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Commit 6476b094 authored by ssomnath's avatar ssomnath
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Cleaned up import statements and PEP8 cleaning

parent a6d61eb4
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pycroscopy/io/translators/AR_hdf5.py
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......@@ -8,22 +8,15 @@ import numpy as np
import h5py
import os
import sys
import re #used to get note values
import re # used to get note values
from pyUSID.io.translator import Translator
from pyUSID.io.write_utils import make_indices_matrix
def install(package):
subprocess.call([sys.executable, "-m", "pip", "install", package])
# Package for downloading online files:
# Finally import pyUSID.
try:
import pyUSID as usid
except ImportError:
warn('pyUSID not found. Will install with pip.')
import pip
install('pyUSID')
import pyUSID as usid
from pyUSID.io.write_utils import Dimension, make_indices_matrix
from pyUSID.io.hdf_utils import create_indexed_group, write_main_dataset, write_simple_attrs, print_tree, get_attributes
if sys.version_info.major == 3:
unicode = str
class ARhdf5(Translator):
'''
......@@ -56,8 +49,7 @@ class ARhdf5(Translator):
self.channels_name = []
self.points_per_sec = None
def translate(self, data_filepath, out_filename,
verbose=False, debug=False):
def translate(self, data_filepath, out_filename, verbose=False, debug=False):
'''
The main function that translates the provided file into a .h5 file
......@@ -81,7 +73,7 @@ class ARhdf5(Translator):
self.debug = debug
#Open the datafile
# Open the datafile
try:
data_filepath = os.path.abspath(data_filepath)
ARh5_file = h5py.File(data_filepath, 'r')
......@@ -89,7 +81,7 @@ class ARhdf5(Translator):
print('Unable to open the file', data_filepath)
raise
#Get info from the origin file like Notes and Segments
# Get info from the origin file like Notes and Segments
self.notes = ARh5_file.attrs['Note']
self.segments = ARh5_file['ForceMap']['Segments'] #shape: (X, Y, 4)
self.segments_name = list(ARh5_file['ForceMap'].attrs['Segments'])
......@@ -109,9 +101,9 @@ class ARhdf5(Translator):
extension_idx = self.segments_name.index('Ext')
short_ext = np.amin(np.array(self.segments[:, :, extension_idx]))
longest_ext = np.amax(np.array(self.segments[:, :, extension_idx]))
difference = longest_ext - short_ext #this is a difference between integers
tot_length = (np.amax(self.segments) - difference) + 1 # +1 otherwise \
# array(tot_length) will be of 1 position shorter
difference = longest_ext - short_ext # this is a difference between integers
tot_length = (np.amax(self.segments) - difference) + 1
# +1 otherwise array(tot_length) will be of 1 position shorter
points_trimmed = np.array(self.segments[:, :, extension_idx]) - short_ext
if self.debug:
print('Data were trimmed in the extension segment of {} points'.format(difference))
......@@ -122,7 +114,7 @@ class ARhdf5(Translator):
h5_file = h5py.File(h5_path, 'w')
# Create the measurement group
h5_meas_group = usid.hdf_utils.create_indexed_group(h5_file, 'Measurement')
h5_meas_group = create_indexed_group(h5_file, 'Measurement')
# Create all channels and main datasets
# at this point the main dataset are just function of time
......@@ -131,15 +123,14 @@ class ARhdf5(Translator):
y_dim = np.linspace(0, np.float(self.note_value('FastScanSize')),
self.map_size['Y'])
z_dim = np.arange(tot_length) / np.float(self.points_per_sec)
pos_dims = [usid.write_utils.Dimension('Cols', 'm', x_dim),
usid.write_utils.Dimension('Rows', 'm', y_dim)]
spec_dims = [usid.write_utils.Dimension('Time', 's', z_dim)]
pos_dims = [Dimension('Cols', 'm', x_dim),
Dimension('Rows', 'm', y_dim)]
spec_dims = [Dimension('Time', 's', z_dim)]
# This is quite time consuming, but on magnetic drive
# is limited from the disk, and therefore is not useful
# This is quite time consuming, but on magnetic drive is limited from the disk, and therefore is not useful
# to parallelize these loops
for index, channel in enumerate(self.channels_name):
cur_chan = usid.hdf_utils.create_indexed_group(h5_meas_group, 'Channel')
cur_chan = create_indexed_group(h5_meas_group, 'Channel')
main_dset = np.empty((self.map_size['X'], self.map_size['Y'], tot_length))
for column in np.arange(self.map_size['X']):
for row in np.arange(self.map_size['Y']):
......@@ -152,7 +143,7 @@ class ARhdf5(Translator):
if index == 0:
first_main_dset = cur_chan
quant_unit = self.get_def_unit(channel)
h5_raw = usid.hdf_utils.write_main_dataset(cur_chan, # parent HDF5 group
h5_raw = write_main_dataset(cur_chan, # parent HDF5 group
main_dset, # 2D array of raw data
'Raw_'+channel, # Name of main dset
channel, # Physical quantity
......@@ -161,7 +152,7 @@ class ARhdf5(Translator):
spec_dims, #spectroscopy dimensions
)
else:
h5_raw = usid.hdf_utils.write_main_dataset(cur_chan, # parent HDF5 group
h5_raw = write_main_dataset(cur_chan, # parent HDF5 group
main_dset, # 2D array of raw data
'Raw_'+channel, # Name of main dset
channel, # Physical quantity
......@@ -178,13 +169,13 @@ class ARhdf5(Translator):
# Make Channels with IMAGES.
# Position indices/values are the same of all other channels
# Spectroscopic indices/valus are they are just one single dimension
img_spec_dims = [usid.write_utils.Dimension('arb', 'a.u.', [1])]
img_spec_dims = [Dimension('arb', 'a.u.', [1])]
for index, image in enumerate(ARh5_file['Image'].keys()):
main_dset = np.reshape(np.array(ARh5_file['Image'][image]), (-1,1), order='F')
cur_chan = usid.hdf_utils.create_indexed_group(h5_meas_group, 'Channel')
cur_chan = create_indexed_group(h5_meas_group, 'Channel')
if index == 0:
first_image_dset = cur_chan
h5_raw = usid.hdf_utils.write_main_dataset(cur_chan, # parent HDF5 group
h5_raw = write_main_dataset(cur_chan, # parent HDF5 group
main_dset, # 2D array of image (shape: P*Q x 1)
'Img_'+image, # Name of main dset
image, # Physical quantity
......@@ -196,7 +187,7 @@ class ARhdf5(Translator):
h5_pos_vals=first_main_dset['Position_Values'],
)
else:
h5_raw = usid.hdf_utils.write_main_dataset(cur_chan, # parent HDF5 group
h5_raw = write_main_dataset(cur_chan, # parent HDF5 group
main_dset, # 2D array of image (shape: P*Q x 1)
'Img_'+image, # Name of main dset
image, # Physical quantity
......@@ -214,24 +205,24 @@ class ARhdf5(Translator):
new_segments = {}
for seg, name in enumerate(self.segments_name):
new_segments.update({name:self.segments[0,0,seg] - short_ext})
usid.hdf_utils.write_simple_attrs(h5_meas_group, {'Segments':new_segments,
write_simple_attrs(h5_meas_group, {'Segments':new_segments,
'Points_trimmed':points_trimmed,
'Notes':self.notes})
usid.hdf_utils.write_simple_attrs(h5_file,
write_simple_attrs(h5_file,
{'translator':'ARhdf5',
'instrument':'Asylum Research '+self.note_value('MicroscopeModel'),
'AR sftware version':self.note_value('Version')})
if self.debug:
print(usid.hdf_utils.print_tree(h5_file))
print(print_tree(h5_file))
print('\n')
for key, val in usid.hdf_utils.get_attributes(h5_meas_group).items():
for key, val in get_attributes(h5_meas_group).items():
if key != 'Notes':
print('{} : {}'.format(key, val))
else:
print('{} : {}'.format(key, 'notes string too long to be written here.'))
#Clean up
# Clean up
ARh5_file.close()
h5_file.close()
self.translated = True
......@@ -254,7 +245,7 @@ class ARhdf5(Translator):
try:
match = re.search(r"^" + name + ":\s+(.+$)", self.notes, re.M)
if not match:
raise
raise Exception
except:
match = re.search(r"^" + name + ":+(.+$)", self.notes, re.M)
if (match):
......@@ -284,7 +275,7 @@ class ARhdf5(Translator):
"""
# Check if chan_name is string
if not isinstance(chan_name, basestring):
if not isinstance(chan_name, (str, unicode)):
raise TypeError('The channel name must be of type string')
# Find the default unit
......
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