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// RF Front End Settings
// Note: The use of these values completely depends on how the PCB is laid out.
//       Please see Device Package and Evaluation Module (EM) Board below.
#define RF_FE_DIFFERENTIAL              0
#define RF_FE_SINGLE_ENDED_RFP          1
#define RF_FE_SINGLE_ENDED_RFN          2
#define RF_FE_ANT_DIVERSITY_RFP_FIRST   3
#define RF_FE_ANT_DIVERSITY_RFN_FIRST   4
#define RF_FE_SINGLE_ENDED_RFP_EXT_PINS 5
#define RF_FE_SINGLE_ENDED_RFN_EXT_PINS 6

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#ifndef USE_DEFAULT_USER_CFG

#include "ble_user_config.h"

// BLE user defined configuration
//bleUserCfg_t user0Cfg = BLE_USER_CFG;


#define MY_RF_FE_MODE_AND_BIAS          ( RF_FE_DIFFERENTIAL | RF_FE_INT_BIAS)

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package main

import (
	"fmt"
	"log"
	"time"

	"golang.org/x/sys/windows/registry"
)

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// Copyright 2017 The Walk Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package main

import (
	"fmt"
	"log"
	"os"

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python C:\Python27\Scripts\hexmerge.py -o .\SOMETHING_MERGED.hex -r 0000:1FFFF .\SOMETHING_APP.hex:0000:1FFFF .\SOMETHING_STACK.hex --overlap=error

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int 8_t b = -10;  // b = 0xf6 for 2's complement 
b = b << 1;       // b=0xec 
b = b >> 1;       // Now b = 0x76 and for demical b = 118 unexpect ouput 

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int 8_t b = -10;  // b = 0xf6 for 2's complement 
b = b << 1;       // b=0xec 
b = b >> 1;       // Now b = 0x76 and for demical b = 118 unexpect ouput 

// The correct way to do that operation

b = b&0x80 | b>>1;// b = 0xf6 for demical b = 10

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utils.importFile("../../../../../src/common/cc26xx/kernel/cc2640/config/cc2640.cfg");
/*
* Extend the cc2640 configuration
*/
var SysStd = xdc.useModule("xdc.runtime.SysStd");
var System = xdc.useModule("xdc.runtime.System");
System.SupportProxy = SysStd;


System.extendedFormats = '%$L%$S%$F%f';

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import tensorflow as tf
iport numpy as np
from tensorflow import keras
# Create an 1*1 layer neuron 
model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])

# Setting optimizer and loss function 
model.compile(optimizer='sgd', loss='mean_squared_error')

# Training data 

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import tensorflow as tf

# Callback function to check model accuracy
class RayCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.99):
      print("\nReached 99% accuracy so cancelling training!")
      self.model.stop_training = True

# Load the MNIST handwrite digit data set