Moved files and reformatted some

doc/conf.py

@@ -15,10 +15,9 @@
 
 import sys
 import os
-import shlex
 
 sys.path.insert(0, os.path.abspath('..'))
-import mdevaluate
+from src import mdevaluate
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the

examples/plot_chi4.py

@@ -21,13 +21,13 @@ calling :func:`~mdevaluate.utils.runningmean` as shown below.
 
 from functools import partial
 import matplotlib.pyplot as plt
-import mdevaluate as md
+from src import mdevaluate as md
 import tudplot
 
 OW = md.open('/data/niels/sim/water/bulk/260K', trajectory='out/*.xtc').subset(atom_name='OW')
 
-t, Fqt = md.correlation.shifted_correlation(
-    partial(md.correlation.isf, q=22.7),
+t, Fqt = src.mdevaluate.correlation.shifted_correlation(
+    partial(src.mdevaluate.correlation.isf, q=22.7),
     OW,
     average=False,
     window=0.2,

examples/plot_isf.py

@@ -8,23 +8,23 @@ Additionally a KWW function is fitted to the results.
 from functools import partial
 import matplotlib.pyplot as plt
 from scipy.optimize import curve_fit
-import mdevaluate as md
+from src import mdevaluate as md
 import tudplot
 
 OW = md.open('/data/niels/sim/water/bulk/260K', trajectory='out/*.xtc').subset(atom_name='OW')
-t, S = md.correlation.shifted_correlation(
-    partial(md.correlation.isf, q=22.7),
+t, S = src.mdevaluate.correlation.shifted_correlation(
+    partial(src.mdevaluate.correlation.isf, q=22.7),
     OW,
     average=True
 )
 # Only include data-points of the alpha-relaxation for the fit
 mask = t > 3e-1
-fit, cov = curve_fit(md.functions.kww, t[mask], S[mask])
-tau = md.functions.kww_1e(*fit)
+fit, cov = curve_fit(src.mdevaluate.functions.kww, t[mask], S[mask])
+tau = src.mdevaluate.functions.kww_1e(*fit)
 
 tudplot.activate()
 plt.figure()
 plt.plot(t, S, '.', label='ISF of Bulk Water')
-plt.plot(t, md.functions.kww(t, *fit), '-', label=r'KWW, $\tau$={:.2f}ps'.format(tau))
+plt.plot(t, src.mdevaluate.functions.kww(t, *fit), '-', label=r'KWW, $\tau$={:.2f}ps'.format(tau))
 plt.xscale('log')
 plt.legend()

examples/plot_spatialisf.py

@@ -8,7 +8,7 @@ In this case the bins describe the shortest distance of an oxygen atom to any wa
 
 import numpy as np
 import matplotlib.pyplot as plt
-import mdevaluate as md
+from src import mdevaluate as md
 import tudplot
 from scipy import spatial
 from scipy.optimize import curve_fit
@@ -73,7 +73,7 @@ wall_atoms = wall_atoms[dist < 0.35]
 SW = traj.subset(indices = wall_atoms)
 
 from functools import partial
-func = partial(md.correlation.isf, q=22.7)
+func = partial(src.mdevaluate.correlation.isf, q=22.7)
 
 #selector function to choose liquid oxygens with a certain distance to wall atoms
 def selector_func(coords, lindices, windices, dmin, dmax):
@@ -93,9 +93,9 @@ for i in range(len(bins)-1):
     selector = partial(selector_func,lindices=LO.atom_subset.indices[0],
                       windices=SW.atom_subset.indices[0],dmin=bins[i],
                       dmax = bins[i+1])
-    t, S[i] = md.correlation.shifted_correlation(
+    t, S[i] = src.mdevaluate.correlation.shifted_correlation(
             func, traj,segments=50, skip=0.1,average=True,
-            correlation=md.correlation.subensemble_correlation(selector),
+            correlation=src.mdevaluate.correlation.subensemble_correlation(selector),
             description=str(bins[i])+','+str(bins[i+1]))
     
 taus = np.zeros(len(S))
@@ -105,10 +105,10 @@ for i,s in enumerate(S):
     pl = plt.plot(t, s, '.', label='d = ' + str(binpos[i]) + ' nm')
     #only includes the relevant data for 1/e fitting
     mask = s < 0.6
-    fit, cov = curve_fit(md.functions.kww, t[mask], s[mask],
+    fit, cov = curve_fit(src.mdevaluate.functions.kww, t[mask], s[mask],
                          p0=[1.0,t[t>1/np.e][-1],0.5])
-    taus[i] = md.functions.kww_1e(*fit)
-    plt.plot(t, md.functions.kww(t, *fit), c=pl[0].get_color())
+    taus[i] = src.mdevaluate.functions.kww_1e(*fit)
+    plt.plot(t, src.mdevaluate.functions.kww(t, *fit), c=pl[0].get_color())
 plt.xscale('log')
 plt.legend()
 #plt.show()

examples/plot_temperature.py

@@ -6,7 +6,7 @@ This example reads an Gromacs energy file and plots the evolultion and mean of t
 """
 
 from matplotlib import pyplot as plt
-import mdevaluate as md
+from src import mdevaluate as md
 import tudplot
 
 tudplot.activate()

src/mdevaluate/correlation.py

@@ -186,9 +186,7 @@ def shifted_correlation(
             correlation = []
             for index in indices:
                 correlation.append(
-                    get_correlation(
-                        frames, start_frame, index, shifted_idx
-                    )
+                    get_correlation(frames, start_frame, index, shifted_idx)
                 )
             return correlation
 
@@ -213,9 +211,13 @@ def shifted_correlation(
     if nodes == 1:
         result = np.array(
             [
-                apply_selector(start_frame, frames=frames, idx=idx,
+                apply_selector(
+                    start_frame,
+                    frames=frames,
+                    idx=idx,
                     selector=selector,
-                               multi_selector=multi_selector)
+                    multi_selector=multi_selector,
+                )
                 for start_frame in start_frames
             ]
         )
@@ -227,11 +229,14 @@ def shifted_correlation(
         try:
             result = np.array(
                 pool.map(
-                    partial(apply_selector,
+                    partial(
+                        apply_selector,
                         frames=frames,
                         idx=idx,
                         selector=selector,
-                            multi_selector=multi_selector), start_frames
+                        multi_selector=multi_selector,
+                    ),
+                    start_frames,
                 )
             )
         finally:

test/test_atoms.py

@@ -2,6 +2,6 @@ from mdevaluate import atoms
 
 
 def test_compare_regex():
-    assert atoms.compare_regex(['OW', ], 'O')[0] == False
-    assert atoms.compare_regex(['WO', ], 'O')[0] == False
-    assert atoms.compare_regex(['O', ], 'O')[0] == True
+    assert not atoms.compare_regex(["OW"], "O")[0]
+    assert not atoms.compare_regex(["WO"], "O")[0]
+    assert atoms.compare_regex(["O"], "O")[0]

test/test_checksum.py

@@ -5,11 +5,11 @@ import numpy as np
 
 def test_checksum():
     salt = checksum.SALT
-    checksum.SALT = ''
+    checksum.SALT = ""
     assert checksum.checksum(1) == 304942582444936629325699363757435820077590259883
-    assert checksum.checksum('42') == checksum.checksum(42)
+    assert checksum.checksum("42") == checksum.checksum(42)
     cs1 = checksum.checksum(999)
-    checksum.SALT = '999'
+    checksum.SALT = "999"
     assert cs1 != checksum.checksum(999)
 
     a = np.array([1, 2, 3])
@@ -19,7 +19,6 @@ def test_checksum():
 
 
 def test_version():
-
     @checksum.version(1)
     def f1():
         pass

test/test_coordinates.py

@@ -7,7 +7,7 @@ from mdevaluate import coordinates
 
 @pytest.fixture
 def trajectory(request):
-    return mdevaluate.open(os.path.join(os.path.dirname(__file__), 'data/water'))
+    return mdevaluate.open(os.path.join(os.path.dirname(__file__), "data/water"))
 
 
 def test_coordinates_getitem(trajectory):

test/test_pbc.py

@@ -9,6 +9,6 @@ def test_pbc_diff():
     y = np.random.rand(10, 3)
     box = np.ones((3,))
 
-    assert (pbc.pbc_diff(x, x, box) == approx(0))
-    dxy = (pbc.pbc_diff(x, y, box)**2).sum(axis=1)**0.5
+    assert pbc.pbc_diff(x, x, box) == approx(0)
+    dxy = (pbc.pbc_diff(x, y, box) ** 2).sum(axis=1) ** 0.5
     assert (dxy <= 0.75**0.5).all()

test/test_utils.py

@@ -8,7 +8,7 @@ from mdevaluate import utils
 @pytest.fixture
 def logdata(request):
     xdata = np.logspace(-1, 3, 50)
-    ydata = np.exp(- (xdata)**0.7)
+    ydata = np.exp(-((xdata) ** 0.7))
     return xdata, ydata
 
 
@@ -18,17 +18,17 @@ def test_filon_fourier_transformation(logdata):
     xdata_zero = copy(xdata)
     xdata_zero[0] = 0
     _, filon = utils.filon_fourier_transformation(xdata_zero, ydata)
-    assert not np.isnan(filon).any(), 'There are NaN values in the filon result!'
+    assert not np.isnan(filon).any(), "There are NaN values in the filon result!"
 
     freqs = np.logspace(-4, 1)
     filon_freqs, filon_imag = utils.filon_fourier_transformation(
-        xdata, xdata, frequencies=freqs, derivative='linear', imag=True
+        xdata, xdata, frequencies=freqs, derivative="linear", imag=True
     )
 
     assert (freqs == filon_freqs).all()
 
     freqs, filon_real = utils.filon_fourier_transformation(
-        xdata, xdata, frequencies=freqs, derivative='linear', imag=False
+        xdata, xdata, frequencies=freqs, derivative="linear", imag=False
     )
     assert np.isclose(filon_imag.real, filon_real).all()