From f774b2e52d4943829835fcbf9c2ddb94bfc0732e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Fri, 1 Jul 2022 08:58:29 +0200 Subject: [PATCH] Clean up before combining pipeline and reservoir code --- .../Ausgleichsbecken.py | 37 - .../Ausgleichsbecken.py | 67 - .../Ausgleichsbecken_class_file.py | 90 - .../Main_Program.ipynb | 145 - .../static_pipeline_pressure/e-RK4-Test.ipynb | 220 - .../pressure_conversion.py | 77 - Messing Around/Durchflussraten.py | 12 - Messing Around/Messy_NB.ipynb | 47 - Messing Around/Messy_py.py | 9 - Messing Around/Zeitreihenvisualisierung.ipynb | 89 - Messing Around/flow_patterns.ipynb | 138 - Messing Around/flow_patterns.py | 67 - Messing Around/pressure_propagation.ipynb | 92 - Messing Around/pressure_propagation.py | 2 - Messing Around/visualize_parameters.py | 1 - Messing Around/visualize_parameters_nb.ipynb | 6977 ----------------- Messing Around/volume_change.py | 140 - Messing Around/volume_change_nb.ipynb | 237 - 18 files changed, 8447 deletions(-) delete mode 100644 Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py delete mode 100644 Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py delete mode 100644 Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb delete mode 100644 Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb delete mode 100644 Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py delete mode 100644 Messing Around/Durchflussraten.py delete mode 100644 Messing Around/Messy_NB.ipynb delete mode 100644 Messing Around/Messy_py.py delete mode 100644 Messing Around/Zeitreihenvisualisierung.ipynb delete mode 100644 Messing Around/flow_patterns.ipynb delete mode 100644 Messing Around/flow_patterns.py delete mode 100644 Messing Around/pressure_propagation.ipynb delete mode 100644 Messing Around/pressure_propagation.py delete mode 100644 Messing Around/visualize_parameters.py delete mode 100644 Messing Around/visualize_parameters_nb.ipynb delete mode 100644 Messing Around/volume_change.py delete mode 100644 Messing Around/volume_change_nb.ipynb diff --git a/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py b/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py index e887617..ac2ddca 100644 --- a/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py +++ b/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py @@ -1,31 +1,5 @@ import numpy as np -def Volume_trend(influx, outflux, timestep=1, V_0=0): - ''' - Returns the trend and the volume and the final volume, defined - by influx and outflux patterns. The optional parameter timestep - defines the time increment over which the fluxes are changing. - ''' - net_flux = influx-outflux - delta_V = net_flux*timestep - V_trend = V_0+np.cumsum(delta_V) - V_end = V_trend[-1] - return V_end, V_trend - -def Height_trend(V_trend, area=1, h_crit_low=-np.inf, h_crit_high=np.inf): - ''' - Returns the trend and the height and the final height, defined - by influx and outflux patterns as well as the crosssection area. - The optional parameters h_crit_low/high indicate limits that the height - should never exceed. If this occures, TRUE is returned in the corresponding - h_crit_flag. - ''' - h_trend = V_trend/area - h_crit_flag_low = np.any(h_trend <= h_crit_low) - h_crit_flag_high = np.any(h_trend >= h_crit_high) - h_end = h_trend[-1] - return h_trend, h_end, h_crit_flag_low, h_crit_flag_high - def get_h_halfstep(initial_height, influx, outflux, timestep, area): h0 = initial_height Q_in = influx @@ -45,7 +19,6 @@ def FODE_function(x, h, alpha, p, rho=1000., g=9.81): def e_RK_4(yn, h, dt, Q0, Q1, A0, A1, p0, p1): alpha = (A1/A0-1) - h_hs = get_h_halfstep(h, Q0, Q1, dt, A0) p_hs = get_p_halfstep(p0, p1) Y1 = yn @@ -55,13 +28,3 @@ def e_RK_4(yn, h, dt, Q0, Q1, A0, A1, p0, p1): ynp1 = yn + dt/6*(FODE_function(Y1, h, alpha, p)+2*FODE_function(Y2, h_hs, alpha, p_hs)+ \ 2*FODE_function(Y3, h_hs, alpha, p_hs)+ FODE_function(Y4, h, alpha, p)) - - - -## testing -# if __name__ == "__main__": -# influx = np.full([1, 100], 6) -# outflux = np.full_like(influx, 4) -# V_end, V_trend = Volume_trend(influx, outflux, timestep=0.5, V_0 = 100) -# print(V_end) -# print(V_trend) diff --git a/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py deleted file mode 100644 index e887617..0000000 --- a/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py +++ /dev/null @@ -1,67 +0,0 @@ -import numpy as np - -def Volume_trend(influx, outflux, timestep=1, V_0=0): - ''' - Returns the trend and the volume and the final volume, defined - by influx and outflux patterns. The optional parameter timestep - defines the time increment over which the fluxes are changing. - ''' - net_flux = influx-outflux - delta_V = net_flux*timestep - V_trend = V_0+np.cumsum(delta_V) - V_end = V_trend[-1] - return V_end, V_trend - -def Height_trend(V_trend, area=1, h_crit_low=-np.inf, h_crit_high=np.inf): - ''' - Returns the trend and the height and the final height, defined - by influx and outflux patterns as well as the crosssection area. - The optional parameters h_crit_low/high indicate limits that the height - should never exceed. If this occures, TRUE is returned in the corresponding - h_crit_flag. - ''' - h_trend = V_trend/area - h_crit_flag_low = np.any(h_trend <= h_crit_low) - h_crit_flag_high = np.any(h_trend >= h_crit_high) - h_end = h_trend[-1] - return h_trend, h_end, h_crit_flag_low, h_crit_flag_high - -def get_h_halfstep(initial_height, influx, outflux, timestep, area): - h0 = initial_height - Q_in = influx - Q_out = outflux - dt = timestep - A = area - - h_halfstep = h0+1/A*(Q_in-Q_out)*dt/2 - -def get_p_halfstep(p0, p1): - p_halfstep = (p0+p1)/2 - -def FODE_function(x, h, alpha, p, rho=1000., g=9.81): - f = x*abs(x)/h*alpha+g-p/(rho*h) - return f - - -def e_RK_4(yn, h, dt, Q0, Q1, A0, A1, p0, p1): - alpha = (A1/A0-1) - - h_hs = get_h_halfstep(h, Q0, Q1, dt, A0) - p_hs = get_p_halfstep(p0, p1) - Y1 = yn - Y2 = yn + dt/2*FODE_function(Y1, h, alpha, p0) - Y3 = yn + dt/2*FODE_function(Y2, h_hs, alpha, p_hs) - Y4 = yn + dt*FODE_function(Y3, h_hs, alpha, p_hs) - ynp1 = yn + dt/6*(FODE_function(Y1, h, alpha, p)+2*FODE_function(Y2, h_hs, alpha, p_hs)+ \ - 2*FODE_function(Y3, h_hs, alpha, p_hs)+ FODE_function(Y4, h, alpha, p)) - - - - -## testing -# if __name__ == "__main__": -# influx = np.full([1, 100], 6) -# outflux = np.full_like(influx, 4) -# V_end, V_trend = Volume_trend(influx, outflux, timestep=0.5, V_0 = 100) -# print(V_end) -# print(V_trend) diff --git a/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py deleted file mode 100644 index b6ce3d5..0000000 --- a/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py +++ /dev/null @@ -1,90 +0,0 @@ -from Ausgleichsbecken import FODE_function, get_h_halfstep, get_p_halfstep -from pressure_conversion import pressure_conversion -class Ausgleichsbecken_class: -# units - area_unit = r'$\mathrm{m}^2$' - area_outflux_unit = r'$\mathrm{m}^2$' - level_unit = 'm' - volume_unit = r'$\mathrm{m}^3$' - flux_unit = r'$\mathrm{m}^3/\mathrm{s}$' - time_unit = 's' - pressure_unit = 'Pa' - -# init - def __init__(self,area,outflux_area,level_min,level_max,timestep = 1): - self.area = area # base area of the rectangular structure - self.area_outflux = outflux_area # area of the outlet towards the pipeline - self.level_min = level_min # lowest allowed water level - self.level_max = level_max # highest allowed water level - self.timestep = timestep # timestep of the simulation - - def update_volume(self): - self.volume = self.level*self.area -# setter - - def set_initial_level(self,initial_level): - self.level = initial_level - self.update_volume() - - def set_influx(self,influx): - self.influx = influx - - def set_outflux(self,outflux): - self.outflux = outflux - -# getter - def get_area(self): - print('The base area of the cuboid reservoir is', self.area, self.area_unit) - - def get_outflux_area(self): - print('The outflux area from the cuboid reservoir to the pipeline is', \ - self.area_outflux, self.area_outflux_unit) - - def get_level(self): - print('The current level in the reservoir is', self.level , self.level_unit) - - def get_crit_levels(self): - print('The critical water levels in the reservoir are: \n',\ - ' Minimum:', self.level_min , self.level_unit , '\n',\ - ' Maximum:', self.level_max , self.level_unit ) - - def get_volume(self): - print('The current water volume in the reservoir is', self.volume, self.volume_unit) - - def get_timestep(self): - print('The timestep for the simulation is' , self.timestep, self.time_unit) - - def get_influx(self): - print('The current influx is', self.influx, self.flux_unit) - - def get_outflux(self): - print('The current outflux is', self.outflux, self.flux_unit) - -# methods - - - def update_level(self,timestep): - # dont update volume here, because update_level gets called to calculate h_halfstep - net_flux = self.influx-self.outflux - delta_V = net_flux*timestep - new_level = (self.volume+delta_V)/self.area - return new_level - - - def e_RK_4(self): - # Update to deal with non constant pipeline pressure! - yn = self.outflux/self.area_outflux - h = self.level - dt = self.timestep - p,_ = pressure_conversion(self.initial_pressure,self.pressure_unit,'Pa') - p_hs,_ = pressure_conversion(self.initial_pressure,self.pressure_unit,'Pa') - alpha = (self.area_outflux/self.area-1) - h_hs = self.update_level(dt/2) - Y1 = yn - Y2 = yn + dt/2*FODE_function(Y1, h, alpha, self.initial_pressure) - Y3 = yn + dt/2*FODE_function(Y2, h_hs, alpha, p_hs) - Y4 = yn + dt*FODE_function(Y3, h_hs, alpha, p_hs) - ynp1 = yn + dt/6*(FODE_function(Y1, h, alpha, p)+2*FODE_function(Y2, h_hs, alpha, p_hs)+ \ - 2*FODE_function(Y3, h_hs, alpha, p_hs)+ FODE_function(Y4, h, alpha, p)) - - self.outflux = ynp1*self.area_outflux \ No newline at end of file diff --git a/Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb b/Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb deleted file mode 100644 index 6e82ef4..0000000 --- a/Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb +++ /dev/null @@ -1,145 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "import matplotlib.pyplot as plt\n", - "from pressure_conversion import pressure_conversion" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# define constants\n", - "initial_level = 5. # m\n", - "initial_influx = 1. # m³/s\n", - "initial_outflux = 0. # m³/s\n", - "initial_pipeline_pressure = 1.\n", - "initial_pressure_unit = 'bar'\n", - "conversion_pressure_unit = 'mWS'\n", - "\n", - "area_base = 1. # m²\n", - "area_outflux = 0.5 # m²\n", - "critical_level_low = 0. # m\n", - "critical_level_high = 10. # m\n", - "simulation_timestep = 0.001 # s\n", - "\n", - "# for while loop\n", - "total_min_level = 0.01 # m\n", - "total_max_time = 150 # s" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib qt\n", - "\n", - "V = Ausgleichsbecken_class(area_base, area_outflux, critical_level_low, critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "V.initial_pressure, V.pressure_unit = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = conversion_pressure_unit)\n", - "\n", - "time_vec = np.arange(0,total_max_time,simulation_timestep)\n", - "outflux_vec = np.empty_like(time_vec)\n", - "outflux_vec[0] = initial_outflux\n", - "level_vec = np.empty_like(time_vec)\n", - "level_vec[0] = initial_level\n", - " \n", - "i_max = -1\n", - "\n", - "for i in range(np.size(time_vec)-1):\n", - " V.e_RK_4()\n", - " V.level = V.update_level(V.timestep)\n", - " V.update_volume()\n", - " outflux_vec[i+1] = V.outflux\n", - " level_vec[i+1] = V.level\n", - " if V.level < total_min_level:\n", - " i_max = i\n", - " break\n", - "\n", - "\n", - "fig1, (ax1, ax2, ax3) = plt.subplots(3, 1)\n", - "fig1.set_figheight(10)\n", - "fig1.suptitle('Ausgleichsbecken')\n", - "\n", - "ax1.plot(time_vec[:i_max],level_vec[:i_max], label='Water level')\n", - "ax1.set_ylabel(r'$h$ ['+V.level_unit+']')\n", - "ax1.set_xlabel(r'$t$ ['+V.time_unit+']')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(time_vec[:i_max],outflux_vec[:i_max], label='Outflux')\n", - "ax2.set_ylabel(r'$Q_{out}$ ['+V.flux_unit+']')\n", - "ax2.set_xlabel(r'$t$ ['+V.time_unit+']')\n", - "ax2.legend()\n", - "\n", - "# plt.subplots_adjust(left=0.2, bottom=0.2)\n", - "ax3.set_axis_off()\n", - "cell_text = np.array([[initial_level, V.level_unit], \\\n", - " [initial_influx, V.flux_unit], \\\n", - " [initial_outflux, V.flux_unit], \\\n", - " [round(V.initial_pressure,2), V.pressure_unit], \\\n", - " [simulation_timestep, V.time_unit], \\\n", - " [area_base, V.area_unit], \\\n", - " [area_outflux, V.area_unit]])\n", - "\n", - "row_labels =['initial_level', \\\n", - " 'initial_influx', \\\n", - " 'initial_outflux', \\\n", - " 'initial_pipeline_pressure', \\\n", - " 'simulation_timestep', \\\n", - " 'area_base', \\\n", - " 'area_outflux']\n", - "\n", - "plt.table(cellText=cell_text, \\\n", - " cellLoc='center', \\\n", - " colWidths=[0.3,0.1,0.3], \\\n", - " rowLabels=row_labels, \\\n", - " loc = 1, \\\n", - " rowLoc='left', \\\n", - " fontsize = 15.)\n", - "\n", - "fig1.tight_layout() " - ] - } - ], - "metadata": { - "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, - "vscode": { - "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb b/Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb deleted file mode 100644 index a857ea4..0000000 --- a/Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# define constants\n", - "initial_level = 5.\n", - "initial_influx = 0. \n", - "initial_outflux = 0. \n", - "initial_pipeline_pressure = 10000. \n", - "\n", - "total_min_level = 0.01\n", - "total_max_time = 150\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "V = Ausgleichsbecken_class(1.,0.5,0.,10.,timestep=0.001)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "V.p0 = initial_pipeline_pressure\n", - "\n", - "outflux_vec = []\n", - "level_vec = []\n", - "\n", - "t = 0\n", - "while V.level > total_min_level:\n", - " t = t+V.timestep\n", - " V.e_RK_4()\n", - " V.level = V.update_level(V.timestep)\n", - " V.set_volume()\n", - " outflux_vec.append(V.outflux)\n", - " level_vec.append(V.level)\n", - " if t > total_max_time:\n", - " break\n", - "\n", - "plt.plot(level_vec)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "V = Ausgleichsbecken_class(1.,0.1,0.,10.,timestep=0.001)\n", - "\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "V.p0 = initial_pipeline_pressure\n", - "\n", - "outflux_vec = []\n", - "level_vec = []\n", - "\n", - "t = 0\n", - "while V.level > total_min_level:\n", - " t = t+V.timestep\n", - " V.e_RK_4()\n", - " V.level = V.update_level(V.timestep)\n", - " V.set_volume()\n", - " outflux_vec.append(V.outflux)\n", - " level_vec.append(V.level)\n", - " if t > total_max_time:\n", - " break\n", - "\n", - "plt.plot(level_vec)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "V = Ausgleichsbecken_class(1.,0.9,0.,10.,timestep=0.001)\n", - "\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "V.p0 = initial_pipeline_pressure\n", - "\n", - "outflux_vec = []\n", - "level_vec = []\n", - "\n", - "t = 0\n", - "while V.level > total_min_level:\n", - " t = t+V.timestep\n", - " V.e_RK_4()\n", - " V.level = V.update_level(V.timestep)\n", - " V.set_volume()\n", - " outflux_vec.append(V.outflux)\n", - " level_vec.append(V.level)\n", - " if t > total_max_time:\n", - " break\n", - "\n", - "plt.plot(level_vec)\n" - ] - } - ], - "metadata": { - "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, - "vscode": { - "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py b/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py deleted file mode 100644 index 96744c4..0000000 --- a/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py +++ /dev/null @@ -1,77 +0,0 @@ -# convert to Pa -def bar_to_pa(p): - return p*1e5 - -def mWS_to_pa(p): - return p*9.80665*1e3 - -def torr_to_pa(p): - return p*133.322 - -def atm_to_pa(p): - return p*101.325*1e3 - -def psi_to_pa(p): - return p*6894.8 - -# convert from Pa -def pa_to_bar(p): - return p*1e-5 - -def pa_to_mWS(p): - return p*1/(9.80665*1e3) - -def pa_to_torr(p): - return p/133.322 - -def pa_to_atm(p): - return p*1/(101.325*1e3) - - # converstion function - -def pa_to_psi(p): - return p/6894.8 - -def pressure_conversion(pressure, input_unit = 'bar', target_unit = 'Pa'): - p = pressure - if input_unit.lower() == 'bar': - p_pa = bar_to_pa(p) - elif input_unit.lower() == 'mws': - p_pa = mWS_to_pa(p) - elif input_unit.lower() == 'torr': - p_pa = torr_to_pa(p) - elif input_unit.lower() == 'atm': - p_pa = atm_to_pa(p) - elif input_unit.lower() == 'psi': - p_pa = psi_to_pa(p) - elif input_unit.lower() == 'pa': - p_pa = p - else: - raise Exception('Given input unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi') - - if target_unit.lower() == 'bar': - return pa_to_bar(p_pa), target_unit - elif target_unit.lower() == 'mws': - return pa_to_mWS(p_pa), target_unit - elif target_unit.lower() == 'torr': - return pa_to_torr(p_pa), target_unit - elif target_unit.lower() == 'atm': - return pa_to_atm(p_pa), target_unit - elif target_unit.lower() =='psi': - return pa_to_psi(p_pa), target_unit - elif target_unit.lower() == 'pa': - return p_pa, target_unit - else: - raise Exception('Given target unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi') - -# testing_pressure_conversion -if __name__ == '__main__': - p = 1 - - unit_dict = ['Pa','Bar','Torr','Atm','MWS','psi'] - - for input_unit in unit_dict: - for target_unit in unit_dict: - converted_p = pressure_conversion(p,input_unit,target_unit) - print(input_unit,target_unit) - print(converted_p) \ No newline at end of file diff --git a/Messing Around/Durchflussraten.py b/Messing Around/Durchflussraten.py deleted file mode 100644 index bbef2d1..0000000 --- a/Messing Around/Durchflussraten.py +++ /dev/null @@ -1,12 +0,0 @@ -import numpy as np -from math import pi - - -def Hagen_Poiseuille(P_above,P_below,dx,constants=[1,1]): - dP = P_above-P_below - r = constants[0] - vis = constants[1] - Q = (pi*r**4)/(8*vis)*dP/dx - return Q - - diff --git a/Messing Around/Messy_NB.ipynb b/Messing Around/Messy_NB.ipynb deleted file mode 100644 index 6b81105..0000000 --- a/Messing Around/Messy_NB.ipynb +++ /dev/null @@ -1,47 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import plotly\n" - ] - } - ], - "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 -} diff --git a/Messing Around/Messy_py.py b/Messing Around/Messy_py.py deleted file mode 100644 index 7d7d927..0000000 --- a/Messing Around/Messy_py.py +++ /dev/null @@ -1,9 +0,0 @@ -class Person: - def __init__(self, name, age): - self.name = name - self.age = age - -p1 = Person("John", 36) - -print(p1.name) -print(p1.age) diff --git a/Messing Around/Zeitreihenvisualisierung.ipynb b/Messing Around/Zeitreihenvisualisierung.ipynb deleted file mode 100644 index f51e563..0000000 --- a/Messing Around/Zeitreihenvisualisierung.ipynb +++ /dev/null @@ -1,89 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib qt\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "n = 10000\n", - "\n", - "t = np.linspace(0,200*np.pi,n)\n", - "omega = 2\n", - "f_t = np.sin(omega*t)*np.cos(50/n*t)\n", - "\n", - "dt_max = 100\n", - "x_s = np.full([dt_max,],np.NaN)\n", - "y_s = np.full([dt_max,],np.NaN)\n", - "\n", - "# fig_ref = plt.figure()\n", - "# ax_ref = fig_ref.add_subplot(111)\n", - "# line_obj_ref = ax_ref.plot(t,f_t, marker='.')\n", - "# plt.show(block=False)\n", - "# plt.pause(3)\n", - "# plt.close(fig_ref)\n", - "\n", - "fig = plt.figure()\n", - "ax1 = fig.add_subplot(211)\n", - "ax2 = fig.add_subplot(212)\n", - "ax1.set_xlim([0,t[100+50]])\n", - "ax1.set_ylim([-1.05,1.05])\n", - "ax2.set_xlim([t[0],t[100+50]])\n", - "ax2.set_ylim([-1.05,1.05])\n", - "line_obj1, = ax1.plot(0,0, marker='.')\n", - "line_obj2, = ax2.plot(0,0, marker='.')\n", - "plt.show(block=False)\n", - "plt.pause(0.01)\n", - "\n", - "for i in range(n):\n", - " if i <= dt_max:\n", - " x_s[:i] = t[:i]\n", - " y_s[:i] = f_t[:i]\n", - " else:\n", - " x_s = t[i-dt_max:i]\n", - " y_s = f_t[i-dt_max:i]\n", - " ax1.set_xlim([t[i-dt_max],t[i]+t[50]])\n", - " ax2.set_xlim([t[0],t[i]+(t[i]-t[0])/2])\n", - " \n", - " line_obj1.set_xdata(x_s)\n", - " line_obj1.set_ydata(y_s)\n", - " line_obj2.set_xdata(t[:i])\n", - " line_obj2.set_ydata(f_t[:i])\n", - " ax1.set_title(str(i))\n", - " fig.canvas.draw()\n", - " plt.pause(0.001)\n", - "\n", - " \n" - ] - } - ], - "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 -} diff --git a/Messing Around/flow_patterns.ipynb b/Messing Around/flow_patterns.ipynb deleted file mode 100644 index e87b12b..0000000 --- a/Messing Around/flow_patterns.ipynb +++ /dev/null @@ -1,138 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "from plotly.subplots import make_subplots\n", - "import plotly.graph_objects as go\n", - "from flow_patterns import return_flux_profiles,make_flux_df\n", - "from volume_change import V_h_test_2,h_V_test_2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# #constant flows\n", - "# #number of steps\n", - "# n = 100\n", - "# #input identifiers\n", - "# i_i_1 = 0\n", - "# #output identifiers\n", - "# o_i_1 = 0\n", - "# # influx and outflux offset\n", - "# i_o = 10\n", - "# o_o = 10\n", - "# #outflux delay\n", - "# o_d = 10\n", - "\n", - "# influx_profile,outflux_profile = return_flux_profiles(n,i_i_1,o_i_1,i_o,o_o,o_d)\n", - "# flux_df = make_flux_df(influx_profile,outflux_profile)\n", - "\n", - "# fig = make_subplots(2,1)\n", - "\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['influx']),row=1,col=1)\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['outflux']),row=2,col=1)\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# #linear increasing flows\n", - "# #number of steps\n", - "# n = 100\n", - "# #input identifiers\n", - "# i_i_2 = 'lin_0010'\n", - "# #output identifiers\n", - "# o_i_2 = 'lin_0010'\n", - "# # influx and outflux offset\n", - "# i_o = 10\n", - "# o_o = 10\n", - "# #outflux delay\n", - "# o_d = 10\n", - "\n", - "# influx_profile,outflux_profile = return_flux_profiles(n,i_i_2,o_i_2,i_o,o_o,o_d)\n", - "# flux_df = make_flux_df(influx_profile,outflux_profile)\n", - "\n", - "# fig = make_subplots(2,1)\n", - "\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['influx']),row=1,col=1)\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['outflux']),row=2,col=1)\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# #sawtooth flows\n", - "# #number of steps\n", - "# n = 100\n", - "# #input identifiers\n", - "# i_i_3 = 'st_0010_0010'\n", - "# #output identifiers\n", - "# o_i_3 = 'st_0010_0010'\n", - "# # influx and outflux offset\n", - "# i_o = 10\n", - "# o_o = 10\n", - "# #outflux delay\n", - "# o_d = 10\n", - "\n", - "# influx_profile,outflux_profile = return_flux_profiles(n,i_i_3,o_i_3,i_o,o_o,o_d)\n", - "# flux_df = make_flux_df(influx_profile,outflux_profile)\n", - "\n", - "# fig = make_subplots(2,1)\n", - "\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['influx']),row=1,col=1)\n", - "# fig.add_trace(go.Scatter(x=flux_df['time'],y=flux_df['outflux']),row=2,col=1)\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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 -} diff --git a/Messing Around/flow_patterns.py b/Messing Around/flow_patterns.py deleted file mode 100644 index e628047..0000000 --- a/Messing Around/flow_patterns.py +++ /dev/null @@ -1,67 +0,0 @@ -import numpy as np -import pandas as pd -import plotly.express as px -from plotly.subplots import make_subplots -import plotly.graph_objects as go - - -def return_flux_profiles(number_of_steps = 1,influx_identifier = 0, outflux_identifier = 0,influx_offset=0,outflux_offset=0, outflux_delay = 0): - ''' Identifier patterns: - 0 ... constant - 'lin_SSSS' ... linear increase with slope int(SSSS) - 'st_SSSS_PPPP' ... sawtooth pattern with slope int(SSSS) and period int(PPPP) steps - ''' - - # case identifiers for if statment - i = influx_identifier - o = outflux_identifier - - n = number_of_steps - #starting value for the influx and outflux - i_o = influx_offset - o_o = outflux_offset - # number of steps, the outflux is held at 0 at the beginning - o_d = outflux_delay - - -# get base profile for the influx (offset will get applied later) - if i == 0: - influx_profile = np.zeros(n) - elif 'lin' in influx_identifier: - k = int(influx_identifier[-4:]) - influx_profile = np.linspace(0,k*(n-1),n) - elif 'st' in influx_identifier: - k = int(influx_identifier[3:7]) - p = int(influx_identifier[-4:]) - influx_profile = np.tile(np.linspace(0,k*(p-1),p),int(np.ceil(n/p))) - - -# apply influx offset - influx_profile = influx_offset + influx_profile - - if o == 0: - outflux_profile = np.zeros(n) - elif 'lin' in outflux_identifier: - k = int(outflux_identifier[-4:]) - outflux_profile = np.linspace(0,k*(n-1),n) - elif 'st' in outflux_identifier: - k = int(outflux_identifier[3:7]) - p = int(outflux_identifier[-4:]) - outflux_profile = np.tile(np.linspace(0,k*(p-1),p),int(np.ceil(n/p))) - -#apply outflux offset and delay (delay means, that the first o_d steps, the outflux will be 0) - outflux_profile = np.concatenate((np.zeros(o_d),outflux_profile[:-o_d]+o_o)) - - return influx_profile,outflux_profile - -def make_flux_df(influx_profile,outflux_profile, time = 0): - if time == 0: - time = np.arange(0,len(influx_profile)) - flux_df = pd.DataFrame(np.transpose([time, influx_profile, outflux_profile]), \ - columns=['time', 'influx', 'outflux']) - return flux_df - - -if __name__ == "__main__": - influx_profile,outflux_profile = return_flux_profiles(100,influx_identifier='st_0010_0010',influx_offset=10) - print(influx_profile) \ No newline at end of file diff --git a/Messing Around/pressure_propagation.ipynb b/Messing Around/pressure_propagation.ipynb deleted file mode 100644 index deb7cbf..0000000 --- a/Messing Around/pressure_propagation.ipynb +++ /dev/null @@ -1,92 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "from pressure_propagation import pressure_update" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Ausbreitungsgeschwindigkeit\n", - "u = 1 # m/s\n", - "# Rohrlänge\n", - "l = 100 # m\n", - "# maximal simulierte Zeitspanne\n", - "t_max = 60 # s\n", - "\n", - "# Zeitschritt\n", - "delta_t = 0.1 # s\n", - "# Diskretisierungslänge = Ausbreitungsgeschwindigkeit*Zeitschritt\n", - "delta_x = u*delta_t\n", - "\n", - "# Anzahl der örtlichen Diskretisierungsintervalle\n", - "n_x = int(np.floor(l/delta_x))\n", - "# Anzahl der zeitlichen Diskretisierungsintervalle\n", - "n_t = int(np.floor(t_max/delta_t))\n", - "\n", - "\n", - "#initiale Druckverteilung (excl hydrostatischer Drucks)\n", - "p_0 = np.ones([n_x,1])\n", - "# np.array das den Verlauf der Druckverteilungen speichert\n", - "pressure_profiles = np.tile(p_0,[1,n_t])\n", - "\n", - "pressure_profiles[-1,0] = 2 # for testing\n", - "# loop\n", - "for i in range(1,n_t): # start at 1 because i reference i-1 in the loop over the control-volumina\n", - " #get boundary pressure from outflux-change and hydrostatic pressure in the pool\n", - " pressure_profiles[-1,i] = 2 # for testing\n", - " \n", - " for j in range(n_x-2,1,-1): # leave out the first and last control-volume because their pressure\n", - " # is set by the boundary conditions\n", - " p = pressure_profiles[j,i-1]\n", - " p1 = pressure_profiles[j+1,i-1]\n", - " p2 = pressure_profiles[j-1,i-1]\n", - " pressure_profiles[j,i] = pressure_update(p,p1,p2)\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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 -} diff --git a/Messing Around/pressure_propagation.py b/Messing Around/pressure_propagation.py deleted file mode 100644 index 9122ec7..0000000 --- a/Messing Around/pressure_propagation.py +++ /dev/null @@ -1,2 +0,0 @@ -def pressure_update(p,p1=-1,p2=-1): - return 1/4*(2*p+p1+p2) diff --git a/Messing Around/visualize_parameters.py b/Messing Around/visualize_parameters.py deleted file mode 100644 index 91f441a..0000000 --- a/Messing Around/visualize_parameters.py +++ /dev/null @@ -1 +0,0 @@ -import plotly \ No newline at end of file diff --git a/Messing Around/visualize_parameters_nb.ipynb b/Messing Around/visualize_parameters_nb.ipynb deleted file mode 100644 index b9d84e3..0000000 --- a/Messing Around/visualize_parameters_nb.ipynb +++ /dev/null @@ -1,6977 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from numpy import cos,exp,sin\n", - "\n", - "\n", - "time_vec = np.linspace(0,100,1000)\n", - "h_vec = cos(time_vec)*exp(-0.03*time_vec)\n", - "\n", - "time_df = pd.DataFrame(time_vec,columns=['time'])\n", - "h_df = pd.DataFrame(h_vec,columns=['height'])\n", - "\n", - "concat_df = pd.concat([time_df,h_df],axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, 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"linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "xaxis 1 title" - } - }, - "xaxis2": { - "anchor": "y2", - "domain": [ - 0, - 1 - ], - "title": { - "text": "xaxis 2 title" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0.575, - 1 - ], - "title": { - "text": "yaxis 1 title" - } - }, - "yaxis2": { - "anchor": "x2", - "domain": [ - 0, - 0.425 - ], - "title": { - "text": "yaxis 2 title" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import plotly.express as px\n", - "from plotly.subplots import make_subplots\n", - "import plotly.graph_objects as go\n", - "\n", - "fig = make_subplots(rows=2, cols=1)\n", - "\n", - "fig.add_trace(go.Scatter(x=concat_df['time'],y=concat_df['height']),\n", - " row=1, col=1)\n", - "fig.add_trace(go.Scatter(x=concat_df['time'],y=-1*np.ones_like(concat_df['time'])),\n", - " row=2, col=1)\n", - "fig.add_trace(go.Scatter(x=concat_df['time'],y=+1*np.ones_like(concat_df['time'])),\n", - " row=2, col=1)\n", - "\n", - "fig.update_xaxes(title_text=\"xaxis 1 title\", row=1, col=1)\n", - "fig.update_xaxes(title_text=\"xaxis 2 title\", row=2, col=1)\n", - "fig.update_yaxes(title_text=\"yaxis 1 title\", row=1, col=1)\n", - "fig.update_yaxes(title_text=\"yaxis 2 title\", row=2, col=1)\n", - "\n", - "\n", - "fig.show()" - ] - } - ], - "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 -} diff --git a/Messing Around/volume_change.py b/Messing Around/volume_change.py deleted file mode 100644 index 98344b4..0000000 --- a/Messing Around/volume_change.py +++ /dev/null @@ -1,140 +0,0 @@ - -# Testvolume -# Depth of the whole structure is constant and given by the variable d -# -# -# { x_1*d*h for h <= h_1 -# V(h) = { x_1*d*(h-h_1)+(x_2-x_1)*d*(h-h_1)**2/(2*(h_2-h_1) + V(h_1)) for h_1 < h <= h_2 -# { x_2*d*(h-h_2)+(x_3-x_2)*d*(h-h_2)**2/(2*(h_3-h_2) + V(h_2)) for h_2 < h <= h_3 -# { x_3*d*(h-h_3) + V(h_3) for h_3 < h -# -# -# { V/(x_1*d) for V <= V_1 -#h(V) = { (-b_2+sqrt(b_2**2-4*a_2*c_2)/(2*a_2)) for V_1 < V <= V_2 -# { (-b_3+sqrt(b_3**2-4*a_3*c_3)/(2*a_3)) for V_2 < V <= V_3 -# { (V-V_3)/(x_1*d) for V_3 < V -# -# with -# a_2 = 0.5*((x_2-x_1)*d)/(h_2-h_1) -# a_3 = 0.5*((x_3-x_2)*d)/(h_3-h_2) -# -# b_2 = x_1*d-((x_2-x_1)*d*h_1)/(h_2-h_1) -# b_3 = x_2*d-((x_3-x_2)*d*h_2)/(h_3-h_2) -# -# c_2 = ((x_2-x_1)*d*h_1**2)/(h_2-h_1)-h_1*x_1*d-(V-V_1) -# c_3 = ((x_3-x_2)*d*h_2**2)/(h_3-h_2)-h_2*x_2*d-(V-V_2) -# -# -# -# -# -# _____ -# | | | -# | | | -# | | | h_4 - h_3 -# | | _|_ -# __| _ _ |__ | -# / x_3 \ | -# / \ | -# / \ | -# / \ | h_3 - h_2 -# / \ | -# / \ | -# / \ | -# / \ | -# / \ _|_ -# <-----------------------------> | -# \ x_2 / | h_2 - h_1 -# \ / | -# \ _ _ _ _ _ _ _ _ _ _ _ / _|_ -# | x_1 | | -# | | | h_1 -# | | | -# |_____________________| _|_ - - - - - -def test_1_parameters(): - h_1 = 10 - h_2 = 5 + h_1 - h_3 = 5 + h_2 - - x_1 = 100 - x_2 = 101 - x_3 = 30 - - d = 5 - - vol_1 = x_1*d*h_1 - vol_2 = x_1*d*(h_2-h_1)+(x_2-x_1)*d*(h_2-h_1)**2/(2*(h_2-h_1)) + vol_1 - vol_3 = x_2*d*(h_3-h_2)+(x_3-x_2)*d*(h_3-h_2)**2/(2*(h_3-h_2)) + vol_2 - - a_2 = 0.5*((x_2-x_1)*d)/(h_2-h_1) - a_3 = 0.5*((x_3-x_2)*d)/(h_3-h_2) - - b_2 = x_1*d-((x_2-x_1)*d*h_1)/(h_2-h_1) - b_3 = x_2*d-((x_3-x_2)*d*h_2)/(h_3-h_2) - - c_2 = ((x_2-x_1)*d*h_1**2)/(2*(h_2-h_1))-h_1*x_1*d - c_3 = ((x_3-x_2)*d*h_2**2)/(2*(h_3-h_2))-h_2*x_2*d - - return h_1,h_2,h_3,x_1,x_2,x_3,d,vol_1,vol_2,vol_3,a_2,a_3,b_2,b_3,c_2,c_3 - -def V_h_test_1(h): - h_1,h_2,h_3,x_1,x_2,x_3,d,vol_1,vol_2,vol_3,a_2,a_3,b_2,b_3,c_2,c_3 = test_1_parameters() - if h <= h_1: - V = x_1*d*h - elif (h_1 < h) and (h <= h_2): - V = x_1*d*(h-h_1)+(x_2-x_1)*d*(h-h_1)**2/(2*(h_2-h_1)) + vol_1 - elif (h_2 < h) and (h <= h_3): - V = x_2*d*(h-h_2)+(x_3-x_2)*d*(h-h_2)**2/(2*(h_3-h_2)) + vol_2 - elif (h_3 < h): - V = x_3*d*(h-h_3) + vol_3 - - return V - -def h_V_test_1(V): - h_1,h_2,h_3,x_1,x_2,x_3,d,vol_1,vol_2,vol_3,a_2,a_3,b_2,b_3,c_2,c_3 =test_1_parameters() - if V <= vol_1: - h = V/(x_1*d) - elif (vol_1 < V) and (V <= vol_2): - h = (-b_2+(b_2**2-4*a_2*(c_2-(V-vol_1)))**0.5)/(2*a_2) - elif (vol_2 < V) and (V <= vol_3): - h = (-b_3+(b_3**2-4*a_3*(c_3-(V-vol_2)))**0.5)/(2*a_3) - elif (vol_3 < V): - h = (V-vol_3)/(x_3*d)+h_3 - return h - - -def test_2_parameters(): - x = 10. - d = 10. - return x,d - -def V_h_test_2(h): - x,d = test_2_parameters() - return x*d*h - -def h_V_test_2(V): - x,d = test_2_parameters() - return V/(x*d) - -def show_parameters(test_version): - h_1,h_2,h_3,x_1,x_2,x_3,d,vol_1,vol_2,vol_3,a_2,a_3,b_2,b_3,c_2,c_3 = test_1_parameters() - x,d = test_2_parameters() - - if test_version == 1: - print('h_1: ', h_1) - print('h_2: ', h_2) - print('h_3: ', h_3) - print('x_1: ', x_1) - print('x_2: ', x_2) - print('x_3: ', x_3) - elif test_version == 2: - print('x: ', x) - print('d: ', d) - - - diff --git a/Messing Around/volume_change_nb.ipynb b/Messing Around/volume_change_nb.ipynb deleted file mode 100644 index 4b3c577..0000000 --- a/Messing Around/volume_change_nb.ipynb +++ /dev/null @@ -1,237 +0,0 @@ -{ - "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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\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 -}