139 lines
3.6 KiB
Plaintext
139 lines
3.6 KiB
Plaintext
{
|
|
"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
|
|
}
|