Merge b9c5864 into b7ae604

.travis.yml

@@ -18,7 +18,7 @@ install:
   - conda info -a
   - conda env create -f ./environment.yml
   - source activate filter_functions
-  - pip install .[fancy_progressbar,doc,tests]
+  - pip install .[all]
 
 script:
   - coverage run --rcfile=coverage.ini -m pytest

README.md

@@ -27,7 +27,8 @@ hadamard = ff.PulseSequence(H_c, H_n, dt)   # Central object representing a cont
 omega = ff.util.get_sample_frequencies(hadamard)
 F = hadamard.get_filter_function(omega)
 
-ff.plot_filter_function(hadamard)           # Filter function cached from before
+from filter_functions import plotting
+plotting.plot_filter_function(hadamard)     # Filter function cached from before
 ```
 
 ![Hadamard dephasing filter function](./doc/source/_static/hadamard.png)
@@ -59,16 +60,16 @@ infidelity = ff.infidelity(hadamard, spectrum, omega)
 ```
 
 ## Installation
-To install the package from PyPI, run `pip install filter_functions`. It is recommended to install QuTiP before by following the [instructions on their website](http://qutip.org/docs/latest/installation.html) rather than installing it through `pip`. To install the package from source run `python setup.py develop` to install using symlinks or `python setup.py install` without.
+To install the package from PyPI, run `pip install filter_functions`. If you require the optional features provided by QuTiP (visualizing Bloch sphere trajectories), it is recommended to install QuTiP before by following the [instructions on their website](http://qutip.org/docs/latest/installation.html) rather than installing it through `pip`. To install the package from source run `python setup.py develop` to install using symlinks or `python setup.py install` without.
 
-To install the optional dependencies (`tqdm` and `requests` for a fancy progress bar), run `pip install -e .[fancy_progressbar]` from the root directory.
+To install dependencies of optional extras (`tqdm` and `requests` for a fancy progress bar, `matplotlib` for plotting, `QuTiP` for Bloch sphere visualization), run `pip install -e .[extra]` where `extra` is one or more of `fancy_progressbar`, `plotting`, `bloch_sphere_visualization` from the root directory. To install all dependencies, including those needed to build the documentation and run the tests, use the extra `all`.
 
 ## Documentation
 You can find the documentation on [Readthedocs](https://filter-functions.readthedocs.io/en/latest/). It is built from Jupyter notebooks that can also be run interactively and are located [here](doc/source/examples). The notebooks explain how to use the package and thus make sense to follow chronologically as a first step. Furthermore, there are also a few example scripts in the [examples](examples) folder.
 
 The documentation including the example notebooks and an automatically generated API documentation can be built by running `make <format>` inside the *doc* directory where `<format>` is for example `html`.
 
-Building the documentation requires the following additional dependencies: `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`, `jupyter_client`, `ipython`, `ipykernel`, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
+Building the documentation requires the following additional dependencies: `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`, `ipython`, `ipykernel`, `jupyter_client`, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
 
 ## References
 [1]: Cywinski, L., Lutchyn, R. M., Nave, C. P., & Das Sarma, S. (2008). How to enhance dephasing time in superconducting qubits. Physical Review B - Condensed Matter and Materials Physics, 77(17), 1–11. [https://doi.org/10.1103/PhysRevB.77.174509](https://doi.org/10.1103/PhysRevB.77.174509)

doc/source/examples/advanced_concatenation.ipynb

@@ -88,7 +88,7 @@
     "d = 2\n",
     "H = np.empty((2, d, d), dtype=complex)\n",
     "\n",
-    "Id, Px, Py, Pz = ff.util.P_np\n",
+    "Id, Px, Py, Pz = ff.util.paulis\n",
     "H[0] = 1/2*Px\n",
     "H[1] = 1/2*Pz\n",
     "\n",
@@ -184,8 +184,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from filter_functions import plotting\n",
+    "\n",
     "for gate in gate_types:\n",
-    "    ff.plot_bloch_vector_evolution(H[gate], n_samples=501, figsize=(4, 4))"
+    "    plotting.plot_bloch_vector_evolution(H[gate], n_samples=501,\n",
+    "                                         figsize=(4, 4))"
    ]
   },
   {
@@ -203,9 +206,8 @@
    "source": [
     "pulses = ('Y', 'X_1', 'X_2')\n",
     "for gate_type, hadamard in H.items():\n",
-    "    fig, ax, leg = ff.plot_pulse_correlation_filter_function(hadamard,\n",
-    "                                                             xscale='linear',\n",
-    "                                                             figsize=(9, 6))\n",
+    "    fig, ax, leg = plotting.plot_pulse_correlation_filter_function(\n",
+    "        hadamard, xscale='linear', figsize=(9, 6))\n",
     "\n",
     "    # Adjust the titles to something more meaningful\n",
     "    for i in range(3):\n",

doc/source/examples/extending_pulses.ipynb

@@ -26,7 +26,7 @@
     "import filter_functions as ff\n",
     "from filter_functions import util\n",
     "\n",
-    "I, X, Y, Z = util.P_np\n",
+    "I, X, Y, Z = util.paulis\n",
     "\n",
     "# In order to be able to remap cached filter functions, we need a separable\n",
     "# basis like the Pauli basis\n",
@@ -209,8 +209,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from filter_functions import plotting\n",
+    "\n",
     "print(swap_14_23.is_cached('F'))\n",
-    "_ = ff.plot_filter_function(swap_14_23)"
+    "_ = plotting.plot_filter_function(swap_14_23)"
    ]
   }
  ],

doc/source/examples/getting_started.ipynb

@@ -126,7 +126,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from filter_functions import analytic\n",
+    "from filter_functions import analytic, plotting\n",
     "\n",
     "# Generate logarithmically spaced frequencies over an interval which generally\n",
     "# captures the important regions of the filter function. Since the filter\n",
@@ -135,7 +135,7 @@
     "omega = ff.util.get_sample_frequencies(FID, n_samples=200, spacing='log',\n",
     "                                       symmetric=False)\n",
     "# Plot the filter function.\n",
-    "fig, ax, legend = ff.plot_filter_function(FID, omega)\n",
+    "fig, ax, legend = plotting.plot_filter_function(FID, omega)\n",
     "# Plot the analytic solution into the same plot\n",
     "ax.plot(omega*tau, analytic.FID(omega*tau)/omega**2,\n",
     "        'x-', label='Analytical', zorder=0)\n",
@@ -161,7 +161,8 @@
     "# Initial state the +1 eigenstate of X\n",
     "psi_i = (qt.basis(2, 0) + qt.basis(2, 1))/np.sqrt(2)\n",
     "\n",
-    "ff.plot_bloch_vector_evolution(FID, psi0=psi_i, n_samples=101, figsize=(4, 4))"
+    "plotting.plot_bloch_vector_evolution(FID, psi0=psi_i, n_samples=101,\n",
+    "                                     figsize=(4, 4))"
    ]
   },
   {
@@ -261,14 +262,15 @@
     "omega = ff.util.get_sample_frequencies(SE, n_samples=400, spacing='log',\n",
     "                                       symmetric=False)\n",
     "# Plot the filter function\n",
-    "fig, ax, legend = ff.plot_filter_function(SE, omega, yscale='log')\n",
+    "fig, ax, legend = plotting.plot_filter_function(SE, omega, yscale='log')\n",
     "ax.set_ylim(bottom=1e-13)\n",
     "# Plot the analytical solution\n",
     "ax.plot(omega*tau, analytic.SE(omega*tau)/omega**2, '--',\n",
     "        label='Analytical (Bang-Bang)', zorder=0)\n",
     "legend = ax.legend()\n",
     "\n",
-    "ff.plot_bloch_vector_evolution(SE, psi0=psi_i, n_samples=101, figsize=(4, 4))"
+    "plotting.plot_bloch_vector_evolution(SE, psi0=psi_i, n_samples=101,\n",
+    "                                     figsize=(4, 4))"
    ]
   },
   {