Initial project version

.gitignore

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+data/
+__pycache__
+*.pyc
+store.egg-info/
+build/
+dist/
+.idea/
+.cache/

README.md

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+# Store.py: A database interface for storing MD evaluation data
+
+A database interface to store evaluations of MD simulations.
+
+## Usage
+
+The package provides interfaces to query records from the database (`get()` function)
+and put new or updated records into the database (`update()` function).
+Both of which will be explained breifly below.
+
+### Automated Evaluation: `eval.yaml`
+
+The package provides a standardized way of running MD evaluations.
+Those evaluations are defined in `eval.yaml` file, located in the respective simulation directory.
+An example format of such a yaml file is as follows:
+
+    namespace: analyse.biwater
+
+    simulation_params:
+        mixingratio: 0.5
+        system: bulk
+        ensemble: nvt
+
+    trajectory-open: open
+
+    evaluations:
+        - subset:
+            residue_name: W100
+            atom_name: OW
+            selection: W100
+          functions:
+            - isf:
+                q: 22.7
+            - msd
+        - subset:
+            residue_name: W100
+            atom_name: OW
+            selection: W100
+          other:
+            residue_name: W075
+            atom_name: OW
+            selection: W075
+          functions:
+            - rdf
+        - subset:
+            residue_name: W100
+            coordinates-map: water_dipole
+            selection: dipole
+          functions:
+            - oaf:
+                order: 1
+
+The first line defines a namespace, which is used to locate evaluation functions (see below).
+The key `simulation_params` defines parameters of the simulation, which are used when storing the results in the database.
+Parameters like the directory and the user are determined automatically from the file path and the current user, respectively.
+With the key `trajectory-open` a function can be specified that is used to open the trajectory.
+If this is omitted the function store.eval.open will be used.
+
+The last key `evaluations` defines a list of evaluations which will be done.
+Each item of the list is a dictionary with the two keys `subset` and und `functions`.
+The parameters defined for the subset will be used to get the subset of the trajectory ,except for the special key `selection` which is used for the store.update function.
+The optional key `other` defines a second subset of atoms, which is passed to the function as the keyword other.
+The functions are again defined as a list. Each item can be either a string or a dictionary with on key value pair.
+In the latter case the key defines the function and the value should be another dictionary of keyword arguments for the function.
+These keyword arguments will also be stored in the database, as evaluation parameters.
+
+The function is located by its name, first in the specified namespace and if not found there, in the store.analyse module, which defines some standard MD evaluation functions.
+The namespace may be used to calculate user defined functions.
+
+The above example doese the following evaluations:
+
+1. All results will be stored in the database with the parameters system=bulk, ensemble=nvt, mixingratio=0.5.
+2. The trajectory will be opened with the function analyse.biwater.open and the path of the yaml file.
+3. The first subset selects all OW atoms of the W100 residues, the selection parameter in the database will be W100.
+4. The first function will either be analyse.biwater.isf (if existent) or store.analyse.isf, with the keyword argument q=22.7.
+5. The second functions msd has no arguments.
+6. A second subset selects all atoms of the W100 residue and runs the functions F1 and F2 with it.
+
+### Included analysis functions
+
+The store package comes with some basic analysis functions, which can also serve as template for writing customized analyses.
+All analysis functions are defind in `store.analyse`, this is also the fall back namespace for eval.yaml evaluations, 
+when no namespace is specified, or functions are not defined in the custom namespace.
+
+Currently, the following functions are defined, some of them are documented, use help(store.analyse.function) to get more info.
+The follwoing list gives the names and required parameters (in parantheses) of the avaliable functions:
+
+- isf(q): Incoherent intermediate scatterig function
+- csf(q): Coherent intermediate scattering function
+- msd: Mean squared displacement
+- oaf(order): Orientational autocorrelation function, use with appropriate vector map (see below) 
+- rdf: Radial pair distribution function
+- tetrahedral_order: Tetrahedral oder parameter
+
+Additionally, some typical vector maps are defined:
+
+- vector: Generic vector map between two atom types
+- water_dipole
+- water_OH_bonds
+
+### Updating
+
+The function `update` handles creation of new records as updating existing ones.
+It will look for a simulation in the database, according to the specified arguments
+and only if no matching record is found, a new simulation will be created.
+
+    import store
+    store.update(
+        'isf', df, directory='/path/to/simulation', user='niels', T=120, selection='OW',
+        simulation_params={'mixingratio': 0.5}, evaluation_params={'q': 22.7}
+    )
+
+Here the variable `df` should be a dataframe with the evaluated data.
+
+### Querying
+
+Users should use the function `get` to retrieve data from the database. For example:
+
+    import store
+
+    store.get(
+        'isf', user='niels', T='100-150',
+        simulation_params={'mixingratio': 0.5}
+    )
+
+Note that the parameters defined as `simulation_params` (or `evaluation_params`) have
+to be defined when the data is put into the database.
+
+## Database organization
+
+The database is organized in two main Tables:
+
+1. Evaluation: Stores the evaluated data, linked to a simulation
+2. Simulation: Stores the metadata of a simulation
+
+### Table schemas
+
+    Evaluation:
+        observable: str
+        selection: str
+        parameters: list of Parameter
+        simulation: Simulation
+        data: object
+
+    Simulation:
+        directory: str
+        user: str
+        temperature: number
+        float_params: list of FloatAttribute
+        string_params: list of StringAttribute
+        evaluations: list of Evaluation
+
+      Parameter:
+          name: str
+          value: float
+
+      FloatAttribute:
+          name: str
+          value: float
+
+      StringAttribute:
+          name: str
+          value: str
+
+The tables Parameter, FloatAttribute and StringAttribute are simple key values pairs,
+allowing float or string values, respectively. They are used to store arbitrary
+attributes of the evaluation and simualtion records.
+
+## Notes for the future
+
+### Clean-Up SQL Database
+
+To delete orphaned evaluations, on the postgresql shell (`psql -h db.cluster -U store`)
+    
+    # Since parameters refernce their respective evaluation, we have to delete them first.
+    DELETE FROM parameters
+    WHERE parameters.evaluation_id IN 
+    (SELECT id FROM evaluations WHERE evaluations.simulation_id IS NULL);
+
+    DELETE FROM evaluations WHERE evaluations.simulation_id IS NULL;
+
+Similarly, one can delete simulations, without any assigned evaluations.
+
+### Database usages
+
+General size info
+
+    SELECT nspname || '.' || relname AS "relation",
+        pg_size_pretty(pg_total_relation_size(C.oid)) AS "total_size"
+    FROM pg_class C
+    LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
+    WHERE nspname NOT IN ('pg_catalog', 'information_schema')
+        AND C.relkind <> 'i'
+        AND nspname !~ '^pg_toast'
+    ORDER BY pg_total_relation_size(C.oid) DESC
+    LIMIT 20;
+
+Number of simulations per User 
+
+    SELECT simulations.user, COUNT(DISTINCT simulations),
+      pg_size_pretty(SUM(pg_column_size(data))) as "data-size",
+      pg_size_pretty(SUM(pg_column_size(data)) / COUNT(DISTINCT simulations)) as "size / sim"
+    FROM evaluations
+    JOIN simulations 
+    ON (simulations.id = evaluations.simulation_id)
+    GROUP BY simulations.user
+    ORDER BY count DESC;
+
+Average size of data per observable
+
+    SELECT observable, 
+      pg_size_pretty(ROUND(AVG(pg_column_size(data)), 0)) as "size-avg",
+      pg_size_pretty(ROUND(SUM(pg_column_size(data)), 0)) as "size-total",
+      AVG(pg_column_size(data)) as "size_bytes"
+    FROM evaluations
+    GROUP BY observable
+    ORDER BY size_bytes DESC;
+
+    
+### SSH tunnel connection
+
+To get a secure connection to the postgrsql server an nas2, one can use SSH tunnels.
+This allows to use SSH certicifates for identification, so no passwords are reuqired.
+Example code fragments:
+
+    import sshtunnel
+    ssh_server = None
+
+    SSH_HOST = 'nas2'
+    SSH_USER = os.getlogin()
+    SSH_KEY = os.environ['HOME'] + '/.ssh/id_rsa'
+    SSH_BIND_PORT = 58222
+    DB_FILE = 'postgresql://localhost:{port}/test'.format(port=SSH_BIND_PORT)
+
+    def open_sshtunnel():
+        global ssh_server
+        ssh_server = sshtunnel.SSHTunnelForwarder(
+            ssh_address_or_host=SSH_HOST,
+            ssh_username=SSH_USER,
+            ssh_pkey=SSH_KEY,
+            remote_bind_address=('localhost', PG_PORT),
+            local_bind_address=('localhost', SSH_BIND_PORT)
+        )
+        ssh_server.start()

debug.cfg

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+[store]
+db_file 	= sqlite:////home/phd/Dokumente/store.db
+
+[eval]
+
+require_trajectory 	= True
+maxcache 			= 1024
+
+topology 	= *.tpr
+trajectory 	= out/*.xtc
+short_dir 	= ../short/*
+
+nojump		= True
+
+[correlation]
+
+skip		= 0.01
+window		= 0.3
+segments	= 300

setup.py

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+from setuptools import setup
+
+
+setup(
+    name='store',
+    description='A database interface to store MD evaluations.',
+    author_email='niels.mueller@physik.tu-darmstadt.de',
+    packages=['store', 'store.webview'],
+    version='0.2',
+    requires=['sqlalchemy', 'psycopg2', 'pandas'],
+    zip_safe=False,
+    include_package_data=True,
+    package_data={'store': ['store.cfg'], 'store.webview': [
+        'templates/*', 'static/bootstrap_v4/*/*', 'static/jquery_v3/*', 'static/popper_v1/*']},
+    entry_points={
+        'console_scripts': [
+            'mdeval = store.eval:cli',
+            'store-dump = store.store:dump_cli'
+        ]
+    },
+)

store/__init__.py

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+import os
+import configparser
+import socket
+
+config = configparser.ConfigParser()
+config.read([
+    os.path.join(os.path.dirname(__file__), 'store.cfg'),
+    os.path.expanduser('~/.store.cfg'),
+    os.environ.get('STORE_CONFIG', '')
+])
+
+PGPASS = '/nfsopt/mdevaluate/pgpass'
+if os.path.exists(PGPASS):
+    with open(PGPASS) as f:
+        for pgpass in f:
+            hostname, *_, username, password = pgpass.split(':')
+            if hostname == 'db.cluster' and username == 'store':
+                config['store']['pg_password'] = password.strip()
+
+# we sometimes have problems with the internal DNS, therefore try to resolve
+# the database host in advance and use IP directly
+try:
+    config['store']['pg_host'] = socket.gethostbyname(config['store']['pg_host'])
+except OSError:
+    pass
+
+
+from .store import get, update, delete, init_db, observables, systems, merge, dump_db
+
+__all__ = ['get', 'update', 'delete', 'init_db', 'observables', 'systems', 'merge', 'dump_db']

store/analyse.py

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+from functools import partial
+import numpy as np
+import pandas as pd
+from scipy.optimize import curve_fit
+
+import mdevaluate as md
+import store
+from .utils import data_frame, traj_slice, set_correlation_defaults, collect_short, excess_entropy, enhanced_bins
+
+
+def openNojump(path, maxcache=500):
+    trajectory = md.open(path, trajectory='nojump.xtc', cached=maxcache)
+    return trajectory
+
+
+def savetxt_newer(filename, newData):
+    if not os.path.isfile(filename):
+        np.savetxt(filename, newData)
+        return
+    f = NamedTemporaryFile()
+    np.savetxt(f.name, newData)
+    if not filecmp.cmp(filename, f.name):
+        np.savetxt(filename, newData)
+    f.close()
+    return
+
+
+def isf(trajectory, q, nojump=True, nav=3, outdir=None, **kwargs):
+    """
+    Compute the incoherent intermediate scattering function and and meta analyses.
+
+    Meta analyses include variance in time, correlation time and susceptibility.
+    """
+    kwargs = set_correlation_defaults(kwargs)
+    kwargs['average'] = False
+    description = 'isf'
+    if 'description' in kwargs:
+        description += '_' + kwargs.pop('description')
+
+    if nojump:
+        trajectory = trajectory.nojump
+
+    t, S = md.correlation.shifted_correlation(
+        partial(md.correlation.isf, q=q), trajectory,
+        description=description, **kwargs)
+
+    N = len(trajectory[0])
+    time = md.utils.runningmean(t[1:], nav)
+    χ4 = md.utils.runningmean(N * S.var(axis=0)[1:], nav)
+
+    S = S.mean(axis=0)
+    freq, suscept = md.correlation.susceptibility(t, S)
+    result = {
+        'isf': data_frame(time=t, cor=S),
+        'isf_var': data_frame(time=time, var=χ4),
+        'isf_sus': data_frame(freq=freq, sus=suscept)
+    }
+    try:
+        m = (S < 0.7)
+        tau = t[np.absolute(S - 1 / np.e).argmin()]
+        fit, _ = curve_fit(md.functions.kww, t[m], S[m], p0=(1, tau, 1), maxfev=5000)
+        tau_1e = md.functions.kww_1e(*fit)
+        result['isf_tau'] = tau_1e
+    except RuntimeError:
+        md.logger.info('Could not estimate tau: %s', trajectory)
+
+    return result
+
+
+# wraps around isf, determines automatic
+def isfwater(trajectory, storeparam, **kwargs):
+    kwargs['segments'] = 10000000 // len(trajectory[0])
+    t, S = md.correlation.shifted_correlation(partial(md.correlation.isf, q=22.7), trajectory,
+                                              segments=min(5, kwargs['segments'] // 50), window=0.5, skip=0.1,
+                                              average=True, description='rough')
+    mask = (S < 0.9) & (S > 0.1)
+    with np.errstate(invalid='ignore'):
+        fit, _ = curve_fit(md.functions.kww, t[mask], S[mask])
+        tau_est = md.functions.kww_1e(*fit)
+    time = trajectory[-1].time - trajectory[0].time
+    kwargs['window'] = max(min(500 * tau_est / time, 0.5), 0.01)
+    kwargs['skip'] = max(0.01, min(0.1, 2 * tau_est / time))
+    return isf(trajectory, storeparam, q=22.7, nojump=False, **kwargs)
+
+
+def csf(trajectory, q, nav=3, **kwargs):
+    """Compute the coherent intermediate scattering function and its variance."""
+    kwargs = set_correlation_defaults(kwargs)
+    kwargs['average'] = False
+
+    t, S = md.correlation.shifted_correlation(
+        partial(md.correlation.coherent_scattering_function, q=q),
+        trajectory.pbc,
+        **kwargs
+    )
+
+    csf_variance = data_frame(
+        time=md.utils.runningmean(t, nav),
+        χ4=md.utils.runningmean(S.var(axis=0), nav)
+    )
+
+    return {'csf': data_frame(time=t, csf=S.mean(axis=0)), 'csf_variance': csf_variance}
+
+
+def msd(trajectory, **kwargs):
+    """Compute the mean squared displacement."""
+    kwargs = set_correlation_defaults(kwargs)
+    kwargs.setdefault('average', True)
+
+    @collect_short
+    def calc(trajectory, **kwargs):
+        t, S = md.correlation.shifted_correlation(
+            md.correlation.msd,
+            trajectory.nojump,
+            **kwargs
+        )
+        return {'msd': data_frame(time=t, msd=S)}
+
+    res = calc(trajectory, **kwargs)
+
+    # Mean diffusivity, calculated over the last half-decade.
+    t = res['msd'].time.values
+    S = res['msd'].msd.values
+    m = t > (t.max() / 3.16)
+    D = (S[m] / (6 * t[m])).mean()
+    res['D'] = D
+    return res
+
+
+def tetrahedral_order(trajectory, other=None, skip=0.1, nr_averages=500, normed=True, **kwargs):
+    if other is None:
+        other = trajectory
+    bins = np.arange(-3, 1, 0.01)
+    q = md.utils.runningmean(bins, 2)
+    sl = traj_slice(len(trajectory), skip, nr_averages)
+    res = md.distribution.time_average(
+        partial(md.distribution.tetrahedral_order_distribution, bins=bins),
+        trajectory.pbc[sl],
+        coordinates_b=other.pbc[sl],
+        **kwargs
+    )
+    N = len(trajectory[0]) if normed else 1
+    res /= N
+    tet_mean = data_frame(**{'<Q>': sum(res * q), 'S_Q': sum(res * np.log(1 - q))})
+    return {'tetrahedral_order': data_frame(q=q, distribution=res),
+            'tetrahedral_mean': tet_mean}
+
+
+def vector(trajectory, atoms=None):
+    """
+    Calcuate the vectors between two types of atoms in the trajectory.
+
+    The names of the atoms between which the vectors should be calculated can be either given
+    as a list of length 2, or the trajectory should contain excactly two atom types.
+    """
+    if atoms is None:
+        atoms = sorted(list(set(trajectory.atom_subset.atom_names)))
+    assert len(atoms) == 2, 'Need to specifiy excactly two atom types for vector calculation.'
+    at_a, at_b = atoms
+    box = trajectory[0].box.diagonal()
+    atoms_a = trajectory.atom_subset.atom_names == at_a
+    atoms_b = trajectory.atom_subset.atom_names == at_b
+    return md.coordinates.vectors(trajectory, atoms_a=atoms_a, atoms_b=atoms_b, normed=True, box=box)
+
+
+def water_dipole(trajectory):
+    """Create a cordinates map of normed water dipole vectors."""
+    sel = trajectory.atom_subset.selection
+    atoms_a = np.where(trajectory.subset(atom_name='OW').atom_subset.selection[sel])[0]
+    atoms_b = np.where(trajectory.subset(atom_name='HW.').atom_subset.selection[sel])[0]
+    atoms_b = atoms_b.reshape(len(atoms_a), 2).T
+    box = trajectory[0].box.diagonal()
+    vec = md.coordinates.vectors(trajectory, atoms_a=atoms_a, atoms_b=atoms_b, normed=True, box=box)
+    vec.description = 'water-dipole'
+    return vec
+
+
+def water_OH_bonds(trajectory):
+    """Create a coordinates map of normed OH vectors for water molecules."""
+    sel = trajectory.atom_subset.selection
+    atoms_a = np.where(trajectory.subset(atom_name='OW').atom_subset.selection[sel])[0]
+    atoms_b = np.where(trajectory.subset(atom_name='HW.').atom_subset.selection[sel])[0]
+    atoms_a = np.vstack([atoms_a] * 2).T.reshape(atoms_b.shape)
+    box = trajectory[0].box.diagonal()
+    vec = md.coordinates.vectors(trajectory, atoms_a=atoms_a, atoms_b=atoms_b, normed=True, box=box)
+    vec.description = 'water-OH-bonds'
+    return vec
+
+
+def oaf(trajectory, order, nav=1, **kwargs):
+    """
+    Calculate the orientational autocorrelation function and meta analyses.
+
+    Use in conjunction with a coordinates map, like water_dipole, to map atom coordinates to the
+    desired vectors. Meta anaylsis include the functions variance in time, correlation time and 
+    the susceptibility. Results are called F{order} (i.e. F1, F2) and accordingly.
+
+    Args:
+        trajectory: The correlated vectore, will be passed automatically in automatic evaluations.
+        order:      Order of the used legendre polynomial, typically 1 or 2.
+        nav (opt.): Running average bevor calculating variance
+        **kwargs:   Any keyowrd arguments are passed to the time_average function.
+    """
+    kwargs = set_correlation_defaults(kwargs)
+
+    fdesc = 'F{}'.format(order)
+    description = fdesc
+    if 'description' in kwargs:
+        description += '_' + kwargs.pop('description')
+
+    # Collect short should apply to the basic compuations only,
+    # additional meta analyses, like tau and susceptibility, are computed from the result.
+    @collect_short
+    def calc_oaf(vectors, order, nav=1, **kwargs):
+        kwargs['average'] = False
+        t, F = md.correlation.shifted_correlation(
+            partial(md.correlation.rotational_autocorrelation, order=order),
+            vectors,
+            description=description,
+            **kwargs
+        )
+        result = {fdesc: data_frame(time=t, cor=F.mean(axis=0))}
+        Na = len(vectors[0])
+        result[fdesc + '_var'] = data_frame(
+            time=md.utils.runningmean(t, nav), var=md.utils.runningmean(Na * F.var(axis=0), nav)
+        )
+        return result
+
+    res = calc_oaf(trajectory, order, nav=nav, **kwargs)
+    t, F = res[fdesc].time.values, res[fdesc].cor.values
+    if F.min() < 0.3:
+        res[fdesc + '_tau'] = md.utils.quick1etau(t, F)
+    freq, sus = md.correlation.susceptibility(t, F)
+    res[fdesc + '_sus'] = data_frame(freq=freq, sus=sus)
+    return res
+
+
+def rdf(trajectory, other=None, skip=0.01, nr_averages=100, q=None, kind=None, **kwargs):
+    """
+    Compute radial pair-distribution function, static structure factor and pair entropy.
+
+    The quantities can be calculated for one ore two sets of atoms. The bins of g(r) are
+    determined automatically, with rmax = L/2 and variable bins with a maximal width of 0.01.
+
+    Args:
+        trajectory: First set of atoms
+        other (opt.): Second set of atoms.
+        skip (opt.): Part of the trajectory to skip at the beginning
+        nr_averages (opt.): How many averages should be taken
+        q (opt.): Scattering vectors of S(q). Default is 2π/(L/2) < q <2π/0.1, with Δq=0.2.
+    """
+    bins = np.arange(0.0, trajectory[0].box.diagonal().min() / 2, 0.001)
+    r = md.utils.runningmean(bins, 2)
+    sl = traj_slice(len(trajectory), skip, nr_averages)
+    if other is not None:
+        kwargs['coordinates_b'] = other.pbc[sl]
+
+    res = md.distribution.time_average(
+        partial(md.distribution.rdf, bins=bins, kind=kind),
+        trajectory.pbc[sl],
+        **kwargs
+    )
+
+    L = trajectory[0].box.diagonal().min()
+    if q is None:
+        q = 2 * np.pi * np.arange(1 / L, 10, 0.5 / L)
+    Nb = len(other[0]) if other is not None else len(trajectory[0])
+    ρ = Nb / trajectory[0].volume
+    Sq = md.utils.Sq_from_gr(r, res, q, ρ)
+
+    ρ = len(trajectory[0]) / trajectory[0].volume
+    S_excess = excess_entropy(r, res, ρ)
+
+    return {'rdf': data_frame(r=r, rdf=res), 'structure_factor': data_frame(q=q, Sq=Sq),
+            'excess_entropy': S_excess}

store/eval.py

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+import os
+import filecmp
+import re
+from tempfile import NamedTemporaryFile
+import argparse
+from psutil import virtual_memory, cpu_count
+import inspect
+import yaml
+from glob import glob
+from multiprocessing.pool import Pool
+import traceback
+from datetime import datetime, timedelta
+import functools
+from enum import Enum
+import time
+from pathlib import Path
+
+from sqlalchemy.exc import OperationalError
+
+import pydoc
+
+import numpy as np
+
+import mdevaluate as md
+from mdevaluate.logging import logger
+
+from . import store
+from . import config
+
+
+def locate(fname, namespace=''):
+    return pydoc.locate(namespace + '.' + fname) or pydoc.locate('store.analyse.' + fname)
+
+
+def open_sim(directory, maxcache=None):
+    tr = None
+    if len(glob(os.path.join(directory, 'nojump.xtc'))) is 1:
+        tr = md.open(directory, trajectory='nojump.xtc', cached=maxcache, reindex=True)
+    else:
+        tr = md.open(directory, trajectory='out/*.xtc', cached=maxcache, reindex=True, nojump=True)
+    if tr is None:
+        raise FileNotFoundError('Can not open trajectory.')
+    return tr
+
+
+def open_energy(directory):
+    return md.open_energy(os.path.join(directory, 'out/*.edr'))
+
+
+def dataframe_to_txt(fname, df):
+    """Save a dataframe to textfile."""
+    header = ' '.join(df.columns)
+    np.savetxt(fname, df.values, header=header)
+    
+
+class RunState:
+    OK = 0
+    ERROR = 1
+    UNKNOWN = 2
+
+    def __init__(self, st, timing=None):
+        if isinstance(st, str):
+            self.state = getattr(self, st)
+        else:
+            self.state = st
+        self.timing = timing
+
+    def __str__(self):
+        if self.state == self.OK:
+            s = '\x1b[0;32m\u2713\x1b[0m'
+            if self.timing is not None:
+                s += '({})'.format(self.timing)
+            return s
+        elif self.state == self.ERROR:
+            return '\x1b[0;31m\u2717\x1b[0m'
+        elif self.state == self.UNKNOWN:
+            return '\x1b[0;33m?\x1b[0m'
+
+
+@functools.total_ordering
+class Report:
+
+    def apply(self, func, *args, err_count=1, **kwargs):
+        if isinstance(func, functools.partial):
+            fname = func.func.__name__
+        else:
+            fname = func.__name__
+        try:
+            start = datetime.now()
+            res = func(*args, **kwargs)
+            time = timedelta(seconds=round((datetime.now() - start).total_seconds()))
+            self.runok(fname, timing=time)
+            return res
+        except Exception as e:
+            self.runerror(fname)
+            self.error(e, err_count=err_count)
+
+    def runok(self, name, timing=None):
+        # st = RunStates.OK.timing(timing) if timing is not None else RunStates.OK
+        self.current['runs'].append((name, RunState('OK', timing)))
+
+    def runerror(self, name):
+        self.current['runs'].append((name, RunState('ERROR')))
+
+    def unknown(self, name):
+        self.current['runs'].append((name, RunState('UNKNOWN')))
+
+    def error(self, e, err_count=1):
+        if err_count > 0:
+            traceback.print_exc()
+            self.current['errors'].append(e)
+            self.err_count += err_count
+
+    def system(self, sys):
+        self.systems.setdefault(sys, {'runs': [], 'errors': []})
+        self.current_system = sys
+
+    def __repr__(self):
+        rep = ''
+        for sys in self.systems:
+            rep += '--= {} =--\n'.format(sys)
+            for run, state in self.systems[sys]['runs']:
+                rep += '{}:{}  '.format(run, state)
+            rep += '\n'
+            for e in self.systems[sys]['errors']:
+                rep += str(e) + '\n'
+            rep += '\n'
+        return rep
+
+    @property
+    def current(self):
+        return self.systems[self.current_system]
+
+    @property
+    def nerrors(self):
+        return sum(len(sys['errors']) for sys in self.systems.values())
+
+    def __init__(self):
+        self.systems = {}
+        self.current_system = None
+        self.err_count = 0
+
+    def __eq__(self, other):
+        return self.systems == other.systems
+
+    def __lt__(self, other):
+        return self.err_count < other.err_count
+
+
+class Loader(yaml.Loader):
+
+    def include(self, node):
+        fname = os.path.abspath(self.construct_scalar(node))
+        with open(fname, 'r') as f:
+            return yaml.load(f, Loader)
+
+    def calc(self, node):
+        return eval(self.construct_scalar(node))
+
+
+Loader.add_constructor('!include', Loader.include)
+Loader.add_constructor('!calc', Loader.calc)
+
+
+def find_user():
+    return os.environ.get('USER') or os.environ.get('LOGNAME')
+
+
+def run_eval(yamlfile, debug=False, txtout=None, autosavedir=None):
+    """
+    Read an eval.yaml file and run the specified functions and store the result.
+    """
+    report = Report()
+    with open(yamlfile) as f:
+        yaml_dict = yaml.load(f.read(), Loader)
+
+    directory = os.path.dirname(yamlfile)
+    user = find_user()
+    namespace = yaml_dict.pop('namespace', '')
+    report.system(directory)
+
+    for k, v in yaml_dict.pop('config', {}).items():
+        config['eval'][k] = str(v)
+
+    temperature = yaml_dict['simulation_params'].pop('T', None)
+    if temperature is None:
+        m = re.match('.*[^\d](\d+)K.*', directory)
+        if m is not None:
+            temperature = int(m.group(1))
+
+    if debug:
+        print('Evaluation for', directory)
+        print('User:', user)
+        print('T:', temperature)
+
+    # dont overdo caching, this allows several parallel analysis with caching
+    MAXCACHE = int(min(3000, (virtual_memory().total / 1024**2 / 5 / cpu_count() / 0.4) // 10 * 10))
+    if autosavedir is not None:
+        md.autosave.enable(autosavedir, verbose=True)
+
+    if 'trajectory-open' in yaml_dict:
+        fopen = locate(yaml_dict['trajectory-open'], namespace)
+        if fopen is None:
+            raise ValueError("Trajectory loader couldn't be located: {}".format(yaml_dict['trajectory-open']))
+    else:
+        fopen = open_sim
+    traj = report.apply(fopen, directory, maxcache=config['eval'].getint('maxcache', MAXCACHE))
+
+    if 'energy-open' in yaml_dict:
+        eopen = locate(yaml_dict['energy-open'], namespace)
+        eopen_err_count = 1
+        if fopen is None:
+            raise ValueError("Energy loader couldn't be located: {}".format(yaml_dict['energy-open']))
+    else:
+        eopen = open_energy
+        eopen_err_count = 0
+    energyfile = report.apply(eopen, directory, err_count=eopen_err_count)
+
+    if config.getboolean('eval', 'require_trajectory') and traj is None:
+        return report
+    if traj is None and energyfile is None:
+        return report
+
+    logger.info('Running evaluations for: %s', directory)
+    for evaluation in yaml_dict['evaluations']:
+        subset = evaluation['subset']
+        selection = subset.pop('selection')
+
+        if 'coordinates-map' in subset:
+            cmap = subset.pop('coordinates-map')
+            crds_map = locate(cmap, namespace)
+            if crds_map is None:
+                report.unknown(cmap)
+                continue
+            subtraj = crds_map(traj.subset(**subset)) if traj is not None else None
+        else:
+            subtraj = traj.subset(**subset) if traj is not None else None
+        subtraj.selection = selection
+
+        other = evaluation.pop('other', None)
+        if other is not None and traj is not None:
+            selection += '-' + other.pop('selection')
+            if 'coordinates-map' in other:
+                cmap = other.pop('coordinates-map')
+                crds_map = locate(cmap, namespace)
+                if crds_map is None:
+                    report.unknown(cmap)
+                    continue
+                othertraj = crds_map(traj.subset(**other))
+            else:
+                othertraj = traj.subset(**other)
+        else:
+            othertraj = None
+
+        functions = evaluation.pop('functions')
+
+        report.system('{}@{}'.format(selection, directory))
+        if debug:
+            print('*****')
+            print('subset:', subset)
+            print('selection:', selection)
+            print('subtraj:', subtraj)
+        for func in functions:
+            if isinstance(func, dict):
+                if len(func) == 1:
+                    (func, params), = func.items()
+                else:
+                    logger.info('Function definition is unclear: %s', str(func))
+            else:
+                params = {}
+            if othertraj is not None:
+                params['other'] = othertraj
+            # Locate the function: First under a specified namespace, then in the default module
+            f = locate(namespace + '.' + func) or locate('store.analyse.' + func)
+            if f is not None:
+                if debug:
+                    print('---')
+                    print('function:', f, func)
+                    print('params:', params)
+                    report.runok(func)
+                else:
+                    logger.info('Run function %s', func)
+                    func_args = inspect.signature(f).parameters
+                    if 'trajectory' in func_args:
+                        params['trajectory'] = subtraj
+                    if 'energyfile' in func_args:
+                        params['energyfile'] = energyfile
+                    res = report.apply(f, **params)
+                    if not isinstance(res, dict):
+                        res = {func: res}
+                    params.pop('trajectory', None)
+                    params.pop('other', None)
+                    params.pop('energyfile', None)
+                    for obs, data in res.items():
+                        if txtout is not None:
+                            dataframe_to_txt(os.path.join(txtout, '{}.dat'.format(obs)), data)
+                        store.update(
+                            obs, data, directory=directory, user=user, selection=selection, T=temperature,
+                            simulation_params=yaml_dict.get('simulation_params', {}),
+                            evaluation_params=params
+                        )
+            else:
+                report.unknown(func)
+                logger.info('Function %s was not found. Namespace was: %s', func, namespace)
+
+    return report
+
+
+def recursive_analysis(basedir, processes=None, debug=False, txtout=None, autosavedir=None):
+    """
+    Run analysis functions recursively for baseidr on several processes.
+    """
+    logger.info('Starting recursive analysis in directory: {}'.format(basedir))
+    reports = []
+
+    def collect_reports(rep):
+        reports.append(rep)
+
+    def catch_error(err):
+        traceback.print_exception(type(err), err, err.__traceback__)
+
+    yaml_files = [str(y) for p in glob(basedir) for y in Path(p).glob('**/eval.yaml')]
+
+    logger.info('Finished walking directories:')
+    for y in yaml_files:
+        logger.info(y)
+
+    # Evaluation is syncronous only if processes=False.
+    if processes is False:
+        for y in yaml_files:
+            print(y)
+            try:
+                reports.append(run_eval(y, debug=debug, txtout=txtout, autosavedir=autosavedir))
+            except FileNotFoundError:
+                print('Skipping evaluation...')
+    else:
+        #pool = Pool(processes=processes)
+        #for y in yaml_files:
+        #    pool.apply_async(run_eval, args=(y,), kwds={'debug': debug, 'txtout': txtout, 'autosavedir': autosavedir},
+        #                     callback=collect_reports, error_callback=catch_error)
+        #pool.close()
+        #pool.join()
+        with Pool(processes=processes) as pool:
+            for y in yaml_files:
+                time.sleep(5)
+                pool.apply_async(run_eval, args=(y,), kwds={'debug': debug, 'txtout': txtout, 'autosavedir': autosavedir},
+                                 callback=collect_reports, error_callback=catch_error)
+            pool.close()
+            pool.join()
+        #    reports = pool.map(
+        #        functools.partial(run_eval, debug=debug, txtout=txtout, autosavedir=autosavedir),
+        #        yaml_files
+        #    )
+
+    print('#*' * 22)
+    print('Finished analysis!: {}'.format(datetime.strftime(datetime.now(), '%c')))
+    print('#*' * 22)
+    for rep in sorted(reports):
+        print(rep)
+    if len(yaml_files) != len(reports):
+        print('#### Error: {} / {} tasks were reported.'.format(len(reports), len(yaml_files)))
+        for y in yaml_files:
+            root = os.path.dirname(y)
+            if not any(root in str(rep) for rep in reports):
+                print('Task not reported: {}'.format(root))
+
+
+# ===========================================
+def cli(args=None):
+    parser = argparse.ArgumentParser(description='Analyse a certain simulation')
+    parser.add_argument('--recursive', '-r', action='store_true', default=False,
+                        help='Perform a recusrive evaluation of the directory.')
+    parser.add_argument('-d', default=None, help='simulation directory; default cwd')
+    parser.add_argument('-o', default=None,
+                        help='default None; output directory for human readable *.dat, if this is not a path ending with "/" then the last part will be a common prefix')
+    parser.add_argument('--autosave', '-a', default=None, help='Autosave directory')
+    parser.add_argument('--verbose', '-v', default=False, action='store_true',
+                        help='Be verbose, i.e. set logging level to DEBUG, default is INFO')
+    parser.add_argument('--processes', '-np', default=None, type=int,
+                        help='Number of sub-processes for the recursive evaluation.')
+    args = parser.parse_args()
+    if args.verbose:
+        md.logging.setlevel('DEBUG')
+    SIMDIR = args.d
+    if SIMDIR is None:
+        SIMDIR = os.getcwd()
+    SIMDIR = os.path.abspath(SIMDIR)
+    OUTDIR = args.o
+    if OUTDIR is not None:
+        OUTDIR = os.path.abspath(OUTDIR)
+        if args.o[-1] == '/':
+            OUTDIR = OUTDIR + '/'
+
+    if args.recursive:
+        recursive_analysis(SIMDIR, txtout=OUTDIR, autosavedir=args.autosave, processes=args.processes)
+    else:
+        yamlfile = os.path.join(SIMDIR, 'eval.yaml')
+        if os.path.exists(yamlfile):
+            rep = run_eval(yamlfile, txtout=OUTDIR, autosavedir=args.autosave)
+            print(rep)
+        else:
+            print('eval.yaml not found, exiting')
+            quit()

store/store.cfg

@@ -0,0 +1,24 @@
+[store]
+# The database connection - change this to sqlite://store.db to use a local sqlite database
+pg_host		= db.cluster
+pg_port		= 5432
+pg_user		= store
+pg_password = none
+db_file 	= postgresql://%(pg_user)s:%(pg_password)s@%(pg_host)s:%(pg_port)s/store
+
+[eval]
+
+require_trajectory 	= True
+maxcache 			= 2000
+
+topology 	= *.tpr
+trajectory 	= out/*.xtc
+short_dir 	= ../short/*
+
+nojump		= True
+
+[correlation]
+
+skip		= 0.01
+window		= 0.3
+segments	= 300

store/store.py

@@ -0,0 +1,500 @@
+import os
+from collections.abc import Iterable
+from logging import info
+import argparse
+import pickle
+
+import pandas as pd
+import sqlalchemy as sql
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.pool import NullPool
+
+
+from .utils import numlike, number_shorthand, lazy_eq
+from . import config
+
+
+Base = declarative_base()
+Engine = sql.create_engine(config['store']['db_file'], poolclass=NullPool)
+Session = sql.orm.sessionmaker(bind=Engine)
+
+
+def init_db():
+    pass
+
+
+class FloatAttribute(Base):
+    __tablename__ = 'floatattributes'
+    id = sql.Column(sql.Integer, primary_key=True)
+    name = sql.Column(sql.String)
+    value = sql.Column(sql.Float)
+    simulation_id = sql.Column(sql.Integer, sql.ForeignKey('simulations.id'))
+
+    def __init__(self, name, value):
+        self.name = name
+        self.value = value
+
+    def __repr__(self):
+        return '<{classname}({name}={value})>'.format(
+            classname=self.__class__.__name__, name=self.name, value=self.value
+        )
+
+
+class StringAttribute(Base):
+    __tablename__ = 'stringattributes'
+    id = sql.Column(sql.Integer, primary_key=True)
+    name = sql.Column(sql.String)
+    value = sql.Column(sql.String)
+    simulation_id = sql.Column(sql.Integer, sql.ForeignKey('simulations.id'))
+
+    def __init__(self, name, value):
+        self.name = name
+        self.value = value
+
+    def __repr__(self):
+        return '<{classname}({name}={value})>'.format(
+            classname=self.__class__.__name__, name=self.name, value=self.value
+        )
+
+
+class Parameter(Base):
+    __tablename__ = 'parameters'
+    id = sql.Column(sql.Integer, primary_key=True)
+    name = sql.Column(sql.String)
+    value = sql.Column(sql.Float)
+    evaluation_id = sql.Column(sql.Integer, sql.ForeignKey('evaluations.id'))
+
+    def __init__(self, name, value):
+        self.name = name
+        self.value = value
+
+    def __repr__(self):
+        return '<{classname}({name}={value})>'.format(
+            classname=self.__class__.__name__, name=self.name, value=self.value
+        )
+
+
+class Simulation(Base):
+    __tablename__ = 'simulations'
+
+    id = sql.Column(sql.Integer, primary_key=True)
+    directory = sql.Column(sql.String)
+    user = sql.Column(sql.String)
+    temperature = sql.Column(sql.Float)
+
+    float_params = sql.orm.relationship(FloatAttribute)
+    string_params = sql.orm.relationship(StringAttribute)
+
+    evaluations = sql.orm.relationship('Evaluation', order_by='Evaluation.id', back_populates='simulation')
+
+    def add_attribute(self, name, value):
+        self.attributes.append(FloatAttribute(name, value))
+
+    def add_parameter(self, name, value):
+        if numlike(value):
+            self.float_params.append(FloatAttribute(name, value))
+        else:
+            self.string_params.append(StringAttribute(name, value))
+
+    def copy(self):
+        params = {p.name: p.value for p in self.float_params + self.string_params}
+
+        return Simulation(self.directory, self.user, self.temperature, **params)
+
+    def delete(self, session=None):
+        if session is None:
+            session = Session()
+            
+        for e in self.evaluations:
+            e.delete(session=session)
+
+        for p in self.float_params + self.string_params:
+            session.delete(p)
+
+        session.delete(self)
+
+    def __init__(self, directory, user, temperature, **parameters):
+        self.directory = directory or ''
+        self.user = user or ''
+        self.temperature = temperature
+        for name, value in parameters.items():
+            self.add_parameter(name, value)
+
+    def __repr__(self):
+        attrs = []
+        for attr, val in self.__dict__.items():
+            if attr[0] is not '_' and attr is not 'type':
+                attrs.append('{}={}'.format(attr, val))
+        return '<{classname}({attributes})>'.format(classname=self.__class__.__name__, attributes=', '.join(attrs))
+
+
+# Dataframes can only be unpickled with the same version of Pandas used to store them
+class PandasPickler:
+
+    @staticmethod
+    def dumps(obj, protocol=None):
+        if isinstance(obj, pd.DataFrame):
+            obj = obj.to_dict()
+        return pickle.dumps(obj, protocol=protocol)
+
+    @staticmethod
+    def loads(buf):
+        obj = pickle.loads(buf)
+        if isinstance(obj, dict):
+            obj = pd.DataFrame(obj)
+        return obj
+
+
+class Evaluation(Base):
+    __tablename__ = 'evaluations'
+
+    id = sql.Column(sql.Integer, primary_key=True)
+    simulation_id = sql.Column(sql.Integer, sql.ForeignKey('simulations.id'))
+    observable = sql.Column(sql.String)
+    selection = sql.Column(sql.String)
+    data = sql.Column(sql.PickleType(comparator=lazy_eq, pickler=PandasPickler))
+    parameters = sql.orm.relationship('Parameter')
+
+    simulation = sql.orm.relationship('Simulation', back_populates='evaluations')
+
+    @property
+    def dataframe(self):
+        """
+        Return a dataframe representing the evaluated data and meta data of the evaluation.
+        """
+        if self.data is None:
+            df = pd.DataFrame()
+        elif numlike(self.data):
+            df = pd.DataFrame({self.observable: self.data}, index=[0])
+        else:
+            df = self.data.copy()
+
+        df['selection'] = self.selection
+        for p in self.parameters:
+            df[p.name] = p.value
+        df['directory'] = self.simulation.directory
+        df['user'] = self.simulation.user
+        df['T'] = self.simulation.temperature
+        for a in self.simulation.float_params:
+            df[a.name] = a.value
+        for a in self.simulation.string_params:
+            df[a.name] = a.value
+
+        return df
+
+    def add_parameter(self, name, value):
+        self.parameters.append(Parameter(name, value))
+
+    def copy(self, simulation=None):
+        return Evaluation(self.observable, simulation or self.simulation, self.data,
+                          parameters={p.name: p.value for p in self.parameters})
+
+    def delete(self, session=None):
+        if session is None:
+            session = Session()
+            
+        for p in self.parameters:
+            session.delete(p)
+        session.delete(self)
+
+    def __init__(self, observable, simulation, data, selection='', parameters={}):
+        self.observable = observable
+        self.simulation = simulation
+        self.data = data
+        self.selection = selection
+        for name, value in parameters.items():
+            self.add_parameter(name, value)
+
+    def __repr__(self):
+        attrs = []
+        for attr, val in self.__dict__.items():
+            if attr[0] is not '_' and attr not in ['type', 'data']:
+                attrs.append('{}={}'.format(attr, val))
+        return '<{classname}({attributes})>'.format(classname=self.__class__.__name__, attributes=', '.join(attrs))
+
+
+Base.metadata.create_all(Engine)
+
+
+def list_query(args, comparator):
+    if isinstance(args, str):
+        args = [args]
+    return sql.or_(*[comparator(a) for a in args])
+
+
+def float_query(value, dbattr):
+    if numlike(value):
+        crit = dbattr == float(value)
+    elif isinstance(value, str):
+        limits = value.split('-')
+        if len(limits) is 2:
+            try:
+                limits = [float(x) for x in limits]
+            except:
+                raise ValueError('Could not parse value={}'.format(value))
+            crit = sql.and_(float(limits[0]) <= dbattr, dbattr <= float(limits[1]))
+        else:
+            raise ValueError('Could not parse value={}'.format(value))
+    elif isinstance(value, Iterable):
+        crit = dbattr.in_(value)
+    else:
+        raise ValueError('Could not parse {} as float value.'.format(value))
+    return crit
+
+
+def query_simulation(directory=None, user=None, T=None, session=None, **attributes):
+    if session is None:
+        session = Session()
+
+    query = session.query(Simulation)
+    if directory is not None:
+        query = query.filter(list_query(directory, Simulation.directory.like))
+    if user is not None:
+        query = query.filter(list_query(user, Simulation.user.like))
+    if T is not None:
+        query = query.filter(float_query(T, Simulation.temperature))
+
+    for name, value in attributes.items():
+        if number_shorthand(value):
+            alias = sql.orm.aliased(FloatAttribute)
+            query = query.join(alias).filter(sql.and_(alias.name == name, float_query(value, alias.value)))
+        else:
+            alias = sql.orm.aliased(StringAttribute)
+            query = query.join(alias).filter(sql.and_(alias.name == name, list_query(value, alias.value.like)))
+    return query
+
+
+def query_evaluation(observable, simulation, selection=None, session=None, **parameters):
+    """
+    Query the database for evaluations.
+
+    Args:
+        observable: Observable of the evaluation
+        directory: Simulation directory as a string, or a list of the former.
+        user: Username os list of users
+        T: Temperature to query, can be a number, a list of numbers or a string giving a range
+        selection: Selection of the simulation
+        **attributes: Any attributes of the simulations as keyword arguments
+
+    This query function allows some shorthands for the definition of query values.
+    For string comparisons the SQL statement LIKE is used, which allows the use of the wildcard '%'.
+    Besides a concrete value, all arguments allow specification of a list of arguments,
+    which will query any of the given values.
+    Number values (i.e. temperature or any simulation attribute) can also be given as strings
+    which represent a range, i. e. '100 - 150'.
+
+    Example:
+        query_observable('isf', ['%/sys1', 'sys2'], T='100-200')
+
+    """
+    if session is None:
+        session = Session()
+
+    if not isinstance(simulation, Iterable):
+        simulation = [simulation]
+    query = session.query(Evaluation).filter(
+        Evaluation.simulation_id.in_([s.id for s in simulation]),
+        Evaluation.observable == observable
+    )
+    if selection is not None:
+        query = query.filter(list_query(selection, Evaluation.selection.like))
+    for name, value in parameters.items():
+        alias = sql.orm.aliased(Parameter)
+        query = query.join(alias).filter(sql.and_(alias.name == name, float_query(value, alias.value)))
+
+    return query
+
+
+def get(observable, directory=None, user=None, T=None, selection=None,
+        evaluation_params={}, simulation_params={}):
+    """
+    Get evaluation data from the database.
+
+    Args:
+        observable: The observable of the evaluation
+        data: The data of the evaluation
+        simulation: The evaluated simulation
+        directory: The simulation directory
+        user: User of the simulation
+        T: Temperature of the simulation
+        selection: Selection of the evaluation
+        evaluation_params: A dict of parameters of the evaluation
+        simualtion_params: A dict of parameters of the evaluation
+    """
+    session = Session()
+    
+    sim_query = query_simulation(directory, user=user, T=T, session=session, **simulation_params)
+    if sim_query.count() == 0:
+        return None
+    #else:
+    #    simulations = sim_query.all()
+
+    if isinstance(observable, str):
+        observable = [observable]
+    results = []
+    for obs in observable:
+        evals = query_evaluation(obs, sim_query, selection=selection, session=session, **evaluation_params)
+        results.extend([ev.dataframe for ev in evals])
+    if len(results) > 0:
+        df = pd.concat(results, ignore_index=True)
+        res = df.sort_values(by=['directory', 'selection', 'T'])
+    else:
+        res = None
+    session.close()
+    return res
+
+
+def merge(observables, *args, **kwargs):
+    """
+    Get the merged datasets of several observables.
+
+    Args:
+        observables: List of observables
+        *args, **kwargs: Additional Argmunets, passed to :func:`get`
+    """
+    if len(observables) > 1:
+        df = pd.merge(get(observables[0], *args, **kwargs), merge(observables[1:], *args, **kwargs))
+        return df.sort_values(by=['system', 'selection', 'T'])
+    else:
+        return get(observables[0], *args, **kwargs)
+
+
+def update(observable, data, simulation=None, directory=None, user=None, T=None, selection=None,
+           evaluation_params={}, simulation_params={}):
+    """
+    Update an existing evaluation record or insert a new one into the database.
+
+    Args:
+        observable: The observable of the evaluation
+        data: The data of the evaluation
+        simulation: The evaluated simulation
+        directory: The simulation directory
+        user: User of the simulation
+        T: Temperature of the simulation
+        selection: Selection of the evaluation
+        evaluation_params: A dict of parameters of the evaluation
+        simualtion_params: A dict of parameters of the evaluation
+
+    """
+    session = Session()
+    
+    if simulation is None:
+        sim_query = query_simulation(directory, user=user, T=T, session=session, **simulation_params)
+        qu_count = sim_query.count()
+        if qu_count == 0:
+            simulation = Simulation(directory, user, T, **simulation_params)
+        elif qu_count == 1:
+            simulation = sim_query.first()
+        else:
+            session.close()
+            raise ValueError('Found multiple simulation records while updating: {}'.format(sim_query.all()))            
+
+    eval_query = query_evaluation(observable, simulation, selection=selection, session=session, **evaluation_params)
+    qu_count = eval_query.count()
+    if qu_count == 0:
+        session.add(Evaluation(observable, simulation, data, selection=selection, parameters=evaluation_params))
+    elif qu_count == 1:
+        eval_query.first().data = data
+    else:
+        session.close()
+        raise ValueError('Found more than one record in database while updating.')
+    session.commit()
+    session.close()
+
+
+def delete(*args, **kwargs):
+    """
+    Delete evaluations from the database.
+    """
+    session = Session()
+    
+    query = query_evaluation(*args, **kwargs)
+    for item in query:
+        session.delete(item)
+        # item.delete()
+
+    session.commit()
+    session.close()
+
+
+def observables(directory=None, user=None, T=None, **parameters):
+    """
+    List all observables, if system is not None for this specific system.
+    """
+    query = query_simulation(directory, user=user, T=T, **parameters)
+
+    obs = set()
+    for sim in query:
+        obs.update({ev.observable for ev in sim.evaluations})
+    return obs
+
+
+def systems(observable=None, user=None):
+    """
+    List all system, if observable is not None for this specific observable.
+    """
+    query = query_simulation(user=user)
+
+    if observable is not None:
+        query = query.join(Evaluation).filter(Evaluation.observable == observable)
+    return {sim.directory for sim in query}
+
+
+def clean_leaves():
+    """Delete objects with missing foreign key from the database."""
+    session = Session()
+    
+
+    for cls in (StringAttribute, FloatAttribute):
+        session.query(cls).filter(cls.simulation_id == None).delete()
+    session.query(Parameter).filter(Parameter.evaluation_id == None).delete()
+    # TODO: Delete evaluation leaves, requires joint query with parameter
+
+    session.commit()
+    session.close()
+
+def dump_db(dbfile, user=None, **kwargs):
+    """
+    Dump the database into a new databse.
+
+    Args:
+        dbfile: File descriptor of the new database.
+        user (opt.): If None, the database of the current user is dumped.
+        **kwargs: Any keyword arguments are passed to query_simulation
+    """
+    if user is None:
+        user = os.getlogin()
+    if '://' not in dbfile:
+        dbfile = 'sqlite:///' + dbfile
+    dump_engine = sql.create_engine(dbfile)
+    Base.metadata.create_all(dump_engine)
+    dump_session = sql.orm.sessionmaker(bind=dump_engine)()
+    for sim in query_simulation(user=user, **kwargs):
+        dump_sim = sim.copy()
+        for ev in sim.evaluations:
+            dump_ev = ev.copy(simulation=dump_sim)
+            dump_session.add(dump_ev)
+    dump_session.commit()
+
+
+def dump_cli(args=None):
+    """Command line interface to dump the database to a file."""
+    parser = argparse.ArgumentParser(description='CLI tool to create a databse dump.')
+
+    parser.add_argument('dbfile', help='The database file for the dump.')
+    parser.add_argument(
+        '--user', '-u',
+        help='The user for which the database is dumped, default is the current user.'
+    )
+    args = parser.parse_args()
+    dump_db(args.dbfile, user=args.user)
+
+
+def delete_dirs(dirpattern):
+    session = Session()
+
+    simulations = query_simulation(directory=dirpattern, session=session)
+    for sim in simulations:
+        sim.delete(session=session)
+    session.commit()
+    session.close()

store/utils.py

@@ -0,0 +1,224 @@
+import os
+from numbers import Number
+from collections.abc import Iterable
+from functools import wraps
+import inspect
+from glob import glob
+import warnings
+import pandas as pd
+import numpy as np
+import traceback
+
+try:
+    import mdevaluate as md
+except ImportError:
+    pass
+
+from . import config
+
+
+def nice_systems(df):
+    return df.replace(['.*bulk_3_1', '.*bulk_1_3', '.*bulk_1_9', '.*bulk'], ['3:1', '1:3', '1:9', '1:1'], regex=True)
+
+
+def numlike(x):
+    return isinstance(x, Number)
+
+
+def number_shorthand(x):
+    if numlike(x):
+        return True
+    elif isinstance(x, str):
+        limits = x.split('-')
+        if len(limits) is 2:
+            try:
+                limits = [float(a) for a in limits]
+            except:
+                return False
+            return True
+        else:
+            return False
+    elif isinstance(x, Iterable):
+        return all(numlike(v) for v in x)
+    else:
+        False
+
+
+def lazy_eq(a, b):
+    try:
+        return a == b
+    except (ValueError, TypeError):
+        return False
+
+
+def data_frame(**kwargs):
+    try:
+        df = pd.DataFrame(kwargs)
+    except ValueError:
+        df = pd.DataFrame(kwargs, index=[0])
+    return df
+
+
+def traj_slice(N, skip, nr_averages):
+    step = int(N * (1 - skip) // nr_averages) or 1
+    return slice(int(skip * N), N, step)
+
+
+def set_correlation_defaults(kwargs):
+    """Set some sensefull defaults for shifted correlation parameters."""
+    for k in config['correlation'].keys():
+        kwargs.setdefault(k, config['correlation'].getfloat(k))
+    return kwargs
+
+
+def merge_timeframes(left, right, on='time'):
+    """Merge two dataframes with overlapping time scales."""
+    merged = pd.merge(left, right, on=on, how='outer', indicator=True, suffixes=('_x', ''))
+    res = pd.concat(
+        (left, merged[left.columns][merged._merge == 'right_only']), ignore_index=True
+    ).sort_values(by=on).reset_index(drop=True)
+    return res
+
+
+def open_sim(directory, maxcache=None):
+    return md.open(
+        directory, topology=config['eval']['topology'], trajectory=config['eval']['trajectory'],
+        reindex=True, nojump=config['eval']['nojump']
+    )
+
+
+def collect_short(func):
+    """
+    Decorator to run an analysis function for the given trajectory and associated short simulations.
+
+    Args:
+        short_subdir (opt.): Directory of short simulations, relative to the main trajectory file.
+
+    Decorated functions will be evaluate for the given trajecotory. Subsequently, this decorator will
+    look for multiple simulations in subdirectories '../short/*' of the trajecotory file. The results
+    for these simulations are then averaged and merged with the result of the original trajectory.
+
+    The simulations should be organized as follows, where the main trajectory, for which the function
+    is called, is basedir/out/long.xtc. Results for short times are then obtained from the two trajectories
+    located in basedir/short/100 and basedir/short/500.
+
+    basedir/
+      topol.tpr
+      out/
+        long.xtc
+      short/
+        100/
+          topol.tpr
+          out/
+            short.xtc
+        500/
+          topol.tpr
+          out/
+            short.xtc
+
+    """
+    @wraps(func)
+    def wrapped(trajectory, *args, **kwargs):
+        res = func(trajectory, *args, **kwargs)
+        indices = trajectory.atom_subset.indices
+        description = trajectory.description
+        directory = os.path.dirname(trajectory.frames.filename)
+        short_dir = os.path.abspath(os.path.join(directory, config['eval']['short_dir']))
+        timestep = trajectory[1].time - trajectory[0].time
+
+        params = inspect.signature(func).parameters
+        has_other = 'other' in params
+        if has_other:
+            args = list(args)
+            other = args.pop(list(params).index('other') - 1)
+            other_indices = other.atom_subset.indices
+            other_description = other.description
+
+
+        res_short = {}
+        N = 0
+        for sd in glob(short_dir):
+            md.logger.debug(sd)
+            try:
+                traj = open_sim(sd)
+            except FileNotFoundError:
+                md.logger.info('Unale to load short simulation: %s', sd)
+                continue
+            if traj is None:
+                print('sim=None')
+                continue
+            N += 1
+            sim = traj.subset(indices=indices)
+            sim.description = description
+            if isinstance(trajectory, md.coordinates.CoordinatesMap):
+                mode = trajectory.coordinates.mode
+                if mode is not None:
+                    sim = getattr(sim, mode)
+                sim = md.coordinates.CoordinatesMap(sim, trajectory.function)
+            else:
+                mode = trajectory.mode
+                if mode is not None:
+                    sim = getattr(sim, mode)
+
+            if has_other:
+                other_sim = traj.subset(indices=other_indices)
+                other_sim.description = other_description
+                if isinstance(other, md.coordinates.CoordinatesMap):
+                    mode = other.coordinates.mode
+                    if mode is not None:
+                        other_sim = getattr(other_sim, mode)
+                    other_sim = md.coordinates.CoordinatesMap(other_sim, other.function)
+                else:
+                    mode = other.mode
+                    if mode is not None:
+                        other_sim = getattr(other_sim, mode)
+
+            short_kwargs = kwargs
+            win = min(round(timestep / (sim[-1].time * 0.9), 3), 0.5)
+            short_kwargs['window'] = win
+            short_kwargs['skip'] = 0.01
+            short_kwargs['segments'] = min(int((0.9 - win) * (len(sim) - 1)), 20)
+            if has_other:
+                sr = func(sim, other_sim, *args, **short_kwargs)
+            else:
+                sr = func(sim, *args, **short_kwargs)
+            for key, val in sr.items():
+                if isinstance(val, pd.DataFrame):
+                    if key in res_short:
+                        res_short[key] += val
+                    else:
+                        res_short[key] = val
+        for key in res_short:
+            res_short[key] /= N
+        if len(res_short) != 0:
+            res.update({key: merge_timeframes(sr, res[key]) for key, sr in res_short.items()})
+        return res
+
+    return wrapped
+
+
+def enhanced_bins(x, y, k=3):
+    """
+    Determine enhanced bins for some x and y data.
+
+    The binssize will be reduced where the derivative of y is steep. The k parameter controlls,
+    how strong the binsize is influenced by the steepness of y.
+    """
+    dy = np.absolute(np.diff(y))
+    dx_max = np.diff(x).mean()
+    xnew = [min(x)]
+    while xnew[-1] < max(x):
+        i = np.where(x > xnew[-1])[0][0]
+        if i >= len(dy):
+            break
+        dx = dx_max / (1 + k * dy[i])
+        xnew.append(xnew[-1] + dx)
+    return np.array(xnew)
+
+
+def excess_entropy(r, gr, ρ=1):
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', category=RuntimeWarning)
+        y = (gr * np.log(gr) - (gr - 1)) * r**2
+    y = np.nan_to_num(y)
+    return -2 * np.pi * ρ * np.trapz(y, r)

store/webview/__init__.py

@@ -0,0 +1,2 @@
+
+from .view import app

store/webview/settings.py

@@ -0,0 +1 @@
+

store/webview/settings_local.py

@@ -0,0 +1,2 @@
+
+DB_FILE = 'sqlite://///home/phd/Projects/store.db'

store/webview/static/bootstrap_v3/css/bootstrap-theme.css

@@ -0,0 +1,587 @@
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+.btn-default,
+.btn-primary,
+.btn-success,
+.btn-info,
+.btn-warning,
+.btn-danger {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
+}
+.btn-default:active,
+.btn-primary:active,
+.btn-success:active,
+.btn-info:active,
+.btn-warning:active,
+.btn-danger:active,
+.btn-default.active,
+.btn-primary.active,
+.btn-success.active,
+.btn-info.active,
+.btn-warning.active,
+.btn-danger.active {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
+          box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
+}
+.btn-default.disabled,
+.btn-primary.disabled,
+.btn-success.disabled,
+.btn-info.disabled,
+.btn-warning.disabled,
+.btn-danger.disabled,
+.btn-default[disabled],
+.btn-primary[disabled],
+.btn-success[disabled],
+.btn-info[disabled],
+.btn-warning[disabled],
+.btn-danger[disabled],
+fieldset[disabled] .btn-default,
+fieldset[disabled] .btn-primary,
+fieldset[disabled] .btn-success,
+fieldset[disabled] .btn-info,
+fieldset[disabled] .btn-warning,
+fieldset[disabled] .btn-danger {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+.btn-default .badge,
+.btn-primary .badge,
+.btn-success .badge,
+.btn-info .badge,
+.btn-warning .badge,
+.btn-danger .badge {
+  text-shadow: none;
+}
+.btn:active,
+.btn.active {
+  background-image: none;
+}
+.btn-default {
+  text-shadow: 0 1px 0 #fff;
+  background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image:      -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));
+  background-image:         linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #dbdbdb;
+  border-color: #ccc;
+}
+.btn-default:hover,
+.btn-default:focus {
+  background-color: #e0e0e0;
+  background-position: 0 -15px;
+}
+.btn-default:active,
+.btn-default.active {
+  background-color: #e0e0e0;
+  border-color: #dbdbdb;
+}
+.btn-default.disabled,
+.btn-default[disabled],
+fieldset[disabled] .btn-default,
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus,
+.btn-default.disabled:active,
+.btn-default[disabled]:active,
+fieldset[disabled] .btn-default:active,
+.btn-default.disabled.active,
+.btn-default[disabled].active,
+fieldset[disabled] .btn-default.active {
+  background-color: #e0e0e0;
+  background-image: none;
+}
+.btn-primary {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #265a88 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #245580;
+}
+.btn-primary:hover,
+.btn-primary:focus {
+  background-color: #265a88;
+  background-position: 0 -15px;
+}
+.btn-primary:active,
+.btn-primary.active {
+  background-color: #265a88;
+  border-color: #245580;
+}
+.btn-primary.disabled,
+.btn-primary[disabled],
+fieldset[disabled] .btn-primary,
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus,
+.btn-primary.disabled:active,
+.btn-primary[disabled]:active,
+fieldset[disabled] .btn-primary:active,
+.btn-primary.disabled.active,
+.btn-primary[disabled].active,
+fieldset[disabled] .btn-primary.active {
+  background-color: #265a88;
+  background-image: none;
+}
+.btn-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image:      -o-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));
+  background-image:         linear-gradient(to bottom, #5cb85c 0%, #419641 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #3e8f3e;
+}
+.btn-success:hover,
+.btn-success:focus {
+  background-color: #419641;
+  background-position: 0 -15px;
+}
+.btn-success:active,
+.btn-success.active {
+  background-color: #419641;
+  border-color: #3e8f3e;
+}
+.btn-success.disabled,
+.btn-success[disabled],
+fieldset[disabled] .btn-success,
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus,
+.btn-success.disabled:active,
+.btn-success[disabled]:active,
+fieldset[disabled] .btn-success:active,
+.btn-success.disabled.active,
+.btn-success[disabled].active,
+fieldset[disabled] .btn-success.active {
+  background-color: #419641;
+  background-image: none;
+}
+.btn-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image:      -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));
+  background-image:         linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #28a4c9;
+}
+.btn-info:hover,
+.btn-info:focus {
+  background-color: #2aabd2;
+  background-position: 0 -15px;
+}
+.btn-info:active,
+.btn-info.active {
+  background-color: #2aabd2;
+  border-color: #28a4c9;
+}
+.btn-info.disabled,
+.btn-info[disabled],
+fieldset[disabled] .btn-info,
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus,
+.btn-info.disabled:active,
+.btn-info[disabled]:active,
+fieldset[disabled] .btn-info:active,
+.btn-info.disabled.active,
+.btn-info[disabled].active,
+fieldset[disabled] .btn-info.active {
+  background-color: #2aabd2;
+  background-image: none;
+}
+.btn-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image:      -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));
+  background-image:         linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #e38d13;
+}
+.btn-warning:hover,
+.btn-warning:focus {
+  background-color: #eb9316;
+  background-position: 0 -15px;
+}
+.btn-warning:active,
+.btn-warning.active {
+  background-color: #eb9316;
+  border-color: #e38d13;
+}
+.btn-warning.disabled,
+.btn-warning[disabled],
+fieldset[disabled] .btn-warning,
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus,
+.btn-warning.disabled:active,
+.btn-warning[disabled]:active,
+fieldset[disabled] .btn-warning:active,
+.btn-warning.disabled.active,
+.btn-warning[disabled].active,
+fieldset[disabled] .btn-warning.active {
+  background-color: #eb9316;
+  background-image: none;
+}
+.btn-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image:      -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));
+  background-image:         linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #b92c28;
+}
+.btn-danger:hover,
+.btn-danger:focus {
+  background-color: #c12e2a;
+  background-position: 0 -15px;
+}
+.btn-danger:active,
+.btn-danger.active {
+  background-color: #c12e2a;
+  border-color: #b92c28;
+}
+.btn-danger.disabled,
+.btn-danger[disabled],
+fieldset[disabled] .btn-danger,
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus,
+.btn-danger.disabled:active,
+.btn-danger[disabled]:active,
+fieldset[disabled] .btn-danger:active,
+.btn-danger.disabled.active,
+.btn-danger[disabled].active,
+fieldset[disabled] .btn-danger.active {
+  background-color: #c12e2a;
+  background-image: none;
+}
+.thumbnail,
+.img-thumbnail {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  background-color: #e8e8e8;
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  background-color: #2e6da4;
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+}
+.navbar-default {
+  background-image: -webkit-linear-gradient(top, #fff 0%, #f8f8f8 100%);
+  background-image:      -o-linear-gradient(top, #fff 0%, #f8f8f8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#f8f8f8));
+  background-image:         linear-gradient(to bottom, #fff 0%, #f8f8f8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image:      -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));
+  background-image:         linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
+}
+.navbar-brand,
+.navbar-nav > li > a {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, .25);
+}
+.navbar-inverse {
+  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image:      -o-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));
+  background-image:         linear-gradient(to bottom, #3c3c3c 0%, #222 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-radius: 4px;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image:      -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));
+  background-image:         linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
+          box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
+}
+.navbar-inverse .navbar-brand,
+.navbar-inverse .navbar-nav > li > a {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);
+}
+.navbar-static-top,
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  border-radius: 0;
+}
+@media (max-width: 767px) {
+  .navbar .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+    background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+    background-repeat: repeat-x;
+  }
+}
+.alert {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, .2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
+}
+.alert-success {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image:      -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));
+  background-image:         linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #b2dba1;
+}
+.alert-info {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image:      -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));
+  background-image:         linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #9acfea;
+}
+.alert-warning {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image:      -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));
+  background-image:         linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #f5e79e;
+}
+.alert-danger {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image:      -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));
+  background-image:         linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dca7a7;
+}
+.progress {
+  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image:      -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));
+  background-image:         linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #286090 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image:      -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));
+  background-image:         linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image:      -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));
+  background-image:         linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image:      -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));
+  background-image:         linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image:      -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));
+  background-image:         linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+}
+.list-group {
+  border-radius: 4px;
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  text-shadow: 0 -1px 0 #286090;
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #2b669a;
+}
+.list-group-item.active .badge,
+.list-group-item.active:hover .badge,
+.list-group-item.active:focus .badge {
+  text-shadow: none;
+}
+.panel {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
+}
+.panel-default > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-primary > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-success > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image:      -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));
+  background-image:         linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-info > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image:      -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));
+  background-image:         linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-warning > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image:      -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));
+  background-image:         linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-danger > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image:      -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));
+  background-image:         linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.well {
+  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image:      -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));
+  background-image:         linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dcdcdc;
+  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
+          box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
+}
+/*# sourceMappingURL=bootstrap-theme.css.map */

store/webview/static/bootstrap_v3/css/bootstrap-theme.css.map

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store/webview/static/bootstrap_v3/css/bootstrap-theme.min.css

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+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] 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store/webview/static/bootstrap_v3/css/bootstrap.min.css

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+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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store/webview/static/bootstrap_v3/js/bootstrap.min.js

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+/*!
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+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under the MIT license
+ */
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store/webview/static/bootstrap_v3/js/npm.js

@@ -0,0 +1,13 @@
+// This file is autogenerated via the `commonjs` Grunt task. You can require() this file in a CommonJS environment.
+require('../../js/transition.js')
+require('../../js/alert.js')
+require('../../js/button.js')
+require('../../js/carousel.js')
+require('../../js/collapse.js')
+require('../../js/dropdown.js')
+require('../../js/modal.js')
+require('../../js/tooltip.js')
+require('../../js/popover.js')
+require('../../js/scrollspy.js')
+require('../../js/tab.js')
+require('../../js/affix.js')

store/webview/static/bootstrap_v4/css/bootstrap-grid.css.map

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store/webview/static/bootstrap_v4/css/bootstrap-grid.min.css

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+/*!
+ * Bootstrap Grid v4.0.0 (https://getbootstrap.com)
+ * Copyright 2011-2018 The Bootstrap Authors
+ * Copyright 2011-2018 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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store/webview/static/bootstrap_v4/css/bootstrap-grid.min.css.map

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store/webview/static/bootstrap_v4/css/bootstrap-reboot.css

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+/*!
+ * Bootstrap Reboot v4.0.0 (https://getbootstrap.com)
+ * Copyright 2011-2018 The Bootstrap Authors
+ * Copyright 2011-2018 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
+ */
+*,
+*::before,
+*::after {
+  box-sizing: border-box;
+}
+
+html {
+  font-family: sans-serif;
+  line-height: 1.15;
+  -webkit-text-size-adjust: 100%;
+  -ms-text-size-adjust: 100%;
+  -ms-overflow-style: scrollbar;
+  -webkit-tap-highlight-color: transparent;
+}
+
+@-ms-viewport {
+  width: device-width;
+}
+
+article, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {
+  display: block;
+}
+
+body {
+  margin: 0;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-size: 1rem;
+  font-weight: 400;
+  line-height: 1.5;
+  color: #212529;
+  text-align: left;
+  background-color: #fff;
+}
+
+[tabindex="-1"]:focus {
+  outline: 0 !important;
+}
+
+hr {
+  box-sizing: content-box;
+  height: 0;
+  overflow: visible;
+}
+
+h1, h2, h3, h4, h5, h6 {
+  margin-top: 0;
+  margin-bottom: 0.5rem;
+}
+
+p {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+abbr[title],
+abbr[data-original-title] {
+  text-decoration: underline;
+  -webkit-text-decoration: underline dotted;
+  text-decoration: underline dotted;
+  cursor: help;
+  border-bottom: 0;
+}
+
+address {
+  margin-bottom: 1rem;
+  font-style: normal;
+  line-height: inherit;
+}
+
+ol,
+ul,
+dl {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+ol ol,
+ul ul,
+ol ul,
+ul ol {
+  margin-bottom: 0;
+}
+
+dt {
+  font-weight: 700;
+}
+
+dd {
+  margin-bottom: .5rem;
+  margin-left: 0;
+}
+
+blockquote {
+  margin: 0 0 1rem;
+}
+
+dfn {
+  font-style: italic;
+}
+
+b,
+strong {
+  font-weight: bolder;
+}
+
+small {
+  font-size: 80%;
+}
+
+sub,
+sup {
+  position: relative;
+  font-size: 75%;
+  line-height: 0;
+  vertical-align: baseline;
+}
+
+sub {
+  bottom: -.25em;
+}
+
+sup {
+  top: -.5em;
+}
+
+a {
+  color: #007bff;
+  text-decoration: none;
+  background-color: transparent;
+  -webkit-text-decoration-skip: objects;
+}
+
+a:hover {
+  color: #0056b3;
+  text-decoration: underline;
+}
+
+a:not([href]):not([tabindex]) {
+  color: inherit;
+  text-decoration: none;
+}
+
+a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {
+  color: inherit;
+  text-decoration: none;
+}
+
+a:not([href]):not([tabindex]):focus {
+  outline: 0;
+}
+
+pre,
+code,
+kbd,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+
+pre {
+  margin-top: 0;
+  margin-bottom: 1rem;
+  overflow: auto;
+  -ms-overflow-style: scrollbar;
+}
+
+figure {
+  margin: 0 0 1rem;
+}
+
+img {
+  vertical-align: middle;
+  border-style: none;
+}
+
+svg:not(:root) {
+  overflow: hidden;
+}
+
+table {
+  border-collapse: collapse;
+}
+
+caption {
+  padding-top: 0.75rem;
+  padding-bottom: 0.75rem;
+  color: #6c757d;
+  text-align: left;
+  caption-side: bottom;
+}
+
+th {
+  text-align: inherit;
+}
+
+label {
+  display: inline-block;
+  margin-bottom: .5rem;
+}
+
+button {
+  border-radius: 0;
+}
+
+button:focus {
+  outline: 1px dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+}
+
+input,
+button,
+select,
+optgroup,
+textarea {
+  margin: 0;
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+
+button,
+input {
+  overflow: visible;
+}
+
+button,
+select {
+  text-transform: none;
+}
+
+button,
+html [type="button"],
+[type="reset"],
+[type="submit"] {
+  -webkit-appearance: button;
+}
+
+button::-moz-focus-inner,
+[type="button"]::-moz-focus-inner,
+[type="reset"]::-moz-focus-inner,
+[type="submit"]::-moz-focus-inner {
+  padding: 0;
+  border-style: none;
+}
+
+input[type="radio"],
+input[type="checkbox"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+
+input[type="date"],
+input[type="time"],
+input[type="datetime-local"],
+input[type="month"] {
+  -webkit-appearance: listbox;
+}
+
+textarea {
+  overflow: auto;
+  resize: vertical;
+}
+
+fieldset {
+  min-width: 0;
+  padding: 0;
+  margin: 0;
+  border: 0;
+}
+
+legend {
+  display: block;
+  width: 100%;
+  max-width: 100%;
+  padding: 0;
+  margin-bottom: .5rem;
+  font-size: 1.5rem;
+  line-height: inherit;
+  color: inherit;
+  white-space: normal;
+}
+
+progress {
+  vertical-align: baseline;
+}
+
+[type="number"]::-webkit-inner-spin-button,
+[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+
+[type="search"] {
+  outline-offset: -2px;
+  -webkit-appearance: none;
+}
+
+[type="search"]::-webkit-search-cancel-button,
+[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+
+::-webkit-file-upload-button {
+  font: inherit;
+  -webkit-appearance: button;
+}
+
+output {
+  display: inline-block;
+}
+
+summary {
+  display: list-item;
+  cursor: pointer;
+}
+
+template {
+  display: none;
+}
+
+[hidden] {
+  display: none !important;
+}
+/*# sourceMappingURL=bootstrap-reboot.css.map */

store/webview/static/bootstrap_v4/css/bootstrap-reboot.css.map

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store/webview/static/bootstrap_v4/css/bootstrap-reboot.min.css

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+/*!
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+ * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
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store/webview/static/bootstrap_v4/css/bootstrap.min.css

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+ * Copyright 2011-2018 The Bootstrap Authors
+ * Copyright 2011-2018 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */:root{--blue:#007bff;--indigo:#6610f2;--purple:#6f42c1;--pink:#e83e8c;--red:#dc3545;--orange:#fd7e14;--yellow:#ffc107;--green:#28a745;--teal:#20c997;--cyan:#17a2b8;--white:#fff;--gray:#6c757d;--gray-dark:#343a40;--primary:#007bff;--secondary:#6c757d;--success:#28a745;--info:#17a2b8;--warning:#ffc107;--danger:#dc3545;--light:#f8f9fa;--dark:#343a40;--breakpoint-xs:0;--breakpoint-sm:576px;--breakpoint-md:768px;--breakpoint-lg:992px;--breakpoint-xl:1200px;--font-family-sans-serif:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";--font-family-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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store/webview/static/bootstrap_v4/js/bootstrap.bundle.min.js

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+/*!
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+  */
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store/webview/static/bootstrap_v4/js/bootstrap.js.map

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store/webview/static/bootstrap_v4/js/bootstrap.min.js

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+/*!
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+  * Copyright 2011-2018 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+  */
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store/webview/static/popper_v1/popper.min.js

@@ -0,0 +1,5 @@
+/*
+ Copyright (C) Federico Zivolo 2017
+ Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT).
+ */(function(e,t){'object'==typeof exports&&'undefined'!=typeof module?module.exports=t():'function'==typeof define&&define.amd?define(t):e.Popper=t()})(this,function(){'use strict';function e(e){return e&&'[object Function]'==={}.toString.call(e)}function t(e,t){if(1!==e.nodeType)return[];var o=getComputedStyle(e,null);return t?o[t]:o}function o(e){return'HTML'===e.nodeName?e:e.parentNode||e.host}function n(e){if(!e)return document.body;switch(e.nodeName){case'HTML':case'BODY':return e.ownerDocument.body;case'#document':return e.body;}var i=t(e),r=i.overflow,p=i.overflowX,s=i.overflowY;return /(auto|scroll)/.test(r+s+p)?e:n(o(e))}function r(e){var o=e&&e.offsetParent,i=o&&o.nodeName;return i&&'BODY'!==i&&'HTML'!==i?-1!==['TD','TABLE'].indexOf(o.nodeName)&&'static'===t(o,'position')?r(o):o:e?e.ownerDocument.documentElement:document.documentElement}function p(e){var t=e.nodeName;return'BODY'!==t&&('HTML'===t||r(e.firstElementChild)===e)}function s(e){return 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+//# sourceMappingURL=popper.min.js.map

store/webview/templates/base.html

@@ -0,0 +1,28 @@
+<!doctype html>
+<head>
+<title>Store Webview</title>
+
+<script defer type='text/javascript' src="{{ url_for('static', filename='jquery_v3/jquery-3.3.1.slim.min.js') }}"></script>
+<script defer type='text/javascript' src="{{ url_for('static', filename='popper_v1/popper.min.js') }}"></script>
+<script defer type='text/javascript' src="{{ url_for('static', filename='bootstrap_v4/js/bootstrap.min.js') }}"></script>
+
+<link rel="stylesheet" href="{{ url_for('static', filename='bootstrap_v4/css/bootstrap.min.css') }}" />
+
+
+{% block head %}{% endblock %}
+</head>
+<body>
+<div class=page>
+  <nav class="navbar navbar-expand-lg navbar-dark bg-dark">  
+    <a class="navbar-brand" href="{{ url_for('view_simulations') }}">Store</a>
+    <a class="nav-link" href="{{ url_for('view_statistics') }}">Statistics</a>
+  </nav>
+    {% for message in get_flashed_messages() %}
+    <div class=flash>{{ message }}</div>
+  {% endfor %}
+  <br />
+  <div class="container-fluid">
+      {% block body %}{% endblock %}
+  </div>
+</div>
+</body>

store/webview/templates/evaluation-details.html

@@ -0,0 +1,58 @@
+{% extends "simulation-details.html" %}
+
+{% block head %}
+<script src="https://d3js.org/d3.v3.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/vega/3.0.0-beta.31/vega.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/vega-lite/2.0.0-beta.3/vega-lite.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/vega-embed/3.0.0-beta.14/vega-embed.js"></script>
+{% endblock %}
+
+{% block evaluation %}
+  <ol class="breadcrumb">
+    <li class="breadcrumb-item">
+      <a href="{{ url_for('view_simulation', sim_id=simulation.id) }}">Evaluations</a>
+    </li>
+    <li class="breadcrumb-item active">{{ evaluation.observable }} </li>
+  </ol>
+<div class="row">
+  <div class="col-md-4">
+    <table class="table table-striped">
+      <th>Evaluation parameters</th><th></th>
+      <tr>
+        <td>Selection</td>
+        <td>{{ evaluation.selection }}</td>
+      </tr>
+      {% for param in evaluation_params %}
+      <tr>
+        <td>{{ param.name }}</td>
+        <td>{{ param.value }}</td>
+      </tr>
+      {% endfor %}
+    </table>
+
+    <div id="vis"></div>
+    <script type="text/javascript">
+      var spec = "{{ url_for('get_evaluation_plot_spec', id=evaluation.id) }}";
+      vega.embed('#vis', spec);
+    </script>
+  </div>
+  <div class="col-md-4">
+    {% if data.to_html %}
+    <div class="card">
+      <div class="card-header"><a data-toggle="collapse" href="#data-table">Data</a></div>
+      <div id="data-table" class="card-collapse collapse">
+        <div class="card-body">
+            {{ data.to_html(index=False) | replace('dataframe', 'table table-sm table-bordered table-striped') | safe }}
+        </div>
+      </div>
+    </div>
+    {% else %}
+      <table class='table'>
+        <tr>
+          <td>Value</td><td>{{ data }}</td>
+        </tr>
+      </table>
+    {% endif %}
+  </div>
+</div>
+{% endblock %}

store/webview/templates/simulation-details.html

@@ -0,0 +1,53 @@
+{% extends "base.html" %}
+
+{% block body %}
+<h2>Simulation Details</h2>
+<div class="row">
+<div class="col-md-4">
+  <div class="card card-default">
+    <div class="card-header">
+      <div class="row">
+        <div class="col-auto">
+          <b>Directory</b>
+        </div>
+        <div class="col-auto">{{ simulation.directory }}</div>
+      </div>
+    </div>
+  
+    <div class="card-body">
+      <table class="table">
+        <tr>
+          <th>Parameters</th><th></th>
+        </tr>
+        <tr>
+          <td>User</td>
+          <td>{{ simulation.user }}</td>
+        </tr>
+        <tr>
+          <td>Temperature</td>
+          <td>{{ simulation.temperature }}</td>
+        </tr>
+        {% for param in sim_params %}
+        <tr>
+          <td>{{ param.name }}</td>
+          <td>{{ param.value }}</td>
+        </tr>
+        {% endfor %}
+      </table>
+    </div>
+  </div>
+  </div>
+  <div class="col-md-8">
+    {% block evaluation %}
+    <ol class="breadcrumb">
+      <li class="breadcrumb-item active">Evaluations</li>
+    </ol>
+    {% for ev_id, obs, sel in evaluations %}
+    <a class="btn btn-info" style="margin: .1em" href="{{ url_for('view_evaluation', id=ev_id )}}">
+      {{ obs }} <span class="badge badge-light">{{ sel }}</span>
+    </a>
+    {% endfor %}
+    {% endblock %}
+  </div>
+  </div>
+{% endblock %}

store/webview/templates/simulations.html

@@ -0,0 +1,46 @@
+{% extends "base.html" %}
+
+{% block body %}
+  <div class="card card-default">
+    <div class="card-header">
+      <h2>Simulations</h2>
+      <form class="row" method="get">
+        <div class="col-md-2">
+        <div class="input-group">
+          <span class="input-group-addon"><span class="glyphicon glyphicon-user"></span></span>
+          <input class="form-control" type="text" placeholder="user", name="user" value="{{ request.args.user }}">
+        </div>
+      </div>
+      <div class="col-md-4">
+    <div class="input-group">
+      <span class="input-group-addon"><span class="glyphicon glyphicon-folder-open"></span></span>
+      <input class="form-control" type="text" placeholder="directory" name="directory" value="{{ request.args.directory}}">
+    </div>
+    </div>
+    <div class="btn-group">
+        <button type="submit" class="btn btn-primary">Reload</button>
+        <button type="reset" name="button" class="btn btn-danger">Reset</button>
+    </div>
+
+
+  </form>
+
+  </div>
+  <div class="card-body">
+  <table class='table table-striped table-sm table-hover'>
+    <tr>
+      <th>#</th><th>User</th><th>Directory</th>
+    </tr>
+    <tbody>
+      {% for sim in simulations %}
+      <tr>
+          <td>
+            <a href="{{ url_for('view_simulation', sim_id=sim.id) }}">{{ sim.id }}</a>
+          </td><td>{{ sim.user}}</td><td>{{ sim.directory }}</td>
+      </tr>
+    {% endfor %}
+  </tbody>
+  </table>
+</div>
+</div>
+{% endblock %}

store/webview/templates/statistics.html

@@ -0,0 +1,36 @@
+{% extends "base.html" %}
+
+{% block body %}
+<h2>Statistics</h2>
+<div class="row">
+  <div class="col-md-5">
+    <div class="card">
+      <div class="card-header">
+        <h4>Table size</h4>
+      </div>
+      <div class="card-body">
+        {{ total_stats.to_html(index=False) | replace('dataframe', 'table table-sm table-bordered table-hover') | safe }}
+      </div>
+    </div>
+    <br />
+    <div class="card">
+      <div class="card-header">
+        <h4>User data</h4>
+      </div>
+      <div class="card-body">
+        {{ user_stats.to_html(index=False) | replace('dataframe', 'table table-sm table-bordered table-hover') | safe }}
+      </div>
+    </div> 
+  </div>
+  <div class="col-md-6">
+    <div class="card">
+      <div class="card-header">
+        <h4>Evaluation data</h4>
+      </div>
+      <div class="card-body">
+        {{ eval_stats.to_html(index=False) | replace('dataframe', 'table table-sm table-bordered table-hover') | safe }}
+      </div>
+    </div>        
+  </div>  
+</div>
+{% endblock %}

store/webview/view.py

@@ -0,0 +1,204 @@
+from flask import Flask, render_template, request, url_for
+
+
+from numbers import Number
+# from datetime import datetime
+import pandas as pd
+import json
+
+try:
+    import altair
+    _ALTAIR = True
+except ImportError:
+    _ALTAIR = False
+
+from .. import store
+
+from . import settings
+
+
+def numlike(x):
+    return isinstance(x, Number)
+
+
+session = store.Session()
+
+app = Flask(__name__)
+app.config.from_object(settings)
+app.config.from_envvar('STOREVIEW_SETTINGS', silent=True)
+
+if 'DB_FILE' in app.config:
+    print('Setting DB_FILE:', app.config['DB_FILE'])
+    store.DB_FILE = app.config['DB_FILE']
+
+
+def db_total_stats():
+    session = store.Session()
+    df = pd.DataFrame(
+        session.execute("""
+            SELECT relname AS "relation",
+                pg_size_pretty(pg_total_relation_size(C.oid)) AS "total_size"
+            FROM pg_class C
+            LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
+            WHERE nspname NOT IN ('pg_catalog', 'information_schema')
+                AND C.relkind <> 'i'
+                AND nspname !~ '^pg_toast'
+                AND relname !~ 'id_seq'
+            ORDER BY pg_total_relation_size(C.oid) DESC
+            LIMIT 20;
+            """).fetchall(), columns=['tabel name', 'total size']
+    )
+    session.close()
+    return df
+
+
+def db_user_stats():
+    session = store.Session()
+    df = pd.DataFrame(
+        session.execute("""
+            SELECT simulations.user, COUNT(DISTINCT simulations),
+               pg_size_pretty(SUM(pg_column_size(data))) as "data-size",
+               pg_size_pretty(SUM(pg_column_size(data)) / COUNT(DISTINCT simulations)) as "size / sim"
+             FROM evaluations
+             JOIN simulations 
+             ON (simulations.id = evaluations.simulation_id)
+             GROUP BY simulations.user
+             ORDER BY count DESC;
+        """).fetchall(), columns=['user', '# of simulations', 'total data', 'data per simulation']
+    )
+    session.close()
+    return df
+
+
+def db_eval_stats():
+    session = store.Session()
+    df = pd.DataFrame(
+        session.execute("""
+            SELECT observable, 
+              pg_size_pretty(ROUND(AVG(pg_column_size(data)), 0)) as "size-avg",
+              pg_size_pretty(ROUND(SUM(pg_column_size(data)), 0)) as "size-total",
+              COUNT(*), AVG(pg_column_size(data)) as "size_bytes"
+            FROM evaluations
+            GROUP BY observable
+            ORDER BY size_bytes DESC;
+        """).fetchall(), columns=['observable', 'Avg. size', 'Total size', '# of evaluations', 'size_bytes']
+    ).drop('size_bytes', axis=1)
+    session.close()
+    return df[~df['Total size'].isnull()]
+
+
+@app.route('/')
+def view_simulations():
+    session = store.Session()
+    simulations = store.query_simulation(
+        directory='%' + request.args.get('directory', '') + '%',
+        user=request.args.get('user', '') + '%',
+        session=session
+    ).all()
+    r = render_template('simulations.html', simulations=simulations)
+    session.close()
+    return r
+
+
+@app.route('/statistics')
+def view_statistics():
+    return render_template('statistics.html', user_stats=db_user_stats(), 
+                           total_stats=db_total_stats(), eval_stats=db_eval_stats())
+
+
+@app.route('/simulation/<int:sim_id>')
+def view_simulation(sim_id):
+    session = store.Session()
+    simulation = store.query_simulation(session=session).filter(store.Simulation.id == sim_id).first()
+    params = simulation.string_params + simulation.float_params
+    evaluations = list(session.query(store.Evaluation).filter(
+        store.Evaluation.simulation_id == simulation.id).order_by('observable').values(
+            'id', 'observable', 'selection'
+        )
+    )
+    r = render_template('simulation-details.html', simulation=simulation,
+                           sim_params=params, evaluations=evaluations)
+    session.close()
+    return r
+
+@app.route('/evaluation/<int:id>')
+def view_evaluation(id):
+    session = store.Session()
+    evaluation = session.query(store.Evaluation).filter(store.Evaluation.id == id).first()
+    df = evaluation.data
+    if numlike(df):
+        data = df
+    elif df is not None:
+        data_columns = list(df.columns)
+        for col in ['directory', 'T', 'user', 'selection', 'system', 'ensemble']:
+            if col in data_columns:
+                data_columns.remove(col)
+        data = df[data_columns]
+    else:
+        data = None
+    simulation = evaluation.simulation
+    sim_params = simulation.string_params + simulation.float_params
+    r = render_template('evaluation-details.html', simulation=simulation, evaluation=evaluation,
+                           sim_params=sim_params, evaluation_params=evaluation.parameters, data=data)
+    session.close()
+    return r
+
+
+@app.route('/evaluation/<int:id>/data.csv')
+def get_evaluation_data(id):
+    session = store.Session()
+    evaluation = session.query(store.Evaluation).filter(store.Evaluation.id == id).first()
+    df = evaluation.data
+    if numlike(df):
+        data = df
+    else:
+        data_columns = list(df.columns)
+        for col in ['directory', 'T', 'user', 'selection', 'system', 'ensemble']:
+            if col in data_columns:
+                data_columns.remove(col)
+        data = df[data_columns]
+    session.close()
+    return data.to_csv()
+
+
+@app.route('/evaluation/<int:id>/spec.json')
+def get_evaluation_plot_spec(id):
+    session = store.Session()
+    evaluation = session.query(store.Evaluation).filter(store.Evaluation.id == id).first()
+    df = evaluation.data
+    if numlike(df) or not _ALTAIR:
+        return '{}'
+    else:
+        data_columns = list(df.columns)
+        for col in ['directory', 'T', 'user', 'selection', 'system', 'ensemble']:
+            if col in data_columns:
+                data_columns.remove(col)
+    plot_spec = {}
+    x = y = None
+    for col in ['time', 'radius', 'r']:
+        if col in data_columns:
+            ycol = data_columns[(data_columns.index(col) + 1) % 2]
+            if col == 'time':
+                x = altair.X(col + ':Q', scale=altair.Scale(type='log'))
+            else:
+                x = altair.X(col + ':Q')
+            if ycol == 'msd':
+                y = altair.Y(ycol + ':Q', scale=altair.Scale(type='log'))
+            else:
+                y = altair.Y(ycol + ':Q')
+
+    if x is None:
+        xcol, ycol = data_columns[:2]
+        x = altair.X(xcol + ':Q')
+        y = altair.Y(ycol + ':Q')
+
+    ch = altair.Chart(
+        url_for('get_evaluation_data', id=id),
+        # data,
+        width=300, height=200).mark_point().encode(
+        x=x, y=y)
+    plot_spec = ch.to_dict()
+    plot_spec["$schema"] = "https://vega.github.io/schema/vega-lite/v1.json"
+    json.codecs
+    session.close()
+    return json.dumps(plot_spec)

test/test_analyse.py

@@ -0,0 +1,26 @@
+import os
+import pytest
+import numpy as np
+
+import mdevaluate as md
+import store.eval as seval
+import store.analyse as sana
+
+
+@pytest.fixture
+def water_trajectory(request, scope='module'):
+    dname = os.environ.get('STORE_TESTDATA', '/autohome/niels/Projects/mdevaluate/test/data/water')
+    return md.open(dname)
+
+
+def test_oaf(water_trajectory):
+    kwargs = {'segments': 10, 'window': 0.5}
+    dipole = sana.water_dipole(water_trajectory)
+    res_1 = sana.oaf(dipole, order=1, **kwargs)['F1'].cor.values
+    res_2 = sana.oaf(dipole, order=2, **kwargs)['F2'].cor.values
+    assert (res_2 <= res_1).all()
+
+    bonds = sana.water_OH_bonds(water_trajectory)
+    res_1 = sana.oaf(bonds, order=1, **kwargs)['F1'].cor.values
+    res_2 = sana.oaf(bonds, order=2, **kwargs)['F2'].cor.values
+    assert (res_2 <= res_1).all()