From 936d64f0d791a26ec6efee73503413b7e7572472 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 16:13:55 +1200 Subject: [PATCH 01/53] Use MyST-NB to render jupyter notebook and markdown files MyST-NB parses jupyter notebooks and markdown in Sphinx, thereby replacing nbsphinx (for .ipynb files) and recommonmark (for .md files). The powerful MyST (Markedly Structured Text) parser also allows much of the documentation to be written in Markdown instead of reStructuredText, which can make it easier for new folks wanting to make contributions to the documentation. --- doc/source/conf.py | 10 ++++------ requirements-docs.txt | 2 ++ 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 46886f59e..05aa90fd6 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -18,8 +18,6 @@ import datetime import icepyx -import recommonmark - # -- Project information ----------------------------------------------------- @@ -35,17 +33,17 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosectionlabel", + "myst_nb", "numpydoc", - "nbsphinx", - "recommonmark", "contributors", # custom extension, from pandas "sphinxcontrib.bibtex", ] source_suffix = { + ".ipynb": "myst-nb", ".rst": "restructuredtext", - ".txt": "markdown", - ".md": "markdown", + ".txt": "myst-nb", + ".md": "myst-nb", } # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] diff --git a/requirements-docs.txt b/requirements-docs.txt index d2f785927..586b2698a 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,6 +1,8 @@ gitpython +myst-nb nbsphinx numpydoc pybtex pygithub +sphinx_rtd_theme sphinxcontrib-bibtex From 5b62759faacfdc36870ba3b9aa8ef685d032589d Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 21:12:32 +1200 Subject: [PATCH 02/53] Make code of conduct link show up on sidebar Use MyST markdown parser instead of reStructuredText parser. --- doc/source/contributing/code_of_conduct_link.md | 2 ++ doc/source/contributing/code_of_conduct_link.rst | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 doc/source/contributing/code_of_conduct_link.md delete mode 100644 doc/source/contributing/code_of_conduct_link.rst diff --git a/doc/source/contributing/code_of_conduct_link.md b/doc/source/contributing/code_of_conduct_link.md new file mode 100644 index 000000000..65e693d93 --- /dev/null +++ b/doc/source/contributing/code_of_conduct_link.md @@ -0,0 +1,2 @@ +```{include} ../../../code_of_conduct.md +``` diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst deleted file mode 100644 index 0f9131439..000000000 --- a/doc/source/contributing/code_of_conduct_link.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../../../code_of_conduct.md \ No newline at end of file From b61a05716a6423515ebb91efaffa90a89e7ffc10 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 21:17:49 +1200 Subject: [PATCH 03/53] Remove unused layout template --- doc/source/_templates/layout.html | 32 ------------------------------- 1 file changed, 32 deletions(-) delete mode 100644 doc/source/_templates/layout.html diff --git a/doc/source/_templates/layout.html b/doc/source/_templates/layout.html deleted file mode 100644 index 4434e08bd..000000000 --- a/doc/source/_templates/layout.html +++ /dev/null @@ -1,32 +0,0 @@ -{# Import the theme's layout. #} -{% extends "!layout.html" %} - - - -{% block htmltitle %} - {% if title == '' or title == 'Home' %} - {{ docstitle|e }} - {% else %} - {{ title|striptags|e }}{{ titlesuffix }} - {% endif %} -{% endblock %} - - -{% block menu %} - {{ super() }} - - - - - {% endif %} -{% endblock %} From 142b94c22e84c11cd02b6b3b92b02dece20a798b Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 21:46:29 +1200 Subject: [PATCH 04/53] Use Sphinx's built-in Napolean extension instead of numpydoc Gets rid of autosummary toctree warnings, see https://stackoverflow.com/questions/12206334/sphinx-autosummary-toctree-contains-reference-to-nonexisting-document-warnings/43237890#43237890. Also need to add sphinx autosummary extension I think. --- doc/source/conf.py | 3 ++- requirements-docs.txt | 1 - 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 05aa90fd6..874171735 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -33,8 +33,9 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosectionlabel", + "sphinx.ext.autosummary", + "sphinx.ext.napoleon", "myst_nb", - "numpydoc", "contributors", # custom extension, from pandas "sphinxcontrib.bibtex", ] diff --git a/requirements-docs.txt b/requirements-docs.txt index 586b2698a..ae78937e8 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,7 +1,6 @@ gitpython myst-nb nbsphinx -numpydoc pybtex pygithub sphinx_rtd_theme From 7998f7dd815038c91625675979e26a599312570b Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 22:46:13 +1200 Subject: [PATCH 05/53] Fix API docs links by putting .rst first in sphinx conf.py API docs didn't have proper links because *.ipynb files were generated instead of *.rst files, some buggy hardcoding apparently. Workaround is to put the ".rst" line first in Sphinx's conf.py. Also removed a numpydoc config, and ensure API docs are generated to doc/source/_icepyx (which is gitignored). --- doc/source/conf.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 874171735..e155394db 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -41,8 +41,9 @@ ] source_suffix = { - ".ipynb": "myst-nb", + # Note, put .rst first so that API docs are linked properly ".rst": "restructuredtext", + ".ipynb": "myst-nb", ".txt": "myst-nb", ".md": "myst-nb", } @@ -62,9 +63,6 @@ # -- Configuration options --------------------------------------------------- autosummary_generate = True -numpydoc_show_class_members = False - - # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for From 6bcb7c1fa8ba22f51e42329f7f8c49efdc4f4571 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 23:35:31 +1200 Subject: [PATCH 06/53] Exclude dev-notebooks folder and turn off MyST jupyter notebook execution --- doc/source/conf.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index e155394db..22b27daf1 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -53,7 +53,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["**.ipynb_checkpoints"] +exclude_patterns = ["**.ipynb_checkpoints", "dev-notebooks"] # location of master document (by default sphinx looks for contents.rst) master_doc = "index" @@ -63,6 +63,8 @@ # -- Configuration options --------------------------------------------------- autosummary_generate = True +jupyter_execute_notebooks = "off" + # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for From 7da1e954d7692723646d9faac3f6994d8b9879c8 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 9 Apr 2021 23:42:34 +1200 Subject: [PATCH 07/53] Move DAAC DataAccess example to doc/source/example_notebooks Put the rendered ICESat-2_DAAC_DataAccess_Example jupyter notebook in the User Guide! No need to visit GitHub to see the jupyter notebook anymore! --- .../example_notebooks}/ICESat-2_DAAC_DataAccess_Example.ipynb | 0 doc/source/index.rst | 4 ++-- 2 files changed, 2 insertions(+), 2 deletions(-) rename {examples => doc/source/example_notebooks}/ICESat-2_DAAC_DataAccess_Example.ipynb (100%) diff --git a/examples/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb similarity index 100% rename from examples/ICESat-2_DAAC_DataAccess_Example.ipynb rename to doc/source/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb diff --git a/doc/source/index.rst b/doc/source/index.rst index 7113ea51c..3e57c5c69 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -23,7 +23,7 @@ icepyx is both a software library and a community composed of ICESat-2 data user :hidden: :caption: User Guide - .. user_guide/Gallery + example_notebooks/ICESat-2_DAAC_DataAccess_Example user_guide/documentation/icepyx user_guide/changelog/index @@ -43,7 +43,7 @@ icepyx is both a software library and a community composed of ICESat-2 data user :caption: Community and Resources community/resources - community/contact + community/contact tracking/tracking **Quick Install** From 6e1e22b9561c3d75f10fce322548cf407abe2c54 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Sat, 10 Apr 2021 11:14:04 +1200 Subject: [PATCH 08/53] Add linkify MyST extension to convert bare URLs to actual hyperlinks See https://myst-parser.readthedocs.io/en/v0.13.6/using/syntax-optional.html#linkify. Converts https URLs into clickable links, e.g. those at the end of the Code of Conduct page. --- doc/source/conf.py | 3 +++ requirements-docs.txt | 1 + 2 files changed, 4 insertions(+) diff --git a/doc/source/conf.py b/doc/source/conf.py index 22b27daf1..c66c0c213 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -39,6 +39,9 @@ "contributors", # custom extension, from pandas "sphinxcontrib.bibtex", ] +myst_enable_extensions = [ + "linkify", +] source_suffix = { # Note, put .rst first so that API docs are linked properly diff --git a/requirements-docs.txt b/requirements-docs.txt index ae78937e8..244285d1b 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,6 +1,7 @@ gitpython myst-nb nbsphinx +linkify-it-py pybtex pygithub sphinx_rtd_theme From 3f5c527f329d0b07c65c55d087bb190181b24a72 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Sat, 10 Apr 2021 11:34:01 +1200 Subject: [PATCH 09/53] Automatically create targets for section headers See https://myst-parser.readthedocs.io/en/v0.13.6/using/howto.html#automatically-create-targets-for-section-headers. Fixes WARNING: duplicate label xxx, other instance in yyy (mostly in the changelog page). --- doc/source/conf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/source/conf.py b/doc/source/conf.py index c66c0c213..62338ec0a 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -65,6 +65,9 @@ bibtex_bibfiles = ["tracking/icepyx_pubs.bib"] # -- Configuration options --------------------------------------------------- +# Prefix document path to section labels, to use: +# `path/to/file:heading` instead of just `heading` +autosectionlabel_prefix_document = True autosummary_generate = True jupyter_execute_notebooks = "off" From d63119ecfacfd61c7799a626fe11a8ba8f126fb0 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Sat, 10 Apr 2021 11:53:05 +1200 Subject: [PATCH 10/53] Sort requirements-docs.txt alphabetically --- requirements-docs.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-docs.txt b/requirements-docs.txt index 244285d1b..4a7f7f3df 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,7 +1,7 @@ gitpython +linkify-it-py myst-nb nbsphinx -linkify-it-py pybtex pygithub sphinx_rtd_theme From 67c213e5125cd692ac3877a5d1ceb97fc8382319 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Mon, 24 May 2021 16:13:02 +1200 Subject: [PATCH 11/53] Move Data Visualization example to doc/source/example_notebooks --- .../example_notebooks}/ICESat-2_Data_Visualization_Example.ipynb | 0 doc/source/index.rst | 1 + 2 files changed, 1 insertion(+) rename {examples => doc/source/example_notebooks}/ICESat-2_Data_Visualization_Example.ipynb (100%) diff --git a/examples/ICESat-2_Data_Visualization_Example.ipynb b/doc/source/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb similarity index 100% rename from examples/ICESat-2_Data_Visualization_Example.ipynb rename to doc/source/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb diff --git a/doc/source/index.rst b/doc/source/index.rst index 3e57c5c69..f1bf56111 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -24,6 +24,7 @@ icepyx is both a software library and a community composed of ICESat-2 data user :caption: User Guide example_notebooks/ICESat-2_DAAC_DataAccess_Example + example_notebooks/ICESat-2_Data_Visualization_Example user_guide/documentation/icepyx user_guide/changelog/index From 1f547e3c84abe6dee5e83382396526b2b5139666 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 16:10:39 -0400 Subject: [PATCH 12/53] migrate rest of examples and add to docs --- doc/source/getting_started/example_link.rst | 4 ---- ...ICESat-2_DAAC_DataAccess2_Subsetting.ipynb | 0 .../ICESat-2_DAAC_DataAccess_Example.ipynb | 0 ...at-2_DEM_comparison_Colombia_working.ipynb | 0 .../ICESat-2_Data_Visualization_Example.ipynb | 0 .../data-access_PineIsland/CITATIONS.txt | 0 .../data-access_PineIsland/README.txt | 0 .../data-access_PineIsland/glims_polygons.dbf | Bin .../data-access_PineIsland/glims_polygons.kml | 0 .../data-access_PineIsland/glims_polygons.prj | 0 .../data-access_PineIsland/glims_polygons.shp | Bin .../data-access_PineIsland/glims_polygons.shx | Bin doc/source/getting_started/examples.rst | 14 ++++++++++++ doc/source/index.rst | 6 ++---- examples/README.md | 0 examples/examples.rst | 20 ------------------ 16 files changed, 16 insertions(+), 28 deletions(-) delete mode 100644 doc/source/getting_started/example_link.rst rename {examples => doc/source/getting_started/example_notebooks}/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb (100%) rename doc/source/{ => getting_started}/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/ICESat-2_DEM_comparison_Colombia_working.ipynb (100%) rename doc/source/{ => getting_started}/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/CITATIONS.txt (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/README.txt (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/glims_polygons.dbf (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/glims_polygons.kml (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/glims_polygons.prj (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/glims_polygons.shp (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/data-access_PineIsland/glims_polygons.shx (100%) create mode 100644 doc/source/getting_started/examples.rst create mode 100644 examples/README.md delete mode 100644 examples/examples.rst diff --git a/doc/source/getting_started/example_link.rst b/doc/source/getting_started/example_link.rst deleted file mode 100644 index b4a7d005b..000000000 --- a/doc/source/getting_started/example_link.rst +++ /dev/null @@ -1,4 +0,0 @@ -Examples -======== - -.. include:: ../../../examples/examples.rst diff --git a/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb similarity index 100% rename from examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb diff --git a/doc/source/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb similarity index 100% rename from doc/source/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb diff --git a/examples/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb similarity index 100% rename from examples/ICESat-2_DEM_comparison_Colombia_working.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb diff --git a/doc/source/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb similarity index 100% rename from doc/source/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb diff --git a/examples/supporting_files/data-access_PineIsland/CITATIONS.txt b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/CITATIONS.txt similarity index 100% rename from examples/supporting_files/data-access_PineIsland/CITATIONS.txt rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/CITATIONS.txt diff --git a/examples/supporting_files/data-access_PineIsland/README.txt b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/README.txt similarity index 100% rename from examples/supporting_files/data-access_PineIsland/README.txt rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/README.txt diff --git a/examples/supporting_files/data-access_PineIsland/glims_polygons.dbf b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.dbf similarity index 100% rename from examples/supporting_files/data-access_PineIsland/glims_polygons.dbf rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.dbf diff --git a/examples/supporting_files/data-access_PineIsland/glims_polygons.kml b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.kml similarity index 100% rename from examples/supporting_files/data-access_PineIsland/glims_polygons.kml rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.kml diff --git a/examples/supporting_files/data-access_PineIsland/glims_polygons.prj b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.prj similarity index 100% rename from examples/supporting_files/data-access_PineIsland/glims_polygons.prj rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.prj diff --git a/examples/supporting_files/data-access_PineIsland/glims_polygons.shp b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.shp similarity index 100% rename from examples/supporting_files/data-access_PineIsland/glims_polygons.shp rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.shp diff --git a/examples/supporting_files/data-access_PineIsland/glims_polygons.shx b/doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.shx similarity index 100% rename from examples/supporting_files/data-access_PineIsland/glims_polygons.shx rename to doc/source/getting_started/example_notebooks/supporting_files/data-access_PineIsland/glims_polygons.shx diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst new file mode 100644 index 000000000..d0a952e05 --- /dev/null +++ b/doc/source/getting_started/examples.rst @@ -0,0 +1,14 @@ +.. _examples: + +Example Notebooks +----------------- + +These examples illustrate how to use icepyx. +They demonstrate many of the features of this package, including minimal examples to get you started quickly. +Some include longer analysis workflows and showcase some best-practices. + +.. include:: + example_notebooks/ICESat-2_DAAC_DataAccess_Example + example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting + example_notebooks/ICESat-2_Data_Visualization_Example + example_notebooks/2_DEM_comparison_Colombia_working \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index f1bf56111..318456f17 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -15,16 +15,14 @@ icepyx is both a software library and a community composed of ICESat-2 data user getting_started/origin_purpose getting_started/install - getting_started/example_link + getting_started/examples getting_started/citation_link .. toctree:: :maxdepth: 2 :hidden: :caption: User Guide - - example_notebooks/ICESat-2_DAAC_DataAccess_Example - example_notebooks/ICESat-2_Data_Visualization_Example + user_guide/documentation/icepyx user_guide/changelog/index diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/examples/examples.rst b/examples/examples.rst deleted file mode 100644 index 77ece4d7a..000000000 --- a/examples/examples.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. _examples: - -Example Notebooks ------------------ - -Listed below are example jupyter-notebooks - -`ICESat-2_DAAC_DataAccess_Example `_ - -`ICESat-2_DAAC_DataAccess2_Subsetting `_ - -`Working_with_ICESat-2_Data_Variables `_ - -`ICESat-2_Data_Visualization_Example `_ - -`ICESat-2_Data_Read-in_Example `_ - -`ICESat-2_cloud_data_access_example (BETA ONLY) `_ - -`ICESat-2_DEM_comparison_Colombia_working `_ \ No newline at end of file From 335fcef24550cdb444de0d69e5379bf4b2ea99f1 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 16:15:06 -0400 Subject: [PATCH 13/53] populate examples readme for GitHub findability of examples --- examples/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/README.md b/examples/README.md index e69de29bb..d761ac94e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -0,0 +1,3 @@ +# Examples and Tutorials using icepyx and ICESat-2 data + +Examples are available in the [documentation](https://icepyx.readthedocs.io/en/latest/getting_started/examples.html). Source Jupyter notebooks and supporting materials are in [`doc/source/getting_started/example_notebooks`](https://github.com/icesat2py/icepyx/tree/main/doc/source/getting_started/example_notebooks). \ No newline at end of file From 82c4b183359cba28f12804146d8d39fe23163f9d Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 16:25:05 -0400 Subject: [PATCH 14/53] update examples to eval an rst file --- doc/source/getting_started/examples.rst | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index d0a952e05..f9db4be1b 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -1,14 +1,19 @@ .. _examples: -Example Notebooks ------------------ +Examples +======== These examples illustrate how to use icepyx. They demonstrate many of the features of this package, including minimal examples to get you started quickly. Some include longer analysis workflows and showcase some best-practices. +Example Notebooks +----------------- + +```{eval-rst} .. include:: example_notebooks/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example - example_notebooks/2_DEM_comparison_Colombia_working \ No newline at end of file + example_notebooks/2_DEM_comparison_Colombia_working +``` \ No newline at end of file From 600cc7cbe03fe518ce3eb4cc49467d07eddff5d5 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 16:34:58 -0400 Subject: [PATCH 15/53] use toc tree directive --- doc/source/getting_started/examples.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index f9db4be1b..54dcbbfcd 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -10,10 +10,9 @@ Some include longer analysis workflows and showcase some best-practices. Example Notebooks ----------------- -```{eval-rst} -.. include:: +```{toc-tree} example_notebooks/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example example_notebooks/2_DEM_comparison_Colombia_working -``` \ No newline at end of file +``` From 17080db07d61c2d465f1e55bc7c8f76cc82aad45 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 16:44:17 -0400 Subject: [PATCH 16/53] add rst eval directive to toctree directive --- doc/source/getting_started/examples.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index 54dcbbfcd..80616e746 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -10,7 +10,8 @@ Some include longer analysis workflows and showcase some best-practices. Example Notebooks ----------------- -```{toc-tree} +```{eval-rst} +.. toctree:: example_notebooks/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example From 1453dce6bf7586e50c0fb8a6311b3e7e51ae1423 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 5 Oct 2021 17:04:56 -0400 Subject: [PATCH 17/53] fix typos in docs identified by warnings --- doc/source/getting_started/examples.rst | 11 +++++------ doc/source/user_guide/changelog/v0.3.2.rst | 2 +- doc/source/user_guide/documentation/query.rst | 8 ++++---- 3 files changed, 10 insertions(+), 11 deletions(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index 80616e746..4150a9cb0 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -10,10 +10,9 @@ Some include longer analysis workflows and showcase some best-practices. Example Notebooks ----------------- -```{eval-rst} .. toctree:: - example_notebooks/ICESat-2_DAAC_DataAccess_Example - example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting - example_notebooks/ICESat-2_Data_Visualization_Example - example_notebooks/2_DEM_comparison_Colombia_working -``` + + example_notebooks/ICESat-2_DAAC_DataAccess_Example + example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting + example_notebooks/ICESat-2_Data_Visualization_Example + example_notebooks/2_DEM_comparison_Colombia_working diff --git a/doc/source/user_guide/changelog/v0.3.2.rst b/doc/source/user_guide/changelog/v0.3.2.rst index a04e7457f..99b3c7fe1 100644 --- a/doc/source/user_guide/changelog/v0.3.2.rst +++ b/doc/source/user_guide/changelog/v0.3.2.rst @@ -1,7 +1,7 @@ .. _whatsnew_032: What's new in v0.3.2 (1 December 2020) -------------------------------------- +-------------------------------------- This is a summary of the changes in icepyx v0.3.2. See :ref:`release` for a full changelog including other versions of icepyx. Note that during this time period we transitioned to master + development branches, with mandatory squash commits to the development branch from working branches in order to simplify the git history. diff --git a/doc/source/user_guide/documentation/query.rst b/doc/source/user_guide/documentation/query.rst index 1aac65b49..403dfc085 100644 --- a/doc/source/user_guide/documentation/query.rst +++ b/doc/source/user_guide/documentation/query.rst @@ -21,8 +21,8 @@ Attributes Query.CMRparams Query.cycles - Query.dataset - Query.dataset_version + Query.product + Query.product_version Query.dates Query.end_time Query.file_vars @@ -41,8 +41,8 @@ Methods :toctree: ../../_icepyx/ Query.avail_granules - Query.dataset_all_info - Query.dataset_summary_info + Query.product_all_info + Query.product_summary_info Query.download_granules Query.earthdata_login Query.latest_version From 1be21a0150322fa9a5609c9875f12af2d502bcc3 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 10:29:30 -0400 Subject: [PATCH 18/53] try relocating notebook now that rednering is happening --- doc/source/getting_started/examples.rst | 2 +- .../ICESat-2_DAAC_DataAccess_Example.ipynb | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename {doc/source/getting_started/example_notebooks => examples}/ICESat-2_DAAC_DataAccess_Example.ipynb (100%) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index 4150a9cb0..b34b6560f 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -12,7 +12,7 @@ Example Notebooks .. toctree:: - example_notebooks/ICESat-2_DAAC_DataAccess_Example + ../../../examples/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example example_notebooks/2_DEM_comparison_Colombia_working diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb b/examples/ICESat-2_DAAC_DataAccess_Example.ipynb similarity index 100% rename from doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb rename to examples/ICESat-2_DAAC_DataAccess_Example.ipynb From e6a1555434c38278d952c04729c8f8c7bb680280 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 10:42:27 -0400 Subject: [PATCH 19/53] fix DEM example filename --- doc/source/getting_started/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index b34b6560f..d26164853 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -15,4 +15,4 @@ Example Notebooks ../../../examples/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example - example_notebooks/2_DEM_comparison_Colombia_working + example_notebooks/ICESat-2_DEM_comparison_Colombia_working From eca685184ba49ca2e6fcdd4a62a9ddcd7747aad9 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 10:57:47 -0400 Subject: [PATCH 20/53] revert to numpydoc instead of built-in Sphinx napoleon --- doc/source/conf.py | 5 +++-- doc/source/getting_started/examples.rst | 1 + requirements-docs.txt | 4 ++-- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 62338ec0a..59dd2a2f6 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -33,8 +33,8 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosectionlabel", - "sphinx.ext.autosummary", - "sphinx.ext.napoleon", + "numpydoc", + # "sphinx.ext.autosummary", "myst_nb", "contributors", # custom extension, from pandas "sphinxcontrib.bibtex", @@ -69,6 +69,7 @@ # `path/to/file:heading` instead of just `heading` autosectionlabel_prefix_document = True autosummary_generate = True +numpydoc_show_class_members = False jupyter_execute_notebooks = "off" # -- Options for HTML output ------------------------------------------------- diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index d26164853..b75eb3616 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -11,6 +11,7 @@ Example Notebooks ----------------- .. toctree:: + :maxdepth: 2 ../../../examples/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting diff --git a/requirements-docs.txt b/requirements-docs.txt index 4a7f7f3df..fa9129448 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -2,7 +2,7 @@ gitpython linkify-it-py myst-nb nbsphinx +numpydoc pybtex pygithub -sphinx_rtd_theme -sphinxcontrib-bibtex +sphinxcontrib-bibtex \ No newline at end of file From 9bec2ae84ad02271b4fff7ce1d13eb338b383318 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 11:00:43 -0400 Subject: [PATCH 21/53] reduce toc depth for examples --- doc/source/getting_started/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index b75eb3616..a8229a626 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -11,7 +11,7 @@ Example Notebooks ----------------- .. toctree:: - :maxdepth: 2 + :maxdepth: 1 ../../../examples/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting From 1703a0e6cb000f37fe505e6be9a3c3288f3fe865 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 11:10:11 -0400 Subject: [PATCH 22/53] try re-adding example link --- doc/source/getting_started/example_link.rst | 0 doc/source/getting_started/examples.rst | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 doc/source/getting_started/example_link.rst diff --git a/doc/source/getting_started/example_link.rst b/doc/source/getting_started/example_link.rst new file mode 100644 index 000000000..e69de29bb diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index a8229a626..365b3ac05 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -13,7 +13,7 @@ Example Notebooks .. toctree:: :maxdepth: 1 - ../../../examples/ICESat-2_DAAC_DataAccess_Example + example_link example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting example_notebooks/ICESat-2_Data_Visualization_Example example_notebooks/ICESat-2_DEM_comparison_Colombia_working From c7e991626ff485a10c245c3cace37ac1f44970e8 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 6 Oct 2021 11:11:47 -0400 Subject: [PATCH 23/53] try re-adding example link --- doc/source/getting_started/example_link.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/source/getting_started/example_link.rst b/doc/source/getting_started/example_link.rst index e69de29bb..d9905a53b 100644 --- a/doc/source/getting_started/example_link.rst +++ b/doc/source/getting_started/example_link.rst @@ -0,0 +1 @@ +.. include:: ../../../examples/ICESat-2_DAAC_DataAccess_Example \ No newline at end of file From efb4a648a111ed53832f44027edeff8d85c94951 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 16:42:34 -0500 Subject: [PATCH 24/53] remove dev notebooks --- .../ICESat-2_DAAC_DataAccess_working.ipynb | 4977 ----------------- .../is2_demo_download_restart.ipynb | 439 -- .../spatial_subsetting_vis.ipynb | 1240 ---- 3 files changed, 6656 deletions(-) delete mode 100644 doc/source/dev-notebooks/ICESat-2_DAAC_DataAccess_working.ipynb delete mode 100644 doc/source/dev-notebooks/is2_demo_download_restart.ipynb delete mode 100644 doc/source/dev-notebooks/spatial_subsetting_vis.ipynb diff --git a/doc/source/dev-notebooks/ICESat-2_DAAC_DataAccess_working.ipynb b/doc/source/dev-notebooks/ICESat-2_DAAC_DataAccess_working.ipynb deleted file mode 100644 index 4dce0332e..000000000 --- a/doc/source/dev-notebooks/ICESat-2_DAAC_DataAccess_working.ipynb +++ /dev/null @@ -1,4977 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing ICESat-2 Data\n", - "### Software Development Notebook\n", - "This notebook outlines and begins development for functionality to ease ICESat-2 data access and download from the NASA NSIDC DAAC (NASA National Snow and Ice Data Center Distributed Active Archive Center). This space is meant to be transient and serve as a space for writing and testing code. Documentation and examples will be developed independently.\n", - "\n", - "#### Credits\n", - "* contributers: Jessica Scheick\n", - "* based initially on and modified from the 'NSIDC DAAC ICESat-2 Customize and Access.ipynb' tutorial by Amy Steiker\n", - "* some code from the ICESat-2 Hackweek topolib project was also modified and used in the development of is2_data.py\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/home/jovyan', '/srv/conda/lib/python36.zip', '/srv/conda/lib/python3.6', '/srv/conda/lib/python3.6/lib-dynload', '', '/srv/conda/lib/python3.6/site-packages', '/srv/conda/lib/python3.6/site-packages/IPython/extensions', '/home/jovyan/.ipython', '../../icepyx/core/']\n", - "['/home/jovyan', '/srv/conda/lib/python36.zip', '/srv/conda/lib/python3.6', '/srv/conda/lib/python3.6/lib-dynload', '', '/srv/conda/lib/python3.6/site-packages', '/srv/conda/lib/python3.6/site-packages/IPython/extensions', '/home/jovyan/.ipython', '../../icepyx/core/', '/home/jovyan/icepyx/core']\n" - ] - } - ], - "source": [ - "print(sys.path)\n", - "sys.path.append(os.path.abspath('../../icepyx/core/'))\n", - "print(sys.path)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/srv/conda/lib/python36.zip', '/srv/conda/lib/python3.6', '/srv/conda/lib/python3.6/lib-dynload', '', '/srv/conda/lib/python3.6/site-packages', '/srv/conda/lib/python3.6/site-packages/IPython/extensions', '/home/jovyan/.ipython']\n", - "['/home/jovyan', '/srv/conda/lib/python36.zip', '/srv/conda/lib/python3.6', '/srv/conda/lib/python3.6/lib-dynload', '', '/srv/conda/lib/python3.6/site-packages', '/srv/conda/lib/python3.6/site-packages/IPython/extensions', '/home/jovyan/.ipython']\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "print(sys.path)\n", - "sys.path.insert(0, os.path.abspath('../..'))\n", - "print(sys.path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import packages, including icepyx\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "import getpass\n", - "import socket\n", - "import json\n", - "import zipfile\n", - "import io\n", - "import math\n", - "import os\n", - "import shutil\n", - "from pprint import pprint\n", - "import time\n", - "#import geopandas as gpd\n", - "#import matplotlib.pyplot as plt\n", - "#import fiona\n", - "import h5py\n", - "import re\n", - "# To read KML files with geopandas, we will need to enable KML support in fiona (disabled by default)\n", - "#fiona.drvsupport.supported_drivers['LIBKML'] = 'rw'\n", - "#from shapely.geometry import Polygon, mapping\n", - "#from shapely.geometry.polygon import orient\n", - "from statistics import mean\n", - "from requests.auth import HTTPBasicAuth" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx\n" - ] - } - ], - "source": [ - "#change working directory\n", - "%cd ../../.." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Errno 2] No such file or directory: './Scripts/github/icesat2py/icepyx'\n", - "/home/jovyan/icepyx\n" - ] - } - ], - "source": [ - "cd ./Scripts/github/icesat2py/icepyx" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "from icepyx import query as ipq\n", - "%autoreload 2\n", - "#in order to use \"as ipd\", you have to use autoreload 2, which will automatically reload any module not excluded by being imported with %aimport -[module]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the icesat-2 data object class" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "region_a = ipq.Query('ATL06',[162.0, -78.95, -175, -75.7],['2019-02-20','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59') #, version='2')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bounding box', [162.0, -78.95, -175, -75.7]]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.spatial_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'short_name': 'ATL06',\n", - " 'version': '003',\n", - " 'temporal': '2019-02-20T00:00:00Z,2019-02-28T23:59:59Z',\n", - " 'bounding_box': '-55,68,-48,71'}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.CMRparams" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'page_size': 10,\n", - " 'page_num': 1,\n", - " 'time': '2019-02-20T00:00:00,2019-02-28T23:59:59'}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.reqparams" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'time': '2019-02-20T00:00:00,2019-02-28T23:59:59'}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.subsetparams" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Number of available granules': 22,\n", - " 'Average size of granules (MB)': 20.909271717063636,\n", - " 'Total size of all granules (MB)': 460.00397777539996}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.avail_granules()" - 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" {'inherited': True,\n", - " 'length': '0.0KB',\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1631076765-NSIDC_ECS&q=atl06%20v002&m=-113.62703547966265!-24.431396484375!0!1!0!0%2C2&tl=1556125020!4'},\n", - " {'inherited': True,\n", - " 'length': '0.0KB',\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://openaltimetry.org/'},\n", - " {'inherited': True,\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://doi.org/10.5067/ATLAS/ATL06.002'},\n", - " {'inherited': True,\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://doi.org/10.5067/ATLAS/ATL06.002'}]}]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.granules.avail" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ATL06\n", - "['2019-02-20', '2019-02-28']\n", - "00:00:00\n", - "23:59:59\n", - "002\n", - "['bounding box', [-55, 68, -48, 71]]\n" - ] - } - ], - "source": [ - "print(region_a.dataset)\n", - "print(region_a.dates)\n", - "print(region_a.start_time)\n", - "print(region_a.end_time)\n", - "print(region_a.dataset_version)\n", - "print(region_a.spatial_extent)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "002\n", - "dataset_id : ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V002\n", - "short_name : ATL09\n", - "version_id : 002\n", - "time_start : 2018-10-13T00:00:00.000Z\n", - "coordinate_system : CARTESIAN\n", - "summary : This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n", - "orbit_parameters : {'swath_width': '36.0', 'period': '94.29', 'inclination_angle': '92.0', 'number_of_orbits': '1.0', 'start_circular_latitude': '0.0'}\n" - ] - } - ], - "source": [ - "print(region_a.latest_version())\n", - "region_a.dataset_summary_info()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "region_a.visualize_spatial_extent()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the IS2 Class with polygon inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/icepyx/core/icesat2data.py:115: UserWarning: Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\n", - " warnings.warn(\"Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\")\n", - "/srv/conda/envs/notebook/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " return _prepare_from_string(\" \".join(pjargs))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['-55', '68', '-55', '71', '-48', '71', '-48', '68', '-55', '68']\n" - ] - } - ], - "source": [ - "region_ap = ipd.Icesat2Data('ATL06',[(-55, 68), (-55, 71), (-48, 71), (-48, 68), (-55, 68)],\\\n", - " ['2019-02-20','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='3')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'short_name': 'ATL06',\n", - " 'version': '003',\n", - " 'temporal': '2019-02-20T00:00:00Z,2019-02-28T23:59:59Z',\n", - " 'polygon': '-55.0,68.0,-48.0,68.0,-48.0,71.0,-55.0,71.0,-55.0,68.0'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_ap.CMRparams" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/icepyx/core/icesat2data.py:115: UserWarning: Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\n", - " warnings.warn(\"Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['-55', '68', '-55.2', '70', '-55', '71', '-50', '71.3', '-48', '71', '-47.9', '69', '-48', '68', '-51', '68.5', '-55', '68']\n" - ] - } - ], - "source": [ - "region_ap = ipd.Icesat2Data('ATL06',[(-55, 68), (-55.2, 70), (-55, 71), (-50, 71.3), (-48, 71), (-47.9, 69), (-48, 68), (-51, 68.5), (-55, 68)],\\\n", - " ['2019-02-20','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='3')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['polygon',\n", - " (array('d', [-55.0, -55.2, -55.0, -50.0, -48.0, -47.9, -48.0, -51.0, -55.0]),\n", - " array('d', [68.0, 70.0, 71.0, 71.3, 71.0, 69.0, 68.0, 68.5, 68.0]))]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_ap.spatial_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_ap._spat_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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Please see our documentation pages or example notebooks for updates.\")\n" - ] - } - ], - "source": [ - "region_ap2 = ipd.Icesat2Data('ATL06',[-55, 68, -55, 71, -48, 71, -48, 68, -55, 68],\\\n", - " ['2019-02-20','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='3')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['polygon',\n", - " (array('d', [-55.0, -55.0, -48.0, -48.0, -55.0]),\n", - " array('d', [68.0, 71.0, 71.0, 68.0, 68.0]))]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_ap2.spatial_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_ap2._spat_extent" - ] - }, - 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] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ········\n" - ] - } - ], - "source": [ - "region_ap.earthdata_login('jessica.scheick', 'jessica.scheick@maine.edu')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data request 1 of 1 is submitting to NSIDC\n", - "order ID: 5000000691388\n", - "Initial status of your order request at NSIDC is: pending\n", - "Your order status is still pending at NSIDC. Please continue waiting... this may take a few moments.\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_ap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morder_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/icepyx/icepyx/core/icesat2data.py\u001b[0m in \u001b[0;36morder_granules\u001b[0;34m(self, verbose, subset, **kwargs)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[0;31m#REFACTOR: add checks here to see if the granules object has been created, and also if it already has a list of avail granules (if not, need to create one and add session)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_granules'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_granules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplace_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCMRparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreqparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubsetparams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_filepath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/icepyx/icepyx/core/granules.py\u001b[0m in \u001b[0;36mplace_order\u001b[0;34m(self, CMRparams, reqparams, subsetparams, verbose, subset, session, geom_filepath)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Your order status is still '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' at NSIDC. Please continue waiting... this may take a few moments.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0;31m# print('Status is not complete. Trying again')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0mloop_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatusURL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "region_ap.order_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " return _prepare_from_string(\" \".join(pjargs))\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "region_ap2.visualize_spatial_extent()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/icepyx/core/icesat2data.py:117: UserWarning: Please note: as of 2020-05-05, a major reorganization of the core icepyx code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\n", - " warnings.warn(\"Please note: as of 2020-05-05, a major reorganization of the core icepyx code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\")\n", - "/home/jovyan/icepyx/icepyx/core/validate_inputs.py:25: UserWarning: You are using an old version of this dataset\n", - " warnings.warn(\"You are using an old version of this dataset\")\n" - ] - } - ], - "source": [ - "region_p = ipd.Icesat2Data('ATL06','/home/jovyan/icepyx/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.kml',\\\n", - " ['2019-10-01','2019-10-05'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='2')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['polygon',\n", - " (array('d', [-86.622742, -86.553377, -86.561712, -86.63091, -86.647127, -86.716003, -86.723889, -86.792609, -86.800293, -86.868859, -86.887503, -86.955671, -86.962905, -87.03092, -87.034444, -87.238298, -87.241573, -87.513164, -87.51611, 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"region_p._spat_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " return _prepare_from_string(\" \".join(pjargs))\n" - ] - }, - { - "data": { - "image/png": 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XO6ooitg3fr+fTCYjFCsWykD/NqMoCsFgkNHRUaanp4nH40SjUXQ6Hdu2bftCrEpczuDgIO+99x6JRIKCggLsdjsOh4N0Oi0mYnBpZa29vZ2CggICgQDwG511jUZDbm4uhYWFMuMsuW3uWg2yRqPJAcaAzcDzQFhRlL+60efLAPm3E0VR+Oyzzzhy5Ah6vZ7q6moKCgrmmU34/X6xFFdRUSEkpS43oaiqqsJutxONRvk//+f/oNPpyM3Nxefz8eijj4p6YrUE4vID7OHDh/F4PKIDOxgMYjabhQVxUVERGzdu5N1338VgMIjMrGoSkZ+fL8olwuEwXq+Xl156iV/+8pcMDQ1hMpmwWCwio6waVKgNTKlUikwmg8lkwmQyCYOJeDxOf3+/cG9bv349jz/++A3t397eXk6dOkVfXx8FBQW43W7y8/NRFGWe9fTltstq8K+agTgcDlmjDOzevVvUcre1tVFfX49Op7vnVCtuhmw2i8fjobu7m0AggE6nY+XKlfMmhJLfoDb+eTweJicniUajQsUjkUgQj8cJhUJkMhlcLhcGg4FkMkkymSQQCGA0GvnTP/3TZR0AxmIxAoGAOG6l02kMBgNlZWX4/X6Gh4cZHBwUqwpqY+fatWspKyu7YtvS6TTJZJJUKkUoFOLYsWNCTQSYt2qnGgwVFxdTWVnJ6tWrKSwsvHsbf5OokyFVXUir1YqLwWDA6XTKCeQScTcD5EeAv1AUZZtGo/l/kQGy5AaY6zqnBowGgwG73U55eTlVVVWUlZURDod5++23sdlsVFZWYjKZhHya6mA2NTWF2+2mtbWV7u5uhoeHaWlpoaCggH379qEoCjqdjnQ6zZo1a64IMv/pn/6JYDCIVqslLy+PtWvXztO9PXLkCOvXr2fdunVMTU0xODiI1+vFbreTSqU4deoUdXV11NfXk0gkeOedd4R1dFVVlQig1JKRWCwmTpjpdJpsNjvP0U1RFPR6PWVlZdTW1lJWVnbLB9JEIkF3dzc9PT1MTEzMC351Ot28g7ZWqxX206p+s8/nE0ofTqdTOAT+tqEoCoFAgMnJSXp7e3n++efvmpGKZHkSiUQYGRlhdHSUsbExJicnhba4zWbDaDSi1WqF7GI8HheT0nA4TDabFQo6RUVFrFu37q64aV4PRVHw+XwMDg6KoFS9DA4OYjQaxXEkJyeHZDKJ3+/HYrFQUFAgjhVarVYce2/UHTOVSl1zQp5IJJiZmWF6epqhoSF27NjBli1bFmvTF41wOMw//uM/kk6nsVqt6PX6eckIdUKQk5OD0+nE5XJRWFhIRUUFK1asECsxannKcp403YvczSa9rwL/Nuf6/6PRaJ4DTgAvKYriX+T3k3wB0Ol0ohY4Ho+LgG0uo6OjvPzyy6JRzu/3i2Aym80Kx7J0Oo1Op6Orq0tIT3V2drJp0yY2b95MPB5Hq9UKI4fLaWtrY2hoCIPBwOnTp4VcmprdmJmZobq6mk8//ZRDhw6Rl5eHzWajq6sLs9nMM888wxtvvEF1dTVGo5Hf+73fI5VKMTU1xcTEhMjYqlkXrVY7T01CDVjVADqdTjM9Pc2pU6eora29rSyD0Whk1apVrFq16paen8lkmJqaYmRkhM8//5zx8XHa2toW1Q76XkCj0WAwGITBytDQkAyQfwuJRCJ89NFHDA0NEYlERDNjSUkJK1euRK/Xi9/9+Pi4WPEqKCigvLycwsJCIZFmsViWReATCAQ4evSoSDiMjo6i1WopKCgQEm46nQ673c62bdsWbC7OZDKLIhV5reBYURQRqMfjcRRFWbYZ5D179pDNZlm3bh2FhYVispBMJkmn0+JzTyaThMNhIpEIBw8epKysDIfDwd/93d/Nez2r1YrNZkOv1xMIBHj22WevWZ4nuXUWLUDWaDQG4Engz//9ph8C/x+g/Pvfvwb+YIHnvQC8AFBZWblYw5Esc5LJJFNTU2JZUb0oioLVahWZFzUzUVBQwDPPPEMgEBAZXjWY/Pjjj+e9tipkPzs7K0oDBgYG0Ov1whK4srKSHTt2XDGuDRs2sGHDpclkfn4+IyMjjI+PYzQaMZvN7Nq1i7y8PNGopx7ULBYLk5OTYtb/7rvvotfrsVqtWCwWUTJhMBjEyUPNIKjlFeFwWATQqiRWPB4X2eSFAvq7SU5ODiUlJZSUlFBbW8uBAwfYv38/ubm5bNq06QtbXnA5qpZvJBIhm80u2xOz5M6RTCbp6Ojg9OnT3H///RQUFAjZxrGxMU6fPo3X66WkpITm5ma+/OUv43K5lrVG9oEDBzh48CBVVVXYbDby8vKoqam5aX3iO6WjPjs7y+joKLOzs8zMzGC1WiktLaW1tZWnnnpq2dZsP/jgg7jdbs6dO8fx48eFDCf8pqlcXclX/6+srOSZZ55Bo9Hw2GOPMTY2JmrVI5GIsKwHlvV36l5n0UosNBrNU8AfKYryyAL3VQPvKIrSeq3XkCUWvz2cPHmSd955B4PBQGFhITqdThxY1aAzHo/PyxJrtVpWrFhBa2srzc3NIsNw4sQJBgcHhVpEUVERbrebwsLCBZUF5galavPdQgSDQfr6+sTSp2o3rNVqKS0tJZ1O89lnn9HT08PMzIyo2VWzwna7HZPJRFFRERqNhkgkQjQaFWYK6hicTid2ux2j0SgOmF6vF0VRcLlc6HQ6zGYzubm5QtZNzSAsNdlsVhimtLe3/1aI/b/++uvif6fTSV1dHY899tgSjkhyt+jr6+Pjjz/G6/XidrupqKgQ9bSjo6N8/vnnVFZW0tLSQl1d3bKpK1UbSlXFCDXBoAZX8XicgwcPkslk2L59+7Io75iL1+tl//794npbWxsVFRXMzs5isVjYsmULsViMUCgkGh3VZIjRaJyXmFiIqakp4FJ21mw237GgMxAIEAqFyM/PB2BoaIhoNIqiKFRVVYmJ1tWYmzlXe1Qkt8ddqUHWaDSvAB8qivLTf79eoiiK59///1Ngs6IoX73Wa8gA+bcHRVF45ZVXGB0dZdeuXTe0vBgKhThz5gyTk5Po9XrWr18vlCOuVwubSCQYHR3F7/fz7rvvAr9ZwmttbeXJJ58Uj81ms/zyl7+kr6+PoqIi4vE44XAYvV5PLBYjlUrx3HPP0dXVRW9vL/X19TidTjQajWhwC4VCnD59mmw2y1e+8hUKCws5fvw4Z86cIRQKCRH9dDrN17/+dSG47/P5iMfj2O124vE427Zt46OPPiI3Nxej0SiC6ng8TlVVFS0tLaxcuXLJs7dHjhzhwIEDbNmyZdlmcm6VVCpFT08P2WxWnKCGhoYA+O53v7sslsYld55UKsX3v/99UqkUVVVVYqUpFovx+eefE41Gefrpp6moqFjU97xdY5WJiQl+/OMf43K5MJvNV9TAAqJ0Qm2CXo69BWqCIhwOEwqFCIfDmEwmxsfHxbF0rkGJWsamXrZs2cKaNWuENrO6vVqtlu9973vYbDaxYqfT6cS+KSoqEsfZxXJ93LNnzzzVDvXcAfBf/st/oaioaFHeR3J97ngNskajsQAPAy/OufkvNRrNGi6VWAxedp/ktxhFUXjjjTfw+/3s3Lnzmgf/2dlZxsfHGR0dJR6PA5eWpQoKCvB6vRw5cgSAqqoqZmdnsVqttLW1UVxczLFjxxgfH2flypVEo1E6OzspLi4WqhQGg4F0Os2KFSvE+x0/fpwLFy4wMDAAXCqzUIXw0+m0aLrzer14PB5qamqEtFc8Hqerq0uUgaxatYoHH3yQTCbDP/7jP1JQUMCGDRvIzc1Fq9XS2dmJ1+vls88+Ix6Ps2LFCpqamkQ9YiAQ4MyZM+LAbbfbMZvNhEIhtFotvb299Pb2Mjg4yNNPP31HPqsbZcuWLTgcDt5++20aGhqor69f0vEsFtFoVHSfezweSktL2bBhA9u3b1+wE1/yxeT06dN8+OGHuN1uVqxYgdvtJpvN0tfXR3d3N+vXr2fnzp2LZsLT09PDe++9J8rJ1LpTu93OAw88QGFhIclkkl/84hc4nU527dp11aynwWDAbDbf89rCamB7eXa7urqaaDR6zVKnaDTKiRMnOH/+vAieM5mMaJQ0Go089NBDAELVR8Xn89HT08O+ffv42te+RlVV1W1tRyQSIZVK4XA4rmiMTqVS9Pb2Eo1GMZvNojFPuk8uDdJqWnJXURSFt956C4/HQ3t7+1VPKOFwmHPnzjEzM4NWq8VkMrFixQpcLpcIIJPJJCdPnqSwsBCbzYbVaiUcDjM6OkogEKCiooKCggL6+/uJRqNEIhE2btxIYWEh0WiUs2fPkkwmefHFF8XJZffu3Rw+fBi4lFXZsmXLvNl8PB5ncnKSCxcusHHjRo4ePUpzczOZTIbu7m7a2tpobm6msLBQLK/+/Oc/x2g00tLScsW+6O3tJR6P09LSsuAJTlEUPv74Y1wulwjWAMrLy4lGo0xPT6MoCtXV1axatYr6+volK73IZrO8+eabDA4O8tBDD93ztXGXa0+rXG4uI/niEo/HOXPmDEeOHKG2tpbq6mpxX2dnJ8FgkN/93d+loKBg0d4zGAzyox/9iDVr1lBcXCxWjOLxuFjVeuKJJwgGg/zt3/4tRqOR1atXU1payuDgIJOTk/MCwIKCAnp6emhoaKC1tVUGWpeRzWaBa9fyKorCsWPH0Ol0V+jPK4pyw/s0m83yv/7X/yIvL4/q6mq0Wu28Hhw1661eV8spANHHogbOra2tt9xwLfkN0mr6OnR3d9PX14dOp6OoqIjS0lJcLteyOJCk02l8Ph+5ublfCIkXn8/HmTNnyM3N5dy5c+Tn5+N2u0UtlaIoRKNRPvnkEzZv3iw0V1taWjCbzUJiy+FwYDAYaG9vn/f6drtdOHip5Ofnc/ToUWZnZ8XB0GKxsGnTJg4cOMCpU6fEcukjjzzCtm3b6O7u5vz58xw9elTYNJvNZsLhMNFolHQ6LTIAnZ2dZLNZTCYTNpsNt9s9b4lSFba/HI1Gc91Mq0ajoaqqimw2S1FRkcgizf0eRKNRJicnOXDgAO+88w5PPPEEsViM2dlZSkpKaGpquivfm/fff5/z58+zcuVKLl68iMPhoKSk5J4MlGOxGNlslgceeIBAIIDX62V4eJiioqLryk9JvjiMjo6ye/duSktLhXa4+lvS6/XY7fZFU3GJRqMcPnyYEydO0NDQII5jaubU7/czMzNDY2Mj//Iv/8LIyAiVlZVMT09z5MgRCgsLRQOtWl+s0WiYmZkhPz+f7u5uXC6XdM28jBs5PmUyGfx+PyUlJUQiEdFrEQqF+Od//mcURaG+vp6KigpCoRBTU1OEw2FcLhdutxu73U5PTw+xWEyoIpWXl9/wcXmuRrQaNH/88cecPXuWtrY2amtrl2VZzPVQFIWJiQkmJydZs2bNUg/nCmQGGfjJT37CxMSEyEoCPP/883ddVSMejzMyMsLs7CwGg4GBgQEuXrwo9H61Wi0rV66koaGBUCgkVBkaGhoWbWnvbpBOp/F4PEJIfnx8nLKyMkKhEH6/XzTjPfnkkxw9epQzZ85c4QT3yCOPCAH5G0HVHL78ZObz+Th16hS7du2itrb2iqaHZDLJkSNH6OzsJBqN4nA4qKqqwmw2c+TIEdra2igtLSWbzTI7O0tfXx+zs7M8++yzIvPc2dk5r7Hrd3/3d+9YwHr+/HlmZmawWCwYjUZha60K6RcUFNyxemXVmtjn8+H3+/F6L5lolpaW0tjYKOq07wW6urro7OykvLychoYGnE4nsViMEydO0NbWxtatW5d6iPcU09PTHDx4UFivq+Y6wWCQcDhMXl4eVVVVVFVVUVFRsWxsu1OpFLt37xaykIqiCMWWdDrNuXPn8Pl8tLa2smXLlluW2zp//jxvvfUW5eXl1NfXLxh0d3R0MDk5ycqVKzlx4gRr1qxhxYoVfPrpp/j9fqqqqli/fv1V3yOTyYigWXLzpNNpLly4wODgIJWVldTU1NDR0UFhYSHFxcVMTk4SCAREosRkMhEOh4WplVoDnkgkyGQyNDU13VbyIJPJMDg4yNTUFF6vl+LiYhoaGrBYLPMy0OrfVColxma1WqmqqroimXQ3URSF//E//oeQuvvud7+7JOO4a0Yht8tSBchHjx5ldHQUoxDuD94AACAASURBVNGIy+Wa15l8J/H5fJw9e5aZmRkmJiYIBALk5+djtVpFhrK8vByLxYKiKKLGNRQKYbfbxQ8kJyeH0tJSjEYj4XAYnU5HQUGB0Nk0m81C/cDhcJCTk0MkEiGZTJKXl7eoGT5FUYhEIgQCAUpKSkilUrz99tuYzWY2btw4r1whHA5jtVrp6+tjYmKC4uJikSWdu++7urp45ZVXgEv1x6qBxUJ86UtfEh3CNzrewcFBPB4PPp+P4uJi6urqqKmpobS0dN6+UZe/1OC4r6+Ptra2K15zeHiYs2fP8vTTT9PY2AjA3r172b9/P8XFxTidToxGIxaLBZvNJvSPbwS1Nu1mlvQGBgbw+/0Eg0FCoRBms5mVK1eydevWO9Kt/vrrr+PxeLBYLPh8PmKxGABut/ueqIPs6enh888/B6C+vp6enp5599fX1/Pss88uxdCWPalUisnJSfx+v5Bh7O7uprOzk9raWqxWq1ClMRqN4vek0WiYnp7G4/EIOcOvfvWr4vezXDhw4ABnz54VBjpqRg/AZrPx0ksv3dLr7t+/nwsXLrBt27ZrPq6zsxOHw8GqVav453/+ZzZt2kRpaamomb1TEmuS3xCPx/F6vfh8PiwWCw0NDUs+6Uin03i9XqampoQO9VwTKFUlam4G2uPx0NTUxBNPPHHb759IJETD442iKAqvvfYaiUSCvLy8eY3ydxMZIC8zFEXhV7/6FT09PVRWVmK328nNzb2lYFXVY43FYqTTaUwmE5lMhlAoJGTF5h7Mo9EoGo1G/GgSiQSFhYW43W5CoRAtLS0UFhaSl5eH1Wpd8IefyWTweDzCGSibzdLb20t3d7fIxKvSOhqNBr1ej9PppK+vj9raWjZs2MCpU6fo7Ozk61//Ovn5+Rw6dIhoNIrRaKSsrIy6urp5HcPRaJR/+Zd/wePxAJcCZdXmWS150Gg03H///bcc9KXTacbGxhgbG2NiYoLS0lL+8A//kFQqxfnz5/n444+Jx+M4nU4cDgdDQ0OUl5dTU1Mzb6x+v1/YpD7zzDPic52enqa/v59wOCwav9TAVZ3AzNVqVjutM5mM6OCemZnBZDJRWFiIyWSipqbmpqR+VAvcwcFBRkdH+cY3vrHoS65/+Zd/yX333Sc+h2QyicfjYWJigqKionl1nMuRffv24fP5KCkpIZvNMjk5Ke67nSDoi8rExATHjh0Tq1+5ublYrVah6GKz2WhoaBDf03A4zMGDB0XTreoWWVVVhcVioaOjA4CtW7cyOTlJfX09q1atWjZZ5YVYqA7V5/Px2WefCbv73Nxc7HY7DoeD/Pz8ecf6VCrF3/zN37Bly5ZrTvD7+/vJZDI4HA4uXrzIfffdd0+WMEmWHq/Xy5kzZ3j22WdxOp3MzMwI1z5VscdqtWK327Hb7WSzWRFcO51OUdJx6NAh4UVgMpmwWCxYrVYRP6RSKRG0q7GGupq5HErVZIB8HWKx2DX1cG8H1UpS/dJ5PB5OnTrF+Pj4onY93yhq9kZ932QyyezsLOFwmI6ODtEAEA6HMRqNNDU1iSVm1eGnv78fu90uAmzVLKGoqAiXyyWW8FU744KCgnkduiMjI1RUVBCPx+nv78dqtVJSUoLT6RRWpVNTU0KtQv1B5eTkcPjwYZLJJA6Hg3Q6TW5uLk6nE7fbfc1ZvCq9puqAqnWk6o/fYrGg1Wp54403ACgsLGT16tW0t7fzN3/zN0QiERobG3G5XMzOzhKLxRgfH6e6uprh4WFKSkpYtWoVOp2OyclJhoeHicfjBAIBtm7ditvtZmhoSKg9qGQyGb73ve/R1tYmbGgTiYQQgy8sLKSlpQWbzYbZbKampoapqSk8Hg9jY2NcuHCBtWvXCiWNm/keXLhwgf7+fl544QVcLtfNfpWuyvvvv09HRwd2u510Ok0sFqO8vBy/38+KFSuoqalZtPe6E6jOh5OTk3g8HnJzc4XM02I2Y93LKIpCf38/Bw4cwOv1CqnFsbExvF4vBoOBxsZGqqurSafTaLVadDod8XicTz/9lPz8fFauXCkm2B9//DGZTIbc3FwmJiYAqK2tJT8/X3zfi4qKyM3NxWaziWXi0tLS6/727zZqsPx//+//ZWBggMrKSgwGg3B9i0QiJBIJSkpKqK+vp7m5GaPRyF/91V+h0WgoKipi06ZNCwYPn3/+OVVVVZw9e1ZYEi+XfhnJvUU0GhUleZFIBLvdjtVqFYoZqhGW6qqo1WoxGAzodDpisRhbtmxhy5YtvPXWW/h8PjZt2jRPp1ldWVEVOtRzsJogCoVCOJ1OmpubaW1tXTI3UhkgX4eXX36ZkZER6urqaG5upr6+flEK3jOZDK+//jrd3d2i7s5ms1FWVkZFRcWyE/n+6KOPqKmpoaamBkVRCAaDeDwevF6v+IGsWLFi0cauzlIXWhZUM53qJRKJiHqueDyORqNBq9WSl5dHOp0WzTJzDTjmamCGw2GRpVVLNKxWK0ajUbx2Tk4OGo2G0tLSeZ3KPp+Pc+fOiWY89Yeu1WrZuHEj7e3t/OAHP+CBBx64Int94cIF+vr6RNZ3ZGSE+++/X9Swjo+P8/LLL/M7v/M7V+yDcDjMvn37+OpXv7qgtNDY2Bg//vGPAaioqMBms4kSjmudMC9evEh3dzfpdJrVq1fz4IMP3lQ9942QyWQYHx8nGo3i9/sZGhpidHSUnTt3LqvvfSaTIRqNiqZGr9fL9PQ0ZWVlNDQ00NTU9IXTdb4dMpkMnZ2dHDx4UHTd5+bmYrFYGB8fR6/XC6UVFfW7qJZUFBYWsnnzZjFJj0ajfPjhh+Tm5pKfn09lZeUVE7ZUKsX09PS8k28ymcTn86HRaMRnVV1dfdWkw/Hjx4nH47jdbhwOBw6HA5PJdF1jBp/Px+DgIAMDA/T29pJKpdi4cSObNm1iZmZGHBN8Pp9YRVu1ahWrV6/ml7/8JXa7nfvuu2/e6yYSCaanp8UkzGKx0NjYSGVlJadPn2ZoaIi1a9deUSPa29srVh6j0SjDw8M8+OCD5OXl3dgHKJEswM0occCl5sSLFy/i9XrFCsaGDRtuKoGQzWaZmZnB4/EwMDDAd77znZse92IgVSyuQzqdZs2aNWQyGY4cOcKvf/1rqqqqWLduHQ0NDbdc1+X1eunr68NsNlNYWEh1dfVN1cfebeZOljQajTiJNDU13ZH3u9bSoKr8sFC5RDabJZ1Oc/bsWQYHB8nPz6empkaI6quBbiwWQ6fT4Xa7eeONN9BqtTQ2Ngox+enpafr6+rDb7ZSXlxOPx3G5XDQ1NZFOp8lmswSDQRKJBPX19ezbt4/q6mpxIFFLW1577TUKCwsXHGthYSF6vZ6amhq0Wi16vR6fzwdcclV65ZVXWL169YL74OTJk0JVZSFUFz5Vd9lms3HmzBmhqdzY2LjgQa+iogKNRoPP5+P8+fNMT0+LLv2ioiKqqqpuOyOVk5NDMpnkzTffpLi4GJfLxY4dO5YsOI7H4wwPDwuHwnQ6TTQaJZFIiBKnwsJCdu7cyYoVK5bceGW5kUgkOH78OEeOHBFNwzqdTpR1RaNRLBYLGzZswGaz4fF4uHDhAoFAgKKiIlpbW4nFYsIRci5qeZTad7HQaoZer1+wAU6dSHs8Hnbv3k0gEKCqqorVq1fT0tIy73usloE5nU7RK2GxWHjuuefEcTmTyYgVoMHBQYaHh8UqmOpqmUqlRBOn0+kUjW8mk4nq6mra2tro6+vj9ddfp6amRuioz8VoNFJaWkppaSlr1qwR9ddnz54VwcpCv5W6ujosFgtHjx7l0Ucfxev1LuvSE8m9wc0e7+12Oxs3biQcDgNctRzzWmi1WvG7urzPY7kgM8jAT3/6U6qqqkSKP5VKMTY2xsjICKFQiFWrVt2yQ5iiKExNTdHb28vRo0ex2+2sWbNm2ViQzmXPnj3U1tYu+yVwuJRdPX/+PLFYTDQczm1I0Ol02Gw2kW1Sa6Ha29vnZUuz2axwYsrJycHv9zM9PY3f70ej0WC1WoXzFFyqi1RPXO+++y4Oh4Pq6mrRoXw9uru7GRwcZMOGDRw5coSGhoYFT6BwSWKqo6ODP/qjP1ow+P7bv/1b8b09fvw4VVVVPPXUU/h8Pl5//XXcbvd1ZeRUGcFgMEg0GsXn81FQUMATTzxxW65RPp+Pn/zkJ0J3eilRmyYbGhpoaWkRy4QOh+OKhlDJlfh8Pv71X/8Vm81GTU0Nx44dw+FwiNKna2UvY7EYn3zyyXXNY1KpFGfPnmVgYIAtW7bcdMmQSiKRYGJiQpSBPffcc2Ii7vF4+NnPfsbOnTuFhnooFKK1tZWnn36aV155hcHBQaxWK/n5+eTn519hV59IJERfh8vlWvSmODXgNxgMC65i+v1+Tpw4wYYNG8jLy+Ott95i7dq1t21eIZEsFWpP1nJUsZAZ5AXQ6/VUV1dTXV1NOByms7NTCLKrDVQGgwGr1cqGDRuor6+/6klWrSkrKipi3bp1vPfee7z33nuUlpaKUgt5gr55bDYbmzZtuu7j1AlKOp2mtLT0in2t1WopLy8X19X/s9ksGo3mmp9NQ0MDlZWVN5UVbWhoIDc3l+7ubtrb28nLyxPBuUo6nRbugTqd7qrlPvX19Zw6dQq9Xs+KFSt4+OGHxdhVmZ/rodPpKC4uFtm5bDZLd3c3f//3f09JSQkGg4Hc3Fwee+yxG95GuDQRyGazS9pAFI/HuXDhAn6/n9///d+/q/atqkOXuppxr3Lx4kXefvttmpubqaqqErbn7e3tN/TZms1moV9+LVKpFENDQ1RWVl4RHCuKwvj4OPF4HKvVek1NeKPRSFVVFcXFxezevZtQKCQmetlslpycHMbGxsjNzSWVSvHQQw+h0Wh44403mJmZYdeuXddcPTAajXd0FeRqmumqqZB63DCZTLz//vts3759UfsHJBLJb5AB8r+TTCaZnJwUdWkqNpuNpqYmJicnsdvtbNiwQahCqE5bH330Edu3b7/CpSgQCNDf38/w8DCjo6PMzs4K0fe8vDxOnTqFx+Nh48aNshP5DqFOUG6WG/k8btVOeW5AevbsWfr6+kQpRiwWIxgMUl5eTnt7O42NjVc9YX/5y1/my1/+8rzbenp6eOONN2hpabmmWoRaQ5ZIJIS+tqq20dTUREVFBcFgkPHxcQYHB296G9vb2zl27BjpdPqmn3urpFIp4XTo8XgIBAI0Nzfzla985baCmpGREY4cOUIoFCIajVJXV8fmzZtxOp14vV6xLF5ZWUkymaSzs5P+/n5RpgPw3e9+954KlBVFYd++fZw4cUJs66effkpOTg5btmy5qeOVVqslFApds87RZDJRVFSE1+slmUzO+84nEglhKQ+XTH5SqRR5eXmiyTY3NxeNRkMwGCQYDDI2NobZbOadd95haGgIg8FAeXk5ZWVlBAIBGhoaaGtro6ura16yY7mW1nR0dDAwMEB5eTmHDx/G7XazYcMGGRxLJHcQGSBzaQl2eHgYgKamJtxuN8lkEovFIuTXfud3fkdkMODSUpfRaKSuro6enh7efPNNioqKcLvddHV18dFHHxEOh3G73TidTlavXo3D4SASiXDu3DmCwSBlZWVotVrS6fSyPTBL7gyKotDd3c3IyAjV1dWUlJQI3eobKddQM3KqlJbaIf+v//qvbNy48bomN8PDw6TTaR5++GH8fj+ffPIJZWVlNDc3i+XdgYEBpqam+L3f+72b3r5MJoNer2dgYEBk/BaTcDjM9PS0MJqYnZ0lmUxiNptZsWIFDz74IDU1NbetEjM6OsrPf/5z8vLycLvdlJeXMzMzw49+9CPMZjPJZJLy8nK0Wi09PT1otVpKSkp45JFHMBqN+Hw+Dhw4gM/nW/JSkxslFovxq1/9Cr/fz44dO9Dr9ULGaceOHTe0T+PxuLCKDwaDwo1QNcK4fMKi1WrZsmULBw4cYP/+/Tz00EPiPpPJxNNPP83IyIjIoLa1tQndV7WkQlEU3G43gUBA6ALbbDYeffRRMpkMExMTBINB1q1bh0ajoays7JZLOe42tbW1OBwOkskka9eulecLieQuIANkLmX0JiYmeOKJJzh69KgwU5iZmUGr1bJu3TohLQSXTiCffvoplZWVmM1mVq9ezYYNG0Rtq8PhwGKxEIvFKCkpmbeEb7fb2bJli7iuGoCk0+ll4YZ3L2W57mUuXrzI1NQUWq0Wo9HIwYMHycnJobGxkVWrVlFTU3NVjcienh5ee+010dyk0WiEQgfcmFlARUUFp0+f5oMPPmDTpk08//zzHDp0iA8//JDCwkLC4TAlJSV8+9vfFpPCm0Gn0/Hiiy+yb98+PvnkE3bu3HlLr7MQvb29dHV1CTMXt9tNcXExeXl5i/79NRqNQmpMba588sknaWpqIhgMXlcxxOVy0drayo9+9COefPLJqzZkLhfGx8d59dVXKSoqYvv27aTTafbs2YPFYmHbtm03dIyanZ3l8OHDrFmzhscee0xo/p4/f5533nmHnJwcmpubr3ieqiSyatWqK+7LycmhurpaWNO2t7dTWlrKwMAAiqKwZcsWqqurMRqN/OAHP6C6uprVq1fP+2zuhd6Kq6E2TEskXzQikch1S7CWiqWPyJYBO3bsID8/H7fbzbp168Tt2WyWX//610J7t7y8nNLSUtH5PlcKbC7FxcU8//zzjI6O8uqrr9Ld3Y3dbsdgMIjyDLvdjt/vFyYE+fn5fOlLX7or2ytZOuLxOOfPn8fr9fLUU0/x7rvv0tzczIoVK9BoNIyMjLB3715ef/11SktL2blz57xSiUgkwi9+8Qtqa2spLS2dZwJz8uRJoXhxeV3z5eTm5rJ9+3YmJyfp7Ozko48+orGxkT/+4z+mt7cXk8l02+olOp2OBx98EJ/Px/T09KIEyJFIhIsXL/LCCy/cFUWYwsLCK7J1Xq9XqH5ci0QigdfrZWRkRPQcLFdSqRRHjx7l4MGDtLW1iUl9V1cX8Xichx566IYn8FNTU1RUVIia+LGxMfbv38/w8DCVlZVUVFQs+Dy1Zl510lsIg8GAx+PB4/HQ39/P/v37aWhoEOpDdrsdvV7P1NSUnOxLJMuQ6elpPv30UxETRaNRNm/evNTDWhCpYnGDBAIBjh8/zuDgINlsls2bN9PQ0MD09LSQRFMzzHNJpVL4fD6hJWyxWNDr9YyMjFBUVITD4eBnP/sZ9fX1S55d2rNnD3V1daxYsWJJx/FFIhaL0dXVJRoFU6kU69evZ9OmTbz77rsEg0G2b9+OVqslk8nQ398vmpUSiQRDQ0PYbDbWrVtHe3s7AGfOnOHUqVMkk0mMRiMmkwmj0Sg0nM+fP8/ExIRYvbhcHisYDJJMJudpVqbTaU6cOEF5eTm7du1a1H1w/Phxjh07JrbzdlDr+V944YVFGt2NoSgKP/jBDwgGg5jNZpqbm4WpjtlsRqfTEYlEmJ6eZnp6mpmZGWKxGGVlZaxbt+4KybHlQiaToaOjg3379pGXlydMacbGxjh58iSKomA2m3nwwQevuzIRDAbp6urC6/Xy8MMPU1RUxO7du/H5fNTV1V1To1hlYmKCQ4cO8fjjjy9YRpBMJjl37hzT09PC8GfTpk2Ul5eTzWaZnZ0lm81e4VQnkUiWB5lMhqNHj+LxeHjooYduuOH3TiGNQu4Q//2///d5SwMGgwG73c6zzz67YHZJFZyHS0uGe/fuFVmTpqamq8p93S1kgLz4HDx4kJKSEiEPZzKZ0Ov1/M//+T+F/FxpaSlms5nR0VHKysrYsGEDn332GR6PRzR5lZeX8x//43+84drDcDjM66+/zuDgIA888MA8icKzZ8/S3d0NQGNjI2VlZeTl5ZFMJtmzZ8+iZ2cVReHVV19lZmaG9evX31ImOZVK0dfXR19fH9/4xjeWJBs7PDzMxMQEkUiE2dlZoT8dCoXIZrNYLBbKy8uprq6msrKSoqKiZRmkKYrC6OgoFy5coLOzE6vVSnNzs/jM0+k077//Ps3NzZSUlGAyma4aHKtGGn19fczMzNDe3s6mTZs4cuQIR48eFeoXN7ofurq6uHDhAtXV1bS2tl4zoI7FYkxMTFBRUbEsytMkEsmNMzo6ysWLF/nmN7+5pGZMMkC+Q0xOTuL1eslkMpw+fZrBwUFMJhPf+ta3rpDqmZ2d5cc//rFo1InH4zQ2NmKz2dDr9Yuup3kr7N69m4aGhmuqH0iuTzAY5OzZswQCAVKpFH/8x3+8oHQTXPoevPbaa1RUVNDa2srZs2eF4xfAH/7hH2Kz2cjNzRUyVYBwfXO5XEJ9Ai6VBcXjcVEDf+TIETo6OtBqtUK3trCwkEgkwt69e0Uzk8lkIj8/H7/fz5/8yZ8sehOQoigcPHiQgwcPsnr16qsusy/EwMAA586do6Ghgfvvv3/Z2T2rxiNXqxlfLvh8Pg4fPkxXV5cw3lAnR3MZHR3lzJkzPPbYY9fMekejUY4ePYqiKGzdupW2tjb0ej1Hjx7l6NGjtLe333RzpqIoeDweOjs7SSaTPP7447e0rRKJ5NZQFAW/3z9PqGAxXzsUConXHh0dpbe3l5deemnJYiCpg3yHUPWNAdra2q76uAsXLvD222/T1NREbW3t3Rqe5C6jKAo9PT309PTwwAMP0NTUhNVqvWb2zGQy8dxzzwGXpNcOHDiA3W4nmUyyadMmUfP+D//wD0xMTPD000+j0+l4++23yc/PF9bbdrsdq9WKz+cTms+bN29m586d7NixA4/HQ19fH729vRw7dgyr1UpxcTHT09PE43FaWlqoqanBbDbfkQ55jUbDfffdR01NDa+99hqZTOaGJ2LhcJjm5maeeuqpRR/XYqDRaJZ9cHzq1Cn27NlDTU0N27Ztu6a1uJqNvV5JyMWLF6mrq2PXrl3zHuv1eiksLLwl5RL1dRKJxILOeRKJZHG4muzi559/jtfrJR6PU1FRcc3Y5kaIx+NMTU0J1ZmcnBxsNhvBYJBYLLaoAfhiIwPkO0woFOJXv/oV7e3td1TmSVEUUZMqWRouXLjA9PQ0L7744jXdxa6GzWbjm9/8prCKVj/LVCrFxMQEWq0Wp9MpLHm3bdsGIGyT4/E4ra2tmEwmxsfH+eSTT/joo494+umnhQHD/fffTzqdZmZmRtTLDgwM4HK5WLly5aLuj4UoLS3l2Wef5ac//Sn5+fk3dHB0Op1ChlFya3z66ads3rz5hrLv6gRtdHR0ngLPXMLhMB6Ph2eeeeaKk+z27dv5u7/7uyt04a9HJBKho6ODmZkZGhsbaWxsvOHnSiSSqxMMBhkdHSUWixGNRgmHw2QyGXbs2CEmy+FwmL6+PqampvjqV7/Kz3/+cxoaGm76vVRzrv7+fmZmZshms1RVVdHc3Mzjjz9Ofn6+OC6oK5jLYQV9IWSAfIfR6/XU1tbS0dHBww8/vOiNOtlslv7+fs6cOQPAzp07hXh8OBxmZmaGeDxOSUnJNbNGktuju7ubqakpnn/++VtWa9BoNAtmVe12O//tv/034vE4DoeDRCKB3+8XTnU6nY7c3Nx5wWZFRQXl5eWMj4/z8ssvYzQa0el0FBQU8I1vfAO32y2s1e+7775bGu+t4na7eeSRR9i9ezelpaXzrHxV3XAVRVHo6uri0Ucfvatj/KKhlujcCFarlaamJjo6Oq4aIKsGM+Fw+IpMsapHfaPHukwmQ1dXF93d3bhcruu62Ukkkhtj7qrm2rVrqa+vx+l04nQ6OXfuHB0dHTQ1NdHf38/09DSrVq2irKyMX/ziF9TV1d3UKlAmk2F0dJS+vj60Wi3btm2jrq4Om8121WPBcg2MVWSAfIcIBoPs3bsXuHTCSaVSnDlzhvr6+kXTg81msxw/fpzR0VEA1q9fPy9zuXfvXioqKsjNzeXAgQNYLBZhROFyudBqtaRSKXQ6nchWdnV1MTY2JjKOy7HJaLkRj8fp7u7mW9/61qJ9tpcz1+LWaDRSWFiIx+O5ZrOaaoZQVFREKpUiFotx6NChqz5eURTOnDmDy+W6qRrhW0E9WJ88eZJkMikMT44ePYrL5aKxsZGcnBw8Hg9Wq1VmE2+T/Px8IpHIDT8+Eolc06XN5XLR0tLCT37yE7Zu3crWrVvFyS6bzaIoCp988gmtra243W4URSEajeL3+/H7/YRCISFRODs7S05ODps3b6akpOS2t1UikVyKQTo6OrBYLLzwwgtXNMK1t7dz8eJFzp8/z4YNG4hEIpw6dQqHw0FLS4tIoCiKQiAQwOFwkEqlmJycFJfCwkIaGhrw+Xx0dXVRUlLCrl27qK+vX5aqPTeLDJDvEDMzM5w+fVpcX7VqFbm5uezdu1c46mWzWVauXHnTTXHZbJbh4WG6u7spKChAp9Px8MMPC/k41WEsnU6zfft2qqqqePzxx4Vj4ODgIB0dHcAl5Y1EIiH0CNUv9ZkzZzh9+jRr1qwRwdLIyAj9/f3Y7fZ5etG/7Xg8HuF0dbdYvXo1p0+fviE1B51Oh06nw2AwkMlkrrDyhUvfqddff53x8XESiQTPP//8HXd+s9ls7NixY95tDzzwAAcPHuTQoUPCROWpp576QhxslxKXy4XH47nmY9QVif7+fkZHR9m0adM1H19ZWYndbmfv3r2UlZUJI46ioiJeeukluru7eeutt4QEntlsFmU1jY2NuN1ujEYjo6OjfPrppwwODsoAWSK5ASKRCJOTk8L1NC8vb56Sy9jYGKdOneLBBx9k48aNCx4/NRoNv//7v09vby/vvfceNptN9Cd0dHTQ398v3ECTySQmk4loNEplZSWtra08+eSTdHV1cfDgQXQ6Hf/pP/2nZddAfbtIFYs7iLrkEIvFRE1pKBRiamoKl8tFT08Px48fZ/v27UxMTGAymbBarSJ4URSFRCKByWSa97rHjx9nYmKC//Af/gPl5eV8//vfJy8vj0AgQHV1NU1NTRQVFeFyua547kIoisLMzAzjHFR0xAAAIABJREFU4+OMjY0xODhILBbDZDIxPT0tsk/pdJrKykpGR0exWCy0t7ff0Ot/0VEl03bt2nVTS8u3ysWLF3nrrbdYv379TTcyHThwgPb2dlatWjVveeuDDz5gYGCArVu3Mjo6SldXF9/85jevmUWU3Dv09vby4YcfXjEhgUs17l1dXfT09FBcXMzk5OQNZXMTiQQdHR243W6eeOKJBb/3wWCQTz75hIGBAWKxGHa7HYvFQiqVIhwOi6x2fX09LS0ty37JVSJZbLLZLGNjY4TDYZLJJIlEQlwymQx6vR6dTif+JpNJZmdnqaurI5lM4vF4qKiooLa2lmQySSwWY2xsjL6+Ph566CHRq3I50WiUX//614yNjbF69WpxLhkYGGB4eJiHH36YUCgknEpVGdLLkyvpdJp0On3PxgJS5u3/Z+9No9u6zzv/D3aQILiBBLiA+y6RIilqIyVKsiVZi+0msVM7ju3ETrq3mclpms5/5szMi77omUk7bU+206Y9adKmduIkdqzYsTbLEkVZC/ed4r6BG0iAWIgd9/5fqLgxLUoiZWqz8DlHRxKJi3sB3Iv7/J7l+31AGRwc5Je//CUKhQKdTofVapXktjweD0tLSwA888wzK24+MzMzfPjhh7z44osUFhYyMDCAWq0mLS1tQ05SURQZHR3l/fffZ3Z2Vmqyr66uRqFQ4PP56OjoYGZmhtTUVHbu3PlI65DOzMzQ19eHx+NBoVDcsT3zWggEAnzrW98iLy/vBivdtRDRnvR6vTz55JMolUqam5ux2Wzs3btX+vIbHR1lYGCAL3/5y5+6rMDDRCgU4sSJEwQCARQKBR6Ph+3bt69bMz0cDvO3f/u3bNq0CbPZjEqlIhQKYbFY6O3tpbCwkLq6On76058SFxd3S2crURRpbGyUbtLPPPPMLQPb733veyQnJ5OdnX1DleXdd98lPj6e3bt3R9u5ojxyLCws0NHRgV6vJzs7m7i4OOLi4tDpdMTFxUkBsc/nk4JmpVJJUVGRdM89ffo0H374oZRN1uv1JCYmkpiYSEFBwaq+BkNDQ7z99tukp6dLC1NRFOnp6WFmZoYvfvGLkkLXp51ogPyAIggC09PTaLVaUlJSGBsbk3p8srKyeO2119i2bRvp6emSNuH4+DgWi4Xi4mISEhLIysq6o0nTtSCKIiMjI7S3tzM+Po7b7UYmk0mDYaIoSgH5448/Hr3BAW1tbaSlpUk2u3eDmZkZjh8/jiiK1NbW3lHWbWlpiaamJmJiYsjKyiIzM/OGRc74+Dj9/f28+uqr98TWOcpKBEHg9ddfx+fzYTQamZycxOFw8MILL2AwGG45/LIao6OjfPjhh0xMTKDRaPB6vaSnp/PEE09gNpvx+/3MzMzwi1/8gkOHDt30vFpeXqahoYFvfOMbt7zmA4EAwWCQxcVFfvrTn+L1eikuLiY+Ph6z2YxCoeD9998nPT39niioRInyoODz+ejp6WFhYYEjR46wadOmO648iqKIKIq3vBZDoRCdnZ0sLCwwPz/P7Ows1dXVK4LgiYkJenp6ePnllzGZTI9MW1s0QH7I8Pv9XLhwgebmZo4dOyZl8zQaDSqViqWlJcxmM0tLS9hsNr7yla/c9aEq+O3wTTgcxu/3c/z4ceRyOdPT0+h0unuuhvAgsry8zLlz53j++efx+/0sLy+Tnp6+4a5qkZ7h5eVltm1b9dreEEZGRhgZGeGrX/1qVAXlHtPf38+ZM2fYu3cvPp+PEydOoFAoUKvVeL1eXn311Tu67peXl/F6vVKlyuFwcOnSJXp6eiTNbJVKRWlp6arbT0xMSIH6agiCwG9+8xu6urqQy+W88MILzMzM8MEHHxAfH8/y8jKBQIC4uDjcbjdpaWm3zFhHifJpIaI6de3aNaqrq9m/f/89UWzp7u7mxIkT5ObmEhcXh9FovGG/TqeT7u5uyRFztXasTyNRo5CHBK/XS2trK5cvXyYxMZGSkhJaW1uZn5/ny1/+MkajkZ/+9KfIZDJsNhsymYzPf/7zN5Vi2mgiAV7kJn306FF++MMfotfrsVqtNDc3s3Xr1kc6kxyRyHrrrbckl8SGhga8Xi+ZmZlUVFSwefPmT/ylKJfL+exnP8v/+3//D6/Xe0emDGshPz8fl8vF8ePHefHFF+/KPqKsTldXl6QkExsby1NPPYVKpcLlcnH+/Pk7NtLQ6XTodDrGx8d5/fXX0el0pKamIpPJSExMpKqqih/84AcYjcZVKwczMzOUl5ff9PkbGhqYmpri0KFDOBwOfvKTnxAKhaiurpbKvVarlaWlJRYXF6VWsihRPs1EBvf1ej1f+cpX7voQdISrV69y7tw5tm7desvvjPj4eOrq6uju7n5ksse3Ixog32d8Pp8k4n369GlMJhPV1dV0dXUhk8koLy/n85//PHFxcQA8++yzdHV1kZKSQnZ29n09kZOTk/mzP/szzpw5w+zsLDMzM7z77rskJydLqh2PIgUFBTc4Jvr9fqxWKy0tLZw5c4bDhw9TUlLyiYxdXC4XcL3EdjdxOBwkJibicrmiWeR7SGFhIWfPniU/P19alDqdThobG9m3b98ndu/78MMP2bx5M3l5eZLe9Ntvv81//a//laqqKpqamjh06BByuZyZmRkWFhYQRRGn03lLFZtr164hk8m4cOECoigSGxuLTCajv78fi8VCTk4OmZmZpKamYjAYOHfuHJcuXaKwsPCeBQ1RotwrAoEAvb29zM7O8sQTT1BRUXHP7tuhUIizZ8+uMAS5GcFgkNHRUebn5+9a2+bDRrTF4j5ht9s5f/48vb29BINBABITE4mPj2d2dpYtW7bw5JNP3uejXDsRZzS9Xs/Q0BB2u/2hOv57yeLiIr29vdhsNpKSktiyZQsVFRXExsaiVCrX9OU5MDDAO++8s2owvtG4XC6Gh4eZnJxky5YtHDx4MOrYeI/4u7/7OxQKBRUVFRiNRkZGRhgdHcXlcvH5z3/+pm0Qa+GnP/0pCQkJmM1m2tracLlc+P1+3G43Op0Or9dLZWUlWq2W1tZW6urq8Hq90qDfzfj+978vuUGq1WoCgQDhcBitVsvU1BQOhwO4LjHpdruJjY1Fr9czMzMjyRGmpaVRXV19x6/tYcfn8xEMBomNjY0qezykiKLI5OQk3d3dlJWVcfDgwbtW6bsZ/f39fPDBB9TX19/2sT09PZLi1tatWx+ZZEi0xeIBwefz0d7eTnt7O3Nzc8D1m0RiYiKlpaUYDAZkMhnPP//8Cnexh4H9+/fz3nvvSVJOD9LC60HDYDBQX1+PIAjYbDZGRka4ePEiwWBQkvWJjY3l0KFDbN68WdrObrczMDBAb28vdrudysrKezJprNfrqaqqoqysjO7ubv7+7/8ek8lEamoqBQUFlJaWRktyG4AoijQ1NXHhwgXUajXl5eVkZ2dLk+UpKSnk5+eTn5/P4uIix48fx2Qy3WAAsFby8vK4fPmypFrzwgsv0N/fz9DQEElJSXg8HiYnJ1GpVBw7doyKigppW0EQmJmZQRAEUlNTJXvznp4eSarKarWSkZFBYWGhdH7k5uZis9loaGhAoVDw9NNPS8Ohy8vLBINB7HY77e3tj0yAHJHzdLlcuN1urFYr09PTyOVyQqEQCoWCmpqae9ZKF+WT43K56OjoQBRFXnzxxTXp1d8N5ubm1qzPPzMzw759+x6Z624tRDPI9wibzcZ3vvMd6f9qtZrq6mqqqqoe6onRYDCIKIoolUoEQWBgYIA333yTvXv3RpUP7gBRFAmFQjQ3N7Njxw4qKysBmJ6e5p//+Z9JTU0lPz+f9PT0+5ZZ8ng8OJ1OXC4XFosFo9HIc889d1+O5dPC9PQ07733HsvLyywtLVFbW8vY2Bgej4eKigra29vxeDzk5ORQWVmJXC5naGiIgYEBNm/ezLFjx9b9HSKKIn/9138tDf0lJSVRVlZGWloaly5dIjk5mcrKSvLz8yVb6YGBAa5du8b4+DixsbHI5XIWFxcpLS1lYGAAQRCk1iqv10swGGTfvn2SVKDH4+H06dMYjUYqKytXTQTYbDY++OAD6urqPlXGIZHhZp/PJ/1ZWFjAYrEgk8lQqVSo1Wp0Oh0VFRXo9XoEQaC9vR273U5RURF+v5+UlBQSExNXfN7Ly8tcuXKF/Pz8dRtPRdl4rl69yvT0tORm+tJLL6FUKllYWMDpdBITE0NaWpr0GVosFpaWllYkRDaCkZERTpw4wd69e1f9fSgUor+/n7m5Ofx+P/X19dTW1m7oMTzoRFUsHgAEQZD0DlNTU4mPj39og+KP8qtf/YqOjg4AzGYzr776Kj/+8Y9xu91UVlZGg+Q75MqVK2zbtk0KkEVRpLm5mfPnz3P48OEH5twJh8OcPHmSvXv3UlxcHP2874Bz585x9epVysrKSE5O5syZM8jlcgRBYNu2bTz55JNSljEyVBeRRXO5XDQ1NXHw4ME1SaWFQiF8Ph+Dg4P4fD4uX75MQUEBOTk5LC4ucuHCBeC6G15KSgqzs7MEAgHi4+OxWq2YTCbpT0Ti0Waz4XA4CIfDkl4rXHfqWlpawul0kpiYKJmDpKSk3LbkOzY2RktLCwkJCezevfuel6Y3inA4TGtrKxaLhXA4jFKpRKFQSH/HxMRQUVFxyyyfz+fj1KlTqNVqFAqFZOK0d+9e5ubmGB4exul0StWngwcPPnQVyE8jEcWn1tZWfD4fZrOZvr4+EhMTWV5exmg08uyzzxITE8M//MM/4HA4+MY3viHNG20EwWCQb33rW5Lm/ccZHx9nenqao0ePkpmZ+cDcV+4l0QA5yl3jww8/pLm5mcTERJKTk3nqqacQRZHOzk7effddjh079kibiNwpY2NjOByOG5QjvvOd77Bly5YHyuFufn6eyclJ5ubmeOWVVzAajff7kB4qGhoaGB4eZvv27cBvg9iGhgaeeuop0tLS+N73vieV3Lds2UJRUZG0/ezsLD09PfzJn/zJqoN7y8vLNDU10d/fj9VqRRAE1Gq1ZCjwUVfFSEn/ozfKnp4eRkdHOXr06B1VLa5cuYLVaqW4uJjc3Nw1Kbj4/X5pO6VSyWc+85l17/dBwOPxcOrUKTIzM6mpqdkQhZ/Z2dkVVuyZmZlSL/qlS5ew2+3U1dVFr8Ob4PF4aGpqwuPxEAgEEAQBQHo/dTqdZIW+EQiCgNVqxW63k5mZKVUGuru7mZubIy8vj2vXruHz+XjiiSc2PIP7L//yL+Tl5UnteKIo4nK5cDqdDA0NsWfPHqqqqjZ0nw8T0R7kKHeNXbt2EQ6HOXv2LA6Hg2AwiEqlorKykitXrtDZ2UlOTg7JycmP5Or0TjGbzVy7do2hoaEVrmnl5eVYLJYHKkA2Go0YjUZaWlqYmpqK3pjXSUVFhSTDFMkuRjTHk5KSmJqaIjExkd27d69q6WoymZienuZb3/oWiYmJHDlyZMXg5ltvvUUgEKCkpIS6ujp6enpYXFxk//79NxzLaotZtVqN3++/o7kCURRJSEhgbm5uXZPxS0tLWK1WEhISyM3NRRAEBEF46Bbb09PThMNh5ubmNkz+Mi0tjWeeeWbV3+3bt4+BgQEuXbrE/v3719x/+qjg8/l477330Ol0VFVVERcXh1qtRi6X4/f7pfPuww8/JCkpaUO0gOVyuVR1+ejPtmzZQn9/P9euXWPPnj34fD46Ozs3PEAuKChgdnZW2v/U1BRdXV1kZWWxefPmqEnPLXh0BWujbAhyuZz6+noef/xxbDYb//7v/y797rnnniMrK4srV64wMTFxH4/y4UOpVFJVVcXbb7/N1NSU9PPl5eUHUmfa5/Ph8XgkRZYoa0MQBN5++20KCgqQy+VYrVba29u5cOECR44cITExkYGBAWw2G3Nzc1Lf70eRyWRs3bqVQ4cOSba0Ea5du4bVamXbtm0YDAbsdjtTU1PrGhoqLCwkISGBvr6+db++cDhMf38/JpOJ4eFhOjo6pIy01+tddRu73U5vby9KpRKPx0NXVxdvvfUW7777LrOzs+s+hk9KxKlsvQwNDdHR0UFSUhJPPfXUXTiy1SkuLiY7O5sLFy7c9D2+VywuLtLY2Mj09PR9PY4IarUag8GASqUiPT0dvV4vGXDFxcVhNpuprq7m6NGjLC0tMT4+flePJ1IV0mg0xMTErLh2N4ri4mJJFEAURSwWC4cOHeKFF17gscceuydGJQ8rG7Icl8lkY4ALCAMhURS3yWSyZOBnQC4wBjwniqJ9I/YX5cGjvr6e8vLyFSXexMREHnvssWhwfIeYTCY2bdrEa6+9Rnl5OSkpKXR3d99VG+v14PP5pMBtYmICmUxGT09P1BVtHbjdbtxuN06nk5GREQwGA+Xl5Rw9ehSDwYDb7aanpweFQkFraytut5uysrJVn2tychJRFCkpKcHhcHD+/Hna2towm8309PRgMBgkE6L1SAPKZDKSkpJYXFxc9+tTKpWYzWbGx8ex2WzEx8cTDoexWCy0tbVJvbiRP1qtlvn5ebKyssjLy0OhUJCamopareaDDz7g0qVLHDhw4K5qrIuiyPz8PJ2dnTidTunniYmJZGVlkZGRsaY+UbfbLQVAFovlnioZVFdXS5bge/fuZWFhAZPJdE+DoYWFBS5evIjRaCTSOqlSqYiJicFkMlFSUnLPF/vDw8MsLS3d0ugGQKPRUFVVRXt7OykpKeh0ug0/lqWlJQYHB0lKSmJ+fh6DwSC1fGzk+5KRkSGdg2NjY4ii+InkIR8lNqQH+T8D5G2iKC585GffAmyiKP4fmUz2/wFJoij+t1s9T7QH+dOH0+mkpaWFpqYmKioqSElJeWgHbu4Xfr9f0ssuKCi47+0VNpuNjo4O3G43JpOJpaUldDods7Oz7N69m4MHD97X43tYibQnfZwrV65w4sQJAIqKisjLy0Or1UpKIuFwGLhezvf5fAiCgNvtBq4Ht3FxcSgUChwOB6Io8rnPfW7NN+C+vj4mJydxu93IZDIMBgNGo5Hi4uI1P8fCwgKNjY0cPnx4xbUvCAIul4tgMIjD4cDj8UjvQ3Fx8Q1BqMVioaurC6/Xy86dO8nIyFjT/tdD5Nx2uVxkZ2dTXl4uBblDQ0NYLBaWl5fRarXk5OSQkJDA+Pg4i4uLUi9rRI7Pbrfj9/uZnJxErVZz+PDhDT/eWyEIAmfPnsXpdKJSqQiHw2RkZCAIAktLS5IR1XoCd5/Px9DQEKFQiNLS0hvafT7K8ePHKSwsZNOmTZLBjNvtZmlpiZGREQoLC2+62LsbzM7OcvnyZWpra9csj9nc3IzD4eDAgQMbfjzNzc2UlpZiNpt5/fXXOXLkCA0NDRw8eJCSkpIN3deVK1c4ffo0+/fvp66u7oGsQt4v7lcP8meA/f/57x8D54BbBshRPn10dHTQ0NCARqOhra0NmUyGTCajqKiI4uLiaF/yGtBoNHdVm3J6epqFhQXC4bBUTlYqlWg0GrRaLVqtVlIocDqdzM7OcuTIEcrLy3njjTdWGD6s1tcaZW18PDgWRZH/+I//YHh4mLi4ONLS0pienmZqaopgMIjBYCAlJQWNRoMoilRWVnLx4kVqamokRyyDwUBNTQ1KpZLJyUlaWlrW1C7Q1dXF6OgoSqWSwsJCjEajpJfc09NDX18fKpWK/Px8CgsLb5qZFEWRubk5wuEwDodjRYAsl8ulHtmIDNytyMzMJDMzk+7ubi5duoROp0OpVBIOh9mxY8cd60H7fD4uXrxIVlYWPT09mM1m6uvrV/Q7q9VqNm3axKZNmxAEgeHhYcbHxxkZGSElJYUdO3ZIA4xDQ0M0NzejVCqJiYlBr9ffNmN5N5DL5ezfv5++vj42b96My+Wip6cHtVpNWVkZPp+PpqYmHA4HOTk5N82SCoLA4OCg1BaTmJiIQqHgN7/5DTKZDIVCQWlpKampqeh0OtRqtZQFjbR4yGQyEhISSEhIIDMzE6VSSX9/PzMzMxgMBpxOp2SOAtcrD+np6WzevHlNwZzVauXatWvo9Xo2b958Q6+6IAi0tbVRWlq6Lu348vJySX5xo7PIgUCAlJQUMjMz8Xq9iKJIQUEBH3zwwYbfG3fs2CGZUUVZOxuVQR4F7IAI/JMoij+QyWRLoigmfuQxdlEUb/gGk8lkfwD8AUB2dnbN3e75iXJv8Xq9/PCHP8ThcKDX6yksLJRsa1UqlbSCfpgDZa/X+1BmxUOhEAMDA1gsFrZv345Wq0UQBN577z3guqGJVquVprvj4+NJSEigpqYGnU5Hb28vJ0+eZNOmTVy+fBm4Hsx/9atfxev1Yjabo5mKT0AoFOL73/8+wWCQPXv2EA6HSUtLIy8vj46ODpaWlqioqGB8fJyuri6mp6fJysqSJtIDgQCNjY3Y7XYUCgUajQafz8f+/ftvG0xevHgRm83Gk08+ecNnaLPZWFxcJBQKMTw8jN/vJzs7W7qpq9VqbDYbKSkpNDc3Y7fb2bJlC1lZWRv23ni9Xubm5giFQvj9fq5duwZcP/9iY2OpqqpaU8AsiiINDQ34/X5J0/1e9gvfbyYnJ7l27RoejweDwSA5qeXl5UltPxaLBaVSSWlpKZmZmdJiKBwOEw6HsVqtdHZ2So6JSqVS+s7YtWvXqo5soijicDike0FCQgLx8fHS943b7WZoaAi/38+2bdtu0MMOBALYbDbsdjuTk5N4PB6ys7Ox2+1S9UQURXQ6HXq9HqvVikKhYO/eveuWUfvwww+x2+0kJSWRmppKTk7OhrSqnDp1ipdeegmDwcBf//Vf87nPfU46H+vq6qKGHfeIuy7zJpPJMkRRnJbJZEbgNPA14PhaAuSPEm2x+PTi9Xo5efIkfX19FBYWEgwGGRsbQ6PRoFAoSE5OJhAI3O/DXDd2ux2Px0NxcfEKp7EHGY/HQ29vLzMzM8B1d7NQKITX6yUhIYGUlBQaGxtXbPPf//t/X3FTWFhY4Ec/+hHbtm0jKSmJUCjEzMwMbW1tkhOg3+/nj//4jzdU1/NRZnl5Gbvdjl6vp7Ozk8bGRmQyGUajkYyMDNLS0lZVeRAEgYWFBZqamggEAsTExJCcnExcXNxNs78Oh4OGhgZiYmKora1Fo9EwMTFBTEwMRqMRmUwmTd1bLBbJmjqyP41Gg9/vRyaTcezYsVuW4jcCQRAIhULYbDYuXrxIfn7+mgKMiCnJ4cOHiY2NfSiVMjaCwcFBlpeXUSgUDA8PA9cD4NTUVIqLi9dsZhVpm/F4PJhMpk+0QI5ULDo6OqTKY6TK4vf7JVMVk8lEQUEBarUaURRZWlriwoULFBYWolQqsdls5OTkrDDmWA8+n4/p6WmWlpZYXFzE4/FQUFDwiaoCbrebCxcu8Bd/8RfMzMzwxhtvSIZACoWCUCjEf/kv/yU6QHcPuOstFqIoTv/n3/MymewtYAcwJ5PJ0kVRnJHJZOnA/EbsK8rDSUxMDJ/97Gc5cOAAr7/+Ona7HZ1Ox9LSEkVFRQwODmI0Gu/KMMTdxGQyEQgEGBoaYmJiApVKhVarJTs7m7S0NJaWlkhJSZGku2ZnZ0lJSblvX3xOp5Nz584B10uwMTExaDQaUlJSUKlUXLt2jeHhYf7H//gfuFwuJiYmEEVxRfl/eHiYn/zkJ2zdulUqjSsUCnJzc8nJyZH2c+bMGd58802+9KUv3fPX+WnC6/Xyve99j2AwiF6vx+v1SqXuxx9//Lbby+VyjEYjTz755Ir2gKmpqZtmwxISEjh48CDvv/8+p06dQqlUSu0MESMQuH5dHzp0CL1ez/z8PDMzMywtLZGWliaV81cLOCPZSZfLRVZW1id2f5PL5ajVamnBvVZHsvn5eemalcvlj2zFIzk5mcnJSZaXl0lNTSUcDhMMBqmrq1vXgiHSNrMR8nIymYz8/HzMZrO0AFpeXkYQBOn7arVturu7iY+Pp6ysbEMqk1qtlvz8fOn/c3NztLS04HK5VkiyBQIBZmZmsFqtiKIo6ZqvxvT0tGTBPjMzQ1JSEkNDQ+zatYu4uDjm5uaknvYo949PHCDLZDIdIBdF0fWf/34C+CvgOPBl4P/8599vf9J9RXn40ev1/MEf/AGTk5P88Ic/RKVSMTk5iV6vJyUl5Z4ObWwkPp+P5eVlKcvX29tLa2srarWaQCCAWq0mHA4jl8sJh8Po9XpKSkowm8337Bgj2TKA7Oxs8vPzV+hTWywW3G43L730EiqViuTk5FWd8To7O4HreqyRL/FgMCgpEkRc33bt2kV7ezsLCwtr6jGNsjoqlQqv10tFRYXkwhkKhaThvPUgl8spKipibGyMvLw8BgcHMRgMZGZmSsFhJAvY19dHMBjksccew263k5eXByA57iUlJZGTk0NPTw+zs7PIZDJiY2NJT09namoKuVxOIBDgN7/5DSqVSnKOW15elrLNSqUSh8NBW1sbmZmZ7Nix447eI0EQmJqawuVyrUsFIBQKAdDf349er5cWeI8ao6OjiKJIdXU1mZmZ+Hw+mpubeffdd4mLi8NkMlFaWnpfsusfXcDdrhoVMeUoKipifHwchULByMgIer2eoqKiVds91ovJZGLXrl1cuHCBlpYWAMkxMiYmRjLbuRnT09MMDQ3x0ksvAaDT6bDZbASDQXbu3PlIVjAeVDbikzABb/3nTVYJvCaK4gmZTNYEvCGTyb4KTAC/uwH7ivIpISsri6KiInQ6HQkJCZw/f/6OZKQeFCLDbAaDgezsbLZs2QJcz2iEQiEcDofkXra8vMzY2BhtbW20t7eTnp5ORUXFXc0qj4+P09zcjEwmQy6X43K5sNlsKxQxIlqg2dnZqz6HKIorypwnT55EEATpZ+FwGJ1Oh0qlYmFhgdraWjIzM+no6LgrU+CPCkqlkueff57e3l6ampqQy+Vs3bqVxMTE22/8MQKBAL/+9a+RyWSSGsP4+DgtLS0rAm6FQkE4HCY+Pp7ExMQV+7p48aL02IGBATS86mNYAAAgAElEQVQaDXv27CE1NVV6jFarpbe3l8LCQjIyMvD5fLhcLtxuN9nZ2RiNRvx+P+fOnaOqqgqVSsXly5elQbz14Pf7aWtrY3Z2FqVSSUlJyZqDjPLycnQ6HWNjYwwPD7O4uMjWrVul30cWfg/zjMRqtLS0sLy8LPXV2mw2jEajtGCPiYlhz549LC0tSTKO09PTHDx48IHOssvlctLT05mdnWVmZoZQKERaWhoul4v333+fqqqqT1ytgOsZ96KiIlwuF3A9aK6rq2N5eZnGxka2bVu1Ys/U1BSdnZ28+OKLkgqLQqFALpcjk8no6+t7aFr1HgWiVtNR7hsDAwO8//771NfX09vbS3x8/D3NqN5vIi0Xg4OD2Gw2SktLKSoq2vAMwsDAAF1dXchkMjQaDWazGZVKRX9//wpHLkEQePfddwmHw1RWVrJv3z6USqWUBf+bv/kbXn755RXlxgiiKPJXf/VXAOzdu5empibMZjPT09O88MIL91QD9tOMKIp0dHRw8uRJduzYsSIovR0+n49Tp06RlJRETU2NNNG+sLDA+fPnUSgUK2x31Wo1ycnJpKSkSHJmHo+HxcVFcnNzb9rjGzGH2LlzJ2lpabc8punpaa5evcq+ffukgbD9+/ezuLiIz+ejsLDwhlJ6RNPV4/HgcDiYn59HEATC4TBGo5H6+vo1vycfP+6IzJbdbsdisUgtGAkJCYTDYcrLy1etqjxMeDweTp48SUFBgZT5jI2Npba29qbDxqFQiDNnzpCZmfnQBHAR1Q6PxyPNWIRCIX7nd35n1faMjeDs2bMkJSWtem04HA4uXrzIl770pRXXRWtrKx0dHQiCwPT0NL/3e793w1BilLtH1Go6ygNJZmYmDocD4JG0u5TL5WRkZJCRkUFbWxu9vb0MDw+za9euDW1JyM7OJjExkYsXL7Jv3z7i4uIQRfEGZzS5XE5WVhZjY2O0trbS2toKXB/iiwxQNjc3k5GRccPQlUwm45vf/KbUPpKdnc3AwAD5+fnR4HgDkclkVFVVER8fz89//nN27dq1Zl3sSBk3IinV09MjKVLIZDKpxG42mwmFQkxOTjI3N0dHRwcqlYqkpCSsVisAIyMjLC4uUl5ejlarpb+/X5LqCoVClJeX3zY4husmBjExMVy5coXl5WUATp8+TV5eHrOzs0xPT6/oZ9Xr9fT395OTk0N6ejoFBQWIosg777zDnj171iXh9XF0Oh2CIHD+/Hmys7PZvn07mzZtwu12Y7PZ6O/vx2KxPPQBcldXFyaTSapyrYVIReFOqhb3g+7uboaGhkhPTycrKwuNRgNcHzy8W8Gxz+fD4XCwZ8+eG34XDAbp7Oykrq7uhusiPj6eiYkJDAaD1N4UDZAfDKIBcpT7RmRqPNKj+yhTXV1NWVkZZ8+e5fz585SVlW3YosHr9dLR0YHJZFrRwyeTyZicnMRsNuP3+xEEgby8PKkv3O/3I4ois7OzmEwmCgsLuXbtGmNjY6s6MX1UY7OgoGBdbm1R1kd+fj67du1iampqzQFyWloa+/fv59y5c5w4cUJSs6ioqCArK0uS54LrgYTX68Vuv25+ajQaJSOPPXv2oNPpGBwclJQxAKqqqrBYLCwtLd20TWc1QqEQPp+P1NRUFAoFarWaF198EUEQGBsb4/3332d+fh6dTsfU1BSiKLJp0yZ8Ph/JyclcuXKF9PT0dWXTP4ogCHR2djI3N4der+f3f//3pV7ViP53RNLsZtUdp9PJxMQEVqtVsonfiH7XjUQQBCwWC3Nzc+t2u/R6vfj9/gc6QLZYLIyOjkrmM3v37r3ri5lIZlqn09Ha2kpiYuIN9zKPx8OlS5fIz8+nrq7uhufQ6/Xk5+fzhS98AYvFclcMcKLcGdEAOcp9JdLXGuV63+aRI0ewWCy0tLRgMpnWHPwMDg4yNjZ2w+RzJNDJzMxc0Vspk8nYsWMHV65coampCaVSKYn0l5aWcvjwYebn56UM4/T0NEVFRfzpn/7pQ59B+7QQCASkzNhakMvlGAwG1Go1RUVFt7SbjZxLBw8e5L333sNisUhDeBEJQLlcjkqlklpwlEolu3fv5tKlS5w4cYJjx44hCMJtJd7q6uo4e/YsVquVpKQkvvKVr0jW0xGjEY1GQ21tLUqlknPnzvHrX/9a2l6v13PgwIE76o0VRZHm5mbUajWvvvoqiYmJK57nxIkT0gDf8vKypDEd4eLFi5KLXlJSEiaTCbfbzfvvv49er6eysvK+DqgGAgEuXryIx+OREhFFRUUYjcZ1PU9MTAy5ubmcOXMGmUwmqZrExsaydevWey7lODw8TH9/vzQ34XQ6CQQCmM1m8vLyJHvyu0l/fz/9/f3IZDJp8XTkyJEVj7Hb7Vy+fJndu3dTW1u76r2uqamJ8fFxTpw4sarueJT7RzRAjnLfiOhu3q2S18NIpM1hfHycycnJ2wbIPp+PxsZGvF4vZWVlaDSaG76E4+PjiY+Pl/4f0SweGxtDpVLx7LPP8utf/5rU1FSpL/H06dOoVCoKCwuprKwkNzf3kc/yP2i4XK470heWy+WS1urExATBYJDs7OwV/aeBQIDk5GS2b9+O2+0mNzeXf//3f5dc+CJmGhHr4Pb2drRaLQqFgj179tDY2CgFsatZW0e0mUVRxGQykZKSglarvaE/tLi4mP/1v/4X//f//l9aW1txu91s3ryZgoICTCYTi4uLvPPOOzQ0NJCRkUFBQcG6evgjfcZf//rXV7yXExMT/Ou//isymYydO3fetE3IbrezdetWMjMzV1x3Xq+XwcFBGhsb0el07Nu3775dPw6Hg/LycrKzs+/4GGQyGVu3bqW6uhqfzydJDU5PT3PmzBni4+PJycm5J1WjQCBAZ2cnFRUVKBQK7HY7BoOBwsLCW6pHbDQ2mw2TyURtba2khvLRc89isdDe3s7v/M7v3FKdqb+/n3379tHT08O//uu/Ul9fT05ODi0tLZSUlKw5SRJl44kGyFHuG3a7PWoicROMRiOTk5M3/NxisbCwsABcz35NTEyQlpbG3r17b3nzE0WRsbExZmdnWVhYwGw2s2fPHjZt2sTMzAwKhYKcnBypFeO555674aYf5e7i8Xhoa2uThuMiN9uYmBgMBoOUqYrIp01MTFBZWbnu/RQVFdHZ2UlnZyd5eXkkJCRw5swZUlNT2bVrFwB5eXk0NDQQCoU4cOAAcXFx1NbWcunSJWJiYrh8+TI7d+7EYDBgMBgk/exID/CePXtoa2tjdHSU9957D7VaLUm9+f1+XC6XpA2uVCoJBAJ885vfXDXgl8vl/PEf/zGTk5MUFRWteIzBYODrX/86ExMTXL16lbNnz1JaWoper1+Tk15GRgYWi4W33nqLL3zhC9L5npKSQkZGBi6X67Yl79jY2Buuk5iYGLZs2UJZWRnNzc2cPn1asnv2er0Eg0HC4TDZ2dkboqpwM9RqNUajkdnZWQoLCz/x88lkMmJiYqTFVGZmJmVlZYyPj9PZ2YnRaLzrrSWR6yIifRiRH7zXpKamEnH+/fiibGhoiKGhIV5++eVbnj9Op5NQKERiYiJ1dXVMTk5y5swZrFYrWq2WUCjE3r177+rriHJzogFylPtGf39/tFx/E0RRvEHn1mq1cvnyZYxGo5QpKS4upqSk5LaBbH9/P1arlf3795OXl7ciW5icnIzJZGJiYoLDhw8/kgOT95NwOMy5c+doamoiPT0duVyOx+MhHA5LjnVutxtRFJHL5QiCgCiKq1YLIoiiKA3S2Ww25HI5cXFxxMbG4nA4SE5OZseOHezYsQO3283jjz/OP/3TP+F0OomPj0ev13Po0CF6enr4+c9/ziuvvMITTzxBQkICp0+flhQxIgo0kR7VS5cusby8TE1NDQUFBYyNjZGSkkJqaqrUy6tQKKRzMCI7ZzKZbpkN/7jU3EeRy+Xk5uaSm5tLf38/v/zlL6WMnkajQalUkpCQQFlZ2Q3PIZfL2bZtG8ePHycYDEqLzNjYWL761a/y7W9/m+Hh4RWDXh8lMtx4M1QqlaQH3t/fj06nQ6vVotPpkMvldHZ2MjQ0xJ49e9ZVDejo6GB6ehqtVovZbEatVhMMBqXqQExMDDqdjpiYGAoLC7lw4QJLS0t3pYc4Li6OzZs34/V6uXz58h23u6wFt9tNY2MjKpXqvmVWIw6REUvrtrY2UlJSMJvNWK1WhoeH8Xg8/N7v/d5N3+/FxUV+9rOf4XK5SElJkZwCc3JypGHY6elpZmdn7+VLi/IxojJvUe4bf//3fy9ZFUf5LQ6Hg3PnzrFt2zYyMzNxu91MTU0xMTGBy+Xi2LFjN5VjWo1IMPO1r33tgRscinI9i/Td73531VJ9hIjedOR3oVCI1tZWyS5crVYTCoVISEjAbDZjt9uxWq088cQTmM1mRFHEZrNht9vRarVUVFQgk8no7+/nF7/4BVu3bkWtVnPt2jV27twpBWuCIHDu3DlKS0s5cOAACoWCYDBIf38/b775phQgRtoq4Hr2dWFhgYyMDGZnZ9m9e/dNe15HRkZwOBy8/PLLG/Z+ulwuGhsbuXr1qqRiMDMzw/T0tDSIqNVqKS0tJSMjA0EQOHnyJIcPH75B2WFiYoLGxkbGx8cpLy8nJyeH5eVl6Tp699132blzJykpKQiCwOXLlyX3zPLy8hWDq6sRCARoaGjAYDDcVDbP6/Vy9uxZ/H4/KpUKjUYjLUJcLhcWiwVBEFCpVAiCgCAIBINBKeCPWEZXV1ffVcvvQCDA+fPnJRMYuVyOUqlEpVJJMw6hUEhasGzevPmm74/P58NisWCz2SRTHLfbjdfrJS8vT2qvuNd4PB4aGhokxRWA3bt3093dzfLyMvHx8dTW1lJZWXnL1sEf/ehHkuZ8X18fBoOBrKwsHA4Hly5doqSkRFJpiZos3V1uJfMWDZCj3DfefPNN4HoWNMpvOXXqFFqtFqVSidVqRRAE9Ho9ubm5N81k3Qq/38+pU6cIBAJ87Wtfi2btH0D++Z//menpaQwGA0VFRfj9fikzeCtEUcTlcmG324mNjWViYoKFhQXJRvyP/uiPbrn9t7/9bRISEhAEgVdeeYWzZ8/S2trK1q1bpXYJn89HS0sLMpmMgoICcnNzKSgoIBwO09bWxgcffCApXHycyODeR2/ykUDS6XRK6in/83/+z1UXBl6vl7GxMUmDObJQKCsro6Sk5JZBksvl4rvf/S5Hjx6VArTJyUliYmKw2WwMDg6SlJREeXk5SqWShoYGDh8+vGKYNcIbb7yxQhZRo9Gwbds2Wlpa2L59O1NTU0xPTxMTE0NJSQkTExPMzc1x9OjR2walkYDr0KFDK0r1gUCApqYmKYv41FNPScoioVCIoqKiWz7v4uIiFy9eJBgMcujQoRVzCHeLSCArk8kIBoP4fD5pQFCr1aLRaPB6vVitVkk1RKfTAdflJCMLmUAgQFxcHElJSdLAZmJiImlpaev+/vskOJ1O4uLiGBoaYnh4GJ/Ph1qtluzWv/GNb6DT6SRr7ltVdT7K3/7t31JfX4/NZmN0dJS4uDjpc/7MZz5DSUnJXX1dUX5LNECO8kDS2tpKd3f3qjekR5n29nZGR0cle9ekpKR19wJHgqaINufExAQAzz77LOXl5XfjsKN8Al5//XXm5+cJhUJSxiw9PZ3t27ev+TkiFt8LCwt0dXVRV1fH7t27b7nN8ePH6erqYs+ePezbtw+4ntX91a9+RXJyMllZWSQkJKDRaJiYmGBwcBCHw8Err7xCdnY2MpkMQRD4wQ9+gFarZX5+Hrlczv79+yVNYVEU0Wq1eDweEhMTJTvghIQEtmzZQmtrK2lpaVRXV5OUlERMTAwqlYrm5mYuX74sWZ5rtVqpDztii15dXU1JSYlklR1pSwoGg1y9epUPPvgAtVqNTqdj8+bNxMXF4fF4WFhYwGq1SrrO2dnZWCwWAL7whS/c0K/7q1/9io6ODqqrq+nr6yMjI4OJiQkEQSA2Nha328327dvJysqSrtXf/OY3GAwGYmNjiYuLu2mvrNVqpampSdJET0xMZHh4mIGBAdRqtaRCk5CQQGpq6gp7+I/j8/lwOp10dnbidrvJzMwkJydn3aoV9wKv17ti4TMxMUFycjIlJSUr2sjuF1NTU1y9ehWtVovf78doNFJTU8Pp06el3uI7bSX50Y9+xPz8PIFAgPr6evbt24fVasXr9a5LIjHKJycaIEd5ILl69Sr9/f3RAHkDcTqdnD59esXPPvOZz6DT6UhNTX2gdUwfZYaGhjh16pQ0kPPWW29RWVm5qmvhaoRCIU6dOoUgCKSmpvL444/fdnipo6ODt99+G4CtW7dKyhRwPXsZaS2wWq2UlZVJLpejo6OMj4+jUqk4cOAApaWl/OIXvyA2NpaxsTE8Hg8qlUoaxEtNTZUC0YhmskKhkHST5+bmEEVRKr37/X4CgQBpaWmUlZXddJDX6XRKx+fxeFCr1VLpO2LCYzQa0Wg0zM7OSgFtZCgwouSRmpqK3+8nJyeH5ORkrl69ype//GWMRqMUiDocDv7hH/6BAwcOMDIywtTUFCUlJTgcDpRKJQqFgs2bN6/IAM/OzjIwMIBcLsdms0mtAasxNDTEwMCA1HceyaAKgsDy8jJqtZqYmBjcbjd6vV5yuYwcm0qloru7m6mpKdRqNUlJSezcuXPDXTnvJoIg3BOJM1EUmZmZYWpqioyMjBvcW51OJ729vczOzlJTU0MgEMDr9ZKbm4vNZqO7u5u//Mu//ETHcOXKFc6fP4/BYMBut/Pnf/7nUXm3+0TUSS/KA8nExIRUXouyMUSC48h0eV5eXlR4/iFgaWlJ6it3uVwIgrBmdYNQKMTly5fJz8/n2WefXXO1Qa1Wo1QqOXjwIB988MEKB0e1Ws3jjz8OXA+IGxsbmZycpLq6mvz8fKanp1lcXOSNN95g69at0tBSbm4uzc3NFBUVUV9fz4kTJ4iNjSUQCGA0Gjl8+DBjY2NcuHCBzs5O4LcubeXl5euSCYuPj5cCzsgAYOQ9jATCEdLS0lCr1QwPD1NbW8uVK1fwer08/fTTN7SxpKWl8Y//+I+Ulpby/PPPA7+VxosEvDt27LitU2BaWpr0mOnpaVpaWigrK1s1aC0sLKSwsBBBEGhvb2dmZgaXyyUtDj7az5qeno5MJqOtrY2pqSnC4bA0KHjw4MF70kpxN7gXAeLS0hIdHR2Sas/Vq1cliUFRFBkeHqa7u5vU1FQOHz5MTEwMoiiyuLjI4OAgi4uLfO5zn/vEx9HW1kZNTQ0mk4mGhgYGBwejbRUPINEAOcp9Y9u2bfzsZz8jKyvrtsMsUW5O5As8OTmZpKQkDh8+HP2yfYgIhUJ0dHSQlpaGIAjS0NZaA4aRkRHi4uJ45pln1tWKEyn/R2TKbtbvnJeXR05ODu+//z6NjY3k5OTg9/t55plnOHnyJDabjWPHjvFv//Zvkk5xaWkpgiAQCoUYHBxErVZTUFBAfn4+eXl5TE1NYbVaqa2tJSYmRgpA75SP9/mu9t5lZWUxPz/PuXPnpNc6NjZGUVGRlIXOyckhGAyyZcuWFZWtyACkz+e7I9mt9PR0EhMTee+999i0adNNFwLt7e14PB6CwSAKhUJSSoi4Hu7YsQOZTMaZM2dwu93s3r1b6hUXBOG+tyU8yER0iQ8ePMjmzZv59re/zZYtW6QqxuLiIuFwGIPBQEVFhbTY6u7uxmq1smnTJj7/+c+va0D6ZiQkJEjGTCaTieHh4eh39gNINECOct/Izc3FbDZjs9miAfIdMjIygkwmo7W1Fbieibrd8E6UB4uenh5pKC+icZyVlbXm7efn59mxY8e6M3CRgalz585JJf2bIZfLOXToEEtLS7jdblQqFaOjo/z5n/85oiiiUCj43Oc+x2uvvSaZasjlcsrKyujr6+Ob3/ymlDkNh8OSycK9zHbq9XrkcrnUwgDX3/uJiQmWl5elQSur1cpzzz23QvElKSmJLVu20NzczOzs7G2zxx9HJpOxZ88epqamaGtrY2Jigtra2hWB/fj4uDSwVVtby9DQ0ApJsYWFBYaGhqQguKqqasVxRIPjG/H7/bS0tOBwOBAEgS996Uukp6fT19eH1+ult7eX9PR09u/fT2ZmJl6vlzfeeIOxsTFJ0WRgYIDKykoOHDiwIbrwoiji8Xgk9Sa73b7u8ynKvSEaIEe5r1RWVnLixAmSk5OjQfI6CQQCtLW1IZfLKSwsZGhoiOeffz7ay/aQEQgEkMlk+P1+KaO71ixVpHf3Tnr5ExISOHLkCKIokp+fv6rbXeRngiDgcrkApGGq6urqFdvk5+dz9OhRzp8/z759+9BoNBiNRvr6+mhpaZGyn0qlkpqaGnp7e9d1vBuB0WgkFAqh0+mYmpoCkNz/zp8/T2dnJ/v27VtVDrG+vh5BEBgdHV0R0ETs3W933clkMrKysjAajbS1tXHixAkSExMpKioiPT2d9PR0srOzmZqaIjk5md27dyOKIkNDQ5LWcyQIvnDhAu3t7TgcDrZs2fJQ9RrfSwYHB5HL5fzhH/6hNPwJSHJxe/fuJScnRwp8FQoFVqtVyvC73W7JQGl2dpY//MM//MRB8ujoKE6nk+3btzM6OorL5bpBXjDKg0H0qopyXykvL8fhcHDhwgXq6uqiOr3rIDJ1LwgCQ0NDHDlyJHqjfAipqqpibGyM9vZ26urqCIfD2O32NW0bkcR66aWX7mjfN9PetdlsfPe730WpVKJUKqVMZiQ4+NKXvkRCQsIN29XU1EiybMXFxeh0OmJjYzlx4oQUAML1ICEyJHcvz9mI/NjOnTvZuXPnDcfe0dGBz+dbdduIrq9Wq0UQBC5evIjL5SIQCCAIAjExMeTn51NUVHTLYFmj0bBr1y5cLhfDw8NcvnwZpVLJgQMHqK6uxmKxsLy8TExMDKOjowwMDBAMBvH7/ZKJT01NDVNTU/T29pKXlxfVkr8JRUVFnDt3jtdff51du3ZRWVmJzWbD4XBw+PBhqT0lQmTRNjIywsDAgNQHnpGRwczMDK2trdTU1NzRsYiiSHd3N++9957kgNne3s6f/umfRu97DyjRu2mU+87u3btZWFhgZGTkjqxzH1UiKgWR9oodO3bcz8OJcoeoVCp27tzJr371K7xeL16vF6PRiCiKN81WBQIBLl++jMvl4umnn16TAYQoikxOTrK8vIxSqaSgoIClpSU6OzuZnZ2loKCAnJwcDAaDpESRkJBAWloaZrOZnp4e0tPT2bFjx6rBcYTy8nJOnz5NUVGR5CqWmZm5YiD3C1/4Ar/85S/p7u6mqqpq/W/abV6n0+nE5/NJttbz8/Oo1WpEUcTtduNwOG54DREXs4aGBvr6+tixYwd1dXUrPoOI1vTExASxsbFs3bqV2NhYtFot09PT9PX10dvbi0ajkay4b4bf75f6UEOhEOfPnweut6D09fUxMzODUqmkrKyMoaEhSWO4ra0Nm80mZUPn5+ejAfJN0Gg0HDhwgPHxcU6fPs3U1BTd3d2kp6fT3NyMIAg89thj0ndndXU1Ho+HqakpxsbGiI2N5bHHHkMmk+F0Ojlz5gxFRUVrbg1yuVxcvXqVrVu3curUKWZmZqitrSUpKQlRFBEEIapL/wATlXmL8kDgcrn4/ve/z969e6Or6XVgtVppaGjg1VdfjepnPsR0dXXx5ptvIpfLiY2NRRRFUlJS2LZtG8FgkOHhYSmLG3HFKygo4OjRo1IAFzG/iGTI3G63JJmmVCoRRRFRFImLi8Pn80mGIvn5+cTGxkqqEl/84hcpKioiEAjQ19dHV1cXU1NTyOVyDh48eNtWDlEU+e53v4vBYMDpdJKVlcWxY8duCPa9Xi/f+c53yMjIQCaTYTabMRgMzM7OMjc3JykNRL4PwuEwXV1d0jCaVquVgkibzSbpNdtsNoLBICqVSnJvKy4uJhwOS8N4kUD/ZsfvcDjo7OzE6/WSlJREbGys5E5oNBopLCzEaDSumikOBAIMDw9z7do1zGYzKSkpNyiShMNhjh8/jsFgoLCwEJ/Ph1arlQxGkpOTiYuLkzL8zc3NTE5OSsen0WikTLdCocBsNlNTU7MhPbKfRkRRZH5+HqvVSl5eHk6nU3qvc3Nz+eIXv7ji8aFQiB//+MfMzMzw1FNPSVWO1tZWCgsLqaurW9N+f/7zn2OxWHA6nRQVFbFp0yapTWZxcZHW1la+/vWvRz+3+0hU5i3KA49er6e+vl4qM0cHTtZGamoqubm5WK3WaID8EFNeXk5ZWRmhUEiyjf7Od75DZ2cnFouF/Px8CgoKpKxyeXk5W7ZsIRQKMT8/z5kzZ5ibmyMtLY24uDgSEhIwmUwrbH4j2apIkD0wMMDExASVlZWSEopKpZL6L9VqNZWVlVRWVuLz+ejs7FyTyYxMJuOVV17h3LlzknHIagFATEwMX/ziF5mampKMPTQaDS6Xi/r6enw+Hw0NDaSmphIKhXC5XOTk5JCVlcWJEyek58nPz6e4uJi5uTlCoRCbN28mKyuLgYEBBgYGqK2tXSF16PF4OHXqFFarldTU1FWPPzExkfr6epaXl/F4PPj9ftLT04mPj7+lhXDkfSsrK8NgMDAwMEBLSwtmsxmlUil9XpOTk6hUqhsUMW4myRixkU5MTCQ5ORm9Xk9SUhIGgwGfz8fJkycxm82YTKZosLUKMpkMk8lEamoq4+Pj9PX1STbsq2Vw5XI5DofjBi1pnU4nKYvcjvHxccbHxzl48CCiKN5w3kxNTVFZWRn9vB5gogFylAeGXbt2MT4+TnNzM9u3b48Om60RtVq95p7VKA8mkeG1yM04Epx++OGHqFQqsrKyUCqVxMTEkJGRgc/n4z/+4z8YGxtDr9eTlZV1w9Dc7fYXHx9PKBSS/l9YWMilS5cky9yPotVq19XCo9frefrpp4Hrg1J/8zd/w969exEEAY1GQ0FBAR5rWX0AACAASURBVAkJCZjNZsmoYdeuXUxMTJCamiqVsGtqaujq6sLlcjE0NERPTw/PPPOM5Ba4e/duqb3g425xMplM6h/9KLGxsRQUFHDx4kXKy8tvcM37+Pa3Uve4FUajkdTUVN5++23a2trIysri2rVruFwuEhISJJ3pteDz+TAajdTX19/wO61WS3Z2NleuXEEmk5GSksKWLVvu+Lg/rfh8PhobG9Hr9bz88sukp6ff9LFyuZzq6mpmZmZWPC4yUPvx5718+TLz8/MsLi6ytLSETqcjGAxKNuarEXHNjPLgEg2QozwwyOVyfvd3f5ef/OQnDA0NUVxcfL8P6YEmFApJDmH79++/34cTZQNxu920tLRw6NAhAoEA3d3d0rBcxEI8Ly+Pp59++o6rLT6fb0VAHR8fTzgcvm2GdL04nU48Hs+KrC/A//7f/3tFsPHR7HWEd955h5GREen/Bw4ckKTwSkpKbtl7u7CwcNNe6bKyMiwWCw6H405e0ppxOp2STFtraysajYaDBw+uqWc8giiKyOXyG4YHI4tivV5PVVUVpaWlXLhwgZmZGex2OwcOHFjXfj7ttLe3k5OTw2c/+9k1ZW2dTiexsbGEw2HGxsaYn5/HZrNx+PDhFY957bXX0Gg0mEwm0tLS0Ol0eDweyRwnYgEfDocJh8MsLS0xOTmJz+eTjGiiPJhEA+QoDxRKpZL6+nreeOMNFhYWMBgMUSORVRgaGpI0PJ999tlbZkOiPFyIosg777yzov/248NeHR0dLC7+/+y9WXRb93nu/duYOAAgAZLgBE7iPIizSFGWREmWqMmKLa/ErlOnSe3GTt3Ttc7Xfuusc9qb5pxena6vQ9qTeiVNTp00qZM2amJL1mBJFKmBpERS4jzPMwnOJEAABLC/CxY7oqmBkwbb+3dji9zA3hsAsZ/9/t/3eaY2LY7n5+dpbm6WemOXl5cpKytj165d275yk5+fT35+PuPj44yPj+Pj40NISMi6REpubq7UjpCbmytZn42OjjI5OYlOp1vz2fd4PAwODjIxMcGRI0ce+LxtbW1Sq8qTZGhoCL1ez6FDhzb1+OXlZZaWlhgcHOTo0aPSz72uJ0qlkuXlZem11Gq1fOUrX6Gqqorr169TUlIiL+H/JxEREbS0tHDmzJl1JU6q1WpaW1tpb2/HbDazb98+oqOj0el0uN1urly5wr1790hISECv1zM4OMjMzIzkavLKK6/gcrmoqalhampKSq70WvilpKRs+82ozPYiC2SZ546EhAS+853vMDU1RU1NDRcvXiQ+Pp6MjIwv/RfKwMAA7e3tKJVK3nvvPXl6/QuE1/O2tLQUt9vN3r17H7qtyWRiZGRk0/vq6OhAq9WuEoh2ux2n04nb7X4iMwBhYWFrbLUex86dO9eI2Pr6eiwWC0qlcpWYd7lc9PX10draikKhICMj46EDv97kyc+2kmw3vb29644M/yxut5uzZ88CK0El97dM2O12tFotJSUluFwunE7nqiJCUVER586d29Kxf9GIjY0lKiqKq1evMjw8LLX2PIwDBw6QkZFBYGAgBoNB+vni4iKXL1+moaEBhUJBV1cXoijicrkICgqSblq6uroYGBggKSmJt99+W56r+RwiC2SZ55KgoCCCgoKkGNiysjJu3LjB3r17n/hF7XlEFEWGh4dpbm7md37ndzCbzfIX7heIubk5fv7zn+NyuUhKSiIqKuqRFa6QkBDsdjs2m21TqytarZaxsTEWFhbQ6/Wo1Wr2799PY2MjP/zhD9m/fz+xsbGIooher99wFdJut+Pj47Pt1UuPx8NvfvMbKWwkLCwMj8dDT08PLS0tqFQqdu7cKVkgPozc3FxKS0slC7snVWV1OBxMTExs6rHeFYKwsDCKiopW/S4uLo62tjZmZ2cxGAxr+lw9Hg+CIDAzMyPbiN2HUqkkLi6O69ev8/Wvf/2R77tWq11lTQgrLhYXL14kMjKS4uJi7t27h06nw+l04nA4mJ+fx9fXl9OnT1NdXc2BAwc2HOAj8/wgC2SZ5x6TycTXvvY1zpw5w5UrV1AqlXg8HpaWlqQL+xe5krq0tERdXR1LS0u8/vrrslvFF4zl5WV+8pOfEBUVRVJS0rrEmlqtxmAwUFVVtaFhLy8pKSnY7XZKS0s5duwYvr6+GI1GCgsL+eSTT6isrOTs2bNShTY5OZmdO3eSkJDw0BYMURS5d+8e165dk/pljUYjx44dW9NbvFm6urqAlWpxf38/LpdLCnfw8fHBZDI9VhzDSr91VlYW9+7do6Wlhezs7CcS96vRaDZ17na7ncrKSnbs2PHANhBfX1/i4uK4efMmeXl5awYRGxoaEASB4eFhWSB/hoSEBK5du0ZjY+OGE+xiYmJQKpWEhoYSEhJCbGwsi4uLzMzMEB8fz8mTJ3G73QQEBJCamvqEzkDmaSELZJnPBYIgkJ2dTXNzM4WFhZIdVE9PD+Xl5ahUKgwGAwkJCQ/sx7Xb7QwMDEj/Dg8PX7fZ+7PEZrNx/fp18vLyOHDggFw1/gLS29uLWq1+7FCqNwCjrq6O6elpRFFclxh8EAqFgpycHJxOJ59++imnTp1icXGR8vJyTCYTe/bskba12WwMDw9z/vx50tPTKSkpeeBz3rx5k3v37lFYWIjBYJAszX7xi19gMBiIiooiISFhS32/FRUVAFIKX2trK2azGYVCwdDQkORtq9frycrKeuTfeHx8PHFxcbS0tFBVVcWRI0e23fkhLi6O3t5eYmNj17W9KIrY7XapevwoAZeWlsbIyAj37t1bI5AXFxcxGAxER0dv6fi/iCiVSnbt2sWFCxcICgp6bKvF/YSEhPD666/zs5/9jKCgIAYGBvja174mXXPkJNMvFvK7KfO5wbv07PF4CA0NZXh4mMXFRTQajZRmVVNTgyiK+Pv7r6p0zc3N4fF4pASj1tZWMjMziY+Pf4Zn9Hiqq6spKipi3759z/pQZJ4Qd+7cWddFurm5ma6uLkJCQigpKVnzGd8ogiCwe/duzp49y507dxgbGyMxMZGMjIxV2/n7+5OUlITZbKa0tBR/f39iYmIkYeqltbWVtLQ0aTVHrVZjNpuJjIzEYrEwMzPDmTNn+M1vfgNAYGAgiYmJpKSkEBsbi8vl4vr167S3t2O32wkODiYjI4O4uDhMJpMUYQ0rfsGFhYVcuXKF7u5ugoOD2bFjByaTCbvdzujoKKWlpej1evLy8h66wqRQKNi5cycjIyMMDw+TkpKy6dfzQYSEhNDd3U1ZWdkjnWYcDge9vb309fWxtLSEIAiPrWh3dHQAK84enyUiIoKenh55QO8hGAwGMjIyuHz5Mm+99daGHhsUFIRCoZBCap5ki47Ms0UWyDKfK44dO8aHH35IZGQkKpWK3bt3k5OTwy9+8QspTWp2dpbJyUm8KZGCIBAdHY3JZJKGLYaGhrh79y6Dg4Ps37//ufRc9va0PWpYS+bzz8jICElJSQ/9/fLyMiMjIwwODhIdHU1+fv627t9kMjE2NkZ0dPQjq7v+/v4UFhbS19dHbW0tS0tLJCYmsnv3biIiItBoNA8MURAEgdDQUBwOBz4+Phw/fhxYSc8cGxvjwoULLCwsACsrOzk5OWg0Gjo6Orhw4QI6nQ6bzYbH45EqdF77th07dtDY2EhKSsoqQWk2m8nIyKCjo4Py8nISEhLIzMx86Lk5HA46Ozu3XSCHh4dz8OBBrl69KqXlfRa32015eTkejwe9Xs+RI0ceW4n0eDxMT08TFBT0wOdMSUlhcXGRa9euERcXR2pqqmz5dh8Oh4OxsTH8/Pw2/Fi9Xk9GRgZTU1P8zu/8jiyOv8DIAlnmc0VUVBTx8fF0dHRgMBiYmJggNDSUrKws+vv7CQ8Px2g0PrYn2RsBW1ZWRkNDAzk5OU/0uDs6Ouju7paCEpRKJTabDT8/P1JSUoiMjJTidb3pXt7EM7fbLS/dfYEJDQ1lfn5+zUAQIKXJuVwuIiIiHinyNotOp0MUxXU5TISGhkqBHN7Wi1/+8pe4XC6pivswXC7XqjAU799pWlqaVDW9X8RlZ2eTmZmJQqHA4/Hg8Xj45JNPVh2rwWCQVoQ+W3H18/MjOzubqKgoqqqqGBsbY//+/WuEot1uJyYmhp6eHsrKyqRo6fDw8G2JvW9vb8fX1xeNRvPA31utVux2O6dOnVrXjbooilRXV2O1Wle1wtyPQqGgoKCApKQkabDMO9ycmJj4yF7yLwN2u52xsTHefvvtDT9WoVBw+vTpJ3BUMs8b8lVX5nNHSUkJ77//PllZWYyPj1NdXc3u3buprq5mcnKSkJCQdT2Pr68veXl5VFZWEh0djdFo5MqVK9hsNpRKJcnJyeuqKM3Pz9Pb28v09DQOh4OQkBApWjQjIwOVSsXY2BjJyckcPnwYm83G8vIyAQEBWCwWfv3rX9PY2IhWq2VpaYn4+HgSExPRaDQYDAa6u7u3vbIl83ywsLDA3Nyc1EMqiiIjIyMMDQ0xNzeH1WolMDCQI0eOPDFBYzab6ezs3PCQmrf1IiEhgcXFxce6XXiHah/Egyp5giBQXV2NUqmkoKAApVJJdHQ0/f39+Pv7S6I3NDSU3bt3P3S/wcHBHD16lHv37vHpp5+SnZ0t9QS73W5u3LiBw+HA4/GgVqsJCQmRPJv37t27pQrh8PAww8PDmM1mJiYmUKlU2Gw23G43DocDh8PB4OAgGo1m3e9vd3c3FouFo0ePPlR0ezEYDMTGxtLQ0IDZbEar1dLe3k5PTw9ZWVmEhoZuea7Bu1IHYLFYCAkJee7Fd2BgIOnp6XzwwQf8j//xP57745V5NsgCWeZzR0BAAKdPn+bixYtotVrKy8tZXl7mxIkTXLt2jYMHD677omYymUhMTOT69esolUp0Oh3Hjh1jenqampoaVCrVI6fQh4aGqK+vp6ioiAMHDuDn50dfXx9qtZr+/n4uXbqERqNhYWGB8PDwNdG1er2ed999l/n5eSIjI5mfn+fHP/4x/v7+REZGEhMTQ0VFBcnJyfJS3heMnp4ezp49K/Xp9vf309zcLPXYJyYmEhUV9VgRtFWMRqMUV+5dvdgICoViXQOvUVFRdHZ2bui5tVotHR0dZGVl4ePjw9LSErCSsOfr68vhw4fXVeVVq9UUFhYyODjI3bt3qaurk0SRKIoYjUYsFgtvvvmmFATxj//4j4yNjW0phCcyMpLQ0FBGRkaYnJwEVga5XC4Xvr6+qNVqcnJy1gzZPQyr1UpzczMFBQXr/lx0dnaSnZ0tzVvs2LGD+vp6amtrCQwMJD8/f1NWgd7Alvr6ehwOB76+vthsNhISEsjOzt7w8z1tIiMj6enpkcWxzEORBbLM55KUlBTi4uL42c9+RlhYGOXl5Rw6dAgfHx8aGhrIyspal6AUBIGMjAySk5MZHx/HZDLh4+OD2WyWKljR0dFrLkajo6O0t7fjdrv55je/ueoi6k09y8rKYv/+/TgcDoxG40PbJPR6vXSRDwwM5OTJk1y9elUSyD09PbS3t8u2QV8AvLGztbW1VFRUkJWVhdlslhK3srKynsny9/39vU8KtVq9qtq4HlJSUujo6KCmpoaCggImJydJS0uT/hY2etMYHR1NREQEdrsdt9uNy+XC4XBw7949lEolV65c4fTp0yiVSo4dO8bZs2cJCQnZdECRQqGgsLCQy5cvIwgCmZmZm3aWsNlslJWVERYWtm5BDSufuaamJpaWlkhKSkKj0ZCbm0tmZialpaVcunSJoKAg0tPTpaAL7+MAyVXD+90IK+0y1dXVWCwWEhMTiYiIkKKYu7q60Ov1iKJIbGzsc9ke5na7GRgY2HSIi8yXg+fvkysjs07q6+txOp0EBQWRk5NDVVUVf/AHf8C//du/UVZWJvX2rge1Wr3GSSAyMhKTycS1a9c4fPiw9EXf29tLZ2cnJ0+eJDk5+ZFi5v4EpvVyf/iDQqEgLCyMoaEhWSB/zrFYLPzyl79kfn4eo9HIgQMHpPfZ7XYDPHJY70myvLy87RZn24FGoyElJYX29nauXLmCVquls7OTiIiITf1twUoF97PnGhwczPT0NHV1dVitVrRaLcnJySQlJVFVVcWOHTuIiIjYVDuCRqPh+PHjtLW10dzcvCGBPDc3R1VVFR6PB6fTSWho6JrQkMdx4sQJRkZGJBeUhIQE4uPj8ff35+jRo7hcLmpra6mqqmJ5eVnql/YOVXqdgux2OwqFArPZjMViQaVScfz4cal4YDQaGR4exm6309DQgCiKzM3NkZOT81xVaUVRlOwCX3755Wd8NDLPM7JAlvnc0tnZycTEBDk5Oeh0OkZGRrh06RLf+ta3aGho4MaNG4SHh2/py3n37t1cv36du3fvUlhYKFVj3n33XalSvN2YTCamp6elyF+z2czt27d58cUXpXNxOp2oVKrn6sIj83BcLhcffvghsbGx0lL30tISVqsVlUpFe3s7sOJosZHq4HYd2/NMeno6vb29+Pv7861vfYu+vj4uX77MoUOHti163sfHh4iICKanp/n+97/Pu+++i8Fg4MSJE9y7d4/m5mY6OjooKCiQVntEUWR2dpaZmRlCQkIe2WaiVCoJDw+ns7OTzs7Odd0ITU9Pc+PGDcxms7RCZTabN3V+kZGR0iBwU1MTnZ2daLVa/Pz8CAoKoqCgAIVCgdPpZHJykra2NlwuF3a7nczMTOl4h4eHqampwWg0rnH/8Xg8LC8vS+/J/v37qaioQKfTPdbj+2nS1dWFRqPhrbfekr8/ZR7JlgWyIAjRwE+BcMAD/FAUxe8JgvBd4B3A8p+b/rkoiue3uj8ZGS9vvPEGpaWlXL16FYPBQHZ2No2NjfzHf/wHr732Gh0dHdy4cYPMzMxNp0l5h/Xu3buHx+NBFEVpYv9JER0dTVRUFO3t7aSnp2MwGFCr1YyMjHDhwgVGRkakbf/oj/5oU32jMk+P5eVlPv30U/z9/aV+9srKSsbHx4GV6rF3SK26uprQ0FByc3Ofmi2XVyA/j0vhsLKK4h3I1ev1ZGZm0tvbS11dHQUFBdu6r4yMDHx8fPjBD37AG2+8QWxsLIWFhRQUFFBTU8PVq1cJDQ0lMDCQkZERPB4P4eHhlJeXc+zYsYf2BYuiKPmz9/T0rEsgt7a2IggCO3fu3LbPQlhYGGFhYSwtLTE2NobVaqW/v5+hoSGCg4MxGAwEBgYSERGB1WpFo9HQ2tpKa2urdMPudruJjo5eJS5nZ2eprKzE7XZjMBgwmUyEhIRgNBpxOBzbcuzbgdVqpaOjg3feeUcWxzKPZTu+EV3A/yuK4l1BEPRArSAIl//zd38riuL/tw37kJFZg1KppKSkhIKCAu7cuUNFRQVpaWm0t7dLHpVXrlyhubmZ/fv3b3o/kZGRdHV1cfbsWQwGAx6PZ0NuGZvhxIkTvP/++0RFRREQEIAgCFJ4gL+/PwEBAdy9e1fqDZR5PnG73fzoRz+ShrFgpUVndHSU8PBw1Go1CoUCURQZGhri+PHjVFZWcunSJXJycoiJiXniw5l1dXUPtJh7noiOjpbcF7yRvv/wD/+AxWLZ9s9/YmIiNpuNnp4eyWM5KSmJgoIC0tLS6O7upr+/n6NHj9LT08OdO3dISkpiaGjoocFDt27dknyLPxvE8jCKioooKyujvb1924fe/Pz8JEu+1NRUurq6mJubo6+vD4fDIRUCYMXtx7vysbS0hI+Pz5qhvuHhYVQqFceOHVslPL2i+nlAFEUaGhooKiqS47dl1sWWBbIoiqPA6H/+/4IgCK3A5taBZGQ2gcFg4OjRo2RnZ/PTn/6U6Ohobt68yenTp4mNjeXevXsMDg5uOvFIEASKi4uZmZlhcHCQyclJhoaGnqhADggI4OjRo1y6dIl9+/axuLiI0WjkK1/5CqIosrCwgFqt3nTUsMzTobW1FVhp1REEAbfbTX19PTqdThpaW15exuPxEBcXh4+PDwcPHmRgYID6+nr6+vrYtWvXExWwoaGhjI+PY7Van8s+ZFipImdmZvLRRx/xx3/8x6jVao4fP87Zs2c5dOjQpgIfHoVaraa9vZ25ublVFV+dTkd2djbZ2dlUVlZy584dNBoNu3fv5pe//CVNTU0EBARIlVi9Xo9Go8FisXD48GHcbrd0g+12u7Hb7SwtLaFUKklMTFz1/aRUKomMjKS7u5vk5ORtP0cvKpVqzXzD1NQU5eXlpKamMj8/T39/P2FhYQ+0o1teXqa3t5ekpKQ1yYqzs7NbihbfTvr7+7Hb7VLw0sDAAGaz+bkR8DLPH9u6piYIQhyQC9wG9gJ/LAjCN4EaVqrMM9u5PxmZ+wkKCsJut5OUlMS1a9cYHBwkKSmJ06dPc/XqVWZmZsjKytrUcwuCQFBQkJRg9TRsjHJzc5mbm6OsrIycnBypanPmzBmam5v57//9v0tT5TLPJ6OjoxiNRkn4DA4OIggCiYmJj4w5NxqN+Pn5MTU1xeXLl8nIyFgjoLaL+Ph4uru7GR4eJjEx8bkVDBEREQwMDFBWVkZJSQlpaWl0dHTQ29tLenr6tu4rKSmJgYEBPB7PmtdcFEUuXrxIe3s7Op0Oh8NBTEwMf/Znf4bdbsdisTA8PMzY2Ji0miUIAhUVFfj7+6NUKhEEAbVajV6vJyAggIGBAebn58nLy1u1v5SUFGZmZqitrX2qcfNarRaVSsXo6Ci7du2ipqaGW7duSYEtAQEBHDlyBFgRmiqVapVX+8zMDG1tbRQXFz/RdrT1Mjs7S3NzM2+99RZKpZLLly9z+/ZtkpOTef3115/14ck8p2ybQBYEQQecAf4fURTnBUF4H/hLQPzP//41sCa2RhCEd4F3AWJiYrbrcGS+hCiVSpKSkigrK0Oj0XDx4kVefvllkpOTiY6O5p/+6Z9obGwkIyNj0/1nExMTpKamPjVP4oMHD3LgwIFV+7NYVtr6GxoaKCwsfCrHIbNx7t69S21tLQcOHEAURbq6umhubkapVD7W7mx5eRmr1UpOTg52u5329nb6+/spLCxcl+fwRklOTqa2tpbm5mZSU1NJTU19Lns0s7OzKS8vJzIykoyMDDIyMvj4448JCQmREv62A5VKRX5+Pjdv3mR5eZna2lppAK2vr4/x8XH279/PuXPnMJlMnD9/nlOnTuHn50dMTMyqa5m3//hRr6fT6eRHP/oRIyMjBAcHU1NTg9PpBFZs1p62B3pnZyfLy8ukp6cTGBjI4cOHgZUe3traWpRKJXa7HZfLJfUv38/09DRarRalUrlqcO9ZMDQ0RENDAy+99BKhoaFUV1fT2tpKYmKiXGCQeSTb8g0oCIKaFXH8c1EU/wNAFMVxURTdoih6gH8CHnglF0Xxh6Io7hJFcZfcSymzFRQKBW+88QYvv/wyMzMzTE5O8rOf/Yz5+Xn8/Px45513cLlcVFRUSLZaG8Vutz/R1ooH8dmL4xtvvAHA4uLiUz0OmfXT29vL1atXOXDgAHq9ntraWtra2njhhRfw8/N7rED2bhMfH096ejonT54kICCA0tJSWlpa8Hg823q8sbGxFBcXs3//fnp6emhqanrgPtxu96b/drYDX19fkpOT+eijj4CVfuFDhw5RX18vOYFsFyEhIZw8eRKr1UplZSXd3d20tLRgt9vZs2cPdXV1wIpoHx8f56//+q/5l3/5F+nnXgRBeOzNhkaj4eDBg9y9e5fLly+zvLxMdHQ00dHRJCUlsWvXrm09t8eRkZGBVqtldnZW+pnb7aa0tBSLxUJcXBytra1cunSJ2dlZcnNzVz3e5XKxuLjItWvXaGtre6rH7sVut3Pnzh06Ojr43d/9XXbu3InNZuPatWvk5+djsVieSHS7zBeH7XCxEIAfA62iKP7NfT+P+M/+ZIBXgaat7ktGZj0kJSXxJ3/yJ/z4xz9mcXGRW7duceLECfz8/PjGN77Br371K2pqaigsLNxwZcblcj3RqsPU1BTt7e1MT09jsVjw8/NDpVIRExPDzp078ff3x2g08tZbbz0XS5cyD+by5ctkZmai1+txuVwMDAyQl5fHrVu3AB7rSuDr6yt533r7PgsLC5mamuLOnTsMDAxQWFi4rcNGISEhTExMoFQq6e3tpbu7m+DgYJKSkggNDaWnp4eWlhYEQSAlJYX4+PhnUhnUaDSEhobi8XhQKBTk5uaSmJjI97//faKiora1X1uj0WAymbDb7ej1emlIFlZWkwoLCwkMDGTPnj3YbDZmZma4du0abreb/Pz8De0rPT2dqKgoLl68yMDAAEaj8anfjHtRKBRotVrpJtzraeytajc3NxMYGEhMTAyZmZlrHFC8g43d3d20t7cTFBT0UIs6j8fD4uIiDQ0NzM7OkpWVRXBw8KbfR7fbTW9vL+3t7eTm5q6yA5ycnJTeP6fTKQeFyDyS7Wix2Av8HtAoCIL31vnPga8LgpDDSotFH/CdbdjXFxpRFJ/rQZnPE94ht8rKSvbs2SP9XBAEXn31VX7605/S1tZGWlrahp53aWnpibw/oijy8ccf09TUtMqXNi0tDV9fX5qbm6XKx9LSEgUFBc+988CXFafTycTEhFT1m5qawsfHh76+PhISEsjMzHzsjZkgCGg0GmZnZ1e1DgQHB3Ps2DEaGhq4fv06cXFx7Ny5c8sWbfPz89TX1zM9PU1cXByZmZlYrVa6u7u5ffu25LSxa9cuRFGkubmZtrY2kpKSSExMfOJx2PcTGhpKb28vP/nJT3jttdfQ6XTo9Xp27dpFV1fXts8HZGZm0tbWRldXF/X19SgUClQqFU6nUxJbgiCg1WrRarUEBgZy+fJlnE7nqu+e9RAQEMDrr79OR0cHZ86c4cCBA0+kpWY9ZGVlUVpayq5duxgfH5du7oKDg5mZmWFxcZGioqKH3uwpFAoSEhIYGhqiqalJEsh2u52+vj66u7slxwyVSoXRaCQ2Npbm5mZsNpuUaPg4HA4Hc3SQRwAAIABJREFUra2tKJVK+vv7per722+/vcbhxGQysbi4SEtLC7m5uc9lG5HM88N2uFjcBB70bS97Hm+QiooKrly5wp/+6Z+iUqlQqVTPtHfr8463R/GzqFQqvvrVr/L973+f5OTkDQ0lqVQqFhYWtvMwEUWRv/3bv5WeNy0tjd27d/PBBx/Q1taGv78/4eHhUhy2RqPhJz/5CcXFxRQVFT31/kSZh+PxePjwww+JiopCo9FIrhXBwcGSddZ63y8/Pz/m5ubW9NYqFApycnJISEiQLOF27dpFWFjYho/XbrfT3NzM4OAgoaGhnDhxQhK7er2enJwczGYzs7OzNDU1ERERgUKhICoqirGxMRobG+no6CA+Pp7k5OR1+fVevXp1w8f5WURRZHx8nL/7u7/jz//8z1EoFJhMJoaGhrb83J8lMDCQ3bt3S/u12WzMz8/T3d1NbW0tR48eXbW9Xq8nIiKC0dHRBz3duhAEgeXl5S0d92bxFmocDgcKhYLS0lJEUSQ7O5uioiI++OADfH19sdvtjxXv3psJh8OBxWLB5XLR29vLxMQEgiBQUlIirZJ5iYuL48qVK5JzhsPhwN/ff81na3FxkcHBQfr7+4mIiEAQBN577z3JFvNB+Pn58cILL3Dr1i3efPPNrb9YMl9onk9n+C8pu3btIigoCK1Wy1/+5V8CK/ZQBw8efGqhAV8WAgMDSUxMlMI41kt8fDxXrlwhPT193UJnZmaGhoYG+vr6sFgsKJVKbDYbOTk5vPTSS8DKe+9wONi1axdGoxGAv/iLv8Dj8TA3N0d1dTVtbW3Mzc2RkJDAgQMHqKmpoauri507d5KSkrLGm1Tm6TM2NkZfXx9+fn6cO3duVb+u0+ncUDXw/iXuB6HX6zl69Citra1UVVURGRlJdnb2uqq5oijS399PfX09er2eI0eOPHRlxGQyYTKZaGlpYXh4WIpKDg8PJzw8nKmpKerq6ujp6SEmJobU1NRHfhZjY2Mfe3zrpaenh8HBQWJjY9FqtSwtLW3bcz+I+yvFk5OTD0whHBsbY3R0lFdffXXT+4mPjyc1NZX6+nqKiopQq9UsLi7S19dHeHj4E2u98Kb3+fn5ERgYSEJCAmlpaczNzZGXl4dOp8Pj8eBwOEhOTl7XatqePXu4d+8elZWVkujXarUoFAp6e3vXOAvp9Xpyc3Npamqivr6egIAAlpaWcDqdFBcX09nZyczMDKIokpGRwRtvvEFUVNS6z3Hfvn1kZmbK35cyj0UWyM8RPj4+0pL/7//+79PV1cXNmze5ffs2xcXFHDp06Bkf4ReLzUTsRkRE0NDQwMTExLoqdpOTk/zoRz8iOjqaiIgIUlNTEUWRsbExhoeHgd/6LD8IhUKB0Wjk6NGjHD16lIWFBd5//33cbjfx8fHY7XbKy8v5+OOP2b9/P4WFhXKLzjPEe9MUFBSE0WiUKmjepVxvTPF68Pf3Z35+/rHbpaWlERcXR0VFBRcvXqSwsJDw8PCHbm+326mpqWF6epq8vDxJ8D6O1NRU7t69S0RExKqKX3BwMIcPH2Z2dpa6ujo+/fRTzGYzqampDzzfxMTEde1vPQwODjIxMUFsbCwxMTE4HI6nEtctiiIDAwOrxJ0oiszMzFBTU8PXv/71LbVGKJVKvva1r3Hu3DmuXr2Kv78/CwsLhISEPFEPdofDQVhYGN/+9rcfuk1ISAijo6PrSgOElVW3goIC7t27x8DAAD4+PgQHBxMfH8/169dZWFhAqVSSnZ2Nn58fHo+HmZkZ1Go1r776KikpKYiiyP/6X/+LyclJBEHgD//wD9Hr9ZtaPfN+p8rIPA7ld7/73Wd9DBI//OEPv/vuu+8+68N4LjAYDMTHx7N//36cTift7e1YrVaioqKeW5/SzxNLS0ucPXtWmtbeCKIocufOHbKysh7b+9ne3o7NZiM3N1cKDXC73dTU1HD69GkMBsOG9u29ierv76ehoYHIyEgiIiLQaDR0dXVRXl5Ofn4+09PTOJ1O1Go1FouF5eXlJxY0IPNbvINcXgs+b9yuwWDAYDBs6D1YWFhgcnLykX7JXtRqNfHx8QiCQF1dHUajEa1Wu0ZADA4OcvPmTXx8fDh8+PCGhEJQUBADAwNMT08/sGLn6+tLXFwcUVFRDA4O0tLSwszMDAEBAUxOTjI1NYXFYlkTSrEVhoaGyMrKwmg0SsEan376KW63m5CQkCfSfiSKIqOjowwPD7Nr1y5pH21tbdTV1bF///5N+63fjyAIJCcnk5iYSHR0NCdOnCAoKIiWlpZtrcLDb1tWGhoa2Lt3LxEREQ/dNjk5mcbGRpRKJUFBQY99jefn57l16xYTExNkZmZSWFiI2WzG398ff39/KTjFK87r6+vxeDy8/fbbUhHCbrczODhId3c3MTEx5OTkyK1lMtvC//yf/3P0u9/97g8f9Du5gvyc472zrqqqkqZ8t7J0J7OCn58fmZmZDA4OYjKZNvRlm5iYyNzcHNevX6ekpOSR246MjDwwljUyMnLTF7mgoCBpmnx6elpa8jSZTOTn59Pd3S3ZYAmCgEqlQqlUotfrKS4uJiEhQRbLT5DExESWlpYea+X2OPz8/Da8ypGcnMzS0hJ37tzBz8+PI0eOsLy8zNjYGL29vczOzpKdnb2p6X1BELDb7Y+1HNPpdBQXF7O0tERtbS2lpaVSuMR2O6/odDrGx8elRMmYmBjee+89fvGLX9Dc3LxtKW6iKDI7O8vg4KAUIBIbG7tqyGt0dJRvfvObG1rufxyCIBAWFiYJxSdRHLFYLDQ2NqJQKDh+/Phjo7D1ej1vv/02//zP/0xzczOnT59+4HYzMzPcvHkTl8uF2WzGaDTS0dGBRqORVi28r2Fvby82m43h4WHm5+f59re/LbkF9ff384tf/EIqGrW2tjI4OCjnJsg8cWSB/DnAa+W0uLi4qUEcmQdz6tQp/uVf/oWysjLS09PX/doKgkBqairl5eVERUURHx+/yvrN7XYzOjpKY2MjTU1Na1pjXC7Xlvvffu/3fu+BP7fZbCgUCgYHBxkZGcFmsxEdHU1ycjK9vb2cOXMGpVLJN77xDdni6AmhVCrRaDQ4nc4t3Yj4+/tvqg0oOzubtLQ0zp49y9mzZ3G5XPj6+hISEkJRUdGmHSe8on+9ItfPz499+/ZhtVq5ePHiY28mN0NoaChtbW0UFRVJPwsMDOSb3/wmf/d3f0dSUtKGbBlFUcThcGC1WrFarSwuLrKwsIDFYpFEfnZ2NmazWRLHHo+Hnp4e5ufnsdvt236O96NUKjf1mXgYFouFO3fu8PLLL28oAMloNPKd73yHv//7v2dhYeGBrTRtbW0YjUYKCwvRaDRYrVYmJiZoa2tb1dbT1dWF2+2WBj9zcnJWvWcVFRWkpaVJ4njHjh3behMiI/MwZIH8OUCj0fDmm28SHBws905tIxqNhrfffpvW1lbOnz/Pjh07iImJWZeo0el05OXlUV5ezq9//WsKCwux2WyMjIwwPT2NTqfDZDJx+PBhacBycXGRq1ev4nK5pLCPzXD9+nWqqqokW6S9e/ei1WqxWCz84z/+45rtvUlgSqWS0NBQJicn+fnPf85/+2//jZ/+9KfMz89LS+7bufz9ZUUQBIxGI1ardUsCeTMVZC8ajYa9e/cyOztLd3c3J06c2PRxeJmdnd2UB7hGo3liy+FhYWHU1dVRXl7Ovn37pAqrn58fycnJdHV1PbYi6mV8fJzGxkYWFhbQaDSo1Wo0Gg1KpRKn08lLL730wPOvr6/H7Xbz0ksvbXvrw2cJCwtjfn4el8u1ZWu/yclJ7ty5w2uvvbauNp7PotVqefHFF6mpqaG4uHjVe9za2sr4+DhHjx6Vbsi8w406nQ6n04lKpWJgYIDIyEiCgoKoq6tDp9NRWVlJTk6O1Prm6+vLyMgIExMTTE1N8fbbb2/Jnm1sbIxf/epX+Pr6otVq2bdv37r78GW+XMgC+XPCdg62yPwWQRBIT0/HZDJRVlbG1atXUSqVpKWlERsb+8gLe0REBBEREVitVpqbmzEYDKSnp6PT6dBoNExNTVFWVgasVJm8E/YxMTEkJCRs+pgdDgdLS0sIgiBdOL7xjW+g1+tJS0uThv+8ProdHR3ExcURFBREbm4uMzMzhIWFoVKppG29VTJZIG8PPj4+W7bp8vHxWRUWslHCw8PxeDzbJk69N1Ib5Un2iqpUKg4ePMjVq1fp6uriD/7gD6TflZSU8P7772M0GomMjGRubo6AgACp9eV+kTU9PU1lZSVxcXG8+OKLawTYjRs3qK+vXxXt3tfXR19fHwsLC7z33nsbnifYDGq1GpPJxMzMzBqP3/XidTBpbm7mq1/96qbEsZfCwkLu3LnDzMzMqtAar3XbZ92XXC4XFouFTz75BEEQEAQBPz8/lpeXKSoqoqKigsDAQH7961/z5ptvIggCJ06coKGhAafTSU5OzpaHkO/cuSPNBlRVVa1JAZSR8SILZBkZVqysXnvtNTweD6Ojo/zmN79hcHCQpKQkwsLCHnmR12q1qy6cXq5fv45SqWTXrl0sLCzQ0tKC2WwmJSVlS9WfkpISdu7cSV9fH1FRUdKqgq+vL6+//vqqbScmJqirq6OkpOSB5/Bnf/ZnwIqIkT23twdvv+pWb2q9YSHeG5pnjcPh2JRQFwRhy/3YD+PWrVtSOpp3ENDbAhIQEMDXv/51/vVf/xWn00ltbe2qY/L398fHx4fAwEDGxsbYsWPHQ0NG0tLSuHXrFi6Xi5mZGZxOJ/X19bz22muEhYURGBj4RM7vQURHRzM9Pb0pgex9HZxOJ2+99dYaj+2NIggC0dHRawRyeHi45MJ0v0NPUFAQ4+PjHDx4EIVCgZ+fHxqNhvLycrq7u0lOTqa7uxun00l1dTWFhYX4+vo+8Pt1swwMDJCTk4PBYCAlJYWuri65MCDzQGSBLCNzHwqFArPZzHvvvUd9fT1VVVVS0IPZbCY8PHzdFbGAgABmZ2fRaDQEBgai0WhYXl7elmQsb/X6cYSGhq4JMrifp5mA9mVhYGCA2dlZ5ufn6enpQRTFdXvGfhZvWMhmBfLIyMi2vMeiKLKwsLCpm6gnVUGemZnBYrHw4osvotfraW1tpbKyklOnTknbxMTEUFhYyI0bN9Dr9ezbtw9/f3/sdjsWiwWr1SrFOj8qgc973hcvXkSr1RIQEEBJSQnJyclP5NweRVRUFNXV1Rt+nCiKdHR04OPjw9tvv71tA3+Tk5PSkKTL5aK+vp7BwUGKi4upqKigtbVVsi99mLtHZGQkAwMD7Nmzh9HRUVwuF1evXkWr1a67RWY92Gw2rFartBKi1WqxWq3b9vwyXyxkgSwj8wAUCgW5ubnk5ORgsVgYHBzk9u3btLS0EBoaKg2J9PT0MDExgUajwWw2k5iYiEqloq6uDofDAUBHRwfh4eHY7XZmZ2dZXFykoaEBg8FAd3c3sFLZ8YYsyPZFn29mZmYAaGpqQqfT4Xa7uXz5MhERERQUFGxImOh0OoaHhzclxKxWK4ODg7z44osbfuz9LC0tUVVVhc1me6hf98MQRRGPxwOstBltZ7Tv6OgoAQEB0g1nVFQUFRUVHD9+fNUKzd69e7l58yYZGRnScKyvr6/Ud/q46qHb7ebmzZsUFRWxY8cO4uLinmlEcVRUFBcvXlz36zk3N8fAwAAjIyPodDq+8pWvbJs4djqdku+0N1I6ISGBvLw8BgcHycrKorGxURLIDyMsLIzm5maGhobYv38/V65cQRAELl26RE9PD6dOndqW78V//dd/xW63o1arGR0dpbW1lVdeeWXLzyvzxUQWyDIyj0AQBEJDQwkNDSUvL4+BgQG6urq4e/cuADk5ObzyyissLCxQVVVFWVkZWVlZ9Pb2UlBQgFqtRq/Xs7y8jI+PD3a7ncrKSmlQxeuNLAgCnZ2dVFZWcvLkyUcGPcg8v4iiSGNjIxkZGauEl91u59q1a9TW1pKfn09TU5PkAzs+Pi6Jau9zeLHZbI+0s/L2KPv6+uJ0OqmpqcFut+N2u3E4HAQGBm55+f/mzZtoNBpOnDixYWHoTdh7Ejd9IyMjq9wM9Ho9wcHBfPDBB7z22mvSeVutVkRR3HB4iCiKTE9P09nZSUBAAIcPH97W498sBoMBk8lEV1fXum6c6uvriY2N5cCBA9seoKJWqzl16hSNjY04HA6++tWvsmPHDvr7+zl37hwOh2NdPvMBAQHk5+dTXV3NiRMnOHr0KBcuXCAjI4O2tjbu3r1Lfn7+lo83ODgYX19fVCoVXV1dnDx5kpSUlC0/r8wXE1kgy8isE0EQiI2NJTY2ds3F0mAwEB0dza1bt7hy5Qo+Pj6rLt4LCws4nU4SEhLIyclZ9Vi3201LSwvh4eE0Nzfzgx/8gL/4i794Kucks324XC4+/PBDZmZm1izX+/r6cujQIS5fvsyNGzeYnZ2VbNx8fX0JDQ2VBpq8w0uwYuv1IIFssVhoaGhgfn4eURRRqVS43W6pFUitVqPT6bbserO4uIjVauXw4cMbFsdzc3P09/dz7Ngx/P39t73qarPZiIiIoKenh7a2NmJjY/Hx8WFpaYn333+fkydPkpmZKXmGu1yudbWITE1N0d/fz9jYGL6+vuTl5ZGXl7etx74VBEHg9OnT/OAHPyA8PPyRLVveEI533313y64XDzuWB70+QUFBTE5OAnDkyJF1PVd0dDR3797F5XIhiiL+/v5YrVZycnKoqKjYFoFsNpupqKhgYGAAm8225R5smS82skCWkdlG9u7di9VqpbKykqGhIYKDg1EqlZSXl6NQKPD392dubk6qbs3MzNDb20tvby96vZ7Q0FCmp6ef8VnIbJbh4WGCg4NXeeR6//9+kZydnb1p94C+vj7q6uqIj49nz549AJSWlpKamkpSUtJDq7WbaXGYnJzc9HJ8d3c3/v7+TzT6vLy8HFEUCQoKYnh4WPIx9ng8/PrXv2ZiYoLDhw+zY8cOxsfHH+qf63a7mZ+fx2Kx0NXVRXFxMadOnVo1ePY8YTAY2LNnD93d3Y90YfB+5ywsLDxVi1CdTodWq8Vut9PU1MTevXvXbGO1WqWbPLvdLrXitLe309fXh0qlQq/XExQUxOLiIouLi1v+LOXn53PhwgWqq6uJiIjgo48+QqVSkZycTH5+/hrXDZkvN7JAlpHZZo4ePUpycjJXrlyhoaGBpaUlFAoFMTExNDY20tjYyK5du2hubmZpaQm1Ws2LL76I0WiktbV1XcN3Ms8fKpWKP/3TP+Wv/uqvuHTpEi6XC7vdLsWLHz9+HJ1Oh0ql2vCgpsvloru7G4vFwvT0NHl5easqy9HR0XR3d5OYmLhKII+Pj1NdXS1Zzul0Onbu3ElYWJjUmuHF4/FIrR7BwcEMDw/T39+Px+Ohvb2dlJSUDQlsi8XyxMSxx+PB5XKxf/9+7HY7kZGRkpAXRRGLxUJVVRW3bt1Co9GQnp5OfX39KoFst9ux2+3Mz8/T3t6OIAgEBwfzzjvvfC785lNSUlY5czwMvV7P1NTUUz0nQRBITExkcXGR/v5+RkZG8PX1xePxSMfhfW9SUlIIDAxkYmKCoaEhhoaGKC4upqamBqvViiAImEwm+vv7tzywp1QqOXLkCDdu3GB5eZnJyUmioqKoqanhxo0b/OEf/uFTseuT+XwgC2QZmSdAXFwc3/72t4GVQb6GhgZ6e3vR6XQsLi5SU1MDwMmTJ/Hx8ZGEh81me+ikt8zzj7effHFxEVgRCvcv8cOKBVZVVRXHjh1btyvE9evXsdvt+Pr6cvDgwTUCOzMzk7GxMWpra8nNzZWW05ubm4mKiiItLQ2Px0NfXx+3b99GqVSyvLyMv78/ZrOZsbEx5ufnUSqVCIKAQqHA7XZLbSAtLS2o1ep1W9d5PB6Wl5ef2M1ea2srer3+gVZn3rmBgwcPUl5ezrVr18jJyZF8yJ1OJ21tbfT19REYGIjRaOT48eOPHSR73jCZTNhsNpaXlx/5OdJoNNK5P01eeOEF/vmf/5mAgAD6+/sZHx9HqVSSn5+PSqXC4XBw5MgR6bvPaDSu6gfW6/WSw4RWq922lbWOjg48Hg9Wq5WTJ09y/vx5yYpwdHRUFsgyErJAlpF5wsTHxxMfH48oikxNTdHS0sK1a9fIzMxck7QmiuK2XcxEUaS2thaPx0NBQQFut1sSQDJPjvDwcEZHR6V/C4KAx+Ph0qVL6PV6FAoFLpeLrq4uUlJSWFxcpKuri/T09Acu8dpsNhYWFiguLn5oFVChUJCUlERbWxsff/wxCQkJREVFMT8/T2FhoWRrlZaWhtlslgb4BgYGGBoaIiQkhIMHD0rCenJyUgp7OH/+PKIoMjExsW6BPDc3J0U2b5X+/n6MRqN0U7CwsEBPTw+ZmZmPfFxAQABFRUXcunWLhoYGVCoVFy9exGazERUVxX/5L//lqfoXbzcKhYLIyEhGR0cfOsjp7UHebKjIVggNDUWj0aDRaJifnyckJARRFKmurn5sjLpXwHp7hF0u15ZSKe/n1KlT/N//+38RRZH8/Hyys7Opr6/n/PnzXLx4kZs3b3LgwIFnYuEn83whC2QZmaeEIAiEhIRQXFyMyWTi448/XhMyMDU1tS2xp0tLS3z66afU1dUBKz2LH374IcXFxRw6dGjLzy/zcBISEqRlYp1Oh8Ph4Nq1awQHBxMZGYnD4WBubo6Wlha6u7ulSm55eTklJSWr2hicTielpaVERUU9trIVHx+P2WymrKyMrq4uenp6pGjf+7m/+pyYmPhA0Xu/oPLapN0v+h+HwWAgMzOT3t7exwrZh2G32/nkk0+AFT/okydP4vF4KCsrIyYmZl2xzq2trfj6+uJwONDr9bz55psEBAQ8sq/a7XYzNjZGeXk5w8PD/Mmf/MkTGXDbDg4dOsSZM2cwm80PPKfx8XGMRuMzc8VxuVzExMRQW1tLbGwsr7zyitQudPbsWSYnJ1cNyrlcLu7du8fo6Ci+vr7SCoTT6ZQs+raKyWTilVdeYWhoiHPnzlFYWCi5zvz7v/87c3NznD9//pH9/DJfDp7Pv3oZmS84aWlpuN1uzp07R3R0NBERESgUChYXF6UksI0giiI9PT00NjYC0NXVtcourKurC4DKykpqa2s5duzYpoWLzKM5ePAgk5OTVFZWEhYWxtDQEL6+vmRmZkrv7c6dO6XURoPBgEaj4ezZszQ1Na1qsenv70ehUJCXlyctAy8tLeHxeKShu8XFRam/88aNG2g0Gl566SXu3LkjDT5thf7+foANDesJgoBer2dxcZHe3l4pSGIjaDQaVCoVgYGBUptKXV0dWq2WrKysdYmXmZkZEhMTaW9vx2634+fnt+Y8rFYrnZ2ddHd309TUBCCJSpvN9sRSALeDuLg4YmNjaW5ufmBrlsvlQq/XP4MjWyEjI4M7d+6gVCrJzc1FrVajVqvZuXMndrud2tpaTCaT9F42NTUxODjI3r17JeHc1dXF1NTUlkV+b28vXV1dlJSUkJqaSnh4ON/73veoq6sjODiYvXv3Mjg4CKx8fj+bDijz5UMWyDIyz4idO3dy9+5denp66O/vl5YcJyYmiIuLW/fziKLIv//7vzMyMiL1VEZERNDb24tCocDj8TA7Oysljs3Pz3P+/HnJZWNxcZGgoCCys7Nxu91cvXoVm82GzWajq6sLg8HA7Owse/bseWQqn8xv+drXvsbk5CQ//vGPCQsLY9++fat+LwgCSqVyjauCxWIBVtoqvO0PsbGxkoAYGRnh9u3bqFQqqXXD+3yiKGIymXjhhReA38Yji6K46UpYXV0dFouF4uJibt68KfVBP462tjba2trQaDQMDAxIAtnj8TA/P09AQMBjB/5GR0elc+rp6aG7u5vBwUF27dq17vMxGo10dnZKbg6ffPIJeXl59Pf3MzQ0xMLCArOzs4SFhWE0GgkMDESn01FUVITD4aC7u/u5j2A/fvw4//AP/0B6evpzV+n2BiG53e41N0k5OTncvn1bWuUaHh7G4XCgVqvx9/dnZmaGxsZGlErllgcnRVHkV7/6FTabjZKSEuC3CY/+/v5MT0/z0UcfSX9H4eHh23JzKfP55vn6a5KR+ZKRkJDA0tISAQEBhIeHMzg4yMcff8xbb7217srP7Ows/f39HD16VKqOXbhwQQosmZ2dJTExUVqi9Iphi8XC1NQUGo2GpqYmbt++LUUKp6amYrfbpeeHFdEms35CQkL4oz/6I/7P//k/WCwWTCYTLpeLpqYmpqen0Wq17N69W9p+586dNDU1ce7cOVwuF263G1jp542OjsbpdEqPO3bsmPQ4q9XKlStXcLlcq0TnzMwMarV6S8vEQ0NDpKamMjs7K1WovQLjYczMzNDW1sbevXvx9fWltLSUzs5OQkNDuXbtGm63+4E3DRMTE9y+fRsfHx/y8/OlgcPo6GgWFxdpamoiJSUFs9m87uMvLi5mYGCAuro68vPzaW1t5eLFi1LYRmxsLHq9XhKWMzMz0ms4PDy8Le1OTxqdTkdMTAzDw8Or2k5GR0el13ArN0lbYXp6GkEQKCoqWnNDpFKpePPNN6murpaSR19++WXUajX9/f20tLRw5MgRsrOzt+yhffPmTWw22yprRX9/f3bs2MHs7KzUK79nzx4qKirYu3cvISEhW9qnzOcfWSDLyDxDMjIyaGhooKOjg8nJSXJzc6mrq+Nv/uZv+K//9b9iMBgQRZGxsTEuXbrE1NQUe/bsISsrC51OhyiKVFZWYjAYJHHsdDqlnkur1Up8fDyjo6MkJCRI+42KilpVvUxJSWFqakpyHlAoFMTFxaHX6xkaGpKstGQ2hl6v5/Tp05w7d47s7GyamprweDzs2LFDcjfxLo37+/ujVqvZtWsXKpUKHx8fBEGgtbWVK1euSCLus0v+3j7jsLAwOjo6sNlsaDQaurq6NiQmP0tg+0hTAAAgAElEQVR/fz9utxuLxcLExAQ6ne6xw15TU1NUVlaSlJQkbWs2m5mcnJQEXHJyMlevXuXcuXN4PB40Gg1KpZKFhQWSk5NxOByUl5dL7QOwUg0fGhra1PnExMTQ09NDdXU1+/bte2SV9X6vaIvFQmFh4Yb39yzIysqiqqpqlUBuamri1Vdf5cKFC5L4fJoVZlEUpcG6wMBAaUj4fgwGAyUlJRw6dIi/+qu/wmazMTk5SWdnJ2+99da2iNQ7d+5QWlpKREQEv/u7vyv9XK1Wk5eXx5kzZ4iNjcVkMknzIefPn2d5eXlNqJPMlwtZIMvIPEMMBgPvvPMO09PT9PT0UFpaKgmj733ve+Tl5TE+Pr6qylFbW0tFRQUlJSV0d3czPDy8yh90dHQUt9vN9PQ0YWFhDAwMPDYmV6lUrkmVEgSB9PR00tPTmZiY4NatW2RlZclm+hskLS2NgIAAPvjgA5RKJUePHkWj0aBWq+no6JAEstls5u7du6hUqlXCoKCggNTUVElsfPTRRzidTjQajbSNRqNBFEWMRiMtLS3Y7Xb8/f23ZBnY1tZGeno6Op2O8fFxDAbDAwXDvXv36O3tRa1W43a7iY+PX2WZNjc3h0KhYH5+noyMDLRaLYcPH2Z6ehq9Xs/w8DAul4udO3dKQ1nenmsv3rjizYaW7Nu3j2vXrvHJJ58QHR1NXFwcRqNx1T5mZ2cZGxvjhRdeYHl5mYmJiU31Tj8LkpOTOXv2LDabDR8fH2pra5mfn2dqagqlUsnU1BSXLl3ipZdeemrH5A2ZSUhI4Pr164yNjfHKK688cFun04koirS3t7O0tMRbb721qVmMz3L58mUqKirQ6/V861vfWvP5SUtLY8eOHUxMTPDKK68wPj5OVVUVCoWCCxcuEB8fv2HPcpkvDrJAlpF5xqhUKkJDQwkNDaWuro7x8XFgxS5sZmYGg8GAy+XCaDSSkZHBpUuXMJlMnD17lqioKPbv37+qMhQbG4tCoeDu3bscO3aMgoKCLS9RmkwmRFHkf//v//3/s/fewXGd573/52wBFovee+8gKkESBAtIiwC7RMuSLIlyIjfRGcvJTJxk7JSZzGTyR37zS5tcO6NQsaUosq51r22RkiiKFSwgAQIgSBAgOokOLMousCiL7ef+Ae8JIRSikgR5PjOcIbCnvAd79uzzPu/zfL+kpqZy5MiRVZNdehaIjIwkLCxMkr6CaTOOyclJfvvb35KdnU1gYCB2ux29Xj8rc/ZguY2npyf9/f0zsoVBQUF0d3cTFBSE1WplbGyMvLy8Zb/v5eXlWK1WoqKi8PDwYNeuXVKQ82CzVF9fH11dXSQlJREXF4dGo5kRuMN0remlS5dmXIenp6cU9M6lzvFg4Gq1WqmurkYQhGVnQFUqFSUlJQwODtLU1ERZWRkqlYq4uDipjKKsrIy4uDhCQkJoa2sjISFhTV0AVxN3d3d27dpFWVmZtAL0k5/8hMrKSkJDQ0lLS+P8+fNs2rSJ0NDQRzKm/v5+qek4Ojqas2fPUlJSMqcahVarpaCggKmpKfbv378qdd9Go5Hr16+TmZnJ17/+9TknV3a7nYSEBHp6evjoo4+kMozc3Fxqa2sZHByUA+RnGDlAlpF5gvj2t7/N559/jtVqxWw2o9PpCAgIYGBggD/7sz/Dy8uLM2fOIIoiTqeTTZs2zXmc6Ohobt++jclkWpWMrys4ccmW/cd//Aff+973HmuH/Hpjw4YN1NbWSk13ra2tUpa+sbERlUqFh4fHDLOEr+K6L74aZHh5ec1QtgCWHRxbrVYGBgbYs2ePNAny9/cnNjaWmpoaSQnCbDZjMpnIyckhLi5u3hrXwMBADh06RHl5OWfPnuXgwYNLCnRbW1slrd+vBt9LxTURdZmm3L9/n5aWFsmuOjc3F6fTyf3793n55ZdXdK5Hzfbt2xEEgVu3bvHqq6+i0WiYmJjAw8MDNzc3EhMTuXr16iO7LpcCRHl5OYWFhQQFBdHR0UFGRsac2xcXF6/q+b29vXn77bcXLNP45JNP0Ov1xMXF0draKpVYwPRnaina3zJPHytLK8nIyKwqGo2Gl19+maNHj/IHf/AHJCQk0NvbCyA1zRUVFTE8PDyj4WQuBEHg7t27NDc3U1dXR1dX14okq4xGIykpKeTn5xMREcFvfvObJ1oC60lj06ZNaLVaysvLpdKYAwcOEBUVhc1mY2pq6qEZy87OTjw8PGbVAo+NjUlSb8sJkEdHRykrK6Onp4dbt24RGBg4a/Kj0+lwOp0olUrUajUJCQns3buX+Pj4hzaAaTQavva1r6FSqaioqJCsrxdDfHw8eXl59Pb2Sp+FlaJQKEhISJBcCb29vdm5cycA3d3d+Pn5zWu+8SSzbdu2GUGhn5+fZDwUFhZGT0/PIxtLVFQUf/Znf8bhw4epqKggMDCQa9euPbJm3/7+/gVLclwW46Ojo+j1esLDw+nu7sbpdHLz5k0MBgNjY2OPZKwyTyZygCwj84SiVqt56aWX+MEPfsBPf/pT6Utv9+7dZGRkPLQes7i4GLvdTm9vLyMjI9TW1lJfX4/D4UAUxSVpvJ45cwan0yktfbq65n/1q19JGrUyC6NSqTh69CiZmZk0Njbi5eWFRqPBy8uLI0eOEBAQ8NDl3MHBwVm14jDdBDU5OYlSqcRisWC325mammJ8fPyhzoyNjY1cvnwZlUpFdXU1AwMDpKWlzdimu7sbi8XC/v37KSoqorCwcIYyymIpLi7GZDJJ8nOLQavVkpCQQFZWFlVVVZIu80pxOp1cv34dm83Gc889J9lrNzc3s3v37lU5x+MmKCgIg8EgqdM8audALy8vMjIyCAoKoqmpif7+fv7pn/6JEydOrKmMWmtrKx9++CHvvvvuvJMqrVZLZmYmb775Jn/8x3+Mh4eHtFrX0dGBwWCQG5OfcYQnKQO0adMmsbq6+nEPQ0bmiaerq4tPP/10SV/kY2NjksyW63MfExPD5s2bH7rfuXPnKCkpmRHAOZ1OampqMBgM5OXlYTKZMBgMuLu7ExYWRmpq6iOrd1xvTE1N8cEHH6BSqcjJyUGj0XD79m3a29tJS0ub0eT2IOfPnycsLIzMzMwZv5+cnOTLL7/Ew8ND0pKFaQe6qakpsrKy5nSeMxqNXLx4kby8POLi4rBarSiVylmTr88//5y0tLRVWW52Op18/vnnZGRkkJiYuCT5sfb2du7evcvBgwdXVFcviiIVFRWMjo5SUlIilXy4VEC+9a1vLfvYTxJOp5Pjx49jMpmw2Wy8+OKLj8VCeXJykn/8x3+UzF9cVudvvfXWqjf9Dg4O8sEHH5CXl4fRaGRkZIQXXnjhoQosd+7c4ezZsyQmJnLnzh2SkpL45je/+cTrYMusDEEQboqiOGetolyDLCOzDhkaGsLd3X1J+/j4+HDgwAEUCgUKhYLz588vqgnJ1XjV0tJCZGSkpDSgUCiIiYmhr6+PwcFBrFYroaGhmEwmLl++zKVLlwgODubgwYOLsgV+lvDw8OB73/sely5d4uLFi2zevJnc3FyCg4OpqamRAmSbzcbExARTU1MolUoSEhJobGyUAmS9Xk9TUxMjIyOkpqayYcMGUlJSZtwbw8PDvPfee3h7e89yBrt16xaxsbGSMc18Nb52u33BbJprwjVfsPugA+DAwACRkZHU19czNDREYWHh4v5oTC/b3759m8uXL89qTl0soihKS+gPBscmk4nW1lbeeuutJR/zSUWhUPD6668zNjZGRETEslVAHsbIyAgff/wxbm5u+Pn5ERAQQGZmprTq5WrK3LBhA62trcTHx2OxWPj1r3/Nm2++uSoaza5Jz+XLl8nOziY0NJSAgABsNhvvv/8+Pj4+vPnmm/MG5NnZ2fj6+lJaWkpCQgJHjx6VraafceQAWUZmHRIYGLis0gZXAOR0OpmYmFhURtDNzY2CggKuXbtGZ2cn+/fvx9PTE51OR3l5OTExMTNk5iorKwkKCmL79u309/fz8ccfs3v37nWjKfuoUKlUFBcXEx8fL2mxuhrguru76erqYnh4GD8/P3x8fLBarfT19aFQKKisrGRqaoqpqSn27NlDZmbmvJmuoKAgduzYwZ07dySXPavVytDQEKOjo2zcuPGhY1UqlUxOTs5ZUjE2NkZ5eTkWi4XExERSUlKksbS3t2OxWLh//75UQ+/p6YkoiiiVSgYGBujp6SEsLAy73c7k5OS88l42m42rV6+i1WoxGo0MDQ0RFBS0qAyf1Wrlxo0bDA0NIYoi7u7uFBcXz/g8VFVVsW3btqfOXtjX13fNSytUKhXDw8NkZmaiVCrp6+ujurqa733ve5IDXmRkJJOTkzz33HPAdEB7+fJlGhsb523cWyw2m43f/e53NDU1IQiCVD+uVqtJS0ujra2N0dHRh646xMbG8u1vf3tFY5F5epADZBmZdYjD4ZCc1pbDfPV/TU1N2O12QkJC0Gg09Pb20t/fj9FoJDg4GIPBQH19PQUFBfT19eHn50d+fv6s46jVahQKBZGRkfj6+nL69GmGh4c5ePDgssf8tJKYmMixY8e4cuUKt27dQqFQMDAwwLZt20hPT5+RJRVFkcnJSVpbW/Hx8SEuLm5RWcEtW7bQ3NxMaWkpdrsds9lMWFgYarUavV7/0Npnb29v7t69O6ukRxRFrl27hr+/PzExMdTX10tj8/X1pbu7Gy8vL5KTk4mNjWVycnKGZXBnZydVVVUzMtA7d+6cpTzgqlt2Op2UlJRw+/ZtyfkxLS2N1NRUKfhxZYJVKhXBwcE0NTUxOjqKt7c3Bw8exM3NbVagVF9fj6+v7yx3P5nF4e3tzaFDh7hw4QI7d+4kPj4eT09PPvjgA/7wD/8Qf39/CgsL+eKLL6T6dkEQCA4OZnBwcMUB8qeffkpTUxMwnTX/4osvgGn5O9eK2Q9/+MMVq6DIPFvIAbKMzDpErVYzMjLC1NTUsvSIXZJiLS0tM7K/Op0OvV5PR0cHVqsVPz8/QkJC2LFjB25ublLNsWsMFosFk8k0I7OYmppKaWkp4+PjeHt7Y7FYUCqV3Lp1i7y8PKlEQ+Z/8PPz44UXXmD//v04HI5531NBEPDy8iIvL29Jx1cqlbzxxhv09fWhUqmIiIhAEAT6+/v54IMPsFgssxrzHqSoqIhPP/2U4eFhAgICKCsrY3x8HDc3N8xmM1u3bgUgIiICvV7P0NCQ1Oz34HG/GqDExsYSGhqKzWbD09OT5uZmysrKEEURT09P6T6H6cyzy05748aNbNy4Eb1eT0VFBV1dXfj7++Pn50dDQwO+vr6YzWba2trw9vaWLKvn4t69ewwPD/PWW2/JS+orIC8vD6vVypUrV8jPzycxMRFRFPnFL37Bq6++SlhYGBMTEwwMDEi9CVardcU1yFVVVXR1dZGQkIBCoWBwcBBfX1+ioqK4e/cuoijyp3/6p7LBkcySkQNkGZl1hsPhoLy8nJSUlBWbdbiCDxfp6encvHmTgwcPzpAMczE0NCQFPBs2bKC3t5fLly9z4MABaRvXErpr6dtV5+rm5kZ9fb0cIC/AWma41Gr1rFrw8PBw3n77bX72s59JRh9zoVAoSElJoaysTDL4yM/Pp6Ojg4mJiRnbBgYGEhgYuGDA/SAajUY6b3p6OmazmZ6eHmJjY7HZbBQUFEhZ35MnT2I0GqWSgcDAQA4cOEBTUxNGo5Guri6Sk5PJyMjgxo0bjI+PS0v6c9HX10dLSwvf//73ZeObVaCgoICAgAB+97vfUVRURFJSEp6envzv//2/ef7553nllVf4zW9+w/79+xEEAa1Wy+jo6JLP09/fT0NDA/39/fT397Nz5068vLwYGhri/v377Ny5E41GQ21tLV//+tfl4FhmWcgBsozMOmJkZITjx4/j7e3N9u3bl30cp9OJxWKRgllX5syV8T116hR5eXmzGrOcTqdU2qFQKCguLubUqVP09vZKhgtGoxFBELh//z5eXl5S3aHRaKSmpobi4mI5U/cE4eHhgd1ul2yuLRYL7u7uiKKIxWLBy8sLDw8PkpKS0Ol0GI1GSkpK8PLyoqenZ0bJxGqgUqnQaDQkJSXNKh+JiIjg7t27Ui01TN+HX12id9VrHz58eM5ziKLIvXv3aG1t5fXXX1/1a3iWSU5OZufOndy8eZOtW7cSHh6ORqPh008/5aWXXsLHx4eKigqpKbW8vJzc3NwZDo0LMT4+zvvvv09CQgLBwcGkpaWh0WgQRZGqqipSUlKkgDgzM5NTp06tuLziwWekzLPDmusgC4KwXxCEZkEQ2gRB+Olan09G5mmmtLSUoKAgioqKltyRPjk5SXNzMy0tLdy7dw+NRoPBYGB8fFzaRqvVEhAQgNlsnlMkPyoqivb2dinAbm1txW63U1FRgcFgkOyUVSoVLS0t1NTUkJWVhUKhkCyzXZrKMk8GfX19OBwO3N3dMZvNkm7u0NAQRqMRq9VKf38/p06dYnx8nJCQELy8vHA4HHR3dy+53ONh2O12xsbGZmWmYVqW0FXisxCugGauoMhsNlNZWUlfXx9vvfUWUVFRqzJumf9h69atpKWlcfHiRUZHR/H39yc/P5+TJ08yMjLC2NgYFy9exGw2k5yczNWrVxc83vDwMP/wD//AyZMn+fLLLwkJCWHDhg2EhYVJ77HBYMBut8+QSIyKisJkMi06Sy2KIl1dXTOeT5WVlfzd3/0dw8PDy/hLyKxn1jSDLAiCEvg5UAL0AFWCIHwqimLDWp5XRuZpQxRFOjs7aW5uRhAEJiYmFiXR9iD9/f3U19fj6emJm5sbGo2Gqakp2tvbycnJobu7m/v372MwGNi7d++cNtLJycl0d3dz6tQprFYrnp6eZGdnS/q5Cy2rC4LA9u3buXLlClu2bHnq1ALWK83Nzfj6+hIUFIRKpcJkMlFQUMDk5CRnz55Fp9MRFhbG9u3b0el00n03Pj6OSqVaVYWE7u5uOjs72bFjx5zH7evrW/J9/9Xj19XVsXHjRnbv3r0smTiZh+NaXfLx8eH27dts27aNsLAwwsLCEEWRtrY2WltbuXPnjlTHvhAfffQRoijS2NhIbGwssbGx3Llzh7a2NhQKBf7+/hiNRgIDA2eUhQmCQGJiIh999BFvvPEGwcHBjIyMcObMGWJiYoiNjcXb21vqpzhz5gxNTU28/vrrkl50TU0N3t7eHD9+nLfffvuRm63IPD7W+umwBWgTRfE+gCAIvwaOAHKALCOzBH7zm9/Q3d1NdnY2er2eM2fOEBsbS2JiIsCilohdpREqlUqqy6yurqajo4Pe3l6sVivx8fEUFBTMW7On0WjYv38/HR0d+Pv7L3lp2iW1tFQHNpm1w2q1YjabaW5uBpihXbtv3z7q6+sZHx/H3d19Rg3z1NTUqgeY9fX1kobtV+nv76enp4c9e/Y89DhfXQ53Op2S7vK3vvUt2SHtEbFx40ZKS0uZnJyUatcFQSA5ORmr1crU1NRDG3fNZjMTExMUFhZSXl5OW1sb7e3tqFQq9u7di91up6uri9TU1Fkuk2NjY3R1dTE1NcXVq1fZsWMH7733HkFBQVRUVHDu3DlpW5VKJel9u56rABMTExQUFFBTU4PJZJID5GeItQ6QI4HuB37uAQrW+JwyMk8VDoeDnp4e8vPzCQwMJDY2lpiYGKqrq+nq6gKmg5qHOdd1d3cTGBg4o15z06ZNZGRkMDIyQmBg4KKaWRQKBQkJCcu6jurqajZu3EhXVxeenp6EhYWtmXmBzOIwGAzk5OTMqfKg1WoZHh4mNTV11mt2u33Vx2KxWOYMllw6xRkZGUvOIIuiSHV1NUqlkmPHjsnNeI8QlUpFeno6zc3N5OXlzZi4pKWlUVtby7lz5/ijP/qjeY/hWp1ymQ45nc5ZpTN+fn5z7tva2sqWLVvYuXMnCoWCn/3sZ5jNZsbHx/Hy8sLPzw9BENDr9ZjNZuLj43n55ZdnPJMiIyO5ePEiarV6RasXMuuPtQ6Q56pqn+FtLQjCMeAYIIl7y8jI/A/19fVotdoZBgpBQUHs378fmM5wXLhwgWvXrpGYmEhOTs6cx/H390en083SmNVqtY8ko6tUKtm8eTMDAwP09vYyMjJCTk4O7u7uxMXFER0dLQfLj5ixsTG6u7tnSP19ldDQUO7duzcjqwbTdaFzleEsl76+PgRBmNMh0qX7nZycvKRjiqLInTt3AHjjjTfkkorHQElJCR9++CG3b98mNzdXCpKVSiVxcXEMDw8vOGnx9vbG4XAsS9LSZrMRFhaGQqFgcnISvV5PeHg4x44dQxRFRkdHJWnLLVu2zFmznpSUREtLC9/85jel+10URcbHx9HpdFRUVDA+Pk5CQgKJiYkkJyfLDX1PCWv9tOgBHkxLRAF9D24giuJx4DjApk2bZgTPMjLPOkajkbNnz5KVlTXvNl5eXvj7+6PRaOjo6ACmbVO/+pDeuHEjp06dQqfTPbYl5sjISCIjIwGoq6ujsrKSuLg46uvrmZiYICMjg7y8PKKiouQvmUdAXV0dERERC7rRbdiwgdOnT0s/u2pBOzs7l2QT/TD6+/tRqVSUlpZiNptRKBSIoojD4cBut0u1qy6924VQKpUIgsCtW7cYGxvj+9//vhwcPyY8PDx48803eeeddxgYGJihVuFwOLDZbPzmN7/hxRdfnDNAFQSB/Px87t69S35+/qKeC65mO71eLwW1jY2NhISEYDKZOHHihKS646p/zs3NnfP8Gzdu5OzZs/T19UnOo+fPn6e8vJzQ0FBCQkKIjY1leHiYzz//nAMHDsxoFJRZv6z1E6MKSBYEIR7oBV4Djq7xOWVknhqUSiUOh0MKKueiv78fg8HAvn37SElJ4erVq/j4+BAfHz9jO0EQUKlU2Gy2tR72ooiOjiY8PFzKaJtMJrq7u/ntb3+Lr68vf/AHfyAHNWtMQkIC165dw2q1zgoOXI2gSqUSURQlXey2tjba2trYvXv3vEvby0Gn0+F0OiUDEtdKiLu7O25ubhgMBu7evUtra6skCzZfsKRUKomPj6e9vZ1XX31V1sF9zLi5uVFcXMz58+cJDQ2V3rfg4GCKi4spLS3l/v378zb57tq1iw8++ICampo5nTu/il6vp6mpiTfffFMKyNva2oiNjSUkJIRbt24RGRmJwWAgJiaG4uLiecsnlEolf/3Xfz3jdwUFBbS0tKDX6wkKCpK0vwE6OjpmBMg9PT1UVFRgtVoJDg5m06ZNsqzgOmFNv31EUbQLgvAj4AygBH4piuLdtTynjMzThNlsxmKxcPv2bcLDw2dphfb09FBdXU1ubi4eHh54eHiQlZVFbW2t5Fql0WhQKBRs2LCBqakpgoODH9PVzOSrwZVWqyU1NVUK8k+cOEFoaCj+/v4kJCTIjX1rQHh4OOnp6dy5c0fKzlksFjo7O6mrqyMpKQmn0yndQ3a7nYaGBgoKClY1ODaZTFgsFjZs2EB3d7fULPUg3t7eREdH09TURFVVFT4+PuTl5c1qmnJlmnU6Hd/4xjeWXJYhszakp6dz/fp17t+/P6Ncx93dnYyMDM6ePcvg4CBbtmyZNaHRaDS8+OKLfPjhh4s6l8VikVQzYLrUoqenh23btqHRaNiyZQsVFRWEh4fz0ksvLXm1ysfHh7fffhuj0ch//Md/UF9fD0yv5rnUeRwOB7/97W/p6uoiKSkJb29vhoeHOX78OPHx8aSmphIREUFQUNCyV8scDgf9/f1ERkbKK25rwJqnZ0RR/AL4Yq3PIyPzNOLv78/mzZupqqrCarVKMklNTU20t7djs9nIz8+f0WAVFxeH2WyWLF3tdjsTExOcP39ecq96knFluhsaGrBarTQ1NfHpp58SEBBAQEAADocDhUJBYWGh3LewCuzbt493332XtrY2kpOTKS0txel0Eh0djV6vx+l0Slm7gYEB1Gr1ok0dFkt7ezswvQzucDjmVTVwmYKkpKRw8+ZNSktLiYyMJDc3VyoT6e3tpaOjg6NHj8qujU8QgiBw5MgRfvnLXxIUFDRjYuN6frW0tNDd3c0bb7wxa38fHx8mJydpampCEARCQkLw8fHBaDTi5+c3o+zGZDLNqI+/efMmvr6++Pj4SPJwRUVFVFZW0tvbO0sL2+l00t/fj81mIzIykmvXrtHc3Iwoivj6+pKUlEReXh7u7u5Ss6qPjw+hoaF0dnZSX19PR0eHZKrj6q2IjIwkLS2Nzs5OampqOHHiBL6+vmzdupWcnJwl11hfu3aN0tJSAN566y1ZnWWVkdcvZWSeYJRKJZGRkdTU1EjqE62trbS1tZGZmUlsbOyc9ZhpaWkzlitdckbrpYP/Qac0mP7CcnWaKxQKLBYL/+f//B+ioqLYv3//qmYznzXc3Nw4evQo7733HgqFAi8vL6xWK1u2bJm1rcPhWJNGyvj4eJqamsjKymJ4eHiGec1cqFQqSau5tLSUzs5OkpKSpNpTl4ObzJOFS4niyy+/pKioaIb0W0xMDKGhoXz55ZdzOte5u7vzwgsvoNPpsNlsXLt2DVEUpWyzn58fmZmZOJ1OWlpaOHTokLRvZGQkV65coaOjg+7ubkRRRKvVEhMTQ21t7awAuaenh/feew+YduNraGiQzEMGBgZoaWnBaDSyZ88e9uzZw8WLF9m4cSNGo5GCggIuXryI3W7na1/72qzPi1qtlmqZe3p6SEtLo7GxkdLSUqKiokhPTyc1NXVRDbDbtm3Dx8eHkydP8u6773LkyBFyc3OX8pbILIAcIMvIPMHU1tZy4sQJYPoLwmq10tDQwPbt2xddKuFwOCRXsoWasZ5kFArFrOuNjY2ltbWVd999l9dff112RFsB/v7+fOc73+H48eM4HI45g+PR0dE5g4nVQKvV4ubmxtTUFDqdjt27dy9qP09PTxISEqivr0cQBAICAjCZTHOOX+bJICsri7GxMaqqqti+ffuMZ9LY2Ib5FsoAACAASURBVBjBwcHzlgtkZ2eTnZ0NQH5+Pt7e3mi1Wnp6eqitraW0tBSbzYbdbicuLk7aLzo6muLiYq5fv467uzvBwcFUVFSQnJxMY2Mju3fvloJ11/bbt2+XtOd3797N1NQUGo0Gk8nEuXPnaG1tpbi4mIKCAm7fvk15eTleXl44nU6ys7Px8/NbVA9FQEAAUVFR2Gw2dDoddXV1nD9/nq1bt1JUVLRgQ6pKpSIrK4vBwUGqqqo4efIk3t7esxRnZJaHIIpPjnDEpk2bxOrq6sc9DBmZJ4Z/+Zd/wdvbm76+PhQKBREREej1eg4cOPDQfScmJrhz5w4DAwOSCH5mZuZTV5PZ399PTU0Ne/fuJSsrS27sWwF9fX188MEHaLVaRkZGCAgIwGAwEBkZycDAAHFxcfPKCK6Uzz77TMpcz6XJ/LBx37x5E6vVyp49e9ixY8eajFFmdRBFkc8//5zm5mays7Ol0gBX2dg3vvGNJR/T4XBw79494uPjsdvts1bLRkZGePfdd9Fqtbz44ot8/PHHeHp6otPpiIuL480331z29RiNRoaHh6XJ2pkzZ7DZbAQEBJCQkDBvQ+knn3zCvn37ZpW9TU1NUV1djb+/P6+88sqcqzZ2ux2FQsHExAT/8i//glqtxmazsXv3bnbt2jVnFl5mNoIg3BRFcdNcr8nfJDIyTzDu7u7ExMSwdetW6uvr6e3tnVV+MBednZ3cunWLoKAgSkpKnmqB+/DwcLZt28aNGzc4ffo0Go2GnJycRTmuycwkIiKC5557TpJ1s1qtZGdn09bWRnx8vJS9WwvCw8OxWq1LDo5hetwWiwW9Xs/27dvXYHQyq4kgCDz//PNkZmZy8uRJRkZGyMjIwOl0LnuVS6lUSvbQcx3j/v37BAQEYLFYsNvtHDhwgPPnz0vNcyvB19dXqqn29/fnxz/+MRaLhdbWVq5fv05dXR2pqakznChdzJWk9PDwYNu2bVy9epW7d+9Kn7vx8XH6+vrw9/fnvffeIzY2Fp1Oh6+vL2azmUOHDpGdnY3D4eDv//7veeONN6RyDpmlIwfIMjJPMDk5OTQ1NREREUFmZiaZmZkP3cfVxJeSkjLDNe9pxt/fnx07dki2yWVlZeTm5s4wV5FZHJs3b+b06dNSs2R7eztKpRKdTodOp0OhULB58+ZVt9xVq9VMTU0te/++vj527twpZ83WEfHx8bz11lu8//77lJeXYzab0Wg0nDlzBk9PT3JyclbNjCYkJASDwYC7uzteXl7Exsbi5ubGqVOn2Lp166qco6Ojg//6r//ipZdeIjMzk+zsbLKysuju7ubjjz/G19d3Rr+EIAhzBsjwPwH/mTNnKC8vJy8vj7GxMa5duwZMK3v09vYyMTGBt7c3xcXFbNo0nQh1HfNXv/oVf/InfyLLyi0TOUCWkXmCGR0dxWg0YjAYJPmghzExMYHJZJpXU/Rpxs3NDTc3N8n9TQ6Ql44gCMTGxtLV1UVUVBQeHh4zvsS7u7upq6tb9TIGVw3ycrFYLFIjlcz6wdPTk+9+97u0trZy69YtJicnmZiYYHBwkLKyMpKTk9m7dy9arRa73U5paSnh4eHEx8fj4+Oz6PNER0fzF3/xFzMmUImJifzJn/zJql1LQ0MDMNOG3dWAWFRUxN27d2fVxy9U5hoREUFYWBgdHR1cvHiRgoICdu3axa1bt7DZbNhsNjZv3kxJScmMrLkgCJSUlHDu3DmOHz/O4cOHiY6OXtLfS0YOkGVknmgOHjxIbGwsX3zxBdnZ2Q9tkHI6nVy5cgWtVvtQt7GnmeDgYNra2uRmrWVy6NAhfvnLXxIdHT2rPtLLy4uysjLsdrtU793b28vdu3cZHx8nICCAmJgYoqOj53Qmmw+XsUd5efmyHPoyMjL47LPP0Gq1JCQkLHl/mceHh4cHCQkJnDhxAo1GQ1paGqmpqWRkZFBdXc3PfvYzbDYbgiAQHBxMX18fp0+fxtvbm29/+9szGuwWYq1XF4KCgvDw8JizFCktLY1Lly4tuTZYoVDQ3d2Nn58fAQEB5OTksGvXLsbGxvDw8Jj3M7Z161Z6e3tpaGigrKwMnU7Hiy++SGpqKgqFYt02bD9K5ABZRuYJRhAEMjMzCQwM5L/+678ICwtbsAmtt7cXs9nMSy+99AhH+eTh4+NDW1vb4x7GuiU4OJiUlBR6e3tnNXUGBgbi7+/PtWvX2LJlC319fdy9e1fKNo+NjVFfX4/RaGTjxo2LPqdGo6GoqIhz585JS+1LISQkBKVSuWpL8jKPFi8vL/7yL/+SpqYmPvvsMzw9PfH398fHxwcvLy98fX0JCQnBw8NDKk2ora3lww8/JDo6moCAADIyMh5rlnTLli3zyiOePHlSckZ1PcMFQZix6iGKIgaDgb6+PtRqNZGRkXh7exMREUF/f7+UIBEE4aElTgqFgpdffhmbzYbT6eTf//3f+eyzz6ioqKC/v59Dhw6xcePGZzqR8jDkAFlGZh0QHh5OQkICdXV15OXlzbudn58fSqVyWQHG08TIyIgsmr9C0tPTuXTp0pyqJ5s3b+b8+fOcPn0arVaLt7c3eXl5Umasrq6OoaGhRZ9Lr9dz//59JicnAbhy5Qp79+5d0ngdDodk5yuzPlGr1WRlZbFhwwYGBgbo6urCbDbj5uYmuTu6DD88PDzQarUYjUY6OzsZGhri0qVLpKenc/DgQWC6Ma+srIzg4GDS0tIIDQ2lrq6OqakpUlNTGRsb48yZMxw5ckRqZhNFkaGhIYaHhxkaGiIgIICsrKwVXdetW7fQ6XQkJSVx9epVPD09Z60GiqJIeXk5U1NTZGdnYzKZKCsrIyIiguzsbJRKJf/5n//JsWPHFl1TLAiClGH+8Y9/DEyX7f3bv/0bp06d4saNG+zatQs3NzepwVHmf5ADZBmZdcKRI0f4+c9/vmA9sre3N5GRkZSWlrJv375nNjug1+tX/KX2rOPKBs/32qFDhxAEYc7lYk9PT/r7+xd1HrvdztWrVwkLCyMkJITw8HDa2tr4/PPP8fHxIScnZ9ENgUqlEqPRuOoNhDKPFoVCQXh4+Ayzl8LCQqxWK729vQwMDDA+Po7ZbCYuLo7e3l50Oh2iKDI2NobNZuPjjz+mq6sLQRDo6enh1q1bbNy4kZqaGgCuX78uHduVTOjr6+Pjjz+Wmn1h2mRkpc8StVotlf187Wtfw+FwcP78eex2O2azWSoR0el0/OQnP8Hd3R2A4uJi/tf/+l9ERkaSkJCA1Wrl1KlTlJSUEBoauqyx+Pn58Td/8zdUV1dz5coVSktLMRgM/PSnP5XOKzONHCDLyKwT3N3d2b59O3V1dQt2XWdlZUnZgYKCAhQKBY2Njfj7+y9oEfxgTel6xmAwMDg4+NTpPT9qmpubF6x5X2jyZbPZFu2419/fj0KhoKCgQAq2U1JSGBwcpKenhwsXLiCKItHR0QvWlKvVauLj47ly5QrPP//8os4ts75wc3MjPj6e+Pj4Wa+NjIyg0Wgk/WMPDw9iYmKIiooiJCSEwcFBdDod6enphISEIAgC4eHhhIWF4e3tzUcffUR3d7cUGCcnJ1NQULAq9ew5OTmz9MNjYmL4xS9+QU1NDVarFaVSOcsZ1d3dneeff56TJ09SUFBAcnIylZWVvPPOO7z66qukpqYuq65aoVAQFBSE1WolKioKg8HARx99xHe+850VX+vTxPr/NpSReYbYuHEjV65cWTBL5lqmvHfvHufOnUMURSYnJ6WO6Adpa2ujubkZm82Gw+GgpKRkXXc6j46OUlFRwQsvvLCur+NJYMOGDfz6178mKytryV/C4eHhNDY2Mjo6+lAb8MjISKqqqrDb7VLjkCAIhIaGEhoaikajoampCZ1OR3NzM6mpqfMeKzExkfPnz1NYWEhQUNCSxiyzvvlq2cFrr70m/d9isVBZWUlfXx8wfX+98sor0us1NTV0dHQQGhqKIAi8+uqri278W8l4//zP/xyYnlBOTk7O+VlJTU3lyJEjnDhxgqSkJJKSklCpVHz88cds3LiRkpKSZZXTKRQKbDYbzc3NpKen093dveJretp4NtdfZWTWKWq1mq1bt9LS0jLvNi79zKKiIuLj40lMTCQsLAy1Wi3JD+l0Ok6fPk1DQwPZ2dns27cPYF3XLY+OjnL9+nUOHTr0TErcrTaRkZH4+vouq9nRx8cHlUqFxWJheHgYvV4/rwSb0WhEqVTO21W/YcMGFAqFpHKxEA6HA7vdzs9//vMF5bNkni3c3d35/ve/z6uvvkp8fPysEjXX5CwkJIRvfetbax4cfxWDwYDBYJj39dTUVL7zne+gVCq5d+8eer0emA7sr169uqxzxsXF8Vd/9Vf86Ec/IigoiImJCf793/8dh8OxrOM9jcgZZBmZdUZBQQE3btx4aHbO09NTarwYHR2lp6eHrq4uSWZow4YNJCUlSUvhbm5ujI6OEhIS8kiuYzUxm81cv36dw4cPPzPmKI+CvXv3cuLEiWWVq1gsFsrKyiQLXJiWwUpISJjhlqfX62dJyT3I4OCgtPx87949uru7pUYtgK6uLtrb2zGbzZhMJmJjY+no6MBqtco1lTISgiCQlpY25+R5PvWJh6HX6/nv//5vkpKSKCwsXJbuusPh4P3330cQBP70T/903oliSEgIX//616Wf29vbGR0dXVEyQK1WExgYSFFRETqdjq6uLiwWy4Kfx2cJOUCWkVlnuLm5UVRUxO3btxdlOw2Qn59PcnIy/f39tLe3zxKWh+kvkK6urnUZIDc2NpKdnS0Hx6uMQqFYdqPn4cOHUSgUqFQqPvvsM0JDQ9Fqtdy8eZP79+9jMpnQarVSqZDT6ZzzXP39/fj7+6NWq3E4HNTX12OxWBAEAYfDgSAIJCUl4enpSWRkJO7u7nR0dCyrnl4URUwm0yPPIMqsT0RRxGg0cvPmTckEJDw8nNDQUCwWC319fURGRi7YUNfT04OHhwcOh4P79+8vWEL0IHPVYS8XlUrF0aNHV+14TwtygCwjsw7Jz8/n+vXr6HS6BRvvXCgUCvz8/PD29qatrY3u7u5ZzSdFRUVcvXqVc+fOERISQmRk5Lqo43Q137z88suPeyhPHTU1Ncu2qX0we6vRaPD19ZWktgYHBwkLC6OpqQm9Xo8oinzyySeEh4fPmvT5+/vT29tLS0sLQUFB7Nq1S1Ir0Gg0uLm5zaqRdnd3x2QyLVkT+fr161y+fJkf/OAHsgujzLzo9Xo+++wzRkdH8fDwIDAwkPT0dDo6OmhqasJoNKJSqVCpVExNTfHjH/943tWMqqoqoqKiEEWRGzduLDpAlll75ABZRmYdolQqOXz4MCdPniQoKGjR2TKlUomXlxdGo3HWaz4+PhQXF9Pa2sro6KikH5qRkbHsIGmt6e3t5fbt27z66qtS97rM6lBRUcH9+/fZtWvXkvcdGxvj5s2bUh3w+Pi4FKyGhIRIqxQpKSnodDpu3LiBh4fHnPdla2srwcHBeHh4SNrKDzNK8PT0ZGxsbMkBckNDAyEhIZw4cYLvfe97S9pX5tnAarXywQcfEBsbS0pKCiqVSipJcDno2e12FAoFAwMD3LlzZ0FFl4GBAeLj4wkPD+fixYt0dXURExPzSK5FZmHkJj0ZmXVKUlISERERCzbszcX4+DhxcXFzvubu7k5mZiY7duyQBOQvX77MqVOnuHLlClardRVGvjrY7Xaqq6s5cuTIvNcjszxGRka4fPkyhYWFy7KknZycZHJykqioKCIjI8nOzp5zmdkltZWVlYXD4Zg1EbPb7YyPj5OWlkZ0dDRGo5G2traHNuAplUqpIXUxGI1Grly5wujoKLm5uRgMhiUZncg8nUxOTs565pWXlzM2NkZycjI+Pj5z1uuqVCq6urq4fv06L7300rwJDKfTyejoKBEREVKT4MDAwJpci8zSkTPIMjLrmPDw8Id29n8Vh8OxqOYlf39/Nm/eTHp6OiaTidu3b9PY2DhLz/NxYTQaCQwMlBUr1oBbt24RHR297Fpcp9OJSqVatDtXYmIiiYmJM/bv7u6mo6MDhUJBaWkpCQkJJCUl0dDQgEqlWnBSpFAolhQgd3V1UVpayu7du9FoNAQFBdHf3y+78j2j1NbWcv78eSwWC25ubhw6dIj09HQGBwepqKhg7969D5U+HB0dZcuWLQvepy5lF9ex3Nzc6O7uZmJigm3btslNpo8ZOUCWkVnH5OfnU1FRwfDw8KLqhV3yQC770cXg5eWFl5cXUVFR9PT0LHusq41CoZClvNYIlzbqcnEppSyV4eFhbt68iclkQqPREB4eTkpKCiaTiaamJkRRJDs7mzt37iwYeHh6etLb2zsj6J4PVzlRdna2VHes1Wqlz4rMs8WZM2eoqKggOjqaTZs2MTIywueff45Op6O/v18yFlkIg8FAX18f3/jGNxbczpVZttvtKJVK4uPjuXDhAt7e3nR2dvLaa689MdKb5eXlDAwM8MILLzwzDq3PxlXKyDyleHt789JLL1FZWUlTU9O8WrMudDod3t7ey+rwHxgYwGQyLXeoq45SqZTkw2RWj8nJyUUZfCzEcgJkV9ZYFEX27dvHgQMHyM3NJTw8nMTERHbu3IndbicmJkYytpnv3EajcVHZ356eHo4fP05oaChJSUnSOOx2O7du3VrS+GXWP1evXuXu3bscPnyYLVu2oFAoCAwMZNu2bfT19WG1WhclgaZSqXA6ndy+fXtBXWFRFHF3d8disQDTE7PDhw9TVFTE5OTkksvn1pKOjg5qa2vp6Oh43EN5ZMgZZKb1BGNiYhZtjSoj8ySRnJzMsWPH+Pzzz7l48SLZ2dnzSrVNTU0t2046KioKm83G1NQUSqUSlUr12DMJcgZ59bl79y7h4eErsh1f6vsyMTHBtWvXMJvN2O12bty4QWhoKGlpadI95lKt0Ol0CIIgZd2+ik6nw+l0zqkGIIoier2e7u5uqqqq0Ov1bNq0ifDw8Bn7t7W1PRW26zKLp7GxkatXr1JSUjKrtMHX13dJpWU+Pj7s3r2bO3fucO3aNXbt2sXmzZtnTRp7enpQqVQzMtKCICAIAl5eXk9Uz8emTZtoaWmhsbFxVey3XdhsNsxm85Ibah8F8hMA+OCDD0hOTpZ1AGXWLX5+fnzrW9+iqamJL774QpLU+mqnf1xcHNeuXVvWOaKiomhububLL79EFEUp0+fl5bUal7BkxsbG1oUM3XrDpTu8Enx8fDCZTIuSIRweHqayshKtVktJSQn37t3Dbrdz//592traSEpKIj09HV9fX8xmM5WVlWRkZMxZnzk0NERNTQ2vvPLKnJO3jo4Ofv3rXxMSEkJcXBz5+fmzjuO6n2UVi2eDO3fuUFVVRU9PDwEBAaumhuPl5cW2bdswGo1UVlZy7949Dhw4MGNlprW1VbK3/iqBgYG0tbWxadOmVRnPSklOTiY+Pp7q6mo2bNiw4sbo8fFxzp49S319PQB/+7d/uwqjXF3kAJnpB+GTKmMlI7MU0tLSSExMpLq6mrKyMkmf0zU7NxgMy1IlAPDw8ODw4cPA9Kz/s88+e6xZNqPRuCgNaJmlMTU1hV6vp7m5GYfDQXR0NFFRUQDSxOjBf35+frOCCj8/P3Jycrhx4waFhYUIgoAoigQEBMy4ZwYGBigvLyc+Pp6srCwUCoXk2peamio1RUVFReHj48O2bdu4d+/ejOyw0+mkp6eH9vZ2LBYLL7744rwZrrq6OhISEtiwYcO81z8wMEBUVJR8bz0D9PT0cPr0acxmM1qtlt27d6/6OXx9fdm5cyd1dXW88847pKSkSI54NTU1FBYWzrlfWFgY58+fp7W1dVlOlmvBa6+9Rk1NzYo/G5OTk3R2dlJfX49KpWLv3r2rNMLVRQ6QQXr4y8g8DajVagoLC8nPz+fatWuUl5eze/duRFGkoaGB/Pz8VTlHdHQ0X375Jfv3738sjSQTExMLBjoyy0OtVmM2m8nKykKtVnP79m16enqkpV8Xrv9brVaSkpLQaDSo1WrUajVOp5OJiQkAyW7aVR+fmJiIh4cHGo2GqqoqMjMzpfrfB1EoFISFhc0oo3hQQxmmm04rKysJDg5mz549pKSkzFv246oJdU3y5mNwcJDnnntukX8tmfVMbW2tdO8Cy2osXQxKpZLc3FyysrIoLy/nn//5n3F3d8fPz29ePW+NRkN6ejq1tbVPTIDs5ubG1q1bV3SMhoYGPvnkE+x2O2lpaXzzm99cs7/7SpEDZBmZpxQ3Nzd27drFxMQE586dkzLHfX19REdHr/j4mzdvlhpJXAL5j4qxsTH0er0sqL8GxMfHMzg4SGRkJAqFgv379y+4fXd3N21tbTgcDpxOJ1NTUwAzZNYcDgcajQZBEGhtbcXLywuLxUJSUtKcwbELp9OJw+GYs9Z4YGCA6upqXnzxxUXJyblsrxdCFEXGx8flFcVnAJvNRm9vL9HR0avyPFwMSqWSHTt2MDY2htlsfmgjqbu7uzTRfFowGAxotVqUSiVarfaJDY5BDpBlZJ5qFAoFzz//PCEhIVy7do2dO3dSVlY2b9CxVCwWy2OpQR4eHiYlJQUfH59Hfu6nndzcXOrq6qivr1/UxOfBAMPpdPLJJ59IjaK+vr4MDw/jcDgIDQ3Fbrdz5swZgoODyc3Nfeixh4aGUCqVM/SYXSshHR0dvPbaa8TGxi762kJDQ9Hr9TOa8h7EYDDg7u4u20w/A/zud79DrVYTERHxyM/t4+OzqGeXr68vd+7cYWho6KnR5C4sLJT+5k96gkMOkGVkngGSkpK4du0a/v7+aDQaysvL2bFjx6L2bWlpoa+vD7PZjM1mQxRF1Go1UVFRmM3mx/KQc7m0yaw+CoWCV199lX/9138lKSlpUbJWLlzybg8uCT/YSKlSqSgoKKCsrIzw8PA53fUeZGpqakbNvN1up66ujqmpKX70ox8t2cgkJyeH27dvzxsg9/T0kJmZ+URntWRWhsVioaqqiu7ubkpKSp5o9Spvb2/S0tL45S9/SVxcHAcOHFj3SQGlUrmqKhhriRwgy8g8A/j5+TExMYEgCAQGBkq6mw9Dp9PR0NBAcnIyvr6+eHt7IwgCg4OD3LlzB0EQHnmj3sTEBF1dXWvSUCMzjUajISMjg87OziUZhixG3i0oKIjs7GzKy8vZsmXLghm8oKAgJicnpcC7sbERURT5wz/8w2XVvXt6es6rFe5q9jtw4MCSjyvz5OJwOOjq6uLSpUuYTCaMRiNBQUHs3LnziQ6OXSQkJBATE0NLSwvvvPMOxcXF5OXlrfkkzmVYkp+fvy7+TmuBHCDLyDwDKJVKFAoFTqeTsbGxeXWSv0p9fT0pKSlkZGTM+L2Pjw9ms5nu7u61GO68OBwOKisr2b1797xZQJnVYevWrbz//vukpKQs+gtysfrHCQkJKBQKysvLiYmJYfPmzXNud/v2bYKDg6VgwGw2s2HDhmUFx06nk9LS0nnlqQYHB/H39ycgIGDJx5Z5shgeHqa6uprx8XG6urqkpuKkpKRlGyU9TlQqFRkZGURERPDZZ5+h1WpJS0tb03Nev34dp9PJzZs3ef3111dkHLRekZ30ZGSeEdRqNXa7ndTUVO7fvz+jiWo+rFbrvAFDSEjIIzfq6OnpwdfXly1btjzS8z6LhISEEBERwb1795a032IzW3FxcWzcuJGhoaE5HRrHx8fR6/UzJmfx8fFcuXJlWQYKFy9eZGpqat6GrL6+PjIzM5d8XJknB6fTyYULF/jFL37B2NgYKpWKLVu28Nxzz5GcnIy/v/+6C44fxM/Pj6ysLC5cuLCgQ99q8Pbbb+Pp6YnJZOI///M/aW9vX9PzPYnIAbKMzDOAKIrY7XYUCgWRkZGo1Wr6+vqAaTWAW7du0d7ezsTEhFTC0NraisVimdeMwxVg2+12hoaGFhVwr/Qa9Ho9cXFxco3oI+LgwYO0trYyMjLy0G3b29upqqpa0vFjYmLw9PTkzJkznDt3DoPBgMPhYHJykkuXLhEfHz/j/gsKCsLb23vJNtAmk4nKykpJk/mrOJ1O+vv7Z62UyKwPGhoaeO+99/jnf/5nWltb2bNnDxkZGVJQ/DSRnJyMQqHg7t27axokBwQE8N3vfhc3NzccDgf//d//zfXr19fsfE8i63cqJSMjs2gmJydRqVRSw1NgYCD9/f1Sw15gYCDDw8PcuXMHtVqNIAiYzeYZ+rVfxcPDA1EU+fTTTyXt2dzc3BU7LM2F0+mkrKwMQRDIyspa9ePLzE1AQAD79++nrKyMXbt2LbhtR0cHwJKysEqlkq1bt1JfX09HRwdXr16VvvTDw8PnfK+9vLwoKyujoKBg0ecZHR3Fy8tr3gan4eFhfH19n8ll5PWOKIr83//7fwEoKip6atQe5kMQBKKiovjkk0+4cOECL7zwAomJiWtyroCAAH7wgx9QWlpKRUUF586dIzs7+7G5pz5q5ABZRuYZwGAwzHioJSYmcunSJfr6+ggLC5PcnGpqaujr6+PgwYMAlJaWcvHiRYqKimapGfj7+3Po0CEpM93W1kZra+uqBMhWq5Xh4WH0ej3j4+PY7XZUKhVvvfWWnD1+xKSmpnLq1KlFbRsREbEkU4P+/n4qKyvx9PTkueeew9/fH7PZTGNjI2NjY3O+1y65uKXg7+/P5OQkNpttTifJgYGBGe58MuuPzZs3P/XBsYu4uDhCQ0O5evUqt27dWrMAGab19Pft20dhYSEOh+OZCY5hhSUWgiD8/4IgNAmCcEcQhE8EQfD7/e/jBEGYEgTh9u//vbM6w5WRkVkOBoNhhiRWYGAg+/fvR6FQEB8fL/1+48aNHD58GIVCgUKhYM+ePfj6+nLhwgX0ev2cNccqlQqFQoGPjw82mw2dTresGlGYlvVqbm7m7Nmz9PX1ERkZyde+9jX27dvH0aNH5eD4MaBSqdZkKbezs5MbuVWXUQAAIABJREFUN26QlZVFcXGxtBTuchAzGAxzqq0YjcZ53fLmw8PDg4SEBLq6uuZ8fWhoaEHDEpknF6PRiFqtJjIy8nEP5ZEhCAJarZZNmzbR1dXF2NjYmp/Tx8fnqStXeRgrzSCfA/5SFEW7IAj/H/CXwE9+/9o9URQfrgQvIyOz5gwNDc0IkEdHR2lqapLsfBeisLCQ27dvU1ZWhtPpJC0tbU5lA39/f+x2OxUVFbi5uUlZ6MWOr66uDpPJREJCAseOHZPVBJ4QXAoo82Vfl4PNZqO2tpa8vLw5jT40Gg1arZaenp4Z2TFRFDGZTMuSnYqNjZ2z4XBsbAyLxfJMBVhPE9XV1cTFxT2TUmQBAQHExcXx7rvv8vrrrz8W05OnmRUFyKIonn3gxwrg5ZUNR0ZGZi0YHh6WZv92u53S0lKCg4N57rnnFrV/bm4uubm5DA4OcvPmTdrb20lLSyMqKgo3Nzdg2hb1+eefx2AwUF5evqjjiqJIfX09vb29HDp0iNTU1CVnB2XWFkEQCAsLw2AwPNTYY3Jy8qHH6+zspLa2Fq1Wu6ALnqenJ+Pj48B0QN3R0UFHRwdarZbXXnttaRfBdDBRVVU1w0XSarXS2tpKTk6OfN+tUzQajWRv/qwhCAIpKSl4enry4YcfcvToUdlAaRVZzSfCd4HTD/wcLwjCLUEQLguCsHO+nQRBOCYIQrUgCNVDQ0OrOBwZGRkXw8PDeHt7Sz+LosiOHTuW7EQWEhLCgQMHSExMpKWlhTNnzsxo4hMEgeHhYURRXJQEXENDAxMTE/zwhz8kPT1dDlKeUKKjozEYDAtuI4oinZ2dM4Lknp4eLly4wJ07dxgfH2dsbIy6ujqSkpIWnJyZTCYmJibw8fGhtbWVM2fOYLVaeemllzh27Bi+vr5Lvobk5GRCQ0P58ssvaWhooKamhjNnzkiNgjLrk5ycHPr7+xeltPK0EhkZSV5eHh999NGckokyy+OhGWRBEM4Dc63B/rUoiid/v81fA3bgV79/rR+IEUVRLwhCPnBCEIQNoijOKpQRRfE4cBxg06ZNj1ZUVUbmGUAURYxGoxQMC4KwYv3i1NRU/Pz8KC8vx263S1lkmA6KrFYrtbW15ObOX2Wl0+no6+vj2LFjS7Izlnn0JCUl8cUXXyzoqicIAmq1mrt377JlyxbJrCEpKQmdTifpqPr4+JCWljbvZMhqtXLmzBlgWhljdHSUH/7wh/PKDS4WQRD45je/yfDwMDdu3CAkJIRXXnkFDw+PFR1X5vFy8uRJHA4HV69e5YUXXnjcw3lshIeH09PTw927d+c13pFZGg8NkEVRLF7odUEQ3gQOA3vE33/riqJoASy///9NQRDuASlA9YpHLCMjsyTMZjNKpVISyB8bG1txvZ7T6aSqqoqMjIwZwbHNZmNsbIyCggKqq6sXDJCHhobIz89fchZb5tETFxfH+Pg4FosFd3f3ebdTq9VMTU3R1NREU1MTSUlJZGZmLkn67ebNmwQHB5OQkEBNTQ2iKDI5ObniANlFUFAQhw4dWpVjyTx+XHXlcs/CdB+ITqd73MN4alipisV+ppvyXhBF0fTA74MFQVD+/v8JQDJwfyXnkpGRWR4TExMzsmQmkwmn07mipbi6ujpsNhtarZaJiQkcDgcjIyNcuHABLy8vWlpaHmoHrFKp5OXAdYJCoSAiIoKHlcGFhoYyOTlJZ2cnhYWFy3Km8/PzQ6/X09nZiSiKKJVKOfiRmZcf/vCHwHSpxbOO0+lc106BTxor/Uv+DHAHzv1efqlCFMU/AoqAvxMEwQ44gD8SRXHhAjYZGZk1YWRkRApWR0ZGqKqqIisra0VlDXFxcUxMTFBXV4fVasVut6NWq3Fzc2NycpKwsDC2b9++4DHkeuP1xebNmzl//jzh4eEIgjDn++fh8f/au/PguK/rwPff2/sGdAONfScI7qtIiiJpbhK1W7JjWY5kqzwuy5InL3G5KpX3x0zluTw1eVlq5nlclef3YluJnbGrPE7KimNZj5ZC2bIoiosokiAJECAJEgux72j0vt33B9G/ABJIgkSD2M6nqotEL78+fdlonr6/c8913lX3kulkatH7+vpYsWIF0Wh0Sv28EJMVFhayd+9e2tvbl/1W4R6Ph6tXr5JOp+XzNQtm28Vi2saRWuvXgddnc2whRHacPHmS8vJyUqkUJ0+epKam5q42c5iO1+udkgCn02k+/PBD+vv72bVr1x1bx8HNZF16zy4e69at4+LFixw7dozh4WHWrVvH2rVrgZt17rPplRyNRqeccVizZg1r1qyhubn5jp0zhKiurub8+fOsXLlyWdeUl5aW0tzcTHt7+5T+9uLeyFcMIZawgYEB+vr6qK6uprOzE631beuC70U6nebo0aOMjIxw6NChOybHWmuuXLnC2NjYbRd9iYVFKcXzzz+PxWLB6XTS1NTEe++9R0NDA++88w7RaPSu+7D29vby5ptvcvjwYU6dOvWJ265du5b196tYeurq6lixYgUtLS3zHcq8UkphsViWZU/ouSAJshBLWCqVwm63YzKZaG9vz3oj+XQ6zfvvv08sFuORRx6Z0YK71tZWenp6eOWVV6Ys8BMLn9ls5vnnnyeZTLJ3717y8/MZHBykuLiYp59++q5KIXp7ezl58iRr167l4YcfprOzk6GhIeDmwqvz58/zwgsvUFpaOlcvRywhBw8epL29/Z538VwK4vE4Y2Njy2bL7bkm1dxCLGFut9toom8ymbK+VfPvfvc7xsbG2LVr1227G2REIhGampr46le/ek+9bMX88/v9bNq0ia6urlnN7l64cIHCwkIKCgq4cOECOTk5+P1+RkZGuHLlCq+++io+ny+LkYulLC8vD7PZ/Im2k8tJX18flZWVy7rMJJtkBlmIJayjowOfz0c6nWZ8fHxGSexMZWYrMjuUNTQ03HEziYsXL7J9+3aKioqyFoe4/x555JFZb86we/duhoaGePfdd1FKsXfvXlKpFPX19TzyyCOSHIu7VlRUZJyFWI5GR0epqqqa7zCWDEmQhVjCent78fl8XLt2Da01q1evztqxr127hs/n4+GHH2bfvn309vby/vvvc/r06U+c5sxsKR2JRDhw4EDWYhDzw+l08sQTT3DmzJl7XpyXk5PDo48+yqc+9Sn27duH3W7n9OnTFBcXs23btixHLJaDPXv2cPny5VlvhHQrqVSKQCDA0NAQfX19jI6Ozsnz3KtEInHH9ppi5qTEQoglzO/3c+PGDbTWuFyurLb+6e3tNepD/X4/jz76KOFwmBMnTvD222+za9cuCgsLicfjnDt3jnQ6zVe/+lWsVmvWYhDzZ9OmTVy4cIH29nZqa2vv6RgulwuXy0UikeDUqVPk5eXx3HPPZb0USCwPdXV1OBwO+vv7Z939JJlMYjKZCIfD9Pb2MjAwwMDAAB6PB7vdjt1uZ2hoiNzcXPLz88nPz6egoGDe3rupVIre3l7ZBCeLJEEWYgnLy8sjFApRVVVFW1tbVo8di8U+UUfscrk4dOgQTU1NfPDBBxw4cID6+npqa2t5/PHHJTleYrTWJJPJWR0j86WqtraWT3/609K/VdwzpRRFRUX3vAGR1pqhoSFaWlro6ekBwOFwUFdXx549e6itrZ1S35tMJjl//jwDAwNcvHgRuLlhid/vn/2Lucu4r1y5Qnl5+X1/7qVMEmQhlrD+/n7cbjd+v59EIkFfX1/W+sreLjFat24dLS0tnDlzBpvNxtNPPy2zgkuM1prW1tY7bghzK4lEgra2Nq5cucK+ffvYvXu3vEfErI2OjlJdXX3XjxscHOTkyZO43W4eeughvvKVrxi70t3qfWmxWNi+fTsAjz/+OH//93/P8ePHefLJJ6dMBly5coVEIsGGDRvu4RXd2ZkzZwiFQrz44otzcvzlShJkIZawK1euUFRUhNlspqamhsbGxqwkyIFAgEQiccvFdul0mng8Tjwe58tf/rIkPkuQyWTioYceYnh4eNr3VDweZ2RkhEQigdaa3NxcxsfHGR8fZ3R0lMHBQWpqanj55ZelLZXIiiNHjtDb23vL/urJZJJwOEwkEiGZTOL1ekmlUiQSCRobG3n00UfZsWPHPT23yWTi5Zdf5te//jUffvghu3fvBm5+Bmfrc3c6g4ODjI6O8o1vfEO2mc4yGU0hlqhIJEJHR4ex25nT6ZxV14GPH1spRVtbG263m+Li4inN6TMdM5577rl7rk8VC5/L5eLkyZP4fD5KSkoIh8N0dXUxOjpKX18fhYWFOBwOzGYzV69exefzEY1G2bZtG+vXr5/VdudCfFxTUxNVVVWfaHOWTqfp6emhoaEBi8VCTk4ONpuNhoYGbDYbTqeT2tpaYzb4XlksFj772c/y/e9/n4GBAS5fvozL5cLpdOJyuYjH41ltQae15sKFCxw8eHDBJMfJZJKuri4qKioW/YYlC2NEhRBZ99FHH1FWVobdbieRSHDlyhU2btyYlWPn5ORgtVq5ceMGbreb+vp6fD4fZrMZpRSDg4M88cQTWe2aIRaevXv34na7OX78OJcvXyYUCrF+/Xq2bdvGmjVrpB+ruK+ef/55jh07xjvvvMPq1aux2Wyk02mam5txOBwcOnRozndmNJlMrFmzhtOnT+PxeDhw4AA/+clP6OjowOl0ZnX30MHBQcxmM1u2bMnaMWerubmZ119/nXXr1vHUU0/d1eZBC42aq3Yo92LHjh36o48+mu8whFj00uk03/3ud9m5cyd5eXm0t7dz6dIlnnrqqaw9Rzwep76+HrPZzFNPPcXIyAixWIxIJMLWrVuz2nNZLGzJZJLW1laKiopkAxgx79ra2jh9+jTJZJJkMsnOnTtZs2bNfY2hu7sbs9nM97//fQB8Ph92u50HH3wwK5+NWms++OADtmzZYpRzLASpVIrvfe97jI6OYrPZeOaZZ9i0adN8h3VLSqkzWutp62pkBlmIJailpQWHw0FeXh4A0Wg066fgbDYblZWVXLlyhby8PNn8YxmzWCysWrVqvsMQAoCamhpqamrmNYaysjIAvvWtb2EymTh27BjNzc0cOXKEHTt2UFJSMqvj9/b2orXmoYceyka4WWM2m/nyl7/MP/zDP+D3+zl79uyCTpBvR/rpCLEEnT17lsrKSuDmbnpNTU1zUu5QWFiI3W7n8OHDWT+2EEIsdpm2hXv37uWVV17hhRdeoL6+ftabmfT29vLAAw8syLaI+fn5vPTSS3R1ddHW1sZ3vvMd6uvr5zusu7bwRlYIMStHjhyhtbXVSJC7u7upqKi4p9ZHd2I2mzGZTHg8nqwfWwghlpoVK1Zgs9kYGxub1XFGRkaMWeqFqKysjEOHDmEymQgGg/zqV78inU4bty+k8t5bkRILIZaY9vZ2HnjgAaMPp8ViIZFIzMlztba2Eo1G2b9//5wcXwghlpqtW7dy9uxZdu7cOe3kwtDQELFYbEoCHI1GaW1tJRgMkkgkiEQiRgndQvXQQw/x7rvvsn//fhoaGvjRj37E2rVraWtr48aNG/zBH/xBVhctZpskyEIsMW63e8q38+LiYhoaGrJ2/EQiQX9/P52dnQwODvK1r31NdsgTQogZ2rdvH0opjh49is1mIzc3F7/fT0lJCdevX+fKlSsAPProo+Tm5tLa2sqlS5fYsGEDa9euxWq1UlNTg8PhmOdXcnuZ/ve5ubns27ePnp4ebty4gcvlMlrtLWSSIAuxxFRXV3Pt2jUqKyuJxWKcP3/+ntttnTt3jqGhIWw2G/F4nHA4jNaa0tJSHnjgAdavX7/gP6SFEGIhUUqxb98+9u7dS29vLwMDA1y9epV3332XkpIS/viP/5hz585x/Phx4/P11VdfXVTbSIdCIX7zm98YLUEBysvLjX784XCYqqqqeY7y9iRBFmKJMZlMRKNRAHp6erBYLBw6dOiejmWxWFixYgVbt27F6XSSm5uL3W6XnfGEEGKWlFKUlpZSWlrK5s2b0Vobn62PP/44Bw8e5OrVq6xevXrRnaVrbGyksbGRXbt2Ga8rGo3ywQcfUFdXx7PPPrvgX5MkyEIsIel0mt///vdGTXBPTw8FBQX3fLxwOMyGDRvmZIGfEEKIf/fxiQebzcaGDRvmKZp7l0ql2Lx5M8lkktOnTxMIBFi3bh0jIyPY7Xa+8IUvzHeIMyIJshBLyPDwsFHTBjd3WnrwwQdn9NhUKsW1a9fIycmhqKiIaDRKJBJhfHx8LkMWQggxyZkzZ7h27RpVVVWsW7eO0dFRuru7icVibN++fcHuTheNRnnjjTfo7OzklVdeoaamBpfLxbFjx6itraW+vp7nnntuvsOcMUmQhVhCent7p+xklk6nZ7xrU1tbG319fQwODnLixAncbjd1dXU88MADcxWuEEKIj2lvb2d4eJhIJMLvf/97PB4PXq+X0dFRcnJy2L59+3yHOK2TJ08SCATIycnhb//2b7FYLMRiMUpKSjh16hSbN29m5cqV8x3mjEmCLMQS0tPTg81mY3R0FK/Xi91uZ2hoaEbtgOx2O06nk2effZb+/v4F3X5HCCGWqn379vGjH/2Ibdu2TUmGL1y4YKwvWWiam5v58MMP2bNnD06nk0gkwocffsj+/fv58MMP2blz54Lb9e9OJEEWYgm5ceMGIyMjtLe343K5iEajM94CuqioiI6ODr73ve8B8Cd/8iezql8WQghx9woLC1m9ejU9PT3GjGsikeDGjRvs3bt3nqP7pEAgwOHDh9mxYwc+nw+tNefPn6eyspLdu3ezZ8+e+Q7xnshOekIsEel0mp6eHhKJBMXFxeTn52MymYx65Dsxm804HA7cbje7du3C7XbPccRCCCGms3HjRq5du0YikSAYDPLGG28Qj8cXXGu05uZmXnvtNWpqaigqKiKVSnHmzBkAPve5zy3qjkcygyzEEqGUYufOnQwNDdHf38+aNWvo6+vjxIkT7N69+46Pb2xsxGKx8M1vfnPBN3AXQoilbNWqVaxatYq3336bRCLBww8/vGC2ltZa09nZybvvvsvw8DAPPPAARUVFJBIJLl26RHt7O3/6p3+KxbK4U8zFHb0QwhAKhTh//jzl5eWEQiHy8vIoKCigr6+PdDqNyXT7E0aBQIDHH39ckmMhhFgAnnnmGQ4ePEgqlcLn8813OMDNheD/9E//hNaampoatm7dislkoquri3PnzlFVVcWaNWswm83zHeqsSYIsxBLR2dmJ2+0mJyeHDRs2oJSirKyMzs5OhoeHp60n1lrT3d1NNBplZGSEkpKSeYhcCCHExymlFlRLtwsXLvCb3/yGTZs2TSn10FrT3NzM5z//eVatWjWPEWaXJMhCLBFdXV34/X5qamqM68LhMC6Xi/z8/E/cP5FIcOrUKeDmFqBPPvkkLpfrfoUrhBBikWhtbeXtt99m7969U1qJws3F4Q6Hg7q6unmKbm5IgizEEtDT08PZs2c/sSlIPB7H5XJNW15x48YNPB4PX/rSl+5YfiGEEGL5Onr0KAUFBQSDQZxOJzabDa017e3tNDY28sUvfnFRL8ibjiTIQixyY2Nj/OM//iM+n490Om3sew/Q3d19y2/1qVSKwcFBent7F8ziDyGEEAvPvn37qK+vp6+vj/r6evLy8ohEIjidTl566SUqKirmO8SskwRZiEXO4/Hw1FNPcfToUY4fP85jjz2G2+2mv7+faDRKdXX1tI9btWoVTqeTn/zkJ7hcLvbv38/WrVvvc/RCCCEWutraWmprawEYGRlhaGgIi8VCdXX1kps5zpAEWYhFzmw2EwwGSafTPPzww0b/4gsXLlBdXX3bVjsVFRWUlZVx/Phxksnk/QpZCCHEIpWXlzej3VkXu1kVHiql/otSqkspVT9xeXrSbf9ZKdWilLqslHpi9qEKIaaTTCY5fvw4O3funLJ4IpVK3XHR3eDgICdOnCAajbJx48a5DlUIMQPxeHzBbiksxHKRjRnk72qt/6/JVyil1gMvAhuAMuAdpdRqrXUqC88nhJhkYGAAu91u7JiXadYejUaNU2LTCQQCnDp1iscee4wtW7Ysib6VQix2yWSSv/7rv8ZsNlNZWUldXR2f+tSn5jssIZaduVq6/lng51rrmNa6FWgBds7RcwmxrDkcjinlEdevX+fGjRscOHDgluUVWmsaGxvZu3cv27Ztk+RYiAViZGQEh8PBihUr6O/v5+zZs/MdkhDLUjZmkL+hlPoPwEfAn2mtR4By4OSk+3ROXPcJSqmvA18HFtwe40IsBk6nk2g0anSvMJvNOByOW+681NXVRWNjI16vl4ceeug+RyuEuB2fz8emTZswmUyUlpayadOm+Q5JiGXpjgmyUuodYLrttf4c+DvgLwA98ed3gJeB6ZY06umOr7X+IfBDgB07dkx7HyHErdntdvLz87l+/TorV66ksLCQhoYG3njjDdavXz+lzdv4+Dj19fW8+OKLt+xuMROpVIpUKiXbUguRZVarlaeffvrOdxRCzKk7Jsha60dnciCl1GvAmxM/dgKVk26uALrvOjohxB0ppdi5cydnzpxh5cqVeL1eHnvsMU6fPk0gEJhyX5vNRjqdprx82hM6t5RKpbhw4QInTpzA5/MxMjJCIBDgwIED7NmzJ5svRwghhJh3syqxUEqVaq17Jn78HNAw8fc3gJ8ppf4HNxfprQI+nM1zCSFubXR0dErHCrfbTW1tLefOnWNkZASz2YzFYsFkMpFIJPjFL37BCy+8MOP+la+//jpNTU2YTCbjuUwmE+fOnZMEWQghxJIz2xrk/6aU2srN8ok24D8CaK0blVL/DFwCksCfSAcLIebOzp07+eEPf8ixY8dYsWIFZWVlVFZWYjabCYfD9Pf309vba9z/8uXLdHV1zXj3o0996lOMj4+zadMm+vv7sdlsVFVV3bZLhhBCCLFYzSpB1lp/+Ta3/SXwl7M5vhBiZnJyctiyZQsffPABfX19lJSUsGXLFsrLyxkbG6O5uRmr1crKlSvp7+9ndHT0rjpXlJeX87WvfW0OX4EQQgixcMhOekIsEYcOHcLlcnHkyBH6+vo4cuQIPp8Pu92O2Wxm48aNVFVVYbfbCQQC/Mu//Atf/OIXyc/Pn+/QhRBCiAVFEmQhlgilFHv27KG4uJg33niDcDhMKpVibGyM0tJSKiv/fd2sx+OhuLiY1157jZqaGtasWUNxcTFFRUXSE1kIIcSyp7ReOJ3VduzYoT/66KP5DkOIRS+ZTHL27Fk++OAD7HY7ZWVleDwe/H4/HR0dXLx4EavVSiwWw+l0YrVaGRsb48CBAxw8eHC+wxdCCCHmnFLqjNZ6x3S3yQyyEEuQxWJh586d7Nixg+bmZlpaWujo6OCjjz7C7/fj9XoZHh4GwO/3E4lEWLNmjXSkEEIIIZAEWYglzWQysX79etavXw9AIBCgo6ODQCBAYWEhK1euxGSaqx3nhRBCiMVJEmQhlpHc3Fw2btw432EIIYQQC5pMHQkhhBBCCDGJJMhCCCGEEEJMIgmyEEIIIYQQk0iCLIQQQgghxCSSIAshhBBCCDGJJMhCCCGEEEJMIgmyEEIIIYQQk0iCLIQQQgghxCSSIAshhBBCCDGJJMhCCCGEEEJMIltNCyHu2sDAAFeuXCEWi7F161by8/PnOyQhhBAiayRBFkLcUSqVoru7m8HBQZqbm2lvb8fhcDA2Nsa1a9d49dVX5ztEIYQQImskQRZCfILWmq6uLsbHxzl//jyXL1+ecrvVasVut7Nv3z527949T1EKIYQQc0MSZCHEFBcvXuTdd98llUoRCAQAqKurY/fu3ZSUlBCPx4nFYhQXF89zpEIIsfDE43FsNtt8hyFmSRJkIQQAw8PDnD59msbGRrZu3YpSisbGRl599VXsdrtxP5fLNY9RCjE/tNYEg0HGx8eJxWI4HA7cbjcejweTSda7341kMsnx48cZHh6mtraWtWvX3nNCmUwmiUQi5OTkZDnKe5NOp/nrv/5rqqur+dKXviSJ8iImCbIQy1x/fz9Hjhyhs7OTyspK9u7dC8DQ0BBDQ0Ncu3aN9evXz3OUQtx/fX19XLp0ievXr9PX14fZbMbtdmOxWIwzKYlEgpUrV/LMM8/gdrvnO+SsaGxsxGq1snr16qweN5VK0dTUxDvvvENOTg4FBQWcOnWKw4cPs2vXLnbt2oXD4bjtMYaHh/noo4/o6OggmUwyPDyMyWTC6XTy8MMPs3Hjxhl/YdFaE4vFiMVieDwezGazcVs6nQb4xLHS6TSjo6OMjY2RSCSM+0UiEcbHx+nv78fv9xOLxTh16hT79u27myESC4jSWs93DIYdO3bojz76aL7DEGJZiMfjvPXWWzQ1NbF69Wpqa2sJh8M0NjYyNDSE1+uloKCARx55RLpUiGUhmUwSDodJpVJcvHiRkydPUllZSWFhIfn5+dPOBsZiMc6dO4fdbmfv3r3U1NSglJqH6O+N1ppoNEoqlcJqtXLlyhUOHz6MyWRiz549bNmyBY/HA9wcn3Q6jdVqRSllPNZut0+blCaTSQDMZjOXL1/m8OHDOJ1OVq1aRUlJiXG/YDBIc3MzfX19bNu2jaKiIuN4kUiEsbExRkZGGBgYIBgMUlVVRXFxMRaLBY/Hg9VqZXBwkEuXLhGNRnE4HJhMJtauXUttbS2lpaVT/u3i8TgXLlzg+PHjBINBrFYrsVgMt9uNy+UiFAoRDAbRWmO1WsnJySEnJ4dIJMLQ0BBOpxOXy4XFYjHitFgs2O127HY7ZWVlALz//vusXLmSqqoqtNbE43HS6TSpVIpYLEY8Hjf+DTKJtsViwWaz4XQ6KS4upq6ublG9nxYbpdQZrfWOaW+TBFmI5emnP/0pqVSKzZs3Y7VaaW5u5vr16+zZs4fdu3djscgJJrF0pdNp+vv7MZlMaK05d+4c58+fNxIev9/PmjVr8Hq9dzxWIpGgra2N9vYw7IjFAAAgAElEQVR2TCYTfr+fvLw8tmzZQllZ2ZQEp7u7m8bGRoqLiykpKcHr9U4pYZorPT091NfX09bWRiQSweVykUgkGB8fB24msclkkvz8fNauXYvD4aChoYGhoSFSqRQWi4VEIoFSinQ6jcViIZ1Oo5QimUxSWFhITk4OIyMjpNNpo/RBa43NZsPhcLBx48bbrl0IBoO0trYaiSTcXBDscDhwuVx4PB7y8vJumzAGAgHj+bu7uxkZGWFkZMQ4TiqVIhKJUFxcTG1tLYWFhSiljOtjsRh2u904G5BIJIhEIkQiEaxWK16vd8afjdFolBs3bhAIBDCZTJjNZpRSKKWwWq3Gz4DxZyb2RCLB9evXqaio4Ctf+cqMnk/cPUmQhRCf8JOf/AS/3091dTV9fX1cvHiRr33tawumlk+IbNJaMz4+TmtrK1euXOH69etGYppKpSgvL6empsaYLb3X5xgdHSUcDhMIBLhx4wZms5mKigqsVivd3d0Eg0EqKiqM+4RCIcxmM7m5ubhcLqxWK1arlSeffJLc3Nx7jiWdTtPb28v169dpbW2lp6eHFStWUFRUhMPhIBqNYrFYcDqdt62T1VqTSqVIJpPY7XYjmUylUkail06nGR4eNmZhzWazUfaglDJmdedrJjSdThOPx4nH45jNZux2+6KYAOju7ubEiRPU1dVRXV1NZWUlZWVlWK3W+Q5tyZAEWYhlTmtNMpk0Pli11vzyl78klUqxbt06IpEIv//979mwYQMPPPAAJSUlclpPLBhXr17l9OnTeL1e1qxZQ11d3R0fMz4+TnNzMx0dHQwNDTE8PIxSisLCQgoLCykuLp7zBaeZhDkQCJBMJvF6veTn508pR8iceg+Hw8RiMdLpNAMDA4TDYb761a/eUyLX3NzMW2+9hVKKgoIC/H4/xcXFklgtQvF4nL6+PoaHhxkdHWV0dJScnBzy8vLIy8vD5/Ph8/koKCjA7XYbi6vD4TDr1q2juLhYPstvQxJkIZapSCTCL3/5S1pbWzGZTDzzzDNs3LiRw4cPc/XqVfbv32/MHsViMa5evUpPTw8mk4m9e/eyffv2eX4FYjlLp9McP36cU6dOsWbNGhKJBC0tLZSUlFBeXk4kEqGwsJBt27ZhsVjQWvO73/2OixcvEolEKC0tpaCgAI/HQ05Ozn0pZcgGrTXHjh1j+/bt7Ny5c8aPS6fTXLp0icOHD7Njxw6KiormMEoxH1KpFMFgkFAoRDgcJhwOE41Gje4q6XTaSJa7u7txuVxs3LiRqqoqPB4PbrfbOBMgbp8gL/xzDEKIe/ab3/yGZDLJ008/TTgc5vDhw6RSKS5fvszBgwennFq12+1s3LiRDRs2MDw8zPvvv08ymeShhx6ax1cg5sL4+Dh9fX3U1NQQjUZRSn2iA4PWmoGBAaOna6b2c2BggEAgQDQaJZlMorXGbDbjcrnIyckxFjDdbTKqtaa7u5sbN27Q29tLIBCgr68Pl8vFvn37jNne6upq+vv7GRwcxGq1cuHCBd59912qq6sZGBhAa82DDz5Ibm7uokwCIpEInZ2dt21dNjg4SENDA2NjYySTSUKhEGNjY4yNjeHxeNi9ezd5eXn3OXJxP5jNZrxe74xq4zdu3Eh/fz8dHR1cuHCBWCxGNBpFa01JSQnPPPMMRUVFi/L35H6QGWQhlrCf//zneL1eKisrgZsLdc6ePYtSikcfffS2tYfj4+McPXqUl156iYqKivsVspgDoVCI/v5+hoaGaG9vp6WlBY/Hw9DQEHa7Ha01e/fuZc+ePQCcPn2a9957z1iZn1mclUgkjBX2NpvNKBXI1HhGIhESiQTxeJz9+/ezadMmxsbGjBKDzMKrdDpNOp0mGAwyNDTE2NgYw8PDWK1WCgoKyM3Nxel0kpOTM6Oa4FAoxODgILm5uXi93kXbl/jq1as0NTVRV1fHhg0bWL9+/SeSl56eHn76059SUVFh1PvabDZjEdtiqK0V8yuZTNLW1kZLSwvJZJKVK1eyc+dOiouL79hmb6mREgshlgmttTGTZDabef3119m6dSt+v9+4T3NzM93d3djtdnbv3n3bZKK7u5tz587xh3/4h6xYseJ+vIRlJZVKAUzpvxoKhTh69KixuGtyD2qtNeFwmGAwaCyaysvLw2w2k06nGRoaYnBw0KhVHBkZob+/n0gkgs/nw+VykZeXR0lJCS6Xi3Q6jclkIhgMUl9fb5ym9fl8bNu2bcoisa6uLjwez4xmrsbHx2lqaqK/vx+Px4PL5cJkMk15r5lMJqxWq9Fay+12L5k+wveqpaWFixcvkk6neeGFF1i7dq1xWyKR4MSJE5w8eZLNmzfLl1aRFdFolI6ODrq6uhgfHyedTpPJC202m/HZYjKZjM1x8vLyKC4uprKykpKSkkX9pUwSZCHuQqYtUG5uLh6PZ1HUayUSCY4dO8YHH3xAbm6u0YLJ5/OxZcuWKfEnk0neeOMNVqxYgVKKrVu33vbYbW1tBINBXnzxxbl+GctGLBbj8OHDNDU1Gd0Atm3bRnl5OW+99Rbl5eU4nU6uX79OTk4OVqvV6HpgNptxOp2YTCbi8Tgulwuv10tbW5sx6+p0OnE6nbjdbqOH653ew5kuD5n+ruL+S6VSnDt3jlAoxMsvv4zT6QRutj/78Y9/jNvtZu3atbPqbiHErWQ6lmRaHyYSCbTWRmu/RCJBNBolFAoRCAQYHh4mEAjg9/vZvn07Dz744IL/v/LjpAZZiBnq7e3lBz/4AUopnE4n8XjcaHafm5tLbm4uwWAQpRQvvvjiglkEMzY2xtGjR1mzZg35+fmEw2Fjtye4mZBlWkoNDg5SVVXFF77wBf77f//vbNy48bYJkc/n48qVKySTSUmcZklrzbVr13j77bfJzc3liSeewG63E41GuXr1KgMDAxQXF7Np0ybMZjM1NTWEQiFjtjizi9vk47W2tmKxWIxj3SullCRe86y3t5f29na2b99u9GTObIBRU1MzZUZZiGxTSk35fJl8Zivj42eQUqkU169f5+2332br1q1LamttmUEWAjh16hRtbW3k5+fT399PT08P4XAYrTUmk8monfy4b3/72zM6/rlz57hw4QL79++nuro66zWSWmvOnz9PfX09ZrOZvLw8zpw5A9w8lW2xWCgoKCAvLw+Xy0VxcTFNTU0EAgH27dt322/9WmuOHz/Ojh07pKvFXUqn0wwODtLV1UV7eztXr17FbrezatUqKioqFt1si5h7Y2Nj9PT0GB0J3G43BQUFspulWJB6e3s5c+YMTz75JFu2bJnvcO7anM0gK6X+CVgz8aMPGNVab1VK1QBNwOWJ205qrf9oNs8lFrfBwUF+9atf4Xa7qauro6amBr/fbyQIg4ODnD9/nvz8fKO3o8PhYHBwEK21schsrthsNtra2jCbzZjNZsrKyrBYLEaT+2AwyMjICKtXr2b//v1Gc/wbN24YC4wGBweNWdrc3FwqKytJp9OMjIzQ1tZGXV0dv/rVrwgGg+Tk5BiLmVatWsWmTZuMLUYzLamsVitaay5evMixY8dYt24dFRUVRCIR0uk0JSUlxpasmVKJrVu3Mj4+bpxuHx0dZXx8nHA4zNjYGL29vTgcDrq7uyksLGTTpk13TNKUUtTV1fHee+9JgjxDfX19vPPOO7S1tRl1vz6fj71798pGLOK2ZtqhQIj51t3dTX19/ZJdyD2rBFlr/ULm70qp7wBjk26+prW+fXGjWDYCgQCdnZ0AXL582bi+vLycV155hRMnTnD27NlpH+vz+Xj22WcJBoPk5eXh9/sJhUIMDAzQ1NREV1cX27ZtY8+ePfc8M7t582bef/99UqmUsVOR2+2ecrxEIsGlS5f42c9+ZpRdOJ1Och2aYssIntxqfGWF2OxVhCNRent7jYVIBw4cwOFwsHLlSpLJJOFwGIvFgslkor29nWPHjk2p8cq0eMrMXq9du5bu7m6uXr1qnMIaHR0llUqxadMmYrEYwWCQnp4e4vG4se1rpj7Vbrdjt9txOBx3PUaZjQtms8PYchIOh/n+979Pbm4uhw4dknETQiwJmS3VOzs7CYVCWCwWXnrpJcrLy+c7tDmRlRILdXMKqgN4RGt9dWIG+U2t9ca7OY6UWCw8yWSSwcFBuru76e7upre3l9HRUaxWKx6PB6UUoVCIWCyGx+PB5/MZC8QmNy93OBw4nU4cDofR0D8YDGIymfB6vSiljEVIyWSSZDKJzWYzyhwyTf4ziwMyi5EKCwvxer00NTXh8XjYsmWLsSlGZsZ1povsxsfH+bd/+zeifZdIR0ZxWNI8VDSM25IAFBqFNjvRyoTSSUypKOb4CKZUeJqjKVLWXEypCGmzi7iriqhnJQlnOQlHKSnb7WeIMi2w4vH4lJn2jxseHqavrw+r1YrD4cDr9Rr/LrOR2SY3FosxNDSE3+/nueeek2RvBuLxOK+//jpXrlxh69atrFy5cr5DEkKIe6K1pqmpifb2duLxONXV1ezfv5/8/Hxj577FbM67WCil9gP/I/MkEwlyI3AFCAD/h9b6/TsdRxLkuZNMJhkYGGBkZIScnBwqKytJJpOMjIzQ3d1NV1eXUfOWSCSMVlKBQICcnBzjtF8mAUulUkQiEQAj6c2sbs20hMmstrdarUY5QTweJ5FIGPfJrJb9+CXz+Pz8/BmtwE+lUly7do3h4WH8fj/pdJq+vj5GR0dJp9N4PB6jJU0ymSQWixndKjKx2Gy2m1t4uq2sSNXjaH+DYChEQUFB1rdo1SYbo2WfYbzoYFaPO1vhcJiOjg6uXbvGtm3byM/Pp6CggKqqqkX/QTiXxsfH6ezs5Pr16zQ0NJCTk0NpaSl1dXUybkKIRUdrTU9PD5cvX8bj8fCZz3yG/Pz8Jfd5NqsEWSn1DlAyzU1/rrX+1cR9/g5o0Vp/Z+JnO+DRWg8ppbYD/wps0FoHpjn+14GvA1RVVW1vb2+f+SsTM5JOp3nttdeM0/aZpDEajRqzvl6vF6vVaqxitdls2O12PB7PtCtZF5NMScPg4CChUMhIvjO9V81mM/ZQG/6h3+EIXUMlQqRSKVLpNPaJBvxk+0NBmeja8Bd3nEmeK/F4nLGxMaM+ORKJMDw8TCqVoq6ujoMHD8qioGlkznxkFnJ2dXXR1dVlzPTn5eVRUVEhM+1CiEUrkUhw/PhxLBYL+/btY926dUsuMc6Y0xlkpZQF6AK2a607b3Gf3wP/u9b6ttPDMoOcfZkFZj/4wQ/YsGEDdXV1pFIpYrEYTqdzyb7p75nW2MJt5Pb9FtdofXYPbbIRd1URc1UTc68g4tsE6v58+UilUrS2ttLb28vY2BipVIqCggKKiorIz8/H6/VSVlZGQUHBsntPaK2JRCKMjo4SCAQYHx9nbGyMcDhMKpUytvLNlPdkyoIyu7bl5+dnpaxFCCEWgjNnzpCXl8ezzz675D/X5roP8qNA8+TkWClVCAxrrVNKqVpgFXA9C8+1LCSTSd5//33Ky8tZuXKlMYM7+T9qp9NJXl7eJx4bDoentJQaGRkxNgzILO7KzJ6KaShF3L2CsdKncY2eB+7tC6RWVqK560hZ3ERzVpNwVpJwFGd1Jvqtt94iFAoZiwmTyaRRvmI2m43FeKlUinQ6TW1tLY8++ihFRUUzKltZytLpNN3d3Zw+fZrm5mYAPB4PTqfTWMxos9mMrZbz8vKMOvrZ9BoWQoiFrqenh89//vPL+v8IyE6C/CLwvz523X7gvyqlkkAK+COt9XAWnmtZSKfTHD16lPz8fEKhEGazmVgshtYah8OBw+EgHA6zYsUKnE6nsRhuaGiIRCKB3+/H5/Oxdu1a/H5/1nvuLgcJZxldG/9PbKFWLIlhzIkg5sQopmQIS3wYS3wYlY5N/2BlZrxoPwlHKeG8bWjT3DROX7FiBQ0NDYRCIQBKS0tZu3YtO3bswGw2G9sYWywWo33dcpTZIW5oaIgbN27Q2tpKV1cXbrebiooKHnvsMRwOx3yHKYQQC4LP5+OXv/wlX/rSl5bt/xsgG4XMi2QySUdHh7Gv+cd3xwL40Y9+RE5ODtXV1SilsFqtmM1m4xtdPB6no6PDuM1utxtbzC73b31zzR68hnv4IzxDH4CefgORtNlF0u5npPxzxHJWZz2GTA1xIBAgnU6TTCYZHR2lu7sbgG9961sL+otRKpXixIkTtLe3G4tFY7EYPp8Pv99PXV0d69atu6fZ2rGxMc6fP09vby8DAwNTuq7k5eUZCw9lJlgIIT4pnU5z8uRJ8vLy2LRpEy6XC5fLdduOSovVnHexyJblkCAnEgn+6q/+CqUUeXl5RKNR4vE4ZWVlbNmyhc2bN2OxWOjr6+PIkSN0dHTg9/spKChg5cqVstXvAmBKhnCMX8YebMEW6cYSG8CcGJtyn6S9gLGSJwn7tqDNzqw+fygU4re//S0FBQUUFxdjNpunvI9Wr15NWVlZVp8zm9rb23nzzTexWCyUlJTgcrnweDxYrVaCwSDj4+P09fUxMDDApz/9aTZv3nxXx//ud79LYWGh0QElc2whhBAzk0wmuXTpEn19fQQCN/srVFZW8vLLL89zZNklCfIC88EHH3Ds2DHKy8spLy/H6/UyMDDA9evXGRoa4s/+7M9wOm8mVZFIhBs3bnD+/HmuXbuG0+nEYrFgtVqxWq3YbDZjdjlTd2qz2bLWD1fcni3USuH11zAnPtGgZYIimruOoH8XEe/GrJRb9PX1cf78eZ588klWrly5aGZC0+k0x44d4+TJk2zevJny8vLbvj/HxsY4duwY3/zmN43fhzuJx+P8zd/8DQcOHDCOnUqljBaD8XicdDptXKqrq8nNzc3K6xNCiMUoEonQ1NRENBo1PhsjkQjhcBiPx4Pb7TbWYnzmM5+Z73CzShLkBSgcDnPixAmuXr3K0NCQ0Ys3mUzyla98hZqamk88JhAIEIlEiMVixGIxotEo4XCYcDhMIpEgkUgYC/l6e3uJxWJGh4KamhppPTVHrOFOnIEGPEMfYon1T1x7MzGOuyqJ5K4j5qnL2vMlk0laW1vp7+8nlUrx7LPPUl5ePqclFS0tLRw5coSSkhJ27dpFaWnpHR+TSqUYGxtjdHSUnp4ezp07h9lsZvv27TNaJJpJqB977DFWrVo141jfeecdWlpajJ8nt/RzuVxGTXZ9fT0ej4ft27fLmRkhxLIUCAR4//332bFjBzU1NUYbVI/Hg9frXfI1yJIgL3DxeNyoJc6mUChEe3s77733HuFwmMcff1xmlOeQKRnEHmojZfWStOWTtrjn9Pm01ly+fJnu7m7C4TBer9eoFcvJyaGgoAC/309hYeFtvxwlk0n+8i//EgC/34/ZbDZ6YdtsNqMF2oYNG+js7CSdTvPqq6/eNrZf/OIXNDY2GrMPmQVx07WRy/SpDgaDxmV8fJzR0VFyc3P5/Oc/n5WSkUw7t8yXyoGBAd58800AHn/8cXJycmb9HEIIsVjE43FOnTrF+vXrOXDgwHyHMy/mus2bmKVM+7Xp9Pf389Zbb1FRUUF+fj4Wi4VUKmXMHkciETweD1VVVRQUFDA6Okp/fz+9vb10dHQY2wRv3LhRkuM5lrZ4iHjvanf1WVFKsXbtWtauXUskEiESiRhlBKFQiIGBAYLBIGNjY9hsNsrLyykrK6O4uJjCwkJyc3ON2YGNGzfS0NDA0NAQFouFZDI55bmKioro7u5meHiY559//o6xjY3drMl2u904HA601nR1ddHd3W3sZBiNRolEIiQSCXJzc/H5fBQWFlJVVUVhYSElJSXTlo9kvtRndl1MpVKMj48TCAQIBALGrPXo6KixDXqmRjuzAU6mldvatWuNNohCCLFcJBIJfv3rX2MymVi/fv18h7MgyQzyAnf16lV+9rOfTbkuMzvocDiwWq1Eo1GGh4cZHx/H4/EYC5MyO3st9VMk4vYyu78NDw8TCASMpDkSiWAymYyE0eVy4XQ6jS23vV6v0e0hk2Ta7fYZlztEo1Fu3LhhbLiRSCQIBoMkEgmUUsbz2my2KXHY7XacTie5ublYrVa01jQ0NNDU1ER3d7eRfCuljEtm5jzTBtHpdOJ0Oo3fkUyt/kLu7CGEEPeL1pq2tjaGh4fp7e3lgQceYP/+/cuu5aWUWCwBWmuGhoY4efIk7e3tjI6O4nK52L17t9QWi3uSmX2Nx+MkEglisZhRgpA5QzF5172SkhJKS0upqqqiqKjolsccHR2lr6+P/v5+RkZGGBkZMba1ziS+NpvNOKOhlEJrbWx0kplhDofDRgtDs9nMihUrjM4UcjZECCGyIxqN0tDQQG9vL7W1taxatYqamhp8Pt+S/6yVBHkJCofD/OAHP2DdunVUVFTMdzhiCYvFYkbpQiAQoK+vj9LSUp588kmsVistLS10dXXR29vL4ODglC4qmYVxmUVyd3M2Q2ttzFzn5ubK7K8QQsyhaDRq9I8fGBhAKUVFRQUVFRWUl5cb61mWUtIsCfIS9NOf/hSTycTmzZuX1JtVLHypVIrr16/T1NSEyWSipKQEn8+H1+vF6/XetqZeCCHEwqe1JhwOMzw8bKzpCAQCJJNJvF4vfr+fQ4cO3fJs4mIhi/RmIJVK0dnZSX5+vvENaWxsjK6uLoqKiowdZMLhMC0tLUbLtWg0SjQaJRaLUVBQQE1NDdXV1TPu23ov0uk0169fB6C7u9uo28zPz6e2tnbR9MUVi5PZbGbVqlVUV1djtVrlC5oQQiwxSimjA1FlZaVxfSKRMBaB//jHP6agoMA4S+hyufB6vaxbt46cnBzjLGAwGCQcDuNyufD5fIumrabMIHMzyfzXf/1XEokE0WgUrTVOp5NoNEp+fr6xVW1ubi59fX0UFRXhcrmMzTqsVisWi4VAIMDQ0BBDQ0N4PB7Ky8vx+/1TWl3l5ORM6R4wG4lEwmhZFQwGaWxspKGhwdgsJLMAKrMIavKipgyr1cqGDRtmHYsQQgghlo9QKEQ4HCYejxOLxYwOSpmJu3A4jMlkMhZ/x+NxgsEgLpeLvLw8/H4/fr+flStXzqi3/lyQEos7eO2113C73WzatAmllDEr7PV6jQVEgUCAcDhMYWHhHb/9pNNpAoEAw8PDU1pvZdpaRSIRcnJyyMvLw+fzGd0DMq2nMpfMm2l8fNz48+Or/ydfPB4PPp8Ph8MxZcHV4OAgw8PDRg1p5rUAWCwWPvvZz96PYRZCCCHEEpdMJolGozgcjk/kS+l0mnA4TCgUIhQKMT4+TktLC9/+9rfnJVYpsbgDi8VCSUmJMbOaSVAzlFJGfeVMmEwmfD4fPp9v2ttTqZQx65vpFDA4OEgymTQu8Xgci8UypfVVZvexZDLJ+Pg4yWSSVCplXKLRKMFgkEgkgtvtRilFMBjEZrMZSXjmm1umFVZeXt4sR08IIYQQ4iaLxXLL7lomkwmPx2PcrrXm2rVr9zO8GZMEeR6YzWZycnLmbOeuzDc0AKfTKX2QhRBCCCHugiTIS1DmG5oQQgghhLh70lhUCCGEEEKISSRBFkIIIYQQYhJJkIUQQgghhJhEEmQhhBBCCCEmkUV6E3p7e43OD0IIIYQQYm4tpL04Pk4SZGDz5s10dHSQSCTmOxQhhBBCiGVj165d8x3CtCRBBrZv38727dvnOwwhhBBCCLEASA2yEEIIIYQQk0iCLIQQQgghxCSSIAshhBBCCDGJJMhCCCGEEEJMIgmyEEIIIYQQk0iCLIQQQgghxCSSIAshhBBCCDGJJMhCCCGEEEJMIgmyEEIIIYQQk0iCLIQQQgghxCSSIAshhBBCCDGJJMhCCCGEEEJMIgmyEEIIIYQQkyit9XzHYFBKDQDt8x3HIlcADM53EEuMjGn2yZhml4xn9smYZpeMZ/bJmM5etda6cLobFlSCLGZPKfWR1nrHfMexlMiYZp+MaXbJeGafjGl2yXhmn4zp3JISCyGEEEIIISaRBFkIIYQQQohJJEFeen443wEsQTKm2Sdjml0yntknY5pdMp7ZJ2M6h6QGWQghhBBCiElkBlkIIYQQQohJJEFexJRSX1BKNSql0kqpHZOur1FKRZRS9ROX70+6bbtS6qJSqkUp9bdKKTU/0S88txrPidv+88SYXVZKPTHpehnPGVJK/RelVNek9+XTk26bdnzFnSmlnpwYtxal1H+a73gWI6VU28Tvcb1S6qOJ6/KVUkeUUlcn/syb7zgXMqXUj5RS/UqphknX3XIM5Xf+9m4xnvIZeh9Jgry4NQDPAUenue2a1nrrxOWPJl3/d8DXgVUTlyfnPsxFY9rxVEqtB14ENnBzvP5fpZR54mYZz7vz3Unvy8Nwx/EVtzExTv8P8BSwHvjixHiKu/fwxPsy8+X4PwG/1VqvAn478bO4tX/kk59/046h/M7PyD8y/f8n8hl6n0iCvIhprZu01pdnen+lVCmQq7U+oW8Wn/8E+IM5C3CRuc14fhb4udY6prVuBVqAnTKeWTPt+M5zTIvFTqBFa31dax0Hfs7N8RSz91ngf078/X8iv9u3pbU+Cgx/7OpbjaH8zt/BLcbzVmQ854AkyEvXCqXUOaXUe0qpfRPXlQOdk+7TOXGduL1y4MaknzPjJuN5976hlLowcfowc7r1VuMr7kzGLjs08G9KqTNKqa9PXFeste4BmPizaN6iW7xuNYbyvr138hl6n1jmOwBxe0qpd4CSaW76c631r27xsB6gSms9pJTaDvyrUmoDMF197LJqY3KP43mrcVv24/lxtxtfbpaj/AU3x+gvgO8ALyPjOBsydtnxKa11t1KqCDiilGqe74CWOHnf3hv5DL2PJEFe4LTWj97DY2JAbOLvZ5RS14DV3PxWWTHprhVAdzbiXCzuZTy5OW6Vk37OjNuyH8+Pm+n4KqVeA96c+PFW4yvuTMYuC7TW3RN/9iulfsnN09N9SqlSrXXPRDlV/7wGuTjdagzlfXsPtEFrPT8AAAGISURBVNZ9mb/LZ+jckxKLJUgpVZgp0FdK1XJz8dj1iVNc40qpXRPdFv4DcKtZU/Hv3gBeVErZlVIruDmeH8p43p2J/yAzPsfNRZFwi/G93/EtUqeBVUqpFUopGzcX6rwxzzEtKkopt1IqJ/N34HFuvjffAL4ycbevIL/b9+JWYyi/8/dAPkPvL5lBXsSUUp8D/m+gEPj/lFL1WusngP3Af1VKJYEU8Eda60yx///GzdWxTuA3ExfBrcdTa92olPpn4BKQBP5Ea52aeJiM58z9N6XUVm6e+msD/iPAHcZX3IbWOqmU+gbwNmAGfqS1bpznsBabYuCXEx0aLcDPtNZvKaVOA/+slPoa0AF8YR5jXPCUUv8LOAgUKKU6gW8Df8M0Yyi/83d2i/E8KJ+h94/spCeEEEIIIcQkUmIhhBBCCCHEJJIgCyGEEEIIMYkkyEIIIYQQQkwiCbIQQgghhBCTSIIshBBCCCHEJJIgCyGEEEIIMYkkyEIIIYQQQkwiCbIQQgghhBCT/P/Z8Q31ewnCRwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "region_p.visualize_spatial_extent()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'region_p' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_p\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavail_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'region_p' is not defined" - ] - } - ], - "source": [ - "region_p.avail_granules()" - ] - }, - { - "cell_type": 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"execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['bounding_box', 'polygon']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'short_name': 'ATL06',\n", - " 'version': '002',\n", - " 'temporal': '2019-10-01T00:00:00Z,2019-10-05T23:59:59Z',\n", - " 'polygon': 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- ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_p.CMRparams" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/icepyx/core/icesat2data.py:115: UserWarning: Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\n", - " warnings.warn(\"Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\")\n", - "/home/jovyan/icepyx/icepyx/core/validate_inputs.py:25: UserWarning: You are using an old version of this dataset\n", - " warnings.warn(\"You are using an old version of this dataset\")\n" - ] - } - ], - "source": [ - "region_t = 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- " ['2019-10-01','2019-10-05'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='2')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "region_t.visualize_spatial_extent()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ········\n" - ] - } - ], - "source": [ - "region_t.earthdata_login('jessica.scheick','jessica.scheick@maine.edu')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'time': '2019-10-01T00:00:00,2019-10-05T23:59:59',\n", - " 'Boundingshape': '{\"type\":\"FeatureCollection\",\"features\":[{\"id\":\"0\",\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-86.622742,-74.908126],[-86.561712,-74.870913],[-86.868859,-74.730522],[-86.962905,-74.605038],[-89.02594,-74.316754],[-89.630517,-74.192147],[-89.830808,-74.065919],[-90.746478,-73.956258],[-91.668214,-74.023169],[-92.049815,-73.929387],[-93.420791,-73.929327],[-93.997163,-73.882768],[-94.277701,-73.714183],[-95.133017,-73.966355],[-96.513501,-74.127404],[-99.889802,-74.085347],[-100.114438,-74.019422],[-100.355131,-74.080906],[-100.462734,-74.240864],[-100.827076,-74.373988],[-101.795349,-74.369597],[-102.424826,-74.497263],[-101.188725,-74.7179],[-101.564382,-75.02971],[-103.37484,-75.273725],[-103.914847,-75.426057],[-104.012128,-75.5223],[-103.029452,-75.748774],[-102.350567,-75.749245],[-101.837882,-75.943066],[-101.899461,-76.014086],[-101.280944,-76.192769],[-101.325735,-76.246168],[-101.190803,-76.27106],[-101.250474,-76.342292],[-101.175067,-76.345822],[-101.402436,-76.52035],[-101.326063,-76.523929],[-101.449791,-76.666392],[-101.310795,-76.691373],[-101.357407,-76.744819],[-101.217404,-76.769752],[-101.295133,-76.85887],[-101.058051,-76.962123],[-100.447336,-77.117686],[-98.433698,-77.320866],[-97.28308,-77.355688],[-97.491148,-77.423178],[-96.514174,-77.485919],[-96.552494,-77.558236],[-96.384656,-77.562336],[-96.441516,-77.670857],[-97.139363,-77.836566],[-97.193451,-77.926901],[-97.64271,-78.080044],[-96.297869,-78.388943],[-96.327803,-78.44329],[-95.721466,-78.511065],[-95.748962,-78.565482],[-94.940425,-78.617072],[-94.988611,-78.726066],[-94.911669,-78.763976],[-95.609268,-78.843079],[-95.637038,-78.897535],[-95.37191,-78.9391],[-95.693408,-79.006456],[-95.269903,-79.124145],[-95.323729,-79.233172],[-95.430206,-79.249633],[-95.155505,-79.291032],[-95.191045,-79.363748],[-94.81352,-79.406486],[-94.847075,-79.479253],[-94.747448,-79.48078],[-94.772403,-79.535367],[-93.90411,-79.638844],[-93.843651,-79.749409],[-93.967323,-79.802836],[-93.788723,-79.87821],[-93.816393,-79.951128],[-93.230546,-80.085534],[-91.707475,-79.87748],[-91.801545,-79.822143],[-91.488897,-79.805457],[-91.465152,-79.641131],[-90.447349,-79.5894],[-90.545492,-79.534464],[-90.042319,-79.37062],[-90.140775,-79.334083],[-90.041814,-79.24285],[-88.982186,-79.076903],[-90.230262,-78.914333],[-90.32191,-78.804808],[-90.689626,-78.676516],[-91.150024,-78.638589],[-92.035347,-78.414844],[-92.106013,-78.30491],[-91.651645,-78.271472],[-91.365784,-78.127206],[-91.188783,-78.128018],[-91.090167,-78.019109],[-90.737076,-77.983849],[-90.909191,-77.946905],[-90.732603,-77.911009],[-90.727088,-77.819973],[-91.070502,-77.800626],[-91.14118,-77.636469],[-91.90279,-77.613923],[-91.984627,-77.595116],[-91.972963,-77.522365],[-92.466819,-77.463587],[-92.199521,-77.374914],[-92.352136,-77.300761],[-92.335283,-77.209895],[-91.434206,-77.234653],[-91.426015,-77.16193],[-91.015545,-77.145686],[-91.008355,-77.054784],[-91.086397,-77.018096],[-91.647835,-76.97871],[-91.640906,-76.924199],[-91.873848,-76.868024],[-91.779021,-76.759619],[-90.823937,-76.710073],[-90.345113,-76.52953],[-86.988029,-75.856983],[-86.945563,-75.711143],[-86.872234,-75.710165],[-87.034102,-75.63967],[-86.965004,-75.620616],[-87.075115,-75.440545],[-87.003154,-75.439609],[-87.021872,-75.349129],[-86.835058,-75.219586],[-86.850654,-75.147247],[-86.717729,-75.109052],[-86.737771,-75.018662],[-86.602149,-74.998483],[-86.622742,-74.908126]]]},\"bbox\":[-104.012128,-80.085534,-86.561712,-73.714183]}],\"bbox\":[-104.012128,-80.085534,-86.561712,-73.714183]}'}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_t.subsetparams()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data request 1 of 2 is submitting to NSIDC\n", - "order ID: 5000000691393\n", - "Initial status of your order request at NSIDC is: pending\n", - "Your order status is still pending at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n" - ] - } - ], - "source": [ - "region_t.order_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Number of available granules': 12,\n", - " 'Average size of granules (MB)': 69.47542500495832,\n", - " 'Total size of all granules (MB)': 833.7051000595}" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_p.avail_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "path='/home/jovyan/icepyx/dev-notebooks/fakedir'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ·········\n" - ] - } - ], - "source": [ - "region_a.earthdata_login('icepyx_devteam','jessica.scheick@maine.edu')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Subsetting options\n", - "[{'id': 'ICESAT2',\n", - " 'maxGransAsyncRequest': '2000',\n", - " 'maxGransSyncRequest': '100',\n", - " 'spatialSubsetting': 'true',\n", - " 'spatialSubsettingShapefile': 'true',\n", - " 'temporalSubsetting': 'true',\n", - " 'type': 'both'}]\n", - "Data File Formats (Reformatting Options)\n", - "['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3']\n", - "Reprojection Options\n", - "[]\n", - "Data File (Reformatting) Options Supporting Reprojection\n", - "['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3', 'No reformatting']\n", - "Data File (Reformatting) Options NOT Supporting Reprojection\n", - "[]\n", - "Data Variables (also Subsettable)\n", - "['ancillary_data/atlas_sdp_gps_epoch',\n", - " 'ancillary_data/control',\n", - " 'ancillary_data/data_end_utc',\n", - " 'ancillary_data/data_start_utc',\n", - " 'ancillary_data/end_cycle',\n", - " 'ancillary_data/end_delta_time',\n", - " 'ancillary_data/end_geoseg',\n", - " 'ancillary_data/end_gpssow',\n", - " 'ancillary_data/end_gpsweek',\n", - " 'ancillary_data/end_orbit',\n", - " 'ancillary_data/end_region',\n", - " 'ancillary_data/end_rgt',\n", - " 'ancillary_data/granule_end_utc',\n", - " 'ancillary_data/granule_start_utc',\n", - " 'ancillary_data/qa_at_interval',\n", - " 'ancillary_data/release',\n", - " 'ancillary_data/start_cycle',\n", - " 'ancillary_data/start_delta_time',\n", - " 'ancillary_data/start_geoseg',\n", - " 'ancillary_data/start_gpssow',\n", - " 'ancillary_data/start_gpsweek',\n", - " 'ancillary_data/start_orbit',\n", - " 'ancillary_data/start_region',\n", - " 'ancillary_data/start_rgt',\n", - " 'ancillary_data/version',\n", - " 'ancillary_data/land_ice/dt_hist',\n", - " 'ancillary_data/land_ice/fit_maxiter',\n", - " 'ancillary_data/land_ice/fpb_maxiter',\n", - " 'ancillary_data/land_ice/maxiter',\n", - " 'ancillary_data/land_ice/max_res_ids',\n", - " 'ancillary_data/land_ice/min_dist',\n", - " 'ancillary_data/land_ice/min_gain_th',\n", - " 'ancillary_data/land_ice/min_n_pe',\n", - " 'ancillary_data/land_ice/min_n_sel',\n", - " 'ancillary_data/land_ice/min_signal_conf',\n", - " 'ancillary_data/land_ice/n_hist',\n", - " 'ancillary_data/land_ice/nhist_bins',\n", - " 'ancillary_data/land_ice/n_sigmas',\n", - " 'ancillary_data/land_ice/proc_interval',\n", - " 'ancillary_data/land_ice/rbin_width',\n", - " 'ancillary_data/land_ice/sigma_beam',\n", - " 'ancillary_data/land_ice/sigma_tx',\n", - " 'ancillary_data/land_ice/t_dead',\n", - " 'ancillary_data/land_ice/win_nsig',\n", - " 'gt1l/land_ice_segments/atl06_quality_summary',\n", - " 'gt1l/land_ice_segments/delta_time',\n", - " 'gt1l/land_ice_segments/h_li',\n", - " 'gt1l/land_ice_segments/h_li_sigma',\n", - " 'gt1l/land_ice_segments/latitude',\n", - " 'gt1l/land_ice_segments/longitude',\n", - " 'gt1l/land_ice_segments/segment_id',\n", - " 'gt1l/land_ice_segments/sigma_geo_h',\n", - " 'gt1l/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt1l/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt1l/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt1l/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt1l/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt1l/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt1l/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt1l/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt1l/land_ice_segments/dem/dem_flag',\n", - " 'gt1l/land_ice_segments/dem/dem_h',\n", - " 'gt1l/land_ice_segments/dem/geoid_h',\n", - " 'gt1l/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt1l/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt1l/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt1l/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt1l/land_ice_segments/fit_statistics/h_rms_misfit',\n", - " 'gt1l/land_ice_segments/fit_statistics/h_robust_sprd',\n", - " 'gt1l/land_ice_segments/fit_statistics/n_fit_photons',\n", - " 'gt1l/land_ice_segments/fit_statistics/n_seg_pulses',\n", - " 'gt1l/land_ice_segments/fit_statistics/sigma_h_mean',\n", - " 'gt1l/land_ice_segments/fit_statistics/signal_selection_source',\n", - " 'gt1l/land_ice_segments/fit_statistics/signal_selection_source_status',\n", - " 'gt1l/land_ice_segments/fit_statistics/snr',\n", - " 'gt1l/land_ice_segments/fit_statistics/snr_significance',\n", - " 'gt1l/land_ice_segments/fit_statistics/w_surface_window_final',\n", - " 'gt1l/land_ice_segments/geophysical/bckgrd',\n", - " 'gt1l/land_ice_segments/geophysical/bsnow_conf',\n", - " 'gt1l/land_ice_segments/geophysical/bsnow_h',\n", - " 'gt1l/land_ice_segments/geophysical/bsnow_od',\n", - " 'gt1l/land_ice_segments/geophysical/cloud_flg_asr',\n", - " 'gt1l/land_ice_segments/geophysical/cloud_flg_atm',\n", - " 'gt1l/land_ice_segments/geophysical/dac',\n", - " 'gt1l/land_ice_segments/geophysical/e_bckgrd',\n", - " 'gt1l/land_ice_segments/geophysical/msw_flag',\n", - " 'gt1l/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt1l/land_ice_segments/geophysical/r_eff',\n", - " 'gt1l/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt1l/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt1l/land_ice_segments/geophysical/tide_earth',\n", - " 'gt1l/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt1l/land_ice_segments/geophysical/tide_load',\n", - " 'gt1l/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt1l/land_ice_segments/geophysical/tide_pole',\n", - " 'gt1l/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt1l/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt1l/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt1l/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt1l/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt1l/land_ice_segments/ground_track/x_atc',\n", - " 'gt1l/land_ice_segments/ground_track/y_atc',\n", - " 'gt1l/residual_histogram/bckgrd_per_bin',\n", - " 'gt1l/residual_histogram/count',\n", - " 'gt1l/residual_histogram/delta_time',\n", - " 'gt1l/residual_histogram/dh',\n", - " 'gt1l/residual_histogram/ds_segment_id',\n", - " 'gt1l/residual_histogram/lat_mean',\n", - " 'gt1l/residual_histogram/lon_mean',\n", - " 'gt1l/residual_histogram/pulse_count',\n", - " 'gt1l/residual_histogram/segment_id_list',\n", - " 'gt1l/residual_histogram/x_atc_mean',\n", - " 'gt1l/segment_quality/delta_time',\n", - " 'gt1l/segment_quality/record_number',\n", - " 'gt1l/segment_quality/reference_pt_lat',\n", - " 'gt1l/segment_quality/reference_pt_lon',\n", - " 'gt1l/segment_quality/segment_id',\n", - " 'gt1l/segment_quality/signal_selection_source',\n", - " 'gt1l/segment_quality/signal_selection_status/signal_selection_status_all',\n", - " 'gt1l/segment_quality/signal_selection_status/signal_selection_status_backup',\n", - " 'gt1l/segment_quality/signal_selection_status/signal_selection_status_confident',\n", - " 'gt1r/land_ice_segments/atl06_quality_summary',\n", - " 'gt1r/land_ice_segments/delta_time',\n", - " 'gt1r/land_ice_segments/h_li',\n", - " 'gt1r/land_ice_segments/h_li_sigma',\n", - " 'gt1r/land_ice_segments/latitude',\n", - " 'gt1r/land_ice_segments/longitude',\n", - " 'gt1r/land_ice_segments/segment_id',\n", - " 'gt1r/land_ice_segments/sigma_geo_h',\n", - " 'gt1r/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt1r/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt1r/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt1r/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt1r/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt1r/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt1r/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt1r/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt1r/land_ice_segments/dem/dem_flag',\n", - " 'gt1r/land_ice_segments/dem/dem_h',\n", - " 'gt1r/land_ice_segments/dem/geoid_h',\n", - " 'gt1r/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt1r/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt1r/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt1r/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt1r/land_ice_segments/fit_statistics/h_rms_misfit',\n", - " 'gt1r/land_ice_segments/fit_statistics/h_robust_sprd',\n", - " 'gt1r/land_ice_segments/fit_statistics/n_fit_photons',\n", - " 'gt1r/land_ice_segments/fit_statistics/n_seg_pulses',\n", - " 'gt1r/land_ice_segments/fit_statistics/sigma_h_mean',\n", - " 'gt1r/land_ice_segments/fit_statistics/signal_selection_source',\n", - " 'gt1r/land_ice_segments/fit_statistics/signal_selection_source_status',\n", - " 'gt1r/land_ice_segments/fit_statistics/snr',\n", - " 'gt1r/land_ice_segments/fit_statistics/snr_significance',\n", - " 'gt1r/land_ice_segments/fit_statistics/w_surface_window_final',\n", - " 'gt1r/land_ice_segments/geophysical/bckgrd',\n", - " 'gt1r/land_ice_segments/geophysical/bsnow_conf',\n", - " 'gt1r/land_ice_segments/geophysical/bsnow_h',\n", - " 'gt1r/land_ice_segments/geophysical/bsnow_od',\n", - " 'gt1r/land_ice_segments/geophysical/cloud_flg_asr',\n", - " 'gt1r/land_ice_segments/geophysical/cloud_flg_atm',\n", - " 'gt1r/land_ice_segments/geophysical/dac',\n", - " 'gt1r/land_ice_segments/geophysical/e_bckgrd',\n", - " 'gt1r/land_ice_segments/geophysical/msw_flag',\n", - " 'gt1r/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt1r/land_ice_segments/geophysical/r_eff',\n", - " 'gt1r/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt1r/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt1r/land_ice_segments/geophysical/tide_earth',\n", - " 'gt1r/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt1r/land_ice_segments/geophysical/tide_load',\n", - " 'gt1r/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt1r/land_ice_segments/geophysical/tide_pole',\n", - " 'gt1r/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt1r/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt1r/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt1r/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt1r/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt1r/land_ice_segments/ground_track/x_atc',\n", - " 'gt1r/land_ice_segments/ground_track/y_atc',\n", - " 'gt1r/residual_histogram/bckgrd_per_bin',\n", - " 'gt1r/residual_histogram/count',\n", - " 'gt1r/residual_histogram/delta_time',\n", - " 'gt1r/residual_histogram/dh',\n", - " 'gt1r/residual_histogram/ds_segment_id',\n", - " 'gt1r/residual_histogram/lat_mean',\n", - " 'gt1r/residual_histogram/lon_mean',\n", - " 'gt1r/residual_histogram/pulse_count',\n", - " 'gt1r/residual_histogram/segment_id_list',\n", - " 'gt1r/residual_histogram/x_atc_mean',\n", - " 'gt1r/segment_quality/delta_time',\n", - " 'gt1r/segment_quality/record_number',\n", - " 'gt1r/segment_quality/reference_pt_lat',\n", - " 'gt1r/segment_quality/reference_pt_lon',\n", - " 'gt1r/segment_quality/segment_id',\n", - " 'gt1r/segment_quality/signal_selection_source',\n", - " 'gt1r/segment_quality/signal_selection_status/signal_selection_status_all',\n", - " 'gt1r/segment_quality/signal_selection_status/signal_selection_status_backup',\n", - " 'gt1r/segment_quality/signal_selection_status/signal_selection_status_confident',\n", - " 'gt2l/land_ice_segments/atl06_quality_summary',\n", - " 'gt2l/land_ice_segments/delta_time',\n", - " 'gt2l/land_ice_segments/h_li',\n", - " 'gt2l/land_ice_segments/h_li_sigma',\n", - " 'gt2l/land_ice_segments/latitude',\n", - " 'gt2l/land_ice_segments/longitude',\n", - " 'gt2l/land_ice_segments/segment_id',\n", - " 'gt2l/land_ice_segments/sigma_geo_h',\n", - " 'gt2l/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt2l/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt2l/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt2l/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt2l/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt2l/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt2l/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt2l/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt2l/land_ice_segments/dem/dem_flag',\n", - " 'gt2l/land_ice_segments/dem/dem_h',\n", - " 'gt2l/land_ice_segments/dem/geoid_h',\n", - " 'gt2l/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt2l/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt2l/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt2l/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt2l/land_ice_segments/fit_statistics/h_rms_misfit',\n", - " 'gt2l/land_ice_segments/fit_statistics/h_robust_sprd',\n", - " 'gt2l/land_ice_segments/fit_statistics/n_fit_photons',\n", - " 'gt2l/land_ice_segments/fit_statistics/n_seg_pulses',\n", - " 'gt2l/land_ice_segments/fit_statistics/sigma_h_mean',\n", - " 'gt2l/land_ice_segments/fit_statistics/signal_selection_source',\n", - " 'gt2l/land_ice_segments/fit_statistics/signal_selection_source_status',\n", - " 'gt2l/land_ice_segments/fit_statistics/snr',\n", - " 'gt2l/land_ice_segments/fit_statistics/snr_significance',\n", - " 'gt2l/land_ice_segments/fit_statistics/w_surface_window_final',\n", - " 'gt2l/land_ice_segments/geophysical/bckgrd',\n", - " 'gt2l/land_ice_segments/geophysical/bsnow_conf',\n", - " 'gt2l/land_ice_segments/geophysical/bsnow_h',\n", - " 'gt2l/land_ice_segments/geophysical/bsnow_od',\n", - " 'gt2l/land_ice_segments/geophysical/cloud_flg_asr',\n", - " 'gt2l/land_ice_segments/geophysical/cloud_flg_atm',\n", - " 'gt2l/land_ice_segments/geophysical/dac',\n", - " 'gt2l/land_ice_segments/geophysical/e_bckgrd',\n", - " 'gt2l/land_ice_segments/geophysical/msw_flag',\n", - " 'gt2l/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt2l/land_ice_segments/geophysical/r_eff',\n", - " 'gt2l/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt2l/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt2l/land_ice_segments/geophysical/tide_earth',\n", - " 'gt2l/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt2l/land_ice_segments/geophysical/tide_load',\n", - " 'gt2l/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt2l/land_ice_segments/geophysical/tide_pole',\n", - " 'gt2l/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt2l/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt2l/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt2l/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt2l/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt2l/land_ice_segments/ground_track/x_atc',\n", - " 'gt2l/land_ice_segments/ground_track/y_atc',\n", - " 'gt2l/residual_histogram/bckgrd_per_bin',\n", - " 'gt2l/residual_histogram/count',\n", - " 'gt2l/residual_histogram/delta_time',\n", - " 'gt2l/residual_histogram/dh',\n", - " 'gt2l/residual_histogram/ds_segment_id',\n", - " 'gt2l/residual_histogram/lat_mean',\n", - " 'gt2l/residual_histogram/lon_mean',\n", - " 'gt2l/residual_histogram/pulse_count',\n", - " 'gt2l/residual_histogram/segment_id_list',\n", - " 'gt2l/residual_histogram/x_atc_mean',\n", - " 'gt2l/segment_quality/delta_time',\n", - " 'gt2l/segment_quality/record_number',\n", - " 'gt2l/segment_quality/reference_pt_lat',\n", - " 'gt2l/segment_quality/reference_pt_lon',\n", - " 'gt2l/segment_quality/segment_id',\n", - " 'gt2l/segment_quality/signal_selection_source',\n", - " 'gt2l/segment_quality/signal_selection_status/signal_selection_status_all',\n", - " 'gt2l/segment_quality/signal_selection_status/signal_selection_status_backup',\n", - " 'gt2l/segment_quality/signal_selection_status/signal_selection_status_confident',\n", - " 'gt2r/land_ice_segments/atl06_quality_summary',\n", - " 'gt2r/land_ice_segments/delta_time',\n", - " 'gt2r/land_ice_segments/h_li',\n", - " 'gt2r/land_ice_segments/h_li_sigma',\n", - " 'gt2r/land_ice_segments/latitude',\n", - " 'gt2r/land_ice_segments/longitude',\n", - " 'gt2r/land_ice_segments/segment_id',\n", - " 'gt2r/land_ice_segments/sigma_geo_h',\n", - " 'gt2r/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt2r/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt2r/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt2r/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt2r/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt2r/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt2r/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt2r/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt2r/land_ice_segments/dem/dem_flag',\n", - " 'gt2r/land_ice_segments/dem/dem_h',\n", - " 'gt2r/land_ice_segments/dem/geoid_h',\n", - " 'gt2r/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt2r/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt2r/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt2r/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt2r/land_ice_segments/fit_statistics/h_rms_misfit',\n", - " 'gt2r/land_ice_segments/fit_statistics/h_robust_sprd',\n", - " 'gt2r/land_ice_segments/fit_statistics/n_fit_photons',\n", - " 'gt2r/land_ice_segments/fit_statistics/n_seg_pulses',\n", - " 'gt2r/land_ice_segments/fit_statistics/sigma_h_mean',\n", - " 'gt2r/land_ice_segments/fit_statistics/signal_selection_source',\n", - " 'gt2r/land_ice_segments/fit_statistics/signal_selection_source_status',\n", - " 'gt2r/land_ice_segments/fit_statistics/snr',\n", - " 'gt2r/land_ice_segments/fit_statistics/snr_significance',\n", - " 'gt2r/land_ice_segments/fit_statistics/w_surface_window_final',\n", - " 'gt2r/land_ice_segments/geophysical/bckgrd',\n", - " 'gt2r/land_ice_segments/geophysical/bsnow_conf',\n", - " 'gt2r/land_ice_segments/geophysical/bsnow_h',\n", - " 'gt2r/land_ice_segments/geophysical/bsnow_od',\n", - " 'gt2r/land_ice_segments/geophysical/cloud_flg_asr',\n", - " 'gt2r/land_ice_segments/geophysical/cloud_flg_atm',\n", - " 'gt2r/land_ice_segments/geophysical/dac',\n", - " 'gt2r/land_ice_segments/geophysical/e_bckgrd',\n", - " 'gt2r/land_ice_segments/geophysical/msw_flag',\n", - " 'gt2r/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt2r/land_ice_segments/geophysical/r_eff',\n", - " 'gt2r/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt2r/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt2r/land_ice_segments/geophysical/tide_earth',\n", - " 'gt2r/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt2r/land_ice_segments/geophysical/tide_load',\n", - " 'gt2r/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt2r/land_ice_segments/geophysical/tide_pole',\n", - " 'gt2r/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt2r/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt2r/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt2r/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt2r/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt2r/land_ice_segments/ground_track/x_atc',\n", - " 'gt2r/land_ice_segments/ground_track/y_atc',\n", - " 'gt2r/residual_histogram/bckgrd_per_bin',\n", - " 'gt2r/residual_histogram/count',\n", - " 'gt2r/residual_histogram/delta_time',\n", - " 'gt2r/residual_histogram/dh',\n", - " 'gt2r/residual_histogram/ds_segment_id',\n", - " 'gt2r/residual_histogram/lat_mean',\n", - " 'gt2r/residual_histogram/lon_mean',\n", - " 'gt2r/residual_histogram/pulse_count',\n", - " 'gt2r/residual_histogram/segment_id_list',\n", - " 'gt2r/residual_histogram/x_atc_mean',\n", - " 'gt2r/segment_quality/delta_time',\n", - " 'gt2r/segment_quality/record_number',\n", - " 'gt2r/segment_quality/reference_pt_lat',\n", - " 'gt2r/segment_quality/reference_pt_lon',\n", - " 'gt2r/segment_quality/segment_id',\n", - " 'gt2r/segment_quality/signal_selection_source',\n", - " 'gt2r/segment_quality/signal_selection_status/signal_selection_status_all',\n", - " 'gt2r/segment_quality/signal_selection_status/signal_selection_status_backup',\n", - " 'gt2r/segment_quality/signal_selection_status/signal_selection_status_confident',\n", - " 'gt3l/land_ice_segments/atl06_quality_summary',\n", - " 'gt3l/land_ice_segments/delta_time',\n", - " 'gt3l/land_ice_segments/h_li',\n", - " 'gt3l/land_ice_segments/h_li_sigma',\n", - " 'gt3l/land_ice_segments/latitude',\n", - " 'gt3l/land_ice_segments/longitude',\n", - " 'gt3l/land_ice_segments/segment_id',\n", - " 'gt3l/land_ice_segments/sigma_geo_h',\n", - " 'gt3l/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt3l/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt3l/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt3l/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt3l/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt3l/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt3l/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt3l/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt3l/land_ice_segments/dem/dem_flag',\n", - " 'gt3l/land_ice_segments/dem/dem_h',\n", - " 'gt3l/land_ice_segments/dem/geoid_h',\n", - " 'gt3l/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt3l/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt3l/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt3l/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt3l/land_ice_segments/fit_statistics/h_rms_misfit',\n", - " 'gt3l/land_ice_segments/fit_statistics/h_robust_sprd',\n", - " 'gt3l/land_ice_segments/fit_statistics/n_fit_photons',\n", - " 'gt3l/land_ice_segments/fit_statistics/n_seg_pulses',\n", - " 'gt3l/land_ice_segments/fit_statistics/sigma_h_mean',\n", - " 'gt3l/land_ice_segments/fit_statistics/signal_selection_source',\n", - " 'gt3l/land_ice_segments/fit_statistics/signal_selection_source_status',\n", - " 'gt3l/land_ice_segments/fit_statistics/snr',\n", - " 'gt3l/land_ice_segments/fit_statistics/snr_significance',\n", - " 'gt3l/land_ice_segments/fit_statistics/w_surface_window_final',\n", - " 'gt3l/land_ice_segments/geophysical/bckgrd',\n", - " 'gt3l/land_ice_segments/geophysical/bsnow_conf',\n", - " 'gt3l/land_ice_segments/geophysical/bsnow_h',\n", - " 'gt3l/land_ice_segments/geophysical/bsnow_od',\n", - " 'gt3l/land_ice_segments/geophysical/cloud_flg_asr',\n", - " 'gt3l/land_ice_segments/geophysical/cloud_flg_atm',\n", - " 'gt3l/land_ice_segments/geophysical/dac',\n", - " 'gt3l/land_ice_segments/geophysical/e_bckgrd',\n", - " 'gt3l/land_ice_segments/geophysical/msw_flag',\n", - " 'gt3l/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt3l/land_ice_segments/geophysical/r_eff',\n", - " 'gt3l/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt3l/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt3l/land_ice_segments/geophysical/tide_earth',\n", - " 'gt3l/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt3l/land_ice_segments/geophysical/tide_load',\n", - " 'gt3l/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt3l/land_ice_segments/geophysical/tide_pole',\n", - " 'gt3l/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt3l/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt3l/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt3l/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt3l/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt3l/land_ice_segments/ground_track/x_atc',\n", - " 'gt3l/land_ice_segments/ground_track/y_atc',\n", - " 'gt3l/residual_histogram/bckgrd_per_bin',\n", - " 'gt3l/residual_histogram/count',\n", - " 'gt3l/residual_histogram/delta_time',\n", - " 'gt3l/residual_histogram/dh',\n", - " 'gt3l/residual_histogram/ds_segment_id',\n", - " 'gt3l/residual_histogram/lat_mean',\n", - " 'gt3l/residual_histogram/lon_mean',\n", - " 'gt3l/residual_histogram/pulse_count',\n", - " 'gt3l/residual_histogram/segment_id_list',\n", - " 'gt3l/residual_histogram/x_atc_mean',\n", - " 'gt3l/segment_quality/delta_time',\n", - " 'gt3l/segment_quality/record_number',\n", - " 'gt3l/segment_quality/reference_pt_lat',\n", - " 'gt3l/segment_quality/reference_pt_lon',\n", - " 'gt3l/segment_quality/segment_id',\n", - " 'gt3l/segment_quality/signal_selection_source',\n", - " 'gt3l/segment_quality/signal_selection_status/signal_selection_status_all',\n", - " 'gt3l/segment_quality/signal_selection_status/signal_selection_status_backup',\n", - " 'gt3l/segment_quality/signal_selection_status/signal_selection_status_confident',\n", - " 'gt3r/land_ice_segments/atl06_quality_summary',\n", - " 'gt3r/land_ice_segments/delta_time',\n", - " 'gt3r/land_ice_segments/h_li',\n", - " 'gt3r/land_ice_segments/h_li_sigma',\n", - " 'gt3r/land_ice_segments/latitude',\n", - " 'gt3r/land_ice_segments/longitude',\n", - " 'gt3r/land_ice_segments/segment_id',\n", - " 'gt3r/land_ice_segments/sigma_geo_h',\n", - " 'gt3r/land_ice_segments/bias_correction/fpb_mean_corr',\n", - " 'gt3r/land_ice_segments/bias_correction/fpb_mean_corr_sigma',\n", - " 'gt3r/land_ice_segments/bias_correction/fpb_med_corr',\n", - " 'gt3r/land_ice_segments/bias_correction/fpb_med_corr_sigma',\n", - " 'gt3r/land_ice_segments/bias_correction/fpb_n_corr',\n", - " 'gt3r/land_ice_segments/bias_correction/med_r_fit',\n", - " 'gt3r/land_ice_segments/bias_correction/tx_mean_corr',\n", - " 'gt3r/land_ice_segments/bias_correction/tx_med_corr',\n", - " 'gt3r/land_ice_segments/dem/dem_flag',\n", - " 'gt3r/land_ice_segments/dem/dem_h',\n", - " 'gt3r/land_ice_segments/dem/geoid_h',\n", - " 'gt3r/land_ice_segments/fit_statistics/dh_fit_dx',\n", - " 'gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n", - " 'gt3r/land_ice_segments/fit_statistics/dh_fit_dy',\n", - " 'gt3r/land_ice_segments/fit_statistics/h_expected_rms',\n", - " 'gt3r/land_ice_segments/fit_statistics/h_mean',\n", - " 'gt3r/land_ice_segments/fit_statistics/h_rms_misfit',\n", - 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" 'gt3r/land_ice_segments/geophysical/msw_flag',\n", - " 'gt3r/land_ice_segments/geophysical/neutat_delay_total',\n", - " 'gt3r/land_ice_segments/geophysical/r_eff',\n", - " 'gt3r/land_ice_segments/geophysical/solar_azimuth',\n", - " 'gt3r/land_ice_segments/geophysical/solar_elevation',\n", - " 'gt3r/land_ice_segments/geophysical/tide_earth',\n", - " 'gt3r/land_ice_segments/geophysical/tide_equilibrium',\n", - " 'gt3r/land_ice_segments/geophysical/tide_load',\n", - " 'gt3r/land_ice_segments/geophysical/tide_ocean',\n", - " 'gt3r/land_ice_segments/geophysical/tide_pole',\n", - " 'gt3r/land_ice_segments/ground_track/ref_azimuth',\n", - " 'gt3r/land_ice_segments/ground_track/ref_coelv',\n", - " 'gt3r/land_ice_segments/ground_track/seg_azimuth',\n", - " 'gt3r/land_ice_segments/ground_track/sigma_geo_at',\n", - " 'gt3r/land_ice_segments/ground_track/sigma_geo_xt',\n", - " 'gt3r/land_ice_segments/ground_track/x_atc',\n", - " 'gt3r/land_ice_segments/ground_track/y_atc',\n", - " 'gt3r/residual_histogram/bckgrd_per_bin',\n", - 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" 'quality_assessment/gt3l/signal_selection_source_fraction_0',\n", - " 'quality_assessment/gt3l/signal_selection_source_fraction_1',\n", - " 'quality_assessment/gt3l/signal_selection_source_fraction_2',\n", - " 'quality_assessment/gt3l/signal_selection_source_fraction_3',\n", - " 'quality_assessment/gt3r/delta_time',\n", - " 'quality_assessment/gt3r/lat_mean',\n", - " 'quality_assessment/gt3r/lon_mean',\n", - " 'quality_assessment/gt3r/signal_selection_source_fraction_0',\n", - " 'quality_assessment/gt3r/signal_selection_source_fraction_1',\n", - " 'quality_assessment/gt3r/signal_selection_source_fraction_2',\n", - " 'quality_assessment/gt3r/signal_selection_source_fraction_3']\n" - ] - } - ], - "source": [ - "region_a.show_custom_options()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'short_name': 'ATL06',\n", - " 'version': '002',\n", - " 'temporal': '2019-02-20T00:00:00Z,2019-02-28T23:59:59Z',\n", - " 'bounding_box': '-55,68,-48,71'}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.CMRparams" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'time': '2019-02-20T00:00:00,2019-02-28T23:59:59',\n", - " 'bounding_box': '-55,68,-48,71'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.subsetparams()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "obs_keys = region_a.CMRparams.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['page_size', 'page_num', 'request_mode', 'include_meta'])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.reqparams.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "Your search returned no results; try different search parameters", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavail_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/icepyx/icepyx/core/is2class.py\u001b[0m in \u001b[0;36mavail_granules\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreqparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'page_num'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranules\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Your search returned no results; try different search parameters\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranule_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAssertionError\u001b[0m: Your search returned no results; try different search parameters" - ] - } - ], - "source": [ - "region_a.avail_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'producer_granule_id': 'ATL06_20190221121851_08410203_002_01.h5',\n", - " 'time_start': '2019-02-21T12:19:05.000Z',\n", - " 'orbit': {'ascending_crossing': '-40.35812957405553',\n", - " 'start_lat': '59.5',\n", - " 'start_direction': 'A',\n", - " 'end_lat': '80',\n", - " 'end_direction': 'A'},\n", - " 'updated': '2019-10-24T13:18:53.725Z',\n", - " 'orbit_calculated_spatial_domains': [{'equator_crossing_date_time': '2019-02-21T12:03:18.922Z',\n", - " 'equator_crossing_longitude': '-40.35812957405553',\n", - " 'orbit_number': '2429'}],\n", - " 'dataset_id': 'ATLAS/ICESat-2 L3A Land Ice Height V002',\n", - " 'data_center': 'NSIDC_ECS',\n", - 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" 'length': '0.0KB',\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://openaltimetry.org/'},\n", - " {'inherited': True,\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://doi.org/10.5067/ATLAS/ATL06.002'},\n", - " {'inherited': True,\n", - " 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#',\n", - " 'hreflang': 'en-US',\n", - " 'href': 'https://doi.org/10.5067/ATLAS/ATL06.002'}]}]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.granules" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['5000000500408']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.orderIDs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "region_a.download_granules('/Users/jessica/Scripts/github/icesat2py/icepyx/download/', verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "source": [ - "## Steps required by the user\n", - "- create icesat2data object with the minimum inputs (dataset, time period, spatial extent)\n", - "- enter Earthdata login credentials and open an active session\n", - "- download data (querying can be done prior to logging in)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submitting the request - behind the scenes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the search query\n", - "\n", - "#### We will now populate dictionaries to be applied to our search query below based on spatial and temporal inputs. For additional search parameters, see the [The Common Metadata Repository API documentation](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html \"CMR API documentation\").\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'aoi' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Create CMR parameters used for granule search. Modify params depending on bounding_box or polygon input.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0maoi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'1'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;31m# bounding box input:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m params = {\n", - "\u001b[0;31mNameError\u001b[0m: name 'aoi' is not defined" - ] - } - ], - "source": [ - "#Create CMR parameters used for granule search. Modify params depending on bounding_box or polygon input.\n", - "\n", - "if aoi == '1':\n", - "# bounding box input:\n", - " params = {\n", - " 'short_name': short_name,\n", - " 'version': latest_version,\n", - " 'temporal': temporal,\n", - " 'page_size': 100,\n", - " 'page_num': 1,\n", - " 'bounding_box': bounding_box\n", - " }\n", - "else:\n", - " \n", - "# If polygon input (either via coordinate pairs or shapefile/KML/KMZ):\n", - " params = {\n", - " 'short_name': short_name,\n", - " 'version': latest_version,\n", - " 'temporal': temporal,\n", - " 'page_size': 100,\n", - " 'page_num': 1,\n", - " 'polygon': polygon,\n", - " }\n", - "\n", - "print('CMR search parameters: ', params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Input the parameter dictionary to the CMR granule search to query all granules that meet the criteria based on the granule metadata. Print the number of granules returned." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Query number of granules using our (paging over results)\n", - "\n", - "granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n", - "\n", - "granules = []\n", - "while True:\n", - " response = requests.get(granule_search_url, params=params, headers=headers)\n", - " results = json.loads(response.content)\n", - "\n", - " if len(results['feed']['entry']) == 0:\n", - " # Out of results, so break out of loop\n", - " break\n", - "\n", - " # Collect results and increment page_num\n", - " granules.extend(results['feed']['entry'])\n", - " params['page_num'] += 1\n", - "\n", - " \n", - "# Get number of granules over my area and time of interest\n", - "len(granules)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "granules = region_a.granules\n", - "len(granules)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Although subsetting, reformatting, or reprojecting can alter the size of the granules, this \"native\" granule size can still be used to guide us towards the best download method to pursue, which we will come back to later on in this tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Request data from the NSIDC data access API." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### We will now set up our data download request. The data access and service API (labeled EGI below) incorporates the CMR parameters that we explored above, plus customization service parameters as well as a few configuration parameters.\n", - "\n", - "![Data Access Service API diagram](https://gsfc-ngap-developer.s3.amazonaws.com/be03ae4ddbe19c8ea7734df6941385b8baba4741f6c7ec62fd4230eccdc31fc0)\n", - "\n", - "#### As described above, the API is structured as a URL with a base plus individual key-value-pairs (KVPs) separated by ‘&’. The base URL of the NSIDC API is:
\n", - "`https://n5eil02u.ecs.nsidc.org/egi/request`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Set NSIDC data access base URL\n", - "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's go over the configuration parameters:\n", - "\n", - "* `request_mode`\n", - "* `page_size`\n", - "* `page_num`\n", - "\n", - "`request_mode` is \"synchronous\" by default, meaning that the request relies on a direct, continous connection between you and the API endpoint. Outputs are directly downloaded, or \"streamed\" to your working directory. For this tutorial, we will set the request mode to asynchronous, which will allow concurrent requests to be queued and processed without the need for a continuous connection.\n", - "\n", - "**Use the streaming `request_mode` with caution: While it can be beneficial to stream outputs directly to your local directory, note that timeout errors can result depending on the size of the request, and your request will not be queued in the system if NSIDC is experiencing high request volume. For best performance, I recommend setting `page_size=1` to download individual outputs, which will eliminate extra time needed to zip outputs and will ensure faster processing times per request. An example streaming request loop is available at the bottom of the tutorial below. **\n", - "\n", - "Recall that we queried the total number and volume of granules prior to applying customization services. `page_size` and `page_num` can be used to adjust the number of granules per request up to a limit of 2000 granules for asynchronous, and 100 granules for synchronous (streaming). For now, let's select 10 granules to be processed in each zipped request. For ATL06, the granule size can exceed 100 MB so we want to choose a granule count that provides us with a reasonable zipped download size. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set number of granules requested per order, which we will initially set to 10.\n", - "page_size = 10\n", - "\n", - "#Determine number of pages basd on page_size and total granules. Loop requests by this value\n", - "page_num = math.ceil(len(granules)/page_size)\n", - "\n", - "#Set request mode. \n", - "request_mode = 'async'\n", - "\n", - "# Determine how many individual orders we will request based on the number of granules requested\n", - "\n", - "print(page_num)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### After all of these KVP inputs, what does our request look like? Here's a summary of all possible KVPs that we explored, both for CMR searching and for the subsetter:\n", - "\n", - "#### CMR search keys:\n", - "* `short_name=`\n", - "* `version=`\n", - "* `temporal=`\n", - "* `bounding_box=`\n", - "* `polygon=`\n", - "\n", - "#### Customization service keys:\n", - "* `time=`\n", - "* `bbox=`\n", - "* `bounding_shape=` \n", - "* `format=`\n", - "* `projection=`\n", - "* `projection_parameters=`\n", - "* `Coverage=`\n", - "\n", - "#### No customization (access only):\n", - "* `agent=` \n", - "* `include_meta=` \n", - " * `Y` by default. `N` for No metadata requested.\n", - "\n", - "#### Request configuration keys:\n", - "* `request_mode=` \n", - "* `page_size=`\n", - "* `page_num=`\n", - "* `token=`\n", - "* `email=`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### If we were to create an API request based on our request parameters and submit into a web browser for example, here's what we end up with:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Print API base URL + request parameters --> for polygon\n", - "API_request = f'{base_url}?short_name={short_name}&version={latest_version}&temporal={temporal}&time={timevar}&polygon={polygon}&Coverage={coverage}&request_mode={request_mode}&page_size={page_size}&page_num={page_num}&token={token}&email={email}'\n", - "print(API_request)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Print API base URL + request parameters --> for bbox\n", - "API_request = f'{base_url}?short_name={short_name}&version={latest_version}&temporal={temporal}&time={timevar}\\\n", - "&bbox={bbox}&Coverage={coverage}&request_mode={request_mode}&page_size={page_size}&page_num={page_num}&token={token}&email={email}'\n", - "print(API_request)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### We'll also create a new dictionary of NSIDC API KVPs to be used in our subset request. Because we are looping through each page of requests, we'll add the `page_num` KVP to our dictionary within the loop below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subset_params = {\n", - " 'short_name': short_name, \n", - " 'version': latest_version, \n", - " 'temporal': temporal, \n", - " 'time': timevar, \n", - " 'polygon': polygon, \n", - " 'Coverage': coverage, \n", - " 'request_mode': request_mode, \n", - " 'page_size': page_size, \n", - " 'token': token, \n", - " 'email': email, \n", - " }\n", - "print(subset_params)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "subset_params = {\n", - " 'short_name': short_name, \n", - " 'version': latest_version, \n", - " 'temporal': temporal, \n", - " 'time': timevar, \n", - " 'bbox': bbox, \n", - " 'Coverage': coverage, \n", - " 'request_mode': request_mode, \n", - " 'page_size': page_size, \n", - " 'token': token, \n", - " 'email': email, \n", - " }\n", - "print(subset_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### We'll request the same data but without any subsetting services applied. Let's create another request parameter dictionary with the `time` and `coverage` service keys removed, and we'll add `agent=NO` instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "request_params = {\n", - " 'short_name': short_name, \n", - " 'version': latest_version, \n", - " 'temporal': temporal, \n", - " 'bbox': bbox, #'polygon': polygon, \n", - " 'agent' : 'NO',\n", - " 'include_meta' : 'Y',\n", - " 'request_mode': request_mode, \n", - " 'page_size': page_size, \n", - " 'token': token, \n", - " 'email': email, \n", - " }\n", - "\n", - "print(request_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Request Data\n", - "\n", - "#### Finally, we'll download the data directly to this notebook directory in a new Outputs folder. The progress of each order will be reported.\n", - "\n", - "We'll start by creating an output folder if the folder does not already exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "path = str(os.getcwd() + '/Outputs')\n", - "if not os.path.exists(path):\n", - " os.mkdir(path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we'll submit our request without subsetting services:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Request data service for each page number, and unzip outputs\n", - "\n", - "for i in range(page_num):\n", - " page_val = i + 1\n", - " print('Order: ', page_val)\n", - " request_params.update( {'page_num': page_val} )\n", - " \n", - "# For all requests other than spatial file upload, use get function\n", - " request = session.get(base_url, params=request_params)\n", - " \n", - " print('Request HTTP response: ', request.status_code)\n", - "\n", - "# Raise bad request: Loop will stop for bad response code.\n", - " request.raise_for_status()\n", - " print('Order request URL: ', request.url)\n", - " esir_root = ET.fromstring(request.content)\n", - " print('Order request response XML content: ', request.content)\n", - "\n", - "#Look up order ID\n", - " orderlist = [] \n", - " for order in esir_root.findall(\"./order/\"):\n", - " orderlist.append(order.text)\n", - " orderID = orderlist[0]\n", - " print('order ID: ', orderID)\n", - "\n", - "#Create status URL\n", - " statusURL = base_url + '/' + orderID\n", - " print('status URL: ', statusURL)\n", - "\n", - "#Find order status\n", - " request_response = session.get(statusURL) \n", - " print('HTTP response from order response URL: ', request_response.status_code)\n", - " \n", - "# Raise bad request: Loop will stop for bad response code.\n", - " request_response.raise_for_status()\n", - " request_root = ET.fromstring(request_response.content)\n", - " statuslist = []\n", - " for status in request_root.findall(\"./requestStatus/\"):\n", - " statuslist.append(status.text)\n", - " status = statuslist[0]\n", - " print('Data request ', page_val, ' is submitting...')\n", - " print('Initial request status is ', status)\n", - "\n", - "#Continue loop while request is still processing\n", - " while status == 'pending' or status == 'processing': \n", - " print('Status is not complete. Trying again.')\n", - " time.sleep(10)\n", - " loop_response = session.get(statusURL)\n", - "\n", - "# Raise bad request: Loop will stop for bad response code.\n", - " loop_response.raise_for_status()\n", - " loop_root = ET.fromstring(loop_response.content)\n", - "\n", - "#find status\n", - " statuslist = []\n", - " for status in loop_root.findall(\"./requestStatus/\"):\n", - " statuslist.append(status.text)\n", - " status = statuslist[0]\n", - " print('Retry request status is: ', status)\n", - " if status == 'pending' or status == 'processing':\n", - " continue\n", - "\n", - "#Order can either complete, complete_with_errors, or fail:\n", - "# Provide complete_with_errors error message:\n", - " if status == 'complete_with_errors' or status == 'failed':\n", - " messagelist = []\n", - " for message in loop_root.findall(\"./processInfo/\"):\n", - " messagelist.append(message.text)\n", - " print('error messages:')\n", - " pprint.pprint(messagelist)\n", - "\n", - "# Download zipped order if status is complete or complete_with_errors\n", - " if status == 'complete' or status == 'complete_with_errors':\n", - " downloadURL = 'https://n5eil02u.ecs.nsidc.org/esir/' + orderID + '.zip'\n", - " print('Zip download URL: ', downloadURL)\n", - " print('Beginning download of zipped output...')\n", - " zip_response = session.get(downloadURL)\n", - " # Raise bad request: Loop will stop for bad response code.\n", - " zip_response.raise_for_status()\n", - " with zipfile.ZipFile(io.BytesIO(zip_response.content)) as z:\n", - " z.extractall(path)\n", - " print('Data request', page_val, 'is complete.')\n", - " else: print('Request failed.')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run our request loop again, this time with subsetting services applied. We will post the KML file directly to the API:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Request data service for each page number, and unzip outputs\n", - "\n", - "for i in range(page_num):\n", - " page_val = i + 1\n", - " print('Order: ', page_val)\n", - " subset_params.update( {'page_num': page_val} )\n", - " \n", - "# Post polygon to API endpoint for polygon subsetting to subset based on original, non-simplified KML file\n", - "\n", - "# shape_post = {'shapefile': open(kml_filepath, 'rb')}\n", - "# request = session.post(base_url, params=subset_params, files=shape_post) \n", - " \n", - "# FOR ALL OTHER REQUESTS THAT DO NOT UTILIZED AN UPLOADED POLYGON FILE, USE A GET REQUEST INSTEAD OF POST:\n", - " request = session.get(base_url, params=request_params)\n", - " \n", - " print('Request HTTP response: ', request.status_code)\n", - "\n", - "# Raise bad request: Loop will stop for bad response code.\n", - " request.raise_for_status()\n", - " print('Order request URL: ', request.url)\n", - " esir_root = ET.fromstring(request.content)\n", - " print('Order request response XML content: ', request.content)\n", - "\n", - "# Look up order ID\n", - " orderlist = [] \n", - " for order in esir_root.findall(\"./order/\"):\n", - " orderlist.append(order.text)\n", - " orderID = orderlist[0]\n", - " print('order ID: ', orderID)\n", - "\n", - "# Create status URL\n", - " statusURL = base_url + '/' + orderID\n", - " print('status URL: ', statusURL)\n", - "\n", - "# Find order status\n", - " request_response = session.get(statusURL) \n", - " print('HTTP response from order response URL: ', request_response.status_code)\n", - " \n", - "# Raise bad request: Loop will stop for bad response code.\n", - " request_response.raise_for_status()\n", - " request_root = ET.fromstring(request_response.content)\n", - " statuslist = []\n", - " for status in request_root.findall(\"./requestStatus/\"):\n", - " statuslist.append(status.text)\n", - " status = statuslist[0]\n", - " print('Data request ', page_val, ' is submitting...')\n", - " print('Initial request status is ', status)\n", - "\n", - "# Continue to loop while request is still processing\n", - " while status == 'pending' or status == 'processing': \n", - " print('Status is not complete. Trying again.')\n", - " time.sleep(10)\n", - " loop_response = session.get(statusURL)\n", - "\n", - "# Raise bad request: Loop will stop for bad response code.\n", - " loop_response.raise_for_status()\n", - " loop_root = ET.fromstring(loop_response.content)\n", - "\n", - "# Find status\n", - " statuslist = []\n", - " for status in loop_root.findall(\"./requestStatus/\"):\n", - " statuslist.append(status.text)\n", - " status = statuslist[0]\n", - " print('Retry request status is: ', status)\n", - " if status == 'pending' or status == 'processing':\n", - " continue\n", - "\n", - "# Order can either complete, complete_with_errors, or fail:\n", - "# Provide complete_with_errors error message:\n", - " if status == 'complete_with_errors' or status == 'failed':\n", - " messagelist = []\n", - " for message in loop_root.findall(\"./processInfo/\"):\n", - " messagelist.append(message.text)\n", - " print('error messages:')\n", - " pprint.pprint(messagelist)\n", - "\n", - "# Download zipped order if status is complete or complete_with_errors\n", - " if status == 'complete' or status == 'complete_with_errors':\n", - " downloadURL = 'https://n5eil02u.ecs.nsidc.org/esir/' + orderID + '.zip'\n", - " print('Zip download URL: ', downloadURL)\n", - " print('Beginning download of zipped output...')\n", - " zip_response = session.get(downloadURL)\n", - " # Raise bad request: Loop will stop for bad response code.\n", - " zip_response.raise_for_status()\n", - " with zipfile.ZipFile(io.BytesIO(zip_response.content)) as z:\n", - " z.extractall(path)\n", - " print('Data request', page_val, 'is complete.')\n", - " else: print('Request failed.')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Why did we get an error? \n", - "\n", - "Errors can occur when our search filter overestimates the extent of the data contained within the granule. CMR uses orbit metadata to determine the extent of the file, including the following parameters:\n", - "\n", - "Collection-level:\n", - "* `SwathWidth`\n", - "* `Period`\n", - "* `InclinationAngle`\n", - "* `NumberOfOrbits` \n", - "* `StartCircularLatitude` \n", - "\n", - "Granule level: \n", - "* `AscendingCrossing`\n", - "* `StartLatitude`\n", - "* `StartDirection`\n", - "* `EndLatitude`\n", - "* `EndDirection` \n", - "\n", - "However, the values themselves are not inspected during our search. This can be a relatively common error for ICESat-2 search and access because of the limitations of the metadata, but it only means that more data were returned in the search results as a \"false positive\" compared to what the subsetter found when cropping the data values. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Clean up the Output folder by removing individual order folders:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Clean up Outputs folder by removing individual granule folders \n", - "\n", - "for root, dirs, files in os.walk(path, topdown=False):\n", - " for file in files:\n", - " try:\n", - " shutil.move(os.path.join(root, file), path)\n", - " except OSError:\n", - " pass\n", - " \n", - "for root, dirs, files in os.walk(path):\n", - " for name in dirs:\n", - " os.rmdir(os.path.join(root, name))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#List files\n", - "sorted(os.listdir(path))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you're interested in the streaming request method, an example loop is below: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set page size to 1 to improve performance\n", - "page_size = 1\n", - "request_params.update( {'page_size': page_size})\n", - "\n", - "# No metadata to only return a single output\n", - "request_params.update( {'include_meta': 'N'})\n", - "\n", - "#Determine number of pages basd on page_size and total granules. Loop requests by this value\n", - "page_num = math.ceil(len(granules)/page_size)\n", - "print(page_num)\n", - "\n", - "#Set request mode. \n", - "request_params.update( {'request_mode': 'stream'})\n", - "\n", - "print(request_params)\n", - "\n", - "os.chdir(path)\n", - "\n", - "for i in range(page_num):\n", - " page_val = i + 1\n", - " print('Order: ', page_val)\n", - " request_params.update( {'page_num': page_val})\n", - " request = session.get(base_url, params=request_params)\n", - " print('HTTP response from order response URL: ', request.status_code)\n", - " request.raise_for_status()\n", - " d = request.headers['content-disposition']\n", - " fname = re.findall('filename=(.+)', d)\n", - " open(eval(fname[0]), 'wb').write(request.content)\n", - " print('Data request', page_val, 'is complete.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Before we request the data and download the outputs, let's explore some simple comparisons of the data from s3 that we've already requested." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define paths for output folders\n", - "\n", - "opath = '/home/jovyan/data-access/data-access-outputs'\n", - "sopath = '/home/jovyan/data-access/data-access-subsetted-outputs'\n", - "\n", - "# Choose the same native/subsetted file to compare\n", - "\n", - "native_file = opath + '/ATL06_20190222031203_08500210_001_01.h5'\n", - "processed_file = sopath + '/processed_ATL06_20190222031203_08500210_001_01.h5'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare file sizes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "os.path.getsize(native_file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "os.path.getsize(processed_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read the files using h5py and compare the HDF5 groups and datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read files using h5py package\n", - "\n", - "native = h5py.File(native_file, 'r')\n", - "processed = h5py.File(processed_file, 'r')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Native file groups:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "printGroups = True\n", - "groups = list(native.keys())\n", - "for g in groups:\n", - " group = native[g]\n", - " if printGroups:\n", - " print('---')\n", - " print('Group: {}'.format(g))\n", - " print('---')\n", - " for d in group.keys():\n", - " print(group[d])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Subsetted file groups:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "printGroups = True\n", - "groups = list(processed.keys())\n", - "for g in groups:\n", - " group = processed[g]\n", - " if printGroups:\n", - " print('---')\n", - " print('Group: {}'.format(g))\n", - " print('---')\n", - " for d in group.keys():\n", - " print(group[d])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/dev-notebooks/is2_demo_download_restart.ipynb b/doc/source/dev-notebooks/is2_demo_download_restart.ipynb deleted file mode 100644 index 8c7b74506..000000000 --- a/doc/source/dev-notebooks/is2_demo_download_restart.ipynb +++ /dev/null @@ -1,439 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import pandas as pd\n", - "\n", - "import h5py\n", - "import os,json\n", - "from pprint import pprint" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx\n" - ] - } - ], - "source": [ - "#change working directory\n", - "%cd ../" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "from icepyx import icesat2data as ipd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose a region for subsetting as well. Use the same region as in the core demo." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/icepyx/core/icesat2data.py:115: UserWarning: Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\n", - " warnings.warn(\"Please note: as of 2020-05-05, a major reorganization of the core icepyx.icesat2data code may result in errors produced by now depricated functions. Please see our documentation pages or example notebooks for updates.\")\n" - ] - } - ], - "source": [ - "region_a = ipd.Icesat2Data('ATL07',[-170, 70, -130, 80],['2019-02-22','2019-02-23'], \\\n", - " start_time='00:00:00', end_time='23:59:59', version='2')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "region_a.earthdata_login('liuzheng','liuzheng@apl.uw.edu')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ········\n" - ] - } - ], - "source": [ - "region_a.earthdata_login('jessica.scheick','jessica.scheick@maine.edu')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use `latitude` only to reduce data request volume" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'atlas_sdp_gps_epoch': ['ancillary_data/atlas_sdp_gps_epoch'],\n", - " 'data_end_utc': ['ancillary_data/data_end_utc'],\n", - " 'data_start_utc': ['ancillary_data/data_start_utc'],\n", - " 'end_delta_time': ['ancillary_data/end_delta_time'],\n", - " 'granule_end_utc': ['ancillary_data/granule_end_utc'],\n", - " 'granule_start_utc': ['ancillary_data/granule_start_utc'],\n", - " 'latitude': ['gt2r/sea_ice_segments/latitude'],\n", - " 'sc_orient': ['orbit_info/sc_orient'],\n", - " 'sc_orient_time': ['orbit_info/sc_orient_time'],\n", - " 'start_delta_time': ['ancillary_data/start_delta_time']}\n" - ] - } - ], - "source": [ - "var_dict = region_a.order_vars.append(beam_list=['gt2r'],var_list=['latitude'])\n", - "pprint(region_a.order_vars.wanted)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'time': '2019-02-22T00:00:00,2019-02-23T23:59:59',\n", - " 'Coverage': '/orbit_info/sc_orient,/orbit_info/sc_orient_time,/ancillary_data/atlas_sdp_gps_epoch,/ancillary_data/data_start_utc,/ancillary_data/data_end_utc,/ancillary_data/granule_start_utc,/ancillary_data/granule_end_utc,/ancillary_data/start_delta_time,/ancillary_data/end_delta_time,/gt2r/sea_ice_segments/latitude',\n", - " 'bbox': '-170,70,-130,80'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.subsetparams(Coverage=region_a.order_vars.wanted)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'page_size': 2,\n", - " 'page_num': 1,\n", - " 'request_mode': 'async',\n", - " 'email': 'jessica.scheick@maine.edu',\n", - " 'include_meta': 'Y'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.reqparams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup download (found a bugs here but not sure how to fix)\n", - "* Use small `page_size` to generate more orders\n", - "* Bug?: HAVE to set `page_num` to 1. \n", - " * For repeated calling to order_granules, `page_num` might be set to larger than 1 at the end. \n", - " * With `page_num` larger than 1, the the total number of available granules is wrong and the resulting `page_num` and `page_size` are modified to values that do not make sense. \n", - " * Eg., set `page_num` to 2. \n", - " * Any idea why is this happenning?\n", - " \n", - " Yes: \n", - " when order_granules() is called, if it did not already have all of the required parameters for a download (versus a granule search), it was simply re-generating the reqparams, thus overwriting any that had already been set...\n", - " I think I've corrected this issue, as well as updated the code so that it will actually check that an acceptable key has been submitted." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of data order requests is 5 for 10 granules.\n", - "Data request 1 of 5 is submitting to NSIDC\n", - "order ID: 5000000693988\n", - "Initial status of your order request at NSIDC is: processing\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order is: complete_with_errors\n", - "NSIDC provided these error messages:\n", - "['166321086:NoMatchingData - No data found that matched subset constraints. '\n", - " 'Exit code 3.',\n", - " 'PT45.903S',\n", - " 'ICESAT2']\n", - "Your order is: complete_with_errors\n", - "Data request 2 of 5 is submitting to NSIDC\n", - "order ID: 5000000693989\n", - "Initial status of your order request at NSIDC is: processing\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order is: complete\n", - "Data request 3 of 5 is submitting to NSIDC\n", - "order ID: 5000000693991\n", - "Initial status of your order request at NSIDC is: processing\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order is: complete\n", - "Data request 4 of 5 is submitting to NSIDC\n", - "order ID: 5000000693992\n", - "Initial status of your order request at NSIDC is: processing\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order is: complete_with_errors\n", - "NSIDC provided these error messages:\n", - "['166321065:NoMatchingData - No data found that matched subset constraints. '\n", - " 'Exit code 3.',\n", - " 'PT17.313S',\n", - " 'ICESAT2']\n", - "Your order is: complete_with_errors\n", - "Data request 5 of 5 is submitting to NSIDC\n", - "order ID: 5000000693993\n", - "Initial status of your order request at NSIDC is: processing\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order status is still processing at NSIDC. Please continue waiting... this may take a few moments.\n", - "Your order is: complete\n" - ] - } - ], - "source": [ - "region_a.reqparams['page_size'] = 2\n", - "region_a.order_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['5000000693988',\n", - " '5000000693989',\n", - " '5000000693991',\n", - " '5000000693992',\n", - " '5000000693993']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_a.granules.orderIDs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check the content of the restart file to see if it matches the records in region_a" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"orderIDs\": [\"5000000693988\", \"5000000693989\", \"5000000693991\", \"5000000693992\", \"5000000693993\"]}\n", - "\u001b[K\u001b[7m(END)\u001b[m\u001b[K (END)\u001b[m\u001b[K\u0007" - ] - } - ], - "source": [ - "!less .order_restart" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start download and interrupt the kernel after downloading one order or two" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beginning download of zipped output...\n", - "Data request 5000000693988 of 5 order(s) is complete.\n", - "Beginning download of zipped output...\n", - "Data request 5000000693989 of 5 order(s) is complete.\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./down'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/icepyx/icepyx/core/icesat2data.py\u001b[0m in \u001b[0;36mdownload_granules\u001b[0;34m(self, path, verbose, subset, restart, **kwargs)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_granules\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'orderIDs'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_granules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morderIDs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morder_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 739\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_granules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrestart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/icepyx/icepyx/core/granules.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(self, verbose, path, session, restart)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;31m# if extract is True:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;31m# update the current finished order id and save to file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/zipfile.py\u001b[0m in \u001b[0;36mextractall\u001b[0;34m(self, path, members, pwd)\u001b[0m\n\u001b[1;32m 1644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1645\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mzipinfo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmembers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_member\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzipinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1648\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/zipfile.py\u001b[0m in \u001b[0;36m_extract_member\u001b[0;34m(self, member, targetpath, pwd)\u001b[0m\n\u001b[1;32m 1699\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpwd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1700\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargetpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1701\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1703\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtargetpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "region_a.download_granules('./down')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now, restart. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restarting download ... \n", - "Beginning download of zipped output...\n", - "Data request 5000000693989 of 4 order(s) is complete.\n", - "Beginning download of zipped output...\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregion_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_granules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./down'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrestart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - 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"\u001b[0;32m/srv/conda/envs/notebook/lib/python3.8/site-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_peek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1838\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1839\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1840\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "region_a.download_granules('./down',restart=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/source/dev-notebooks/spatial_subsetting_vis.ipynb b/doc/source/dev-notebooks/spatial_subsetting_vis.ipynb deleted file mode 100644 index 45c2ffc78..000000000 --- a/doc/source/dev-notebooks/spatial_subsetting_vis.ipynb +++ /dev/null @@ -1,1240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring data visualization to look for subsetting\n", - "\n", - "#### Credits\n", - "* notebook by Jessica Scheick, derived from DEM example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup\n", - "##### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", - "##### For full functionality,\n", - "- Please install [icepyx](https://github.com/icesat2py/icepyx), [topolib](https://github.com/ICESAT-2HackWeek/topohack), [contextily](https://github.com/darribas/contextily) using `git clone xxxxx`, `pip install -e .` workflow (see below; **you must restart your kernel after installing the packages**)\n", - "- Download [NASA ASP](https://github.com/NeoGeographyToolkit/StereoPipeline) tar ball and unzip, we execute the commands from the notebook, using the path to the untared bin folder for the given commands." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtaining file:///home/jovyan/contextily\n", - "Collecting geopy (from contextily==1.0rc2)\n", - " Using cached https://files.pythonhosted.org/packages/53/fc/3d1b47e8e82ea12c25203929efb1b964918a77067a874b2c7631e2ec35ec/geopy-1.21.0-py2.py3-none-any.whl\n", - "Requirement already satisfied: matplotlib in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (3.1.0)\n", - "Requirement already satisfied: mercantile in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (1.0.4)\n", - "Requirement already satisfied: pillow in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (6.0.0)\n", - "Requirement already satisfied: rasterio in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (1.0.24)\n", - "Requirement already satisfied: requests in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (2.21.0)\n", - "Requirement already satisfied: joblib in /srv/conda/lib/python3.6/site-packages (from contextily==1.0rc2) (0.13.2)\n", - "Collecting geographiclib<2,>=1.49 (from geopy->contextily==1.0rc2)\n", - " Using cached https://files.pythonhosted.org/packages/8b/62/26ec95a98ba64299163199e95ad1b0e34ad3f4e176e221c40245f211e425/geographiclib-1.50-py3-none-any.whl\n", - "Requirement already satisfied: numpy>=1.11 in /srv/conda/lib/python3.6/site-packages (from matplotlib->contextily==1.0rc2) (1.16.4)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /srv/conda/lib/python3.6/site-packages (from matplotlib->contextily==1.0rc2) (2.4.0)\n", - "Requirement already satisfied: cycler>=0.10 in /srv/conda/lib/python3.6/site-packages (from matplotlib->contextily==1.0rc2) (0.10.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /srv/conda/lib/python3.6/site-packages (from matplotlib->contextily==1.0rc2) (1.1.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /srv/conda/lib/python3.6/site-packages (from matplotlib->contextily==1.0rc2) (2.7.5)\n", - "Requirement already satisfied: click>=3.0 in /srv/conda/lib/python3.6/site-packages (from mercantile->contextily==1.0rc2) (7.0)\n", - "Requirement already satisfied: affine in /srv/conda/lib/python3.6/site-packages (from rasterio->contextily==1.0rc2) (2.2.2)\n", - "Requirement already satisfied: attrs in /srv/conda/lib/python3.6/site-packages (from rasterio->contextily==1.0rc2) (18.2.0)\n", - "Requirement already satisfied: cligj>=0.5 in /srv/conda/lib/python3.6/site-packages (from rasterio->contextily==1.0rc2) (0.5.0)\n", - "Requirement already satisfied: snuggs>=1.4.1 in /srv/conda/lib/python3.6/site-packages (from rasterio->contextily==1.0rc2) (1.4.6)\n", - "Requirement already satisfied: click-plugins in /srv/conda/lib/python3.6/site-packages (from rasterio->contextily==1.0rc2) (1.1.1)\n", - "Requirement already satisfied: idna<2.9,>=2.5 in /srv/conda/lib/python3.6/site-packages (from requests->contextily==1.0rc2) (2.8)\n", - "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /srv/conda/lib/python3.6/site-packages (from requests->contextily==1.0rc2) (1.24.1)\n", - "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /srv/conda/lib/python3.6/site-packages (from requests->contextily==1.0rc2) (3.0.4)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /srv/conda/lib/python3.6/site-packages (from requests->contextily==1.0rc2) (2019.3.9)\n", - "Requirement already satisfied: six in /srv/conda/lib/python3.6/site-packages (from cycler>=0.10->matplotlib->contextily==1.0rc2) (1.12.0)\n", - "Requirement already satisfied: setuptools in /srv/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->contextily==1.0rc2) (40.8.0)\n", - "Installing collected packages: geographiclib, geopy, contextily\n", - " Found existing installation: contextily 0.9.2\n", - " Uninstalling contextily-0.9.2:\n", - " Successfully uninstalled contextily-0.9.2\n", - " Running setup.py develop for contextily\n", - "Successfully installed contextily geographiclib-1.50 geopy-1.21.0\n", - "Obtaining file:///home/jovyan/topohack\n", - "Requirement already satisfied: requests in /srv/conda/lib/python3.6/site-packages (from topolib==0.1) (2.21.0)\n", - "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /srv/conda/lib/python3.6/site-packages (from requests->topolib==0.1) (3.0.4)\n", - "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /srv/conda/lib/python3.6/site-packages (from requests->topolib==0.1) (1.24.1)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /srv/conda/lib/python3.6/site-packages (from requests->topolib==0.1) (2019.3.9)\n", - "Requirement already satisfied: idna<2.9,>=2.5 in /srv/conda/lib/python3.6/site-packages (from requests->topolib==0.1) (2.8)\n", - "Installing collected packages: topolib\n", - " Running setup.py develop for topolib\n", - "Successfully installed topolib\n", - "Obtaining file:///home/jovyan/icepyx\n", - "Installing collected packages: icepyx\n", - " Running setup.py develop for icepyx\n", - "Successfully installed icepyx\n" - ] - } - ], - "source": [ - "%%bash\n", - "cd ~\n", - "# git clone https://github.com/icesat2py/icepyx.git\n", - "# git clone https://github.com/ICESAT-2HackWeek/topohack.git\n", - "# git clone https://github.com/darribas/contextily.git\n", - "\n", - "cd contextily\n", - "pip install -e .\n", - "cd ../topohack\n", - "pip install -e .\n", - "cd ../icepyx\n", - "pip install -e ." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan\n" - ] - } - ], - "source": [ - "%cd ~\n", - "#needs to be wherever icepyx, contextily, and topolib are installed in the previous step (ideally $HOME)\n", - "# %pwd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", - "- Along track heights for canopy (land and vegitation) and terrain\n", - "- Terrain heights provided are aggregated over every 100 m along track interval, output contains \"h_te_best_fit: height from best fit algorithm for all photons in the range\", median height and others. Here we use h_te_best_fit.\n", - "- See this preliminary introduction and quality assessment [paper](https://www.mdpi.com/2072-4292/11/14/1721) for more detail" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import packages, including icepyx" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", - " data = yaml.load(f.read()) or {}\n", - "/srv/conda/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", - " defaults = yaml.load(f)\n" - ] - } - ], - "source": [ - "#from icepyx import is2class as ipd\n", - "import os\n", - "import shutil\n", - "import h5py\n", - "import xarray as xr\n", - "# depedencies\n", - "import getpass\n", - "#from topolib.subsetDat import subsetBBox;\n", - "from topolib import icesat2_data\n", - "import glob\n", - "import rasterio\n", - "from topolib import gda_lib\n", - "from topolib import dwnldArctic\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "from multiprocessing import Pool\n", - "import contextily as ctx\n", - "import pandas as pd\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "from icepyx import is2class as ipd\n", - "%autoreload 2\n", - "#in order to use \"as ipd\", you have to use autoreload 2, which will automatically reload any module not excluded by being imported with %aimport -[module]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/dev-notebooks\n" - ] - } - ], - "source": [ - "%cd ~/icepyx/dev-notebooks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## subset and non data objects" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "region_areg = ipd.Icesat2Data('ATL08', [-73.9, 10.7, -73.4, 11.1], ['2018-12-01','2019-09-01'], \\\n", - " start_time='00:00:00', end_time='23:59:59')\n", - "#2019-01-04; 2019-01-06 works for subsetting" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "region_asub = ipd.Icesat2Data('ATL08', [-73.9, 10.7, -73.4, 11.1], ['2018-12-01','2019-09-01'], \\\n", - " start_time='00:00:00', end_time='23:59:59')\n", - "#2019-02-01; 2019-02-04 doesn't work for subsetting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "above: bounding box in Colombia\n", - "below: shapefile in Antarctica" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "region_areg = ipd.Icesat2Data('ATL06', '/home/jovyan/icepyx/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.kml',\\\n", - " ['2019-02-22','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "region_asub = ipd.Icesat2Data('ATL06', '/home/jovyan/icepyx/doc/examples/supporting_files/data-access_PineIsland/glims_polygons.kml',\\\n", - " ['2019-02-22','2019-02-28'], \\\n", - " start_time='00:00:00', end_time='23:59:59')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "region_areg=None\n", - "region_asub=None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Log in to Earthdata" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Earthdata Login password: ········\n", - "Earthdata Login password: ········\n" - ] - } - ], - "source": [ - "earthdata_uid = 'Jessica.scheick'\n", - "email = 'jessica.scheick@maine.edu'\n", - "sessionr=region_areg.earthdata_login(earthdata_uid, email)\n", - "sessions=region_asub.earthdata_login(earthdata_uid, email)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Number of available granules': 22,\n", - " 'Average size of granules (MB)': 57.7880039648591,\n", - " 'Total size of all granules (MB)': 1271.3360872269}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#search for available granules\n", - "region_areg.avail_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Number of available granules': 22,\n", - " 'Average size of granules (MB)': 57.7880039648591,\n", - " 'Total size of all granules (MB)': 1271.3360872269}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_asub.avail_granules()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Number of available granules': 2, 'Average size of granules (MB)': 27.7243270874, 'Total size of all granules (MB)': 55.4486541748}\n", - "{'Number of available granules': 2, 'Average size of granules (MB)': 27.7243270874, 'Total size of all granules (MB)': 55.4486541748}\n" - ] - } - ], - "source": [ - "print(region_areg.granule_info)\n", - "print(region_asub.granule_info)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Place the order" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'short_name': 'ATL06', 'version': '002', 'temporal': '2019-02-22T00:00:00Z,2019-02-28T23:59:59Z', 'polygon': '-86.622742,-74.908126,-86.561712,-74.870913,-86.868859,-74.730522,-86.962905,-74.605038,-89.02594,-74.316754,-89.630517,-74.192147,-89.830808,-74.065919,-90.746478,-73.956258,-91.668214,-74.023169,-92.049815,-73.929387,-93.420791,-73.929327,-93.997163,-73.882768,-94.277701,-73.714183,-95.133017,-73.966355,-96.513501,-74.127404,-99.889802,-74.085347,-100.114438,-74.019422,-100.355131,-74.080906,-100.462734,-74.240864,-100.827076,-74.373988,-101.795349,-74.369597,-102.424826,-74.497263,-101.188725,-74.7179,-101.564382,-75.02971,-103.37484,-75.273725,-103.914847,-75.426057,-104.012128,-75.5223,-103.029452,-75.748774,-102.350567,-75.749245,-101.837882,-75.943066,-101.899461,-76.014086,-101.280944,-76.192769,-101.325735,-76.246168,-101.190803,-76.27106,-101.250474,-76.342292,-101.175067,-76.345822,-101.402436,-76.52035,-101.326063,-76.523929,-101.449791,-76.666392,-101.310795,-76.691373,-101.357407,-76.744819,-101.217404,-76.769752,-101.295133,-76.85887,-101.058051,-76.962123,-100.447336,-77.117686,-98.433698,-77.320866,-97.28308,-77.355688,-97.491148,-77.423178,-96.514174,-77.485919,-96.552494,-77.558236,-96.384656,-77.562336,-96.441516,-77.670857,-97.139363,-77.836566,-97.193451,-77.926901,-97.64271,-78.080044,-96.297869,-78.388943,-96.327803,-78.44329,-95.721466,-78.511065,-95.748962,-78.565482,-94.940425,-78.617072,-94.988611,-78.726066,-94.911669,-78.763976,-95.609268,-78.843079,-95.637038,-78.897535,-95.37191,-78.9391,-95.693408,-79.006456,-95.269903,-79.124145,-95.323729,-79.233172,-95.430206,-79.249633,-95.155505,-79.291032,-95.191045,-79.363748,-94.81352,-79.406486,-94.847075,-79.479253,-94.747448,-79.48078,-94.772403,-79.535367,-93.90411,-79.638844,-93.843651,-79.749409,-93.967323,-79.802836,-93.788723,-79.87821,-93.816393,-79.951128,-93.230546,-80.085534,-91.707475,-79.87748,-91.801545,-79.822143,-91.488897,-79.805457,-91.465152,-79.641131,-90.447349,-79.5894,-90.545492,-79.534464,-90.042319,-79.37062,-90.140775,-79.334083,-90.041814,-79.24285,-88.982186,-79.076903,-90.230262,-78.914333,-90.32191,-78.804808,-90.689626,-78.676516,-91.150024,-78.638589,-92.035347,-78.414844,-92.106013,-78.30491,-91.651645,-78.271472,-91.365784,-78.127206,-91.188783,-78.128018,-91.090167,-78.019109,-90.737076,-77.983849,-90.909191,-77.946905,-90.732603,-77.911009,-90.727088,-77.819973,-91.070502,-77.800626,-91.14118,-77.636469,-91.90279,-77.613923,-91.984627,-77.595116,-91.972963,-77.522365,-92.466819,-77.463587,-92.199521,-77.374914,-92.352136,-77.300761,-92.335283,-77.209895,-91.434206,-77.234653,-91.426015,-77.16193,-91.015545,-77.145686,-91.008355,-77.054784,-91.086397,-77.018096,-91.647835,-76.97871,-91.640906,-76.924199,-91.873848,-76.868024,-91.779021,-76.759619,-90.823937,-76.710073,-90.345113,-76.52953,-86.988029,-75.856983,-86.945563,-75.711143,-86.872234,-75.710165,-87.034102,-75.63967,-86.965004,-75.620616,-87.075115,-75.440545,-87.003154,-75.439609,-87.021872,-75.349129,-86.835058,-75.219586,-86.850654,-75.147247,-86.717729,-75.109052,-86.737771,-75.018662,-86.602149,-74.998483,-86.622742,-74.908126', 'email': 'jessica.scheick@maine.edu', 'token': 'AF883333-4409-8028-BB64-6777373E7A7B', 'page_size': 10, 'page_num': 1, 'request_mode': 'async', 'include_meta': 'Y', 'agent': 'NO'}\n", - "b'\\n\\n \\n 5000000454318\\n You may receive an email about your order if you specified an EMAIL address. <br/><br/>The instructions used to process this order are: Processing tool=NO. Include metadata and processing history=Y. Granule id(s)=SC:ATL06.002:166270642,SC:ATL06.002:166249906,SC:ATL06.002:166254985,SC:ATL06.002:166287851,SC:ATL06.002:166250215,SC:ATL06.002:166237548,SC:ATL06.002:166237531,SC:ATL06.002:166239099,SC:ATL06.002:166272636,SC:ATL06.002:166236007. Email address=jessica.scheick@maine.edu.\\n \\n \\n NSIDC User Services\\n nsidc@nsidc.org\\n \\n \\n PT0.115S\\n NO\\n \\n \\n processing\\n 0\\n 10\\n \\n\\n'\n", - "[]\n", - "order ID: 5000000454318\n", - "Data request 1 is submitting...\n", - "Initial request status is processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: complete\n" - ] - } - ], - "source": [ - "region_areg.order_granules(sessionr, subset=False)\n", - "#region_a.order_granules(session, verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'short_name': 'ATL06', 'version': '002', 'temporal': '2019-02-22T00:00:00Z,2019-02-28T23:59:59Z', 'polygon': 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'email': 'jessica.scheick@maine.edu', 'token': 'AF883333-4409-8028-BB64-6777373E7A7B', 'page_size': 10, 'page_num': 1, 'request_mode': 'async', 'include_meta': 'Y', 'time': '2019-02-22T00:00:00,2019-02-28T23:59:59'}\n", - "Order: 1\n", - "into shapefile subsetting loop\n", - "b'\\n\\n \\n 5000000454322\\n You may receive an email about your order if you specified an EMAIL address. <br/><br/>The instructions used to process this order are: Bounding 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Include metadata and processing history=Y. Granule id(s)=SC:ATL06.002:166270642,SC:ATL06.002:166249906,SC:ATL06.002:166254985,SC:ATL06.002:166287851,SC:ATL06.002:166250215,SC:ATL06.002:166237548,SC:ATL06.002:166237531,SC:ATL06.002:166239099,SC:ATL06.002:166272636,SC:ATL06.002:166236007. Temporal search start=2019-02-22T00:00:00 end=2019-02-28T23:59:59. Email address=jessica.scheick@maine.edu. Processing tool=ICESAT2.\\n \\n \\n NSIDC User Services\\n nsidc@nsidc.org\\n \\n \\n PT0.130S\\n ICESAT2\\n \\n \\n processing\\n 0\\n 10\\n \\n\\n'\n", - "[]\n", - "Request HTTP response: 201\n", - "Order request URL: 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- "Order request response XML content: b'\\n\\n \\n 5000000454322\\n You may receive an email about your order if you specified an EMAIL address. <br/><br/>The instructions used to process this order are: Bounding Shape={\"type\":\"FeatureCollection\",\"crs\":{\"type\":\"name\",\"properties\":{\"name\":\"urn:ogc:def:crs:OGC:1.3:CRS84\"}},\"features\":[{\"type\":\"Feature\",\"properties\":{\"Name\":null,\"description\":null,\"timestamp\":null,\"begin\":null,\"end\":null,\"altitudeMode\":null,\"tessellate\":-1,\"extrude\":0,\"visibility\":-1,\"drawOrder\":null,\"icon\":null,\"line_type\":\"glac_bound\",\"anlys_id\":528486,\"glac_id\":\"G263560E76894S\",\"anlys_time\":\"2018-07-19T00:00:00\",\"area\":165078,\"db_area\":165079,\"width\":0,\"length\":0,\"primeclass\":0,\"min_elev\":9,\"mean_elev\":1085,\"max_elev\":2181,\"src_date\":\"2001-10-01T00:00:00\",\"rec_status\":\"okay\",\"glac_name\":\"Pine Island 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Include metadata and processing history=Y. Granule id(s)=SC:ATL06.002:166270642,SC:ATL06.002:166249906,SC:ATL06.002:166254985,SC:ATL06.002:166287851,SC:ATL06.002:166250215,SC:ATL06.002:166237548,SC:ATL06.002:166237531,SC:ATL06.002:166239099,SC:ATL06.002:166272636,SC:ATL06.002:166236007. Temporal search start=2019-02-22T00:00:00 end=2019-02-28T23:59:59. Email address=jessica.scheick@maine.edu. Processing tool=ICESAT2.\\n \\n \\n NSIDC User Services\\n nsidc@nsidc.org\\n \\n \\n PT0.130S\\n ICESAT2\\n \\n \\n processing\\n 0\\n 10\\n \\n\\n'\n", - "\n", - "\n", - "order ID: 5000000454322\n", - "status URL: https://n5eil02u.ecs.nsidc.org/egi/request/5000000454322\n", - "HTTP response from order response URL: 201\n", - "Data request 1 is submitting...\n", - "Initial request status is processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: processing\n", - "Status is not complete. Trying again.\n", - "Retry request status is: complete_with_errors\n", - "error messages:\n", - "['166249906:NoMatchingData - No data found that matched subset constraints. '\n", - " 'Exit code 3.',\n", - " '166237531:NoMatchingData - No data found that matched subset constraints. '\n", - " 'Exit code 3.',\n", - " '166272636:NoMatchingData - No data found that matched subset constraints. '\n", - " 'Exit code 3.',\n", - " 'PT2M13.976S',\n", - " 'ICESAT2']\n" - ] - } - ], - "source": [ - "region_asub.order_granules(sessions, subset=True, verbose=True)\n", - "#region_a.order_granules(session, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the order" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "wd=%pwd\n", - "pathreg = wd + '/downloadreg'\n", - "pathsub = wd + '/downloadsub'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beginning download of zipped output...\n", - "Data request 5000000454318 of 1 order(s) is complete.\n" - ] - } - ], - "source": [ - "region_areg.download_granules(sessionr, pathreg)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beginning download of zipped output...\n", - "Data request 5000000454322 of 1 order(s) is complete.\n" - ] - } - ], - "source": [ - "region_asub.download_granules(sessions, pathsub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Clean up the download folder by removing individual order folders:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "#Clean up Outputs folder by removing individual granule folders \n", - "path=pathsub\n", - "for root, dirs, files in os.walk(path, topdown=False):\n", - " for file in files:\n", - " try:\n", - " shutil.move(os.path.join(root, file), path)\n", - " except OSError:\n", - " pass\n", - " \n", - "for root, dirs, files in os.walk(path):\n", - " for name in dirs:\n", - " os.rmdir(os.path.join(root, name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocess #2\n", - "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/icepyx/dev-notebooks\n" - ] - } - ], - "source": [ - "%cd /home/jovyan/icepyx/dev-notebooks" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190222031203_08500210_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190222031944_08500211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190222155404_08580211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190222155947_08580212_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190223024624_08650210_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190223025405_08650211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190223152825_08730211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190223153408_08730212_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190224022046_08800210_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadreg/ATL06_20190224022827_08800211_002_01.h5']\n" - ] - } - ], - "source": [ - "# glob to list of files (run block of code creating wd and path variables if starting processing here)\n", - "ATL08_list = sorted(glob.glob(pathreg+'/*.h5'))\n", - "print(ATL08_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190222031203_08500210_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190222155404_08580211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190222155947_08580212_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190223024624_08650210_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190223025405_08650211_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190223153408_08730212_002_01.h5', '/home/jovyan/icepyx/dev-notebooks/downloadsub/processed_ATL06_20190224022827_08800211_002_01.h5']\n" - ] - } - ], - "source": [ - "# glob to list of files (run block of code creating wd and path variables if starting processing here)\n", - "ATL08_listsub = sorted(glob.glob(pathsub+'/*.h5'))\n", - "print(ATL08_listsub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Examine content of 1 ATLO8 hdf file" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# dict containing data entries to retrive (ATL08)\n", - "dataset_dict = {'land_segments':['delta_time','longitude','latitude','atl06_quality_summary','quality','terrain_flg'], 'land_segments/terrain':['h_te_best_fit']}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "#gda_lib.ATL08_to_dict(ATL08_list[0],dataset_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "local variable 'df_final' referenced before assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtemp_gdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgda_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mATL08_2_gdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mATL08_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataset_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/topohack/topolib/gda_lib.py\u001b[0m in \u001b[0;36mATL08_2_gdf\u001b[0;34m(ATL06_fn, dataset_dict)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0mdf_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0mgdf_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_final\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'geometry'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'init'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'epsg:4326'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 144\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgdf_final\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'df_final' referenced before assignment" - ] - } - ], - "source": [ - "## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\n", - "temp_gdf = gda_lib.ATL08_2_gdf(ATL08_list[0],dataset_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "local variable 'df_final' referenced before assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtemp_gdfsub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgda_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mATL08_2_gdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mATL08_listsub\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataset_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/topohack/topolib/gda_lib.py\u001b[0m in \u001b[0;36mATL08_2_gdf\u001b[0;34m(ATL06_fn, dataset_dict)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0mdf_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0mgdf_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_final\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'geometry'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'init'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'epsg:4326'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 144\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgdf_final\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'df_final' referenced before assignment" - ] - } - ], - "source": [ - "## the data can be converted to geopandas dataframe, see ATL08_2_gdf function in topolib gda_lib\n", - "temp_gdfsub = gda_lib.ATL08_2_gdf(ATL08_listsub[0],dataset_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " delta_time longitude latitude terrain_flg h_te_best_fit pair beam \\\n", - "0 3.439855e+07 -72.739960 22.674255 0.0 -43.186260 1.0 0.0 \n", - "1 3.439855e+07 -72.740051 22.673355 0.0 -43.869007 1.0 0.0 \n", - "2 3.439855e+07 -72.755119 22.531675 0.0 -42.931442 1.0 0.0 \n", - "3 3.439855e+07 -72.774513 22.350164 1.0 -36.861523 1.0 0.0 \n", - "4 3.439855e+07 -72.774612 22.349264 1.0 -36.810665 1.0 0.0 \n", - "\n", - " p_b geometry \n", - "0 1.0_0.0 POINT (-72.73995971679688 22.67425537109375) \n", - "1 1.0_0.0 POINT (-72.74005126953125 22.67335510253906) \n", - "2 1.0_0.0 POINT (-72.75511932373047 22.53167533874512) \n", - "3 1.0_0.0 POINT (-72.77451324462891 22.35016441345215) \n", - "4 1.0_0.0 POINT (-72.77461242675781 22.34926414489746) " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "temp_gdfsub.plot()\n", - "#plt.ylim(10.5,11.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-75.08763123, 1.08277035, -72.62879181, 24.57600975])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdf.total_bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bounding box', [-73.9, 10.7, -73.4, 11.1]]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_areg.spatial_extent" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-73.68257904, 10.70025349, -73.58360291, 11.09985828])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdfsub.total_bounds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "colombia_crs = {'init':'epsg:32618'}\n", - "plot_web = {'init':'epsg:3857'}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['delta_time', 'longitude', 'latitude', 'terrain_flg', 'h_te_best_fit',\n", - " 'pair', 'beam', 'p_b', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_gdf.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_list = [(gda_lib.ATL08_2_gdf(x,dataset_dict)) for x in ATL08_list]\n", - "gdf_colombia = gda_lib.concat_gdf(gdf_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_listsub = [(gda_lib.ATL08_2_gdf(x,dataset_dict)) for x in ATL08_listsub]\n", - "gdf_colombiasub = gda_lib.concat_gdf(gdf_listsub)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Bounding box data (Colombia)\n", - "- Visualise data footprints" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,10))\n", - "temp_web = gdf_colombia.to_crs(plot_web)\n", - "clim = np.percentile(temp_web['h_te_best_fit'].values,(2,98))\n", - "temp_web.plot('h_te_best_fit',ax=ax,s=3,legend=True,cmap='inferno',vmin=clim[0],vmax=clim[1])\n", - "ctx.add_basemap(ax=ax)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,10))\n", - "temp_websub = gdf_colombiasub.to_crs(plot_web)\n", - "climsub = np.percentile(temp_websub['h_te_best_fit'].values,(2,98))\n", - "temp_websub.plot('h_te_best_fit',ax=ax,s=3,legend=True,cmap='inferno',vmin=climsub[0],vmax=climsub[1])\n", - "ctx.add_basemap(ax=ax)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Polygon data (Antarctica)\n", - "- Visualise data footprints" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert the list of hdf5 files into more familiar Pandas Dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# dict containing data entries to retrive (ATL06)\n", - "dataset_dict={'land_ice_segments':['atl06_quality_summary','delta_time','h_li','hli_sigma',\\\n", - " 'latitude','longitude','segment_id','sigma_geo_h'], 'land_ice_segments/ground_track':['x_atc']}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "ant_crs = {'init':'epsg:3031'}\n", - "plot_web = {'init':'epsg:3857'}" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_list = [(gda_lib.ATL06_2_gdf(x,dataset_dict)) for x in ATL08_list]\n", - "gdf_colombia = gda_lib.concat_gdf(gdf_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "gdf_listsub = [(gda_lib.ATL06_2_gdf(x,dataset_dict)) for x in ATL08_listsub]\n", - "gdf_colombiasub = gda_lib.concat_gdf(gdf_listsub)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,10))\n", - "temp_web = gdf_colombia.to_crs(plot_web)\n", - "clim = np.percentile(temp_web['x_atc'].values,(2,98))\n", - "temp_web.plot('x_atc',ax=ax,s=3,legend=True,cmap='inferno',vmin=clim[0],vmax=clim[1])\n", - "ctx.add_basemap(ax=ax)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(10,10))\n", - "temp_websub = gdf_colombiasub.to_crs(plot_web)\n", - "climsub = np.percentile(temp_websub['x_atc'].values,(2,98))\n", - "temp_websub.plot('x_atc',ax=ax,s=3,legend=True,cmap='inferno',vmin=climsub[0],vmax=climsub[1])\n", - "ctx.add_basemap(ax=ax)\n", - "ax.set_xticks([])\n", - "ax.set_yticks([])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 2a6299be40517ae25890d523b0637d281cc4e0cd Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 16:45:52 -0500 Subject: [PATCH 25/53] move all example notebooks into doc after rebase --- .../ICESat-2_DAAC_DataAccess_Example.ipynb | 0 .../ICESat-2_Data_Read-in_Example.ipynb | 0 .../ICESat-2_cloud_data_access_example.ipynb | 0 .../Working_with_ICESat-2_Data_Variables.ipynb | 0 .../supporting_files/simple_test_poly.gpkg | Bin 5 files changed, 0 insertions(+), 0 deletions(-) rename {examples => doc/source/getting_started/example_notebooks}/ICESat-2_DAAC_DataAccess_Example.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/ICESat-2_Data_Read-in_Example.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/ICESat-2_cloud_data_access_example.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/Working_with_ICESat-2_Data_Variables.ipynb (100%) rename {examples => doc/source/getting_started/example_notebooks}/supporting_files/simple_test_poly.gpkg (100%) diff --git a/examples/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb similarity index 100% rename from examples/ICESat-2_DAAC_DataAccess_Example.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb diff --git a/examples/ICESat-2_Data_Read-in_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb similarity index 100% rename from examples/ICESat-2_Data_Read-in_Example.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb diff --git a/examples/ICESat-2_cloud_data_access_example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb similarity index 100% rename from examples/ICESat-2_cloud_data_access_example.ipynb rename to doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb diff --git a/examples/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb similarity index 100% rename from examples/Working_with_ICESat-2_Data_Variables.ipynb rename to doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb diff --git a/examples/supporting_files/simple_test_poly.gpkg b/doc/source/getting_started/example_notebooks/supporting_files/simple_test_poly.gpkg similarity index 100% rename from examples/supporting_files/simple_test_poly.gpkg rename to doc/source/getting_started/example_notebooks/supporting_files/simple_test_poly.gpkg From b1f1b80792287b63f3d67359efd36863b3440805 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 16:47:50 -0500 Subject: [PATCH 26/53] remove dev-notebooks from exclusion list since dir was removed --- doc/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 59dd2a2f6..03f918bd2 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -56,7 +56,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["**.ipynb_checkpoints", "dev-notebooks"] +exclude_patterns = ["**.ipynb_checkpoints"] # location of master document (by default sphinx looks for contents.rst) master_doc = "index" From 841be708fcef6bb04764861c8d9b0142e6b99103 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 16:57:22 -0500 Subject: [PATCH 27/53] revert code of conduct link to rst --- doc/source/contributing/code_of_conduct_link.md | 2 -- doc/source/contributing/code_of_conduct_link.rst | 1 + 2 files changed, 1 insertion(+), 2 deletions(-) delete mode 100644 doc/source/contributing/code_of_conduct_link.md create mode 100644 doc/source/contributing/code_of_conduct_link.rst diff --git a/doc/source/contributing/code_of_conduct_link.md b/doc/source/contributing/code_of_conduct_link.md deleted file mode 100644 index 65e693d93..000000000 --- a/doc/source/contributing/code_of_conduct_link.md +++ /dev/null @@ -1,2 +0,0 @@ -```{include} ../../../code_of_conduct.md -``` diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst new file mode 100644 index 000000000..b40b02486 --- /dev/null +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -0,0 +1 @@ +{include} ../../../code_of_conduct.md \ No newline at end of file From 663b477c285396e32177a2d65280202259801578 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 16:58:54 -0500 Subject: [PATCH 28/53] fix file to previous syntax --- doc/source/contributing/code_of_conduct_link.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst index b40b02486..0f9131439 100644 --- a/doc/source/contributing/code_of_conduct_link.rst +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -1 +1 @@ -{include} ../../../code_of_conduct.md \ No newline at end of file +.. include:: ../../../code_of_conduct.md \ No newline at end of file From f5dc0e575fb4a8c6a9ed81002bf7571a29815b77 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:08:01 -0500 Subject: [PATCH 29/53] add examples.rst from dev branch to retain git tracking with edits --- doc/source/getting_started/examples.rst | 19 ------------------- examples/examples.rst | 20 ++++++++++++++++++++ 2 files changed, 20 insertions(+), 19 deletions(-) delete mode 100644 doc/source/getting_started/examples.rst create mode 100644 examples/examples.rst diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst deleted file mode 100644 index 365b3ac05..000000000 --- a/doc/source/getting_started/examples.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. _examples: - -Examples -======== - -These examples illustrate how to use icepyx. -They demonstrate many of the features of this package, including minimal examples to get you started quickly. -Some include longer analysis workflows and showcase some best-practices. - -Example Notebooks ------------------ - -.. toctree:: - :maxdepth: 1 - - example_link - example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting - example_notebooks/ICESat-2_Data_Visualization_Example - example_notebooks/ICESat-2_DEM_comparison_Colombia_working diff --git a/examples/examples.rst b/examples/examples.rst new file mode 100644 index 000000000..77ece4d7a --- /dev/null +++ b/examples/examples.rst @@ -0,0 +1,20 @@ +.. _examples: + +Example Notebooks +----------------- + +Listed below are example jupyter-notebooks + +`ICESat-2_DAAC_DataAccess_Example `_ + +`ICESat-2_DAAC_DataAccess2_Subsetting `_ + +`Working_with_ICESat-2_Data_Variables `_ + +`ICESat-2_Data_Visualization_Example `_ + +`ICESat-2_Data_Read-in_Example `_ + +`ICESat-2_cloud_data_access_example (BETA ONLY) `_ + +`ICESat-2_DEM_comparison_Colombia_working `_ \ No newline at end of file From a7aebca557f3ee3f5009ba235aefc9cc10ac6c3a Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:11:45 -0500 Subject: [PATCH 30/53] update and move examples.rst --- doc/source/getting_started/examples.rst | 22 ++++++++++++++++++++++ examples/examples.rst | 20 -------------------- 2 files changed, 22 insertions(+), 20 deletions(-) create mode 100644 doc/source/getting_started/examples.rst delete mode 100644 examples/examples.rst diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst new file mode 100644 index 000000000..540b36df3 --- /dev/null +++ b/doc/source/getting_started/examples.rst @@ -0,0 +1,22 @@ +.. _examples: + +Examples +======== + +These examples illustrate how to use icepyx. +They demonstrate many of the features of this package, including minimal examples to get you started quickly. +Some include longer analysis workflows and showcase some best-practices. + +Example Notebooks +----------------- + +.. toctree:: + :maxdepth: 1 + + example_notebooks/ICESat-2_DAAC_DataAccess_Example + example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting + example_notebooks/Working_with_ICESat-2_Data_Variables + example_notebooks/ICESat-2_Data_Visualization_Example + example_notebooks/ICESat-2_Data_Read-in_Example + example_notebooks/ICESat-2_cloud_data_access_example (BETA ONLY) + example_notebooks/ICESat-2_DEM_comparison_Colombia_working \ No newline at end of file diff --git a/examples/examples.rst b/examples/examples.rst deleted file mode 100644 index 77ece4d7a..000000000 --- a/examples/examples.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. _examples: - -Example Notebooks ------------------ - -Listed below are example jupyter-notebooks - -`ICESat-2_DAAC_DataAccess_Example `_ - -`ICESat-2_DAAC_DataAccess2_Subsetting `_ - -`Working_with_ICESat-2_Data_Variables `_ - -`ICESat-2_Data_Visualization_Example `_ - -`ICESat-2_Data_Read-in_Example `_ - -`ICESat-2_cloud_data_access_example (BETA ONLY) `_ - -`ICESat-2_DEM_comparison_Colombia_working `_ \ No newline at end of file From b47f44c0e808294fb0caa3caa6e986a4c1f8f566 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:22:04 -0500 Subject: [PATCH 31/53] remove example link rst --- doc/source/getting_started/example_link.rst | 1 - 1 file changed, 1 deletion(-) delete mode 100644 doc/source/getting_started/example_link.rst diff --git a/doc/source/getting_started/example_link.rst b/doc/source/getting_started/example_link.rst deleted file mode 100644 index d9905a53b..000000000 --- a/doc/source/getting_started/example_link.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../../../examples/ICESat-2_DAAC_DataAccess_Example \ No newline at end of file From 72cb9d40010079bc52a48b32fc24c38766d606d9 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:32:19 -0500 Subject: [PATCH 32/53] update heading levels for myst rendering --- .../ICESat-2_DAAC_DataAccess2_Subsetting.ipynb | 8 ++++---- .../ICESat-2_DAAC_DataAccess_Example.ipynb | 2 +- .../ICESat-2_DEM_comparison_Colombia_working.ipynb | 12 ++++++------ .../ICESat-2_Data_Read-in_Example.ipynb | 2 +- .../ICESat-2_Data_Visualization_Example.ipynb | 4 ++-- 5 files changed, 14 insertions(+), 14 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb index 24be472c2..d5a5f62c3 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb @@ -133,7 +133,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About Data Variables in a query object\n", + "### About Data Variables in a query object\n", "\n", "A given ICESat-2 product may have over 200 variable + path combinations.\n", "icepyx includes a custom `Variables` module that is \"aware\" of the ATLAS sensor and how the ICESat-2 data products are stored.\n", @@ -219,7 +219,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Applying variable subsetting to your order and download\n", + "### Applying variable subsetting to your order and download\n", "\n", "In order to have your wanted variable list included with your order, you must pass it as a keyword argument to the `subsetparams()` attribute or the `order_granules()` or `download_granules()` (which calls `order_granules` under the hood if you have not already placed your order) functions." ] @@ -278,7 +278,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Check the variable list in your downloaded file\n", + "### Check the variable list in your downloaded file\n", "\n", "Compare the available variables associated with the full product relative to those in your downloaded data file." ] @@ -343,7 +343,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb index ea12186f0..6cd8108cd 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb @@ -508,7 +508,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb index a7c7b0aab..6ef50b001 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb @@ -112,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Preprocess #1\n", + "### Preprocess #1\n", "- Download using icepyx" ] }, @@ -265,7 +265,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Preprocess #2\n", + "### Preprocess #2\n", "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" ] }, @@ -416,7 +416,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Preprocess #3\n", + "### Preprocess #3\n", "- Visualise data footprints" ] }, @@ -715,7 +715,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Going fancy, include only if you want to :)" + "### Going fancy, include only if you want to :)" ] }, { @@ -874,7 +874,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -888,7 +888,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.4" } }, "nbformat": 4, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb index 9e5de36b4..2a8170bbc 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb @@ -641,7 +641,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb index 9c47a7923..ba1c1b012 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb @@ -243,7 +243,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -257,7 +257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.4" } }, "nbformat": 4, From d690453b896193a8741f8e58a93bfffc068ed10e Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:37:12 -0500 Subject: [PATCH 33/53] update heading levels for myst rendering --- .../ICESat-2_DEM_comparison_Colombia_working.ipynb | 6 +++--- .../Working_with_ICESat-2_Data_Variables.ipynb | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb index 6ef50b001..c64de33b5 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb @@ -540,7 +540,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 1\n", + "### Section 1\n", "- This contains demonstration of elevation profile along 1 track, which has 6 beams" ] }, @@ -612,7 +612,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 2:\n", + "### Section 2:\n", "- Compare ICESat-2 Elevation with that of reference DEM (in this case TANDEM-X)" ] }, @@ -706,7 +706,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 3\n", + "### Section 3\n", "- Application of ICESat-2 as control surface for DEMs coregistration\n", "- Or, to find offsets and align ICESat-2 tracks to a control surface" ] diff --git a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb index 8a45da785..ae58c8bd6 100644 --- a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb +++ b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb @@ -72,7 +72,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Interacting with ICESat-2 Data Variables\n", + "### Interacting with ICESat-2 Data Variables\n", "\n", "Each variables instance (which is actually an associated Variables class object) contains two variable list attributes.\n", "One is the list of possible or available variables (`avail` attribute) and is unmutable, or unchangeable, as it is based on the input product specifications or files.\n", @@ -764,7 +764,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From d226b4f2638d6c67853a46e363cbd3bfe9da3621 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 14 Dec 2021 17:42:11 -0500 Subject: [PATCH 34/53] update heading levels for myst rendering --- .../ICESat-2_cloud_data_access_example.ipynb | 4 ++-- .../Working_with_ICESat-2_Data_Variables.ipynb | 2 +- doc/source/getting_started/examples.rst | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb index f437b9d35..7ba75b2aa 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ICESat-2 AWS cloud data access with icepyx\n", + "## ICESat-2 AWS cloud data access with icepyx (BETA ONLY)\n", "### Utilizing icepyx capabilities to enable cloud data access\n", "This notebook illustrates the use of icepyx for access ICESat-2 data currently available through the AWS (Amazon Web Services) us-west2 hub s3 data bucket.\n", "\n", @@ -175,7 +175,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb index ae58c8bd6..478b307fa 100644 --- a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb +++ b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb @@ -693,7 +693,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using your wanted variable list\n", + "### Using your wanted variable list\n", "\n", "Now that you have your wanted variables list, you need to use it within your icepyx object (`Query` or `Read`) will automatically use it. " ] diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index 540b36df3..b025caee3 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -18,5 +18,5 @@ Example Notebooks example_notebooks/Working_with_ICESat-2_Data_Variables example_notebooks/ICESat-2_Data_Visualization_Example example_notebooks/ICESat-2_Data_Read-in_Example - example_notebooks/ICESat-2_cloud_data_access_example (BETA ONLY) + example_notebooks/ICESat-2_cloud_data_access_example example_notebooks/ICESat-2_DEM_comparison_Colombia_working \ No newline at end of file From 66032ac354bce605f92df82c514db48e3f69d06b Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Wed, 15 Dec 2021 16:03:44 -0500 Subject: [PATCH 35/53] fix heading level --- .../example_notebooks/ICESat-2_cloud_data_access_example.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb index 7ba75b2aa..c4d4d35ff 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb @@ -8,7 +8,7 @@ "### Utilizing icepyx capabilities to enable cloud data access\n", "This notebook illustrates the use of icepyx for access ICESat-2 data currently available through the AWS (Amazon Web Services) us-west2 hub s3 data bucket.\n", "\n", - "## Critical Caveats\n", + "### Critical Caveats\n", "***Please do not contact us saying this does not work until you have read this section in detail***\n", "1. ICESat-2 data is not currently publicly available on the cloud (and will not likely be until at least the end of 2021). A limited subset is currently available in an s3 bucket to developers and beta testers who have been registered with NSIDC.\n", "2. This example and the code it describes are part of ongoing development. Current limitations to using these features are described throughout the example, as appropriate.\n", From fa815d6b7b2a93fca5cca6dd80b4c15c5deab5e5 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Thu, 16 Dec 2021 09:45:00 -0500 Subject: [PATCH 36/53] removed example notebooks subheading --- doc/source/getting_started/examples.rst | 3 --- 1 file changed, 3 deletions(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index b025caee3..560710ef5 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -7,9 +7,6 @@ These examples illustrate how to use icepyx. They demonstrate many of the features of this package, including minimal examples to get you started quickly. Some include longer analysis workflows and showcase some best-practices. -Example Notebooks ------------------ - .. toctree:: :maxdepth: 1 From 323235f49db1ef17a10e8be77a71bac418dd7416 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Thu, 16 Dec 2021 09:51:30 -0500 Subject: [PATCH 37/53] try decreasing maxdepth --- doc/source/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/index.rst b/doc/source/index.rst index 318456f17..9e4f9b7b2 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -9,7 +9,7 @@ icepyx is both a software library and a community composed of ICESat-2 data user .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :hidden: :caption: Getting Started From 2072419ebd31158f10f1a5b058f354114add20c6 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Thu, 16 Dec 2021 09:54:53 -0500 Subject: [PATCH 38/53] decrease examples.rst maxdepth --- doc/source/getting_started/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index 560710ef5..f00938493 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -8,7 +8,7 @@ They demonstrate many of the features of this package, including minimal example Some include longer analysis workflows and showcase some best-practices. .. toctree:: - :maxdepth: 1 + :maxdepth: 0 example_notebooks/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting From 3c04e2efeefb9381e9c33f25359f29657e72ce90 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Thu, 16 Dec 2021 09:59:44 -0500 Subject: [PATCH 39/53] Revert "decrease examples.rst maxdepth" This reverts commit 2072419ebd31158f10f1a5b058f354114add20c6. --- doc/source/getting_started/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst index f00938493..560710ef5 100644 --- a/doc/source/getting_started/examples.rst +++ b/doc/source/getting_started/examples.rst @@ -8,7 +8,7 @@ They demonstrate many of the features of this package, including minimal example Some include longer analysis workflows and showcase some best-practices. .. toctree:: - :maxdepth: 0 + :maxdepth: 1 example_notebooks/ICESat-2_DAAC_DataAccess_Example example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting From fd237e73207601009f0622451e1cea93fa389cd9 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Tue, 21 Dec 2021 11:58:44 +1300 Subject: [PATCH 40/53] Fix code of conduct link --- doc/source/contributing/code_of_conduct_link.md | 2 ++ doc/source/contributing/code_of_conduct_link.rst | 1 - doc/source/contributing/contribution_guidelines.rst | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 doc/source/contributing/code_of_conduct_link.md delete mode 100644 doc/source/contributing/code_of_conduct_link.rst diff --git a/doc/source/contributing/code_of_conduct_link.md b/doc/source/contributing/code_of_conduct_link.md new file mode 100644 index 000000000..65e693d93 --- /dev/null +++ b/doc/source/contributing/code_of_conduct_link.md @@ -0,0 +1,2 @@ +```{include} ../../../code_of_conduct.md +``` diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst deleted file mode 100644 index 0f9131439..000000000 --- a/doc/source/contributing/code_of_conduct_link.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../../../code_of_conduct.md \ No newline at end of file diff --git a/doc/source/contributing/contribution_guidelines.rst b/doc/source/contributing/contribution_guidelines.rst index 2a227ce89..1a6d260d9 100644 --- a/doc/source/contributing/contribution_guidelines.rst +++ b/doc/source/contributing/contribution_guidelines.rst @@ -6,7 +6,7 @@ Thank you for your interest in contributing to icepyx! We welcome and invite con Here we provide a set of guidelines and information for contributing to icepyx. This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. |Contributor Covenant| .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg - :target: ../../../code_of_conduct.md + :target: ../../../../code_of_conduct.md Ways to Contribute From 846d7cfaf198b1315fc856f8462d49bdf4c5d05f Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Tue, 21 Dec 2021 12:15:32 +1300 Subject: [PATCH 41/53] Fix changelog rst errors --- doc/source/user_guide/changelog/v0.4.1.rst | 4 ++-- doc/source/user_guide/changelog/v0.5.0.rst | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/source/user_guide/changelog/v0.4.1.rst b/doc/source/user_guide/changelog/v0.4.1.rst index 04262ce35..1d39e5b2e 100644 --- a/doc/source/user_guide/changelog/v0.4.1.rst +++ b/doc/source/user_guide/changelog/v0.4.1.rst @@ -1,7 +1,7 @@ -.. _whatsnew_0x0: +.. _whatsnew_041: What's new in 0.4.1 (01 December 2021) ------------------------------------ +-------------------------------------- These are the changes in icepyx 0.4.1 See :ref:`release` for a full changelog including other versions of icepyx. diff --git a/doc/source/user_guide/changelog/v0.5.0.rst b/doc/source/user_guide/changelog/v0.5.0.rst index cf964b80b..c94403329 100644 --- a/doc/source/user_guide/changelog/v0.5.0.rst +++ b/doc/source/user_guide/changelog/v0.5.0.rst @@ -1,7 +1,7 @@ .. _whatsnew_050: What's new in 0.5.0 (8 December 2021) ------------------------------------ +------------------------------------- These are the changes in icepyx 0.5.0 See :ref:`release` for a full changelog including other versions of icepyx. @@ -17,7 +17,7 @@ New Features * add ability to get list of variables from a file * add variables example notebook; trim variables module details out of subsetting example * update examples from 2020 Hackweek tutorials -- preliminary AWS access (#213) +- preliminary AWS access (#213) * add basic cloud data access capabilities * add weak check for AWS instance @@ -61,4 +61,4 @@ Other Contributors ~~~~~~~~~~~~ -.. contributors:: v0.4.1..v0.5.0|HEAD \ No newline at end of file +.. contributors:: v0.4.1..v0.5.0|HEAD From db480e7a387b026fe606efe586f17c57a4995e99 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Tue, 21 Dec 2021 12:46:01 +1300 Subject: [PATCH 42/53] Increase jupyter notebook section heading levels Prevents `WARNING: Non-consecutive header level increase; 0 to 2 [myst.header]` when building docs. --- ...ICESat-2_DAAC_DataAccess2_Subsetting.ipynb | 34 ++++---- .../ICESat-2_DAAC_DataAccess_Example.ipynb | 30 +++---- ...at-2_DEM_comparison_Colombia_working.ipynb | 54 ++++++------- .../ICESat-2_Data_Read-in_Example.ipynb | 44 +++++------ .../ICESat-2_Data_Visualization_Example.ipynb | 22 +++--- .../ICESat-2_cloud_data_access_example.ipynb | 18 ++--- ...Working_with_ICESat-2_Data_Variables.ipynb | 78 +++++++++---------- .../tracking/pypistats/get_pypi_stats.ipynb | 2 +- 8 files changed, 139 insertions(+), 143 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb index d5a5f62c3..ceeb7194a 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb @@ -4,15 +4,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Subsetting ICESat-2 Data with the NSIDC Subsetter\n", - "### How to Use the NSIDC Subsetter Example Notebook\n", + "# Subsetting ICESat-2 Data with the NSIDC Subsetter\n", + "## How to Use the NSIDC Subsetter Example Notebook\n", "This notebook illustrates the use of icepyx for subsetting ICESat-2 data ordered through the NSIDC DAAC. We'll show how to find out what subsetting options are available and how to specify the subsetting options for your order.\n", "\n", "For more information on using icepyx to find, order, and download data, see our complimentary [ICESat-2_DAAC_DataAccess_Example Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb).\n", "\n", "Questions? Be sure to check out the FAQs throughout this notebook, indicated as italic headings.\n", "\n", - "#### Credits\n", + "### Credits\n", "* notebook contributors: Zheng Liu, Jessica Scheick, and Amy Steiker\n", "* some source material: [NSIDC Data Access Notebook](https://github.com/ICESAT-2HackWeek/ICESat2_hackweek_tutorials/tree/main/03_NSIDCDataAccess_Steiker) by Amy Steiker and Bruce Wallin" ] @@ -21,7 +21,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### _What is SUBSETTING anyway?_\n", + "## _What is SUBSETTING anyway?_\n", "\n", "Anyone who's worked with geospatial data has probably encountered subsetting. Typically, we search for data wherever it is stored and download the chunks (aka granules, scenes, passes, swaths, etc.) that contain something we are interested in. Then, we have to extract from each chunk the pieces we actually want to analyze. Those pieces might be geospatial (i.e. an area of interest), temporal (i.e. certain months of a time series), and/or certain variables. This process of extracting the data we are going to use is called subsetting.\n", "\n", @@ -32,7 +32,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import packages, including icepyx" + "## Import packages, including icepyx" ] }, { @@ -56,7 +56,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create a query object and log in to Earthdata\n", + "## Create a query object and log in to Earthdata\n", "\n", "For this example, we'll be working with a sea ice product (ATL09) for an area along West Greenland (Disko Bay)." ] @@ -84,7 +84,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Discover Subsetting Options\n", + "## Discover Subsetting Options\n", "\n", "You can see what subsetting options are available for a given product by calling `show_custom_options()`. The options are presented as a series of headings followed by available values in square brackets. Headings are:\n", "* **Subsetting Options**: whether or not temporal and spatial subsetting are available for the data product\n", @@ -119,7 +119,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### _Why do I have to provide spatial bounds to icepyx even if I don't use them to subset my data order?_\n", + "## _Why do I have to provide spatial bounds to icepyx even if I don't use them to subset my data order?_\n", "\n", "Because they're still needed for the granule level search.\n", "Spatial inputs are usually required for any data search, on any platform, even if your search parameters cover the entire globe.\n", @@ -133,7 +133,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### About Data Variables in a query object\n", + "## About Data Variables in a query object\n", "\n", "A given ICESat-2 product may have over 200 variable + path combinations.\n", "icepyx includes a custom `Variables` module that is \"aware\" of the ATLAS sensor and how the ICESat-2 data products are stored.\n", @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Determine what variables are available for your data product\n", + "## Determine what variables are available for your data product\n", "There are multiple ways to get a complete list of available variables.\n", "To increase readability, some display options (2 and 3, below) show the 200+ variable + path combinations as a dictionary where the keys are variable names and the values are the paths to that variable.\n", "\n", @@ -184,7 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### _Why not just download all the data and subset locally? What if I need more variables/granules?_\n", + "## _Why not just download all the data and subset locally? What if I need more variables/granules?_\n", "\n", "Taking advantage of the NSIDC subsetter is a great way to reduce your download size and thus your download time and the amount of storage required, especially if you're storing your data locally during analysis. By downloading your data using icepyx, it is easy to go back and get additional data with the same, similar, or different parameters (e.g. you can keep the same spatial and temporal bounds but change the variable list). Related tools (e.g. [`captoolkit`](https://github.com/fspaolo/captoolkit)) will let you easily merge files if you're uncomfortable merging them during read-in for processing." ] @@ -193,7 +193,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Building the default wanted variable list" + "## Building the default wanted variable list" ] }, { @@ -219,7 +219,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Applying variable subsetting to your order and download\n", + "## Applying variable subsetting to your order and download\n", "\n", "In order to have your wanted variable list included with your order, you must pass it as a keyword argument to the `subsetparams()` attribute or the `order_granules()` or `download_granules()` (which calls `order_granules` under the hood if you have not already placed your order) functions." ] @@ -267,7 +267,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### _Why does the subsetter say no matching data was found?_\n", + "## _Why does the subsetter say no matching data was found?_\n", "Sometimes, granules (\"files\") returned in our initial search end up not containing any data in our specified area of interest.\n", "This is because the initial search is completed using summary metadata for a granule.\n", "You've likely encountered this before when viewing available imagery online: your spatial search turns up a bunch of images with only a few border or corner pixels, maybe even in no data regions, in your area of interest.\n", @@ -278,7 +278,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Check the variable list in your downloaded file\n", + "## Check the variable list in your downloaded file\n", "\n", "Compare the available variables associated with the full product relative to those in your downloaded data file." ] @@ -298,7 +298,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Check the downloaded data\n", + "### Check the downloaded data\n", "Get all `latitude` variables in your downloaded file:" ] }, @@ -328,7 +328,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Compare to the variable paths available in the original data" + "### Compare to the variable paths available in the original data" ] }, { diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb index 6cd8108cd..16eda5f7a 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Accessing ICESat-2 Data\n", - "### Data Query and Basic Download Example Notebook\n", + "# Accessing ICESat-2 Data\n", + "## Data Query and Basic Download Example Notebook\n", "This notebook illustrates the use of icepyx for ICESat-2 data access and download from the NASA NSIDC DAAC (NASA National Snow and Ice Data Center Distributed Active Archive Center).\n", "A complimentary notebook demonstrates in greater detail the [subsetting](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb) options available when ordering data.\n", "\n", - "#### Credits\n", + "### Credits\n", "* original notebook by: Jessica Scheick\n", "* notebook contributors: Amy Steiker and Tyler Sutterley\n", "* source material: [NSIDC Data Access Notebook](https://github.com/ICESAT-2HackWeek/ICESat2_hackweek_tutorials/tree/master/03_NSIDCDataAccess_Steiker) by Amy Steiker and Bruce Wallin and [2020 Hackweek Data Access Notebook](https://github.com/ICESAT-2HackWeek/2020_ICESat-2_Hackweek_Tutorials/blob/main/06-07.Data_Access/02-Data_Access_rendered.ipynb) by Jessica Scheick and Amy Steiker" @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import packages, including icepyx" + "## Import packages, including icepyx" ] }, { @@ -38,7 +38,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Quick-Start\n", + "## Quick-Start\n", "\n", "The entire process of getting ICESat-2 data (from query to download) can ultimately be accomplished in three minimal lines of code:\n", "\n", @@ -57,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Key Steps for Programmatic Data Access\n", + "## Key Steps for Programmatic Data Access\n", "\n", "There are several key steps for accessing data from the NSIDC API:\n", "1. Define your parameters (spatial, temporal, dataset, etc.)\n", @@ -74,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create an ICESat-2 data object with the desired search parameters\n", + "## Create an ICESat-2 data object with the desired search parameters\n", "\n", "There are three required inputs, depending on how you want to search for data. Two are required in all cases:\n", "- `short_name` = the data product of interest, known as its \"short name\".\n", @@ -269,7 +269,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Built in methods allow us to get more information about our data product\n", + "## Built in methods allow us to get more information about our data product\n", "In addition to viewing the stored object information shown above (e.g. product short name, start and end date and time, version, etc.), we can also request summary information about the data product itself or confirm that we have manually specified the latest version." ] }, @@ -305,7 +305,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Querying a data product\n", + "## Querying a data product\n", "In order to search the product collection for available data granules, we need to build our search parameters. This is done automatically behind the scenes when you run `region_a.avail_granules()`, but you can also build and view them by calling `region_a.CMRparams`. These are formatted as a dictionary of key:value pairs according to the [CMR documentation](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html)." ] }, @@ -366,7 +366,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Log in to NASA Earthdata\n", + "## Log in to NASA Earthdata\n", "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login. This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. Once you have successfully logged in for a given query instance, the token and session will be passed behind the scenes as needed for you to order and download data. Passwords are entered but not shown or stored in plain text by the system.\n", "\n", "There are multiple ways to provide your Earthdata credentials via icepyx.\n", @@ -388,7 +388,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Additional Parameters and Subsetting\n", + "## Additional Parameters and Subsetting\n", "\n", "Once we have generated our session, we must build the required configuration parameters needed to actually download data. These will tell the system how we want to download the data. As with the CMR search parameters, these will be built automatically when you run `region_a.order_granules()`, but you can also create and view them with `region_a.reqparams`. The default parameters, given below, should work for most users.\n", "- `page_size` = 2000. This is the number of granules we will request per order.\n", @@ -397,7 +397,7 @@ "- `agent` = 'NO'\n", "- `include_meta` = 'Y'\n", "\n", - "#### More details about the configuration parameters\n", + "### More details about the configuration parameters\n", "`request_mode` is \"asynchronous\" by default, which allows concurrent requests to be queued and processed without the need for a continuous connection between you and the API endpoint.\n", "In contrast, using a \"synchronous\" `request_mode` means that the request relies on a direct, continous connection between you and the API endpoint.\n", "Outputs are directly downloaded, or \"streamed\", to your working directory.\n", @@ -423,7 +423,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Subsetting\n", + "### Subsetting\n", "\n", "In addition to the required parameters (CMRparams and reqparams) that are submitted with our order, for ICESat-2 data products we can also submit subsetting parameters to NSIDC.\n", "For a deeper dive into subsetting, please see our [Subsetting Tutorial Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb), which covers subsetting in more detail, including how to get a list of subsetting options, how to build your list of subsetting parameters, and how to generate a list of desired variables (most datasets have more than 200 variable fields!), including using pre-built default lists (these lists are still in progress and we welcome contributions!).\n", @@ -463,7 +463,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Place the order\n", + "### Place the order\n", "Then, we can send the order to NSIDC using the order_granules function. Information about the granules ordered and their status will be printed automatically. Status information can also be emailed to the address provided when the `email` kwarg is set to `True`. Additional information on the order, including request URLs, can be viewed by setting the optional keyword input 'verbose' to True." ] }, @@ -491,7 +491,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the order\n", + "## Download the order\n", "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword `verbose`." ] }, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb index c64de33b5..2a0784aa3 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Comparing ICESat-2 Altimetry Elevations with DEM\n", - "### Example Notebook\n", + "# Comparing ICESat-2 Altimetry Elevations with DEM\n", + "## Example Notebook\n", "This notebook compares elevations from ICESat-2 to those from a DEM.\n", "\n", - "#### Credits\n", + "### Credits\n", "* notebook by: [Jessica Scheick](https://github.com/JessicaS11) and [Shashank Bhushan](https://github.com/ShashankBice)\n" ] }, @@ -16,9 +16,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Setup\n", - "##### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", - "##### For full functionality,\n", + "### Setup\n", + "#### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", + "#### For full functionality,\n", "- Please install [icepyx](https://github.com/icesat2py/icepyx), [topolib](https://github.com/ICESAT-2HackWeek/topohack), [contextily](https://github.com/darribas/contextily) using `git clone xxxxx`, `pip install -e .` workflow (see below; **you must restart your kernel after installing the packages**)\n", "- Download [NASA ASP](https://github.com/NeoGeographyToolkit/StereoPipeline) tar ball and unzip, we execute the commands from the notebook, using the path to the untared bin folder for the given commands." ] @@ -58,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", + "### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", "- Along track heights for canopy (land and vegitation) and terrain\n", "- Terrain heights provided are aggregated over every 100 m along track interval, output contains \"h_te_best_fit: height from best fit algorithm for all photons in the range\", median height and others. Here we use h_te_best_fit.\n", "- See this preliminary introduction and quality assessment [paper](https://www.mdpi.com/2072-4292/11/14/1721) for more detail" @@ -68,7 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import packages, including icepyx" + "## Import packages, including icepyx" ] }, { @@ -112,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Preprocess #1\n", + "## Preprocess #1\n", "- Download using icepyx" ] }, @@ -120,7 +120,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Create an ICESat-2 data object with the desired search parameters\n", + "### Create an ICESat-2 data object with the desired search parameters\n", "- See the ICESat-2 DAAC Data Access notebook for more details on downloading data from the NSIDC" ] }, @@ -138,7 +138,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Finding and downloading data\n", + "## Finding and downloading data\n", "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login (available for free on their website). This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. The token is attached to the data object and stored, but the session must be passed to the download function. Then we can order the granules." ] }, @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Log in to Earthdata" + "### Log in to Earthdata" ] }, { @@ -185,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Place the order" + "### Place the order" ] }, { @@ -212,7 +212,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Download the order\n", + "### Download the order\n", "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword 'verbose'." ] }, @@ -239,7 +239,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Clean up the download folder by removing individual order folders:" + "### Clean up the download folder by removing individual order folders:" ] }, { @@ -265,7 +265,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Preprocess #2\n", + "## Preprocess #2\n", "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" ] }, @@ -283,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Examine content of 1 ATLO8 hdf file" + "## Examine content of 1 ATLO8 hdf file" ] }, { @@ -399,7 +399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Convert the list of hdf5 files into more familiar Pandas Dataframe" + "## Convert the list of hdf5 files into more familiar Pandas Dataframe" ] }, { @@ -416,7 +416,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Preprocess #3\n", + "## Preprocess #3\n", "- Visualise data footprints" ] }, @@ -439,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", + "## We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", "- TANDEM-X DEM for the region was downloaded and preprocessed, filtered using scripts from the [tandemx](https://github.com/dshean/tandemx) repository" ] }, @@ -540,7 +540,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Section 1\n", + "## Section 1\n", "- This contains demonstration of elevation profile along 1 track, which has 6 beams" ] }, @@ -612,7 +612,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Section 2:\n", + "## Section 2:\n", "- Compare ICESat-2 Elevation with that of reference DEM (in this case TANDEM-X)" ] }, @@ -620,7 +620,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " + "### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " ] }, { @@ -645,7 +645,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" + "### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" ] }, { @@ -706,7 +706,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Section 3\n", + "## Section 3\n", "- Application of ICESat-2 as control surface for DEMs coregistration\n", "- Or, to find offsets and align ICESat-2 tracks to a control surface" ] @@ -715,14 +715,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Going fancy, include only if you want to :)" + "## Going fancy, include only if you want to :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Application of ICESat-2 as control for DEM co-registration ?\n", + "### Application of ICESat-2 as control for DEM co-registration ?\n", "- Can use point cloud alignment techniques to align DEMs to points, for now as a starting point we can use the transformation matrix to inform on the horizontal and vertical offset between ICESat-2 tracks and DEMs\n", "- We will be using a flavor of Iterative Closest Point alignment algorithm, implemented in [Ames Stereo Pipeline](https://github.com/NeoGeographyToolkit/StereoPipeline)" ] diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb index 2a8170bbc..219573a80 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb @@ -5,14 +5,14 @@ "id": "552e9ef9", "metadata": {}, "source": [ - "## Reading ICESat-2 Data in for Analysis\n", - "### Example notebook to showcase ICESat-2 data read-in using icepyx\n", + "# Reading ICESat-2 Data in for Analysis\n", + "## Example notebook to showcase ICESat-2 data read-in using icepyx\n", "This notebook illustrates the use of icepyx for reading ICESat-2 data files, loading them into a data object.\n", "Currently the default data object is an Xarray Dataset, with ongoing work to provide support for other data object types.\n", "\n", "For more information on how to order and download ICESat-2 data, see the [icepyx data access tutorial](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb).\n", "\n", - "### Motivation\n", + "## Motivation\n", "Most often, when you open a data file, you must specify the underlying data structure and how you'd like the information to be read in.\n", "A simple example of this, for instance when opening a csv or similarly delimited file, is letting the software know if the data contains a header row, what the data type is (string, double, float, boolean, etc.) for each column, what the delimeter is, and which columns or rows you'd like to be loaded.\n", "Many ICESat-2 data readers are quite manual in nature, requiring that you accurately type out a list of string paths to the various data variables.\n", @@ -20,13 +20,13 @@ "icepyx simplifies this process by relying on its awareness of ICESat-2 specific data file variable storage structure.\n", "Instead of needing to manually iterate through the beam pairs, you can provide a few options to the `Read` object and icepyx will do the heavy lifting for you (as detailed in this notebook).\n", "\n", - "### Approach\n", + "## Approach\n", "If you're interested in what's happening under the hood: icepyx turns your instructions into something called a catalog, then uses the Intake library and the catalog to actually load the data into memory. Specifically, icepyx creates an [Intake](https://intake.readthedocs.io/en/latest/) data [catalog](https://intake.readthedocs.io/en/latest/catalog.html) for each requested variable and then merges the read-in data from each of the variables to create a single data object.\n", "\n", "Intake catalogs are powerful (and the tool we selected) because they can be saved, shared, modified, and reused to reproducibly read in a set of data files in a consistent way as part of an analysis workflow.\n", "This approach streamlines the transition between data sources (local/downloaded files or, ultimately, cloud/bucket access) and data object types (e.g. [Xarray Dataset](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html) or [GeoPandas GeoDataFrame](https://geopandas.org/docs/reference/api/geopandas.GeoDataFrame.html)).\n", "\n", - "#### Credits\n", + "### Credits\n", "* original notebook by: Jessica Scheick\n", "* notebook contributors: Wei Ji and Tian\n", "* templates for default ICESat-2 Intake catalogs from: [Wei Ji](https://github.com/icesat2py/icepyx/issues/106) and [Tian](https://github.com/icetianli/ICESat2_xarray).\n" @@ -37,7 +37,7 @@ "id": "0d360de3", "metadata": {}, "source": [ - "### Import packages, including icepyx" + "## Import packages, including icepyx" ] }, { @@ -57,7 +57,7 @@ "source": [ "---------------------------------\n", "\n", - "### Quick Start Guide\n", + "## Quick Start Guide\n", "For those who might be looking into playing with this (but don't want all the details/explanations)" ] }, @@ -110,7 +110,7 @@ "metadata": {}, "source": [ "---------------------------------------\n", - "### Key steps for loading (reading) ICESat-2 data\n", + "## Key steps for loading (reading) ICESat-2 data\n", "\n", "Reading in ICESat-2 data with icepyx happens in a few simple steps:\n", "1. Let icepyx know where to find your data (this might be local files or urls to data in cloud storage)\n", @@ -127,7 +127,7 @@ "id": "9bf6d38c", "metadata": {}, "source": [ - "### Step 0: Get some data if you haven't already\n", + "## Step 0: Get some data if you haven't already\n", "Here are a few lines of code to get you set up with a few data files if you don't already have some on your local system." ] }, @@ -167,7 +167,7 @@ "id": "e8da42c1", "metadata": {}, "source": [ - "### Step 1: Set data source path\n", + "## Step 1: Set data source path\n", "\n", "Provide a full path to the data to be read in (i.e. opened).\n", "Currently accepted inputs are:\n", @@ -217,7 +217,7 @@ "id": "92743496", "metadata": {}, "source": [ - "### Step 2: Create a filename pattern for your data files\n", + "## Step 2: Create a filename pattern for your data files\n", "\n", "Files provided by NSIDC typically match the format `\"ATL{product:2}_{datetime:%Y%m%d%H%M%S}_{rgt:4}{cycle:2}{orbitsegment:2}_{version:3}_{revision:2}.h5\"` where the parameters in curly brackets indicate a parameter name (left of the colon) and character length or format (right of the colon).\n", "Some of this information is used during data opening to help correctly read and label the data within the data structure, particularly when multiple files are opened simultaneously.\n", @@ -263,7 +263,7 @@ "id": "4275b04c", "metadata": {}, "source": [ - "### Step 3: Create an icepyx read object\n", + "## Step 3: Create an icepyx read object\n", "\n", "The `Read` object has two required inputs:\n", "- `path` = a string with the full file path or full directory path to your hdf5 (.h5) format files.\n", @@ -301,7 +301,7 @@ "id": "da8d8024", "metadata": {}, "source": [ - "### Step 4: Specify variables to be read in\n", + "## Step 4: Specify variables to be read in\n", "\n", "To load your data into memory or prepare it for analysis, icepyx needs to know which variables you'd like to read in.\n", "If you've used icepyx to download data from NSIDC with variable subsetting (which is the default), then you may already be familiar with the icepyx `Variables` module and how to create and modify lists of variables.\n", @@ -396,7 +396,7 @@ "id": "473de4d7", "metadata": {}, "source": [ - "### Step 5: Loading your data\n", + "## Step 5: Loading your data\n", "\n", "Now that you've set up all the options, you're ready to read your ICESat-2 data into memory!" ] @@ -405,9 +405,7 @@ "cell_type": "code", "execution_count": null, "id": "eaabc976", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "ds = reader.load()" @@ -440,7 +438,7 @@ "id": "b1d7de2d", "metadata": {}, "source": [ - "### On to data analysis!\n", + "## On to data analysis!\n", "\n", "From here, you can begin your analysis.\n", "Ultimately, icepyx aims to include an Xarray extension with ICESat-2 aware functions that allow you to do things like easily use only data from strong beams.\n", @@ -473,7 +471,7 @@ "id": "6edfbb25", "metadata": {}, "source": [ - "### More on Intake catalogs and the read object\n", + "## More on Intake catalogs and the read object\n", "\n", "As anyone familiar with ICESat-2 hdf5 files knows, one of the challenges to reading in data is looping through all of the beam pairs for each track.\n", "The icepyx read module takes advantage of icepyx's variables module, which has some awareness of ICESat-2 data and uses that to save the user the trouble of having to loop through each beam pair.\n", @@ -486,7 +484,7 @@ "id": "0f0076f9", "metadata": {}, "source": [ - "#### Viewing the template catalog\n", + "### Viewing the template catalog\n", "\n", "You can access the ICESat-2 catalog template as an attribute of the read object.\n", "\n", @@ -520,7 +518,7 @@ "id": "fef43556", "metadata": {}, "source": [ - "#### Use an existing catalog\n", + "### Use an existing catalog\n", "If you already have a catalog for your data, you can supply that when you create the read object." ] }, @@ -579,7 +577,7 @@ "id": "d56fc41c", "metadata": {}, "source": [ - "#### More customization options\n", + "### More customization options\n", "\n", "If you'd like to use the icepyx ICESat-2 Catalog template to create your own customized catalog, we recommend that you access the `build_catalog` function directly, which returns an Intake Catalog instance.\n", "\n", @@ -621,7 +619,7 @@ "id": "bab9c949", "metadata": {}, "source": [ - "#### Saving your catalog\n", + "### Saving your catalog\n", "If you create a highly customized ICESat-2 catalog, you can use Intake's `save` to export it as a .yml file.\n", "\n", "Don't forget you can easily use an existing catalog (such as this highly customized one you just made) to read in your data with `reader = ipx.Read(filepath, pattern, catalog)` (so it's as easy as re-creating your reader object with your modified catalog)." diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb index ba1c1b012..ccc9ba278 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb @@ -5,12 +5,12 @@ "id": "1e29ff05", "metadata": {}, "source": [ - "## Visualizing ICESat-2 Data\n", - "### Elevation Visualization Example Notebook\n", + "# Visualizing ICESat-2 Data\n", + "## Elevation Visualization Example Notebook\n", "\n", "This notebook demonstrates interactive ICESat-2 elevation visualization by requesting data from [OpenAltimetry](https://www.openaltimetry.org/) based on metadata provided by [icepyx](https://icepyx.readthedocs.io/en/latest/). We will show how to plot spatial extent and elevation interactively. \n", "\n", - "#### Credits\n", + "### Credits\n", "* Notebook by: [Tian Li](https://github.com/icetianli), [Jessica Scheick](https://github.com/JessicaS11) and \n", "[Wei Ji](https://github.com/weiji14)\n", "* Source material: [READ_ATL06_DEM Notebook](https://github.com/ICESAT-2HackWeek/Assimilation/blob/master/contributors/icetianli/READ_ATL06_DEM.ipynb) by Tian Li and [Friedrich Knuth](https://github.com/friedrichknuth)" @@ -21,7 +21,7 @@ "id": "6333399a", "metadata": {}, "source": [ - "### Import packages" + "## Import packages" ] }, { @@ -39,7 +39,7 @@ "id": "57f2cfd8", "metadata": {}, "source": [ - "### Create an ICESat-2 query object\n", + "## Create an ICESat-2 query object\n", "Set the desired parameters for your data visualization.\n", "\n", "For details on minimum required inputs, please refer to [ICESat-2_DAAC_DataAccess_Example](https://github.com/icesat2py/icepyx/blob/main/examples/ICESat-2_DAAC_DataAccess_Example.ipynb). If you are using a spatial extent input other than a bounding box for your search, it will automatically be converted to a bounding box for the purposes of visualization ONLY (your query object will not be affected)." @@ -126,7 +126,7 @@ "id": "1b178836", "metadata": {}, "source": [ - "### Visualize spatial extent \n", + "## Visualize spatial extent \n", "By calling function `visualize_spatial_extent`, it will plot the spatial extent in red outline overlaid on a basemap, try zoom-in/zoom-out to see where is your interested region and what the geographic features look like in this region." ] }, @@ -147,9 +147,9 @@ "id": "71ca513d", "metadata": {}, "source": [ - "### Visualize ICESat-2 elevation using OpenAltimetry API\n", + "## Visualize ICESat-2 elevation using OpenAltimetry API\n", "\n", - "#### **Note: this function currently only supports products `ATL06, ATL07, ATL08, ATL10, ATL12, ATL13`**\n", + "### **Note: this function currently only supports products `ATL06, ATL07, ATL08, ATL10, ATL12, ATL13`**\n", "\n", "Now that we have produced an interactive map showing the spatial extent of ICESat-2 data to be requested from NSIDC using icepyx, what if we want to have a quick check on the ICESat-2 elevations we plan to download from NSIDC? [OpenAltimetry API](https://openaltimetry.org/data/swagger-ui/#/) provides a nice way to achieve this. By sending metadata (product, date, bounding box, trackId) of each ICESat-2 file to the API, it can return elevation data almost instantaneously. The major drawback is requests are limited to 5x5 degree spatial bounding box selection for most of the ICESat-2 L3A products [ATL06, ATL07, ATL08, ATL10, ATL12, ATL13](https://icesat-2.gsfc.nasa.gov/science/data-products). To solve this issue, if you input spatial extent exceeds the 5 degree maximum in either horizontal dimension, your input spatial extent will be splited into 5x5 degree lat/lon grids first, use icepyx to query the metadata of ICESat-2 files located in each grid, and send each request to OpenAltimetry. Data sampling rates are 1/50 for ATL06 and 1/20 for other products.\n", "\n", @@ -174,7 +174,7 @@ "id": "9ee72a5c", "metadata": {}, "source": [ - "#### Plot elevation for individual RGT\n", + "### Plot elevation for individual RGT\n", "\n", "The visualization tool also provides the option to view elevation data by latitude for each ground track." ] @@ -194,7 +194,7 @@ "id": "b7082edd", "metadata": {}, "source": [ - "### Move on to data downloading from NSIDC if these are the products of interest\n", + "## Move on to data downloading from NSIDC if these are the products of interest\n", "\n", "For more details on the data ordering and downloading process, see [ICESat-2_DAAC_DataAccess_Example](https://github.com/icesat2py/icepyx/blob/main/examples/ICESat-2_DAAC_DataAccess_Example.ipynb)" ] @@ -223,7 +223,7 @@ "id": "textile-casting", "metadata": {}, "source": [ - "### Alternative Access Options to Visualize ICESat-2 elevation using OpenAltimetry API\n", + "## Alternative Access Options to Visualize ICESat-2 elevation using OpenAltimetry API\n", "\n", "You can also view elevation data by importing the visualization module directly and initializing it with your query object or a list of parameters:\n", " ```\n", diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb index c4d4d35ff..e59b58b94 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb @@ -4,17 +4,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ICESat-2 AWS cloud data access with icepyx (BETA ONLY)\n", - "### Utilizing icepyx capabilities to enable cloud data access\n", + "# ICESat-2 AWS cloud data access with icepyx (BETA ONLY)\n", + "## Utilizing icepyx capabilities to enable cloud data access\n", "This notebook illustrates the use of icepyx for access ICESat-2 data currently available through the AWS (Amazon Web Services) us-west2 hub s3 data bucket.\n", "\n", - "### Critical Caveats\n", + "## Critical Caveats\n", "***Please do not contact us saying this does not work until you have read this section in detail***\n", "1. ICESat-2 data is not currently publicly available on the cloud (and will not likely be until at least the end of 2021). A limited subset is currently available in an s3 bucket to developers and beta testers who have been registered with NSIDC.\n", "2. This example and the code it describes are part of ongoing development. Current limitations to using these features are described throughout the example, as appropriate.\n", "3. You **MUST** be working within an AWS instance. Otherwise, you will get a permissions error.\n", "\n", - "#### Credits\n", + "### Credits\n", "* notebook by: Jessica Scheick\n", "* source material: [is2-nsidc-cloud.py](https://gist.github.com/bradlipovsky/80ab6a7aff3d3524b9616a9fc176065e#file-is2-nsidc-cloud-py-L28) by Brad Lipovsky" ] @@ -34,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create an icepyx Query object\n", + "## Create an icepyx Query object\n", "In order to develop and test cloud data access functionality, here we search for an arbitrary granule over Greenland that was previously determined to be available on s3 using [Earthdata Search](https://search.earthdata.nasa.gov/). s3 availability is not yet included in CMR metadata, so it cannot be determined programmatically." ] }, @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Construct the granule s3 urls\n", + "## Construct the granule s3 urls\n", "Since cloud data available is not yet included as part of the standard granule metadata, there is no way for us to check whether or not these s3 bucket urls are valid, since they are constructed from other granule metadata. Thus, you may get FileNotFound Errors when trying to use these urls." ] }, @@ -82,7 +82,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Log in to Earthdata and generate an s3 token\n", + "## Log in to Earthdata and generate an s3 token\n", "You can use icepyx's existing login functionality to generate your s3 data access token, which should be good for five hours. We currently do not have this set up to automatically renew, but if you're interested in adding this functionality please get in touch or submit a PR!" ] }, @@ -110,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Set up your s3 access using your credentials" + "## Set up your s3 access using your credentials" ] }, { @@ -137,7 +137,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Select an s3 url and access the data\n", + "## Select an s3 url and access the data\n", "Development is underway for data read in capabilities, which will include options for cloud data access. Stay tuned and we'd love for you to join us and contribute!\n", "\n", "**Note: If you get a PermissionDenied Error when trying to read in the data, you may not be sending your request from an AWS hub in us-west2. We're currently working on how to alert users if they will not be able to access ICESat-2 data in the cloud for this reason**" diff --git a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb index 478b307fa..ee7d431dd 100644 --- a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb +++ b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Working with ICESat-2's Nested Variables\n", - "### Get a list of available variables and choose the ones you want to work with\n", + "# Working with ICESat-2's Nested Variables\n", + "## Get a list of available variables and choose the ones you want to work with\n", "\n", "This notebook illustrates the use of icepyx for managing lists of available and wanted ICESat-2 data variables.\n", "The two use cases for variable management within your workflow are:\n", @@ -22,7 +22,7 @@ "\n", "Questions? Be sure to check out the FAQs throughout this notebook, indicated as italic headings.\n", "\n", - "#### Credits\n", + "### Credits\n", "* based on the subsetting notebook by: Jessica Scheick and Zheng Liu" ] }, @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### _Why do ICESat-2 products need a custom variable manager?_\n", + "## _Why do ICESat-2 products need a custom variable manager?_\n", "\n", "It can be confusing and cumbersome to comb through the 200+ variable and path combinations contained in ICESat-2 data products.\n", "The icepyx `Variables` module makes it easier for users to quickly find and extract the specific variables they would like to work with across multiple beams, keywords, and variables and provides reader-friendly formatting to browse variables.\n", @@ -42,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Some technical details about the Variables module\n", + "### Some technical details about the Variables module\n", "For those eager to push the limits or who want to know more implementation details...\n", "\n", "The only required input to the `Variables` module is `vartype`.\n", @@ -55,7 +55,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import packages, including icepyx" + "## Import packages, including icepyx" ] }, { @@ -72,7 +72,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Interacting with ICESat-2 Data Variables\n", + "## Interacting with ICESat-2 Data Variables\n", "\n", "Each variables instance (which is actually an associated Variables class object) contains two variable list attributes.\n", "One is the list of possible or available variables (`avail` attribute) and is unmutable, or unchangeable, as it is based on the input product specifications or files.\n", @@ -136,13 +136,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### ICESat-2 data variables\n", + "## ICESat-2 data variables\n", "\n", "ICESat-2 data is natively stored in a nested file format called hdf5.\n", "Much like a directory-file system on a computer, each variable (file) has a unique path through the heirarchy (directories) within the file.\n", "Thus, some variables (e.g. `'latitude'`, `'longitude'`) have multiple paths (one for each of the six beams in most products).\n", "\n", - "### Determine what variables are available\n", + "## Determine what variables are available\n", "`region_a.order_vars.avail` will return a list of all valid path+variable strings." ] }, @@ -192,7 +192,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Building your wanted variable list\n", + "## Building your wanted variable list\n", "\n", "Now that you know which variables and path components are available, you need to build a list of the ones you'd like included.\n", "There are several options for generating your initial list as well as modifying it, giving the user complete control.\n", @@ -246,7 +246,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Modifying your wanted variable list\n", + "## Modifying your wanted variable list\n", "\n", "Generating and modifying your variable request list, which is stored in `region_a.order_vars.wanted`, is controlled by the `append` and `remove` functions that operate on `region_a.order_vars.wanted`. The input options to `append` are as follows (the full documentation for this function can be found by executing `help(region_a.order_vars.append)`).\n", "* `defaults` (default False) - include the default variable list for your product (not yet fully implemented for all products; please submit your default variable list for inclusion!)\n", @@ -275,7 +275,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Examples (Overview)\n", + "## Examples (Overview)\n", "Below are a series of examples to show how you can use `append` and `remove` to modify your wanted variable list.\n", "For clarity, `region_a.order_vars.wanted` is cleared at the start of many examples.\n", "However, multiple `append` and `remove` commands can be called in succession to build your wanted variable list (see Examples 3+).\n", @@ -291,18 +291,16 @@ "metadata": {}, "source": [ "------------------\n", - "### Example Track 1 (Land Ice - run with ATL06 dataset)\n", + "## Example Track 1 (Land Ice - run with ATL06 dataset)\n", "\n", - "#### Example 1: choose variables\n", + "### Example 1: choose variables\n", "Add all `latitude` and `longitude` variables across all six beam groups. Note that the additional required variables for time and spacecraft orientation are included by default." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "region_a.order_vars.append(var_list=['latitude','longitude'])\n", @@ -313,7 +311,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 2: specify beams and variable\n", + "### Example 2: specify beams and variable\n", "Add `latitude` for only `gt1l` and `gt2l`" ] }, @@ -341,7 +339,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 3: add/remove selected beams+variables\n", + "### Example 3: add/remove selected beams+variables\n", "Add `latitude` for `gt3l` and remove it for `gt2l`" ] }, @@ -360,7 +358,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 4: `keyword_list`\n", + "### Example 4: `keyword_list`\n", "Add `latitude` and `longitude` for all beams and with keyword `land_ice_segments`" ] }, @@ -378,7 +376,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 5: target a specific variable + path\n", + "### Example 5: target a specific variable + path\n", "Remove `gt1r/land_ice_segments/longitude` (but keep `gt1r/land_ice_segments/latitude`)" ] }, @@ -396,7 +394,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 6: add variables not specific to beams/profiles\n", + "### Example 6: add variables not specific to beams/profiles\n", "Add `rgt` under `orbit_info`." ] }, @@ -414,7 +412,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 7: add all variables+paths of a group\n", + "### Example 7: add all variables+paths of a group\n", "In addition to adding specific variables and paths, we can filter all variables with a specific keyword as well. Here, we add all variables under `orbit_info`. Note that paths already in `region_a.order_vars.wanted`, such as `'orbit_info/rgt'`, are not duplicated." ] }, @@ -432,7 +430,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 8: add all possible values for variables+paths\n", + "### Example 8: add all possible values for variables+paths\n", "Append all `longitude` paths and all variables/paths with keyword `land_ice_segments`.\n", "\n", "Similarly to what is shown in Example 4, if you submit only one `append` call as `region_a.order_vars.append(var_list=['longitude'], keyword_list=['land_ice_segments'])` rather than the two `append` calls shown below, you will only add the variable `longitude` and only paths containing `land_ice_segments`, not ALL paths for `longitude` and ANY variables with `land_ice_segments` in their path." @@ -455,7 +453,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 9: remove all variables+paths associated with a beam\n", + "### Example 9: remove all variables+paths associated with a beam\n", "Remove all paths for `gt1l` and `gt3r`" ] }, @@ -475,7 +473,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 10: generate a default list for the rest of the tutorial\n", + "### Example 10: generate a default list for the rest of the tutorial\n", "Generate a reasonable variable list prior to download" ] }, @@ -497,9 +495,9 @@ "metadata": {}, "source": [ "------------------\n", - "### Example Track 2 (Atmosphere - run with ATL09 dataset commented out at the start of the notebook)\n", + "## Example Track 2 (Atmosphere - run with ATL09 dataset commented out at the start of the notebook)\n", "\n", - "#### Example 1: choose variables\n", + "### Example 1: choose variables\n", "Add all `latitude` and `longitude` variables" ] }, @@ -517,7 +515,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 2: specify beams/profiles and variable\n", + "### Example 2: specify beams/profiles and variable\n", "Add `latitude` for only `profile_1` and `profile_2`" ] }, @@ -545,7 +543,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 3: add/remove selected beams+variables\n", + "### Example 3: add/remove selected beams+variables\n", "Add `latitude` for `profile_3` and remove it for `profile_2`" ] }, @@ -564,7 +562,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 4: `keyword_list`\n", + "### Example 4: `keyword_list`\n", "Add `latitude` for all profiles and with keyword `low_rate`" ] }, @@ -582,7 +580,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 5: target a specific variable + path\n", + "### Example 5: target a specific variable + path\n", "Remove `'profile_1/high_rate/latitude'` (but keep `'profile_3/high_rate/latitude'`)" ] }, @@ -600,7 +598,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 6: add variables not specific to beams/profiles\n", + "### Example 6: add variables not specific to beams/profiles\n", "Add `rgt` under `orbit_info`." ] }, @@ -618,7 +616,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 7: add all variables+paths of a group\n", + "### Example 7: add all variables+paths of a group\n", "In addition to adding specific variables and paths, we can filter all variables with a specific keyword as well. Here, we add all variables under `orbit_info`. Note that paths already in `region_a.order_vars.wanted`, such as `'orbit_info/rgt'`, are not duplicated." ] }, @@ -636,7 +634,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 8: add all possible values for variables+paths\n", + "### Example 8: add all possible values for variables+paths\n", "Append all `longitude` paths and all variables/paths with keyword `high_rate`.\n", "Simlarly to what is shown in Example 4, if you submit only one `append` call as `region_a.order_vars.append(var_list=['longitude'], keyword_list=['high_rate'])` rather than the two `append` calls shown below, you will only add the variable `longitude` and only paths containing `high_rate`, not ALL paths for `longitude` and ANY variables with `high_rate` in their path." ] @@ -656,7 +654,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 9: remove all variables+paths associated with a profile\n", + "### Example 9: remove all variables+paths associated with a profile\n", "Remove all paths for `profile_1` and `profile_3`" ] }, @@ -674,7 +672,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Example 10: generate a default list for the rest of the tutorial\n", + "### Example 10: generate a default list for the rest of the tutorial\n", "Generate a reasonable variable list prior to download" ] }, @@ -693,7 +691,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Using your wanted variable list\n", + "## Using your wanted variable list\n", "\n", "Now that you have your wanted variables list, you need to use it within your icepyx object (`Query` or `Read`) will automatically use it. " ] @@ -702,7 +700,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### With a `Query` object\n", + "### With a `Query` object\n", "In order to have your wanted variable list included with your order, you must pass it as a keyword argument to the `subsetparams()` attribute or the `order_granules()` or `download_granules()` (which calls `order_granules` under the hood if you have not already placed your order) functions." ] }, @@ -749,7 +747,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### With a `Read` object\n", + "### With a `Read` object\n", "Calling the `load()` method on your `Read` object will automatically look for your wanted variable list and use it.\n", "Please see the [read-in example Jupyter Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_Data_Read-in_Example.ipynb) for a complete example of this usage.\n" ] diff --git a/doc/source/tracking/pypistats/get_pypi_stats.ipynb b/doc/source/tracking/pypistats/get_pypi_stats.ipynb index 3a719e27c..1170766a0 100644 --- a/doc/source/tracking/pypistats/get_pypi_stats.ipynb +++ b/doc/source/tracking/pypistats/get_pypi_stats.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## icepyx PyPI Statistics\n", + "# icepyx PyPI Statistics\n", "Use PyPIStats library to get data on PyPI downloads of icepyx (or any other package)\n", "\n", "See the [pypistats website](https://github.com/hugovk/pypistats) for potential calls, options, and formats (e.g. markdown, rst, html, json, numpy, pandas)\n", From 340d47c550ecf1c6fce2025618c41c87b2e5e87d Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Tue, 21 Dec 2021 12:47:00 +1300 Subject: [PATCH 43/53] Remove duplicate headings in data variables notebook Fixes `WARNING: duplicate label getting_started/example_notebooks/working_with_icesat-2_data_variables:example 1: choose variables, other instance in ...` --- ...Working_with_ICESat-2_Data_Variables.ipynb | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb index ee7d431dd..ba5b43a34 100644 --- a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb +++ b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb @@ -293,7 +293,7 @@ "------------------\n", "## Example Track 1 (Land Ice - run with ATL06 dataset)\n", "\n", - "### Example 1: choose variables\n", + "### Example 1.1: choose variables\n", "Add all `latitude` and `longitude` variables across all six beam groups. Note that the additional required variables for time and spacecraft orientation are included by default." ] }, @@ -311,7 +311,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 2: specify beams and variable\n", + "### Example 1.2: specify beams and variable\n", "Add `latitude` for only `gt1l` and `gt2l`" ] }, @@ -339,7 +339,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 3: add/remove selected beams+variables\n", + "### Example 1.3: add/remove selected beams+variables\n", "Add `latitude` for `gt3l` and remove it for `gt2l`" ] }, @@ -358,7 +358,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 4: `keyword_list`\n", + "### Example 1.4: `keyword_list`\n", "Add `latitude` and `longitude` for all beams and with keyword `land_ice_segments`" ] }, @@ -376,7 +376,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 5: target a specific variable + path\n", + "### Example 1.5: target a specific variable + path\n", "Remove `gt1r/land_ice_segments/longitude` (but keep `gt1r/land_ice_segments/latitude`)" ] }, @@ -394,7 +394,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 6: add variables not specific to beams/profiles\n", + "### Example 1.6: add variables not specific to beams/profiles\n", "Add `rgt` under `orbit_info`." ] }, @@ -412,7 +412,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 7: add all variables+paths of a group\n", + "### Example 1.7: add all variables+paths of a group\n", "In addition to adding specific variables and paths, we can filter all variables with a specific keyword as well. Here, we add all variables under `orbit_info`. Note that paths already in `region_a.order_vars.wanted`, such as `'orbit_info/rgt'`, are not duplicated." ] }, @@ -430,7 +430,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 8: add all possible values for variables+paths\n", + "### Example 1.8: add all possible values for variables+paths\n", "Append all `longitude` paths and all variables/paths with keyword `land_ice_segments`.\n", "\n", "Similarly to what is shown in Example 4, if you submit only one `append` call as `region_a.order_vars.append(var_list=['longitude'], keyword_list=['land_ice_segments'])` rather than the two `append` calls shown below, you will only add the variable `longitude` and only paths containing `land_ice_segments`, not ALL paths for `longitude` and ANY variables with `land_ice_segments` in their path." @@ -453,7 +453,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 9: remove all variables+paths associated with a beam\n", + "### Example 1.9: remove all variables+paths associated with a beam\n", "Remove all paths for `gt1l` and `gt3r`" ] }, @@ -473,7 +473,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 10: generate a default list for the rest of the tutorial\n", + "### Example 1.10: generate a default list for the rest of the tutorial\n", "Generate a reasonable variable list prior to download" ] }, @@ -497,7 +497,7 @@ "------------------\n", "## Example Track 2 (Atmosphere - run with ATL09 dataset commented out at the start of the notebook)\n", "\n", - "### Example 1: choose variables\n", + "### Example 2.1: choose variables\n", "Add all `latitude` and `longitude` variables" ] }, @@ -515,7 +515,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 2: specify beams/profiles and variable\n", + "### Example 2.2: specify beams/profiles and variable\n", "Add `latitude` for only `profile_1` and `profile_2`" ] }, @@ -543,7 +543,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 3: add/remove selected beams+variables\n", + "### Example 2.3: add/remove selected beams+variables\n", "Add `latitude` for `profile_3` and remove it for `profile_2`" ] }, @@ -562,7 +562,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 4: `keyword_list`\n", + "### Example 2.4: `keyword_list`\n", "Add `latitude` for all profiles and with keyword `low_rate`" ] }, @@ -580,7 +580,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 5: target a specific variable + path\n", + "### Example 2.5: target a specific variable + path\n", "Remove `'profile_1/high_rate/latitude'` (but keep `'profile_3/high_rate/latitude'`)" ] }, @@ -598,7 +598,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 6: add variables not specific to beams/profiles\n", + "### Example 2.6: add variables not specific to beams/profiles\n", "Add `rgt` under `orbit_info`." ] }, @@ -616,7 +616,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 7: add all variables+paths of a group\n", + "### Example 2.7: add all variables+paths of a group\n", "In addition to adding specific variables and paths, we can filter all variables with a specific keyword as well. Here, we add all variables under `orbit_info`. Note that paths already in `region_a.order_vars.wanted`, such as `'orbit_info/rgt'`, are not duplicated." ] }, @@ -634,7 +634,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 8: add all possible values for variables+paths\n", + "### Example 2.8: add all possible values for variables+paths\n", "Append all `longitude` paths and all variables/paths with keyword `high_rate`.\n", "Simlarly to what is shown in Example 4, if you submit only one `append` call as `region_a.order_vars.append(var_list=['longitude'], keyword_list=['high_rate'])` rather than the two `append` calls shown below, you will only add the variable `longitude` and only paths containing `high_rate`, not ALL paths for `longitude` and ANY variables with `high_rate` in their path." ] @@ -654,7 +654,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 9: remove all variables+paths associated with a profile\n", + "### Example 2.9: remove all variables+paths associated with a profile\n", "Remove all paths for `profile_1` and `profile_3`" ] }, @@ -672,7 +672,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 10: generate a default list for the rest of the tutorial\n", + "### Example 2.10: generate a default list for the rest of the tutorial\n", "Generate a reasonable variable list prior to download" ] }, From a161b428ea2c1ccabc848ad16d3f7cd743771b44 Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Tue, 21 Dec 2021 13:08:23 +1300 Subject: [PATCH 44/53] Turn examples into a dedicated section Increase visibility of the jupyter notebook examples on the readthedocs page. --- doc/source/getting_started/examples.rst | 19 ------------------- doc/source/index.rst | 25 ++++++++++++++++++------- examples/README.md | 4 +++- 3 files changed, 21 insertions(+), 27 deletions(-) delete mode 100644 doc/source/getting_started/examples.rst diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst deleted file mode 100644 index 560710ef5..000000000 --- a/doc/source/getting_started/examples.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. _examples: - -Examples -======== - -These examples illustrate how to use icepyx. -They demonstrate many of the features of this package, including minimal examples to get you started quickly. -Some include longer analysis workflows and showcase some best-practices. - -.. toctree:: - :maxdepth: 1 - - example_notebooks/ICESat-2_DAAC_DataAccess_Example - example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting - example_notebooks/Working_with_ICESat-2_Data_Variables - example_notebooks/ICESat-2_Data_Visualization_Example - example_notebooks/ICESat-2_Data_Read-in_Example - example_notebooks/ICESat-2_cloud_data_access_example - example_notebooks/ICESat-2_DEM_comparison_Colombia_working \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index 9e4f9b7b2..e6883fc24 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -15,14 +15,26 @@ icepyx is both a software library and a community composed of ICESat-2 data user getting_started/origin_purpose getting_started/install - getting_started/examples getting_started/citation_link +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: Examples + + getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example + getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting + getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables + getting_started/example_notebooks/ICESat-2_Data_Visualization_Example + getting_started/example_notebooks/ICESat-2_Data_Read-in_Example + getting_started/example_notebooks/ICESat-2_cloud_data_access_example + getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working + .. toctree:: :maxdepth: 2 :hidden: :caption: User Guide - + user_guide/documentation/icepyx user_guide/changelog/index @@ -44,14 +56,13 @@ icepyx is both a software library and a community composed of ICESat-2 data user community/resources community/contact tracking/tracking - + **Quick Install** -.. |Conda install| image:: https://anaconda.org/conda-forge/icepyx/badges/installer/conda.svg +.. |Conda install| image:: https://anaconda.org/conda-forge/icepyx/badges/installer/conda.svg :target: https://anaconda.org/conda-forge/icepyx - + .. |Pypi install| image:: https://badge.fury.io/py/icepyx.svg :target: https://pypi.org/project/icepyx/ - -|Conda install| |Pypi install| +|Conda install| |Pypi install| diff --git a/examples/README.md b/examples/README.md index d761ac94e..2e7a9e91a 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,3 +1,5 @@ # Examples and Tutorials using icepyx and ICESat-2 data -Examples are available in the [documentation](https://icepyx.readthedocs.io/en/latest/getting_started/examples.html). Source Jupyter notebooks and supporting materials are in [`doc/source/getting_started/example_notebooks`](https://github.com/icesat2py/icepyx/tree/main/doc/source/getting_started/example_notebooks). \ No newline at end of file +Examples are available in the [documentation](https://icepyx.readthedocs.io). +Source Jupyter notebooks and supporting materials are in +[`doc/source/getting_started/example_notebooks`](https://github.com/icesat2py/icepyx/tree/main/doc/source/getting_started/example_notebooks). From 3b003bbb3e8f5da3ae37e96151355562a894f6c9 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 10:36:48 -0500 Subject: [PATCH 45/53] Revert "Fix code of conduct link" This reverts commit fd237e73207601009f0622451e1cea93fa389cd9. --- doc/source/contributing/code_of_conduct_link.md | 2 -- doc/source/contributing/code_of_conduct_link.rst | 1 + doc/source/contributing/contribution_guidelines.rst | 2 +- 3 files changed, 2 insertions(+), 3 deletions(-) delete mode 100644 doc/source/contributing/code_of_conduct_link.md create mode 100644 doc/source/contributing/code_of_conduct_link.rst diff --git a/doc/source/contributing/code_of_conduct_link.md b/doc/source/contributing/code_of_conduct_link.md deleted file mode 100644 index 65e693d93..000000000 --- a/doc/source/contributing/code_of_conduct_link.md +++ /dev/null @@ -1,2 +0,0 @@ -```{include} ../../../code_of_conduct.md -``` diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst new file mode 100644 index 000000000..0f9131439 --- /dev/null +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -0,0 +1 @@ +.. include:: ../../../code_of_conduct.md \ No newline at end of file diff --git a/doc/source/contributing/contribution_guidelines.rst b/doc/source/contributing/contribution_guidelines.rst index 1a6d260d9..2a227ce89 100644 --- a/doc/source/contributing/contribution_guidelines.rst +++ b/doc/source/contributing/contribution_guidelines.rst @@ -6,7 +6,7 @@ Thank you for your interest in contributing to icepyx! We welcome and invite con Here we provide a set of guidelines and information for contributing to icepyx. This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. |Contributor Covenant| .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg - :target: ../../../../code_of_conduct.md + :target: ../../../code_of_conduct.md Ways to Contribute From 541caf60bf6ae7314ffdcae332de57c855e67724 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 10:42:54 -0500 Subject: [PATCH 46/53] fix code of conduct link --- doc/source/contributing/contribution_guidelines.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/contributing/contribution_guidelines.rst b/doc/source/contributing/contribution_guidelines.rst index 2a227ce89..1a6d260d9 100644 --- a/doc/source/contributing/contribution_guidelines.rst +++ b/doc/source/contributing/contribution_guidelines.rst @@ -6,7 +6,7 @@ Thank you for your interest in contributing to icepyx! We welcome and invite con Here we provide a set of guidelines and information for contributing to icepyx. This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. |Contributor Covenant| .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg - :target: ../../../code_of_conduct.md + :target: ../../../../code_of_conduct.md Ways to Contribute From e47858839e2ae4b2f7099e43f78df61c3cf6f9a7 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 11:10:12 -0500 Subject: [PATCH 47/53] Revert "Turn examples into a dedicated section" This reverts commit a161b428ea2c1ccabc848ad16d3f7cd743771b44. --- doc/source/getting_started/examples.rst | 19 +++++++++++++++++++ doc/source/index.rst | 25 +++++++------------------ examples/README.md | 4 +--- 3 files changed, 27 insertions(+), 21 deletions(-) create mode 100644 doc/source/getting_started/examples.rst diff --git a/doc/source/getting_started/examples.rst b/doc/source/getting_started/examples.rst new file mode 100644 index 000000000..560710ef5 --- /dev/null +++ b/doc/source/getting_started/examples.rst @@ -0,0 +1,19 @@ +.. _examples: + +Examples +======== + +These examples illustrate how to use icepyx. +They demonstrate many of the features of this package, including minimal examples to get you started quickly. +Some include longer analysis workflows and showcase some best-practices. + +.. toctree:: + :maxdepth: 1 + + example_notebooks/ICESat-2_DAAC_DataAccess_Example + example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting + example_notebooks/Working_with_ICESat-2_Data_Variables + example_notebooks/ICESat-2_Data_Visualization_Example + example_notebooks/ICESat-2_Data_Read-in_Example + example_notebooks/ICESat-2_cloud_data_access_example + example_notebooks/ICESat-2_DEM_comparison_Colombia_working \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index e6883fc24..9e4f9b7b2 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -15,26 +15,14 @@ icepyx is both a software library and a community composed of ICESat-2 data user getting_started/origin_purpose getting_started/install + getting_started/examples getting_started/citation_link -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: Examples - - getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example - getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting - getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables - getting_started/example_notebooks/ICESat-2_Data_Visualization_Example - getting_started/example_notebooks/ICESat-2_Data_Read-in_Example - getting_started/example_notebooks/ICESat-2_cloud_data_access_example - getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working - .. toctree:: :maxdepth: 2 :hidden: :caption: User Guide - + user_guide/documentation/icepyx user_guide/changelog/index @@ -56,13 +44,14 @@ icepyx is both a software library and a community composed of ICESat-2 data user community/resources community/contact tracking/tracking - + **Quick Install** -.. |Conda install| image:: https://anaconda.org/conda-forge/icepyx/badges/installer/conda.svg +.. |Conda install| image:: https://anaconda.org/conda-forge/icepyx/badges/installer/conda.svg :target: https://anaconda.org/conda-forge/icepyx - + .. |Pypi install| image:: https://badge.fury.io/py/icepyx.svg :target: https://pypi.org/project/icepyx/ + +|Conda install| |Pypi install| -|Conda install| |Pypi install| diff --git a/examples/README.md b/examples/README.md index 2e7a9e91a..d761ac94e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,5 +1,3 @@ # Examples and Tutorials using icepyx and ICESat-2 data -Examples are available in the [documentation](https://icepyx.readthedocs.io). -Source Jupyter notebooks and supporting materials are in -[`doc/source/getting_started/example_notebooks`](https://github.com/icesat2py/icepyx/tree/main/doc/source/getting_started/example_notebooks). +Examples are available in the [documentation](https://icepyx.readthedocs.io/en/latest/getting_started/examples.html). Source Jupyter notebooks and supporting materials are in [`doc/source/getting_started/example_notebooks`](https://github.com/icesat2py/icepyx/tree/main/doc/source/getting_started/example_notebooks). \ No newline at end of file From 77a93b19377f2e9b0bc4ed2e27dee977f36cf29a Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 11:21:41 -0500 Subject: [PATCH 48/53] Revert "Increase jupyter notebook section heading levels" This reverts commit db480e7a387b026fe606efe586f17c57a4995e99. --- ...ICESat-2_DAAC_DataAccess2_Subsetting.ipynb | 34 ++++++------ .../ICESat-2_DAAC_DataAccess_Example.ipynb | 30 +++++------ ...at-2_DEM_comparison_Colombia_working.ipynb | 54 +++++++++---------- .../ICESat-2_Data_Read-in_Example.ipynb | 44 +++++++-------- .../ICESat-2_Data_Visualization_Example.ipynb | 22 ++++---- .../ICESat-2_cloud_data_access_example.ipynb | 18 +++---- ...Working_with_ICESat-2_Data_Variables.ipynb | 38 ++++++------- .../tracking/pypistats/get_pypi_stats.ipynb | 2 +- 8 files changed, 123 insertions(+), 119 deletions(-) diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb index ceeb7194a..d5a5f62c3 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb @@ -4,15 +4,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Subsetting ICESat-2 Data with the NSIDC Subsetter\n", - "## How to Use the NSIDC Subsetter Example Notebook\n", + "## Subsetting ICESat-2 Data with the NSIDC Subsetter\n", + "### How to Use the NSIDC Subsetter Example Notebook\n", "This notebook illustrates the use of icepyx for subsetting ICESat-2 data ordered through the NSIDC DAAC. We'll show how to find out what subsetting options are available and how to specify the subsetting options for your order.\n", "\n", "For more information on using icepyx to find, order, and download data, see our complimentary [ICESat-2_DAAC_DataAccess_Example Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb).\n", "\n", "Questions? Be sure to check out the FAQs throughout this notebook, indicated as italic headings.\n", "\n", - "### Credits\n", + "#### Credits\n", "* notebook contributors: Zheng Liu, Jessica Scheick, and Amy Steiker\n", "* some source material: [NSIDC Data Access Notebook](https://github.com/ICESAT-2HackWeek/ICESat2_hackweek_tutorials/tree/main/03_NSIDCDataAccess_Steiker) by Amy Steiker and Bruce Wallin" ] @@ -21,7 +21,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## _What is SUBSETTING anyway?_\n", + "### _What is SUBSETTING anyway?_\n", "\n", "Anyone who's worked with geospatial data has probably encountered subsetting. Typically, we search for data wherever it is stored and download the chunks (aka granules, scenes, passes, swaths, etc.) that contain something we are interested in. Then, we have to extract from each chunk the pieces we actually want to analyze. Those pieces might be geospatial (i.e. an area of interest), temporal (i.e. certain months of a time series), and/or certain variables. This process of extracting the data we are going to use is called subsetting.\n", "\n", @@ -32,7 +32,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages, including icepyx" + "### Import packages, including icepyx" ] }, { @@ -56,7 +56,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create a query object and log in to Earthdata\n", + "### Create a query object and log in to Earthdata\n", "\n", "For this example, we'll be working with a sea ice product (ATL09) for an area along West Greenland (Disko Bay)." ] @@ -84,7 +84,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Discover Subsetting Options\n", + "### Discover Subsetting Options\n", "\n", "You can see what subsetting options are available for a given product by calling `show_custom_options()`. The options are presented as a series of headings followed by available values in square brackets. Headings are:\n", "* **Subsetting Options**: whether or not temporal and spatial subsetting are available for the data product\n", @@ -119,7 +119,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## _Why do I have to provide spatial bounds to icepyx even if I don't use them to subset my data order?_\n", + "### _Why do I have to provide spatial bounds to icepyx even if I don't use them to subset my data order?_\n", "\n", "Because they're still needed for the granule level search.\n", "Spatial inputs are usually required for any data search, on any platform, even if your search parameters cover the entire globe.\n", @@ -133,7 +133,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About Data Variables in a query object\n", + "### About Data Variables in a query object\n", "\n", "A given ICESat-2 product may have over 200 variable + path combinations.\n", "icepyx includes a custom `Variables` module that is \"aware\" of the ATLAS sensor and how the ICESat-2 data products are stored.\n", @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Determine what variables are available for your data product\n", + "### Determine what variables are available for your data product\n", "There are multiple ways to get a complete list of available variables.\n", "To increase readability, some display options (2 and 3, below) show the 200+ variable + path combinations as a dictionary where the keys are variable names and the values are the paths to that variable.\n", "\n", @@ -184,7 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## _Why not just download all the data and subset locally? What if I need more variables/granules?_\n", + "### _Why not just download all the data and subset locally? What if I need more variables/granules?_\n", "\n", "Taking advantage of the NSIDC subsetter is a great way to reduce your download size and thus your download time and the amount of storage required, especially if you're storing your data locally during analysis. By downloading your data using icepyx, it is easy to go back and get additional data with the same, similar, or different parameters (e.g. you can keep the same spatial and temporal bounds but change the variable list). Related tools (e.g. [`captoolkit`](https://github.com/fspaolo/captoolkit)) will let you easily merge files if you're uncomfortable merging them during read-in for processing." ] @@ -193,7 +193,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Building the default wanted variable list" + "### Building the default wanted variable list" ] }, { @@ -219,7 +219,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Applying variable subsetting to your order and download\n", + "### Applying variable subsetting to your order and download\n", "\n", "In order to have your wanted variable list included with your order, you must pass it as a keyword argument to the `subsetparams()` attribute or the `order_granules()` or `download_granules()` (which calls `order_granules` under the hood if you have not already placed your order) functions." ] @@ -267,7 +267,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## _Why does the subsetter say no matching data was found?_\n", + "### _Why does the subsetter say no matching data was found?_\n", "Sometimes, granules (\"files\") returned in our initial search end up not containing any data in our specified area of interest.\n", "This is because the initial search is completed using summary metadata for a granule.\n", "You've likely encountered this before when viewing available imagery online: your spatial search turns up a bunch of images with only a few border or corner pixels, maybe even in no data regions, in your area of interest.\n", @@ -278,7 +278,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Check the variable list in your downloaded file\n", + "### Check the variable list in your downloaded file\n", "\n", "Compare the available variables associated with the full product relative to those in your downloaded data file." ] @@ -298,7 +298,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Check the downloaded data\n", + "#### Check the downloaded data\n", "Get all `latitude` variables in your downloaded file:" ] }, @@ -328,7 +328,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Compare to the variable paths available in the original data" + "#### Compare to the variable paths available in the original data" ] }, { diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb index 16eda5f7a..6cd8108cd 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DAAC_DataAccess_Example.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Accessing ICESat-2 Data\n", - "## Data Query and Basic Download Example Notebook\n", + "## Accessing ICESat-2 Data\n", + "### Data Query and Basic Download Example Notebook\n", "This notebook illustrates the use of icepyx for ICESat-2 data access and download from the NASA NSIDC DAAC (NASA National Snow and Ice Data Center Distributed Active Archive Center).\n", "A complimentary notebook demonstrates in greater detail the [subsetting](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb) options available when ordering data.\n", "\n", - "### Credits\n", + "#### Credits\n", "* original notebook by: Jessica Scheick\n", "* notebook contributors: Amy Steiker and Tyler Sutterley\n", "* source material: [NSIDC Data Access Notebook](https://github.com/ICESAT-2HackWeek/ICESat2_hackweek_tutorials/tree/master/03_NSIDCDataAccess_Steiker) by Amy Steiker and Bruce Wallin and [2020 Hackweek Data Access Notebook](https://github.com/ICESAT-2HackWeek/2020_ICESat-2_Hackweek_Tutorials/blob/main/06-07.Data_Access/02-Data_Access_rendered.ipynb) by Jessica Scheick and Amy Steiker" @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages, including icepyx" + "### Import packages, including icepyx" ] }, { @@ -38,7 +38,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Quick-Start\n", + "### Quick-Start\n", "\n", "The entire process of getting ICESat-2 data (from query to download) can ultimately be accomplished in three minimal lines of code:\n", "\n", @@ -57,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Key Steps for Programmatic Data Access\n", + "### Key Steps for Programmatic Data Access\n", "\n", "There are several key steps for accessing data from the NSIDC API:\n", "1. Define your parameters (spatial, temporal, dataset, etc.)\n", @@ -74,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create an ICESat-2 data object with the desired search parameters\n", + "### Create an ICESat-2 data object with the desired search parameters\n", "\n", "There are three required inputs, depending on how you want to search for data. Two are required in all cases:\n", "- `short_name` = the data product of interest, known as its \"short name\".\n", @@ -269,7 +269,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Built in methods allow us to get more information about our data product\n", + "### Built in methods allow us to get more information about our data product\n", "In addition to viewing the stored object information shown above (e.g. product short name, start and end date and time, version, etc.), we can also request summary information about the data product itself or confirm that we have manually specified the latest version." ] }, @@ -305,7 +305,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Querying a data product\n", + "### Querying a data product\n", "In order to search the product collection for available data granules, we need to build our search parameters. This is done automatically behind the scenes when you run `region_a.avail_granules()`, but you can also build and view them by calling `region_a.CMRparams`. These are formatted as a dictionary of key:value pairs according to the [CMR documentation](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html)." ] }, @@ -366,7 +366,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Log in to NASA Earthdata\n", + "### Log in to NASA Earthdata\n", "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login. This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. Once you have successfully logged in for a given query instance, the token and session will be passed behind the scenes as needed for you to order and download data. Passwords are entered but not shown or stored in plain text by the system.\n", "\n", "There are multiple ways to provide your Earthdata credentials via icepyx.\n", @@ -388,7 +388,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Additional Parameters and Subsetting\n", + "### Additional Parameters and Subsetting\n", "\n", "Once we have generated our session, we must build the required configuration parameters needed to actually download data. These will tell the system how we want to download the data. As with the CMR search parameters, these will be built automatically when you run `region_a.order_granules()`, but you can also create and view them with `region_a.reqparams`. The default parameters, given below, should work for most users.\n", "- `page_size` = 2000. This is the number of granules we will request per order.\n", @@ -397,7 +397,7 @@ "- `agent` = 'NO'\n", "- `include_meta` = 'Y'\n", "\n", - "### More details about the configuration parameters\n", + "#### More details about the configuration parameters\n", "`request_mode` is \"asynchronous\" by default, which allows concurrent requests to be queued and processed without the need for a continuous connection between you and the API endpoint.\n", "In contrast, using a \"synchronous\" `request_mode` means that the request relies on a direct, continous connection between you and the API endpoint.\n", "Outputs are directly downloaded, or \"streamed\", to your working directory.\n", @@ -423,7 +423,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Subsetting\n", + "#### Subsetting\n", "\n", "In addition to the required parameters (CMRparams and reqparams) that are submitted with our order, for ICESat-2 data products we can also submit subsetting parameters to NSIDC.\n", "For a deeper dive into subsetting, please see our [Subsetting Tutorial Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess2_Subsetting.ipynb), which covers subsetting in more detail, including how to get a list of subsetting options, how to build your list of subsetting parameters, and how to generate a list of desired variables (most datasets have more than 200 variable fields!), including using pre-built default lists (these lists are still in progress and we welcome contributions!).\n", @@ -463,7 +463,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Place the order\n", + "#### Place the order\n", "Then, we can send the order to NSIDC using the order_granules function. Information about the granules ordered and their status will be printed automatically. Status information can also be emailed to the address provided when the `email` kwarg is set to `True`. Additional information on the order, including request URLs, can be viewed by setting the optional keyword input 'verbose' to True." ] }, @@ -491,7 +491,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Download the order\n", + "### Download the order\n", "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword `verbose`." ] }, diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb index 2a0784aa3..c64de33b5 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_DEM_comparison_Colombia_working.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Comparing ICESat-2 Altimetry Elevations with DEM\n", - "## Example Notebook\n", + "## Comparing ICESat-2 Altimetry Elevations with DEM\n", + "### Example Notebook\n", "This notebook compares elevations from ICESat-2 to those from a DEM.\n", "\n", - "### Credits\n", + "#### Credits\n", "* notebook by: [Jessica Scheick](https://github.com/JessicaS11) and [Shashank Bhushan](https://github.com/ShashankBice)\n" ] }, @@ -16,9 +16,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Setup\n", - "#### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", - "#### For full functionality,\n", + "#### Setup\n", + "##### The Notebook was run on ICESat2 Hackweek 2019 pangeo image\n", + "##### For full functionality,\n", "- Please install [icepyx](https://github.com/icesat2py/icepyx), [topolib](https://github.com/ICESAT-2HackWeek/topohack), [contextily](https://github.com/darribas/contextily) using `git clone xxxxx`, `pip install -e .` workflow (see below; **you must restart your kernel after installing the packages**)\n", "- Download [NASA ASP](https://github.com/NeoGeographyToolkit/StereoPipeline) tar ball and unzip, we execute the commands from the notebook, using the path to the untared bin folder for the given commands." ] @@ -58,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", + "#### ICESat-2 product being explored : [ATL08](https://nsidc.org/data/atl08)\n", "- Along track heights for canopy (land and vegitation) and terrain\n", "- Terrain heights provided are aggregated over every 100 m along track interval, output contains \"h_te_best_fit: height from best fit algorithm for all photons in the range\", median height and others. Here we use h_te_best_fit.\n", "- See this preliminary introduction and quality assessment [paper](https://www.mdpi.com/2072-4292/11/14/1721) for more detail" @@ -68,7 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages, including icepyx" + "### Import packages, including icepyx" ] }, { @@ -112,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Preprocess #1\n", + "### Preprocess #1\n", "- Download using icepyx" ] }, @@ -120,7 +120,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create an ICESat-2 data object with the desired search parameters\n", + "##### Create an ICESat-2 data object with the desired search parameters\n", "- See the ICESat-2 DAAC Data Access notebook for more details on downloading data from the NSIDC" ] }, @@ -138,7 +138,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finding and downloading data\n", + "### Finding and downloading data\n", "In order to download any data from NSIDC, we must first authenticate ourselves using a valid Earthdata login (available for free on their website). This will create a valid token to interface with the DAAC as well as start an active logged-in session to enable data download. The token is attached to the data object and stored, but the session must be passed to the download function. Then we can order the granules." ] }, @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Log in to Earthdata" + "#### Log in to Earthdata" ] }, { @@ -185,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Place the order" + "#### Place the order" ] }, { @@ -212,7 +212,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the order\n", + "#### Download the order\n", "Finally, we can download our order to a specified directory (which needs to have a full path but doesn't have to point to an existing directory) and the download status will be printed as the program runs. Additional information is again available by using the optional boolean keyword 'verbose'." ] }, @@ -239,7 +239,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Clean up the download folder by removing individual order folders:" + "#### Clean up the download folder by removing individual order folders:" ] }, { @@ -265,7 +265,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Preprocess #2\n", + "### Preprocess #2\n", "- Convert data into geopandas dataframe, which allows for doing basing geospatial opertaions" ] }, @@ -283,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Examine content of 1 ATLO8 hdf file" + "### Examine content of 1 ATLO8 hdf file" ] }, { @@ -399,7 +399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Convert the list of hdf5 files into more familiar Pandas Dataframe" + "### Convert the list of hdf5 files into more familiar Pandas Dataframe" ] }, { @@ -416,7 +416,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Preprocess #3\n", + "### Preprocess #3\n", "- Visualise data footprints" ] }, @@ -439,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", + "### We will use the TANDEM-X Global DEM for our comparison. The resolution of the globally avaialable product is 90 m, with *horizontal* and *vertical* accuracy better than 2 to 3 m.\n", "- TANDEM-X DEM for the region was downloaded and preprocessed, filtered using scripts from the [tandemx](https://github.com/dshean/tandemx) repository" ] }, @@ -540,7 +540,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 1\n", + "### Section 1\n", "- This contains demonstration of elevation profile along 1 track, which has 6 beams" ] }, @@ -612,7 +612,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 2:\n", + "### Section 2:\n", "- Compare ICESat-2 Elevation with that of reference DEM (in this case TANDEM-X)" ] }, @@ -620,7 +620,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " + "#### Sample elevations from DEM at ATLO8-locations using nearest neighbour algorithm " ] }, { @@ -645,7 +645,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" + "#### Plot elevation differences (ICESat-2 minus TANDEM-X) as a function of elevation\n" ] }, { @@ -706,7 +706,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Section 3\n", + "### Section 3\n", "- Application of ICESat-2 as control surface for DEMs coregistration\n", "- Or, to find offsets and align ICESat-2 tracks to a control surface" ] @@ -715,14 +715,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Going fancy, include only if you want to :)" + "### Going fancy, include only if you want to :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Application of ICESat-2 as control for DEM co-registration ?\n", + "#### Application of ICESat-2 as control for DEM co-registration ?\n", "- Can use point cloud alignment techniques to align DEMs to points, for now as a starting point we can use the transformation matrix to inform on the horizontal and vertical offset between ICESat-2 tracks and DEMs\n", "- We will be using a flavor of Iterative Closest Point alignment algorithm, implemented in [Ames Stereo Pipeline](https://github.com/NeoGeographyToolkit/StereoPipeline)" ] diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb index 219573a80..2a8170bbc 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Read-in_Example.ipynb @@ -5,14 +5,14 @@ "id": "552e9ef9", "metadata": {}, "source": [ - "# Reading ICESat-2 Data in for Analysis\n", - "## Example notebook to showcase ICESat-2 data read-in using icepyx\n", + "## Reading ICESat-2 Data in for Analysis\n", + "### Example notebook to showcase ICESat-2 data read-in using icepyx\n", "This notebook illustrates the use of icepyx for reading ICESat-2 data files, loading them into a data object.\n", "Currently the default data object is an Xarray Dataset, with ongoing work to provide support for other data object types.\n", "\n", "For more information on how to order and download ICESat-2 data, see the [icepyx data access tutorial](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_DAAC_DataAccess_Example.ipynb).\n", "\n", - "## Motivation\n", + "### Motivation\n", "Most often, when you open a data file, you must specify the underlying data structure and how you'd like the information to be read in.\n", "A simple example of this, for instance when opening a csv or similarly delimited file, is letting the software know if the data contains a header row, what the data type is (string, double, float, boolean, etc.) for each column, what the delimeter is, and which columns or rows you'd like to be loaded.\n", "Many ICESat-2 data readers are quite manual in nature, requiring that you accurately type out a list of string paths to the various data variables.\n", @@ -20,13 +20,13 @@ "icepyx simplifies this process by relying on its awareness of ICESat-2 specific data file variable storage structure.\n", "Instead of needing to manually iterate through the beam pairs, you can provide a few options to the `Read` object and icepyx will do the heavy lifting for you (as detailed in this notebook).\n", "\n", - "## Approach\n", + "### Approach\n", "If you're interested in what's happening under the hood: icepyx turns your instructions into something called a catalog, then uses the Intake library and the catalog to actually load the data into memory. Specifically, icepyx creates an [Intake](https://intake.readthedocs.io/en/latest/) data [catalog](https://intake.readthedocs.io/en/latest/catalog.html) for each requested variable and then merges the read-in data from each of the variables to create a single data object.\n", "\n", "Intake catalogs are powerful (and the tool we selected) because they can be saved, shared, modified, and reused to reproducibly read in a set of data files in a consistent way as part of an analysis workflow.\n", "This approach streamlines the transition between data sources (local/downloaded files or, ultimately, cloud/bucket access) and data object types (e.g. [Xarray Dataset](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html) or [GeoPandas GeoDataFrame](https://geopandas.org/docs/reference/api/geopandas.GeoDataFrame.html)).\n", "\n", - "### Credits\n", + "#### Credits\n", "* original notebook by: Jessica Scheick\n", "* notebook contributors: Wei Ji and Tian\n", "* templates for default ICESat-2 Intake catalogs from: [Wei Ji](https://github.com/icesat2py/icepyx/issues/106) and [Tian](https://github.com/icetianli/ICESat2_xarray).\n" @@ -37,7 +37,7 @@ "id": "0d360de3", "metadata": {}, "source": [ - "## Import packages, including icepyx" + "### Import packages, including icepyx" ] }, { @@ -57,7 +57,7 @@ "source": [ "---------------------------------\n", "\n", - "## Quick Start Guide\n", + "### Quick Start Guide\n", "For those who might be looking into playing with this (but don't want all the details/explanations)" ] }, @@ -110,7 +110,7 @@ "metadata": {}, "source": [ "---------------------------------------\n", - "## Key steps for loading (reading) ICESat-2 data\n", + "### Key steps for loading (reading) ICESat-2 data\n", "\n", "Reading in ICESat-2 data with icepyx happens in a few simple steps:\n", "1. Let icepyx know where to find your data (this might be local files or urls to data in cloud storage)\n", @@ -127,7 +127,7 @@ "id": "9bf6d38c", "metadata": {}, "source": [ - "## Step 0: Get some data if you haven't already\n", + "### Step 0: Get some data if you haven't already\n", "Here are a few lines of code to get you set up with a few data files if you don't already have some on your local system." ] }, @@ -167,7 +167,7 @@ "id": "e8da42c1", "metadata": {}, "source": [ - "## Step 1: Set data source path\n", + "### Step 1: Set data source path\n", "\n", "Provide a full path to the data to be read in (i.e. opened).\n", "Currently accepted inputs are:\n", @@ -217,7 +217,7 @@ "id": "92743496", "metadata": {}, "source": [ - "## Step 2: Create a filename pattern for your data files\n", + "### Step 2: Create a filename pattern for your data files\n", "\n", "Files provided by NSIDC typically match the format `\"ATL{product:2}_{datetime:%Y%m%d%H%M%S}_{rgt:4}{cycle:2}{orbitsegment:2}_{version:3}_{revision:2}.h5\"` where the parameters in curly brackets indicate a parameter name (left of the colon) and character length or format (right of the colon).\n", "Some of this information is used during data opening to help correctly read and label the data within the data structure, particularly when multiple files are opened simultaneously.\n", @@ -263,7 +263,7 @@ "id": "4275b04c", "metadata": {}, "source": [ - "## Step 3: Create an icepyx read object\n", + "### Step 3: Create an icepyx read object\n", "\n", "The `Read` object has two required inputs:\n", "- `path` = a string with the full file path or full directory path to your hdf5 (.h5) format files.\n", @@ -301,7 +301,7 @@ "id": "da8d8024", "metadata": {}, "source": [ - "## Step 4: Specify variables to be read in\n", + "### Step 4: Specify variables to be read in\n", "\n", "To load your data into memory or prepare it for analysis, icepyx needs to know which variables you'd like to read in.\n", "If you've used icepyx to download data from NSIDC with variable subsetting (which is the default), then you may already be familiar with the icepyx `Variables` module and how to create and modify lists of variables.\n", @@ -396,7 +396,7 @@ "id": "473de4d7", "metadata": {}, "source": [ - "## Step 5: Loading your data\n", + "### Step 5: Loading your data\n", "\n", "Now that you've set up all the options, you're ready to read your ICESat-2 data into memory!" ] @@ -405,7 +405,9 @@ "cell_type": "code", "execution_count": null, "id": "eaabc976", - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "ds = reader.load()" @@ -438,7 +440,7 @@ "id": "b1d7de2d", "metadata": {}, "source": [ - "## On to data analysis!\n", + "### On to data analysis!\n", "\n", "From here, you can begin your analysis.\n", "Ultimately, icepyx aims to include an Xarray extension with ICESat-2 aware functions that allow you to do things like easily use only data from strong beams.\n", @@ -471,7 +473,7 @@ "id": "6edfbb25", "metadata": {}, "source": [ - "## More on Intake catalogs and the read object\n", + "### More on Intake catalogs and the read object\n", "\n", "As anyone familiar with ICESat-2 hdf5 files knows, one of the challenges to reading in data is looping through all of the beam pairs for each track.\n", "The icepyx read module takes advantage of icepyx's variables module, which has some awareness of ICESat-2 data and uses that to save the user the trouble of having to loop through each beam pair.\n", @@ -484,7 +486,7 @@ "id": "0f0076f9", "metadata": {}, "source": [ - "### Viewing the template catalog\n", + "#### Viewing the template catalog\n", "\n", "You can access the ICESat-2 catalog template as an attribute of the read object.\n", "\n", @@ -518,7 +520,7 @@ "id": "fef43556", "metadata": {}, "source": [ - "### Use an existing catalog\n", + "#### Use an existing catalog\n", "If you already have a catalog for your data, you can supply that when you create the read object." ] }, @@ -577,7 +579,7 @@ "id": "d56fc41c", "metadata": {}, "source": [ - "### More customization options\n", + "#### More customization options\n", "\n", "If you'd like to use the icepyx ICESat-2 Catalog template to create your own customized catalog, we recommend that you access the `build_catalog` function directly, which returns an Intake Catalog instance.\n", "\n", @@ -619,7 +621,7 @@ "id": "bab9c949", "metadata": {}, "source": [ - "### Saving your catalog\n", + "#### Saving your catalog\n", "If you create a highly customized ICESat-2 catalog, you can use Intake's `save` to export it as a .yml file.\n", "\n", "Don't forget you can easily use an existing catalog (such as this highly customized one you just made) to read in your data with `reader = ipx.Read(filepath, pattern, catalog)` (so it's as easy as re-creating your reader object with your modified catalog)." diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb index ccc9ba278..ba1c1b012 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_Data_Visualization_Example.ipynb @@ -5,12 +5,12 @@ "id": "1e29ff05", "metadata": {}, "source": [ - "# Visualizing ICESat-2 Data\n", - "## Elevation Visualization Example Notebook\n", + "## Visualizing ICESat-2 Data\n", + "### Elevation Visualization Example Notebook\n", "\n", "This notebook demonstrates interactive ICESat-2 elevation visualization by requesting data from [OpenAltimetry](https://www.openaltimetry.org/) based on metadata provided by [icepyx](https://icepyx.readthedocs.io/en/latest/). We will show how to plot spatial extent and elevation interactively. \n", "\n", - "### Credits\n", + "#### Credits\n", "* Notebook by: [Tian Li](https://github.com/icetianli), [Jessica Scheick](https://github.com/JessicaS11) and \n", "[Wei Ji](https://github.com/weiji14)\n", "* Source material: [READ_ATL06_DEM Notebook](https://github.com/ICESAT-2HackWeek/Assimilation/blob/master/contributors/icetianli/READ_ATL06_DEM.ipynb) by Tian Li and [Friedrich Knuth](https://github.com/friedrichknuth)" @@ -21,7 +21,7 @@ "id": "6333399a", "metadata": {}, "source": [ - "## Import packages" + "### Import packages" ] }, { @@ -39,7 +39,7 @@ "id": "57f2cfd8", "metadata": {}, "source": [ - "## Create an ICESat-2 query object\n", + "### Create an ICESat-2 query object\n", "Set the desired parameters for your data visualization.\n", "\n", "For details on minimum required inputs, please refer to [ICESat-2_DAAC_DataAccess_Example](https://github.com/icesat2py/icepyx/blob/main/examples/ICESat-2_DAAC_DataAccess_Example.ipynb). If you are using a spatial extent input other than a bounding box for your search, it will automatically be converted to a bounding box for the purposes of visualization ONLY (your query object will not be affected)." @@ -126,7 +126,7 @@ "id": "1b178836", "metadata": {}, "source": [ - "## Visualize spatial extent \n", + "### Visualize spatial extent \n", "By calling function `visualize_spatial_extent`, it will plot the spatial extent in red outline overlaid on a basemap, try zoom-in/zoom-out to see where is your interested region and what the geographic features look like in this region." ] }, @@ -147,9 +147,9 @@ "id": "71ca513d", "metadata": {}, "source": [ - "## Visualize ICESat-2 elevation using OpenAltimetry API\n", + "### Visualize ICESat-2 elevation using OpenAltimetry API\n", "\n", - "### **Note: this function currently only supports products `ATL06, ATL07, ATL08, ATL10, ATL12, ATL13`**\n", + "#### **Note: this function currently only supports products `ATL06, ATL07, ATL08, ATL10, ATL12, ATL13`**\n", "\n", "Now that we have produced an interactive map showing the spatial extent of ICESat-2 data to be requested from NSIDC using icepyx, what if we want to have a quick check on the ICESat-2 elevations we plan to download from NSIDC? [OpenAltimetry API](https://openaltimetry.org/data/swagger-ui/#/) provides a nice way to achieve this. By sending metadata (product, date, bounding box, trackId) of each ICESat-2 file to the API, it can return elevation data almost instantaneously. The major drawback is requests are limited to 5x5 degree spatial bounding box selection for most of the ICESat-2 L3A products [ATL06, ATL07, ATL08, ATL10, ATL12, ATL13](https://icesat-2.gsfc.nasa.gov/science/data-products). To solve this issue, if you input spatial extent exceeds the 5 degree maximum in either horizontal dimension, your input spatial extent will be splited into 5x5 degree lat/lon grids first, use icepyx to query the metadata of ICESat-2 files located in each grid, and send each request to OpenAltimetry. Data sampling rates are 1/50 for ATL06 and 1/20 for other products.\n", "\n", @@ -174,7 +174,7 @@ "id": "9ee72a5c", "metadata": {}, "source": [ - "### Plot elevation for individual RGT\n", + "#### Plot elevation for individual RGT\n", "\n", "The visualization tool also provides the option to view elevation data by latitude for each ground track." ] @@ -194,7 +194,7 @@ "id": "b7082edd", "metadata": {}, "source": [ - "## Move on to data downloading from NSIDC if these are the products of interest\n", + "### Move on to data downloading from NSIDC if these are the products of interest\n", "\n", "For more details on the data ordering and downloading process, see [ICESat-2_DAAC_DataAccess_Example](https://github.com/icesat2py/icepyx/blob/main/examples/ICESat-2_DAAC_DataAccess_Example.ipynb)" ] @@ -223,7 +223,7 @@ "id": "textile-casting", "metadata": {}, "source": [ - "## Alternative Access Options to Visualize ICESat-2 elevation using OpenAltimetry API\n", + "### Alternative Access Options to Visualize ICESat-2 elevation using OpenAltimetry API\n", "\n", "You can also view elevation data by importing the visualization module directly and initializing it with your query object or a list of parameters:\n", " ```\n", diff --git a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb index e59b58b94..c4d4d35ff 100644 --- a/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb +++ b/doc/source/getting_started/example_notebooks/ICESat-2_cloud_data_access_example.ipynb @@ -4,17 +4,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ICESat-2 AWS cloud data access with icepyx (BETA ONLY)\n", - "## Utilizing icepyx capabilities to enable cloud data access\n", + "## ICESat-2 AWS cloud data access with icepyx (BETA ONLY)\n", + "### Utilizing icepyx capabilities to enable cloud data access\n", "This notebook illustrates the use of icepyx for access ICESat-2 data currently available through the AWS (Amazon Web Services) us-west2 hub s3 data bucket.\n", "\n", - "## Critical Caveats\n", + "### Critical Caveats\n", "***Please do not contact us saying this does not work until you have read this section in detail***\n", "1. ICESat-2 data is not currently publicly available on the cloud (and will not likely be until at least the end of 2021). A limited subset is currently available in an s3 bucket to developers and beta testers who have been registered with NSIDC.\n", "2. This example and the code it describes are part of ongoing development. Current limitations to using these features are described throughout the example, as appropriate.\n", "3. You **MUST** be working within an AWS instance. Otherwise, you will get a permissions error.\n", "\n", - "### Credits\n", + "#### Credits\n", "* notebook by: Jessica Scheick\n", "* source material: [is2-nsidc-cloud.py](https://gist.github.com/bradlipovsky/80ab6a7aff3d3524b9616a9fc176065e#file-is2-nsidc-cloud-py-L28) by Brad Lipovsky" ] @@ -34,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create an icepyx Query object\n", + "### Create an icepyx Query object\n", "In order to develop and test cloud data access functionality, here we search for an arbitrary granule over Greenland that was previously determined to be available on s3 using [Earthdata Search](https://search.earthdata.nasa.gov/). s3 availability is not yet included in CMR metadata, so it cannot be determined programmatically." ] }, @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Construct the granule s3 urls\n", + "### Construct the granule s3 urls\n", "Since cloud data available is not yet included as part of the standard granule metadata, there is no way for us to check whether or not these s3 bucket urls are valid, since they are constructed from other granule metadata. Thus, you may get FileNotFound Errors when trying to use these urls." ] }, @@ -82,7 +82,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Log in to Earthdata and generate an s3 token\n", + "### Log in to Earthdata and generate an s3 token\n", "You can use icepyx's existing login functionality to generate your s3 data access token, which should be good for five hours. We currently do not have this set up to automatically renew, but if you're interested in adding this functionality please get in touch or submit a PR!" ] }, @@ -110,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up your s3 access using your credentials" + "### Set up your s3 access using your credentials" ] }, { @@ -137,7 +137,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Select an s3 url and access the data\n", + "### Select an s3 url and access the data\n", "Development is underway for data read in capabilities, which will include options for cloud data access. Stay tuned and we'd love for you to join us and contribute!\n", "\n", "**Note: If you get a PermissionDenied Error when trying to read in the data, you may not be sending your request from an AWS hub in us-west2. We're currently working on how to alert users if they will not be able to access ICESat-2 data in the cloud for this reason**" diff --git a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb index ba5b43a34..d25b6ebbd 100644 --- a/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb +++ b/doc/source/getting_started/example_notebooks/Working_with_ICESat-2_Data_Variables.ipynb @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working with ICESat-2's Nested Variables\n", - "## Get a list of available variables and choose the ones you want to work with\n", + "## Working with ICESat-2's Nested Variables\n", + "### Get a list of available variables and choose the ones you want to work with\n", "\n", "This notebook illustrates the use of icepyx for managing lists of available and wanted ICESat-2 data variables.\n", "The two use cases for variable management within your workflow are:\n", @@ -22,7 +22,7 @@ "\n", "Questions? Be sure to check out the FAQs throughout this notebook, indicated as italic headings.\n", "\n", - "### Credits\n", + "#### Credits\n", "* based on the subsetting notebook by: Jessica Scheick and Zheng Liu" ] }, @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## _Why do ICESat-2 products need a custom variable manager?_\n", + "### _Why do ICESat-2 products need a custom variable manager?_\n", "\n", "It can be confusing and cumbersome to comb through the 200+ variable and path combinations contained in ICESat-2 data products.\n", "The icepyx `Variables` module makes it easier for users to quickly find and extract the specific variables they would like to work with across multiple beams, keywords, and variables and provides reader-friendly formatting to browse variables.\n", @@ -42,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Some technical details about the Variables module\n", + "#### Some technical details about the Variables module\n", "For those eager to push the limits or who want to know more implementation details...\n", "\n", "The only required input to the `Variables` module is `vartype`.\n", @@ -55,7 +55,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages, including icepyx" + "### Import packages, including icepyx" ] }, { @@ -72,7 +72,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Interacting with ICESat-2 Data Variables\n", + "### Interacting with ICESat-2 Data Variables\n", "\n", "Each variables instance (which is actually an associated Variables class object) contains two variable list attributes.\n", "One is the list of possible or available variables (`avail` attribute) and is unmutable, or unchangeable, as it is based on the input product specifications or files.\n", @@ -136,13 +136,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ICESat-2 data variables\n", + "### ICESat-2 data variables\n", "\n", "ICESat-2 data is natively stored in a nested file format called hdf5.\n", "Much like a directory-file system on a computer, each variable (file) has a unique path through the heirarchy (directories) within the file.\n", "Thus, some variables (e.g. `'latitude'`, `'longitude'`) have multiple paths (one for each of the six beams in most products).\n", "\n", - "## Determine what variables are available\n", + "### Determine what variables are available\n", "`region_a.order_vars.avail` will return a list of all valid path+variable strings." ] }, @@ -192,7 +192,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Building your wanted variable list\n", + "### Building your wanted variable list\n", "\n", "Now that you know which variables and path components are available, you need to build a list of the ones you'd like included.\n", "There are several options for generating your initial list as well as modifying it, giving the user complete control.\n", @@ -246,7 +246,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Modifying your wanted variable list\n", + "### Modifying your wanted variable list\n", "\n", "Generating and modifying your variable request list, which is stored in `region_a.order_vars.wanted`, is controlled by the `append` and `remove` functions that operate on `region_a.order_vars.wanted`. The input options to `append` are as follows (the full documentation for this function can be found by executing `help(region_a.order_vars.append)`).\n", "* `defaults` (default False) - include the default variable list for your product (not yet fully implemented for all products; please submit your default variable list for inclusion!)\n", @@ -275,7 +275,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Examples (Overview)\n", + "### Examples (Overview)\n", "Below are a series of examples to show how you can use `append` and `remove` to modify your wanted variable list.\n", "For clarity, `region_a.order_vars.wanted` is cleared at the start of many examples.\n", "However, multiple `append` and `remove` commands can be called in succession to build your wanted variable list (see Examples 3+).\n", @@ -291,7 +291,7 @@ "metadata": {}, "source": [ "------------------\n", - "## Example Track 1 (Land Ice - run with ATL06 dataset)\n", + "### Example Track 1 (Land Ice - run with ATL06 dataset)\n", "\n", "### Example 1.1: choose variables\n", "Add all `latitude` and `longitude` variables across all six beam groups. Note that the additional required variables for time and spacecraft orientation are included by default." @@ -300,7 +300,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ "region_a.order_vars.append(var_list=['latitude','longitude'])\n", @@ -495,7 +497,7 @@ "metadata": {}, "source": [ "------------------\n", - "## Example Track 2 (Atmosphere - run with ATL09 dataset commented out at the start of the notebook)\n", + "### Example Track 2 (Atmosphere - run with ATL09 dataset commented out at the start of the notebook)\n", "\n", "### Example 2.1: choose variables\n", "Add all `latitude` and `longitude` variables" @@ -691,7 +693,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using your wanted variable list\n", + "### Using your wanted variable list\n", "\n", "Now that you have your wanted variables list, you need to use it within your icepyx object (`Query` or `Read`) will automatically use it. " ] @@ -700,7 +702,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### With a `Query` object\n", + "#### With a `Query` object\n", "In order to have your wanted variable list included with your order, you must pass it as a keyword argument to the `subsetparams()` attribute or the `order_granules()` or `download_granules()` (which calls `order_granules` under the hood if you have not already placed your order) functions." ] }, @@ -747,7 +749,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### With a `Read` object\n", + "#### With a `Read` object\n", "Calling the `load()` method on your `Read` object will automatically look for your wanted variable list and use it.\n", "Please see the [read-in example Jupyter Notebook](https://github.com/icesat2py/icepyx/blob/main/doc/examples/ICESat-2_Data_Read-in_Example.ipynb) for a complete example of this usage.\n" ] diff --git a/doc/source/tracking/pypistats/get_pypi_stats.ipynb b/doc/source/tracking/pypistats/get_pypi_stats.ipynb index 1170766a0..3a719e27c 100644 --- a/doc/source/tracking/pypistats/get_pypi_stats.ipynb +++ b/doc/source/tracking/pypistats/get_pypi_stats.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# icepyx PyPI Statistics\n", + "## icepyx PyPI Statistics\n", "Use PyPIStats library to get data on PyPI downloads of icepyx (or any other package)\n", "\n", "See the [pypistats website](https://github.com/hugovk/pypistats) for potential calls, options, and formats (e.g. markdown, rst, html, json, numpy, pandas)\n", From 2cbf85b9d6671d6eea88628b6a90fefd91e7b760 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 11:42:23 -0500 Subject: [PATCH 49/53] add heading to code of conduct link --- doc/source/contributing/code_of_conduct_link.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst index 0f9131439..9bc133498 100644 --- a/doc/source/contributing/code_of_conduct_link.rst +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -1 +1,4 @@ +Code of Conduct +--------------- + .. include:: ../../../code_of_conduct.md \ No newline at end of file From 4ca39d8f6ef5c10e50980543c68118266cfcd2db Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 12:10:03 -0500 Subject: [PATCH 50/53] add parser to code of conduct link --- doc/source/contributing/code_of_conduct_link.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst index 9bc133498..da8b8e5be 100644 --- a/doc/source/contributing/code_of_conduct_link.rst +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -1,4 +1,5 @@ Code of Conduct --------------- -.. include:: ../../../code_of_conduct.md \ No newline at end of file +.. include:: ../../../code_of_conduct.md + :parser: myst_parser.sphinx_ \ No newline at end of file From 22db87d79357e2b9dca11b03996a5281c1baf416 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 12:50:23 -0500 Subject: [PATCH 51/53] re-fix code of conduct badge target link --- doc/source/contributing/contribution_guidelines.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/contributing/contribution_guidelines.rst b/doc/source/contributing/contribution_guidelines.rst index 1a6d260d9..2a227ce89 100644 --- a/doc/source/contributing/contribution_guidelines.rst +++ b/doc/source/contributing/contribution_guidelines.rst @@ -6,7 +6,7 @@ Thank you for your interest in contributing to icepyx! We welcome and invite con Here we provide a set of guidelines and information for contributing to icepyx. This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. |Contributor Covenant| .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg - :target: ../../../../code_of_conduct.md + :target: ../../../code_of_conduct.md Ways to Contribute From 01fd47f90c098943ff1ac2a08f83d4108e321da9 Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Tue, 21 Dec 2021 12:59:11 -0500 Subject: [PATCH 52/53] try removing extra header level for coc --- doc/source/contributing/code_of_conduct_link.rst | 3 --- 1 file changed, 3 deletions(-) diff --git a/doc/source/contributing/code_of_conduct_link.rst b/doc/source/contributing/code_of_conduct_link.rst index da8b8e5be..7f538460b 100644 --- a/doc/source/contributing/code_of_conduct_link.rst +++ b/doc/source/contributing/code_of_conduct_link.rst @@ -1,5 +1,2 @@ -Code of Conduct ---------------- - .. include:: ../../../code_of_conduct.md :parser: myst_parser.sphinx_ \ No newline at end of file From 388f8432b3e91ee9368303b3fb185ded8d4050b0 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Tue, 21 Dec 2021 18:06:04 +0000 Subject: [PATCH 53/53] GitHub action UML generation auto-update --- doc/source/user_guide/documentation/classes_dev_uml.svg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/user_guide/documentation/classes_dev_uml.svg b/doc/source/user_guide/documentation/classes_dev_uml.svg index a9be2ac8d..3d095c114 100644 --- a/doc/source/user_guide/documentation/classes_dev_uml.svg +++ b/doc/source/user_guide/documentation/classes_dev_uml.svg @@ -18,7 +18,7 @@ capability_url email netrc : NoneType -pswd : str, NoneType +pswd : NoneType, str session : Session uid