Initial project version

.gitignore

@@ -0,0 +1,8 @@
+data/
+__pycache__
+*.pyc
+store.egg-info/
+build/
+dist/
+.idea/
+.cache/

README.md

@@ -0,0 +1,244 @@
+# 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

@@ -0,0 +1,19 @@
+[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

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -0,0 +1,275 @@
+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

@@ -0,0 +1,403 @@
+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/fonts/glyphicons-halflings-regular.svg

@@ -0,0 +1,288 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata></metadata>
+<defs>
+<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
+<font-face units-per-em="1200" ascent="960" descent="-240" />
+<missing-glyph horiz-adv-x="500" />
+<glyph horiz-adv-x="0" />
+<glyph horiz-adv-x="400" />
+<glyph unicode=" " />
+<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
+<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xa0;" />
+<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
+<glyph unicode="&#x2000;" horiz-adv-x="650" />
+<glyph unicode="&#x2001;" horiz-adv-x="1300" />
+<glyph unicode="&#x2002;" horiz-adv-x="650" />
+<glyph unicode="&#x2003;" horiz-adv-x="1300" />
+<glyph unicode="&#x2004;" horiz-adv-x="433" />
+<glyph unicode="&#x2005;" horiz-adv-x="325" />
+<glyph unicode="&#x2006;" horiz-adv-x="216" />
+<glyph unicode="&#x2007;" horiz-adv-x="216" />
+<glyph unicode="&#x2008;" horiz-adv-x="162" />
+<glyph unicode="&#x2009;" horiz-adv-x="260" />
+<glyph unicode="&#x200a;" horiz-adv-x="72" />
+<glyph unicode="&#x202f;" horiz-adv-x="260" />
+<glyph unicode="&#x205f;" horiz-adv-x="325" />
+<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
+<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
+<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
+<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
+<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
+<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
+<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
+<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
+<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
+<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
+<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
+<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
+<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
+<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
+<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
+<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
+<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
+<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
+<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
+<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
+<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
+<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
+<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
+<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
+<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
+<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
+<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
+<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
+<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
+<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
+<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
+<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
+<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
+<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
+<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
+<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
+<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
+<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
+<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
+<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
+<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
+<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
+<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
+<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
+<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
+<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
+<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
+<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
+<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
+<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
+<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
+<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
+<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
+<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
+<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
+<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
+<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
+<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
+<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
+<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
+<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
+<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
+<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
+<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
+<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
+<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
+<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
+<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
+<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
+<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
+<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
+<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
+<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
+<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
+<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
+<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
+<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
+<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
+<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
+<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
+<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
+<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
+<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
+<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
+<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
+<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
+<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
+<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
+<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
+<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
+<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
+<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
+<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
+<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
+<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
+<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
+<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
+<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
+<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
+<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
+<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
+<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
+<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
+<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
+<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
+<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
+<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
+<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
+<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
+<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
+<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
+<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
+<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
+<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
+<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
+<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
+<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
+<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
+<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
+<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
+<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
+<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
+<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
+<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
+<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
+<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
+<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
+<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
+<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
+<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
+<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
+<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
+<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
+<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
+<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
+<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
+<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
+<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
+<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
+<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
+<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
+<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
+<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
+<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
+<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
+<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
+<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
+<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
+<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
+<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
+<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
+<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
+<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
+<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
+<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
+<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
+<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
+<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
+<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
+<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
+<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
+<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
+<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
+<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
+<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
+<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
+<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
+<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
+<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
+<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
+<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
+<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
+<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
+<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
+<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
+<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
+<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
+<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
+<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
+<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
+<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
+<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
+<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
+<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
+<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
+<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
+<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
+<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
+<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
+<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
+<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
+<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
+<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
+<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
+<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
+<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
+<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
+<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
+<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
+<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
+<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
+<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
+<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
+<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
+<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
+<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
+<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
+<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
+<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
+<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
+<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
+<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
+<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
+<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
+<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
+<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
+<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
+<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
+</font>
+</defs></svg> 

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-reboot.css

@@ -0,0 +1,330 @@
+/*!
+ * 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.min.css

@@ -0,0 +1,8 @@
+/*!
+ * 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)
+ */*,::after,::before{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:.5rem}p{margin-top:0;margin-bottom:1rem}abbr[data-original-title],abbr[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}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{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]):focus,a:not([href]):not([tabindex]):hover{color:inherit;text-decoration:none}a:not([href]):not([tabindex]):focus{outline:0}code,kbd,pre,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:.75rem;padding-bottom:.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}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,input{overflow:visible}button,select{text-transform:none}[type=reset],[type=submit],button,html [type=button]{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{padding:0;border-style:none}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}input[type=date],input[type=datetime-local],input[type=month],input[type=time]{-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.min.css.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()