Naming convention (binsize -> bin_size, etc.) (NeuralEnsemble#316)

doc/style_guide.rst

@@ -42,6 +42,10 @@ throughout Elephant.
 
 .. code-block:: python
 
+    def pair_of_signals_example(spiketrain_i, spiketrain_j):
+        # Add '_i' and '_j' suffixes to a pair of signals, spiketrains or any
+        # other variables that come in pairs.
+
     def perfect_naming_of_parameters(spiketrains, spiketrain, reference_spiketrain,
                          target_spiketrain, signal, signals, max_iterations,
                          min_threshold, n_bins, n_surrogates, bin_size, max_size,
@@ -119,9 +123,8 @@ throughout Elephant.
             Therefore, do not use terms as `n` or `n_surr`, that are not
             immediately understood.
         bin_size : pq.Quantity or int
-            Separate the words by underscore. Do not use `binsize`. Old functions
-            which use `binsize` will be gradually refactored to conform to the new
-            convention.
+            Separate the words by underscore. Do not use `bin_size`. Old functions
+            which use `binsize` are deprecated.
         max_size : float
             Another example showing that words should be separated by underscores.
             This intersects with the naming convention for a maximum value.

doc/tutorials/asset.ipynb

@@ -185,7 +185,7 @@
    ],
    "source": [
     "# 2.1.1) create ASSET analysis object\n",
-    "# hint: try different binsizes, e.g. binsize=2.5, 3.5, 4.0 ms\n",
+    "# hint: try different bin sizes, e.g. bin_size=2.5, 3.5, 4.0 ms\n",
     "asset_obj = asset.ASSET(spiketrains, bin_size=3*pq.ms)\n",
     "\n",
     "# 2.1.2) compute the intersection matrix\n",

doc/tutorials/parallel.ipynb

@@ -256,7 +256,7 @@
    "source": [
     "### Example 2. Custom functions and positional argument\n",
     "\n",
-    "Sometimes you might want to iterate over the second (or third, etc.) argument of a function. To do this, you need to create a custom function that passes its first input argument into the right position of the original function. Below is an example of how to compute time histograms of spiketrains with different `binsize` values (the second argument)."
+    "Sometimes you might want to iterate over the second (or third, etc.) argument of a function. To do this, you need to create a custom function that passes its first input argument into the right position of the original function. Below is an example of how to compute time histograms of spiketrains with different `bin_size` values (the second argument)."
    ]
   },
   {
@@ -269,9 +269,9 @@
     "spiketrains = [homogeneous_poisson_process(rate, t_stop=10*pq.s) for _ in range(8)]\n",
     "\n",
     "# step 2: define your custom function\n",
-    "def my_custom_function(binsize):\n",
+    "def my_custom_function(bin_size):\n",
     "    # specify all custom key-word options here\n",
-    "    return time_histogram(spiketrains, binsize, output='counts')"
+    "    return time_histogram(spiketrains, bin_size, output='counts')"
    ]
   },
   {
@@ -282,9 +282,9 @@
    },
    "outputs": [],
    "source": [
-    "binsize_list = np.linspace(0.1, 1, num=8) * pq.s\n",
+    "bin_size_list = np.linspace(0.1, 1, num=8) * pq.s\n",
     "\n",
-    "time_hist = ProcessPoolExecutor().execute(my_custom_function, binsize_list)"
+    "time_hist = ProcessPoolExecutor().execute(my_custom_function, bin_size_list)"
    ]
   },
   {
@@ -334,7 +334,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`time_hist` contains 8 AnalogSignals - one AnalogSignal per `binsize` from `binsize_list`.\n",
+    "`time_hist` contains 8 AnalogSignals - one AnalogSignal per `bin_size` from `bin_size_list`.\n",
     "\n",
     "### Benchmark\n",
     "\n",
@@ -351,7 +351,7 @@
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "# initialize the iteration list\n",
-    "binsize_list = np.linspace(0.1, 1, 100) * pq.s"
+    "bin_size_list = np.linspace(0.1, 1, 100) * pq.s"
    ]
   },
   {
@@ -369,7 +369,7 @@
    ],
    "source": [
     "# sequential processing\n",
-    "%timeit [time_histogram(spiketrains, binsize) for binsize in binsize_list]"
+    "%timeit [time_histogram(spiketrains, bin_size) for bin_size in bin_size_list]"
    ]
   },
   {
@@ -387,7 +387,7 @@
    ],
    "source": [
     "# with parallelization\n",
-    "%timeit ProcessPoolExecutor(max_workers=4).execute(my_custom_function, binsize_list)"
+    "%timeit ProcessPoolExecutor(max_workers=4).execute(my_custom_function, bin_size_list)"
    ]
   }
  ],

doc/tutorials/unitary_event_analysis.ipynb

@@ -97,7 +97,7 @@
    },
    "outputs": [],
    "source": [
-    "def plot_UE(data,Js_dict,Js_sig,binsize,winsize,winstep, pat,N,t_winpos,**kwargs):\n",
+    "def plot_UE(data,Js_dict,Js_sig,bin_size,winsize,winstep, pat,N,t_winpos,**kwargs):\n",
     "    \"\"\"\n",
     "    Examples:\n",
     "    ---------\n",
@@ -150,9 +150,9 @@
     "                if len(x)>0:\n",
     "                    xx = []\n",
     "                    for j in sig_idx_win:\n",
-    "                        xx =np.append(xx,x[np.where((x*binsize>=t_winpos[j]) &(x*binsize<t_winpos[j] + winsize))])\n",
+    "                        xx =np.append(xx,x[np.where((x*bin_size>=t_winpos[j]) &(x*bin_size<t_winpos[j] + winsize))])\n",
     "                    plt.plot(\n",
-    "                        np.unique(xx)*binsize, np.ones_like(np.unique(xx))*tr + n*(num_tr + 1) + 1,\n",
+    "                        np.unique(xx)*bin_size, np.ones_like(np.unique(xx))*tr + n*(num_tr + 1) + 1,\n",
     "                        ms=arg_dict['marker_size'], marker = 's', ls = '',mfc='none', mec='r')\n",
     "        plt.axhline((tr + 2)*(n+1) ,lw = 2, color = 'k')\n",
     "    y_ticks_pos = np.arange(num_tr/2 + 1,N*(num_tr+1), num_tr+1)\n",
@@ -181,7 +181,7 @@
     "                     np.ones_like(data_tr[n].magnitude)*tr + n*(num_tr + 1) + 1,\n",
     "                     '.', markersize=0.5, color = 'k')\n",
     "            plt.plot(\n",
-    "                np.unique(Js_dict['indices']['trial'+str(tr)])*binsize,\n",
+    "                np.unique(Js_dict['indices']['trial'+str(tr)])*bin_size,\n",
     "                np.ones_like(np.unique(Js_dict['indices']['trial'+str(tr)]))*tr + n*(num_tr + 1) + 1,\n",
     "                ls = '',ms=arg_dict['marker_size'], marker = 's', markerfacecolor='none', markeredgecolor='c')\n",
     "        plt.axhline((tr + 2)*(n+1) ,lw = 2, color = 'k')\n",
@@ -300,10 +300,10 @@
    "outputs": [],
    "source": [
     "UE = ue.jointJ_window_analysis(\n",
-    "    spiketrains, binsize=5*pq.ms, winsize=100*pq.ms, winstep=10*pq.ms, pattern_hash=[3])\n",
+    "    spiketrains, bin_size=5*pq.ms, winsize=100*pq.ms, winstep=10*pq.ms, pattern_hash=[3])\n",
     "\n",
     "plot_UE(\n",
-    "        spiketrains, UE, ue.jointJ(0.05),binsize=5*pq.ms,winsize=100*pq.ms,winstep=10*pq.ms,\n",
+    "        spiketrains, UE, ue.jointJ(0.05),bin_size=5*pq.ms,winsize=100*pq.ms,winstep=10*pq.ms,\n",
     "        pat=ue.inverse_hash_from_pattern([3], N=2), N=2,\n",
     "        t_winpos=ue._winpos(0*pq.ms,spiketrains[0][0].t_stop,winsize=100*pq.ms,winstep=10*pq.ms))\n",
     "plt.show()"