotlib as mpl + import cp2kdata.plots.colormaps + ``` + ```stdout + #output + color blind friendly colormap registered as cp2kdata_cb_lcmap + color blind friendly colormap registered as cp2kdata_cb_lscmap + ``` + +2. Get the colormaps + + There are two colormaps in the package. + The first one is a listed colormap, which can also be understood as a discrete colormap. + ```python + mpl.colormaps['cp2kdata_cb_lcmap'] + ``` + ![cbl_cbar](./figures/cp2kdata_cb_lcmap.png) + + The second one is a linear segmented colormap, which can also be understood as a continuous colormap. + ```python + mpl.colormaps['cp2kdata_cb_lscmap'] + ``` + ![cbls_cbar](./figures/cp2kdata_cb_lscmap.png) + +3. Example for using the listed colormap + ```python + import matplotlib.pyplot as plt + import matplotlib as mpl + import numpy as np + import cp2kdata.plots.colormaps + plt.style.use('cp2kdata.matplotlibstyle.jcp') + + + cp2kdata_cb_lcmap = mpl.colormaps['cp2kdata_cb_lcmap'] + plt.rcParams["axes.prop_cycle"] = plt.cycler("color", cp2kdata_cb_lcmap.colors) + row = 1 + col = 1 + fig = plt.figure(figsize=(3.37*col, 1.89*row), dpi=300, facecolor='white') + gs = fig.add_gridspec(row,col) + ax = fig.add_subplot(gs[0]) + + t = np.linspace(-10, 10, 100) + def sigmoid(t, t0): + return 1 / (1 + np.exp(-(t - t0))) + + nb_colors = len(plt.rcParams['axes.prop_cycle']) + + shifts = np.linspace(-5, 5, nb_colors) + amplitudes = np.linspace(1, 1.5, nb_colors) + for t0, a in zip(shifts, amplitudes): + ax.plot(t, a * sigmoid(t, t0), '-') + ax.set_xlim(-10, 10) + + fig.savefig("cb_lcmap_plot.png", dpi=100) + ``` + ![cbl_plot](./figures/cb_lcmap_plot.png) +4. Example for using the linear segmented colormap + ```python + import matplotlib.pyplot as plt + import matplotlib as mpl + import numpy as np + import cp2kdata.plots.colormaps + plt.style.use('cp2kdata.matplotlibstyle.jcp') + + + cp2kdata_cb_lscmap = mpl.colormaps['cp2kdata_cb_lscmap'] + N = 13 + plt.rcParams["axes.prop_cycle"] = plt.cycler("color", cp2kdata_cb_lscmap(np.linspace(0,1,N))) + row = 1 + col = 1 + fig = plt.figure(figsize=(3.37*col, 1.89*row), dpi=300, facecolor='white') + gs = fig.add_gridspec(row,col) + ax = fig.add_subplot(gs[0]) + + t = np.linspace(-10, 10, 100) + def sigmoid(t, t0): + return 1 / (1 + np.exp(-(t - t0))) + + nb_colors = len(plt.rcParams['axes.prop_cycle']) + + shifts = np.linspace(-5, 5, nb_colors) + amplitudes = np.linspace(1, 1.5, nb_colors) + for t0, a in zip(shifts, amplitudes): + ax.plot(t, a * sigmoid(t, t0), '-') + ax.set_xlim(-10, 10) + + fig.savefig("cb_lscmap_plot.png", dpi=100) + ``` + ![cbls_plot](./figures/cb_lscmap_plot.png) diff --git a/docs/references.md b/docs/references.md new file mode 100644 index 0000000..98239ac --- /dev/null +++ b/docs/references.md @@ -0,0 +1,3 @@ +# Bibliography +```{bibliography} +``` \ No newline at end of file diff --git a/jupyter-book/_toc.yml b/jupyter-book/_toc.yml index b169ec3..1cf16c2 100644 --- a/jupyter-book/_toc.yml +++ b/jupyter-book/_toc.yml @@ -12,6 +12,9 @@ parts: - caption: Parameter Test chapters: - file: docs/input_test + - caption: Plots + chapters: + - file: docs/plots - caption: Plugin chapters: - file: docs/dpdata_plugin diff --git a/jupyter-book/references.bib b/jupyter-book/references.bib index 783ec6a..f9b78d1 100644 --- a/jupyter-book/references.bib +++ b/jupyter-book/references.bib @@ -1,56 +1,12 @@ --- --- - -@inproceedings{holdgraf_evidence_2014, - address = {Brisbane, Australia, Australia}, - title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, - booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, - publisher = {Frontiers in Neuroscience}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, - year = {2014} -} - -@article{holdgraf_rapid_2016, - title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, - volume = {7}, - issn = {2041-1723}, - url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, - doi = {10.1038/ncomms13654}, - number = {May}, - journal = {Nature Communications}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, - year = {2016}, - pages = {13654}, - file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} -} - -@inproceedings{holdgraf_portable_2017, - title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, - volume = {Part F1287}, - isbn = {978-1-4503-5272-7}, - doi = {10.1145/3093338.3093370}, - abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, - booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}}, - author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, - year = {2017}, - keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} -} - -@article{holdgraf_encoding_2017, - title = {Encoding and decoding models in cognitive electrophysiology}, - volume = {11}, - issn = {16625137}, - doi = {10.3389/fnsys.2017.00061}, - abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, - journal = {Frontiers in Systems Neuroscience}, - author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, - year = {2017}, - keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} -} - -@book{ruby, - title = {The Ruby Programming Language}, - author = {Flanagan, David and Matsumoto, Yukihiro}, - year = {2008}, - publisher = {O'Reilly Media} -} +@article{wong2010points, + title={Points of view: Color coding}, + author={Wong, Bang}, + journal={nature methods}, + volume={7}, + number={8}, + pages={573}, + year={2010}, + publisher={Nature Publishing Group} +} \ No newline at end of file