{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# # only need to import the function you want to use\n", "# # secondary functions, that are called within the imported one, don't need to be importex explicitly\n", "# from volume_change import V_von_h\n", "\n", "# V_von_h(10)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# create in and outflux vectors\n", "import pandas as pd\n", "import numpy as np\n", "from numpy import cos,sin\n", "from volume_change import V_h_test_1,h_V_test_1,V_h_test_2,h_V_test_2\n", "from flow_patterns import return_flux_profiles,make_flux_df\n", "\n", "\n", "t_max = 100\n", "timestep = 1\n", "time = np.arange(0,t_max,timestep)\n", "#input identifiers\n", "i_i = 'st_0010_0010'\n", "#output identifiers\n", "o_i = 'st_0010_0010'\n", "# influx and outflux offset\n", "i_o = 7.5\n", "o_o = 8.\n", "#outflux delay\n", "o_d = 5\n", "\n", "influx, outflux = return_flux_profiles(len(time),i_i,o_i,i_o,o_o,o_d)\n", "\n", "\n", "h_0 = 0.\n", "\n", "V_t = np.empty_like(time,dtype=float)\n", "h_t = np.empty_like(time,dtype=float)\n", "delta_Q = np.empty_like(time,dtype=float)\n", "delta_V = np.empty_like(time,dtype=float)\n", "\n", "for i in range(len(time)):\n", " delta_Q[i] = influx[i]-outflux[i]\n", " delta_V[i] = delta_Q[i]*timestep\n", " if i == 0:\n", " V_t[0] = V_h_test_2(h_0)\n", " else:\n", " V_t[i] = V_t[i-1]+delta_V[i]\n", " \n", " h_t[i] = h_V_test_2(V_t[i])\n", "\n", "df = pd.DataFrame(np.transpose([time,influx,outflux,h_t,V_t]),columns=['time','influx','outflux','h_t','V_t'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5101051e4acd4cfeace5f79c193a6a04", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# try to draw h_t in an animated way\n", "%matplotlib widget\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "\n", "fig, ax = plt.subplots()\n", "xdata, ydata = [], []\n", "ln, = plt.plot([], [], 'ro')\n", "\n", "def init():\n", " ax.set_xlim(0, time[-1])\n", " ax.set_ylim(np.min(h_t)-1,np.max(h_t)+1)\n", " return ln,\n", "\n", "def update(frame):\n", " xdata.append(time[frame])\n", " ydata.append(h_t[frame])\n", " ln.set_data(xdata, ydata)\n", " return ln,\n", "\n", "ani = FuncAnimation(fig, update, frames=np.arange(np.size(time)),\n", " init_func=init, blit=True,repeat=False)\n", "plt.show()\n", "\n", "frames=np.arange(np.size(time))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# # plot np.arrays with matplotlib\n", "# %matplotlib widget\n", "# from matplotlib import pyplot as plt\n", "\n", "# #define color for plot\n", "# iter_colors = iter(['blue','green','red','yellow'])\n", "\n", "# #create figure with 3 stacked subplots\n", "# fig_data,axs_data = plt.subplots(3,1,figsize=(7,10))\n", "\n", "# #preparation for figure legend\n", "\n", "\n", "# #fill subplots with data and assign handles to the line_objects (, is necessary, because )\n", "# handle0, = axs_data[0].plot(time,h_t,marker='.',color=next(iter_colors))\n", "# handle1, = axs_data[1].plot(time,V_t,marker='.',color=next(iter_colors))\n", "# handle2, = axs_data[2].plot(time,influx,marker='.',color=next(iter_colors))\n", "# handle3, = axs_data[2].plot(time,outflux,marker='.',color=next(iter_colors))\n", "\n", "\n", "# #set subplot axis labels\n", "# axs_data[0].set_ylabel(r'$h(t) \\, / \\, \\mathrm{m}$')\n", "# axs_data[0].set_xlabel(r'$t \\, / \\, \\mathrm{s}$')\n", "# axs_data[1].set_ylabel(r'$V(t) \\, / \\, \\mathrm{m}$')\n", "# axs_data[1].set_xlabel(r'$t \\, / \\, \\mathrm{s}$')\n", "# axs_data[2].set_ylabel(r'$Q(t)\\, / \\, \\mathrm{m^3s^{-1}}$')\n", "# axs_data[2].set_xlabel(r'$t \\, / \\, \\mathrm{s}$')\n", "\n", "# #give the axis labels enough space to be shown\n", "# plt.tight_layout()\n", "\n", "# # resize subplot widths, so that the legend can be shown besides them\n", "# plt.subplots_adjust(right=0.8)\n", "\n", "# # add legend\n", "# handles = [handle0,handle1,handle2,handle3]\n", "# legend_names = [r'$h(t)$',r'$V(t)$',r'$Q_{in}(t)$',r'$Q_{out}(t)$']\n", "# fig_data.legend(handles,legend_names,loc = 'upper right')\n", "\n", "# plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# #plot with plolty\n", "# import plotly.express as px\n", "# from plotly.subplots import make_subplots\n", "# import plotly.graph_objects as go\n", "# import plotly.io as pio\n", "# pio.renderers.default = \"vscode\"\n", "\n", "\n", "# fig1 = make_subplots(3,1,subplot_titles=('Height','Volume','Fluxes'))\n", "\n", "# fig1.add_trace(go.Scatter(x=df['time'],y=df['h_t'],name='height',mode='lines+markers',marker=dict(size=5)),row=1,col=1)\n", "# fig1.add_trace(go.Scatter(x=df['time'],y=df['V_t'],name='volume',mode='lines+markers',marker=dict(size=5)),row=2,col=1)\n", "# fig1.add_trace(go.Scatter(x=df['time'],y=df['influx'],name='influx',mode='lines+markers',marker=dict(size=5)),row=3,col=1)\n", "# fig1.add_trace(go.Scatter(x=df['time'],y=df['outflux'],name='outlfux',mode='lines+markers',marker=dict(size=5)),row=3,col=1)\n", "\n", "# fig1.update_xaxes(title_text = 'time',row=1,col=1)\n", "# fig1.update_xaxes(title_text = 'time',row=2,col=1)\n", "# fig1.update_xaxes(title_text = 'time',row=3,col=1)\n", "# fig1.update_yaxes(title_text = 'h(t)',row=1,col=1)\n", "# fig1.update_yaxes(title_text = 'V(t)',row=2,col=1)\n", "# fig1.update_yaxes(title_text = 'Q(t)',row=3,col=1)\n", "\n", "# fig1.update_layout(height=700)\n", "\n", "# fig2 = px.scatter(df,x='time',y='h_t',animation_frame='time')\n", "# fig2.update_xaxes(range=[0,100])\n", "# fig2.update_yaxes(range=[0,5])\n", "# fig1.show()\n", "# fig2.show('notebook')" ] } ], "metadata": { "interpreter": { "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" }, "kernelspec": { "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "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": "3.8.13" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }