Requesting review of my logical data model

I am working on a personal project and I want to make sure that my data model is correct and following the best practices. Receiving feedback on my data model is important in improving my ability and knowledge of databases.

I am designing a stock market program of sorts. My model for it is pretty simple.

The workflow of my project:

  • A user creates a user account and enters their address
  • A user then can create an account for a specific stock at a share and USD amount

My plan is to make the account table a type two table. Valid from will be set to the date the account was created and valid to will be initially null. Valid to will be set to current date once the account is closed.

enter image description here

ADO.NET entity data model wizard crashes when creating a Postgresql data model

I am having trouble creating an ado.net data model from an existing Postgres database. I have used Npgsql and connected successfully but the wizard crashes without any error when I try to add a new entity data model. I have installed the Npgsql PostgreSQL Integration extension(Version 4.1.3.1) and the following packages:

<packages>   <package id="EntityFramework" version="6.2.0" targetFramework="net472" />   <package id="EntityFramework6.Npgsql" version="3.2.1.1" targetFramework="net472" />   <package id="Microsoft.Bcl.AsyncInterfaces" version="1.0.0" targetFramework="net472" />   <package id="Npgsql" version="4.1.3.1" targetFramework="net472" />   <package id="System.Buffers" version="4.5.0" targetFramework="net472" />   <package id="System.Memory" version="4.5.3" targetFramework="net472" />   <package id="System.Numerics.Vectors" version="4.5.0" targetFramework="net472" />   <package id="System.Runtime.CompilerServices.Unsafe" version="4.6.0" targetFramework="net472" />   <package id="System.Text.Encodings.Web" version="4.6.0" targetFramework="net472" />   <package id="System.Text.Json" version="4.6.0" targetFramework="net472" />   <package id="System.Threading.Tasks.Extensions" version="4.5.3" targetFramework="net472" />   <package id="System.ValueTuple" version="4.5.0" targetFramework="net472" /> </packages> 

The wizard crashes after clicking next on this window:

Entity Data Model wizard

Further technical details Npgsql version: Version 4.1.3.1 PostgreSQL version: 12.5 Operating system: Windows 10

Note:

  • I am using Npgsql -Version 4.1.3.1 instead of the latest stable version because the latest Npgsql PostgreSQL Integration extension in the VS marketplace is also of this version.
  • I am using EntityFramework6.Npgsql -Version 3.2.1.1 instead of the latest stable version because my project relies on EntityFramework -Version 6.2.0 and it is the latest version to have EntityFramework -Version 6.2.0 as the minimum dependency.
  • I have tried using all the latest stable versions of EntityFramework, Npgsql, and EntityFramework6.Npgsql and still had the same results

Thank you.

Fitting model to intensity plot

I am trying to fit a model to the following dataset to extract numerical values for 4 parameters $ J_x$ , $ J_y$ , $ J_z$ , and $ g$ . I also know that $ g \approx 2$ in this case. The dataset is a list of quadruples: {x, energy, intensity, error} as shown below

dataset = {{0.01299648, 0.01203211, 0.1263361, 0.005950636}, {0.01299648, 0.04910681, 0.0336076, 0.002947696}, {0.01299648, 0.09977061, 0.001322289, 0.000413821}, {0.01299648, 0.1508783, 0.000499663, 0.000258259}, {0.01299648, 0.2008796, 0.000419055, 0.00024877}, {0.01299648, 0.2510364, 0.000421737, 0.000272571}, {0.01299648, 0.3009251, 0.000178943, 0.000156955}, {0.01299648, 0.3508747, 0.0000992, 0.0000883}, {0.01299648, 0.3999321, 0.000430162, 0.000468312}, {0.01299648, 0.4489179, 0.001252234, 0.000846992}, {0.01299648, 0.5002585, 0.000617269, 0.000553035}, {0.01299648, 0.5509165, 0.001468457, 0.000842178}, {0.01299648, 0.6011173, 0.003723728, 0.001349723}, {0.01299648, 0.6498302, 0.004062989, 0.001265983}, {0.01299648, 0.6988636, 0.001993023, 0.000906512}, {0.01299648, 0.7499531, 0.000721637, 0.000587884}, {0.01299648, 0.8010127, 0.000252952, 0.000316284}, {0.05334629, 0.01203211, 0.1305249, 0.004184997}, {0.05334629, 0.04910681, 0.03503799, 0.002187056}, {0.05334629, 0.09977061, 0.001494748, 0.000322744}, {0.05334629, 0.1508783, 0.000631434, 0.000216124}, {0.05334629, 0.2008796, 0.000516482, 0.000212526}, {0.05334629, 0.2510364, 0.000452927, 0.000203133}, {0.05334629, 0.3009251, 0.00038714, 0.000173926}, {0.05334629, 0.3508747, 0.000419254, 0.000179236}, {0.05334629, 0.3999321, 0.000425151, 0.000310161}, {0.05334629, 0.4489179, 0.000511058, 0.000412408}, {0.05334629, 0.5002585, 0.000683154, 0.000400352}, {0.05334629, 0.5509165, 0.001937698, 0.000617178}, {0.05334629, 0.6011173, 0.003902016, 0.000892543}, {0.05334629, 0.6498302, 0.00309874, 0.000839511}, {0.05334629, 0.6988636, 0.001156821, 0.000561058}, {0.05334629, 0.7499531, 0.000876003, 0.000445513}, {0.05334629, 0.8010127, 0.000494271, 0.000353135}, {0.05334629, 0.8507249, 0.000468474, 0.000405826}, {0.05334629, 0.9009042, 0.000227273, 0.000284132}, {0.09946869, 0.01203211, 0.1314684, 0.002607759}, {0.09946869, 0.04910681, 0.03540794, 0.001393436}, {0.09946869, 0.09977061, 0.001480958, 0.000215194}, {0.09946869, 0.1508783, 0.000518701, 0.000144396}, {0.09946869, 0.2008796, 0.00039427, 0.000139293}, {0.09946869, 0.2510364, 0.000395253, 0.000148412}, {0.09946869, 0.3009251, 0.000357242, 0.000144663}, {0.09946869, 0.3508747, 0.000435539, 0.000173756}, {0.09946869, 0.3999321, 0.000440639, 0.000186949}, {0.09946869, 0.4489179, 0.000453975, 0.000189049}, {0.09946869, 0.5002585, 0.000817151, 0.000265822}, {0.09946869, 0.5509165, 0.002821699, 0.000522746}, {0.09946869, 0.6011173, 0.005799377, 0.000790837}, {0.09946869, 0.6498302, 0.003142505, 0.000619193}, {0.09946869, 0.6988636, 0.00089875, 0.000377157}, {0.09946869, 0.7499531, 0.000935933, 0.000440654}, {0.09946869, 0.8010127, 0.000741281, 0.00040915}, {0.09946869, 0.8507249, 0.000379727, 0.000311241}, {0.09946869, 0.9009042, 0.000452129, 0.000372186}, {0.09946869, 0.9499643, 0.000321497, 0.000298012}, {0.09946869, 0.9987899, 0.000216047, 0.000265462}, {0.09946869, 1.049146, 0.000321083, 0.000405456}, {0.09946869, 1.100007, 0.00007, 0.000197968}, {0.09946869, 1.151003, 0.000371132, 0.000898138}, {0.09946869, 1.20088, 0.001603127, 0.001909191}, {0.1513923, 0.01203211, 0.1271927, 0.002382964}, {0.1513923, 0.04910681, 0.03466543, 0.001207942}, {0.1513923, 0.09977061, 0.001471831, 0.000180972}, {0.1513923, 0.1508783, 0.000458085, 0.000108564}, {0.1513923, 0.2008796, 0.000323088, 0.0000968}, {0.1513923, 0.2510364, 0.000262988, 0.0000927}, {0.1513923, 0.3009251, 0.000208331, 0.0000952}, {0.1513923, 0.3508747, 0.000261884, 0.000132211}, {0.1513923, 0.3999321, 0.000318829, 0.000154663}, {0.1513923, 0.4489179, 0.000390393, 0.000170512}, {0.1513923, 0.5002585, 0.000796483, 0.000230864}, {0.1513923, 0.5509165, 0.003169181, 0.00045884}, {0.1513923, 0.6011173, 0.006016299, 0.000634249}, {0.1513923, 0.6498302, 0.002961305, 0.000479318}, {0.1513923, 0.6988636, 0.00089255, 0.00030299}, {0.1513923, 0.7499531, 0.000601942, 0.000292041}, {0.1513923, 0.8010127, 0.000622036, 0.000308231}, {0.1513923, 0.8507249, 0.00059582, 0.000311122}, {0.1513923, 0.9009042, 0.000342461, 0.000258311}, {0.1513923, 0.9499643, 0.000365842, 0.000264302}, {0.1513923, 0.9987899, 0.000383168, 0.000282596}, {0.1513923, 1.049146, 0.000158197, 0.000218464}, {0.1513923, 1.100007, 0.0000797, 0.0001487}, {0.1513923, 1.151003, 0.000272186, 0.000602807}, {0.1513923, 1.20088, 0.000791483, 0.000943182}, {0.1513923, 1.24981, 0.000810134, 0.000846855}, {0.1513923, 1.298876, 0.001098106, 0.000878741}, {0.1513923, 1.35012, 0.001020097, 0.00091243}, {0.1513923, 1.400843, 0.00099628, 0.001472998}, {0.1513923, 1.45113, 0.001696679, 0.002615917}, {0.1513923, 1.487999, 0.00068497, 0.001417412}, {0.200413, 0.01203211, 0.1251366, 0.002421743}, {0.200413, 0.04910681, 0.03396657, 0.001239589}, {0.200413, 0.09977061, 0.001455514, 0.000186787}, {0.200413, 0.1508783, 0.000471295, 0.000107087}, {0.200413, 0.2008796, 0.000356116, 0.0000954}, {0.200413, 0.2510364, 0.000255785, 0.0000834}, {0.200413, 0.3009251, 0.000192659, 0.0000751}, {0.200413, 0.3508747, 0.000195415, 0.000086}, {0.200413, 0.3999321, 0.000212672, 0.0000972}, {0.200413, 0.4489179, 0.000201986, 0.0000998}, {0.200413, 0.5002585, 0.000452148, 0.00014457}, {0.200413, 0.5509165, 0.002283648, 0.000302595}, {0.200413, 0.6011173, 0.005130702, 0.000435712}, {0.200413, 0.6498302, 0.002947664, 0.000341739}, {0.200413, 0.6988636, 0.000885883, 0.00019889}, {0.200413, 0.7499531, 0.000353508, 0.000142375}, {0.200413, 0.8010127, 0.000337989, 0.000159135}, {0.200413, 0.8507249, 0.000294789, 0.00016511}, {0.200413, 0.9009042, 0.000287179, 0.00017037}, {0.200413, 0.9499643, 0.000311635, 0.000194681}, {0.200413, 0.9987899, 0.000207756, 0.000158586}, {0.200413, 1.049146, 0.000158257, 0.00013942}, {0.200413, 1.100007, 0.000190184, 0.000146221}, {0.200413, 1.151003, 0.000213257, 0.000208235}, {0.200413, 1.20088, 0.000336925, 0.000306631}, {0.200413, 1.24981, 0.000487695, 0.000424801}, {0.200413, 1.298876, 0.000638927, 0.000549385}, {0.200413, 1.35012, 0.001054225, 0.000776587}, {0.200413, 1.400843, 0.001720866, 0.001358017}, {0.200413, 1.45113, 0.00200075, 0.001706196}, {0.200413, 1.487999, 0.001241234, 0.001336194}, {0.249747, 0.01203211, 0.1205826, 0.002361229}, {0.249747, 0.04910681, 0.03260196, 0.001191631}, {0.249747, 0.09977061, 0.001261705, 0.000170178}, {0.249747, 0.1508783, 0.000333223, 0.00009}, {0.249747, 0.2008796, 0.000305626, 0.0000888}, {0.249747, 0.2510364, 0.000248531, 0.0000826}, {0.249747, 0.3009251, 0.000227554, 0.0000828}, {0.249747, 0.3508747, 0.000242098, 0.0000856}, {0.249747, 0.3999321, 0.000213059, 0.0000796}, {0.249747, 0.4489179, 0.000171657, 0.0000691}, {0.249747, 0.5002585, 0.000269242, 0.0000875}, {0.249747, 0.5509165, 0.001302021, 0.000202058}, {0.249747, 0.6011173, 0.003488679, 0.000339568}, {0.249747, 0.6498302, 0.003051286, 0.000322039}, {0.249747, 0.6988636, 0.001461859, 0.000227405}, {0.249747, 0.7499531, 0.000595035, 0.000144464}, {0.249747, 0.8010127, 0.000388456, 0.000118404}, {0.249747, 0.8507249, 0.000291368, 0.00010498}, {0.249747, 0.9009042, 0.000241225, 0.000107813}, {0.249747, 0.9499643, 0.000237664, 0.00011481}, {0.249747, 0.9987899, 0.000151976, 0.0000917}, {0.249747, 1.049146, 0.000151201, 0.000093}, {0.249747, 1.100007, 0.000175435, 0.000106715}, {0.249747, 1.151003, 0.000146402, 0.00010687}, {0.249747, 1.20088, 0.000207344, 0.0001413}, {0.249747, 1.24981, 0.000247186, 0.00017871}, {0.249747, 1.298876, 0.000406102, 0.000266496}, {0.249747, 1.35012, 0.000704678, 0.00045794}, {0.249747, 1.400843, 0.000829369, 0.000665319}, {0.249747, 1.45113, 0.000937308, 0.000875315}, {0.249747, 1.487999, 0.001484896, 0.00116244}, {0.2999469, 0.01203211, 0.1134484, 0.002063204}, {0.2999469, 0.04910681, 0.03021775, 0.001057232}, {0.2999469, 0.09977061, 0.00106177, 0.000140285}, {0.2999469, 0.1508783, 0.00030537, 0.0000768}, {0.2999469, 0.2008796, 0.000213005, 0.0000649}, {0.2999469, 0.2510364, 0.000185747, 0.0000657}, {0.2999469, 0.3009251, 0.000164455, 0.000065}, {0.2999469, 0.3508747, 0.000142963, 0.0000636}, {0.2999469, 0.3999321, 0.000130967, 0.0000592}, {0.2999469, 0.4489179, 0.000167198, 0.0000687}, {0.2999469, 0.5002585, 0.000180923, 0.0000726}, {0.2999469, 0.5509165, 0.000541689, 0.000134283}, {0.2999469, 0.6011173, 0.001467756, 0.000226035}, {0.2999469, 0.6498302, 0.00203471, 0.000272955}, {0.2999469, 0.6988636, 0.001823599, 0.000254749}, {0.2999469, 0.7499531, 0.001339045, 0.000208357}, {0.2999469, 0.8010127, 0.0009652, 0.000182255}, {0.2999469, 0.8507249, 0.00046882, 0.000122285}, {0.2999469, 0.9009042, 0.000292738, 0.000102422}, {0.2999469, 0.9499643, 0.000180685, 0.0000821}, {0.2999469, 0.9987899, 0.000126662, 0.0000694}, {0.2999469, 1.049146, 0.000182039, 0.000087}, {0.2999469, 1.100007, 0.000243545, 0.0000985}, {0.2999469, 1.151003, 0.000167506, 0.0000907}, {0.2999469, 1.20088, 0.000221046, 0.000117207}, {0.2999469, 1.24981, 0.000266948, 0.000130003}, {0.2999469, 1.298876, 0.000361939, 0.000171356}, {0.2999469, 1.35012, 0.000423017, 0.000222659}, {0.2999469, 1.400843, 0.000497565, 0.000299089}, {0.2999469, 1.45113, 0.000841964, 0.00069896}, {0.2999469, 1.487999, 0.001636413, 0.001292695}, {0.351399, 0.01203211, 0.1023983, 0.001765024}, {0.351399, 0.04910681, 0.02760775, 0.000890958}, {0.351399, 0.09977061, 0.001013449, 0.000120122}, {0.351399, 0.1508783, 0.000314347, 0.0000681}, {0.351399, 0.2008796, 0.000218643, 0.0000587}, {0.351399, 0.2510364, 0.000145264, 0.0000501}, {0.351399, 0.3009251, 0.0000988, 0.0000431}, {0.351399, 0.3508747, 0.0000886, 0.0000424}, {0.351399, 0.3999321, 0.000078, 0.0000407}, {0.351399, 0.4489179, 0.000113312, 0.0000492}, {0.351399, 0.5002585, 0.0000947, 0.0000463}, {0.351399, 0.5509165, 0.000150304, 0.0000649}, {0.351399, 0.6011173, 0.000443105, 0.000115168}, {0.351399, 0.6498302, 0.000857112, 0.000160557}, {0.351399, 0.6988636, 0.001158728, 0.000179657}, {0.351399, 0.7499531, 0.001562672, 0.000200422}, {0.351399, 0.8010127, 0.001445169, 0.000188441}, {0.351399, 0.8507249, 0.000932427, 0.0001463}, {0.351399, 0.9009042, 0.00044006, 0.00010092}, {0.351399, 0.9499643, 0.000182093, 0.00007}, {0.351399, 0.9987899, 0.000100358, 0.0000596}, {0.351399, 1.049146, 0.000133863, 0.0000725}, {0.351399, 1.100007, 0.000144129, 0.000072}, {0.351399, 1.151003, 0.00010871, 0.0000655}, {0.351399, 1.20088, 0.000123393, 0.0000805}, {0.351399, 1.24981, 0.000193871, 0.000102966}, {0.351399, 1.298876, 0.000241517, 0.000134903}, {0.351399, 1.35012, 0.000296181, 0.000181252}, {0.351399, 1.400843, 0.00026523, 0.000220609}, {0.351399, 1.45113, 0.000646058, 0.000572446}, {0.351399, 1.487999, 0.000963321, 0.000939469}, {0.4027665, 0.01203211, 0.09461402, 0.001366129}, {0.4027665, 0.04910681, 0.0254652, 0.000706637}, {0.4027665, 0.09977061, 0.001031896, 0.0000992}, {0.4027665, 0.1508783, 0.000384627, 0.0000604}, {0.4027665, 0.2008796, 0.000268799, 0.0000534}, {0.4027665, 0.2510364, 0.000208381, 0.0000464}, {0.4027665, 0.3009251, 0.000199062, 0.0000445}, {0.4027665, 0.3508747, 0.000186287, 0.0000418}, {0.4027665, 0.3999321, 0.000159836, 0.0000416}, {0.4027665, 0.4489179, 0.000107729, 0.000037}, {0.4027665, 0.5002585, 0.0000923, 0.0000347}, {0.4027665, 0.5509165, 0.0000702, 0.0000317}, {0.4027665, 0.6011173, 0.000146609, 0.0000496}, {0.4027665, 0.6498302, 0.000280341, 0.0000714}, {0.4027665, 0.6988636, 0.000459356, 0.0000931}, {0.4027665, 0.7499531, 0.000744988, 0.00011817}, {0.4027665, 0.8010127, 0.001140008, 0.000143582}, {0.4027665, 0.8507249, 0.001200483, 0.000141774}, {0.4027665, 0.9009042, 0.000922343, 0.000119712}, {0.4027665, 0.9499643, 0.000375711, 0.0000757}, {0.4027665, 0.9987899, 0.000140653, 0.0000492}, {0.4027665, 1.049146, 0.000098, 0.0000461}, {0.4027665, 1.100007, 0.0000772, 0.0000404}, {0.4027665, 1.151003, 0.0000815, 0.0000407}, {0.4027665, 1.20088, 0.0000899, 0.0000479}, {0.4027665, 1.24981, 0.0000992, 0.0000589}, {0.4027665, 1.298876, 0.000142484, 0.0000848}, {0.4027665, 1.35012, 0.000189181, 0.000109538}, {0.4027665, 1.400843, 0.000172183, 0.000138258}, {0.4027665, 1.45113, 0.000275446, 0.000293852}, {0.4027665, 1.487999, 0.000381214, 0.000439755}, {0.4496463, 0.01203211, 0.1094655, 0.001215262}, {0.4496463, 0.04910681, 0.03120829, 0.000643984}, {0.4496463, 0.09977061, 0.001421187, 0.0000962}, {0.4496463, 0.1508783, 0.000589879, 0.0000633}, {0.4496463, 0.2008796, 0.000401797, 0.0000524}, {0.4496463, 0.2510364, 0.000392448, 0.0000514}, {0.4496463, 0.3009251, 0.000374653, 0.0000495}, {0.4496463, 0.3508747, 0.000443615, 0.0000546}, {0.4496463, 0.3999321, 0.000371195, 0.0000521}, {0.4496463, 0.4489179, 0.000203434, 0.0000406}, {0.4496463, 0.5002585, 0.000117548, 0.0000317}, {0.4496463, 0.5509165, 0.0000764, 0.000026}, {0.4496463, 0.6011173, 0.0000956, 0.0000299}, {0.4496463, 0.6498302, 0.000120406, 0.0000352}, {0.4496463, 0.6988636, 0.000175619, 0.000043}, {0.4496463, 0.7499531, 0.000325115, 0.0000604}, {0.4496463, 0.8010127, 0.0005661, 0.0000836}, {0.4496463, 0.8507249, 0.000928296, 0.000105136}, {0.4496463, 0.9009042, 0.001128707, 0.000111962}, {0.4496463, 0.9499643, 0.000719185, 0.0000871}, {0.4496463, 0.9987899, 0.000239313, 0.0000504}, {0.4496463, 1.049146, 0.0000817, 0.0000313}, {0.4496463, 1.100007, 0.0000617, 0.0000287}, {0.4496463, 1.151003, 0.0000532, 0.0000284}, {0.4496463, 1.20088, 0.0000802, 0.0000369}, {0.4496463, 1.24981, 0.000094, 0.0000454}, {0.4496463, 1.298876, 0.00010737, 0.0000566}, {0.4496463, 1.35012, 0.0000915, 0.0000647}, {0.4496463, 1.400843, 0.00016223, 0.000102268}, {0.4496463, 1.45113, 0.000256105, 0.000193461}, {0.4496463, 1.487999, 0.000406342, 0.000335206}, {0.4967034, 0.01203211, 0.1515964, 0.001494316}, {0.4967034, 0.04910681, 0.03684591, 0.000740697}, {0.4967034, 0.09977061, 0.001324794, 0.000099}, {0.4967034, 0.1508783, 0.000433766, 0.0000559}, {0.4967034, 0.2008796, 0.000275803, 0.0000457}, {0.4967034, 0.2510364, 0.000212638, 0.0000404}, {0.4967034, 0.3009251, 0.000196184, 0.0000388}, {0.4967034, 0.3508747, 0.000205123, 0.0000397}, {0.4967034, 0.3999321, 0.000232467, 0.0000434}, {0.4967034, 0.4489179, 0.000401981, 0.0000576}, {0.4967034, 0.5002585, 0.000176261, 0.0000392}, {0.4967034, 0.5509165, 0.0000896, 0.0000293}, {0.4967034, 0.6011173, 0.0000941, 0.0000303}, {0.4967034, 0.6498302, 0.000100446, 0.0000324}, {0.4967034, 0.6988636, 0.000110629, 0.0000342}, {0.4967034, 0.7499531, 0.000192725, 0.0000444}, {0.4967034, 0.8010127, 0.000359514, 0.0000619}, {0.4967034, 0.8507249, 0.000674937, 0.0000873}, {0.4967034, 0.9009042, 0.001039717, 0.000108854}, {0.4967034, 0.9499643, 0.000770872, 0.0000938}, {0.4967034, 0.9987899, 0.000225213, 0.0000509}, {0.4967034, 1.049146, 0.0000582, 0.000027}, {0.4967034, 1.100007, 0.0000442, 0.0000254}, {0.4967034, 1.151003, 0.0000482, 0.0000287}, {0.4967034, 1.20088, 0.0000665, 0.0000361}, {0.4967034, 1.24981, 0.0000707, 0.0000395}, {0.4967034, 1.298876, 0.0000618, 0.0000412}, {0.4967034, 1.35012, 0.0000937, 0.0000635}, {0.4967034, 1.400843, 0.000150926, 0.0000974}, {0.4967034, 1.45113, 0.000135016, 0.000147398}, {0.4967034, 1.487999, 0.000233144, 0.000242054}, {0.5504108, 0.01203211, 0.09064252, 0.0014725}, {0.5504108, 0.04910681, 0.02602461, 0.000745193}, {0.5504108, 0.09977061, 0.000835608, 0.0000957}, {0.5504108, 0.1508783, 0.000236157, 0.0000513}, {0.5504108, 0.2008796, 0.000142981, 0.0000427}, {0.5504108, 0.2510364, 0.000107481, 0.0000387}, {0.5504108, 0.3009251, 0.0000878, 0.0000356}, {0.5504108, 0.3508747, 0.0000884, 0.0000348}, {0.5504108, 0.3999321, 0.000127604, 0.0000402}, {0.5504108, 0.4489179, 0.000139343, 0.0000394}, {0.5504108, 0.5002585, 0.000122183, 0.0000388}, {0.5504108, 0.5509165, 0.0000747, 0.0000328}, {0.5504108, 0.6011173, 0.0000848, 0.0000356}, {0.5504108, 0.6498302, 0.000123968, 0.0000454}, {0.5504108, 0.6988636, 0.000148527, 0.0000523}, {0.5504108, 0.7499531, 0.000235699, 0.0000658}, {0.5504108, 0.8010127, 0.000372422, 0.0000797}, {0.5504108, 0.8507249, 0.000677797, 0.000105258}, {0.5504108, 0.9009042, 0.000967788, 0.000128001}, {0.5504108, 0.9499643, 0.000557506, 0.0000969}, {0.5504108, 0.9987899, 0.000136945, 0.0000475}, {0.5504108, 1.049146, 0.0000519, 0.0000324}, {0.5504108, 1.100007, 0.0000573, 0.0000335}, {0.5504108, 1.151003, 0.0000676, 0.0000375}, {0.5504108, 1.20088, 0.0000507, 0.0000342}, {0.5504108, 1.24981, 0.0000355, 0.0000311}, {0.5504108, 1.298876, 0.0000463, 0.0000433}, {0.5504108, 1.35012, 0.000113439, 0.0000779}, {0.5504108, 1.400843, 0.000125893, 0.0001}, {0.5504108, 1.45113, 0.000127608, 0.000143131}, {0.5504108, 1.487999, 0.000108575, 0.000174224}, {0.5999553, 0.01203211, 0.06634204, 0.001353167}, {0.5999553, 0.04910681, 0.01829561, 0.000704955}, {0.5999553, 0.09977061, 0.000690618, 0.0000958}, {0.5999553, 0.1508783, 0.000167709, 0.0000499}, {0.5999553, 0.2008796, 0.000126736, 0.0000463}, {0.5999553, 0.2510364, 0.0000828, 0.0000358}, {0.5999553, 0.3009251, 0.0000879, 0.0000358}, {0.5999553, 0.3508747, 0.0000923, 0.0000381}, {0.5999553, 0.3999321, 0.0000769, 0.0000375}, {0.5999553, 0.4489179, 0.0000776, 0.0000401}, {0.5999553, 0.5002585, 0.0000767, 0.0000387}, {0.5999553, 0.5509165, 0.0000877, 0.0000394}, {0.5999553, 0.6011173, 0.000166282, 0.0000555}, {0.5999553, 0.6498302, 0.000232257, 0.0000684}, {0.5999553, 0.6988636, 0.000360469, 0.0000907}, {0.5999553, 0.7499531, 0.00039869, 0.000098}, {0.5999553, 0.8010127, 0.00053909, 0.00010748}, {0.5999553, 0.8507249, 0.000729154, 0.000115996}, {0.5999553, 0.9009042, 0.000611591, 0.000102393}, {0.5999553, 0.9499643, 0.000244657, 0.000068}, {0.5999553, 0.9987899, 0.0000664, 0.0000389}, {0.5999553, 1.049146, 0.0000428, 0.0000316}, {0.5999553, 1.100007, 0.0000545, 0.0000332}, {0.5999553, 1.151003, 0.0000771, 0.0000403}, {0.5999553, 1.20088, 0.0000709, 0.0000418}, {0.5999553, 1.24981, 0.000079, 0.0000527}, {0.5999553, 1.298876, 0.0000836, 0.0000684}, {0.5999553, 1.35012, 0.000146739, 0.0000985}, {0.5999553, 1.400843, 0.000180494, 0.000125324}, {0.5999553, 1.45113, 0.000171035, 0.000194538}, {0.5999553, 1.487999, 0.000283511, 0.000326393}, {0.6503139, 0.01203211, 0.08897097, 0.002566025}, {0.6503139, 0.04910681, 0.02465827, 0.001434306}, {0.6503139, 0.09977061, 0.000902531, 0.00018245}, {0.6503139, 0.1508783, 0.00021565, 0.0000701}, {0.6503139, 0.2008796, 0.000186667, 0.0000717}, {0.6503139, 0.2510364, 0.000151591, 0.0000644}, {0.6503139, 0.3009251, 0.000120543, 0.0000513}, {0.6503139, 0.3508747, 0.000108839, 0.0000451}, {0.6503139, 0.3999321, 0.0000846, 0.0000418}, {0.6503139, 0.4489179, 0.000115256, 0.0000494}, {0.6503139, 0.5002585, 0.000128018, 0.0000502}, {0.6503139, 0.5509165, 0.000132044, 0.0000477}, {0.6503139, 0.6011173, 0.000235156, 0.0000634}, {0.6503139, 0.6498302, 0.000448416, 0.0000941}, {0.6503139, 0.6988636, 0.000524899, 0.000111871}, {0.6503139, 0.7499531, 0.000584105, 0.000120938}, {0.6503139, 0.8010127, 0.000550121, 0.000109041}, {0.6503139, 0.8507249, 0.000462607, 0.000095}, {0.6503139, 0.9009042, 0.000262414, 0.0000695}, {0.6503139, 0.9499643, 0.000116691, 0.000049}, {0.6503139, 0.9987899, 0.0000866, 0.0000478}, {0.6503139, 1.049146, 0.0000572, 0.0000395}, {0.6503139, 1.100007, 0.0000328, 0.0000283}, {0.6503139, 1.151003, 0.0000645, 0.0000398}, {0.6503139, 1.20088, 0.0000764, 0.0000465}, {0.6503139, 1.24981, 0.0000914, 0.0000615}, {0.6503139, 1.298876, 0.000131544, 0.0000916}, {0.6503139, 1.35012, 0.000152969, 0.000107919}, {0.6503139, 1.400843, 0.000160366, 0.000125925}, {0.6503139, 1.45113, 0.000227675, 0.000244772}, {0.6503139, 1.487999, 0.000410726, 0.000420443}, {0.6990286, 0.01203211, 0.1305426, 0.004546241}, {0.6990286, 0.04910681, 0.03146357, 0.002106031}, {0.6990286, 0.09977061, 0.001037019, 0.00026412}, {0.6990286, 0.1508783, 0.000231548, 0.000091}, {0.6990286, 0.2008796, 0.000298593, 0.000118439}, {0.6990286, 0.2510364, 0.000231503, 0.000103976}, {0.6990286, 0.3009251, 0.000136526, 0.0000669}, {0.6990286, 0.3508747, 0.000123731, 0.0000975}, {0.6990286, 0.3999321, 0.000157325, 0.000139099}, {0.6990286, 0.4489179, 0.000276389, 0.000156038}, {0.6990286, 0.5002585, 0.000241361, 0.000137575}, {0.6990286, 0.5509165, 0.000154788, 0.0000892}, {0.6990286, 0.6011173, 0.000268714, 0.0000745}, {0.6990286, 0.6498302, 0.000475129, 0.000104437}, {0.6990286, 0.6988636, 0.000607848, 0.000120112}, {0.6990286, 0.7499531, 0.000470629, 0.00011044}, {0.6990286, 0.8010127, 0.000318165, 0.0000894}, {0.6990286, 0.8507249, 0.000204583, 0.0000658}, {0.6990286, 0.9009042, 0.000127872, 0.0000515}, {0.6990286, 0.9499643, 0.0000904, 0.0000453}, {0.6990286, 0.9987899, 0.000180306, 0.0000659}, {0.6990286, 1.049146, 0.000121658, 0.000054}, {0.6990286, 1.100007, 0.000130731, 0.000056}, {0.6990286, 1.151003, 0.00015566, 0.0000637}, {0.6990286, 1.20088, 0.000097, 0.0000527}, {0.6990286, 1.24981, 0.0000895, 0.0000586}, {0.6990286, 1.298876, 0.00014314, 0.0000869}, {0.6990286, 1.35012, 0.000181547, 0.000118287}, {0.6990286, 1.400843, 0.000167488, 0.000131239}, {0.6990286, 1.45113, 0.000292798, 0.000288394}, {0.6990286, 1.487999, 0.000433997, 0.000458083}, {0.7477285, 0.3009251, 0.000053, 0.0000462}, {0.7477285, 0.3508747, 0.0000956, 0.000133103}, {0.7477285, 0.3999321, 0.000344739, 0.000279699}, {0.7477285, 0.4489179, 0.000390532, 0.000270478}, {0.7477285, 0.5002585, 0.000392541, 0.000259302}, {0.7477285, 0.5509165, 0.000149147, 0.000128412}, {0.7477285, 0.6011173, 0.000165453, 0.0000795}, {0.7477285, 0.6498302, 0.000452263, 0.000147439}, {0.7477285, 0.6988636, 0.000415388, 0.000125599}, {0.7477285, 0.7499531, 0.000297813, 0.000243738}, {0.7477285, 0.8010127, 0.000194161, 0.000304051}, {0.7477285, 0.8507249, 0.000154568, 0.00027359}, {0.7477285, 0.9009042, 0.000134434, 0.000251633}, {0.7477285, 0.9499643, 0.000110127, 0.00019129}, {0.7477285, 0.9987899, 0.000110382, 0.00010382}, {0.7477285, 1.049146, 0.000129385, 0.0000827}, {0.7477285, 1.100007, 0.000353017, 0.0000981}, {0.7477285, 1.151003, 0.000538544, 0.00013636}, {0.7477285, 1.20088, 0.00015782, 0.0000889}, {0.7477285, 1.24981, 0.0000696, 0.0000608}, {0.7477285, 1.298876, 0.000146578, 0.00013496}, {0.7477285, 1.35012, 0.000300833, 0.000205088}, {0.7477285, 1.400843, 0.000445233, 0.000245628}, {0.7477285, 1.45113, 0.000371585, 0.000403792}, {0.7477285, 1.487999, 0.00069879, 0.000703723}, {0.79343, 0.8010127, 0.000545414, 0.000697371}, {0.79343, 0.8507249, 0.000225278, 0.000428143}, {0.79343, 0.9009042, 0.000404924, 0.000554523}, {0.79343, 0.9499643, 0.000148193, 0.000297461}, {0.79343, 0.9987899, 0.000165649, 0.000213896}, {0.79343, 1.049146, 0.0000836, 0.000110634}, {0.79343, 1.100007, 0.000175279, 0.0000915}, {0.79343, 1.151003, 0.000331701, 0.000166089}, {0.79343, 1.20088, 0.000197516, 0.000136657}, {0.79343, 1.24981, 0.0000473, 0.0000663}, {0.79343, 1.298876, 0.000169997, 0.000187402}, {0.79343, 1.35012, 0.000461569, 0.000344723}, {0.79343, 1.400843, 0.000334411, 0.000273181}, {0.79343, 1.45113, 0.000547673, 0.000501559}, {0.79343, 1.487999, 0.000890756, 0.000845645}, {0.8292615, 1.35012, 0.000118945, 0.000182641}, {0.8292615, 1.400843, 0.000102903, 0.000170622}, {0.8292615, 1.45113, 0.000157944, 0.000294485}, {0.8292615, 1.487999, 0.000248875, 0.000441169}} 

My model yields the energy at a specific coordinate $ \boldsymbol{k} =\{k_x, k_y, k_z\}$ where $ \boldsymbol{k} = x(\boldsymbol{b}_1 + \boldsymbol{b}_2)$ . Here $ x$ is the first entry of each quadruple in the dataset, $ \boldsymbol{b}_1 = \{ 2 \pi, 2\pi/\sqrt{3}, 0 \}$ , and $ \boldsymbol{b}_2 = \{ 0, 4\pi/\sqrt{3}, 0 \}$

(* First obtain the measured energy values and store them in a list *) ω = DeleteDuplicates@*Flatten@dataset[[All, 2]]; b1 = {2π, (2π)/Sqrt[3], 0}; b2 = {0, (4π)/Sqrt[3], 0}; xKtoM = DeleteDuplicates@*Flatten@dataset[[All, 1]]; k = Table[xKtoM[[i]] (b1 + b2), {i, 1, Length[xKtoM]}]; 

I then go on to define my model

(* Bohr magneton μ in units of eV*T^-1 pulled from wikipedia*) μ = 5.7883818012*10^-5;  (* This is the model where we have taken the positive branch of the energy spectrum *) d[kx_, ky_, kz_, g_, Bz_, Jx_, Jy_, Jz_] := {1/2 (Jx + Jy) (Cos[k[[1]]] + Cos[k[[2]]] + Cos[k[[1]] + k[[2]]]) - 3 Jz + g \[Mu] Bz,  1/2 (Jx - Jy) (E^(-I 2 \[Pi]/3) Cos[k[[1]]] + E^(I 2 \[Pi]/3) Cos[k[[2]]] + Cos[k[[1]] + k[[2]]])};  spectrum[{kx_, ky_, kz_}] := Norm[d[kx, ky, kz, g, 4, Jx, Jy, Jz]]; (* Get the spectrum going from the high symmetry K \[Rule] M path using the k vector/list we defined above. This is the model we use for the dataset *) model = Map[spectrum, k]; 

From here I would have the energy values at 18 separate points if I had specified values for Jx, Jy, Jz, and g. I then thought to define a cost function that takes my model and subtracts the corresponding measured energy value and then try to find the minimum using the built-in FindMinimum function

(* Define a cost function between the model and the measured energy values Subscript[\[Omega], i] *) CostFunction[Jx_, Jy_, Jz_, g_] := Sum[Abs[model[[i]] - ω[[i]]]^2, {i, 1, Length[ω]}] (* Minimize this cost function to attempt to extract the parameters Jx, Jy, Jz, and g *) params = FindMinimum[model, {{Jx, 0.5}, {Jy, 0.5}, {Jz, 0.5}, {g, 2}}] 

My only issue with this is that

  1. My model is a list of functions while the measured energy values $ \omega$ is a list of real numbers
  2. Using FindMinimum yields quite a lot of errors

Is it possible to get around these issues using something like NonLinearModelFit or something similar? Let me know if I should clarify anything or add any additional info and thanks in advance!

SQL Server log file in “Simple” recovery model

Can someone please shed some light on log growth in simple recovery model. If I understand correctly, even in "Simple" recovery model the transaction log can grow? So if I have open transaction(s) (Update, Insert, Delete, Index Rebuilds, etc.) these start to re-use inactive VLF’s and if none are present the log will start growing based on log configuration until it reaches the configured size or hits disc space available limit? Can any of the active VLF’s be marked as "inactive" due to a checkpoint and\or full backups?

In short, it was requested that I change all databases to "Simple" recovery model and issue a "DBCC SHRINKFILE (N’LogName’ , 0, TRUNCATEONLY)" every hour. I received a Error 9002 and the Full database backup and dbcc shrink failed. I guess, eventually, the transactions completed and the checkpoint, full backup and dbcc shrinkfile were able to complete.

How to model inheritance in MySQL for adding and deleting users?

I currently have a MySQL database and our ERD has inheritance for various tables. One is User which has as children: administrators, students and professors. The tables look as follow:

CREATE TABLE `Users` (   `id_number` int DEFAULT NULL,   `First_name` varchar(50) DEFAULT NULL,   `Last_name` varchar(50) DEFAULT NULL,   `User_Type` varchar(14) DEFAULT NULL ) ENGINE=InnoDB CREATE TABLE `Advisor` (   `Faculty_id` varchar(2) DEFAULT NULL,   `Student_id` varchar(5) DEFAULT NULL,   `Date_Assigned` date DEFAULT NULL ) ENGINE=InnoDB  CREATE TABLE `Faculty` (   `Faculty_id` varchar(13) DEFAULT NULL,   `Room_id` varchar(2) DEFAULT NULL,   `Faculty_Speciality` varchar(16) DEFAULT NULL,   `Faculty_Rank` int DEFAULT NULL,   `Faculty_Type` varchar(9) DEFAULT NULL,   `user_type` varchar(13) DEFAULT NULL ) ENGINE=InnoDB 

I’m aware that things such as PRIMARY KEY are missing for the ids and REFERENCES for foreign keys. Similar is found on this post How to model inheritance of two tables MySQL. However, what I want to know is when adding users how to populate both tables (parent and children)? Meaning that both tables user and student get the same information? How would this work on data that gets over 3000 students?

non-linear model fit function not working

I have attempted to exercise a non-linear model fit to my data, dadtAbs, and got the following puzzle

nlm =    NonlinearModelFit[     Transpose[{Table[t, {t, 1, tmax}],dadtAbs}],      (1 - aaa)*bbb*NSPI[bbb, ddd, population, t] *        (1 - ddd)*(population - NSPI[bbb, ddd, population, t]),      {aaa, bbb, ddd},      t] 

where dadtAbs is a list, population is a known constant, aaa, bbb, ddd are the desired answers, and t is the variable.

When I queried nlm, I got

[0.] 

Here is the function NSPI:

NSPI[alpha_, delta_, population_, tt_] :=    population/(1 + (population - 1)*Exp[-alpha*population*(1 - delta)*tt]) 

population is a large constant, for example, 1000000

How do I model the fighter’s Great Weapon Fighting fighting style in Anydice?

I was trying to create an AnyDice function to model the Great Weapon Fighting fighting style (which lets you reroll 1s and 2s), but I couldn’t get it to work on any arbitrary dice.

I’ve found this one:

function: reroll R:n under N:n {    if R < N { result: d12 } else {result: R} } output [reroll 1d12 under 3] named "greataxe weapon fighting" 

And it works fine. But I don’t know how to make the function generic so i don’t need to change the d12 every time i want a different dice to reroll.

I’ve tried

function: reroll R:n under N:n {    if R < N { result: d{1..R} } else {result: R} } output [reroll 1d12 under 3] named "greataxe weapon fighting" 

but it is not giving the right probabilities. Maybe if I could fetch the die size inside the function…

Is it possible to model these probabilities in AnyDice?

I was asked by a pal to help him model a dice mechanic in AnyDice. I must admit I am an absolute neophyte with it, and I offered to solve it using software I’m better versed in. I’d like to be able to help them do this in AnyDice.

The mechanic is as follows:

  • The player and their opponent are each assigned a pool of dice. This is done via other mechanics of the game, of which details are not germane. Suffice to say, the player will have some set of dice (say 2D6, 1D8, and 1D12) that will face off against the opponents pool (which will generally be different from the player’s, say 3D6, 2D8 and 1D12).
  • The player and their oppoent roll their pools.
  • The opponent notes their highest value die. This is the target.
  • The player counts the number of their dice that have a higher value than the target, if any.
  • The count of the dice exceeding the target, if any, is the number of success points.

I searched the AnyDice tag here for questions that might be similar, the closest I found was "Modelling an opposed dice pool mechanic in AnyDice", specifically the answer by Ilmari Karonen.

That question and answer, however, deals with only a single die type.

Can a question like "What are the probabilities of N successes when rolling 4D6 and 6D20 as the player against 6D6 and 4D20 for the opponent?", be handled in AnyDice and produce a chart similar to that below?

enter image description here

Problem Lotka Volterra Model – Modelling & Plotting in Python

I urgently need your help. Currently I’m conducting a research in regards of the revenue calculation and the dynamics within revenue calculation for my masters. I thought of revenue/profit margin as of a dynamical system – Lotka Volterra differential equations. I thought of a contribution margin calculation as within this simple formula:

https://www.accountingformanagement.org/wp-content/uploads/2012/07/contribution-margin-ratio-img4.png

In consequence my idea was the following, but I receive an error:
enter image description here

enter image description here

enter image description here

  • Can anyone help me?
  • Do you think it’s a bad idea to use Lotka Volterra equations for this nonlinear purpose of Sales/Cost/Margin Simulation?
  • How would you model it and why? Am I missing equations or does the simple system already fit the requirements?
  • How do I generate a valid plot out of these results (Phase Portrait etc.)?

Signal translation with Seq2Seq model

I’m currently doing some research on signal processing and I got a dataset which includes the signal in itself and its "translation".

A signal and its translation

So I want to use a Many-to-Many RNN to translate the first into the second.

After spending a week reading about the different option I have, I ended up learning about RNN and Seq2Seq models. I believe this is the right solution for the problem (correct me if I’m wrong).

Now, as the input and the output are of the same length, I don’t need to add padding and thus I tried a simple LSTM layer and TimeDistributed Dense layer (Keras):

model = Sequential([     LSTM(256, return_sequences=True, input_shape=SHAPE, dropout=0.2),     TimeDistributed(Dense(units=1, activation="softmax")) ])  model.compile(optimizer='adam', loss='categorical_crossentropy') 

But the model seems to learn nothing from the sequence and when I plot the "prediction", it nothing but values between 0 and 1.

As you can see, I’m a beginner and the code I wrote might not make sense to you but I need guidance on few questions:

  • Does the model make sense for the problem I’m trying to solve ?
  • Am I’m using the right loss/activation functions ?
  • And finally, please correct/teach me