Dapid

import numpy as np
import keras_cv


model = keras_cv.models.RetinaNet(backbone=keras_cv.models.MobileNetV3Backbone.from_preset('mobilenet_v3_small_imagenet'),
                                              num_classes=2,
                                              bounding_box_format="xywh",
                                              )
image= np.random.randint(0, 255, size=(1, 640, 480, 3), dtype=np.uint8)
model(image)

Dapid

import numpy as np
import scipy.optimize as spo
import scipy.stats as sps
import matplotlib.pyplot as plt

x, y = np.array([[3.16275414, 3.79136358],
                 [3.06332232, 3.56686702],
                 [2.71045949, 3.65764056],
                 [3.31620986, 3.9009491 ],
                 [3.0538026 , 3.77374607],

Dapid

import pandas as pd
import pylab as plt

df_allcauses = pd.read_excel('datasetfinalcorrected3.xlsx', sheet_name='Table 2', header=4, nrows=38)
df_covid = pd.read_excel('datasetfinalcorrected3.xlsx', sheet_name='Table 1', header=4, nrows=38)


for k in df_covid.keys():
    if k.startswith('Age-st'):
        df_covid[k] = pd.to_numeric(df_covid[k], errors='coerce').fillna(0)

Dapid

import numpy as np
import matplotlib.pyplot as plt

import cartopy.crs as ccrs
import cartopy.feature as cfeature


lon = 10 + 15 * np.random.random(30)
lat = 55 + 15 * np.random.random(30)
data = np.random.randn(30)

Dapid

# Run this to get the example file

import tables
import numpy as np

h5 = tables.open_file('onefile.h5', 'w', filters=tables.Filters(8, 'lzo'))
g = h5.create_group(h5.root, 'data')

h5.create_carray(g, 'data_array', obj=np.random.random((int(1e6), 20)))
h5.close()

Dapid

[Version]
AppVersion=5.7
Version=346

[General]
Rank=-1
ColorLabel=0
InTrash=false

[Exposure]

Dapid

\version "2.19.83"
\language "espanol"

\header {
  title = "Amazing Grace"
  composer = "Trad. Scottish"
  %tagline = ""
}

Dapid

import numpy as np
import netCDF4 as nc
from scipy import stats

import pylab as plt
import seaborn as sns

rootgrp = nc.Dataset('HadCRUT.4.6.0.0.median.nc')  

t = rootgrp['time'][:] / 365 + 1850

Dapid

import os

# os.environ['CUDA_VISIBLE_DEVICES'] = '1'

import time

import numpy

import jax.numpy as np
from jax import random, grad, jit

Dapid

import time

import numpy

import jax.numpy as np
from jax import random, grad, jit
from jax import vmap


def _compute_single_loss(h, J, sigma, N, lambda_h, lambda_j):