Copyright (C)  FastTrack.
Permission is granted to copy, distribute and/or modify this document.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Get started with FastAnalysis

FastAnalysis is a python library that simplifies the importation of a tracking analysis performed with FastTrack. Easily select data for a given object or a given timepoint.

Package installation

The FastAnalysis package can be installed following the provided instruction at https://github.com/FastTrackOrg/FastAnalysis.

import sys
!{sys.executable} -m pip install fastanalysis

Package usage

import fastanalysis as fa

Load the tracking data and get the basic information.

data = fa.Load("tracking.txt")

print("Total objects: ", data.getObjectNumber())

Total objects:  14

print("Keys: ", data.getKeys())

Keys:  ['xHead', 'yHead', 'tHead', 'xTail', 'yTail', 'tTail', 'xBody', 'yBody', 'tBody', 'curvature', 'areaBody', 'perimeterBody', 'headMajorAxisLength', 'headMinorAxisLength', 'headExcentricity', 'tailMajorAxisLength', 'tailMinorAxisLength', 'tailExcentricity', 'bodyMajorAxisLength', 'bodyMinorAxisLength', 'bodyExcentricity', 'imageNumber', 'id']

Select the tracking data for a given object

data.getObjects(0)

	xHead	yHead	tHead	xTail	yTail	tTail	xBody	yBody	tBody	curvature	...	headMinorAxisLength	headExcentricity	tailMajorAxisLength	tailMinorAxisLength	tailExcentricity	bodyMajorAxisLength	bodyMinorAxisLength	bodyExcentricity	imageNumber	id
0	514.327	333.120	5.816190	499.960	327.727	6.102260	508.345	330.876	5.943950	0.000348	...	2.98626	0.921388	11.40750	2.06461	0.983485	17.7578	2.86227	0.986924	0	0
14	512.965	332.575	5.866170	499.435	327.759	6.052000	507.626	330.673	5.951020	0.000370	...	2.94920	0.921589	10.63010	1.79632	0.985619	16.5912	2.55900	0.988034	1	0
28	510.519	331.417	5.888830	495.784	327.366	6.128890	504.484	329.758	6.020880	0.000285	...	3.02647	0.931937	11.80820	1.97610	0.985898	17.9581	2.76152	0.988106	2	0
42	508.379	330.542	5.978240	493.210	327.460	6.159440	502.213	329.291	6.083920	0.000217	...	3.10027	0.928649	11.67010	1.97623	0.985558	18.0820	2.70641	0.988735	3	0
56	503.990	331.764	4.889370	496.990	322.509	6.183440	501.029	327.856	5.378760	0.001812	...	2.92067	0.914680	8.25174	3.01276	0.930966	13.0947	5.19418	0.917965	4	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2414	515.720	332.233	0.047758	504.152	332.758	6.219550	510.844	332.456	0.031550	0.000053	...	2.83620	0.891031	7.96985	2.21498	0.960604	13.3794	2.48061	0.982662	195	0
2428	514.518	331.229	6.173800	502.533	333.448	0.410982	509.848	332.091	0.188133	0.000454	...	3.01448	0.895229	9.27558	1.86710	0.979531	14.0909	3.11860	0.975201	196	0
2442	515.022	333.301	6.039930	503.432	330.360	6.110450	510.456	332.144	6.046190	0.000040	...	2.86268	0.894578	8.45494	1.97392	0.972365	13.7423	2.44318	0.984069	197	0
2456	514.364	332.322	0.089009	501.409	333.271	6.061220	509.183	332.699	0.030277	0.000309	...	2.70609	0.926143	9.87331	2.72056	0.961288	15.0990	3.04030	0.979518	198	0
2470	514.682	333.174	6.045460	503.418	331.482	0.110999	510.000	332.472	6.164560	0.000434	...	2.90509	0.881178	8.31982	2.31197	0.960614	13.2119	2.86264	0.976245	199	0

190 rows × 23 columns

Select the tracking data for a list of objects

data.getObjects([0, 2, 4])

	xHead	yHead	tHead	xTail	yTail	tTail	xBody	yBody	tBody	curvature	...	headMinorAxisLength	headExcentricity	tailMajorAxisLength	tailMinorAxisLength	tailExcentricity	bodyMajorAxisLength	bodyMinorAxisLength	bodyExcentricity	imageNumber	id
0	514.3270	333.120	5.816190	499.9600	327.727	6.102260	508.3450	330.876	5.943950	0.000348	...	2.98626	0.921388	11.40750	2.06461	0.983485	17.7578	2.86227	0.986924	0	0
2	23.9978	287.715	3.706460	34.9722	278.836	3.998190	29.2056	283.505	3.848440	0.000714	...	3.02224	0.878284	11.09250	2.61304	0.971858	16.5670	3.00962	0.983361	0	2
4	480.5800	213.482	1.282360	478.1250	228.520	1.533030	479.4280	220.543	1.425670	0.000539	...	3.45208	0.873828	11.80360	2.22489	0.982075	17.9204	3.02978	0.985604	0	4
14	512.9650	332.575	5.866170	499.4350	327.759	6.052000	507.6260	330.673	5.951020	0.000370	...	2.94920	0.921589	10.63010	1.79632	0.985619	16.5912	2.55900	0.988034	1	0
16	19.4579	293.022	4.288610	25.5579	281.206	4.183790	21.8962	288.302	4.233790	0.000041	...	3.10527	0.893777	8.95453	1.95308	0.975924	15.1497	2.60690	0.985084	1	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2449	477.5150	253.259	0.494507	466.0410	261.018	0.831531	472.8820	256.382	0.622416	0.000357	...	3.18330	0.890322	9.94944	2.33195	0.972145	15.8464	3.12130	0.980409	198	2
2456	514.3640	332.322	0.089009	501.4090	333.271	6.061220	509.1830	332.699	0.030277	0.000309	...	2.70609	0.926143	9.87331	2.72056	0.961288	15.0990	3.04030	0.979518	198	0
2462	413.9010	332.386	5.582420	401.1560	322.815	5.799440	408.5330	328.356	5.657920	0.000666	...	3.24954	0.920555	11.51030	2.47230	0.976660	18.4979	3.05143	0.986300	199	4
2463	479.2630	250.716	0.644442	465.5060	258.476	0.362501	473.8190	253.784	0.503416	0.000200	...	2.96435	0.930262	11.55250	1.76407	0.988273	18.1329	2.75170	0.988419	199	2
2470	514.6820	333.174	6.045460	503.4180	331.482	0.110999	510.0000	332.472	6.164560	0.000434	...	2.90509	0.881178	8.31982	2.31197	0.960614	13.2119	2.86264	0.976245	199	0

544 rows × 23 columns

Select the tracking data for a given image

data.getFrames(100)

	xHead	yHead	tHead	xTail	yTail	tTail	xBody	yBody	tBody	curvature	...	headMinorAxisLength	headExcentricity	tailMajorAxisLength	tailMinorAxisLength	tailExcentricity	bodyMajorAxisLength	bodyMinorAxisLength	bodyExcentricity	imageNumber	id
1220	471.9020	331.89000	0.269094	460.4110	335.5360	0.205151	467.340	333.3400	0.285930	0.000054	...	3.02325	0.879342	9.4287	2.36285	0.968090	14.0688	2.71188	0.981246	100	0
1221	394.0560	311.18300	5.862420	380.7090	302.9660	5.533100	388.653	307.8680	5.710570	0.000782	...	3.18665	0.911141	12.0567	2.13218	0.984238	18.0280	3.11668	0.984943	100	9
1222	294.9150	323.62100	5.928470	280.3880	319.3940	6.078460	289.216	321.9640	6.006340	0.000511	...	3.08521	0.908041	11.1557	1.66443	0.988807	17.2867	2.57567	0.988838	100	2
1223	219.1200	191.08900	0.631238	205.3180	198.1280	0.088934	213.337	194.0410	0.437513	0.000843	...	3.96058	0.881927	10.6798	3.26638	0.952081	17.7698	4.40737	0.968753	100	3
1224	191.9850	48.83960	1.338080	185.1600	62.8722	0.851539	188.648	55.6479	1.073240	0.001664	...	3.49889	0.867101	12.0051	2.62074	0.975881	18.3914	3.78692	0.978572	100	5
1225	504.0520	9.82832	0.558700	489.4620	18.4243	0.514300	497.878	13.4694	0.531725	0.000089	...	3.41134	0.912303	12.3365	1.99489	0.986839	19.5719	2.78473	0.989826	100	13
1226	304.1520	202.55400	5.721260	290.9760	193.5960	5.719690	298.600	198.7770	5.695240	0.000052	...	3.28029	0.905315	11.6325	1.98547	0.985326	18.4733	2.68715	0.989364	100	7
1227	413.6920	329.85100	0.026077	399.8350	331.4840	0.161337	408.339	330.4820	0.113899	0.000115	...	3.07354	0.914786	10.4648	1.93615	0.982736	16.1656	2.63191	0.986657	100	12
1228	84.2595	119.73900	3.797330	96.9216	110.7160	3.734220	90.450	115.3290	3.756420	0.000192	...	3.52603	0.853670	12.6427	2.15830	0.985321	18.5339	2.84253	0.988169	100	10
1229	364.4060	35.89410	0.353083	348.8500	40.4274	6.276460	357.959	37.7740	0.251617	0.000484	...	3.37384	0.920171	11.3458	3.33696	0.955770	18.6191	3.79543	0.979003	100	4
1230	490.6630	44.94220	0.503424	476.7040	52.5656	0.472187	485.125	47.9706	0.495529	0.000124	...	3.27567	0.922368	10.9915	1.97134	0.983785	18.2792	2.72458	0.988829	100	11
1231	510.1740	40.70270	5.941170	497.3000	34.7202	5.673400	505.110	38.3486	5.830410	0.000431	...	3.11621	0.902134	10.2866	2.08310	0.979281	16.2374	2.87259	0.984227	100	6

12 rows × 23 columns

Select the tracking data for a list of images

data.getFrames([100, 102])

	xHead	yHead	tHead	xTail	yTail	tTail	xBody	yBody	tBody	curvature	...	headMinorAxisLength	headExcentricity	tailMajorAxisLength	tailMinorAxisLength	tailExcentricity	bodyMajorAxisLength	bodyMinorAxisLength	bodyExcentricity	imageNumber	id
1220	471.9020	331.89000	0.269094	460.4110	335.5360	0.205151	467.3400	333.3400	0.285930	0.000054	...	3.02325	0.879342	9.42870	2.36285	0.968090	14.0688	2.71188	0.981246	100	0
1221	394.0560	311.18300	5.862420	380.7090	302.9660	5.533100	388.6530	307.8680	5.710570	0.000782	...	3.18665	0.911141	12.05670	2.13218	0.984238	18.0280	3.11668	0.984943	100	9
1222	294.9150	323.62100	5.928470	280.3880	319.3940	6.078460	289.2160	321.9640	6.006340	0.000511	...	3.08521	0.908041	11.15570	1.66443	0.988807	17.2867	2.57567	0.988838	100	2
1223	219.1200	191.08900	0.631238	205.3180	198.1280	0.088934	213.3370	194.0410	0.437513	0.000843	...	3.96058	0.881927	10.67980	3.26638	0.952081	17.7698	4.40737	0.968753	100	3
1224	191.9850	48.83960	1.338080	185.1600	62.8722	0.851539	188.6480	55.6479	1.073240	0.001664	...	3.49889	0.867101	12.00510	2.62074	0.975881	18.3914	3.78692	0.978572	100	5
1225	504.0520	9.82832	0.558700	489.4620	18.4243	0.514300	497.8780	13.4694	0.531725	0.000089	...	3.41134	0.912303	12.33650	1.99489	0.986839	19.5719	2.78473	0.989826	100	13
1226	304.1520	202.55400	5.721260	290.9760	193.5960	5.719690	298.6000	198.7770	5.695240	0.000052	...	3.28029	0.905315	11.63250	1.98547	0.985326	18.4733	2.68715	0.989364	100	7
1227	413.6920	329.85100	0.026077	399.8350	331.4840	0.161337	408.3390	330.4820	0.113899	0.000115	...	3.07354	0.914786	10.46480	1.93615	0.982736	16.1656	2.63191	0.986657	100	12
1228	84.2595	119.73900	3.797330	96.9216	110.7160	3.734220	90.4500	115.3290	3.756420	0.000192	...	3.52603	0.853670	12.64270	2.15830	0.985321	18.5339	2.84253	0.988169	100	10
1229	364.4060	35.89410	0.353083	348.8500	40.4274	6.276460	357.9590	37.7740	0.251617	0.000484	...	3.37384	0.920171	11.34580	3.33696	0.955770	18.6191	3.79543	0.979003	100	4
1230	490.6630	44.94220	0.503424	476.7040	52.5656	0.472187	485.1250	47.9706	0.495529	0.000124	...	3.27567	0.922368	10.99150	1.97134	0.983785	18.2792	2.72458	0.988829	100	11
1231	510.1740	40.70270	5.941170	497.3000	34.7202	5.673400	505.1100	38.3486	5.830410	0.000431	...	3.11621	0.902134	10.28660	2.08310	0.979281	16.2374	2.87259	0.984227	100	6
1244	471.4650	333.53700	0.001856	455.8950	333.1370	0.067247	465.2880	333.3780	6.274380	0.000043	...	3.02042	0.922201	11.22160	2.37528	0.977341	17.8254	2.66739	0.988741	102	0
1245	396.0970	314.66900	5.861210	381.8960	308.4470	5.874390	390.4180	312.1800	5.870340	0.000174	...	3.30117	0.899737	11.37300	1.84361	0.986774	17.8003	2.68429	0.988564	102	9
1246	294.3930	324.25600	5.943700	279.2910	320.1950	6.066590	288.5890	322.6960	6.020880	0.000204	...	3.05422	0.916951	11.74010	1.61688	0.990471	17.8923	2.52835	0.989966	102	2
1247	220.4620	188.58500	0.373316	205.5400	192.7970	0.203012	214.4550	190.2810	0.274169	0.000333	...	4.10433	0.859915	10.67680	2.45992	0.973096	17.7399	3.50993	0.980231	102	3
1248	440.7830	245.34100	0.994409	432.8300	259.2290	1.086010	437.0160	251.9210	1.057480	0.000134	...	3.14810	0.901165	12.80690	1.98403	0.987927	19.2185	2.57322	0.990996	102	8
1249	181.7160	48.26740	1.795220	183.2810	64.6947	1.403630	182.4580	56.2288	1.618700	0.003078	...	3.54904	0.877756	13.01390	3.97463	0.952220	19.4517	4.32784	0.974935	102	5
1250	501.1650	7.83423	0.516324	484.9430	16.9856	0.489818	494.5580	11.5641	0.510098	0.000011	...	3.43780	0.925195	12.71500	1.93735	0.988324	21.2212	2.82628	0.991092	102	13
1251	299.4490	204.64200	5.764900	288.1070	193.9180	5.171220	294.7050	200.1600	5.506650	0.004240	...	3.51118	0.918436	10.65060	2.64429	0.968689	17.9687	4.10568	0.973546	102	7
1252	423.3440	226.69300	1.685010	423.8160	242.8190	1.563020	423.5670	234.3430	1.596200	0.000203	...	3.48268	0.857366	12.90370	2.01238	0.987764	19.0144	2.79258	0.989156	102	1
1253	419.6690	331.30500	0.240837	405.4400	333.2920	6.160820	413.7010	332.1370	0.104904	0.000355	...	3.16441	0.904444	10.92710	2.83584	0.965737	16.7111	3.40928	0.978968	102	12
1254	74.8227	119.96300	3.814450	87.7565	110.9120	3.722070	80.8288	115.7600	3.749350	0.000395	...	3.66070	0.858662	12.48400	2.12660	0.985384	18.7389	2.97267	0.987337	102	10
1255	372.3560	33.29750	0.324467	357.0870	35.5002	6.115410	365.9440	34.2238	0.109167	0.000586	...	3.46947	0.902759	11.64000	2.45400	0.977524	17.8863	3.81849	0.976946	102	4
1256	486.9070	43.34160	0.455006	472.0110	50.9601	0.484798	480.8850	46.4257	0.472028	0.000043	...	3.40055	0.920011	10.94680	2.10120	0.981405	19.0397	2.83045	0.988888	102	11
1257	506.5980	42.84610	5.625700	494.8000	35.6051	5.914810	501.8730	39.9492	5.749680	0.000309	...	3.31937	0.889443	9.90312	2.08601	0.977563	15.9151	3.01796	0.981856	102	6

26 rows × 23 columns

Select the tracking data for a list of images and a list of objects

data.getObjectsInFrames(ids=[0,2,4], indexes=[100,102])

	xHead	yHead	tHead	xTail	yTail	tTail	xBody	yBody	tBody	curvature	...	headMinorAxisLength	headExcentricity	tailMajorAxisLength	tailMinorAxisLength	tailExcentricity	bodyMajorAxisLength	bodyMinorAxisLength	bodyExcentricity	imageNumber	id
1220	471.902	331.8900	0.269094	460.411	335.5360	0.205151	467.340	333.3400	0.285930	0.000054	...	3.02325	0.879342	9.4287	2.36285	0.968090	14.0688	2.71188	0.981246	100	0
1222	294.915	323.6210	5.928470	280.388	319.3940	6.078460	289.216	321.9640	6.006340	0.000511	...	3.08521	0.908041	11.1557	1.66443	0.988807	17.2867	2.57567	0.988838	100	2
1229	364.406	35.8941	0.353083	348.850	40.4274	6.276460	357.959	37.7740	0.251617	0.000484	...	3.37384	0.920171	11.3458	3.33696	0.955770	18.6191	3.79543	0.979003	100	4
1244	471.465	333.5370	0.001856	455.895	333.1370	0.067247	465.288	333.3780	6.274380	0.000043	...	3.02042	0.922201	11.2216	2.37528	0.977341	17.8254	2.66739	0.988741	102	0
1246	294.393	324.2560	5.943700	279.291	320.1950	6.066590	288.589	322.6960	6.020880	0.000204	...	3.05422	0.916951	11.7401	1.61688	0.990471	17.8923	2.52835	0.989966	102	2
1255	372.356	33.2975	0.324467	357.087	35.5002	6.115410	365.944	34.2238	0.109167	0.000586	...	3.46947	0.902759	11.6400	2.45400	0.977524	17.8863	3.81849	0.976946	102	4

6 rows × 23 columns

Check if a list of objects is in a frame

data.isObjectsInFrame(ids=[0,2,4], index=100)

[True, True, True]

Basic plotting

• Plot the velocity distribution for given objects

import matplotlib.pyplot as plt
%matplotlib inline

plotObj = fa.Plot(data)
plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=True);

plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=False);

plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=True);

Using seaborn on extracted data

• Plot the distribution of positions.

import seaborn as sns

data_object_0 = data.getObjects(0)
sns.jointplot(data=data_object_0, x="xBody", y="yBody");

# All objects pooled
sns.kdeplot(data=data.getObjects(list(range(data.getObjectNumber()))), x="xBody", y="yBody", shade=True);

• Compare the distributions of displacements.

tmp = data.getObjects([0,2,4])
tmp["displacement"] = (tmp["xBody"]**2 + tmp["yBody"]**2)**0.5
sns.violinplot(data=tmp, x="id", y="displacement");

sns.boxplot(data=tmp, x="id", y="displacement");

sns.boxenplot(data=tmp, x="id", y="displacement");