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Change name of column and some whitespace fixes #635

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Jan 28, 2025
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Change name of column and some whitespace fixes #635

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26 changes: 13 additions & 13 deletions tutorials/Models-Quick-Fit/Models-Quick-Fit.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,7 @@
"from astroquery.vizier import Vizier\n",
"import scipy.optimize\n",
"# Make plots display in notebooks\n",
"%matplotlib inline "
"%matplotlib inline"
]
},
{
Expand Down Expand Up @@ -85,7 +85,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This catalog has a lot of information, but for this tutorial we are going to work only with periods and magnitudes. Let's grab them using the keywords `'Period'` and `__Ksmag__`. Note that `'e__Ksmag_'` refers to the error bars in the magnitude measurements."
"This catalog has a lot of information, but for this tutorial we are going to work only with periods and magnitudes. Let's grab them using the keywords `'Period'` and `<Ksmag>`. Note that `'e_<Ksmag>'` refers to the error bars in the magnitude measurements."
]
},
{
Expand All @@ -94,10 +94,10 @@
"metadata": {},
"outputs": [],
"source": [
"period = np.array(catalog[0]['Period']) \n",
"period = np.array(catalog[0]['Period'])\n",
"log_period = np.log10(period)\n",
"k_mag = np.array(catalog[0]['__Ksmag_'])\n",
"k_mag_err = np.array(catalog[0]['e__Ksmag_'])"
"k_mag = np.array(catalog[0]['<Ksmag>'])\n",
"k_mag_err = np.array(catalog[0]['e_<Ksmag>'])"
]
},
{
Expand Down Expand Up @@ -214,7 +214,7 @@
"metadata": {},
"outputs": [],
"source": [
"fitter = fitting.LinearLSQFitter() "
"fitter = fitting.LinearLSQFitter()"
]
},
{
Expand Down Expand Up @@ -257,7 +257,7 @@
"outputs": [],
"source": [
"plt.errorbar(log_period,k_mag,k_mag_err,fmt='k.')\n",
"plt.plot(log_period, best_fit(log_period), color='g', linewidth=3) \n",
"plt.plot(log_period, best_fit(log_period), color='g', linewidth=3)\n",
"plt.xlabel(r'$\\log_{10}$(Period [days])')\n",
"plt.ylabel('Ks')"
]
Expand Down Expand Up @@ -318,11 +318,11 @@
"source": [
"N = 100\n",
"x1 = np.linspace(0, 4, N) # Makes an array from 0 to 4 of N elements\n",
"y1 = x1**3 - 6*x1**2 + 12*x1 - 9 \n",
"y1 = x1**3 - 6*x1**2 + 12*x1 - 9\n",
"# Now we add some noise to the data\n",
"y1 += np.random.normal(0, 2, size=len(y1)) #One way to add random gaussian noise\n",
"sigma = 1.5\n",
"y1_err = np.ones(N)*sigma "
"y1_err = np.ones(N)*sigma"
]
},
{
Expand All @@ -339,7 +339,7 @@
"outputs": [],
"source": [
"plt.errorbar(x1, y1, yerr=y1_err,fmt='k.')\n",
"plt.xlabel('$x_1$') \n",
"plt.xlabel('$x_1$')\n",
"plt.ylabel('$y_1$')"
]
},
Expand All @@ -357,7 +357,7 @@
"outputs": [],
"source": [
"model_poly = models.Polynomial1D(degree=3)\n",
"fitter_poly = fitting.LinearLSQFitter() \n",
"fitter_poly = fitting.LinearLSQFitter()\n",
"best_fit_poly = fitter_poly(model_poly, x1, y1, weights = 1.0/y1_err)"
]
},
Expand Down Expand Up @@ -477,7 +477,7 @@
"outputs": [],
"source": [
"plt.errorbar(x1, y1, yerr=y1_err,fmt='k.')\n",
"plt.plot(x1, best_fit_poly(x1), color='r', linewidth=3, label='LinearLSQFitter()') \n",
"plt.plot(x1, best_fit_poly(x1), color='r', linewidth=3, label='LinearLSQFitter()')\n",
"plt.plot(x1, best_fit_poly_2(x1), color='g', linewidth=3, label='SimplexLSQFitter()')\n",
"plt.xlabel(r'$\\log_{10}$(Period [days])')\n",
"plt.ylabel('Ks')\n",
Expand Down Expand Up @@ -753,7 +753,7 @@
"y3 = 5.0 * np.sin(2 * np.pi * x3)\n",
"y3 = np.array([y_point + np.random.normal(0, 1) for y_point in y3])\n",
"sigma = 1.5\n",
"y3_err = np.ones(N)*sigma "
"y3_err = np.ones(N)*sigma"
]
},
{
Expand Down
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