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Using numpy assets when possible, and formatted with black

master
Gabriele Girelli 4 years ago
parent
commit
55627552d4
1 changed files with 63 additions and 25 deletions
  1. +63
    -25
      nd2reader/parser.py

+ 63
- 25
nd2reader/parser.py View File

@ -247,27 +247,37 @@ class Parser(object):
"""
return {channel: n for n, channel in enumerate(self.metadata["channels"])}
def _check_unwanted_bytes(self, image_group_data, image_data_start, height, width):
def _get_unwanted_bytes_ids(
self, image_group_data, image_data_start, height, width
):
# Check if the byte array size conforms to the image axes size. If not, check
# that the number of unexpected (unwanted) bytes is a multiple of the number of
# rows (height), as the same unmber of unwanted bytes is expected to be
# appended at the end of each row. Then, returns the indexes of the unwanted
# bytes.
number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
n_unwanted_bytes = (len(image_group_data[image_data_start:]))%(height*width)
n_unwanted_bytes = (len(image_group_data[image_data_start:])) % (height * width)
if not n_unwanted_bytes:
return False
assert 0 == n_unwanted_bytes % height, "An unexpected number of extra bytes was encountered based on the expected frame size, therefore the file could not be parsed."
byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-n_unwanted_bytes+1, height*number_of_true_channels)
all_zero_bytes = all([0 == image_group_data[byte_ids[i]+i] for i in range(len(byte_ids))])
if not all_zero_bytes:
raise Exception(f"{n_unwanted_bytes} unexpected non-zero bytes were found in the ND2 file, the file could not be parsed.")
return all_zero_bytes
def _remove_unwanted_bytes(self, image_group_data, image_data_start, height, width):
# Remove unwanted 0-bytes that can appear in stitched images
number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
n_unwanted_bytes = (len(image_group_data[image_data_start:]))%(height*width)
unwanted_byte_per_step = n_unwanted_bytes // height
byte_ids = range(image_data_start+height*number_of_true_channels, len(image_group_data)-n_unwanted_bytes+1, height*number_of_true_channels)
warnings.warn(f"{n_unwanted_bytes} ({unwanted_byte_per_step}*{height}) unexpected zero bytes were found in the ND2 file and removed to allow further parsing.")
return np.arange(0)
assert 0 == n_unwanted_bytes % height, (
"An unexpected number of extra bytes was encountered based on the expected"
+ " frame size, therefore the file could not be parsed."
)
return np.arange(
image_data_start + height * number_of_true_channels,
len(image_group_data) - n_unwanted_bytes + 1,
height * number_of_true_channels,
)
def _remove_bytes_by_id(self, byte_ids, image_group_data, height):
# Remove bytes by ID.
bytes_per_row = len(byte_ids) // height
warnings.warn(
f"{len(byte_ids)} ({bytes_per_row}*{height}) unexpected zero "
+ "bytes were found in the ND2 file and removed to allow further parsing."
)
for i in range(len(byte_ids)):
del image_group_data[byte_ids[i]:(byte_ids[i]+unwanted_byte_per_step)]
del image_group_data[byte_ids[i] : (byte_ids[i] + bytes_per_row)]
def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
"""Reads the raw bytes and the timestamp of an image.
@ -291,16 +301,41 @@ class Parser(object):
image_group_data = array.array("H", data)
image_data_start = 4 + channel_offset
# Stitched ND2 files have been reported to contain unexpected (according to
# image shape) zero bytes at the end of each image data row. This hinders
# proper reshaping of the data. Hence, here the unwanted zero bytes are
# identified and removed.
unwanted_byte_ids = self._get_unwanted_bytes_ids(
image_group_data, image_data_start, height, width
)
if 0 != len(unwanted_byte_ids):
assert np.all(
image_group_data[unwanted_byte_ids + np.arange(len(unwanted_byte_ids))]
== 0
), (
f"{len(unwanted_byte_ids)} unexpected non-zero bytes were found"
+ " in the ND2 file, the file could not be parsed."
)
self._remove_bytes_by_id(unwanted_byte_ids, image_group_data, height)
# The images for the various channels are interleaved within the same array. For example, the second image
# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
# a data structure that way, please send the author of this library a message.
number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
if self._check_unwanted_bytes(image_group_data, image_data_start, height, width):
self._remove_unwanted_bytes(image_group_data, image_data_start, height, width)
try:
image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width))
image_data = np.reshape(
image_group_data[image_data_start::number_of_true_channels],
(height, width),
)
except ValueError:
image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, int(round(len(image_group_data[image_data_start::number_of_true_channels])/height))))
image_data = np.reshape(
image_group_data[image_data_start::number_of_true_channels],
(
height,
len(image_group_data[image_data_start::number_of_true_channels])
// height,
),
)
# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
# don't have the same number of images each cycle. We discovered this because we only took GFP images every
@ -309,11 +344,14 @@ class Parser(object):
if np.any(image_data):
return timestamp, image_data
# If a blank "gap" image is encountered, generate an array of corresponding height and width to avoid
# errors with ND2-files with missing frames. Array is filled with nan to reflect that data is missing.
# If a blank "gap" image is encountered, generate an array of corresponding height and width to avoid
# errors with ND2-files with missing frames. Array is filled with nan to reflect that data is missing.
else:
empty_frame = np.full((height, width), np.nan)
warnings.warn('ND2 file contains gap frames which are represented by np.nan-filled arrays; to convert to zeros use e.g. np.nan_to_num(array)')
warnings.warn(
"ND2 file contains gap frames which are represented by np.nan-filled"
+ " arrays; to convert to zeros use e.g. np.nan_to_num(array)"
)
return timestamp, image_data
def _get_frame_metadata(self):


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