Update NB 7.0 with f-strings

Author: jsdodge

notebooks/7.0-The-error-matrix.ipynb

@@ -68,7 +68,7 @@
     "\\chi^2(m,c;\\{x_k\\},\\{y_k\\},\\{\\alpha_k\\}) = \\sum_{k=1}^N\\frac{[y_k - (mx_k +  c)]^2}{\\alpha_k^2} \\label{eq:lsq}\\tag{1}\n",
     "$$\n",
     "\n",
-    "with respect to the parameters $m$ and $c$. We use a semicolon to emphasize the distinction between the *model parameters*, $m$ and $c$, and the *measurements*, $\\{x_k\\},\\{y_k\\}$, and $\\{\\alpha_k\\}$. Typically we are interested only in the functional dependence of $\\chi^2$ on the model parameters, holding the measurement variables fixed."
+    "with respect to the parameters $m$ and $c$. We use a semicolon to emphasize the distinction between the *model parameters*, $m$ and $c$, and the *measurements*, $\\{x_k\\},\\{y_k\\}$, and $\\{\\alpha_k\\}$. Typically, we are interested only in the functional dependence of $\\chi^2$ on the model parameters, holding the measurement variables fixed."
    ]
   },
   {
@@ -119,8 +119,8 @@
     "cAlpha = np.sqrt(pCov[1, 1])\n",
     "\n",
     "# Display formatted results\n",
-    "print(\"Model slope (mV/Hz):     {0:.2f} ± {1:.2f}\".format(mHat, mAlpha))\n",
-    "print(\"Model intercept (mV):    {0:.0f} ± {1:.0f}\".format(cHat, cAlpha))\n",
+    "print(f\"Model slope (mV/Hz):     {mHat:.2f} ± {mAlpha:.2f}\")\n",
+    "print(f\"Model intercept (mV):    {cHat:.0f} ± {cAlpha:.0f}\")\n",
     "print()\n",
     "\n",
     "# Evaluate the chi-squared function at the minimum and define contour levels\n",
@@ -231,7 +231,7 @@
     "print(\"Covariance matrix:\")\n",
     "print(np.array_str(pCov, precision=4, suppress_small=True))\n",
     "print()\n",
-    "print(\"Correlation coefficient: {0:.3f}\".format(rho_mc))\n",
+    "print(f\"Correlation coefficient: {rho_mc:.3f}\")\n",
     "print()"
    ]
   },
@@ -251,14 +251,14 @@
    "outputs": [],
    "source": [
     "trial = np.arange(1,77)\n",
-    "noiseScale = 0.1;\n",
+    "noiseScale = 0.1\n",
     "\n",
-    "mRange = [0.925, 1.075];\n",
-    "cRange = [-0.35, 0.35];\n",
-    "m = np.linspace(mRange[0], mRange[1], Nm);\n",
-    "c = np.linspace(cRange[0], cRange[1], Nc);\n",
+    "mRange = [0.925, 1.075]\n",
+    "cRange = [-0.35, 0.35]\n",
+    "m = np.linspace(mRange[0], mRange[1], Nm)\n",
+    "c = np.linspace(cRange[0], cRange[1], Nc)\n",
     "\n",
-    "chi2grid_trial = np.zeros([Nc, Nm]);\n",
+    "chi2grid_trial = np.zeros([Nc, Nm])\n",
     "\n",
     "fig, ax = plt.subplots(5, 5, sharex=True, sharey=True, figsize=[12.8, 9.6])\n",
     "\n",
@@ -293,7 +293,7 @@
     "           linewidths=1, linestyles='dotted')\n",
     "        ax[i][j].vlines(mHat_trial + mAlpha_trial*np.array([-1, 1]), c[0], c[-1], \n",
     "           linewidths=1, linestyles='dotted')\n",
-    "        ax[i][j].text(1.05, 0.25, \"({0:d})\".format(5*i+j+1))\n",
+    "        ax[i][j].text(1.05, 0.25, f\"({5*i+j+1:d})\")\n",
     "\n",
     "fig.text(0.5, 0.04, 'Gradient (trial/mean)', ha='center')\n",
     "fig.text(0.04, 0.5, 'Intercept (trial)', va='center', rotation='vertical')\n",
@@ -351,7 +351,7 @@
    "source": [
     "N_sim = 500\n",
     "trial = np.arange(1,77)\n",
-    "noiseScale = 0.1;\n",
+    "noiseScale = 0.1\n",
     "\n",
     "mHat_sim = np.zeros(N_sim)\n",
     "cHat_sim = np.zeros(N_sim)\n",
@@ -367,17 +367,17 @@
     "Nm = 50\n",
     "Nc = 50\n",
     "\n",
-    "mRange = [0.95, 1.06];\n",
-    "cRange = [-0.35, 0.35];\n",
-    "m = np.linspace(mRange[0], mRange[1], Nm);\n",
-    "c = np.linspace(cRange[0], cRange[1], Nc);\n",
+    "mRange = [0.95, 1.06]\n",
+    "cRange = [-0.35, 0.35]\n",
+    "m = np.linspace(mRange[0], mRange[1], Nm)\n",
+    "c = np.linspace(cRange[0], cRange[1], Nc)\n",
     "\n",
     "# Define the chi-squared function for m and c, given the data\n",
     "def chi2fun(m, c):\n",
     "    normres = (meanVal - (m*trial + c))/(noiseScale*trial)\n",
     "    return np.sum(normres**2)\n",
     "\n",
-    "chi2grid_sim = np.zeros([Nc, Nm]);\n",
+    "chi2grid_sim = np.zeros([Nc, Nm])\n",
     "for i in range(Nc):\n",
     "    for j in range(Nm):\n",
     "        chi2grid_sim[i, j] = chi2fun(m[j], c[i])\n",