From 8d618101afd6c9b2e6e090313e366058da649f35 Mon Sep 17 00:00:00 2001 From: jbloom Date: Sun, 24 Mar 2024 16:12:43 -0700 Subject: [PATCH 1/4] add RMSD and fix coefficient of determination for edge case Add `HillCurve.rmsd` and fix calculation for `HillCurve.r2` in the edge case when there is no variation in the data. --- CHANGELOG.rst | 4 ++++ neutcurve/hillcurve.py | 22 +++++++++++++++++++++- 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 24fad0e..ad5ec30 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -15,6 +15,10 @@ The format is based on `Keep a Changelog `_. - Add ``no_curve_fit_first`` argument to ``HillCurve`` to aid debugging/development. +- Improvements to metrics for assessing curve fit (see [here](https://github.com/jbloomlab/neutcurve/issues/55#issuecomment-2016975219)): + - The coefficient of determination (``r2``) now is one if all points are fit by a straight line, rather than engative infinity. + - A root-mean-square-deviation (square root of mean residual) is now calculated as the ``rmsd`` attribute of ``HillCurve`` objects and reported in fit parameter summaries from ``CurveFits``. + 1.1.2 ----- diff --git a/neutcurve/hillcurve.py b/neutcurve/hillcurve.py index 793ca37..01bfcd7 100644 --- a/neutcurve/hillcurve.py +++ b/neutcurve/hillcurve.py @@ -120,6 +120,9 @@ class HillCurve: `r2` (float) Coefficient of determination indicating how well the curve fits the data (https://en.wikipedia.org/wiki/Coefficient_of_determination). + `rmsd` (float) + Root mean square deviation of fitted to actual values (square root of mean + residual). `params_stdev` (dict or `None`) If standard deviations can be estimated on the fit parameters, keyed by 'bottom', 'top', 'midpoint', @@ -322,6 +325,10 @@ class HillCurve: >>> round(neut.r2, 3) 1.0 + We can also quantify the goodness of fit with :attr:`HillCurve.rmsd`: + >>> round(neut.rmsd, 3) + 0.0 + Now fit with bounds on the parameters. First, we make bounds cover the true values: >>> neut_bounds_cover = HillCurve( @@ -337,6 +344,8 @@ class HillCurve: True >>> round(neut_bounds_cover.r2, 3) 1.0 + >>> round(neut_bounds_cover.rmsd, 3) + 0.0 Next fit with bounds that do not cover the true parameters: >>> neut_bounds_nocover = HillCurve( @@ -352,6 +361,8 @@ class HillCurve: 0.05 >>> round(neut_bounds_nocover.r2, 2) 0.99 + >>> round(neut_bounds_nocover.rmsd, 3) + 0.045 Now fit with `infectivity_or_neutralized='neutralized'`, which is useful when the signal **increases** rather than decreases with increasing @@ -629,7 +640,16 @@ def __init__( ssres = ( (numpy.array([self.fracinfectivity(c) for c in self.cs]) - self.fs) ** 2 ).sum() - self.r2 = 1 - ssres / sstot + if sstot == 0: + if ssres == 0: + self.r2 = 1.0 + else: + self.r2 = 0.0 + else: + self.r2 = 1.0 - ssres / sstot + + # compute rmsd + self.rmsd = math.sqrt(ssres / len(self.cs)) def _fit_curve( self, From 2598137b2cf786843518bda83c2ed48599eb5186 Mon Sep 17 00:00:00 2001 From: jbloom Date: Sun, 24 Mar 2024 16:31:25 -0700 Subject: [PATCH 2/4] `CurveFits` reports `rmsd` --- neutcurve/curvefits.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/neutcurve/curvefits.py b/neutcurve/curvefits.py index 99fe78e..d143e2a 100644 --- a/neutcurve/curvefits.py +++ b/neutcurve/curvefits.py @@ -537,6 +537,7 @@ def fitParams( - 'top': top of curve. - 'bottom': bottom of curve. - 'r2': coefficient of determination of fit + - 'rmsd': root-mean square deviation of fits """ if ic50_error not in {None, "fit_stdev"}: @@ -566,6 +567,7 @@ def fitParams( "top", "bottom", "r2", + "rmsd", ] for serum in self.sera: for virus in self.viruses[serum]: From 68d259836fb9305824540db520cab30a93ed4135 Mon Sep 17 00:00:00 2001 From: jbloom Date: Sun, 24 Mar 2024 16:31:38 -0700 Subject: [PATCH 3/4] update examples to add `rmsd` output --- notebooks/combine_curvefits.ipynb | 96 ++--- notebooks/constrain_params_range.ipynb | 91 +++-- notebooks/curvefits_example.ipynb | 520 ++++++++++++++----------- notebooks/test_curves.ipynb | 148 ++++--- 4 files changed, 477 insertions(+), 378 deletions(-) diff --git a/notebooks/combine_curvefits.ipynb b/notebooks/combine_curvefits.ipynb index b5edb3e..5df6be9 100644 --- a/notebooks/combine_curvefits.ipynb +++ b/notebooks/combine_curvefits.ipynb @@ -17,11 +17,11 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:21.759483Z", - "iopub.status.busy": "2024-03-23T12:50:21.758464Z", - "iopub.status.idle": "2024-03-23T12:50:33.647118Z", - "shell.execute_reply": "2024-03-23T12:50:33.645350Z", - "shell.execute_reply.started": "2024-03-23T12:50:21.759449Z" + "iopub.execute_input": "2024-03-24T23:23:59.705113Z", + "iopub.status.busy": "2024-03-24T23:23:59.704634Z", + "iopub.status.idle": "2024-03-24T23:24:02.093882Z", + "shell.execute_reply": "2024-03-24T23:24:02.092535Z", + "shell.execute_reply.started": "2024-03-24T23:23:59.705078Z" } }, "outputs": [], @@ -43,11 +43,11 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:33.649985Z", - "iopub.status.busy": "2024-03-23T12:50:33.648874Z", - "iopub.status.idle": "2024-03-23T12:50:33.661552Z", - "shell.execute_reply": "2024-03-23T12:50:33.660523Z", - "shell.execute_reply.started": "2024-03-23T12:50:33.649938Z" + "iopub.execute_input": "2024-03-24T23:24:02.099466Z", + "iopub.status.busy": "2024-03-24T23:24:02.099124Z", + "iopub.status.idle": "2024-03-24T23:24:02.108968Z", + "shell.execute_reply": "2024-03-24T23:24:02.108253Z", + "shell.execute_reply.started": "2024-03-24T23:24:02.099428Z" } }, "outputs": [], @@ -68,11 +68,11 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:33.664360Z", - "iopub.status.busy": "2024-03-23T12:50:33.663975Z", - "iopub.status.idle": "2024-03-23T12:50:34.022519Z", - "shell.execute_reply": "2024-03-23T12:50:34.021361Z", - "shell.execute_reply.started": "2024-03-23T12:50:33.664331Z" + "iopub.execute_input": "2024-03-24T23:24:02.113186Z", + "iopub.status.busy": "2024-03-24T23:24:02.112951Z", + "iopub.status.idle": "2024-03-24T23:24:02.455126Z", + "shell.execute_reply": "2024-03-24T23:24:02.454289Z", + "shell.execute_reply.started": "2024-03-24T23:24:02.113160Z" }, "tags": [] }, @@ -94,11 +94,11 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:34.026980Z", - "iopub.status.busy": "2024-03-23T12:50:34.026650Z", - "iopub.status.idle": "2024-03-23T12:50:34.581382Z", - "shell.execute_reply": "2024-03-23T12:50:34.580282Z", - "shell.execute_reply.started": "2024-03-23T12:50:34.026955Z" + "iopub.execute_input": "2024-03-24T23:24:02.462043Z", + "iopub.status.busy": "2024-03-24T23:24:02.461682Z", + "iopub.status.idle": "2024-03-24T23:24:03.071796Z", + "shell.execute_reply": "2024-03-24T23:24:03.070966Z", + "shell.execute_reply.started": "2024-03-24T23:24:02.462015Z" }, "tags": [] }, @@ -132,11 +132,11 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:34.582583Z", - "iopub.status.busy": "2024-03-23T12:50:34.582359Z", - "iopub.status.idle": "2024-03-23T12:50:34.677224Z", - "shell.execute_reply": "2024-03-23T12:50:34.676140Z", - "shell.execute_reply.started": "2024-03-23T12:50:34.582562Z" + "iopub.execute_input": "2024-03-24T23:24:03.075999Z", + "iopub.status.busy": "2024-03-24T23:24:03.075781Z", + "iopub.status.idle": "2024-03-24T23:24:03.182389Z", + "shell.execute_reply": "2024-03-24T23:24:03.181609Z", + "shell.execute_reply.started": "2024-03-24T23:24:03.075975Z" }, "tags": [] }, @@ -162,11 +162,11 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:34.678443Z", - "iopub.status.busy": "2024-03-23T12:50:34.678228Z", - "iopub.status.idle": "2024-03-23T12:50:34.752574Z", - "shell.execute_reply": "2024-03-23T12:50:34.751838Z", - "shell.execute_reply.started": "2024-03-23T12:50:34.678421Z" + "iopub.execute_input": "2024-03-24T23:24:03.186844Z", + "iopub.status.busy": "2024-03-24T23:24:03.186527Z", + "iopub.status.idle": "2024-03-24T23:24:03.257297Z", + "shell.execute_reply": "2024-03-24T23:24:03.256546Z", + "shell.execute_reply.started": "2024-03-24T23:24:03.186819Z" }, "scrolled": true }, @@ -206,6 +206,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -225,6 +226,7 @@ " 1.0\n", " 0.0\n", " 0.996\n", + " 0.028\n", " \n", " \n", " 1\n", @@ -242,6 +244,7 @@ " 1.0\n", " 0.0\n", " 0.986\n", + " 0.053\n", " \n", " \n", " 2\n", @@ -259,6 +262,7 @@ " 1.0\n", " 0.0\n", " 0.982\n", + " 0.060\n", " \n", " \n", " 3\n", @@ -276,6 +280,7 @@ " 1.0\n", " 0.0\n", " 0.992\n", + " 0.039\n", " \n", " \n", " 4\n", @@ -293,6 +298,7 @@ " 1.0\n", " 0.0\n", " 0.980\n", + " 0.069\n", " \n", " \n", " 5\n", @@ -310,6 +316,7 @@ " 1.0\n", " 0.0\n", " 0.994\n", + " 0.035\n", " \n", " \n", " 6\n", @@ -327,6 +334,7 @@ " 1.0\n", " 0.0\n", " 0.990\n", + " 0.047\n", " \n", " \n", "\n", @@ -342,14 +350,14 @@ "5 FI6v3 P80D 3 0.013 interpolated 0.0128 0.013 \n", "6 FI6v3 P80D average 2 0.012 interpolated 0.0125 0.012 \n", "\n", - " midpoint_bound midpoint_bound_type slope top bottom r2 \n", - "0 0.017 interpolated 2.505 1.0 0.0 0.996 \n", - "1 0.019 interpolated 2.513 1.0 0.0 0.986 \n", - "2 0.015 interpolated 1.878 1.0 0.0 0.982 \n", - "3 0.017 interpolated 2.279 1.0 0.0 0.992 \n", - "4 0.012 interpolated 2.025 1.0 0.0 0.980 \n", - "5 0.013 interpolated 2.059 1.0 0.0 0.994 \n", - "6 0.012 interpolated 2.035 1.0 0.0 0.990 " + " midpoint_bound midpoint_bound_type slope top bottom r2 rmsd \n", + "0 0.017 interpolated 2.505 1.0 0.0 0.996 0.028 \n", + "1 0.019 interpolated 2.513 1.0 0.0 0.986 0.053 \n", + "2 0.015 interpolated 1.878 1.0 0.0 0.982 0.060 \n", + "3 0.017 interpolated 2.279 1.0 0.0 0.992 0.039 \n", + "4 0.012 interpolated 2.025 1.0 0.0 0.980 0.069 \n", + "5 0.013 interpolated 2.059 1.0 0.0 0.994 0.035 \n", + "6 0.012 interpolated 2.035 1.0 0.0 0.990 0.047 " ] }, "execution_count": 6, @@ -395,11 +403,11 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-23T12:50:34.753685Z", - "iopub.status.busy": "2024-03-23T12:50:34.753493Z", - "iopub.status.idle": "2024-03-23T12:50:35.875072Z", - "shell.execute_reply": "2024-03-23T12:50:35.873283Z", - "shell.execute_reply.started": "2024-03-23T12:50:34.753666Z" + "iopub.execute_input": "2024-03-24T23:24:03.261594Z", + "iopub.status.busy": "2024-03-24T23:24:03.261369Z", + "iopub.status.idle": "2024-03-24T23:24:04.077435Z", + "shell.execute_reply": "2024-03-24T23:24:04.076116Z", + "shell.execute_reply.started": "2024-03-24T23:24:03.261569Z" } }, "outputs": [ @@ -411,7 +419,7 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# NBVAL_RAISES_EXCEPTION\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mneutcurve\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCurveFits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombineCurveFits\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfit1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfit2_invalid\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/neutcurve/neutcurve/curvefits.py:204\u001b[0m, in \u001b[0;36mCurveFits.combineCurveFits\u001b[0;34m(curvefits_list, sera, viruses, serum_virus_replicates_to_drop)\u001b[0m\n\u001b[1;32m 193\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39mdf \u001b[38;5;241m=\u001b[39m combined_fits\u001b[38;5;241m.\u001b[39m_get_avg_and_stderr_df(combined_fits\u001b[38;5;241m.\u001b[39mdf)\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(combined_fits\u001b[38;5;241m.\u001b[39mdf) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\n\u001b[1;32m 195\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 196\u001b[0m [\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 202\u001b[0m )\n\u001b[1;32m 203\u001b[0m ):\n\u001b[0;32m--> 204\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mduplicated sera/virus/replicate in `curvefits_list`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 206\u001b[0m \u001b[38;5;66;03m# combine sera\u001b[39;00m\n\u001b[1;32m 207\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39msera \u001b[38;5;241m=\u001b[39m combined_fits\u001b[38;5;241m.\u001b[39mdf[combined_fits\u001b[38;5;241m.\u001b[39mserum_col]\u001b[38;5;241m.\u001b[39munique()\u001b[38;5;241m.\u001b[39mtolist()\n", + "File \u001b[0;32m~/neutcurve/neutcurve/curvefits.py:207\u001b[0m, in \u001b[0;36mCurveFits.combineCurveFits\u001b[0;34m(curvefits_list, sera, viruses, serum_virus_replicates_to_drop)\u001b[0m\n\u001b[1;32m 196\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39mdf \u001b[38;5;241m=\u001b[39m combined_fits\u001b[38;5;241m.\u001b[39m_get_avg_and_stderr_df(combined_fits\u001b[38;5;241m.\u001b[39mdf)\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(combined_fits\u001b[38;5;241m.\u001b[39mdf) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\n\u001b[1;32m 198\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 199\u001b[0m [\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 205\u001b[0m )\n\u001b[1;32m 206\u001b[0m ):\n\u001b[0;32m--> 207\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mduplicated sera/virus/replicate in `curvefits_list`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;66;03m# combine sera\u001b[39;00m\n\u001b[1;32m 210\u001b[0m combined_fits\u001b[38;5;241m.\u001b[39msera \u001b[38;5;241m=\u001b[39m combined_fits\u001b[38;5;241m.\u001b[39mdf[combined_fits\u001b[38;5;241m.\u001b[39mserum_col]\u001b[38;5;241m.\u001b[39munique()\u001b[38;5;241m.\u001b[39mtolist()\n", "\u001b[0;31mValueError\u001b[0m: duplicated sera/virus/replicate in `curvefits_list`" ] } diff --git a/notebooks/constrain_params_range.ipynb b/notebooks/constrain_params_range.ipynb index c3d8850..5d1c627 100644 --- a/notebooks/constrain_params_range.ipynb +++ b/notebooks/constrain_params_range.ipynb @@ -31,11 +31,11 @@ "id": "23c10a08-2722-4db9-9617-46863a56afa1", "metadata": { "execution": { - "iopub.execute_input": "2024-03-24T16:37:22.863661Z", - "iopub.status.busy": "2024-03-24T16:37:22.863301Z", - "iopub.status.idle": "2024-03-24T16:37:24.607780Z", - "shell.execute_reply": "2024-03-24T16:37:24.606763Z", - "shell.execute_reply.started": "2024-03-24T16:37:22.863625Z" + "iopub.execute_input": "2024-03-24T23:28:35.806905Z", + "iopub.status.busy": "2024-03-24T23:28:35.806385Z", + "iopub.status.idle": "2024-03-24T23:28:37.422100Z", + "shell.execute_reply": "2024-03-24T23:28:37.421136Z", + "shell.execute_reply.started": "2024-03-24T23:28:35.806867Z" } }, "outputs": [], @@ -62,11 +62,11 @@ "id": "2bef2888-15c1-4120-915b-8bf45f31ea70", "metadata": { "execution": { - "iopub.execute_input": "2024-03-24T16:37:24.615995Z", - "iopub.status.busy": "2024-03-24T16:37:24.615602Z", - "iopub.status.idle": "2024-03-24T16:37:30.485109Z", - "shell.execute_reply": "2024-03-24T16:37:30.484284Z", - "shell.execute_reply.started": "2024-03-24T16:37:24.615961Z" + "iopub.execute_input": "2024-03-24T23:28:37.424629Z", + "iopub.status.busy": "2024-03-24T23:28:37.424138Z", + "iopub.status.idle": "2024-03-24T23:28:43.610548Z", + "shell.execute_reply": "2024-03-24T23:28:43.609805Z", + "shell.execute_reply.started": "2024-03-24T23:28:37.424598Z" } }, "outputs": [ @@ -103,9 +103,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1157: RuntimeWarning: invalid value encountered in power\n", + "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power\n", " return b + (t - b) / (1 + (c / m) ** s)\n", - "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1157: RuntimeWarning: divide by zero encountered in divide\n", + "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide\n", " return b + (t - b) / (1 + (c / m) ** s)\n" ] }, @@ -201,11 +201,11 @@ "id": "f23eb9e4-de3d-4e25-ba96-b044159d45d8", "metadata": { "execution": { - "iopub.execute_input": "2024-03-24T16:37:30.489649Z", - "iopub.status.busy": "2024-03-24T16:37:30.489322Z", - "iopub.status.idle": "2024-03-24T16:37:30.532526Z", - "shell.execute_reply": "2024-03-24T16:37:30.531447Z", - "shell.execute_reply.started": "2024-03-24T16:37:30.489625Z" + "iopub.execute_input": "2024-03-24T23:28:43.612591Z", + "iopub.status.busy": "2024-03-24T23:28:43.612128Z", + "iopub.status.idle": "2024-03-24T23:28:43.648660Z", + "shell.execute_reply": "2024-03-24T23:28:43.647806Z", + "shell.execute_reply.started": "2024-03-24T23:28:43.612565Z" } }, "outputs": [ @@ -248,6 +248,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -263,6 +264,7 @@ " 0.94\n", " 0\n", " 0.86\n", + " 0.12\n", " \n", " \n", " 1\n", @@ -276,6 +278,7 @@ " 0.8\n", " 0\n", " 0.67\n", + " 0.19\n", " \n", " \n", " 2\n", @@ -289,6 +292,7 @@ " 0.92\n", " 0\n", " 0.95\n", + " 0.061\n", " \n", " \n", " 3\n", @@ -302,6 +306,7 @@ " 0.9\n", " 0\n", " 0.87\n", + " 0.11\n", " \n", " \n", " 4\n", @@ -315,6 +320,7 @@ " 0.81\n", " 0\n", " 0.87\n", + " 0.092\n", " \n", " \n", " 5\n", @@ -328,6 +334,7 @@ " 0.82\n", " 0\n", " 0.95\n", + " 0.056\n", " \n", " \n", " 6\n", @@ -341,6 +348,7 @@ " 0.87\n", " 0\n", " 0.52\n", + " 0.2\n", " \n", " \n", " 7\n", @@ -354,6 +362,7 @@ " 0.8\n", " 0\n", " 0.35\n", + " 0.15\n", " \n", " \n", " 8\n", @@ -367,6 +376,7 @@ " 0.87\n", " 0\n", " 0.97\n", + " 0.043\n", " \n", " \n", "\n", @@ -384,16 +394,16 @@ "7 A/Oman/3011/2023 plate11_r32_75k-TACGAAAATCAAGAGC 0.00035 \n", "8 A/Oman/3011/2023 plate11_r32_75k-TCCTTTAACTAATCGA 0.00021 \n", "\n", - " ic50_bound midpoint midpoint_bound_type slope top bottom r2 \n", - "0 interpolated 0.001 interpolated 1 0.94 0 0.86 \n", - "1 interpolated 0.0022 interpolated 1.4 0.8 0 0.67 \n", - "2 interpolated 0.0006 interpolated 1 0.92 0 0.95 \n", - "3 interpolated 0.0011 interpolated 1.1 0.9 0 0.87 \n", - "4 interpolated 0.00082 interpolated 1 0.81 0 0.87 \n", - "5 interpolated 0.00054 interpolated 1 0.82 0 0.95 \n", - "6 interpolated 0.0013 interpolated 1 0.87 0 0.52 \n", - "7 interpolated 0.00059 interpolated 1 0.8 0 0.35 \n", - "8 interpolated 0.00027 interpolated 1.2 0.87 0 0.97 " + " ic50_bound midpoint midpoint_bound_type slope top bottom r2 rmsd \n", + "0 interpolated 0.001 interpolated 1 0.94 0 0.86 0.12 \n", + "1 interpolated 0.0022 interpolated 1.4 0.8 0 0.67 0.19 \n", + "2 interpolated 0.0006 interpolated 1 0.92 0 0.95 0.061 \n", + "3 interpolated 0.0011 interpolated 1.1 0.9 0 0.87 0.11 \n", + "4 interpolated 0.00082 interpolated 1 0.81 0 0.87 0.092 \n", + "5 interpolated 0.00054 interpolated 1 0.82 0 0.95 0.056 \n", + "6 interpolated 0.0013 interpolated 1 0.87 0 0.52 0.2 \n", + "7 interpolated 0.00059 interpolated 1 0.8 0 0.35 0.15 \n", + "8 interpolated 0.00027 interpolated 1.2 0.87 0 0.97 0.043 " ] }, "metadata": {}, @@ -438,6 +448,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -453,6 +464,7 @@ " 0.83\n", " 0\n", " 0.83\n", + " 0.14\n", " \n", " \n", " 10\n", @@ -466,6 +478,7 @@ " 0.8\n", " 0\n", " 0.93\n", + " 0.067\n", " \n", " \n", " 11\n", @@ -479,6 +492,7 @@ " 0.9\n", " 0\n", " 0.92\n", + " 0.091\n", " \n", " \n", " 12\n", @@ -492,6 +506,7 @@ " 1\n", " 0\n", " 0.85\n", + " 0.17\n", " \n", " \n", " 13\n", @@ -505,6 +520,7 @@ " 1\n", " 0\n", " 0.77\n", + " 0.16\n", " \n", " \n", " 14\n", @@ -518,6 +534,7 @@ " 1\n", " 0\n", " 0.85\n", + " 0.11\n", " \n", " \n", " 15\n", @@ -531,6 +548,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0091\n", " \n", " \n", " 16\n", @@ -544,6 +562,7 @@ " 1\n", " 0\n", " 0.93\n", + " 0.023\n", " \n", " \n", "\n", @@ -560,15 +579,15 @@ "15 A/Hong_Kong/4801/2014_(15/192) plate11_r32_75k-CACAGACAATAAAAAA 7.8e-05 \n", "16 A/Hong_Kong/4801/2014_(15/192) plate11_r32_75k-GGTTAACTTTGGAAGC 7.8e-05 \n", "\n", - " ic50_bound midpoint midpoint_bound_type slope top bottom r2 \n", - "9 interpolated 0.0025 interpolated 2 0.83 0 0.83 \n", - "10 interpolated 0.00097 interpolated 1.1 0.8 0 0.93 \n", - "11 interpolated 0.0014 interpolated 1.5 0.9 0 0.92 \n", - "12 interpolated 0.00022 interpolated 2 1 0 0.85 \n", - "13 interpolated 0.00011 interpolated 2 1 0 0.77 \n", - "14 interpolated 0.0001 interpolated 2 1 0 0.85 \n", - "15 upper 4.8e-05 upper 2 1 0 0.99 \n", - "16 upper 4.4e-05 upper 2 1 0 0.93 " + " ic50_bound midpoint midpoint_bound_type slope top bottom r2 rmsd \n", + "9 interpolated 0.0025 interpolated 2 0.83 0 0.83 0.14 \n", + "10 interpolated 0.00097 interpolated 1.1 0.8 0 0.93 0.067 \n", + "11 interpolated 0.0014 interpolated 1.5 0.9 0 0.92 0.091 \n", + "12 interpolated 0.00022 interpolated 2 1 0 0.85 0.17 \n", + "13 interpolated 0.00011 interpolated 2 1 0 0.77 0.16 \n", + "14 interpolated 0.0001 interpolated 2 1 0 0.85 0.11 \n", + "15 upper 4.8e-05 upper 2 1 0 0.99 0.0091 \n", + "16 upper 4.4e-05 upper 2 1 0 0.93 0.023 " ] }, "metadata": {}, diff --git a/notebooks/curvefits_example.ipynb b/notebooks/curvefits_example.ipynb index 488fcfd..19519e4 100644 --- a/notebooks/curvefits_example.ipynb +++ b/notebooks/curvefits_example.ipynb @@ -26,11 +26,11 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:56.247402Z", - "iopub.status.busy": "2024-01-01T00:05:56.246683Z", - "iopub.status.idle": "2024-01-01T00:05:58.121799Z", - "shell.execute_reply": "2024-01-01T00:05:58.120722Z", - "shell.execute_reply.started": "2024-01-01T00:05:56.247361Z" + "iopub.execute_input": "2024-03-24T23:28:50.121734Z", + "iopub.status.busy": "2024-03-24T23:28:50.120911Z", + "iopub.status.idle": "2024-03-24T23:28:51.692109Z", + "shell.execute_reply": "2024-03-24T23:28:51.691182Z", + "shell.execute_reply.started": "2024-03-24T23:28:50.121683Z" } }, "outputs": [], @@ -53,11 +53,11 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.124616Z", - "iopub.status.busy": "2024-01-01T00:05:58.123366Z", - "iopub.status.idle": "2024-01-01T00:05:58.129203Z", - "shell.execute_reply": "2024-01-01T00:05:58.128442Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.124561Z" + "iopub.execute_input": "2024-03-24T23:28:51.695094Z", + "iopub.status.busy": "2024-03-24T23:28:51.694579Z", + "iopub.status.idle": "2024-03-24T23:28:51.699981Z", + "shell.execute_reply": "2024-03-24T23:28:51.699157Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.695063Z" } }, "outputs": [], @@ -86,11 +86,11 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.131988Z", - "iopub.status.busy": "2024-01-01T00:05:58.131528Z", - "iopub.status.idle": "2024-01-01T00:05:58.135968Z", - "shell.execute_reply": "2024-01-01T00:05:58.135170Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.131943Z" + "iopub.execute_input": "2024-03-24T23:28:51.701609Z", + "iopub.status.busy": "2024-03-24T23:28:51.701224Z", + "iopub.status.idle": "2024-03-24T23:28:51.705056Z", + "shell.execute_reply": "2024-03-24T23:28:51.704337Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.701577Z" } }, "outputs": [], @@ -103,11 +103,11 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.140172Z", - "iopub.status.busy": "2024-01-01T00:05:58.139765Z", - "iopub.status.idle": "2024-01-01T00:05:58.155315Z", - "shell.execute_reply": "2024-01-01T00:05:58.154423Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.140132Z" + "iopub.execute_input": "2024-03-24T23:28:51.706740Z", + "iopub.status.busy": "2024-03-24T23:28:51.706175Z", + "iopub.status.idle": "2024-03-24T23:28:51.716290Z", + "shell.execute_reply": "2024-03-24T23:28:51.715550Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.706711Z" } }, "outputs": [], @@ -130,11 +130,11 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.156867Z", - "iopub.status.busy": "2024-01-01T00:05:58.156481Z", - "iopub.status.idle": "2024-01-01T00:05:58.174558Z", - "shell.execute_reply": "2024-01-01T00:05:58.173744Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.156832Z" + "iopub.execute_input": "2024-03-24T23:28:51.719377Z", + "iopub.status.busy": "2024-03-24T23:28:51.719035Z", + "iopub.status.idle": "2024-03-24T23:28:51.734805Z", + "shell.execute_reply": "2024-03-24T23:28:51.734103Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.719352Z" } }, "outputs": [ @@ -241,11 +241,11 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.176178Z", - "iopub.status.busy": "2024-01-01T00:05:58.175637Z", - "iopub.status.idle": "2024-01-01T00:05:58.185823Z", - "shell.execute_reply": "2024-01-01T00:05:58.185050Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.176143Z" + "iopub.execute_input": "2024-03-24T23:28:51.736086Z", + "iopub.status.busy": "2024-03-24T23:28:51.735773Z", + "iopub.status.idle": "2024-03-24T23:28:51.746173Z", + "shell.execute_reply": "2024-03-24T23:28:51.745374Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.736062Z" } }, "outputs": [ @@ -384,11 +384,11 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.187177Z", - "iopub.status.busy": "2024-01-01T00:05:58.186831Z", - "iopub.status.idle": "2024-01-01T00:05:58.305733Z", - "shell.execute_reply": "2024-01-01T00:05:58.305005Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.187147Z" + "iopub.execute_input": "2024-03-24T23:28:51.747633Z", + "iopub.status.busy": "2024-03-24T23:28:51.747261Z", + "iopub.status.idle": "2024-03-24T23:28:51.869718Z", + "shell.execute_reply": "2024-03-24T23:28:51.869031Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.747602Z" } }, "outputs": [], @@ -408,11 +408,11 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.309205Z", - "iopub.status.busy": "2024-01-01T00:05:58.308838Z", - "iopub.status.idle": "2024-01-01T00:05:58.313755Z", - "shell.execute_reply": "2024-01-01T00:05:58.313136Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.309175Z" + "iopub.execute_input": "2024-03-24T23:28:51.871302Z", + "iopub.status.busy": "2024-03-24T23:28:51.870985Z", + "iopub.status.idle": "2024-03-24T23:28:51.875379Z", + "shell.execute_reply": "2024-03-24T23:28:51.874820Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.871278Z" } }, "outputs": [ @@ -443,11 +443,11 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.314873Z", - "iopub.status.busy": "2024-01-01T00:05:58.314557Z", - "iopub.status.idle": "2024-01-01T00:05:58.318968Z", - "shell.execute_reply": "2024-01-01T00:05:58.318256Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.314848Z" + "iopub.execute_input": "2024-03-24T23:28:51.876443Z", + "iopub.status.busy": "2024-03-24T23:28:51.876235Z", + "iopub.status.idle": "2024-03-24T23:28:51.881520Z", + "shell.execute_reply": "2024-03-24T23:28:51.880721Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.876423Z" } }, "outputs": [ @@ -483,11 +483,11 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.320259Z", - "iopub.status.busy": "2024-01-01T00:05:58.319891Z", - "iopub.status.idle": "2024-01-01T00:05:58.325321Z", - "shell.execute_reply": "2024-01-01T00:05:58.324642Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.320231Z" + "iopub.execute_input": "2024-03-24T23:28:51.882793Z", + "iopub.status.busy": "2024-03-24T23:28:51.882468Z", + "iopub.status.idle": "2024-03-24T23:28:51.888287Z", + "shell.execute_reply": "2024-03-24T23:28:51.887506Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.882768Z" } }, "outputs": [ @@ -524,11 +524,11 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.326583Z", - "iopub.status.busy": "2024-01-01T00:05:58.326245Z", - "iopub.status.idle": "2024-01-01T00:05:58.902358Z", - "shell.execute_reply": "2024-01-01T00:05:58.901738Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.326554Z" + "iopub.execute_input": "2024-03-24T23:28:51.889752Z", + "iopub.status.busy": "2024-03-24T23:28:51.889376Z", + "iopub.status.idle": "2024-03-24T23:28:52.469895Z", + "shell.execute_reply": "2024-03-24T23:28:52.469205Z", + "shell.execute_reply.started": "2024-03-24T23:28:51.889723Z" } }, "outputs": [ @@ -572,11 +572,11 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:58.903555Z", - "iopub.status.busy": "2024-01-01T00:05:58.903246Z", - "iopub.status.idle": "2024-01-01T00:05:59.202285Z", - "shell.execute_reply": "2024-01-01T00:05:59.201686Z", - "shell.execute_reply.started": "2024-01-01T00:05:58.903529Z" + "iopub.execute_input": "2024-03-24T23:28:52.471139Z", + "iopub.status.busy": "2024-03-24T23:28:52.470843Z", + "iopub.status.idle": "2024-03-24T23:28:52.775650Z", + "shell.execute_reply": "2024-03-24T23:28:52.775071Z", + "shell.execute_reply.started": "2024-03-24T23:28:52.471116Z" } }, "outputs": [ @@ -624,11 +624,11 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.203818Z", - "iopub.status.busy": "2024-01-01T00:05:59.203506Z", - "iopub.status.idle": "2024-01-01T00:05:59.271706Z", - "shell.execute_reply": "2024-01-01T00:05:59.271006Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.203792Z" + "iopub.execute_input": "2024-03-24T23:28:52.776774Z", + "iopub.status.busy": "2024-03-24T23:28:52.776486Z", + "iopub.status.idle": "2024-03-24T23:28:52.846554Z", + "shell.execute_reply": "2024-03-24T23:28:52.845707Z", + "shell.execute_reply.started": "2024-03-24T23:28:52.776752Z" } }, "outputs": [ @@ -667,6 +667,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -686,6 +687,7 @@ " 1\n", " 0\n", " 0.992\n", + " 0.0389\n", " \n", " \n", " 1\n", @@ -703,6 +705,7 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0592\n", " \n", " \n", " 2\n", @@ -720,6 +723,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0467\n", " \n", " \n", " 3\n", @@ -737,6 +741,7 @@ " 1\n", " 0\n", " 0.995\n", + " 0.0328\n", " \n", " \n", " 4\n", @@ -754,6 +759,7 @@ " 1\n", " 0\n", " 0.981\n", + " 0.0655\n", " \n", " \n", " 5\n", @@ -771,6 +777,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0376\n", " \n", " \n", " 6\n", @@ -788,6 +795,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0375\n", " \n", " \n", " 7\n", @@ -805,6 +813,7 @@ " 1\n", " 0\n", " 0.997\n", + " 0.0228\n", " \n", " \n", " 8\n", @@ -822,6 +831,7 @@ " 1\n", " 0\n", " 0.989\n", + " 0.0483\n", " \n", " \n", " 9\n", @@ -839,6 +849,7 @@ " 1\n", " 0\n", " 0.982\n", + " 0.0674\n", " \n", " \n", " 10\n", @@ -856,6 +867,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0518\n", " \n", " \n", " 11\n", @@ -873,25 +885,26 @@ " 1\n", " 0\n", " 0.708\n", + " 0.052\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.017 0.017 interpolated 2.28 1 0 0.992\n", - "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0283 0.0283 interpolated 2.4 1 0 0.986\n", - "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0123 0.0123 interpolated 2.05 1 0 0.99\n", - "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.0229 0.0229 interpolated 1.83 1 0 0.995\n", - "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0106 0.0106 interpolated 1.86 1 0 0.981\n", - "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.0428 0.0428 interpolated 2 1 0 0.993\n", - "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.0348 0.0348 interpolated 1.82 1 0 0.993\n", - "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.0845 0.0845 interpolated 1.8 1 0 0.997\n", - "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.0198 0.0198 interpolated 2.06 1 0 0.989\n", - "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.0348 0.0348 interpolated 2.6 1 0 0.982\n", - "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.106 0.106 interpolated 4.04 1 0 0.99\n", - "11 H17-L19 V135T average 2 11.4 lower >11.4 16.9 11.4 lower 2.36 1 0 0.708" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.017 0.017 interpolated 2.28 1 0 0.992 0.0389\n", + "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0283 0.0283 interpolated 2.4 1 0 0.986 0.0592\n", + "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0123 0.0123 interpolated 2.05 1 0 0.99 0.0467\n", + "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.0229 0.0229 interpolated 1.83 1 0 0.995 0.0328\n", + "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0106 0.0106 interpolated 1.86 1 0 0.981 0.0655\n", + "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.0428 0.0428 interpolated 2 1 0 0.993 0.0376\n", + "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.0348 0.0348 interpolated 1.82 1 0 0.993 0.0375\n", + "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.0845 0.0845 interpolated 1.8 1 0 0.997 0.0228\n", + "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.0198 0.0198 interpolated 2.06 1 0 0.989 0.0483\n", + "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.0348 0.0348 interpolated 2.6 1 0 0.982 0.0674\n", + "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.106 0.106 interpolated 4.04 1 0 0.99 0.0518\n", + "11 H17-L19 V135T average 2 11.4 lower >11.4 16.9 11.4 lower 2.36 1 0 0.708 0.052" ] }, "execution_count": 13, @@ -931,11 +944,11 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.273090Z", - "iopub.status.busy": "2024-01-01T00:05:59.272857Z", - "iopub.status.idle": "2024-01-01T00:05:59.433498Z", - "shell.execute_reply": "2024-01-01T00:05:59.432801Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.273065Z" + "iopub.execute_input": "2024-03-24T23:28:52.852344Z", + "iopub.status.busy": "2024-03-24T23:28:52.851979Z", + "iopub.status.idle": "2024-03-24T23:28:53.017716Z", + "shell.execute_reply": "2024-03-24T23:28:53.016664Z", + "shell.execute_reply.started": "2024-03-24T23:28:52.852316Z" } }, "outputs": [ @@ -974,6 +987,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -993,6 +1007,7 @@ " 1\n", " 0\n", " 0.996\n", + " 0.0284\n", " \n", " \n", " 1\n", @@ -1010,6 +1025,7 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0527\n", " \n", " \n", " 2\n", @@ -1027,6 +1043,7 @@ " 1\n", " 0\n", " 0.982\n", + " 0.0597\n", " \n", " \n", " 3\n", @@ -1044,6 +1061,7 @@ " 1\n", " 0\n", " 0.992\n", + " 0.0389\n", " \n", " \n", " 4\n", @@ -1061,18 +1079,19 @@ " 1\n", " 0\n", " 0.977\n", + " 0.077\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 FI6v3 WT 1 0.0167 interpolated 0.0167 0.0167 0.0167 interpolated 2.5 1 0 0.996\n", - "1 FI6v3 WT 2 0.019 interpolated 0.019 0.019 0.019 interpolated 2.51 1 0 0.986\n", - "2 FI6v3 WT 3 0.0152 interpolated 0.0152 0.0152 0.0152 interpolated 1.88 1 0 0.982\n", - "3 FI6v3 WT average 3 0.017 interpolated 0.017 0.017 0.017 interpolated 2.28 1 0 0.992\n", - "4 FI6v3 K(-8T) 1 0.0308 interpolated 0.0308 0.0308 0.0308 interpolated 2.62 1 0 0.977" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 FI6v3 WT 1 0.0167 interpolated 0.0167 0.0167 0.0167 interpolated 2.5 1 0 0.996 0.0284\n", + "1 FI6v3 WT 2 0.019 interpolated 0.019 0.019 0.019 interpolated 2.51 1 0 0.986 0.0527\n", + "2 FI6v3 WT 3 0.0152 interpolated 0.0152 0.0152 0.0152 interpolated 1.88 1 0 0.982 0.0597\n", + "3 FI6v3 WT average 3 0.017 interpolated 0.017 0.017 0.017 interpolated 2.28 1 0 0.992 0.0389\n", + "4 FI6v3 K(-8T) 1 0.0308 interpolated 0.0308 0.0308 0.0308 interpolated 2.62 1 0 0.977 0.077" ] }, "execution_count": 14, @@ -1098,11 +1117,11 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.434804Z", - "iopub.status.busy": "2024-01-01T00:05:59.434418Z", - "iopub.status.idle": "2024-01-01T00:05:59.452081Z", - "shell.execute_reply": "2024-01-01T00:05:59.451385Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.434777Z" + "iopub.execute_input": "2024-03-24T23:28:53.019505Z", + "iopub.status.busy": "2024-03-24T23:28:53.019032Z", + "iopub.status.idle": "2024-03-24T23:28:53.045448Z", + "shell.execute_reply": "2024-03-24T23:28:53.044848Z", + "shell.execute_reply.started": "2024-03-24T23:28:53.019466Z" } }, "outputs": [ @@ -1141,6 +1160,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -1160,6 +1180,7 @@ " 1\n", " 0\n", " 0.996\n", + " 0.0284\n", " \n", " \n", " 1\n", @@ -1177,6 +1198,7 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0527\n", " \n", " \n", " 2\n", @@ -1194,6 +1216,7 @@ " 1\n", " 0\n", " 0.982\n", + " 0.0597\n", " \n", " \n", " 3\n", @@ -1211,6 +1234,7 @@ " 1\n", " 0\n", " 0.977\n", + " 0.077\n", " \n", " \n", " 4\n", @@ -1228,18 +1252,19 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0601\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 FI6v3 WT 1 0.0167 interpolated 0.0167 0.0167 0.0167 interpolated 2.5 1 0 0.996\n", - "1 FI6v3 WT 2 0.019 interpolated 0.019 0.019 0.019 interpolated 2.51 1 0 0.986\n", - "2 FI6v3 WT 3 0.0152 interpolated 0.0152 0.0152 0.0152 interpolated 1.88 1 0 0.982\n", - "3 FI6v3 K(-8T) 1 0.0308 interpolated 0.0308 0.0308 0.0308 interpolated 2.62 1 0 0.977\n", - "4 FI6v3 K(-8T) 2 0.0284 interpolated 0.0284 0.0284 0.0284 interpolated 2.55 1 0 0.986" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 FI6v3 WT 1 0.0167 interpolated 0.0167 0.0167 0.0167 interpolated 2.5 1 0 0.996 0.0284\n", + "1 FI6v3 WT 2 0.019 interpolated 0.019 0.019 0.019 interpolated 2.51 1 0 0.986 0.0527\n", + "2 FI6v3 WT 3 0.0152 interpolated 0.0152 0.0152 0.0152 interpolated 1.88 1 0 0.982 0.0597\n", + "3 FI6v3 K(-8T) 1 0.0308 interpolated 0.0308 0.0308 0.0308 interpolated 2.62 1 0 0.977 0.077\n", + "4 FI6v3 K(-8T) 2 0.0284 interpolated 0.0284 0.0284 0.0284 interpolated 2.55 1 0 0.986 0.0601" ] }, "execution_count": 15, @@ -1264,11 +1289,11 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.453737Z", - "iopub.status.busy": "2024-01-01T00:05:59.453410Z", - "iopub.status.idle": "2024-01-01T00:05:59.476054Z", - "shell.execute_reply": "2024-01-01T00:05:59.475339Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.453712Z" + "iopub.execute_input": "2024-03-24T23:28:53.046575Z", + "iopub.status.busy": "2024-03-24T23:28:53.046271Z", + "iopub.status.idle": "2024-03-24T23:28:53.068642Z", + "shell.execute_reply": "2024-03-24T23:28:53.067940Z", + "shell.execute_reply.started": "2024-03-24T23:28:53.046553Z" } }, "outputs": [ @@ -1310,6 +1335,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -1332,6 +1358,7 @@ " 1\n", " 0\n", " 0.992\n", + " 0.0389\n", " \n", " \n", " 1\n", @@ -1352,6 +1379,7 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0592\n", " \n", " \n", " 2\n", @@ -1372,6 +1400,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0467\n", " \n", " \n", " 3\n", @@ -1392,6 +1421,7 @@ " 1\n", " 0\n", " 0.995\n", + " 0.0328\n", " \n", " \n", " 4\n", @@ -1412,6 +1442,7 @@ " 1\n", " 0\n", " 0.981\n", + " 0.0655\n", " \n", " \n", " 5\n", @@ -1432,6 +1463,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0376\n", " \n", " \n", " 6\n", @@ -1452,6 +1484,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0375\n", " \n", " \n", " 7\n", @@ -1472,6 +1505,7 @@ " 1\n", " 0\n", " 0.997\n", + " 0.0228\n", " \n", " \n", " 8\n", @@ -1492,6 +1526,7 @@ " 1\n", " 0\n", " 0.989\n", + " 0.0483\n", " \n", " \n", " 9\n", @@ -1512,6 +1547,7 @@ " 1\n", " 0\n", " 0.982\n", + " 0.0674\n", " \n", " \n", " 10\n", @@ -1532,6 +1568,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0518\n", " \n", " \n", " 11\n", @@ -1552,25 +1589,26 @@ " 1\n", " 0\n", " 0.708\n", + " 0.052\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str ic95 ic95_bound ic95_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.062 interpolated 0.062 0.017 0.017 interpolated 2.28 1 0 0.992\n", - "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0967 interpolated 0.0967 0.0283 0.0283 interpolated 2.4 1 0 0.986\n", - "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0516 interpolated 0.0516 0.0123 0.0123 interpolated 2.05 1 0 0.99\n", - "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.114 interpolated 0.114 0.0229 0.0229 interpolated 1.83 1 0 0.995\n", - "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0516 interpolated 0.0516 0.0106 0.0106 interpolated 1.86 1 0 0.981\n", - "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.186 interpolated 0.186 0.0428 0.0428 interpolated 2 1 0 0.993\n", - "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.176 interpolated 0.176 0.0348 0.0348 interpolated 1.82 1 0 0.993\n", - "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.433 interpolated 0.433 0.0845 0.0845 interpolated 1.8 1 0 0.997\n", - "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.083 interpolated 0.083 0.0198 0.0198 interpolated 2.06 1 0 0.989\n", - "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.108 interpolated 0.108 0.0348 0.0348 interpolated 2.6 1 0 0.982\n", - "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.221 interpolated 0.221 0.106 0.106 interpolated 4.04 1 0 0.99\n", - "11 H17-L19 V135T average 2 11.4 lower >11.4 11.4 lower >11.4 16.9 11.4 lower 2.36 1 0 0.708" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str ic95 ic95_bound ic95_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.062 interpolated 0.062 0.017 0.017 interpolated 2.28 1 0 0.992 0.0389\n", + "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0967 interpolated 0.0967 0.0283 0.0283 interpolated 2.4 1 0 0.986 0.0592\n", + "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0516 interpolated 0.0516 0.0123 0.0123 interpolated 2.05 1 0 0.99 0.0467\n", + "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.114 interpolated 0.114 0.0229 0.0229 interpolated 1.83 1 0 0.995 0.0328\n", + "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0516 interpolated 0.0516 0.0106 0.0106 interpolated 1.86 1 0 0.981 0.0655\n", + "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.186 interpolated 0.186 0.0428 0.0428 interpolated 2 1 0 0.993 0.0376\n", + "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.176 interpolated 0.176 0.0348 0.0348 interpolated 1.82 1 0 0.993 0.0375\n", + "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.433 interpolated 0.433 0.0845 0.0845 interpolated 1.8 1 0 0.997 0.0228\n", + "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.083 interpolated 0.083 0.0198 0.0198 interpolated 2.06 1 0 0.989 0.0483\n", + "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.108 interpolated 0.108 0.0348 0.0348 interpolated 2.6 1 0 0.982 0.0674\n", + "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.221 interpolated 0.221 0.106 0.106 interpolated 4.04 1 0 0.99 0.0518\n", + "11 H17-L19 V135T average 2 11.4 lower >11.4 11.4 lower >11.4 16.9 11.4 lower 2.36 1 0 0.708 0.052" ] }, "execution_count": 16, @@ -1595,11 +1633,11 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.477581Z", - "iopub.status.busy": "2024-01-01T00:05:59.477081Z", - "iopub.status.idle": "2024-01-01T00:05:59.497918Z", - "shell.execute_reply": "2024-01-01T00:05:59.497265Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.477555Z" + "iopub.execute_input": "2024-03-24T23:28:53.069895Z", + "iopub.status.busy": "2024-03-24T23:28:53.069566Z", + "iopub.status.idle": "2024-03-24T23:28:53.089878Z", + "shell.execute_reply": "2024-03-24T23:28:53.089240Z", + "shell.execute_reply.started": "2024-03-24T23:28:53.069871Z" } }, "outputs": [ @@ -1639,6 +1677,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -1659,6 +1698,7 @@ " 1\n", " 0\n", " 0.992\n", + " 0.0389\n", " \n", " \n", " 1\n", @@ -1677,6 +1717,7 @@ " 1\n", " 0\n", " 0.986\n", + " 0.0592\n", " \n", " \n", " 2\n", @@ -1695,6 +1736,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0467\n", " \n", " \n", " 3\n", @@ -1713,6 +1755,7 @@ " 1\n", " 0\n", " 0.995\n", + " 0.0328\n", " \n", " \n", " 4\n", @@ -1731,6 +1774,7 @@ " 1\n", " 0\n", " 0.981\n", + " 0.0655\n", " \n", " \n", " 5\n", @@ -1749,6 +1793,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0376\n", " \n", " \n", " 6\n", @@ -1767,6 +1812,7 @@ " 1\n", " 0\n", " 0.993\n", + " 0.0375\n", " \n", " \n", " 7\n", @@ -1785,6 +1831,7 @@ " 1\n", " 0\n", " 0.997\n", + " 0.0228\n", " \n", " \n", " 8\n", @@ -1803,6 +1850,7 @@ " 1\n", " 0\n", " 0.989\n", + " 0.0483\n", " \n", " \n", " 9\n", @@ -1821,6 +1869,7 @@ " 1\n", " 0\n", " 0.982\n", + " 0.0674\n", " \n", " \n", " 10\n", @@ -1839,6 +1888,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.0518\n", " \n", " \n", " 11\n", @@ -1857,25 +1907,26 @@ " 1\n", " 0\n", " 0.708\n", + " 0.052\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str ic50_error midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.0254 0.017 0.017 interpolated 2.28 1 0 0.992\n", - "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0411 0.0283 0.0283 interpolated 2.4 1 0 0.986\n", - "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0193 0.0123 0.0123 interpolated 2.05 1 0 0.99\n", - "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.0382 0.0229 0.0229 interpolated 1.83 1 0 0.995\n", - "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0176 0.0106 0.0106 interpolated 1.86 1 0 0.981\n", - "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.0683 0.0428 0.0428 interpolated 2 1 0 0.993\n", - "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.0582 0.0348 0.0348 interpolated 1.82 1 0 0.993\n", - "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.142 0.0845 0.0845 interpolated 1.8 1 0 0.997\n", - "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.0313 0.0198 0.0198 interpolated 2.06 1 0 0.989\n", - "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.0477 0.0348 0.0348 interpolated 2.6 1 0 0.982\n", - "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.144 0.106 0.106 interpolated 4.04 1 0 0.99\n", - "11 H17-L19 V135T average 2 11.4 lower >11.4 NaN 16.9 11.4 lower 2.36 1 0 0.708" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str ic50_error midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 FI6v3 WT average 3 0.017 interpolated 0.017 0.0254 0.017 0.017 interpolated 2.28 1 0 0.992 0.0389\n", + "1 FI6v3 K(-8T) average 3 0.0283 interpolated 0.0283 0.0411 0.0283 0.0283 interpolated 2.4 1 0 0.986 0.0592\n", + "2 FI6v3 P80D average 3 0.0123 interpolated 0.0123 0.0193 0.0123 0.0123 interpolated 2.05 1 0 0.99 0.0467\n", + "3 FI6v3 V135T average 3 0.0229 interpolated 0.0229 0.0382 0.0229 0.0229 interpolated 1.83 1 0 0.995 0.0328\n", + "4 FI6v3 K280A average 3 0.0106 interpolated 0.0106 0.0176 0.0106 0.0106 interpolated 1.86 1 0 0.981 0.0655\n", + "5 FI6v3 K280S average 3 0.0428 interpolated 0.0428 0.0683 0.0428 0.0428 interpolated 2 1 0 0.993 0.0376\n", + "6 FI6v3 K280T average 3 0.0348 interpolated 0.0348 0.0582 0.0348 0.0348 interpolated 1.82 1 0 0.993 0.0375\n", + "7 FI6v3 N291S average 3 0.0845 interpolated 0.0845 0.142 0.0845 0.0845 interpolated 1.8 1 0 0.997 0.0228\n", + "8 FI6v3 M17L-HA2 average 3 0.0198 interpolated 0.0198 0.0313 0.0198 0.0198 interpolated 2.06 1 0 0.989 0.0483\n", + "9 FI6v3 G47R-HA2 average 3 0.0348 interpolated 0.0348 0.0477 0.0348 0.0348 interpolated 2.6 1 0 0.982 0.0674\n", + "10 H17-L19 WT average 2 0.106 interpolated 0.106 0.144 0.106 0.106 interpolated 4.04 1 0 0.99 0.0518\n", + "11 H17-L19 V135T average 2 11.4 lower >11.4 NaN 16.9 11.4 lower 2.36 1 0 0.708 0.052" ] }, "execution_count": 17, @@ -1899,11 +1950,11 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.499721Z", - "iopub.status.busy": "2024-01-01T00:05:59.499402Z", - "iopub.status.idle": "2024-01-01T00:05:59.527822Z", - "shell.execute_reply": "2024-01-01T00:05:59.527138Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.499695Z" + "iopub.execute_input": "2024-03-24T23:28:53.091751Z", + "iopub.status.busy": "2024-03-24T23:28:53.091282Z", + "iopub.status.idle": "2024-03-24T23:28:53.120985Z", + "shell.execute_reply": "2024-03-24T23:28:53.120141Z", + "shell.execute_reply.started": "2024-03-24T23:28:53.091712Z" } }, "outputs": [ @@ -2122,11 +2173,11 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:05:59.528986Z", - "iopub.status.busy": "2024-01-01T00:05:59.528757Z", - "iopub.status.idle": "2024-01-01T00:06:01.374498Z", - "shell.execute_reply": "2024-01-01T00:06:01.372819Z", - "shell.execute_reply.started": "2024-01-01T00:05:59.528961Z" + "iopub.execute_input": "2024-03-24T23:28:53.123112Z", + "iopub.status.busy": "2024-03-24T23:28:53.122328Z", + "iopub.status.idle": "2024-03-24T23:28:55.017870Z", + "shell.execute_reply": "2024-03-24T23:28:55.016458Z", + "shell.execute_reply.started": "2024-03-24T23:28:53.123069Z" } }, "outputs": [ @@ -2166,11 +2217,11 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:01.377261Z", - "iopub.status.busy": "2024-01-01T00:06:01.376694Z", - "iopub.status.idle": "2024-01-01T00:06:03.037653Z", - "shell.execute_reply": "2024-01-01T00:06:03.036988Z", - "shell.execute_reply.started": "2024-01-01T00:06:01.377203Z" + "iopub.execute_input": "2024-03-24T23:28:55.020462Z", + "iopub.status.busy": "2024-03-24T23:28:55.019891Z", + "iopub.status.idle": "2024-03-24T23:28:56.728031Z", + "shell.execute_reply": "2024-03-24T23:28:56.727408Z", + "shell.execute_reply.started": "2024-03-24T23:28:55.020413Z" } }, "outputs": [ @@ -2212,11 +2263,11 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:03.038807Z", - "iopub.status.busy": "2024-01-01T00:06:03.038593Z", - "iopub.status.idle": "2024-01-01T00:06:03.980102Z", - "shell.execute_reply": "2024-01-01T00:06:03.979370Z", - "shell.execute_reply.started": "2024-01-01T00:06:03.038787Z" + "iopub.execute_input": "2024-03-24T23:28:56.729096Z", + "iopub.status.busy": "2024-03-24T23:28:56.728833Z", + "iopub.status.idle": "2024-03-24T23:28:57.772193Z", + "shell.execute_reply": "2024-03-24T23:28:57.771318Z", + "shell.execute_reply.started": "2024-03-24T23:28:56.729060Z" } }, "outputs": [ @@ -2251,11 +2302,11 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:03.981304Z", - "iopub.status.busy": "2024-01-01T00:06:03.980980Z", - "iopub.status.idle": "2024-01-01T00:06:04.551552Z", - "shell.execute_reply": "2024-01-01T00:06:04.550908Z", - "shell.execute_reply.started": "2024-01-01T00:06:03.981282Z" + "iopub.execute_input": "2024-03-24T23:28:57.774166Z", + "iopub.status.busy": "2024-03-24T23:28:57.773719Z", + "iopub.status.idle": "2024-03-24T23:28:58.389679Z", + "shell.execute_reply": "2024-03-24T23:28:58.388990Z", + "shell.execute_reply.started": "2024-03-24T23:28:57.774127Z" } }, "outputs": [ @@ -2287,11 +2338,11 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:04.553112Z", - "iopub.status.busy": "2024-01-01T00:06:04.552774Z", - "iopub.status.idle": "2024-01-01T00:06:05.303861Z", - "shell.execute_reply": "2024-01-01T00:06:05.303108Z", - "shell.execute_reply.started": "2024-01-01T00:06:04.553086Z" + "iopub.execute_input": "2024-03-24T23:28:58.391251Z", + "iopub.status.busy": "2024-03-24T23:28:58.390971Z", + "iopub.status.idle": "2024-03-24T23:28:59.152103Z", + "shell.execute_reply": "2024-03-24T23:28:59.151344Z", + "shell.execute_reply.started": "2024-03-24T23:28:58.391228Z" } }, "outputs": [ @@ -2338,11 +2389,11 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:05.305764Z", - "iopub.status.busy": "2024-01-01T00:06:05.305395Z", - "iopub.status.idle": "2024-01-01T00:06:08.065139Z", - "shell.execute_reply": "2024-01-01T00:06:08.064516Z", - "shell.execute_reply.started": "2024-01-01T00:06:05.305737Z" + "iopub.execute_input": "2024-03-24T23:28:59.154307Z", + "iopub.status.busy": "2024-03-24T23:28:59.153863Z", + "iopub.status.idle": "2024-03-24T23:29:01.935927Z", + "shell.execute_reply": "2024-03-24T23:29:01.935144Z", + "shell.execute_reply.started": "2024-03-24T23:28:59.154273Z" } }, "outputs": [ @@ -2373,11 +2424,11 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:08.066681Z", - "iopub.status.busy": "2024-01-01T00:06:08.066383Z", - "iopub.status.idle": "2024-01-01T00:06:08.795665Z", - "shell.execute_reply": "2024-01-01T00:06:08.794904Z", - "shell.execute_reply.started": "2024-01-01T00:06:08.066659Z" + "iopub.execute_input": "2024-03-24T23:29:01.937398Z", + "iopub.status.busy": "2024-03-24T23:29:01.937004Z", + "iopub.status.idle": "2024-03-24T23:29:02.676089Z", + "shell.execute_reply": "2024-03-24T23:29:02.675397Z", + "shell.execute_reply.started": "2024-03-24T23:29:01.937374Z" } }, "outputs": [ @@ -2412,11 +2463,11 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:08.801121Z", - "iopub.status.busy": "2024-01-01T00:06:08.800716Z", - "iopub.status.idle": "2024-01-01T00:06:09.483700Z", - "shell.execute_reply": "2024-01-01T00:06:09.483012Z", - "shell.execute_reply.started": "2024-01-01T00:06:08.801093Z" + "iopub.execute_input": "2024-03-24T23:29:02.678373Z", + "iopub.status.busy": "2024-03-24T23:29:02.677760Z", + "iopub.status.idle": "2024-03-24T23:29:03.433247Z", + "shell.execute_reply": "2024-03-24T23:29:03.432589Z", + "shell.execute_reply.started": "2024-03-24T23:29:02.678318Z" } }, "outputs": [ @@ -2454,11 +2505,11 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:09.485315Z", - "iopub.status.busy": "2024-01-01T00:06:09.484978Z", - "iopub.status.idle": "2024-01-01T00:06:13.697509Z", - "shell.execute_reply": "2024-01-01T00:06:13.696725Z", - "shell.execute_reply.started": "2024-01-01T00:06:09.485289Z" + "iopub.execute_input": "2024-03-24T23:29:03.434786Z", + "iopub.status.busy": "2024-03-24T23:29:03.434493Z", + "iopub.status.idle": "2024-03-24T23:29:07.660199Z", + "shell.execute_reply": "2024-03-24T23:29:07.659437Z", + "shell.execute_reply.started": "2024-03-24T23:29:03.434766Z" } }, "outputs": [ @@ -2489,11 +2540,11 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:13.699634Z", - "iopub.status.busy": "2024-01-01T00:06:13.699273Z", - "iopub.status.idle": "2024-01-01T00:06:18.277227Z", - "shell.execute_reply": "2024-01-01T00:06:18.276632Z", - "shell.execute_reply.started": "2024-01-01T00:06:13.699599Z" + "iopub.execute_input": "2024-03-24T23:29:07.661926Z", + "iopub.status.busy": "2024-03-24T23:29:07.661625Z", + "iopub.status.idle": "2024-03-24T23:29:12.201263Z", + "shell.execute_reply": "2024-03-24T23:29:12.200521Z", + "shell.execute_reply.started": "2024-03-24T23:29:07.661905Z" } }, "outputs": [ @@ -2537,11 +2588,11 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:18.278598Z", - "iopub.status.busy": "2024-01-01T00:06:18.278100Z", - "iopub.status.idle": "2024-01-01T00:06:21.283429Z", - "shell.execute_reply": "2024-01-01T00:06:21.282510Z", - "shell.execute_reply.started": "2024-01-01T00:06:18.278577Z" + "iopub.execute_input": "2024-03-24T23:29:12.202978Z", + "iopub.status.busy": "2024-03-24T23:29:12.202667Z", + "iopub.status.idle": "2024-03-24T23:29:14.913196Z", + "shell.execute_reply": "2024-03-24T23:29:14.912464Z", + "shell.execute_reply.started": "2024-03-24T23:29:12.202958Z" } }, "outputs": [ @@ -2585,17 +2636,17 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:21.285888Z", - "iopub.status.busy": "2024-01-01T00:06:21.285453Z", - "iopub.status.idle": "2024-01-01T00:06:23.971162Z", - "shell.execute_reply": "2024-01-01T00:06:23.970445Z", - "shell.execute_reply.started": "2024-01-01T00:06:21.285852Z" + "iopub.execute_input": "2024-03-24T23:29:14.914693Z", + "iopub.status.busy": "2024-03-24T23:29:14.914335Z", + "iopub.status.idle": "2024-03-24T23:29:17.035703Z", + "shell.execute_reply": "2024-03-24T23:29:17.034880Z", + "shell.execute_reply.started": "2024-03-24T23:29:14.914669Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2807,11 +2858,11 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:23.972858Z", - "iopub.status.busy": "2024-01-01T00:06:23.972530Z", - "iopub.status.idle": "2024-01-01T00:06:23.989874Z", - "shell.execute_reply": "2024-01-01T00:06:23.989191Z", - "shell.execute_reply.started": "2024-01-01T00:06:23.972835Z" + "iopub.execute_input": "2024-03-24T23:29:17.037109Z", + "iopub.status.busy": "2024-03-24T23:29:17.036813Z", + "iopub.status.idle": "2024-03-24T23:29:17.054253Z", + "shell.execute_reply": "2024-03-24T23:29:17.053476Z", + "shell.execute_reply.started": "2024-03-24T23:29:17.037086Z" } }, "outputs": [ @@ -2968,11 +3019,11 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:23.991189Z", - "iopub.status.busy": "2024-01-01T00:06:23.990973Z", - "iopub.status.idle": "2024-01-01T00:06:24.565881Z", - "shell.execute_reply": "2024-01-01T00:06:24.565158Z", - "shell.execute_reply.started": "2024-01-01T00:06:23.991167Z" + "iopub.execute_input": "2024-03-24T23:29:17.055895Z", + "iopub.status.busy": "2024-03-24T23:29:17.055163Z", + "iopub.status.idle": "2024-03-24T23:29:17.616740Z", + "shell.execute_reply": "2024-03-24T23:29:17.616200Z", + "shell.execute_reply.started": "2024-03-24T23:29:17.055834Z" } }, "outputs": [ @@ -2997,11 +3048,11 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:24.567201Z", - "iopub.status.busy": "2024-01-01T00:06:24.566870Z", - "iopub.status.idle": "2024-01-01T00:06:24.581012Z", - "shell.execute_reply": "2024-01-01T00:06:24.580316Z", - "shell.execute_reply.started": "2024-01-01T00:06:24.567178Z" + "iopub.execute_input": "2024-03-24T23:29:17.618441Z", + "iopub.status.busy": "2024-03-24T23:29:17.618019Z", + "iopub.status.idle": "2024-03-24T23:29:17.638098Z", + "shell.execute_reply": "2024-03-24T23:29:17.637432Z", + "shell.execute_reply.started": "2024-03-24T23:29:17.618409Z" } }, "outputs": [ @@ -3040,6 +3091,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -3059,14 +3111,15 @@ " 1\n", " 0\n", " -0.932\n", + " 0.101\n", " \n", " \n", "\n", "" ], "text/plain": [ - " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2\n", - "0 BF520.1 H330R average 2 5 lower >5 1.63e+04 5 lower 3.56 1 0 -0.932" + " serum virus replicate nreplicates ic50 ic50_bound ic50_str midpoint midpoint_bound midpoint_bound_type slope top bottom r2 rmsd\n", + "0 BF520.1 H330R average 2 5 lower >5 1.63e+04 5 lower 3.56 1 0 -0.932 0.101" ] }, "execution_count": 33, @@ -3095,11 +3148,11 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:24.582729Z", - "iopub.status.busy": "2024-01-01T00:06:24.582419Z", - "iopub.status.idle": "2024-01-01T00:06:26.203029Z", - "shell.execute_reply": "2024-01-01T00:06:26.202406Z", - "shell.execute_reply.started": "2024-01-01T00:06:24.582700Z" + "iopub.execute_input": "2024-03-24T23:29:17.639284Z", + "iopub.status.busy": "2024-03-24T23:29:17.638956Z", + "iopub.status.idle": "2024-03-24T23:29:18.825307Z", + "shell.execute_reply": "2024-03-24T23:29:18.824431Z", + "shell.execute_reply.started": "2024-03-24T23:29:17.639262Z" } }, "outputs": [ @@ -3143,11 +3196,11 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-01-01T00:06:26.204480Z", - "iopub.status.busy": "2024-01-01T00:06:26.204174Z", - "iopub.status.idle": "2024-01-01T00:06:27.576273Z", - "shell.execute_reply": "2024-01-01T00:06:27.575566Z", - "shell.execute_reply.started": "2024-01-01T00:06:26.204458Z" + "iopub.execute_input": "2024-03-24T23:29:18.827017Z", + "iopub.status.busy": "2024-03-24T23:29:18.826624Z", + "iopub.status.idle": "2024-03-24T23:29:20.245100Z", + "shell.execute_reply": "2024-03-24T23:29:20.244415Z", + "shell.execute_reply.started": "2024-03-24T23:29:18.826980Z" } }, "outputs": [ @@ -3185,13 +3238,6 @@ "makes all axes share the same x-ticks so they can be set for just the last\n", "plot. Otherwise you need to set the ticks for each axis separately." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -3210,7 +3256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/notebooks/test_curves.ipynb b/notebooks/test_curves.ipynb index d8c29a8..c253ea1 100644 --- a/notebooks/test_curves.ipynb +++ b/notebooks/test_curves.ipynb @@ -15,11 +15,11 @@ "id": "03b86d38-d5c6-4b72-90d1-55327515f187", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:27.964328Z", - "iopub.status.busy": "2023-12-30T01:06:27.963802Z", - "iopub.status.idle": "2023-12-30T01:06:33.704971Z", - "shell.execute_reply": "2023-12-30T01:06:33.703618Z", - "shell.execute_reply.started": "2023-12-30T01:06:27.964296Z" + "iopub.execute_input": "2024-03-24T23:24:28.589958Z", + "iopub.status.busy": "2024-03-24T23:24:28.588937Z", + "iopub.status.idle": "2024-03-24T23:24:30.211282Z", + "shell.execute_reply": "2024-03-24T23:24:30.210489Z", + "shell.execute_reply.started": "2024-03-24T23:24:28.589920Z" } }, "outputs": [], @@ -43,11 +43,11 @@ "id": "44ec22b1-6464-4a4b-881c-3ea16dfd5fd0", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:33.707349Z", - "iopub.status.busy": "2023-12-30T01:06:33.706676Z", - "iopub.status.idle": "2023-12-30T01:06:33.717627Z", - "shell.execute_reply": "2023-12-30T01:06:33.716626Z", - "shell.execute_reply.started": "2023-12-30T01:06:33.707307Z" + "iopub.execute_input": "2024-03-24T23:24:30.218575Z", + "iopub.status.busy": "2024-03-24T23:24:30.218231Z", + "iopub.status.idle": "2024-03-24T23:24:30.225788Z", + "shell.execute_reply": "2024-03-24T23:24:30.225096Z", + "shell.execute_reply.started": "2024-03-24T23:24:30.218549Z" } }, "outputs": [], @@ -69,11 +69,11 @@ "id": "0872eb41-2cfa-4ed7-97b0-045a0170518d", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:33.720382Z", - "iopub.status.busy": "2023-12-30T01:06:33.719987Z", - "iopub.status.idle": "2023-12-30T01:06:33.879366Z", - "shell.execute_reply": "2023-12-30T01:06:33.878368Z", - "shell.execute_reply.started": "2023-12-30T01:06:33.720353Z" + "iopub.execute_input": "2024-03-24T23:24:30.230668Z", + "iopub.status.busy": "2024-03-24T23:24:30.229932Z", + "iopub.status.idle": "2024-03-24T23:24:30.363601Z", + "shell.execute_reply": "2024-03-24T23:24:30.362888Z", + "shell.execute_reply.started": "2024-03-24T23:24:30.230600Z" } }, "outputs": [ @@ -110,6 +110,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -127,6 +128,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 1\n", @@ -142,6 +144,7 @@ " 1\n", " 0\n", " 0.91\n", + " 0.12\n", " \n", " \n", " 2\n", @@ -157,6 +160,7 @@ " 1\n", " 0\n", " 0.78\n", + " 0.19\n", " \n", " \n", " 3\n", @@ -172,6 +176,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 4\n", @@ -187,6 +192,7 @@ " 1\n", " 0\n", " 0.78\n", + " 0.18\n", " \n", " \n", " 5\n", @@ -202,6 +208,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.037\n", " \n", " \n", " 6\n", @@ -217,6 +224,7 @@ " 1\n", " 0\n", " 1\n", + " 0.0073\n", " \n", " \n", " 7\n", @@ -232,6 +240,7 @@ " 1\n", " 0\n", " 0.95\n", + " 0.099\n", " \n", " \n", " 8\n", @@ -247,6 +256,7 @@ " 1\n", " 0\n", " 0.98\n", + " 0.064\n", " \n", " \n", " 9\n", @@ -262,6 +272,7 @@ " 1\n", " 0\n", " 0.74\n", + " 0.2\n", " \n", " \n", " 10\n", @@ -277,6 +288,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 11\n", @@ -292,6 +304,7 @@ " 1\n", " 0\n", " 0.91\n", + " 0.12\n", " \n", " \n", "\n", @@ -326,19 +339,19 @@ "10 interpolated 0.00097 0.00097 interpolated 2.8 1 \n", "11 interpolated 0.0022 0.0022 interpolated 4.6 1 \n", "\n", - " bottom r2 \n", - "0 0 0.86 \n", - "1 0 0.91 \n", - "2 0 0.78 \n", - "3 0 0.86 \n", - "4 0 0.78 \n", - "5 0 0.99 \n", - "6 0 1 \n", - "7 0 0.95 \n", - "8 0 0.98 \n", - "9 0 0.74 \n", - "10 0 0.86 \n", - "11 0 0.91 " + " bottom r2 rmsd \n", + "0 0 0.86 0.15 \n", + "1 0 0.91 0.12 \n", + "2 0 0.78 0.19 \n", + "3 0 0.86 0.15 \n", + "4 0 0.78 0.18 \n", + "5 0 0.99 0.037 \n", + "6 0 1 0.0073 \n", + "7 0 0.95 0.099 \n", + "8 0 0.98 0.064 \n", + "9 0 0.74 0.2 \n", + "10 0 0.86 0.15 \n", + "11 0 0.91 0.12 " ] }, "metadata": {}, @@ -376,11 +389,11 @@ "id": "aeebb9f7-5b71-4fe3-9f29-d21bda9cce8c", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:33.884634Z", - "iopub.status.busy": "2023-12-30T01:06:33.884263Z", - "iopub.status.idle": "2023-12-30T01:06:36.574397Z", - "shell.execute_reply": "2023-12-30T01:06:36.573644Z", - "shell.execute_reply.started": "2023-12-30T01:06:33.884604Z" + "iopub.execute_input": "2024-03-24T23:24:30.367300Z", + "iopub.status.busy": "2024-03-24T23:24:30.367089Z", + "iopub.status.idle": "2024-03-24T23:24:32.514053Z", + "shell.execute_reply": "2024-03-24T23:24:32.512526Z", + "shell.execute_reply.started": "2024-03-24T23:24:30.367278Z" } }, "outputs": [ @@ -419,11 +432,11 @@ "id": "dda8bf71-d4c0-40bd-8acf-f0eb45ec9228", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:36.576463Z", - "iopub.status.busy": "2023-12-30T01:06:36.575955Z", - "iopub.status.idle": "2023-12-30T01:06:36.810295Z", - "shell.execute_reply": "2023-12-30T01:06:36.809260Z", - "shell.execute_reply.started": "2023-12-30T01:06:36.576426Z" + "iopub.execute_input": "2024-03-24T23:24:32.521181Z", + "iopub.status.busy": "2024-03-24T23:24:32.520524Z", + "iopub.status.idle": "2024-03-24T23:24:32.641075Z", + "shell.execute_reply": "2024-03-24T23:24:32.640326Z", + "shell.execute_reply.started": "2024-03-24T23:24:32.521125Z" } }, "outputs": [ @@ -460,6 +473,7 @@ " top\n", " bottom\n", " r2\n", + " rmsd\n", " \n", " \n", " \n", @@ -477,6 +491,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 1\n", @@ -492,6 +507,7 @@ " 1\n", " 0\n", " 0.91\n", + " 0.12\n", " \n", " \n", " 2\n", @@ -507,6 +523,7 @@ " 1\n", " 0\n", " -0.7\n", + " 0.54\n", " \n", " \n", " 3\n", @@ -522,6 +539,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 4\n", @@ -537,6 +555,7 @@ " 1\n", " 0\n", " -1.2\n", + " 0.56\n", " \n", " \n", " 5\n", @@ -552,6 +571,7 @@ " 1\n", " 0\n", " 0.99\n", + " 0.037\n", " \n", " \n", " 6\n", @@ -567,6 +587,7 @@ " 1\n", " 0\n", " 1\n", + " 0.0073\n", " \n", " \n", " 7\n", @@ -582,6 +603,7 @@ " 1\n", " 0\n", " 0.95\n", + " 0.099\n", " \n", " \n", " 8\n", @@ -597,6 +619,7 @@ " 1\n", " 0\n", " 0.98\n", + " 0.064\n", " \n", " \n", " 9\n", @@ -612,6 +635,7 @@ " 1\n", " 0\n", " 0.74\n", + " 0.2\n", " \n", " \n", " 10\n", @@ -627,6 +651,7 @@ " 1\n", " 0\n", " 0.86\n", + " 0.15\n", " \n", " \n", " 11\n", @@ -642,6 +667,7 @@ " 1\n", " 0\n", " 0.91\n", + " 0.12\n", " \n", " \n", "\n", @@ -676,19 +702,19 @@ "10 interpolated 0.00097 0.00097 interpolated 2.8 1 \n", "11 interpolated 0.0022 0.0022 interpolated 4.6 1 \n", "\n", - " bottom r2 \n", - "0 0 0.86 \n", - "1 0 0.91 \n", - "2 0 -0.7 \n", - "3 0 0.86 \n", - "4 0 -1.2 \n", - "5 0 0.99 \n", - "6 0 1 \n", - "7 0 0.95 \n", - "8 0 0.98 \n", - "9 0 0.74 \n", - "10 0 0.86 \n", - "11 0 0.91 " + " bottom r2 rmsd \n", + "0 0 0.86 0.15 \n", + "1 0 0.91 0.12 \n", + "2 0 -0.7 0.54 \n", + "3 0 0.86 0.15 \n", + "4 0 -1.2 0.56 \n", + "5 0 0.99 0.037 \n", + "6 0 1 0.0073 \n", + "7 0 0.95 0.099 \n", + "8 0 0.98 0.064 \n", + "9 0 0.74 0.2 \n", + "10 0 0.86 0.15 \n", + "11 0 0.91 0.12 " ] }, "metadata": {}, @@ -719,11 +745,11 @@ "id": "a52c2a3f-278f-4a8d-8a58-384effab51f3", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:36.811806Z", - "iopub.status.busy": "2023-12-30T01:06:36.811526Z", - "iopub.status.idle": "2023-12-30T01:06:38.892637Z", - "shell.execute_reply": "2023-12-30T01:06:38.891750Z", - "shell.execute_reply.started": "2023-12-30T01:06:36.811781Z" + "iopub.execute_input": "2024-03-24T23:24:32.644351Z", + "iopub.status.busy": "2024-03-24T23:24:32.644081Z", + "iopub.status.idle": "2024-03-24T23:24:34.799892Z", + "shell.execute_reply": "2024-03-24T23:24:34.798710Z", + "shell.execute_reply.started": "2024-03-24T23:24:32.644327Z" } }, "outputs": [ @@ -762,11 +788,11 @@ "id": "323edb09-e9f2-44ff-b2c6-5700e1927782", "metadata": { "execution": { - "iopub.execute_input": "2023-12-30T01:06:38.893969Z", - "iopub.status.busy": "2023-12-30T01:06:38.893639Z", - "iopub.status.idle": "2023-12-30T01:06:38.917442Z", - "shell.execute_reply": "2023-12-30T01:06:38.916557Z", - "shell.execute_reply.started": "2023-12-30T01:06:38.893950Z" + "iopub.execute_input": "2024-03-24T23:24:34.804772Z", + "iopub.status.busy": "2024-03-24T23:24:34.804439Z", + "iopub.status.idle": "2024-03-24T23:24:34.830467Z", + "shell.execute_reply": "2024-03-24T23:24:34.829482Z", + "shell.execute_reply.started": "2024-03-24T23:24:34.804746Z" } }, "outputs": [ From 394335b8d43ae190bc674c52f4314fd09712f1a7 Mon Sep 17 00:00:00 2001 From: jbloom Date: Sun, 24 Mar 2024 17:18:26 -0700 Subject: [PATCH 4/4] document `HillCurve.rmsd` in docs --- notebooks/hillcurve_example.ipynb | 184 +++++++++++++++++++----------- 1 file changed, 119 insertions(+), 65 deletions(-) diff --git a/notebooks/hillcurve_example.ipynb b/notebooks/hillcurve_example.ipynb index 6079517..7a141a0 100644 --- a/notebooks/hillcurve_example.ipynb +++ b/notebooks/hillcurve_example.ipynb @@ -65,11 +65,11 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:23:59.496495Z", - "iopub.status.busy": "2023-12-25T15:23:59.496192Z", - "iopub.status.idle": "2023-12-25T15:24:01.463409Z", - "shell.execute_reply": "2023-12-25T15:24:01.462274Z", - "shell.execute_reply.started": "2023-12-25T15:23:59.496467Z" + "iopub.execute_input": "2024-03-25T00:16:10.313359Z", + "iopub.status.busy": "2024-03-25T00:16:10.312974Z", + "iopub.status.idle": "2024-03-25T00:16:17.050633Z", + "shell.execute_reply": "2024-03-25T00:16:17.049017Z", + "shell.execute_reply.started": "2024-03-25T00:16:10.313325Z" } }, "outputs": [], @@ -91,11 +91,11 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.472497Z", - "iopub.status.busy": "2023-12-25T15:24:01.472109Z", - "iopub.status.idle": "2023-12-25T15:24:01.476785Z", - "shell.execute_reply": "2023-12-25T15:24:01.475930Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.472464Z" + "iopub.execute_input": "2024-03-25T00:16:17.063376Z", + "iopub.status.busy": "2024-03-25T00:16:17.062854Z", + "iopub.status.idle": "2024-03-25T00:16:17.068135Z", + "shell.execute_reply": "2024-03-25T00:16:17.067334Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.063333Z" } }, "outputs": [], @@ -121,11 +121,11 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.481096Z", - "iopub.status.busy": "2023-12-25T15:24:01.480654Z", - "iopub.status.idle": "2023-12-25T15:24:01.485640Z", - "shell.execute_reply": "2023-12-25T15:24:01.484960Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.481054Z" + "iopub.execute_input": "2024-03-25T00:16:17.071840Z", + "iopub.status.busy": "2024-03-25T00:16:17.071520Z", + "iopub.status.idle": "2024-03-25T00:16:17.075348Z", + "shell.execute_reply": "2024-03-25T00:16:17.074582Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.071817Z" } }, "outputs": [], @@ -138,11 +138,11 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.489752Z", - "iopub.status.busy": "2023-12-25T15:24:01.489457Z", - "iopub.status.idle": "2023-12-25T15:24:01.517264Z", - "shell.execute_reply": "2023-12-25T15:24:01.516549Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.489726Z" + "iopub.execute_input": "2024-03-25T00:16:17.079066Z", + "iopub.status.busy": "2024-03-25T00:16:17.078720Z", + "iopub.status.idle": "2024-03-25T00:16:17.107888Z", + "shell.execute_reply": "2024-03-25T00:16:17.107223Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.079038Z" } }, "outputs": [ @@ -284,11 +284,11 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.521396Z", - "iopub.status.busy": "2023-12-25T15:24:01.520995Z", - "iopub.status.idle": "2023-12-25T15:24:01.529641Z", - "shell.execute_reply": "2023-12-25T15:24:01.528847Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.521363Z" + "iopub.execute_input": "2024-03-25T00:16:17.111916Z", + "iopub.status.busy": "2024-03-25T00:16:17.111686Z", + "iopub.status.idle": "2024-03-25T00:16:17.118923Z", + "shell.execute_reply": "2024-03-25T00:16:17.118202Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.111894Z" } }, "outputs": [], @@ -309,11 +309,11 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.533698Z", - "iopub.status.busy": "2023-12-25T15:24:01.533124Z", - "iopub.status.idle": "2023-12-25T15:24:01.538339Z", - "shell.execute_reply": "2023-12-25T15:24:01.537341Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.533672Z" + "iopub.execute_input": "2024-03-25T00:16:17.123353Z", + "iopub.status.busy": "2024-03-25T00:16:17.123020Z", + "iopub.status.idle": "2024-03-25T00:16:17.127871Z", + "shell.execute_reply": "2024-03-25T00:16:17.127123Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.123326Z" } }, "outputs": [ @@ -355,11 +355,11 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.542463Z", - "iopub.status.busy": "2023-12-25T15:24:01.542065Z", - "iopub.status.idle": "2023-12-25T15:24:01.552644Z", - "shell.execute_reply": "2023-12-25T15:24:01.551763Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.542428Z" + "iopub.execute_input": "2024-03-25T00:16:17.131390Z", + "iopub.status.busy": "2024-03-25T00:16:17.131080Z", + "iopub.status.idle": "2024-03-25T00:16:17.139788Z", + "shell.execute_reply": "2024-03-25T00:16:17.138992Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.131365Z" } }, "outputs": [ @@ -403,11 +403,11 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.553918Z", - "iopub.status.busy": "2023-12-25T15:24:01.553601Z", - "iopub.status.idle": "2023-12-25T15:24:01.558469Z", - "shell.execute_reply": "2023-12-25T15:24:01.557582Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.553890Z" + "iopub.execute_input": "2024-03-25T00:16:17.141011Z", + "iopub.status.busy": "2024-03-25T00:16:17.140692Z", + "iopub.status.idle": "2024-03-25T00:16:17.145309Z", + "shell.execute_reply": "2024-03-25T00:16:17.144530Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.140985Z" } }, "outputs": [ @@ -446,11 +446,11 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.559816Z", - "iopub.status.busy": "2023-12-25T15:24:01.559500Z", - "iopub.status.idle": "2023-12-25T15:24:01.565449Z", - "shell.execute_reply": "2023-12-25T15:24:01.564069Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.559791Z" + "iopub.execute_input": "2024-03-25T00:16:17.146608Z", + "iopub.status.busy": "2024-03-25T00:16:17.146265Z", + "iopub.status.idle": "2024-03-25T00:16:17.150767Z", + "shell.execute_reply": "2024-03-25T00:16:17.150014Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.146585Z" } }, "outputs": [ @@ -484,11 +484,11 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.567122Z", - "iopub.status.busy": "2023-12-25T15:24:01.566762Z", - "iopub.status.idle": "2023-12-25T15:24:01.572287Z", - "shell.execute_reply": "2023-12-25T15:24:01.571332Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.567094Z" + "iopub.execute_input": "2024-03-25T00:16:17.152404Z", + "iopub.status.busy": "2024-03-25T00:16:17.152081Z", + "iopub.status.idle": "2024-03-25T00:16:17.157138Z", + "shell.execute_reply": "2024-03-25T00:16:17.156321Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.152379Z" } }, "outputs": [ @@ -528,11 +528,11 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:01.573693Z", - "iopub.status.busy": "2023-12-25T15:24:01.573324Z", - "iopub.status.idle": "2023-12-25T15:24:02.167031Z", - "shell.execute_reply": "2023-12-25T15:24:02.166146Z", - "shell.execute_reply.started": "2023-12-25T15:24:01.573665Z" + "iopub.execute_input": "2024-03-25T00:16:17.158441Z", + "iopub.status.busy": "2024-03-25T00:16:17.158117Z", + "iopub.status.idle": "2024-03-25T00:16:17.779460Z", + "shell.execute_reply": "2024-03-25T00:16:17.778697Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.158415Z" } }, "outputs": [ @@ -570,11 +570,11 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:02.168582Z", - "iopub.status.busy": "2023-12-25T15:24:02.168173Z", - "iopub.status.idle": "2023-12-25T15:24:02.388597Z", - "shell.execute_reply": "2023-12-25T15:24:02.387854Z", - "shell.execute_reply.started": "2023-12-25T15:24:02.168557Z" + "iopub.execute_input": "2024-03-25T00:16:17.781246Z", + "iopub.status.busy": "2024-03-25T00:16:17.780860Z", + "iopub.status.idle": "2024-03-25T00:16:17.980355Z", + "shell.execute_reply": "2024-03-25T00:16:17.979647Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.781219Z" } }, "outputs": [ @@ -600,7 +600,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Quantifying goodness of fit\n", "We can quantify the goodness of fit by the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination) ($R^2$) using `HillCurve.r2`, which will be one if the curve perfectly fits the data and less than one otherwise.\n", + "Essentially, this corresponds to the fraction of the variation in the data explained by the curve.\n", "A value of <0 means the fit is worse than just drawing a straight line through the data:" ] }, @@ -609,11 +611,11 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-12-25T15:24:02.389972Z", - "iopub.status.busy": "2023-12-25T15:24:02.389625Z", - "iopub.status.idle": "2023-12-25T15:24:02.395258Z", - "shell.execute_reply": "2023-12-25T15:24:02.394557Z", - "shell.execute_reply.started": "2023-12-25T15:24:02.389949Z" + "iopub.execute_input": "2024-03-25T00:16:17.981777Z", + "iopub.status.busy": "2024-03-25T00:16:17.981546Z", + "iopub.status.idle": "2024-03-25T00:16:17.986876Z", + "shell.execute_reply": "2024-03-25T00:16:17.986270Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.981755Z" } }, "outputs": [ @@ -631,6 +633,58 @@ "source": [ "round(curve.r2, 3)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also quantify how well each data point is fit on average with the root mean square deviation (the square root of the mean residual)\n", + "A value of zero means the curve perfectly fits the data.\n", + "This is returned by `HillCurve.rmsd`:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2024-03-25T00:16:17.988494Z", + "iopub.status.busy": "2024-03-25T00:16:17.988229Z", + "iopub.status.idle": "2024-03-25T00:16:17.993775Z", + "shell.execute_reply": "2024-03-25T00:16:17.993054Z", + "shell.execute_reply.started": "2024-03-25T00:16:17.988469Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.028" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(curve.rmsd, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, you might consider a curve fit to be \"good\" if **either** the `r2` is close to one, or the `rmsd` is close to zero.\n", + "Usually they will be correlated, but if the data essentially fall on a flat line (no or complete neutralization at all tested concentrations), then you could have a poor `r2` close to zero but still a small `rmsd` because in this case the curve would fit the data well (small `rmsd`) but would not be much better than a line (so poor `r2`) simply because the data are basically linear." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {