Commit ad7ebfea authored by CompPhysChris's avatar CompPhysChris Committed by GitHub
Browse files

Merge pull request #124 from pycroscopy/cades_dev

Cades dev
parents 838aa7b3 31c6b611
......@@ -132,15 +132,17 @@ dataset would be structured as:
| iN-1, j0 | iN-1, j1 | iN-1, j2 | .... | iN-1, jP-1 | iN-1, jP-1 |
+------------+------------+------------+--------+--------------+--------------+
* If the same voltage sweep were performed twice at each location, the data would be represented as N x 2 P.
The data is still saved as a long (2*P) 1D array at each location. The number of spectroscopic dimensions
would change from just ['Voltage'] to ['Voltage', 'Cycle'] where the second spectroscopic dimension would
account for repetitions of this bias sweep.
* **The spectroscopic data would be stored as it would be recorded as volt_0-cycle_0, volt_1-cycle_0.....
volt_P-1-cycle_0, volt_0-cycle_1.....volt_P-1-cycle-1. Just like the positions**
* Now, if the bias was swept thrice from -1 to +1V and then thrice again from -2 to 2V, the data bacomes
N x 2 * 3 P. The data now has two position dimensions (X, Y) and three spectrosocpic dimensions ['Voltage',
'Cycle', 'Step']. The data is still saved as a (P * 2 * 3) 1D array at each location.
* If the same voltage sweep were performed twice at each location, the data would be represented as N x 2 P.
The data is still saved as a long (2*P) 1D array at each location. The number of spectroscopic dimensions
would change from just ['Voltage'] to ['Voltage', 'Cycle'] where the second spectroscopic dimension would
account for repetitions of this bias sweep.
* **The spectroscopic data would be stored as it would be recorded as volt_0-cycle_0, volt_1-cycle_0.....
volt_P-1-cycle_0, volt_0-cycle_1.....volt_P-1-cycle-1. Just like the positions**
* Now, if the bias was swept thrice from -1 to +1V and then thrice again from -2 to 2V, the data bacomes
N x 2 * 3 P. The data now has two position dimensions (X, Y) and three spectrosocpic dimensions ['Voltage',
'Cycle', 'Step']. The data is still saved as a (P * 2 * 3) 1D array at each location.
- A collection of ``k`` chosen spectra would also be considered
``main`` datasets since the data is still structured as
......@@ -357,24 +359,18 @@ comfortably accomodates the pycroscopy format and offers several
advantageous features.
Information can be stored in HDF5 files in several ways:
* ``Datasets`` allow the storageo of data matricies and these are the
vessels used for storing the ``main``, ``ancillary``, and any extra data
matricies
* ``Datagroups`` are similar to folders in conventional
file systems and can be used to store any number of datasets or
* ``Datasets`` allow the storageo of data matricies and these are the vessels used for storing the ``main``,
``ancillary``, and any extra data matricies
* ``Datagroups`` are similar to folders in conventional file systems and can be used to store any number of datasets or
datagroups themselves
* ``Attributes`` are small pieces of
information, such as experimental or analytical parameters, that are
stored in key-value pairs in the same way as dictionaries in python.
Both datagroups and datasets can store attributes.
* While they are not
means to store data, ``Links`` or ``references`` can be used to
provide shortcuts and aliases to datasets and datagroups. This feature
is especially useful for avoiding duplication of datasets when two
``main`` datasets use the same ancillary datasets.
Among the `various
benefits <http://extremecomputingtraining.anl.gov/files/2015/03/HDF5-Intro-aug7-130.pdf>`__
* ``Attributes`` are small pieces of information, such as experimental or analytical parameters, that are stored in
key-value pairs in the same way as dictionaries in python. Both datagroups and datasets can store attributes.
* While they are not means to store data, ``Links`` or ``references`` can be used to provide shortcuts and aliases to
datasets and datagroups. This feature is especially useful for avoiding duplication of datasets when two ``main``
datasets use the same ancillary datasets.
Among the `various benefits <http://extremecomputingtraining.anl.gov/files/2015/03/HDF5-Intro-aug7-130.pdf>`__
that they offer, HDF5 files:
* are readily compatible with high-performance computing facilities
......@@ -479,9 +475,10 @@ time)
* ``labels`` - list of strings for the column names like ['Bias', 'Cycle']
* ``units`` – list of strings for units like ['V', ''].
Empty string for dimensionless quantities
Empty string for dimensionless quantities
* Optional attributes:
* Region references based on row names
Attributes
......@@ -557,7 +554,7 @@ Measurement data
- Datasets common to Channel\_000 and Channel\_001
- ....
- ...
Tool (analysis / processing)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
......
pycroscopy.analysis.be_loop_model
=================================
pycroscopy\.analysis\.be\_loop\_model
=====================================
.. automodule:: pycroscopy.analysis.be_loop_model
......@@ -9,15 +9,12 @@ pycroscopy.analysis.be_loop_model
.. rubric:: Classes
Classes
-------
.. autosummary::
.. autoclass:: BELoopModel
:members:
.. autoclass:: LoopOptimize
:members:
BELoopModel
LoopOptimize
......
pycroscopy.analysis.be_sho_model
================================
pycroscopy\.analysis\.be\_sho\_model
====================================
.. automodule:: pycroscopy.analysis.be_sho_model
......@@ -9,13 +9,11 @@ pycroscopy.analysis.be_sho_model
.. rubric:: Classes
Classes
-------
.. autosummary::
.. autoclass:: BESHOmodel
:members:
BESHOmodel
......
pycroscopy\.analysis\.fit\_methods
==================================
.. automodule:: pycroscopy.analysis.fit_methods
.. rubric:: Classes
.. autosummary::
BE_Fit_Methods
Fit_Methods
forc_iv_fit_methods
\ No newline at end of file
pycroscopy\.analysis\.guess\_methods
====================================
.. automodule:: pycroscopy.analysis.guess_methods
.. rubric:: Functions
.. autosummary::
r_square
.. rubric:: Classes
.. autosummary::
GuessMethods
\ No newline at end of file
pycroscopy.analysis.model
=========================
pycroscopy\.analysis\.model
===========================
.. automodule:: pycroscopy.analysis.model
......@@ -9,13 +9,11 @@ pycroscopy.analysis.model
.. rubric:: Classes
Classes
-------
.. autosummary::
.. autoclass:: Model
:members:
Model
......
pycroscopy\.analysis\.optimize
==============================
.. automodule:: pycroscopy.analysis.optimize
.. rubric:: Functions
.. autosummary::
targetFuncFit
targetFuncGuess
.. rubric:: Classes
.. autosummary::
Optimize
\ No newline at end of file
pycroscopy\.io\.be\_hdf\_utils
==============================
.. automodule:: pycroscopy.io.be_hdf_utils
.. rubric:: Functions
.. autosummary::
generateTestSpectroscopicData
getActiveUDVSsteps
getDataIndicesForUDVSstep
getForExcitWfm
getIndicesforPlotGroup
getSliceForExcWfm
getSpecSliceForUDVSstep
isReshapable
isSimpleDataset
maxReadPixels
reshapeToNsteps
reshapeToOneStep
\ No newline at end of file
pycroscopy\.io\.hdf\_utils
==========================
.. automodule:: pycroscopy.io.hdf_utils
.. rubric:: Functions
.. autosummary::
buildReducedSpec
calc_chunks
checkAndLinkAncillary
checkIfMain
check_for_old
copyAttributes
copyRegionRefs
copy_main_attributes
createRefFromIndices
create_empty_dataset
create_spec_inds_from_vals
findDataset
findH5group
getAuxData
getDataSet
getH5DsetRefs
getH5GroupRefs
getH5RegRefIndices
get_all_main
get_attr
get_attributes
get_data_descriptor
get_dimensionality
get_formatted_labels
get_sort_order
get_source_dataset
get_unit_values
linkRefAsAlias
linkRefs
link_as_main
print_tree
reducingRefCopy
reshape_from_Ndims
reshape_to_Ndims
simpleRefCopy
\ No newline at end of file
pycroscopy.io.io_hdf5
=====================
pycroscopy\.io\.io\_hdf5
========================
.. automodule:: pycroscopy.io.io_hdf5
......@@ -9,13 +9,11 @@ pycroscopy.io.io_hdf5
.. rubric:: Classes
Classes
-------
.. autosummary::
.. autoclass:: ioHDF5
:members:
ioHDF5
......
pycroscopy\.io\.io\_utils
=========================
.. automodule:: pycroscopy.io.io_utils
.. rubric:: Functions
.. autosummary::
check_dtype
check_ssh
complex_to_float
compound_to_scalar
getAvailableMem
getTimeStamp
realToComplex
realToCompound
recommendCores
transformToReal
transformToTargetType
uiGetFile
\ No newline at end of file
pycroscopy\.io\.microdata
=========================
.. automodule:: pycroscopy.io.microdata
.. rubric:: Classes
.. autosummary::
MicroData
MicroDataGroup
MicroDataset
\ No newline at end of file
pycroscopy.io.translators
=========================
pycroscopy\.io\.translators
===========================
.. automodule:: pycroscopy.io.translators
......
pycroscopy\.processing\.atom\_finding
=====================================
.. automodule:: pycroscopy.processing.atom_finding
.. rubric:: Functions
.. autosummary::
apply_binarization_filter
apply_binarization_filter_select
apply_find
apply_gaussian_corr_filter
apply_invert_filter
apply_select_channel
apply_wiener_filter
cluster_2d_oleg
cluster_2d_oleg_return_geo_center
cluster_into_atomic_columns
fun_2d_gaussian
return_img
return_pos
run_PCA_atoms
\ No newline at end of file
pycroscopy\.processing\.cluster
===============================
.. automodule:: pycroscopy.processing.cluster
.. rubric:: Functions
.. autosummary::
reorder_clusters
.. rubric:: Classes
.. autosummary::
Cluster
\ No newline at end of file
pycroscopy.processing.decomposition
===================================
pycroscopy\.processing\.decomposition
=====================================
.. automodule:: pycroscopy.processing.decomposition
......@@ -9,13 +9,11 @@ pycroscopy.processing.decomposition
.. rubric:: Classes
Classes
-------
.. autosummary::
.. autoclass:: Decomposition
:members:
Decomposition
......
pycroscopy\.processing\.feature\_extraction
===========================================
.. automodule:: pycroscopy.processing.feature_extraction
.. rubric:: Functions
.. autosummary::
pickle_keypoints
.. rubric:: Classes
.. autosummary::
FeatureExtractorParallel
FeatureExtractorSerial
\ No newline at end of file
pycroscopy\.processing\.fft
===========================
.. automodule:: pycroscopy.processing.fft
.. rubric:: Functions
.. autosummary::
downSample
getNoiseFloor
get_fft_stack
harmonicsPassFilter
makeLPF
noiseBandFilter
\ No newline at end of file
pycroscopy\.processing\.giv\_utils
==================================
.. automodule:: pycroscopy.processing.giv_utils
.. rubric:: Functions
.. autosummary::
bayesian_inference_dataset
bayesian_inference_unit
do_bayesian_inference
plot_bayesian_results
plot_bayesian_spot_from_h5
\ No newline at end of file
pycroscopy\.processing\.gmode\_utils
====================================
.. automodule:: pycroscopy.processing.gmode_utils
.. rubric:: Functions
.. autosummary::
decompress_response