Moved files and reformatted some

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()