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How to force NumPy to always use a precision (float32, float64 …)?
2019 Community Moderator ElectionHow do I print a double value with full precision using cout?How can I force division to be floating point? Division keeps rounding down to 0?How can the Euclidean distance be calculated with NumPy?How to deal with floating point number precision in JavaScript?How to print the full NumPy array, without truncation?How to access the ith column of a NumPy multidimensional array?What should I worry about if I compress float64 array to float32 in numpy?Golang floating point precision float32 vs float64Python+numpy: a lucky floating point case?NumPy casts lower precision to higher precision in floating point arithmetic
I am trying to study a little FIR example written in Python. See https://scipy-cookbook.readthedocs.io/items/FIRFilter.html
My goal is to study how output precision varies for each float16, float32 and float64 (available in numpy). So for the first case I need to keep all my computations done in float16 only. The thing is i should each time cast the data to ensure that I'm using the right format. Is there a method to consistently use a unified context for the whole computations i.e. to perform all computations (additions, substractions, cos, sin ...etc) using float16 for example without re-writing code with casts?
python numpy floating-point precision
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
add a comment |
I am trying to study a little FIR example written in Python. See https://scipy-cookbook.readthedocs.io/items/FIRFilter.html
My goal is to study how output precision varies for each float16, float32 and float64 (available in numpy). So for the first case I need to keep all my computations done in float16 only. The thing is i should each time cast the data to ensure that I'm using the right format. Is there a method to consistently use a unified context for the whole computations i.e. to perform all computations (additions, substractions, cos, sin ...etc) using float16 for example without re-writing code with casts?
python numpy floating-point precision
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
1
one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
1
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49
add a comment |
I am trying to study a little FIR example written in Python. See https://scipy-cookbook.readthedocs.io/items/FIRFilter.html
My goal is to study how output precision varies for each float16, float32 and float64 (available in numpy). So for the first case I need to keep all my computations done in float16 only. The thing is i should each time cast the data to ensure that I'm using the right format. Is there a method to consistently use a unified context for the whole computations i.e. to perform all computations (additions, substractions, cos, sin ...etc) using float16 for example without re-writing code with casts?
python numpy floating-point precision
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
I am trying to study a little FIR example written in Python. See https://scipy-cookbook.readthedocs.io/items/FIRFilter.html
My goal is to study how output precision varies for each float16, float32 and float64 (available in numpy). So for the first case I need to keep all my computations done in float16 only. The thing is i should each time cast the data to ensure that I'm using the right format. Is there a method to consistently use a unified context for the whole computations i.e. to perform all computations (additions, substractions, cos, sin ...etc) using float16 for example without re-writing code with casts?
python numpy floating-point precision
python numpy floating-point precision
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
asked Mar 6 at 14:31
noureddine-asnoureddine-as
236
236
1
one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
1
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49
add a comment |
1
one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
1
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49
1
1
one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
1
1
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49
add a comment |
1 Answer
1
active
oldest
votes
From the numpy basics:
When operating with arrays of different types, the type of the
resulting array corresponds to the more general or precise one (a
behavior known as upcasting).
You can define the data type on the array creation. Applying a sum, multiplication or substraction, result will upcast to the "larger" type, it will also keep the dtype if you perform operations on the array, e.g.:
x = np.ones(10, dtype=np.float16)
y = np.ones(10, dtype=np.float32)
print((x + y).dtype, (x - y).dtype, (x * y).dtype)
print(np.sin(x).dtype, np.sin(y).dtype)
>> float32 float32 float32
float16 float32
An exception is when passing an integer, in which case, by default, numpy upcasts to float64
print(np.sin(np.ones(10, dtype=int)).dtype)
>> float64
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
add a comment |
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From the numpy basics:
When operating with arrays of different types, the type of the
resulting array corresponds to the more general or precise one (a
behavior known as upcasting).
You can define the data type on the array creation. Applying a sum, multiplication or substraction, result will upcast to the "larger" type, it will also keep the dtype if you perform operations on the array, e.g.:
x = np.ones(10, dtype=np.float16)
y = np.ones(10, dtype=np.float32)
print((x + y).dtype, (x - y).dtype, (x * y).dtype)
print(np.sin(x).dtype, np.sin(y).dtype)
>> float32 float32 float32
float16 float32
An exception is when passing an integer, in which case, by default, numpy upcasts to float64
print(np.sin(np.ones(10, dtype=int)).dtype)
>> float64
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
add a comment |
From the numpy basics:
When operating with arrays of different types, the type of the
resulting array corresponds to the more general or precise one (a
behavior known as upcasting).
You can define the data type on the array creation. Applying a sum, multiplication or substraction, result will upcast to the "larger" type, it will also keep the dtype if you perform operations on the array, e.g.:
x = np.ones(10, dtype=np.float16)
y = np.ones(10, dtype=np.float32)
print((x + y).dtype, (x - y).dtype, (x * y).dtype)
print(np.sin(x).dtype, np.sin(y).dtype)
>> float32 float32 float32
float16 float32
An exception is when passing an integer, in which case, by default, numpy upcasts to float64
print(np.sin(np.ones(10, dtype=int)).dtype)
>> float64
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
add a comment |
From the numpy basics:
When operating with arrays of different types, the type of the
resulting array corresponds to the more general or precise one (a
behavior known as upcasting).
You can define the data type on the array creation. Applying a sum, multiplication or substraction, result will upcast to the "larger" type, it will also keep the dtype if you perform operations on the array, e.g.:
x = np.ones(10, dtype=np.float16)
y = np.ones(10, dtype=np.float32)
print((x + y).dtype, (x - y).dtype, (x * y).dtype)
print(np.sin(x).dtype, np.sin(y).dtype)
>> float32 float32 float32
float16 float32
An exception is when passing an integer, in which case, by default, numpy upcasts to float64
print(np.sin(np.ones(10, dtype=int)).dtype)
>> float64
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
From the numpy basics:
When operating with arrays of different types, the type of the
resulting array corresponds to the more general or precise one (a
behavior known as upcasting).
You can define the data type on the array creation. Applying a sum, multiplication or substraction, result will upcast to the "larger" type, it will also keep the dtype if you perform operations on the array, e.g.:
x = np.ones(10, dtype=np.float16)
y = np.ones(10, dtype=np.float32)
print((x + y).dtype, (x - y).dtype, (x * y).dtype)
print(np.sin(x).dtype, np.sin(y).dtype)
>> float32 float32 float32
float16 float32
An exception is when passing an integer, in which case, by default, numpy upcasts to float64
print(np.sin(np.ones(10, dtype=int)).dtype)
>> float64
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
answered Mar 6 at 14:57
MstainoMstaino
1,8451412
1,8451412
add a comment |
add a comment |
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one way to do it as a 1-time hassle, is to assign a dtype to all your operations using a variable, that you can then set and change at top of the file as needed.
– Paritosh Singh
Mar 6 at 14:33
1
See Issue #6860: How to set float32 as default.
– jdehesa
Mar 6 at 14:49