# Overflow encountered in double_scalars [SOLVED]

When working with numerical calculations, especially in programming and scientific computing, you may encounter the error message: “Overflow encountered in double_scalars“.

`RuntimeWarning: overflow encountered in double_scalar `

This error is common among many programming languages, including Python, and this error occurs when working with double-precision floating-point numbers, a common data type used to represent real numbers. While double-precision numbers offer high precision, they come with finite limits, and when these limits are breached, programmers encounter this “overflow” error. The error message is as follows:

In this article, we will explain what this problem implies, why and when it happens, how to fix it like using arbitrary precision arithmetic libraries or data types to handle huge numbers; applying numerical approaches to control exponential growth; rigorously validating and preparing data to handle incorrect input data, and checking algorithm design for numerical stability and provide important best practices to avoid error in the future.

## What is the Overflow Encountered in Double Scalars Error?

The error message “Overflow encountered in double_scalars” typically arises when performing arithmetic operations, such as addition, subtraction, multiplication, or division, on floating-point numbers (often represented as double data type) and the result exceeds the maximum or minimum representable value for that data type.

## What Causes the Error to Occur?

Depending on the specific code being used and the data being processed, many scenarios might result in the “Overflow encountered in double_scalars” issue. The following are some common situations where you could run into this error:

### 1. Multiplying Large Numbers

```import numpy as np
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
result = np.float64(1.0e34)
for i in range(1, 1000):
result = np.multiply(result, np.float64(2.0))
print (result)```

In this example, we use `'np.seterr(over='raise')'` to modify NumPy’s error handling to raise an error on overflow. We used NumPy’s `'np.float64'` data type to represent the result. We also use NumPy’s `'np.multiply'` function for multiplication to ensure that the result remains within NumPy’s numerical precision. However, after a few iterations, the result still becomes so large that it exceeds the maximum value that can be represented with double-precision floating-point numbers, triggering the “Overflow encountered in double_scalars” error.

### 2. Dividing by Very Small Numbers

```import numpy as np
np.seterr(all='warn')
value = np.float64(1.0)
for i in range(1, 1000):
value = np.divide(value, np.float64(1e-10))
print (value)```

In this case, we represent values using the NumPy `'np.float64'` data type, and we divide values using the NumPy `'np.divide'` function. Despite using NumPy, the result continues to approach positive infinity as the denominator gets smaller and smaller. Eventually, this causes the representational limitations to be exceeded and the “Overflow encountered in double_scalars” error to appear.

### 3. Accumulating Large Numbers

```import numpy as np
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
total = np.float64(0.0)
for i in range(1, 1000000):
total += np.float64(1e1056)  # Using a larger value for addition
print (total)```

In this code, we use NumPy’s `'np.float64'` data type to represent the total. We also set NumPy’s error handling to raise an error on overflows using `'np.seterr(over='raise')'`. As a result, the code will now trigger the “Overflow encountered in double_scalars” error when the total becomes too large due to the accumulation of small values.

### 4. Exponential Functions

```import numpy as np
np.seterr(over='raise')
x = np.float64(1000.0)
result = np.exp(x)
print (result)```

We represent x using the NumPy `'np.float64'` data type. Using `'np.seterr(over='raise')'`, we also used NumPy’s error handling to report an error when an overflow occurs. Now, when computing the exponential of a very big x, the code will produce the “Overflow encountered in double_scalars” error because the result exceeds the representational bounds of double-precision floating-point integers.

## Solutions to Fix the Error

Let’s look into each solution with code examples and explanations:

### 1. Using a Data Type with Greater Precision

To resolve this error, you can use a data type that has greater precision. Here’s the code:

```import numpy as np
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
result = np.float64(1.0)
for i in range(1, 1000):
result = np.multiply(result, np.float64(2.0))
print (result)```

Output:

Tip: To avoid overflow when multiplying large numbers, consider using a data type with greater precision, such as Python’s decimal module. This module provides arbitrary-precision decimal arithmetic.

### 2. Using Approximations or Limits

Another way to solve this issue is by using approximation or limits.

```import numpy as np
import math
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
x = np.float64(1000.0)
result = np.float64(0.0)
for n in range(0, 100):
term = np.power(x, n) / math.factorial(n)
result += term
print (result)```

Output:

In the code example, we calculate the exponential of x by approximating it using a Taylor series expansion. We use NumPy’s np.float64 data type and set NumPy’s error handling to raise on overflows to ensure that the code raises the “Overflow encountered in double_scalars” error if the result becomes too large.

Caution: Be cautious when dealing with large or small numbers in iterative calculations.

### 3. Implement a Check for Overflow

You can also implement a check for overflow beforehand to avoid the error. The code is as shown:

```import numpy as np
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
result = np.float64(1.0)
for i in range(1, 1000):
if result > 1e-308:  # A threshold to prevent overflow
raise OverflowError("Overflow encountered in double_scalars")
result = np.multiply(result, np.float64(2.0))
print (result)```

Output:

In this example, we still use NumPy’s np.float64 data type and have NumPy set up to raise errors on overflows for error handling. The loop now has an explicit check to determine whether the result has grown too big. If it exceeds a threshold (in this case, 1e-308), we raise an OverflowError with a custom error message to indicate that overflow has occurred.

### 4. Implement a Check for Extremely Small Denominators

```import numpy as np
# Set NumPy error handling to raise on overflows
np.seterr(over='raise')
value = np.float64(1.0)
for i in range(1, 1000):
if abs(value) < 1e100:  # Check for extremely small denominator
raise OverflowError("Overflow encountered in double_scalars")
value = np.divide(value, np.float64(1e-10))
print (value)```

Output:

In this code example, we use NumPy’s np.float64 data type and set NumPy’s error handling to raise on overflows. We have also added a check within the loop to detect when the value becomes extremely small. If the absolute value of value falls below a threshold (e.g., 1e100), we raise an OverflowError with a custom error message to indicate that overflow has occurred.

## Wrapping Up

The error message “Overflow encountered in double_scalars” typically occurs in programming, particularly in languages like Python, when there is an attempt to perform a mathematical operation that results in a value too large to be represented as a double-precision floating-point number. This error arises because double-precision floating-point numbers have a finite range, and when a result exceeds this range, it “overflows,” meaning it cannot be accurately represented. Understanding the causes and implications of this error, as well as employing appropriate solutions and best practices, is crucial for writing robust and reliable software. The following practices can be adopted to avoid errors:

1. Validate and pre-process input data rigorously. Implement data scaling or normalization techniques to keep data within reasonable calculation ranges.
2. Consider using specialized numerical libraries like GSL (GNU Scientific Library) for this purpose.
3. Choose data types (e.g., `'float'``'decimal'``'numpy.float64'`) that can handle the expected range of values in your calculations without sacrificing precision.