Merge branch 'master' into feature/fast_complex_exponential

Author: thangleiter

.readthedocs.yml

@@ -1,8 +1,16 @@
 version: 2
+
+build:
+  image: stable
+
 conda:
   environment: environment.yml
+  
 python:
-  version: 3
+  system_packages: true
   install:
     - method: pip
       path: .
+      extra_requirements:
+         - doc
+         - fancy_progressbar

.travis.yml

@@ -15,13 +15,12 @@ install:
   - hash -r
   - conda config --set always_yes yes --set changeps1 no
   - conda config --append channels conda-forge
-  - conda update -q conda
   # Useful for debugging any issues with conda
   - conda info -a
 
   - conda env create -f ./environment.yml
-  - source activate test-environment
-  - python setup.py install
+  - source activate filter_functions
+  - pip install .[fancy_progressbar,doc,tests]
 
 script:
   - "coverage run --rcfile=coverage.ini -m pytest"

README.md

@@ -1,11 +1,11 @@
-# `filter_functions`: A package for efficient numeric calculation of generalized filter functions
+# `filter_functions`: A package for efficient numerical calculation of generalized filter functions
 [![Coverage Status](https://coveralls.io/repos/github/qutech/filter_functions/badge.svg?branch=master)](https://coveralls.io/github/qutech/filter_functions?branch=master)
 [![Build Status](https://travis-ci.org/qutech/filter_functions.svg?branch=master)](https://travis-ci.org/qutech/filter_functions)
 [![Documentation Status](https://readthedocs.org/projects/filter-functions/badge/?version=latest)](https://filter-functions.readthedocs.io/en/latest/?badge=latest)
 
 Simply put, filter functions characterize a pulse's susceptibility to noise at a given frequency and can thus be used to gain insight into the decoherence of the system. The formalism allows for efficient calculation of several quantities of interest such as average gate fidelity. Moreover, the filter function of a composite pulse can be easily derived from those of the constituent pulses, allowing for efficient assembly and characterization of pulse sequences.
 
-Previously, filter functions have only been computed analytically for select pulses such as dynamical decoupling sequences [1], [2]. With this project we aim to provide a toolkit for calculating and inspecting filter functions for arbitrary pulses including pulses without analytic form such as one might get from numeric pulse optimization algorithms. 
+Previously, filter functions have only been computed analytically for select pulses such as dynamical decoupling sequences [1], [2]. With this project we aim to provide a toolkit for calculating and inspecting filter functions for arbitrary pulses including pulses without analytic form such as one might get from numerical pulse optimization algorithms. 
 
 The package is built to interface with [QuTiP](http://qutip.org/), a widely-used quantum toolbox for Python, and comes with extensive documentation and a test suite.
 

doc/source/examples/advanced_concatenation.ipynb

@@ -244,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

doc/source/examples/calculating_error_transfer_matrices.ipynb

@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import qutip as qt\n",
+    "from qutip.qip import operations\n",
     "\n",
     "import filter_functions as ff\n",
     "from filter_functions import util\n",
@@ -139,7 +139,7 @@
    "outputs": [],
    "source": [
     "cnot_12 = hadamard_2 @ cphase_12 @ hadamard_2\n",
-    "util.oper_equiv(cnot_12.total_Q, qt.cnot(control=0, target=1))"
+    "util.oper_equiv(cnot_12.total_Q, operations.cnot(control=0, target=1))"
    ]
   },
   {
@@ -168,7 +168,7 @@
     "\n",
     "cnot_21 = ff.remap(cnot_12, (1, 0), oper_identifier_mapping=mapping)\n",
     "\n",
-    "util.oper_equiv(cnot_21.total_Q, qt.cnot(control=1, target=0))\n",
+    "util.oper_equiv(cnot_21.total_Q, operations.cnot(control=1, target=0))\n",
     "print(cnot_21.c_oper_identifiers, cnot_21.n_oper_identifiers, sep='\\t')"
    ]
   },
@@ -230,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

doc/source/examples/extending_pulses.ipynb

@@ -252,6 +252,13 @@
    "source": [
     "The discrepancy to the analytical solution is due to the finite pulse width we have chosen here. We can see that, contrary to the FID-like filter function along $\\sigma_x$, the dephasing filter function of the SE pulse drops to (almost) zero for DC noise, affirming that indeed the pulse enhances coherence as it filters out frequencies slower than the pulse duration."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -270,7 +277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

doc/source/examples/getting_started.ipynb

@@ -138,7 +138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,