From e255e3c1bbf8bfa3b327925a72961600d4104a3a Mon Sep 17 00:00:00 2001 From: Joseph Hellerstein Date: Sat, 18 May 2024 20:21:40 -0700 Subject: [PATCH] Fix noise, disturbance and filters --- ...nalysis-of-Immune-Response-Pneumocci.ipynb | 1259 +++++++++++++---- notebooks/differential_control.ipynb | 174 ++- src/controlSBML/antimony_builder.py | 57 +- src/controlSBML/control_sbml.py | 2 + src/controlSBML/point_evaluator.py | 12 +- src/controlSBML/siso_closed_loop_designer.py | 10 +- tests/test_antimony_builder.py | 30 +- tests/test_control_sbml.py | 67 +- todo.txt | 9 +- 9 files changed, 1260 insertions(+), 360 deletions(-) diff --git a/examples/Analysis-of-Immune-Response-Pneumocci.ipynb b/examples/Analysis-of-Immune-Response-Pneumocci.ipynb index aab66ad..c82f07e 100644 --- a/examples/Analysis-of-Immune-Response-Pneumocci.ipynb +++ b/examples/Analysis-of-Immune-Response-Pneumocci.ipynb @@ -261,12 +261,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████| 1/1 [00:01<00:00, 1.55s/it]\n" + "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 1.08it/s]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -346,12 +346,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.37it/s]\n" + "100%|██████████| 100/100 [00:03<00:00, 29.64it/s]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -390,12 +390,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████| 1/1 [00:01<00:00, 1.51s/it]\n" + "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 1.15it/s]\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -424,324 +424,1013 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/100 [00:00 7\u001b[0m design_result \u001b[38;5;241m=\u001b[39m \u001b[43mctlsb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplotGridDesign\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTIMES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msetpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSETPOINT\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/home/Technical/repos/controlSBML/src/controlSBML/control_sbml.py:733\u001b[0m, in \u001b[0;36mControlSBML.plotGridDesign\u001b[0;34m(self, grid, setpoint, sign, times, num_process, num_restart, selections, **kwargs)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m cn\u001b[38;5;241m.\u001b[39mO_TITLE \u001b[38;5;129;01min\u001b[39;00m plot_dct) \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(plot_dct[cn\u001b[38;5;241m.\u001b[39mO_TITLE]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 727\u001b[0m plot_dct[cn\u001b[38;5;241m.\u001b[39mO_TITLE] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getParameterStr([cn\u001b[38;5;241m.\u001b[39mCP_KP, cn\u001b[38;5;241m.\u001b[39mCP_KI, cn\u001b[38;5;241m.\u001b[39mCP_KF, cn\u001b[38;5;241m.\u001b[39mCP_KD],\n\u001b[1;32m 728\u001b[0m kP\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, cn\u001b[38;5;241m.\u001b[39mCP_KP),\n\u001b[1;32m 729\u001b[0m kI\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, cn\u001b[38;5;241m.\u001b[39mCP_KI),\n\u001b[1;32m 730\u001b[0m kD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, cn\u001b[38;5;241m.\u001b[39mCP_KD),\n\u001b[1;32m 731\u001b[0m kF\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, cn\u001b[38;5;241m.\u001b[39mCP_KF),\n\u001b[1;32m 732\u001b[0m )\n\u001b[0;32m--> 733\u001b[0m response_ts, antimony_builder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plotClosedLoop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43msetpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msetpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 736\u001b[0m \u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mkP\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkP\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m \u001b[49m\u001b[43mkI\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkI\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 739\u001b[0m \u001b[43m \u001b[49m\u001b[43mkD\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 740\u001b[0m \u001b[43m \u001b[49m\u001b[43mkF\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkF\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[43m \u001b[49m\u001b[43mselections\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselections\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 742\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mplot_dct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m GridDesignResult(timeseries\u001b[38;5;241m=\u001b[39mresponse_ts, antimony_builder\u001b[38;5;241m=\u001b[39mantimony_builder,\n\u001b[1;32m 744\u001b[0m designs\u001b[38;5;241m=\u001b[39mdesigns)\n", - "File \u001b[0;32m~/home/Technical/repos/controlSBML/src/controlSBML/control_sbml.py:656\u001b[0m, in \u001b[0;36mControlSBML._plotClosedLoop\u001b[0;34m(self, kP, kI, kD, kF, setpoint, sign, selections, times, **kwargs)\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[38;5;66;03m# Plot the response\u001b[39;00m\n\u001b[1;32m 651\u001b[0m response_ts, builder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sbml_system\u001b[38;5;241m.\u001b[39msimulateSISOClosedLoop(input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_name,\n\u001b[1;32m 652\u001b[0m output_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_name, sign\u001b[38;5;241m=\u001b[39msign,\n\u001b[1;32m 653\u001b[0m kP\u001b[38;5;241m=\u001b[39mkP, kI\u001b[38;5;241m=\u001b[39mkI, kD\u001b[38;5;241m=\u001b[39mkD, kF\u001b[38;5;241m=\u001b[39mkF, setpoint\u001b[38;5;241m=\u001b[39msetpoint, selections\u001b[38;5;241m=\u001b[39mselections,\n\u001b[1;32m 654\u001b[0m times\u001b[38;5;241m=\u001b[39mtimes,\n\u001b[1;32m 655\u001b[0m )\n\u001b[0;32m--> 656\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sbml_system\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplotSISOClosedLoop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msetpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mplot_dct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response_ts, builder\n", - "File \u001b[0;32m~/home/Technical/repos/controlSBML/src/controlSBML/sbml_system.py:535\u001b[0m, in \u001b[0;36mSBMLSystem.plotSISOClosedLoop\u001b[0;34m(self, timeseries, setpoint, selections, mgr, **kwargs)\u001b[0m\n\u001b[1;32m 533\u001b[0m mgr \u001b[38;5;241m=\u001b[39m OptionManager(new_kwargs)\n\u001b[1;32m 534\u001b[0m new_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_plot\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 535\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mtimeseries\u001b[49m\u001b[43m[\u001b[49m\u001b[43moutput_name\u001b[49m\u001b[43m]\u001b[49m, columns\u001b[38;5;241m=\u001b[39m[output_name])\n\u001b[1;32m 536\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39msetdefault(cn\u001b[38;5;241m.\u001b[39mO_AX2, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 537\u001b[0m plot_result \u001b[38;5;241m=\u001b[39m util\u001b[38;5;241m.\u001b[39mplotOneTS(df, colors\u001b[38;5;241m=\u001b[39m[cn\u001b[38;5;241m.\u001b[39mSIMULATED_COLOR], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnew_kwargs)\n", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] - } - ], - "source": [ - "noise_spec = ctl.NoiseSpec(random_mag=0.0001, random_std=0.001, offset=1)\n", - "ctlsb = ctl.ControlSBML(URL, times=TIMES, is_fixed_input_species=True, figsize=FIGSIZE,\n", - " input_name=INPUT_NAME, output_name=OUTPUT_NAME, noise_spec=noise_spec)\n", - "grid = ctlsb.getGrid()\n", - "grid.addAxis(\"kP\", min_value=0.5, max_value=10, num_coordinate=10)\n", - "grid.addAxis(\"kI\", min_value=0.002, max_value=0.02, num_coordinate=10)\n", - "design_result = ctlsb.plotGridDesign(grid, times=TIMES, setpoint=SETPOINT)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Narrative about noise\n", - "2. Plot noise in design (as an option)\n", - "3. PID vs. filter vs. both" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explore" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: transformers in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (4.40.2)\n", - "Requirement already satisfied: filelock in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (3.14.0)\n", - "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (0.23.0)\n", - "Requirement already satisfied: numpy>=1.17 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (1.26.0)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (23.2)\n", - "Requirement already satisfied: pyyaml>=5.1 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (6.0.1)\n", - "Requirement already satisfied: regex!=2019.12.17 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (2024.5.10)\n", - "Requirement already satisfied: requests in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (2.31.0)\n", - "Requirement already satisfied: tokenizers<0.20,>=0.19 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (0.19.1)\n", - "Requirement already satisfied: safetensors>=0.4.1 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (0.4.3)\n", - "Requirement already satisfied: tqdm>=4.27 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from transformers) (4.66.1)\n", - "Requirement already satisfied: fsspec>=2023.5.0 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2024.5.0)\n", - "Requirement already satisfied: typing-extensions>=3.7.4.3 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.8.0)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from requests->transformers) (3.3.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from requests->transformers) (3.4)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from requests->transformers) (2.0.6)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /Users/jlheller/home/Technical/repos/controlSBML/ctl/lib/python3.9/site-packages (from requests->transformers) (2023.7.22)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_TOO_MUCH_WORK: The solver took mxstep (20000) internal steps but could not reach tout.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n", + "CVODE Error: CV_CONV_FAILURE: Convergence test failures occurred too many times (= MXNCF = 10) during one internal timestep or occurred with |h| = hmin.; In virtual double rr::CVODEIntegrator::integrate(double, double)\n" ] - } - ], - "source": [ - "!pip install transformers" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": 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", "text/plain": [ - "['',\n", - " 'Users',\n", - " 'jlheller',\n", - " 'home',\n", - " 'Technical',\n", - " 'repos',\n", - " 'controlSBML',\n", - " 'ctl',\n", - " 'lib',\n", - " 'python3.9',\n", - " 'site-packages',\n", - " 'transformers']" + "
" ] }, - "execution_count": 33, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "transformers.__file__.split('/')[:-1]\n", - "#\"/models/llama/convert_llama_weights_to_hf.py\"" + "noise_spec = ctl.NoiseSpec(random_mag=1, random_std=0.1, offset=1)\n", + "ctlsb = ctl.ControlSBML(URL, times=TIMES, is_fixed_input_species=True, figsize=FIGSIZE,\n", + " input_name=INPUT_NAME, output_name=OUTPUT_NAME, noise_spec=noise_spec)\n", + "grid = ctlsb.getGrid()\n", + "grid.addAxis(\"kP\", min_value=0.5, max_value=10, num_coordinate=10)\n", + "grid.addAxis(\"kI\", min_value=0.002, max_value=0.02, num_coordinate=10)\n", + "design_result = ctlsb.plotGridDesign(grid, times=TIMES, setpoint=SETPOINT, num_restart=5)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "TRANSFORM=`python -c \"import transformers;print('/'.join(transformers.__file __.split('/')[:-1])+'/models/llama/convert_llama_weights_to_hf.py')\"`" + "1. Narrative about noise\n", + "2. Plot noise in design (as an option)\n", + "3. PID vs. filter vs. both" ] } ], diff --git a/notebooks/differential_control.ipynb b/notebooks/differential_control.ipynb index 1f8012a..2aca9ca 100644 --- a/notebooks/differential_control.ipynb +++ b/notebooks/differential_control.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "c4fcb146-6457-48a5-97fa-6feb1bcf6fa7", "metadata": { "scrolled": true @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "'1.1.03'" + "'1.1.04'" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -374,13 +374,13 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 38, "id": "15ccef64-2c84-4923-bfb2-813626ec50e9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -405,7 +405,7 @@ "S1 = 10\n", "S2 = 0\n", "S3 = 0\n", - "end\n", + "\n", "\n", "//^^^^^^^^^Added by ControlSBML^^^^^^^^^^\n", "const S1\n", @@ -415,15 +415,16 @@ "S3 = 0.0\n", "\n", "// Make sinusoid: amplitude=1, frequency=2\n", - "noise_S1_S3_ot := 1.000000*sin(2*pi*2.000000*time) + 1.000000\n", + "noise_S1_S3_ot := 0*sin(2*pi*2.000000*time) + 1.000000\n", "\n", "// Make sinusoid: amplitude=2, frequency=3\n", - "disturbance_S1_S3_ot := 2.000000*sin(2*pi*3.000000*time) + 2.000000\n", + "disturbance_S1_S3_ot := 0\n", "\n", "// Make filter: kF=0.1\n", "\n", "// Filter: kF=0.1\n", - " -> filter_S1_S3_ot; -0.100000*filter_S1_S3_ot + 0.100000*filter_S1_S3_in \n", + "kF = 1e3\n", + " -> filter_S1_S3_ot; -kF*filter_S1_S3_ot + kF*filter_S1_S3_in \n", "filter_S1_S3_ot = 0\n", "\n", "// Make the PID controller\n", @@ -442,9 +443,10 @@ "controller_S1_S3_in := control_error_S1_S3_ot\n", "S1 := controller_S1_S3_ot + disturbance_S1_S3_ot\n", "filter_S1_S3_in := S3 + noise_S1_S3_ot\n", + "end\n", "\"\"\"\n", "rr = te.loada(MODEL2)\n", - "rr.simulate(0, 100, 1000, selections=[\"time\", \"setpoint_S1_S3\", \"S1\", \"filter_S1_S3_ot\", \"control_error_S1_S3_ot\", \"S3\"])\n", + "rr.simulate(0, 1000, 10000, selections=[\"time\", \"setpoint_S1_S3\", \"S1\", \"filter_S1_S3_ot\", \"control_error_S1_S3_ot\", \"S3\"])\n", "rr.plot()" ] }, @@ -504,6 +506,158 @@ "source": [ "np.std(data[\"Y\"])" ] + }, + { + "cell_type": "markdown", + "id": "7b322c03-2881-43ca-bbcf-ff7a77c6da93", + "metadata": {}, + "source": [ + "# Assess equivalence of reaction and species" + ] + }, + { + "cell_type": "markdown", + "id": "679b326a-d7a2-4df8-893b-1578a2a2c71f", + "metadata": {}, + "source": [ + "This equivalence works well for smooth functions, such as sine." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a97eae32-7b93-452a-ad37-c3b4318abcfe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = \"\"\"\n", + "k1 := sin(100*time)\n", + "J1: S2->S2; k1\n", + "S2 = 1\n", + "\"\"\"\n", + "rr = te.loada(model)\n", + "selections = [\"time\", \"k1\", \"J1\"]\n", + "data = rr.simulate(0, 100, 1000, selections=selections)\n", + "plt.scatter(data[\"k1\"], data[\"J1\"])" + ] + }, + { + "cell_type": "markdown", + "id": "374b3a00-1472-4e9f-850b-1a4613fba71b", + "metadata": {}, + "source": [ + "But it works poory if $k_1$ is purely random. Suppose $k_1 = a t + e(t)$, where\n", + "$log(e(t))$ is $N(0, 1)$." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "dc0bfd64-b3ae-4029-9fea-eff5399668e7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = \"\"\"\n", + "k1 := lognormal(0, 1) + a*time\n", + "J1: S2->S2; k1\n", + "S2 = 1\n", + "a = 0\n", + "\"\"\"\n", + "_, axes = plt.subplots(2, 2)\n", + "pos_dct = {0: (0, 0), 1: (0, 1), 2: (1, 0), 3: (1, 1)}\n", + "for idx, a in enumerate([0, 2, 4, 8]):\n", + " ax = axes[pos_dct[idx]]\n", + " rr = te.loada(model)\n", + " rr[\"a\"] = a\n", + " selections = [\"time\", \"k1\", \"J1\"]\n", + " data = rr.simulate(0, 100, 1000, selections=selections)\n", + " ax.scatter(data[\"k1\"], data[\"J1\"])\n", + " ax.set_title(f\"a: {a}\")\n", + " if idx < 2:\n", + " ax.set_xticklabels([])" + ] + }, + { + "cell_type": "markdown", + "id": "93fe72e8-70ce-45e4-9174-c4e764d63025", + "metadata": {}, + "source": [ + "# rateOf for a random variable" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ea9d43c0-39de-4697-9495-ec85ad1aa886", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = \"\"\"\n", + "k1 := lognormal(0, 1) + a*time\n", + "J1: S1->S1; k1\n", + "rJ1 := rateOf(J1)\n", + "S1 = 1\n", + "a = 0\n", + "\"\"\"\n", + "_, axes = plt.subplots(2, 2)\n", + "pos_dct = {0: (0, 0), 1: (0, 1), 2: (1, 0), 3: (1, 1)}\n", + "for idx, a in enumerate([0, 2, 4, 8]):\n", + " ax = axes[pos_dct[idx]]\n", + " rr = te.loada(model)\n", + " rr[\"a\"] = a\n", + " selections = [\"time\", \"k1\", \"J1\", \"rJ1\"]\n", + " data = rr.simulate(0, 100, 1000, selections=selections)\n", + " ax.plot(data[\"time\"], data[\"rJ1\"])\n", + " ax.plot(data[\"time\"], data[\"J1\"])\n", + " ax.set_title(f\"a: {a}\")\n", + " if idx < 2:\n", + " ax.set_xticklabels([])\n", + " if idx == 3:\n", + " ax.legend([\"rJ1\", \"J1\"])" + ] } ], "metadata": { diff --git a/src/controlSBML/antimony_builder.py b/src/controlSBML/antimony_builder.py index 114b0ef..bf13d13 100644 --- a/src/controlSBML/antimony_builder.py +++ b/src/controlSBML/antimony_builder.py @@ -339,7 +339,7 @@ def _makeInputOutputName(self, prefix, suffix): self.closed_loop_symbols.append(name_ot) return name_in, name_ot - def makeFilterElement(self, kF:float, prefix:str="filter", suffix:str=""): + def makeFilterElement(self, kF:float, prefix:str="filter", suffix:str="", kF_nofilter=100): """ Makes a filter. prefix + suffix + IN is the input and prefix + suffix + OT is the output. Prefix is used to scope within a control loop. Suffix is used to scope between control loops. @@ -348,6 +348,7 @@ def makeFilterElement(self, kF:float, prefix:str="filter", suffix:str=""): kF: float prefix: str (beginning of the name) suffix: str (ending of the name) + kF_nofilter: float (value of kF when no filter is used) Returns: str: name of the filter input str: name of the filter output @@ -358,17 +359,18 @@ def makeFilterElement(self, kF:float, prefix:str="filter", suffix:str=""): self.makeAddition(name_in, "S3") # S3 is the output of the system self.makeAddition("control_error", setpoint, "-"+name_ot) """ + # self.addStatement("") self.makeComment("Make filter: kF=%s" % (str(kF))) kF_name = self._makeScopedName("kF", suffix) name_in, name_ot = self._makeInputOutputName(prefix, suffix) if (kF is None) or np.isclose(kF, 0): - kF = 10e6 # Use a large value to approximate a filter with no effect - comment = "Filter with no effect" + new_kF = kF_nofilter else: - comment = "" - self.makeAdditionStatement(kF_name, kF, is_assignment=False, comment=comment) - calculation = "-%s*%s + %s*%s" % (kF_name, name_ot, kF_name, name_in) + new_kF = kF + self.makeAdditionStatement(kF_name, new_kF, is_assignment=False) + # Construct the filter calculation + calculation = f"-{kF_name}*{name_ot} + {kF_name}*{name_in}" # Use a dummy reaction to integrate instead of "'" to avoid antimony limitations with # combining rate rules and assignment rules statement = " -> %s; %s " % (name_ot, calculation) @@ -433,29 +435,42 @@ def makePIDControllerElement(self, str: name of the controller output """ base_name = prefix + "_" + "%s" + suffix # type: ignore + def makeControllerScopedName(name): + scoped_name = base_name % name + self.closed_loop_symbols.append(scoped_name) + return scoped_name + # self.addStatement("") self.makeComment("Make the PID controller") name_in, name_ot = self._makeInputOutputName(prefix, suffix) # Constants for parameters - kP_name = base_name % "kP" - kI_name = base_name % "kI" - kD_name = base_name % "kD" if kP is not None: + kP_name = makeControllerScopedName("kP") self.makeAdditionStatement(kP_name, str(kP), is_assignment=False) - self.closed_loop_symbols.append(kP_name) if kI is not None: + kI_name = makeControllerScopedName("kI") self.makeAdditionStatement(kI_name, str(kI), is_assignment=False) - self.closed_loop_symbols.append(kI_name) if kD is not None: + kD_name = makeControllerScopedName("kD") self.makeAdditionStatement(kD_name, str(kD), is_assignment=False) - self.closed_loop_symbols.append(kD_name) # Make the derivative of the control error if kD is not None: derivative_error_name = base_name % "derivative_error" self.closed_loop_symbols.append(derivative_error_name) if filter_calculation is None: - filter_calculation = "rateOf(%s)" % (output_name) # type: ignore - sign_filter_calculation = "%d*(%s)" % (sign, filter_calculation) # type: ignore + # Step 1: Make a dummy reaction to get the rate of the output + # J: S->S; output_name + # S = 1 + # filter_calculation = rateOf(J) + reaction_name = makeControllerScopedName("reaction") + species_name = makeControllerScopedName("species") + reaction_stmt = f"{reaction_name}: {species_name} -> {species_name}" + reaction_stmt += f"; {output_name}" + self.addStatement(reaction_stmt) + self.makeAdditionStatement(species_name, 1, is_assignment=False) # rate_law = 0 + # Step 2: Make the rate law + filter_calculation = f"rateOf({reaction_name})" + sign_filter_calculation = f"{sign}*{filter_calculation}" statement = "%s := %s" % (derivative_error_name, sign_filter_calculation) # type: ignore self.addStatement(statement) # Make the integral of the control error @@ -481,7 +496,8 @@ def makeSISOClosedLoopSystem(self, input_name, output_name, kP=None, kI=None, kD noise_spec=cn.NoiseSpec(), disturbance_spec=cn.DisturbanceSpec(), initial_output_value=None, sign=-1): """ - Creates a closed loop system with a single input and a single output. + Creates a closed loop system with a single input and a single output. Does not create a filter + if kF is in [0, None] Args: input_name: str (input to system) @@ -512,14 +528,21 @@ def makeSISOClosedLoopSystem(self, input_name, output_name, kP=None, kI=None, kD controller_in, controller_ot = self.makePIDControllerElement(output_name, filter_calculation=filter_calculation, kP=kP, kI=kI, kD=kD, prefix="controller", suffix=suffix, sign=sign) - control_error_name = self.makeControlErrorSignal(setpoint_name, filter_ot, sign, prefix="control_error", + if filter_ot is None: + comparison_signal_str = "(" + output_name + " + " + noise_ot + ")" + else: + comparison_signal_str = filter_ot + control_error_name = self.makeControlErrorSignal(setpoint_name, + comparison_signal_str, + sign, prefix="control_error", suffix=suffix) # Connect the pieces by specifying assignment statements self.addStatement("") self.makeComment("Connect the elements of the closed loop") self.makeAdditionStatement(controller_in, control_error_name) self.makeAdditionStatement(input_name, controller_ot, disturbance_ot) - self.makeAdditionStatement(filter_in, output_name, noise_ot) + if filter_in is not None: + self.makeAdditionStatement(filter_in, output_name, noise_ot) def getInputManipulationName(self, input_name): """ diff --git a/src/controlSBML/control_sbml.py b/src/controlSBML/control_sbml.py index 268d1c1..de9ef3a 100644 --- a/src/controlSBML/control_sbml.py +++ b/src/controlSBML/control_sbml.py @@ -749,6 +749,8 @@ def plotGridDesign(self, @staticmethod def setSpec(val): + if isinstance(val, bool): + return val if isinstance(val, int): return float(val) else: diff --git a/src/controlSBML/point_evaluator.py b/src/controlSBML/point_evaluator.py index 47544ca..9d46e5e 100644 --- a/src/controlSBML/point_evaluator.py +++ b/src/controlSBML/point_evaluator.py @@ -93,11 +93,13 @@ def _calculateMse(self, **parameter_dct:dict)->Tuple[str, object]: return cn.DESIGN_RESULT_CANNOT_SIMULATE, None outputs = response_ts[self.output_name].values max_value = np.max([np.max(outputs), np.abs(np.min(outputs))]) - if max_value > max_output: - return cn.DESIGN_RESULT_OUTPUT_TOO_LARGE, None - min_value = np.min([np.max(outputs), np.abs(np.min(outputs))]) - if min_value < min_output: - return cn.DESIGN_RESULT_OUTPUT_TOO_SMALL, None + if False: + # Disable these checks + if max_value > max_output: + return cn.DESIGN_RESULT_OUTPUT_TOO_LARGE, None + min_value = np.min([np.max(outputs), np.abs(np.min(outputs))]) + if min_value < min_output: + return cn.DESIGN_RESULT_OUTPUT_TOO_SMALL, None # residuals = self.setpoint - response_ts[self.output_name].values mse = np.mean(residuals**2) diff --git a/src/controlSBML/siso_closed_loop_designer.py b/src/controlSBML/siso_closed_loop_designer.py index 2b4b98c..09d950f 100644 --- a/src/controlSBML/siso_closed_loop_designer.py +++ b/src/controlSBML/siso_closed_loop_designer.py @@ -322,12 +322,13 @@ def getValue(val): parallel_search.search() search_result_df = parallel_search.getSearchResults() if len(search_result_df) == 0: - df = pd.DataFrame([[None, None, None, None, cn.DESIGN_RESULT_CANNOT_SIMULATE]], + df = pd.DataFrame([[None, None, None, None, None, cn.DESIGN_RESULT_CANNOT_SIMULATE]], columns=[CP_kP, CP_kI, CP_kD, CP_kF, cn.SCORE, cn.REASON]) return DesignResult(dataframe=df, max_count=0) # Merge the results and sort by score search_result_df = search_result_df.sort_values(cn.SCORE, ascending=True) # type: ignore search_result_df = search_result_df.reset_index(drop=True) + result_df = search_result_df.copy() # Handle replications of the same design parameters and select successful designs search_result_df = search_result_df[search_result_df[cn.SCORE].notna()] del search_result_df[cn.REASON] @@ -342,6 +343,11 @@ def getValue(val): sorted_mean_df = threshold_mean_df.reset_index() sorted_mean_df = sorted_mean_df.sort_values(cn.SCORE, ascending=True) sorted_mean_df = sorted_mean_df.reset_index() + # Check for no result + if len(sorted_mean_df) == 0: + df = pd.DataFrame([[None, None, None, None, None, cn.DESIGN_RESULT_CANNOT_SIMULATE]], + columns=[CP_kP, CP_kI, CP_kD, CP_kF, cn.SCORE, cn.REASON]) + return DesignResult(dataframe=df, max_count=0) # Record the result self.residual_mse = sorted_mean_df.loc[0, cn.SCORE] if CP_kP in sorted_mean_df.columns: @@ -356,7 +362,7 @@ def getValue(val): if self.save_path is not None: sorted_mean_df.to_csv(self.save_path, index=False) self._design_result_df = sorted_mean_df - return DesignResult(dataframe=sorted_mean_df, max_count=max_count) + return DesignResult(dataframe=result_df, max_count=max_count) @property def design_result_df(self): diff --git a/tests/test_antimony_builder.py b/tests/test_antimony_builder.py index 387ccdc..66a44f8 100644 --- a/tests/test_antimony_builder.py +++ b/tests/test_antimony_builder.py @@ -44,16 +44,18 @@ def setUp(self): def init(self): self.builder = ab.AntimonyBuilder(LINEAR_MDL, symbol_dct=SYMBOL_DCT) - def check(self, builder=None): + def check(self, builder=None, times=None): if builder is None: builder = self.builder + if times is None: + times = np.linspace(0, 20, 2000) rr = te.loada(str(builder)) selections = ["time", "S1", "S2", "S3"] if "setpoint_S1_S3" in rr.keys(): selections.append("setpoint_S1_S3") if "noise_S1_S3_ot" in rr.keys(): selections.append("noise_S1_S3_ot") - data = rr.simulate(0,20, 2000, selections=selections) + data = rr.simulate(times[0], times[-1], len(times), selections=selections) self.assertTrue(len(data) > 0) if IS_PLOT: rr.plot() @@ -141,6 +143,16 @@ def testMakeFilterElement(self): self.builder.makeAdditionStatement(filter_in, sin_ot) self.check() + def testMakeFilterElementNofilter(self): + if IGNORE_TEST: + return + self.init() + filter_in, filter_ot, calculation = self.builder.makeFilterElement(0, suffix="_S1_S3") + noise_spec = cn.NoiseSpec(sine_amp=1, sine_freq=2) + sin_ot = self.builder.makeNoiseElement(noise_spec, suffix="_S1_S2") + self.builder.makeAdditionStatement(filter_in, sin_ot) + self.check() + def testMakeControlErrorSignal(self): if IGNORE_TEST: return @@ -166,6 +178,16 @@ def testMakePIDController(self): self.builder.makeAdditionStatement("S1", name_ot) self.builder.makeAdditionStatement(name_in, 3, "-"+"S3") self.check() + + def testMakePIDController4(self): + if IGNORE_TEST: + return + self.init() + name_in, name_ot = self.builder.makePIDControllerElement("S3", kP=7, suffix="_S1_S3") + self.builder.makeBoundarySpecies("S1") + self.builder.makeAdditionStatement("S1", name_ot) + self.builder.makeAdditionStatement(name_in, 3, "-"+"S3") + self.check() def testMakePIDController3(self): # Filter without differential control @@ -275,8 +297,8 @@ def testMakeSISClosedLoop(self): return self.init() self.builder.makeBoundarySpecies("S1") - self.builder.makeSISOClosedLoopSystem("S1", "S3", kP=10000, kI=1, setpoint=4) - data = self.check() + self.builder.makeSISOClosedLoopSystem("S1", "S3", kP=10, kI=1, setpoint=4) + data = self.check(times=np.linspace(0, 100, 1000)) self.assertTrue(np.isclose(data["S3"][-1], 4, atol=0.01)) def testCopyAndEqual(self): diff --git a/tests/test_control_sbml.py b/tests/test_control_sbml.py index ba33169..28b40ac 100644 --- a/tests/test_control_sbml.py +++ b/tests/test_control_sbml.py @@ -14,8 +14,8 @@ import unittest -IGNORE_TEST = True -IS_PLOT = True +IGNORE_TEST = False +IS_PLOT = False TIMES = cn.TIMES FIGSIZE = (5, 5) SAVE1_PATH = os.path.join(cn.TEST_DIR, "control_sbml_save_path.csv") @@ -138,7 +138,7 @@ def testPlotDesign(self): ctlsb.setSystem(input_name="S1", output_name="S3") result = ctlsb.plotDesign(setpoint=setpoint, kP_spec=True, kI_spec=True, figsize=FIGSIZE, is_plot=IS_PLOT, min_parameter_value=0.001, max_parameter_value=10, num_restart=2, - num_coordinate=5, num_process=10) + num_coordinate=3, num_process=10) # Show that kP, kI are now the defaults _ = ctlsb._plotClosedLoop(setpoint=setpoint, is_plot=IS_PLOT, kP=1, figsize=FIGSIZE, times=np.linspace(0, 100, 1000)) @@ -176,10 +176,10 @@ def testPlotGridDesign(self): return setpoint = 5 ctlsb = ControlSBML(LINEAR_MDL, setpoint=setpoint, final_value=10, input_name="S1", output_name="S3") - grid = Grid(min_value=0.1, max_value=10, num_coordinate=10) - grid.addAxis("kP") - grid.addAxis("kI") - grid.addAxis("kD") + grid = Grid(min_value=0.1, max_value=10, num_coordinate=3) + grid.addAxis("kP", num_coordinate=3) + grid.addAxis("kI", num_coordinate=3) + grid.addAxis("kD", num_coordinate=3) _ = ctlsb.plotGridDesign(grid, is_plot=IS_PLOT) def testPlotDesign1(self): @@ -218,22 +218,23 @@ def testFullAPI(self): OUTPUT_NAME = "pmTORC1" INPUT_NAME = "pIRS" path = util.getModelPath("Varusai2018") - CTLSB = ControlSBML(path, figsize=FIGSIZE, times=TIMES, markers=False, - input_name=INPUT_NAME, output_name=OUTPUT_NAME) # Specify default value of options - _ = CTLSB.plotModel(ax2=0, is_plot=IS_PLOT) + if False: + CTLSB = ControlSBML(path, figsize=FIGSIZE, times=TIMES, markers=False, + input_name=INPUT_NAME, output_name=OUTPUT_NAME) # Specify default value of options + _ = CTLSB.plotModel(ax2=0, is_plot=IS_PLOT) # Define the system and plot response over a controlled range CTLSB = ControlSBML(path, figsize=FIGSIZE, input_name=INPUT_NAME, output_name=OUTPUT_NAME, times=np.linspace(0, 1000, 10000), markers=False, sign=-1, is_plot=False) + _ = CTLSB.plotDesign(setpoint=150, kP_spec=True, kI_spec=True, kF_spec=False, + num_restart=1, is_plot=IS_PLOT, selections=[INPUT_NAME, OUTPUT_NAME], + num_process=5) if False: _, builder = CTLSB.plotStaircaseResponse(is_plot=IS_PLOT) _, builder = CTLSB.plotStaircaseResponse(initial_value=20, final_value=25, is_plot=IS_PLOT) _ = CTLSB.plotTransferFunctionFit(figsize=FIGSIZE, num_zero=1, num_pole=2, initial_value=20, final_value=25, fit_start_time=200, is_plot=IS_PLOT) _ = CTLSB._plotClosedLoop(setpoint=150, kP=1, kF=None, is_plot=IS_PLOT) - _ = CTLSB.plotDesign(setpoint=150, kP_spec=True, kI_spec=True, kF_spec=False, - num_restart=1, is_plot=IS_PLOT, selections=[INPUT_NAME, OUTPUT_NAME], - num_process=5) _ = CTLSB._plotClosedLoop(setpoint=120, kP=0.002, kI=0.019, is_plot=IS_PLOT) _ = CTLSB._plotClosedLoop(setpoint=150, kP=1, is_plot=IS_PLOT) @@ -345,8 +346,9 @@ def testBug5(self): times = np.linspace(0, 50, 500) WOLF_CTLSB = ControlSBML(cn.WOLF_PATH, input_name="s1", output_name="s5", times=times) - _ = WOLF_CTLSB.plotDesign(kP_spec=.001, kI_spec=False, times=np.linspace(0, 5, 50), num_restart=1, - num_process=1, is_plot=IS_PLOT) + _ = WOLF_CTLSB.plotDesign(kP_spec=True, kI_spec=True, num_restart=1, + is_plot=IS_PLOT, num_coordinate=20, + min_parameter_value=0.001, max_parameter_value=5) def testBug6(self): # Bug with setting inputs that are fixed @@ -395,7 +397,7 @@ def testBug8(self): ctlsb = ControlSBML(path, times=np.linspace(0, 30, 300), input_name="Vitamins", output_name="Normal_cells", is_fixed_input_species=True) result = ctlsb.plotDesign(setpoint=2, kP_spec=0.2, kI_spec=0.1, figsize=FIGSIZE, times=np.linspace(0, 100, 1000), min_parameter_value=1, - max_parameter_value=100,is_plot=IS_PLOT) + max_parameter_value=100,is_plot=IS_PLOT, num_coordinate=2) self.assertTrue(isinstance(result.timeseries, Timeseries)) def testBug9(self): @@ -407,7 +409,7 @@ def testBug9(self): times=np.linspace(0, 100, 1000), figsize=FIGSIZE) def test(kP_spec, kI_spec): result = ctlsb.plotDesign(setpoint=2, kP_spec=kP_spec, kI_spec=kI_spec, min_parameter_value=1, - max_parameter_value=100, num_restart=1, is_plot=IS_PLOT) + max_parameter_value=100, num_restart=1, is_plot=IS_PLOT, num_coordinate=2) self.assertTrue("kP" in result.designs.dataframe.columns) self.assertTrue("kI" in result.designs.dataframe.columns) if result.timeseries is not None: @@ -426,8 +428,8 @@ def testBug10(self): OUTPUT_NAME = "EI_P" CTLSB.setSystem(input_name=INPUT_NAME, output_name=OUTPUT_NAME) grid = CTLSB.getGrid() - grid.addAxis("kP", min_value=0.0, max_value=0.005, num_coordinate=11) - grid.addAxis("kI", min_value=0.0, max_value=0.002, num_coordinate=11) + grid.addAxis("kP", min_value=0.0, max_value=0.005, num_coordinate=3) + grid.addAxis("kI", min_value=0.0, max_value=0.002, num_coordinate=3) result = CTLSB.plotGridDesign(grid, setpoint=0.0000003, is_plot=IS_PLOT) self.assertEqual(result.designs.dataframe.loc[0, cn.REASON], cn.DESIGN_RESULT_SUCCESS) @@ -439,7 +441,7 @@ def testBug11(self): path = util.getModelPath("Tsai2014") CTLSB = ControlSBML(path, times=np.linspace(0, 100, 1000)) CTLSB.setSystem(input_name="Plx1_active", output_name="APC_C_active") - result = CTLSB.plotTransferFunctionFit(fit_start_time=20, final_value=1.0, + _ = CTLSB.plotTransferFunctionFit(fit_start_time=20, final_value=1.0, is_plot=IS_PLOT) def testBug12(self): @@ -452,7 +454,7 @@ def testBug12(self): ctlsb= ControlSBML(path, figsize=(5, 5), times=np.linspace(0, 2000, 20000), markers=False, input_name=INPUT_NAME, output_name=OUTPUT_NAME) _ = ctlsb.plotDesign(setpoint=80, kP_spec=1, kI_spec=0.01, times=np.linspace(0, 2000, 20000), - is_plot=IS_PLOT) + is_plot=IS_PLOT, num_coordinate=2) def testBug13(self): # Bad time axis @@ -464,7 +466,8 @@ def testBug13(self): CTLSB = ControlSBML(path, figsize=(5, 5), times=np.linspace(0, 10, 100), markers=False, is_fixed_input_species=True, save_path="data.csv", input_name=INPUT_NAME, output_name=OUTPUT_NAME) # Specify default value of options TIMES = np.linspace(0, 10**5, 10**6) - _ = CTLSB.plotDesign(setpoint=0.1, kP_spec=1, kI_spec=0.1, times=TIMES, num_restart=1, is_plot=IS_PLOT) + _ = CTLSB.plotDesign(setpoint=0.1, kP_spec=1, kI_spec=0.1, times=TIMES, num_restart=1, is_plot=IS_PLOT, + num_coordinate=2) def testBug14(self): # Not using the correct design parameters @@ -479,25 +482,29 @@ def testBug14(self): SETPOINT = 1000 _ = CTLSB.plotDesign(kP_spec=1.5555, kI_spec=0.018, times=TIMES, setpoint=SETPOINT, is_plot=IS_PLOT) grid = CTLSB.getGrid() - grid.addAxis("kP", min_value=0.5, max_value=10, num_coordinate=11) - grid.addAxis("kI", min_value=0.002, max_value=0.02, num_coordinate=11) + grid.addAxis("kP", min_value=0.5, max_value=10, num_coordinate=3) + grid.addAxis("kI", min_value=0.002, max_value=0.02, num_coordinate=3) _ = CTLSB.plotGridDesign(grid, times=TIMES, setpoint=SETPOINT, is_plot=IS_PLOT) def testBug15(self): # Not using the correct design parameters - #if IGNORE_TEST: - # return + if IGNORE_TEST: + return + TIMES = np.linspace(0, 200, 2000) INPUT_NAME = 'Pneumococci___P' OUTPUT_NAME = 'Neutrophils__N' path = util.getModelPath("Smith2011_V1") - noise_spec = cn.NoiseSpec(random_mag=0.0001, random_std=0.001, offset=1) + noise_spec = cn.NoiseSpec(random_mag=10.0, random_std=1, offset=1) ctlsb = ControlSBML(path, times=TIMES, is_fixed_input_species=True, figsize=FIGSIZE, input_name=INPUT_NAME, output_name=OUTPUT_NAME, noise_spec=noise_spec) grid = ctlsb.getGrid() - grid.addAxis("kP", min_value=0.5, max_value=10, num_coordinate=10) - grid.addAxis("kI", min_value=0.002, max_value=0.02, num_coordinate=10) + grid.addAxis("kP", min_value=0.5, max_value=10, num_coordinate=5) + grid.addAxis("kD", min_value=0.0, max_value=0.2, num_coordinate=5) + grid.addAxis("kI", min_value=0.0, max_value=0.2, num_coordinate=5) + #grid.addAxis("kF", min_value=0.01, max_value=0.02, num_coordinate=2) SETPOINT = 1000 - design_result = ctlsb.plotGridDesign(grid, times=TIMES, setpoint=SETPOINT, num_restart=10) + _ = ctlsb.plotGridDesign(grid, times=TIMES, setpoint=SETPOINT, num_restart=5, + is_plot=IS_PLOT) diff --git a/todo.txt b/todo.txt index fabfb18..274c4dd 100644 --- a/todo.txt +++ b/todo.txt @@ -1,10 +1,5 @@ -NoiseSpec, DisturbanceSpec - a. sine_freq, sine_amp, random_std, random_amp, dc_offset, trend_slope - b. default dc_offset = sine_amp - c. Classes in constants: DisturbanceSpec inherits from NoiseSpec - d. __init__.py: from constants import NoiseSpec, DistrubanceSpec - e. makeSineElement takes NoiseSpec, DisturbanceSpec as arguments -Remove the units from time on plots. +Resolve rateOf + Use filter? Update docs on REAMDE Create pypi version CTLSB.plotSBMLSystem - does a plot with multiple inputs and multiple outputs.