diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..3e99ede
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "."
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}
\ No newline at end of file
diff --git a/src/pylibsparseir/core.py b/src/pylibsparseir/core.py
index e18e72f..99cd890 100644
--- a/src/pylibsparseir/core.py
+++ b/src/pylibsparseir/core.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 from .ctypes_wrapper import spir_kernel, spir_sve_result, spir_basis, spir_funcs, spir_sampling
-from .constants import COMPUTATION_SUCCESS, ORDER_ROW_MAJOR, SPIR_TWORK_FLOAT64, SPIR_TWORK_FLOAT64X2
+from pylibsparseir.constants import COMPUTATION_SUCCESS, ORDER_ROW_MAJOR, SPIR_TWORK_FLOAT64, SPIR_TWORK_FLOAT64X2
 
 def _find_library():
     """Find the SparseIR shared library."""
diff --git a/src/pylibsparseir/__init__.py b/src/sparse_ir/__init__.py
similarity index 100%
rename from src/pylibsparseir/__init__.py
rename to src/sparse_ir/__init__.py
diff --git a/src/pylibsparseir/abstract.py b/src/sparse_ir/abstract.py
similarity index 100%
rename from src/pylibsparseir/abstract.py
rename to src/sparse_ir/abstract.py
diff --git a/src/pylibsparseir/augment.py b/src/sparse_ir/augment.py
similarity index 100%
rename from src/pylibsparseir/augment.py
rename to src/sparse_ir/augment.py
diff --git a/src/pylibsparseir/basis.py b/src/sparse_ir/basis.py
similarity index 93%
rename from src/pylibsparseir/basis.py
rename to src/sparse_ir/basis.py
index 8ab93e1..35bcaa2 100644
--- a/src/pylibsparseir/basis.py
+++ b/src/sparse_ir/basis.py
@@ -3,8 +3,8 @@
 """
 from typing import Optional
 import numpy as np
-from .core import basis_new, basis_get_size, basis_get_svals, basis_get_u, basis_get_v, basis_get_uhat, basis_get_default_tau_sampling_points, basis_get_default_omega_sampling_points, basis_get_default_matsubara_sampling_points
-from .constants import STATISTICS_FERMIONIC, STATISTICS_BOSONIC
+from pylibsparseir.core import basis_new, basis_get_size, basis_get_svals, basis_get_u, basis_get_v, basis_get_uhat, basis_get_default_tau_sampling_points, basis_get_default_omega_sampling_points, basis_get_default_matsubara_sampling_points
+from pylibsparseir.constants import STATISTICS_FERMIONIC, STATISTICS_BOSONIC
 from .kernel import LogisticKernel
 from .abstract import AbstractBasis
 from .sve import SVEResult
@@ -137,7 +137,7 @@ def default_omega_sampling_points(self, npoints=None):
         ndarray
             Default omega sampling points
         """
-        from .core import basis_get_default_omega_sampling_points
+        from pylibsparseir.core import basis_get_default_omega_sampling_points
         return basis_get_default_omega_sampling_points(self._ptr)
 
     def default_matsubara_sampling_points(self, npoints=None, positive_only=False):
diff --git a/src/pylibsparseir/basis_set.py b/src/sparse_ir/basis_set.py
similarity index 100%
rename from src/pylibsparseir/basis_set.py
rename to src/sparse_ir/basis_set.py
diff --git a/src/pylibsparseir/dlr.py b/src/sparse_ir/dlr.py
similarity index 96%
rename from src/pylibsparseir/dlr.py
rename to src/sparse_ir/dlr.py
index f7ff6ee..7ee6659 100644
--- a/src/pylibsparseir/dlr.py
+++ b/src/sparse_ir/dlr.py
@@ -8,9 +8,9 @@
 import ctypes
 import numpy as np
 from .abstract import AbstractBasis
-from .core import basis_get_default_omega_sampling_points
-from .core import _lib, COMPUTATION_SUCCESS
-from .constants import SPIR_ORDER_ROW_MAJOR
+from pylibsparseir.core import basis_get_default_omega_sampling_points
+from pylibsparseir.core import _lib, COMPUTATION_SUCCESS
+from pylibsparseir.constants import SPIR_ORDER_ROW_MAJOR
 
 class DiscreteLehmannRepresentation(AbstractBasis):
     """
diff --git a/src/pylibsparseir/kernel.py b/src/sparse_ir/kernel.py
similarity index 92%
rename from src/pylibsparseir/kernel.py
rename to src/sparse_ir/kernel.py
index dd968c8..de2abf9 100644
--- a/src/pylibsparseir/kernel.py
+++ b/src/sparse_ir/kernel.py
@@ -8,10 +8,10 @@
 from ctypes import c_int, c_double, byref
 import numpy as np
 
-from .core import _lib
-from .core import logistic_kernel_new, reg_bose_kernel_new
-from .constants import COMPUTATION_SUCCESS
-from pylibsparseir.abstract import AbstractKernel
+from pylibsparseir.core import _lib
+from pylibsparseir.core import logistic_kernel_new, reg_bose_kernel_new
+from pylibsparseir.constants import COMPUTATION_SUCCESS
+from .abstract import AbstractKernel
 
 
 def kernel_domain(kernel: AbstractKernel):
diff --git a/src/pylibsparseir/poly.py b/src/sparse_ir/poly.py
similarity index 100%
rename from src/pylibsparseir/poly.py
rename to src/sparse_ir/poly.py
diff --git a/src/pylibsparseir/sampling.py b/src/sparse_ir/sampling.py
similarity index 98%
rename from src/pylibsparseir/sampling.py
rename to src/sparse_ir/sampling.py
index 328aacb..20b54a9 100644
--- a/src/pylibsparseir/sampling.py
+++ b/src/sparse_ir/sampling.py
@@ -4,8 +4,8 @@
 
 import numpy as np
 from ctypes import POINTER, c_double, c_int, byref
-from .core import c_double_complex, tau_sampling_new, matsubara_sampling_new, _lib
-from .constants import COMPUTATION_SUCCESS, SPIR_ORDER_ROW_MAJOR
+from pylibsparseir.core import c_double_complex, tau_sampling_new, matsubara_sampling_new, _lib
+from pylibsparseir.constants import COMPUTATION_SUCCESS, SPIR_ORDER_ROW_MAJOR
 
 
 class TauSampling:
diff --git a/src/pylibsparseir/sve.py b/src/sparse_ir/sve.py
similarity index 91%
rename from src/pylibsparseir/sve.py
rename to src/sparse_ir/sve.py
index 640020d..846aa76 100644
--- a/src/pylibsparseir/sve.py
+++ b/src/sparse_ir/sve.py
@@ -7,8 +7,8 @@
 import ctypes
 from ctypes import c_int, byref
 
-from .core import _lib, sve_result_new, sve_result_get_svals, sve_result_get_size
-from .constants import COMPUTATION_SUCCESS, ORDER_ROW_MAJOR
+from pylibsparseir.core import _lib, sve_result_new, sve_result_get_svals, sve_result_get_size
+from pylibsparseir.constants import COMPUTATION_SUCCESS, ORDER_ROW_MAJOR
 from .abstract import AbstractKernel
 from .kernel import LogisticKernel, RegularizedBoseKernel
 
diff --git a/tests/c_api/dlr_tests.py b/tests/c_api/dlr_tests.py
index eb87db6..6913939 100644
--- a/tests/c_api/dlr_tests.py
+++ b/tests/c_api/dlr_tests.py
@@ -9,7 +9,7 @@
 import numpy as np
 import ctypes
 from ctypes import c_int, c_double, byref, POINTER
-from pylibsparseir.kernel import LogisticKernel, RegularizedBoseKernel
+from sparse_ir.kernel import LogisticKernel, RegularizedBoseKernel
 
 from pylibsparseir.core import (
     _lib,
diff --git a/tests/conftest.py b/tests/conftest.py
index 920744f..8c02210 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -9,7 +9,7 @@
 import numpy as np
 import pylibsparseir
 
-from pylibsparseir.kernel import LogisticKernel, RegularizedBoseKernel
+from sparse_ir.kernel import LogisticKernel, RegularizedBoseKernel
 
 @pytest.fixture(scope="session")
 def sve_logistic():
diff --git a/tests/test_advanced_features.py b/tests/test_advanced_features.py
index 1938517..6ca5d53 100644
--- a/tests/test_advanced_features.py
+++ b/tests/test_advanced_features.py
@@ -7,241 +7,241 @@
 
 import pytest
 import numpy as np
-import pylibsparseir
+import sparse_ir
 from .conftest import BASIS_PARAMS
 
 
 class TestNoiseResilience:
     """Test noise resilience following sparse-ir patterns."""
-    
+
     @pytest.mark.parametrize("statistics,beta,wmax", BASIS_PARAMS[:2])  # Limit to avoid long tests
     def test_tau_noise_resilience(self, statistics, beta, wmax, rng):
         """Test that sampling is resilient to noise in tau domain."""
         eps = 1e-6
-        
+
         try:
-            basis = pylibsparseir.FiniteTempBasis(statistics, beta, wmax, eps)
-            sampling = pylibsparseir.TauSampling(basis)
+            basis = sparse_ir.FiniteTempBasis(statistics, beta, wmax, eps)
+            sampling = sparse_ir.TauSampling(basis)
         except Exception as e:
             pytest.skip(f"Failed to create basis/sampling: {e}")
-        
+
         # Create synthetic IR coefficients (mimicking sparse-ir test)
         try:
             # Evaluate v functions at test frequencies
             omega_test = np.array([-0.999 * wmax, -0.01 * wmax, 0.5 * wmax])
             v_vals = basis.v(omega_test)
-            
+
             # Create IR coefficients as linear combination
             rhol = v_vals @ np.array([0.8, -0.2, 0.5])
             Gl = basis.s * rhol
             Gl_magn = np.linalg.norm(Gl)
-            
+
             # Evaluate to tau domain
             Gtau = sampling.evaluate(Gl)
-            
+
             # Add noise
             noise_level = 1e-5
             Gtau_noisy = Gtau + noise_level * np.linalg.norm(Gtau) * rng.randn(*Gtau.shape)
-            
+
             # Fit back
             Gl_recovered = sampling.fit(Gtau_noisy)
-            
+
             # Check that recovery is reasonable despite noise
             recovery_error = np.linalg.norm(Gl - Gl_recovered)
             expected_error = 12 * noise_level * Gl_magn  # Following sparse-ir pattern
-            
+
             assert recovery_error <= expected_error, \
                 f"Recovery error {recovery_error} exceeds expected {expected_error}"
-                
+
         except Exception as e:
             pytest.skip(f"Noise resilience test failed: {e}")
 
 
 class TestEdgeCases:
     """Test edge cases and boundary conditions."""
-    
+
     def test_small_lambda(self):
         """Test with very small Lambda values."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 1.0, 1.0, 1e-4)  # Lambda = 1
+            basis = sparse_ir.FiniteTempBasis('F', 1.0, 1.0, 1e-4)  # Lambda = 1
             assert basis.size >= 1
             assert len(basis.s) == basis.size
-            
+
             # Should still be able to create sampling
-            sampling = pylibsparseir.TauSampling(basis)
+            sampling = sparse_ir.TauSampling(basis)
             assert len(sampling.tau) == basis.size
-            
+
         except Exception as e:
             pytest.skip(f"Small lambda test failed: {e}")
-    
+
     def test_large_lambda(self):
         """Test with large Lambda values."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 10.0, 100.0, 1e-6)  # Lambda = 1000
+            basis = sparse_ir.FiniteTempBasis('F', 10.0, 100.0, 1e-6)  # Lambda = 1000
             assert basis.size > 10  # Should have reasonable number of basis functions
-            
+
             # Test basic functionality
             tau_points = np.linspace(0, basis.beta, 3)
             u_vals = basis.u(tau_points)
             assert u_vals.shape == (basis.size, 3)
-            
+
         except Exception as e:
             pytest.skip(f"Large lambda test failed: {e}")
-    
+
     def test_high_precision(self):
         """Test with very high precision requirements."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-12)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-12)
         assert basis.accuracy <= 1e-11  # Allow some tolerance
-        
+
         # High precision should give more basis functions
-        basis_low = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        basis_low = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
         assert basis.size >= basis_low.size
-    
+
     def test_boundary_tau_points(self):
         """Test evaluation at boundary tau points."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
-            
+            basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+
             # Test at boundaries
             tau_boundary = np.array([0.0, basis.beta])
             u_vals = basis.u(tau_boundary)
             assert u_vals.shape == (basis.size, 2)
             assert np.all(np.isfinite(u_vals))
-            
+
             # Test very close to boundaries
             eps_tau = 1e-10
             tau_near_boundary = np.array([eps_tau, basis.beta - eps_tau])
             u_vals_near = basis.u(tau_near_boundary)
             assert np.all(np.isfinite(u_vals_near))
-            
+
         except Exception as e:
             pytest.skip(f"Boundary tau test failed: {e}")
-    
+
     def test_zero_frequency(self):
         """Test evaluation at zero frequency."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
-            
+            basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+
             # Test v function at omega = 0
             v_zero = basis.v(np.array([0.0]))
             assert v_zero.shape == (basis.size, 1)
             assert np.all(np.isfinite(v_zero))
-            
+
         except Exception as e:
             pytest.skip(f"Zero frequency test failed: {e}")
 
 
 class TestConsistencyChecks:
     """Test internal consistency of the implementation."""
-    
+
     def test_reconstruction_accuracy(self):
         """Test that evaluate/fit operations are consistent."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
-        sampling = pylibsparseir.TauSampling(basis)
-        
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        sampling = sparse_ir.TauSampling(basis)
+
         # Create test coefficients with different magnitudes
         al_test = np.zeros(basis.size)
         al_test[0] = 1.0      # Large coefficient
-        al_test[1] = 0.1      # Medium coefficient  
+        al_test[1] = 0.1      # Medium coefficient
         al_test[2] = 0.01     # Small coefficient
-        
+
         # Test roundtrip accuracy
         ax = sampling.evaluate(al_test)
         al_recovered = sampling.fit(ax)
-        
+
         # Should recover with high accuracy
         error = np.max(np.abs(al_test - al_recovered))
         assert error < 1e-12, f"Reconstruction error too large: {error}"
-        
+
         # Test that the reconstruction preserves the structure
         for i in range(3):
             rel_error = abs(al_test[i] - al_recovered[i]) / max(abs(al_test[i]), 1e-14)
             assert rel_error < 1e-12, f"Relative error for coefficient {i}: {rel_error}"
-    
+
     def test_singular_value_ordering(self):
         """Test that singular values are properly ordered."""
         for statistics in ['F', 'B']:
             for lambda_val in [10, 42]:
                 try:
-                    basis = pylibsparseir.FiniteTempBasis(statistics, 1.0, lambda_val, 1e-6)
-                    
+                    basis = sparse_ir.FiniteTempBasis(statistics, 1.0, lambda_val, 1e-6)
+
                     s = basis.s
                     # Check decreasing order
                     assert np.all(s[:-1] >= s[1:]), f"Singular values not decreasing for {statistics}, λ={lambda_val}"
-                    
+
                     # Check positivity
                     assert np.all(s > 0), f"Non-positive singular values for {statistics}, λ={lambda_val}"
-                    
+
                     # Check normalization (first should be largest)
                     assert s[0] == np.max(s), f"First singular value not largest for {statistics}, λ={lambda_val}"
-                    
+
                 except Exception as e:
                     pytest.skip(f"Singular value test failed for {statistics}, λ={lambda_val}: {e}")
-    
+
     def test_significance_properties(self):
         """Test properties of significance array."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
-            
+            basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+
             sig = basis.significance
-            
+
             # First significance should be 1
             assert abs(sig[0] - 1.0) < 1e-14, f"First significance {sig[0]} != 1"
-            
+
             # Should be decreasing
             assert np.all(sig[:-1] >= sig[1:]), "Significance not decreasing"
-            
+
             # Should be between 0 and 1
             assert np.all(sig >= 0), "Negative significance values"
             assert np.all(sig <= 1), "Significance values > 1"
-            
+
         except Exception as e:
             pytest.skip(f"Significance test failed: {e}")
 
 
 class TestMemoryAndPerformance:
     """Test memory usage and performance characteristics."""
-    
+
     def test_large_basis_creation(self):
         """Test creating large basis without memory issues."""
         try:
             # Create a moderately large basis
-            basis = pylibsparseir.FiniteTempBasis('F', 1.0, 100.0, 1e-8)
-            
+            basis = sparse_ir.FiniteTempBasis('F', 1.0, 100.0, 1e-8)
+
             # Should be able to access all properties
             size = basis.size
             assert size > 20  # Should be reasonably large
-            
+
             s = basis.s
             assert len(s) == size
-            
+
             # Should be able to evaluate functions
             tau_test = np.array([0.1, 0.5, 0.9])
             u_vals = basis.u(tau_test)
             assert u_vals.shape == (size, 3)
-            
+
         except MemoryError:
             pytest.skip("Not enough memory for large basis test")
         except Exception as e:
             pytest.skip(f"Large basis test failed: {e}")
-    
+
     def test_repeated_operations(self):
         """Test that repeated operations don't leak memory or degrade performance."""
         try:
-            basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
-            sampling = pylibsparseir.TauSampling(basis)
-            
+            basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+            sampling = sparse_ir.TauSampling(basis)
+
             # Test coefficients
             al_test = np.random.random(basis.size)
-            
+
             # Perform many evaluate/fit cycles
             for _ in range(10):
                 ax = sampling.evaluate(al_test)
                 al_recovered = sampling.fit(ax)
-                
+
                 # Should maintain accuracy
                 error = np.max(np.abs(al_test - al_recovered))
                 assert error < 1e-12, f"Accuracy degraded after repeated operations: {error}"
-            
+
         except Exception as e:
             pytest.skip(f"Repeated operations test failed: {e}")
\ No newline at end of file
diff --git a/tests/test_basis.py b/tests/test_basis.py
index 399054a..6b81808 100644
--- a/tests/test_basis.py
+++ b/tests/test_basis.py
@@ -2,9 +2,8 @@
 Test cases for FiniteTempBasis functionality
 """
 
-import pytest
 import numpy as np
-import pylibsparseir
+import sparse_ir
 
 
 class TestFiniteTempBasis:
@@ -17,7 +16,7 @@ def test_basic_creation(self):
         eps = 1e-6
 
         # Test fermion basis
-        basis_f = pylibsparseir.FiniteTempBasis('F', beta, wmax, eps)
+        basis_f = sparse_ir.FiniteTempBasis('F', beta, wmax, eps)
         assert basis_f.statistics == 'F'
         assert basis_f.beta == beta
         assert basis_f.wmax == wmax
@@ -26,14 +25,14 @@ def test_basic_creation(self):
         assert len(basis_f.s) == basis_f.size
 
         # Test boson basis
-        basis_b = pylibsparseir.FiniteTempBasis('B', beta, wmax, eps)
+        basis_b = sparse_ir.FiniteTempBasis('B', beta, wmax, eps)
         assert basis_b.statistics == 'B'
         assert basis_b.beta == beta
         assert basis_b.wmax == wmax
 
     def test_singular_values(self):
         """Test singular values properties."""
-        basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
 
         # Singular values should be positive and decreasing
         s = basis.s
@@ -51,7 +50,7 @@ def test_singular_values(self):
 
     def test_basis_function_evaluation(self):
         """Test basis function evaluation."""
-        basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
 
         # Test u functions (imaginary time)
         tau_points = np.linspace(0, basis.beta, 5)
@@ -74,7 +73,7 @@ def test_basis_function_evaluation(self):
 
     def test_default_sampling_points(self):
         """Test default sampling points."""
-        basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
 
         # Test tau sampling points
         tau_points = basis.default_tau_sampling_points()
@@ -94,7 +93,7 @@ def test_default_sampling_points(self):
 
     def test_repr(self):
         """Test string representation."""
-        basis = pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
         repr_str = repr(basis)
         assert 'FiniteTempBasis' in repr_str
         assert 'F' in repr_str
@@ -108,7 +107,7 @@ def test_finite_temp_bases():
     wmax = 4.0
     eps = 1e-8
 
-    f_basis, b_basis = pylibsparseir.finite_temp_bases(beta, wmax, eps)
+    f_basis, b_basis = sparse_ir.finite_temp_bases(beta, wmax, eps)
 
     assert f_basis.statistics == 'F'
     assert b_basis.statistics == 'B'
@@ -121,7 +120,7 @@ class TestBasisFunctionEvaluation:
     """Test basis function evaluation accuracy."""
     def test_u_function_finite(self):
         """Test that u functions evaluate to finite values."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
 
         # Test at various tau points
         tau_points = np.array([0.0, 0.25, 0.5, 0.75, 1.0])
@@ -135,7 +134,7 @@ def test_u_function_finite(self):
 
     def test_v_function_finite(self):
         """Test that v functions evaluate to finite values."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
 
         # Test at various omega points
         omega_points = np.linspace(-8, 8, 9)
diff --git a/tests/test_basis_set.py b/tests/test_basis_set.py
index 56e811b..4033aba 100644
--- a/tests/test_basis_set.py
+++ b/tests/test_basis_set.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: MIT
 import numpy as np
 
-from pylibsparseir import FiniteTempBasisSet,\
+from sparse_ir import FiniteTempBasisSet,\
     TauSampling, MatsubaraSampling, finite_temp_bases
 
 
diff --git a/tests/test_dlr.py b/tests/test_dlr.py
index 46da089..4866b76 100644
--- a/tests/test_dlr.py
+++ b/tests/test_dlr.py
@@ -1,7 +1,7 @@
 # Copyright (C) 2020-2022 Markus Wallerberger, Hiroshi Shinaoka, and others
 # SPDX-License-Identifier: MIT
-from pylibsparseir import DiscreteLehmannRepresentation
-from pylibsparseir import FiniteTempBasis, MatsubaraSampling, TauSampling, LogisticKernel, SVEResult
+from sparse_ir import DiscreteLehmannRepresentation
+from sparse_ir import FiniteTempBasis, MatsubaraSampling, TauSampling
 import numpy as np
 import pytest
 
diff --git a/tests/test_kernel.py b/tests/test_kernel.py
index adb7d01..c79205a 100644
--- a/tests/test_kernel.py
+++ b/tests/test_kernel.py
@@ -5,7 +5,7 @@
 import pytest
 import numpy as np
 import pylibsparseir
-from pylibsparseir.kernel import LogisticKernel, RegularizedBoseKernel, kernel_domain
+from sparse_ir.kernel import LogisticKernel, RegularizedBoseKernel, kernel_domain
 from .conftest import KERNEL_LAMBDAS
 
 
diff --git a/tests/test_poly.py b/tests/test_poly.py
index 3435325..0868cd7 100644
--- a/tests/test_poly.py
+++ b/tests/test_poly.py
@@ -1,13 +1,11 @@
-import importlib
 import numpy as np
-import pylibsparseir
-importlib.reload(pylibsparseir)
+import sparse_ir
 
 def test_poly():
     eps = 1e-6
     beta = 4.2
     wmax = 10
-    basis = pylibsparseir.FiniteTempBasis('F', beta, wmax, eps)
+    basis = sparse_ir.FiniteTempBasis('F', beta, wmax, eps)
 
     u1 = basis.u[1]
     assert np.allclose(u1(np.array([0.5, 0.3, 1.0, 2.0])), np.array([-0.43049722, -0.67225263, -0.18450157, -0.01225698]))
@@ -17,7 +15,7 @@ def test_poly_v():
     beta = 2
     wmax = 21
     eps = 1e-7
-    basis_b = pylibsparseir.FiniteTempBasis("B", beta, wmax, eps=eps)
+    basis_b = sparse_ir.FiniteTempBasis("B", beta, wmax, eps=eps)
 
     omega_p = np.array([2.2, -1.0])
     o = basis_b.v(omega_p)
diff --git a/tests/test_sampling.py b/tests/test_sampling.py
index 867e816..9f31a34 100644
--- a/tests/test_sampling.py
+++ b/tests/test_sampling.py
@@ -4,7 +4,7 @@
 
 import pytest
 import numpy as np
-import pylibsparseir
+import sparse_ir
 
 
 class TestTauSampling:
@@ -13,11 +13,11 @@ class TestTauSampling:
     @pytest.fixture
     def basis(self):
         """Create a test basis."""
-        return pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        return sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
 
     def test_creation_default_points(self, basis):
         """Test TauSampling creation with default points."""
-        sampling = pylibsparseir.TauSampling(basis)
+        sampling = sparse_ir.TauSampling(basis)
 
         assert len(sampling.tau) == basis.size
         # Note: tau points can extend beyond [0, beta] for numerical reasons
@@ -26,14 +26,14 @@ def test_creation_default_points(self, basis):
     def test_creation_custom_points(self, basis):
         """Test TauSampling creation with custom points."""
         custom_points = np.linspace(0, basis.beta, 10)
-        sampling = pylibsparseir.TauSampling(basis, custom_points)
+        sampling = sparse_ir.TauSampling(basis, custom_points)
 
         assert len(sampling.tau) == 10
         np.testing.assert_array_almost_equal(sampling.tau, custom_points)
 
     def test_evaluate_fit_roundtrip(self, basis):
         """Test evaluate/fit roundtrip accuracy."""
-        sampling = pylibsparseir.TauSampling(basis)
+        sampling = sparse_ir.TauSampling(basis)
 
         # Test with different coefficient patterns
         test_cases = [
@@ -53,7 +53,7 @@ def test_evaluate_fit_roundtrip(self, basis):
 
     def test_evaluate_shape(self, basis):
         """Test evaluate output shape."""
-        sampling = pylibsparseir.TauSampling(basis)
+        sampling = sparse_ir.TauSampling(basis)
 
         al = np.ones(basis.size)
         ax = sampling.evaluate(al)
@@ -63,7 +63,7 @@ def test_evaluate_shape(self, basis):
 
     def test_fit_shape(self, basis):
         """Test fit output shape."""
-        sampling = pylibsparseir.TauSampling(basis)
+        sampling = sparse_ir.TauSampling(basis)
 
         ax = np.ones(len(sampling.tau))
         al = sampling.fit(ax)
@@ -73,7 +73,7 @@ def test_fit_shape(self, basis):
 
     def test_repr(self, basis):
         """Test string representation."""
-        sampling = pylibsparseir.TauSampling(basis)
+        sampling = sparse_ir.TauSampling(basis)
         repr_str = repr(sampling)
         assert 'TauSampling' in repr_str
         assert str(len(sampling.tau)) in repr_str
@@ -85,12 +85,12 @@ class TestMatsubaraSampling:
     @pytest.fixture
     def basis(self):
         """Create a test basis."""
-        return pylibsparseir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
+        return sparse_ir.FiniteTempBasis('F', 10.0, 8.0, 1e-6)
 
     def test_creation_default_points(self, basis):
         """Test MatsubaraSampling creation with default points."""
         # MatsubaraSampling creation works fine
-        sampling = pylibsparseir.MatsubaraSampling(basis)
+        sampling = sparse_ir.MatsubaraSampling(basis)
 
         # Check that we have sampling points
         assert hasattr(sampling, 'wn')
@@ -104,14 +104,14 @@ def test_creation_custom_points(self, basis):
         """Test MatsubaraSampling creation with custom points."""
         # Custom points for fermionic frequencies (odd integers)
         custom_wn = np.array([1, 3, 5, 7, 9], dtype=np.int64)
-        sampling = pylibsparseir.MatsubaraSampling(basis, custom_wn)
+        sampling = sparse_ir.MatsubaraSampling(basis, custom_wn)
 
         assert len(sampling.wn) == len(custom_wn)
         np.testing.assert_array_equal(sampling.wn, custom_wn)
 
     def test_repr(self, basis):
         """Test string representation."""
-        sampling = pylibsparseir.MatsubaraSampling(basis)
+        sampling = sparse_ir.MatsubaraSampling(basis)
         repr_str = repr(sampling)
         assert 'MatsubaraSampling' in repr_str
         assert str(len(sampling.wn)) in repr_str
\ No newline at end of file
diff --git a/tests/test_sampling_advanced.py b/tests/test_sampling_advanced.py
index 0b7f824..decd6f7 100644
--- a/tests/test_sampling_advanced.py
+++ b/tests/test_sampling_advanced.py
@@ -4,7 +4,7 @@
 
 import pytest
 import numpy as np
-import pylibsparseir
+import sparse_ir
 from .conftest import BASIS_PARAMS
 
 
@@ -15,8 +15,8 @@ class TestTauSamplingAccuracy:
     def test_tau_noise_tolerance(self, stat, beta, wmax, rng):
         """Test tau sampling noise tolerance following sparse-ir pattern."""
         eps = 1e-6
-        basis = pylibsparseir.FiniteTempBasis(stat, beta, wmax, eps)
-        smpl = pylibsparseir.TauSampling(basis)
+        basis = sparse_ir.FiniteTempBasis(stat, beta, wmax, eps)
+        smpl = sparse_ir.TauSampling(basis)
 
         # Create test coefficients using basis functions
         # This follows the sparse-ir pattern: rhol = basis.v([-.999, -.01, .5]) @ [0.8, -.2, 0.5]
@@ -51,8 +51,8 @@ def test_tau_noise_tolerance(self, stat, beta, wmax, rng):
 
     def test_tau_perfect_roundtrip(self):
         """Test perfect roundtrip without noise."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
-        smpl = pylibsparseir.TauSampling(basis)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        smpl = sparse_ir.TauSampling(basis)
 
         # Test with different coefficient patterns
         test_coefficients = [
@@ -70,8 +70,8 @@ def test_tau_perfect_roundtrip(self):
 
     def test_tau_sampling_matrix_properties(self):
         """Test properties of the tau sampling matrix."""
-        basis = pylibsparseir.FiniteTempBasis('F', 2.0, 10.0, 1e-6)
-        smpl = pylibsparseir.TauSampling(basis)
+        basis = sparse_ir.FiniteTempBasis('F', 2.0, 10.0, 1e-6)
+        smpl = sparse_ir.TauSampling(basis)
 
         # The sampling should be well-conditioned for default points
         # We can't directly access the condition number yet, but we can test
@@ -95,14 +95,14 @@ class TestMatsubaraSamplingBasic:
 
     def test_matsubara_creation_simple(self):
         """Test basic Matsubara sampling creation."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
 
         # This might fail due to current implementation issues
         # But let's test what we can
         try:
             # Test with very simple custom points
             simple_points = np.array([1, 3], dtype=np.int64)
-            smpl = pylibsparseir.MatsubaraSampling(basis, sampling_points=simple_points)
+            smpl = sparse_ir.MatsubaraSampling(basis, sampling_points=simple_points)
 
             assert len(smpl.wn) == 2
             np.testing.assert_array_equal(smpl.wn, simple_points)
@@ -118,14 +118,14 @@ class TestSamplingEdgeCases:
 
     def test_out_of_bounds_tau(self):
         """Test tau points outside [0, beta] range."""
-        basis = pylibsparseir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis('F', 1.0, 10.0, 1e-6)
 
         # The C++ library enforces tau to be within [0, beta]
         # Test that out-of-bounds points raise an error
         out_of_bounds_points = np.array([-0.5, 1.5])  # beta = 1.0
 
         # Creating sampling with out-of-bounds points is allowed
-        smpl = pylibsparseir.TauSampling(basis, out_of_bounds_points)
+        smpl = sparse_ir.TauSampling(basis, out_of_bounds_points)
         assert len(smpl.tau) == 2
         np.testing.assert_array_equal(smpl.tau, out_of_bounds_points)
 
@@ -139,8 +139,8 @@ def test_out_of_bounds_tau(self):
     @pytest.mark.parametrize("stat", ['F', 'B'])
     def test_different_statistics(self, stat):
         """Test sampling works for both fermions and bosons."""
-        basis = pylibsparseir.FiniteTempBasis(stat, 1.0, 10.0, 1e-6)
-        smpl = pylibsparseir.TauSampling(basis)
+        basis = sparse_ir.FiniteTempBasis(stat, 1.0, 10.0, 1e-6)
+        smpl = sparse_ir.TauSampling(basis)
 
         # Basic operations should work for both statistics
         assert len(smpl.tau) == basis.size
diff --git a/tests/test_sve.py b/tests/test_sve.py
index e776c1f..0e2e687 100644
--- a/tests/test_sve.py
+++ b/tests/test_sve.py
@@ -4,7 +4,7 @@
 
 import pytest
 import numpy as np
-import pylibsparseir
+import sparse_ir
 from .conftest import BASIS_PARAMS
 
 
@@ -15,7 +15,7 @@ class TestSVEAccuracy:
     def test_accuracy_bounds(self, stat, beta, wmax):
         """Test that basis accuracy meets expected bounds."""
         eps = 1e-6
-        basis = pylibsparseir.FiniteTempBasis(stat, beta, wmax, eps)
+        basis = sparse_ir.FiniteTempBasis(stat, beta, wmax, eps)
 
         # Basic properties
         assert 0 < basis.accuracy <= basis.significance[-1]
@@ -28,7 +28,7 @@ def test_accuracy_bounds(self, stat, beta, wmax):
     @pytest.mark.parametrize("stat,beta,wmax", BASIS_PARAMS)
     def test_singular_value_properties(self, stat, beta, wmax):
         """Test singular value properties."""
-        basis = pylibsparseir.FiniteTempBasis(stat, beta, wmax, 1e-6)
+        basis = sparse_ir.FiniteTempBasis(stat, beta, wmax, 1e-6)
 
         s = basis.s
 
@@ -56,7 +56,7 @@ def test_basis_size_scaling(self, lambda_):
         wmax = lambda_ / beta
         eps = 1e-6
 
-        basis = pylibsparseir.FiniteTempBasis('F', beta, wmax, eps)
+        basis = sparse_ir.FiniteTempBasis('F', beta, wmax, eps)
 
         # Basis size should be reasonable for the given Lambda
         # The exact scaling depends on the kernel and implementation
@@ -70,9 +70,9 @@ def test_epsilon_vs_size(self):
         """Test that smaller epsilon gives larger basis size."""
         beta, wmax = 1.0, 42.0
 
-        basis_loose = pylibsparseir.FiniteTempBasis('F', beta, wmax, 1e-4)
-        basis_medium = pylibsparseir.FiniteTempBasis('F', beta, wmax, 1e-6)
-        basis_tight = pylibsparseir.FiniteTempBasis('F', beta, wmax, 1e-8)
+        basis_loose = sparse_ir.FiniteTempBasis('F', beta, wmax, 1e-4)
+        basis_medium = sparse_ir.FiniteTempBasis('F', beta, wmax, 1e-6)
+        basis_tight = sparse_ir.FiniteTempBasis('F', beta, wmax, 1e-8)
 
         # Sizes should increase (or stay same) as epsilon decreases
         assert basis_loose.size <= basis_medium.size <= basis_tight.size, \
@@ -81,7 +81,7 @@ def test_epsilon_vs_size(self):
     @pytest.mark.parametrize("stat", ['F', 'B'])
     def test_statistics_consistency(self, stat):
         """Test that basis statistics are consistent."""
-        basis = pylibsparseir.FiniteTempBasis(stat, 1.0, 10.0, 1e-6)
+        basis = sparse_ir.FiniteTempBasis(stat, 1.0, 10.0, 1e-6)
 
         assert basis.statistics == stat
 
@@ -96,8 +96,8 @@ def test_same_parameters_same_result(self):
         """Test that same parameters give same results."""
         params = ('F', 2.0, 15.0, 1e-6)
 
-        basis1 = pylibsparseir.FiniteTempBasis(*params)
-        basis2 = pylibsparseir.FiniteTempBasis(*params)
+        basis1 = sparse_ir.FiniteTempBasis(*params)
+        basis2 = sparse_ir.FiniteTempBasis(*params)
 
         # Should have identical properties
         assert basis1.size == basis2.size
@@ -112,7 +112,7 @@ def test_finite_temp_bases_factory(self):
         """Test finite_temp_bases factory function."""
         beta, wmax, eps = 2.0, 20.0, 1e-6
 
-        f_basis, b_basis = pylibsparseir.finite_temp_bases(beta, wmax, eps)
+        f_basis, b_basis = sparse_ir.finite_temp_bases(beta, wmax, eps)
 
         # Check types
         assert f_basis.statistics == 'F'
diff --git a/tests/test_sve_advanced.py b/tests/test_sve_advanced.py
index 3b3953b..d34a5c8 100644
--- a/tests/test_sve_advanced.py
+++ b/tests/test_sve_advanced.py
@@ -7,7 +7,7 @@
 import pylibsparseir
 from pylibsparseir.core import *
 from .conftest import KERNEL_LAMBDAS
-from pylibsparseir.kernel import LogisticKernel, RegularizedBoseKernel
+from sparse_ir.kernel import LogisticKernel, RegularizedBoseKernel
 
 class TestSVEProperties:
     """Test SVE computation and properties."""
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new file mode 100644
index 0000000..9db0c80
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+++ b/uv.lock
@@ -0,0 +1,1756 @@
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