pax_global_header 0000666 0000000 0000000 00000000064 14654366563 0014534 g ustar 00root root 0000000 0000000 52 comment=556494356cdd0fdb2a2d0226f7503a869716bdb5
PlotPy-2.6.2/ 0000775 0000000 0000000 00000000000 14654366563 0012772 5 ustar 00root root 0000000 0000000 PlotPy-2.6.2/.coveragerc 0000664 0000000 0000000 00000000555 14654366563 0015120 0 ustar 00root root 0000000 0000000 [run]
parallel = True
concurrency = multiprocessing,thread
omit =
*/plotpy/tests/*
*/plotpy/external/*
*/plotpy/plot/interactive.py
*.vscode/extensions*
*/qwt/*
*/guidata/*
[report]
exclude_also =
def __repr__
raise AssertionError
raise NotImplementedError
if __name__ == .__main__.:
if TYPE_CHECKING:
PlotPy-2.6.2/.env.template 0000664 0000000 0000000 00000000014 14654366563 0015370 0 ustar 00root root 0000000 0000000 PYTHONPATH=. PlotPy-2.6.2/.github/ 0000775 0000000 0000000 00000000000 14654366563 0014332 5 ustar 00root root 0000000 0000000 PlotPy-2.6.2/.github/workflows/ 0000775 0000000 0000000 00000000000 14654366563 0016367 5 ustar 00root root 0000000 0000000 PlotPy-2.6.2/.github/workflows/build.yml 0000664 0000000 0000000 00000001072 14654366563 0020211 0 ustar 00root root 0000000 0000000 name: Build
on:
workflow_dispatch:
jobs:
build_wheels:
name: Build wheels on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
matrix:
# macos-13 is an intel runner, macos-14 is apple silicon
os: [ubuntu-latest, windows-latest, macos-13, macos-14]
steps:
- uses: actions/checkout@v4
- name: Build wheels
run: pipx run cibuildwheel==2.20.0
- uses: actions/upload-artifact@v4
with:
name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
PlotPy-2.6.2/.github/workflows/build_deploy.yml 0000664 0000000 0000000 00000002663 14654366563 0021574 0 ustar 00root root 0000000 0000000 name: Build and upload to PyPI
on:
workflow_dispatch:
release:
types:
- published
jobs:
build_wheels:
name: Build wheels on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
matrix:
# macos-13 is an intel runner, macos-14 is apple silicon
os: [ubuntu-latest, windows-latest, macos-13, macos-14]
steps:
- uses: actions/checkout@v4
- name: Build wheels
uses: pypa/cibuildwheel@v2.20.0
- uses: actions/upload-artifact@v4
with:
name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
build_sdist:
name: Build source distribution
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build sdist
run: pipx run build --sdist
- uses: actions/upload-artifact@v4
with:
name: cibw-sdist
path: dist/*.tar.gz
upload_pypi:
needs: [build_wheels, build_sdist]
runs-on: ubuntu-latest
environment: pypi
permissions:
id-token: write
if: github.event_name == 'release' && github.event.action == 'published'
steps:
- uses: actions/download-artifact@v4
with:
# unpacks all CIBW artifacts into dist/
pattern: cibw-*
path: dist
merge-multiple: true
- uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }} PlotPy-2.6.2/.github/workflows/test.yml 0000664 0000000 0000000 00000003446 14654366563 0020100 0 ustar 00root root 0000000 0000000 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
# Inspired from https://pytest-qt.readthedocs.io/en/latest/troubleshooting.html#github-actions
name: Python package
on:
push:
branches: [ "master" ]
pull_request:
branches: [ "master" ]
jobs:
build:
env:
DISPLAY: ':99.0'
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.8", "3.11", "3.12"]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v3
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
sudo apt install libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 x11-utils
/sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX
python -m pip install --upgrade pip
python -m pip install flake8 pytest
pip install PyQt5 setuptools numpy Cython
python setup.py build_ext --inplace
pip install .[test]
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Test with pytest
run: |
pytest
PlotPy-2.6.2/.gitignore 0000664 0000000 0000000 00000001135 14654366563 0014762 0 ustar 00root root 0000000 0000000 # Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# Visual Studio Code
.env
.venv
# C extensions
*.so
*.pyd
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
eggs/
.eggs/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# Tests
.tox/
.hypothesis/
.coverage
.coverage.*
.cache
unittests.xml
coverage.xml
coverage/
.pytest_cache/
pylint_report.txt
/*.h5
/*.png
/*.tif
/*.pickle
# Sphinx documentation
docs/build/
# Generated cython c file
src/histogram2d.c
src/mandelbrot.c
# qt.conf files
doc/source/deployment_example/qt.conf
# Coverage
htmlcov
# Other
*.bak
*_ui.py
PlotPy-2.6.2/.pylintrc 0000664 0000000 0000000 00000001122 14654366563 0014633 0 ustar 00root root 0000000 0000000 [FORMAT]
# Essential to be able to compare code side-by-side (`black` default setting)
# and best compromise to minimize file size
max-line-length=88
[TYPECHECK]
ignored-modules=qtpy.QtWidgets,qtpy.QtCore,qtpy.QtGui,qtpy.QtSvg,qtpy.QtPrintSupport,qtpy.QtDesigner,plotpy._scaler,plotpy.mandelbrot,plotpy.histogram2d,PyQt5.QtWidgets
[MESSAGES CONTROL]
disable=wrong-import-order
[DESIGN]
max-args=8 # default: 5
max-attributes=12 # default: 7
max-branches=17 # default: 12
max-locals=20 # default: 15
min-public-methods=0 # default: 2
max-public-methods=25 # default: 20 PlotPy-2.6.2/.readthedocs.yaml 0000664 0000000 0000000 00000000733 14654366563 0016224 0 ustar 00root root 0000000 0000000 # Read the Docs configuration file for Sphinx projects
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
version: 2
build:
os: ubuntu-22.04
tools:
python: "3.11"
jobs:
post_create_environment:
- pip install PyQt5 setuptools numpy Cython
- python setup.py build_ext --inplace
sphinx:
configuration: doc/conf.py
formats:
- pdf
python:
install:
- method: pip
path: .
extra_requirements:
- doc
PlotPy-2.6.2/.vscode/ 0000775 0000000 0000000 00000000000 14654366563 0014333 5 ustar 00root root 0000000 0000000 PlotPy-2.6.2/.vscode/launch.json 0000664 0000000 0000000 00000003537 14654366563 0016510 0 ustar 00root root 0000000 0000000 {
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Run Test Launcher",
"type": "debugpy",
"request": "launch",
"program": "${workspaceFolder}/plotpy/tests/__init__.py",
"console": "integratedTerminal",
"envFile": "${workspaceFolder}/.env",
"python": "${config:python.defaultInterpreterPath}",
"justMyCode": true,
"env": {
"QT_COLOR_MODE": "light",
}
},
{
"name": "Run current file",
"type": "debugpy",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"envFile": "${workspaceFolder}/.env",
"python": "${config:python.defaultInterpreterPath}",
"justMyCode": false,
"pythonArgs": [
"-W error::DeprecationWarning",
"-W error::RuntimeWarning",
],
"env": {}
},
{
"name": "Run current file (unattended)",
"type": "debugpy",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"envFile": "${workspaceFolder}/.env",
"python": "${config:python.defaultInterpreterPath}",
"pythonArgs": [
"-W error::DeprecationWarning",
],
"justMyCode": false,
"args": [
"--unattended"
],
"env": {
"GUIDATA_PARSE_ARGS": "1",
}
}
]
} PlotPy-2.6.2/.vscode/settings.json 0000664 0000000 0000000 00000001616 14654366563 0017072 0 ustar 00root root 0000000 0000000 {
"[bat]": {
"files.encoding": "cp850",
},
"editor.rulers": [
88
],
"files.exclude": {
"**/__pycache__": true,
"**/.pytest_cache": true,
"**/.hypothesis": true,
"**/*.pyc": true,
"**/*.pyo": true,
"**/*.pyd": true,
".venv": true
},
"files.trimFinalNewlines": true,
"files.trimTrailingWhitespace": true,
"python.defaultInterpreterPath": "${env:PPSTACK_PYTHONEXE}",
"editor.formatOnSave": true,
"python.analysis.autoFormatStrings": true,
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true,
"python.testing.pytestPath": "pytest",
"python.testing.pytestArgs": [
"plotpy"
],
"[python]": {
"editor.defaultFormatter": "charliermarsh.ruff"
},
"editor.codeActionsOnSave": {
"source.organizeImports": "explicit",
},
} PlotPy-2.6.2/.vscode/tasks.json 0000664 0000000 0000000 00000027315 14654366563 0016363 0 ustar 00root root 0000000 0000000 {
// See https://go.microsoft.com/fwlink/?LinkId=733558
// for the documentation about the tasks.json format
"version": "2.0.0",
"tasks": [
{
"label": "gettext - Scan",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"gettext.bat",
"rescan",
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHONUTF8": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": false
}
},
{
"label": "gettext - Compile",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"gettext.bat",
"compile",
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": false
}
},
{
"label": "Run Coverage",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"run_coverage.bat",
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "dedicated",
"showReuseMessage": true,
"clear": true
}
},
{
"label": "Run pytest",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"run_pytest.bat"
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}"
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "dedicated",
"showReuseMessage": true,
"clear": true
}
},
{
"label": "Run Pylint",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"run_pylint.bat",
// "--disable=R0801,C0103,C0114,C0115,C0116,W0612,W0613",
"--disable=fixme,C,R,W",
"--ignore=external",
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "dedicated",
"showReuseMessage": true,
"clear": true
}
},
{
"label": "Run Ruff",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"run_ruff.bat",
],
"options": {
"cwd": "scripts",
"env": {
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
"UNATTENDED": "1"
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "dedicated",
"showReuseMessage": true,
"clear": true
}
},
{
"label": "Clean Up",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"clean_up.bat"
],
"options": {
"cwd": "scripts",
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": false
}
},
{
"label": "Upgrade PlotPyStack packages",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"upgrade_stack.bat"
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": false
}
},
{
"label": "â ī¸ Upgrade whole environment â ī¸",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"upgrade_env.bat"
],
"options": {
"cwd": "scripts",
"env": {
"UNATTENDED": "1",
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
}
},
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": false
}
},
{
"label": "Build documentation",
"type": "shell",
"command": "cmd",
"options": {
"cwd": "scripts",
"env": {
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
"UNATTENDED": "1",
}
},
"args": [
"/c",
"build_doc.bat"
],
"problemMatcher": [],
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": true
}
},
{
"label": "Build Python packages",
"type": "shell",
"command": "cmd",
"options": {
"cwd": "scripts",
"env": {
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
"UNATTENDED": "1",
}
},
"args": [
"/c",
"build_dist.bat"
],
"problemMatcher": [],
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": true
},
"dependsOrder": "sequence",
"dependsOn": [
"Clean Up",
]
},
{
"label": "Build all wheels",
"type": "shell",
"command": "cmd",
"options": {
"cwd": "scripts",
"env": {
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
"UNATTENDED": "1",
}
},
"args": [
"/c",
"build_wheels.bat"
],
"problemMatcher": [],
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": true
},
"dependsOrder": "sequence",
"dependsOn": [
"Clean Up",
]
},
{
"label": "Build extensions in place",
"type": "shell",
"command": "cmd",
"options": {
"cwd": "scripts",
"env": {
"PYTHON": "${env:PPSTACK_PYTHONEXE}",
"UNATTENDED": "1",
}
},
"args": [
"/c",
"build_inplace.bat"
],
"problemMatcher": [],
"group": {
"kind": "build",
"isDefault": true
},
"presentation": {
"echo": true,
"reveal": "always",
"focus": false,
"panel": "shared",
"showReuseMessage": true,
"clear": true
},
"dependsOrder": "sequence",
"dependsOn": [
"Clean Up",
]
},
{
"label": "Display Coverage",
"type": "shell",
"command": "cmd",
"args": [
"/c",
"display_cov.bat",
],
"options": {
"cwd": "scripts",
},
"group": {
"kind": "build",
"isDefault": false
},
"presentation": {
"echo": true,
"reveal": "never",
"focus": false,
"panel": "shared",
"showReuseMessage": false,
"clear": true
}
},
]
} PlotPy-2.6.2/CHANGELOG.md 0000664 0000000 0000000 00000063554 14654366563 0014620 0 ustar 00root root 0000000 0000000 # Changelog #
## Version 2.6.2 ##
đĨ New features / Enhancements:
* Added Wheel packages for all major platforms on PyPI:
* Windows (32/64bits), MacOS, Linux
* Python 3.8 to 3.12
đ ī¸ Bug fixes:
* Fixed color theme support (dark/light mode), leveraging the new `guidata` V3.6 feature
## Version 2.6.1 ##
âšī¸ Release V2.6.0 was a fugitive release that was replaced by V2.6.1 due to a critical bug in the segment line cross section computation for masked images.
đĨ New features / Enhancements:
* Added support for color theme change at execution (relies on guidata V3.6)
* Changed strategy for default plot widget size:
* No default size is applied to the plot widget anymore (before, the default size was 800x600 pixels)
* Added parameter `size` to `PlotDialog`, `PlotWindow` classes, and `make.dialog`, `make.window` functions to set the initial size of the plot widget
đ ī¸ Bug fixes:
* Fixed segment line cross section computation for masked images:
* Avoided warning message when encountering masked values in the image data
* Replacing masked values by NaNs when computing the segment line cross section (as before, but explicitely, to avoid the warning message)
## Version 2.5.1 ##
âšī¸ Release V2.5.0 was a fugitive release that was replaced by V2.5.1 due to packaging issues.
In this release, test coverage is 79%.
đĨ New features / Enhancements:
* Alternative dictionary argument for plot options:
* This new feature was introduced in the context of the cyclic import bug fix, to avoid importing the `plotpy.plot` module just to get the `PlotOptions` or `BasePlotOptions` classes
* All classes (and a few functions) that used to take an `options` argument as a `BasePlotOptions` or `PlotOptions` instance now also accept a dictionary argument with the same keys as the `BasePlotOptions` or `PlotOptions` class attributes, and the same values as the corresponding attributes
* This concerns the following classes and functions:
* `plotpy.plot.BasePlot`
* `plotpy.plot.PlotWidget`
* `plotpy.plot.PlotDialog`
* `plotpy.plot.PlotWindow`
* `plotpy.plot.SubPlotWidget`
* `plotpy.plot.SyncPlotWindow`
* `plotpy.tools.RotateCropTool`
* `plotpy.widgets.fit.FitDialog`
* `plotpy.widgets.fliprotate.FlipRotateDialog`
* `plotpy.widgets.rotatecrop.RotateCropDialog`
* `plotpy.widgets.selectdialog.SelectDialog`
* `plotpy.widgets.selectdialog.select_with_shape_tool`
* Added "Lock LUT range" option for image items:
* This new option is disabled by default, which matches the previous behavior: when updating an image item data, the LUT range is automatically adjusted to the new data range (if not passed as an argument to the `BaseImageItem.set_data` method)
* When enabled, the LUT range is locked and the LUT range is not adjusted when updating the image item data
* The option is available in image parameters dialog
* A new tool `LockLUTRangeTool` has been implemented to toggle the option from the plot context menu: the tool is not registered by default in the plot widget, but can be added by the host application if needed
* See test script `tests.features.test_image_data_update` for an example of usage of the new option and tool
* Added missing `set_style` method to `XRangeSelection` class: this method is used to set the style of the range selection item from configuration options
đ ī¸ Bug fixes:
* [Issue #19](https://github.com/PlotPyStack/PlotPy/issues/19) - Fix `distutils` deprecation in setup.py: replaced `distutils.core` by `setuptools` in the setup.py script to avoid the deprecation warning when building the package with Python 3.10 and 3.11, and to ensure compatibility with earlier Python versions (PlotPy is already compatible with the most recent Python versions: this only concerns the build system)
* Fix cyclic import in `plotpy.tools` module:
* Some tools in `plotpy.tools` subpackage were importing the `plotpy.plot` module, which was importing the `plotpy.tools` module, causing a cyclic import issue
* This is now fixed by introducing new constants for axis IDs in the `plotpy.constants` module, and using them everywhere in the code, thus avoiding to import the `plotpy.plot` module just to get the axis IDs
* Fix empty label in X/Y cross section plots:
* This is a regression introduced in V2.1.0
* When showing the X/Y cross section plots (using the plot context menu), an empty label was displayed at the center of each of those plots
* The label now shows "Enable a marker" as previously
* Fix historic unexpected behavior of interactive tools:
* When triggering an interactive tool (e.g. by clicking on the corresponding toolbar button), the tool `activate` method was called twice, which was not expected, but was not causing any issue given the current implementation
* However, when defining custom interactive tools, this behavior could lead to unexpected results (i.e. really executing activation actions twice)
* This is now fixed: the `activate` method is called only once when triggering an interactive tool
## Version 2.4.2 ##
In this release, test coverage is 79%.
đ ī¸ Bug fixes:
* [Issue #17](https://github.com/PlotPyStack/PlotPy/issues/17):
* Debian's Python team has reported that the contour unit test was failing on `arm64` architecture
* This is the opportunity to replace the `contour2d` Cython extension by scikit-image's `find_contours` function, thus avoiding to reinvent the wheel by relying on a more robust and tested implementation
* The `contour2d` Cython extension is removed from the source code
* The contour related features remain the same, but the implementation is now based on scikit-image's `find_contours` function
* The scikit-image dependency is added to the package requirements
## Version 2.4.1 ##
In this release, test coverage is 79%.
đ ī¸ Bug fixes:
* Contrast adjustment panel:
* A regression was introduced in V2.0.0: levels histogram was no longer removed from contrast adjustment panel when the associated image was removed from the plot
* This is now fixed: when an image is removed, the histogram is removed as well and the contrast panel is refreshed (which was not the case even before the regression)
## Version 2.4.0 ##
In this release, test coverage is 79%.
đĨ New features / Enhancements:
* Contrast adjustment panel:
* New layout: the vertical toolbar (which was constrained in a small area on the right side of the panel) is now a horizontal toolbar at the top of the panel, beside the title
* New "Set range" button: allows the user to set manually the minimum and maximum values of the histogram range
* New Z-axis logarithmic scale feature:
* Added new tool `ZAxisLogTool` to toggle the Z-axis logarithmic scale
* The tool is registered by default in the plot widget, like the `ColormapTool`
* When enabled, the active image item is displayed after applying a base-10 logarithm to its pixel values
* Curve statistics tool `CurveStatsTool` is now customizable:
* When adding the tool: `plot_widget.manager.add_tool(CurveStatsTool, labelfuncs=(...))`
* Or after: `plot_widget.manager.get_tool(CurveStatsTool).set_labelfuncs(...)`
* The `labelfuncs` parameter is a list of tuples `(label, func)` where `label` is the label displayed in the statistics table, and `func` is a function that takes the curve data and returns the corresponding statistic value (see the documentation for more details)
* Image statistics tool `ImageStatsTool` is now customizable:
* When adding the tool: `plot_widget.manager.add_tool(ImageStatsTool, stats_func=...)`
* Or after: `plot_widget.manager.get_tool(ImageStatsTool).set_stats_func(...)`
* The `stats_func` parameter is a function that takes the image item and selected rectangle coordinates, and returns a string with the statistics to display
* New `SIG_AXIS_PARAMETERS_CHANGED` signal emitted by `BasePlot` when the axes parameters are changed (e.g. when the axes are inverted, or the scale is changed)
* New "Reverse X axis" feature:
* Added new tool `ReverseXAxisTool` to toggle the X-axis direction
* The tool is registered by default in the plot widget, like its Y-axis counterpart
đ ī¸ Bug fixes:
* Contrast adjustment panel:
* Fixed histogram update issues when no image was currently selected (even if the an image was displayed and was selected before)
* Histogram range was not updated when either the minimum or maximum value was set using the "Minimum value" or "Maximum value" buttons (which have been renamed to "Min." and "Max." in this release)
* Histogram range was not updated when the "Set full range" button was clicked
* Image parameters: contrast range was not updated when the image Z axis bounds were changed using the "Parameters" dialog
đ§š API cleanup:
* Deprecated `AnnotationParam.update_annotation` method: use `update_item` instead
* Deprecated `AxesShapeParam.update_axes` method: use `update_item` instead
* Deprecated `AxesParam.update_axes` method: use `update_item` instead
* Deprecated `ImageAxesParam.update_axes` method: use `update_item` instead
* Deprecated `LabelParam.update_label` method: use `update_item` instead
* Deprecated `MarkerParam.update_marker` method: use `update_item` instead
* Deprecated `RangeShapeParam.update_range` method: use `update_item` instead
* Deprecated `ShapeParam.update_shape` method: use `update_item` instead
## Version 2.3.5 ##
This release is mainly intended to fix the Windows binary distribution, which was not supporting NumPy 2.0.
đ ī¸ Bug fixes:
* Moved back `conftest.py` to the `tests` folder (was in the root folder), so that `pytest` can be executed with proper configuration when running the test suite from the installed package
* Removed benchmarks from automated test suite (not relevant for the end user): added `plotpy-benchmarks` script to run the benchmarks
## Version 2.3.4 ##
In this release, test coverage is 79%.
đ ī¸ Bug fixes:
* Add support for NumPy 2.0:
* Updated build system to use NumPy 2.0 on Python > 3.8 and NumPy 1 on Python 3.8
* Use `numpy.asarray` instead of `numpy.array(..., copy=False)`
* Use `numpy.isin` instead of `numpy.in1d`
* Use `scipy.integrate.trapezoid` instead of `numpy.trapz`
* `ColorMapManager`: fix segmentation fault with PySide6 on Linux
## Version 2.3.3 ##
In this release, test coverage is 79%.
đ ī¸ Bug fixes:
* Moved up `LineCrossSection` import for consistency with other cross-section panels
* Unexpected behavior regarding `io.load_items` and `io.save_items` functions:
* Those functions were serializing and deserializing most of the parameters of the plot items, but not their visibility state
* This is now fixed: the visibility state of the plot items is now saved and restored as expected
âšī¸ Other changes:
* Explicitely exclude NumPy V2 from the dependencies (not compatible yet)
## Version 2.3.2 ##
In this release, test coverage is 79%.
Version 2.3.2 fixes a blocking issue with the colormap editor unit test introduced in version 2.3.1. The latter is a fugitive release that was not announced.
đĨ New features / Enhancements:
* Colormap: added "Apply" button to the colormap manager
* Automated test suite:
* Test coverage has been improved from 75% to 79%
* The following features are now covered by unit tests:
* Panning with the mouse move events
* Zooming with the mouse wheel/move events
* Curve statistics tool
* Image rotation and translations via `SelectTool` (simulating mouse events)
* Masked areas in images and `ImageMaskTool`
* `LockTrImageTool`
* Cursor tools (`HCursorTool`, `VCursorTool`, `XCursorTool` and `HRangeTool`)
* `DisplayCoordsTool`: simulation of 'Alt' and 'Ctrl' keys
* Complete coverage for `MultiLineTool` and `FreeFormTool`
* Selection tools (`RectangularSelectionTool` and `SelectTool`)
đ ī¸ Bug fixes:
* Image statistics tool: fixed "No available data" message when the tool rectangular region top Y coordinate is above the image top Y coordinate
* Label items (`LabelItem`, `LegendBoxItem`, `DataInfoLabel`, ...) were not emitting the `SIG_ITEM_MOVED` signal when moved interactively (with the mouse) if the item anchor was attached to the canvas
* Colormap: fixed context menu entry update (colormap icon was updated as expected, but the colormap name was not)
* Rotate/crop dialog: added missing toolbar on plot widget
* Flip/rotate dialog: added missing toolbar on plot widget
* Fixed issue with oblique averaged cross section computation (`AttributeError` when clicking on the empty cross section plot)
## Version 2.3.0 ##
In this release, test coverage is 75%.
đĨ New features:
* Added support for colormap inversion:
* The user can now invert the colormap of an image item:
* From the image parameters dialog ("Invert colormap" checkbox)
* From the plot context menu (right-click on the image item)
* `BaseImageItem.set_color_map` method takes a new `invert` parameter (which defaults to `None`, meaning that the behavior is unchanged)
* New `ReverseColormapTool`: registered by default in the plot widget, like the `ColormapTool` (add the "Invert colormap" entry in the context menu of the image)
đ ī¸ Bug fixes:
* `ErrorBarCurveItem`: fixed NumPy deprecation warning ("Conversion of an array with ndim > 0 to a scalar is deprecated [...]")
âšī¸ Other changes:
* Image plot items deserialization:
* When an image plot item is deserialized, and needs to be reloaded from a file, the file path is adapted to the current working directory if file is not found (this is the legacy behavior).
* An unnecessary call to `ImageIOHandler.adapt_path` method was removed from the `RawImageItem.deserialize` method: this issue has to be handled by the host application, not by the PlotPy library.
* `ImageIOHandler`: removed `add_change_path` and `adapt_path` methods
* Fix typo in `tests.features.test_colormap_editor` module: renamed function `test_colormap_manager` to `test_colormap_editor`
* Removed unnecessary `BaseImageItem.get_color_map_name` method
## Version 2.2.0 ##
In this release, test coverage is 75%.
New features:
* Added `SIG_ITEM_PARAMETERS_CHANGED` signal to `BasePlot` class:
* This signal is emitted when the parameters of an item are changed using the parameters dialog, or a specific tool (e.g. the colormap selection tool, or the lock/unlock tool for image items)
* This signal is emitted with the item as argument
* It is often emitted before the `SIG_ITEMS_CHANGED` signal, which is global to all items, but not necessarily. For example, when the colormap of an image is changed, the `SIG_ITEM_PARAMETERS_CHANGED` signal is emitted for the image item, but the `SIG_ITEMS_CHANGED` signal is not emitted.
* Added new colormap presets:
* `viridis`, `plasma`, `inferno`, `magma`, `cividis`
* `afmhot`
* `coolwarm`, `bwr`, `seismic`
* `gnuplot2`, `CMRmap`, `rainbow`, `turbo`
* Fixed all qualitative colormaps:
* All qualitative colormaps have been re-computed because they are not supposed to be interpolated, which was the case and made them unusable
* The qualitative colormaps are now usable and look like the ones from Matplotlib
* Colormap manager:
* Added a button to remove a custom colormap
* The preset colormaps *and* the currently selected colormap are read-only
* Added automatic unit tests for interactive tools:
* `AnnotatedCircleTool`, `AnnotatedEllipseTool`, `AnnotatedObliqueRectangleTool`, `AnnotatedPointTool`, `AnnotatedRectangleTool`, `AnnotatedSegmentTool`
* `AverageCrossSectionTool`, `CrossSectionTool`, `ObliqueCrossSectionTool`, `LineCrossSectionTool`
* `EditPointTool`, `SelectPointsTool`, `SelectPointTool`
* `AspectRatioTool`, `ImageStatsTool`, `SnapshotTool`
* `DisplayCoordsTool`, `RectZoomTool`
* `CircleTool`, `EllipseTool`, `FreeFormTool`, `MultiLineTool`, `ObliqueRectangleTool`, `PointTool`, `RectangleTool`, `SegmentTool`
* Internal package reorganization: moved icons to `plotpy/data/icons` folder
## Version 2.1.2 ##
New features:
* Added `Echelon` alpha function to the image parameters:
* The `Echelon` alpha function is a step function, so that the alpha channel is 0 (full transparency) for the lowest value of the Lookup Table (LUT) and opaque (transparency level set by the `Global alpha` parameter) for the other values
* This feature is added to the other existing alpha functions: `Constant`, `Linear`, `Sigmoid`, and `Hyperbolic tangent`
Bug fixes:
* Compatibility with PythonQwt 0.12.
## Version 2.1.1 ##
Bug fixes:
* API breakage (unintentional) in V2.1.0:
* In V2.1.0, `mathutils.colormap` module was renamed to `mathutils.colormaps`
* Original `mathutils.colormap` module naming is restored in this release
* Colormap selection from the toolbar was not triggering the `SIG_ITEMS_CHANGED` signal (every time an item parameter is changed, the `SIG_ITEMS_CHANGED` signal has to be emitted by the `BasePlot` instance to notify the application that the plot has been modified)
## Version 2.1.0 ##
In this release, test coverage is 71%.
New features:
* Curve-related features:
* New `tools.SelectPointsTool` to select graphically multiple points on a plot
* New `tools.EditPointTool` to edit graphically the position of a point on a plot
* New downsampling feature:
* The user may enable it to reduce the number of points displayed on a curve (e.g. when the curve is too dense)
* The downsampling factor is adjustable (default to 10, i.e. 1 point out of 10 is displayed)
* The feature is disabled by default
* Image-related features:
* New "Colormap Manager":
* Before this release, the colormap selection was limited to presets (e.g. "gray", "jet", etc.)
* Now, the user can select a preset, edit it, or create a new one from scratch thanks to the new "Colormap Manager" dialog
* New line cross section feature:
* Before this release, the cross section feature was limited to either horizontal or vertical lines, or an average cross section withing a rectangle (aligned with the axes, or oblique)
* Now, the user can draw a line cross section with the new "Line Cross Section" tool: the intensity profile associated to the drawn segment is displayed in a dedicated plot
* Added support for gestures:
* Zooming in/out with the a two-finger pinch gesture
* Panning with a two-finger drag gesture
Documentation:
* Reorganized some sections
* Added sections on new features
Bug fixes:
* Fixed critical bug in oblique cross section feature (regression introduced in 2.0.0)
* Removed dependency to `pytest-qt` for the test suite (due to Qt6 compatibility issues)
## Version 2.0.3 ##
Bug fixes:
* [Issue #9](https://github.com/PlotPyStack/PlotPy/issues/9) - MacOS: `error: a space is required between consecutive right angle brackets (use '> >')`
## Version 2.0.2 ##
Bug fixes:
* [Issue #3](https://github.com/PlotPyStack/PlotPy/issues/3) - `PlotWidget`: `ZeroDivisionError` on resize while ignoring constraints
* [Issue #4](https://github.com/PlotPyStack/PlotPy/issues/4) - Average cross section: `RuntimeWarning: Mean of empty slice.`
* [Issue #5](https://github.com/PlotPyStack/PlotPy/issues/5) - Contrast panel: levels histogram is sometimes not updated
* [Issue #6](https://github.com/PlotPyStack/PlotPy/issues/6) - 1D Histogram items are not properly drawn
* [Issue #7](https://github.com/PlotPyStack/PlotPy/issues/7) - Contrast panel: histogram may contains zeros periodically due to improper bin sizes
* [Issue #8](https://github.com/PlotPyStack/PlotPy/issues/8) - Contrast panel: switch back to default tool after selecting min/max
## Version 2.0.1 ##
Bug fixes:
* Fixed `plotpy.tools.AnnotatedEllipseTool`: `AttributeError` when finalizing the shape
* `plotpy.widgets.select_with_shape_tool`: added missing `toolbar` and `options` parameters
* `items.XRangeSelection` is now serializable, as expected
Documentation:
* `plotpy.plot.SyncPlotWindow`: added missing documentation
* Added more information on PlotPyStack
* New "Motivation" section explaining the reasons behind the creation of PlotPy
## Version 2.0.0 ##
This version is the first release of the 2.0 series, which is distributed under the [BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause).
PlotPy 2.0 is a major release that brings a lot of new features and bug fixes.
When initiated in 2016, PlotPy 1.0 was the result of the merge of two projects (as well as some other changes, e.g. a redesign of the API):
* [guidata](https://pypi.org/project/guidata/), a Python library generating graphical user interfaces for easy dataset editing and display
* [guiqwt](https://pypi.org/project/guiqwt/), a Python library providing efficient 2D data-plotting features (curve/image visualization and related tools) for interactive computing and signal/image processing application development
With PlotPy 2.0, the [guidata](https://pypi.org/project/guidata/) code base has been reextracted: PlotPy now relies on [guidata](https://pypi.org/project/guidata/) as a dependency, like before the merge.
PlotPy 2.0 also integrates all the bug fixes (>30) and new features that were added to [guiqwt](https://pypi.org/project/guiqwt/) since the merge (i.e. between 2016 and 2023).
Supported versions of Python and Qt bindings have been updated:
* Python: 3.8, 3.9, and 3.10 (3.11 should work too, but will be officially supported when dropping support for Python 3.8, to keep a 3-year support period)
* Qt bindings: PyQt5 (even if PyQt6 and PySide6 are not officially supported, efforts have been made and will continue to be made to support them)
PlotPy 2.0 is a major release because it also brings a lot of new features:
* `plot.PlotWidget`, `plot.PlotDialog`, and `plot.PlotWindow`: API overhaul (simple, more consistent, more flexible, more extensible - see documentation for details)
* `plot.SyncPlotWindow`: new class to show multiple plots in a single window, in a synchronized way (zoom, pan, etc.)
* `widgets.selectdialog.SelectDialog`: a dialog box to select items using a shape tool (segment, rectangle or custom)
* Image lookup table (LUT):
* Initially, the LUT alpha channel was either constant (input parameter `alpha` was a float between 0 and 1) or linearly dependent on the image pixel values (when the `alpha_mask` parameter was enabled)
* Now, the LUT may be either constant (same as before) or dependent on the image pixel values but not only linearly: the LUT alpha channel may follow a linear, a sigmoid or an hyperbolic tangent function (see the new `alpha_function` parameter). The old `alpha_mask` parameter was removed
* Image pixels are now centered on their coordinates:
* This means that the pixel at row `i` and column `j` is centered on the point `(j, i)` (before, the top-left corner of the pixel at row `i` and column `j` was centered on the point `(j, i)`)
* This convention is more consistent with the way images are displayed in other scientific image processing tools
* This is one of the benefits of porting back [guiqwt](https://pypi.org/project/guiqwt/) changes since the merge (i.e. between 2016 and 2023)
* New SVG-based shapes:
* `items.RectangleSVGShape`: rectangle shape based on SVG data or file
* `items.SquareSVGShape`: square shape based on SVG data or file
* `items.CircleSVGShape`: circle shape based on SVG data or file
* `builder.PlotBuilder`:
* Renamed `PlotBuilder` (originally `guiqwt.builder.PlotItemBuilder`)
* Builder instance is still available using `from plotpy.builder import make`
* Plot widget creation is now supported:
* `make.widget()` for `plot.PlotWidget`
* `make.dialog()` for `plot.PlotDialog`
* `make.window()` for `plot.PlotWindow`
* Added support for more plot items:
* `make.contours()` for generating a list of `items.ContourItem` objects
* `make.annotated_point()` for `items.AnnotatedPoint`
* `make.polygon()` for `items.PolygonShape`
* `make.svg()` for `items.RectangleSVGShape`, `items.SquareSVGShape`, and `items.CircleSVGShape`
* Added JSON serialization support for all plot items (curve, image, etc.)
* Brand new documentation, based on Sphinx with links to other projects API, examples and tutorials (e.g. on development related topics).
* Black code formatting on all Python files
* New automated test suite:
* Automatic execution: `--unattended` command line option (Qt loop is bypassed)
* Test suite based on `pytest`, supporting `pytest-cov` for coverage testing, `pytest-xvfb` for headless testing, and `pytest-qt` for Qt testing
* Added support for Coverage: test coverage improved up to 70%
* Added typing annotations on (almost) all Python files
* Distribution: switched to `pyproject.toml` (still relying on `setuptools` and `setup.py` for building Cython/C++ extensions)
* Added code quality configuration files:
* `.pylintrc`: pylint configuration file
* `.isort.cfg`: isort configuration file
* `.coveragerc`: coverage configuration file
* Added Visual Studio Code configuration files:
* `.vscode/settings.json`: Python interpreter, code formatting, etc.
* `.vscode/tasks.json`: build, test, etc.
* `.vscode/launch.json`: run current file, run tests, etc.
PlotPy 2.0 also brings a lot of bug fixes and improvements:
* Handled all Cython/C++ extensions compilation warnings
* Fixed all NumPy deprecation issues (e.g. `numpy.matrix`)
* Fixed (annotated) circle/ellipse item creation/test
* Fixed all documentation build warnings
* Fixed regressions introduced by PlotPy V1 on top of guiqwt:
* Global references for the Debian package management
* Major aspect ratio issues:
* When resizing the plot widget (images were downsized indefinitely)
* When auto-scaling the plot widget (images were not displayed entirely)
* `TrImageItem` rotation algorithm
* Oblique cross-section test
* About dialog, version informations
* Ported all [guiqwt](https://pypi.org/project/guiqwt/) bug fixes since the merge (i.e. between 2016 and 2023):
* Added support for Visual Studio 2015 and earlier
* Speeding-up image alpha channel calculation
* Optimized colormap icon caching
* X-axis direction and auto-scale
* Added load test (with a very large number of plot widgets)
* Coordinates inversion in `EllipseShape`
* ValueError with levels histogram
* Various fixes regarding plot item creation, cross-section features, PyQt5 support, DICOM support, TIFF support, etc.
* Etc.
PlotPy-2.6.2/LICENSE 0000664 0000000 0000000 00000002776 14654366563 0014013 0 ustar 00root root 0000000 0000000 BSD 3-Clause License
Copyright (c) 2023, CEA-Codra, Pierre Raybaut.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
PlotPy-2.6.2/MANIFEST.in 0000664 0000000 0000000 00000000172 14654366563 0014530 0 ustar 00root root 0000000 0000000 graft doc
graft qtdesigner
graft src
graft colormaps
include *.desktop
include CHANGELOG.md
include requirements.txt PlotPy-2.6.2/README.md 0000664 0000000 0000000 00000010317 14654366563 0014253 0 ustar 00root root 0000000 0000000 # PlotPy: Curve and image plotting tools for Python/Qt applications
[](https://pypi.org/project/plotpy/)
[](https://github.com/PlotPyStack/plotpy/)
[](https://pypi.python.org/pypi/plotpy/)
[](https://www.anaconda.com/download/)
âšī¸ Created in 2016 by [Pierre Raybaut](https://github.com/PierreRaybaut) and maintained by the [PlotPyStack](https://github.com/PlotPyStack) organization.
âšī¸ PlotPy ***V2*** is the new major release of [`guiqwt`](https://github.com/PierreRaybaut/guiqwt): same team đī¸, same goal đ¯, same long-term support âŗ.
## Overview
`plotpy` is is a Python library providing efficient 2D data-plotting features
for interactive computing and signal/image processing application development.
It is part of the [PlotPyStack](https://github.com/PlotPyStack) project, aiming at
providing a unified framework for creating scientific GUIs with Python and Qt.
`plotpy` is based on:
* [Python](http://www.python.org) language and [Qt](https://doc.qt.io/) GUI toolkit (via [PySide](https://doc.qt.io/qtforpython-6/) or [PyQt](http://www.riverbankcomputing.co.uk/software/pyqt/intro))
* [guidata](https://pypi.python.org/pypi/guidata) automatic GUI generation library
* [PythonQwt](https://pypi.python.org/pypi/PythonQwt) plotting widgets library
* [NumPy](https://pypi.python.org/pypi/NumPy) and [SciPy](https://pypi.python.org/pypi/SciPy) scientific computing libraries
See [documentation](https://plotpy.readthedocs.io/en/latest/) for more details on
the library and [changelog](https://github.com/PlotPyStack/PlotPy/blob/master/CHANGELOG.md)
for recent history of changes.
Copyrights and licensing:
* Copyright Š 2023 [CEA](https://www.cea.fr), [Codra](https://codra.net/), [Pierre Raybaut](https://github.com/PierreRaybaut).
* Licensed under the terms of the BSD 3-Clause (see [LICENSE](https://github.com/PlotPyStack/PlotPy/blob/master/LICENSE)).
## Features
The `plotpy` library also provides the following features.
General plotting features:
* Ready-to-use [plot widgets and dialog boxes](https://plotpy.readthedocs.io/en/latest/features/plot/index.html)
* [pyplot](https://plotpy.readthedocs.io/en/latest/features/pyplot.html): interactive
plotting widgets, equivalent to `matplotlib.pyplot`, at least for the implemented functions
* Supported [plot items](https://plotpy.readthedocs.io/en/latest/features/items/index.html):
curves, images, contours, histograms, labels, shapes, annotations, ...
Interactive features (i.e. not only programmatic plotting but also with mouse/keyboard):
* Multiple object selection for moving objects or editing their properties through
automatically generated dialog boxes
* Item list panel: move objects from foreground to background, show/hide objects,
remove objects, ...
* Customizable aspect ratio for images
* Tons of ready-to-use tools: plot canvas export to image file, image snapshot,
interval selection, image rectangular filter, etc.
* Curve fitting tool with automatic fit, manual fit with sliders, ...
* Contrast adjustment panel for images: select the LUT by moving a range selection
object on the image levels histogram, eliminate outliers, ...
* X-axis and Y-axis cross-sections: support for multiple images, average
cross-section tool on a rectangular area, ...
* Apply any affine transform to displayed images in real-time (rotation,
magnification, translation, horizontal/vertical flip, ...)
Application development helpers:
* Ready-to-use [plot widgets and dialog boxes](https://plotpy.readthedocs.io/en/latest/features/plot/index.html)
* Load/save graphical objects (curves, images, shapes) into HDF5, JSON or INI files
* A lot of test scripts which demonstrate `plotpy` features
(see [examples](https://plotpy.readthedocs.io/en/latest/intro/examples.html))
## Dependencies and installation
See [Installation](https://plotpy.readthedocs.io/en/latest/intro/installation.html)
section in the documentation for more details.
PlotPy-2.6.2/colormaps/ 0000775 0000000 0000000 00000000000 14654366563 0014771 5 ustar 00root root 0000000 0000000 PlotPy-2.6.2/colormaps/README.md 0000664 0000000 0000000 00000000251 14654366563 0016246 0 ustar 00root root 0000000 0000000 This directory contains functions and data useful to generate default colormaps used
in Plotpy. Launch `colormap.py` to create/overwrite the colormaps_default.json file. PlotPy-2.6.2/colormaps/_cm.py 0000664 0000000 0000000 00001451174 14654366563 0016116 0 ustar 00root root 0000000 0000000 # -*- coding: utf-8 -*-
#
# Licensed under the terms of the BSD 3-Clause
# (see plotpy/LICENSE for details)
"""
Color data and pre-defined cmap objects.
This is a helper for cm.py, originally part of that file.
Separating the data (this file) from cm.py makes both easier
to deal with.
Objects visible in cm.py are the individual cmap objects ('autumn',
etc.) and a dictionary, 'datad', including all of these objects.
"""
# pylint: disable-msg=C0103
# pylint: disable-msg=C0301
# pylint: disable-msg=C0302
_binary_data = {
"red": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"green": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
}
_bone_data = {
"red": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"blue": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
}
_autumn_data = {
"red": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"blue": ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)),
}
_bone_data = {
"red": ((0.0, 0.0, 0.0), (0.746032, 0.652778, 0.652778), (1.0, 1.0, 1.0)),
"green": (
(0.0, 0.0, 0.0),
(0.365079, 0.319444, 0.319444),
(0.746032, 0.777778, 0.777778),
(1.0, 1.0, 1.0),
),
"blue": ((0.0, 0.0, 0.0), (0.365079, 0.444444, 0.444444), (1.0, 1.0, 1.0)),
}
_cool_data = {
"red": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
}
_copper_data = {
"red": ((0.0, 0.0, 0.0), (0.809524, 1.000000, 1.000000), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.0, 0.0), (1.0, 0.7812, 0.7812)),
"blue": ((0.0, 0.0, 0.0), (1.0, 0.4975, 0.4975)),
}
_flag_data = {
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}
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"green": ((0.0, 0, 0), (1.0, 1, 1)),
"blue": ((0.0, 0, 0), (1.0, 1, 1)),
}
_hot_data = {
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"green": (
(0.0, 0.0, 0.0),
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(0.746032, 1.000000, 1.000000),
(1.0, 1.0, 1.0),
),
"blue": ((0.0, 0.0, 0.0), (0.746032, 0.000000, 0.000000), (1.0, 1.0, 1.0)),
}
_hsv_data = {
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(0.158730, 1.000000, 1.000000),
(0.174603, 0.968750, 0.968750),
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(0.841270, 0.968750, 0.968750),
(0.857143, 1.000000, 1.000000),
(1.0, 1.0, 1.0),
),
"green": (
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(0.507937, 1.000000, 1.000000),
(0.666667, 0.062500, 0.062500),
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(1.0, 0.0, 0.0),
),
"blue": (
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(0.349206, 0.062500, 0.062500),
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(0.841270, 1.000000, 1.000000),
(0.857143, 0.937500, 0.937500),
(1.0, 0.09375, 0.09375),
),
}
_jet_data = {
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"green": (
(0.0, 0, 0),
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(0.64, 1, 1),
(0.91, 0, 0),
(1, 0, 0),
),
"blue": ((0.0, 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65, 0, 0), (1, 0, 0)),
}
_pink_data = {
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}
_prism_data = {
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(0.158730, 0.000000, 0.000000),
(0.174603, 0.666667, 0.666667),
(0.190476, 1.000000, 1.000000),
(0.222222, 1.000000, 1.000000),
(0.238095, 0.000000, 0.000000),
(0.253968, 0.000000, 0.000000),
(0.269841, 0.666667, 0.666667),
(0.285714, 1.000000, 1.000000),
(0.317460, 1.000000, 1.000000),
(0.333333, 0.000000, 0.000000),
(0.349206, 0.000000, 0.000000),
(0.365079, 0.666667, 0.666667),
(0.380952, 1.000000, 1.000000),
(0.412698, 1.000000, 1.000000),
(0.428571, 0.000000, 0.000000),
(0.444444, 0.000000, 0.000000),
(0.460317, 0.666667, 0.666667),
(0.476190, 1.000000, 1.000000),
(0.507937, 1.000000, 1.000000),
(0.523810, 0.000000, 0.000000),
(0.539683, 0.000000, 0.000000),
(0.555556, 0.666667, 0.666667),
(0.571429, 1.000000, 1.000000),
(0.603175, 1.000000, 1.000000),
(0.619048, 0.000000, 0.000000),
(0.634921, 0.000000, 0.000000),
(0.650794, 0.666667, 0.666667),
(0.666667, 1.000000, 1.000000),
(0.698413, 1.000000, 1.000000),
(0.714286, 0.000000, 0.000000),
(0.730159, 0.000000, 0.000000),
(0.746032, 0.666667, 0.666667),
(0.761905, 1.000000, 1.000000),
(0.793651, 1.000000, 1.000000),
(0.809524, 0.000000, 0.000000),
(0.825397, 0.000000, 0.000000),
(0.841270, 0.666667, 0.666667),
(0.857143, 1.000000, 1.000000),
(0.888889, 1.000000, 1.000000),
(0.904762, 0.000000, 0.000000),
(0.920635, 0.000000, 0.000000),
(0.936508, 0.666667, 0.666667),
(0.952381, 1.000000, 1.000000),
(0.984127, 1.000000, 1.000000),
(1.0, 0.0, 0.0),
),
"green": (
(0.0, 0.0, 0.0),
(0.031746, 1.000000, 1.000000),
(0.047619, 1.000000, 1.000000),
(0.063492, 0.000000, 0.000000),
(0.095238, 0.000000, 0.000000),
(0.126984, 1.000000, 1.000000),
(0.142857, 1.000000, 1.000000),
(0.158730, 0.000000, 0.000000),
(0.190476, 0.000000, 0.000000),
(0.222222, 1.000000, 1.000000),
(0.238095, 1.000000, 1.000000),
(0.253968, 0.000000, 0.000000),
(0.285714, 0.000000, 0.000000),
(0.317460, 1.000000, 1.000000),
(0.333333, 1.000000, 1.000000),
(0.349206, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),
(0.412698, 1.000000, 1.000000),
(0.428571, 1.000000, 1.000000),
(0.444444, 0.000000, 0.000000),
(0.476190, 0.000000, 0.000000),
(0.507937, 1.000000, 1.000000),
(0.523810, 1.000000, 1.000000),
(0.539683, 0.000000, 0.000000),
(0.571429, 0.000000, 0.000000),
(0.603175, 1.000000, 1.000000),
(0.619048, 1.000000, 1.000000),
(0.634921, 0.000000, 0.000000),
(0.666667, 0.000000, 0.000000),
(0.698413, 1.000000, 1.000000),
(0.714286, 1.000000, 1.000000),
(0.730159, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),
(0.793651, 1.000000, 1.000000),
(0.809524, 1.000000, 1.000000),
(0.825397, 0.000000, 0.000000),
(0.857143, 0.000000, 0.000000),
(0.888889, 1.000000, 1.000000),
(0.904762, 1.000000, 1.000000),
(0.920635, 0.000000, 0.000000),
(0.952381, 0.000000, 0.000000),
(0.984127, 1.000000, 1.000000),
(1.0, 1.0, 1.0),
),
"blue": (
(0.0, 0.0, 0.0),
(0.047619, 0.000000, 0.000000),
(0.063492, 1.000000, 1.000000),
(0.079365, 1.000000, 1.000000),
(0.095238, 0.000000, 0.000000),
(0.142857, 0.000000, 0.000000),
(0.158730, 1.000000, 1.000000),
(0.174603, 1.000000, 1.000000),
(0.190476, 0.000000, 0.000000),
(0.238095, 0.000000, 0.000000),
(0.253968, 1.000000, 1.000000),
(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),
(0.333333, 0.000000, 0.000000),
(0.349206, 1.000000, 1.000000),
(0.365079, 1.000000, 1.000000),
(0.380952, 0.000000, 0.000000),
(0.428571, 0.000000, 0.000000),
(0.444444, 1.000000, 1.000000),
(0.460317, 1.000000, 1.000000),
(0.476190, 0.000000, 0.000000),
(0.523810, 0.000000, 0.000000),
(0.539683, 1.000000, 1.000000),
(0.555556, 1.000000, 1.000000),
(0.571429, 0.000000, 0.000000),
(0.619048, 0.000000, 0.000000),
(0.634921, 1.000000, 1.000000),
(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),
(0.714286, 0.000000, 0.000000),
(0.730159, 1.000000, 1.000000),
(0.746032, 1.000000, 1.000000),
(0.761905, 0.000000, 0.000000),
(0.809524, 0.000000, 0.000000),
(0.825397, 1.000000, 1.000000),
(0.841270, 1.000000, 1.000000),
(0.857143, 0.000000, 0.000000),
(0.904762, 0.000000, 0.000000),
(0.920635, 1.000000, 1.000000),
(0.936508, 1.000000, 1.000000),
(0.952381, 0.000000, 0.000000),
(1.0, 0.0, 0.0),
),
}
_spring_data = {
"red": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
}
_summer_data = {
"red": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.5, 0.5), (1.0, 1.0, 1.0)),
"blue": ((0.0, 0.4, 0.4), (1.0, 0.4, 0.4)),
}
_winter_data = {
"red": ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)),
"green": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 0.5, 0.5)),
}
_spectral_data = {
"red": [
(0.0, 0.0, 0.0),
(0.05, 0.4667, 0.4667),
(0.10, 0.5333, 0.5333),
(0.15, 0.0, 0.0),
(0.20, 0.0, 0.0),
(0.25, 0.0, 0.0),
(0.30, 0.0, 0.0),
(0.35, 0.0, 0.0),
(0.40, 0.0, 0.0),
(0.45, 0.0, 0.0),
(0.50, 0.0, 0.0),
(0.55, 0.0, 0.0),
(0.60, 0.0, 0.0),
(0.65, 0.7333, 0.7333),
(0.70, 0.9333, 0.9333),
(0.75, 1.0, 1.0),
(0.80, 1.0, 1.0),
(0.85, 1.0, 1.0),
(0.90, 0.8667, 0.8667),
(0.95, 0.80, 0.80),
(1.0, 0.80, 0.80),
],
"green": [
(0.0, 0.0, 0.0),
(0.05, 0.0, 0.0),
(0.10, 0.0, 0.0),
(0.15, 0.0, 0.0),
(0.20, 0.0, 0.0),
(0.25, 0.4667, 0.4667),
(0.30, 0.6000, 0.6000),
(0.35, 0.6667, 0.6667),
(0.40, 0.6667, 0.6667),
(0.45, 0.6000, 0.6000),
(0.50, 0.7333, 0.7333),
(0.55, 0.8667, 0.8667),
(0.60, 1.0, 1.0),
(0.65, 1.0, 1.0),
(0.70, 0.9333, 0.9333),
(0.75, 0.8000, 0.8000),
(0.80, 0.6000, 0.6000),
(0.85, 0.0, 0.0),
(0.90, 0.0, 0.0),
(0.95, 0.0, 0.0),
(1.0, 0.80, 0.80),
],
"blue": [
(0.0, 0.0, 0.0),
(0.05, 0.5333, 0.5333),
(0.10, 0.6000, 0.6000),
(0.15, 0.6667, 0.6667),
(0.20, 0.8667, 0.8667),
(0.25, 0.8667, 0.8667),
(0.30, 0.8667, 0.8667),
(0.35, 0.6667, 0.6667),
(0.40, 0.5333, 0.5333),
(0.45, 0.0, 0.0),
(0.5, 0.0, 0.0),
(0.55, 0.0, 0.0),
(0.60, 0.0, 0.0),
(0.65, 0.0, 0.0),
(0.70, 0.0, 0.0),
(0.75, 0.0, 0.0),
(0.80, 0.0, 0.0),
(0.85, 0.0, 0.0),
(0.90, 0.0, 0.0),
(0.95, 0.0, 0.0),
(1.0, 0.80, 0.80),
],
}
datad = {
"autumn": _autumn_data,
"bone": _bone_data,
"binary": _binary_data,
"cool": _cool_data,
"copper": _copper_data,
"flag": _flag_data,
"gray": _gray_data,
"hot": _hot_data,
"hsv": _hsv_data,
"jet": _jet_data,
"pink": _pink_data,
"prism": _prism_data,
"spring": _spring_data,
"summer": _summer_data,
"winter": _winter_data,
"spectral": _spectral_data,
}
# 34 colormaps based on color specifications and designs
# developed by Cynthia Brewer (http://colorbrewer.org).
# The ColorBrewer palettes have been included under the terms
# of an Apache-stype license (for details, see the file
# LICENSE_COLORBREWER in the license directory of the matplotlib
# source distribution).
_Accent_data = {
"blue": [
(0.0, 0.49803921580314636, 0.49803921580314636),
(0.14285714285714285, 0.83137255907058716, 0.83137255907058716),
(0.2857142857142857, 0.52549022436141968, 0.52549022436141968),
(0.42857142857142855, 0.60000002384185791, 0.60000002384185791),
(0.5714285714285714, 0.69019609689712524, 0.69019609689712524),
(0.7142857142857143, 0.49803921580314636, 0.49803921580314636),
(0.8571428571428571, 0.090196080505847931, 0.090196080505847931),
(1.0, 0.40000000596046448, 0.40000000596046448),
],
"green": [
(0.0, 0.78823530673980713, 0.78823530673980713),
(0.14285714285714285, 0.68235296010971069, 0.68235296010971069),
(0.2857142857142857, 0.75294119119644165, 0.75294119119644165),
(0.42857142857142855, 1.0, 1.0),
(0.5714285714285714, 0.42352941632270813, 0.42352941632270813),
(0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254),
(0.8571428571428571, 0.35686275362968445, 0.35686275362968445),
(1.0, 0.40000000596046448, 0.40000000596046448),
],
"red": [
(0.0, 0.49803921580314636, 0.49803921580314636),
(0.14285714285714285, 0.7450980544090271, 0.7450980544090271),
(0.2857142857142857, 0.99215686321258545, 0.99215686321258545),
(0.42857142857142855, 1.0, 1.0),
(0.5714285714285714, 0.21960784494876862, 0.21960784494876862),
(0.7142857142857143, 0.94117647409439087, 0.94117647409439087),
(0.8571428571428571, 0.74901962280273438, 0.74901962280273438),
(1.0, 0.40000000596046448, 0.40000000596046448),
],
}
_Blues_data = {
"blue": [
(0.0, 1.0, 1.0),
(0.125, 0.9686274528503418, 0.9686274528503418),
(0.25, 0.93725490570068359, 0.93725490570068359),
(0.375, 0.88235294818878174, 0.88235294818878174),
(0.5, 0.83921569585800171, 0.83921569585800171),
(0.625, 0.7764706015586853, 0.7764706015586853),
(0.75, 0.70980393886566162, 0.70980393886566162),
(0.875, 0.61176472902297974, 0.61176472902297974),
(1.0, 0.41960784792900085, 0.41960784792900085),
],
"green": [
(0.0, 0.9843137264251709, 0.9843137264251709),
(0.125, 0.92156863212585449, 0.92156863212585449),
(0.25, 0.85882353782653809, 0.85882353782653809),
(0.375, 0.7921568751335144, 0.7921568751335144),
(0.5, 0.68235296010971069, 0.68235296010971069),
(0.625, 0.57254904508590698, 0.57254904508590698),
(0.75, 0.44313725829124451, 0.44313725829124451),
(0.875, 0.31764706969261169, 0.31764706969261169),
(1.0, 0.18823529779911041, 0.18823529779911041),
],
"red": [
(0.0, 0.9686274528503418, 0.9686274528503418),
(0.125, 0.87058824300765991, 0.87058824300765991),
(0.25, 0.7764706015586853, 0.7764706015586853),
(0.375, 0.61960786581039429, 0.61960786581039429),
(0.5, 0.41960784792900085, 0.41960784792900085),
(0.625, 0.25882354378700256, 0.25882354378700256),
(0.75, 0.12941177189350128, 0.12941177189350128),
(0.875, 0.031372550874948502, 0.031372550874948502),
(1.0, 0.031372550874948502, 0.031372550874948502),
],
}
_BrBG_data = {
"blue": [
(0.0, 0.019607843831181526, 0.019607843831181526),
(0.10000000000000001, 0.039215687662363052, 0.039215687662363052),
(0.20000000000000001, 0.17647059261798859, 0.17647059261798859),
(0.29999999999999999, 0.49019607901573181, 0.49019607901573181),
(0.40000000000000002, 0.76470589637756348, 0.76470589637756348),
(0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.89803922176361084, 0.89803922176361084),
(0.69999999999999996, 0.75686275959014893, 0.75686275959014893),
(0.80000000000000004, 0.56078433990478516, 0.56078433990478516),
(0.90000000000000002, 0.36862745881080627, 0.36862745881080627),
(1.0, 0.18823529779911041, 0.18823529779911041),
],
"green": [
(0.0, 0.18823529779911041, 0.18823529779911041),
(0.10000000000000001, 0.31764706969261169, 0.31764706969261169),
(0.20000000000000001, 0.5058823823928833, 0.5058823823928833),
(0.29999999999999999, 0.7607843279838562, 0.7607843279838562),
(0.40000000000000002, 0.90980392694473267, 0.90980392694473267),
(0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.91764706373214722, 0.91764706373214722),
(0.69999999999999996, 0.80392158031463623, 0.80392158031463623),
(0.80000000000000004, 0.59215688705444336, 0.59215688705444336),
(0.90000000000000002, 0.40000000596046448, 0.40000000596046448),
(1.0, 0.23529411852359772, 0.23529411852359772),
],
"red": [
(0.0, 0.32941177487373352, 0.32941177487373352),
(0.10000000000000001, 0.54901963472366333, 0.54901963472366333),
(0.20000000000000001, 0.74901962280273438, 0.74901962280273438),
(0.29999999999999999, 0.87450981140136719, 0.87450981140136719),
(0.40000000000000002, 0.96470588445663452, 0.96470588445663452),
(0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.78039216995239258, 0.78039216995239258),
(0.69999999999999996, 0.50196081399917603, 0.50196081399917603),
(0.80000000000000004, 0.20784313976764679, 0.20784313976764679),
(0.90000000000000002, 0.0039215688593685627, 0.0039215688593685627),
(1.0, 0.0, 0.0),
],
}
_BuGn_data = {
"blue": [
(0.0, 0.99215686321258545, 0.99215686321258545),
(0.125, 0.97647058963775635, 0.97647058963775635),
(0.25, 0.90196079015731812, 0.90196079015731812),
(0.375, 0.78823530673980713, 0.78823530673980713),
(0.5, 0.64313727617263794, 0.64313727617263794),
(0.625, 0.46274510025978088, 0.46274510025978088),
(0.75, 0.27058824896812439, 0.27058824896812439),
(0.875, 0.17254902422428131, 0.17254902422428131),
(1.0, 0.10588235408067703, 0.10588235408067703),
],
"green": [
(0.0, 0.98823529481887817, 0.98823529481887817),
(0.125, 0.96078431606292725, 0.96078431606292725),
(0.25, 0.92549020051956177, 0.92549020051956177),
(0.375, 0.84705883264541626, 0.84705883264541626),
(0.5, 0.7607843279838562, 0.7607843279838562),
(0.625, 0.68235296010971069, 0.68235296010971069),
(0.75, 0.54509806632995605, 0.54509806632995605),
(0.875, 0.42745098471641541, 0.42745098471641541),
(1.0, 0.26666668057441711, 0.26666668057441711),
],
"red": [
(0.0, 0.9686274528503418, 0.9686274528503418),
(0.125, 0.89803922176361084, 0.89803922176361084),
(0.25, 0.80000001192092896, 0.80000001192092896),
(0.375, 0.60000002384185791, 0.60000002384185791),
(0.5, 0.40000000596046448, 0.40000000596046448),
(0.625, 0.25490197539329529, 0.25490197539329529),
(0.75, 0.13725490868091583, 0.13725490868091583),
(0.875, 0.0, 0.0),
(1.0, 0.0, 0.0),
],
}
_BuPu_data = {
"blue": [
(0.0, 0.99215686321258545, 0.99215686321258545),
(0.125, 0.95686274766921997, 0.95686274766921997),
(0.25, 0.90196079015731812, 0.90196079015731812),
(0.375, 0.85490196943283081, 0.85490196943283081),
(0.5, 0.7764706015586853, 0.7764706015586853),
(0.625, 0.69411766529083252, 0.69411766529083252),
(0.75, 0.61568629741668701, 0.61568629741668701),
(0.875, 0.48627451062202454, 0.48627451062202454),
(1.0, 0.29411765933036804, 0.29411765933036804),
],
"green": [
(0.0, 0.98823529481887817, 0.98823529481887817),
(0.125, 0.92549020051956177, 0.92549020051956177),
(0.25, 0.82745099067687988, 0.82745099067687988),
(0.375, 0.73725491762161255, 0.73725491762161255),
(0.5, 0.58823531866073608, 0.58823531866073608),
(0.625, 0.41960784792900085, 0.41960784792900085),
(0.75, 0.25490197539329529, 0.25490197539329529),
(0.875, 0.058823529630899429, 0.058823529630899429),
(1.0, 0.0, 0.0),
],
"red": [
(0.0, 0.9686274528503418, 0.9686274528503418),
(0.125, 0.87843137979507446, 0.87843137979507446),
(0.25, 0.74901962280273438, 0.74901962280273438),
(0.375, 0.61960786581039429, 0.61960786581039429),
(0.5, 0.54901963472366333, 0.54901963472366333),
(0.625, 0.54901963472366333, 0.54901963472366333),
(0.75, 0.53333336114883423, 0.53333336114883423),
(0.875, 0.5058823823928833, 0.5058823823928833),
(1.0, 0.30196079611778259, 0.30196079611778259),
],
}
_Dark2_data = {
"blue": [
(0.0, 0.46666666865348816, 0.46666666865348816),
(0.14285714285714285, 0.0078431377187371254, 0.0078431377187371254),
(0.2857142857142857, 0.70196080207824707, 0.70196080207824707),
(0.42857142857142855, 0.54117649793624878, 0.54117649793624878),
(0.5714285714285714, 0.11764705926179886, 0.11764705926179886),
(0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254),
(0.8571428571428571, 0.11372549086809158, 0.11372549086809158),
(1.0, 0.40000000596046448, 0.40000000596046448),
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"green": [
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(0.5, 0.99607843160629272, 0.99607843160629272),
(0.625, 0.92549020051956177, 0.92549020051956177),
(0.75, 0.80000001192092896, 0.80000001192092896),
(0.875, 0.60000002384185791, 0.60000002384185791),
(1.0, 0.40000000596046448, 0.40000000596046448),
],
}
_YlOrRd_data = {
"blue": [
(0.0, 0.80000001192092896, 0.80000001192092896),
(0.125, 0.62745100259780884, 0.62745100259780884),
(0.25, 0.46274510025978088, 0.46274510025978088),
(0.375, 0.29803922772407532, 0.29803922772407532),
(0.5, 0.23529411852359772, 0.23529411852359772),
(0.625, 0.16470588743686676, 0.16470588743686676),
(0.75, 0.10980392247438431, 0.10980392247438431),
(0.875, 0.14901961386203766, 0.14901961386203766),
(1.0, 0.14901961386203766, 0.14901961386203766),
],
"green": [
(0.0, 1.0, 1.0),
(0.125, 0.92941176891326904, 0.92941176891326904),
(0.25, 0.85098040103912354, 0.85098040103912354),
(0.375, 0.69803923368453979, 0.69803923368453979),
(0.5, 0.55294120311737061, 0.55294120311737061),
(0.625, 0.30588236451148987, 0.30588236451148987),
(0.75, 0.10196078568696976, 0.10196078568696976),
(0.875, 0.0, 0.0),
(1.0, 0.0, 0.0),
],
"red": [
(0.0, 1.0, 1.0),
(0.125, 1.0, 1.0),
(0.25, 0.99607843160629272, 0.99607843160629272),
(0.375, 0.99607843160629272, 0.99607843160629272),
(0.5, 0.99215686321258545, 0.99215686321258545),
(0.625, 0.98823529481887817, 0.98823529481887817),
(0.75, 0.89019608497619629, 0.89019608497619629),
(0.875, 0.74117648601531982, 0.74117648601531982),
(1.0, 0.50196081399917603, 0.50196081399917603),
],
}
# The next 7 palettes are from the Yorick scientific visalisation package,
# an evolution of the GIST package, both by David H. Munro.
# They are released under a BSD-like license (see LICENSE_YORICK in
# the license directory of the matplotlib source distribution).
_gist_earth_data = {
"blue": [
(0.0, 0.0, 0.0),
(0.0042016808874905109, 0.18039216101169586, 0.18039216101169586),
(0.0084033617749810219, 0.22745098173618317, 0.22745098173618317),
(0.012605042196810246, 0.27058824896812439, 0.27058824896812439),
(0.016806723549962044, 0.31764706969261169, 0.31764706969261169),
(0.021008403971791267, 0.36078432202339172, 0.36078432202339172),
(0.025210084393620491, 0.40784314274787903, 0.40784314274787903),
(0.029411764815449715, 0.45490196347236633, 0.45490196347236633),
(0.033613447099924088, 0.45490196347236633, 0.45490196347236633),
(0.037815127521753311, 0.45490196347236633, 0.45490196347236633),
(0.042016807943582535, 0.45490196347236633, 0.45490196347236633),
(0.046218488365411758, 0.45490196347236633, 0.45490196347236633),
(0.050420168787240982, 0.45882353186607361, 0.45882353186607361),
(0.054621849209070206, 0.45882353186607361, 0.45882353186607361),
(0.058823529630899429, 0.45882353186607361, 0.45882353186607361),
(0.063025213778018951, 0.45882353186607361, 0.45882353186607361),
(0.067226894199848175, 0.45882353186607361, 0.45882353186607361),
(0.071428574621677399, 0.46274510025978088, 0.46274510025978088),
(0.075630255043506622, 0.46274510025978088, 0.46274510025978088),
(0.079831935465335846, 0.46274510025978088, 0.46274510025978088),
(0.08403361588716507, 0.46274510025978088, 0.46274510025978088),
(0.088235296308994293, 0.46274510025978088, 0.46274510025978088),
(0.092436976730823517, 0.46666666865348816, 0.46666666865348816),
(0.09663865715265274, 0.46666666865348816, 0.46666666865348816),
(0.10084033757448196, 0.46666666865348816, 0.46666666865348816),
(0.10504201799631119, 0.46666666865348816, 0.46666666865348816),
(0.10924369841814041, 0.46666666865348816, 0.46666666865348816),
(0.11344537883996964, 0.47058823704719543, 0.47058823704719543),
(0.11764705926179886, 0.47058823704719543, 0.47058823704719543),
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(0.13445378839969635, 0.47450980544090271, 0.47450980544090271),
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(0.1428571492433548, 0.47450980544090271, 0.47450980544090271),
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(0.23529411852359772, 0.49019607901573181, 0.49019607901573181),
(0.23949579894542694, 0.49411764740943909, 0.49411764740943909),
(0.24369747936725616, 0.49411764740943909, 0.49411764740943909),
(0.24789915978908539, 0.49411764740943909, 0.49411764740943909),
(0.25210085511207581, 0.49411764740943909, 0.49411764740943909),
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(1.0, 0.0039215688593685627, 0.0039215688593685627),
],
}
datad["Accent"] = _Accent_data
datad["Blues"] = _Blues_data
datad["BrBG"] = _BrBG_data
datad["BuGn"] = _BuGn_data
datad["BuPu"] = _BuPu_data
datad["Dark2"] = _Dark2_data
datad["GnBu"] = _GnBu_data
datad["Greens"] = _Greens_data
datad["Greys"] = _Greys_data
datad["Oranges"] = _Oranges_data
datad["OrRd"] = _OrRd_data
datad["Paired"] = _Paired_data
datad["Pastel1"] = _Pastel1_data
datad["Pastel2"] = _Pastel2_data
datad["PiYG"] = _PiYG_data
datad["PRGn"] = _PRGn_data
datad["PuBu"] = _PuBu_data
datad["PuBuGn"] = _PuBuGn_data
datad["PuOr"] = _PuOr_data
datad["PuRd"] = _PuRd_data
datad["Purples"] = _Purples_data
datad["RdBu"] = _RdBu_data
datad["RdGy"] = _RdGy_data
datad["RdPu"] = _RdPu_data
datad["RdYlBu"] = _RdYlBu_data
datad["RdYlGn"] = _RdYlGn_data
datad["Reds"] = _Reds_data
datad["Set1"] = _Set1_data
datad["Set2"] = _Set2_data
datad["Set3"] = _Set3_data
datad["Spectral"] = _Spectral_data
datad["YlGn"] = _YlGn_data
datad["YlGnBu"] = _YlGnBu_data
datad["YlOrBr"] = _YlOrBr_data
datad["YlOrRd"] = _YlOrRd_data
datad["gist_earth"] = _gist_earth_data
datad["gist_gray"] = _gist_gray_data
datad["gist_heat"] = _gist_heat_data
datad["gist_ncar"] = _gist_ncar_data
datad["gist_rainbow"] = _gist_rainbow_data
datad["gist_stern"] = _gist_stern_data
datad["gist_yarg"] = _gist_yarg_data
PlotPy-2.6.2/colormaps/colormap.py 0000664 0000000 0000000 00000005636 14654366563 0017171 0 ustar 00root root 0000000 0000000 # -*- coding: utf-8 -*-
#
# Licensed under the terms of the BSD 3-Clause
# (see plotpy/LICENSE for details)
# pylint: disable=C0103
"""
Colormap utilities
------------------
"""
from __future__ import annotations
import _cm
import numpy as np
from qtpy import QtGui as QG
from qwt import QwtInterval
from plotpy.mathutils.colormap import (
DEFAULT_COLORMAPS,
DEFAULT_COLORMAPS_PATH,
save_colormaps,
)
from plotpy.widgets.colormap.widget import EditableColormap # Reuse matplotlib data
# usefull to obtain a full color map
FULLRANGE = QwtInterval(0.0, 1.0)
COLORMAPS = {}
EXTRA_COLORMAPS = [] # custom build colormaps
ICON_CACHE = {}
def _interpolate(val, vmin, vmax):
"""Interpolate a color component between to values as provided
by matplotlib colormaps
"""
interp = (val - vmin[0]) / (vmax[0] - vmin[0])
return (1 - interp) * vmin[1] + interp * vmax[2]
def _setup_colormap(cmap: EditableColormap, cmdata):
"""Setup a CustomQwtLinearColorMap according to
matplotlib's data
"""
red = np.array(cmdata["red"])
green = np.array(cmdata["green"])
blue = np.array(cmdata["blue"])
qmin = QG.QColor()
qmin.setRgbF(red[0, 2], green[0, 2], blue[0, 2])
qmax = QG.QColor()
qmax.setRgbF(red[-1, 2], green[-1, 2], blue[-1, 2])
cmap.setColorInterval(qmin, qmax)
indices = sorted(set(red[:, 0]) | set(green[:, 0]) | set(blue[:, 0]))
for i in indices[1:-1]:
idxr = red[:, 0].searchsorted(i)
idxg = green[:, 0].searchsorted(i)
idxb = blue[:, 0].searchsorted(i)
compr = _interpolate(i, red[idxr - 1], red[idxr])
compg = _interpolate(i, green[idxg - 1], green[idxg])
compb = _interpolate(i, blue[idxb - 1], blue[idxb])
col = QG.QColor()
col.setRgbF(compr, compg, compb)
cmap.addColorStop(i, col)
def get_cmap(name: str) -> EditableColormap:
"""Get a colormap from its name
Args:
name: colormap name
Returns:
Return a QwtColormap based on matplotlib's colormap of the same name
We avoid rebuilding the cmap by keeping it in cache
"""
if name in COLORMAPS:
return COLORMAPS[name]
colormap = EditableColormap()
COLORMAPS[name] = colormap
COLORMAPS[colormap] = name
data = getattr(_cm, "_" + name + "_data")
_setup_colormap(colormap, data)
return colormap
def get_colormap_list() -> list[str]:
"""Builds a list of available colormaps by introspection of the _cm module
Returns:
list of colormap names
"""
cmlist = []
cmlist += EXTRA_COLORMAPS
for name in dir(_cm):
if name.endswith("_data"):
obj = getattr(_cm, name)
if isinstance(obj, dict):
cmlist.append(name[1:-5])
return cmlist
if __name__ == "__main__":
for cmap in get_colormap_list():
DEFAULT_COLORMAPS[cmap] = get_cmap(cmap)
save_colormaps(DEFAULT_COLORMAPS_PATH, DEFAULT_COLORMAPS)
PlotPy-2.6.2/colormaps/matplotlib_cmaps.py 0000664 0000000 0000000 00000021543 14654366563 0020702 0 ustar 00root root 0000000 0000000 from __future__ import annotations
import copy
import sys
from typing import Callable
import matplotlib.colors as pltc
import matplotlib.pyplot as plt
import numpy as np
from plotpy.mathutils.colormap import (
DEFAULT_COLORMAPS,
DEFAULT_COLORMAPS_PATH,
CmapDictType,
EditableColormap,
save_colormaps,
)
PERCEPTUALLY_UNIFORM_CMAPS = ["viridis", "plasma", "inferno", "magma", "cividis"]
SEQUENTIAL_CMAPS = [
"Greys",
"Purples",
"Blues",
"Greens",
"Oranges",
"Reds",
"YlOrBr",
"YlOrRd",
"OrRd",
"PuRd",
"RdPu",
"BuPu",
"GnBu",
"PuBu",
"YlGnBu",
"PuBuGn",
"BuGn",
"YlGn",
]
SEQUENTIAL_CMAPS2 = [
"binary",
"gist_yarg",
"gist_gray",
"gray",
"bone",
"pink",
"spring",
"summer",
"autumn",
"winter",
"cool",
"Wistia",
"hot",
"afmhot",
"gist_heat",
"copper",
]
DIVERGING_CMAPS = [
"PiYG",
"PRGn",
"BrBG",
"PuOr",
"RdGy",
"RdBu",
"RdYlBu",
"RdYlGn",
"Spectral",
"coolwarm",
"bwr",
"seismic",
]
CYCLIC_CMAPS = ["twilight", "twilight_shifted", "hsv"]
QUALITATIVE_CMAPS = [
"Pastel1",
"Pastel2",
"Paired",
"Accent",
"Dark2",
"Set1",
"Set2",
"Set3",
"tab10",
"tab20",
"tab20b",
"tab20c",
]
MISCELLANEOUS_CMAPS = [
"flag",
"prism",
"ocean",
"gist_earth",
"terrain",
"gist_stern",
"gnuplot",
"gnuplot2",
"CMRmap",
"cubehelix",
"brg",
"gist_rainbow",
"rainbow",
"jet",
"turbo",
"nipy_spectral",
"gist_ncar",
]
SORTED_MATPLOTLIB_COLORMAPS: list[str] = [
*PERCEPTUALLY_UNIFORM_CMAPS,
*SEQUENTIAL_CMAPS,
*SEQUENTIAL_CMAPS2,
*DIVERGING_CMAPS,
*CYCLIC_CMAPS,
*QUALITATIVE_CMAPS,
*MISCELLANEOUS_CMAPS,
]
def rgb_colors_to_hex_list(
colors: list[tuple[int, int, int]]
) -> list[tuple[float, str]]:
"""Convert a list of RGB colors to a list of tuples with the position of the color
and the color in hex format. Positions evenly distributed between 0 and 1.
Args:
colors: list of RGB colors
Returns:
list of tuples with the position of the color and the color in hex format
"""
return [(i / len(colors), pltc.to_hex(color)) for i, color in enumerate(colors)]
def _interpolate(
val: float, vmin: tuple[float, float, float], vmax: tuple[float, float, float]
):
"""Interpolate between two level of a color.
Args:
val: value to interpolate
vmin: R, G or B tuple from a matplotlib segmented colormap
vmax: R, G or B tuple from matplotlib segmented colormap
Returns:
The interpolated R, G or B component
"""
interp = (val - vmin[0]) / (vmax[0] - vmin[0])
return (1 - interp) * vmin[1] + interp * vmax[2]
def std_segmented_cmap_to_hex_list(cmdata: dict[str, list[tuple[float, float, float]]]):
"""Convert a matplotlib segmented colormap to a list of tuples with the position of
the color and the color in hex format.
Args:
cmdata: segmented colormap data
Returns:
list of tuples with the position of the color and the color in hex format
"""
colors: list[tuple[float, str]] = []
red = np.array(cmdata["red"])
green = np.array(cmdata["green"])
blue = np.array(cmdata["blue"])
indices = sorted(set(red[:, 0]) | set(green[:, 0]) | set(blue[:, 0]))
for i in indices:
idxr = red[:, 0].searchsorted(i)
idxg = green[:, 0].searchsorted(i)
idxb = blue[:, 0].searchsorted(i)
compr = _interpolate(i, red[idxr - 1], red[idxr])
compg = _interpolate(i, green[idxg - 1], green[idxg])
compb = _interpolate(i, blue[idxb - 1], blue[idxb])
colors.append((i, pltc.to_hex((compr, compg, compb))))
return colors
InterpFuncT = Callable[[np.ndarray], np.ndarray]
def func_segmented_cmap_to_hex_list(
n: int,
cmap: pltc.LinearSegmentedColormap,
) -> list[tuple[float, str]]:
"""Convert a matplotlib segmented colormap to a list of tuples with the position of
the color and the color in hex format. The input colormap contains function for each
color RGB component instead of a list of colors.
Args:
n: number of colors to generate
cmap: segmented colormap
Returns:
list of tuples with the position of the color and the color in hex format
"""
colors = []
arr = np.linspace(0, 1, n, dtype=float)
colors = [(i, pltc.to_hex(rgba)) for i, rgba in zip(arr, cmap(arr))]
return colors
def continuous_to_descrete_cmap(cmap: EditableColormap) -> EditableColormap:
"""Convert a continuous colormap to a descrete one.
Args:
cmap: colormap to convert
Returns:
descrete colormap
"""
raw_cmap: tuple[tuple[float, str], ...] = cmap.to_tuples()
new_raw_cmap: list[tuple[float, str]] = [raw_cmap[0]]
n = len(raw_cmap)
coeff = (n - 1) / n
for i, (pos, color) in enumerate(raw_cmap[1:]):
prev_pos, prev_color = raw_cmap[i]
curr_pos, curr_color = pos, color
new_pos = curr_pos * coeff
new_raw_cmap.append((new_pos, prev_color))
new_raw_cmap.append((new_pos, curr_color))
new_raw_cmap.append(raw_cmap[-1])
return EditableColormap.from_iterable(new_raw_cmap, name=cmap.name)
def sort_mpl_colormaps(colormaps: CmapDictType) -> CmapDictType:
"""Filter and sort input colormaps to follow the same order (by category) as in the
matplotlib colormaps documentation. Colormaps not found in the matplotlib
are filtered out.
Args:
colormaps: Dictionnary of colormaps to extract and order
Returns:
Filtered and sorted colormaps dictionnary
"""
ordered_colormaps: CmapDictType = {}
lower_cmap_names = [cm.lower() for cm in SORTED_MATPLOTLIB_COLORMAPS]
for lower_name in lower_cmap_names:
cmap = colormaps.get(lower_name, None)
if lower_name.endswith("_r"):
continue
if cmap is None:
print(f"Colormap {lower_name} not found in input colormaps.")
continue
ordered_colormaps[lower_name] = cmap
return ordered_colormaps
def append_non_mpl_colormaps(mpl_colormaps: CmapDictType, colormaps: CmapDictType):
"""Append colormaps not found in the matplotlib colormaps to the input colormaps.
Mutate the input in place.
Args:
mpl_colormaps: dictionnary of matplotlib colormaps. Mutated in place.
colormaps: dictionnary of colormaps to append to the matplotlib colormaps
"""
colormap_names = set(SORTED_MATPLOTLIB_COLORMAPS)
for colormap in colormaps.values():
if colormap.name not in colormap_names:
print(f"{colormap} not in matplotlib colormaps.")
mpl_colormaps[colormap.name.lower()] = colormap
def main(cmaps: CmapDictType, out_json_path: str = DEFAULT_COLORMAPS_PATH):
new_cmaps: dict[str, list[tuple[float, str]]] = {}
# Uniform colormaps with a .colors attribute that return a list of RGB colors
cmaps_with_colors = [
"magma",
"viridis",
"inferno",
"plasma",
"cividis",
]
# Discrete colormaps, same as uniform colormaps but the colormap must be post
# processed to become descrete
descrete_cmaps = [
"Pastel1",
"Pastel2",
"Paired",
"Accent",
"Dark2",
"Set1",
"Set2",
"Set3",
]
cmaps_with_colors.extend(descrete_cmaps)
# Colormaps with a _segmented_data attribute that contains the R, G and B components
# as lists of tuples
segmented_cmaps = [
"coolwarm",
"bwr",
"seismic",
]
# Colormaps with a _segmentdata attribute that contains the R, G and B components as
# functions that return the color for a given position
interp_cmaps = ["gnuplot2", "CMRmap", "rainbow", "turbo", "afmhot"]
for cm_name in cmaps_with_colors:
cmap = plt.get_cmap(cm_name)
new_cmaps[cm_name] = rgb_colors_to_hex_list(cmap.colors)
for cm_name in descrete_cmaps:
cmap = EditableColormap.from_iterable(new_cmaps[cm_name], name=cm_name)
new_cmaps[cm_name] = list(continuous_to_descrete_cmap(cmap).to_tuples())
for cm_name in segmented_cmaps:
cmap = plt.get_cmap(cm_name)
new_cmaps[cm_name] = std_segmented_cmap_to_hex_list(cmap._segmentdata)
n = 128
for cm_name in interp_cmaps:
cmap = plt.get_cmap(cm_name)
new_cmaps[cm_name] = func_segmented_cmap_to_hex_list(n, cmap)
for name, raw_cm in new_cmaps.items():
cmaps[name.lower()] = EditableColormap.from_iterable(raw_cm, name=name)
ordered_cmaps = sort_mpl_colormaps(cmaps)
append_non_mpl_colormaps(ordered_cmaps, cmaps)
save_colormaps(out_json_path, ordered_cmaps)
if __name__ == "__main__":
cmaps = copy.deepcopy(DEFAULT_COLORMAPS)
out_json = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_COLORMAPS_PATH
main(cmaps, out_json)
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tŗÔ
2˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ !Ŗú˙˙ė
˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ų˙˙˙˙Y˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ?Ú˙˙˙˙˙˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙˙˙˙Ā˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ !Ę˙˙˙˙˙˙ß˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ Mí˙˙˙˙˙˙ō
.˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ũ˙˙˙˙˙˙üL˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ Ã˙˙˙˙˙˙˙˙i˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙
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2˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ \õ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ūšJ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙îĸM˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ #Í˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙īŖR
˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ Tī˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ö°_˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ °˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ûŊc˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ 8ĸú˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ö§I˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ qÔ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ßw˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ &÷˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙÷ĸ
2˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙
2¯ũ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ūŗ6˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ 'ĸũ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ÄB˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ û˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙š
,˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ^ę˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙û˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ -Ë˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙v˙g ˙X~Ē˙K~´˙<|ŋ˙0|É˙-}Ę˙'Đ˙$Ņ˙Ô˙Õ˙$Ņ˙(
Î˙-Ę˙3Å˙;ž˙Hĩ˙UĢ˙aŖ˙o˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ík˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ĒĒĒwø˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙
˙f~Ą˙Lx´˙5sĮ˙oŲ˙kę˙ lî˙ nî˙ pî˙ rî˙ tí˙ ví˙ xí˙ zė˙ |ė˙ ~ë˙ ė˙ ë˙ ę˙ ë˙ ę˙ ę˙ ę˙ é˙ é˙ é˙ č˙Ū˙#Î˙8ŋ˙N°˙i˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙Ķ
A˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ "Å˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙
˙az¤˙Apž˙ hØ˙aí˙ bđ˙ cī˙ fī˙ hī˙ iî˙ lī˙ nī˙ pî˙ rî˙ tí˙ vė˙ xí˙ zė˙ }ė˙ ~ė˙ ė˙ ë˙ ë˙ ë˙ ę˙ é˙ ę˙ é˙ é˙ é˙ č˙ č˙ į˙ č˙ į˙ į˙ ä˙Ō˙9ž˙W¨˙|˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ū˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ Lî˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙}˙Ut¯˙)eŅ˙Zî˙ Zņ˙ \ņ˙ ^ņ˙ `đ˙ ađ˙ dđ˙ fđ˙ hī˙ jī˙ lî˙ nî˙ pí˙ rî˙ tí˙ wí˙ xí˙ zė˙ |ė˙ ~ë˙ ë˙ ë˙ ę˙ ę˙ ę˙ ę˙ é˙ ę˙ é˙ é˙ č˙ č˙ č˙ č˙ į˙ į˙ æ˙ Ŗæ˙ ¤æ˙ §æ˙¨ä˙¤Ō˙Cĩ˙j˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙áE˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ĒĒĒ
ū˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙}˙y}
˙w|˙&aŅ˙ Uņ˙ Xō˙ Zō˙ [ņ˙ ^ņ˙ `ņ˙ bđ˙ dđ˙ fī˙ hī˙ jî˙ lī˙ nî˙ pî˙ rî˙ tí˙ ví˙ xí˙ zė˙ }ė˙ ~ė˙ ė˙ ë˙ ę˙ ë˙ ę˙ ę˙ é˙ é˙ é˙ é˙ č˙ č˙ į˙ į˙ į˙ į˙ æ˙ Ŗæ˙ ¤å˙ §æ˙ Šæ˙ Ģå˙ å˙ ¯å˙ĢÚ˙=š˙n˙˙˙˙˙˙˙˙˙˙˙˙˙˙ū˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ĩ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙n{˙{~
˙˙˙˙˙,cË˙ Vō˙ Xō˙ Yņ˙ \ņ˙ ^ņ˙ _đ˙ bđ˙ dđ˙ fđ˙ hī˙ jī˙ lī˙ nî˙ pí˙ rî˙ tí˙ ví˙ xí˙ zė˙ |ė˙ ~ė˙ ë˙ ë˙ ë˙ ę˙ ę˙ ę˙ é˙ é˙ é˙ č˙ é˙ č˙ č˙ į˙ į˙ į˙ æ˙ Ŗæ˙ Ĩæ˙ Ļå˙ Šå˙ Ēä˙ å˙ ¯ä˙ ąä˙ ŗä˙ ĩã˙ŽĶ˙I˛˙}˙˙˙˙˙˙˙˙˙˙˙˙˙Í'˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙
Í˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙]rĒ˙TÜ˙ Hô˙w|˙˙˙˙˙,cĖ˙ Vō˙ Xō˙ Yņ˙ \ņ˙ ^ņ˙ `ņ˙ká˙nŪ˙qß˙qŪ˙tŪ˙pį˙ nī˙ pî˙ rî˙ tî˙ ví˙ xė˙ zė˙ |ė˙ ~ė˙ ė˙ ë˙ ë˙ ę˙ ę˙ ę˙ ę˙ é˙ã˙Ų˙Ų˙Ų˙Ų˙Ú˙ į˙ į˙ Ąį˙ ĸæ˙ ¤æ˙ §æ˙ Šå˙ Ēå˙ å˙ Žä˙ ąä˙ ˛ã˙ ĩã˙ ˇã˙ šã˙šß˙8Ļŧ˙s˙˙˙˙˙˙˙˙˙˙˙˙ķS˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ (ß˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙Xn˙Nâ˙ Dõ˙ Eô˙ Hô˙w|˙˙˙˙˙,cË˙ Uō˙ Xō˙ Yņ˙ \ņ˙ ]đ˙ _đ˙Ŗ˙˙˙˙˙>~Ė˙ nî˙ pî˙ rí˙ tí˙ ví˙ xí˙ zė˙ |ė˙ ~ë˙ ë˙ ë˙ ë˙ ë˙ ę˙ ę˙ é˙ ę˙;Ę˙˙˙˙˙Ą˙ į˙ į˙ æ˙ Ŗæ˙ Ĩæ˙ §æ˙ ¨å˙ Ģå˙ å˙ ¯ä˙ ąä˙ ŗä˙ ĩä˙ ˇã˙ ¸â˙ ģâ˙ Ŋã˙žā˙˙˙˙˙˙˙˙˙˙˙˙˙ū
˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ *č˙˙˙˙˙˙˙˙˙˙˙˙˙˙[nŦ˙Iå˙ @ö˙ Bõ˙ Cô˙ Eô˙ Gô˙w|˙˙˙˙˙,cË˙ Vō˙ Xņ˙ Zō˙ \ņ˙ ^ņ˙ `ņ˙Ŗ˙˙˙˙˙>~Ė˙ nî˙ pí˙ rî˙ tî˙ ví˙ xí˙ zí˙ }í˙ ~ė˙ ė˙ ë˙
ë˙ ę˙ ę˙ ę˙ é˙ é˙;Ę˙˙˙˙˙Ą˙ į˙ į˙ Ąæ˙ Ŗæ˙ ¤å˙ §å˙ Šæ˙ Ģå˙ å˙ Žä˙ ąä˙ ŗä˙ ĩã˙ ˇã˙ šã˙ ģâ˙ Ŋâ˙ žá˙ĸ˙˙˙˙˙˙˙˙˙˙˙˙˙˙ļ
˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ *č˙˙˙˙˙˙˙˙˙˙˙˙˙oy˙"LŲ˙ ;ö˙ >ö˙ ?õ˙ Aõ˙ Dõ˙ Fõ˙ Hô˙w|˙˙˙˙˙,cË˙ Vō˙ Xō˙ Yņ˙ [ņ˙ ^ņ˙ `ņ˙Ŗ˙˙˙˙˙>~Ė˙ nî˙ pî˙ rî˙ tí˙ ví˙ xė˙ zė˙ |ė˙ ~ė˙ ė˙ ë˙ ë˙ ę˙ ę˙ ę˙ ę˙ ę˙;Ę˙˙˙˙˙Ą˙ į˙ į˙ Ąį˙ ĸæ˙ Ĩæ˙ §æ˙ ¨å˙ Ēå˙ å˙ ¯å˙ °ä˙ ŗä˙ ĩã˙ ļã˙ šã˙ ģâ˙ Ŋâ˙ ŋâ˙ĸ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙Î
˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ (č˙˙˙˙˙˙˙˙˙˙˙˙~˙9XĮ˙8ô˙ :÷˙ ;ö˙ >ö˙ ?õ˙ Bõ˙ Cô˙ Eô˙ Gô˙w|˙˙˙˙˙,cË˙ Uņ˙ Xō˙ Zņ˙ \ņ˙ ^ņ˙ `đ˙Ŗ˙˙˙˙˙>~Ė˙ nî˙ pî˙ rî˙ tí˙ ví˙ xí˙ zí˙ }ė˙ ~ė˙ ë˙ ė˙
ë˙ ę˙ ę˙ ę˙ é˙ é˙;Ę˙˙˙˙˙Ą˙ į˙ æ˙ æ˙ Ŗæ˙ ¤æ˙ §æ˙ Šå˙ Ģå˙ å˙ ¯ä˙ ąä˙ ˛ã˙ ĩä˙ ˇã˙ šã˙ ģã˙ Ŋâ˙ ŋâ˙ĸ˙˙˙˙˙Gŗž˙c˙˙˙˙˙˙˙˙˙˙ß(˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙ ˙˙˙
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