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kandi X-RAY | xxy Summary

xxy is a JavaScript library typically used in User Interface, Menu applications. xxy has no bugs, it has no vulnerabilities and it has low support. However xxy has a Non-SPDX License. You can download it from GitHub.

xxy

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xxy has a low active ecosystem.

It has 84 star(s) with 17 fork(s). There are 1 watchers for this library.

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There are 1 open issues and 1 have been closed. On average issues are closed in 340 days. There are no pull requests.

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Installation instructions, examples and code snippets are available.

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kandi has reviewed xxy and discovered the below as its top functions. This is intended to give you an instant insight into xxy implemented functionality, and help decide if they suit your requirements.

Called on a banner
Initialize a new FastClick event .
Core core implementation .
function setup banner
set multiple other styles
Add a style element to head .
Play playback to play
start slider
Change the index of the first group
Clean up the box

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xxy Key Features

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Community Discussions

Trending Discussions on xxy

Plotting continuous distribution in horizontal bar plot

Multiple variable distribution plot using ggplot2

parsing nested semi structured json data with pandas and json_normalize with null value

Iterate through upper triangular matrix in Python

Use numpy vectorize or map to speed up a loop - Python NumPy 3D matrix "get rid of a loop" Python question, Monte Carlo

Pandas (with def and np.where): error with values in a dataframe row conditioned on another dataframe row

Database does not exist when migration create in mikroORM

Scala 3 macros trait arguments

Union inside json_agg with json_build_object

How can I get a list of Tensorflow Dataset objects from a directory, each containing images from just one ID?

QUESTION

Plotting continuous distribution in horizontal bar plot

Asked 2022-Mar-14 at 18:59

This was my earlier question where it was solved using multiple distribution.

I want to plot the continuous variable like age or tumor mutation burden as shown in first figure with a range like a window such 20-30 age group or some mutational burden range

The frequencies are calculated for all the variables of the metadata, but when plotting the age is not mapped to the final plot as show in the second plot.

Does the age need to be converted into other class before plotting?

...

ANSWER

Answered 2022-Mar-14 at 09:14

Rename Diagnosis-Age and use cut to convert to a factor. Add labels as required for appearance of age groups in legend.

Note I have swapped name and perc in the call to aes to avoid the call to coord_flip.

Source https://stackoverflow.com/questions/71463663

QUESTION

Multiple variable distribution plot using ggplot2

Asked 2022-Mar-02 at 12:15

I have different categorical variable which I would like to show in terms of distribution.

So in my data-frame I have like 147 patients and their traits such as age,gender,disease subtypes etc etc.

This is my dataframe subset

...

ANSWER

Answered 2022-Mar-02 at 12:15

library(tidyverse)
library(ggnewscale)

plot_meta <- structure(list(
  patient = structure(c(
    36L, 33L, 122L, 95L, 66L,
    49L
  ), .Label = c(
    "TCGA-AB-2805", "TCGA-AB-2806", "TCGA-AB-2808",
    "TCGA-AB-2810", "TCGA-AB-2811", "TCGA-AB-2812", "TCGA-AB-2813",
    "TCGA-AB-2814", "TCGA-AB-2815", "TCGA-AB-2817", "TCGA-AB-2818",
    "TCGA-AB-2819", "TCGA-AB-2820", "TCGA-AB-2821", "TCGA-AB-2822",
    "TCGA-AB-2823", "TCGA-AB-2825", "TCGA-AB-2826", "TCGA-AB-2828",
    "TCGA-AB-2830", "TCGA-AB-2834", "TCGA-AB-2835", "TCGA-AB-2836",
    "TCGA-AB-2839", "TCGA-AB-2840", "TCGA-AB-2841", "TCGA-AB-2842",
    "TCGA-AB-2843", "TCGA-AB-2844", "TCGA-AB-2845", "TCGA-AB-2846",
    "TCGA-AB-2847", "TCGA-AB-2849", "TCGA-AB-2851", "TCGA-AB-2853",
    "TCGA-AB-2856", "TCGA-AB-2857", "TCGA-AB-2858", "TCGA-AB-2859",
    "TCGA-AB-2861", "TCGA-AB-2862", "TCGA-AB-2863", "TCGA-AB-2865",
    "TCGA-AB-2866", "TCGA-AB-2867", "TCGA-AB-2869", "TCGA-AB-2870",
    "TCGA-AB-2871", "TCGA-AB-2872", "TCGA-AB-2873", "TCGA-AB-2874",
    "TCGA-AB-2875", "TCGA-AB-2876", "TCGA-AB-2877", "TCGA-AB-2878",
    "TCGA-AB-2880", "TCGA-AB-2881", "TCGA-AB-2882", "TCGA-AB-2883",
    "TCGA-AB-2884", "TCGA-AB-2885", "TCGA-AB-2886", "TCGA-AB-2888",
    "TCGA-AB-2889", "TCGA-AB-2890", "TCGA-AB-2891", "TCGA-AB-2892",
    "TCGA-AB-2893", "TCGA-AB-2894", "TCGA-AB-2895", "TCGA-AB-2896",
    "TCGA-AB-2897", "TCGA-AB-2898", "TCGA-AB-2899", "TCGA-AB-2900",
    "TCGA-AB-2901", "TCGA-AB-2908", "TCGA-AB-2910", "TCGA-AB-2911",
    "TCGA-AB-2912", "TCGA-AB-2913", "TCGA-AB-2914", "TCGA-AB-2915",
    "TCGA-AB-2916", "TCGA-AB-2917", "TCGA-AB-2918", "TCGA-AB-2919",
    "TCGA-AB-2920", "TCGA-AB-2921", "TCGA-AB-2924", "TCGA-AB-2925",
    "TCGA-AB-2927", "TCGA-AB-2928", "TCGA-AB-2929", "TCGA-AB-2930",
    "TCGA-AB-2931", "TCGA-AB-2932", "TCGA-AB-2933", "TCGA-AB-2934",
    "TCGA-AB-2935", "TCGA-AB-2936", "TCGA-AB-2937", "TCGA-AB-2938",
    "TCGA-AB-2939", "TCGA-AB-2940", "TCGA-AB-2941", "TCGA-AB-2942",
    "TCGA-AB-2943", "TCGA-AB-2944", "TCGA-AB-2946", "TCGA-AB-2948",
    "TCGA-AB-2949", "TCGA-AB-2950", "TCGA-AB-2952", "TCGA-AB-2955",
    "TCGA-AB-2956", "TCGA-AB-2959", "TCGA-AB-2963", "TCGA-AB-2965",
    "TCGA-AB-2966", "TCGA-AB-2970", "TCGA-AB-2971", "TCGA-AB-2973",
    "TCGA-AB-2975", "TCGA-AB-2976", "TCGA-AB-2977", "TCGA-AB-2979",
    "TCGA-AB-2980", "TCGA-AB-2981", "TCGA-AB-2982", "TCGA-AB-2983",
    "TCGA-AB-2984", "TCGA-AB-2986", "TCGA-AB-2987", "TCGA-AB-2988",
    "TCGA-AB-2990", "TCGA-AB-2991", "TCGA-AB-2992", "TCGA-AB-2994",
    "TCGA-AB-2995", "TCGA-AB-2996", "TCGA-AB-2998", "TCGA-AB-2999",
    "TCGA-AB-3000", "TCGA-AB-3001", "TCGA-AB-3002", "TCGA-AB-3007",
    "TCGA-AB-3008", "TCGA-AB-3009", "TCGA-AB-3011", "TCGA-AB-3012"
  ), class = "factor"), Sex = structure(c(2L, 2L, 1L, 1L, 2L, 2L), .Label = c("Female", "Male"), class = "factor"), FAB = structure(c(
    5L,
    1L, 5L, 3L, 2L, 4L
  ), .Label = c(
    "M0", "M1", "M2", "M3", "M4",
    "M5", "M6", "M7", "nc"
  ), class = "factor"), `Diagnosis-Age` = c(
    63L,
    39L, 76L, 62L, 42L, 42L
  ), `Bone-Marrow-Blast-Percentage` = c(
    82L,
    83L, 91L, 72L, 68L, 88L
  ), Cytogenetics = structure(c(
    75L, 93L,
    51L, 27L, 21L, 57L
  ), .Label = c(
    "37~49,XY,+Y,der(1)add(1)(p13)del(1)(q21q25),-5,der(7)inv(7)(p15q11.2)?inv(7)(q22q32),+17,add(17)(p13),+21,+mar[cp20]",
    "39~47,XX,del(5)(q13q33),-7,der(8)t(8;?8;8)(p23;?p11.2p23;q11.2),der(14)t(1;14)(p12;p11.2)der(1)t(7;16)(p15;q22),+2mar[cp19]",
    "41~44,X,?i(X)(p10),-7,der(12)t(8;12)(q11.2;p11.2),-8 [cp11]/46,XX[8[",
    "42,XY,-5,-7,add(12)(p13),t(14;15)(q10;q10),der(17)t(5;17)(p13;p11.2),-18[6]/40,idem,-11,-add(12)(p13),der(12)t(?;12)(?;p13),-19[6]/41,idem,-der(17)[3]/41,idem,-der(17),+mar1,+mar[3]/41,idem,der(1)der(1)(p12)add(1)(p12),+der(1)(q21)add(1)(q21),-3,-8[2]",
    "43,XY-3,del(5)(q12q33),-7,der(10)t(10;11)(q26;q13),-12,-18,+2mar[20]",
    "44-45,X,-Y,-5,add(16)(q22),-17,-18,iso(21),+mars[cp5]/82-84,XX,-Y,-3,-4,-11,-12,-19,-21,+21[cp5}",
    "44~46,XX,del(11)(q23),der(19)?t(11;19)(q23;p13.1)[cp11]/44~45,XX,-19[cp4]/46,XX [5]",
    "44~47,XX,t(1;15)(q32;q26)[14],del(5)(q13q33)[19],-7[20],+8[7],del(12)(p11.2p11.2)[15],del(17)(q21)[8],der(22)t(1;22)(p13;p11.2)[20],+mar[13][cp20]",
    "44~47,XY,del(5)(q22q35)[20],-7[14],-8[6],der(12)t(10;12)(p11.2q21)[2],add(14)(p12)[11],-17[13],der(17)t(10;17)(q11.2;p13)[14],-18[7],add(18)(p11.2)[7],-21[10],i(21)(q10)[4],-22[4],+mar[10],+mar1x2[6][cp20]",
    "45,X,-X,t(8;21)(q22;q22)[20]", "45,X,-Y, t(8;7;21)(q22;p15;q22[22]/46,XY[3]",
    "45,X,-Y,t(8;21)(q22;q22)[13]/45,idem,del(9)(q22;q32)[7]", "45,X,-Y,t(8;21)(q22;q22)[19]/46,XY[1]",
    "45,X,-Y[3]/46,XY [17]", "45,XX-7[5]-only 5 metaphases", "45,XX,-7,t(9;11)(p22;q23)[19]/46,XX[1]",
    "45,XX,-7[12]/46,XX[8]", "45,XX,-7[20]", "45,XY,-7, t(9;22)(q34;q11.20) [19]/46,XY[1]",
    "45,XY,-7[20]", "45,XY,der(7)(t:7;12)(p11.1;p11.2),-12,-13,+mar[19]/46,XY[1]",
    "45~46,XY,add(X)(q22)[7],Y[4],der(5)t(5;17)(q13;21)[18],-7[18],+8[17],del(12)(q23)[16],-17[18],add(18)(p11.2)[14][cp18]",
    "46, XX[14]", "46, XX[15]", "46, XX[16]", "46, XX[19]", "46, XX[20]",
    "46, XY[15]", "46, XY[20]", "46,XX,1~50dmin[12]/46,idem,der(6)t(6;?)(q22;?)[2]/46,XX[6]",
    "46,XX,9qh+[20]", "46,XX,del(3)(q23q26.2),der(7)t(1:7)(q32;q32),del(10)(q22q25),t(13;16)(q34;p11.2)dup(21)(q22)[cp20]",
    "46,XX,del(5)(q11.2q33)[1]/48~52,idem,+1,+?del(5)(q15q33),+11,+11,?t(12;22)(p13;q12),-13,-17,+i(22)(q10),+i(22)(q10),+mar[cp19]",
    "46,XX,del(5)(q22q33)[4]/46,XX[16]", "46,XX,i(17)(q10)[1]/45,sl-7[2]/48,sl,+13,+19[3]/46,XX[15]",
    "46,XX,inv(16)(p13q22)[15]/46,XX[2]", "46,XX,inv(16)(p13q22)[19]/46,XX[1]",
    "46,XX,inv(16)(p13q22)[20]", "46,XX,inv(16)(p13q22)[5]/46,idem,t(3;3)(p13;q?28)[5]/46,XX[6]",
    "46,XX,t(15;17)(q22;q21.1)[19]/47,idem,+8 [1]", "46,XX,t(15;17)(q22;q21),t(16;19)(p13.3;p13.1)[17]/46,XX[3]",
    "46,XX,t(15;17)(q22;q21)[11]/46,XX[9]", "46,XX,t(15;17)(q22;q21)[12]/46,XX[8]",
    "46,XX,t(15;17)(q22;q21)[20]", "46,XX,t(8;21)(q22;q22)[17]/46,XX[3]",
    "46,XX,t(8;21)(q22;q22)[20]", "46,XX,t(8;21)[15]/46,idem,del(9)(q12q22)[5]",
    "46,XX[15]", "46,XX[18]", "46,XX[19]/46,XX,add(7)(p?22)[1]",
    "46,XX[20]", "46,XX+13,21[cp17]/46,XX[3]", "46,XY,9qh+[19]",
    "46,XY,del(11)(p12)[2]/46,XY[18]", "46,XY,del(20)(q11.2)[23]/92,XXYY,del(20)(q11.2)x2[2]/46,XY[3]",
    "46,XY,del(7)(q21q36)[18]/46,XY[2]", "46,XY,del(9)(q13:q22),t(11:21)(p13;q22),t(15;17)(q22;q210[20]",
    "46,XY,i(17)(q10)[15]/47,XY,idem+13[3]/46,XY[2]", "46,XY,inv(16)(p13;q22)[20]",
    "46,XY,inv(16)(p13q22)[17]/46,XY[3]", "46,XY,inv(16)(p13q22)[9]/46,XY[10]",
    "46,XY,t(11;19)(q23;p13)[17]/46,XY,t(11;19)(q23;p13),inv(12)(p12p13)[3]",
    "46,XY,t(11;19)(q23;p13)[20]", "46,XY,t(15;17)(q22;q21)[19]/46,XY[1]",
    "46,XY,t(15;17)(q22;q21)[20]", "46,XY,t(15;17)(q22:q21)[11]/46,XY[9]",
    "46,XY,t(2;4)(q34;q21)inv(16)(p13q22) [20]", "46,XY,t(6;11)(q27;q23)[15]",
    "46,XY,t(9;11)(p22;q23)[7]/47,XY,t(9;11)(p22;q23)[7]/46,XY[4]",
    "46,XY,t(9;22)(q34;q11.2)[13]/34~37,idem,-3,del(4),-4,-5,-7,-9,-10,t?(11;12),-12,-14,-14,-16,-17,-22[cp6]/46,XY[1]",
    "46,XY,t(9;22)(q34;q11.2[4]/50,idem,+8,+10,+21,+der(22)(t(9;22)(q34;q11.2)[16]",
    "46,XY[13]", "46,XY[15]", "46,XY[19]", "46,XY[20]", "46,XY[30]",
    "46~49,XY,del(3)(p14),del(5)(p11.2q33),del(17)(q21q21),add(21)(p11.2),+22,mar[cp20]",
    "47,XX,+der(5)t(2;5)(p11.2;q11.2)?,t(8;16)(p11.2;p13.3)[19]",
    "47,XX,i(11)(q10)[18]/46,XX [2]", "47,XX,t(15;17)(q22:q21)+mar[20]",
    "47,XX+11 [20]", "47,XX+8 [20]", "47,XXY [17]", "47,XY,+13[5]/46,XY[15]",
    "47,XY,+21 [6]/46,XY[13]", "47,XY,+21[11]/48,XY,+3,+21[8]", "47,XY,+22[10]/47,XY,+8[7]/45,XY,del(3)(p21),del(4)(p12p15),-7,?dup(7)(q11.2q36)[3]",
    "47,XY,+8 [10]/46,XY [10]", "47,XY,+8 [19]", "47,XY,+8 [20]",
    "47,XY,+8[15]/46,+8,-17[3]", "47,XY,+9[10]/46,XY[10]", "47,XY,del(5)(q22q33),t(10;11)(p13~p15;q22~23),i(17)(q10)[3]/46,XY[17]",
    "47,XY,del(7)(q22),+8,t(15;17)(q22;q21)[18]/46,XY,del(7)(q22),t(15;17)(q22;q21)[2]",
    "47,XY+8 [15]/48,XY+8+8[4]/46,XY[1]", "48,XY,+8,+8[16]/46,XY[4]",
    "52~54,XY,+2,+4,+6,+8,del(11)(q23),+19,+19,+21[17]/46,XY[3]",
    "53~56,XY,+1,del(2)(q33q34),+8,+10,+11x2,+13x1-2,+14,del(17)(p11.2),+19,add(21)(q22),+22[cp20]",
    "incomplete-46,XY,del(12)(p11.20[2]/46,XY[3]", "N.D.", "ND",
    "Outside hospital with inv(16)"
  ), class = "factor"), `Cytogenetic-Code--Other-` = structure(c(
    8L,
    3L, 8L, 8L, 3L, 9L
  ), .Label = c(
    "BCR-ABL1", "CBFB-MYH11", "Complex Cytogenetics",
    "Intermediate Risk Cytogenetic Abnormality", "MLL translocation, poor risk",
    "MLL translocation, t(9;11)", "N.D.", "Normal Karyotype", "PML-RARA",
    "Poor Risk Cytogenetic Abnormality", "RUNX1-RUNX1T1"
  ), class = "factor"),
  Induction = structure(c(11L, 4L, 1L, 8L, 4L, 9L), .Label = c(
    "7+3",
    "7+3, dauna", "7+3, IT", "7+3+3", "7+3+3, gleevec", "7+3+3, then 5+2+2",
    "7+3+3+PSC", "7+3+AMD", "7+3+ATRA", "7+3+dauno", "7+3+Genasense",
    "7+3+study drug", "7+4+ATRA", "Azacitidine", "CLAM", "Cytarabine only",
    "Decitabine", "Decitabine then 7+3", "Hydrea & Idarubicin",
    "Hydrea, ATRA started", "hydrea, didn't get add'l chemo",
    "LBH/Decitabine", "low dose Ara C", "no treatment", "Revlimid",
    "Revlmd then Decitbne,7+3,5+2"
  ), class = "factor")
), row.names = c(
  NA,
  6L
), class = "data.frame")

df <-
  plot_meta %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(everything()) %>%
  count(name, value) %>%
  group_by(name) %>%
  mutate(perc = n / sum(n) * 100)
df
#> # A tibble: 38 × 4
#> # Groups:   name [8]
#>    name                         value                                    n  perc
#>                                                             
#>  1 Bone-Marrow-Blast-Percentage 68                                       1  16.7
#>  2 Bone-Marrow-Blast-Percentage 72                                       1  16.7
#>  3 Bone-Marrow-Blast-Percentage 82                                       1  16.7
#>  4 Bone-Marrow-Blast-Percentage 83                                       1  16.7
#>  5 Bone-Marrow-Blast-Percentage 88                                       1  16.7
#>  6 Bone-Marrow-Blast-Percentage 91                                       1  16.7
#>  7 Cytogenetic-Code--Other-     Complex Cytogenetics                     2  33.3
#>  8 Cytogenetic-Code--Other-     Normal Karyotype                         3  50  
#>  9 Cytogenetic-Code--Other-     PML-RARA                                 1  16.7
#> 10 Cytogenetics                 45,XY,der(7)(t:7;12)(p11.1;p11.2),-…     1  16.7
#> # … with 28 more rows

df %>%
  ggplot(aes(name, perc)) +
  geom_col(data = ~ filter(.x, name == "FAB") %>% rename(FAB = value), mapping = aes(fill = FAB)) +
  new_scale_fill() +
  geom_col(data = ~ filter(.x, name == "Sex") %>% rename(Sex = value), mapping = aes(fill = Sex)) +
  new_scale_fill() +
  geom_col(data = ~ filter(.x, name == "Induction") %>% rename(Induction = value), mapping = aes(fill = Induction)) +
  coord_flip()

Source https://stackoverflow.com/questions/71318781

QUESTION

parsing nested semi structured json data with pandas and json_normalize with null value

Asked 2022-Feb-02 at 05:54

I am trying to flatten a semi-structured json dataset having the following structure, and containing null values for some key/values.

A sample of this dataset is the following:

...

ANSWER

Answered 2022-Feb-02 at 05:54

I ended converting it to a csv file:

Source https://stackoverflow.com/questions/70898069

QUESTION

Iterate through upper triangular matrix in Python

Asked 2021-Oct-31 at 17:41

I am using Python and I have a XxY matrix where X=Y and I want to iterate over the upper triangular matrix in a specific way such that it starts with and proceeds with and and so on and so forth until the last row and column. Therefore, I tried to create a double loop which loops over the columns one by one and within that loop I created another loop which loops over the rows always adding one row. However, I got stuck in defining how to add the next row for every column in the second loop. Here is what I got so far (for simplicity I just created an array of zeros):

...

ANSWER

Answered 2021-Oct-31 at 17:41

With Numpy, you can get the indices for the upper triangular matrix with triu_indices_from and index into the array with that:

Source https://stackoverflow.com/questions/69789032

QUESTION

Use numpy vectorize or map to speed up a loop - Python NumPy 3D matrix "get rid of a loop" Python question, Monte Carlo

Asked 2021-Oct-29 at 10:51

Now I have 1 loop that populates a 3D NumPy matrix. I'm not exactly the best at understanding a 3D array structure even though I know it's really just a XxYxZ representation of the normal XxY that I'm used to thinking in (2D). So if you want to know what this is it is a Brownian Bridge (BB) construction used in Monte Carlo simulations for financial problems. Credit for the original code (derived from the commentary which fixed the original post by author Kenta Oono located here): https://gist.github.com/delta2323/6bb572d9473f3b523e6e. You don't really need to know anything about the math behind it; it just basically chops up a path of steps (21 in this example), begins at 0, has normally distributed shocks (hence np.random.randn) applied until it reaches the end, which is also 0. Each path is applied to a simulated price to randomly "shock it" over time, generating a potential path the asset could follow on its way to expiration. Although these are totally uncorrelated, so I suppose I would pass a V matrix in as well to correlate the paths to be correct, however, let us keep it simple:

...

ANSWER

Answered 2021-Oct-29 at 10:51

One simple solution to speed up this code is to parallelize it using Numba. You only need to use the decorator @nb.njit('float64[:,:,::1](int64, int64, int64)', parallel=True) for the function sample_path_batches (where nb is the Numba module). Note that dtype=float must be replaced with dtype=np.float64 in the function so that Numba can compile the code correctly. Note that parallel=True should automatically parallelize the np.random.randn call as well as the basic following operation in the loop. On a 10-core machine this is 7 times faster (it takes 0.253 second with Numpy and 0.036 with a parallel implementation of Numba). If you do not see any improvement, you could also try to parallelize it manually using prange.

Additionally, you can use np.float32 types for significantly faster performance (up to 2 times faster theoretically). However, Numpy do not currently support such types for np.random.randn. Instead, np.random.default_rng().random(size=underlyings*sims, dtype=np.float32).reshape(underlyings, sims) should be used. Unfortunately, it is probably not yet supported by Numba since Numpy add this quite recently...

If you have an Nvidia GPU, another solution is to use CUDA to execute the function on the GPU. This should be much faster. Note that Numba have specific optimized functions to generate random np.float32 values on the GPU using CUDA (see here).

Source https://stackoverflow.com/questions/69747403

QUESTION

Pandas (with def and np.where): error with values in a dataframe row conditioned on another dataframe row

Asked 2021-Oct-07 at 17:07

I have dataframes A of shape XxY with values and dataframes B of shape ZxY to be filled with statistics calculated from A.

As an example:

...

ANSWER

Answered 2021-Oct-07 at 17:07

I found the error. I erroneously had the same label twice in the index. Essentially my dataframe B was something like:

Source https://stackoverflow.com/questions/69483057

QUESTION

Database does not exist when migration create in mikroORM

Asked 2021-Oct-04 at 09:53

I tried to mikroORM create migrations but it seems that I cannot create the table itself. I don't know what I missed and the error says database "crm" does not exist.

Please see code below:

mikro-config.ts

...

ANSWER

Answered 2021-Sep-28 at 11:06

You need to create a postgres database separately. Mikro-orm will not create a database for you.

Create a postgres database with name "crm" and then run and apply migrations your tables will be created.

Source https://stackoverflow.com/questions/69338561

QUESTION

Scala 3 macros trait arguments

Asked 2021-Jul-17 at 16:53

Here is the basic setup:

...

ANSWER

Answered 2021-Jul-17 at 15:03

If you do

Source https://stackoverflow.com/questions/68412253

QUESTION

Union inside json_agg with json_build_object

Asked 2021-May-21 at 09:57

CREATE TABLE test1 (
    id integer, value text);

INSERT INTO test1
    VALUES (1, 'xyz'), (2, 'xxy');

CREATE TABLE test2 (
    id integer, value text);

INSERT INTO test2
    VALUES (3, 'yyy'), (4, 'yxy');

...

ANSWER

Answered 2021-May-21 at 09:57

Don't put the subquery with the union in the json_agg(), but in the FROM statement:

Source https://stackoverflow.com/questions/67471391

QUESTION

How can I get a list of Tensorflow Dataset objects from a directory, each containing images from just one ID?

Asked 2021-Apr-28 at 07:33

In Using Keras APIs, how can I import images in batches with exactly K instances of each ID in a given batch?, the answer from Dmytro Prylipko requires that I have a list of tf.data.Dataset objects to pass into tf.data.Dataset.zip.

I need each tf.data.Dataset object to only contain only instances of one ID, and for there to be an equal number of tf.data.Dataset objects as there are IDs.

My data consists of images imported from a directory using the following structure:

...

ANSWER

Answered 2021-Apr-28 at 07:33

Using the answers in the following links, I have come up with an example to implement this requirement:

TensorFlow: training on my own image

Create tensorflow dataset from image local directory

https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices

Example implementation:

Given the following directory structure:

Source https://stackoverflow.com/questions/67288581

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