Merge branch 'develop' into feature/v2.0

README.md

@@ -11,7 +11,7 @@ It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/he
 * implementation of the matrix-pencil-method [IEEE Trans. Acoust. 38, 814-824 (1990)](https://ieeexplore.ieee.org/document/56027) for the extraction of energy levels, especially suited for noisy data and excited states
 
 There exist similar implementations of gamma method error analysis suites in
-- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors).
+- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
 - [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
 - [Python 3](https://github.com/mbruno46/pyobs)
 
@@ -34,15 +34,15 @@ The basic objects of a pyerrors analysis are instances of the class `Obs`. They
 import numpy as np
 import pyerrors as pe
 
-observable1 = pe.Obs([samples1], ['ensemble1'])
-observable1.gamma_method()
-observable1.print()
+obs1 = pe.Obs([samples1], ['ensemble1'])
+obs1.gamma_method()
+obs1.print()
 ```
 Often one is interested in secondary observables which can be arbitrary functions of primary observables. `pyerrors` overloads most basic math operations and numpy functions such that the user can work with `Obs` objects as if they were floats
 ```python
-observable3 = 12.0 / observable1 ** 2 - np.exp(-1.0 / observable2)
-observable3.gamma_method()
-observable3.print()
+obs3 = 12.0 / obs1 ** 2 - np.exp(-1.0 / obs2)
+obs3.gamma_method()
+obs3.print()
 ```
 
 More detailed examples can be found in  the `/examples` folder:

examples/05_matrix_operations.ipynb

@@ -6,8 +6,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
     "import pyerrors as pe\n",
     "import numpy as np\n",
     "import scipy"
@@ -169,7 +167,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Obs[7.2(1.7)] Obs[-1.00(47)]]\n"
+      "[Obs[7.2(1.7)] Obs[-1.00(45)]]\n"
      ]
     }
    ],
@@ -229,7 +227,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cond    \t Obs[6.23(59)]\n",
+      "cond    \t Obs[6.23(58)]\n",
       "expm_cond    \t Obs[4.45(19)]\n"
      ]
     }
@@ -261,7 +259,7 @@
      "output_type": "stream",
      "text": [
       "[[Obs[2.025(49)] Obs[0.0]]\n",
-      " [Obs[-0.494(50)] Obs[0.870(29)]]]\n"
+      " [Obs[-0.494(51)] Obs[0.870(29)]]]\n"
      ]
     }
    ],
@@ -349,20 +347,20 @@
      "output_type": "stream",
      "text": [
       "orth\n",
-      "[[Obs[-0.9592(75)] Obs[0.283(25)]]\n",
-      " [Obs[0.283(25)] Obs[0.9592(75)]]]\n",
+      "[[Obs[-0.9592(76)] Obs[0.283(26)]]\n",
+      " [Obs[0.283(26)] Obs[0.9592(76)]]]\n",
       "expm\n",
-      "[[Obs[75(15)] Obs[-21.4(4.2)]]\n",
-      " [Obs[-21.4(4.2)] Obs[8.3(1.4)]]]\n",
+      "[[Obs[75(15)] Obs[-21.4(4.1)]]\n",
+      " [Obs[-21.4(4.1)] Obs[8.3(1.4)]]]\n",
       "logm\n",
       "[[Obs[1.334(57)] Obs[-0.496(61)]]\n",
       " [Obs[-0.496(61)] Obs[-0.203(50)]]]\n",
       "sinhm\n",
       "[[Obs[37.3(7.4)] Obs[-10.8(2.1)]]\n",
-      " [Obs[-10.8(2.1)] Obs[3.94(69)]]]\n",
+      " [Obs[-10.8(2.1)] Obs[3.94(68)]]]\n",
       "sqrtm\n",
       "[[Obs[1.996(51)] Obs[-0.341(37)]]\n",
-      " [Obs[-0.341(37)] Obs[0.940(15)]]]\n"
+      " [Obs[-0.341(37)] Obs[0.940(14)]]]\n"
      ]
     }
    ],
@@ -397,8 +395,8 @@
       "Eigenvalues:\n",
       "[Obs[0.705(57)] Obs[4.39(19)]]\n",
       "Eigenvectors:\n",
-      "[[Obs[-0.283(25)] Obs[-0.9592(75)]]\n",
-      " [Obs[-0.9592(75)] Obs[0.283(25)]]]\n"
+      "[[Obs[-0.283(26)] Obs[-0.9592(76)]]\n",
+      " [Obs[-0.9592(76)] Obs[0.283(26)]]]\n"
      ]
     }
    ],
@@ -467,7 +465,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.11"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

examples/base_style.mplstyle

@@ -0,0 +1,30 @@
+# Base mplstyle, Fabian Joswig, 2021
+font.size:    12
+
+lines.marker : o
+lines.markersize : 6
+lines.linewidth : 1.0
+lines.linestyle : none
+
+font.family: serif
+
+axes.labelsize : 14
+
+xtick.labelsize : 12
+
+ytick.labelsize : 12
+
+figure.dpi : 100
+figure.figsize : 6.4, 3.9555    # figure size in inches, golden ratio
+figure.subplot.left :   0.125  # the left side of the subplots of the figure
+figure.subplot.right :  0.95    # the right side of the subplots of the figure
+figure.subplot.bottom : 0.125   # the bottom of the subplots of the figure
+figure.subplot.top :    0.95   # the top of the subplots of the figure
+
+errorbar.capsize : 3.4641016 # sqrt(markersize * 2)
+
+legend.fontsize : 12
+legend.frameon : False
+
+savefig.transparent : True
+savefig.dpi : 300

pyerrors/correlators.py

@@ -354,7 +354,7 @@ class Corr:
         return
 
     # Plotting routine for correlator
-    def show(self, x_range=None, comp=None, logscale=False, plateau=None, fit_res=None, save=None, ylabel=None):
+    def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=None, fit_res=None, save=None, ylabel=None):
         """Plots the correlator, uses tag as label if available.
 
         Parameters
@@ -367,7 +367,7 @@ class Corr:
         if self.N!=1:
             raise Exception("Correlator must be projected before plotting")
         if  x_range is None:
-            x_range=[0, self.T]
+            x_range = [0, self.T]
 
         fig = plt.figure()
         ax1 = fig.add_subplot(111)
@@ -378,12 +378,15 @@ class Corr:
             ax1.set_yscale('log')
         else:
             # we generate ylim instead of using autoscaling.
-            try:
-                y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-                y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
-                ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
-            except:
-                pass
+            if y_range is None:
+                try:
+                    y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+                    y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
+                    ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
+                except:
+                    pass
+            else:
+                ax1.set_ylim(y_range)
         if comp:
             if isinstance(comp, Corr) or isinstance(comp, list):
                 for corr in comp if isinstance(comp, list) else [comp]:

pyerrors/fits.py

@@ -575,7 +575,7 @@ def residual_plot(x, y, func, fit_res):
     gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
     ax0 = plt.subplot(gs[0])
     ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
-    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10)
+    ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
     ax0.set_xticklabels([])
     ax0.set_xlim([xstart, xstop])
     ax0.set_xticklabels([])