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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md index afe06f3..5989325 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ micromamba activate soluenv rm -rf subprojects git restore subprojects meson setup build --wipe -pip install -e . --no-build-isolation +pip install -e . #--no-build-isolation ``` ## Usage diff --git a/environment.yml b/environment.yml index 1753548..a983ef0 100644 --- a/environment.yml +++ b/environment.yml @@ -42,8 +42,12 @@ dependencies: - pybind11 - meson-python - h5py + - scipy # Examples - gdown + - matplotlib - pip: # works for regular pip packages - lammps-logfile + # Required for plotting + - spirit-extras diff --git a/examples/hydrogen_bond_tcf/hbond_tcf.py b/examples/hydrogen_bond_tcf/hbond_tcf.py index 51dd0c0..478bb2e 100644 --- a/examples/hydrogen_bond_tcf/hbond_tcf.py +++ b/examples/hydrogen_bond_tcf/hbond_tcf.py @@ -1,16 +1,70 @@ from pathlib import Path import gdown +import soluanalysis as solu +from soluanalysis.io import read_lammps_dump +import numpy as np hbond_example_dir = Path(__file__).resolve().parent input_folder = hbond_example_dir / "input" # Create the folder if it does not exist input_folder.mkdir(parents=True, exist_ok=True) +# Create an output folder if it does not exist +output_folder = hbond_example_dir / "output" +output_folder.mkdir(parents=True, exist_ok=True) +output_file = output_folder / 'tcf.txt' # URL for the trajectory file (571 MB) file_id = '15sv3cc_Mx281C1RHDTzUiqMoVBjwcWNu' url = f"https://drive.google.com/uc?id={file_id}" -# Output file path -output_file = input_folder / 'dump-tcf.lammpstrj' +# File path for the trajectory +input_file = input_folder / 'dump-tcf.lammpstrj' # Download the file into the 'input' folder (if it does not already exist) -if not output_file.exists(): - gdown.download(url, str(output_file), quiet=False) \ No newline at end of file +if not input_file.exists(): + gdown.download(url, str(input_file), quiet=False) + +# Read in the trajectory +systems, timesteps = read_lammps_dump(input_file, ':') +print("Trajectory read.\n") +# General system information +fe_type = 3 +o_type = 1 +h_type = 2 +cl_type = 4 +# Cutoffs, types etc needed for hydrogen bonds +donor_atom_types = [o_type] +acceptor_atom_types = [cl_type, o_type] +h_atom_types = [h_type] +donor_acceptor_cutoff = 3.2 +max_angle_deg = 30 # in degrees +# List that will hold UndirectedNetwork objects corresponding to hydrogen bonds +networks = [] + +# Loop through the trajectory +for system in systems: + n_atoms = system.n_atoms() + network = solu.graphlib.UndirectedNetwork(n_atoms) # Create the network + # We will ignore hydrogens so there is no need for intramolecular hydrogen bonds + solu.james.add_hbonds( + network, + system, + donor_atom_types, + acceptor_atom_types, + h_atom_types, + donor_acceptor_cutoff, + max_angle_deg, + True, + ) + networks.append(network) +print("Network list generated\n") +# Now that you have a list of UndirectedNetwork objects and the timesteps, +# the time autocorrelation function can be calculated + +# Calculate the time correlation function (using default values of start_t0 etc) +tau_values, tcf_avg, tcf_error = solu.james.time_correlation_function( + networks, timesteps, 0, 1, 1, None + ) +print("Calculated the time correlation function\n") +# Write these out to a CSV +# Write out to file +header_string = 'tau\ttcf\ttcf_stderr' +np.savetxt(output_file, np.column_stack((tau_values, tcf_avg, tcf_error)), delimiter=' ', header = header_string) \ No newline at end of file diff --git a/examples/hydrogen_bond_tcf/lineplot.py b/examples/hydrogen_bond_tcf/lineplot.py new file mode 100644 index 0000000..4d66de5 --- /dev/null +++ b/examples/hydrogen_bond_tcf/lineplot.py @@ -0,0 +1,143 @@ +from spirit_extras.plotting import Paper_Plot +import numpy as np +from pathlib import Path +import matplotlib.pyplot as plt +from PIL import Image +import os +from os.path import isfile, join +import matplotlib.patheffects as path_effects +import matplotlib.lines as mlines +from matplotlib.legend_handler import HandlerTuple +from soluanalysis.misc import fit_biexponential + +def insert_inset(image_path, axis, rel_height, + rel_width, margin_x, margin_y, x_align, y_align): + image = pplot.open_image(image_path) # Read the image as a numpy array + # image = pplot.crop_to_content(image) # crop the image to content + # image = pplot.crop(image, 950,950) + + image = pplot.replace_background_color(image, [0,0,0,0], background_color=None) + + # Create an axis to hold the inset + # The inset shall be located in the top right corner + ax_inset = pplot.create_inset_axis( + containing_ax=axis, + rel_height=rel_height, + rel_width=rel_width, + margin_x=margin_x, + margin_y=margin_y, + x_align=x_align, + y_align=y_align, + ) + return ax_inset, image + +# Figure attributes +hbond_example_dir = Path(__file__).resolve().parent +input_dir = hbond_example_dir / "output" +fname = "tcf.txt" + +# Lengths in inches in general +CM = Paper_Plot.cm # Use this to give lengths in cm... + +# Params +params = { +"font.size": 8, +"font.family": ("Arial","sans-serif"), +"mathtext.fontset": "dejavuserif", +"xtick.labelsize": 7, +"ytick.labelsize": 7, +"axes.labelsize": 8 +} + +# For legend labels +font = {'family': ("Arial","sans-serif"), + 'weight': 'normal', + 'size': 7.5, + } + +# Either set the absolute height here or set the aspect ratio +# inside apply_absolute_margins + +pplot = Paper_Plot( + width=3.25, height=2.75,nrows=1, ncols=1, rcParams=params +) + +# Vertical margin: bottom and then top +# horizontal margin: left and then right +# Golden ratio aspect ratio=1.618 +# hspace -> height space + +pplot.apply_absolute_margins( + aspect_ratio=None, + abs_horizontal_margins=[1.3 * CM, 0.12 * CM], + abs_vertical_margins=[1.0 * CM, 0.5 * CM], + abs_wspace=0.0 * CM, + abs_hspace=0.0 * CM, +) + +print(pplot.info_string()) + +# Get the figure and gridpsec objects +fig = pplot.fig() +gs = pplot.gs() + +# DATA +# Get the data (with counterions) +infile = os.path.join(input_dir,fname) +data = np.loadtxt(infile,skiprows=1) # Load the text file +tau_val = np.array(data[:,0]) # Tau values in fs +tau_val = tau_val/1000 # Tau values in ps +tcf_val = np.array(data[:,1]) # Time correlation function values +tcf_val_err = np.array(data[:,2]) # Error bars for the TCF + +# --------------------------------------------- +# Curve fitting +# initial guess A, tau1, tau2 +params, fit_t, fit_ac, lifetime = fit_biexponential(tau_val, tcf_val, [0.5, 1, 2]) +print("Lifetime is ", lifetime, "ps \n") +A, tau1, tau2 = params +# If A+B is around 1.01, the fit is bad even if it looks okay +print(f"The time constants are {tau1} ps and {tau2} ps\n") +# --------------------------------------------- +ax1 = fig.add_subplot(gs[0,0]) +ax1.set_xlabel(r'$\tau$ (ps)') +ax1.set_ylabel(r'$\mathrm{C_{HB}} (\tau)$',labelpad=7) # we already handled the x-label with ax1 + +# Data points and line for non-octahedral state +# (tcf_1,) = ax1.plot(tau_val, tcf_val, marker=".", markersize=6, +# color="orangered",markeredgecolor='black',markeredgewidth=0.6,zorder=4, +# label=r'Data') +(tcf_1,) = ax1.plot(tau_val, tcf_val, marker=None, + color="orangered",zorder=4, + label=r'Data') +(fit,) = ax1.plot(tau_val, tcf_val, marker=None, + color="grey",zorder=6, linestyle = '--', + label=r'Fit') +# Shaded error region for non-octahedral states +ax1.fill_between(tau_val, tcf_val-tcf_val_err, tcf_val+tcf_val_err,linewidth=0.1,color="peachpuff", alpha=0.8,zorder=0) + +# Horizontal line through 0.0 +plt.axhline(y = 0.0, color = 'black', linestyle = '--', linewidth=1) + +# ax1.set_xlim([0,2.25]) # in percent +# xtick_vec = np.arange(0.0,2.5,0.5) +# xtick_vec = np.append(xtick_vec, 2.25) +# ax1.set_xticks(xtick_vec) +# ax1.set_ylim([0.0,25]) +# ax1.set_yscale("log") +ax1.set_xlim([0.0,10]) + +# --------------------------------------------------------------------- + +# PLOT LABEL (lifetime) +text = r'$\tau_C=$'+ "{:.2f} ps".format(lifetime) +ax1.text(4, 0.4, text, fontsize=7.5) + +# LEGEND + +ax1.legend(handles=[tcf_1, fit], fontsize=7.4) + +# --------------------------------------------------------------------- + +# plt.show() +fig.savefig(input_dir/'tcf.png', dpi=300) diff --git a/meson.build b/meson.build index 43a2180..37315d8 100644 --- a/meson.build +++ b/meson.build @@ -53,11 +53,20 @@ py.install_sources([ subdir: 'soluanalysis' ) -# IO and extras +# Extras py.install_sources([ - 'soluanalysis/io.py', - 'soluanalysis/ion_pairs.py' + 'soluanalysis/ion_pairs.py', + 'soluanalysis/hdf5_io.py', + 'soluanalysis/misc.py' ], pure: false, subdir: 'soluanalysis' +) + +# Thirdparty ASE and IO +py.install_sources([ + 'soluanalysis/thirdparty/ase/io.py', + ], + pure: false, + subdir: 'soluanalysis/' ) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 6a30dc8..05ff9ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,8 @@ authors = [ dependencies = [ "ase >= 3.22.1", "numpy", - "h5py" + "h5py", + "scipy" ] requires-python = ">=3.10" readme = "README.md" @@ -19,6 +20,8 @@ license = {text = "MIT"} [project.optional-dependencies] example = [ "gdown", + "spirit-extras", + "matplotlib" ] [project.urls] diff --git a/python_bindings/bindings.cpp b/python_bindings/bindings.cpp index 164317c..0d35553 100644 --- a/python_bindings/bindings.cpp +++ b/python_bindings/bindings.cpp @@ -1,6 +1,4 @@ #pragma once -#include "network_base.hpp" -#include "undirected_network.hpp" #include #include #include @@ -9,9 +7,11 @@ #include #include #include +#include #include // Basics +#include "bondcorrel.hpp" #include "bondfinder.hpp" #include "directed_network.hpp" #include "network_base.hpp" @@ -77,6 +77,8 @@ PYBIND11_MODULE(james, m) { .def_readwrite("boxLo", &James::Atoms::System::boxLo) .def("n_atoms", &James::Atoms::System::n_atoms) .def("collect_ids", &James::Atoms::System::collect_ids) + .def("collect_positions", &James::Atoms::System::collect_positions) + .def("reset_positions", &James::Atoms::System::reset_positions) .def("delete", static_cast( &James::Atoms::System::del), @@ -140,6 +142,49 @@ PYBIND11_MODULE(james, m) { py::enum_(m, "WriteIdentifier") .value("AtomID", James::Path::WriteIdentifier::AtomID) .value("Index", James::Path::WriteIdentifier::Index); + // Binding for the templated time_correlation_function, which is templated on + // the network type + // For a vector of UndirectedNetwork objects + m.def( + "time_correlation_function", + [](const std::vector> + &network_time_series, + const std::vector &time, int start_t0 = 0, int start_tau = 1, + int delta_tau = 1, std::optional calc_upto_tau = std::nullopt, + bool continuous_bond = true) { + return James::Bond::Correlation::time_correlation_function< + Graph::UndirectedNetwork>(network_time_series, time, + start_t0, start_tau, delta_tau, + calc_upto_tau, continuous_bond); + }, + pybind11::arg("network_time_series"), pybind11::arg("time"), + pybind11::arg("start_t0") = 0, pybind11::arg("start_tau") = 1, + pybind11::arg("delta_tau") = 1, + pybind11::arg("calc_upto_tau") = std::nullopt, + pybind11::arg("continuous_bond") = true, + "Time correlation function returning tau values, the normalized " + "correlation function values, and the standard error in the " + "correlation function values."); + // For a vector of DirectedNetwork objects + m.def( + "time_correlation_function", + [](const std::vector> &network_time_series, + const std::vector &time, int start_t0 = 0, int start_tau = 1, + int delta_tau = 1, std::optional calc_upto_tau = std::nullopt, + bool continuous_bond = true) { + return James::Bond::Correlation::time_correlation_function< + Graph::DirectedNetwork>(network_time_series, time, start_t0, + start_tau, delta_tau, calc_upto_tau, + continuous_bond); + }, + pybind11::arg("network_time_series"), pybind11::arg("time"), + pybind11::arg("start_t0") = 0, pybind11::arg("start_tau") = 1, + pybind11::arg("delta_tau") = 1, + pybind11::arg("calc_upto_tau") = std::nullopt, + pybind11::arg("continuous_bond") = true, + "Time correlation function returning tau values, the normalized " + "correlation function values, and the standard error in the " + "correlation function values."); } PYBIND11_MODULE(graphlib, m) { diff --git a/resources/Fe3_water_cluster.lammpstrj b/resources/Fe3_water_cluster.lammpstrj new file mode 100644 index 0000000..3c1cb7b --- /dev/null +++ b/resources/Fe3_water_cluster.lammpstrj @@ -0,0 +1,308 @@ +ITEM: TIMESTEP +0 +ITEM: NUMBER OF ATOMS +19 +ITEM: BOX BOUNDS pp pp pp +0.9025605765 49.0974394233 +0.9025605765 49.0974394233 +0.9025605765 49.0974394233 +ITEM: ATOMS id type mol x y z +14 3 14 22.4684 31.2259 29.1658 +4163 1 1559 23.9672 30.088 28.4023 +4164 2 1559 24.7243 30.2167 27.9883 +4165 2 1559 23.9445 29.2246 28.5247 +5678 1 2064 20.9277 32.2228 29.9101 +5679 2 2064 20.1984 32.4722 29.5014 +5680 2 2064 20.7315 32.3158 30.755 +5846 1 2120 21.2571 29.6878 28.7283 +5847 2 2120 21.4117 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+8753 1 3089 23.0279 30.4746 30.848 +8754 2 3089 22.4495 30.1298 31.4027 +8755 2 3089 23.7849 30.4148 31.2774 +9260 1 3258 22.1368 31.978 27.2436 +9261 2 3258 21.5115 31.7441 26.6819 +9262 2 3258 22.5734 32.6011 26.8167 diff --git a/soluanalysis/hdf5_io.py b/soluanalysis/hdf5_io.py new file mode 100644 index 0000000..ca98faa --- /dev/null +++ b/soluanalysis/hdf5_io.py @@ -0,0 +1,196 @@ +import numpy as np +from typing import Dict, List, Optional, TextIO, Tuple, Union +from pathlib import Path +from collections import deque +from os.path import splitext +from soluanalysis.james import Atom, System +import h5py +import soluanalysis as solu +import numbers + + +def save_system_to_hdf5(system: solu.james.System, hdf5_group: h5py.Group): + """Save a System object to an HDF5 group. + + Args: + system (solu.james.System): System object to serialize + hdf5_group (h5py.Group): The HDF5 group to be saved to + """ + + atom_ids = np.array([atom.id for atom in system.atoms], dtype=np.int32) + atom_types = np.array([atom.type for atom in system.atoms], dtype=np.int32) + mol_ids = np.array( + [atom.id if atom.mol_id is None else atom.mol_id for atom in system.atoms], + dtype=np.int32, + ) + positions = np.array([atom.position for atom in system.atoms], dtype=np.float64) + + hdf5_group.create_dataset("atom_ids", data=atom_ids) + hdf5_group.create_dataset("atom_types", data=atom_types) + hdf5_group.create_dataset("mol_ids", data=mol_ids) + hdf5_group.create_dataset("positions", data=positions) + + if system.box is not None: + hdf5_group.create_dataset("box", data=np.array(system.box, dtype=np.float64)) + if system.boxLo is not None: + hdf5_group.create_dataset( + "boxLo", data=np.array(system.boxLo, dtype=np.float64) + ) + + +def read_ion_pairs_from_hdf5( + file_path: Path, +) -> Tuple[ + Dict[int, Dict[int, List[List[int]]]], + List[int], + solu.james.System, + int, + solu.james.WriteIdentifier, +]: + """Reads the HDF5 file and reconstructs a dictionary with the time series information about the ion pairs + + Args: + file_path (Path): The HDF5 file to read from + + Returns: + Tuple[Dict[int, Dict[int, List[List[int]]]], List[int], int, solu.james.WriteIdentifier]: A tuple containing + 1) the dictionary with the ion pairs, + 2) timesteps, + 3) System object + 4) max_depth, + 5) writeIdentifier + """ + time_series_dict = {} + + enum_mapping = { + "WriteIdentifier.AtomID": solu.james.WriteIdentifier.AtomID, + "WriteIdentifier.Index": solu.james.WriteIdentifier.Index, + } + + with h5py.File(file_path, "r") as file: + # Read the metadata + max_depth = file.attrs["max_depth"] + identifier_str = file.attrs["writeIdentifier"] + + # Read the timesteps + timesteps = file["timesteps"][:].tolist() + + # Read the representative System object + system_group = file["system"] + system = read_system_from_hdf5(system_group) + + # Iterate over the timesteps + for timestep in timesteps: + timestep_group = file[str(timestep)] + groups = {} + + # Iterate over the lengths within each timestep + for length in timestep_group.keys(): + length_group = timestep_group[length] + + # Read the numpy array and convert it back to a list of lists + data_array = length_group["ion_pairs"][:] + lists = data_array.tolist() + + groups[int(length)] = lists + + time_series_dict[int(timestep)] = groups + + # Return the time series, timesteps, max_depth, the enum, and the number of atoms + return ( + time_series_dict, + timesteps, + system, + max_depth, + enum_mapping.get(identifier_str), + ) + + +def read_system_from_hdf5(hdf5_group: h5py.Group) -> solu.james.System: + """Read a System object from an HDF5 group. + + Args: + hdf5_group (h5py.Group): HDF5 group, from which the System object will be reconstructed + + Returns: + solu.james.System: Reconstructed System object + """ + atom_ids = hdf5_group["atom_ids"][:] + atom_types = hdf5_group["atom_types"][:] + mol_ids = hdf5_group["mol_ids"][:] + positions = hdf5_group["positions"][:] + + # Reconstruct atoms list + atoms = [ + solu.james.Atom(atom_id, atom_type, mol_id, position) + for atom_id, atom_type, mol_id, position in zip( + atom_ids, atom_types, mol_ids, positions + ) + ] + + # Read optional attributes + box = hdf5_group["box"][:] if "box" in hdf5_group else None + boxLo = hdf5_group["boxLo"][:] if "boxLo" in hdf5_group else None + + # Reconstruct the System object + system = solu.james.System(atoms, box, boxLo) + + return system + + +def save_ion_pairs_to_hdf5( + file_path: Path, + time_series_dict: Dict[int, Dict[int, List[List[int]]]], + system: solu.james.System, + max_depth: int, + write_identifier: solu.james.WriteIdentifier, + **compression_kwargs: Union[str, int], +) -> None: + """Save the ion pairs per time step, sorted according to length into an HDF5 file. + + Args: + file_path (Path): File path of the HDF5 file to write to + time_series_dict (Dict[int, Dict[int, List[List[int]]]]): Dictionary containing timesteps and ion pairs. + The keys of the outer dictionary are timesteps, and the keys of the inner dictionary are ion pair lengths + system (solu.james.System): Representative System object, containing indices, atom IDs, atom types, molecular IDs + max_depth (int): Maximum length of the ion pair + write_identifier (solu.james.WriteIdentifier): enum class which describes whether the elements correspond to + atom IDs or indices in the System object. + compression_kwargs(Union[str, int]): additional compression options for the create_dataset command in h5py. + For instance, compression="gzip" and compression_opts=4 + """ + # Extract timesteps from the keys of time_series_dict and sort + timesteps = sorted(time_series_dict.keys()) + + with h5py.File(file_path, "w") as file: + # Save metadata + file.attrs["max_depth"] = max_depth + file.attrs["writeIdentifier"] = str(write_identifier) # convert enum to string + + # Save the timesteps as a separate dataset + file.create_dataset( + "timesteps", data=np.array(timesteps, dtype=np.int32), **compression_kwargs + ) + + # Save the System object + system_group = file.create_group("system") + save_system_to_hdf5(system, system_group) + + # Now save the ion pairs per timestep into separate groups (each length would be in a different group) + for timestep in timesteps: + groups = time_series_dict[timestep] + + # Create a group for each timestep (timesteps are unique) + timestep_group = file.create_group(str(timestep)) + + for length, data in groups.items(): + # Create a subgroup for each length (can go upto max_length) + length_group = timestep_group.create_group(str(length)) + + # Convert the list of lists to a numpy array + ion_pair_data = np.array(data, dtype=np.int32) + + # Save the numpy array to the HDF5 file + length_group.create_dataset( + "ion_pairs", data=ion_pair_data, **compression_kwargs + ) diff --git a/soluanalysis/misc.py b/soluanalysis/misc.py new file mode 100644 index 0000000..018aa71 --- /dev/null +++ b/soluanalysis/misc.py @@ -0,0 +1,112 @@ +from scipy.optimize import curve_fit +import numpy as np +import numpy.typing as npt +from typing import Union, List, Tuple + + +def biexponential_model(t: float, A: float, tau1: float, tau2: float) -> float: + """Fit data to a biexponential function (sum of two exponential decays). A and B should sum to 1.0 + C(t) = A*exp(-t/tau1) + B*exp(-t/tau2) + + Args: + t (float): time (independent variable) + A (float): Preexponential factor + tau1 (float): time constant + B (float): Preexponential factor + tau2 (float): time constant + + Returns: + float: Result of the expression, used for fitting + """ + return A * np.exp(-t / tau1) + (1 - A) * np.exp(-t / tau2) + +def exponential_model(t: float, tau1: float) -> float: + """Fit data to an exponential decay + + Args: + t (float): time (independent variable) + tau1 (float): time constant + + Returns: + float: Result of the expression, used for fitting + """ + return np.exp(-t / tau1) + +def fit_exponential( + tau_timeseries: Union[List[float], npt.NDArray], + tcf_timeseries: Union[List[float], npt.NDArray], + initial_guess: Union[List[float], npt.NDArray] = [1], +) -> Tuple[Tuple[float, float, float], npt.NDArray, npt.NDArray, float]: + """Fit + + Args: + tau_timeseries (Union[List[float], npt.NDArray]): Lag times for the TCF + tcf_timeseries (Union[List[float], npt.NDArray]): Time autocorrelation function values (this fit seems to work for the continuous bond definition). + Once a bond breaks, it is considered broken even if reformed. + initial_guess (Union[List[float], npt.NDArray], optional): Initial guess for parameters A, tau1 B, and tau2. Defaults to [0.5, 1, 2]. + + Returns: + Tuple[Tuple[float, float, float], npt.NDArray, npt.NDArray, float]: Returns a tuple of fitted parameters (A, tau1 and tau2), times, TCF values and the lifetime. + """ + + # Initial guess for the parameters [A, tau1, tau2] + + # Increase the maxfev parameter to allow more iterations + params, params_covariance = curve_fit( + exponential_model, + tau_timeseries, + tcf_timeseries, + p0=initial_guess, + maxfev=10000, + ) + + fit_t = np.linspace(tau_timeseries[0], tau_timeseries[-1], 1000) + fit_ac = exponential_model(fit_t, *params) + + # Extract the parameters + tau1 = params + + # Compute the lifetime + lifetime = tau1 + + return params, fit_t, fit_ac, lifetime + +def fit_biexponential( + tau_timeseries: Union[List[float], npt.NDArray], + tcf_timeseries: Union[List[float], npt.NDArray], + initial_guess: Union[List[float], npt.NDArray] = [0.5, 1, 2], +) -> Tuple[Tuple[float, float, float], npt.NDArray, npt.NDArray, float]: + """Fit a biexponential function of the form (A*exp(-t/tau1) + B*exp(-t/tau2)) to the tau values and time autocorrelation function values (using the continuous bond definition). + Using the relation from Gowers et al. (2015). + + Args: + tau_timeseries (Union[List[float], npt.NDArray]): Lag times for the TCF + tcf_timeseries (Union[List[float], npt.NDArray]): Time autocorrelation function values (this fit seems to work for the continuous bond definition). + Once a bond breaks, it is considered broken even if reformed. + initial_guess (Union[List[float], npt.NDArray], optional): Initial guess for parameters A, tau1 B, and tau2. Defaults to [0.5, 1, 2]. + + Returns: + Tuple[Tuple[float, float, float], npt.NDArray, npt.NDArray, float]: Returns a tuple of fitted parameters (A, tau1 and tau2), times, TCF values and the lifetime. + """ + + # Initial guess for the parameters [A, tau1, tau2] + + # Increase the maxfev parameter to allow more iterations + params, params_covariance = curve_fit( + biexponential_model, + tau_timeseries, + tcf_timeseries, + p0=initial_guess, + maxfev=10000, + ) + + fit_t = np.linspace(tau_timeseries[0], tau_timeseries[-1], 1000) + fit_ac = biexponential_model(fit_t, *params) + + # Extract the parameters + A, tau1, tau2 = params + + # Compute the lifetime + lifetime = A * tau1 + (1 - A) * tau2 + + return params, fit_t, fit_ac, lifetime diff --git a/soluanalysis/thirdparty/ase/LICENSE b/soluanalysis/thirdparty/ase/LICENSE new file mode 100644 index 0000000..d159169 --- /dev/null +++ b/soluanalysis/thirdparty/ase/LICENSE @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/soluanalysis/thirdparty/ase/__init__.py b/soluanalysis/thirdparty/ase/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/soluanalysis/io.py b/soluanalysis/thirdparty/ase/io.py similarity index 57% rename from soluanalysis/io.py rename to soluanalysis/thirdparty/ase/io.py index 0bd8483..6a5f0cd 100644 --- a/soluanalysis/io.py +++ b/soluanalysis/thirdparty/ase/io.py @@ -1,3 +1,6 @@ +# Licensed under GNU Lesser General Public License v2.1s +# from ASE : https://gitlab.com/ase/ase +# Last modified 6-09-2024 import numpy as np from typing import Dict, List, Optional, TextIO, Tuple, Union from pathlib import Path @@ -6,6 +9,29 @@ from soluanalysis.james import Atom, System import h5py import soluanalysis as solu +import numbers + + +def string2index(stridx: str) -> Union[int, slice, str]: + """Convert index string to either int or slice. From ASE""" + if ":" not in stridx: + # may contain database accessor + try: + return int(stridx) + except ValueError: + return stridx + i = [None if s == "" else int(s) for s in stridx.split(":")] + return slice(*i) + + +def index2range(index, length): + """Convert slice or integer to range. From ASE + + If index is an integer, range will contain only that integer.""" + obj = range(length)[index] + if isinstance(obj, numbers.Integral): + obj = range(obj, obj + 1) + return obj def lammps_data_to_system( @@ -205,6 +231,7 @@ def read_lammps_dump(file_path: Path, index=-1) -> tuple[List[System], List[int] Args: file_path (pathlib.Path): Path to the LAMMPS trajectory file. index: integer or slice object (by default, gets the last timestep) + index=':' or index=slice(None) : all Returns: List[System], List[int]: A list of System objects representing each frame in the trajectory within the specified range, and a list of timesteps read from the LAMMPS dump file. @@ -215,6 +242,18 @@ def read_lammps_dump(file_path: Path, index=-1) -> tuple[List[System], List[int] lower_box_limits = None timesteps = [] + if isinstance(index, str): + try: + index = string2index(index) + except ValueError: + pass + + if index is None or index == ":": + index = slice(None, None, None) + + if not isinstance(index, (slice, str)): + index = slice(index, (index + 1) or None) + try: with open(file_path, "r") as file: lines = deque(file.readlines()) @@ -270,190 +309,3 @@ def read_lammps_dump(file_path: Path, index=-1) -> tuple[List[System], List[int] return images[index], timesteps[index] else: return [images[index]], [timesteps[index]] - - -def save_system_to_hdf5(system: solu.james.System, hdf5_group: h5py.Group): - """Save a System object to an HDF5 group. - - Args: - system (solu.james.System): System object to serialize - hdf5_group (h5py.Group): The HDF5 group to be saved to - """ - - atom_ids = np.array([atom.id for atom in system.atoms], dtype=np.int32) - atom_types = np.array([atom.type for atom in system.atoms], dtype=np.int32) - mol_ids = np.array( - [atom.id if atom.mol_id is None else atom.mol_id for atom in system.atoms], - dtype=np.int32, - ) - positions = np.array([atom.position for atom in system.atoms], dtype=np.float64) - - hdf5_group.create_dataset("atom_ids", data=atom_ids) - hdf5_group.create_dataset("atom_types", data=atom_types) - hdf5_group.create_dataset("mol_ids", data=mol_ids) - hdf5_group.create_dataset("positions", data=positions) - - if system.box is not None: - hdf5_group.create_dataset("box", data=np.array(system.box, dtype=np.float64)) - if system.boxLo is not None: - hdf5_group.create_dataset( - "boxLo", data=np.array(system.boxLo, dtype=np.float64) - ) - - -def read_system_from_hdf5(hdf5_group: h5py.Group) -> solu.james.System: - """Read a System object from an HDF5 group. - - Args: - hdf5_group (h5py.Group): HDF5 group, from which the System object will be reconstructed - - Returns: - solu.james.System: Reconstructed System object - """ - atom_ids = hdf5_group["atom_ids"][:] - atom_types = hdf5_group["atom_types"][:] - mol_ids = hdf5_group["mol_ids"][:] - positions = hdf5_group["positions"][:] - - # Reconstruct atoms list - atoms = [ - solu.james.Atom(atom_id, atom_type, mol_id, position) - for atom_id, atom_type, mol_id, position in zip( - atom_ids, atom_types, mol_ids, positions - ) - ] - - # Read optional attributes - box = hdf5_group["box"][:] if "box" in hdf5_group else None - boxLo = hdf5_group["boxLo"][:] if "boxLo" in hdf5_group else None - - # Reconstruct the System object - system = solu.james.System(atoms, box, boxLo) - - return system - - -def save_ion_pairs_to_hdf5( - file_path: Path, - time_series_dict: Dict[int, Dict[int, List[List[int]]]], - system: solu.james.System, - max_depth: int, - write_identifier: solu.james.WriteIdentifier, - **compression_kwargs: Union[str, int], -) -> None: - """Save the ion pairs per time step, sorted according to length into an HDF5 file. - - Args: - file_path (Path): File path of the HDF5 file to write to - time_series_dict (Dict[int, Dict[int, List[List[int]]]]): Dictionary containing timesteps and ion pairs. - The keys of the outer dictionary are timesteps, and the keys of the inner dictionary are ion pair lengths - system (solu.james.System): Representative System object, containing indices, atom IDs, atom types, molecular IDs - max_depth (int): Maximum length of the ion pair - write_identifier (solu.james.WriteIdentifier): enum class which describes whether the elements correspond to - atom IDs or indices in the System object. - compression_kwargs(Union[str, int]): additional compression options for the create_dataset command in h5py. - For instance, compression="gzip" and compression_opts=4 - """ - # Extract timesteps from the keys of time_series_dict and sort - timesteps = sorted(time_series_dict.keys()) - - with h5py.File(file_path, "w") as file: - # Save metadata - file.attrs["max_depth"] = max_depth - file.attrs["writeIdentifier"] = str(write_identifier) # convert enum to string - - # Save the timesteps as a separate dataset - file.create_dataset( - "timesteps", data=np.array(timesteps, dtype=np.int32), **compression_kwargs - ) - - # Save the System object - system_group = file.create_group("system") - save_system_to_hdf5(system, system_group) - - # Now save the ion pairs per timestep into separate groups (each length would be in a different group) - for timestep in timesteps: - groups = time_series_dict[timestep] - - # Create a group for each timestep (timesteps are unique) - timestep_group = file.create_group(str(timestep)) - - for length, data in groups.items(): - # Create a subgroup for each length (can go upto max_length) - length_group = timestep_group.create_group(str(length)) - - # Convert the list of lists to a numpy array - ion_pair_data = np.array(data, dtype=np.int32) - - # Save the numpy array to the HDF5 file - length_group.create_dataset( - "ion_pairs", data=ion_pair_data, **compression_kwargs - ) - - -def read_ion_paird_from_hdf5( - file_path: Path, -) -> Tuple[ - Dict[int, Dict[int, List[List[int]]]], - List[int], - solu.james.System, - int, - solu.james.WriteIdentifier, -]: - """Reads the HDF5 file and reconstructs a dictionary with the time series information about the ion pairs - - Args: - file_path (Path): The HDF5 file to read from - - Returns: - Tuple[Dict[int, Dict[int, List[List[int]]]], List[int], int, solu.james.WriteIdentifier]: A tuple containing - 1) the dictionary with the ion pairs, - 2) timesteps, - 3) System object - 4) max_depth, - 5) writeIdentifier - """ - time_series_dict = {} - - enum_mapping = { - "WriteIdentifier.AtomID": solu.james.WriteIdentifier.AtomID, - "WriteIdentifier.Index": solu.james.WriteIdentifier.Index, - } - - with h5py.File(file_path, "r") as file: - # Read the metadata - max_depth = file.attrs["max_depth"] - identifier_str = file.attrs["writeIdentifier"] - - # Read the timesteps - timesteps = file["timesteps"][:].tolist() - - # Read the representative System object - system_group = file["system"] - system = read_system_from_hdf5(system_group) - - # Iterate over the timesteps - for timestep in timesteps: - timestep_group = file[str(timestep)] - groups = {} - - # Iterate over the lengths within each timestep - for length in timestep_group.keys(): - length_group = timestep_group[length] - - # Read the numpy array and convert it back to a list of lists - data_array = length_group["ion_pairs"][:] - lists = data_array.tolist() - - groups[int(length)] = lists - - time_series_dict[int(timestep)] = groups - - # Return the time series, timesteps, max_depth, the enum, and the number of atoms - return ( - time_series_dict, - timesteps, - system, - max_depth, - enum_mapping.get(identifier_str), - ) diff --git a/tests/test_avg_chunk.py b/tests/test_avg_chunk.py new file mode 100644 index 0000000..b7f6b16 --- /dev/null +++ b/tests/test_avg_chunk.py @@ -0,0 +1,69 @@ +import pytest +from pathlib import Path +import soluanalysis as solu +from soluanalysis.io import read_lammps_dump, write_lammps_dump +from soluanalysis.hdf5_io import read_ion_pairs_from_hdf5, save_ion_pairs_to_hdf5 +import numpy as np + + +def test_average_frames(): + """ + Tests that you can read in a trajectory in "chunks", and also average frames, thereby creating a + new list of System objects. + """ + read_chunk_size = 10 + + n_avg_frames = 5 + + n_frames = 11 # known here, but there could be a function to get this? + + test_dir = Path(__file__).resolve().parent + infilename = test_dir / "../resources/Fe3_water_cluster.lammpstrj" + + averaged_systems = [] + timesteps_avg = [] + + for ichunk in range(0, n_frames, read_chunk_size): + chunk_start = ichunk + chunk_end = ichunk + read_chunk_size + slice_str = str(chunk_start) + ":" + str(chunk_end) + # Read in the trajectory (the last index in the slice is non inclusive) + systems, timesteps = solu.io.read_lammps_dump(infilename, slice_str) + + n_frames_chunk = len(systems) + + # Now you have chunks of size 10 + # average every 5 frames + for jchunk in range(0, n_frames_chunk, n_avg_frames): + avg_chunk_start = jchunk + avg_chunk_end = avg_chunk_start + n_avg_frames + if avg_chunk_end > n_frames_chunk: + avg_chunk_end = n_frames_chunk + # Get the system (assume that all atoms in the correct order TODO: handle incorrect order) + current_system = systems[avg_chunk_start] + avg_pos = np.array(current_system.collect_positions()) + # Number of frames in averaging chunk + n_chunk_avg = avg_chunk_end - avg_chunk_start + + for frame in range(avg_chunk_start + 1, avg_chunk_end, 1): + avg_pos = avg_pos + np.array(systems[frame].collect_positions()) + + # Get the mean by dividing the number + avg_pos = avg_pos / n_chunk_avg + # Reset the positions + current_system.reset_positions(avg_pos) + # save the timesteps and the averaged system + timesteps_avg.append(timesteps[avg_chunk_start]) + averaged_systems.append(current_system) + + # Check that the timesteps are what are expected + assert timesteps_avg == [0, 25, 50] + assert averaged_systems[0].atoms[0].position == pytest.approx( + [22.50546, 31.19614, 29.12514] + ) + assert averaged_systems[1].atoms[0].position == pytest.approx( + [22.58648, 31.1157, 29.0308] + ) + assert averaged_systems[2].atoms[0].position == pytest.approx( + [22.64, 31.0671, 28.9615] + ) diff --git a/tests/test_bonds.py b/tests/test_bonds.py index ff853c2..8776a92 100644 --- a/tests/test_bonds.py +++ b/tests/test_bonds.py @@ -37,6 +37,7 @@ def test_bond_formation(octahedral_system): h_atom_types = [h_type] donor_acceptor_cutoff = 3.2 max_angle_deg = 30 # in degrees + # Continuous bonds solu.james.add_hbonds( network, system, diff --git a/tests/test_correlation.py b/tests/test_correlation.py new file mode 100644 index 0000000..7398a44 --- /dev/null +++ b/tests/test_correlation.py @@ -0,0 +1,73 @@ +import pytest +import soluanalysis as solu + + +def create_network_list(): + """Generate a list of networks for the test case""" + network_list = [] + n_atoms = 4 + weight_edge = 1 + + # First time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(0, 1, weight_edge) + network.push_back_neighbour_and_weight(0, 2, weight_edge) + network.push_back_neighbour_and_weight(0, 3, weight_edge) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + + # Second time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(0, 1, weight_edge) + network.push_back_neighbour_and_weight(0, 3, weight_edge) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + # Third time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(0, 1, weight_edge) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + # Fourth time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + # 5th time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + # 6th time step + network = solu.graphlib.UndirectedNetwork(n_atoms) + network.push_back_neighbour_and_weight(1, 2, weight_edge) + network.push_back_neighbour_and_weight(1, 3, weight_edge) + network.push_back_neighbour_and_weight(2, 3, weight_edge) + network_list.append(network) + + return network_list + + +def test_hbond_correlation(): + """Test to obtain the correlation function given a list of UndirectedNetwork objects""" + times = [0, 10, 20] # Times at timesteps required for the correlation function + networks = create_network_list() + # Check that the network list was created properly + assert solu.graphlib.get_neighbours(networks[0], 0) == [1, 2, 3] + assert solu.graphlib.get_neighbours(networks[-1], 0) == [] + + # Get the time correlation function + tau_values_expected = [0, 10, 20] + tcf_avg_expected = [1.0, 0.7944444444444444, 0.6722222222222222] + tau_values, tcf_avg, tcf_error = solu.james.time_correlation_function( + networks, times, 0, 1, 1, None + ) + assert tau_values == tau_values_expected + assert tcf_avg == tcf_avg_expected diff --git a/tests/test_io.py b/tests/test_io.py index 456b079..dfef433 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -2,6 +2,7 @@ from pathlib import Path import soluanalysis as solu from soluanalysis.io import read_lammps_dump, write_lammps_dump +from soluanalysis.hdf5_io import read_ion_pairs_from_hdf5, save_ion_pairs_to_hdf5 @pytest.fixture @@ -36,6 +37,20 @@ def small_system(): return system +def test_read_slice(): + """ + Tests that you can read in a slice of frames from a trajectory file + """ + test_dir = Path(__file__).resolve().parent + infilename = test_dir / "../resources/Fe3_water_cluster.lammpstrj" + # Read in the trajectory (the last index in the slice is non inclusive) + systems, timesteps = solu.io.read_lammps_dump(infilename, "1:3") + + assert len(systems) == 2 + assert timesteps == [5, 10] + assert systems[0].atoms[0].position == [22.4864, 31.2099, 29.1459] + + def test_write_read_single_dump(small_system): """ Tests that you can write out and then read back in a System object containing two molecules. @@ -121,12 +136,12 @@ def test_hdf5_files(octahedral_system): max_depth = 3 write_identifier = solu.james.WriteIdentifier.AtomID - solu.io.save_ion_pairs_to_hdf5( + save_ion_pairs_to_hdf5( file_path, time_series_data, systems[0], max_depth, write_identifier ) time_series_read, timesteps_read, system_read, max_depth_read, identifier_read = ( - solu.io.read_ion_paird_from_hdf5(file_path) + read_ion_pairs_from_hdf5(file_path) ) assert time_series_read == time_series_data