cotengra v0.5.6, markdown readme

README.md

@@ -0,0 +1,38 @@
+<img src="https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/logo-banner.png?raw=true" alt="quimb" width="800px">
+
+[![Tests](https://github.com/jcmgray/quimb/actions/workflows/tests.yml/badge.svg)](https://github.com/jcmgray/quimb/actions/workflows/tests.yml)
+[![Code Coverage](https://codecov.io/gh/jcmgray/quimb/branch/main/graph/badge.svg)](https://codecov.io/gh/jcmgray/quimb)
+[![Code Quality](https://app.codacy.com/project/badge/Grade/3c7462a3c45f41fd9d8f0a746a65c37c)](https://www.codacy.com/gh/jcmgray/quimb/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=jcmgray/quimb&amp;utm_campaign=Badge_Grade)
+[![Documentation Status](https://readthedocs.org/projects/quimb/badge/?version=latest)](http://quimb.readthedocs.io/en/latest/?badge=latest)
+[![JOSS Paper](http://joss.theoj.org/papers/10.21105/joss.00819/status.svg)](https://doi.org/10.21105/joss.00819)
+[![PyPI](https://img.shields.io/pypi/v/quimb?color=teal)](https://pypi.org/project/quimb/)
+[![Anaconda-Server Badge](https://anaconda.org/conda-forge/quimb/badges/version.svg)](https://anaconda.org/conda-forge/quimb)
+
+[`quimb`](https://github.com/jcmgray/quimb) is an easy but fast python library for *'quantum information many-body'* calculations, focusing primarily on **tensor networks**. The code is hosted on [github](https://github.com/jcmgray/quimb), and docs are hosted on [readthedocs](http://quimb.readthedocs.io/en/latest/). Functionality is split in two:
+
+---
+
+The `quimb.tensor` module contains tools for working with **tensors and tensor networks**. It has a particular focus on automatically handling arbitrary geometry, e.g. beyond 1D and 2D lattices. With this you can:
+
+- construct and manipulate arbitrary (hyper) graphs of tensor networks
+- automatically [contract](https://cotengra.readthedocs.io), optimize and draw networks
+- use various backend array libraries such as [jax](https://jax.readthedocs.io) and [torch](https://pytorch.org/) via [autoray](https://github.com/jcmgray/autoray/)
+- run specific MPS, PEPS, MERA and quantum circuit algorithms, such as DMRG & TEBD
+
+<img src="https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-tensor.svg?raw=true" width="300px">
+
+---
+
+The core `quimb` module contains tools for reference **'exact'** quantum calculations, where the states and operator are represented as either `numpy.ndarray` or `scipy.sparse` **matrices**. With this you can:
+
+- construct operators in complicated tensor spaces
+- find groundstates, excited states and do time evolutions, including with [slepc](https://slepc.upv.es/)
+- compute various quantities including entanglement measures
+- take advantage of [numba](https://numba.pydata.org) accelerations
+- stochastically estimate $\mathrm{Tr}f(X)$ quantities
+
+<img src="https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-herm-matrix.svg?raw=true" width="300px">
+
+---
+
+The **full documentation** can be found at: [quimb.readthedocs.io](https://quimb.readthedocs.io). Contributions of any sort are very welcome - please see the [contributing guide](https://github.com/jcmgray/quimb/blob/main/.github/CONTRIBUTING.md). [Issues](https://github.com/jcmgray/quimb/issues) and [pull requests](https://github.com/jcmgray/quimb/pulls) are hosted on [github](https://github.com/jcmgray/quimb). For other questions and suggestions, please use the [discussions page](https://github.com/jcmgray/quimb/discussions).

ci/requirements/py-base.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - autoray>=0.6.7
-  # - cotengra>=0.5.3
+  - cotengra>=0.5.6
   - coverage
   - cytoolz
   - joblib
@@ -16,5 +16,3 @@ dependencies:
   - pytest-cov
   - scipy
   - tqdm
-  - pip:
-    - git+https://github.com/jcmgray/cotengra.git@althash

ci/requirements/py-jax.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - autoray>=0.6.7
-  # - cotengra>=0.5.3
+  - cotengra>=0.5.6
   - coverage
   - cytoolz
   - joblib
@@ -19,4 +19,3 @@ dependencies:
   - pip
   - pip:
     - jax[cpu]
-    - git+https://github.com/jcmgray/cotengra.git@althash

ci/requirements/py-slepc.yml

@@ -3,7 +3,7 @@ channels:
   - defaults
 dependencies:
   - autoray>=0.6.7
-  # - cotengra>=0.5.3
+  - cotengra>=0.5.6
   - coverage
   - cytoolz
   - joblib
@@ -21,6 +21,4 @@ dependencies:
   - scipy
   - slepc=*=*complex*
   - slepc4py
-  - tqdm
-  - pip:
-    - git+https://github.com/jcmgray/cotengra.git@althash
+  - tqdm

ci/requirements/py-tensorflow.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - autoray>=0.6.7
-  # - cotengra>=0.5.3
+  - cotengra>=0.5.6
   - coverage
   - cytoolz
   - joblib
@@ -19,4 +19,3 @@ dependencies:
   - pip
   - pip:
     - tensorflow
-    - git+https://github.com/jcmgray/cotengra.git@althash

ci/requirements/py-torch.yml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - autoray>=0.6.7
-  # - cotengra>=0.5.3
+  - cotengra>=0.5.6
   - coverage
   - cpuonly
   - cytoolz
@@ -18,6 +18,4 @@ dependencies:
   - pytest-cov
   - pytorch
   - scipy
-  - tqdm
-  - pip:
-    - git+https://github.com/jcmgray/cotengra.git@althash
+  - tqdm

setup.py

@@ -23,7 +23,7 @@ def readme():
     packages=find_packages(exclude=["deps", "tests*"]),
     install_requires=[
         "autoray>=0.6.7",
-        "cotengra>=0.5.3",
+        "cotengra>=0.5.6",
         "cytoolz>=0.8.0",
         "numba>=0.39",
         "numpy>=1.17",