From ff2fa03f8fa30061f76569126dad17318c633d67 Mon Sep 17 00:00:00 2001 From: qian-chu Date: Tue, 26 Nov 2024 20:09:50 +0000 Subject: [PATCH] deploy: 69b995249f5840157f58260bbd8e5577664ee6dd --- _images/tutorials_read_recording_26_2.png | Bin 8614 -> 0 bytes _images/tutorials_read_recording_28_0.png | Bin 0 -> 51605 bytes _images/tutorials_read_recording_30_0.png | Bin 0 -> 49574 bytes _sources/tutorials/read_recording.ipynb.txt | 416 +++++++++--------- genindex.html | 20 +- objects.inv | Bin 3677 -> 3741 bytes reference/data.html | 65 +++ searchindex.js | 2 +- tutorials/read_recording.html | 444 ++++++++------------ tutorials/read_recording.ipynb | 416 +++++++++--------- 10 files changed, 653 insertions(+), 710 deletions(-) delete mode 100644 _images/tutorials_read_recording_26_2.png create mode 100644 _images/tutorials_read_recording_28_0.png create mode 100644 _images/tutorials_read_recording_30_0.png diff --git a/_images/tutorials_read_recording_26_2.png 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This project (collection of recordings on Pupil Cloud) contains two recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) object pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." + "We will use a sample recording produced by the NCC Lab, called `boardView`. This project (collection of recordings on Pupil Cloud) contains two recordings downloaded with the `Timeseries Data + Scene Video` option and a marker mapper enrichment. It can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) instance pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." ] }, { @@ -20,7 +20,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "D:\\GitHub\\pyneon\\data\\OfficeWalk\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\n" ] } ], @@ -28,7 +28,7 @@ "from pyneon import get_sample_data, NeonDataset, NeonRecording\n", "\n", "# Download sample data (if not existing) and return the path\n", - "sample_dir = get_sample_data(\"OfficeWalk\")\n", + "sample_dir = get_sample_data(\"boardView\")\n", "print(sample_dir)" ] }, @@ -39,31 +39,29 @@ "The `OfficeWalk` data has the following structure:\n", "\n", "```text\n", - "OfficeWalk\n", - "├── Timeseries Data\n", - "│ ├── walk1-e116e606\n", + "boardView\n", + "├── Timeseries Data + Scene Video\n", + "│ ├── boardview1-d4fd9a27\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", - "│ ├── walk2-93b8c234\n", + "│ ├── boardview2-713532d5\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", "| ├── enrichment_info.txt\n", "| └── sections.csv\n", - "├── OfficeWalk_FACE-MAPPER_FaceMap\n", - "├── OfficeWalk_MARKER-MAPPER_TagMap_csv\n", - "└── OfficeWalk_STATIC-IMAGE-MAPPER_ManualMap_csv\n", + "└── boardView_MARKER-MAPPER_boardMapping_csv\n", "```\n", "\n", - "The `Timeseries Data` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`." + "The `Timeseries Data + Scene Video` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To load a `NeonDataset`, specify the path to the `Timeseries Data` folder:" + "To load a `NeonDataset`, specify the path to the `Timeseries Data + Scene Video` folder:" ] }, { @@ -80,7 +78,7 @@ } ], "source": [ - "dataset_dir = sample_dir / \"Timeseries Data\"\n", + "dataset_dir = sample_dir / \"Timeseries Data + Scene Video\"\n", "dataset = NeonDataset(dataset_dir)\n", "print(dataset)" ] @@ -102,14 +100,14 @@ "output_type": "stream", "text": [ "\n", - "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview2-713532d5\n" ] } ], "source": [ - "first_recording = dataset[0]\n", - "print(type(first_recording))\n", - "print(first_recording.recording_dir)" + "recording = dataset[0]\n", + "print(type(recording))\n", + "print(recording.recording_dir)" ] }, { @@ -129,12 +127,12 @@ "output_type": "stream", "text": [ "\n", - "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\n" ] } ], "source": [ - "recording_dir = dataset_dir / \"walk1-e116e606\"\n", + "recording_dir = dataset_dir / \"boardview1-d4fd9a27\"\n", "recording = NeonRecording(recording_dir)\n", "print(type(recording))\n", "print(recording.recording_dir)" @@ -158,23 +156,23 @@ "output_type": "stream", "text": [ "\n", - "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n", - "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n", + "Recording ID: d4fd9a27-3e28-45bf-937f-b9c14c3c1c5e\n", + "Wearer ID: af6cd360-443a-4d3d-adda-7dc8510473c2\n", "Wearer name: Qian\n", - "Recording start time: 2024-08-30 17:37:01.527000\n", - "Recording duration: 98.213s\n", - " exist filename path\n", - "3d_eye_states True 3d_eye_states.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n", - "blinks True blinks.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n", - "events True events.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n", - "fixations True fixations.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n", - "gaze True gaze.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n", - "imu True imu.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n", - "labels True labels.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n", - "saccades True saccades.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n", - "world_timestamps True world_timestamps.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n", - "scene_video_info False None None\n", - "scene_video False None None\n", + "Recording start time: 2024-11-26 12:44:48.937000\n", + "Recording duration: 32.046s\n", + " exist filename path\n", + "3d_eye_states True 3d_eye_states.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\3d_eye_states.csv\n", + "blinks True blinks.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\blinks.csv\n", + "events True events.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\events.csv\n", + "fixations True fixations.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\fixations.csv\n", + "gaze True gaze.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\gaze.csv\n", + "imu True imu.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\imu.csv\n", + "labels True labels.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\labels.csv\n", + "saccades True saccades.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\saccades.csv\n", + "world_timestamps True world_timestamps.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\world_timestamps.csv\n", + "scene_video_info True scene_camera.json C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\scene_camera.json\n", + "scene_video True 182240fd_0.0-32.046.mp4 C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\182240fd_0.0-32.046.mp4\n", "\n" ] } @@ -187,9 +185,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As seen in the output, this recording includes all data files except the scene video and its metadata because we downloaded only the \"Timeseries Data\" instead of \" \"Timeseries Data + Scene Video\" from Pupil Cloud. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", + "As seen in the output, this recording includes all data files. This tutorial will focus on non-video data. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", "\n", - "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data like the video, it will simply return `None` and a warning message." + "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data, PyNeon will return `None` and a warning message." ] }, { @@ -201,17 +199,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "recording.gaze is \n", - "recording.fixations is \n", - "recording.video is None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\GitHub\\pyneon\\pyneon\\recording.py:271: UserWarning: Scene video not loaded because not all video-related files (video, scene_camera.json, world_timestamps.csv) are found.\n", - " warnings.warn(\n" + "recording.gaze is \n", + "recording.saccades is \n", + "recording.video is < cv2.VideoCapture 0000027AE592DB90>\n" ] } ], @@ -219,10 +209,8 @@ "# Gaze and fixation data are available\n", "gaze = recording.gaze\n", "print(f\"recording.gaze is {gaze}\")\n", - "fixations = recording.fixations\n", - "print(f\"recording.fixations is {fixations}\")\n", - "\n", - "# Video is not available\n", + "saccades = recording.saccades\n", + "print(f\"recording.saccades is {saccades}\")\n", "video = recording.video\n", "print(f\"recording.video is {video}\")" ] @@ -269,19 +257,19 @@ "text": [ " gaze x [px] gaze y [px] worn fixation id blink id \\\n", "timestamp [ns] \n", - "1725032224852161732 1067.486 620.856 1 1 \n", - "1725032224857165732 1066.920 617.117 1 1 \n", - "1725032224862161732 1072.699 615.780 1 1 \n", - "1725032224867161732 1067.447 617.062 1 1 \n", - "1725032224872161732 1071.564 613.158 1 1 \n", + "1732621490425631343 697.829 554.242 1 1 \n", + "1732621490430625343 698.096 556.335 1 1 \n", + "1732621490435625343 697.810 556.360 1 1 \n", + "1732621490440625343 695.752 557.903 1 1 \n", + "1732621490445625343 696.108 558.438 1 1 \n", "\n", " azimuth [deg] elevation [deg] \n", "timestamp [ns] \n", - "1725032224852161732 16.213030 -0.748998 \n", - "1725032224857165732 16.176285 -0.511733 \n", - "1725032224862161732 16.546413 -0.426618 \n", - "1725032224867161732 16.210049 -0.508251 \n", - "1725032224872161732 16.473521 -0.260388 \n", + "1732621490425631343 -7.581023 3.519804 \n", + "1732621490430625343 -7.563214 3.385485 \n", + "1732621490435625343 -7.581576 3.383787 \n", + "1732621490440625343 -7.713686 3.284294 \n", + "1732621490445625343 -7.690596 3.250055 \n", "gaze x [px] float64\n", "gaze y [px] float64\n", "worn Int32\n", @@ -307,43 +295,43 @@ "name": "stdout", "output_type": "stream", "text": [ - " fixation id end timestamp [ns] duration [ms] \\\n", - "start timestamp [ns] \n", - "1725032224852161732 1 1725032225007283732 155 \n", - "1725032225027282732 2 1725032225282527732 255 \n", - "1725032225347526732 3 1725032225617770732 270 \n", - "1725032225667907732 4 1725032225798022732 130 \n", - "1725032225833015732 5 1725032225958137732 125 \n", + " saccade id end timestamp [ns] duration [ms] \\\n", + "start timestamp [ns] \n", + "1732621490876132343 1 1732621490891115343 15 \n", + "1732621491241357343 2 1732621491291481343 50 \n", + "1732621491441602343 3 1732621491516601343 75 \n", + "1732621491626723343 4 1732621491696847343 70 \n", + "1732621491917092343 5 1732621491977090343 60 \n", "\n", - " fixation x [px] fixation y [px] azimuth [deg] \\\n", - "start timestamp [ns] \n", - "1725032224852161732 1069.932 614.843 16.369094 \n", - "1725032225027282732 906.439 538.107 5.878844 \n", - "1725032225347526732 694.843 533.982 -7.781338 \n", - "1725032225667907732 572.983 488.800 -15.679003 \n", - "1725032225833015732 601.861 491.125 -13.813521 \n", + " amplitude [px] amplitude [deg] mean velocity [px/s] \\\n", + "start timestamp [ns] \n", + "1732621490876132343 14.938179 0.962102 1025.709879 \n", + "1732621491241357343 130.743352 8.378644 2700.713283 \n", + "1732621491441602343 241.003342 15.391730 3615.380044 \n", + "1732621491626723343 212.619205 13.608618 3757.394092 \n", + "1732621491917092343 220.842812 13.914266 4220.180601 \n", "\n", - " elevation [deg] \n", - "start timestamp [ns] \n", - "1725032224852161732 -0.367312 \n", - "1725032225027282732 4.561914 \n", - "1725032225347526732 4.819739 \n", - "1725032225667907732 7.636408 \n", - "1725032225833015732 7.512433 \n", - "fixation id Int32\n", - "end timestamp [ns] Int64\n", - "duration [ms] Int64\n", - "fixation x [px] float64\n", - "fixation y [px] float64\n", - "azimuth [deg] float64\n", - "elevation [deg] float64\n", + " peak velocity [px/s] \n", + "start timestamp [ns] \n", + "1732621490876132343 1191.520740 \n", + "1732621491241357343 3687.314947 \n", + "1732621491441602343 5337.244676 \n", + "1732621491626723343 6164.040944 \n", + "1732621491917092343 6369.217052 \n", + "saccade id Int32\n", + "end timestamp [ns] Int64\n", + "duration [ms] Int64\n", + "amplitude [px] float64\n", + "amplitude [deg] float64\n", + "mean velocity [px/s] float64\n", + "peak velocity [px/s] float64\n", "dtype: object\n" ] } ], "source": [ - "print(fixations.data.head())\n", - "print(fixations.data.dtypes)" + "print(saccades.data.head())\n", + "print(saccades.data.dtypes)" ] }, { @@ -360,7 +348,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` returns the gaze x-coordinate column." + "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` (or `gaze['gaze x [px]']`) returns the gaze x-coordinate column." ] }, { @@ -373,35 +361,35 @@ "output_type": "stream", "text": [ "First row of gaze data:\n", - "gaze x [px] 1067.486\n", - "gaze y [px] 620.856\n", + "gaze x [px] 697.829\n", + "gaze y [px] 554.242\n", "worn 1.0\n", "fixation id 1.0\n", "blink id \n", - "azimuth [deg] 16.21303\n", - "elevation [deg] -0.748998\n", - "Name: 1725032224852161732, dtype: Float64\n", + "azimuth [deg] -7.581023\n", + "elevation [deg] 3.519804\n", + "Name: 1732621490425631343, dtype: Float64\n", "\n", "All gaze x values:\n", "timestamp [ns]\n", - "1725032224852161732 1067.486\n", - "1725032224857165732 1066.920\n", - "1725032224862161732 1072.699\n", - "1725032224867161732 1067.447\n", - "1725032224872161732 1071.564\n", - " ... \n", - "1725032319717194732 800.364\n", - "1725032319722198732 799.722\n", - "1725032319727194732 799.901\n", - "1725032319732194732 796.982\n", - "1725032319737194732 797.285\n", - "Name: gaze x [px], Length: 18769, dtype: float64\n" + "1732621490425631343 697.829\n", + "1732621490430625343 698.096\n", + "1732621490435625343 697.810\n", + "1732621490440625343 695.752\n", + "1732621490445625343 696.108\n", + " ... \n", + "1732621520958946343 837.027\n", + "1732621520964071343 836.595\n", + "1732621520969071343 836.974\n", + "1732621520974075343 835.169\n", + "1732621520979070343 833.797\n", + "Name: gaze x [px], Length: 6091, dtype: float64\n" ] } ], "source": [ "print(f\"First row of gaze data:\\n{gaze.data.iloc[0]}\\n\")\n", - "print(f\"All gaze x values:\\n{gaze.data['gaze x [px]']}\")" + "print(f\"All gaze x values:\\n{gaze['gaze x [px]']}\")" ] }, { @@ -423,10 +411,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1725032224852161732 1725032224857165732 1725032224862161732 ...\n", - " 1725032319727194732 1725032319732194732 1725032319737194732]\n", - "[0.0000000e+00 5.0040000e-03 1.0000000e-02 ... 9.4875033e+01 9.4880033e+01\n", - " 9.4885033e+01]\n" + "[1732621490425631343 1732621490430625343 1732621490435625343 ...\n", + " 1732621520969071343 1732621520974075343 1732621520979070343]\n", + "[0.0000000e+00 4.9940000e-03 9.9940000e-03 ... 3.0543440e+01 3.0548444e+01\n", + " 3.0553439e+01]\n" ] } ], @@ -439,7 +427,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Timestamps (UTC, in ns) and relative time (relative to the stream start, in s) are thus the two units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes two arguments: `start` and `end`:" + "Timestamps (UTC, in ns), relative time (relative to the stream start, in s), and index are the three units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes `start` and `end` of the crop window in either UTC timestamps or relative time, and uses `by` to specify whether " ] }, { @@ -451,18 +439,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "94.885033\n", - "9.999289\n" + "Gaze data points before cropping: 6091\n", + "Gaze data points after cropping: 999\n" ] } ], "source": [ - "# Last data time of the original gaze data\n", - "print(gaze.times[-1])\n", + "print(f\"Gaze data points before cropping: {len(gaze)}\")\n", "\n", - "# Crop the gaze data to the first 10 seconds\n", - "gaze_cropped = gaze.crop(0, 10, by=\"time\") # Crop by time\n", - "print(gaze_cropped.times[-1])" + "# Crop the gaze data to 5-10 seconds\n", + "gaze_crop = gaze.crop(5, 10, by=\"time\") # Crop by time\n", + "print(f\"Gaze data points after cropping: {len(gaze_crop)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also want to restrict one stream to the temporal range of another stream. This can be done by calling the `restrict()` method. The method takes another `NeonStream` instance as an argument and crops the stream to the intersection of the two streams' temporal ranges." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IMU first timestamp: 1732621495435389343 > Gaze first timestamp: 1732621495430263343\n", + "IMU last timestamp: 1732621500421101343 < Gaze last timestamp: 1732621500424901343\n" + ] + } + ], + "source": [ + "imu_crop = recording.imu.restrict(gaze_crop)\n", + "saccades_crop = saccades.restrict(gaze_crop)\n", + "print(\n", + " f\"IMU first timestamp: {imu_crop.first_ts} > Gaze first timestamp: {gaze_crop.first_ts}\"\n", + ")\n", + "print(\n", + " f\"IMU last timestamp: {imu_crop.last_ts} < Gaze last timestamp: {gaze_crop.last_ts}\"\n", + ")" ] }, { @@ -476,52 +495,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Data streams and events\n", + "## An example plot of cropped data\n", "\n", - "Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let's plot some samples from the `gaze` and `eye_states` streams and a saccade from the `saccades` events." + "Below we show how to easily plot the gaze and saccade data we cropped just now. Since PyNeon data are stored in `pandas.DataFrame`, you can use any plotting library that supports `pandas.DataFrame` as input. Here we use `seaaborn` and `matplotlib` to plot the gaze x, y coordinates and the saccade durations (shadowed areas)." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saccade id 2.0\n", - "end timestamp [ns] 1725032225347526656.0\n", - "duration [ms] 65.0\n", - "amplitude [px] 228.36139\n", - "amplitude [deg] 14.676102\n", - "mean velocity [px/s] 3685.269894\n", - "peak velocity [px/s] 5411.775481\n", - "Name: 1725032225282527732, dtype: Float64\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unhashable type: 'numpy.ndarray'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m 17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m fixations\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m 21\u001b[0m (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m 22\u001b[0m \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m 23\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 24\u001b[0m horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 25\u001b[0m )\n\u001b[0;32m 27\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m 1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m 2583\u001b[0m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m 2584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 2587\u001b[0m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m 2588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m 1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1758\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 107\u001b[0m axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:214\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[0;32m 213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m 216\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" - ] - }, { "data": { - "image/png": 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", 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", 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    " + "
    " ] }, "metadata": {}, @@ -532,69 +520,33 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", - "gaze_color = \"royalblue\"\n", - "gyro_color = \"darkorange\"\n", - "\n", - "imu = recording.imu\n", - "fixations = recording.saccades\n", - "\n", "# Create a figure\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax2 = ax.twinx()\n", - "ax.yaxis.label.set_color(gaze_color)\n", - "ax2.yaxis.label.set_color(gyro_color)\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", - "# Visualize the 2nd saccade\n", - "saccade = fixations.data.iloc[1]\n", - "print(saccade)\n", - "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n", - "ax.text(\n", - " (saccade.index.values + saccade[\"end timestamp [ns]\"]) / 2,\n", - " 1050,\n", - " \"Saccade\",\n", - " horizontalalignment=\"center\",\n", - ")\n", + "# Visualize the 1st saccade\n", + "for _, sac in saccades_crop.data.iterrows():\n", + " ax.axvspan(sac.name, sac[\"end timestamp [ns]\"], color=\"lightgray\")\n", "\n", - "# Visualize gaze x and pupil diameter left\n", - "sns.scatterplot(\n", + "# Visualize gaze x and y\n", + "sns.lineplot(\n", " ax=ax,\n", - " data=gaze.data.head(100),\n", - " x=gaze.data.index,\n", + " data=gaze_crop.data,\n", + " x=gaze_crop.data.index,\n", " y=\"gaze x [px]\",\n", - " color=gaze_color,\n", + " color=\"b\",\n", + " label=\"Gaze x\",\n", ")\n", - "sns.scatterplot(\n", - " ax=ax2,\n", - " data=imu.data.head(60),\n", - " x=imu.data.index,\n", - " y=\"gyro x [deg/s]\",\n", - " color=gyro_color,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's apparent that at the beginning of the recording, there are some missing data points in both the `gaze` and `imu` streams. This is presumably due to the time it takes for the sensors to start up and stabilize. We will show how to handle missing data using resampling in the next tutorial. For now, it's important to be aware of these gaps and that it will require great caution to assume the data is continuously and equally sampled.\n", - "\n", - "PyNeon also calculates the effective (as opposed to the nominal) sampling frequency of each stream by dividing the number of samples by the duration of the recording." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\n", - " f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n", - " f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\n", + "sns.lineplot(\n", + " ax=ax,\n", + " data=gaze_crop.data,\n", + " x=gaze_crop.data.index,\n", + " y=\"gaze y [px]\",\n", + " color=\"g\",\n", + " label=\"Gaze y\",\n", ")\n", - "print(\n", - " f\"IMU: nominal sampling frequency = {recording.imu.sampling_freq_nominal}, \"\n", - " f\"effective sampling frequency = {recording.imu.sampling_freq_effective}\"\n", - ")" + "ax.set_ylabel(\"Gaze location (pixels)\")\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -602,14 +554,36 @@ "metadata": {}, "source": [ "## Visualizing gaze heatmap\n", - "Finally, we will show how to plot a heatmap of the gaze/fixation data." + "Finally, we will show how to plot a heatmap of the gaze/fixation data. Since it requires gaze, fixation, and video data, the input it takes is an instance of `NeonRecording` that contains all necessary data. The method `plot_heatmap()`, by default, plots a gaze heatmap with fixations overlaid as circles." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Gg2Gl0Eqg1eItk6bOzj5m06ZNDA0Nceedd0brZmZmuPfeeznxxBMBOPHEE5mamuKBBx6ItvnBD36AUooTTjgh2ubuu++mVqtF29xxxx28+tWvZt26dXvpbAwGw4HDYnwAa8RXYNj7mJidttinYiefz7Nt2za2bdsG+EHJ27ZtY9euXQghuPzyy/nUpz7Fv/3bv/HII49w/vnns3Hjxihja/PmzZx++um85z3v4b777uMnP/kJl112Geeeey4bN24E4N3vfjfJZJKLL76YHTt28M1vfpMvfvGLXHnllfvorA0Gw+qllWjZk5AxQsewgmgBagmLXhsZxvvUjfXzn/+ct771rdHjUIBccMEF3HLLLXzkIx+hUChwySWXMDU1xUknncTtt99OOp2O9vna177GZZddxsknn4yUknPOOYcbbrgher6np4f/+q//4tJLL+W1r30t69ev55prrmmoxWMwGAwL046gWRs3DYNhNbLf1NnZnzF1dgyGA5l2PtPtfk02j2W+Xtcme6/Ozui1abrTS6izU9Zs+NPyAV9nZ78NUDYYDIaVZzmFjsGwDwjdUYvef/mmsj+z3wYoGwwGw+pjDRUuMRhWEcayYzAY1igradUxgsewl9BLDDJeI29VI3YMBoPBYFilmDo77WHEjsFgWGO0e2NYIz95DYY1gBE7BoPBsGyYbCzDXkbJJQYor433qBE7BoPBYDCsVkw2VluYbCyDwWBYFlrdcExdLoNhf8BYdgwGwxpipeJ1Fho3fG5tuAsMexetBXoJ2VhrpaywETsGg2GNsC+ETvN2a+TOYth7mJidtjBix2AwGPYaRvAYlhetWGLq+dp4P5qYHYPBYIhYTqvOfF+vJo7HYNjbGMuOwWAwLIr5RIts8Xdzyoux8BiWCb3EbKylVF9eRRixYzAY1gB7q+HnfNYciRE8hpVg6QHKa0PsGDeWwWA4wFkJodNqzD19ncoW26yNG43BsK8xlh2DwXAAY8SE4QBHSX9Z9P7LN5X9GSN2DAbDAci+FzkiNgfdYDlqdmkZd5Zh8Sy9Eei+/6zsDYwby2AwGFYYMUd8ma9eg2FvYiw7hlXEQr9AzC9jA+w9i47e47E0uoXIMRiWFxOg3B5G7BhWCXv6QMafN8JnbbKvv7QVzRabUPDoPb4njSvLsEhMzE5bGFuqYRWwr29iBkMr2hMnexY6BoNhpTGWHcN+Trspvs0Bn2B+Ka8V9iSGW9W4ibOU90mzOys8zp5+RzbPx7xXDYvDBCi3R1tiZ2ZmpuOBu7u7O97HYFiYhW4ge7qhGQ5M2hE6K82e43cMhpXCxOy0R1tip7e3FyHavyBCCB5//HEOO+ywRU/MYOic5tL8xsJz4LJcX9DL9d6Yz8IDjYLLCHLDMmNidtqibTfWt771Lfr6+va4ndaaM888c0mTMhjmUv8wt8pwMXVM1grtipx9ITDC91jzHPd0fPPeNBhWmrbEzite8Qre/OY309/f39aghx12GIlEYkkTMxg6YW7GSyvBA+bGsprppO1DaGlZqVidPc2hXVFm3o+GpWFidtqjLbHzzDPPdDTo9u3bFzUZg6GRzj6EexY84ZjmBrO6aF84DA4O4Dg5CoU8w8MjC+y70u+B+PjNczDvP8PyYWJ22mNZs7GKxSLZbHY5hzQYGthTkTZT02St4gudLVu2ks1mKRaLAPMInr392pv3msGwr+k4qunkk0/mhRdemLP+vvvu49hjj12OORkMy4gpJbV6EXRi3XOcHNlsFiEE2WwWx8mt3NQMhv0FLetByotZ9Nr4juz4LNPpNEcffTTf/OY3AVBK8YlPfIKTTjrJBCYb9i5C1pf46rb6EK0N0+3aQAKSQiFPsVhEa02xWKRQyDdtpzFWFsOBRhizs5RlLdCxG+t73/seX/7yl7nooov4f//v//GrX/2KZ599lu9+97uceuqpKzFHg6Fj2nNnGfZf2vkCjotYEbisaDNmx2AwrCUWFbNz6aWX8vzzz/PZz34W27b50Y9+xK//+q8v99wMhoXRao5Vx7DaWUic7Om1DgXPSIuxjPA1HJhovbQgY71GPhod3ykmJyc555xzuPHGG/n7v/973vWud3Hqqafyd3/3dysxP8OaZqU/heZX/+qh3a8qQaexPgbDqmapLizjxmrNkUceyaZNm/jFL37Bpk2beM973sM3v/lN3v/+9/O9732P733veysxT4MBqHeR7hzTTmL/p90+aJ2yRn66GgyGeen4m+S9730vd999N5s2bYrW/e///b956KGHqFaryzo5g2HlWRu/avZ/lip05hOyRugYDmy0lkte1gIdW3auvvrqlutf/vKXc8cddyx5QgZDaxQtb34mbsdgLHaGtcxSXVHGjTU/k5OTfOUrX+HRRx8FYPPmzVx00UVt9c4yGJYd3e7Nbj5XlmklsW9pp1RAp5jX0rA2MBWU26Pjb5XQhXXDDTcwOTnJ5OQkX/rSl9i0aRN33333SszRYGhg5VLK18aHfv9iqdfcWHUMhr3N3Xffzdve9jY2btyIEILbbrut4fkLL7wQIUTDcvrpp++byQZ0bNm59NJLede73sWNN96IZVkAeJ7H+9//fi699FIeeeSRZZ+kYS3TSVPFRlrX2tlToHKrYxkrwcqwEnE65rUyrC32RSPQQqHAMcccw0UXXcTZZ5/dcpvTTz+dr371q9HjVCq16DkuBx1bdp588kn++I//OBI6AJZlceWVV/Lkk08u6+Q8z+Pqq69m06ZNZDIZXvnKV/KXf/mX6FhhAK0111xzDQcddBCZTIZTTjmFJ554omGciYkJzjvvPLq7u+nt7eXiiy8mn2+urmrY/6nf3Nq17rTO3Or0bW9SmedH7GFZaJ+lYISOwQD7JkD5jDPO4FOf+hTvfOc7590mlUoxNDQULevWrVvKaS6Zjs/yNa95TRSrE+fRRx/lmGOOWZZJhXz2s5/lxhtv5G//9m959NFH+exnP8v111/Pl770pWib66+/nhtuuIGbbrqJe++9F8dxOO200yiXy9E25513Hjt27OCOO+7gu9/9LnfffTeXXHLJss7VsJLEb2KdC57WyDaWZozoqdPutWhXAEH7X0fGdWUwLDczMzMNS6VSWdJ4P/rRjxgYGODVr34173vf+xgfH1+mmS6Ojt1YH/zgB/nQhz7Ek08+yRve8AYAfvazn/HlL3+Zz3zmMzz88MPRtkcfffSSJvfTn/6Us846i9/+7d8G4NBDD+XrX/869913H+Bbdb7whS/w53/+55x11lkA3HrrrQwODnLbbbdx7rnn8uijj3L77bdz//33c/zxxwPwpS99iTPPPJO//uu/ZuPGjUuao2H/p53WEc0WIH/7VjdfxdqtzLuYQOJOgscXO9Zaeg0MhkaWy4118MEHN6z/+Mc/zic+8YlFjXn66adz9tlns2nTJp566in+9E//lDPOOIN77rmnwSu0N+lY7Pze7/0eAB/5yEdaPieEQGuNEALP85Y0uV//9V/n5ptv5vHHH+fXfu3XeOihh/if//kfPv/5zwPwzDPPsHv3bk455ZRon56eHk444QTuuecezj33XO655x56e3sjoQNwyimnIKXk3nvvbWmGq1QqDap2ZmZmSedhWA7isTv1NPR2iwyG2+jYDVMscIONj9kolExxQp92rTDtXK+lZF8ZoWNY2yxXNtZzzz1Hd3d3tH4pMTbnnntu9PdRRx3F0UcfzStf+Up+9KMfcfLJJy963KXQsdh55plnVmIeLbnqqquYmZnhiCOOwLIsPM/j05/+NOeddx4Au3fvBmBwcLBhv8HBwei53bt3MzAw0PC8bdv09fVF2zRz3XXX8Rd/8RfLfTqGZaVzwaNRDA4OkHNy5INGkYK5XdObiUYOUtx1w805fiM/UG+88WsrY2vbuebzWcg6wVh0DIaVpru7u0HsLCeHHXYY69ev58knn1w9YucVr3jFSsyjJf/yL//C1772Nf75n/+ZrVu3sm3bNi6//HI2btzIBRdcsGLH/djHPsaVV14ZPZ6ZmZlj4jPsC8KbW+cWHo1mcHCArVu2kM06FEslYAfDw2PBXm3ckIPhhVYxa89asvS0EDrzCcVAGNYtasstTIzQMRhgddTZef755xkfH+eggw5a8WPNR1s/uf7t3/6NWq3W9qD/8R//QalUWvSkQv7kT/6Eq666inPPPZejjjqKP/iDP+CKK67guuuuA2BoaAiA4eHhhv2Gh4ej54aGhhgZGWl43nVdJiYmom2aSaVSkcpdSbVr2Lvkcl1knS6EtMhmHXK5HL6C8T8GAmvexScIXBYSEfyrr4e1EMAcnbeIW8RaBHaLRouZ6OCfwWBoH62X1gh0MWInn8+zbds2tm3bBvgen23btrFr1y7y+Tx/8id/ws9+9jN+9atfceedd3LWWWdx+OGHc9pppy3z2bdPW2Lnne98J1NTU20Peu655/LSSy8tdk4RxWIRKRunaFkWSvm/Gjdt2sTQ0BB33nln9PzMzAz33nsvJ554IgAnnngiU1NTPPDAA9E2P/jBD1BKccIJJyx5joZ9QXN2Vuhe0q0tCMK/CefzeYrFAlprisUi+XwRge0LGpHwRYyw51kSMeEjQdhNoif+Pj2Qbth1QddozZHBuvp1abg+MWHY1tJwxIVEj8ZYdQyGfcvPf/5zjjvuOI477jgArrzySo477jiuueYaLMvi4Ycf5u1vfzu/9mu/xsUXX8xrX/tafvzjH+/TWjttubG01lx44YVtTzSe9r0U3va2t/HpT3+aQw45hK1bt/KLX/yCz3/+81x00UUACCG4/PLL+dSnPsWrXvUqNm3axNVXX83GjRt5xzveAfitLE4//XTe8573cNNNN1Gr1bjssss499xzTSbWGkIIwfDwKPAouZxDPl9gZHgcIax5XTFCSHTkjpHoyI0FGg//Zk6sXUXcpSVY/TflZssVbfUhE1jB9YnvuwdXXzhu260/DAYDsOhaOfX9O/+eestb3rLgft///vcXPZ+Voi2x02l8TFjAb6l86Utf4uqrr+b9738/IyMjbNy4kT/6oz/immuuibb5yEc+QqFQ4JJLLmFqaoqTTjqJ22+/nXQ6HW3zta99jcsuu4yTTz4ZKSXnnHMON9xww5LnZ9iXNFdW3nP8jhCS4eExhodHfYtOTOiEmVmiwTUTf6x8waMVWgTba+XLGRGP4znQBE8rZOTa81+F0HVV3yK8PnXmTzetC6PYGFrFSgashWtqMCyOfVFBeTUi9GJk3RpjZmaGnp4e/C/dtfHGWD3MX/uldRBtPT6ntdCJ7R/bT0c3bhU91qhA8Hj+el13p9Vvzqv549XkwopdHyESaO0xNLghspQNh5YyGlP82yK6dqHwme96+msMhv0b/z07PT29YjGf4X1p+ztfQ1di8bVrZmseR37nwRWd6/7AorqeGwz7D4vvnbUQojmOJHJp1a0MAolGBW4b5nFpHXiWCIGF1oqhwQ1s3bqFrJOjWCyA2MnI8GSwzR7S+ZuFpAiuZVsuQoPBYOgMI3YMBwCtCw7Wn1ZtxZrMR5SNJQKLjvZdK0IA2LGbNPO4tFaj4BGxv+J/W8H/klzOIevkEMImm+0il8sxMjwVWHfiwmSutazuBvMQwXX1r5Lrb7+gi3A1Xk+DYWVYDann+wNG7BgOWPZYbLBDEaS1y8DAOpwowHmU+RIa22lRsTqY//rkC0WKxQLZbBfFYoF8vkjLoGYaRU7cqhPGQdVfJgkaPxhcL60Cu8GwFtBqaXE3ayUnwIgdwwFCs3UHQNYFT/iJFuF6z7cuBAHH4RAi+DuMZBPC31ZrxcDAOrZu3Uo261Ao5oGdfkZXkKkVt+4ccO6seDyTkEhhMzI8DvySXM6hkC8wOjKJlPWvlMgKJKS/xGKjGjO2QGkXAE+BHwyuUC2tOwaDwdA5HYudp59+msMOO2wl5mIwLJHW8TtzLTy+qysueMKYESKRg7+NVoElQuPkHLJZByEkTjZHLpdlZHjMd9toYvE7/ngH5A06bpURSUZHphkdmUJgI0VjaYpQ5IBvwZEiAYAlwq8d//94MLMWCkXNF5tCBgJypX56rtWGroYDCePGao+OAxkOP/xw3vrWt/J//+//XbZ6OgbD8tFccLD56aasqtC6oMPMqmDRYbFCFcXpFPKFoCihauG2YQGXWLzC8mpiPhddYwyOFMlG11RoyQmsOaHQkUJiCTv4u3GxwqKNgdUnsgQ1ZcgtDdG0zPecwbB6COvsLGVZC3R8lg8++CBHH300V155JUNDQ/zRH/0R991330rMzWBYJHMrLDdUV9ZhWnP4XM13VeE1iB6l3Uj0CAHDw+Ns37GDnY9uZ/uOHUFRwlYfodX+5dF0wxdhtWQrJnRkJEygSeDEhE5dzNhYIoUt09gyhS1TSGEhEEis4LkUUthIadMc2+PPailCpLmZ6XxLuK0RPQbDgUTH38rHHnssX/ziF3nxxRf5p3/6J1566SVOOukkjjzySD7/+c8zOjq6EvM0GDqktVuiwa0UiR5oaemBmJXHv6GPDE/y9FPPMzI8iRAHYsibaHrUujjjQo/9/errpJAIYQXiRiKxUKpC//oUhxzaT9+GFJ4qB/tZrcdZQjZdZ8LlQG37YThQUVoseVkLLPobxLZtzj77bP71X/+Vz372szz55JN8+MMf5uCDD+b8889flt5YBsPSWLiH1hxLj3ZpsPTo2jxWHhHE9BzIaQyxXlhhxeQoBkc2uJtgrusqXCdjriwR7Ke1on9Dji1bjmTrluM4cstRbBjobmjN0dpithji2WGhhSr2T1j+Evs318pjMOzHLKUJqBKwRiooL/ob5ec//znvf//7Oeigg/j85z/Phz/8YZ566inuuOMOXnzxRc4666zlnKfBsEiaLTx10eM/O4/wiVxcXst4nublwKDFl17cVdVQnbpugWlHmEjhCyOJhdDgOFm6nHXYwsZxesk5ji+Hlk3k+LNsfNTUsT1+rBYNSecbx2DYnwgDlJeyrAU6tsN//vOf56tf/SqPPfYYZ555Jrfeeitnnnlm1J1806ZN3HLLLRx66KHLPVeDYZG0ytKaW3ywIWsrqsGjIrkkggwhHfTJatw37hJbjTTWx2luD0FkufG/MuIWncZRWgsGpT0EEiVACEGhUGS2MInj9DBbmCBfKPhSUqsG12GUidXxtW1xPsF5LPgbL0hz949tqjYbDAcKHYudG2+8kYsuuogLL7yQgw46qOU2AwMDfOUrX1ny5AyG5SOSLLF1zTcy2RDTE970Iv0DoL16ennLo3gtxt3faRGnEw9KFnbdfRVmSsXcVXsisoiFqf1CMj6aZ/vOh3GcLPl8kZGRKaRIRDWNwv8bx2k3PXyeuKNQqNGi030kcGK766BtxYFQJ8lwwGJSz9ujY7HzxBNP7HGbZDLZcad0g2HvsFAvrUZrT0NBQiGZU59nzsirseJvY5ZSy9YQTXE5/nNzW0C0cucprSLXlNIeMtAMUqaYGK0wPlICQSB0Gl2CdUHZSjx2KD7mc1HF6vjMTUY/UKpgGw5kjNhpjwMxncRg2AN7EjwhdUtPSyvPfPs2dOven2lOx4Z6J/hEzGVVTwWXgZWnldiLDxcXLUoroEbYOBVA6OBvAUrX8HQNrRWeqqJ0jbDr+cICslnwzD2f1uItKGaoPQaH1vtd22fz7B4eJWrq6s88Npax7hgMqxkjdgxrlHa6pdctPa3bTrTL/u7WahYGMpZ5RYPQCasgtwokVrpu3YqsPcFjpUFGDT+DFhzBdVFaRe0iQjegjgudBStRt/FCNMUehQwOrWfr1iPJZjMUC377j93DY02jG+uOYf9GaYlaQmHApey7mjBix2BYkEbBAzS5ttphfwt0nccCMqd4oC94/CJ/9SrIfmp43bKjg4adMmixoeaxyKh5ahq1ym6b34XVzrVsyrJq+ttPf/f8ru1B+4+skyOXc2B4jOa+XQbD/ozWYmmNQI0by2A4kOnkAx53Z7Sw8nR83H1pKdiD0BGJQMz4VpzIdYUVa/VQT0X3XVEJlPaCmBs/LkfFazeG1anDxzq06Lixv33XldJuzH01X4bbnkWmaDrPuenzFvl8Keja7vjtPwrFpkHkKs+wMxgMIUbsGNYg+/qXzL4WPK1oUQVZ1IsHxqsgQ6zwX3AakcAJA5CDS6y0arCUNFptfOKuq/lo5U6az8XUTlsJvxr2OLCTXC5LPp+vt/8wAsewijAByu2xKLHzrW99i3/5l39h165dVKvVhucefPDBZZmYwbC8rNAHegFXlmjILIpvFwa71p9deZrPP5Z5FQYcB1YcIpeVH4wcWnSsYPFHkw3DahRKBGnjQkWWHhGKHCSerkUVlMOlwaIDjVad5jMQc4Oi9/yqLvD6CJuR4UlGhicAjRA2OpyHwbBKMGKnPTqOTLrhhhv4wz/8QwYHB/nFL37B61//evr7+3n66ac544wzVmKOBsMK0aoJ5HKMFxCrytvsVmlkb3/ZNMexyIbnojidyKqTiCog+207Ew2LxKr/H4ikUBiF7RigseoytLDotBI6c6odx9s+JOjkNWxVF0gEFqvla09hMBiWg6effnpZx+v4E/53f/d33HzzzXzpS18imUzykY98hDvuuIMPfvCDTE9PL+vkDIal09zBeqEb40LdsOOtEmItB2I3Xj+w1wr+TtRvxsIOspvqPZjmzmElO23PM24s24oo8LgeoFyP06k38JTBc1JYkaCxRcoXOVoilMbSFrZINSyWSAWdzxMNxwnF1cLI+jUV/iJFKogrSgV/B8/Fr3sUbN1cRHBujSCDYbVyoDYCPfzww3nrW9/K//2//5dyubzk8Tr+pO/atYtf//VfByCTyTA7OwvAH/zBH/D1r399yRMyGJaPuSKnZRPIFs0gW3b7Dtc3iBwruNHa0SJlMva46SYcCp89ip7mZTmuQXgd5qaZy3C+WJFVxxJ2cF2kL2you7Is7Miyk9AplKrRtz7DIYeup299BqEVSZElKbK+4JEpbJmOgp4Xrr7cJCKD6ytlEimSWDKNlEksmfHXBetl7DUIr3tzzyvR4u+Gdcti4TMY9h4Ham+sBx98kKOPPporr7ySoaEh/uiP/oj77rtv0eN1/MkeGhpiYmICgEMOOYSf/exnADzzzDNovb8FXRrWLgsUmJvT9DG4uTa5TPYsfposIsGNFsK/w0ymFq6YOe6tPX0Ul8/ys9Axw7RsP6bGo7kDedTRHAupJVJLtHbpX5/myC1bOWrLazly65GsW58BrSORJCNrUVPLiTm9taw5f9fn4McSha41//rWHy9kJZpzHnO2MyLHsDo5UMXOscceyxe/+EVefPFF/umf/omXXnqJk046iSOPPJLPf/7zjI6OdjRex5/w3/zN3+Tf/u3fAPjDP/xDrrjiCn7rt36L//2//zfvfOc7Ox3OYFhm4qIgZs0RgStD2DGXSCpYQutBqskCEy4y2jfaP7Q2iNCykMKS/mJbGWwr41seRCr4P7Q+pCJLz/xWnoU+lp1Ye1pbdfyn6qIlXlNH47FhoIdDNw3Sv8HBU+Vgu7o1RuKLHInExkarGtlskp5sHymRpi87SK/TS1IlSWuHNDlSIudbemSWhMz4ri2ZDESLHVllfHdfkzUnsOLYVoaE5ZCwHJKWQ8rqIhk8tq10/ZrLZFAA0Y7EZiiWwvOsuxytmMCKi7oWFaINBsNex7Ztzj77bP71X/+Vz372szz55JN8+MMf5uCDD+b888/npZdeam+cTg988803o5QfQHjppZfS39/PT3/6U97+9rfzR3/0R50OZzCsKM1WjGZrTBwdFArUKL/DOWEQbWxfaNg/qigcFN6DutVA42cmaa2QJOpjadAChI41ntSKxlTqdornLS2FvTmWRWvFwOA6tm45kq5cD6ViiUd3/pKZcR3pJhlYZmTwr6rydPVb2CmPqphAe91UilUq+SpJ0ghsVPAPAUJLPFHzj69dX3hoC40bpbKHXeXj1px4ZljoWqufRw2wcQGFiySBFytg2GzRaSVkwiwx/4FJPzesHpYad7O/xuyE/PznP+ef/umf+MY3voHjOHz4wx/m4osv5vnnn+cv/uIvOOuss9pyb3UkdlzX5dprr+Wiiy7i5S9/OQDnnnsu55577uLOwmBYVuZpahnEojSKnLpQie8eddsORIrAnlMXRkauqnp1Yf9G3JiW7adgK7TwgtRqG6UsFDUECoWLQKJ1LZp6mK7ui57wJr18N956zFFsXehO0pBzHBwnF1QV7iLjpJkcmw3OGRQqmJWNp6v0bkiydfNWPM9jenKCqelnmBl2KY3apK1cUE3Zn38NiRQSDz8FXemaLwAlaOVfdyHC6xev8WNjyxQCSUJmAaJrrfDQ2m8iigIP//pqEXZObzzP+N++uFH+NqJ5vcGwOtB6aenj+2v0yec//3m++tWv8thjj3HmmWdy6623cuaZZyKl/znetGkTt9xyC4ceemhb43Ukdmzb5vrrr+f888/veOIGw8qyUFNLK4qhaQjCnce6o1Eo5aKDujGiSWzExwjTrKVIBBYcX0xYIumPE9SbUbrmt0uQvnVD6RqS0JqU8AVPeBqLsvLsiXY81oJCoUShkA8sOwUK+XpVYU0oXPyxlPbIOA49Tj+WsKhW1rH72XEqowm6ZR9oUME5WNjY2NSoooTyG3/KukDxxUm9zUT8NUrITFTnJymyhNlgYQVnjxoqEE0EbjctFTpoNhofE6i76wZ7yDkO+XwhKihohI7BsH9w4403ctFFF3HhhRdy0EEHtdxmYGCAr3zlK22N17Eb6+STT+auu+5qW00ZDCtP68wpn0bXUhQcG7PshE0tlVYIYdWtDVr6VoIWv3zi4wF4qszAQK9/8yyUmBgrYolUvbKw1oAX/Yzyqwr7lgzdVGW4tRtlOQRP83hzz2lkZBLBDnK5HKV8mbGxAimrB4UfrGyRiLmlBKVCmdnCJN1OH9VaGV2xSYuML/riR9MCKSQWNhKJDsSKEBYy7moKmokK0ehqjNf5kcICrdHaRQvtr4OG8bwFfq1qrdgw2MPWLVtxnBz5wizo7QyPTC7XxTUY9hoHalHBO+64g0MOOSSy5IRorXnuuec45JBDSCaTXHDBBW2N17HYOeOMM7jqqqt45JFHeO1rX4vjOA3Pv/3tb+90SINhCSxQGTjK/KmnVYdBsZawsWU6GEHWK/sG1hhPV4Ju3Ha9I3cgQEK3j2/V8T9CGwa62Lr1KLqcXgqFWXbu3MnkWBUhbDxVoX9DDieXIT87y/DIBELafusnPN/CE1hM4haeRpfWYthDZ/cWmUtSJBgdmWF0dBZb+NfK0zUkFgiQOqyz4wuLydEK2/V2HCdDJV+jOArdMo0ViMB66K9Aal/seLi4uL4LSig8JEqouvtJ1+OhpJDYMu0HNJMgIVLUVIn+9WkcJ8NsocjkWBFLprFIRP25/Ay42GvWEKejIncdCJxsDifnwMjEIq+zwbDv0EuM2dlfxc4rX/lKXnrpJQYGBhrWT0xMsGnTJjyvs2a9HYud97///YDvT2tGCNHxBAyGxdH+BzReTyUUOmHQa5gSDUGMTWDBCFsd6OBx83iN6zQ5x6HL6UUKmy5nHY6TZXK0AsD6gRybNx9B1smRz0+heYSRkamGgFyCoFwWtPAst3VnHoRAChuNiFxXSvuiUAkvmIUfu5OQDoVxKIyWSZMlIzOx2davkkZi+ScbCUQprEig+Na1hN8+IorbqWeBRdlgStO/Ps3WLVvpdvqZKYzz8M5HmB5369vH2krE+3fVkeQLBfKFWZxsjkIxTz4/y1IDvg0Gw/IxXymbfD5POp3ueLyOxU6YiWUw7D0W98sjnm0Uuq9Ci0Rz/RdQeMrDEpYfQxPczq1AXPixNcwT0yHIFwrMFqYiy06hUAQh0NrDcXI4TjdCQNbpwsllYXQ6skD5x7fxI1xkPWg5OPW5QcuL+QzWY22ix2H2WRCo659f4NLDRQrwdBjv0lSjRhO1g7CxsawkaBsXD6F9x5KK7enpKh41PMDWNgiiwoRKeIG1KOghpoleq2Y8amScHDmnN7ie3WSdJJNjlSDA3LfshGhdj9mpv1oWI8OToLbj5BwK+QLDw+P4vbHm67RuMOyfHGhurCuvvBLwjSfXXHMN2Ww2es7zPO69916OPfbYjsddUtfzcrm8KIVlMLTHEj6ETUUD43E6YRXghEgjkHiqQu/6JFknR6FQZGKsjC1SfmNL5WdUhUJnTkBzYJEZHZlhBzvI5bKUCmXGR4vYMouLR6FQpFCYIevkKBZmKeZLyDDLS4DQVjC+n+kVBi37vaLql0FotUTBsxB+VpKfKWYFa+pNMcMYIz8tX4IET9cCV1QKpX3rjK1tJAIVpKaDoKAmSW2AXC5JIV9icqxGQjpBRWYXpT2k8K06InCXze2jFQRHC5t8ocRMYYJup4/Z4iSFQjEQp14UEK6DnltRdl14HkFwMuDH6IxMACJ4XeuZYxovuP7G0mPYvznQxM4vfvELwLfsPPLIIySTyei5ZDLJMcccw4c//OGOx+1Y7Hiex7XXXstNN93E8PAwjz/+OIcddhhXX301hx56KBdffHHHkzAY5rLED2AgUPaE0jV61yc5cstWnFwPs4UJtu/YzvhYqcGdEg0bCB7tB9z49h8BUiaZGCsxMVrys7RkKnKJjY3k2a624+SyFGZnGR2dQQTZW1EQbhBeozWIwJvSUItnXnGzRNdLYN0hOkYgBoIhowylINBa4EXCTwWWFA8XBHgxi42rFTa+RSe1ATZv3UyP0894/iV+sWMbpfE80mp2D1oQxkcFNYkiN1pwLW2RYmqsyPadO8g5GWYLBcZGC9gyg9aVwOU2vxBsjruKCyGtY0Kn9d4dXlyDwdApP/zhDwG/aPEXv/hFuru7l2Xcjisof/rTn+aWW27h+uuvb1BcRx55JP/4j/+4LJMyrGWWry1C/aYV3sQaXRzgp087Tpqc04sUkpzTi+Nk52zXMK72U9OVdlG6hqsquKqCp6soXDxdo6ZK1FQJV1dASMZH8zz7zG7Gxgp+zyYaA539NPZEsIRNLmN9noKKy2EPr8ZKywtdL930SEdumvD6RFYQrfwMJx2mytfwVAVPVf1FV4LzKlJTRapenorOU9VFyjpPRZQoiSIlUaQiqlSoUaZMKpegy1lHRRdYN5RjwyEOiXUlqsrvqyebrTjB/LwgXd9VZf8a6wpVXcQTMD5W4NlfjTI2VkBLQVX7c3JVJZq30rXgfBoX34rVuE5Ff9eC90vcqqPmXEeDYX/hQG0E+tWvfnXZhA4swrJz6623cvPNN3PyySfz3ve+N1p/zDHH8Mtf/nLZJmZYi6zAh66FC0ppLzqUFBaFQpl8YQon10O+MEWxUAxS0JuEQiAI4o9VUPlX6RqQAJSfSt38O0IIBNa8VocoI2xOxWUF2kULQMcDlxeTCBCL24mqRXu+9SiwYoWF/fzziyaH1h6SRGRI8nD9AoG6Fs3dw0Wg0CQja4+lLQr5MhP53fQNdZHtTtHXO8i6zS/n0ScfZXy8dTfjejVjP45H49fmaUxFt9DUAtEZuq+8uiuq+fUKrWOxl7W+rul1iVVgNhj2Zw4kN9bZZ5/NLbfcQnd3N2efffaC237729/uaOyOxc4LL7zA4YcfPme9UopardbpcAbDAsxneFzoBhQG+dY/wDqoYqy1xAvcJK6uBPVZLMbH8jyy8yEyTppCocj4aBGkjCw3YdxHyxiQIKBWBkHNAEo31vZZCD9w2grmGQQDCwHCjiwuSodp8YFbR9cCV5e/zr8ei3FnNYqf6DtP15+vu3oknq5EVY219OclhX9tpUhQlVaU8q1QWNikZJqpsRoP73iY/tks/b1DCDdNz7o+0s5TeKMzaCkiq1uzWAkFmBR+TJBW8bBnGgo2eroSWaVCC5UOA7FjNFfE9tfFrIBRrI4ROob9nwOpXURPT4///Rf8vZx0LHa2bNnCj3/8Y17xilc0rP/Wt77Fcccdt2wTM6wlFmhYuUg0OrAy+OP5VpgaSvtveRGzSFgiwcRYBW807wsPKXFVJbAmhC4OFVhvmq07vqjxRVS9CF68Wm/DmcbS1uNFDZWqMTDQ48f15IuMjkz7cT9BrR+NwlMV/yaOROlKlKm1VMGjw/lG7q16HE+9Tk14HhKNi1ZeMHcXJWykcIPHYf8wv4+WRiGsBNMTNSpqkp4tB7Gut5eZwjizhTxK6EjohNdaqVjcjvbnpkTQR4tKw3UNBU1d5AQuqki0tGMBa7TuzA1KNi4sg2Fv8NWvfrXl38tBx2Lnmmuu4YILLuCFF15AKcW3v/1tHnvsMW699Va++93vLuvkDIbWdJKNFKZY+9lGoatD6Ro6CBJGBDdpIdHo2A3Ua7DqxIVOg3UgCuYlsoLoyCrizRE+YQYXBNWbtWbDQC9bt24l5/RQKMywU+xkbLSILRO4QfFBvw4QgOtncGGhRS1waS1F8PjjN2RAxZtixuYcBWjH/g/7UPlWMIUUtt8OQli4uL79TKYpTLk8/sQTpJ1nyRdKjI8WENKO9mueT9yVha67IuOBxXFXVZRCH58/0NZ7ZY7QMVYdw+rAr4e1BDfWSoQPLAOlUgmtdZR6/uyzz/Kd73yHLVu2cOqpp3Y8Xsc/oc866yz+/d//nf/+7//GcRyuueYaHn30Uf793/+d3/qt3+p4AnvihRde4Pd///fp7+8nk8lw1FFH8fOf/zx6XmvNNddcw0EHHUQmk+GUU07hiSeeaBhjYmKC8847j+7ubnp7e7n44ovJ5/PLPlfDchDvZt34b77t5tL8S92LAlA9VcH1ytRUiYqXp+LlqXp5yt401eDvqsoHQbglXK8cBLtWYkGsfiBruCjtW1yUqvqLDpbocSxoVrnRzVkGVh5LJOju6qYnN0Ba5ujr2khvrp+s7CUr1+HY/WTtflJWF0mrG9tykDKNlEkEiaagZf/KNS57umb16zTHEhLWnYlZTMLr4J9breGahtetovOU9Sxl8pRFgZIooC2L8fEKu56dYGKshJA2rvYDj70gINoP/G4MLFaqiqdK0eJ6hWiJr1eqHF1vTQ1Nzc/uip3DfIsO/jW8f9AYq45hfyeM2VnKsj9y1llnceuttwIwNTXF61//ej73uc9x1llnceONN3Y83qL8BW9605u44447GBkZoVgs8j//8z+LUlp7YnJykje+8Y0kEgn+8z//k507d/K5z32OdevWRdtcf/313HDDDdx0003ce++9OI7DaaedRrlcD3w877zz2LFjB3fccQff/e53ufvuu7nkkkuWfb6GpdIodJqZK3raePuGv/Zj7o7QahBlHGk3Wuo33sB9FQQJ6+im6EXr4ku4PqzPEh2PulUo3A4aLUNC2JQKVUqFWQQWxcIs5YJLQmRJigw2KRKksWU6qP5sR53bafi/zWuyp0sWO5c5Aqjp3Ihf05g7yQ3EoUuFmi7j6Roeru8yC4KLo7o4eu41CR83L+HrEl8iYRR/bdosDtgocsBYdAyG/YMHH3yQN73pTYAfJjM0NMSzzz7Lrbfeyg033NDxeEsqKpjP5+dUVF7OVLHPfvazHHzwwQ2+u02bNkV/a635whe+wJ//+Z9z1llnAX622ODgILfddhvnnnsujz76KLfffjv3338/xx9/PABf+tKXOPPMM/nrv/5rNm7cuGzzNexf+HE7Ivg7yDrCj63xAvcLgBJu434Ngcj+DbYe/zFP9k48ziUM/A1cWDoKmq4Xrqs3urRIyAwzYx6P7XyKHmeEcr5KbTTBOrkOrRVVUcXDxZIJqqJIVRUjV5vr+a4trWuE3dn1AoInEostxFFzIb92CLO3tAal3CB+KYyn8RBSRnWJLJ1A0VjduHFuLea9x9o30CBQ5o272RPNr6ex6BhWBwdSgHKcYrFIV1cXAP/1X//F2WefjZSSN7zhDTz77LMdj9fxz8BnnnmG3/7t38ZxHHp6eli3bh3r1q2jt7e3weKyHPzbv/0bxx9/PL/7u7/LwMAAxx13HP/wD//QMJfdu3dzyimnROt6eno44YQTuOeeewC455576O3tjYQOwCmnnIKUknvvvXdZ52tYIZqDfOdYd5rfxvXH9boyyndtRO4X3zWidCVwg1Tq7hBdqbujVNV3V8VcIn4sT4t/um7VIaznQi2y9oSWkMZzkdikcMR6qmNpZp8RMNrNgHgZvTpHt87RrbrpVetwdA9ZsY6M7CFp5UjIDFKkAkuPX4cHIedYxRpcgWKuFUhgLUro+NdXxURhWJunGtQd8i1l/t81apSDlhH+suC4MctMgyVNu3MWHViH4q9L+8x9TYzQMawmDlQ31uGHH85tt93Gc889x/e///3IezQyMrIoo0rHlp3f//3fR2vNP/3TPzE4OBilia0ETz/9NDfeeCNXXnklf/qnf8r999/PBz/4wait++7duwEYHBxs2G9wcDB6bvfu3XO6ptq2TV9fX7RNM5VKhUqlEj2emZlZztMyLAMC0XRTa63bm2/8zYG49Yq6jZaGxmye9l0bcWtSvTqxgjDbSYQ3b79AoAwXLUmRxhFO0DFKYgmJpX2R4uGR1Ek0Ck+ksMJMLmmjPD+zLMw8C7O0Gi/EApacNlLk56OlNWYewuKB4T6eqGHhn4eF8v8JN3he+hYqPddqA3uy3Mz3ei3dxWcwGPYO11xzDe9+97u54oorOPnkkznxxBMB38qzmMzvjsXOQw89xAMPPMCrX/3qjg/WKUopjj/+eK699loAjjvuOLZv385NN93EBRdcsGLHve666/iLv/iLFRvfsAjiN+QoHTp0UbW+8bWK+wlTrf0MprjoiQ3fVHm5tWuk1Q1VNmwnEJHgmZPtRFBIMLDspHSajM6QEykSQpKSElv4gq6mNJ7WWMqirFNY0sYTNVxRCdLhwVMV3/ARZZ+1uiD1TuLRqqYeYovDd9stVFdICIlFwq9tJCzCzvIKD0/UqGnLL/YIeFqilBVkeuG/VsuSLdXOPsaqY1hdKJboxtpPs7F+53d+h5NOOomXXnqJY445Jlp/8skn8853vrPj8Tr+dnvd617Hc8891/GBFsNBBx3Eli1bGtZt3ryZXbt2ATA0NATA8PBwwzbDw8PRc0NDQ4yMjDQ877ouExMT0TbNfOxjH2N6ejpa9tb5GhZHc+By6+ytOI3ZR3ODcRtTmNsPYm10iUT7xNwxc+JUhERqGZQ3lCSExBICWwhSFmgquBRBVEkgSWCR0Mmoa3somKRI1Gv4iLmLEHa0bZi+LYRN3Q0YX9/eUmfhrxEpLMLu8lJYWNjB+dr17vPC/zvMUIuO03LsVtdfNy0Gw9rgQHVjgX//Pu6445Cy/j3w+te/niOOOKLjsTq27PzjP/4j733ve3nhhRc48sgjSSQSDc8fffTRHU9iPt74xjfy2GOPNax7/PHHo4KGmzZtYmhoiDvvvDNq+T4zM8O9997L+973PgBOPPFEpqameOCBB3jta18LwA9+8AOUUpxwwgktj5tKpUilUst2HoZ2CQJ7m4ncQQGiyb0Rrm4lcDpy0bR2mXRGc1f00K01z7kF+NWcBZYQJAKhU1ST6PWKXDZJKV/AG9ak6MbVSRI6BQISMuu7tnS9YWerYN64VSkuVOYUPWwxx1YWm3i9oXhzTfA7twssLGFjBy63pMiS1BnfXafTDWO5wqUsUiihgl5hpajHFSiEdv1g8pavyXzCJtbrwmAwrDoKhQKf+cxnuPPOOxkZGZmTDPX00093NF7HYmd0dJSnnnqKP/zDP4zWCSHQWiOEwPMW07OnNVdccQW//uu/zrXXXsu73vUu7rvvPm6++WZuvvnm6LiXX345n/rUp3jVq17Fpk2buPrqq9m4cSPveMc7AN8SdPrpp/Oe97yHm266iVqtxmWXXca5555rMrH2S8KifLpRvLQSPIsivt88gmaPsSGdFDVsMXzTTVsisbWFhYUtBAkpULqCvUGxefMWep1+JvLj/FxvpzriksQmRRqpJVVRxBN+zA5WowiZz13V7E6TLa5luG/YymLuOdTTxVVTpWIraGgadim3SGApC1uBLZJkyYY2JgBq2sMWNq4IRI30j+/poC6R8IOQifcHC46+ZzoRPcYiZFh9KMSSXFH7qxvr//v//j/uuusu/uAP/oCDDjpoyfHBHYudiy66iOOOO46vf/3rKx6g/LrXvY7vfOc7fOxjH+OTn/wkmzZt4gtf+ALnnXdetM1HPvIRCoUCl1xyCVNTU5x00kncfvvtpNP1X49f+9rXuOyyyzj55JORUnLOOecsKk/fsFL4DQvmrtWtrTUdE6ZCh/2ORPC+DW/yLYJ5Y3FB8wuexdPQ5kAov22C1iitUVrgiRrpbJJ+px8tJb1OP2knRUnXEKL+3pZYUbCyhW/ZCZuNhiKmUfTMn3UV3y6Mq/H3aW3Z8YWOh6AG2FGdnFDohC4qpSv0rM/Qnc1Qy7vURvNkZF/sHCRK+720KqJMghpaqEZ3lrZidXiWJjZbY4SOYZWyVFfUfurG+s///E++973v8cY3vnFZxhNa644+5Y7j8NBDD7VsBnqgMjMzEzQlkxiz+ErSumDgHMGzR6tO65vz4OAGcjmHfL7A8PBoi5v4wm6szmu3+EQp34AIUsWlSJG2e7Blmi65gZzqIauz9MssCSFIyCp6fYEtW3zLzlh+nG3bt1MdyVLDYlrMUhVVimLWL+CHF6Vzx7OeZNx9FWue2XqesY7iyChTyg76icngeYXyrTB4eLh+Ecbg+GFLBz8iJ0VCJ+lZLzhu82vY4AySL0zy5KO/xBrPIYXA0y5aS2raxkUzLWaZlTNUdZG8N4KrK9S8Ap4qo7Wf/l9v0tnp67HQZ9eIHcNy4r9Hp6enl7X2XJzwvvQvx/4OWSux5x3moejVeNe2b63oXBfDpk2b+I//+A82b968LON1bNn5zd/8zTUndgz7lj26tBpoJXQ0g4Mb2Lp1C9msQ7FYAHYyPDw2v2WyRVxQQyf1Zbg5hpYRP+Va1S07QmCJNJXxEo/s2EnKSVLKV6iMChIiRS3o3C6DfyKSIQTr68HAkYCJbaEWsIoEkUNILbHxg5ptbc/Z38JvHVEV1SDVPRBSgUVG4qfUC6VxnBy9znoSwqbf2cBLuV8xPjZO14YMOSdFIV+kMgxp0YOFja1t3KDKst8DzMcv0OjOmbPBYDjw+Mu//EuuueYa/s//+T9Rf6yl0LHYedvb3sYVV1zBI488wlFHHTUnQPntb3/7kidlWKvE3Vlxl8U8ggfajN3RdOW6cLI5hJBksw65nMPw8ChzrUkxIRCO3W4wdHS0FkIoFGih+0p4eEHaeFUXKYkESEipBAlPorVNUvZSHS1THnERZEmQpKI9arEbfkKnGlxTcZEitYysMnFCsaOEatgPIEESqQUWFknsKEMsXqpQAxXlodCUqVIRFVzhUhLFKHbHF0QJksKmkq9SKEyRdQaYKU5QmM2TG7A5eutW+px+xgrj/EI/gjdaI6FtEiJJjUrgCrODoocyskj5rkX/r/qMFsJYYw0HLkvNqNpfs7E+97nP8dRTTzE4OMihhx46R2s8+OCDHY3Xsdh573vfC8AnP/nJOc8td4CyYS3SHFBaz2JqqF/TAQKbfL5AsVgkm81SKpbI5wu0vgm2iONZSFDNI4TmEzxa+B3KddDXCcDVFaoUkUgKokxC2ygFKW0BCSCBh6aiXWq4BLYgAD+NW8sGC0xCJ7CwkAjspmBkf09/bkrrSESGAcMJbCSQFBYZy8ISkJR+lhiAFOBpTdmz8DQUPZuiTlDVLlLIwL2lcHGxg0Dq4miBR3c8you5X1EtVnEnFOs29bA+148tJeudfpxcmtkxD1tb2Novq2iJBFFrDREUGYwKDcaF6UKd3vfPL3KDYbloVQO80/33R8Iko+WiY7HTnP5lMKw8C6Rtz3FptdhWKHYPD6NRe4jZiY/RJvNYf+KCp5VVqt6UVPlNMkWNmqhQoYwKrCtKhzWRRdB4whc6rqj/oJCBAPAFgo2tLXypYwUOqTDjqYJHDYmNJZLRvMK5eroKuCQkpGWGtJRkbYElIGuDJeqywdOCkieoKX89LiS0xNMeEl/w+OP61yYte3DHBLNjkJYOPXY3pXyesfw4/U4/44VxivkyAids99n+9Y9d8aWxkGAyGAx7m49//OPLOt6SGoEaDCtHBxaeOS6tRhcY+BlFw8NjketqweDkxTBP7Z+5hHYViacqKFGLGmW6ouK3UJAJijoTuJRisTJi7vgJnUQiSekEKRJYCFLSIiEEUvhiZcabILlekXZSFPNlKqMVcrIvOuOinsQZ0GRzaSiVsSarHJJeR3fSIyk1XbZHQiqE8GfuKsl0zaaqBBMVi8mqpOzZiKqgqj1KukJJlqK5KxRSpLGw8fPjLcqjBR7esYOMk6KQL1MaFaREEo8SrnDxhB/srGOp5r4rq94OQ7eywi3Inl5zI3gMq48D1Y0FMDU1xbe+9S2eeuop/uRP/oS+vj4efPBBBgcHednLXtbRWIsSO4VCgbvuuotdu3ZRrVYbnvvgBz+4mCENhjbYQxxPy+3jgkfQaAHYh1ZKrUAov1u4dlHaj8SxRAKtFVL46dhSyKbwYx8RhSf7NXpsLL/6MoKUlFgCLCFwdZn0Bs3mrUeyLtvPeGGM7Tt24I1VSIo0VV0ms0FzzJFH0u/0U6qM8/jOnVilEt0Jm5RUdCdqJC1fdEg0FeW7xipKUvUkJc+/rgkh/bYWQYBzY1Bz2AbVD8J2ZB+1sTLFMRe0Q0YkcXXoZOv0dZmvdlI7WXtG8BhWN0ovrXO52k/f7g8//DCnnHIKPT09/OpXv+I973kPfX19fPvb32bXrl3ceuutHY3Xsdj5xS9+wZlnnkmxWKRQKNDX18fY2BjZbJaBgQEjdgzLTKuicPNYeVoGLe8fbtdonlGVY1/kCCRKudQoIYUfsCyQ1GQ5bAca1dEJBY6lEyTwXVGh2yqJRVpaKCooyiREgrSVpqRckl0pNjj9CClZ76wnm0tTHlckpS+osl1pXtbdT9KSvCy7jsl1FoM6z6/1QMp26XNmSSXrP2oq1STj+W5KtQRpmcMSSWZdSdG1sT2BpxSVoO5O6NKSSDytSGiLsuuhcEmIJCmRwdWKMhVcFCVZoiJKVHXJd+/pVtlXrZud+te43Yaw4fuqlXXICB6DYV9z5ZVXcuGFF3L99dfT1dUVrT/zzDN597vf3fF4HYudK664gre97W3cdNNN9PT08LOf/YxEIsHv//7v86EPfajjCRgM7dFceHAPcTzLyRK6grckbNQZNLpUuoJWvgAI69QoXfNTr4PifGEjTYuE3yZC+y4uC0kCm4SQVPUUqQ2KbFeKUmGS4rhLt+zGLdWYLY3Tm+1npjROrVAhI3OkLBAqgS7nyZfHeVlXP6XyCJlqgVd0KQ5ZP0M6VaFn/SSJbDmYusAtp8js3kCpnEYhqCqJLS0mKxYaSUlZvrihbqmpiDIeNjNqlnXrk2RzaUr5aaZHp0nKbiqiioef0VXTFWqUUbo2p5+Yn37uB3nXk7EaG8O2w9wyAs2ixwgew+pAI9BLiFlbyr4ryf3338/f//3fz1n/spe9jN27d3c8XsdiZ9u2bfz93/89Ukosy6JSqXDYYYdx/fXXc8EFF3D22Wd3PAmDYaksX7XlVoMvVNdn/vm0O3bo4BEiqBKswcP186gE0TotwvDd5nYT4FEhtUGxdetWnGyKGWuMxx9/nNJkgdJIhe1qJ0knSSlfxRuXJK00AkHGSlOdKPLYzh280JUg5xbYWKrx8kFNd88MyXSF7OAEdq4Iyr8Gbj5DtZjGtl26CzkydoaaEtixWKH6dfAzswBqlMkNaF69+dfodtYzU5hg584dFMbyeMG5edSi4oRKqzmtNfxLEhM8dP7axK+9P55osvQYwWNYPSi9xK7n+2nMTiqVYmZmZs76xx9/nA0bNnQ8XsdiJ5FIRB1IBwYG2LVrF5s3b6anp8d0BzesMAtbdxZKTV9MEcD2Cxkujno/KT/fSulavdm6VIF1R6JF0JRWZkGARaLemgER3Kw9Mk6K7mwXorfK0S8/Fu2BN6V4Tr6AHk1QHRckccjINFqDlH421WCiDys/AYVZfq2nyuYByUFDu9lwxK+wuwvIV3WhezeC8kAp7Mlx+jO/ojqVo1ZLMFNNYwmNYydwlSDhStB+YHJVVPGoUaWEq8v0OD3IrgQlUcDqSiByiunx0eDXqaKqin5F5mDR1Ptu1dPPQUQvZz21vlUD1AURdWETd4k1Bj63W8vHYDAsJ29/+9v55Cc/yb/8y78Afszlrl27+OhHP8o555zT8Xgdf3sfd9xx3H///QD8xm/8Btdccw1f+9rXuPzyyznyyCM7noDB0Bmd33QWW+14KVWS57MyzTemb8Go39i1rgfqNv8fRyKRwhc7U9OTvDC9CyspKRWLeBWXga5BUk4SpCQjc9giPWeUaXecUm8N+2UO412KFyuTpDJlX+is81DrBxhNbOBXbj9jdj+6fwNWd5FErkQqVSEpXVJSYQuN1fSNovEC8eKhUcwWZpjOj1HTFaYLY8wW8n7LicCiEwodT7tRen7jdQ0O0LJ5qdXREl5B3yUml99daTDsBfwA5aUt+yOf+9znyOfzbNiwgVKpxG/8xm9w+OGH09XVxac//emOx+vYsnPttdcyOzsLwKc//WnOP/983ve+9/GqV72Kr3zlKx1PwGDY98xNVZ9DdCNcaJvg/wWaiDaLoPCmO1/NHx1kKEkRztRraDHhao8Zb5rcgCCZS/LL5x/l0Re384qDDmW9PciMV2Z2tgJeDoRGab8ooBQCIaBChcSAZuuWLWxa14ulf8WzTz9AOTmDHNCwro+HSy/n6dENgA1egcN7LY7u24Wt8qSzZdK2S9LysGJhNGH/rLC+j4dvpRkemcHVvyDnZMgXSoyP5rFkCqV8MeQLncCNpdzoGjRes/kFT8truFAvsKhLfLQysvK0tvA0bG0w7HMO1Jidnp4e7rjjDn7yk5/w0EMPkc/nec1rXsMpp5yyqPE6FjvHH3989PfAwAC33377og5sMKwEncXtNN8Em1LVw7GahI6gdefw+A3TX6Hmr6Yco95tfOGbt99hXKKFIvxX0SWcDZrNW7Yy1DXAWOElfrH9fp6dfJGZ3gqlQoXaqCAjkpSVH+2jtMYWgqS0qFHDySYZ7OonIRWH9OR4cSRBNVWAdb2MJ/t5euJgjj3+NzjkkH6efuZ57vvxf2KVsmy08yTSFRK2S0J6WIJIlGnqMTiurkQWG4DRkSlGmPDPXVi4Xj6yaCntRuKmlVuqtShstS4e1Nz4fFw8heEKoVtM+zsE6+KipzlbK9raYNinHIgxO0opbrnlFr797W/zq1/9CiEEmzZtYmhoCK31/D0NF6BjsfPMM8/gui6vetWrGtY/8cQTJBIJDj300I4nYTC0z/xv8vmEzvyCo/kmNp/QiYmcsHt58w0UFVkKGkRPIHgaJzS/aBJYwQ1dB3FCc89J4aGEwtMuVTTrcmlyTh8e0JsdpK9rkJlfVclPgSBLSqSpKIVHvE2EpKY0QtiUCwWGZ8fJruvluakZVKlMxrLBcynkq6AUBx/SixA1JB67dnm4pQ30e1n6x2ooJfGUxNPgaX/8UIzppkVFQsN3vUWWlXhV6ZbZdC3cVk3CJ7ye9eBlorEb94s9CEVOTPS0ev1aFzDcH0VP8/tlf5qbwbBntNa8/e1v5z/+4z845phjOOqoo9Ba8+ijj3LhhRfy7W9/m9tuu63jcTsWOxdeeCEXXXTRHLFz77338o//+I/86Ec/6ngSBkN7NH+RtxAn8+7pPz9X9DTfMButOYIEiKBzVCB8hJBzhEqUIi1U1PNK69oc11aDlSg8hpCIoOGllDZK1xgYWEcu51DMlxkbnQWRarSW4FdbRqaYLtQYLrzEBmeQ6cIE4zNFtMriiSQKjaYSNJqon7utJZZKk5IpamMlHt6+k191J9hoj/IqWaWbNBTH6LKTUBnjqSeepL83ybafP8Ngb5q3HX04kyOKnz0xQXdFUVEWNeW3kKhphStdvxKy8t1SrqrUA47jlpWGwGBvzro5r2NwzWRYeyh4beLPN45ZP15ri1HocozHRcVEaxDEXA9gtpreQ4txce3NX9ILHcsIoQMBrf1lKfvvT9xyyy3cfffd3Hnnnbz1rW9teO4HP/gB73jHO7j11ls5//zzOxp3UUUF3/jGN85Z/4Y3vIHLLrus0+EMhuWn2dXRUKdlz24ln3pcSCh0QkESNqaMo4J7ntZxq48V2lLmjS8R1C1HAFp7DAysY+uWI8k53RQKszy681HGx4ogEijtIYUd/O/Pb2qszCM7t9PtPEM5X6U0CjmZoIYb2VQ8PFTgq5GBGcPTGldr+q0+emeLOKUyr9pgc2hfF8obh6rLOsflld2z7Hz4fmamqjy/q8BZbx5gvajSXUvws0SFfE2BFoFVxxeU4VF9seHF/lYtBU79/BcWOvEYp/A6h8Knvp1VH7c5gS9cFxw7fB39L3xVF0tCBlaeuTV95k9VJ3aw1dCY1KTWHwhoBOoAitn5+te/zp/+6Z/OEToAv/mbv8lVV13F1772tY7FTsfpB0KIKEA5zvT0tOl4btiLhJYX0WiNaSUqwvWicZ85/0TgphI2QiSQIoUUSSyZwZIZEpZDyu4iZXWRtnobF7uHpNVN0u7CkhmkTCJlEiESvnUoEjWBdQgrulmHN3H/f0Eu55BzupHSIpfrxsllo1PxXUEuVYpUdZGyKOBZgvHxCruenWJsokrVlkyJGabkDFNymhk5Q17OkhczlEQxqFJcpaI9qkpR8UDoDEnRjVRdFEpZihM9uC+k0LumOEY8yW/0b+P47P28Mvk0zswI5eEMjz8uKUxCxXWYqSYpe1BVGpe6GyvMwgpFju+u8sWP0rXYehetXeo9nOu9nENxGVq+LJnEEikSMkPSypGycqStbjJ2Lxm7l7TVTdrqIW31kLRyJKVDQmb8/YLFHyeFFIloEcJGCBspksjo71TjayhsXwgJK/bei7+20ZtuniX+/u1k2dNnYTH7tTNPg2Eud999N29729vYuHEjQog5biWtNddccw0HHXQQmUyGU045hSeeeKKtsR9++GFOP/30eZ8/44wzeOihhzqec8eWnTe/+c1cd911fP3rX8ey/F9Znudx3XXXcdJJJ3U8AYNhWVgwcFU1btdsPYi5lkLLgRQ2ILFkCksmEVgkZAYpLKSwSZAORvYFvofvqmnoZq6CIFfh153ReHX3V8yFFf4vhERiU8yXKRbzOE4PxUKBYr7kBybr8FgElp3AyoFECgtLJhB41Ki7z1SDewYsbBI6hRKKik4hNVSV39Sz7ElKtQTFSopiPkvXeA92JUlBv0QpMcHLqGElFNv/J88DEwdTmVZ0TeUo6xwF16IaiB3fojS3x1UkdKLr39qd1SoIORSDMrCu2TKFLVIIIUmQjq5B+Jr4cqse0O0FwdHha+VfTxU1YQ2vow6rW0cWm7CgYz17qzkQvR7I3KoScyv27H7dk7u1febbb09Vxlc69qddQWUsT3tiXzQCLRQKHHPMMVx00UUtCwlff/313HDDDfyf//N/2LRpE1dffTWnnXYaO3fuJJ1OLzj2xMQEg4OD8z4/ODjI5ORkx3PuWOx89rOf5c1vfjOvfvWredOb3gTAj3/8Y2ZmZvjBD37Q8QQMhuWn+Qu+ydWwUMpy5LYKYmiEjS18C0BSZrFIYIuU37YhQONR0xWktPysI1nzq/8Kf/Hr58hYIbzgUGEMUGTV8S0GY6OzPLrjUZyuHIV8kdHRWSyZqh9PeyDA0/WbtN9iImzW2TpbLNoONWcLFfj9a0pS9SzKlRTV2Sy/HPV4fuwghJNGlwu80p7kdXaVF2YKUM0xYa3nSc+iogQ17Tf5nO+4IhJ9c0VQa+qB3KHQsUQCgfQtNUH7jKTI+o1HY13WPVHzU/axInET77KlIlEDaCLR47ux5ooeEYrY+URPS/eWP5vGx+Eu7cWY+dNbiRv+whlsc2k134XmtVzWIZPyvyf2RTbWGWecwRlnnNHyOa01X/jCF/jzP/9zzjrrLABuvfVWBgcHue222zj33HMXHNvzPGx7fmliWRau26pn3sJ0LHa2bNnCww8/zN/+7d/y0EMPkclkOP/887nsssvo6+vreAIGw95hvl/cwXotQYhI6PguDpukzJGUWWxSZOkmQZKESpLUiWA0f7yKqFARZWqyQkHIKEgZ8CshBzfKkHisTih0pEhgCRshU0yM15gan0KhsS3fjaUDlxCAF9SgcakgmzLERMziAyCDHltCSJI6G3VWj6MBTwuKnk2ylmS26FB7oZudsodjDzuMTUMZntld4pGHH+dorZCzGylU0oyWHCarNrM1ScXDFzxoJP45RU1MtVu3YlEPIp6bKSXn/C2ljUBiyxQJmUUKm4zoIaFTJEiSUVmkFsExBR4ervDwcP3XRFSoUYnicTwkUqj69RREWWIaL4i9imWHBfFHQhC42toJZG629DQJnHYLGLbK5msYfzlpZ06t4pNW4jjtHM+In/2RZ555ht27dzfUw+np6eGEE07gnnvu2aPY0Vpz4YUXkkqlWj5fqVQWNa+OxQ7Axo0bufbaaxd1QINh3xJaeWJfuBoGB/vp6uoiXygzOjLpiwMspLCwSWGJBAmdJKF9oZMKLDtVXaJGDYEmIZIoraJ2DjKMNdFWZNXwD9fosokHPEfNP2WiPtuYyAH/xhwP7PUCi07kgiNu8bHwtPJFR9Bfq34lxJwbaWgSdz2LfFWgu3K8rKsPaoqX5bI8JLuZnsmja0lKtQQVJakpgadFEE+kY+OHrjkLKSRKS5qtOgu/TvVzqgtCO7LoJEiS1EkS2g6klS94XPy28lJIXFyU9gswWjqBJ2pILL9qbCw+NyraGE4/fK4p3jhyeQXPa8HcQOamxqR77tvWfPPfg9s1mmL7bVHaqT/VUeB+269jfJ/FsJjjrS00S5N94b7NfahSqdS8gmMhwiadza6owcHBthp4XnDBBXvcptPgZFik2DEYVjfx7CfN0OAAW4/ciuN0USqW2SkeY3rcxZYZUiJHmhwpnaZbdZHAJiUsUkKSVxN0D2jSTprp2RJToy45qxtP1qiRQMsgVkSC8mr1+i66Meg26mwe+z8UCVHcSRAdNDeNWs2Jd/F03XVk4Y9H2DldJEjqJCmdJk2CrGWTtQQZS5OyFAmpsKRCaUFCpajOuDz5QpVD+rt4ZrRIYRzKlT4KZYdCLcFMNUHZE1SVLxSkENhaktJpJJIaFVxRQQm/NpAQMgiDCXt/tX594kHbofvKlmmS+JYpR+VI6RQpEiTQaO0ihMLyHY1kyOAGwsvGRgaxO1Jb1EQ5uqYqeC9EvcZE4CbE9os3aoUWXr19R3Cpw7kLJAq30bXVopK2/0f8pr+QAGi6wS8geJpZSjPcdvdttlgt9/hzj0XT8eq1muJbrlWWy4118MEHN6z/+Mc/zic+8YmlTG1RfPWrX12RcY3YMawS9vBhbmjU2WS5WXBUSa7LwcnmEMLGcXroyuWYncj7N1iRIqOyvruEBElhkbEsEBVyGwSbtxzJumwfw/lRfrF9O6UxSIkMCKhi1904QjakNzfE6sTcWFLYyEAAhf97wo85CSsoEwv+VcptTN/W9RiX0I0VziEcz7dQJUgISUIIkhakpMbVJabdPJal6NWCDFkGijM89PDjbM9kKM+49M5KSPQwU01Scm2KnqQa1NcJbzd+y4cqSEVCpkCDkvV2EEq40bWvG4LmxraE18aKMqZsEiKFpROkdIoMKWpqhtygwEuUESmPlM7gFS0KI5AR66jphO+mQlGjErisPL9Gka5bdMKGokrX09GVrmEFzUfDuB+kLzQlCTxVIQhCChq2EitKOH+5gUbX1nzVuOOFEWNjtSl62mIxvcAWcKst2xyaSkVAs9WpOd3fsBw899xzdHd3R48XY9UBGBoaAmB4eJiDDjooWj88PMyxxx67pDkuBSN2DKua+V0EbfS7AkCQzxcoFgtknW6KpRL5QjFIRfYtIxKJrS0SwsIWAktAmQIi5eIk0iQsi77cepxciuJYre4Ci6eXYwWxIMFRY+4rX5z4LjP/eIHYERZC1y0PUlj+/6iWLqEwuyj+t4jG8t0/YZxLApuMZZG2BEkJU944us/D6hXMVMt4pRJHZW0OyzlscKuUZvJQc7CSXZRci6pnUVESL1ZfRwBFNUFivcdQLsVMoUhxrEzCSvuB24HIkaLmW0N0eE2a2nS0KNpYF39WZKlRukZ2AA7/tVeikiU2vepQSjNlpnbn2aEfxx2t+K6tMJ5HWITFAyUWSgRiMbhWUtgx8RNzN+qYOy2K8akxOLgOJ5cln88zvHuM0EwlovpK8ffh3PfifEJnMbRyQS0oShbb9DS+X7vCq9NjtRB2c+tjNQuetVszqF6kYfH7A3R3dzeIncUStnW48847I3EzMzPDvffey/ve974lj79YjNgx7Oe0F2sgEMytUgzzfw2EMTKC4eFxBDvJdXVTLFYYH8njJAZ9h4i2fSsIlm8FkZIKk9jra8geeK74BP3VQbRIUC1UyZChojWaJBUSiEDAeKLaEAMSt75YoZtGpEiINFGaufLjfqRI+80/RVhheL5CfMoXU4KYyPFv8gnSJEWGjMqS0xlSwiJnSxwbklYJtd7l2K1H8Or1OcaLIzzx2MPkvSrdMkGGLGkpmNUZZqspim6CgmtT9iRlT+Aqv02ES5nUBs3Bhx9GIpmhUJ1F80tmxwVKZoPzlihR82/00i/G2FAEMP7KN1i8rOj1sLBJYCGpksul6U7lqGQUmwYP4xn3SYSzDieXZnbMw9JJLBQSP1tLBwLWo4ZSRfo2pHGcLIVCkfHRIrbMzJmHh2/3CUOvtfIYGFzHls1H4GRz5IszwHaGd49H772GOJ7m82oWOS3cVL5garomc4LKF765h8+3Z4mRaB1Gf4g2eg8tZLma/xh7Zp6syZhFaeGCjmtP8OyL1PN8Ps+TTz4ZPX7mmWfYtm0bfX19HHLIIVx++eV86lOf4lWvelWUer5x40be8Y53LHqeS2VRYsd1XX70ox/x1FNP8e53v5uuri5efPFFuru7yeVyyz1Hw5qjnQ9f+66qPR5NWAwPT0Qp3vE078btBC5lZL/imK3HkMqkmGCMXz75COVRQW0sSUKkkLSXLVBPyY5bgCSeKtK7PhN1Bp8aKyFlGqmtID3amtMWoU6TlSQWuiuwAruIL4VsAbYETY1UNsVB3f0krAov61nHM5k0pdI0Sglma5q0SCC0pKYkbmDRqWnREBzpahcvUUanqqi0BlFF2C7omMUqcq+p6PzndjWPp+I3ZprFsUSCcqFIsVbApcqzw09TKVaZKRYoFyrYwqE6z71PaZf+DVm2bNmK43RTKMywc+cOJsfcmIUmSEdHNgoPIcg5oetTks065HIOw4zPOY9WIq4d6vvtDXeN/xoMDm4gl3PI5wuMDI9HWX2N86nvs+e5LebzaVxUnbIvUs9//vOfN1Q4vvLKKwE/uPiWW27hIx/5CIVCgUsuuYSpqSlOOukkbr/99j3W2FlJOhY7zz77LKeffjq7du2iUqnwW7/1W3R1dfHZz36WSqXCTTfdtBLzNBxwLG+V1gZ31nzm9TkWH/8mJsIsGvzqsfV046AKsNC4WlFTfuRHdzZFd7YfKSX9bhKrsgt3HFKih4r2cIUb7NnCWhHPLgrcNZZI+JYdUkgl6F3vcOSWo+h2+pgpTLB95yPMjIEtUr7bB4WL5QfTChUJgdBFFrrgZBDUK4VNUmTJ6CwZnSItbdJSkrEhY2mytqRSLjFWGKUv7bB7Zhy3VObFQpXZXBKVc6gVKmRnSuREN2XPouhJSq5v2akpqKmgwGLS45WHv4KNg6/g2d3P8OQzT5DQCaQWeMKP33FFJXjNvLqIaS66OOf19a9nWJnZRZEgSWFE8Lh4CjdR4vFnniCl07GYnSQ1ytRE0KcrqL0Txg5lnXU4TjdSSBynm6yTZWJ0Ekuk5nntQuElyBcKFEpFnEyWYrFAPl9qKFAYf71DoTCv22qOxaaF0JmT0t4+e7LwaO0xODjA1q1byGYdSsUSO9jpWzxjrrm581ueHxudsnC7DsPe4C1veUtgCWyNEIJPfvKTfPKTn9yLs1qYjsXOhz70IY4//ngeeugh+vv7o/XvfOc7ec973rOskzMciKxcKfo9mu11s4m8Xiul2SQfuo3C7uJ2EIchhU2pUGAsP06f0890cRavAEmRpaaCHlTBP/BvJKqF+IraH4SuLHxXjVSCnJOlJ9tPggTrshvIOTkKo0WQKbRWKOFhCdvPugpS2+N9nuLusTBWJxmkzSewSUlJSvqxOmlL05tIUcsrHtu5g5d6JLJaIJcvMtud44gtW1mf3cBzU9M88NATVGZcLJKUPUElEDle0A9LYpHUGYozJXbVnqZUrGDrNInga0YSBhsHFiptAW5k3ZlX6GgPgiBrJVRQK8e/0mnRgx5zEToFQuFhobF8oaP9bTzc6DWJKisLi2KhSKEwE1l2ioVigzXDfy9JaBI+QliMjkyzU+/EcTLM5mcDS0isaGLMNdVJbE5LS9AShE675HIO2ayDEIJsNutbqobHaPV5bewub9jXLFfq+YFOx2Lnxz/+MT/96U9JJpMN6w899FBeeOGFZZuYwbAwK/GrMqySG/76d3FFhaqoIpBUtEeCBOVheJjtJLMpyoUKlVGBRYIqLjU8alSpiQpe0OV7ISJXFvUifOV8lWJhmh5nPfnCNJV8lSRpqtrFFipoFRHc/HUt2r8elCwD647vPkqINJa2sbXlu7AEWLKxtsxQah3VUpFUrUBPMklFdvFiMsUGZwM1L0W/swGVeoFpV5EWvkUnTDmvKR2ku9u4RcnU7iLpLEzmJ6jmPSRpwA1ukm50ruH5z3eNNGGrBhVlUNV0EVeX8HPWgrpCOoEkidCBq1ErKtrFw6MqalRFlRrV6PXwxYhgdDTP9p2PRDE7k6NlpKxbdcJ5zflfK6RIMDI8hWYMrfVcd1yHWVOtOrE3jzO/0Gl1rLmfj5bWz0Ag5/OzfpB+1gksVQUW+mGy/EKn/eu1UoJvtbIv3FirkY7FjlKqZcPP559/nq6urmWZlOFAZL4P1PKLlrYCMyNrjl8bRejQVVID5btatPCoiqJvRcFFIklomxRdFIZdpnQVSYqESJGnQklUqIkqVVGipsu4KhA8sQaY8V/5YeVjST0TK0WWwmiBx3Y8Ti73HIV8mcqoRVZ2ISj5LREEeLKGCmJ4vIb2BmDLFGFLhaTwW1ykdJpUkDqflIKEEFhCR1ff1YKUzOBIG6EUrltldqbCk2N5erNZXpieZGrGJVXrpoTFVNUXOlVPU1Z+mwihk1RGBdu8X9A1lKLm1qi4JTxtIXWSsMhg/PzVHu5bvuhUCGrUvCrrBhLknBxuIc/4aJ6cWE9VJ5HByUstqAUiR6MoiaJfQVlXcKmgqNfMkSLB+GiJ8ZFS5PaL1y+qNzH18LSf4h+vrIzQkYCLz3f+c9mTQGjad49CZyGB0Dobcc5nIxDIw8OjwHZyuRz5fJHh4dHgtbKa9l8ukbMHcdO2WDQuLEN7dCx2Tj31VL7whS9w8803A/4vqXw+z8c//nHOPPPMZZ+g4UBm3/j858MPRvW/0P1F4ukannCDSsQuCP+GmsDGEjYaTQWXGi61FhaEqBjdPMRbO0gtkUiych3VMY/pMY0gQ04mUWhsbeMJNwr2RYQByFaDlowymLCiLCRbB38LQVWVcXHJKZu0lULht4pASWrKn09SZOiarvLQ9sfwUrsoF2rYkxaWyFAJMrBqSlPTGldrtPbPMiGyeJQZzB3Cuq5BCkMFHtmxnenxakcezFB0+HEwHkpr1m/oZsuWzThOL7Vilcd2PEZ5LI8U3cjg16lERq0i3DBWJ+rDXhcrUR8xYTfMK/5aNVt0wr9D8Rqf54Ln0pZAWE6h0yGR4BkLXFd1UbrXLTh7uJ7GqjOX5Uo9P9DpWOx87nOf47TTTmPLli2Uy2Xe/e5388QTT7B+/Xq+/vWvr8QcDQckKyd0Flep1QusOy4gcb0ynihHUb+WSODJWkP8C/gxJL49yLfoeLpGRefxdI2aKqK0X8dFhV23xdwYjqiHFdLPltIWKZEmEaz3v8x8QRG6gDyR86szW0Td1kPCbuA2qaiujhVIn6KawN4gcLpSTJQLlKaLWGJdsB+UXBtLBDVydZb0RI2JmockS01lGa35nc1na4qa1nhaU4ulwpd1mZ6uHEP9m/yspmSCVC5JdXwa1RTAq1oIwXgwb9zK4uKSdZKksik0NRLZJJYDs2OTuMK3asngevkCxxecNV3208y1G3U+j6fuz+tC02ERRBWILd96o4KeZP7rObea9fzBxq1ocewO08v3TOusxfi4YdmG6FMjlhoFsgfaEoh7On7zGGtXBO2L1PPVSMdi5+UvfzkPPfQQ3/jGN3j44YfJ5/NcfPHFnHfeeWQyc2tUGAxzf9LPL3TmFg/r9EidmA/qrqxwThrl11EZWkcu10WpWGVidJKElQMJFgncoGFniELhat9lVRc5fsxPJHLmcSsQrQ3+ab9flYXEiir5VvB0FQEkg75bNVJIIaN2FHELkiWC3lEiFcTq2NhYKF0lPaDZuvVI1uf6KZbHefzRnczkK0iRxBJQUUFIrvZdW65KoJWkqiBfg1lX4WkoKo+a9kN+PbzoHMAmny8yWRijy1nHTGGCYqGEFHYgOrx63Ez4MsSbcIbrApef1n7uFWhm87PM5idwnB7KxSlmClO4QJl848uqFR7+dXd1JTpm/bXwhZaMxdmI2OsfEoqjULCG84pbcxosdw3WmPlEzjw3+hYCoLMYnYVYuExDK9dWRAcNSxdD55/1tWKHMCw3i6qzY9s2v//7v7/cczGsUZoFypzmlAt8IS6pdL1ojmfwQMPgYB9btm7ByeYolsr8cudjTI4VAeo9rIJGoP5+KhI6StciS0tYnTcMfPZRQdXg+UWP0lWqVJGkqeoiqQ2KnJNiJl9mdrRESnb51gttB9liNT+IN7jBhpldlvaL8IU3co8aaSdFT7YfLSQ9mX5kJsn0pBsUM/SDliUw65Upey6QwFNpagoKriLvenhoirqK69uborlbaKSwyY8qtu/YTjqXIp8vMjPmIa0EaoH6Qw1CJ3CrROuCrLORkUl2sAMnl6VUqDI+WiBhOdEcZNjyIWaJCV+T8O84no7HUM0VKg2dzwMLT/h6xp/3HywkdJYrPiWkneaYnVtOW8a6LWd7CjoVN+0ee+1adcA/+6W8Smvl6i1K7DzxxBP88Ic/ZGRkBKUaL/M111yzLBMzGEKWtRdPNGirTtN+unDOcchm0oAik0mRySYZ9sbxtNuQMh7tGXN1hC6r8KYY3nTr5+JXOa4H3jbeGEtqiu4NFj05h3JhCu2WOObIE+lx+hmbHWPHzh2URzWOcnCFX6cmTK32hH8zt3QCqWXQFiIZsxYlKOSLjObH6Hf6GS9NMD1TJV3LIQJTttIw5U6g+hSJbJJiYZbC8AwpsY6SdilSRglNRZbx22gGLrjgeFILErKL8liVwlgNVyRIyQwlipFFR2kvcBPNtZK0+ptYYdzhkQkYGQ+qKiepqeKcgoPhtfX/9lgobko0rY9nXDVac9yGeTW7rFZe4LRisa7ghUXSysfFLPXc18rtuT00S3RjrWA5kP2JjsXOP/zDP/C+972P9evXMzQ01FBWXAhhxI5hVaNR5PMFCsV8YNkpks/P+g0idZg27bcNgMabX2QBmGPNIXKZxftXNePpKus2pNi6dSsbnAGmCi/x5PBOMokslpD05frJOinKoy4WfoG+JEmqAmSQpg1+l2/wBUjYFwrAFknKo2W279hONpemWqzCmCRrpyh7/s5VVcZd5/HqzVvpyfYxXhjjF+4j5MeKuEJQCwrzhQJLh2nkwhd9YX0ZW6QQ2CC8BQO049dw4dcltMKIIKhb+A403boQYVxIxQVMnFZFAOOBx3ELT33dvrDk7E3ic1vOuLrlOGcjcgyLp2Ox86lPfYpPf/rTfPSjH12J+RgOSGJNoYDlbPXQMQvGIPhCZPfwMOAXWssXCowMT/rZWKo+77hgaenaiN0s6xv6xfFEEOkSZXxRw8NFaEUq10WXsw4hBH3OBhKWxejMbg5dfxj54iS1YpUu6eDqBBpNQlu4Oo2Hhysaj2drK6pFo/FzzVKim8pojfKIiy0ypGSaqZq/n9KakirjpG2sdDczugYZB5WD8bFxlJTURIWwOB+Ahe27y4J/8ddVNhXka9XyYb7XoU5dIIZuxng9IX/cFhlUbWRLtXquUeDML3LmrYuzEB2KnKXGr81Pu1WH94YoMwJmqaigEe9S9l8LdCx2Jicn+d3f/d2VmIvB0BmL7dy8IAohYPfwCAxrBDZCCLR2I2uOX8Cvca+4W2P+vkZxa5AKgm9jN2ohKBZKTBXGSTmDFIsz5Eeq/GrkacZHxygXyuhxi4SU1LwSAptUUNvZQ2LpurDx5ymi+jP+bHQgMxMIkcADimF2ERqNpoxHNV9id+Elsk4XM8UJJotTFC0PhKCmy42nJNIIYUVuI9+1tXDF4LCCcvw+1xDT1IA/47jgiWdqRdc+hl7Q6rIwzSInGn8xImcZLDgrJ3gWIjzecrk31sjddB9hKii3R8di53d/93f5r//6L9773veuxHwMa5DOujO3M+AibjJNwkmI8CtA1b/6gz9auWVa3gTnZLUEFh8RhqHUY308aljSZnq0xvYd23k+9yxuoQrjOdKWQ3nSwxZduBRhfZ7uTJJioUBxRJCRvSgtqQWSJWyMEOI73mJZRk1nFmZUKaHxhGJ6rMS2R7eRdmzyhRKjo9PYMhMFYQsh5wgaGbizQmuS0lU8XNBgySQWdtQuIpxa2PDT/zvMjJrf6jCf4GnVTLQ54Hk+5u67BGvOfu2eaofm295auQ2ubkwF5fboWOwcfvjhXH311fzsZz/jqKOOIpFINDz/wQ9+cNkm18xnPvMZPvaxj/GhD32IL3zhCwCUy2X++I//mG984xtUKhVOO+00/u7v/o7BwcFov127dvG+972PH/7wh+RyOS644AKuu+46bHtR8dmGRdH8azG8McTcQcshehZ7w5l3PxUJoXljT9qojSLCG6ao1/NRygXpZwnVRBmhJWmrj8I4eGM1smTJWVlsJEkJmgqpDYotW4+kN9vPZHGch7dvR467SFLUlC9fSqqEoorAxhLJSPyE1pvQ3VWjGtUJCmNwaqKClorZsSLuaAUt/OymqvItOlr7faXCWj7gxwbFhU5VzZDbIMnkkuTzZWZHK9hWCosESii/kB/gttkdvuUlj1Wjbpm63ub7IG7JaRxrPqHTFIcVzaepbs0ysnesO0bYGA5sOr7b33zzzeRyOe666y7uuuuuhueEECsmdu6//37+/u//nqOPPrph/RVXXMH3vvc9/vVf/5Wenh4uu+wyzj77bH7yk58A4Hkev/3bv83Q0BA//elPeemllzj//PNJJBJce+21KzJXQyfMjd9p6OGzEHvrl3S7N85F3DD8gGavoSeXEEkSZLFJ4On6qFXtknFS9Dl+x/W+bD/ZXJrimIstUkghmPEmSG5QOE6SQr5EYbRAWvZEQqehIWZM6LjCDQJ+g+rPAkIrS9gyYT6ijKwgbT63QXDk1q10OX1MFcZ4ZMd2ZsddpJDRthC2i5i/N1Zb16+Fm6pdoePTykq3kEWn1RwUg4MD5Jwc+UKe4eGRDuKTDIal0SjXF7f/WqBjsfPMM8+sxDwWJJ/Pc9555/EP//APfOpTn4rWT09P85WvfIV//ud/5jd/8zcB+OpXv8rmzZv52c9+xhve8Ab+67/+i507d/Lf//3fDA4Ocuyxx/KXf/mXfPSjH+UTn/jEnIamhpWmVTzA/FYeoq0X92t578Y7tPraCK1CoYALnUcSpWtIEri6gtC+S6ciSri4URq30pBQAiUEUtiUC3kmC+OkE2lGZoYpzMySsfqwEFS9MtkBzas3b6Xb6WMyEBrVsQpa2CgUrvCoiSph1Wcl/AJ8rvb7Rnn4afNhfZp4FeEo5T6IN/dbcdpY2H5VaSxcquRyGfqc9QghSCeyJFIaT1WxZAqlFVZQGDEKMA46t/v1h9r/6q1nXDUHwM/3WuxhvD0ee65VR6MZHBxg65YtURNNwO8ttQwWHtMewbAnTAXl9lj0z49qtcpjjz2G67p73niJXHrppfz2b/82p5xySsP6Bx54gFqt1rD+iCOO4JBDDuGee+4B4J577uGoo45qcGuddtppzMzMsGPHjpbHq1QqzMzMNCyGvcH8v1E6+dLXsX8rg5pnmW/bFoRBysENW+mgpQGu392bKjU/kgY3aMuQECm8McnPfvFT7t/1E16Y/RXKqlHVRSwhAA8nl6Yv148tLHqcfjK5VOSiUkLXa/IEx/Hw43C8oF+7qypBNegKXtBiIawb5McX1eNkwr5cYeFCiSRJkkqhykxhgvHp3YwUnyLdI+laL6ipfLBVLFanZZB5e19LWrsMDPSw6bCNbBjoiWrh7G1yTo5s1kEIQTbrkHNyyzJuey0TTEq3wdAOHYudYrHIxRdfTDabZevWrezatQuAD3zgA3zmM59Z9gl+4xvf4MEHH+S6666b89zu3btJJpP09vY2rB8cHGT37t3RNnGhEz4fPteK6667jp6enmg5+OCDl+FMDI0slEPQ+ktct/mvfeYTLXtaOiEMwG369STqmVmh9cTTtaCXkxt0T69RxaWm/T5UNaWxRIZ0Msurh7by+lecxGu2vh6rX1FWZaSwKebLTOTHcbXHVGGcQr4cxbbEg5RlIFQk9QDhsPt62H/LXx8PIvYtO0JYgVXHr9Ac9vOykAgE+eEy27bfz1Ojj7Cudx1bDzmeo448hp71iUiQ+IKn3gR17uvSmrCOkVIuGwbWsWXrFjZv3srWrVsYGFwXjL84ATBf/aO5k2gcP1/IUywW0FpTLBbIF/Lz7Njm8Au+j1u9Dzt9f64Vx8XaYLHfYov9RlutdCx2Pvaxj/HQQw/xox/9iHQ6Ha0/5ZRT+OY3v7msk3vuuef40Ic+xNe+9rWGY600H/vYx5ieno6W5557bq8de+2hmV/4LPYjuTc+4pqF5w4NQkfIQODIOUXw6pYd351U1UWqokpFlKmIKlU8KtqjohRlr0Y669CT3UBNaLKZLhJOiqqqIUlRGRXs2LmDB3fey/YdOyiMaqRIosTcOcqgZYUMxEuDyBFNzUqF9GNuRAJLJLBJBT24fKGTwKaqp0lsKDL0yl48z8Ura3rZiGN10+v04zgZP6Oq46+dVq+ZJpfLks1mEAKy2Qy5nNNQ2HF+t5RsWhrpZH4CwfDwCDt27mTnozvZsXNnELPTnmugfbHeyXu2k/e9seqsdrRe+rIW6Dhm57bbbuOb3/wmb3jDGxqqJ2/dupWnnnpqWSf3wAMPMDIywmte85poned53H333fzt3/4t3//+96lWq0xNTTVYd4aHhxkaGgJgaGiI++67r2Hc4aBoXLhNM6lUilQqtaznYmiHVrEXcebG9sx9biXp5FuhlUVnrtBpRRQ7Q9IPHg4CZL3A+jI+O8oYLzLYP8Dw2AiTM+OkGEBrTVb2kR8tUhqtAhlS0ppzA9XaxdVVtPD7aBFkXEl8UWORwNO1qCVGWHQsFEMCGQik+j+lqzgbNFu2bKU/18/I9Av88oVHmC5M0OtsCBqCVqJMrPpYVocByuG2mnzet6iEsTL52XYsKvO5zZby/vEFT/zxnrKz2rdArpXf3QbDytKx2BkdHWVgYGDO+kKh0CB+loOTTz6ZRx55pGHdH/7hH3LEEUfw0Y9+lIMPPphEIsGdd97JOeecA8Bjjz3Grl27OPHEEwE48cQT+fSnP83IyEg07zvuuIPu7m62bNmyrPM1LAfNN4FW76mVvgF0ImrmdnSPbm6ibjkQWBDGuUTxKsGNPxIVvivLIoFHjYoooVCR9URpBdqjVq0yOjGB62kmpycp1moopZC4KEALG6I6zV5sZpKyKpDbIMk6OQqFErOjLgnLIUHKP5ZI+BE9ooIbFCkMu4ZbIoEUNha+RSehUyR0EguJQpF1UvTn+rGkxcbeg3n06R1s3/4wua5upvMFimOQsNJIUWqvRcQethnePQZ6B04uQz5fYHh4fI5FKv66xK/13GMRPE9kfdIC0CoQZC2yvhreJ/N/9y1/Z++Fxmv3O3iN/JxfAygEagnB8EvZdzXRsdg5/vjj+d73vscHPvABgEjg/OM//mMkMJaLrq4ujjzyyIZ1juPQ398frb/44ou58sor6evro7u7mw984AOceOKJvOENbwDg1FNPZcuWLfzBH/wB119/Pbt37+bP//zPufTSS431ZlXQ3g2l/TFWkuYbacyS0yB0YgKoBRo/QyqMa6lRRWMjtURRpm/deoayr8Qt1xjK9jHVW2JqqoItLD/jilhxwZj7ytUVujdYbNniZ2vNFCbYvnM75TGQMhkEL6f8hqKBkU3hNxvVqJjQSfjNRoN/NhaKJJVimanCOH259UwVJki6GUrjNoVxUDikpaamq/X6PGHMTgsBsmCbh1AMCMHu4TEYDh9a0fOtXFHx48SvvcaLFReUkeBpffCVEtrtjNvO+7id6sdG6BxImHYR7dGx2Ln22ms544wz2LlzJ67r8sUvfpGdO3fy05/+dE7dnb3B3/zN3yCl5JxzzmkoKhhiWRbf/e53ed/73seJJ56I4zhccMEFfPKTn9zrczUslVafynYE0N745dIUnxMInbr1xo46pjdnIklh09xJHQKLivB8ESKCG7jWzOaL5Gt5epx+pgvjTOeLVEnhUYsEjmq6eUokaI+ck2Gdsx5bWPQ7G+hxcrijNWyVwRUeFjYeLlVRhaA7e9hNXeC7uCxRD0wOO6qnRIraaIVHd+4MmoxW8MYsuq0eFCCUi9J+qnvoOqtRxhI2ng6uRXC8OYX9WhFmhS3wijQIy6Zr37Bd1I7CirLjIvETNDZtj5USQou5G62RO5jB0CZC687Dk5566ik+85nP8NBDD5HP53nNa17DRz/6UY466qiVmOM+Z2Zmhp6eHvwvzrVh8lvd7M3XqFnkgCABQgbxKTII7k3U07VFo2VHRustv50CYAu/4rBAkhApBBZJ7bddqKgZetcnyTppioUys6MeSdmFEv7NNhQ6Eul3PkeS0H4mVHp9hSO3Hsk6Zz0zhXF27tiJN5ZFiCQ1XaZECSUEntRUKKOEwqWC0h5S+HV1BBYZncXWNmmdJkOShJCkpYWigsYlIRJ+qrwGpTVF5VHWLlVcpuUULi4FJqmoPJ6uUFMllHKDNHc/Y6tVh3F/fesif3ErmoyLzEDkNDQPjWXDhceKApsDweP3Q1PUm7qqhvo6dZZb5BihsvrRgGJ6epru7u4VOUJ4X7rq5ZeQkouvF1dRVT7z/M0rOtf9gUX1S3jlK1/JP/zDPyz3XAyGVcR8GTsyclmFQse32tTjc+JWnHj2Uz3dO4jhwcOKHccXMS4JmSM/7jIzVkQKG9tK4+LOsYT4Tpn6/rZIURyt8NiOR8nkUlQKVWpjFmmRJq8mSG2ATM6mkC8xNVZFWn4fK1+s1ecltYxaREhEUJPHwxYpMjKDJQS2CCWAX0zRQpBA4rcJtYNYpETQFDQBlCLrTqteVyGdNfaUc4ROeP0jQl0ci42PV4sOXXjzs5xCx4gcQ+eYmJ326Fjs/Md//AeWZXHaaac1rP/+97+PUoozzjhj2SZnMLTPfB/YxdXNbLv6beQWkVEQcmjRsWTKd1EJiS38+DApElGsin+c5gakcs56DxeB8qsWR+niEoSNi8INLDChEAhv5ra2QQT/BzPMiByVsTLV0SpdVg7HzlBVJboHJUds3kLG6WWqMMZDOx5mekJjiSRo8ETNF2m6noEFUFbT9GywyOXSeKUChbEy/Yk+EjK8fn4VaEsIqkqSUBauylETvmtOC0VNlHFVBUUNLfw0/Oa4mbkiJy4yYtewSWhKGRQ8FAmsSHDG4nV0mOnmt8vQ1C09oZ1MBy7AuVlbrYSOESyGvctS08fXSup5x3eCq666Cs9r1Y9Gc9VVVy3LpAyG5aH121u08W+PRHVz/OM0ZlvZdWuC8Dt9C2FFNWqksKMlvl3YTVw2BM/Wb74eNTxR811Lwu9n5YlavRIyQQXmoBpydLsWComkpKaw15foPzSFMyioUcIWAo3n99zK9ZMQFr3OepxcBqmIhE0odPyzDbPHamQ3wNatWzhuywkcuXUriQ0KV1ewBCRk8yJISkmKBAltkyDpX4+oWGHdxTc/7dWbiWJ0IjeibHgNrOi1CF+b8HVqdx7NLFRvyWAw7Gs6tuw88cQTLVO2jzjiCJ588sllmZTB0D5zU7/rz7QQLQvexDq3AoVWlFDgxF0nUtjRDTW07Fgi0TJTaO5M6tWFI/dRk+gAUKI+c6/ZjSWCasNAVZfIbIDNWzcz4AwwURhm587tzI5PYYsU5UKeifw4jrOO6cIYpXwFhJ9SroIk9vBYrnZJoPB0hXTOodvpRwrJumw/GScFUzWSMo0UEBp4LCFIKEHC09SUja0FNZ2lRhUEuLKCUGW/GWpgpZIEFZMFhElldTkxv+CJByNHYhMZxUOFAshTVdAaIQRSJPyO7liRFc13p4VWttbp5wbDvmapJVLXSiWnjsVOT08PTz/9NIceemjD+ieffBLHcZZrXgZDh7QQOQ3CpsldNF9xvw5+0cdFSzzbypLJ6CZriZQf2BvccMNKxXFXlgpcKfHYkEjgBNtJZJTuHT5WKNBEgclhfEndGuShUEjApUYml6TH6aNCge6hDN0zSSa9adxJB3dEsEPvwHYSFPIlpsdctJWIsrFU/CtVJHBxEUJSzJeZLozT72xgsjRFpVChV3aR8vt6krI0ZVVGKZeElSAh09S0RUVJal4ajxwSSVUUEbI+f60lXpAVhXYJexUKHbq0QpfS/C6suHVHhha14LGriqzf4JB10uQLecZGZpAiGRw/iB1qVfCwZXaWsegY9h0m9bw9OhY7Z511Fpdffjnf+c53eOUrXwn4QueP//iPefvb377sEzQYOmGu0GldW6Vxm/j+HYideN2WFqnN4Vh+F/FKzI3VWmjNFwwb72MV72VlhZJHK1wBBPVl4mP4YkeCsCjky4znd7NhqJtsV5r1vRsZyqZ48vGnYczBHdXMjJbQpEjKNGWKMctOY/yMRiGlTX60xiM7dtCVyyDKNRi3SKX81i5SwERtArdPYadTFAt5amNFLLEOWxA47Oxg8dPRfVHid1vXURySjPSEX0PHigmezlHapW9Dli1btuA4XcwWptihH2FstLjnnVeszo7BYFhJOhY7119/PaeffjpHHHEEL3/5ywF4/vnnedOb3sRf//VfL/sEDYb5Cd1Uc2vc+I/rqcj+40ZxUmfhAnQLz6BeGC/M+PFv2H6MiNZV+jZ0053rolgoMzlaIGUtnN4Zt/xY2JFFJ0GyocM4EHUxt3CpIVFCBa0mfJFCYJURUjI5VuEXO7YxONvFht4hUm43PT0DWM4upkeLWCKDKyw83KAvVwmNCpqT1hpcawj/3B0rR3HMojrmst7OkbMyUWJTVZVQfYojt26h31nP8Ow4D2/fiTdaIWOlKHkWNZ1EakFNVqmJip/MFgg2V1R865R2USoIINZhvJC7KOGh8NDaxXG6cJwuEOA43WScNHp0NtpOUw/4Nhj2Z5YaLbZGDDuLc2P99Kc/5Y477uChhx4ik8lw9NFH8+Y3v3kl5mcwzEMLodPwrLUHkdNa+LTTu6p5n/BxQzAsfvr0+g3dbN26lS6nj1Jhlp07H2V2vIYM4kcWPsO6y0oiGwr5RSnlAj/GRIMnXL8wn5AN8TuhJSZh5SiNlxn2ZunfcjDdvf1MFcaYzZdQpFDC77buEQQ/61oUGK10PbXdEokoQ0wJhSUyJEkgSeFpjQp8TjXtknKSDOb6QUgGcv2knSRTo3nQNRASS1tYKOxYOrotU/7xhOe7lIKXS2uJoqmdA8QKDLYpUIVFoVCiUJgh4+Qo5Kcp5It1N6Cx3hhWEb4bawmp52tE7Syqzo4QglNPPZVTTz11uedjMCyeFl3F40X8WvVHElhBxdz48/4z8/dZWihlvLENgeOkcLJdIBQZxyHjJBkfnfLT0rFQLa0HHugwuDfMhvIXW1sIBDYSV1fQuupbXGQghAJx4h9fRaKnKqpI7WJbNjPTVR594jF2ObsoFsrMjLlYlkBTjvpxhd3XNX6/rnjtGU8kIgtMKbBiKa1IKxutJUkpSEiBR4JSfpaXZiboz/Uzmh9nfHYCrydJtiuJnslTHdYkZTdpnSWhk8ggTT9MSdfBXLywK7yq+FaroAAhTS626NqHxf+Ehxd8mbvKj4ESSEZGpvD0IzhOitl8gdGRKaRMRAKv3jVdzbHydN7rymAw7GvaFjv33HMP4+Pj/K//9b+idbfeeisf//jHKRQKvOMd7+BLX/qS6Tdl2As0W3Hq1Yv9FXWLTpQh1UKMCCFRusbA0DpyjkM+P4vnevSu66OQLzI6Mo0lM23Pak6hQCSz+QKzhUlyTg/FQp6Z/GwQUFyLRMmccYLAWL8+Td1tZWu/M5VEUlXTZDZATy5JIV9iZtTDtlK4wg3O0Y0aeAKRqLJEAmlJRidKjI4XEcJG2pIy0yjtRcLCXyoo7de9ifebEqISPZbSAulnaGVUCk9LEp4kIQUJmcYbK/LQ9p0knST5QoGa6/Gao46lJ9vPS7NjbNOPUBt1yZDCCxqMetqvwVOjikJRkxV/XtSoiqIvekQFT8kg26wCxOvyhFWQPYSWvuUrfEbXfBErJGOjs4yMTARuOSsQVHWXGaGVJzZunXr3dYNhX2Lq7LRH22Lnk5/8JG95y1sisfPII49w8cUXc+GFF7J582b+6q/+io0bN/KJT3xipeZqMOyB1taWVoRVegcG17F1y1acrIOQLlOT02Szvbg1l53sZGy0sKCFpxWhwBBIxkZn2LFjO07OoVQoMz6ax5IZP+akeU7B/JWoByDXg4Pr5+LqCpkNsGXrFnJOH1OFMR7ZsZ3p8SoIGcS5eJELKhxbYgU1cBK++UqIICanFsX5uNpvDaECweO3T1DR/2GVY6VrCGRU08fDpkINSFBTiqonUFqQs/twJ8vUJmoIlaDv4A30ZPtBStY5fqp6ddTFJo1EILTAwwpyovwYJKl916BHAiU9lLbQyg9gVrig6xa5yKLT0N/Kt7J52hekkRsMvx9WKOjC/UIrTnjOPq3aRBgM+x6Tet4ebYudbdu28Zd/+ZfR42984xuccMIJUduIgw8+mI9//ONG7Bj2Ke3G3IB/c8w5DjmnC4Rg/YZ1aAWViiaX6yaXcxgfK8xp0Dkfak6shwIEIyPTiNFpwv5XSrXO+gmf9+Nu/Js5Glx8YaSERmlNjSrduRTdTj8I6Hb6SOWSuOOzSJKRQPKo4epKMDf/Bi6D8etz9gIx48VcVsq3cEQCIC7MZGARcyMx6Unf4iKlJKGTuMqhUkuREJKSJ/30e1J42mZ6Js9wfoyebD8ThTGqxSpp6fjZWDE04GqF1poKLjU8qqJGXmTwZI2SmKEqini6QhWCOQdurcAt6WfB+aJHUZ9vXATHxVHYEysco96jy2sKhjZWHYNhtdG22JmcnGRwcDB6fNdddzW0hnjd617Hc889t7yzMxgWST2mZqFMK0G+UKBQyOM4OcZHx5iZnsZx+ikUZinki0jseQNfF+qZ1JDJI8KMCcVCGUQisOX4wbfKzxwKgoCVrieBW9iU8hWmC+N0OeuYKUxQypej/lt+JpbnLzExA0QCJn4OKnajV9r9/9s78/io6nP/f75nJjOTzGQhhCQghEBFkFUBgaCIVjQqXkC4tbWooFQrDUWk5SKvqoi+FNDeWtsilvurwsUFSi+gchXkBmXRsMhqAMMiiyhJ2JIwCZntPL8/zpwz50xmkiH7ZJ43r/MiOev3mcmc85nn+yy6B7//Ya96dXS1bWQAgkzwChcEKQLTJSnxPkrWmAk+kiDBDJMQkISAgBVXSitRSAdhTbCgqtIF9zkBh2SDUAKllCo5QkAmggwJRAQTSXCRD4IEPMIGH5nhgQs+f4q69n7qtIcqYhTRA238aoxW8PuknwKT9SLHv075nRA734OZaIGnsSIjYrGTkZGBEydOoEuXLnC73dizZw/mzZunbb98+TLi4urOMGGYxoZA/igeWUl7JiiZOqQGGysfaOGf4lEfikJIKC25hEIUItHuwGWnEz6vB+3apaHSWYXz5y7DJFnDiho5xENTuZaxk3akKB4TxXNCsqxU9RUyTCIOJHxw+T+uZphQds6D/Qe/gc0Rh0pnNS6dd0E2meETLnioWhd749LETPD49GNU1+m9I3pxphxv8v+s5IIRvPDJ6tSbB5VCQhxs/nibBH+Hdps/bV6CgIAkknCl1IUq8sIs7JrQsUpKm444Sam2rH9vq30SPLIJ1bIZkizggRdeyauFbnmFFTI8kIUH8GfBaYUVSTYIX0LN6UP9+2W0N9B1veb0VYw8IZhWD09jRUbEYufee+/FM888g4ULF2Lt2rVISEjAiBEjtO0HDhzQigwyTNOia1GtrZKNQcr+NgMg1ZMjB77B6DpdC5hwrqQC50WFVhSwrOys/8FsCTE1pR5qzKIKfsCS4aGJINETfM7AuNWUdJJkSEIGJMBNVTBB6eFEkGESZsSJBFRccOPi+UpAkgCTBW5RqQUZK7E3geyl2sek22bwaujGSf5pNf/rpn9dZPIoGVoA3HDCY6pGtUiAScShmuwQkGAhC0wwB4KshdIkVQYQB8AiCZiEgM0ErfpytVwNL3kgRBzssKHKKwFeG1zkg4c8kCXlNfeIKv/b6g6IGZ2t2ttOoac4je9lQOAo28i4Xn0BGKaVQA2soMyenSBeeukljB8/HiNHjoTD4cCyZctgsVi07W+//TanojOtAL93R/NCyAbBo37zV708hqrHQkDJ7RIBL1AYgj02ZBAMcpCgkMMep45ZHYOSLaS2TQgECsvCBxPi1JrJihoQgPoR9gq3sh98Wpq2ml0UekzQxmUMxA0tdAz/614b1T4ZirCSRJzyv6RMF7n8laUJMuLIAp+QALJCQEYcKc0bVPebJACTf7nkvQhKlRGXYIHb6YTnXBWsoh1MQsBEwp+Gb4Zbi3MK4bEJFqS1FgmsaS8LHYZpO0QsdtLS0rBlyxaUl5fD4XDAZDJ+S1q1ahUcDkejD5BhahKmgJbm3VEED/yNHbV+R6gZy6MXQ/pv/pFWUA7tLQkhIoL2q2GRem0hQVBAkMmSFTIp3hySZLiF4uVRm1rKusBit6zUxfHIVVoKtX5aSrNX/3oZxhTOqxFstE9L51Zid7yKAPKP2UcuuGWnoTeYWVhhFlaYRBwSKBFmUoojChIwk1CahgIwS4CMK5Da+9C3Tx+kOdrjx4oL+ObgIUjnXbBKyhcsq2yBR1gQJ6xK5poww+vPFAvUxak9RspgUg1buf8VEx1wBeXIqFcF5VCkpqY2eDAMc/X4vTUgpd6OTvAo6xXBoz3AdVNYgOpUCKQoA4FCg3UROg4mSFiEExQ1zqWMV5BekJghy15/g0wfBJm0LCs1e0mtn6NmT5E/wDa4VkxwcHRtUzfK9nAPf0nbrlSt1sVJwQtAgk+GEmAtvP4Ky2ZAAmThQxzZ4IFSNNBH8fDBGBEloLTAsNqtSE9MheSvvBxvt+DKeQ9MwqIEPENAIsWrIwlTjcBrgzct4tt5uPc8Vh4HTDTCjUAjo14VlBmmZQjl0QnhgTEInqBHlc7bo3kjanh7vKHPayBoGiuCINe6UeNiAh4KQAbkQF0gte+WFtvj9+zoi//5ZJeWOq2OJ6TYqjEVV5d3Q7/OLzDVVG0h+4v4mSCEDBLKa0iSDJ+Q4CMvTMIM2R9YbBZWxAkLBAlIMlDllWCVALMkIElxqHI6cbbiItIc7VF8+QIqnW4IcsAjE3yk9HP3Cq+/lYWacRY0XkNg8dWGYcbIE4BhYgQWO0wLEixegh8wdfV7MQqSgLcBqFEXRV9bBaqgUAVAIMsocOXwYidkdlYogWNYr7ctfICyXjyAlDYMJPxTWsIMAbfWdBSAIU5IXxcnIHI8hnEEbAj1ML8aQaAXkpI2tUXwal4odSzK2L3wSWal7o8J8KAaccKq5HURlFYTkGD2CSQIG7znq3Cg8BCsdgucTje85yTYhBU+8sFDMrzCL3D8i6x7HWCoeqy3iwUM0/bg1PPIYLHDNDO1CZhIm9mFqJ1T17G64FoFGQFPjm6qS+hieeoi7LSQcXvtQke/TtL2FVrlX/96kv3p3v56MaqYCIoV0k9ZaWOqdXqqtnHVF1l3FX/FZXj8mlOCzy/KPJILblj8KerKWC2ygOQTiBepkC9Ww3XBA7PsgEWyotpHcJEMD8nwCS+88Gp9u5Ssq+CAZK6Lw7R9jCkQ9Ts+FmCxwzQD9enIW0urh3qdz08Yj08gkDiywGTDOUKcv3aBE0pwCMN+ZPCW+IOBawg243UNU1URC63GILSXR1mnBIcLKNNqqvdJEnFKuwnhgUuqBmQgTjaj2m2BTVIKEZqFBYAFHlmGl7xwkw9OuOAWHlyWKuAiJ9xU5U+z9yoeLfL6hY/e7hj56sowTFhY7DAtxNWKihAiJ1zGVIj4GP3Dz3CekB6fcGOsRSyE9Z5EmtUTXARIF3itBVcrRfPU6Tcl9ih0oHHziJxQBMYN6DxU5AFEHAQCFYp9/oaeEkxwiWrIsMBMEiADJuFPSwfg8dvjghcu4dJidXzwGJt3BqXUM0wswAHKkcFih2li9AIltDiJyFNjEDb62BrjdE6NS6ptA/wryf+vxjVDek2ubipLPX/o4yO9o+hFj+od8afN62v2+GvxRH79qx1HMFfjTQsRxOwXPDIAyC7IIlBPyCc8gKR0eHeRHRay+OvoKO+tGp/jFm5Ui0r44MUVuRweWfHqGAOyPbrXRG6AvQwTHXDqeWSw2GGakNBC5+rFTeB4rdFn0HYtoJhkQPN4+Kep6uPpqQeN600JJXoA7XWk4Bihpk6bDvY8RUKQd0oneARJ8PpT/L1CaW1hEnGoFk7ESTZ/l3Z/oUX/VJ6+lpDL51RaYcheyLIbWm0dzavF3h2GYQKw2GGalYinojRqenEgpPDNOQUAkv1TPECw4NFfP6SHp9UR3Boj0od46/++plSI9nd0J7MWm6QGYUv+91ttiOojpYs7kVJZWi2aGPBwscBhYg+exooMFjtMM6B6ZXQP7TCem2BCCRwl7Vr/M/zTGD4ImBVtQ2p6uf8bv1ZMUNadux5CJ4ynqGlpS3cjNVNMhizDX3ww0NZCEmbtPQ2eotR3ZZfJpWWekb9KdE2vTlt63RgmNJx6HhksdpgmohYhESb+puYZjFNWgRYOkuGBSORBRno7JDjiUemsREnJOQhh1gJWtSKCDSXCFhJMZCgeHCVdXpMnFOgTFr6WkKyL0Qm0hWChw8QinHoeGSx2mBakDm8OEDRlpfwviTid8CF0yGiPPr37ICHBjsrKCgCFKCm5pBwnJH8nb7XZp3pYhB/xUAJHXVfrOQIVnGOTIG+ezhuniVd9U1b/e6T2KjMSEDuBzvKewLZm97QxDBNtsNhhmpc6vCPhhI6+arD6s/A/HBPtDtgT7BBCgsORDIcjEedKL0Ntt0Bazymf4dy1PiQj8eIEnUNA1FK0L5bQCR29N0733upjrmp0YQ/RYqO2dhehW0IIsHeHiQVkNDBmp9FG0rphvzzTRITrmB2qjL/+KJ9h37qmn0j2QggfbAkmOBKtcFaWo7LyCsJPo0X4Jx+JtyCiFgyxRoj4LFXoCEkTsJpg1YnYcNRWnTrUtQMI1K+gJcNED9QISyzAnh2m2ajZmRwI7qat7OczenhCoEyB+JCR0R7tUzNQdrEcwgR8f/okSksuQJKs8Mm+wL5QM7RCPDjDDjhWvvM0FjU9OsLfnV3oA4+F8bajTGUFflZ+CNO8VCVshl2oQpCGwktXZRHDMG0DFjtMM+DPjNJjEDxh9qkLIWB3JCAjozM8Hg/c3mr4vIAQtQulliEWp1WMMTohywgov/gzQmrrGxaCELFT+uKRgWvy9BbTdiFq2FQUZ2MxTIPR14gJaiOgenhUhPGhpJWwI7V2DqCP6SChpJo7nU44KytgtyfA5fahqroaauPMwLnU43QdyRvJaxN59/C2fkcJ9uoEhI4ybWX2x1mZ/AHJag6JOsUVJHiE2hYjkiB25T+9J0jz5WhFJvVxPVqUegPsZZjWAVEDKyjHyMeAxQ7TxNQUPMraoArGYYQPISB4hCaYfCCSAAiUll4CoRB2ezyclZUoLbmkm66SA2nLQbFAdY86Ru4AjUKoOB1FkOjjcyQR598iIz0jBQ57Ai5XOnGutFzp4i785yI1Ld1YZyc4eD0YAUknbnzqSuU/LTVdL6rZy8MwsQKLHaaZCRWjo/P2ACGmuKAFKwuhfpPxx2sICedKy1FKF6F6CRQxJAdSlfXZPmEbdjambUw4iBSh06d3b9jtDlRWOlGIQpQWl4WNJQ5fWLLmudWeYcL/1uqraGsNVWO+LADTluA6O5HBYodpBvSiQu/l0SMZvT1aY05V2Kjf+JUHnqxNgegfej5tekQvdPSF55TRRCpyGuM2EAueg8jjrYQAHPYE2O0OCCHBbnfAYbejFKpHzh+srE5fAjCWHzD715lqCB69F4+0NhRe/0bZ3zVe9fIEB8jHwvvEtEVkUgpsNOT4WIDFDtNKMHp8NG+P5uVRpidUr4+awVNzvlkOkdFTm9CJle81rQUBZ2UVKiudmmfHWVmJUG4dvZBVW4SoXh5JMtfYR4njkrVpMHU7Gf62+P1mmFiExQ7TzASLjeCHnP5hpOuYDWjfzKFNUegq8YYsNhfcSiDUNRqbtv41KbgxqX5L6MaqmrfN/7qrcVYOu12Ls1I8eIG4KiEC1ZXV9iACJpgkCwClirYU5NmRSQYJxbsnSDneR7K/1KPkH5kJJDy6GB6GiW4aWisnVj4Frbqo4Pz583HTTTchMTER6enpGDduHIqKigz7VFdXIy8vD+3bt4fD4cCECRNQUlJi2Of06dMYPXo0EhISkJ6ejlmzZsHr9TanKUxYaitxpT4klX8gVbh4/FMVHuV38ga2kce/3QP410cmdBqjNFes3DbCZ6DpX2vydys3/q7UUDpXUoET3/2A0uKyQCCybgoSAAwVs2GCJJkhiTiYJSvMkhUmYfUHQQsImJT1wuoXQmZAEDIz2qP7T7oiI7M9DOnoQvILM/UWyMUHmehE7XrekCUWaNViZ/PmzcjLy8P27duxceNGeDwe3HXXXaisrNT2efrpp/Hxxx9j1apV2Lx5M3788UeMHz9e2+7z+TB69Gi43W589dVXWLZsGZYuXYrnn3++JUxiIiL0VJP2sNLSlmXtAaouwanlxpTjYKETayKlmSDda61VwZa1oHHZ39eKQIAgLaDcUHogKGNObQ+ienOUopJupHVIQNfsDkjrkABZdkMIk38fQnp6O/Tu2xe9r++DPn36IiOjPb/TTJuDGuFfLCCIoifL/ty5c0hPT8fmzZtx6623ory8HB06dMD777+Pf//3fwcAfPvtt7j++utRUFCAYcOG4dNPP8V9992HH3/8ERkZGQCAt956C7Nnz8a5c+dgsVjqvG5FRQWSk5OhaEP+Btj8BL/m+uJ0xm11x+REzZ97lGBsC6GsMTb/VFtF1JlJBWOsjbqfJKx+r44ZZmFVBA1MSO1gRe/efZBoT8HlyjIcPHgQF89fAYHg9jmR3S0Dva7vBRBBJi8OHTqAY8dPASGnN2Xw3wbTeBAAGeXl5UhKSmqSK6jPpfsSn0CcqPs5Fg4PubHu8pImHWtroFV7doIpLy8HAKSmpgIAdu/eDY/Hg1GjRmn79OrVC1lZWSgoKAAAFBQUoF+/fprQAYDc3FxUVFTg4MGDIa/jcrlQUVFhWJiWJLyAqf0bil7osAen6QnlgfPH4vg9PKTz8BgWXUxPJBDJsNsTkGhPgSQkJNpT4LDHa9c0CTMqnUogNJGMqqpKOJ2VdZyVYaIPnsaKjKgJUJZlGTNmzMDNN9+Mvn37AgCKi4thsViQkpJi2DcjIwPFxcXaPnqho25Xt4Vi/vz5mDdvXiNbwDQM9RMZLnW9ruOYpiHU+6KrlE1erfYNSP1uFbqdh74nmlo1W4hAEUmCHLiMACorq3C5sgx2exIqKyvgrKxUUnBJBvkLTvroIOz2eFx2XkZJyQV/4HNwfzTO0GKiF66zExlRI3by8vJQWFiIbdu2Nfm15syZg5kzZ2q/V1RUoEuXLk1+XSYS6srmYnHTMgRXygaCs+kCbR3C31416UR6waPfLvvXxeF8qRMH6SAc9ng4K6/gXGkFTJINMikB7EKYca60AiV0HkRBJ2IYJqaIimmsadOmYd26dfj888/RuXNnbX1mZibcbjfKysoM+5eUlCAzM1PbJzg7S/1d3ScYq9WKpKQkw8K0VmIxG6q1EkFguX4JSSDwXDlWH9jshY888JEXSvFBM86fc+LkyRKcP+eEJFkgq9tJPy0mAjpHDYTmjvZMG4GIGrxcDS+88AKEEIalV69eTWRd49GqxQ4RYdq0aVizZg02bdqEbt26GbYPGjQIcXFxyM/P19YVFRXh9OnTyMnJAQDk5OTgm2++QWlpqbbPxo0bkZSUhN69ezePIQwTM4QrH+CrmQNCvhpLQAjJhtICMnn8ixc+csEjV8EjX9Gd1wePXAUvueAjF3yyG7LshUwu//Fef1kCX1CmHldQZqIbuRGWq6VPnz44e/astjTHjEtDadXTWHl5eXj//ffx4YcfIjExUYuxSU5ORnx8PJKTkzFlyhTMnDkTqampSEpKwm9/+1vk5ORg2LBhAIC77roLvXv3xsMPP4xXX30VxcXFePbZZ5GXlwer1dqS5jFMGyY4lgcILhgZ+ihjYULFwyNBQFZ6oumztgTg08XfaIHOFPAM1eRqW4YwDBOM2WwOOzPSWmnVYmfx4sUAgNtuu82w/p133sHkyZMBAK+//jokScKECRPgcrmQm5uLN998U9vXZDJh3bp1mDp1KnJycmC32zFp0iS8+OKLzWUGw8QwelERTvjo0cX56KaeZHghIMEnKwHQsvAYu6AjUIGZ1EKTQOD/sOnmwWNkmOiCqGG1cupTfebo0aPo1KkTbDYbcnJyMH/+fGRlZdV7DM1BVNXZaSm4zg7DNDU1aynVVq8nUKtH7yEKVF+uIXIAFjpMM9J8dXbuSPgVzA2os+MlN/Kr/h++//57w1itVmvI2Y9PP/0UTqcTPXv2xNmzZzFv3jz88MMPKCwsRGJiYr3H0dS06pgdhmFihZqxPoY2IWrQshbD49U8OOoi+wOXibwwVNSmcB4dhmFUunTpguTkZG2ZP39+yP3uuece/OxnP0P//v2Rm5uLTz75BGVlZfjnP//ZzCO+Olr1NBbDMLGGPtZHja+RAPIpcTtC1najGt/VQjWDDdcXjT06TNtAJqUkZ0OOBxDSsxMJKSkpuO6663Ds2LF6j6E5YM8OwzCtkJo3b30zWIUQOSU6Lw4LHSYWaKzeWMHlViIVO06nE8ePH0fHjh2b0swGw2KHYZhWijq1pW/8Gj5tncinu3mHSq7lWkxM26O5U89///vfY/PmzTh58iS++uor3H///TCZTHjwwQcbxZ6mgqexGIZp5dSc2rr6YxmGaQzOnDmDBx98EBcuXECHDh1wyy23YPv27ejQoUNLD61WWOwwDBMl6FtSRLIvw7R91Da6DTn+alixYkW9r9WSsNhhGCaKYBHDMHoaK0C5rcMxOwzDMAzDtGnYs8MwDMMwUUrNzMOrPz4WYLHDMAzDMFFKc8fsRCs8jcUwDMMwTJuGPTsMwzAME6WwZycyWOwwDMMwTJTCMTuRwdNYDMMwDMO0adizwzAMwzBRCjVwGitWPDssdhiGYRgmSpGFDCGuto2K7virbsESnbDYYRiGYZgoRQZBcIBynXDMDsMwDMMwbRr27DAMwzBMlEL+5POGHB8LsNhhGIZhmChFBho4jRUb8DQWwzAMwzBtGvbsMAzDMEyUwtlYkcFih2EYhmGiFBkyRAMES6yIHZ7GYhiGYRimTcOeHYZhGIaJUtizExksdhiGYRgmSuHU88jgaSyGYRiGYdo07NlhGIZhmCiFs7Eig8UOwzAMw0QpBLlBgiVWprFY7DAMwzBMlELwgRoQkULwNeJoWi8cs8MwDMMwTJuGPTsMwzAME6UoU1gcs1MXLHYYhmEYJkqRQWiY2Kl/E9FogqexGIZhGIZp07Bnh2EYhmGiFCVAWTTo+FiAxQ7DMAzDRCkcsxMZPI3FMAzDMEybhj07DMMwDBOlcG+syIgpz86iRYuQnZ0Nm82GoUOHYufOnS09JIZhGIapNzJ8DV5igZgROytXrsTMmTMxd+5c7NmzBwMGDEBubi5KS0tbemgMwzAMwzQhMSN2/vSnP+Hxxx/Ho48+it69e+Ott95CQkIC3n777ZYeGsMwDMPUC3UaqyFLLBATYsftdmP37t0YNWqUtk6SJIwaNQoFBQUtODKGYRiGqT8y+Rq8xAIxEaB8/vx5+Hw+ZGRkGNZnZGTg22+/rbG/y+WCy+XSfi8vL/f/FBuVJhmGYZiGoDwriJr+mcEBypERE2Lnapk/fz7mzZsXYguBBQ/DMAwTCRcuXEBycnJLD4NBjIidtLQ0mEwmlJSUGNaXlJQgMzOzxv5z5szBzJkztd/LysrQtWtXnD59Oqr/cCsqKtClSxd8//33SEpKaunh1Ju2YEdbsAFoG3a0BRsAtqM1UV5ejqysLKSmpjb5tRTPTv2notiz04awWCwYNGgQ8vPzMW7cOACALMvIz8/HtGnTauxvtVphtVprrE9OTo7aD5+epKQktqOV0BZsANqGHW3BBoDtaE1IUtOHxRLJkBvSLoJY7LQpZs6ciUmTJmHw4MEYMmQI/vznP6OyshKPPvpoSw+NYRiGYZgmJGbEzs9//nOcO3cOzz//PIqLi3HDDTdg/fr1NYKWGYZhGCZaUKahGtIIlD07bY5p06aFnLaqC6vVirlz54ac2oom2I7WQ1uwAWgbdrQFGwC2ozXRnDZQA1PHG3p8tCCoOXLjGIZhGIZpNCoqKpCcnIxkW28IYar3eYh8KK8+hPLy8qiPkaqNmPLsMAzDMExbQglP5mmsumCxwzAMwzBRipJNxdlYdRET7SIYhmEYholdWOxEwKJFi5CdnQ2bzYahQ4di586dLT0kjfnz5+Omm25CYmIi0tPTMW7cOBQVFRn2qa6uRl5eHtq3bw+Hw4EJEybUKLB4+vRpjB49GgkJCUhPT8esWbPg9Xqb0xSNBQsWQAiBGTNmaOuixYYffvgBDz30ENq3b4/4+Hj069cPX3/9tbadiPD888+jY8eOiI+Px6hRo3D06FHDOS5evIiJEyciKSkJKSkpmDJlCpxOZ7PZ4PP58Nxzz6Fbt26Ij4/HT37yE7z00kuG0vetzY4tW7bg3/7t39CpUycIIbB27VrD9sYa74EDBzBixAjYbDZ06dIFr776arPZ4fF4MHv2bPTr1w92ux2dOnXCI488gh9//DGq7AjmySefhBACf/7zn1uVHZHYcPjwYYwZMwbJycmw2+246aabcPr0aW17c9y3CL4GLzEBMbWyYsUKslgs9Pbbb9PBgwfp8ccfp5SUFCopKWnpoRERUW5uLr3zzjtUWFhI+/bto3vvvZeysrLI6XRq+zz55JPUpUsXys/Pp6+//pqGDRtGw4cP17Z7vV7q27cvjRo1ivbu3UuffPIJpaWl0Zw5c5rdnp07d1J2djb179+fnnrqqaiy4eLFi9S1a1eaPHky7dixg7777jvasGEDHTt2TNtnwYIFlJycTGvXrqX9+/fTmDFjqFu3bnTlyhVtn7vvvpsGDBhA27dvp61bt9K1115LDz74YLPZ8fLLL1P79u1p3bp1dOLECVq1ahU5HA564403Wq0dn3zyCf3hD3+g1atXEwBas2aNYXtjjLe8vJwyMjJo4sSJVFhYSB988AHFx8fT3//+92axo6ysjEaNGkUrV66kb7/9lgoKCmjIkCE0aNAgwzlaux16Vq9eTQMGDKBOnTrR66+/3qrsqMuGY8eOUWpqKs2aNYv27NlDx44dow8//NDwbGjK+1Z5eTkBoARLd7Jbr633kmDpTgCovLy8UV631gqLnToYMmQI5eXlab/7fD7q1KkTzZ8/vwVHFZ7S0lICQJs3byYi5QYZFxdHq1at0vY5fPgwAaCCggIiUj7UkiRRcXGxts/ixYspKSmJXC5Xs4398uXL1KNHD9q4cSONHDlSEzvRYsPs2bPplltuCbtdlmXKzMyk1157TVtXVlZGVquVPvjgAyIiOnToEAGgXbt2aft8+umnJISgH374oekGr2P06NH02GOPGdaNHz+eJk6cSESt347gB1NjjffNN9+kdu3aGf6eZs+eTT179mwWO0Kxc+dOAkCnTp0iouiy48yZM3TNNddQYWEhde3a1SB2WpsdoWz4+c9/Tg899FDYY5r6vqWKnXhLNiVYu9d7ibdkx4TY4WmsWnC73di9ezdGjRqlrZMkCaNGjUJBQUELjiw8aod2tSfL7t274fF4DDb06tULWVlZmg0FBQXo16+focBibm4uKioqcPDgwWYbe15eHkaPHm0YKxA9Nnz00UcYPHgwfvaznyE9PR033ngj/uu//kvbfuLECRQXFxvsSE5OxtChQw12pKSkYPDgwdo+o0aNgiRJ2LFjR7PYMXz4cOTn5+PIkSMAgP3792Pbtm245557osoOlcYab0FBAW699VZYLBZtn9zcXBQVFeHSpUvNZI2R8vJyCCGQkpKijTEa7JBlGQ8//DBmzZqFPn361Nje2u2QZRn/+7//i+uuuw65ublIT0/H0KFDDVNd0XLfihVY7NTC+fPn4fP5alRZzsjIQHFxcQuNKjyyLGPGjBm4+eab0bdvXwBAcXExLBaLdjNU0dtQXFwc0kZ1W3OwYsUK7NmzB/Pnz6+xLVps+O6777B48WL06NEDGzZswNSpUzF9+nQsW7bMMI7a/p6Ki4uRnp5u2G42m5GamtpsdjzzzDP4xS9+gV69eiEuLg433ngjZsyYgYkTJ2pjVMetp7XZodJY420Nf2N6qqurMXv2bDz44INafZRosWPhwoUwm82YPn16yO2t3Y7S0lI4nU4sWLAAd999Nz777DPcf//9GD9+PDZv3qyNoTnuW0S+Bi+xAKeetyHy8vJQWFiIbdu2tfRQrorvv/8eTz31FDZu3AibzdbSw6k3sixj8ODBeOWVVwAAN954IwoLC/HWW29h0qRJLTy6yPnnP/+J9957D++//z769OmDffv2YcaMGejUqVNU2dGW8Xg8eOCBB0BEWLx4cUsP56rYvXs33njjDezZswdC1D9luiWRZSVde+zYsXj66acBADfccAO++uorvPXWWxg5cmSzjaWhqeOces4gLS0NJpOpRvR8SUkJMjMzW2hUoZk2bRrWrVuHzz//HJ07d9bWZ2Zmwu12o6yszLC/3obMzMyQNqrbmprdu3ejtLQUAwcOhNlshtlsxubNm/GXv/wFZrMZGRkZrd4GAOjYsSN69+5tWHf99ddr2RnqOGr7e8rMzERpaalhu9frxcWLF5vNjlmzZmnenX79+uHhhx/G008/rXndosUOlcYab2v4GwMCQufUqVPYuHGjoeptNNixdetWlJaWIisrS/u8nzp1Cr/73e+QnZ0dFXakpaXBbDbX+XmPhvtWrMBipxYsFgsGDRqE/Px8bZ0sy8jPz0dOTk4LjiwAEWHatGlYs2YNNm3ahG7duhm2Dxo0CHFxcQYbioqKcPr0ac2GnJwcfPPNN4abi3oTDf4wNwV33HEHvvnmG+zbt09bBg8ejIkTJ2o/t3YbAODmm2+ukfZ/5MgRdO3aFQDQrVs3ZGZmGuyoqKjAjh07DHaUlZVh9+7d2j6bNm2CLMsYOnRoM1gBVFVVQZKMtwaTyaR9m40WO1Qaa7w5OTnYsmULPB6Pts/GjRvRs2dPtGvXrllsUYXO0aNH8X//939o3769YXs02PHwww/jwIEDhs97p06dMGvWLGzYsCEq7LBYLLjppptq/bw3172XIDd4iQlaOkK6tbNixQqyWq20dOlSOnToED3xxBOUkpJiiJ5vSaZOnUrJycn0xRdf0NmzZ7WlqqpK2+fJJ5+krKws2rRpE3399deUk5NDOTk52nY1/fGuu+6iffv20fr166lDhw4tknquos/GIooOG3bu3Elms5lefvllOnr0KL333nuUkJBA7777rrbPggULKCUlhT788EM6cOAAjR07NmQK9I033kg7duygbdu2UY8ePZo19XzSpEl0zTXXaKnnq1evprS0NPqP//iPVmvH5cuXae/evbR3714CQH/6059o7969WpZSY4y3rKyMMjIy6OGHH6bCwkJasWIFJSQkNGrKdm12uN1uGjNmDHXu3Jn27dtn+LzrM3daux2hCM7Gag121GXD6tWrKS4ujpYsWUJHjx6lv/71r2QymWjr1q3aOZryvqVmY8WZMshi7ljvJc6UERPZWCx2IuCvf/0rZWVlkcVioSFDhtD27dtbekgaAEIu77zzjrbPlStX6De/+Q21a9eOEhIS6P7776ezZ88aznPy5Em65557KD4+ntLS0uh3v/sdeTyeZrYmQLDYiRYbPv74Y+rbty9ZrVbq1asXLVmyxLBdlmV67rnnKCMjg6xWK91xxx1UVFRk2OfChQv04IMPksPhoKSkJHr00Ufp8uXLzWZDRUUFPfXUU5SVlUU2m426d+9Of/jDHwwP1NZmx+effx7yczBp0qRGHe/+/fvplltuIavVStdccw0tWLCg2ew4ceJE2M/7559/HjV2hCKU2GlpOyKx4R//+Adde+21ZLPZaMCAAbR27VrDOZryvsVi5+rgrucMwzAME2WoXc/Npg4Qov4RKUQyvL5z3PWcYRiGYZjWiZI6Xn+fBWdjMQzDMAzDtAHYs8MwDMMwUQsBDcqoio1IFhY7DMMwDBOlKNNQ9S/OGCthuyx2GIZhGCZKUerkNEDsxIhnh2N2GIZhGIZp07Bnh2EYhmGiloZ5djhmh2EYhmGY1k0DY3YQIzE7PI3FMEzMMXnyZAghIITA2rVrG/Xc2dnZ2rmDm0AyDNMysNhhmAg4d+4cpk6diqysLFitVmRmZiI3NxdffvllSw+NqSd33303zp49i3vuuadRz7tr1y78z//8T6Oek2HCwY1AI4PFDsNEwIQJE7B3714sW7YMR44cwUcffYTbbrsNFy5caOmhtRn03aubA1W0Wq3WRj1vhw4dkJqa2qjnZJjwyI2wXD2LFi1CdnY2bDYbhg4dip07dzbQjqaFxQ7D1EFZWRm2bt2KhQsX4vbbb0fXrl0xZMgQzJkzB2PGjDHs9+tf/xoZGRmw2Wzo27cv1q1bp23ftm0bRowYgfj4eHTp0gXTp09HZWWltj07OxuvvPIKHnvsMSQmJiIrKwtLliwxjOX777/HAw88gJSUFKSmpmLs2LE4efJkreM/ePAg7rvvPiQlJSExMREjRozA8ePHASheiDvvvBNpaWlITk7GyJEjsWfPHsPxQgj8/e9/x3333YeEhARcf/31KCgowLFjx3DbbbfBbrdj+PDh2jlVPvzwQwwcOBA2mw3du3fHvHnz4PV6DeddvHgxxowZA7vdjpdffhk+nw9TpkxBt27dEB8fj549e+KNN96o1b4XX3wRnTp1MgjP0aNH4/bbb4csR34jP3nyJIQQWLFiBYYPH669h5s3b270azFMNLNy5UrMnDkTc+fOxZ49ezBgwADk5uaitLS0pYcWnhZuRMowrR6Px0MOh4NmzJhB1dXVIffx+Xw0bNgw6tOnD3322Wd0/Phx+vjjj+mTTz4hIqJjx46R3W6n119/nY4cOUJffvkl3XjjjTR58mTtHF27dqXU1FRatGgRHT16lObPn0+SJNG3335LRERut5uuv/56euyxx+jAgQN06NAh+uUvf0k9e/Y0dCTXc+bMGUpNTaXx48fTrl27qKioiN5++23tnPn5+bR8+XI6fPgwHTp0iKZMmUIZGRlUUVGhnQMAXXPNNbRy5UoqKiqicePGUXZ2Nv30pz+l9evX06FDh2jYsGF09913a8ds2bKFkpKSaOnSpXT8+HH67LPPKDs7m1544QXDedPT0+ntt9+m48eP06lTp8jtdtPzzz9Pu3btou+++47effddSkhIoJUrV4Z9f7xeL+Xk5NC4ceOIiOhvf/sbpaSk0KlTp8IeM2nSJBo7dqxhndpVvHPnzvSvf/2LDh06RL/61a8oMTGRzp8/f1XXUjtmX7p0KewYGKYhqF3PATMJxNV7AcxX3fV8yJAhlJeXp/3u8/moU6dONH/+/KYwtVFgscMwEfCvf/2L2rVrRzabjYYPH05z5syh/fv3a9s3bNhAkiRRUVFRyOOnTJlCTzzxhGHd1q1bSZIkunLlChEpYuehhx7StsuyTOnp6bR48WIiIlq+fDn17NmTZFnW9nG5XBQfH08bNmwIed05c+ZQt27dyO12R2Snz+ejxMRE+vjjj7V1AOjZZ5/Vfi8oKCAA9I9//ENb98EHH5DNZtN+v+OOO+iVV14xnHv58uXUsWNHw3lnzJhR55jy8vJowoQJte5z/PhxSkxMpNmzZ1N8fDy99957te5fm9hZsGCBts7j8VDnzp1p4cKFV3UtFjtMUxMQOya/YKnvYroqseNyuchkMtGaNWsM6x955BEaM2ZME1jaOPA0FsNEwIQJE/Djjz/io48+wt13340vvvgCAwcOxNKlSwEA+/btQ+fOnXHdddeFPH7//v1YunQpHA6HtuTm5kKWZZw4cULbr3///trPQghkZmZqruH9+/fj2LFjSExM1M6RmpqK6urqGlNIKvv27cOIESMQFxcXcntJSQkef/xx9OjRA8nJyUhKSoLT6cTp06cN++nHlZGRAQDo16+fYV11dTUqKiq0sb744osGex9//HGcPXsWVVVV2nGDBw+uMaZFixZh0KBB6NChAxwOB5YsWVJjPMF0794df/zjH7Fw4UKMGTMGv/zlL2vdvzZycnK0n81mMwYPHozDhw83ybUYpnGgBiwKFRUVhsXlcoW80vnz5+Hz+bT7gEpGRgaKi4sb2a7Gg+vsMEyE2Gw23Hnnnbjzzjvx3HPP4Ve/+hXmzp2LyZMnIz4+vtZjnU4nfv3rX2P69Ok1tmVlZWk/B4sSIYQWC+J0OjFo0CC89957Nc7RoUOHkNeta1yTJk3ChQsX8MYbb6Br166wWq3IycmB2+027KcflxAi7Dr9WOfNm4fx48fXuKbNZtN+ttvthm0rVqzA73//e/znf/4ncnJykJiYiNdeew07duyo1Q4A2LJlC0wmE06ePAmv1wuzuelub815LYYJhcViQWZmZqMIDIfDgS5duhjWzZ07Fy+88EKDz91a4E8ow9ST3r17azVa+vfvjzNnzuDIkSMhvTsDBw7EoUOHcO2119b7egMHDsTKlSuRnp6OpKSkiI7p378/li1bBo/HE9K78+WXX+LNN9/EvffeC0AJgD5//ny9x6gfa1FR0VXb++WXX2L48OH4zW9+o60L57XSs3LlSqxevRpffPEFHnjgAbz00kuYN2/eVY8bALZv345bb70VAOD1erF7925MmzatSa7FMPXFZrPhxIkTNb6Y1Aci0r6wqITLUkxLS4PJZEJJSYlhfUlJCTIzMxs8lqaCp7EYpg4uXLiAn/70p3j33Xdx4MABnDhxAqtWrcKrr76KsWPHAgBGjhyJW2+9FRMmTMDGjRtx4sQJfPrpp1i/fj0AYPbs2fjqq68wbdo07Nu3D0ePHsWHH35oeIjWxcSJE5GWloaxY8di69atOHHiBL744gtMnz4dZ86cCXnMtGnTUFFRgV/84hf4+uuvcfToUSxfvhxFRUUAgB49emD58uU4fPgwduzYgYkTJ9bpDYqE559/Hv/93/+NefPm4eDBgzh8+DBWrFiBZ599ttbjevToga+//hobNmzAkSNH8Nxzz2HXrl21HnPmzBlMnToVCxcuxC233IJ33nkHr7zyCrZv316vsS9atAhr1qzBt99+i7y8PFy6dAmPPfZYk1yLYRqCzWZDUlJSgxd1Clu/hBM7FosFgwYNQn5+vrZOlmXk5+cbpoBbHS0dNMQwrZ3q6mp65plnaODAgZScnEwJCQnUs2dPevbZZ6mqqkrb78KFC/Too49S+/btyWazUd++fWndunXa9p07d9Kdd95JDoeD7HY79e/fn15++WVte9euXen11183XHvAgAE0d+5c7fezZ8/SI488QmlpaWS1Wql79+70+OOP1xpcuH//frrrrrsoISGBEhMTacSIEXT8+HEiItqzZw8NHjyYbDYb9ejRg1atWlVjHAAMwYhqIO/evXu1daECctevX0/Dhw+n+Ph4SkpKoiFDhtCSJUvCnld9rSdPnkzJycmUkpJCU6dOpWeeeYYGDBgQ0jZZlumOO+6g3NxcQ+D2b3/7W/rJT35Cly9fDnlcbQHK77//Pg0ZMoQsFgv17t2bNm3adNXX4gBlpi2zYsUKslqttHTpUjp06BA98cQTlJKSQsXFxS09tLAIohhpjMEwDONn8uTJKCsrM7SKOHnyJLp164a9e/fihhtuaND5v/jiC9x+++24dOkSUlJSGnQuhmmN/O1vf8Nrr72G4uJi3HDDDfjLX/6CoUOHtvSwwsLTWAzDxCTr1q2Dw+EwFH5sDPr06dPoLSgYprUxbdo0nDp1Ci6XCzt27GjVQgcA2LPDMEzMUVpaqqXJd+zYEXa7vdE8O6dOndJaX3Tv3h2SxN8pGaalYbHDMAzDMEybhr9yMAzDMAzTpmGxwzAMwzBMm4bFDsMwDMMwbRoWOwzDMAzDtGlY7DAMwzAM06ZhscMwDMMwTJuGxQ7DMAzDMG0aFjsMwzAMw7RpWOwwDMMwDNOmYbHDMAzDMEyb5v8D94LwoRZIdQkAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
    ,\n", + " )" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "recording.plot_distribution()" ] @@ -618,13 +592,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "we can neatly see that the recorded data shows a centre-bias, which is a well-known effect from eye statistics. In y, we can see that fixations tend to occur below the horizon, which is indicative of a walking task where a participant looks at the floor in front of them more often" + "We can see a clear centre-bias, as participants tend to look more centrally relative to head position." ] } ], "metadata": { "kernelspec": { - "display_name": "pyneon", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -638,7 +612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/genindex.html b/genindex.html index 1e66e46..6406a49 100644 --- a/genindex.html +++ b/genindex.html @@ -342,16 +342,20 @@

    C

  • (pyneon.NeonRecording method)
    • - - + @@ -629,12 +633,18 @@

    R

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    +
    +restrict(other: NeonStream)#
    +

    Temporally restrict the stream to the timestamps of another stream. +In others words, crop the stream to tmin and tmax of the other stream.

    +
    +
    Parameters:
    +

    other (NeonStream) – The other stream whose timestamps are used to restrict the data.

    +
    +
    Returns:
    +

    Stream with data restricted to the timestamps of the other stream.

    +
    +
    Return type:
    +

    NeonStream

    +
    +
    +
    +
    interpolate(new_ts: ndarray | None = None, float_kind: str = 'linear', other_kind: str = 'nearest', inplace: bool = False) → NeonStream | None#
    @@ -624,6 +642,50 @@

    Event ID.

    +
    +
    +crop(tmin: Number | None = None, tmax: Number | None = None, by: Literal['timestamp', 'row'] = 'timestamp', inplace: bool = False) → NeonEV | None#
    +

    Crop data to a specific time range based on timestamps or row numbers.

    +
    +
    Parameters:
    +
      +

    • tmin (number, optional) – Start timestamp/row to crop the data to. If None, +the minimum timestamp/row in the data is used. Defaults to None.

      • +

    • tmax (number, optional) – End timestamp/row to crop the data to. If None, +the maximum timestamp/row in the data is used. Defaults to None.

      • +

    • by ("timestamp" or "row", optional) – Whether tmin and tmax are UTC timestamps in nanoseconds +or row numbers of the stream data. +Defaults to “timestamp”.

      • +

    • inplace (bool, optional) – Whether to replace the data in the object with the cropped data. +Defaults to False.

      • +
    +
    +
    Returns:
    +

    Cropped stream if inplace=False, otherwise None.

    +
    +
    Return type:
    +

    NeonEV or None

    +
    +
    +
    + +
    +
    +restrict(other: NeonStream) → NeonEV#
    +

    Restrict events to a time range defined by another stream.

    +
    +
    Parameters:
    +

    other (NeonStream) – Stream to restrict to.

    +
    +
    Returns:
    +

    Restricted event data.

    +
    +
    Return type:
    +

    NeonEV

    +
    +
    +
    +
    @@ -878,6 +940,7 @@

    Returns:#
  • NeonStream.is_uniformly_sampled
  • NeonStream.time_to_ts()
  • NeonStream.crop()
    • +
  • NeonStream.restrict()
  • NeonStream.interpolate()
  • NeonStream.window_average()
    • @@ -891,6 +954,8 @@

    Returns:#
  • NeonEV.end_ts
  • NeonEV.durations
  • NeonEV.id
    • +
  • NeonEV.crop()
    • +
  • NeonEV.restrict()
  • NeonBlinks
    • diff --git a/searchindex.js b/searchindex.js index 41b15c2..d9a2831 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Setup: Loading a Neon Recording": [[13, "1.-Setup:-Loading-a-Neon-Recording"]], "2. Mapping Gaze Data to Video Frames": [[13, "2.-Mapping-Gaze-Data-to-Video-Frames"]], "3. Estimating the Scanpath": [[13, "3.-Estimating-the-Scanpath"]], "4. Understanding Fixation Status": [[13, "4.-Understanding-Fixation-Status"]], "5. Overlaying Fixations on the Video": [[13, "5.-Overlaying-Fixations-on-the-Video"]], "Classes for individual data types": [[1, null]], "Concatenating different streams": [[11, "Concatenating-different-streams"]], "Conclusion": [[8, "Conclusion"]], "Data and metadata of a NeonRecording": [[12, "Data-and-metadata-of-a-NeonRecording"]], "Data as dataframes": [[12, "Data-as-dataframes"]], "Data format": [[0, "data-format"]], "Data streams and events": [[12, "Data-streams-and-events"]], "Export Neon data recording to BIDS formats": [[9, null]], "Exportation module": [[3, null]], "Installation": [[0, "installation"]], "Interpolate Data and Concatenate Channels": [[11, null]], "Interpolating data streams": [[11, "Interpolating-data-streams"]], "License": [[0, "license"]], "Mapping Scanpath to video": [[13, null]], "NeonDataset class": [[2, null]], "NeonRecording class": [[6, null]], "Parameters:": [[1, "parameters"], [6, "parameters"], [6, "id1"]], "Preprocessing module": [[5, null]], "PyNeon API": [[4, null]], "PyNeon Tutorials": [[10, null]], "Reading a Neon dataset/recording": [[12, null]], "Reading sample data": [[12, "Reading-sample-data"]], "Returns:": [[1, "returns"]], "Step 1: Loading Sample Data": [[8, "Step-1:-Loading-Sample-Data"]], "Step 2: Constructing Event Times": [[8, "Step-2:-Constructing-Event-Times"]], "Step 3: Verifying Event Intervals": [[8, "Step-3:-Verifying-Event-Intervals"]], "Step 4: Creating Epochs from the Data": [[8, "Step-4:-Creating-Epochs-from-the-Data"]], "Step 5: Initializing the Epoch Class": [[8, "Step-5:-Initializing-the-Epoch-Class"]], "Step 6: Converting Epochs to NumPy Array": [[8, "Step-6:-Converting-Epochs-to-NumPy-Array"]], "Step 7: Averaging Across Epochs": [[8, "Step-7:-Averaging-Across-Epochs"]], "Step 8: Averaging Over Time": [[8, "Step-8:-Averaging-Over-Time"]], "Summary": [[13, "Summary"]], "Tutorial: Processing Eye-Tracking Data with PyNeon": [[8, null]], "Unequally sampled data": [[11, "Unequally-sampled-data"]], "Useful attributes and methods for NeonStream and NeonEV": [[12, "Useful-attributes-and-methods-for-NeonStream-and-NeonEV"]], "Visualization module": [[7, null]], "Visualizing gaze heatmap": [[12, "Visualizing-gaze-heatmap"]], "Welcome to PyNeon documentation": [[0, null]]}, "docnames": ["index", "reference/data", "reference/dataset", "reference/export", "reference/index", "reference/preprocess", "reference/recording", "reference/vis", "tutorials/epoching", "tutorials/export_to_bids", "tutorials/index", "tutorials/interpolate_and_concat", "tutorials/read_recording", "tutorials/video"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["index.rst", "reference/data.rst", 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"zeros_lik": 11}, "titles": ["Welcome to PyNeon documentation", "Classes for individual data types", "NeonDataset class", "Exportation module", "PyNeon API", "Preprocessing module", "NeonRecording class", "Visualization module", "Tutorial: Processing Eye-Tracking Data with PyNeon", "Export Neon data recording to BIDS formats", "PyNeon Tutorials", "Interpolate Data and Concatenate Channels", "Reading a Neon dataset/recording", "Mapping Scanpath to video"], "titleterms": {"1": [8, 13], "2": [8, 13], "3": [8, 13], "4": [8, 13], "5": [8, 13], "6": 8, "7": 8, "8": 8, "across": 8, "an": 12, "api": 4, "arrai": 8, "attribut": 12, "averag": 8, "bid": 9, "channel": 11, "class": [1, 2, 6, 8], "concaten": 11, "conclus": 8, "construct": 8, "convert": 8, "creat": 8, "crop": 12, "data": [0, 1, 8, 9, 11, 12, 13], "datafram": 12, "dataset": 12, "differ": 11, "document": 0, "epoch": 8, "estim": 13, "event": 8, "exampl": 12, "export": [3, 9], "ey": 8, "fixat": 13, "format": [0, 9], "frame": 13, "from": 8, "gaze": [12, 13], "heatmap": 12, "individu": 1, "initi": 8, "instal": 0, "interpol": 11, "interv": 8, "licens": 0, "load": [8, 13], "map": 13, "metadata": 12, "method": 12, "modul": [3, 5, 7], "neon": [9, 12, 13], "neondataset": 2, "neonev": 12, "neonrecord": [6, 12], "neonstream": 12, "numpi": 8, "over": 8, "overlai": 13, "paramet": [1, 6], "plot": 12, "preprocess": 5, "process": 8, "pyneon": [0, 4, 8, 10], "read": 12, "record": [9, 12, 13], "return": 1, "sampl": [8, 11, 12], "scanpath": 13, "setup": 13, "statu": 13, "step": 8, "stream": 11, "summari": 13, "time": 8, "track": 8, "tutori": [8, 10], "type": 1, "understand": 13, "unequ": 11, "us": 12, "verifi": 8, "video": 13, "visual": [7, 12], "welcom": 0}}) \ No newline at end of file diff --git a/tutorials/read_recording.html b/tutorials/read_recording.html index 9ea81d8..613eae4 100644 --- a/tutorials/read_recording.html +++ b/tutorials/read_recording.html @@ -351,8 +351,8 @@

    Reading a Neon dataset/recordingPupil Cloud and give an overview of the data structure.

    Reading sample data#

    -

    We will use a sample recording produced by the NCC Lab, called OfficeWalk. This project (collection of recordings on Pupil Cloud) contains two recordings and multiple enrichments and can be downloaded with the get_sample_data() function. The function returns a Pathlib.Path (reference) object pointing to the downloaded and unzipped directory. PyNeon accepts both Path and string objects but internally always uses -Path.

    +

    We will use a sample recording produced by the NCC Lab, called boardView. This project (collection of recordings on Pupil Cloud) contains two recordings downloaded with the Timeseries Data + Scene Video option and a marker mapper enrichment. It can be downloaded with the get_sample_data() function. The function returns a Pathlib.Path (reference) instance pointing to the downloaded and unzipped directory. PyNeon +accepts both Path and string objects but internally always uses Path.

    [1]:
    @@ -360,7 +360,7 @@

    Reading sample data
    from pyneon import get_sample_data, NeonDataset, NeonRecording
 
 # Download sample data (if not existing) and return the path
-sample_dir = get_sample_data("OfficeWalk")
+sample_dir = get_sample_data("boardView")
 print(sample_dir)

    @@ -370,34 +370,32 @@

    Reading sample data 
    -D:\GitHub\pyneon\data\OfficeWalk
+C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView

    The OfficeWalk data has the following structure:

    -
    OfficeWalk
-├── Timeseries Data
-│   ├── walk1-e116e606
+
    boardView
+├── Timeseries Data + Scene Video
+│   ├── boardview1-d4fd9a27
 │   │   ├── info.json
 │   │   ├── gaze.csv
 │   │   └── ....
-│   ├── walk2-93b8c234
+│   ├── boardview2-713532d5
 │   │   ├── info.json
 │   │   ├── gaze.csv
 │   │   └── ....
 |   ├── enrichment_info.txt
 |   └── sections.csv
-├── OfficeWalk_FACE-MAPPER_FaceMap
-├── OfficeWalk_MARKER-MAPPER_TagMap_csv
-└── OfficeWalk_STATIC-IMAGE-MAPPER_ManualMap_csv
+└── boardView_MARKER-MAPPER_boardMapping_csv
 
    
 
    
-
    The Timeseries Data folder contains what PyNeon refers to as a NeonDataset. It consists of two recordings, each with its own info.json file and data files. These recordings can be loaded either individually as a NeonRecording as a collective NeonDataset.
    
-
    To load a NeonDataset, specify the path to the Timeseries Data folder:
    
+
    The Timeseries Data + Scene Video folder contains what PyNeon refers to as a NeonDataset. It consists of two recordings, each with its own info.json file and data files. These recordings can be loaded either individually as a NeonRecording as a collective NeonDataset.
    
+
    To load a NeonDataset, specify the path to the Timeseries Data + Scene Video folder:
    
 
    
 
    [2]:
 
    
 
    
-
    dataset_dir = sample_dir / "Timeseries Data"
+
    dataset_dir = sample_dir / "Timeseries Data + Scene Video"
 dataset = NeonDataset(dataset_dir)
 print(dataset)
 
    
@@ -416,9 +414,9 @@ 
    Reading sample data
    [3]:
 
    
 
    
-
    first_recording = dataset[0]
-print(type(first_recording))
-print(first_recording.recording_dir)
+
    recording = dataset[0]
+print(type(recording))
+print(recording.recording_dir)
 
    
 
    
 
    
@@ -428,7 +426,7 @@ 
    Reading sample data
 
     <class 'pyneon.recording.NeonRecording'>
-D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk2-93b8c234
+C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview2-713532d5
 
    
 
    
 
    Alternatively, you can directly load a single NeonRecording by specifying the recording’s folder path:
    
@@ -436,7 +434,7 @@ 
    Reading sample data
    [4]:
 
    
 
    
-
    recording_dir = dataset_dir / "walk1-e116e606"
+
    recording_dir = dataset_dir / "boardview1-d4fd9a27"
 recording = NeonRecording(recording_dir)
 print(type(recording))
 print(recording.recording_dir)
@@ -449,7 +447,7 @@ 
    Reading sample data
 
     <class 'pyneon.recording.NeonRecording'>
-D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606
+C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27
    @@ -470,28 +468,28 @@

    Data and metadata of a NeonRecording 
     
-Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8
-Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561
+Recording ID: d4fd9a27-3e28-45bf-937f-b9c14c3c1c5e
+Wearer ID: af6cd360-443a-4d3d-adda-7dc8510473c2
 Wearer name: Qian
-Recording start time: 2024-08-30 17:37:01.527000
-Recording duration: 98.213s
-                  exist              filename                                                                                  path
-3d_eye_states      True     3d_eye_states.csv     D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\3d_eye_states.csv
-blinks             True            blinks.csv            D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\blinks.csv
-events             True            events.csv            D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\events.csv
-fixations          True         fixations.csv         D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\fixations.csv
-gaze               True              gaze.csv              D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\gaze.csv
-imu                True               imu.csv               D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\imu.csv
-labels             True            labels.csv            D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\labels.csv
-saccades           True          saccades.csv          D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\saccades.csv
-world_timestamps   True  world_timestamps.csv  D:\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\world_timestamps.csv
-scene_video_info  False                  None                                                                                  None
-scene_video       False                  None                                                                                  None
+Recording start time: 2024-11-26 12:44:48.937000
+Recording duration: 32.046s
+                 exist                 filename                                                                                                                                path
+3d_eye_states     True        3d_eye_states.csv        C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\3d_eye_states.csv
+blinks            True               blinks.csv               C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\blinks.csv
+events            True               events.csv               C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\events.csv
+fixations         True            fixations.csv            C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\fixations.csv
+gaze              True                 gaze.csv                 C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\gaze.csv
+imu               True                  imu.csv                  C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\imu.csv
+labels            True               labels.csv               C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\labels.csv
+saccades          True             saccades.csv             C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\saccades.csv
+world_timestamps  True     world_timestamps.csv     C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\world_timestamps.csv
+scene_video_info  True        scene_camera.json        C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\scene_camera.json
+scene_video       True  182240fd_0.0-32.046.mp4  C:\Users\qian.chu\Documents\GitHub\pyneon\data\boardView\Timeseries Data + Scene Video\boardview1-d4fd9a27\182240fd_0.0-32.046.mp4
    -

    As seen in the output, this recording includes all data files except the scene video and its metadata because we downloaded only the “Timeseries Data” instead of “ “Timeseries Data + Scene Video” from Pupil Cloud. For processing video, refer to the Neon video tutorial.

    -

    Individual data streams can be accessed as properties of the NeonRecording object. For example, the gaze data can be accessed as recording.gaze, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data like the video, it will simply return None and a warning message.

    +

    As seen in the output, this recording includes all data files. This tutorial will focus on non-video data. For processing video, refer to the Neon video tutorial.

    +

    Individual data streams can be accessed as properties of the NeonRecording object. For example, the gaze data can be accessed as recording.gaze, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data, PyNeon will return None and a warning message.

    [6]:
    @@ -499,32 +497,21 @@

    Data and metadata of a NeonRecording
    # Gaze and fixation data are available
 gaze = recording.gaze
 print(f"recording.gaze is {gaze}")
-fixations = recording.fixations
-print(f"recording.fixations is {fixations}")
-
-# Video is not available
+saccades = recording.saccades
+print(f"recording.saccades is {saccades}")
 video = recording.video
 print(f"recording.video is {video}")

    -
    -
    -
    -
    -
    -recording.gaze is <pyneon.stream.NeonGaze object at 0x000001B2167FC830>
-recording.fixations is <pyneon.events.NeonFixations object at 0x000001B236FA6DB0>
-recording.video is None
-
    -
    -
    +
    -D:\GitHub\pyneon\pyneon\recording.py:271: UserWarning: Scene video not loaded because not all video-related files (video, scene_camera.json, world_timestamps.csv) are found.
-  warnings.warn(
+recording.gaze is <pyneon.stream.NeonGaze object at 0x0000027AE3680C20>
+recording.saccades is <pyneon.events.NeonSaccades object at 0x0000027AC22E0B90>
+recording.video is < cv2.VideoCapture 0000027AE592DB90>

    PyNeon reads tabular CSV file into specialized classes (e.g., gaze.csv to NeonGaze) which all have a data attribute that holds the tabular data as a pandas.DataFrame (reference). Depending on the nature of the data, such classes could be of NeonStream or NeonEV super classes. NeonStream contains (semi)-continuous data streams, while NeonEV (dubbed so to avoid confusion with the @@ -562,19 +549,19 @@

    Data as dataframes
                          gaze x [px]  gaze y [px]  worn  fixation id  blink id  \
 timestamp [ns]
-1725032224852161732     1067.486      620.856     1            1      <NA>
-1725032224857165732     1066.920      617.117     1            1      <NA>
-1725032224862161732     1072.699      615.780     1            1      <NA>
-1725032224867161732     1067.447      617.062     1            1      <NA>
-1725032224872161732     1071.564      613.158     1            1      <NA>
+1732621490425631343      697.829      554.242     1            1      <NA>
+1732621490430625343      698.096      556.335     1            1      <NA>
+1732621490435625343      697.810      556.360     1            1      <NA>
+1732621490440625343      695.752      557.903     1            1      <NA>
+1732621490445625343      696.108      558.438     1            1      <NA>
 
                      azimuth [deg]  elevation [deg]
 timestamp [ns]
-1725032224852161732      16.213030        -0.748998
-1725032224857165732      16.176285        -0.511733
-1725032224862161732      16.546413        -0.426618
-1725032224867161732      16.210049        -0.508251
-1725032224872161732      16.473521        -0.260388
+1732621490425631343      -7.581023         3.519804
+1732621490430625343      -7.563214         3.385485
+1732621490435625343      -7.581576         3.383787
+1732621490440625343      -7.713686         3.284294
+1732621490445625343      -7.690596         3.250055
 gaze x [px]        float64
 gaze y [px]        float64
 worn                 Int32
@@ -589,8 +576,8 @@ 
    Data as dataframes
    [8]:

    -
    print(fixations.data.head())
-print(fixations.data.dtypes)
+
    print(saccades.data.head())
+print(saccades.data.dtypes)
    @@ -599,48 +586,48 @@

    Data as dataframes 
    -                      fixation id   end timestamp [ns]  duration [ms]  \
+                      saccade id   end timestamp [ns]  duration [ms]  \
 start timestamp [ns]
-1725032224852161732             1  1725032225007283732            155
-1725032225027282732             2  1725032225282527732            255
-1725032225347526732             3  1725032225617770732            270
-1725032225667907732             4  1725032225798022732            130
-1725032225833015732             5  1725032225958137732            125
+1732621490876132343            1  1732621490891115343             15
+1732621491241357343            2  1732621491291481343             50
+1732621491441602343            3  1732621491516601343             75
+1732621491626723343            4  1732621491696847343             70
+1732621491917092343            5  1732621491977090343             60
 
-                      fixation x [px]  fixation y [px]  azimuth [deg]  \
+                      amplitude [px]  amplitude [deg]  mean velocity [px/s]  \
 start timestamp [ns]
-1725032224852161732          1069.932          614.843      16.369094
-1725032225027282732           906.439          538.107       5.878844
-1725032225347526732           694.843          533.982      -7.781338
-1725032225667907732           572.983          488.800     -15.679003
-1725032225833015732           601.861          491.125     -13.813521
+1732621490876132343        14.938179         0.962102           1025.709879
+1732621491241357343       130.743352         8.378644           2700.713283
+1732621491441602343       241.003342        15.391730           3615.380044
+1732621491626723343       212.619205        13.608618           3757.394092
+1732621491917092343       220.842812        13.914266           4220.180601
 
-                      elevation [deg]
+                      peak velocity [px/s]
 start timestamp [ns]
-1725032224852161732         -0.367312
-1725032225027282732          4.561914
-1725032225347526732          4.819739
-1725032225667907732          7.636408
-1725032225833015732          7.512433
-fixation id             Int32
-end timestamp [ns]      Int64
-duration [ms]           Int64
-fixation x [px]       float64
-fixation y [px]       float64
-azimuth [deg]         float64
-elevation [deg]       float64
+1732621490876132343            1191.520740
+1732621491241357343            3687.314947
+1732621491441602343            5337.244676
+1732621491626723343            6164.040944
+1732621491917092343            6369.217052
+saccade id                Int32
+end timestamp [ns]        Int64
+duration [ms]             Int64
+amplitude [px]          float64
+amplitude [deg]         float64
+mean velocity [px/s]    float64
+peak velocity [px/s]    float64
 dtype: object

    PyNeon performs the following preprocessing when reading the CSV files: 1. Removes the redundant section id and recording id columns that are present in the raw CSVs. 2. Sets the timestamp [ns] (or start timestamp [ns] for most event files) column as the DataFrame index. 3. Automatically assigns appropriate data types to columns. For instance, Int64 type is assigned to timestamps, Int32 to event IDs (blink/fixation/saccade ID), and float64 to float data (e.g. gaze location, pupil size).

    -

    Just like any other pandas.DataFrame, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, gaze.data.iloc[0] returns the first row of the gaze data, and gaze.data['gaze x [px]'] returns the gaze x-coordinate column.

    +

    Just like any other pandas.DataFrame, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, gaze.data.iloc[0] returns the first row of the gaze data, and gaze.data['gaze x [px]'] (or gaze['gaze x [px]']) returns the gaze x-coordinate column.

    [9]:
 
    print(f"First row of gaze data:\n{gaze.data.iloc[0]}\n")
-print(f"All gaze x values:\n{gaze.data['gaze x [px]']}")
+print(f"All gaze x values:\n{gaze['gaze x [px]']}")
    @@ -650,29 +637,29 @@

    Data as dataframes 
     First row of gaze data:
-gaze x [px]        1067.486
-gaze y [px]         620.856
+gaze x [px]         697.829
+gaze y [px]         554.242
 worn                    1.0
 fixation id             1.0
 blink id               <NA>
-azimuth [deg]      16.21303
-elevation [deg]   -0.748998
-Name: 1725032224852161732, dtype: Float64
+azimuth [deg]     -7.581023
+elevation [deg]    3.519804
+Name: 1732621490425631343, dtype: Float64
 
 All gaze x values:
 timestamp [ns]
-1725032224852161732    1067.486
-1725032224857165732    1066.920
-1725032224862161732    1072.699
-1725032224867161732    1067.447
-1725032224872161732    1071.564
-                         ...
-1725032319717194732     800.364
-1725032319722198732     799.722
-1725032319727194732     799.901
-1725032319732194732     796.982
-1725032319737194732     797.285
-Name: gaze x [px], Length: 18769, dtype: float64
+1732621490425631343    697.829
+1732621490430625343    698.096
+1732621490435625343    697.810
+1732621490440625343    695.752
+1732621490445625343    696.108
+                        ...
+1732621520958946343    837.027
+1732621520964071343    836.595
+1732621520969071343    836.974
+1732621520974075343    835.169
+1732621520979070343    833.797
+Name: gaze x [px], Length: 6091, dtype: float64
    @@ -694,23 +681,22 @@

    Useful attributes and methods for NeonStream and NeonEV 
    -[1725032224852161732 1725032224857165732 1725032224862161732 ...
- 1725032319727194732 1725032319732194732 1725032319737194732]
-[0.0000000e+00 5.0040000e-03 1.0000000e-02 ... 9.4875033e+01 9.4880033e+01
- 9.4885033e+01]
+[1732621490425631343 1732621490430625343 1732621490435625343 ...
+ 1732621520969071343 1732621520974075343 1732621520979070343]
+[0.0000000e+00 4.9940000e-03 9.9940000e-03 ... 3.0543440e+01 3.0548444e+01
+ 3.0553439e+01]
    -

    Timestamps (UTC, in ns) and relative time (relative to the stream start, in s) are thus the two units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the crop() method. The method takes two arguments: start and end:

    +

    Timestamps (UTC, in ns), relative time (relative to the stream start, in s), and index are the three units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the crop() method. The method takes start and end of the crop window in either UTC timestamps or relative time, and uses by to specify whether

    [11]:
    -
    # Last data time of the original gaze data
-print(gaze.times[-1])
+
    print(f"Gaze data points before cropping: {len(gaze)}")
 
-# Crop the gaze data to the first 10 seconds
-gaze_cropped = gaze.crop(0, 10, by="time")  # Crop by time
-print(gaze_cropped.times[-1])
+# Crop the gaze data to 5-10 seconds
+gaze_crop = gaze.crop(5, 10, by="time")  # Crop by time
+print(f"Gaze data points after cropping: {len(gaze_crop)}")
    @@ -719,180 +705,114 @@

    Useful attributes and methods for NeonStream and NeonEV 
    -94.885033
-9.999289
+Gaze data points before cropping: 6091
+Gaze data points after cropping: 999

    -

    There are many other attributes and methods available for NeonStream and NeonEV classes. For a full list, refer to the API reference. We will also cover some of them in the following tutorials (e.g., interpolation and concatenation of streams).

    - -
    -

    Data streams and events#

    -

    Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let’s plot some samples from the gaze and eye_states streams and a saccade from the saccades events.

    +

    You may also want to restrict one stream to the temporal range of another stream. This can be done by calling the restrict() method. The method takes another NeonStream instance as an argument and crops the stream to the intersection of the two streams’ temporal ranges.

    [12]:
    -
    import matplotlib.pyplot as plt
-import seaborn as sns
-
-gaze_color = "royalblue"
-gyro_color = "darkorange"
-
-imu = recording.imu
-fixations = recording.saccades
-
-# Create a figure
-fig, ax = plt.subplots(figsize=(10, 5))
-ax2 = ax.twinx()
-ax.yaxis.label.set_color(gaze_color)
-ax2.yaxis.label.set_color(gyro_color)
-
-# Visualize the 2nd saccade
-saccade = fixations.data.iloc[1]
-print(saccade)
-ax.axvspan(saccade.index.values, saccade["end timestamp [ns]"], color="lightgray")
-ax.text(
-    (saccade.index.values + saccade["end timestamp [ns]"]) / 2,
-    1050,
-    "Saccade",
-    horizontalalignment="center",
-)
-
-# Visualize gaze x and pupil diameter left
-sns.scatterplot(
-    ax=ax,
-    data=gaze.data.head(100),
-    x=gaze.data.index,
-    y="gaze x [px]",
-    color=gaze_color,
+
    imu_crop = recording.imu.restrict(gaze_crop)
+saccades_crop = saccades.restrict(gaze_crop)
+print(
+    f"IMU first timestamp: {imu_crop.first_ts} > Gaze first timestamp: {gaze_crop.first_ts}"
 )
-sns.scatterplot(
-    ax=ax2,
-    data=imu.data.head(60),
-    x=imu.data.index,
-    y="gyro x [deg/s]",
-    color=gyro_color,
+print(
+    f"IMU last timestamp: {imu_crop.last_ts} < Gaze last timestamp: {gaze_crop.last_ts}"
 )
    -
    +
    -saccade id                                2.0
-end timestamp [ns]      1725032225347526656.0
-duration [ms]                            65.0
-amplitude [px]                      228.36139
-amplitude [deg]                     14.676102
-mean velocity [px/s]              3685.269894
-peak velocity [px/s]              5411.775481
-Name: 1725032225282527732, dtype: Float64
+IMU first timestamp: 1732621495435389343 > Gaze first timestamp: 1732621495430263343
+IMU last timestamp: 1732621500421101343 < Gaze last timestamp: 1732621500424901343
    -
    -
    +

    There are many other attributes and methods available for NeonStream and NeonEV classes. For a full list, refer to the API reference. We will also cover some of them in the following tutorials (e.g., interpolation and concatenation of streams).

    +
    +
    +

    An example plot of cropped data#

    +

    Below we show how to easily plot the gaze and saccade data we cropped just now. Since PyNeon data are stored in pandas.DataFrame, you can use any plotting library that supports pandas.DataFrame as input. Here we use seaaborn and matplotlib to plot the gaze x, y coordinates and the saccade durations (shadowed areas).

    +
    +
    [13]:
+
    +
    +
    import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Create a figure
+fig, ax = plt.subplots(figsize=(10, 4))
+
+# Visualize the 1st saccade
+for _, sac in saccades_crop.data.iterrows():
+    ax.axvspan(sac.name, sac["end timestamp [ns]"], color="lightgray")
+
+# Visualize gaze x and y
+sns.lineplot(
+    ax=ax,
+    data=gaze_crop.data,
+    x=gaze_crop.data.index,
+    y="gaze x [px]",
+    color="b",
+    label="Gaze x",
+)
+sns.lineplot(
+    ax=ax,
+    data=gaze_crop.data,
+    x=gaze_crop.data.index,
+    y="gaze y [px]",
+    color="g",
+    label="Gaze y",
+)
+ax.set_ylabel("Gaze location (pixels)")
+plt.legend()
+plt.show()
+
    -
    -
    ----------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-Cell In[12], line 19
-     17 saccade = fixations.data.iloc[1]
-     18 print(saccade)
----> 19 ax.axvspan(saccade.index.values, saccade["end timestamp [ns]"], color="lightgray")
-     20 ax.text(
-     21     (saccade.index.values + saccade["end timestamp [ns]"]) / 2,
-     22     1050,
-     23     "Saccade",
-     24     horizontalalignment="center",
-     25 )
-     27 # Visualize gaze x and pupil diameter left
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axes\_axes.py:1087, in Axes.axvspan(self, xmin, xmax, ymin, ymax, **kwargs)
-   1085 # Strip units away.
-   1086 self._check_no_units([ymin, ymax], ['ymin', 'ymax'])
--> 1087 (xmin, xmax), = self._process_unit_info([("x", [xmin, xmax])], kwargs)
-   1089 p = mpatches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, **kwargs)
-   1090 p.set_transform(self.get_xaxis_transform(which="grid"))
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axes\_base.py:2585, in _AxesBase._process_unit_info(self, datasets, kwargs, convert)
-   2583     # Update from data if axis is already set but no unit is set yet.
-   2584     if axis is not None and data is not None and not axis.have_units():
--> 2585         axis.update_units(data)
-   2586 for axis_name, axis in axis_map.items():
-   2587     # Return if no axis is set.
-   2588     if axis is None:
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axis.py:1756, in Axis.update_units(self, data)
-   1754 neednew = self.converter != converter
-   1755 self.converter = converter
--> 1756 default = self.converter.default_units(data, self)
-   1757 if default is not None and self.units is None:
-   1758     self.set_units(default)
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:105, in StrCategoryConverter.default_units(data, axis)
-    103 # the conversion call stack is default_units -> axis_info -> convert
-    104 if axis.units is None:
---> 105     axis.set_units(UnitData(data))
-    106 else:
-    107     axis.units.update(data)
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:181, in UnitData.__init__(self, data)
-    179 self._counter = itertools.count()
-    180 if data is not None:
---> 181     self.update(data)
-
-File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:214, in UnitData.update(self, data)
-    212 # check if convertible to number:
-    213 convertible = True
---> 214 for val in OrderedDict.fromkeys(data):
-    215     # OrderedDict just iterates over unique values in data.
-    216     _api.check_isinstance((str, bytes), value=val)
-    217     if convertible:
-    218         # this will only be called so long as convertible is True.
-
-TypeError: unhashable type: 'numpy.ndarray'
-
    -../_images/tutorials_read_recording_26_2.png -
    -
    -

    It’s apparent that at the beginning of the recording, there are some missing data points in both the gaze and imu streams. This is presumably due to the time it takes for the sensors to start up and stabilize. We will show how to handle missing data using resampling in the next tutorial. For now, it’s important to be aware of these gaps and that it will require great caution to assume the data is continuously and equally sampled.

    -

    PyNeon also calculates the effective (as opposed to the nominal) sampling frequency of each stream by dividing the number of samples by the duration of the recording.

    -
    -
    [ ]:
-
    -
    -
    print(
-    f"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, "
-    f"effective sampling frequency = {gaze.sampling_freq_effective}"
-)
-print(
-    f"IMU: nominal sampling frequency = {recording.imu.sampling_freq_nominal}, "
-    f"effective sampling frequency = {recording.imu.sampling_freq_effective}"
-)
-
    +../_images/tutorials_read_recording_28_0.png

    Visualizing gaze heatmap#

    -

    Finally, we will show how to plot a heatmap of the gaze/fixation data.

    -
    -
    [ ]:
+
    Finally, we will show how to plot a heatmap of the gaze/fixation data. Since it requires gaze, fixation, and video data, the input it takes is an instance of NeonRecording that contains all necessary data. The method plot_heatmap(), by default, plots a gaze heatmap with fixations overlaid as circles.
    
+
    
+
    [14]:
 
    
 
    
 
    recording.plot_distribution()
 
    
 
    
 
    
-
    we can neatly see that the recorded data shows a centre-bias, which is a well-known effect from eye statistics. In y, we can see that fixations tend to occur below the horizon, which is indicative of a walking task where a participant looks at the floor in front of them more often
    
+
    
+
    
+
    
+
    
+../_images/tutorials_read_recording_30_0.png
+
    
+
    
+
    
+
    [14]:
+
    
+
    
+
    
+
    +(<Figure size 640x480 with 2 Axes>,
+ <Axes: xlabel='Scene camera x [px]', ylabel='Scene camera y [px]'>)
+
    
+
    
+
    We can see a clear centre-bias, as participants tend to look more centrally relative to head position.
    @@ -947,7 +867,7 @@

    Visualizing gaze heatmapData and metadata of a NeonRecording
  • Data as dataframes
  • Useful attributes and methods for NeonStream and NeonEV
    • -
  • Data streams and events
    • +
  • An example plot of cropped data
  • Visualizing gaze heatmap

    • diff --git a/tutorials/read_recording.ipynb b/tutorials/read_recording.ipynb index 7456ffa..1b11a07 100644 --- a/tutorials/read_recording.ipynb +++ b/tutorials/read_recording.ipynb @@ -8,7 +8,7 @@ "In this tutorial, we will show how to load a single Neon recording downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/) and give an overview of the data structure.\n", "\n", "## Reading sample data\n", - "We will use a sample recording produced by the NCC Lab, called `OfficeWalk`. This project (collection of recordings on Pupil Cloud) contains two recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) object pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." + "We will use a sample recording produced by the NCC Lab, called `boardView`. This project (collection of recordings on Pupil Cloud) contains two recordings downloaded with the `Timeseries Data + Scene Video` option and a marker mapper enrichment. It can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) instance pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." ] }, { @@ -20,7 +20,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "D:\\GitHub\\pyneon\\data\\OfficeWalk\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\n" ] } ], @@ -28,7 +28,7 @@ "from pyneon import get_sample_data, NeonDataset, NeonRecording\n", "\n", "# Download sample data (if not existing) and return the path\n", - "sample_dir = get_sample_data(\"OfficeWalk\")\n", + "sample_dir = get_sample_data(\"boardView\")\n", "print(sample_dir)" ] }, @@ -39,31 +39,29 @@ "The `OfficeWalk` data has the following structure:\n", "\n", "```text\n", - "OfficeWalk\n", - "├── Timeseries Data\n", - "│ ├── walk1-e116e606\n", + "boardView\n", + "├── Timeseries Data + Scene Video\n", + "│ ├── boardview1-d4fd9a27\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", - "│ ├── walk2-93b8c234\n", + "│ ├── boardview2-713532d5\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", "| ├── enrichment_info.txt\n", "| └── sections.csv\n", - "├── OfficeWalk_FACE-MAPPER_FaceMap\n", - "├── OfficeWalk_MARKER-MAPPER_TagMap_csv\n", - "└── OfficeWalk_STATIC-IMAGE-MAPPER_ManualMap_csv\n", + "└── boardView_MARKER-MAPPER_boardMapping_csv\n", "```\n", "\n", - "The `Timeseries Data` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`." + "The `Timeseries Data + Scene Video` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To load a `NeonDataset`, specify the path to the `Timeseries Data` folder:" + "To load a `NeonDataset`, specify the path to the `Timeseries Data + Scene Video` folder:" ] }, { @@ -80,7 +78,7 @@ } ], "source": [ - "dataset_dir = sample_dir / \"Timeseries Data\"\n", + "dataset_dir = sample_dir / \"Timeseries Data + Scene Video\"\n", "dataset = NeonDataset(dataset_dir)\n", "print(dataset)" ] @@ -102,14 +100,14 @@ "output_type": "stream", "text": [ "\n", - "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview2-713532d5\n" ] } ], "source": [ - "first_recording = dataset[0]\n", - "print(type(first_recording))\n", - "print(first_recording.recording_dir)" + "recording = dataset[0]\n", + "print(type(recording))\n", + "print(recording.recording_dir)" ] }, { @@ -129,12 +127,12 @@ "output_type": "stream", "text": [ "\n", - "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\n" + "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\n" ] } ], "source": [ - "recording_dir = dataset_dir / \"walk1-e116e606\"\n", + "recording_dir = dataset_dir / \"boardview1-d4fd9a27\"\n", "recording = NeonRecording(recording_dir)\n", "print(type(recording))\n", "print(recording.recording_dir)" @@ -158,23 +156,23 @@ "output_type": "stream", "text": [ "\n", - "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n", - "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n", + "Recording ID: d4fd9a27-3e28-45bf-937f-b9c14c3c1c5e\n", + "Wearer ID: af6cd360-443a-4d3d-adda-7dc8510473c2\n", "Wearer name: Qian\n", - "Recording start time: 2024-08-30 17:37:01.527000\n", - "Recording duration: 98.213s\n", - " exist filename path\n", - "3d_eye_states True 3d_eye_states.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n", - "blinks True blinks.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n", - "events True events.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n", - "fixations True fixations.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n", - "gaze True gaze.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n", - "imu True imu.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n", - "labels True labels.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n", - "saccades True saccades.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n", - "world_timestamps True world_timestamps.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n", - "scene_video_info False None None\n", - "scene_video False None None\n", + "Recording start time: 2024-11-26 12:44:48.937000\n", + "Recording duration: 32.046s\n", + " exist filename path\n", + "3d_eye_states True 3d_eye_states.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\3d_eye_states.csv\n", + "blinks True blinks.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\blinks.csv\n", + "events True events.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\events.csv\n", + "fixations True fixations.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\fixations.csv\n", + "gaze True gaze.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\gaze.csv\n", + "imu True imu.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\imu.csv\n", + "labels True labels.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\labels.csv\n", + "saccades True saccades.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\saccades.csv\n", + "world_timestamps True world_timestamps.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\world_timestamps.csv\n", + "scene_video_info True scene_camera.json C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\scene_camera.json\n", + "scene_video True 182240fd_0.0-32.046.mp4 C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\182240fd_0.0-32.046.mp4\n", "\n" ] } @@ -187,9 +185,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As seen in the output, this recording includes all data files except the scene video and its metadata because we downloaded only the \"Timeseries Data\" instead of \" \"Timeseries Data + Scene Video\" from Pupil Cloud. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", + "As seen in the output, this recording includes all data files. This tutorial will focus on non-video data. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", "\n", - "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data like the video, it will simply return `None` and a warning message." + "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data, PyNeon will return `None` and a warning message." ] }, { @@ -201,17 +199,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "recording.gaze is \n", - "recording.fixations is \n", - "recording.video is None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\GitHub\\pyneon\\pyneon\\recording.py:271: UserWarning: Scene video not loaded because not all video-related files (video, scene_camera.json, world_timestamps.csv) are found.\n", - " warnings.warn(\n" + "recording.gaze is \n", + "recording.saccades is \n", + "recording.video is < cv2.VideoCapture 0000027AE592DB90>\n" ] } ], @@ -219,10 +209,8 @@ "# Gaze and fixation data are available\n", "gaze = recording.gaze\n", "print(f\"recording.gaze is {gaze}\")\n", - "fixations = recording.fixations\n", - "print(f\"recording.fixations is {fixations}\")\n", - "\n", - "# Video is not available\n", + "saccades = recording.saccades\n", + "print(f\"recording.saccades is {saccades}\")\n", "video = recording.video\n", "print(f\"recording.video is {video}\")" ] @@ -269,19 +257,19 @@ "text": [ " gaze x [px] gaze y [px] worn fixation id blink id \\\n", "timestamp [ns] \n", - "1725032224852161732 1067.486 620.856 1 1 \n", - "1725032224857165732 1066.920 617.117 1 1 \n", - "1725032224862161732 1072.699 615.780 1 1 \n", - "1725032224867161732 1067.447 617.062 1 1 \n", - "1725032224872161732 1071.564 613.158 1 1 \n", + "1732621490425631343 697.829 554.242 1 1 \n", + "1732621490430625343 698.096 556.335 1 1 \n", + "1732621490435625343 697.810 556.360 1 1 \n", + "1732621490440625343 695.752 557.903 1 1 \n", + "1732621490445625343 696.108 558.438 1 1 \n", "\n", " azimuth [deg] elevation [deg] \n", "timestamp [ns] \n", - "1725032224852161732 16.213030 -0.748998 \n", - "1725032224857165732 16.176285 -0.511733 \n", - "1725032224862161732 16.546413 -0.426618 \n", - "1725032224867161732 16.210049 -0.508251 \n", - "1725032224872161732 16.473521 -0.260388 \n", + "1732621490425631343 -7.581023 3.519804 \n", + "1732621490430625343 -7.563214 3.385485 \n", + "1732621490435625343 -7.581576 3.383787 \n", + "1732621490440625343 -7.713686 3.284294 \n", + "1732621490445625343 -7.690596 3.250055 \n", "gaze x [px] float64\n", "gaze y [px] float64\n", "worn Int32\n", @@ -307,43 +295,43 @@ "name": "stdout", "output_type": "stream", "text": [ - " fixation id end timestamp [ns] duration [ms] \\\n", - "start timestamp [ns] \n", - "1725032224852161732 1 1725032225007283732 155 \n", - "1725032225027282732 2 1725032225282527732 255 \n", - "1725032225347526732 3 1725032225617770732 270 \n", - "1725032225667907732 4 1725032225798022732 130 \n", - "1725032225833015732 5 1725032225958137732 125 \n", + " saccade id end timestamp [ns] duration [ms] \\\n", + "start timestamp [ns] \n", + "1732621490876132343 1 1732621490891115343 15 \n", + "1732621491241357343 2 1732621491291481343 50 \n", + "1732621491441602343 3 1732621491516601343 75 \n", + "1732621491626723343 4 1732621491696847343 70 \n", + "1732621491917092343 5 1732621491977090343 60 \n", "\n", - " fixation x [px] fixation y [px] azimuth [deg] \\\n", - "start timestamp [ns] \n", - "1725032224852161732 1069.932 614.843 16.369094 \n", - "1725032225027282732 906.439 538.107 5.878844 \n", - "1725032225347526732 694.843 533.982 -7.781338 \n", - "1725032225667907732 572.983 488.800 -15.679003 \n", - "1725032225833015732 601.861 491.125 -13.813521 \n", + " amplitude [px] amplitude [deg] mean velocity [px/s] \\\n", + "start timestamp [ns] \n", + "1732621490876132343 14.938179 0.962102 1025.709879 \n", + "1732621491241357343 130.743352 8.378644 2700.713283 \n", + "1732621491441602343 241.003342 15.391730 3615.380044 \n", + "1732621491626723343 212.619205 13.608618 3757.394092 \n", + "1732621491917092343 220.842812 13.914266 4220.180601 \n", "\n", - " elevation [deg] \n", - "start timestamp [ns] \n", - "1725032224852161732 -0.367312 \n", - "1725032225027282732 4.561914 \n", - "1725032225347526732 4.819739 \n", - "1725032225667907732 7.636408 \n", - "1725032225833015732 7.512433 \n", - "fixation id Int32\n", - "end timestamp [ns] Int64\n", - "duration [ms] Int64\n", - "fixation x [px] float64\n", - "fixation y [px] float64\n", - "azimuth [deg] float64\n", - "elevation [deg] float64\n", + " peak velocity [px/s] \n", + "start timestamp [ns] \n", + "1732621490876132343 1191.520740 \n", + "1732621491241357343 3687.314947 \n", + "1732621491441602343 5337.244676 \n", + "1732621491626723343 6164.040944 \n", + "1732621491917092343 6369.217052 \n", + "saccade id Int32\n", + "end timestamp [ns] Int64\n", + "duration [ms] Int64\n", + "amplitude [px] float64\n", + "amplitude [deg] float64\n", + "mean velocity [px/s] float64\n", + "peak velocity [px/s] float64\n", "dtype: object\n" ] } ], "source": [ - "print(fixations.data.head())\n", - "print(fixations.data.dtypes)" + "print(saccades.data.head())\n", + "print(saccades.data.dtypes)" ] }, { @@ -360,7 +348,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` returns the gaze x-coordinate column." + "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` (or `gaze['gaze x [px]']`) returns the gaze x-coordinate column." ] }, { @@ -373,35 +361,35 @@ "output_type": "stream", "text": [ "First row of gaze data:\n", - "gaze x [px] 1067.486\n", - "gaze y [px] 620.856\n", + "gaze x [px] 697.829\n", + "gaze y [px] 554.242\n", "worn 1.0\n", "fixation id 1.0\n", "blink id \n", - "azimuth [deg] 16.21303\n", - "elevation [deg] -0.748998\n", - "Name: 1725032224852161732, dtype: Float64\n", + "azimuth [deg] -7.581023\n", + "elevation [deg] 3.519804\n", + "Name: 1732621490425631343, dtype: Float64\n", "\n", "All gaze x values:\n", "timestamp [ns]\n", - "1725032224852161732 1067.486\n", - "1725032224857165732 1066.920\n", - "1725032224862161732 1072.699\n", - "1725032224867161732 1067.447\n", - "1725032224872161732 1071.564\n", - " ... \n", - "1725032319717194732 800.364\n", - "1725032319722198732 799.722\n", - "1725032319727194732 799.901\n", - "1725032319732194732 796.982\n", - "1725032319737194732 797.285\n", - "Name: gaze x [px], Length: 18769, dtype: float64\n" + "1732621490425631343 697.829\n", + "1732621490430625343 698.096\n", + "1732621490435625343 697.810\n", + "1732621490440625343 695.752\n", + "1732621490445625343 696.108\n", + " ... \n", + "1732621520958946343 837.027\n", + "1732621520964071343 836.595\n", + "1732621520969071343 836.974\n", + "1732621520974075343 835.169\n", + "1732621520979070343 833.797\n", + "Name: gaze x [px], Length: 6091, dtype: float64\n" ] } ], "source": [ "print(f\"First row of gaze data:\\n{gaze.data.iloc[0]}\\n\")\n", - "print(f\"All gaze x values:\\n{gaze.data['gaze x [px]']}\")" + "print(f\"All gaze x values:\\n{gaze['gaze x [px]']}\")" ] }, { @@ -423,10 +411,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1725032224852161732 1725032224857165732 1725032224862161732 ...\n", - " 1725032319727194732 1725032319732194732 1725032319737194732]\n", - "[0.0000000e+00 5.0040000e-03 1.0000000e-02 ... 9.4875033e+01 9.4880033e+01\n", - " 9.4885033e+01]\n" + "[1732621490425631343 1732621490430625343 1732621490435625343 ...\n", + " 1732621520969071343 1732621520974075343 1732621520979070343]\n", + "[0.0000000e+00 4.9940000e-03 9.9940000e-03 ... 3.0543440e+01 3.0548444e+01\n", + " 3.0553439e+01]\n" ] } ], @@ -439,7 +427,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Timestamps (UTC, in ns) and relative time (relative to the stream start, in s) are thus the two units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes two arguments: `start` and `end`:" + "Timestamps (UTC, in ns), relative time (relative to the stream start, in s), and index are the three units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes `start` and `end` of the crop window in either UTC timestamps or relative time, and uses `by` to specify whether " ] }, { @@ -451,18 +439,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "94.885033\n", - "9.999289\n" + "Gaze data points before cropping: 6091\n", + "Gaze data points after cropping: 999\n" ] } ], "source": [ - "# Last data time of the original gaze data\n", - "print(gaze.times[-1])\n", + "print(f\"Gaze data points before cropping: {len(gaze)}\")\n", "\n", - "# Crop the gaze data to the first 10 seconds\n", - "gaze_cropped = gaze.crop(0, 10, by=\"time\") # Crop by time\n", - "print(gaze_cropped.times[-1])" + "# Crop the gaze data to 5-10 seconds\n", + "gaze_crop = gaze.crop(5, 10, by=\"time\") # Crop by time\n", + "print(f\"Gaze data points after cropping: {len(gaze_crop)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also want to restrict one stream to the temporal range of another stream. This can be done by calling the `restrict()` method. The method takes another `NeonStream` instance as an argument and crops the stream to the intersection of the two streams' temporal ranges." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IMU first timestamp: 1732621495435389343 > Gaze first timestamp: 1732621495430263343\n", + "IMU last timestamp: 1732621500421101343 < Gaze last timestamp: 1732621500424901343\n" + ] + } + ], + "source": [ + "imu_crop = recording.imu.restrict(gaze_crop)\n", + "saccades_crop = saccades.restrict(gaze_crop)\n", + "print(\n", + " f\"IMU first timestamp: {imu_crop.first_ts} > Gaze first timestamp: {gaze_crop.first_ts}\"\n", + ")\n", + "print(\n", + " f\"IMU last timestamp: {imu_crop.last_ts} < Gaze last timestamp: {gaze_crop.last_ts}\"\n", + ")" ] }, { @@ -476,52 +495,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Data streams and events\n", + "## An example plot of cropped data\n", "\n", - "Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let's plot some samples from the `gaze` and `eye_states` streams and a saccade from the `saccades` events." + "Below we show how to easily plot the gaze and saccade data we cropped just now. Since PyNeon data are stored in `pandas.DataFrame`, you can use any plotting library that supports `pandas.DataFrame` as input. Here we use `seaaborn` and `matplotlib` to plot the gaze x, y coordinates and the saccade durations (shadowed areas)." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saccade id 2.0\n", - "end timestamp [ns] 1725032225347526656.0\n", - "duration [ms] 65.0\n", - "amplitude [px] 228.36139\n", - "amplitude [deg] 14.676102\n", - "mean velocity [px/s] 3685.269894\n", - "peak velocity [px/s] 5411.775481\n", - "Name: 1725032225282527732, dtype: Float64\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unhashable type: 'numpy.ndarray'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m 17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m fixations\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m 21\u001b[0m (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m 22\u001b[0m \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m 23\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 24\u001b[0m horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 25\u001b[0m )\n\u001b[0;32m 27\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m 1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m 2583\u001b[0m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m 2584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 2587\u001b[0m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m 2588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m 1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1758\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 107\u001b[0m axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:214\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[0;32m 213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m 216\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" - ] - }, { "data": { - "image/png": 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", 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", 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    " + "
    " ] }, "metadata": {}, @@ -532,69 +520,33 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", - "gaze_color = \"royalblue\"\n", - "gyro_color = \"darkorange\"\n", - "\n", - "imu = recording.imu\n", - "fixations = recording.saccades\n", - "\n", "# Create a figure\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax2 = ax.twinx()\n", - "ax.yaxis.label.set_color(gaze_color)\n", - "ax2.yaxis.label.set_color(gyro_color)\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", - "# Visualize the 2nd saccade\n", - "saccade = fixations.data.iloc[1]\n", - "print(saccade)\n", - "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n", - "ax.text(\n", - " (saccade.index.values + saccade[\"end timestamp [ns]\"]) / 2,\n", - " 1050,\n", - " \"Saccade\",\n", - " horizontalalignment=\"center\",\n", - ")\n", + "# Visualize the 1st saccade\n", + "for _, sac in saccades_crop.data.iterrows():\n", + " ax.axvspan(sac.name, sac[\"end timestamp [ns]\"], color=\"lightgray\")\n", "\n", - "# Visualize gaze x and pupil diameter left\n", - "sns.scatterplot(\n", + "# Visualize gaze x and y\n", + "sns.lineplot(\n", " ax=ax,\n", - " data=gaze.data.head(100),\n", - " x=gaze.data.index,\n", + " data=gaze_crop.data,\n", + " x=gaze_crop.data.index,\n", " y=\"gaze x [px]\",\n", - " color=gaze_color,\n", + " color=\"b\",\n", + " label=\"Gaze x\",\n", ")\n", - "sns.scatterplot(\n", - " ax=ax2,\n", - " data=imu.data.head(60),\n", - " x=imu.data.index,\n", - " y=\"gyro x [deg/s]\",\n", - " color=gyro_color,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's apparent that at the beginning of the recording, there are some missing data points in both the `gaze` and `imu` streams. This is presumably due to the time it takes for the sensors to start up and stabilize. We will show how to handle missing data using resampling in the next tutorial. For now, it's important to be aware of these gaps and that it will require great caution to assume the data is continuously and equally sampled.\n", - "\n", - "PyNeon also calculates the effective (as opposed to the nominal) sampling frequency of each stream by dividing the number of samples by the duration of the recording." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\n", - " f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n", - " f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\n", + "sns.lineplot(\n", + " ax=ax,\n", + " data=gaze_crop.data,\n", + " x=gaze_crop.data.index,\n", + " y=\"gaze y [px]\",\n", + " color=\"g\",\n", + " label=\"Gaze y\",\n", ")\n", - "print(\n", - " f\"IMU: nominal sampling frequency = {recording.imu.sampling_freq_nominal}, \"\n", - " f\"effective sampling frequency = {recording.imu.sampling_freq_effective}\"\n", - ")" + "ax.set_ylabel(\"Gaze location (pixels)\")\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -602,14 +554,36 @@ "metadata": {}, "source": [ "## Visualizing gaze heatmap\n", - "Finally, we will show how to plot a heatmap of the gaze/fixation data." + "Finally, we will show how to plot a heatmap of the gaze/fixation data. Since it requires gaze, fixation, and video data, the input it takes is an instance of `NeonRecording` that contains all necessary data. The method `plot_heatmap()`, by default, plots a gaze heatmap with fixations overlaid as circles." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Gg2Gl0Eqg1eItk6bOzj5m06ZNDA0Nceedd0brZmZmuPfeeznxxBMBOPHEE5mamuKBBx6ItvnBD36AUooTTjgh2ubuu++mVqtF29xxxx28+tWvZt26dXvpbAwGw4HDYnwAa8RXYNj7mJidttinYiefz7Nt2za2bdsG+EHJ27ZtY9euXQghuPzyy/nUpz7Fv/3bv/HII49w/vnns3Hjxihja/PmzZx++um85z3v4b777uMnP/kJl112Geeeey4bN24E4N3vfjfJZJKLL76YHTt28M1vfpMvfvGLXHnllfvorA0Gw+qllWjZk5AxQsewgmgBagmLXhsZxvvUjfXzn/+ct771rdHjUIBccMEF3HLLLXzkIx+hUChwySWXMDU1xUknncTtt99OOp2O9vna177GZZddxsknn4yUknPOOYcbbrgher6np4f/+q//4tJLL+W1r30t69ev55prrmmoxWMwGAwL046gWRs3DYNhNbLf1NnZnzF1dgyGA5l2PtPtfk02j2W+Xtcme6/Ozui1abrTS6izU9Zs+NPyAV9nZ78NUDYYDIaVZzmFjsGwDwjdUYvef/mmsj+z3wYoGwwGw+pjDRUuMRhWEcayYzAY1igradUxgsewl9BLDDJeI29VI3YMBoPBYFilmDo77WHEjsFgWGO0e2NYIz95DYY1gBE7BoPBsGyYbCzDXkbJJQYor433qBE7BoPBYDCsVkw2VluYbCyDwWBYFlrdcExdLoNhf8BYdgwGwxpipeJ1Fho3fG5tuAsMexetBXoJ2VhrpaywETsGg2GNsC+ETvN2a+TOYth7mJidtjBix2AwGPYaRvAYlhetWGLq+dp4P5qYHYPBYIhYTqvOfF+vJo7HYNjbGMuOwWAwLIr5RIts8Xdzyoux8BiWCb3EbKylVF9eRRixYzAY1gB7q+HnfNYciRE8hpVg6QHKa0PsGDeWwWA4wFkJodNqzD19ncoW26yNG43BsK8xlh2DwXAAY8SE4QBHSX9Z9P7LN5X9GSN2DAbDAci+FzkiNgfdYDlqdmkZd5Zh8Sy9Eei+/6zsDYwby2AwGFYYMUd8ma9eg2FvYiw7hlXEQr9AzC9jA+w9i47e47E0uoXIMRiWFxOg3B5G7BhWCXv6QMafN8JnbbKvv7QVzRabUPDoPb4njSvLsEhMzE5bGFuqYRWwr29iBkMr2hMnexY6BoNhpTGWHcN+Trspvs0Bn2B+Ka8V9iSGW9W4ibOU90mzOys8zp5+RzbPx7xXDYvDBCi3R1tiZ2ZmpuOBu7u7O97HYFiYhW4ge7qhGQ5M2hE6K82e43cMhpXCxOy0R1tip7e3FyHavyBCCB5//HEOO+ywRU/MYOic5tL8xsJz4LJcX9DL9d6Yz8IDjYLLCHLDMmNidtqibTfWt771Lfr6+va4ndaaM888c0mTMhjmUv8wt8pwMXVM1grtipx9ITDC91jzHPd0fPPeNBhWmrbEzite8Qre/OY309/f39aghx12GIlEYkkTMxg6YW7GSyvBA+bGsprppO1DaGlZqVidPc2hXVFm3o+GpWFidtqjLbHzzDPPdDTo9u3bFzUZg6GRzj6EexY84ZjmBrO6aF84DA4O4Dg5CoU8w8MjC+y70u+B+PjNczDvP8PyYWJ22mNZs7GKxSLZbHY5hzQYGthTkTZT02St4gudLVu2ks1mKRaLAPMInr392pv3msGwr+k4qunkk0/mhRdemLP+vvvu49hjj12OORkMy4gpJbV6EXRi3XOcHNlsFiEE2WwWx8mt3NQMhv0FLetByotZ9Nr4juz4LNPpNEcffTTf/OY3AVBK8YlPfIKTTjrJBCYb9i5C1pf46rb6EK0N0+3aQAKSQiFPsVhEa02xWKRQyDdtpzFWFsOBRhizs5RlLdCxG+t73/seX/7yl7nooov4f//v//GrX/2KZ599lu9+97uceuqpKzFHg6Fj2nNnGfZf2vkCjotYEbisaDNmx2AwrCUWFbNz6aWX8vzzz/PZz34W27b50Y9+xK//+q8v99wMhoXRao5Vx7DaWUic7Om1DgXPSIuxjPA1HJhovbQgY71GPhod3ykmJyc555xzuPHGG/n7v/973vWud3Hqqafyd3/3dysxP8OaZqU/heZX/+qh3a8qQaexPgbDqmapLizjxmrNkUceyaZNm/jFL37Bpk2beM973sM3v/lN3v/+9/O9732P733veysxT4MBqHeR7hzTTmL/p90+aJ2yRn66GgyGeen4m+S9730vd999N5s2bYrW/e///b956KGHqFaryzo5g2HlWRu/avZ/lip05hOyRugYDmy0lkte1gIdW3auvvrqlutf/vKXc8cddyx5QgZDaxQtb34mbsdgLHaGtcxSXVHGjTU/k5OTfOUrX+HRRx8FYPPmzVx00UVt9c4yGJYd3e7Nbj5XlmklsW9pp1RAp5jX0rA2MBWU26Pjb5XQhXXDDTcwOTnJ5OQkX/rSl9i0aRN33333SszRYGhg5VLK18aHfv9iqdfcWHUMhr3N3Xffzdve9jY2btyIEILbbrut4fkLL7wQIUTDcvrpp++byQZ0bNm59NJLede73sWNN96IZVkAeJ7H+9//fi699FIeeeSRZZ+kYS3TSVPFRlrX2tlToHKrYxkrwcqwEnE65rUyrC32RSPQQqHAMcccw0UXXcTZZ5/dcpvTTz+dr371q9HjVCq16DkuBx1bdp588kn++I//OBI6AJZlceWVV/Lkk08u6+Q8z+Pqq69m06ZNZDIZXvnKV/KXf/mX6FhhAK0111xzDQcddBCZTIZTTjmFJ554omGciYkJzjvvPLq7u+nt7eXiiy8mn2+urmrY/6nf3Nq17rTO3Or0bW9SmedH7GFZaJ+lYISOwQD7JkD5jDPO4FOf+hTvfOc7590mlUoxNDQULevWrVvKaS6Zjs/yNa95TRSrE+fRRx/lmGOOWZZJhXz2s5/lxhtv5G//9m959NFH+exnP8v111/Pl770pWib66+/nhtuuIGbbrqJe++9F8dxOO200yiXy9E25513Hjt27OCOO+7gu9/9LnfffTeXXHLJss7VsJLEb2KdC57WyDaWZozoqdPutWhXAEH7X0fGdWUwLDczMzMNS6VSWdJ4P/rRjxgYGODVr34173vf+xgfH1+mmS6Ojt1YH/zgB/nQhz7Ek08+yRve8AYAfvazn/HlL3+Zz3zmMzz88MPRtkcfffSSJvfTn/6Us846i9/+7d8G4NBDD+XrX/869913H+Bbdb7whS/w53/+55x11lkA3HrrrQwODnLbbbdx7rnn8uijj3L77bdz//33c/zxxwPwpS99iTPPPJO//uu/ZuPGjUuao2H/p53WEc0WIH/7VjdfxdqtzLuYQOJOgscXO9Zaeg0MhkaWy4118MEHN6z/+Mc/zic+8YlFjXn66adz9tlns2nTJp566in+9E//lDPOOIN77rmnwSu0N+lY7Pze7/0eAB/5yEdaPieEQGuNEALP85Y0uV//9V/n5ptv5vHHH+fXfu3XeOihh/if//kfPv/5zwPwzDPPsHv3bk455ZRon56eHk444QTuuecezj33XO655x56e3sjoQNwyimnIKXk3nvvbWmGq1QqDap2ZmZmSedhWA7isTv1NPR2iwyG2+jYDVMscIONj9kolExxQp92rTDtXK+lZF8ZoWNY2yxXNtZzzz1Hd3d3tH4pMTbnnntu9PdRRx3F0UcfzStf+Up+9KMfcfLJJy963KXQsdh55plnVmIeLbnqqquYmZnhiCOOwLIsPM/j05/+NOeddx4Au3fvBmBwcLBhv8HBwei53bt3MzAw0PC8bdv09fVF2zRz3XXX8Rd/8RfLfTqGZaVzwaNRDA4OkHNy5INGkYK5XdObiUYOUtx1w805fiM/UG+88WsrY2vbuebzWcg6wVh0DIaVpru7u0HsLCeHHXYY69ev58knn1w9YucVr3jFSsyjJf/yL//C1772Nf75n/+ZrVu3sm3bNi6//HI2btzIBRdcsGLH/djHPsaVV14ZPZ6ZmZlj4jPsC8KbW+cWHo1mcHCArVu2kM06FEslYAfDw2PBXm3ckIPhhVYxa89asvS0EDrzCcVAGNYtasstTIzQMRhgddTZef755xkfH+eggw5a8WPNR1s/uf7t3/6NWq3W9qD/8R//QalUWvSkQv7kT/6Eq666inPPPZejjjqKP/iDP+CKK67guuuuA2BoaAiA4eHhhv2Gh4ej54aGhhgZGWl43nVdJiYmom2aSaVSkcpdSbVr2Lvkcl1knS6EtMhmHXK5HL6C8T8GAmvexScIXBYSEfyrr4e1EMAcnbeIW8RaBHaLRouZ6OCfwWBoH62X1gh0MWInn8+zbds2tm3bBvgen23btrFr1y7y+Tx/8id/ws9+9jN+9atfceedd3LWWWdx+OGHc9pppy3z2bdPW2Lnne98J1NTU20Peu655/LSSy8tdk4RxWIRKRunaFkWSvm/Gjdt2sTQ0BB33nln9PzMzAz33nsvJ554IgAnnngiU1NTPPDAA9E2P/jBD1BKccIJJyx5joZ9QXN2Vuhe0q0tCMK/CefzeYrFAlprisUi+XwRge0LGpHwRYyw51kSMeEjQdhNoif+Pj2Qbth1QddozZHBuvp1abg+MWHY1tJwxIVEj8ZYdQyGfcvPf/5zjjvuOI477jgArrzySo477jiuueYaLMvi4Ycf5u1vfzu/9mu/xsUXX8xrX/tafvzjH+/TWjttubG01lx44YVtTzSe9r0U3va2t/HpT3+aQw45hK1bt/KLX/yCz3/+81x00UUACCG4/PLL+dSnPsWrXvUqNm3axNVXX83GjRt5xzveAfitLE4//XTe8573cNNNN1Gr1bjssss499xzTSbWGkIIwfDwKPAouZxDPl9gZHgcIax5XTFCSHTkjpHoyI0FGg//Zk6sXUXcpSVY/TflZssVbfUhE1jB9YnvuwdXXzhu260/DAYDsOhaOfX9O/+eestb3rLgft///vcXPZ+Voi2x02l8TFjAb6l86Utf4uqrr+b9738/IyMjbNy4kT/6oz/immuuibb5yEc+QqFQ4JJLLmFqaoqTTjqJ22+/nXQ6HW3zta99jcsuu4yTTz4ZKSXnnHMON9xww5LnZ9iXNFdW3nP8jhCS4eExhodHfYtOTOiEmVmiwTUTf6x8waMVWgTba+XLGRGP4znQBE8rZOTa81+F0HVV3yK8PnXmTzetC6PYGFrFSgashWtqMCyOfVFBeTUi9GJk3RpjZmaGnp4e/C/dtfHGWD3MX/uldRBtPT6ntdCJ7R/bT0c3bhU91qhA8Hj+el13p9Vvzqv549XkwopdHyESaO0xNLghspQNh5YyGlP82yK6dqHwme96+msMhv0b/z07PT29YjGf4X1p+ztfQ1di8bVrZmseR37nwRWd6/7AorqeGwz7D4vvnbUQojmOJHJp1a0MAolGBW4b5nFpHXiWCIGF1oqhwQ1s3bqFrJOjWCyA2MnI8GSwzR7S+ZuFpAiuZVsuQoPBYOgMI3YMBwCtCw7Wn1ZtxZrMR5SNJQKLjvZdK0IA2LGbNPO4tFaj4BGxv+J/W8H/klzOIevkEMImm+0il8sxMjwVWHfiwmSutazuBvMQwXX1r5Lrb7+gi3A1Xk+DYWVYDann+wNG7BgOWPZYbLBDEaS1y8DAOpwowHmU+RIa22lRsTqY//rkC0WKxQLZbBfFYoF8vkjLoGYaRU7cqhPGQdVfJgkaPxhcL60Cu8GwFtBqaXE3ayUnwIgdwwFCs3UHQNYFT/iJFuF6z7cuBAHH4RAi+DuMZBPC31ZrxcDAOrZu3Uo261Ao5oGdfkZXkKkVt+4ccO6seDyTkEhhMzI8DvySXM6hkC8wOjKJlPWvlMgKJKS/xGKjGjO2QGkXAE+BHwyuUC2tOwaDwdA5HYudp59+msMOO2wl5mIwLJHW8TtzLTy+qysueMKYESKRg7+NVoElQuPkHLJZByEkTjZHLpdlZHjMd9toYvE7/ngH5A06bpURSUZHphkdmUJgI0VjaYpQ5IBvwZEiAYAlwq8d//94MLMWCkXNF5tCBgJypX56rtWGroYDCePGao+OAxkOP/xw3vrWt/J//+//XbZ6OgbD8tFccLD56aasqtC6oMPMqmDRYbFCFcXpFPKFoCihauG2YQGXWLzC8mpiPhddYwyOFMlG11RoyQmsOaHQkUJiCTv4u3GxwqKNgdUnsgQ1ZcgtDdG0zPecwbB6COvsLGVZC3R8lg8++CBHH300V155JUNDQ/zRH/0R991330rMzWBYJHMrLDdUV9ZhWnP4XM13VeE1iB6l3Uj0CAHDw+Ns37GDnY9uZ/uOHUFRwlYfodX+5dF0wxdhtWQrJnRkJEygSeDEhE5dzNhYIoUt09gyhS1TSGEhEEis4LkUUthIadMc2+PPailCpLmZ6XxLuK0RPQbDgUTH38rHHnssX/ziF3nxxRf5p3/6J1566SVOOukkjjzySD7/+c8zOjq6EvM0GDqktVuiwa0UiR5oaemBmJXHv6GPDE/y9FPPMzI8iRAHYsibaHrUujjjQo/9/errpJAIYQXiRiKxUKpC//oUhxzaT9+GFJ4qB/tZrcdZQjZdZ8LlQG37YThQUVoseVkLLPobxLZtzj77bP71X/+Vz372szz55JN8+MMf5uCDD+b8889flt5YBsPSWLiH1hxLj3ZpsPTo2jxWHhHE9BzIaQyxXlhhxeQoBkc2uJtgrusqXCdjriwR7Ke1on9Dji1bjmTrluM4cstRbBjobmjN0dpithji2WGhhSr2T1j+Evs318pjMOzHLKUJqBKwRiooL/ob5ec//znvf//7Oeigg/j85z/Phz/8YZ566inuuOMOXnzxRc4666zlnKfBsEiaLTx10eM/O4/wiVxcXst4nublwKDFl17cVdVQnbpugWlHmEjhCyOJhdDgOFm6nHXYwsZxesk5ji+Hlk3k+LNsfNTUsT1+rBYNSecbx2DYnwgDlJeyrAU6tsN//vOf56tf/SqPPfYYZ555Jrfeeitnnnlm1J1806ZN3HLLLRx66KHLPVeDYZG0ytKaW3ywIWsrqsGjIrkkggwhHfTJatw37hJbjTTWx2luD0FkufG/MuIWncZRWgsGpT0EEiVACEGhUGS2MInj9DBbmCBfKPhSUqsG12GUidXxtW1xPsF5LPgbL0hz949tqjYbDAcKHYudG2+8kYsuuogLL7yQgw46qOU2AwMDfOUrX1ny5AyG5SOSLLF1zTcy2RDTE970Iv0DoL16ennLo3gtxt3faRGnEw9KFnbdfRVmSsXcVXsisoiFqf1CMj6aZ/vOh3GcLPl8kZGRKaRIRDWNwv8bx2k3PXyeuKNQqNGi030kcGK766BtxYFQJ8lwwGJSz9ujY7HzxBNP7HGbZDLZcad0g2HvsFAvrUZrT0NBQiGZU59nzsirseJvY5ZSy9YQTXE5/nNzW0C0cucprSLXlNIeMtAMUqaYGK0wPlICQSB0Gl2CdUHZSjx2KD7mc1HF6vjMTUY/UKpgGw5kjNhpjwMxncRg2AN7EjwhdUtPSyvPfPs2dOven2lOx4Z6J/hEzGVVTwWXgZWnldiLDxcXLUoroEbYOBVA6OBvAUrX8HQNrRWeqqJ0jbDr+cICslnwzD2f1uItKGaoPQaH1vtd22fz7B4eJWrq6s88Npax7hgMqxkjdgxrlHa6pdctPa3bTrTL/u7WahYGMpZ5RYPQCasgtwokVrpu3YqsPcFjpUFGDT+DFhzBdVFaRe0iQjegjgudBStRt/FCNMUehQwOrWfr1iPJZjMUC377j93DY02jG+uOYf9GaYlaQmHApey7mjBix2BYkEbBAzS5ttphfwt0nccCMqd4oC94/CJ/9SrIfmp43bKjg4adMmixoeaxyKh5ahq1ym6b34XVzrVsyrJq+ttPf/f8ru1B+4+skyOXc2B4jOa+XQbD/ozWYmmNQI0by2A4kOnkAx53Z7Sw8nR83H1pKdiD0BGJQMz4VpzIdYUVa/VQT0X3XVEJlPaCmBs/LkfFazeG1anDxzq06Lixv33XldJuzH01X4bbnkWmaDrPuenzFvl8Keja7vjtPwrFpkHkKs+wMxgMIUbsGNYg+/qXzL4WPK1oUQVZ1IsHxqsgQ6zwX3AakcAJA5CDS6y0arCUNFptfOKuq/lo5U6az8XUTlsJvxr2OLCTXC5LPp+vt/8wAsewijAByu2xKLHzrW99i3/5l39h165dVKvVhucefPDBZZmYwbC8rNAHegFXlmjILIpvFwa71p9deZrPP5Z5FQYcB1YcIpeVH4wcWnSsYPFHkw3DahRKBGnjQkWWHhGKHCSerkUVlMOlwaIDjVad5jMQc4Oi9/yqLvD6CJuR4UlGhicAjRA2OpyHwbBKMGKnPTqOTLrhhhv4wz/8QwYHB/nFL37B61//evr7+3n66ac544wzVmKOBsMK0aoJ5HKMFxCrytvsVmlkb3/ZNMexyIbnojidyKqTiCog+207Ew2LxKr/H4ikUBiF7RigseoytLDotBI6c6odx9s+JOjkNWxVF0gEFqvla09hMBiWg6effnpZx+v4E/53f/d33HzzzXzpS18imUzykY98hDvuuIMPfvCDTE9PL+vkDIal09zBeqEb40LdsOOtEmItB2I3Xj+w1wr+TtRvxsIOspvqPZjmzmElO23PM24s24oo8LgeoFyP06k38JTBc1JYkaCxRcoXOVoilMbSFrZINSyWSAWdzxMNxwnF1cLI+jUV/iJFKogrSgV/B8/Fr3sUbN1cRHBujSCDYbVyoDYCPfzww3nrW9/K//2//5dyubzk8Tr+pO/atYtf//VfByCTyTA7OwvAH/zBH/D1r399yRMyGJaPuSKnZRPIFs0gW3b7Dtc3iBwruNHa0SJlMva46SYcCp89ip7mZTmuQXgd5qaZy3C+WJFVxxJ2cF2kL2you7Is7Miyk9AplKrRtz7DIYeup299BqEVSZElKbK+4JEpbJmOgp4Xrr7cJCKD6ytlEimSWDKNlEksmfHXBetl7DUIr3tzzyvR4u+Gdcti4TMY9h4Ham+sBx98kKOPPporr7ySoaEh/uiP/oj77rtv0eN1/MkeGhpiYmICgEMOOYSf/exnADzzzDNovb8FXRrWLgsUmJvT9DG4uTa5TPYsfposIsGNFsK/w0ymFq6YOe6tPX0Ul8/ys9Axw7RsP6bGo7kDedTRHAupJVJLtHbpX5/myC1bOWrLazly65GsW58BrSORJCNrUVPLiTm9taw5f9fn4McSha41//rWHy9kJZpzHnO2MyLHsDo5UMXOscceyxe/+EVefPFF/umf/omXXnqJk046iSOPPJLPf/7zjI6OdjRex5/w3/zN3+Tf/u3fAPjDP/xDrrjiCn7rt36L//2//zfvfOc7Ox3OYFhm4qIgZs0RgStD2DGXSCpYQutBqskCEy4y2jfaP7Q2iNCykMKS/mJbGWwr41seRCr4P7Q+pCJLz/xWnoU+lp1Ye1pbdfyn6qIlXlNH47FhoIdDNw3Sv8HBU+Vgu7o1RuKLHInExkarGtlskp5sHymRpi87SK/TS1IlSWuHNDlSIudbemSWhMz4ri2ZDESLHVllfHdfkzUnsOLYVoaE5ZCwHJKWQ8rqIhk8tq10/ZrLZFAA0Y7EZiiWwvOsuxytmMCKi7oWFaINBsNex7Ztzj77bP71X/+Vz372szz55JN8+MMf5uCDD+b888/npZdeam+cTg988803o5QfQHjppZfS39/PT3/6U97+9rfzR3/0R50OZzCsKM1WjGZrTBwdFArUKL/DOWEQbWxfaNg/qigcFN6DutVA42cmaa2QJOpjadAChI41ntSKxlTqdornLS2FvTmWRWvFwOA6tm45kq5cD6ViiUd3/pKZcR3pJhlYZmTwr6rydPVb2CmPqphAe91UilUq+SpJ0ghsVPAPAUJLPFHzj69dX3hoC40bpbKHXeXj1px4ZljoWqufRw2wcQGFiySBFytg2GzRaSVkwiwx/4FJPzesHpYad7O/xuyE/PznP+ef/umf+MY3voHjOHz4wx/m4osv5vnnn+cv/uIvOOuss9pyb3UkdlzX5dprr+Wiiy7i5S9/OQDnnnsu55577uLOwmBYVuZpahnEojSKnLpQie8eddsORIrAnlMXRkauqnp1Yf9G3JiW7adgK7TwgtRqG6UsFDUECoWLQKJ1LZp6mK7ui57wJr18N956zFFsXehO0pBzHBwnF1QV7iLjpJkcmw3OGRQqmJWNp6v0bkiydfNWPM9jenKCqelnmBl2KY3apK1cUE3Zn38NiRQSDz8FXemaLwAlaOVfdyHC6xev8WNjyxQCSUJmAaJrrfDQ2m8iigIP//pqEXZObzzP+N++uFH+NqJ5vcGwOtB6aenj+2v0yec//3m++tWv8thjj3HmmWdy6623cuaZZyKl/znetGkTt9xyC4ceemhb43Ukdmzb5vrrr+f888/veOIGw8qyUFNLK4qhaQjCnce6o1Eo5aKDujGiSWzExwjTrKVIBBYcX0xYIumPE9SbUbrmt0uQvnVD6RqS0JqU8AVPeBqLsvLsiXY81oJCoUShkA8sOwUK+XpVYU0oXPyxlPbIOA49Tj+WsKhW1rH72XEqowm6ZR9oUME5WNjY2NSoooTyG3/KukDxxUm9zUT8NUrITFTnJymyhNlgYQVnjxoqEE0EbjctFTpoNhofE6i76wZ7yDkO+XwhKihohI7BsH9w4403ctFFF3HhhRdy0EEHtdxmYGCAr3zlK22N17Eb6+STT+auu+5qW00ZDCtP68wpn0bXUhQcG7PshE0tlVYIYdWtDVr6VoIWv3zi4wF4qszAQK9/8yyUmBgrYolUvbKw1oAX/Yzyqwr7lgzdVGW4tRtlOQRP83hzz2lkZBLBDnK5HKV8mbGxAimrB4UfrGyRiLmlBKVCmdnCJN1OH9VaGV2xSYuML/riR9MCKSQWNhKJDsSKEBYy7moKmokK0ehqjNf5kcICrdHaRQvtr4OG8bwFfq1qrdgw2MPWLVtxnBz5wizo7QyPTC7XxTUY9hoHalHBO+64g0MOOSSy5IRorXnuuec45JBDSCaTXHDBBW2N17HYOeOMM7jqqqt45JFHeO1rX4vjOA3Pv/3tb+90SINhCSxQGTjK/KmnVYdBsZawsWU6GEHWK/sG1hhPV4Ju3Ha9I3cgQEK3j2/V8T9CGwa62Lr1KLqcXgqFWXbu3MnkWBUhbDxVoX9DDieXIT87y/DIBELafusnPN/CE1hM4haeRpfWYthDZ/cWmUtSJBgdmWF0dBZb+NfK0zUkFgiQOqyz4wuLydEK2/V2HCdDJV+jOArdMo0ViMB66K9Aal/seLi4uL4LSig8JEqouvtJ1+OhpJDYMu0HNJMgIVLUVIn+9WkcJ8NsocjkWBFLprFIRP25/Ay42GvWEKejIncdCJxsDifnwMjEIq+zwbDv0EuM2dlfxc4rX/lKXnrpJQYGBhrWT0xMsGnTJjyvs2a9HYud97///YDvT2tGCNHxBAyGxdH+BzReTyUUOmHQa5gSDUGMTWDBCFsd6OBx83iN6zQ5x6HL6UUKmy5nHY6TZXK0AsD6gRybNx9B1smRz0+heYSRkamGgFyCoFwWtPAst3VnHoRAChuNiFxXSvuiUAkvmIUfu5OQDoVxKIyWSZMlIzOx2davkkZi+ScbCUQprEig+Na1hN8+IorbqWeBRdlgStO/Ps3WLVvpdvqZKYzz8M5HmB5369vH2krE+3fVkeQLBfKFWZxsjkIxTz4/y1IDvg0Gw/IxXymbfD5POp3ueLyOxU6YiWUw7D0W98sjnm0Uuq9Ci0Rz/RdQeMrDEpYfQxPczq1AXPixNcwT0yHIFwrMFqYiy06hUAQh0NrDcXI4TjdCQNbpwsllYXQ6skD5x7fxI1xkPWg5OPW5QcuL+QzWY22ix2H2WRCo659f4NLDRQrwdBjv0lSjRhO1g7CxsawkaBsXD6F9x5KK7enpKh41PMDWNgiiwoRKeIG1KOghpoleq2Y8amScHDmnN7ie3WSdJJNjlSDA3LfshGhdj9mpv1oWI8OToLbj5BwK+QLDw+P4vbHm67RuMOyfHGhurCuvvBLwjSfXXHMN2Ww2es7zPO69916OPfbYjsddUtfzcrm8KIVlMLTHEj6ETUUD43E6YRXghEgjkHiqQu/6JFknR6FQZGKsjC1SfmNL5WdUhUJnTkBzYJEZHZlhBzvI5bKUCmXGR4vYMouLR6FQpFCYIevkKBZmKeZLyDDLS4DQVjC+n+kVBi37vaLql0FotUTBsxB+VpKfKWYFa+pNMcMYIz8tX4IET9cCV1QKpX3rjK1tJAIVpKaDoKAmSW2AXC5JIV9icqxGQjpBRWYXpT2k8K06InCXze2jFQRHC5t8ocRMYYJup4/Z4iSFQjEQp14UEK6DnltRdl14HkFwMuDH6IxMACJ4XeuZYxovuP7G0mPYvznQxM4vfvELwLfsPPLIIySTyei5ZDLJMcccw4c//OGOx+1Y7Hiex7XXXstNN93E8PAwjz/+OIcddhhXX301hx56KBdffHHHkzAY5rLED2AgUPaE0jV61yc5cstWnFwPs4UJtu/YzvhYqcGdEg0bCB7tB9z49h8BUiaZGCsxMVrys7RkKnKJjY3k2a624+SyFGZnGR2dQQTZW1EQbhBeozWIwJvSUItnXnGzRNdLYN0hOkYgBoIhowylINBa4EXCTwWWFA8XBHgxi42rFTa+RSe1ATZv3UyP0894/iV+sWMbpfE80mp2D1oQxkcFNYkiN1pwLW2RYmqsyPadO8g5GWYLBcZGC9gyg9aVwOU2vxBsjruKCyGtY0Kn9d4dXlyDwdApP/zhDwG/aPEXv/hFuru7l2Xcjisof/rTn+aWW27h+uuvb1BcRx55JP/4j/+4LJMyrGWWry1C/aYV3sQaXRzgp087Tpqc04sUkpzTi+Nk52zXMK72U9OVdlG6hqsquKqCp6soXDxdo6ZK1FQJV1dASMZH8zz7zG7Gxgp+zyYaA539NPZEsIRNLmN9noKKy2EPr8ZKywtdL930SEdumvD6RFYQrfwMJx2mytfwVAVPVf1FV4LzKlJTRapenorOU9VFyjpPRZQoiSIlUaQiqlSoUaZMKpegy1lHRRdYN5RjwyEOiXUlqsrvqyebrTjB/LwgXd9VZf8a6wpVXcQTMD5W4NlfjTI2VkBLQVX7c3JVJZq30rXgfBoX34rVuE5Ff9eC90vcqqPmXEeDYX/hQG0E+tWvfnXZhA4swrJz6623cvPNN3PyySfz3ve+N1p/zDHH8Mtf/nLZJmZYi6zAh66FC0ppLzqUFBaFQpl8YQon10O+MEWxUAxS0JuEQiAI4o9VUPlX6RqQAJSfSt38O0IIBNa8VocoI2xOxWUF2kULQMcDlxeTCBCL24mqRXu+9SiwYoWF/fzziyaH1h6SRGRI8nD9AoG6Fs3dw0Wg0CQja4+lLQr5MhP53fQNdZHtTtHXO8i6zS/n0ScfZXy8dTfjejVjP45H49fmaUxFt9DUAtEZuq+8uiuq+fUKrWOxl7W+rul1iVVgNhj2Zw4kN9bZZ5/NLbfcQnd3N2efffaC237729/uaOyOxc4LL7zA4YcfPme9UopardbpcAbDAsxneFzoBhQG+dY/wDqoYqy1xAvcJK6uBPVZLMbH8jyy8yEyTppCocj4aBGkjCw3YdxHyxiQIKBWBkHNAEo31vZZCD9w2grmGQQDCwHCjiwuSodp8YFbR9cCV5e/zr8ei3FnNYqf6DtP15+vu3oknq5EVY219OclhX9tpUhQlVaU8q1QWNikZJqpsRoP73iY/tks/b1DCDdNz7o+0s5TeKMzaCkiq1uzWAkFmBR+TJBW8bBnGgo2eroSWaVCC5UOA7FjNFfE9tfFrIBRrI4ROob9nwOpXURPT4///Rf8vZx0LHa2bNnCj3/8Y17xilc0rP/Wt77Fcccdt2wTM6wlFmhYuUg0OrAy+OP5VpgaSvtveRGzSFgiwcRYBW807wsPKXFVJbAmhC4OFVhvmq07vqjxRVS9CF68Wm/DmcbS1uNFDZWqMTDQ48f15IuMjkz7cT9BrR+NwlMV/yaOROlKlKm1VMGjw/lG7q16HE+9Tk14HhKNi1ZeMHcXJWykcIPHYf8wv4+WRiGsBNMTNSpqkp4tB7Gut5eZwjizhTxK6EjohNdaqVjcjvbnpkTQR4tKw3UNBU1d5AQuqki0tGMBa7TuzA1KNi4sg2Fv8NWvfrXl38tBx2Lnmmuu4YILLuCFF15AKcW3v/1tHnvsMW699Va++93vLuvkDIbWdJKNFKZY+9lGoatD6Ro6CBJGBDdpIdHo2A3Ua7DqxIVOg3UgCuYlsoLoyCrizRE+YQYXBNWbtWbDQC9bt24l5/RQKMywU+xkbLSILRO4QfFBvw4QgOtncGGhRS1waS1F8PjjN2RAxZtixuYcBWjH/g/7UPlWMIUUtt8OQli4uL79TKYpTLk8/sQTpJ1nyRdKjI8WENKO9mueT9yVha67IuOBxXFXVZRCH58/0NZ7ZY7QMVYdw+rAr4e1BDfWSoQPLAOlUgmtdZR6/uyzz/Kd73yHLVu2cOqpp3Y8Xsc/oc866yz+/d//nf/+7//GcRyuueYaHn30Uf793/+d3/qt3+p4AnvihRde4Pd///fp7+8nk8lw1FFH8fOf/zx6XmvNNddcw0EHHUQmk+GUU07hiSeeaBhjYmKC8847j+7ubnp7e7n44ovJ5/PLPlfDchDvZt34b77t5tL8S92LAlA9VcH1ytRUiYqXp+LlqXp5yt401eDvqsoHQbglXK8cBLtWYkGsfiBruCjtW1yUqvqLDpbocSxoVrnRzVkGVh5LJOju6qYnN0Ba5ujr2khvrp+s7CUr1+HY/WTtflJWF0mrG9tykDKNlEkEiaagZf/KNS57umb16zTHEhLWnYlZTMLr4J9breGahtetovOU9Sxl8pRFgZIooC2L8fEKu56dYGKshJA2rvYDj70gINoP/G4MLFaqiqdK0eJ6hWiJr1eqHF1vTQ1Nzc/uip3DfIsO/jW8f9AYq45hfyeM2VnKsj9y1llnceuttwIwNTXF61//ej73uc9x1llnceONN3Y83qL8BW9605u44447GBkZoVgs8j//8z+LUlp7YnJykje+8Y0kEgn+8z//k507d/K5z32OdevWRdtcf/313HDDDdx0003ce++9OI7DaaedRrlcD3w877zz2LFjB3fccQff/e53ufvuu7nkkkuWfb6GpdIodJqZK3raePuGv/Zj7o7QahBlHGk3Wuo33sB9FQQJ6+im6EXr4ku4PqzPEh2PulUo3A4aLUNC2JQKVUqFWQQWxcIs5YJLQmRJigw2KRKksWU6qP5sR53bafi/zWuyp0sWO5c5Aqjp3Ihf05g7yQ3EoUuFmi7j6Roeru8yC4KLo7o4eu41CR83L+HrEl8iYRR/bdosDtgocsBYdAyG/YMHH3yQN73pTYAfJjM0NMSzzz7Lrbfeyg033NDxeEsqKpjP5+dUVF7OVLHPfvazHHzwwQ2+u02bNkV/a635whe+wJ//+Z9z1llnAX622ODgILfddhvnnnsujz76KLfffjv3338/xx9/PABf+tKXOPPMM/nrv/5rNm7cuGzzNexf+HE7Ivg7yDrCj63xAvcLgBJu434Ngcj+DbYe/zFP9k48ziUM/A1cWDoKmq4Xrqs3urRIyAwzYx6P7XyKHmeEcr5KbTTBOrkOrRVVUcXDxZIJqqJIVRUjV5vr+a4trWuE3dn1AoInEostxFFzIb92CLO3tAal3CB+KYyn8RBSRnWJLJ1A0VjduHFuLea9x9o30CBQ5o272RPNr6ex6BhWBwdSgHKcYrFIV1cXAP/1X//F2WefjZSSN7zhDTz77LMdj9fxz8BnnnmG3/7t38ZxHHp6eli3bh3r1q2jt7e3weKyHPzbv/0bxx9/PL/7u7/LwMAAxx13HP/wD//QMJfdu3dzyimnROt6eno44YQTuOeeewC455576O3tjYQOwCmnnIKUknvvvXdZ52tYIZqDfOdYd5rfxvXH9boyyndtRO4X3zWidCVwg1Tq7hBdqbujVNV3V8VcIn4sT4t/um7VIaznQi2y9oSWkMZzkdikcMR6qmNpZp8RMNrNgHgZvTpHt87RrbrpVetwdA9ZsY6M7CFp5UjIDFKkAkuPX4cHIedYxRpcgWKuFUhgLUro+NdXxURhWJunGtQd8i1l/t81apSDlhH+suC4MctMgyVNu3MWHViH4q9L+8x9TYzQMawmDlQ31uGHH85tt93Gc889x/e///3IezQyMrIoo0rHlp3f//3fR2vNP/3TPzE4OBilia0ETz/9NDfeeCNXXnklf/qnf8r999/PBz/4wait++7duwEYHBxs2G9wcDB6bvfu3XO6ptq2TV9fX7RNM5VKhUqlEj2emZlZztMyLAMC0XRTa63bm2/8zYG49Yq6jZaGxmye9l0bcWtSvTqxgjDbSYQ3b79AoAwXLUmRxhFO0DFKYgmJpX2R4uGR1Ek0Ck+ksMJMLmmjPD+zLMw8C7O0Gi/EApacNlLk56OlNWYewuKB4T6eqGHhn4eF8v8JN3he+hYqPddqA3uy3Mz3ei3dxWcwGPYO11xzDe9+97u54oorOPnkkznxxBMB38qzmMzvjsXOQw89xAMPPMCrX/3qjg/WKUopjj/+eK699loAjjvuOLZv385NN93EBRdcsGLHve666/iLv/iLFRvfsAjiN+QoHTp0UbW+8bWK+wlTrf0MprjoiQ3fVHm5tWuk1Q1VNmwnEJHgmZPtRFBIMLDspHSajM6QEykSQpKSElv4gq6mNJ7WWMqirFNY0sYTNVxRCdLhwVMV3/ARZZ+1uiD1TuLRqqYeYovDd9stVFdICIlFwq9tJCzCzvIKD0/UqGnLL/YIeFqilBVkeuG/VsuSLdXOPsaqY1hdKJboxtpPs7F+53d+h5NOOomXXnqJY445Jlp/8skn8853vrPj8Tr+dnvd617Hc8891/GBFsNBBx3Eli1bGtZt3ryZXbt2ATA0NATA8PBwwzbDw8PRc0NDQ4yMjDQ877ouExMT0TbNfOxjH2N6ejpa9tb5GhZHc+By6+ytOI3ZR3ODcRtTmNsPYm10iUT7xNwxc+JUhERqGZQ3lCSExBICWwhSFmgquBRBVEkgSWCR0Mmoa3somKRI1Gv4iLmLEHa0bZi+LYRN3Q0YX9/eUmfhrxEpLMLu8lJYWNjB+dr17vPC/zvMUIuO03LsVtdfNy0Gw9rgQHVjgX//Pu6445Cy/j3w+te/niOOOKLjsTq27PzjP/4j733ve3nhhRc48sgjSSQSDc8fffTRHU9iPt74xjfy2GOPNax7/PHHo4KGmzZtYmhoiDvvvDNq+T4zM8O9997L+973PgBOPPFEpqameOCBB3jta18LwA9+8AOUUpxwwgktj5tKpUilUst2HoZ2CQJ7m4ncQQGiyb0Rrm4lcDpy0bR2mXRGc1f00K01z7kF+NWcBZYQJAKhU1ST6PWKXDZJKV/AG9ak6MbVSRI6BQISMuu7tnS9YWerYN64VSkuVOYUPWwxx1YWm3i9oXhzTfA7twssLGFjBy63pMiS1BnfXafTDWO5wqUsUiihgl5hpajHFSiEdv1g8pavyXzCJtbrwmAwrDoKhQKf+cxnuPPOOxkZGZmTDPX00093NF7HYmd0dJSnnnqKP/zDP4zWCSHQWiOEwPMW07OnNVdccQW//uu/zrXXXsu73vUu7rvvPm6++WZuvvnm6LiXX345n/rUp3jVq17Fpk2buPrqq9m4cSPveMc7AN8SdPrpp/Oe97yHm266iVqtxmWXXca5555rMrH2S8KifLpRvLQSPIsivt88gmaPsSGdFDVsMXzTTVsisbWFhYUtBAkpULqCvUGxefMWep1+JvLj/FxvpzriksQmRRqpJVVRxBN+zA5WowiZz13V7E6TLa5luG/YymLuOdTTxVVTpWIraGgadim3SGApC1uBLZJkyYY2JgBq2sMWNq4IRI30j+/poC6R8IOQifcHC46+ZzoRPcYiZFh9KMSSXFH7qxvr//v//j/uuusu/uAP/oCDDjpoyfHBHYudiy66iOOOO46vf/3rKx6g/LrXvY7vfOc7fOxjH+OTn/wkmzZt4gtf+ALnnXdetM1HPvIRCoUCl1xyCVNTU5x00kncfvvtpNP1X49f+9rXuOyyyzj55JORUnLOOecsKk/fsFL4DQvmrtWtrTUdE6ZCh/2ORPC+DW/yLYJ5Y3FB8wuexdPQ5kAov22C1iitUVrgiRrpbJJ+px8tJb1OP2knRUnXEKL+3pZYUbCyhW/ZCZuNhiKmUfTMn3UV3y6Mq/H3aW3Z8YWOh6AG2FGdnFDohC4qpSv0rM/Qnc1Qy7vURvNkZF/sHCRK+720KqJMghpaqEZ3lrZidXiWJjZbY4SOYZWyVFfUfurG+s///E++973v8cY3vnFZxhNa644+5Y7j8NBDD7VsBnqgMjMzEzQlkxiz+ErSumDgHMGzR6tO65vz4OAGcjmHfL7A8PBoi5v4wm6szmu3+EQp34AIUsWlSJG2e7Blmi65gZzqIauz9MssCSFIyCp6fYEtW3zLzlh+nG3bt1MdyVLDYlrMUhVVimLWL+CHF6Vzx7OeZNx9FWue2XqesY7iyChTyg76icngeYXyrTB4eLh+Ecbg+GFLBz8iJ0VCJ+lZLzhu82vY4AySL0zy5KO/xBrPIYXA0y5aS2raxkUzLWaZlTNUdZG8N4KrK9S8Ap4qo7Wf/l9v0tnp67HQZ9eIHcNy4r9Hp6enl7X2XJzwvvQvx/4OWSux5x3moejVeNe2b63oXBfDpk2b+I//+A82b968LON1bNn5zd/8zTUndgz7lj26tBpoJXQ0g4Mb2Lp1C9msQ7FYAHYyPDw2v2WyRVxQQyf1Zbg5hpYRP+Va1S07QmCJNJXxEo/s2EnKSVLKV6iMChIiRS3o3C6DfyKSIQTr68HAkYCJbaEWsIoEkUNILbHxg5ptbc/Z38JvHVEV1SDVPRBSgUVG4qfUC6VxnBy9znoSwqbf2cBLuV8xPjZO14YMOSdFIV+kMgxp0YOFja1t3KDKst8DzMcv0OjOmbPBYDjw+Mu//EuuueYa/s//+T9Rf6yl0LHYedvb3sYVV1zBI488wlFHHTUnQPntb3/7kidlWKvE3Vlxl8U8ggfajN3RdOW6cLI5hJBksw65nMPw8ChzrUkxIRCO3W4wdHS0FkIoFGih+0p4eEHaeFUXKYkESEipBAlPorVNUvZSHS1THnERZEmQpKI9arEbfkKnGlxTcZEitYysMnFCsaOEatgPIEESqQUWFknsKEMsXqpQAxXlodCUqVIRFVzhUhLFKHbHF0QJksKmkq9SKEyRdQaYKU5QmM2TG7A5eutW+px+xgrj/EI/gjdaI6FtEiJJjUrgCrODoocyskj5rkX/r/qMFsJYYw0HLkvNqNpfs7E+97nP8dRTTzE4OMihhx46R2s8+OCDHY3Xsdh573vfC8AnP/nJOc8td4CyYS3SHFBaz2JqqF/TAQKbfL5AsVgkm81SKpbI5wu0vgm2iONZSFDNI4TmEzxa+B3KddDXCcDVFaoUkUgKokxC2ygFKW0BCSCBh6aiXWq4BLYgAD+NW8sGC0xCJ7CwkAjspmBkf09/bkrrSESGAcMJbCSQFBYZy8ISkJR+lhiAFOBpTdmz8DQUPZuiTlDVLlLIwL2lcHGxg0Dq4miBR3c8you5X1EtVnEnFOs29bA+148tJeudfpxcmtkxD1tb2Novq2iJBFFrDREUGYwKDcaF6UKd3vfPL3KDYbloVQO80/33R8Iko+WiY7HTnP5lMKw8C6Rtz3FptdhWKHYPD6NRe4jZiY/RJvNYf+KCp5VVqt6UVPlNMkWNmqhQoYwKrCtKhzWRRdB4whc6rqj/oJCBAPAFgo2tLXypYwUOqTDjqYJHDYmNJZLRvMK5eroKuCQkpGWGtJRkbYElIGuDJeqywdOCkieoKX89LiS0xNMeEl/w+OP61yYte3DHBLNjkJYOPXY3pXyesfw4/U4/44VxivkyAids99n+9Y9d8aWxkGAyGAx7m49//OPLOt6SGoEaDCtHBxaeOS6tRhcY+BlFw8NjketqweDkxTBP7Z+5hHYViacqKFGLGmW6ouK3UJAJijoTuJRisTJi7vgJnUQiSekEKRJYCFLSIiEEUvhiZcabILlekXZSFPNlKqMVcrIvOuOinsQZ0GRzaSiVsSarHJJeR3fSIyk1XbZHQiqE8GfuKsl0zaaqBBMVi8mqpOzZiKqgqj1KukJJlqK5KxRSpLGw8fPjLcqjBR7esYOMk6KQL1MaFaREEo8SrnDxhB/srGOp5r4rq94OQ7eywi3Inl5zI3gMq48D1Y0FMDU1xbe+9S2eeuop/uRP/oS+vj4efPBBBgcHednLXtbRWIsSO4VCgbvuuotdu3ZRrVYbnvvgBz+4mCENhjbYQxxPy+3jgkfQaAHYh1ZKrUAov1u4dlHaj8SxRAKtFVL46dhSyKbwYx8RhSf7NXpsLL/6MoKUlFgCLCFwdZn0Bs3mrUeyLtvPeGGM7Tt24I1VSIo0VV0ms0FzzJFH0u/0U6qM8/jOnVilEt0Jm5RUdCdqJC1fdEg0FeW7xipKUvUkJc+/rgkh/bYWQYBzY1Bz2AbVD8J2ZB+1sTLFMRe0Q0YkcXXoZOv0dZmvdlI7WXtG8BhWN0ovrXO52k/f7g8//DCnnHIKPT09/OpXv+I973kPfX19fPvb32bXrl3ceuutHY3Xsdj5xS9+wZlnnkmxWKRQKNDX18fY2BjZbJaBgQEjdgzLTKuicPNYeVoGLe8fbtdonlGVY1/kCCRKudQoIYUfsCyQ1GQ5bAca1dEJBY6lEyTwXVGh2yqJRVpaKCooyiREgrSVpqRckl0pNjj9CClZ76wnm0tTHlckpS+osl1pXtbdT9KSvCy7jsl1FoM6z6/1QMp26XNmSSXrP2oq1STj+W5KtQRpmcMSSWZdSdG1sT2BpxSVoO5O6NKSSDytSGiLsuuhcEmIJCmRwdWKMhVcFCVZoiJKVHXJd+/pVtlXrZud+te43Yaw4fuqlXXICB6DYV9z5ZVXcuGFF3L99dfT1dUVrT/zzDN597vf3fF4HYudK664gre97W3cdNNN9PT08LOf/YxEIsHv//7v86EPfajjCRgM7dFceHAPcTzLyRK6grckbNQZNLpUuoJWvgAI69QoXfNTr4PifGEjTYuE3yZC+y4uC0kCm4SQVPUUqQ2KbFeKUmGS4rhLt+zGLdWYLY3Tm+1npjROrVAhI3OkLBAqgS7nyZfHeVlXP6XyCJlqgVd0KQ5ZP0M6VaFn/SSJbDmYusAtp8js3kCpnEYhqCqJLS0mKxYaSUlZvrihbqmpiDIeNjNqlnXrk2RzaUr5aaZHp0nKbiqiioef0VXTFWqUUbo2p5+Yn37uB3nXk7EaG8O2w9wyAs2ixwgew+pAI9BLiFlbyr4ryf3338/f//3fz1n/spe9jN27d3c8XsdiZ9u2bfz93/89Ukosy6JSqXDYYYdx/fXXc8EFF3D22Wd3PAmDYaksX7XlVoMvVNdn/vm0O3bo4BEiqBKswcP186gE0TotwvDd5nYT4FEhtUGxdetWnGyKGWuMxx9/nNJkgdJIhe1qJ0knSSlfxRuXJK00AkHGSlOdKPLYzh280JUg5xbYWKrx8kFNd88MyXSF7OAEdq4Iyr8Gbj5DtZjGtl26CzkydoaaEtixWKH6dfAzswBqlMkNaF69+dfodtYzU5hg584dFMbyeMG5edSi4oRKqzmtNfxLEhM8dP7axK+9P55osvQYwWNYPSi9xK7n+2nMTiqVYmZmZs76xx9/nA0bNnQ8XsdiJ5FIRB1IBwYG2LVrF5s3b6anp8d0BzesMAtbdxZKTV9MEcD2Cxkujno/KT/fSulavdm6VIF1R6JF0JRWZkGARaLemgER3Kw9Mk6K7mwXorfK0S8/Fu2BN6V4Tr6AHk1QHRckccjINFqDlH421WCiDys/AYVZfq2nyuYByUFDu9lwxK+wuwvIV3WhezeC8kAp7Mlx+jO/ojqVo1ZLMFNNYwmNYydwlSDhStB+YHJVVPGoUaWEq8v0OD3IrgQlUcDqSiByiunx0eDXqaKqin5F5mDR1Ptu1dPPQUQvZz21vlUD1AURdWETd4k1Bj63W8vHYDAsJ29/+9v55Cc/yb/8y78Afszlrl27+OhHP8o555zT8Xgdf3sfd9xx3H///QD8xm/8Btdccw1f+9rXuPzyyznyyCM7noDB0Bmd33QWW+14KVWS57MyzTemb8Go39i1rgfqNv8fRyKRwhc7U9OTvDC9CyspKRWLeBWXga5BUk4SpCQjc9giPWeUaXecUm8N+2UO412KFyuTpDJlX+is81DrBxhNbOBXbj9jdj+6fwNWd5FErkQqVSEpXVJSYQuN1fSNovEC8eKhUcwWZpjOj1HTFaYLY8wW8n7LicCiEwodT7tRen7jdQ0O0LJ5qdXREl5B3yUml99daTDsBfwA5aUt+yOf+9znyOfzbNiwgVKpxG/8xm9w+OGH09XVxac//emOx+vYsnPttdcyOzsLwKc//WnOP/983ve+9/GqV72Kr3zlKx1PwGDY98xNVZ9DdCNcaJvg/wWaiDaLoPCmO1/NHx1kKEkRztRraDHhao8Zb5rcgCCZS/LL5x/l0Re384qDDmW9PciMV2Z2tgJeDoRGab8ooBQCIaBChcSAZuuWLWxa14ulf8WzTz9AOTmDHNCwro+HSy/n6dENgA1egcN7LY7u24Wt8qSzZdK2S9LysGJhNGH/rLC+j4dvpRkemcHVvyDnZMgXSoyP5rFkCqV8MeQLncCNpdzoGjRes/kFT8truFAvsKhLfLQysvK0tvA0bG0w7HMO1Jidnp4e7rjjDn7yk5/w0EMPkc/nec1rXsMpp5yyqPE6FjvHH3989PfAwAC33377og5sMKwEncXtNN8Em1LVw7GahI6gdefw+A3TX6Hmr6Yco95tfOGbt99hXKKFIvxX0SWcDZrNW7Yy1DXAWOElfrH9fp6dfJGZ3gqlQoXaqCAjkpSVH+2jtMYWgqS0qFHDySYZ7OonIRWH9OR4cSRBNVWAdb2MJ/t5euJgjj3+NzjkkH6efuZ57vvxf2KVsmy08yTSFRK2S0J6WIJIlGnqMTiurkQWG4DRkSlGmPDPXVi4Xj6yaCntRuKmlVuqtShstS4e1Nz4fFw8heEKoVtM+zsE6+KipzlbK9raYNinHIgxO0opbrnlFr797W/zq1/9CiEEmzZtYmhoCK31/D0NF6BjsfPMM8/gui6vetWrGtY/8cQTJBIJDj300I4nYTC0z/xv8vmEzvyCo/kmNp/QiYmcsHt58w0UFVkKGkRPIHgaJzS/aBJYwQ1dB3FCc89J4aGEwtMuVTTrcmlyTh8e0JsdpK9rkJlfVclPgSBLSqSpKIVHvE2EpKY0QtiUCwWGZ8fJruvluakZVKlMxrLBcynkq6AUBx/SixA1JB67dnm4pQ30e1n6x2ooJfGUxNPgaX/8UIzppkVFQsN3vUWWlXhV6ZbZdC3cVk3CJ7ye9eBlorEb94s9CEVOTPS0ev1aFzDcH0VP8/tlf5qbwbBntNa8/e1v5z/+4z845phjOOqoo9Ba8+ijj3LhhRfy7W9/m9tuu63jcTsWOxdeeCEXXXTRHLFz77338o//+I/86Ec/6ngSBkN7NH+RtxAn8+7pPz9X9DTfMButOYIEiKBzVCB8hJBzhEqUIi1U1PNK69oc11aDlSg8hpCIoOGllDZK1xgYWEcu51DMlxkbnQWRarSW4FdbRqaYLtQYLrzEBmeQ6cIE4zNFtMriiSQKjaYSNJqon7utJZZKk5IpamMlHt6+k191J9hoj/IqWaWbNBTH6LKTUBnjqSeepL83ybafP8Ngb5q3HX04kyOKnz0xQXdFUVEWNeW3kKhphStdvxKy8t1SrqrUA47jlpWGwGBvzro5r2NwzWRYeyh4beLPN45ZP15ri1HocozHRcVEaxDEXA9gtpreQ4txce3NX9ILHcsIoQMBrf1lKfvvT9xyyy3cfffd3Hnnnbz1rW9teO4HP/gB73jHO7j11ls5//zzOxp3UUUF3/jGN85Z/4Y3vIHLLrus0+EMhuWn2dXRUKdlz24ln3pcSCh0QkESNqaMo4J7ntZxq48V2lLmjS8R1C1HAFp7DAysY+uWI8k53RQKszy681HGx4ogEijtIYUd/O/Pb2qszCM7t9PtPEM5X6U0CjmZoIYb2VQ8PFTgq5GBGcPTGldr+q0+emeLOKUyr9pgc2hfF8obh6rLOsflld2z7Hz4fmamqjy/q8BZbx5gvajSXUvws0SFfE2BFoFVxxeU4VF9seHF/lYtBU79/BcWOvEYp/A6h8Knvp1VH7c5gS9cFxw7fB39L3xVF0tCBlaeuTV95k9VJ3aw1dCY1KTWHwhoBOoAitn5+te/zp/+6Z/OEToAv/mbv8lVV13F1772tY7FTsfpB0KIKEA5zvT0tOl4btiLhJYX0WiNaSUqwvWicZ85/0TgphI2QiSQIoUUSSyZwZIZEpZDyu4iZXWRtnobF7uHpNVN0u7CkhmkTCJlEiESvnUoEjWBdQgrulmHN3H/f0Eu55BzupHSIpfrxsllo1PxXUEuVYpUdZGyKOBZgvHxCruenWJsokrVlkyJGabkDFNymhk5Q17OkhczlEQxqFJcpaI9qkpR8UDoDEnRjVRdFEpZihM9uC+k0LumOEY8yW/0b+P47P28Mvk0zswI5eEMjz8uKUxCxXWYqSYpe1BVGpe6GyvMwgpFju+u8sWP0rXYehetXeo9nOu9nENxGVq+LJnEEikSMkPSypGycqStbjJ2Lxm7l7TVTdrqIW31kLRyJKVDQmb8/YLFHyeFFIloEcJGCBspksjo71TjayhsXwgJK/bei7+20ZtuniX+/u1k2dNnYTH7tTNPg2Eud999N29729vYuHEjQog5biWtNddccw0HHXQQmUyGU045hSeeeKKtsR9++GFOP/30eZ8/44wzeOihhzqec8eWnTe/+c1cd911fP3rX8ey/F9Znudx3XXXcdJJJ3U8AYNhWVgwcFU1btdsPYi5lkLLgRQ2ILFkCksmEVgkZAYpLKSwSZAORvYFvofvqmnoZq6CIFfh153ReHX3V8yFFf4vhERiU8yXKRbzOE4PxUKBYr7kBybr8FgElp3AyoFECgtLJhB41Ki7z1SDewYsbBI6hRKKik4hNVSV39Sz7ElKtQTFSopiPkvXeA92JUlBv0QpMcHLqGElFNv/J88DEwdTmVZ0TeUo6xwF16IaiB3fojS3x1UkdKLr39qd1SoIORSDMrCu2TKFLVIIIUmQjq5B+Jr4cqse0O0FwdHha+VfTxU1YQ2vow6rW0cWm7CgYz17qzkQvR7I3KoScyv27H7dk7u1febbb09Vxlc69qddQWUsT3tiXzQCLRQKHHPMMVx00UUtCwlff/313HDDDfyf//N/2LRpE1dffTWnnXYaO3fuJJ1OLzj2xMQEg4OD8z4/ODjI5ORkx3PuWOx89rOf5c1vfjOvfvWredOb3gTAj3/8Y2ZmZvjBD37Q8QQMhuWn+Qu+ydWwUMpy5LYKYmiEjS18C0BSZrFIYIuU37YhQONR0xWktPysI1nzq/8Kf/Hr58hYIbzgUGEMUGTV8S0GY6OzPLrjUZyuHIV8kdHRWSyZqh9PeyDA0/WbtN9iImzW2TpbLNoONWcLFfj9a0pS9SzKlRTV2Sy/HPV4fuwghJNGlwu80p7kdXaVF2YKUM0xYa3nSc+iogQ17Tf5nO+4IhJ9c0VQa+qB3KHQsUQCgfQtNUH7jKTI+o1HY13WPVHzU/axInET77KlIlEDaCLR47ux5ooeEYrY+URPS/eWP5vGx+Eu7cWY+dNbiRv+whlsc2k134XmtVzWIZPyvyf2RTbWGWecwRlnnNHyOa01X/jCF/jzP/9zzjrrLABuvfVWBgcHue222zj33HMXHNvzPGx7fmliWRau26pn3sJ0LHa2bNnCww8/zN/+7d/y0EMPkclkOP/887nsssvo6+vreAIGw95hvl/cwXotQYhI6PguDpukzJGUWWxSZOkmQZKESpLUiWA0f7yKqFARZWqyQkHIKEgZ8CshBzfKkHisTih0pEhgCRshU0yM15gan0KhsS3fjaUDlxCAF9SgcakgmzLERMziAyCDHltCSJI6G3VWj6MBTwuKnk2ylmS26FB7oZudsodjDzuMTUMZntld4pGHH+dorZCzGylU0oyWHCarNrM1ScXDFzxoJP45RU1MtVu3YlEPIp6bKSXn/C2ljUBiyxQJmUUKm4zoIaFTJEiSUVmkFsExBR4ervDwcP3XRFSoUYnicTwkUqj69RREWWIaL4i9imWHBfFHQhC42toJZG629DQJnHYLGLbK5msYfzlpZ06t4pNW4jjtHM+In/2RZ555ht27dzfUw+np6eGEE07gnnvu2aPY0Vpz4YUXkkqlWj5fqVQWNa+OxQ7Axo0bufbaaxd1QINh3xJaeWJfuBoGB/vp6uoiXygzOjLpiwMspLCwSWGJBAmdJKF9oZMKLDtVXaJGDYEmIZIoraJ2DjKMNdFWZNXwD9fosokHPEfNP2WiPtuYyAH/xhwP7PUCi07kgiNu8bHwtPJFR9Bfq34lxJwbaWgSdz2LfFWgu3K8rKsPaoqX5bI8JLuZnsmja0lKtQQVJakpgadFEE+kY+OHrjkLKSRKS5qtOgu/TvVzqgtCO7LoJEiS1EkS2g6klS94XPy28lJIXFyU9gswWjqBJ2pILL9qbCw+NyraGE4/fK4p3jhyeQXPa8HcQOamxqR77tvWfPPfg9s1mmL7bVHaqT/VUeB+269jfJ/FsJjjrS00S5N94b7NfahSqdS8gmMhwiadza6owcHBthp4XnDBBXvcptPgZFik2DEYVjfx7CfN0OAAW4/ciuN0USqW2SkeY3rcxZYZUiJHmhwpnaZbdZHAJiUsUkKSVxN0D2jSTprp2RJToy45qxtP1qiRQMsgVkSC8mr1+i66Meg26mwe+z8UCVHcSRAdNDeNWs2Jd/F03XVk4Y9H2DldJEjqJCmdJk2CrGWTtQQZS5OyFAmpsKRCaUFCpajOuDz5QpVD+rt4ZrRIYRzKlT4KZYdCLcFMNUHZE1SVLxSkENhaktJpJJIaFVxRQQm/NpAQMgiDCXt/tX594kHbofvKlmmS+JYpR+VI6RQpEiTQaO0ihMLyHY1kyOAGwsvGRgaxO1Jb1EQ5uqYqeC9EvcZE4CbE9os3aoUWXr19R3Cpw7kLJAq30bXVopK2/0f8pr+QAGi6wS8geJpZSjPcdvdttlgt9/hzj0XT8eq1muJbrlWWy4118MEHN6z/+Mc/zic+8YmlTG1RfPWrX12RcY3YMawS9vBhbmjU2WS5WXBUSa7LwcnmEMLGcXroyuWYncj7N1iRIqOyvruEBElhkbEsEBVyGwSbtxzJumwfw/lRfrF9O6UxSIkMCKhi1904QjakNzfE6sTcWFLYyEAAhf97wo85CSsoEwv+VcptTN/W9RiX0I0VziEcz7dQJUgISUIIkhakpMbVJabdPJal6NWCDFkGijM89PDjbM9kKM+49M5KSPQwU01Scm2KnqQa1NcJbzd+y4cqSEVCpkCDkvV2EEq40bWvG4LmxraE18aKMqZsEiKFpROkdIoMKWpqhtygwEuUESmPlM7gFS0KI5AR66jphO+mQlGjErisPL9Gka5bdMKGokrX09GVrmEFzUfDuB+kLzQlCTxVIQhCChq2EitKOH+5gUbX1nzVuOOFEWNjtSl62mIxvcAWcKst2xyaSkVAs9WpOd3fsBw899xzdHd3R48XY9UBGBoaAmB4eJiDDjooWj88PMyxxx67pDkuBSN2DKua+V0EbfS7AkCQzxcoFgtknW6KpRL5QjFIRfYtIxKJrS0SwsIWAktAmQIi5eIk0iQsi77cepxciuJYre4Ci6eXYwWxIMFRY+4rX5z4LjP/eIHYERZC1y0PUlj+/6iWLqEwuyj+t4jG8t0/YZxLApuMZZG2BEkJU944us/D6hXMVMt4pRJHZW0OyzlscKuUZvJQc7CSXZRci6pnUVESL1ZfRwBFNUFivcdQLsVMoUhxrEzCSvuB24HIkaLmW0N0eE2a2nS0KNpYF39WZKlRukZ2AA7/tVeikiU2vepQSjNlpnbn2aEfxx2t+K6tMJ5HWITFAyUWSgRiMbhWUtgx8RNzN+qYOy2K8akxOLgOJ5cln88zvHuM0EwlovpK8ffh3PfifEJnMbRyQS0oShbb9DS+X7vCq9NjtRB2c+tjNQuetVszqF6kYfH7A3R3dzeIncUStnW48847I3EzMzPDvffey/ve974lj79YjNgx7Oe0F2sgEMytUgzzfw2EMTKC4eFxBDvJdXVTLFYYH8njJAZ9h4i2fSsIlm8FkZIKk9jra8geeK74BP3VQbRIUC1UyZChojWaJBUSiEDAeKLaEAMSt75YoZtGpEiINFGaufLjfqRI+80/RVhheL5CfMoXU4KYyPFv8gnSJEWGjMqS0xlSwiJnSxwbklYJtd7l2K1H8Or1OcaLIzzx2MPkvSrdMkGGLGkpmNUZZqspim6CgmtT9iRlT+Aqv02ES5nUBs3Bhx9GIpmhUJ1F80tmxwVKZoPzlihR82/00i/G2FAEMP7KN1i8rOj1sLBJYCGpksul6U7lqGQUmwYP4xn3SYSzDieXZnbMw9JJLBQSP1tLBwLWo4ZSRfo2pHGcLIVCkfHRIrbMzJmHh2/3CUOvtfIYGFzHls1H4GRz5IszwHaGd49H772GOJ7m82oWOS3cVL5garomc4LKF765h8+3Z4mRaB1Gf4g2eg8tZLma/xh7Zp6syZhFaeGCjmtP8OyL1PN8Ps+TTz4ZPX7mmWfYtm0bfX19HHLIIVx++eV86lOf4lWvelWUer5x40be8Y53LHqeS2VRYsd1XX70ox/x1FNP8e53v5uuri5efPFFuru7yeVyyz1Hw5qjnQ9f+66qPR5NWAwPT0Qp3vE078btBC5lZL/imK3HkMqkmGCMXz75COVRQW0sSUKkkLSXLVBPyY5bgCSeKtK7PhN1Bp8aKyFlGqmtID3amtMWoU6TlSQWuiuwAruIL4VsAbYETY1UNsVB3f0krAov61nHM5k0pdI0Sglma5q0SCC0pKYkbmDRqWnREBzpahcvUUanqqi0BlFF2C7omMUqcq+p6PzndjWPp+I3ZprFsUSCcqFIsVbApcqzw09TKVaZKRYoFyrYwqE6z71PaZf+DVm2bNmK43RTKMywc+cOJsfcmIUmSEdHNgoPIcg5oetTks065HIOw4zPOY9WIq4d6vvtDXeN/xoMDm4gl3PI5wuMDI9HWX2N86nvs+e5LebzaVxUnbIvUs9//vOfN1Q4vvLKKwE/uPiWW27hIx/5CIVCgUsuuYSpqSlOOukkbr/99j3W2FlJOhY7zz77LKeffjq7du2iUqnwW7/1W3R1dfHZz36WSqXCTTfdtBLzNBxwLG+V1gZ31nzm9TkWH/8mJsIsGvzqsfV046AKsNC4WlFTfuRHdzZFd7YfKSX9bhKrsgt3HFKih4r2cIUb7NnCWhHPLgrcNZZI+JYdUkgl6F3vcOSWo+h2+pgpTLB95yPMjIEtUr7bB4WL5QfTChUJgdBFFrrgZBDUK4VNUmTJ6CwZnSItbdJSkrEhY2mytqRSLjFWGKUv7bB7Zhy3VObFQpXZXBKVc6gVKmRnSuREN2XPouhJSq5v2akpqKmgwGLS45WHv4KNg6/g2d3P8OQzT5DQCaQWeMKP33FFJXjNvLqIaS66OOf19a9nWJnZRZEgSWFE8Lh4CjdR4vFnniCl07GYnSQ1ytRE0KcrqL0Txg5lnXU4TjdSSBynm6yTZWJ0Ekuk5nntQuElyBcKFEpFnEyWYrFAPl9qKFAYf71DoTCv22qOxaaF0JmT0t4+e7LwaO0xODjA1q1byGYdSsUSO9jpWzxjrrm581ueHxudsnC7DsPe4C1veUtgCWyNEIJPfvKTfPKTn9yLs1qYjsXOhz70IY4//ngeeugh+vv7o/XvfOc7ec973rOskzMciKxcKfo9mu11s4m8Xiul2SQfuo3C7uJ2EIchhU2pUGAsP06f0890cRavAEmRpaaCHlTBP/BvJKqF+IraH4SuLHxXjVSCnJOlJ9tPggTrshvIOTkKo0WQKbRWKOFhCdvPugpS2+N9nuLusTBWJxmkzSewSUlJSvqxOmlL05tIUcsrHtu5g5d6JLJaIJcvMtud44gtW1mf3cBzU9M88NATVGZcLJKUPUElEDle0A9LYpHUGYozJXbVnqZUrGDrNInga0YSBhsHFiptAW5k3ZlX6GgPgiBrJVRQK8e/0mnRgx5zEToFQuFhobF8oaP9bTzc6DWJKisLi2KhSKEwE1l2ioVigzXDfy9JaBI+QliMjkyzU+/EcTLM5mcDS0isaGLMNdVJbE5LS9AShE675HIO2ayDEIJsNutbqobHaPV5bewub9jXLFfq+YFOx2Lnxz/+MT/96U9JJpMN6w899FBeeOGFZZuYwbAwK/GrMqySG/76d3FFhaqoIpBUtEeCBOVheJjtJLMpyoUKlVGBRYIqLjU8alSpiQpe0OV7ISJXFvUifOV8lWJhmh5nPfnCNJV8lSRpqtrFFipoFRHc/HUt2r8elCwD647vPkqINJa2sbXlu7AEWLKxtsxQah3VUpFUrUBPMklFdvFiMsUGZwM1L0W/swGVeoFpV5EWvkUnTDmvKR2ku9u4RcnU7iLpLEzmJ6jmPSRpwA1ukm50ruH5z3eNNGGrBhVlUNV0EVeX8HPWgrpCOoEkidCBq1ErKtrFw6MqalRFlRrV6PXwxYhgdDTP9p2PRDE7k6NlpKxbdcJ5zflfK6RIMDI8hWYMrfVcd1yHWVOtOrE3jzO/0Gl1rLmfj5bWz0Ag5/OzfpB+1gksVQUW+mGy/EKn/eu1UoJvtbIv3FirkY7FjlKqZcPP559/nq6urmWZlOFAZL4P1PKLlrYCMyNrjl8bRejQVVID5btatPCoiqJvRcFFIklomxRdFIZdpnQVSYqESJGnQklUqIkqVVGipsu4KhA8sQaY8V/5YeVjST0TK0WWwmiBx3Y8Ti73HIV8mcqoRVZ2ISj5LREEeLKGCmJ4vIb2BmDLFGFLhaTwW1ykdJpUkDqflIKEEFhCR1ff1YKUzOBIG6EUrltldqbCk2N5erNZXpieZGrGJVXrpoTFVNUXOlVPU1Z+mwihk1RGBdu8X9A1lKLm1qi4JTxtIXWSsMhg/PzVHu5bvuhUCGrUvCrrBhLknBxuIc/4aJ6cWE9VJ5HByUstqAUiR6MoiaJfQVlXcKmgqNfMkSLB+GiJ8ZFS5PaL1y+qNzH18LSf4h+vrIzQkYCLz3f+c9mTQGjad49CZyGB0Dobcc5nIxDIw8OjwHZyuRz5fJHh4dHgtbKa9l8ukbMHcdO2WDQuLEN7dCx2Tj31VL7whS9w8803A/4vqXw+z8c//nHOPPPMZZ+g4UBm3/j858MPRvW/0P1F4ukannCDSsQuCP+GmsDGEjYaTQWXGi61FhaEqBjdPMRbO0gtkUiych3VMY/pMY0gQ04mUWhsbeMJNwr2RYQByFaDlowymLCiLCRbB38LQVWVcXHJKZu0lULht4pASWrKn09SZOiarvLQ9sfwUrsoF2rYkxaWyFAJMrBqSlPTGldrtPbPMiGyeJQZzB3Cuq5BCkMFHtmxnenxakcezFB0+HEwHkpr1m/oZsuWzThOL7Vilcd2PEZ5LI8U3cjg16lERq0i3DBWJ+rDXhcrUR8xYTfMK/5aNVt0wr9D8Rqf54Ln0pZAWE6h0yGR4BkLXFd1UbrXLTh7uJ7GqjOX5Uo9P9DpWOx87nOf47TTTmPLli2Uy2Xe/e5388QTT7B+/Xq+/vWvr8QcDQckKyd0Flep1QusOy4gcb0ynihHUb+WSODJWkP8C/gxJL49yLfoeLpGRefxdI2aKqK0X8dFhV23xdwYjqiHFdLPltIWKZEmEaz3v8x8QRG6gDyR86szW0Td1kPCbuA2qaiujhVIn6KawN4gcLpSTJQLlKaLWGJdsB+UXBtLBDVydZb0RI2JmockS01lGa35nc1na4qa1nhaU4ulwpd1mZ6uHEP9m/yspmSCVC5JdXwa1RTAq1oIwXgwb9zK4uKSdZKksik0NRLZJJYDs2OTuMK3asngevkCxxecNV3208y1G3U+j6fuz+tC02ERRBWILd96o4KeZP7rObea9fzBxq1ocewO08v3TOusxfi4YdmG6FMjlhoFsgfaEoh7On7zGGtXBO2L1PPVSMdi5+UvfzkPPfQQ3/jGN3j44YfJ5/NcfPHFnHfeeWQyc2tUGAxzf9LPL3TmFg/r9EidmA/qrqxwThrl11EZWkcu10WpWGVidJKElQMJFgncoGFniELhat9lVRc5fsxPJHLmcSsQrQ3+ab9flYXEiir5VvB0FQEkg75bNVJIIaN2FHELkiWC3lEiFcTq2NhYKF0lPaDZuvVI1uf6KZbHefzRnczkK0iRxBJQUUFIrvZdW65KoJWkqiBfg1lX4WkoKo+a9kN+PbzoHMAmny8yWRijy1nHTGGCYqGEFHYgOrx63Ez4MsSbcIbrApef1n7uFWhm87PM5idwnB7KxSlmClO4QJl848uqFR7+dXd1JTpm/bXwhZaMxdmI2OsfEoqjULCG84pbcxosdw3WmPlEzjw3+hYCoLMYnYVYuExDK9dWRAcNSxdD55/1tWKHMCw3i6qzY9s2v//7v7/cczGsUZoFypzmlAt8IS6pdL1ojmfwQMPgYB9btm7ByeYolsr8cudjTI4VAeo9rIJGoP5+KhI6StciS0tYnTcMfPZRQdXg+UWP0lWqVJGkqeoiqQ2KnJNiJl9mdrRESnb51gttB9liNT+IN7jBhpldlvaL8IU3co8aaSdFT7YfLSQ9mX5kJsn0pBsUM/SDliUw65Upey6QwFNpagoKriLvenhoirqK69uborlbaKSwyY8qtu/YTjqXIp8vMjPmIa0EaoH6Qw1CJ3CrROuCrLORkUl2sAMnl6VUqDI+WiBhOdEcZNjyIWaJCV+T8O84no7HUM0VKg2dzwMLT/h6xp/3HywkdJYrPiWkneaYnVtOW8a6LWd7CjoVN+0ee+1adcA/+6W8Smvl6i1K7DzxxBP88Ic/ZGRkBKUaL/M111yzLBMzGEKWtRdPNGirTtN+unDOcchm0oAik0mRySYZ9sbxtNuQMh7tGXN1hC6r8KYY3nTr5+JXOa4H3jbeGEtqiu4NFj05h3JhCu2WOObIE+lx+hmbHWPHzh2URzWOcnCFX6cmTK32hH8zt3QCqWXQFiIZsxYlKOSLjObH6Hf6GS9NMD1TJV3LIQJTttIw5U6g+hSJbJJiYZbC8AwpsY6SdilSRglNRZbx22gGLrjgeFILErKL8liVwlgNVyRIyQwlipFFR2kvcBPNtZK0+ptYYdzhkQkYGQ+qKiepqeKcgoPhtfX/9lgobko0rY9nXDVac9yGeTW7rFZe4LRisa7ghUXSysfFLPXc18rtuT00S3RjrWA5kP2JjsXOP/zDP/C+972P9evXMzQ01FBWXAhhxI5hVaNR5PMFCsV8YNkpks/P+g0idZg27bcNgMabX2QBmGPNIXKZxftXNePpKus2pNi6dSsbnAGmCi/x5PBOMokslpD05frJOinKoy4WfoG+JEmqAmSQpg1+l2/wBUjYFwrAFknKo2W279hONpemWqzCmCRrpyh7/s5VVcZd5/HqzVvpyfYxXhjjF+4j5MeKuEJQCwrzhQJLh2nkwhd9YX0ZW6QQ2CC8BQO049dw4dcltMKIIKhb+A403boQYVxIxQVMnFZFAOOBx3ELT33dvrDk7E3ic1vOuLrlOGcjcgyLp2Ox86lPfYpPf/rTfPSjH12J+RgOSGJNoYDlbPXQMQvGIPhCZPfwMOAXWssXCowMT/rZWKo+77hgaenaiN0s6xv6xfFEEOkSZXxRw8NFaEUq10WXsw4hBH3OBhKWxejMbg5dfxj54iS1YpUu6eDqBBpNQlu4Oo2Hhysaj2drK6pFo/FzzVKim8pojfKIiy0ypGSaqZq/n9KakirjpG2sdDczugYZB5WD8bFxlJTURIWwOB+Ahe27y4J/8ddVNhXka9XyYb7XoU5dIIZuxng9IX/cFhlUbWRLtXquUeDML3LmrYuzEB2KnKXGr81Pu1WH94YoMwJmqaigEe9S9l8LdCx2Jicn+d3f/d2VmIvB0BmL7dy8IAohYPfwCAxrBDZCCLR2I2uOX8Cvca+4W2P+vkZxa5AKgm9jN2ohKBZKTBXGSTmDFIsz5Eeq/GrkacZHxygXyuhxi4SU1LwSAptUUNvZQ2LpurDx5ymi+jP+bHQgMxMIkcADimF2ERqNpoxHNV9id+Elsk4XM8UJJotTFC0PhKCmy42nJNIIYUVuI9+1tXDF4LCCcvw+1xDT1IA/47jgiWdqRdc+hl7Q6rIwzSInGn8xImcZLDgrJ3gWIjzecrk31sjddB9hKii3R8di53d/93f5r//6L9773veuxHwMa5DOujO3M+AibjJNwkmI8CtA1b/6gz9auWVa3gTnZLUEFh8RhqHUY308aljSZnq0xvYd23k+9yxuoQrjOdKWQ3nSwxZduBRhfZ7uTJJioUBxRJCRvSgtqQWSJWyMEOI73mJZRk1nFmZUKaHxhGJ6rMS2R7eRdmzyhRKjo9PYMhMFYQsh5wgaGbizQmuS0lU8XNBgySQWdtQuIpxa2PDT/zvMjJrf6jCf4GnVTLQ54Hk+5u67BGvOfu2eaofm295auQ2ubkwF5fboWOwcfvjhXH311fzsZz/jqKOOIpFINDz/wQ9+cNkm18xnPvMZPvaxj/GhD32IL3zhCwCUy2X++I//mG984xtUKhVOO+00/u7v/o7BwcFov127dvG+972PH/7wh+RyOS644AKuu+46bHtR8dmGRdH8azG8McTcQcshehZ7w5l3PxUJoXljT9qojSLCG6ao1/NRygXpZwnVRBmhJWmrj8I4eGM1smTJWVlsJEkJmgqpDYotW4+kN9vPZHGch7dvR467SFLUlC9fSqqEoorAxhLJSPyE1pvQ3VWjGtUJCmNwaqKClorZsSLuaAUt/OymqvItOlr7faXCWj7gxwbFhU5VzZDbIMnkkuTzZWZHK9hWCosESii/kB/gttkdvuUlj1Wjbpm63ub7IG7JaRxrPqHTFIcVzaepbs0ysnesO0bYGA5sOr7b33zzzeRyOe666y7uuuuuhueEECsmdu6//37+/u//nqOPPrph/RVXXMH3vvc9/vVf/5Wenh4uu+wyzj77bH7yk58A4Hkev/3bv83Q0BA//elPeemllzj//PNJJBJce+21KzJXQyfMjd9p6OGzEHvrl3S7N85F3DD8gGavoSeXEEkSZLFJ4On6qFXtknFS9Dl+x/W+bD/ZXJrimIstUkghmPEmSG5QOE6SQr5EYbRAWvZEQqehIWZM6LjCDQJ+g+rPAkIrS9gyYT6ijKwgbT63QXDk1q10OX1MFcZ4ZMd2ZsddpJDRthC2i5i/N1Zb16+Fm6pdoePTykq3kEWn1RwUg4MD5Jwc+UKe4eGRDuKTDIal0SjXF7f/WqBjsfPMM8+sxDwWJJ/Pc9555/EP//APfOpTn4rWT09P85WvfIV//ud/5jd/8zcB+OpXv8rmzZv52c9+xhve8Ab+67/+i507d/Lf//3fDA4Ocuyxx/KXf/mXfPSjH+UTn/jEnIamhpWmVTzA/FYeoq0X92t578Y7tPraCK1CoYALnUcSpWtIEri6gtC+S6ciSri4URq30pBQAiUEUtiUC3kmC+OkE2lGZoYpzMySsfqwEFS9MtkBzas3b6Xb6WMyEBrVsQpa2CgUrvCoiSph1Wcl/AJ8rvb7Rnn4afNhfZp4FeEo5T6IN/dbcdpY2H5VaSxcquRyGfqc9QghSCeyJFIaT1WxZAqlFVZQGDEKMA46t/v1h9r/6q1nXDUHwM/3WuxhvD0ee65VR6MZHBxg65YtURNNwO8ttQwWHtMewbAnTAXl9lj0z49qtcpjjz2G67p73niJXHrppfz2b/82p5xySsP6Bx54gFqt1rD+iCOO4JBDDuGee+4B4J577uGoo45qcGuddtppzMzMsGPHjpbHq1QqzMzMNCyGvcH8v1E6+dLXsX8rg5pnmW/bFoRBysENW+mgpQGu392bKjU/kgY3aMuQECm8McnPfvFT7t/1E16Y/RXKqlHVRSwhAA8nl6Yv148tLHqcfjK5VOSiUkLXa/IEx/Hw43C8oF+7qypBNegKXtBiIawb5McX1eNkwr5cYeFCiSRJkkqhykxhgvHp3YwUnyLdI+laL6ipfLBVLFanZZB5e19LWrsMDPSw6bCNbBjoiWrh7G1yTo5s1kEIQTbrkHNyyzJuey0TTEq3wdAOHYudYrHIxRdfTDabZevWrezatQuAD3zgA3zmM59Z9gl+4xvf4MEHH+S6666b89zu3btJJpP09vY2rB8cHGT37t3RNnGhEz4fPteK6667jp6enmg5+OCDl+FMDI0slEPQ+ktct/mvfeYTLXtaOiEMwG369STqmVmh9cTTtaCXkxt0T69RxaWm/T5UNaWxRIZ0Msurh7by+lecxGu2vh6rX1FWZaSwKebLTOTHcbXHVGGcQr4cxbbEg5RlIFQk9QDhsPt62H/LXx8PIvYtO0JYgVXHr9Ac9vOykAgE+eEy27bfz1Ojj7Cudx1bDzmeo448hp71iUiQ+IKn3gR17uvSmrCOkVIuGwbWsWXrFjZv3srWrVsYGFwXjL84ATBf/aO5k2gcP1/IUywW0FpTLBbIF/Lz7Njm8Au+j1u9Dzt9f64Vx8XaYLHfYov9RlutdCx2Pvaxj/HQQw/xox/9iHQ6Ha0/5ZRT+OY3v7msk3vuuef40Ic+xNe+9rWGY600H/vYx5ieno6W5557bq8de+2hmV/4LPYjuTc+4pqF5w4NQkfIQODIOUXw6pYd351U1UWqokpFlKmIKlU8KtqjohRlr0Y669CT3UBNaLKZLhJOiqqqIUlRGRXs2LmDB3fey/YdOyiMaqRIosTcOcqgZYUMxEuDyBFNzUqF9GNuRAJLJLBJBT24fKGTwKaqp0lsKDL0yl48z8Ura3rZiGN10+v04zgZP6Oq46+dVq+ZJpfLks1mEAKy2Qy5nNNQ2HF+t5RsWhrpZH4CwfDwCDt27mTnozvZsXNnELPTnmugfbHeyXu2k/e9seqsdrRe+rIW6Dhm57bbbuOb3/wmb3jDGxqqJ2/dupWnnnpqWSf3wAMPMDIywmte85poned53H333fzt3/4t3//+96lWq0xNTTVYd4aHhxkaGgJgaGiI++67r2Hc4aBoXLhNM6lUilQqtaznYmiHVrEXcebG9sx9biXp5FuhlUVnrtBpRRQ7Q9IPHg4CZL3A+jI+O8oYLzLYP8Dw2AiTM+OkGEBrTVb2kR8tUhqtAhlS0ppzA9XaxdVVtPD7aBFkXEl8UWORwNO1qCVGWHQsFEMCGQik+j+lqzgbNFu2bKU/18/I9Av88oVHmC5M0OtsCBqCVqJMrPpYVocByuG2mnzet6iEsTL52XYsKvO5zZby/vEFT/zxnrKz2rdArpXf3QbDytKx2BkdHWVgYGDO+kKh0CB+loOTTz6ZRx55pGHdH/7hH3LEEUfw0Y9+lIMPPphEIsGdd97JOeecA8Bjjz3Grl27OPHEEwE48cQT+fSnP83IyEg07zvuuIPu7m62bNmyrPM1LAfNN4FW76mVvgF0ImrmdnSPbm6ibjkQWBDGuUTxKsGNPxIVvivLIoFHjYoooVCR9URpBdqjVq0yOjGB62kmpycp1moopZC4KEALG6I6zV5sZpKyKpDbIMk6OQqFErOjLgnLIUHKP5ZI+BE9ooIbFCkMu4ZbIoEUNha+RSehUyR0EguJQpF1UvTn+rGkxcbeg3n06R1s3/4wua5upvMFimOQsNJIUWqvRcQethnePQZ6B04uQz5fYHh4fI5FKv66xK/13GMRPE9kfdIC0CoQZC2yvhreJ/N/9y1/Z++Fxmv3O3iN/JxfAygEagnB8EvZdzXRsdg5/vjj+d73vscHPvABgEjg/OM//mMkMJaLrq4ujjzyyIZ1juPQ398frb/44ou58sor6evro7u7mw984AOceOKJvOENbwDg1FNPZcuWLfzBH/wB119/Pbt37+bP//zPufTSS431ZlXQ3g2l/TFWkuYbacyS0yB0YgKoBRo/QyqMa6lRRWMjtURRpm/deoayr8Qt1xjK9jHVW2JqqoItLD/jilhxwZj7ytUVujdYbNniZ2vNFCbYvnM75TGQMhkEL6f8hqKBkU3hNxvVqJjQSfjNRoN/NhaKJJVimanCOH259UwVJki6GUrjNoVxUDikpaamq/X6PGHMTgsBsmCbh1AMCMHu4TEYDh9a0fOtXFHx48SvvcaLFReUkeBpffCVEtrtjNvO+7id6sdG6BxImHYR7dGx2Ln22ms544wz2LlzJ67r8sUvfpGdO3fy05/+dE7dnb3B3/zN3yCl5JxzzmkoKhhiWRbf/e53ed/73seJJ56I4zhccMEFfPKTn9zrczUslVafynYE0N745dIUnxMInbr1xo46pjdnIklh09xJHQKLivB8ESKCG7jWzOaL5Gt5epx+pgvjTOeLVEnhUYsEjmq6eUokaI+ck2Gdsx5bWPQ7G+hxcrijNWyVwRUeFjYeLlVRhaA7e9hNXeC7uCxRD0wOO6qnRIraaIVHd+4MmoxW8MYsuq0eFCCUi9J+qnvoOqtRxhI2ng6uRXC8OYX9WhFmhS3wijQIy6Zr37Bd1I7CirLjIvETNDZtj5USQou5G62RO5jB0CZC687Dk5566ik+85nP8NBDD5HP53nNa17DRz/6UY466qiVmOM+Z2Zmhp6eHvwvzrVh8lvd7M3XqFnkgCABQgbxKTII7k3U07VFo2VHRustv50CYAu/4rBAkhApBBZJ7bddqKgZetcnyTppioUys6MeSdmFEv7NNhQ6Eul3PkeS0H4mVHp9hSO3Hsk6Zz0zhXF27tiJN5ZFiCQ1XaZECSUEntRUKKOEwqWC0h5S+HV1BBYZncXWNmmdJkOShJCkpYWigsYlIRJ+qrwGpTVF5VHWLlVcpuUULi4FJqmoPJ6uUFMllHKDNHc/Y6tVh3F/fesif3ErmoyLzEDkNDQPjWXDhceKApsDweP3Q1PUm7qqhvo6dZZb5BihsvrRgGJ6epru7u4VOUJ4X7rq5ZeQkouvF1dRVT7z/M0rOtf9gUX1S3jlK1/JP/zDPyz3XAyGVcR8GTsyclmFQse32tTjc+JWnHj2Uz3dO4jhwcOKHccXMS4JmSM/7jIzVkQKG9tK4+LOsYT4Tpn6/rZIURyt8NiOR8nkUlQKVWpjFmmRJq8mSG2ATM6mkC8xNVZFWn4fK1+s1ecltYxaREhEUJPHwxYpMjKDJQS2CCWAX0zRQpBA4rcJtYNYpETQFDQBlCLrTqteVyGdNfaUc4ROeP0jQl0ci42PV4sOXXjzs5xCx4gcQ+eYmJ326Fjs/Md//AeWZXHaaac1rP/+97+PUoozzjhj2SZnMLTPfB/YxdXNbLv6beQWkVEQcmjRsWTKd1EJiS38+DApElGsin+c5gakcs56DxeB8qsWR+niEoSNi8INLDChEAhv5ra2QQT/BzPMiByVsTLV0SpdVg7HzlBVJboHJUds3kLG6WWqMMZDOx5mekJjiSRo8ETNF2m6noEFUFbT9GywyOXSeKUChbEy/Yk+EjK8fn4VaEsIqkqSUBauylETvmtOC0VNlHFVBUUNLfw0/Oa4mbkiJy4yYtewSWhKGRQ8FAmsSHDG4nV0mOnmt8vQ1C09oZ1MBy7AuVlbrYSOESyGvctS08fXSup5x3eCq666Cs9r1Y9Gc9VVVy3LpAyG5aH121u08W+PRHVz/OM0ZlvZdWuC8Dt9C2FFNWqksKMlvl3YTVw2BM/Wb74eNTxR811Lwu9n5YlavRIyQQXmoBpydLsWComkpKaw15foPzSFMyioUcIWAo3n99zK9ZMQFr3OepxcBqmIhE0odPyzDbPHamQ3wNatWzhuywkcuXUriQ0KV1ewBCRk8yJISkmKBAltkyDpX4+oWGHdxTc/7dWbiWJ0IjeibHgNrOi1CF+b8HVqdx7NLFRvyWAw7Gs6tuw88cQTLVO2jzjiCJ588sllmZTB0D5zU7/rz7QQLQvexDq3AoVWlFDgxF0nUtjRDTW07Fgi0TJTaO5M6tWFI/dRk+gAUKI+c6/ZjSWCasNAVZfIbIDNWzcz4AwwURhm587tzI5PYYsU5UKeifw4jrOO6cIYpXwFhJ9SroIk9vBYrnZJoPB0hXTOodvpRwrJumw/GScFUzWSMo0UEBp4LCFIKEHC09SUja0FNZ2lRhUEuLKCUGW/GWpgpZIEFZMFhElldTkxv+CJByNHYhMZxUOFAshTVdAaIQRSJPyO7liRFc13p4VWttbp5wbDvmapJVLXSiWnjsVOT08PTz/9NIceemjD+ieffBLHcZZrXgZDh7QQOQ3CpsldNF9xvw5+0cdFSzzbypLJ6CZriZQf2BvccMNKxXFXlgpcKfHYkEjgBNtJZJTuHT5WKNBEgclhfEndGuShUEjApUYml6TH6aNCge6hDN0zSSa9adxJB3dEsEPvwHYSFPIlpsdctJWIsrFU/CtVJHBxEUJSzJeZLozT72xgsjRFpVChV3aR8vt6krI0ZVVGKZeElSAh09S0RUVJal4ajxwSSVUUEbI+f60lXpAVhXYJexUKHbq0QpfS/C6suHVHhha14LGriqzf4JB10uQLecZGZpAiGRw/iB1qVfCwZXaWsegY9h0m9bw9OhY7Z511Fpdffjnf+c53eOUrXwn4QueP//iPefvb377sEzQYOmGu0GldW6Vxm/j+HYideN2WFqnN4Vh+F/FKzI3VWmjNFwwb72MV72VlhZJHK1wBBPVl4mP4YkeCsCjky4znd7NhqJtsV5r1vRsZyqZ48vGnYczBHdXMjJbQpEjKNGWKMctOY/yMRiGlTX60xiM7dtCVyyDKNRi3SKX81i5SwERtArdPYadTFAt5amNFLLEOWxA47Oxg8dPRfVHid1vXURySjPSEX0PHigmezlHapW9Dli1btuA4XcwWptihH2FstLjnnVeszo7BYFhJOhY7119/PaeffjpHHHEEL3/5ywF4/vnnedOb3sRf//VfL/sEDYb5Cd1Uc2vc+I/rqcj+40ZxUmfhAnQLz6BeGC/M+PFv2H6MiNZV+jZ0053rolgoMzlaIGUtnN4Zt/xY2JFFJ0GyocM4EHUxt3CpIVFCBa0mfJFCYJURUjI5VuEXO7YxONvFht4hUm43PT0DWM4upkeLWCKDKyw83KAvVwmNCpqT1hpcawj/3B0rR3HMojrmst7OkbMyUWJTVZVQfYojt26h31nP8Ow4D2/fiTdaIWOlKHkWNZ1EakFNVqmJip/MFgg2V1R865R2USoIINZhvJC7KOGh8NDaxXG6cJwuEOA43WScNHp0NtpOUw/4Nhj2Z5YaLbZGDDuLc2P99Kc/5Y477uChhx4ik8lw9NFH8+Y3v3kl5mcwzEMLodPwrLUHkdNa+LTTu6p5n/BxQzAsfvr0+g3dbN26lS6nj1Jhlp07H2V2vIYM4kcWPsO6y0oiGwr5RSnlAj/GRIMnXL8wn5AN8TuhJSZh5SiNlxn2ZunfcjDdvf1MFcaYzZdQpFDC77buEQQ/61oUGK10PbXdEokoQ0wJhSUyJEkgSeFpjQp8TjXtknKSDOb6QUgGcv2knSRTo3nQNRASS1tYKOxYOrotU/7xhOe7lIKXS2uJoqmdA8QKDLYpUIVFoVCiUJgh4+Qo5Kcp5It1N6Cx3hhWEb4bawmp52tE7Syqzo4QglNPPZVTTz11uedjMCyeFl3F40X8WvVHElhBxdz48/4z8/dZWihlvLENgeOkcLJdIBQZxyHjJBkfnfLT0rFQLa0HHugwuDfMhvIXW1sIBDYSV1fQuupbXGQghAJx4h9fRaKnKqpI7WJbNjPTVR594jF2ObsoFsrMjLlYlkBTjvpxhd3XNX6/rnjtGU8kIgtMKbBiKa1IKxutJUkpSEiBR4JSfpaXZiboz/Uzmh9nfHYCrydJtiuJnslTHdYkZTdpnSWhk8ggTT9MSdfBXLywK7yq+FaroAAhTS626NqHxf+Ehxd8mbvKj4ESSEZGpvD0IzhOitl8gdGRKaRMRAKv3jVdzbHydN7rymAw7GvaFjv33HMP4+Pj/K//9b+idbfeeisf//jHKRQKvOMd7+BLX/qS6Tdl2As0W3Hq1Yv9FXWLTpQh1UKMCCFRusbA0DpyjkM+P4vnevSu66OQLzI6Mo0lM23Pak6hQCSz+QKzhUlyTg/FQp6Z/GwQUFyLRMmccYLAWL8+Td1tZWu/M5VEUlXTZDZATy5JIV9iZtTDtlK4wg3O0Y0aeAKRqLJEAmlJRidKjI4XEcJG2pIy0yjtRcLCXyoo7de9ifebEqISPZbSAulnaGVUCk9LEp4kIQUJmcYbK/LQ9p0knST5QoGa6/Gao46lJ9vPS7NjbNOPUBt1yZDCCxqMetqvwVOjikJRkxV/XtSoiqIvekQFT8kg26wCxOvyhFWQPYSWvuUrfEbXfBErJGOjs4yMTARuOSsQVHWXGaGVJzZunXr3dYNhX2Lq7LRH22Lnk5/8JG95y1sisfPII49w8cUXc+GFF7J582b+6q/+io0bN/KJT3xipeZqMOyB1taWVoRVegcG17F1y1acrIOQLlOT02Szvbg1l53sZGy0sKCFpxWhwBBIxkZn2LFjO07OoVQoMz6ax5IZP+akeU7B/JWoByDXg4Pr5+LqCpkNsGXrFnJOH1OFMR7ZsZ3p8SoIGcS5eJELKhxbYgU1cBK++UqIICanFsX5uNpvDaECweO3T1DR/2GVY6VrCGRU08fDpkINSFBTiqonUFqQs/twJ8vUJmoIlaDv4A30ZPtBStY5fqp6ddTFJo1EILTAwwpyovwYJKl916BHAiU9lLbQyg9gVrig6xa5yKLT0N/Kt7J52hekkRsMvx9WKOjC/UIrTnjOPq3aRBgM+x6Tet4ebYudbdu28Zd/+ZfR42984xuccMIJUduIgw8+mI9//ONG7Bj2Ke3G3IB/c8w5DjmnC4Rg/YZ1aAWViiaX6yaXcxgfK8xp0Dkfak6shwIEIyPTiNFpwv5XSrXO+gmf9+Nu/Js5Glx8YaSERmlNjSrduRTdTj8I6Hb6SOWSuOOzSJKRQPKo4epKMDf/Bi6D8etz9gIx48VcVsq3cEQCIC7MZGARcyMx6Unf4iKlJKGTuMqhUkuREJKSJ/30e1J42mZ6Js9wfoyebD8ThTGqxSpp6fjZWDE04GqF1poKLjU8qqJGXmTwZI2SmKEqini6QhWCOQdurcAt6WfB+aJHUZ9vXATHxVHYEysco96jy2sKhjZWHYNhtdG22JmcnGRwcDB6fNdddzW0hnjd617Hc889t7yzMxgWST2mZqFMK0G+UKBQyOM4OcZHx5iZnsZx+ikUZinki0jseQNfF+qZ1JDJI8KMCcVCGUQisOX4wbfKzxwKgoCVrieBW9iU8hWmC+N0OeuYKUxQypej/lt+JpbnLzExA0QCJn4OKnajV9r9/9s78/io6nP/f75nJjOTzGQhhCQghEBFkFUBgaCIVjQqXkC4tbWooFQrDUWk5SKvqoi+FNDeWtsilvurwsUFSi+gchXkBmXRsMhqAMMiiyhJ2JIwCZntPL8/zpwz50xmkiH7ZJ43r/MiOev3mcmc85nn+yy6B7//Ya96dXS1bWQAgkzwChcEKQLTJSnxPkrWmAk+kiDBDJMQkISAgBVXSitRSAdhTbCgqtIF9zkBh2SDUAKllCo5QkAmggwJRAQTSXCRD4IEPMIGH5nhgQs+f4q69n7qtIcqYhTRA238aoxW8PuknwKT9SLHv075nRA734OZaIGnsSIjYrGTkZGBEydOoEuXLnC73dizZw/mzZunbb98+TLi4urOMGGYxoZA/igeWUl7JiiZOqQGGysfaOGf4lEfikJIKC25hEIUItHuwGWnEz6vB+3apaHSWYXz5y7DJFnDiho5xENTuZaxk3akKB4TxXNCsqxU9RUyTCIOJHxw+T+uZphQds6D/Qe/gc0Rh0pnNS6dd0E2meETLnioWhd749LETPD49GNU1+m9I3pxphxv8v+s5IIRvPDJ6tSbB5VCQhxs/nibBH+Hdps/bV6CgIAkknCl1IUq8sIs7JrQsUpKm444Sam2rH9vq30SPLIJ1bIZkizggRdeyauFbnmFFTI8kIUH8GfBaYUVSTYIX0LN6UP9+2W0N9B1veb0VYw8IZhWD09jRUbEYufee+/FM888g4ULF2Lt2rVISEjAiBEjtO0HDhzQigwyTNOia1GtrZKNQcr+NgMg1ZMjB77B6DpdC5hwrqQC50WFVhSwrOys/8FsCTE1pR5qzKIKfsCS4aGJINETfM7AuNWUdJJkSEIGJMBNVTBB6eFEkGESZsSJBFRccOPi+UpAkgCTBW5RqQUZK7E3geyl2sek22bwaujGSf5pNf/rpn9dZPIoGVoA3HDCY6pGtUiAScShmuwQkGAhC0wwB4KshdIkVQYQB8AiCZiEgM0ErfpytVwNL3kgRBzssKHKKwFeG1zkg4c8kCXlNfeIKv/b6g6IGZ2t2ttOoac4je9lQOAo28i4Xn0BGKaVQA2soMyenSBeeukljB8/HiNHjoTD4cCyZctgsVi07W+//TanojOtAL93R/NCyAbBo37zV708hqrHQkDJ7RIBL1AYgj02ZBAMcpCgkMMep45ZHYOSLaS2TQgECsvCBxPi1JrJihoQgPoR9gq3sh98Wpq2ml0UekzQxmUMxA0tdAz/614b1T4ZirCSRJzyv6RMF7n8laUJMuLIAp+QALJCQEYcKc0bVPebJACTf7nkvQhKlRGXYIHb6YTnXBWsoh1MQsBEwp+Gb4Zbi3MK4bEJFqS1FgmsaS8LHYZpO0QsdtLS0rBlyxaUl5fD4XDAZDJ+S1q1ahUcDkejD5BhahKmgJbm3VEED/yNHbV+R6gZy6MXQ/pv/pFWUA7tLQkhIoL2q2GRem0hQVBAkMmSFTIp3hySZLiF4uVRm1rKusBit6zUxfHIVVoKtX5aSrNX/3oZxhTOqxFstE9L51Zid7yKAPKP2UcuuGWnoTeYWVhhFlaYRBwSKBFmUoojChIwk1CahgIwS4CMK5Da+9C3Tx+kOdrjx4oL+ObgIUjnXbBKyhcsq2yBR1gQJ6xK5poww+vPFAvUxak9RspgUg1buf8VEx1wBeXIqFcF5VCkpqY2eDAMc/X4vTUgpd6OTvAo6xXBoz3AdVNYgOpUCKQoA4FCg3UROg4mSFiEExQ1zqWMV5BekJghy15/g0wfBJm0LCs1e0mtn6NmT5E/wDa4VkxwcHRtUzfK9nAPf0nbrlSt1sVJwQtAgk+GEmAtvP4Ky2ZAAmThQxzZ4IFSNNBH8fDBGBEloLTAsNqtSE9MheSvvBxvt+DKeQ9MwqIEPENAIsWrIwlTjcBrgzct4tt5uPc8Vh4HTDTCjUAjo14VlBmmZQjl0QnhgTEInqBHlc7bo3kjanh7vKHPayBoGiuCINe6UeNiAh4KQAbkQF0gte+WFtvj9+zoi//5ZJeWOq2OJ6TYqjEVV5d3Q7/OLzDVVG0h+4v4mSCEDBLKa0iSDJ+Q4CMvTMIM2R9YbBZWxAkLBAlIMlDllWCVALMkIElxqHI6cbbiItIc7VF8+QIqnW4IcsAjE3yk9HP3Cq+/lYWacRY0XkNg8dWGYcbIE4BhYgQWO0wLEixegh8wdfV7MQqSgLcBqFEXRV9bBaqgUAVAIMsocOXwYidkdlYogWNYr7ctfICyXjyAlDYMJPxTWsIMAbfWdBSAIU5IXxcnIHI8hnEEbAj1ML8aQaAXkpI2tUXwal4odSzK2L3wSWal7o8J8KAaccKq5HURlFYTkGD2CSQIG7znq3Cg8BCsdgucTje85yTYhBU+8sFDMrzCL3D8i6x7HWCoeqy3iwUM0/bg1PPIYLHDNDO1CZhIm9mFqJ1T17G64FoFGQFPjm6qS+hieeoi7LSQcXvtQke/TtL2FVrlX/96kv3p3v56MaqYCIoV0k9ZaWOqdXqqtnHVF1l3FX/FZXj8mlOCzy/KPJILblj8KerKWC2ygOQTiBepkC9Ww3XBA7PsgEWyotpHcJEMD8nwCS+88Gp9u5Ssq+CAZK6Lw7R9jCkQ9Ts+FmCxwzQD9enIW0urh3qdz08Yj08gkDiywGTDOUKcv3aBE0pwCMN+ZPCW+IOBawg243UNU1URC63GILSXR1mnBIcLKNNqqvdJEnFKuwnhgUuqBmQgTjaj2m2BTVIKEZqFBYAFHlmGl7xwkw9OuOAWHlyWKuAiJ9xU5U+z9yoeLfL6hY/e7hj56sowTFhY7DAtxNWKihAiJ1zGVIj4GP3Dz3CekB6fcGOsRSyE9Z5EmtUTXARIF3itBVcrRfPU6Tcl9ih0oHHziJxQBMYN6DxU5AFEHAQCFYp9/oaeEkxwiWrIsMBMEiADJuFPSwfg8dvjghcu4dJidXzwGJt3BqXUM0wswAHKkcFih2li9AIltDiJyFNjEDb62BrjdE6NS6ptA/wryf+vxjVDek2ubipLPX/o4yO9o+hFj+od8afN62v2+GvxRH79qx1HMFfjTQsRxOwXPDIAyC7IIlBPyCc8gKR0eHeRHRay+OvoKO+tGp/jFm5Ui0r44MUVuRweWfHqGAOyPbrXRG6AvQwTHXDqeWSw2GGakNBC5+rFTeB4rdFn0HYtoJhkQPN4+Kep6uPpqQeN600JJXoA7XWk4Bihpk6bDvY8RUKQd0oneARJ8PpT/L1CaW1hEnGoFk7ESTZ/l3Z/oUX/VJ6+lpDL51RaYcheyLIbWm0dzavF3h2GYQKw2GGalYinojRqenEgpPDNOQUAkv1TPECw4NFfP6SHp9UR3Boj0od46/++plSI9nd0J7MWm6QGYUv+91ttiOojpYs7kVJZWi2aGPBwscBhYg+exooMFjtMM6B6ZXQP7TCem2BCCRwl7Vr/M/zTGD4ImBVtQ2p6uf8bv1ZMUNadux5CJ4ynqGlpS3cjNVNMhizDX3ww0NZCEmbtPQ2eotR3ZZfJpWWekb9KdE2vTlt63RgmNJx6HhksdpgmohYhESb+puYZjFNWgRYOkuGBSORBRno7JDjiUemsREnJOQhh1gJWtSKCDSXCFhJMZCgeHCVdXpMnFOgTFr6WkKyL0Qm0hWChw8QinHoeGSx2mBakDm8OEDRlpfwviTid8CF0yGiPPr37ICHBjsrKCgCFKCm5pBwnJH8nb7XZp3pYhB/xUAJHXVfrOQIVnGOTIG+ezhuniVd9U1b/e6T2KjMSEDuBzvKewLZm97QxDBNtsNhhmpc6vCPhhI6+arD6s/A/HBPtDtgT7BBCgsORDIcjEedKL0Ntt0Bazymf4dy1PiQj8eIEnUNA1FK0L5bQCR29N0733upjrmp0YQ/RYqO2dhehW0IIsHeHiQVkNDBmp9FG0rphvzzTRITrmB2qjL/+KJ9h37qmn0j2QggfbAkmOBKtcFaWo7LyCsJPo0X4Jx+JtyCiFgyxRoj4LFXoCEkTsJpg1YnYcNRWnTrUtQMI1K+gJcNED9QISyzAnh2m2ajZmRwI7qat7OczenhCoEyB+JCR0R7tUzNQdrEcwgR8f/okSksuQJKs8Mm+wL5QM7RCPDjDDjhWvvM0FjU9OsLfnV3oA4+F8bajTGUFflZ+CNO8VCVshl2oQpCGwktXZRHDMG0DFjtMM+DPjNJjEDxh9qkLIWB3JCAjozM8Hg/c3mr4vIAQtQulliEWp1WMMTohywgov/gzQmrrGxaCELFT+uKRgWvy9BbTdiFq2FQUZ2MxTIPR14gJaiOgenhUhPGhpJWwI7V2DqCP6SChpJo7nU44KytgtyfA5fahqroaauPMwLnU43QdyRvJaxN59/C2fkcJ9uoEhI4ybWX2x1mZ/AHJag6JOsUVJHiE2hYjkiB25T+9J0jz5WhFJvVxPVqUegPsZZjWAVEDKyjHyMeAxQ7TxNQUPMraoArGYYQPISB4hCaYfCCSAAiUll4CoRB2ezyclZUoLbmkm66SA2nLQbFAdY86Ru4AjUKoOB1FkOjjcyQR598iIz0jBQ57Ai5XOnGutFzp4i785yI1Ld1YZyc4eD0YAUknbnzqSuU/LTVdL6rZy8MwsQKLHaaZCRWjo/P2ACGmuKAFKwuhfpPxx2sICedKy1FKF6F6CRQxJAdSlfXZPmEbdjambUw4iBSh06d3b9jtDlRWOlGIQpQWl4WNJQ5fWLLmudWeYcL/1uqraGsNVWO+LADTluA6O5HBYodpBvSiQu/l0SMZvT1aY05V2Kjf+JUHnqxNgegfej5tekQvdPSF55TRRCpyGuM2EAueg8jjrYQAHPYE2O0OCCHBbnfAYbejFKpHzh+srE5fAjCWHzD715lqCB69F4+0NhRe/0bZ3zVe9fIEB8jHwvvEtEVkUgpsNOT4WIDFDtNKMHp8NG+P5uVRpidUr4+awVNzvlkOkdFTm9CJle81rQUBZ2UVKiudmmfHWVmJUG4dvZBVW4SoXh5JMtfYR4njkrVpMHU7Gf62+P1mmFiExQ7TzASLjeCHnP5hpOuYDWjfzKFNUegq8YYsNhfcSiDUNRqbtv41KbgxqX5L6MaqmrfN/7qrcVYOu12Ls1I8eIG4KiEC1ZXV9iACJpgkCwClirYU5NmRSQYJxbsnSDneR7K/1KPkH5kJJDy6GB6GiW4aWisnVj4Frbqo4Pz583HTTTchMTER6enpGDduHIqKigz7VFdXIy8vD+3bt4fD4cCECRNQUlJi2Of06dMYPXo0EhISkJ6ejlmzZsHr9TanKUxYaitxpT4klX8gVbh4/FMVHuV38ga2kce/3QP410cmdBqjNFes3DbCZ6DpX2vydys3/q7UUDpXUoET3/2A0uKyQCCybgoSAAwVs2GCJJkhiTiYJSvMkhUmYfUHQQsImJT1wuoXQmZAEDIz2qP7T7oiI7M9DOnoQvILM/UWyMUHmehE7XrekCUWaNViZ/PmzcjLy8P27duxceNGeDwe3HXXXaisrNT2efrpp/Hxxx9j1apV2Lx5M3788UeMHz9e2+7z+TB69Gi43W589dVXWLZsGZYuXYrnn3++JUxiIiL0VJP2sNLSlmXtAaouwanlxpTjYKETayKlmSDda61VwZa1oHHZ39eKQIAgLaDcUHogKGNObQ+ienOUopJupHVIQNfsDkjrkABZdkMIk38fQnp6O/Tu2xe9r++DPn36IiOjPb/TTJuDGuFfLCCIoifL/ty5c0hPT8fmzZtx6623ory8HB06dMD777+Pf//3fwcAfPvtt7j++utRUFCAYcOG4dNPP8V9992HH3/8ERkZGQCAt956C7Nnz8a5c+dgsVjqvG5FRQWSk5OhaEP+Btj8BL/m+uJ0xm11x+REzZ97lGBsC6GsMTb/VFtF1JlJBWOsjbqfJKx+r44ZZmFVBA1MSO1gRe/efZBoT8HlyjIcPHgQF89fAYHg9jmR3S0Dva7vBRBBJi8OHTqAY8dPASGnN2Xw3wbTeBAAGeXl5UhKSmqSK6jPpfsSn0CcqPs5Fg4PubHu8pImHWtroFV7doIpLy8HAKSmpgIAdu/eDY/Hg1GjRmn79OrVC1lZWSgoKAAAFBQUoF+/fprQAYDc3FxUVFTg4MGDIa/jcrlQUVFhWJiWJLyAqf0bil7osAen6QnlgfPH4vg9PKTz8BgWXUxPJBDJsNsTkGhPgSQkJNpT4LDHa9c0CTMqnUogNJGMqqpKOJ2VdZyVYaIPnsaKjKgJUJZlGTNmzMDNN9+Mvn37AgCKi4thsViQkpJi2DcjIwPFxcXaPnqho25Xt4Vi/vz5mDdvXiNbwDQM9RMZLnW9ruOYpiHU+6KrlE1erfYNSP1uFbqdh74nmlo1W4hAEUmCHLiMACorq3C5sgx2exIqKyvgrKxUUnBJBvkLTvroIOz2eFx2XkZJyQV/4HNwfzTO0GKiF66zExlRI3by8vJQWFiIbdu2Nfm15syZg5kzZ2q/V1RUoEuXLk1+XSYS6srmYnHTMgRXygaCs+kCbR3C31416UR6waPfLvvXxeF8qRMH6SAc9ng4K6/gXGkFTJINMikB7EKYca60AiV0HkRBJ2IYJqaIimmsadOmYd26dfj888/RuXNnbX1mZibcbjfKysoM+5eUlCAzM1PbJzg7S/1d3ScYq9WKpKQkw8K0VmIxG6q1EkFguX4JSSDwXDlWH9jshY888JEXSvFBM86fc+LkyRKcP+eEJFkgq9tJPy0mAjpHDYTmjvZMG4GIGrxcDS+88AKEEIalV69eTWRd49GqxQ4RYdq0aVizZg02bdqEbt26GbYPGjQIcXFxyM/P19YVFRXh9OnTyMnJAQDk5OTgm2++QWlpqbbPxo0bkZSUhN69ezePIQwTM4QrH+CrmQNCvhpLQAjJhtICMnn8ixc+csEjV8EjX9Gd1wePXAUvueAjF3yyG7LshUwu//Fef1kCX1CmHldQZqIbuRGWq6VPnz44e/astjTHjEtDadXTWHl5eXj//ffx4YcfIjExUYuxSU5ORnx8PJKTkzFlyhTMnDkTqampSEpKwm9/+1vk5ORg2LBhAIC77roLvXv3xsMPP4xXX30VxcXFePbZZ5GXlwer1dqS5jFMGyY4lgcILhgZ+ihjYULFwyNBQFZ6oumztgTg08XfaIHOFPAM1eRqW4YwDBOM2WwOOzPSWmnVYmfx4sUAgNtuu82w/p133sHkyZMBAK+//jokScKECRPgcrmQm5uLN998U9vXZDJh3bp1mDp1KnJycmC32zFp0iS8+OKLzWUGw8QwelERTvjo0cX56KaeZHghIMEnKwHQsvAYu6AjUIGZ1EKTQOD/sOnmwWNkmOiCqGG1cupTfebo0aPo1KkTbDYbcnJyMH/+fGRlZdV7DM1BVNXZaSm4zg7DNDU1aynVVq8nUKtH7yEKVF+uIXIAFjpMM9J8dXbuSPgVzA2os+MlN/Kr/h++//57w1itVmvI2Y9PP/0UTqcTPXv2xNmzZzFv3jz88MMPKCwsRGJiYr3H0dS06pgdhmFihZqxPoY2IWrQshbD49U8OOoi+wOXibwwVNSmcB4dhmFUunTpguTkZG2ZP39+yP3uuece/OxnP0P//v2Rm5uLTz75BGVlZfjnP//ZzCO+Olr1NBbDMLGGPtZHja+RAPIpcTtC1najGt/VQjWDDdcXjT06TNtAJqUkZ0OOBxDSsxMJKSkpuO6663Ds2LF6j6E5YM8OwzCtkJo3b30zWIUQOSU6Lw4LHSYWaKzeWMHlViIVO06nE8ePH0fHjh2b0swGw2KHYZhWijq1pW/8Gj5tncinu3mHSq7lWkxM26O5U89///vfY/PmzTh58iS++uor3H///TCZTHjwwQcbxZ6mgqexGIZp5dSc2rr6YxmGaQzOnDmDBx98EBcuXECHDh1wyy23YPv27ejQoUNLD61WWOwwDBMl6FtSRLIvw7R91Da6DTn+alixYkW9r9WSsNhhGCaKYBHDMHoaK0C5rcMxOwzDMAzDtGnYs8MwDMMwUUrNzMOrPz4WYLHDMAzDMFFKc8fsRCs8jcUwDMMwTJuGPTsMwzAME6WwZycyWOwwDMMwTJTCMTuRwdNYDMMwDMO0adizwzAMwzBRCjVwGitWPDssdhiGYRgmSpGFDCGuto2K7virbsESnbDYYRiGYZgoRQZBcIBynXDMDsMwDMMwbRr27DAMwzBMlEL+5POGHB8LsNhhGIZhmChFBho4jRUb8DQWwzAMwzBtGvbsMAzDMEyUwtlYkcFih2EYhmGiFBkyRAMES6yIHZ7GYhiGYRimTcOeHYZhGIaJUtizExksdhiGYRgmSuHU88jgaSyGYRiGYdo07NlhGIZhmCiFs7Eig8UOwzAMw0QpBLlBgiVWprFY7DAMwzBMlELwgRoQkULwNeJoWi8cs8MwDMMwTJuGPTsMwzAME6UoU1gcs1MXLHYYhmEYJkqRQWiY2Kl/E9FogqexGIZhGIZp07Bnh2EYhmGiFCVAWTTo+FiAxQ7DMAzDRCkcsxMZPI3FMAzDMEybhj07DMMwDBOlcG+syIgpz86iRYuQnZ0Nm82GoUOHYufOnS09JIZhGIapNzJ8DV5igZgROytXrsTMmTMxd+5c7NmzBwMGDEBubi5KS0tbemgMwzAMwzQhMSN2/vSnP+Hxxx/Ho48+it69e+Ott95CQkIC3n777ZYeGsMwDMPUC3UaqyFLLBATYsftdmP37t0YNWqUtk6SJIwaNQoFBQUtODKGYRiGqT8y+Rq8xAIxEaB8/vx5+Hw+ZGRkGNZnZGTg22+/rbG/y+WCy+XSfi8vL/f/FBuVJhmGYZiGoDwriJr+mcEBypERE2Lnapk/fz7mzZsXYguBBQ/DMAwTCRcuXEBycnJLD4NBjIidtLQ0mEwmlJSUGNaXlJQgMzOzxv5z5szBzJkztd/LysrQtWtXnD59Oqr/cCsqKtClSxd8//33SEpKaunh1Ju2YEdbsAFoG3a0BRsAtqM1UV5ejqysLKSmpjb5tRTPTv2notiz04awWCwYNGgQ8vPzMW7cOACALMvIz8/HtGnTauxvtVphtVprrE9OTo7aD5+epKQktqOV0BZsANqGHW3BBoDtaE1IUtOHxRLJkBvSLoJY7LQpZs6ciUmTJmHw4MEYMmQI/vznP6OyshKPPvpoSw+NYRiGYZgmJGbEzs9//nOcO3cOzz//PIqLi3HDDTdg/fr1NYKWGYZhGCZaUKahGtIIlD07bY5p06aFnLaqC6vVirlz54ac2oom2I7WQ1uwAWgbdrQFGwC2ozXRnDZQA1PHG3p8tCCoOXLjGIZhGIZpNCoqKpCcnIxkW28IYar3eYh8KK8+hPLy8qiPkaqNmPLsMAzDMExbQglP5mmsumCxwzAMwzBRipJNxdlYdRET7SIYhmEYholdWOxEwKJFi5CdnQ2bzYahQ4di586dLT0kjfnz5+Omm25CYmIi0tPTMW7cOBQVFRn2qa6uRl5eHtq3bw+Hw4EJEybUKLB4+vRpjB49GgkJCUhPT8esWbPg9Xqb0xSNBQsWQAiBGTNmaOuixYYffvgBDz30ENq3b4/4+Hj069cPX3/9tbadiPD888+jY8eOiI+Px6hRo3D06FHDOS5evIiJEyciKSkJKSkpmDJlCpxOZ7PZ4PP58Nxzz6Fbt26Ij4/HT37yE7z00kuG0vetzY4tW7bg3/7t39CpUycIIbB27VrD9sYa74EDBzBixAjYbDZ06dIFr776arPZ4fF4MHv2bPTr1w92ux2dOnXCI488gh9//DGq7AjmySefhBACf/7zn1uVHZHYcPjwYYwZMwbJycmw2+246aabcPr0aW17c9y3CL4GLzEBMbWyYsUKslgs9Pbbb9PBgwfp8ccfp5SUFCopKWnpoRERUW5uLr3zzjtUWFhI+/bto3vvvZeysrLI6XRq+zz55JPUpUsXys/Pp6+//pqGDRtGw4cP17Z7vV7q27cvjRo1ivbu3UuffPIJpaWl0Zw5c5rdnp07d1J2djb179+fnnrqqaiy4eLFi9S1a1eaPHky7dixg7777jvasGEDHTt2TNtnwYIFlJycTGvXrqX9+/fTmDFjqFu3bnTlyhVtn7vvvpsGDBhA27dvp61bt9K1115LDz74YLPZ8fLLL1P79u1p3bp1dOLECVq1ahU5HA564403Wq0dn3zyCf3hD3+g1atXEwBas2aNYXtjjLe8vJwyMjJo4sSJVFhYSB988AHFx8fT3//+92axo6ysjEaNGkUrV66kb7/9lgoKCmjIkCE0aNAgwzlaux16Vq9eTQMGDKBOnTrR66+/3qrsqMuGY8eOUWpqKs2aNYv27NlDx44dow8//NDwbGjK+1Z5eTkBoARLd7Jbr633kmDpTgCovLy8UV631gqLnToYMmQI5eXlab/7fD7q1KkTzZ8/vwVHFZ7S0lICQJs3byYi5QYZFxdHq1at0vY5fPgwAaCCggIiUj7UkiRRcXGxts/ixYspKSmJXC5Xs4398uXL1KNHD9q4cSONHDlSEzvRYsPs2bPplltuCbtdlmXKzMyk1157TVtXVlZGVquVPvjgAyIiOnToEAGgXbt2aft8+umnJISgH374oekGr2P06NH02GOPGdaNHz+eJk6cSESt347gB1NjjffNN9+kdu3aGf6eZs+eTT179mwWO0Kxc+dOAkCnTp0iouiy48yZM3TNNddQYWEhde3a1SB2WpsdoWz4+c9/Tg899FDYY5r6vqWKnXhLNiVYu9d7ibdkx4TY4WmsWnC73di9ezdGjRqlrZMkCaNGjUJBQUELjiw8aod2tSfL7t274fF4DDb06tULWVlZmg0FBQXo16+focBibm4uKioqcPDgwWYbe15eHkaPHm0YKxA9Nnz00UcYPHgwfvaznyE9PR033ngj/uu//kvbfuLECRQXFxvsSE5OxtChQw12pKSkYPDgwdo+o0aNgiRJ2LFjR7PYMXz4cOTn5+PIkSMAgP3792Pbtm245557osoOlcYab0FBAW699VZYLBZtn9zcXBQVFeHSpUvNZI2R8vJyCCGQkpKijTEa7JBlGQ8//DBmzZqFPn361Nje2u2QZRn/+7//i+uuuw65ublIT0/H0KFDDVNd0XLfihVY7NTC+fPn4fP5alRZzsjIQHFxcQuNKjyyLGPGjBm4+eab0bdvXwBAcXExLBaLdjNU0dtQXFwc0kZ1W3OwYsUK7NmzB/Pnz6+xLVps+O6777B48WL06NEDGzZswNSpUzF9+nQsW7bMMI7a/p6Ki4uRnp5u2G42m5GamtpsdjzzzDP4xS9+gV69eiEuLg433ngjZsyYgYkTJ2pjVMetp7XZodJY420Nf2N6qqurMXv2bDz44INafZRosWPhwoUwm82YPn16yO2t3Y7S0lI4nU4sWLAAd999Nz777DPcf//9GD9+PDZv3qyNoTnuW0S+Bi+xAKeetyHy8vJQWFiIbdu2tfRQrorvv/8eTz31FDZu3AibzdbSw6k3sixj8ODBeOWVVwAAN954IwoLC/HWW29h0qRJLTy6yPnnP/+J9957D++//z769OmDffv2YcaMGejUqVNU2dGW8Xg8eOCBB0BEWLx4cUsP56rYvXs33njjDezZswdC1D9luiWRZSVde+zYsXj66acBADfccAO++uorvPXWWxg5cmSzjaWhqeOces4gLS0NJpOpRvR8SUkJMjMzW2hUoZk2bRrWrVuHzz//HJ07d9bWZ2Zmwu12o6yszLC/3obMzMyQNqrbmprdu3ejtLQUAwcOhNlshtlsxubNm/GXv/wFZrMZGRkZrd4GAOjYsSN69+5tWHf99ddr2RnqOGr7e8rMzERpaalhu9frxcWLF5vNjlmzZmnenX79+uHhhx/G008/rXndosUOlcYab2v4GwMCQufUqVPYuHGjoeptNNixdetWlJaWIisrS/u8nzp1Cr/73e+QnZ0dFXakpaXBbDbX+XmPhvtWrMBipxYsFgsGDRqE/Px8bZ0sy8jPz0dOTk4LjiwAEWHatGlYs2YNNm3ahG7duhm2Dxo0CHFxcQYbioqKcPr0ac2GnJwcfPPNN4abi3oTDf4wNwV33HEHvvnmG+zbt09bBg8ejIkTJ2o/t3YbAODmm2+ukfZ/5MgRdO3aFQDQrVs3ZGZmGuyoqKjAjh07DHaUlZVh9+7d2j6bNm2CLMsYOnRoM1gBVFVVQZKMtwaTyaR9m40WO1Qaa7w5OTnYsmULPB6Pts/GjRvRs2dPtGvXrllsUYXO0aNH8X//939o3769YXs02PHwww/jwIEDhs97p06dMGvWLGzYsCEq7LBYLLjppptq/bw3172XIDd4iQlaOkK6tbNixQqyWq20dOlSOnToED3xxBOUkpJiiJ5vSaZOnUrJycn0xRdf0NmzZ7WlqqpK2+fJJ5+krKws2rRpE3399deUk5NDOTk52nY1/fGuu+6iffv20fr166lDhw4tknquos/GIooOG3bu3Elms5lefvllOnr0KL333nuUkJBA7777rrbPggULKCUlhT788EM6cOAAjR07NmQK9I033kg7duygbdu2UY8ePZo19XzSpEl0zTXXaKnnq1evprS0NPqP//iPVmvH5cuXae/evbR3714CQH/6059o7969WpZSY4y3rKyMMjIy6OGHH6bCwkJasWIFJSQkNGrKdm12uN1uGjNmDHXu3Jn27dtn+LzrM3daux2hCM7Gag121GXD6tWrKS4ujpYsWUJHjx6lv/71r2QymWjr1q3aOZryvqVmY8WZMshi7ljvJc6UERPZWCx2IuCvf/0rZWVlkcVioSFDhtD27dtbekgaAEIu77zzjrbPlStX6De/+Q21a9eOEhIS6P7776ezZ88aznPy5Em65557KD4+ntLS0uh3v/sdeTyeZrYmQLDYiRYbPv74Y+rbty9ZrVbq1asXLVmyxLBdlmV67rnnKCMjg6xWK91xxx1UVFRk2OfChQv04IMPksPhoKSkJHr00Ufp8uXLzWZDRUUFPfXUU5SVlUU2m426d+9Of/jDHwwP1NZmx+effx7yczBp0qRGHe/+/fvplltuIavVStdccw0tWLCg2ew4ceJE2M/7559/HjV2hCKU2GlpOyKx4R//+Adde+21ZLPZaMCAAbR27VrDOZryvsVi5+rgrucMwzAME2WoXc/Npg4Qov4RKUQyvL5z3PWcYRiGYZjWiZI6Xn+fBWdjMQzDMAzDtAHYs8MwDMMwUQsBDcqoio1IFhY7DMMwDBOlKNNQ9S/OGCthuyx2GIZhGCZKUerkNEDsxIhnh2N2GIZhGIZp07Bnh2EYhmGiloZ5djhmh2EYhmGY1k0DY3YQIzE7PI3FMEzMMXnyZAghIITA2rVrG/Xc2dnZ2rmDm0AyDNMysNhhmAg4d+4cpk6diqysLFitVmRmZiI3NxdffvllSw+NqSd33303zp49i3vuuadRz7tr1y78z//8T6Oek2HCwY1AI4PFDsNEwIQJE7B3714sW7YMR44cwUcffYTbbrsNFy5caOmhtRn03aubA1W0Wq3WRj1vhw4dkJqa2qjnZJjwyI2wXD2LFi1CdnY2bDYbhg4dip07dzbQjqaFxQ7D1EFZWRm2bt2KhQsX4vbbb0fXrl0xZMgQzJkzB2PGjDHs9+tf/xoZGRmw2Wzo27cv1q1bp23ftm0bRowYgfj4eHTp0gXTp09HZWWltj07OxuvvPIKHnvsMSQmJiIrKwtLliwxjOX777/HAw88gJSUFKSmpmLs2LE4efJkreM/ePAg7rvvPiQlJSExMREjRozA8ePHASheiDvvvBNpaWlITk7GyJEjsWfPHsPxQgj8/e9/x3333YeEhARcf/31KCgowLFjx3DbbbfBbrdj+PDh2jlVPvzwQwwcOBA2mw3du3fHvHnz4PV6DeddvHgxxowZA7vdjpdffhk+nw9TpkxBt27dEB8fj549e+KNN96o1b4XX3wRnTp1MgjP0aNH4/bbb4csR34jP3nyJIQQWLFiBYYPH669h5s3b270azFMNLNy5UrMnDkTc+fOxZ49ezBgwADk5uaitLS0pYcWnhZuRMowrR6Px0MOh4NmzJhB1dXVIffx+Xw0bNgw6tOnD3322Wd0/Phx+vjjj+mTTz4hIqJjx46R3W6n119/nY4cOUJffvkl3XjjjTR58mTtHF27dqXU1FRatGgRHT16lObPn0+SJNG3335LRERut5uuv/56euyxx+jAgQN06NAh+uUvf0k9e/Y0dCTXc+bMGUpNTaXx48fTrl27qKioiN5++23tnPn5+bR8+XI6fPgwHTp0iKZMmUIZGRlUUVGhnQMAXXPNNbRy5UoqKiqicePGUXZ2Nv30pz+l9evX06FDh2jYsGF09913a8ds2bKFkpKSaOnSpXT8+HH67LPPKDs7m1544QXDedPT0+ntt9+m48eP06lTp8jtdtPzzz9Pu3btou+++47effddSkhIoJUrV4Z9f7xeL+Xk5NC4ceOIiOhvf/sbpaSk0KlTp8IeM2nSJBo7dqxhndpVvHPnzvSvf/2LDh06RL/61a8oMTGRzp8/f1XXUjtmX7p0KewYGKYhqF3PATMJxNV7AcxX3fV8yJAhlJeXp/3u8/moU6dONH/+/KYwtVFgscMwEfCvf/2L2rVrRzabjYYPH05z5syh/fv3a9s3bNhAkiRRUVFRyOOnTJlCTzzxhGHd1q1bSZIkunLlChEpYuehhx7StsuyTOnp6bR48WIiIlq+fDn17NmTZFnW9nG5XBQfH08bNmwIed05c+ZQt27dyO12R2Snz+ejxMRE+vjjj7V1AOjZZ5/Vfi8oKCAA9I9//ENb98EHH5DNZtN+v+OOO+iVV14xnHv58uXUsWNHw3lnzJhR55jy8vJowoQJte5z/PhxSkxMpNmzZ1N8fDy99957te5fm9hZsGCBts7j8VDnzp1p4cKFV3UtFjtMUxMQOya/YKnvYroqseNyuchkMtGaNWsM6x955BEaM2ZME1jaOPA0FsNEwIQJE/Djjz/io48+wt13340vvvgCAwcOxNKlSwEA+/btQ+fOnXHdddeFPH7//v1YunQpHA6HtuTm5kKWZZw4cULbr3///trPQghkZmZqruH9+/fj2LFjSExM1M6RmpqK6urqGlNIKvv27cOIESMQFxcXcntJSQkef/xx9OjRA8nJyUhKSoLT6cTp06cN++nHlZGRAQDo16+fYV11dTUqKiq0sb744osGex9//HGcPXsWVVVV2nGDBw+uMaZFixZh0KBB6NChAxwOB5YsWVJjPMF0794df/zjH7Fw4UKMGTMGv/zlL2vdvzZycnK0n81mMwYPHozDhw83ybUYpnGgBiwKFRUVhsXlcoW80vnz5+Hz+bT7gEpGRgaKi4sb2a7Gg+vsMEyE2Gw23Hnnnbjzzjvx3HPP4Ve/+hXmzp2LyZMnIz4+vtZjnU4nfv3rX2P69Ok1tmVlZWk/B4sSIYQWC+J0OjFo0CC89957Nc7RoUOHkNeta1yTJk3ChQsX8MYbb6Br166wWq3IycmB2+027KcflxAi7Dr9WOfNm4fx48fXuKbNZtN+ttvthm0rVqzA73//e/znf/4ncnJykJiYiNdeew07duyo1Q4A2LJlC0wmE06ePAmv1wuzuelub815LYYJhcViQWZmZqMIDIfDgS5duhjWzZ07Fy+88EKDz91a4E8ow9ST3r17azVa+vfvjzNnzuDIkSMhvTsDBw7EoUOHcO2119b7egMHDsTKlSuRnp6OpKSkiI7p378/li1bBo/HE9K78+WXX+LNN9/EvffeC0AJgD5//ny9x6gfa1FR0VXb++WXX2L48OH4zW9+o60L57XSs3LlSqxevRpffPEFHnjgAbz00kuYN2/eVY8bALZv345bb70VAOD1erF7925MmzatSa7FMPXFZrPhxIkTNb6Y1Aci0r6wqITLUkxLS4PJZEJJSYlhfUlJCTIzMxs8lqaCp7EYpg4uXLiAn/70p3j33Xdx4MABnDhxAqtWrcKrr76KsWPHAgBGjhyJW2+9FRMmTMDGjRtx4sQJfPrpp1i/fj0AYPbs2fjqq68wbdo07Nu3D0ePHsWHH35oeIjWxcSJE5GWloaxY8di69atOHHiBL744gtMnz4dZ86cCXnMtGnTUFFRgV/84hf4+uuvcfToUSxfvhxFRUUAgB49emD58uU4fPgwduzYgYkTJ9bpDYqE559/Hv/93/+NefPm4eDBgzh8+DBWrFiBZ599ttbjevToga+//hobNmzAkSNH8Nxzz2HXrl21HnPmzBlMnToVCxcuxC233IJ33nkHr7zyCrZv316vsS9atAhr1qzBt99+i7y8PFy6dAmPPfZYk1yLYRqCzWZDUlJSgxd1Clu/hBM7FosFgwYNQn5+vrZOlmXk5+cbpoBbHS0dNMQwrZ3q6mp65plnaODAgZScnEwJCQnUs2dPevbZZ6mqqkrb78KFC/Too49S+/btyWazUd++fWndunXa9p07d9Kdd95JDoeD7HY79e/fn15++WVte9euXen11183XHvAgAE0d+5c7fezZ8/SI488QmlpaWS1Wql79+70+OOP1xpcuH//frrrrrsoISGBEhMTacSIEXT8+HEiItqzZw8NHjyYbDYb9ejRg1atWlVjHAAMwYhqIO/evXu1daECctevX0/Dhw+n+Ph4SkpKoiFDhtCSJUvCnld9rSdPnkzJycmUkpJCU6dOpWeeeYYGDBgQ0jZZlumOO+6g3NxcQ+D2b3/7W/rJT35Cly9fDnlcbQHK77//Pg0ZMoQsFgv17t2bNm3adNXX4gBlpi2zYsUKslqttHTpUjp06BA98cQTlJKSQsXFxS09tLAIohhpjMEwDONn8uTJKCsrM7SKOHnyJLp164a9e/fihhtuaND5v/jiC9x+++24dOkSUlJSGnQuhmmN/O1vf8Nrr72G4uJi3HDDDfjLX/6CoUOHtvSwwsLTWAzDxCTr1q2Dw+EwFH5sDPr06dPoLSgYprUxbdo0nDp1Ci6XCzt27GjVQgcA2LPDMEzMUVpaqqXJd+zYEXa7vdE8O6dOndJaX3Tv3h2SxN8pGaalYbHDMAzDMEybhr9yMAzDMAzTpmGxwzAMwzBMm4bFDsMwDMMwbRoWOwzDMAzDtGlY7DAMwzAM06ZhscMwDMMwTJuGxQ7DMAzDMG0aFjsMwzAMw7RpWOwwDMMwDNOmYbHDMAzDMEyb5v8D94LwoRZIdQkAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
    ,\n", + " )" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "recording.plot_distribution()" ] @@ -618,13 +592,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "we can neatly see that the recorded data shows a centre-bias, which is a well-known effect from eye statistics. In y, we can see that fixations tend to occur below the horizon, which is indicative of a walking task where a participant looks at the floor in front of them more often" + "We can see a clear centre-bias, as participants tend to look more centrally relative to head position." ] } ], "metadata": { "kernelspec": { - "display_name": "pyneon", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -638,7 +612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.6" } }, "nbformat": 4,