Revised examples: PEP8, docstring, minor edits

Author: spors

nonrecursive_filters/introduction.ipynb

@@ -111,7 +111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

quantization/introduction.ipynb

@@ -1429,14 +1429,14 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
     "\n",
     "A = 1.2  # amplitude of signal\n",
     "Q = 1/10  # quantization stepsize\n",
     "N = 2000  # number of samples\n",
     "\n",
     "\n",
     "def uniform_midtread_quantizer(x, Q):\n",
+    "    '''Uniform mid-tread quantizer with limiter.'''\n",
     "    # limiter\n",
     "    x = np.copy(x)\n",
     "    idx = np.where(np.abs(x) >= 1)\n",
@@ -1448,6 +1448,7 @@
     "\n",
     "\n",
     "def plot_signals(x, xQ):\n",
+    "    '''Plot continuous, quantized and error signal.'''\n",
     "    e = xQ - x\n",
     "    plt.figure(figsize=(10, 6))\n",
     "    plt.plot(x, label=r'signal $x[k]$')\n",
@@ -2883,6 +2884,7 @@
     "\n",
     "\n",
     "def uniform_midrise_quantizer(x, Q):\n",
+    "    '''Uniform mid-rise quantizer with limiter.'''\n",
     "    # limiter\n",
     "    x = np.copy(x)\n",
     "    idx = np.where(np.abs(x) >= 1)\n",
@@ -2941,7 +2943,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

quantization/linear_uniform_characteristic.ipynb

@@ -3662,6 +3662,7 @@
     "\n",
     "\n",
     "def uniform_midtread_quantizer(x, Q):\n",
+    "    '''Uniform mid-tread quantizer with limiter.'''\n",
     "    # limiter\n",
     "    x = np.copy(x)\n",
     "    idx = np.where(x <= -1)\n",
@@ -3675,6 +3676,7 @@
     "\n",
     "\n",
     "def analyze_quantizer(x, e):\n",
+    "    '''Compute and plot PDF, CCF and PSD of quantizer.'''\n",
     "    # estimated PDF of error signal\n",
     "    pe, bins = np.histogram(e, bins=20, density=True, range=(-Q, Q))\n",
     "    # estimate cross-correlation between input and error\n",
@@ -4910,6 +4912,7 @@
     "\n",
     "\n",
     "def compute_SNR(a):\n",
+    "    '''Numerically evaluate SNR of a quantized normally distributed signal.'''\n",
     "    # compute input signal\n",
     "    x = np.random.normal(size=N, scale=a)\n",
     "    # quantize signal\n",
@@ -4922,6 +4925,7 @@
     "\n",
     "\n",
     "def plot_SNR(A, SNR):\n",
+    "    '''Plot SNR.'''\n",
     "    # plot results\n",
     "    plt.figure(figsize=(8, 4))\n",
     "    plt.plot(20*np.log10(A), SNR)\n",
@@ -6202,6 +6206,7 @@
     "\n",
     "\n",
     "def compute_SNR(a):\n",
+    "    '''Numerically evaluate SNR of a quantized Laplace distributed signal.'''\n",
     "    # compute input signal\n",
     "    x = np.random.laplace(size=N, scale=a/np.sqrt(2))\n",
     "    # quantize signal\n",