Merge pull request #7 from SpM-lab/sparse-ir

Rename irbasis3 to sparse-ir

.github/workflows/pytest.yml

@@ -1,4 +1,4 @@
-name: irbasis3 python package
+name: sparse_ir python package
 
 on: [push]
 

README.md

@@ -1,4 +1,4 @@
-irbasis3 - A library for the intermediate representation of propagators
+sparse-ir - A library for the intermediate representation of propagators
 =======================================================================
 This library provides routines for constructing and working with the
 intermediate representation of correlation functions.  It provides:
@@ -10,20 +10,21 @@ intermediate representation of correlation functions.  It provides:
 Installation
 ------------
 
-    pip install irbasis3 xprec
+    pip install sparse-ir[xprec]
 
 Though not strictly required, we strongly recommend installing the `xprec`
-package alongside `irbasis3` as it allows to compute the IR basis functions
-with greater accuracy.
+package alongside `sparse-ir` as it allows to compute the IR basis functions
+with greater accuracy.  If you do not want to do this, simply remove `[xprec]`
+from the above line.
 
 Quick start
 -----------
 Here is some python code illustrating the API:
 
     # Compute IR basis for fermions and β = 10, W <= 4.2
-    import irbasis3, numpy
-    K = irbasis3.KernelFFlat(lambda_=42)
-    basis = irbasis3.FiniteTempBasis(K, statistics='F', beta=10)
+    import sparse_ir, numpy
+    K = sparse_ir.KernelFFlat(lambda_=42)
+    basis = sparse_ir.FiniteTempBasis(K, statistics='F', beta=10)
 
     # Assume spectrum is a single pole at ω = 2.5, compute G(iw)
     # on the first few Matsubara frequencies. (Fermionic/bosonic Matsubara
@@ -32,7 +33,7 @@ Here is some python code illustrating the API:
     giw = gl @ basis.uhat([1, 3, 5, 7])
 
     # Reconstruct same coefficients from sparse sampling on the Matsubara axis:
-    smpl_iw = irbasis3.MatsubaraSampling(basis)
+    smpl_iw = sparse_ir.MatsubaraSampling(basis)
     giw = -1/(1j * numpy.pi/basis.beta * smpl_iw.wn - 2.5)
     gl_rec = smpl_iw.fit(giw)
 

doc/conf.py

@@ -4,7 +4,7 @@
 
 # === Project information
 
-project = 'irbasis3'
+project = 'sparse-ir'
 copyright = '2021, Markus Wallerberger and others'
 author = ', '.join([
     'Markus Wallerberger',
@@ -14,8 +14,8 @@
     'Chikano Naoya',
     ])
 
-release = '3.0-alpha1'
-version = '3.0-alpha1'
+release = '0.2'
+version = '0.2'
 
 # === General configuration
 

doc/hacking.md

@@ -3,8 +3,8 @@ Hacking/Development
 For development, we recommend installing a "editable" version of the
 repository with unit test dependencies::
 
-    git checkout git@github.com:SpM-lab/irbasis3
-    cd irbasis3
+    git checkout git@github.com:SpM-lab/sparse_ir
+    cd sparse_ir
     pip install -e .[test]
 
 Now the installed package automatically updates as you are working on the code.

doc/kernels.md

@@ -3,28 +3,28 @@ Kernels
 Defines integral kernels for the analytic continuation problem.  Specifically,
 it defines two kernels:
 
- - `irbasis3.KernelFFlat`: continuation of *fermionic* spectral functions.
- - `irbasis3.KernelFFlat`: continuation of *bosonic* spectral functions.
+ - `sparse_ir.KernelFFlat`: continuation of *fermionic* spectral functions.
+ - `sparse_ir.KernelFFlat`: continuation of *bosonic* spectral functions.
 
-These can be fed directly into `irbasis3.IRBasis` or `irbasis3.FiniteTempBasis`
+These can be fed directly into `sparse_ir.IRBasis` or `sparse_ir.FiniteTempBasis`
 to get the intermediate representation.
 
 Predefined kernels
 ------------------
 
 ```{eval-rst}
-.. autoclass:: irbasis3.kernel.KernelFFlat
+.. autoclass:: sparse_ir.kernel.KernelFFlat
     :members:
     :special-members: __call__
 
-.. autoclass:: irbasis3.kernel.KernelBFlat
+.. autoclass:: sparse_ir.kernel.KernelBFlat
     :members:
     :special-members: __call__
 ```
 
 Custom kernels
 --------------
-Adding kernels to `irbasis3` is simple - at the very basic levle, the library
+Adding kernels to `sparse_ir` is simple - at the very basic levle, the library
 expects a kernel `K` to be able to provide two things:
 
  1. the values through `K(x, y)`
@@ -35,13 +35,13 @@ axis instead of the default (flat) one.  We create a new kernel by inheriting
 from `irbasis.kernel.KernelBase` and then simply wrap around a fermionic
 kernel, modifying the values as needed:
 
-    import irbasis3
-    import irbasis3.kernel
+    import sparse_ir
+    import sparse_ir.kernel
 
-    class KernelFGauss(irbasis3.kernel.KernelBase):
+    class KernelFGauss(sparse_ir.kernel.KernelBase):
         def __init__(self, lambda_, std):
             super().__init__(self)
-            self._inner = irbasis3.KernelFFlat(lambda_)
+            self._inner = sparse_ir.KernelFFlat(lambda_)
             self.lambda_ = lambda_
             self.std = std
 
@@ -58,7 +58,7 @@ kernel, modifying the values as needed:
 You can feed this kernel now directly to `irbasis.IRBasis`:
 
     K = GaussFKernel(10., 1.)
-    basis = irbasis3.IRBasis(K, 'F')
+    basis = sparse_ir.IRBasis(K, 'F')
     print(basis.s)
 
 This should get you started.  For a fully-fledged and robust implementation,
@@ -82,14 +82,14 @@ Base classes
 ------------
 
 ```{eval-rst}
-.. autoclass:: irbasis3.kernel.KernelBase
+.. autoclass:: sparse_ir.kernel.KernelBase
     :members:
     :special-members: __call__
 
-.. autoclass:: irbasis3.kernel.ReducedKernel
+.. autoclass:: sparse_ir.kernel.ReducedKernel
     :members:
     :special-members: __call__
 
-.. autoclass:: irbasis3.kernel.SVEHints
+.. autoclass:: sparse_ir.kernel.SVEHints
     :members:
 ```

doc/porting.md

@@ -1,11 +1,11 @@
 Porting
 =======
-For porting codes from irbasis, version 2, to irbasis3, you have two options:
+For porting codes from irbasis, version 2, to sparse_ir, you have two options:
 
-  * using the `irbasis3.adapter` module (easier)
-  * porting to the new `irbasis3` API (cleaner)
+  * using the `sparse_ir.adapter` module (easier)
+  * porting to the new `sparse_ir` API (cleaner)
 
-In both cases, please note that `irbasis3` now computes the basis on-the-fly
+In both cases, please note that `sparse_ir` now computes the basis on-the-fly
 rather than loading it from a file.  This has some important consequences:
 
   * Singular values and basis functions may slightly differ from the old
@@ -21,19 +21,19 @@ basis coefficients or store the sampling points together with the data.
 
 Adapter module
 --------------
-For ease of porting, we provide the `irbasis3.adapter` module.  This module
+For ease of porting, we provide the `sparse_ir.adapter` module.  This module
 is API-compatible with the `irbasis` package, version 2.  This means you
 simply have to replace::
 
     import irbasis
 
 with the following::
 
-    import irbasis3.adapter as irbasis
+    import sparse_ir.adapter as irbasis
 
 and everything should work as expected.
 
 ```{eval-rst}
-.. autoclass:: irbasis3.adapter.Basis
+.. autoclass:: sparse_ir.adapter.Basis
     :members:
 ```

doc/sampling.md

@@ -33,20 +33,20 @@ Sparse sampling transformers
 ----------------------------
 
 ```{eval-rst}
-.. autoclass:: irbasis3.sampling.TauSampling
+.. autoclass:: sparse_ir.sampling.TauSampling
     :members: tau, evaluate, fit
 
-.. autoclass:: irbasis3.sampling.MatsubaraSampling
+.. autoclass:: sparse_ir.sampling.MatsubaraSampling
     :members: wn, evaluate, fit
 ```
 
 Base classes
 -------------
 
 ```{eval-rst}
-.. autoclass:: irbasis3.sampling.SamplingBase
+.. autoclass:: sparse_ir.sampling.SamplingBase
     :members:
 
-.. autoclass:: irbasis3.sampling.DecomposedMatrix
+.. autoclass:: sparse_ir.sampling.DecomposedMatrix
     :members:
 ```

setup.py

@@ -12,21 +12,21 @@ def readfile(*parts):
         return f.read()
 
 
-def extract_varval(*parts, varname):
+def extract_varval(varname):
     """Extract value of __version__ variable by parsing python script"""
-    initfile = readfile(*parts)
-    var_re = re.compile(r"(?m)^__" + varname + r"__\s*=\s*['\"]([^'\"]*)['\"]")
+    initfile = readfile('src', 'sparse_ir', '__init__.py')
+    var_re = re.compile(rf"(?m)^{varname}\s*=\s*['\"]([^'\"]*)['\"]")
     match = var_re.search(initfile)
     return match.group(1)
 
 
-VERSION = extract_varval('src', 'irbasis3', '__init__.py', varname='version')
-MIN_XPREC_VERSION = extract_varval('src', 'irbasis3', '__init__.py', varname='min_xprec_version')
-REPO_URL = "https://github.com/SpM-lab/irbasis3"
+REPO_URL = "https://github.com/SpM-lab/sparse-ir"
+VERSION = extract_varval('__version__')
+MIN_XPREC_VERSION = extract_varval('min_xprec_version')
 LONG_DESCRIPTION = readfile('README.md')
 
 setup(
-    name='irbasis3',
+    name='sparse-ir',
     version=VERSION,
 
     description=

src/irbasis3/__init__.py → src/sparse_ir/__init__.py

@@ -4,15 +4,16 @@
 """
 __copyright__ = "Copyright (C) 2020-2021 Markus Wallerberger and others"
 __license__ = "MIT"
-__version__ = "3.0-alpha6"
-__min_xprec_version__ = "1.1.1"
+__version__ = "0.2"
+
+min_xprec_version = "1.0"
 
 try:
     import xprec as xprec
     from pkg_resources import parse_version
     from warnings import warn
-    if parse_version(xprec.__version__) < parse_version(__min_xprec_version__):
-        warn(f"xprec is too old! Please use xprec>={__min_xprec_version__}.")
+    if parse_version(xprec.__version__) < parse_version(min_xprec_version):
+        warn(f"xprec is too old! Please use xprec>={min_xprec_version}.")
 except ImportError:
     pass
 

src/irbasis3/adapter.py → src/sparse_ir/adapter.py

@@ -5,7 +5,7 @@
 
 This is designed to be a drop-in replacement for `irbasis`, where the basis
 can be computed on-the-fly for arbitrary values of Lambda.  In other words,
-you should be able to replace `irbasis` with `irbasis3.adapter` and
+you should be able to replace `irbasis` with `sparse_ir.adapter` and
 everything should hopefully still work.
 
 Note however that on-the-fly computation typically has lower accuracy.  Thus,

src/irbasis3/basis.py → src/sparse_ir/basis.py

@@ -33,9 +33,9 @@ class IRBasis:
     at x = 0.2::
 
         # Compute IR basis suitable for fermions and β*W <= 42
-        import irbasis3
-        K = irbasis3.KernelFFlat(lambda_=42)
-        basis = irbasis3.IRBasis(K, statistics='F')
+        import sparse_ir
+        K = sparse_ir.KernelFFlat(lambda_=42)
+        basis = sparse_ir.IRBasis(K, statistics='F')
 
         # Assume spectrum is a single pole at x = 0.2, compute G(iw)
         # on the first few Matsubara frequencies
@@ -146,9 +146,9 @@ class FiniteTempBasis:
     at ω = 2.5::
 
         # Compute IR basis for fermions and β = 10, W <= 4.2
-        import irbasis3
-        K = irbasis3.KernelFFlat(lambda_=42)
-        basis = irbasis3.FiniteTempBasis(K, statistics='F', beta=10)
+        import sparse_ir
+        K = sparse_ir.KernelFFlat(lambda_=42)
+        basis = sparse_ir.FiniteTempBasis(K, statistics='F', beta=10)
 
         # Assume spectrum is a single pole at ω = 2.5, compute G(iw)
         # on the first few Matsubara frequencies

test/test_augmentation.py

@@ -1,8 +1,8 @@
 # Copyright (C) 2020-2021 Markus Wallerberger and others
 # SPDX-License-Identifier: MIT
 import numpy as np
-import irbasis3
-from irbasis3 import augmentation
+import sparse_ir
+from sparse_ir import augmentation
 from scipy.special import eval_legendre, spherical_jn
 
 import pytest
@@ -51,11 +51,11 @@ def test_legendre_basis(stat):
     # G(tau) = -e^{-tau*pole}/(1 + e^{-beta*pole}) [F]
     #        = -e^{-tau*pole}/(1 - e^{-beta*pole}) [B]
     pole = 1.0
-    tau_smpl = irbasis3.TauSampling(basis)
+    tau_smpl = sparse_ir.TauSampling(basis)
     gtau = -np.exp(-tau_smpl.sampling_points * pole)/(1 - sign * np.exp(-beta * pole))
     gl_from_tau = tau_smpl.fit(gtau)
 
-    matsu_smpl = irbasis3.MatsubaraSampling(basis)
+    matsu_smpl = sparse_ir.MatsubaraSampling(basis)
     giv = 1/(1J*matsu_smpl.sampling_points*np.pi/beta - pole)
     gl_from_matsu = matsu_smpl.fit(giv)
 

test/test_compare.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from irbasis3 import adapter
+from sparse_ir import adapter
 
 try:
     import irbasis