From 312a827388a6fef3260bb3fa81d94444cd396b74 Mon Sep 17 00:00:00 2001 From: robfatland Date: Tue, 13 Aug 2024 17:30:29 -0700 Subject: [PATCH] Mike Yamada --- book/chapters/charts.py | 9 +- book/chapters/data.ipynb | 507 ++++--------------------------- book/chapters/oceanscience.ipynb | 122 ++++---- book/chapters/oceanscience.py | 35 +++ 4 files changed, 155 insertions(+), 518 deletions(-) create mode 100644 book/chapters/oceanscience.py diff --git a/book/chapters/charts.py b/book/chapters/charts.py index 678205d..a8bd18d 100644 --- a/book/chapters/charts.py +++ b/book/chapters/charts.py @@ -17,14 +17,15 @@ def dt64_from_doy(year, doy): return dt64(str(year) + '-01-01') + td64(doy-1, 'D def day_of_month_to_string(d): return str(d) if d > 9 else '0' + str(d) -def ProfilerDepthChart(t0, t1, fnm): +def RenderShallowProfilerTwoDayDepthChart(): ''' This is a very hardcoded function that generates a two-day span of profiles with some annotations indicating what is going on, particularly with midnight / noon profiles. ''' - ds = xr.open_dataset(fnm).sel(time=slice(dt64(t0), dt64(t1))) # this is not profiler metadata. It is actual sensor data. - fig, axs = plt.subplots(figsize=(12,4), tight_layout=True) - axs.plot(ds.time, ds.z, marker='.', ms=11., color='k', mfc='r', linewidth='.0001') + t0, t1, fnm = '2022-01-01', '2022-01-03', './data/rca/sensors/osb/conductivity_jan_2022.nc' + ds = xr.open_dataset(fnm).sel(time=slice(dt64(t0), dt64(t1))) + fig, axs = plt.subplots(figsize=(12,4), tight_layout=True) + axs.plot(ds.time, -ds.depth, marker=',', ms=36., color='k', mfc='r', linewidth='.001') axs.set(ylim = (-210., 0.), title='Shallow profiler depth over two days', ylabel='depth (m)', xlabel='Hours (UTM)') axs.text(dt64('2021-12-31 22:15'), -184, 'AT') axs.text(dt64('2021-12-31 22:05'), -193, 'REST') diff --git a/book/chapters/data.ipynb b/book/chapters/data.ipynb index 2eaaf98..1c47c92 100644 --- a/book/chapters/data.ipynb +++ b/book/chapters/data.ipynb @@ -10,49 +10,50 @@ "# Data\n", "\n", "\n", - "This chapter describes the initial observational data we associate \n", - "with the upper water column: As provided by the regional cabled array \n", - "'shallow profiler' systems. The enthusiastic reader is encouraged to \n", - "skip ahead to the next chapter and retain this chapter as a reference\n", - "if and when it is needed later. \n", + "The second part of this oceanography Jupyter Book is \n", + "concerned with methods and technical details in support of the \n", + "science presented in the first part. \n", "\n", "\n", - "## Data types\n", + "The **`Epipelargosy`** chapter concerned the upper water column as observed\n", + "by the regional cabled array shallow profilers.\n", "\n", "\n", - "There are two central data *concepts*:\n", + "## Shallow Profiler Data\n", + "\n", + "\n", + "### Data types\n", + "\n", + "\n", + "There are two data concepts:\n", "\n", "\n", "- Platform metadata: A record of when a profiler is at rest / ascending / descending\n", - "- Sensor data: Temperature as a function of depth for a given profile, and so on across salinity and the many other sensors\n", + "- Sensor data: Sensor values as a function of depth for a given profile: Salinity, temperature etcetera\n", "\n", "\n", - "### Profile metadata\n", + "We identify the sensor data of interest by pulling a time range from the platform metadata.\n", + "For this purpose we refer to a rest interval followed by ascent followed by descent as\n", + "a single shallow profiler *profile*.\n", "\n", "\n", - "Profiles are not acquired instantaneously. One profile takes on the order of an hour\n", - "as the Science Pod rises due to positive buoyancy from a depth of 200 meters to near the surface.\n", - "Once it reaches the top of its ascent, ideally within 10 meters of the surface, the platform\n", - "winch engages and it is drawn back down to its cradle on the shallow profiler platform at\n", - "a depth of roughly 200 meters.\n", - "Typically nine of these profiles run per day. We can view each profile \n", - "as an *observation* of the state of the epipelagic water column.\n", - "Correspondingly, charts of sensor data do not feature a time axis. Rather the vertical \n", - "axis is depth with the surface at the top. The horizontal axis is then the sensor \n", - "parameter: Temperature, salinity, dissolved oxygen, fluorescence etcetera.\n", + "### Selecting sensor data from profile metadata\n", "\n", "\n", - "Isolating sensor time-series data to a particular profile time interval is a necessary task. \n", - "This is done using bounding times, typically start of ascent to end of ascent. This information\n", - "in stored as a table of profile metadata timestamps. (Sensor data includes timestamps and depth \n", - "but no information on the state of the science pod.) \n", - "A single profile is viewed in three consecutive stages: Rest, Ascent, and Descent.\n", - "The Rest stage consists of the Science Pod parked on the platform at a depth of 200 meters. \n", + "Profiles take on the order of an hour for one ascent/descent cycle as the \n", + "Science Pod traverses from the platform at a depth of 200 meters to near the surface\n", + "and back again. Typically nine profiles run per day. We consier each profile \n", + "to be an *observation* of the state of the epipelagic water column.\n", + "A data chart of one such observation features a vertical axis for depth (with the surface at\n", + "the top) and a horizontal axis for the sensor parameter. Note that there is no time axis \n", + "in this chart scheme.\n", "\n", "\n", - "Profile stage metadata describing the timestamps is stored as CSV files in this\n", - "repository, sorted by time range and location. That information is read in \n", - "as a pandas DataFrame for use in time-boxing profiles.\n", + "Profile metadata is stored in a CSV file as a table of timestamps. A single profile consists\n", + "of three consecutive stages as noted: Rest, Ascent, and Descent.\n", + "(The Rest stage has the Science Pod parked in the platform at a depth of 200 meters.)\n", + "Profile metadata is read into memory as a pandas DataFrame where each row corresponds\n", + "to an entire profile. \n", "\n", "\n", "### Sensor data\n", @@ -166,7 +167,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Sensor dictionary with abbreviations\n", + "### Sensor dictionary with abbreviations\n", "\n", "\n", "The following table lists sensors in relation to instruments. \n", @@ -246,7 +247,7 @@ "# def WriteProfile(date_id):\n", "# fnm = '../data/osb_ctd_' + date_id + '_pressure.nc' \n", "# a0, a1, d0, d1, r0, r1 = ProfileGenerator(fnm, 'z', True)\n", - " # last 2 days chart check: xr.open_dataset(fnm)['z'][-1440*2:-1].plot()\n", + "# # last 2 days chart check: xr.open_dataset(fnm)['z'][-1440*2:-1].plot()\n", "# if not ProfileWriter('../profiles/osb_profiles_' + date_id + '.csv', a0, a1, d0, d1, r0, r1): print('ProfileWriter() is False')\n", "# \n", "# for date_id in ['apr21', 'jul21', 'jan22']: WriteProfile(date_id) # !!!!! hard coded flag\n", @@ -284,39 +285,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Jupyter Notebook running Python 3\n", "\n", "Jupyter Notebook running Python 3\n" ] }, { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: b'/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_key]\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key]\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n", - "\u001b[0;31mKeyError\u001b[0m: [, ('/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'aeba5f41-b198-417e-bfc2-04b4fa646b79']", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mJupyter Notebook running Python \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(sys\u001b[38;5;241m.\u001b[39mversion_info[\u001b[38;5;241m0\u001b[39m]))\n\u001b[0;32m----> 8\u001b[0m ProfilerDepthChart(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-03\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/rca/sensors/osb/ctd_jan22_conductivity.nc\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;66;03m# index [0] is arbitrary; any dataset will include z data\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Enable this code for a more expansive view of shallow profiler depth history.\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m: VisualizeProfiles(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjan22\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m31\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m01\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJanuary\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOregon Slope Base\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mosb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mctd_jan22_conductivity.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/oceanography/book/chapters/charts.py:25\u001b[0m, in \u001b[0;36mProfilerDepthChart\u001b[0;34m(t0, t1, fnm)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mProfilerDepthChart\u001b[39m(t0, t1, fnm):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m This is a very hardcoded function that generates a two-day span of profiles with some\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m annotations indicating what is going on, particularly with midnight / noon profiles.\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m '''\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(fnm)\u001b[38;5;241m.\u001b[39msel(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(dt64(t0), dt64(t1))) \u001b[38;5;66;03m# this is not profiler metadata. It is actual sensor data.\u001b[39;00m\n\u001b[1;32m 26\u001b[0m fig, axs \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m4\u001b[39m), tight_layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 27\u001b[0m axs\u001b[38;5;241m.\u001b[39mplot(ds\u001b[38;5;241m.\u001b[39mtime, ds\u001b[38;5;241m.\u001b[39mz, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m, ms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m11.\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m, mfc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.0001\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 555\u001b[0m decode_cf,\n\u001b[1;32m 556\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 562\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 563\u001b[0m )\n\u001b[1;32m 565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[1;32m 567\u001b[0m filename_or_obj,\n\u001b[1;32m 568\u001b[0m drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[1;32m 569\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecoders,\n\u001b[1;32m 570\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 571\u001b[0m )\n\u001b[1;32m 572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 573\u001b[0m backend_ds,\n\u001b[1;32m 574\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 585\u001b[0m )\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 571\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 587\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 589\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m store \u001b[38;5;241m=\u001b[39m NetCDF4DataStore\u001b[38;5;241m.\u001b[39mopen(\n\u001b[1;32m 591\u001b[0m filename_or_obj,\n\u001b[1;32m 592\u001b[0m mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m,\n\u001b[1;32m 594\u001b[0m group\u001b[38;5;241m=\u001b[39mgroup,\n\u001b[1;32m 595\u001b[0m clobber\u001b[38;5;241m=\u001b[39mclobber,\n\u001b[1;32m 596\u001b[0m diskless\u001b[38;5;241m=\u001b[39mdiskless,\n\u001b[1;32m 597\u001b[0m persist\u001b[38;5;241m=\u001b[39mpersist,\n\u001b[1;32m 598\u001b[0m lock\u001b[38;5;241m=\u001b[39mlock,\n\u001b[1;32m 599\u001b[0m autoclose\u001b[38;5;241m=\u001b[39mautoclose,\n\u001b[1;32m 600\u001b[0m )\n\u001b[1;32m 602\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 386\u001b[0m clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m 387\u001b[0m )\n\u001b[1;32m 388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m 389\u001b[0m netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(manager, group\u001b[38;5;241m=\u001b[39mgroup, mode\u001b[38;5;241m=\u001b[39mmode, lock\u001b[38;5;241m=\u001b[39mlock, autoclose\u001b[38;5;241m=\u001b[39mautoclose)\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire()\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m 395\u001b[0m ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire_with_cache_info(needs_lock)\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m file\n", - "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 215\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 216\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opener(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2463\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2026\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc'" - ] + "data": { + "image/png": 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42022-01-01 10:12:00-192.02022-01-01 11:07:00-193.02022-01-01 11:07:00-193.02022-01-01 12:17:00-23.02022-01-01 12:17:00-23.02022-01-01 12:56:00-198.0
.......................................
2742022-01-31 12:53:00-190.02022-01-31 11:07:00-191.02022-01-31 11:07:00-191.02022-01-31 12:15:00-23.02022-01-31 12:15:00-23.02022-01-31 15:15:00-191.0
2752022-01-31 15:15:00-191.02022-01-31 13:27:00-190.02022-01-31 13:27:00-190.02022-01-31 14:36:00-20.02022-01-31 14:36:00-20.02022-01-31 17:31:00-193.0
2762022-01-31 17:31:00-193.02022-01-31 15:42:00-191.02022-01-31 15:42:00-191.02022-01-31 16:52:00-20.02022-01-31 16:52:00-20.02022-01-31 19:56:00-193.0
2772022-01-31 19:56:00-193.02022-01-31 18:07:00-193.02022-01-31 18:07:00-193.02022-01-31 19:17:00-20.02022-01-31 19:17:00-20.02022-01-31 23:32:00-193.0
2782022-01-31 23:32:00-193.02022-01-31 20:37:00-194.02022-01-31 20:37:00-194.02022-01-31 21:50:00-15.02022-01-31 21:50:00-15.02022-01-31 23:32:00-193.0
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279 rows × 12 columns

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" - ], - "text/plain": [ - " r0t r0z r1t r1z a0t \\\n", - "0 2022-01-01 00:00:00 -191.0 2022-01-01 00:22:00 -191.0 2022-01-01 00:22:00 \n", - "1 2022-01-01 02:11:00 -194.0 2022-01-01 02:37:00 -194.0 2022-01-01 02:37:00 \n", - "2 2022-01-01 03:38:00 -196.0 2022-01-01 04:47:00 -196.0 2022-01-01 04:47:00 \n", - "3 2022-01-01 06:36:00 -196.0 2022-01-01 07:17:00 -194.0 2022-01-01 07:17:00 \n", - "4 2022-01-01 10:12:00 -192.0 2022-01-01 11:07:00 -193.0 2022-01-01 11:07:00 \n", - ".. ... ... ... ... ... \n", - "274 2022-01-31 12:53:00 -190.0 2022-01-31 11:07:00 -191.0 2022-01-31 11:07:00 \n", - "275 2022-01-31 15:15:00 -191.0 2022-01-31 13:27:00 -190.0 2022-01-31 13:27:00 \n", - "276 2022-01-31 17:31:00 -193.0 2022-01-31 15:42:00 -191.0 2022-01-31 15:42:00 \n", - "277 2022-01-31 19:56:00 -193.0 2022-01-31 18:07:00 -193.0 2022-01-31 18:07:00 \n", - "278 2022-01-31 23:32:00 -193.0 2022-01-31 20:37:00 -194.0 2022-01-31 20:37:00 \n", - "\n", - " a0z a1t a1z d0t d0z \\\n", - "0 -191.0 2022-01-01 01:32:00 -19.0 2022-01-01 01:32:00 -19.0 \n", - "1 -194.0 2022-01-01 03:15:00 -105.0 2022-01-01 03:15:00 -105.0 \n", - "2 -196.0 2022-01-01 05:57:00 -24.0 2022-01-01 05:57:00 -24.0 \n", - "3 -194.0 2022-01-01 08:30:00 -12.0 2022-01-01 08:30:00 -12.0 \n", - "4 -193.0 2022-01-01 12:17:00 -23.0 2022-01-01 12:17:00 -23.0 \n", - ".. ... ... ... ... ... \n", - "274 -191.0 2022-01-31 12:15:00 -23.0 2022-01-31 12:15:00 -23.0 \n", - "275 -190.0 2022-01-31 14:36:00 -20.0 2022-01-31 14:36:00 -20.0 \n", - "276 -191.0 2022-01-31 16:52:00 -20.0 2022-01-31 16:52:00 -20.0 \n", - "277 -193.0 2022-01-31 19:17:00 -20.0 2022-01-31 19:17:00 -20.0 \n", - "278 -194.0 2022-01-31 21:50:00 -15.0 2022-01-31 21:50:00 -15.0 \n", - "\n", - " d1t d1z \n", - "0 2022-01-01 02:11:00 -194.0 \n", - "1 2022-01-01 03:38:00 -196.0 \n", - "2 2022-01-01 06:36:00 -196.0 \n", - "3 2022-01-01 10:12:00 -192.0 \n", - "4 2022-01-01 12:56:00 -198.0 \n", - ".. ... ... \n", - "274 2022-01-31 15:15:00 -191.0 \n", - "275 2022-01-31 17:31:00 -193.0 \n", - "276 2022-01-31 19:56:00 -193.0 \n", - "277 2022-01-31 23:32:00 -193.0 \n", - "278 2022-01-31 23:32:00 -193.0 \n", - "\n", - "[279 rows x 12 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "profiles = ReadProfileMetadata()\n", "profiles" @@ -650,20 +375,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-01-01 00:22:00 2022-01-01 01:32:00 Time difference: 0 days 01:10:00\n", - "Type of these times: \n" - ] - } - ], + "outputs": [], "source": [ "# profile time access syntax:\n", "profile_row_index = 0\n", @@ -674,32 +390,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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IyFxMgIjIbXy35xwA4KaeEU5d99OYWqWEh9JQwM1hMCLzMQEiIrdQXavDjwfyAAC3XxcpczTWxUJoIssxASIit7D+UD4qtfWIaueN/k644OGVcD8wIssxASIit2Ac/prcJxIKhXOu+dMS7gdGZDkmQEQuqLpWh91ZxZwV1OB8aTW2nroIALjtWtca/gIabYfBHiAis7lGFSARmZwsrMBDX+zFycJKDIsPwX/+0gcBXh5yhyWrVftyIQQwoHM7RLf3kTscqzOtBq3latBE5mIPEJEL+fHAedzyn604WVgJAPjf8QuY/N42nC3SyByZfIQQpuEvVyt+NjLtB8YeICKzMQEicgG19Xq88sMhzPxqH6pqdUjp0h6f3dcf4QFeOFlYiYmLt2LH6SK5w5TF3uwSnLmogY+nEjf1jJA7HJu41APEBIjIXEyAiJxcflkN7vpwB5ZuzQIAPDy8Kz6/vz+ujw/BmpmD0SsyEKVVdbj7o534ele2vMHKwNj7M66H66z982esASKyHBMgIie2/VQRxr/7O/acLYG/lwofpF6HZ8d2h0pp+NUODfDC1w+mYHxyBOr1As+uyMS/fjzsNsXR1bU6/LjfNdf+aYzrABFZjgkQkZMqq67D/Z/twsXKWnQP98cPM4dgTFL4Zffz8lDi3bv64O83dAMAfLTlDJ7+dr+9w5XFhsP5qNDWIzLYGwM6u9baP42Z1gFiAkRkNiZARE7qf8cvoKpWh84dfLHqkcGI7eDb4n0lScLfb4jHf/7SB5IErNyXi+yiKjtGKw/j8Ndt17re2j+N+XEIjMhiTICInNRvRwsBAGMSw+DtqTTrMeOTO2Jw1w4AgO/2nrNZbI4gr6waW0667to/jfmzCJrIYmZVBB44cMDiEycmJkKlcs2CQyK56fQC6ccMCdCI7qEWPfaOvpHYcvIiVuw5h7+P6uayPSMr97r22j+NmVaCZg8QkdnMylB69+4NSZIghHmFkwqFAsePH0eXLl3aFBwRNS8jpxQlVXUI8FLhuphgix57Y1I4/L1UyC2txvbTRRgc18FGUcpHCIEVxuEvFy5+NuI0eCLLmd1Fs3PnToSEhFz1fkII9OjRo01BEdGVbWoY/hoWHwIPpWUj2V4eSkzo1RFf7czGt7tzXDIB+vD30zh9UQNvD9dd+6exSwshciVoInOZlQBdf/31iIuLQ1BQkFknHTZsGLy9vdsSFxFdgbH+Z6SFw19Gd1wXia92ZmPtwXy8WlPnUltlpP2RjTd+PgoAeGJ0vCk5cGWNa4CEEJAk1xzWJLIms/503LRpk9nJDwD8/PPPiIhw/b+6iOSQV1aNw3nlkCTg+vir98o2p3dUEOJC/aCt15vWyXEFP2fm4blVmQCAB6/vgr8Nc49heGOSpxdAdZ1O5miInANngRE5mU1HLwAA+kQFob2fulXnkCQJdzTUxny7J8dqsclp8/ELmJW2D3oB3NU/Gv83trvcIdmNj6cSxk4fFkITmcdqCVBOTg7uu+8+a52OiFrQ1uEvo1uv7QSlQsK+7FKcLKywRmiy2XO2GA99vgd1OoGbkyPwr0k93GoYSJKkS3VALIQmMovVEqDi4mJ89tln1jodETWjpk6HrQ1r21g6/f3PQv29MLxhCO3bPc67JtCRvHLMWLoL1XU6XB8fgnfu7A2li07tvxLuB0ZkGbOrA9esWXPF20+fPt3mYIjoynacLkJ1nQ7hAV5IjAho8/nu6BuJjUcLsXJvLp4ek2DaQ8yRCCHw7w3HcPh8Obw9lfBSKeFl/O6hwDe7z6G8ph59Y4Lx/t3XwVPleG2wBz8vFVDGqfBE5jI7AZo0adJV1wJypy5nIjkYp7+P6B5ild+3kd3D0M7XExcqtPjfiQsY2T2szee0tu2ni7B406kr3ueaiAB8fG8/s1fEdkWXpsIzASIyh9kJUEREBBYvXoxJkyY1e3tGRgauu+46a8VFRH8ihMBvx4z1P9ZJVDxVCkzs3RFLt2bh293nHDIB+nz7WQCGmqfhCSGoqdOhulaPmnodqmt18PdS4d5BsQj0dp2p/K3h17CUAXuAiMxjdgJ03XXXYe/evS0mQJasFE1Eljt1oRI5xdXwVCkwOK691c57x3VRWLo1C78eKUCxphbtfD2tdu62yiurxobDBQCAZ8d2R0K4v8wROa5LNUBcDBEALlRosXTrGdTp9AjxVyPEX40OfobvIX5qBPt4uuw2MGQesxOgp59+GhqNpsXb4+LisGnTJqsERUSX23jE0PszsEt7+Hhab3G/xI4BSOoYgEPny/F9Ri5mDO5stXO31Vc7s6HTCwzo3I7Jz1WYdoRnDxAyz5Xhgc93I6+s5or381OrDF9ehu/+Xir4eCqh0wto6/WordejVmf4rq3Xo0fHALw0IQnBDvRHArWe2e+iQ4cOveLtvr6+uP7669scEBE1zzT9PaF1ix9eyR3XReLQ+cP4dvc5h0mAauv1WP6HYY2i6Smx8gbjBIz7gbn7NPjvM3LxzHcHoK3Xo0uIL0Z1D8XFylpcqNAaviq1KNbUAjAki5XaeqDcvHOfLKzErqwSvH/3degZGdimODXaeuiEgGngRBi/CdTrBep1AnU6fcOX4d9eHgpEt/N120J/a2vTn5HLly/HLbfcAl9fX2vFQ0TNKKuuw+6zJQCsV//T2MTenfDGz0dxOK8cqR/vRK/IIPToFIiekYHoGOglywSHtQfzcLFSi7AANcYkOV5tkqPxc/Np8Dq9wFvrj+H9zYaC+REJIVh4V59mt3mp0+lRWlUHTUMCVF5Th8oaw781tTqoFBI8lQp4qhRQqwzf63QC//rpMM4WVeG297fhtYlJmNIv2uI4t526iLc3HDf9PltKpZDQJcQX3cL8kRDmj/gwf3QL84O/WgWFQoJKIV36LhnawaG+5rUpAXrwwQcxYMAA7vpOZGO/n7gAnV4gLtQP0e19rH7+YF9PTOzdEd/uOYffT1zE7ycumm5r5+uJHp0CcfeAaIxJCrf6c7fEWPx8V/9oizd8dUf+brwjfFl1HWal7UP6McMq6Q8P74qnxiS0uB6Uh1JhqguyRP/O7fDkNxn49Ughnl2RiX3ZpXj5liR4eVx99uGesyWYv+EYtp0qMuu5VAoJKqUED6Wi4UtCZY0hQTteUInjBZX4CVffxsbbQ4n4MD8khBuSpe7hAUgI97e47a6oTQkQi56J7MNaqz9fybzbkjFtYAwyc8uQea4UmbnlOFFQgWJNLf53/AL+d/wCHhneFU9e4YPFWg6dL8PusyVQKST8pb/lf2W7I3fsAdJo67EvuxQvfn8Qpy9q4OWhwLzbkjGxdyebPF+gtwc+SO2L99JPYv4vx5G2KweHzpdjyd3XIjK4+T9MDuaWYf6GY9jUkJx5KCXc1T8aD13fFR0abWXTuJNVKUnN9toIIXC+rAbH8ytwvKACxwoM309f0KCmTgd9Mx/J1XU67D9Xhv3nypocD/LxgKdSAdFwXiEAvRAQMCSIfmpDPZRvQ52Ur1oFbw+FaXiuXn9paK5eJxAaoEZyQ69xYkRgs0tS6PUCeeU1OFVYiVMXKhHo7YHJ10Ze/cLbiOtvk0zk5HR6YfrLdkSC7RIghUJC76gg9I4KAhADwLDy9NH8Cqzel4tPt2XhvfRTOJpfgQVTe9t0B3lj78+NPcIRGuBls+dxJa5eA1Sv0+NYQQX255Rhf04pMnJKcaKwwvSh3zHQCx9M74sendpWm3M1CoWEmSO7ITkyCI+n7UNmbhnGLfgd0e19oGwYdlIqDF91Oj32ZZcCAJQKCbdfG4nHRsW1mCxdjSRJ6BTkjU5B3s2uBC+EgE4voGv4Xq8XuFChxbH8ChzNr8Cx/HIcL6hEVpEGpVVXni14oUJrcXwr9+YCABQS0C3UHz06BaJTkBfOFlfhZGElTl/QNNms97qYYOdNgNauXYuOHTtaKxYiasb+c6Uo1tTC30uFvrHBdn1uLw+lKSnqHRWEZ1ccwG9HCzFp8VZ8OL0vuob4Wf05y6rqsDrD8EY6fWCM1c/vqly5B6iwogY3L9rS7Idyx0AvDOzaHrPHXWPXYZ1h8SH48bEheOTLvThwrgyHzjdfSS1JwMReHTHrhnh07mDbellJMgybNf5gD/DyQNcQP9zUM8J0rLpWh7PFGuj0AgpJgiQBEozfgVqdHhqtDpraemi09Q21UjrU1OkahuYMQ3IqheG7UiHhbFEVDuaW4UBumSHpauih+jOVQkJsB190DfFFcmSQTa/H1bQpARoyZIi14iCiFmw8YlgHZ1i3EFlrYSb16YSuIX544PPdOH1Bg0n/2YpFd/Vp855kf/btnhzU1OnRPdwf/Tu3s+q5XZmxBkhT63oJ0K4zJbhQoYVapUDf2GD0jgpCr0hDUi5nD2FksA9WPDwIe86WoLpOB31Dr4u+US9Mj06BNvlDoS28PZXoHt72rXRaUlBeg8xzhmSosLwGMe19ERfqh64hvohq5+MwNX0WJ0BFRUV48cUXsWnTJhQWFkKv1ze5vbi42GrBEbk7IQR+zswHAIeYCdUzMhBrZg7BI1/uwa6sEtz32S48fWMCHr6+q1Vmiun1Al/sMAx/pabEcHsdC/ipG1aCdsEeoLPFhjXobu4Zgben9JY3mD/xUCowsIv1FiZ1BWEBXghL9MINifK/Z12JxQnQ3XffjVOnTuH+++9HWFhYm96glixZgiVLliArKwsAkJSUhBdffBHjxo0DANx7772X7TA/YMAA7Nixo8Vzfvrpp5gxY8Zlx6urq+HlxVoCci5H8ipw5qIGapUCo65xjDeTEH81vvzrQLz8wyF8tTMbb647hqN5FXjz9mSzZsNcye8nLyKrqAr+ahUm2aiQ1VW5cg3Q2YtVAGCTGZDkvixOgLZs2YItW7agV69ebX7yyMhIzJ07F3FxcQCAzz77DBMnTsS+ffuQlJQEABg7diyWLl1qeoyn59VX4AwICMCxY8eaHGPyQ87op8zzAIDhCSGmGg9H4KlS4I1beyIxIgAvrzmENfvP48xFDT6Yfh0iAr1bfd7Pt2cBAG67LhK+DtReZ2B8fRhWLdZBrXKdjWGNPUAxTIDIiix+h+nevTuqq6ut8uQTJkxo8vPrr7+OJUuWYMeOHaYESK1WIzzcsrVHJEmy+DFEjqbx8FfjAkZHcvfAGMSF+uHhL/YgM7cMt/xnK/6beh2ujba8WDunuAobG6b7p6aw+NlSjRNkjda1EqDsIkMPUEx7LrpL1mNxJdJ7772H559/Hps3b0ZRURHKy8ubfLWWTqdDWloaNBoNUlJSTMfT09MRGhqK+Ph4/O1vf0NhYeFVz1VZWYmYmBhERkZi/Pjx2Ldv3xXvr9VqrdYOImtxxOGv5gzs0h5rZg5B93B/XKjQYup/d+C7PecsOsf2U0WY/skfEAIYEtfB4YpGnYFSIcGnYe0VV6oD0tbrkFdu2NMrph17gMh6LE6AgoKCUFZWhpEjRyI0NBTBwcEIDg5GUFAQgoMt/6svMzMTfn5+UKvVeOihh7Bq1SokJiYCAMaNG4cvv/wSv/32G+bPn49du3Zh5MiR0GpbXp+ge/fu+PTTT7FmzRosX74cXl5eGDx4ME6cONHiY+bMmYPAwEDTV1RUlMXtILI2Rx3+ak5UO8NsmBuTwlCr0+Opb/fjXz8eRr1Of8XHlVXV4dnvDuCuD3fgzEUNQv3VmH1TdztF7XqMr5MKrevsCJ9TXA0hDG1rx01IyYokYeFyzv3794dKpcKsWbOaLYK2dEPU2tpaZGdno7S0FCtWrMBHH32EzZs3m5KgxvLy8hATE4O0tDRMnjzZrPPr9Xpce+21GDZsGBYtWtTsfbRabZOkqry8HFFRUSgrK0NAgO2mChK1RAiBkfM348xFDRZO7W2zlW2tTa8XWLDxBBZtNPzB0c7XE8MTQjCqexiGxncwLZ4ohMBPmXl4ec1hXKw0/O5NGxCNZ8d1t+kCi65u5Px0nL6gwdcPDMQAF5mZ9NvRAtz36W4kRgTg51lX3pSbqLy8HIGBgWZ9flv8Z+XBgwexb98+JCQktDrAxjw9PU1F0H379sWuXbuwcOFC/Pe//73svhEREYiJiblib86fKRQK9OvX74qPUavVUKu5Lwo5DmcZ/vozhULCE6PjkRDmj+dXZ6JYU4uVe3Oxcm8uVAoJ/Tu3w8juodhxugi/HjEMZ3cN8cXc25LRL5Zr/rSVv9r19gPLumis/+HwF1mXxQlQ3759kZOTY7UE6M+EEC0OcRUVFSEnJwcREeYXhAohkJGRgZ49e1orRCKbc6bhr+bcnByBMUlh2HO2BBuPFGDj0UKcvqDBtlNFps0gPZQSHhkeh0dGdHWpgl05+bnghqjZxZwCT7Zh8TvrY489hlmzZuHpp59Gz5494eHRtLs6OTnZ7HM999xzGDduHKKiolBRUYG0tDSkp6dj3bp1qKysxMsvv4zbbrsNERERyMrKwnPPPYcOHTrg1ltvNZ1j+vTp6NSpE+bMmQMAeOWVVzBw4EB069YN5eXlWLRoETIyMrB48WJLm0okC2eY/WUO4wJxA7u0x/M3J+LMRQ1+O1qI9GOF8PFU4skxCYgP85c7TJdiqgFyoSLos0UNU+DbcQYYWZfFCdCUKVMAAPfdd5/pmCRJEEJAkiTodLqWHnqZgoICpKamIi8vD4GBgUhOTsa6deswevRoVFdXIzMzE8uWLUNpaSkiIiIwYsQIfP311/D3v/SmmZ2dDYXiUi13aWkpHnjgAeTn5yMwMBB9+vTB//73P/Tv39/SphLJwlmHv66mcwdf3D+kM+4f0lnuUFyWaTVoF+oBOtvQAxTLHiCyMosToDNnzljtyT/++OMWb/P29sb69euveo709PQmP7/zzjt455132hoakWycffiL5GPcD8xVpsHr9ALnig3rznEIjKzN4nfXmBguUEZkK64y/EXy8HOxIuj88hrU6vTwUEptWmGcqDkWrwM0Z84cfPLJJ5cd/+STTzBv3jyrBEXkrlx1+Ivsw7QfmIv0AJ29aKj/iQr2gVLBjXHJuixOgP773/+ie/fLFypLSkrC+++/b5WgiNwVh7+oLXxNPUCusRDiWc4AIxuyOAHKz89vdhp6SEgI8vLyrBIUkTvi8Be1lautA3TWuAcYt8AgG7A4AYqKisLWrVsvO75161Z07NjRKkERuSMOf1FbmWqAXGQILNu0CzynwJP1WdzH/te//hV///vfUVdXh5EjRwIANm7ciGeeeQZPPvmk1QMkchcc/qK2MtUAuVoPEIfAyAYsfpd95plnUFxcjEceeQS1tbUAAC8vLzz77LOYPXu21QMkchdrOfxFbeRKPUBCCGQzASIbsjgBkiQJ8+bNwwsvvIAjR47A29sb3bp1415aRG1wsVKL0w0zXoYnhMocDTkrfxfaCqNYU4sKbT0kCYgMZgJE1tfqfnY/Pz/069fPmrEQua3M3DIAQJcQXwR6czd0ah1jD1BVrQ46vXDqqePGGWDhAV7w8uBecWR9FhdBt+S9997Dq6++aq3TEbmVzHOGBCi5U6DMkZAzM9YAAc7fC2Qc/ormDDCyEaslQCtWrMCnn35qrdMRuZUDDQlQz8ggeQMhp6ZWKeGpNLytO3sCxAJosjWrTTXZuHGjtU5F5HYyc0sBAMmR7AGitvHzUqFYU+v0hdBnOQWebMxqPUBE1DqF5TUoKNdCIQGJEQFyh0NOzs9FVoPmDDCyNbMToJMnT2LPnj1Njm3cuBEjRoxA//798cYbb1g9OCJ3YCyAjgv1M21lQNRaxgTI2fcDyzKtAs0eILINsxOgp59+GqtXrzb9fObMGUyYMAGenp5ISUnBnDlzsGDBAhuESOTaTPU/nYLkDYRcgp8LTIXXaOtxsVILgPuAke2Y/efm7t278cwzz5h+/vLLLxEfH4/169cDAJKTk/Huu+/i73//u9WDJHJlxh4g1v+QNfi7wGKI2Q1T4IN8PLgsBNmM2T1AFy9eRGRkpOnnTZs2YcKECaafhw8fjqysLKsGR+TqhBCNZoAxAaK2c4UeIG6CSvZgdgLUrl07027ver0eu3fvxoABA0y319bWQghh/QiJXFh+eQ0uVmqhVEgsgCarcIUaIG6CSvZgdgJ0/fXX47XXXkNOTg4WLFgAvV6PESNGmG4/fPgwYmNjbREjkcsy9v50C/XjardkFS7VA8T6H7Ihs2uAXn/9dYwePRqxsbFQKBRYtGgRfH0vZeeff/65aXd4IjKPaQVoDn+RlbhCDdBZrgJNdmB2AtS5c2ccOXIEhw8fRkhICDp27Njk9ldeeaVJjRARXd2BXK4ATdZ1aR0gJ06AOARGdmDRoiMeHh7o1atXs7e1dJyImieEQOa5UgDcA4ysx8/LMGuqwkkToDqdHudLawBwCIxsy+JV14QQ+O6777Bp0yYUFhZCr9c3uX3lypVWC47IlZ0rqUZJVR08lBK6R/jLHQ65CFMPUI1zrgSdW1INnV7Ay0OBUH+13OGQC7M4AZo1axY++OADjBgxAmFhYZAkyRZxEbk84/o/CeH+UKtYAE3W4e/kRdBniy/V//DzhWzJ4gToiy++wMqVK3HTTTfZIh4it8EVoMkW/Jy8CDq7iPU/ZB8Wb4YaGBiILl262CIWIrfCHeDJFozT4J21BiiLiyCSnVicAL388st45ZVXUF1dbYt4iNxCkxWgWQBNVuTfaBaYMy5OyzWAyF4sHgK74447sHz5coSGhiI2NhYeHk33adm7d6/VgiNyVWeLqlBRUw9PlQLxYSyAJusx9gAJAVTV6uCrtvhtXlbGVaCjOQRGNmbxb8a9996LPXv24O6772YRNFErGdf/uSYiAJ4qiztiiVrk7aGEQgL0wtAL5EwJkBDCtBEqh8DI1iz+zfjpp5+wfv16DBkyxBbxELkFrv9DtiJJEvzUKpTX1KOiph5hTrTFXGGFFjV1eigVEjoFe8sdDrk4i//0jIqKQkCAE/1GkdMrLK9BsaZW7jCsijvAky35NyyG6GxT4bMuGoa/OgZ5wUPJnlGyLYt7gObPn49nnnkG77//Pjc/JasSQqCgXIvM3DJk5pbhYMP3CxWG3dKnp8Tg76PiEejjcfWTOTC9XuBgLvcAI9tx1qnwxjWAYln/Q3ZgcQJ09913o6qqCl27doWPj89lRdDFxcVWC47cx4ZD+Xjlh8PILb18dqEkATq9wNKtWVi9LxdPjknAXf2joVQ4Z/3Z6YsaaGp18PJQIC7ET+5wyAVd2hHeuVaDzuYmqGRHFidACxYssEEY5K5q6nSY8/MRfLb9LABAIQHdQv3Ro1MgenYKQM/IQFwTEYA9Z0vw6g+HcaKwEv9cfRBf7DiLl29JwsAu7WVugeWM6/8kdQyEit38ZAPGHqAKJ+0B4hR4sgeLE6B77rnHFnGQGzpZWInHlu/DkbxyAMDfhnbGP0bHw8fz8pfl0G4hWDtrKL7YcRZv/3IcR/MrMPWDHbi5ZwRempCI0AAve4ffalz/h2zNz0m3wyhpqPXr4Mc9wMj2zPrzs7y83KKTVlRUtCoYcg9CCHyzOwcT3t2CI3nlaO/riaUz+uH5mxObTX6MVEoF7h3cGelPj8DdA6OhkICfMvNw63vbcPpCpR1b0DaZ51j/Q7bl76Q1QJpaQ7zONHWfnJdZCVBwcDAKCwvNPmmnTp1w+vTpVgdFrquipg5//zoDz3x3ANV1OgyOa4+1s4ZiREKo2edo5+uJf03qiZ8eH4ouHXyRW1qN29/fjgMNU8sdmU4vcOi84Q8KJkBkK35q5+wB0jTE68cEiOzArFeZEAIfffQR/PzMK9isq3OuwjuyPZ1e4Ls9OZi/4TgKG2Z1PTE6Hg9d37XVxczXRATgm4dSMGPpLmTmluGuD3bgg+l9MTiug5Wjt55TFypRXaeDr6cSnTuwAJpsw1mHwDRaHQDAx1MpcyTkDsxKgKKjo/Hhhx+afdLw8PDLZoeR+/r9xAW8/tMRHM03DI3GtPfB23f2wnUx7dp87g5+aix/YCAe/Hw3tp4swoylu/DOlN64OTmizee2tqraery57igAQwG0s85iI8fnrD1AVRwCIzsy61WWlZVl4zDIFZ0oqMAbPx/BpmMXAAABXio8PqobUlNioFZZ7y88P7UKn9zbD//4OgM/Z+Zj5vK9KK7qgdSBMVZ7jrYqKK/B/Z/twsHccngqFXhkRFe5QyIX5u/lrDVA7AEi+2GaTVanrdfh9Z+O4Mud2dDpBVQKCakpMXh8ZDcE+3ra5DnVKiXevetaBPscxJc7s/HC6oMor67DoyPibPJ8ljh0vgz3f7ob+eU1aOfriQ9Sr0Pf2Lb3fhG1xE9t6IGvcKIeoDqdHrX1egCA7xUmQxBZC19lZFV1Oj1mfrUPvxwuAADcmBSG/xt3DTp3sP3KrkqFhH9N6oEOfmos3HgC/95wDLddG4nwQPmmyP92tAAzv9qHqloduob44pN7+yGGq9ySjfk5YQ9QVUP9D8AhMLIPrsJGVqPTCzz17X78crgAnioFPrm3L/6b2tcuyY+RJEn4x+h49IoKghDA5uPmz160tqVbz+Cvn+1GVa0Og7q2x8qHBzP5Ibtwxhog4xR4D6UETxU/msj2+CojqxBC4J+rM/F9xnmoFBKWTLsWI7uHyRbPiIQQAMCmoxdkef6Fv57AKz8chl4AU/pG4bP7+jv9HmbkPPydcBaYsQD6SmuBEVkTEyBqMyEEXvvxCJb/kQOFBCyY2hujrpEv+QFgWldoy8mLproCe9HW6/DB/04BAJ6+MQFzb+vJna3JrpxxM1TjFHhfFkCTnVj8rhwbG4tXX30V2dnZtoiHnNA7vxzHJ1vPAADm3ZaM8ckdZY7IsM1Ee19PVGrrsedsiV2fe/upImhqdQj1V+Ph67tCkjjdnezLWANUq9NDW6+7yr0dg3EIzIf1P2QnFidATz75JL7//nt06dIFo0ePRlpaGrRarS1iIyfw/uZTWPTbSQDAK7ck4Y6+UTJHZKBQSLi+YRgs/Zh964B+PWIoAB91TRgUXOuHZNB4FpWz9AIZi6BZAE32YnEC9Nhjj2HPnj3Ys2cPEhMT8fjjjyMiIgIzZ87E3r17bREjOSAhBD76/TTmrjUs7PfM2ATcMyhW3qD+xDgMtsmOCZAQAr8eNjzfmER5hwHJfSkVkmkoyVnqgEz7gHEIjOyk1YUJvXr1wsKFC5Gbm4uXXnoJH330Efr164devXrhk08+gRDCmnGSA6nX6fHSmkP4109HAACPjuiKR4bLv97Onw3t1gEKCTheUIlzJVV2ec5D58uRX14Dbw8lUrq2t8tzEjXHOAxW4SQ9QJe2wWAPENlHqxOguro6fPPNN7jlllvw5JNPom/fvvjoo49w55134vnnn8e0adOsGSc5iEptPf62bDeWbT8LAJg9rjueGpMgc1TNC/LxxLXRwQCA9GP2mQ1mXP9oWHwHeHnwL1mSj6+TTYW/tA0Gf2/IPixOtffu3YulS5di+fLlUCqVSE1NxTvvvIPu3bub7jNmzBgMGzbMqoGS/PLKqnHfp7txJK8capUCC6b0xriejrfnVmMjuodi99kSpB+7gLvtsDWGsf7nBplnwRG18/HEaWhQUF4jdyhmYQ8Q2ZvFPUD9+vXDiRMnsGTJEpw7dw7//ve/myQ/AJCYmIipU6daLUiS38HcMkz8z1YcyStHBz81vn4wxeGTHwAY3lAIvfXkRZvPhjlfWo1D58shScDI7qE2fS6iq0nqGAAAOHCuTOZIzFPFGiCyM4tT7dOnTyMm5sp/Sfv6+mLp0qWtDoocy6+HC/DY8n2ortMhPswPn9zbD5HBPnKHZZbEiACE+qtRWKHFH2eKMbRbiM2ea2ND78910cFo76e22fMQmaNXVBCw/Sz255TKHYpZjEN1nAVG9mJxD9CIESNQVFR02fHS0lJ06dLFKkGRYxBCYEn6Kfzt892ortNhaLcO+O7hQU6T/ACGrTFMs8FsvCr0L0cMs79u4OwvcgC9ooIAAAfPl6FOZ9/FQFujqtY4DZ49QGQfFidAWVlZ0OkuH0rQarXIzc21SlAkv+paHWalZWDeuqMQApg2IBqf3NsPAV7Ot53DcDusB1RRU4ftpy4CYP0POYbO7X3h76VCTZ0exwsq5A7nqjRaboVB9mX2K23NmjWmf69fvx6BgYGmn3U6HTZu3IjY2FirBkfyyCurxgPL9iAztwwqhYSXbklCqh0KiG1lcLcOUCkknL6owdkijU02JP3f8Yuo0wl07uCLriHc8JTkp1BI6BUZhC0nL2J/ThmSOgZe/UEyYg8Q2ZvZCdCkSZMAGIYU7rnnnia3eXh4IDY2FvPnz7dqcGR/e84W48HP9+JipRbBPh5Ycvd1GNjFudezCfDyQN/YYOw4XYz0YxdwzyDLEhQhBMpr6hHo3XLvl3H21+jEMG59QQ6jV1RgQwJUir8MiJY7nCvScDNUsjOzh8D0ej30ej2io6NRWFho+lmv10Or1eLYsWMYP368LWMlG/tmVw6mfrADFyu16B7ujzUzhzh98mPU2lWhz5dW4/b3t+Pa137Bt7tzmr1PvU6P34421P9w+IscSK/IIADA/nOlssZhDuNWGH4sgiY7sfiVdubMGVvEQTLJuqjB2oP5WHswzzRddlyPcPz7jl4uNRtjRPdQzFl7FNtPFaG6VgdvM6baph8rxD++zkBJVR0AYPbKTEQEemNItw5N7rf7bAnKqusQ7OOBa6ODbBE+Uav0biiEPl5QAY223qF/pytNNUAcAiP7MOu3YdGiRXjggQfg5eWFRYsWXfG+jz/+uFUCI9s5WViJtZl5WHswH4fzyk3HlQoJj42Mw+Mju7ncJp7dQv3QKcgbuaXV2HG6CCOusE6PTi+w4Nfj+M+mkxAC6NEpAJ2CvLH+UAEe/mIPvn04Bd3DA0z3/7Vh9ecR3UOhUrZ6cXUiqwsN8EJEoBfyympwMLcMAxy4R/fSStCOm6SRazHrlfbOO+9g2rRp8PLywjvvvNPi/SRJsigBWrJkCZYsWYKsrCwAQFJSEl588UWMGzcOAHDvvffis88+a/KYAQMGYMeOHVc874oVK/DCCy/g1KlT6Nq1K15//XXceuutZsdlK6cuVOL5VZnw8VTB21MJbw8lfBq+e3sqoVYpIUmAMfUwlpJIkNBSWUm9XqCmToeaOj1q6nTQ1hv+XV2rQ3WdDtW1OlTV1aOqVoeaWh00tTqUVdeZHq9USEjp0h7jeoZjTGI4Qvxdc/0aSTLsDv/VzmykHytsMQEqrKjBrOUZ2H7asNTDtAHReGF8IiQJSP3oD/yRVYz7lu7CqkcHIyzAC0II/GKs/+HwFzmg3lFByCvLx/5zpQ6dAGlqjStBsweI7MOsBKjxsJc1h8AiIyMxd+5cxMUZNtL87LPPMHHiROzbtw9JSUkAgLFjxzZZVNHT0/OK59y+fTumTJmC1157DbfeeitWrVqFO++8E1u2bMGAAQOsFntrlGhqseN0sawxAICHUsLguA64qUcERieGIdj3ytfUVYxICMVXO7Ox6dgFvCxEk2Llipo67MoqxrMrMnGhQgsfTyXmTO6Jib07me7zwfTrMHnJNpy+oMF9n+7CNw+mIK+sGmeLquCpVGBovO0WWSRqrV5RQVh7MB8ZDrwgYp1Oj9p6w1pFviyCJjux+JW2efNmXH/99VZ58gkTJjT5+fXXX8eSJUuwY8cOUwKkVqsRHh5u9jkXLFiA0aNHY/bs2QCA2bNnY/PmzViwYAGWL19ulbhbq3MHXyy6qw9qanWoqq1HdZ0e1bX1qK7ToapWB229HkIAAsLwAAEIGGYhtTSzSCFJ8PJQwMtDafiuUpr+7e2putTL5Gn47uOpRFiAF/ydcD2fthrUtT08lQpkF1fh3d9O4mKlFqcuVOJkYSUKyrWm+3UL9cOSu69FXKh/k8cH+Xji03v749b3tuLQ+XLM/GovrosxbLaa0rU9izfJIZkKoXMcd0sM4xR4gENgZD8Wv9JGjx6N8PBw/OUvf8G0adPQs2dPqwSi0+nw7bffQqPRICUlxXQ8PT0doaGhCAoKwvXXX4/XX38doaEt129s374d//jHP5ocu/HGG7FgwQKrxNkW7f3UuKVXR7nDcFu+ahUGdGmH309cxNu/HL/s9g5+atyYFIbnb76mxam40e198NE9fXHXhzuw6dgF/H6iYfFDrv5MDqpnZCAkCcgtrUZhRQ1C/b3kDukyxkUQPZQSPFWsoyP7sDgBOn/+PNLS0rB8+XK8+eab6NGjB+6++2785S9/QWRkpMUBZGZmIiUlBTU1NfDz88OqVauQmJgIABg3bhzuuOMOxMTE4MyZM3jhhRcwcuRI7NmzB2p187Uq+fn5CAtr+mEUFhaG/Pz8FmPQarXQai/1AJSXl7d4X3JujwyPQ6W2Hh381Oga4oeuIb7oGuqHrh38EOhjXq9Yn+hgLJjSBw9/uQf1ekNv3Q3XcPNTckx+ahW6hfrheEElDuSU4YZEx0uAqrgGEMnA4lS7Q4cOmDlzJrZu3YpTp05hypQpWLZsGWJjYzFy5EiLA0hISEBGRgZ27NiBhx9+GPfccw8OHz4MAJgyZQpuvvlm9OjRAxMmTMDatWtx/Phx/PTTT1c855+Hi640hAQAc+bMQWBgoOkrKirK4naQc0jp2h6rHhmMD6f3xf+N6447+kbh2uhgs5Mfo7E9wvHPmw2Jet+YYEQEetsiXCKrcPT1gDQNawBxJ3iypzb1NXbu3Bn/93//h7lz56Jnz57YvHmzxefw9PREXFwc+vbtizlz5qBXr15YuHBhs/eNiIhATEwMTpw40eL5wsPDL+vtKSwsvKxXqLHZs2ejrKzM9JWT0/yCd0SN3T+kM1Y/Ohjvp14ndyhEV2TcGNVRC6FNq0Cz/ofsqNUJ0NatW/HII48gIiICf/nLX5CUlIQff/yxzQEJIZoMRzVWVFSEnJwcREREtPj4lJQU/PLLL02ObdiwAYMGDWrxMWq1GgEBAU2+iMzROyoIHfxcc+kAch3GBRH355RCCCFvMM2oYg8QycDidPu5557D8uXLcf78edxwww1YsGABJk2aBB8fH4uf/LnnnsO4ceMQFRWFiooKpKWlIT09HevWrUNlZSVefvll3HbbbYiIiEBWVhaee+45dOjQocmaPtOnT0enTp0wZ84cAMCsWbMwbNgwzJs3DxMnTsT333+PX3/9FVu2bLE4PiIiV5AQ7g9PlQLlNfXIKqpC5w6OtWGvhosgkgwsfrWlp6fjqaeewpQpU9ChQ4erP+AKCgoKkJqairy8PAQGBiI5ORnr1q3D6NGjUV1djczMTCxbtgylpaWIiIjAiBEj8PXXX8Pf/9L05OzsbCgUlzqyBg0ahLS0NPzzn//ECy+8gK5du+Lrr7+WfQ0gIiK5eCgV6NExAHuzS7E/p9TxEiCtcRFEJkBkPxa/2rZt22a1J//4449bvM3b2xvr16+/6jnS09MvO3b77bfj9ttvb0toREQupVdUEPZmlyIjpxST+nS6+gPs6NI2GBwCI/sxKwFas2YNxo0bBw8PD6xZs+aK973lllusEhgREVmPqQ7IAWeCsQeI5GDWq23SpEnIz89HaGgoJk2a1OL9JEmCTqdr8XYiIpKHcSr8ofPlqK3XO9SCg6YeIBZBkx2ZlQDp9fpm/01ERM4hpr0Pgnw8UFpVh2P5FegZGSh3SCYsgiY5WPwnwLJly5qdpl5bW4tly5ZZJSgiIrIuSZJMvUAZDjYMZloIkTVAZEcWJ0AzZsxAWdnlm+pVVFRgxowZVgmKiIisr1ej9YAciXEvMNYAkT1ZnAC1tK3EuXPnEBjoOF2qRETUVO8ow3u0oyVAxt3g2QNE9mR2ut2nTx9IkgRJkjBq1CioVJceqtPpcObMGYwdO9YmQRIRUdslNwyBnbxQiYqaOvh7WbYHnq1ouBkqycDsV5tx9ldGRgZuvPFG+Pn5mW7z9PREbGwsbrvtNqsHSERE1tHBT43IYG+cK6lG5rkyDIpr22K21mLcCsOPRdBkR2a/2l566SUAQGxsLKZOnQq1mvsfERE5m15RQThXUo2Mc6UOkwBd6gHiEBjZj8U1QImJicjIyLjs+M6dO7F7925rxERERDbSu2EY7EDO5ZNZ5GIsguY0eLInixOgRx99FDk5OZcdz83NxaOPPmqVoIiIyDZi2hs2rs4vr5E5kks0tcaVoNkDRPZjcQJ0+PBhXHvttZcd79OnDw4fPmyVoIiIyDaCfT0BAKVVtTJHYlCn06O23rDAri+LoMmOLE6A1Go1CgoKLjuel5fXZGYYERE5nmAfw8yvkqo6mSMxME6BBwAfToMnO7I4ARo9ejRmz57dZDHE0tJSPPfccxg9erRVgyMiIusK8jH0AJXX1EGnFzJHc2kfMA+lBLWKCRDZj8VdNvPnz8ewYcMQExODPn36ADBMjQ8LC8Pnn39u9QCJiMh6Ar0NPUBCAOXVdaYhMblwFWiSi8WvuE6dOuHAgQP48ssvsX//fnh7e2PGjBm466674OHhGItqERFR8zyUCvirVajQ1qOkqtYBEqCGVaBZAE121qqU29fXFw888IC1YyEiIjsI8vVoSIDkrwMyrQHEKfBkZxa/4q624/v06dNbHQwREdlesI8ncoqrHWImWBV7gEgmFidAs2bNavJzXV0dqqqq4OnpCR8fHyZAREQOzlgI7Ug9QFwEkezN4llgJSUlTb4qKytx7NgxDBkyBMuXL7dFjEREZEVBDYXQjtADZKwBYhE02ZvFCVBzunXrhrlz517WO0RERI7HuBZQqQP0AFWZeoA4BEb2ZZUECACUSiXOnz9vrdMREZGNXBoCYw8QuS+LX3Fr1qxp8rMQAnl5efjPf/6DwYMHWy0wIiKyDYfsAWIRNNmZxQnQpEmTmvwsSRJCQkIwcuRIzJ8/31pxERGRjRh7gEqrHaAHiNPgSSYWv+L0er0t4iAiIjsJMu4HpnGAHqCGITA/1gCRnVmtBoiIiJxDsI/j7Ahfya0wSCZmveKeeOIJs0/49ttvtzoYIiKyvWAHWgfIuBs8Z4GRvZmVAO3bt6/Jz3v27IFOp0NCQgIA4Pjx41AqlbjuuuusHyEREVlVYMMQWHWdDjV1Onh5yJd8mGqA2ANEdmbWK27Tpk2mf7/99tvw9/fHZ599huDgYACGxRFnzJiBoUOH2iZKIiKymgAvFZQKCTq9QFl1nawJ0KWtMJgAkX1ZXAM0f/58zJkzx5T8AEBwcDD+9a9/cRYYEZETkCTJtBq03GsBabgQIsnE4gSovLwcBQUFlx0vLCxERUWFVYIiIiLbcpSZYBot9wIjeVicAN16662YMWMGvvvuO5w7dw7nzp3Dd999h/vvvx+TJ0+2RYxERGRljjITTFNrXAmaPUBkXxan3O+//z6eeuop3H333airM/zloFKpcP/99+Ott96yeoBERGR9xh6g0mr5eoDqdHrU1hvWlmMNENmbxa84Hx8fvPfee3jrrbdw6tQpCCEQFxcHX19fW8RHREQ24Aj7gRmnwAOAD2uAyM5anXL7+voiOTnZmrEQEZGdOMJ+YMZ9wDyUEtQqJkBkXxYnQBqNBnPnzsXGjRtRWFh42dYYp0+ftlpwRERkG6YeII18PUDcCZ7kZPGr7q9//Ss2b96M1NRUREREQJIkW8RFREQ2ZJoFJmMPkGkGGAugSQYWJ0Br167FTz/9hMGDB9siHiIisgPjLLAyGXeE507wJCeLp8EHBwejXbt2toiFiIjsxBF6gC6tAs0eILI/ixOg1157DS+++CKqqqpsEQ8REdmBI6wDxH3ASE4Wv+rmz5+PU6dOISwsDLGxsfDw8Ghy+969e60WHBER2calBKgOQghZ6jkv7QTPBIjsz+JX3aRJk2wQBhER2ZNxCKxeL1CprYe/l8dVHmF9l7bB4BAY2Z/FCdBLL71kiziIiMiOvDyU8PJQoKZOj9KqOpkSIE6DJ/m0+lW3Z88eHDlyBJIkITExEX369LFmXEREZGPBPp7IK6tBSVUtotr52P35jQshsgia5GBxAlRYWIipU6ciPT0dQUFBEEKgrKwMI0aMQFpaGkJCQmwRJxERWVmQKQGSZyYYp8GTnCyeBfbYY4+hvLwchw4dQnFxMUpKSnDw4EGUl5fj8ccft0WMRERkA0Hexu0w5JkJZpwG78caIJKBxWn3unXr8Ouvv+Kaa64xHUtMTMTixYsxZswYqwZHRES2E+wr735gnAZPcrK4B0iv11829R0APDw8LtsXjIiIHJfcO8Ibi6A5C4zkYHECNHLkSMyaNQvnz583HcvNzcU//vEPjBo1yqrBERGR7ci9Izx7gEhOFidA//nPf1BRUYHY2Fh07doVcXFx6Ny5MyoqKvDuu+/aIkYiIrKBIG95e4AubYXBBIjsz+JXXVRUFPbu3YtffvkFR48ehRACiYmJuOGGG2wRHxER2UiQg/QAcQiM5NDqtHv06NEYPXq0NWMhIiI7kns/MG6FQXIyewjst99+Q2JiIsrLyy+7raysDElJSfj999+tGhwREdmOcRaYXOsAVWqNNUDsASL7MzsBWrBgAf72t78hICDgstsCAwPx4IMP4u2337ZqcEREZDtyzgKr0+lRW2+YOcwaIJKD2QnQ/v37MXbs2BZvHzNmDPbs2WOVoIiIyPaMCyFW1NSjXmffZUyMw18A4MMaIJKB2QlQQUFBs+v/GKlUKly4cMEqQRERke0Fel96Ty+rtu8wmHEfMJVCgqfS4gnJRG1m9quuU6dOyMzMbPH2AwcOICIiwipBERGR7amUCgR4GYaf7F0HdGkRRBUkSbLrcxMBFiRAN910E1588UXU1NRcdlt1dTVeeukljB8/3qrBERGRbQX7yjMTjDvBk9zMrjz75z//iZUrVyI+Ph4zZ85EQkICJEnCkSNHsHjxYuh0Ojz//PO2jJWIiKwsyNsDZ2H/HiDTDDBOgSeZmP3KCwsLw7Zt2/Dwww9j9uzZEEIAACRJwo033oj33nsPYWFhNguUiIisL0imtYAurQLNHiCSh0Wpd0xMDH7++WeUlJTg5MmTEEKgW7duCA4OtlV8RERkQ3LtB8Z9wEhurSq9Dw4ORr9+/dC/f/82JT9LlixBcnIyAgICEBAQgJSUFKxdu7bZ+z744IOQJAkLFiy44jk//fRTSJJ02VdztUtERO5OrrWAuAo0yU3WV15kZCTmzp2LuLg4AMBnn32GiRMnYt++fUhKSjLdb/Xq1di5cyc6duxo1nkDAgJw7NixJse8vLysFzgRkYsINiVA9p4Fxn3ASF6yJkATJkxo8vPrr7+OJUuWYMeOHaYEKDc3FzNnzsT69etx8803m3VeSZIQHh5u9XiJiFzNpQ1R7dsDZJwGzyEwkovDrD6l0+mQlpYGjUaDlJQUAIBer0dqaiqefvrpJj1CV1NZWYmYmBhERkZi/Pjx2Ldv3xXvr9VqUV5e3uSLiMgdyLUjPKfBk9xkT4AyMzPh5+cHtVqNhx56CKtWrUJiYiIAYN68eVCpVHj88cfNPl/37t3x6aefYs2aNVi+fDm8vLwwePBgnDhxosXHzJkzB4GBgaavqKioNreLiMgZBMtUA2QqgmYNEMlE9ldeQkICMjIyUFpaihUrVuCee+7B5s2bUV1djYULF2Lv3r0WrRI6cOBADBw40PTz4MGDce211+Ldd9/FokWLmn3M7Nmz8cQTT5h+Li8vZxJERG4h2DQN3s49QJwGTzKTPQHy9PQ0FUH37dsXu3btwsKFC3HNNdegsLAQ0dHRpvvqdDo8+eSTWLBgAbKyssw6v0KhQL9+/a7YA6RWq6FWq9vUDiIiZ2QcApOrB4izwEguDvfKE0JAq9UiNTUVN9xwQ5PbbrzxRqSmpmLGjBkWnS8jIwM9e/a0dqhERE7PmABp6/WortXB2049MpemwbMHiOQhawL03HPPYdy4cYiKikJFRQXS0tKQnp6OdevWoX379mjfvn2T+3t4eCA8PBwJCQmmY9OnT0enTp0wZ84cAMArr7yCgQMHolu3bigvL8eiRYuQkZGBxYsX27VtRETOwE+tgkohoV4vUFpdC29Pb7s8r2krDM4CI5nI+sorKChAamoq8vLyEBgYiOTkZKxbtw6jR482+xzZ2dlQKC7VcpeWluKBBx5Afn4+AgMD0adPH/zvf/9D//79bdEEIiKnJkkSgnw8cbFSixJNHSIC7ZMAXaoBYgJE8pD1lffxxx9bdP/m6n7S09Ob/PzOO+/gnXfeaUNURETuJcjHAxcrtXZdC+jSLDAOgZE8ZJ8GT0RE8go2FULbbyaYsQbIj0XQJBMmQEREbs60I3y1HXuATDVA7AEieTABIiJyc/beEb5ep4e2Xg+ANUAkHyZARERuzrQatMY+PUCahuEvgDVAJB8mQEREbi7QzjVAxn3AVAoJnkp+DJE8+MojInJzl7bDsFMPkHEKvFpl0VZHRNbEBIiIyM2ZaoCq7dsDxH3ASE5MgIiI3FyQnXeEN/YAcSd4khMTICIiNxdk51lgxinw7AEiOTEBIiJyc41rgPR6YfPnM60CzSnwJCMmQEREbs7YA6QXQEVD74wtcSd4cgRMgIiI3JxapTStyGyPmWCmITDWAJGMmAAREdGlxRDtUAdk7AHiEBjJiQkQEREh0Nu4GKIde4BYBE0yYgJEREQI9jXOBLNDAmQsguYQGMmICRAREV3aEd4eQ2DGlaDZA0QyYgJERESm1aDtUQNk7AFiETTJiQkQEREhyNt++4FxGjw5AiZARERkWgvIHj1AlVouhEjyYwJERER23RH+Ug0QEyCSDxMgIiJqNAvMfjVAPhwCIxkxASIiIgR6229HeGMNkB+LoElGTICIiMg0C8wuPUCmGiD2AJF8mAAREZGpBqhSW4/aer3Nnqdep4e24fysASI5MQEiIiIEeHtAkgz/Lqu2XS+QpmH4C2ANEMmLCRAREUGpkBDUsB/Y+dJqmz1PVUMBtEohwVPJjyCSD199REQEAOjfuR0A4PuM8zZ7jmKNocg60NsDkrHLiUgGTICIiAgAMLVfNABg5b5z0NbrrnLv1skuqgIARLXzscn5iczFBIiIiAAAw+JDEB7ghdKqOmw4VGCT5zhbbEiAopkAkcyYABEREQBDHdCdfSMBAF/vyrHJc5xt6AGKac8EiOTFBIiIiEzu6BsFSQK2nLyInIbeGmvKYQ8QOQgmQEREZBLVzgdD4joAAL7Zbf1eoLPFGgBATHtfq5+byBJMgIiIqIkp/aIAAN/uPod6nfUWRazT6XG+tAYAh8BIfkyAiIioidGJYQj28UB+eQ3+d+KC1c6bW1INnV5ArVIgxE9ttfMStQYTICIiakKtUmLytYZi6LQ/rDcM1ngGmELBNYBIXkyAiIjoMlMbhsE2Hi1EYUWNVc6ZXcwZYOQ4mAAREdFluoX547qYYOj0Aiv25FrlnNlFhgLo6HYsgCb5MQEiIqJmGYuhv96VDSFEm89nXAMoup13m89F1FZMgIiIqFk394yAn1qFrKIq7DxT3ObzXRoCYw8QyY8JEBERNctXrcKEXh0BtH1laCGEKQGKZg0QOQAmQERE1CJjMfTPmXkoq6pr9XkuVtaiqlYHSQIigzkERvJjAkRERC1KjgxE93B/aOv1WJ3R+mLo7IYVoDsGekOtUlorPKJWYwJEREQtkiTJVAy9cu+5Vp/HWAAdxQJochBMgIiI6Iom9OoIpULC/nNlOH2hslXnMBVAcwo8OQgmQEREdEUd/NQY2s2wQerqjPOtOkd2EQugybEwASIioqua1LsTAOD7jNxWrQl0lqtAk4NhAkRERFc1JikMPp5KnC2qQkZOqcWPv7QIIhMgcgxMgIiI6Kp8PFUYkxgGAFi9z7LZYFW19bhYqQXAGiByHEyAiIjILBP7GIbBfjyQhzqd3uzHGQugA709EOjjYZPYiCzFBIiIiMwyNK4D2vt6okhTiy0nL5r9OOPwF+t/yJEwASIiIrOolArT1hiWDINlm9YAYgJEjoMJEBERmW1ib0MCtOFQATTaerMec7ZhFegYJkDkQJgAERGR2XpHBSGmvQ+q63T45XCBWY/JLq4GwCEwcixMgIiIyGySJJnWBFpl5jBYdpGhByiaM8DIgTABIiIii0xqmA225eRF0/T2ltTr9DhXwh4gcjxMgIiIyCKdO/iiV2QgdHqBH/dfeWuMvLIa1OsFPJUKhAV42SlCoqtjAkRERBYz9gKtusreYMY1gCLbeUOpkGweF5G5mAAREZHFxic37BCfU4ozFzUt3s+0BhBngJGDYQJEREQWC/FXY3CcYYf47zNaLoY2TYFvzwJocixMgIiIqFVu7XNpUcSWdojnIojkqGRNgJYsWYLk5GQEBAQgICAAKSkpWLt2bbP3ffDBByFJEhYsWHDV865YsQKJiYlQq9VITEzEqlWrrBw5ERGNSQyHr6cSWUVV2HSssNn7GGuAOARGjkbWBCgyMhJz587F7t27sXv3bowcORITJ07EoUOHmtxv9erV2LlzJzp27HjVc27fvh1TpkxBamoq9u/fj9TUVNx5553YuXOnrZpBROSWfNUqTBsYAwD4z28nL+sFEkKYeoA4BZ4cjawJ0IQJE3DTTTchPj4e8fHxeP311+Hn54cdO3aY7pObm4uZM2fiyy+/hIfH1XcRXrBgAUaPHo3Zs2eje/fumD17NkaNGmVWzxEREVnmr0M6w1OlwN7sUuw4XdzktpKqOlQ0bJfBITByNA5TA6TT6ZCWlgaNRoOUlBQAgF6vR2pqKp5++mkkJSWZdZ7t27djzJgxTY7deOON2LZtm9VjJiJyd6EBXpjSNwoAsHjTySa3nW1YATo8wAteHkq7x0Z0JbInQJmZmfDz84NarcZDDz2EVatWITExEQAwb948qFQqPP7442afLz8/H2FhYU2OhYWFIT8/v8XHaLValJeXN/kiIiLzPHh9F6gUEracvIiMnFLTcWP9TzR7f8gByZ4AJSQkICMjAzt27MDDDz+Me+65B4cPH8aePXuwcOFCfPrpp5AkyxbP+vP9hRBXPMecOXMQGBho+oqKimpVW4iI3FFksA8mNuwP9p/fLvUCGet/oln/Qw5I9gTI09MTcXFx6Nu3L+bMmYNevXph4cKF+P3331FYWIjo6GioVCqoVCqcPXsWTz75JGJjY1s8X3h4+GW9PYWFhZf1CjU2e/ZslJWVmb5ycnKs1TwiIrfwyIiukCTg1yMFOJpv6EU/yxlg5MBkT4D+TAgBrVaL1NRUHDhwABkZGaavjh074umnn8b69etbfHxKSgp++eWXJsc2bNiAQYMGtfgYtVptmopv/CIiIvN1DfHDTT0iAACLN50CwB4gcmwqOZ/8ueeew7hx4xAVFYWKigqkpaUhPT0d69atQ/v27dG+ffsm9/fw8EB4eDgSEhJMx6ZPn45OnTphzpw5AIBZs2Zh2LBhmDdvHiZOnIjvv/8ev/76K7Zs2WLXthERuZtHRnTFT5l5+OnAeTwxOt60CjRrgMgRyZoAFRQUIDU1FXl5eQgMDERycjLWrVuH0aNHm32O7OxsKBSXOrIGDRqEtLQ0/POf/8QLL7yArl274uuvv8aAAQNs0QQiImqQ1DEQI7uH4rejhVjw63EUlGsBcBsMckySaGn9cjdWXl6OwMBAlJWVcTiMiMgCe86W4LYll5Yd8VercODlMRZPZiFqDUs+vx2uBoiIiJzXdTHBSOlyqXwhur0Pkx9ySEyAiIjIqh4dEWf6N+t/yFExASIiIqsaHNcevaKCAACxHVj/Q46JCRAREVmVJEmYf0cypvSNwvSUGLnDIWqWrLPAiIjINcWF+mPe7clyh0HUIvYAERERkdthAkRERERuhwkQERERuR0mQEREROR2mAARERGR22ECRERERG6HCRARERG5HSZARERE5HaYABEREZHbYQJEREREbocJEBEREbkdJkBERETkdpgAERERkdvhbvDNEEIAAMrLy2WOhIiIiMxl/Nw2fo5fCROgZlRUVAAAoqKiZI6EiIiILFVRUYHAwMAr3kcS5qRJbkav1+P8+fPw9/eHJEkt3q+8vBxRUVHIyclBQECAHSN0DO7efoDXwN3bD/AasP3u3X7Asa6BEAIVFRXo2LEjFIorV/mwB6gZCoUCkZGRZt8/ICBA9v90Obl7+wFeA3dvP8BrwPa7d/sBx7kGV+v5MWIRNBEREbkdJkBERETkdpgAtYFarcZLL70EtVotdyiycPf2A7wG7t5+gNeA7Xfv9gPOew1YBE1ERERuhz1ARERE5HaYABEREZHbYQJEREREbocJEBEREbkdJkBX4O714Wy/e7cf4DVw9/YDvAZsv+u2nwlQC3Q6HaqqquQOQzZsv3u3H+A1cPf2A7wGbL9rt58JUDPmz5+Pfv36YcKECZgzZw7OnTsHwLUz4cbYfvduP8Br4O7tB3gN2H7Xbz/XAfqTWbNmYfXq1Xj11Vexe/dubN26FUqlEtu2bYOHh4fc4dkc2+/e7Qd4Ddy9/QCvAdvvJu0XZJKXlyeSk5PF0qVLTccyMzNFWFiYePzxx0VdXZ18wdkB2+/e7dfr9W5/Ddy9/ULwGrD97tN+JkDC8MYvhBD5+flCkiSxZ8+eJseXL18uPD09xbZt22SL0R7ctf06nU4I4b7tz8/Pb/Jvd7wG7v4eYGynELwG7tp+I3dqv9vWAK1fvx4//PADTp8+jfr6egCARqNB//79sWLFCgCAJEkAgKlTp6Jv3754++23AQB6vV6eoK1o5cqVePvtt7Fx40aUlZUBACorK92m/Z999hmSkpKQn58PhcLwa1BTU+M27QeAd999FyEhIdi8ebPpmDtdg2+//RaLFi3C7t27TYWeGo0G/fr1c4v2A8DOnTsBXGon4F6vAX4OuPfngNv1AP3xxx+iZ8+eIi4uTiQmJoq4uDgxZ84cIYQQtbW14t577xU333yzOHTokBBCmLr7vv32W+Hn5ycKCwtli90aduzYIXr06CG6dOkihg8fLqKiosS0adOEEO7RfqNhw4YJSZLEX//6V9Oxuro6t2j/li1bRHx8vIiIiBAfffRRk9vc4RqcPn1a9OvXT0RGRop+/fqJjh07ismTJwshhKivrxczZswQN910k8u2XwghDhw4IAYNGiQkSRLffPONEOJSO93hNcDPAX4OCOFmPUDr16/H/fffj3HjxmHXrl344YcfcMcdd+C7777DwYMH4eHhgcmTJ+P8+fP45ptvAAAqlQoA4Ovri5CQEOTm5srZhDb5/vvvMXXqVEyYMAEHDhzAypUr8fzzz2Pbtm04ceIEPDw8cNttt7ls+40KCgoghMCiRYvwySefYMeOHQAMbZ08eTLy8vJctv0ffPABhg4dihtuuAHnz5/H/fffj7q6OtPtKpUKkyZNcunXwHfffQcPDw8cOXIEGzZswOeff44NGzZg1qxZUCqVmDp1qku3f/fu3XjsscfQvn17TJgwAe+99x7q6+uhUqlQV1cHlUrl0u8D/Bzg54CR2yRAOp0OOTk5GDZsGJ555hkEBgaiS5cuGDVqFMrKykzDIBMmTMDQoUOxbt06fPnll6bHFxQUwM/PD127dpWrCW2WmJiIp556CrNnz4aPjw+Cg4Ph4eGBoUOHolu3bgCA8ePHY8iQIVi7dq3Ltd9IrVZDkiSMHDkSN998Mx5//HHTbRMmTMD111+Pn3/+2SXbP27cOADAqFGjAABvvfUWHnjgATzyyCNIS0sDAEycONFlXwM6nQ5paWlISUmBn58fgoKCMHLkSHzwwQf473//ix9++AFjxozB8OHDXbL9ABAXF4eePXti7ty5SE1NRUlJiWlYw/g+OH78eJd8H+TnAD8HmpC7C8qeDh8+LIqLi5scy8nJEeHh4eLYsWOmY2fOnBEzZ84UCoVC3HPPPWLWrFkiKChIvPTSS6K+vr5JwaCzqa2tNf37iy++EMHBwSIpKUlMmzZNfP7550IIwxCBq7ZfiEvdv0IYhoPUarVYvXq1SE9PFwcPHhT5+fni0Ucfdbn219fXCyGEeOKJJ4RKpRJJSUni2muvFc8++6zo16+f6NChg3jhhReEEEKcOnXK5V4DxmL3cePGidtvv73JMSGEGDp0qBg1apTQ6/UiKyvL5dovxKWCVo1GI4QQoqSkRPz9738XPXv2FNnZ2UIIIbRarRDCdd8H+TnAzwEjl0+AjG/6jen1etN/3tKlS0VSUpKora1t8mYohBCLFy8WDz/8sBg9erRYs2aNXeK1NmP7//xi/fjjj0ViYqJ44403xJdffin+8Y9/CEmSxO7du033d8X26/V6sWPHDnHzzTeb7vOXv/xFKJVK0aFDB7Fp0ybTcVdovxCXrkHj6avXXHONeOqpp0RFRYUQwvCGuHjxYqFSqUROTo4QwjVfA/X19eLf//63SE5OFpmZmUIIIWpqaoQQhroQSZLEwYMHTfd3hfYL0fz7gPH9btOmTWLw4MHi4YcfbvaxrnAN+Dng3p8DLXGpBKjxC7fxf7RGoxG5ublCiEsfAsbbZ8yYIR588EE7Rmk75rTfmPmXlJSIkpKSJo/v27dvi2+CzuBK7T937pzp5y+//FLcd999or6+Xtx1111CpVIJPz8/U9udeZ0Lc6/BoUOHxIkTJ5o8tqCgQMTGxor58+fbPlA702g0ph6ODRs2iKFDh4rHH3/cdLtOpxM6nU4kJiaKefPmyRWmTTV+DTROCLRarXjjjTdEQkKC2LJlixDC8DvgCn/hN+YunwMtcZfPAUu4TA3Qm2++ibvuuguzZ8/GyZMnTcfz8vIwceJE3H777QAuFXMZZWZmYvDgwQAM0//+/HhnYW77jat4BgYGIigoyLSseXZ2Nqqrq9GpUycAzrfc+dXaf8cdd5iOFRQUYO3atfDx8cG5c+ewefNmvPnmm/jkk09w5syZy14jzsKSa5CYmIi4uDgAl6azZmdno7i4GNHR0fYN3Eq++eYb7N+//7LjhYWFmDhxIu68804AwOjRozFs2DD8/vvvpqm+CoUC586dQ0VFBWJiYgA43+8AcPVrYHwNKJVKAIY2enp6Yvz48YiPj8f8+fORnZ2N1NRUrF+/3q6xW8PV2u/qnwPmtt9VPwcs5fQJUGFhIYYPH46PP/4Y0dHRWLlyJSZOnIjFixcDAMLCwjBx4kSMHDkSNTU1psdJkoQzZ86gqKgIQ4YMwSeffIKoqCj88MMPTrXUd1vab/yu1+vx9ddfIygoyPQL0nhdEEdmSfs1Gg0AoEePHkhMTMSXX36J3377DYMGDcLtt9+OqKgoLFu2TM7mtEprXwNGCoUCOp0O33//PQYOHIhhw4bZuwltsnv3bgwYMABTp07Fhx9+iMrKSgCX3rxDQkIwadIkjBo1ynTbPffcg969e+OBBx7ADz/8gJMnT2LlypXw8/NDz549ATjP7wBg/jVo6X2gZ8+eGD16NNauXYuuXbvijz/+cKpC17a03xU+B9r6/+/snwOtJlfXk7WsXr1adO/evUn3/oMPPih69uwp1q9fL4QQpjqHP3v33XeFSqUSMTExIjg4WHzxxRd2idmaWtt+rVYrfv/9d7Fo0SLRu3dvER0dLdatW2e3uK2lte03FoE2VlBQYLtAbai116CmpkZs3rxZvPPOO6JXr16ic+fOYuPGjXaL2xoKCgrEzJkzxQMPPCDmzp0rfHx8mrTBOCRorPNprLCwUEydOlV07txZxMbGioiICLF69Wq7xW4tbbkGQhiGg3788UcREREhYmNjxffff2+XuK2lre139s+BtrTfVT4HWstpEyDjf+rChQtFQkJCkzf4gwcPismTJ4uhQ4de9rjGxbCvvfaa8PHxEa+99pp9graitra/tLRULFu2TIwYMcIt2//nQkdn1NZrUFJSIj766CMxcOBA8corr9gnaCvTaDRi1apVYteuXUIIIfr37y/Gjh17xYXa/vx/X1hYKDZv3mzTOG2pNdegcX1PdXW1GDt2rJg9e7bNY7WFtrTf2T8HhGhb+539c6CtnCoB+vXXX8W+fftEZWWl6dicOXNEcnKyOHXqVJP7fvXVV+Kaa64xTekTwvChcMcdd5j+Us7IyBDl5eX2Cd4KrN3+0tJSUV1dbZ/grcBa7TcWAjpjkae1XwPFxcWiqqrKPsFbQXPtb1y0vnfvXiFJkli2bFmzSe6fXwPOWPBu7WsgRNNp0Y7OWu13pc+BtrTf2T4HrMkpEqAdO3aI+Ph4ER0dLTp16iRGjBghVqxYIYQQIjc3VygUiiY71wohRFZWlrj55pvFzJkzTTMeNmzYIMLDw51uloe12//mm2/auwltYu32v/XWW/ZuQpvxNXB5+1etWiWEuJTIGj8Epk2bJhITEy9LCIUQYv369S71GnCna2Dt9rvC50Bb2u9s7wG24PAJUGVlpZg0aZJ44IEHRGFhodi+fbu44447RLdu3cTvv/8uhDBMYezWrVuTv2iEEGLKlCmmPX6EMIyBGvc2cRZsv3u3Xwhegyu137gzdV1dnemv3bKyMuHl5SVee+01U8+GcQp8VVWV07VfCF4Dtt+9228rDp8AHT58WCiVSpGenm46duTIETFp0iTRs2dPIYQQRUVFIjAwUDzyyCNNxj1vv/128be//c3uMVsT2+/e7ReC1+BK7U9OTm5yX+NfwK+//roICwsTX331lRg/fryYPHmyKC0ttWvc1uTu14Dtd+/224rDJ0D79u0TPXr0ED/++GOT4xs3bhQBAQHi/fffF0IIsXz5ctG5c2cxfPhwsXz5cvH888+LDh06OH1FO9vv3u0Xgtfgau3/8MMPhRCiyfL8JSUlQpIkIUmSGDFiRJMZcs7I3a8B2+/e7bcVh0+AcnNzRbdu3cTcuXObFGqVlJSIhx9+WPTr189U37Bu3Tpx6623isGDB4tevXqJX3/9Va6wrYbtd+/2C8FrcLX29+/f39T1r9frxZo1a4SPj49ISkoSW7dulStsq3L3a8D2u3f7bcWhEyDjf+hjjz0munbtatq7x+jDDz8U/fr1EydPnmxy3FnXc/kztt+92y8Er4G57T99+rQQwvDmv3btWrFw4UK7x2or7n4N2H73br8tyZoArVix4rJ9SBprvIBTUFCQeOKJJ0ReXp7p9i+++EL4+vqaah6cbVoz2+/e7ReC18Da7XdG7n4N2H73br+cZEmANm/eLBITE4UkSWLx4sVXvK+xoOujjz4SXbp0Mf3nl5eXi7/97W8iNTVVaLVae4RtNWy/e7dfCF4Dd2+/ELwGbL97t98RSELYd7ez48eP45VXXkFgYCAkScLq1avxxx9/mDZfu5KFCxdi4cKF8PDwQF1dHfR6PZYvX46UlBQ7RG4dbL97tx/gNXD39gO8Bmy/e7ffYdg74yosLBQfffSROHjwoNBoNCI6OlrMmjXrio9pvJplVlaW+Pnnn8Xy5cttHKltsP3u3X4heA3cvf1C8Bqw/e7dfkdh8wTo999/F2fPnm1yrPGy3cuWLROenp5iz549tg5FFmy/e7dfCF4Dd2+/ELwGbL97t99R2SwB2rhxo+jcubOIiYkRERERIjU1Vezdu1cIcXmh5uDBg8VNN93kVPvRXA3b797tF4LXwN3bLwSvAdvv3u13dDZJgHJyckRKSop4/vnnxdmzZ8UPP/wgevfuLUaNGiVOnDghhBCmdUuEEGLbtm1CoVCIlStXCiEMXX0XL160RWh2wfa7d/uF4DVw9/YLwWvA9rt3+52BTRKgDRs2CC8vL3H8+HHTsfXr14sRI0aIKVOmNPuYe++9VyQnJ4tff/1VjB07VsyePVvU1NTYIjybY/vdu/1C8Bq4e/uF4DVg+927/c7AJglQWlqa6N27tzh69KjpmE6nE4sXLxaxsbFi/fr1Qoim2e/OnTtNy3aPHj1aFBUV2SI0u2D73bv9QvAauHv7heA1YPvdu/3OwCYJUGZmplCr1WL16tVNjh89elRMmjRJ3HvvvaZj9fX14ssvvxRqtVr07dtX7Nq1yxYh2RXb797tF4LXwN3bLwSvAdvv3u13BgpbTK3v0aMHRo4ciXfeeQeVlZWm4wkJCYiOjkZ+fj6qq6sBALW1tSgqKsKCBQuwa9cu9O3b1xYh2RXb797tB3gN3L39AK8B2+/e7XcKtsqsMjIyhEqlEkuWLGkyhvnaa6+J6OjoJlMAXRHb797tF4LXwN3bLwSvAdvv3u13dCpbJVa9evXCs88+i1dffRVKpRJ33XUX9Ho9/vjjD0ybNg0qlc2e2iGw/e7dfoDXwN3bD/AasP3u3X5HZ/OtMB599FGsWLEC0dHRKCwshI+PD7755hv06NHDlk/rMNh+924/wGvg7u0HeA3Yfvduv6OyeQKk1Wpx+PBhZGRkwNPTE9OmTbPl0zkctt+92w/wGrh7+wFeA7bfvdvvqOy+GSoRERGR3GwyC4yIiIjIkTEBIiIiIrfDBIiIiIjcDhMgIiIicjtMgIiIiMjtMAEiIiIit8MEiIiIiNwOEyAiIiJyO0yAiMhlpKenQ5IklJaWyh0KETk4rgRNRE5r+PDh6N27NxYsWAAAqK2tRXFxMcLCwiBJkrzBEZFD41a0ROQyPD09ER4eLncYROQEOARGRE7p3nvvxebNm7Fw4UJIkgRJkvDpp582GQL79NNPERQUhB9//BEJCQnw8fHB7bffDo1Gg88++wyxsbEIDg7GY489Bp1OZzp3bW0tnnnmGXTq1Am+vr4YMGAA0tPT5WkoEdkEe4CIyCktXLgQx48fR48ePfDqq68CAA4dOnTZ/aqqqrBo0SKkpaWhoqICkydPxuTJkxEUFISff/4Zp0+fxm233YYhQ4ZgypQpAIAZM2YgKysLaWlp6NixI1atWoWxY8ciMzMT3bp1s2s7icg2mAARkVMKDAyEp6cnfHx8TMNeR48evex+dXV1WLJkCbp27QoAuP322/H555+joKAAfn5+SExMxIgRI7Bp0yZMmTIFp06dwvLly3Hu3Dl07NgRAPDUU09h3bp1WLp0Kd544w37NZKIbIYJEBG5NB8fH1PyAwBhYWGIjY2Fn59fk2OFhYUAgL1790IIgfj4+Cbn0Wq1aN++vX2CJiKbYwJERC7Nw8Ojyc+SJDV7TK/XAwD0ej2USiX27NkDpVLZ5H6NkyYicm5MgIjIaXl6ejYpXraGPn36QKfTobCwEEOHDrXquYnIcXAWGBE5rdjYWOzcuRNZWVm4ePGiqRenLeLj4zFt2jRMnz4dK1euxJkzZ7Br1y7MmzcPP//8sxWiJiJHwASIiJzWU089BaVSicTERISEhCA7O9sq5126dCmmT5+OJ598EgkJCbjllluwc+dOREVFWeX8RCQ/rgRNREREboc9QEREROR2mAARERGR22ECRERERG6HCRARERG5HSZARERE5HaYABEREZHbYQJEREREbocJEBEREbkdJkBERETkdpgAERERkdthAkRERERuhwkQERERuZ3/B9pzXEFB6DqVAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ds = xr.open_dataset('./data/rca/sensors/osb/ctd_jan22_conductivity.nc')\n", "ds_timebox = ds.sel(time=slice(tA0, tA1))\n", @@ -723,19 +418,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-rw-r--r-- 1 rob rob 894104 Jun 16 17:20 ./data/rca/sensors/osb/ctd_jan22_conductivity.nc\n" - ] - } - ], + "outputs": [], "source": [ "base_path = './data/rca/sensors/'\n", "date_id = 'jan22'\n", @@ -781,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [] }, @@ -815,30 +502,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# temperature and salinity\n", "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['conductivity']], [0], \n", @@ -858,30 +526,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# temperature: ascent versus descent\n", "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n", @@ -909,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "tags": [] }, @@ -941,30 +590,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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UqZNK2ZEjR9CgQQM0adJEJe5u3bqpHBZVnJb66KOPVNYfNmxYmWLV5syZMzA3N8eAAQNUyhVXPCkOadeqVQuenp44deoUAG4fNWzYEMOHD8ezZ8/w5MkT5OTk4MKFC3jvvfdU2hsQEAAXFxeV9ir6qISGhqq8bt++fSEUCnWKXSwWY/fu3UhISECzZs3AGMOff/6p/PwBBf2iCn8+FMLDw9G3b1/Y2tqCz+dDKBRi5MiRyM/PVzsN+u2336J+/foICAjAuXPnsH37djg7OxcbX6tWrZCcnIyhQ4fif//7H16/fq21roODA+RyOeLi4ordZnp6Or788kvUrl0bAoEAAoEAFhYWyMjIwP3799Xqa/rMA1CeztP2ORs0aBAEgvLpnln0O4OV4jqZonV1/Tzp8t7/9ddfCAgIQL169bS+vq5/rwoBAQGwtLRUzjs6OsLBwUHt9GlpxMTEwN7eHjweT6U8NDQUDRo0UOtLN3ToUI3beZvvoyNHjoDH42H48OEq9ZycnNC4cWO198HJyQmtWrVSKWvUqNFbvQ+l0b9/f5iamirnLS0t0adPH5w/f17tlJe2z77ib+PEiRPIy8vDyJEjVdpuamoKf39/jadWP/zwQ51jHTRoECZMmIAZM2bg66+/xpw5c1S6exT3u5uXl4elS5fC19cXIpEIAoEAIpEIjx49Uvs+qF27Nn777TccPHgQvXv3Rvv27Uu8mEiX32sFoVAIa2vrMl0JXGWTpi1btkAoFKpMCoq+Rh988AGSk5ORnJwMqVSKdu3aYd++fcrLa3k8Hk6fPo1u3brhm2++QbNmzWBvb48pU6Yoz4UqzicX14Fc0edgwIABajGtWLECjDEkJiaqrGNra6syr+hgnJWVVab3Q7Fe4T/OwjT9qMbHx+PWrVtqMVtaWoIxpvxyT0hIgEAgUIvZycmpTLFqk5CQoBwiojAHBwcIBAIkJCQoyzp37qxMok6dOoUuXbqgYcOGcHR0xKlTp/DPP/8gKytLJWmKj4/H4cOH1dqr6B9V9MespESkqNq1a6N9+/bIzs7GRx99pLZ+586dVV5X0Y8jIiIC7du3R3R0NL7//nv8/fffuHr1Kn766ScA6p8JsViMYcOGITs7G02aNFHrw6bJiBEjlFeBffjhh3BwcMC7776rsb+V4jNU0mdx2LBh+PHHHzF27FicOHECV65cwdWrV2Fvb69x3ZI+84r9W/RzpemzV1ZF931prhJ88eIFxGIxatSoAUD3z5Mu7/2rV69KvEhF179XBU3vmVgsLvN3DMDtK03fMQkJCXB0dFQr11QGvN33UXx8PBhjcHR0VKt76dKlCnkfSkPT96STkxNyc3ORnp5ebF3FZ1/xt6H4rWnZsqVa23ft2qXWdjMzs1JdeQZww0nIZDIIBAJMmTJFZVloaKja6z5//hwAEBQUhHnz5qFfv344fPgwLl++jKtXr6Jx48Ya3+tevXrB0dER2dnZCAoKUvkHUxNdfq8LMzU1LdM+rpRXz+miT58+uHr1qlp5SkoK9u3bB4D7YGmyY8cOTJw4EQDg4eGhTLIePnyI3bt3Y8GCBcjNzcX69ethb28PAIiKitJ6NZGdnR0AYO3atVp7+2v78igvihiKJmcKRRMRxToSiUTl6Jumbdra2iIvLw8JCQkqX0AlHYkoLVtbW1y+fBmMMZV4X758iby8PGU8AJeAbNiwAVeuXMHly5fx1VdfAeAumT958iRevHgBCwsLlf1hZ2eHRo0aYcmSJRpf38XFRWVe03tWnN9//x1Hjx5Fq1at8OOPP2Lw4MF49913lct/+eUXlT9uRXsOHjyIjIwM7N+/Hx4eHsrlRTs3Kty5cwfz589Hy5YtcfXqVaxZswZBQUElxjd69GiMHj0aGRkZOH/+PIKDg9G7d288fPhQ5XUVn6HC73dRKSkpOHLkCIKDgzFr1ixleU5OjtbPYEkUn624uDi4uroqyxWfvfJQ9DvDy8tLp/Wio6Nx/fp1+Pv7K496lebzVNJ7b29vj6ioqGJj0PXvVZ/s7Ozw77//qpXb2tqqdFhX0PYd8TbfR3Z2duDxePj77781Xs2q7ytcTU1NVTpLK2g7eqvpPYiLi4NIJIKFhYVauabPvuJvQ/Ee7N27V+VvVpvSfodlZGRgxIgReOeddxAfH4+xY8fif//7n3J58+bN1f6GFJ/z7du3Y+TIkVi6dKnK8tevX8Pa2lrttT799FOkpaWhfv36mDJlCtq3bw8bG5ti4yvp97qwpKSkMv1NVNmkydbWVuN/EDt27EBWVhYWL16Mdu3aqS0fOHAgNm7cqEyaCnvnnXfw1VdfYd++fcovhq5du4LP52PdunXw8/PTGEvbtm1hbW2Ne/fuYfLkyW/ZsgKl+W/Iw8MDEokET5480Xn7vXv3xtKlS2Fra1vsj0dAQAC++eYb/PHHHyr/eezYsUPn19JF586dsXv3bhw8eBAffPCBsnzr1q3K5YXr8ng8zJs3DyYmJujQoQMA4L333sOMGTPw4sULdOjQQeUIZO/evXHs2DHUqlWrxD/O0rp9+zamTJmCkSNH4rfffkObNm0wePBghIeHK19L25ghii+2wl/2jDGNV7BlZGRg4MCB8PT0xNmzZzFr1izMmjULbdu2VUnQimNubo4ePXogNzcX/fr1w927d1W+gJ8+fQpbW9tiE30ejwfGmNoP1O+//17iVTbaKK5I+uOPP9C8eXNl+e7du9WuRiurslwmn5WVhbFjxyIvLw8zZ85Ulpfl86Ttve/Rowe2bduG//77T+vnRNe/19Io7RHuunXr4s8//0RKSgqkUqmy3N/fH6tWrVKOq6Owc+dOnWPRtX29e/fG8uXLER0djUGDBum8/fLi6emJPXv2ICcnR/n+JSQk4OLFixqP6uzfvx8rV65UHqFLS0vD4cOH0b59e7WjK9o++4q/jW7dukEgEODJkyelOu2mq08//RQRERG4cuUKHjx4gAEDBuDbb7/FtGnTAHCnFrX9DfF4PLXvg6NHjyI6Ohq1a9dWKf/999+xfft2bNy4Ef7+/mjWrBlGjx5dqsGcNf1eK8TExCA7O7tMQ69U2aRJmw0bNsDGxgZffPGFxsPII0eOxJo1a3Dz5k3lZbkDBw5EnTp1IBKJcObMGdy6dUv537OnpyfmzJmDxYsXIysrSzlMwL179/D69WssXLgQFhYWWLt2LQIDA5GYmIgBAwbAwcEBr169ws2bN/Hq1SusW7eu1G1p2LAhzp07h8OHD8PZ2RmWlpZav1BFIhH8/PzUBgYtztSpU7Fv3z506NAB06ZNQ6NGjSCXyxEREYGQkBBMnz4d7777Lrp27YoOHTpg5syZyMjIQIsWLfDPP/9g27ZtGrfL4/G0nl8vzsiRI/HTTz8hMDAQz58/R8OGDXHhwgUsXboUPXv2VDnV5uDggAYNGiAkJAQBAQHKsUfee+89JCYmIjExEWvWrFHZ/qJFi3Dy5Em0adMGU6ZMgY+PD7Kzs/H8+XMcO3YM69evL9M4XhkZGRg0aBC8vLzw888/QyQSYffu3Tp/EXTp0gUikQhDhw7FzJkzkZ2djXXr1iEpKUmtbuEvNXNzc6xevRphYWEYMmQIwsPDNf5HBwDjxo2DRCJB27Zt4ezsjLi4OCxbtgxSqVTtiOylS5fg7+9f7H+pVlZW6NChA1auXAk7Ozt4enoiNDQUGzZs0BpDSerVq4fhw4fju+++g1AoxHvvvYc7d+5g1apVpT7FUFYRERG4dOkS5HI5UlJSVAa3XL16Nbp27aqsq+vnSZf3ftGiRfjrr7/QoUMHzJkzBw0bNkRycjKOHz+OoKAg1K1bV+e/19Jo2LAhAOD7779HYGAghEIhfHx8VPpCFdaxY0cwxnD58mWV92Lq1KnYuHEjevTogUWLFsHR0RE7duzAgwcPAAAmJiX3FNG1fW3btsUnn3yC0aNH49q1a+jQoQPMzc0RGxuLCxcuoGHDhpgwYUKp3gdF20JDQ0vs5zZixAj88ssvGD58OMaNG4eEhAR88803Wj+jfD4fXbp0QVBQEORyOVasWIHU1FQsXLhQre7+/fshEAjQpUsX3L17F/PmzUPjxo2VyaGnpycWLVqEuXPn4unTp+jevTtsbGwQHx+v/E7QtF1dKBKZTZs2oX79+qhfvz4mT56ML7/8Em3btlXrF1ZU7969sXnzZtStWxeNGjXC9evXsXLlSrXvVMU/mIGBgRg9ejQA7nd7wIAB+O6777SO5n3r1q0Sf68VFL+DhQe21Vmpu44bIV2vnrt58yYDwKZOnaq1zoMHD5SX/8bHx7NRo0axunXrMnNzc2ZhYcEaNWrEvv32W7WrnrZu3cpatmzJTE1NmYWFBWvatCnbtGmTSp3Q0FDWq1cvVqNGDSYUCpmrqyvr1auXyhU8iqskCl+SzVjBFUaFr2K5ceMGa9u2LTMzM2MANF6xUdiGDRsYn8/XeCVV/fr1Na6Tnp7OvvrqK+bj48NEIhGTSqWsYcOGbNq0aSqX7iYnJ7MxY8Ywa2trZmZmxrp06aJ8LwtfPZeWlsYAsCFDhhQbqyKuom1KSEhgn376KXN2dmYCgYB5eHiw2bNns+zsbLX1p02bxgCwJUuWqJTXqVOHAWC3bt1SW+fVq1dsypQpzMvLiwmFQlajRg3WvHlzNnfuXOXVYrpcSVXY8OHDmZmZmdqVQ3v27GEA2LffflviNg4fPswaN27MTE1NmaurK5sxY4byqkXF1SO//fYbA6D2uXv8+LHyShSFou/tli1bWEBAAHN0dGQikYi5uLiwQYMGqb1HiisT9+3bV2LMUVFR7MMPP2Q2NjbM0tKSde/end25c4d5eHioXOmm+GwXvSpK0yXFOTk5bPr06czBwYGZmpqy1q1bs7CwMLVtlqSsV88pJj6fz2xsbFjz5s3Z1KlTtQ73ocvnSdf3PjIyko0ZM4Y5OTkxoVCorKe4Wo8x3f9eAbBJkyapxavpfZw9ezZzcXFhJiYmavujqPz8fObp6al2JSRjjN25c4e99957zNTUlNWoUYN9/PHHbMuWLQwAu3nzprJeeXwfMcbYxo0b2bvvvsvMzc2ZRCJhtWrVYiNHjmTXrl0r8bWKXunGGGPNmzdnTk5OWtte2JYtW1i9evWYqakp8/X1Zbt27dJ69dyKFSvYwoULWc2aNZlIJGJNmzZVu/Rf8btw/fp11qdPH2ZhYcEsLS3Z0KFDVfa/wsGDB1lAQACzsrJiYrGYeXh4sAEDBqgMb1Oaq85v3brFJBKJ2mcjOzubNW/enHl6eiqHB9AmKSmJffzxx8zBwYGZmZmxdu3asb///lvluyg9PZ3VrVuX+fr6soyMDJX1J02axIRCofJKx6LfD6X5vR4xYgRr2LChTm0vqkokTUQ3WVlZzN7eXuOwAxXl6NGjjMfjaUxYiPH76quvmLu7u3IsIkKKWrVqFbOxsWGZmZkl1h03bhyzsLBgOTk5FRBZ2aWmpjKBQMB+/PHHcttmaf750vbPNCm9lJQUZm5uzn799dcyrV9lr54j6kxNTbFw4UKsWbNGbYiDinL27FkMGTJEedifVB7Jycn46aefsHTp0nK7xJ9UPZMmTYJUKlVe3amwaNEi/P777zhz5gwOHTqETz75BL///jumTZsGkUhkoGh1c/78ebi6umLcuHGGDoW8pW+//Rbu7u7KU3+lRd981cwnn3yC5ORkPH361CCJS9HbTJDK49mzZ5g9e3a5j79FqhZTU1Ns27ZN7bYditvMREVFIS8vD3Xq1MGaNWvw+eefGyhS3fXq1Qu9evUydBikHFhZWWHz5s1l/sePx1gpRm8jhBBCCKmm6PQcIYQQQogOKGkihBBCCNEBJU2EEEIIITqgpIkQQgghRAeUNBFCCCGE6KDSJ02enp7g8Xhq06RJk7SuExoaiubNm8PU1BTe3t5qN/KrLErb9nPnzmmsr7iVQWWTl5eHr776Cl5eXpBIJPD29saiRYsgl8uLXa8q7P+ytL0q7f+0tDRMnTpVeU/FNm3aaLxBd2FVYb8DpW97Zd7v58+fR58+feDi4gIej6d2yyHGGBYsWAAXFxdIJBJ07NgRd+/eLXG7+/btg6+vL8RiMXx9fXHgwAE9taDs9NH2zZs3a/wsZGdn67ElZVNS+/fv349u3bopb9Cs7QbmRb31vi/HgTYN4uXLlyw2NlY5nTx5stih/p8+fcrMzMzY559/zu7du8d+++03JhQK2d69eys28HJQ2rYrhp3/77//VNYrOsR8ZfH1118zW1tbduTIEfbs2TO2Z88eZmFhwb777jut61SV/V+Wtlel/T9o0CDm6+vLQkND2aNHj1hwcDCzsrJiUVFRGutXlf3OWOnbXpn3+7Fjx9jcuXPZvn37GAB24MABleXLly9nlpaWbN++fez27dts8ODBzNnZmaWmpmrd5sWLFxmfz2dLly5l9+/fZ0uXLmUCgYBdunRJz60pHX20fdOmTczKykrlcxAbG6vnlpRNSe3funUrW7hwofL2UeHh4SVuszz2faVPmor6/PPPWa1atZhcLte4fObMmaxu3boqZePHj2etW7euiPD0qqS2K748S7pHUGXRq1cvNmbMGJWy/v37s+HDh2tdp6rs/7K0vars/8zMTMbn89mRI0dUyhs3bszmzp2rcZ2qst/L0vaqst+L/nDK5XLm5OSkcluo7OxsJpVK2fr167VuZ9CgQax79+4qZd26ddPpfpiGUl5t37RpE5NKpXqMVD80JU0KitvR6JI0lce+r/Sn5wrLzc3F9u3bMWbMGK13YA8LC1O5+zYAdOvWDdeuXYNMJquIMPVCl7YrNG3aFM7OzujcuTPOnj1bQRGWv3bt2uH06dN4+PAhAODmzZu4cOECevbsqXWdqrL/y9J2hcq+//Py8pCfnw9TU1OVcolEggsXLmhcp6rs97K0XaGy7/einj17hri4OJX9KhaL4e/vj4sXL2pdT9tnobh1jE1Z2w4A6enp8PDwQM2aNdG7d2+1kdursvLY91UqaTp48CCSk5MxatQorXXi4uLg6OioUubo6Ii8vDy8fv1azxHqjy5td3Z2xq+//op9+/Zh//798PHxQefOnXH+/PmKC7Qcffnllxg6dCjq1q0LoVCIpk2bYurUqRg6dKjWdarK/i9L26vK/re0tISfnx8WL16MmJgY5OfnY/v27bh8+TJiY2M1rlNV9ntZ2l5V9ntRcXFxAKBxvyqWaVuvtOsYm7K2vW7duti8eTMOHTqEP//8E6ampmjbti0ePXqk13iNRXns+yp177kNGzagR48ecHFxKbZe0SMx7M2dZEo6QmPMdGm7j48PfHx8lPN+fn6IjIzEqlWr0KFDh4oIs1zt2rUL27dvx44dO1C/fn3cuHEDU6dOhYuLCwIDA7WuVxX2f1naXpX2/7Zt2zBmzBi4urqCz+ejWbNmGDZsGP7991+t61SF/Q6Uvu1Vab9romm/lrRPy7KOMSptO1q3bo3WrVsr59u2bYtmzZph7dq1+OGHH/QWpzF5231fZY40vXjxAqdOncLYsWOLrefk5KSWVb58+RICgQC2trb6DFFvdG27Jq1bt660/2XMmDEDs2bNwpAhQ9CwYUOMGDEC06ZNw7Jly7SuU1X2f1narkll3f+1atVCaGgo0tPTERkZiStXrkAmk8HLy0tj/aqy34HSt12TyrrfC3NycgIAjfu16NGEouuVdh1jU9a2F2ViYoKWLVtW+s+Crspj31eZpGnTpk1wcHAo8U7Ufn5+OHnypEpZSEgIWrRoAaFQqM8Q9UbXtmsSHh4OZ2dnPUSlf5mZmTAxUf0I8/n8Yi+7ryr7vyxt16Qy738AMDc3h7OzM5KSknDixAm8//77GutVlf1emK5t16Sy73cA8PLygpOTk8p+zc3NRWhoKNq0aaN1PW2fheLWMTZlbXtRjDHcuHGj0n8WdFUu+17nLuNGLD8/n7m7u7Mvv/xSbdmsWbPYiBEjlPOKS4+nTZvG7t27xzZs2FBpLz1mrHRt//bbb9mBAwfYw4cP2Z07d9isWbMYALZv376KDLncBAYGMldXV+Vl9/v372d2dnZs5syZyjpVdf+Xpe1Vaf8fP36c/fXXX+zp06csJCSENW7cmLVq1Yrl5uYyxqrufmes9G2vzPs9LS2NhYeHs/DwcAaArVmzhoWHh7MXL14wxrjL7qVSKdu/fz+7ffs2Gzp0qNpl9yNGjGCzZs1Szv/zzz+Mz+ez5cuXs/v377Ply5cb5ZAD+mj7ggUL2PHjx9mTJ09YeHg4Gz16NBMIBOzy5csV3r6SlNT+hIQEFh4ezo4ePcoAsJ07d7Lw8HCVIRT0se+rRNJ04sQJ5TgkRQUGBjJ/f3+VsnPnzrGmTZsykUjEPD092bp16yoo0vJXmravWLGC1apVi5mamjIbGxvWrl07dvTo0QqMtnylpqayzz//nLm7uzNTU1Pm7e3N5s6dy3JycpR1qur+L0vbq9L+37VrF/P29mYikYg5OTmxSZMmseTkZOXyqrrfGSt92yvzflcMl1B0CgwMZIxxl94HBwczJycnJhaLWYcOHdjt27dVtuHv76+sr7Bnzx7m4+PDhEIhq1u3rlEmkPpo+9SpU5m7uzsTiUTM3t6ede3alV28eLECW6W7ktq/adMmjcuDg4OV29DHvucx9qY3JCGEEEII0arK9GkihBBCCNEnSpoIIYQQQnRASRMhhBBCiA4oaSKEEEII0QElTYQQQgghOqCkiRBCCCFEB5Q0EUIIIYTooFokTTk5OViwYAFycnIMHUqFq85tB6p3+6nt1bPtQPVuP7Wd2q5PlWZwy59//hkrV65EbGws6tevj++++w7t27fXad3U1FRIpVKkpKTAyspKz5Eal+rcdqB6t5/aXj3bDlTv9lPbqe36bHulONK0a9cuTJ06FXPnzkV4eDjat2+PHj16ICIiwtChEUIIIaSaqBRJ05o1a/Dxxx9j7NixqFevHr777ju4ublh3bp1hg6NEEIIIdWEwNABlCQ3NxfXr1/HrFmzVMq7du2KixcvalwnJydH5bxmYmIiACAyMhJSqVR/wRqhtLQ0AEB0dDRSU1MNHE3Fq87tp7ZXz7YD1bv91Pbq2faUlBQAQF5enn5f6C1uQlwhoqOjGQD2zz//qJQvWbKEvfPOOxrXCQ4O1nj3Y5poookmmmiiqepOf//9t15zEqM/0qTA4/FU5hljamUKs2fPRlBQkHI+MjISDRo0wJUrV+Ds7KzXOAkhhBBSsWJjY9GqVSu4u7vr9XWMPmmys7MDn89HXFycSvnLly/h6OiocR2xWAyxWKycV5ySc3Z2Rs2aNfUXLCGEEEIMxsREv121jb4juEgkQvPmzXHy5EmV8pMnT6JNmzYGiooQQggh1Y3RH2kCgKCgIIwYMQItWrSAn58ffv31V0RERODTTz81dGiEEEIIqSaM/kgTAAwePBjfffcdFi1ahCZNmuD8+fM4duwYPDw8DB0aeUsvX77E+PHj4e7uDrFYDCcnJ3Tr1g1hYWHKOjweDwcPHiyX13v+/Dl4PB5u3LhRbL1z586Bx+MhOTlZbVmTJk2wYMECZZ3ips2bNwMA9u3bh44dO0IqlcLCwgKNGjXCokWLlFd26mL//v3o0qUL7O3tYWVlBT8/P5w4cUKt3r59++Dr6wuxWAxfX18cOHBAZfmyZcvQsmVLWFpawsHBAf369cN///2nXC6TyfDll1+iYcOGMDc3h4uLC0aOHImYmJgSY0xKSsKIESMglUohlUoxYsQItffw888/R/PmzSEWi9GkSROd2x8aGormzZvD1NQU3t7eWL9+vcryu3fv4sMPP4Snpyd4PB6+++47nbZ7+/Zt+Pv7QyKRwNXVFYsWLQIrMuZvSa9dVGJiIj777DP4+PjAzMwM7u7umDJlivIKH4D7LH788cfw8vKCRCJBrVq1EBwcjNzc3BJjLmkfA9yAwF5eXjA1NUXz5s3x999/6/R+EEK0qxRJEwBMnDgRz58/R05ODq5fv44OHToYOiRSDj788EPcvHkTW7ZswcOHD3Ho0CF07NixVMmErnT5MSqNNm3aIDY2VjkNGjQI3bt3VykbPHgw5s6di8GDB6Nly5b466+/cOfOHaxevRo3b97Etm3bdH698+fPo0uXLjh27BiuX7+OgIAA9OnTB+Hh4co6YWFhGDx4MEaMGIGbN29ixIgRGDRoEC5fvqysExoaikmTJuHSpUs4efIk8vLy0LVrV2RkZAAAMjMz8e+//2LevHn4999/sX//fjx8+BB9+/YtMcZhw4bhxo0bOH78OI4fP44bN25gxIgRKnUYYxgzZgwGDx6sc9ufPXuGnj17on379ggPD8ecOXMwZcoU7Nu3T1knMzMT3t7eWL58OZycnHTabmpqKrp06QIXFxdcvXoVa9euxapVq7BmzZpSvXZRMTExiImJwapVq3D79m1s3rwZx48fx8cff6ys8+DBA8jlcvzyyy+4e/cuvv32W6xfvx5z5swpNmZd9jENCEyInuj12jwjERkZyQCwyMhIQ4dCCklKSmIA2Llz57TW8fDwULmc1MPDgzHG2OPHj1nfvn2Zg4MDMzc3Zy1atGAnT55UW3fx4sUsMDCQWVlZsZEjR6pdnurv76/xdc+ePcsAsKSkJLVljRs3ZsHBwWrlgYGB7P3331cpu3z5MgPAvvvuO63vwdvw9fVlCxcuVM4PGjSIde/eXaVOt27d2JAhQ7Ru4+XLlwwACw0N1VrnypUrDAB78eKF1jr37t1jANilS5eUZWFhYQwAe/DggVr94OBg1rhxY63bK2zmzJmsbt26KmXjx49nrVu31ljfw8ODffvttyVu9+eff2ZSqZRlZ2cry5YtW8ZcXFyYXC4v02trs3v3biYSiZhMJtNa55tvvmFeXl7FbkeXfdyqVSv26aefqtSpW7cumzVrVqliJqSyqKjf+UpzpIlUPRYWFrCwsMDBgwe13mTx6tWrAIBNmzYhNjZWOZ+eno6ePXvi1KlTCA8PR7du3dCnTx+1/6RXrlyJBg0a4Pr165g3bx6uXLkCADh16hRiY2Oxf/9+PbYQ+OOPP2BhYYGJEydqXG5tbQ2g4LThuXPndN62XC5HWloaatSooSwLCwtD165dVep169ZN60CwQMGgcIW3o6kOj8dTxqtJWFgYpFIp3n33XWVZ69atIZVKi319XWhr17Vr1yCTyd5qu/7+/ipX23br1g0xMTF4/vy5zq+tOFWrWEcTxT2xBALtXUlTUlLU9oOnpycWLFigEnNx+1gxIHDROsUNCEwI0Q0lTcRgBAIBNm/ejC1btsDa2hpt27bFnDlzcOvWLWUde3t7AFxy4eTkpJxv3Lgxxo8fj4YNG6JOnTr4+uuv4e3tjUOHDqm8RqdOnfDFF1+gdu3aqF27tnJ9W1tbODk5FZsolIdHjx7B29sbQqGw2HpCoVDZ/0VXq1evRkZGBgYNGqQsi4uLUxuKw9HRUW3IDgXGGIKCgtCuXTs0aNBAY53s7GzMmjULw4YNK/ZGmHFxcXBwcFArd3Bw0Pr6utLWrry8PLx+/brct6tYputrm5mZwcfHR+t+TkhIwOLFizF+/HitsTx58gRr165Vu8ClVq1asLOzKzFmRbyvX79Gfn5+qT4HRF18erxa3zZCKGkiBvXhhx8iJiYGhw4dQrdu3XDu3Dk0a9ZM2YFam4yMDMycORO+vr6wtraGhYUFHjx4oHakqUWLFnqMvmSsmEFYC3N1dcWDBw/QqlUrnbb7559/YsGCBdi1a5daolKagWAnT56MW7du4c8//9S4XCaTYciQIZDL5fj555+V5Z9++qnySKGFhYXW1y7p9TUpvN3CCYSmdml7zdLQZbsl1WnVqhUePHgAV1dXte2npqaiV69e8PX1RXBwsMYYYmJi0L17dwwcOBBjx45VWXb69GlMnjy5xJiLlpXmc0BU9dvZD06rnTBgzwBDh0KMTKUYcoBUbaampujSpQu6dOmC+fPnY+zYsQgODsaoUaO0rjNjxgycOHECq1atQu3atSGRSDBgwAC1zt7m5uZliklxRCUlJUXtlFRycrLO9zB85513cOHCBchkshKPNulq165d+Pjjj7Fnzx689957KsucnJx0Hgj2s88+w6FDh3D+/HmNg77KZDIMGjQIz549w5kzZ1SOMi1atAhffPGF2mvHx8erbefVq1daB6LVpPCVjYrX1NYugUAAW1tbnbddlLbtAgVHnN7mtdPS0tC9e3dYWFjgwIEDGj8DMTExCAgIUA6nUtaYFfGWZUBgoup///0PALD/vn5P35PKh440EaPj6+urvJIL4E5d5efnq9T5+++/MWrUKHzwwQdo2LAhnJyciu1PoiASiQBAbXtF1alTByYmJso+VAqxsbGIjo6Gj4+PTm0ZNmwY0tPTVY7SFKZpSIPi/Pnnnxg1ahR27NiBXr16qS338/NTGwg2JCREZSBYxhgmT56M/fv348yZM/Dy8lLbjiJhevToEU6dOqWWHDg4OChPedauXVv52ikpKcp+YwBw+fJlpKSklGog2sLbVRxF09auFi1avFUy6ufnh/Pnz6sk2yEhIXBxcYGnp+dbvXZqaiq6du0KkUiEQ4cOwdTUVK1OdHQ0OnbsiGbNmmHTpk06jWZc0j6mAYEJ0SO9djM3EnT1nHF6/fo1CwgIYNu2bWM3b95kT58+Zbt372aOjo5szJgxynp16tRhEyZMYLGxsSwxMZExxli/fv1YkyZNWHh4OLtx4wbr06cPs7S0ZJ9//rlyPU1XUMlkMiaRSNjXX3/N4uLiWHJystb4JkyYwNzd3dmBAwfY06dP2YULF5i/vz9r2LChxiugNF09xxh39RWfz2czZsxgFy9eZM+fP2enTp1iAwYMUF5VFxUVxXx8fNjly5e1xrNjxw4mEAjYTz/9xGJjY5VT4Tb8888/jM/ns+XLl7P79++z5cuXM4FAoHJF24QJE5hUKmXnzp1T2U5mZqbyPerbty+rWbMmu3HjhkqdnJwcrfExxlj37t1Zo0aNWFhYGAsLC2MNGzZkvXv3Vqnz6NEjFh4ezsaPH8/eeecdFh4ezsLDw4vd9tOnT5mZmRmbNm0au3fvHtuwYQMTCoVs7969yjo5OTnKbTk7O7MvvviChYeHs0ePHmndbnJyMnN0dGRDhw5lt2/fZvv372dWVlZs1apVpXrty5cvMx8fHxYVFcUYYyw1NZW9++67rGHDhuzx48cq72FeXh5jjLsZee3atVmnTp1YVFSUSp3COnXqxNauXauc12Uf79y5kwmFQrZhwwZ27949NnXqVGZubs6eP3+u9b0gBbAAyolUDhX1O18tPhGUNBmn7OxsNmvWLNasWTMmlUqZmZkZ8/HxYV999ZXyB5wxxg4dOsRq167NBAKBcsiBZ8+esYCAACaRSJibmxv78ccfmb+/f4lJE2OM/fbbb8zNzY2ZmJhoHXJAEd+iRYtYvXr1mEQiYR4eHmzUqFFqP2oK2pImxhjbtWsX69ChA7O0tGTm5uasUaNGbNGiRcohB549e8YAsLNnz2qNx9/fX+NdvQMDA1Xq7dmzh/n4+DChUMjq1q3L9u3bp7Jc0zYAsE2bNqnEomkqLj7GGEtISGAfffQRs7S0ZJaWluyjjz5SG1ZBWzuePXtW7LbPnTvHmjZtykQiEfP09GTr1q1TWa4t7uL2MWOM3bp1i7Vv356JxWLm5OTEFixYoBxuQNfXVgxRoWiDYr64dm7atElrncI8PDzUhrgoaR8zxthPP/3EPDw8mEgkYs2aNSt2SAmiipKmyqeifud5jFX9ywOioqLg5uaGyMhIumEvIYQQrRhjMFlUcJqUBVf5n8gqoaJ+56lPEyGEEPJGPiu+vyOp3ihpIoQQQt5Iy0lTmX+e/NwwgRCjREkTIYQQ8kZ8huqwGd22dzNQJMQYUdJECCGEvGEmVB2V38lCt5s/k+qBkiZCCCHkjX9j/1WZP//ivIEiIcaIkiZCCCHkjUxZpqFDIEaMkiZCCCHkjUcJjwwdAjFilDQRQgghbwR4BRg6BGLEKGkihBBC3hDzxSrzwf7BBoqEGCNKmgghhJA3xALVpKmWTS0DRUKMESVNhBBCyBsivkhlftfdXQaKhBgjSpoIIYSQN4qenjv66KiBIiHGiJImQggh5A1xUqqhQyBGjJImQggh5I3LfZqpzE9uOdlAkRBjJDB0AIQQQohRSE1Fk7iC2UNDDqGPTx/DxUOMDh1pIoQQQgAgIwO1kgpmHcwdDBcLMUqUNBFCCCEA4OysMrs+7AcDBUKMlcGSpufPn+Pjjz+Gl5cXJBIJatWqheDgYOTm5qrU4/F4atP69esNFDUhhJAqbcQI5dPN93YYMBBijAzWp+nBgweQy+X45ZdfULt2bdy5cwfjxo1DRkYGVq1apVJ306ZN6N69u3JeKpVWdLiEEEKqgyVLgI3bCuZTUgD6zSFvGCxp6t69u0oi5O3tjf/++w/r1q1TS5qsra3h5ORU0SESQgipbtzc0Ps/4IjPm3lLS4OGQ4yLUfVpSklJQY0aNdTKJ0+eDDs7O7Rs2RLr16+HXC43QHSEEEKqg3YCr4KZv/82XCDE6BjNkANPnjzB2rVrsXr1apXyxYsXo3PnzpBIJDh9+jSmT5+O169f46uvvtK6rZycHOTk5Cjn09LS9BY3IYSQqqXpzG+Bff0AAHmdOkKQzwwbEDEa5X6kacGCBRo7bxeerl27prJOTEwMunfvjoEDB2Ls2LEqy7766iv4+fmhSZMmmD59OhYtWoSVK1cWG8OyZcsglUqVk6+vb3k3kxBCSBXV5kXB2Yxw6hlCCuExxso1hX79+jVev35dbB1PT0+YmpoC4BKmgIAAvPvuu9i8eTNMTIrP4/755x+0a9cOcXFxcHR01Fin6JGm6Oho+Pr6IjIyEjVr1ixliwghhFQrJibgBXM/jVvQDyODDxg4IFKSqKgouLm56f13vtxPz9nZ2cHOzk6nutHR0QgICEDz5s2xadOmEhMmAAgPD4epqSmsra211hGLxRCLC266mJpK9xIihBCimxRRwbGEOoMnGTASYmwM1qcpJiYGHTt2hLu7O1atWoVXr14plymulDt8+DDi4uLg5+cHiUSCs2fPYu7cufjkk09UkiJCCCGkvByc0x9g+wEAlv5dgHjq00Q4BkuaQkJC8PjxYzx+/FjtUJrijKFQKMTPP/+MoKAgyOVyeHt7Y9GiRZg0iTJ/Qggh+sH69AYOcUlT3eJ7m5BqxmBJ06hRozBq1Khi6xQdy4kQQgjRt2aJBWcy4prWAfWEJQpGNU4TIYQQYmgNPd9VPp88im7aSwpQ0kQIIYQUwrOzg3sy91yQnVtsXVK9UNJECCGEFBYaii8uck9znOlIEylASRMhhBBS2J9/olks9/Rq7LXi65JqhZImQgghpLAmTWCfyT2Nz4hHYlaiYeMhRoOSJkIIIaQwgQC1C+VJ556fM1goxLhQ0kQIIYQU1qwZcvkFs5Q0EQVKmgghhJDCAgJwqdDgTAN8BxguFmJUKGkihBBCClu3Ds+tC2avx1w3WCjEuFDSRAghhCjExwMTJ2LobYAv54qCQoIMGxMxGpQ0EUIIIQpWVoBEAnE+sG9XQfGjhEeGi4kYDUqaCCGEEAWJBEhPBwB0eFFQHJMWY6CAiDGhpIkQQggpLCcHACDNKSi6/fK2gYIhxoSSJkIIIaQwkQgAYMKALk+4IjOhmQEDIsaCkiZCCCGkMD4fmDwZAPCvM1c09fhUw8VDjAYlTYQQQkhRn34KAKiZys2m5aYZMBhiLChpIoQQQory9wcAjHszRBMNcEkASpoIIYQQdSkpAABJHjebKcs0YDDEWFDSRAghhBSWnw/kcdkSe1PEA89w8RCjQUkTIYQQUtgXXyifsje5kgmPfi4JJU2EEEKIqn37lE/lA7m+TJQ0EYCSJkIIIUTVzJnKp/J9ewFQ0kQ49CkghBBCCvvwQ+VT+ZvTczwe9WkilDQRQgghBTIzARcX5ayiIzgdaSIAJU2EEEJIgb59VWblP3wPgJImwqFPASGEEAIAUVHA6dMF83v24M6ruwBoyAHCoaSJEEIIAYCIiILnL17gV69E/PrvrwCAenb1DBQUMSYGTZo8PT3B4/FUplmzZqnUiYiIQJ8+fWBubg47OztMmTIFubm5BoqYEEJIlZVZaNRvPh/jj4xXzs5sO1PDCqS6ERg6gEWLFmHcuHHKeQsLC+Xz/Px89OrVC/b29rhw4QISEhIQGBgIxhjWrl1riHAJIYRUVYV+f+Dqiu61u+P44+PwtvGGRCgxXFzEaBg8abK0tISTk5PGZSEhIbh37x4iIyPh8uZqhtWrV2PUqFFYsmQJrKysKjJUQgghVdnNmwXP8/PxQd0PcPzxcXhIPQwXEzEqBu/TtGLFCtja2qJJkyZYsmSJyqm3sLAwNGjQQJkwAUC3bt2Qk5OD69evGyJcQgghVVWTJgXPmzVDw8vPAQDnX5zHlegrBgmJGBeDHmn6/PPP0axZM9jY2ODKlSuYPXs2nj17ht9//x0AEBcXB0dHR5V1bGxsIBKJEBcXp3W7OTk5yMnJUc6npaXppwGEEEKqDkmhU3C3bsFv0i28NwI4VSsfO+/sRCvXVoaLjRiFcj/StGDBArXO3UWna9euAQCmTZsGf39/NGrUCGPHjsX69euxYcMGJCQkKLenaRRWxlixo7MuW7YMUqlUOfn6+pZ3MwkhhFQ1jRoBhw+r3LDXL4p7lOVmGSgoYkzK/UjT5MmTMWTIkGLreHp6aixv3bo1AODx48ewtbWFk5MTLl++rFInKSkJMplM7QhUYbNnz0ZQUJByPjo6mhInQgghJevdm5tWrgR27ULIqTe/Z6z41Uj1UO5Jk52dHezs7Mq0bnh4OADA2dkZAODn54clS5YgNjZWWRYSEgKxWIzmzZtr3Y5YLIZYLFbOp6amlikeQggh1ZiDAy7X5J7eiL9h0FCIcTBYn6awsDBcunQJAQEBkEqluHr1KqZNm4a+ffvC3d0dANC1a1f4+vpixIgRWLlyJRITE/HFF19g3LhxdOUcIYQQ/cnLA1asgEc94IU10L9mV0NHRIyAwa6eE4vF2LVrFzp27AhfX1/Mnz8f48aNw59//qmsw+fzcfToUZiamqJt27YYNGgQ+vXrh1WrVhkqbEIIIdXBnTvAiROoncjNOtrRsAPEgEeamjVrhkuXLpVYz93dHUeOHKmAiAghhJA3GjcGAAjk3KxMyDdgMMRYGHycJkIIIcTo8HhA7dqwejN6TVoO9Y0llDQRQgghmrm4wDqbe5qUmVB8XVItUNJECCGEaHL+vPJIU0RatGFjIUaBkiZCCCFEEwsLZL3p+ZuYnWjYWIhRoKSJEEII0WT4cAjfdASvxbc3bCzEKFDSRAghhGhiawv+m6SJyeWGjYUYBUqaCCGEEE3++w8vrLmnTjxLg4ZCjAMlTYQQQogmiYm46sI9bWhZy7CxEKNASRMhhBBSVGwscOYM0kXcrKMtjQhOKGkihBBCOHI5sGUL0LMn4OKCZFMg2ZRbZOvuY9jYiFEw2G1UCCGEEKORkADUrAlkZyuL5DxA/ubQglTqaKDAiDGhI02EEEKqt+RkwM5OJWECAOuTfyufp9JtVAgoaSKEEFLdLV+uXrZqFUzatoONqQ0A4FXmqwoOihgjOj1HCCGk+pLJgMxM1TLGAAAhT0KQlJ0EAEjKSqroyIgRoqSJEEJI9XPuHPD115CfOQ2ZCSATgXvkA7LUaOTJ8/C/B/9TVm/p2tJwsRKjQUkTIYSQKoUxhuBzwVh5cSWEJkLwTfgQmggBADK5DHl5MsiyMyBrC8jba9jAtzVVZjt4dICFyKICIifGjpImQggxIMYYHrx+AB6PBz6PDwDIk+dpnPJZPhhjYGBlfpQz+Vtvo/CjnMnLbVtliU/O5JDJZcjNz0Vu5HPIXsfjSd4r/I0XAIBsZGt+44v59ePz+BDyhRCaCOFs6YwJLSaU924nlRQlTYQQYkC9dvTCX4//MnQY1cr9HwEzGSDMB4TygkfBqDEQ/vI7eDyeoUMkRoqSJkKI0ZEzOcIiw5CUnYRMWSYyZZlIzk5GTl5OmY5OlHQ0pMzrl7D8adJTXIm+otK2Fi4tkC/PVx45uvfqXoW/v+3c24EHHngM4AFvHhn3qJwYt0z+5pG9WS5n4F2+DJ6cwURRF+XzaFLGdURvkh5R/pvn+YWeywGBHMgWcANV9r8PeBfu0+3oyF0916AB0KJFBe0BUllR0kQIMToNfm6A+6/vGzoMvbgWc82gr//HPmDY7QsGjUHJ0hLg8wETE+5R8TwrC0gqxdVqEyYAH30EiEQFk1is+Tmfr7/2kCqPkiZCSNkxBuTlAbm53KXbubnK53FpsdjweDeYXM7VY+zNc27+cXoktsZyp6XcxY5w4lshOzYSt6Ra+qAU46MEV1jIeNxRELkcvHw5eHI5TOSsoEwuL/ScccsyMkt1tKO0y/JNgB/eBV5Yc3GuPQZ4JnNHPgRygK94ZKrzupYVnjfqE0qKRIUxwNQUGD8emDEDcHY2bFyElBIlTYRURbm5wP/+B7x+zSUzAMDjAfn5QGoqkJjIjX7s4qJMdNJy0/Ew/yWy87KRl5eDvJws5OVmI0+Wg7zcHO5RMeXlIE+WizyWj7w3l2nnmRRMmUJgmaarkrSIyIlHBOIBaemb2jwG2P5rdOlXrCBBYW+eeHkB7dsDZmaAuTk3FX4ukXBHWXg8zROgfVlJEwDcuwdERABCIXfERdOjUFj219D0mh06ANIy7FRCjBQlTYToUaYsE6efnoaQL4SIL4LQRAgejwdZvgx58jzu8md5HmT5MsiZXDkp+tnImRxyWS7Y//4H+YW/C8ogB5PLIc97sx4PYHhzryy7GpAnJnJlvDdlbyZlHR6X3HzjDED4ZjI36FtVZnaevsDvQQU/+oUngaBg4vNVHwVvvv6OH+du1CqRcEdBJBLuh97RkUtmFKd1hMKC00fapsLLCycPxqBTJ0NHQEilR0kTIXoiy5ehzto6iEmLefuNCQB01LVyYplfxhYS2MAMAhM+BDwBBCYCCPgCCPhCCPkiCPhCCAQi1UmlTAiBiZBb780kLDKvyyTkc+vweXyY8ExgwjMBj8cDDzzwTfgQ88UQC8QwFZiigUMDgPcWd4Rq0KDs6xJCqhVKmghRYEylX44sKwMvkp8jOzsdspxMyP4OheyntdyIwSZArpU5ZK2aQ5Yvgyw/F7L8XOTKZdy8XAaZPA8xDYpPmJrHcFf6KPqnKPrGmBTqI6Myr+vyzp1hIhSBxxfARCCAiYkAPJ4JTPh8mIjEMBGKwRMKcfv1XZx5dgYAsHvAbgysP1D/7zMhhFRSlDSRqu35cyA+nuu/k5VVMCnm584FUlIAAOc8gYN1gRw+sF7bHRM+LjyTAeB8mUO79yNQ73UpV/rzT6BlS+5UkalpwaToi0IIIURvKGkiVdfGjcDHH5dcD8BFNyBglPblzuk8CPOYctwXxWB4ImtbCK1sIHxzGkrZd+nN6Sw+X4gXuS8LTm8JuMe5jSaj3vjmgJNT+bSVEEKI3hksaTp37hwCAgI0Lrty5QpatuT+1dc0Muu6devw6aef6jU+UgXUqKFeVrcud5mzRMJNIhHw55+okyaCs9ACsTL1/kANHBrgdvDtCgiYEEKIMTNY0tSmTRvExsaqlM2bNw+nTp1CiyKjsm7atAndu3dXzkvpElaii379uFNzjo4FZYMGAQsXKmdz83Ox68seGHlwJKAhYZrSagqC/IIqIFhCCCHGzmBJk0gkglOhUxMymQyHDh3C5MmT1Y4uWVtbq9QlRGcODlz/JVNTbn71amDBAoDHw4yQGVgVtkptFXOhOSzFlpjdbjamvDulYuMlhBBitN7iOt3ydejQIbx+/RqjRo1SWzZ58mTY2dmhZcuWWL9+PeRyebHbysnJQWpqqnJKS0vTU9SkUhCLuWQJADIygO+/x5XoK2oJ07CGw5D7VS7S56QjdnosJUyEEEJUGE1H8A0bNqBbt25wc3NTKV+8eDE6d+4MiUSC06dPY/r06Xj9+jW++uorrdtatmwZFhY6BUMIRo4Epk8HADxePA3vphQssjezx+mRp9HQsaGBgiOEEFIZ8BhjrDw3uGDBghITlqtXr6r0W4qKioKHhwd2796NDz/8sNh1V69ejUWLFiElJUVrnZycHOTk5Cjno6Oj4evri8jISNSsWVPHlpAq581p32XtgDnvcUWfNPsEP/T4AWKB2ICBEUIIeRtRUVFwc3PT++98uR9pmjx5MoYMGVJsHU9PT5X5TZs2wdbWFn379i1x+61bt0Zqairi4+PhWLiDbyFisRhiccGPYGpqasmBk6rPxgZISoJNofvBPkp8RAkTIYQQnZR70mRnZwc7Ozud6zPGsGnTJowcORJCobDE+uHh4TA1NYW1tfVbREmqpW3bgN698Z9tQZHDvRfcSOA0MCQhhJASGLwj+JkzZ/Ds2TN8rGEQwsOHD+O3337DnTt38OTJE/z++++YO3cuPvnkE5UjSYTopFcvYOFCfBxeUPRv1lPu5qpnzxouLkIIIZWCwZOmDRs2oE2bNqhXr57aMqFQiJ9//hl+fn5o1KgRvv/+eyxatAirFVdCEVJa8+ejQUwetqd1BQA8sgWuuIK73QohhBBSjHLvCG6MKqqDGKk8cjt3hLhDKACgcRxwo9fhglHCFZOjI9cPihBCiFGrtB3BCakMRA8e4fp9oPl44KYTEPVRH9TUdL1Ahw7A++8D5uaAVMpNVlbcvL094Opa4bETQggxDEqaSPX0++9o1LuncrbhBODUVsA1DbDKASQygAcA589zkzbTpgGLF3NJFCGEkCqNkiZSPfXoAcHV62h4sBtu818jWQK0GF+wmMcAIUwgZCYQ5eZDmMcgygeEckCYD+VzUdq3uLvkW6S/uS6hAXPA3x8cgnXjdw3TLkIIIXpDSROpvpo1w8UGzzDn9Bxcjr6MiJQIxKXHAQAYD8iFHLk8OTJMdd/kHd5L2BxsDdsdgIWcD/N8bnLKFaF2thkc8yVwYGZwYGZw4VnCJd8cFqFhMMsDeKYS7pYviokxwM4O+PlnoFYtPb0JhBBCdEVJE6nWLEQW+KHHD8p5OZMjIzcDGbIMyPJlkMllyM3PhSz/zaNcBlluNnJD/oLsyUPkHj6IkR8AKUUSqwQzIAH5APLflGQB0DKKvQ/3YJYLmMu4I1kxVlxZn/+A7kNrw1xkDgu5ABZyAczlfFjIhXDLMYVtvggQCAomPl+35yYmQEoKIJcXLOPzgRYtuFOOIlF5vs2EEFIlUNJESCEmPBNYii1hKbYsvuKnHbnHFXIkv3oFMIasrDRkZCQhPSsFGVmpyMhKQXp2Kl5nJSAiIwYvcxK5SZaM+LxkROUl4SUruJl0poibCjvsw01AhsYwmscAzWIBcR4gzi/0KAPE2dx8qhh4aQ5MvArUSirhDdi7F0hKApYvL6EiIYRUP5Q0EfI2TEy4oQkASOAECQDdx8PnjmxlyjK5o1u56cjITEFGRhLWhf+Krf/txlDPPsjNz0V6bjoy8jKRLsvEnbTHyGPcEazrLtykizVtgMOW42GZC1j88Ass3hzZksgASR5gmgeYMABPnpTqLSCEkOqCxmkipBK6FnMNl6MuI0+eh7TcNOTk5SAnP6fg8c3zE09OIDErUeft3voZaPgSXDJobQ34+QHTpwMdO9KtZgghRovGaSKEaNXCpQVauLTQqe7rzNcIPhuMR4mPEJ0WjXuv7mmte6L2m6RJLgcSE4GjR7kJ4Po8CYXcJBCoPzc15QYFNTMrmCIjgRcvgA8+4Dq183hcQmZiUvC86GNJyxR9svj8gikrC/j3X6BrV6BvX0rwCCF6QUeaCKlG5EyOPn/2wbFHx4qt98MxYNQNwDK3YuIqd6amqkmVLlPRREzbxBhw5QqQl8c9l8mA7GzV13d1BY4fBxo0MEz7CalmKup3npImQqqpnLwcXIu5hnab2mmtU9+2HpraNUSTGr5wN3OCGUQwgxBmPCHMmODNJIQZ48NUxiDMyoVJVjaQmclN164B//wDdOrEHY2Sy7mJMdVHXZ4zBuTnc8/z8wue5+QAoaEV+M6VQv36BUfkkpKAiAjAxwe4cIEbWZ4QUi4oaSpHlDQRUrLErETsuL0Dc8/MRWqOpnvK6EZgIoCILyr7ZFL8clOBKRo7NUZLl5YQ8oUFL5yUxJ2mK5pYlXYqad3Ro8vh3QbQti1QuzZ3VMzEhEsKXVyAceMAJ6fyeQ1CqglKmsoRJU2ElA5jDDFpMQiPC0d4bDhuxt/E68zXyJRlqk0ZsgzImdyg8TpbOCN8fDgcLRwr7kVlMi5JUxxVy83lyhRT0fk+fXTf9saNQMuWgLMzUKMG9dEipASUNJUjSpoI0R/GGGRyGbJkWcrBQMt7WntlrU6xdK/dHfdf3Ud6bjoEJgJs/WArutbqqud3QEdZWcAvv3Ad7F1duaNWZ89yY2MVp25d4Pp1rmM9IUQjSprKESVNhFRu225uw8iDI8u07oa+GyAVS7lR1UUWMBeaw1xkDjFfDFOBKcQCMcR8McQCMUx4JuUcuQ5SUoBbt4AOHbTXOX8eaN++4mIipJKhIQcIIeSNEY1HYETjEQCAfHk+MmQZSMtJQ3puOtJyuceU7BT029VPbd2PD32s8+sITYTKJEqRUJkKTOFg7oCaVjVRp0YdNHBogJpWNdHYsTHEAvHbNezhQ6BfP+D+ffVlHTpwR6RatODGyyKEGBwlTYSQSoVvwoeV2ApWYvWrz+Tz5fjr8V+YdmIaHiY8xPs+7yv7XSnuKZiRm4FMWSZy8nOQnac6VAB3b0EZ0pGuUq5tbCsvay9ITaWwEltBKi54dLJwgrWpNfgmfABAc+fmaOXaCryifZP++ktzwjR6NNeXSSLhhjNYu5a7Ao8xIDoa6NWLjjwRYgB0eo4QUm0p+mMpRlLPzstWGV09Oy8bWXlZiEuPQ2RKJG7G38TDhIe4Hnu91K/lV9MP45uPh5nQDGZCM0iEEpjJ+TCbuwCSk+dgJgPMZIBNdsnbUtG9O7ByJY0JRao16tNUjihpIoSUJ1m+DBEpEYjPiMfWm1vxy/Vfym3bDeCIHswbn2U1gts3ZdzurFnc7W/sSnMnREIqL0qayhElTYQQffrfg//h1NNTYGBQfKUyMMiZHPnyfGTlZSFTlql8zJRlIktW6HleFjJyM5D/5kbMCqlfpsAy7DqQllYwYjljwOPHwOeflxzYlSvc0AWEVHGUNJUjSpoIIZVBZEok3L9zVykb12wc3KXucLNyQ32H+mju3Fy1b1R6OtfPadMmYMUK9Y126sQNa2Bjo+foCTEcSprKESVNhJDKgjGGej/Vw38J/2lcXrtGbSzrvAwDfAeoL8zL4zqNBwWpls+aBSxbpodoCTEOFfU7b4BBSQghhGjD4/HwYPID3J90H7/3+R3B/sEY02QM6trVBQA8TnyMgXsGYunfS9VXFgiAsWPVyy0s9Bw1IdUDDTlACCFGqK5dXWWiBAC8harDFcw9MxeRKZGoIamhnGwkNjDv9QGamgF2mW8qWlgAM2dWYOSEVF2UNBFCSCXQ16cvDv13SKVs/fX16hXfDJw+Ohzw8GoCm76DUetZCNq5t4PUVFoBkRJSdVHSRAghlcDBwQdxM/4mYtJikJiVWGhKQNLRffjTNhb5hTpcbGoKADeA8zeUZfsH7ccH9T6o4MgJqTooaSKEkEqAx+OhiVMTNHFqUlCYmAgMGQKcjMU2AHccgNDZw5DZsgkeJT7Cb//+prKNFykvKjRmQqoavXYEX7JkCdq0aQMzMzNYW1trrBMREYE+ffrA3NwcdnZ2mDJlCnJzc1Xq3L59G/7+/pBIJHB1dcWiRYtQDS76I4QQzRgDrl4FbG2BkyeVxQ3mfo9JU//AZ+9+Bk9rT2W5i6ULDgw+gM/f1WFsJ0KIVno90pSbm4uBAwfCz88PGzZsUFuen5+PXr16wd7eHhcuXEBCQgICAwPBGMPatWsBAKmpqejSpQsCAgJw9epVPHz4EKNGjYK5uTmmT5+uz/AJIcR4yOXcPeeOH1dfZm0N3LwJuLsjMSsRtt/YKhd523jj1qe3YC4yr7hYCami9Jo0LVy4EACwefNmjctDQkJw7949REZGwsXFBQCwevVqjBo1CkuWLIGVlRX++OMPZGdnY/PmzRCLxWjQoAEePnyINWvWICgoSP0GmIQQUpXs2sWdggMg5wGvzYHXZoWmwIF43aYJblz+AiE7QpCSk6KyetjHYZQwEVJODNqnKSwsDA0aNFAmTADQrVs35OTk4Pr16wgICEBYWBj8/f0hFotV6syePRvPnz+Hl5eX2nZzcnKQk5OjnE9LS9NvQwghpLQeP+Zuc5KVBWRmco9vnsuy0vGfLA63WRzuPbyIe4OA/+yAxzWAnKLf2tl7gDN7VIp87X0R2DgQgY0D4WDuUHFtIqSKM2jSFBcXB0dHR5UyGxsbiEQixMXFKet4enqq1FGsExcXpzFpWrZsmfIoFyGEGJXISMDdXeOiV2bAp72BI+8AuYpvZ2fVOjzwYCOxgZ2ZXcEksYOtmS1qWtVEv7r94C7VvH1CyNspddK0YMGCEhOSq1evokWLFjptT9PpNcaYSnnROopO4NpOzc2ePRtBhW4jEB0dDV9fX53iIYQQvdq/X2NxogToOxS45MbNW0GMBibOqC9yha91HdTt0B8+Tg3gJnWDwIQufCbEEEr9lzd58mQMeXN+XZuiR4a0cXJywuXLl1XKkpKSIJPJlEeTnJyclEedFF6+fAkAakepFMRiscrpvNTUVJ3iIYSQMmGMO8WWlgakpnI30c3OLphycgqeX7yotvrWxsDU7kCSBLDO5uHYuHNo7dWe+mwSYmRKnTTZ2dnBzs6uXF7cz88PS5YsQWxsLJyduWPQISEhEIvFaN68ubLOnDlzkJubC5FIpKzj4uKic3JGCCFl9uoV8OABEBsLfPQRd1NcAKhfn0uQUlO5ZEkuL/Wm83nA7vrAqH4A4wH1XwJ/oD8ae3co3zYQQsqFXo/xRkREIDExEREREcjPz8eNGzcAALVr14aFhQW6du0KX19fjBgxAitXrkRiYiK++OILjBs3DlZWVgCAYcOGYeHChRg1ahTmzJmDR48eYenSpZg/fz79F0YI0Z+bN4FmzbQnQ3fvqpfxeICVFbKk5kiUipBkwUeiBR9J5iZIlHBHkhIjHyLWHLjfpg5uy6KRLksHAPR5pw8OzDsAvglfj40ihLwNvSZN8+fPx5YtW5TzTZs2BQCcPXsWHTt2BJ/Px9GjRzFx4kS0bdsWEokEw4YNw6pVq5TrSKVSnDx5EpMmTUKLFi1gY2ODoKAglT5LhBBSbqKiADc39XInJ+4UXGoqMGsW0KULYGUFWFoix1yMj85PxcXYK0jMSkROfor6+gqKPtqZ/wEALEQWGOQ7CD/1+okSJkKMHI9Vg6G1o6Ki4ObmhsjISNSsWdPQ4RBCjEViIvD8OfDwIXf6TdM/Y507c+U9eyqLIlIicPThUZx+dhp3Xt7Bfwn/qa3G5/FhI7FBDUkN2Jhyj4rndmZ2qGtXF/Ud6qOeXT1Klgh5SxX1O0+XYBBCqq7kZODJE26KigISErhEKSEB2LOnxNXx0UfA9u0AuKt2/439F9NOTMPfEX+rVZWKpejr0xeLAxbDRmIDS5EldSEgpIqhpIkQUnllZABffgkcOsRdoaaYsrNLt53WrQEvLyA/H7hwAWjUCKxTJ1wa2BrHzsxDeFw4rsZcxcsM7spdHnho49YGver0QguXFqjvUB/OFs6UJBFSxVHSRAipvH7/Hfjpp7fbhlDInZp7/RqwtAT++gto1AgTjnyKX7bMVKkqEUjQ2bszvu32LWrXqP12r0sIqXQoaSKEVC75+UBMDHDmDDe9LZkMePGiYL5jRyAhAa8yX6lV/brT1wjyo4tQCKmuKGkihBgvuRz49Vdg61ZALAYiIrjbkMhk+nvN4GCAx8OklpOw/77q6N18HnXYJqQ6o6SJEGK8jhwBJkxQLxcIgHr1gG7dAFdXwMGBm6RSQCIBTE1VJ7GYW6cUfY4239isfD680XAM9B2IPu/0KYdGEUIqK0qaCCHG6/Vr1fnffuMSJRcXgK+/oz4JmQnYeWcnAOCH7j/gs3c/09trEUIqD0qaCCGGlZMDrFsHHD0K2NlxwwQkJHBTTIxqXSsrzQNPlpPc/FxsDN+ICUcLjm5pGoOJEFI9UdJECKl4jHHJUWwsMHUqcPKk9roSCVC7NjB0KPDhh6V6mfTcdNyIu4HHiY8RmRKJ5OxkpOSkcFN2SsF8NleWnac+VEFLl5alaxshpMqipIkQol85OcD06cCGDYC7O9eJOza2+LGU/vc/wNaWu3WJp2eZTsUN3TdUeYqtLNq4tcHegXvhbOlc5m0QQqoWSpoIIeVPcSQpJgb4+GPg8mWu/OFD1Xo2Nlz/JHt7rv9Ss2bc1XJisdZN58vzEZMWg9eZr5XTq8xXiE+Px7PkZ3iW/AxPk54qB6IEgPe834On1BM2EhtIxVJITaWwNrVWPi/8aCW2otuaEEI0oqSJEFJ2eXnA3bvApUvAjRvAo0fA06dcspSTo3mdlSu502zOztyVbTraFL4JYw6NKVV4fB4f2/tvx5AGQ0q1HiGEaEJJEyGk9F69Ar7+GtiyBUhJ0V7Pxgbw9uaSJH9/oFEjwMJCa3XGGO68vIPI1EjEpsUiJi0GsemxeJr0FCeenFDWE5gIYG9mDzszO+Vkb2YPT2tPeNt4w9vGG142XrA2tS7HRhNCqjtKmgghpRcYyN1uBOCuaGvZkpt8fLhO2zVrcv2RdDiSlC/Px7ILy3D88XH8E/lPifUXdlyIue3n0ik0QkiFo6SJEFI6r14BJ94c9dm/H+jb963GTDr26BjmnZ2nVt6zTk84WzjDxdIFzhbOcJO6oaNnR1iItB+pIoQQfaKkiRCim4wMYO5c7pScXM4dVfrgg7faZFhkGN7f+b5y3lJkiaigKFiJrd42WkIIKXeUNBFCSnb2LNCpU8G8kxPwww9vvVknCycwMOX8g8kPKGEihBgtE0MHQAgxcqdPFyRMHh7A2rXcTXO7dn3rTXvZeGFLvy0AuKNMLpYub71NQgjRF0qaCCHF++MP7rFxY+D2bWDyZO7mt+Wkf73+AIC03DTcf3W/3LZLCCHljU7PEUKK98+bK9pu3gS2beOujqtTh7sHXBmTp3x5PjaEb8CasDWITotWlrtauZZHxIQQoheUNBFS3WVlcYNSvngBpKVx85mZ3JSVpTqK96RJ6uvv31/qDuFXoq9g/JHxKmUNHBrgesx1NHNuBqmptCwtIYQQvaKkiZDqIDsbCA3lEqOUFGD9em7k7vJw/Hipk6aWri3xSbNP8Ou/vyrL7ry8g05bCzqb/9TzJ0xsObF8YiSEkHLAY4yxkqtVblFRUXBzc0NkZCRq1qxp6HAIqThxccA773BHkIpjYcGdcrOxAczMAImEe9T2XCzmki8PD6BHD0AkKlN4jDGcfHoSYw+NRWRqpMqyRo6NcPPTm2XaLiGkeqmo33k60kRIVfXqFXcLk6ws1fLhw7mr3xgDli4FfH0Ba2uAx6vwEKPTotF/V39kyDJUyt2s3HBk6JEKj4cQQopDSRMhVVFsLOCi5fL91asBB4eKjUcLoYkQJjz1i3g/bvoxUnJS4CJ3odulEEKMBg05QEhVdOiQ6vyIEdxpur17jSZhAgBHC0fcnnAbK95boVK+IHQBGq5rCMFiAQ4+OGiY4AghpAi9Jk1LlixBmzZtYGZmBmtra7XlN2/exNChQ+Hm5gaJRIJ69erh+++/V6nz/Plz8Hg8ten48eP6DJ2QyikzE5gzB1i4sKBs/Hhg61bgv/+ADz80XGxaeFh7YGbbmWDBDDFBMfil9y/o4t1FufyDXR+g67auqAbdLwkhRk6vp+dyc3MxcOBA+Pn5YcOGDWrLr1+/Dnt7e2zfvh1ubm64ePEiPvnkE/D5fEyePFml7qlTp1C/fn3lfI0aNfQZOiGVC2PcrU46d1Ytb9MG+Pxzw8RUBs6Wzvik+Sf4pPknuBl3E01+aQIAOPn0JA79dwjv132/+A0QQoge6TVpWvjmv93NmzdrXD5mzBiVeW9vb4SFhWH//v1qSZOtrS2cnJz0EichldrDh0Dr1kBSUkHZrFnAvHnclW6VVGOnxpDPl2PkwZHYfms79t3fR0kTIcSgjK5PU0pKisajSH379oWDgwPatm2LvXv3GiAyQowMY9yglD4+BQnTuHHcCN7LllXqhEmBx+OhtWtrAFC7wo4QQiqaUV09FxYWht27d+Po0aPKMgsLC6xZswZt27aFiYkJDh06hMGDB2PLli0YPny4xu3k5OQgJydHOZ9W0hg1hFQ2ubncWElF/fqrelklk5KdgpvxN/Ei+QUiUiKw8cZGAIBEIDFwZISQ6q7USdOCBQuUp920uXr1Klq0aFGq7d69exfvv/8+5s+fjy5dCjqB2tnZYdq0acr5Fi1aICkpCd98843WpGnZsmUlxkhIpXbxYsHzGjWAMWOAKVMMF89bepH8Ande3sGC0AW4FnNNY53CncMJIcQQSp00TZ48GUOGDCm2jqenZ6m2ee/ePXTq1Anjxo3DV199VWL91q1b4/fff9e6fPbs2QgKClLOR0dHw9fXt1QxEWLUbhYaKTs2tswjcleUhMwERKVGISYtRjlFp0XjRcoLPE58jMeJj9XW6eTVCR5SD3hIPdD7nd5o7tLcAJETQkiBUidNdnZ2sLOzK7cA7t69i06dOiEwMBBLlizRaZ3w8HA4OztrXS4WiyEudOoiNTX1reMkxKj8/DP3uHCh0SZMSVlJeJr0FL129EJ8Rnyxdfk8Pho4NICXjRfau7fHtNbTwDPACOWEEFIcvfZpioiIQGJiIiIiIpCfn48bN24AAGrXrg0LCwvcvXsXAQEB6Nq1K4KCghAXFwcA4PP5sLe3BwBs2bIFQqEQTZs2hYmJCQ4fPowffvgBK1as0PayhFR96encY9euFf7SRx4eQejzUDhaOCIjNwPpuelIyUlBcnYyUnJSkJKdgtj0WESkRKitW8+uHjytPeFq6QoXSxe4Sd1Qy6YWGjg0gL25fYW3hRBCSkOvSdP8+fOxZcsW5XzTpk0BAGfPnkXHjh2xZ88evHr1Cn/88Qf++OMPZT0PDw88f/5cOf/111/jxYsX4PP5eOedd7Bx40at/ZkIqRY8PYGYGODLL4EffgAaN9bryy04twALQ0vfT9DZwhn25va4FX8LOz/cicENBushOkIIqRg8Vg2G2a2oux8TUmHOnQO6dAHy8rj5zp2B//0PMDcv95fKl+dDsFjz/1fjm4+HhcgCUrEU1qbWkJq+eRRL0dipMaxNrcs9HkIIKaqifueNasgBQoiOOnYE/v0XWLoU2LkTOH0acHcHEhLK/aX4JnyYC81VxknaN2gfer/TGyK+cfanIoQQfaCkiZDKqmFDbpiBnTu5+cREQC4HTMp/zNqr465iyvEpOPX0FADgw93cPezedX0XzZybwc7MDvZm9hCYCOBo4Yj3fd4H34Rf7nEQQoghUdJESGV2/77qfOPG3BhOlpbl+jL17Ovh5IiTuBh5Eeuvrce2W9sAAJejL+Ny9GW1+os6LsI8/3nlGgMhhBia0d1GhRBSCqNHA598UjB/5w5gZQVEqF+5Vh7auLXB1g+2InVWKmpaae830N6jvV5enxBCDImSJkIqMx4PKDogbI0a3H3p9CQtJw3eP3gjKjVKbdnGvhvBghk6enbU2+sTQoih0Ok5QiqTK1e4U3KxsUBcHDdFFUle1q0DPDz0FkLX7V3xOvO1cv7zdz/H152+hoXIQm+vSQghxoCSJkIqi59+AiZPLr4Onw/UqaPXMC5FXVKZvxh5EZOPTYajuSN87X0xvNFw6gROCKmSKGkipLKwttZc/sUXQO/egJMTUKsWINDvn/WMNjOw8uJK5fzVmKu4GnNVOT/qf6PgV9MPXjZeyM7LhruVO5Z0XgIzoZle4yKEEH2jwS0JqUxSUoC//wbWrgVCQlTLrawqLozsFDxJeoL49HjEZ8QjLj0Ov//7O54nP0c+y9e4zvpe6zG04VBYiSsuTkJI9VBRv/OUNBFSWdy8yY3LFBYGyGQF5TwecOsW0KCB4WJ7Iz03HY8SHuFR4iM8TnyMuWfmqtWJmBoBN6mbAaIjhFRVFfU7T1fPEVJZ7NwJnD9fkDCZmQGjRgFnzgD16xs0NAULkQWaOjfFoPqDMKf9HKTPTsdfH/2lUsf9O3d02tIJd1/eNVCUhBBSNpQ0EVJZjBkDmJoWzGdmAps3AwEBXHnNmly/Jx4P6N+fu8JOLjdUtAAAc5E5utfujvz5+VjZpaAf1NnnZ/HR/o8MGBkhhJQedQQnpLKoU4fru3TiBLBrF3DtGhAfDyQnA7m5QHR0Qd0DB7jJxASwsQFsbblJKgUsLDRPUinXmbxJE+6xHGXKMnH44WGVsg/rfViur0EIIfpGSRMhlYlIBPTpw00K2dlc8vT6NTcswaZNXLmJCXekKSGh9DfyjY4GXFzKHGZKdgpWXVyFcy/OITo1Gs+Tn4OB6z7Zo3YP/N73d7hYln37hBBiCJQ0EVLZmZpyg1l6eAAbN3ITAOTlcYmUImlKSADS0rjp0SPg+++1bzMvr1Qh5Obn4nnyc1yNvopnyc8w76z6fedcLF0wqeUkzG43Gzwer1TbJ4QQY0BJEyFVlUDAnWYrcqqNMYZXvQPw2h5IEQMpViIkv+OO1Jr2yHSsgcxa7sh8/Cuy7mchU5aJzLxMZMoykSV7My/LRFZeoeeyLCRnJyuPJBUV7B+MSS0nwd7cviJaTQghekNJEyHVyMHbe/DB/kFAK3ATACAXwGNuygfw8M1USqYCU3jbeKO5c3M0dGiIRo6N0MChAVytXMspekIIMSxKmgipDq5fBz77DCM6hgHigmLvTFNIvetBaiqFVCyFucgcZgIzSIQSmAnNYCY0g0RQ6LmGcolQghqSGrA3s6fTboSQKo2SJkKqg3ffBfLzsfsV0HN4QfHV6Q9Qw0F/N/clhJCqhMZpIqSqyssDXrwA9uwB8rlbm/R4DGzpu0lZxcbe3VDREUJIpUNJEyFVzdOnQGAgN/aSpycwaFDBMj8/9PTprZy9+4pG5SaEEF3R6TlCqorERGDmTGDDhoIyoRBwcwNcXQEvL2D2bHxy+BMAAJ/Hp7GSCCGkFChpIsQYMQZkZXGjfScncyOBK8ZY0jS9fMmNAK4YX0kq5UYN79KFG+Tyjd13d+PAgwMAgPW916OGpEbFt40QQiopSpoIMZTnz7mb8N6+zY3onZJSkCQlJ5d6gEkAgLc3MG8ed3quyJVst+JvYfDewQCA0U1GY2yzsW/bAkIIqVYoaSLEECIiuNNlJeHzuZvwSqWApSU3WVgUPFfMW1lx94wLCOBOyRVxNfoqOm7pCACwEFlgZtuZ5dkaQgipFihpIqSiyOVATg433bpVfN1du4CePQFzc7UjRmWxOmw1MmWZAID9g/ajrl3dt94mIYRUN3T1HCFva88eoFEj7qhQ8+ZAgwZAnTpcB2x7e+4okEjELTczA2xsVG+4q8m773JHkMppsMgJLSbAXGgOAOi6vStWXVxVLtslhJDqRK9J05IlS9CmTRuYmZnB2tpaYx0ej6c2rV+/XqXO7du34e/vD4lEAldXVyxatAiMab7PFSF6d/488OOPwJdfAkOHcpf0377NHUn691/g7l3g8WMgKoq7YW5aGiCTqW6Dx+NutGttDTg6cjfbBbjkKi6uYL6c+Hv6Y+eAncr5GSdnlOv2CSGkOtDr6bnc3FwMHDgQfn5+2FD4MugiNm3ahO7duyvnpVKp8nlqaiq6dOmCgIAAXL16FQ8fPsSoUaNgbm6O6dOn6zN8QtTZ2nKX9mvj6cld8i8Wc0mR4rHwc7GY63dUQbccSclOwdQTU7Ht5jZlGV01RwghpafXpGnhwoUAgM2bNxdbz9raGk5F7sSu8McffyA7OxubN2+GWCxGgwYN8PDhQ6xZswZBQUF0rytSsYpLmADuKJSbW8XEooPHiY9RZ20dlbJlnZdhWutpBoqIEEIqL6Po0zR58mTY2dmhZcuWWL9+PeRyuXJZWFgY/P39IRYX3GW0W7duiImJwfPnzw0QLalWZDIuEVq8GOjXj+tnVJwdOyokrJIwxrD77m6VhOnDeh8if34+ZrWbBbFAXMzahBBCNDH41XOLFy9G586dIZFIcPr0aUyfPh2vX7/GV199BQCIi4uDp6enyjqOjo7KZV4aLtvOyclBTk6Ocj4tLU1/DSBVQ0YGsHAh1x9JJOL6Ib16BTx7xj0vytIScHHhJjs7biDKgABgzJiKj72IM8/OoPPWzsp5G1MbhIwIQQuXFgaMihBCKr9SJ00LFixQnnbT5urVq2jRQrcvaEVyBABNmjQBACxatEilvOgpOEUncG2n5pYtW1ZijKSayMkBYmK402qpqVwClJpaMCnmf/xR+zbs7ID33gNateKujmvUiOvAbYQO/3cYfXf2Vc43dGiIvz76C65WrgaMihBCqoZSJ02TJ0/GkCFDiq1T9MhQabRu3RqpqamIj4+Ho6MjnJycEBcXp1Ln5cuXAAqOOBU1e/ZsBAUFKeejo6Ph6+tb5phIJcAY8NtvwPjx3OX+EgkQHQ0kJJR9m05OwMmTgK+vyq1IjNmaS2sAAJ29OmNa62noWacn9fsjhJByUuqkyc7ODnZ2dvqIBQAQHh4OU1NT5RAFfn5+mDNnDnJzcyESiQAAISEhcHFx0ZqcicVilT5QqampeouXGEhuLpfQ3LwJ3L8PbN9esOzRI9W6YjF3tMjKSn2ytFSfZ4xb5/33NY6ubaxSslNw7vk5AMAvvX9BrRq1DBsQIYRUMXrt0xQREYHExEREREQgPz8fN27cAADUrl0bFhYWOHz4MOLi4uDn5weJRIKzZ89i7ty5+OSTT5RJz7Bhw7Bw4UKMGjUKc+bMwaNHj7B06VLMnz+f/oOuzsaPB4q7KvP4ca6/kasrN5hkNfisXIm+onxuwqscR8YIIaQy0WvSNH/+fGzZskU537RpUwDA2bNn0bFjRwiFQvz8888ICgqCXC6Ht7c3Fi1ahEmTJinXkUqlOHnyJCZNmoQWLVrAxsYGQUFBKqffSDX0998Fzxcv5gaITE4GnJ2BDz7gbj9STciZHPdf3cfqsNUAgC7eXeBlo8N97QghhJQKj1WDobWjoqLg5uaGyMhI1KxZ09DhkLcVHg40a8Y9b9sWuHDBsPEY0PWY62jxm+pFF0eHHUXPOj0NFBEhhFS8ivqdp2P4pPLp0aPg+fDhhovDgLJkWZh/dr5awrSl3xZKmAghRE8MPk4TIWqysoBz57hbjmRlAdnZ3KPieW5uQd0JE7jp77+Bdu0MFnJFuh5zHR02d0CmLFOl/OanN9HIsZGBoiKEkKqPkiZiGIwBt25x4yjJ5UBeHrB+PfDHH2Xb3ldfcYlWFReXHqdydOn77t+jqVNTNHJsBKmptJg1CSGEvC1Kmoh+HTsGDB7MXbpfuzZ3W5K8PODOndJt5733uLGXFDe/VTx/+RLIzAS++UY/8RuR5OxkuH/rrpz/uefPmNByggEjIoSQ6oWSJqJfvXoVPL96VXs9qRRISdG8bNUqYPr08o2rEsmUZWLp30uxMXwjZHIZAGCg70BKmAghpIJR0kT0SyDgjiwp1KsHrF3LHXkyMQHi4rgr4JydDRejkZtwdAK23twKAKhlUwvL31uO/vX6GzgqQgipfihpIvrz4IFqwgRwo3e/917B/Jw5gI73KayOjj06hq03t4IHHn7t8yuGNxoOU4GpocMihJBqiYYcIPpjZwe0aVN8naVLucEoiYrUnFSM/t9o9NrBnd4c12wcxjYbSwkTIYQYECVNRH/s7IB//uGulGOMO+qUnMxdNRcUBNSowdW7cQNITzdkpEaDMYZrMddQ/+f62Hxjs7J8+XvLDRcUIYQQAJQ0kYrE53Mdvhs2BFavBn75pWCZpSU39EA1FpMWg1a/t0LL31oiKjUK1qbW+KP/H8ifnw8biY2hwyOEkGqP+jQRw8jM5MZoUvD05OYlEoOFZAiyfBmuxVzD/vv7sSpsFQBAYCLAAN8BWNllJWpa0W1/CCHEWFDSRCre4sXA/PkF840bA6dPV6uEKTw2HNNOTEPoi1CVchFfhFuf3oKPnY+BIiOEEKINJU2k4oSEAFOmAP/9V1C2YAEwc2a1SZjy5HnYe28vhu4bqiyzldjC39MfHT06YmjDobAzszNghIQQQrShpInoV1YWsGcPMHUqkJSkumzgQCA42CBh6QtjDEnZSYhJi0FCZgJeZ75GQlaC8vmaS2vU1nk54yVMeNS9kBBCjB0lTaT8hYcDw4cD9+5pXt6jB/D990CdOhUbVzkKjw3Hk6QniEiJwPPk5ypTWm6aTtvY+eFODPAdQAkTIYRUEpQ0kfIXFKSeMAUFccmSnx9gbm6YuMrJr9d/xfgj44utYyuxhZ2ZHWzN3jxKbGErsYW9uT28bbzRs05PmAnNKihiQggh5YGSJlL+pk0Dzp1TLevbF/D3N0g4usiUZSImLYY7nZaZgISsBCRmJSIhk3tMzklGSnYKUnJScCHigsq6TZ2aYnzz8fC09oSntSfcpe6QCKtHHy1CCKlOKGki5a9PH2D2bGDZsoKyjh2BkSO5oQXc3bmpQYMKvedcRm4GwuPCcSPuBm7F30JUahSiUqMQnRaNxKzEMm1zTJMx+K3vb3SKjRBCqgFKmkj54/G426O4uQETJxaUb92qXrdzZ+7ec25uQP36QLt23E1+y1FydjJ+uPwDvr30LZKzk7XWMxOawcHcAbYSW9SQ1ICtma3yubWpNaxNrSEVSyE1lcLG1AZ1bOvASmxVrrESQggxXpQ0Ef2ZMIGbXr8GQkO5G/hGRgIREcCzZ9zQA6dPc5NCmzbAmTOAWFwuIXx36TssOLcAKTkpAABnC2c0cWqCFi4t4GntCVdLV9S0qglXK1dIxVLweLxyeV1CCCFVDyVNRP/s7IAPP1Qvf/YM2L8f+OKLgrKLF7kxm2rWBGrVAnx9gVatgPbtAW/vUr3svnv7MO3ENABAffv6mNdhHgb4DgDfhP82rSGEEFJNUdJEDMfLC5g+HXjnHWDFCu7mvmIxdzuVyEhuOncO+Pln7pTfzp3AoEEAgJy8HDxNeor4jHi8zHiJ+PQ3jxnxiEuPQ3hcOKJSowAAwxsNx5Z+W6jfESGEkLdCSRMxvD59uAkAGANevQKePAEePwZu3ADWrOHKBw8GBg3C+RfnMWTvEMSmxxa7WT6Pjx51euCnnj9RwkQIIeStUdJEjAuPBzg4cJOfH9CpE5c0vSH7bT16xgchIz8LEoEE7lJ3OFo4wsHcAQ5mDsrnde3qoqVLS5iLKveYUIQQQowHJU3EuDk7A126ACdPAgB4n05A5jwAPKC5fWNsGfgHvG1K19eJEEIIKQs6Z0GMm4kJcPw4cOQI8NlnEAR0xuy/Ab4cuBB7CY3XN8bjxMeGjpIQQkg1QEkTMX4mJkCvXsAPPwAHDmDJGeD4dm5Rem466qytg38i/jFsjIQQQqo8vSZNS5YsQZs2bWBmZgZra2u15Zs3bwaPx9M4vXz5EgDw/PlzjcuPHz+uz9CJsTp4EADQJE61uOv2rsjOy674eAghhFQbeu3TlJubi4EDB8LPzw8bNmxQWz548GB0795dpWzUqFHIzs6Gg4ODSvmpU6dQv3595XyNGjX0EzQxbl27Ap07w+70adhnAK/e9PO2TsqGaVQcd5sWQgghRA/0mjQtXLgQAHdESROJRAKJpODGpq9evcKZM2c0Jli2trZwcnLSS5ykEnF0BE6dAl68wJ/BI/Ce198AgCYxcuDaNUqaCCGE6I1R9WnaunUrzMzMMGDAALVlffv2hYODA9q2bYu9e/caIDpiVDw80HlfOI7+wc3+4yNB3gfvGzYmQgghVZpRJU0bN27EsGHDVI4+WVhYYM2aNdi7dy+OHTuGzp07Y/Dgwdi+fbvW7eTk5CA1NVU5paWlVUT4pCLl5QHp6ej2GKiRBaSwLFyOvmLoqAghhFRhpU6aFixYoLXztmK6du1aqQMJCwvDvXv38PHHH6uU29nZYdq0aWjVqhVatGiBRYsWYeLEifjmm2+0bmvZsmWQSqXKydfXt9TxECMnEACffgo+AzrFmgIAvjj5BRKzEg0cGCGEkKqKxxhjpVnh9evXeP36dbF1PD09YWpqqpzfvHkzpk6diuTkZK3rfPzxx/j3338RHh5eYgx//PEHxo4di6ysLI3Lc3JykJOTo5yPjo6Gr68vIiMjUbNmzRK3TyqJ2FjAxQVXXIGOYwXI4uXhw3ofYu8gOn1LCCHVSVRUFNzc3PT+O1/qjuB2dnaws7Mr1yDS09Oxe/duLFu2TKf64eHhcHZ21rpcLBZDLBYr51NTU986RmJEXrzgbq2ycSMAoFU0cIANQnfeDuy7vw/77u3Dh74fGjhIQgghVY1er56LiIhAYmIiIiIikJ+fjxs3bgAAateuDQsLC2W9Xbt2IS8vDx999JHaNrZs2QKhUIimTZvCxMQEhw8fxg8//IAVK1boM3RirJ48AZo0AdLTufl69YBPPkG3iRMx5bQdfrjyAyYcnYCedXpCIpQUuylCCCGkNPSaNM2fPx9btmxRzjdt2hQAcPbsWXTs2FFZvmHDBvTv3x82NjYat/P111/jxYsX4PP5eOedd7Bx40YMHz5cn6ETY3X4MJcwOTsDmzdz96Xj8QAA33T5Brvu7kJ8Rjz+ifwH73m/Z9hYCSGEVCl6TZo2b96sdYymwi5evKh1WWBgIAIDA8sxKlJpZWcD169zz/39uYEuCxELxKghqYH4jHjwwDNAgIQQQqoyoxpygBA1GRnAuXNAx46ARAIohppo2VJj9bh07v4qThY0ECohhJDypdcjTYSUSUwMcPcucPEi8P33QFKS6vK5c4EpUzSuKmdyAECePE/fURJCCKlmKGkixuW334BPPlEtq1ED6NABaNoUmDQJsLXVurqXjRduxN1AREoEGjs11nOwhBBCqhNKmojxuHlTNWHy9QXGjwcmTuQGs9SBuZC7g29ufq4+IiSEEFKNUdJEjMfJk9yjuTnw4AFQhgHKsvOyAQCmAtMSahJCCCGlQx3BifFQJE0DB5YpYQIKOoLbSDQPX0EIIYSUFR1pIobHGLBsGRASws2/+24ZNsEw+dhkRKdFAwA8pB7lGSEhhBBCSRMxsDNnuD5L//3HzQsEwLhxpd7Mk6Qn+PnazwCA0U1Gw8XSpTyjJIQQQuj0HDGQtDRg+HCgc2cuYTIxAfr3Bx4+BPj8Um1q/bX1qLO2TsF87/Xg8WhwS0IIIeWLjjSRipWfzw1QOWpUQdk77wBXrgBSaZk2OeHoBOXzH3v8CBFf9JZBEkIIIeroSBOpOBkZwIQJqglTjRrArVtlSpgYY/j8r8+V8yveW4FJrSaVQ6CEEEKIOjrSRCpGejpgaVkwP2QIsHgxUKuW8oa7pXUp6hJ+uPIDAGBQ/UGY8q7mUcIJIYSQ8kBJE6kY77yjOj93LlC79ltt8tB/hwBw95nbNWDXW22LEEIIKQmdniP6xxh3VKmw4cPLvLnUnFR8f+l7LP9nOQAgIzfjbaIjhBBCdEJHmkj5e/4cWLWKG9U7JgaIjOROzxU2YkSpN3vv1T0EnQjC6WenVW7Ie27UubeLlxBCCNEBJU2k/AUGAufPa17m4wMcPAjUrVvqza4JW4MTT04AAOra1cXUd6dieKPhMBeZv0WwhBBCiG4oaSLlz82t4HmrVsC2bVyZRFLmTTLGsCF8g3L+3sR7NBYTIYSQCkVJEylf778PHOI6aEMoBNasUe8EXgYLQxcqn//10V+UMBFCCKlw1BGclI8nT4DevQsSpho1gGvXgLZty2XzCZkJAAARX4TutbuXyzYJIYSQ0qAjTaTsPvsM+PFH9fKuXYG9e1XHZXpLK7qsQF27upjYcmK5bZMQQggpDTrSRMquaMJkbQ0cOwacOFGuCRMAmAnNMKnVJDotRwghxGAoaSJld/Uq9/jpp8CePUBcHNCjh2FjIoQQQvSETs+RsmvRghu4khBCCKkG6EgTIYQQQogOKGkihBBCCNEBJU2EEEIIITqgpIkQQgghRAd6S5qeP3+Ojz/+GF5eXpBIJKhVqxaCg4ORm5urUi8iIgJ9+vSBubk57OzsMGXKFLU6t2/fhr+/PyQSCVxdXbFo0SIw6oBMCCGEkAqkt6vnHjx4ALlcjl9++QW1a9fGnTt3MG7cOGRkZGDVqlUAgPz8fPTq1Qv29va4cOECEhISEBgYCMYY1q5dCwBITU1Fly5dEBAQgKtXr+Lhw4cYNWoUzM3NMX36dH2FTwghhBCigscq8JDNypUrsW7dOjx9+hQA8Ndff6F3796IjIyEi4sLAGDnzp0YNWoUXr58CSsrK6xbtw6zZ89GfHw8xGIxAGD58uVYu3YtoqKidBrsMCoqCm5uboiMjETNmjX110BCCCGEVLiK+p2v0HGaUlJSUKNGDeV8WFgYGjRooEyYAKBbt27IycnB9evXERAQgLCwMPj7+ysTJkWd2bNn4/nz5/Dy8lJ7nZycHOTk5Ki8LgDExsbqo1mEEEIIMSDF77tcLtfr61RY0vTkyROsXbsWq1evVpbFxcXB0dFRpZ6NjQ1EIhHi4uKUdTw9PVXqKNaJi4vTmDQtW7YMCxcuVCtv1arV2zaDEEIIIUYqMjIS7u7uett+qZOmBQsWaExICrt69SpatGihnI+JiUH37t0xcOBAjB07VqWuptNrjDGV8qJ1FGcUtZ2amz17NoKCgpTziYmJ8PLywp07dyCVSouNvapJS0uDr68v7t27B8tyvh9cZVCd209tr55tB6p3+6nt1bPtKSkpaNCgAerVq6fX1yl10jR58mQMGTKk2DqFjwzFxMQgICAAfn5++PXXX1XqOTk54fLlyyplSUlJkMlkyqNJTk5OyqNOCi9fvgQAtaNUCmKxWOV0noKbmxusrKyKjb2qSU1NBQC4urpWu7YD1bv91Pbq2Xageref2l49265or0Cg3xNopd66nZ0d7OzsdKobHR2NgIAANG/eHJs2bYKJieoIB35+fliyZAliY2Ph7OwMAAgJCYFYLEbz5s2VdebMmYPc3FyIRCJlHRcXF7XTdoQQQggh+qK3cZpiYmLQsWNHuLm5YdWqVXj16hXi4uJUjhp17doVvr6+GDFiBMLDw3H69Gl88cUXGDdunDJrHDZsGMRiMUaNGoU7d+7gwIEDWLp0KYKCgnS6co4QQgghpDzo7ThWSEgIHj9+jMePH6td/qfok8Tn83H06FFMnDgRbdu2hUQiwbBhw5TjOAGAVCrFyZMnMWnSJLRo0QI2NjYICgpS6bNUErFYjODgYI2n7Kq66tx2oHq3n9pePdsOVO/2U9up7fpUoeM0EUIIIYRUVnTvOUIIIYQQHVDSRAghhBCiA0qaCCGEEEJ0QEkTIYQQQogOKn3S5OnpCR6PpzZNmjRJ6zqhoaFo3rw5TE1N4e3tjfXr11dgxOWntG0/d+6cxvoPHjyo4MjLR15eHr766it4eXlBIpHA29sbixYtKvHeQ1Vh/5el7VVp/6elpWHq1Knw8PCARCJBmzZtcPXq1WLXqQr7HSh92yvzfj9//jz69OkDFxcX8Hg8HDx4UGU5YwwLFiyAi4sLJBIJOnbsiLt375a43X379sHX1xdisRi+vr44cOCAnlpQdvpo++bNmzV+FrKzs/XYkrIpqf379+9Ht27dYGdnBx6Phxs3bui03bfe96ySe/nyJYuNjVVOJ0+eZADY2bNnNdZ/+vQpMzMzY59//jm7d+8e++2335hQKGR79+6t2MDLQWnbfvbsWQaA/ffffyrr5eXlVWzg5eTrr79mtra27MiRI+zZs2dsz549zMLCgn333Xda16kq+78sba9K+3/QoEHM19eXhYaGskePHrHg4GBmZWXFoqKiNNavKvudsdK3vTLv92PHjrG5c+eyffv2MQDswIEDKsuXL1/OLC0t2b59+9jt27fZ4MGDmbOzM0tNTdW6zYsXLzI+n8+WLl3K7t+/z5YuXcoEAgG7dOmSnltTOvpo+6ZNm5iVlZXK5yA2NlbPLSmbktq/detWtnDhQvbbb78xACw8PLzEbZbHvq/0SVNRn3/+OatVqxaTy+Ual8+cOZPVrVtXpWz8+PGsdevWFRGeXpXUdsWXZ1JSUsUGpie9evViY8aMUSnr378/Gz58uNZ1qsr+L0vbq8r+z8zMZHw+nx05ckSlvHHjxmzu3Lka16kq+70sba8q+73oD6dcLmdOTk5s+fLlyrLs7GwmlUrZ+vXrtW5n0KBBrHv37ipl3bp1Y0OGDCn3mMtLebV906ZNTCqV6jFS/dCUNCk8e/ZM56SpPPZ9pT89V1hubi62b9+OMWPGaB0tPCwsDF27dlUp69atG65duwaZTFYRYeqFLm1XaNq0KZydndG5c2ecPXu2giIsf+3atcPp06fx8OFDAMDNmzdx4cIF9OzZU+s6VWX/l6XtCpV9/+fl5SE/Px+mpqYq5RKJBBcuXNC4TlXZ72Vpu0Jl3+9FPXv2DHFxcSr7VSwWw9/fHxcvXtS6nrbPQnHrGJuyth0A0tPT4eHhgZo1a6J3794IDw/Xd7hGozz2fZVKmg4ePIjk5GSMGjVKa524uDi1G/06OjoiLy8Pr1+/1nOE+qNL252dnfHrr79i37592L9/P3x8fNC5c2ecP3++4gItR19++SWGDh2KunXrQigUomnTppg6dSqGDh2qdZ2qsv/L0vaqsv8tLS3h5+eHxYsXIyYmBvn5+di+fTsuX76M2NhYjetUlf1elrZXlf1elOKWXJr2a9GbvBddr7TrGJuytr1u3brYvHkzDh06hD///BOmpqZo27YtHj16pNd4jUV57Hv93g64gm3YsAE9evSAi4tLsfWKHolhbwZFr8z3stOl7T4+PvDx8VHO+/n5ITIyEqtWrUKHDh0qIsxytWvXLmzfvh07duxA/fr1cePGDUydOhUuLi4IDAzUul5V2P9laXtV2v/btm3DmDFj4OrqCj6fj2bNmmHYsGH4999/ta5TFfY7UPq2V6X9romm/VrSPi3LOsaotO1o3bo1WrdurZxv27YtmjVrhrVr1+KHH37QW5zG5G33fZU50vTixQucOnUKY8eOLbaek5OTWlb58uVLCAQC2Nra6jNEvdG17Zq0bt260v6XMWPGDMyaNQtDhgxBw4YNMWLECEybNg3Lli3Tuk5V2f9labsmlXX/16pVC6GhoUhPT0dkZCSuXLkCmUwGLy8vjfWryn4HSt92TSrrfi/MyckJADTu16JHE4quV9p1jE1Z216UiYkJWrZsWek/C7oqj31fZZKmTZs2wcHBAb169Sq2np+fH06ePKlSFhISghYtWkAoFOozRL3Rte2ahIeHw9nZWQ9R6V9mZiZMTFQ/wnw+v9jL7qvK/i9L2zWpzPsfAMzNzeHs7IykpCScOHEC77//vsZ6VWW/F6Zr2zWp7PsdALy8vODk5KSyX3NzcxEaGoo2bdpoXU/bZ6G4dYxNWdteFGMMN27cqPSfBV2Vy77Xucu4EcvPz2fu7u7syy+/VFs2a9YsNmLECOW84tLjadOmsXv37rENGzZU2kuPGStd27/99lt24MAB9vDhQ3bnzh02a9YsBoDt27evIkMuN4GBgczV1VV52f3+/fuZnZ0dmzlzprJOVd3/ZWl7Vdr/x48fZ3/99Rd7+vQpCwkJYY0bN2atWrViubm5jLGqu98ZK33bK/N+T0tLY+Hh4Sw8PJwBYGvWrGHh4eHsxYsXjDHusnupVMr279/Pbt++zYYOHap22f2IESPYrFmzlPP//PMP4/P5bPny5ez+/fts+fLlRjnkgD7avmDBAnb8+HH25MkTFh4ezkaPHs0EAgG7fPlyhbevJCW1PyEhgYWHh7OjR48yAGznzp0sPDxcZQgFfez7KpE0nThxQjkOSVGBgYHM399fpezcuXOsadOmTCQSMU9PT7Zu3boKirT8labtK1asYLVq1WKmpqbMxsaGtWvXjh09erQCoy1fqamp7PPPP2fu7u7M1NSUeXt7s7lz57KcnBxlnaq6/8vS9qq0/3ft2sW8vb2ZSCRiTk5ObNKkSSw5OVm5vKrud8ZK3/bKvN8VwyUUnQIDAxlj3KX3wcHBzMnJiYnFYtahQwd2+/ZtlW34+/sr6yvs2bOH+fj4MKFQyOrWrWuUCaQ+2j516lTm7u7ORCIRs7e3Z127dmUXL16swFbprqT2b9q0SePy4OBg5Tb0se95jL3pDUkIIYQQQrSqMn2aCCGEEEL0iZImQgghhBAdUNJECCGEEKIDSpoIIYQQQnRASRMhhBBCiA4oaSKEEEII0QElTYQQQgghOqCkiRBCCCFEB5Q0EUIIIYTogJImQgghhBAdUNJECCGEEKKD/2+00TQKRsEoGAWjYBSMglFABAAAufGFB5Gc11UAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ds_zarr = xr.open_dataset('./data/rca/sensors/osb/temperature_jan_2022.nc')\n", "\n", @@ -978,30 +608,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attempting 1 charts\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# temperature: ascent versus descent\n", "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n", diff --git a/book/chapters/oceanscience.ipynb b/book/chapters/oceanscience.ipynb index f8ee8b2..f21957e 100644 --- a/book/chapters/oceanscience.ipynb +++ b/book/chapters/oceanscience.ipynb @@ -37,8 +37,15 @@ "\n", "The purpose of this Jupyter Book is to explore research ideas in oceanography relative \n", "to observational data from sensors. The underlying agenda is to document a reproducible \n", - "methodology for this exploration. However *method* can take up all the oxygen in the \n", - "room so before diving into the data let us take a moment to frame the science, beginning \n", + "methodology for this exploration. However *method* in all its fine details has a tendency\n", + "to overwhelm the science. To try and address this the book is formatted as two parts.\n", + "The first half, starting with this chapter, attempts to minimize the technical\n", + "emphasis and focus on a science narrative. This means that a great deal of the\n", + "code is placed in Python modules such as `charts.py`. The second half of the book,\n", + "starting at the chapter on **`data`** goes into the technical means behind the science.\n", + "\n", + "\n", + "Let us begin, then, by attempting to frame the science, beginning \n", "with an ambitious question:\n", "
" ] @@ -50,18 +57,22 @@ "${\\Large \\textrm{How stable is the epipelagic ocean?}}$\n", "\n", "\n", - "This question by itself is simplistic so let's qualify its meaning in these ensuing sections.\n", + "This question by itself is simplistic so let's qualify its meaning.\n", "\n", "\n", "\n", "### *Epipelagic ocean* defined\n", "\n", - "*Epipelagic* is more or less synomymous with *sun illuminated* or *photic*, \n", - "appearing most often as *epipelagic zone* or *photic zone*. \n", - "This is approximately the upper 200 meter layer of the water column subjected \n", - "to downwelling sunlight. Sunlight is in turn the energy source of primary production\n", - "by photosynthesizing plankton. This is the ecosystem of the upper ocean:\n", - "The engine powering the marine food web.\n", + "\n", + "Pelagic refers to the ocean water column, particularly away from the shore. \n", + "*Epipelagic* is then the *upper* water column and the term is synonymous with \n", + "*sun illuminated* or *photic*. The most common expressions are \n", + "*epipelagic zone* and *photic zone*.\n", + "This is the upper 200 meters of the water column subjected \n", + "to downwelling sunlight. Sunlight is in turn the energy source of primary \n", + "production: Photosynthesis primarily by plankton. So we are looking at\n", + "the ecosystem of the upper ocean: The biological engine powering\n", + "life in the ocean.\n", "\n", "\n", "Our observational starting point is three observing sites located in the \n", @@ -77,16 +88,13 @@ "``` \n", "\n", "\n", - "### Limits on observation\n", - "\n", "\n", - "Our observational focus is three\n", - "[shallow profilers](https://interactiveoceans.washington.edu/technology/shallow-profiler-moorings/) \n", - "maintained by the Regional Cabled Array program. \n", - "This amounts to a direct record of the state of the upper ocean at two fine scales:\n", - "Time and depth.\n", - "From this starting point we will add other observing resources: ARGO drifters, satellite data, \n", - "NOAA buoys and so forth." + "Our initial observational focus is a\n", + "[shallow profiler](https://interactiveoceans.washington.edu/technology/shallow-profiler-moorings/) \n", + "maintained and by the Regional Cabled Array program located at the Oregon Slope Base site. \n", + "The shallow profiler generates a record of the state of the upper ocean with both time and \n", + "depth at fine scale. From this observational starting point we proceed to add other resources\n", + "including ARGO drifters, satellites, and NOAA buoys." ] }, { @@ -99,7 +107,7 @@ "We define *stability* very broadly in terms of interpretive parameters and dimensions:\n", "\n", "\n", - "- depth dimension: Through the upper 200 meters on scales of centimeters to tens of meters\n", + "- depth: Through the upper 200 meters; on scales of centimeters to tens of meters\n", "- physical stability: temperature, density of water, available light, turbulence\n", "- chemical stability: salinity, dissolved oxygen, inorganic carbon\n", "- biological stability: nutrient concentration (nitrates), particulate distribution, fluorescence, ...\n", @@ -195,11 +203,12 @@ "|17|Hydroxide ion OH- | -no direct observation-\n", "|18|Water H2O | temperature and salinity sensors\n", "|46|carbon dioxide CO2 | 'partial pressure' pCO2 sensor\n", - "|62|carbonic acid H2CO3 | -no direct observation-\n", - "|61|bicarbonate anion HCO3- | -no direct observation-\n", - "|60|carbonate CO32- | -no direct observation-\n", + "|62|carbonic acid H2CO3 | by inference\n", + "|61|bicarbonate anion HCO3- | by inference\n", + "|60|carbonate CO32- | by inference\n", "|62|nitrate NO3- | nitrate sensor\n", "|180|glucose C6H12O6 | -no direct observation-\n", + "|240|Cystine (amino acid) C6H12N2O4S2| -no direct observation-\n", "|894|chlorophyll C55H72MgN4O5 | fluorescence sensor" ] }, @@ -437,20 +446,29 @@ "\n", "Ascent data are\n", "considered more pristine; \n", - "although pH and pCO2 are unique in that they are recorded on *descent*.\n", - "\n", - "\n", - "\n", - "## Introducing Python code \n", - "\n", - "\n", - "Before concluding with the agenda view of subsequent chapters: Here are a few lines of \n", - "Python code for an example calculation." + "although pH and pCO2 are unique in that they are recorded on *descent*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A DOC Calculation\n", + "\n", + "The [Ocean Carbon and Biogeochemistry (OCB)](https://us-ocb.org)\n", + "organization is concerned with the science of the ocean carbon cycle.\n", + "This includes carbon in various chemical forms considered as distributed reservoirs. By far the largest\n", + "of these is dissolved inorganic carbon (DIC) associated with carbonate chemistry. A second important\n", + "carbon reservoir is Dissolved Organic Carbon, referring to biologically important carbon compounds. \n", + "The following cell -- in part to illustrate Python utility -- gives an estimate of the \n", + "total mass of the ocean's dissolved organic carbon reservoir. \n", + "More on DOC can be found at this \n", + "[OCB web resource](https://www.us-ocb.org/what-controls-the-distribution-of-dissolved-organic-carbon-in-the-surface-ocean/)." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "tags": [] }, @@ -459,44 +477,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mass of earth's oceans: 1.34e+21 kg\n", - "Carbon grams per kilogram seawater: 0.00048\n", - "Grams of dissolved organic carbon in the ocean: 6.45e+17\n", - "Dissolved organic carbon mass, earth's oceans, Gtons or Petagrams: 644.6 PgC\n", - " (Gigatons and Petagrams are equivalent: 1 gram being 1 millionth of a metric ton\n", - " and Giga is 1 millionth of Peta.)\n" + "Mass of earth's oceans: 1.34e+09 GTons\n", + "Organic carbon (kg) dissolved per kg of seawater: 4.8e-07\n", + "Dissolved organic carbon mass, earth's oceans: 644.6 GTons\n", + "\n" ] } ], "source": [ - "# how much dissolved organic carbon in the ocean? Supposed to be about 1000 GT; and 38,000 GT inorganic\n", - "# 100 - 500 umoles carbon per kg seawater near the surface\n", - "# This is reduced by a factor of 5 - 10 in below-surface waters\n", - "surface_carbon_conc = 280e-6\n", - "depth_attenuation = (1./7.)\n", - "C_gm_per_mole = 12\n", - "\n", - "radius_earth_meters = 6378000\n", - "pi_approx = 3.141592654\n", - "ocean_percent = 71\n", - "ocean_mean_depth_meters = 3700\n", - "kg_per_m3 = 1000\n", - "\n", - "ocean_mass_kg = 4. * pi_approx * radius_earth_meters**2 * (ocean_percent / 100.) * ocean_mean_depth_meters * kg_per_m3\n", - "\n", - "GTons_per_gm = 1e-15\n", - "carbon_gm_per_kg = surface_carbon_conc * depth_attenuation * C_gm_per_mole\n", - "\n", - "gm_C_in_ocean = ocean_mass_kg * carbon_gm_per_kg\n", - "\n", - "PgC = GTons_per_gm * gm_C_in_ocean\n", - "\n", - "print(\"Mass of earth's oceans:\", '{:0.2e}'.format(ocean_mass_kg), 'kg')\n", - "print(\"Carbon grams per kilogram seawater:\", round(carbon_gm_per_kg, 6))\n", - "print(\"Grams of dissolved organic carbon in the ocean:\", '{:0.2e}'.format(gm_C_in_ocean))\n", - "print(\"Dissolved organic carbon mass, earth's oceans, Gtons or Petagrams:\", round(PgC, 1), 'PgC')\n", - "print(\" (Gigatons and Petagrams are equivalent: 1 gram being 1 millionth of a metric ton\")\n", - "print(\" and Giga is 1 millionth of Peta.)\")" + "import oceanscience\n", + "oceanscience.OceanScienceCalculations()" ] }, { diff --git a/book/chapters/oceanscience.py b/book/chapters/oceanscience.py new file mode 100644 index 0000000..19e49b4 --- /dev/null +++ b/book/chapters/oceanscience.py @@ -0,0 +1,35 @@ +def OceanScienceCalculations(): + ''' + The following is an estimate of DOC in the ocean. + DOC Dissolved organic carbon in the ocean is ~1000 GTons. Inorganic carbon: 38,000 GTons. + Near the ocean surface: 100 - 500 micromoles of carbon per kg seawater. The concentration + decreases with depth so for the entire water column we can use an attenuation factor + of (1/7.5). + ''' + radius_earth_meters = 6378000 + pi = 3.141592654 + ocean_percent = 71 + ocean_mean_depth_meters = 3700 + water_kg_per_m3 = 1000 + ocean_volume_m3 = 4. * pi * radius_earth_meters**2 * (ocean_percent / 100.) * ocean_mean_depth_meters + GTons_per_kg = 1e-12 + ocean_mass_kg = ocean_volume_m3 * water_kg_per_m3 + ocean_mass_GTons = ocean_mass_kg * GTons_per_kg + + carbon_surface_um_per_kg = 300 + micromoles_per_mole = 1e6 + moles_per_micromole = 1/micromoles_per_mole + depth_attenuation = 1/7.5 + carbon_moles_per_kg = carbon_surface_um_per_kg * moles_per_micromole * depth_attenuation + carbon_gm_per_mole = 12 + gm_per_kg = 1000 + kg_per_gm = 1 / gm_per_kg + + carbon_kg_per_kg = carbon_moles_per_kg * carbon_gm_per_mole * kg_per_gm + carbon_in_the_ocean_kg = ocean_mass_kg * carbon_kg_per_kg + carbon_in_ocean_GTons = GTons_per_kg * carbon_in_the_ocean_kg + + print("Mass of earth's oceans:", '{:0.2e}'.format(ocean_mass_GTons), 'GTons') + print("Organic carbon (kg) dissolved per kg of seawater:", round(carbon_kg_per_kg, 12)) + print("Dissolved organic carbon mass, earth's oceans:", round(carbon_in_ocean_GTons, 1), 'GTons') + print() \ No newline at end of file