Merge pull request #3 from ZviBaratz/dev

Integrated CircleCI and codecov

README.md

@@ -1,13 +1,13 @@
-[![Documentation Status](https://readthedocs.org/projects/dicom-parser/badge/?version=latest)](http://dicom-parser.readthedocs.io/?badge=latest)
 [![PyPI version](https://img.shields.io/pypi/v/dicom_parser.svg)](https://pypi.python.org/pypi/pylabber/)
 [![PyPI status](https://img.shields.io/pypi/status/dicom_parser.svg)](https://pypi.python.org/pypi/pylabber/)
-![Coverage](coverage.svg)
+[![CircleCI](https://circleci.com/gh/ZviBaratz/dicom_parser.svg?style=shield)](https://app.circleci.com/pipelines/github/ZviBaratz/dicom_parser)
+[![codecov.io](https://codecov.io/gh/ZviBaratz/dicom_parser/coverage.svg?branch=master)](https://codecov.io/github/ZviBaratzabbingProject/dicom_parser?branch=master)
+[![Documentation Status](https://readthedocs.org/projects/dicom-parser/badge/?version=latest)](http://dicom-parser.readthedocs.io/?badge=latest)
 
 # dicom_parser
 
 _dicom_parser_ is a utility python package meant to facilitate access to
-[DICOM](https://www.dicomstandard.org/) header information by extending the functionality of
-_[pydicom]_.
+[DICOM](https://www.dicomstandard.org/) header information by extending the functionality of _[pydicom]_.
 
 Essentially, _dicom_parser_ uses [DICOM](https://www.dicomstandard.org/)'s
 [data-element](https://northstar-www.dartmouth.edu/doc/idl/html_6.2/DICOM_Attributes.html)
@@ -23,7 +23,7 @@ For more information, please see [the documentation].
 
 To install the latest version of `dicom_parser`, simply run:
 
-```shell
+```bash
     pip install dicom_parser
 ```
 
@@ -36,10 +36,8 @@ an [Image](https://dicom-parser.readthedocs.io/en/latest/modules/dicom_parser.ht
 instance.
 
 ```python
-    from dicom_parser import Image
-
-    # Create a DICOM Image object
-    image = Image('/path/to/dicom/file.dcm')
+    >>> from dicom_parser import Image
+    >>> image = Image('/path/to/dicom/file.dcm')
 ```
 
 ### Coversion to Python's native types
@@ -55,153 +53,150 @@ Decimal String (DS) to _float_ using the [Header] class's
 method:
 
 ```python
-    raw_value = image.header.raw['ImagingFrequency'].value
-    raw_value
-    >> "123.25993"
-    type(raw_value)
-    >> str
-
-    parsed_value = image.header.get('ImagingFrequency')
-    parsed_value
-    >> 123.25993
-    type(parsed_value)
-    >> float
+    >>> raw_value = image.header.raw['ImagingFrequency'].value
+    >>> raw_value
+    "123.25993"
+    >>> type(raw_value)
+    str
+
+    >>> parsed_value = image.header.get('ImagingFrequency')
+    >>> parsed_value
+    123.25993
+    >>> type(parsed_value)
+    float
 ```
 
 Age String (AS) to _float_:
 
 ```python
-    raw_value = image.header.raw['PatientAge'].value
-    raw_value
-    >> "027Y"
-    type(raw_value)
-    >> str
-
-    parsed_value = image.header.get('PatientAge')
-    parsed_value
-    >> 27.0
-    type(parsed_value)
-    >> float
+    >>> raw_value = image.header.raw['PatientAge'].value
+    >>> raw_value
+    "027Y"
+    >>> type(raw_value)
+    str
+
+    >>> parsed_value = image.header.get('PatientAge')
+    >>> parsed_value
+    27.0
+    >>> type(parsed_value)
+    float
 ```
 
 Date String (DA) to _[datetime.date]_ using the [Header] class's
 indexing operator/subscript notation:
 
 ```python
-    raw_value = image.header.raw['PatientBirthDate'].value
-    raw_value
-    >> "19901214"
-    type(raw_value)
-    >> str
-
-    parsed_value = image.header['PatientBirthDate']
-    parsed_value
-    >> datetime.date(1990, 12, 14)
-    type(parsed_value)
-    >> datetime.date
+    >>> raw_value = image.header.raw['PatientBirthDate'].value
+    >>> raw_value
+    "19901214"
+    >>> type(raw_value)
+    str
+
+    >>> parsed_value = image.header['PatientBirthDate']
+    >>> parsed_value
+    datetime.date(1990, 12, 14)
+    >>> type(parsed_value)
+    datetime.date
 ```
 
 Code String (CS) to a verbose value or set of values:
 
 ```python
-    raw_value = image.header.raw['SequenceVariant'].value
-    raw_value
-    >> ['SP', 'OSP']
-    type(raw_value)
-    >> pydicom.multival.MultiValue
-
-    parsed_value = image.header['SequenceVariant']
-    parsed_value
-    >> {'Oversampling Phase', 'Spoiled'}
-    type(parsed_value)
-    >> set
+    >>> raw_value = image.header.raw['SequenceVariant'].value
+    >>> raw_value
+    ['SP', 'OSP']
+    >>> type(raw_value)
+    pydicom.multival.MultiValue
+
+    >>> parsed_value = image.header['SequenceVariant']
+    >>> parsed_value
+    {'Oversampling Phase', 'Spoiled'}
+    >>> type(parsed_value)
+    set
 ```
 
 Et cetera.
 
 > The _dict_-like functionality also includes safe getting:
 >
 > ```python
->     image.header.get('MissingKey')
->     >> None
->     image.header.get('MissingKey', 'DefaultValue')
->     >> 'DefaultValue'
+>     >>> image.header.get('MissingKey')
+>     None
+>     >>> image.header.get('MissingKey', 'DefaultValue')
+>     'DefaultValue'
 > ```
 >
 > As well as raising a KeyError for missing keys with the indexing operator:
 >
 > ```python
->     image.header['MissingKey']
->     >> ...
->     >> KeyError: "The keyword: 'MissingKey' does not exist in the header!"
+>     >>> image.header['MissingKey']
+>     KeyError: "The keyword: 'MissingKey' does not exist in the header!"
 > ```
 
 ### Read DICOM series directory as a `Series`
 
 Another useful class this package offers is the `Series` class:
 
 ```python
-    from dicom_parser import Series
-
-    series = Series('/some/dicom/series/')
+    >>> from dicom_parser import Series
+    >>> series = Series('/some/dicom/series/')
 ```
 
 The `Series` instance allows us to easily
 query the underlying images' headers using its `get` method:
 
 ```python
     # Single value
-    series.get('EchoTime')
-    >> 3.04
+    >>> series.get('EchoTime')
+    3.04
 
     # Multiple values
-    series.get('InstanceNumber')
-    >> [1, 2, 3]
+    >>> series.get('InstanceNumber')
+    [1, 2, 3]
 
     # No value
-    series.get('MissingKey')
-    >> None
+    >>> series.get('MissingKey')
+    None
 
     # Default value
-    series.get('MissingKey', 'default_value')
-    >> 'default_value'
+    >>> series.get('MissingKey', 'default_value')
+    'default_value'
 ```
 
 Similarly to the `Image` class, we can also use the indexing operator:
 
 ```python
     # Single value
-    series['RepetitionTime']
-    >> 7.6
+    >>> series['RepetitionTime']
+    7.6
 
     # Multiple values
-    series['SOPInstanceUID']
-    >> ["1.123.1241.123124124.12.1",
-        "1.123.1241.123124124.12.2",
-        "1.123.1241.123124124.12.3"]
+    >>> series['SOPInstanceUID']
+    ["1.123.1241.123124124.12.1",
+     "1.123.1241.123124124.12.2",
+     "1.123.1241.123124124.12.3"]
 
     # No value
-    series['MissingKey']
-    ...
+    >>> series['MissingKey']
     KeyError: "The keyword: 'MissingKey' does not exist in the header!"
 ```
 
 Another useful feature of the indexing operator is for querying an `Image` instance based on its index in the series:
 
 ```python
-    series[6]
-    >> dicom_parser.image.Image
-    series[6].header['InstanceNumber]
-    >> 7   # InstanceNumber is 1-indexed
+    >>> series[6]
+    dicom_parser.image.Image
+    >>> series[6].header['InstanceNumber]
+    7   # InstanceNumber is 1-indexed
 ```
 
 The `data` property returns a stacked volume of the images' data:
 
 ```python
-    type(series.data)
-    >>> numpy.ndarray
-    series.data.shape
-    >> (224, 224, 208)
+    >>> type(series.data)
+    numpy.ndarray
+    >>> series.data.shape
+    (224, 224, 208)
 ```
 
 #### Siemens 4D data
@@ -211,9 +206,9 @@ Reading Siemens 4D data
 is also supported:
 
 ```python
-    fmri_series = Series('/path/to/dicom/fmri/')
-    fmri_series.data.shape
-    >> (96, 96, 64, 200)
+    >>> fmri_series = Series('/path/to/dicom/fmri/')
+    >>> fmri_series.data.shape
+    (96, 96, 64, 200)
 ```
 
 [datetime.date]: https://docs.python.org/3/library/datetime.html#available-types