%matplotlib inline
-import nibabel as nb
-nb.load('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz').orthoview();
+from nilearn.plotting import plot_anat
+%matplotlib inline
+
+
+plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', dim=-1);
Example of an already imple
-
@@ -11936,7 +11948,7 @@ Example of interface: FSL's BETIn [ ]:
BETIn [ ]:nb.load('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w_bet.nii.gz').orthoview();
+plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w_bet.nii.gz', dim=-1);
Example of interface: FSL's BETIn [ ]:
nb.load(res.outputs.out_file).orthoview();
+plot_anat(res.outputs.out_file, dim=-1);
The CommandLine interfaceIn [ ]:
nipype_ls = CommandLine('ls', args='-lh /data/ds000114', terminal_output='allatonce')
+nipype_ls = CommandLine('ls', args='-lh', terminal_output='allatonce')
The CommandLine interface
-Now, we have a Python object nipype_ls that is a runnable nipype interface. After execution, Nipype interface return a result object. We can retrieve the output of our ls invocation from the result.runtime property:
+Now, we have a Python object nipype_ls that is a runnable nipype interface. After execution, Nipype interface returns a result object. We can retrieve the output of our ls invocation from the result.runtime property:
@@ -12264,7 +12276,7 @@ The CommandLine interface
-Create your own CommandLine interface¶
Let's create a Nipype Interface for a very simple tool called antsTransformInfo from the ANTs package. This tool is so simple it does not even have an usage description for bash. Using it with a file, gives us the following result:
+Create your own CommandLine interface¶
Let's create a Nipype Interface for a very simple tool called antsTransformInfo from the ANTs package. This tool is so simple it does not even have a usage description for bash. Using it with a file, gives us the following result:
@@ -12298,20 +12310,20 @@ Create your own CommandLine
Reference Count: 3
Modified Time: 660
Debug: Off
- Object Name:
- Observers:
+ Object Name:
+ Observers:
none
- Matrix:
- 1.0201 -0.00984231 0.00283729
- -0.245557 0.916396 0.324585
- -0.0198016 -0.00296066 0.988634
+ Matrix:
+ 1.0201 -0.00984231 0.00283729
+ -0.245557 0.916396 0.324585
+ -0.0198016 -0.00296066 0.988634
Offset: [2.00569, -15.15, -1.26341]
Center: [-3.37801, 17.4338, 8.46811]
Translation: [1.79024, -13.0295, -1.34439]
- Inverse:
- 0.982713 0.0105343 -0.00627888
- 0.256084 1.09282 -0.359526
- 0.0204499 0.00348366 1.01029
+ Inverse:
+ 0.982713 0.0105343 -0.00627888
+ 0.256084 1.09282 -0.359526
+ 0.0204499 0.00348366 1.01029
Singular: 0
@@ -12421,7 +12433,7 @@ So let's plan our implementation:
-Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the intputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
+Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the inputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
The inputs are implemented using the Enthought traits package. For now, we'll use the File trait extension of nipype:
@@ -12590,7 +12602,7 @@ Specifying the inputs
-Nipype will make sure that the parameters fullfil their prescribed attributes. For instance in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
+Nipype will make sure that the parameters fulfill their prescribed attributes. For instance, in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
@@ -12700,7 +12712,7 @@ Specifying the outputsclass TransformInfoOutputSpec(TraitedSpec):
translation = traits.List(traits.Float, desc='the translation component of the input transform')
-
+
class TransformInfo(CommandLine):
_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
@@ -12835,20 +12847,20 @@ We are almost there - final needs
@@ -12863,7 +12875,7 @@ We are almost there - final needs
-We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
+We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
@@ -12878,25 +12890,25 @@ We are almost there - final needs_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13000,25 +13012,25 @@ Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13087,16 +13099,16 @@ Wrapping up
# Do not set exists=True for output files!
out_file = File(mandatory=True, argstr='%s', position=1, desc='the output image')
-
+
class CustomBETOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
mask_file = File(desc="path/name of binary brain mask (if generated)")
-
+
class CustomBET(CommandLine):
_cmd = 'bet'
input_spec = CustomBETInputSpec
output_spec = CustomBETOutputSpec
-
+
def _list_outputs(self):
# Get the attribute saved during _run_interface
@@ -13131,7 +13143,7 @@ Wrapping up
In [ ]:
-nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
@@ -13179,7 +13191,7 @@ Wrapping up
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
-Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
@@ -13194,21 +13206,21 @@ Create your own Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
@@ -13278,7 +13290,7 @@ Create your own Python in
In [ ]:
-nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
@@ -13326,7 +13338,7 @@ Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
@@ -13481,7 +13493,7 @@ Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
@@ -13699,7 +13711,7 @@ Complete approach - pure
It crashed with...
-NotImplementedError:
+NotImplementedError:
Now, we have a Python object nipype_ls that is a runnable nipype interface. After execution, Nipype interface return a result object. We can retrieve the output of our ls invocation from the result.runtime property:
Now, we have a Python object nipype_ls that is a runnable nipype interface. After execution, Nipype interface returns a result object. We can retrieve the output of our ls invocation from the result.runtime property:
CommandLine interface
Create your own CommandLine interface¶
Let's create a Nipype Interface for a very simple tool called antsTransformInfo from the ANTs package. This tool is so simple it does not even have an usage description for bash. Using it with a file, gives us the following result:
Create your own CommandLine interface¶
Let's create a Nipype Interface for a very simple tool called antsTransformInfo from the ANTs package. This tool is so simple it does not even have a usage description for bash. Using it with a file, gives us the following result:
Create your own CommandLine
Reference Count: 3
Modified Time: 660
Debug: Off
- Object Name:
- Observers:
+ Object Name:
+ Observers:
none
- Matrix:
- 1.0201 -0.00984231 0.00283729
- -0.245557 0.916396 0.324585
- -0.0198016 -0.00296066 0.988634
+ Matrix:
+ 1.0201 -0.00984231 0.00283729
+ -0.245557 0.916396 0.324585
+ -0.0198016 -0.00296066 0.988634
Offset: [2.00569, -15.15, -1.26341]
Center: [-3.37801, 17.4338, 8.46811]
Translation: [1.79024, -13.0295, -1.34439]
- Inverse:
- 0.982713 0.0105343 -0.00627888
- 0.256084 1.09282 -0.359526
- 0.0204499 0.00348366 1.01029
+ Inverse:
+ 0.982713 0.0105343 -0.00627888
+ 0.256084 1.09282 -0.359526
+ 0.0204499 0.00348366 1.01029
Singular: 0
@@ -12421,7 +12433,7 @@ So let's plan our implementation:
-Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the intputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
+Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the inputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
The inputs are implemented using the Enthought traits package. For now, we'll use the File trait extension of nipype:
@@ -12590,7 +12602,7 @@ Specifying the inputs
-Nipype will make sure that the parameters fullfil their prescribed attributes. For instance in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
+Nipype will make sure that the parameters fulfill their prescribed attributes. For instance, in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
@@ -12700,7 +12712,7 @@ Specifying the outputsclass TransformInfoOutputSpec(TraitedSpec):
translation = traits.List(traits.Float, desc='the translation component of the input transform')
-
+
class TransformInfo(CommandLine):
_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
@@ -12835,20 +12847,20 @@ We are almost there - final needs
@@ -12863,7 +12875,7 @@ We are almost there - final needs
-We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
+We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
@@ -12878,25 +12890,25 @@ We are almost there - final needs_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13000,25 +13012,25 @@ Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13087,16 +13099,16 @@ Wrapping up
# Do not set exists=True for output files!
out_file = File(mandatory=True, argstr='%s', position=1, desc='the output image')
-
+
class CustomBETOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
mask_file = File(desc="path/name of binary brain mask (if generated)")
-
+
class CustomBET(CommandLine):
_cmd = 'bet'
input_spec = CustomBETInputSpec
output_spec = CustomBETOutputSpec
-
+
def _list_outputs(self):
# Get the attribute saved during _run_interface
@@ -13131,7 +13143,7 @@ Wrapping up
In [ ]:
-nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
@@ -13179,7 +13191,7 @@ Wrapping up
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
-Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
@@ -13194,21 +13206,21 @@ Create your own Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
@@ -13278,7 +13290,7 @@ Create your own Python in
In [ ]:
-nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
@@ -13326,7 +13338,7 @@ Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
@@ -13481,7 +13493,7 @@ Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
@@ -13699,7 +13711,7 @@ Complete approach - pure
It crashed with...
-NotImplementedError:
+NotImplementedError:
So let's plan our implementation:
-Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the intputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
+Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the inputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
The inputs are implemented using the Enthought traits package. For now, we'll use the File trait extension of nipype:
@@ -12590,7 +12602,7 @@ Specifying the inputs
-Nipype will make sure that the parameters fullfil their prescribed attributes. For instance in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
+Nipype will make sure that the parameters fulfill their prescribed attributes. For instance, in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
@@ -12700,7 +12712,7 @@ Specifying the outputsclass TransformInfoOutputSpec(TraitedSpec):
translation = traits.List(traits.Float, desc='the translation component of the input transform')
-
+
class TransformInfo(CommandLine):
_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
@@ -12835,20 +12847,20 @@ We are almost there - final needs
@@ -12863,7 +12875,7 @@ We are almost there - final needs
-We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
+We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
@@ -12878,25 +12890,25 @@ We are almost there - final needs_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13000,25 +13012,25 @@ Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13087,16 +13099,16 @@ Wrapping up
# Do not set exists=True for output files!
out_file = File(mandatory=True, argstr='%s', position=1, desc='the output image')
-
+
class CustomBETOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
mask_file = File(desc="path/name of binary brain mask (if generated)")
-
+
class CustomBET(CommandLine):
_cmd = 'bet'
input_spec = CustomBETInputSpec
output_spec = CustomBETOutputSpec
-
+
def _list_outputs(self):
# Get the attribute saved during _run_interface
@@ -13131,7 +13143,7 @@ Wrapping up
In [ ]:
-nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
@@ -13179,7 +13191,7 @@ Wrapping up
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
-Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
@@ -13194,21 +13206,21 @@ Create your own Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
@@ -13278,7 +13290,7 @@ Create your own Python in
In [ ]:
-nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
@@ -13326,7 +13338,7 @@ Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
@@ -13481,7 +13493,7 @@ Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
@@ -13699,7 +13711,7 @@ Complete approach - pure
It crashed with...
-NotImplementedError:
+NotImplementedError:
Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the intputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
Specifying the inputs¶
However, the args argument is too generic and does not deviate much from just running it in bash, or directly using subprocess.Popen. Let's define the inputs specification for the interface, extending the nipype.interfaces.base.CommandLineInputSpec class.
The inputs are implemented using the Enthought traits package. For now, we'll use the File trait extension of nipype:
Specifying the inputs
-Nipype will make sure that the parameters fullfil their prescribed attributes. For instance in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
+Nipype will make sure that the parameters fulfill their prescribed attributes. For instance, in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
@@ -12700,7 +12712,7 @@ Specifying the outputsclass TransformInfoOutputSpec(TraitedSpec):
translation = traits.List(traits.Float, desc='the translation component of the input transform')
-
+
class TransformInfo(CommandLine):
_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
@@ -12835,20 +12847,20 @@ We are almost there - final needs
@@ -12863,7 +12875,7 @@ We are almost there - final needs
-We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
+We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
@@ -12878,25 +12890,25 @@ We are almost there - final needs_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13000,25 +13012,25 @@ Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
Nipype will make sure that the parameters fullfil their prescribed attributes. For instance in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
Nipype will make sure that the parameters fulfill their prescribed attributes. For instance, in_file is mandatory. An error is issued if we build the command line or try to run this interface without it:
We are almost there - final needs
@@ -12863,7 +12875,7 @@ We are almost there - final needs
-We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
+We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
@@ -12878,25 +12890,25 @@ We are almost there - final needs_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
@@ -13000,25 +13012,25 @@ Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
We need to complete the functionallity of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
We need to complete the functionality of the run() member of our interface to parse the standard output. This is done extending its _run_interface() member.
When we define outputs, generally they need to be explicitly wired in the _list_outputs() member of the core class. Let's see how we can complete those:
We are almost there - final needs_cmd = 'antsTransformInfo' input_spec = TransformInfoInputSpec output_spec = TransformInfoOutputSpec - + def _run_interface(self, runtime): import re - + # Run the command line as a natural CommandLine interface runtime = super(TransformInfo, self)._run_interface(runtime) # Search transform in the standard output expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]') trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')] - + # Save it for later use in _list_outputs setattr(self, '_result', trans) - + # Good to go return runtime - + def _list_outputs(self): - + # Get the attribute saved during _run_interface return {'translation': getattr(self, '_result')}
Summary of a CommandLine_cmd = 'antsTransformInfo'
input_spec = TransformInfoInputSpec
output_spec = TransformInfoOutputSpec
-
+
def _run_interface(self, runtime):
import re
-
+
# Run the command line as a natural CommandLine interface
runtime = super(TransformInfo, self)._run_interface(runtime)
# Search transform in the standard output
expr_tra = re.compile('Translation:\s+\[(?P<translation>[0-9\.-]+,\s[0-9\.-]+,\s[0-9\.-]+)\]')
trans = [float(v) for v in expr_tra.search(runtime.stdout).group('translation').split(', ')]
-
+
# Save it for later use in _list_outputs
setattr(self, '_result', trans)
-
+
# Good to go
return runtime
-
+
def _list_outputs(self):
-
+
# Get the attribute saved during _run_interface
return {'translation': getattr(self, '_result')}
Wrapping up
In [ ]:
-nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
@@ -13179,7 +13191,7 @@ Wrapping up
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
-Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
@@ -13194,21 +13206,21 @@ Create your own Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
@@ -13278,7 +13290,7 @@ Create your own Python in
In [ ]:
-nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
@@ -13326,7 +13338,7 @@ Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
@@ -13481,7 +13493,7 @@ Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
@@ -13699,7 +13711,7 @@ Complete approach - pure
It crashed with...
-NotImplementedError:
+NotImplementedError:
nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
Wrapping up
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
-Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
@@ -13194,21 +13206,21 @@ Create your own Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
Create your own Python interface¶
CommandLine interface is great, but my tool is already in Python - can I wrap it natively?
Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and than writes a resampled image after the translation has been applied:
+Sure. Let's solve the following problem: Let's say we have a Python function that takes an input image and a list of three translations (x, y, z) in mm, and then writes a resampled image after the translation has been applied:
Python in
import nibabel as nb
import numpy as np
from scipy.ndimage.interpolation import affine_transform
-
+
# Load the data
nii = nb.load(img)
data = nii.get_data()
-
+
# Create the transformation matrix
matrix = np.eye(3)
trans = (np.array(translation) / nii.header.get_zooms()[:3]) * np.array([1.0, -1.0, -1.0])
-
+
# Apply the transformation matrix
newdata = affine_transform(data, matrix=matrix, offset=trans)
-
+
# Save the new data in a new NIfTI image
nb.Nifti1Image(newdata, nii.affine, nii.header).to_filename(out_file)
-
+
print('Translated file now is here: %s' % out_file)
Create your own Python in
In [ ]:
-nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
@@ -13326,7 +13338,7 @@ Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
@@ -13481,7 +13493,7 @@ Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
@@ -13699,7 +13711,7 @@ Complete approach - pure
It crashed with...
-NotImplementedError:
+NotImplementedError:
nb.load(orig_image).orthoview()
+plot_anat(orig_image, dim=-1);
Create your own Python in
In [ ]:
-nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
@@ -13382,7 +13394,7 @@ Create your own Python in
-Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
+Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
@@ -13412,8 +13424,8 @@ Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
nb.load('translated.nii.gz').orthoview()
+plot_anat('translated.nii.gz', dim=-1);
Create your own Python in
Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If, like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
Quick approach - Function interface¶
Don't reinvent the wheel if it's not necessary. If like in this case, we have a well-defined function we want to run with Nipype, it is fairly easy to solve it with the Function interface:
Quick approach - Function
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
-For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
+For more, check out the Function Node notebook.
The arguments of translate_image should ideally be listed in the same order and with the same names as in the signature of the function. The same should be the case for the outputs. Finally, the Function interface takes a function input that is pointed to your python code.
Note: The inputs and outputs do not pass any kind of conformity checking: the function node will take any kind of data type for their inputs and outputs.
-There are some other limitations for the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
For more, checkout the Function Node notebook.
+There are some other limitations to the Function interface when used inside workflows. Additionally, the function must be totally self-contained, since it will run with no global context. In practice, it means that all the imported modules and variables must be defined within the context of the function.
For more, check out the Function Node notebook.
Quick approach - Function
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
@@ -13529,7 +13541,7 @@ Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
# Plot the result
-nb.load('translated_functioninterface.nii.gz').orthoview()
+plot_anat('translated_functioninterface.nii.gz', dim=-1);
Quick approach - Function
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
-Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
@@ -13547,7 +13559,7 @@ Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
Complete approach - pure Python interface¶
Now, we face the problem of interfacing something different from a command line. Therefore, the CommandLine base class will not help us here. The specification of the inputs and outputs, though, will work the same way.
Let's start from that point. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
+Let's start from that point on. Our Python function takes in three inputs: (1) the input image, (2) the translation and (3) an output image.
The specification of inputs and outputs must be familiar to you at this point. Please note that now, input specification is derived from BaseInterfaceInputSpec, which is a bit thinner than CommandLineInputSpec. The output specification can be derived from TraitedSpec as before:
Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
It crashed with... -NotImplementedError: +NotImplementedError:
Complete approach - pure class TranslateImage(BaseInterface):
input_spec = TranslateImageInputSpec
output_spec = TranslateImageOutputSpec
-
+
def _run_interface(self, runtime):
-
+
# Call our python code here:
translate_image(
self.inputs.in_file,
self.inputs.translation,
self.inputs.out_file
)
-
+
# And we are done
return runtime
class TranslateImage(BaseInterface):
input_spec = TranslateImageInputSpec
output_spec = TranslateImageOutputSpec
-
+
def _run_interface(self, runtime):
-
+
# Call our python code here:
translate_image(
self.inputs.in_file,
self.inputs.translation,
self.inputs.out_file
)
-
+
# And we are done
return runtime
Complete approach - pure
Translated file now is here: translated_nipype.nii.gz
It crashed with...
-NotImplementedError:
+NotImplementedError:
Translated file now is here: translated_nipype.nii.gz It crashed with... -NotImplementedError: +NotImplementedError:
Complete approach - pure out_file = File(mandatory=True, desc='the output image') # Do not set exists=True !!
translation = traits.List([50.0, 0.0, 0.0], traits.Float, usedefault=True,
desc='the translation component of the input transform')
-
+
class TranslateImageOutputSpec(TraitedSpec):
out_file = File(desc='the output image')
class TranslateImage(BaseInterface):
input_spec = TranslateImageInputSpec
output_spec = TranslateImageOutputSpec
-
+
def _run_interface(self, runtime):
# Call our python code here:
@@ -13907,7 +13919,7 @@ Complete approach - pure In [ ]:
Complete approach - pure In [ ]:
nb.load(result.outputs.out_file).orthoview()
+plot_anat(result.outputs.out_file, dim=-1);
Create your own MATLAB in
load input_image.mat;
total = sum(data(:) > 0)
-
load input_image.mat;
total = sum(data(:) > 0)The following example, uses scipy.io.savemat to convert the input image to MATLAB format. Once the file is loaded we can quickly extract the estimated total volume.
The following example uses scipy.io.savemat to convert the input image to MATLAB format. Once the file is loaded we can quickly extract the estimated total volume.
Note: For the purpose of this example, we will be using the freely available MATLAB alternative Octave. But the implementation of a MATLAB interface will be identical.
Preparation¶
class BrainVolumeMATLABInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True)
script_file = File(exists=True, mandatory=True)
-
+
class BrainVolumeMATLABOutputSpec(TraitedSpec):
volume = traits.Int(desc='brain volume')
@@ -14062,7 +14074,7 @@ Step by step implementation
-load input_image.mat;
+load input_image.mat;
total = sum(data(:) > 0)
load input_image.mat; +load input_image.mat; total = sum(data(:) > 0)