diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 5e215110..3a8563f4 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -13,7 +13,7 @@ jobs:
   pytest:
     runs-on: ${{ matrix.os }}
     strategy:
-      fail-fast: true
+      fail-fast: false
       matrix:
         os: [ubuntu-latest]
         python-version: ["3.7", "3.8", "3.9", "3.10"]
diff --git a/pyerrors/obs.py b/pyerrors/obs.py
index c8373d5e..9c99f617 100644
--- a/pyerrors/obs.py
+++ b/pyerrors/obs.py
@@ -267,18 +267,6 @@ class Obs:
                 self.e_windowsize[e_name] = 0
                 continue
 
-            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
-            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
-            # Make sure no entry of tauint is smaller than 0.5
-            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
-            # hep-lat/0306017 eq. (42)
-            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) + 0.5 - self.e_n_tauint[e_name]) / e_N)
-            self.e_n_dtauint[e_name][0] = 0.0
-
-            def _compute_drho(i):
-                tmp = self.e_rho[e_name][i + 1:w_max] + np.concatenate([self.e_rho[e_name][i - 1::-1], self.e_rho[e_name][1:w_max - 2 * i]]) - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i]
-                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
-
             gaps = []
             for r_name in e_content[e_name]:
                 if isinstance(self.idl[r_name], range):
@@ -291,6 +279,18 @@ class Obs:
             else:
                 gapsize = gaps[0]
 
+            self.e_rho[e_name] = e_gamma[e_name][:w_max] / e_gamma[e_name][0]
+            self.e_n_tauint[e_name] = np.cumsum(np.concatenate(([0.5], self.e_rho[e_name][1:])))
+            # Make sure no entry of tauint is smaller than 0.5
+            self.e_n_tauint[e_name][self.e_n_tauint[e_name] <= 0.5] = 0.5 + np.finfo(np.float64).eps
+            # hep-lat/0306017 eq. (42)
+            self.e_n_dtauint[e_name] = self.e_n_tauint[e_name] * 2 * np.sqrt(np.abs(np.arange(w_max) / gapsize + 0.5 - self.e_n_tauint[e_name]) / e_N)
+            self.e_n_dtauint[e_name][0] = 0.0
+
+            def _compute_drho(i):
+                tmp = self.e_rho[e_name][i + 1:w_max] + np.concatenate([self.e_rho[e_name][i - 1::-1], self.e_rho[e_name][1:w_max - 2 * i]]) - 2 * self.e_rho[e_name][i] * self.e_rho[e_name][1:w_max - i]
+                self.e_drho[e_name][i] = np.sqrt(np.sum(tmp ** 2) / e_N)
+
             _compute_drho(gapsize)
             if self.tau_exp[e_name] > 0:
                 texp = self.tau_exp[e_name]
diff --git a/setup.py b/setup.py
index b68e0408..ad1aa746 100644
--- a/setup.py
+++ b/setup.py
@@ -24,8 +24,8 @@ setup(name='pyerrors',
       author_email='fabian.joswig@ed.ac.uk',
       license="MIT",
       packages=find_packages(),
-      python_requires='>=3.6.0',
-      install_requires=['numpy>=1.19', 'autograd>=1.5', 'numdifftools', 'matplotlib>=3.3', 'scipy>=1.5', 'iminuit>=2', 'h5py>=3', 'lxml>=4', 'python-rapidjson>=1', 'pandas>=1.1', 'pysqlite3>=0.4'],
+      python_requires='>=3.7.0',
+      install_requires=['numpy>=1.19', 'autograd>=1.5', 'numdifftools', 'matplotlib>=3.5', 'scipy>=1.7', 'iminuit>=2.17', 'h5py>=3.7', 'lxml>=4.9', 'python-rapidjson>=1.9', 'pandas>=1.1'],
       classifiers=[
           'Development Status :: 5 - Production/Stable',
           'Intended Audience :: Science/Research',
diff --git a/tests/obs_test.py b/tests/obs_test.py
index 29ee67ba..5c6565fe 100644
--- a/tests/obs_test.py
+++ b/tests/obs_test.py
@@ -669,6 +669,23 @@ def test_gamma_method_irregular():
     assert np.isclose(tau_a, tau_b)
 
 
+def test_irregular_gapped_dtauint():
+    my_idl = list(range(0, 5010, 10))
+    my_idl.remove(400)
+    my_idl2 = list(range(0, 501, 1))
+    my_idl2.remove(40)
+
+    my_data = np.random.normal(1.1, 0.2, 500)
+    obs = pe.Obs([my_data], ["B1"], idl=[my_idl])
+    obs.gamma_method()
+
+    obs2 = pe.Obs([my_data], ["B2"], idl=[my_idl2])
+    obs2.gamma_method()
+
+    assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
+    assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
+
+
 def test_covariance_is_variance():
     value = np.random.normal(5, 10)
     dvalue = np.abs(np.random.normal(0, 1))