tprak

commit c39b561596629c34b66ddd1bcf7675e589631fff
parent 2ae590c0123a3e613005cd3e586b2fde6170fc2b
Author: miksa <milutin@popovic.xyz>
Date:   Thu,  8 Apr 2021 11:59:25 +0200

plot fix

Diffstat:
M
sesh1/prog/Untitled Folder/Untitled.ipynb
 | 
20
+++++++++-----------
D
sesh1/prog/automated.png
 | 
0

M
sesh1/prog/fit_single.py
 | 
16
+++++++++-------

3 files changed, 18 insertions(+), 18 deletions(-)
diff --git a/sesh1/prog/Untitled Folder/Untitled.ipynb b/sesh1/prog/Untitled Folder/Untitled.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "collectible-genius",
+   "id": "automatic-recommendation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,14 +16,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "higher-looking",
+   "execution_count": 22,
+   "id": "demographic-distributor",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d1e1b835069141a68e30bf401c53cdb4",
+       "model_id": "fdcefe067eeb404086e0067ef6450be9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -37,10 +37,10 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.somefunc(m_q=0.7, g_q=0.2, m_w=0.8, g_w=0.2, e_w=0.002, a=-522.9, b=191.4, c=0.5)>"
+       "<function __main__.somefunc(m_q=0.7, g_q=0.2, m_w=0.8, g_w=0.2, e_w=0.002, a=-500, b=200, c=0.2)>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -55,9 +55,7 @@
     "    part3 = c*(1 + a*s + b*s**2)**2\n",
     "    return part1 * part2 * part3\n",
     "\n",
-    "def somefunc(m_q=0.7, g_q=0.2, m_w=0.8, g_w=0.2, e_w=2e-3, a=-522.90, b=191.40, c=0.5):\n",
-    "    data = np.loadtxt('../data/SND-VFF.txt')\n",
-    "    s = data[:,0]\n",
+    "def somefunc(m_q=0.7, g_q=0.2, m_w=0.8, g_w=0.2, e_w=2e-3, a=-500, b=200, c=0.2): data = np.loadtxt('../data/SND-VFF.txt') s = data[:,0]\n",
     "    F2 = data[:, 1]\n",
     "\n",
     "    p0 = [m_q, g_q, m_w, g_w, e_w, a, b, c]   # in GeV\n",
@@ -83,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "nonprofit-bolivia",
+   "id": "stock-julian",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -91,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "automotive-belgium",
+   "id": "elect-storm",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/sesh1/prog/automated.png b/sesh1/prog/automated.png
Binary files differ.
diff --git a/sesh1/prog/fit_single.py b/sesh1/prog/fit_single.py
@@ -24,19 +24,21 @@ def main():
     popt, pcov = curve_fit(model, s, F2, p0)
     popt, uncert = np.round(popt, 3), np.round(np.sqrt(np.diagonal(pcov)), 3)
 
+    s_model = np.linspace(s[0], s[-1], 500)
+
     plt.figure(figsize=[10, 7])
     plt.title('SND DATA FIT')
     plt.scatter(s, F2, marker='.', c='black')
-    plt.plot(s, model(s, *popt), color='red')
-    plt.annotate('Very Bad, A guessing game with the parameters', (0.15, 40))
-    plt.annotate(r'No $\omega$ resonance recognized by the fit', (0.15, 35))
+    plt.plot(s_model, model(s_model, *popt), color='red')
+    plt.annotate('A guessing game with the parameters', (0.15, 40))
+    plt.annotate(r'$\Gamma_{\omega}$ bad fit', (0.15, 38))
 
     plt.annotate(r'$M_{\rho} = $' + f'({popt[0]}' + r'$\pm$' + f'{uncert[0]}) GeV', (0.7, 40))
-    plt.annotate(r'$\Gamma_{\rho} = $' + f'({popt[1]}' + r'$\pm$' + f'{uncert[1]}) GeV', (0.7, 36))
-    plt.annotate(r'$M_{\omega} = $' + f'({popt[2]}' + r'$\pm$' + f'{uncert[2]}) GeV', (0.7, 34))
-    plt.annotate(r'$\Gamma_{\omega} = $' + f'({popt[3]}' + r'$\pm$' + f'{uncert[3]}) GeV', (0.7, 32))
+    plt.annotate(r'$\Gamma_{\rho} = $' + f'({popt[1]}' + r'$\pm$' + f'{uncert[1]}) GeV', (0.7, 38))
+    plt.annotate(r'$M_{\omega} = $' + f'({popt[2]}' + r'$\pm$' + f'{uncert[2]}) GeV', (0.7, 36))
+    plt.annotate(r'$\Gamma_{\omega} = $' + f'({popt[3]}' + r'$\pm$' + f'{uncert[3]}) GeV', (0.7, 34))
 
-    plt.savefig('automated.png')
+    plt.savefig('fit_single.png')
 
 
 if __name__ == "__main__":