louismullie

-- Function to split the input query and generate all contiguous combinations
CREATE FUNCTION split_to_table(@input_query VARCHAR(MAX))
RETURNS @result TABLE (combination VARCHAR(MAX))
AS
BEGIN
    DECLARE @words TABLE (id INT IDENTITY(1,1), word VARCHAR(255))
    DECLARE @total_words INT, @i INT, @j INT, @current_combination VARCHAR(MAX)

    -- Step 1: Splitting the input string into words
    INSERT INTO @words (word)

louismullie

import re
from struct import iter_unpack
from quopri import decodestring

import magic
import matplotlib.pyplot as plt
import numpy as np

# Constants
UNITS = {

louismullie

Hey dahani9091 (ChatGPT project)

louismullie

novo_test = pd.read_csv("test.csv")
novo_test

df_cavity = pd.read_csv('/content/gdrive/MyDrive/Kaggle/cavity_pred_CUSTOM_A.csv', low_memory=False)
df_cavity = df_cavity.rename(columns={'variant': 'mutation_key'})
df_cavity

novo_test2 = novo_test.copy().rename({'protein_sequence': 'mutant_seq', 'seq_id': 'source_df_id'}, axis = 1)
novo_test2['sequence'] = 'VPVNPEPDATSVENVALKTGSGDSQSDPIKADLEVKGQSALPFDVDCWAILCKGAPNVLQRVNEKTKNSNRDRSGANKGPFKDPQKWGIKALPPKNPSWSAQDFKSPEEYAFASSLQGGTNAILAPVNLASQNSQGGVLNGFYSANKVAQFDPSKPQQTKGTWFQITKFTGAAGPYCKALGSNDKSVCDKNKNIAGDWGFDPAKWAYQYDEKNNKFNYVGK'
novo_test2 = novo_test2.apply(find_mut,axis=1)

louismullie

// Requête no. 1
{
  "header": {},
  "body": {
    "selectors": [
      {
        "resource": "Patient",
        "filters": [
          { "path": "code", 
            "operator": "matches",

louismullie

def auc_roc(y_true, y_pred):
# can be any tensorflow metric
value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)
    # find all variables created for this metric
    metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]
    
    # Add metric variables to GLOBAL_VARIABLES collection.
    # They will be initialized for new session.
    for v in metric_vars:
    tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)

louismullie

Import libraries (tensorflow backend)

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np

louismullie

# Lab data
query = "SELECT lab_data.patient_site_uid, lab_name, lab_sample_time, lab_result_value, lab_sample_type from lab_data " + \
  " INNER JOIN patient_data ON " + \
  " lab_data.patient_site_uid = patient_data.patient_site_uid WHERE " + \
  " (lab_data.lab_sample_type = 'venous_blood' OR " + \
  " lab_data.lab_sample_type = 'arterial_blood' OR " + \
  " lab_data.lab_sample_type = 'unspecified_blood') AND " + \
  " lab_data.lab_result_status = 'resulted' " + \
  inclusion_flag
  

louismullie

const R = require('r-script')
const express = require('express')
const app = module.exports = express()
 
app.use(app.router)

app.get("/dashboard", function (req, res, next) {
  res.send("Some HTML with D3.js code")
})

louismullie

{
  
  // The type of resource
  "resourceType" : "Observation",
  
  // The status of the observation
  "status": "final",
  
  // Time of the observation
  "effectiveDateTime": "YYYY-MM-DDThh:mm:ss+zz:zz",