adnipy package¶
Submodules¶
adnipy.adni module¶
Pandas dataframe extension for ADNI.
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class
adnipy.adni.
ADNI
(pandas_dataframe)[source]¶ Bases:
object
Methods
drop_dynamic
(self)Remove images which are dynamic. groups
(self[, grouped_mci])Create a dataframe for each group and save it to a csv file. longitudinal
(self)Keep only longitudinal data. rid
(self)Add a roster ID column. standard_column_names
(self)Rename dataframe columns to module standard. standard_dates
(self)Change type of date columns to datetime. standard_index
(self[, index])Process dataframes into a standardized format. timepoints
(self[, second])Extract timepoints from a dataframe. -
drop_dynamic
(self)[source]¶ Remove images which are dynamic.
Drops all rows, in which the Description contains ‘Dynamic’.
Returns: - pd.DataFrame
All images that are not dynamic.
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groups
(self, grouped_mci=True)[source]¶ Create a dataframe for each group and save it to a csv file.
Parameters: - grouped_mci : bool, default True
If true, ‘LMCI’ and ‘EMCI’ are treated like ‘MCI’. However, the original values will stills be in the output.
Returns: - dict
Dictionnairy with a dataframe for each group.
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longitudinal
(self)[source]¶ Keep only longitudinal data.
This requires an ‘RID’ or ‘Subject ID’ column in the dataframe. Do not use if multiple images are present for a single timepoint.
Parameters: - images : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
A dataframe with only longitudinal data.
See also
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rid
(self)[source]¶ Add a roster ID column.
Will not work if ‘RID’ is already present or ‘Subject ID’ is missing.
Returns: - pd.DataFrame
Dataframe with a ‘RID’ column.
Examples
>>> subjects = {"Subject ID": ["100_S_1000", "101_S_1001"]} >>> collection = pd.DataFrame(subjects) >>> collection Subject ID 0 100_S_1000 1 101_S_1001 >>> collection.rid() Subject ID RID 0 100_S_1000 1000 1 101_S_1001 1001
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standard_column_names
(self)[source]¶ Rename dataframe columns to module standard.
This function helps when working with multiple dataframes, since the same data can have different names. It will also call rid() on the dataframe.
Returns: - pd.DataFrame
This will have standardized columns names.
See also
Examples
>>> subjects = pd.DataFrame({"Subject": ["101_S_1001", "102_S_1002"]}) >>> subjects Subject 0 101_S_1001 1 102_S_1002 >>> subjects.standard_column_names() Subject ID RID 0 101_S_1001 1001 1 102_S_1002 1002
>>> images = pd.DataFrame({"Image": [100001, 100002]}) >>> images Image 0 100001 1 100002 >>> images.standard_column_names() Image ID 0 100001 1 100002
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standard_dates
(self)[source]¶ Change type of date columns to datetime.
Returns: - pd.DataFrame
Dates will have the appropriate dtype.
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standard_index
(self, index=None)[source]¶ Process dataframes into a standardized format.
The output is easy to read. Applying functions the the output may not work as expected.
Parameters: - index : list of str, default None
These columns will be the new index.
Returns: - pd.DataFrame
An easy to read dataframe for humans.
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adnipy.adnipy module¶
Process ADNI study data with adnipy.
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adnipy.adnipy.
drop_dynamic
(images)[source]¶ Remove images which are dynamic.
Drops all rows, in which the Description contains ‘Dynamic’.
Parameters: - images : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
All images that are not dynamic.
-
adnipy.adnipy.
get_matching_images
(left, right)[source]¶ Match different scan types based on closest date.
The columns ‘Subject ID’ and ‘SCANDATE’ are required.
Parameters: - left : pd.DataFrame
Dataframe containing the tau scans.
- right : pd.DataFrame
Dataframe containing the mri scans.
Returns: - pd.DataFrame
For each timepoint there is a match from both inputs.
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adnipy.adnipy.
groups
(collection, grouped_mci=True)[source]¶ Create a dataframe for each group and save it to a csv file.
Parameters: - collection : pd.DataFrame
DataFrame has to have a Group column.
- grouped_mci : bool, default True
If true, ‘LMCI’ and ‘EMCI’ are treated like ‘MCI’. However, the original values will stills be in the output.
Returns: - dict
Dictionnairy with a dataframe for each group.
-
adnipy.adnipy.
longitudinal
(images)[source]¶ Keep only longitudinal data.
This requires an ‘RID’ or ‘Subject ID’ column in the dataframe. Do not use if multiple images are present for a single timepoint.
Parameters: - images : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
A dataframe with only longitudinal data.
See also
-
adnipy.adnipy.
read_csv
(file)[source]¶ Return a csv file as a pandas.DataFrame.
Recognizes missing values used in the ADNI database.
Parameters: - file : str, pathlib.Path
The path to the .csv file.
Returns: - pd.DataFrame
Returns the file as a dataframe.
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adnipy.adnipy.
rid
(collection)[source]¶ Add a roster ID column.
Will not work if ‘RID’ is already present or ‘Subject ID’ is missing.
Parameters: - collection : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
Dataframe with a ‘RID’ column.
Examples
>>> collection = pd.DataFrame({"Subject ID": ["100_S_1000", "101_S_1001"]}) >>> collection Subject ID 0 100_S_1000 1 101_S_1001 >>> rid(collection) Subject ID RID 0 100_S_1000 1000 1 101_S_1001 1001
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adnipy.adnipy.
standard_column_names
(dataframe)[source]¶ Rename dataframe columns to module standard.
This function helps when working with multiple dataframes, since the same data can have different names. It will also call rid() on the dataframe.
Parameters: - dataframe : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
This will have standardized columns names.
See also
Examples
>>> subjects = pd.DataFrame({"Subject": ["101_S_1001", "102_S_1002"]}) >>> subjects Subject 0 101_S_1001 1 102_S_1002 >>> standard_column_names(subjects) Subject ID RID 0 101_S_1001 1001 1 102_S_1002 1002
>>> images = pd.DataFrame({"Image": [100001, 100002]}) >>> images Image 0 100001 1 100002 >>> standard_column_names(images) Image ID 0 100001 1 100002
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adnipy.adnipy.
standard_dates
(dataset)[source]¶ Change type of date columns to datetime.
Parameters: - dataset : pd.DataFrame
This dataframe will be modified.
Returns: - pd.DataFrame
Dates will have the appropriate dtype.
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adnipy.adnipy.
standard_index
(df, index=None)[source]¶ Process dataframes into a standardized format.
The output is easy to read. Applying functions the the output may not work as expected.
Parameters: - df : pd.DataFrame
This dataframe will be modified.
- index : list of str, default None
These columns will be the new index.
Returns: - pd.DataFrame
An easy to read dataframe for humans.
adnipy.data module¶
Process data created in Matlab.
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adnipy.data.
image_id_from_filename
(filename)[source]¶ Extract image ID of single ADNI .nii filename.
Images from the ADNI database have a specific formatting. Using regular expressions the image ID can be extracted from filenames.
Parameters: - filename : str
It must contain the Image ID at the end.
Returns: - numpy.int64
Image as a integer.
Examples
>>> image_id_from_filename("*_I123456.nii") 123456
Module contents¶
Process ADNI study data with adnipy.