diff --git a/docs/source/_static/recipes/descr_from_trj.py b/docs/source/_static/recipes/descr_from_trj.py deleted file mode 100644 index 57993e94..00000000 --- a/docs/source/_static/recipes/descr_from_trj.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Copiable code from Recipe #1.""" - -from pathlib import Path - -from dynsight.trajectory import Trj - - -def main() -> None: - """Code from the Recipe #1.""" - # Loading an example trajectory - files_path = Path("../tests/systems/") - trj = Trj.init_from_xtc( - traj_file=files_path / "balls_7_nvt.xtc", - topo_file=files_path / "balls_7_nvt.gro", - ) - - _ = trj.get_soap( - r_cut=2.0, # cutoff radius for neighbors list - n_max=4, # n_max SOAP parameter - l_max=4, # l_max SOAP parameter - selection="all", # compute on a selection of particles - centers="all", # compute for a selection of centers - respect_pbc=False, # consider PBC - n_jobs=1, # use multiprocessing to speed up calculations - ) - - # Loading an example trajectory - files_path = Path("../tests/systems/") - trj = Trj.init_from_xtc( - traj_file=files_path / "balls_7_nvt.xtc", - topo_file=files_path / "balls_7_nvt.gro", - ) - - # Computing number of neighbors from scratch - neigcounts, n_neig = trj.get_coord_number( - r_cut=2.0, # cutoff radius for neighbors list - selection="all", # compute on a selection of particles - neigcounts=None, # it will be computed and returned - ) - - # Computing LENS - lens = trj.get_lens( # noqa: F841 - r_cut=2.0, # cutoff radius for neighbors list - selection="all", # compute on a selection of particles - ) - - -if __name__ == "__main__": - main() diff --git a/docs/source/_static/recipes/entropy.py b/docs/source/_static/recipes/entropy.py deleted file mode 100644 index bc12e793..00000000 --- a/docs/source/_static/recipes/entropy.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Copiable code from Recipe #4.""" - -import numpy as np - -import dynsight - -rng = np.random.default_rng(42) # set the random seed - -# Entropy of a discrete variable -n_sample = 10000 -rolls = rng.integers(1, 7, size=n_sample) -dice_entropy = dynsight.analysis.compute_shannon( - data=rolls.astype(float), - data_range=(1, 6), - n_bins=6, - units="bit", -) - -# Entropy of a discrete multivariate variable -n_sample = 10000 -rolls = rng.integers(1, 7, size=(n_sample, 2)) -dices_entropy = dynsight.analysis.compute_shannon_multi( - data=rolls.astype(float), - data_ranges=[(1, 6), (1, 6)], - n_bins=[6, 6], - units="bit", -) - - -# Entropy of a continuous variable -n_sample = 10000000 -data_1 = rng.normal(loc=0.0, scale=1.0, size=n_sample) -data_2 = rng.normal(loc=0.0, scale=2.0, size=n_sample) - -gauss_entropy_1 = dynsight.analysis.compute_kl_entropy( - data=data_1, - units="bit", -) -gauss_entropy_2 = dynsight.analysis.compute_kl_entropy( - data=data_2, - units="bit", -) -diff = gauss_entropy_2 - gauss_entropy_1 - - -# Entropy of a continuous multivariate variable -n_sample = 100000 -mean = [1, 1] -cov = np.array([[1, 0], [0, 1]]) -data_1 = rng.multivariate_normal( - mean=mean, - cov=cov, - size=n_sample, -) -data_2 = rng.multivariate_normal( - mean=mean, - cov=cov * 4.0, - size=n_sample, -) - -gauss_entropy_1 = dynsight.analysis.compute_kl_entropy_multi( - data=data_1, - units="bit", -) -gauss_entropy_2 = dynsight.analysis.compute_kl_entropy_multi( - data=data_2, - units="bit", -) -diff_2d = gauss_entropy_2 - gauss_entropy_1 diff --git a/docs/source/_static/recipes/info_gain.py b/docs/source/_static/recipes/info_gain.py deleted file mode 100644 index 97eccbe1..00000000 --- a/docs/source/_static/recipes/info_gain.py +++ /dev/null @@ -1,179 +0,0 @@ -"""Copiable code from Recipe #3.""" - -from __future__ import annotations - -from pathlib import Path -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from numpy.typing import NDArray - -import matplotlib.pyplot as plt -import numpy as np - -import dynsight - - -def info_gain_with_onion( - delta_t_list: NDArray[np.int64] | list[int], - data: NDArray[np.float64], - n_bins: int = 40, -) -> tuple[ - NDArray[np.int64], - NDArray[np.float64], - NDArray[np.float64], - NDArray[np.float64], - float, -]: - """Performs full information gain analysis with Onion clustering.""" - data_range = (float(np.min(data)), float(np.max(data))) - - n_clusters = np.zeros(len(delta_t_list), dtype=int) - clusters_frac = [] - info_gain = np.zeros(len(delta_t_list)) - clusters_entr = [] - - for i, delta_t in enumerate(delta_t_list): - state_list, labels = dynsight.onion.onion_uni_smooth( - data, - delta_t=delta_t, - ) - - n_clusters[i] = len(state_list) - tmp_frac = [0.0] + [state.perc for state in state_list] - tmp_frac[0] = 1.0 - np.sum(tmp_frac) - clusters_frac.append(tmp_frac) - - flat_data = data.flatten() - flat_labels = labels.flatten() - info_gain[i], _, h_0, _ = dynsight.analysis.compute_entropy_gain( - flat_data, - flat_labels, - n_bins=n_bins, - ) - - tmp_entr = [] - label_list = np.unique(labels) - if label_list[0] != -1: - tmp_entr.append(-1.0) - - for _, lab in enumerate(label_list): - mask = labels == lab - selected_points = data[mask] - tmp_entr.append( - dynsight.analysis.compute_shannon( - selected_points, - data_range, - n_bins=n_bins, - ) - ) - clusters_entr.append(tmp_entr) - - max_n_envs = np.max([len(elem) for elem in clusters_entr]) - for i, elem in enumerate(clusters_entr): - while len(elem) < max_n_envs: - elem.append(-1.0) - clusters_frac[i].append(0.0) - - cl_frac = np.array(clusters_frac, dtype=float) - cl_entr = np.array(clusters_entr) - - return n_clusters, cl_frac, info_gain, cl_entr, h_0 - - -def plot_info_results( - delta_t_list: NDArray[np.int64] | list[int], - cl_frac: NDArray[np.float64], - cl_entr: NDArray[np.float64], - h_0: float, - file_path: Path, -) -> None: - """Plot information gain as a function of ∆t.""" - frac = cl_frac.T - entr = cl_entr.T - s_list = [] - for i, st_fr in enumerate(frac): - s_list.append(st_fr * entr[i]) - s_cumul = [s_list[0]] - for _, tmp_s in enumerate(s_list[1:]): - s_cumul.append(s_cumul[-1] + tmp_s) - - fig, ax = plt.subplots() - - i_0 = (1 - h_0) * np.ones(len(delta_t_list)) - ax.plot(delta_t_list, i_0, ls="--", c="black", marker="") # I_0 - ax.fill_between( - delta_t_list, - 1, - 1 - s_cumul[0], - alpha=0.5, - ) - for i, tmp_s in enumerate(s_cumul[1:]): - ax.fill_between( - delta_t_list, - 1 - s_cumul[i], - 1 - tmp_s, - alpha=0.5, - ) - ax.fill_between( - delta_t_list, - 1 - s_cumul[-1], - 1 - h_0, - color="gainsboro", - ) - ax.plot( - delta_t_list, - 1 - s_cumul[-1], - c="black", - marker="", - ) # I_clust - - ax.set_ylim(0.0, 1.0) - ax.set_xlabel(r"Time resolution $\Delta t$") - ax.set_ylabel(r"Information $I$") - ax.set_xscale("log") - - fig.savefig(file_path, dpi=600) - plt.close() - - -def main() -> None: - """Copiable code from Recipe #3.""" - rng = np.random.default_rng(1234) - - n_atoms = 10 - num_blocks = 10 - block_size = 100 - sigma = 0.1 - - # Generate the array - tmp_data = [ - np.concatenate( - [ - rng.normal(loc=(i % 2), scale=sigma, size=block_size) - for i in range(num_blocks) - ] - ) - for _ in range(n_atoms) - ] - data = np.array(tmp_data) - - _, n_frames = data.shape - delta_t_list = np.unique(np.geomspace(2, n_frames, 10, dtype=int)) - - n_cl, cl_frac, info_gain, cl_entr, h_0 = info_gain_with_onion( - delta_t_list, - data, - ) - - plot_info_results( - delta_t_list, - cl_frac, - cl_entr, - h_0, - Path("./source/_static/info_plot.png"), - ) - - -if __name__ == "__main__": - main() diff --git a/docs/source/_static/recipes/soap_dim_red.py b/docs/source/_static/recipes/soap_dim_red.py deleted file mode 100644 index 5b600fd6..00000000 --- a/docs/source/_static/recipes/soap_dim_red.py +++ /dev/null @@ -1,179 +0,0 @@ -"""Copiable code from Recipe #2.""" - -from __future__ import annotations - -from pathlib import Path - -from sklearn.decomposition import PCA - -import dynsight -from dynsight.trajectory import Insight, Trj -from dynsight.utilities import load_or_compute_soap - - -def compute_soap_pca( - trj: Trj, - r_cut: float, - n_max: int, - l_max: int, - n_components: int, - soap_path: Path | None = None, - pca_path: Path | None = None, - selection: str = "all", - centers: str = "all", - respect_pbc: bool = True, - n_core: int = 1, -) -> Insight: - """Computes the PCA of SOAP on a Trajectory.""" - if pca_path is not None and pca_path.exists(): - return Insight.load_from_json(pca_path) - - soap = load_or_compute_soap( - trj=trj, - r_cut=r_cut, - n_max=n_max, - l_max=l_max, - selection=selection, - centers=centers, - respect_pbc=respect_pbc, - n_core=n_core, - soap_path=soap_path, - ) - - n_atom, n_frames, n_dims = soap.dataset.shape - reshaped_soap = soap.dataset.reshape(n_atom * n_frames, n_dims) - pca = PCA(n_components=n_components) - transformed_soap = pca.fit_transform(reshaped_soap) - pca_ds = transformed_soap.reshape(n_atom, n_frames, -1) - - soap_pca = Insight(pca_ds, meta=soap.meta.copy()) - - if pca_path is not None: - soap_pca.dump_to_json(pca_path) - - return soap_pca - - -def compute_soap_tica( - trj: Trj, - r_cut: float, - n_max: int, - l_max: int, - lag_time: int, - tica_dim: int, - soap_path: Path | None = None, - tica_path: Path | None = None, - selection: str = "all", - centers: str = "all", - respect_pbc: bool = True, - n_core: int = 1, -) -> Insight: - """Computes the tICA of SOAP on a Trajectory.""" - if tica_path is not None and tica_path.exists(): - return Insight.load_from_json(tica_path) - - soap = load_or_compute_soap( - trj=trj, - r_cut=r_cut, - n_max=n_max, - l_max=l_max, - selection=selection, - centers=centers, - respect_pbc=respect_pbc, - n_core=n_core, - soap_path=soap_path, - ) - - rel_times, _, tica_ds = dynsight.descriptors.many_body_tica( - soap.dataset, - lag_time=lag_time, - tica_dim=tica_dim, - ) - - meta = soap.meta.copy() - meta.update( - { - "lag_time": lag_time, - "rel_times": rel_times, - } - ) - soap_tica = Insight(tica_ds, meta=meta) - - if tica_path is not None: - soap_tica.dump_to_json(tica_path) - - return soap_tica - - -def compute_timesoap( - trj: Trj, - r_cut: float, - n_max: int, - l_max: int, - delay: int = 1, - soap_path: Path | None = None, - tsoap_path: Path | None = None, - selection: str = "all", - centers: str = "all", - respect_pbc: bool = True, - n_core: int = 1, -) -> Insight: - """Computes timeSOAP on a Trajectory.""" - if tsoap_path is not None and tsoap_path.exists(): - return Insight.load_from_json(tsoap_path) - - soap = load_or_compute_soap( - trj=trj, - r_cut=r_cut, - n_max=n_max, - l_max=l_max, - selection=selection, - centers=centers, - respect_pbc=respect_pbc, - n_core=n_core, - soap_path=soap_path, - ) - - tsoap = soap.get_angular_velocity(delay=delay) - - if tsoap_path is not None: - tsoap.dump_to_json(tsoap_path) - - return tsoap - - -def main() -> None: - """Copiable code from Recipe #2.""" - files_path = Path("../tests/systems/") - trj = Trj.init_from_xtc( - traj_file=files_path / "balls_7_nvt.xtc", - topo_file=files_path / "balls_7_nvt.gro", - ) - - _ = compute_soap_pca( - trj=trj, - r_cut=2.0, - n_max=4, - l_max=4, - n_components=1, - ) - - _ = compute_soap_tica( - trj=trj, - r_cut=10.0, - n_max=4, - l_max=4, - lag_time=10, - tica_dim=1, - ) - - _ = compute_timesoap( - trj=trj, - r_cut=10.0, - n_max=4, - l_max=4, - ) - - -if __name__ == "__main__": - main() diff --git a/examples/analysis_workflow.py b/examples/analysis_workflow.py deleted file mode 100644 index 13c7a0a5..00000000 --- a/examples/analysis_workflow.py +++ /dev/null @@ -1,56 +0,0 @@ -"""An example for the trajectory module.""" - -from pathlib import Path - -from dynsight.trajectory import Insight, OnionSmoothInsight, Trj - - -def main() -> None: - """An example for the trajectory module.""" - files_path = Path("analysis_workflow") - - # Create a Trj object - trj = Trj.init_from_xtc( - traj_file=files_path / "oxygens.xtc", - topo_file=files_path / "oxygens.gro", - ) - - # We want, for instance, compute LENS on this trajectory - # From here, we work with an Insight, containing data computed from a Trj - lens_file = files_path / "lens.json" - if lens_file.exists(): - lens = Insight.load_from_json(lens_file, mmap_mode="r") - else: - lens = trj.get_lens(r_cut=7.5, n_jobs=6) - lens.dump_to_json(lens_file) - - # We can do spatial average on the computed LENS - trj_lens = trj.with_slice(slice(0, -1, 1)) - lens_aver = lens.spatial_average(trj=trj_lens, r_cut=7.5, n_jobs=6) - - # And we can perform onion-clustering - lens_onion = lens_aver.get_onion_smooth(delta_t=10) - - lens_onion.plot_output( - file_path=files_path / "fig1.png", - data_insight=lens_aver, - ) - lens_onion.plot_one_trj( - file_path=files_path / "fig2.png", - data_insight=lens_aver, - particle_id=1234, - ) - lens_onion.dump_colored_trj( - trj=trj_lens, - file_path=files_path / "colored_trj.xyz", - ) - - # Save/load the Insight with all the results - lens_onion.dump_to_json(files_path / "onion.json") - _ = OnionSmoothInsight.load_from_json( - files_path / "onion.json", mmap_mode="r" - ) - - -if __name__ == "__main__": - main() diff --git a/examples/analysis_workflow/fig1.png b/examples/analysis_workflow/fig1.png deleted file mode 100644 index 8d6d8de0..00000000 Binary files a/examples/analysis_workflow/fig1.png and /dev/null differ diff --git a/examples/analysis_workflow/fig2.png b/examples/analysis_workflow/fig2.png deleted file mode 100644 index 9d19980a..00000000 Binary files a/examples/analysis_workflow/fig2.png and /dev/null differ diff --git a/examples/analysis_workflow/lens.npy b/examples/analysis_workflow/lens.npy deleted file mode 100644 index 2b4643af..00000000 Binary files a/examples/analysis_workflow/lens.npy and /dev/null differ diff --git a/examples/analysis_workflow/onion_labels.npy b/examples/analysis_workflow/onion_labels.npy deleted file mode 100644 index be616cc9..00000000 Binary files a/examples/analysis_workflow/onion_labels.npy and /dev/null differ diff --git a/examples/analysis_workflow/oxygens.gro b/examples/analysis_workflow/oxygens.gro deleted file mode 100644 index 71d63ed4..00000000 --- a/examples/analysis_workflow/oxygens.gro +++ /dev/null @@ -1,2051 +0,0 @@ -Written by MDAnalysis - 2048 - 1water OW 1 2.821 2.796 0.322 - 2water OW 2 0.886 0.538 0.090 - 3water OW 3 0.437 0.277 0.294 - 4water OW 4 0.326 0.168 0.509 - 5water OW 5 0.042 2.806 0.537 - 6water OW 6 0.057 0.254 0.535 - 7water OW 7 1.456 0.756 4.370 - 8water OW 8 0.328 2.886 0.554 - 9water OW 9 3.124 0.206 0.759 - 10water OW 10 3.108 0.154 0.338 - 11water OW 11 3.137 1.596 5.455 - 12water OW 12 1.614 1.934 4.807 - 13water OW 13 0.440 2.799 0.309 - 14water OW 14 0.484 2.833 0.784 - 15water OW 15 0.459 0.282 0.720 - 16water OW 16 3.120 2.940 0.746 - 17water OW 17 1.226 2.525 0.340 - 18water OW 18 1.979 1.201 4.961 - 19water OW 19 1.157 0.931 0.147 - 20water OW 20 1.083 0.177 0.565 - 21water OW 21 0.843 2.843 0.606 - 22water OW 22 0.845 0.275 0.522 - 23water OW 23 1.682 1.004 0.127 - 24water OW 24 0.703 2.892 0.344 - 25water OW 25 0.731 0.202 0.767 - 26water OW 26 0.699 0.177 0.301 - 27water OW 27 0.069 0.225 6.531 - 28water OW 28 2.342 2.347 3.579 - 29water OW 29 1.124 2.896 0.585 - 30water OW 30 1.259 2.818 0.824 - 31water OW 31 1.233 0.241 0.819 - 32water OW 32 0.748 2.932 0.841 - 33water OW 33 1.764 2.376 4.114 - 34water OW 34 3.199 0.293 2.717 - 35water OW 35 2.068 0.527 0.326 - 36water OW 36 1.861 0.212 0.544 - 37water OW 37 2.838 2.442 4.102 - 38water OW 38 1.579 0.278 0.563 - 39water OW 39 2.395 2.058 4.150 - 40water OW 40 1.660 2.798 0.559 - 41water OW 41 1.474 0.165 0.777 - 42water OW 42 1.067 0.814 5.484 - 43water OW 43 2.284 2.871 0.337 - 44water OW 44 1.131 0.118 6.117 - 45water OW 45 1.919 2.929 0.560 - 46water OW 46 2.055 2.788 0.771 - 47water OW 47 2.022 0.281 0.771 - 48water OW 48 1.512 2.881 0.787 - 49water OW 49 0.056 0.244 6.082 - 50water OW 50 0.076 1.004 6.433 - 51water OW 51 2.853 0.259 0.341 - 52water OW 52 2.718 0.143 0.563 - 53water OW 53 2.465 2.806 0.548 - 54water OW 54 2.487 0.289 0.566 - 55water OW 55 0.970 2.330 5.974 - 56water OW 56 2.737 2.861 0.592 - 57water OW 57 2.285 0.167 0.757 - 58water OW 58 2.337 0.146 0.353 - 59water OW 59 3.062 2.421 6.271 - 60water OW 60 2.449 0.570 0.099 - 61water OW 61 2.429 2.792 0.126 - 62water OW 62 2.912 2.779 0.799 - 63water OW 63 2.847 0.234 0.784 - 64water OW 64 2.323 2.903 0.757 - 65water OW 65 3.145 0.627 4.549 - 66water OW 66 1.770 2.024 4.353 - 67water OW 67 0.466 0.559 0.360 - 68water OW 68 0.319 0.889 0.526 - 69water OW 69 0.041 0.526 0.543 - 70water OW 70 0.053 1.005 0.538 - 71water OW 71 3.070 1.660 2.574 - 72water OW 72 0.289 0.630 0.558 - 73water OW 73 3.100 0.924 0.756 - 74water OW 74 1.616 1.660 3.494 - 75water OW 75 3.122 1.704 0.331 - 76water OW 76 1.595 1.093 5.188 - 77water OW 77 0.728 0.622 0.308 - 78water OW 78 0.473 0.574 0.772 - 79water OW 79 0.436 1.038 0.729 - 80water OW 80 3.096 0.654 0.740 - 81water OW 81 1.259 1.059 0.367 - 82water OW 82 0.083 1.723 6.464 - 83water OW 83 1.522 0.964 0.340 - 84water OW 84 1.154 0.928 0.597 - 85water OW 85 0.871 0.557 0.565 - 86water OW 86 0.838 1.006 0.546 - 87water OW 87 2.019 2.033 6.749 - 88water OW 88 1.142 0.643 0.569 - 89water OW 89 0.701 0.940 0.777 - 90water OW 90 0.703 0.893 0.324 - 91water OW 91 0.054 1.296 2.772 - 92water OW 92 2.687 2.175 6.096 - 93water OW 93 1.282 0.553 0.334 - 94water OW 94 1.225 0.534 0.818 - 95water OW 95 1.285 1.060 0.815 - 96water OW 96 0.747 0.669 0.779 - 97water OW 97 0.923 2.524 5.655 - 98water OW 98 1.708 0.033 5.397 - 99water OW 99 1.573 0.629 6.759 - 100water OW 100 1.921 0.917 0.530 - 101water OW 101 1.604 0.553 0.614 - 102water OW 102 1.640 1.055 0.579 - 103water OW 103 2.474 0.220 6.088 - 104water OW 104 1.909 0.632 0.534 - 105water OW 105 1.534 0.946 0.812 - 106water OW 106 2.062 0.011 5.854 - 107water OW 107 2.898 1.888 4.536 - 108water OW 108 2.263 2.159 6.736 - 109water OW 109 1.328 0.906 5.525 - 110water OW 110 2.068 0.569 0.755 - 111water OW 111 2.067 0.955 0.757 - 112water OW 112 1.456 0.672 0.821 - 113water OW 113 2.343 0.638 0.341 - 114water OW 114 1.825 1.361 5.160 - 115water OW 115 2.845 1.032 0.281 - 116water OW 116 2.732 0.952 0.502 - 117water OW 117 2.507 0.550 0.545 - 118water OW 118 2.473 1.002 0.560 - 119water OW 119 2.162 1.686 3.645 - 120water OW 120 2.776 0.662 0.538 - 121water OW 121 2.334 0.912 0.805 - 122water OW 122 2.371 0.899 0.312 - 123water OW 123 1.760 2.031 5.272 - 124water OW 124 0.353 1.378 6.512 - 125water OW 125 2.850 0.537 0.286 - 126water OW 126 2.863 0.524 0.775 - 127water OW 127 2.852 1.008 0.743 - 128water OW 128 2.329 0.643 0.744 - 129water OW 129 0.103 1.276 6.456 - 130water OW 130 1.453 0.162 5.353 - 131water OW 131 1.267 1.772 0.324 - 132water OW 132 0.319 1.720 0.556 - 133water OW 133 0.090 1.298 0.545 - 134water OW 134 0.038 1.765 0.585 - 135water OW 135 0.480 1.042 0.343 - 136water OW 136 0.329 1.440 0.564 - 137water OW 137 3.151 1.660 0.826 - 138water OW 138 3.107 1.374 0.324 - 139water OW 139 2.768 0.686 5.383 - 140water OW 140 1.379 1.476 5.203 - 141water OW 141 1.843 2.833 3.497 - 142water OW 142 0.469 1.318 0.763 - 143water OW 143 0.444 1.788 0.801 - 144water OW 144 3.152 1.390 0.771 - 145water OW 145 3.127 0.934 6.698 - 146water OW 146 1.509 0.719 4.637 - 147water OW 147 2.770 2.548 2.996 - 148water OW 148 0.832 2.523 0.137 - 149water OW 149 0.889 1.284 0.566 - 150water OW 150 0.844 1.814 0.536 - 151water OW 151 3.136 0.722 3.779 - 152water OW 152 1.119 1.449 0.591 - 153water OW 153 0.693 1.699 0.747 - 154water OW 154 0.297 1.660 0.150 - 155water OW 155 1.101 1.726 0.553 - 156water OW 156 2.010 0.581 2.960 - 157water OW 157 0.473 1.779 0.345 - 158water OW 158 1.247 1.339 0.806 - 159water OW 159 1.244 1.811 0.780 - 160water OW 160 0.714 1.422 0.747 - 161water OW 161 1.658 2.038 0.549 - 162water OW 162 0.277 2.846 0.095 - 163water OW 163 1.652 1.460 5.757 - 164water OW 164 1.914 1.658 0.562 - 165water OW 165 1.676 1.333 0.602 - 166water OW 166 1.662 1.756 0.608 - 167water OW 167 3.158 0.544 5.721 - 168water OW 168 1.937 1.364 0.548 - 169water OW 169 1.517 1.673 0.842 - 170water OW 170 0.488 0.612 3.598 - 171water OW 171 0.389 0.265 2.932 - 172water OW 172 1.417 0.879 5.223 - 173water OW 173 0.887 0.985 0.141 - 174water OW 174 2.043 1.234 0.768 - 175water OW 175 2.047 1.768 0.783 - 176water OW 176 1.535 1.362 0.843 - 177water OW 177 1.539 2.586 2.841 - 178water OW 178 0.913 2.355 3.442 - 179water OW 179 2.846 1.783 0.318 - 180water OW 180 2.737 1.653 0.551 - 181water OW 181 2.456 1.281 0.581 - 182water OW 182 2.458 1.734 0.575 - 183water OW 183 2.491 1.736 4.444 - 184water OW 184 2.720 1.370 0.562 - 185water OW 185 2.299 1.627 0.782 - 186water OW 186 0.831 0.174 6.086 - 187water OW 187 1.162 2.692 4.208 - 188water OW 188 0.495 0.955 6.279 - 189water OW 189 2.816 1.311 0.294 - 190water OW 190 2.895 1.292 0.746 - 191water OW 191 2.873 1.732 0.783 - 192water OW 192 2.299 1.343 0.796 - 193water OW 193 1.746 2.088 5.858 - 194water OW 194 2.736 0.944 0.058 - 195water OW 195 0.414 2.526 0.326 - 196water OW 196 0.285 2.469 0.592 - 197water OW 197 0.062 2.054 0.595 - 198water OW 198 0.010 2.520 0.565 - 199water OW 199 2.935 2.275 5.482 - 200water OW 200 0.303 2.176 0.575 - 201water OW 201 3.098 2.411 0.805 - 202water OW 202 3.124 2.088 0.347 - 203water OW 203 1.851 1.958 3.747 - 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624water OW 624 1.504 0.633 2.540 - 625water OW 625 2.292 0.642 2.089 - 626water OW 626 2.671 0.938 1.901 - 627water OW 627 2.822 1.038 2.127 - 628water OW 628 2.679 0.931 2.344 - 629water OW 629 2.418 0.563 2.335 - 630water OW 630 2.401 1.026 2.321 - 631water OW 631 2.674 0.655 1.856 - 632water OW 632 2.672 0.669 2.366 - 633water OW 633 2.331 0.924 2.568 - 634water OW 634 2.270 0.918 2.055 - 635water OW 635 2.416 1.032 1.841 - 636water OW 636 2.426 0.563 1.851 - 637water OW 637 2.824 0.633 2.093 - 638water OW 638 2.866 0.552 2.540 - 639water OW 639 2.821 1.064 2.555 - 640water OW 640 2.261 0.653 2.590 - 641water OW 641 3.081 1.388 2.079 - 642water OW 642 0.342 1.676 1.915 - 643water OW 643 0.490 1.780 2.145 - 644water OW 644 0.287 1.741 2.350 - 645water OW 645 0.032 1.343 2.321 - 646water OW 646 0.020 1.784 2.379 - 647water OW 647 0.320 1.385 1.932 - 648water OW 648 0.280 1.470 2.342 - 649water OW 649 0.026 1.771 2.769 - 650water OW 650 3.104 1.669 2.144 - 651water OW 651 0.085 1.752 1.911 - 652water OW 652 0.039 1.293 1.887 - 653water OW 653 0.467 1.322 2.164 - 654water OW 654 0.435 1.363 2.560 - 655water OW 655 0.427 1.791 2.565 - 656water OW 656 3.097 1.389 2.561 - 657water OW 657 0.724 1.417 2.115 - 658water OW 658 1.127 1.685 1.886 - 659water OW 659 1.215 1.759 2.126 - 660water OW 660 1.146 1.694 2.382 - 661water OW 661 0.821 1.322 2.333 - 662water OW 662 0.858 1.796 2.365 - 663water OW 663 1.121 1.422 1.923 - 664water OW 664 1.088 1.414 2.362 - 665water OW 665 0.688 1.688 2.547 - 666water OW 666 0.745 1.682 2.117 - 667water OW 667 0.867 1.748 1.876 - 668water OW 668 0.852 1.349 1.875 - 669water OW 669 1.241 1.335 2.147 - 670water OW 670 1.254 1.290 2.563 - 671water OW 671 2.219 2.797 5.539 - 672water OW 672 0.704 1.404 2.566 - 673water OW 673 1.514 1.421 2.122 - 674water OW 674 1.888 1.642 1.923 - 675water OW 675 2.072 1.790 2.097 - 676water OW 676 1.902 1.653 2.303 - 677water OW 677 1.624 1.327 2.345 - 678water OW 678 1.655 1.789 2.347 - 679water OW 679 1.928 1.381 1.883 - 680water OW 680 1.902 1.384 2.324 - 681water OW 681 2.717 1.432 2.780 - 682water OW 682 1.484 1.702 2.144 - 683water OW 683 1.624 1.730 1.909 - 684water OW 684 1.673 1.315 1.915 - 685water OW 685 2.056 1.302 2.109 - 686water OW 686 2.051 1.279 2.546 - 687water OW 687 2.091 1.762 2.499 - 688water OW 688 1.704 1.797 2.627 - 689water OW 689 2.294 1.426 2.074 - 690water OW 690 2.704 1.687 1.861 - 691water OW 691 2.855 1.741 2.078 - 692water OW 692 2.695 1.649 2.283 - 693water OW 693 2.389 1.301 2.336 - 694water OW 694 2.464 1.798 2.308 - 695water OW 695 2.679 1.420 1.882 - 696water OW 696 2.636 1.382 2.335 - 697water OW 697 2.339 1.678 2.517 - 698water OW 698 2.320 1.699 2.100 - 699water OW 699 2.451 1.791 1.872 - 700water OW 700 2.423 1.313 1.859 - 701water OW 701 2.797 1.307 2.112 - 702water OW 702 2.815 1.325 2.532 - 703water OW 703 2.836 1.772 2.493 - 704water OW 704 2.286 1.400 2.550 - 705water OW 705 3.053 2.122 2.061 - 706water OW 706 0.286 2.448 1.894 - 707water OW 707 0.422 2.547 2.103 - 708water OW 708 0.306 2.420 2.320 - 709water OW 709 3.190 2.051 2.294 - 710water OW 710 0.057 2.499 2.291 - 711water OW 711 0.299 2.155 1.896 - 712water OW 712 0.263 2.121 2.334 - 713water OW 713 0.907 2.571 2.805 - 714water OW 714 3.105 2.392 2.101 - 715water OW 715 0.032 2.493 1.875 - 716water OW 716 0.053 2.040 1.865 - 717water OW 717 0.436 2.047 2.129 - 718water OW 718 0.383 2.065 2.584 - 719water OW 719 0.399 2.532 2.563 - 720water OW 720 3.099 2.173 2.524 - 721water OW 721 0.698 2.136 2.146 - 722water OW 722 1.091 2.446 1.919 - 723water OW 723 1.220 2.525 2.131 - 724water OW 724 1.104 2.446 2.372 - 725water OW 725 0.847 2.085 2.365 - 726water OW 726 0.809 2.511 2.325 - 727water OW 727 1.083 2.130 1.892 - 728water OW 728 1.116 2.138 2.334 - 729water OW 729 0.737 2.474 2.588 - 730water OW 730 0.679 2.401 2.099 - 731water OW 731 0.816 2.529 1.911 - 732water OW 732 0.816 2.016 1.914 - 733water OW 733 1.234 2.037 2.091 - 734water OW 734 1.254 2.063 2.575 - 735water OW 735 1.282 2.596 2.548 - 736water OW 736 0.803 2.067 2.786 - 737water OW 737 1.505 2.140 2.075 - 738water OW 738 1.902 2.448 1.893 - 739water OW 739 2.047 2.528 2.131 - 740water OW 740 1.852 2.457 2.353 - 741water OW 741 1.627 2.063 2.314 - 742water OW 742 1.599 2.567 2.344 - 743water OW 743 1.889 2.154 1.891 - 744water OW 744 1.886 2.184 2.315 - 745water OW 745 1.520 2.169 2.552 - 746water OW 746 1.481 2.419 2.137 - 747water OW 747 1.653 2.512 1.924 - 748water OW 748 1.642 2.006 1.874 - 749water OW 749 2.024 2.063 2.114 - 750water OW 750 2.044 2.041 2.542 - 751water OW 751 0.339 2.220 3.240 - 752water OW 752 1.894 2.144 2.748 - 753water OW 753 2.293 2.147 2.115 - 754water OW 754 2.739 2.429 1.916 - 755water OW 755 2.866 2.516 2.149 - 756water OW 756 2.712 2.427 2.369 - 757water OW 757 2.449 2.093 2.345 - 758water OW 758 2.445 2.543 2.373 - 759water OW 759 2.724 2.143 1.865 - 760water OW 760 2.722 2.145 2.353 - 761water OW 761 2.022 2.522 2.559 - 762water OW 762 2.313 2.429 2.122 - 763water OW 763 2.456 2.522 1.919 - 764water OW 764 2.442 2.079 1.901 - 765water OW 765 2.803 2.023 2.095 - 766water OW 766 2.843 2.037 2.570 - 767water OW 767 2.866 2.583 2.549 - 768water OW 768 2.293 2.144 2.555 - 769water OW 769 3.023 0.701 5.017 - 770water OW 770 0.262 0.165 2.709 - 771water OW 771 1.078 2.090 2.789 - 772water OW 772 1.935 2.694 5.214 - 773water OW 773 1.686 2.846 4.899 - 774water OW 774 0.239 2.896 2.736 - 775water OW 775 3.179 2.534 2.771 - 776water OW 776 0.734 2.900 5.334 - 777water OW 777 3.127 0.153 6.740 - 778water OW 778 1.210 0.552 2.990 - 779water OW 779 2.818 1.913 4.278 - 780water OW 780 3.081 2.885 2.490 - 781water OW 781 0.788 1.777 2.778 - 782water OW 782 1.602 2.014 0.138 - 783water OW 783 1.938 1.032 5.677 - 784water OW 784 2.653 1.551 4.294 - 785water OW 785 2.706 0.924 2.764 - 786water OW 786 0.138 0.452 4.529 - 787water OW 787 3.134 1.308 3.425 - 788water OW 788 2.761 2.390 5.944 - 789water OW 789 2.004 1.434 3.649 - 790water OW 790 2.162 2.052 4.391 - 791water OW 791 1.489 2.965 2.556 - 792water OW 792 2.132 2.973 3.799 - 793water OW 793 2.883 1.930 5.324 - 794water OW 794 1.026 1.394 4.265 - 795water OW 795 1.507 0.637 3.018 - 796water OW 796 3.045 1.498 5.717 - 797water OW 797 2.174 2.748 4.223 - 798water OW 798 0.439 0.187 6.262 - 799water OW 799 1.882 2.299 5.302 - 800water OW 800 1.106 0.640 2.752 - 801water OW 801 2.427 2.023 2.762 - 802water OW 802 2.815 0.337 2.998 - 803water OW 803 0.228 2.371 4.186 - 804water OW 804 1.581 1.593 6.307 - 805water OW 805 2.344 1.372 3.456 - 806water OW 806 0.515 2.939 4.643 - 807water OW 807 0.411 1.795 3.025 - 808water OW 808 0.448 0.288 4.674 - 809water OW 809 1.507 2.444 5.992 - 810water OW 810 1.659 1.121 4.730 - 811water OW 811 2.502 2.914 5.000 - 812water OW 812 0.060 1.642 5.893 - 813water OW 813 0.595 1.217 5.195 - 814water OW 814 1.127 2.164 6.468 - 815water OW 815 1.270 0.190 4.065 - 816water OW 816 0.299 2.303 3.768 - 817water OW 817 0.191 1.679 3.622 - 818water OW 818 2.715 0.194 2.793 - 819water OW 819 2.256 0.906 4.642 - 820water OW 820 2.202 1.819 4.932 - 821water OW 821 1.940 0.967 6.543 - 822water OW 822 2.299 2.863 6.312 - 823water OW 823 2.296 2.413 2.589 - 824water OW 824 0.288 0.204 5.072 - 825water OW 825 1.193 0.692 4.280 - 826water OW 826 1.267 0.073 3.817 - 827water OW 827 2.433 0.255 2.759 - 828water OW 828 2.724 0.137 6.019 - 829water OW 829 0.499 2.914 5.607 - 830water OW 830 0.498 1.825 3.407 - 831water OW 831 2.033 1.076 5.940 - 832water OW 832 3.169 2.705 4.508 - 833water OW 833 0.075 0.570 3.199 - 834water OW 834 0.346 0.848 2.793 - 835water OW 835 0.759 2.048 5.006 - 836water OW 836 0.755 2.664 4.352 - 837water OW 837 0.281 1.403 3.595 - 838water OW 838 0.850 2.356 5.426 - 839water OW 839 0.264 0.601 2.727 - 840water OW 840 2.811 2.090 4.857 - 841water OW 841 0.839 0.249 6.501 - 842water OW 842 2.896 1.815 4.885 - 843water OW 843 1.501 2.854 3.413 - 844water OW 844 2.314 0.930 3.021 - 845water OW 845 0.323 2.865 5.417 - 846water OW 846 2.421 0.488 5.752 - 847water OW 847 1.629 1.023 2.806 - 848water OW 848 1.996 1.852 3.502 - 849water OW 849 0.597 0.204 3.576 - 850water OW 850 0.636 0.270 5.714 - 851water OW 851 2.317 0.647 6.706 - 852water OW 852 2.766 0.125 4.546 - 853water OW 853 0.959 1.086 5.130 - 854water OW 854 1.132 1.724 3.549 - 855water OW 855 2.446 1.281 3.209 - 856water OW 856 3.125 0.939 2.951 - 857water OW 857 2.514 1.327 6.077 - 858water OW 858 1.299 1.286 2.970 - 859water OW 859 2.326 0.223 3.015 - 860water OW 860 0.705 1.017 2.944 - 861water OW 861 2.972 0.520 5.274 - 862water OW 862 2.004 2.226 3.215 - 863water OW 863 2.715 0.705 4.381 - 864water OW 864 1.460 0.392 4.106 - 865water OW 865 2.090 2.745 3.594 - 866water OW 866 2.141 0.763 4.277 - 867water OW 867 1.236 2.815 6.294 - 868water OW 868 0.544 1.619 4.286 - 869water OW 869 1.778 2.754 5.440 - 870water OW 870 2.511 1.017 3.229 - 871water OW 871 0.613 0.150 5.301 - 872water OW 872 0.251 2.812 3.145 - 873water OW 873 1.994 2.498 6.295 - 874water OW 874 1.113 0.193 6.538 - 875water OW 875 1.907 0.153 2.773 - 876water OW 876 2.595 1.029 4.750 - 877water OW 877 3.027 0.266 4.543 - 878water OW 878 0.190 0.286 3.671 - 879water OW 879 1.149 0.901 6.525 - 880water OW 880 0.492 1.335 3.032 - 881water OW 881 1.399 1.799 4.402 - 882water OW 882 3.186 0.604 2.757 - 883water OW 883 1.936 2.154 4.526 - 884water OW 884 0.358 1.063 4.583 - 885water OW 885 3.072 1.530 4.852 - 886water OW 886 0.355 1.632 4.780 - 887water OW 887 3.079 0.242 2.978 - 888water OW 888 1.688 0.315 6.493 - 889water OW 889 2.119 0.062 5.257 - 890water OW 890 2.311 1.400 3.000 - 891water OW 891 1.297 0.121 5.575 - 892water OW 892 2.723 0.653 2.763 - 893water OW 893 0.315 2.118 4.107 - 894water OW 894 2.316 2.065 6.045 - 895water OW 895 1.652 1.522 4.998 - 896water OW 896 2.450 0.220 5.674 - 897water OW 897 2.427 2.823 2.786 - 898water OW 898 1.618 2.962 5.687 - 899water OW 899 1.085 0.949 3.569 - 900water OW 900 0.681 0.715 2.914 - 901water OW 901 1.438 2.486 4.492 - 902water OW 902 2.568 0.670 5.599 - 903water OW 903 2.979 1.705 3.580 - 904water OW 904 2.276 1.703 4.606 - 905water OW 905 0.699 0.119 6.309 - 906water OW 906 0.811 1.105 3.189 - 907water OW 907 0.256 2.446 2.779 - 908water OW 908 1.428 1.465 3.445 - 909water OW 909 3.201 2.381 3.012 - 910water OW 910 1.549 1.388 3.041 - 911water OW 911 2.336 2.705 6.038 - 912water OW 912 0.926 1.129 5.524 - 913water OW 913 3.081 2.634 3.505 - 914water OW 914 0.109 0.367 5.447 - 915water OW 915 1.096 1.349 3.769 - 916water OW 916 3.113 2.712 5.107 - 917water OW 917 0.843 0.147 4.989 - 918water OW 918 0.519 2.541 5.414 - 919water OW 919 2.765 1.398 5.996 - 920water OW 920 2.735 2.046 3.401 - 921water OW 921 0.780 2.467 6.503 - 922water OW 922 0.699 2.163 3.017 - 923water OW 923 3.074 2.963 4.579 - 924water OW 924 1.050 0.907 2.763 - 925water OW 925 1.302 2.648 2.977 - 926water OW 926 0.956 2.277 3.700 - 927water OW 927 0.665 2.928 2.954 - 928water OW 928 0.746 2.332 4.975 - 929water OW 929 1.917 0.924 2.344 - 930water OW 930 2.120 1.454 5.238 - 931water OW 931 1.663 0.285 2.770 - 932water OW 932 2.818 0.499 3.776 - 933water OW 933 1.501 1.442 2.567 - 934water OW 934 0.055 2.452 3.981 - 935water OW 935 2.395 0.665 4.232 - 936water OW 936 3.054 0.253 5.384 - 937water OW 937 2.942 2.534 4.567 - 938water OW 938 3.060 2.460 4.963 - 939water OW 939 2.013 2.039 2.975 - 940water OW 940 2.301 1.049 4.062 - 941water OW 941 1.187 0.701 3.651 - 942water OW 942 0.414 0.884 5.578 - 943water OW 943 0.341 0.926 6.515 - 944water OW 944 2.276 1.267 5.678 - 945water OW 945 2.833 0.605 3.013 - 946water OW 946 2.116 0.580 3.960 - 947water OW 947 0.312 1.734 4.394 - 948water OW 948 1.752 1.570 3.706 - 949water OW 949 0.097 1.261 3.211 - 950water OW 950 0.248 0.075 3.214 - 951water OW 951 2.427 1.735 2.754 - 952water OW 952 1.668 0.054 4.689 - 953water OW 953 0.912 1.359 3.231 - 954water OW 954 1.660 1.303 2.768 - 955water OW 955 1.326 1.829 3.099 - 956water OW 956 0.296 0.107 6.026 - 957water OW 957 1.997 2.176 5.750 - 958water OW 958 0.329 0.556 4.701 - 959water OW 959 1.552 0.188 6.706 - 960water OW 960 1.710 3.000 4.188 - 961water OW 961 1.377 1.839 5.196 - 962water OW 962 1.627 1.017 3.963 - 963water OW 963 1.926 2.883 2.778 - 964water OW 964 0.356 0.620 3.179 - 965water OW 965 0.241 1.826 5.655 - 966water OW 966 1.839 0.133 5.173 - 967water OW 967 1.192 2.482 2.780 - 968water OW 968 2.710 1.739 2.725 - 969water OW 969 1.384 2.222 4.536 - 970water OW 970 2.312 0.257 5.167 - 971water OW 971 0.430 2.789 2.911 - 972water OW 972 1.671 2.435 3.272 - 973water OW 973 2.276 2.149 6.308 - 974water OW 974 0.467 2.259 5.669 - 975water OW 975 1.202 1.133 3.404 - 976water OW 976 1.241 2.384 6.041 - 977water OW 977 0.322 1.415 2.831 - 978water OW 978 0.701 2.264 4.480 - 979water OW 979 2.132 2.483 3.693 - 980water OW 980 0.368 1.670 6.519 - 981water OW 981 0.077 0.643 5.076 - 982water OW 982 2.453 1.341 2.779 - 983water OW 983 0.644 2.192 2.562 - 984water OW 984 1.101 2.415 4.181 - 985water OW 985 2.608 2.983 3.135 - 986water OW 986 2.378 1.723 3.809 - 987water OW 987 0.507 1.235 5.610 - 988water OW 988 1.081 1.698 2.812 - 989water OW 989 2.564 2.432 3.140 - 990water OW 990 1.843 1.717 4.329 - 991water OW 991 0.832 2.101 6.054 - 992water OW 992 3.150 1.990 3.254 - 993water OW 993 3.091 2.122 6.700 - 994water OW 994 0.605 1.893 5.835 - 995water OW 995 0.463 1.374 3.800 - 996water OW 996 1.333 0.610 5.879 - 997water OW 997 1.221 2.280 4.795 - 998water OW 998 0.624 1.068 4.307 - 999water OW 999 1.682 2.404 2.977 - 1000water OW 1000 0.271 2.545 3.604 - 1001water OW 1001 0.088 0.372 5.141 - 1002water OW 1002 0.499 1.821 3.938 - 1003water OW 1003 2.168 0.336 4.336 - 1004water OW 1004 1.759 1.834 3.342 - 1005water OW 1005 2.234 1.906 3.991 - 1006water OW 1006 0.729 0.373 5.153 - 1007water OW 1007 1.231 0.248 5.034 - 1008water OW 1008 0.865 1.078 6.131 - 1009water OW 1009 0.734 1.386 3.890 - 1010water OW 1010 2.729 2.421 2.751 - 1011water OW 1011 0.200 0.093 3.483 - 1012water OW 1012 2.813 0.369 5.845 - 1013water OW 1013 0.134 0.167 4.314 - 1014water OW 1014 0.466 2.136 4.760 - 1015water OW 1015 0.080 1.757 6.138 - 1016water OW 1016 0.571 0.513 3.311 - 1017water OW 1017 0.938 2.980 4.726 - 1018water OW 1018 2.876 1.287 6.235 - 1019water OW 1019 2.700 2.139 2.787 - 1020water OW 1020 2.964 2.881 3.370 - 1021water OW 1021 2.033 1.014 3.017 - 1022water OW 1022 1.945 2.424 2.833 - 1023water OW 1023 2.495 0.501 6.019 - 1024water OW 1024 2.024 1.306 3.910 - 1025water OW 1025 2.848 2.802 6.312 - 1026water OW 1026 1.543 1.572 5.410 - 1027water OW 1027 0.499 1.326 3.412 - 1028water OW 1028 2.068 1.308 6.276 - 1029water OW 1029 2.680 2.375 0.120 - 1030water OW 1030 2.547 0.287 4.476 - 1031water OW 1031 1.196 2.025 4.888 - 1032water OW 1032 2.077 2.557 4.897 - 1033water OW 1033 0.974 1.785 5.856 - 1034water OW 1034 2.223 0.919 4.907 - 1035water OW 1035 2.264 1.185 5.005 - 1036water OW 1036 0.851 1.294 3.663 - 1037water OW 1037 0.299 2.861 6.508 - 1038water OW 1038 1.861 0.359 4.007 - 1039water OW 1039 1.545 0.669 0.368 - 1040water OW 1040 2.829 0.832 4.143 - 1041water OW 1041 2.066 0.254 6.721 - 1042water OW 1042 2.771 2.628 5.794 - 1043water OW 1043 0.344 2.064 4.980 - 1044water OW 1044 2.856 1.269 3.360 - 1045water OW 1045 0.387 2.062 3.457 - 1046water OW 1046 1.780 1.251 4.002 - 1047water OW 1047 0.959 0.423 3.281 - 1048water OW 1048 2.379 1.162 4.609 - 1049water OW 1049 1.005 0.779 4.972 - 1050water OW 1050 2.029 0.959 4.118 - 1051water OW 1051 1.244 1.327 5.673 - 1052water OW 1052 1.109 0.400 5.225 - 1053water OW 1053 2.773 2.487 5.539 - 1054water OW 1054 0.089 1.026 2.744 - 1055water OW 1055 1.040 1.579 5.620 - 1056water OW 1056 0.360 2.480 4.393 - 1057water OW 1057 0.588 1.025 4.743 - 1058water OW 1058 1.224 1.752 5.757 - 1059water OW 1059 1.509 2.134 6.717 - 1060water OW 1060 1.264 2.814 0.336 - 1061water OW 1061 1.664 1.744 0.119 - 1062water OW 1062 0.038 1.742 3.245 - 1063water OW 1063 0.881 2.774 3.237 - 1064water OW 1064 0.365 1.331 6.094 - 1065water OW 1065 2.780 2.023 3.019 - 1066water OW 1066 2.061 2.258 4.842 - 1067water OW 1067 2.183 0.684 5.087 - 1068water OW 1068 0.743 0.899 6.733 - 1069water OW 1069 1.642 2.112 5.031 - 1070water OW 1070 2.850 1.050 6.652 - 1071water OW 1071 0.765 2.491 5.980 - 1072water OW 1072 0.158 0.848 3.872 - 1073water OW 1073 0.445 1.815 4.992 - 1074water OW 1074 2.049 0.542 6.740 - 1075water OW 1075 3.083 2.722 3.774 - 1076water OW 1076 0.214 2.453 5.081 - 1077water OW 1077 2.024 2.786 6.730 - 1078water OW 1078 0.384 0.132 5.727 - 1079water OW 1079 0.284 2.164 2.808 - 1080water OW 1080 0.694 1.522 5.223 - 1081water OW 1081 3.078 1.627 6.290 - 1082water OW 1082 1.672 1.061 6.519 - 1083water OW 1083 0.444 2.750 3.312 - 1084water OW 1084 0.484 1.296 6.735 - 1085water OW 1085 2.306 0.581 5.326 - 1086water OW 1086 0.729 1.192 4.532 - 1087water OW 1087 1.534 2.684 5.372 - 1088water OW 1088 3.139 1.105 5.035 - 1089water OW 1089 1.202 1.034 2.988 - 1090water OW 1090 0.852 2.791 0.131 - 1091water OW 1091 2.496 1.753 5.732 - 1092water OW 1092 2.817 0.062 5.268 - 1093water OW 1093 1.358 0.812 4.872 - 1094water OW 1094 0.488 0.720 5.929 - 1095water OW 1095 2.022 2.516 6.726 - 1096water OW 1096 2.364 2.017 3.181 - 1097water OW 1097 1.032 1.980 3.926 - 1098water OW 1098 0.761 1.399 0.359 - 1099water OW 1099 0.748 0.706 3.659 - 1100water OW 1100 1.905 2.984 4.578 - 1101water OW 1101 0.941 1.678 5.269 - 1102water OW 1102 2.829 2.000 6.721 - 1103water OW 1103 3.097 2.453 2.513 - 1104water OW 1104 2.477 0.030 4.217 - 1105water OW 1105 0.639 0.377 4.509 - 1106water OW 1106 2.672 2.917 0.109 - 1107water OW 1107 1.502 2.153 0.369 - 1108water OW 1108 0.361 0.631 5.668 - 1109water OW 1109 1.305 0.674 3.415 - 1110water OW 1110 2.442 0.883 5.139 - 1111water OW 1111 0.977 0.277 4.328 - 1112water OW 1112 0.042 2.031 0.096 - 1113water OW 1113 2.748 1.273 3.611 - 1114water OW 1114 3.016 0.672 4.022 - 1115water OW 1115 0.824 0.611 6.244 - 1116water OW 1116 2.423 1.482 3.912 - 1117water OW 1117 2.078 1.020 3.468 - 1118water OW 1118 1.211 0.294 3.019 - 1119water OW 1119 1.369 2.118 5.117 - 1120water OW 1120 0.394 2.483 6.731 - 1121water OW 1121 0.156 0.927 4.447 - 1122water OW 1122 1.236 2.067 0.319 - 1123water OW 1123 1.121 2.323 5.064 - 1124water OW 1124 2.334 1.241 5.275 - 1125water OW 1125 0.056 2.772 4.252 - 1126water OW 1126 2.370 1.788 5.303 - 1127water OW 1127 0.781 1.563 6.163 - 1128water OW 1128 2.052 1.262 0.297 - 1129water OW 1129 0.680 0.156 4.751 - 1130water OW 1130 0.093 2.410 4.838 - 1131water OW 1131 0.880 1.021 4.704 - 1132water OW 1132 1.604 1.249 5.987 - 1133water OW 1133 2.088 1.930 5.592 - 1134water OW 1134 0.614 1.426 4.819 - 1135water OW 1135 1.142 0.081 3.585 - 1136water OW 1136 2.574 1.980 3.623 - 1137water OW 1137 1.794 2.556 4.670 - 1138water OW 1138 0.406 2.517 2.990 - 1139water OW 1139 2.049 1.087 5.244 - 1140water OW 1140 2.918 0.918 5.391 - 1141water OW 1141 2.445 2.955 5.462 - 1142water OW 1142 1.585 0.987 4.231 - 1143water OW 1143 0.169 0.477 6.103 - 1144water OW 1144 1.515 2.465 2.583 - 1145water OW 1145 1.473 2.626 4.941 - 1146water OW 1146 1.493 0.034 5.922 - 1147water OW 1147 2.679 2.886 3.404 - 1148water OW 1148 0.740 0.494 5.386 - 1149water OW 1149 2.458 2.737 3.942 - 1150water OW 1150 1.118 0.207 3.261 - 1151water OW 1151 1.502 1.924 3.930 - 1152water OW 1152 2.591 2.404 4.249 - 1153water OW 1153 2.433 1.763 0.111 - 1154water OW 1154 0.650 2.581 4.698 - 1155water OW 1155 1.045 1.979 5.399 - 1156water OW 1156 0.506 0.145 3.293 - 1157water OW 1157 1.888 1.375 0.120 - 1158water OW 1158 3.080 2.566 4.209 - 1159water OW 1159 2.973 2.269 4.680 - 1160water OW 1160 1.834 0.222 5.599 - 1161water OW 1161 1.791 0.698 4.344 - 1162water OW 1162 0.999 2.658 3.716 - 1163water OW 1163 1.364 0.553 5.595 - 1164water OW 1164 2.196 1.491 5.842 - 1165water OW 1165 0.257 2.813 4.755 - 1166water OW 1166 1.009 0.342 4.889 - 1167water OW 1167 2.771 1.854 6.017 - 1168water OW 1168 0.840 2.345 4.255 - 1169water OW 1169 1.619 2.483 6.479 - 1170water OW 1170 0.870 1.763 0.104 - 1171water OW 1171 0.882 2.922 4.453 - 1172water OW 1172 0.362 2.258 5.181 - 1173water OW 1173 0.752 1.793 6.499 - 1174water OW 1174 2.691 0.028 4.791 - 1175water OW 1175 1.093 0.161 0.130 - 1176water OW 1176 0.812 1.851 3.795 - 1177water OW 1177 0.644 0.814 4.387 - 1178water OW 1178 0.505 0.880 3.226 - 1179water OW 1179 1.944 1.238 5.494 - 1180water OW 1180 2.914 1.434 5.479 - 1181water OW 1181 3.191 2.072 2.824 - 1182water OW 1182 2.423 2.709 5.323 - 1183water OW 1183 0.503 1.027 6.720 - 1184water OW 1184 2.117 2.288 5.112 - 1185water OW 1185 1.312 1.953 6.088 - 1186water OW 1186 2.801 2.187 5.724 - 1187water OW 1187 2.899 2.453 3.420 - 1188water OW 1188 2.446 0.337 3.243 - 1189water OW 1189 1.228 2.850 3.444 - 1190water OW 1190 2.472 2.533 2.756 - 1191water OW 1191 1.902 0.918 3.270 - 1192water OW 1192 3.169 2.189 3.913 - 1193water OW 1193 0.689 1.713 3.021 - 1194water OW 1194 1.973 0.848 4.838 - 1195water OW 1195 0.410 1.080 6.027 - 1196water OW 1196 2.834 1.514 3.695 - 1197water OW 1197 0.175 1.415 4.544 - 1198water OW 1198 3.082 2.365 5.889 - 1199water OW 1199 0.291 1.700 2.782 - 1200water OW 1200 1.793 1.689 3.108 - 1201water OW 1201 1.910 0.656 3.200 - 1202water OW 1202 1.727 0.515 5.945 - 1203water OW 1203 2.317 0.632 6.252 - 1204water OW 1204 1.838 2.533 3.473 - 1205water OW 1205 0.867 0.619 5.842 - 1206water OW 1206 2.584 2.815 5.895 - 1207water OW 1207 1.285 1.300 4.122 - 1208water OW 1208 1.000 0.559 4.415 - 1209water OW 1209 1.338 2.241 4.259 - 1210water OW 1210 0.871 0.063 3.864 - 1211water OW 1211 1.289 0.994 6.734 - 1212water OW 1212 1.884 2.719 4.482 - 1213water OW 1213 0.589 2.083 4.000 - 1214water OW 1214 2.324 1.412 6.708 - 1215water OW 1215 0.303 2.115 0.122 - 1216water OW 1216 0.664 2.417 6.769 - 1217water OW 1217 1.392 2.453 3.168 - 1218water OW 1218 2.089 0.248 0.343 - 1219water OW 1219 0.093 1.380 5.972 - 1220water OW 1220 2.518 2.361 3.827 - 1221water OW 1221 1.762 2.787 5.852 - 1222water OW 1222 0.162 2.652 3.823 - 1223water OW 1223 0.935 0.529 5.081 - 1224water OW 1224 2.419 1.056 6.041 - 1225water OW 1225 1.340 1.816 4.849 - 1226water OW 1226 1.264 1.785 5.434 - 1227water OW 1227 2.652 1.047 5.031 - 1228water OW 1228 1.466 2.733 6.462 - 1229water OW 1229 1.147 2.572 4.754 - 1230water OW 1230 0.764 1.450 3.024 - 1231water OW 1231 1.009 2.078 5.128 - 1232water OW 1232 2.904 2.154 4.440 - 1233water OW 1233 1.276 1.506 3.657 - 1234water OW 1234 2.337 0.128 4.436 - 1235water OW 1235 0.945 1.736 6.310 - 1236water OW 1236 2.298 0.148 6.314 - 1237water OW 1237 0.958 1.141 5.816 - 1238water OW 1238 0.880 2.471 4.548 - 1239water OW 1239 3.021 1.929 4.083 - 1240water OW 1240 0.023 2.765 6.517 - 1241water OW 1241 2.617 2.614 3.760 - 1242water OW 1242 2.863 0.315 5.148 - 1243water OW 1243 1.887 2.895 0.097 - 1244water OW 1244 0.057 0.820 3.290 - 1245water OW 1245 0.440 1.700 3.684 - 1246water OW 1246 2.663 2.487 4.948 - 1247water OW 1247 2.328 1.613 5.525 - 1248water OW 1248 0.080 1.015 0.068 - 1249water OW 1249 0.079 1.675 5.245 - 1250water OW 1250 0.958 2.461 3.927 - 1251water OW 1251 2.896 1.485 3.959 - 1252water OW 1252 2.397 1.679 5.076 - 1253water OW 1253 0.453 0.374 3.731 - 1254water OW 1254 2.060 0.755 5.672 - 1255water OW 1255 1.668 1.127 5.618 - 1256water OW 1256 3.093 2.360 0.355 - 1257water OW 1257 0.408 2.034 6.265 - 1258water OW 1258 1.561 0.719 5.501 - 1259water OW 1259 2.863 0.230 6.266 - 1260water OW 1260 0.714 1.665 6.739 - 1261water OW 1261 2.802 0.728 4.864 - 1262water OW 1262 1.642 0.238 3.187 - 1263water OW 1263 0.931 2.542 3.088 - 1264water OW 1264 1.109 0.204 2.786 - 1265water OW 1265 1.748 2.167 3.327 - 1266water OW 1266 2.660 2.128 3.844 - 1267water OW 1267 0.507 2.389 4.850 - 1268water OW 1268 0.636 0.812 4.932 - 1269water OW 1269 1.023 1.191 4.879 - 1270water OW 1270 1.868 2.942 3.956 - 1271water OW 1271 0.736 1.678 0.339 - 1272water OW 1272 0.208 0.765 4.856 - 1273water OW 1273 0.963 2.172 5.588 - 1274water OW 1274 2.172 2.271 5.937 - 1275water OW 1275 1.236 0.219 4.420 - 1276water OW 1276 2.098 2.993 4.781 - 1277water OW 1277 1.177 1.655 6.459 - 1278water OW 1278 1.302 2.229 2.923 - 1279water OW 1279 0.031 2.511 6.076 - 1280water OW 1280 1.320 1.683 6.220 - 1281water OW 1281 2.389 2.319 4.075 - 1282water OW 1282 3.018 1.830 5.559 - 1283water OW 1283 3.105 0.913 0.302 - 1284water OW 1284 2.180 0.346 5.397 - 1285water OW 1285 2.843 0.393 3.387 - 1286water OW 1286 1.484 0.247 2.968 - 1287water OW 1287 2.480 1.273 6.494 - 1288water OW 1288 1.512 2.926 6.685 - 1289water OW 1289 0.071 2.955 5.503 - 1290water OW 1290 1.538 2.220 6.338 - 1291water OW 1291 1.269 1.766 2.620 - 1292water OW 1292 0.685 0.847 5.805 - 1293water OW 1293 2.231 2.985 4.072 - 1294water OW 1294 0.035 0.859 4.677 - 1295water OW 1295 0.094 1.200 3.621 - 1296water OW 1296 1.369 2.393 3.442 - 1297water OW 1297 2.998 2.201 4.156 - 1298water OW 1298 0.727 1.475 4.568 - 1299water OW 1299 1.086 0.671 3.221 - 1300water OW 1300 2.319 0.097 5.908 - 1301water OW 1301 2.432 2.019 4.416 - 1302water OW 1302 0.735 0.558 4.326 - 1303water OW 1303 1.986 2.981 5.586 - 1304water OW 1304 2.779 0.647 3.267 - 1305water OW 1305 0.801 0.793 3.915 - 1306water OW 1306 0.400 1.264 4.256 - 1307water OW 1307 1.001 1.452 4.981 - 1308water OW 1308 2.088 0.977 0.308 - 1309water OW 1309 1.453 0.385 5.189 - 1310water OW 1310 2.812 2.529 6.297 - 1311water OW 1311 2.748 2.262 5.070 - 1312water OW 1312 2.657 2.019 4.096 - 1313water OW 1313 2.485 1.299 3.683 - 1314water OW 1314 1.336 2.125 3.389 - 1315water OW 1315 2.633 1.043 4.346 - 1316water OW 1316 2.202 2.440 4.218 - 1317water OW 1317 1.983 0.604 6.058 - 1318water OW 1318 0.525 2.704 4.502 - 1319water OW 1319 3.114 0.048 4.963 - 1320water OW 1320 0.899 1.016 6.525 - 1321water OW 1321 0.290 1.384 4.876 - 1322water OW 1322 1.940 0.351 5.830 - 1323water OW 1323 0.296 1.403 5.517 - 1324water OW 1324 1.522 0.957 6.276 - 1325water OW 1325 2.361 2.938 3.305 - 1326water OW 1326 2.334 1.361 0.332 - 1327water OW 1327 1.880 0.149 4.350 - 1328water OW 1328 0.568 2.857 4.994 - 1329water OW 1329 1.160 1.043 4.580 - 1330water OW 1330 0.701 2.682 5.571 - 1331water OW 1331 1.188 2.180 3.793 - 1332water OW 1332 1.879 2.123 6.539 - 1333water OW 1333 0.734 1.652 3.975 - 1334water OW 1334 0.842 2.498 5.179 - 1335water OW 1335 2.848 1.185 5.356 - 1336water OW 1336 3.017 0.475 4.758 - 1337water OW 1337 1.338 1.263 6.249 - 1338water OW 1338 2.283 0.455 3.751 - 1339water OW 1339 2.022 0.552 4.456 - 1340water OW 1340 2.134 1.080 6.716 - 1341water OW 1341 0.044 1.344 4.770 - 1342water OW 1342 0.446 0.439 5.133 - 1343water OW 1343 3.073 2.867 6.758 - 1344water OW 1344 2.347 0.589 3.217 - 1345water OW 1345 1.095 2.427 0.102 - 1346water OW 1346 0.264 1.550 5.739 - 1347water OW 1347 0.036 1.766 0.096 - 1348water OW 1348 1.386 2.840 4.393 - 1349water OW 1349 2.956 0.017 4.150 - 1350water OW 1350 2.729 1.300 5.124 - 1351water OW 1351 3.133 2.078 5.216 - 1352water OW 1352 1.108 0.510 4.657 - 1353water OW 1353 1.820 0.336 4.534 - 1354water OW 1354 2.405 2.059 6.532 - 1355water OW 1355 1.762 2.336 4.912 - 1356water OW 1356 0.484 1.101 3.733 - 1357water OW 1357 2.727 0.682 0.081 - 1358water OW 1358 3.082 2.963 2.945 - 1359water OW 1359 1.351 1.207 5.218 - 1360water OW 1360 3.002 1.161 5.633 - 1361water OW 1361 2.300 0.411 4.104 - 1362water OW 1362 2.461 2.624 4.345 - 1363water OW 1363 1.266 0.248 6.769 - 1364water OW 1364 1.063 2.180 3.309 - 1365water OW 1365 1.547 2.180 4.074 - 1366water OW 1366 0.887 1.295 6.477 - 1367water OW 1367 1.494 1.659 2.725 - 1368water OW 1368 0.648 2.922 3.227 - 1369water OW 1369 0.745 0.389 5.932 - 1370water OW 1370 0.348 0.220 5.430 - 1371water OW 1371 2.817 2.831 2.994 - 1372water OW 1372 2.229 0.257 4.904 - 1373water OW 1373 2.692 1.661 6.496 - 1374water OW 1374 0.427 0.336 4.054 - 1375water OW 1375 0.844 0.760 4.586 - 1376water OW 1376 2.403 1.670 6.204 - 1377water OW 1377 0.140 1.167 4.281 - 1378water OW 1378 1.106 0.353 5.638 - 1379water OW 1379 2.387 0.188 3.472 - 1380water OW 1380 2.159 1.737 4.347 - 1381water OW 1381 2.558 0.631 5.211 - 1382water OW 1382 0.949 2.977 5.175 - 1383water OW 1383 0.011 1.958 5.732 - 1384water OW 1384 1.316 2.819 6.039 - 1385water OW 1385 1.381 1.525 5.826 - 1386water OW 1386 2.861 1.011 6.186 - 1387water OW 1387 1.134 1.791 4.098 - 1388water OW 1388 0.749 1.311 6.239 - 1389water OW 1389 0.883 0.184 5.610 - 1390water OW 1390 2.464 2.007 5.486 - 1391water OW 1391 2.180 2.017 5.145 - 1392water OW 1392 2.518 2.105 5.087 - 1393water OW 1393 0.437 2.755 6.722 - 1394water OW 1394 1.129 0.893 5.213 - 1395water OW 1395 1.939 1.433 6.462 - 1396water OW 1396 0.276 1.757 4.117 - 1397water OW 1397 2.927 2.889 4.808 - 1398water OW 1398 2.105 2.466 4.470 - 1399water OW 1399 1.540 0.077 3.747 - 1400water OW 1400 1.387 0.473 4.686 - 1401water OW 1401 2.896 1.445 4.667 - 1402water OW 1402 0.146 1.508 4.079 - 1403water OW 1403 1.415 0.285 5.843 - 1404water OW 1404 1.611 1.331 6.243 - 1405water OW 1405 0.822 0.573 2.723 - 1406water OW 1406 3.180 0.625 6.295 - 1407water OW 1407 1.144 2.571 3.480 - 1408water OW 1408 0.931 0.226 5.337 - 1409water OW 1409 2.545 0.075 5.219 - 1410water OW 1410 2.328 2.143 0.328 - 1411water OW 1411 2.532 0.788 4.852 - 1412water OW 1412 0.369 0.034 4.396 - 1413water OW 1413 1.820 1.570 4.583 - 1414water OW 1414 0.958 0.997 4.000 - 1415water OW 1415 1.058 0.929 4.255 - 1416water OW 1416 2.542 2.725 4.610 - 1417water OW 1417 2.522 0.218 4.883 - 1418water OW 1418 3.017 1.056 4.187 - 1419water OW 1419 1.269 1.813 6.688 - 1420water OW 1420 1.709 1.361 5.495 - 1421water OW 1421 1.903 0.352 5.362 - 1422water OW 1422 0.305 2.809 6.026 - 1423water OW 1423 0.762 1.795 3.301 - 1424water OW 1424 1.091 2.158 0.094 - 1425water OW 1425 3.084 2.884 0.319 - 1426water OW 1426 2.889 2.534 3.839 - 1427water OW 1427 2.691 0.244 3.217 - 1428water OW 1428 2.446 0.527 2.747 - 1429water OW 1429 1.213 2.733 5.772 - 1430water OW 1430 1.733 1.685 6.099 - 1431water OW 1431 1.272 1.520 4.888 - 1432water OW 1432 1.266 0.291 3.491 - 1433water OW 1433 0.432 2.016 6.719 - 1434water OW 1434 1.969 0.680 0.122 - 1435water OW 1435 1.942 0.872 6.135 - 1436water OW 1436 1.328 2.754 3.740 - 1437water OW 1437 1.762 2.488 5.151 - 1438water OW 1438 0.294 0.783 3.456 - 1439water OW 1439 1.720 2.166 3.611 - 1440water OW 1440 0.248 0.706 4.253 - 1441water OW 1441 0.719 0.115 6.712 - 1442water OW 1442 1.631 2.797 0.073 - 1443water OW 1443 0.409 2.218 4.492 - 1444water OW 1444 2.016 2.280 4.043 - 1445water OW 1445 0.272 2.352 6.501 - 1446water OW 1446 1.972 1.787 4.045 - 1447water OW 1447 1.269 2.787 6.702 - 1448water OW 1448 2.016 1.743 2.956 - 1449water OW 1449 0.859 1.889 4.300 - 1450water OW 1450 1.040 1.344 4.626 - 1451water OW 1451 1.630 0.989 3.244 - 1452water OW 1452 3.044 2.960 3.895 - 1453water OW 1453 2.158 2.729 4.499 - 1454water OW 1454 2.689 2.919 2.750 - 1455water OW 1455 1.716 0.583 0.157 - 1456water OW 1456 3.019 2.537 3.174 - 1457water OW 1457 0.828 0.264 0.075 - 1458water OW 1458 1.410 0.711 3.809 - 1459water OW 1459 2.229 2.261 4.619 - 1460water OW 1460 2.493 1.390 5.588 - 1461water OW 1461 1.581 2.732 4.073 - 1462water OW 1462 1.409 1.045 3.586 - 1463water OW 1463 2.123 0.762 5.405 - 1464water OW 1464 0.690 2.149 6.703 - 1465water OW 1465 2.449 0.493 4.651 - 1466water OW 1466 0.662 2.477 3.518 - 1467water OW 1467 2.936 1.421 4.408 - 1468water OW 1468 0.861 1.684 3.553 - 1469water OW 1469 1.096 0.751 5.943 - 1470water OW 1470 3.032 2.848 5.885 - 1471water OW 1471 1.873 0.952 4.428 - 1472water OW 1472 1.879 1.742 5.490 - 1473water OW 1473 0.414 0.694 5.229 - 1474water OW 1474 2.926 0.844 3.620 - 1475water OW 1475 0.011 2.161 4.730 - 1476water OW 1476 0.382 2.694 3.990 - 1477water OW 1477 3.176 1.351 5.229 - 1478water OW 1478 0.314 0.805 6.141 - 1479water OW 1479 1.121 1.091 3.792 - 1480water OW 1480 2.014 1.341 6.004 - 1481water OW 1481 1.634 0.556 2.774 - 1482water OW 1482 1.618 1.279 3.298 - 1483water OW 1483 0.055 1.053 4.049 - 1484water OW 1484 0.341 0.619 6.509 - 1485water OW 1485 2.984 0.764 5.764 - 1486water OW 1486 3.134 2.646 5.492 - 1487water OW 1487 1.512 1.398 6.745 - 1488water OW 1488 3.131 0.658 6.722 - 1489water OW 1489 0.204 2.456 3.180 - 1490water OW 1490 1.602 2.217 5.683 - 1491water OW 1491 1.637 2.697 4.370 - 1492water OW 1492 1.323 2.173 5.746 - 1493water OW 1493 0.777 0.502 4.729 - 1494water OW 1494 2.463 0.282 0.127 - 1495water OW 1495 1.276 2.313 5.298 - 1496water OW 1496 1.476 0.476 3.431 - 1497water OW 1497 0.841 0.303 2.708 - 1498water OW 1498 1.318 1.088 4.308 - 1499water OW 1499 2.212 2.104 5.386 - 1500water OW 1500 1.573 0.701 5.102 - 1501water OW 1501 1.425 2.977 4.149 - 1502water OW 1502 2.865 2.990 5.561 - 1503water OW 1503 0.991 0.197 4.065 - 1504water OW 1504 2.016 2.835 3.029 - 1505water OW 1505 1.550 0.910 6.728 - 1506water OW 1506 0.120 1.673 4.618 - 1507water OW 1507 0.018 2.478 6.492 - 1508water OW 1508 2.486 1.013 0.097 - 1509water OW 1509 0.734 2.126 3.769 - 1510water OW 1510 1.417 1.930 5.826 - 1511water OW 1511 2.003 2.135 6.296 - 1512water OW 1512 3.181 0.621 5.448 - 1513water OW 1513 1.579 2.725 6.054 - 1514water OW 1514 0.825 1.072 2.713 - 1515water OW 1515 0.514 0.439 5.539 - 1516water OW 1516 0.776 1.855 5.429 - 1517water OW 1517 1.824 0.725 5.020 - 1518water OW 1518 1.934 1.683 0.108 - 1519water OW 1519 0.087 1.771 3.857 - 1520water OW 1520 0.483 1.321 0.382 - 1521water OW 1521 0.080 0.244 0.127 - 1522water OW 1522 1.527 1.655 2.984 - 1523water OW 1523 0.898 1.687 4.888 - 1524water OW 1524 1.183 2.555 4.480 - 1525water OW 1525 2.386 1.364 4.814 - 1526water OW 1526 0.059 1.267 5.479 - 1527water OW 1527 1.545 2.882 0.326 - 1528water OW 1528 1.882 2.861 6.501 - 1529water OW 1529 2.888 1.314 2.975 - 1530water OW 1530 0.605 1.862 4.414 - 1531water OW 1531 2.229 0.108 5.542 - 1532water OW 1532 0.787 1.458 5.635 - 1533water OW 1533 0.989 2.653 4.971 - 1534water OW 1534 1.389 1.453 3.900 - 1535water OW 1535 2.836 2.498 6.742 - 1536water OW 1536 1.877 1.549 5.901 - 1537water OW 1537 0.499 1.223 6.311 - 1538water OW 1538 1.316 0.519 6.766 - 1539water OW 1539 2.627 0.410 5.043 - 1540water OW 1540 0.205 0.944 5.868 - 1541water OW 1541 1.859 1.332 4.739 - 1542water OW 1542 1.824 2.927 3.222 - 1543water OW 1543 2.688 0.435 4.277 - 1544water OW 1544 1.236 2.079 6.719 - 1545water OW 1545 1.591 2.804 3.163 - 1546water OW 1546 1.797 0.263 3.552 - 1547water OW 1547 2.252 2.366 5.463 - 1548water OW 1548 2.462 0.833 4.016 - 1549water OW 1549 2.447 2.516 4.795 - 1550water OW 1550 1.688 1.356 6.528 - 1551water OW 1551 0.666 0.756 5.347 - 1552water OW 1552 1.535 0.296 6.250 - 1553water OW 1553 0.308 2.825 3.530 - 1554water OW 1554 1.160 1.542 5.368 - 1555water OW 1555 1.220 0.261 0.341 - 1556water OW 1556 1.484 2.965 2.962 - 1557water OW 1557 1.236 1.477 6.067 - 1558water OW 1558 1.073 2.882 3.865 - 1559water OW 1559 1.676 0.088 6.133 - 1560water OW 1560 2.060 1.523 5.501 - 1561water OW 1561 2.530 2.458 5.402 - 1562water OW 1562 2.094 1.746 5.809 - 1563water OW 1563 2.466 1.807 6.468 - 1564water OW 1564 2.709 1.579 5.353 - 1565water OW 1565 2.690 0.374 3.994 - 1566water OW 1566 0.447 0.554 2.919 - 1567water OW 1567 0.743 0.167 4.382 - 1568water OW 1568 0.592 0.538 6.102 - 1569water OW 1569 2.023 2.512 3.273 - 1570water OW 1570 1.983 1.197 4.261 - 1571water OW 1571 2.443 2.040 0.108 - 1572water OW 1572 3.082 2.392 6.709 - 1573water OW 1573 1.917 0.177 0.136 - 1574water OW 1574 1.593 0.047 5.059 - 1575water OW 1575 2.683 0.974 5.980 - 1576water OW 1576 0.508 1.780 6.317 - 1577water OW 1577 0.481 2.470 5.875 - 1578water OW 1578 2.870 1.778 2.942 - 1579water OW 1579 2.257 2.429 6.287 - 1580water OW 1580 0.034 2.755 0.123 - 1581water OW 1581 3.104 0.636 0.306 - 1582water OW 1582 3.156 1.365 3.007 - 1583water OW 1583 1.409 2.957 4.629 - 1584water OW 1584 1.524 0.796 5.882 - 1585water OW 1585 2.813 1.737 6.273 - 1586water OW 1586 0.943 0.573 5.586 - 1587water OW 1587 2.073 0.765 3.762 - 1588water OW 1588 0.711 2.149 0.340 - 1589water OW 1589 1.088 2.894 6.521 - 1590water OW 1590 3.072 0.310 4.982 - 1591water OW 1591 2.301 2.079 3.595 - 1592water OW 1592 1.331 2.064 5.467 - 1593water OW 1593 2.332 2.439 2.985 - 1594water OW 1594 2.887 2.732 4.267 - 1595water OW 1595 1.534 1.977 3.240 - 1596water OW 1596 0.232 0.167 3.906 - 1597water OW 1597 0.196 2.143 5.368 - 1598water OW 1598 0.262 0.978 3.657 - 1599water OW 1599 1.649 2.846 6.298 - 1600water OW 1600 1.270 1.326 0.390 - 1601water OW 1601 1.901 0.950 2.777 - 1602water OW 1602 0.434 2.061 3.002 - 1603water OW 1603 0.860 0.530 6.501 - 1604water OW 1604 0.017 0.494 3.653 - 1605water OW 1605 1.716 2.538 3.901 - 1606water OW 1606 1.279 1.273 6.708 - 1607water OW 1607 1.545 1.999 4.529 - 1608water OW 1608 2.281 0.664 2.945 - 1609water OW 1609 3.074 0.395 3.879 - 1610water OW 1610 1.723 2.878 3.728 - 1611water OW 1611 1.599 2.036 5.494 - 1612water OW 1612 2.695 2.393 6.528 - 1613water OW 1613 2.088 1.764 6.717 - 1614water OW 1614 2.811 2.864 6.035 - 1615water OW 1615 2.405 2.344 5.181 - 1616water OW 1616 0.539 0.517 4.886 - 1617water OW 1617 0.989 1.167 4.416 - 1618water OW 1618 2.215 2.601 3.958 - 1619water OW 1619 2.535 0.579 3.880 - 1620water OW 1620 2.042 1.328 6.702 - 1621water OW 1621 2.186 1.367 4.518 - 1622water OW 1622 2.873 0.567 6.692 - 1623water OW 1623 0.614 1.084 3.364 - 1624water OW 1624 2.959 1.447 5.089 - 1625water OW 1625 1.846 2.385 4.402 - 1626water OW 1626 2.319 0.911 6.269 - 1627water OW 1627 1.630 0.468 3.670 - 1628water OW 1628 0.654 2.429 3.077 - 1629water OW 1629 1.276 2.809 4.813 - 1630water OW 1630 2.279 2.939 3.023 - 1631water OW 1631 2.745 0.164 6.511 - 1632water OW 1632 2.247 0.519 4.852 - 1633water OW 1633 1.943 2.804 4.960 - 1634water OW 1634 1.516 0.675 6.260 - 1635water OW 1635 1.632 1.767 5.589 - 1636water OW 1636 2.113 0.507 3.332 - 1637water OW 1637 1.916 0.137 6.519 - 1638water OW 1638 1.561 2.457 4.742 - 1639water OW 1639 2.420 2.506 0.124 - 1640water OW 1640 0.192 2.045 3.862 - 1641water OW 1641 0.837 2.079 4.625 - 1642water OW 1642 1.655 2.863 2.781 - 1643water OW 1643 2.891 1.291 4.149 - 1644water OW 1644 2.308 1.026 4.348 - 1645water OW 1645 1.854 2.124 0.144 - 1646water OW 1646 0.596 1.708 6.048 - 1647water OW 1647 2.126 1.522 4.747 - 1648water OW 1648 0.320 0.125 6.528 - 1649water OW 1649 0.636 2.680 5.205 - 1650water OW 1650 2.926 2.244 3.814 - 1651water OW 1651 1.734 1.770 3.910 - 1652water OW 1652 2.132 0.684 4.677 - 1653water OW 1653 2.707 1.403 0.065 - 1654water OW 1654 2.661 1.446 4.839 - 1655water OW 1655 1.098 0.419 3.668 - 1656water OW 1656 3.179 2.030 6.063 - 1657water OW 1657 1.093 2.584 6.217 - 1658water OW 1658 0.909 2.957 5.930 - 1659water OW 1659 1.440 1.018 5.755 - 1660water OW 1660 1.053 2.941 2.732 - 1661water OW 1661 2.937 2.195 3.520 - 1662water OW 1662 1.280 0.583 4.034 - 1663water OW 1663 2.963 0.840 3.354 - 1664water OW 1664 2.841 1.626 5.854 - 1665water OW 1665 2.634 1.704 4.918 - 1666water OW 1666 1.229 2.004 4.272 - 1667water OW 1667 1.029 1.898 6.104 - 1668water OW 1668 0.771 1.360 5.887 - 1669water OW 1669 1.276 1.629 3.298 - 1670water OW 1670 1.272 0.264 6.334 - 1671water OW 1671 0.223 0.139 4.809 - 1672water OW 1672 2.071 0.209 4.103 - 1673water OW 1673 1.757 0.860 4.671 - 1674water OW 1674 1.373 0.534 4.966 - 1675water OW 1675 2.227 2.704 3.336 - 1676water OW 1676 0.398 0.874 5.015 - 1677water OW 1677 0.085 0.515 6.526 - 1678water OW 1678 2.603 2.172 3.189 - 1679water OW 1679 0.276 2.607 5.542 - 1680water OW 1680 2.631 1.127 5.540 - 1681water OW 1681 0.566 1.699 4.636 - 1682water OW 1682 0.057 1.951 4.220 - 1683water OW 1683 3.156 1.384 6.218 - 1684water OW 1684 3.127 0.103 3.484 - 1685water OW 1685 2.867 1.027 2.982 - 1686water OW 1686 2.242 1.468 4.259 - 1687water OW 1687 3.148 0.469 4.151 - 1688water OW 1688 3.134 1.640 3.000 - 1689water OW 1689 1.680 0.328 0.093 - 1690water OW 1690 0.740 0.724 3.202 - 1691water OW 1691 0.836 2.042 0.100 - 1692water OW 1692 0.646 1.638 4.980 - 1693water OW 1693 0.420 2.479 6.318 - 1694water OW 1694 0.221 1.454 5.141 - 1695water OW 1695 3.172 0.381 3.401 - 1696water OW 1696 1.458 0.925 3.005 - 1697water OW 1697 0.179 2.572 4.619 - 1698water OW 1698 0.165 0.678 5.872 - 1699water OW 1699 2.037 0.277 6.307 - 1700water OW 1700 1.630 1.271 0.144 - 1701water OW 1701 0.085 1.283 0.109 - 1702water OW 1702 1.691 1.744 6.505 - 1703water OW 1703 2.311 2.882 6.730 - 1704water OW 1704 0.053 0.054 4.080 - 1705water OW 1705 0.027 2.075 4.457 - 1706water OW 1706 1.865 0.663 2.740 - 1707water OW 1707 1.050 1.982 4.462 - 1708water OW 1708 2.875 2.618 4.837 - 1709water OW 1709 1.254 0.561 6.355 - 1710water OW 1710 0.548 2.267 5.385 - 1711water OW 1711 2.453 0.285 3.905 - 1712water OW 1712 2.900 0.790 4.583 - 1713water OW 1713 1.890 0.918 3.884 - 1714water OW 1714 0.378 2.642 4.953 - 1715water OW 1715 2.641 2.211 4.438 - 1716water OW 1716 3.111 0.923 5.594 - 1717water OW 1717 1.836 1.026 3.632 - 1718water OW 1718 1.165 0.018 5.788 - 1719water OW 1719 0.357 0.947 0.108 - 1720water OW 1720 1.037 0.459 3.946 - 1721water OW 1721 0.203 0.422 4.259 - 1722water OW 1722 2.794 2.833 4.527 - 1723water OW 1723 2.461 1.566 5.952 - 1724water OW 1724 0.442 1.344 4.520 - 1725water OW 1725 1.617 0.304 3.900 - 1726water OW 1726 0.006 2.784 6.092 - 1727water OW 1727 3.178 2.266 5.653 - 1728water OW 1728 1.915 0.181 3.169 - 1729water OW 1729 1.216 2.523 6.701 - 1730water OW 1730 3.065 0.910 5.988 - 1731water OW 1731 0.343 0.677 0.131 - 1732water OW 1732 3.178 1.142 5.852 - 1733water OW 1733 2.694 1.605 5.613 - 1734water OW 1734 2.811 2.786 6.738 - 1735water OW 1735 1.377 1.683 4.076 - 1736water OW 1736 3.084 2.158 6.286 - 1737water OW 1737 0.656 0.201 2.930 - 1738water OW 1738 0.836 2.914 5.657 - 1739water OW 1739 0.968 1.626 4.629 - 1740water OW 1740 0.152 0.340 3.090 - 1741water OW 1741 3.102 2.913 6.319 - 1742water OW 1742 0.083 1.991 4.998 - 1743water OW 1743 2.616 2.691 5.114 - 1744water OW 1744 2.456 0.539 6.492 - 1745water OW 1745 1.630 1.209 3.580 - 1746water OW 1746 1.757 0.758 3.626 - 1747water OW 1747 0.266 2.078 6.493 - 1748water OW 1748 0.542 2.909 3.675 - 1749water OW 1749 2.062 1.093 4.600 - 1750water OW 1750 1.769 0.694 5.746 - 1751water OW 1751 0.307 1.696 3.246 - 1752water OW 1752 2.320 2.930 4.625 - 1753water OW 1753 0.076 1.704 4.950 - 1754water OW 1754 0.859 0.839 3.434 - 1755water OW 1755 0.743 1.440 3.444 - 1756water OW 1756 1.106 1.802 5.072 - 1757water OW 1757 1.954 1.696 6.466 - 1758water OW 1758 1.519 1.681 6.720 - 1759water OW 1759 1.331 1.215 5.914 - 1760water OW 1760 2.345 1.699 6.682 - 1761water OW 1761 2.641 1.808 4.649 - 1762water OW 1762 1.384 0.988 3.850 - 1763water OW 1763 0.099 0.544 0.138 - 1764water OW 1764 0.670 2.468 4.085 - 1765water OW 1765 1.494 2.433 0.363 - 1766water OW 1766 2.727 0.973 3.902 - 1767water OW 1767 0.365 2.940 5.153 - 1768water OW 1768 2.719 1.712 4.051 - 1769water OW 1769 1.969 2.360 5.549 - 1770water OW 1770 1.580 1.636 4.281 - 1771water OW 1771 2.652 2.605 3.483 - 1772water OW 1772 1.566 0.130 3.454 - 1773water OW 1773 0.565 0.564 3.892 - 1774water OW 1774 2.131 1.540 3.988 - 1775water OW 1775 3.001 2.314 5.186 - 1776water OW 1776 1.487 0.239 4.907 - 1777water OW 1777 1.667 0.506 3.227 - 1778water OW 1778 3.130 1.667 6.687 - 1779water OW 1779 0.222 2.353 5.554 - 1780water OW 1780 1.225 1.981 3.600 - 1781water OW 1781 1.119 0.810 4.698 - 1782water OW 1782 0.407 2.711 5.781 - 1783water OW 1783 2.600 2.787 4.181 - 1784water OW 1784 2.912 0.962 5.090 - 1785water OW 1785 2.790 1.025 3.255 - 1786water OW 1786 0.735 1.150 4.068 - 1787water OW 1787 0.371 1.991 4.346 - 1788water OW 1788 3.105 0.663 3.005 - 1789water OW 1789 0.009 1.995 6.481 - 1790water OW 1790 0.162 1.980 3.602 - 1791water OW 1791 2.603 2.997 5.679 - 1792water OW 1792 3.027 2.593 5.768 - 1793water OW 1793 0.398 2.750 6.288 - 1794water OW 1794 1.895 1.801 5.212 - 1795water OW 1795 2.443 1.286 0.097 - 1796water OW 1796 2.483 0.271 6.518 - 1797water OW 1797 2.777 0.916 6.438 - 1798water OW 1798 2.069 2.752 6.232 - 1799water OW 1799 1.165 0.652 0.118 - 1800water OW 1800 0.673 1.703 5.651 - 1801water OW 1801 0.866 0.452 3.560 - 1802water OW 1802 1.187 1.353 3.233 - 1803water OW 1803 2.740 0.255 3.645 - 1804water OW 1804 2.555 2.041 4.794 - 1805water OW 1805 2.633 0.408 5.443 - 1806water OW 1806 0.734 1.065 3.609 - 1807water OW 1807 2.501 1.440 5.181 - 1808water OW 1808 0.495 1.481 5.902 - 1809water OW 1809 0.612 1.132 5.848 - 1810water OW 1810 2.419 0.911 5.588 - 1811water OW 1811 0.082 0.179 4.579 - 1812water OW 1812 2.700 1.675 0.116 - 1813water OW 1813 0.326 0.131 0.090 - 1814water OW 1814 2.343 0.158 6.737 - 1815water OW 1815 3.085 0.052 5.712 - 1816water OW 1816 2.113 2.112 3.816 - 1817water OW 1817 0.664 2.130 6.270 - 1818water OW 1818 2.454 0.725 4.493 - 1819water OW 1819 1.116 2.886 0.086 - 1820water OW 1820 0.415 0.728 4.512 - 1821water OW 1821 1.141 1.699 0.079 - 1822water OW 1822 0.702 0.322 3.180 - 1823water OW 1823 2.485 0.994 6.472 - 1824water OW 1824 1.157 1.411 0.126 - 1825water OW 1825 3.034 2.149 3.039 - 1826water OW 1826 2.350 0.930 6.697 - 1827water OW 1827 1.863 2.393 3.704 - 1828water OW 1828 2.892 1.315 6.695 - 1829water OW 1829 2.548 2.000 5.908 - 1830water OW 1830 1.935 2.319 6.098 - 1831water OW 1831 1.879 0.853 5.246 - 1832water OW 1832 1.680 0.530 5.298 - 1833water OW 1833 1.149 1.420 2.786 - 1834water OW 1834 2.039 1.610 5.018 - 1835water OW 1835 1.053 1.530 3.986 - 1836water OW 1836 0.255 1.855 5.374 - 1837water OW 1837 2.823 1.137 4.666 - 1838water OW 1838 1.353 2.623 4.006 - 1839water OW 1839 0.013 1.427 4.293 - 1840water OW 1840 1.718 1.235 4.274 - 1841water OW 1841 1.432 1.126 4.568 - 1842water OW 1842 1.282 2.196 6.226 - 1843water OW 1843 0.490 2.474 3.294 - 1844water OW 1844 0.454 1.748 6.763 - 1845water OW 1845 2.709 0.731 5.823 - 1846water OW 1846 0.821 0.896 5.565 - 1847water OW 1847 0.535 2.245 3.619 - 1848water OW 1848 2.663 1.159 4.101 - 1849water OW 1849 1.312 0.582 5.337 - 1850water OW 1850 0.177 1.041 5.575 - 1851water OW 1851 0.865 2.050 6.491 - 1852water OW 1852 0.007 2.152 3.464 - 1853water OW 1853 0.734 2.737 3.550 - 1854water OW 1854 1.664 2.230 6.075 - 1855water OW 1855 1.941 0.179 6.061 - 1856water OW 1856 2.067 2.063 0.355 - 1857water OW 1857 0.849 2.760 6.501 - 1858water OW 1858 2.756 1.180 5.798 - 1859water OW 1859 1.159 1.362 6.471 - 1860water OW 1860 0.471 0.527 6.755 - 1861water OW 1861 1.109 2.938 3.223 - 1862water OW 1862 2.559 2.410 6.204 - 1863water OW 1863 1.979 0.496 5.087 - 1864water OW 1864 1.820 2.586 5.682 - 1865water OW 1865 1.535 0.840 3.464 - 1866water OW 1866 2.671 2.898 6.506 - 1867water OW 1867 2.719 2.109 0.120 - 1868water OW 1868 3.196 2.499 5.266 - 1869water OW 1869 0.322 1.419 3.238 - 1870water OW 1870 1.657 1.844 5.868 - 1871water OW 1871 2.777 0.561 6.042 - 1872water OW 1872 1.211 2.947 5.068 - 1873water OW 1873 1.180 0.122 4.669 - 1874water OW 1874 2.081 1.333 3.409 - 1875water OW 1875 1.399 2.360 3.908 - 1876water OW 1876 0.731 0.624 6.717 - 1877water OW 1877 2.149 0.543 5.826 - 1878water OW 1878 1.550 1.483 4.541 - 1879water OW 1879 1.180 1.798 4.609 - 1880water OW 1880 0.304 2.413 6.079 - 1881water OW 1881 2.567 0.040 3.612 - 1882water OW 1882 0.923 2.295 4.765 - 1883water OW 1883 0.375 0.760 4.007 - 1884water OW 1884 2.952 0.303 4.277 - 1885water OW 1885 1.225 1.132 5.431 - 1886water OW 1886 2.319 2.580 5.041 - 1887water OW 1887 1.060 2.733 5.562 - 1888water OW 1888 1.486 2.410 6.708 - 1889water OW 1889 2.835 2.024 6.297 - 1890water OW 1890 0.782 0.884 6.315 - 1891water OW 1891 0.186 1.029 5.140 - 1892water OW 1892 1.083 1.910 2.986 - 1893water OW 1893 2.997 0.178 3.728 - 1894water OW 1894 1.596 2.503 0.117 - 1895water OW 1895 0.709 2.829 6.720 - 1896water OW 1896 2.735 1.676 3.166 - 1897water OW 1897 1.867 2.395 0.099 - 1898water OW 1898 1.078 2.435 6.480 - 1899water OW 1899 2.532 2.190 5.679 - 1900water OW 1900 2.887 0.376 5.558 - 1901water OW 1901 1.254 1.004 6.265 - 1902water OW 1902 1.938 1.960 4.724 - 1903water OW 1903 1.997 2.802 0.332 - 1904water OW 1904 1.683 0.954 5.416 - 1905water OW 1905 2.369 2.435 6.017 - 1906water OW 1906 2.317 1.671 2.993 - 1907water OW 1907 0.844 2.087 4.125 - 1908water OW 1908 0.684 2.825 6.290 - 1909water OW 1909 0.411 0.458 6.291 - 1910water OW 1910 2.888 0.293 6.712 - 1911water OW 1911 2.497 2.275 4.670 - 1912water OW 1912 0.128 2.864 4.998 - 1913water OW 1913 2.174 0.201 3.637 - 1914water OW 1914 1.637 2.004 6.518 - 1915water OW 1915 1.146 2.581 5.364 - 1916water OW 1916 2.855 1.723 6.707 - 1917water OW 1917 1.996 0.607 3.562 - 1918water OW 1918 0.151 0.811 5.282 - 1919water OW 1919 1.922 0.701 6.542 - 1920water OW 1920 1.917 1.862 6.230 - 1921water OW 1921 0.885 2.717 6.071 - 1922water OW 1922 0.367 1.391 0.129 - 1923water OW 1923 1.098 2.335 5.747 - 1924water OW 1924 0.794 2.809 2.728 - 1925water OW 1925 0.680 2.434 6.259 - 1926water OW 1926 1.583 0.513 4.327 - 1927water OW 1927 1.952 2.677 4.053 - 1928water OW 1928 0.673 2.051 5.273 - 1929water OW 1929 2.021 1.286 2.994 - 1930water OW 1930 2.463 1.042 2.796 - 1931water OW 1931 1.482 0.450 6.043 - 1932water OW 1932 2.276 2.162 2.956 - 1933water OW 1933 2.702 0.748 3.718 - 1934water OW 1934 1.635 2.021 2.792 - 1935water OW 1935 1.318 1.369 4.681 - 1936water OW 1936 1.864 2.388 6.516 - 1937water OW 1937 2.270 2.424 6.723 - 1938water OW 1938 1.124 0.626 6.594 - 1939water OW 1939 0.365 2.031 5.799 - 1940water OW 1940 1.952 0.381 3.753 - 1941water OW 1941 3.156 1.377 6.678 - 1942water OW 1942 3.112 1.097 4.762 - 1943water OW 1943 2.380 1.656 3.464 - 1944water OW 1944 2.502 0.006 3.885 - 1945water OW 1945 1.277 1.091 4.941 - 1946water OW 1946 1.657 0.591 6.504 - 1947water OW 1947 2.030 0.247 3.420 - 1948water OW 1948 1.882 1.404 3.194 - 1949water OW 1949 2.165 2.576 5.749 - 1950water OW 1950 1.221 2.675 5.120 - 1951water OW 1951 1.086 0.538 6.137 - 1952water OW 1952 0.402 2.419 3.979 - 1953water OW 1953 2.712 0.489 4.783 - 1954water OW 1954 0.793 2.779 4.839 - 1955water OW 1955 2.291 0.977 5.342 - 1956water OW 1956 1.479 1.824 3.656 - 1957water OW 1957 0.560 2.907 3.968 - 1958water OW 1958 1.129 2.975 4.345 - 1959water OW 1959 2.074 0.292 2.954 - 1960water OW 1960 2.267 1.140 3.806 - 1961water OW 1961 0.974 1.352 5.247 - 1962water OW 1962 0.497 1.815 5.283 - 1963water OW 1963 3.137 1.868 4.717 - 1964water OW 1964 1.649 1.727 5.171 - 1965water OW 1965 1.076 0.975 3.224 - 1966water OW 1966 1.555 1.684 0.358 - 1967water OW 1967 0.538 0.125 4.202 - 1968water OW 1968 1.542 2.371 3.663 - 1969water OW 1969 1.213 2.906 2.957 - 1970water OW 1970 2.162 2.041 3.370 - 1971water OW 1971 1.914 1.385 2.753 - 1972water OW 1972 2.170 2.592 5.322 - 1973water OW 1973 2.090 2.576 3.012 - 1974water OW 1974 3.127 0.172 6.309 - 1975water OW 1975 2.284 1.031 5.793 - 1976water OW 1976 2.234 2.858 5.105 - 1977water OW 1977 1.142 0.335 5.911 - 1978water OW 1978 1.183 2.935 5.415 - 1979water OW 1979 1.514 1.245 4.911 - 1980water OW 1980 0.574 0.942 3.953 - 1981water OW 1981 2.269 0.906 3.644 - 1982water OW 1982 0.180 1.012 3.099 - 1983water OW 1983 1.493 2.478 5.197 - 1984water OW 1984 1.446 2.900 5.230 - 1985water OW 1985 2.894 1.013 4.427 - 1986water OW 1986 0.857 1.598 4.228 - 1987water OW 1987 3.046 1.863 3.802 - 1988water OW 1988 2.887 1.710 5.148 - 1989water OW 1989 2.608 1.918 5.282 - 1990water OW 1990 3.162 1.218 3.844 - 1991water OW 1991 0.708 1.289 5.436 - 1992water OW 1992 0.888 1.343 2.792 - 1993water OW 1993 2.873 1.817 3.350 - 1994water OW 1994 1.946 0.253 4.947 - 1995water OW 1995 0.467 0.240 6.716 - 1996water OW 1996 3.175 1.579 3.447 - 1997water OW 1997 2.453 0.701 3.600 - 1998water OW 1998 1.530 2.135 3.019 - 1999water OW 1999 2.975 2.023 5.875 - 2000water OW 2000 0.847 2.025 5.796 - 2001water OW 2001 1.540 1.412 0.377 - 2002water OW 2002 0.316 1.225 5.277 - 2003water OW 2003 3.175 2.333 4.323 - 2004water OW 2004 1.580 2.483 5.724 - 2005water OW 2005 2.479 1.372 4.151 - 2006water OW 2006 0.411 1.457 4.055 - 2007water OW 2007 0.253 2.129 6.053 - 2008water OW 2008 2.591 1.669 3.639 - 2009water OW 2009 2.862 2.814 5.158 - 2010water OW 2010 1.585 0.360 5.619 - 2011water OW 2011 2.700 0.082 4.050 - 2012water OW 2012 0.873 2.008 3.142 - 2013water OW 2013 0.752 1.383 6.732 - 2014water OW 2014 2.534 0.732 3.349 - 2015water OW 2015 0.324 2.774 4.253 - 2016water OW 2016 0.528 1.585 5.429 - 2017water OW 2017 2.671 2.111 6.507 - 2018water OW 2018 2.426 2.461 5.762 - 2019water OW 2019 0.912 1.283 0.129 - 2020water OW 2020 2.040 2.750 5.928 - 2021water OW 2021 2.752 1.365 6.463 - 2022water OW 2022 1.131 1.018 6.022 - 2023water OW 2023 0.038 0.864 6.202 - 2024water OW 2024 2.745 0.186 0.096 - 2025water OW 2025 3.163 1.504 3.885 - 2026water OW 2026 2.481 1.763 3.222 - 2027water OW 2027 0.664 1.946 4.761 - 2028water OW 2028 2.367 1.416 6.279 - 2029water OW 2029 2.872 2.749 5.415 - 2030water OW 2030 2.704 0.650 6.473 - 2031water OW 2031 0.968 1.539 5.976 - 2032water OW 2032 1.728 0.307 4.795 - 2033water OW 2033 2.689 1.403 3.165 - 2034water OW 2034 1.568 2.660 3.636 - 2035water OW 2035 2.220 1.964 4.697 - 2036water OW 2036 3.147 2.787 3.186 - 2037water OW 2037 0.732 2.725 4.075 - 2038water OW 2038 0.088 2.430 3.438 - 2039water OW 2039 1.139 2.366 3.127 - 2040water OW 2040 0.487 1.211 4.936 - 2041water OW 2041 2.440 2.752 6.528 - 2042water OW 2042 3.154 0.017 5.269 - 2043water OW 2043 0.269 2.399 0.143 - 2044water OW 2044 1.691 0.683 3.883 - 2045water OW 2045 2.403 2.506 3.355 - 2046water OW 2046 0.872 0.187 3.613 - 2047water OW 2047 2.923 1.089 3.748 - 2048water OW 2048 1.991 2.052 4.166 - 3.20000 3.00000 6.82880 diff --git a/examples/analysis_workflow/oxygens.xtc b/examples/analysis_workflow/oxygens.xtc deleted file mode 100644 index f5f25724..00000000 Binary files a/examples/analysis_workflow/oxygens.xtc and /dev/null differ diff --git a/examples/info_gain/Information_gains.png b/examples/info_gain/Information_gains.png deleted file mode 100644 index 42bea1e4..00000000 Binary files a/examples/info_gain/Information_gains.png and /dev/null differ diff --git a/examples/info_gain/tmp_0_1D.png b/examples/info_gain/tmp_0_1D.png deleted file mode 100644 index 47508f0c..00000000 Binary files a/examples/info_gain/tmp_0_1D.png and /dev/null differ diff --git a/examples/info_gain/tmp_0_2D.png b/examples/info_gain/tmp_0_2D.png deleted file mode 100644 index 52ed66b8..00000000 Binary files a/examples/info_gain/tmp_0_2D.png and /dev/null differ diff --git a/examples/info_gain/tmp_1_1D.png b/examples/info_gain/tmp_1_1D.png deleted file mode 100644 index 0a108eaf..00000000 Binary files a/examples/info_gain/tmp_1_1D.png and /dev/null differ diff --git a/examples/info_gain/tmp_1_2D.png b/examples/info_gain/tmp_1_2D.png deleted file mode 100644 index 3c6e93ad..00000000 Binary files a/examples/info_gain/tmp_1_2D.png and /dev/null differ diff --git a/examples/info_gain_example.py b/examples/info_gain_example.py deleted file mode 100644 index 46c28213..00000000 --- a/examples/info_gain_example.py +++ /dev/null @@ -1,209 +0,0 @@ -"""How to use the code for the information gain in clustering analysis.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from numpy.typing import NDArray - -from pathlib import Path -from typing import Callable - -import matplotlib.pyplot as plt -import numpy as np - -import dynsight - - -def energy_landscape_1(x: float, y: float) -> float: - """A potential energy landscape with 2 minima.""" - sigma = 0.12 # Width of the Gaussian wells - gauss1 = np.exp(-(x**2 + y**2) / (2 * sigma**2)) - gauss2 = np.exp(-(x**2 + (y - 1) ** 2) / (2 * sigma**2)) - return -np.log(gauss1 + gauss2 + 1e-6) - - -def energy_landscape_2(x: float, y: float) -> float: - """A potential energy landscape with 4 minima.""" - sigma = 0.12 # Width of the Gaussian wells - gauss1 = np.exp(-(x**2 + y**2) / (2 * sigma**2)) - gauss2 = np.exp(-((x - 1) ** 2 + y**2) / (2 * sigma**2)) - gauss3 = np.exp(-(x**2 + (y - 1) ** 2) / (2 * sigma**2)) - gauss4 = np.exp(-((x - 1) ** 2 + (y - 1) ** 2) / (2 * sigma**2)) - return -np.log(gauss1 + gauss2 + gauss3 + gauss4 + 1e-6) - - -def numerical_gradient( - f: Callable[[float, float], float], x: float, y: float, h: float = 1e-5 -) -> tuple[float, float]: - """Compute numerical gradient using finite differences.""" - grad_x = (f(x + h, y) - f(x - h, y)) / (2 * h) - grad_y = (f(x, y + h) - f(x, y - h)) / (2 * h) - return -grad_x, -grad_y - - -def create_trajectory( - energy_landscape: Callable[[float, float], float], file_name: Path -) -> NDArray[np.float64]: - """Simulate Langevin Dynamics on a given energy landscape.""" - rng = np.random.default_rng(0) - n_atoms = 100 - time_steps = 10000 - dt = 0.01 # Time step - diffusion_coeff = 0.6 # Diffusion coefficient (random noise strength) - - if energy_landscape == energy_landscape_1: - particles = rng.standard_normal((n_atoms, 2)) * 0.2 - particles[n_atoms // 2 :, 1] += 1.0 - else: - particles = rng.standard_normal((n_atoms, 2)) * 0.2 - n_group = n_atoms // 4 - particles[n_group : 2 * n_group, 1] += 1 # (0, 1) - particles[2 * n_group : 3 * n_group, 0] += 1 # (1, 0) - particles[3 * n_group :, 0] += 1 # (1, 1) - particles[3 * n_group :, 1] += 1 - - trajectory = np.zeros((time_steps, n_atoms, 2)) - for t in range(time_steps): - for i in range(n_atoms): - x, y = particles[i] - fx, fy = numerical_gradient(energy_landscape, x, y) - noise_x = np.sqrt(2 * diffusion_coeff * dt) * rng.standard_normal() - noise_y = np.sqrt(2 * diffusion_coeff * dt) * rng.standard_normal() - - # Update position with deterministic force and stochastic term - particles[i, 0] += fx * dt + noise_x - particles[i, 1] += fy * dt + noise_y - - trajectory[t, i] = particles[i] - - plt.figure() - plt.plot(trajectory[:, :, 0], trajectory[:, :, 1]) - plt.show() - - dataset = np.transpose(trajectory, (1, 0, 2)) - np.save(file_name, dataset) - return dataset - - -def main() -> None: - """How to use the code for the information gain in clustering analysis.""" - cwd = Path.cwd() - folder_name = "info_gain" - folder_path = cwd / folder_name - if not folder_path.exists(): - folder_path.mkdir() - - # Let's build bidimensional variables - Langevin Dynamics in 2D - file_1 = folder_path / "trj_2.npy" # With 2 minima - file_2 = folder_path / "trj_4.npy" # With 4 minima - - if not file_1.exists(): - dataset_1 = create_trajectory(energy_landscape_1, file_1) - dataset_1 = np.load(file_1) - - if not file_2.exists(): - dataset_2 = create_trajectory(energy_landscape_2, file_2) - dataset_2 = np.load(file_2) - - results = [] - env0 = [] - delta_t_list = np.unique(np.geomspace(2, 1000, 45, dtype=int)) - example_delta_t = 4 # Choosing a ∆t which works well to plot results - - for i, dataset in enumerate([dataset_1, dataset_2]): - n_atoms, n_frames, n_dims = dataset.shape - - # We can do clustering on one single variable: - y_positions = dataset[:, :, 1] - info_gain_y = np.zeros(delta_t_list.size) - tmp_env0 = np.zeros(delta_t_list.size) - - for j, delta_t in enumerate(delta_t_list): - reshaped_data = dynsight.onion.helpers.reshape_from_nt( - y_positions, delta_t - ) - state_list, labels = dynsight.onion.onion_uni(reshaped_data) - tmp_env0[j] = np.sum(labels == -1) / labels.size - - if j == example_delta_t: - dynsight.onion.plot.plot_output_uni( - f"info_gain/tmp_{i}_1D.png", - reshaped_data, - n_atoms, - state_list, - ) - - # and compute the information gain: - info_gain_y[j], *_ = dynsight.analysis.compute_entropy_gain( - reshaped_data, labels, n_bins=40 - ) - results.append(info_gain_y) - env0.append(tmp_env0) - - # Or we can do clustering on both variables: - info_gain_xy = np.zeros(delta_t_list.size) - tmp_env0 = np.zeros(delta_t_list.size) - tmp1_dataset = np.transpose(dataset, (2, 0, 1)) - for j, delta_t in enumerate(delta_t_list): - reshaped_data = dynsight.onion.helpers.reshape_from_dnt( - tmp1_dataset, delta_t - ) - state_list, labels = dynsight.onion.onion_multi(reshaped_data) - tmp_env0[j] = np.sum(labels == -1) / labels.size - - if j == example_delta_t: - dynsight.onion.plot.plot_output_multi( - f"info_gain/tmp_{i}_2D.png", - tmp1_dataset, - state_list, - labels, - delta_t, - ) - - # and compute the information gain: - # We need an array (n_samples, n_dims), and labels (n_samples,) - n_sequences = int(n_frames / delta_t) - long_labels = np.repeat(labels, delta_t) - tmp = dataset[:, : n_sequences * delta_t, :] - ds_reshaped = tmp.reshape((-1, n_dims)) - - info_gain_xy[j], *_ = dynsight.analysis.compute_entropy_gain_multi( - ds_reshaped, long_labels, n_bins=[40, 40] - ) - # Need to multiply by two because it's 2 dimensional, and the output - # of the info_gain functions is normalized by the log volume of the - # phase space, which is 2D is doubled - info_gain_xy *= 2 - results.append(info_gain_xy) - env0.append(tmp_env0) - - colorlist = ["C0", "C2", "C1", "C3"] - markerlist = ["s", "o", "d", "o"] - labellist = [ - "2 peaks - 1D clustering", - "2 peaks - 2D clustering", - "4 peaks - 1D clustering", - "4 peaks - 2D clustering", - ] - - fig, ax = plt.subplots() - for i, system in enumerate(results): - ax.plot( - delta_t_list, - system, - label=labellist[i], - c=colorlist[i], - marker=markerlist[i], - ) - - ax.set_xlabel(r"Time resolution $\Delta t$ [frame]") - ax.set_ylabel(r"Information gain $\Delta H$ [bit]") - ax.set_xscale("log") - ax.legend() - fig.savefig("info_gain/Information_gains.png", dpi=600) - - -if __name__ == "__main__": - main() diff --git a/examples/lens.py b/examples/lens.py deleted file mode 100644 index c83af14a..00000000 --- a/examples/lens.py +++ /dev/null @@ -1,93 +0,0 @@ -"""An example for the lens module. - -Details on LENS and additional examples can be found in the paper: -M. Crippa, A. Cardellini, C. Caruso, & G.M. Pavan, -Detecting dynamic domains and local fluctuations in complex molecular -systems via timelapse neighbors shuffling, PNAS 120 (30) e2300565120, -https://doi.org/10.1073/pnas.2300565120 (2023). -""" - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -from MDAnalysis import Universe - -from dynsight.lens import compute_lens, list_neighbours_along_trajectory - - -def example_for_lens(universe: Universe, r_cut: float) -> None: - """Compute LENS on a trajectory.""" - lens = compute_lens( - universe=universe, - r_cut=r_cut, - delay=1, - centers="all", - selection="all", - respect_pbc=True, - n_jobs=4, - ) - - # Plot the LENS time-series and the cumulative distribution - fig, ax = plt.subplots(1, 2, sharey=True) - fig.suptitle(f"LENS computed with cutoff radius {r_cut} Å") - for particle in lens[::200]: - ax[0].plot(particle, c="k", lw=1.0, alpha=0.5) - ax[1].hist(lens.ravel(), bins=50, density=True, orientation="horizontal") - ax[0].set_xlabel("Time") - ax[0].set_ylabel("LENS") - ax[1].set_xlabel("Probability density") - plt.show() - plt.close() - - -def example_for_number_of_neighbors(universe: Universe, r_cut: float) -> None: - """Compute number of neighbors on a trajectory.""" - neighlist = list_neighbours_along_trajectory( - universe=universe, - r_cut=r_cut, - centers="all", - selection="all", - respect_pbc=True, - n_jobs=4, - ) # list[list[AtomGroup]] - - # Count the neighbors for each molecule at each frame - n_neigh = np.array( - [[len(atom_group) for atom_group in frame] for frame in neighlist] - ).T - - # Plot the n_neigh time-series and the cumulative distribution - fig, ax = plt.subplots(1, 2, sharey=True) - fig.suptitle(f"Number of neighbors within a cutoff radius {r_cut} Å") - for particle in n_neigh[::200]: - ax[0].plot(particle, c="k", lw=1.0, alpha=0.5) - ax[1].hist( - n_neigh.ravel(), - bins=np.unique(n_neigh).size, - density=True, - orientation="horizontal", - ) - ax[0].set_xlabel("Time") - ax[0].set_ylabel(r"$n_{neigh}$") - ax[1].set_xlabel("Probability density") - plt.show() - plt.close() - - -def main() -> None: - """An example for the lens module.""" - # Get the trajectory data (as an MDAnalysis.Universe) - files_path = Path("analysis_workflow") - universe = Universe( - files_path / "oxygens.gro", - files_path / "oxygens.xtc", - ) - - r_cut = 7.5 # Angstrom - example_for_lens(universe, r_cut) - example_for_number_of_neighbors(universe, r_cut) - - -if __name__ == "__main__": - main() diff --git a/examples/onion_analysis.py b/examples/onion_analysis.py deleted file mode 100644 index 64a76e4e..00000000 --- a/examples/onion_analysis.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Analysis of particles moving between Gaussian energy minima. - -In this example, a dataset is created composed by the (x, y) coordinates -of particles moving in a 2D energy landscape with 4 energy minima. - -Onion clustering is applied initially to the univariate dataset of x -positions, finding only two clusters. - -Then, applying it on the full (x, y) bivariate dataset, all the 4 minima are -found. -""" - -from __future__ import annotations - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np - -from dynsight.trajectory import Insight - - -def energy_landscape(x: float, y: float) -> float: - """A 2-dimensional potential energy landscape with 4 minima.""" - sigma = 0.12 # Width of the Gaussian wells - gauss1 = np.exp(-(x**2 + y**2) / (2 * sigma**2)) - gauss2 = np.exp(-((x - 1) ** 2 + y**2) / (2 * sigma**2)) - gauss3 = np.exp(-(x**2 + (y - 1) ** 2) / (2 * sigma**2)) - gauss4 = np.exp(-((x - 1) ** 2 + (y - 1) ** 2) / (2 * sigma**2)) - return -np.log(gauss1 + gauss2 + gauss3 + gauss4 + 1e-6) - - -def numerical_gradient( - x: float, y: float, h: float = 1e-5 -) -> tuple[float, float]: - """Compute numerical gradient using finite differences.""" - grad_x = (energy_landscape(x + h, y) - energy_landscape(x - h, y)) / ( - 2 * h - ) - grad_y = (energy_landscape(x, y + h) - energy_landscape(x, y - h)) / ( - 2 * h - ) - return -grad_x, -grad_y - - -def create_trajectory( - n_atoms: int, - time_steps: int, - file_path: Path, -) -> Insight: - """Simulate Langevin Dynamics on a given energy landscape.""" - rng = np.random.default_rng(0) - dt = 0.01 # Time step - diffusion_coeff = 0.8 # Diffusion coefficient (random noise strength) - - # Initialize particles' positions - particles = rng.standard_normal((n_atoms, 2)) * 0.2 - n_group = n_atoms // 4 - particles[n_group : 2 * n_group, 1] += 1 # (0, 1) - particles[2 * n_group : 3 * n_group, 0] += 1 # (1, 0) - particles[3 * n_group :, 0] += 1 # (1, 1) - particles[3 * n_group :, 1] += 1 - - trajectory = np.zeros((time_steps, n_atoms, 2)) - for t in range(time_steps): - for i in range(n_atoms): - x, y = particles[i] - fx, fy = numerical_gradient(x, y) - noise_x = np.sqrt(2 * diffusion_coeff * dt) * rng.standard_normal() - noise_y = np.sqrt(2 * diffusion_coeff * dt) * rng.standard_normal() - - # Update position with deterministic force and stochastic term - particles[i, 0] += fx * dt + noise_x - particles[i, 1] += fy * dt + noise_y - - trajectory[t, i] = particles[i] - - dataset = np.transpose(trajectory, (1, 0, 2)) - insight = Insight(dataset) - insight.dump_to_json(file_path) - - return insight - - -def main() -> None: - """Analysis of particles moving between Gaussian energy minima.""" - data_path = Path("onion_analysis") - - # Load or create the input dataset - # Here we are using the (x,y) coordinates as the descriptor for the system - n_atoms = 100 - n_frames = 10000 - file_path = data_path / "data.json" - if file_path.exists(): - coord_2d = Insight.load_from_json(file_path) - else: - coord_2d = create_trajectory(n_atoms, n_frames, file_path) - coord_1d = Insight(coord_2d.dataset[:, :, 0]) - - # Test onion clustering on a wide range of time resolutions - delta_t_list, n_clust, unclass_frac, envs = coord_1d.get_onion_analysis( - fig1_path=data_path / "time-res_1d.png", - fig2_path=data_path / "pop_fracs_1d.png", - ) - - # Perform onion clustering at delta_t = 100 - onion_results = coord_1d.get_onion_smooth(delta_t=100) - onion_results.plot_output(data_path / "output_1d.png", coord_1d) - onion_results.plot_one_trj(data_path / "example_1d.png", coord_1d, 10) - onion_results.plot_state_populations(data_path / "state_pops_1d.png") - - # Test onion clustering on a wide range of time resolutions - delta_t_list, n_clust, unclass_frac, envs = coord_2d.get_onion_analysis( - fig1_path=data_path / "time-res_2d.png", - fig2_path=data_path / "pop_fracs_2d.png", - ) - - # Perform onion clustering at delta_t = 10 - onion_results = coord_2d.get_onion_smooth(delta_t=10) - onion_results.plot_output(data_path / "output_2d.png", coord_2d) - onion_results.plot_one_trj(data_path / "example_2d.png", coord_2d, 10) - onion_results.plot_state_populations(data_path / "state_pops_2d.png") - - plt.show() - - -if __name__ == "__main__": - main() diff --git a/examples/onion_analysis/example_1d.png b/examples/onion_analysis/example_1d.png deleted file mode 100644 index 5b04643c..00000000 Binary files a/examples/onion_analysis/example_1d.png and /dev/null differ diff --git a/examples/onion_analysis/example_2d.png b/examples/onion_analysis/example_2d.png deleted file mode 100644 index 6f7c7fa2..00000000 Binary files a/examples/onion_analysis/example_2d.png and /dev/null differ diff --git a/examples/onion_analysis/output_1d.png b/examples/onion_analysis/output_1d.png deleted file mode 100644 index d6ce20c8..00000000 Binary files a/examples/onion_analysis/output_1d.png and /dev/null differ diff --git a/examples/onion_analysis/output_2d.png b/examples/onion_analysis/output_2d.png deleted file mode 100644 index 53d62562..00000000 Binary files a/examples/onion_analysis/output_2d.png and /dev/null differ diff --git a/examples/onion_analysis/pop_fracs_1d.png b/examples/onion_analysis/pop_fracs_1d.png deleted file mode 100644 index d377bcb8..00000000 Binary files a/examples/onion_analysis/pop_fracs_1d.png and /dev/null differ diff --git a/examples/onion_analysis/pop_fracs_2d.png b/examples/onion_analysis/pop_fracs_2d.png deleted file mode 100644 index 9208460b..00000000 Binary files a/examples/onion_analysis/pop_fracs_2d.png and /dev/null differ diff --git a/examples/onion_analysis/state_pops_1d.png b/examples/onion_analysis/state_pops_1d.png deleted file mode 100644 index 93e8c980..00000000 Binary files a/examples/onion_analysis/state_pops_1d.png and /dev/null differ diff --git a/examples/onion_analysis/state_pops_2d.png b/examples/onion_analysis/state_pops_2d.png deleted file mode 100644 index a2d46bf1..00000000 Binary files a/examples/onion_analysis/state_pops_2d.png and /dev/null differ diff --git a/examples/onion_analysis/time-res_1d.png b/examples/onion_analysis/time-res_1d.png deleted file mode 100644 index 4cc87480..00000000 Binary files a/examples/onion_analysis/time-res_1d.png and /dev/null differ diff --git a/examples/onion_analysis/time-res_2d.png b/examples/onion_analysis/time-res_2d.png deleted file mode 100644 index 6b6c12f1..00000000 Binary files a/examples/onion_analysis/time-res_2d.png and /dev/null differ diff --git a/examples/samp_en/Fig1.png b/examples/samp_en/Fig1.png deleted file mode 100644 index a59ed949..00000000 Binary files a/examples/samp_en/Fig1.png and /dev/null differ diff --git a/examples/samp_en/Fig2.png b/examples/samp_en/Fig2.png deleted file mode 100644 index 3e9dc617..00000000 Binary files a/examples/samp_en/Fig2.png and /dev/null differ diff --git a/examples/samp_en/Fig3.png b/examples/samp_en/Fig3.png deleted file mode 100644 index ee31ae67..00000000 Binary files a/examples/samp_en/Fig3.png and /dev/null differ diff --git a/examples/sample_entropy.py b/examples/sample_entropy.py deleted file mode 100644 index 0efa9746..00000000 --- a/examples/sample_entropy.py +++ /dev/null @@ -1,246 +0,0 @@ -"""How to use the code for the sample entropy computation.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from numpy.typing import NDArray - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np - -import dynsight - - -def extract_sequences_for_label( - data: NDArray[np.float64], - reshaped_data: NDArray[np.float64], - labels: NDArray[np.int64], - delta_t: int, - target_label: int, -) -> list[NDArray[np.float64]]: - """Creates the list of sequences clustered in a specific cluster. - - Extracts sequences corresponding to a specific label from the original - data, merging only consecutive windows with the same label. - - Parameters: - data (np.ndarray): - Original data of shape (n_atoms, n_frames). - - reshaped_data (np.ndarray): - Windowed data of shape (n_atoms * n_windows, delta_t). - - labels (np.ndarray): - Cluster labels of shape (n_atoms * n_windows,). - - delta_t (int): - The length of each time window. - - target_label (int): - The label for which sequences should be extracted. - - Returns: - list: A list of sequences (np.ndarray) where each entry is a - concatenated segment from `data` corresponding to consecutive - occurrences of `target_label`. - """ - n_atoms, n_frames = data.shape - sequences = [] # List to store sequences for the target label - - # Reshape labels to match (n_atoms, n_windows) - n_windows = n_frames // delta_t - labels = labels.reshape((n_atoms, n_windows)) - - for atom_idx in range(n_atoms): # Iterate over each particle - current_sequence: list[NDArray[np.float64]] = [] - - for window_idx in range(n_windows): - if labels[atom_idx, window_idx] == target_label: - # Add the current window to the sequence - current_sequence.extend( - reshaped_data[atom_idx * n_windows + window_idx, :] - ) - # If we had an active sequence, store it and reset - elif current_sequence: - sequences.append(np.array(current_sequence)) - current_sequence = [] - - # Append the last sequence if it was still active - if current_sequence: - sequences.append(np.array(current_sequence)) - - return sequences - - -def combined_sample_entropies( - data: list[NDArray[np.float64]] | NDArray[np.float64], - r_factor: np.float64 | float, - m_par: int = 2, -) -> float: - """Compute the average sample entropy of a time-series dataset. - - The average is computed ignoring possible nan values. - - Parameters: - data : np.ndarray of shape (n_particles, n_frames) - - r_factor : float - The similarity threshold between signal windows. A common choice - is 0.2 * the standard deviation of the dataset. - - m_par : int (default 2) - The m parameter (length of the considered overlapping windows). - - Returns: - float - The sample entropy of the dataset (average over all the particles). - """ - if isinstance(data, np.ndarray) and data.ndim == 1: - data = [data] - - sampen = [] - for particle in data: - try: - tmp = dynsight.analysis.sample_entropy(particle, r_factor, m_par) - sampen.append(tmp) - except RuntimeError: # noqa: PERF203 - continue - - return np.mean(np.array(sampen)) - - -def main() -> None: - """How to use the code for the sample entropy computation.""" - cwd = Path.cwd() - folder_name = "samp_en" - folder_path = cwd / folder_name - if not folder_path.exists(): - folder_path.mkdir() - - delta_t_list = np.unique(np.geomspace(6, 499, 20, dtype=int)) - t_samp = 0.1 - - # Let's import the LENS signals for water/ice coexistence - data_directory = "onion_example_files/data/univariate_time-series.npy" - data = np.load(data_directory)[::10, 1:] # First frame of LENS is zero - - # We need to set a unique closeness threshold, to use for each cluster - r_fact = 0.2 # This is the default value - r_fact *= np.std(data) - - # We start computing the average sample entropy of the entire dataset - aver_samp_en = combined_sample_entropies(data, r_factor=r_fact) - - # Then we can perform Onion Clustering at different ∆t and compute - # the sample entropy of the different clusters - samp_en_list = [] - fractions = [] - for _, delta_t in enumerate(delta_t_list): - reshaped_data = dynsight.onion.helpers.reshape_from_nt(data, delta_t) - _, labels = dynsight.onion.onion_uni(reshaped_data) - - tmp_list = [] - tmp_frac = [] - for label in np.unique(labels): - # This function is necessary to extract and concatenate all the - # sequences clustered in the cluster under analysis - selected_data = extract_sequences_for_label( - data, - reshaped_data, - labels, - delta_t, - label, - ) - - tmp_sampen = combined_sample_entropies( - selected_data, r_factor=r_fact - ) - tmp_list.append(tmp_sampen) - fraction = np.sum(labels == label) / labels.size - tmp_frac.append(fraction) - - samp_en_list.append(tmp_list) - fractions.append(tmp_frac) - - max_n_states = np.max([len(tmp) for tmp in samp_en_list]) - for i, tmp in enumerate(samp_en_list): - while len(tmp) < max_n_states: - tmp.append(0.0) - fractions[i].append(0.0) - - samp_en_array = np.array(samp_en_list).T - frac_array = np.array(fractions).T - - labels = ["Unclassified", "Ice", "Interface", "Liquid", "Total SampEn"] - - fig, ax = plt.subplots() - for i, state in enumerate(samp_en_array): - mask = state != 0.0 - ax.plot( - delta_t_list * t_samp, - frac_array[i], - label=labels[i], - marker="o", - ) - ax.set_xlabel(r"Time resolution $\Delta t$ [ns]") - ax.set_ylabel("State population fraction") - ax.set_xscale("log") - ax.legend() - fig.savefig(folder_path / "Fig1.png", dpi=600) - - fig, ax = plt.subplots() - for i, state in enumerate(samp_en_array): - mask = state != 0.0 - ax.plot( - delta_t_list[mask] * t_samp, - state[mask], - label=labels[i], - marker="o", - ) - ax.plot( - delta_t_list * t_samp, - aver_samp_en * np.ones(len(delta_t_list)), - label=labels[-1], - ls="--", - c="k", - ) - ax.set_xlabel(r"Time resolution $\Delta t$ [ns]") - ax.set_ylabel("Sample Entropy") - ax.set_xscale("log") - ax.set_ylim(bottom=0.0) - ax.legend() - fig.savefig(folder_path / "Fig2.png", dpi=600) - - fig, ax = plt.subplots() - y_val = np.zeros(samp_en_array.shape[1]) - for i, state in enumerate(samp_en_array): - ax.fill_between( - delta_t_list * 0.1, - y_val, - y_val + state * frac_array[i], - label=labels[i], - alpha=0.8, - ) - y_val += state * frac_array[i] - ax.plot( - delta_t_list * 0.1, - aver_samp_en * np.ones(len(delta_t_list)), - label=labels[-1], - ls="--", - c="k", - ) - ax.set_xlabel(r"Time resolution $\Delta t$ [ns]") - ax.set_ylabel("Weighted Sample Entropy") - ax.set_xscale("log") - ax.legend() - fig.savefig(folder_path / "Fig3.png", dpi=600) - - plt.show() - - -if __name__ == "__main__": - main() diff --git a/examples/soap.py b/examples/soap.py deleted file mode 100644 index 95694ba1..00000000 --- a/examples/soap.py +++ /dev/null @@ -1,75 +0,0 @@ -from __future__ import annotations - -import argparse -import logging -import pathlib - -import matplotlib.pyplot as plt -import MDAnalysis - -import dynsight - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s | %(levelname)s | %(message)s", -) - - -def _parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument("data_path", type=str, help="path with test files") - return parser.parse_args() - - -def main() -> None: - """Run the example. - - Paper: https://doi.org/10.1063/5.0147025 - - """ - # trajectory (it is a 55 gold atoms nanoparticle NVT at few temperatures): - # wget https://github.com/GMPavanLab/SOAPify/releases/download/0.1.0rc0/SmallExample.zip - # We'll start by caclulating the SOAP fingerprints of the simulation - # using lMax=8, nMax=8, and cutoff=4.48023312 that is 10% more than the Au - # cell - - args = _parse_args() - - data_path = pathlib.Path(args.data_path) - - universe = MDAnalysis.Universe( - data_path / "ih55.data", - [data_path / "ih55-T_100.lammpsdump"], - atom_style="id type x y z", - ) - universe.atoms.types = ["Au"] * len(universe.atoms) - - soap = dynsight.soap.saponify_trajectory( - universe, - soaprcut=4.48023312, - soapnmax=8, - soaplmax=8, - n_core=4, - ) - - tsoap = dynsight.soap.timesoap(soap) - - natoms = tsoap.shape[0] - fig, axes = plt.subplots(2, sharey=True) - for i in range(4): - axes[0].plot(tsoap[i], label=f"Atom {i}") - axes[1].plot(tsoap[-1 - i], label=f"Atom {natoms - 1 - i}") - - for ax in axes: - ax.legend() - fig.tight_layout() - fig.savefig( - "tsoap.png", - dpi=360, - bbox_inches="tight", - ) - plt.show() - - -if __name__ == "__main__": - main()