Merge branch 'develop' into documentation

CHANGELOG.md

@@ -2,10 +2,13 @@
 
 All notable changes to this project will be documented in this file.
 
-## [2.x.x] - 2022-xx-xx
+## [2.2.0] - 2022-xx-xx
 ### Added
+- New submodule `input.pandas` added which adds the possibility to read and write pandas DataFrames containing `Obs` or `Corr` objects to csv files or SQLite databases.
+- `hash` method for `Obs` objects added.
 - `Obs.reweight` method added in analogy to `Corr.reweight` which allows for a more convenient reweighting of individual observables.
 - `Corr.show` now has the additional argument `title` which allows to add a title to the figure. Figures are now saved with `bbox_inches='tight'`.
+- Function for the extraction of the gradient flow coupling added (see 1607.06423 for details).
 
 ### Fixed
 - `Corr.m_eff` can now deal with correlator entries which are exactly zero.

examples/01_basic_example.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Import pyerrors, as well as autograd wrapped numpy and matplotlib."
+    "Import numpy, matplotlib and pyerrors."
    ]
   },
   {
@@ -91,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper resampling is performed in the background via automatic differentiation."
+    "`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper error propagation is performed in the background via automatic differentiation."
    ]
   },
   {
@@ -179,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This figure shows the windowsize dependence of the integrated autocorrelation time. The red vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
+    "This figure shows the windowsize dependence of the integrated autocorrelation time. The vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
     "Choosing a larger windowsize would not significantly alter $\\tau_\\text{int}$, so everything seems to be correct here."
    ]
   },
@@ -283,7 +283,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function to take into account long range autocorrelations"
+    "We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function via the parameter `tau_exp` to take into account long range autocorrelations"
    ]
   },
   {

examples/02_correlators.ipynb

@@ -242,7 +242,7 @@
    "id": "4a9d13b2",
    "metadata": {},
    "source": [
-    "We can also use the priodicity of the lattice in order to obtain the cosh effective mass"
+    "We can also use the periodicity of the lattice in order to obtain the cosh effective mass"
    ]
   },
   {
@@ -387,8 +387,7 @@
    "source": [
     "## Missing Values \n",
     "\n",
-    "Apart from the build-in functions, there is another reason, why one should use a **Corr** instead of a list of **Obs**. \n",
-    "Missing values are handled for you. \n",
+    "Apart from the build-in functions, another benefit of using ``Corr`` objects is that they can handle missing values. \n",
     "We will create a second correlator with missing values. "
    ]
   },
@@ -459,18 +458,6 @@
     "The important thing is that, whatever you do, correlators keep their length **T**. So there will never be confusion about how you count timeslices. You can also take a plateau or perform a fit, even though some values might be missing."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f3c4609c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert first_derivative.T == my_correlator.T == len(first_derivative.content) == len(my_correlator.content)\n",
-    "assert first_derivative.content[0] is None\n",
-    "assert first_derivative.content[-1] is None"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "7fcbcac4",

pyerrors/input/hadrons.py

@@ -163,7 +163,7 @@ def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None
                 Nt = h5file["DistillationContraction/Metadata"].attrs.get("Nt")[0]
 
                 identifier = []
-                for in_file in range(4):
+                for in_file in range(len(h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.keys()) - 1):
                     encoded_info = h5file["DistillationContraction/Metadata/DmfInputFiles"].attrs.get("DmfInputFiles_" + str(in_file))
                     full_info = encoded_info[0].decode().split("/")[-1].replace(".h5", "").split("_")
                     my_tuple = (full_info[0], full_info[1][1:], full_info[2], full_info[3])
@@ -174,8 +174,8 @@ def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None
             for diagram in diagrams:
                 real_data = np.zeros(Nt)
                 for x0 in range(Nt):
-                    raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)]
-                    real_data += np.roll(raw_data[:]["re"].astype(np.double), -x0)
+                    raw_data = h5file["DistillationContraction/Correlators/" + diagram + "/" + str(x0)][:]["re"].astype(np.double)
+                    real_data += np.roll(raw_data, -x0)
                 real_data /= Nt
 
                 corr_data[diagram].append(real_data)

setup.py

@@ -25,7 +25,7 @@ setup(name='pyerrors',
       license="MIT",
       packages=find_packages(),
       python_requires='>=3.6.0',
-      install_requires=['numpy>=1.16', 'autograd>=1.4', 'numdifftools', 'matplotlib>=3.3', 'scipy>=1', 'iminuit>=2', 'h5py>=3', 'lxml>=4', 'python-rapidjson>=1', 'pandas>=1.1', 'pysqlite3>=0.4'],
+      install_requires=['numpy>=1.19', 'autograd>=1.4', 'numdifftools', 'matplotlib>=3.3', 'scipy>=1.5', 'iminuit>=2', 'h5py>=3', 'lxml>=4', 'python-rapidjson>=1', 'pandas>=1.1', 'pysqlite3>=0.4'],
       classifiers=[
           'Development Status :: 5 - Production/Stable',
           'Intended Audience :: Science/Research',