Note
Click here to download the full example code
Bulk processing with ListOfTracks¶
This example will guide you through the process of importing and analyzing multiple tracks from a single .csv file in bulk.
The ListOfTracks class can load and store multiple tracks in bulk.
from trait2d.analysis import ListOfTracks
We can now import data from a .csv file. In order to separate tracks, the file has to contain the column id which assigns a unique integer track identifier to each row. You can have a look at Project Maintenance for a complete documentation. Alternative column names can also be supplied using the col_name_* keyword arguments. (See trait2d.analysis.ListOfTracks.from_file() and trait2d.analysis.Track.from_file() for this.)
tracks = ListOfTracks.from_file("three_tracks.csv", unit_length='micrometres')
The tracks are now imported and can be used for analysis.
The representation of the ListOfTracks instance holds some information about it.
tracks
Out:
<ListOfTracks instance at 140667696617456>
Number of tracks: 3
To get an overview, we can use trait2d.analysis.ListOfTracks.plot_trajectories()
tracks.plot_trajectories()
from trait2d.analysis.models import ModelBrownian, ModelConfined, ModelHop
from trait2d.analysis import ModelDB
ModelDB().add_model(ModelBrownian)
ModelDB().add_model(ModelConfined)
ModelDB().add_model(ModelHop)
For analysis, trait2d.analysis.ListOfTracks uses the same semantics as Track for analysis. You can get a summary of the analysis results using trait2d.analysis.ListOfTracks.adc_summary().
tracks.adc_analysis(fraction_fit_points = 0.15)
tracks.adc_summary(plot_dapp=True, plot_pie_chart=True)
Out:
{'sectors': {'ModelHop': 0.3333333333333333, 'ModelConfined': 0.6666666666666666, 'not catergorized': 0.0}, 'average_params': {'ModelHop': array([3.14967424e-13, 3.42032577e-11, 6.45676387e-10, 1.21317270e-05]), 'ModelConfined': array([1.46622083e-12, 1.63824707e-08, 1.50744939e-01])}, 't': array([0. , 0.0005, 0.001 , 0.0015, 0.002 , 0.0025, 0.003 , 0.0035,
0.004 , 0.0045, 0.005 , 0.0055, 0.006 , 0.0065, 0.007 , 0.0075,
0.008 , 0.0085, 0.009 , 0.0095, 0.01 , 0.0105, 0.011 , 0.0115,
0.012 , 0.0125, 0.013 , 0.0135, 0.014 , 0.0145, 0.015 , 0.0155,
0.016 , 0.0165, 0.017 , 0.0175, 0.018 , 0.0185, 0.019 , 0.0195,
0.02 , 0.0205, 0.021 , 0.0215, 0.022 , 0.0225, 0.023 , 0.0235,
0.024 , 0.0245, 0.025 , 0.0255, 0.026 , 0.0265, 0.027 , 0.0275,
0.028 , 0.0285, 0.029 , 0.0295, 0.03 , 0.0305, 0.031 , 0.0315,
0.032 , 0.0325, 0.033 , 0.0335, 0.034 , 0.0345, 0.035 , 0.0355,
0.036 , 0.0365, 0.037 , 0.0375, 0.038 , 0.0385, 0.039 , 0.0395,
0.04 , 0.0405, 0.041 , 0.0415, 0.042 , 0.0425, 0.043 , 0.0435,
0.044 , 0.0445, 0.045 , 0.0455, 0.046 , 0.0465, 0.047 , 0.0475,
0.048 , 0.0485, 0.049 , 0.0495, 0.05 , 0.0505, 0.051 , 0.0515,
0.052 , 0.0525, 0.053 , 0.0535, 0.054 , 0.0545, 0.055 , 0.0555,
0.056 , 0.0565, 0.057 , 0.0575, 0.058 , 0.0585, 0.059 , 0.0595,
0.06 , 0.0605, 0.061 , 0.0615, 0.062 , 0.0625, 0.063 , 0.0635,
0.064 , 0.0645, 0.065 , 0.0655, 0.066 , 0.0665, 0.067 , 0.0675,
0.068 , 0.0685, 0.069 , 0.0695, 0.07 , 0.0705, 0.071 , 0.0715,
0.072 , 0.0725, 0.073 , 0.0735, 0.074 , 0.0745, 0.075 , 0.0755,
0.076 , 0.0765, 0.077 , 0.0775, 0.078 , 0.0785, 0.079 , 0.0795,
0.08 , 0.0805, 0.081 , 0.0815, 0.082 , 0.0825, 0.083 , 0.0835,
0.084 , 0.0845, 0.085 , 0.0855, 0.086 , 0.0865, 0.087 , 0.0875,
0.088 , 0.0885, 0.089 , 0.0895, 0.09 , 0.0905, 0.091 , 0.0915,
0.092 , 0.0925, 0.093 , 0.0935, 0.094 , 0.0945, 0.095 , 0.0955,
0.096 , 0.0965, 0.097 , 0.0975, 0.098 , 0.0985, 0.099 , 0.0995,
0.1 , 0.1005, 0.101 , 0.1015, 0.102 , 0.1025, 0.103 , 0.1035,
0.104 , 0.1045, 0.105 , 0.1055, 0.106 , 0.1065, 0.107 , 0.1075,
0.108 , 0.1085, 0.109 , 0.1095, 0.11 , 0.1105, 0.111 , 0.1115,
0.112 , 0.1125, 0.113 , 0.1135, 0.114 , 0.1145, 0.115 , 0.1155,
0.116 , 0.1165, 0.117 , 0.1175, 0.118 , 0.1185, 0.119 , 0.1195,
0.12 , 0.1205, 0.121 , 0.1215, 0.122 , 0.1225, 0.123 , 0.1235,
0.124 , 0.1245, 0.125 , 0.1255, 0.126 , 0.1265, 0.127 , 0.1275,
0.128 , 0.1285, 0.129 , 0.1295, 0.13 , 0.1305, 0.131 , 0.1315,
0.132 , 0.1325, 0.133 , 0.1335, 0.134 , 0.1345, 0.135 , 0.1355,
0.136 , 0.1365, 0.137 , 0.1375, 0.138 , 0.1385, 0.139 , 0.1395,
0.14 , 0.1405, 0.141 , 0.1415, 0.142 , 0.1425, 0.143 , 0.1435,
0.144 , 0.1445, 0.145 , 0.1455, 0.146 , 0.1465, 0.147 , 0.1475,
0.148 , 0.1485, 0.149 , 0.1495, 0.15 , 0.1505, 0.151 , 0.1515,
0.152 , 0.1525, 0.153 , 0.1535, 0.154 , 0.1545, 0.155 , 0.1555,
0.156 , 0.1565, 0.157 , 0.1575, 0.158 , 0.1585, 0.159 , 0.1595,
0.16 , 0.1605, 0.161 , 0.1615, 0.162 , 0.1625, 0.163 , 0.1635,
0.164 , 0.1645, 0.165 , 0.1655, 0.166 , 0.1665, 0.167 , 0.1675,
0.168 , 0.1685, 0.169 , 0.1695, 0.17 , 0.1705, 0.171 , 0.1715,
0.172 , 0.1725, 0.173 , 0.1735, 0.174 , 0.1745, 0.175 , 0.1755,
0.176 , 0.1765, 0.177 , 0.1775, 0.178 , 0.1785, 0.179 , 0.1795,
0.18 , 0.1805, 0.181 , 0.1815, 0.182 , 0.1825, 0.183 , 0.1835,
0.184 , 0.1845, 0.185 , 0.1855, 0.186 , 0.1865, 0.187 , 0.1875,
0.188 , 0.1885, 0.189 , 0.1895, 0.19 , 0.1905, 0.191 , 0.1915,
0.192 , 0.1925, 0.193 , 0.1935, 0.194 , 0.1945, 0.195 , 0.1955,
0.196 , 0.1965, 0.197 , 0.1975, 0.198 , 0.1985, 0.199 , 0.1995,
0.2 , 0.2005, 0.201 , 0.2015, 0.202 , 0.2025, 0.203 , 0.2035,
0.204 , 0.2045, 0.205 , 0.2055, 0.206 , 0.2065, 0.207 , 0.2075,
0.208 , 0.2085, 0.209 , 0.2095, 0.21 , 0.2105, 0.211 , 0.2115,
0.212 , 0.2125, 0.213 , 0.2135, 0.214 , 0.2145, 0.215 , 0.2155,
0.216 , 0.2165, 0.217 , 0.2175, 0.218 , 0.2185, 0.219 , 0.2195,
0.22 , 0.2205, 0.221 , 0.2215, 0.222 , 0.2225, 0.223 , 0.2235,
0.224 , 0.2245, 0.225 , 0.2255, 0.226 , 0.2265, 0.227 , 0.2275,
0.228 , 0.2285, 0.229 , 0.2295, 0.23 , 0.2305, 0.231 , 0.2315,
0.232 , 0.2325, 0.233 , 0.2335, 0.234 , 0.2345, 0.235 , 0.2355,
0.236 , 0.2365, 0.237 , 0.2375, 0.238 , 0.2385, 0.239 , 0.2395,
0.24 , 0.2405, 0.241 , 0.2415, 0.242 , 0.2425, 0.243 , 0.2435,
0.244 , 0.2445, 0.245 , 0.2455, 0.246 , 0.2465, 0.247 , 0.2475,
0.248 , 0.2485, 0.249 , 0.2495, 0.25 , 0.2505, 0.251 , 0.2515,
0.252 , 0.2525, 0.253 , 0.2535, 0.254 , 0.2545, 0.255 , 0.2555,
0.256 , 0.2565, 0.257 , 0.2575, 0.258 , 0.2585, 0.259 , 0.2595,
0.26 , 0.2605, 0.261 , 0.2615, 0.262 , 0.2625, 0.263 , 0.2635,
0.264 , 0.2645, 0.265 , 0.2655, 0.266 , 0.2665, 0.267 , 0.2675,
0.268 , 0.2685, 0.269 , 0.2695, 0.27 , 0.2705, 0.271 , 0.2715,
0.272 , 0.2725, 0.273 , 0.2735, 0.274 , 0.2745, 0.275 , 0.2755,
0.276 , 0.2765, 0.277 , 0.2775, 0.278 , 0.2785, 0.279 , 0.2795,
0.28 , 0.2805, 0.281 , 0.2815, 0.282 , 0.2825, 0.283 , 0.2835,
0.284 , 0.2845, 0.285 , 0.2855, 0.286 , 0.2865, 0.287 , 0.2875,
0.288 , 0.2885, 0.289 , 0.2895, 0.29 , 0.2905, 0.291 , 0.2915,
0.292 , 0.2925, 0.293 , 0.2935, 0.294 , 0.2945, 0.295 , 0.2955,
0.296 , 0.2965, 0.297 , 0.2975, 0.298 , 0.2985, 0.299 , 0.2995,
0.3 , 0.3005, 0.301 , 0.3015, 0.302 , 0.3025, 0.303 , 0.3035,
0.304 , 0.3045, 0.305 , 0.3055, 0.306 , 0.3065, 0.307 , 0.3075,
0.308 , 0.3085, 0.309 , 0.3095, 0.31 , 0.3105, 0.311 , 0.3115,
0.312 , 0.3125, 0.313 , 0.3135, 0.314 , 0.3145, 0.315 , 0.3155,
0.316 , 0.3165, 0.317 , 0.3175, 0.318 , 0.3185, 0.319 , 0.3195,
0.32 , 0.3205, 0.321 , 0.3215, 0.322 , 0.3225, 0.323 , 0.3235,
0.324 , 0.3245, 0.325 , 0.3255, 0.326 , 0.3265, 0.327 , 0.3275,
0.328 , 0.3285, 0.329 , 0.3295, 0.33 , 0.3305, 0.331 , 0.3315,
0.332 , 0.3325, 0.333 , 0.3335, 0.334 , 0.3345, 0.335 , 0.3355,
0.336 , 0.3365, 0.337 , 0.3375, 0.338 , 0.3385, 0.339 , 0.3395,
0.34 , 0.3405, 0.341 , 0.3415, 0.342 , 0.3425, 0.343 , 0.3435,
0.344 , 0.3445, 0.345 , 0.3455, 0.346 , 0.3465, 0.347 , 0.3475,
0.348 , 0.3485, 0.349 , 0.3495, 0.35 , 0.3505, 0.351 , 0.3515,
0.352 , 0.3525, 0.353 , 0.3535, 0.354 , 0.3545, 0.355 , 0.3555,
0.356 , 0.3565, 0.357 , 0.3575, 0.358 , 0.3585, 0.359 , 0.3595,
0.36 , 0.3605, 0.361 , 0.3615, 0.362 , 0.3625, 0.363 , 0.3635,
0.364 , 0.3645, 0.365 , 0.3655, 0.366 , 0.3665, 0.367 , 0.3675,
0.368 , 0.3685, 0.369 , 0.3695, 0.37 , 0.3705, 0.371 , 0.3715,
0.372 , 0.3725, 0.373 , 0.3735, 0.374 , 0.3745, 0.375 , 0.3755,
0.376 , 0.3765, 0.377 , 0.3775, 0.378 , 0.3785, 0.379 , 0.3795,
0.38 , 0.3805, 0.381 , 0.3815, 0.382 , 0.3825, 0.383 , 0.3835,
0.384 , 0.3845, 0.385 , 0.3855, 0.386 , 0.3865, 0.387 , 0.3875,
0.388 , 0.3885, 0.389 , 0.3895, 0.39 , 0.3905, 0.391 , 0.3915,
0.392 , 0.3925, 0.393 , 0.3935, 0.394 , 0.3945, 0.395 , 0.3955,
0.396 , 0.3965, 0.397 , 0.3975, 0.398 , 0.3985, 0.399 , 0.3995,
0.4 , 0.4005, 0.401 , 0.4015, 0.402 , 0.4025, 0.403 , 0.4035,
0.404 , 0.4045, 0.405 , 0.4055, 0.406 , 0.4065, 0.407 , 0.4075,
0.408 , 0.4085, 0.409 , 0.4095, 0.41 , 0.4105, 0.411 , 0.4115,
0.412 , 0.4125, 0.413 , 0.4135, 0.414 , 0.4145, 0.415 , 0.4155,
0.416 , 0.4165, 0.417 , 0.4175, 0.418 , 0.4185, 0.419 , 0.4195,
0.42 , 0.4205, 0.421 , 0.4215, 0.422 , 0.4225, 0.423 , 0.4235,
0.424 , 0.4245, 0.425 , 0.4255, 0.426 , 0.4265, 0.427 , 0.4275,
0.428 , 0.4285, 0.429 , 0.4295, 0.43 , 0.4305, 0.431 , 0.4315,
0.432 , 0.4325, 0.433 , 0.4335, 0.434 , 0.4345, 0.435 , 0.4355,
0.436 , 0.4365, 0.437 , 0.4375, 0.438 , 0.4385, 0.439 , 0.4395,
0.44 , 0.4405, 0.441 , 0.4415, 0.442 , 0.4425, 0.443 , 0.4435,
0.444 , 0.4445, 0.445 , 0.4455, 0.446 , 0.4465, 0.447 , 0.4475,
0.448 , 0.4485, 0.449 , 0.4495, 0.45 , 0.4505, 0.451 , 0.4515,
0.452 , 0.4525, 0.453 , 0.4535, 0.454 , 0.4545, 0.455 , 0.4555,
0.456 , 0.4565, 0.457 , 0.4575, 0.458 , 0.4585, 0.459 , 0.4595,
0.46 , 0.4605, 0.461 , 0.4615, 0.462 , 0.4625, 0.463 , 0.4635,
0.464 , 0.4645, 0.465 , 0.4655, 0.466 , 0.4665, 0.467 , 0.4675,
0.468 , 0.4685, 0.469 , 0.4695, 0.47 , 0.4705, 0.471 , 0.4715,
0.472 , 0.4725, 0.473 , 0.4735, 0.474 , 0.4745, 0.475 , 0.4755,
0.476 , 0.4765, 0.477 , 0.4775, 0.478 , 0.4785, 0.479 , 0.4795,
0.48 , 0.4805, 0.481 , 0.4815, 0.482 , 0.4825, 0.483 , 0.4835,
0.484 , 0.4845, 0.485 , 0.4855, 0.486 , 0.4865, 0.487 , 0.4875,
0.488 , 0.4885, 0.489 , 0.4895, 0.49 , 0.4905, 0.491 , 0.4915,
0.492 , 0.4925, 0.493 , 0.4935, 0.494 , 0.4945, 0.495 , 0.4955,
0.496 , 0.4965, 0.497 , 0.4975, 0.498 , 0.4985, 0.499 , 0.4995]), 'average_msd': {'ModelHop': array([1.42050049e-15, 2.10697886e-15, 2.85239047e-15, 3.67889479e-15,
4.45101089e-15, 5.16773439e-15, 5.88477851e-15, 6.52131322e-15,
6.99849207e-15, 7.57761988e-15, 8.10736992e-15, 8.71398418e-15,
9.32469328e-15, 9.93457814e-15, 1.07800113e-14, 1.16671803e-14,
1.25816682e-14, 1.34652685e-14, 1.44105645e-14, 1.51610106e-14,
1.59214795e-14, 1.65151578e-14, 1.70719328e-14, 1.76394012e-14,
1.80952117e-14, 1.85983338e-14, 1.90606138e-14, 1.96552393e-14,
2.03236730e-14, 2.10936686e-14, 2.18430124e-14, 2.26462591e-14,
2.34235414e-14, 2.40558822e-14, 2.48284030e-14, 2.54113397e-14,
2.59891335e-14, 2.66172317e-14, 2.71463867e-14, 2.77059403e-14,
2.82326653e-14, 2.88471839e-14, 2.95487800e-14, 3.03215467e-14,
3.11110071e-14, 3.19329743e-14, 3.27308048e-14, 3.33578421e-14,
3.41048495e-14, 3.48347464e-14, 3.55500906e-14, 3.61979964e-14,
3.69035724e-14, 3.76070923e-14, 3.83868185e-14, 3.91767385e-14,
4.00187769e-14, 4.09010761e-14, 4.17861808e-14, 4.26071620e-14,
4.35185770e-14, 4.43017094e-14, 4.51286639e-14, 4.59037532e-14,
4.66860163e-14, 4.74517334e-14, 4.81717309e-14, 4.88677493e-14,
4.94547069e-14, 5.00831982e-14, 5.07375355e-14, 5.14191300e-14,
5.20216746e-14, 5.26730135e-14, 5.31759872e-14, 5.35997733e-14,
5.40748776e-14, 5.45280258e-14, 5.48419241e-14, 5.52211519e-14,
5.56554169e-14, 5.59453451e-14, 5.63072053e-14, 5.66368804e-14,
5.69503103e-14, 5.73724718e-14, 5.78212631e-14, 5.82691829e-14,
5.88144976e-14, 5.93908196e-14, 5.99213278e-14, 6.04564554e-14,
6.10397069e-14, 6.15011409e-14, 6.19662845e-14, 6.24203214e-14,
6.27224020e-14, 6.31544616e-14, 6.35934577e-14, 6.41345338e-14,
6.47244075e-14, 6.53592101e-14, 6.59723882e-14, 6.66293727e-14,
6.72542877e-14, 6.78696654e-14, 6.84435775e-14, 6.90030760e-14,
6.95597397e-14, 6.99166260e-14, 7.02697492e-14, 7.06320068e-14,
7.09916768e-14, 7.14714675e-14, 7.19774695e-14, 7.24894397e-14,
7.30708905e-14, 7.36803062e-14, 7.43139763e-14, 7.48706622e-14,
7.54012080e-14, 7.58636329e-14, 7.62453153e-14, 7.66232838e-14,
7.69655039e-14, 7.74246453e-14, 7.79342621e-14, 7.83599872e-14,
7.88848702e-14, 7.95058074e-14, 8.01338672e-14, 8.08201046e-14,
8.14237480e-14, 8.18930331e-14, 8.23155827e-14, 8.27738241e-14,
8.30989410e-14, 8.33896882e-14, 8.36270733e-14, 8.37812388e-14,
8.41878416e-14, 8.45836303e-14, 8.50439130e-14, 8.55248770e-14,
8.59231944e-14, 8.64409166e-14, 8.68660059e-14, 8.72909573e-14,
8.76725911e-14, 8.80520440e-14, 8.83662733e-14, 8.86721069e-14,
8.89116772e-14, 8.91120868e-14, 8.94053028e-14, 8.97790893e-14,
9.02189780e-14, 9.06847808e-14, 9.11262160e-14, 9.15864471e-14,
9.20067139e-14, 9.24696695e-14, 9.29234087e-14, 9.33678309e-14,
9.35894393e-14, 9.39813811e-14, 9.42089756e-14, 9.44829533e-14,
9.47576067e-14, 9.50339313e-14, 9.53309198e-14, 9.56208215e-14,
9.58709751e-14, 9.60501488e-14, 9.63298968e-14, 9.64904934e-14,
9.66905077e-14, 9.68314054e-14, 9.69013248e-14, 9.69549468e-14,
9.70397269e-14, 9.69128631e-14, 9.68463502e-14, 9.68183919e-14,
9.67858343e-14, 9.68253627e-14, 9.68412320e-14, 9.69056574e-14,
9.70339024e-14, 9.72545419e-14, 9.74449981e-14, 9.75382801e-14,
9.76696821e-14, 9.77663894e-14, 9.78791949e-14, 9.79513367e-14,
9.79277776e-14, 9.79637678e-14, 9.79106682e-14, 9.79291312e-14,
9.79350917e-14, 9.79969406e-14, 9.81316640e-14, 9.82093141e-14,
9.83112171e-14, 9.84644266e-14, 9.86454208e-14, 9.88071338e-14,
9.89526319e-14, 9.89469645e-14, 9.90098484e-14, 9.90614069e-14,
9.89937081e-14, 9.88890702e-14, 9.88158267e-14, 9.88947846e-14,
9.88357384e-14, 9.89347749e-14, 9.89876203e-14, 9.90503899e-14,
9.92040591e-14, 9.92073812e-14, 9.92604573e-14, 9.92185682e-14,
9.90991901e-14, 9.88918382e-14, 9.87339959e-14, 9.85159413e-14,
9.84604575e-14, 9.83129043e-14, 9.82754848e-14, 9.82564963e-14,
9.83097311e-14, 9.83162449e-14, 9.83428088e-14, 9.83242649e-14,
9.82730050e-14, 9.80903731e-14, 9.79021607e-14, 9.75718270e-14,
9.72440616e-14, 9.69474215e-14, 9.68469643e-14, 9.67025040e-14,
9.67384499e-14, 9.67329700e-14, 9.68729516e-14, 9.68846939e-14,
9.69252996e-14, 9.70297360e-14, 9.70121314e-14, 9.69182510e-14,
9.67399680e-14, 9.66205206e-14, 9.65834473e-14, 9.65625760e-14,
9.65372922e-14, 9.66173049e-14, 9.67173702e-14, 9.68468915e-14,
9.69867888e-14, 9.70050267e-14, 9.71304689e-14, 9.72560957e-14,
9.74543752e-14, 9.75069407e-14, 9.76368574e-14, 9.78052532e-14,
9.78371048e-14, 9.79907055e-14, 9.81314288e-14, 9.83371097e-14,
9.85719657e-14, 9.88597477e-14, 9.91964449e-14, 9.94880959e-14,
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Single tracks can also be retrieved and viewed easily. It is also possible to run individual analysis on these tracks.
tracks.get_track(0).plot_adc_analysis_results()
trait2d.analysis.ListOfTracks.adc_summary()’s way of averaging the parameters as well as Dapp for each model after analysing each track can be inaccurate. It may thus be desirable to get an average of the categorised curves first and then run an additional analysis. trait2d.analysis.ListOfTracks.average() can help with this.
It returns a special trait2d.analysis.MSDTrack object which does not store trajectory data but instead the average MSD values. It can be used for analysis like an ordinary trait2d.analysis.Track object.
Here, we analyse the average of all tracks classified as Confined Diffusion.
confined_tracks = tracks.get_sublist(ModelConfined).average()
confined_tracks.adc_analysis()
confined_tracks.plot_adc_analysis_results()
It is a good idea to use ModelDB().cleanup() at the end of your notebooks to remove all models again. Otherwise they may carry over into other open notebooks.
ModelDB().cleanup()
Total running time of the script: ( 0 minutes 1.396 seconds)