Note
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Analyse an inventory¶
This example shows the different types of statistics that can be applied to an existing inventory for analysis.
Necessary imports
from spatiocoexistence import Inventory
from spatiocoexistence.data import inventory_file
Initialize the inventory
inv = Inventory.from_data(inventory_file)
Access the data from the inventory.
all_data = inv.data
x = inv.x
dbh = inv.dbh
species = inv.species
crowding_conspecifics_BA = inv.CI_CS
crowding_conspecifics = inv.CI_C
Get the species abundance of that inventory. The index refers to that species.
inv.species_abundance()[10]
np.int64(129)
4. Get the amount of reproductive individuals per species that are alive. The reproduction is defined by a tree being larger than the reproductive size. In this example we set it to = 2dm, and assume it holds for all tree species.
inv.count_reproductives(2)
(array([ 0, 29, 105, 454, 143, 28, 1, 171, 84, 24, 90, 12, 25,
170, 45, 36, 157, 9, 5, 68, 52, 21, 41, 8, 12, 57,
168, 15, 123, 21, 228, 38, 97, 170, 80, 16, 36, 7, 168,
46, 74, 59, 9, 96, 1, 46, 35, 200, 89, 165, 11, 176,
36, 478, 11, 110, 39, 3, 31, 51, 6, 40, 198, 28, 179,
13, 989, 45, 24, 25, 224, 112, 11, 75, 38, 897], dtype=int32), array([ 2, 3, 4, ..., 20515, 20516, 20517], shape=(7684,)))
Get the summary statistics of the plot. More information how to calculate them can be found in … ToDo
inv.get_BA_and_k()
(array([ 2.5350216 , 5.72626862, 4.7558197 , 6.4313602 , 0. ,
1.64784671, 7.53022908, 15.64648526, 1.09822482, 7.8630367 ,
2.22380728, 1.98102032, 5.39247401, 2.98440828, 4.13861385,
4.19205058, 1.48804531, 1.59443177, 7.03923928, 3.103917 ,
4.3342495 , 2.55024865, 1.21389167, 1.17654637, 0. ,
3.89232701, 1.2560269 , 3.94285989, 2.16756748, 2.18731194,
1.87837071, 2.50698635, 2.3764987 , 41.1362981 , 2.32417211,
4.16847158, 1.09698991, 8.33869431, 3.48354924, 2.35825732,
5.71830032, 1.39442405, 12.2888562 , 1.19249398, 8.3054285 ,
1.19997058, 3.75313683, 2.99568669, 14.68735626, 1.51154357,
3.92766453, 1.35058536, 13.64581685, 1.64229196, 5.95414774,
3.40562048, 1.47416314, 7.93548456, 2.35126361, 2.07658206,
12.76626178, 6.2770597 , 1.3402427 , 7.40210651, 1.65191719,
5.0383856 , 2.37988218, 2.67879672, 1.86108429, 3.00701361,
6.27202722, 2.2433842 , 6.44153831, 3.10601604, 10.20857836]), array([5.90758954, 5.08752063, 5.69982887, 5.38651354, 7.85793112,
6.33134646, 7.07446448, 6.28096888, 5.46172053, 5.56389605,
6.02894799, 8.27618822, 4.03834144, 4.79437134, 5.06290242,
4.81607605, 6.2466325 , 3.63640657, 7.1625957 , 8.29118164,
6.61197696, 6.21393474, 7.52333069, 6.60224667, 6.23121126,
6.86527958, 8.3568061 , 7.33854142, 6.70779604, 5.94834094,
7.09196612, 8.1363333 , 7.20440694, 5.08248881, 6.63012037,
5.02001295, 6.78319929, 4.54380425, 6.46466454, 5.51783699,
5.10281631, 6.69679866, 7.53112227, 6.66044357, 6.12226063,
5.97925271, 7.13246896, 6.52352474, 6.37932903, 6.68226073,
7.10968036, 5.72514605, 7.15129205, 6.52804434, 6.25205638,
8.96772691, 6.49060014, 4.29412862, 7.30376756, 6.99373505,
5.97062498, 5.53180638, 6.59641942, 6.85485563, 5.88031301,
8.20320975, 6.31724548, 6.64008416, 8.48562408, 6.44811979,
6.91019766, 9.49152756, 4.74124138, 5.26695417, 5.48911063]), array([1.51051883, 1.98482017, 1.83285556, 1.78796788, 0. ,
1.18300472, 2.55852563, 4.73526673, 0.383275 , 2.69554008,
1.36491466, 1.05006378, 1.97484844, 1.37478768, 1.57172499,
1.81294576, 1.11501691, 1.11896385, 2.46549827, 1.76699476,
2.38793082, 1.45411667, 0.98840474, 0.79228712, 0. ,
1.86447676, 0.81457812, 1.64733581, 1.3288138 , 1.1655271 ,
1.21321332, 1.35337776, 1.33815419, 5.54636437, 1.25143879,
1.87125104, 0.77429224, 2.224403 , 1.46131462, 0.96476724,
1.42604871, 1.04772658, 3.80245902, 1.06759929, 2.22475864,
0.21952256, 2.11176605, 1.13115502, 3.95608645, 1.05128917,
1.82569503, 0.63683063, 3.73995502, 1.17400954, 2.55257984,
1.33328794, 1.09218013, 2.52948563, 1.42428891, 1.52449653,
4.68681597, 2.12815127, 0.51428139, 2.66717745, 1.04606137,
2.12661324, 1.27084488, 1.27280347, 1.19726066, 1.60438308,
1.77610739, 1.51011983, 1.86992486, 1.30235237, 3.77965877]), array([2.59673734, 2.24425932, 2.52463228, 2.38657495, 3.46252103,
2.77516803, 3.12808103, 2.7759844 , 2.40591127, 2.4536383 ,
2.65145538, 3.64107203, 1.78199219, 2.11009145, 2.22973307,
2.12052228, 2.72492471, 1.59897258, 3.15702772, 3.64055523,
2.90839606, 2.73113438, 3.20470348, 2.90257889, 2.74926476,
3.01771222, 3.6733024 , 3.23281864, 2.9494011 , 2.60389528,
3.10953354, 3.57332479, 3.1524134 , 2.26163074, 2.91728413,
2.20966239, 2.98223944, 2.01496351, 2.84628572, 2.43019668,
2.25402643, 2.93603463, 3.32659615, 2.87724756, 2.70062668,
2.64132087, 3.11044313, 2.87561732, 2.83557766, 2.9362603 ,
3.12683797, 2.51938593, 3.21610253, 2.86114047, 2.75306947,
3.94966648, 2.83713933, 1.89228554, 3.20632912, 3.0719007 ,
2.63019275, 2.4460011 , 2.90544223, 3.02844496, 2.58570521,
3.62684363, 2.77781894, 2.92238191, 3.72694989, 2.82395087,
3.05560072, 4.17132476, 2.09270623, 2.31890666, 2.47596806]), array([ 9.46097371, 6.99000943, 1.55998418, 0.87795643, 0. ,
2.07062529, 2.71320664, 0.50973253, 1.88558577, 2.70210348,
4.59685102, 1.09056731, 13.31604717, 0.69859657, 5.94055188,
4.2053578 , 1.79242665, 20.11784151, 1.7508829 , 3.5800251 ,
1.32733654, 3.33298513, 1.38514602, 1.31689595, 0. ,
1.35567395, 1.0421509 , 1.12349835, 1.11971527, 10.38348323,
2.64431099, 1.49966545, 1.63039396, 1.1379443 , 3.10190789,
5.9204547 , 1.62808115, 6.52889285, 5.18621149, 0.54767118,
0.84675654, 1.48038026, 6.63302399, 2.09356976, 0.43613966,
1.06052981, 2.77475292, 0.78908126, 1.98430405, 1.99897832,
0.96871284, 0.49111883, 1.3647738 , 2.74746419, 4.38010144,
2.30535182, 8.40747165, 9.37764521, 1.29069196, 0.97636966,
1.30949723, 1.60204177, 0.32312057, 1.65198829, 2.06197958,
1.65989253, 7.41549836, 1.50296838, 1.14998958, 1.32581722,
2.57180347, 6.9277689 , 11.89223095, 1.47732397, 1.04290659]), array([0.84276739, 0.87016185, 0.95080395, 0.93782846, 0.98695416,
0.85498654, 0.90318239, 0.89016167, 0.87125045, 0.94503105,
0.93527443, 0.84821539, 1.0211858 , 0.92587957, 1.0349787 ,
0.93143892, 0.926617 , 0.81935567, 0.87586605, 0.82153776,
0.93245914, 0.90492154, 0.87747729, 0.88119853, 0.93011775,
0.85408237, 0.88658749, 0.92567075, 0.93951381, 0.85523744,
0.88020223, 0.84140003, 0.79814146, 0.86119662, 0.93937029,
0.89400338, 0.88839459, 0.98124687, 0.84325599, 0.96229458,
0.87385577, 0.88607039, 0.88599882, 0.78766865, 0.96588658,
0.98477064, 0.83079559, 0.92153698, 0.82755301, 0.81332964,
0.86809334, 0.94592841, 0.86325998, 0.89327895, 0.95944619,
0.87601818, 0.93183596, 0.95285935, 0.85426466, 0.88955188,
1.029276 , 0.884409 , 0.95901931, 0.822559 , 0.89900076,
0.81410874, 0.94220999, 0.83418973, 0.8967298 , 0.93522333,
0.89870992, 0.87563905, 0.9517751 , 0.9987204 , 0.88559478]), array([ 114, 159, 853, 225, 51, 225, 304, 179, 54, 129, 68,
70, 243, 86, 60, 258, 444, 61, 102, 227, 77, 139,
1392, 76, 91, 362, 93, 279, 83, 640, 253, 255, 551,
97, 50, 72, 68, 229, 78, 137, 92, 199, 165, 740,
66, 51, 851, 169, 308, 100, 347, 101, 693, 231, 250,
86, 351, 57, 204, 112, 75, 302, 81, 329, 73, 1890,
127, 56, 146, 586, 165, 97, 106, 69, 2322]), array([20417, 20372, 19678, 20306, 20480, 20306, 20227, 20352, 20477,
20402, 20463, 20461, 20288, 20445, 20471, 20273, 20087, 20470,
20429, 20304, 20454, 20392, 19139, 20455, 20440, 20169, 20438,
20252, 20448, 19891, 20278, 20276, 19980, 20434, 20481, 20459,
20463, 20302, 20453, 20394, 20439, 20332, 20366, 19791, 20465,
20480, 19680, 20362, 20223, 20431, 20184, 20430, 19838, 20300,
20281, 20445, 20180, 20474, 20327, 20419, 20456, 20229, 20450,
20202, 20458, 18641, 20404, 20475, 20385, 19945, 20366, 20434,
20425, 20462, 18209]))
First, use the dedicated plotting helpers on the Inventory object to show individual diagnostics and the spatial map.
# Spatial map of the trees:
inv.plot_map(scale=5, filter=2)
# Species abundance distribution (SAD):
inv.plot_SAD()
# Aggregation (k_ff) versus abundance for well-represented species:
inv.plot_k_vs_abundance(threshold=50)
# Reduced growth, survival, and recruitment distributions:
inv.plot_reduced_growth()
inv.plot_reduced_survival()
inv.plot_reduced_recruitment()
# Crowding index distributions for conspecifics and heterospecifics:
inv.plot_CI_CS_distribution()
inv.plot_CI_HS_distribution()
# Size-class relationships: mean crowding per size class, size distribution,
# and size-dependent mortality.
inv.plot_size_mean_CI_CS()
inv.plot_size_mean_CI_HS()
inv.plot_size_distribution()
inv.plot_size_mortality()
/builds/heinzej/CrowdingIndex/spatiocoexistence/inventory/_plotting.py:142: RuntimeWarning: divide by zero encountered in log
np.log(k_ff[mask_abund]),
You can also restrict the inventory to a subset of trees using apply_focus.
This changes the inventory in place.
Here we focus on reproductive trees (“rep”) and then re-plot the size distribution.
inv.apply_focus("rep", reproductive_size=2.0)
inv.plot_size_distribution()
Alternatively, all these diagnostics can be shown at once in a single composite “display” plot using Inventory.plot().
inv.plot(scale=5, filter=3)
/builds/heinzej/CrowdingIndex/spatiocoexistence/inventory/_plotting.py:142: RuntimeWarning: divide by zero encountered in log
np.log(k_ff[mask_abund]),
/builds/heinzej/CrowdingIndex/spatiocoexistence/inventory/_plotting.py:218: RuntimeWarning: divide by zero encountered in log
np.log(BA_ff[mask_abund]),
/builds/heinzej/CrowdingIndex/spatiocoexistence/inventory/_plotting.py:291: RuntimeWarning: divide by zero encountered in log
np.log(sorted_k),
/builds/heinzej/CrowdingIndex/spatiocoexistence/inventory/_plotting.py:685: RuntimeWarning: divide by zero encountered in log
log_abundance = np.log(sorted_abundance)
Total running time of the script: (0 minutes 2.782 seconds)