From 4b8b7ea8e4e9668c32f5636664b3e34899cd6c1f Mon Sep 17 00:00:00 2001
From: jrob93 <jrobinson72@qub.ac.uk>
Date: Tue, 30 Jul 2024 14:43:09 +0100
Subject: [PATCH] demo nb

---
 notebooks/colour_functions_demo.ipynb | 32 +++++++++++++--------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/notebooks/colour_functions_demo.ipynb b/notebooks/colour_functions_demo.ipynb
index 0feeaa5..902f481 100644
--- a/notebooks/colour_functions_demo.ipynb
+++ b/notebooks/colour_functions_demo.ipynb
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1b35e509",
+   "id": "73871e56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "483aad4b",
+   "id": "63980010",
    "metadata": {},
    "source": [
     "Now we can inpsect how the colour of the object varies (or not) as a function of time. The adler function `col_obs_ref` will compare the latest observation in a given filter with observations in another filter. By setting parameter `N_ref` one can set how many past obsevrations to use when calculating the latest colour."
@@ -188,7 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2a32cb06",
+   "id": "bd0e0185",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,19 +223,19 @@
     "ax1 = plt.subplot(gs[0, 0])\n",
     "\n",
     "col_dict_list = []\n",
-    "for i in range(len(t_app) - 1):\n",
+    "for app_i in range(len(t_app) - 1):\n",
     "    # consider only one apparition\n",
-    "    if i != 3:\n",
+    "    if app_i != 3:\n",
     "        continue\n",
     "\n",
-    "    time_min = t_app[i]\n",
-    "    time_max = t_app[i + 1]\n",
+    "    time_min = t_app[app_i]\n",
+    "    time_max = t_app[app_i + 1]\n",
     "    print(time_min, time_max)\n",
     "\n",
     "    _df_obs_all = df_obs_all[\n",
     "        (df_obs_all[\"midPointMjdTai\"] >= time_min) & (df_obs_all[\"midPointMjdTai\"] < time_max)\n",
     "    ]\n",
-    "    print(i, len(_df_obs_all))\n",
+    "    print(app_i, len(_df_obs_all))\n",
     "    _time_max = np.amax(_df_obs_all[\"midPointMjdTai\"])\n",
     "\n",
     "    # get the phase curve model and observations for each filter\n",
@@ -259,9 +259,9 @@
     "\n",
     "    # simulate stepping through each filt_obs observation\n",
     "    x1 = time_min\n",
-    "    for i in range(len(df_obs)):\n",
-    "        x2 = df_obs.iloc[i][x_plot]\n",
-    "        print(i, x1, x2)\n",
+    "    for xi in range(len(df_obs)):\n",
+    "        x2 = df_obs.iloc[xi][x_plot]\n",
+    "        print(xi, x1, x2)\n",
     "\n",
     "        # do the colour finding function here\n",
     "        col_dict = col_obs_ref(\n",
@@ -279,7 +279,7 @@
     "        col_dict_list.append(col_dict)\n",
     "\n",
     "        # plot some lines to show the colour and mean reference\n",
-    "        ax1.vlines(df_obs.iloc[i][x_plot], df_obs.iloc[i][y_plot], col_dict[y_ref_col], color=\"k\", ls=\":\")\n",
+    "        ax1.vlines(df_obs.iloc[xi][x_plot], df_obs.iloc[xi][y_plot], col_dict[y_ref_col], color=\"k\", ls=\":\")\n",
     "        ax1.hlines(col_dict[y_ref_col], col_dict[x1_ref_col], col_dict[x2_ref_col], color=\"k\", ls=\"--\")\n",
     "\n",
     "# store running colour parameters as a dataframe\n",
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ab8b05a",
+   "id": "a7734122",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd42d97c",
+   "id": "0cd867d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1c71812",
+   "id": "05abfee3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cded6f36",
+   "id": "605a043a",
    "metadata": {},
    "outputs": [],
    "source": []