Georg_Brantegger/Python-DT_Slot_3
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validation and KW Hammer Lastfälle

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Georg ´Brantegger
2022-09-27 15:31:36 +02:00
parent e1c7bc9d07
commit a951ea9b64
45 changed files with 1378443 additions and 1273749 deletions
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2
.gitignore vendored
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@@ -5,3 +5,5 @@
Messing Around/ Messing Around/
Messing Around/messy_nb.ipynb Messing Around/messy_nb.ipynb
Validation Data/raw data Tieferbach/*.txt Validation Data/raw data Tieferbach/*.txt
Druckrohrleitung/Gif Plots

36
Druckrohrleitung/Druckstoß Visualisierung.ipynb
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@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -70,14 +70,14 @@
" # for general simulation\n", " # for general simulation\n",
"flux_init = Tur_Q_nenn/1.1 # [m³/s] initial flux through whole system for steady state initialization \n", "flux_init = Tur_Q_nenn/1.1 # [m³/s] initial flux through whole system for steady state initialization \n",
"level_init = Con_targetLevel # [m] initial water level in upstream reservoir for steady state initialization\n", "level_init = Con_targetLevel # [m] initial water level in upstream reservoir for steady state initialization\n",
"simTime_target = 10. # [s] target for total simulation time (will vary slightly to fit with Pip_dt)\n", "simTime_target = 3. # [s] target for total simulation time (will vary slightly to fit with Pip_dt)\n",
"nt = int(simTime_target//Pip_dt) # [1] Number of timesteps of the whole system\n", "nt = int(simTime_target//Pip_dt) # [1] Number of timesteps of the whole system\n",
"t_vec = np.arange(0,nt+1,1)*Pip_dt # [s] time vector. At each step of t_vec the system parameters are stored\n" "t_vec = np.arange(0,nt+1,1)*Pip_dt # [s] time vector. At each step of t_vec the system parameters are stored\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@@ -94,7 +94,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -111,7 +111,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -156,7 +156,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -164,7 +164,7 @@
"# Time loop\n", "# Time loop\n",
"\n", "\n",
"# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n", "# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n",
"fig1,axs1 = plt.subplots(3,1)\n", "fig1,axs1 = plt.subplots(3,1, figsize=(16,9))\n",
"fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n", "fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n",
"axs1[0].set_title('Pressure distribution in pipeline')\n", "axs1[0].set_title('Pressure distribution in pipeline')\n",
"axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", "axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
@@ -195,7 +195,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -247,16 +247,24 @@
" lo_2, = axs1[2].plot(Pip_x_vec,pipe.get_current_flux_distribution(),marker='.',c='blue')\n", " lo_2, = axs1[2].plot(Pip_x_vec,pipe.get_current_flux_distribution(),marker='.',c='blue')\n",
" lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n", " lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n",
" lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n", " lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n",
" fig1.suptitle(str(round(t_vec[it_pipe],2))+ ' s / '+str(round(t_vec[-1],2)) + ' s' )\n", " fig1.suptitle(str(round(t_vec[it_pipe]-1,2))+ ' s / '+str(round(t_vec[-1]-1,2)) + ' s' )\n",
" fig1.canvas.draw()\n", " fig1.canvas.draw()\n",
" fig1.tight_layout()\n", " fig1.tight_layout()\n",
" fig1.show()\n", " fig1.show()\n",
" plt.pause(0.1) " " # if int(it_pipe/100) < 10:\n",
" # figname = 'GIF Plots\\ GIF00'+str(int(it_pipe/100))+'.png'\n",
" # elif int(it_pipe/100) < 100:\n",
" # figname = 'GIF Plots\\ GIF0'+str(int(it_pipe/100))+'.png'\n",
" # else:\n",
" # figname = 'GIF Plots\\ GIF'+str(int(it_pipe/100))+'.png'\n",
" # print(figname)\n",
" # fig1.savefig(fname=figname)\n",
" plt.pause(0.000001) "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -292,7 +300,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "display_name": "Python 3.8.13 ('DT_Slot_3')",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@@ -311,7 +319,7 @@
"orig_nbformat": 4, "orig_nbformat": 4,
"vscode": { "vscode": {
"interpreter": { "interpreter": {
"hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
} }
} }
}, },

330
KW Hammer.ipynb
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@@ -2,7 +2,28 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.4648308563541388\n",
"1.9761415556839106\n",
"0.4819009688403728\n"
]
}
],
"source": [
"print(level_vec[0]-np.min(level_vec))\n",
"print(level_vec[np.argmin(np.abs(t_vec-600))])\n",
"print(np.max(level_vec)-level_vec[0])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -24,7 +45,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"i = 7\n",
"j = 2\n",
"\n",
"Kp_list = np.arange(0.1,2.1,0.1)\n",
"Area_list = np.arange(20.,140.,20.)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -48,14 +82,17 @@
" # for KW UL\n", " # for KW UL\n",
"UL_T1_Q_nenn = 3.75 # [m³/s] nominal flux of turbine \n", "UL_T1_Q_nenn = 3.75 # [m³/s] nominal flux of turbine \n",
"UL_T1_p_nenn = pressure_conversion(2.711,'bar',pUnit_calc) # [Pa] nominal pressure of turbine \n", "UL_T1_p_nenn = pressure_conversion(2.711,'bar',pUnit_calc) # [Pa] nominal pressure of turbine \n",
"UL_T1_closingTime = 80. # [s] closing time of turbine\n", "UL_T1_closingTime = 160. # [s] closing time of turbine\n",
"\n", "\n",
"UL_T2_Q_nenn = 3.75 # [m³/s] nominal flux of turbine \n", "UL_T2_Q_nenn = 3.75 # [m³/s] nominal flux of turbine \n",
"UL_T2_p_nenn = pressure_conversion(2.711,'bar',pUnit_calc) # [Pa] nominal pressure of turbine \n", "UL_T2_p_nenn = pressure_conversion(2.711,'bar',pUnit_calc) # [Pa] nominal pressure of turbine \n",
"UL_T2_closingTime = 80. # [s] closing time of turbine\n", "UL_T2_closingTime = 160. # [s] closing time of turbine\n",
"\n", "\n",
" # for PI controller\n", " # for PI controller\n",
"Con_targetLevel = 2. # [m]\n", "Con_targetLevel = 2. # [m]\n",
"Con_K_p = Kp_list[i] # [-] proportional constant of PI controller\n",
"Con_T_i = 200. # [s] timespan in which a steady state error is corrected by the intergal term\n",
"Con_deadbandRange = 0.00 # [m] Deadband range around targetLevel for which the controller does NOT intervene\n",
"\n", "\n",
" # for pipeline\n", " # for pipeline\n",
"Pip_length = 2300. # [m] length of pipeline\n", "Pip_length = 2300. # [m] length of pipeline\n",
@@ -74,7 +111,7 @@
"Pip_h_vec = np.arange(0,Pip_nn,1)*Pip_head/Pip_n_seg # [m] vector holding the vertival distance of each node from the upstream reservoir\n", "Pip_h_vec = np.arange(0,Pip_nn,1)*Pip_head/Pip_n_seg # [m] vector holding the vertival distance of each node from the upstream reservoir\n",
"\n", "\n",
" # for reservoir\n", " # for reservoir\n",
"Res_area_base = 100. # [m²] total base are of the cuboid reservoir \n", "Res_area_base = Area_list[j] # [m²] total base are of the cuboid reservoir \n",
"Res_area_out = Pip_area # [m²] outflux area of the reservoir, given by pipeline area\n", "Res_area_out = Pip_area # [m²] outflux area of the reservoir, given by pipeline area\n",
"Res_level_crit_lo = 0. # [m] for yet-to-be-implemented warnings\n", "Res_level_crit_lo = 0. # [m] for yet-to-be-implemented warnings\n",
"Res_level_crit_hi = np.inf # [m] for yet-to-be-implemented warnings\n", "Res_level_crit_hi = np.inf # [m] for yet-to-be-implemented warnings\n",
@@ -86,14 +123,14 @@
"# flux_init = (OL_T1_Q_nenn+OL_T2_Q_nenn) # [m³/s] initial flux through whole system for steady state initialization \n", "# flux_init = (OL_T1_Q_nenn+OL_T2_Q_nenn) # [m³/s] initial flux through whole system for steady state initialization \n",
"OL_LAs_init = [1.,0.3] # [vec] initial guide vane openings of OL-KW\n", "OL_LAs_init = [1.,0.3] # [vec] initial guide vane openings of OL-KW\n",
"level_init = Con_targetLevel # [m] initial water level in upstream reservoir for steady state initialization\n", "level_init = Con_targetLevel # [m] initial water level in upstream reservoir for steady state initialization\n",
"simTime_target = 600. # [s] target for total simulation time (will vary slightly to fit with Pip_dt)\n", "simTime_target = 1200. # [s] target for total simulation time (will vary slightly to fit with Pip_dt)\n",
"nt = int(simTime_target//Pip_dt) # [1] Number of timesteps of the whole system\n", "nt = int(simTime_target//Pip_dt) # [1] Number of timesteps of the whole system\n",
"t_vec = np.arange(0,nt+1,1)*Pip_dt # [s] time vector. At each step of t_vec the system parameters are stored\n" "t_vec = np.arange(0,nt+1,1)*Pip_dt # [s] time vector. At each step of t_vec the system parameters are stored\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -127,12 +164,16 @@
"KW_UL.add_turbine(UL_T1)\n", "KW_UL.add_turbine(UL_T1)\n",
"KW_UL.add_turbine(UL_T2)\n", "KW_UL.add_turbine(UL_T2)\n",
"\n", "\n",
"KW_UL.set_steady_state_by_flux(flux_init,pipe.get_current_pressure_distribution()[-1])\n" "KW_UL.set_steady_state_by_flux(flux_init,pipe.get_current_pressure_distribution()[-1])\n",
"\n",
"# level controller\n",
"level_control = PI_controller_class(Con_targetLevel,Con_deadbandRange,Con_K_p,Con_T_i,Pip_dt)\n",
"level_control.set_control_variable(UL_T1.get_current_LA(),display_warning=False)\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -177,10 +218,12 @@
" # manual input to modulate influx\n", " # manual input to modulate influx\n",
"OL_T1_LA_soll_vec = np.full_like(t_vec,OL_T1.get_current_LA()) # storing the target value for the guide van opening\n", "OL_T1_LA_soll_vec = np.full_like(t_vec,OL_T1.get_current_LA()) # storing the target value for the guide van opening\n",
"OL_T1_LA_soll_vec[np.argmin(np.abs(t_vec-100)):] = 0.\n", "OL_T1_LA_soll_vec[np.argmin(np.abs(t_vec-100)):] = 0.\n",
"OL_T1_LA_soll_vec[np.argmin(np.abs(t_vec-600)):] = 1.\n",
"\n", "\n",
"\n", "\n",
"OL_T2_LA_soll_vec = np.full_like(t_vec,OL_T2.get_current_LA()) # storing the target value for the guide van opening\n", "OL_T2_LA_soll_vec = np.full_like(t_vec,OL_T2.get_current_LA()) # storing the target value for the guide van opening\n",
"\n", "\n",
"\n",
"OL_T1_LA_ist_vec = np.zeros_like(t_vec) # storing the actual value of the guide vane opening\n", "OL_T1_LA_ist_vec = np.zeros_like(t_vec) # storing the actual value of the guide vane opening\n",
"OL_T1_LA_ist_vec[0] = OL_T1.get_current_LA() # storing the initial value of the guide vane opening\n", "OL_T1_LA_ist_vec[0] = OL_T1.get_current_LA() # storing the initial value of the guide vane opening\n",
"\n", "\n",
@@ -188,11 +231,11 @@
"OL_T2_LA_ist_vec[0] = OL_T2.get_current_LA() # storing the initial value of the guide vane opening\n", "OL_T2_LA_ist_vec[0] = OL_T2.get_current_LA() # storing the initial value of the guide vane opening\n",
"\n", "\n",
"# UL KW\n", "# UL KW\n",
"UL_T1_LA_soll_vec = np.full_like(t_vec,UL_T1.get_current_LA()) # storing the target value of the guide vane opening\n", "UL_T1_LA_soll_vec = np.zeros_like(t_vec) # storing the target value of the guide vane opening\n",
"UL_T1_LA_soll_vec[np.argmin(np.abs(t_vec-105)):] -= 0.25\n", "UL_T1_LA_soll_vec[0] = UL_T1.get_current_LA()\n",
"\n", "\n",
"UL_T2_LA_soll_vec = np.full_like(t_vec,UL_T2.get_current_LA()) # storing the target value of the guide vane opening\n", "UL_T2_LA_soll_vec = np.zeros_like(t_vec) # storing the target value of the guide vane opening\n",
"UL_T2_LA_soll_vec[np.argmin(np.abs(t_vec-105)):] = 0.\n", "UL_T2_LA_soll_vec[0] = UL_T2.get_current_LA()\n",
"\n", "\n",
"UL_T1_LA_ist_vec = np.zeros_like(t_vec) # storing the actual value of the guide vane opening\n", "UL_T1_LA_ist_vec = np.zeros_like(t_vec) # storing the actual value of the guide vane opening\n",
"UL_T1_LA_ist_vec[0] = UL_T1.get_current_LA() # storing the initial value of the guide vane opening\n", "UL_T1_LA_ist_vec[0] = UL_T1.get_current_LA() # storing the initial value of the guide vane opening\n",
@@ -203,7 +246,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -225,49 +268,49 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib qt5\n", "%matplotlib qt5\n",
"# Time loop\n", "# Time loop\n",
"\n", "\n",
"# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n", "# # create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n",
"fig1,axs1 = plt.subplots(3,1)\n", "# fig1,axs1 = plt.subplots(3,1)\n",
"fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n", "# fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n",
"axs1[0].set_title('Pressure distribution in pipeline')\n", "# axs1[0].set_title('Pressure distribution in pipeline')\n",
"axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", "# axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"axs1[0].set_ylabel(r'$p$ ['+pUnit_conv+']')\n", "# axs1[0].set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"axs1[0].set_ylim([-2,50])\n", "# axs1[0].set_ylim([-2,50])\n",
"axs1[1].set_title('Pressure distribution in pipeline \\n Difference to t=0')\n", "# axs1[1].set_title('Pressure distribution in pipeline \\n Difference to t=0')\n",
"axs1[1].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", "# axs1[1].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"axs1[1].set_ylabel(r'$p$ ['+pUnit_conv+']')\n", "# axs1[1].set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"axs1[1].set_ylim([-2,20])\n", "# axs1[1].set_ylim([-2,20])\n",
"axs1[2].set_title('Flux distribution in pipeline')\n", "# axs1[2].set_title('Flux distribution in pipeline')\n",
"axs1[2].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", "# axs1[2].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"axs1[2].set_ylabel(r'$Q$ [$\\mathrm{m}^3 / \\mathrm{s}$]')\n", "# axs1[2].set_ylabel(r'$Q$ [$\\mathrm{m}^3 / \\mathrm{s}$]')\n",
"axs1[2].set_ylim([-1,10])\n", "# axs1[2].set_ylim([-1,10])\n",
"lo_0, = axs1[0].plot(Pip_x_vec,pressure_conversion(p_old,pUnit_calc, pUnit_conv),marker='.')\n", "# lo_0, = axs1[0].plot(Pip_x_vec,pressure_conversion(p_old,pUnit_calc, pUnit_conv),marker='.')\n",
"lo_0min, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n", "# lo_0min, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n",
"lo_0max, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n", "# lo_0max, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n",
"lo_1, = axs1[1].plot(Pip_x_vec,pressure_conversion(p_old-p_0,pUnit_calc, pUnit_conv),marker='.')\n", "# lo_1, = axs1[1].plot(Pip_x_vec,pressure_conversion(p_old-p_0,pUnit_calc, pUnit_conv),marker='.')\n",
"lo_1min, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n", "# lo_1min, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n",
"lo_1max, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n", "# lo_1max, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n",
"lo_2, = axs1[1].plot(Pip_x_vec,Q_old,marker='.')\n", "# lo_2, = axs1[1].plot(Pip_x_vec,Q_old,marker='.')\n",
"lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n", "# lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n",
"lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n", "# lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n",
"\n", "\n",
"# axs1[0].autoscale()\n", "# # axs1[0].autoscale()\n",
"# axs1[1].autoscale()\n", "# # axs1[1].autoscale()\n",
"\n", "\n",
"fig1.tight_layout()\n", "# fig1.tight_layout()\n",
"fig1.show()\n", "# fig1.show()\n",
"plt.pause(1)\n" "# plt.pause(1)\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -290,7 +333,11 @@
" for it_res in range(Res_nt):\n", " for it_res in range(Res_nt):\n",
" reservoir.timestep_reservoir_evolution() \n", " reservoir.timestep_reservoir_evolution() \n",
" level_vec[it_pipe] = reservoir.get_current_level() \n", " level_vec[it_pipe] = reservoir.get_current_level() \n",
" volume_vec[it_pipe] = reservoir.get_current_volume() \n", " volume_vec[it_pipe] = reservoir.get_current_volume() \n",
"\n",
" level_control.update_control_variable(level_vec[it_pipe])\n",
" UL_T1_LA_soll_vec[it_pipe] = level_control.get_current_control_variable() \n",
" UL_T2_LA_soll_vec[it_pipe] = level_control.get_current_control_variable() \n",
" \n", " \n",
" # change the guide vane opening based on the target value and closing time limitation\n", " # change the guide vane opening based on the target value and closing time limitation\n",
" KW_UL.update_LAs([UL_T1_LA_soll_vec[it_pipe],UL_T2_LA_soll_vec[it_pipe]])\n", " KW_UL.update_LAs([UL_T1_LA_soll_vec[it_pipe],UL_T2_LA_soll_vec[it_pipe]])\n",
@@ -322,115 +369,119 @@
" v_old = pipe.get_current_velocity_distribution()\n", " v_old = pipe.get_current_velocity_distribution()\n",
" Q_old = pipe.get_current_flux_distribution()\n", " Q_old = pipe.get_current_flux_distribution()\n",
"\n", "\n",
" # plot some stuff\n", " # # plot some stuff\n",
" # remove line-objects to autoscale axes (there is definetly a better way, but this works ¯\\_(ツ)_/¯ )\n", " # # remove line-objects to autoscale axes (there is definetly a better way, but this works ¯\\_(ツ)_/¯ )\n",
" if it_pipe%50 == 0:\n", " # if it_pipe%50 == 0:\n",
" lo_0.remove()\n", " # lo_0.remove()\n",
" lo_0min.remove()\n", " # lo_0min.remove()\n",
" lo_0max.remove()\n", " # lo_0max.remove()\n",
" lo_1.remove()\n", " # lo_1.remove()\n",
" lo_1min.remove()\n", " # lo_1min.remove()\n",
" lo_1max.remove()\n", " # lo_1max.remove()\n",
" lo_2.remove()\n", " # lo_2.remove()\n",
" lo_2min.remove()\n", " # lo_2min.remove()\n",
" lo_2max.remove()\n", " # lo_2max.remove()\n",
" # plot new pressure and velocity distribution in the pipeline\n", " # # plot new pressure and velocity distribution in the pipeline\n",
" lo_0, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_current_pressure_distribution(),pUnit_calc,pUnit_conv),marker='.',c='blue')\n", " # lo_0, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_current_pressure_distribution(),pUnit_calc,pUnit_conv),marker='.',c='blue')\n",
" lo_0min, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n", " # lo_0min, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red')\n",
" lo_0max, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red') \n", " # lo_0max, = axs1[0].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node(),pUnit_calc,pUnit_conv),c='red') \n",
" lo_1, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_current_pressure_distribution()-p_0,pUnit_calc,pUnit_conv),marker='.',c='blue')\n", " # lo_1, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_current_pressure_distribution()-p_0,pUnit_calc,pUnit_conv),marker='.',c='blue')\n",
" lo_1min, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n", " # lo_1min, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_lowest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n",
" lo_1max, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n", " # lo_1max, = axs1[1].plot(Pip_x_vec,pressure_conversion(pipe.get_highest_pressure_per_node()-p_0,pUnit_calc,pUnit_conv),c='red')\n",
" lo_2, = axs1[2].plot(Pip_x_vec,pipe.get_current_flux_distribution(),marker='.',c='blue')\n", " # lo_2, = axs1[2].plot(Pip_x_vec,pipe.get_current_flux_distribution(),marker='.',c='blue')\n",
" lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n", " # lo_2min, = axs1[2].plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n",
" lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n", " # lo_2max, = axs1[2].plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n",
" fig1.suptitle(str(round(t_vec[it_pipe],2))+ ' s / '+str(round(t_vec[-1],2)) + ' s' )\n", " # fig1.suptitle(str(round(t_vec[it_pipe],2))+ ' s / '+str(round(t_vec[-1],2)) + ' s' )\n",
" fig1.canvas.draw()\n", " # fig1.canvas.draw()\n",
" fig1.tight_layout()\n", " # fig1.tight_layout()\n",
" fig1.show()\n", " # fig1.show()\n",
" plt.pause(0.1) " " # plt.pause(0.1) "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"fig2,axs2 = plt.subplots(1,1)\n", "# fig2,axs2 = plt.subplots(1,1)\n",
"axs2.set_title('Level and Volume reservoir')\n", "# axs2.set_title('Level and Volume reservoir')\n",
"axs2.plot(t_vec,level_vec,label='level')\n", "# axs2.plot(t_vec,level_vec,label='level')\n",
"axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", "# axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"axs2.set_ylabel(r'$h$ [m]')\n", "# axs2.set_ylabel(r'$h$ [m]')\n",
"x_twin_00 = axs2.twinx()\n", "# x_twin_00 = axs2.twinx()\n",
"x_twin_00.set_ylabel(r'$V$ [$\\mathrm{m}^3$]')\n", "# x_twin_00.set_ylabel(r'$V$ [$\\mathrm{m}^3$]')\n",
"x_twin_00.plot(t_vec,volume_vec)\n", "# x_twin_00.plot(t_vec,volume_vec)\n",
"axs2.legend()\n", "# axs2.legend()\n",
"\n",
"fig2,axs2 = plt.subplots(1,1)\n",
"axs2.set_title('LA')\n",
"axs2.plot(t_vec,100*OL_T1_LA_soll_vec,label='OL_T1 Target',c='b')\n",
"axs2.scatter(t_vec[::200],100*OL_T1_LA_ist_vec[::200],label='OL_T1 Actual',c='b',marker='+')\n",
"axs2.plot(t_vec,100*OL_T2_LA_soll_vec,label='OL_T2 Target',c='g')\n",
"axs2.scatter(t_vec[::200],100*OL_T2_LA_ist_vec[::200],label='OL_T2 Actual',c='g',marker='+')\n",
"axs2.plot(t_vec,100*UL_T1_LA_soll_vec,label='UL_T1 Target',c='r')\n",
"axs2.scatter(t_vec[::200],100*UL_T1_LA_ist_vec[::200],label='UL_T1 Actual',c='r',marker='+')\n",
"axs2.plot(t_vec,100*UL_T2_LA_soll_vec,label='UL_T2 Target',c='k')\n",
"axs2.scatter(t_vec[::200],100*UL_T2_LA_ist_vec[::200],label='UL_T2 Actual',c='k',marker='+')\n",
"axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"axs2.set_ylabel(r'$LA$ [%]')\n",
"axs2.legend()\n",
"\n",
"fig2,axs2 = plt.subplots(1,1)\n",
"axs2.set_title('Pressure change vs t=0 at reservoir and turbine')\n",
"axs2.plot(t_vec,pressure_conversion(p_boundary_res-p_boundary_res[0],pUnit_calc, pUnit_conv),label='Reservoir')\n",
"axs2.plot(t_vec,pressure_conversion(p_boundary_tur-p_boundary_tur[0],pUnit_calc, pUnit_conv),label='Turbine')\n",
"axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"axs2.set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"axs2.legend()\n",
"\n",
"fig2,axs2 = plt.subplots(1,1)\n",
"axs2.set_title('Fluxes')\n",
"axs2.plot(t_vec,Q_in_vec,label='Influx')\n",
"axs2.plot(t_vec,Q_boundary_res,label='Outflux')\n",
"axs2.scatter(t_vec[::200],Q_boundary_tur[::200],label='Flux Turbine',c='g',marker='+')\n",
"axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"axs2.set_ylabel(r'$Q$ [$\\mathrm{m}^3/\\mathrm{s}$]')\n",
"axs2.legend()\n",
"\n",
"fig2,axs2 = plt.subplots(1,1)\n",
"axs2.set_title('Min and Max Pressure')\n",
"axs2.plot(Pip_x_vec,pipe.get_lowest_pressure_per_node(disp_flag=True),c='red')\n",
"axs2.plot(Pip_x_vec,pipe.get_highest_pressure_per_node(disp_flag=True),c='red')\n",
"axs2.set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"axs2.set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"\n", "\n",
"# fig2,axs2 = plt.subplots(1,1)\n", "# fig2,axs2 = plt.subplots(1,1)\n",
"# axs2.set_title('Min and Max Fluxes')\n", "# axs2.set_title('LA')\n",
"# axs2.plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n", "# axs2.plot(t_vec,100*OL_T1_LA_soll_vec,label='OL_T1 Target',c='b')\n",
"# axs2.plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n", "# axs2.scatter(t_vec[::200],100*OL_T1_LA_ist_vec[::200],label='OL_T1 Actual',c='b',marker='+')\n",
"# axs2.set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", "# axs2.plot(t_vec,100*OL_T2_LA_soll_vec,label='OL_T2 Target',c='g')\n",
"# axs2.scatter(t_vec[::200],100*OL_T2_LA_ist_vec[::200],label='OL_T2 Actual',c='g',marker='+')\n",
"# axs2.plot(t_vec,100*UL_T1_LA_soll_vec,label='UL_T1 Target',c='r')\n",
"# axs2.scatter(t_vec[::200],100*UL_T1_LA_ist_vec[::200],label='UL_T1 Actual',c='r',marker='+')\n",
"# axs2.plot(t_vec,100*UL_T2_LA_soll_vec,label='UL_T2 Target',c='k')\n",
"# axs2.scatter(t_vec[::200],100*UL_T2_LA_ist_vec[::200],label='UL_T2 Actual',c='k',marker='+')\n",
"# axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"# axs2.set_ylabel(r'$LA$ [%]')\n",
"# axs2.legend()\n",
"\n",
"# fig2,axs2 = plt.subplots(1,1)\n",
"# axs2.set_title('Pressure change vs t=0 at reservoir and turbine')\n",
"# axs2.plot(t_vec,pressure_conversion(p_boundary_res-p_boundary_res[0],pUnit_calc, pUnit_conv),label='Reservoir')\n",
"# axs2.plot(t_vec,pressure_conversion(p_boundary_tur-p_boundary_tur[0],pUnit_calc, pUnit_conv),label='Turbine')\n",
"# axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"# axs2.set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"# axs2.legend()\n",
"\n",
"# fig2,axs2 = plt.subplots(1,1)\n",
"# axs2.set_title('Fluxes')\n",
"# axs2.plot(t_vec,Q_in_vec,label='Influx')\n",
"# axs2.plot(t_vec,Q_boundary_res,label='Outflux')\n",
"# axs2.scatter(t_vec[::200],Q_boundary_tur[::200],label='Flux Turbine',c='g',marker='+')\n",
"# axs2.set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"# axs2.set_ylabel(r'$Q$ [$\\mathrm{m}^3/\\mathrm{s}$]')\n", "# axs2.set_ylabel(r'$Q$ [$\\mathrm{m}^3/\\mathrm{s}$]')\n",
"# axs2.legend()\n",
"\n",
"# fig2,axs2 = plt.subplots(1,1)\n",
"# axs2.set_title('Min and Max Pressure')\n",
"# axs2.plot(Pip_x_vec,pipe.get_lowest_pressure_per_node(disp_flag=True),c='red')\n",
"# axs2.plot(Pip_x_vec,pipe.get_highest_pressure_per_node(disp_flag=True),c='red')\n",
"# axs2.set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"# axs2.set_ylabel(r'$p$ ['+pUnit_conv+']')\n",
"\n",
"# # fig2,axs2 = plt.subplots(1,1)\n",
"# # axs2.set_title('Min and Max Fluxes')\n",
"# # axs2.plot(Pip_x_vec,pipe.get_lowest_flux_per_node(),c='red')\n",
"# # axs2.plot(Pip_x_vec,pipe.get_highest_flux_per_node(),c='red')\n",
"# # axs2.set_xlabel(r'$x$ [$\\mathrm{m}$]')\n",
"# # axs2.set_ylabel(r'$Q$ [$\\mathrm{m}^3/\\mathrm{s}$]')\n",
"\n", "\n",
"\n", "\n",
"fig2.tight_layout()\n", "# fig2.tight_layout()\n",
"plt.show()" "# plt.show()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"fig3,axs3 = plt.subplots(2,2)\n", "\n",
"fig3,axs3 = plt.subplots(2,2,figsize=(16,9))\n",
"fig3.suptitle('Fläche = '+str(Res_area_base)+'\\n'+'Kp = '+str(round(Con_K_p,1))+' Ti = '+str(Con_T_i) )\n",
"axs3[0,0].set_title('Level and Volume reservoir')\n", "axs3[0,0].set_title('Level and Volume reservoir')\n",
"axs3[0,0].plot(t_vec,level_vec,label='level')\n", "axs3[0,0].plot(t_vec,level_vec,label='level')\n",
"axs3[0,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", "axs3[0,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n",
"axs3[0,0].set_ylabel(r'$h$ [m]')\n", "axs3[0,0].set_ylabel(r'$h$ [m]')\n",
"axs3[0,0].set_ylim(0,3.5)\n",
"x_twin_00 = axs3[0,0].twinx()\n", "x_twin_00 = axs3[0,0].twinx()\n",
"x_twin_00.set_ylabel(r'$V$ [$\\mathrm{m}^3$]')\n", "x_twin_00.set_ylabel(r'$V$ [$\\mathrm{m}^3$]')\n",
"x_twin_00.plot(t_vec,volume_vec)\n", "x_twin_00.plot(t_vec,volume_vec)\n",
"x_twin_00.set_ylim(0,3.5*Res_area_base)\n",
"axs3[0,0].legend()\n", "axs3[0,0].legend()\n",
"\n", "\n",
"axs3[0,1].set_title('LA')\n", "axs3[0,1].set_title('LA')\n",
@@ -462,30 +513,37 @@
"axs3[1,1].legend()\n", "axs3[1,1].legend()\n",
"\n", "\n",
"fig3.tight_layout()\n", "fig3.tight_layout()\n",
"plt.show()" "plt.show()\n",
"\n",
"figname = 'Simulation Hammer\\KW_Hammer_Fläche_'+str(Res_area_base)+'_Ti_'+str(Con_T_i)+'_Kp'+str(round(Con_K_p,1))+'.png'\n",
"fig3.savefig(figname)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"0.015478260869565217\n" "0.42240906887823404\n",
"1.9673287975884344\n",
"0.43120621785509305\n"
] ]
} }
], ],
"source": [ "source": [
"print(np.sin(Pip_angle))" "print(level_vec[0]-np.min(level_vec))\n",
"print(level_vec[np.argmin(np.abs(t_vec-600))])\n",
"print(np.max(level_vec)-level_vec[0])"
] ]
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "display_name": "Python 3.8.13 ('DT_Slot_3')",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@@ -504,7 +562,7 @@
"orig_nbformat": 4, "orig_nbformat": 4,
"vscode": { "vscode": {
"interpreter": { "interpreter": {
"hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
} }
} }
}, },

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Validation Data/consolidated pandas dataframes/consolidate_validation_data.ipynb
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@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -14,7 +14,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -37,7 +37,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -46,7 +46,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -56,7 +56,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -71,12 +71,13 @@
"UT_M1_p = UT_df['UL_T1_p'].to_numpy()\n", "UT_M1_p = UT_df['UL_T1_p'].to_numpy()\n",
"UT_M2_p = UT_df['UL_T2_p'].to_numpy()\n", "UT_M2_p = UT_df['UL_T2_p'].to_numpy()\n",
"UT_M1_LA = UT_df['UL_T1_LA'].to_numpy()\n", "UT_M1_LA = UT_df['UL_T1_LA'].to_numpy()\n",
"UT_M2_LA = UT_df['UL_T2_LA'].to_numpy()" "UT_M2_LA = UT_df['UL_T2_LA'].to_numpy()\n",
"UT_Ausl = UT_df['Ausl'].to_numpy()\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@@ -85,7 +86,7 @@
"(1657542740.9878564, 1657553169.3173888)" "(1657542740.9878564, 1657553169.3173888)"
] ]
}, },
"execution_count": 21, "execution_count": 16,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -116,30 +117,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"ename": "TypeError",
"evalue": "Cannot construct a dtype from an array",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\Validation Data\\consolidated pandas dataframes\\consolidate_validation_data.ipynb Cell 7\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(UT_t_vec[mask_UT],TB_level[mask_UT])\n\u001b[1;32m----> <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m validation_data_UT \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49marray([UT_t_vec[mask_UT],UT_M1_LA[mask_UT],UT_M2_LA[mask_UT],UT_M1_p[mask_UT],UT_M2_p[mask_UT]],TB_level[mask_UT])\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m validation_data_TB \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([TB_t_vec[mask_TB],TB_M1_LA[mask_TB],TB_M2_LA[mask_TB],TB_M1_p[mask_TB],TB_M2_p[mask_TB]])\n",
"\u001b[1;31mTypeError\u001b[0m: Cannot construct a dtype from an array"
]
}
],
"source": [ "source": [
"mask_UT = np.logical_and(t1<UT_t_vec,UT_t_vec <t2)\n", "mask_UT = np.logical_and(t1<UT_t_vec,UT_t_vec <t2)\n",
"mask_TB = np.logical_and(t1<TB_t_vec,TB_t_vec <t2)\n", "mask_TB = np.logical_and(t1<TB_t_vec,TB_t_vec <t2)\n",
"\n", "\n",
"\n", "\n",
"fig = plt.figure()\n", "\n",
"plt.plot(UT_t_vec[mask_UT],TB_level[mask_UT])\n", "validation_data_UT = UT_df[mask_UT]\n",
"validation_data_UT = np.array([UT_t_vec[mask_UT];UT_M1_LA[mask_UT];UT_M2_LA[mask_UT];UT_M1_p[mask_UT];UT_M2_p[mask_UT]];TB_level[mask_UT])\n", "validation_data_TB = TB_df[mask_TB]\n",
"validation_data_TB = np.array([TB_t_vec[mask_TB];TB_M1_LA[mask_TB];TB_M2_LA[mask_TB];TB_M1_p[mask_TB];TB_M2_p[mask_TB]])" "\n",
"validation_data_UT.to_csv('Validation_data_UT.csv')\n",
"validation_data_TB.to_csv('Validation_data_TB.csv')\n",
"\n"
] ]
} }
], ],

61313
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Validation Data/raw data Untertweng/Schnellschluss/Validation_Untertweng_Schnellschluss.ipynb
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@@ -35,7 +35,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\georg\\AppData\\Local\\Temp\\ipykernel_29732\\1290488230.py:2: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n", "C:\\Users\\georg\\AppData\\Local\\Temp\\ipykernel_24336\\3411177260.py:2: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
" raw_data = pd.read_csv(\"2015_08_25 15.20 M12 SS100%.csv\",sep=\";\",header=7,index_col=False)\n" " raw_data = pd.read_csv(\"2015_08_25 15.20 M12 SS100%.csv\",sep=\";\",header=7,index_col=False)\n"
] ]
} }
@@ -53,7 +53,7 @@
"df['M1-LA'] = pd.to_numeric(raw_data['M1-LA'])/100\n", "df['M1-LA'] = pd.to_numeric(raw_data['M1-LA'])/100\n",
"df['M2-LA'] = pd.to_numeric(raw_data['M2-LA'])/100\n", "df['M2-LA'] = pd.to_numeric(raw_data['M2-LA'])/100\n",
"df['Druck'] = pd.to_numeric(raw_data['P-DRL'])\n", "df['Druck'] = pd.to_numeric(raw_data['P-DRL'])\n",
"df['Pegel'] = pd.to_numeric(raw_data['Pegel-UW'])\n", "df['Pegel'] = pd.to_numeric(raw_data['PEGEL-UW'])\n",
"\n", "\n",
"val_t_vec_raw = np.array(df['timestamp']-df['timestamp'][0])\n", "val_t_vec_raw = np.array(df['timestamp']-df['timestamp'][0])\n",
"val_LA1_vec_raw = np.array(df['M1-LA']) \n", "val_LA1_vec_raw = np.array(df['M1-LA']) \n",
@@ -501,7 +501,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[<matplotlib.lines.Line2D at 0x1e74e242940>]" "[<matplotlib.lines.Line2D at 0x18a94bcf820>]"
] ]
}, },
"execution_count": 10, "execution_count": 10,

15
Validation Data/raw data Untertweng/consolidate_validation_data_Untertweng.ipynb
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@@ -21,7 +21,8 @@
"M1_p_df = pd.read_csv('M1_Druck.txt',delimiter=';')\n", "M1_p_df = pd.read_csv('M1_Druck.txt',delimiter=';')\n",
"M2_p_df = pd.read_csv('M2_Druck.txt',delimiter=';')\n", "M2_p_df = pd.read_csv('M2_Druck.txt',delimiter=';')\n",
"M1_LA_df = pd.read_csv('M1_LA.txt',delimiter=';')\n", "M1_LA_df = pd.read_csv('M1_LA.txt',delimiter=';')\n",
"M2_LA_df = pd.read_csv('M2_LA.txt',delimiter=';')" "M2_LA_df = pd.read_csv('M2_LA.txt',delimiter=';')\n",
"Ausl_df = pd.read_csv('AL_SeitAuslPos.txt',delimiter=';')"
] ]
}, },
{ {
@@ -34,7 +35,8 @@
"M1_p_df['Timestamp'] = M1_p_df['TIMESTAMP UNIX']+M1_p_df['TIMESTAMP MS']/1000.\n", "M1_p_df['Timestamp'] = M1_p_df['TIMESTAMP UNIX']+M1_p_df['TIMESTAMP MS']/1000.\n",
"M2_p_df['Timestamp'] = M2_p_df['TIMESTAMP UNIX']+M2_p_df['TIMESTAMP MS']/1000.\n", "M2_p_df['Timestamp'] = M2_p_df['TIMESTAMP UNIX']+M2_p_df['TIMESTAMP MS']/1000.\n",
"M1_LA_df['Timestamp'] = M1_LA_df['TIMESTAMP UNIX']+M1_LA_df['TIMESTAMP MS']/1000.\n", "M1_LA_df['Timestamp'] = M1_LA_df['TIMESTAMP UNIX']+M1_LA_df['TIMESTAMP MS']/1000.\n",
"M2_LA_df['Timestamp'] = M2_LA_df['TIMESTAMP UNIX']+M2_LA_df['TIMESTAMP MS']/1000." "M2_LA_df['Timestamp'] = M2_LA_df['TIMESTAMP UNIX']+M2_LA_df['TIMESTAMP MS']/1000.\n",
"Ausl_df['Timestamp'] = Ausl_df['TIMESTAMP UNIX']+Ausl_df['TIMESTAMP MS']/1000."
] ]
}, },
{ {
@@ -47,7 +49,8 @@
"M1_p_df.set_index('Timestamp',inplace=True)\n", "M1_p_df.set_index('Timestamp',inplace=True)\n",
"M2_p_df.set_index('Timestamp',inplace=True)\n", "M2_p_df.set_index('Timestamp',inplace=True)\n",
"M1_LA_df.set_index('Timestamp',inplace=True)\n", "M1_LA_df.set_index('Timestamp',inplace=True)\n",
"M2_LA_df.set_index('Timestamp',inplace=True)" "M2_LA_df.set_index('Timestamp',inplace=True)\n",
"Ausl_df.set_index('Timestamp',inplace=True)"
] ]
}, },
{ {
@@ -61,12 +64,14 @@
"M2_p_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n", "M2_p_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
"M1_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n", "M1_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
"M2_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n", "M2_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
"Ausl_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
"\n", "\n",
"pegel_df.rename(columns={'VALUE': 'TB-Pegel'},inplace=True)\n", "pegel_df.rename(columns={'VALUE': 'TB-Pegel'},inplace=True)\n",
"M1_p_df.rename(columns={'VALUE': 'M1-Druck'},inplace=True)\n", "M1_p_df.rename(columns={'VALUE': 'M1-Druck'},inplace=True)\n",
"M2_p_df.rename(columns={'VALUE': 'M2-Druck'},inplace=True)\n", "M2_p_df.rename(columns={'VALUE': 'M2-Druck'},inplace=True)\n",
"M1_LA_df.rename(columns={'VALUE': 'M1-LA'},inplace=True)\n", "M1_LA_df.rename(columns={'VALUE': 'M1-LA'},inplace=True)\n",
"M2_LA_df.rename(columns={'VALUE': 'M2-LA'},inplace=True)\n" "M2_LA_df.rename(columns={'VALUE': 'M2-LA'},inplace=True)\n",
"Ausl_df.rename(columns={'VALUE': 'Ausl'},inplace=True)\n"
] ]
}, },
{ {
@@ -75,7 +80,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"UT_df = pegel_df.join([M1_LA_df,M1_p_df,M2_LA_df,M2_p_df],how='outer').sort_index()" "UT_df = pegel_df.join([M1_LA_df,M1_p_df,M2_LA_df,M2_p_df,Ausl_df],how='outer').sort_index()"
] ]
}, },
{ {