What measure of uncertainty is the default that is used in the gratia draw() function? - ggplot2

I have a data set that looks like this:
structure(list(landings = c(116, 31, 0, 0, 0,
0, 0, 0, 0, 120, 0, 241, 9, 0, 64, 326, 142, 605, 139, 410,
212, 470, 416, 309, 1269, 474, 22, 135, 395, 464, 451, 32,
2537, 210, 299, 1522, 184, 550, 666, 429, 1372, 184, 147,
1208, 159, 951, 1000, 1100, 301, 144, 244, 0, 0, 281, 0,
0, 0, 0, 0, 0, 0, 0, 0, 42, 594, 26, 747, 436, 0, 914, 182,
8, 275, 175, 766, 130, 930, 31, 177, 123, 895, 88, 107, 0,
4, 481, 909, 511, 877, 402, 295, 336, 645, 310, 301, 398,
411, 0, 205, 293, 49, 454, 162, 138, 1171, 0, 138, 0, 111,
0, 0, 36, 78, 114, 0, 0, 134, 44, 549, 0, 378, 716, 739,
393, 203, 839, 70, 454, 132, 651, 63, 1850, 217, 403, 55,
0, 408, 43, 17, 12, 26, 2, 811, 581, 1216, 154, 1059, 89,
1862, 1310, 297, 29, 680, 0, 0, 29, 0, 0, 0, 0, 0, 0, 17,
6, 0, 0, 0, 44, 909, 0, 0, 0, 194, 0, 212, 18, 46, 44, 56,
365, 37, 0, 73, 11, 16, 19, 0, 0, 0, 23, 0, 92, 0, 216, 0,
16, 0, 80, 319, 59, 35, 929, 47, 0, 0, 356, 0, 0, 33, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 91, 362, 0,
0, 0, 0, 0, 29, 0, 0, 392, 105, 0, 94, 15, 222, 34, 44, 178,
1867, 0, 224, 241, 23, 1502, 492, 168, 0, 234, 299, 453,
0, 406, 149, 0, 39, 57, 86, 0, 28, 23, 265, 0, 0, 0, 168,
31, 20, 0, 28, 78, 244, 13, 0, 99, 168, 861, 52, 649, 0,
174, 0, 0, 2462, 64, 178, 0, 61, 0, 321, 391, 33, 17, 227,
241, 248, 294, 1119, 37, 90, 0, 85, 37, 89, 0, 0, 0), Date = c(2014,
2014.01916495551, 2014.03832991102, 2014.05749486653, 2014.07665982204,
2014.09582477755, 2014.11498973306, 2014.13415468857, 2014.15331964408,
2014.17248459959, 2014.1916495551, 2014.21081451061, 2014.22997946612,
2014.24914442163, 2014.26830937714, 2014.28747433265, 2014.30663928816,
2014.32580424367, 2014.34496919918, 2014.36413415469, 2014.3832991102,
2014.40246406571, 2014.42162902122, 2014.44079397673, 2014.45995893224,
2014.47912388775, 2014.49828884326, 2014.51745379877, 2014.53661875428,
2014.55578370979, 2014.5749486653, 2014.59411362081, 2014.61327857632,
2014.63244353183, 2014.65160848734, 2014.67077344285, 2014.68993839836,
2014.70910335387, 2014.72826830938, 2014.74743326489, 2014.7665982204,
2014.78576317591, 2014.80492813142, 2014.82409308693, 2014.84325804244,
2014.86242299795, 2014.88158795346, 2014.90075290897, 2014.91991786448,
2014.93908281999, 2014.9582477755, 2014.97741273101, 2014.99657768652,
2015.01574264203, 2015.03490759754, 2015.05407255305, 2015.07323750856,
2015.09240246407, 2015.11156741958, 2015.13073237509, 2015.1498973306,
2015.16906228611, 2015.18822724162, 2015.20739219713, 2015.22655715264,
2015.24572210815, 2015.26488706366, 2015.28405201916, 2015.30321697467,
2015.32238193018, 2015.34154688569, 2015.3607118412, 2015.37987679671,
2015.39904175222, 2015.41820670773, 2015.43737166324, 2015.45653661875,
2015.47570157426, 2015.49486652977, 2015.51403148528, 2015.53319644079,
2015.5523613963, 2015.57152635181, 2015.59069130732, 2015.60985626283,
2015.62902121834, 2015.64818617385, 2015.66735112936, 2015.68651608487,
2015.70568104038, 2015.72484599589, 2015.7440109514, 2015.76317590691,
2015.78234086242, 2015.80150581793, 2015.82067077344, 2015.83983572895,
2015.85900068446, 2015.87816563997, 2015.89733059548, 2015.91649555099,
2015.9356605065, 2015.95482546201, 2015.97399041752, 2015.99315537303,
2016.01232032854, 2016.03148528405, 2016.05065023956, 2016.06981519507,
2016.08898015058, 2016.10814510609, 2016.1273100616, 2016.14647501711,
2016.16563997262, 2016.18480492813, 2016.20396988364, 2016.22313483915,
2016.24229979466, 2016.26146475017, 2016.28062970568, 2016.29979466119,
2016.3189596167, 2016.33812457221, 2016.35728952772, 2016.37645448323,
2016.39561943874, 2016.41478439425, 2016.43394934976, 2016.45311430527,
2016.47227926078, 2016.49144421629, 2016.5106091718, 2016.52977412731,
2016.54893908282, 2016.56810403833, 2016.58726899384, 2016.60643394935,
2016.62559890486, 2016.64476386037, 2016.66392881588, 2016.68309377139,
2016.7022587269, 2016.72142368241, 2016.74058863792, 2016.75975359343,
2016.77891854894, 2016.79808350445, 2016.81724845996, 2016.83641341547,
2016.85557837098, 2016.87474332649, 2016.893908282, 2016.91307323751,
2016.93223819302, 2016.95140314853, 2016.97056810404, 2016.98973305955,
2017.00889801506, 2017.02806297057, 2017.04722792608, 2017.06639288159,
2017.0855578371, 2017.10472279261, 2017.12388774812, 2017.14305270363,
2017.16221765914, 2017.18138261465, 2017.20054757016, 2017.21971252567,
2017.23887748118, 2017.25804243669, 2017.2772073922, 2017.29637234771,
2017.31553730322, 2017.33470225873, 2017.35386721424, 2017.37303216975,
2017.39219712526, 2017.41136208077, 2017.43052703628, 2017.44969199179,
2017.4688569473, 2017.48802190281, 2017.50718685832, 2017.52635181383,
2017.54551676934, 2017.56468172485, 2017.58384668036, 2017.60301163587,
2017.62217659138, 2017.64134154689, 2017.6605065024, 2017.67967145791,
2017.69883641342, 2017.71800136893, 2017.73716632444, 2017.75633127995,
2017.77549623546, 2017.79466119097, 2017.81382614648, 2017.83299110199,
2017.85215605749, 2017.871321013, 2017.89048596851, 2017.90965092402,
2017.92881587953, 2017.94798083504, 2017.96714579055, 2017.98631074606,
2018.00547570157, 2018.02464065708, 2018.04380561259, 2018.0629705681,
2018.08213552361, 2018.12046543463, 2018.13963039014, 2018.15879534565,
2018.17796030116, 2018.19712525667, 2018.21629021218, 2018.23545516769,
2018.2546201232, 2018.27378507871, 2018.29295003422, 2018.31211498973,
2018.33127994524, 2018.35044490075, 2018.36960985626, 2018.38877481177,
2018.40793976728, 2018.42710472279, 2018.4462696783, 2018.46543463381,
2018.48459958932, 2018.50376454483, 2018.52292950034, 2018.54209445585,
2018.56125941136, 2018.58042436687, 2018.59958932238, 2018.61875427789,
2018.6379192334, 2018.65708418891, 2018.67624914442, 2018.69541409993,
2018.71457905544, 2018.73374401095, 2018.75290896646, 2018.77207392197,
2018.79123887748, 2018.81040383299, 2018.8295687885, 2018.84873374401,
2018.86789869952, 2018.88706365503, 2018.90622861054, 2018.92539356605,
2018.94455852156, 2018.96372347707, 2018.98288843258, 2019.00205338809,
2019.0212183436, 2019.04038329911, 2019.05954825462, 2019.07871321013,
2019.09787816564, 2019.11704312115, 2019.13620807666, 2019.15537303217,
2019.17453798768, 2019.19370294319, 2019.2128678987, 2019.23203285421,
2019.25119780972, 2019.27036276523, 2019.28952772074, 2019.30869267625,
2019.32785763176, 2019.34702258727, 2019.36618754278, 2019.38535249829,
2019.4045174538, 2019.42368240931, 2019.44284736482, 2019.46201232033,
2019.48117727584, 2019.50034223135, 2019.51950718686, 2019.53867214237,
2019.55783709788, 2019.57700205339, 2019.5961670089, 2019.61533196441,
2019.63449691992, 2019.65366187543, 2019.67282683094, 2019.69199178645,
2019.71115674196, 2019.73032169747, 2019.74948665298, 2019.76865160849,
2019.787816564, 2019.80698151951, 2019.82614647502, 2019.84531143053,
2019.86447638604, 2019.88364134155, 2019.90280629706, 2019.92197125257,
2019.94113620808, 2019.96030116359, 2019.9794661191))
I am running a GAM that looks like this:
gam1<-gam(landings~s(Date))
I am using draw to plot my data:
draw(gam1)
I have been looking to figure out what the uncertainty is measured by in draw() with no success. Is this a 95% confidence interval or standard error that is used to plot the uncertainty in this plot?


It's an approximate 95% credible interval (drawn at 2 * the standard error of the smooth), the same as you'd get from mgcv:::plot.gam().
I should make this clearer, and allow users to control what coverage they want for the interval, in the package.

Related

Null values at the end of rows after INSERT INTO

I am currently trying to INSERT INTO my SQL database a row of 144 columns.
The problem is that the last 10 values of the new row are NULL while they are supposed to be float and int.
That's an example of what I have in my DB after the INSERT INTO :
First column
Before last column
Last column
1
NULL
NULL
That's the SQL request I am using
INSERT INTO "historic_data2"
VALUES (28438, 163, 156, 1, 'FIST 2', 91, 81, 82, 84, 90, 6, '2 Pts Int M', 'Offensive', 0, '91_81_82_84_90', 86, 85, 0, 36, 62, 24, 0, 132, 86, 0, 83, 0, 0, 0, 0, 42, 77, 24, 0, 173, 107, 0, 204, 0, 0, 0, 0, 42, 77, 24, 0, 173, 107, 0, 204, 0, 0, 0, 81, 62, 34, 23, 19, 45, 32, 18, 9, 19, 0.5555555555555556, 0.5161290322580645, 0.5294117647058824, 0.391304347826087, 1.0, 82, 54, 34, 18, 28, 49, 27, 17, 8, 28, 0.5975609756097561, 0.5, 0.5, 0.4444444444444444, 1.0, 302, 233, 132, 89, 69, 168, 116, 69, 35, 69, 0.5562913907284768, 0.4978540772532189, 0.5227272727272727, 0.39325842696629215, 1.0, 214, 161, 84, 73, 53, 119, 79, 39, 36, 53, 0.5560747663551402, 0.4906832298136646, 0.4642857142857143, 0.4931506849315068, 1.0, 717, 544, 298, 233, 173, 416, 285, 175, 97, 173, 0.5801952580195258, 0.5238970588235294, 0.587248322147651, 0.41630901287553645, 1.0, 466, 315, 183, 128, 151, 357, 233, 138, 91, 151, 0.7660944206008584, 0.7396825396825397, 0.7540983606557377, 0.7109375,1.0,112)
I can't figure out how to solve this issue. My guess would be that there is a hard limit on how much column you can insert at once but I don't know how to solve that.
Thank you in advance for your help

I need to filter the column from the beginning of a sentence

In my code, I can filter a column from exact texts, and it works without problems. However, it is necessary to filter another column with the beginning of a sentence.
The phrases in this column are:
A_2020.092222
A_2020.090787
B_2020.983898
B_2020.209308
So, I need to receive everything that starts with A_20 and B_20.
Thanks in advance
My code:
from bs4 import BeautifulSoup
import pandas as pd
import zipfile, urllib.request, shutil, time, csv, datetime, os, sys, os.path
#location
dt = datetime.datetime.now()
file_csv = "/home/Downloads/source.CSV"
file_csv_new = "/var/www/html/Data/Test.csv"
#open CSV
with open(file_csv, 'r', encoding='CP1251') as file:
reader = csv.reader(file, delimiter=';')
data = list(reader)
#list to dataframe
df = pd.DataFrame(data)
#filter UF
df = df.loc[df[9].isin(['PR','SC','RS'])]
#filter key
# A_ & B_
df = df.loc[df[35].isin(['A_20','B_20'])]
#print (df)
#Empty DataFrame
#Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]
#Index: []
#[0 rows x 119 columns]```

Give the following a try:
lst1 = ['A_2020.092222', 'A_2020.090787 ', 'B_2020.983898', 'B_2020.209308', 'C_2020.209308', 'D_2020.209308']
df = pd.DataFrame(lst1, columns =['Name'])
df.loc[df.Name.str.startswith(('A_20','B_20'))]

Preventing an animation from looping

I want my animation to only play once and not loop. My understanding is that you can do that by setting "next" to false. However, my animation is still looping. Here is my sprite sheet json file:
{
"images": [
"ressources/atlas/apparition.png"
],
"framerate": 12,
"frames": [
[1, 1, 170, 172, 0, -15, -15],
[1, 175, 164, 165, 0, -19, -18],
[1, 342, 156, 160, 0, -23, -21],
[159, 342, 147, 146, 0, -27, -28],
[167, 175, 134, 128, 0, -33, -37],
[173, 1, 122, 96, 0, -40, -52],
[173, 99, 96, 64, 0, -52, -68]
],
"animations": {
"apparition": { "frames": [6, 5, 4, 3, 2, 1, 0], "next": false }
}
}
Ideas?

Well... it seems that you must use gotoAndPlay() if you want to prevent looping. I was using play().

Convert ECC PKCS#8 public and private keys to traditional format

I have ECC public and private generated with BouncyCastle:
Security.addProvider(new org.bouncycastle.jce.provider.BouncyCastleProvider());
ECNamedCurveParameterSpec ecSpec = ECNamedCurveTable
.getParameterSpec("secp192r1");
KeyPairGenerator g = KeyPairGenerator.getInstance("ECDSA", "BC");
g.initialize(ecSpec, new SecureRandom());
KeyPair pair = g.generateKeyPair();
System.out.println(Arrays.toString(pair.getPrivate().getEncoded()));
System.out.println(Arrays.toString(pair.getPublic().getEncoded()));
byte[] privateKey = new byte[]{48, 123, 2, 1, 0, 48, 19, 6, 7, 42, -122, 72, -50, 61, 2, 1, 6, 8, 42, -122, 72, -50, 61, 3, 1, 1, 4, 97, 48, 95, 2, 1, 1, 4, 24, 14, 117, 7, -120, 15, 109, -59, -35, 72, -91, 99, -2, 51, -120, 112, -47, -1, -115, 25, 48, -104, -93, 78, -7, -96, 10, 6, 8, 42, -122, 72, -50, 61, 3, 1, 1, -95, 52, 3, 50, 0, 4, 64, 48, -104, 32, 41, 13, 1, -75, -12, -51, -24, -13, 56, 75, 19, 74, -13, 75, -82, 35, 1, -50, -93, -115, -115, -34, -81, 119, -109, -50, -39, -57, -20, -67, 65, -50, 66, -122, 96, 84, 117, -49, -101, 54, -30, 77, -110, -122}
byte[] publicKey = new byte[]{48, 73, 48, 19, 6, 7, 42, -122, 72, -50, 61, 2, 1, 6, 8, 42, -122, 72, -50, 61, 3, 1, 1, 3, 50, 0, 4, 64, 48, -104, 32, 41, 13, 1, -75, -12, -51, -24, -13, 56, 75, 19, 74, -13, 75, -82, 35, 1, -50, -93, -115, -115, -34, -81, 119, -109, -50, -39, -57, -20, -67, 65, -50, 66, -122, 96, 84, 117, -49, -101, 54, -30, 77, -110, -122}
How to convert them into traditional format which can be reused later in https://github.com/kmackay/micro-ecc/blob/master/uECC.h? I need 24 bytes private and 48 public key while now it is 125 and 75.

Gives 24 and 48, sometimes when 0 is added at the beginning 25 or 49:
ECPrivateKey ecPrivateKey = (ECPrivateKey)privateKey;
System.out.println(ecPrivateKey.getS().toByteArray().length);
ECPublicKey ecPublicKey = (ECPublicKey)publicKey;
System.out.println(ecPublicKey.getW().getAffineX().toByteArray().length + ecPublicKey.getW().getAffineY().toByteArray().length);

BytesArray. ObjC to Swift

I am trying to write Swift implementation of the following ObjC(header file) code.
#include <stddef.h>
#ifndef VO_CERTIFICATE_TYPE
#define VO_CERTIFICATE_TYPE
typedef struct _voCertificate
{
const char* bytes;
size_t length;
}
voCertificate;
#endif
static const char myCertificate_BYTES[] =
{
103, 92, -99, 33, 72, 48, 119, -72,
-77, 75, -88, 81, 113, -46, -119, -119,
5, 42, -33, 94, 23, 3, -112, 34,
-63, 75, -77, 26, -41, -69, 50, 71,
19, 121, 109, -60, 40, 18, 46, -86,
..........
};
voCertificate const myCertificate =
{
myCertificate_BYTES,
sizeof(myCertificate_BYTES)
};
//////////////////////////////////////
NSData *certificate = [NSData dataWithBytes:myCertificate.bytes length:myCertificate.length];
My best assumption was:
let myCertificate = [
103, 92, -99, 33, 72, 48, 119, -72,
-77, 75, -88, 81, 113, -46, -119, -119,
5, 42, -33, 94, 23, 3, -112, 34,
-63, 75, -77, 26, -41, -69, 50, 71,
19, 121, 109, -60, 40, 18, 46, -86,
........................]
var certificate = NSData(bytes: myCertificate as [Byte], length: myCertificate.count)
I tried to reach ObjC variable through Bridging-Header too, but there was "Undefined symbols for architecture armv7" error.
I would really appreciate any help.

Your biggest problem is that the type of your myCertificate array is Int not Int8. Here is something that is working for me. Note I reconstructed the array from the NSData object to see if everything came out ok.
let myCertificate = Array<Int8>(arrayLiteral:
103, 92, -99, 33, 72, 48, 119, -72,
-77, 75, -88, 81, 113, -46, -119, -119,
5, 42, -33, 94, 23, 3, -112, 34,
-63, 75, -77, 26, -41, -69, 50, 71,
19, 121, 109, -60, 40, 18, 46, -86)
var certificate = NSData(bytes: myCertificate, length: myCertificate.count)
var buffer = [Int8](count: certificate.length, repeatedValue: 0)
certificate.getBytes(&buffer, length: certificate.length)