sinh effective mass implemented

.github/workflows/flake8.yml

@@ -16,6 +16,6 @@ jobs:
       - name: flake8 Lint
         uses: py-actions/flake8@v1
         with:
-          ignore: "E501"
+          ignore: "E501,E722"
           exclude: "__init__.py, input/__init__.py"
           path: "pyerrors"

pyerrors/correlators.py

@@ -252,14 +252,21 @@ class Corr:
 
             return np.log(Corr(newcontent, padding_back=1))
 
-        elif variant == 'periodic':
+        elif variant in ['periodic', 'cosh', 'sinh']:
+            if variant in ['periodic', 'cosh']:
+                func = anp.cosh
+            else:
+                func = anp.sinh
+
+            def root_function(x, d):
+                return func(x * (t - self.T / 2)) / func(x * (t + 1 - self.T / 2)) - d
+
             newcontent = []
             for t in range(self.T - 1):
                 if (self.content[t] is None) or (self.content[t + 1] is None):
                     newcontent.append(None)
                 else:
-                    func = lambda x, d: anp.cosh(x * (t - self.T / 2)) / anp.cosh(x * (t + 1 - self.T / 2)) - d
+                    newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess)))
-                    newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], func, guess=guess)))
             if(all([x is None for x in newcontent])):
                 raise Exception('m_eff is undefined at all timeslices')
 

pyerrors/input/bdio.py

@@ -65,7 +65,7 @@ def read_ADerrors(file_path, bdio_path='./libbdio.so', **kwargs):
 
     print('Reading of bdio file started')
     while 1 > 0:
-        record = bdio_seek_record(fbdio)
+        bdio_seek_record(fbdio)
         ruinfo = bdio_get_ruinfo(fbdio)
 
         if ruinfo == 7:
@@ -75,13 +75,13 @@ def read_ADerrors(file_path, bdio_path='./libbdio.so', **kwargs):
         if ruinfo < 0:
             # EOF reached
             break
-        rlen = bdio_get_rlen(fbdio)
+        bdio_get_rlen(fbdio)
 
         def read_c_double():
             d_buf = ctypes.c_double
             pd_buf = d_buf()
             ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-            iread = bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
+            bdio_read_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
             return pd_buf.value
 
         mean = read_c_double()
@@ -91,7 +91,7 @@ def read_ADerrors(file_path, bdio_path='./libbdio.so', **kwargs):
             d_buf = ctypes.c_size_t
             pd_buf = d_buf()
             ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-            iread = bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+            bdio_read_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
             return pd_buf.value
 
         neid = read_c_size_t()
@@ -137,7 +137,7 @@ def read_ADerrors(file_path, bdio_path='./libbdio.so', **kwargs):
         d_buf = ctypes.c_double * np.sum(ndata)
         pd_buf = d_buf()
         ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-        iread = bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio))
+        bdio_read_f64(ppd_buf, ctypes.c_size_t(8 * np.sum(ndata)), ctypes.c_void_p(fbdio))
         delta = pd_buf[:]
 
         samples = np.split(np.asarray(delta) + mean, np.cumsum([a for su in vrep for a in su])[:-1])
@@ -212,8 +212,7 @@ def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
     fbdio = bdio_open(ctypes.c_char_p(b_path), ctypes.c_char_p(b_write), b_form)
 
     for obs in obs_list:
-
+        # mean = obs.value
-        mean = obs.value
         neid = len(obs.e_names)
         vrep = [[obs.shape[o] for o in sl] for sl in list(obs.e_content.values())]
         vrep_write = [item for sublist in vrep for item in sublist]
@@ -251,12 +250,12 @@ def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
         def write_c_double(double):
             pd_buf = ctypes.c_double(double)
             ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-            iwrite = bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
+            bdio_write_f64(ppd_buf, ctypes.c_size_t(8), ctypes.c_void_p(fbdio))
 
         def write_c_size_t(int32):
             pd_buf = ctypes.c_size_t(int32)
             ppd_buf = ctypes.c_void_p(ctypes.addressof(pd_buf))
-            iwrite = bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
+            bdio_write_int32(ppd_buf, ctypes.c_size_t(4), ctypes.c_void_p(fbdio))
 
         write_c_double(obs.value)
         write_c_size_t(neid)
@@ -365,7 +364,7 @@ def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
 
     print('Reading of bdio file started')
     while 1 > 0:
-        record = bdio_seek_record(fbdio)
+        bdio_seek_record(fbdio)
         ruinfo = bdio_get_ruinfo(fbdio)
         if ruinfo < 0:
             # EOF reached
@@ -530,7 +529,7 @@ def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
     corr_type = []  # Contains correlator data type (important for reading out numerical data)
     corr_props = []  # Contains propagator types (Component of corr_kappa)
     d0 = 0  # tvals
-    d1 = 0  # nnoise
+    # d1 = 0  # nnoise
     prop_kappa = []  # Contains propagator kappas (Component of corr_kappa)
     # Check noise type for multiple replica?
     cnfg_no = -1
@@ -541,7 +540,7 @@ def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
 
     print('Reading of bdio file started')
     while 1 > 0:
-        record = bdio_seek_record(fbdio)
+        bdio_seek_record(fbdio)
         ruinfo = bdio_get_ruinfo(fbdio)
         if ruinfo < 0:
             # EOF reached
@@ -613,7 +612,6 @@ def read_dSdm(file_path, bdio_path='./libbdio.so', **kwargs):
     print('Number of configurations: ', cnfg_no + 1)
 
     corr_kappa = []  # Contains kappa values for both propagators of given correlation function
-    corr_source = []
     for item in corr_props:
         corr_kappa.append(float(prop_kappa[int(item)]))
 

pyerrors/linalg.py

@@ -12,11 +12,14 @@ from functools import partial
 from autograd.extend import defvjp
 
 _dot = partial(anp.einsum, '...ij,...jk->...ik')
+
+
 # batched diag
-_diag = lambda a: anp.eye(a.shape[-1]) * a
+def _diag(a):
+    return anp.eye(a.shape[-1]) * a
+
+
 # batched diagonal, similar to matrix_diag in tensorflow
-
-
 def _matrix_diag(a):
     reps = anp.array(a.shape)
     reps[:-1] = 1