diff --git a/cookbook/cinnabar_absolute.ipynb b/cookbook/cinnabar_absolute.ipynb index a35c3be..f6934db 100644 --- a/cookbook/cinnabar_absolute.ipynb +++ b/cookbook/cinnabar_absolute.ipynb @@ -5,69 +5,29 @@ "id": "ddae61fd-fa15-4be9-9160-ba3e195c67dc", "metadata": {}, "source": [ - "# Plotting OpenFE DG results against experiment using Cinnabar v0.4\n", + "# Plotting OpenFE DG results against experiment using Cinnabar v0.6.0\n", "\n", - "This notebook shows how one would go about creating a cinnabar plot of OpenFE results against known experimental values." + "This notebook shows how one would go about creating a ``cinnabar`` style plot of OpenFE results against known experimental values. For more information on using the ``cinnabar`` API please see the [tutorials](https://cinnabar.openfree.energy/en/latest/tutorials/index.html)." ] }, { "cell_type": "code", "execution_count": 1, "id": "6abcaae7-f5fd-483c-972f-5bb5b6d4908a", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:02.219642Z", + "start_time": "2026-06-09T10:55:02.170335Z" + } + }, "outputs": [], "source": [ "# First we do a set of imports\n", - "import csv\n", - "from pprint import pprint\n", - "from cinnabar import plotting as cinnabar_plotting\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "515561da-b06e-4741-9178-6d5eb75cd140", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_dg(\n", - " exp_data: dict[str, dict[str, float]],\n", - " calc_data: dict[str, dict[str, float]],\n", - " dg_filename: str,\n", - " statistics: list = [\"RMSE\", \"MUE\", \"R2\", \"rho\"],\n", - "):\n", - " shift = sum([i['dG'] for i in exp_data.values()]) / len(exp_data)\n", - " x_data = np.asarray([i['dG'] for i in exp_data.values()])\n", - " y_data = np.asarray([i['dG'] for i in calc_data.values()])\n", - " xerr = np.asarray([i['ddG'] for i in exp_data.values()])\n", - " yerr = np.asarray([i['ddG'] for i in calc_data.values()])\n", - " \n", - " # centralising\n", - " # this should be replaced by providing one experimental result\n", - " x_data = x_data - np.mean(x_data) + shift\n", - " y_data = y_data - np.mean(y_data) + shift\n", - " \n", - " cinnabar_plotting._master_plot(\n", - " x_data,\n", - " y_data,\n", - " xerr=xerr,\n", - " yerr=yerr,\n", - " origins=False,\n", - " statistics=statistics,\n", - " quantity=rf\"$\\Delta$ G\",\n", - " title='Experiment vs OpenFE',\n", - " method_name=\"\",\n", - " target_name=\"\",\n", - " filename=dg_filename,\n", - " bootstrap_x_uncertainty=False,\n", - " bootstrap_y_uncertainty=False,\n", - " statistic_type=\"mle\",\n", - " xy_lim=[-15, -5],\n", - " figsize=5,\n", - " xlabel='experimental',\n", - " ylabel='openfe',\n", - " )" + "%matplotlib inline\n", + "%config InlineBackend.figure_formats = ['svg']\n", + "import pandas as pd\n", + "from cinnabar import FEMap, plotting\n", + "from openff.units import unit" ] }, { @@ -88,44 +48,142 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "8b80f592-f1a3-4c7f-8757-58a88c2e8ee7", - "metadata": {}, + "execution_count": 2, + "id": "6b06e306c51ca4d7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:04.352822Z", + "start_time": "2026-06-09T10:55:04.144955Z" + } + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lig_ejm_42': {'dG': -9.81, 'ddG': 0.18},\n", - " 'lig_ejm_43': {'dG': -8.29, 'ddG': 0.18},\n", - " 'lig_ejm_45': {'dG': -9.59, 'ddG': 0.18},\n", - " 'lig_ejm_46': {'dG': -11.35, 'ddG': 0.17},\n", - " 'lig_ejm_47': {'dG': -9.73, 'ddG': 0.18},\n", - " 'lig_ejm_48': {'dG': -9.03, 'ddG': 0.18},\n", - " 'lig_ejm_50': {'dG': -9.01, 'ddG': 0.18},\n", - " 'lig_ejm_54': {'dG': -10.57, 'ddG': 0.18},\n", - " 'lig_ejm_55': {'dG': -9.24, 'ddG': 0.18},\n", - " 'lig_jmc_23': {'dG': -11.74, 'ddG': 0.18},\n", - " 'lig_jmc_27': {'dG': -11.31, 'ddG': 0.17},\n", - " 'lig_jmc_28': {'dG': -11.01, 'ddG': 0.18}}\n" - ] + "data": { + "text/html": [ + "
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ligandestimate (kcal/mol)uncertainty (kcal/mol)
0lig_ejm_42-9.810.18
1lig_ejm_43-8.290.18
2lig_ejm_45-9.590.18
3lig_ejm_46-11.350.17
4lig_ejm_47-9.730.18
5lig_ejm_48-9.030.18
6lig_ejm_50-9.010.18
7lig_ejm_54-10.570.18
8lig_ejm_55-9.240.18
9lig_jmc_23-11.740.18
10lig_jmc_27-11.310.17
11lig_jmc_28-11.010.18
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" + ], + "text/plain": [ + " ligand estimate (kcal/mol) uncertainty (kcal/mol)\n", + "0 lig_ejm_42 -9.81 0.18\n", + "1 lig_ejm_43 -8.29 0.18\n", + "2 lig_ejm_45 -9.59 0.18\n", + "3 lig_ejm_46 -11.35 0.17\n", + "4 lig_ejm_47 -9.73 0.18\n", + "5 lig_ejm_48 -9.03 0.18\n", + "6 lig_ejm_50 -9.01 0.18\n", + "7 lig_ejm_54 -10.57 0.18\n", + "8 lig_ejm_55 -9.24 0.18\n", + "9 lig_jmc_23 -11.74 0.18\n", + "10 lig_jmc_27 -11.31 0.17\n", + "11 lig_jmc_28 -11.01 0.18" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# read in the experimental data\n", - "\n", - "experimental_data = {}\n", - "experimental_filename = 'assets/experimental.tsv'\n", - "\n", - "with open(experimental_filename, 'r') as fd:\n", - " rd = csv.reader(fd, delimiter=\"\\t\", quotechar='\"')\n", - " headers = next(rd)\n", - " for row in rd:\n", - " experimental_data[row[0]] = {}\n", - " experimental_data[row[0]]['dG'] = float(row[1])\n", - " experimental_data[row[0]]['ddG'] = float(row[2])\n", - "\n", - "pprint(experimental_data)" + "experimental_data = pd.read_csv('assets/experimental.tsv', sep=\"\\t\")\n", + "experimental_data" ] }, { @@ -135,75 +193,2315 @@ "source": [ "## Loading free energy results\n", "\n", - "Next we load in results from the TSV file created by `openfe gather --report dg`.\n", + "Next we load in results from the TSV file created by running `openfe gather --report dg` on a network of results from [relative binding free energy calculations](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html#overview).\n", "\n", "Please see the following tutorial for more information on how to run the gather command: https://github.com/OpenFreeEnergy/ExampleNotebooks/blob/main/rbfe_tutorial/cli_tutorial.md\n", "\n", - "**Important note:**\n", - "If there is no experimental binding free energy for a ligand, that ligand's calcuated data will be excluded from the plot." + "
Note: If there is no experimental binding free energy for a ligand, that ligand's calcuated data will be excluded from the plot.
" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "63a920b3-6ef3-4214-b3cc-433319053721", - "metadata": {}, + "execution_count": 3, + "id": "58d3dbf698013805", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:13.025826Z", + "start_time": "2026-06-09T10:55:12.960934Z" + } + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lig_ejm_42': {'dG': 0.7, 'ddG': 0.1},\n", - " 'lig_ejm_43': {'dG': 2.0, 'ddG': 0.2},\n", - " 'lig_ejm_46': {'dG': -0.98, 'ddG': 0.05},\n", - " 'lig_ejm_47': {'dG': -0.1, 'ddG': 0.1},\n", - " 'lig_ejm_48': {'dG': 0.53, 'ddG': 0.09},\n", - " 'lig_ejm_50': {'dG': 0.91, 'ddG': 0.06},\n", - " 'lig_jmc_23': {'dG': -0.68, 'ddG': 0.09},\n", - " 'lig_jmc_27': {'dG': -1.1, 'ddG': 0.1},\n", - " 'lig_jmc_28': {'dG': -1.25, 'ddG': 0.08}}\n" - ] + "data": { + "text/html": [ + "
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ligandDG(MLE) (kcal/mol)uncertainty (kcal/mol)
0lig_ejm_31-0.090.05
1lig_ejm_420.700.10
2lig_ejm_46-0.980.05
3lig_ejm_47-0.100.10
4lig_ejm_480.530.09
5lig_ejm_500.910.06
6lig_ejm_432.000.20
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8lig_jmc_27-1.100.10
9lig_jmc_28-1.250.08
\n", + "
" + ], + "text/plain": [ + " ligand DG(MLE) (kcal/mol) uncertainty (kcal/mol)\n", + "0 lig_ejm_31 -0.09 0.05\n", + "1 lig_ejm_42 0.70 0.10\n", + "2 lig_ejm_46 -0.98 0.05\n", + "3 lig_ejm_47 -0.10 0.10\n", + "4 lig_ejm_48 0.53 0.09\n", + "5 lig_ejm_50 0.91 0.06\n", + "6 lig_ejm_43 2.00 0.20\n", + "7 lig_jmc_23 -0.68 0.09\n", + "8 lig_jmc_27 -1.10 0.10\n", + "9 lig_jmc_28 -1.25 0.08" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Read in calculated results\n", - "\n", - "calc_data = {}\n", - "calculated_filename = 'assets/calc_dg.tsv'\n", + "calculated_data = pd.read_csv('assets/calc_dg.tsv', sep=\"\\t\")\n", + "calculated_data" + ] + }, + { + "cell_type": "markdown", + "id": "a70b17e4", + "metadata": {}, + "source": [ + "
Note: The estimated absolute binding afinites are centered around zero rather than the experimental mean, thus an offset should be applied to the calculated results before comparing them.
\n", "\n", - "with open(calculated_filename, 'r') as fd:\n", - " rd = csv.reader(fd, delimiter=\"\\t\", quotechar='\"')\n", - " headers = next(rd)\n", - " for row in rd:\n", - " # Only add ligand to dict if it has an exp. value\n", - " if row[0] in experimental_data:\n", - " calc_data[row[0]] = {}\n", - " calc_data[row[0]]['dG'] = float(row[1])\n", - " calc_data[row[0]]['ddG'] = float(row[2])\n", + "You can read more about absolute estimators in the [cinnabar guide](https://cinnabar.openfree.energy/en/latest/concepts/estimators.html#centering-of-results)\n", "\n", - "pprint(calc_data)" + "Next we will use the ``cinnabar`` API to gather the experiemtnal and calculated results into a single object:" ] }, { "cell_type": "code", "execution_count": 5, - "id": "d67089d8-1984-479f-b750-b51ab2ab174d", - "metadata": {}, + "id": "a65699b71a68cfe4", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:16.713105Z", + "start_time": "2026-06-09T10:55:16.673656Z" + } + }, "outputs": [], "source": [ - "# Bring exp data in same order as calc data\n", - "sorted_exp_data = {k: experimental_data[k] for k in calc_data}" + "# Create FEMap\n", + "fe_map = FEMap()\n", + "\n", + "# add the experimental data\n", + "for index, row in experimental_data.iterrows():\n", + " fe_map.add_experimental_measurement(\n", + " label=row['ligand'],\n", + " value=row['estimate (kcal/mol)'] * unit.kilocalories_per_mole,\n", + " uncertainty=row['uncertainty (kcal/mol)'] * unit.kilocalories_per_mole,\n", + " source=\"Experimental\"\n", + " )\n", + "\n", + "# add the computed data\n", + "for index, row in calculated_data.iterrows():\n", + " fe_map.add_absolute_calculation(\n", + " label=row['ligand'],\n", + " value=row[\"DG(MLE) (kcal/mol)\"] * unit.kilocalories_per_mole,\n", + " uncertainty=row[\"uncertainty (kcal/mol)\"] * unit.kilocalories_per_mole,\n", + " source=\"openfe\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "95f86cdb", + "metadata": {}, + "source": [ + "We can then check that the absolute measurments have been correctly added:" ] }, { "cell_type": "code", "execution_count": 6, - "id": "f5468e6f-ee73-45e7-8665-dd3e4da9342e", + "id": "e9a4e3a67460c0f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:18.505512Z", + "start_time": "2026-06-09T10:55:18.407406Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_ejm_42-9.810.18ExperimentalFalse
1lig_ejm_43-8.290.18ExperimentalFalse
2lig_ejm_45-9.590.18ExperimentalFalse
3lig_ejm_46-11.350.17ExperimentalFalse
4lig_ejm_47-9.730.18ExperimentalFalse
5lig_ejm_48-9.030.18ExperimentalFalse
6lig_ejm_50-9.010.18ExperimentalFalse
7lig_ejm_54-10.570.18ExperimentalFalse
8lig_ejm_55-9.240.18ExperimentalFalse
9lig_jmc_23-11.740.18ExperimentalFalse
10lig_jmc_27-11.310.17ExperimentalFalse
11lig_jmc_28-11.010.18ExperimentalFalse
12lig_ejm_31-0.090.05openfeTrue
13lig_ejm_420.700.10openfeTrue
14lig_ejm_432.000.20openfeTrue
15lig_ejm_46-0.980.05openfeTrue
16lig_ejm_47-0.100.10openfeTrue
17lig_ejm_480.530.09openfeTrue
18lig_ejm_500.910.06openfeTrue
19lig_jmc_23-0.680.09openfeTrue
20lig_jmc_27-1.100.10openfeTrue
21lig_jmc_28-1.250.08openfeTrue
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" + ], + "text/plain": [ + " label DG (kcal/mol) uncertainty (kcal/mol) source \\\n", + "0 lig_ejm_42 -9.81 0.18 Experimental \n", + "1 lig_ejm_43 -8.29 0.18 Experimental \n", + "2 lig_ejm_45 -9.59 0.18 Experimental \n", + "3 lig_ejm_46 -11.35 0.17 Experimental \n", + "4 lig_ejm_47 -9.73 0.18 Experimental \n", + "5 lig_ejm_48 -9.03 0.18 Experimental \n", + "6 lig_ejm_50 -9.01 0.18 Experimental \n", + "7 lig_ejm_54 -10.57 0.18 Experimental \n", + "8 lig_ejm_55 -9.24 0.18 Experimental \n", + "9 lig_jmc_23 -11.74 0.18 Experimental \n", + "10 lig_jmc_27 -11.31 0.17 Experimental \n", + "11 lig_jmc_28 -11.01 0.18 Experimental \n", + "12 lig_ejm_31 -0.09 0.05 openfe \n", + "13 lig_ejm_42 0.70 0.10 openfe \n", + "14 lig_ejm_43 2.00 0.20 openfe \n", + "15 lig_ejm_46 -0.98 0.05 openfe \n", + "16 lig_ejm_47 -0.10 0.10 openfe \n", + "17 lig_ejm_48 0.53 0.09 openfe \n", + "18 lig_ejm_50 0.91 0.06 openfe \n", + "19 lig_jmc_23 -0.68 0.09 openfe \n", + "20 lig_jmc_27 -1.10 0.10 openfe \n", + "21 lig_jmc_28 -1.25 0.08 openfe \n", + "\n", + " computational \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "5 False \n", + "6 False \n", + "7 False \n", + "8 False \n", + "9 False \n", + "10 False \n", + "11 False \n", + "12 True \n", + "13 True \n", + "14 True \n", + "15 True \n", + "16 True \n", + "17 True \n", + "18 True \n", + "19 True \n", + "20 True \n", + "21 True " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fe_map.get_absolute_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "b8b74cd4", "metadata": {}, + "source": [ + "Finally we can use ``cinnabar`` to plot the calculated vs experimental results with some accuracy and ranking metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ea1b79eabcdec37a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T10:55:25.346034Z", + "start_time": "2026-06-09T10:55:23.918986Z" + } + }, "outputs": [ { "data": { - "image/png": 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" ] @@ -213,13 +2511,19 @@ } ], "source": [ - "plot_dg(sorted_exp_data, calc_data, 'dg.png')" + "plotting.plot_DGs(\n", + " fe_map, \n", + " source=\"openfe\", # the name of the computational source to plot\n", + " figsize=5, \n", + " shift=experimental_data[\"estimate (kcal/mol)\"].mean(), # shift the computed values by the mean of the experimental values to put them on a similar scale\n", + " xy_lim=[-15, -5]\n", + " )" ] }, { "cell_type": "code", "execution_count": null, - "id": "db474531-7c3b-4fac-a224-2e71b22a6586", + "id": "b7a7fa721a373142", "metadata": {}, "outputs": [], "source": [] @@ -227,7 +2531,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "openfe_dev", "language": "python", "name": "python3" }, @@ -241,7 +2545,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/environment.yaml b/environment.yaml index 6b1d00b..383d230 100644 --- a/environment.yaml +++ b/environment.yaml @@ -8,7 +8,7 @@ dependencies: - nbval - nglview - notebook - - numpy <2.4.0 # https://github.com/OpenFreeEnergy/cinnabar/issues/198 + - numpy - pip - pytest - pytest-cov @@ -16,3 +16,5 @@ dependencies: - python - openfe==1.11.0 - openff-interchange-base != 0.5.1 # https://github.com/openforcefield/openff-interchange/issues/1450 + - pip: + - git+https://github.com/OpenFreeEnergy/cinnabar@main diff --git a/plotting_rbfes_with_cinnabar/PlottingFreeEnergiesUsingCinnabar.ipynb b/plotting_rbfes_with_cinnabar/PlottingFreeEnergiesUsingCinnabar.ipynb index 9ae7d7b..9d7ee0d 100644 --- a/plotting_rbfes_with_cinnabar/PlottingFreeEnergiesUsingCinnabar.ipynb +++ b/plotting_rbfes_with_cinnabar/PlottingFreeEnergiesUsingCinnabar.ipynb @@ -5,9 +5,9 @@ "id": "ddae61fd-fa15-4be9-9160-ba3e195c67dc", "metadata": {}, "source": [ - "# Plotting OpenFE results against experiment using Cinnabar v0.4\n", + "# Plotting OpenFE results against experiment using Cinnabar v0.6.0\n", "\n", - "This notebook shows how one would go about creating a cinnabar plot of OpenFE results against known experimental values." + "This notebook shows how one would go about creating a ``cinnabar`` style plot of OpenFE results against known experimental values. For more information on using the ``cinnabar`` API and the different plot styles available please see the [tutorials](https://cinnabar.openfree.energy/en/latest/tutorials/index.html)." ] }, { @@ -17,7 +17,7 @@ "source": [ "## 0. Setup for Google Colab\n", "\n", - "If you are running this example in Google Colab, run the following cells to setup the environment. If you are running this notebook locally, skip down to `1. Overview`" + "If you are running this example in Google Colab, run the following cells to setup the environment. If you are running this notebook locally, skip down to `1. Loading experimental data`" ] }, { @@ -96,11 +96,11 @@ "outputs": [], "source": [ "# First we do a set of imports\n", - "import csv\n", - "from pprint import pprint\n", - "import cinnabar\n", - "from cinnabar import plotting as cinnabar_plotting\n", - "from cinnabar import femap, stats" + "%matplotlib inline\n", + "%config InlineBackend.figure_formats = ['svg']\n", + "import pandas as pd\n", + "from cinnabar import FEMap, plotting\n", + "from openff.units import unit" ] }, { @@ -110,40 +110,140 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lig_ejm_31': {'dG': -9.57, 'ddG': 0.18},\n", - " 'lig_ejm_42': {'dG': -9.81, 'ddG': 0.18},\n", - " 'lig_ejm_43': {'dG': -8.29, 'ddG': 0.18},\n", - " 'lig_ejm_45': {'dG': -9.59, 'ddG': 0.18},\n", - " 'lig_ejm_46': {'dG': -11.35, 'ddG': 0.17},\n", - " 'lig_ejm_47': {'dG': -9.73, 'ddG': 0.18},\n", - " 'lig_ejm_48': {'dG': -9.03, 'ddG': 0.18},\n", - " 'lig_ejm_50': {'dG': -9.01, 'ddG': 0.18},\n", - " 'lig_ejm_54': {'dG': -10.57, 'ddG': 0.18},\n", - " 'lig_ejm_55': {'dG': -9.24, 'ddG': 0.18},\n", - " 'lig_jmc_23': {'dG': -11.74, 'ddG': 0.18},\n", - " 'lig_jmc_27': {'dG': -11.31, 'ddG': 0.17},\n", - " 'lig_jmc_28': {'dG': -11.01, 'ddG': 0.18}}\n" - ] + "data": { + "text/html": [ + "
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ligandestimate (kcal/mol)uncertainty (kcal/mol)
0lig_ejm_31-9.570.18
1lig_ejm_42-9.810.18
2lig_ejm_43-8.290.18
3lig_ejm_45-9.590.18
4lig_ejm_46-11.350.17
5lig_ejm_47-9.730.18
6lig_ejm_48-9.030.18
7lig_ejm_50-9.010.18
8lig_ejm_54-10.570.18
9lig_ejm_55-9.240.18
10lig_jmc_23-11.740.18
11lig_jmc_27-11.310.17
12lig_jmc_28-11.010.18
\n", + "
" + ], + "text/plain": [ + " ligand estimate (kcal/mol) uncertainty (kcal/mol)\n", + "0 lig_ejm_31 -9.57 0.18\n", + "1 lig_ejm_42 -9.81 0.18\n", + "2 lig_ejm_43 -8.29 0.18\n", + "3 lig_ejm_45 -9.59 0.18\n", + "4 lig_ejm_46 -11.35 0.17\n", + "5 lig_ejm_47 -9.73 0.18\n", + "6 lig_ejm_48 -9.03 0.18\n", + "7 lig_ejm_50 -9.01 0.18\n", + "8 lig_ejm_54 -10.57 0.18\n", + "9 lig_ejm_55 -9.24 0.18\n", + "10 lig_jmc_23 -11.74 0.18\n", + "11 lig_jmc_27 -11.31 0.17\n", + "12 lig_jmc_28 -11.01 0.18" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# read in the experimental data\n", - "\n", - "experimental_data = {}\n", - "experimental_filename = 'experimental.tsv'\n", - "\n", - "with open(experimental_filename, 'r') as fd:\n", - " rd = csv.reader(fd, delimiter=\"\\t\", quotechar='\"')\n", - " headers = next(rd)\n", - " for row in rd:\n", - " experimental_data[row[0]] = {}\n", - " experimental_data[row[0]]['dG'] = float(row[1])\n", - " experimental_data[row[0]]['ddG'] = float(row[2])\n", - "\n", - "pprint(experimental_data)" + "experimental_data = pd.read_csv('experimental.tsv', sep=\"\\t\")\n", + "experimental_data" ] }, { @@ -153,78 +253,134 @@ "source": [ "## Loading free energy results\n", "\n", - "Next we load in results from the TSV file created by `openfe gather --report ddg`.\n", + "Next we load in results from the TSV file created by running `openfe gather --report ddg` on a network of results from [relative binding free energy calculations](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html#overview).\n", "\n", - "Please see the following tutorial for more information on how to run the gather command: https://github.com/OpenFreeEnergy/ExampleNotebooks/blob/main/rbfe_tutorial/cli_tutorial.md" + "Please see the full [gather command tutorial](https://github.com/OpenFreeEnergy/ExampleNotebooks/blob/main/rbfe_tutorial/cli_tutorial.md) for more information." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "63a920b3-6ef3-4214-b3cc-433319053721", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lig_ejm_31->lig_ejm_42': {'dG': 0.49,\n", - " 'ddG': 0.09,\n", - " 'ligand_i': 'lig_ejm_31',\n", - " 'ligand_j': 'lig_ejm_42'},\n", - " 'lig_ejm_31->lig_ejm_46': {'dG': -0.73,\n", - " 'ddG': 0.1,\n", - " 'ligand_i': 'lig_ejm_31',\n", - " 'ligand_j': 'lig_ejm_46'},\n", - " 'lig_ejm_31->lig_ejm_47': {'dG': 0.0,\n", - " 'ddG': 0.2,\n", - " 'ligand_i': 'lig_ejm_31',\n", - " 'ligand_j': 'lig_ejm_47'},\n", - " 'lig_ejm_31->lig_ejm_48': {'dG': 0.5,\n", - " 'ddG': 0.2,\n", - " 'ligand_i': 'lig_ejm_31',\n", - " 'ligand_j': 'lig_ejm_48'},\n", - " 'lig_ejm_31->lig_ejm_50': {'dG': 0.94,\n", - " 'ddG': 0.07,\n", - " 'ligand_i': 'lig_ejm_31',\n", - " 'ligand_j': 'lig_ejm_50'},\n", - " 'lig_ejm_42->lig_ejm_43': {'dG': 1.2,\n", - " 'ddG': 0.1,\n", - " 'ligand_i': 'lig_ejm_42',\n", - " 'ligand_j': 'lig_ejm_43'},\n", - " 'lig_ejm_46->lig_jmc_23': {'dG': -0.39,\n", - " 'ddG': 0.05,\n", - " 'ligand_i': 'lig_ejm_46',\n", - " 'ligand_j': 'lig_jmc_23'},\n", - " 'lig_ejm_46->lig_jmc_27': {'dG': -0.6,\n", - " 'ddG': 0.1,\n", - " 'ligand_i': 'lig_ejm_46',\n", - " 'ligand_j': 'lig_jmc_27'},\n", - " 'lig_ejm_46->lig_jmc_28': {'dG': -0.12,\n", - " 'ddG': 0.04,\n", - " 'ligand_i': 'lig_ejm_46',\n", - " 'ligand_j': 'lig_jmc_28'}}\n" - ] + "data": { + "text/html": [ + "
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ligand_iligand_jDDG(i->j) (kcal/mol)uncertainty (kcal/mol)
0lig_ejm_31lig_ejm_420.490.09
1lig_ejm_31lig_ejm_46-0.730.10
2lig_ejm_31lig_ejm_470.000.20
3lig_ejm_31lig_ejm_480.500.20
4lig_ejm_31lig_ejm_500.940.07
5lig_ejm_42lig_ejm_431.200.10
6lig_ejm_46lig_jmc_23-0.390.05
7lig_ejm_46lig_jmc_27-0.600.10
8lig_ejm_46lig_jmc_28-0.120.04
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" + ], + "text/plain": [ + " ligand_i ligand_j DDG(i->j) (kcal/mol) uncertainty (kcal/mol)\n", + "0 lig_ejm_31 lig_ejm_42 0.49 0.09\n", + "1 lig_ejm_31 lig_ejm_46 -0.73 0.10\n", + "2 lig_ejm_31 lig_ejm_47 0.00 0.20\n", + "3 lig_ejm_31 lig_ejm_48 0.50 0.20\n", + "4 lig_ejm_31 lig_ejm_50 0.94 0.07\n", + "5 lig_ejm_42 lig_ejm_43 1.20 0.10\n", + "6 lig_ejm_46 lig_jmc_23 -0.39 0.05\n", + "7 lig_ejm_46 lig_jmc_27 -0.60 0.10\n", + "8 lig_ejm_46 lig_jmc_28 -0.12 0.04" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Read in calculated results\n", - "\n", - "calculated_data = {}\n", - "calculated_filename = 'final_results.tsv'\n", - "\n", - "with open(calculated_filename, 'r') as fd:\n", - " rd = csv.reader(fd, delimiter=\"\\t\", quotechar='\"')\n", - " headers = next(rd)\n", - " for row in rd:\n", - " tag = row[0] + \"->\" + row[1]\n", - " calculated_data[tag] = {}\n", - " calculated_data[tag]['ligand_i'] = row[0]\n", - " calculated_data[tag]['ligand_j'] = row[1]\n", - " calculated_data[tag]['dG'] = float(row[2])\n", - " calculated_data[tag]['ddG'] = float(row[3])\n", - "\n", - "pprint(calculated_data)" + "# Read in calculated relative free energy results\n", + "calculated_data = pd.read_csv('final_results.tsv', sep=\"\\t\")\n", + "calculated_data" ] }, { @@ -232,9 +388,11 @@ "id": "a1daf9cf-5107-4564-a200-b9fe670837cb", "metadata": {}, "source": [ - "## Creating a Cinnabar CSV file\n", + "## Creating a Cinnabar ``FEMap``\n", "\n", - "Next we take the data we've gathered above and write it out as `cinnabar` \"csv\" file." + "Next we will use the ``cinnabar`` API to gather the experiemtnal and calculated results into a single object which we can use to plot and analyse the network of results.\n", + "\n", + "**Note**: The ``FEMap`` object requires estimates and uncertainties to have units." ] }, { @@ -244,24 +402,26 @@ "metadata": {}, "outputs": [], "source": [ - "cinnabar_filename = 'cinnabar_input.csv'\n", + "fe_map = FEMap()\n", + "\n", + "# add the calculated data\n", + "for _, row in calculated_data.iterrows():\n", + " fe_map.add_relative_calculation(\n", + " labelA=row['ligand_i'], # the ligand we are transforming from\n", + " labelB=row['ligand_j'], # the ligand we are transforming to\n", + " value=row[\"DDG(i->j) (kcal/mol)\"] * unit.kilocalories_per_mole, # the computed relative free energy change for the transformation\n", + " uncertainty=row[\"uncertainty (kcal/mol)\"] * unit.kilocalories_per_mole, # the uncertainty in the computed relative free energy change\n", + " source=\"openfe\", # the source of the data, can be any string but should be consistent across all data points from the same source\n", + " )\n", "\n", - "with open(cinnabar_filename, 'w') as f:\n", - " f.write(\"# Experimental block\\n\")\n", - " f.write(\"# Ligand, expt_DDG, expt_dDDG\\n\")\n", - " for entry in experimental_data:\n", - " dG = experimental_data[entry]['dG']\n", - " ddG = experimental_data[entry]['ddG']\n", - " f.write(f\"{entry},{dG:.2f},{ddG:.2f}\\n\")\n", - " f.write('\\n')\n", - " f.write('# Calculated block\\n')\n", - " f.write('# Ligand1,Ligand2,calc_DDG,calc_dDDG(MBAR),calc_dDDG(additional)\\n')\n", - " for entry in calculated_data:\n", - " dG = calculated_data[entry]['dG']\n", - " ddG = calculated_data[entry]['ddG']\n", - " molA = calculated_data[entry]['ligand_i']\n", - " molB = calculated_data[entry]['ligand_j']\n", - " f.write(f\"{molA},{molB},{dG:.2f},0,{ddG:.2f}\\n\")" + "# add the experimental data\n", + "for _, row in experimental_data.iterrows():\n", + " fe_map.add_experimental_measurement(\n", + " label=row['ligand'], # the ligand for which we have an experimental measurement\n", + " value=row['estimate (kcal/mol)'] * unit.kilocalories_per_mole, # the experimental free energy for that ligand\n", + " uncertainty=row['uncertainty (kcal/mol)'] * unit.kilocalories_per_mole, # the uncertainty in the experimental free energy\n", + " source=\"Experimental\", # the source of the data, can be any string but should be consistent across all data points from the same source\n", + " )" ] }, { @@ -271,17 +431,7 @@ "source": [ "## Plotting the results using Cinnabar\n", "\n", - "Once we've created the appropriate Cinnabar input file we can go ahead and plot out our results." - ] - }, - { - "cell_type": "markdown", - "id": "ce62492e-da4b-4b48-bbcd-2bc92a08804b", - "metadata": {}, - "source": [ - "### Generating a Cinnabar FEMap and plotting out the network\n", - "\n", - "First let's load the data into cinnabar and draw out the network of free energy results." + "Once we've created the ``FEMap`` we can inspect the alchemical network:" ] }, { @@ -290,17 +440,987 @@ "id": "86c004bc-bc58-4a55-92e2-151beb994d65", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hannahbaumann/cinnabar/cinnabar/femap.py:21: UserWarning: Assuming kcal/mol units on measurements\n", - " warnings.warn(\"Assuming kcal/mol units on measurements\")\n" - ] - }, { "data": { - "image/png": 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" ] @@ -310,9 +1430,7 @@ } ], "source": [ - "fe = femap.FEMap.from_csv('cinnabar_input.csv')\n", - "fe.generate_absolute_values() # Get MLE generated estimates of the absolute values\n", - "fe.draw_graph()" + "fe_map.draw_graph()" ] }, { @@ -327,13 +1445,1655 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "2edeabb9-9c07-49be-a83f-9622ef742046", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -344,7 +3104,11 @@ ], "source": [ "# note you can pass the filename argument to write things out to file\n", - "cinnabar_plotting.plot_DDGs(fe.to_legacy_graph(), figsize=5)" + "plotting.plot_DDGs(\n", + " fe_map, \n", + " source=\"openfe\", # the name of the computational source to plot\n", + " figsize=5\n", + " )" ] }, { @@ -354,18 +3118,1806 @@ "source": [ "### Plotting out the absolute free energy results\n", "\n", - "Finally let's go ahead and plot out the MLE derived absolute free energies" + "Finally let's generate an estimate of the absolute binding free energy for each ligand using the [MLE estimator](https://cinnabar.openfree.energy/en/latest/concepts/estimators.html#maximum-likelihood-estimation-mle) and plot out the derived values:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "f39ef3c0-4d74-4c53-83bc-94d820428d47", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -376,7 +4928,12 @@ ], "source": [ "# note you can pass the filename argument to write to file\n", - "cinnabar_plotting.plot_DGs(fe.to_legacy_graph(), figsize=5)" + "fe_map.generate_absolute_values() # Get MLE generated estimates of the absolute values\n", + "plotting.plot_DGs(\n", + " fe_map, \n", + " source=\"MLE\", # the name of the computational source to plot\n", + " figsize=5\n", + " )" ] }, { @@ -389,21 +4946,1890 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "7fed7b2c-076f-4f98-818d-abdbdaf3121b", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hannahbaumann/cinnabar/cinnabar/femap.py:21: UserWarning: Assuming kcal/mol units on measurements\n", - " warnings.warn(\"Assuming kcal/mol units on measurements\")\n" - ] - }, { "data": { - "image/png": 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" ] @@ -413,11 +6839,14 @@ } ], "source": [ - "data = femap.read_csv('cinnabar_input.csv')\n", - "exp_DG_sum = sum([data['Experimental'][i].DG for i in data['Experimental'].keys()])\n", - "shift = exp_DG_sum / len(data['Experimental'].keys())\n", + "shift = experimental_data[\"estimate (kcal/mol)\"].mean() # shift the computed values by the mean of the experimental values to put them on a similar scale\n", "\n", - "cinnabar_plotting.plot_DGs(fe.to_legacy_graph(), figsize=5, shift=shift.m)" + "plotting.plot_DGs(\n", + " fe_map, \n", + " source=\"MLE\", \n", + " figsize=5, \n", + " shift=shift\n", + ")" ] }, { @@ -427,106 +6856,450 @@ "source": [ "## Writing out the MLE derived absolute free energies\n", "\n", - "Finally, we can also write out the MLE derived absolute free energies in the following manner.\n", - "\n", - "**Note: you can obtain this directly from your results by calling `openfe gather --report dg`**" + "Finally, we can also extract the MLE derived absolute free energies along with the experimental results into a pandas ``DataFrame``." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "26b09073-f05f-401c-a10f-95c6c957f4da", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lig_ejm_31': {'calculated_error': 0.05,\n", - " 'calculated_estimate': 0.04,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -9.57},\n", - " 'lig_ejm_42': {'calculated_error': 0.09,\n", - " 'calculated_estimate': 0.53,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -9.81},\n", - " 'lig_ejm_43': {'calculated_error': 0.13,\n", - " 'calculated_estimate': 1.73,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -8.29},\n", - " 'lig_ejm_46': {'calculated_error': 0.07,\n", - " 'calculated_estimate': -0.69,\n", - " 'experimental_error': 0.17,\n", - " 'experimental_estimate': -11.35},\n", - " 'lig_ejm_47': {'calculated_error': 0.19,\n", - " 'calculated_estimate': 0.04,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -9.73},\n", - " 'lig_ejm_48': {'calculated_error': 0.19,\n", - " 'calculated_estimate': 0.54,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -9.03},\n", - " 'lig_ejm_50': {'calculated_error': 0.08,\n", - " 'calculated_estimate': 0.98,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -9.01},\n", - " 'lig_jmc_23': {'calculated_error': 0.08,\n", - " 'calculated_estimate': -1.08,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -11.74},\n", - " 'lig_jmc_27': {'calculated_error': 0.11,\n", - " 'calculated_estimate': -1.29,\n", - " 'experimental_error': 0.17,\n", - " 'experimental_estimate': -11.31},\n", - " 'lig_jmc_28': {'calculated_error': 0.08,\n", - " 'calculated_estimate': -0.81,\n", - " 'experimental_error': 0.18,\n", - " 'experimental_estimate': -11.01}}\n" - ] + "data": { + "text/html": [ + "
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labelDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_ejm_31-9.5700.180000ExperimentalFalse
1lig_ejm_42-9.8100.180000ExperimentalFalse
2lig_ejm_43-8.2900.180000ExperimentalFalse
3lig_ejm_45-9.5900.180000ExperimentalFalse
4lig_ejm_46-11.3500.170000ExperimentalFalse
5lig_ejm_47-9.7300.180000ExperimentalFalse
6lig_ejm_48-9.0300.180000ExperimentalFalse
7lig_ejm_50-9.0100.180000ExperimentalFalse
8lig_ejm_54-10.5700.180000ExperimentalFalse
9lig_ejm_55-9.2400.180000ExperimentalFalse
10lig_jmc_23-11.7400.180000ExperimentalFalse
11lig_jmc_27-11.3100.170000ExperimentalFalse
12lig_jmc_28-11.0100.180000ExperimentalFalse
13lig_ejm_310.0410.054900MLETrue
14lig_ejm_420.5310.088736MLETrue
15lig_ejm_431.7310.125992MLETrue
16lig_ejm_46-0.6890.070810MLETrue
17lig_ejm_470.0410.187120MLETrue
18lig_ejm_480.5410.187120MLETrue
19lig_ejm_500.9810.083271MLETrue
20lig_jmc_23-1.0790.083750MLETrue
21lig_jmc_27-1.2890.114079MLETrue
22lig_jmc_28-0.8090.079335MLETrue
\n", + "
" + ], + "text/plain": [ + " label DG (kcal/mol) uncertainty (kcal/mol) source \\\n", + "0 lig_ejm_31 -9.570 0.180000 Experimental \n", + "1 lig_ejm_42 -9.810 0.180000 Experimental \n", + "2 lig_ejm_43 -8.290 0.180000 Experimental \n", + "3 lig_ejm_45 -9.590 0.180000 Experimental \n", + "4 lig_ejm_46 -11.350 0.170000 Experimental \n", + "5 lig_ejm_47 -9.730 0.180000 Experimental \n", + "6 lig_ejm_48 -9.030 0.180000 Experimental \n", + "7 lig_ejm_50 -9.010 0.180000 Experimental \n", + "8 lig_ejm_54 -10.570 0.180000 Experimental \n", + "9 lig_ejm_55 -9.240 0.180000 Experimental \n", + "10 lig_jmc_23 -11.740 0.180000 Experimental \n", + "11 lig_jmc_27 -11.310 0.170000 Experimental \n", + "12 lig_jmc_28 -11.010 0.180000 Experimental \n", + "13 lig_ejm_31 0.041 0.054900 MLE \n", + "14 lig_ejm_42 0.531 0.088736 MLE \n", + "15 lig_ejm_43 1.731 0.125992 MLE \n", + "16 lig_ejm_46 -0.689 0.070810 MLE \n", + "17 lig_ejm_47 0.041 0.187120 MLE \n", + "18 lig_ejm_48 0.541 0.187120 MLE \n", + "19 lig_ejm_50 0.981 0.083271 MLE \n", + "20 lig_jmc_23 -1.079 0.083750 MLE \n", + "21 lig_jmc_27 -1.289 0.114079 MLE \n", + "22 lig_jmc_28 -0.809 0.079335 MLE \n", + "\n", + " computational \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "5 False \n", + "6 False \n", + "7 False \n", + "8 False \n", + "9 False \n", + "10 False \n", + "11 False \n", + "12 False \n", + "13 True \n", + "14 True \n", + "15 True \n", + "16 True \n", + "17 True \n", + "18 True \n", + "19 True \n", + "20 True \n", + "21 True \n", + "22 True " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "dG_results = {}\n", - "nodes = list(fe.to_legacy_graph().nodes.data())\n", - "\n", - "for key in range(len(nodes)):\n", - " dG_results[nodes[key][0]] = {\n", - " 'experimental_estimate': nodes[key][1]['exp_DG'],\n", - " 'experimental_error': nodes[key][1]['exp_dDG'],\n", - " 'calculated_estimate': round(nodes[key][1]['calc_DG'],2),\n", - " 'calculated_error': round(nodes[key][1]['calc_dDG'],2), \n", - " }\n", - "\n", - "# We can now print out the results\n", - "pprint(dG_results)\n", - "\n", - "# write out the calculated results\n", - "with open('dG_calculated_results.dat', 'w') as f:\n", - " writer = csv.writer(f, delimiter=\"\\t\", lineterminator=\"\\n\")\n", - " writer.writerow([\"ligand\", \"DG(MLE)\", \"uncertainty (kcal/mol)\",])\n", - " for ligand in dG_results:\n", - " writer.writerow([\n", - " ligand,\n", - " dG_results[ligand]['calculated_estimate'],\n", - " dG_results[ligand]['calculated_error'],\n", - " ])\n", - "\n", - "# write out the experimental results\n", - "with open('dG_experimental_results.dat', 'w') as f:\n", - " writer = csv.writer(f, delimiter=\"\\t\", lineterminator=\"\\n\")\n", - " writer.writerow([\"ligand\", \"DG\", \"uncertainty (kcal/mol)\",])\n", - " for ligand in dG_results:\n", - " writer.writerow([\n", - " ligand,\n", - " dG_results[ligand]['experimental_estimate'],\n", - " dG_results[ligand]['experimental_error'],\n", - " ])" + "abs_df = fe_map.get_absolute_dataframe()\n", + "abs_df" + ] + }, + { + "cell_type": "markdown", + "id": "d668de5f", + "metadata": {}, + "source": [ + "We can then extract the MLE dervied values.\n", + "**Note: you can obtain this directly from your results by calling `openfe gather --report dg`**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8069c254", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_ejm_310.0410.054900MLETrue
1lig_ejm_420.5310.088736MLETrue
2lig_ejm_431.7310.125992MLETrue
3lig_ejm_46-0.6890.070810MLETrue
4lig_ejm_470.0410.187120MLETrue
5lig_ejm_480.5410.187120MLETrue
6lig_ejm_500.9810.083271MLETrue
7lig_jmc_23-1.0790.083750MLETrue
8lig_jmc_27-1.2890.114079MLETrue
9lig_jmc_28-0.8090.079335MLETrue
\n", + "
" + ], + "text/plain": [ + " label DG (kcal/mol) uncertainty (kcal/mol) source computational\n", + "0 lig_ejm_31 0.041 0.054900 MLE True\n", + "1 lig_ejm_42 0.531 0.088736 MLE True\n", + "2 lig_ejm_43 1.731 0.125992 MLE True\n", + "3 lig_ejm_46 -0.689 0.070810 MLE True\n", + "4 lig_ejm_47 0.041 0.187120 MLE True\n", + "5 lig_ejm_48 0.541 0.187120 MLE True\n", + "6 lig_ejm_50 0.981 0.083271 MLE True\n", + "7 lig_jmc_23 -1.079 0.083750 MLE True\n", + "8 lig_jmc_27 -1.289 0.114079 MLE True\n", + "9 lig_jmc_28 -0.809 0.079335 MLE True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mle_df = abs_df[abs_df[\"source\"] == \"MLE\"].reset_index(drop=True)\n", + "mle_df" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "openfe_dev", "language": "python", "name": "python3" }, @@ -540,7 +7313,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.9" } }, "nbformat": 4,