commit 981d06d34cde53fcf8831896970d939c9c6bb5ab Author: Johanne Trotin Date: Sun Mar 30 12:32:15 2025 +0200 Expé E4C v6 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1e6c9a1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +venv +staticfiles +./db.sqlite3 +.idea +*~ +*.sqlite3 +_static_root +_bots*s +__temp* +__pycache__/ +*.py[cod] +.DS_Store +*.otreezip \ No newline at end of file diff --git a/.ipynb_checkpoints/Procfile-checkpoint b/.ipynb_checkpoints/Procfile-checkpoint new file mode 100644 index 0000000..9156b92 --- /dev/null +++ b/.ipynb_checkpoints/Procfile-checkpoint @@ -0,0 +1,2 @@ +web: otree prodserver1of2 +worker: otree prodserver2of2 diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/data-checkpoint.txt b/.ipynb_checkpoints/data-checkpoint.txt new file mode 100644 index 0000000..e2777c8 --- /dev/null +++ b/.ipynb_checkpoints/data-checkpoint.txt @@ -0,0 +1 @@ +{"people": [{"name": "Scott", "website": "stackabuse.com", "from": "Nebraska"}, {"name": "Larry", "website": "google.com", "from": "Michigan"}, {"name": "Tim", "website": "apple.com", "from": "Alabama"}]} \ No newline at end of file diff --git a/.ipynb_checkpoints/fig_groupe2-checkpoint.png b/.ipynb_checkpoints/fig_groupe2-checkpoint.png new file mode 100644 index 0000000..76a94dd Binary files /dev/null and b/.ipynb_checkpoints/fig_groupe2-checkpoint.png differ diff --git a/.ipynb_checkpoints/manage-checkpoint.py b/.ipynb_checkpoints/manage-checkpoint.py new file mode 100644 index 0000000..d6f4caa --- /dev/null +++ b/.ipynb_checkpoints/manage-checkpoint.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python +import os +import sys + + +if __name__ == "__main__": + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "settings") + + from otree.management.cli import execute_from_command_line + + execute_from_command_line(sys.argv, script_file=__file__) diff --git a/.ipynb_checkpoints/multiples_chocs_figures-checkpoint.ipynb b/.ipynb_checkpoints/multiples_chocs_figures-checkpoint.ipynb new file mode 100644 index 0000000..cd93544 --- /dev/null +++ b/.ipynb_checkpoints/multiples_chocs_figures-checkpoint.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "from matplotlib.colors import colorConverter as cc\n", + "import numpy as np\n", + " \n", + "def plot_mean_and_CI(mean, lb, ub, color_mean=None, color_shading=None):\n", + " # plot the shaded range of the confidence intervals\n", + " plt.fill_between(range(mean.shape[0]), ub, lb,\n", + " color=color_shading, alpha=.5)\n", + " # plot the mean on top\n", + " plt.plot(mean, color_mean)\n", + " \n", + "# generate 3 sets of random means and confidence intervals to plot\n", + "mean0 = np.random.random(50)\n", + "ub0 = mean0 + np.random.random(50) + .5\n", + "lb0 = mean0 - np.random.random(50) - .5\n", + " \n", + "mean1 = np.random.random(50) + 2\n", + "ub1 = mean1 + np.random.random(50) + .5\n", + "lb1 = mean1 - np.random.random(50) - .5\n", + " \n", + "mean2 = np.random.random(50) -1\n", + "ub2 = mean2 + np.random.random(50) + .5\n", + "lb2 = mean2 - np.random.random(50) - .5\n", + " \n", + "# plot the data\n", + "fig = plt.figure(1, figsize=(7, 2.5))\n", + "plot_mean_and_CI(mean0, ub0, lb0, color_mean='k', color_shading='k')\n", + "plot_mean_and_CI(mean1, ub1, lb1, color_mean='b', color_shading='b')\n", + "plot_mean_and_CI(mean2, ub2, lb2, color_mean='g--', color_shading='g')\n", + " \n", + "class LegendObject(object):\n", + " def __init__(self, facecolor='red', edgecolor='white', dashed=False):\n", + " self.facecolor = facecolor\n", + " self.edgecolor = edgecolor\n", + " self.dashed = dashed\n", + " \n", + " def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n", + " x0, y0 = handlebox.xdescent, handlebox.ydescent\n", + " width, height = handlebox.width, handlebox.height\n", + " patch = mpatches.Rectangle(\n", + " # create a rectangle that is filled with color\n", + " [x0, y0], width, height, facecolor=self.facecolor,\n", + " # and whose edges are the faded color\n", + " edgecolor=self.edgecolor, lw=3)\n", + " handlebox.add_artist(patch)\n", + " \n", + " # if we're creating the legend for a dashed line,\n", + " # manually add the dash in to our rectangle\n", + " if self.dashed:\n", + " patch1 = mpatches.Rectangle(\n", + " [x0 + 2*width/5, y0], width/5, height, facecolor=self.edgecolor,\n", + " transform=handlebox.get_transform())\n", + " handlebox.add_artist(patch1)\n", + " \n", + " return patch\n", + " \n", + "bg = np.array([1, 1, 1]) # background of the legend is white\n", + "colors = ['black', 'blue', 'green']\n", + "# with alpha = .5, the faded color is the average of the background and color\n", + "colors_faded = [(np.array(cc.to_rgb(color)) + bg) / 2.0 for color in colors]\n", + " \n", + "plt.legend([0, 1, 2], ['Data 0', 'Data 1', 'Data 2'],\n", + " handler_map={\n", + " 0: LegendObject(colors[0], colors_faded[0]),\n", + " 1: LegendObject(colors[1], colors_faded[1]),\n", + " 2: LegendObject(colors[2], colors_faded[2], dashed=True),\n", + " })\n", + " \n", + "plt.title('Example mean and confidence interval plot')\n", + "plt.tight_layout()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.37747772, 0.07693753, 0.1940634 , 0.15176569, 0.14293999,\n", + " 0.60894227, 0.09985447, 0.41118113, 0.68274936, 0.85285374,\n", + " 0.4541564 , 0.63333913, 0.35620657, 0.51059929, 0.85266049,\n", + " 0.23237531, 0.23283028, 0.47911222, 0.40544178, 0.81126309,\n", + " 0.86811543, 0.35334637, 0.86403165, 0.86593378, 0.25916208,\n", + " 0.21492518, 0.92107401, 0.32022366, 0.93038498, 0.4327189 ,\n", + " 0.24912534, 0.77540019, 0.13939365, 0.79231252, 0.65351859,\n", + " 0.11080199, 0.48425663, 0.7100898 , 0.02117045, 0.41127722,\n", + " 0.16392522, 0.32550661, 0.95452695, 0.42164106, 0.22468096,\n", + " 0.65491521, 0.36794668, 0.78792547, 0.17727132, 0.82141194])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/nb_multiples_chocs-checkpoint.ipynb b/.ipynb_checkpoints/nb_multiples_chocs-checkpoint.ipynb new file mode 100644 index 0000000..0411718 --- /dev/null +++ b/.ipynb_checkpoints/nb_multiples_chocs-checkpoint.ipynb @@ -0,0 +1,5117 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "x1 = [1, 2, 2, 3, 4, 4, 4, 4, 4, 5, 5]\n", + "x2 = [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 5, 5, 5]\n", + "bins = [x + 0.5 for x in range(0, 6)]\n", + "plt.hist([x1, x2], bins = bins, color = ['yellow', 'green'],\n", + " edgecolor = 'red', hatch = '/', label = ['x1', 'x2'],\n", + " histtype = 'bar') # bar est le defaut\n", + "plt.ylabel('valeurs')\n", + "plt.xlabel('nombres')\n", + "plt.title('2 series')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.read_stata('/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_Avril_2021/plm_MC_190712_v1.dta')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
var1periodnmarketdirec_shockpost_shock1_npost_shock2_nuuddt0t_shock...r6_negr11_negr6_shockr11_shockstd_riskmean_risk_v1median_risk_v1numvarnumvarupnumvardown
0170703_08501100.0NaN1001...0.00.00.00.02.8644.0883.75NaNNaNNaN
1170703_08502100.0NaN1001...0.00.00.00.03.0764.9385.002.00.02.0
2170703_08503100.0NaN1001...0.00.00.00.03.1704.4753.751.01.00.0
3170703_08504100.0NaN1001...0.00.00.00.03.5955.6886.002.00.02.0
4170703_08505100.0NaN1001...0.00.00.00.03.7535.0753.751.01.00.0
\n", + "

5 rows × 65 columns

\n", + "
" + ], + "text/plain": [ + " var1 period nmarket direc_shock post_shock1_n post_shock2_n \\\n", + "0 170703_0850 1 1 0 0.0 NaN \n", + "1 170703_0850 2 1 0 0.0 NaN \n", + "2 170703_0850 3 1 0 0.0 NaN \n", + "3 170703_0850 4 1 0 0.0 NaN \n", + "4 170703_0850 5 1 0 0.0 NaN \n", + "\n", + " uu dd t0 t_shock ... r6_neg r11_neg r6_shock r11_shock std_risk \\\n", + "0 1 0 0 1 ... 0.0 0.0 0.0 0.0 2.864 \n", + "1 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.076 \n", + "2 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.170 \n", + "3 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.595 \n", + "4 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.753 \n", + "\n", + " mean_risk_v1 median_risk_v1 numvar numvarup numvardown \n", + "0 4.088 3.75 NaN NaN NaN \n", + "1 4.938 5.00 2.0 0.0 2.0 \n", + "2 4.475 3.75 1.0 1.0 0.0 \n", + "3 5.688 6.00 2.0 0.0 2.0 \n", + "4 5.075 3.75 1.0 1.0 0.0 \n", + "\n", + "[5 rows x 65 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ptftrad_nrd_nt0uuddperiod
0-10.003.333333-3.3333330101
10.001.666667-1.6666670102
25.001.666667-0.5555560103
310.002.0833330.4166670104
45.052.0033330.6700000105
\n", + "
" + ], + "text/plain": [ + " ptft rad_n rd_n t0 uu dd period\n", + "0 -10.00 3.333333 -3.333333 0 1 0 1\n", + "1 0.00 1.666667 -1.666667 0 1 0 2\n", + "2 5.00 1.666667 -0.555556 0 1 0 3\n", + "3 10.00 2.083333 0.416667 0 1 0 4\n", + "4 5.05 2.003333 0.670000 0 1 0 5" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ptft = df[['ptft','rad_n', 'rd_n','t0','uu','dd','period']]\n", + "ptft.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ptft_p1 = ptft[ptft['period']<6]\n", + "ptft_p2 = ptft[(ptft.period >5) & (ptft.period < 11) ]\n", + "ptft_p3 = ptft[(ptft.period >10)]\n", + "ptft_p1_t0 = ptft_p1[ptft_p1.t0 == 1]\n", + "ptft_p1_uu = ptft_p1[ptft_p1.uu == 1]\n", + "ptft_p1_dd = ptft_p1[ptft_p1.dd == 1]\n", + "ptft_p2_t0 = ptft_p2[ptft_p2.t0 == 1]\n", + "ptft_p2_uu = ptft_p2[ptft_p2.uu == 1]\n", + "ptft_p2_dd = ptft_p2[ptft_p2.dd == 1]\n", + "ptft_p3_t0 = ptft_p3[ptft_p3.t0 == 1]\n", + "ptft_p3_uu = ptft_p3[ptft_p3.uu == 1]\n", + "ptft_p3_dd = ptft_p3[ptft_p3.dd == 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import statistics as st\n", + "v11 = st.mean(ptft_p1_t0.ptft)\n", + "v12 = st.mean(ptft_p2_t0.ptft)\n", + "v13 = st.mean(ptft_p3_t0.ptft)\n", + "v21 = st.mean(ptft_p1_uu.ptft)\n", + "v22 = st.mean(ptft_p2_uu.ptft)\n", + "v23 = st.mean(ptft_p3_uu.ptft)\n", + "v31 = st.mean(ptft_p1_dd.ptft)\n", + "v32 = st.mean(ptft_p2_dd.ptft)\n", + "v33 = st.mean(ptft_p3_dd.ptft)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "data = [[v11, v21, v31],\n", + "[v12, v22, v32],\n", + "[v13, v23, v33]]\n", + "X = np.arange(3)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n", + "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n", + "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import statistics as st\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "v11 = st.mean(ptft_p1_t0.rad_n)\n", + "v12 = st.mean(ptft_p2_t0.rad_n)\n", + "v13 = st.mean(ptft_p3_t0.rad_n)\n", + "v21 = st.mean(ptft_p1_uu.rad_n)\n", + "v22 = st.mean(ptft_p2_uu.rad_n)\n", + "v23 = st.mean(ptft_p3_uu.rad_n)\n", + "v31 = st.mean(ptft_p1_dd.rad_n)\n", + "v32 = st.mean(ptft_p2_dd.rad_n)\n", + "v33 = st.mean(ptft_p3_dd.rad_n)\n", + "data = [[v11, v21, v31],\n", + "[v12, v22, v32],\n", + "[v13, v23, v33]]\n", + "X = np.arange(3)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n", + "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n", + "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import statistics as st\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "v11 = st.median(ptft_p1_t0.rad_n)\n", + "v12 = st.median(ptft_p2_t0.rad_n)\n", + "v13 = st.median(ptft_p3_t0.rad_n)\n", + "v21 = st.median(ptft_p1_uu.rad_n)\n", + "v22 = st.median(ptft_p2_uu.rad_n)\n", + "v23 = st.median(ptft_p3_uu.rad_n)\n", + "v31 = st.median(ptft_p1_dd.rad_n)\n", + "v32 = st.median(ptft_p2_dd.rad_n)\n", + "v33 = st.median(ptft_p3_dd.rad_n)\n", + "data = [[v11, v21, v31],\n", + "[v12, v22, v32],\n", + "[v13, v23, v33]]\n", + "X = np.arange(3)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n", + "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n", + "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import statistics as st\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "v11 = st.mean(ptft_p1_t0.rd_n)\n", + "v12 = st.mean(ptft_p2_t0.rd_n)\n", + "v13 = st.mean(ptft_p3_t0.rd_n)\n", + "v21 = st.mean(ptft_p1_uu.rd_n)\n", + "v22 = st.mean(ptft_p2_uu.rd_n)\n", + "v23 = st.mean(ptft_p3_uu.rd_n)\n", + "v31 = st.mean(ptft_p1_dd.rd_n)\n", + "v32 = st.mean(ptft_p2_dd.rd_n)\n", + "v33 = st.mean(ptft_p3_dd.rd_n)\n", + "data = [[v11, v21, v31],\n", + "[v12, v22, v32],\n", + "[v13, v23, v33]]\n", + "X = np.arange(3)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n", + "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n", + "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import statistics as st\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "v11 = st.median(ptft_p1_t0.rd_n)\n", + "v12 = st.median(ptft_p2_t0.rd_n)\n", + "v13 = st.median(ptft_p3_t0.rd_n)\n", + "v21 = st.median(ptft_p1_uu.rd_n)\n", + "v22 = st.median(ptft_p2_uu.rd_n)\n", + "v23 = st.median(ptft_p3_uu.rd_n)\n", + "v31 = st.median(ptft_p1_dd.rd_n)\n", + "v32 = st.median(ptft_p2_dd.rd_n)\n", + "v33 = st.median(ptft_p3_dd.rd_n)\n", + "data = [[v11, v21, v31],\n", + "[v12, v22, v32],\n", + "[v13, v23, v33]]\n", + "X = np.arange(3)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n", + "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n", + "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "

df1

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroup
0BobAccounting
1JakeEngineering
2LisaEngineering
3SueHR
\n", + "
\n", + "
\n", + "
\n", + "

df2

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeehire_date
0Lisa2004
1Bob2008
2Jake2012
3Sue2014
\n", + "
\n", + "
" + ], + "text/plain": [ + "df1\n", + " employee group\n", + "0 Bob Accounting\n", + "1 Jake Engineering\n", + "2 Lisa Engineering\n", + "3 Sue HR\n", + "\n", + "df2\n", + " employee hire_date\n", + "0 Lisa 2004\n", + "1 Bob 2008\n", + "2 Jake 2012\n", + "3 Sue 2014" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "class display(object):\n", + " \"\"\"Display HTML representation of multiple objects\"\"\"\n", + " template = \"\"\"
\n", + "

{0}

{1}\n", + "
\"\"\"\n", + " def __init__(self, *args):\n", + " self.args = args\n", + " \n", + " def _repr_html_(self):\n", + " return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n", + " for a in self.args)\n", + " \n", + " def __repr__(self):\n", + " return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n", + " for a in self.args)\n", + "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n", + " 'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n", + "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n", + " 'hire_date': [2004, 2008, 2012, 2014]})\n", + "display('df1', 'df2') " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegrouphire_date
0BobAccounting2008
1JakeEngineering2012
2LisaEngineering2004
3SueHR2014
\n", + "
" + ], + "text/plain": [ + " employee group hire_date\n", + "0 Bob Accounting 2008\n", + "1 Jake Engineering 2012\n", + "2 Lisa Engineering 2004\n", + "3 Sue HR 2014" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = pd.merge(df1, df2)\n", + "#df3" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "df4 = pd.concat([df1, df1])\n", + "#df4" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegrouptest
0BobAccounting1
1JakeEngineering1
2LisaEngineering3
3SueHR3
0BobAccounting1
1JakeEngineering1
2LisaEngineering3
3SueHR3
\n", + "
" + ], + "text/plain": [ + " employee group test\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 1\n", + "2 Lisa Engineering 3\n", + "3 Sue HR 3\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 1\n", + "2 Lisa Engineering 3\n", + "3 Sue HR 3" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupAddress
0BobAccounting0
1JakeEngineering0
2LisaEngineering0
3SueHR0
\n", + "
" + ], + "text/plain": [ + " employee group Address\n", + "0 Bob Accounting 0\n", + "1 Jake Engineering 0\n", + "2 Lisa Engineering 0\n", + "3 Sue HR 0" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "df3 = df1.copy()\n", + "df3['Address']=0\n", + "df3 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16\n" + ] + } + ], + "source": [ + "b=0\n", + "a=1\n", + "for i in range(0,4):\n", + " df3.at[b,'Address']=a\n", + " a+=5\n", + " b+=1\n", + " print(b) \n", + "print(df3) " + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupAddress
0BobAccounting1
1JakeEngineering6
2LisaEngineering11
3SueHR16
\n", + "
" + ], + "text/plain": [ + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4=df3.copy()\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupAddress
0BobAccounting2
1JakeEngineering7
2LisaEngineering12
3SueHR17
\n", + "
" + ], + "text/plain": [ + " employee group Address\n", + "0 Bob Accounting 2\n", + "1 Jake Engineering 7\n", + "2 Lisa Engineering 12\n", + "3 Sue HR 17" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b=0\n", + "a=2\n", + "for i in range(0,4):\n", + " df3.at[b,'Address']=a\n", + " a+=5\n", + " b+=1 \n", + "df3 " + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupAddress
0BobAccounting1
1JakeEngineering6
2LisaEngineering11
3SueHR16
0BobAccounting2
1JakeEngineering7
2LisaEngineering12
3SueHR17
\n", + "
" + ], + "text/plain": [ + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16\n", + "0 Bob Accounting 2\n", + "1 Jake Engineering 7\n", + "2 Lisa Engineering 12\n", + "3 Sue HR 17" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4 = pd.concat([df4, df3])\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "b=0\n", + "c+=1\n", + "a=c" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A\n", + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16\n", + "0 Bob Accounting 2\n", + "1 Jake Engineering 7\n", + "2 Lisa Engineering 12\n", + "3 Sue HR 17\n", + "A\n", + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16\n", + "0 Bob Accounting 2\n", + "1 Jake Engineering 7\n", + "2 Lisa Engineering 12\n", + "3 Sue HR 17\n", + "0 Bob Accounting 3\n", + "1 Jake Engineering 8\n", + "2 Lisa Engineering 13\n", + "3 Sue HR 18\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
employeegroupAddress
0BobAccounting1
1JakeEngineering6
2LisaEngineering11
3SueHR16
0BobAccounting2
1JakeEngineering7
2LisaEngineering12
3SueHR17
0BobAccounting3
1JakeEngineering8
2LisaEngineering13
3SueHR18
\n", + "
" + ], + "text/plain": [ + " employee group Address\n", + "0 Bob Accounting 1\n", + "1 Jake Engineering 6\n", + "2 Lisa Engineering 11\n", + "3 Sue HR 16\n", + "0 Bob Accounting 2\n", + "1 Jake Engineering 7\n", + "2 Lisa Engineering 12\n", + "3 Sue HR 17\n", + "0 Bob Accounting 3\n", + "1 Jake Engineering 8\n", + "2 Lisa Engineering 13\n", + "3 Sue HR 18" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "df3 = df1.copy()\n", + "df3['Address']=0\n", + "#\n", + "b=0\n", + "a=1\n", + "for i in range(0,4):\n", + " df3.at[b,'Address']=a\n", + " a+=5\n", + " b+=1 \n", + "# \n", + "df4=df3.copy()\n", + "b=0\n", + "a=2\n", + "c=2\n", + "for i in range(1,3):\n", + " for i in range(0,4):\n", + " df3.at[b,'Address']=a\n", + " a+=5\n", + " b+=1 \n", + " df4 = pd.concat([df4, df3])\n", + " b=0\n", + " c+=1\n", + " a=c\n", + " print(\"A\")\n", + " print(df4)\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df1' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# création de la colonne count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined" + ] + } + ], + "source": [ + "import numpy as np\n", + "df3 = df1.copy()\n", + "df3['count']=0\n", + "# création de la colonne count\n", + "b=0\n", + "a=0 #1\n", + "n=4 # 4 lignes // 2160 lignes \n", + "for i in range(0,n):\n", + " df3.at[b,'count']=a\n", + " a+= 5 #+=240\n", + " b+=1 \n", + "# on va répliquer la même chose 240 fois pour avoir une observation \n", + "# par seconde par sujet \n", + "df3['secnd']=0 #@\n", + "df4=df3.copy()\n", + "b=0\n", + "a=1 # 2\n", + "c=1 #2\n", + "n=4 # 4: répéter 5 fois // 240 / nbr de secondes 2min\n", + "v=4 # 2160 / (8 sujets * 18 marchés * 15 périodes)\n", + "for i in range(0,n):\n", + " for i in range(0,v):\n", + " df3.at[b,'count']=a\n", + " a+= 5 # +=120\n", + " b+=1\n", + " df3['secnd']=c #@ \n", + " df4 = pd.concat([df4, df3])\n", + " b=0\n", + " c+=1\n", + " a=c\n", + "# print(\"A\")\n", + "# print(df4)\n", + "df4\n", + "df5 = df4.sort_values(by=['count'])\n", + "df5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# pour importer une table Excel\n", + "import pandas as pd\n", + "ddf = pd.read_excel('/Users/waelbousselmi/Desktop/data_mc_2021.xlsx')\n", + "#print(ddf)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "170703_0850 2160\n", + "nmarket 2160\n", + "traitement 2160\n", + "treament 2160\n", + "Period 2160\n", + "Subject 2160\n", + "UpperBound1 2160\n", + "MedianP1 2160\n", + "LowerBound1 2160\n", + "StartWealth 2160\n", + "EndWealth 2160\n", + "variation_wealth 2160\n", + "GainPrevision 2160\n", + "GainPrevisionCumule 2160\n", + "Risk_debutperiode 2160\n", + "Risk_finperiode 2160\n", + "variation_risk 2160\n", + "StartMoney 2160\n", + "StartStock 2160\n", + "EndMoney 2160\n", + "EndStock 2160\n", + "VarMoney 2160\n", + "VarStock 2160\n", + "TResult 2160\n", + "TotalDividende 2160\n", + "DividendeCumule 2160\n", + "MaxPrice 2160\n", + "MinPrice 2160\n", + "MeanPrice 2160\n", + "MedianPrice 2160\n", + "GainFinal 2160\n", + "GainFinalEUR 2160\n", + "GainForecastECU 2160\n", + "GainForecastEUR 2160\n", + "CompteEpargne 2160\n", + "CompteEpargneEUR 2160\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
170703_0850nmarkettraitementtreamentPeriodSubjectUpperBound1MedianP1LowerBound1StartWealth...MedianPriceGainFinalGainFinalEURGainForecastECUGainForecastEURCompteEpargneCompteEpargneEURcountsecndsecnd2
0170703_08501HH011320.0320.0320.06750.0...290.007350.021.781012.503.00877.502.60000
0170703_08501HH011320.0320.0320.06750.0...290.007350.021.781012.503.00877.502.60111
0170703_08501HH011320.0320.0320.06750.0...290.007350.021.781012.503.00877.502.60222
0170703_08501HH011320.0320.0320.06750.0...290.007350.021.781012.503.00877.502.60333
0170703_08501HH011320.0320.0320.06750.0...290.007350.021.781012.503.00877.502.60444
..................................................................
2159171026_083818NO2158-111.0-111.0-111.07795.0...300.757810.023.142413.447.152128.446.312613551161796
2159171026_083818NO2158-111.0-111.0-111.07795.0...300.757810.023.142413.447.152128.446.312613561171797
2159171026_083818NO2158-111.0-111.0-111.07795.0...300.757810.023.142413.447.152128.446.312613571181798
2159171026_083818NO2158-111.0-111.0-111.07795.0...300.757810.023.142413.447.152128.446.312613581191799
2159171026_083818NO2158-111.0-111.0-111.07795.0...300.757810.023.142413.447.152128.446.312613591201800
\n", + "

261360 rows × 39 columns

\n", + "
" + ], + "text/plain": [ + " 170703_0850 nmarket traitement treament Period Subject UpperBound1 \\\n", + "0 170703_0850 1 HH 0 1 1 320.0 \n", + "0 170703_0850 1 HH 0 1 1 320.0 \n", + "0 170703_0850 1 HH 0 1 1 320.0 \n", + "0 170703_0850 1 HH 0 1 1 320.0 \n", + "0 170703_0850 1 HH 0 1 1 320.0 \n", + "... ... ... ... ... ... ... ... \n", + "2159 171026_0838 18 NO 2 15 8 -111.0 \n", + "2159 171026_0838 18 NO 2 15 8 -111.0 \n", + "2159 171026_0838 18 NO 2 15 8 -111.0 \n", + "2159 171026_0838 18 NO 2 15 8 -111.0 \n", + "2159 171026_0838 18 NO 2 15 8 -111.0 \n", + "\n", + " MedianP1 LowerBound1 StartWealth ... MedianPrice GainFinal \\\n", + "0 320.0 320.0 6750.0 ... 290.00 7350.0 \n", + "0 320.0 320.0 6750.0 ... 290.00 7350.0 \n", + "0 320.0 320.0 6750.0 ... 290.00 7350.0 \n", + "0 320.0 320.0 6750.0 ... 290.00 7350.0 \n", + "0 320.0 320.0 6750.0 ... 290.00 7350.0 \n", + "... ... ... ... ... ... ... \n", + "2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n", + "2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n", + "2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n", + "2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n", + "2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n", + "\n", + " GainFinalEUR GainForecastECU GainForecastEUR CompteEpargne \\\n", + "0 21.78 1012.50 3.00 877.50 \n", + "0 21.78 1012.50 3.00 877.50 \n", + "0 21.78 1012.50 3.00 877.50 \n", + "0 21.78 1012.50 3.00 877.50 \n", + "0 21.78 1012.50 3.00 877.50 \n", + "... ... ... ... ... \n", + "2159 23.14 2413.44 7.15 2128.44 \n", + "2159 23.14 2413.44 7.15 2128.44 \n", + "2159 23.14 2413.44 7.15 2128.44 \n", + "2159 23.14 2413.44 7.15 2128.44 \n", + "2159 23.14 2413.44 7.15 2128.44 \n", + "\n", + " CompteEpargneEUR count secnd secnd2 \n", + "0 2.60 0 0 0 \n", + "0 2.60 1 1 1 \n", + "0 2.60 2 2 2 \n", + "0 2.60 3 3 3 \n", + "0 2.60 4 4 4 \n", + "... ... ... ... ... \n", + "2159 6.31 261355 116 1796 \n", + "2159 6.31 261356 117 1797 \n", + "2159 6.31 261357 118 1798 \n", + "2159 6.31 261358 119 1799 \n", + "2159 6.31 261359 120 1800 \n", + "\n", + "[261360 rows x 39 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#1ere étape (note garder l'ordre)\n", + "import numpy as np\n", + "ddf3 = ddf.copy()\n", + "ddf3['count']=0\n", + "# création de la colonne count\n", + "b=0\n", + "a=0\n", + "n=2160 # 4 lignes // 2160 lignes \n", + "for i in range(0,n):\n", + " ddf3.at[b,'count']=a\n", + " a+= 121 # +=121\n", + " b+=1\n", + "# 2eme étape \n", + "# on va répliquer la même chose 120 fois pour avoir une observation \n", + "# par seconde par sujet \n", + "ddf3['secnd']=0 #@\n", + "ddf4=ddf3.copy()\n", + "b=0\n", + "a=1\n", + "c=1\n", + "n=120 # 4: répéter 5 fois // 120 nbr de secondes 2min (avec sec=0 avant de commencer le marché)\n", + "v=2160 # 2160 / (8 sujets * 18 marchés * 15 périodes)\n", + "for i in range(0,n):\n", + " for i in range(0,v):\n", + " ddf3.at[b,'count']=a\n", + " a+=121 # +=121 2min\n", + " b+=1\n", + " ddf3['secnd']=c #@ \n", + " ddf4 = pd.concat([ddf4, ddf3])\n", + " b=0\n", + " c+=1\n", + " a=c\n", + "#ddf4\n", + "ddf4['secnd2']= ddf4['secnd'] + (120* (ddf4['Period']-1))\n", + "ddf5 = ddf4.sort_values(by=['count'])\n", + "ddf5" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ddf5' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mddf5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nmarket'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Subject'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Period'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'secnd'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mddf5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'/Users/waelbousselmi/Desktop/ddf5.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ddf5' is not defined" + ] + } + ], + "source": [ + "ddf5.to_csv(r'/Users/waelbousselmi/Desktop/ddf5.csv', index = False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pour importer une table Excel\n", + "import pandas as pd\n", + "df6 = pd.read_excel('/Users/waelbousselmi/Desktop/risque_continu.xlsx')\n", + "#print(ddf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df7 = pd.read_csv('/Users/waelbousselmi/Desktop/ddf5.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "875" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df6.shape[0] #875\n", + "#df7.shape[0] # 261360" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alpha = 34 sur 34\n" + ] + } + ], + "source": [ + "###### pour intégrer les valeurs risque \n", + "df7['risk_test']= \"\"\n", + "a = df7.shape[0] # 875\n", + "b = 34 # df6.shape[0] # 261360\n", + "alpha = 0\n", + "c=0\n", + "for i in range (0,b):\n", + " for i in range (0,a):\n", + " if (df6.at[c,\"nmarket\"]==df7.at[i,\"nmarket\"] and \n", + " df6.at[c,\"Subject\"]==df7.at[i,\"Subject\"] and\n", + " df6.at[c,\"Period\"]==df7.at[i,\"Period\"] and \n", + " df6.at[c,\"treatment\"]==df7.at[i,\"traitement\"] and \n", + " df6.at[c,\"timerisk_start\"]==df7.at[i,\"secnd2\"]):\n", + "# print(\"A\",i, \"/ sujet=\",df6.at[c,\"Subject\"], \"/ période=\",df6.at[c,\"Period\"],\"/ traitement\",df6.at[c,\"treatment\"], \"/ nmarket\",df6.at[c,\"nmarket\"],\n", + "# \"/ q1_risk=\", df6.at[c,\"q1_risk\"], \"/ temps_sec:\",df6.at[c,\"timerisk_start\"] )\n", + " df7.at[i,'risk_test'] = df6.at[c,\"q1_risk\"]\n", + " alpha +=1 \n", + " c+=1\n", + "# print(\"c=\",c)\n", + "print(\"alpha = \", alpha, \"sur\",b)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# ça pris > 30 min pour faire la boucle !!!\n", + "# df7.to_csv(r'/Users/waelbousselmi/Desktop/df7_test.csv', index = False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df7 = pd.read_csv('/Users/waelbousselmi/Desktop/df7.csv')\n", + "#/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_MC_juin2021/df13.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "df8 = df7.sort_values(by=['nmarket', 'Subject', 'Period', 'secnd2']) #, ascending = (False) !!! " + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "## très très important (after sorting or concat) !!!!!\n", + "df9 = df8.reset_index(drop=True)\n", + "#df9.at[150,\"Subject\"] # pour vérifier" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "## très important pour remplacer les NaN !!!!\n", + "df9['risk_test'] = df9['risk_test'].fillna(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "#df9.at[3,\"risk_test\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "#len(df9)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "#len(df9[(df9['secnd']== 120)])" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "#len(df9[(df9['secnd2']== 120)])" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "# pour supprimer les doublants avec eg. secnd2 = 120 deux fois !!!!!!!! (important)\n", + "df10 = df9[(df9['secnd']== 0) & (df9['secnd2']!= 0)].index\n", + "df9.drop(df10 , inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "df11 = df9.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "df11['risk_test2']=df11['risk_test']" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "## très très important (after sorting or concat) !!!!!\n", + "df12 = df11.reset_index(drop=True) # faut créer un nouveau df si non ça ne marche pas " + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "962\n" + ] + } + ], + "source": [ + "a = df12.shape[0]\n", + "count=0\n", + "for i in range (0,a):\n", + " if df12.at[i,'Period'] != \"\":\n", + " val = df12.at[i,'risk_test2']\n", + "# print(i,val)\n", + " count +=1\n", + " else :\n", + " df12.at[i,'risk_test2'] = val \n", + "print(count) " + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
170703_0850nmarkettraitementtreamentPeriodSubjectUpperBound1MedianP1LowerBound1StartWealth...GainFinalEURGainForecastECUGainForecastEURCompteEpargneCompteEpargneEURcountsecndsecnd2risk_testrisk_test2
0170703_08501HH011320.0320.0320.06750.0...21.781012.503.00877.502.600001010
1170703_08501HH011320.0320.0320.06750.0...21.781012.503.00877.502.6011110
2170703_08501HH011320.0320.0320.06750.0...21.781012.503.00877.502.6022210
3170703_08501HH011320.0320.0320.06750.0...21.781012.503.00877.502.6033310
4170703_08501HH011320.0320.0320.06750.0...21.781012.503.00877.502.6044410
..................................................................
259339171026_083818NO2158-111.0-111.0-111.07795.0...23.142413.447.152128.446.3126135511617963
259340171026_083818NO2158-111.0-111.0-111.07795.0...23.142413.447.152128.446.3126135611717973
259341171026_083818NO2158-111.0-111.0-111.07795.0...23.142413.447.152128.446.3126135711817983
259342171026_083818NO2158-111.0-111.0-111.07795.0...23.142413.447.152128.446.3126135811917993
259343171026_083818NO2158-111.0-111.0-111.07795.0...23.142413.447.152128.446.31261359120180000
\n", + "

259344 rows × 41 columns

\n", + "
" + ], + "text/plain": [ + " 170703_0850 nmarket traitement treament Period Subject \\\n", + "0 170703_0850 1 HH 0 1 1 \n", + "1 170703_0850 1 HH 0 1 1 \n", + "2 170703_0850 1 HH 0 1 1 \n", + "3 170703_0850 1 HH 0 1 1 \n", + "4 170703_0850 1 HH 0 1 1 \n", + "... ... ... ... ... ... ... \n", + "259339 171026_0838 18 NO 2 15 8 \n", + "259340 171026_0838 18 NO 2 15 8 \n", + "259341 171026_0838 18 NO 2 15 8 \n", + "259342 171026_0838 18 NO 2 15 8 \n", + "259343 171026_0838 18 NO 2 15 8 \n", + "\n", + " UpperBound1 MedianP1 LowerBound1 StartWealth ... GainFinalEUR \\\n", + "0 320.0 320.0 320.0 6750.0 ... 21.78 \n", + "1 320.0 320.0 320.0 6750.0 ... 21.78 \n", + "2 320.0 320.0 320.0 6750.0 ... 21.78 \n", + "3 320.0 320.0 320.0 6750.0 ... 21.78 \n", + "4 320.0 320.0 320.0 6750.0 ... 21.78 \n", + "... ... ... ... ... ... ... \n", + "259339 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n", + "259340 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n", + "259341 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n", + "259342 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n", + "259343 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n", + "\n", + " GainForecastECU GainForecastEUR CompteEpargne CompteEpargneEUR \\\n", + "0 1012.50 3.00 877.50 2.60 \n", + "1 1012.50 3.00 877.50 2.60 \n", + "2 1012.50 3.00 877.50 2.60 \n", + "3 1012.50 3.00 877.50 2.60 \n", + "4 1012.50 3.00 877.50 2.60 \n", + "... ... ... ... ... \n", + "259339 2413.44 7.15 2128.44 6.31 \n", + "259340 2413.44 7.15 2128.44 6.31 \n", + "259341 2413.44 7.15 2128.44 6.31 \n", + "259342 2413.44 7.15 2128.44 6.31 \n", + "259343 2413.44 7.15 2128.44 6.31 \n", + "\n", + " count secnd secnd2 risk_test risk_test2 \n", + "0 0 0 0 10 10 \n", + "1 1 1 1 10 \n", + "2 2 2 2 10 \n", + "3 3 3 3 10 \n", + "4 4 4 4 10 \n", + "... ... ... ... ... ... \n", + "259339 261355 116 1796 3 \n", + "259340 261356 117 1797 3 \n", + "259341 261357 118 1798 3 \n", + "259342 261358 119 1799 3 \n", + "259343 261359 120 1800 0 0 \n", + "\n", + "[259344 rows x 41 columns]" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df12" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "#df12.to_csv(r'/Users/waelbousselmi/Desktop/df12.csv', index = False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "df13 = df12.rename(columns={'170703_0850': 'ref_date'}) ## pour renommer une colonne\n", + "#df13.to_stata('/Users/waelbousselmi/Desktop/df13.dta') ### je n'ai pas besoin d'avoir un format dta " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df13['choc1']= \"\"\n", + "a = df13.shape[0] \n", + "for i in range (0,a):\n", + " if (df13.at[i,\"Period\"] > 5 and df13.at[i,\"traitement\"] != \"NO\"):\n", + " df13.at[i,\"choc1\"] = 1\n", + " else:\n", + " df13.at[i,\"choc1\"] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "df13['choc2']= \"\"\n", + "a = df13.shape[0] \n", + "for i in range (0,a):\n", + " if (df13.at[i,\"Period\"] > 10 and df13.at[i,\"traitement\"] != \"NO\"):\n", + " df13.at[i,\"choc2\"] = 1\n", + " else:\n", + " df13.at[i,\"choc2\"] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df13['choc1_nn']= \"\"\n", + "a = df13.shape[0] \n", + "for i in range (0,a):\n", + " if (df13.at[i,\"Period\"] > 5 and df13.at[i,\"Period\"] < 11 and df13.at[i,\"traitement\"] != \"NO\"):\n", + " df13.at[i,\"choc1_nn\"] = 1\n", + " elif (df13.at[i,\"Period\"] <6 and df13.at[i,\"traitement\"] != \"NO\"):\n", + " df13.at[i,\"choc1_nn\"] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "df13['choc2']= \"\"\n", + "a = df13.shape[0] \n", + "for i in range (0,a):\n", + " if (df13.at[i,\"Period\"] < 11):\n", + " df13.at[i,\"choc2\"] = 0\n", + " else:\n", + " df13.at[i,\"choc2\"] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ref_datenmarkettraitementtreamentPeriodSubjectUpperBound1MedianP1LowerBound1StartWealth...CompteEpargneCompteEpargneEURcountsecndsecnd2risk_testrisk_test2choc1choc2newid
0170703_08501HH011320.0320.0320.06750.0...877.502.6000010.010.0001
1170703_08501HH011320.0320.0320.06750.0...877.502.60111NaN10.0001
2170703_08501HH011320.0320.0320.06750.0...877.502.60222NaN10.0001
3170703_08501HH011320.0320.0320.06750.0...877.502.60333NaN10.0001
4170703_08501HH011320.0320.0320.06750.0...877.502.60444NaN10.0001
..................................................................
259339171026_083818NO2158-111.0-111.0-111.07795.0...2128.446.312613551161796NaN3.011144
259340171026_083818NO2158-111.0-111.0-111.07795.0...2128.446.312613561171797NaN3.011144
259341171026_083818NO2158-111.0-111.0-111.07795.0...2128.446.312613571181798NaN3.011144
259342171026_083818NO2158-111.0-111.0-111.07795.0...2128.446.312613581191799NaN3.011144
259343171026_083818NO2158-111.0-111.0-111.07795.0...2128.446.3126135912018000.00.011144
\n", + "

259344 rows × 44 columns

\n", + "
" + ], + "text/plain": [ + " ref_date nmarket traitement treament Period Subject \\\n", + "0 170703_0850 1 HH 0 1 1 \n", + "1 170703_0850 1 HH 0 1 1 \n", + "2 170703_0850 1 HH 0 1 1 \n", + "3 170703_0850 1 HH 0 1 1 \n", + "4 170703_0850 1 HH 0 1 1 \n", + "... ... ... ... ... ... ... \n", + "259339 171026_0838 18 NO 2 15 8 \n", + "259340 171026_0838 18 NO 2 15 8 \n", + "259341 171026_0838 18 NO 2 15 8 \n", + "259342 171026_0838 18 NO 2 15 8 \n", + "259343 171026_0838 18 NO 2 15 8 \n", + "\n", + " UpperBound1 MedianP1 LowerBound1 StartWealth ... CompteEpargne \\\n", + "0 320.0 320.0 320.0 6750.0 ... 877.50 \n", + "1 320.0 320.0 320.0 6750.0 ... 877.50 \n", + "2 320.0 320.0 320.0 6750.0 ... 877.50 \n", + "3 320.0 320.0 320.0 6750.0 ... 877.50 \n", + "4 320.0 320.0 320.0 6750.0 ... 877.50 \n", + "... ... ... ... ... ... ... \n", + "259339 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n", + "259340 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n", + "259341 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n", + "259342 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n", + "259343 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n", + "\n", + " CompteEpargneEUR count secnd secnd2 risk_test risk_test2 choc1 \\\n", + "0 2.60 0 0 0 10.0 10.0 0 \n", + "1 2.60 1 1 1 NaN 10.0 0 \n", + "2 2.60 2 2 2 NaN 10.0 0 \n", + "3 2.60 3 3 3 NaN 10.0 0 \n", + "4 2.60 4 4 4 NaN 10.0 0 \n", + "... ... ... ... ... ... ... ... \n", + "259339 6.31 261355 116 1796 NaN 3.0 1 \n", + "259340 6.31 261356 117 1797 NaN 3.0 1 \n", + "259341 6.31 261357 118 1798 NaN 3.0 1 \n", + "259342 6.31 261358 119 1799 NaN 3.0 1 \n", + "259343 6.31 261359 120 1800 0.0 0.0 1 \n", + "\n", + " choc2 newid \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 \n", + "... ... ... \n", + "259339 1 144 \n", + "259340 1 144 \n", + "259341 1 144 \n", + "259342 1 144 \n", + "259343 1 144 \n", + "\n", + "[259344 rows x 44 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#df14 = df13.groupby('secnd2').risk_test2.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#df14" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# calcul interval de confiance (autre méthode de calcul) \n", + "#import numpy as np\n", + "#import scipy.stats as st\n", + "#data = df14\n", + "#st.t.interval(alpha=0.95, df=len(data)-1, loc=np.mean(data), scale=st.sem(data)) " + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [], + "source": [ + "# nouvelle variable \"newid\", id pour chaque sujet \n", + "df13['newid']= \"\"\n", + "a = df13.shape[0]\n", + "var = 1\n", + "cc = df13.at[0,\"Subject\"]\n", + "for i in range (0,a):\n", + " if (df13.at[i,\"Subject\"] == cc):\n", + " df13.at[i,\"newid\"] = var\n", + " else:\n", + " cc = df13.at[i,\"Subject\"]\n", + " var += 1\n", + " df13.at[i,\"newid\"] = var" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df13 = pd.read_csv('/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_MC_juin2021/df13.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df13.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dfhh = df13[(df13[\"traitement\"] == \"HH\")]\n", + "dfbb = df13[(df13[\"traitement\"] == \"BB\")]\n", + "dft0 = df13[(df13[\"traitement\"] == \"NO\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# pour créer deux groupe par traitement (groupe 1: ceux qui sont plus averses au risque et \n", + "#groupe 2: ceux qui sont plus amateurs de risque)\n", + "import statistics as st\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "median1 = st.median(dfhh[\"risk_test2\"])\n", + "median2 = st.median(dfbb[\"risk_test2\"])\n", + "median3 = st.median(dft0[\"risk_test2\"])\n", + "dfhh_g1 = dfhh[(dfhh[\"risk_test2\"] < median1)]\n", + "dfhh_g2 = dfhh[(dfhh[\"risk_test2\"] >= median1)]\n", + "dfbb_g1 = dfbb[(dfbb[\"risk_test2\"] < median2)]\n", + "dfbb_g2 = dfbb[(dfbb[\"risk_test2\"] >= median2)]\n", + "dft0_g1 = dft0[(dft0[\"risk_test2\"] < median3)]\n", + "dft0_g2 = dft0[(dft0[\"risk_test2\"] >= median3)]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# pour calculer l'interval de confiance à 95%\n", + "import pandas as pd\n", + "import numpy as np\n", + "import math\n", + "\n", + "stats_hh = dfhh.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_bb = dfbb.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_t0 = dft0.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "\n", + "stats_hh_g1 = dfhh_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_bb_g1 = dfbb_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_t0_g1 = dft0_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "\n", + "stats_hh_g2 = dfhh_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_bb_g2 = dfbb_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "stats_t0_g2 = dft0_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n", + "\n", + "\n", + "ci95_hi_hh = []\n", + "ci95_lo_hh = []\n", + "ci95_hi_bb = []\n", + "ci95_lo_bb = []\n", + "ci95_hi_t0 = []\n", + "ci95_lo_t0 = []\n", + "\n", + "ci95_hi_hh_g1 = []\n", + "ci95_lo_hh_g1 = []\n", + "ci95_hi_bb_g1 = []\n", + "ci95_lo_bb_g1 = []\n", + "ci95_hi_t0_g1 = []\n", + "ci95_lo_t0_g1 = []\n", + "\n", + "ci95_hi_hh_g2 = []\n", + "ci95_lo_hh_g2 = []\n", + "ci95_hi_bb_g2 = []\n", + "ci95_lo_bb_g2 = []\n", + "ci95_hi_t0_g2 = []\n", + "ci95_lo_t0_g2 = []\n", + "\n", + "for i in stats_hh.index:\n", + " m, c, s = stats_hh.loc[i]\n", + " ci95_hi_hh.append(m + 1.95*s/math.sqrt(c))\n", + " ci95_lo_hh.append(m - 1.95*s/math.sqrt(c))\n", + "\n", + "for i in stats_bb.index:\n", + " m1, c1, s1 = stats_bb.loc[i]\n", + " ci95_hi_bb.append(m1 + 1.95*s1/math.sqrt(c1))\n", + " ci95_lo_bb.append(m1 - 1.95*s1/math.sqrt(c1))\n", + "\n", + "for i in stats_t0.index:\n", + " m2, c2, s2 = stats_t0.loc[i]\n", + " ci95_hi_t0.append(m2 + 1.95*s2/math.sqrt(c2))\n", + " ci95_lo_t0.append(m2 - 1.95*s2/math.sqrt(c2))\n", + "\n", + " \n", + "for i in stats_hh_g1.index:\n", + " m_g1_g1, c_g1, s_g1 = stats_hh_g1.loc[i]\n", + " ci95_hi_hh_g1.append(m_g1 + 1.95*s_g1/math.sqrt(c_g1))\n", + " ci95_lo_hh_g1.append(m_g1 - 1.95*s_g1/math.sqrt(c_g1))\n", + "\n", + "for i in stats_bb_g1.index:\n", + " m1_g1, c1_g1, s1_g1 = stats_bb_g1.loc[i]\n", + " ci95_hi_bb_g1.append(m1_g1 + 1.95*s1_g1/math.sqrt(c1_g1))\n", + " ci95_lo_bb_g1.append(m1_g1 - 1.95*s1_g1/math.sqrt(c1_g1))\n", + "\n", + "for i in stats_t0_g1.index:\n", + " m2_g1, c2_g1, s2_g1 = stats_t0_g1.loc[i]\n", + " ci95_hi_t0_g1.append(m2_g1 + 1.95*s2_g1/math.sqrt(c2_g1))\n", + " ci95_lo_t0_g1.append(m2_g1 - 1.95*s2_g1/math.sqrt(c2_g1))\n", + "\n", + " \n", + "for i in stats_hh_g2.index:\n", + " m_g2, c_g2, s_g2 = stats_hh_g2.loc[i]\n", + " ci95_hi_hh_g2.append(m_g2 + 1.95*s_g2/math.sqrt(c_g2))\n", + " ci95_lo_hh_g2.append(m_g2 - 1.95*s_g2/math.sqrt(c_g2))\n", + "\n", + "for i in stats_bb_g2.index:\n", + " m1_g2, c1_g2, s1_g2 = stats_bb_g2.loc[i]\n", + " ci95_hi_bb_g2.append(m1_g2 + 1.95*s1_g2/math.sqrt(c1_g2))\n", + " ci95_lo_bb_g2.append(m1_g2 - 1.95*s1_g2/math.sqrt(c1_g2))\n", + "\n", + "for i in stats_t0_g2.index:\n", + " m2_g2, c2_g2, s2_g2 = stats_t0_g2.loc[i]\n", + " ci95_hi_t0_g2.append(m2_g2 + 1.95*s2_g2/math.sqrt(c2_g2))\n", + " ci95_lo_t0_g2.append(m2_g2 - 1.95*s2_g2/math.sqrt(c2_g2))\n", + "\n", + " \n", + "stats_hh['ci95_hi_hh'] = ci95_hi_hh\n", + "stats_hh['ci95_lo_hh'] = ci95_lo_hh\n", + "stats_bb['ci95_hi_bb'] = ci95_hi_bb\n", + "stats_bb['ci95_lo_bb'] = ci95_lo_bb\n", + "stats_t0['ci95_hi_t0'] = ci95_hi_t0\n", + "stats_t0['ci95_lo_t0'] = ci95_lo_t0\n", + "\n", + "stats_hh_g1['ci95_hi_hh_g1'] = ci95_hi_hh_g1\n", + "stats_hh_g1['ci95_lo_hh_g1'] = ci95_lo_hh_g1\n", + "stats_bb_g1['ci95_hi_bb_g1'] = ci95_hi_bb_g1\n", + "stats_bb_g1['ci95_lo_bb_g1'] = ci95_lo_bb_g1\n", + "stats_t0_g1['ci95_hi_t0_g1'] = ci95_hi_t0_g1\n", + "stats_t0_g1['ci95_lo_t0_g1'] = ci95_lo_t0_g1\n", + "\n", + "stats_hh_g2['ci95_hi_hh_g2'] = ci95_hi_hh_g2\n", + "stats_hh_g2['ci95_lo_hh_g2'] = ci95_lo_hh_g2\n", + "stats_bb_g2['ci95_hi_bb_g2'] = ci95_hi_bb_g2\n", + "stats_bb_g2['ci95_lo_bb_g2'] = ci95_lo_bb_g2\n", + "stats_t0_g2['ci95_hi_t0_g2'] = ci95_hi_t0_g2\n", + "stats_t0_g2['ci95_lo_t0_g2'] = ci95_lo_t0_g2\n", + "\n", + "#print(stats)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "from matplotlib.colors import colorConverter as cc\n", + "import numpy as np\n", + "from matplotlib.collections import LineCollection\n", + " \n", + "def plot_mean_and_CI(mean, lb, ub, color_mean=None, color_shading=None):\n", + " # plot the shaded range of the confidence intervals\n", + " plt.fill_between(range(mean.shape[0]), ub, lb,\n", + " color=color_shading, alpha=.3)\n", + " # plot the mean on top\n", + " plt.plot(mean, color_mean, lw=5)\n", + " \n", + "# generate 3 sets of random means and confidence intervals to plot\n", + "mean0 = stats_hh[\"mean\"]\n", + "ub0 = stats_hh['ci95_hi_hh']\n", + "lb0 = stats_hh['ci95_lo_hh']\n", + " \n", + "mean1 = stats_bb[\"mean\"]\n", + "ub1 = stats_bb['ci95_hi_bb']\n", + "lb1 = stats_bb['ci95_lo_bb']\n", + " \n", + "mean2 = stats_t0[\"mean\"]\n", + "ub2 = stats_t0['ci95_hi_t0']\n", + "lb2 = stats_t0['ci95_lo_t0']\n", + " \n", + " \n", + "# plot the data\n", + "fig = plt.figure(1, figsize=(10, 10))\n", + "plot_mean_and_CI(mean0, ub0, lb0, color_mean='g', color_shading='green')\n", + "plot_mean_and_CI(mean1, ub1, lb1, color_mean='r', color_shading='red')\n", + "plot_mean_and_CI(mean2, ub2, lb2, color_mean='b', color_shading='blue')\n", + " \n", + "class LegendObject(object):\n", + " def __init__(self, facecolor='red', edgecolor='white', dashed=False):\n", + " self.facecolor = facecolor\n", + " self.edgecolor = edgecolor\n", + " self.dashed = dashed\n", + " \n", + " def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n", + " x0, y0 = handlebox.xdescent, handlebox.ydescent\n", + " width, height = handlebox.width, handlebox.height\n", + " patch = mpatches.Rectangle(\n", + " # create a rectangle that is filled with color\n", + " [x0, y0], width, height, facecolor=self.facecolor,\n", + " # and whose edges are the faded color\n", + " edgecolor=self.edgecolor, lw=3)\n", + " handlebox.add_artist(patch)\n", + " \n", + " # if we're creating the legend for a dashed line,\n", + " # manually add the dash in to our rectangle\n", + " if self.dashed:\n", + " patch1 = mpatches.Rectangle(\n", + " [x0 + 2*width/5, y0], width/5, height, facecolor=self.edgecolor,\n", + " transform=handlebox.get_transform())\n", + " handlebox.add_artist(patch1)\n", + " \n", + " return patch\n", + " \n", + "bg = np.array([1, 1, 1]) # background of the legend is white\n", + "colors = ['g', 'r', 'b']\n", + "# with alpha = .5, the faded color is the average of the background and color\n", + "colors_faded = [(np.array(cc.to_rgb(color)) + bg) / 2.0 for color in colors]\n", + " \n", + "plt.legend([0, 1, 2], ['Up-Up', 'Down-Down', 'T0'],\n", + " handler_map={\n", + " 0: LegendObject(colors[0], colors_faded[0]),\n", + " 1: LegendObject(colors[1], colors_faded[1]),\n", + " 2: LegendObject(colors[2], colors_faded[2], dashed=True),\n", + " })\n", + " \n", + "plt.title('Mean risk preferences by second')\n", + "plt.tight_layout()\n", + "plt.ylim(3,6)\n", + "ax = plt.gca()\n", + "#ax.axes.xaxis.set_visible(False)\n", + "ax.xaxis.set_ticks(np.arange(0, 1800, 120))\n", + "#ax.set_xticks(ax.get_xticks()[::120])\n", + "#plt.xlim(0,1800)\n", + "\n", + "#plt.grid()\n", + "\n", + "l3 = list(range(120,1800,120))\n", + "plt.vlines(x=l3, ymin=0, ymax=10, colors='purple', ls='--', lw=1)\n", + "\n", + "\n", + "# les deux lignes verticales pour choc1 et choc2\n", + "l1 = [(600,0 ), (600, 10)]\n", + "l2 = [(1200,0), (1200, 10)]\n", + "lc = LineCollection([l1, l2], color=[\"k\",\"k\"], lw=2)\n", + "plt.gca().add_collection(lc)\n", + "\n", + "#\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "l3 = list(range(120,1800,120))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[120, 240, 360, 480, 600, 720, 840, 960, 1080, 1200, 1320, 1440, 1560, 1680]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[120, 240, 360, 480, 600, 720, 840, 960, 1080, 1200, 1320, 1440, 1560, 1680]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.collections import LineCollection\n", + "\n", + "np.random.seed(5)\n", + "x = np.arange(1, 101)\n", + "y = 20 + 3 * x + np.random.normal(0, 60, 100)\n", + "plt.plot(x, y, \"o\")\n", + "\n", + "# Takes list of lines, where each line is a sequence of coordinates\n", + "l1 = [(70, 100), (70, 250)]\n", + "l2 = [(70, 90), (90, 200)]\n", + "lc = LineCollection([l1, l2], color=[\"k\",\"blue\"], lw=2)\n", + "\n", + "plt.gca().add_collection(lc)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "\n", + "# you can change each line separately, like:\n", + "#ax.spines['right'].set_linewidth(0.5)\n", + "# to change all, just write:\n", + "\n", + "for axis in ['top','bottom','left','right']:\n", + " ax.spines[axis].set_linewidth(2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "#df13.to_csv(r'/Users/waelbousselmi/Desktop/df13.csv', index = False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "df14_1 = df14[(df14[\"nmarket\"] == 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(15,6))\n", + "for name, group in df14_1.groupby('Subject'):\n", + " group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(15,6))\n", + "for name, group in df14.groupby('nmarket'):\n", + " group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [], + "source": [ + "#import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "#sns.lineplot(data=df14_1, x=\"secnd2\", y=\"risk_test2\", hue=\"Subject\")" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df14_1.at[2, \"Subject\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "may_flights = df14_1.query(\"Subject == '5'\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ref_datenmarkettraitementtreamentPeriodSubjectUpperBound1MedianP1LowerBound1StartWealth...CompteEpargneEURcountsecndsecnd2risk_testrisk_test2choc1choc2newidchoc1_nn
7204170703_08501HH015350.0300.0250.06750.0...1.2748400550050
7205170703_08501HH015350.0300.0250.06750.0...1.274851150050
7206170703_08501HH015350.0300.0250.06750.0...1.274862250050
7207170703_08501HH015350.0300.0250.06750.0...1.274873350050
7208170703_08501HH015350.0300.0250.06750.0...1.274884450050
..................................................................
9000170703_08501HH01550.00.00.010369.9...-1.8914152116179651151
9001170703_08501HH01550.00.00.010369.9...-1.8914153117179751151
9002170703_08501HH01550.00.00.010369.9...-1.8914154118179851151
9003170703_08501HH01550.00.00.010369.9...-1.8914155119179951151
9004170703_08501HH01550.00.00.010369.9...-1.8914156120180051151
\n", + "

1801 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + " ref_date nmarket traitement treament Period Subject UpperBound1 \\\n", + "7204 170703_0850 1 HH 0 1 5 350.0 \n", + "7205 170703_0850 1 HH 0 1 5 350.0 \n", + "7206 170703_0850 1 HH 0 1 5 350.0 \n", + "7207 170703_0850 1 HH 0 1 5 350.0 \n", + "7208 170703_0850 1 HH 0 1 5 350.0 \n", + "... ... ... ... ... ... ... ... \n", + "9000 170703_0850 1 HH 0 15 5 0.0 \n", + "9001 170703_0850 1 HH 0 15 5 0.0 \n", + "9002 170703_0850 1 HH 0 15 5 0.0 \n", + "9003 170703_0850 1 HH 0 15 5 0.0 \n", + "9004 170703_0850 1 HH 0 15 5 0.0 \n", + "\n", + " MedianP1 LowerBound1 StartWealth ... CompteEpargneEUR count secnd \\\n", + "7204 300.0 250.0 6750.0 ... 1.27 484 0 \n", + "7205 300.0 250.0 6750.0 ... 1.27 485 1 \n", + "7206 300.0 250.0 6750.0 ... 1.27 486 2 \n", + "7207 300.0 250.0 6750.0 ... 1.27 487 3 \n", + "7208 300.0 250.0 6750.0 ... 1.27 488 4 \n", + "... ... ... ... ... ... ... ... \n", + "9000 0.0 0.0 10369.9 ... -1.89 14152 116 \n", + "9001 0.0 0.0 10369.9 ... -1.89 14153 117 \n", + "9002 0.0 0.0 10369.9 ... -1.89 14154 118 \n", + "9003 0.0 0.0 10369.9 ... -1.89 14155 119 \n", + "9004 0.0 0.0 10369.9 ... -1.89 14156 120 \n", + "\n", + " secnd2 risk_test risk_test2 choc1 choc2 newid choc1_nn \n", + "7204 0 5 5 0 0 5 0 \n", + "7205 1 5 0 0 5 0 \n", + "7206 2 5 0 0 5 0 \n", + "7207 3 5 0 0 5 0 \n", + "7208 4 5 0 0 5 0 \n", + "... ... ... ... ... ... ... ... \n", + "9000 1796 5 1 1 5 1 \n", + "9001 1797 5 1 1 5 1 \n", + "9002 1798 5 1 1 5 1 \n", + "9003 1799 5 1 1 5 1 \n", + "9004 1800 5 1 1 5 1 \n", + "\n", + "[1801 rows x 45 columns]" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "may_flights" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [], + "source": [ + "import statistics as st\n", + "v11 = st.mean(may_flights.risk_test2)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.021654636313159" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v11" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(data=may_flights, x=\"secnd2\", y=\"secnd\")" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "flights_wide = df14_1.pivot(\"secnd2\", \"Subject\", \"risk_test2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Subject12345678
secnd2
0102.2125552.5
1102.2125552.5
2102.2125552.5
3102.2125552.5
4102.2125552.5
...........................
1796107.1212581010
1797107.1212581010
1798107.1212581010
1799107.1212581010
1800107.1212581010
\n", + "

1801 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + "Subject 1 2 3 4 5 6 7 8\n", + "secnd2 \n", + "0 10 2.2 1 2 5 5 5 2.5\n", + "1 10 2.2 1 2 5 5 5 2.5\n", + "2 10 2.2 1 2 5 5 5 2.5\n", + "3 10 2.2 1 2 5 5 5 2.5\n", + "4 10 2.2 1 2 5 5 5 2.5\n", + "... .. ... .. .. .. .. .. ...\n", + "1796 10 7.12 1 2 5 8 10 10\n", + "1797 10 7.12 1 2 5 8 10 10\n", + "1798 10 7.12 1 2 5 8 10 10\n", + "1799 10 7.12 1 2 5 8 10 10\n", + "1800 10 7.12 1 2 5 8 10 10\n", + "\n", + "[1801 rows x 8 columns]" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flights_wide" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# https://seaborn.pydata.org/generated/seaborn.lineplot.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#sns.lineplot(data=flights_wide[\"Subject\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(15,6))\n", + "for name, group in df14_1.groupby('Subject'):\n", + " group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/requirements-checkpoint.txt b/.ipynb_checkpoints/requirements-checkpoint.txt new file mode 100644 index 0000000..ce16730 --- /dev/null +++ b/.ipynb_checkpoints/requirements-checkpoint.txt @@ -0,0 +1,6 @@ +# oTree-may-overwrite-this-file +# IF YOU MODIFY THIS FILE, remove these comments. +# otherwise, oTree will automatically overwrite it. +otree[mturk]>=3.3.11,<5 +psycopg2>=2.8.4 +sentry-sdk==0.7.9 diff --git a/.ipynb_checkpoints/requirements_base-checkpoint.txt b/.ipynb_checkpoints/requirements_base-checkpoint.txt new file mode 100644 index 0000000..ac998e5 --- /dev/null +++ b/.ipynb_checkpoints/requirements_base-checkpoint.txt @@ -0,0 +1,2 @@ +# You should put your requirements in requirements.txt instead. +# You can delete this file. diff --git a/.ipynb_checkpoints/settings-checkpoint.py b/.ipynb_checkpoints/settings-checkpoint.py new file mode 100644 index 0000000..e87a65a --- /dev/null +++ b/.ipynb_checkpoints/settings-checkpoint.py @@ -0,0 +1,339 @@ +from os import environ + +# if you set a property in SESSION_CONFIG_DEFAULTS, it will be inherited by all configs +# in SESSION_CONFIGS, except those that explicitly override it. +# the session config can be accessed from methods in your apps as self.session.config, +# e.g. self.session.config['participation_fee'] + +#### WB MTURK / session config MTurk ######################### + +mturk_hit_settings = { + 'keywords': ['Survey', 'Game', 'Demographics'], # Questionnaire démographique #'Questionnaire', 'Français' + 'title': 'Survey (gain $3.0, duration 7 min)',# Questionnaire démographique + 'description': 'You can earn up to $3.0 in approximately 7 minutes.', # Questionnaire démographique: Gain de $0.60 pour une durée de 1 minute. + 'frame_height': 500, + 'template': 'global/mturk_template.html', + 'minutes_allotted_per_assignment': 60, + 'expiration_hours': 7*24, # 7 days +# 'grant_qualification_id': '3O3C52ZQEUFOKIZH0662Y397HAFSHV',# Mturk reel Exp G-J ###to prevent retakes +# 'grant_qualification_id': '3ZSPHI510NGVHARBJTEPE3ECGE6IJ2', # Sandbox Exp G-J + 'grant_qualification_id': '3IMKSS0I9C91UAW9OSPZY1N2EAQW6A' , # Mturk exp Covid +# 'grant_qualification_id': '3LKGAM13UYJQHAONM1Z1QYBBX0W7ES', # Sandbox exp Covid + +# 'grant_qualification_id': '3C2W12C2LIJHOP5OLGDWR8T1PGZURB',# Mturk reel Exp Ines +# 'grant_qualification_id': '3MTUBPNNZX0I8DMRLCRIX5T88L6XMM', # Sandbox Exp Ines + + 'qualification_requirements': [ + { + 'QualificationTypeId': "00000000000000000071", # que pour expé Ines ##### workers from the US, 00000000000000000071 is the code for a location-based qualification + 'Comparator': "EqualTo", +# 'LocaleValues': [{'Country': "FR"}] + 'LocaleValues': [{'Country': "US"}] + }, + + { +# 'QualificationTypeId': "3O3C52ZQEUFOKIZH0662Y397HAFSHV", # Mturk reel Exp G-J ###To prevent a worker from participating in your study twice +# 'QualificationTypeId': "3ZSPHI510NGVHARBJTEPE3ECGE6IJ2", # Sandbox Exp G-J +# 'QualificationTypeId': '3C2W12C2LIJHOP5OLGDWR8T1PGZURB',# Mturk reel Exp Ines +# 'QualificationTypeId': '3MTUBPNNZX0I8DMRLCRIX5T88L6XMM', # Sandbox Exp Ines + 'QualificationTypeId': '3IMKSS0I9C91UAW9OSPZY1N2EAQW6A', # Mturk exp Covid +# 'QualificationTypeId':'3LKGAM13UYJQHAONM1Z1QYBBX0W7ES', # Sandbox exp Covid + + 'Comparator': "DoesNotExist", + } + +] + } +############################################################################# +SESSION_CONFIG_DEFAULTS = { + 'real_world_currency_per_point': 0.001,# 0.001 pour exp realeffort et exp covid sinon = 1 + 'participation_fee': 0,# 0.5 # 0 Expé police + 'doc': "", + 'mturk_hit_settings': mturk_hit_settings, +} +############################################################################# +SESSION_CONFIGS = [ + +# dict( +# name='part1_trust', +# display_name='part1_trust', +# num_demo_participants=2, +# app_sequence=['exp_police_part1_trust_v4'], +# ), + +# dict( +# name='part2_dictator', +# display_name='part2_dictator', +## num_demo_participants=2, +# app_sequence=['exp_police_part2_dictator_v2'], +# ), + +# dict( +# name='part3_TAI', +# display_name='part3_TAI', +# num_demo_participants=2, +# app_sequence=['exp_police_part3_tai'], +# ), + +# dict( +# name='part4_trust', +# display_name='part4_trust', +# num_demo_participants=2, +# app_sequence=['exp_police_part4_trust_v3'], +# ), + +# dict( +# name='part4bis_trust', +# display_name='part4bis_trust', +# num_demo_participants=2, +# app_sequence=['exp_police_part4bis_trust3'], +# ), + +# dict( +# name='part5_6_7_questionnaire', +# display_name='part5_6_7_questionnaire', +# num_demo_participants=2, +# app_sequence=['exp_police_part5_6_7_questionnaire_v2'], +# ), + + dict( + name='exp_police_all', + display_name='exp_police_all', + num_demo_participants=2, + app_sequence=['exp_police_part1_trust_v4', 'exp_police_part2_dictator_v2', 'exp_police_part3_tai', 'exp_police_part4_trust_v3', 'exp_police_part4bis_trust3', 'exp_police_part5_6_7_questionnaire_v2', 'payment_info'], + ), + + + +# dict( +# name='exp_covid_trust_v3', +# display_name='exp_covid_trust_v3', +# num_demo_participants=1, +# app_sequence=['exp_covid_trust_v3', 'payment_info_mturk'], +# ), + +# dict( +# name='exp_e4c_questionnaire', +# display_name='exp_e4c_questionnaire', +# num_demo_participants=1, +# app_sequence=['exp_e4c_questionnaire'], +# ), + + + +# dict( +# name='exp_e4c_risk_binswanger_FR', +# display_name='exp_e4c_risk_binswanger_FR', +# num_demo_participants=1, +# app_sequence=['exp_e4c_risk_binswanger_FR'], +# ), + +# dict( +# name='exp_e4c_risk_binswanger', +# display_name='exp_e4c_risk_binswanger', +# num_demo_participants=1, +# app_sequence=['exp_e4c_risk_binswanger'], +# ), + +# dict( +# name='exp_e4c_risk_gneezy', +# display_name='exp_e4c_risk_gneezy', +# num_demo_participants=1, +# app_sequence=['exp_e4c_risk_gneezy'], +# ), + +# dict( +# name='exp_e4c_questionnaire_final', +# display_name='exp_e4c_questionnaire_final', +# num_demo_participants=1, +# app_sequence=['exp_e4c_questionnaire_final'], +# ), + +# dict( +# name='exp_e4c_final_FR', +# display_name='exp_e4c_final_FR', +# num_demo_participants=2, +# app_sequence=['exp_e4c_questionnaire_FR_v2', +# 'exp_e4c_risk_gneezy_FR_v2', +# 'exp_e4c_risk_binswanger_FR_v2', +# 'exp_e4c_risk_gneezy_FR_v2_end', +# 'exp_e4c_questionnaire_final_FR_v2'], +# ), + + + + dict( + name='exp_contact_ih_all', + display_name='exp_contact_ih_all', + num_demo_participants=2, + app_sequence=['exp_contact_ih_part1_v3', 'exp_contact_ih_part2_v3', 'exp_contact_ih_part3_v3', 'exp_contact_ih_part4_v3'], + ), + +# dict( +# name='exp_contact_ih_part1_test', +# display_name='exp_contact_ih_part1_test', +# num_demo_participants=1, +# app_sequence=['exp_contact_ih_part1_test'], +# ), + +# dict( +# name='exp_e4c_final_FR_test', +# display_name='exp_e4c_final_FR_test', +# num_demo_participants=2, +# app_sequence=['exp_e4c_risk_gneezy_FR_v2', +# 'exp_e4c_risk_binswanger_FR_v2', +# 'exp_e4c_risk_gneezy_FR_v2_end', +# 'exp_e4c_questionnaire_final_FR_v2'], +# ), + +# dict( +# name='exp_e4c_final_FR_bot', +# display_name='exp_e4c_final_FR_bot', +# num_demo_participants=2, +# app_sequence=['exp_e4c_questionnaire_FR_v2', +# 'exp_e4c_risk_gneezy_FR_v2', +# 'exp_e4c_risk_binswanger_FR_v2', +# 'exp_e4c_risk_gneezy_FR_v2_end', +# 'exp_e4c_questionnaire_final_FR_v2'], +# use_browser_bots=True +# ), + +# dict( +# name='exp_e4c_final_EN_bot', +# display_name='exp_e4c_final_EN_bot', +# num_demo_participants=2, +# app_sequence=['exp_e4c_en_p1_quest', +# 'exp_e4c_en_p2_risk_gneezy', +# 'exp_e4c_en_risk_binswanger_v2', +# 'exp_e4c_en_p2bis_risk_gneezy_end', +# 'exp_e4c_en_p5_questfin'], +# use_browser_bots=True +# ), + +# dict( +# name='exp_e4c_final_EN', +# display_name='exp_e4c_final_EN', +# num_demo_participants=2, +# app_sequence=['exp_e4c_en_p1_quest', +# 'exp_e4c_en_p2_risk_gneezy', +# 'exp_e4c_en_risk_binswanger_v2', +# 'exp_e4c_en_p2bis_risk_gneezy_end', +# 'exp_e4c_en_p5_questfin'], +# ), + +# dict( +# name='exp_e4c_final_test_EN_test', +# display_name='exp_e4c_final_EN_test', +# num_demo_participants=2, +# app_sequence=['exp_e4c_en_p2_risk_gneezy', +# 'exp_e4c_en_risk_binswanger_v2', +# 'exp_e4c_en_p2bis_risk_gneezy_end', +# 'exp_e4c_en_p5_questfin'], +# ), + +# dict( +# name='exp_covid_trust_cadrage_v3', +# display_name='exp_covid_trust_cadrage_v3', +# num_demo_participants=1, +# app_sequence=['exp_covid_trust_cadrage_v3', 'payment_info_mturk'], +# ), + +# dict( +# name='exp_covid_trust_v3_bot', +# display_name='exp_covid_trust_v3_bot', +# num_demo_participants=1, +# app_sequence=['exp_covid_trust_v3'], +# use_browser_bots=True +# ), + +# dict( +# name='exp_covid_trust_cadrage_v3_bot', +# display_name='exp_covid_trust_cadrage_v3_bot', +# num_demo_participants=1, +# app_sequence=['exp_covid_trust_cadrage_v3'], +# use_browser_bots=True +# ), + + + +# dict( +# name='exp_police_all_bot', +# display_name='exp_police_all_bot', +# num_demo_participants=2, +# app_sequence=['exp_police_part1_trust_v4', 'exp_police_part2_dictator_v2', 'exp_police_part3_tai', +# 'exp_police_part4_trust_v3', 'exp_police_part4bis_trust3', +# 'exp_police_part5_6_7_questionnaire_v2', 'payment_info'], +# use_browser_bots=True +# ), + + +# dict( +# name='exp_etienne_test4', +# display_name='exp_etienne Off_the_record (test)', +# num_demo_participants=1, +# app_sequence=['exp_etienne_test4'], +# ), +# dict( +# name='trust', +# display_name='trust', +# num_demo_participants=2, +# app_sequence=['trust'], +# ), + + dict( + name='expines_test17', + display_name='expines_test17', + num_demo_participants=2, + app_sequence=['expines_test17'], + ), + + + + +] + +# ISO-639 code +# for example: de, fr, ja, ko, zh-hans +LANGUAGE_CODE = 'en' #en # 'fr' pour Exp Mindfullness et exp police + +# e.g. EUR, GBP, CNY, JPY +REAL_WORLD_CURRENCY_CODE = 'USD' # EUR pour Exp Mindfullness +USE_POINTS = True # False + +ROOMS = [ + dict( + name="labeds", + display_name="labeds", + participant_label_file="_rooms/labeds.txt" + ), +] + +#ADMIN_USERNAME = 'admin' +ADMIN_USERNAME = 'wael bousselmi' +# for security, best to set admin password in an environment variable +ADMIN_PASSWORD = environ.get('OTREE_ADMIN_PASSWORD') + + +DEMO_PAGE_INTRO_HTML = """ +Here are some oTree games. +""" + +# don't share this with anybody. +SECRET_KEY = 'nm5eaornndp(_0bt_64k+03(-f-n%4bir_+==$8i7&-o$th2@)' + +INSTALLED_APPS = ['otree'] + + +AWS_ACCESS_KEY_ID = environ.get('AWS_ACCESS_KEY_ID') +AWS_SECRET_ACCESS_KEY = environ.get('AWS_SECRET_ACCESS_KEY') + + + + + + + + + + + diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..d9506ce --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12.5 diff --git a/Procfile b/Procfile new file mode 100644 index 0000000..9156b92 --- /dev/null +++ b/Procfile @@ -0,0 +1,2 @@ +web: otree prodserver1of2 +worker: otree prodserver2of2 diff --git a/_rooms/labeds.txt b/_rooms/labeds.txt new file mode 100644 index 0000000..4789336 --- /dev/null +++ b/_rooms/labeds.txt @@ -0,0 +1,40 @@ +labeds1 +labeds2 +labeds3 +labeds4 +labeds5 +labeds6 +labeds7 +labeds8 +labeds9 +labeds10 +labeds11 +labeds12 +labeds13 +labeds14 +labeds15 +labeds16 +labeds17 +labeds18 +labeds19 +labeds20 +labeds21 +labeds22 +labeds23 +labeds24 +labeds25 +labeds26 +labeds27 +labeds28 +labeds29 +labeds30 +labeds31 +labeds32 +labeds33 +labeds34 +labeds35 +labeds36 +labeds37 +labeds38 +labeds39 +labeds40 \ No newline at end of file diff --git a/_static/Police_car.jpg b/_static/Police_car.jpg new file mode 100644 index 0000000..7e97867 Binary files /dev/null and b/_static/Police_car.jpg differ diff --git a/_static/asso1.png b/_static/asso1.png new file mode 100644 index 0000000..f64cb1f Binary files /dev/null and b/_static/asso1.png differ diff --git a/_static/asso2.png b/_static/asso2.png new file mode 100644 index 0000000..f8ee7cd Binary files /dev/null and b/_static/asso2.png differ diff --git a/_static/asso3.png b/_static/asso3.png new file mode 100644 index 0000000..dcfbe97 Binary files /dev/null and b/_static/asso3.png differ diff --git a/_static/contact/image1.png b/_static/contact/image1.png new file mode 100644 index 0000000..8035163 Binary files /dev/null and b/_static/contact/image1.png differ diff --git a/_static/contact/image2.png b/_static/contact/image2.png new file mode 100644 index 0000000..92ab5e4 Binary files /dev/null and b/_static/contact/image2.png differ diff --git a/_static/dictator_game.png b/_static/dictator_game.png new file mode 100644 index 0000000..9eeceb9 Binary files /dev/null and b/_static/dictator_game.png differ diff --git a/_static/e4c/crt1.png b/_static/e4c/crt1.png new file mode 100644 index 0000000..ce8d274 Binary files /dev/null and b/_static/e4c/crt1.png differ diff --git a/_static/e4c/crt2.png b/_static/e4c/crt2.png new file mode 100644 index 0000000..57a3e15 Binary files /dev/null and b/_static/e4c/crt2.png differ diff --git a/_static/e4c/crt3.png b/_static/e4c/crt3.png new file mode 100644 index 0000000..c3f8ea7 Binary files /dev/null and b/_static/e4c/crt3.png differ diff --git a/_static/e4c/gneezy_potters.png b/_static/e4c/gneezy_potters.png new file mode 100644 index 0000000..6f0eac8 Binary files /dev/null and b/_static/e4c/gneezy_potters.png differ diff --git a/_static/e4c/gneezy_potters_en.png b/_static/e4c/gneezy_potters_en.png new file mode 100644 index 0000000..d437cb8 Binary files /dev/null and b/_static/e4c/gneezy_potters_en.png differ diff --git a/_static/expines/coin-dollar2.jpg b/_static/expines/coin-dollar2.jpg new file mode 100644 index 0000000..9418699 Binary files /dev/null and b/_static/expines/coin-dollar2.jpg differ diff --git a/_static/expines/coin.png b/_static/expines/coin.png new file mode 100644 index 0000000..55e2411 Binary files /dev/null and b/_static/expines/coin.png differ diff --git a/_static/expines/radio_button.png b/_static/expines/radio_button.png new file mode 100644 index 0000000..ddb90ea Binary files /dev/null and b/_static/expines/radio_button.png differ diff --git a/_static/figurea_precaution.png b/_static/figurea_precaution.png new file mode 100644 index 0000000..c8d497f Binary files /dev/null and b/_static/figurea_precaution.png differ diff --git a/_static/global/empty.css b/_static/global/empty.css new file mode 100644 index 0000000..e69de29 diff --git a/_static/global/matrix.css b/_static/global/matrix.css new file mode 100644 index 0000000..6384e9d --- /dev/null +++ b/_static/global/matrix.css @@ -0,0 +1,9 @@ +.rotate90 { + -webkit-transform: rotate(-90deg); + -moz-transform: rotate(-90deg); + -ms-transform: rotate(-90deg); + -o-transform: rotate(-90deg); + transform: rotate(-90deg); + float: left; + margin-top: 1em; // TODO: do not hardcode to center align +} diff --git a/_static/police1.jpg b/_static/police1.jpg new file mode 100644 index 0000000..7e97867 Binary files /dev/null and b/_static/police1.jpg differ diff --git a/_static/police10.jpg b/_static/police10.jpg new file mode 100644 index 0000000..83a2e40 Binary files /dev/null and b/_static/police10.jpg differ diff --git a/_static/police2.jpg b/_static/police2.jpg new file mode 100644 index 0000000..7ac74cd Binary files /dev/null and b/_static/police2.jpg differ diff --git a/_static/police3.jpg b/_static/police3.jpg new file mode 100644 index 0000000..aa4f408 Binary files /dev/null and b/_static/police3.jpg differ diff --git a/_static/police4.jpg b/_static/police4.jpg new file mode 100644 index 0000000..d25943f Binary files /dev/null and b/_static/police4.jpg differ diff --git a/_static/police5.jpg b/_static/police5.jpg new file mode 100644 index 0000000..83a2e40 Binary files /dev/null and b/_static/police5.jpg differ diff --git a/_static/police6.jpg b/_static/police6.jpg new file mode 100644 index 0000000..a40b8e0 Binary files /dev/null and b/_static/police6.jpg differ diff --git a/_static/police7.jpg b/_static/police7.jpg new file mode 100644 index 0000000..7e97867 Binary files /dev/null and b/_static/police7.jpg differ diff --git a/_static/police8.jpg b/_static/police8.jpg new file mode 100644 index 0000000..7ac74cd Binary files /dev/null and b/_static/police8.jpg differ diff --git a/_static/police9.jpg b/_static/police9.jpg new file mode 100644 index 0000000..aa4f408 Binary files /dev/null and b/_static/police9.jpg differ diff --git a/_static/sante1.jpg b/_static/sante1.jpg new file mode 100644 index 0000000..d80a502 Binary files /dev/null and b/_static/sante1.jpg differ diff --git a/_static/sante10.jpg b/_static/sante10.jpg new file mode 100644 index 0000000..871613d Binary files /dev/null and b/_static/sante10.jpg differ diff --git a/_static/sante2.jpg b/_static/sante2.jpg new file mode 100644 index 0000000..bcb69f6 Binary files /dev/null and b/_static/sante2.jpg differ diff --git a/_static/sante3.jpg b/_static/sante3.jpg new file mode 100644 index 0000000..f6aa637 Binary files /dev/null and b/_static/sante3.jpg differ diff --git a/_static/sante4.jpg b/_static/sante4.jpg new file mode 100644 index 0000000..a063830 Binary files /dev/null and b/_static/sante4.jpg differ diff --git a/_static/sante5.jpg b/_static/sante5.jpg new file mode 100644 index 0000000..871613d Binary files /dev/null and b/_static/sante5.jpg differ diff --git a/_static/sante6.jpg b/_static/sante6.jpg new file mode 100644 index 0000000..145ee43 Binary files /dev/null and b/_static/sante6.jpg differ diff --git a/_static/sante7.jpg b/_static/sante7.jpg new file mode 100644 index 0000000..d80a502 Binary files /dev/null and b/_static/sante7.jpg differ diff --git a/_static/sante8.jpg b/_static/sante8.jpg new file mode 100644 index 0000000..bcb69f6 Binary files /dev/null and b/_static/sante8.jpg differ diff --git a/_static/sante9.jpg b/_static/sante9.jpg new file mode 100644 index 0000000..f6aa637 Binary files /dev/null and b/_static/sante9.jpg differ diff --git a/_static/trust_game.png b/_static/trust_game.png new file mode 100644 index 0000000..c98b7b3 Binary files /dev/null and b/_static/trust_game.png differ diff --git a/_static/trust_game_covid2.png b/_static/trust_game_covid2.png new file mode 100644 index 0000000..459791d Binary files /dev/null and b/_static/trust_game_covid2.png differ diff --git a/_templates/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/_templates/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/_templates/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_templates/Untitled.ipynb b/_templates/Untitled.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/_templates/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_templates/global/Page.html b/_templates/global/Page.html new file mode 100644 index 0000000..236a97b --- /dev/null +++ b/_templates/global/Page.html @@ -0,0 +1,8 @@ +{% extends "otree/Page.html" %} +{% load otree static %} + +{% block global_styles %} +{% endblock %} + +{% block global_scripts %} +{% endblock %} \ No newline at end of file diff --git a/_templates/global/mturk_template.html b/_templates/global/mturk_template.html new file mode 100644 index 0000000..2aaeef8 --- /dev/null +++ b/_templates/global/mturk_template.html @@ -0,0 +1,23 @@ + + + +
+

+ Cette tâche consiste à répondre à un questionnaire d'une durée de 1 minute. + Après avoir terminé ce HIT, vous recevrez votre rémunération. +

+ +

+ L'URL de l'étude apparaît ici: link. +

+

+

Important

+
+ A la dernière page, vous recevrez un code. + Veuillez copier / coller ce code ci-dessous. +

+ + +
+
+ diff --git a/a1_en_p1_quest/__init__.py b/a1_en_p1_quest/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a1_en_p1_quest/_builtin/__init__.py b/a1_en_p1_quest/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a1_en_p1_quest/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a1_en_p1_quest/models.py b/a1_en_p1_quest/models.py new file mode 100644 index 0000000..d98c8bf --- /dev/null +++ b/a1_en_p1_quest/models.py @@ -0,0 +1,797 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a1_en_p1_quest' + players_per_group = None + num_rounds = 1 + + showupfee = 0 + + tauxchange = 0.8 # 1 ecu = 0.8 € + + +class Subsession(BaseSubsession): + + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict(id=p.participant.id_in_session, + label=p.participant.label, + )) + return dict(report=report) + +class Group(BaseGroup): + pass + + + +class Player(BasePlayer): + + def role(self): + return {1: 'A', 2: 'B'}[self.id_in_group] + + treatment = models.IntegerField() +################################################ page Questionnaire 0 + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + questionnaire_before = models.IntegerField() + + language = models.StringField() + + q_1 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_2_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + q_2_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_22 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + + q_2_e_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_e_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_e_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_h = models.StringField( + label="", + blank=True + ) + + q_3 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_4 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_5 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + # q_6 = models.IntegerField( + # choices=[ + # [1, '1'], + # [2, '2'], + # [3, '3'], + # [4, '4'], + # [5, '5'], + # [6, '6'], + # ], + # widget=widgets.RadioSelectHorizontal, + # label="", + # blank=False + # ) + + q_6 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t drink coffee (or the) at school/work'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_7 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_8 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_9 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_10 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + # q_11 = models.IntegerField( + # choices=[ + # [1, '1 - Jamais'], + # [2, '2 - Rarement'], + # [3, '3 - Souvent'], + # [4, '4 - Toujours'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + + q_11 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t use batteries'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) +################################################ page Questionnaire 1 + + q_12 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_13 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_14 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_15 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + # q_16 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_17 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_18 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + + q_16 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + q_16_a = models.IntegerField( + label="", + blank=True + ) + + q_16_b = models.StringField( + label="", + blank=True + ) + + q_17 = models.IntegerField( + choices=[ + [1, 'a- Green (100% electric or hybrid) (€26 per ride)'], + [2, 'b- UberX (Economical, fast and reliable) (at €22 per ride) '], + [3, 'c- Comfort (spacious vehicles and top rated drivers) (at €30 per ride)'], + + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_18 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_19_p2 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_20 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t drive'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + +################################################# Page : Questionnaire2 + + q_21 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_22_n = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_23 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_24 = models.IntegerField( + choices=[ + [0, 'Oui'], + [1, 'Non'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_24_a = models.StringField( + label="If yes which one? ", + blank=True + ) + + q_25 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + q_26_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + +# ------------- à modifier ici !!!!!! + + q_27_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + q_28_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_e = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_f = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_29_a = models.IntegerField( + label="", + blank=False + ) + + q_29_b = models.IntegerField( + label="", + blank=False + ) + + q_29_c = models.IntegerField( + label="", + blank=False + ) + + q_29_d = models.IntegerField( + label="", + blank=False + ) + + + + # q_30 = models.IntegerField( + # choices=[ + # [0, 'Yes'], + # [1, 'No'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_31 = models.StringField( + # label="31) What does this mean for you?", + # ) + + + + + + diff --git a/a1_en_p1_quest/pages.py b/a1_en_p1_quest/pages.py new file mode 100644 index 0000000..d9a3668 --- /dev/null +++ b/a1_en_p1_quest/pages.py @@ -0,0 +1,68 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 1 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + +class Questionnaire0(Page): + form_model = 'player' + form_fields = ['q_1', 'q_2_a', 'q_2_b', 'q_2_c', + 'q_22', 'q_2_e_a', 'q_2_e_b', 'q_2_e_c', 'q_2_h', + 'q_3', 'q_4', 'q_5', 'q_6', + 'q_7', 'q_8', 'q_9', 'q_10', 'q_11', + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 1 + +class Questionnaire1(Page): + form_model = 'player' + form_fields = [ + 'q_12', 'q_13', 'q_14', 'q_15', + 'q_16', + 'q_16_a','q_16_b', 'q_17', + 'q_18', + 'q_19_p2', 'q_20', + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 1 + + +class Questionnaire2(Page): + form_model = 'player' + form_fields = [ 'q_21', 'q_22_n', 'q_23', 'q_24', + 'q_24_a', 'q_25', + 'q_26_a', 'q_26_b', + 'q_26_c', 'q_26_d', + 'q_27_a', 'q_27_b', 'q_27_c', + 'q_27_d', + 'q_28_a', 'q_28_b', 'q_28_c', + 'q_28_d', 'q_28_e', 'q_28_f', + 'q_29_a', 'q_29_b','q_29_c', 'q_29_d', + + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 1 + + +page_sequence = [ + Bienvenue, + Questionnaire0, + Questionnaire1, + Questionnaire2, + + +] diff --git a/a1_en_p1_quest/templates/a1_en_p1_quest/Bienvenue.html b/a1_en_p1_quest/templates/a1_en_p1_quest/Bienvenue.html new file mode 100644 index 0000000..b743cdd --- /dev/null +++ b/a1_en_p1_quest/templates/a1_en_p1_quest/Bienvenue.html @@ -0,0 +1,28 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire0.html b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire0.html new file mode 100644 index 0000000..0018e16 --- /dev/null +++ b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire0.html @@ -0,0 +1,215 @@ +{% load otree %} + +{% block title %} + Questionnaire (1/3) +{% endblock %} + +{% block content %} + +
+
+ +The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. + +

+
+
+ +

+ 1) + Do you buy organic and / or ecological products? + + + {% formfield player.q_1 %} +

+ +
+

+ 2) For each of the following criteria, please indicate if it can influence your choice to buy a product: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a. The price can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_a %} Strongly agree
b. The origin can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_b %} Strongly agree
c. The quality can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_c %} Strongly agree
d. The taste (if it is a food product) can influence your choices of products to buy
Strongly disagree {% formfield player.q_22 %} Strongly agree
e. Label influence:
+           * The organic label can influence your choices of products to buy +
* Organic label: + An organic product is a product from organic farming (it is an agricultural production + method that excludes the use of most synthetic chemicals, toxic products, etc.). + There is a label (AB) in France that authenticates products from this method of + agriculture. +
        Strongly disagree {% formfield player.q_2_e_a %} Strongly agree
+           * The ecological label can influence your choices of products to buy +
* Ecological label: + An ecological product is a product that will be thought out, designed, + packaged, distributed, recycled in such a way as to minimize the impact on the environment. + It must comply with many rules related to these steps during specifications drawn up by + an organization such as ECOLABEL or ECOCERT. +An ecological product must be designed with maximum respect for the environment + (respect linked to the use of raw materials and machines during manufacture). + The life cycle of the product as well as its recycling are also important points + to consider. +
        Strongly disagree {% formfield player.q_2_e_b %} Strongly agree
+           * The equitable trade label can influence your choices of products to buy +
* Equitable trade label: + Equitable trade is a commercial and international partnership, based on concrete principles and launched + almost 20 years ago. Its objective is to be able to guarantee a better balance between + producers and resellers, by ensuring better working conditions and better remuneration + for small producers. +
        Strongly disagree {% formfield player.q_2_e_c %} Strongly agree
+

+           * + Other label can influence your choices of + products to buy (if yes, to indicate) + {% formfield player.q_2_h %} + +

+ +

+ 3) Do you shop online ? + {% formfield player.q_3 %} +

+ + +

+ 4) Do you shop clothes online? + {% formfield player.q_4 %} +

+ + + +

+ 5) + Do you agree that it is less + environmentally friendly to shop online rather than in-store? + + + + + + + + + +
Strongly disagree {% formfield player.q_5 %} Strongly agree
+

+ +

+ 6) Do you bring your own cup of coffee (tea) to school/work (instead of using the + cups from the coffee machine)? + {% formfield player.q_6 %} +

+ + +

+ 7) Do you sort your household waste? + {% formfield player.q_7 %} +

+ +

+ 8) Do you use a compost bin? + {% formfield player.q_8 %} +

+

+ 9) Do you collect and recycle used paper? + {% formfield player.q_9 %} +

+ +

+ 10) Do you reuse your shopping bags? + {% formfield player.q_10 %} +

+ +

+ 11) Do you return used batteries to appropriate recycling points? + {% formfield player.q_11 %} +

+ + + + + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire1.html b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire1.html new file mode 100644 index 0000000..b4bed5a --- /dev/null +++ b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire1.html @@ -0,0 +1,137 @@ +{% load otree %} + +{% block title %} + Questionnaire (2/3) +{% endblock %} + +{% block content %} + +
+
+

+The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. +

+ The questions in this section relate to your habits as a student, i.e. they do not refer to the facilities in your accommodation prior to building 103. On the other hand, the "you" refers to yourself, and not to all the residents of your flat if it is a shared flat. +

+

+
+
+ +

+ 12) When buying new electronic devices, do you make sure to buy the ones that use the least amount of energy? + {% formfield player.q_12 %} +

+ +

+ 13) Do you switch off or completely disconnect electrical devices when not in use (without using the + standby mode)? + {% formfield player.q_13 %} +

+ +

+ 14) Do you use a dryer for your clothes? + {% formfield player.q_14 %} +

+ +

+ 15) Do you wait until you have a full charge before doing your laundry? + {% formfield player.q_15 %} +

+ + +

16) Do you regularly defrost the fridge?

+ + 1 - Never
+
+

        a - Because I bought a frost free fridge on purpose
+         b - I share the refrigerator with other people and someone else is responsible for it
+         c - Other reason
+         (If other reason please specify) :
+ +

+

+ 2 - Rarely
+ 3 - Often
+ 4 - Always
+ +

+ + +

+ 17) Suppose you are about to book a taxi (e.g. UBER) to Orly airport. +
Which option would you choose? + + {% formfield player.q_17 %} +

+ +

+ 18) Electric cars are less polluting than petrol or diesel cars. +
(Indicate your degree of agreement) + + + + + + +
Strongly disagree {% formfield player.q_18 %} Strongly agree
+

+ +

+ 19) Would you be interested in buying an alternative fuel car? + + + + + + + +
Strongly disagree {% formfield player.q_19_p2 %} Strongly agree
+

+ +

+ 20) Do you adopt eco-responsible driving? +
+ + Eco-responsible driving consists of: Checking the tire pressure. + Make sure the vehicle is in good condition. Adopt a smooth driving + (Start smoothly and change without waiting for a higher gear to + avoid over-revs). Stop the engine during a prolonged stop. Do not + abuse the air conditioning. + + {% formfield player.q_20 %} +

+ + + + + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire2.html b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire2.html new file mode 100644 index 0000000..c9c00b1 --- /dev/null +++ b/a1_en_p1_quest/templates/a1_en_p1_quest/Questionnaire2.html @@ -0,0 +1,313 @@ +{% load otree %} + +{% block title %} + Questionnaire (3/3) +{% endblock %} + +{% block content %} + +
+
+ +The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. + + +

+ +

+ 21) + Support higher taxes for environmentally damaging behavior. +
(Indicate your degree of agreement) + + + + + + + +
Strongly disagree {% formfield player.q_21 %} Strongly agree
+

+ +

+ 22) +In a political program, the place given to environmental issues influences my vote. +
(Indicate your degree of agreement) + + + + + + + +
Strongly disagree {% formfield player.q_22_n %} Strongly agree
+ +

+ + +

+ 23) Do you often discuss environmental issues with friends? + + {% formfield player.q_23 %} +

+ +

24) Are you a member of an environmental organisation? If so, which one? ?

+No
+Yes
+
+

{% formfield player.q_24_a %} +

+
+ +

+ + +

+ 25) Do you support financial incentives for environmentally beneficial technology? +
(Indicate your degree of agreement) + + + + + + + +
Strongly disagree {% formfield player.q_25 %} Strongly agree
+

+ +

+ 26) For each of the following actions, please indicate whether it can reduce the + individual carbon footprint to a small or very large extent: + +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a. Avoid air travel. Reduced way:
Not very important {% formfield player.q_26_a %} Very important
b. Have fewer children. Reduced way:
Not very important {% formfield player.q_26_b %} Very important
c. Not having a car. Reduced way:
Not very important {% formfield player.q_26_c %} Very important
d. a Vegetarian diet. Reduced way:
Not very important {% formfield player.q_26_d %} Very important
+

+ +

+

+ +

+ 27) For each of these actions, please indicate whether you would be prepared to adopt it: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a- Avoid air travel:
Not prepared at all {% formfield player.q_27_a %} Very prepared
b- Have fewer children:
Not prepared at all {% formfield player.q_27_b %} Very prepared
c- Not owning a car:
Not prepared at all {% formfield player.q_27_c %} Very prepared
d- Adopt a vegetarian diet:
Not prepared at all {% formfield player.q_27_d %} Very prepared
+

+ +

+ 28) For each of the following questions, please indicate whether it concerns you: + + + + > + + + + + + + + > + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a- Global warming:
Not concerned at all {% formfield player.q_28_a %} Very concerned
b- Air pollution:
Not concerned at all {% formfield player.q_28_b %} Very concerned
c- The disappearance of animal and plant species:
Not concerned at all {% formfield player.q_28_c %} Very concerned
d- The abundance of non-recyclable waste:
Not concerned at all {% formfield player.q_28_d %} Very concerned
e- The use of pesticides:
Not concerned at all {% formfield player.q_28_e %} Very concerned
f- Depletion of resources (drinking water, fossil fuels):
Not concerned at all {% formfield player.q_28_f %} Very concerned
+

+ + +

+ 29) Rank the consumption items (for a household) from the highest CO2 emissions to the lowest. +
(1: highest CO2 emissions; ...; 4: lowest CO2 emissions) + + + + + + + + + + + + + + + + + + + + + + + +
a- Transport: +
+ +
b- Food: +
+ +
c- Energy consumption in the home (heating, lighting): +
+ +
d- Energy consumption of various equipment + (appliances installed and used in the home or elsewhere): +
+ +
+

+ + + + +

{% next_button %}

+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest/templates/a1_en_p1_quest/QuestionnaireEnd.html b/a1_en_p1_quest/templates/a1_en_p1_quest/QuestionnaireEnd.html new file mode 100644 index 0000000..26368c6 --- /dev/null +++ b/a1_en_p1_quest/templates/a1_en_p1_quest/QuestionnaireEnd.html @@ -0,0 +1,29 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + First part : End + +{% endblock %} + +{% block content %} +
+
+ + +

+ You have completed the first part. + +
You will now begin the second part. + +

+ + +
+
+ +

{% next_button %}

+ + +{% endblock %} diff --git a/a1_en_p1_quest/tests.py b/a1_en_p1_quest/tests.py new file mode 100644 index 0000000..c110a27 --- /dev/null +++ b/a1_en_p1_quest/tests.py @@ -0,0 +1,81 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + + if self.participant.vars['questionnaire_before'] == 1: + yield (pages.Bienvenue) + yield (pages.Questionnaire0, { + 'q_1': 1, + 'q_2_a': 1, + 'q_2_b': 1, + 'q_2_c': 1, + 'q_22': 1, + 'q_2_e_a': 1, + 'q_2_e_b': 1, + 'q_2_e_c': 1, + 'q_2_h': 1, + 'q_3': 1, + 'q_4': 1, + 'q_5': 1, + 'q_6': 1, + 'q_7': 1, + 'q_8': 1, + 'q_9': 1, + 'q_10': 1, + 'q_11': 1, + }) + yield (pages.Questionnaire1, { + 'q_12': 1, + 'q_13': 1, + 'q_14': 1, + 'q_15': 1, + # 'q_16': 1, + # 'q_17': 1, + # 'q_18': 1, + 'q_16': 1, + 'q_16_a': 1, + 'q_16_b': 1, + 'q_17': 1, + 'q_18': 1, + 'q_19_p2': 1, + 'q_20': 1, + }) + yield (pages.Questionnaire2, { + 'q_21':1, + 'q_22_n':1, + 'q_23':1, + 'q_24':1, + 'q_24_a':1, + 'q_25':1, + 'q_26_a':1, + 'q_26_b':1, + 'q_26_c':1, + 'q_26_d':1, + 'q_27_a':1, + 'q_27_b':1, + 'q_27_c':1, + 'q_27_d':1, + 'q_28_a':1, + 'q_28_b':1, + 'q_28_c':1, + 'q_28_d':1, + 'q_28_e':1, + 'q_28_f':1, + 'q_29_a':1, + 'q_29_b':1, + 'q_29_c':1, + 'q_29_d':1, + + }) + + + diff --git a/a1_en_p1_quest_v2/__init__.py b/a1_en_p1_quest_v2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a1_en_p1_quest_v2/_builtin/__init__.py b/a1_en_p1_quest_v2/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a1_en_p1_quest_v2/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a1_en_p1_quest_v2/models.py b/a1_en_p1_quest_v2/models.py new file mode 100644 index 0000000..18fafab --- /dev/null +++ b/a1_en_p1_quest_v2/models.py @@ -0,0 +1,797 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a1_en_p1_quest_v2' + players_per_group = None + num_rounds = 1 + + showupfee = 0 + + tauxchange = 0.8 # 1 ecu = 0.8 € + + +class Subsession(BaseSubsession): + + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict(id=p.participant.id_in_session, + label=p.participant.label, + )) + return dict(report=report) + +class Group(BaseGroup): + pass + + + +class Player(BasePlayer): + + def role(self): + return {1: 'A', 2: 'B'}[self.id_in_group] + + treatment = models.IntegerField() +################################################ page Questionnaire 0 + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + questionnaire_before = models.IntegerField() + + language = models.StringField() + + q_1 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_2_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + q_2_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_22 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + + q_2_e_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_e_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_e_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_2_h = models.StringField( + label="", + blank=True + ) + + q_3 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_4 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_5 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + # q_6 = models.IntegerField( + # choices=[ + # [1, '1'], + # [2, '2'], + # [3, '3'], + # [4, '4'], + # [5, '5'], + # [6, '6'], + # ], + # widget=widgets.RadioSelectHorizontal, + # label="", + # blank=False + # ) + + q_6 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t drink coffee (or the) at school/work'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_7 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_8 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_9 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_10 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + # q_11 = models.IntegerField( + # choices=[ + # [1, '1 - Jamais'], + # [2, '2 - Rarement'], + # [3, '3 - Souvent'], + # [4, '4 - Toujours'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + + q_11 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t use batteries'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) +################################################ page Questionnaire 1 + + q_12 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_13 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_14 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_15 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + # q_16 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_17 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_18 = models.IntegerField( + # choices=[ + # [1, '1 - Never'], + # [2, '2 - Rarely'], + # [3, '3 - Often'], + # [4, '4 - Always'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + + q_16 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + q_16_a = models.IntegerField( + label="", + blank=True + ) + + q_16_b = models.StringField( + label="", + blank=True + ) + + q_17 = models.IntegerField( + choices=[ + [1, 'a- Green (100% electric or hybrid) (€26 per ride)'], + [2, 'b- UberX (Economical, fast and reliable) (at €22 per ride) '], + [3, 'c- Comfort (spacious vehicles and top rated drivers) (at €30 per ride)'], + + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_18 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_19_p2 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_20 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + [5, '5 - I don’t drive'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + +################################################# Page : Questionnaire2 + + q_21 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_22_n = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_23 = models.IntegerField( + choices=[ + [1, '1 - Never'], + [2, '2 - Rarely'], + [3, '3 - Often'], + [4, '4 - Always'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_24 = models.IntegerField( + choices=[ + [0, 'Oui'], + [1, 'Non'], + ], + widget=widgets.RadioSelect, + label="", + blank=False + ) + + q_24_a = models.StringField( + label="If yes which one? ", + blank=True + ) + + q_25 = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + q_26_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_26_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'], + [8, '8'], + [9, '9'], + [10, '10'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + +# ------------- à modifier ici !!!!!! + + q_27_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_27_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + + q_28_a = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_b = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_c = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_d = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_e = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_28_f = models.IntegerField( + choices=[ + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + ], + widget=widgets.RadioSelectHorizontal, + label="", + blank=False + ) + + q_29_a = models.IntegerField( + label="", + blank=False + ) + + q_29_b = models.IntegerField( + label="", + blank=False + ) + + q_29_c = models.IntegerField( + label="", + blank=False + ) + + q_29_d = models.IntegerField( + label="", + blank=False + ) + + + # + # q_30 = models.IntegerField( + # choices=[ + # [0, 'Yes'], + # [1, 'No'], + # ], + # widget=widgets.RadioSelect, + # label="", + # blank=False + # ) + # + # q_31 = models.StringField( + # label="31) What does this mean for you?", + # ) + + + + + + diff --git a/a1_en_p1_quest_v2/pages.py b/a1_en_p1_quest_v2/pages.py new file mode 100644 index 0000000..0a36bb2 --- /dev/null +++ b/a1_en_p1_quest_v2/pages.py @@ -0,0 +1,72 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 0 + + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + +class Questionnaire0(Page): + form_model = 'player' + form_fields = ['q_1', 'q_2_a', 'q_2_b', 'q_2_c', + 'q_22', 'q_2_e_a', 'q_2_e_b', 'q_2_e_c', 'q_2_h', + 'q_3', 'q_4', 'q_5', 'q_6', + 'q_7', 'q_8', 'q_9', 'q_10', 'q_11', + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 0 + + +class Questionnaire1(Page): + form_model = 'player' + form_fields = [ + 'q_12', 'q_13', 'q_14', 'q_15', + 'q_16', + 'q_16_a','q_16_b', 'q_17', + 'q_18', + 'q_19_p2', 'q_20', + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 0 + + + +class Questionnaire2(Page): + form_model = 'player' + form_fields = [ 'q_21', 'q_22_n', 'q_23', 'q_24', + 'q_24_a', 'q_25', + 'q_26_a', 'q_26_b', + 'q_26_c', 'q_26_d', + 'q_27_a', 'q_27_b', 'q_27_c', + 'q_27_d', + 'q_28_a', 'q_28_b', 'q_28_c', + 'q_28_d', 'q_28_e', 'q_28_f', + 'q_29_a', 'q_29_b','q_29_c', 'q_29_d', + ] + def is_displayed(self): + return self.player.participant.vars['questionnaire_before'] == 0 + + + + +page_sequence = [ + Bienvenue, + Questionnaire0, + Questionnaire1, + Questionnaire2, + + +] diff --git a/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Bienvenue.html b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Bienvenue.html new file mode 100644 index 0000000..b743cdd --- /dev/null +++ b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Bienvenue.html @@ -0,0 +1,28 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire0.html b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire0.html new file mode 100644 index 0000000..0018e16 --- /dev/null +++ b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire0.html @@ -0,0 +1,215 @@ +{% load otree %} + +{% block title %} + Questionnaire (1/3) +{% endblock %} + +{% block content %} + +
+
+ +The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. + +

+
+
+ +

+ 1) + Do you buy organic and / or ecological products? + + + {% formfield player.q_1 %} +

+ +
+

+ 2) For each of the following criteria, please indicate if it can influence your choice to buy a product: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a. The price can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_a %} Strongly agree
b. The origin can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_b %} Strongly agree
c. The quality can influence your choices of products to buy
Strongly disagree {% formfield player.q_2_c %} Strongly agree
d. The taste (if it is a food product) can influence your choices of products to buy
Strongly disagree {% formfield player.q_22 %} Strongly agree
e. Label influence:
+           * The organic label can influence your choices of products to buy +
* Organic label: + An organic product is a product from organic farming (it is an agricultural production + method that excludes the use of most synthetic chemicals, toxic products, etc.). + There is a label (AB) in France that authenticates products from this method of + agriculture. +
        Strongly disagree {% formfield player.q_2_e_a %} Strongly agree
+           * The ecological label can influence your choices of products to buy +
* Ecological label: + An ecological product is a product that will be thought out, designed, + packaged, distributed, recycled in such a way as to minimize the impact on the environment. + It must comply with many rules related to these steps during specifications drawn up by + an organization such as ECOLABEL or ECOCERT. +An ecological product must be designed with maximum respect for the environment + (respect linked to the use of raw materials and machines during manufacture). + The life cycle of the product as well as its recycling are also important points + to consider. +
        Strongly disagree {% formfield player.q_2_e_b %} Strongly agree
+           * The equitable trade label can influence your choices of products to buy +
* Equitable trade label: + Equitable trade is a commercial and international partnership, based on concrete principles and launched + almost 20 years ago. Its objective is to be able to guarantee a better balance between + producers and resellers, by ensuring better working conditions and better remuneration + for small producers. +
        Strongly disagree {% formfield player.q_2_e_c %} Strongly agree
+

+           * + Other label can influence your choices of + products to buy (if yes, to indicate) + {% formfield player.q_2_h %} + +

+ +

+ 3) Do you shop online ? + {% formfield player.q_3 %} +

+ + +

+ 4) Do you shop clothes online? + {% formfield player.q_4 %} +

+ + + +

+ 5) + Do you agree that it is less + environmentally friendly to shop online rather than in-store? + + + + + + + + + +
Strongly disagree {% formfield player.q_5 %} Strongly agree
+

+ +

+ 6) Do you bring your own cup of coffee (tea) to school/work (instead of using the + cups from the coffee machine)? + {% formfield player.q_6 %} +

+ + +

+ 7) Do you sort your household waste? + {% formfield player.q_7 %} +

+ +

+ 8) Do you use a compost bin? + {% formfield player.q_8 %} +

+

+ 9) Do you collect and recycle used paper? + {% formfield player.q_9 %} +

+ +

+ 10) Do you reuse your shopping bags? + {% formfield player.q_10 %} +

+ +

+ 11) Do you return used batteries to appropriate recycling points? + {% formfield player.q_11 %} +

+ + + + + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire1.html b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire1.html new file mode 100644 index 0000000..b4bed5a --- /dev/null +++ b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire1.html @@ -0,0 +1,137 @@ +{% load otree %} + +{% block title %} + Questionnaire (2/3) +{% endblock %} + +{% block content %} + +
+
+

+The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. +

+ The questions in this section relate to your habits as a student, i.e. they do not refer to the facilities in your accommodation prior to building 103. On the other hand, the "you" refers to yourself, and not to all the residents of your flat if it is a shared flat. +

+

+
+
+ +

+ 12) When buying new electronic devices, do you make sure to buy the ones that use the least amount of energy? + {% formfield player.q_12 %} +

+ +

+ 13) Do you switch off or completely disconnect electrical devices when not in use (without using the + standby mode)? + {% formfield player.q_13 %} +

+ +

+ 14) Do you use a dryer for your clothes? + {% formfield player.q_14 %} +

+ +

+ 15) Do you wait until you have a full charge before doing your laundry? + {% formfield player.q_15 %} +

+ + +

16) Do you regularly defrost the fridge?

+ + 1 - Never
+
+

        a - Because I bought a frost free fridge on purpose
+         b - I share the refrigerator with other people and someone else is responsible for it
+         c - Other reason
+         (If other reason please specify) :
+ +

+

+ 2 - Rarely
+ 3 - Often
+ 4 - Always
+ +

+ + +

+ 17) Suppose you are about to book a taxi (e.g. UBER) to Orly airport. +
Which option would you choose? + + {% formfield player.q_17 %} +

+ +

+ 18) Electric cars are less polluting than petrol or diesel cars. +
(Indicate your degree of agreement) + + + + + + +
Strongly disagree {% formfield player.q_18 %} Strongly agree
+

+ +

+ 19) Would you be interested in buying an alternative fuel car? + + + + + + + +
Strongly disagree {% formfield player.q_19_p2 %} Strongly agree
+

+ +

+ 20) Do you adopt eco-responsible driving? +
+ + Eco-responsible driving consists of: Checking the tire pressure. + Make sure the vehicle is in good condition. Adopt a smooth driving + (Start smoothly and change without waiting for a higher gear to + avoid over-revs). Stop the engine during a prolonged stop. Do not + abuse the air conditioning. + + {% formfield player.q_20 %} +

+ + + + + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire2.html b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire2.html new file mode 100644 index 0000000..7ba6c84 --- /dev/null +++ b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/Questionnaire2.html @@ -0,0 +1,312 @@ +{% load otree %} + +{% block title %} + Questionnaire (3/3) +{% endblock %} + +{% block content %} + +
+
+ +The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. + + +

+ +

+ 21) + Support higher taxes for environmentally damaging behavior. +
(Indicate your degree of agreement) + + + + + + + +
Strongly disagree {% formfield player.q_21 %} Strongly agree
+

+ +

+ 22) +In a political program, the place given to environmental issues influences my vote. +
(Indicate your degree of agreement) + + + + + + + + +
Strongly disagree {% formfield player.q_22_n %} Strongly agree
+

+ + +

+ 23) Do you often discuss environmental issues with friends? + + {% formfield player.q_23 %} +

+ +

24) Are you a member of an environmental organisation? If so, which one? ?

+No
+Yes
+
+

{% formfield player.q_24_a %} +

+
+ +

+ + +

+ 25) Do you support financial incentives for environmentally beneficial technology? +
(Indicate your degree of agreement) + + + + + + + +
Strongly disagree {% formfield player.q_25 %} Strongly agree
+

+ +

+ 26) For each of the following actions, please indicate whether it can reduce the + individual carbon footprint to a small or very large extent: + +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a. Avoid air travel. Reduced way:
Not very important {% formfield player.q_26_a %} Very important
b. Have fewer children. Reduced way:
Not very important {% formfield player.q_26_b %} Very important
c. Not having a car. Reduced way:
Not very important {% formfield player.q_26_c %} Very important
d. a Vegetarian diet. Reduced way:
Not very important {% formfield player.q_26_d %} Very important
+

+ +

+

+ +

+ 27) For each of these actions, please indicate whether you would be prepared to adopt it: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a- Avoid air travel:
Not prepared at all {% formfield player.q_27_a %} Very prepared
b- Have fewer children:
Not prepared at all {% formfield player.q_27_b %} Very prepared
c- Not owning a car:
Not prepared at all {% formfield player.q_27_c %} Very prepared
d- Adopt a vegetarian diet:
Not prepared at all {% formfield player.q_27_d %} Very prepared
+

+ +

+ 28) For each of the following questions, please indicate whether it concerns you: + + + > + + + + + + + + > + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
a- Global warming:
Not concerned at all {% formfield player.q_28_a %} Very concerned
b- Air pollution:
Not concerned at all {% formfield player.q_28_b %} Very concerned
c- The disappearance of animal and plant species:
Not concerned at all {% formfield player.q_28_c %} Very concerned
d- The abundance of non-recyclable waste:
Not concerned at all {% formfield player.q_28_d %} Very concerned
e- The use of pesticides:
Not concerned at all {% formfield player.q_28_e %} Very concerned
f- Depletion of resources (drinking water, fossil fuels):
Not concerned at all {% formfield player.q_28_f %} Very concerned
+

+ + +

+ 29) Rank the consumption items (for a household) from the highest CO2 emissions to the lowest. +
(1: highest CO2 emissions; ...; 4: lowest CO2 emissions) + + + + + + + + + + + + + + + + + + + + + + + +
a- Transport: +
+ +
b- Food: +
+ +
c- Energy consumption in the home (heating, lighting): +
+ +
d- Energy consumption of various equipment + (appliances installed and used in the home or elsewhere): +
+ +
+

+ + + + +

{% next_button %}

+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/QuestionnaireEnd.html b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/QuestionnaireEnd.html new file mode 100644 index 0000000..26368c6 --- /dev/null +++ b/a1_en_p1_quest_v2/templates/a1_en_p1_quest_v2/QuestionnaireEnd.html @@ -0,0 +1,29 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + First part : End + +{% endblock %} + +{% block content %} +
+
+ + +

+ You have completed the first part. + +
You will now begin the second part. + +

+ + +
+
+ +

{% next_button %}

+ + +{% endblock %} diff --git a/a1_en_p1_quest_v2/tests.py b/a1_en_p1_quest_v2/tests.py new file mode 100644 index 0000000..5cb21aa --- /dev/null +++ b/a1_en_p1_quest_v2/tests.py @@ -0,0 +1,81 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + + if self.participant.vars['questionnaire_before'] == 0: + yield (pages.Bienvenue) + yield (pages.Questionnaire0, { + 'q_1': 1, + 'q_2_a': 1, + 'q_2_b': 1, + 'q_2_c': 1, + 'q_22': 1, + 'q_2_e_a': 1, + 'q_2_e_b': 1, + 'q_2_e_c': 1, + 'q_2_h': 1, + 'q_3': 1, + 'q_4': 1, + 'q_5': 1, + 'q_6': 1, + 'q_7': 1, + 'q_8': 1, + 'q_9': 1, + 'q_10': 1, + 'q_11': 1, + }) + yield (pages.Questionnaire1, { + 'q_12': 1, + 'q_13': 1, + 'q_14': 1, + 'q_15': 1, + # 'q_16': 1, + # 'q_17': 1, + # 'q_18': 1, + 'q_16': 1, + 'q_16_a': 1, + 'q_16_b': 1, + 'q_17': 1, + 'q_18': 1, + 'q_19_p2': 1, + 'q_20': 1, + }) + yield (pages.Questionnaire2, { + 'q_21':1, + 'q_22_n':1, + 'q_23':1, + 'q_24':1, + 'q_24_a':1, + 'q_25':1, + 'q_26_a':1, + 'q_26_b':1, + 'q_26_c':1, + 'q_26_d':1, + 'q_27_a':1, + 'q_27_b':1, + 'q_27_c':1, + 'q_27_d':1, + 'q_28_a':1, + 'q_28_b':1, + 'q_28_c':1, + 'q_28_d':1, + 'q_28_e':1, + 'q_28_f':1, + 'q_29_a':1, + 'q_29_b':1, + 'q_29_c':1, + 'q_29_d':1, + + }) + + + diff --git a/a3_gp/__init__.py b/a3_gp/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a3_gp/_builtin/__init__.py b/a3_gp/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a3_gp/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a3_gp/models.py b/a3_gp/models.py new file mode 100644 index 0000000..58ba2a9 --- /dev/null +++ b/a3_gp/models.py @@ -0,0 +1,140 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a3_gp' + players_per_group = None + num_rounds = 1 + + showupfee = 0 + + instructions_GP_template = 'a3_gp/instructions.html' + + + +class Subsession(BaseSubsession): + + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict(id=p.participant.id_in_session, + label=p.participant.label, + )) + return dict(report=report) + +class Group(BaseGroup): + pass + + + +class Player(BasePlayer): + + # def role(self): + # return {1: 'A', 2: 'B'}[self.id_in_group] + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + + treatment = models.IntegerField() + + + risk11_qc_a = models.IntegerField( + choices=[ + [1, '3'], + [2, '4'], + [3, '5'], + [4, '6'], + [5, '7'], + ], + widget=widgets.RadioSelectHorizontal, + blank=False + ) + risk11_qc_b = models.IntegerField( + choices=[ + [1, '4'], + [2, '6'], + [3, '16'], + [4, '22'], + [5, '28'], + ], + widget=widgets.RadioSelectHorizontal, + blank=False + ) + risk11_qc_c = models.IntegerField( + choices=[ + [1, '4'], + [2, '6'], + [3, '16'], + [4, '22'], + [5, '28'], + ], + widget=widgets.RadioSelectHorizontal, + blank=False + ) + ########################################################################################## + risk11_a = models.IntegerField( + label="investment dans le projet risqué", + blank=False, + min=0, + max=10 + ) + risk11_b = models.IntegerField( + label="investment dans le projet non-risqué", + blank=False, + min=0, + max=10 + ) + + risk_assessment_gp = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + confidence_level_gp = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + memory_recall_gp = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + + + + + + tiragerisk = models.FloatField() + gainrisk = models.FloatField() + tirageriskresult = models.StringField() + + task_start_19 = models.FloatField() + task_time_19 = models.FloatField() + task_start_20 = models.FloatField() + task_time_20 = models.FloatField() + + + diff --git a/a3_gp/pages.py b/a3_gp/pages.py new file mode 100644 index 0000000..0067831 --- /dev/null +++ b/a3_gp/pages.py @@ -0,0 +1,100 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + # def is_displayed(self): + # return self.participant.vars['versionexp'] == 2 + + def is_displayed(self): + return self.round_number == 1 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n =self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + +########################################################################################### Jeu 5 : Risk - Gneezy Potters- Questionnaire compréhension +class L_risk11_qc(Page): + form_model = 'player' + form_fields = ['risk11_qc_a', 'risk11_qc_b', 'risk11_qc_c'] + + # def is_displayed(self): + # return self.player.id_in_group % 2 == 1 and self.participant.vars['versionexp'] == 2 + # + + def vars_for_template(self): + self.player.task_start_19 = round(time.time(),2) ###### get_time + + return { + 'page': self.subsession.round_number + 10, + } + + + def error_message(self, values): + print('values is', values) + if values['risk11_qc_a'] != 2: + return 'Please correct your answer 1 !!' + elif values['risk11_qc_b'] != 4: + return 'Please correct your answer 2 !!' + elif values['risk11_qc_c'] != 1: + return 'Please correct your answer 3 !!' + + def before_next_page(self): + self.player.task_time_19 = round(time.time() - self.player.task_start_19,2) ######### count_time + #self.player.treatment = self.player.participant.vars['treatment'] + +class M_risk11(Page): + # timeout_seconds = 1 # WB + + form_model = 'player' + form_fields = ['risk11_a', 'risk11_b', 'risk_assessment_gp', 'confidence_level_gp', 'memory_recall_gp'] + + # def is_displayed(self): + # return self.player.id_in_group % 2 == 1 and self.participant.vars['versionexp'] == 2 + + def vars_for_template(self): + self.player.task_start_20 = round(time.time(),2) ###### get_time + + return { + 'page': self.subsession.round_number + 11, + } + + def error_message(self, values): + print('values is', values) + if values['risk11_a'] + values['risk11_b'] != 10: + return 'The sum must be equal to 10 !!' + + def before_next_page(self): + self.player.task_time_20 = round(time.time() - self.player.task_start_20,2) ######### count_time + + self.player.tiragerisk = round(random.random(), 2) ## + if self.player.tiragerisk < 0.5: + self.player.gainrisk = (self.player.risk11_a * 3) + self.player.risk11_b + self.player.tirageriskresult = "Succès" + else: + self.player.gainrisk = self.player.risk11_b + self.player.tirageriskresult = "Echec" + + + self.participant.vars['tirageriskresult_j1'] = self.player.tirageriskresult + self.participant.vars['risk11_a_j1'] = self.player.risk11_a + self.participant.vars['risk11_b_j1'] = self.player.risk11_b + self.participant.vars['gainrisk_j1'] = self.player.gainrisk + +page_sequence = [ + Bienvenue, + L_risk11_qc, + M_risk11, + +] diff --git a/a3_gp/templates/a3_gp/Bienvenue.html b/a3_gp/templates/a3_gp/Bienvenue.html new file mode 100644 index 0000000..b743cdd --- /dev/null +++ b/a3_gp/templates/a3_gp/Bienvenue.html @@ -0,0 +1,28 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a3_gp/templates/a3_gp/Jeu1End.html b/a3_gp/templates/a3_gp/Jeu1End.html new file mode 100644 index 0000000..817680e --- /dev/null +++ b/a3_gp/templates/a3_gp/Jeu1End.html @@ -0,0 +1,28 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + Second part: End + +{% endblock %} + +{% block content %} +
+
+ +

+ You have completed the second part. + +
You will now begin the third part. + +

+ + +
+
+ +

{% next_button %}

+ + +{% endblock %} diff --git a/a3_gp/templates/a3_gp/L_risk11_qc.html b/a3_gp/templates/a3_gp/L_risk11_qc.html new file mode 100644 index 0000000..17938ed --- /dev/null +++ b/a3_gp/templates/a3_gp/L_risk11_qc.html @@ -0,0 +1,74 @@ +{% load otree %} + +{% block title %} + Game {{player.game_num}} +{% endblock %} + +{% block content %} + +

+ This game can be taken into account when calculating your winnings. +

+ {% include Constants.instructions_GP_template %} + + +
+
+ +

+ +

Comprehension test:

+ + +

If you have invested 6 ECUs in the risky project:

+ +

1) How many ECUs have you kept?

+ +

{% formfield player.risk11_qc_a label=""%} +

+

2) If the investment is successful, how many ECUs in your possession at the end of this game?

+

{% formfield player.risk11_qc_b label=""%} +

+

3) If the investment is unsuccessful, how many ECUs in your possession at the end of this game?

+

{% formfield player.risk11_qc_c label=""%} +

+ + + +

+ + +{% next_button %} + + + +
+
+ + +{% endblock %} + + + +{% block scripts %} + + + + + + + +{% endblock %} \ No newline at end of file diff --git a/a3_gp/templates/a3_gp/M_risk11.html b/a3_gp/templates/a3_gp/M_risk11.html new file mode 100644 index 0000000..01e369c --- /dev/null +++ b/a3_gp/templates/a3_gp/M_risk11.html @@ -0,0 +1,108 @@ +{% load otree %} + +{% block title %} + Game {{player.game_num}} +{% endblock %} + +{% block content %} + + +
+
+ +

+

+ Votre choix : +

+ +
+
+ +
+

Please enter the amount of your investment in the project (in ECUs) (an amount between 0 and 10):

+

{% formfield player.risk11_a label=""%} +

+ +

So, how many ECUs did you keep (an amount between 0 and 10)?

+

{% formfield player.risk11_b label=""%} +

+ +

+ Note: the sum of the two amounts must be equal to 10. +

+ +

+ + +
+ +


+
+
+ +

Please answer the following questions:

+

+ + 1) On a scale from 1 (not at all risky) to 7 (very risky):
+ How risky do you consider your own decision (indicated above) to be? + + + + + + + + +
Not at all risky {% formfield player.risk_assessment_gp %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated above? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_gp %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_gp%} I remembered it very well
+ +
+ +
+ {% next_button %} +
+ + + + +

+
+
+ + {% include Constants.instructions_GP_template %} + +
+ + + +{% endblock %} \ No newline at end of file diff --git a/a3_gp/templates/a3_gp/M_risk11_result.html b/a3_gp/templates/a3_gp/M_risk11_result.html new file mode 100644 index 0000000..0358d57 --- /dev/null +++ b/a3_gp/templates/a3_gp/M_risk11_result.html @@ -0,0 +1,43 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} +{% endblock %} + +{% block content %} + +
+
+ + +

Jeu 1 : RESULTAT

+ + + +

Le tirage au sort est : {{player.tirageriskresult}} +

+

Votre investissement dans l’actif risqué : {{player.risk11_a}}

+

Vous avez gardé : {{player.risk11_b}}

+ +
+ +

Votre gain pour ce jeu est de : {{player.gainrisk}} ECU(s)

+ + +

+ +
+
+ +

+

+ Rappel des instructions : +

+
+
+ + {% include Constants.instructions_GP_template %} + +
+ +{% endblock %} diff --git a/a3_gp/templates/a3_gp/instructions.html b/a3_gp/templates/a3_gp/instructions.html new file mode 100644 index 0000000..b790c7c --- /dev/null +++ b/a3_gp/templates/a3_gp/instructions.html @@ -0,0 +1,45 @@ +{% load otree %} + + + +
+
+

+ Rules of the game : +

+ +

You have a capital of 10 ECUs, and you must decide the amount of your investment in the project described below. + You can invest any amount between 0 ECUs and 10 ECUs (in whole numbers).

+ +
Project description:
+ +

This project earns 3 times the amount invested in the event of success and 0 in the event of failure. + Success and failure have the same probability of happening: 1 in 2 chance.

+
+

Your gain from this task = amount retained + gain from your investment in the project.

+
+

Example 1: You decide to invest 4 ECUs in the project. The project is a success. Your project gain is + of 3 x 4 ECUs = 12 ECUs. Your gain from this task is therefore 6 ECUs kept + 12 ECUs from the project = 18 ECUs. +

+ +

Example 2: You decide to invest 4 ECUs in the project. The project is a failure. Your project gain is + of 0 x 4 ECUs = 0 ECUs. Your gain from this game is therefore 6 ECUs kept + 0 ECUs from the project = 6 ECUs. +

+

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+
+ +
+ +
+
+ +

+

Summary of the game:

+ +
+
+

+ + +
+
\ No newline at end of file diff --git a/a3_gp/tests.py b/a3_gp/tests.py new file mode 100644 index 0000000..ac2d599 --- /dev/null +++ b/a3_gp/tests.py @@ -0,0 +1,36 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + yield (pages.Bienvenue) + + + # if self.player.id_in_group % 2 == 1 and self.participant.vars['versionexp'] == 2: + yield (pages.L_risk11_qc, { + 'risk11_qc_a': 2, + 'risk11_qc_b': 4, + 'risk11_qc_c': 1, + }) + + yield (pages.M_risk11, { + 'risk11_a': 0, #random.randint(0, 10), + 'risk11_b': 10 , + 'risk_assessment_gp':1, + 'confidence_level_gp':1, + 'memory_recall_gp':1, + }) + + + + + + + diff --git a/a3_welcome/__init__.py b/a3_welcome/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a3_welcome/_builtin/__init__.py b/a3_welcome/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a3_welcome/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a3_welcome/models.py b/a3_welcome/models.py new file mode 100644 index 0000000..331114f --- /dev/null +++ b/a3_welcome/models.py @@ -0,0 +1,43 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a3_welcome' + players_per_group = None + num_rounds = 1 + + +class Subsession(BaseSubsession): + pass + +class Group(BaseGroup): + pass + +class Player(BasePlayer): + prolific_id = models.StringField(default=str(" ")) # prolific + game_num = models.IntegerField(initial=0) + app_sequence_select = models.IntegerField(initial=1) + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField(initial=0) + + + + + + + + diff --git a/a3_welcome/pages.py b/a3_welcome/pages.py new file mode 100644 index 0000000..bd2182d --- /dev/null +++ b/a3_welcome/pages.py @@ -0,0 +1,42 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + +class BienvenueBis(Page): # version anglaise + form_model = 'player' + form_fields = [] + + def before_next_page(self): + self.player.game_num = 0 + self.player.app_sequence_select = random.randint(1,4) + self.player.questionnaire_before = random.randint(0,1) + self.player.participant.vars['game_num'] = self.player.game_num + self.player.participant.vars['app_sequence_select'] = self.player.app_sequence_select + self.player.participant.vars['questionnaire_before'] = self.player.questionnaire_before + if self.player.app_sequence_select == 1: + self.player.app_sequence_select_n = "gp;bw;MDG_asso;MDG_class" + elif self.player.app_sequence_select == 2: + self.player.app_sequence_select_n = "gp;bw;MDG_class;MDG_asso" + elif self.player.app_sequence_select == 3: + self.player.app_sequence_select_n = "bw;gp;MDG_asso;MDG_class" + elif self.player.app_sequence_select == 4: + self.player.app_sequence_select_n = "bw;gp;MDG_class;MDG_asso" + self.player.participant.vars['app_sequence_select_n'] = self.player.app_sequence_select_n + +class Demographics(Page): + form_model = 'player' + form_fields = ['age', 'gender', 'education', "income"] + + @staticmethod + def before_next_page(self, timeout_happened): + self.prolific_id = self.participant.label +pass + + +page_sequence = [ + BienvenueBis, + +] diff --git a/a3_welcome/templates/a3_welcome/Bienvenue.html b/a3_welcome/templates/a3_welcome/Bienvenue.html new file mode 100644 index 0000000..d345c6a --- /dev/null +++ b/a3_welcome/templates/a3_welcome/Bienvenue.html @@ -0,0 +1,68 @@ + + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Expérience en économie +{% endblock %} + +{% block content %} + + +
+
+ +

Bienvenue

+ +

+ Merci d’avoir accepté de participer à cette expérience. + Une rémunération fixe (de 5 €) vous sera versée + pour votre participation, et ce, quelles que soient vos réponses durant + cette expérience. À cela s’ajoutera une rémunération variable liée + à vos réponses au cours + de l’expérience. +

+ +

+ L’expérience dure environ 30 min. Elle se compose de deux types de tâches : + les questionnaires et les jeux. + La rémunération variable que vous percevrez à la fin de l’expérience + ne dépendra pas de vos réponses + aux questionnaires. + En effet, dans ces questionnaires, il n’y a ni bonne, ni mauvaise réponse. + En revanche, votre rémunération + variable dépendra + de la qualité de vos réponses aux jeux. Plus précisément, à la fin de l’expérience, + un seul des jeux + sera sélectionné + aléatoirement pour être payé pour de bon. +

+
La monnaie utilisée dans cette expérience est l'ECU. +
Taux de change 1 ECU = {{ Constants.tauxchange}} €.
+ +
+ Important : Toutes vos réponses seront traitées anonymement. + Personne n’aura la possibilité d’identifier directement vos décisions. + + + + + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a3_welcome/templates/a3_welcome/Bienvenue0.html b/a3_welcome/templates/a3_welcome/Bienvenue0.html new file mode 100644 index 0000000..122e029 --- /dev/null +++ b/a3_welcome/templates/a3_welcome/Bienvenue0.html @@ -0,0 +1,41 @@ + + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Expérience en économie / Experiment in economics +{% endblock %} + +{% block content %} + + +
+
+ + + +

+

Choix de la langue / Choice of language

+ {% formfield player.versionexp %} + +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a3_welcome/templates/a3_welcome/BienvenueBis.html b/a3_welcome/templates/a3_welcome/BienvenueBis.html new file mode 100644 index 0000000..c07cfb5 --- /dev/null +++ b/a3_welcome/templates/a3_welcome/BienvenueBis.html @@ -0,0 +1,42 @@ + +{% load otree %} + +{% block title %} + Experiment in economics +{% endblock %} + +{% block content %} +
+
+ +

Welcome

+ +

Thank you for agreeing to participate in this experiment. +You will receive a fixed payment (of 5 €) for your participation, +regardless of your responses during this experiment. In addition, there will be a variable payment based on your responses during the experiment. +The experiment consists of two types of tasks: questionnaires and games. +

+

+These two tasks are independent of each other, and you will receive instructions at the beginning of each task. +The variable payment you will receive at the end of the experiment will not depend on your responses to the questionnaires. In fact, in these questionnaires, there are no right or wrong answers. However, your variable payment will depend on the quality of your responses to the games. +Specifically, at the end of the experiment, only one of the games will be randomly selected to be paid for real. +The currency used in this experiment is the ECU. +

+

+In case of non-integer winnings, the amount will be rounded (winnings of 1.4 → final winnings of 1€, winnings of 2.8 → final winnings of 3€). +

+

+The experiment lasts about 45 minutes, probably less. You are free to leave the experiment at any time, but if you decide to leave the experiment before completing it, you will not be paid. +

+
+Important: All your answers will be treated anonymously. No one will be able to directly identify your decisions. + + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a3_welcome/tests.py b/a3_welcome/tests.py new file mode 100644 index 0000000..8f3736d --- /dev/null +++ b/a3_welcome/tests.py @@ -0,0 +1,23 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + # yield (pages.Bienvenue0, { + # 'versionexp': random.randint(1,2), + # }) + # + # if self.participant.vars['versionexp'] == 1: + # yield (pages.Bienvenue) + # elif self.participant.vars['versionexp'] == 2: + yield (pages.BienvenueBis) + + + diff --git a/a3_welcome0/__init__.py b/a3_welcome0/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a3_welcome0/_builtin/__init__.py b/a3_welcome0/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a3_welcome0/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a3_welcome0/models.py b/a3_welcome0/models.py new file mode 100644 index 0000000..2c4d3b8 --- /dev/null +++ b/a3_welcome0/models.py @@ -0,0 +1,56 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a3_welcome0' + players_per_group = None + num_rounds = 1 + + +class Subsession(BaseSubsession): + pass + +class Group(BaseGroup): + pass + +class Player(BasePlayer): + q1_participation = models.IntegerField( + choices=[ + [1, 'Yes'], + [0, 'No'], + ], + widget=widgets.RadioSelect, + label="A) Have you already participated in one of our previous experiments?", + blank=False + ) + + q2_previoussession = models.IntegerField( + choices=[ + [1, 'March 2024'], + [2, 'September 2024'], + [3, "I'm not concerned by this question"] + ], + widget=widgets.RadioSelect, + label="B) Which session did you participate in?", + blank=False + ) + + + + + + diff --git a/a3_welcome0/pages.py b/a3_welcome0/pages.py new file mode 100644 index 0000000..6e4440c --- /dev/null +++ b/a3_welcome0/pages.py @@ -0,0 +1,17 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + +class Bienvenue0(Page): # version anglaise + form_model = 'player' + form_fields = ["q1_participation", "q2_previoussession"] + + + +page_sequence = [ + Bienvenue0, + +] diff --git a/a3_welcome0/templates/a3_welcome0/Bienvenue0.html b/a3_welcome0/templates/a3_welcome0/Bienvenue0.html new file mode 100644 index 0000000..1ab85e0 --- /dev/null +++ b/a3_welcome0/templates/a3_welcome0/Bienvenue0.html @@ -0,0 +1,37 @@ +{% load otree %} + +{% block title %} + Experiment in economics +{% endblock %} + +{% block content %} + + +
+
+ +

Welcome

+ + +

This experiment is exclusively for individuals who have previously + participated in our online experiment (around March 2024 or September 2024).

+ + {% formfield player.q1_participation %} + + {% formfield player.q2_previoussession %} + +

+If you answered no to question A, please exit the experiment by simply closing this page. +

+ + + + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a3_welcome0/tests.py b/a3_welcome0/tests.py new file mode 100644 index 0000000..88d5bf3 --- /dev/null +++ b/a3_welcome0/tests.py @@ -0,0 +1,23 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + # yield (pages.Bienvenue0, { + # 'versionexp': random.randint(1,2), + # }) + # + # if self.participant.vars['versionexp'] == 1: + # yield (pages.Bienvenue) + # elif self.participant.vars['versionexp'] == 2: + yield (pages.Bienvenue0) + + + diff --git a/a4_questappli/__init__.py b/a4_questappli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a4_questappli/_builtin/__init__.py b/a4_questappli/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a4_questappli/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a4_questappli/models.py b/a4_questappli/models.py new file mode 100644 index 0000000..c4254fc --- /dev/null +++ b/a4_questappli/models.py @@ -0,0 +1,119 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a4_questappli' + players_per_group = None + num_rounds = 1 + + +class Subsession(BaseSubsession): + pass + +class Group(BaseGroup): + pass + +class Player(BasePlayer): + q1_app_installation = models.IntegerField( + choices=[ + [0, "I didn't know such an app existed"], + [1, "I didn't install it because I wasn't interested"], + [2, "I didn't install it because I couldn't"], + [3, "I installed it but never used it"], + [4, "I installed it and used it a few times"], + [5, "I installed it and used it regularly"], + ], + widget=widgets.RadioSelect, + label="1) A mobile application for residents of Building 103 was developed last spring. Did you install it?", + blank=False + ) + + q2_satisfaction = models.IntegerField( + choices=[ + [1, "Very satisfied"], + [2, "Satisfied"], + [3, "Dissatisfied"], + [4, "Very dissatisfied"], + [5, "Not applicable"], + ], + widget=widgets.RadioSelect, + label="2) If yes, how satisfied are you with it?", + blank=False + ) + + q3_comments = models.LongStringField( + label="3) Any comments about this application?", + blank=True + ) + + q4_access_data = models.IntegerField( + choices=[ + [0, "I did not download the application"], + [1, "I downloaded the application, but I did not have access to this data"], + [2, "I downloaded the application, and I had access to this data"], + ], + widget=widgets.RadioSelect, + label="4) Did the application give you access to your apartment’s water and energy consumption?", + blank=False + ) + + q5_weekly_reports = models.IntegerField( + choices=[ + [0, "No"], + [1, "Yes, by mail"], + [2, "Yes, by email"], + [3, "Yes, by both mail and email"], + ], + widget=widgets.RadioSelect, + label="5) Did you receive weekly reports, either by mail or email, in addition to the application, regarding your apartment’s water and energy consumption?", + blank=False + ) + + q6_energy_comparison = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 7)], + label="", + widget=widgets.RadioSelectHorizontal, + blank = False + ) + + q7_water_comparison = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 7)], + label="", + widget=widgets.RadioSelectHorizontal, + blank = False + ) + + + + + + + + + + + + + + + + + + + + + diff --git a/a4_questappli/pages.py b/a4_questappli/pages.py new file mode 100644 index 0000000..bd28181 --- /dev/null +++ b/a4_questappli/pages.py @@ -0,0 +1,20 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + +class questappli(Page): # version anglaise + form_model = 'player' + form_fields = [ 'q1_app_installation', 'q2_satisfaction', 'q3_comments', + 'q4_access_data', 'q5_weekly_reports', + 'q6_energy_comparison', + 'q7_water_comparison' + ] + + + +page_sequence = [ + questappli, +] diff --git a/a4_questappli/templates/a4_questappli/questappli.html b/a4_questappli/templates/a4_questappli/questappli.html new file mode 100644 index 0000000..f4fc6f8 --- /dev/null +++ b/a4_questappli/templates/a4_questappli/questappli.html @@ -0,0 +1,85 @@ +{% load otree %} + +{% block title %} + Questionnaire: +{% endblock %} + +{% block content %} + +
+
+ +The answers to the following questions will not be taken into account when calculating your winnings. + However, we ask that you answer as honestly as possible. As a reminder, all your answers will + be treated anonymously, and no one will have access to your answers. +
+ +
+ +
+
+ +

Questionnaire about the mobile application

+ + + {% formfield player.q1_app_installation %} + + {% formfield player.q2_satisfaction%} + {% formfield player.q3_comments %} + {% formfield player.q4_access_data%} + {% formfield player.q5_weekly_reports %} + +
+ +
+ +
+
+ +

Questionnaire Consumption

+ + 6) How would you rate your flat's energy consumption (heating, lighting, + household appliances, etc.) compared with that of other flats in your building? + + + + + + + + + +
Much lower than other
apartments on average		{% formfield player.q6_energy_comparison %} Much higher than other
apartments on average
+ +
+ + 7) How would you rate your apartment’s water consumption (hot and cold) + compared with that of other flats in your building? + + + + + + + + + +
Much lower than other
apartments on average		{% formfield player.q7_water_comparison %} Much higher than other
apartments on average
+ + + + + + + + + +
+

+ +

{% next_button %}

+ + + +{% endblock %} + diff --git a/a4_questappli/tests.py b/a4_questappli/tests.py new file mode 100644 index 0000000..e298234 --- /dev/null +++ b/a4_questappli/tests.py @@ -0,0 +1,28 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + yield (pages.questappli, { + 'q1_app_installation': 1, + 'q2_satisfaction':1, + 'q3_comments': 'blabli', + 'q4_access_data':1, + 'q5_weekly_reports':1, + 'q6_energy_comparison':1, + 'q7_water_comparison':1, + }) + + + + + + + diff --git a/a5_questfin/__init__.py b/a5_questfin/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a5_questfin/_builtin/__init__.py b/a5_questfin/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a5_questfin/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a5_questfin/models.py b/a5_questfin/models.py new file mode 100644 index 0000000..2943e9d --- /dev/null +++ b/a5_questfin/models.py @@ -0,0 +1,734 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a5_questfin' + players_per_group = None + num_rounds = 1 + + app_seq1 = ["gp", "bw", "MDG_asso", "MDG_class"] + app_seq2 = ["gp", "bw", "MDG_class", "MDG_asso"] + app_seq3 = ["bw", "gp", "MDG_asso", "MDG_class"] + app_seq4 = ["bw", "gp", "MDG_class", "MDG_asso"] + + exchangerate_gp_bw = 0.8 + exchangerate_DGM = 0.1 + + showupfee = 5 + + instructions_GP_template = 'a5_questfin/instructions_gp.html' + instructions_bw_template = 'a5_questfin/instructions_bw.html' + instructions_DCL_template = 'a5_questfin/instructions_dcl.html' + instructions_DAS_template = 'a5_questfin/instructions_das.html' + instructions_colors_template = 'a5_questfin/instructions_colors.html' + + + +class Subsession(BaseSubsession): + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict( + gainrisk_eur = p.field_maybe_none('gainrisk_eur') or 0.0, + gain_bw_eur = p.field_maybe_none('gain_bw_eur') or 0.0, + mdgasso_gain_dgm_eur = p.field_maybe_none('mdgasso_gain_dgm_eur') or 0.0, + mdgasso_gain_asso_eur = p.field_maybe_none('mdgasso_gain_asso_eur') or 0.0, + mdgclass_gain_dgm_eur = p.field_maybe_none('mdgclass_gain_dgm_eur') or 0.0, + mdgclass_dgm_selected = p.field_maybe_none('mdgclass_dgm_selected') or 0, + mdgclass_round_selected = p.field_maybe_none('mdgclass_round_selected') or 0, + mdgclass_dot1_select = p.field_maybe_none('mdgclass_dot1_select') or 0, + mdgclass_s1_select = p.field_maybe_none('mdgclass_s1_select') or 0, + mdgclass_g1_select = p.field_maybe_none('mdgclass_g1_select') or 0, + mdgclass_keep_select = p.field_maybe_none('mdgclass_keep_select') or 0, + mdgclass_sent_select = p.field_maybe_none('mdgclass_sent_select') or 0, + mdgclass_gain_joueur2 = p.field_maybe_none('mdgclass_gain_joueur2') or 0.0, + mdgclass_gain_joueur2_eur = p.field_maybe_none('mdgclass_gain_joueur2_eur') or 0.0, + dictatorYES = p.field_maybe_none('dictatorYES') or False, + app_sequence_select_n = p.field_maybe_none('app_sequence_select_n') or 0, + game_num_select = p.field_maybe_none('game_num_select') or 0, + firstname = p.field_maybe_none('firstname') or "", + lastname = p.field_maybe_none('lastname') or "", + email = p.field_maybe_none('email') or "", + room = p.field_maybe_none('room') or "", + game_select_n = p.field_maybe_none('room') or "", + gainfinal_eur_f = p.field_maybe_none('gainfinal_eur_f') or 0.0, + gainfinal_eur_showupfee_f2025 = p.field_maybe_none('gainfinal_eur_showupfee_f2025') or 0.0, + gainfinal_asso_eur_f = p.field_maybe_none('gainfinal_asso_eur_f') or 0.0, + )) + return dict(report=report) + +class Group(BaseGroup): + pass + +class Player(BasePlayer): + app_sequence_select = models.IntegerField(blank=True, null=True) + app_sequence_select_n = models.StringField(blank=True, null=True) + questionnaire_before = models.IntegerField(blank=True, null=True) + game_num_select = models.IntegerField(blank=True, null=True) + game_select_n = models.StringField(blank=True, null=True) + game_select = models.IntegerField(blank=True, null=True) + + # result of GP + tirageriskresult = models.StringField(blank=True, null=True) + tirageriskresult_en = models.StringField(blank=True, null=True) + risk11_a = models.FloatField(blank=True, null=True) + risk11_b = models.FloatField(blank=True, null=True) + gainrisk = models.FloatField(blank=True, null=True) + gainrisk_eur = models.FloatField(blank=True, null=True) + + # result of BW + tirage_color_n = models.StringField(blank=True, null=True) + gain_bw = models.FloatField(blank=True, null=True) + gain_bw_eur = models.FloatField(blank=True, null=True) + + # result of MDG_asso + mdgasso_dgm_selected = models.IntegerField(blank=True, null=True) + mdgasso_round_selected = models.IntegerField(blank=True, null=True) + mdgasso_dot1_select = models.IntegerField(blank=True, null=True) + mdgasso_s1_select = models.IntegerField(blank=True, null=True) + mdgasso_g1_select = models.IntegerField(blank=True, null=True) + mdgasso_keep_select = models.FloatField(blank=True, null=True) + mdgasso_sent_select = models.FloatField(blank=True, null=True) + mdgasso_gain_dgm = models.FloatField(blank=True, null=True) + mdgasso_gain_asso = models.FloatField(blank=True, null=True) + mdgasso_gain_dgm_eur = models.FloatField(blank=True, null=True) + mdgasso_gain_asso_eur = models.FloatField(blank=True, null=True) + + # result of MDG_classique + mdgclass_dgm_selected = models.IntegerField(blank=True, null=True) + mdgclass_round_selected = models.IntegerField(blank=True, null=True) + mdgclass_dot1_select = models.IntegerField(blank=True, null=True) + mdgclass_s1_select = models.IntegerField(blank=True, null=True) + mdgclass_g1_select = models.IntegerField(blank=True, null=True) + mdgclass_keep_select = models.FloatField(blank=True, null=True) + mdgclass_sent_select = models.FloatField(blank=True, null=True) + mdgclass_gain_dgm = models.FloatField(blank=True, null=True) + mdgclass_gain_joueur2 = models.FloatField(blank=True, null=True) + mdgclass_gain_dgm_eur = models.FloatField(blank=True, null=True) + mdgclass_gain_joueur2_eur = models.FloatField(blank=True, null=True) + dictatorYES = models.IntegerField(blank=True, null=True) + dictatorYES_n = models.StringField(blank=True, null=True) + + + # new 2025 + # g1_easy_to_understand = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g1_involves_risk = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g1_annoying_boring = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # + # g2_easy_to_understand = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g2_involves_risk = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g2_annoying_boring = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # + # g3_easy_to_understand = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g3_involves_altruism = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g3_annoying_boring = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # + # g4_easy_to_understand = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g4_involves_altruism = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + # g4_annoying_boring = models.IntegerField( + # choices=[(1, 'Strongly Disagree'), (2, 'Disagree'), (3, 'Neutral'), (4, 'Agree'), (5, 'Strongly Agree')], + # label="", + # blank=False + # ) + + crt1 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 5. + + crt2 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 5 + + crt3 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 47 + + gain_crt = models.IntegerField() + + +# demographic quetsionnaire + q_40_etude = models.IntegerField( + choices=[ + [1, '1- Arts and / or literature'], + [2, '2- Economy and / or management'], + [3, '3- Scientific / engineering'], + [4, '4- Other'], + ], + widget=widgets.RadioSelect, + label="a. Your field of study ", + blank=False + ) + + q_41_gender = models.IntegerField( + choices=[ + [1, '1- Woman'], + [2, '2- Male'], + [3, '3- Non-binary'], + [4, '4- Do not wish to answer'], + ], + widget=widgets.RadioSelect, + label="b. You are ", + blank=False + ) + + q_42_age = models.IntegerField( + label="c. How old are you?", + blank=False, + min=15, + max=100, + ) + + q_43_nationalite = models.IntegerField( + choices=[ + [1, "Afghanistan"], + [2, "Albania"], + [3, "Algeria"], + [4, "Andorra"], + [5, "Angola"], + [6, "Antigua and Barbuda"], + [7, "Argentina"], + [8, "Armenia"], + [9, "Australia"], + [10, "Austria"], + [11, "Azerbaijan"], + [12, "Bahamas"], + [13, "Bahrain"], + [14, "Bangladesh"], + [15, "Barbados"], + [16, "Belarus"], + [17, "Belgium"], + [18, "Belize"], + [19, "Benin"], + [20, "Bhutan"], + [21, "Bolivia"], + [22, "Bosnia and Herzegovina"], + [23, "Botswana"], + [24, "Brazil"], + [25, "Brunei Darussalam"], + [26, "Bulgaria"], + [27, "Burkina Faso"], + [28, "Burundi"], + [29, "Cambodia"], + [30, "Cameroon"], + [31, "Canada"], + [32, "Cape Verde"], + [33, "Central African Republic"], + [34, "Chad"], + [35, "Chile"], + [36, "China"], + [37, "Colombia"], + [38, "Comoros"], + [39, "Congo, Republic of the..."], + [40, "Costa Rica"], + [41, "Côte d'Ivoire"], + [42, "Croatia"], + [43, "Cuba"], + [44, "Cyprus"], + [45, "Czech Republic"], + [46, "Democratic People's Republic of Korea"], + [47, "Democratic Republic of the Congo"], + [48, "Denmark"], + [49, "Djibouti"], + [50, "Dominica"], + [51, "Dominican Republic"], + [52, "Ecuador"], + [53, "Egypt"], + [54, "El Salvador"], + [55, "Equatorial Guinea"], + [56, "Eritrea"], + [57, "Estonia"], + [58, "Ethiopia"], + [59, "Fiji"], + [60, "Finland"], + [61, "France"], + [62, "Gabon"], + [63, "Gambia"], + [64, "Georgia"], + [65, "Germany"], + [66, "Ghana"], + [67, "Greece"], + [68, "Grenada"], + [69, "Guatemala"], + [70, "Guinea"], + [71, "Guinea-Bissau"], + [72, "Guyana"], + [73, "Haiti"], + [74, "Honduras"], + [75, "Hong Kong (S.A.R.)"], + [76, "Hungary"], + [77, "Iceland"], + [78, "India"], + [79, "Indonesia"], + [80, "Iran, Islamic Republic of..."], + [81, "Iraq"], + [82, "Ireland"], + [83, "Israel"], + [84, "Italy"], + [85, "Jamaica"], + [86, "Japan"], + [87, "Jordan"], + [88, "Kazakhstan"], + [89, "Kenya"], + [90, "Kiribati"], + [91, "Kuwait"], + [92, "Kyrgyzstan"], + [93, "Lao People's Democratic Republic"], + [94, "Latvia"], + [95, "Lebanon"], + [96, "Lesotho"], + [97, "Liberia"], + [98, "Libyan Arab Jamahiriya"], + [99, "Liechtenstein"], + [100, "Lithuania"], + [101, "Luxembourg"], + [102, "Madagascar"], + [103, "Malawi"], + [104, "Malaysia"], + [105, "Maldives"], + [106, "Mali"], + [107, "Malta"], + [108, "Marshall Islands"], + [109, "Mauritania"], + [110, "Mauritius"], + [111, "Mexico"], + [112, "Micronesia, Federated States of..."], + [113, "Monaco"], + [114, "Mongolia"], + [115, "Montenegro"], + [116, "Morocco"], + [117, "Mozambique"], + [118, "Myanmar"], + [119, "Namibia"], + [120, "Nauru"], + [121, "Nepal"], + [122, "Netherlands"], + [123, "New Zealand"], + [124, "Nicaragua"], + [125, "Niger"], + [126, "Nigeria"], + [127, "North Korea"], + [128, "Norway"], + [129, "Oman"], + [130, "Pakistan"], + [131, "Palau"], + [132, "Panama"], + [133, "Papua New Guinea"], + [134, "Paraguay"], + [135, "Peru"], + [136, "Philippines"], + [137, "Poland"], + [138, "Portugal"], + [139, "Qatar"], + [140, "Republic of Korea"], + [141, "Republic of Moldova"], + [142, "Romania"], + [143, "Russian Federation"], + [144, "Rwanda"], + [145, "Saint Kitts and Nevis"], + [146, "Saint Lucia"], + [147, "Saint Vincent and the Grenadines"], + [148, "Samoa"], + [149, "San Marino"], + [150, "Sao Tome and Principe"], + [151, "Saudi Arabia"], + [152, "Senegal"], + [153, "Serbia"], + [154, "Seychelles"], + [155, "Sierra Leone"], + [156, "Singapore"], + [157, "Slovakia"], + [158, "Slovenia"], + [159, "Solomon Islands"], + [160, "Somalia"], + [161, "South Africa"], + [162, "South Korea"], + [163, "Spain"], + [164, "Sri Lanka"], + [165, "Sudan"], + [166, "Suriname"], + [167, "Swaziland"], + [168, "Sweden"], + [169, "Switzerland"], + [170, "Syrian Arab Republic"], + [171, "Tajikistan"], + [172, "Thailand"], + [173, "The former Yugoslav Republic of Macedonia"], + [174, "Timor-Leste"], + [175, "Togo"], + [176, "Tonga"], + [177, "Trinidad and Tobago"], + [178, "Tunisia"], + [179, "Turkey"], + [180, "Turkmenistan"], + [181, "Tuvalu"], + [182, "Uganda"], + [183, "Ukraine"], + [184, "United Arab Emirates"], + [185, "United Kingdom of Great Britain and Northern Ireland"], + [186, "United Republic of Tanzania"], + [187, "United States of America"], + [188, "Uruguay"], + [189, "Uzbekistan"], + [190, "Vanuatu"], + [191, "Venezuela, Bolivarian Republic of..."], + [192, "Viet Nam"], + [193, "Yemen"], + [194, "Zambia"], + [195, "Zimbabwe"], + ], + blank=False, + label="d. Country of citizenship", + + ) + + q_44_revenu = models.IntegerField( + choices=[ + [1, '1- Less than 5,000 Euros'], + [2, '2- Between 5,000 and 10,000 Euros'], + [3, '3- Between 10,000 and 15,000 Euros'], + [4, '4- Between 15,000 and 20,000 Euros'], + [5, '5- Between 20,000 and 25,000 Euros'], + [6, '6- Greater than 25,000 Euros'], + ], + widget=widgets.RadioSelect, + label="e. Please estimate your gross annual income (including your scholarships and student loan) in Euros. (Note: Your own income, not that of your parents)", + blank=False + ) + + + q3_countries = models.IntegerField( + choices=[ + [1, "Afghanistan"], + [2, "Albania"], + [3, "Algeria"], + [4, "Andorra"], + [5, "Angola"], + [6, "Antigua and Barbuda"], + [7, "Argentina"], + [8, "Armenia"], + [9, "Australia"], + [10, "Austria"], + [11, "Azerbaijan"], + [12, "Bahamas"], + [13, "Bahrain"], + [14, "Bangladesh"], + [15, "Barbados"], + [16, "Belarus"], + [17, "Belgium"], + [18, "Belize"], + [19, "Benin"], + [20, "Bhutan"], + [21, "Bolivia"], + [22, "Bosnia and Herzegovina"], + [23, "Botswana"], + [24, "Brazil"], + [25, "Brunei Darussalam"], + [26, "Bulgaria"], + [27, "Burkina Faso"], + [28, "Burundi"], + [29, "Cambodia"], + [30, "Cameroon"], + [31, "Canada"], + [32, "Cape Verde"], + [33, "Central African Republic"], + [34, "Chad"], + [35, "Chile"], + [36, "China"], + [37, "Colombia"], + [38, "Comoros"], + [39, "Congo, Republic of the..."], + [40, "Costa Rica"], + [41, "Côte d'Ivoire"], + [42, "Croatia"], + [43, "Cuba"], + [44, "Cyprus"], + [45, "Czech Republic"], + [46, "Democratic People's Republic of Korea"], + [47, "Democratic Republic of the Congo"], + [48, "Denmark"], + [49, "Djibouti"], + [50, "Dominica"], + [51, "Dominican Republic"], + [52, "Ecuador"], + [53, "Egypt"], + [54, "El Salvador"], + [55, "Equatorial Guinea"], + [56, "Eritrea"], + [57, "Estonia"], + [58, "Ethiopia"], + [59, "Fiji"], + [60, "Finland"], + [61, "France"], + [62, "Gabon"], + [63, "Gambia"], + [64, "Georgia"], + [65, "Germany"], + [66, "Ghana"], + [67, "Greece"], + [68, "Grenada"], + [69, "Guatemala"], + [70, "Guinea"], + [71, "Guinea-Bissau"], + [72, "Guyana"], + [73, "Haiti"], + [74, "Honduras"], + [75, "Hong Kong (S.A.R.)"], + [76, "Hungary"], + [77, "Iceland"], + [78, "India"], + [79, "Indonesia"], + [80, "Iran, Islamic Republic of..."], + [81, "Iraq"], + [82, "Ireland"], + [83, "Israel"], + [84, "Italy"], + [85, "Jamaica"], + [86, "Japan"], + [87, "Jordan"], + [88, "Kazakhstan"], + [89, "Kenya"], + [90, "Kiribati"], + [91, "Kuwait"], + [92, "Kyrgyzstan"], + [93, "Lao People's Democratic Republic"], + [94, "Latvia"], + [95, "Lebanon"], + [96, "Lesotho"], + [97, "Liberia"], + [98, "Libyan Arab Jamahiriya"], + [99, "Liechtenstein"], + [100, "Lithuania"], + [101, "Luxembourg"], + [102, "Madagascar"], + [103, "Malawi"], + [104, "Malaysia"], + [105, "Maldives"], + [106, "Mali"], + [107, "Malta"], + [108, "Marshall Islands"], + [109, "Mauritania"], + [110, "Mauritius"], + [111, "Mexico"], + [112, "Micronesia, Federated States of..."], + [113, "Monaco"], + [114, "Mongolia"], + [115, "Montenegro"], + [116, "Morocco"], + [117, "Mozambique"], + [118, "Myanmar"], + [119, "Namibia"], + [120, "Nauru"], + [121, "Nepal"], + [122, "Netherlands"], + [123, "New Zealand"], + [124, "Nicaragua"], + [125, "Niger"], + [126, "Nigeria"], + [127, "North Korea"], + [128, "Norway"], + [129, "Oman"], + [130, "Pakistan"], + [131, "Palau"], + [132, "Panama"], + [133, "Papua New Guinea"], + [134, "Paraguay"], + [135, "Peru"], + [136, "Philippines"], + [137, "Poland"], + [138, "Portugal"], + [139, "Qatar"], + [140, "Republic of Korea"], + [141, "Republic of Moldova"], + [142, "Romania"], + [143, "Russian Federation"], + [144, "Rwanda"], + [145, "Saint Kitts and Nevis"], + [146, "Saint Lucia"], + [147, "Saint Vincent and the Grenadines"], + [148, "Samoa"], + [149, "San Marino"], + [150, "Sao Tome and Principe"], + [151, "Saudi Arabia"], + [152, "Senegal"], + [153, "Serbia"], + [154, "Seychelles"], + [155, "Sierra Leone"], + [156, "Singapore"], + [157, "Slovakia"], + [158, "Slovenia"], + [159, "Solomon Islands"], + [160, "Somalia"], + [161, "South Africa"], + [162, "South Korea"], + [163, "Spain"], + [164, "Sri Lanka"], + [165, "Sudan"], + [166, "Suriname"], + [167, "Swaziland"], + [168, "Sweden"], + [169, "Switzerland"], + [170, "Syrian Arab Republic"], + [171, "Tajikistan"], + [172, "Thailand"], + [173, "The former Yugoslav Republic of Macedonia"], + [174, "Timor-Leste"], + [175, "Togo"], + [176, "Tonga"], + [177, "Trinidad and Tobago"], + [178, "Tunisia"], + [179, "Turkey"], + [180, "Turkmenistan"], + [181, "Tuvalu"], + [182, "Uganda"], + [183, "Ukraine"], + [184, "United Arab Emirates"], + [185, "United Kingdom of Great Britain and Northern Ireland"], + [186, "United Republic of Tanzania"], + [187, "United States of America"], + [188, "Uruguay"], + [189, "Uzbekistan"], + [190, "Vanuatu"], + [191, "Venezuela, Bolivarian Republic of..."], + [192, "Viet Nam"], + [193, "Yemen"], + [194, "Zambia"], + [195, "Zimbabwe"], + ], + label="f. Country - previous accommodation", + blank=False + ) + + q_46_logement_ville = models.StringField( + label="g. City - previous accommodation", + blank=False + ) + + q_31 = models.IntegerField() # nbre d'enfant + + q_47_residence = models.IntegerField( + label="i. You currently live in:", + choices=[ + [1, 'Bâtiment 103'], + [2, 'Another residence '], + ], + widget=widgets.RadioSelect, + blank=False + ) + + q_48_satisfaction = models.IntegerField( + choices=[ + [1, '1 - Very satisfied'], + [2, '2 - Satisfied '], + [3, '3 - Unsatisfied '], + [4, '4 - Very unsatisfied'], + ], + widget=widgets.RadioSelect, + label="j. How satisfied are you with your place of residence? ", + blank=True + ) + + + q_49_propositions = models.StringField( + label="k. Do you have any ideas or suggestions for improvements concerning your place of residence?", + blank=True + ) + + + lastname = models.StringField( + label="Last Name", + blank=False + ) + + firstname = models.StringField( + label="First Name", + blank=False + ) + + email = models.StringField( + label="Email", + blank=False + ) + + room = models.StringField( + label="Room number (for example: 103.3.09)", + blank=False + ) + id_in_group_nn = models.IntegerField(blank=True, null=True) + + gainfinal_eur = models.FloatField(blank=True, null=True) + gainfinal_eur_showupfee = models.FloatField(blank=True, null=True) + gainfinal_asso_eur = models.FloatField(blank=True, null=True) + + gainfinal_eur_f = models.FloatField(blank=True, null=True) + gainfinal_eur_showupfee_f = models.FloatField(blank=True, null=True) + gainfinal_asso_eur_f = models.FloatField(blank=True, null=True) + + gainfinal_eur_showupfee_f2025 = models.FloatField(blank=True, null=True) + + + + + + + diff --git a/a5_questfin/pages.py b/a5_questfin/pages.py new file mode 100644 index 0000000..f16d295 --- /dev/null +++ b/a5_questfin/pages.py @@ -0,0 +1,179 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def before_next_page(self): + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n =self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + self.player.game_num_select = random.randint(1,4) + + self.player.game_select_n = getattr(Constants, f'app_seq{self.player.app_sequence_select}')[self.player.game_num_select-1] + + # result of GP + self.player.tirageriskresult = self.participant.vars['tirageriskresult_j1'] + if self.player.tirageriskresult == "Succès": + self.player.tirageriskresult_en = "Success" + elif self.player.tirageriskresult == "Echec": + self.player.tirageriskresult_en = "Failure" + + self.player.risk11_a = self.participant.vars['risk11_a_j1'] + self.player.risk11_b = self.participant.vars['risk11_b_j1'] + self.player.gainrisk = self.participant.vars['gainrisk_j1'] + self.player.gainrisk_eur = self.player.gainrisk * Constants.exchangerate_gp_bw + + # result of BW + self.player.tirage_color_n = self.participant.vars['tirage_color_n'] + self.player.gain_bw = self.participant.vars['gain_bw'] + self.player.gain_bw_eur = self.participant.vars['gain_bw_eur'] + + # result of MDG_asso + self.player.mdgasso_dgm_selected = self.player.participant.vars['mdgasso_dgm_selected'] + self.player.mdgasso_round_selected = self.player.participant.vars['mdgasso_round_selected'] + self.player.mdgasso_dot1_select = self.player.participant.vars['mdgasso_dot1_select'] + self.player.mdgasso_s1_select = self.player.participant.vars['mdgasso_s1_select'] + self.player.mdgasso_g1_select = self.player.participant.vars['mdgasso_g1_select'] + self.player.mdgasso_keep_select = self.player.participant.vars['mdgasso_keep_select'] + self.player.mdgasso_sent_select = self.player.participant.vars['mdgasso_sent_select'] + self.player.mdgasso_gain_dgm = self.player.participant.vars['mdgasso_gain_dgm'] + self.player.mdgasso_gain_asso = self.player.participant.vars['mdgasso_gain_asso'] + self.player.mdgasso_gain_dgm_eur = self.player.participant.vars['mdgasso_gain_dgm_eur'] + self.player.mdgasso_gain_asso_eur = self.player.participant.vars['mdgasso_gain_asso_eur'] + + # result of MDG_classique + self.player.mdgclass_dgm_selected = self.player.participant.vars['mdgclass_dgm_selected'] + self.player.mdgclass_round_selected = self.player.participant.vars['mdgclass_round_selected'] + self.player.mdgclass_dot1_select = self.player.participant.vars['mdgclass_dot1_select'] + self.player.mdgclass_s1_select = self.player.participant.vars['mdgclass_s1_select'] + self.player.mdgclass_g1_select = self.player.participant.vars['mdgclass_g1_select'] + self.player.mdgclass_keep_select = self.player.participant.vars['mdgclass_keep_select'] + self.player.mdgclass_sent_select = self.player.participant.vars['mdgclass_sent_select'] + self.player.mdgclass_gain_dgm = self.player.participant.vars['mdgclass_gain_dgm'] + self.player.mdgclass_gain_joueur2 = self.player.participant.vars['mdgclass_gain_joueur2'] + self.player.mdgclass_gain_dgm_eur = self.player.participant.vars['mdgclass_gain_dgm_eur'] + self.player.mdgclass_gain_joueur2_eur = self.player.participant.vars['mdgclass_gain_joueur2_eur'] + self.player.dictatorYES = random.randint(0, 1) + if self.player.dictatorYES == 1: + self.player.dictatorYES_n = "player_1" + elif self.player.dictatorYES == 0: + self.player.dictatorYES_n = "player_2" + + + if self.player.game_select_n == "gp": + self.player.game_select = 1 + self.player.gainfinal_eur = self.player.gainrisk_eur + self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + elif self.player.game_select_n == "bw": + self.player.game_select = 2 + self.player.gainfinal_eur = self.player.gain_bw_eur + self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + elif self.player.game_select_n == "MDG_asso": + self.player.game_select = 3 + self.player.gainfinal_eur = self.player.mdgasso_gain_dgm_eur + self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + self.player.gainfinal_asso_eur = self.player.mdgasso_gain_asso_eur + self.player.gainfinal_asso_eur_f = round(self.player.gainfinal_asso_eur, 0) + elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 1: + self.player.game_select = 4 + self.player.gainfinal_eur = self.player.mdgclass_gain_dgm_eur + self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 0: + self.player.game_select = 4 + self.player.gainfinal_eur = -999 + self.player.gainfinal_eur_showupfee = -999 + + self.player.gainfinal_eur_f = round(self.player.gainfinal_eur,0) + self.player.gainfinal_eur_showupfee_f = round(self.player.gainfinal_eur_showupfee,0) + + + + # if self.player.game_select_n != "MDG_class" : + # self.player.payoff = self.player.gainfinal_eur_showupfee + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 1: + # self.player.payoff = self.player.gainfinal_eur_showupfee + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 0: + # self.player.payoff = -999 + +class Questionnaire4(Page): + form_model = 'player' + form_fields = ['q_40_etude', 'q_41_gender', 'q_42_age', + 'q_43_nationalite', 'q_44_revenu', + 'q3_countries', 'q_46_logement_ville', 'q_31', + 'q_47_residence', 'q_48_satisfaction', 'q_49_propositions' + ] + +class Questionnaire5(Page): + pass + +class Questionnaire6(Page): + form_model = 'player' + form_fields = [ + 'g1_easy_to_understand', 'g1_involves_risk', 'g1_annoying_boring', + 'g2_easy_to_understand', 'g2_involves_risk', 'g2_annoying_boring', + 'g3_easy_to_understand', 'g3_involves_altruism', 'g3_annoying_boring', + 'g4_easy_to_understand', 'g4_involves_altruism', 'g4_annoying_boring', + ] + +class Questionnaire7crt(Page): + form_model = 'player' + form_fields = ['crt1', 'crt2', 'crt3', + ] + timeout_seconds = 180 # 3 minutes + + def before_next_page(self): + # Enregistre automatiquement les réponses si le temps expire + self.player.gain_crt = 0 + if self.player.crt1 == 5: + self.player.gain_crt +=1 + if self.player.crt2 == 5: + self.player.gain_crt +=1 + if self.player.crt3 == 47: + self.player.gain_crt += 1 + if self.player.gainfinal_eur_showupfee != -999: + self.player.gainfinal_eur_showupfee_f2025 = self.player.gainfinal_eur_showupfee_f + self.player.gain_crt + 3 + else: + self.player.gainfinal_eur_showupfee_f2025 = -999 + + +class Result(Page): + form_model = 'player' + form_fields = [ + 'lastname', + 'firstname', + 'email', + 'room', + ] + +class FinalPage(Page): + pass + +class FinalPage2(Page): + + def vars_for_template(self): + + return dict( + game_select=self.player.game_select, + + ) + + + + +page_sequence = [ + Bienvenue, + #Questionnaire4, + #Questionnaire5, + #Questionnaire6, + Questionnaire7crt, + Result, + #FinalPage, # page test à désactiver + FinalPage2, +] diff --git a/a5_questfin/templates/a5_questfin/AdminReport.html b/a5_questfin/templates/a5_questfin/AdminReport.html new file mode 100644 index 0000000..85fce9e --- /dev/null +++ b/a5_questfin/templates/a5_questfin/AdminReport.html @@ -0,0 +1,68 @@ +{% load otree %} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + {% for row in report %} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + {% endfor %} + +
gainrisk_eur gain_bw_eur mdgasso_gain_dgm_eur mdgasso_gain_asso_eur mdgclass_gain_dgm_eur dgm_select round_select dotation s1 g1 keep_byj1 sent_toj2 mdgclass_gain_j2_points mdgclass_gain_j2_eur app_seq game_num_select firstname lastname email room dictatorYES game_select_n gainfinal_eur gainfinalshowupfee_eur2025 gainfinal_asso_eur
{{row.gainrisk_eur}} {{row.gain_bw_eur }} {{row.mdgasso_gain_dgm_eur}} {{row.mdgasso_gain_asso_eur}} {{row.mdgclass_gain_dgm_eur}} {{row.mdgclass_dgm_selected }} {{row.mdgclass_round_selected}} {{row.mdgclass_dot1_select }} {{row.mdgclass_s1_select}} {{row.mdgclass_g1_select}} {{row.mdgclass_keep_select}} {{row.mdgclass_sent_select}} {{row.mdgclass_gain_joueur2}} {{row.mdgclass_gain_joueur2_eur}} {{row.app_sequence_select_n }} {{row.game_num_select}} {{row.firstname}} {{row.lastname}} {{row.email}} {{row.room}} {{row.dictatorYES}} {{row.game_select_n}} {{row.gainfinal_eur_f}} {{row.gainfinal_eur_showupfee_f2025}} {{row.gainfinal_asso_eur_f}}
diff --git a/a5_questfin/templates/a5_questfin/Bienvenue.html b/a5_questfin/templates/a5_questfin/Bienvenue.html new file mode 100644 index 0000000..1ba1066 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Bienvenue.html @@ -0,0 +1,29 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a5_questfin/templates/a5_questfin/FinalPage.html b/a5_questfin/templates/a5_questfin/FinalPage.html new file mode 100644 index 0000000..3b71dca --- /dev/null +++ b/a5_questfin/templates/a5_questfin/FinalPage.html @@ -0,0 +1,97 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + YOUR GAIN (test page to disable) + +{% endblock %} + +{% block content %} +
+
+ + + +
+ +
+
+ +

+ The game is over. + The randomly selected game is game number: {{ player.game_num_select}} // {{player.game_select_n}} +

+ +
GP ---------------------
+ +

+ The random draw is: {{player.tirageriskresult}} +
Your investment in the risky asset: {{player.risk11_a}} +
You kept: {{player.risk11_b}} +
Your gain for this game is: {{player.gainrisk}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gainrisk_eur}} EUROS(s) +

+ +
BW ---------------------
+ +

The selected color is: {{player.tirage_color_n}} +
Your winnings for this game are: {{player.gain_bw}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gain_bw_eur }} EUROS(s) +

+ +
MDG-asso ---------------------
+

+ The randomly selected round is: {{player.mdgasso_round_selected}} +
Your endowment during this round was {{player.mdgasso_dot1_select}} ECUs. +
The value of S was {{player.mdgasso_s1_select}}. +
The value of G was {{player.mdgasso_g1_select}}. +

You chose to keep {{player.mdgasso_keep_select}} POINTs and give {{player.mdgasso_sent_select}} POINTs to the organization. +

Your gain is {{player.mdgasso_gain_dgm}} POINTs ({{player.mdgasso_gain_dgm_eur}} EUROs). +
The organization's gain is {{player.mdgasso_gain_asso}} POINTs ({{player.mdgasso_gain_asso_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + +
MDG-class ---------------------
+

+ + Your role in this game is: {{player.dictatorYES_n}} + +
if player.dictatorYES == 1 : + +
+ + The randomly selected round is: {{player.mdgclass_round_selected}} +
Your endowment during this round was {{player.mdgclass_dot1_select}} ECUs. +
The value of S was {{player.mdgclass_s1_select}}. +
The value of G was {{player.mdgclass_g1_select}}. +

You chose to keep {{player.mdgclass_keep_select}} POINTs and give {{player.mdgclass_sent_select}} POINTs to player 2. +

Your gain is {{player.mdgclass_gain_dgm}} POINTs ({{player.mdgclass_gain_dgm_eur}} EUROs). +
Player 2's gain is {{player.mdgclass_gain_joueur2}} POINTs ({{player.mdgclass_gain_joueur2_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + +
if player.dictatorYES == 0 : + +
We must wait for the response of the second player before communicating your gain to you. + An email will be sent to you shortly to announce your gain and the procedures for retrieving your gain. + +

+ +

+ + + + + + +
+
+ +

{% next_button %}

+ + + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/FinalPage2.html b/a5_questfin/templates/a5_questfin/FinalPage2.html new file mode 100644 index 0000000..cc309a9 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/FinalPage2.html @@ -0,0 +1,119 @@ +{% load otree %} + +{% block title %} + + YOUR GAIN + +{% endblock %} + +{% block content %} +
+
+ + {% if player.game_select_n != "MDG_class" %} +
+

You have completed the experiment and earned a total of {{ player.gainfinal_eur_showupfee_f2025|to2}} EUROS
+ = Gain games (rounded amount)= {{ player.gainfinal_eur_f|to2}} +
+ questionnaire tasks = 3 +
+ questionnaire CRT = {{ player.gain_crt|to2}} +
+ showup-fee = {{ Constants.showupfee|to2}}

+
+ {% elif player.game_select_n == "MDG_class" and player.dictatorYES == 1 %} +
+

You have completed the experiment and earned a total of {{ player.gainfinal_eur_showupfee_f2025|to2}} EUROS
+ = Gain games (rounded amount)= {{ player.gainfinal_eur_f|to2}} +
+ questionnaire tasks = 3 +
+ questionnaire CRT = {{ player.gain_crt|to2}} +
+ showup-fee = {{ Constants.showupfee|to2}}

+
+ {% endif %} + +
+ +
+ +
+
+ +

+

+ The game is over. + The randomly selected game is game number: {{ player.game_num_select}} // {{player.game_select_n}} +

+

+ + {% if player.game_select_n == "gp" %} + +

+ The random draw is: {{player.tirageriskresult_en}} +
Your investment in the risky asset: {{player.risk11_a}} +
You kept: {{player.risk11_b}} +
Your gain for this game is: {{player.gainrisk}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gainrisk_eur}} EUROS(s) +

+ + {% elif player.game_select_n == "bw" %} + +

The selected color is: {{player.tirage_color_n}} +
Your winnings for this game are: {{player.gain_bw}} ECU(s) - (exchange rate: 1 ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gain_bw_eur}} EUROS(s) +

+ + {% elif player.game_select_n == "MDG_asso" %} + +

+ The randomly selected round is: {{player.mdgasso_round_selected}} +
Your endowment during this round was {{player.mdgasso_dot1_select}} ECUs. +
The value of S was {{player.mdgasso_s1_select}}. +
The value of G was {{player.mdgasso_g1_select}}. +

You chose to keep {{player.mdgasso_keep_select}} POINTs and give {{player.mdgasso_sent_select}} POINTs to the organization. +

Your gain is {{player.mdgasso_gain_dgm}} POINTs ({{player.mdgasso_gain_dgm_eur}} EUROs). +
The organization's gain is {{player.mdgasso_gain_asso}} POINTs ({{player.mdgasso_gain_asso_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + {% elif player.game_select_n == "MDG_class" %} + + + Your role in this game is : {{player.dictatorYES_n}} + + {% if player.dictatorYES == 1 %} +

+ +
+ + The randomly selected round is: {{player.mdgclass_round_selected}} +
Your endowment during this round was {{player.mdgclass_dot1_select}} ECUs. +
The value of S was {{player.mdgclass_s1_select}}. +
The value of G was {{player.mdgclass_g1_select}}. +

You chose to keep {{player.mdgclass_keep_select}} POINTs and give {{player.mdgclass_sent_select}} POINTs to player 2. +

Your gain is {{player.mdgclass_gain_dgm}} POINTs ({{player.mdgclass_gain_dgm_eur}} EUROs). +
Player 2's gain is {{player.mdgclass_gain_joueur2}} POINTs ({{player.mdgclass_gain_joueur2_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + {% elif player.dictatorYES == 0 %} + +

+ +
We must wait for the response of the second player before communicating your gain to you. + An email will be sent to you shortly to announce your gain and the procedures for retrieving your gain. +

+ + {% endif %} + + + {% endif %} + + + + + +
+
+ +

{% next_button %}

+ + + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/Questionnaire4.html b/a5_questfin/templates/a5_questfin/Questionnaire4.html new file mode 100644 index 0000000..f460097 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Questionnaire4.html @@ -0,0 +1,98 @@ + + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} +Final Questionnaire +{% endblock %} + +{% block content %} + + +
+
+ +The answers to the following questions will not be taken into account in the calculation of + your earnings. We nevertheless ask you to respond as sincerely as possible. + As a reminder, all your responses will be treated anonymously, and no one + will not have access to your answers. + +

+
+
+ + {% formfield player.q_40_etude %} + {% formfield player.q_41_gender %} + {% formfield player.q_42_age%} + {% formfield player.q_43_nationalite %} + {% formfield player.q_44_revenu %} +
+

+ +Indicate the City and Country where your previous accommodation was: +

+ {% formfield player.q3_countries %} + + + {% formfield player.q_46_logement_ville %} + + + +
+
+ + +

+
+ + + {% formfield player.q_47_residence %} + + {% formfield player.q_48_satisfaction %} + + {% formfield player.q_49_propositions %} + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/Questionnaire5.html b/a5_questfin/templates/a5_questfin/Questionnaire5.html new file mode 100644 index 0000000..3989f10 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Questionnaire5.html @@ -0,0 +1,144 @@ +{% load otree %} + +{% block title %} +Questionnaire +{% endblock %} + +{% block content %} + +
+
+ +In this part, you are asked to compare the decisions made in the four tasks in Part 1 and to answer questions about specific decisions made in each task. +
+To do this, you are first briefly reminded of the tasks by examining a summary of the corresponding instructions. + For answering the questions in Part 2 carefully and truthfully, you will receive €3.00. +
+ Each game will be given a colour so that you can recognise it later in the questionnaire: +
+ Task "orange" , Task "blue" , + Task "green" and + Task "red" . + + +
+ + + +
+
+ + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} +
+ {% next_button %} +
+ +
+ + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/Questionnaire6.html b/a5_questfin/templates/a5_questfin/Questionnaire6.html new file mode 100644 index 0000000..a0073e0 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Questionnaire6.html @@ -0,0 +1,244 @@ +{% load otree %} + +{% block title %} +Comparison of Tasks +{% endblock %} + + +{% block content %} +
+
+ + +Please answer the following questions for each of the four tasks.
+For each question, please indicate how much you agree to the respective statements using a scale from 1 (not agree at all) to 7 (fully agree). + + +
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Game 1:
Task "orange"
Game 2:
Task "blue"
Game 3:
Task "green"
Game 4:
Task "red"
The task is easy to understand and can be answered straightforwardly + {% formfield player.g1_easy_to_understand%} + + {% formfield player.g2_easy_to_understand%} + + {% formfield player.g3_easy_to_understand%} + + {% formfield player.g4_easy_to_understand%} +
The task involves complex calculations and requires + deliberating on the trade-off between expected outcomes + and the inherent “riskiness” of the different outcomes. + {% formfield player.g1_involves_risk%} + + + {% formfield player.g2_involves_risk%} +
The task involves complex calculations and requires deliberating + on the trade-off between expected outcomes and the degree of + “altruism” in different choices. + + + {% formfield player.g3_involves_altruism%} + + {% formfield player.g4_involves_altruism%} +
Completing the task is annoying and boring. + {% formfield player.g1_annoying_boring %} + + + {% formfield player.g2_annoying_boring %} + + + {% formfield player.g3_annoying_boring %} + + + {% formfield player.g4_annoying_boring %} +
+ +
+ {% next_button %} +
+ +
+ +

+ + +

Recall of tasks and their colors

+ +
+
+ + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} + +
+ + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/Questionnaire7crt.html b/a5_questfin/templates/a5_questfin/Questionnaire7crt.html new file mode 100644 index 0000000..6871fa9 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Questionnaire7crt.html @@ -0,0 +1,49 @@ +{% load otree %} + +{% block title %} +Questionnaire +{% endblock %} + + +{% block content %} + +
+
+ +Below, you will find 3 questions to answer.
+ +You have 3 minutes to complete them.
+Each correct answer earns you 1 euro.
+Once the time is up, your responses will be submitted automatically. +
+ +
+
+
+ + + + +{% formfield player.crt1%} + + + +{% formfield player.crt2%} + + + +{% formfield player.crt3%} + +
+ + + + {% next_button %} +
+ + + + + + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/Result.html b/a5_questfin/templates/a5_questfin/Result.html new file mode 100644 index 0000000..b8e753a --- /dev/null +++ b/a5_questfin/templates/a5_questfin/Result.html @@ -0,0 +1,58 @@ +{% load otree %} + +{% block title %} + + FINAL QUESTIONNAIRE + +{% endblock %} + +{% block content %} +
+
+ +
+
+ NB : Useful information to receive your gain: +
+
+ +
+ +
+
+ +

+ Please fill in the following fields in order to be able to take the + amount you won during this experience. + {% formfield player.lastname %} + {% formfield player.firstname %} + {% formfield player.email %} + {% formfield player.room %} + +

+ +

+ + Reminder for those who have not yet registered + + : to receive your gain you must subscribe to the list of voluntary participants via the link : + + +

https://ipel.crest.fr/public/participant_create.php

+ +
Note : Your personal data are strictly confidential. + All your responses will be treated anonymously. + + +

+ + +
+
+ +

{% next_button %}

+ + + +{% endblock %} + diff --git a/a5_questfin/templates/a5_questfin/instructions_bw.html b/a5_questfin/templates/a5_questfin/instructions_bw.html new file mode 100644 index 0000000..b1ad614 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/instructions_bw.html @@ -0,0 +1,529 @@ +{% load otree %} + + + + +

+ Rules of the game: + +

+ +

+ Look at the table below. It presents 9 different situations. Look at them carefully. + Situations A, B, C, D, E, F, G, H or I contain two possible gains (in ECUS): the red gains on the left and + green gains on the right. You will receive one of these two amounts of ECUS depending on the color that + will be assigned to you at the end of the game. +

+ +

+At the end of the game, the ECU you have won will be converted into € using the following rule:

+ +

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+ +

+So you will first have to choose the situation you prefer the most: A, B, C, D, E, F, G, H or I.

+ + +

+ At the end of the experiment, a random draw will be made by the programme to determine your colour (50% chance of red and 50% chance of green). +

+Example: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ +
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ +
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ +
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ +
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ +
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + +
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + +
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + +
(0 ECUs) (24 ECUs)
+ + + +

+ + + + + +
+ + + +
+ + + + + + diff --git a/a5_questfin/templates/a5_questfin/instructions_colors.html b/a5_questfin/templates/a5_questfin/instructions_colors.html new file mode 100644 index 0000000..94cbc01 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/instructions_colors.html @@ -0,0 +1,116 @@ +{% extends "global/Page.html" %} +{% load otree %} + + +{% block content %} + + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} + + + +{% endblock %} \ No newline at end of file diff --git a/a5_questfin/templates/a5_questfin/instructions_das.html b/a5_questfin/templates/a5_questfin/instructions_das.html new file mode 100644 index 0000000..7aaa43e --- /dev/null +++ b/a5_questfin/templates/a5_questfin/instructions_das.html @@ -0,0 +1,41 @@ +{% load otree %} +

+ Instructions +

+

+Game Rule: This game consists of 8 rounds. +
At the end of the game, the POINTS you have earned will be converted to € according to the following rule: +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+For each of these rounds, you have a starting amount in POINTs. + You can choose to give some to a non-profit organization Agir pour l'Environnement + working for environmental protection. + So you will choose two amounts: one that you give and one that you keep. The sum of the amounts you give and the amounts you keep must be equal to the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, + and a value S ("self") will be assigned to the POINTs you keep. Thus, the amount you keep will be multiplied by S, and the one you give will be multiplied by G. +
Your final gain will be: +
Amount you chose to keep x S
+
And the final amount given to the organization Agir pour l'Environnement will be: +
Amount you chose to give x G
+
At the end of the game, one of the rounds will be randomly chosen to determine your gain, and the amount given to the organization "Agir pour l'Environnement". +

+

+
-------------------------
+

+ Presentation of the Agir pour l'Environnement association: +
Agir pour l'Environnement is a citizens' organisation working for a livable planet. + The association puts pressure on politicians and economic decision-makers by conducting campaigns + that bring together a wide network of associations and citizens. + In order to maintain its independence of action, the association refuses all funding from + the public authorities. + + + + + Link to Agir pour l'environnement + + +

You can visit the Agir pour l'Environnement website by clicking the link below:

+ Agir pour l'environnement + +

+ diff --git a/a5_questfin/templates/a5_questfin/instructions_dcl.html b/a5_questfin/templates/a5_questfin/instructions_dcl.html new file mode 100644 index 0000000..24a2881 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/instructions_dcl.html @@ -0,0 +1,38 @@ +{% load otree %} + +

+ Instructions +

+

+Rules of the game: +
At the end of the game, the POINTs you have earned will be converted into € according to the following rule: + +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+ +This game consists of 8 rounds and is played with one of the other participants in the experiment, +whom we will refer to as "your partner." +Your partner is anonymous and has no means of identifying you either. +
For each of these rounds, you have a starting amount in POINT. +You can decide to give some of it to your partner. +Therefore, you will choose two amounts: one that you give and one that you keep. +The sum of the amounts you give and the amounts you keep must equal the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, +and a value S ("self") will be assigned to the POINTs you keep. +Thus, the amount you keep will be multiplied by S, and the amount you give will be multiplied by G. +
Your partner plays the same game on their side. +
At the end of the game, one round will be randomly chosen to determine your payoff, + and one of the two players will be randomly named the "dictator" (player1). +
-------------------------------------------
+ Case 1: +
If you are the dictator (player 1), your final payoff will be: +
Amount you chose to keep x S
+
And your partner's (player 2) final payoff will be: +
Amount you chose to give x G
 +
-------------------------------------------
+ Case 2: +
If you are not named dictator (player 2), your final payoff will be: +
Amount your partner chose to give you x G
+
And your partner's (player 1) final payoff will be: +
Amount your partner chose to keep x S
 + +

diff --git a/a5_questfin/templates/a5_questfin/instructions_gp.html b/a5_questfin/templates/a5_questfin/instructions_gp.html new file mode 100644 index 0000000..651bc29 --- /dev/null +++ b/a5_questfin/templates/a5_questfin/instructions_gp.html @@ -0,0 +1,32 @@ +{% load otree %} + + +

+ Rules of the game : +

+ +

You have a capital of 10 ECUs, and you must decide the amount of your investment in the project described below. + You can invest any amount between 0 ECUs and 10 ECUs (in whole numbers).

+ +
Project description:
+ +

This project earns 3 times the amount invested in the event of success and 0 in the event of failure. + Success and failure have the same probability of happening: 1 in 2 chance.

+ +

Your gain from this task = amount retained + gain from your investment in the project.

+ +

Example 1: You decide to invest 4 ECUs in the project. The project is a success. Your project gain is + of 3 x 4 ECUs = 12 ECUs. Your gain from this task is therefore 6 ECUs kept + 12 ECUs from the project = 18 ECUs. +

+ +

Example 2: You decide to invest 4 ECUs in the project. The project is a failure. Your project gain is + of 0 x 4 ECUs = 0 ECUs. Your gain from this game is therefore 6 ECUs kept + 0 ECUs from the project = 6 ECUs. +

+

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+ +

Summary of the game:

+ +
+
+

+
diff --git a/a5_questfin/tests.py b/a5_questfin/tests.py new file mode 100644 index 0000000..3430b16 --- /dev/null +++ b/a5_questfin/tests.py @@ -0,0 +1,62 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + yield (pages.Bienvenue) + #yield (pages.Questionnaire5) + + + # yield (pages.Questionnaire4, { + # 'q_40_etude': 1, + # 'q_41_gender': 1, + # 'q_42_age': random.randint(18, 40), + # 'q_43_nationalite': 5, + # 'q_44_revenu': 1, + # 'q3_countries': 1, + # 'q_46_logement_ville': 'fr', + # 'q_31': 3, + # 'q_47_residence': 1, + # 'q_48_satisfaction': 2, + # 'q_49_propositions': 'sasasadad' + # }) + + # yield (pages.Questionnaire6, { + # 'g1_easy_to_understand':1, + # 'g1_involves_risk':1, + # 'g1_annoying_boring':1, + # 'g2_easy_to_understand':1, + # 'g2_involves_risk':1, + # 'g2_annoying_boring':1, + # 'g3_easy_to_understand':1, + # 'g3_involves_altruism':1, + # 'g3_annoying_boring':1, + # 'g4_easy_to_understand':1, + # 'g4_involves_altruism':1, + # 'g4_annoying_boring':1, + # }) + + yield (pages.Questionnaire7crt, { + 'crt1':5, + 'crt2':5, + 'crt3':47, + }) + + + yield (pages.Result, { + 'lastname': 'a', + 'firstname': 'b', + 'email': '@', + 'room': 'a', + }) + + #yield (pages.FinalPage2) + + diff --git a/a5_survey/__init__.py b/a5_survey/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/a5_survey/_builtin/__init__.py b/a5_survey/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/a5_survey/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/a5_survey/models.py b/a5_survey/models.py new file mode 100644 index 0000000..cb5df68 --- /dev/null +++ b/a5_survey/models.py @@ -0,0 +1,155 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) + +doc = """ +author = 'Wael Bousselmi' +Exp E4C +""" + + +class Constants(BaseConstants): + name_in_url = 'a5_survey' + players_per_group = None + num_rounds = 1 + + app_seq1 = ["gp", "bw", "MDG_asso", "MDG_class"] + app_seq2 = ["gp", "bw", "MDG_class", "MDG_asso"] + app_seq3 = ["bw", "gp", "MDG_asso", "MDG_class"] + app_seq4 = ["bw", "gp", "MDG_class", "MDG_asso"] + + exchangerate_gp_bw = 0.8 + exchangerate_DGM = 0.1 + + showupfee = 5 + + instructions_GP_template = 'a5_survey/instructions_gp.html' + instructions_bw_template = 'a5_survey/instructions_bw.html' + instructions_DCL_template = 'a5_survey/instructions_dcl.html' + instructions_DAS_template = 'a5_survey/instructions_das.html' + instructions_colors_template = 'a5_survey/instructions_colors.html' + +class Subsession(BaseSubsession): + pass + +class Group(BaseGroup): + pass + +class Player(BasePlayer): + + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + #questionnaire_before = models.IntegerField() + #game_num_select = models.IntegerField() + #game_select_n = models.StringField() + #game_select = models.IntegerField() + + # new 2025 + g1_easy_to_understand = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g1_involves_risk = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g1_annoying_boring = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + + g2_easy_to_understand = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g2_involves_risk = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g2_annoying_boring = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + + g3_easy_to_understand = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g3_involves_altruism = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g3_annoying_boring = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + + g4_easy_to_understand = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g4_involves_altruism = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + g4_annoying_boring = models.IntegerField( + choices=[(1, '1-Strongly Disagree'), (2, '2'), (3, '3'), (4, '4'),(5, '5'), (6, '6-Strongly Agree')], + label="", + blank=False + ) + + crt1 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 5. + + crt2 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 5 + + crt3 = models.FloatField( + label="", + blank=True + ) # Correct Answer: 47 + + gain_crt = models.IntegerField() + + + + + + + + + + + + + + + + + + + + + diff --git a/a5_survey/pages.py b/a5_survey/pages.py new file mode 100644 index 0000000..3e75e94 --- /dev/null +++ b/a5_survey/pages.py @@ -0,0 +1,179 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def before_next_page(self): + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n =self.player.participant.vars['app_sequence_select_n'] + #self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + # self.player.game_num_select = random.randint(1,4) + # + # self.player.game_select_n = getattr(Constants, f'app_seq{self.player.app_sequence_select}')[self.player.game_num_select-1] + # + # # result of GP + # self.player.tirageriskresult = self.participant.vars['tirageriskresult_j1'] + # if self.player.tirageriskresult == "Succès": + # self.player.tirageriskresult_en = "Success" + # elif self.player.tirageriskresult == "Echec": + # self.player.tirageriskresult_en = "Failure" + # + # self.player.risk11_a = self.participant.vars['risk11_a_j1'] + # self.player.risk11_b = self.participant.vars['risk11_b_j1'] + # self.player.gainrisk = self.participant.vars['gainrisk_j1'] + # self.player.gainrisk_eur = self.player.gainrisk * Constants.exchangerate_gp_bw + # + # # result of BW + # self.player.tirage_color_n = self.participant.vars['tirage_color_n'] + # self.player.gain_bw = self.participant.vars['gain_bw'] + # self.player.gain_bw_eur = self.participant.vars['gain_bw_eur'] + # + # # result of MDG_asso + # self.player.mdgasso_dgm_selected = self.player.participant.vars['mdgasso_dgm_selected'] + # self.player.mdgasso_round_selected = self.player.participant.vars['mdgasso_round_selected'] + # self.player.mdgasso_dot1_select = self.player.participant.vars['mdgasso_dot1_select'] + # self.player.mdgasso_s1_select = self.player.participant.vars['mdgasso_s1_select'] + # self.player.mdgasso_g1_select = self.player.participant.vars['mdgasso_g1_select'] + # self.player.mdgasso_keep_select = self.player.participant.vars['mdgasso_keep_select'] + # self.player.mdgasso_sent_select = self.player.participant.vars['mdgasso_sent_select'] + # self.player.mdgasso_gain_dgm = self.player.participant.vars['mdgasso_gain_dgm'] + # self.player.mdgasso_gain_asso = self.player.participant.vars['mdgasso_gain_asso'] + # self.player.mdgasso_gain_dgm_eur = self.player.participant.vars['mdgasso_gain_dgm_eur'] + # self.player.mdgasso_gain_asso_eur = self.player.participant.vars['mdgasso_gain_asso_eur'] + # + # # result of MDG_classique + # self.player.mdgclass_dgm_selected = self.player.participant.vars['mdgclass_dgm_selected'] + # self.player.mdgclass_round_selected = self.player.participant.vars['mdgclass_round_selected'] + # self.player.mdgclass_dot1_select = self.player.participant.vars['mdgclass_dot1_select'] + # self.player.mdgclass_s1_select = self.player.participant.vars['mdgclass_s1_select'] + # self.player.mdgclass_g1_select = self.player.participant.vars['mdgclass_g1_select'] + # self.player.mdgclass_keep_select = self.player.participant.vars['mdgclass_keep_select'] + # self.player.mdgclass_sent_select = self.player.participant.vars['mdgclass_sent_select'] + # self.player.mdgclass_gain_dgm = self.player.participant.vars['mdgclass_gain_dgm'] + # self.player.mdgclass_gain_joueur2 = self.player.participant.vars['mdgclass_gain_joueur2'] + # self.player.mdgclass_gain_dgm_eur = self.player.participant.vars['mdgclass_gain_dgm_eur'] + # self.player.mdgclass_gain_joueur2_eur = self.player.participant.vars['mdgclass_gain_joueur2_eur'] + # self.player.dictatorYES = random.randint(0, 1) + # if self.player.dictatorYES == 1: + # self.player.dictatorYES_n = "player_1" + # elif self.player.dictatorYES == 0: + # self.player.dictatorYES_n = "player_2" + # + # + # if self.player.game_select_n == "gp": + # self.player.game_select = 1 + # self.player.gainfinal_eur = self.player.gainrisk_eur + # self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + # elif self.player.game_select_n == "bw": + # self.player.game_select = 2 + # self.player.gainfinal_eur = self.player.gain_bw_eur + # self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + # elif self.player.game_select_n == "MDG_asso": + # self.player.game_select = 3 + # self.player.gainfinal_eur = self.player.mdgasso_gain_dgm_eur + # self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + # self.player.gainfinal_asso_eur = self.player.mdgasso_gain_asso_eur + # self.player.gainfinal_asso_eur_f = round(self.player.gainfinal_asso_eur, 0) + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 1: + # self.player.game_select = 4 + # self.player.gainfinal_eur = self.player.mdgclass_gain_dgm_eur + # self.player.gainfinal_eur_showupfee = self.player.gainfinal_eur + Constants.showupfee + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 0: + # self.player.game_select = 4 + # self.player.gainfinal_eur = -999 + # self.player.gainfinal_eur_showupfee = -999 + # + # self.player.gainfinal_eur_f = round(self.player.gainfinal_eur,0) + # self.player.gainfinal_eur_showupfee_f = round(self.player.gainfinal_eur_showupfee,0) + + + # if self.player.game_select_n != "MDG_class" : + # self.player.payoff = self.player.gainfinal_eur_showupfee + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 1: + # self.player.payoff = self.player.gainfinal_eur_showupfee + # elif self.player.game_select_n == "MDG_class" and self.player.dictatorYES == 0: + # self.player.payoff = -999 + +class Questionnaire4(Page): + form_model = 'player' + form_fields = ['q_40_etude', 'q_41_gender', 'q_42_age', + 'q_43_nationalite', 'q_44_revenu', + 'q3_countries', 'q_46_logement_ville', 'q_31', + 'q_47_residence', 'q_48_satisfaction', 'q_49_propositions' + ] + +class Questionnaire5(Page): + pass + +class Questionnaire6(Page): + form_model = 'player' + form_fields = [ + 'g1_easy_to_understand', 'g1_involves_risk', 'g1_annoying_boring', + 'g2_easy_to_understand', 'g2_involves_risk', 'g2_annoying_boring', + 'g3_easy_to_understand', 'g3_involves_altruism', 'g3_annoying_boring', + 'g4_easy_to_understand', 'g4_involves_altruism', 'g4_annoying_boring', + ] + +class Questionnaire7crt(Page): + form_model = 'player' + form_fields = ['crt1', 'crt2', 'crt3', + ] + timeout_seconds = 180 # 3 minutes + + def before_next_page(self): + # Enregistre automatiquement les réponses si le temps expire + self.player.gain_crt = 0 + if self.player.crt1 == 5: + self.player.gain_crt +=1 + if self.player.crt2 == 5: + self.player.gain_crt +=1 + if self.player.crt3 == 47: + self.player.gain_crt += 1 + if self.player.gainfinal_eur_showupfee != -999: + self.player.gainfinal_eur_showupfee_f2025 = self.player.gainfinal_eur_showupfee_f + self.player.gain_crt + 3 + else: + self.player.gainfinal_eur_showupfee_f2025 = -999 + + +class Result(Page): + form_model = 'player' + form_fields = [ + 'lastname', + 'firstname', + 'email', + 'room', + ] + +class FinalPage(Page): + pass + +class FinalPage2(Page): + + def vars_for_template(self): + + return dict( + game_select=self.player.game_select, + + ) + + + + +page_sequence = [ + Bienvenue, + #Questionnaire4, + Questionnaire5, + Questionnaire6, + #Questionnaire7crt, + #Result, + #FinalPage, # page test à désactiver + #FinalPage2, +] diff --git a/a5_survey/templates/a5_survey/Bienvenue.html b/a5_survey/templates/a5_survey/Bienvenue.html new file mode 100644 index 0000000..1ba1066 --- /dev/null +++ b/a5_survey/templates/a5_survey/Bienvenue.html @@ -0,0 +1,29 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/a5_survey/templates/a5_survey/FinalPage.html b/a5_survey/templates/a5_survey/FinalPage.html new file mode 100644 index 0000000..3b71dca --- /dev/null +++ b/a5_survey/templates/a5_survey/FinalPage.html @@ -0,0 +1,97 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + YOUR GAIN (test page to disable) + +{% endblock %} + +{% block content %} +
+
+ + + +
+ +
+
+ +

+ The game is over. + The randomly selected game is game number: {{ player.game_num_select}} // {{player.game_select_n}} +

+ +
GP ---------------------
+ +

+ The random draw is: {{player.tirageriskresult}} +
Your investment in the risky asset: {{player.risk11_a}} +
You kept: {{player.risk11_b}} +
Your gain for this game is: {{player.gainrisk}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gainrisk_eur}} EUROS(s) +

+ +
BW ---------------------
+ +

The selected color is: {{player.tirage_color_n}} +
Your winnings for this game are: {{player.gain_bw}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gain_bw_eur }} EUROS(s) +

+ +
MDG-asso ---------------------
+

+ The randomly selected round is: {{player.mdgasso_round_selected}} +
Your endowment during this round was {{player.mdgasso_dot1_select}} ECUs. +
The value of S was {{player.mdgasso_s1_select}}. +
The value of G was {{player.mdgasso_g1_select}}. +

You chose to keep {{player.mdgasso_keep_select}} POINTs and give {{player.mdgasso_sent_select}} POINTs to the organization. +

Your gain is {{player.mdgasso_gain_dgm}} POINTs ({{player.mdgasso_gain_dgm_eur}} EUROs). +
The organization's gain is {{player.mdgasso_gain_asso}} POINTs ({{player.mdgasso_gain_asso_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + +
MDG-class ---------------------
+

+ + Your role in this game is: {{player.dictatorYES_n}} + +
if player.dictatorYES == 1 : + +
+ + The randomly selected round is: {{player.mdgclass_round_selected}} +
Your endowment during this round was {{player.mdgclass_dot1_select}} ECUs. +
The value of S was {{player.mdgclass_s1_select}}. +
The value of G was {{player.mdgclass_g1_select}}. +

You chose to keep {{player.mdgclass_keep_select}} POINTs and give {{player.mdgclass_sent_select}} POINTs to player 2. +

Your gain is {{player.mdgclass_gain_dgm}} POINTs ({{player.mdgclass_gain_dgm_eur}} EUROs). +
Player 2's gain is {{player.mdgclass_gain_joueur2}} POINTs ({{player.mdgclass_gain_joueur2_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + +
if player.dictatorYES == 0 : + +
We must wait for the response of the second player before communicating your gain to you. + An email will be sent to you shortly to announce your gain and the procedures for retrieving your gain. + +

+ +

+ + + + + + +
+
+ +

{% next_button %}

+ + + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/FinalPage2.html b/a5_survey/templates/a5_survey/FinalPage2.html new file mode 100644 index 0000000..cc309a9 --- /dev/null +++ b/a5_survey/templates/a5_survey/FinalPage2.html @@ -0,0 +1,119 @@ +{% load otree %} + +{% block title %} + + YOUR GAIN + +{% endblock %} + +{% block content %} +
+
+ + {% if player.game_select_n != "MDG_class" %} +
+

You have completed the experiment and earned a total of {{ player.gainfinal_eur_showupfee_f2025|to2}} EUROS
+ = Gain games (rounded amount)= {{ player.gainfinal_eur_f|to2}} +
+ questionnaire tasks = 3 +
+ questionnaire CRT = {{ player.gain_crt|to2}} +
+ showup-fee = {{ Constants.showupfee|to2}}

+
+ {% elif player.game_select_n == "MDG_class" and player.dictatorYES == 1 %} +
+

You have completed the experiment and earned a total of {{ player.gainfinal_eur_showupfee_f2025|to2}} EUROS
+ = Gain games (rounded amount)= {{ player.gainfinal_eur_f|to2}} +
+ questionnaire tasks = 3 +
+ questionnaire CRT = {{ player.gain_crt|to2}} +
+ showup-fee = {{ Constants.showupfee|to2}}

+
+ {% endif %} + +
+ +
+ +
+
+ +

+

+ The game is over. + The randomly selected game is game number: {{ player.game_num_select}} // {{player.game_select_n}} +

+

+ + {% if player.game_select_n == "gp" %} + +

+ The random draw is: {{player.tirageriskresult_en}} +
Your investment in the risky asset: {{player.risk11_a}} +
You kept: {{player.risk11_b}} +
Your gain for this game is: {{player.gainrisk}} ECU(s) - (exchange rate: 1ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gainrisk_eur}} EUROS(s) +

+ + {% elif player.game_select_n == "bw" %} + +

The selected color is: {{player.tirage_color_n}} +
Your winnings for this game are: {{player.gain_bw}} ECU(s) - (exchange rate: 1 ECU = 0.8 EUROs) +
Your gain in euros is: {{player.gain_bw_eur}} EUROS(s) +

+ + {% elif player.game_select_n == "MDG_asso" %} + +

+ The randomly selected round is: {{player.mdgasso_round_selected}} +
Your endowment during this round was {{player.mdgasso_dot1_select}} ECUs. +
The value of S was {{player.mdgasso_s1_select}}. +
The value of G was {{player.mdgasso_g1_select}}. +

You chose to keep {{player.mdgasso_keep_select}} POINTs and give {{player.mdgasso_sent_select}} POINTs to the organization. +

Your gain is {{player.mdgasso_gain_dgm}} POINTs ({{player.mdgasso_gain_dgm_eur}} EUROs). +
The organization's gain is {{player.mdgasso_gain_asso}} POINTs ({{player.mdgasso_gain_asso_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + {% elif player.game_select_n == "MDG_class" %} + + + Your role in this game is : {{player.dictatorYES_n}} + + {% if player.dictatorYES == 1 %} +

+ +
+ + The randomly selected round is: {{player.mdgclass_round_selected}} +
Your endowment during this round was {{player.mdgclass_dot1_select}} ECUs. +
The value of S was {{player.mdgclass_s1_select}}. +
The value of G was {{player.mdgclass_g1_select}}. +

You chose to keep {{player.mdgclass_keep_select}} POINTs and give {{player.mdgclass_sent_select}} POINTs to player 2. +

Your gain is {{player.mdgclass_gain_dgm}} POINTs ({{player.mdgclass_gain_dgm_eur}} EUROs). +
Player 2's gain is {{player.mdgclass_gain_joueur2}} POINTs ({{player.mdgclass_gain_joueur2_eur}} EUROs). +
Recall: - (exchange rate: 1 POINT = 0.1 EUROs) +

+ + {% elif player.dictatorYES == 0 %} + +

+ +
We must wait for the response of the second player before communicating your gain to you. + An email will be sent to you shortly to announce your gain and the procedures for retrieving your gain. +

+ + {% endif %} + + + {% endif %} + + + + + +
+
+ +

{% next_button %}

+ + + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/Questionnaire4.html b/a5_survey/templates/a5_survey/Questionnaire4.html new file mode 100644 index 0000000..f460097 --- /dev/null +++ b/a5_survey/templates/a5_survey/Questionnaire4.html @@ -0,0 +1,98 @@ + + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} +Final Questionnaire +{% endblock %} + +{% block content %} + + +
+
+ +The answers to the following questions will not be taken into account in the calculation of + your earnings. We nevertheless ask you to respond as sincerely as possible. + As a reminder, all your responses will be treated anonymously, and no one + will not have access to your answers. + +

+
+
+ + {% formfield player.q_40_etude %} + {% formfield player.q_41_gender %} + {% formfield player.q_42_age%} + {% formfield player.q_43_nationalite %} + {% formfield player.q_44_revenu %} +
+

+ +Indicate the City and Country where your previous accommodation was: +

+ {% formfield player.q3_countries %} + + + {% formfield player.q_46_logement_ville %} + + + +
+
+ + +

+
+ + + {% formfield player.q_47_residence %} + + {% formfield player.q_48_satisfaction %} + + {% formfield player.q_49_propositions %} + + +

{% next_button %}

+ +
+ +
+ +{% endblock %} + + +{% block scripts %} + + + + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/Questionnaire5.html b/a5_survey/templates/a5_survey/Questionnaire5.html new file mode 100644 index 0000000..3989f10 --- /dev/null +++ b/a5_survey/templates/a5_survey/Questionnaire5.html @@ -0,0 +1,144 @@ +{% load otree %} + +{% block title %} +Questionnaire +{% endblock %} + +{% block content %} + +
+
+ +In this part, you are asked to compare the decisions made in the four tasks in Part 1 and to answer questions about specific decisions made in each task. +
+To do this, you are first briefly reminded of the tasks by examining a summary of the corresponding instructions. + For answering the questions in Part 2 carefully and truthfully, you will receive €3.00. +
+ Each game will be given a colour so that you can recognise it later in the questionnaire: +
+ Task "orange" , Task "blue" , + Task "green" and + Task "red" . + + +
+ + + +
+
+ + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template%} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} +
+ {% next_button %} +
+ +
+ + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/Questionnaire6.html b/a5_survey/templates/a5_survey/Questionnaire6.html new file mode 100644 index 0000000..d9c3606 --- /dev/null +++ b/a5_survey/templates/a5_survey/Questionnaire6.html @@ -0,0 +1,245 @@ +{% load otree %} + +{% block title %} +Comparison of Tasks +{% endblock %} + + +{% block content %} +
+
+ + +Please answer the following questions for each of the four tasks.
+For each question, please indicate how much you agree to the + respective statements using a scale from 1 (Strongly Disagree) to 6 (Strongly Agree). + + +
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Game 1:
Task "orange"
Game 2:
Task "blue"
Game 3:
Task "green"
Game 4:
Task "red"
The task is easy to understand and can be answered straightforwardly + {% formfield player.g1_easy_to_understand%} + + {% formfield player.g2_easy_to_understand%} + + {% formfield player.g3_easy_to_understand%} + + {% formfield player.g4_easy_to_understand%} +
The task involves complex calculations and requires + deliberating on the trade-off between expected outcomes + and the inherent “riskiness” of the different outcomes. + {% formfield player.g1_involves_risk%} + + + {% formfield player.g2_involves_risk%} +
The task involves complex calculations and requires deliberating + on the trade-off between expected outcomes and the degree of + “altruism” in different choices. + + + {% formfield player.g3_involves_altruism%} + + {% formfield player.g4_involves_altruism%} +
Completing the task is annoying and boring. + {% formfield player.g1_annoying_boring %} + + + {% formfield player.g2_annoying_boring %} + + + {% formfield player.g3_annoying_boring %} + + + {% formfield player.g4_annoying_boring %} +
+ +
+ {% next_button %} +
+ +
+ +

+ + +

Recall of tasks and their colors

+ +
+
+ + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_bw_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} + +
+ + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/Questionnaire7crt.html b/a5_survey/templates/a5_survey/Questionnaire7crt.html new file mode 100644 index 0000000..6871fa9 --- /dev/null +++ b/a5_survey/templates/a5_survey/Questionnaire7crt.html @@ -0,0 +1,49 @@ +{% load otree %} + +{% block title %} +Questionnaire +{% endblock %} + + +{% block content %} + +
+
+ +Below, you will find 3 questions to answer.
+ +You have 3 minutes to complete them.
+Each correct answer earns you 1 euro.
+Once the time is up, your responses will be submitted automatically. +
+ +
+
+
+ + + + +{% formfield player.crt1%} + + + +{% formfield player.crt2%} + + + +{% formfield player.crt3%} + +
+ + + + {% next_button %} +
+ + + + + + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/Result.html b/a5_survey/templates/a5_survey/Result.html new file mode 100644 index 0000000..b8e753a --- /dev/null +++ b/a5_survey/templates/a5_survey/Result.html @@ -0,0 +1,58 @@ +{% load otree %} + +{% block title %} + + FINAL QUESTIONNAIRE + +{% endblock %} + +{% block content %} +
+
+ +
+
+ NB : Useful information to receive your gain: +
+
+ +
+ +
+
+ +

+ Please fill in the following fields in order to be able to take the + amount you won during this experience. + {% formfield player.lastname %} + {% formfield player.firstname %} + {% formfield player.email %} + {% formfield player.room %} + +

+ +

+ + Reminder for those who have not yet registered + + : to receive your gain you must subscribe to the list of voluntary participants via the link : + + +

https://ipel.crest.fr/public/participant_create.php

+ +
Note : Your personal data are strictly confidential. + All your responses will be treated anonymously. + + +

+ + +
+
+ +

{% next_button %}

+ + + +{% endblock %} + diff --git a/a5_survey/templates/a5_survey/instructions_bw.html b/a5_survey/templates/a5_survey/instructions_bw.html new file mode 100644 index 0000000..b1ad614 --- /dev/null +++ b/a5_survey/templates/a5_survey/instructions_bw.html @@ -0,0 +1,529 @@ +{% load otree %} + + + + +

+ Rules of the game: + +

+ +

+ Look at the table below. It presents 9 different situations. Look at them carefully. + Situations A, B, C, D, E, F, G, H or I contain two possible gains (in ECUS): the red gains on the left and + green gains on the right. You will receive one of these two amounts of ECUS depending on the color that + will be assigned to you at the end of the game. +

+ +

+At the end of the game, the ECU you have won will be converted into € using the following rule:

+ +

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+ +

+So you will first have to choose the situation you prefer the most: A, B, C, D, E, F, G, H or I.

+ + +

+ At the end of the experiment, a random draw will be made by the programme to determine your colour (50% chance of red and 50% chance of green). +

+Example: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ +
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ +
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ +
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ +
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ +
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + +
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + +
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + +
(0 ECUs) (24 ECUs)
+ + + +

+ + + + + +
+ + + +
+ + + + + + diff --git a/a5_survey/templates/a5_survey/instructions_colors.html b/a5_survey/templates/a5_survey/instructions_colors.html new file mode 100644 index 0000000..94cbc01 --- /dev/null +++ b/a5_survey/templates/a5_survey/instructions_colors.html @@ -0,0 +1,116 @@ +{% extends "global/Page.html" %} +{% load otree %} + + +{% block content %} + + {% if player.app_sequence_select == 1%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + {% elif player.app_sequence_select == 2%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% elif player.app_sequence_select == 3%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DAS_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DCL_template %} +
+ + + {% elif player.app_sequence_select == 4%} + +

Game 1: Task "orange"

+
+
+ {% include Constants.instructions_BW_template %} +
+
+

Game 2: Task "blue"

+
+
+ {% include Constants.instructions_GP_template %} +
+
+

Game 3: Task "green"

+
+
+ {% include Constants.instructions_DCL_template %} +
+
+

Game 4: Task "red"

+
+
+ {% include Constants.instructions_DAS_template %} +
+ + {% endif %} + + + +{% endblock %} \ No newline at end of file diff --git a/a5_survey/templates/a5_survey/instructions_das.html b/a5_survey/templates/a5_survey/instructions_das.html new file mode 100644 index 0000000..7aaa43e --- /dev/null +++ b/a5_survey/templates/a5_survey/instructions_das.html @@ -0,0 +1,41 @@ +{% load otree %} +

+ Instructions +

+

+Game Rule: This game consists of 8 rounds. +
At the end of the game, the POINTS you have earned will be converted to € according to the following rule: +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+For each of these rounds, you have a starting amount in POINTs. + You can choose to give some to a non-profit organization Agir pour l'Environnement + working for environmental protection. + So you will choose two amounts: one that you give and one that you keep. The sum of the amounts you give and the amounts you keep must be equal to the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, + and a value S ("self") will be assigned to the POINTs you keep. Thus, the amount you keep will be multiplied by S, and the one you give will be multiplied by G. +
Your final gain will be: +
Amount you chose to keep x S
+
And the final amount given to the organization Agir pour l'Environnement will be: +
Amount you chose to give x G
+
At the end of the game, one of the rounds will be randomly chosen to determine your gain, and the amount given to the organization "Agir pour l'Environnement". +

+

+
-------------------------
+

+ Presentation of the Agir pour l'Environnement association: +
Agir pour l'Environnement is a citizens' organisation working for a livable planet. + The association puts pressure on politicians and economic decision-makers by conducting campaigns + that bring together a wide network of associations and citizens. + In order to maintain its independence of action, the association refuses all funding from + the public authorities. + + + + + Link to Agir pour l'environnement + + +

You can visit the Agir pour l'Environnement website by clicking the link below:

+ Agir pour l'environnement + +

+ diff --git a/a5_survey/templates/a5_survey/instructions_dcl.html b/a5_survey/templates/a5_survey/instructions_dcl.html new file mode 100644 index 0000000..24a2881 --- /dev/null +++ b/a5_survey/templates/a5_survey/instructions_dcl.html @@ -0,0 +1,38 @@ +{% load otree %} + +

+ Instructions +

+

+Rules of the game: +
At the end of the game, the POINTs you have earned will be converted into € according to the following rule: + +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+ +This game consists of 8 rounds and is played with one of the other participants in the experiment, +whom we will refer to as "your partner." +Your partner is anonymous and has no means of identifying you either. +
For each of these rounds, you have a starting amount in POINT. +You can decide to give some of it to your partner. +Therefore, you will choose two amounts: one that you give and one that you keep. +The sum of the amounts you give and the amounts you keep must equal the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, +and a value S ("self") will be assigned to the POINTs you keep. +Thus, the amount you keep will be multiplied by S, and the amount you give will be multiplied by G. +
Your partner plays the same game on their side. +
At the end of the game, one round will be randomly chosen to determine your payoff, + and one of the two players will be randomly named the "dictator" (player1). +
-------------------------------------------
+ Case 1: +
If you are the dictator (player 1), your final payoff will be: +
Amount you chose to keep x S
+
And your partner's (player 2) final payoff will be: +
Amount you chose to give x G
 +
-------------------------------------------
+ Case 2: +
If you are not named dictator (player 2), your final payoff will be: +
Amount your partner chose to give you x G
+
And your partner's (player 1) final payoff will be: +
Amount your partner chose to keep x S
 + +

diff --git a/a5_survey/templates/a5_survey/instructions_gp.html b/a5_survey/templates/a5_survey/instructions_gp.html new file mode 100644 index 0000000..651bc29 --- /dev/null +++ b/a5_survey/templates/a5_survey/instructions_gp.html @@ -0,0 +1,32 @@ +{% load otree %} + + +

+ Rules of the game : +

+ +

You have a capital of 10 ECUs, and you must decide the amount of your investment in the project described below. + You can invest any amount between 0 ECUs and 10 ECUs (in whole numbers).

+ +
Project description:
+ +

This project earns 3 times the amount invested in the event of success and 0 in the event of failure. + Success and failure have the same probability of happening: 1 in 2 chance.

+ +

Your gain from this task = amount retained + gain from your investment in the project.

+ +

Example 1: You decide to invest 4 ECUs in the project. The project is a success. Your project gain is + of 3 x 4 ECUs = 12 ECUs. Your gain from this task is therefore 6 ECUs kept + 12 ECUs from the project = 18 ECUs. +

+ +

Example 2: You decide to invest 4 ECUs in the project. The project is a failure. Your project gain is + of 0 x 4 ECUs = 0 ECUs. Your gain from this game is therefore 6 ECUs kept + 0 ECUs from the project = 6 ECUs. +

+

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+ +

Summary of the game:

+ +
+
+

+
diff --git a/a5_survey/tests.py b/a5_survey/tests.py new file mode 100644 index 0000000..7c27b3c --- /dev/null +++ b/a5_survey/tests.py @@ -0,0 +1,62 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + yield (pages.Bienvenue) + yield (pages.Questionnaire5) + + + # yield (pages.Questionnaire4, { + # 'q_40_etude': 1, + # 'q_41_gender': 1, + # 'q_42_age': random.randint(18, 40), + # 'q_43_nationalite': 5, + # 'q_44_revenu': 1, + # 'q3_countries': 1, + # 'q_46_logement_ville': 'fr', + # 'q_31': 3, + # 'q_47_residence': 1, + # 'q_48_satisfaction': 2, + # 'q_49_propositions': 'sasasadad' + # }) + + yield (pages.Questionnaire6, { + 'g1_easy_to_understand':1, + 'g1_involves_risk':1, + 'g1_annoying_boring':1, + 'g2_easy_to_understand':1, + 'g2_involves_risk':1, + 'g2_annoying_boring':1, + 'g3_easy_to_understand':1, + 'g3_involves_altruism':1, + 'g3_annoying_boring':1, + 'g4_easy_to_understand':1, + 'g4_involves_altruism':1, + 'g4_annoying_boring':1, + }) + + # yield (pages.Questionnaire7crt, { + # 'crt1':5, + # 'crt2':5, + # 'crt3':47, + # }) + # + # + # yield (pages.Result, { + # 'lastname': 'a', + # 'firstname': 'b', + # 'email': '@', + # 'room': 'a', + # }) + + #yield (pages.FinalPage2) + + diff --git a/b3_bw/__init__.py b/b3_bw/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/b3_bw/_builtin/__init__.py b/b3_bw/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/b3_bw/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/b3_bw/models.py b/b3_bw/models.py new file mode 100644 index 0000000..6f8b620 --- /dev/null +++ b/b3_bw/models.py @@ -0,0 +1,174 @@ +import random +from otree.api import ( + models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer, + Currency as c, currency_range +) + + +doc = """ +Author : Wael Bousselmi +""" + +class Constants(BaseConstants): + name_in_url = 'b3_bw' + players_per_group = None + num_rounds = 1 + + tauxdechange = 1 + + instructions_BW_template = 'b3_bw/Risk_BinsWanger_qc.html' + + gainrisk_red = [8, 7, 6, 5, 4, 3, 2, 1, 0] + gainrisk_green = [8, 10, 12, 14, 16, 18, 20, 22, 24] + + + + tauxchange = 0.8 + +class Subsession(BaseSubsession): + + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict(id=p.participant.id_in_session, label=p.participant.label, answers=p.gainrisk)) + return dict(report=report) + + +class Group(BaseGroup): + pass + + +############################# +def imi_inv(label): + return models.IntegerField( + choices=[ + [0, '0'], + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'] + ], + label=label, + widget=widgets.RadioSelect, + blank=False + ) + +# Define template for Goal commitment questions +def goal_com(label): + return models.IntegerField( + choices=[ + [0, '0'], + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + ], + label=label, + widget=widgets.RadioSelect, + blank=False + ) +############################# + + + +class Player(BasePlayer): + + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + + id_in_group_nn = models.IntegerField() + treatment = models.IntegerField() + riskqc_a = models.IntegerField( + choices=[ + [1, '10 ECUs'], + [2, '0 ECUs'], + [3, '4 ECUs'], + [4, '7 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + riskqc_b = models.IntegerField( + choices=[ + [1, '20 ECUs'], + [2, '0 ECUs'], + [3, '5 ECUs'], + [4, '14 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + riskqc_c = models.IntegerField( + choices=[ + [1, '0 ECUs'], + [2, '2 ECUs'], + [3, '24 ECUs'], + [4, '8 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + choicerisk = models.IntegerField( + blank=False, + ) + + choicerisknn = models.StringField() + + + risk_assessment_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False, + ) + confidence_level_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False, + ) + memory_recall_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank = False, + ) + + + tc3 = models.StringField() + + tirage_color = models.IntegerField() + tirage_color_n = models.StringField() + + gain_bw = models.FloatField() + gain_bw_eur = models.FloatField() + + ##### + + task_start_risk = models.FloatField() + task_time_risk = models.FloatField() + + + + + + + + + + diff --git a/b3_bw/pages.py b/b3_bw/pages.py new file mode 100644 index 0000000..20a0afd --- /dev/null +++ b/b3_bw/pages.py @@ -0,0 +1,107 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] < 3 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n = self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + +class Risk_BinsWanger_qc(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] < 3 # 1 ou 2 + + def before_next_page(self): + self.player.tirage_color = random.randint(0, 1) + if self.player.tirage_color == 0: + self.player.tirage_color_n = 'red' + else: + self.player.tirage_color_n = 'green' + + +class Risk_BinsWanger_qc2(Page): + form_model = 'player' + form_fields = ['riskqc_a', 'riskqc_b', 'riskqc_c'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] < 3 # 1 ou 2 + + def error_message(self, values): + print('values is', values) + if values['riskqc_a'] != 1: + return 'Please correct your answer 1!!' + elif values['riskqc_b'] != 3: + return 'Please correct your answer 2!!' + elif values['riskqc_c'] != 1: + return 'Please correct your answer 3!!' + +class Risk_BinsWanger(Page): + form_model = 'player' + form_fields = ['choicerisk', 'risk_assessment_bw', 'confidence_level_bw', 'memory_recall_bw'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] < 3 # 1 ou 2 + + def vars_for_template(self): + self.player.task_start_risk = time.time() ###### get_time + + def before_next_page(self): + + if self.player.choicerisk == 1: + self.player.choicerisknn = "A" + elif self.player.choicerisk == 2: + self.player.choicerisknn = "B" + elif self.player.choicerisk == 3: + self.player.choicerisknn = "C" + elif self.player.choicerisk == 4: + self.player.choicerisknn = "D" + elif self.player.choicerisk == 5: + self.player.choicerisknn = "E" + elif self.player.choicerisk == 6: + self.player.choicerisknn = "F" + elif self.player.choicerisk == 7: + self.player.choicerisknn = "G" + elif self.player.choicerisk == 8: + self.player.choicerisknn = "H" + elif self.player.choicerisk == 9: + self.player.choicerisknn = "I" + + self.player.task_time_risk = time.time() - self.player.task_start_risk ######### count_time + + self.participant.vars['choicerisk'] = self.player.choicerisk + self.participant.vars['choicerisknn'] = self.player.choicerisknn + + if self.player.tirage_color == 0: + self.player.gain_bw = Constants.gainrisk_red[self.player.choicerisk-1] + else: + self.player.gain_bw = Constants.gainrisk_green[self.player.choicerisk-1] + self.player.gain_bw_eur = round(self.player.gain_bw * Constants.tauxchange,1) + + self.participant.vars['tirage_color_n'] = self.player.tirage_color_n + self.participant.vars['gain_bw'] = self.player.gain_bw + self.participant.vars['gain_bw_eur'] = self.player.gain_bw_eur + + +page_sequence = [ + Bienvenue, + Risk_BinsWanger_qc, + Risk_BinsWanger_qc2, + Risk_BinsWanger, +] diff --git a/b3_bw/templates/b3_bw/Bienvenue.html b/b3_bw/templates/b3_bw/Bienvenue.html new file mode 100644 index 0000000..1ba1066 --- /dev/null +++ b/b3_bw/templates/b3_bw/Bienvenue.html @@ -0,0 +1,29 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/b3_bw/templates/b3_bw/Jeu2End.html b/b3_bw/templates/b3_bw/Jeu2End.html new file mode 100644 index 0000000..cc12151 --- /dev/null +++ b/b3_bw/templates/b3_bw/Jeu2End.html @@ -0,0 +1,34 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + + + First Part : End + + +{% endblock %} + +{% block content %} +
+
+ + + +

+ You have completed the first part. + +
You will now begin the second part. + +

+ + + +
+
+ +

{% next_button %}

+ + +{% endblock %} diff --git a/b3_bw/templates/b3_bw/Risk_BinsWanger.html b/b3_bw/templates/b3_bw/Risk_BinsWanger.html new file mode 100644 index 0000000..5b04eb4 --- /dev/null +++ b/b3_bw/templates/b3_bw/Risk_BinsWanger.html @@ -0,0 +1,604 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + + Game {{player.game_num}}: Your choice + +{% endblock %} + +{% block content %} + + + +
+
+ + + + +

+You will now play the game for real. The winning of this game will count towards the final gain.

+ + + +

Now please choose the situation you prefer the most from the following choices (A, B, C, D, E, F, G, H or I). +

+ +

+ +

Please make a choice

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+ 		A
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ + 		B
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ + 		C
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ + 		D
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ + 		E
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ + 		F
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + + 		G
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + + 		H
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + 		I
(0 ECUs) (24 ECUs)
+ +




+ +
+
+ +


+
+
+ +

Please answer the following questions:

+

+ + 1) On a scale from 1 (not at all risky) to 7 (very risky):
+ How risky do you consider your own decision (indicated above) to be? + + + + + + + + +
Not at all risky {% formfield player.risk_assessment_bw %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated above? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_bw %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_bw%} I remembered it very well
+ +
+ + + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw/templates/b3_bw/Risk_BinsWanger_color.html b/b3_bw/templates/b3_bw/Risk_BinsWanger_color.html new file mode 100644 index 0000000..b3f9dd0 --- /dev/null +++ b/b3_bw/templates/b3_bw/Risk_BinsWanger_color.html @@ -0,0 +1,161 @@ + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + Jeu 2 : Choix de votre couleur + + + +{% endblock %} + +{% block content %} + + +
+
+ + + + + +

Veuillez choisir entre les deux boutons suivants :

+ +

Une couleur (rouge ou verte) est cachée derrière chaque bouton. + Lorsque vous cliquez sur l'un des boutons, sa couleur apparaît. + Si la couleur qui apparaît est verte, vous recevrez les gains verts de la sélection que vous avez faite au début. + S'il est rouge, vous recevrez les gains rouges.

+

+ +
+ + + +
+ +

+

+ +

+ +

+

+

+ + + +
+ + +{% endblock %} + + +{% block scripts %} + + + + + +{% endblock %} \ No newline at end of file diff --git a/b3_bw/templates/b3_bw/Risk_BinsWanger_qc.html b/b3_bw/templates/b3_bw/Risk_BinsWanger_qc.html new file mode 100644 index 0000000..9034958 --- /dev/null +++ b/b3_bw/templates/b3_bw/Risk_BinsWanger_qc.html @@ -0,0 +1,565 @@ +{% load otree %} + +{% block title %} + + Game {{player.game_num}} + + +{% endblock %} + +{% block content %} + +

+This game can be taken into account when calculating your gains.

+ +
+
+ + + + + +

+ Rules of the game: + +

+ +

+ Look at the table below. It presents 9 different situations. Look at them carefully. + Situations A, B, C, D, E, F, G, H or I contain two possible gains (in ECUS): the red gains on the left and + green gains on the right. You will receive one of these two amounts of ECUS depending on the color that + will be assigned to you at the end of the game. +

+ +

+At the end of the game, the ECU you have won will be converted into € using the following rule:

+ +

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+
+ +

+So you will first have to choose the situation you prefer the most: A, B, C, D, E, F, G, H or I.

+ + +

+ At the end of the experiment, a random draw will be made by the programme to determine your colour (50% chance of red and 50% chance of green). +

+ +

+ +

Warning! The image below is an example, your choice will be saved later.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ +
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ +
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ +
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ +
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ +
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + +
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + +
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + +
(0 ECUs) (24 ECUs)
+ + + +

+ + + + + +
+ + + +
+ + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw/templates/b3_bw/Risk_BinsWanger_qc2.html b/b3_bw/templates/b3_bw/Risk_BinsWanger_qc2.html new file mode 100644 index 0000000..39fe52b --- /dev/null +++ b/b3_bw/templates/b3_bw/Risk_BinsWanger_qc2.html @@ -0,0 +1,325 @@ +{% load otree %} + +{% block title %} + Comprehension Questionnaire +{% endblock %} + +{% block content %} + + +
+
+ + + + + +

Now we want to test if you have understood the in-game instructions.

+ + +
+ +

+ Example 1: + +

+ + +

If you have chosen situation B, then the color Green appears. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
B + + +

+ + + + +
+ + + +

+ + 		+ +

+ + + + + +
+ + + + + +

+ + 		+ + B + +
(7 ECUs) (10 ECUs)
+ +
+ +

+Question 1: What would your earnings be?

+ {% formfield player.riskqc_a label="" %} + +

+ + +

+ Example 2: +

+ + +

If you have chosen situation D, then the color Red appears. + +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + + + +

+ + D + +
(5 ECU) (14 ECUs)
+ +
+

+ Question 2: What would your earnings be?

+ + {% formfield player.riskqc_b label=""%} + +

+ + + +

+ + Example 3: +

+ + +

If you have chosen situation I, then the color Red + appears. + +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + 		+ + E + +
(0 ECUs) (24 ECUs)
+ +

+ Question 3: What would your earnings be?

+ + {% formfield player.riskqc_c label=""%} + +

+ + + + + +




+ + + + + + +
+ + + +
+ + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw/templates/b3_bw/Risk_BinsWanger_result.html b/b3_bw/templates/b3_bw/Risk_BinsWanger_result.html new file mode 100644 index 0000000..29e5901 --- /dev/null +++ b/b3_bw/templates/b3_bw/Risk_BinsWanger_result.html @@ -0,0 +1,50 @@ +{% load otree %} + + +{% block title %} + + Gain of game 1 : Details + + +{% endblock %} + +{% block content %} + + +
+
+ + + +

Your choice was: {{player.choicerisknn}}

+ +

The selected color is: {{player.colorselectnn}}

+ + +

Your winnings for this game are: {{player.gainrisk}} ECU (s)

+ + +

Your winnings for this game are: {{player.gainriskeur}} ECU (s)

+

-----------------------------------------

+ +
+ + + +
+
+ +

+

+Reminder of the instructions: +

+
+
+ + {% include Constants.instructions_BW_template %} + +
+ +{% endblock %} + + diff --git a/b3_bw/tests.py b/b3_bw/tests.py new file mode 100644 index 0000000..f64ef43 --- /dev/null +++ b/b3_bw/tests.py @@ -0,0 +1,36 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + if self.round_number == 1 and self.participant.vars['app_sequence_select'] < 3: + if self.round_number == 1: + yield (pages.Bienvenue) + + + if self.player.participant.vars['app_sequence_select'] < 3: + yield (pages.Risk_BinsWanger_qc) + + yield (pages.Risk_BinsWanger_qc2, { + 'riskqc_a': 1, + 'riskqc_b': 3, + 'riskqc_c': 1, + }) + + yield (pages.Risk_BinsWanger, { + 'choicerisk': random.randint(1, 9), + 'risk_assessment_bw':1, + 'confidence_level_bw':1, + 'memory_recall_bw':1, + }) + + + + diff --git a/b3_bw_v2/__init__.py b/b3_bw_v2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/b3_bw_v2/_builtin/__init__.py b/b3_bw_v2/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/b3_bw_v2/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/b3_bw_v2/models.py b/b3_bw_v2/models.py new file mode 100644 index 0000000..3783bdd --- /dev/null +++ b/b3_bw_v2/models.py @@ -0,0 +1,172 @@ +import random +from otree.api import ( + models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer, + Currency as c, currency_range +) + + +doc = """ +Author : Wael Bousselmi +""" + +class Constants(BaseConstants): + name_in_url = 'b3_bw_v2' + players_per_group = None + num_rounds = 1 + + tauxdechange = 1 + + instructions_BW_template = 'b3_bw_v2/Risk_BinsWanger_qc.html' + + gainrisk_red = [8, 7, 6, 5, 4, 3, 2, 1, 0] + gainrisk_green = [8, 10, 12, 14, 16, 18, 20, 22, 24] + + + + tauxchange = 0.8 + +class Subsession(BaseSubsession): + + def vars_for_admin_report(self): + report = list() + for p in self.get_players(): + report.append(dict(id=p.participant.id_in_session, label=p.participant.label, answers=p.gainrisk)) + return dict(report=report) + + +class Group(BaseGroup): + pass + + +############################# +def imi_inv(label): + return models.IntegerField( + choices=[ + [0, '0'], + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + [6, '6'], + [7, '7'] + ], + label=label, + widget=widgets.RadioSelect, + blank=False + ) + +# Define template for Goal commitment questions +def goal_com(label): + return models.IntegerField( + choices=[ + [0, '0'], + [1, '1'], + [2, '2'], + [3, '3'], + [4, '4'], + [5, '5'], + ], + label=label, + widget=widgets.RadioSelect, + blank=False + ) +############################# + + + +class Player(BasePlayer): + + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + + id_in_group_nn = models.IntegerField() + treatment = models.IntegerField() + riskqc_a = models.IntegerField( + choices=[ + [1, '10 ECUs'], + [2, '0 ECUs'], + [3, '4 ECUs'], + [4, '7 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + riskqc_b = models.IntegerField( + choices=[ + [1, '20 ECUs'], + [2, '0 ECUs'], + [3, '5 ECUs'], + [4, '14 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + riskqc_c = models.IntegerField( + choices=[ + [1, '0 ECUs'], + [2, '2 ECUs'], + [3, '24 ECUs'], + [4, '8 ECUs'], + ], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + choicerisk = models.IntegerField( + blank=False, + ) + + choicerisknn = models.StringField() + + risk_assessment_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + confidence_level_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + memory_recall_bw = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + tc3 = models.StringField() + + tirage_color = models.IntegerField() + tirage_color_n = models.StringField() + + gain_bw = models.FloatField() + gain_bw_eur = models.FloatField() + + ##### + + task_start_risk = models.FloatField() + task_time_risk = models.FloatField() + + + + + + + + + + diff --git a/b3_bw_v2/pages.py b/b3_bw_v2/pages.py new file mode 100644 index 0000000..edc77db --- /dev/null +++ b/b3_bw_v2/pages.py @@ -0,0 +1,107 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants + +import random +import time + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] > 2 #3 ou 4 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n = self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + +class Risk_BinsWanger_qc(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] > 2 #3 ou 4 + + def before_next_page(self): + self.player.tirage_color = random.randint(0, 1) + if self.player.tirage_color == 0: + self.player.tirage_color_n = 'red' + else: + self.player.tirage_color_n = 'green' + + +class Risk_BinsWanger_qc2(Page): + form_model = 'player' + form_fields = ['riskqc_a', 'riskqc_b', 'riskqc_c'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] > 2 #3 ou 4 + + def error_message(self, values): + print('values is', values) + if values['riskqc_a'] != 1: + return 'Please correct your answer 1!!' + elif values['riskqc_b'] != 3: + return 'Please correct your answer 2!!' + elif values['riskqc_c'] != 1: + return 'Please correct your answer 3!!' + +class Risk_BinsWanger(Page): + form_model = 'player' + form_fields = ['choicerisk', 'risk_assessment_bw', 'confidence_level_bw', 'memory_recall_bw'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] > 2 #3 ou 4 + + def vars_for_template(self): + self.player.task_start_risk = time.time() ###### get_time + + def before_next_page(self): + + if self.player.choicerisk == 1: + self.player.choicerisknn = "A" + elif self.player.choicerisk == 2: + self.player.choicerisknn = "B" + elif self.player.choicerisk == 3: + self.player.choicerisknn = "C" + elif self.player.choicerisk == 4: + self.player.choicerisknn = "D" + elif self.player.choicerisk == 5: + self.player.choicerisknn = "E" + elif self.player.choicerisk == 6: + self.player.choicerisknn = "F" + elif self.player.choicerisk == 7: + self.player.choicerisknn = "G" + elif self.player.choicerisk == 8: + self.player.choicerisknn = "H" + elif self.player.choicerisk == 9: + self.player.choicerisknn = "I" + + self.player.task_time_risk = time.time() - self.player.task_start_risk ######### count_time + + self.participant.vars['choicerisk'] = self.player.choicerisk + self.participant.vars['choicerisknn'] = self.player.choicerisknn + + if self.player.tirage_color == 0: + self.player.gain_bw = Constants.gainrisk_red[self.player.choicerisk-1] + else: + self.player.gain_bw = Constants.gainrisk_green[self.player.choicerisk-1] + self.player.gain_bw_eur = round(self.player.gain_bw * Constants.tauxchange,1) + + self.participant.vars['tirage_color_n'] = self.player.tirage_color_n + self.participant.vars['gain_bw'] = self.player.gain_bw + self.participant.vars['gain_bw_eur'] = self.player.gain_bw_eur + + +page_sequence = [ + Bienvenue, + Risk_BinsWanger_qc, + Risk_BinsWanger_qc2, + Risk_BinsWanger, +] diff --git a/b3_bw_v2/templates/b3_bw_v2/Bienvenue.html b/b3_bw_v2/templates/b3_bw_v2/Bienvenue.html new file mode 100644 index 0000000..b743cdd --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Bienvenue.html @@ -0,0 +1,28 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/b3_bw_v2/templates/b3_bw_v2/Jeu2End.html b/b3_bw_v2/templates/b3_bw_v2/Jeu2End.html new file mode 100644 index 0000000..cc12151 --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Jeu2End.html @@ -0,0 +1,34 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + + + First Part : End + + +{% endblock %} + +{% block content %} +
+
+ + + +

+ You have completed the first part. + +
You will now begin the second part. + +

+ + + +
+
+ +

{% next_button %}

+ + +{% endblock %} diff --git a/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger.html b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger.html new file mode 100644 index 0000000..84b536c --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger.html @@ -0,0 +1,612 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + + Game {{player.game_num}}: Your choice + +{% endblock %} + +{% block content %} + + + +
+
+ + + + +

+You will now play the game for real. The winning of this game will count towards the final gain.

+ + + +

Now please choose the situation you prefer the most from the following choices (A, B, C, D, E, F, G, H or I). +

+ +

+ +

Please make a choice

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+ 		A
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ + 		B
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ + 		C
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ + 		D
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ + 		E
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ + 		F
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + + 		G
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + + 		H
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + 		I
(0 ECUs) (24 ECUs)
+ +




+ + + + + +
+
+ + + +


+
+
+ +

Please answer the following questions:

+

+ + 1) On a scale from 1 (not at all risky) to 7 (very risky):
+ How risky do you consider your own decision (indicated above) to be? + + + + + + + + +
Not at all risky {% formfield player.risk_assessment_bw %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated above? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_bw %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_bw%} I remembered it very well
+ +
+ + + + + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_color.html b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_color.html new file mode 100644 index 0000000..b3f9dd0 --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_color.html @@ -0,0 +1,161 @@ + + + + +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + + Jeu 2 : Choix de votre couleur + + + +{% endblock %} + +{% block content %} + + +
+
+ + + + + +

Veuillez choisir entre les deux boutons suivants :

+ +

Une couleur (rouge ou verte) est cachée derrière chaque bouton. + Lorsque vous cliquez sur l'un des boutons, sa couleur apparaît. + Si la couleur qui apparaît est verte, vous recevrez les gains verts de la sélection que vous avez faite au début. + S'il est rouge, vous recevrez les gains rouges.

+

+ +
+ + + +
+ +

+

+ +

+ +

+

+

+ + + +
+ + +{% endblock %} + + +{% block scripts %} + + + + + +{% endblock %} \ No newline at end of file diff --git a/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc.html b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc.html new file mode 100644 index 0000000..9034958 --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc.html @@ -0,0 +1,565 @@ +{% load otree %} + +{% block title %} + + Game {{player.game_num}} + + +{% endblock %} + +{% block content %} + +

+This game can be taken into account when calculating your gains.

+ +
+
+ + + + + +

+ Rules of the game: + +

+ +

+ Look at the table below. It presents 9 different situations. Look at them carefully. + Situations A, B, C, D, E, F, G, H or I contain two possible gains (in ECUS): the red gains on the left and + green gains on the right. You will receive one of these two amounts of ECUS depending on the color that + will be assigned to you at the end of the game. +

+ +

+At the end of the game, the ECU you have won will be converted into € using the following rule:

+ +

The exchange rate for this game is (1 ECU = 0.8 EUROS)

+
+ +

+So you will first have to choose the situation you prefer the most: A, B, C, D, E, F, G, H or I.

+ + +

+ At the end of the experiment, a random draw will be made by the programme to determine your colour (50% chance of red and 50% chance of green). +

+ +

+ +

Warning! The image below is an example, your choice will be saved later.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
A +

+ + + + +
+ + + + +

+ 		+

+ + + + +
+ + + + +

+
(8 ECUs) (8 ECUs)
B + +

+ + + + +
+ + + + + +

+ + 		+ +

+ + + + + + +
+ + + + + +

+ +
(7 ECUs) (10 ECUs)
C + +

+ + + +
+ + + + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + +

+ +
(6 ECUs) (12 ECUs)
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + +

+ +
(5 ECUs) (14 ECUs)
E + +

+ + +
+ + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +

+ +
(4 ECUs) (16 ECUs)
F + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + +

+ +
(3 ECUs) (18 ECUs)
G + +

+ + + +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + +

+ + +
(2 ECU) (20 ECUs)
H + +

+ +

+ + 		+ +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + +

+ + +
(1 ECU) (22 ECUs)
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + +
(0 ECUs) (24 ECUs)
+ + + +

+ + + + + +
+ + + +
+ + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc2.html b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc2.html new file mode 100644 index 0000000..3c98c20 --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_qc2.html @@ -0,0 +1,326 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Comprehension Questionnaire +{% endblock %} + +{% block content %} + + +
+
+ + + + + +

Now we want to test if you have understood the in-game instructions.

+ + +
+ +

+ Example 1: + +

+ + +

If you have chosen situation B, then the color Green appears. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
B + + +

+ + + + +
+ + + +

+ + 		+ +

+ + + + + +
+ + + + + +

+ + 		+ + B + +
(7 ECUs) (10 ECUs)
+ +
+ +

+Question 1: What would your earnings be?

+ {% formfield player.riskqc_a label="" %} + +

+ + +

+ Example 2: +

+ + +

If you have chosen situation D, then the color Red appears. + +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
D + +

+ + + +
+ + + +

+ + 		+ +

+ + + + + + + +
+ + + + + + + + + +

+ + D + +
(5 ECU) (14 ECUs)
+ +
+

+ Question 2: What would your earnings be?

+ + {% formfield player.riskqc_b label=""%} + +

+ + + +

+ + Example 3: +

+ + +

If you have chosen situation I, then the color Red + appears. + +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Options Red Green
I + + +

+ + + + + + + + +
+ + + + + + + + +
+ + + + + + + + +

+ + + 		+ + E + +
(0 ECUs) (24 ECUs)
+ +

+ Question 3: What would your earnings be?

+ + {% formfield player.riskqc_c label=""%} + +

+ + + + + +




+ + + + + + +
+ + + +
+ + + {% next_button %} + +{% endblock %} + + + + + + +{% block scripts %} + + + + + + + +{% endblock %} + diff --git a/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_result.html b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_result.html new file mode 100644 index 0000000..ee231fc --- /dev/null +++ b/b3_bw_v2/templates/b3_bw_v2/Risk_BinsWanger_result.html @@ -0,0 +1,51 @@ +{% extends "global/Page.html" %} +{% load otree %} + + +{% block title %} + + Gain of game 1 : Details + + +{% endblock %} + +{% block content %} + + +
+
+ + + +

Your choice was: {{player.choicerisknn}}

+ +

The selected color is: {{player.colorselectnn}}

+ + +

Your winnings for this game are: {{player.gainrisk}} ECU (s)

+ + +

Your winnings for this game are: {{player.gainriskeur}} ECU (s)

+

-----------------------------------------

+ +
+ + + +
+
+ +

+

+Reminder of the instructions: +

+
+
+ + {% include Constants.instructions_BW_template %} + +
+ +{% endblock %} + + diff --git a/b3_bw_v2/tests.py b/b3_bw_v2/tests.py new file mode 100644 index 0000000..9e7a599 --- /dev/null +++ b/b3_bw_v2/tests.py @@ -0,0 +1,37 @@ +from otree.api import Currency as c, currency_range, Submission +from . import pages +from ._builtin import Bot +from .models import Constants +import random + + +# ********************************************************************************************************************** +# *** BOT +# ********************************************************************************************************************** +class PlayerBot(Bot): + def play_round(self): + + if self.participant.vars['app_sequence_select'] > 2: + + if self.round_number == 1: + yield (pages.Bienvenue) + + + if self.player.participant.vars['app_sequence_select'] > 2: #3 ou 4 + yield (pages.Risk_BinsWanger_qc) + + yield (pages.Risk_BinsWanger_qc2, { + 'riskqc_a': 1, + 'riskqc_b': 3, + 'riskqc_c': 1, + }) + + yield (pages.Risk_BinsWanger, { + 'choicerisk': random.randint(1, 9), + 'risk_assessment_bw': 1, + 'confidence_level_bw': 1, + 'memory_recall_bw': 1, + }) + + + diff --git a/dictator_modif_asso/__init__.py b/dictator_modif_asso/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dictator_modif_asso/_builtin/__init__.py b/dictator_modif_asso/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/dictator_modif_asso/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/dictator_modif_asso/models.py b/dictator_modif_asso/models.py new file mode 100644 index 0000000..453f187 --- /dev/null +++ b/dictator_modif_asso/models.py @@ -0,0 +1,195 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) +import random + + +doc = """ +Modified dictator game +""" + + +class Constants(BaseConstants): + name_in_url = 'dictator_modif_asso' + players_per_group = None + num_rounds = 8 + + instructions_template = 'dictator_modif_asso/instructions.html' + + exchangerate = 0.1 + + # Initial amount allocated to each player + #endowment = c(100) + #multiplier = 3 + + dgm1 = {1: [40, 3, 1], 2: [40, 1, 3], 3: [60, 2, 1], 4: [60, 1, 2], + 5: [75, 2, 1], 6: [75, 1, 2], 7: [60, 1, 1], 8: [100, 1, 1]} + + dgm2 = {8: [40, 3, 1], 7: [40, 1, 3], 6: [60, 2, 1], 5: [60, 1, 2], + 4: [75, 2, 1], 3: [75, 1, 2], 2: [60, 1, 1], 1: [100, 1, 1]} + + dgm3 = {8: [40, 3, 1], 2: [40, 1, 3], 1: [60, 2, 1], 4: [60, 1, 2], + 3: [75, 2, 1], 6: [75, 1, 2], 5: [60, 1, 1], 7: [100, 1, 1]} + + dgm4 = {5: [40, 3, 1], 6: [40, 1, 3], 7: [60, 2, 1], 8: [60, 1, 2], + 1: [75, 2, 1], 2: [75, 1, 2], 3: [60, 1, 1], 4: [100, 1, 1]} + + dgm5 = {4: [40, 3, 1], 1: [40, 1, 3], 6: [60, 2, 1], 3: [60, 1, 2], + 8: [75, 2, 1], 5: [75, 1, 2], 2: [60, 1, 1], 7: [100, 1, 1]} + + + +class Subsession(BaseSubsession): + pass + # def creating_session(subsession): + # # Random Matching + # subsession.group_randomly() + +class Group(BaseGroup): + pass + # dot1 = models.IntegerField() + # s1 = models.IntegerField() + # g1 = models.IntegerField() + # send1 = models.IntegerField() + # send2 = models.IntegerField() + # keep1 = models.IntegerField() + # keep2 = models.IntegerField() + # roundselect = models.IntegerField() + # def dict_dgm(self): + # if self.id_in_subsession == 1 or self.id_in_subsession == 6 or self.id_in_subsession == 11: + # self.dot1 = Constants.dgm1[self.round_number-1][0] + # self.s1 = Constants.dgm1[self.round_number-1][1] + # self.g1 = Constants.dgm1[self.round_number-1][2] + # elif self.id_in_subsession == 2 or self.id_in_subsession == 7 or self.id_in_subsession == 12: + # self.dot1 = Constants.dgm2[self.round_number-1][0] + # self.s1 = Constants.dgm2[self.round_number-1][1] + # self.g1 = Constants.dgm2[self.round_number-1][2] + # elif self.id_in_subsession == 3 or self.id_in_subsession == 8 or self.id_in_subsession == 13: + # self.dot1 = Constants.dgm3[self.round_number-1][0] + # self.s1 = Constants.dgm3[self.round_number-1][1] + # self.g1 = Constants.dgm3[self.round_number-1][2] + # elif self.id_in_subsession == 4 or self.id_in_subsession == 9 or self.id_in_subsession == 14: + # self.dot1 = Constants.dgm4[self.round_number-1][0] + # self.s1 = Constants.dgm4[self.round_number-1][1] + # self.g1 = Constants.dgm4[self.round_number-1][2] + # elif self.id_in_subsession == 5 or self.id_in_subsession == 10 or self.id_in_subsession == 15: + # self.dot1 = Constants.dgm5[self.round_number-1][0] + # self.s1 = Constants.dgm5[self.round_number-1][1] + # self.g1 = Constants.dgm5[self.round_number-1][2] + # sent_amount = models.CurrencyField( + # min=0, max=Constants.endowment, doc="""Amount sent by P1""" + # ) + # + # sent_back_amount = models.CurrencyField(doc="""Amount sent back by P2""", min=c(0)) + + def sent_back_amount_max(self): + return self.sent_amount * Constants.multiplier + + def set_payoffs(self): + p1 = self.get_player_by_id(1) + p2 = self.get_player_by_id(2) + + self.send1 = p1.sent_amount + self.send2 = p2.sent_amount + self.keep1 = p1.keep_amount + self.keep2 = p2.keep_amount + + if self.round_number == 8: + self.roundselect = random.randint(1, 8) + if p1.dictator == 1: + p1.gain_dgm = 23 + p2.gain_dgm = 25 + else: + p1.gain_dgm = 233 + p2.gain_dgm = 255 + + + # p1.payoff = self.keep1 * self.s1 + # p2.payoff = self.send1 * self.g1 + # + # p1.payoff = Constants.endowment - self.sent_amount + self.sent_back_amount + # p2.payoff = self.sent_amount * Constants.multiplier - self.sent_back_amount + +class Player(BasePlayer): + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + # def role(self): + # if self.id_in_group == 1: + # return 'dictator1' + # if self.id_in_group == 2: + # return 'dictator2' + # role1 = models.StringField() + # dictator = models.IntegerField() + dgm_selected = models.IntegerField() + round_selected = models.IntegerField() + + r1 = models.IntegerField( + blank=False + ) + r2 = models.IntegerField( + blank=False + ) + r3 = models.IntegerField( + blank=False + ) + r4 = models.IntegerField( + blank=False + ) + keep_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous gardez""" ) + sent_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous donnez""") + + + altruistic_assessment_das = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + confidence_level_das = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + memory_recall_das = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + dot1 = models.IntegerField() + s1 = models.IntegerField() + g1 = models.IntegerField() + + dot1_select = models.IntegerField() + s1_select = models.IntegerField() + g1_select = models.IntegerField() + keep_select = models.IntegerField() + sent_select = models.IntegerField() + + gain_dgm = models.IntegerField() + gain_asso = models.IntegerField() + + gain_dgm_eur = models.FloatField() + gain_asso_eur = models.FloatField() + + + + + + diff --git a/dictator_modif_asso/pages.py b/dictator_modif_asso/pages.py new file mode 100644 index 0000000..6fdeb96 --- /dev/null +++ b/dictator_modif_asso/pages.py @@ -0,0 +1,161 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants +import random + + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + # def is_displayed(self): + # return self.participant.vars['versionexp'] == 2 + + def is_displayed(self): + return self.round_number == 1 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n = self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + +class Introduction(Page): + form_model = 'player' + form_fields = ['r1','r2','r3','r4'] + + def is_displayed(self): + return self.round_number == 1 + + def error_message(self, values): + print('values is', values) + if values['r1'] != 98: + return 'Merci de corriger votre réponse 1 !!' + elif values['r2'] != 2: + return 'Merci de corriger votre réponse 2 !!' + elif values['r3'] != 110: + return 'Merci de corriger votre réponse 3 !!' + elif values['r4'] != 5: + return 'Merci de corriger votre réponse 4 !!' + + def before_next_page(self): + # selection of random dotation/s/g and round to be paid + self.player.dgm_selected = random.randint(1, 5) + self.player.round_selected = random.randint(1, 8) + self.player.participant.vars['mdgasso_dgm_selected'] = self.player.dgm_selected + self.player.participant.vars['mdgasso_round_selected'] = self.player.round_selected + +class NextPage(Page): + form_model = 'player' + form_fields = [] + + def vars_for_template(self): + return dict( + round = self.round_number, + ) + + + def before_next_page(self): + self.player.dgm_selected = self.player.participant.vars['mdgasso_dgm_selected'] + self.player.round_selected =self.player.participant.vars['mdgasso_round_selected'] + self.player.dot1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][0] + self.player.s1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][1] + self.player.g1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][2] + +class Send(Page): + """comment + """ + form_model = 'player' + form_fields = ['keep_amount', 'sent_amount'] + + def error_message(self, values): + print('values is', values) + if values['keep_amount'] < 0: + return 'the amount must be positive ' + elif values['keep_amount'] > self.player.dot1 : + return f'please choose an amount less than {self.player.dot1}' + elif values['sent_amount'] < 0: + return 'the amount must be positive ' + elif values['sent_amount'] + values['keep_amount'] != self.player.dot1: + return f'the sum of the two amounts must be equal to {self.player.dot1}' + + + + + def vars_for_template(self): + return dict( + dot1 = self.player.dot1, + s1 = self.player.s1, + g1 = self.player.g1, + round = self.round_number, + game_num = self.player.participant.vars['game_num'] + ) + + def before_next_page(self): + self.player.participant.vars[f'dot1_r{self.round_number}'] = self.player.dot1 + self.player.participant.vars[f's1_r{self.round_number}'] = self.player.s1 + self.player.participant.vars[f'g1_r{self.round_number}'] = self.player.g1 + self.player.participant.vars[f'keep_r{self.round_number}'] = self.player.keep_amount + self.player.participant.vars[f'sent_r{self.round_number}'] = self.player.sent_amount + + if self.round_number == 8: + self.player.dot1_select = self.player.participant.vars[f'dot1_r{self.player.round_selected}'] + self.player.s1_select = self.player.participant.vars[f's1_r{self.player.round_selected}'] + self.player.g1_select = self.player.participant.vars[f'g1_r{self.player.round_selected}'] + self.player.keep_select = self.player.participant.vars[f'keep_r{self.player.round_selected}'] + self.player.sent_select = self.player.participant.vars[f'sent_r{self.player.round_selected}'] + self.player.gain_dgm = self.player.keep_select * self.player.s1_select + self.player.gain_asso = self.player.sent_select * self.player.g1_select + self.player.gain_dgm_eur = round(self.player.gain_dgm * Constants.exchangerate,1) + self.player.gain_asso_eur = round(self.player.gain_asso * Constants.exchangerate,1) + self.player.payoff = round(self.player.gain_dgm_eur,1) + + # to final page: + self.player.participant.vars['mdgasso_dot1_select'] = self.player.dot1_select + self.player.participant.vars['mdgasso_s1_select'] = self.player.s1_select + self.player.participant.vars['mdgasso_g1_select'] = self.player.g1_select + self.player.participant.vars['mdgasso_keep_select'] = self.player.keep_select + self.player.participant.vars['mdgasso_sent_select'] = self.player.sent_select + self.player.participant.vars['mdgasso_gain_dgm'] = self.player.gain_dgm + self.player.participant.vars['mdgasso_gain_asso'] = self.player.gain_asso + self.player.participant.vars['mdgasso_gain_dgm_eur'] = self.player.gain_dgm_eur + self.player.participant.vars['mdgasso_gain_asso_eur'] = self.player.gain_asso_eur + +class Questionnaire(Page): + form_model = 'player' + form_fields = ['altruistic_assessment_das', 'confidence_level_das', 'memory_recall_das'] + def is_displayed(self): + return self.round_number == 8 + + +class Results(Page): + def is_displayed(self): + return self.round_number == 8 + + def vars_for_template(self): + #group = player.group + return dict( + dot1_select = self.player.dot1_select, + s1_select = self.player.s1_select, + g1_select = self.player.g1_select, + round = self.round_number, + round_selected = self.player.round_selected, + keep_select = self.player.keep_select, + sent_select = self.player.sent_select, + gain_dgm = self.player.gain_dgm, + gain_asso = self.player.gain_asso, + gain_dgm_eur = self.player.gain_dgm_eur, + gain_asso_eur = self.player.gain_asso_eur, + + ) + +page_sequence = [ + Bienvenue, + Introduction, + NextPage, + Send, + Questionnaire, +] + + diff --git a/dictator_modif_asso/templates/dictator_modif_asso/Bienvenue.html b/dictator_modif_asso/templates/dictator_modif_asso/Bienvenue.html new file mode 100644 index 0000000..1ba1066 --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/Bienvenue.html @@ -0,0 +1,29 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/dictator_modif_asso/templates/dictator_modif_asso/Introduction.html b/dictator_modif_asso/templates/dictator_modif_asso/Introduction.html new file mode 100644 index 0000000..6d31978 --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/Introduction.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{player.game_num}} +{% endblock %} + +{% block content %} + + {% include Constants.instructions_template %} + +
+
+ +

+

Comprehension questionnaire

+ +
Q1. If your starting amount is 100 POINTs, S = 1 and G = 1, and you choose to give 2 +to the association: +
{% formfield player.r1 label="Your gain is (in POINTs)"%} + {% formfield player.r2 label="You donate to the association (in POINTs)"%} + +
Q2. If your starting amount is 60 POINTs, S = 2 and G = 1, and you choose to give 5 +to the association: + +
{% formfield player.r3 label="Your gain is (in POINTs)"%} + {% formfield player.r4 label="You donate to the association (in POINTs)"%} + +
+ {% next_button %} + +{% endblock %} diff --git a/dictator_modif_asso/templates/dictator_modif_asso/NextPage.html b/dictator_modif_asso/templates/dictator_modif_asso/NextPage.html new file mode 100644 index 0000000..b6ff090 --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/NextPage.html @@ -0,0 +1,22 @@ +{% load otree %} + +{% block title %} + Round: {{round}} +{% endblock %} + +{% block content %} + + {% if round == 1 %} +

Start the first round

+ {% else %} + +

Go to the next round

+ + {% endif %} + + + +

{% next_button %}

+ + +{% endblock %} diff --git a/dictator_modif_asso/templates/dictator_modif_asso/Questionnaire.html b/dictator_modif_asso/templates/dictator_modif_asso/Questionnaire.html new file mode 100644 index 0000000..3756362 --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/Questionnaire.html @@ -0,0 +1,73 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + + +


+
+
+ +

Please answer the following questions:

+

+ +1) On a scale from 1 (not at all altruistic) to 7 (very altruistic):
+How altruistic do you consider your own decision (in the task just completed) to be? + + + + + + + + + +
Not at all risky {% formfield player.altruistic_assessment_das %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated in the task just completed? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_das %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made, in the same task, during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_das%} I remembered it very well
+ +
+ + + + {% next_button %} + +{% endblock %} + + + + + diff --git a/dictator_modif_asso/templates/dictator_modif_asso/Results.html b/dictator_modif_asso/templates/dictator_modif_asso/Results.html new file mode 100644 index 0000000..7d5154d --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/Results.html @@ -0,0 +1,30 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Results +{% endblock %} + +{% block content %} +

+ +

+ The randomly selected round is: {{round_selected}} +
Your endowment during this round was {{dot1_select}} ECUs. +
The value of S was {{s1_select}}. +
The value of G was {{g1_select}}. +

You chose to keep {{keep_select}} POINTs and give {{sent_select}} POINTs to the organization. +

Your gain is {{gain_dgm}} POINTs ({{gain_dgm_eur}} EUROs). +
The organization's gain is {{gain_asso}} POINTs ({{gain_asso_eur}} EUROs). +

+

+

+

+

+

+ +

{% next_button %}

+ + {% include Constants.instructions_template %} + +{% endblock %} \ No newline at end of file diff --git a/dictator_modif_asso/templates/dictator_modif_asso/Send.html b/dictator_modif_asso/templates/dictator_modif_asso/Send.html new file mode 100644 index 0000000..6125084 --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/Send.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{game_num}} - Your Choice - (Round: {{round}}) +{% endblock %} + +{% block content %} + + +

+ Your starting amount is {{dot1}} POINTs. +
The value S is {{s1}}: what you keep for yourself will therefore be multiplied by {{s1}}. +
The value G is {{g1}}: what you give to your partner will therefore be multiplied by {{g1}}. + + +

+ +

Out of the {{dot1}} POINTs: + {% formfield group.keep_amount label="You keep:" %} + + {% formfield group.sent_amount label="You give to the association:" %} +

+ + +

+ {% next_button %} +

+ + {% include Constants.instructions_template %} + +{% endblock %} diff --git a/dictator_modif_asso/templates/dictator_modif_asso/instructions.html b/dictator_modif_asso/templates/dictator_modif_asso/instructions.html new file mode 100644 index 0000000..a8a702c --- /dev/null +++ b/dictator_modif_asso/templates/dictator_modif_asso/instructions.html @@ -0,0 +1,56 @@ +{% load otree %} + +
+
+ +

+ Instructions +

+

+Game Rule: This game consists of 8 rounds. +
At the end of the game, the POINTS you have earned will be converted to € according to the following rule: + +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+
For each of these rounds, you have a starting amount in POINTs. + You can choose to give some to a non-profit organization Agir pour l'Environnement + working for environmental protection. + So you will choose two amounts: one that you give and one that you keep. The sum of the amounts you give and the amounts you keep must be equal to the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, + and a value S ("self") will be assigned to the POINTs you keep. Thus, the amount you keep will be multiplied by S, and the one you give will be multiplied by G. +
Your final gain will be: +
Amount you chose to keep x S
+
And the final amount given to the organization Agir pour l'Environnement will be: +
Amount you chose to give x G
+
At the end of the game, one of the rounds will be randomly chosen to determine your gain, and the amount given to the organization "Agir pour l'Environnement". + +

+ + +

+
-------------------------
+

+ Presentation of the Agir pour l'Environnement association: +
Agir pour l'Environnement is a citizens' organisation working for a livable planet. + The association puts pressure on politicians and economic decision-makers by conducting campaigns + that bring together a wide network of associations and citizens. + In order to maintain its independence of action, the association refuses all funding from + the public authorities. + + + + + Link to Agir pour l'environnement + + +

You can visit the Agir pour l'Environnement website by clicking the link below:

+ Agir pour l'environnement + + + + +

+ + + +
+
\ No newline at end of file diff --git a/dictator_modif_asso/tests.py b/dictator_modif_asso/tests.py new file mode 100644 index 0000000..bc8ec67 --- /dev/null +++ b/dictator_modif_asso/tests.py @@ -0,0 +1,43 @@ +from otree.api import Currency as c, currency_range +from . import pages +from ._builtin import Bot +from .models import Constants +import random + +class PlayerBot(Bot): + def play_round(self): + + if self.round_number == 1: + yield (pages.Bienvenue) + yield (pages.Introduction, { + "r1": 98, + "r2":2, + "r3":110, + "r4":5, + }) + + yield (pages.NextPage, { + }) + + keep_amount = random.randint(0, self.player.dot1) + sent_amount = self.player.dot1 - keep_amount + + yield (pages.Send, { + "keep_amount": keep_amount, + "sent_amount": sent_amount, + }) + + if self.round_number == 8: + yield (pages.Questionnaire, { + 'altruistic_assessment_das':1, + 'confidence_level_das':1, + 'memory_recall_das':1, + }) + + # if self.round_number == 8: + # yield (pages.Results, { + # }) + + + #if self.player.id_in_group == 1: + diff --git a/dictator_modif_class/__init__.py b/dictator_modif_class/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dictator_modif_class/_builtin/__init__.py b/dictator_modif_class/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/dictator_modif_class/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/dictator_modif_class/models.py b/dictator_modif_class/models.py new file mode 100644 index 0000000..171b593 --- /dev/null +++ b/dictator_modif_class/models.py @@ -0,0 +1,194 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) +import random + + +doc = """ +Modified dictator game +""" + + +class Constants(BaseConstants): + name_in_url = 'dictator_modif_class' + players_per_group = None + num_rounds = 8 + + instructions_template = 'dictator_modif_class/instructions.html' + + exchangerate = 0.1 + + # Initial amount allocated to each player + #endowment = c(100) + #multiplier = 3 + + dgm1 = {1: [40, 3, 1], 2: [40, 1, 3], 3: [60, 2, 1], 4: [60, 1, 2], + 5: [75, 2, 1], 6: [75, 1, 2], 7: [60, 1, 1], 8: [100, 1, 1]} + + dgm2 = {8: [40, 3, 1], 7: [40, 1, 3], 6: [60, 2, 1], 5: [60, 1, 2], + 4: [75, 2, 1], 3: [75, 1, 2], 2: [60, 1, 1], 1: [100, 1, 1]} + + dgm3 = {8: [40, 3, 1], 2: [40, 1, 3], 1: [60, 2, 1], 4: [60, 1, 2], + 3: [75, 2, 1], 6: [75, 1, 2], 5: [60, 1, 1], 7: [100, 1, 1]} + + dgm4 = {5: [40, 3, 1], 6: [40, 1, 3], 7: [60, 2, 1], 8: [60, 1, 2], + 1: [75, 2, 1], 2: [75, 1, 2], 3: [60, 1, 1], 4: [100, 1, 1]} + + dgm5 = {4: [40, 3, 1], 1: [40, 1, 3], 6: [60, 2, 1], 3: [60, 1, 2], + 8: [75, 2, 1], 5: [75, 1, 2], 2: [60, 1, 1], 7: [100, 1, 1]} + + + +class Subsession(BaseSubsession): + pass + # def creating_session(subsession): + # # Random Matching + # subsession.group_randomly() + +class Group(BaseGroup): + pass + # dot1 = models.IntegerField() + # s1 = models.IntegerField() + # g1 = models.IntegerField() + # send1 = models.IntegerField() + # send2 = models.IntegerField() + # keep1 = models.IntegerField() + # keep2 = models.IntegerField() + # roundselect = models.IntegerField() + # def dict_dgm(self): + # if self.id_in_subsession == 1 or self.id_in_subsession == 6 or self.id_in_subsession == 11: + # self.dot1 = Constants.dgm1[self.round_number-1][0] + # self.s1 = Constants.dgm1[self.round_number-1][1] + # self.g1 = Constants.dgm1[self.round_number-1][2] + # elif self.id_in_subsession == 2 or self.id_in_subsession == 7 or self.id_in_subsession == 12: + # self.dot1 = Constants.dgm2[self.round_number-1][0] + # self.s1 = Constants.dgm2[self.round_number-1][1] + # self.g1 = Constants.dgm2[self.round_number-1][2] + # elif self.id_in_subsession == 3 or self.id_in_subsession == 8 or self.id_in_subsession == 13: + # self.dot1 = Constants.dgm3[self.round_number-1][0] + # self.s1 = Constants.dgm3[self.round_number-1][1] + # self.g1 = Constants.dgm3[self.round_number-1][2] + # elif self.id_in_subsession == 4 or self.id_in_subsession == 9 or self.id_in_subsession == 14: + # self.dot1 = Constants.dgm4[self.round_number-1][0] + # self.s1 = Constants.dgm4[self.round_number-1][1] + # self.g1 = Constants.dgm4[self.round_number-1][2] + # elif self.id_in_subsession == 5 or self.id_in_subsession == 10 or self.id_in_subsession == 15: + # self.dot1 = Constants.dgm5[self.round_number-1][0] + # self.s1 = Constants.dgm5[self.round_number-1][1] + # self.g1 = Constants.dgm5[self.round_number-1][2] + # sent_amount = models.CurrencyField( + # min=0, max=Constants.endowment, doc="""Amount sent by P1""" + # ) + # + # sent_back_amount = models.CurrencyField(doc="""Amount sent back by P2""", min=c(0)) + + def sent_back_amount_max(self): + return self.sent_amount * Constants.multiplier + + def set_payoffs(self): + p1 = self.get_player_by_id(1) + p2 = self.get_player_by_id(2) + + self.send1 = p1.sent_amount + self.send2 = p2.sent_amount + self.keep1 = p1.keep_amount + self.keep2 = p2.keep_amount + + if self.round_number == 8: + self.roundselect = random.randint(1, 8) + if p1.dictator == 1: + p1.gain_dgm = 23 + p2.gain_dgm = 25 + else: + p1.gain_dgm = 233 + p2.gain_dgm = 255 + + + # p1.payoff = self.keep1 * self.s1 + # p2.payoff = self.send1 * self.g1 + # + # p1.payoff = Constants.endowment - self.sent_amount + self.sent_back_amount + # p2.payoff = self.sent_amount * Constants.multiplier - self.sent_back_amount + +class Player(BasePlayer): + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + # def role(self): + # if self.id_in_group == 1: + # return 'dictator1' + # if self.id_in_group == 2: + # return 'dictator2' + # role1 = models.StringField() + # dictator = models.IntegerField() + dgm_selected = models.IntegerField() + round_selected = models.IntegerField() + + r1 = models.IntegerField( + blank=False + ) + r2 = models.IntegerField( + blank=False + ) + r3 = models.IntegerField( + blank=False + ) + r4 = models.IntegerField( + blank=False + ) + keep_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous gardez""" ) + sent_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous donnez""") + + altruistic_assessment_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + confidence_level_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + memory_recall_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + dot1 = models.IntegerField() + s1 = models.IntegerField() + g1 = models.IntegerField() + + dot1_select = models.IntegerField() + s1_select = models.IntegerField() + g1_select = models.IntegerField() + keep_select = models.IntegerField() + sent_select = models.IntegerField() + + gain_dgm = models.IntegerField() + gain_joueur2 = models.IntegerField() + + gain_dgm_eur = models.FloatField() + gain_joueur2_eur = models.FloatField() + + + + + + diff --git a/dictator_modif_class/pages.py b/dictator_modif_class/pages.py new file mode 100644 index 0000000..a967e26 --- /dev/null +++ b/dictator_modif_class/pages.py @@ -0,0 +1,169 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants +import random + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] % 2 == 1 + + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n = self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + +class Introduction(Page): + form_model = 'player' + form_fields = ['r1','r2','r3','r4'] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] % 2 == 1 + + + def error_message(self, values): + print('values is', values) + if values['r1'] != 98: + return 'Please correct your answer 1 !!' + elif values['r2'] != 2: + return 'Please correct your answer 2 !!' + elif values['r3'] != 5: + return 'Please correct your answer 3 !!' + elif values['r4'] != 110: + return 'Please correct your answer 4 !!' + + def before_next_page(self): + # selection of random dotation/s/g and round to be paid + self.player.dgm_selected = random.randint(1, 5) + self.player.round_selected = random.randint(1, 8) + self.player.participant.vars['mdgclass_dgm_selected'] = self.player.dgm_selected + self.player.participant.vars['mdgclass_round_selected'] = self.player.round_selected + +class NextPage(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] % 2 == 1 + + + def vars_for_template(self): + return dict( + round = self.round_number, + ) + + + def before_next_page(self): + self.player.dgm_selected = self.player.participant.vars['mdgclass_dgm_selected'] + self.player.round_selected =self.player.participant.vars['mdgclass_round_selected'] + self.player.dot1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][0] + self.player.s1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][1] + self.player.g1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][2] + +class Send(Page): + """comment + """ + form_model = 'player' + form_fields = ['keep_amount', 'sent_amount'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] % 2 == 1 + + + def error_message(self, values): + print('values is', values) + if values['keep_amount'] < 0: + return 'the amount must be positive ' + elif values['keep_amount'] > self.player.dot1 : + return f'please choose an amount less than {self.player.dot1}' + elif values['sent_amount'] < 0: + return 'the amount must be positive ' + elif values['sent_amount'] + values['keep_amount'] != self.player.dot1: + return f'the sum of the two amounts must be equal to {self.player.dot1}' + + + + + def vars_for_template(self): + return dict( + dot1 = self.player.dot1, + s1 = self.player.s1, + g1 = self.player.g1, + round = self.round_number, + game_num=self.player.participant.vars['game_num'] + + ) + + def before_next_page(self): + self.player.participant.vars[f'dot1_r{self.round_number}'] = self.player.dot1 + self.player.participant.vars[f's1_r{self.round_number}'] = self.player.s1 + self.player.participant.vars[f'g1_r{self.round_number}'] = self.player.g1 + self.player.participant.vars[f'keep_r{self.round_number}'] = self.player.keep_amount + self.player.participant.vars[f'sent_r{self.round_number}'] = self.player.sent_amount + + if self.round_number == 8: + self.player.dot1_select = self.player.participant.vars[f'dot1_r{self.player.round_selected}'] + self.player.s1_select = self.player.participant.vars[f's1_r{self.player.round_selected}'] + self.player.g1_select = self.player.participant.vars[f'g1_r{self.player.round_selected}'] + self.player.keep_select = self.player.participant.vars[f'keep_r{self.player.round_selected}'] + self.player.sent_select = self.player.participant.vars[f'sent_r{self.player.round_selected}'] + self.player.gain_dgm = self.player.keep_select * self.player.s1_select + self.player.gain_joueur2 = self.player.sent_select * self.player.g1_select + self.player.gain_dgm_eur = round(self.player.gain_dgm * Constants.exchangerate,1) + self.player.gain_joueur2_eur = round(self.player.gain_joueur2 * Constants.exchangerate,1) + self.player.payoff = round(self.player.gain_dgm_eur,1) + + # to final page: + self.player.participant.vars['mdgclass_dot1_select'] = self.player.dot1_select + self.player.participant.vars['mdgclass_s1_select'] = self.player.s1_select + self.player.participant.vars['mdgclass_g1_select'] = self.player.g1_select + self.player.participant.vars['mdgclass_keep_select'] = self.player.keep_select + self.player.participant.vars['mdgclass_sent_select'] = self.player.sent_select + self.player.participant.vars['mdgclass_gain_dgm'] = self.player.gain_dgm + self.player.participant.vars['mdgclass_gain_joueur2'] = self.player.gain_joueur2 + self.player.participant.vars['mdgclass_gain_dgm_eur'] = self.player.gain_dgm_eur + self.player.participant.vars['mdgclass_gain_joueur2_eur'] = self.player.gain_joueur2_eur + +class Questionnaire(Page): + form_model = 'player' + form_fields = ['altruistic_assessment_dcl', 'confidence_level_dcl', 'memory_recall_dcl'] + def is_displayed(self): + return self.round_number == 8 and self.player.participant.vars['app_sequence_select'] % 2 == 1 + + +class Results(Page): + def is_displayed(self): + return self.round_number == 8 and self.player.participant.vars['app_sequence_select'] % 2 == 1 + + + def vars_for_template(self): + #group = player.group + return dict( + dot1_select = self.player.dot1_select, + s1_select = self.player.s1_select, + g1_select = self.player.g1_select, + round = self.round_number, + round_selected = self.player.round_selected, + keep_select = self.player.keep_select, + sent_select = self.player.sent_select, + gain_dgm = self.player.gain_dgm, + gain_joueur2 = self.player.gain_joueur2, + gain_dgm_eur = self.player.gain_dgm_eur, + gain_joueur2_eur = self.player.gain_joueur2_eur, + + ) + +page_sequence = [ + Bienvenue, + Introduction, + NextPage, + Send, + Questionnaire, +] + + diff --git a/dictator_modif_class/templates/dictator_modif_class/Bienvenue.html b/dictator_modif_class/templates/dictator_modif_class/Bienvenue.html new file mode 100644 index 0000000..1ba1066 --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/Bienvenue.html @@ -0,0 +1,29 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/dictator_modif_class/templates/dictator_modif_class/Introduction.html b/dictator_modif_class/templates/dictator_modif_class/Introduction.html new file mode 100644 index 0000000..490206c --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/Introduction.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{player.game_num}} +{% endblock %} + +{% block content %} + + {% include Constants.instructions_template %} + +
+
+ +

+

Comprehension Test

+ +
Q1. If you are named "dictator" (player 1), your starting amount is 100 POINTs, +S = 1, G = 1, and you choose to give 2 to your partner (player 2), what is your payoff? What about your partner's? + +
{% formfield player.r1 label="You gain (POINTs)"%} + {% formfield player.r2 label="Your partner gains (POINTs)"%} +
Q2. If you are not named "dictator" (player 2), and your partner's (player 1) starting +amount is 60 POINTs, S = 2, and G = 1. If your partner decides to give you 5, +what is your payoff? What about your partner's? +
{% formfield player.r3 label="You gain (POINTs)"%} + {% formfield player.r4 label="Your partner gains (POINTs)"%} + +
+ {% next_button %} + +{% endblock %} diff --git a/dictator_modif_class/templates/dictator_modif_class/NextPage.html b/dictator_modif_class/templates/dictator_modif_class/NextPage.html new file mode 100644 index 0000000..b6ff090 --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/NextPage.html @@ -0,0 +1,22 @@ +{% load otree %} + +{% block title %} + Round: {{round}} +{% endblock %} + +{% block content %} + + {% if round == 1 %} +

Start the first round

+ {% else %} + +

Go to the next round

+ + {% endif %} + + + +

{% next_button %}

+ + +{% endblock %} diff --git a/dictator_modif_class/templates/dictator_modif_class/Questionnaire.html b/dictator_modif_class/templates/dictator_modif_class/Questionnaire.html new file mode 100644 index 0000000..0742aed --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/Questionnaire.html @@ -0,0 +1,73 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + + +


+
+
+ +

Please answer the following questions:

+

+ +1) On a scale from 1 (not at all altruistic) to 7 (very altruistic):
+How altruistic do you consider your own decision (in the task just completed) to be? + + + + + + + + + +
Not at all risky {% formfield player.altruistic_assessment_dcl %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated in the task just completed? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_dcl %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made, in the same task, during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_dcl%} I remembered it very well
+ +
+ + + + {% next_button %} + +{% endblock %} + + + + + diff --git a/dictator_modif_class/templates/dictator_modif_class/Results.html b/dictator_modif_class/templates/dictator_modif_class/Results.html new file mode 100644 index 0000000..e7d8392 --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/Results.html @@ -0,0 +1,30 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Results +{% endblock %} + +{% block content %} +

+ +

+ The randomly selected round is: {{round_selected}} +
Your endowment during this round was {{dot1_select}} ECUs. +
The value of S was {{s1_select}}. +
The value of G was {{g1_select}}. +

You chose to keep {{keep_select}} POINTs and give {{sent_select}} POINTs to player 2. +

Your gain is {{gain_dgm}} POINTs ({{gain_dgm_eur}} EUROs). +
The player 2 gain is {{gain_joueur2}} POINTs ({{gain_joueur2_eur}} EUROs). +

+

+

+

+

+

+ +

{% next_button %}

+ + {% include Constants.instructions_template %} + +{% endblock %} \ No newline at end of file diff --git a/dictator_modif_class/templates/dictator_modif_class/Send.html b/dictator_modif_class/templates/dictator_modif_class/Send.html new file mode 100644 index 0000000..0658340 --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/Send.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{game_num}} - Your Choice - (Round: {{round}}) +{% endblock %} + +{% block content %} + + +

+ Your starting amount is {{dot1}} POINTs. +
The value S is {{s1}}: what you keep for yourself will therefore be multiplied by {{s1}}. +
The value G is {{g1}}: what you give to your partner will therefore be multiplied by {{g1}}. + + +

+ +

Out of the {{dot1}} POINTs: + {% formfield group.keep_amount label="You keep:" %} + + {% formfield group.sent_amount label="You give to the other player:" %} +

+ + +

+ {% next_button %} +

+ + {% include Constants.instructions_template %} + +{% endblock %} diff --git a/dictator_modif_class/templates/dictator_modif_class/instructions.html b/dictator_modif_class/templates/dictator_modif_class/instructions.html new file mode 100644 index 0000000..9b791ad --- /dev/null +++ b/dictator_modif_class/templates/dictator_modif_class/instructions.html @@ -0,0 +1,44 @@ +{% load otree %} + +
+
+ +

+ Instructions +

+

+Rules of the game: +
At the end of the game, the POINTs you have earned will be converted into € according to the following rule: + +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+ + +
This game consists of 8 rounds and is played with one of the other participants in the experiment, +whom we will refer to as "your partner." +Your partner is anonymous and has no means of identifying you either. +
For each of these rounds, you have a starting amount in POINT. +You can decide to give some of it to your partner. +Therefore, you will choose two amounts: one that you give and one that you keep. +The sum of the amounts you give and the amounts you keep must equal the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, +and a value S ("self") will be assigned to the POINTs you keep. +Thus, the amount you keep will be multiplied by S, and the amount you give will be multiplied by G. +
Your partner plays the same game on their side. +
At the end of the game, one round will be randomly chosen to determine your payoff, + and one of the two players will be randomly named the "dictator" (player1). +
-------------------------------------------
+ Case 1: +
If you are the dictator (player 1), your final payoff will be: +
Amount you chose to keep x S
+
And your partner's (player 2) final payoff will be: +
Amount you chose to give x G
 +
-------------------------------------------
+ Case 2: +
If you are not named dictator (player 2), your final payoff will be: +
Amount your partner chose to give you x G
+
And your partner's (player 1) final payoff will be: +
Amount your partner chose to keep x S
 + +

+
+
\ No newline at end of file diff --git a/dictator_modif_class/tests.py b/dictator_modif_class/tests.py new file mode 100644 index 0000000..7c22d32 --- /dev/null +++ b/dictator_modif_class/tests.py @@ -0,0 +1,42 @@ +from otree.api import Currency as c, currency_range +from . import pages +from ._builtin import Bot +from .models import Constants +import random + +class PlayerBot(Bot): + def play_round(self): + + if self.participant.vars['app_sequence_select'] % 2 == 1: + if self.round_number == 1: + yield (pages.Bienvenue) + yield (pages.Introduction, { + "r1": 98, + "r2":2, + "r3":5, + "r4":110, + }) + + yield (pages.NextPage, {}) + + keep_amount = random.randint(0, self.player.dot1) + sent_amount = self.player.dot1 - keep_amount + + yield (pages.Send, { + "keep_amount": keep_amount, + "sent_amount": sent_amount, + }) + + if self.round_number == 8: + yield (pages.Questionnaire, { + 'altruistic_assessment_dcl': 1, + 'confidence_level_dcl': 1, + 'memory_recall_dcl': 1, + }) + # if self.round_number == 8: + # yield (pages.Results, { + # }) + + + #if self.player.id_in_group == 1: + diff --git a/dictator_modif_class_v2/__init__.py b/dictator_modif_class_v2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dictator_modif_class_v2/_builtin/__init__.py b/dictator_modif_class_v2/_builtin/__init__.py new file mode 100644 index 0000000..3505705 --- /dev/null +++ b/dictator_modif_class_v2/_builtin/__init__.py @@ -0,0 +1,20 @@ +# Don't change anything in this file. +from .. import models +import otree.api + + +class Page(otree.api.Page): + subsession: models.Subsession + group: models.Group + player: models.Player + + +class WaitPage(otree.api.WaitPage): + subsession: models.Subsession + group: models.Group + + +class Bot(otree.api.Bot): + subsession: models.Subsession + group: models.Group + player: models.Player diff --git a/dictator_modif_class_v2/models.py b/dictator_modif_class_v2/models.py new file mode 100644 index 0000000..bf4ffa7 --- /dev/null +++ b/dictator_modif_class_v2/models.py @@ -0,0 +1,194 @@ +from otree.api import ( + models, + widgets, + BaseConstants, + BaseSubsession, + BaseGroup, + BasePlayer, + Currency as c, + currency_range, +) +import random + + +doc = """ +Modified dictator game +""" + + +class Constants(BaseConstants): + name_in_url = 'dictator_modif_class_v2' + players_per_group = None + num_rounds = 8 + + instructions_template = 'dictator_modif_class_v2/instructions.html' + + exchangerate = 0.1 + + # Initial amount allocated to each player + #endowment = c(100) + #multiplier = 3 + + dgm1 = {1: [40, 3, 1], 2: [40, 1, 3], 3: [60, 2, 1], 4: [60, 1, 2], + 5: [75, 2, 1], 6: [75, 1, 2], 7: [60, 1, 1], 8: [100, 1, 1]} + + dgm2 = {8: [40, 3, 1], 7: [40, 1, 3], 6: [60, 2, 1], 5: [60, 1, 2], + 4: [75, 2, 1], 3: [75, 1, 2], 2: [60, 1, 1], 1: [100, 1, 1]} + + dgm3 = {8: [40, 3, 1], 2: [40, 1, 3], 1: [60, 2, 1], 4: [60, 1, 2], + 3: [75, 2, 1], 6: [75, 1, 2], 5: [60, 1, 1], 7: [100, 1, 1]} + + dgm4 = {5: [40, 3, 1], 6: [40, 1, 3], 7: [60, 2, 1], 8: [60, 1, 2], + 1: [75, 2, 1], 2: [75, 1, 2], 3: [60, 1, 1], 4: [100, 1, 1]} + + dgm5 = {4: [40, 3, 1], 1: [40, 1, 3], 6: [60, 2, 1], 3: [60, 1, 2], + 8: [75, 2, 1], 5: [75, 1, 2], 2: [60, 1, 1], 7: [100, 1, 1]} + + + +class Subsession(BaseSubsession): + pass + # def creating_session(subsession): + # # Random Matching + # subsession.group_randomly() + +class Group(BaseGroup): + pass + # dot1 = models.IntegerField() + # s1 = models.IntegerField() + # g1 = models.IntegerField() + # send1 = models.IntegerField() + # send2 = models.IntegerField() + # keep1 = models.IntegerField() + # keep2 = models.IntegerField() + # roundselect = models.IntegerField() + # def dict_dgm(self): + # if self.id_in_subsession == 1 or self.id_in_subsession == 6 or self.id_in_subsession == 11: + # self.dot1 = Constants.dgm1[self.round_number-1][0] + # self.s1 = Constants.dgm1[self.round_number-1][1] + # self.g1 = Constants.dgm1[self.round_number-1][2] + # elif self.id_in_subsession == 2 or self.id_in_subsession == 7 or self.id_in_subsession == 12: + # self.dot1 = Constants.dgm2[self.round_number-1][0] + # self.s1 = Constants.dgm2[self.round_number-1][1] + # self.g1 = Constants.dgm2[self.round_number-1][2] + # elif self.id_in_subsession == 3 or self.id_in_subsession == 8 or self.id_in_subsession == 13: + # self.dot1 = Constants.dgm3[self.round_number-1][0] + # self.s1 = Constants.dgm3[self.round_number-1][1] + # self.g1 = Constants.dgm3[self.round_number-1][2] + # elif self.id_in_subsession == 4 or self.id_in_subsession == 9 or self.id_in_subsession == 14: + # self.dot1 = Constants.dgm4[self.round_number-1][0] + # self.s1 = Constants.dgm4[self.round_number-1][1] + # self.g1 = Constants.dgm4[self.round_number-1][2] + # elif self.id_in_subsession == 5 or self.id_in_subsession == 10 or self.id_in_subsession == 15: + # self.dot1 = Constants.dgm5[self.round_number-1][0] + # self.s1 = Constants.dgm5[self.round_number-1][1] + # self.g1 = Constants.dgm5[self.round_number-1][2] + # sent_amount = models.CurrencyField( + # min=0, max=Constants.endowment, doc="""Amount sent by P1""" + # ) + # + # sent_back_amount = models.CurrencyField(doc="""Amount sent back by P2""", min=c(0)) + + def sent_back_amount_max(self): + return self.sent_amount * Constants.multiplier + + def set_payoffs(self): + p1 = self.get_player_by_id(1) + p2 = self.get_player_by_id(2) + + self.send1 = p1.sent_amount + self.send2 = p2.sent_amount + self.keep1 = p1.keep_amount + self.keep2 = p2.keep_amount + + if self.round_number == 8: + self.roundselect = random.randint(1, 8) + if p1.dictator == 1: + p1.gain_dgm = 23 + p2.gain_dgm = 25 + else: + p1.gain_dgm = 233 + p2.gain_dgm = 255 + + + # p1.payoff = self.keep1 * self.s1 + # p2.payoff = self.send1 * self.g1 + # + # p1.payoff = Constants.endowment - self.sent_amount + self.sent_back_amount + # p2.payoff = self.sent_amount * Constants.multiplier - self.sent_back_amount + +class Player(BasePlayer): + + game_num = models.IntegerField() + app_sequence_select = models.IntegerField() + app_sequence_select_n = models.StringField() + questionnaire_before = models.IntegerField() + + + # def role(self): + # if self.id_in_group == 1: + # return 'dictator1' + # if self.id_in_group == 2: + # return 'dictator2' + # role1 = models.StringField() + # dictator = models.IntegerField() + dgm_selected = models.IntegerField() + round_selected = models.IntegerField() + + r1 = models.IntegerField( + blank=False + ) + r2 = models.IntegerField( + blank=False + ) + r3 = models.IntegerField( + blank=False + ) + r4 = models.IntegerField( + blank=False + ) + keep_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous gardez""" ) + sent_amount = models.IntegerField( + blank=False, initial=0, doc="""Vous donnez""") + + altruistic_assessment_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + confidence_level_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + memory_recall_dcl = models.IntegerField( + choices=[[i, str(i)] for i in range(1, 8)], + label="", + widget=widgets.RadioSelectHorizontal, + blank=False + ) + + dot1 = models.IntegerField() + s1 = models.IntegerField() + g1 = models.IntegerField() + + dot1_select = models.IntegerField() + s1_select = models.IntegerField() + g1_select = models.IntegerField() + keep_select = models.IntegerField() + sent_select = models.IntegerField() + + gain_dgm = models.IntegerField() + gain_joueur2 = models.IntegerField() + + gain_dgm_eur = models.FloatField() + gain_joueur2_eur = models.FloatField() + + + + + + diff --git a/dictator_modif_class_v2/pages.py b/dictator_modif_class_v2/pages.py new file mode 100644 index 0000000..46887a1 --- /dev/null +++ b/dictator_modif_class_v2/pages.py @@ -0,0 +1,166 @@ +from otree.api import Currency as c, currency_range +from ._builtin import Page, WaitPage +from .models import Constants +import random + +class Bienvenue(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] % 2 == 0 # 2 ou 4 + + def before_next_page(self): + self.player.game_num = self.player.participant.vars['game_num'] + 1 + self.player.participant.vars['game_num'] = self.player.game_num + self.player.app_sequence_select = self.player.participant.vars['app_sequence_select'] + self.player.app_sequence_select_n = self.player.participant.vars['app_sequence_select_n'] + self.player.questionnaire_before = self.player.participant.vars['questionnaire_before'] + + +class Introduction(Page): + form_model = 'player' + form_fields = ['r1','r2','r3','r4'] + + def is_displayed(self): + return self.round_number == 1 and self.player.participant.vars['app_sequence_select'] % 2 == 0 # 2 ou 4 + + def error_message(self, values): + print('values is', values) + if values['r1'] != 98: + return 'Please correct your answer 1 !!' + elif values['r2'] != 2: + return 'Please correct your answer 2 !!' + elif values['r3'] != 5: + return 'Please correct your answer 3 !!' + elif values['r4'] != 110: + return 'Please correct your answer 4 !!' + + def before_next_page(self): + # selection of random dotation/s/g and round to be paid + self.player.dgm_selected = random.randint(1, 5) + self.player.round_selected = random.randint(1, 8) + self.player.participant.vars['mdgclass_dgm_selected'] = self.player.dgm_selected + self.player.participant.vars['mdgclass_round_selected'] = self.player.round_selected + +class NextPage(Page): + form_model = 'player' + form_fields = [] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] % 2 == 0 # 2 ou 4 + + def vars_for_template(self): + return dict( + round = self.round_number, + ) + + + def before_next_page(self): + self.player.dgm_selected = self.player.participant.vars['mdgclass_dgm_selected'] + self.player.round_selected =self.player.participant.vars['mdgclass_round_selected'] + self.player.dot1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][0] + self.player.s1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][1] + self.player.g1 = getattr(Constants, f'dgm{self.player.dgm_selected}')[self.round_number][2] + +class Send(Page): + """comment + """ + form_model = 'player' + form_fields = ['keep_amount', 'sent_amount'] + + def is_displayed(self): + return self.player.participant.vars['app_sequence_select'] % 2 == 0 # 2 ou 4 + + def error_message(self, values): + print('values is', values) + if values['keep_amount'] < 0: + return 'the amount must be positive ' + elif values['keep_amount'] > self.player.dot1 : + return f'please choose an amount less than {self.player.dot1}' + elif values['sent_amount'] < 0: + return 'the amount must be positive ' + elif values['sent_amount'] + values['keep_amount'] != self.player.dot1: + return f'the sum of the two amounts must be equal to {self.player.dot1}' + + def vars_for_template(self): + return dict( + dot1 = self.player.dot1, + s1 = self.player.s1, + g1 = self.player.g1, + round = self.round_number, + game_num=self.player.participant.vars['game_num'] + + ) + + def before_next_page(self): + self.player.participant.vars[f'dot1_r{self.round_number}'] = self.player.dot1 + self.player.participant.vars[f's1_r{self.round_number}'] = self.player.s1 + self.player.participant.vars[f'g1_r{self.round_number}'] = self.player.g1 + self.player.participant.vars[f'keep_r{self.round_number}'] = self.player.keep_amount + self.player.participant.vars[f'sent_r{self.round_number}'] = self.player.sent_amount + + if self.round_number == 8: + self.player.dot1_select = self.player.participant.vars[f'dot1_r{self.player.round_selected}'] + self.player.s1_select = self.player.participant.vars[f's1_r{self.player.round_selected}'] + self.player.g1_select = self.player.participant.vars[f'g1_r{self.player.round_selected}'] + self.player.keep_select = self.player.participant.vars[f'keep_r{self.player.round_selected}'] + self.player.sent_select = self.player.participant.vars[f'sent_r{self.player.round_selected}'] + self.player.gain_dgm = self.player.keep_select * self.player.s1_select + self.player.gain_joueur2 = self.player.sent_select * self.player.g1_select + self.player.gain_dgm_eur = round(self.player.gain_dgm * Constants.exchangerate,1) + self.player.gain_joueur2_eur = round(self.player.gain_joueur2 * Constants.exchangerate,1) + self.player.payoff = round(self.player.gain_dgm_eur,1) + + # to final page: + self.player.participant.vars['mdgclass_dot1_select'] = self.player.dot1_select + self.player.participant.vars['mdgclass_s1_select'] = self.player.s1_select + self.player.participant.vars['mdgclass_g1_select'] = self.player.g1_select + self.player.participant.vars['mdgclass_keep_select'] = self.player.keep_select + self.player.participant.vars['mdgclass_sent_select'] = self.player.sent_select + self.player.participant.vars['mdgclass_gain_dgm'] = self.player.gain_dgm + self.player.participant.vars['mdgclass_gain_joueur2'] = self.player.gain_joueur2 + self.player.participant.vars['mdgclass_gain_dgm_eur'] = self.player.gain_dgm_eur + self.player.participant.vars['mdgclass_gain_joueur2_eur'] = self.player.gain_joueur2_eur + + +class Questionnaire(Page): + form_model = 'player' + form_fields = ['altruistic_assessment_dcl', 'confidence_level_dcl', 'memory_recall_dcl'] + def is_displayed(self): + return self.round_number == 8 and self.player.participant.vars['app_sequence_select'] % 2 == 0 + + + +class Results(Page): + def is_displayed(self): + return self.round_number == 8 and self.player.participant.vars['app_sequence_select'] % 2 == 0 + + def vars_for_template(self): + #group = player.group + return dict( + dot1_select = self.player.dot1_select, + s1_select = self.player.s1_select, + g1_select = self.player.g1_select, + round = self.round_number, + round_selected = self.player.round_selected, + keep_select = self.player.keep_select, + sent_select = self.player.sent_select, + gain_dgm = self.player.gain_dgm, + gain_joueur2 = self.player.gain_joueur2, + gain_dgm_eur = self.player.gain_dgm_eur, + gain_joueur2_eur = self.player.gain_joueur2_eur, + + ) + + + +page_sequence = [ + Bienvenue, + Introduction, + NextPage, + Send, + Questionnaire, +] + + diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/Bienvenue.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Bienvenue.html new file mode 100644 index 0000000..9505e95 --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Bienvenue.html @@ -0,0 +1,38 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + + + + +
+
+ + +

+

+ Please click on the "Next" button to go to the next step +

+ +

+ + +

{% next_button %}

+ +
+ + + +{% endblock %} + diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/Introduction.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Introduction.html new file mode 100644 index 0000000..ccd6570 --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Introduction.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{player.game_num}} +{% endblock %} + +{% block content %} + + {% include Constants.instructions_template %} + +
+
+ +

+

Comprehension Test

+ +
Q1. If you are named "dictator", your starting amount is 100 POINTs, +S = 1, G = 1, and you choose to give 2 to your partner, what is your payoff? What about your partner's? + +
{% formfield player.r1 label="You gain (POINTs)"%} + {% formfield player.r2 label="Your partner gains (POINTs)"%} +
Q2. If you are not named "dictator", and your partner's starting +amount is 60 POINTs, S = 2, and G = 1. If your partner decides to give you 5, +what is your payoff? What about your partner's? +
{% formfield player.r3 label="You gain (POINTs)"%} + {% formfield player.r4 label="Your partner gains (POINTs)"%} + +
+ {% next_button %} + +{% endblock %} diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/NextPage.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/NextPage.html new file mode 100644 index 0000000..b6ff090 --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/NextPage.html @@ -0,0 +1,22 @@ +{% load otree %} + +{% block title %} + Round: {{round}} +{% endblock %} + +{% block content %} + + {% if round == 1 %} +

Start the first round

+ {% else %} + +

Go to the next round

+ + {% endif %} + + + +

{% next_button %}

+ + +{% endblock %} diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/Questionnaire.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Questionnaire.html new file mode 100644 index 0000000..0742aed --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Questionnaire.html @@ -0,0 +1,73 @@ +{% load otree %} + +{% block title %} + +{% endblock %} + +{% block content %} + + + +


+
+
+ +

Please answer the following questions:

+

+ +1) On a scale from 1 (not at all altruistic) to 7 (very altruistic):
+How altruistic do you consider your own decision (in the task just completed) to be? + + + + + + + + + +
Not at all risky {% formfield player.altruistic_assessment_dcl %} Very risky
+ +

+ + 2) On a scale from 1 (not at all confident) to 7 (very confident):
+How confident do you feel with your decision indicated in the task just completed? + + + + + + + + + +
Not at all confident {% formfield player.confidence_level_dcl %} Very confident
+ +

+ +3) On a scale from 1 (I did not remember it at all) to 7 (I remembered it very well):
+Did you remember the choice you made, in the same task, during the previous experimental session you participated in (March or September 2024)? + + + + + + + + + + +
I did not remember it at all {% formfield player.memory_recall_dcl%} I remembered it very well
+ +
+ + + + {% next_button %} + +{% endblock %} + + + + + diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/Results.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Results.html new file mode 100644 index 0000000..e7d8392 --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Results.html @@ -0,0 +1,30 @@ +{% extends "global/Page.html" %} +{% load otree %} + +{% block title %} + Results +{% endblock %} + +{% block content %} +

+ +

+ The randomly selected round is: {{round_selected}} +
Your endowment during this round was {{dot1_select}} ECUs. +
The value of S was {{s1_select}}. +
The value of G was {{g1_select}}. +

You chose to keep {{keep_select}} POINTs and give {{sent_select}} POINTs to player 2. +

Your gain is {{gain_dgm}} POINTs ({{gain_dgm_eur}} EUROs). +
The player 2 gain is {{gain_joueur2}} POINTs ({{gain_joueur2_eur}} EUROs). +

+

+

+

+

+

+ +

{% next_button %}

+ + {% include Constants.instructions_template %} + +{% endblock %} \ No newline at end of file diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/Send.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Send.html new file mode 100644 index 0000000..0658340 --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/Send.html @@ -0,0 +1,31 @@ +{% load otree %} + +{% block title %} + Game {{game_num}} - Your Choice - (Round: {{round}}) +{% endblock %} + +{% block content %} + + +

+ Your starting amount is {{dot1}} POINTs. +
The value S is {{s1}}: what you keep for yourself will therefore be multiplied by {{s1}}. +
The value G is {{g1}}: what you give to your partner will therefore be multiplied by {{g1}}. + + +

+ +

Out of the {{dot1}} POINTs: + {% formfield group.keep_amount label="You keep:" %} + + {% formfield group.sent_amount label="You give to the other player:" %} +

+ + +

+ {% next_button %} +

+ + {% include Constants.instructions_template %} + +{% endblock %} diff --git a/dictator_modif_class_v2/templates/dictator_modif_class_v2/instructions.html b/dictator_modif_class_v2/templates/dictator_modif_class_v2/instructions.html new file mode 100644 index 0000000..9b791ad --- /dev/null +++ b/dictator_modif_class_v2/templates/dictator_modif_class_v2/instructions.html @@ -0,0 +1,44 @@ +{% load otree %} + +
+
+ +

+ Instructions +

+

+Rules of the game: +
At the end of the game, the POINTs you have earned will be converted into € according to the following rule: + +

The exchange rate for this game is (1 POINT = 0.1 EUROS)

+ + +
This game consists of 8 rounds and is played with one of the other participants in the experiment, +whom we will refer to as "your partner." +Your partner is anonymous and has no means of identifying you either. +
For each of these rounds, you have a starting amount in POINT. +You can decide to give some of it to your partner. +Therefore, you will choose two amounts: one that you give and one that you keep. +The sum of the amounts you give and the amounts you keep must equal the starting amount. +
During each round, a value G ("give") will be assigned to the POINTs you give, +and a value S ("self") will be assigned to the POINTs you keep. +Thus, the amount you keep will be multiplied by S, and the amount you give will be multiplied by G. +
Your partner plays the same game on their side. +
At the end of the game, one round will be randomly chosen to determine your payoff, + and one of the two players will be randomly named the "dictator" (player1). +
-------------------------------------------
+ Case 1: +
If you are the dictator (player 1), your final payoff will be: +
Amount you chose to keep x S
+
And your partner's (player 2) final payoff will be: +
Amount you chose to give x G
 +
-------------------------------------------
+ Case 2: +
If you are not named dictator (player 2), your final payoff will be: +
Amount your partner chose to give you x G
+
And your partner's (player 1) final payoff will be: +
Amount your partner chose to keep x S
 + +

+
+
\ No newline at end of file diff --git a/dictator_modif_class_v2/tests.py b/dictator_modif_class_v2/tests.py new file mode 100644 index 0000000..855acd6 --- /dev/null +++ b/dictator_modif_class_v2/tests.py @@ -0,0 +1,42 @@ +from otree.api import Currency as c, currency_range +from . import pages +from ._builtin import Bot +from .models import Constants +import random + +class PlayerBot(Bot): + def play_round(self): + + if self.participant.vars['app_sequence_select'] % 2 == 0: + + if self.round_number == 1: + yield (pages.Bienvenue) + yield (pages.Introduction, { + "r1": 98, + "r2":2, + "r3":5, + "r4":110, + }) + + yield (pages.NextPage, {}) + + keep_amount = random.randint(0, self.player.dot1) + sent_amount = self.player.dot1 - keep_amount + yield (pages.Send, { + "keep_amount": keep_amount, + "sent_amount": sent_amount, + }) + + if self.round_number == 8: + yield (pages.Questionnaire, { + 'altruistic_assessment_dcl': 1, + 'confidence_level_dcl': 1, + 'memory_recall_dcl': 1, + }) + # if self.round_number == 8: + # yield (pages.Results, { + # }) + + + #if self.player.id_in_group == 1: + diff --git a/manage.py b/manage.py new file mode 100644 index 0000000..d6f4caa --- /dev/null +++ b/manage.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python +import os +import sys + + +if __name__ == "__main__": + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "settings") + + from otree.management.cli import execute_from_command_line + + execute_from_command_line(sys.argv, script_file=__file__) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..bc0b61d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,17 @@ +# oTree-may-not-overwrite-this-file +aiofiles==0.6.0 +click==7.1.2 +h11==0.14.0 +itsdangerous==1.1.0 +MarkupSafe==1.1.1 +otree==5.11.1 +python-multipart==0.0.5 +six==1.17.0 +SQLAlchemy==1.3.22 +starlette==0.14.1 +uvicorn==0.13.4 +websockets==10.1 +WTForms==2.3.3 +WTForms-SQLAlchemy==0.2 +psycopg2 == 2.9.10 +sentry-sdk==1.39.1 diff --git a/requirements_base.txt b/requirements_base.txt new file mode 100644 index 0000000..ac998e5 --- /dev/null +++ b/requirements_base.txt @@ -0,0 +1,2 @@ +# You should put your requirements in requirements.txt instead. +# You can delete this file. diff --git a/runtime.txt b/runtime.txt new file mode 100644 index 0000000..14a2d25 --- /dev/null +++ b/runtime.txt @@ -0,0 +1 @@ +python-3.12.5 \ No newline at end of file diff --git a/settings.py b/settings.py new file mode 100644 index 0000000..9fcadb6 --- /dev/null +++ b/settings.py @@ -0,0 +1,197 @@ +from os import environ + + +# if you set a property in SESSION_CONFIG_DEFAULTS, it will be inherited by all configs +# in SESSION_CONFIGS, except those that explicitly override it. +# the session config can be accessed from methods in your apps as self.session.config, +# e.g. self.session.config['participation_fee'] + +#### WB MTURK / session config MTurk ######################### + +mturk_hit_settings = { + 'keywords': ['Survey', 'Game', 'Demographics'], # Questionnaire démographique #'Questionnaire', 'Français' + 'title': 'Survey (gain $3.0, duration 7 min)',# Questionnaire démographique + 'description': 'You can earn up to $3.0 in approximately 7 minutes.', # Questionnaire démographique: Gain de $0.60 pour une durée de 1 minute. + 'frame_height': 500, + 'template': 'global/mturk_template.html', + 'minutes_allotted_per_assignment': 60, + 'expiration_hours': 7*24, # 7 days +# 'grant_qualification_id': '3O3C52ZQEUFOKIZH0662Y397HAFSHV',# Mturk reel Exp G-J ###to prevent retakes +# 'grant_qualification_id': '3ZSPHI510NGVHARBJTEPE3ECGE6IJ2', # Sandbox Exp G-J + 'grant_qualification_id': '3IMKSS0I9C91UAW9OSPZY1N2EAQW6A' , # Mturk exp Covid +# 'grant_qualification_id': '3LKGAM13UYJQHAONM1Z1QYBBX0W7ES', # Sandbox exp Covid + +# 'grant_qualification_id': '3C2W12C2LIJHOP5OLGDWR8T1PGZURB',# Mturk reel Exp Ines +# 'grant_qualification_id': '3MTUBPNNZX0I8DMRLCRIX5T88L6XMM', # Sandbox Exp Ines + + 'qualification_requirements': [ + { + 'QualificationTypeId': "00000000000000000071", # que pour expé Ines ##### workers from the US, 00000000000000000071 is the code for a location-based qualification + 'Comparator': "EqualTo", +# 'LocaleValues': [{'Country': "FR"}] + 'LocaleValues': [{'Country': "US"}] + }, + + { +# 'QualificationTypeId': "3O3C52ZQEUFOKIZH0662Y397HAFSHV", # Mturk reel Exp G-J ###To prevent a worker from participating in your study twice +# 'QualificationTypeId': "3ZSPHI510NGVHARBJTEPE3ECGE6IJ2", # Sandbox Exp G-J +# 'QualificationTypeId': '3C2W12C2LIJHOP5OLGDWR8T1PGZURB',# Mturk reel Exp Ines +# 'QualificationTypeId': '3MTUBPNNZX0I8DMRLCRIX5T88L6XMM', # Sandbox Exp Ines + 'QualificationTypeId': '3IMKSS0I9C91UAW9OSPZY1N2EAQW6A', # Mturk exp Covid +# 'QualificationTypeId':'3LKGAM13UYJQHAONM1Z1QYBBX0W7ES', # Sandbox exp Covid + + 'Comparator': "DoesNotExist", + } + +] + } +############################################################################# +SESSION_CONFIG_DEFAULTS = { + 'real_world_currency_per_point': 0.001,# 0.001 pour exp realeffort et exp covid sinon = 1 + 'participation_fee': 0,# 0.5 # 0 Expé police + 'doc': "", + 'mturk_hit_settings': mturk_hit_settings, +} +############################################################################# + + + + +# # Liste des jeux +# games = [ +# 'a3_gp', +# 'b3_bw', +# 'dictator_modif_asso', +# 'dictator_modif_class', +# ] +# +# # Mélanger l'ordre des jeux +# random.shuffle(games) + + +def custom_app_sequence(num_participants): + # Define the tasks + tasks = ['A', 'B', 'C', 'D'] + + # Create a list to store randomized sequences for each participant + participant_sequences = [] + + # Generate a random sequence for each participant + for _ in range(num_participants): + participant_sequence = tasks[:] # Copy the tasks list + shuffle(participant_sequence) # Shuffle the sequence for each participant + participant_sequences.append(participant_sequence) + + return participant_sequences + + + +SESSION_CONFIGS = [ + + dict( + name='exp2_e4c_random', + display_name='exp2_e4c_random', + num_demo_participants=1, + app_sequence=['a3_welcome0', + 'a3_welcome', + 'a4_questappli', + 'a1_en_p1_quest', + 'b3_bw_v2', # game 2 (v2) + 'a3_gp', # game 1 + 'b3_bw', # game 2 + 'dictator_modif_class_v2', # game 4 (v2) + 'dictator_modif_asso', # game 3 + 'dictator_modif_class', # game 4 + 'a5_survey', + 'a1_en_p1_quest_v2', # questionnaire (after) + 'a5_questfin', + ], + # completionlink= + # 'https://app.prolific.co/submissions/complete?cc=11111111', # prolific + ), # + + + dict( + name='exp2_e4c_random_BOT', + display_name='exp2_e4c_random_BOT', + num_demo_participants=1, + app_sequence=['a3_welcome', + 'a1_en_p1_quest', + 'b3_bw_v2', # game 2 (v2) + 'a3_gp', # game 1 + 'b3_bw', # game 2 + 'dictator_modif_class_v2', # game 4 (v2) + 'dictator_modif_asso', # game 3 + 'dictator_modif_class', # game 4 + 'a5_survey', + 'a1_en_p1_quest_v2', # questionnaire (after) + 'a5_questfin', + ], + # completionlink= + # 'https://app.prolific.co/submissions/complete?cc=11111111', # prolific + use_browser_bots=True, + + + ), # + + + + + +] + + + +# ISO-639 code +# for example: de, fr, ja, ko, zh-hans +LANGUAGE_CODE = 'en' #en # 'fr' pour Exp Mindfullness et exp police + +# e.g. EUR, GBP, CNY, JPY +REAL_WORLD_CURRENCY_CODE = 'USD' # EUR pour Exp Mindfullness +USE_POINTS = True # False + +ROOMS = [ + dict( + name="labeds", + display_name="labeds", + participant_label_file="_rooms/labeds.txt" + ), + +# prolific + dict( + name='exp2_e4c_random', + display_name='exp2_e4c_random', + + ), +] + +#ADMIN_USERNAME = 'admin' +ADMIN_USERNAME = 'wael bousselmi' +# for security, best to set admin password in an environment variable +ADMIN_PASSWORD = environ.get('OTREE_ADMIN_PASSWORD') + + +DEMO_PAGE_INTRO_HTML = """ +Here are some oTree games. +""" + +# don't share this with anybody. +SECRET_KEY = 'nm5eaornndp(_0bt_64k+03(-f-n%4bir_+==$8i7&-o$th2@)' + +# List of additional apps besides otree default apps +INSTALLED_APPS = ['otree', 'a3_welcome', 'a3_gp', 'b3_bw', 'dictator_modif_asso', 'dictator_modif_class'] + + + +AWS_ACCESS_KEY_ID = environ.get('AWS_ACCESS_KEY_ID') +AWS_SECRET_ACCESS_KEY = environ.get('AWS_SECRET_ACCESS_KEY') + + +DEBUG = False + + + + + + +