From 258aebabfb18d01a5d4af5f798db7a00e1f04938 Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Thu, 3 Dec 2020 22:21:24 +0000 Subject: [PATCH 1/6] Make notebooks downloadable --- Makefile | 1 + docs/conf.py | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index ab85ccf8c..0db0dfbf7 100644 --- a/Makefile +++ b/Makefile @@ -63,6 +63,7 @@ notebooks: jupytext --pipe black --pipe-fmt py:percent examples/*.ipynb jupyter nbconvert --execute --inplace examples/*.ipynb jupytext --sync examples/*.ipynb + # jupytext --to script examples/*.md docs-clean: mkdir -p docs/api diff --git a/docs/conf.py b/docs/conf.py index 9873cd4bd..ae9947479 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -145,6 +145,10 @@ napoleon_use_param = True nbsphinx_custom_formats = {".md": lambda s: jupytext.reads(s, ".md")} +nbsphinx_prolog = """ +This Jupyter notebook can be downloaded from `{{ env.docname.split("/")[-1] }}.ipynb <{{ env.docname.split("/")[-1] }}.ipynb#http://>`_ + +""" # -- apidoc ---------------------------------------------------------- @@ -287,7 +291,6 @@ def setup(app): # Output file base name for HTML help builder. htmlhelp_basename = "pintdoc" - # -- Options for LaTeX output ------------------------------------------ latex_elements = { From 60661c2456447037a283c22b3cb7d7cc04c037a5 Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Fri, 4 Dec 2020 13:31:32 +0000 Subject: [PATCH 2/6] Changed notebook format to py:percent --- Makefile | 2 +- docs/examples/PINT_walkthrough.md | 267 -------------- docs/examples/PINT_walkthrough.py | 252 +++++++++++++ docs/examples/build_model_from_scratch.md | 369 ------------------- docs/examples/build_model_from_scratch.py | 361 ++++++++++++++++++ docs/examples/understanding_fitters.md | 222 ----------- docs/examples/understanding_fitters.py | 203 ++++++++++ docs/examples/understanding_parameters.md | 230 ------------ docs/examples/understanding_parameters.py | 218 +++++++++++ docs/examples/understanding_timing_models.md | 183 --------- docs/examples/understanding_timing_models.py | 173 +++++++++ 11 files changed, 1208 insertions(+), 1272 deletions(-) delete mode 100644 docs/examples/PINT_walkthrough.md create mode 100644 docs/examples/PINT_walkthrough.py delete mode 100644 docs/examples/build_model_from_scratch.md create mode 100644 docs/examples/build_model_from_scratch.py delete mode 100644 docs/examples/understanding_fitters.md create mode 100644 docs/examples/understanding_fitters.py delete mode 100644 docs/examples/understanding_parameters.md create mode 100644 docs/examples/understanding_parameters.py delete mode 100644 docs/examples/understanding_timing_models.md create mode 100644 docs/examples/understanding_timing_models.py diff --git a/Makefile b/Makefile index 0db0dfbf7..5b941fbe2 100644 --- a/Makefile +++ b/Makefile @@ -59,7 +59,7 @@ coverage: ## check code coverage quickly with the default Python $(BROWSER) htmlcov/index.html notebooks: - jupytext --sync examples/*.md + jupytext --sync examples/*.py jupytext --pipe black --pipe-fmt py:percent examples/*.ipynb jupyter nbconvert --execute --inplace examples/*.ipynb jupytext --sync examples/*.ipynb diff --git a/docs/examples/PINT_walkthrough.md b/docs/examples/PINT_walkthrough.md deleted file mode 100644 index ee67b00c2..000000000 --- a/docs/examples/PINT_walkthrough.md +++ /dev/null @@ -1,267 +0,0 @@ ---- -jupyter: - jupytext: - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# PINT Example Session - - -The PINT homepage is at: https://github.com/nanograv/PINT. - -The documentation is availble here: https://nanograv-pint.readthedocs.io/en/latest/index.html - -PINT can be run via a Python script, in an interactive session with ipython or jupyter, or using one of the command-line tools provided. - - -## Times of Arrival (TOAs) - - -The raw data for PINT are TOAs, which can be read in from files in a variety of formats, or constructed programatically. PINT currently can read TEMPO, Tempo2, and ITOA text files, as well as a range of spacecraft FITS format event files (e.g. Fermi "FT1" and NICER .evt files). - -Note: The first time TOAs get read in, lots of processing (can) happen, which can take some time. However, a "pickle" file can be saved, so the next time the same file is loaded (if nothing has changed), the TOAs will be loaded from the pickle file, which is much faster. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:39.132757Z", "iopub.status.busy": "2020-09-10T16:29:39.132213Z", "iopub.status.idle": "2020-09-10T16:29:39.423718Z", "shell.execute_reply": "2020-09-10T16:29:39.423106Z"} -from __future__ import print_function, division -import numpy as np -import astropy.units as u -from pprint import pprint -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:39.428349Z", "iopub.status.busy": "2020-09-10T16:29:39.427787Z", "iopub.status.idle": "2020-09-10T16:29:40.002429Z", "shell.execute_reply": "2020-09-10T16:29:40.001957Z"} -%matplotlib inline -import matplotlib.pyplot as plt - -# Turn on quantity support for plotting. This is very helpful! -from astropy.visualization import quantity_support - -quantity_support() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:40.006715Z", "iopub.status.busy": "2020-09-10T16:29:40.006154Z", "iopub.status.idle": "2020-09-10T16:29:41.145039Z", "shell.execute_reply": "2020-09-10T16:29:41.145529Z"} -# Here is how to create a single TOA in Python -# The first argument is an MJD(UTC) as a 2-double tuple to allow extended precision -# and the second argument is the TOA uncertainty -# Wherever possible, it is good to use astropy units on the values, -# but there are sensible defaults if you leave them out (us for uncertainty, MHz for freq) -import pint.toa as toa - -a = toa.TOA( - (54567, 0.876876876876876), - 4.5 * u.us, - freq=1400.0 * u.MHz, - obs="GBT", - backend="GUPPI", -) -print(a) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.149363Z", "iopub.status.busy": "2020-09-10T16:29:41.148810Z", "iopub.status.idle": "2020-09-10T16:29:41.579135Z", "shell.execute_reply": "2020-09-10T16:29:41.579710Z"} -# An example of reading a TOA file -import pint.toa as toa - -t = toa.get_TOAs("NGC6440E.tim", usepickle=False) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.596914Z", "iopub.status.busy": "2020-09-10T16:29:41.596300Z", "iopub.status.idle": "2020-09-10T16:29:41.600398Z", "shell.execute_reply": "2020-09-10T16:29:41.600851Z"} -# You can print a summary of the loaded TOAs -t.print_summary() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.605483Z", "iopub.status.busy": "2020-09-10T16:29:41.604935Z", "iopub.status.idle": "2020-09-10T16:29:41.607757Z", "shell.execute_reply": "2020-09-10T16:29:41.607286Z"} -# The get_mjds() method returns an array of the MJDs for the TOAs -# Here is the MJD of the first TOA. Notice that is has the units of days -pprint(t.get_mjds()) -``` - -TOAs are stored in a [Astropy Table](https://astropy.readthedocs.org/latest/table/) in an instance of the TOAs class. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.611991Z", "iopub.status.busy": "2020-09-10T16:29:41.611442Z", "iopub.status.idle": "2020-09-10T16:29:41.614625Z", "shell.execute_reply": "2020-09-10T16:29:41.614163Z"} -# List the table columns, which include pre-computed TDB times and -# solar system positions and velocities -t.table.colnames -``` - -Lots of cool things that tables can do... - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.617601Z", "iopub.status.busy": "2020-09-10T16:29:41.617068Z", "iopub.status.idle": "2020-09-10T16:29:41.619770Z", "shell.execute_reply": "2020-09-10T16:29:41.619182Z"} -# This pops open a browser window showing the contents of the table -# t.table.show_in_browser() -``` - -Can do fancy sorting, selecting, re-arranging very easily. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.623825Z", "iopub.status.busy": "2020-09-10T16:29:41.623278Z", "iopub.status.idle": "2020-09-10T16:29:41.625580Z", "shell.execute_reply": "2020-09-10T16:29:41.626100Z"} -select = t.get_errors() < 20 * u.us -print(select) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.630816Z", "iopub.status.busy": "2020-09-10T16:29:41.630273Z", "iopub.status.idle": "2020-09-10T16:29:41.633189Z", "shell.execute_reply": "2020-09-10T16:29:41.632689Z"} -pprint(t.table["tdb"][select]) -``` - -TOAs objects have a select() method to select based on a boolean mask. This selection can be undone later with unselect. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.664597Z", "iopub.status.busy": "2020-09-10T16:29:41.647943Z", "iopub.status.idle": "2020-09-10T16:29:41.673985Z", "shell.execute_reply": "2020-09-10T16:29:41.673406Z"} -t.print_summary() -t.select(select) -t.print_summary() -t.unselect() -t.print_summary() -``` - -PINT routines / classes / functions use [Astropy Units](https://astropy.readthedocs.org/latest/units/) internally and externally as much as possible: - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.678293Z", "iopub.status.busy": "2020-09-10T16:29:41.677752Z", "iopub.status.idle": "2020-09-10T16:29:41.681143Z", "shell.execute_reply": "2020-09-10T16:29:41.680693Z"} -pprint(t.get_errors()) -``` - -The times in each row contain (or are derived from) [Astropy Time](https://astropy.readthedocs.org/latest/time/) objects: - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.684616Z", "iopub.status.busy": "2020-09-10T16:29:41.684084Z", "iopub.status.idle": "2020-09-10T16:29:41.686828Z", "shell.execute_reply": "2020-09-10T16:29:41.686285Z"} -toa0 = t.table["mjd"][0] -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.690991Z", "iopub.status.busy": "2020-09-10T16:29:41.690450Z", "iopub.status.idle": "2020-09-10T16:29:41.693862Z", "shell.execute_reply": "2020-09-10T16:29:41.693304Z"} -toa0.tai -``` - -But the most useful timescale, TDB is also stored in its own column as a long double numpy array, to maintain precision and keep from having to redo the conversion. -*Note that is is the TOA time converted to the TDB timescale, but the Solar System delays have not been applied, so this is NOT what people call "barycentered times"* - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.697857Z", "iopub.status.busy": "2020-09-10T16:29:41.697295Z", "iopub.status.idle": "2020-09-10T16:29:41.700215Z", "shell.execute_reply": "2020-09-10T16:29:41.699660Z"} -pprint(t.table["tdbld"][:3]) -``` - -## Timing Models - - -Now let's define and load a timing model - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.703464Z", "iopub.status.busy": "2020-09-10T16:29:41.702914Z", "iopub.status.idle": "2020-09-10T16:29:41.967700Z", "shell.execute_reply": "2020-09-10T16:29:41.968159Z"} -import pint.models as models - -m = models.get_model("NGC6440E.par") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.973653Z", "iopub.status.busy": "2020-09-10T16:29:41.973019Z", "iopub.status.idle": "2020-09-10T16:29:41.975935Z", "shell.execute_reply": "2020-09-10T16:29:41.975460Z"} -# Printing a model gives the parfile representation -print(m) -``` - -Timing models are composed of "delay" terms and "phase" terms, which are computed by the Components of the model. The delay terms are evaluated in order, going from terms local to the Solar System, which are needed for computing 'barycenter-corrected' TOAs, through terms for the binary system. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.980773Z", "iopub.status.busy": "2020-09-10T16:29:41.980219Z", "iopub.status.idle": "2020-09-10T16:29:41.983587Z", "shell.execute_reply": "2020-09-10T16:29:41.983140Z"} -# delay_funcs lists all the delay functions in the model, and the order is important! -m.delay_funcs -``` - -The phase functions include the spindown model and an absolute phase definition (if the TZR parameters are specified). - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.987415Z", "iopub.status.busy": "2020-09-10T16:29:41.986745Z", "iopub.status.idle": "2020-09-10T16:29:41.990377Z", "shell.execute_reply": "2020-09-10T16:29:41.989759Z"} -# And phase_funcs holds a list of all the phase functions -m.phase_funcs -``` - -You can easily show/compute individual terms... - -```python execution={"iopub.execute_input": "2020-09-10T16:29:41.999568Z", "iopub.status.busy": "2020-09-10T16:29:41.999005Z", "iopub.status.idle": "2020-09-10T16:29:42.001706Z", "shell.execute_reply": "2020-09-10T16:29:42.001251Z"} -ds = m.solar_system_shapiro_delay(t) -pprint(ds) -``` - -The `get_mjds()` method can return the TOA times as either astropy Time objects (for high precision), or as double precisions Quantities (for easy plotting). - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.027533Z", "iopub.status.busy": "2020-09-10T16:29:42.026975Z", "iopub.status.idle": "2020-09-10T16:29:42.409792Z", "shell.execute_reply": "2020-09-10T16:29:42.409192Z"} -plt.plot(t.get_mjds(high_precision=False), ds.to(u.us), "+") -plt.xlabel("MJD") -plt.ylabel("Solar System Shapiro Delay ($\mu$s)") -``` - -Here are all of the terms added together: - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.431540Z", "iopub.status.busy": "2020-09-10T16:29:42.430971Z", "iopub.status.idle": "2020-09-10T16:29:42.433970Z", "shell.execute_reply": "2020-09-10T16:29:42.433308Z"} -pprint(m.delay(t)) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.458375Z", "iopub.status.busy": "2020-09-10T16:29:42.457824Z", "iopub.status.idle": "2020-09-10T16:29:42.460655Z", "shell.execute_reply": "2020-09-10T16:29:42.460149Z"} -pprint(m.phase(t)) -``` - -## Residuals - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.463981Z", "iopub.status.busy": "2020-09-10T16:29:42.463446Z", "iopub.status.idle": "2020-09-10T16:29:42.467428Z", "shell.execute_reply": "2020-09-10T16:29:42.466942Z"} -import pint.residuals -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.492576Z", "iopub.status.busy": "2020-09-10T16:29:42.492016Z", "iopub.status.idle": "2020-09-10T16:29:42.494589Z", "shell.execute_reply": "2020-09-10T16:29:42.493984Z"} -rs = pint.residuals.Residuals(t, m) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.517012Z", "iopub.status.busy": "2020-09-10T16:29:42.516449Z", "iopub.status.idle": "2020-09-10T16:29:42.689756Z", "shell.execute_reply": "2020-09-10T16:29:42.689161Z"} -# Note that the Residuals object contains a toas member that has the TOAs used to compute -# the residuals, so you can use that to get the MJDs and uncertainties for each TOA -# Also note that plotting astropy Quantities must be enabled using -# astropy quanity_support() first (see beginning of this notebook) -plt.errorbar( - rs.toas.get_mjds(), - rs.time_resids.to(u.us), - yerr=rs.toas.get_errors().to(u.us), - fmt=".", -) -plt.title("%s Pre-Fit Timing Residuals" % m.PSR.value) -plt.xlabel("MJD") -plt.ylabel("Residual (us)") -plt.grid() -``` - -## Fitting and Post-Fit residuals - - -The fitter is *completely* separate from the model and the TOA code. So you can use any type of fitter with some easy coding to create a new subclass of `Fitter`. This example uses PINT's Weighted Least Squares fitter. The return value for this fitter is the chi^2 after the fit. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.693910Z", "iopub.status.busy": "2020-09-10T16:29:42.693349Z", "iopub.status.idle": "2020-09-10T16:29:42.890759Z", "shell.execute_reply": "2020-09-10T16:29:42.891296Z"} -import pint.fitter - -f = pint.fitter.WLSFitter(t, m) -f.fit_toas() # fit_toas() returns the final reduced chi squared -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.894577Z", "iopub.status.busy": "2020-09-10T16:29:42.893996Z", "iopub.status.idle": "2020-09-10T16:29:42.935805Z", "shell.execute_reply": "2020-09-10T16:29:42.936250Z"} -# You can now print a nice human-readable summary of the fit -f.print_summary() -``` - - -```python execution={"iopub.execute_input": "2020-09-10T16:29:42.956235Z", "iopub.status.busy": "2020-09-10T16:29:42.955676Z", "iopub.status.idle": "2020-09-10T16:29:43.130677Z", "shell.execute_reply": "2020-09-10T16:29:43.130112Z"} -# Lets plot the post-fit residuals -plt.errorbar( - t.get_mjds(), f.resids.time_resids.to(u.us), t.get_errors().to(u.us), fmt="x" -) -plt.title("%s Post-Fit Timing Residuals" % m.PSR.value) -plt.xlabel("MJD") -plt.ylabel("Residual (us)") -plt.grid() -``` - -## Other interesting things - - -We can make Barycentered TOAs in a single line, if you have a model and a TOAs object! These are TDB times with the Solar System delays applied (precisely which of the delay components are applied is changeable -- the default applies all delays before the ones associated with the binary system) - -```python execution={"iopub.execute_input": "2020-09-10T16:29:43.152582Z", "iopub.status.busy": "2020-09-10T16:29:43.152023Z", "iopub.status.idle": "2020-09-10T16:29:43.155240Z", "shell.execute_reply": "2020-09-10T16:29:43.154585Z"} -pprint(m.get_barycentric_toas(t)) -``` - -```python - -``` diff --git a/docs/examples/PINT_walkthrough.py b/docs/examples/PINT_walkthrough.py new file mode 100644 index 000000000..9fe991b7a --- /dev/null +++ b/docs/examples/PINT_walkthrough.py @@ -0,0 +1,252 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # PINT Example Session + +# %% [markdown] +# The PINT homepage is at: https://github.com/nanograv/PINT. +# +# The documentation is availble here: https://nanograv-pint.readthedocs.io/en/latest/index.html +# +# PINT can be run via a Python script, in an interactive session with ipython or jupyter, or using one of the command-line tools provided. + +# %% [markdown] +# ## Times of Arrival (TOAs) + +# %% [markdown] +# The raw data for PINT are TOAs, which can be read in from files in a variety of formats, or constructed programatically. PINT currently can read TEMPO, Tempo2, and ITOA text files, as well as a range of spacecraft FITS format event files (e.g. Fermi "FT1" and NICER .evt files). +# +# Note: The first time TOAs get read in, lots of processing (can) happen, which can take some time. However, a "pickle" file can be saved, so the next time the same file is loaded (if nothing has changed), the TOAs will be loaded from the pickle file, which is much faster. + +# %% +from __future__ import print_function, division +import numpy as np +import astropy.units as u +from pprint import pprint + +# %% +# %matplotlib inline +import matplotlib.pyplot as plt + +# Turn on quantity support for plotting. This is very helpful! +from astropy.visualization import quantity_support + +quantity_support() + +# %% +# Here is how to create a single TOA in Python +# The first argument is an MJD(UTC) as a 2-double tuple to allow extended precision +# and the second argument is the TOA uncertainty +# Wherever possible, it is good to use astropy units on the values, +# but there are sensible defaults if you leave them out (us for uncertainty, MHz for freq) +import pint.toa as toa + +a = toa.TOA( + (54567, 0.876876876876876), + 4.5 * u.us, + freq=1400.0 * u.MHz, + obs="GBT", + backend="GUPPI", +) +print(a) + +# %% +# An example of reading a TOA file +import pint.toa as toa + +t = toa.get_TOAs("NGC6440E.tim", usepickle=False) + +# %% +# You can print a summary of the loaded TOAs +t.print_summary() + +# %% +# The get_mjds() method returns an array of the MJDs for the TOAs +# Here is the MJD of the first TOA. Notice that is has the units of days +pprint(t.get_mjds()) + +# %% [markdown] +# TOAs are stored in a [Astropy Table](https://astropy.readthedocs.org/latest/table/) in an instance of the TOAs class. + +# %% +# List the table columns, which include pre-computed TDB times and +# solar system positions and velocities +t.table.colnames + +# %% [markdown] +# Lots of cool things that tables can do... + +# %% +# This pops open a browser window showing the contents of the table +# t.table.show_in_browser() + +# %% [markdown] +# Can do fancy sorting, selecting, re-arranging very easily. + +# %% +select = t.get_errors() < 20 * u.us +print(select) + +# %% +pprint(t.table["tdb"][select]) + +# %% [markdown] +# TOAs objects have a select() method to select based on a boolean mask. This selection can be undone later with unselect. + +# %% +t.print_summary() +t.select(select) +t.print_summary() +t.unselect() +t.print_summary() + +# %% [markdown] +# PINT routines / classes / functions use [Astropy Units](https://astropy.readthedocs.org/latest/units/) internally and externally as much as possible: + +# %% +pprint(t.get_errors()) + +# %% [markdown] +# The times in each row contain (or are derived from) [Astropy Time](https://astropy.readthedocs.org/latest/time/) objects: + +# %% +toa0 = t.table["mjd"][0] + +# %% +toa0.tai + +# %% [markdown] +# But the most useful timescale, TDB is also stored in its own column as a long double numpy array, to maintain precision and keep from having to redo the conversion. +# *Note that is is the TOA time converted to the TDB timescale, but the Solar System delays have not been applied, so this is NOT what people call "barycentered times"* + +# %% +pprint(t.table["tdbld"][:3]) + +# %% [markdown] +# ## Timing Models + +# %% [markdown] +# Now let's define and load a timing model + +# %% +import pint.models as models + +m = models.get_model("NGC6440E.par") + +# %% +# Printing a model gives the parfile representation +print(m) + +# %% [markdown] +# Timing models are composed of "delay" terms and "phase" terms, which are computed by the Components of the model. The delay terms are evaluated in order, going from terms local to the Solar System, which are needed for computing 'barycenter-corrected' TOAs, through terms for the binary system. + +# %% +# delay_funcs lists all the delay functions in the model, and the order is important! +m.delay_funcs + +# %% [markdown] +# The phase functions include the spindown model and an absolute phase definition (if the TZR parameters are specified). + +# %% +# And phase_funcs holds a list of all the phase functions +m.phase_funcs + +# %% [markdown] +# You can easily show/compute individual terms... + +# %% +ds = m.solar_system_shapiro_delay(t) +pprint(ds) + +# %% [markdown] +# The `get_mjds()` method can return the TOA times as either astropy Time objects (for high precision), or as double precisions Quantities (for easy plotting). + +# %% +plt.plot(t.get_mjds(high_precision=False), ds.to(u.us), "+") +plt.xlabel("MJD") +plt.ylabel("Solar System Shapiro Delay ($\mu$s)") + +# %% [markdown] +# Here are all of the terms added together: + +# %% +pprint(m.delay(t)) + +# %% +pprint(m.phase(t)) + +# %% [markdown] +# ## Residuals + +# %% +import pint.residuals + +# %% +rs = pint.residuals.Residuals(t, m) + +# %% +# Note that the Residuals object contains a toas member that has the TOAs used to compute +# the residuals, so you can use that to get the MJDs and uncertainties for each TOA +# Also note that plotting astropy Quantities must be enabled using +# astropy quanity_support() first (see beginning of this notebook) +plt.errorbar( + rs.toas.get_mjds(), + rs.time_resids.to(u.us), + yerr=rs.toas.get_errors().to(u.us), + fmt=".", +) +plt.title("%s Pre-Fit Timing Residuals" % m.PSR.value) +plt.xlabel("MJD") +plt.ylabel("Residual (us)") +plt.grid() + +# %% [markdown] +# ## Fitting and Post-Fit residuals + +# %% [markdown] +# The fitter is *completely* separate from the model and the TOA code. So you can use any type of fitter with some easy coding to create a new subclass of `Fitter`. This example uses PINT's Weighted Least Squares fitter. The return value for this fitter is the chi^2 after the fit. + +# %% +import pint.fitter + +f = pint.fitter.WLSFitter(t, m) +f.fit_toas() # fit_toas() returns the final reduced chi squared + +# %% +# You can now print a nice human-readable summary of the fit +f.print_summary() + + +# %% +# Lets plot the post-fit residuals +plt.errorbar( + t.get_mjds(), f.resids.time_resids.to(u.us), t.get_errors().to(u.us), fmt="x" +) +plt.title("%s Post-Fit Timing Residuals" % m.PSR.value) +plt.xlabel("MJD") +plt.ylabel("Residual (us)") +plt.grid() + +# %% [markdown] +# ## Other interesting things + +# %% [markdown] +# We can make Barycentered TOAs in a single line, if you have a model and a TOAs object! These are TDB times with the Solar System delays applied (precisely which of the delay components are applied is changeable -- the default applies all delays before the ones associated with the binary system) + +# %% +pprint(m.get_barycentric_toas(t)) + +# %% diff --git a/docs/examples/build_model_from_scratch.md b/docs/examples/build_model_from_scratch.md deleted file mode 100644 index 8e553d0b8..000000000 --- a/docs/examples/build_model_from_scratch.md +++ /dev/null @@ -1,369 +0,0 @@ ---- -jupyter: - jupytext: - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Building a timing model from scratch - -This example includes: - * Constructing a timing model object from scratch - * Adding and deleting components - * Assigning parameter values - * Adding prefix-able parameters - -```python execution={"iopub.execute_input": "2020-09-10T16:29:10.940420Z", "iopub.status.busy": "2020-09-10T16:29:10.939842Z", "iopub.status.idle": "2020-09-10T16:29:12.880949Z", "shell.execute_reply": "2020-09-10T16:29:12.880344Z"} -import astropy.units as u # Astropy units is a very useful module. -from pint.models import ( - parameter as p, -) # We would like to add parameters to the model, so we need parameter module. -from pint.models.timing_model import ( - TimingModel, - Component, -) # Interface for timing model -import pint -from astropy.time import Time # PINT uses astropy Time objects to represent times -``` - -Typically, timing models are built by reading a par file with the `get_model()` function, but it is possible to construct them entirely programmatically from scratch. Also, once you have a `TimingModel` object (no matter how you built it), you can modify it by adding or removing parameters or entire components. This example show how this is done. - -We are going to build the model for "NGC6440E.par" from scratch - -### First let us see all the possible components we can use - -All built-in component classes can be viewed from `Component` class, which uses the meta-class to collect the built-in component class. For how to make a component class, see example "make_component_class" (in preparation) - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.885203Z", "iopub.status.busy": "2020-09-10T16:29:12.884653Z", "iopub.status.idle": "2020-09-10T16:29:12.889654Z", "shell.execute_reply": "2020-09-10T16:29:12.890171Z"} -# list all the existing components -# all_components is a dictionary, with the component name as the key and component class as the value. -all_components = Component.component_types -# Print the component class names. -_ = [print(x) for x in all_components] # The "_ =" just suppresses excess output -``` - -### Choose your components - -Let's start from a relatively simple model, with -`AbsPhase`: The absolute phase of the pulsar, typical parameters, `TZRMJD`, `TZRFREQ`... -`AstrometryEquatorial`: The ICRS equatorial coordinate, parameters, `RAJ`, `DECJ`, `PMRA`, `PMDEC`... -`Spindown`: The pulsar spin-down model, parameters, `F0`, `F1`... - -We will add a dispersion model as a demo. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.895407Z", "iopub.status.busy": "2020-09-10T16:29:12.894757Z", "iopub.status.idle": "2020-09-10T16:29:12.897240Z", "shell.execute_reply": "2020-09-10T16:29:12.896676Z"} -selected_components = ["AbsPhase", "AstrometryEquatorial", "Spindown"] -component_instances = [] - -# Initiate the component instances -for cp_name in selected_components: - component_class = all_components[cp_name] # Get the component class - component_instance = component_class() # Instantiate a component object - component_instances.append(component_instance) -``` - - -### Construct timing model (i.e., `TimingModel` instance) - -`TimingModel` class provides the storage and interface for the components. It also manages the components internally. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.900892Z", "iopub.status.busy": "2020-09-10T16:29:12.900332Z", "iopub.status.idle": "2020-09-10T16:29:12.902900Z", "shell.execute_reply": "2020-09-10T16:29:12.902431Z"} -# Construct timing model instance, given a name and a list of components to include (that we just created above) -tm = TimingModel("NGC6400E", component_instances) -``` - -### View the components in the timing model instance - -To view all the components in `TimingModel` instance, we can use the property `.components`, which returns a dictionary (name as the key, component instance as the value). - -Internally, the components are stored in a list(ordered list, you will see why this is important below) according to their types. All the delay type of components (subclasses of `DelayComponent` class) are stored in the `DelayComponent_list`, and the phase type of components (subclasses of `PhaseComponent` class) in the `PhaseComponent_list`. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.906500Z", "iopub.status.busy": "2020-09-10T16:29:12.905839Z", "iopub.status.idle": "2020-09-10T16:29:12.909968Z", "shell.execute_reply": "2020-09-10T16:29:12.909491Z"} -# print the components in the timing model -for (cp_name, cp_instance) in tm.components.items(): - print(cp_name, cp_instance) -``` - -### Useful methods of `TimingModel` - -* `TimingModel.components()` : List all the components in the timing model. -* `TimingModel.add_component()` : Add component into the timing model. -* `TimingModel.remove_component()` : Remove a component from the timing model. -* `TimingModel.params()` : List all the parameters in the timing model from all components. -* `TimingModel.setup()` : Setup the components (e.g., register the derivatives). -* `TimingModel.validate()` : Validate the components see if the parameters are setup properly. -* `TimingModel.delay()` : Compute the total delay. -* `TimingModel.phase()` : Compute the total phase. -* `TimingModel.delay_funcs()` : List all the delay functions from all the delay components. -* `TimingModel.phase_funcs()` : List all the phase functions from all the phase components. -* `TimingModel.get_component_type()` : Get all the components from one category -* `TimingModel.map_component()` : Get the component location. It returns the component's instance, order in the list, host list and its type. -* `TimingModel.get_params_mapping()` : Report which component each parameter comes from. -* `TimingModel.get_prefix_mapping()` : Get the index mapping for one prefix parameters. -* `TimingModel.param_help()` : Print the help line for all available parameters. - - - -### Component order - -Since the times that are given to a delay component include all the delays from the previously-evaluted delay components, the order of delay components is important. For example, the solar system delays need to be applied to get to barycentric time, which is needed to evaluate the binary delays, then the binary delays must be applied to get to pulsar proper time. - -PINT provides a default ordering for the components. In most cases this should be correct, but can be modified by expert users for a particular purpose. - -Here is the default order: - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.913890Z", "iopub.status.busy": "2020-09-10T16:29:12.913351Z", "iopub.status.idle": "2020-09-10T16:29:12.917358Z", "shell.execute_reply": "2020-09-10T16:29:12.916791Z"} -from pint.models.timing_model import DEFAULT_ORDER - -_ = [print(order) for order in DEFAULT_ORDER] -``` - -### Add parameter values - -Initially, the parameters have no values or the default values, so we must add them before validating the model. - -Please note, PINT's convention for fitting flag is defined in the `Parameter.frozen` attribute. `Parameter.frozen = True` means "do **not** fit this parameter". This is the opposite of TEMPO/TEMPO2 .par file flag where "1" means the parameter is fitted. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:12.999203Z", "iopub.status.busy": "2020-09-10T16:29:12.998520Z", "iopub.status.idle": "2020-09-10T16:29:13.012885Z", "shell.execute_reply": "2020-09-10T16:29:13.013341Z"} -# We build a dictionary with a key for each parameter we want to set. -# The dictionary entries can be either -# {'pulsar name': (parameter value, TEMPO_Fit_flag, uncertainty)} akin to a TEMPO par file form -# or -# {'pulsar name': (parameter value, )} for parameters that can't be fit -# NOTE: The values here are assumed to be in the default units for each parameter -# Notice that we assign values with units, and pint defines a special hourangle_second unit that can be use for -# right ascensions. Also, angles can be specified as strings that will be parsed by astropy. -params = { - "PSR": ("1748-2021E",), - "RAJ": ("17:48:52.75", 1, 0.05 * pint.hourangle_second), - "DECJ": ("-20:21:29.0", 1, 0.4 * u.arcsec), - "F0": (61.485476554 * u.Hz, 1, 5e-10 * u.Hz), - "F1": (-1.181e-15 * u.Hz / u.s, 1, 1e-18 * u.Hz / u.s), - "PEPOCH": (Time(53750.000000, format="mjd", scale="tdb"),), - "POSEPOCH": (Time(53750.000000, format="mjd", scale="tdb"),), - "TZRMJD": (Time(53801.38605120074849, format="mjd", scale="tdb"),), - "TZRFRQ": (1949.609 * u.MHz,), - "TZRSITE": (1,), -} - -# Assign the parameters -for name, info in params.items(): - par = getattr(tm, name) # Get parameter object from name - par.quantity = info[0] # set parameter value - if len(info) > 1: - if info[1] == 1: - par.frozen = False # Frozen means not fit. - par.uncertainty = info[2] -``` -### Validating the model - -Validating model checks if there is any important parameter values missing, and if the -parameters are assigned correctly. If there is anything not assigned correctly, it will raise an exception. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.019206Z", "iopub.status.busy": "2020-09-10T16:29:13.018657Z", "iopub.status.idle": "2020-09-10T16:29:13.021147Z", "shell.execute_reply": "2020-09-10T16:29:13.021666Z"} -tm.validate() -# You should see all the assigned parameters. -# Printing a TimingModel object shows the parfile representation -print(tm) -``` -The validate function is also integrated into the add_component() function. When adding a component it will validate the timing model by default; however, it can be switched off by setting flag `validate=False`. We will use this flag in the next section. - - -### Add a component to the timing model - -We will add the dispersion component to the timing model. The steps are: -1. Instantiate the Dispersion class -2. Add dispersion instance into the timing model, with validation as False. -

Since the dispersion model's parameter have not set yet, validation would fail. We will validate it after the parameters filled in.

-3. Add parameters and set values -4. Validate the timing model. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.026594Z", "iopub.status.busy": "2020-09-10T16:29:13.026041Z", "iopub.status.idle": "2020-09-10T16:29:13.028635Z", "shell.execute_reply": "2020-09-10T16:29:13.028164Z"} -dispersion_class = all_components["DispersionDM"] -dispersion = dispersion_class() # Make the dispersion instance. - -# Using validate=False here allows a component being added first and validate later. -tm.add_component(dispersion, validate=False) -``` -Let us examine the components in the timing model. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.039490Z", "iopub.status.busy": "2020-09-10T16:29:13.038920Z", "iopub.status.idle": "2020-09-10T16:29:13.045112Z", "shell.execute_reply": "2020-09-10T16:29:13.044663Z"} -# print the components out, DispersionDM should be there. -print("All components in timing model:") -display(tm.components) - -print("\n") -print("Delay components in the DelayComponent_list (order matters!):") - -# print the delay component order, dispersion should be after the astrometry -display(tm.DelayComponent_list) -``` - -The DM value can be set as we set the parameters above. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.049841Z", "iopub.status.busy": "2020-09-10T16:29:13.049283Z", "iopub.status.idle": "2020-09-10T16:29:13.051933Z", "shell.execute_reply": "2020-09-10T16:29:13.051377Z"} -tm.DM.quantity = 223.9 * u.pc / u.cm ** 3 -tm.DM.frozen = False # Frozen means not fit. -tm.DM.uncertainty = 0.3 * u.pc / u.cm ** 3 -``` - -Run validate again and just make sure everything is setup good. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.055482Z", "iopub.status.busy": "2020-09-10T16:29:13.054933Z", "iopub.status.idle": "2020-09-10T16:29:13.057224Z", "shell.execute_reply": "2020-09-10T16:29:13.057645Z"} -tm.validate() # If this fails, that means the DM model was not setup correctly. -``` - -Now the dispersion model component is added and you are now set for your analysis. - - -### Delete a component - -Deleting a component will remove the component from component list. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.061000Z", "iopub.status.busy": "2020-09-10T16:29:13.060458Z", "iopub.status.idle": "2020-09-10T16:29:13.063019Z", "shell.execute_reply": "2020-09-10T16:29:13.062478Z"} -# Remove by name -tm.remove_component("DispersionDM") -``` -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.067602Z", "iopub.status.busy": "2020-09-10T16:29:13.067068Z", "iopub.status.idle": "2020-09-10T16:29:13.070624Z", "shell.execute_reply": "2020-09-10T16:29:13.070075Z"} -display(tm.components) -``` - -Dispersion model should disappear from the timing model. - - -### Add prefix-style parameters - -Prefix style parameters are used in certain models (e.g., DMX_nnnn or Fn). - -Let us use the `DispersionDMX` model to demonstrate how it works. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.074918Z", "iopub.status.busy": "2020-09-10T16:29:13.074363Z", "iopub.status.idle": "2020-09-10T16:29:13.077082Z", "shell.execute_reply": "2020-09-10T16:29:13.076582Z"} -tm.add_component(all_components["DispersionDMX"]()) -``` -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.080905Z", "iopub.status.busy": "2020-09-10T16:29:13.080334Z", "iopub.status.idle": "2020-09-10T16:29:13.083559Z", "shell.execute_reply": "2020-09-10T16:29:13.083063Z"} -_ = [print(cp) for cp in tm.components] -# "DispersionDMX" should be there. -``` - -### Display the existing DMX parameters - -What do we have in DMX model. - -Note, `Component` class also has the attribute `params`, which is only for the parameters in the component. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.087066Z", "iopub.status.busy": "2020-09-10T16:29:13.086532Z", "iopub.status.idle": "2020-09-10T16:29:13.088955Z", "shell.execute_reply": "2020-09-10T16:29:13.089374Z"} -print(tm.components["DispersionDMX"].params) -``` - -### Add DMX parameters - -Since we already have DMX_0001, we will add DMX_0003, just want to show that for DMX model, DMX index('0001' part) does not have to follow the consecutive order. - -The prefix type of parameters have to use `prefixParameter` class from `pint.models.parameter` module. -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.093687Z", "iopub.status.busy": "2020-09-10T16:29:13.093134Z", "iopub.status.idle": "2020-09-10T16:29:13.095614Z", "shell.execute_reply": "2020-09-10T16:29:13.095158Z"} -# Add prefix parameters -dmx_0003 = p.prefixParameter( - parameter_type="float", name="DMX_0003", value=None, units=u.pc / u.cm ** 3 -) - -tm.components["DispersionDMX"].add_param(dmx_0003, setup=True) -# tm.add_param_from_top(dmx_0003, "DispersionDMX", setup=True) -# # Component should given by its name string. use setup=True make sure new parameter get registered. -``` - -### Check if the parameter and component setup correctly. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.099633Z", "iopub.status.busy": "2020-09-10T16:29:13.099099Z", "iopub.status.idle": "2020-09-10T16:29:13.103358Z", "shell.execute_reply": "2020-09-10T16:29:13.102805Z"} -display(tm.params) -display(tm.delay_deriv_funcs.keys()) # the derivative function should be added. -``` - -However only adding DMX_0003 is not enough, since one DMX parameter also need a DMX range, `DMXR1_0003`, `DMXR2_0003` in this case. Without them, the validation will fail. So let us add them as well. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.108224Z", "iopub.status.busy": "2020-09-10T16:29:13.107671Z", "iopub.status.idle": "2020-09-10T16:29:13.110449Z", "shell.execute_reply": "2020-09-10T16:29:13.109872Z"} -dmxr1_0003 = p.prefixParameter( - parameter_type="mjd", name="DMXR1_0003", value=None, units=u.day -) # DMXR1 is a type of MJD parameter internally. -dmxr2_0003 = p.prefixParameter( - parameter_type="mjd", name="DMXR2_0003", value=None, units=u.day -) # DMXR1 is a type of MJD parameter internally. - -tm.components["DispersionDMX"].add_param(dmxr1_0003, setup=True) -tm.components["DispersionDMX"].add_param(dmxr2_0003, setup=True) -``` - - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.114424Z", "iopub.status.busy": "2020-09-10T16:29:13.113891Z", "iopub.status.idle": "2020-09-10T16:29:13.117105Z", "shell.execute_reply": "2020-09-10T16:29:13.117589Z"} -tm.params -``` - -Then validate it. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.121195Z", "iopub.status.busy": "2020-09-10T16:29:13.120662Z", "iopub.status.idle": "2020-09-10T16:29:13.122715Z", "shell.execute_reply": "2020-09-10T16:29:13.123262Z"} -tm.validate() -``` - -### Remove a parameter - -Remove a parameter is just use the `remove_param()` function. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.127660Z", "iopub.status.busy": "2020-09-10T16:29:13.127120Z", "iopub.status.idle": "2020-09-10T16:29:13.130512Z", "shell.execute_reply": "2020-09-10T16:29:13.130064Z"} -tm.remove_param("DMX_0003") -tm.remove_param("DMXR1_0003") -tm.remove_param("DMXR2_0003") -display(tm.params) -``` -### Add higher order derivatives of spin frequency to timing model - -Adding higher order derivatives of spin frequency (e.g., F2, F3, F4) is a common use case. `Fn` is a prefixParameter, but unlike the `DMX_` parameters, all indexes up to the maximum order must exist, since it represents the coefficients of a Taylor expansion. - -Let us list the current spindown model parameters: - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.134039Z", "iopub.status.busy": "2020-09-10T16:29:13.133501Z", "iopub.status.idle": "2020-09-10T16:29:13.136915Z", "shell.execute_reply": "2020-09-10T16:29:13.136362Z"} -display(tm.components["Spindown"].params) -``` - -Let us add `F2` to the model. `F2` needs a very high precision, we use longdouble=True flag to specify the `F2` value to be a longdouble type. - -Note, if we add `F3` directly without `F2`, the validation will fail. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.141103Z", "iopub.status.busy": "2020-09-10T16:29:13.140542Z", "iopub.status.idle": "2020-09-10T16:29:13.143078Z", "shell.execute_reply": "2020-09-10T16:29:13.142576Z"} -f2 = p.prefixParameter( - parameter_type="float", - name="F2", - value=0.0, - units=u.Hz / (u.s) ** 2, - longdouble=True, -) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.146522Z", "iopub.status.busy": "2020-09-10T16:29:13.145987Z", "iopub.status.idle": "2020-09-10T16:29:13.147894Z", "shell.execute_reply": "2020-09-10T16:29:13.148366Z"} -tm.components["Spindown"].add_param(f2, setup=True) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.151865Z", "iopub.status.busy": "2020-09-10T16:29:13.151329Z", "iopub.status.idle": "2020-09-10T16:29:13.153670Z", "shell.execute_reply": "2020-09-10T16:29:13.153167Z"} -tm.validate() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.157714Z", "iopub.status.busy": "2020-09-10T16:29:13.157166Z", "iopub.status.idle": "2020-09-10T16:29:13.160650Z", "shell.execute_reply": "2020-09-10T16:29:13.160049Z"} -display(tm.params) -``` - -Now `F2` can be used in the timing model. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:13.164571Z", "iopub.status.busy": "2020-09-10T16:29:13.164039Z", "iopub.status.idle": "2020-09-10T16:29:13.166943Z", "shell.execute_reply": "2020-09-10T16:29:13.167360Z"} -tm.F2.quantity = 2e-10 * u.Hz / u.s ** 2 -display(tm.F2) -``` - -```python - -``` diff --git a/docs/examples/build_model_from_scratch.py b/docs/examples/build_model_from_scratch.py new file mode 100644 index 000000000..f54ab431f --- /dev/null +++ b/docs/examples/build_model_from_scratch.py @@ -0,0 +1,361 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Building a timing model from scratch +# +# This example includes: +# * Constructing a timing model object from scratch +# * Adding and deleting components +# * Assigning parameter values +# * Adding prefix-able parameters + +# %% +import astropy.units as u # Astropy units is a very useful module. +from pint.models import ( + parameter as p, +) # We would like to add parameters to the model, so we need parameter module. +from pint.models.timing_model import ( + TimingModel, + Component, +) # Interface for timing model +import pint +from astropy.time import Time # PINT uses astropy Time objects to represent times + +# %% [markdown] +# Typically, timing models are built by reading a par file with the `get_model()` function, but it is possible to construct them entirely programmatically from scratch. Also, once you have a `TimingModel` object (no matter how you built it), you can modify it by adding or removing parameters or entire components. This example show how this is done. +# +# We are going to build the model for "NGC6440E.par" from scratch +# +# ### First let us see all the possible components we can use +# +# All built-in component classes can be viewed from `Component` class, which uses the meta-class to collect the built-in component class. For how to make a component class, see example "make_component_class" (in preparation) + +# %% +# list all the existing components +# all_components is a dictionary, with the component name as the key and component class as the value. +all_components = Component.component_types +# Print the component class names. +_ = [print(x) for x in all_components] # The "_ =" just suppresses excess output + +# %% [markdown] +# ### Choose your components +# +# Let's start from a relatively simple model, with +# `AbsPhase`: The absolute phase of the pulsar, typical parameters, `TZRMJD`, `TZRFREQ`... +# `AstrometryEquatorial`: The ICRS equatorial coordinate, parameters, `RAJ`, `DECJ`, `PMRA`, `PMDEC`... +# `Spindown`: The pulsar spin-down model, parameters, `F0`, `F1`... +# +# We will add a dispersion model as a demo. + +# %% +selected_components = ["AbsPhase", "AstrometryEquatorial", "Spindown"] +component_instances = [] + +# Initiate the component instances +for cp_name in selected_components: + component_class = all_components[cp_name] # Get the component class + component_instance = component_class() # Instantiate a component object + component_instances.append(component_instance) + + +# %% [markdown] +# ### Construct timing model (i.e., `TimingModel` instance) +# +# `TimingModel` class provides the storage and interface for the components. It also manages the components internally. + +# %% +# Construct timing model instance, given a name and a list of components to include (that we just created above) +tm = TimingModel("NGC6400E", component_instances) + +# %% [markdown] +# ### View the components in the timing model instance +# +# To view all the components in `TimingModel` instance, we can use the property `.components`, which returns a dictionary (name as the key, component instance as the value). +# +# Internally, the components are stored in a list(ordered list, you will see why this is important below) according to their types. All the delay type of components (subclasses of `DelayComponent` class) are stored in the `DelayComponent_list`, and the phase type of components (subclasses of `PhaseComponent` class) in the `PhaseComponent_list`. + +# %% +# print the components in the timing model +for (cp_name, cp_instance) in tm.components.items(): + print(cp_name, cp_instance) + +# %% [markdown] +# ### Useful methods of `TimingModel` +# +# * `TimingModel.components()` : List all the components in the timing model. +# * `TimingModel.add_component()` : Add component into the timing model. +# * `TimingModel.remove_component()` : Remove a component from the timing model. +# * `TimingModel.params()` : List all the parameters in the timing model from all components. +# * `TimingModel.setup()` : Setup the components (e.g., register the derivatives). +# * `TimingModel.validate()` : Validate the components see if the parameters are setup properly. +# * `TimingModel.delay()` : Compute the total delay. +# * `TimingModel.phase()` : Compute the total phase. +# * `TimingModel.delay_funcs()` : List all the delay functions from all the delay components. +# * `TimingModel.phase_funcs()` : List all the phase functions from all the phase components. +# * `TimingModel.get_component_type()` : Get all the components from one category +# * `TimingModel.map_component()` : Get the component location. It returns the component's instance, order in the list, host list and its type. +# * `TimingModel.get_params_mapping()` : Report which component each parameter comes from. +# * `TimingModel.get_prefix_mapping()` : Get the index mapping for one prefix parameters. +# * `TimingModel.param_help()` : Print the help line for all available parameters. +# + +# %% [markdown] +# ### Component order +# +# Since the times that are given to a delay component include all the delays from the previously-evaluted delay components, the order of delay components is important. For example, the solar system delays need to be applied to get to barycentric time, which is needed to evaluate the binary delays, then the binary delays must be applied to get to pulsar proper time. +# +# PINT provides a default ordering for the components. In most cases this should be correct, but can be modified by expert users for a particular purpose. +# +# Here is the default order: + +# %% +from pint.models.timing_model import DEFAULT_ORDER + +_ = [print(order) for order in DEFAULT_ORDER] + +# %% [markdown] +# ### Add parameter values +# +# Initially, the parameters have no values or the default values, so we must add them before validating the model. +# +# Please note, PINT's convention for fitting flag is defined in the `Parameter.frozen` attribute. `Parameter.frozen = True` means "do **not** fit this parameter". This is the opposite of TEMPO/TEMPO2 .par file flag where "1" means the parameter is fitted. + +# %% +# We build a dictionary with a key for each parameter we want to set. +# The dictionary entries can be either +# {'pulsar name': (parameter value, TEMPO_Fit_flag, uncertainty)} akin to a TEMPO par file form +# or +# {'pulsar name': (parameter value, )} for parameters that can't be fit +# NOTE: The values here are assumed to be in the default units for each parameter +# Notice that we assign values with units, and pint defines a special hourangle_second unit that can be use for +# right ascensions. Also, angles can be specified as strings that will be parsed by astropy. +params = { + "PSR": ("1748-2021E",), + "RAJ": ("17:48:52.75", 1, 0.05 * pint.hourangle_second), + "DECJ": ("-20:21:29.0", 1, 0.4 * u.arcsec), + "F0": (61.485476554 * u.Hz, 1, 5e-10 * u.Hz), + "F1": (-1.181e-15 * u.Hz / u.s, 1, 1e-18 * u.Hz / u.s), + "PEPOCH": (Time(53750.000000, format="mjd", scale="tdb"),), + "POSEPOCH": (Time(53750.000000, format="mjd", scale="tdb"),), + "TZRMJD": (Time(53801.38605120074849, format="mjd", scale="tdb"),), + "TZRFRQ": (1949.609 * u.MHz,), + "TZRSITE": (1,), +} + +# Assign the parameters +for name, info in params.items(): + par = getattr(tm, name) # Get parameter object from name + par.quantity = info[0] # set parameter value + if len(info) > 1: + if info[1] == 1: + par.frozen = False # Frozen means not fit. + par.uncertainty = info[2] +# %% [markdown] +# ### Validating the model +# +# Validating model checks if there is any important parameter values missing, and if the +# parameters are assigned correctly. If there is anything not assigned correctly, it will raise an exception. + +# %% +tm.validate() +# You should see all the assigned parameters. +# Printing a TimingModel object shows the parfile representation +print(tm) +# %% [markdown] +# The validate function is also integrated into the add_component() function. When adding a component it will validate the timing model by default; however, it can be switched off by setting flag `validate=False`. We will use this flag in the next section. + +# %% [markdown] +# ### Add a component to the timing model +# +# We will add the dispersion component to the timing model. The steps are: +# 1. Instantiate the Dispersion class +# 2. Add dispersion instance into the timing model, with validation as False. +#

Since the dispersion model's parameter have not set yet, validation would fail. We will validate it after the parameters filled in.

+# 3. Add parameters and set values +# 4. Validate the timing model. + +# %% +dispersion_class = all_components["DispersionDM"] +dispersion = dispersion_class() # Make the dispersion instance. + +# Using validate=False here allows a component being added first and validate later. +tm.add_component(dispersion, validate=False) +# %% [markdown] +# Let us examine the components in the timing model. + +# %% +# print the components out, DispersionDM should be there. +print("All components in timing model:") +display(tm.components) + +print("\n") +print("Delay components in the DelayComponent_list (order matters!):") + +# print the delay component order, dispersion should be after the astrometry +display(tm.DelayComponent_list) + +# %% [markdown] +# The DM value can be set as we set the parameters above. + +# %% +tm.DM.quantity = 223.9 * u.pc / u.cm ** 3 +tm.DM.frozen = False # Frozen means not fit. +tm.DM.uncertainty = 0.3 * u.pc / u.cm ** 3 + +# %% [markdown] +# Run validate again and just make sure everything is setup good. + +# %% +tm.validate() # If this fails, that means the DM model was not setup correctly. + +# %% [markdown] +# Now the dispersion model component is added and you are now set for your analysis. + +# %% [markdown] +# ### Delete a component +# +# Deleting a component will remove the component from component list. + +# %% +# Remove by name +tm.remove_component("DispersionDM") +# %% +display(tm.components) + +# %% [markdown] +# Dispersion model should disappear from the timing model. + +# %% [markdown] +# ### Add prefix-style parameters +# +# Prefix style parameters are used in certain models (e.g., DMX_nnnn or Fn). +# +# Let us use the `DispersionDMX` model to demonstrate how it works. + +# %% +tm.add_component(all_components["DispersionDMX"]()) +# %% +_ = [print(cp) for cp in tm.components] +# "DispersionDMX" should be there. + +# %% [markdown] +# ### Display the existing DMX parameters +# +# What do we have in DMX model. +# +# Note, `Component` class also has the attribute `params`, which is only for the parameters in the component. + +# %% +print(tm.components["DispersionDMX"].params) + +# %% [markdown] +# ### Add DMX parameters +# +# Since we already have DMX_0001, we will add DMX_0003, just want to show that for DMX model, DMX index('0001' part) does not have to follow the consecutive order. +# +# The prefix type of parameters have to use `prefixParameter` class from `pint.models.parameter` module. +# %% +# Add prefix parameters +dmx_0003 = p.prefixParameter( + parameter_type="float", name="DMX_0003", value=None, units=u.pc / u.cm ** 3 +) + +tm.components["DispersionDMX"].add_param(dmx_0003, setup=True) +# tm.add_param_from_top(dmx_0003, "DispersionDMX", setup=True) +# # Component should given by its name string. use setup=True make sure new parameter get registered. + +# %% [markdown] +# ### Check if the parameter and component setup correctly. + +# %% +display(tm.params) +display(tm.delay_deriv_funcs.keys()) # the derivative function should be added. + +# %% [markdown] +# However only adding DMX_0003 is not enough, since one DMX parameter also need a DMX range, `DMXR1_0003`, `DMXR2_0003` in this case. Without them, the validation will fail. So let us add them as well. + +# %% +dmxr1_0003 = p.prefixParameter( + parameter_type="mjd", name="DMXR1_0003", value=None, units=u.day +) # DMXR1 is a type of MJD parameter internally. +dmxr2_0003 = p.prefixParameter( + parameter_type="mjd", name="DMXR2_0003", value=None, units=u.day +) # DMXR1 is a type of MJD parameter internally. + +tm.components["DispersionDMX"].add_param(dmxr1_0003, setup=True) +tm.components["DispersionDMX"].add_param(dmxr2_0003, setup=True) + + +# %% +tm.params + +# %% [markdown] +# Then validate it. + +# %% +tm.validate() + +# %% [markdown] +# ### Remove a parameter +# +# Remove a parameter is just use the `remove_param()` function. + +# %% +tm.remove_param("DMX_0003") +tm.remove_param("DMXR1_0003") +tm.remove_param("DMXR2_0003") +display(tm.params) +# %% [markdown] +# ### Add higher order derivatives of spin frequency to timing model +# +# Adding higher order derivatives of spin frequency (e.g., F2, F3, F4) is a common use case. `Fn` is a prefixParameter, but unlike the `DMX_` parameters, all indexes up to the maximum order must exist, since it represents the coefficients of a Taylor expansion. +# +# Let us list the current spindown model parameters: + +# %% +display(tm.components["Spindown"].params) + +# %% [markdown] +# Let us add `F2` to the model. `F2` needs a very high precision, we use longdouble=True flag to specify the `F2` value to be a longdouble type. +# +# Note, if we add `F3` directly without `F2`, the validation will fail. + +# %% +f2 = p.prefixParameter( + parameter_type="float", + name="F2", + value=0.0, + units=u.Hz / (u.s) ** 2, + longdouble=True, +) + +# %% +tm.components["Spindown"].add_param(f2, setup=True) + +# %% +tm.validate() + +# %% +display(tm.params) + +# %% [markdown] +# Now `F2` can be used in the timing model. + +# %% +tm.F2.quantity = 2e-10 * u.Hz / u.s ** 2 +display(tm.F2) + +# %% diff --git a/docs/examples/understanding_fitters.md b/docs/examples/understanding_fitters.md deleted file mode 100644 index d8139f2d2..000000000 --- a/docs/examples/understanding_fitters.md +++ /dev/null @@ -1,222 +0,0 @@ ---- -jupyter: - jupytext: - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Understanding Fitters - - - -```python execution={"iopub.execute_input": "2020-09-10T16:29:46.396063Z", "iopub.status.busy": "2020-09-10T16:29:46.395515Z", "iopub.status.idle": "2020-09-10T16:29:46.680836Z", "shell.execute_reply": "2020-09-10T16:29:46.680224Z"} -from __future__ import print_function, division -import numpy as np -import astropy.units as u -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:46.684438Z", "iopub.status.busy": "2020-09-10T16:29:46.683898Z", "iopub.status.idle": "2020-09-10T16:29:48.340734Z", "shell.execute_reply": "2020-09-10T16:29:48.341188Z"} -import pint.toa -import pint.models -import pint.fitter -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:48.345797Z", "iopub.status.busy": "2020-09-10T16:29:48.345215Z", "iopub.status.idle": "2020-09-10T16:29:48.636498Z", "shell.execute_reply": "2020-09-10T16:29:48.635991Z"} -%matplotlib inline -import matplotlib.pyplot as plt - -# Turn on quantity support for plotting. This is very helpful! -from astropy.visualization import quantity_support - -quantity_support() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:48.640228Z", "iopub.status.busy": "2020-09-10T16:29:48.639664Z", "iopub.status.idle": "2020-09-10T16:29:49.131499Z", "shell.execute_reply": "2020-09-10T16:29:49.131925Z"} -# Load some TOAs and a model to fit -t = pint.toa.get_TOAs("NGC6440E.tim", usepickle=False) -m = pint.models.get_model("NGC6440E.par") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.135573Z", "iopub.status.busy": "2020-09-10T16:29:49.134964Z", "iopub.status.idle": "2020-09-10T16:29:49.138294Z", "shell.execute_reply": "2020-09-10T16:29:49.137746Z"} -# You can check if a model includes a noise model with correlated errors (e.g. ECORR or TNRED) by checking the has_correlated_errors property -m.has_correlated_errors -``` - -There are several fitters in PINT, each of which is a subclass of `Fitter` - -* `WLSFitter` - PINT's workhorse fitter, which does a basic weighted least-squares minimization of the residuals. -* `GLSFitter` - A generalized least squares fitter, like "tempo -G", that can handle noise processes like ECORR and red noise that are specified by their correlation function properties. -* `PowellFitter` - A very simple example fitter that uses the Powell method implemented in scipy. One notable feature is that it does not require evaluating derivatives w.r.t the model parameters. -* `MCMCFitter` - A fitter that does an MCMC fit using the [emcee](https://emcee.readthedocs.io/en/stable/) package. This can be very slow, but accomodates Priors on the parameter values and can produce corner plots and other analyses of the posterior distributions of the parameters. - - - - -## Weighted Least Squares Fitter - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.179940Z", "iopub.status.busy": "2020-09-10T16:29:49.169313Z", "iopub.status.idle": "2020-09-10T16:29:49.211172Z", "shell.execute_reply": "2020-09-10T16:29:49.210676Z"} -# Instantiate a fitter -wlsfit = pint.fitter.WLSFitter(toas=t, model=m) -``` - -A fit is performed by calling `fit_toas()` - -For most fitters, multiple iterations can be done by setting the `maxiter` keyword argument - -The return value of most fitters is the final chi^2 value - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.333439Z", "iopub.status.busy": "2020-09-10T16:29:49.235196Z", "iopub.status.idle": "2020-09-10T16:29:49.337152Z", "shell.execute_reply": "2020-09-10T16:29:49.336573Z"} -wlsfit.fit_toas(maxiter=1) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.340545Z", "iopub.status.busy": "2020-09-10T16:29:49.339987Z", "iopub.status.idle": "2020-09-10T16:29:49.423914Z", "shell.execute_reply": "2020-09-10T16:29:49.423449Z"} -# A summary of the fit and resulting model parameters can easily be printed -# Only free parameters will have values and uncertainties in the Postfit column -wlsfit.print_summary() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.427735Z", "iopub.status.busy": "2020-09-10T16:29:49.427187Z", "iopub.status.idle": "2020-09-10T16:29:49.430728Z", "shell.execute_reply": "2020-09-10T16:29:49.430162Z"} -# The WLS fitter doesn't handle correlated errors -wlsfit.resids.model.has_correlated_errors -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.434815Z", "iopub.status.busy": "2020-09-10T16:29:49.434228Z", "iopub.status.idle": "2020-09-10T16:29:49.437688Z", "shell.execute_reply": "2020-09-10T16:29:49.437217Z"} -# You can request a pretty-printed covariance matrix -cov = wlsfit.get_covariance_matrix(pretty_print=True) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.455410Z", "iopub.status.busy": "2020-09-10T16:29:49.454785Z", "iopub.status.idle": "2020-09-10T16:29:49.700825Z", "shell.execute_reply": "2020-09-10T16:29:49.700298Z"} -# plot() will make a plot of the post-fit residuals -wlsfit.plot() -``` - -## Powell fitter - -The Powell fitter takes much longer to run! It also doesn't find quite as good of a minimum as the WLS fitter. - -This uses scipy's modification of Powell’s method, which is a conjugate direction method. It performs sequential one-dimensional minimizations along each vector of the directions, which is updated at each iteration of the main minimization loop. The function need not be differentiable, and no derivatives are taken. - -The default number of iterations is 20, but this can be changed with the `maxiter` parameter - -```python execution={"iopub.execute_input": "2020-09-10T16:29:49.770034Z", "iopub.status.busy": "2020-09-10T16:29:49.732333Z", "iopub.status.idle": "2020-09-10T16:29:49.772781Z", "shell.execute_reply": "2020-09-10T16:29:49.772199Z"} -powfit = pint.fitter.PowellFitter(toas=t, model=m) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:50.271975Z", "iopub.status.busy": "2020-09-10T16:29:49.922152Z", "iopub.status.idle": "2020-09-10T16:30:04.467925Z", "shell.execute_reply": "2020-09-10T16:30:04.468442Z"} -powfit.fit_toas() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.472612Z", "iopub.status.busy": "2020-09-10T16:30:04.471972Z", "iopub.status.idle": "2020-09-10T16:30:04.484548Z", "shell.execute_reply": "2020-09-10T16:30:04.483972Z"} -powfit.print_summary() -``` - -***!!! Note that the Powell fitter does not produce a covariance matrix or estimates of the uncertainties. !!!*** - -## Comparing models - -There also a convenience function for pretty printing a comparison of two models with the differences measured in sigma. - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.490927Z", "iopub.status.busy": "2020-09-10T16:30:04.490380Z", "iopub.status.idle": "2020-09-10T16:30:04.492804Z", "shell.execute_reply": "2020-09-10T16:30:04.493406Z"} -print(wlsfit.model.compare(powfit.model)) -``` - -## Generalized Least Squares fitter - -The GLS fitter is capable of handling correlated noise models. - -It has some more complex options using the `maxiter`, `threshold`, and `full_cov` keyword arguments to `fit_toas()`. - -If `maxiter` is less than one, **no fitting is done**, just the -chi-squared computation. In this case, you must provide the `residuals` -argument. - -If `maxiter` is one or more, so fitting is actually done, the -chi-squared value returned is only approximately the chi-squared -of the improved(?) model. In fact it is the chi-squared of the -solution to the linear fitting problem, and the full non-linear -model should be evaluated and new residuals produced if an accurate -chi-squared is desired. - -A first attempt is made to solve the fitting problem by Cholesky -decomposition, but if this fails singular value decomposition is -used instead. In this case singular values below threshold are removed. - -`full_cov` determines which calculation is used. If True, the full -covariance matrix is constructed and the calculation is relatively -straightforward but the full covariance matrix may be enormous. -If False, an algorithm is used that takes advantage of the structure -of the covariance matrix, based on information provided by the noise -model. The two algorithms should give the same result to numerical -accuracy where they both can be applied. - - -To test this fitter properly, we need a model that includes correlated noise components, so we will load one from NANOGrav 9yr data release. - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.497034Z", "iopub.status.busy": "2020-09-10T16:30:04.496484Z", "iopub.status.idle": "2020-09-10T16:30:04.870981Z", "shell.execute_reply": "2020-09-10T16:30:04.871526Z"} -m1855 = pint.models.get_model("B1855+09_NANOGrav_9yv1.gls.par") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.875258Z", "iopub.status.busy": "2020-09-10T16:30:04.874699Z", "iopub.status.idle": "2020-09-10T16:30:04.878088Z", "shell.execute_reply": "2020-09-10T16:30:04.877510Z"} -# You can check if a model includes a noise model with correlated errors (e.g. ECORR or TNRED) by checking the has_correlated_errors property -m1855.has_correlated_errors -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.914831Z", "iopub.status.busy": "2020-09-10T16:30:04.914208Z", "iopub.status.idle": "2020-09-10T16:30:04.916868Z", "shell.execute_reply": "2020-09-10T16:30:04.917319Z"} -print(m1855) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:04.920970Z", "iopub.status.busy": "2020-09-10T16:30:04.920362Z", "iopub.status.idle": "2020-09-10T16:30:13.987324Z", "shell.execute_reply": "2020-09-10T16:30:13.986743Z"} -ts1855 = pint.toa.get_TOAs("B1855+09_NANOGrav_9yv1.tim") -ts1855.print_summary() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:13.991886Z", "iopub.status.busy": "2020-09-10T16:30:13.991313Z", "iopub.status.idle": "2020-09-10T16:30:16.407409Z", "shell.execute_reply": "2020-09-10T16:30:16.407866Z"} -glsfit = pint.fitter.GLSFitter(toas=ts1855, model=m1855) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:16.427618Z", "iopub.status.busy": "2020-09-10T16:30:16.421670Z", "iopub.status.idle": "2020-09-10T16:30:26.038804Z", "shell.execute_reply": "2020-09-10T16:30:26.038179Z"} -glsfit.fit_toas(maxiter=1) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:26.042140Z", "iopub.status.busy": "2020-09-10T16:30:26.041512Z", "iopub.status.idle": "2020-09-10T16:30:26.043906Z", "shell.execute_reply": "2020-09-10T16:30:26.043397Z"} -# Not sure how to do this properly yet. -# glsfit2 = pint.fitter.GLSFitter(toas=t, model=glsfit.model, residuals=glsfit.resids) -# glsfit2.fit_toas(maxiter=0) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:26.055501Z", "iopub.status.busy": "2020-09-10T16:30:26.054912Z", "iopub.status.idle": "2020-09-10T16:30:26.156579Z", "shell.execute_reply": "2020-09-10T16:30:26.156000Z"} -glsfit.print_summary() -``` - -The GLS fitter produces two types of residuals, the normal residuals to the deterministic model and those from the noise model. - -```python execution={"iopub.execute_input": "2020-09-10T16:30:26.161363Z", "iopub.status.busy": "2020-09-10T16:30:26.160747Z", "iopub.status.idle": "2020-09-10T16:30:26.163858Z", "shell.execute_reply": "2020-09-10T16:30:26.164305Z"} -glsfit.resids.time_resids -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:26.168927Z", "iopub.status.busy": "2020-09-10T16:30:26.168368Z", "iopub.status.idle": "2020-09-10T16:30:26.171958Z", "shell.execute_reply": "2020-09-10T16:30:26.171319Z"} -glsfit.resids.noise_resids -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:30:26.196241Z", "iopub.status.busy": "2020-09-10T16:30:26.195623Z", "iopub.status.idle": "2020-09-10T16:30:26.658631Z", "shell.execute_reply": "2020-09-10T16:30:26.658052Z"} -# Here we can plot both the residuals to the deterministic model as well as the realization of the noise model residuals -# The difference will be the "whitened" residuals -fig, ax = plt.subplots(figsize=(16, 9)) -mjds = glsfit.toas.get_mjds() -ax.plot(mjds, glsfit.resids.time_resids, ".") -ax.plot(mjds, glsfit.resids.noise_resids["pl_red_noise"], ".") -``` - -The MCMC fitter is considerably more complicated, so it has its own dedicated walkthroughs in `MCMC_walkthrough.ipynb` (for photon data) and `examples/fit_NGC6440E_MCMC.py` (for fitting TOAs). - -```python - -``` diff --git a/docs/examples/understanding_fitters.py b/docs/examples/understanding_fitters.py new file mode 100644 index 000000000..dc88d6b4b --- /dev/null +++ b/docs/examples/understanding_fitters.py @@ -0,0 +1,203 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Understanding Fitters +# +# + +# %% +from __future__ import print_function, division +import numpy as np +import astropy.units as u + +# %% +import pint.toa +import pint.models +import pint.fitter + +# %% +# %matplotlib inline +import matplotlib.pyplot as plt + +# Turn on quantity support for plotting. This is very helpful! +from astropy.visualization import quantity_support + +quantity_support() + +# %% +# Load some TOAs and a model to fit +t = pint.toa.get_TOAs("NGC6440E.tim", usepickle=False) +m = pint.models.get_model("NGC6440E.par") + +# %% +# You can check if a model includes a noise model with correlated errors (e.g. ECORR or TNRED) by checking the has_correlated_errors property +m.has_correlated_errors + +# %% [markdown] +# There are several fitters in PINT, each of which is a subclass of `Fitter` +# +# * `WLSFitter` - PINT's workhorse fitter, which does a basic weighted least-squares minimization of the residuals. +# * `GLSFitter` - A generalized least squares fitter, like "tempo -G", that can handle noise processes like ECORR and red noise that are specified by their correlation function properties. +# * `PowellFitter` - A very simple example fitter that uses the Powell method implemented in scipy. One notable feature is that it does not require evaluating derivatives w.r.t the model parameters. +# * `MCMCFitter` - A fitter that does an MCMC fit using the [emcee](https://emcee.readthedocs.io/en/stable/) package. This can be very slow, but accomodates Priors on the parameter values and can produce corner plots and other analyses of the posterior distributions of the parameters. +# +# + +# %% [markdown] +# ## Weighted Least Squares Fitter + +# %% +# Instantiate a fitter +wlsfit = pint.fitter.WLSFitter(toas=t, model=m) + +# %% [markdown] +# A fit is performed by calling `fit_toas()` +# +# For most fitters, multiple iterations can be done by setting the `maxiter` keyword argument +# +# The return value of most fitters is the final chi^2 value + +# %% +wlsfit.fit_toas(maxiter=1) + +# %% +# A summary of the fit and resulting model parameters can easily be printed +# Only free parameters will have values and uncertainties in the Postfit column +wlsfit.print_summary() + +# %% +# The WLS fitter doesn't handle correlated errors +wlsfit.resids.model.has_correlated_errors + +# %% +# You can request a pretty-printed covariance matrix +cov = wlsfit.get_covariance_matrix(pretty_print=True) + +# %% +# plot() will make a plot of the post-fit residuals +wlsfit.plot() + +# %% [markdown] +# ## Powell fitter +# +# The Powell fitter takes much longer to run! It also doesn't find quite as good of a minimum as the WLS fitter. +# +# This uses scipy's modification of Powell’s method, which is a conjugate direction method. It performs sequential one-dimensional minimizations along each vector of the directions, which is updated at each iteration of the main minimization loop. The function need not be differentiable, and no derivatives are taken. +# +# The default number of iterations is 20, but this can be changed with the `maxiter` parameter + +# %% +powfit = pint.fitter.PowellFitter(toas=t, model=m) + +# %% +powfit.fit_toas() + +# %% +powfit.print_summary() + +# %% [markdown] +# ***!!! Note that the Powell fitter does not produce a covariance matrix or estimates of the uncertainties. !!!*** +# +# ## Comparing models +# +# There also a convenience function for pretty printing a comparison of two models with the differences measured in sigma. + +# %% +print(wlsfit.model.compare(powfit.model)) + +# %% [markdown] +# ## Generalized Least Squares fitter +# +# The GLS fitter is capable of handling correlated noise models. +# +# It has some more complex options using the `maxiter`, `threshold`, and `full_cov` keyword arguments to `fit_toas()`. +# +# If `maxiter` is less than one, **no fitting is done**, just the +# chi-squared computation. In this case, you must provide the `residuals` +# argument. +# +# If `maxiter` is one or more, so fitting is actually done, the +# chi-squared value returned is only approximately the chi-squared +# of the improved(?) model. In fact it is the chi-squared of the +# solution to the linear fitting problem, and the full non-linear +# model should be evaluated and new residuals produced if an accurate +# chi-squared is desired. +# +# A first attempt is made to solve the fitting problem by Cholesky +# decomposition, but if this fails singular value decomposition is +# used instead. In this case singular values below threshold are removed. +# +# `full_cov` determines which calculation is used. If True, the full +# covariance matrix is constructed and the calculation is relatively +# straightforward but the full covariance matrix may be enormous. +# If False, an algorithm is used that takes advantage of the structure +# of the covariance matrix, based on information provided by the noise +# model. The two algorithms should give the same result to numerical +# accuracy where they both can be applied. + +# %% [markdown] +# To test this fitter properly, we need a model that includes correlated noise components, so we will load one from NANOGrav 9yr data release. + +# %% +m1855 = pint.models.get_model("B1855+09_NANOGrav_9yv1.gls.par") + +# %% +# You can check if a model includes a noise model with correlated errors (e.g. ECORR or TNRED) by checking the has_correlated_errors property +m1855.has_correlated_errors + +# %% +print(m1855) + +# %% +ts1855 = pint.toa.get_TOAs("B1855+09_NANOGrav_9yv1.tim") +ts1855.print_summary() + +# %% +glsfit = pint.fitter.GLSFitter(toas=ts1855, model=m1855) + +# %% +glsfit.fit_toas(maxiter=1) + +# %% +# Not sure how to do this properly yet. +# glsfit2 = pint.fitter.GLSFitter(toas=t, model=glsfit.model, residuals=glsfit.resids) +# glsfit2.fit_toas(maxiter=0) + +# %% +glsfit.print_summary() + +# %% [markdown] +# The GLS fitter produces two types of residuals, the normal residuals to the deterministic model and those from the noise model. + +# %% +glsfit.resids.time_resids + +# %% +glsfit.resids.noise_resids + +# %% +# Here we can plot both the residuals to the deterministic model as well as the realization of the noise model residuals +# The difference will be the "whitened" residuals +fig, ax = plt.subplots(figsize=(16, 9)) +mjds = glsfit.toas.get_mjds() +ax.plot(mjds, glsfit.resids.time_resids, ".") +ax.plot(mjds, glsfit.resids.noise_resids["pl_red_noise"], ".") + +# %% [markdown] +# The MCMC fitter is considerably more complicated, so it has its own dedicated walkthroughs in `MCMC_walkthrough.ipynb` (for photon data) and `examples/fit_NGC6440E_MCMC.py` (for fitting TOAs). + +# %% diff --git a/docs/examples/understanding_parameters.md b/docs/examples/understanding_parameters.md deleted file mode 100644 index 483467fbb..000000000 --- a/docs/examples/understanding_parameters.md +++ /dev/null @@ -1,230 +0,0 @@ ---- -jupyter: - jupytext: - cell_metadata_json: true - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Understanding Parameters - -```python execution={"iopub.execute_input": "2020-09-10T16:29:05.962203Z", "iopub.status.busy": "2020-09-10T16:29:05.961161Z", "iopub.status.idle": "2020-09-10T16:29:09.110676Z", "shell.execute_reply": "2020-09-10T16:29:09.110045Z"} jupyter={"outputs_hidden": false} -import pint.models -import pint.models.parameter as pp -import astropy.units as u -from astropy.coordinates.angles import Angle -from astropy.time import Time -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.114066Z", "iopub.status.busy": "2020-09-10T16:29:09.113524Z", "iopub.status.idle": "2020-09-10T16:29:09.336889Z", "shell.execute_reply": "2020-09-10T16:29:09.336282Z"} jupyter={"outputs_hidden": false} -# Load a model to play with -model = pint.models.get_model("B1855+09_NANOGrav_dfg+12_TAI.par") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.348075Z", "iopub.status.busy": "2020-09-10T16:29:09.347518Z", "iopub.status.idle": "2020-09-10T16:29:09.350913Z", "shell.execute_reply": "2020-09-10T16:29:09.350441Z"} jupyter={"outputs_hidden": false} -# This model has a large number of parameters of various types -model.params -``` - -## Attributes of Parameters - -Each parameter has attributes that specify the name and type of the parameter, its units, and the uncertainty. -The `par.quantity` and `par.uncertainty` are both astropy quantities with units. If you need the bare values, -access `par.value` and `par.uncertainty_value`, which will be numerical values in the units of `par.units` - -Let's look at those for each of the types of parameters in this model. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.360328Z", "iopub.status.busy": "2020-09-10T16:29:09.359604Z", "iopub.status.idle": "2020-09-10T16:29:09.377048Z", "shell.execute_reply": "2020-09-10T16:29:09.377476Z"} jupyter={"outputs_hidden": false} -printed = [] -for p in model.params: - par = getattr(model, p) - if type(par) in printed: - continue - print("Name ", par.name) - print("Type ", type(par)) - print("Quantity ", par.quantity, type(par.quantity)) - print("Value ", par.value) - print("units ", par.units) - print("Uncertainty ", par.uncertainty) - print("Uncertainty_value", par.uncertainty_value) - print("Summary ", par) - print("Parfile Style ", par.as_parfile_line()) - print() - printed.append(type(par)) -``` - -Note that DMX_nnnn is an example of a `prefixParameter`. These are parameters that are indexed by a numerical value and a componenent can have an arbitrary number of them. -In some cases, like `Fn` they are coefficients of a Taylor expansion and so all indices up to the maximum must be present. For others, like `DMX_nnnn` some indices can be missing without a problem. - -`prefixParameter`s can be used to hold indexed parameters of various types ( float, bool, str, MJD, angle ). Each one will instantiate a parameter of that type as `par.param_comp`. -When you print the parameter it looks like the `param_comp` type. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.381843Z", "iopub.status.busy": "2020-09-10T16:29:09.381295Z", "iopub.status.idle": "2020-09-10T16:29:09.384568Z", "shell.execute_reply": "2020-09-10T16:29:09.384053Z"} -# Note that for each instance of a prefix parameter is of type `prefixParameter` -print("Type = ", type(model.DMX_0016)) -print("param_comp type = ", type(model.DMX_0016.param_comp)) -print("Printing gives : ", model.DMX_0016) -``` - -## Constructing a parameter - -You can make a Parameter instance by calling its constructor - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.388561Z", "iopub.status.busy": "2020-09-10T16:29:09.388009Z", "iopub.status.idle": "2020-09-10T16:29:09.390425Z", "shell.execute_reply": "2020-09-10T16:29:09.390875Z"} jupyter={"outputs_hidden": false} -# You can specify the vaue as a number -t = pp.floatParameter(name="TEST", value=100, units="Hz", uncertainty=0.03) -print(t) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.394886Z", "iopub.status.busy": "2020-09-10T16:29:09.394261Z", "iopub.status.idle": "2020-09-10T16:29:09.397410Z", "shell.execute_reply": "2020-09-10T16:29:09.396920Z"} jupyter={"outputs_hidden": false} -# Or as a string that will be parsed -t2 = pp.floatParameter(name="TEST", value="200", units="Hz", uncertainty=".04") -print(t2) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.402455Z", "iopub.status.busy": "2020-09-10T16:29:09.401844Z", "iopub.status.idle": "2020-09-10T16:29:09.404895Z", "shell.execute_reply": "2020-09-10T16:29:09.404422Z"} jupyter={"outputs_hidden": false} -# Or as an astropy Quantity with units (this is the preferred method!) -t3 = pp.floatParameter( - name="TEST", value=0.3 * u.kHz, units="Hz", uncertainty=4e-5 * u.kHz -) -print(t3) -print(t3.quantity) -print(t3.value) -print(t3.uncertainty) -print(t3.uncertainty_value) -``` - -## Setting Parameters - -The value of a parameter can be set in multiple ways. As usual, the preferred method is to set it using an astropy Quantity, so units will be checked and respected - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.409561Z", "iopub.status.busy": "2020-09-10T16:29:09.409014Z", "iopub.status.idle": "2020-09-10T16:29:09.411819Z", "shell.execute_reply": "2020-09-10T16:29:09.412262Z"} jupyter={"outputs_hidden": false} -par = model.F0 -# Here we set it using a Quantity in kHz. Because astropy Quantities are used, it does the right thing! -par.quantity = 0.3 * u.kHz -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.416589Z", "iopub.status.busy": "2020-09-10T16:29:09.416031Z", "iopub.status.idle": "2020-09-10T16:29:09.419490Z", "shell.execute_reply": "2020-09-10T16:29:09.418920Z"} jupyter={"outputs_hidden": false} -# Here we set it with a bare number, which is interpreted as being in the units `par.units` -print(par) -par.quantity = 200 -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.424527Z", "iopub.status.busy": "2020-09-10T16:29:09.423970Z", "iopub.status.idle": "2020-09-10T16:29:09.426589Z", "shell.execute_reply": "2020-09-10T16:29:09.427036Z"} jupyter={"outputs_hidden": false} -# If you try to set the parameter to a quantity that isn't compatible with the units, it raises an exception -try: - print(par) - par.value = 100 * u.second # SET F0 to seconds as time. - print("Quantity ", par.quantity, type(par.quantity)) - print("Value ", par.value) - print(par) -except u.UnitConversionError as e: - print("Exception raised:", e) -else: - raise ValueError("That was supposed to raise an exception!") -``` - -### MJD parameters - -These parameters hold a date as an astropy `Time` object. Numbers will be interpreted as MJDs in the default time scale of the parameter (which is UTC for the TZRMJD parameter) - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.433972Z", "iopub.status.busy": "2020-09-10T16:29:09.433400Z", "iopub.status.idle": "2020-09-10T16:29:09.437110Z", "shell.execute_reply": "2020-09-10T16:29:09.437563Z"} jupyter={"outputs_hidden": false} -par = model.TZRMJD -print(par) -par.quantity = 54000 -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -par.quantity -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.443202Z", "iopub.status.busy": "2020-09-10T16:29:09.442648Z", "iopub.status.idle": "2020-09-10T16:29:09.446875Z", "shell.execute_reply": "2020-09-10T16:29:09.446314Z"} jupyter={"outputs_hidden": false} -# And of course, you can set them with a `Time` object -par.quantity = Time.now() -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -par.quantity -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.452453Z", "iopub.status.busy": "2020-09-10T16:29:09.451797Z", "iopub.status.idle": "2020-09-10T16:29:09.455744Z", "shell.execute_reply": "2020-09-10T16:29:09.455178Z"} -# I wonder if this should get converted to UTC? -par.quantity = Time(58000.0, format="mjd", scale="tdb") -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -par.quantity -``` - -### AngleParameters - -These store quanities as angles using astropy coordinates - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.461206Z", "iopub.status.busy": "2020-09-10T16:29:09.460628Z", "iopub.status.idle": "2020-09-10T16:29:09.463885Z", "shell.execute_reply": "2020-09-10T16:29:09.463434Z"} jupyter={"outputs_hidden": false} -# The unit for RAJ is hourangle -par = model.RAJ -print(par) -par.quantity = 12 -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.470200Z", "iopub.status.busy": "2020-09-10T16:29:09.469585Z", "iopub.status.idle": "2020-09-10T16:29:09.473559Z", "shell.execute_reply": "2020-09-10T16:29:09.473085Z"} jupyter={"outputs_hidden": false} -# Best practice is to set using a quantity with units -print(par) -par.quantity = 30.5 * u.hourangle -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -par.quantity -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.479653Z", "iopub.status.busy": "2020-09-10T16:29:09.479085Z", "iopub.status.idle": "2020-09-10T16:29:09.483177Z", "shell.execute_reply": "2020-09-10T16:29:09.482702Z"} jupyter={"outputs_hidden": false} -# But a string will work -par.quantity = "20:30:00" -print("Quantity ", par.quantity, type(par.quantity)) -print("Value ", par.value) -print(par) -par.quantity -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.490228Z", "iopub.status.busy": "2020-09-10T16:29:09.489668Z", "iopub.status.idle": "2020-09-10T16:29:09.493522Z", "shell.execute_reply": "2020-09-10T16:29:09.494042Z"} jupyter={"outputs_hidden": false} -# And the units can be anything that is convertable to hourangle -print(par) -par.quantity = 30 * u.deg -print("Quantity ", par.quantity, type(par.quantity)) -print("Quantity in deg", par.quantity.to(u.deg)) -print("Value ", par.value) -print(par) -par.quantity -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:09.499142Z", "iopub.status.busy": "2020-09-10T16:29:09.498578Z", "iopub.status.idle": "2020-09-10T16:29:09.501075Z", "shell.execute_reply": "2020-09-10T16:29:09.501562Z"} jupyter={"outputs_hidden": false} -# Here, setting RAJ to an incompatible unit will raise an exception -try: - # Example for RAJ - print(par) - par.quantity = 30 * u.hour # Here hour is in the unit of time, not hourangle - print("Quantity ", par.quantity, type(par.quantity)) - print(par) - par.quantity -except u.UnitConversionError as e: - print("Exception raised:", e) -else: - raise ValueError("That was supposed to raise an exception!") -``` diff --git a/docs/examples/understanding_parameters.py b/docs/examples/understanding_parameters.py new file mode 100644 index 000000000..1b28a0cd7 --- /dev/null +++ b/docs/examples/understanding_parameters.py @@ -0,0 +1,218 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_json: true +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Understanding Parameters + +# %% {"jupyter": {"outputs_hidden": false}} +import pint.models +import pint.models.parameter as pp +import astropy.units as u +from astropy.coordinates.angles import Angle +from astropy.time import Time + +# %% {"jupyter": {"outputs_hidden": false}} +# Load a model to play with +model = pint.models.get_model("B1855+09_NANOGrav_dfg+12_TAI.par") + +# %% {"jupyter": {"outputs_hidden": false}} +# This model has a large number of parameters of various types +model.params + +# %% [markdown] +# ## Attributes of Parameters +# +# Each parameter has attributes that specify the name and type of the parameter, its units, and the uncertainty. +# The `par.quantity` and `par.uncertainty` are both astropy quantities with units. If you need the bare values, +# access `par.value` and `par.uncertainty_value`, which will be numerical values in the units of `par.units` +# +# Let's look at those for each of the types of parameters in this model. + +# %% {"jupyter": {"outputs_hidden": false}} +printed = [] +for p in model.params: + par = getattr(model, p) + if type(par) in printed: + continue + print("Name ", par.name) + print("Type ", type(par)) + print("Quantity ", par.quantity, type(par.quantity)) + print("Value ", par.value) + print("units ", par.units) + print("Uncertainty ", par.uncertainty) + print("Uncertainty_value", par.uncertainty_value) + print("Summary ", par) + print("Parfile Style ", par.as_parfile_line()) + print() + printed.append(type(par)) + +# %% [markdown] +# Note that DMX_nnnn is an example of a `prefixParameter`. These are parameters that are indexed by a numerical value and a componenent can have an arbitrary number of them. +# In some cases, like `Fn` they are coefficients of a Taylor expansion and so all indices up to the maximum must be present. For others, like `DMX_nnnn` some indices can be missing without a problem. +# +# `prefixParameter`s can be used to hold indexed parameters of various types ( float, bool, str, MJD, angle ). Each one will instantiate a parameter of that type as `par.param_comp`. +# When you print the parameter it looks like the `param_comp` type. + +# %% +# Note that for each instance of a prefix parameter is of type `prefixParameter` +print("Type = ", type(model.DMX_0016)) +print("param_comp type = ", type(model.DMX_0016.param_comp)) +print("Printing gives : ", model.DMX_0016) + +# %% [markdown] +# ## Constructing a parameter +# +# You can make a Parameter instance by calling its constructor + +# %% {"jupyter": {"outputs_hidden": false}} +# You can specify the vaue as a number +t = pp.floatParameter(name="TEST", value=100, units="Hz", uncertainty=0.03) +print(t) + +# %% {"jupyter": {"outputs_hidden": false}} +# Or as a string that will be parsed +t2 = pp.floatParameter(name="TEST", value="200", units="Hz", uncertainty=".04") +print(t2) + +# %% {"jupyter": {"outputs_hidden": false}} +# Or as an astropy Quantity with units (this is the preferred method!) +t3 = pp.floatParameter( + name="TEST", value=0.3 * u.kHz, units="Hz", uncertainty=4e-5 * u.kHz +) +print(t3) +print(t3.quantity) +print(t3.value) +print(t3.uncertainty) +print(t3.uncertainty_value) + +# %% [markdown] +# ## Setting Parameters +# +# The value of a parameter can be set in multiple ways. As usual, the preferred method is to set it using an astropy Quantity, so units will be checked and respected + +# %% {"jupyter": {"outputs_hidden": false}} +par = model.F0 +# Here we set it using a Quantity in kHz. Because astropy Quantities are used, it does the right thing! +par.quantity = 0.3 * u.kHz +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) + +# %% {"jupyter": {"outputs_hidden": false}} +# Here we set it with a bare number, which is interpreted as being in the units `par.units` +print(par) +par.quantity = 200 +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) + +# %% {"jupyter": {"outputs_hidden": false}} +# If you try to set the parameter to a quantity that isn't compatible with the units, it raises an exception +try: + print(par) + par.value = 100 * u.second # SET F0 to seconds as time. + print("Quantity ", par.quantity, type(par.quantity)) + print("Value ", par.value) + print(par) +except u.UnitConversionError as e: + print("Exception raised:", e) +else: + raise ValueError("That was supposed to raise an exception!") + +# %% [markdown] +# ### MJD parameters +# +# These parameters hold a date as an astropy `Time` object. Numbers will be interpreted as MJDs in the default time scale of the parameter (which is UTC for the TZRMJD parameter) + +# %% {"jupyter": {"outputs_hidden": false}} +par = model.TZRMJD +print(par) +par.quantity = 54000 +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) +par.quantity + +# %% {"jupyter": {"outputs_hidden": false}} +# And of course, you can set them with a `Time` object +par.quantity = Time.now() +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) +par.quantity + +# %% +# I wonder if this should get converted to UTC? +par.quantity = Time(58000.0, format="mjd", scale="tdb") +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) +par.quantity + +# %% [markdown] +# ### AngleParameters +# +# These store quanities as angles using astropy coordinates + +# %% {"jupyter": {"outputs_hidden": false}} +# The unit for RAJ is hourangle +par = model.RAJ +print(par) +par.quantity = 12 +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) + +# %% {"jupyter": {"outputs_hidden": false}} +# Best practice is to set using a quantity with units +print(par) +par.quantity = 30.5 * u.hourangle +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) +par.quantity + +# %% {"jupyter": {"outputs_hidden": false}} +# But a string will work +par.quantity = "20:30:00" +print("Quantity ", par.quantity, type(par.quantity)) +print("Value ", par.value) +print(par) +par.quantity + +# %% {"jupyter": {"outputs_hidden": false}} +# And the units can be anything that is convertable to hourangle +print(par) +par.quantity = 30 * u.deg +print("Quantity ", par.quantity, type(par.quantity)) +print("Quantity in deg", par.quantity.to(u.deg)) +print("Value ", par.value) +print(par) +par.quantity + +# %% {"jupyter": {"outputs_hidden": false}} +# Here, setting RAJ to an incompatible unit will raise an exception +try: + # Example for RAJ + print(par) + par.quantity = 30 * u.hour # Here hour is in the unit of time, not hourangle + print("Quantity ", par.quantity, type(par.quantity)) + print(par) + par.quantity +except u.UnitConversionError as e: + print("Exception raised:", e) +else: + raise ValueError("That was supposed to raise an exception!") diff --git a/docs/examples/understanding_timing_models.md b/docs/examples/understanding_timing_models.md deleted file mode 100644 index fae1d5bbf..000000000 --- a/docs/examples/understanding_timing_models.md +++ /dev/null @@ -1,183 +0,0 @@ ---- -jupyter: - jupytext: - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Understanding Timing Models - - -## Build a timing model starting from a par file - -```python execution={"iopub.execute_input": "2020-09-10T16:29:14.556872Z", "iopub.status.busy": "2020-09-10T16:29:14.556238Z", "iopub.status.idle": "2020-09-10T16:29:16.478705Z", "shell.execute_reply": "2020-09-10T16:29:16.478187Z"} -from pint.models import get_model -from pint.models.timing_model import Component -``` - -One can build a timing model via `get_model()` method. This will read the par file and instantiate all the delay and phase components, using the default ordering. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.482235Z", "iopub.status.busy": "2020-09-10T16:29:16.481688Z", "iopub.status.idle": "2020-09-10T16:29:16.708649Z", "shell.execute_reply": "2020-09-10T16:29:16.709120Z"} -par = "B1855+09_NANOGrav_dfg+12_TAI.par" -m = get_model(par) -``` - - -Each of the parameters in the model can be accessed as an attribute of the `TimingModel` object. -Behind the scenes PINT figures out which component the parameter is stored in. - -Each parameter has attributes like the quantity (which includes units), and a description (see the Understanding Parameters notebook for more detail) - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.713153Z", "iopub.status.busy": "2020-09-10T16:29:16.712587Z", "iopub.status.idle": "2020-09-10T16:29:16.715583Z", "shell.execute_reply": "2020-09-10T16:29:16.715107Z"} -print(m.F0.quantity) -print(m.F0.description) -``` - -We can now explore the structure of the model - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.724225Z", "iopub.status.busy": "2020-09-10T16:29:16.723668Z", "iopub.status.idle": "2020-09-10T16:29:16.727335Z", "shell.execute_reply": "2020-09-10T16:29:16.726852Z"} -# This gives a list of all of the component types (so far there are only delay and phase components) -m.component_types -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.732346Z", "iopub.status.busy": "2020-09-10T16:29:16.731805Z", "iopub.status.idle": "2020-09-10T16:29:16.734790Z", "shell.execute_reply": "2020-09-10T16:29:16.735442Z"} -dir(m) -``` - -The TimingModel class stores lists of the delay model components and phase components that make up the model - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.751102Z", "iopub.status.busy": "2020-09-10T16:29:16.744076Z", "iopub.status.idle": "2020-09-10T16:29:16.761459Z", "shell.execute_reply": "2020-09-10T16:29:16.760879Z"} -# When this list gets printed, it shows the parameters that are associated with each component as well. -m.DelayComponent_list -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.766974Z", "iopub.status.busy": "2020-09-10T16:29:16.766423Z", "iopub.status.idle": "2020-09-10T16:29:16.769860Z", "shell.execute_reply": "2020-09-10T16:29:16.769254Z"} -# Now let's look at the phase components. These include the absolute phase, the spindown model, and phase jumps -m.PhaseComponent_list -``` - -We can add a component to an existing model - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.773615Z", "iopub.status.busy": "2020-09-10T16:29:16.773069Z", "iopub.status.idle": "2020-09-10T16:29:16.774962Z", "shell.execute_reply": "2020-09-10T16:29:16.775606Z"} -from pint.models.astrometry import AstrometryEcliptic -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.779872Z", "iopub.status.busy": "2020-09-10T16:29:16.779230Z", "iopub.status.idle": "2020-09-10T16:29:16.781918Z", "shell.execute_reply": "2020-09-10T16:29:16.781407Z"} -a = AstrometryEcliptic() # init the AstrometryEcliptic instance -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.788033Z", "iopub.status.busy": "2020-09-10T16:29:16.787491Z", "iopub.status.idle": "2020-09-10T16:29:16.790089Z", "shell.execute_reply": "2020-09-10T16:29:16.789531Z"} -# Add the component to the model -# It will be put in the default order -# We set validate=False since we have not set the parameter values yet, which would cause validate to fail -m.add_component(a, validate=False) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.800036Z", "iopub.status.busy": "2020-09-10T16:29:16.799307Z", "iopub.status.idle": "2020-09-10T16:29:16.802907Z", "shell.execute_reply": "2020-09-10T16:29:16.802388Z"} -m.DelayComponent_list # The new instance is added to delay component list -``` - -There are two ways to remove a component from a model. This simplest is to use the `remove_component` method to remove it by name. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.806031Z", "iopub.status.busy": "2020-09-10T16:29:16.805484Z", "iopub.status.idle": "2020-09-10T16:29:16.808055Z", "shell.execute_reply": "2020-09-10T16:29:16.807585Z"} -# We will not do this here, since we'll demonstrate a different method below. -# m.remove_component("AstrometryEcliptic") -``` - -Alternatively, you can have more control using the `map_component()` method, which takes either a string with component name, -or a Component instance and returns a tuple containing the Component instance, its order in the relevant component list, -the list of components of this type in the model, and the component type (as a string) - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.818594Z", "iopub.status.busy": "2020-09-10T16:29:16.817984Z", "iopub.status.idle": "2020-09-10T16:29:16.821353Z", "shell.execute_reply": "2020-09-10T16:29:16.820789Z"} -component, order, from_list, comp_type = m.map_component("AstrometryEcliptic") -print("Component : ", component) -print("Type : ", comp_type) -print("Order : ", order) -print("List : ") -_ = [print(c) for c in from_list] -``` - - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.824668Z", "iopub.status.busy": "2020-09-10T16:29:16.824121Z", "iopub.status.idle": "2020-09-10T16:29:16.826646Z", "shell.execute_reply": "2020-09-10T16:29:16.826109Z"} -# Now we can remove the component by directly manipulating the list -from_list.remove(component) -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.835572Z", "iopub.status.busy": "2020-09-10T16:29:16.834806Z", "iopub.status.idle": "2020-09-10T16:29:16.839094Z", "shell.execute_reply": "2020-09-10T16:29:16.838530Z"} -m.DelayComponent_list # AstrometryEcliptic has been removed from delay list. -``` - -To switch the order of a component, just change the order of the component list - -**NB: that this should almost never be done! In most cases the default order of the delay components is correct. Experts only!** - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.842965Z", "iopub.status.busy": "2020-09-10T16:29:16.842402Z", "iopub.status.idle": "2020-09-10T16:29:16.845286Z", "shell.execute_reply": "2020-09-10T16:29:16.845733Z"} -# Let's look at the order of the components in the delay list first -_ = [print(dc.__class__) for dc in m.DelayComponent_list] -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.849802Z", "iopub.status.busy": "2020-09-10T16:29:16.849263Z", "iopub.status.idle": "2020-09-10T16:29:16.851830Z", "shell.execute_reply": "2020-09-10T16:29:16.851362Z"} -# Now let's swap the order of DispersionDMX and Dispersion -component, order, from_list, comp_type = m.map_component("DispersionDMX") -new_order = 3 -from_list[order], from_list[new_order] = from_list[new_order], from_list[order] -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.855413Z", "iopub.status.busy": "2020-09-10T16:29:16.854751Z", "iopub.status.idle": "2020-09-10T16:29:16.858602Z", "shell.execute_reply": "2020-09-10T16:29:16.858131Z"} -# Print the classes to see the order switch -_ = [print(dc.__class__) for dc in m.DelayComponent_list] -``` - -Delays are always computed in the order of the DelayComponent_list - -```python execution={"iopub.execute_input": "2020-09-10T16:29:16.862129Z", "iopub.status.busy": "2020-09-10T16:29:16.861573Z", "iopub.status.idle": "2020-09-10T16:29:18.484921Z", "shell.execute_reply": "2020-09-10T16:29:18.484349Z"} -# First get the toas -from pint.toa import get_TOAs - -t = get_TOAs("B1855+09_NANOGrav_dfg+12.tim") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:18.492807Z", "iopub.status.busy": "2020-09-10T16:29:18.487988Z", "iopub.status.idle": "2020-09-10T16:29:18.763398Z", "shell.execute_reply": "2020-09-10T16:29:18.762853Z"} -# compute the total delay -total_delay = m.delay(t) -total_delay -``` - -One can get the delay up to some component. For example, if you want the delay computation stop after the Solar System Shapiro delay. - -By default the delay of the specified component *is* included. This can be changed by the keyword parameter `include_last=False`. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:18.776334Z", "iopub.status.busy": "2020-09-10T16:29:18.766704Z", "iopub.status.idle": "2020-09-10T16:29:18.848709Z", "shell.execute_reply": "2020-09-10T16:29:18.848166Z"} -to_jump_delay = m.delay(t, cutoff_component="SolarSystemShapiro") -to_jump_delay -``` - -Here is a list of all the Component types that PINT knows about - -```python execution={"iopub.execute_input": "2020-09-10T16:29:18.853189Z", "iopub.status.busy": "2020-09-10T16:29:18.852646Z", "iopub.status.idle": "2020-09-10T16:29:18.856060Z", "shell.execute_reply": "2020-09-10T16:29:18.855439Z"} -Component.component_types -``` - -When PINT builds a model from a par file, it has to infer what components to include in the model. -This is done by the `component_special_params` of each `Component`. A component will be instantiated -when one of its special parameters is present in the par file. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:18.871044Z", "iopub.status.busy": "2020-09-10T16:29:18.870459Z", "iopub.status.idle": "2020-09-10T16:29:18.873919Z", "shell.execute_reply": "2020-09-10T16:29:18.873425Z"} -from collections import defaultdict - -special = defaultdict(list) -for comp, tp in Component.component_types.items(): - for p in tp().component_special_params: - special[p].append(comp) - - -special -``` diff --git a/docs/examples/understanding_timing_models.py b/docs/examples/understanding_timing_models.py new file mode 100644 index 000000000..701a2b615 --- /dev/null +++ b/docs/examples/understanding_timing_models.py @@ -0,0 +1,173 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Understanding Timing Models + +# %% [markdown] +# ## Build a timing model starting from a par file + +# %% +from pint.models import get_model +from pint.models.timing_model import Component + +# %% [markdown] +# One can build a timing model via `get_model()` method. This will read the par file and instantiate all the delay and phase components, using the default ordering. + +# %% +par = "B1855+09_NANOGrav_dfg+12_TAI.par" +m = get_model(par) + + +# %% [markdown] +# Each of the parameters in the model can be accessed as an attribute of the `TimingModel` object. +# Behind the scenes PINT figures out which component the parameter is stored in. +# +# Each parameter has attributes like the quantity (which includes units), and a description (see the Understanding Parameters notebook for more detail) + +# %% +print(m.F0.quantity) +print(m.F0.description) + +# %% [markdown] +# We can now explore the structure of the model + +# %% +# This gives a list of all of the component types (so far there are only delay and phase components) +m.component_types + +# %% +dir(m) + +# %% [markdown] +# The TimingModel class stores lists of the delay model components and phase components that make up the model + +# %% +# When this list gets printed, it shows the parameters that are associated with each component as well. +m.DelayComponent_list + +# %% +# Now let's look at the phase components. These include the absolute phase, the spindown model, and phase jumps +m.PhaseComponent_list + +# %% [markdown] +# We can add a component to an existing model + +# %% +from pint.models.astrometry import AstrometryEcliptic + +# %% +a = AstrometryEcliptic() # init the AstrometryEcliptic instance + +# %% +# Add the component to the model +# It will be put in the default order +# We set validate=False since we have not set the parameter values yet, which would cause validate to fail +m.add_component(a, validate=False) + +# %% +m.DelayComponent_list # The new instance is added to delay component list + +# %% [markdown] +# There are two ways to remove a component from a model. This simplest is to use the `remove_component` method to remove it by name. + +# %% +# We will not do this here, since we'll demonstrate a different method below. +# m.remove_component("AstrometryEcliptic") + +# %% [markdown] +# Alternatively, you can have more control using the `map_component()` method, which takes either a string with component name, +# or a Component instance and returns a tuple containing the Component instance, its order in the relevant component list, +# the list of components of this type in the model, and the component type (as a string) + +# %% +component, order, from_list, comp_type = m.map_component("AstrometryEcliptic") +print("Component : ", component) +print("Type : ", comp_type) +print("Order : ", order) +print("List : ") +_ = [print(c) for c in from_list] + + +# %% +# Now we can remove the component by directly manipulating the list +from_list.remove(component) + +# %% +m.DelayComponent_list # AstrometryEcliptic has been removed from delay list. + +# %% [markdown] +# To switch the order of a component, just change the order of the component list +# +# **NB: that this should almost never be done! In most cases the default order of the delay components is correct. Experts only!** + +# %% +# Let's look at the order of the components in the delay list first +_ = [print(dc.__class__) for dc in m.DelayComponent_list] + +# %% +# Now let's swap the order of DispersionDMX and Dispersion +component, order, from_list, comp_type = m.map_component("DispersionDMX") +new_order = 3 +from_list[order], from_list[new_order] = from_list[new_order], from_list[order] + +# %% +# Print the classes to see the order switch +_ = [print(dc.__class__) for dc in m.DelayComponent_list] + +# %% [markdown] +# Delays are always computed in the order of the DelayComponent_list + +# %% +# First get the toas +from pint.toa import get_TOAs + +t = get_TOAs("B1855+09_NANOGrav_dfg+12.tim") + +# %% +# compute the total delay +total_delay = m.delay(t) +total_delay + +# %% [markdown] +# One can get the delay up to some component. For example, if you want the delay computation stop after the Solar System Shapiro delay. +# +# By default the delay of the specified component *is* included. This can be changed by the keyword parameter `include_last=False`. + +# %% +to_jump_delay = m.delay(t, cutoff_component="SolarSystemShapiro") +to_jump_delay + +# %% [markdown] +# Here is a list of all the Component types that PINT knows about + +# %% +Component.component_types + +# %% [markdown] +# When PINT builds a model from a par file, it has to infer what components to include in the model. +# This is done by the `component_special_params` of each `Component`. A component will be instantiated +# when one of its special parameters is present in the par file. + +# %% +from collections import defaultdict + +special = defaultdict(list) +for comp, tp in Component.component_types.items(): + for p in tp().component_special_params: + special[p].append(comp) + + +special From 1ff945408b25e120c8b5e28dbd09380250570b4a Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Fri, 4 Dec 2020 13:34:03 +0000 Subject: [PATCH 3/6] Fix Makefile mis-merge --- Makefile | 1 - 1 file changed, 1 deletion(-) diff --git a/Makefile b/Makefile index 5b941fbe2..88cc10456 100644 --- a/Makefile +++ b/Makefile @@ -63,7 +63,6 @@ notebooks: jupytext --pipe black --pipe-fmt py:percent examples/*.ipynb jupyter nbconvert --execute --inplace examples/*.ipynb jupytext --sync examples/*.ipynb - # jupytext --to script examples/*.md docs-clean: mkdir -p docs/api From adb3a790e80a4ee13c043e9b554c35a5e5361592 Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Fri, 4 Dec 2020 13:41:18 +0000 Subject: [PATCH 4/6] Link to .py files on github These files won't be there until this PR gets merged. --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index ae9947479..f21e2e7f1 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -146,7 +146,7 @@ nbsphinx_custom_formats = {".md": lambda s: jupytext.reads(s, ".md")} nbsphinx_prolog = """ -This Jupyter notebook can be downloaded from `{{ env.docname.split("/")[-1] }}.ipynb <{{ env.docname.split("/")[-1] }}.ipynb#http://>`_ +This Jupyter notebook can be downloaded from `{{ env.docname.split("/")[-1] }}.ipynb <{{ env.docname.split("/")[-1] }}.ipynb#http://>`_, or viewed as a python script at `{{ env.docname.split("/")[-1] }}.py `_ """ From a4715cfa141cdc5afea8a3ab5400671ce401c978 Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Fri, 4 Dec 2020 15:15:01 +0000 Subject: [PATCH 5/6] Ensure .py files are picked up as documents --- docs/conf.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index f21e2e7f1..d5a541063 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -144,11 +144,12 @@ napoleon_use_ivar = True # How to format Attributes sections napoleon_use_param = True -nbsphinx_custom_formats = {".md": lambda s: jupytext.reads(s, ".md")} +nbsphinx_custom_formats = {".py": lambda s: jupytext.reads(s, ".py")} nbsphinx_prolog = """ -This Jupyter notebook can be downloaded from `{{ env.docname.split("/")[-1] }}.ipynb <{{ env.docname.split("/")[-1] }}.ipynb#http://>`_, or viewed as a python script at `{{ env.docname.split("/")[-1] }}.py `_ +This Jupyter notebook can be downloaded from `{{ env.docname.split("/")[-1] }}.ipynb <{{ env.docname.split("/")[-1] }}.ipynb#http://>`_, or viewed as a python script at `{{ env.docname.split("/")[-1] }}.py `_. """ +nbsphinx_allow_errors = True # -- apidoc ---------------------------------------------------------- From 8cc31d2c30b438497feb310de8f1c956bfbfd64a Mon Sep 17 00:00:00 2001 From: Anne Archibald Date: Fri, 4 Dec 2020 15:44:15 +0000 Subject: [PATCH 6/6] Added paper validation notebook --- Makefile | 1 + .../paper_validation_example.ipynb | 1409 +++++++++++++++++ docs/examples/Wideband_TOA_walkthrough.md | 170 -- docs/examples/Wideband_TOA_walkthrough.py | 186 +++ docs/tutorials.rst | 2 + 5 files changed, 1598 insertions(+), 170 deletions(-) create mode 100644 docs/examples-rendered/paper_validation_example.ipynb delete mode 100644 docs/examples/Wideband_TOA_walkthrough.md create mode 100644 docs/examples/Wideband_TOA_walkthrough.py diff --git a/Makefile b/Makefile index 88cc10456..07a7f000b 100644 --- a/Makefile +++ b/Makefile @@ -63,6 +63,7 @@ notebooks: jupytext --pipe black --pipe-fmt py:percent examples/*.ipynb jupyter nbconvert --execute --inplace examples/*.ipynb jupytext --sync examples/*.ipynb + jupytext --to py:percent docs/examples-rendered/*.ipynb docs-clean: mkdir -p docs/api diff --git a/docs/examples-rendered/paper_validation_example.ipynb b/docs/examples-rendered/paper_validation_example.ipynb new file mode 100644 index 000000000..e25ac1767 --- /dev/null +++ b/docs/examples-rendered/paper_validation_example.ipynb @@ -0,0 +1,1409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Validation Example for PINT paper\n", + "\n", + "A comparison between PINT result and Tempo/Tempo2 result. This example is presented in the PINT paper. But it can be used for other datasets. \n", + "\n", + "* Requirement\n", + " * Data set: NANOGrav 11-year data J1600-3053\n", + " * TEMPO and its python utils tempo_utils. Download from https://github.com/demorest/tempo_utils\n", + " * TEMPO2 and its python utils tempo2_utils. Download from https://github.com/demorest/tempo_utils\n", + " * TEMPO2 general2 plugins. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Using astropy version 3.2.3. To get most recent IERS data, upgrade to astropy >= 4.0 [pint]\n", + "WARNING: Using astropy version 3.2.3. To get most recent IERS data, upgrade to astropy >= 4.0 [pint.erfautils]\n" + ] + } + ], + "source": [ + "import pint\n", + "import sys\n", + "from pint import toa\n", + "from pint import models\n", + "from pint.fitter import GLSFitter\n", + "import os \n", + "import matplotlib.pyplot as plt\n", + "import astropy.units as u\n", + "import tempo2_utils as t2u\n", + "import tempo_utils\n", + "import tempo2_utils\n", + "import numpy as np\n", + "from astropy.table import Table\n", + "from astropy.io import ascii\n", + "import subprocess\n", + "import tempfile\n", + "from pint import ls\n", + "import astropy.constants as ct\n", + "from pint.solar_system_ephemerides import objPosVel_wrt_SSB\n", + "from astropy.time import Time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print the PINT and TEMPO/TEMPO2 version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PINT version: 0.7+432.g6854a9ec.dirty\n", + "TEMPO version: Tempo v 13.101 (2020-11-04 c5fbddf)\n", + "\n", + "TEMPO2 version: 2019.01.1\n", + "\n" + ] + } + ], + "source": [ + "print(\"PINT version: \", pint.__version__)\n", + "tempo_v = subprocess.check_output([\"tempo\", \"-v\"])\n", + "print(\"TEMPO version: \", tempo_v.decode(\"utf-8\"))\n", + "#Not sure why tempo2_v = subprocess.check_output([\"tempo2\", \"-v\"]) does not work.\n", + "process = subprocess.Popen(['tempo2', '-v'], stdout=subprocess.PIPE)\n", + "tempo2_v = process.communicate()[0]\n", + "print(\"TEMPO2 version: \", tempo2_v.decode(\"utf-8\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Redefine the Tempo2_util function for larger number of observations" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "_nobs = 30000\n", + "def newpar2(parfile,timfile):\n", + " \"\"\"\n", + " Run tempo2, return new parfile (as list of lines). input parfile\n", + " can be either lines or a filename.\n", + " \"\"\"\n", + " orig_dir = os.getcwd()\n", + " try:\n", + " temp_dir = tempfile.mkdtemp(prefix=\"tempo2\")\n", + " try:\n", + " lines = open(parfile,'r').readlines()\n", + " except:\n", + " lines = parfile\n", + " open(\"%s/pulsar.par\" % temp_dir, 'w').writelines(lines)\n", + " timpath = os.path.abspath(timfile)\n", + " os.chdir(temp_dir)\n", + " cmd = \"tempo2 -nobs %d -newpar -f pulsar.par %s -norescale\" % (_nobs, timpath)\n", + " os.system(cmd + \" > /dev/null\")\n", + " outparlines = open('new.par').readlines()\n", + " finally:\n", + " os.chdir(orig_dir)\n", + " os.system(\"rm -rf %s\" % temp_dir)\n", + " for l in outparlines:\n", + " if l.startswith('TRES'): rms = float(l.split()[1])\n", + " elif l.startswith('CHI2R'): (foo, chi2r, ndof) = l.split()\n", + " return float(chi2r)*float(ndof), int(ndof), rms, outparlines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the data file for PSR J1600-3053. \n", + "\n", + "* Note\n", + " * For other data set, one can change the cell below. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "psr = \"J1600-3053\"\n", + "par_file = os.path.join('.', psr + \"_NANOGrav_11yv1.gls.par\")\n", + "tim_file = os.path.join('.', psr + \"_NANOGrav_11yv1.tim\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PINT run\n", + "\n", + "### Load TOAs to PINT" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Applying clock corrections (include_GPS = True, include_BIPM = True) [pint.toa]\n", + "INFO: Observatory gbt, loading clock file \n", + "\t/home/luo/.local/lib/python3.6/site-packages/pint/datafiles/time.dat [pint.observatory.topo_obs]\n", + "INFO: Applying observatory clock corrections. [pint.observatory.topo_obs]\n", + "INFO: Applying GPS to UTC clock correction (~few nanoseconds) [pint.observatory.topo_obs]\n", + "INFO: Observatory gbt, loading GPS clock file \n", + "\t/home/luo/.local/lib/python3.6/site-packages/pint/datafiles/gps2utc.clk [pint.observatory.topo_obs]\n", + "INFO: Applying TT(TAI) to TT(BIPM) clock correction (~27 us) [pint.observatory.topo_obs]\n", + "INFO: Observatory gbt, loading BIPM clock file \n", + "\t/home/luo/.local/lib/python3.6/site-packages/pint/datafiles/tai2tt_bipm2015.clk [pint.observatory.topo_obs]\n", + "INFO: Computing TDB columns. [pint.toa]\n", + "INFO: Doing astropy mode TDB conversion [pint.observatory]\n", + "INFO: Computing PosVels of observatories and Earth, using DE436 [pint.toa]\n", + "INFO: Set solar system ephemeris to link:\n", + "\thttps://data.nanograv.org/static/data/ephem/de436.bsp [pint.solar_system_ephemerides]\n" + ] + } + ], + "source": [ + "t = toa.get_TOAs(tim_file, ephem=\"DE436\", bipm_version=\"BIPM2015\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 12433 TOAs in the dataset.\n" + ] + } + ], + "source": [ + "print(\"There are {} TOAs in the dataset.\".format(t.ntoas))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load timing model from .par file" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n", + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n" + ] + } + ], + "source": [ + "m = models.get_model(par_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make the General Least Square fitter" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "f = GLSFitter(model=m, toas=t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit TOAs for 9 iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Postfit Chi2: 12368.09539037636076\n", + "Degree of freedom: 12307\n" + ] + } + ], + "source": [ + "chi2 = f.fit_toas(9)\n", + "print(\"Postfit Chi2: \", chi2)\n", + "print(\"Degree of freedom: \", f.resids.dof)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The weighted RMS value for pre-fit and post-fit residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9441707008147506 us\n", + "0.9441138158055049 us\n" + ] + } + ], + "source": [ + "print(f.resids_init.rms_weighted())\n", + "print(f.resids.rms_weighted())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the pre-fit and post-fit residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pint_prefit = f.resids_init.time_resids.to_value(u.us)\n", + "pint_postfit = f.resids.time_resids.to_value(u.us)\n", + "\n", + "plt.figure(figsize=(8,5), dpi=150)\n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(t.get_mjds().to_value(u.day), f.resids_init.time_resids.to_value(u.us), \n", + " yerr=t.get_errors().to_value(u.us), fmt='x')\n", + "\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (us)')\n", + "plt.title('PINT pre-fit residuals for PSR J1600-3053 NANOGrav 11-year data')\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(t.get_mjds().to_value(u.day), f.resids.time_resids.to_value(u.us), \n", + " yerr=t.get_errors().to_value(u.us), fmt='x')\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (us)')\n", + "plt.title('PINT post-fit residuals for PSR J1600-3053 NANOGrav 11-year data')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.savefig(\"J1600_PINT\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TEMPO run\n", + "\n", + "### Use tempo_utils to analysis the same data set." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "tempo_toa = tempo_utils.read_toa_file(tim_file)\n", + "tempo_chi2, ndof, rms_t, tempo_par = tempo_utils.run_tempo(tempo_toa ,par_file, get_output_par=True, \n", + " gls=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEMPO postfit chi2: 12368.46\n", + "TEMPO postfit weighted rms: 0.944\n" + ] + } + ], + "source": [ + "print(\"TEMPO postfit chi2: \", tempo_chi2)\n", + "print(\"TEMPO postfit weighted rms: \", rms_t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the TEMPO postfit residuals to a new .par file, for comparison later" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Write out the post fit tempo parfile.\n", + "tempo_parfile = open(psr + '_tempo.par', 'w')\n", + "for line in tempo_par:\n", + " tempo_parfile.write(line)\n", + "tempo_parfile.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the TEMPO residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "tempo_prefit = tempo_toa.get_prefit()\n", + "tempo_postfit = tempo_toa.get_resids()\n", + "mjds = tempo_toa.get_mjd()\n", + "freqs = tempo_toa.get_freq()\n", + "errs = tempo_toa.get_resid_err()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the PINT - TEMPO residual difference." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "tp_diff_pre = (pint_prefit - tempo_prefit) * u.us \n", + "tp_diff_post = (pint_postfit - tempo_postfit) * u.us" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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fU8vsG817JfCZVNlGxtuYUe6tyAWwXgX2i8avRwjQzCRxw5Ax/1iauQGgeADqOELfaSNJnAMJD2m4iNyNddbxVXC7Svi8yt3ugttU7HhryXEH7EfuRvxGwnm4W5S3NaFPPifcEI5IzTsysfylwENEv29R/o6pz8CjfX05oVanRa+dyZ2jFwNbF9gPywnniN+TO+77EM5h8Wd4acY2DiEXjPuAEFCJzws9CX01XgycWMF981iU9x/CEwgtGh/XRD4H+GELj6fk57kodUwZof+V+Hz8PrBBxjL+HuW/Q+hweINofC9CdwHvRfl/z5h3As0EHQgBewfuS43vEX1+K6LPqg5Yr8A+G5MaX+65u1XnwmiaMVHeQsKN8M+JfucItaovSXwmX2/FOWI0zX9Hk39gfqrA96/JOSDxeY2Nhn+U2A+bFCpHieWO11vwWGjBPrgY+CmhD9S+0bj1CH9u3RGtZxkwOGPeseSuP1ZFx0L/KG9jwrkiLuuojPm/l8i/PvHZDojKFQcbi/7+tGBb4/LOKGHaop9vS46taPgkMq5hCOeO+Hv/SSoTgIqvmeckxl2QWO7wFmzHpGiet8rd/3p1rlfNC6CXXrV80UwQiHCR8mE0zQISF12E5meeeL1F6LD6DMKFUffWrptwgXxTYtn3t3L7kj9Go0ucp6TpydVAWkfo6Dr+R3EZ+f86F/qX+eoov66Z9fwqmm5+avzfovEvNTN/fNE+qcxj5cupzzt+1RNuypr8g0q4gXGaqSkDvBxNd0+RzyPet0tS46YAu5RQ/rcLlP9NYPdW7pMJieU0uYAnXIhOjNeTkT8jyltDxj/Yiel+Qe4GJvN7RQheOeFmtXsqL/6uTSjhuB9Z5Ds0tIzjZ3RiOX8rMt0gws3eOuDwAtN0J3dhd3Zi/H6J46To+Se1vJFx2TLyfht//8ioaUgIADQUmX8szdwA0EywppmynxvN+1hLtquE5Za73UW3qdDx1pLjjtyNQpPATWKaQkGFkYnlP0/hGpo9CH+yZH7HE9PdH01zTZHjPvMYIPdb8E5G3h8Tn8OQFnx+5eybuEbSgS09boqsK/l5FjqmdiEXNPthKu+YaPxHwJYF1rEVuRpee6byJtBM0IHQ/4sTbtqT1zr/L/6OER4W4iRqaRECgfE+OzS1zFafuynjXBjljUns88ztJncdMa4Vn2ny2G7yHY3K9kqUv5b8APrIxLxNzgE0DUD1Jvc9vLZQOUosd9F9UskX4U9JBy7MyBubKMvoAvPHn+3vU+N7Ea7JHbi9wLyXJ5Y/tgLbEpd3RgnTJj/fdC3X9OvFjPnzPtPo849rO26XmC7+I3hKNDy0xONqQoFy70eu1tJ9ifFxMHEVqeBzM/vhZnL3CSVfk+jV+V/qA0okg5ltaGaHE6q9Do5GX+vu6+Jp3P3nhH9g4s6dhwHfAG4g/GjOj9rAN9uvT9QXQvxaTvgH9FtR9tuEf9Ham2SfHT8lBDIOcvd+7r4B4YfsFcINzB/MLN3/wQbRe8F25Kn8DVLjy52/pVYSHs87l9zjltcSapRc7e5rM+apRBlvJ9x8bBzt2/6EWgE/Jfx7vxPwiJlt2Mw65kVlX5IY9xpwlru/1sy8zZlJqNGTJ/q+xE+P3NVCp/VZHnb3l7Myon4c4g42r/bC/ZbdR9i2QYQAcHt2eZG8Ewk1Qya5++NZE0T7IO78/uhEVtyPWg/CP8hlifb9CdHgje7+cUZZ3qDMfifK9FD0fqC18kEDaR1hu81sD0JT8NUUeAJn5Pbo/Ygi++cXBc5fEJ4AuyXh3NHkO56xnqOLTJN+kmws7kdxh2QfJxb6CYw/hyvcfWaRZTeqwL6Jv0dblLK+Vih0TL1F7pj6cir7m9H7H939w6yFeniwxRPRYLHPoZCJhBvFDQnNHmOHRe/joxfAqET+AYQb5HpyfSJW4txdzrkwaRVwVYG8+NjbvUB+i1noz/AA4J+E5pwQmpEtaO0yPfQjOiYaPMM6zlNr4/PzwUWmmUnuu5j2QPSe/nyOItfn5qVku4JwTNbaQGCzIq9NmltA9Pn/mVDjaXQi69Tovdi5uVlmNtjMvkb4PqxH9GdCYpL4emJh8j6oBPPjVdC0j1TpwrrXugAi7YUV6YCXEP3/WXqku19vZjcT2vkfRgi6DCdcjA0gBKS+YmYnuPuDRZZfqEPb2wn9ArWHH9G0OIBthAu8Y939/TjT3V80s2MJfT/0IdSSOq7JUjoId7+f6GLVwtMQdyFUS/4JcJqZfd7d/12F9Z6SMW4WcJGZvUL4B3cI8ANCk6RCyzkkTpvZRoTP4lLgcTO7hXCctbZT+gnuXuj78yShhlN3QrPF1zOmeabIsncld+Ey1syKXfzEHbRuQ6jV0R6tJDSnLCS+UB9uZnOKTBd39LtNYtx7hID1MOB5M7uB8CCD14sEGIrZlty+H19kuvGEJkFVYaGD6e8Qbjp2Ipxb08GUPoQL/fmUr11sdzPi42Q9YEqR/nbj/dSXcBPRJPBB8e9fvJ6BwEdF1tMjet+mQH6du79bIG92Ij2QXEB+BOEhFxCaM5eq3H3zIOEPoNvM7JOEm+AXvUinuy3U3DH1VWB3M1vfcw/XiLfpNDM7ucj8A6L3Qp9DQe6+yMxeJgSBRhH+SINcsGk8ud/yUeSCOnH+sx49KTZS7rm7nHNh0pvuXuhJkvGxV+7N8fQix9ljwFllLh9CkOEcwvn9UuBrFVhm2aKA7+mEz2so4bNM74xiDzl5sUhQo9DnE/+hObPQecXdF5vZfwhN02rpMHefUIHl3Ep4utwpFh6g0o/Qf99aCgfwCjm0yD3PauAHBcpc7D4pS2fpCF4qTAEokZy5ifQqwo3My8Cd7v5E9iyN/0z8LXrFwYn9iH4oCBdsfzazHdw98yLK3S2a14DNCf05XAGcTOic9RdlbVl1LE2k/5oMPsXc/UMzu4vw7+0RZtYtcSMcz9/kqR4pcf7S1PhWz29mBxE6mM7yfXf/S7EFRoGa14Gvmtl8wsXlX8xsZ89/4mG521iUu99rZs8QLrCOo0gAKjVfHXCLmT1OOL6+TjjWW/u43Mx/5KN1rTKzBYQg66YFJsu6KY4NTqSb/acw0tz+rqUFzfyDGG9vb3I3VsU0bqu7rzWzLxOanW5LOIdcAawws2cJx/xtLbiRTn5eBT9jQtOQqjCzAwk1CZI1/JaRa/LTjVBzAkIgoRIBqJpvdwni46Qbhf/ASCv0vSjl+9ejxPUUOmaLnduSge/kU9ySj61v8vtSRLn75jxgB8KfSj+IXmujgP9DwE2FaiGVqNi8cV53wk33XAtPtouP8QHkgkzFtPYc+AS5ANSVFp5otz/h85sUlaseOMTMuke/hYcl5k0q99zd6nNhSinHXrn3Q/PJ1YxeTagZ/xqhRtsDRf6gKVl0fv8x4Vrzq2Z2lbu/Wu5yy2HhiYjXkvtD0glNxeJAZG9CVxZ9iyymlM9n/dT4+Bzd3PewlufoinL358zsLcKfn4cTgn19gH+6++xi82aIj1EIn9lKQrDvWeBmD09ITopr721kZuu1oBZUsiZ2XcGppMtREzyRiLtvnnht4+77uPs3iwWfCixnjbs/6+6jyVUN7kvTKvVZ87q7f+TuvyPXL8L/mdmoZmatheQP/1tFpovz4n+ZY/EP5sCsR8smxI+lTv/Azk7lt2T++GYq61XKhW7Sr6L3IYTmKpUqY6niWlfNNvVMi4KGcSDutFasu3FRZcwLuQv3LMmaLpu7u5XwGltmeaqpuZpI8fbeWOK2Dk3OHN2QDCP8M3oTIcDYGziC0K/R20WaQhZT9g1US0XB/D8Rgk+vEGqa9nf3Ddx9M3ffnND8p3GWKhSjzbe7RPFx8naJx4m5+4ysBTVTOy5ez8OlrqeSG9lKZe0bd1/k7qMIHWZfSaghtoYQmLkIeMfMyqn51tJjKnkO/HKJ2zO6lWWLr3cOjgJfnyT08fRkdG1TT/jN6QfsF/127x/Nk67ZVe65u6xzYRvbN3H9OMTd93D3r7n7/ZUIPsXc/V5CDbH1KN6Uu+rMbBdCM631CA8c2Y/wEJKB8b4gBG+hejVh2uv5uVripnankmve2prmd88mjtct3H07dz/Y3c/zpsEnCE+jhnDtvFsL1rNX9D7FW1/DXjohBaBEqut3ifTOLZnRQ/XXPxJ+uH9dqf5NKijZb1Cxi4DkhUdyujcS6WI/aMOj9zdT4+P5NzWzzH9Xo302LD2/u0+oYPAiGYjboUAZdyn0+ZnZpuT+HU5vY1uIy799GcsoWL3ezHqSCzwWq2lRSLLWYGsCJx1NvL2t3lZ3b3D3e939dHf/BOH4OoPwD+QQ4LYSF5X8vIo1oSgWYI0vOnsVmaZQjY4DCc1q1hKa+P7L3dP/lm/edLayVWK7qy0+TraL+kqq9npq8d37KJFuSZOyiuwbd3/a3X/k7gcTgqCfI9R87U2oQVpq7aq0Uo6pNUQ1BqKgz+JofLU/h7jJdF9CYCnZ/C4WB6lGEZpdxU/JezG1rHLP3bU89tqzH0XvnzazQ2tYjuMJQcK3CIHRF929ITVNNc7PkDtHF/suQW3P0dXwR8L383jCny8LyPWTVU3JPti+WMoMUT9le0aDxZodSxekAJRIdSX7HVhVcKrCLiXcfO1CaM7Xbnhodz8tGty1yKS7RO9LyFXjBXiaUO0XwmPNmzCzbRLzP5rKHpdIZ85P+Pc27tg7PX+lJGsepW+O4zJuABxUYP5k2ccVmKaYuAbI9FbMC7nyF+ojoxSHWuEOMA4h17xhUoFpinmDXMfpzdYirIJkVfO2qN0R98dzQHT8l83dF3ioVRnfuOxlZqV0Uj6dXLX5w4pMV6yG5sLofUiRafYvMD6eZ54XbvJ0RJHltlYltrva4uOkB6G2bLXXs6WZFetIuBomEZ40CPDZFsxX8X3j7vXu/gDwhWhUL4p3rFxMsWMqznvNc/0/QW6b/tvMWnPtHp/Hip7DPPSVlOz7KdkBOal0Mv/pVHmh/HN3xc+FnYG7TwT+FQ3+Xw2LEp+fX/XCTbKqcX6G3DE6xMwy/zwzs/60/weStIiHbjz+Ra5J4p0ZQb9qrPd5cn1Xnmlmg4pNH7mQXJzhhqoUTDosBaBEWsHMBplZKT9syaBRsY6HM7n7e0DcH9FPoirx7cnY6P2/zWxoOtPMtiTXSe8/Pf8pgsuJ+s0Cvm1mWbUg4hvmpYQn5TRy92mEIBbAOQX2zfnR+/uEf3ZbJGoC1JwLEumJqbyJ5PouOT+VR1Tmc6LBp6NtSuYXvVkws+PI3QTdn5FftPxmtivhX30Ij+dtra3JCJBGN0r/Gw2+5e5ZHZAXFVXbviUaPKW5m+Cog/VKSj41sLknDVbCHwmB2W7Ab4rVfDSz9ZJPP4xqmxWT7J+s2U7Jo6Yjd0eDZ2RddEbH0PFFFhP3UbKvmTUJQkXNOL6QHh+Ja31sllXbxMy2IjyJtKIqtN3VNonQbxvAzwrVAo2V8b34B7maSNc201y6ot8/D32V/TkaPD/r+Cmg1fvGzLo3E+Bp0XeogELH1M7kjql0P4Q3Re87AT8stnAz62tmPVKj4/NYKeewOMD0X4TOnheQ+x4DvECo8XQguWbnTboqqMC5u9Xnwi7gAkJQcX8Knz+rrbFWXta1ipl9GhhZpXWPI/fnxk8KTHMeLe9SoSP4OeHpnlcDv2nD9Z5HOOYGEfo8Lbhvzewb5JoIjm3NtZ90bgpAibTO5sAkM5toZmeY2c7xD7CZdYuGf0Wuf6D3yQVbWupyQtO1oYSn6lVUFExrfCWy+qXysm48fknYtp7AAxYePRwvdwThaUJ9CBeRWY/KvYhwIbsF8A8z2zGat6+ZXURoNgRwmbsvzJj/PMJNwB6Ejt63jObfyMx+S+7i+Lxm+jkp5Hwzu8PMPp260e9uZvub2b2EjuIB7nL3ycmZo3WeFw1+xsx+G19kR2X9M+Hxwsnpkv5qZleY2QFm1tiEycy2NLOLyd2kfEj248anmNkPzGxY8qbKzDY1s28TAmS9CLXzflriPsmyGLjBzL4VlzO6WfwTuX/If1zG8n9KeMJbd+DhaJsabyrNbICZfcrMbgOeKmM9Tbj7InLNFE8tMShZzvrmkAtWHgOMM7NPxjdfFusI4BwAACAASURBVAwzsx8Qahgcm5j9y2b2jJmdbmaNNfOic9LRhA7JAf4dbVcpLicEgAdFZRmRKMdRhH9ji3Vq/g9C7br1gbujm2zMbH0z+xzhCVHLC8z7dJRn0bw7pbZnAtXrA6Tc7a6qKEh2BuG7uzXhqYfHJ8/T0XniJDMbRytrSkTNv75D2M97A8+Y2dHJAIeZbRsdcy9E01bSjwkdPG8crftL8Y2PmfU0s93N7BcWHiEel7mcfbMVoY+nC81sr+T33cx2JzwRF8Jx2eI/NSLrE46pfaPlmpkdQXhiZU/CY+lvTM7g4Smsf48GrzCzG+LvQ7SMHtFv0v8RfpPTD3yIm4N/Jv6dLCIOJu1DOOfmPeU0qun0NOG3Y4/UPGmtPneXeS7s1Dz09XdXNNiS2oGV9HD0vhshQBhf2/Q1s9MJHbAvKDRzOTw87CW+ZjnFzK6xqFavmfU3s58Q/vwq9Xeuw3D359z93OiV1V9Ttdb7OLk/XEcBL5nZf6fOq3uZ2a3A76NRk4Az26qM0oG4u156ddkXMIZwYe0tnG8Y4Z8AT7xWE35s16TGvwfsWs66CbV/nHBh2rMF5RyaKMcpBabxEl9jiuyLWYnplkav5PCxRcr4GcLFfDz9otQ+vBWwIvN/M9r38fQLU59NZrlbenwktmUeoVlIcvyfCZ1vlrKcdVEZk8fNNwvMNyEx3VpCs6DFqXW/nXV8ZXy2qwk3cktS4z8Gjmrl/onL93PCzYNH+6YutY6fFph/RpQ/uoR1bUvoiDq53IUZ++OdIvt/QpHlx/OPzMi7MJFfD3wQlf3PLdhXo6P5Z5Q4/Q9T34P4yZzpY+/EjHUkyxo/nSke9yEwLLWukXF+gbIcEy0rXsYSck+hm03oELXY/N9IlWtJtD1O6ND4zEL7hhBISH8HV0bpeYSbrzhvaEu2q4TPoNXb3dznXex4i/KHFtqu1HRHRp9xPO2aaDh5TnXg9+XsG+DE1DLj80l9aj0/bulx39y2EgJfyd+YuH+k5Hn+7Ersm1RZ4nkWJI7X+Lt4fAuPpeRyTyB3Hl6aKs9CYESBZfQhBPWT5VsW7Yu1qfFbpubdkdz3Zi2hf6UZ0Wur1LS9U9v7nYyy/CiRvxjoVmTbW33ubu25MJpvDM2f90fG87fi/DC62HHbzLwjE/M2OQeQ+20d28wxlfycSv0ux9OPaek2ZywrfTwuTHxWk4DvUvjcPraEbRxdZP71gNsT646vkeL1/6mUdbRgW8cWKkszn29d9H0r9jq30LHVwjIOLfG4KvidKGEdJ5N/Dbsu2sa8YxG4E+hb7n7Xq3O+VANKpBXc/W1C+/fTCf+Ivka4iBxAOAnPIHQM+E1CcGBy9pJK9rPofatonaVKVsNfWXCqMkT7YjdCDae4Y/JuwBTgOmC4uz9YZP5/EmoB/Z6w33oTglDjCBf5p7q7F5n/ZkI19LsIN9d9CEGV+4DD3X1MGZt3C6GJz72EQE8DMJBwE/o6oVnEIe7+ZQ81BQqVcQzhsbn3RWXrE5X1LuCAaBuy/JzwlJl/E254exH+IZ9NeDT9t4A9ixxfnyXUUnuO0IymL+GYmE3oE+tsYCd3L7d/rIZo+/6X8Ln3JNxcPA4c4+6FqsiXzN2nE5qDnEyoWZfcnumE2gFfJzQLqbSfA98nXFCvJnwPt6F6Hazi7r8gBHd/Rfhe1ROazywjdPZ7JaFfsbsSsz1A2D+3EprMLCack5YSms38BNgt+s62pCwPEYIAfyYcvz2AucCvCU+5md7M/H8gBJrHE268uwNTCbUbDqVwDSjc/UZCIGgCYdu7E7471xNqX1Stan+5290W3H0c4eEHFxBqpSwmHCfrCE8u+gOhKdVZZa7nzmg9lxG+B8ui9dQTggu/JvT3UvE+adz9JUJfgOcTzmVLCd/9WYTj4gfkfw/i+Vqzbz6Mxv2K3HmzH+GmdjKhyctwd7+njE16nnAuuz0qU3xM/x74hLtn9pXn7ivc/SuEWqV/JPTBuF5Uvo8J36/zgB091Weau78TzfcAIXC7MeEctg25PvriaVdG2x7L6kA4Oe4pL1LDuNxzdyvPhZ2ehyc33tjcdFV2IuE64jXCtW83wjn5AkIfnOX0LVmUu69z95MJx9VzhGvc7oTuLs4AvlqtdbfQQAo/dTl+9atZ6VrI3W8n9B36Q0LNx7mE8q8g1ET8NeGpkCd66GpDpAkrcl8nIh2cmR1CrpnAYR6erCdSEWY2gRBAuKTMQJ+IiIiIiHRyqgEl0rkdGr2vI1c7SURERERERKRNVbUzVRGpDQtPVzuW3NNyHnX3uiKziIiIiIiIiFSNAlAinYyZvQjsSe77vYDQRl9ERERERESkJtQET6Tz2YTwNJBphA4q93H3KbUtkoiIiIiIiHRl6oRcRERERERERESqSjWgRERERERERESkqhSAEhERERERERGRqlIASkREREREREREqkoBKBERERERERERqSoFoEREREREREREpKoUgGqHzOwBM3ug1uUQERERERERkfarI8UPzN1rXQZJMbMlQA/gvVqXRURERERERETare2BBnfvX+uCNKd7rQsg2Xr27Nlz++2337XW5eholi9fDkDfvn1rXBLpanTsSa3o2JNa0vEntaJjT2pFx57USqFj77333mPVqlUNtShTSykA1T7N3H777Xd98803a12ODmf8+PEAjBo1qsYlka5Gx57Uio49qSUdf1IrOvakVnTsSa0UOvZ22203Jk+ePLMWZWop9QElIiIiIiIiIiJVpQCUiIiIiIiIiIhUlQJQIiIiIiIiIiJSVQpASZt6ftp8hp7/EEPPf4jnp82vdXFEREREREREpA0oACVtambd8sy0iIiIiIiIiHReCkBJm3rp/brMtIiIiIiIiIh0XgpASZuas7g+My0iIiIiIiIinVf3WhdAOrd0P0/pAFQ6f//tBrVJuURERERERESk7SgAJVV1wk3PF8ybPHdFk/wZVxxT7SKJiIiIiIiISBvrsk3wzKyPmR1nZn8ws9fMbImZLTezV83sIjPrV2Tek83sBTNbZmZ1ZvZPMzuoLcsv0l5MnbOk8cmGU+csqXVxREREREREpB3qsgEo4KvA34GvE/bDw8BTwLbAJcCLZrZpeiYz+yVwGzAceAx4ATgSeNLMPt82RRdpP6bMWZyZFhEREREREYl15QBUA3ADsJO7D3f3L7n7p4CdgZeBYcA1yRnMbBTwP8ACYA93Py6a5/8Ba4FbzWxgW26ESK0tqV+TmRYRERERERGJddkAlLvf7u7fcfd3UuM/As6MBr9gZj0S2edE75cl53P3fwM3AgMINapERERERERERCSiTsizvRq99wQ2Bj4ys17A4dH4ezLmuQf4HvBZ4Oqql1CkRtL9PM1dsiovnc7fafP+bVIuEWkfVjSsYdeLHmkcfvTsQ3QeEBEREREFoArYLnpfDdRF6WGEgNQ8d5+VMc9L0fvuVS5bh/LUeSPzhs+68z+88uFSAPbccgOuP3GfGpRKynHUNU8VzLtu/HtcN/69vHF6sqFI1/K1m57NGx77zHv8/It71ag0IiIiItJeKACV7fvR+8PuHlfv2Dp6zwo+4e7LzWwRMNDMNnD3pdUuZEcwZKO+ecNbD+zVGIDaemCvJvkipZhZt5xDrpwAhCCnjiOR9uP9Bfk/f0+8/RGgAJSIiIhIV6cAVIqZfQb4BqH2008SWf2i9xVFZl8ObBhN22wAyszeLJC1/fLlyxk/fnzzBe5gli5YmUjPq/g2Ll++HKBT7ruOqhqfxesf55r93fvok3xi054VX0dL6diTWmlvx97ilfnDc5Z6uymbVF57O/6k69CxJ7WiY09qpdCxF4/vCBSASjCzXYA7AAN+6O6vJrOjdy+2iGqVrbPYaSN44qNcWqQ1Vq/1zLSItL1Va5zZy3JPwFydyndg+qLc2B7djC030OWHiIiISFejK8CImW0FPAwMBH7p7temJolrNBVr69Mnel9WyjrdfbcCZXmzb9++u44aNaqUxXQoO9Yt53dvTgDgpGMq33QqjgZ3xn3XXkzarz5v+G//mcnl/5oKwAWf3okv7jMkL39Qv14VL8OaN2bD6y8DsPseezJq+OCKr6OldOxJrdT62Bt6/kPNTvPTF9bkDc+44qhqFUfaWK2PP+m6dOxJrejYk1opdOz17dtxuiNRAAows0HAOEI/T7cC52ZM9kH0vlWBZfQlNL9bpP6fChuyUV91St3BpQNK/Xp2z0tXI+A0sy6/Wum8Zavy0ul89QklIiIiIiLSvlQtAGVm6wH9gSXuvq5a6ymXmW0A/IvwlLt7gW+5e1abninAKmATM9sq40l4e0fvr1WtsCJdVNzheJYf3zcZmJw3TkFOERERERGR9mW9Si3IzPY1szFm9riZzQcagAVAg5nNN7PHzOxiM9u3Uussl5n1BO4HRgCPAF9x97VZ07r7SiDu7ev4jEnicQ9Wupxd1fPT5jP0/IcYev5DPD9tfq2LIwVsNbBPZlq6rqlzljR+d6fOWVLr4oiIiIiISDtQVgDKzLqb2elm9jLwHHARcBiwEeGJcLMJT43bCBgFXAw8Z2Yvm9lpZlazJoBm1g34U1Tep4AvuHtDM7P9Mnq/0Mx2TCzrQOB0YAnwhyoUt0t65p15mWlpX3bbckBmWrquKXMWZ6ZFRERERKTranUAyMy+CFwO7EDooPsOYALwAvB2siZRFGgaBuxHCPj8F3AjcK6ZXeDuf2ttOcrwXeDzUXo+8FuzzIfYnevu8wHc/TEzuxb4PvCKmY0DegBHEoJ5J7p7XdVL3kWsXrsuMy3ty6B+vdTkTfIsqV+TmRYRERERka6rnBpIfwVeBb4C3O/u9YUmdPc1wBvR6xYz60UI/vwIuBvoVkY5WmtgIv35glPBGEKACgB3P9vMXiEEsI4kPHH6ceAyd3+6CuUU6fSmzlnCUdc8BcCjZx/CTpv3z8uffOnRecOPT/6Is/4culu7/su7c/iuW7RNQUVERERERKRVyglAfcHd72vNjFGw6k/An8zsc2WUodXcfQwhuNSaeccCYytXGgGa9PM0u25FXjqdv/92g9qkXFJ9M+Yvy0unA1B9euSfqnp275aXTudL20r38/Tx4vq8dDo//fmKiIiIiEjn1+q7ttYGnzKWc38lliMd3wk3PV8w7/43Pub+Nz7OG6dmXyLtQ1x7Lcu1T0zj2iem5Y3Td7dzuf/Mg/jcb56tdTFEREREpJ1TtQER6XA2G9A7My0dy/xl9Yy47HEAJl14OIP69apxiaQ19hgykKfOG9k4POrKCaxO5PcAHk/ki4iIiEjXVLEAlJn1AQYBC9x9eWL8AOB84BPA+8BV7j69UusVkY4n3SRr9qKVeenmmmztuNkGmWnpWF6aUZeXPmr44BqWRsoxZKO+jek+3WFxou/5gf3y80VERESka6pkDagLCZ2K7w9MAjCzHsC/gZ2B+BFzXzSzPdx9bgXXLSIdSLEmW2MefBt4O29cuslWnx7d1YxLpJ3aelBfXp/T+D8Um/ZT8ElEREREKhuAOhyY7u6TEuO+CgwDxgP/B3waOBv4H0KtKOlkXp25sLEvkPvPPIg9hgxsZo6c9JPOfvnIW9z8zAeNwxd8aie+dtC2lSmoiFTMpAsPZ2XD2sbh+1+axVWPvds4/NV9B/Ptw3ZqHF7RsEYdx3dil31xj7w+oY4arqdUioiIiEhlA1BbAy+nxh0HrANGu/ssYJyZfRo4BgWgOqV35i7JS7ckANWnR3fmL8s9PWv12nV5+Q1r17GiIdeuY0XDGvUZI9IODOrXi6HnP1Qw/64XZ3PXi7Pzxk268HAmTMlVhJ0wZS57D92oyXKl44n7hDrkygkAHLf3VrUtkIiIiIi0C5UMQA0EFqbGHQS8HgWfYq8BRyOd0tL6NZnpUsUdEme5ety7XD3u3bxxaoZVmpl1yxtvBp86b2RF+2NZ0bCGXS96BAi12Eqp2fLo2YfkDT/77nzGPPgWAGOO3YWDdhhUsfJJ+5T+rmcFqfT97riGbNRXn5+IiIiI5KlkAGoO0NiDrJntRuiU/K7UdF7BdYpICR6bPCcvferB21ds2U9P/TgvXUpH0ulOxWfMX9aYHrxhryb5IiIiIiIi0rFVMgD1MnCMme3p7q8Q+nly4MHUdDsCs9MzS8f06sz8Sm8fLlyZl07nt6RJnlTOx0vqM9OV8GHiCXbJtIiIiIiIiEiskgGoK4DPApPMbDGhSd4rhA7IATCzTYE9gD9VcL1SQ8mOZtNufuZ9bn7m/bxxapIhIgDfO2w7/jLpfeYuDZ2Xb7ZBN04YsU2NSyUiIiIiItVSsQCUuz9vZp8DfghsAjwE/K+7J3uS/iqwFHi4UuuVziXdN9DfX57JDRNnAPDtQ4fy+b2G1KBUHc/MuuV5w0tWrm5M3/DkDHYd3J89t851+NySPqGSHcEDNKxZl5dO55fSJ9TQQf0y09J5XffEtLzhuUvXNhn3g6N3acsiiYiIiEgXc8+k9zn3njcAuOr44RyvP0SrqqLPwXb3hwiBp0L51wDXVHKd0rmk+/7ZdINeeWn1DVSauMPxQs7682t5wy2pmRZ3OJ7l8oencvnDU1u87J0276/acR3cU+eNzBu+8Yl3uPPFDwE4cd8tOeOwHZs9LkVERERE2tI/XvkwL60AVHVVNAAlIlJtr85c2Nj08/4zD8rsV2z+svrGp6xNuvBwBvXr1WQaqax0LbqN+/XMS1fyyYsiIiIiIpUwJ9GH7Rz1Z1t1VQlAmVl3YGOgZ6Fp3P2Daqxb2s7z0+Zz00l7NQ4/9tZH3P2f3NPWvrT35hyx6xaNw3sP3YiW2qBX98y0dF3vzF2Sl84KQNUta8hLKwDV9oZvOSAzLSIiIiLSXsxfVp+Zluqo6B29mR0BXAgcAKxfZFKv9Lql7Z1w0/NF8+9+aQ53v5QLSLWmidXuWw3MTEtlPT9tPvtvN6jWxWBFw5rGJn6TLz06s/+oVYk+p5LppBnzl+Wl1XSz7fVcv1tmWkRERESkVu6ZlP+QrKX1+el0vprkVVbFgkBmdizwd6AbsBCYBiwrOpNIM9Q3UOv85bT9mw0QJp1w0/Ml7+dqdhT/9NSP89JHDR/c6mVJbW23Sb/MtIiIiIhIrcQdjmdpyMhXAKqyKlkL6WJgPeBs4DfuvraCyxaRFmhJ8KmlqtlR/Lxlq5qkX525MG+aWQtX5qVfnbmQRStyTe5223IAS+pzT+JbUr+mSXVaNcmrviEb9VXwWEREREREGlUyALUb8G93v66CyxSRLi7ucDzLDROnc8PE6UXnP/ee15uMU2BERERERESkbVUyALUMmFvB5Uk7l37s+l9fnMl1T7wHwPcO257/3rf1TbGk4+jRfb3MdClWNKzJG25I9OnUsGZdk3zp2NLNN+97eSa/jZpvfufQoRxXRvNNERERERFp3yoZgHoMONDM1nP37J6BpVNJP1Z9YN/189J67HrXsO/QjTPTpYg7HM9yyYNvc8mDb7e6XNL+pJtnbjmwT15ancWLiIiIiHRelQxA/Qh4EbjazM5VH1AiXYM6ihcRERERkfbk0Tdmc9odLwNw00l76eFG7UQlA1CnAv8Cvgcca2YTgFmAZ0zr7v7TCq5b2oFdBw/ITEvbm3Th4Yy47PFaF6PiJl96NH954f3GmlEXHzuME/bbhnfmLm2cpvf63Xj23fmMefCtxnG3nTqCLQb0bvPySnH9e3XPTIuIiIiIlOO349/JSysA1T5U8op/DCHYZMD20asQBxSA6mQGb9g7My1tr7M+5a1Pj+5N+pzq06M7ewwZmDfdjPnL8oZnLVzBoTtv1iZllNLtvPmAzLSIiIiISDneT9wPJNPpfow/d90E6hIPzN5zcD+uP2lEtYvXZVW6BpR0YXrsevsy+dKjG9M3PvEO1z0xrXH4m5/cmh8cvUstipUn/QNw/8sfctW48G/FuUfuyOf22rIGpZK2ouabIiIiIgLh4URx/7CTLz2aPj3KC1WsashOD9mob96DjrYdtAF1s3KtKRavXMXG/XrmlavcskhOxfaku99WqWWJSPmSJ8rDd908LwC1frf12sWJtDUd2e+w6QaZ6aQBfXpUqIQiIiIiIlJtL05fkJduaeuFR9+YnTe8OpVO5sd9Q2WZvnB1kwcl6Q/Tyqn9HaiIVN2WA/ObRK7fbb0CU7Z/n9hqw8z0zLrljen61fnPQFi4vCEvH5oGv0REREREpDZue2ZaXrqlAahiQaXVzeRL21EASqQLGNSvFxcfO6yx8+6BfdtnDaGeif6dkumkPj26Z/4LcciVEwou96px73LVuHfzxumfDBHpyuYvq298WMWkCw/vtH0HiohIx1CozybpXFpdDcLMnjGzI8tZuZl9ysyeLWcZIlKaE/bbJjPdnuy/3aDMtIiIVNZ3bn8xMy0iItIWVjSsyXvVLc911FS3vKFJvnQO5dSAGgQ8bGYvA7cDf3b3j5ubycwGA18BvgbsDkwpowwiUqK45tDUOUsa2zU/evYh7LR5/xqXLEcd2YuItI3XP1iSmRYREWkL6X6Wkhauapqve4TOoZwA1K7AGcBPgGuAX5rZ28CLhKDSQmAp0B/YCBgG7AfsCBgwFzgTuKmMMohICz3z7ry8dHsKQJXjppP2yht+8p153PH8LABO2n8r/t+Om9SiWCIi7dLKAmkRERGRaml1AMrd1wK/MbPfA/8NfBM4mBCYAvDE5Ba9rwUmAr8H/ubuiQciioi03lHDB+cNz1u2qjG9yxb9m+SLiHQl85fVtyhffUKJiEhHctupI/KGv3HrJOKGe92BPyTyT7l1UtsVTPKU3Ql5FES6E7jTzPoBnyQ0rdsUGAAsBj4GXgWedXf1KCYiIiLShuIOx0vNV1MHERHpSNJPzesBjQGoHhn5UhsVfQpeFFx6JHqJSDswdU5+3x4fL63PS6fzO0uTPBERERER6Zp69YQVq3JpaR8qGoASkfbnqGueKph3w8QZ3DBxRt64zvKv9yb9emamRURERESkc9tzm40YP7WuMZ10/5kH5Q2fcdvzfLRsLQBb9OvGjafs3zaF7IIUgBKRTumo4YOZcYX6fRIRERER6WpO+eR2jQGoUz65XV7eHkMG5g0PHdSPj5Ytbkyn86VyFIASERERERERkU7j0J03K7llx7ab9OXfMxY3pqV6FIAS6eRuOmmvvOHH35rLX/4zG4AT9hnM4buoQz4REZFSzaxbziFXTgDgqfNGMmQj3ayIiLTUpAsPzxsedcXjLIl6De/fHcaff3jGXMHUOUsauxl59OxDyu7DdsfN+gOzE2mpli4dgDKzfYAjgf2A/YHBwCp3L/rsYTM7GfgusCvQADwHXObuz1a3xFKOrnrBeNTw/GZoHy5aSXyCHbZF/yb5IiIiUtjz0+bnpbvK9YSISCUN6pd/y73NoL68Pmd5Yzqdn/Trx97OS1930n5llWXvRB9Re6f6i5LKWq/WBaixnwCXA58nBJ+aZWa/BG4DhgOPAS8QglhPmtnnq1ROqYB/vjY7My0iIiJSqvGT52SmRUSk9XYZvEFmOstTU+dlpltrjyEDmXHFMcy44hj1/1RlXboGFPBv4FXgxehV9CrCzEYB/wMsAA5093ei8QcCE4BbzWyCuy+sZqGldRataMhMi4hI27n+sSlc/di7AJxzxA6cdcTONS5R1zD50qPzhne96JGi+V1dsVrTr3+4MDNdyMQpcznl1kkA3HbqCA7dWU3fRUTSDtlpU+5+aU5jupg1a7LT0v516QCUu/9fctjMmpvlnOj9sjj4FC3n32Z2I/A94OvA1ZUsp0glbblh78y0iEhbuO2Zd/PSCkC1jT498i/5+gArEul0fld3wxNTG9O/+Oeb/PAzuzUOL1+1Ni89s2553rzpJnl3/nt6XloBKBGRpg7cYZPMNIQ+n5Lq1+Wn0/nl9gkl1VOxqw0z2xE4EHjK3acnxu8HXAt8Angf+F93v79S620rZtYLiHtCuydjknsIAajPogBUu5C+IFxc35CXbu6CsbM6avhgZlyhfp9EpDZWrsxOS9vafvNcXxvbb941fv9aYtybuab6D7wxjwfemJA5XV09jTWlYumnLs1csCIzLSIiOYP69Sr41Lq4w/EsqzPyS336nbS9Sv7ddQ7wTWDbeISZbQI8CvQHHNgF+KuZ7evur1Zw3W1hGNATmOfuszLyX4red2+7Ikkx6QvCpLtemM1dL+T3A6UTlYhI9TUUSEvb+vIB2/D6fZMb05JvaQXjRHWJSGudoq4iItKFVTIAdTDwmrvPTIz7OiH4dDXwY+DTwL2EYNXJFVx3W9g6es8KPuHuy81sETDQzDZw96VtVzQREZH26frHpuQNr06l0/lqktc2Pr/3EH4cBaA+v/eQGpem9tK1otcVmK4UE6fMzRuuW5afTuerSZ6IiHQV5u6VWZDZAuBJd/98YtwEYH9gE3dfFo17JhreqSIrriAzc2CVuzd55qOZfRW4E3jG3Q8uMP8sYEtgsLt/VML63iyQtf0222zT85Zbbim98ALA8uXhArJv3758/dGW/ct4y1HqD0laL3nsibSl9n7s6VzcubX3469ULT1OK0nHfOt0lmNPOp7ksbdk1TrOnrgKgGsO7Un/nl39IfPVoWuJoNB57+tf/zrvv//+ZHffLWu+9qSS35BeQH08YGbdgBHAc3HwKTKDEKTpaOIeyotF7JrtxVxEREREREQ6vnfrGjLTIpKtkk3wZhL6SYodQniwyhOp6XoDy+l44iZ1xf5m6RO9LysyTaNCEUoze7Nv3767jho1qgXFE4Dx48cDMGrUKJ7aM/8wu/GJd7jzxQ8bhx89+xB69+jWONxVOiGX6kgeeyJtqd0fe48+1KLJ2+12SKZ2f/yVqoXHaSV1+H1XI53m2JMOJ3ns/f7Gp4HFAPxnaX/OPTGzoYyUS9cSQOHzXkeqCVrJANTjAU0JvwAAIABJREFUwBlm9n1C0OkyQm2h9BPvPkEIVnU0H0TvW2VlmllfYENgkfp/ah/SAaX+vdfPG35t1kKOH6GOV0VERLLMX1bPiMseB2DShYczqF+THgpERLq0V2YszkxLZV187LC84UsefLtovrRflQxAXQ58CfhlNGzAX5JPuzOz3YDtgV9XcL1tZQqwCtjEzLbKeBLe3tH7a21bLGmtpfVral0EERGRivvHKzM568/hcuT6L+/OZ/dsXSfjE96em5fWnzZN9QB+fvzwvHHn3vNGbQojIlU3f1k9S1ata0wneyZaGY1LUuC+MtIBp+byTz14+2oWR8pQsQCUu88ysz2BbwGbAP8BxqYm24tQI+ruSq23rbj7SjMbT3iS3/HANalJjo/eH2zTgknJNuzTo9ZFkHbm1ZkL+dxvngXg/jMPYo8hA2tcIhFJevSN2Zx2x8sA3HTSXhw1fHCNS9QxXDduSl66tQGoR9/4KC/dmQNQF3wq/9k4lz88tWD+rx6e2tjp6YA+NNkvCkCJdF5xrVAAJj5ePB+YccUx1S6SSIdSyRpQuPuHwJgi+XcAd1RynW3sl4QA1IVm9pC7vwNgZgcCpwNLgD/UsHySsqIhV8vpsGGb5V1QNqxZl5cP0KdHRb8S0s7NXbwyP60AlEi7cudzM/LSCkCVZu6iVZnplnp//vLMdGd0+sgd84bTAahk/oOvfMjrc8L+2Lx/x+l3Q0REpNa69N22mR0D/CQ1uoeZPZcY/qm7PwTg7o+Z2bXA94FXzGwcofb1kYQnCp7o7nVtUHQp0a4XPVIw7/KHpza5wNS/FCIi7cfk2Qsz01Lc2rXZ6eakm47MXlifl1bTkuCwXTbj9TnTGtNpky48PG941BWPsyT6v6t/dxh//uFN5hEREekKWh2AMrOty1mxu3/Q/FRVtwmwf2qcpcZtksx097PN7BXgu4TA02pCB+yXufvTVSyriIhIh/PUeSPzhg+5ckLR/KQVK7LTku8fr+Q/26U+lU7nF2qSl246krR0beduWpKuEV0sf7MBucDb1hv3aTJtOjA3ZKM+vPnxisZ0Vw3ciXRVKxrWNP4pPvnSo9XiQrq0co7+GYSn3LWGl7nuinD3sTTtp6pq84lIbb06M78GxfS6FXnpdL76hBIpX/qJpL2hsdPW3qn8R9+YzVuzc08RSjb6Wh7lJ6lJXhB3OJ5lbUZ+a/uE6syK1ZjOym9J8G2bQbkA1DaDQsBq4pS5nHLrJABuO3UEh+7ctCaViHQONz7xTl76B0fvUsPSdEzpP6uOvHJC458tvYBxRf7MkvalnCDQk7Q+ACUi0ubiDsezXP7PKU3GdaZ/9zs63ax1Hj26w8o1uXRS3OF4Ien8GVcoACXt3y6DN+Sfk+c3pgEeSQRTH3ljts5pIp3Yk1M+zksrANVy6T+zuqXS6Xxpv1odgHL3kRUsh0hVpPth+NNz73P1Y+8CcM4RO/CVAzrvE31EOpPbnpmWl9bNWse1z3YbMX5qXWNapLP7ygHbNF57xNcdL8/I1bhNplvjH6/MbKzldv2Xd1cNN5F25qMlyzLT0nq9esHy+lxaOo71al0AkWoa1K9X3qtfr1zMtV+v7k3yRaR9euX9usy0dDyXHLd7ZlqksxrUrxczrjiGGVcc03itsag+11NXMt0af3hyWmZaRGpvRcMalif6MFy+IoxLvqTlDt1508y0tH8174dJRKStpNuPPzl1Hj++7828cT87blf+3076IWtvVq7KTkvHM2SjvmreKl3OxClz84brlnpeOp3fklqesxcvy0yLSO2l+49btq68PuUk+J+jd+XeVz9uTEvHUZUAlJntBuwIbEB4qlwT7n57NdYtUkyP7utlpqVriNuHx/829e/Vrck0/Xt1Z+N+PfOm09NK2l76ZqwhlS7nZk1EpK3FfdhlWZWR35Ib0pUrs9NSOj2lTEr1l9P256X/hO/r3vuM4ISbnm+Snx4nlac/szquip5dzewI4LfA9sUmI3RergCUtLmjh2/Bj++b3JiWrqnY045CPxr5T4zSD1zbK3aztjYjX59R9Tw/bX7jxfRfTtuf/bcbVOMSSdrkS4/OG06f49L5hdx26oi84W/eOonVUXp94OZUfmdy/5kH5Q0f/5tn87b9nlR+Lf3jlZl5wyvW5afT+eoTqnl/ePK9vPRZR+xcw9JIe7b9pv34qF/3xnRWvogUVrEAlJmNAB4iBJfuAj4Rva4gBKSOAAYCtwIfVGq9Ii0R98MgIiKleWHagry0AlDtT3O1NUqtzZGuSbhBT6hblUt35pqGewwZmDfcAxoDUD0S+e2hpkzc4XiWtRn5CkA174GXZ+alFYDq2op9z0dc9nhuwomPp2fNzxeRJirZBukCQkDrs+7+NeBlAHf/sbt/GdgBuAc4FrilgusVERER6bJm1i3Pe7U0v5DN+vfOTHcFPXtmp1+cviAzXWlDz3+IV2eW93Q8Kd2chasy09I1/eWF9zPTIlK+Sv5tcxDwsruPy8p090VmdjIwA7gMOKWC6xYREZEOYGbdcg65cgIQHgwQ980mpZs6ZwlHXfMUAI+efUhjupB4f8dKrQm87SZ9eWveysZ0V/KFEVtz8zMfNKZjj7wxOy+drBWW/lx22rx/WWX47h3P89QFnyprGZIt/eSx1evy0+l89QnVtdw0cUpe+tSDi/UuIyItUcmz6UbAxMRwA4CZ9XX35QDuvsrMngKOrOB6RURKEv/zH/f18fDrH3HDk9PzprngUztxwPahidGWA7vWP/4iEPp8Spq5KFdj5urH3mXnzTdgQJ8ejeNa2iTvvLtfzkv/6YyDW1nSrunRN2Zz2h25fXj3i9X7d/6E/Yfyz8nzG9NdyWf33KoxAPXZPbdqHP/u3GWZaYCfPfhGXvq2b5bXb9T8xWvLml8KK9YXZH1GfiW6b2hJ8H3+svrGplyTLjycQf16lb1+Kd2ixFMqk2kRKV8lA1DzgP6pYYDtgNcT43sDAyq4XhGRkqRrAWS5/OGpwFRAHVtL19Tc03uSwQ9o+fdk8qzFmWlo2ln2iIseYUWU7gNMKrEz7c7s5Q/ym2U9/171moEduvNmXfY8uMeQgcy44pjGmjDx+7xlucfMzVu2Mq+mzMvTc5/NKzMq23wuGZCQjum+l2blpYv1M3XWHZPy0grUV1e6xtvKVDqdLyKtV8kA1LvkP/3uBcIT704HvgtgZjsAo4BpFVyviIiIdBAr1mSnoWkzl549YEVDLq1mMDB+8py84Y8Wl96nk7RcsZoy0+tWF8xvWBOCRuVYDY39QP3vX18pa1lSey3p6PzfMxZnpqU6in3PS8kXkdJV8krun8AVZjbc3d8AHgbeB75tZvsCswjBp17AHyq4XhEREWmn0p1er06l0/nJZimb9u/Fwvn1jemuauqcJY3peUvzgxpLVqanlvZgJeU/DWsN8LnfPNtkfD+De79/SOPwsdc8RRSnpQfw4NmHNJlHam/mglWZaRGRrqSSAajbgcWAA7h7g5n9F3A3sG/0WgfcDFxbwfWKiJTkppP2yht+cuo87nhhVt64q44fTv9e67dlsUTalZtO2osl9bkw0WNvzuXht+blTXPV8cMb0/OX1Rftn6S5pq/FOsg+YIdBTJk/qzHdFaX7fEprKJgjndU6J6+D8349oW5VLl1u5+dSGengen0qncyvW95QtN/JdG069QklIh1VxQJQ7j4H+F1q3OvALmY2DBgIvOvu87LmFxGptqOGD84bnrcs9w/kz47blRMP2LatiyTS7hw1fDBDz3+o6DTn3vNGYuiNqvUT9KPP7MZtz81qTHdFxYJPhXQD1ibSt5w6opJFyjN90Wp++sIaePQh7j/zIPYYMrBq65JgHfkBiZ23GNDYTOsT2v/tRkuD78Wka9N11b7ZmrOiYU1jc7nJlx6d2Wz71qff45IH3wbg4mOH6Ql3Im2sTTpTcPe322I9IiIi0nn06dG9S91oTZwyl1NuDZ0P33bqCA7debNWLae3wTLPpVu7nFI8PC3XkdcfJr7DdSftV7V1SVBP4eZ9B+2wSdsWRqQd+csL7+el08Gl+cvqG4NPAL8d/3arAlDXHNoTgIMPOZj/z96dx1dR3f0D/3whJCEJWwh7AmFHBEFFQRYFpGBVHnGrPtZWaX1Q68/WqqXaWquIS632sVq78FiVVq0LtbhRpBJAFgER2WVfkrAnAbInBM7vj7l3MjOZe3OXuXfu8nm/XnnlO/fMvTMkh5u53znne677w1IUntRS/j3bt8T7/288Fwwg8oPVPImIKOYsnzlej+976yt8VVyhb6cB+MzQTpQoPlhfaIpDTRx1at8KlSdO63Ek7TYswvdVYeRW5Et0t43KBQB9xF+oJgyKXLKRKNZ9sfu4KbYml2bN32TarqhGSNqmtQCgTYXs0T4LhSe1EYg92mchJysd6x653LT/zX9ajt2l2oTpfh1T8fbdzddpM658ue6RyzntkhKGYwkoEXk1iN2VUuqHTh2biCgUnbLSbGNyl7VuxjFL0eWWlv2NRauJ4tne49W2cbDO6dYO+06U6HGkvLl6H06oxu2T5WcjdiwnbCw6oRf1jrXpgt4V0d5eXYxQylMn00hBN3inJb8zYyRG9knOenTxYP2BUtvY6/MdzleC6dYurUlsTRa1a50Gb8W+dq3TAkomWZNpU4fnOXC2RO5zcgTU7QHsowCI5zsTUETkqslDumP/M92b35Giqrm6GNWWffjBixLF4fJK2zhYN43Mx4JtJXrspI1FJ/T49//ZZmqrs7QDiKkkz2//vc0UvzFjjItnY8YpO5FVXd/Q/E4BuP/tL7HyF9925LUofNbfa1WNOa6ub8DfV+3THzt52rQ7aqHVhArHhHO64v2Nx/TYTo/26fpI7h7tAxvJ9M7aQlPMBBQlCicTUBN8PN4CQB6AKQBuAvC/AD5y8LhERESUYJJh+sGyHUdN28bpIBXVTdsD8dwNQ3DZwC4RS8x6RxDZabBpj6UE8fHyWlMcSMFiSgze33O4Dsb4KL9k4+/3WtNMu5exJhSg1d/z1uIzPmZ05uBWPb7EUHftEh812Eb264QPtxzX40AcPlltGxPFOydXwVvWzC5/E5F/A3gVwIdOHZeIiIgSz0cbDpriRFypyPohx6j6rP92X24Y0SucU0poZdW1pnjBpsY+tmDTQdufXZNaLn9cgd1l2iS5ftlpePtHY/W2CU8tRoUnP9GmBbDkF43PnfLcYpR6Dt8xHZj/4/FBrYJmlALgzRkjQ3ouRVYoSWOvbbOm6LE1cWJso8iyq71nfazAkIDKyUpvNtE+ZUg3/HL+Nj22Y1zZEgBKyutNsbU9EW/KUHKI6q0epdQbIvIAgMcATIzmsYmIiCj6llsKxls/dFvbvd76Yp8pTsQEVLx5c/W+5ncK07x1B/DgvC0AtNFc4STUrNMBT1Wb44JtR/Ttgm1HbI9l/ZDXMydTT0D1zMk0tZ+T2xZrC8v12NiWk5WO0tpaPc7LztQ/tHrrCwUqFWAdIhet2Vti2jb+LkJJGnv5G4HH0XnRlQmgyhCHK5Aklb9puKca7NtZk4zikRvvZrsAXOHCcYmIiCgA1rvt4dyNtxaJ9xaC9Ma+isgfKq2zjZPZtllTcPlv/4PDFY3TgCYOyMYfbr0oKsf33sGPpPnri01xOAkof9MF6wG9ThYssT99OmWhYGeZHhvNvm4YJr+wXI+NRvXLwY6SYj0ORwsJbv9FWw5hxhtfAwDm3Ho+Jg+Jj9qHL322A89/thsA8MCkfnqhdrfdNGeNaTuWppmSM/I7Z2DrsWo9jlUvfbYDI2cwAUXxJaoJKBFpAeA8AJxATUREFKOsd9utSaNw7sZnoPHOsvGy3jq9oMESc/qB9nO/qFdHvZYIAFx5XveEGh1RVFZpG0eDtaCx3c913IDOeGVloR4bDeja1mcyYkR+NuauLtZjI+s0v9GzF8M7+SYVwKpHLsedr6/VixgP7RXcyoZ/WbrbFMdLAur1FbtNcX5OBu59exMA4KWbz3NkZIrbfI0AvW5YZ72w9XXDOtvuQ857+IoBAICZVw3WR7PNvGqwm6fk17aDJ90+BaKgReWKRUQyAAwA8DCA/gA+jsZxiYLFgqRERE19e3COPkLk24PDu9t6bs/GaUrn9myrP+5v+kGdTXuyjjo4N7eDnoB6+IoBCVfzqaquwTaOButIP7s+dm6PdrZxc/wVKrYmU1tY4pysdMy8crA+8ua+yYMCPi4AFJ2osI1jXW2tOZ63rkjfnreuCLf1ceGkHPbEh5tN23NuHwUAmH39+Xh/46d6TNFx5/j+ABDRhRycVMvBwRSHHPt0LSJnAtkNwHEAP3PquERO2lx80hRzXjUREfDw1UOwYNtSPQ7H8zdfoNeBev7mC8I8s+Rz/Yg8PL1wpx7HMl+jO4zmrTtg2i6vMcfWdrcTboHUcgn3edYEFKDVGQr1A3F1tX0cb7YdOmGO+7R28WwaeWt4BdLfrRZtL7V9PCM1JS4SIOSuBvivSUYUi5wc3lGExhH6VvUADgNYBuBlpdQxB49L5JiisipTzDdxIiKgY1aabRwKY/FlCp5xZG60R+neNirXtO2dUuZrH1/1vYy8Bcft1Nu0u52AiobehvozvUOoP7NoyyHTdrUyx9b2WJmS99JnO0zbNZa4ypA8O+4nkWYdzW6dWhkp//tp5GukkZm1HuGFj36q95vWMPehRFQP1iSj+OPYlYtSKt+p1yJyw5q9JaYL3Yra6A79JyKKVbwbHxnWERP/9eJSnPBMO+qQDnz44/FNnuPm7+LxaebC2u+uLjZ9wOvfMa3JPhS8uTMu0aeczp1xSdDP9xYct3PWpn3/M7GRgPIWHLdjVzx2zUGt91Vt0KbmTR2ujQhcsOmgvs+CTQf9Jjmd9OlG3l+PNmsSPisDqKluGhNR7GCBG0pqxrtiu4+Z6yLUN5wNqCApERFRKKwjhHLbZ+LEkSo9DmQEUSy5bFAXt0/B5Mlp5uLB0VjFzwmhTvNLNn/Z6gm2aoXJvQko60qK0VLV/C4UYZ2zMnDcM8+0c1YG/nDrUFO7dbTQOzNGRu3ciEjDT9OU1KwFR42eXrhTr7PhxQtCIiKKlN45GdjsSUD1zondpb+9MlsDNYYhULeNia2q0N8d1du0HS8JKArP8fJa2zgYxpUJrQsgLLpvnB5PfmF5SK9PkXHL6Hz9//kto/OblNJIQeMKqymIrXpJxumE/j6fEMW7kBNQIvJoGMdVSqknwng+ERElKOu0pFvmLEfRSW2di7z2LfHWjHE2zyKKf5cO6qKvcHdpjI0msnPVsFy9DtRto3LjbsQWJRbvqPWy6sakkzEOhnVlQiMnk063XBQb0x8TxbUX5OkJqGsvaLpIQxoaE1DhVTN01pur97l9CkRRE84IqMegFR0Xy+PGQuTi4zEFgAkocpV11Qgiig1NpyVloejkKT3mh1xKVGkpLWzjWPX4tGG4rK22itfEiaz9ROGZc+v5pm1/tazs2I0a8Ves3JcHJvUzbRtHzTjtqevPb34nClhzNfIG5bbBV8UVehwrwhmd+cCkfhjYNXb+LUTNCScBNd3msdEA/gdAIYB5nu8A0BPA9QB6Afg/AKvCOC6RI6zzwIkoNuVlt8YX+0/pMVGi+s+Ww6bYW9OGKBk0XY0vuASUUy4d2Nm0ffuYnnhlZaGPvSmezLxysH79P/PKwc3sHR+W7zyGeycNdPs0iAIWcgJKKTXXuC0iowD8BdrIpllKqTOW9p8D+BWAhwC8HupxiYgouVzcJwfvrj+ix0SJavPBk7ZxIjPW0wGaTm+ytofDWNcHAK57aRkKT2ljW3q2S8H7917m2LHcZP2ZXfXCcpz2xK0AfOLgzzQR3H1pPn54aV+91lP/LubRJPdPOYcJqAQxsk9OwtVz3XOs3O1TIAqKk0XIZwHYoZT6tV2jJyH1mIhcC+BxAJMdPDZRxD18xQDkP/QJAG1lHWtxUyKKDGOR0FgqGErOMhZgBYALHv0U3uot6QDWW9oTUWXdads4kTVXT8faHs6HR2tdn0Hd2qHwVKke+6v7E08GdG1r2s5sBZw83Rhb22PFxqITfttbAaZE2osTtd/X3QWh1XnyunfSQL9Tt4xt3uvAUF05OAcLtpXoMVG4QixzRuQaJxNQFwMI5F15C4DESj1TUqhvOKvHZZX1Lp4JUWCW7TiK215bp29vmzUFGanxt/hpXnZmwt2xjIaSylr9jv66Ry6P+Q/X1r7Z0hLHY99tjrUW4Ylqc2xtZwLWWemGOlvpcVBzK1Tn9GinT2M+p0c7l8/Gt2te9l+h47QlTkuxlqGNfX/8/ki3T4ESTD3i529FdX2DXqstXq9JKXxO/9YHObQPERGFqeCbI6btv6/ahzvH93fpbCjafjFvgymec/soF88meJmZQFVVY+xLvCXajPzVImywaWci1ln7Sqpt40Qzul8nPQE1ul8nR187Hj9QTh7UEYu2l+pxPJwzkS9nED9/Kx6bv9EUP/udC108G3KLk++4KwB8W0R+ppT6rd0OIvIggPMBLHDwuFEnIukAHgbw39AKrJcBWAjgUaVUsZvnRoF77oYhpu2/fr4f3xyr1LcvyW+H60c0FoA9fJJjXCm+nKisM22frObIvWSyelepbRwvxg/sqtf+Gj+wq8/9/vr5HlP88yvPjfi5kbv+snQXnl64E4A2PT7UxHp9wxnbONFMGdINz3+2W4+9nEge/X3VPlMcDzc5XrhlhP7vfuGWERE9Vv+OadhVWqfHRHasU9DtVnRMBJ9tPWIbU3JxMgH1CwDjATwjItPRuAqegrb63fXQRj9Ve/aNS57k02JoK/4dBvABgHxoqwJeLSKXKKX2+H4FihU3jOhl2l605bApAdU6tSXqDNPuNhSfNMVvrt5nej5rQhFRLCk/Yx/Hi8emDdMTUI9NG+Zzv6/2l9nGlLg+3nDQFAea9CipNN9IOlZRa4qt7fE0ms6fAV3b2o6IWLHzmCluugpd8xZvO2KK4yEB5a/ek9Ne/t7Feh2zl793cVSOSfEnWUbh1dTZx5RcHOvtSqlNInI5tBXuBgF4BFryCQC8k7R3ArhdKbXJqeO64BfQkk9fAJislKoEABG5H8DzAF4FkBjLqCSBZTuO6nHDWWVqK9hZhoKd9h9mFm8vweLt5vnWTECR25btOIq6042ZhoOnzB+m9hyrwKIth/TtsQM6J81FTzKwfnhurj3WP1wH8iGxqKwKawsbVwDaX8rVgGLRziPl+ofwRfeNC7sIdklVtW3cHO9UTTsn6pq2x+o0lmDMW3cAD87bAkAb+W28+fbER5tMcSgJqMOnqmzjaPrgntGm7e/NWYVyT8Gotq2Av88YbfOs0FhXUrTrU1cOzsGs67SkeU5WekL0I4quFgDO2sTxprq+wbStLLG1ndekycHR37JSao2IDIY2EmosgO7Qkk+HoE3RW6KUUr5fIbaJSCsA93o27/EmnwBAKfU7EbkNwKUicqFS6itXTpKCYizQTBTvmuvPi7aX6nUvvHhhnDj8fbi2a4/X370xkfawoc4VAFRVx1+iLRm88vkuUxxu3Y/aOmUbU1O/XbjFFBsTUEWnztjG/lg/MJ6oOGOKw/lA+c4Mc4Hu785ZA++rpQB409JetV9LoA3L62B6vH+XNviquEKPre3hsL6fXJjbeCyvBdtK8Mfv832HQvf/JvTBi0v2NomDEQv12fxNJay1aY/X6xIKjuM90ZNgWuL5SjRjAbQHsEcp9bVN+zwA5wGYCoAJKCKiKIqFiy2KPH+JtiqVOIm2RLK56JRtDDQdUTJm9mJ4Z2akAVj5yOX428rGKe9/WboLJw3l7E7Wa48ZxcM0sGipbKwsgIrKpgkko0CSR/4+UFaeDe8DpXXlrlRAT0Cl2rQX7Ld/nR7t0/WkUI/2kU0End+rQ5MEFFG47prQX0863TWhP3p2zDC1z5y3xTRC6llLXVsA+Nf6IlPMmRoUK3h1HhxvEYr1PtrXW/ajBHXHmJ64bUwft0+DiAwWbztsiqcOz/OzN5H7fn21eWHgxz/e7rc9Xh2vrLGNgaYjSrLSgbraxjgnK93v3X8F6AXJvZiAslcN/wmkWBuN0NIw96hli8Cfd25uB3y45bgeR9LFvTvilZWFET2GP0VlVRj37FIAwPKZ45GX7WfJUIob1ino1rq1v5q3Bd530jSbdgB4b22hKWYCimJFyAkoEbnUE65VStUatgOilPo81GO7qKfnu6+V7oot+1GMs9YN+GRDMeZ4LiRmjOmJq4bn6m3vrN2Pt77U6ue0a53KP/JEMeat1QdMMRNQiaeozJ0aM6FqblTe9LF9TdsvLNyOU54hH+1SmrbHi51HzLW46urMsbXdWBNqZJ8cLNhWoscUnHnrDpi2a3zsFw9SUgDUG+IAjeqbA63srDd23v3/+BIAUHvavjqPt93rd/99UUTO49Y5K0zxsoem+NmbEkVqClDT0Bjb2Xyo0jYmcls4I6CWQrvxdA60d3nvdqBahnFst2R5vvuqeFll2c8vEdnqo6lvVVUVCgoKgjk3AlBVpf0KQv3ZlR+rMcSFKN11XN+uLmls27NnNwpaHASRV7h9zwlPXNIKZTWN9Tg+2XsWOw2f8y7MAS7NbbyN3DUrJaHeZ/YdrjHEpxLq3+aPt+9pA/EDF48/nx8sCv7jtJv/ziUHGs/36Tc/xYRerf3u37ol9ARU65bx8Tuye+/z93uqOAu9ILnXq5Mbfy5tT9cY4pKQfgZO/dzi4edv9WAI/0d8ceLfH85rVNabY+tr+fu76+1Tpbu+RsGuJs1he3+j/5/z+xuPmbav7hKZvnTgZIMpjsc+G4/cvubrkgGcKm+MCwoKUNdg/hh+xhL/e5F5enpaiiDWsP82z1ffa7wWjH3hJKD+Bi3hdMqynci8/1N9/Ttj738yESWNHm1S0KNN49v6imLzBXJpaz/ZAAAgAElEQVSn1sDQzmnRPq2IaXKxddYcW9tj8WLLTd4kwaB2wMyR/hMjFJolheZ4QtNZEiYD2gNHjjbGyWhU9zS8vatOjyl5NfiIKXDHq8/g5yu0TN5vxqaiU0Y83v8nq7E9oN9gHNtD+353gf+VcK3txsR/pNx5rnn7L1v9t1NyCDkBpZS63d92gvJWGfQ198pbIS6gcY5KKdv/diKyNTMzc/DEiRODPD3yZoND/dkdWLEH2KXV4Bg4cBAmGqY/9B9ehfmeefYP3Mh59smmuWXEw+17kTD/0FqgpHEUX+/e+Zg4MXH+2uc/9InPtuOnm15suV3PJFIa74KFNvJh+6nY6rd+LfL9O/fFzX/bfYsbz7e8vvlzOZC6B5976kBNuMj8NyhW2b73Bfl7sv5cpn3bskOYr+f468SyEP6P+GL774/gz9C6gmXqosaC9KkAzrvYXDZh09pVQR/DMS71pSbTkBctNW32H25eKXDu+xvhnce44Egm5t5h/hlSaNy+5psI4FHrgzH4/mY9wt8e+kS/UmkN4OHvJeZ1WST56nuZmfHzuZRFyIPjvZeZ66M917IfxZkLemXbxgCQl52ZsB9gqXmbik+YYmsCKhbld2oDoDEB1SqYKq5EFDTrKmKnz5jj5lYZmz62b9zWfYoH1hX3Jj27WF9Rr30q8NnMy22eRV6zP9oc0df3t8JlnU17NEZwxBpvwfFA29sZ3mI27D8BIjeJj5iSS1QSUCIyCMAQAIVKqbXROGaEbPR8v8BHu/fxTVE4F4qAYXkdmGQiW3UNZ23jWHb1sB6m1aNuvIjrIySybbPMxWetK1oZ2/2thEWh8/dzrbFp59+bwMydPsK0fdtr6/y2+2JdcS8/JwsbPMV583OymrTHI+vKiU98vB1O/cX6cEPj/dUOqcDr/zMaN768ylsnHKkA3ruHI2xiyekG+5jIDd2yU7GnrF6PKTk5loASkZsA3AngYaXUGsPjzwD4mWH7fQA3KaXi4xOc2UpoNa/6isj5SqmvLe03eL5/HN3TIiJqakDXtvyAm0Sso2lSYK6bMuP1NfDl1jkrTdtvzBjj4JkRheeygV3CavclOyvVNo5n1hF0z328HU6Vpq00vFBdvXbTrl0GcNyzNE+7DO2xZPDktMGm7V/O3+a33S3WQtTWKXwsJ0HRdN/kQbj37U16TMnJyRFQtwI4H8AG7wMiMhLATADlAD4BMArAdQD+G8CbDh47KpRS9SLyBwC/BPAHEZmslKoCABG5H8B5AFYopb709zpEFPusy4QfK681xdZ2olhzQc+2WFvY2E9X7D3pc19/bYmguRpu5K6dR8qD+p2kAXptoHDKlF8+uCsKdpbpMQWvd05bHPe8z/TOiY3/V9X1Dfpow22zpjRJzjvhu6N6m7atCShru1vqLLF1ih5vUlE0TR2eh6nD89w+DXKZk+/IQwBsUkoZ3+u+D23FuO8opRaJSAcA+wDMQBwmoDxmA5gEYDSAXSKyHEAvACMBlAKY7uK5URRsLDqBa17WCl9+cM/opLnbl2ysy4Qb/b5gL35fsNf0WDLWoqDYdselfbH2DetA3eT0zIKtpvjVH1zi4tkkvkX3jfP7Hmo1+YXlfj8IW4tTK0tsbQ90Kt2UId30xMGUId0Cek68ycgAqjwjlLLTgA9+Ml5vsyYjls8cb9q21nyqtsSzP9qMU9X1+mPndI+NBNSX+0pNcagj5IiIyHlOJqA6A/jC8tgEAMeUUosAQCl1wpOwudDB40aVUqpWRCYAeBjALQCmATgBYC6AXymlitw8P4q89QfKTDETUETuCKbmUTIaO6Cz26fgupduPg8AsG5P4/u2MabIcHqEmb/i1PU27YGO6sjJSk/4ESA/mjgIj3tWVrz3W4NMU646pgOltY2xdTrWKyv9r6ljbZ+7uhg/v/LckEccPXfDENP2z+Zt0ZONAuC3lnaU77F9nU1FJ01xNBJQOa2BkprGOFLuGGOu5djc74iSh/fvnZd3qpuvdiK3OJmAqgaQ4d0QkWwAAwG8Z9nvJIC4/sSulKqBtvplkxUwiYgoOv68ZFdQ7fdPOSeSpxNzMlJT9A/X+Q85tzS7mwL58GXcxzvUv8FQCMUYu626viEi04OIvPytrHj18FzMXV2sx054Z+0B0/FmvvsV3l1/BADwnQu64tnv+L4HfcOIXqbtX87bok8hS7VpLyiwT0At3X7UFN87aWAQ/4LQ/HTKYH003U+nRK7+0yNTh5q2mYAiL+vUNmsCilPfKFY4edWzF8AlItJKKXUaWq0nAFhk2a8rgGMOHpeIiJKQcYW/QNqTLQFldFk/832fZbtP+G2PVdYPX2+tLDRNC+rfMQ2PTB2q12jzfjcWY29A0xpvTo3YCXbq2eBHP034ETgUu0bkZ+sJqBH52Y685scbDpoSUAu+PmKKn/1O4K9lrV/kS3W9eXm3fccrTLG1PVI1oWKl7lMyi0b9LyIKj5P/K18F8CcAn4vIF9BqIVUC+MC7g4ikABgBYJ3tKxDFoI1F5g9qh07WmGJrO6fkJQbr9K131h7QpzH8+upBuOli853Y1Ss+j9q5EQVr7h3mpdGtI6Ks7fHqnG5aIslfEqjept2pJBCLm1M8Gd4z2zYOR0lljWn7jLKPQ2VMMPxpYjrSUqTJ9Gujsrqm07MTOen7wT2jsXDzIX37T5/vN7ULgLsuzde331y9L6ESZ5uLT5rikX1yXDwbd7VtCZSfaYyJYoWTCai/Qqv59B1oBbmrAPyPUqrUsM9UAO0AFDh4XKKI8hYct/N/Kw7g/1YcMD2WyBc2ycR61yw1pYUp5l01IvdlZgDVhiFQ30rQQtJEkZCXnen3muXXV5uXSffehPF6+IoBAIDnF+6EtxR5eU2DacSRAzknE2OB8Z1l9RjaOZx1EOPfHWN66tPw7hjTE8PyOvi9blVompRKpATUC4u2m+J/3DXWxbNx15M3nqdPw3vyRtZ/otjh2CcopVQDgJtF5OfQCpJvV0pVWHbbB+BaAKudOi4RERElp2vON3/4Yo0LIudYa0f99uPtpimvTy/c2eQ5J2xGHHnVQKsJZWSsCWVczXDWfHP9mjRP+9yVjVOrlxWdxdAkX2vhkalDm0xNTmaFZRW2cTKaOjyPfxMpJjl+C18pdQDAAR9tGwBscPqYRESRltshwzYm97wzY6Rp+6dvr8Whcu1+e/e2gv+9+WI3TisuXNavg14HKl7qP9nhhy+i+OItSN64/QmWzxyPvOxMv6sd1qHpaodltfb7UvKw1vcqrzpriqNR/4uIghOR/4WeFfAuBJAD4IBSyvdYUKIYN3f6CNP2ywW7sPbAKQDAxb3a4Z6J/d04LYqyi3p3tI3JPdbaDpmtUuEtVZvZKjWpaz80J1FqPsWiJ6eZV8Dyrozlqz1RLZ853rQ97aWlKPWUB+rYGph/7/gmz6HY1ql9Cg6cbGh+xyC992VhSItElFUBdQ1OT/KLL0VlVRj37FIA0BN5sWDN3hLcNGcNAO1mUaT+Hvur/1VxJrnqfxHFC0cTUCLSBcCL0FbA8xZMmQtglaf9RwBmA7hGKRX4MjFELrpsYBfT9ptf7NPj9q1TmrRTYjIuaU9E5Iu1nsqT87fp05YybNoTlfWDcN9ObVFaWK7HwXxQXnTfONP2TX9ajhOeZdE6pAHv3D3O5lnktNvH9mtSB8oJK3cdDykBVQ7g7oLkHgZlnJI4d+XemBkR+tfPd5ti3hAiIq8Wze8SGBHJgZZouhHAJgAvQ1tswWg+gDYAbnDquERERAAwNLedbUzkJvERJ5vcDum2cSAGdG1r+srt0Ji8yu2Q2aSdImP62L4RuRFz6FRlk9pQFJitB0/Zxm77an+pbUxE5OQIqF8B6A3gUaXUbAAQkf9n3EEpdUhEvgFwqYPHJYqoNXtLTNsnqk+bYms77/IQueOmkb3x/sZjehxpJZW1ek2SdY9cjpys4D5UR5pxufJts6aw9oVL0tOBqtrGOFkNzcvW/38OzcsO67X6d8nE5iNVekzOieT7RksAb3lq903/vzWo9syeq6xRTWpDUWBOVtU3idc9crlpnwlPL0bFGS1u0xJY8rC5PRLq6+1jIiInr0b/C8A33uSTHwcAjHLwuEQR5Z3DbufLwvIm7W5P0+KHTkpWI/vkRPX/331vfWWK35gxJmrHDsSfl+wyxaFMcaHw3X/FYL0O1P1XJEf9JzuTBnfVp29NGtw1rNdyMplFZpuLT5piJ2+qnYH9NVWF82WlElZRWZVp+1hVjSm2tudlZ6J9VktUnNIyUO2zWkbkZon1ZmztWXPMm7VE5OXkJ9NuAD4IYL9aaNPwiCgCisuqTTGnIxBFxqYDJ23jWPHW6r2mOJkSUNYC2BOfXQrv2NVWAAos7ZH03VG9k6bukz952ZmOJYjH9OsEYLshJqfMXbHHFDNREFu8BcftlFY3bd//zFXo1bENik5pf6N6dYzMRzB/N2tP27S7fbOWiNzjZALqFIAeAezXHwDH2RJFyJeGufZf7i9lAoooQmrP2MexoqTGPk4G1gLXyhLHykpRFJoBXdvyA2yEfHP4lG1sZC0Kf/Ofl6PMM800Ox14+67G9skvcM0ht40b0Bkr9p7U41iQ/9AnAIDnbhiCG0b0cvlsiCianExArQJwlYicq5TaareDiIwBcB6ANxw8LhERUcSVVJpXW6q3xNb2aNeEqq73P4/F2p5M03MbfMREyc76vlB1+rQptnvfsN7YGprbAct2n9BjY3srAKdBbrp+RB6eXrhTj2PJ3BX7wkpAveOpKeb13Tlr9Pf4FABvWtqJyH1OXn0+D60O1IciMgPAEmOjiIwF8Ddo137/6+BxiSLKWszxH2sO4Pn/aMvLPvCtfvjvke7eubFeHNY3nDXFyfyh0ymxXmyaosPbBwJtj/YIDW/tt0DbOYKEiPy9bxyrDOx9o3+XNnoCqn8X8xSvlohuAmrbrCm47JlPcdxTjaBTBrDsoSlRPIPYk5OVHrPv97uPVDW/kx/WKaIZLYHyM40xp5C6Y2PRCVzz8ioAwAf3jMawvA4unxHFEsc+iSqlVojITwH8DsAiABXQRrpfJyJTAWR7tn+slPraqeMSRZo12ZCVlmKK3U5G+Lt4fPzj7XrRV69YvQiJZXuOVZpit3/nFH9mf7QZr6wsBADcMaYnHpk61OUzSi6tAdQYYiIKTf5Dn+CdGSNNH+wv7t1Rf38rr6nHa4Y6UnURPJcWAH52YUtccOEI/bGM1BT069wOx/dr0wf7dW7HG28xrAbajVSnfkf9u7XBV8UVekzu+OuyXab4xVsvdvFsKNY4+o6slHpRRNYCeAjARAACoC20vz+LADyllOJkcCKKK6eq621jokD9w/PhzBszARVZy3YcNW3XWGJr+2UDu0T+pIgSxIPvrMPyh6/Qt/NzsvT43fVHgPXRKfXaOQ0Y2DG1ySiXAV3b4AtPAmpAVyYhYt2fl+xCz44Z+nY4U/LO79VBT0Cd34ujbtzyzeFy25gIcDgBBQBKqdUApomIAOgIbfRtiVIqBku0EgVvcPd2tjERkS9VPmKKjNteWxdUO0eGEgWu7JT5kt5YFN5bXDoa2vsYjHzdhT0xd3WxHlNse3HJXtN2OAmob53bTR+N961zu4V1XhS6g6V1tjEREIEElJdSSgEosWsTkRSlFOuAUlzq2znLNqbEYS0mXV7bYIrdLjZNRETklrPN7xKUzpmCY1Wq+R0t+rW3f9xYh8pak4rsFZVVYdyzSwEAy2eOj9uVQkf2yeENBRdsLDph2q62xNZ21oRKblGdFC0i6QBmALgfQH40j03klFgr5jh3+gjTdsGOo5i7qkjfvuWi7pgypHu0Tyuu+Ss2/eC8zU0ei6X+QLFj9kdN+4qvNk7JI6J4UQ9gzV7zPeZwij23S0/FsargR0mcm9PS9vGM1JSE/bu8fOZ40/aNf1qGIxVa8q5rG8F7d18W0uvOXbnXFLv5N8nJmlDk32sr9ui1Yn999SBMH9s3pNfxFhwPtD1R/39SYBz53y0iqQA6ACi1G9kkIm0A3APgPgCdnDgmEWmstUuKT1Sbtvt3acv6JkQueMVQ96m5NiagiChenAFw05w1pse8Hyhfuvk80+M/eXuTPmKqBYDfe9p/8e4mVHgaTtUGn3zaNmsKVq/4POjnxTvryKSeHdrgSEW5Hoc6cunTLQdNsZt/kwY/+ikTFFHyx4LtpjjUBBRRMMJKQInIEGir3l3meS0lIp8BuE8ptd1TB+p+AA9DS1AJgPUAHgvnuERkZrwTue94paltf2mVo3cqieJddX2DvnrktllTAr7TOnlQR9P2ou2lPtutbcH4aEMR7n17EwDtw9zU4XkBPe+De0abtq13HK3tREROs75f/eztTfBOWk81tD/8ziZ9n2rzfbOA/Gt9EVjhB8jJSrWNg2Ws62Wt8RUI69+X615eBRb/jX3Hq+1jokgKOQElIvkAVgBoAy2xBM/3yQCWisiFAP4GYLzn8Q0AHlNKfRj66RKRHeudSKO5XxRh7hdFpsd4Z4mS2Z+X7DLF9085J6Dnzbl9lGnbWmzX2B5OId7XV+wzxYEmoJqrqcCaC84LNZlJlOzOKHO8bdYU/f9SIH45fxtendw6AmcWX4b1zMaCbSV6HKiiMvNyGPWW2Nre3Mgq69+XdHDBDSKyF86V0kMA2gJYBuDnALZCS0Z9G8BvASyHVuepBMCPlVJvh3WmRERRsu6Ry03bS7cfxYPztgAAnrthCMYPiq0pjct2HNVX9Zo7fQSnXDbjjVV7TXGgCSirFoBpaolTvEtIW2OKPdX1DaaYCSgis6zWQG2NFreENsITAIyT7uoA/GbB1mifWkK48rzueHrhTj0OlLfguJ3TNu3B3rjMyACqOKIm5ry2Yk9Q7ZySR5EQzpXSRADHAfyXUsp7hVwF4DURqQfwdwANAMYrpbaFd5pERNFjXdWubXorUxxrq96V19TbxmSvus4+DlY6Gld6ia0eoRnaNRObj1TpMTlv1vxNpvjFWy928WyIYs+vrzlPn1JcBeix0VkAc1cXR/fEEkRedmbMjGo3JuSvPC+Xv9MY5C04Hmg7E1AUCeEkoHIBfGZIPhkt9Hz/nMknIqLIKq9tsI1JY7woBgBlia3tgY5iGTuoo17raaylPpS1EK/1Q5exfd3+Mn1UgB1rW6BT8mZfP0yvAzX7+mEBPYeCs3rfcduYKJGlAVhpGSnsy9TheZg6PC+sackUH4KZQklEySucBFQ6tOl1TSilSrX642DqmygKrMvyzvuyEL9fok0z+smEPrjhop4unFXiaJeRahvHivqGs7YxafxdFNfZtAd6N9laE8rImiSyJqCM7fe+vcnvnWJ/z/VnWF6HmLkzniisycqKKnMcajKTKJ60QNORwkRERIGI9JURPwkRRYG1OGT7zFRTHOqyvKTp2znLNnbLsh1HTdu7jlWaYms7a0JRsll03zjT9uQXlvttD5S/ZGaNTTsTgBTrrDUPx81eDE/JJrQGsNzTPuGpxajwXNUzr0pERKEK909IPxH5fijtSqm/hXlsIqKoyMlKj6kPkt6C43beWluMt9aaR9PE0rknq0w0rgjEdHDkDeja1rSdJUClaoyt7UTJyjqSKbdjGnaV1umxt/2mS3rilZWFAIAbR3JUNTknE8CXs6Y0u9/OI+X6zYRF943j+zhRnAo3ATXG8+XLWD/tTEBR0iuprMWI2YsBaHchnRrSPrh7O9uY4g+XeU8Mz9zcWIj3GUt9KIq8btmNH6q7Zae5fDZEsSuvY6b+fyWvY2O6/K4J/fUE1F0T+rtybuQcY+mGa19aipKaxrZWAAospR0iKad9SkDXNit3HzfFTEDFDutIyknPLsZJz5o47VOBz2YGVjOOkkM4n2TmOnYWRElq6fajpviGEb0ced3u7VvbxhR/isuqTTEvuOLTJf062cYUHZcN6oJdng/Plw3ilFQiX4b0aI+CnWV67BXuSGBrrcrLn10KrtnqHmNphpoac1sqmpZ2iKRzu7dvficAx8prbWMK3PKZ4/H5zmP69i/nN10r7Mlpg/V40ZZDmDyke7Ova72BPqBrW6wtLNdj1owjo5ATUEqp6U6eCFEyqjCsWFbh4OplsbQsL4Vnf0mlKWYCKnjP3TDEtP3gvC1+2yPhqY82m+Lf/fdFET8mNfrORb300RvfuciZRD9RIvr+mN540bOIyffH9Hbsda0JjcxUoJ4ZqJigLNvt27YI6XXm3Hq+afuuN77WiwG3APBnQ/vCzQfx/kYtETKsZ3ZAr3+wrMo2psDlZWfaJp2MrO37n2k+AWWV2yFdT0DldmDyicw4l4OIKIYdr6yzjSlwI/vkhNXuhIJtx2zjnUfKI35s0u7AMilP1Lxo1TzsbxghcU6n1vjmeE0zzzB7ctpgoHpfJE4t4b252vxzs15Z9GifZdrnu6MCS0RaR8qk4Wu9oH2apX3sgM54f6NWXuB7o+1f3/r3cV9pjSm2tvMGXey4YmgPPcF4xdAeLp8NxRomoIiiqKTSPGT4m0OnTLG1nUNWk491Gff6hrOmuLq+wTTXPiM1BX9ftQ9PL9wJAHj4igE+L+aS1bhnlwbVHokPXydP28fW1dma88E9ox06IyIi91xzQS7WFmojLW4d07vZURlW3x3VGwUFTECFormf9drCcv13AwSegLJq30ZQU6H02CgjNaXZv7X+/j5uPlzZpJ03GWLHBfnZtjERwAQUUVR5C47beXf9Yby7/rDpMf4xTT7+lnl//OPtePzj7abH9j9zFVJTGofLp6a0YKHyGGBNJgfb7suwvA4hPY+IKJZcOqAzgG1NYkoc53TrgMMVZXpMySPWVo+m2MJPKUREce6CXtm2MbnHX7I5kHYiokTGWpWJ7+aLe+kF7W++mLX3iEjDBBQRUZzLzky1jYmIiIjcMHlI95AKWFNkGcs4AMDY2YvhHZOdDmCFpZ3IaUxAERE5ZP3hWvzgoU8AaKvBBLJ0rRM6ZqXZxkRERPHA+qHYOkrU2k5EobHWlxVLzPqzFGlJm4ASkUwA1wG4GMBIAMMApAJ4WCn1TDPPzQUwC8AVALIBFAJ4G8BTSqnQCnsQUdw7Vd+4mHG0VqzL9yS81j1yOS8afPj11YP0+HcLtqOisa47BMCjhvZ48ebqfXoh2e8NBCb0au3yGRERha65v1/8++acbbOmmLYvfPRTfbW61gC+srRHW1FZlavHTzbpqUBNfWNMFGlJm4AC0B/A34J9koj0BfAFgE4AtgBYDmAEgF8BmCQiE5RSXCudiEKyfOZ40/b89cV4/rPdAIAHJvXDtAty9ba3Vu/Hnz7fDwD457oi3Dm+f7ROM25sLDphqouVng5UVDe2t4C5bla8FPn+eMNBPV5zGJjA8hoRUV3foC8MYP3QRkQUj6wLlVhHwLi9kElzK9eSs749rDve+vKQHhNFmuPvMCKSAuBqABcByAGwRin1qqetu+exbUqpBt+vEhUVAP4KYC2ALwFcD+CXATzvVWjJpxeVUj8B9H/zuwCuBfALAL+OxAkTUeLLy840bWdnpZpiY/tBw13CrcUnIn9yceial1f5bT9j2SdeiuJ+sf+UHu8sd/FEEtwPX11tGxNRZA3MSceOklo9psjJzACqqxvjeGO9cUfBuX/KOXoC6v4p5/jc76MNRbj37U0AgJduPg9Th+dF5fwo8TiagBKRywD8HUAPaEl0BaAVtKQNAFwO4HUA3wHwTyePHSyl1B4Ad3i3ReSa5p4jIhcBuBTAMQAzDa/VICJ3Q0u83Ssis5VSp50/a4p378wYadr+/X92YNW+kwCA0b3b4yffGujGaVGIFm05pMebDteisKKx7ZtD5aZ2ACHVhEpLaWEbU2JZdN84AMDkF5bbPk7u2Fp0yjYmosh6864xeh2oN+8a4/LZJLY7Lh2Apxfu1GM789YdwIPztgAAnrthCG4YETvDbq037ig4OVnpAd18+8NnO00xE1AUKscSUCIyFMACAC0B/B7ASgDvWXb7J4A/Qhtt5GoCKkRXe75/ZJ1mp5Q6KiLLAUwEMAbA0iifG8WBkX1yTNuX9C3VE1CX9M1p0k6xbcYbX/tse2NtMd5YW2x6LJTVYDq1abzzm5bSAiWVjWXmahvOmmJjG8CaGW6ac+v5pm1rX7G2D+ja1vZ1vI+/uXofAODL/aW2+y05UIPDnn0A4Lujegd3wmTr9Blz/MCkfqZ27/RYL2s7EYUm0A/FFL7vje6tJ6C+N9r+b8eTH24xxbGUgKLo8I5ItMZEwXJyBNSjANIATFZKFQCAiJh2UEpVi8g3AM5v+vS4MMzzfb2P9vXQElDDwAQUBSArPcU2JvLq0ylLj71Dn+0s2l6KRZZVg3jx7p6mo92+bqZd0yULOFrZGHt5C4778vcdAHY07sMEVGiq683VAZQl/uGlfU3tf/xst6l4772TOIqViOJLRmpKs9cL9fX2MRFRsJz8xHsZgNXe5JMfhQC+5eBxo6mn53uxj/Ziy35ERGHJy87ULwy9K95R4H48oY9p+8Ule/22u+3CnjlYsK1Ejym6vAXH7dTatHPtQSIiSkQfbSgKqp1T8ihQTiag2gI42Oxe2iiplg4eN5q896OrfbRXWfbzS0S2+mjqW1VVhYKC5nJ5ZFVVpf0K4uVnV1vaeBup9vAuFBQccPFsKFi/GdtYIPyr4lq8u7+xRtO03sAlPczr2Ua7X8bL/4NIGt6qufbDpu2CgsM+9gxPnyxgb2Vj7Ot3c0m7BizQ41Mh/w75u4+OVoA+AqoVkvPnHm9/dylxsO9FzoqiGtN2lSWeNdd8Q2xsnnPp+J9faP6Y+Juvzvhtd+P3nwx9795FNf7bLaPyM8t2RfJ0yMNX3/M+Hg+cTEAdBuC7dH6jIQDC/pQtIvM8rxWM7yul1oZzWM931Uw7UUAGdkzFq5PdPgsKRV2DQtvUxoRTa22oZaIAACAASURBVMu7aXoKTO1pKXx7cMPBCv8Lrlrbe7SJzFTYK3sL/rBZ6bEvPdqk4NXJnI4bLyb2Bubva4yJiBLBq98E1z7WwcEvAzuab961RA3O6HHTdiKKL05e5S4CcIeIXKuU+pfdDiJyO4BeAJ5z4Hj5AIItthDu4qLeNa58Lbfgff3KQF5MKXWu3eMisjUzM3PwxIkTgzw98maD+bOjSGs6Hc68Qt3bu4C3dzUWaXSiHtNL2ebhzq+v2I+vissBABfmtsXtY/NN7RM5HLrZaYu/+sK8YOn+ZyKTEZ4I4MFwXmBRcNMv+R4YoiB/zi/ceRVeiNCpxAv+3SW3sO9FUAz9zclf9x/sKdNmDORnp8bE7zsp+l4M9QFq5KvvZWbGz2qQTiagngJwM4B/iMhvAXzgeTxDRIYAmAbgFwBKAfwu3IMppUaE+xohKIRWQD3XR3uuYT8iIkdZ59f/Z8thfOWpPNejfRrn3xMREVFCmXBOV+xZWajHRBTfWjS/S2CUUgcAXAXgBIBfAlgDbarajQA2ApgFbQTRNUqpI04dN8o2er5f4KPd+7jvpaqIiIiIiIioWd86t5ttTETxydFCE0qpFSIyAMAPAUyCNk2uJbTV4T4D8Bel1EknjxllnwB4FMBUEUlTStV5G0SkC4BxAE4BWOHS+RFREklPbWkbU+K5bZR54O3c1ebFWCd0A3r28jU4l4iIKHDvzBhp2r51zhp4J6y3AvCGpT2SRvbJcaSMARHFBscrnSqlKgC84PlKKEqptSKyEsAYAL8BcB8AiEgKgD9Ce09+SSl12verEFEisF6cvf3ZWvxrr1Zk+sHJ/XFRfnbEz2HcgM54d/0RPaam7hjT07T91qpCVHuWkcgQ4JbRPW2eFXusCSerJYcBHG7c5/FpwyJ8RkRElKhG9skxbacCegLqNIANhSdw5/j+0T4tipKisvhZUY3iT1IvtSMi/wLgHcvpvXX8IxGZ5okPK6WutTxtOoAvAPxERCYC2AbgIgB9oE07fDKyZ01EscB6cbbcUPtvQOesJu2RMHV4Hus+NeORqUNN2//eXIzq8rMAgA5tWjRpJyIiIv9eX7mLCagENu7ZpUHtv+6RyyNzIpSQIpKAEpGe0BI7ab72UUp9HoljB+l8aKvyGeV5vgDggPUJSqldInI+tJpWVwC4FkARgNkAnlJK1VqfQ0REREREFI8yMoCq6sbtwxXKvZOhmJOTle72KVAccTQBJSI/hFaA3JrUseN6wRKlVH6IzyuCNhKKiAgA0KqlQFt3AUhr5frbG/mQnpICoN4QEzXaNmsKrn1xGXaUNL2XlA5g/awp0T8pIiKX/eHWkbhpzhq3T4OIEoBjV98icjeAPwAQAF8D2AOAE0iJKCmkthQ9TmcCKmZxOWfyJyM1BYfK7Acyi6ediCgZ/GXprqDaOSWPiALh5JXU/dBuK1+llCpw8HWJiGLewI6peHUyMHHiRLdPhfx4ZOrQsOs+Vdc3YPCjnwLQRswwKZFYGs7aP85fMxElk6cX7gyqnQkoIgqEk5dTuQCWMPlERESJbNfRClM8LK9DxI/5wT2jTdvXvLzKtP2ri1Mw4qKLI34eiei1FXtM2zU+9svNzsBrK/Zg+ti+kT8pIiIilyyfOd60/a1nl8I7NjgdwH8s7UTBcDIBVQhthDoREVHC2nLwpCmORgKquWP0bt8qKueRiB7/eHtA+209Vo2tH29nAoqIiBJaXnamabulJba2EwWjhYOvNRfAJSLSzcHXJCIiiqrq+gbkP/QJ8h/6BNX1DU3aV+48ZhsTERERJZqWLe1jolA4mYD6DYClABaKyKUOvi4REVHU/H3VPlNcXd9g+jpQ2rgW9YHS6ibt0dC/Y5oed06NyiGJiIgoCV19QXfbmCgUjk3BU0qdEZE7ASwGsERE6gEcgXdd8ia7K45hJyKimDP/qyJT7K8Q69aj1XpBcq/9z1wVsXPzeu7mC/U6UHcOZ3VsIiIiioy7JwzAW18e0mOicDh21Soi5wBYAqATtFpQaQB6OfX6RERE0VBZX28bx5JheR30RFdBAdf+ICIiZ82dPsK0fdtr6/y2U+LKy86Mys01Sg5O3jZ9HkBnAK8D+B2AvUqpar/PICIicpl12txZpWxjIiKiZHHZwC5htRMR2XEyATUGwCal1A8cfE0iArBmbwlumrMGAPDOjJEY2SfH5TMiShzWKXRGB8vPRvFMiIiIiIgSl5NFyOsB7HLw9YjIY9uhU7YxJa6Sylp9JbaSylq3T4eIiIiS2NCumbYxEVEwnExALQEw3MHXIyJKWuv3l9nGRERERNH24LfPsY2JiILh5BS8hwCsFZFnATyklOK8BSIiIiIiojh32cAuLERNRGFzMgF1K4CPATwAYJqILAVwEIBdBVellHrCwWMTJZQ1e0tM2/tLK02xtZ01oYiIQrN85njT9qRnl6LOE6cB+MzSTkREREShcTIB9Ri0ZJMA6Of58kUBYAKKyAdvwXE7c78oxtwvik2P8Y5U/LPWeSqvbTDF1vacrPSonBc11b9jOnaV1urxBz+5zOUzonDkZZtrmbS0xNZ2IiIiIgqNkwmo6Q6+FhFRUhkxe7HPtgfnbW7yGJOO7umQmQp4ElAdMlORkerkn1JyW6f2KThwskGPiYiIiMgZjl1ZKaXmOvVaREREseq8vPZYW1iux5RYRvbJwYH1R/SYiIiIiJzBW3tEMeidGSNN288v3IG1hScBABf3bI8HrhjoxmkREYCLe3fEKysL9ZgSy72TBuFdTwLq3kmDXD4bIiIiosTBBBRRDLLedc/J2mOIU3hXPgFZk47r9pXit//ZDQD42bf6YQQTHRHz8BUDTNv/u3AnvBW30gH81NI+dkBn25gSQ152Jqe4EhEREUVAyAkoESmAVkz8NqVUsWc7UEopdXmoxyZKdEVlVabtqtNnTbG1nUVy4581qXiqul6P+3dpw6RjBN05vr9p+40v9qDo1BkAQKd2LZu0A6zBRUREREQUrHBGQI2HloDKMGwHSoVxXKKEN+7ZpT7blu0qa9LOD8NEREREREQUy8JJQPX2fD9o2SYiIopbWampAGoMMRERERERhSvkBJRS6oC/bSIiCl27jFTbmCKrpLIW3xyv0benXZjn4tkQERERESWOcGpA7QXwnlLq5w6eDxERQasJxamV0feP1Y33Uh6Y1M+2/hMREREREQUvnCl4+QA6OXQeRGSwfOZ40/b8DcV4fpG2ItoDk/th2vBcF86KKPEt2HjQFN87aaCLZ0NERERElDjCSUARUYRYV7XLNkzBys5I5ap3RBFyvLLGNiYiIiIiovAwAUVEFCXV9Q0Y/OinAIBts6YgI5VvwW4rqaw1bZfXmGNre05WejROi4iIiIgo4fDTD1Ec6JSVZhtTfNlcfNIUj+yT4+LZEACMmL3YZ1u9TTvrchERERERhSbcBNRwEXk0lCcqpWaFeWyipHFO93a2McWX3ccqTDETUERERERElCzCTUAN83wFQwAoAExAEQUoLzuTIy+IiIiIiIgoboWbgNoDYKUTJ0JElGiq6xtM2/UNZ02xtZ01oYiIiIiIKFGF+2lnhVLqB46cCRFRgvEWHLfz+Mfb8fjH202PcZQbERERERElqhZunwARERERERERESU2zvcgIqKkdfel+abtP32+3287ERERERGFhgkoIiJKWtaEU3PtP7/y3MidDBERERFRAmMCiogoQj64Z7Rpe+Hmw/jT5/sAAHdf2htXDO3mxmkRERERERFFXcgJKKUU60cREfkxLK+DaXvLwZN6nJvdukk7ERERERFRokraJJKIDBKRn4vIYhEpFJE6ETkiIu+LyLhmnpsrIq+KyCERqRWRnSIyS0TSo3X+RBR/6hvO2sZERERERESJLmkTUAA+A/AMgBEAtgOYD+A4gGsBLBOR++yeJCJ9AawHMB1AKYAPALQE8CsABSKSFvlTJ6J4dKy81jYmIiIiIiJKdMmcgNoG4BYAnZRSk5VSNymlhgK4C4AAeE5EBts871UAnQC8qJQaqpS6CcBAAP8CcAmAX0Tn9Iko3rTPSLWNiYiIiIiIEl3SJqA8Sad/KKXqLY//BcAiaKOabjS2ichFAC4FcAzATMNzGgDcDeA0gHtFpFWET5+I4lDvnEzbmIiIiIiIKNFxFTx7GwFMBtDd8vjVnu8fKaXqjA1KqaMishzARABjACyN9EkSUWyrrm8wbdcZ6j7VNZxt0p6RyrfkaFv3yOWm7UtnL0a1J84A8LmlnYiIiIiIQsNPO/b6eL4fsTw+zPN9vY/nrYeWgBoGJqCIkt7gRz/12Xbv2xuh5bob7X/mqgifEVnlZJnXjkhPA6rrGmNrOxERERERhSZpp+D54iky7h3p9KGluafne7GPpxdb9iMiojgyvFe2bUxEREREROHhCCgDEUkB8DqANADvKKW+suyS5fleDXtVlv2aO95WH019q6qqUFBQEMjLkEFVlfYr4M+Oos2Jvsd+676MuhpDXBYXvxO+75Gb2P/ILex75Bb2PXKLr77nfTwexG0CSkTmARgS5NO+r5Ra66f9JQBjAewF8CO7w3q+K1+nFeT5EBFRDJmUn4aPD9TpMREREREROSNuE1AA8gEMDPI5Gb4aRORRAHcBOApgilKqzGa3Cs93X8tXeV+/MpCTUUqd6+NctmZmZg6eOHFiIC9DBt5sMH92FG12fW/dxbWmfZZuP4oH520BADx3wxCMH9TF1M56Q7Fh2rfdPoPg8H2P3MT+R25h3yO3sO+RW3z1vczM+FldO24TUEqpEU69lojcA+BxAKcAXKGU2u1j10IA5wPI9dGea9iPiJKcNaHUNr2VKWbCiYiIiIiIkkXSFyEXke9Cm3pXDeAqpdQGP7t7l6y6wEe79/FNDp0eEREREREREVHcS+oElIhcCa3o+GkA1yqlVjbzlE8836eKiKk4iIh0ATAO2iiqFQ6fKhElgLRWLW1jIiIiIiKiRJe0CSgRGQNgnmfzJqXUouae4ylgvhJAZwC/MbxWCoA/AmgF4CWl1Gnnz5iI4t25PdrZxkRERERERIkubmtAOeBjAK0B7AMwTUSm2eyzQin1iuWx6QC+APATEZkIYBuAiwD0AbAGwJORO2Uiimc5WenY/8xVbp8GERERERFR1CVzAqq953tvz5cvpgSUUmqXiJwPYBaAKwBcC6AIwGwATymlapu+BBERERERERFR8kraBJRSSsJ4bhG0kVBERERERERERNSMpK0BRURERERERERE0SFKKbfPgSxEpDwtLa1N37593T6VuFNVVQUAyMzMdPlMKNmw75Fb2PfITex/5Bb2PXIL+x65xVff27NnD+rq6iqUUm3dOK9gMAEVg0TkCIAMaLWlKDjerN0eV8+CkhH7HrmFfY/cxP5HbmHfI7ew75FbfPW9PADVSqmuUT6foDEBRQlFRLYCgFLqXLfPhZIL+x65hX2P3MT+R25h3yO3sO+RWxKh77EGFBERERERERERRRQTUEREREREREREFFFMQBERERERERERUUQxAUVERERERERERBHFBBQREREREREREUUUV8EjIiIiIiIiIqKI4ggoIiIiIiIiIiKKKCagiIiIiIiIiIgoopiAIiIiIiIiIiKiiGICioiIiIiIiIiIIooJKCIiIiIiIiIiiigmoIiIiIiIiIiIKKKYgCIiIiIiIiIioohiAooiQkSWiojy83VFAK/xmWH/rjbt45s5xmo/r50rIq+KyCERqRWRnSIyS0TS/TwnXUQe9+xb63nuqyKSG/hPhqIh2P4nIo81s/8zfo41WkQWiEiZiFSKyFoRua2Z82P/S1DR6Ht87yM7of7dFZE0EXlQRNaJSLnnfWyHiPxVRHr4eM45IvKeiBwXkRoR2SwiPxURn9eVItJeRF4QkQMiUuf5/nsRae/nOS1E5D7P69d4jveeiAwO/idEkRKNvici+c0c44if82PfS2Ah/N31t6/3q8DHsXjNR7po9D1JwGu+FKdeiMiHfwKotHn8oL8nicjtAC4HoABIM8fYA2CFj8ftXrsvgC8AdAKwBcByACMA/ArAJBGZoJSqszwnHcBiAKMBHAbwAYB8ANMBXC0ilyilbI9Hrgq2/60EsNvm8a/sdhaRawG8By2Z/zmAEmj99nURGaaUut/mOex/ySGifc+D731kJ+C+JyKdAXwGYCiAI54YAPoB+AGA16zPE5FR0PpEBoC1APYDuBTA7wCMEZEblVLK8pyO0PpefwB7AcwHcC6AHwO4UkRGKaVKLc8RAO8AuAHASQCfAMgBcD2Aqzz9dU3zPw6Kooj2PY+jABbaPH7K7oTY95JKoP1vrp/XuAra73q5tYHXfORHRPueR+Jc8yml+MUvx78ALIWWPMoP4bmdoL2pfwrtwlYB6Gqz33hP2+tBvv4yz/N+b3gsBcD7nscft3nOLE/bKgBZhsfv9zy+zO2fOb9C738AHvPsf3sQx+gA7cJUAbjO8HgXALs8j0+weR77XwJ/Ranv8b2PX3a/32D7XgtoF6cKwGwAKZb2PgByLI+lQEuUKgA/NTye5ekjCsB0m2P9zdP2T+NxALzoeXyuzXN+4GnbCaCL4fHrPY/vtp4zvxK67+V79l8a5Lmx7yX4V7D9z8/rtAdQ63mt/pY2XvPxy62+Nx4Jds3n+i+OX4n5Fc5/SABvAqgB0BcOJ6AAXOR5zlEAaZa2LgDqAZQBaGV4vBWAE57nnW/zmhs9bRe6/XPnV2j9D6ElAX7mec58m7ZrPW0fWR5n/0vwryj1Pb738cvudxxs3/N+yJ4XxDFu9Dxng03b+Z62zZbHuwI44+ljXSxtaQCOAWiwadvqeb1pNsf6wNN2vds/d35Fre/lI8gEFPtecnwF2//8vM7/eF7nC5s2XvPxy62+Nx4Jds3HGlAUU0RkCoBbADypIjO89GrP94+UZdihUuootOGJHQCMMTSNhZaZ3qOU+trmNed5vk91+Fwptnn70jybtk+g3cmYZJlnzf5HbmHfI6s7Pd+fD+I5Pt/3PH1kL4AhIpJvaPo2PFNWPH3N+Jw6AB8BaOnZDwAgIr0BDIZ2M+oTm/Ng34tvofS9ULDvUTBu9Xz/u00br/kokvz1vVDEdN9jDSiKtB965t+fhTaUeb5SqtBuRxHJAPBnANsBPBvEMfqLyNMAOkKburcCwEKl1FmbfYd5vq/38VrrAUz07Lc0iOcY96PYEXD/85goIsMBpAMoBvBvpZSvGjzneb436RdKqXoR2QJtrvVAaHcNAPa/ZBLJvufF9z6y02zfE5E20N6fKgCsEZFLAPwXgGwAhQA+UEptsXntQPpEH89++4N4zg9g7kfeeItS6rSP5xj3o9gQyb7n1UVEHgfQDVrdpzUAPlRK1dvsy76XXIL9u6sTkZ4AxgE4Da3+lxWv+cifSPY9r4S55mMCiiLtEcv2cyLyhFLqCZt9n4A2xHqCjwsJX0Z7vow2i8j1Sqldlsd7er4X+3itYst+oT6HYkMw/Q8AvmfZfkJE/gltepReXFBE2kK7SwD47xcjoPUL78UI+1/yiEjfs+B7H9kJpO8NhjYyZDe0Wjj3WJ7zhIg8p5SaaXk8Wv2IfS8+RbLveQ0C8KjlsUIR+Y5qWhicfS+5BPt31+i70BY9+rdqWpSe13zUnIj0PYuEuebjFDyKlM+hfaDqC22lnIEAfgltrv0sEfmJcWcRuQDAT6AVg1wa4DFOAfgtgFHQssEdoa1GsRraqir/EZF2ludkeb5X+3jNKst+oT6H3BVU/4N2IfwgtJVxsgDkQfuDcBBa0VHrkFjj7zrSfYn9L75Euu8BfO8je8H0vQ6e70OhJQCeA9Ab2iIg/wNt+tHPROQuyzGi1Y/Y9+JLNPpeHYA/QauH0gVAOwCXAFgA7QPRQsvUT4B9L1kE+3fXjr8pULzmI18i3feABLzm4wgoigillPXu1E4AT4nIOmir2z0uInOUUjUi0hLA/0FbXeLBII7xNQDrHNUCERkLYAm04Yz3AHjK0C7ep/t4WfHzWDDPIRcF0/88+79h2b8KwFsisgTAZgDTRGS0UmqVpz2Q37lTfYn9L45Eoe/xvY9sBdn3Wnr2SQHwD6XUzwzPe0VE0gD8AdqF9J/tDufjNKL1vkcxJBp9Tyl1GMCPLMdZDeAqEXkTWv3QXwCYYWhn30sCwf7dtfLcBB8M7XPIR3a7BHAavOZLQlHoewl5zccRUBRVSqlFANZBu3M1yvPwfQAuADBTKVXiwDHOAPiNZ3OKpbnC8z3Tx9MzPN+NU15CeQ7FIB/9z9/+hwG85tk09qUKQ5wBe071Jfa/BOBg3/P3HL73URM++p7xPexVm6e9Bu0iNFdE+hke9/6uI92PmnuO93H2vRjmcN/zx/vBKxrve+x7cSKIv7veESjvWQs2e/Ca7/+zd9/xclT1/8dfnySkB0IIAYFoEIEIKL0qX0IRFBAQELBBRFT8KUqxoCKGoiAKKhYUFVEQUBFBCQhIDNUgiNRIT6hS0nPTbsrn98c5c3d27my7u3v3lvfz8ZjHzs6cM3Nm9uzszGfPnJGaNLDulVtHrz3nUwBKWiG5T/VN8fX9hJON48xsenogPEIX4Lo47d1dXEci6RBukxL5Nsmk62oe6blK1Y2q07v7IkKTWGh+XVL96zvqrnt15FHd69+y9WJ2at7z2cTuvhR4I74dl5rVXfVIda/vaFTdq2UdCdU9Kfs7Gu/COCa+zbZIBnTOJ11Wd92rYx09uu4pACWtkNz/n46gGvB/wF6ZYUicv3t8P7aOdUChY8AdSuRLpj9SZx7puUrVjVrTl6wXZrYWsA2hz4onq8mTma761zc1qu51JY/qXv9WVC/i03mSzk7HZBOb2QAKne6m61J31aMkzzbxeFpNHumZGlX3ql5HiuqeVPod3Zdw8f484dH0peicT2rVqLrXlXX06LqnAJR0KzNbn3CvKsTHObr7JHe3vIHCv2NvitOur3JVR8TX7GPMp8bX98d+BtJl2yCWbSHh0ZaJe+K0zcxs+5x1HRlfb6yybNIiefWvQnoDPhDflqpLR9LZwcBQ4HZ3X56TR/Wvn2lw3StHxz4pUqbuJf1N7J2TbQ9gMKFD6CdS00se92IdeSsw091npWb9jfBo6j3NbFwmzxBCK+g1wM3J9Jj/v8Aw4KCc8qnu9QINrnvllDruqe71Y1X+7ia3QF3p7uX6/dI5n1StwXWvnN55zufuGjQ0dCDc67o3YJnpE2JFd+CGKpc1O6bfMGfep4H1MtMsTl9JOKnYMSdfUoYfpKYNAv4Up5+Tk+fcOO8eYERq+qlx+l2t3u8aulb/CK3qjgWGZNKPJHSA6sD/gOGZ+WMIB2oHDk9NH0doEuvAvqp//WfoxrqnY5+GuupenLcV4Uk9c4HtU9PHEU5mHfhxJs9awHNx3imp6SOAe+P0T+SU78o471pgUGr6D+P0K3LynBDnPQWMS00/PE5/Dlir1fu+vw/dWPeOBTbJWf/hwKKY5wOqe/1r6Er9S6UZTuj3xoGJFdajcz4Nrap7fe6cr+Ufnoa+NwCTYyV9BZgOXBO/BMvi9MfSP+gVljWb0gGo2UA78BBwQxySE+PVwEkllrk5MCemeySW79n4fgYwNCfP0Dgv2a7fp97PAd7W6v2uoWv1L/5QOOHEYgbwB+DWVB2ZD7yrxLqOiHVtDeFJFH+M6R34oepf/xq6q+7p2Keh3rqXyndSnL88HsP+CsyL0/4NjMrJswfhMc1Jvfl9XK8DfwYG5OQZCzwT0zwTy/do6v3YnDwDgOtimnnx+PoPwvF2GbBHq/e7hu6re3HZq4HHCf/u/4nQUsnjcEGJ8qnu9eGhq/Uv5v1wTPOvKtelcz4N3V736IPnfC3/8DT0vQF4O/BTwgnE64To7ALgn4QI6rAaljWb0gGokwgnLM8R7n1dEdNfAexcYbnjCU9a+V/M9wxwTrmyEZpjnx3TrgBeBS4Hxrd6n2voev0DRgHnxx+Plwgnw0viD8f3gI0rrO9dhOb782O+B4CPq/71v6G76p6OfRrqrXuZvPsTAp8LYh18HDiTTMu7TJ6tCS1K5qTynAYMLJNnXeBiQgemK+Lrj4AxZfIMjOV/jHBSP4cQeNi61ftcQ/fWPeAjhAv+pwlB+3bg5Vgf9qtQRtW9PjrUWf9uIlxjfL6G9emcT0O31j364DmfxRWJiIiIiIiIiIg0hTohFxERERERERGRplIASkREREREREREmkoBKBERERERERERaSoFoEREREREREREpKkUgBIRERERERERkaZSAEpERERERERERJpKASgREREREREREWkqBaBERERERERERKSpFIASEREREREREZGmUgBKRERERERERESaSgEoERERERERERFpKgWgREREpF8wM08Nu5dJd1Qq3ezMvAkVpqeHFWb2upn928x+bmbvMTOro/yjzWyumV1dY75O5e0OZrZ9XPeXunvdIiIi0vMoACUiIiL90UfKzPtoHctdAvwmDn8A7gPGAJ8CbgXuN7MturjsrwOjgXPqKF+3cff/AH8BvmZmY1pdHhEREWktBaBERESkP1kBzASONrNB2Zlmth7wXuDBLi5/jrtPjsPH3P397r4psB0wDdgRuNPMxteyUDN7E3AS8Bd3n9nFsrXCeYSg2VdaXRARERFpLQWgREREpL/5HTAWOCBn3tHAWsCVjVyhuz8M7E9oBbUB8MMaF3E8MAT4bSPL1WzuPgN4BjjezAa3ujwiIiLSOgpAiYiISH/zO8DJv9Xuo0AbcEOjV+ruq4HPxnUfZmZvriZf7DfqE8ACYGqJNCPM7Dtm9oKZLTezJ8zs1FJ9TpnZUDP7hJndYGbPmdkyM1tgZnea2TE56afG/pzeU2b9i8xsoZmNyMy+mhDw+0A12ysiIiJ9kwJQIiIi0q+4+/PAPcAhZjYymW5mmwK7A9cBcZJ1nQAAIABJREFUS5u07meAfwMG7FVltq2ATYF/unt7dqaZDSG0rPoyMAz4KzAbOB/4cYllTgB+CewKvEAIuD0E7AZcbWZTMul/Fl8/WWJ5xwCjgKvcfUlm3vT4elCJvCIiItIPKAAlIiIi/dGVwHDg8NS0pEXU75q87ofi69urTL9nfL2/xPxTgT2AfwFvc/cPuvt7CcGkUh2qv0G4BXEjd5/k7se4+yRgS0Lw6htmNiGV/ibgReBQM1s/Z3lJYOoXOfPuB9aktkNERET6IQWgREREpD/6A9BO8dPwPgK8Ctze5HXPia/rVpn+nfH1yRLzPxNfT3H3hclEd38Q+EleBnef6+63uvuazPRZwLcI54jvT01fTWgxNRg4Np3HzLYhtKT6T1xndl2Lgf8BE8xs7VIbKSIiIn2bAlAiIiLS77j7fEKrnn3NbEMz25nQ+ufqGGxppqRfJq8y/bj4Or/TgkI/UuOBl9393py8V5ctiNm7zewMM7vEzH5tZpcDH4yzN88k/yWwCjghMz1p/XRpmVXNi695radERESkH+j0+GERERGRfuJK4DBC/0WbpqY129j4Oq9sqoJ14uvinHkbxdcXSuTNnW5m6xD6utqnzHpHpd+4+ytmdiOhA/U93f2u2P/URwl9Zl1VZlmL4us6ZdKIiIhIH6YWUCIiItJf3Uh4styxwNHAf/NuIWuC7eLrzCrTJ7fV5d2+Vqk1Vanp3yEEn+4EJhGCYoPc3Qh9Q6WXnZZ0Rp60gjoCGAP83t0X5aRPJIGnhWXSiIiISB+mAJSIiIj0S+6+ArgW2B7YgG5o/WRmmwM7EDrlvrPKbK/H1zE5816Jr28pkbfU9A8Aq4FD3P2O2CdUcuvhW8uU5VbgOeCDZjaa8p2PpyX9Xb1RIZ2IiIj0UQpAiYiISH/2W2AuoWPwpj79zswGAj8mtCz6k7u/VGXWh+PrxOwMd38eeAnY2Mx2z8l7TIllrgssTndannJUqYK4uxOCTcOAbwJ7AY+7+z9L5Ykdj28EzKrQSkpERET6MAWgREREpN9y97vcfay7rx+DOU1hZu8ktB7an/BEuFNqyH5XfN2lxPyfx9cL00+ZM7PtgM+WyPMUMNrMjs6U8xRg7wrluYzwBMGTCcG0Sq2fdo7p7qqQTkRERPowdUIuIiIiUrs1JaaPjU+SAxhI6Ptoawq3td0PfNjdX65hXf8FZgG7mdlQd1+emf9d4GBgd+BZM/sHoQPxfYBfAZ/JWeZ5hFsOrzGzzxJaUW1LaGX1fcoEyNz9dTO7ntBSagVwRYXyT4qvN1VIJyIiIn2YWkCJiIiIVG9ofF1SYv4I4Lg4HE0ICi0ALiW0ftrV3Z+pZYXxtrdfEoJK78+ZvwLYD/geISB0KCHgdQbwuRLL/B1wEDCD0Cn6+wj9Se0D/KWKYt0eX//k7pWe5vchwi2Of65iuSIiItJHWTinEREREZFKzOxAYCpwk7sf1I3r3ZDQCuo2dz+ku9Zbpjy3Au8B9nb36WXS7Q7cC1zg7l/ppuKJiIhID6QWUCIiIiJVMLO1gBPj2+nduW53fxX4EXCwmW3dnevOMrNdCC2uHi8XfIpOJ7QAu6DZ5RIREZGeTS2gRERERMows4mEJ77tBLwNeBnYxt0XdHM5RgPPElpBlXq6XTPXfz7wZsKte2sDh7p7ydv1zGx74EHgK+6uAJSIiEg/pwCUiIiISBlmNgmYRujH6O/A19x9divL1ApmNhsYD8wm3FL387IZRERERFIUgBIRERERERERkaZSH1AiIiIiIiIiItJUCkCJiIiIiIiIiEhTKQAlIiIiIiIiIiJNpQCUiIiIiIiIiIg0lQJQIiIiIiIiIiLSVApAiYiIiIiIiIhIUykAJSIiIiIiIiIiTaUAlIiIiIiIiIiINJUCUCIiIiIiIiIi0lQKQImIiIiIiIiISFMpACUiIiIiIiIiIk2lAJSIiIiIiIiIiDSVAlAiIiIiIiIiItJUCkCJiIiIiIiIiEhTKQAlIiIiIiIiIiJNpQCUiIiIiIiIiIg0lQJQIiIiIiIiIiLSVApAiYiIiIiIiIhIUykAJSIiIiIiIiIiTaUAlIiIiIiIiIiINJUCUCIiIiIiIiIi0lQKQImIiIiIiIiISFMpACUiIv2WmQ00s1PN7D9mtsTMPA6HxfnT4/spLS5qS6T2x6RW5G+WZper1PLNbEJq3oQSed9hZn8ws/+Z2aqY9qFMmj3NbKqZvWFmq2Oa65uxLdJcZjY7fn6TW10Wkf7AzCbH79zsVpdFpD9SAEr6BTObkjrpTw/LzewlM/uLmR1lZlZmGfub2VVm9ky8UF0WTxxnmNlPzexDZrZ+DeteYWavmNktZnaCma1Vx/alL2om58wfbWaHmtnZZnZjvLApmb7Cut5mZhea2WNmtjDui+fM7Hoz+38V8m5mZj83s1lx378et/+IKte9g5ldGT+zFXE7/mxm+9SyDSWW/REzu9jM7o6f65JYxhfiOqot494x/f9iGV+KZd6hQr68OpIdriyRd3MzO93MrjWzx+N+XWlmC8zsATM7z8w26cp+6Y3MbFL83k2uIvkPgAuB7YBBwGtxWF7Fek6O69munvKKJMxsU+Ae4IPAhsBCQn2ck0qzGzANOBBYD5gX08zv7vJK71bjsVKqkDkfSw+rzWyemf3TzM40s7El8ifpp+TMS59PLjGzjaosx6Q4bVKV5xqlhgmN2Uu9jxUCxV0ZpqSW06V8MW+2bv2tinJ/sNwyY5q6r5HicgZaOJf+o4Xz/CVmttjCddMVZnZo1Ttc+rRBrS6ASAu8lhpfB9g4Du8HJpvZB9x9RZLAzIYAVxAuCBJrgAXARsBbgF2BzwBnAVOqXPco4E1x2B/4tJnt7+7NuIg4DPh1vQsxs5OB84EhcdIyYBWwaRwmAT8tkfdA4I/A8DhpEeHiaX9gfzP7NfAJd/cS+U8ALqFw3FoIbEDYtsPM7Cx3n1LH5v0cGJF6vzCua3wcDjOzW4HD3X1JiTJOAb4Z3zphGzcGPgIcbWafcfdfVijHfKC9xLwFJaYfCpyXet8OtAGjgR3jcJKZfdTd+0MriUmEz+EO4PJSicxsFPDp+PbLwPdy6t8LwJOkAgApJxO+/7OBh3Lm9wVPxtelLS1F37GSwj5dmTP/04TfhmeAvd39pZw0JxOOTfcAh7j7vGYUVPqFSVRxrJQuW0Q4TwIYDKwL7BaHz5rZQe7+QBeXPZzw2X26UsKUdorPQ9PGAGsRjkuljimra1hXX/MGMDRn+jBg7Tg+h/x91JYzbUmJ6ZXypb3HzDYp8TuROL7CMrJqukZKmNn2wFXAxNTkNkJjl83i8FEz+xdwjLvPqrFc0oeoBZT0O+6+YTIQAg7bALfF2e8Dzs1k+S6F4NNlhJYSQ9x9PcKP0UTgJOBuQtChqnW7+wjCxesv4uydgIvr2rjyXgVuBr4FVNWaJ83MTgW+TziJ+gkw0d2Hu/vahJOq/YHc4Er8V/8PhBOme4At3X0dwo/b2THZx4Evlci/O/AzwkXX9cB4dx8NrE8IHAF808yOqnW7Un4JHAdsDgyNyx9K+NH8UUyzP6G1TF4Zj6IQfPo5sH5cxvhY5kHAz+K2lHN4pp6kh8+VyPNf4Axgb2A9dx/i7usSTowOB54l1PWr4mchwUTCCTfAJXnBT3c/1t0nuvuPu7doPUPc9onu/q9Wl6UvcPeXU/v05Zwk74ivN5S5qEjSXKPgk0iP9oXU7/cYQpDnDEKQZxxwnZnlBTWqdbyZbVFtYne/t9T5BXBvTFYyjbu/WEdZezV337nEfvtCKlluGnf/Xs4iv1dmP5fLl5hNuI4/tlQCM9sYeA8h2JX3J1redtZ6jYSZ/R9wF+Gcaj5wGvAmdx8Vr3UmEM6PlwK7ADPMbGJ2OdJ/KAAl/Zq7r3H3x4FDCP84Q2iJNAg6Wkh8Kk7/ubt/wt0fdvdVqfxPuvuP3X1P4IIa1/+Cu38KuD1OOsrMRta7XTmudPc3ufuB7n6Gu19XS2Yzeweh5RPAye7+OXdP/sXH3Re4+23u/sUSizib8EP2KnCwuz8V87W5+zeBS2O6r5vZujn5LwAGAo8CRyUXZu4+191PBG5J0pnZwFq2LbUNJ7v7b939meTfHQ+ec/fPA1fHpMda5nbJuM7ks7/F3U9097lxGS8BR8eyp9M1jLtPdfdvufv09AWpuy939z8TbtWBEJD6cKPX34slrfFw90r/NIp0h6ROlquP1aQRkR7G3ee7+7eA78RJ4wktmGv1IvAI4Y+tbzeoeNK7/Ca+frxMmuMI551/JAShalLpGgnAzMYBvyec478E7OLuF7n7q6nlPO/uZwN7EVryjwOurTP4Kr2YAlAihAt1wgEawu0PSWR+IoXbzW6oYjnLKqUpIQmgDCa0wGmoJGBWh68RWor8y91raqVlZiMotLi6xN3zbiNLbh9bm3BLXTr/W4F3x7ffc/e821aS/G8B/q+W8tVgRnwdRvgXM22vuG7IORl093YKLafeHbep28SAX3JrZ819QVmqI24zG2yhv6lH4v39883sNjN7XxXLOdxCH2SvmVl7fL3RzD5QId8BZnZd7Iug3cwWWeh37FYz+6KZjYnpJpiZU2iJtldOnwaT4+DA9NQ60mnS0zt1Qp70l0DhM/91dj3V7dnOnVJb6Cft0th/wgrL6STVzDY0s/PN7GEL/bAtj/vjl2a2VZl1bWJm37fQT9gSK/RD9+84feecPGU76zazdc3su2b2bCzH/yz0/7Bjhe2u2Alsdt9k5g0ws3fF/TAjVTfmmtkdZnZiNlDcSHVsd+42WexfhHBLFIQWnek61dF3C+HfZOhc7yaQYmZDzezzcX/MifvnVQv99b23TBnT6xxnZheZ2VNmtjSvbpvZOmb2dTO7z8LxYIWZvWhmV1vor6rifjCzDczsh1boH/A1M7vGKvxLHuvBUXGbXo7rfiPW6fPNbJsS+bq6b4ZZOOb8M27ryri+mWb2G6uyr8Ayyx9loc++Jy30MzknlmnXKvJub2aXxTq51MzaLBwjzrVMf0NW5bEypn0svu/UAtfMdk+lvzZn/lqxHG4l+mo0s8PiNr4SP4f5ZnZnNd9h68KxMPNdSvq1vCzW2aTfxl9YaD3SLFekxjsdd6uwBvhqHD/CzHapv0iNE78nh8T9+FD8jiS/N9dbmfMFy/w2mNmOVngow4r4+V5k+X9WppezW1zXnPhdetLMvmXN+ZO3Fe4AngPeZmZ7lkgzOb7W1QVHmWskgK8Q+isE+Ji7P0MJHm43/Xx8uzXwiXrKJb2Yu2vQ0OcHQr9MHqp8yTT/L0kD7BGn7Zya9skmrvvLqfXs1IV1TEjln1xlnqrSE/7VaI9pP92Fsh2QWtfOZdLNjGmuzkz/dCr/uBJ5BxL6WnDgvCbVoT/G5S8GLDPvPAp9Pg0skX9cajs67cfUvElNKPvWqeV/sQv5p8e83wbujOMrCUEtTw1TSuQfDFyTSrea0MfE6tS0q4C1cvKemVnHkvgZpKdNimnHE1rZtcXp7fF9ejg6Dq/GMiTLSKe5Lmfbp6SmfTGmS8q/MLueGvbthFQZPpzatqR/iNmZ9Adntj/p7yt5vwI4Nmc922a2d1V8vyY17fJa6mUs++zMuhemxg8plZ9wYuzZ7SuzbyaUmZfUx4WZaXcCw0osu8vftzq3O3ebgPtj3UmOtW2ZOrVHarxUvRufWt7mwFOpdSX9Fqb3z08r7JsT4nKd0I/NIjK/Y4T+D19N5VlF4VicrPerFT7bgwj9jiT1fnlq3kJg2xLlHEu4CEtv04LUPnTg+px8Xdo3hAuvhzL55se6l0wrWZ/L1KekLp0CPJFTp5Jj5vFllnEWxd/lJXEZyftXgO1T6as6Vsa0F8c01+Ws9+updcyh82/ju+K85WS+i8BI4K+Z/b4wsx33AuuW2OauHgsnpdLsnVrGosxn+TKwcRePD8kyJpdIMzyV5tIS378pOfmmpOsZhd+naRXKUfE4l1rW9Fq3OWdZkzOf69JYJ9PTvlch72zCb2LyfV5A8TnDY8DIEss4PpN2AYXvw38J37UufV8rbOuEKtKX/HxrrFuTKJwfXZaTds847xnAKBxnytUrL7PuvGuktSgcp/5R5TZYLJMDM+vd/xp659DyAmjQ0B1DlQfXC1IH14lx2rDUj+ZLlAmg1Lnu2ymc0K7XhXWkf5QmV5mnqvSEE7SO/RLf30g42VxO+AfmV8DWJfKflso/vMx6/hDTPJKZ/qM4/bUK5fxXTPeXBtabdQh9c12W2oazctIlJ9H3VVje6zHdxWU+jwdjunZCp5e3A58tt+9KrGsQobXTccDzcdnzgLFd2A/TKZzELScEBYfGeeMpBOec0ClyNv/3UvX7bGB0nL4uoU+yJO/5mXxvoXASeSGwUeazeTehP7IdS3znplfYrklVfDeTbZ+SM292Ld+5EsufkNr+xYSWdjul5m+RGt+Fwkn0zwjfx4Fx3pvjvnDCRdROmfX8Pc77N6EDXIvTk1aXpwFfKlMvJ2WmDyQETZJ69UFgUJy3FSH4M79M/slUuACgfABqE0LfakcRHgYxIE4fGZf9csx3UYll55aris+r3u0uuU2V6lu19Y7w8IFZMc3thAuRIanvzSkULrq/UGbfLCYERPZJ7d8tMtuSbOsfgR1S+2Ic4bueXNAfVmY/zCP0obhTnDcI2I8QNHHgzpwyDqLQ7+Jywp8466c+owmE2+e/3ah9Q+i7x4G5hL71knwDYh38GJlgQpV1Kvk8F+TUqben6sRKYIec/CdTCKCcDmyY2g87Uji/eJHMBTtVHCuBD6Q+pwEljivJReh2JfZZp+UDf47zngY+BIyK04cSgrjPxvl/zslbz7FwUqbu3UDhnG8w4ZiSBFF/24XPc0Jq+aW+o1ul0mR/95LpnY4BdA5A7ZpK/94y5ah4nKOxAajDCH1hTiJ1Tkt46M6ZFIJKeecLk+O8JBj9C2JwnRC4+2wq/9k5+XegcNz5R+qzXQs4hnDMSo5bsxuwrUl5c4/pOelLfr411q1Jsa6vJhyzst/tX8d0X4/vZ1dRr7zMuvOukXZPTftcDdvxvVS+Dev9DDT0vqHlBdCgoTuGSgdXwq1fyQXLXFInWYTbzzw1/JfQYfWJhJO7QV1dd/zxuDS17Bu6uH3pH6XJVeapKj2FFkhrCCeTyb+TbRT/+1jq38YL4/x5Fdbz/ZhuTmb6n+L0ByvkT05mH6izrhyT+byTYTmhpVOnFk6Ei3on5x/iTLr/xHTXlvk8kn27KDPtSeDtVZT/iRLlfxx4Zxf3yfTUcjr9C0+4AEtaIjyembcxhZPBb5dYflJH2gkdVybTj0q2vcbyJt+56RXSTSr13czZ9ik582bX8p0rsfz0d3c2Jf7RjWmTIGunk+5Umh/GNNdnpi+N03evsXy5FzCpz8aBfXPyDafwL2de/snJNle5bybUWO6dUt+lodVuVxXLrXe7y25TufpWbb0jPDjDCYGH3N8nCkGFN7JpUuVbCGxSphxJ4LnkRTqFlgYPldkP/yWnpRrhqUtJmk0y8z5B4XfpwBo+vy7vG+CmOL1Ti656Bopb0+XVqWEUWmxNzcwbS7hQX5OXN6YZBDwQ85+cmTeFCsdKwh8FyR8BO6SmD6HQsiXZr6dm8k6L07+ZmX5QnP4/SrQyIgSZkxZN2cBWPcfCSan9PY1MUC2mOSnOX1qqnpRZb7pul/qO/jiVJhucTaZ3OgaQCUDFadfFaf8h1QKNFgagqljXF+O6/p4zb3Kq3JeXyJ+cMzydMy/5nj5J/nEl3Sp/dj3bkVPeCVWkT9JmW7nmDeMzeTt9poQOwh34eCrdyLj81RSCd7OrqFdeosy510iEVrJJed5Vwz77aCpf7nFLQ98e1AeU9GtmNtrM9iWchGwUJ//Q3dckadz924R7lpPOnScSTn4vIZzUzYn9DVTs1yf2MZEMSwgtUz4ZZz9BaOLa06Tvsz+HEMjYw91Huvsowj+RDxH+OfyVme2UyT8qvlZ6jHsyf1Rmer35a7WMcDvIaxQep7ua8O/Phe6e94jdRpTxt4ST8vXivl2b0LroHEIAZwvgFjMbXWEdb8SyL0pNewQ4yd0fqZC3khfJ6Usgfl+SJ6NsZaHT+sQRhAug5RQ6ss86lxDAXAs4MjU96S9slIW+xPq6H3uJztDNbFvCLcErKfEkxui38XU/K+6QP9mXb6q7lMEx8fUed789O9Pdl9KEDver5aGvidcJtxBv18BF9+jtNjOj8NjtC710/3/XE44RYwl/pOS5wks8ic9Cv2uHx7elvtdQqI/bmtkGJdJc6Pn9J95MCEpD4cl/iWQbb3L3m8qsv0MD9k2jv0NZperUMkKAB+C9ZrZOavZHCEHPB/LyxvyrKDxE44BaC+Xu84GH49t0P067EYJj9wB/y843syGEFhIQWqKknRBfr/D8p0ES616Sr6PcDTgWpn07fb6XkvT5OYwG9ctpof/Et5vZxcBn4uSnCS3K6/E1wjnKdoSWZL3B1Pi6e5nPBnKeuBYln8/bzKzjYSLx/CipK9/NO664+y3AP2ssbzOMADaoMFTzUJ3L4mu6M/Kj4vJv8zqeXFjFNdJ6qeRza1h0+ol865VMJX2WAlDS76Q72SQ0w/07hZPMKwm3BBVx9x8R/o07ktC0+35CoAJCs/1PAI+a2cEVVp/+YRmemv5bQv8MuSdiLZYcJ4wQJDjY3Tt+vN39fkJfDEsJgYYzur2EDeTuN3jhEbRDgXcSbg/8BvCYme1edgFdX+9x7n6TFz/F7iV3P5PCRe944NQKy9kzln8dwg/7J+Lr7Wb2K0s9vaQLpruHv69y3Eno/wVC6xMy4/e7+yJyxAucB3Ly/otwovIm4D4z+5yZTYwXkn3RPWXmJR3xDwCezASzOwYKF4IjKD6xSy5yfmNmF5rZXukT9y5IPqdpZdKUm1e3eEF3ooXO6F+JHRCnj+/jYtKaO94vo+XbXcFWFB6ScHmZevI/wr/kUOhMP6tcfdydwm/DtDLreTyVp9R67subGAMnb8S3HQ9+iMewpOPmv5YpY1a9+yb5Dn3OQgfrh1mmc+86VVOnBhBuMUokx4VtSm1P3KYzY7pSn0G1ZUsHoPZJzbuXcH7wf6nfmD0Iv6HL6PwZJ+X+VIVy75dT7nqPhWm5dY9w+2ci+9CRWnQ8KICwf2YSWlcNINwKekiZQGhV3P0JCn8MnWNNfPhCLSw8WOAsCx32zzWzVal9MTMmG07xn5xp87x0h9bpzyedfwdSx6UyxWvlMTpxlrtbhWF2Fcv5MyE4vqeZJcHSJNDe6Q/DSrpyjRSVOjfMXU2t5ZK+pZ4LEZHe6rXU+ArCBe5/gN+5e/Zfug7xn5Q/xSE5Cd6F0M/EcYQf0mvM7G2eevxoZhkW8xrhqRGHEP49PpbQoeJ38/K12OLU+B/d/flsAnd/2cyuIvyruZ+ZDUy1FEryV7rYTeYvzkzvcn4z24PQPD3PF9z99+UWGE8MHwU+bGZzCCeOvzezLTP/rNW7jWW5+3Vmdg+hQ9fDKFxMVMo3D7jMzG4n1K/jCXX9x7WsP6VkgNTdV5jZXEJwdVxqVjJeKbiatLToyOvuC8zsQ4QOyrcm9AcGsNDM7iQEBn/v+U9G7I1eLzMv+fdxIGEfVyNdH78MvI3Qh9upcVhtZg8R/o2+tMYAeDWfa27rmUaw8Ojnv1PcMmY54XieHHvWJ1yMNLL1XEu3uwobpcbXrzJPqeNWNfURulYf08odD5OL8/RF9Xqp951+j8qoa9+4+1UWnjZ2EuFPgWMAzOwZ4FZCR8D/rqE8WeXqVHpe+viabNOwOFTS1aDzPwj9xO1pZoPib+Pecd40d19mZjMIT4TdidCXXTL/XndfkSwoBkiSwN06cail3PUeCzu4e27dc/dVqf856gnoLKLwZ+Xq+P4pQnDst+6+pI5lp00htIZ7K6F7iB+VTd1k8Y+6mwh9riXaKNwKPpBCHRhBcYuYRDXHBSj+fNLfjZ56jG4od19uZlcTWtVNNrPLCeeK8wmtOWtVyzVSutXTWMJtj9Xoassp6SPUAkr6naR1Sxze4u47uvsJ5YJPJZazyt3vdffJhM5WIfyQHlM6V0ded/f/ufvPKfQ38R0r8ZjiFkv/iP+3TLpkXvbfxuSfqnUrtLhIHnn8Smb6K5n5teQfTOmmzdWcrKd9P76OB7KPEK6njNVKWp1VvNUzKwYNk0Dcp7qw7o5FdUPeonTu/ndgU0KQ9jeEWxbWIfQPcwXwH2vu47K7U97tnYmkKf4TVfxr2unfU3df4O77EDpdvoDQumUV4Z/NM4GnY7CvVuU+13rqSyXfJwSf5hICq29y92Huvn6qBWPyPWvGv62t2u5K0rdsbFhlPbm8xLKqqY/LaqiP0xuxgRm17Ou69427nwxsSbjt6WZCy4O3EW6ff8DMftCkbSk1L9mmn1W5PRO6WLakhetIYJf4W74roZ+wJOiWbSW1T2Z6tswAx1RZ7sk5+bt0LOxmX0idb27s7m9390Pd/ZIGBp+Ifx4kQaczzGxkufTNFP+cvZoQfHoIOBBY291HufsG8di8WzpLC4rZ1yQtnY6lcHvrVenAb7VqvEaamRrfIWd+Kdunxh8vmUr6LAWgRBrj56nxLWvJGE/KryD8CP+4wv3wrZDuN6jcCXL6JCKd7rHU+NZl8m8TX7M/Rkn+cWaW+6913GcTs/ndfXoXLrpKSQfi3laijG8v9fnFFhtJ+Vvxg5uUf7M6llHyViYL/X0kgcd0y4lkfHyVy34jO8Pdl7j7Fe4+2d23iGm/Qmjxkm4Z1ZclrSrfanX0h+Xud7v7V9z93YQLhEMJrfyGEVrLVduiIPlcy93eVm5e8g/20DJpcltGxBYUSf9Dn3P3X3um1Wn8Hjby9qhEvdvdbOn9kO03qRnrGWZm2eNhs80l9P8DoVPeajVk37j7M+49AjpKAAAgAElEQVR+nrsfSDjm7U6hpcEXzOyQLi662jqVPr4m29TMzzppKZQEmvYh3AY3mPCEwiRQmVyg7hOPUbtkpifLWk4IXEHXyt2QY2EfdB6h1cs4Qmu1VtmdwhNsD3b3m71zS7MNm7Tu9Hej3B9TfeVPK6CjK4zHCMeJk+Pkmm+/64L7KbRUO6KaDPEOkMPi2/9mf7ulf1AASqQx0h0H1/yPA6EF1WrCI5ePa0iJGsTDPfjPxbdblUn69vi6iOImtXdTaIL+3ryMZvaWVP5bM7NvS43n5ic0N0469s7mb5R0y6PsyVRSxlGEfi/ypMt+W4k05ST/GM7qQl4olD+3k+sq7VWm/6U9KdzW/UBqekffTlbceW4HCx2HdvQVVakQ7v6yu19AoQPa92SSJB1kNvuf1e5aDxT64xlMaDVZN3df7u5/oRDMGUqhf5VKks917zJpyrXonB9fx8XgZZ5dS0xfn0Lg6j8l0ryb8sGtrqp3u5vtMQoPIKjYGrcO91L4o6GZ6+nEw+1f/4pv319D1obvG3df4+4zCP1DvhAnZ49H1SpXp5J5ayiu88lxYbf4O1qrWo5hHQGmVHnSrZtmEG6x2gPYl3BrVBv5x/Sk3B80s1qvRRp+LOwL3H0BhQcCnEbx7WjdKfmz6Q0vfVv3fiWm1+tBCnW6px6jmyUJOA0GHvH6bgeuiofuD34Z3+5lZpOqyPZRCuejP21GuaTnUwBKpAwzG2tmpZ4QlJYOGj1Y63rc/Vkg6Y/oGz2lE8mUy+PrB81sQnZmvAUquX3nJi9+iuASYr9ZwGdKBCG+El8Xk7ln3d2fIwSxAE4rsW9Oj6/PE24VqIlV1zH3V1Pjd2Tm3UGhL5LTM/OSFhvJP5J3x21Kzy978m9mh1EIDNyQM79s+c1sK0JLFwiPWu6qN5MTII0XEF+Lb//r7o+mZv+J0NplKIXPOetrhEd6r6RQVygTmEik+9ZISy4yKz0xsF7dtR4IgY/kwvNbpVoDJiw8pSwZH1ThIi/dn1m5267SkuPVu/NOOs1sGPClMvmTp2oZOReRMf8pJfIuohD82DYn7yBKd5Rar3q3u6licCZ5KtJxZlY2oJiuJzWu53UKx6IvmdkWzVhPGb+Krwea2YHVZKh335Q7HsVWQMkT+6r9DmWVqlNDKfx+3BIDDYkrCN/fgcBPyrWgNrMB1vkpqrUcw5Jg0+4UbkPvCEDFi9F7CK0pk9+Duz2/k+1L4+sWVPi+mNkIMxucmtTlY2E/cDGhf6NRtO6BMEnrtg3yWtSa2SaEJ0s3XPxuJH9CfjF+d7Lr34/SfxT2ZlcQ/pS7kJzz0Cb6DoWWZ1eYWclW9vF66uL49r8UjuPSzygAJVLehoR+He6w8LSlLZNggZkNjO+/T6F/oOdJXUDX6DzCRdUEwpPLGioG0zqG1KyRmXl5/TRdRNi2IcBfzKzj/n0z24nwdKDhhBPhs3PynwksITzN7K8Wn9QRTyzPJHSaCXCuhyeiZX2ZcFK/LaGj941j/jFm9lMKJ8NfTt0OUIvTzexKM3tf+gQ9XrjvambXEe6th3Bfffq+9+Ti48vx7YFm9tPkpDeW9RrC0/TS6dL+aGbnm9lu6RMmM9vYzL5J4aL3ZfIfO/2kmZ1q4QlxA1L5x5nZZwgBsqGE1nnnVLlP8iwELjGzTyblNLPxhP4ekn8bv57OEP8B/WF8e7qFp+KMjnlHm9k5FC5ALnL3/6Wyf8XMbjazj8WT1mS7hpjZUal82cewJ7dEbm2hI/pmSdZzpJmVepJPQ7i7E74nKwiBwPvM7EgrfgT1xmb2UTO7jXBSmNiE0MfTGWa2fTpgaWbvJDzZBsJ3tNoA7p8oBNv/ZGZHJBe/ZvZ2Qv84Jf+B9/CI9SSwfJGZ7ZfKvyOhg/Hc/O7eRqEVxEVmtk9S781sG0J92CluT6PVtd3d5BzgWUKLxL/FY0PHRbqZrWNm7zWz3wB31bGe0witXdcG7jaz4y31B0P8PTk8Hj+vrmM9ea4g1B8jfA5fSn7X4m/zBDM7xcy+k8lXz765z8wuNrNJlrr1y8w2MrMfUbg1O3s8qtbCuC1HJt9RM5tIeEjARMLvR9EDKOLtK8nF5kHAbWb2rlSdtPi7cCrheJV9Um8tx8p7CEG2oYTf4jmE23fTkoBU0noxt29Nd7+B8PQugPPN7BJLBTEtPOFy1/j5PU/xwynqORb2afH2xinxbS2tAxvpbsKx14A/JJ9r/F4eQPgTrJn95H2D8F2ZCEw1sy3j+gfF84Y/EPpu61Pc/Q13/2Icbu7G9b4GHE1o/bgJcH889nYEH81svJl9g3B+MZpw7DjSix/mI/2Ju2vQ0OcHwg+yE89dasg3kdCc11PDSsJJ96rM9GeBrepZN6H1jwMvAkNqKOeEVDmOK5HGqxymlNkXL6XSLY5D+v3BZcp4IOGkJEm/ILMPfw1YmfwnxH2fpJ+f+Wxyy11r/UhtyxuEk+309GuAoVUuZ00sY7renFAi3/RUutXAPMLFSHrdT+TVr5zPdiXhx31RZvrrwP5d3D9J+b5NuCjzuG/mZdZxTon8gwlBtOw2rk5NuwpYq8LnspTw3Ut/7jMJHQqn8w2K+ytJMw+YHYcjU+kmJWmq2PZO9Qv4v1RZVhE6vZ4NzO7id3dCFenfEz9fT613DsXfLQd+UWIdSZ65hAu4ZNqK9L7JqVuTcua9lXDbUZJmOeF7nSzvkAr5t8vU02WEW3ac0M/LgaX2DaHz9LbMupNlrQQ+Fj8LBybXsl1VfAZd3u5Kn3e5+pZKU3K7Umk2JXQAnP7c59P5uPJ0PfuG0JnsrFSeNYTv2+LMem7rSr2v8BmOJVzQZI+56eP29Y3aN6mypNfVlslzURfqU7LcUygct9J1KlnfJ8ss40sU/54mT6/K/oZ9JJOvqmNlKn16f/8hZ/6umfXtXKbMwwmByXT6Njr/NjiwcSOOhTHfpGRehc+lS8eITN0u+R2tYr2djgEUfhNnl8k/kNC6xGvZBgrHnum1ljlnWSdm1r+YcHx3wrnV+1PzJmTyTq5iGyeUyh/nf4ri84QFhO+Ux31zSqV11LCtk8uVpczn20b4nSs3XFdmu2utl7OrqFdlvxMVlr8T4Sl46c99EZ2PkfcDm9W73zX07kEtoETKcPcnCPezf5rQSuARwsnNOoQTvNnAXwjBka080zKmC5LbRjaJ66xWunl6U/5RiPtia0ILp6Rj8oGEH5yLgW3c/cYy+W8itAL6BWG/DSOcFNxGONH9uLt7mfy/JJzcXkVoCTScEFS5HtjX3afUsXmXEZqEX0c4GW8H1iUEPB4l3C6wp7sf4+EfxlJlnELo++L6WLbhsaxXAbvFbcjzbeAHhCfdvUL4h3lIHL8J+CSwXZn69X5CK7UZwP8ITyIcHPPfSuiUcgt3r7d/rPa4fV8jfO5DCBdttwMHufs38jK5e7u7H03opPJmQvBjVHy9GTjc3T/s4RaOtEsJJ5JXE/6pX0pobTGfEAg7GdjBM51YerjlY19C3wSzCfvjLXFo2NOB3P1OQquDvxP2wwap9TSFu99GaGnxVcI/zQsJ/yiuIQTjfkUIgJyUyvZynPZ9CnVkJOGCbSbwE8L399oay/IcIYh0ESEIYYST/GuBPTz0L1Uu/0OEjoqvIXxfBhAuIH8Sl1vyeOqhf4tdCP9mz4l5F8f3e7j7FbVsSy3q3e7u4O6zCBcExxJaqKaPC7MIrU+OJ9xOVc96/kPoG/BzhO/BHMJ3ewDhiZVXEfpbOrzUMupY9xxCMOGjhOPIG4RtnE/oMPt8CreCpfN1dd8cA3yTcLybFdOvRWih83vC79CpdWzSfEKdPp8Q4BxCCMb8FXiXu/+iVEZ3/y7hT6LvE36flxOOC0kfTBcQbju6KpOv1mPlP1Lj2afbQbg9LrmtbxFluiRw96Xu/iFC69krCH1NDojrfT0u/8vA5p7Tl1AXj4V9nocW2Z3qfTeX4WeE38bphDo4iPA79CNC67lsy7lGr/9SQt+gfyV8h4YQvqfnEb5jeS3tu9sISj+lORl6ze2j7v4A4RrhWMK59PMU+gV9jnDsORzYxUO3I9KPWZnrPRHpJcxsTwq3zuztzXnctfRTZjYd2As4q85An4iIiIiI9FNqASXSN+wVX9dQaJ0kIiIiIiIi0iNU8+QnEemhLDxd7WAKnTHf6u7zWlgkERERERERkU4UgBLppczsfkI/JMn3eC6hTxwRERERERGRHkW34In0XusTnhTzHPAzYEd3f7K1RRIRERERERHpTJ2Qi4iIiIiIiIhIU6kFlIiIiIiIiIiINJUCUCIiIiIiIiIi0lQKQImIiIiIiIiISFMpACUiIiIiIiIiIk2lAJSIiIiIiIiIiDTVoFYXQDozs1eB4cCLrS6LiIiIiIiIiPRY44Gl7r5hqwtSibl7q8sgGWa2aMiQIaM222yzVhel11myZAkAI0aMaHFJpL9R3ZNWUd2TVlL9k1ZR3ZNWUd2TVilV95599llWrFix2N3XbkW5aqEWUD3Ti5ttttlWjz/+eKvL0etMmzYNgH322afFJZH+RnVPWkV1T1pJ9U9aRXVPWkV1T1qlVN3beuutmTlzZq+4e0p9QImIiIiIiIiISFMpACUiIiIiIiIiIk2lAJSIiIiIiIiIiDSV+oASERHpZZa2r2KrM28B4JJ9hjJkkLW4RCIiIiIi5akFlIiISC+ztH1Vx/iK1XqarYiIiIj0fApAiYiI9DLL2ld3jCsAJSKNsrR9FRNOn8qE06cWBbpFREQaQQEoERGRXmbekvaO8bb2NS0siYiIiIhIddQHlIiISA+XbYmwfGUh6NS+Glas8qI0wwfr511EREREehadoYqIiPRwSYfjeb7z71XAKphWSDP7/IO6oVQi0tekA9lL21cpmC0iIg2lXxURERERkX4o27ry5fnLi8azASgFpEREpB76FRERERER6YfKta489Cf3dJqm1pUiUsnS9lUdx5aZZx+gwLUUUSfkIiIiIiIiIlK37K28ImkKR4qIiIiI9EMPnLFv0fvHX1nEcZfdD8Bvjt+ZrTdauxXFEpFebF5be9H42JFDW1ga6WkUgBIRERER6Yd2Ovf2kvOSQFSabsETEZF6KAAlIiIiIiIiIjXL3mY3f+nKovHsfPUJ1b/p0xcRERERERGRmpV7mMHRl87oNE0tKfs3BaBERERERPqhu748qej97TNfZcqNTwAw5eCJ7LvVhi0olYiI9FUKQImIiIiI9EN7XjC95LwpNz7REYxKqOWCiIjUY0CrCyAiIiIiIt1Lj0cXEZHupgCUiIiIiEg/owCUiIh0NwWgRERERERERESkqZoWgDKzAWY22swU5BIRERER6UHmtbW3uggiItLPNKwTcjPbGTgI2BPYFhgNGOBmtgB4CLgLuMnd72/UekVEREREpLzsLXfLVq5pUUlERKS/qisAZWaDgE8AJwLvJAScEouBRcA6wBhgnzicaWaPAJcAl7m7bkAXEREREWmirc68pdVFEBGRfq7Lt8eZ2RHATEIg6a3AlYRg1DuAtdx9HXcf7+5rA4MJAaoTgKti+p8BM+NyRERERERERESkj6qnBdQfgYeBDwE3uPvyUgljK6fH4nCZmQ0FPgB8BfgDMLCOcoiIiIiIiIiISA9WTwDqcHe/visZY7DqauBqMzu0jjKIiIi0xJy25ex07u0APHDGvowdObTFJRIRERER6bm6fAteV4NPOcu5oRHLERER6U7pJ0jpaVIiIiIiIuV1OQAlIiLSny1buTp3XEREREREOmtYAMrMhpvZm81sRGb6OmZ2npndaGY/MbNNG7VOERGRVnlt4bLccRERERER6ayePqCyziB0Kr4r8ACAmQ0G/glsCVhMd4SZbevurzVw3SIiIk01p634WRuz5iwpGs/OV59QIiIiIiIFjQxA7QvMcvcHUtM+DEwEpgHfAd4HnAycApzewHWLiIg0VdLheJ7z/vYU5/3tqaJps88/qNlFEhERERHpNRrZB9Sbgacy0w4D1gCT3f02dz8VeBLQWbmIiIiIiIiISD/RyADUusD8zLQ9gEfd/aXUtEeA8Q1cr4iIiIiI5FjavooJp09tdTFEREQaGoB6FdgoeWNmWwNjgTsy6byB6xQRERERERERkR6ukQGo/wDvMrPt4vtTCMGmGzPpNgdeaeB6RUREqpa0Bphw+lSWtq9qdXFERJpqbtuKVhdBREQEaGwA6vy4vAfMbC5wPPAwoQNyAMxsHLAt8O8GrldERKRq6YsxXZiJiIiIiHSPhj0Fz93vM7NDgS8B6wNTga+5+5pUsg8Di4G/NWq99TCzHYH3ALsAuxJuIVzh7mWfnW1mxwKfA7YC2oEZwLnufm9zSywiIvVa1r46d1xEpC/Itux87o0lXVrOV9+3JXtvOa4RRRIREQEaGIACcPephMBTqfk/AH7QyHXW6RvAobVkMLOLCLcXLgNuBYYSglj7m9kH3f3PDS+liIg0zLKVq3PHRUT6gq3OvKUhyznv5ic57+Yni6bNPl8PshYRka5raACqF/on4TbB++PwarnEZrYPIfg0F9jd3Z+O03cHpgO/NrPp7p59GqCIiLRItjXAgqUri8az84cP7u8/jSIiIiIijdeUs2wzGwSsBwwplcbdX2jGumvh7t9JvzezSllOi6/nJsGnuJx/mtnPgM8T+r66sJHlFBGRrivXGuC4X9/faZr+4RcRgc/sNYEPbD++1cUQEZE+pKEBKDPbDzgD2A1Yq0xSb/S6m83MhgL7xrfX5iS5lhCAej8KQImIiIhIL3bJHbO55I7ZRdMUoBcRkXo0LAhkZgcDfwYGAvOB54C2Ri2/B5hIaNH1hru/lDP/wfj6zu4rkoiIiIiIiIhIz9fIVkjfBAYAJwM/cfe+1rPrm+NrXvAJd19iZguAdc1slLsv7r6iiYiIiIiIiIj0XI0MQG0N/NPdL27gMnuSkfF1aZk0S4DRMW3FAJSZPV5i1mZLlixh2rRptZVQWLIkPGpY+066m+pe39Goz7CVdUH1ULqLjn39S0/6nFX3pFVU9+qj/dZ1pepeMr03GNDAZbUBrzVweT1N0kO5V5FGRERERERERESiRraA+juwu5kNcPc1DVxuT5G0aBpRJs3w+FpV31fuvnXedDN7fMSIEVvts88+NRSvd1javqrjiVQzzz6g4Y87T6LBfXHfSc+muteD3Tq1puQlP8NGLacrWrlukTJ07OuBajxe1KInfc6qe9Iq/a3uvThvCXteMB2Au748ifFjMpfDOkfpNqXq3ogR5UIUPUsjW0B9BRgGXGhmAxu43J7ihfi6Sd5MMxtBuP1uQX/u/2lp+yomnD6VCadPZWn7qlYXR0RERERERER6gEY2P/k4cDPweeBgM5tO6LA775Y1d/dzGrju7vAksAJY38w2yXkS3g7x9ZHuLVbPMrdtRdH48DHFVeyleUuLxrfYcO1uK5uIiIiIiIiItEYjA1BTCMEmAzaLQykO9KoAlLsvM7NpwPuAI4EfZJIcGV9v7NaC9TCvLFhWNJ5tojl/aXvuuIiIiIiIiPQsy9pX546LdEWjW0D1dRcRAlBnmNlUd38awMx2Bz4NLAJ+1cLy9TjZ2/BenLekaPwdm4wumt/oPqFERERERESkOunrNYDZc5cUjQ8b3Bd725Hu0rCrfXf/TaOW1V3M7CDgG5nJg81sRur9Oe4+FcDd/25mPwS+ADxkZrcBg4H3EPrT+oi7z+uGovcYlQJMR196X8m8X7z2Mb547WNF02aff1BjCygiIiIiIiJVSTocz/OpKx7svoJIn9Tfm5usD+yamWaZaeunZ7r7yWb2EPA5QuBpJXA7cK67393EsvZIyRPt8mSDS9IzNPtJhCIiIiIiIiJZ/frK090vBy7vrnwiIiIiIiIiPdWtJ+9Z9P6Wx1/lwtueBuC092zOAVtvWDR//x/c1W1lk96vywEoM7sHmOLut9WxjPcCZ7r7Hl1dhojU5unXFheNbzt+3RaWRkREREREeopyAaULb3u6Ixgl0hUD6sg7FvibmT1gZp83s3HVZDKzjczstHgb202Arn5FutFrC5fljouIiIj0FHc8+RoTTp/KhNOncseTr7W6OCIi0gD13IK3FXAioRPvHwAXmdkTwP3Ak8B8YDGwNjAGmAjsAmxO6GfpNeCzwKV1lEGkYfpq30hJR/FL21ex07m3F817ecGyTh3J95XtFhERkd7rT/c/XzS+15YbtLA0IiLSCF2+0nT31cBPzOwXwAeBE4B3EwJTAJ5KbvF1NXAH8AvgT+7e3tX1i0h1ynUUf9aNT3DWjU8UTdOTCEVERKTVZsx6I3dcpB5z2pZ3/CH7wBn7Mnbk0BaXSKR/qbupQwwi/Q74nZmNBN4FvBMYB6wDLAReBx4G7nX3tnrXKdIMc9tWFI0PH6OWQCIiIiKtsHRp/rhIPZ59va1oXAEoke7V0CvsGFy6JQ4iIiIiIiIVZft5WuHF4+oHShph4dL23HER6R5q4iF1mXn2AUXvb3rkZb547WMAfO/IbZg0sfh+/X8+M4eTrnkYgB8dsy37blX8GM/ulO37aP6S9qLx9UYOKZrf2/pGym6fiIiISE913K8fKDlvZc78y/Yf1uQSSV80a86S3HER6R6964paepxsUGb8mBFF49lmrePWHlo03sqgTrm+kQ75yb2dpvW2vpHKbZ+IiIiISF83p2150fvn57UVjWfn65Y8keZSAEq61Uajh+WOi4iIiIiINFL2CdBpV/3rFa761ytF03rbH84ivY0CUNJQlQJMwwYPzB0XERERERGR3iXbJcvdT73Op678DwCXfnR73r3FuFYUS3ooBaCkodL9JmX7UILiW/Z6Y59KyW1tM88+oNeVv5Lsj4dIT9ITvn/Z78gV987ivL89BcBX37sFH9tj024vk4iIiEgrZc/J1hk+uGi8r10zSX1UG6Shhg8eVLbpaqX5PdncthVF48PH9K2vj34cpCd79KUFReO7vnVs09Y14fSpQOdAV/Y7MvFNaxeN6zskIiIi/c3S9lVF50BD1xqYOy4CCkCJVO2VBcuKxtMdrotIcz3z+uKi8WYGoBLZE6qs0al/+NLjIiJSu2wr0x3OvIWke+ihwIOZ+TPuvrN7CiYiZW115i1FDQyGpYJOwxSAkgwFoEREpMeb9UZb7ngzLWtfXXb+xusOyx0XEZHaZQP+KzPjamUq0jtsMmZ47rgINDAAZWabA7sDd7n7rNT0XYAfAu8Ange+5u43NGq9It3l9UXLisaXtq8qmq8TI5HGyX6/WvH9qxSA6s192omIJNItj+6fNY/jfn0/AL/5+M7svOmYVhWL1SXGpaAn9I8oktWbu1yR5mvkUeo04ASgoxdWM1sfuBVYG3Dg7cAfzWxnd3+4gesWabqTrnkkM/5I0XwdaEUaJzmhzvOXR9/gL48Wz2/G92/+0pVlA106wRJd/ElfkK63o4evVTSuOi0iIo3UyF+VdwOPuPuLqWnHE4JPFwJfB94HXEcIVh3bwHWLiIg01NGXzug0TQEnkf6hvwYXx4wYnDveHea0LS96PwRYkRrPzu9t5rQtZ6dzbwfggTP2ZezIoXUvM/0nSaV+C0VEeoJGHqXeBGR7A3wf4bfjLHdvB24wsxnAbg1cr4iIiPQQ/enC/aV5S4vGt9hw7TKppTfpzidv9iTDBg/MHe8OSXAmz4qc+Zft37v63nv29bai8UYEoBqlPx23RaS1BjRwWUOh42EVmNlAYCdghrune4ydDWzcwPWKiIhIDzG3bUXueF+0bOXq3HHp216ct4QJp09lwulTeXHeklYXp6HUt13zzHxlYe54LZa2ryoalrWv6Zi3rH1Np/nV6k/HbRFprUb+srwITEy93xMYDvwjk24Y0Ld+rUVERASAVxYsKxofP2ZEC0vTWNkLuhfmthWNb77BqKL5uoDvvZ55fXHReLoFVF+u4+rbrnmefm1R7ngtyvWPuOcF2Uuu6m8bTz90o9IDOERE6tHIM6PbgRPN7AuEoNO5hI7Hs0+8ewchWCUiIiJ9zMKl7bnjfUG5iz89nKIxWnUrUDa42LZ8VdF4en66Xi9XyzcpIdtn1TOvtRWNZ+e38pa8R16aXzSu24lFpFka+at+HnAUcFF8b8Dv00+7M7Otgc2AHzdwvSIiItIi2Qv3WXOWFI2Xe5KgSFarOlUuF1w8729Pcd7fnsqd99wbbey15QbNKlaXzDz7gKL3f37wRb5+/UwAvnXYVnxgh/GtKFa/U65Pq3+9sKjT/O4MWGePy4tTAdfFyzvfvqfjtog0SsOOJu7+kpltB3wSWB/4N3B5Jtn2hBZRf2jUekW6KnuC9uhLCzueevX7T+2W+wQsEZFWKHdB+bEtYY+Nh7LXpL1aUbSaL9zVKkjKefzlhUXje23ZczpqzjN/aXunfqBafUteNliw/sghReMKJki54/ZZNz7BWTc+UTRNx20RaZSG/gK5+8vAlDLzrwSubOQ6RboqewK20eihueMiIq2WPV5tsu7wjvGxwwYwZJDpolL6hEXL2nPHe6qLpz3HxdOeK5rW0y7WN1hnWO54T5MNtO945i0kvW0NA/6dmT/j7uzDt/u+W0/es+j9P558nfNufhKAr75vS/beclwriiUiUjWdrYqU8MAZ+xa9v+OJ1zjt2scAuPDIbdhrYs9qci8i/cfo4YM7xkcObuQDbUW6V/ZWn1cWLC8a161A9RszYnDueE+T/WwHGqE32Tiuzx72/8FdJeedd/OTHcGoRE8LhoqIdPlIbmZvrmfF7v5CPflFGm3Y4IFF49nOIEcNXatovJWdRVYjG0B78Pn5fOqKBwG49GM7sMNb1m1Fsfq1ZnWu26pOe6V1Nl630IphvWEDy6QU6dl0C2fzrZe6BS893tOtM2oAbYvWdIyLiEjvV89Vymw6/peomde5bpGGS1+0513Ar5NqcZAe72H/Se8AACAASURBVKmyAbJ1hq1VNN7TA2h9UbM6153btqJofPgYHV77unTdGTLQWlgSke43p215RwfOD5yxr37PKhg+eFCvDNwdseMELv7Hcx3jIl2RvbXzOzc9zm9mvATAcbttwlcO3LoVxRLpt+q5SrmTrgegRHqcSido66aCTuv2ggBU1tC1BuaOS+/3yoJlReOt7gBXmi99vJo2bVpT16UWdtLTvDx/WdG4AlB904l7b94RgDpx781bXBrprbK/WVtsuHbRuH7TRLpXl79x7j6pgeUQ6fHGjBycO95b9JY+IPqyZl00LVzanjsuvc+L85aw5wXTAbjry5MUTBTJ8djLC4rGtx1f3S3lajnVuzSr1bD0b9tsPDp3XPqvh1+cz6E/uReAGz67R9W/KdI1uqFapEpjRw5l9vkHMfv8g3rlSWu2j6tGWdq+igmnT2XC6VM7dRbb3y1tX1U0LF+5umPe8pWrO83v6nJfTrWAennBsi4vV1pv3pL23PFGm9O2vON7O6dtedm0T7+2OHdcpLdZ1r46d7zZ7ntuTsf37b7n5nTbenu7394zK3dcpB6bbzAqd1z6r+z5uTSX/koQ6SeSAFqjPfrSgqLxXd86tuHr6K3Kda579KX3dZpW7edTbrln3fgEZ934RJeWK63X1ZYdtZrX1l40Xi6oPuPZOUXj+mdQWmlO23JmvdHW8X7WG22dgqil6nM2wNtdLQyfeX1x0bh+J6sz9eGXisZPPeDtLSyNiPRVOkZ3r6YEoMxsa2BzYBSQ2zuqu/+2GesWke6l279EGue1hctzx7tTttXckhWrisbVqk5aKbmFLvHLe17gl/cUP1i5VNB9Qeo3akE3/l49/MK8ovGP7LZpt627N3ttfnvuuEg9emun/M2gPh6DtuWrcselORpay8xsP+Cn/H/27jxMiurcH/j3wDA7M8Mwwz7sm4AoCKICiqCDcbkaY9Sba2I0hvxMoiEJMW4xideoMZoYTeINMRg0N1FjvJq4ICqgoIAgAuLIzsDMsM0Csy8MnN8fvcyp09U9vVR3VXV/P8/Dw9tdPd013TVdVW+95z3AqFAPg6d5ORNQREmgWpmBTY2JqHsVdc2G27uVIW67jzQGLE9ExUaoCrsnVu71NwUmcjq9MmqvUjm1t7oJEwfnJ2g9OkxjMtI/r05pjLsbLkxEkUnVWZS3VBwz3NYn89GXs/LbWpZtZUKIaQBehye59DcAp3v/PQxPQuoiAH0APAPgQJCnISKH06sf9KsG+vJkuZqyfm+Nf9jcCwtmsDw3zl5YMMNw++5/foI9tZ4Tt1F90/Hgl6bYsVqW8zUcN/NGWTXeKDMuj+SqrXplc/Udcwy93w4p1VWH6ttcObECUXf0aimV2XBlq+h9nirqWgyxvpz7E49Qn1ebyfIlpVlxXiOi5Jaqsyj7Go6bWbquAkvXVRjuY8Wctaw8M7zL+3yXSCnfFkI8A+B0KeU9ACCEKADwRwCXA5hm4esSUQKFqo54aNlOPLRsp+G+ZPnSVitRKuqaecIQZ/r7OzA/25+AGpifHdH7X3b/fMPtN7ZWYdFL22JfSYdTk8GhEl03PrMhAWtDlDrMevz57KppC1ju5v3kzsMNKH18NQBg+cLZhinuich6Vs7mGezYtuz++SGP992CQwydycpZ8M4D8ImU8m2zhVLK4wC+BuAUgAcsfF0iorhr7zxlGlNi5GWmmcbhyE5PM/wr7u2+WSyjUcehPkQUZ/e+vMU0JqL40CcRiYQ+S7I+kRBnT6ZEsDINWAjgPeV2BwAIIXKklM0AIKVsF0KsBnCxha9LRERJbnT/PKCspiuOQUF2agw3K69p6v5BLhfvq5v68xORUVlFg2lMnqHPqmufWo1DjZ4p3gf27okXb51tw1qR2+04XG+II6k6DFXVtHRdJZauqwy63I12KX01dx1pZC8nh7DySK0aQJ52GwBGAvhUuT8LQGK6PhJRyoq1RFnv0bFPOZnfV9MUsDyR9BPhFz/aj595e5n87PLxuPbsYXasll+4730kyYOZY4r9za9njimOaf0Kc5IzAaVftVQnBfjhRaMxf9JA/+23PjuEx97eDQC4+5Kx+MLkQf5loYbrOY1+9ZZDY4kSS28UTl30fjqnDczHocY6f5wq/XbIWruONJnGycDqi0p2zXxKoVmZgNoN4+x3H8Ez4923AHwXAIQQowHMBcApdIjIb0vFMX9DwFe/c54lVyg2ldcZ4tJJg0I8OlCoHh5PrzmAp9cY51JIZDNUfYfcV2kg3Tc33fYx7p9V1RviC8aZJ6AiSR7MGFlkWZ+UvrkZljyP04S6svnYO7vx2Du7TZc9uGwnHtR6t6lJzv9ZtQtPrPDstm+fOxL/b86YsF833j7aW2uInZiAsrJfB5Hddh42Vjmd1GJ9eTx7QrV7M16+5Lvd+77ulBRmA6hTYnepqGv2X6BYfcccJtASRJ/9cfuhBkOsL3fzPqZSmbChsq4l4u8P/UKc/l5NH9E3thUkS1j5Tf0GgIeFEJOklNsALAOwH8CtQojpACrhST5lAvizha9LRC53pL7VGFuQgFKrP9Q4GfXLyzKN7dLQ2mEa6+xq7J6dnuZPZg2/8/WEvKbbqCdyOVoc60melVc4D9W3mMZWUQ9m2ReDCP6G42ZOmCxffcecsJMW+pC17qoxb13hPfFe4fk+cXozd/Vk2o3N2uuaOwwxE1CJEWp2yOXba7BcW+70v4NQWk+cNI3DFelESWQPKxNQzwKoByABQErZIYT4DwAvApju/XcKwNMAfmvh6xJRAunDv577cJ//C/2uS8biq+eNsGO1HEl/ryrrWgyzBQ2x6ApoH6WnUR8H9DdqaOs0jYmA2Hoy6EmgmsYOQ6wvjzS5pf98a8cp0zgSsVZjErnZ3uomQxwqaZHsCY3zx/YDUKbE7pJKw5nW763xV8K/sGCGI6trk1EkF6Q5w517WfZJSSkPA/ijdt+nAE4TQowH0AfAbilltdnPE5E76F/wucqMZLmZsVdHREsvQW5SEh9NbZ0hS5TjtRPTnycrvachtup11ESWVUmtSAS+9ycNsW95a4fnft8QOP0zijV5QPExqCDTNI7Wuj01hjiSBFSoq5vLt9cELI/0SnCo55/9yMqInstnX02zaUyUCiqPtZjGqaikMMfV1Sl6MvGCcf1tXJv4emNrlSFmAio+QvWurG5qD3lcqFco85jRPRLySUkptyfidYgo8Ub3620ah7Kl4pjhdrlyUlZe0xywPJwT1FAlymZlt+pBoNmYc7166YWP9uPn3kbfP718PK7TGn2vW/N+t+uoJ6Csog4pUyXq6lDo934HHlq2o9vn+Nlr2/2N1H3cfKCeTOZNGAhgqxLHZseh46ZxPNnVh6mmqQ3r93X1qVq/rxZfmlZieIyb+3UQdUftCaX3hyL3qKhrxufK9/Xnh44bhtEDyVXBtvNwo2lM1gp10eeeV8pwzytlhvvU48Jw+42S8zBVSEQxiWb4l6/huBlPM+TgyaJ4UKerL69pwtgBeQHJmvS0HobYDVdaXvhovyG+adaoEI8mMhcswRmtymNthjgRlW/hDoPbeO88w+3PDtbjxiUbAQBLb57mj8OlJ2dX7KgNuC+a95bDQ8gtymubDXEyJy2Smd6P68WPD+PFjw8b7kumi0YHjzebxuQcWyuOG+JkrshLNpYd5QkhlkTwcCml/IZVr01E9rF7+JebqCfWiUhgfX7wuGlMicdeBV0+OtBgiGMdNheOTw4cM8TBElChqvkiTT7F0x9X7TLE0SSguE1SNJYvnG24fdnjq3HCG/cC/LHPe7uOGWI9kZFMSQsKn9NmB12/t8Zwu7ah0xDry8ke6gWriromQ8yJQtzDyqONr4fxGAlAeP9nAoq6xQNkD6ftqFVWV0ckirqjau88ZYjNKjImDS7w31bjSCT6vWrrOGkaU+LVKn0NapvakV2Ymt9ldtlztNE0dqvN++tM40i8W3bIEF9xZkmIRxN56LO39URX0kmNY6VXSlFyqTrWaojtPq71VZSaaToVuHxJqf0zDqeiYEP2Xtx0GC9uOmy6jJzHyiPgC4Pc3wNACYD5AK4D8BsA/7bwdSmJ8aTNo66pwxDbvaOOlT7V8uqdR3G3d5z3g1dNwOwoZofRn/PVTyrx6Nu7AQCLLh6NK6cM8S+b/ciqoDux257fAmCL4b7yhy/DmP5d/a3U2En0xFnnKWmI2eTbPpy+2h6+Bvj7a7v6vO2vbQk5KYEbtLWbxz7hDNHbqAxL3FheF/cElJMvpFD00nyXlbU4Vm9sPWjNE4Up2gueFXXN/qqu1XfMcc13ezh9LkP154nVgdomQxxOr09elCZKDlbOgvdeNw95VgjxJoAlAP5l1etScmtVqjZaU7iCY8fhekOsX4F0G/0Arcg7K5ovjuYATv+ZPjnphjjWg0I3VHqFOlh8o6wGb8RpqJPeN+fljRXeXl7A3ZeMxdXepsuhhjclu2gOtlOdemIHBJ4whcNsm9tR3WpJH6ZE0od/dGixvrzsYL0hNktAVTe0mcbx8ta2Q4b4v84ZEffXtIt+QeQ3y8vw8uajAICrz+yH75dOsGGt4mNCSZ5/WK0aR0NtUv5mghNQz324zxB/a86YsH5OnxlOP9ZwauJVT95MGJRviGNN7uw83IDSx1cD8Azb1I9ba5QLq2rsRvH4jNVkG1GySWjqWEr5VyHEDwH8DMDcRL42udMHu6sNsdsTL9HSh4gROYl+sJWTmWaInXLAnUh6tZlebWLFbHLJ7p0yltP7hBoe0mmy/OozuqpIP62oAzAqYJts7jhliONdIakmFN7cejCpE1B6EqIwJ9MQu6VKJhwPXH2GP9GgxtGI5Wdj9crHFYY43ARUQ2uHaezjlgup0UwoE8rK7UcMcWGu8Tn12Y/dXJW6SvldV20/gmumDQvx6PBsUGZPtdOr3znPH/97cxWe/sAzuc0tM4fhijMHR/x8ehWbW7R0dLLizkJ2vJO7AFxiw+uSC+gHwB1KsqUjSG+eZKTviGubThhiN++oEyVPSYKoMcUf3/vQ1WhL11Vi6brKBK5NaIsuHoPSiQPsXo0A6uyUALDriPt7N3Wnu6qBcH1aWR8Qh9om39tdF/dm8NuVKdzV2O3u/sJYzPHOvtR64iSu/P2HGH7n64YhQoP7dE3QocbJYOyAPEu2Fbt7Ph2objWNu9NdJc/mA3WG2KnJR6snlPm4vNYQP7RsZ9DHLl1XgaXrKgz3mW1TlXUthtgpF6Vf/OiAIbYiAVXdGHtVqhXDQ9Vq7U1Kv8HBfbKiquTWW6u4xYT73nJ8pbSbJPTMQAjRA8BkACzhIFOhDpAfWrYzYAcWyZeBE8ugg41nDzVU6aFlO/DQsh2G+9z+pXiaUvqtxrE4c2ihaQwEzuLz4e5q/Mzb++Bnl4/HeaOLLVmHVDVuQL5pTM706Nu78Ojbuwz3OeE7pUI52QCAI/XhnxS61V8+2GOIH/zSlKiep6q23RDbMYOTfqGkqdUY68vd6sE3d+LBNwNPrtfsPOqfbXFkca7/fjU2Y1US0m302fHiTb+geVKLg13w1BNl5bXNhjhgeU2LaZzsqupaTeNI6J9B64mThtgpF6W3VzWYxrGwogfa+zuPGuJYq06LlCq2otzoquT0fpiUmhLylyqEyAYwFsBdAMYAeC0Rr0ukcmIjb6dezUm0ksIcy094Qz2n/j6rlRaDCrJS9nOwilpqr5fdxxMblCYHXx+YilrjydqOw8lfAfVpRb1pHKkWLQ41hC9eQl1IaetmeTLYcbgRpZM8cSQJKHV/VF7TxP1RnIS64Nlmstx3PBEqUbZ0bQWWrjVW8lx9Zld16b6apoRXsIe7X7Siz6Xaw6umuc00jkSoz+jK338YcF8iL5w0tHtqKWqa2tCmZC/bTgYm18P5jPVk2ifl0c1uqtqn9CdT42jlZaWbxkSRsuzoXAgRzsBmAaAawI+sel2icB1r6TCN7aRfzbECT8JTl94M/O5/bsHyzz2VD6WnFeHBL52RkPUoys00PRBUewkAwGtbqvCnNZ5+At+cNQyXnxF5PwHVGuVqn1p9QO4SrA/MY+/sTvCaJN7hhmbTmNzn08pj/tjsgsjybQex4K+fAAAW3zDF/3319PtdVXBPv7/HMd9jbjq2+OOqXf6K+bsuGRt2P6dQht/5elQ/9/Lmw4ZYvQ3EP2nyaeVxQ2w2IYFVgn13H2lO3MCXRG2nC9/zVpm+Z0ykdyAwuR7OZxyPhuNbK46bxtEqUHqDFYTZJ0xPrOkTslBqsvKvsgLBJ1/tAHAIwHsAfi+lPBrkcUQpJR4zY6mNCzfsq8UF3t4UySIeBxcZvXqaxnaK5vfUr7Jl9hSG2O6qP3373lbVdUA0vCgn5u3/lU0VhjgeJ241TW22v4/kHLfMGoqn1xzwxz8oPS3iEwm1aqBFuXDe0ta17KeXjwcADC7IAgDc+tdP/EOGesI4fIicobGtM+QQoY+UffVH+2r931eH6rsSj2psN/V3mXDfW45pZWBGb3ivJ6CWb0vsDHvRsGooZr1ywbXeIRdfw6W3S9haVY9F/9gKAHj0y5MxebB1Q/z1C3hX/HYlDjV6EmcDe/fAv793oX+Z06s3fcNA99V2VQ3vq21ERV0zXlgww3/foIIsfOOZtdhZ7UmmjS3OwJ9vOjfo855R0ifihGmo/eFtz2+N6LkoeViWgJJSDrfquZxOCJEJz3DC/wQwFEAdgGUA7pNSOqezbIrTS2DVcfkVdc0Y1c9YBp+IAyn9YPRgfZsh1pdHQ21caEUTQ6eJR5XLwPws09hOeqPG7MLIv67TlASUGjtFRloP0zhae442mcYqvRdcpKY98K4j+iOpCcqn5mYiIy36z/f2uSPx5WlDw349p1c/RENNAkUiu1eaIc5OT8PqO+YYHnPzn9dhV63nu3hM30ws+cY5/mWzH1kVtGqg+VRgRYFv28vAJ/5hdhkwDrmzUjgnwVWNnfjJ2hPA8tdTqmdRd9aW14ds7L79UNc2t3FfnX8brK7vSidW158M2Dbten8/qzIOCXVKKwMzuw42mcY+v3qzLJGrE5UNShPvDeW1UX/u25TPbVtVvWMq6sIRalZEXyJKFcu+Wd+WB+Xn4FBjoz926rZuxmyYaHWzDLi//OHLMLq4tz8BNbq4t2Ob5FNySa4jyATwJp/eBXAePFVdrwIYDuAmAJcLIc6VUu4J/gwUyuIbjA1X39l+BC9u9FypunbaIFw0PvxqnlBXKBa9tA3ANsN9iTipDNlk/c2deMikkWmk2pWZA9U4WTS0nTCNY2HVLD7tndJfpu+Ek/TzxvTDy1uO+mOnycvsZRqHK6CJ7ClpiM2qD/RecAQ8sWIvnlix13CfE5JsiRTt9O99ctINcUtHJ/rmZvjvy05Pw5j+uV0JqP65lhzgp/cCWk4Exlbbqgwj21p5zPQk+OPDXS/+1rZDUZ8o+6odVm4/Yhg+deH4/lF/Pk7j2z9svHeeYSbAzQebTH/HNgRPQiaamy5onQoS++yqdf7sWy9vrDDE0TaQLq9uNMROadwdL931Vg13ZrhxA3vj48pGf5ysZo7thzfKavwxUSIk17dOYtwNT/JpLYBSKWUTAAghfgDgMQBLAFxg3+q5m35lpup4KwBPAuq0AXmuunJjhbL75wMAXvhoP37unaXtp5ePx3Vnd03x2tJhLPPvUJJOHZ2nku5gw8kJts+quw5qY63O0mcKieakdc74/vAlWudEkLxNlP5KtVn/KCrPQiV0d9e1m1YfxKPvmh3Ug+ya1pMY3Du+f9fsr2XO973si9XbgOc73HdwDwAFOdY0bh1enIvN3soONbbaP5WT4H9urAiYXryloxOv7Ou6/caWKtx20bioXsssAWM2+20k9L5zZo2L7fDn9/egMcpJHYff+XrAifMza/YYjhFumjUq5nXUjx0OHTcmoD7YXY0hhdn+29EeWwy/83X84qoJUf2s7o+rPLN5qm9tq3K/m+w61GgaR+pf26oN8b+2Be4XnThLNNCVlG49cTLgb3fRxWNQOnGAYT/e0tEZUDmmbqNA+MdW544qwt82HPTHsfrfdftwzyueyrtfXDUh5hnprDJpcIFpTBRPUR+xCiHui+F1pZTyv2P4eVsIIXoBuM178zu+5BMASCl/LYS4EcD5QoizpJQf27KSSSZdGZaTbsEQHbfxHdDpU5+qB3qhTsLNTojcVtmgHwQ7OcFW39FVgVPdFNkVVv33UKecP1Lfipb+xitw4faEcvLnHU0/gVi1KQerbQ5KQPmGbS38+8f4uKLrZOMXV03A+Q64Kum5GBAYU2SsOJEBgG/PHeNvYK3GVttTXW8a+/z5fWPB9yGHbRtOSTjpnnq/PKafv/flLVh6S1dy7fl15YZYTUBFO3y2u15m+vFFLImLJ94pMwxdXfDMenxeHfm2FCxZ2V0S89FrJnkr4+2jD7VsOGmMox0mHI7WjpOmsd1CVT4++vYuPPp26MTiPa+U+ZM+PktvmuaPj4foiXXu6GLTOFqPLy8zxE5JQBUqF0UKLbpAQtSdWM7UfgZP03G98YTaiFwEuU8CcF0CCsAsAAUA9kgpzY72XgIwGcAVAJiAskBeZpppnGpSeerTeCbYrGryCXim5H1uR9Q/HvL3NDu5dHJiCXBmz6CapjYcbei6iq/GdvNdhR03oLchAVXX5LlC60tQ+v4/pPSPq2s9haKsYHOAREdPiOpTw1vRry4VqDNQAcCZQwtjfs6dhxv83wm+760XFhj3C19dvB6+U6t0AM8tmIHrFq+P+HXUifgamj33qdvC+r01hp9pPdF1ouxrdjuqXy72HG0yvH4mgDXeHmxOb+jrRFsPHDPcDvW9pv6ttnR0xu27OJb+eI1NMFSiVNUlNpFpd/IJ6H4YcDyHoe6tbjLE6j4n2YTbJ/Xfm6sMsZrUfWHBDGz6eCMAYOpZ0wK+W9VG3/7XajGP7WY2QydRvMWyF7rJ5L7zAHwTwAF4kjEHvPcPBfAlAMMA/AmAMy9Jdc83h/mmIMs3aY+jGGWk9TSNU81EZaaPiRbO+pHqYh2OpR6gHWoKrNRyUnUWBZ7o3vb8FpvWJJBv0gS9guBQfYtheISZ32w+CavnQQuVEF26rhJL13G+jXAcbeg6kX7y+smW9H/6ywd7DPGDX5oSMLW61OJopl7XT3jN+hHp2k0eU/7wZXjyHWN2PiczMRN/JKuOjuC39WWRVreoFw8i5etxFak2eL4Dn/3AM56zwTlFOCmhobUjII52G7DT8oWzcdc/N/sv4pxV0hu3a0OCPy6v88cHalsMExQBXYnQv63tGlv8t7X7DAmoGSOL0Fye7o910XzfEqWSqM+GpJRL1dtCiHMA/BGeyqb7pZQnteU/BvATAHcC+Eu0r2sz3zRBwY68K7XHUYxKJw1C+cPsNaImLpjEsI4+zA0lfSL6+WQf/hiL7hqBklGwBNPfNhz096FwMnVGwX9vrjL0o9H/DlLJQaVvzsHj1sx0+vZnBw3xg1+aEvAYESS2y8a9dYbbJYW5QR5J4WgG8Ou3Pjfc9mmBpyeUT9Wxru/icPoJ2lH5cgrRVcI9foGn4f/C95zfVNypapra0NDW9Zk3tHUGzCJth2i2Qz35/XFFI258ZmPQxz+xci+eWGk+AcfhY13blBoDnkbm1S0n/bGuoq4Zr2zqOlUszA0cufC/6/YF3GcXp/YAo+QlpLSmbF8IsRxAfyllyOofIcQWAEeklKWWvHACCSEWw1Ph9Qsp5b0my0cD2AVgp5Sy2y6cQojPgiwaNWzYsIwlS5bEtL6pqLnZsyPIycnBzcsjK+FeUhp5E+RI+XZYPqsrO/BauSe+fDgwe4hxJ1Wc3X3VlxN/TyvF8/fbdKgNv/vU8x343dMFpg6MbKdr5bol2+f46dF2/Gazp1/X98/sgdP7ZQQ8pqH9lP/E4fELMpCXEVmft0jfM6uE897btW7kLr4TaJ/vv9fur14SAH6jLc/L6IFblrf6Z/bqAeDp0iy0dxqP5W5dYTyBfGpuZsB9ibKkNAu3Lm+Fegp3/RigdITn70jfL+6o68SSMs9vePOEHhhXmIYfr4lsxkrf3yj/Do2+f2ZPjC00zjiakWZMUVa3nIz4/bbLkzM928ltH7i3Ryi3VWfxfR4LlrfClwJLA7BY2e876bO6eQIwfYDn2DXS73jf77rv+An890ee3/YnZ6dhREHksxLr2julbfuceHHKcbd6rqu6+eabsX///jIp5UQ71isSVpZSnA0gnNrbbQDcWgZg1tPKbDlZQD0I+uWs9LCSMU6n/w59MoxxNL/jU3ONSZP3K9vwd6Xf5vfP7IGxhanVNyqYqkbjFTV1Juba9sDloWYWa2i3dwY+3wmnfvJgF/0EuKbtlCHWl2ekCdS2dp141raejDgBpW/7TjrpJgqHvs33QNdgyh7e5er3UmPHqYAp5qsaO/GTtSdCvo6dfwdVjZ3Q125YXk//7xVq3T2JKHckQ9zAbLiuflLVcdLafnKJoCdy3VQRpe8byV7L93mSS+rRYKdyv9MUZ/WM+TjwmHIsdqz1ZNAElJpUempuZsjXbehw1izV5CxWj+UZb9FjnMrXGTZY/bJvrs+w5kMOlqEUQnyWk5MzYe7cuRGuXnJ5b8cRYI2ndHbAmMm4YFz308ivWLECADB37lxgeWS9COx4vzu3HcRzOzzNZGdPn4K5FkxtXrduH7Cza7aNM6dMDeu9c6qyWcak0Asf7TcM6Vl8wxTMUmYJCzVEMVR/ir/vlIB2mlT+cPBCzWh6XYTcxiLcXn0HAU4Z1hfq/XhuO/DcduMJcPnDl2H5toPAR57tv2Ts6bFv/yuM6/CF0nkB91khrO+KCD/PVHL73JF4YsVef/y18zyzAaViM2p9W8p+53U0eo/bs3t4lnf3XdNd8sluZuv3y4+j71tWdv98bNxXi695h9Y8e9M0bM9XpwAAIABJREFUTBvR1/AY/36Af4fd8lVzbLx3HrLT07Ct8jiwNrKG9XbxVQDof0d3vfc6fOmCLADOTB148CKJszwfZGK9YPfbbepZ07p6TkV53lO3cT/wqacR//BxEzF32jDTx9c0tQErPPvp6efNDDlUr6KuGVizKqL1cTqnnJcbznUVekWUk1mZgFoD4AtCiB9JKX9l9gAhxCIAUwC8YeHrJpKvqfqQIMuHaI8jCimjV0/TOKbnTDNeUd9b3eTqBFR2epqhH0FHp/GqSkNbZ8AsP04cv65OMU3x0QMwDE2ixFF7QO092oxrF68DALy44ByM7Gc8KFq1/Yg/HlqY7ci/V7v07An/RtzT/UW/cZGdnoYB+V1VOwPys+LeG/HFBTMwsl9X36qqY6248vdunU/HXDIlgHvnAK3NgTGRUy1fONtw+5o/rEaDt/gzLx146dtdy8trmvwzofbJjn6Eg+/Y+oDSs/NAXUtADzDfPjqSCQ3CmfCAUpeVe+y7AcwB8LAQ4iZ0zYIn4Zn97kvwVD+1eB/rRr4pk6YGWe67f2sC1iXpmc3KkWxGFueaxrGYPCSyRtpuEOrAeNFLnwbc55SqIFV3jV/L7p8PwPqZZ5zaXDKcxqfdrevybcbm3JkCaJFdsb6c4kf9rNQDz4EFmQGfo/q3YMWscKH4/q6efHcHnnqvHABw6wXDcdu8rjaNds72tH5vjeF2xwljrC9PVr7PCQB2HWn0J3de/c55GNO/d9TPqyZGgeiTLAXZ6Y757iSP5Qtno7LMvMH0aQP74OjuYwExkVPpE7UMzs9CQ3WrP1aXDynMNo0jZfZ9+MSKvf4KZR/fMXWoBJTeNP5YnM7b1Iu5T63chb9tqAIAfGX6YNx64RjMfmRVXF6XrGVZAkpKuVUIMQ+eGe7GA7gXMPTSBICdAL4upXRrguYDAPUARgkhpkgpP9GWX+P9/7XErlZy0GeSOFjfboiDTZUajHpACwDvlh02TLv+08vH47qzzctME+Xg8VZDHO3JmHqS0nbCuFMor20OOInhFLHOE68r+Gq1yartR3BNkNLqRNMPfKJJJPquAJppkaGXk33UK7axXL0Nh+/vapSS4B9VnOuY2USvWxx8qFNLN8vjbfnC2fiPx1fDlxbOBBCvwULq5zG4T5YhNvus1FmlzGaY8ok04aQeN1TWtfhn1cpKN5ajOWX7SWVjB+Shssx82WPXT/V/9mqcivrlChxt6uoz1bsH/MN8yblOL8nH594E1Okl+YZl2elpCb3Q6ksuHVJmjj5U32pIfiXqQo56npSf1csQlxTmBIw2+MfGA4Yh/1+e5pmonokqe1m6B5VSrhdCTICnEmoWgEHwJJ8OwjNEb6W0ato9G0gpO4QQvwNwD4DfCSFKpZTNACCE+AGAyQDWSCk32LmebhXqy+ChN3fgoTd3GO7r7stXP0DMywpsqmf3QaSaVKuoa446MRTqJGXp2gosXVthuM+JFUKJoO+Y3t95FPe84jmC/cVVE3C+0ksq0uf607/ew7Pbu77e7rpkLC6dHHtPr1gdqG0xjYnsEm7yIBw/vXy8vyfcTy8fj4smDDB9XCKrrtxg+cLZuOvlLfj4QAMA4KyheXjoauMkxmMH5CE7A2jzXgvKzgB+8oUJhsf85JUyQ1P0HAB3XzXB/70aL+q+28r9uPpc6rZploAqf/iyqHoBRmL1HXPQN9fTYPus+97y9zLKAPCOsg+a98gqtmpX1DV1GOKy++fbWulop1NaU3mb509JSUtvmoYbn9kYcF8oJYW5prEdzP529N8nUfSWG2rc0tEZ8lzSrLqL7GH52bc3wbTS+y8ZPQDgIgDnAdglhFgNzxDDGQBqAdxk47qRRv1y0oe4dXSeCigZTXRCqlEZgqTGFB/6iWdxboYhjuTEVH9snpbfvHTyIEec6J44eco0JooX9WRdP3EHPMP1rEqC68OYg/3NJbLqyg18lT0+Hx9oCLiv/OHLMKK4N+oqPfOvjCjujf86Z4ThMb94pQxqWnvm+L74r3NGJCQBlewXUvTfL01pcpfeQ6sEyAaqU+D6wo3neFqtnj6kIKAqXvW/6/YZ4p9fdUbQxzqN7+JW6SOrDI3Ul3vvj7Ryo0brwB7PRKV6Ye4n/7cVq3bVxfHV4sNXBTn9p2+h2Zu7yxHAhp93VUdGmsw068PaXW/W+ZMG4rF3dvtj8gj23i9dV4ml6yoTvDYULdYQR0hK2SaEuBDAXQC+AuAqAMcALAXwEyllRaifp8QKtZN4aNlOPLRsp+G+ZD+gpcRYfMOUqJNPvgO41o6TASeEiy4ejSunBJsDwUPvo9SijNNv6TgZcZ8lJ3BqHysyZ2WCqTsFSjKpIERiyaqeGXYru38+pt73lmFY3KY4VnfMGd8fH3sTUHPGB54w9c4FWpR5f6/xDm/QK0SDnUybiVd1U6TC2Y71HlP/3FgRcFxhlbGDevs/i7GDjH2xzh1ZjH9tq47L61rBP+upIpoh0l0nmJ7/l5RmmT5uxeeHDLGbElC+Y4eC3gKtjdIfO+GCVnfUdTxtYJ4rE1C+7xyh3Cdg/C7aeO88rFm9BgAwa/asgCGe+vdCNMYOyMPqO+Zg9iOrUPr4aqy+Y44rtoFY3XXJWP93qD6SgMPmkkfUe3YhxPne8CNvUub8kD+gkVK+H+1r201K2QrgPu8/orDpvZj21zYZYvZqSqzhRbmmcTRy0nvAN614fgwVFuoBRvnDl+Gljfux6CXP9LgD8jO7PQAJ1eti6boKLF3njOGYS2+ahn9s2I/XvCdNl08qxpenm/en2lReZ4hLJ9k/tJGcIUuZPTQrxEyiyVIxk52ehl49gDZvJUyvHp779ITPJb9aZbh6v+xHc6I6eO/uKnxhVhaONHWVWEwdXgggsEI0oxfQeqIrDvU95qbPSk+GDyqIX3L89ovH+Ye93H7xOMOyy88c4pgEVCaANSYn4Pr3dgY+QXvAo6xRU3/SNHaTc0cW4+UtR/2x0+l9V92c6AeA3rkCTd4EYO9cYVhWlJuJvIwe/lhndt/pA3Lw6eFmfxyOuuYOQ5wKCSg1gW9WKGAXffum2MRyaWkVPE3GT4Onubjvdrg4wTClnFC9mv6ythJ/WWssHw33IPzV75xnuP3vLZV4es0BAMAts4biijNCV804XWBD+UO47XnPXAZPXj8Z8ybYX55cmNUTvgTUoALzq7LRyEjrYRq7nd4/4LVt1f5klI9v+69SmvWrsX7SfcOfPsD+Y56z3GF9euGv35zJK2YuEk2flu6G+yWjovxeaPRu50X5nrG/+olJYV5PNHtPvAvzekZ94jJ2QF7I/dANM0cY+ugFq04szsvA8dp2f5woeiXCqu1H/An9R6+ZZFrVFYu8rK6LD09ePxnnju5KHMTaCHv6iL6mMdCV+HOCngivsja9h3X9iHYebjDcbtVifbkbjBtYAHgTUOMGFti8Nt3TqxVH94t+9konKJ042F9xVzpxcMjHFmV1DXUsCnL4t+gLp/mPexZ94bSw1uGAcpH6QG0TzihJvlmu46ns/vmorGtF6eOempflC8/HkELPBxTpsYbdPYOTTSzv5rPwJJzqtdtElGD6TmnT/q6KkcEF2a7faelf/P3ysgxxtDuG8pomQ6xPgxuJ4uyeWFKahblz50b9HGaG9s01jVPJ6p1HDfFNs0YBCDzpTu/RwxCnwtXCZBLN33FJYY5rqmWs8rWZo/yN1782c5TpYwqyMlFR3+yP4+W/zhmBgS2efjtztf5QqtOH5GNX7VF/nCihkj6eRNQ2w32xbksD87v2TeMG5Fs6XDhUZZg6XDDejdG7kxvmbmr66EKs2GnNEC19uHqky53o0smD/NUfTpjQJFJWXoizw48vnehPQP340okhHzumfz5qyuv9sZkLxvWP+Pul7GCDIb7izIh+POVlp6chK73ruDArvQcTSQ4R9acgpfx6qNtEZJ1U6YFTUdfsr1gJNd5dPbCJ5SCnuqndNHaSwpx009hp9Cq1N7ZWGSoNLp08OOIrTr5+VQdqu5rNHqhtDtrHqqh3JnZ5qyyKeifn3wgllyevn2y4/aPntxr6O/1KWw4A1509zJ+Auu5s82GrV08rwafex1w9rcSy9Y3W9+dP8A8n+v78Cd082r1CzfCoz9hYXtPkP8G98Zwh3Z7kusW808JLllx/9jDLElDJyO3J9ZLCHNx+4Ug8sdKds45FMhT4yRum+Y/Rn7wh9Ox2oeiN9XccbjTE+nJeZIuefsyqVkoBnpElY/q7u4rPyZgGpKSmf8E89+E+Q3O7r54X/Iqtk6zdXW2Irzgz9AnFhEH5pnE8tHR0+hMLZffPj+nqwucH6w1xsJ1rX2X2OjWOFyt/x0i55SBUf0/0ae+jec/Mqhd217YH3O97f+aM74+13quQVg+tIYrGxH7Z+Oxoiz/+x3dnG5brfxdPvL3Dn0Qt6Zth+l0fzonRRRMG+JMdF00YEPX6W8Ut32OxCtW4XJ+xUTW8KNewLbR0dPormRK9z4nVD+aHN7yodNIglD/sSVZFU7V1+4UjARzq9nGpruz++Tjn/rfQ4J1oOS8N/jjeKuuaun9QErBq4o1QLQNW7KzFCm15KnynxirY+YL+napWSgHAmP69XfW96zYJeWeFEOMBTAJwQEr5USJekwgI/IJJV3ropKclvhRT70fx93X7/Q1ef3jRaPznOeZXs2uaOkzjYDKVZryZIRrzWqFWqRyqbWpHdmH072m8K5JaOoxHXR2dpwyxvty3fVj5O8abnnT99fLPDf3AflAa3slBrMymvde3//e2H8EPvVVSj10zCRd4k0bR9kpx0pCF5QuNiYZr/7Aax71/ugXpwIvf7lruxuEhyUTtJWZ1z7BR/XL8CahR/bpPxJb0zVESUNFf3U6VhI+bTBycb4jbT3Q1xx6sVfJW1rUY4liGh8fLxnvn2Tpj4eVnDEZlGRNQ3clOT0N+Tk80eHvCqbHV9Aqd/XVd1cpnDu6NR77sGUP2zb+sxf7jnuOtMX0z8Or35hh+Ll6zepLz/fCiMXjsnV3+eP6krgsoiTpWWn3HHCaf4syyd1cIcR2AbwG4S0q5Xrn/YQA/Um6/DOA6KaVFrQcpWYQzhCdWegIq0fShc7mZaYbYqqF1Z5T0SeqTj2hnSQp1UPPz17b7KwZ8fK9xUGl8ffB4q6PLnvWdZi+lL1KvHolLupoNRdG3796ZvQxxrNu/k0669RPG/Ow0HPcmOPOz0+J+Qll2/3zM/MVbOBZlHlf/Pp7/63dR4T1hKClIw1s/iH2aaaeI59/zmAH5gLe5/pgB3VejThpc4B+WNGmw8xsPU/j0KolQs7BuKK81xHYloL45cxi+PH0oAOB/Vu7wD6O8ZeZQS1sB+Kabj0Tp46uxpNTdfYYSZVjf3qioPx4QWy3UZ7i5qtE0gTBlWB+e7CcJ9cLb/22qwFPvl0f8HL7kky9Wb0cj3POFRI+sSHVW/sXfAGAKgM2+O4QQMwDcAaABwOsAzgFwNYD/BPC/Fr42JQF9B1Ss9HAp7p1pyQ5KPaB38sG93uOmqa3TEAfrgeM2+tUyfcpZp4x3r2/pMI3dwK4r1HZeGXeiUf3ysP94nT+Ot+z0NAzIy8Kx6tbuHxzk51X9crNQcbzRH/MzDc9VU4f4q1yvmtr9bKRj+ueaxqkkVXoeqtPU61PW6xVQdvnTB/vxpw/2B9x/Rom1x08lhTmGHlludfbQPPzllnMN9zmhmueha870J4fU2AlKCmP/nlNngr7vn1ux5bBn6N8ZA3L9cSrQZwb+x4YKPLFyDwDg9gtH4cvTPUO64/X5s5qbwmXlEeQkAFullOr11q/BMzPetVLK5UKIPgD2AVgAJqCoG/EYRqY2lHNCc7lgvZpCDUF67J3d/hMan0RWfejD1A4p1UGHjrcGXDkIdaIaaif42Nu78djb9v2eQNfv2q4M1WsPMVTPic4e2RfAbiVOjHCuOoWqAHCyaHqCXTp5kL+yRR8eqB48A8BXnvoQzd5NLqcH8Ldbz8OVv/8w4vU8bVBvfB5lAkpXOmkgPq5s9McUnkivql5xZkm3Pf4oOajfkS0dnYb9yn5lhtb9NU2O2+fEY0ZWtyefAKAgu5ftn40ZJ1d3jBsQ+7G473g+Oz0NE0vy/EknNU4F+kXaoX2zDLHTqvdvPKcES9dV2L0aAKIfWUHRsfJbsh+Atdp9FwI4KqVcDgBSymNCiNUAzrLwdSlJWTXTmcppXzCj+uWaxk4W6mretYvXB9znpPc7Uma/623PbwGwxXCfk3/H04cUmMZOEGrGKCfbdaTREJ9R0qfbn5k8pI9pDCDg54f2zfInjob2zQrr+c1cN2OEf8hMrJzUX8tNnLbPIWcKtV99o6wGb2jL7d6mnHABz4nGDwwcZvvoNZMM7ST++1/bUO8tpM5PBxZ/fUbc18vJ30OzxvaL+Tl8fz/lD1+GMf3zABwEAIzpn4flC0cZHvvt59Zjd63nAxjdNx1/+Gr833+75CltDtTYKYoclgylxLEyAdUCwF9DLIQoBDAOwD+0xx0HEN3RNKUUJ1+xsYpVM2eQUbAKFb08+dVPKvGot8pq0cWjceUUzxAZJ5WnhxJOJY6TDzzduv0fV4ZhHg9zSObYAXlh/64FOemANwFVkBN9Ys7KhGMqfB+TM+w52mSIk3UInlu58Ts7UWaOKQ6475ppw3DNtK4JZv6+br+/mnR0v96YMbIoYevnox8L3fGPzVi7z9MX6twRBXjky4kZphePbUmfbVLvnzayuDd219b6Yyc2+LdKvjIZjBrrE8J8+9kN+OhAAwDPMNI/fG06gOgnhAnXwAJ+t6cqKxNQewGcK4ToJaU8AU+vJwBYrj1uAABrLslSUnPyiTM5mzpcoaWj05+Y0cuP+ygn931y0uNenhzN0K1Q3DQ7XzJRZ69SY6uUThqIteX1/jha6ndoNNOcB3suIivpPQ0rlJ5HFXUtGNUvOXoeUvILJ+k/ql+OPwE1qp89Q6L0Y52SPplYu68rLinMCUhS3PHiZqzY6UnczB3bF49ce2ZC1jVSBUqiRY19hvbNAVCrxMkr2EgS/Tu0SKlAL8pNT9h3rF4NTqnDyrOVJQCeAvC+EGItgJsANAF41fcAIUQagGkANlr4ukQJlYgGqYGNBCvxxEpPpc7tF47Gl6d338w2XvTp5VfuOIKH3vQMzbnrC2Nx4bj+YT9XYEVSFR592zPjxaKLx+DKKdHNfNjacdI0tpvVU2vvrW4yxE4b35+sqpS+Z1XHWy3vzzJ1WKFpTJSMQl1lX/TSpwH3MRFKgOf4a+tHnt54+qydZ973Fny1qekANt8/P67NwCPZJs8YWogXNx32x06gNgL3xfqxbUlhliF2aiK4u1mgrzhzCJ7+4IA/Tmbhzgw8un8eUFbTFSeIPvkCpQ4rE1B/hqfn07UAZgBoBvBNKWWt8pgrAOQDWGHh6xIlVCKGBwRW6qQZYjsTDaFmuXjozZ3+ZJRPqJ1f4O/ZyxBH+3seVBIEB4+3Bn2ejLQepnG8tCrVMq0WVM6s3nnUEF8QQfKPoqc2y/35a9sDmufGeoLc3QE0Rc7q6kNKTk5r9k3Bqcde+ueU2RPwXXvK7OlZriepHvj3p/jbBk+voK9MH4R7rzjdv6yyrgVZ6V2T3zz57na8+LEnaXTtWQNw27zxUa/36H69TeNw6b/H1/+8Fh/t9w6fGpaHv3zjXLMfC0ltBB6sKbhbJw3RZSmTGmVZNMGRXXyVzbHu084aXgjPQCZfHH++Yxx9e951pNE/6cqr3znP0G/OCTNKkjUs27NKKTsBXC+E+DE8Dcm3SykbtYftA/BFAOusel2iRNt9tNEQ2zF+n4z0k4Z6pS9PfUtH0JMK9bNTY730fNX2I4YmonPGR5fo2VZ13BBH2lxa/z0OHmsxxDx5olQUTlVqNI3jwzG+Xza2H23xx7/8sjOHpTjZCwuMTYDfLjuMp9fsBwDcMmsYLp4wIOrn1k9uujuB0ZczEexOJX2z8Zn377Kkr6fKQt8fTh1W6E9ATR1WaFiuVyePKs4DcNgfx3IRcMbIopi2K/33mD2mnz8BNXtMv6j2+6cpszCrsWqwMoRrsEUTA9khkn6MbhFrRf3EwfmmcSLo26ueIORxbHKy/FOVUu4HsD/Iss0ANlv9mkSJ1NF5yjSOp96ZaaZxsilWmhsXR9DoONRJxYK/fhJwn+/gI1h5sn4Cq88kEm7Vm54Q0redSBNGbpspicgpdh1pMMRWJaAmD8nzJ6AmD8mz7HlTyXUms6f6PL1mvz8Z5RPJ9xpPXlLT90rH+ff93ysdZ/qYMcpQozHdDDvKVY67ch12DDa8KNs0jkQ4Q7WmKpUxU+NcJeNLHPuOkf78/h489X654TGLb5jiX49U/zs/VN8aUwLKSRPCqJWHakzJJS5/sd4Z8M4CUARgv5Tyw3i8DpGVgl1F1xukNrd3GmJ9eTyG5AWr1Ek24VyFs0OGckUmI4KS7VAJo3gM3aLYLV84G4+//Tne+MzTD+HSiUX+mOJPr1h5t+wQbnt+KwDgyesnY96EwKbsdU0dhtjsO7ixrdM0jtWA/GzTmIjsM2tsP9NYpQ7tUWMzQ/pkm8ZOMLRvrmlstUQmKXwJJd//Zr2C+uc7tw9VolU3tnX/IJdgAio1WJqAEkL0B/AEPDPg+RqqLAXwoXf5twE8AOBKKWXwRjJENqg61mqIfTu2UA1SH317Nx59e7fhvnjsoMNtJOh2Tv09u5tVhZKH3uOMyafE0q9kjxuQb4jNrnQfqm81xGMH5AVUF1Ypw1WrLByuOrRvtmlMRPaxetbOkcW5prETRJJIcyuzini392+yUmNbZ9K0YFDX262/A3XPsk9WCFEET6JpBIBPAHwA4Lvaw14B8FsA1wBgAooc5UBtkyFOpqEUbMAbm8F9skxjig+9CuaKx1dhT107AGBUYQb+vXCODWtFTqFXnVY3thtitZrVzNMfHPDPguQT7clqJMN4iCi4V79znuG2rxFxsOWxiiRJ5dSLY4D1yTYnMmt6zhnUuiRTRX0qbM9kbQXUT+BJPt0npXwAAIQQhgSUlPKgEOJzAOdb+LpElmhQhmU0WDhEg9zPSePjU4GeIO2Xl+lPQPXLy4wogao3lP/py1vxelk1AOCyCcX4+dWTQyYryF7qSYYvDvV5LXrp07ivk4ozFhJZo7uLfsl0UZAiw2STO6gXDxc9vwlveI+1Lp1QjEevn2rXapEDWZmA+g8An/uSTyHsB3COha9LFBX9Kvqx5q4+Ive8UoYhfbITPhtEvKiluS0dnayAIleZODgfa8vr/XEk9B4RA5UKtoF92EOCiCiewpmlkigUVsW4Q21TVzVyuzLpTnvnKcMyAMgu5HlIKrPy0x8I4NUwHtcGIDkHKZOrdFf1cOMzGxO0JvHX2nHSNKb4snp6cX0Iwq+WfY41e44BAGaN6oMfXXJaDGvrXCOUnhsjIuy/saXimOH2iVPSEG+pOIalN03z3zdxcD7W7q7Bbc9vAQA8ef0Z/pisEcmQ4ESfeOjDP5/7cB8eWrYTAHDXJWPx1fNGJGxdiKKhV33+z8pdAUNOEymcSQLMnDu868LDucOT42JgstO3PXX2ulvPH45vnD/KhrWiRJn9yCrT+9/dWYt3tWVMKKY2KxNQ9QAGh/G4MQAOW/i6RAnhOzH55RufYem6SgDAjecMwY8vnWjnapnSmxG2njhpiGNpVqifoH1adRzX/dEzjfYL35qB0wcXRLq6MdPX6YOdR/FN7xTMf7phCmYGmQUn3vQZC3cfbfTHI4pyIp7RUB+C0C+3lyFO1iEKX5xagnteKfPHkdD7iKiWrq3A0rUVhvvKH74MeVld76sakzV2HWk0xE7abvXvwkEFmYaY1aPkdHqCJz+ra+KMr0wfhOvOHu6//dibn+P9vcYkvdXKa5oMcbjTxd952UT/9/edlznvOIsC6duePoSa1W9EBFibgPoQwGVCiIlSys/MHiCEmAlgMoC/Wvi6RAnhO/FoVZI3rQ4dzuarLjBT+pvA/v+RXIkIOEHLzzLEdrwf+mvmKTPV5WWnO+YzykjrYRpHK1P5vTId8jvGA8vvk8um/XWG2EkJKF2/vCzT2I30RP3/barwJ3Z/cdWEiJO7ZJ3lC2cbbv/u3R3416dHAQD/cXo/fHfeuKife3hRVxLg3FFFhr+3sQN7xz0B1dB2wjTuDvuruV9eZpppTESpzcpvg8fg6QP1LyHEAgAr1YVCiFkAngXQCeA3Fr4uUUJsqTjm6BMlu2Sl9zSN7ZSpTM+b6aCpeot7Z5rG0VKvJvLKonUKlASmGpM19IoIJzt9SIFp7EZ6In5In2xD7JREfSrSq4KmDCv0J6CmDCsMu2rITKgkqlodZRW9v+aBuhZDrC/nvit55SnbV14ctrVks/qOOYbb/9hwAE+s3GvPyhDFkWVHG1LKNUKI7wP4NYDlABoBSABXCyGuAFDovX27lPITq16XKFGu/P2HKH/4MmQpB+lZPGA3nLQ45QRmsNJoWo3tZnVibN6EAf6Dk3kR9pNKFXpPCr2/07mjA4dBOnX7cSt9yG9ja4chjmVIMEVvoFK9qsbUZfidrwPovleZ1Ypy003jaIzql2saA8DAAuuTP6H6az6xYi+eWGE8oWaVU/JSJw1Jlkl94qmkMMdwe0z/yHpeWk2vmn3ynR2Gnl63XdRVmRlq5AWRztK9qZTyCSHERwDuBDAXgACQB6AdnqTUg1LKwPE/RC6xpeIYIETXHUIENDlmlZT9nJgUAzw9oaw82OYQhe7pV9f1/k5mV9+LcjOT7n3VE3G/futz/G3DQQCevjA/mB+/BvahDkxf3nIUL28xLnfSe5/Mwz8LlcRGYYxJjmSX6NljrRz6Ger7bEz/6CuriLqTjPvSRLK7akz/ztOrw510fE3uYvmWI6VcB+AqIYQA0BdATwA1UkpOvUVkSZE6AAAgAElEQVSOUnb//Igz9npD42BNjFOJE0/QnLhORHbSE22eE8+D/pjDYFKPUxP14YhkJkUrRDJ7mxX6KCd6feI4DJgXzIicy0ktJABg/MA805goUnHbY0spJYAas2VCiDQpZafZMqJEcdsBdyT0stkVZYfw3ee3AgB+d/1kzJ0w0I7VIiKHsHKID7mTmxP1lUpfocq6lph6JIXjUH1r3F9DNXZAnms/GyKyxqACZw2NHlmcaxoTRSqhZ+BCiEwACwD8AMDwRL42USpr6zxlGhOlmmh6UujD1/69uQo/f207AOCnl4/HFWcOtm4FE2Ro31zTmMgNWk+cNI3jpb3zFHuVEVFClRTm+BPRvn50duqbm2EaA4HHSUve34M/ePtFffv84bj5/FFxXz9yD0v2nkKIdAB9ANSaVTYJIXoD+A6AhQCKrXhNIiuoX5hVx1oNQ+wWXTQa158zLGRDTacKNbRw0UvbsOilbYb7eKWVUkU0PSn0oTd69ZAbh6+x0Tq52fGWDtM4XhY8tyngvmTZb/LEkYjCEapqVj8OyleGDudnu/M4ieInpgSUEGISPLPeXeB9LimEeAfAQinldm8fqB8AuAueBJUAsAnAz2J5XSKrqF+I+tXMnMw0fmESUYBkmFqazWHJrVo6OtF+oquSt/0Eq5NioR/nDC7MNsQ8DiKiSLFfFIUS9R5aCDEcwBoAveFJLMH7fymAVUKIswA8C2CO9/7NAH4mpfxX9KtLFD/Z6WlYfMMULPjrJwCAwd6x1/rVwb+v34/H3t4NAPjhxaPxnzOGJXZFichW+kwwRJQ4eoXvgr8mb3WSHfIy00xjIqJwxeM4Se9v+8bWKv+IjkevmYRLJ7uvHUKqimXPcieAPADvAfgxgM/gSUZ9AcCvAKyGp89TDYDbpZTPx7SmlBQSPXNNpDKUGSd8sX71LzcjzRDz6iBRauHwNSJKVslQ4UlE9orHcZJ+zljcO9MQO+2ckoKL5ZOaC6AawH9IKRu99zUDeEYI0QHgOQCdAOZIKctiW02ixGhXmpm2B2lsylkgiFIbh68RUbKKZqIGIiJVIo6TWI3uXrEkoIYAeEdJPqmWef9/n8kncpOGthOmsWpgfpZpTEQUL3rp+c9e2YIXNx0GAMzqB/zXpExcMOcCO1aNiJIIE+xE5AZZyqgVNSbniyUBlQnP8LoAUspaT/9xVMbw/JSEapvaDXF2ob3lknrj0tqmDkNs1ti0UJkBS42JiOJFLy0fkN/VKLggC8hIEyw/J6Kkp7ZyeGpuJjLSRDc/QUTJaIgyYYIak/PF+2j1VPcPoWSmJ3BalWFtrSdO2j5zjd7MVPXQsp14aNlOw33lD1/Gq4NEZLs+OV3Jb+bBiYiIKJVkp6fxfMylYj3bHy2E+Fo0y6WUz8b42uQCoRI8pb9ZHXAfv0iIiLo3dVihPx7Tx3mVT2qVAhGRVSrrWvxxTetJDO7tvO8/IiIKLtZv7Znef8HMCrGcCSgiIqIojOnf2x8PynXeCZhe3UpEZIVjLV2tEpo6ONCCiMhtYjlqXWrZWlDSWn3HHMPt7Yca8M3nNgEA/vTVqRg/MM+GtSIicje19HzFihU2rw0RUXzoyezdR7vmPjrYdBLD86XhMeyFR0TkbFF/S0spb7JyRSg5zX5kVdBlvkSUikPwiIjcr06Z0IEolI33zjPcfmvbIdzzimcC5V9cNQHzJw3EtAfetWPVyAFCDeV9bgfw3I42YEXXY3gcSUTkbLxMQERERDHRqxQO1bfatCbkNqGSS/e8UuZPRhEREZH7MQFFcaUPwfv8UAMWeCufFn91Kk6zeQiefuX17+v347G3dwMAfnjxaPznjGF2rBYRkauw4TgRERERdYcJKIqrUEPwFjhgCF5RbqbhdqEytXlhTnrAciIicpfVd8zB0g/24ukPDgAAbpk5FDfOHGnzWpHbDb/zdZTdP589h4iIiCLAvSaRIi8zzTQmIiJ3KinMwYjiXP/tEcW5KCnMsXGNKFm0dHQyAUVERBSBHnavAJGT9MvLMo2JiMi9eHGB4qG146Tdq0BEROQqPAqjuCq7f77h9ppd1YYeULPGFNuxWkGN6pdrGhMRkXsN7ZtrGhPFggkoIiKiyDABRXGlz4yU0auHIdaX213KXpSbySl8iYiSjN7fj8gKx1o6HHccQ0RE5GTcS1JchZpe+cYlGwPuY/KHiIisVlKYw/0LWe66xesD7uN2RkREFJzlCSghRBqAywFMB1AEYL2Ucol32SDvfWVSys7gz0Ju1NLR6Z+KmzPDEBEREREREZGPpRkCIcQFAJ4DMBiAACAB9AKwxPuQeQD+AuBaAP+08rXJfmoZOmeGISIiIifR+1Ju2FeLG58JrMYmInKSgJ66O49iwV8/AQAsvmEKZo3tZ8dqEUXFsgyBEOJ0AG8A6AngtwA+APAP7WH/BPAHAF8CE1ApIfBgrw43PrMBALD0pumYPqLQjtUiIiIHuGXWUPy/OWPsXg1KEfqFsYJs9gMjIufTv7vyle+u/Ox0XvQnV7Fya70PQAaAUinlCgAQQhgeIKVsEUJ8DmCKha9LDqHOBhN8ZhgZJCYiolQzfkAeinIz7V4NSlFsSE9EbpTZq6dpTOQGViagLgCwzpd8CuEAgIstfF2yiT7zi56AUntCmTEre2fzTiKi1DGmf57dq0ApLCudJ25E5D6c2ZXczMoEVB6AqjAelwHPMD1yuVDJpdLHVydwTUi3+o45htvr99Zi0UufAgAeveZ0zBjZ14a1IiIy4oEz2YnDVojIjfrmZpjGRG5g5Z73EIDTwnjcJAD7LXzdqAghcgBcDeBsADMAnAEgHcBdUsqHu/nZIQDuB3AJgEJ4qrqeB/CglLItnutNFI7Zj6wKusyXiFKx8oyI7MAKFLJTdnqaf/83/M7XbV4bIqLwqN9dRG5jZQJqOYBbhBBflFL+n9kDhBBfBzAMwKMWvm60xgB4NtIfEkKMArAWQDGAbQBWA5gG4CcALhJCXCilbLdyRYmIiJIJD5yJklPgbF3VWPDXTQCAxTdMxayxxXasFhEROYSVCagHAVwP4O9CiF8BeNV7f7YQYhKAqwDcDaAWwK8tfN1oNQL4M4CPAGyAZ2a+e8L4uSXwJJ+ekFJ+DwCEEGkAXgTwRXh+x5/GY4XdbvUdc7D9YD2+6Z029E83TMH4Qfk2r1Vy0ofg7atuwte8PbeevWkaRhTn2rBWRERERssXzjbe/uwQHn17NwBg0cWjUTpxIIf1u0jgbF29DDGHPRIRpTbL9gJSyv1CiMsA/AOeRM7d8Exz9mXvPwGgGsAXpZSHrXrdaEkp9wC4xXdbCHFldz8jhJgO4HwARwHcoTxXpxDiVgCXA7hNCPGAlPKE9WvtbvqwMF8iSsWr4tYINQTva2z+TkREDhEqufTo27v9yah423jvPMPtf2+uws9f256Q105mfZTp4tWYiIhSk6WXIaSUa4QQYwF8A8BFAIbD03C8EsA7AP4opTxu5Wsm2OXe//+tD7OTUh4RQqwGMBfATACrErxuRERERBSFotxM7TaTJVYoVN7HQr6nREQpz/I6WCllI4DHvf+SzRne/zcFWb4JngTUGWACimyk92CorGtF6ePvAwCWLzwfQwqz7FgtIiIiR6ppMs4h09F5yqY1SS7qkDsOvyMiIu4JIjPU+39lkOWV2uOSml6uvml/HRY85xlWt/irUzB1WCGmPfCuHauW8vSDvKz0HoaYB4FERERdeLwSH5yti4iIVHE5CxVCDAUwEEBGsMdIKd+Px2vHma9zc0uQ5c3a40ISQnwWZNGo5uZmrFixIpJ1S7ibl7cGXeZLRKkevyADBxpO4tefdAIAfjAlDUPzjFNwx/o7Nzc3W/I8yaa9U/rjbRvXYVeasHFtkhO3PbKLG7e9RK6rm94XN3Li9mfnujjpfUhFfP8pEZz4vUepIdi257vfDSxNQAkhvgFPA/JhYTy8Z/cPCflaLwGYFOGPfU1K+VEsL+v9X3aznEwsfM/QNsubiOo03LeklEPDiIiIiIiIiJKNZQko7yxwv4MnCfMJgD3oqgiKh+EAxkX4M9kxvmaj9/+cbp6/KZwnk1JONLtfCPFZTk7OhLlz50a4egm2/HXLnzLW39mXDXb8e5dgLR2dwIq3AAAXzLmAQ/DigNse2cUR216E+4OY1jWRr0Xdinn7c8ixxOozjYesWw4cx3ef35yQ16YQ+PdODuSI/S6lpGDbXk5OsPSE81h5FvoDAB0ALpNSxr0eUUo5Ld6vYeIAgCkAhgRZPkR5XNIL7AF1HAue+xgAsPirZ2HqsAL2VHAI9mAgomShT7KwrfI4rl28HgDw4oIZmDSkwI7VIpeb/cgqu1eBiIgo6VmZgBoCYGUikk822gLgSgBTgyz33b81Matjr1DJJV8iioiIyEp6BWdGr56GmBWeRERERM5k5VHaASR/D6TXAdwH4AohRIaU0t/USAjRH8BsAPUA1ti0fo628d55qDrehit/9wEA4NXvzsTggkyb14qIiNysMCfdNCZ30Kup91Q347o/rgMAvPCtczCqOCch1dT6eizfdgh3v1IW99clIiJKJVYmoJYCuEMIMVBKecjC53UMKeVHQogPAMwE8EsACwFACJEG4A8AegF4Ukp5wr61dK6i3Ey0dpz03y7M7oWiXCagiIgoelnpPU1jcgf9OKCuqcMf98lK3HECWwYQERHFn5UJqF8COBvAMiHEbVLK9y187rgQQvwfgIHem77+Td8WQlzljQ9JKb+o/dhNANYC+J4QYi6AMgDTAYwEsB7AL+K71kREROSjDrnj8Dv3M0soLl842/CY17ccxG9X7gEAfO/CUbjsjEGJW0EiIiKKmmVHalLKk0KIbwF4F8BKIUQHgMMApPnD5SirXjsGUwAM0+4r8f4DgP36D0gpdwkhpgC4H8AlAL4IoALAAwAelFK2xW913Y9XqomIyEqcZCG5mB0nlD6+Oujjf7tyjz8Z5RPN9qAnuTaW13IIHhERkcUsS0AJIU4DsBJAMTy9oDIQmNxxFCnl8Ch/rgKeSigiIiIicrlQSa5g7r5kLL4wmdVXRERE4bKyVv0xAP0A/AXArwHslVK2WPj8lASKcjN5pZqIiIhMuek44cFlO/Hgsp2G+9yy7kRERHawMgE1E8BWKeXNFj4nOVjZ/fMNtz+tOo7r/rgeAPDCt2bg9MEFdqwWEREl2AsLZhhvb9iPlz85DAC4esoAXDfd0QXRRERERJQAViagOgDssvD5yG2kMI+JiCipXbd4fdBlL39y2J+M8mGVCEVi9R1zDLffKTuCn7/2OQDgp5efhosm9LdhrSgRNt47z3B71fYjWPTSNgDAzRMEJhenY9bsWXasGhERRcHKBNRKeJp6U4qYcN9bQZddt3hdwH084SAiIqJIzX5kVdBlP3/tc38yyofHG8mjKDfTcLukMMcfF2f1QF5Gj4DHEBGRc/Ww8LnuBNBHCPGIEMLK5yUiIiIiohTXJzvdH+em83SDiMhtrKyAugHAawB+COAqIcQqAFUApMljpZTyvy18bSIiIiIiSmJZ6T39cXpPtnsgInIbKxNQP4Mn2SQAjPb+C0YCYAKKiIiIiGyn95lav7cOi17aas/KUFBqAiqDCSgiItexMgF1k4XPRS6gz4JX29Tu79Ow+o456JubYcNaEREREUUmVJ8pco6i3Ex/j68VK1bYvDZERBQpyxJQUsqlVj0XuUN2unHzaUnv9MdZ6T0DlhMRERERERFRamKGgIiIiIgcS6+4/rTyOK5bvB4A8MKCGTh9SIEdq0VEREQRYgKKLKNWPLH6iYiIiKygH1MMKsgyxDzmICIicoeo99hCiBXwNBO/UUpZ6b0dLimlnBftaxMRERFRalIbUasxEREROVssl4zmwJOAylZuh0vG8LrkUNnpaf7GkERERETxwIprIiIid4plrz3C+3+VdpuIiIiIKKnddclYXDp5kN2rQURE5BpRJ6CklPtD3SYiIiIisppTKq4fWrYTDy3babjPCetFRETkVD2i/UEhxF4hxC+tXBkiIiIiIiIiIko+USegAAwHUGzRehARERERERERUZKKJQFFRERERERERETULSagiIiIiIiIiIgorpiAIiIiIiIiIiKiuIp6FjyvM4UQ90Xzg1LK+2N8bSIiIiIiIiIicoFYE1BneP9FQgCQAJiAIiIiIiIiIiJKAbEmoPYA+MCKFSEiIiIiIiIiouQUawJqjZTyZkvWhIiIiIiIiIiIkhKbkBMRERERERERUVwxAUVERERERERERHHFBBQREREREREREcUVE1BERERERERERBRXUTchl1IyeUVERERYfcccw+2nVu7E3zYcBAB8Zfog3HrhWBvWioiIiIicJNZZ8IiIiCjFzX5kVdBlf9tw0J+M8il/+LI4rxFRZMrun2+4XVnXitLH37dpbYiIiJITE1BERERElNKy042HxFnpLPQnIiKyGveuRERERESKrPSedq8CERFR0mEFFBERERFREK9+5zwU5qQDAFbvPIq7XykDADx41QTMHtvPzlUjIiJyFSagiIiIiIgU6pC8K3//oeljPImoMsN97G9GREQUHBNQREREFBO9gfMbW6uw6KVtAIBHr5mESycPtmO1iKKWnZ6G8ocvw/A7X7d7VYiIiJIGE1BEREQUk5aOTsPt9s5Thlhfrjd8JiIiIqLkxyNAIiIiism0B94NuuyeV8pwzyscpkRERESU6piAIiIiIiIyoQ8vraxrRenj7wMAli88H0MKs+xYLSIiIldiAoqIiIhisvHeeYbbVcfbcOXvPgAAvPrdmRhckGnHahHFTB8umpXewxBzOCkREVH4uNckIiKimBTlBk8wDS7IDLmciIiIiFJDj+4fQkREREREfXMzTGMiIiLqHhNQREREREREREQUVxyCR0REREQUhuz0NM7iSEREFCUmoIiIiMhSRbmZPEknIiIiIgMOwSMiIiIiIiIiorhiAoqIiIiIiIiIiOKKCSgiIiIiIiIiIoorIaW0ex1II4RoyMjI6D1q1Ci7V8V1mpubAQA5OTk2rwmlGm57ZBdue2Qnbn9kF257ZBdue2SXYNvenj170N7e3iilzLNjvSLBBJQDCSEOA8gGUGH3uriQL2u3x9a1oFTEbY/swm2P7MTtj+zCbY/swm2P7BJs2ysB0CKlHJDg9YkYE1CUVIQQnwGAlHKi3etCqYXbHtmF2x7Zidsf2YXbHtmF2x7ZJRm2PfaAIiIiIiIiIiKiuGICioiIiIiIiIiI4ooJKCIiIiIiIiIiiismoIiIiIiIiIiIKK6YgCIiIiIiIiIiorjiLHhERERERERERBRXrIAiIiIiIiIiIqK4YgKKiIiIiIiIiIjiigkoIiIiIiIiIiKKKyagiIiIiIiIiIgorpiAIiIiIiIiIiKiuGICioiIiIiIiIiI4ooJKCIiIiIiIiIiiismoCguhBCrhBAyxL9LwniOd5THDzBZPqeb11gX4rmHCCGWCCEOCiHahBA7/3979x0mWVUmYPz9YHBGgqSBQQV3UFgVMGBkUNcBs4AKiLBmMS9rgDXsY1oRs7gr6q45g6iAggi6mEZQwIQBMA44sCCIZEQYFL7945yS8nKruqunq7qn6v09z3kufcI9d7o/bp0+fe65EfGmiFjUp82iiDi01r2xtv14RGw9/e+MRmHQ+IuIN05R/+19+to1Ik6OiCsj4k8R8YOIeNYU12f8jalRxJ73PrWZ6eduRCyMiFdExI8i4tp6H/t1RHwsIu7co809I+KYiPhjRNwQEWdHxMER0XNcGRGbRMR7IuKCiFhdj0dExCZ92qwTES+v57+h9ndMROww+HdIwzKK2IuIpVP0cWmf6zP2xtgMPnf71e2kb/XoyzGf/mYUsRdjOOZbMFsnkno4DvhTS/7F/RpFxLOBRwAJxBR9nAd8t0d+27nvBpwBbAGcA5wGPAB4PfDIiNgtM1c32iwCvgnsClwCnAAsBZ4D7BkRyzKztT/NqUHj73vAypb8H7dVjoi9gWMok/mnApdT4vaTEXGfzDykpY3xNxmGGnuV9z61mXbsRcSWwDeAewGX1v8G2A44EPhEs11E7EKJifWBHwCrgH8C/hN4SETsl5nZaLM5Jfa2B84Hjgd2BF4KPD4idsnMKxptAvg88GTgauAkYDGwL7BHjdfvT/3t0AgNNfaqPwBfa8m/pu2CjL2JMt34+1Sfc+xB+Vmf1ixwzKc+hhp71fiM+TLTZJr1BKygTB4tnUHbLSg39f+lDGwT2Kql3vJa9skBz/+d2u6IrrwFwBdr/qEtbd5Uy04HNuzKP6Tmf2euv+emmccf8MZa/9kD9LEpZWCawD5d+UuA39b83VraGX9jnEYUe977TG0/30Fjbx3K4DSBNwMLGuV3BRY38hZQJkoTOLgrf8MaIwk8p6WvT9ey47r7Ad5b8z/V0ubAWvYbYElX/r41f2Xzmk1jHXtLa/0VA16bsTfmadD463OeTYAb67m2b5Q55jPNVewtZ8zGfHP+gzONZ1qT/yGBo4AbgLsxyxNQwANrmz8ACxtlS4CbgCuB9bry1wOuqu12bjnnz2rZ/ef6+26aWfwxs0mAV9Y2x7eU7V3LTmzkG39jnkYUe977TG0/40Fjr/NL9rED9LFfbfPTlrKda9nZjfytgJtrjC1plC0ELgP+2lJ2bj3fk1r6OqGW7TvX33fTyGJvKQNOQBl7k5EGjb8+53l+Pc8ZLWWO+UxzFXvLGbMxn3tAaV6JiMcATwXeksNZXrpnPZ6YjWWHmfkHyvLETYGHdBU9lDIzfV5m/qTlnMfW416zfK2a3zqxdGxL2UmUv2Q8svGctfGnuWLsqemF9fjuAdr0vO/VGDkf2CkilnYVPY76yEqNte42q4ETgXVrPQAiYltgB8ofo05quQ5jb+02k9ibCWNPg3h6PX6mpcwxn4apX+zNxLyOPfeA0rA9tz5/fwtlKfPxmXlhW8WIWB/4IPAr4J0D9LF9RLwN2Jzy6N53ga9l5i0tde9Tj2f1ONdZwO613ooB2nTX0/wx7firdo+I+wKLgIuAr2Zmrz147l2Pt4mLzLwpIs6hPGt9d8pfDcD4myTDjL0O731qM2XsRcRGlPvTdcD3I2IZ8ARgM+BC4ITMPKfl3NOJibvWeqsGaHMgfx9Hnf8+JzP/0qNNdz3ND8OMvY4lEXEocEfKvk/fB76cmTe11DX2Jsugn7t/ExF3AR4G/IWy/1eTYz71M8zY6xibMZ8TUBq21zW+PjwiDsvMw1rqHkZZYr1bj4FEL7vW1O3siNg3M3/byL9LPV7U41wXNerNtI3mh0HiD+AZja8Pi4jjKI9H/W1zwYi4A+WvBNA/Lh5AiYvOYMT4mxxDib0G731qM53Y24GyMmQlZS+cgxptDouIwzPzVY38UcWRsbd2GmbsddwDeEMj78KIeEredmNwY2+yDPq52+1plJcefTVvuym9Yz5NZSix1zA2Yz4fwdOwnEr5hepulDfl3B14LeVZ+zdFxMu6K0fE/YCXUTaDXDHNPq4B3gXsQpkN3pzyNoozKW9V+XpEbNxos2E9/rnHOa9v1JtpG82tgeKPMhB+BeXNOBsC21A+EC6mbDraXBLb/bMediwZf2uXYcceeO9Tu0Fib9N6vBdlAuBwYFvKS0CeT3n86JUR8aJGH6OKI2Nv7TKK2FsNfICyH8oSYGNgGXAy5ReirzUe/QRjb1IM+rnbpt8jUI751MuwYw/GcMznCigNRWY2/zr1G+CtEfEjytvtDo2ID2fmDRGxLvARytslXjFAHz8Bms+ofisiHgp8m7Kc8SDgrV3l0Wne47TRJ2+QNppDg8RfrX9ko/71wGcj4tvA2cCTImLXzDy9lk/nZz5bsWT8rUVGEHve+9RqwNhbt9ZZABydma/savfRiFgIvJ8ykP5gW3c9LmNU9z3NI6OIvcy8BPiXRj9nAntExFGU/UNfA7ygq9zYmwCDfu421T+C70D5PeTEtirTuAzHfBNoBLE3lmM+V0BppDLzFOBHlL9c7VKzXw7cD3hVZl4+C33cDLyjfvmYRvF19bhBj+br12P3Iy8zaaN5qEf89at/CfCJ+mV3LF3X9d/r0262Ysn4GwOzGHv92njv0230iL3ue9jHW5p9gjII3ToituvK7/yshx1HU7Xp5Bt789gsx14/nV+8RnHfM/bWEgN87nZWoBzT3LC5csyngcxi7PXrY60d8zkBpbnQeU71jvW4F2Ww8ayIWNGdKK/QBfhizXvoDPvo6GwIt3WPdls36s20jeavXrEx7fqZeS1lSSwMP5aMv/GxxrG3Bm2MvcnWjItVXWUXNCtn5p+BP9Yvt+wqGlUcGXvjY7Zib5A+Oow99f0crU9hHFC/bK5IBhzzacbWOPbWoI95HXtOQGkudJ7/755BDeCfgIc30sJavqx+vXgN+oBbNwa8X492nfyfr2EbzV+9YmPQ+j3jIiLWA3ai7Fnx6+m0aeQbf+NptmJvJm2Mvcn2d3FR387T2ex0s2bliFiHWzfd7Y6lUcVRp81O9X46nTaan2Yr9qbdRxdjT1N9jj6C8sv7BZRX0/fimE+Dmq3Ym0kf8zr2nIDSSEXEFpRnVaG+zjEzl2dmtCVu/evYHWve8dPsat96bL7G/KR63KvuM9B9bUvqtV1DebVlx/dq3t0iYueWvp5cj1+Z5rVpjrTF3xT1A9i7ftkrlp7Mbe0JLAK+mZk3trQx/ibMLMdeP9779Hf6xF5nv4ndWprtCtyOsiH0r7rye973aozcFfhFZv6uq+hrlFdTPywitmy0WUhZBX0L8NVOfm3/S+D2wB4t12fsrQVmOfb66XXfM/Ym2DQ/dzuPQB2Zmf32/XLMp2mb5djrZ+0c82WmyTSrifKs625ANPKX1kBP4IRpnmtVrb9VS9kLgc0beVHz/0IZVNy/pV3nGt7TlXn+1SkAAApvSURBVLcAOK7mH9bS5s217HvABl35h9T80+b6+26aWfxRVtU9E1jYqL8hZQPUBC4B1m+Ub0a5USewT1f+lpQlsQk8wvibnDTC2PPeZ1qj2KtlO1De1HMFsHNX/paUwWwC72+0WQ84v5Yd3JW/AXB6zX9uy/UdWcuOBRZ05R9R8z/T0uZ5tew3wJZd+fvU/POB9eb6ez/paYSx90xg65b+9wGurW32NvYmK80k/rrqrE/Z9yaBe0zRj2M+01zF3tiN+eb8h2cavwQ8uwbp74EVwOfq/wQ31Pxzuj/QpzjXKnpPQK0CbgJ+CpxQU2dgfDPwkh7n3B64vNb7eb2+8+rXZwKLWtosqmWdf9fnu76+HNhurr/vppnFX/2gSMrA4kzgC8ApXTFyFfCQHn3tW2PtFsqbKI6p9RM4wvibrDSq2PPeZ1rT2Otq95JafmO9h50IXFnzfgxs1NJmV8prmjtx8/nabwJfAtZpabMYWFnrrKzXd3bX14tb2qwDfLHWubLeX79Nud/eAOw619930+hir577ZuBcyl/3j6OsVMqa3tnj+oy9MU4zjb/a9qm1zg+m2ZdjPtPIY48xHPPN+Q/PNH4JuCfwP5QBxGWU2dmrgTMoM6i3H+Bcq+g9AfUSyoDlfMqzr6tr/c8AD5zivNtQ3rRySW23Ejis37VRlmO/qdZdDVwKfBLYZq6/56aZxx+wEfD2+uFxEWUwfH394DgcuPMU/T2Esnz/qtruR8BzjL/JS6OKPe99pjWNvUbbR1MmPq+uMXgu8AYaK+8abXakrCi5vKvNvwHr9mmzKfBeygamq+vxfcBmfdqsW6//HMqg/nLKxMOOc/09N4029oCnUX7h/y1l0v4m4OIaD4+c4hqNvTFNaxh/J1N+x3jpAP055jONNPYYwzFf1I4kSZIkSZKkoXATckmSJEmSJA2VE1CSJEmSJEkaKiegJEmSJEmSNFROQEmSJEmSJGmonICSJEmSJEnSUDkBJUmSJEmSpKFyAkqSJEmSJElD5QSUJEmSJEmShsoJKEmSJEmSJA2VE1CSJEmSJEkaKiegJEmSJEmSNFROQEmSpIkQEdmVlvWp95SueqsaZUunyO9OqyPisoj4cUR8KCIeFRGxBte/SURcERFHD9juNtc7ChGxc+37laPuW5IkzT9OQEmSpEn0tD5lT1+D814PfKqmLwDfBzYDXgCcAvwwIv5xhud+LbAJcNgaXN/IZOZPgC8Dr4mIzeb6eiRJ0txyAkqSJE2S1cAvgP0jYkGzMCI2Bx4LnDXD81+emc+u6RmZuVdmbgvcF/gWcH/g1IjYZpCTRsQdgZcAX87MX8zw2ubC2yiTZq+e6wuRJElzywkoSZI0aY4CFgOPaSnbH1gPOHI2O8zMnwGPpqyCWgIcMeApDgQWAp+ezesatsw8E1gJHBgRt5vr65EkSXPHCShJkjRpjgKS9kftng78CThhtjvNzJuBg2rfT4qIu0ynXd036rnA1cBJPepsEBHviIgLI+LGiPhVRBzSa8+piFgUEc+NiBMi4vyIuCEiro6IUyPigJb6J9X9nB7Vp/9rI+KaiNigUXw0ZcJv7+n8eyVJ0nhyAkqSJE2UzLwA+B7whIjYsJMfEdsCy4AvAn8eUt8rgR8DATx8ms12ALYFzsjMm5qFEbGQsrLqVcDtgROBVcDbgff3OOdS4KPAg4ELKRNuPwV2AY6OiDc26n+wHp/f43wHABsBn83M6xtlK+pxjx5tJUnSBHACSpIkTaIjgfWBfbryOiuijhpy3z+tx3tOs/7D6vGHPcoPAXYFfgBsl5n7ZeZjKZNJvTZU/yPlEcQ7ZebyzDwgM5cDd6dMXr0+IpZ21T8Z+D/giRGxRcv5OhNTH2kp+yFwS9e/Q5IkTSAnoCRJ0iT6AnATf/82vKcBlwLfHHLfl9fjptOsf+96/HWP8hfX48GZeU0nMzPPAv67rUFmXpGZp2TmLY383wFvoYwR9+rKv5myYup2wDO720TETpSVVD+pfTb7ug64BFgaEXfo9Y+UJEnjzQkoSZI0cTLzKsqqnkdExFYR8UDK6p+j62TLMHX2Zcpp1t+yHq+6zYnKPlLbABdn5uktbY/ueyERD42I10XEByLiExHxSWC/Wrx9o/pHgb8Cz2vkd1Y/fbhPV1fWY9vqKUmSNAFu8/phSZKkCXEk8CTK/kXbduUN2+J6vLJvrVttXI/XtZTdqR4v7NG2NT8iNqbsdbV7n3436v4iM38fEV+hbKD+sMw8re4/9XTKnlmf7XOua+tx4z51JEnSGHMFlCRJmlRfobxZ7pnA/sAv2x4hG4L71uMvplm/81hd2+NrU62m6pX/Dsrk06nAcsqk2ILMDMreUN3n7tbZjLyzCmpfYDPg85l5bUv9js7E0zV96kiSpDHmBJQkSZpImbkaOBbYGVjCCFY/RcT2wP0om3KfOs1ml9XjZi1lv6/Hf+jRtlf+3sDNwBMy8zt1T6jOo4d37XMtpwDnA/tFxCb033y8W2e/qz9OUU+SJI0pJ6AkSdIk+zRwBWVj8KG+/S4i1gXeT1lZdFxmXjTNpj+rx3s0CzLzAuAi4M4Rsayl7QE9zrkpcF33puVdntLrQjIzKZNNtwf+A3g4cG5mntGrTd14/E7A76ZYJSVJksaYE1CSJGliZeZpmbk4M7eokzlDERH3pqweejTljXAHD9D8tHp8UI/yD9Xju7vfMhcR9wUO6tHmN8AmEbF/4zoPBnab4no+TnmD4Mspk2lTrX56YK132hT1JEnSGHMTckmSpMHd0iN/cX2THMC6lL2PduTWx9p+CDw1My8eoK9fAr8DdomIRZl5Y6P8XcCewDLgvIj4NmUD8d2BjwEvbjnn2yiPHH4uIg6irKK6D2WV1X/RZ4IsMy+LiOMpK6VWA5+Z4vqX1+PJU9STJEljzBVQkiRJ07eoHq/vUb4B8Kya9qdMCl0NfJiy+unBmblykA7rY28fpUwq7dVSvhp4JHA4ZULoiZQJr9cB/9rjnEcBewBnUjZFfxxlP6ndgS9P47K+WY/HZeZUb/P7Z8ojjl+axnklSdKYijKmkSRJ0lQi4vHAScDJmbnHCPvdirIK6uuZ+YRR9dvnek4BHgXslpkr+tRbBpwOvDMzXz2iy5MkSfOQK6AkSZKmISLWA15Uv1wxyr4z81LgfcCeEbHjKPtuiogHUVZcndtv8qn6d8oKsHcO+7okSdL85gooSZKkPiLiHpQ3vj0A2A64GNgpM68e8XVsApxHWQXV6+12w+z/7cBdKI/u3QF4Ymb2fFwvInYGzgJenZlOQEmSNOGcgJIkSeojIpYD36LsY/QN4DWZuWour2kuRMQqYBtgFeWRug/1bSBJktTFCShJkiRJkiQNlXtASZIkSZIkaaicgJIkSZIkSdJQOQElSZIkSZKkoXICSpIkSZIkSUPlBJQkSZIkSZKGygkoSZIkSZIkDZUTUJIkSZIkSRoqJ6AkSZIkSZI0VE5ASZIkSZIkaaicgJIkSZIkSdJQOQElSZIkSZKkoXICSpIkSZIkSUPlBJQkSZIkSZKGygkoSZIkSZIkDdX/A0axd7KA5kirAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5), dpi=150)\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(mjds, (tp_diff_pre - tp_diff_pre.mean()).to_value(u.ns), '+')\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (ns)')\n", + "plt.title('PSR J1600-3053 prefit residual differences between PINT and TEMPO')\n", + "plt.grid(True)\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(mjds, (tp_diff_post - tp_diff_post.mean()).to_value(u.ns), '+')\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (ns)')\n", + "plt.title('PSR J1600-3053 postfit residual differences between PINT and TEMPO')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.savefig(\"J1600_PINT_tempo.eps\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the parameter between TEMPO and PINT\n", + "\n", + "* Reported quantities\n", + " * TEMPO value\n", + " * TEMPO uncertainty \n", + " * Parameter units\n", + " * TEMPO parameter value - PINT parameter value\n", + " * TEMPO/PINT parameter absolute difference divided by TEMPO uncertainty \n", + " * PINT uncertainty divided by TEMPO uncertainty\n", + " * If TEMPO provides the uncertainty value" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n", + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n" + ] + } + ], + "source": [ + "# Create the parameter compare table\n", + "tv = []\n", + "tu = []\n", + "tv_pv = []\n", + "tv_pv_tc = []\n", + "tc_pc = []\n", + "units = []\n", + "names = []\n", + "no_t_unc = []\n", + "tempo_new_model = models.get_model(psr + '_tempo.par')\n", + "for param in tempo_new_model.params:\n", + " t_par = getattr(tempo_new_model, param)\n", + " pint_par = getattr(f.model, param)\n", + " tempoq = t_par.quantity \n", + " pintq = pint_par.quantity\n", + " try:\n", + " diffq = tempoq - pintq\n", + " if t_par.uncertainty_value != 0.0:\n", + " diff_tcq = np.abs(diffq) / t_par.uncertainty\n", + " uvsu = pint_par.uncertainty / t_par.uncertainty\n", + " no_t_unc.append(False)\n", + " else:\n", + " diff_tcq = np.abs(diffq) / pint_par.uncertainty\n", + " uvsu = t_par.uncertainty\n", + " no_t_unc.append(True)\n", + " except TypeError:\n", + " continue\n", + " uvsu = pint_par.uncertainty / t_par.uncertainty\n", + " tv.append(tempoq.value)\n", + " tu.append(t_par.uncertainty.value)\n", + " tv_pv.append(diffq.value)\n", + " tv_pv_tc.append(diff_tcq.value)\n", + " tc_pc.append(uvsu)\n", + " units.append(t_par.units)\n", + " names.append(param)\n", + " \n", + "compare_table = Table((names, tv, tu, units, tv_pv, tv_pv_tc, tc_pc, no_t_unc), names = ('name', 'Tempo Value', 'Tempo uncertainty', 'units', \n", + " 'Tempo_V-PINT_V', \n", + " 'Tempo_PINT_diff/unct', \n", + " 'PINT_unct/Tempo_unct', \n", + " 'no_t_unc')) \n", + "compare_table.sort('Tempo_PINT_diff/unct')\n", + "compare_table = compare_table[::-1]\n", + "compare_table.write('parameter_compare.t.html', format='html', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print the parameter difference in a table.\n", + "\n", + "The table is sorted by relative difference in descending order. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=125\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
nameTempo ValueTempo uncertaintyunitsTempo_V-PINT_VTempo_PINT_diff/unctPINT_unct/Tempo_unctno_t_unc
str8str32float128objectfloat128float128float128bool
ELONG244.3476778440795.9573e-09deg-5.921065165948036224e-100.099391757439578940530.9999766504295133643False
ELAT-10.07183902536513.36103e-08deg-3.1913434074201663115e-090.0949513514434612696571.000072183713741608False
PMELONG0.46260.010399999999999999523mas / yr0.000711879058279796250730.0684499094499804172641.0031591779004100928False
F0277.93771124297461485.186e-13Hz-1.471045507628332416e-140.0283657058933346011571.0000736554074983135False
PX0.5040.07349999999999999589mas-0.00207030297070254221130.0281673873564971741220.99982582356450722116False
ECC0.00017372948.9000000000000002855e-09-2.384406823461443503e-100.026791087904061160891.0022775207693099819False
DMX_00100.000669275610.00020051850499999999489pc / cm3-5.0847948039543485257e-060.0253582321689180198440.99999786016599179206False
DMX_00010.00164320560.00022434462499999998828pc / cm3-5.328772561611662406e-060.0237526197100182932811.0000068575953371397False
DMX_00020.001360248720.00020941304000000001188pc / cm3-4.905837050632510035e-060.0234266072954793548591.000010656028552436False
OM181.849568165780.01296546975deg-0.000263761958789854311650.0203434170821195515610.9909562505170320566False
........................
DMX_00453.64190777e-050.00020164094999999999935pc / cm3-1.0880461652431374893e-070.000539595833704977802251.0000006821705129667False
DMX_0071-0.0001769126030.00019118353399999999634pc / cm3-9.349008259535601141e-080.00048900698004335468441.0000046190658971046False
DMX_00752.00017094e-060.00019663653799999999744pc / cm3-9.082493292551379154e-080.000461892453199688594360.9999419961581833549False
DMX_00940.0009298491210.00019402737299999999105pc / cm35.00029611804828078e-080.000257710860108809557650.9999940905421160764False
DMX_0073-0.0001569538350.00019724444300000000259pc / cm34.9749872529263657744e-080.000252224456986418924061.0000039385363455047False
DMX_00170.0001787627570.00021197504699999999088pc / cm3-2.7382927343715330118e-080.000129179956467778640550.9999889282854504957False
DMX_0043-0.0004948486480.0001997188189999999947pc / cm32.5596058013453541757e-080.000128160471514972973540.9999848366739377825False
DMX_00838.70047706e-060.00020486178099999999887pc / cm32.416989696640591326e-080.0001179814841422564440041.0000060508320367525False
DMX_0069-0.0002513683560.00019942850700000000919pc / cm32.1310941707771303283e-080.0001068600574128116095961.0000028555921218754False
DMX_0067-0.0003779679840.00019749766400000001308pc / cm3-1.27852614923047724904e-086.473626691759135337e-050.999976952183460277False
" + ], + "text/plain": [ + "\n", + " name Tempo Value ... PINT_unct/Tempo_unct no_t_unc\n", + " str8 str32 ... float128 bool \n", + "-------- -------------------- ... ---------------------- --------\n", + " ELONG 244.347677844079 ... 0.9999766504295133643 False\n", + " ELAT -10.0718390253651 ... 1.000072183713741608 False\n", + " PMELONG 0.4626 ... 1.0031591779004100928 False\n", + " F0 277.9377112429746148 ... 1.0000736554074983135 False\n", + " PX 0.504 ... 0.99982582356450722116 False\n", + " ECC 0.0001737294 ... 1.0022775207693099819 False\n", + "DMX_0010 0.00066927561 ... 0.99999786016599179206 False\n", + "DMX_0001 0.0016432056 ... 1.0000068575953371397 False\n", + "DMX_0002 0.00136024872 ... 1.000010656028552436 False\n", + " OM 181.84956816578 ... 0.9909562505170320566 False\n", + " ... ... ... ... ...\n", + "DMX_0045 3.64190777e-05 ... 1.0000006821705129667 False\n", + "DMX_0071 -0.000176912603 ... 1.0000046190658971046 False\n", + "DMX_0075 2.00017094e-06 ... 0.9999419961581833549 False\n", + "DMX_0094 0.000929849121 ... 0.9999940905421160764 False\n", + "DMX_0073 -0.000156953835 ... 1.0000039385363455047 False\n", + "DMX_0017 0.000178762757 ... 0.9999889282854504957 False\n", + "DMX_0043 -0.000494848648 ... 0.9999848366739377825 False\n", + "DMX_0083 8.70047706e-06 ... 1.0000060508320367525 False\n", + "DMX_0069 -0.000251368356 ... 1.0000028555921218754 False\n", + "DMX_0067 -0.000377967984 ... 0.999976952183460277 False" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### If one wants the Latex output please use the cell below. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#ascii.write(compare_table, sys.stdout, Writer = ascii.Latex,\n", + "# latexdict = {'tabletype': 'table*'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check out the maximum DMX difference" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Row index=6\n", + "
\n", + "\n", + "\n", + "\n", + "
nameTempo ValueTempo uncertaintyunitsTempo_V-PINT_VTempo_PINT_diff/unctPINT_unct/Tempo_unctno_t_unc
str8str32float128objectfloat128float128float128bool
DMX_00100.000669275610.00020051850499999999489pc / cm3-5.0847948039543485257e-060.0253582321689180198440.99999786016599179206False
" + ], + "text/plain": [ + "\n", + " name Tempo Value Tempo uncertainty units Tempo_V-PINT_V Tempo_PINT_diff/unct PINT_unct/Tempo_unct no_t_unc\n", + " str8 str32 float128 object float128 float128 float128 bool \n", + "-------- ------------- ------------------------- -------- -------------------------- ----------------------- ---------------------- --------\n", + "DMX_0010 0.00066927561 0.00020051850499999999489 pc / cm3 -5.0847948039543485257e-06 0.025358232168918019844 0.99999786016599179206 False" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_dmx = 0\n", + "max_dmx_index = 0\n", + "for ii, row in enumerate(compare_table):\n", + " if row['name'].startswith('DMX_'):\n", + " if row['Tempo_PINT_diff/unct'] > max_dmx:\n", + " max_dmx = row['Tempo_PINT_diff/unct']\n", + " max_dmx_index = ii\n", + "\n", + "dmx_max = compare_table[max_dmx_index]['name']\n", + "\n", + "compare_table[max_dmx_index]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Output the table in the paper" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=20\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
nameTempo ValueTempo uncertaintyunitsTempo_V-PINT_VTempo_PINT_diff/unctPINT_unct/Tempo_unctno_t_unc
str8str32float128objectfloat128float128float128bool
F0277.93771124297461485.186e-13Hz-1.471045507628332416e-140.0283657058933346011571.0000736554074983135False
F1-7.338737472765e-164.619148184227e-21Hz / s6.3513794158537125015e-230.0137501096794031429841.0001125509037762049False
FD13.98314325e-051.6566479199999999207e-06s-2.5078110490474835037e-090.00151378637474611004930.99999722077930985886False
FD2-1.47296057e-051.1922595999999999884e-06s1.3481392263201306481e-090.00113074302469037000010.9999985886156963488False
JUMP1-8.789e-061.2999999999999999941e-07s-4.662519179964242028e-100.00358655321535710945241.0037094614491930411False
PX0.5040.07349999999999999589mas-0.00207030297070254221130.0281673873564971741220.99982582356450722116False
ELONG244.3476778440795.9573e-09deg-5.921065165948036224e-100.099391757439578940530.9999766504295133643False
ELAT-10.07183902536513.36103e-08deg-3.1913434074201663115e-090.0949513514434612696571.000072183713741608False
PMELONG0.46260.010399999999999999523mas / yr0.000711879058279796250730.0684499094499804172641.0031591779004100928False
PMELAT-7.15550.058200000000000001732mas / yr-0.000504438977884547057330.0086673363897688503190.9992173055526453185False
PB14.348465725503022.1222661e-06d-3.4563402588790037573e-080.0162860833468479930841.0000724303266271833False
A18.8016531228.1100000000000004906e-07ls1.4914297352675021102e-080.0183900090661837482820.98441828361417216264False
A1DOT-4e-156.260000000000000155e-16ls / s8.913933368296104875e-180.01423951017299697301140.99986819306556440345False
ECC0.00017372948.9000000000000002855e-09-2.384406823461443503e-100.026791087904061160891.0022775207693099819False
T055878.26189804510000000.0005167676d-1.0512816950025705154e-050.0203434134609555729770.9909421696656756166False
OM181.849568165780.01296546975deg-0.000263761958789854311650.0203434170821195515610.9909562505170320566False
OMDOT0.00522090.0013554deg / yr-2.2104443267939444245e-050.0163084279680828126351.0000991630450569873False
M20.2718940.089418999999999998485solMass-0.00164145012184002681010.018356838276429247870.978663730015661093False
SINI0.9062850.033993000000000002380.000543831174441344877830.0159983283158692916880.9838897673347273276False
DMX_00100.000669275610.00020051850499999999489pc / cm3-5.0847948039543485257e-060.0253582321689180198440.99999786016599179206False
" + ], + "text/plain": [ + "\n", + " name Tempo Value ... PINT_unct/Tempo_unct no_t_unc\n", + " str8 str32 ... float128 bool \n", + "-------- ---------------------- ... ---------------------- --------\n", + " F0 277.9377112429746148 ... 1.0000736554074983135 False\n", + " F1 -7.338737472765e-16 ... 1.0001125509037762049 False\n", + " FD1 3.98314325e-05 ... 0.99999722077930985886 False\n", + " FD2 -1.47296057e-05 ... 0.9999985886156963488 False\n", + " JUMP1 -8.789e-06 ... 1.0037094614491930411 False\n", + " PX 0.504 ... 0.99982582356450722116 False\n", + " ELONG 244.347677844079 ... 0.9999766504295133643 False\n", + " ELAT -10.0718390253651 ... 1.000072183713741608 False\n", + " PMELONG 0.4626 ... 1.0031591779004100928 False\n", + " PMELAT -7.1555 ... 0.9992173055526453185 False\n", + " PB 14.34846572550302 ... 1.0000724303266271833 False\n", + " A1 8.801653122 ... 0.98441828361417216264 False\n", + " A1DOT -4e-15 ... 0.99986819306556440345 False\n", + " ECC 0.0001737294 ... 1.0022775207693099819 False\n", + " T0 55878.2618980451000000 ... 0.9909421696656756166 False\n", + " OM 181.84956816578 ... 0.9909562505170320566 False\n", + " OMDOT 0.0052209 ... 1.0000991630450569873 False\n", + " M2 0.271894 ... 0.978663730015661093 False\n", + " SINI 0.906285 ... 0.9838897673347273276 False\n", + "DMX_0010 0.00066927561 ... 0.99999786016599179206 False" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paper_params = ['F0', 'F1', 'FD1', 'FD2', 'JUMP1', 'PX', \n", + " 'ELONG', 'ELAT', 'PMELONG', 'PMELAT', 'PB', \n", + " 'A1', 'A1DOT', 'ECC', 'T0', 'OM', 'OMDOT', 'M2',\n", + " 'SINI', dmx_max]\n", + "# Get the table index of the parameters above\n", + "paper_param_index = []\n", + "for pp in paper_params:\n", + " # We assume the parameter name are unique in the table\n", + " idx = np.where(compare_table['name'] == pp)[0][0]\n", + " paper_param_index.append(idx)\n", + "paper_param_index = np.array(paper_param_index)\n", + "compare_table[paper_param_index]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TEMPO2 run\n", + "\n", + "Before TEMPO2 run, the .par file has to be modified for a more accurate TEMPO2 vs PINT comparison. We save the modified .par file in a file named \"[PSR name]_tempo2.par\". In this case, \"J1600-3053_tempo2.par\"\n", + "\n", + "* Modified parameters \n", + " * ECL IERS2010 ----> ECL IERS 2003 (TEMPO2 use IERS 2003 Obliquity angle as default)\n", + " * T2CMETHOD TEMPO ----> # T2CMETHOD TEMPO (Make TEMPO2 ues the new precession and nutation model IAU 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "tempo2_par = \"J1600-3053_tempo2.par\"" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "less J1600-3053_tempo2.par" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PINT refit using the modified tempo2-style parfile" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n", + "INFO: Parameter A1DOT's value will be scaled by 1e-12 [pint.models.parameter]\n" + ] + } + ], + "source": [ + "m_t2 = models.get_model(tempo2_par)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12368.094237187552177" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_t2 = GLSFitter(toas=t, model=m_t2)\n", + "f_t2.fit_toas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tempo2 fit" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEMPO2 chi2: 12265.156200000001\n", + "TEMPO2 rms: 0.944\n" + ] + } + ], + "source": [ + "tempo2_chi2, ndof, rms_t2, tempo2_new_par = newpar2(tempo2_par, tim_file)\n", + "print(\"TEMPO2 chi2: \", tempo2_chi2)\n", + "print(\"TEMPO2 rms: \", rms_t2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get TEMPO2 residuals, toa value, observing frequencies, and data error" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "tempo2_result = t2u.general2(tempo2_par, tim_file, ['sat', 'pre', 'post', 'freq', 'err'])\n", + "# TEMPO2's residual unit is second\n", + "tp2_diff_pre = f_t2.resids_init.time_resids - tempo2_result['pre'] * u.s\n", + "tp2_diff_post = f_t2.resids.time_resids - tempo2_result['post'] * u.s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the TEMPO2 - PINT residual difference" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5), dpi=150)\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(mjds, (tp2_diff_pre - tp2_diff_pre.mean()).to_value(u.ns), '+')\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (ns)')\n", + "plt.title('PSR J1600-3053 prefit residual differences between PINT and TEMPO2')\n", + "plt.grid(True)\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(mjds, (tp2_diff_post - tp2_diff_post.mean()).to_value(u.ns), '+')\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Time Residuals (ns)')\n", + "plt.title('PSR J1600-3053 postfit residual differences between PINT and TEMPO2')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.savefig(\"J1600_PINT_tempo2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write out the TEMPO2 postfit parameter to a new file\n", + "\n", + "* Note, since the ECL parameter is hard coded in tempo2, we will have to add it manually " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Write out the post fit tempo parfile.\n", + "tempo2_parfile = open(psr + '_new_tempo2.2.par', 'w')\n", + "for line in tempo2_new_par:\n", + " tempo2_parfile.write(line)\n", + "tempo2_parfile.write(\"ECL IERS2003\")\n", + "tempo2_parfile.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the parameter between TEMPO2 and PINT\n", + "\n", + "* Reported quantities\n", + " * TEMPO2 value\n", + " * TEMPO2 uncertainty \n", + " * Parameter units\n", + " * TEMPO2 parameter value - PINT parameter value\n", + " * TEMPO2/PINT parameter absolute difference divided by TEMPO2 uncertainty \n", + " * PINT uncertainty divided by TEMPO2 uncertainty\n", + " * If TEMPO2 provides the uncertainty value" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: EPHVER 5 does nothing in PINT [pint.models.timing_model]\n", + "WARNING: Unrecognized parfile line 'NE_SW 0' [pint.models.timing_model]\n", + "WARNING: Unrecognized parfile line 'NE_SW2 0.000' [pint.models.timing_model]\n", + "/home/luo/.local/lib/python3.6/site-packages/astropy/units/quantity.py:464: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + } + ], + "source": [ + "# Create the parameter compare table\n", + "tv = []\n", + "t2_unc = []\n", + "tv_pv = []\n", + "tv_pv_tc = []\n", + "tc_pc = []\n", + "units = []\n", + "names = []\n", + "no_t2_unc = []\n", + "tempo2_new_model = models.get_model(psr + '_new_tempo2.2.par')\n", + "for param in tempo2_new_model.params:\n", + " t2_par = getattr(tempo2_new_model, param)\n", + " pint2_par = getattr(f_t2.model, param)\n", + " tempo2q = t2_par.quantity \n", + " pint2q = pint2_par.quantity\n", + " try:\n", + " diff2q = tempo2q - pint2q\n", + " if t2_par.uncertainty_value != 0.0:\n", + " diff_tcq = np.abs(diff2q) / t2_par.uncertainty\n", + " uvsu = pint2_par.uncertainty / t2_par.uncertainty\n", + " no_t2_unc.append(False)\n", + " else:\n", + " diff_tcq = np.abs(diff2q) / pint2_par.uncertainty\n", + " uvsu = t2_par.uncertainty\n", + " no_t2_unc.append(True)\n", + " except TypeError:\n", + " continue\n", + " uvsu = pint2_par.uncertainty / t2_par.uncertainty\n", + " tv.append(tempo2q.value)\n", + " t2_unc.append(t2_par.uncertainty.value)\n", + " tv_pv.append(diff2q.value)\n", + " tv_pv_tc.append(diff_tcq.value)\n", + " tc_pc.append(uvsu)\n", + " units.append(t2_par.units)\n", + " names.append(param)\n", + " \n", + "compare_table2 = Table((names, tv, t2_unc,units, tv_pv, tv_pv_tc, tc_pc, no_t2_unc), names = ('name', 'Tempo2 Value', 'T2 unc','units', \n", + " 'Tempo2_V-PINT_V', \n", + " 'Tempo2_PINT_diff/unct', \n", + " 'PINT_unct/Tempo2_unct', \n", + " 'no_t_unc')) \n", + "compare_table2.sort('Tempo2_PINT_diff/unct')\n", + "compare_table2 = compare_table2[::-1]\n", + "compare_table2.write('parameter_compare.t2.html', format='html', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print the parameter difference in a table.\n", + "The table is sorted by relative difference in descending order. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=125\n", + "
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nameTempo2 ValueT2 uncunitsTempo2_V-PINT_VTempo2_PINT_diff/unctPINT_unct/Tempo2_unctno_t_unc
str8str32float128objectfloat128float128float128bool
ECC0.000173729661575211688.922286680669999241e-094.168033894912624715e-110.00467148618295644760261.0000400789683185909False
DMX_00980.00133946131224894170.00019579968831114546654pc / cm3-5.162393215032545085e-070.00263656865828559502930.99999926235860314705False
DMX_0070-0.000237479639065179730.00019767137320477682749pc / cm3-4.6318680163804021657e-070.00234321639056052789111.0000006066661308868False
DMX_00970.00139283306619874460.00019620100461426303326pc / cm3-4.3591375636898264945e-070.00222177127597283181550.99999985479541497746False
DMX_0055-0.00053077049044036210.00019675128861832102923pc / cm3-3.936735762570617997e-070.00200086911258178087521.0000000155376389532False
DMX_0063-0.000484105710728255740.00019894769104906708185pc / cm3-3.8388090987831295642e-070.00192955699990323206741.0000001737671666557False
DMX_00790.000189767952940002160.00019490725481464179483pc / cm3-3.6413058978400727507e-070.00186822491615459915921.0000001869460202197False
DMX_00100.000674033569559790.00020051850482404336064pc / cm3-3.734867366063333513e-070.0018626048350703144560.9999998791435175116False
F1-7.3387383041227678664e-164.619148404392432094e-21Hz / s-8.212906306513322778e-240.0017780130854214428440.999998198306420979False
DMX_00860.000295253466908306440.0001961188165133768578pc / cm3-3.4086588203348670498e-070.00173805802060934301571.0000003760250906204False
........................
DMX_0024-6.464357906175583e-050.00019594538945981657802pc / cm3-3.0733074786039684713e-080.000156845102968560835831.000000040794174927False
DMX_00920.00132072951385398940.00019585216459019454951pc / cm3-2.987272381023420298e-080.000152526901465401508451.0000000598805378615False
DMX_0058-0.00053775814687447930.00019927530538964258904pc / cm3-2.7240830902737663e-080.000136699481400737144711.0000003494124634074False
DMX_00890.00074956144462958460.00021586616414944812654pc / cm32.664672352724588994e-080.000123440946070630470831.0000000318049040438False
DMX_0032-6.265675469663684e-050.00019561483985536690729pc / cm32.0624768732425491705e-080.000105435603697935035421.0000001069330806125False
DMX_0040-0.00052424493853935320.00020212647115737782458pc / cm3-1.3087848262614831807e-086.4750787898654266965e-050.99999999750656676234False
DMX_00010.00164843721682323250.00022434462780433157077pc / cm3-1.1670286121810355406e-085.2019458794390748143e-051.0000004809807354622False
DMX_0027-0.000182880825351814140.00019391445756469536201pc / cm3-9.576702027013850663e-094.938621981715206116e-051.0000001643637170812False
DMX_00838.544780315309648e-060.00020486177918444288125pc / cm3-3.8797037898631337458e-091.8938153350558018293e-051.0000001513452474455False
DMX_0044-0.00033900236624910280.00021062295971768858391pc / cm31.0720102164903794195e-095.0897120519399367266e-061.0000001883055240626False
" + ], + "text/plain": [ + "\n", + " name Tempo2 Value ... PINT_unct/Tempo2_unct no_t_unc\n", + " str8 str32 ... float128 bool \n", + "-------- -------------------------- ... ---------------------- --------\n", + " ECC 0.00017372966157521168 ... 1.0000400789683185909 False\n", + "DMX_0098 0.0013394613122489417 ... 0.99999926235860314705 False\n", + "DMX_0070 -0.00023747963906517973 ... 1.0000006066661308868 False\n", + "DMX_0097 0.0013928330661987446 ... 0.99999985479541497746 False\n", + "DMX_0055 -0.0005307704904403621 ... 1.0000000155376389532 False\n", + "DMX_0063 -0.00048410571072825574 ... 1.0000001737671666557 False\n", + "DMX_0079 0.00018976795294000216 ... 1.0000001869460202197 False\n", + "DMX_0010 0.00067403356955979 ... 0.9999998791435175116 False\n", + " F1 -7.3387383041227678664e-16 ... 0.999998198306420979 False\n", + "DMX_0086 0.00029525346690830644 ... 1.0000003760250906204 False\n", + " ... ... ... ... ...\n", + "DMX_0024 -6.464357906175583e-05 ... 1.000000040794174927 False\n", + "DMX_0092 0.0013207295138539894 ... 1.0000000598805378615 False\n", + "DMX_0058 -0.0005377581468744793 ... 1.0000003494124634074 False\n", + "DMX_0089 0.0007495614446295846 ... 1.0000000318049040438 False\n", + "DMX_0032 -6.265675469663684e-05 ... 1.0000001069330806125 False\n", + "DMX_0040 -0.0005242449385393532 ... 0.99999999750656676234 False\n", + "DMX_0001 0.0016484372168232325 ... 1.0000004809807354622 False\n", + "DMX_0027 -0.00018288082535181414 ... 1.0000001643637170812 False\n", + "DMX_0083 8.544780315309648e-06 ... 1.0000001513452474455 False\n", + "DMX_0044 -0.0003390023662491028 ... 1.0000001883055240626 False" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_table2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### If one wants to get the latex version, please use the line below." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#ascii.write(compare_table2, sys.stdout, Writer = ascii.Latex,\n", + "# latexdict = {'tabletype': 'table*'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check out the maximum DMX difference" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Row index=1\n", + "
\n", + "\n", + "\n", + "\n", + "
nameTempo2 ValueT2 uncunitsTempo2_V-PINT_VTempo2_PINT_diff/unctPINT_unct/Tempo2_unctno_t_unc
str8str32float128objectfloat128float128float128bool
DMX_00980.00133946131224894170.00019579968831114546654pc / cm3-5.162393215032545085e-070.00263656865828559502930.99999926235860314705False
" + ], + "text/plain": [ + "\n", + " name Tempo2 Value T2 unc units Tempo2_V-PINT_V Tempo2_PINT_diff/unct PINT_unct/Tempo2_unct no_t_unc\n", + " str8 str32 float128 object float128 float128 float128 bool \n", + "-------- --------------------- ------------------------- -------- ------------------------- ------------------------ ---------------------- --------\n", + "DMX_0098 0.0013394613122489417 0.00019579968831114546654 pc / cm3 -5.162393215032545085e-07 0.0026365686582855950293 0.99999926235860314705 False" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_dmx = 0\n", + "max_dmx_index = 0\n", + "for ii, row in enumerate(compare_table2):\n", + " if row['name'].startswith('DMX_'):\n", + " if row['Tempo2_PINT_diff/unct'] > max_dmx:\n", + " max_dmx = row['Tempo2_PINT_diff/unct']\n", + " max_dmx_index = ii\n", + "\n", + "dmx_max2 = compare_table2[max_dmx_index]['name']\n", + "\n", + "compare_table2[max_dmx_index]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Output the table in the paper" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=20\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
nameTempo2 ValueT2 uncunitsTempo2_V-PINT_VTempo2_PINT_diff/unctPINT_unct/Tempo2_unctno_t_unc
str8str32float128objectfloat128float128float128bool
F0277.937711242974627885.1859268946902080184e-13Hz-6.6613381477509392425e-160.00128450290237823877811.0000082417695045875False
F1-7.3387383041227678664e-164.619148404392432094e-21Hz / s-8.212906306513322778e-240.0017780130854214428440.999998198306420979False
FD13.983282287426775e-051.6566478062738200598e-06s-1.6031694728325007922e-090.00096771894832516980931.0000000032094340519False
FD2-1.4729805752137882e-051.1922596055992699934e-06s1.4162333940147552079e-090.0011878565602353929541.0000000136320319477False
JUMP1-8.7887456483184e-060.0s-5.0316350075314342574e-110.0003856129036950526755infTrue
PX0.50612420123220640.07348886965486496614mas1.9718059246720542887e-050.000268313546518336016031.0000000164253699531False
ELONG244.347677842553825.95727548431e-09deg9.322320693172514439e-120.00156486311867316137561.0000013810109530107False
ELAT-10.0718390470430653.361025894297e-08deg-1.5125678487493132707e-110.000450031596399136187830.9999926235884047247False
PMELONG0.46190960156254910.010433361011620021289mas / yr7.3610413870994761965e-060.000705529251686126028871.0000025739354863052False
PMELAT-7.1551456742758220.058156247552489513664mas / yr-7.1059018702079868035e-050.00122186388724521513040.99999263990213360653False
PB14.3484657546613667862.12226632065849e-06d-1.9218603731011030256e-090.00090556984031334688540.9999977436618808823False
A18.801653122864638.114047416773300209e-07ls-8.203180357213568641e-100.0010109850159682348160.9999811897467071331False
A1DOT-4.008979189463729e-156.2586911221949290846e-16ls / s-1.0370977784914106416e-180.00165705218270573400971.0000003677433377813False
ECC0.000173729661575211688.922286680669999241e-094.168033894912624715e-110.00467148618295644760261.0000400789683185909False
T055878.26189947384950700.00051676746764245482d4.890245969835227413e-070.000946314595255971034961.000008278658846269False
OM181.849604015494514780.01296564244572522874deg1.2275557313340401677e-050.000946775862802514033941.0000088591412276617False
OMDOT0.00523955285176455407780.00135543635075636363deg / yr-1.2270890653582061121e-060.00090530924943355925340.99999775911651708066False
M20.27176338143833560.08941866471282471085solMass0.0001041874283656540889350.00116516421599741461581.0000236859702187342False
SINI0.90642005682258460.03399283139781983376-4.2613468352326044908e-050.00125360161539997817631.0000254830135359985False
DMX_00100.000674033569559790.00020051850482404336064pc / cm3-3.734867366063333513e-070.0018626048350703144560.9999998791435175116False
" + ], + "text/plain": [ + "\n", + " name Tempo2 Value ... PINT_unct/Tempo2_unct no_t_unc\n", + " str8 str32 ... float128 bool \n", + "-------- -------------------------- ... ---------------------- --------\n", + " F0 277.93771124297462788 ... 1.0000082417695045875 False\n", + " F1 -7.3387383041227678664e-16 ... 0.999998198306420979 False\n", + " FD1 3.983282287426775e-05 ... 1.0000000032094340519 False\n", + " FD2 -1.4729805752137882e-05 ... 1.0000000136320319477 False\n", + " JUMP1 -8.7887456483184e-06 ... inf True\n", + " PX 0.5061242012322064 ... 1.0000000164253699531 False\n", + " ELONG 244.34767784255382 ... 1.0000013810109530107 False\n", + " ELAT -10.071839047043065 ... 0.9999926235884047247 False\n", + " PMELONG 0.4619096015625491 ... 1.0000025739354863052 False\n", + " PMELAT -7.155145674275822 ... 0.99999263990213360653 False\n", + " PB 14.348465754661366786 ... 0.9999977436618808823 False\n", + " A1 8.80165312286463 ... 0.9999811897467071331 False\n", + " A1DOT -4.008979189463729e-15 ... 1.0000003677433377813 False\n", + " ECC 0.00017372966157521168 ... 1.0000400789683185909 False\n", + " T0 55878.2618994738495070 ... 1.000008278658846269 False\n", + " OM 181.84960401549451478 ... 1.0000088591412276617 False\n", + " OMDOT 0.0052395528517645540778 ... 0.99999775911651708066 False\n", + " M2 0.2717633814383356 ... 1.0000236859702187342 False\n", + " SINI 0.9064200568225846 ... 1.0000254830135359985 False\n", + "DMX_0010 0.00067403356955979 ... 0.9999998791435175116 False" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paper_params = ['F0', 'F1', 'FD1', 'FD2', 'JUMP1', 'PX', \n", + " 'ELONG', 'ELAT', 'PMELONG', 'PMELAT', 'PB', \n", + " 'A1', 'A1DOT', 'ECC', 'T0', 'OM', 'OMDOT', 'M2',\n", + " 'SINI', dmx_max]\n", + "# Get the table index of the parameters above\n", + "paper_param_index = []\n", + "for pp in paper_params:\n", + " # We assume the parameter name are unique in the table\n", + " idx = np.where(compare_table2['name'] == pp)[0][0]\n", + " paper_param_index.append(idx)\n", + "paper_param_index = np.array(paper_param_index)\n", + "compare_table2[paper_param_index]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The residual difference between PINT and TEMPO2 is at the level of ~1ns. \n", + "\n", + "* We believe the discrepancy is mainly from the solar system geometric delay. \n", + "* We will use the tempo2 postfit parameters, which are wrote out to `J1600-3053_new_tempo2.2.par`" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: EPHVER 5 does nothing in PINT [pint.models.timing_model]\n", + "WARNING: Unrecognized parfile line 'NE_SW 0' [pint.models.timing_model]\n", + "WARNING: Unrecognized parfile line 'NE_SW2 0.000' [pint.models.timing_model]\n" + ] + } + ], + "source": [ + "tempo2_result2 = t2u.general2('J1600-3053_new_tempo2.2.par', tim_file, ['sat', 'pre', 'post', 'freq', 'err'])\n", + "m_t22 = models.get_model('J1600-3053_new_tempo2.2.par')\n", + "f_t22 = GLSFitter(toas=t, model=m_t22)\n", + "f_t22.fit_toas()\n", + "tp2_diff_pre2 = f_t22.resids_init.time_resids - tempo2_result2['pre'] * u.s\n", + "tp2_diff_post2 = f_t22.resids.time_resids - tempo2_result2['post'] * u.s" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "PINT_solar = m_t22.solar_system_geometric_delay(t)\n", + "tempo2_solar = t2u.general2('J1600-3053_new_tempo2.2.par', tim_file, ['roemer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "diff_solar = PINT_solar + tempo2_solar['roemer'] * u.s\n", + "plt.figure(figsize=(8,2.5), dpi=150)\n", + "plt.plot(mjds, (tp2_diff_post2 - tp2_diff_post2.mean()).to_value(u.ns), '+')\n", + "plt.plot(mjds, (diff_solar - diff_solar.mean()).to_value(u.ns, equivalencies=[(ls, u.s)]), 'x')\n", + "\n", + "plt.xlabel('MJD (day)')\n", + "plt.ylabel('Discrepancies (ns)')\n", + "#plt.title('PSR J1600-3053 postfit residual differences between PINT and TEMPO2')\n", + "plt.grid(True)\n", + "plt.legend(['Postfit Residual Differences', 'Solar System Geometric Delay Difference'],\n", + " loc='upper center', bbox_to_anchor=(0.5, -0.3), shadow=True, ncol=2)\n", + "plt.tight_layout()\n", + "plt.savefig(\"solar_geo\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/Wideband_TOA_walkthrough.md b/docs/examples/Wideband_TOA_walkthrough.md deleted file mode 100644 index cce0387fe..000000000 --- a/docs/examples/Wideband_TOA_walkthrough.md +++ /dev/null @@ -1,170 +0,0 @@ ---- -jupyter: - jupytext: - formats: ipynb,md - text_representation: - extension: .md - format_name: markdown - format_version: '1.2' - jupytext_version: 1.5.2 - kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Wideband TOA fitting - -```python execution={"iopub.execute_input": "2020-09-10T16:29:20.198689Z", "iopub.status.busy": "2020-09-10T16:29:20.198111Z", "iopub.status.idle": "2020-09-10T16:29:22.547401Z", "shell.execute_reply": "2020-09-10T16:29:22.547856Z"} -import os - -from pint.models import get_model -from pint.toa import get_TOAs -from pint.fitter import WidebandTOAFitter -import matplotlib.pyplot as plt -import astropy.units as u -``` - -## Setup your inputs - -```python execution={"iopub.execute_input": "2020-09-10T16:29:22.551487Z", "iopub.status.busy": "2020-09-10T16:29:22.550933Z", "iopub.status.idle": "2020-09-10T16:29:24.214947Z", "shell.execute_reply": "2020-09-10T16:29:24.214428Z"} -model = get_model("J1614-2230_NANOGrav_12yv3.wb.gls.par") -toas = get_TOAs("J1614-2230_NANOGrav_12yv3.wb.tim", ephem="de436") -``` - -## Setup the fitter like old time - -```python execution={"iopub.execute_input": "2020-09-10T16:29:24.231849Z", "iopub.status.busy": "2020-09-10T16:29:24.225575Z", "iopub.status.idle": "2020-09-10T16:29:24.723913Z", "shell.execute_reply": "2020-09-10T16:29:24.723416Z"} -fitter = WidebandTOAFitter(toas, model) -``` - -## Run your fits like old time - -```python execution={"iopub.execute_input": "2020-09-10T16:29:24.760908Z", "iopub.status.busy": "2020-09-10T16:29:24.760345Z", "iopub.status.idle": "2020-09-10T16:29:28.292646Z", "shell.execute_reply": "2020-09-10T16:29:28.292141Z"} -fitter.fit_toas() -``` - -## What are the difference? - - -### Concept of fitting different types of data together -#### Residuals are combined with TOA/time residuals and dm residuals - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.296487Z", "iopub.status.busy": "2020-09-10T16:29:28.295938Z", "iopub.status.idle": "2020-09-10T16:29:28.299335Z", "shell.execute_reply": "2020-09-10T16:29:28.298731Z"} -type(fitter.resids) -``` - -#### If we look into the resids attribute, it has two independent Residual objects. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.303156Z", "iopub.status.busy": "2020-09-10T16:29:28.302609Z", "iopub.status.idle": "2020-09-10T16:29:28.305446Z", "shell.execute_reply": "2020-09-10T16:29:28.305874Z"} -fitter.resids.residual_objs -``` - -#### Each of them can be used independently - -* Time residual - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.342821Z", "iopub.status.busy": "2020-09-10T16:29:28.330288Z", "iopub.status.idle": "2020-09-10T16:29:28.520180Z", "shell.execute_reply": "2020-09-10T16:29:28.519607Z"} -time_resids = fitter.resids.residual_objs["toa"].time_resids -plt.errorbar( - toas.get_mjds().value, - time_resids.to_value(u.us), - yerr=toas.get_errors().to_value(u.us), - fmt="x", -) -plt.ylabel("us") -plt.xlabel("MJD") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.525251Z", "iopub.status.busy": "2020-09-10T16:29:28.524698Z", "iopub.status.idle": "2020-09-10T16:29:28.527648Z", "shell.execute_reply": "2020-09-10T16:29:28.527083Z"} -# Time RMS -print(fitter.resids.residual_objs["toa"].rms_weighted()) -print(fitter.resids.residual_objs["toa"].chi2) -``` - -* DM residual - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.556722Z", "iopub.status.busy": "2020-09-10T16:29:28.535404Z", "iopub.status.idle": "2020-09-10T16:29:28.698341Z", "shell.execute_reply": "2020-09-10T16:29:28.697831Z"} -dm_resids = fitter.resids.residual_objs["dm"].resids -dm_error = fitter.resids.residual_objs["dm"].get_data_error() -plt.errorbar(toas.get_mjds().value, dm_resids.value, yerr=dm_error.value, fmt="x") -plt.ylabel("pc/cm^3") -plt.xlabel("MJD") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.703699Z", "iopub.status.busy": "2020-09-10T16:29:28.703155Z", "iopub.status.idle": "2020-09-10T16:29:28.705717Z", "shell.execute_reply": "2020-09-10T16:29:28.706203Z"} -# DM RMS -print(fitter.resids.residual_objs["dm"].rms_weighted()) -print(fitter.resids.residual_objs["dm"].chi2) -``` - -#### However, in the combined residuals, one can access rms and chi2 as well - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.710647Z", "iopub.status.busy": "2020-09-10T16:29:28.710098Z", "iopub.status.idle": "2020-09-10T16:29:28.713817Z", "shell.execute_reply": "2020-09-10T16:29:28.713259Z"} -print(fitter.resids.rms_weighted()) -print(fitter.resids.chi2) -``` - -#### The initial residuals is also a combined residual object - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.792714Z", "iopub.status.busy": "2020-09-10T16:29:28.779022Z", "iopub.status.idle": "2020-09-10T16:29:28.937303Z", "shell.execute_reply": "2020-09-10T16:29:28.936720Z"} -time_resids = fitter.resids_init.residual_objs["toa"].time_resids -plt.errorbar( - toas.get_mjds().value, - time_resids.to_value(u.us), - yerr=toas.get_errors().to_value(u.us), - fmt="x", -) -plt.ylabel("us") -plt.xlabel("MJD") -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:28.957488Z", "iopub.status.busy": "2020-09-10T16:29:28.956731Z", "iopub.status.idle": "2020-09-10T16:29:29.107675Z", "shell.execute_reply": "2020-09-10T16:29:29.107097Z"} -dm_resids = fitter.resids_init.residual_objs["dm"].resids -dm_error = fitter.resids_init.residual_objs["dm"].get_data_error() -plt.errorbar(toas.get_mjds().value, dm_resids.value, yerr=dm_error.value, fmt="x") -plt.ylabel("pc/cm^3") -plt.xlabel("MJD") -``` - -#### Design Matrix are combined - -```python execution={"iopub.execute_input": "2020-09-10T16:29:29.121833Z", "iopub.status.busy": "2020-09-10T16:29:29.115596Z", "iopub.status.idle": "2020-09-10T16:29:32.307439Z", "shell.execute_reply": "2020-09-10T16:29:32.307892Z"} -d_matrix = fitter.get_designmatrix() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:32.318273Z", "iopub.status.busy": "2020-09-10T16:29:32.311723Z", "iopub.status.idle": "2020-09-10T16:29:32.327689Z", "shell.execute_reply": "2020-09-10T16:29:32.327089Z"} -print("Number of TOAs:", toas.ntoas) -print("Number of DM measurments:", len(fitter.resids.residual_objs["dm"].dm_data)) -print("Number of fit params:", len(fitter.model.free_params)) -print("Shape of design matrix:", d_matrix.shape) -``` - -#### Covariance Matrix are combined - -```python execution={"iopub.execute_input": "2020-09-10T16:29:32.339963Z", "iopub.status.busy": "2020-09-10T16:29:32.339244Z", "iopub.status.idle": "2020-09-10T16:29:37.638810Z", "shell.execute_reply": "2020-09-10T16:29:37.638235Z"} -c_matrix = fitter.get_noise_covariancematrix() -``` - -```python execution={"iopub.execute_input": "2020-09-10T16:29:37.642426Z", "iopub.status.busy": "2020-09-10T16:29:37.641863Z", "iopub.status.idle": "2020-09-10T16:29:37.645045Z", "shell.execute_reply": "2020-09-10T16:29:37.644512Z"} -print("Shape of covariance matrix:", c_matrix.shape) -``` - -### NOTE the matrix are PINTMatrix object right now, here are the difference - - -If you want to access the matrix data - -```python execution={"iopub.execute_input": "2020-09-10T16:29:37.648769Z", "iopub.status.busy": "2020-09-10T16:29:37.648232Z", "iopub.status.idle": "2020-09-10T16:29:37.651476Z", "shell.execute_reply": "2020-09-10T16:29:37.650922Z"} -print(d_matrix.matrix) -``` - -PINT matrix has labels that marks all the element in the matrix. It has the label name, index of range of the matrix, and the unit. - -```python execution={"iopub.execute_input": "2020-09-10T16:29:37.654999Z", "iopub.status.busy": "2020-09-10T16:29:37.654448Z", "iopub.status.idle": "2020-09-10T16:29:37.657207Z", "shell.execute_reply": "2020-09-10T16:29:37.656755Z"} -print("labels for dimension 0:", d_matrix.labels[0]) -``` - -```python - -``` diff --git a/docs/examples/Wideband_TOA_walkthrough.py b/docs/examples/Wideband_TOA_walkthrough.py new file mode 100644 index 000000000..1a7e9c073 --- /dev/null +++ b/docs/examples/Wideband_TOA_walkthrough.py @@ -0,0 +1,186 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.7.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Wideband TOA fitting +# +# Traditional pulsar timing involved measuring only the arrival time of each pulse. But as receivers have covered wider and wider contiguous bandwidths, it became necessary to generate many TOAs for each time interval, covering different subbands. This frequency coverage allowed better handling of changing dispersion measures, but resulted in a large number of TOAs and had certain limitations. A new approach measures the pulse arrival time and the dispersion measure simultaneously from a frequency-resolved data cube. This produces TOAs, each of which has an associated dispersion measure and uncertainty. Working with this data requires different handling from PINT. This notebook demonstrates that. + +# %% +import os + +import astropy.units as u +import matplotlib.pyplot as plt +from astropy.visualization import quantity_support + +from pint.fitter import WidebandTOAFitter +from pint.models import get_model +from pint.toa import get_TOAs + +quantity_support() + +# %% [markdown] +# ## Set up your inputs + +# %% +model = get_model("J1614-2230_NANOGrav_12yv3.wb.gls.par") +toas = get_TOAs("J1614-2230_NANOGrav_12yv3.wb.tim", ephem="de436") + +# %% [markdown] +# The DM and its uncertainty are recorded as flags, `pp_dm` and `pp_dme` on the TOAs that have them, They are not currently available as Columns in the Astropy object. On the other hand, it is not necessary that every observation have a measured DM. +# +# (The name, `pp_dm`, refers to the fact that they are obtained using "phase portraits", like profiles but in one more dimension.) + +# %% +print(open(toas.filename).readlines()[-1]) + +# %% +toas.table[-1] + +# %% +toas.table["flags"][0] + +# %% [markdown] +# ## Do the fit +# +# As before, but now we need a fitter adapted to wideband TOAs. + +# %% +fitter = WidebandTOAFitter(toas, model) + +# %% +fitter.fit_toas() + +# %% [markdown] +# ## What is new, compared to narrowband fitting? + +# %% [markdown] +# ### Residual objects combine TOA and time data + +# %% +type(fitter.resids) + +# %% [markdown] +# #### If we look into the resids attribute, it has two independent Residual objects. + +# %% +fitter.resids.residual_objs + +# %% [markdown] +# #### Each of them can be used independently +# +# * Time residual + +# %% +time_resids = fitter.resids.residual_objs["toa"].time_resids +plt.errorbar( + toas.get_mjds().value, + time_resids.to_value(u.us), + yerr=toas.get_errors().to_value(u.us), + fmt="x", +) +plt.ylabel("us") +plt.xlabel("MJD") + +# %% +# Time RMS +print(fitter.resids.residual_objs["toa"].rms_weighted()) +print(fitter.resids.residual_objs["toa"].chi2) + +# %% [markdown] +# * DM residual + +# %% +dm_resids = fitter.resids.residual_objs["dm"].resids +dm_error = fitter.resids.residual_objs["dm"].get_data_error() +plt.errorbar(toas.get_mjds().value, dm_resids.value, yerr=dm_error.value, fmt="x") +plt.ylabel("pc/cm^3") +plt.xlabel("MJD") + +# %% +# DM RMS +print(fitter.resids.residual_objs["dm"].rms_weighted()) +print(fitter.resids.residual_objs["dm"].chi2) + +# %% [markdown] +# #### However, in the combined residuals, one can access rms and chi2 as well + +# %% +print(fitter.resids.rms_weighted()) +print(fitter.resids.chi2) + +# %% [markdown] +# #### The initial residuals is also a combined residual object + +# %% +time_resids = fitter.resids_init.residual_objs["toa"].time_resids +plt.errorbar( + toas.get_mjds().value, + time_resids.to_value(u.us), + yerr=toas.get_errors().to_value(u.us), + fmt="x", +) +plt.ylabel("us") +plt.xlabel("MJD") + +# %% +dm_resids = fitter.resids_init.residual_objs["dm"].resids +dm_error = fitter.resids_init.residual_objs["dm"].get_data_error() +plt.errorbar(toas.get_mjds().value, dm_resids.value, yerr=dm_error.value, fmt="x") +plt.ylabel("pc/cm^3") +plt.xlabel("MJD") + +# %% [markdown] +# ### Matrices +# +# We're now fitting a mixed set of data, so the matrices used in fitting now have different units in different parts, and some care is needed to keep track of which part goes where. + +# %% [markdown] +# #### Design Matrix are combined + +# %% +d_matrix = fitter.get_designmatrix() + +# %% +print("Number of TOAs:", toas.ntoas) +print("Number of DM measurments:", len(fitter.resids.residual_objs["dm"].dm_data)) +print("Number of fit params:", len(fitter.model.free_params)) +print("Shape of design matrix:", d_matrix.shape) + +# %% [markdown] +# #### Covariance Matrix are combined + +# %% +c_matrix = fitter.get_noise_covariancematrix() + +# %% +print("Shape of covariance matrix:", c_matrix.shape) + +# %% [markdown] +# NOTE the matrix are PINTMatrix object right now, here are the difference + +# %% [markdown] +# If you want to access the matrix data + +# %% +print(d_matrix.matrix) + +# %% [markdown] +# PINT matrix has labels that marks all the element in the matrix. It has the label name, index of range of the matrix, and the unit. + +# %% +print("labels for dimension 0:", d_matrix.labels[0]) + +# %% diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 23fcd29a0..086a0063e 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -17,3 +17,5 @@ We don't really have any proper tutorials yet. But for the moment, we have a few examples/understanding_fitters.ipynb examples/Wideband_TOA_walkthrough.ipynb examples/build_model_from_scratch.ipynb + examples-rendered/paper_validation_example.ipynb +