{"cells":[{"cell_type":"markdown","metadata":{"id":"pmM0zX-SW4M4"},"source":["# Tutorial on `edu-convokit` for the NCTE dataset\n","\n","Welcome to the tutorial on `edu-convokit` for the [NCTE dataset](https://github.com/ddemszky/classroom-transcript-analysis). This tutorial will walk you through the process of using `edu-convokit` to pre-process, annotate and analyze the NCTE dataset.\n","\n","If you are looking for a tutorial on the individual components of `edu-convokit`, please refer to the following tutorials to get started:\n","- [Text Pre-processing Colab](https://colab.research.google.com/drive/1a-EwYwkNYHSNcNThNTXe6DNpsis0bpQK)\n","- [Annotation Colab](https://colab.research.google.com/drive/1rBwEctFtmQowZHxralH2OGT5uV0zRIQw)\n","- [Analysis Colab](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe)\n","\n","This tutorial will use all of the components!"]},{"cell_type":"markdown","metadata":{"id":"fxcdKqHeW4M6"},"source":["## Installation\n","\n","Let's start by installing `edu-convokit` and importing the necessary modules.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16955,"status":"ok","timestamp":1703933634344,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"EHd_2SRNW4M6","outputId":"e21ff79d-1a0a-4883-9ab1-84d1b809fd6a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting git+https://github.com/rosewang2008/edu-convokit.git\n","  Cloning https://github.com/rosewang2008/edu-convokit.git to /tmp/pip-req-build-ncsfguml\n","  Running command git clone --filter=blob:none --quiet https://github.com/rosewang2008/edu-convokit.git /tmp/pip-req-build-ncsfguml\n","  Resolved https://github.com/rosewang2008/edu-convokit.git to commit 5c1128c8f94d7574bc61cc56f29ce64bdca4ae30\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) 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emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for emoji: filename=emoji-1.7.0-py3-none-any.whl size=171033 sha256=0a5d69f4ac14f376271ac4011aefc99eb821dcd5b2584b28eefd54bca2b8cfc0\n","  Stored in directory: /root/.cache/pip/wheels/31/8a/8c/315c9e5d7773f74b33d5ed33f075b49c6eaeb7cedbb86e2cf8\n","Successfully built edu-convokit docopt emoji\n","Installing collected packages: emoji, docopt, num2words, ftfy, clean-text, edu-convokit\n","Successfully installed clean-text-0.6.0 docopt-0.6.2 edu-convokit-0.0.1 emoji-1.7.0 ftfy-6.1.3 num2words-0.5.10\n"]}],"source":["!pip install edu-convokit"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14881,"status":"ok","timestamp":1703933649222,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"x0Q58WdEW4M7","outputId":"cc67e4d1-0930-46f8-cc6a-ad3c19164f04"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:root:Since the GPL-licensed package `unidecode` is not installed, using Python's `unicodedata` package which yields worse results.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]}],"source":["from edu_convokit.preprocessors import TextPreprocessor\n","from edu_convokit.annotation import Annotator\n","from edu_convokit.analyzers import (\n","    QualitativeAnalyzer,\n","    QuantitativeAnalyzer,\n","    LexicalAnalyzer,\n","    TemporalAnalyzer,\n","    GPTConversationAnalyzer\n",")\n","# For helping us load data\n","from edu_convokit import utils\n","\n","import os\n","import tqdm"]},{"cell_type":"markdown","metadata":{"id":"plFdw3csW4M7"},"source":["## 📑 Data\n","\n","Let's download the dataset under `raw_data/`.\n","Note we're only download a subsample of the NCTE dataset for this tutorial; this cuts down the annotation time.\n","If you would like to annotate the entire dataset, feel free to upload the entire dataset to this Colab!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":701,"status":"ok","timestamp":1703933659477,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"8w8_hOq8W4M7","outputId":"8e8ca0e5-c4a9-4a40-8f6a-9eebe0e10333"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-12-30 10:54:18--  https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/ncte.zip\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 346717 (339K) [application/zip]\n","Saving to: ‘ncte.zip’\n","\n","\rncte.zip              0%[                    ]       0  --.-KB/s               \rncte.zip            100%[===================>] 338.59K  --.-KB/s    in 0.03s   \n","\n","2023-12-30 10:54:19 (9.57 MB/s) - ‘ncte.zip’ saved [346717/346717]\n","\n"]}],"source":["# We will put the data here:\n","DATA_DIR = \"raw_data\"\n","!mkdir -p $DATA_DIR\n","\n","# We will put the annotated data here:\n","ANNOTATIONS_DIR = \"annotations\"\n","!mkdir -p $ANNOTATIONS_DIR\n","\n","# # Download the data\n","!wget \"https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/ncte.zip\"\n","\n","# # Unzip the data\n","!unzip -n -q ncte.zip -d $DATA_DIR\n","\n","# Data directory is then raw_data/talkmoves\n","DATA_DIR = \"raw_data/ncte\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dATniCmtW4M7"},"outputs":[],"source":["# We'll set the important variables specific to this dataset. If you open one of the files, you'll see that the\n","# speaker and text columns are defined as:\n","TEXT_COLUMN = \"text\"\n","SPEAKER_COLUMN = \"speaker\"\n","\n","# We will also define the annotation columns.\n","# For the purposes of this tutorial, we will only be using talktime, student_reasoning, and uptake.\n","TALK_TIME_COLUMN = \"talktime\"\n","STUDENT_REASONING_COLUMN = \"student_reasoning\"\n","UPTAKE_COLUMN = \"uptake\""]},{"cell_type":"markdown","metadata":{"id":"hGYZAQzKW4M7"},"source":["One thing that will be important is knowing how the teacher/tutor and student are represented in the dataset.\n","Let's load some examples and see how they are represented."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":223},"executionInfo":{"elapsed":257,"status":"ok","timestamp":1703933669958,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"KrBZyToSW4M7","outputId":"764874f7-b558-4251-ec99-f286e27f8349"},"outputs":[{"name":"stdout","output_type":"stream","text":["['teacher' 'multiple students' 'student']\n"]},{"data":{"text/html":["\n","  <div id=\"df-e2a6c5cf-842e-4704-bc65-4be7fdc42beb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>speaker</th>\n","      <th>talktime_words</th>\n","      <th>math_density</th>\n","      <th>uptake</th>\n","      <th>student_reasoning</th>\n","      <th>focusing_questions</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Okay.  I think it’s working.  Alright, so the ...</td>\n","      <td>teacher</td>\n","      <td>17</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Yes.</td>\n","      <td>multiple students</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Student M, you don’t have your homework?</td>\n","      <td>teacher</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>No.</td>\n","      <td>student</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Did you hand it in?</td>\n","      <td>teacher</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2a6c5cf-842e-4704-bc65-4be7fdc42beb')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e2a6c5cf-842e-4704-bc65-4be7fdc42beb button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Alright, so the ...            teacher   \n","1                                               Yes.  multiple students   \n","2           Student M, you don’t have your homework?            teacher   \n","3                                                No.            student   \n","4                                Did you hand it in?            teacher   \n","\n","   talktime_words  math_density  uptake  student_reasoning  focusing_questions  \n","0              17             0     NaN                NaN                 0.0  \n","1               1             0     NaN                NaN                 NaN  \n","2               7             0     NaN                NaN                 0.0  \n","3               1             0     NaN                NaN                 NaN  \n","4               5             0     NaN                NaN                 0.0  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["files = os.listdir(DATA_DIR)\n","files = [os.path.join(DATA_DIR, f) for f in files if utils.is_valid_file_extension(f)]\n","df = utils.merge_dataframes_in_list(files)\n","print(df[SPEAKER_COLUMN].unique())\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"2c7-JGp4W4M8"},"source":["There are two speakers: one is the teacher and the other is the student/multiple students.\n","Let's defined these variables as well."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ykqN9avUW4M8"},"outputs":[],"source":["STUDENT_SPEAKER = ['student', 'multiple students']\n","TEACHER_SPEAKER = 'teacher'"]},{"cell_type":"markdown","metadata":{"id":"3LT7a6syW4M8"},"source":["## 📝 Text Pre-Processing and Annotation\n","\n","Let's first preprocess and annotate the dataset with `edu-convokit`. The following section will:\n","- Read each file in the dataset and preprocess it using `edu-convokit`'s `Preprocessor`.\n","- Then, annotate the file using `edu-convokit`'s `Annotator` for talktime, student reasoning and uptake.\n","- Finally, save the annotated file under `data/annotated/`.\n","\n","Let's get started!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["478afdc591fe4717b816d4b8999b20f9","438e1e3248f840cc80f868bdc9d6f055","b481a2dd11ec4b3492fa630d54aeec5e","6098c18d2e19483f80f674d7c3da495d","c72001496dd34958a49e871566ceda21","727b4b33e2194d2490ecaf9ee5c55755","4975d1aad66f42f2b8d327cfb64815f6","f4c4b907198e460a8388132636e0bf3f","8bd6b18a737443bb95c5203eb3543049","154686b7a0814306b14f41c546528814","9ddbc9ebb4174aeaade38c8a04fa79f3","32567c294e094c5a92fe985acabdaef4","ee114eb89811467b8027be7f094e8c46","d6714101182048619cca0346ad386f1b","98983133bd48476f91c3ce46693a39d0","ee0ab175bea84a35a999011696045ac7","f1fb0968b90b4e719f8a2ed200275cda","d12e9c2b17a34fd5812174abee025196","56c2d940a7fa42faa25edaf380db5d7b","4dd893dde3c84322ae8eaaa5fc8509a5","9f80803f4ba64b01aada90f268f3ee97","15e6c40a84154e9eb2c18c2624e60e91","6c35b75aaa3746cdbce04bfdf653904e","db2330f69fa645a3beeacd333abff97b","3eb38197657a4443bc7b16ed0dbeb052","4a7397ce40cf4875adb95ee0c6338cff","97caabac0ca8435ab931b1f19efe6a73","7ad12fd270234f84874b096cd09b284d","c3d0bcfa22ad4b30a93ed9f4c495dcd8","27370b9b75594bc0adebd5c660d1ce06","a016b13faf5240c9a6df8bc37b984d82","7b2ce202138d469db770b752cc58be91","6c60afe2fe5a4fe49684139c197ef309","9b84a1889c894cc78623ba9d213982b4","01a9cd65ca10474382117fb832734377","277b6ad600474bc8a7746f6aaca4a5b1","11fbcb21e8c94691aef35f1a85f8b43c","822aeacfa582438eb2089d3f54838d5e","37f1737da8904f10b1ebe5626a13120e","ed70e63306ca438da9b4b6b8c796372f","2d039f0ecd5d4ed7900ec55c5fe670c2","6e78f68175bb43cf9282aa8f0e0e7d9a","18db32367fcc4501ba2b655704dc6adb","88362c746da243d3b6eea7df74b78908","c521dae35d204f0ab7e7cdac598370b2","8d7e519f388349b3bc7f73c9ee48fe2a","88bc713728604916959a88910a8be97f","d8d938b4f27d470a9ec1ba28f0610e6b","cd23fcf16be1428f8314531ffadda28c","96c8dfd124f64684858a903a83a88ed4","ead0c7c4a61b4907ad090e80416d9061","7a620997d037457da80aeae328f64e16","f9e1db47741b40c682fe51e6f4f54400","8a674a85e5e24fb8a5862d28258be5ee","d848376790fe4f40949741b855847df0","f4c6112a4afd4ff285b883ca8eacdefe","22d19190e0b449a097da103386730120","d46c6d4bfd6c48d881294365737c2b27","52cded88639a447c8fb7466f53825214","46923b22da424156b76a02c73c9fb032","77b8d071e0cb47e2820f4862e8e1c848","fde34952d6ca455793bd663d4b43ed15","70013f4b11cd4b8f8e067695c4dfa498","8c321b4c74ad4c6ea8d3a70dfbd63468","df3f5f67947b45bf9707ec5c5d3bd41f","52f879cff0f44d799ae44f0ee06c6c40","eb4a7c9b210443f68fe1279b4010aacd","36886c8d78fc41b699cc59f374a7e26f","256f7cc6524b48888acf7edb88ab1c49","23d7df6808ea4c55ac1763f5d137d60c","ac646a3905434b9cb677f4ec7f61c2be","3b3637d8890e4fb6afc4cef9065d5cfa","a8af9b08015f427fa9c3e656a77c3de2","820b13ddd89049ac85f37fe1c962f6e7","211012efbf094cca8ae6bb08ca007918","7692175b1de846f2a3a27482d905bcaf","47e51cf393fb430e8e378d97d24e5dfc","3522036c734a46168ea54fa4be48123d","95e6c262155b48f6af185730b0c7102a","c6f210a5df554b9e924761dd50107270","bde2f459e85a48fea8bd9a59c84444b8","20e0ec7162204d5c88790c6ea8732e7b","815fded1286240c7afc44eea95078a5b","1bc2effe5f294e0cbde214d446c44ab6","0bc4afe16cd84660978efe9577a5c4cc","3d61070309294c6688f0b85bdd1b5a98","c7febd7f46f94beba741a8c99293567e","43955496caaf41ecbf5dc0ce4369ea3d","23117cdc20964a93b70a9ca146021d29","ce8d54da8e274079bd7f8a5c94608030","ed59d6932c014175831a797ac2da59b2","185f516c843d48ebbc142509345aed80","e422cd4bfd484eb89b5de04b74d60728","8ff6e25179da4f89b2e7a2967b976c82","e35a28ded01748e581d8916ac39f3fe1","10688d2331974877982f7c6c09651f76","3e1dfcc0cdee4d288cea8d307a576068","d0d883d301214f90bae1a9330fabf5c7","e2d74aa0c1e54c37a2245800449bdae0","58482a44819d42d1a8a790df1b9a9e72","a816d28763464753890859222f89e234","83663ebaa26647c3911d29f9d147bac8","a0e799679d9140969e8c62f2e3f88b7d","9f08ae117c214863ad339d9b1e56e7ad","8917378aff6f427a82f2285bbe304312","ae089ab13bf448d59a645fc7cb370762","783cd9ea44e9410f972aa7151392b13e","e570bf1c7c434b978129dbdfb25467a5","057b1a08111544f2bdbcd1b7c30a9477","39fad1ff23b445848d2ed756cbb16940","4658d8da259e432fa0dd9bd5d9d277b2","981363f695cc4feeabcd83310d782420","6a9901898f694198a8fc889fd9d7f97c","f1eb2bdaee9d4c5bb447ead2cfc3c9ba","4c0f42714122472aa5eec2f45d493f67","7857c7de78e3449c927675769a0e17d2","5942ef7bf09d4c3785f2a1d2858a3591","f6e8f19f1e714bc1832356b54df1c769","0081e98c727a459d9e0e46aa33ccc10b","73aeeb41cbd947c9b5eba7b986975237","aa8700ccfc2d4f1aa9a9cc5057cbba7b"]},"executionInfo":{"elapsed":402968,"status":"ok","timestamp":1703934077738,"user":{"displayName":"Rose 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So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f4c6112a4afd4ff285b883ca8eacdefe","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"eb4a7c9b210443f68fe1279b4010aacd","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3522036c734a46168ea54fa4be48123d","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"23117cdc20964a93b70a9ca146021d29","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"58482a44819d42d1a8a790df1b9a9e72","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/585 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4658d8da259e432fa0dd9bd5d9d277b2","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["\r  3%|▎         | 1/29 [00:26<12:22, 26.51s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","  7%|▋         | 2/29 [00:35<07:16, 16.17s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 10%|█         | 3/29 [00:48<06:25, 14.81s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 14%|█▍        | 4/29 [01:04<06:17, 15.08s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 17%|█▋        | 5/29 [01:19<06:05, 15.24s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 21%|██        | 6/29 [01:29<05:04, 13.25s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 24%|██▍       | 7/29 [01:48<05:37, 15.36s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 28%|██▊       | 8/29 [01:55<04:24, 12.58s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 31%|███       | 9/29 [02:01<03:33, 10.69s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 34%|███▍      | 10/29 [02:21<04:17, 13.53s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 38%|███▊      | 11/29 [02:39<04:26, 14.78s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 41%|████▏     | 12/29 [02:50<03:51, 13.63s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 45%|████▍     | 13/29 [03:03<03:33, 13.36s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 48%|████▊     | 14/29 [03:11<02:56, 11.73s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 52%|█████▏    | 15/29 [03:27<03:02, 13.02s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 55%|█████▌    | 16/29 [03:47<03:16, 15.13s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 59%|█████▊    | 17/29 [03:55<02:35, 12.99s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 62%|██████▏   | 18/29 [04:10<02:32, 13.84s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 66%|██████▌   | 19/29 [04:38<02:58, 17.83s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 69%|██████▉   | 20/29 [04:54<02:36, 17.35s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 72%|███████▏  | 21/29 [05:06<02:06, 15.76s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 76%|███████▌  | 22/29 [05:16<01:39, 14.18s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 79%|███████▉  | 23/29 [05:21<01:07, 11.31s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 83%|████████▎ | 24/29 [05:41<01:09, 13.98s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 86%|████████▌ | 25/29 [05:56<00:56, 14.10s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 90%|████████▉ | 26/29 [06:06<00:38, 12.88s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 93%|█████████▎| 27/29 [06:18<00:25, 12.71s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 97%|█████████▋| 28/29 [06:23<00:10, 10.38s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","100%|██████████| 29/29 [06:42<00:00, 13.89s/it]\n"]}],"source":["# Initialize the preprocessor and annotator\n","processor = TextPreprocessor()\n","annotator = Annotator()\n","\n","# This takes about 7 minutes on Colab, CPU\n","# Though this time varies depending on bandwidth\n","for filename in tqdm.tqdm(os.listdir(DATA_DIR)):\n","    if utils.is_valid_file_extension(filename):\n","        df = utils.load_data(os.path.join(DATA_DIR, filename))\n","\n","    # Preprocess the data. We're just going to merge the utterances of the same speaker together and directly update the dataframe.\n","    df = processor.merge_utterances_from_same_speaker(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        target_text_column=TEXT_COLUMN\n","    )\n","\n","    # Now we're going to annotate the data.\n","    df = annotator.get_talktime(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        output_column=TALK_TIME_COLUMN\n","    )\n","\n","    df = annotator.get_student_reasoning(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=STUDENT_REASONING_COLUMN,\n","        # We just want to annotate the student utterances. So we're going to specify the speaker value as STUDENT_SPEAKER.\n","        speaker_value=STUDENT_SPEAKER\n","    )\n","\n","    df = annotator.get_uptake(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=UPTAKE_COLUMN,\n","        # We want to annotate the teacher's uptake of the student's utterances.\n","        # So we're looking for instances where the student first speaks, then the teacher speaks.\n","        speaker1=STUDENT_SPEAKER,\n","        speaker2=TEACHER_SPEAKER\n","    )\n","\n","    # And we're done! Let's now save the annotated data as a csv file.\n","    filename = filename.split(\".\")[0] + \".csv\"\n","    df.to_csv(os.path.join(ANNOTATIONS_DIR, filename), index=False)"]},{"cell_type":"markdown","metadata":{"id":"8dn1YsvkW4M8"},"source":["## Analysis\n","\n","Now that we have annotated the dataset, let's analyze it using `edu-convokit`'s `Analyzer`. We'll be doing the following:\n","- We'll use `QualitativeAnalyzer` to look at some examples of the talktime, student reasoning and uptake annotations.\n","- We'll use `QuantitativeAnalyzer` to look at the aggregate statistics of the talktime, student reasoning and uptake annotations.\n","- We'll use `LexicalAnalyzer` to compare the student and tutor's vocabulary.\n","- We'll use `TemporalAnalyzer` to look at the temporal trends of the talktime, student reasoning and uptake annotations.\n","\n","Let's get started!!!"]},{"cell_type":"markdown","metadata":{"id":"ky8MSYjcW4M8"},"source":["### 🔍 Qualitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":249,"status":"ok","timestamp":1703934083611,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"xIC04X5MW4M8","outputId":"e2e17c12-612a-4484-bb90-91ef527ff908"},"outputs":[{"name":"stdout","output_type":"stream","text":["talktime: 17\n",">> teacher: Okay.  I think it’s working.  Alright, so the first thing – everyone got their homework back, correct?\n","\n","talktime: 17\n",">> teacher: Okay.  That could be your problem.  Student [G], do you have a question?  Student N, sit up.\n","\n","talktime: 17\n",">> student: A year has 52 weeks so 52 times 4 equals 52 times 2 plus 52 times 2.\n","\n","talktime: 1\n",">> multiple students: Yes.\n","\n","talktime: 1\n",">> student: No.\n","\n","talktime: 1\n",">> student: Yes.\n","\n","talktime: 7\n",">> teacher: Student M, you don’t have your homework?\n","\n","talktime: 7\n",">> student: [Inaudible]. She just didn’t really – oh.\n","\n","talktime: 7\n",">> student: 60 times 11; and 3 times 11.\n","\n"]}],"source":["# We're going to look at examples from the entire dataset.\n","qualitative_analyzer = QualitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Examples of talktime. Will show random examples from the dataset.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=TALK_TIME_COLUMN,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703934084491,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"Bf9c-iY2W4M8","outputId":"4827a8ad-9392-4c76-8d02-afdeffbbf3be"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 1.0\n",">> student: You could – so 76 times 10 is double the final problem so you –\n","\n","student_reasoning: 1.0\n",">> student: They all will add up to a whole.\n","\n","student_reasoning: 1.0\n",">> student: Because I know that three-sixths is equivalent to one-half in fractions.\n","\n"]}],"source":["# Examples of student reasoning. Let's look at positive examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=1.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":113,"status":"ok","timestamp":1703934086073,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"upy6Io5PW4M9","outputId":"7ef617ea-10e9-4a3b-8fe1-c7ee024d8dc0"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 0.0\n",">> student: No.  This is Student D.D.; this is Student D.C.\n","\n","student_reasoning: 0.0\n",">> student: Do you want me to go that way?\n","\n","student_reasoning: 0.0\n",">> student: I did 63 times 10 equals 630 and then 63 to that equals [Inaudible] and then I got, I got 693.\n","\n"]}],"source":["# We can also look at negative examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=0.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703934086891,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"bd-Zn76jW4M9","outputId":"fda8e3cf-8184-4451-f915-dfe19c6f1171"},"outputs":[{"name":"stdout","output_type":"stream","text":["uptake: 1.0\n","student: No.  This is Student D.D.; this is Student D.C.\n",">> teacher: Okay, give Student D.C. his.  And what are you on Student D?\n","\n","uptake: 1.0\n","student: Can I leave my desk?\n",">> teacher: You may go get it.  Student A, can you please get Student N’s folder out of her desk, and Student J, can you please turn off a light.\n","\n","uptake: 1.0\n","student: 63 times 10 is 630.\n",">> teacher: 630.  Good.  Um, Student H, what’s 60 times 11?\n","\n"]}],"source":["# Examples of uptake.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=UPTAKE_COLUMN,\n","    # I want to look at positive examples of uptake (uptake = 1.0)\n","    feature_value=1.0,\n","    # ... and look at the previous student utterance (show_k_previous_lines = 1).\n","    # This is interesting because it will show us how the teacher is responding to the student's utterance.\n","    show_k_previous_lines=1,\n",")"]},{"cell_type":"markdown","metadata":{"id":"Hnqz2mbFW4M9"},"source":["### 📊 Quantitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"executionInfo":{"elapsed":499,"status":"ok","timestamp":1703934088845,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"ERhtObqhW4M9","outputId":"7649b694-d4b7-489c-c011-e1a51f3e52ee"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["talktime\n","\n","Proportion statistics\n","                   count      mean       std       min       25%       50%       75%       max\n","speaker                                                                                       \n","multiple students   29.0  0.003755  0.004297  0.000127  0.000892  0.002291  0.004942  0.017520\n","student             29.0  0.145835  0.054895  0.040153  0.105824  0.139083  0.176111  0.279475\n","teacher             29.0  0.850410  0.056019  0.709941  0.821598  0.858346  0.890292  0.959623\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["quantitative_analyzer = QuantitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Let's plot the talk time ratio between the speakers.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\"\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":747},"executionInfo":{"elapsed":650,"status":"ok","timestamp":1703934093286,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"uue-7HjKW4M9","outputId":"44706530-0c0a-4c0d-93c9-8d7567ad3034"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["student_reasoning\n","\n","Average statistics\n","                   count      mean       std  min       25%       50%       75%       max\n","speaker                                                                                  \n","multiple students    1.0  0.000000       NaN  0.0  0.000000  0.000000  0.000000  0.000000\n","student             29.0  0.208633  0.163928  0.0  0.085106  0.166667  0.321429  0.615385\n","teacher              0.0       NaN       NaN  NaN       NaN       NaN       NaN       NaN\n","\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# What about the student reasoning? How often does the student use reasoning?\n","quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We change this to \"avg\" because we're now looking at within-speaker statistics.\n","    value_as=\"avg\",\n","    # We can set the y-axis limits to [0, 1] because the student reasoning column is a binary column.\n","    yrange=(0, 1)\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\"\n",")"]},{"cell_type":"markdown","metadata":{"id":"U7gV6jBIW4M9"},"source":["Note, the tutor has no `student_reasoning` because we did not annotate the tutor's utterances for student reasoning.\n","We can easily remove the tutor from the plot by dropping na values:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":574},"executionInfo":{"elapsed":374,"status":"ok","timestamp":1703934099027,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"R33PTU5eW4M9","outputId":"b478931f-fac6-4997-b7dd-4ac59014fd12"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"elapsed":254,"status":"ok","timestamp":1703934100425,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"6TbigOpBW4M-","outputId":"4250489f-64e8-4426-bc05-86a578b3ec5f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"WbspNjE5W4M-"},"source":["## 💬 Lexical Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13462,"status":"ok","timestamp":1703934118097,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"g2fny_laW4M-","outputId":"0704565c-7e1a-4cd2-8a89-c79e220a0dd0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Top Words By Speaker\n","teacher\n","student: 2127\n","okay: 2100\n","one: 1811\n","right: 1428\n","times: 1243\n","going: 952\n","know: 707\n","two: 650\n","good: 618\n","want: 569\n","\n","\n","multiple students\n","yes: 101\n","inaudible: 89\n","yeah: 29\n","percent: 18\n","two: 10\n","one: 7\n","half: 6\n","three: 5\n","whole: 4\n","unlike: 4\n","\n","\n","student\n","inaudible: 1453\n","times: 501\n","one: 420\n","two: 242\n","yeah: 225\n","three: 195\n","like: 180\n","yes: 177\n","four: 175\n","percent: 155\n","\n","\n","\n"]}],"source":["lexical_analyzer = LexicalAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Let's look at the most common words per speaker in the dataset.\n","lexical_analyzer.print_word_frequency(\n","    text_column=TEXT_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We want to look at the top 10 words per speaker.\n","    topk=10,\n","    # Let's also format the text (e.g., remove punctuation, lowercase the text, etc.)\n","    run_text_formatting=True\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"mh5zb9PpW4M-"},"source":["The language between the two looks pretty similar. Let's run a log-odds analysis to see what words that are more likely to be used by the tutor vs. student."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":516},"executionInfo":{"elapsed":13657,"status":"ok","timestamp":1703934135016,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"YamPzZVUW4M-","outputId":"2d397848-ad74-4d3a-eea8-add6a8120a69"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# This returns the merged dataframe of the annotated files in DATA_DIR.\n","df = lexical_analyzer.get_df()\n","\n","# We want to create two groups of df: one for the student and one for the tutor.\n","student_df = df[df[SPEAKER_COLUMN].isin(STUDENT_SPEAKER)]\n","tutor_df = df[df[SPEAKER_COLUMN] == TEACHER_SPEAKER]\n","\n","# Now we can run the log-odds analysis:\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=tutor_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    # Let's name the df groups to show on the plot\n","    group1_name=\"Student\",\n","    group2_name=\"Teacher\",\n","    # Let's also run the text formatting\n","    run_text_formatting=True,\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":901},"executionInfo":{"elapsed":14036,"status":"ok","timestamp":1703934152139,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"6G2s9ls7W4M-","outputId":"328406db-be2d-42c2-efab-5c6a7544cc98"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# We might also be interested in other n-grams. Let's look at the top 10 bigrams per speaker.\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=tutor_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    group1_name=\"Student\",\n","    group2_name=\"Tutor\",\n","    run_text_formatting=True,\n","    # n-grams:\n","    run_ngrams=True,\n","    n=2,\n","    topk=10\n",")"]},{"cell_type":"markdown","metadata":{"id":"N-p9P-gGW4M-"},"source":["## 📈 Temporal Analysis\n","\n","Let's look at the temporal trends of the talktime, student reasoning and uptake annotations!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":477},"executionInfo":{"elapsed":1584,"status":"ok","timestamp":1703934161496,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"2U5UAkWtW4M-","outputId":"d16f6662-8669-4a1b-f09a-7aac04f11e0f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["temporal_analyzer = TemporalAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# First let's look at the talk time ratio between the speakers over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"prop\",\n","    # Let's create 10 bins for the x-axis.\n","    num_bins=10\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":1130,"status":"ok","timestamp":1703934170074,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"w0WT9reqW4M_","outputId":"cc996522-4709-43b7-98a7-15194db520be"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Now student reasoning over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 5 bins for the x-axis.\n","    num_bins=10,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":1190,"status":"ok","timestamp":1703934176532,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"gLWNo4r_W4M_","outputId":"45c0c81a-8f60-4dfd-eb8f-c3e7d778c780"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlIAAAG7CAYAAAAIfHukAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADC7klEQVR4nOzdd3zT1f748dcnO2m6yyh7lC17C4hMGVevioB6Rb2gXr3i/Mq9cMGrIjhQ/F2ui+tVUbl6xQHqFVAoKEuGbFCglLZ00t20aZJmfX5/1IaGDtqQkrQ9z8eDh+YzzucknyZ554z3kWRZlhEEQRAEQRDqTRHoCgiCIAiCIDRWIpASBEEQBEHwkQikBEEQBEEQfCQCKUEQBEEQBB+JQEoQBEEQBMFHIpASBEEQBEHwkQikBEEQBEEQfKQKdAWaMqfTiclkQqvVolCImFUQBEEQGgO3201ZWRnh4eGoVLWHSiKQakAmk4mUlJRAV0MQBEEQBB906tSJ6OjoWo8RgVQD0mq1QPmN0Ov1fi3b5XKRkJBA9+7dUSqVfi1bqD9xP4KLuB/BRdyP4CPuSe2sVispKSme7/HaiECqAVV05+n1egwGg1/LdrlcABgMBvEmCALifgQXcT+Ci7gfwUfck7qpy7AcMXBHEARBEATBRyKQEgRBEARB8JEIpARBEARBEHwkAilBEARBEAQfiUBKEARBEATBR2LWniAIwlUky7LnX3NRMUOs4r9C4DW3eyJJkuefv4lAShAE4SqwWq3k5+djNpubVRAF5cGjSqUiMTGxQb7IhPprjvdEkiSMRiPR0dF+ze0Y1IHUwYMHWbVqFYmJiSgUCkaMGMGCBQto3br1Zc8dP348Foul2vwYxcXFhIeHs3v3bs+2hQsXsnXrVnQ6XZXjH374Ye68884rezKCIDRbVquV1NRUIiIi6NSpE2q1OtBVuqpkWcZqtaLX65vNl3awa473xOFwYDKZSE1NpUOHDn4LpoI2kNq3bx/z5s1j3rx5rFmzBovFwpNPPsmsWbP48ssvadGixWXL+OKLL2jXrp3XNrfbzcSJExk/fnyV4xcvXsytt97qt+cgCIIAkJ+fT0REBK1atQp0VQJClmUUCgVKpbLZfGkHu+Z4T5RKpaexJD8/v0p84KugHGzudDp5+umn6dChA48//jgqlYqwsDCWL19OXl4er7322mXLGDp0aLWtS3v37iUjI4OZM2c2RNUFQRC8yLKM2WwmPDw80FURBAEIDw/3axd7UAZS+/fvJzU1lcmTJ3ulZ2/VqhUDBgxg48aNlJaW1lrGyy+/TExMTJXtX375Jf369aNHjx5+r7cgCMKlKgaWN7fuPEEIVmq12q8TPoIykDpw4ABAtcFOz549KSsr49ixY/Uu12QyER8fz6xZs664joIgCHXR3AaWC0Jj4a/3ZlCOkUpOTgagZcuWVfZVbEtJSeHaa6+tV7nffvstSqWSadOmVbt/z549bNiwgdTUVFwuF7169eLuu+9mzJgx9XwGgiAIgiA0B0EZSJnNZoBqxzhVbCspKal3uV9++SXTpk0jJCSk2v25ubk8++yzdO3alaysLF599VXuu+8+Fi1axL333lvv61VwuVx+z9XR3HKABDtxP4JLMN0Pl8vVLHNHVVbxvJvr8w9GzfmeVLwXa/t8qM9nR1AGUg3h9OnT/PLLLzzzzDPV7l+0aBEGg8EzjiE2NpYVK1Zw8uRJXn31VaZMmVKntAvVSUhI8LneNbE73eQVOyk5cBSDVoFS0TxmXQS7EydOBLoKQiXBcj9UKhVWq9VrzGdzZLVaA10F4RLN8Z643W4cDoffPh+CMpAyGo0A2Gy2KvsqtoWGhtarzC+++ILu3bvTv3//avdXN6NGqVQybtw41qxZw65du3ye6de9e3cMBoNP59akuNTGph8OE92qPSqNipZRBqLCdGjVVfNmCQ3P5XJx4sQJ+vbtW23uMuHqCqb74XK5SExMRK/XB7wugdLUcxa9/fbbrFq1yvP49OnTAaxN3TT1e1Ibl8uFWq2mV69eNb4nLRZLnRtBgjKQ6ty5MwA5OTlV9lVs69SpU53Ls9vt/O9//+PPf/5zvetSka8qPz+/3udWUCqVfv8AVSqUKCSJ6HA9LjdcyLdyId9KdLiOFhF6QvTqZvfmCAYNca8F3wXL/WjI5Skak6b6Gvz5z3/mz3/+M3PmzOHAgQON6jk21XtSm4rnXNvnQ30+N4KynXnYsGEAnDlzpsq+M2fOoNVqa2xZqs62bduwWCz8/ve/r3Z/eno6n332WbX78vLyAIiKiqrz9a42jUpJhFFLqEFDUXEZp5ILOJ1SQGGxDZfLHejqCYIgCEKTFZSB1PDhw+nQoQNbtmzB7b4YCGRnZ3PkyBGmT5/uNWA8JycHp9NZY3lffvklkydPJiIiotr9GRkZrFy5Ervd7rXd5XLx448/olKp6j1DMBCUConQEA0RoVqcTpmzaUUcP5dHZl4pNnvNr48gCIIgCL4JykBKpVKxdOlSUlNTWbVqFS6Xi5KSEhYvXkxMTAxPPPGE59hNmzYxZswY5s+fX21Z2dnZ7Nmzh9tuu63WaxYVFbFkyRIKCgoAKCgoYMmSJSQlJfHwww/7LZX81SBJEnqdyjNmKjOnhBPn8kjONFFisTfLWRqCIDQe3377LbNnz2bYsGEMHTqUGTNm8I9//IOUlBTS09Pp0aOH598XX3zBmjVrmDp1Kn379mX06NE8//zzNc7s3rFjB3PmzGHQoEH079+fW265hY8//tjrRzuUD8L+73//y9y5c7nuuuu45ppruP766/n73/9e56Eel9a1R48eLFy4sN71OXr0qFcZ+/bt46OPPmLq1Klcc8011ZYrXD1BGUgBjBw5kg8//JDDhw8zevRobrjhBsLCwli3bp1XfqnIyEiMRiOxsbHVlrN+/XratWvHiBEjarzWwIEDee211zCbzcycOZMRI0YwefJkMjIyeOONN3waWxUsNColEaE6wg3a8m6/lAJ+TSmgoNiGU3T7CYIQZNauXcv//d//MXHiRLZs2cIPP/zAfffdx4cffsjq1atp164dZ86c4cUXXwTg3Xff5ezZs3zwwQccOHCAxx57jE8//ZQ//vGPVXoZ3nvvPR544AHatWvH999/z549e7j11ltZtmwZTz/9tNexiYmJPPvss8TFxfH555/z888/s3LlSo4dO8btt9/uSdNTm3bt2rFp0yZat27Nyy+/zJkzZ3jppZfqXZ8BAwZw5swZT4PBO++8w4ULF/jggw+Ij48nLi7O59dbuHJBOdi8wpAhQ1i7dm2tx4wcOZJDhw7VuP+hhx7ioYceqrUMjUbD9OnTmT59uk/1bAwUv3X7AVjLnCSmF6FWKmj122w/nTao/xQEQWgm1q9fT0REBPfff79n29SpUzlz5gwXLlyocrzD4WDZsmWe1BIzZ84kKSmJ999/nw8//NBTzqlTp3j11Vdp164dy5Yt8wwmnjNnDidOnOCLL75g2rRpjBo1CgC9Xs91113H3/72N8+1Bg8ezMsvv8zvf/97PvvsM+bOnVvrczl79izz5s3jL3/5C7/73e+89tW3PpW53W7+8pe/eB7/6U9/qlNgJzSMoG2REhqOXqsiKlSHTqMiM8/MyXP5JGWYKC4V3X6CIASWJEmYTCb27Nnjtf2+++5jwYIFVY6/4YYbquTnmjp1KgBff/21Z9u6detwu93ccsstVWZkVfyIXr9+vWdbXFwc//73v6tcr3v37gAcPny41udx5swZ5s6dy8KFC6sEUb7Up7rnV+Gmm27izjvvrLU+QsMRzRDNmFqlIMKow+2WMZnLyCuyEKJT0yo6hIhQLSqliLMFQbi67r77bhYuXMi8efMYOnQo06ZNY9KkScTExHhyDFbWpk2bKtu6dOkClHfP2e12NBoNx48fB6BXr15Vjq9Itnzy5Emv7QcPHuTdd9/lzJkzZGdne2W7NplMNT6HU6dO8cc//hGgxmElvtTn0v1CcBCBlFDe7Wco7/azlTlJzjShUipoGaknKlyPXnT7CYJwldx88820bduWf//73+zevZsDBw6wbNkyJk+ezN/+9jdPbr8K1SU71uv1QHnSydLSUjQajafrq7Yxr5UHkX/zzTf85S9/oV+/frz++ut0794djab8c7JHjx61Pof77ruPgQMHsn37dp599ln++c9/VjmmvvWprLrl04TAEd+QghedVoVOq8LpdJOVbyEzz0JkmIaWkSEY9WoUYikaQRAa2NChQxk6dCgFBQV89913fPzxx2zatInTp0/zzTffeJbygvIM1JeqWPZEoVB4UuVUrIaxZs2aOqWzefPNN5FlmaVLl9KzZ8961f/ZZ59l3Lhx3H777Xz//fds3Lixyhjc+tZHCF6i70aolkqlIMKoJdyowVzq4HRKPr8m55NXZMHhFLP9BEFoGLt376a0tBQoT4R85513smHDBuLi4khKSiIxMdHr+MzMzCplJCUlAeXjnCpakSqSOGdkZFR73dOnT3Ps2DHP44rjLl1Fo7qlyy41adIkVCoVL7/8MlqtlqVLl5Kbm+t1TH3rIwQvEUgJtVJIEkaDhqiw8qbypMxiTpzLIz2nBIvNEeDaCYLQ1Pz973/n4MGDXts0Gg0dOnQAQKvVeu377rvvquSA2rRpEwC33HKLZ9vs2bNRKpVs2LChyjUtFgvz5s1j165dnm0VKXUuXWGjtlnil+ratStPPvkkRUVFVdIr1Lc+QvASgZRQZzpN+Ww/g1bFhXwLJ5PyOZtaiMlchtstZvsJguAfL7zwAvv376e0tJTi4mLWr1/Prl27GDNmjGcgeYV27dqxZMkScnJysFqtfP7556xdu5b+/ftz1113eY7r0aMHf/nLXzh06BALFy7k3Llz2Gw2Tpw4wQMPPECLFi245557PMffe++9ACxZsoTjx49jtVo5cOAAzzzzTL2eyz333MPw4cP54Ycf+PLLL32ujxC8JFnMd28wFouFU6dO0atXr2oHRF6JYrONjdsPMqh/L5SKwCzK6pZlLDYndocLvU5F6ygDEaE61KrmF5+7XC6OHj3KgAEDgmKR3OYumO6Hy+UiISGB7t27B7wugSLLMhaLBYPBcNkFcg8dOsTXX3/NoUOHyMrKQpIk2rVrx0033cQf/vAHz0Dr9evXs2jRIl588UUUCgXvv/8+ycnJhIWFMWXKFJ544olqZ/nt3r2b9957jxMnTuB0OmnTpg2TJ0/mj3/8I+Hh4V7Hbty4kffff5+kpCQkSeKaa67hT3/6k1f+qIrEoIsWLfI695ZbbuGll15i/PjxVbrvPvroI4YPH17n+qSnpzNhwoQqz+XFF1/k1ltvrfX1rEl97klTU5f3ZH2+v8Vgc8FnCknCqFeDXk2Z3UVKVjGK7BJaRhmIDtNh0KkvX4ggCEIlgwcPZvDgwfU65+abb+bmm2+u07GjR49m9OjRdTq2pkTNl3b3ATUGNNu3b7/i+lRkcxeCkwikBL/QapRoNUqcLjfZBRYu5JUSbtTSKspAqEEjZvsJgiAITZIIpAS/UikVRIRoccsyVpuTM6mF6DSq35ai0aJWNc+uDUEQBKFpan6DWYSrQiFJhOjVRIXqUEoSadnFHE/MI/WCmO0nCILv0tPT6dGjh2dM0qJFi+jRowfp6ekBrpnQXIkWKaHBVXT7uVxucossZBeUEhaioVVUCKEhGpSi208QhDoS44WEYCMCKeGqUSoVhIdoy2eLlDlJSC1Aq1HROjpEdPsJgiAIjZIIpISrTpIkQnRqQnRqyhwu0rKLSc+RaB1lIDpCj04j/iwFQRCExkF8YwkBpVUr0aqVv63tV0pmfiktI/S0iDSI9AmCIAhC0BOBlBAUytf20+Fyy+SZbOQUWokI1dI6unyx5OaWME4QBEFoHEQgJQQVpUIi/Lf0CaUWB6dKCggzqGkdbSQsROSjEgRBEIKLCKSEoFSxWLIsy1h/G5hu0KlpHR1CZKgWpVJk7hAEQRACTwRSQlCTJAmDTo1Bp8Zmd5KUaUKjUhIbbSAqXCdm+gmCIAgBJQIpodHQaVToNCrsThdpOSWk55rLZ/qF69FpxZ+yIAiCcPWJ/hGh0dGolEQYdYTo1FzIt3AiKY/zWSZKrSJjuiAINZs4cSKDBw+mR48e7N+/P9DVEZoIEUgJjZZKqSDcqCXcoCXfVMYvSfmcTS2kuNSOLMuBrp4gNGs9evRg4cKFga6Gl/j4eBYvXhzoagh+FujPe9EfIjR6CoVEWIimfKafzcmplHzCDBpax4QQFqIVS9AIgiA0UQ6nC1kGjTpw42VFICU0GQpJwqhXE6JTYStzcTa1EJ1WTZsYAxGhOlRipp8gCEKT4XbLOJzugP9YFoGU0ORIkoRep0KvU1Fmd5GcWYxKZaZ1dAjRYbqA/nIRhKbuk08+4c033wRg06ZN7Nq1C4CePXvy7rvvAvDpp5/yySefcOHCBSRJom/fvvz5z39m0KBBnnLKyspYu3Yt8fHxZGRkYLVaiY2N5a677mL27NlVrltaWsrq1avZvHkzFosFjUZDp06dmDRpEjNnzkSj0Xgdb7fbeemll9iyZQslJSUMGDCAZ599lrZt23od53Q6ee+991i/fj2FhYUolUqGDh3Ko48+SlxcHACnT59m3rx5lJSUUFZWxs6dO3nhhRc4evQoFy5coG3btmzfvt1/L7IAgMPlxuWWRSAlCA1Jq1Gi1SixO12kZ5eQmWumZZSBFmKmnxAkXG6Z5EwTZXZXoKviodUo6dwm3KcvqDvvvJM777yTHj16MG3aNF566aXyhcotFgCWLVvGunXrWLlyJRMnTsRqtfLCCy9w99138+9//5uRI0cCkJubyyuvvMKiRYu4++67Adi8eTMLFizAbDYzb948zzXLysq4++67yc/PZ/Xq1fTs2ZPi4mKee+45li5dyqBBg+jVq5dXPV9//XXmz5/PX//6VzIzM5kzZw4PP/wwX331lecYWZZ57LHHOHjwIK+//jrDhg2joKCARYsWMXPmTD777DO6detGz5492bNnDwsXLmTDhg0888wzPPbYY/Tq1Yt169bxr3/9q96vo1A7l9uN0+lGEQSrXohvEqFZ0KiUaEKVOF1usvMtZOdZiI7Q0TLSQIherOknBIbD6Wbhm7tISC0KdFWq6N4hgpceHoNa5b8u8SNHjrB27VrmzJnD5MmTAQgJCeHvf/8727dv58UXX+Sbb74BQKPRMH78eO69917P+dOnTyc+Pp53332XuXPnepaOWrNmDSdPnuT//b//R8+ePQEICwtj2bJlbNu2rdq69O3bl+uuuw6Atm3bctNNN/H222+Tnp5Ou3btANi4cSPx8fEsWrSIYcOGARAVFcXy5cu5/vrree2113j77berlH3jjTd6ArcpU6agVIpWcH+SZRm7w00QxFCAmLUnNDMqpYIIo5awEA2FxWX8mixm+gmBk11QGpRBFEBCahHZBaV+LXPTpk0AjB492mu7Vqule/funDlzhuzsbABatmxZbZDSqVMnCgoKyM/P92zbuHFjteXq9Xref/992rdvX6WcwYMHez2OjY0F8Fy/crmjRo3yOjYmJobWrVvz008/4XQ6q5Q9dOhQz/+Hh4dz2223VTlG8J3LJeN2u4NmyTDRIiU0SxUz/eTfZvqdTsknxKChjZjpJ1xFraJC6N4hIiiDqe4dImgVFeLXMlNSUgD461//ikrl/fVTVlaGwWAgOzubVq1aAbBt2zY++eQTkpOTsdlsSJLk6SK02Wyec8+fP49erycsLKzKNSuPu6osMjLS67FaXd4yXTkwqqjvPffcU2XhdJvNhkKhwGQyER0d7bUvJiam2msKV84ty9idLhSK4GkHEoGU0KxJv830Q6/GanOSkFqIXqOiTYsQMdNPaHBqlYIVj1zXpMZI1cWbb77JkCFDaj3m448/ZunSpdx4442sWLHCE6y8/vrrvPHGG1WOdzqdyLJcJeCpSX2+iNevX0/r1q3rfHwwfck3NU6nG1mGYHqJgzqQOnjwIKtWrSIxMRGFQsGIESNYsGBBnf6gx48fj8ViqbZvuri4mPDwcHbv3u21/cKFC7zyyivs27cPt9tNXFwcjz322GXf8ELTUHmmX1JmMWqlmdYxYqaf0LCUCom4dhGBrsZV0alTJ3bt2kVOTk6VfSaTiV9++YVhw4ahUqn4/PPPAVi0aFGVFp/qyj1z5gyFhYVERUV57cvLy0On02E0Gn2qb1JSEtnZ2VW+d3JyckhOTmb48OH1LlfwjcvtDop0B5cKopjO2759+7jnnnsYOHAgu3btYvPmzZhMJmbNmkVubm6dyvjiiy/Ys2eP179du3bRokULpkyZ4nVsbm4us2fPpqioiM2bN7Nr1y4GDx7MPffcw759+xriKQpBSqtREhWqQ6dRkZFj5nhiHmk5JVjLqo6FEAShenq9HoejfNkmp9PJSy+95BlgvmXLlirHf/jhh7z00kueLr+KdAWXtjBlZmZWOXfatGkAVQaWl5SUMH78eI4fP+7Tc/jd735XY31XrlzJBx984FO5Qv3JsoyjYoB5cMVRwRlIOZ1Onn76aTp06MDjjz+OSqUiLCyM5cuXk5eXx2uvvXbZMoYOHYpOp6uyfe/evWRkZDBz5kyv7StXriQ3N5dly5YRFhaGSqXiscceo1OnTjzzzDO4XMHT7C5cHWpV+cD0UL2a7AILJ8/lk5xpwizW9BOEy4qLiyMpKQmXy8XPP//M+vXr6dq1K3fffTdbtmzhyy+/xOVyIcsy33//PWvWrOGvf/2r5/ypU6cCsGLFCs+4qB07dvDtt99WudYf//hHrrnmGt566y1Onz4NQFFREQsXLmTAgAGMGDHCp+cwbdo0Jk6cyCeffMIPP/yALMs4nU4+/vhj4uPjeeyxx3wqV6g/l1vG5ZaDIt3BpSQ5CKcq7dmzh7lz5/Lggw/yxBNPeO278847OXnyJHv37iUkpP4DIZ988knS0tI8zcYAZrOZkSNH0qdPHz799FOv4//xj3/w9ttvs2bNGq699tp6XctisXDq1Cl69eqFwWCod11rU2y2sXH7QQb174VSIbqdrga3W6bU6sDhdhFh1NEqykBYiAZJknC5XBw9epQBAwaIqc5BIJjuh8vlIiEhge7duwe8LlfT0aNHefbZZ7lw4QIGg4F58+Zx5513IkkSn3/+OZ988gmZmZno9Xo6d+7MQw895EkxAOUtEGvWrOGzzz7jwoULREVFMWTIEHQ6HevWrSMyMpIZM2awYMECoDwh59tvv83mzZux2WzodDomTJjAI488QmhoKACzZ88mMTERs9lMeHg4Y8aMYeXKlcyZM4dff/21ynYo/2G/Zs0a1q9fT0FBAQaDgd69e/PII494Ui3k5ORwyy23eBJyxsTE0LJlSzZs2HCVX/W6q8jtZTAY6jyuLFBkWcZa5kQCpEu69dy/JeTUauo+Uqku78n6fH8H5RipAwcOAOWLXl6qZ8+eHDp0iGPHjtU7sDGZTMTHx/P00097bT927Bh2u73G6wHs37+/3tcTmhaFQiL0t5l+FpuT0+cLMBo0tIkOIUQflG8lQQiYAQMGeJJbVk7ICTBz5swqvQKXkiSJuXPnMnfu3Cr7li5dWmVbSEgITz31FE899VSNZa5bt67a7WvXrq3xHJVKxf3338/9999f4zEtW7Zkz549Ne4XroyjYoC5MjgDvqD89E9OTgbK/zgvVbEtJSWl3oHNt99+i1Kp9PSnV6iY4lrb9c6fP1+va1Xmcrn83jXocpeX53a7/VquUDc6rQKdVoO1zMmZ8wVoVBKmUqfoAg4SFfchGO5HRfdVxb/mqOJ5N9fnH4wayz0pX0/PVZ4zqpqqVlS/Ps+j4r1Y2+dDfT47gjKQMpvNANWOcarYVlJSUu9yv/zyS6ZNm1alS7CiLL1eX+Wcim2+XK9CQkKCz+fWxGYvD6ASExP9XrZQfw6njM3u5udDx9BpgnLoYbN04sSJQFcBKG/VsFqtzX5avNVqDXQVhEsE+z1xuGTcbrnG5JtuNzgV4HTUvbXK7XbjcDj89vkQlIFUQzh9+jS//PILzzzzzFW/dvfu3f0/RqrURuqPR4iLi2v2H87BwO12c/yXBKJjO9OtfeTlTxAalMvl4sSJE/Tt2zfg45JcLheJiYno9fqA1yVQZFnGarWi1+uDfjxOc9EY7onL5Ua2u1AqJaQapuq53DIqpVSvFDUulwu1Wk2vXr1qHSNV10aQoAykKvJ9VM5cW6FiW8Xgwbr64osv6N69O/3796+yr6Ks6iLzim31vV5lSqXS7x+gFQPMFQqFGGweJPQaCZPZTpnDjUEn1u8LBg3x3vOFJEmef82ZeA2CT7DeE1mWsTvLl4GprX4Vu+rzHCqec22fD/X53AjKpozOnTsDVJu0rWJbp06d6lye3W7nf//7X43rHVWUVdv1OnbsWOfrCc2TJEkoFBLZBZbLHywIgiDUyOlyI8s1d+kFk6AMpCqmwJ45c6bKvjNnzqDVaqttWarJtm3bsFgs/P73v692f//+/dFoNDVeDxDZa4U6MerV5BZZsdhErilBEARfuN0ydoe70QxbCcpaDh8+nA4dOrBlyxavWWnZ2dkcOXKE6dOnew0Yz8nJqXYF7gpffvklkydPJiIiotr9RqOR6dOnc/z4cS5cuODZLssyW7dupWPHjl75TQShJgqFhFIhkVsoWqUEQRB84XCWz5gLwh7HagVlIKVSqVi6dCmpqamsWrUKl8tFSUkJixcvJiYmxitJ56ZNmxgzZgzz58+vtqzs7Gz27NlTY7dehSeffJKYmBgWL15McXExLpeLVatWkZyczHPPPVdlpXJBqEmoXkNOoU0sKSMIglBPLpcbp0sOuvX0ahOUgRTAyJEj+fDDDzl8+DCjR4/mhhtuICwsjHXr1nnle4qMjMRoNBIbG1ttOevXr6ddu3aXXSKgZcuWrFu3jvDwcKZMmcLo0aM5dOgQH374ISNHjvTrcxOaNoVCQqGAHNEqJQiCUGcVA8yDcT292gR1M8uQIUNqzTgL5QHXoUOHatz/0EMP8dBDD9XperGxsXVax08QLseo15BTaKFVpAGdNqjfZoIgCEHB6XKXL/kSpBnMaxK0LVKC0Jgpf5uym1MY3MnuBEEQgkF5BnN3o5ildykRSAlCAwn9rVXKZhdjpQRBEGpTsZ5eYxlgXpkIpAShgSgV5bl4c4tEq5QgNKRffvmFUaNG0a9fv2oXn6/NqlWrGD16NLm5uT5dOz4+nlGjRnHNNdcwfvx4n8oIdkVFRYwaNYqBAwfSo0cP0tPT/Vp++QBzd6MaYF6ZCKQEoQEZ9Wpy8i2UOQK/eK4gNFV9+vRhz549VRakrzB+/HjmzJlT7T6TyYTZbMZut/t07YkTJ7Jnzx4GDhzo0/n+MmfOnAYL5CIiItizZw9z5871e9mXG2Dev28fnl78N79f159EICUIDUipVCAjkydm8AlCUHr66afZu3cvbdu2DXRVmiWnS8blbpxjoyqIQEoQGphRryG7wIJdtEoJQtCRJAm9Xh/oajRLblnG4XShbCQZzGsi5mULQgNTKRW43DJ5RVbatDAGujqC0KB++OEHlixZgslkomXLlqxevZrnn3+e06dPExkZyfz587npppv46aefeO2110hOTqZbt24888wz9OrVCyhfreKWW26hpKSEsrIyz1JdRUVFTJ8+vcr2y9WloKCAnJwcRo0aBUBUVBT/+9//mDFjBmlpaZhMJl588UVuvfVWCgoKuPHGG7FYLFgsFj7++GP+85//cOTIEYqLi+nZsydPPfUUgwcPrtPrkZ+fz6pVq9i+fTt2ux2j0cj48eN55JFHCA8Pv+z5GRkZrFq1ip9//hmHw4HBYGDAgAHccsstjBw5ktOnTzNv3jxMJhMul8vzHCue/5w5c0hMTMRsNvPRRx95ljubM2cOp06dqrIdwO128+9//5t169ZRWlpKixYtauwaBXA6nbz33nusX7+ewsJClEolQ4cO5dFHHyUuLg7AU8+Ke7dz506eefZZjh87hkKpZNy4cTz5fws8Qe1n6z5l9dtvAfD999+xZ89uAHr37sMbb70NwI8/bOf9998jIz0dhUJBixYtGDt2LLfffjutWrWq0/3xh8YdBgpCIxFq0JCVX+pZ+kAQKshuF2VZSdjSTgXNv7KsJGS3b3+r48aN84wZKi0tZfXq1fzzn/9k27ZtjBo1ir/85S989dVXHDhwgE8++YQff/wRp9PJww8/7Fnqq2XLltWOeaoYq1PTWKia6hIbG8vAgQPZs2cPe/bs4X//+x9QvnzY66+/7nVOVFSU13igxYsXc+utt/LDDz/w448/EhISwr333suxY8cue/3CwkJmz57NyZMn+fTTTzlw4ABvv/02O3fu5K677sJqrX0iitPpZO7cueTn5/PVV1+xe/duPvzwQ5KSknjrrfIgo2fPnp7XOzY21vMc9+zZg0ajYd26dSxevLhK2WvXruWpp56q9rrLly/ntdde44EHHmDv3r189tlnnD17ls2bN1c5VpZlHnvsMd5//32ef/55Dhw4wMaNGykrK2PmzJmcPXvWq54V927ZsmU89tj/sf2HnSxb9gJffvEFb/xzlafcWbNvZ/uPOwG44YYpbP9xJ9t/3OkJon7++QBPPP4Y06b/jvhtP7Br1y7mz5/Pv//9b/bs2XO5W+NXokVKEK4ClVKB2y2TW2SjTUzI5U8QmgXZ5SDzo6cpyzwb6KpUoW3TjTZ3P4+kVPtcRlFREQ888AARERFYLBbmzp3LJ598wquvvsoPP/yAWq1Go9Ewe/ZslixZwokTJwI+aPtSN954I9dddx0A4eHhPPPMM0yaNIkXX3yRTz/9tNZz/9//+3+kpaWxYcMG2rVrB0CPHj146qmneOSRR1i3bh333ntvjecnJiaSkpLCXXfd5Wm9io2N5bHHHvMEg/527tw5Pv74Y4YNG8btt98OgMFgYNGiRUyePLnK8Rs3biQ+Pp5FixZ51qSNiopi+fLlXH/99bz22mu8/fbbVc674YapdOzUESQYMXIkffv1Y/v2bSz468I61fPH7dtxu91Mn36jZ3Hj8ePHc+uttxIaGurr0/eJaJEShKskVK/hgmiVEipxFOUEZRAFUJZ5FkdRzhWVodfr6dmzp+dxxfJevXr1Qq2+GKBVLPFVedH4YHHpEmHt27enW7duHDlyhIKCghrPc7vdbN68maioKHr37u21r2/fvgDs3Lmz1mtHRkaiVCpZs2YNO3bswO12AzBmzBhWrFjhy9O5rO3btyPLMtdff73XdqVS6dX9V2Hjxo0AXl2KADExMbRu3ZqffvrJ09JYWb8BA1BUShoV2zqWnJy6/71FRUcDsPTZv5OWlurZvnTpUiZNmlTncvxBtEgJwlWiUilwWd3kmWzERotWKQHUES3RtukWlMGUtk031BEtL39gLSIiIrweazQaoDxAqKwiqLpcV1cgtGjRosq2li1bkpCQQFpaGlFRUdWeV1BQQHFxMSqVqkqQAeWtPEVFRbVeu1WrVjz33HO88MILPPDAA8TExDB+/HhmzJjBgAEDfHk6l5WWlgbU/LwvlZKSAsA999yDdEk2TZvNhkKhwGQyEf1b4CPLMgDRUTFe6Q7UanW1AVdN7rjzD/zyy0m2bPmeLVu+p0+fPkyZMoVbb72VmJiYOpfjDyKQEoSryKjXcCGvlJhwPWqVaBBu7iSlmjb3LMeefR7ZWRbo6nhIKi2aVh2RFMorKkdRw2ysmrbXVUXLTGPQpk0btm7d6vP5M2fOZOrUqWzevJnNmzfzxRdf8Nlnn3HPPffwt79dWX4lf76O69evp3Xr1pe/ZnkchfIKP/8MBgOv/b9VJCYmsuW7zWzatJGVK1fyzjvv8NZbb3m6Ga8GEUgJwlWkVikwW90UmKy0Eq1SAiAplGhjuwS6GkFJpSr/inI4HF5dgfn5+VetDnl5eXTo0MFrW0UXVPv27Ws8LyoqirCwMHJycpBluUprTWJiIk6n06vr81KyLON2uzEajcycOZOZM2eSlJTEgw8+yIcffshdd91VpW7VqXgdL23xKSwsrHJsRXnVdbNVl/29U6dOJCUlkZ2dXSWQysnJITk52dMl6HK7kSsiqSvkdruRJIkuXbry8PxHeOKJx1m/fj1/+9vf+Mc//sEnn3zil+vUhfhJLAhXWYheTWZ+KU5X4/lVLQiBUDGFPSsry7PNbrdz8uTJepWj1+txOByex6+99hq//vprnc7dt2+f1+O0tDTOnj3LwIEDa+zWg/JWt6lTp2Kz2aqMhXK5XDz44INVyr7UgQMHuPHGG722denShQkTJgBQXFzs2X7pc3z//ffZu3cvcPF1zMzM9CrryJEjVa45fvx4JElix44dXtvdbjcHDhyocvzvfvc7ALZs2VJl38qVK/nggw+A8qDQ4az/Z55Or/cEgE6nkxeWLyMrK5Nnnl7CB2ve9xwnSRIzZswgIiKCkpKSel/nSlxxIFVcXMznn3/OM888wyOPPALA+fPnOXjw4BVXThCaIo1KicPpJt9kC3RVBCGoVXypv/vuuzgcDmw2GytWrKj3GJi4uDjS0tKw2WwkJyfz7rvv1rl7cefOnezevRtZljGZTCxduhS1Wl2nbrUnnniCDh068NJLL5GYmAhASUkJS5YsQa/Xc9ttt122jHPnzvGf//zHE0ycP3+e+Ph4unXr5sm7VfEcCwoKyM/Pp6CggLfffpvS0lIABg4cSHR0NOvWraOwsBCXy8VXX31VbatTly5duPvuuzlw4ACffvopbrcbq9XKyy+/XO0YtmnTpjFx4kQ++eQTfvjhB2RZxul08vHHHxMfH89jjz0GgMst43S5670ocdeuXUlKTsLlcnHo4EG+/OJztFodAOs+/S8JCeW5xGRZZsOGDRQVFVUJPhuaJFeM/PLB119/zfPPP09paamn6fLUqVPs37+fe+65hxkzZrB8+XJ/1rdRsVgsnDp1il69emEwGPxadrHZxsbtBxnUvxfKKxzHIFw5l9tFQkIC3bt3r9P9sDtc2J1urukajUopGob9zeVycfToUQYMGIBSGdj3h8tV6W8jwHW5Go4fP85DDz3kSRAZFRXFiy++yPnz51m9ejV5eXlotVpCQ0P5/vvvWb58Odu2bcNkMmE0GomLi2PdunWe8jZu3Mhbb71FdnY27du35+GHHyY+Pp4NGzYQExPDrFmzmDhxIg888IAn2WPF9oov8eTkZBYvXkxSUhJarZbbb7+dhx56yCshp9FoJCYmhu+//x6A119/nTfeeIMNGzawdu1a9u3bR1FRUZWEnPHx8TzzzDNez/exxx5j1qxZQPmg89dff51t27bhcrnQ6/WMHTuWP//5z54B2DUxm8189tlnxMfHk5GRgdPpxGAwcP311/Pggw96nZ+Xl8eiRYs4ceIEarWaSZMm8fTTT3u6FE+dOsXzzz/PmTNniImJ4ZZbbiEsLIznnnuO8PBwevfu7Wk9crvdvPvuu3z66aeYzWYiIyOZMWMGNpuNN998k8jISMaMGcMrr7wClLcUrVmzhvXr11NQUIDBYKB379488sgj9OzZE1mWSU3P4o7Zt2E2mykrKyM6Opr7H/gT06ZP55bf31Rl+x13/qH87+nYMZYtW0pOdjZ6g4H7H/gTt946gzNnTvPlF1/w84EDlJQUI8sysbGx3HHHHcyYMaPW17Uu78n6fH/7HEjt3buXuXPn0qZNG6ZMmULr1q154YUXOHXqFAAnTpzg0Ucf5eGHH65T1N0UiUCq+ahvIAVQUGylc5twWkT6929DEIFUsJFlGYvFgsFgqDJWKFhVBFLbtm3z5IBqSq7mPbE7XTgcbpRK/1/H7ZZRKiS0mroP+fZ3IOXzT+F///vf3HrrrWzdupUFCxZUSR/ft29f/v73v/PZZ5/5eglBaNJC9Boy80pxibFSgiA0UW63jMPRuBclvhyfA6mTJ0/y+OOP19rPPGzYMM6fP+/rJQShSdOqlZQ5XBSWiLFSgiA0TRUJiBtJQ6RPfA6kysrKMBprX4DVbDZjt9t9vYQgNHkhWjWZuaJVShCCRUFBAaNGjeL998tnhN12220sWLAgwLVqnJwuN05XeddbU+ZzHqnOnTuzfv16/vCHP9R4zMaNG+nSReRHEYSaaDVKCoodFJaUEROhD3R1BKHZq1i0WLgysizjcLjKW6KadhzleyB1yy238MILL3Dy5Eluu+02evToAZQP4srMzGT9+vW89957LFq0yG+VFYSmyKBTkZlXSmSYrsn/chMEoXlwuty4ZRpkgHmw8TmQuuuuu9i3bx8bNmzgq6++8mzv27evZy2dSZMmeVaPFgShejqNioISG0UlNqLDRauUIAiNm9stY2/iA8wr83mMlFKp5K233mLx4sV07twZWZY96ezj4uJ4+umnWbVqVaOZ6ioIgWTQqMjKK8Xlp+UTBEEQAqU5DDCv7IrW2pMkiTlz5jBnzhxKS0sxm82EhYWh14tf1YJQHzqtivxiK8XmMiLDdIGujiAIgk9cLjdOlxvlFS5M3Zj4/Ewrd+cBhISE0KpVqypBVOUU9oIg1MygVZOZa8YtWqUEQWiEZFnG7ixfTLipDzCvzOdAqi6DyM+dO8cVrEAjCM2KXqui1OaguFSkDBEEofFxuty43XKzGRtVwedASpZlzp49W+P+Dz74gBkzZogxUoJQD3qNmow80SolCELj4nbLOJzNZ4B5ZVfUiTlv3jzOnTvntS0tLY277rqLl19+2e/rywlCU6fXqSi1OCixiFYpQRAaD4fLjSw3nwHmlV1RILVkyRLmzp1LUlISAJ988gm///3vOXjwIBMnTuTbb78VXXuCUE86jZLMXLN47wjCFZozZw7Dhg2jR48erF+/PtDVabJcbjdOp7vZ5sHzedbeiy++yOTJk3E6ncydO5dOnTqxb98+QkNDeemll7j55psB2LZtm7/qKgjNgkGnLp/BV2on3KgNdHUEocG8/vrrvPHGG2zbto127dr5vfy1a9eyf/9+7r77br+XXZs5c+aQkZHB9u3br+p1A0GWy3NGNYcM5jW5oszmANOmTcPpdLJw4UIGDhzIa6+9RmxsrOe4n3/+mbZt2/p0jYMHD7Jq1SoSExNRKBSMGDGCBQsW0Lp16zqXkZCQwNtvv83Ro0exWCxotVp69+7Nfffdx5AhQzzHvf7667z//vvVdkfOmDGDJ5980qfnIAi+0GlUZOWZCQvRiHGGgiAELZerPH9kc0p3cCm/PPObbrqJF154gQsXLuBwOLz2+bpEzL59+7jnnnsYOHAgu3btYvPmzZhMJmbNmkVubm6dytixYwd33HEHY8eOZevWrezfv5/nn3+ePXv2cPjw4SrHz507lz179lT5F2xB1PmsYt768jg/nzWTkFooFrxtggxaFcWldkosjssfLAiCEABuWcbudKFQKJptaxTUo0WqLk2jFouF22+/nbi4uCuqlNPp5Omnn6ZDhw48/vjjKBQKwsLCWL58OePGjeO1117jxRdfrLWMgoICFixYwMMPP+zpZgQYO3Ysd999Ny1atLiiOgbSK/85yPkLJQCc/eoX9FoVvTtH0S8uhh4do9CqlQGuoXClJElCq1aRmWcm1BApWqWERsVms/H222+zefNmLBYLarWaLl26MHXqVG677TYAJk6cSH5+PgC33XYbSmX559bKlSv58ccf+eqrrygsLGT+/Pk88sgjALz22mt89tlnVbZXiI+P54033iA1NZWoqCgmTZrEyJEja6zn999/z3vvvUdKSgoA3bp14/777+f666/3HDNx4kQKCwsxm828++677Nmzhy1btlBSUsKAAQN49tlnPb0up0+fZt68eZhMJlwuF6NGjfKU88MPP6DRaK7shQ0yTmf5APNm3BgF1COQOnDgQJ0LrXysL18A+/fvJzU1lQcffLA80v1Nq1atGDBgABs3bmTJkiWEhITUWMann36K2WxmxowZVfYtWLCg3nUKJpe+ptYyJ4dO53DodA5qlYKeHSPp2zWGPl2iMejUAaqlcKUMOhWF5jLMVgehhqb1ASxc5Ha7SSlKx+4KnpmaGqWGThHtvD5/6+O5555jz549fPjhh3Tu3JnS0lKWLl3K4sWLPYFUfHy8Z4zUF1984TVGasSIEdx1111MmDDBq9wnn3ySWbNmVdkO8N133/H4449zyy238N///he1Ws23337Lq6++Wm0dP/jgA1588UWeeeYZZs2ahdvtZvXq1fzpT3/ipZde8gxfiY+PZ/369SxatIjXX3+d+fPn89e//pX09HTuueceHn74YU+C6p49e7Jnz55mMUbK5XbjaMYDzCur1xip06dP1/sCPXv2rPc5FYFYjx49qi3v0KFDHDt2jGuvvbbGMuLj42nfvj3h4eH1vn6we+quwaz+8ji/JOVz6bwuh9PNiXP5nDiXj0IhEdcunL5dY+jbNUYMXG5kJElCo1KSlVeKsb1atEo1QU6Xk79vX0liQUqgq1JFXFQnlo7/P1TK+g+ljY+PZ8SIEXTu3BkAg8HA448/zrFjx/xdTQBcLhcvvPACer2eJUuWeFbYuPnmm9m7dy9nzpzxOj4zM5NXX32V8ePHc+edd3q2P/roo8THx7NixQqmT59epQWpb9++XHfddQC0b9+eG2+8kdWrV5Oent4gg+WDlSzLOJr5APPK6vwOqa15tDYVUX19JCcnA9CyZcsq+yq2paSk1BhIuVwuzp49y4ABA9i7dy/vvPMOCQkJKJVKBgwYwJ///OdqA7yTJ09y3333kZSUhM1mo2vXrsyePZvf/e539X4Ol9bH5XJdURmVtWsRwl/mDOTrrYdQ6qP5JamAM6lFOC8ZK+V2yySkFpGQWsSXPyTSsXUo13SN5pqu0bSIEOsh+pPb7fb6r7/oNBIFJgvFkTqMolWqzireb/583/nK5XJ5FnW/NKVFtjkvKIMogMSCFLLNebQJa1Xvc6Ojo/nxxx/573//y0033YRerycsLIxNmzZ5vQYV/1/da1PTvuq2nzx5kuzsbMaOHYvBYPA6fuTIkXz11Vdex3///fc4HA5GjRpV5brXXHMNX375JSdOnGDQoEFe1xw0aJDX8RUTq7Kzs70mVdV0v4NJ5dexvjzr6Sklqvyav8oqql+f51Fxb2r7fKjPZ0edA6k1a9bUudDKLu3Drguz2QyATld18daKbSUlJTWeX1xcjN1uJyEhgYULF7JixQoGDx5MamoqCxYsYNasWXzwwQeeN0mFrKwsli1bRr9+/cjLy+O9997j//7v/zh69ChLliyp9/OokJCQ4PO5NbHZ3WjVCozqYkb1UDGsaxRpeXZSsss4n2vH4az6R3X+QgnnL5SwcU8KkUYlnVtp6dRKS3SoSrR2+EliYqLfy7Ta3eRknadttGhRrK8TJ04EugoAqFQqrFZrla4yo0JPl4j2JBWlBahmNesS0R6jQo/FYqn3uc888wyLFi3i2Wef5aWXXmLkyJFMmzaN66+/3jMWCvBMTrLZbFWuY7PZPMdU3lfd9orE0FFRUVXKCQsLA8But3v2VbxP//GPf/DWW295He9wONDr9WRkZHh+cNvt5d2uBoPBq/yKL2+z2ey13e12I8uyT6/d1Wa1Wut9TplDRpLA6WyACtWT2w1OBTgddf8Oc7vdOBwOv30++Jz+oK4mTJjAqVOnGvoyXsrKygAoKiri5ZdfZvjw4QB06dLF02S7bNkyrwRtf/zjH7n//vs9gVpMTAx//etf+fXXX1m7di033ngj/fv396k+3bt393uW9+JSG6k/HiEuLs7z4dznt31Ol5vENBMnk/L5JSm/2plfhWYXhWYLh89ZiAzT0rdLeUtVp9iwZpni/0q53W4SExO97oe/yLJMUYmduM5RGPVizFtduFwuTpw4Qd++fb2+uANVl8TERPR6fbV1WT7xr6SY0rE7g2iMlEpDp3Dfx0iNGDGC+Ph4duzYwXfffcfWrVv54YcfGDBgAB988IHnc1atLv971ul0VT4jKx9TeZ9Wq62yvaILTqVS1ViORqPx7FOpyr/6nn76aW666abLPp+K8vV6vVf5Fdu1Wq3XdoVCgSRJQb26hyzLWK1W9Hp9vX5IO5xuZIULVZCMMHe5ZVRKCU09Jlm5XC7UajW9evWq8fPBYrHUuRHkigOpX3/9lcOHD2MymfzWjGk0GoGLvzwqq9gWGhpa4/mVW7Iu7ZLs2rUrbdu25ZdffqGgoICoqCiva15q4sSJ7Nu3j+3bt/scSCmVSr9/mCsV5eUpFArP/1fe16dLDH26xOB2y6RkFXPiXB7HE/MoKK76mhYWl7HzaCY7j2ZiNKi5pksM/bpG0619JCpVcLxZGovq7oc/aDVucoushBurttIKNWuI954vJEny/LuUUqmka1THANSq4TidTlQqFRMmTGDChAmYTCYWLFjAjh07+Pbbb5k5cyZwceJMda9NRZDicrm89hUUFFQ5p0OHDgDk5ORUKaciXU7l4yvGbmVnZ1c5vqysjEOHDtGvXz/P90JN9axte033O9jUp54V6+kpgyjdQUXV6/NaVzzn2j4f6vO54XMg5XQ6eeqpp/j++++RZRlJkrwCqSv5A6r4I8/Jyamyr2Jbp06dajw/PDyckJAQSktLiYyMrLI/JiaG9PR0r0CqJhVpEirevI2NQiHRpW04XdqGc9OYLmTmlXI8MY8T5/LIyiutcrzZ4mDfySz2ncxCp1HSu3M0fbvG0KtTFFpN4L+QmqsQvZrC4jJKrQ5CRKuUEOT69OnDTz/9RHR0NFDevXbLLbewY8cOr2EZFYPCnb/1EW3bto2cnBzuuOMOIiMjUavVZGZmepV96NChaq8XGxvLzz//TGlpqdeM7n379lU5/oYbbuDVV19l69atPPDAA177vv32W1555RV27tzp47Mvf16Vcyq+//779OrVy+exxsFA/i1nFFLzXE+vNj43N7z33nvs3buXRYsWsWbNGmRZ5qOPPuKjjz7inXfe4YEHHkCn07F06dJ6lz1s2DCAKjMtKrZptdpaW4ckSWLgwIEAnjwllVUERRVBVHFxMe+++261ZeXl5Xkd25hJkkTbFkamjuzEX+4awt/uHcZNY7rQKTas2uNtdheHz+Tw4aZfWfKvPbz7zUn2/3IBs1UkibzaFJKESqEguyD4x1wIAsCKFSs8QZPJZOLrr79Gr9czceJEzzHdunUD4OzZs7hcLj744APPZCONRsOYMWP48ccfPTPGjx8/zu7du6tcS6lUsnjxYqxWK8uWLcNqteJyufjmm2/Yu3dvleNjY2N56qmnOH78OKtXr/aMgdq/fz8rVqxgwYIFV5TzKS4ujoKCAvLz8ykoKODtt9+mtLTqD9fGxOWWyweYiyiqCkn2sT9u+vTpPProo9xwww0A9OrVq8pYqA0bNrB7925WrlxZr7KdTidTp05FpVKxceNGTz99dnY248aN4/e//71XQs6cnByioqI8/d4AGzdu5Mknn2TZsmWeZmSA1NRUbrjhBvr168e6desASE9PZ+LEifz0009VAqaKbOeffPIJgwcPrtfzsFgsnDp1il69evl/jJTZxsbtBxnUv5dfupJMpWWcPJfP8cQ8EtOLcLtr/rOQJOjaNoK+cTH07RpNZKjobnK5XSQkJNC9e/cG6dqD8izCJnOZyA9WBy6Xi6NHjzJgwICAd+25XJX+NoKgm/Fq+Prrr9m8eTMJCQnY7XZUKhV9+vRh/vz59OrVy3OcLMs8//zzfP/990D598hLL71ETEwMUP6j9/nnn2fv3r1oNBpGjRrFPffcw+9//3sMBgMGg4Hvv//e0wW3bds2Xn/9dVJTU4mIiGD06NFcd911PPzwwxiNRiIjI4mPj/dcPz4+nnfffZekpCR0Oh1t2rThvvvu8wr2Zs+eTWJiImazmfDwcMaMGcPKlSuZM2cOv/76a5XtUP4DfNGiRZw4cQK1Ws2kSZN4+umng6qrr2IwvMFguGy9ZFnGVuZEhqAbQ+t2yygVElpN3TvY6vKerM/3t8+BVP/+/dm9e7dnrFLPnj2r5Jkym83ccMMN7Nmzp97l7927l/vuu4/77ruPRx99FIvFwhNPPEFCQgJffPGFJw3Cpk2beOKJJxg3bhyrV6/2nC/LMg899BAnT57k9ddfZ+DAgWRnZ/PEE09w+vRp1q5dS58+5cOz09PTmTBhAqNHj2bp0qW0bdsWs9nMv//9b1avXs1tt93G8uXL6/0cGlMgVZnF5uDX5AKOJ+Zx+nwBDmftU/rbtwqlX9cY+sbF0CoqeAdXNqSrEUgBFJfaiQzT0rlN08uP5k8ikAou9fnSFq6O+twTh9OF3eFCqQy+MbPBEEj5PEbq0pH+0dHRZGRkeOXSKCoq8qQyqK+RI0fy4YcfsmrVKkaPHo0kSYwYMYJ169Z55ZeKjIzEaDR6LZQM5d1Y//znP3nnnXf4y1/+QlFRESqVipEjR7Js2TK6dOniOTY2Npa3336bb7/9lj/+8Y+YzWZsNhvdunVj2bJlnky8zYVBp2ZIr1YM6dUKu8PFmfOFHD+Xxy9J+VjLqs53TcsuIS27hI0/JdMy0kC/uPKgqn1Lo/jQ9DOjXk1ukZVWUQbRKiU0KrJc/k98JDQubreM3eH2+2zkpsTnQKpjx45s3brVk3CzY8eOvPXWWyxduhSlUondbueVV16hTZs2PlduyJAhrF27ttZjRo4cWe3gQyjvY58/fz7z58+vtQylUsn48eMZP368z3VtqjRqZXkXXlwMLpebxAwTJ34brF5cWnW6dk6hhfifU4n/OZWIUC19u8bQr2sMnduGi6UE/EChkFAqJHILLXSMFa1SQvCT5fKxNXanjNrpqlfLgRB4Dmd5YkoRANfM57/oMWPG8Mwzz5Cens4jjzzC7Nmz+etf/8qOHTto27Yt58+fx2Qy8eijj/qzvkIAKZUKenSIpEeHSG4dF0fqhRJOJOZx/FweeUVVk7oVlZSx62gGu45mEKJT0adLDP3iYujeIRK1SKvgs1C9hpxCGy2jQtBrxZeSELxkubw1w+F0IUnlOYiUSjeqIOwiEqoqz2Auix/Bl+Hzp/DMmTMxGAyewdk33XQTBw8e5PPPP/fMdLvhhhuYN2+ef2oqBBWFJNEpNoxOsWH8bnRnLuRbPLmqMnKrdueW2pwc+PUCB369gFatpGenKPp1jaF35yh0IhioF4VCQqGAnMJSOrYWrVJCcHK53dgd7vIxLEoJp7P8b9fucKGQpKAbtCx4K093INbTqwufv8FatWrF3LlzPY8lSeL5559n/vz5ZGZmEhsbS+vWrf1SSSG4SZJEbEwIsTEhTB7ekXyT9beFk/NIzjBVWYqpzOHi2Nlcjp3NRamU6NM5mlvGxhERKpZAqSujXkNOgZVWkSEiEBWCiqcr77dFbSuvx6aQJFxuGYfThUatFGMog5jTdTEIFmrn90/gVq1a0apVK7755hvatGnDkCFD/H0JIchFh+u5flA7rh/UjpJSOyeTyoOqhNRCXJekVXC5ZI4n5nEuvYg/3NCTXp2jA1TrxkWpkJAUEjmFVjq0rjnLvyBcTW5ZxuFw4XS5f1smpeoxSoWE0yWjUMioVeJLOhhVZDAXrYZ143NHdeVcINX57LPPmDNnjldKAqH5CQ3RMLJvLA/c3Jfn/3Qtc6b0on+3FmjU3n96pTYn73x9ko17kqoEW0L1QvUasgtLsdmDYOVQoUbNpdXF5XJjK3P+Nqam+iAKAOliF19t+eqEwHE43c1ihqW/3ps+B1KXSz/1n//8h/fee49PP/3U10sITYxeq2JQz5bcO703z//pWubd1If2rbxbU+J/TuOtL49RZC4LUC0bD6VCQkIit5qB/kLwqFjXq/KSIU1JxdIhNnv57C6lUrrsmJqKcTd2h8tva7QK/lE+wNzdpAeYOxwOv66F6HMgVZcK9OnTB5PJ5OslhCZMo1JyTZcYHp05gDED2nrtS8owsfLjQ5w+3zjXN7yaQvVqsvMtlDlcga6KUANJkjAajU3ys9Dtlimzu3A43L9Ngqj7F5Pyt/FSTlftCX+Fq6e5DDA3mUwYjf7Lc1jnMVLx8fFs27bNa9vf/va3Gn9N2O12Tp486ZX4UhAupVIpuPX6OLq2DefTrWc8v2rNVgfvbDjBpOEduGF4J9FXX4PyTMMyuYUW2rUUY6WCVXR0NKmpqUD5oupqdeNPpup0uT1dQErFxYSb1ZLB7XbjdruqfEHbytzIshulSPh4VcmyjNvtxuVyeQIKp7M8VYVKKeFuJPGt213+J+VyXf47wuFwYDKZKCoqokOHDn6rQ50DqdOnT7NhwwbPY0mSWL9+fa3ndOzYkaefftr32gnNRv9uLWjbwsiHm34lPac8fYIMbNmfSlKGibum9iI8RMzqq45RryE730LLSAMadfNcgiTY6fV6OnToQH5+PikpKY26O0uWy1MbuNwyEnUdZyLjcDhRq1VcGknJsowkgUqpaDbjyYKBLMs4HA7UajWSJJU/rmiNakTNUbIso1BIdcpNVtE63KFDB/R6vd/qUOdA6p577vFkMZdlmYkTJ1ZpoaogSRJhYWGehSQFoS5iIvQ8OmsgX+88x57jmZ7tienlXX13TelF9w6RAaxhcFIpFciyTF6RlTYtxHsuWOn1etq1a4csy55/jY3N7iT1QjHWUjthYbo6j6NxuV2knk0krltctWtRFhZbiY0yir/fq8jlcnHixAl69eqFUqkkI9dMToGFcGPj+sFqtjoIM2ro0Dqs1uMqxkQ1RLBe50AqNDTUs0AxwNChQ73W1RMEf1CrFNw2vhtd24azblsCZb919ZVYHKxef5zJIzoyeVhH0dV3CaNBQ1Z+KTERetEqFeQa6sO8IcmyTGGxjZSsYiRJIio8pF7nS5KMDEiSAkmq2nIQbtRzocBKRJieUIPGT7UW6kKpVGKzu7lQYCU8RNfo/jYlSYGkUAR0QXCfO6UvtwaeIFyJgT1a8n93DKJtpV+oMvD9vvOs3nCckmrW+WvOylulIM9kC3RVhCbG6XKTeqGEs+lF6DSqBgl0lEoFOrWK5MxiHM5GMjiniZBlmfQcMxqloknP1GtIV5yQs6ysjB07dnDq1ClKSkoIDQ2ld+/ejB07Fo1G/LIQfNci0sBjsweyYUcie09kebafTSvilY8PcffUXsS1jwhcBYOMUacmK89MiwgdapVolRKuXKnVQUpWMRabg0ijrkFbgvU6FYUlNjLzzHS8TDeN4D9F5jJM5jIixcoSPruiQGrLli08++yzFBYWAhWDBsvfaNHR0Tz33HNMmDDhymspNFtqlYJZE7oT1zaCz7YleKb5l1jsvLX+GFNGdGLisA4oGllzdENQqRS4rTJ5Jhux0fXrehGEymRZJrfQyvnsYjRKJZGhuqty3fAQLdn5FsJDtGLJqKvA5ZZJu1BCiE7d6Lr0gonPgdTOnTt5/PHH0Wg0XHfddXTu3Bm9Xo/VaiUpKYkDBw7w6KOP8s477zBq1Ch/1llohgb1bEm7lkY+2PQrWXmlQPnsoc17UziXYeKuKT3F2ArKZ/Bl5ZUSE65HrRLTyYX6czhdpGWbySuyEGrQXtW/I4VCwqBTkZJVTG9dlBjv18AKzU4i9G5CQ8TrfCV8DqTefPNNBg8ezKpVq4iKiqqyPz8/n8cee4x//vOfIpAS/KJllIHHbx/Ihh8T2Xfygmd7QmohKz8+xJypvejaLiJwFQwCapWCUpubApOVVqJVSqinEoud5AwTdqeLyNDADDzWaVSYzGWk55TQuU24aClpIFabk4ISJ+07Nv6cZoHm80+N06dPs2zZsmqDKCjv2lu2bBmnT5/2uXKCcCmNSsnsiT34ww090VT6pWwqtfPml8eIP5CKuxFOK/cng05NZn6pyBgt1JnbLZOVX8rplAIkJCKMgZ29FRqiIa/IRr6YPNEg7A4XSZkmlErpt6S+wpXw+RXU6XS0adOm1mPatGmDwWDw9RKCUKMhvVrx5J2DaB198e9LlmHjT8m8+/VJzNamua5ZXWhUSpxOt/gSEuqkzOHiXHoRaRdKCDVo0OuueA7SFVNIEkaDmtQLxdjKxKLc/uR0uUnKKKLM7kKvEUGUP/j8Kg4ZMoT9+/fXesyBAwcYPXq017ZFixb5eklB8NIqKoQnbh/EsN6tvbafSilg5ceHSMpsemub1VWITk1WnmiVEmpnMpfxa3I+xRY7kaHaOmWHvlo0KiVIEqkXSnC7m3crs7+43DIpWcWUWMqTWAr+4fO75qmnnmLFihV89dVXlJWVee2z2WysX7+ed955h4ULF3rt++qrr3y9pCBUoVEruWNyD+6Y3MNrUGyRuYw3Pz/KtoPNs6tPo1Zid7ooEK1SQjVcbpn0nBLOnC9ArVQQHqINyrFIoXo1ReYycgstga5Ko+d2y6Rnl1BgshLRyLKXBzuf23Dvu+8+zGYzixYtYtGiRURERGAwGLBYLBQVFQHl46RmzZrlr7oKQo2G9W5N+1ahfLjxV7ILyj903TJ8uzuZpAwTd07uSYi+eQ2qNOrUZOWXEh2uE+MgBA9rmZPzWSaKS+2EG+u+zEsgSJJEmEFDWo6Z0BANBl3zeg/7iyzLZOWZuVBQenESQfP7fdlgfP50zcjIICQkhNjYWGJjY9Hr9ciyjF6v92zTaDRe60o1xrWlhMYjNjqEJ+4YxJBerby2/5pcwKufHCIlqzhANQsMjVqJ3eGisES0SgnlX6YFJiunkvOxlrmICtMHdRBVQaVSoFIqSMkqxiW6qn2SU2AhPddMhFEncu41gCsaVbh9+/Z6n9OzZ88ruaQg1EqrVnLn5B7EtQvny+2JOH774C0qKeP1z4/yu1GduX5Qu6DsxmgIBp2ajNzyX6GiVar5crrcZOSUt0iE6jWNLj+TUa+moNjKhfxS2rYMvfwJgke+ycr5C8WEh2gbReDcGPn8yXrLLbdc1fMEoa4kSWJ4n1gev2MgLSP1nu1ut8w3u5J4/3+/YLE1j1l9WrWSMruLwpKyyx8sNEmlVgdnzheSU2gh0qhrdEFUhfAQLZl5pZRYxDqbdWUyl3Euw4TRoAmqiQRNjc+v7IsvvnhVzxOE+moTY+SJOwYxqEdLr+0nk/J59eNDnL/QPLr6QnRqMvNKcYmZT82KLMvkFFj4NSUfp9NNZGjDrpXX0MTCxvVjtjpITCsiRKsunwEpNJgGD1EHDRrU0JcQhBrpNCrumtKTmRO6oVJe/BIpLCnj9c+OsuNwepMfu6fVKLGVOSlqZmOlmvp9rY3D6SI5s5iULBNGnabJTLTQ61SU2Z1k5poDXZWgZi1zcjatEI1KiVYjgqiG5vMYqZ9//vmyx8iyjNVq9fUSguAXkiRxbd82dGwdxocbfyW3qPxv0uWW+WrnOc5lmLhjUo+gSETYUAw6FVl5pUSEBvcsLX+w2Z3kFVlIzi5Dm5RPeKiOEL0arbr8S0XdxH+dB8MyLw0pPERLdoGFcKNY2Lg6ZQ4XielFSDLo9U33My2Y+Pwqz5kzp8m9QYWmrW0LI0/eMYjPtiVwJCHXs/3EuTwycs3cM703HVo1zYGsOo2K/GIrxeYyIsN0ga6O37ndMmarg7wiK/kmK7LbjUopIQP5JpsnJQaUz2Y0GtSE6tVoNSq0GiUalbJRd3tB+WuQXWghPbsEnVpFhLHp3WcoX9g4RKcmOctEH110ox3z1RAcTjfJGUXYHS7CQ0SQebVcUbj68MMPV9lmtVq5cOECu3fvpnfv3gwePPhKLiEIfqXTqsoXN24bwYadibhc5d0/BcU2/vnZEX4/piuj+7dpkj8SQrRqMnPNhBu1jT5oqOBwuigqKSO7wILF5kCtUhIeokXGTbZSQqtRolRc/KKVZRmHy4251EFhcVl5WhZArVQQoldh1JcvkaJVK9GqlY1mpmOZw0VqVjGFJWWEhTT9gcVajRKb2SkWNq7E5XKTkmXCbHEQEdo0g+hgdUWB1Pz582vcV1ZWxsKFC8UYKSHoSJLEqP5t6BhbnsAz77fs3y6XzPofEzmXYWL2xO7otU2rWVynVVFQbMVUWkZkI/6glWUZi81JvslGbpEFt1tGr1URFXZxhmZN6YYkSUKjUlYZfOt0uSkrc1NiKcUtA7KMQpLQ61SEGtQYdL+1XqmVXhn0g4HJXEZSpgm3WyYyNDgzlDeEsN8WNg4L0RITob/8CU2Y2y2TllNCYUkZkSJr+VXn8zfF3r17a92v1Wp58skneeKJJ7j22mt9usbBgwdZtWoViYmJKBQKRowYwYIFC2jduvXlT/5NQkICb7/9NkePHsVisaDVaunduzf33XcfQ4YM8Tq2uLiYlStXsn37dpxOJ+3ateNPf/oTEydO9Kn+QnBr1zKUJ+8czLr4Mxw7m+fZfuxsLhk5Jdw9vTftm1jOGr2mfAZfeEjja5VyudwUl9rJLrRQUmpHoZAw6tR+aTVSKcuTPuorfSS63TJ2p4u8IhtOlwUkkGQJjUZJqEFNiF6NztM1qLjqAYzLXZ6tOjPXjEGnRtfMxsNIlRY2NurV6JrYD5+6kmWZjFwzOQVWIppRIB1MfP4EioyMvOwxoaGhnD171qfy9+3bxz333MPAgQPZtWsXmzdvxmQyMWvWLHJzcy9fALBjxw7uuOMOxo4dy9atW9m/fz/PP/88e/bs4fDhw17HWiwW7rrrLo4ePcoXX3zB3r17ue2223j44YfF+oBNmF6r4p5pvbn1+jiUlWb15ZlsrFp3hD3HMpvU7C+9TkWpxUFxaePJxWMrc5KZV8rxxDzOphVht7uIMGrLEww2YBeWQiGh06gINWiIDNURadQRbtSgUkiYzHbOXygm4XwBx8/mcSwhl7NphWTmlVJUUoa1zNmg6SasZU7Ophb81lWrQ6dpnkGEWNgYLhRYyieSGLUia3mANNi7T5Zl3nvvPcLCwup9rtPp5Omnn6ZDhw48/vjjKBQKwsLCWL58OePGjeO11167bD6qgoICFixYwMMPP8zNN9/s2T527FjuvvtuWrRo4XX8e++9x5kzZ/j0009p1ap8iZHZs2cTHx/PCy+8wPjx4316LkLwkySJMQPa0jG2fFZfQfHFrr4vfjjLuYwiZk3o3mR+8eo0SjLzzIQbNUH769Xtlimx2MktslBUbAdJJkSnIdQQ2G41SZLQqJW/DXC+mFLA6XRjtTkpNts9464qBkWH6NWeWYOaK+walGWZwmIbKVnFSJLk1Z3ZXIXq1RSWlC9s3Co6JNDVuaryiqykXSghPETT6FqY/cXucAU8iPb5m+Huu++ucZ/VaiU1NZXi4mIeeOCBepe9f/9+UlNTefDBB1EoLn7otGrVigEDBrBx40aWLFlCSEjNb5pPP/0Us9nMjBkzquxbsGCB12NZlvn8889p2bIlAwcO9No3efJkdu7cyffff8/MmTPr/VyExqNDq1CeunMw/916hhPnLnb1HUnIJT2nfFZf2xbGANbQPww6NQXFtt8WrQ2u8RQVg8ez8i3YypxoVArCjJqg/6WtUilQXRIgudwyDoeLvEIr2QWlyHJ5IKbTKAnRqz3dURp13boGG/syLw3Fs7BxdvNa2LiopIzkTBOhBk2jmRThT7Is89WOc+w8mkG7lkZWPnZdwO69z4HUgQMHatyn0+no2LEj999/P3PnzvW57B49elTZ17NnTw4dOsSxY8dqHXsVHx9P+/btCQ8Pv+z1zp8/T3Z2NmPGjKn2elAe3IlAqunT61T88Xe92Xk0g//tSvJ0z+QWWfnHp4e55fo4Rl4TG7QtOXWl1SjJyjMTFhL4VilZlim1Ock3WcgttAEyeo2aqEaepkGpkFBqVegqxapuWcbhdGMy28kvslLxO1qjVmLUqQkxqNFrVZ7Wq4pWhlKrg5SsYiw2B5HGxp2hvCFUBLIpWcX06BDZ5AMLs8VOYnoRBp066CY/XC3Hzuax82gGAOk5Zo6dzWNk39iA1OWK+ipOnz7tr3p4SU5OBqBly5ZV9lVsS0lJqTGQcrlcnD17lgEDBrB3717eeecdEhISUCqVDBgwgD//+c9eiyenpKRc9nrnz5+/ouckNB6SJDF2YDs6/dbVV7FOndMl8/m2s5xLNzFrQvdGnTHYoFVRWBLYVimny01JqZ0L+aWYrQ4UColQvX8GjwcrhSR5UitUdA3KsozTJWO2OSg0lyEjg1weiIX81mqVU2hBo1Q26tmWDa25LGxssTk4m1ZU6e+o+Sm1Ofjyx4vjrxUKia5tL99o0lB8DqQacvFhs7k8/b9OV/VDo2JbSUlJjecXFxdjt9tJSEhg4cKFrFixgsGDB5OamsqCBQuYNWsWH3zwgSc1Q0VZen3V8QYV24qLfV+XzeVy4XK5fD6/2jLd5eW53WLNqYbSrmUIT9wxgHVbz/JLcoFn++EzOaTllHD31J7ExpR3L1fch8Z0P1RKicycEkJ0yqvaKmUtc1JYbCO70ILTJaPTKAk1qH6rg+z5274Sje1+KBSg0yjQaS4GkS6XG2uZgxJLGXqNCrVK4ZfXJhCu1v0w6lWk5ZQQoi+fJNDUlNldJKQVIssyGrXyiv4eGtt7pLJvdp7DbLm48PzEoe2JDtf69Xu2PmX5HEhdyeLDEyZMYNu2bT6ffzllZeUtCEVFRbz88ssMHz4cgC5durBixQqmT5/OsmXLWL9+fYPVobKEhAS/l2mzl//xJyYm+r1swdu13ZWEaozsTzBTMYEvt9DKPz49wqjeofRoe3EZjsZ0P2RZxmx1k3dBg0HbsL9s3bKMtcxNUamTUpsbSQKtWuG1/mFDaEz3ozm4GvejzOEmPTWZ9jHaBv/7upqcLpnMfDt2l9uv79fG9h7JyLdz4Nciz+Mwg5KOETaOHj0asDoFZBpSRkZGrfuNxvIBvTZb1UVWK7aFhtbcdFu5JWvkyJFe+7p27Urbtm355ZdfKCgoICoqylNWdesCVmy7khl73bt3x2Aw+Hx+dYpLbaT+eIS4uDivAflCw+jRA4b0K+Y/m89QZC4P1F1u2HmyhFKnnlvGdib1fHKjux+lVgd6rYpu7SMapFXK7vgt83ihBVeZk3bRSvQ6VYMPHne73SQmJja6+9FUXe37UVRSRotIPR1bN42Z1i6Xm6RME1KIg3Cjf1raGuN7xOF08eXeI17bbhvfnWu6taBjrH/vtcViqXMjSEACqct9YHfu3BmAnJycKvsqtnXq1KnG88PDwwkJCaG0tLTafFcxMTGkp6d7AqmKsmq7XseOHWutc22USiVKpX9/8Vcse6FQKLyWwBAaTte2kTz1h8F8suU0v1bq6jt0Ooe07BKu661vdPcj1KAoz3tkd/utK0SWZUqtDvJNVnKLbMiyjEGnxqi/+mOxGtv9aOqu1v2IDNWTV1RGVJiz0S9s7HbLpOeaKbE4GyTdRWN6j2w6cJ5808UGlpF9Y+ncNhKFUuH/79h6lBeUYeiwYcMAOHPmTJV9Z86cQavV0r9//xrPlyTJk8YgPz+/yv6CgvIvwaioKKA8SGrVqlWN1wM83YNC8xaiVzPvpmu4cXQXKk+cyim08tW+ApIzTYGrnA8kSUKtUnAhr/SKy3K63BQU2/g1pYBTyQXkm8o8ySyb66BYITAqL2xsdzTOcWVQ/qMkLaeEvCIrEUGWquRqS88p4cdDaZ7HYSEabhzdJYA1uigoA6nhw4fToUMHtmzZ4jUQLjs7myNHjjB9+nSvHFI5OTk4nU6vMm699VYAdu/e7bU9NTWV9PR0BgwY4AmkJEli5syZ5OTkcOSId7Ph999/T1hYGDfccINfn6PQeCkkifFD2vPwzAFezexOF3yx/VzAk8PVV4heTaHZhtniW7Zzi81BRk4JJxLzOJdehNslExGqJSxEg1JM0xcCRKtR4nKVByKNdXWCrPxSsvPLs5YHOk1JILncMuviE6j80XrbuG5Bsx5qUAZSKpWKpUuXkpqayqpVq3C5XJSUlLB48WJiYmJ44oknPMdu2rSJMWPGVFlAedq0aYwbN45Vq1Z5gqPs7GwWLlyIXq/n73//u9fx8+bNo0ePHjz33HOe7rx169axc+dOFi9eLLKaC1V0aRPOU38YQvcOEZ5t2QUWDp3ODlylfCBJEmqlkgsFdW+VcrllTOYyElIL+SUpn6x8C3qNishQHXqtqll/6AvBIzxEQ36Rzas7qLHIKbCQll3SKNfF9LedR9JJzzF7HveLi6FvXEwAa+QtKAMpKB8k/uGHH3L48GFGjx7NDTfcQFhYGOvWrfPK9xQZGYnRaCQ21jsRlyRJ/POf/+T222/nL3/5C0OHDuXmm2+mdevWfPHFF/Tp08freIPBwNq1a+nXrx+33norI0eO5LPPPuPNN9/0WmJGECoz6tXMmdLLq+vqu33ncboa15TiEL2awuIySq2OWo8rc7jIzi/l5Lk8zpwvwGpzEm7UEmHUVsnsLQiBVnlhY1uZ8/InBImCYispWcWEGxp2PcnGIM9kZfPeFM9jnUbJrePiAlehagRHu1gNhgwZwtq1a2s9ZuTIkRw6dKjafRqNhvnz51dprapJeHg4S5cuZenSpfWuq9B8GQ0arhvYhq0HyvvvC4pt7DuZxej+bQNcs7pTSBIqhYILBaV0bRvhta9i8HhukZV8U/ng8RCdmhCxzpvQCGhUSmx2F+cvlNCtfUTQt+6UWOwkpRdj1Kub/Y8TWZb5fFsCDufFH6Y3jelKeEhwjRdr3ndJEPzkuoFt0aovfkBv2X+eskY2yDXEoKbAZMNiK2+Vcjjd5BVZ+TU5n19TCigqvjh4XKzzJjQmoXo1JnP5wsbBzGJzcDa1EJ1GKd5jwM+nsklILfI87to2nOHXtA5chWoQkECqsQ78E4Sa6LUqBnS5OAGixOJg19Ha86UFG4UkoVQoyMorJS2nhBPn8kjKNCG7ISpUR6gYPC40UpUXNr5c93Wg2MqcJKYVoVQq0AXJIOpAKrHY+XrnOc9jlVJi1sTuQbmAuc+B1IQJE5gwYQLLli2r97kNmdVcEAKlTwc9YSEXZ/FtP5iG1dZ4xmVA+ZivPJON7AILBq2KqFCd+FAXmoSKhY3PXyjGFWRjGO0OF+fSi3C5yrvNBfhqxzkslT4/bxjeiZaR/k1s7S8+B1IZGRkMHDiQGTNm1Pvctm0bz9gRQagrlVJi0rD2nsfWMifbK+U9aQwUConoMB0RIVpUzXyQq9D0GPVqzFYHF/KvPG+avzhdbpIyirA6XISGNL31AX3xS3I+h89cTJDdJiaEcYPbBbBGtfP5k1Kv17N06VJ69erlz/oIQqM2rHcrYsIvLlG080g6JaW+5WcSBMH/wkO0ZOSWUuJj3jR/crllUrKKKbE4iAiyAdSBYrM7+WL7Wc9jSYJZE7sH9exFn2vWo0cPsrMvny9n0aJFvl5CEBodpVLBlJGdPI/tTjdbf04NXIUEQfCiVEjoNSqSM4u9ZoNdbbIsk55dQoFJZC2vbNNPKRSVlHkeXzegbdCvmehzIPXII4/w4osvVruwcGVfffWVr5cQhEZpYI+WxMZcHHj+0/FMCoobX0JAQWiq9DoVdoeLzFzz5Q9uALIsk5lr5kJBKRGhOpHA9jcpmSZ2V5qkExWmY+q1nQNYo7rxeRTpkSNHsNvtjB07ljFjxhAbG4tWK6JqQVBIEtOu7cR73/wClDfff78vhTsm9wxwzQRBqBBm0JBdYClPKHuVFzbOKbSSnmsmwqgLyllogeB0uVm3LYHKc/pnju/WKNbp9DmQeuONNzz//+2339Z4nIi0heaoT+doOsWGkZJVDJTnQxk/pD2tokIuc6YgCFdD5YWN++iir1repgKTlfNZJsJDtCKdSCXbD6ZxIf9inq/BPVvSs1NUAGtUd1c0r/mjjz6qdb8sy9x7771XcglBaJQkSWL6tZ1588tjAMhyed//H3/X5zJnCoJwtWg1SqylTtJySujSJrzBf/ibzGWcyyjGaNCIWbGVZBeUsuXAec/jEL2am8cG1zIwtfE5kNJoNAwbNuyyx126Bp4gNBdx7SPo3iGShNRCAI4n5pGaXUKHVqEBrpkgCBXCDeULG4eHaImJaLhlj0qtDs6lF6HXKtGogr+76mpxyzLr4hNwuS526t0ytitGfePJp+VzSHz8+PE6Hbd9+3ZfLyEIjd70Ud4DJTftSQ5QTQRBqM7VWNjYWubkbFoRKqUCnUYkuK1s74kskjOLPY97doxkUI+WAaxR/fmtbTEvL4/Tp0/7qzhBaBI6tAqlX1yM5/GZ1EIS04oCVyFBEKrQqJRISJy/UILb7d8lzOwOF4npRSDLGETWci9F5jL+tzvJ81ijVjBzQvdGN7b6igIpm83GP/7xD8/MvVtvvRWA3bt3c++995KQkOCXSgpCYzZ1ZCcqfy5s/ClZrDcpCEHGaPD/wsYOZ3nWcrvDhdEgspZXJssyX24/S5n94uLu00Z2JipMV8tZwcnnQMpsNnP77bezevVqsrOzCQsL83w5xMXFodPpmDNnDufPn79MSYLQtLWODmFIz1aexylZxfyaXBDAGgmCcCl/L2zscrlJyTJhtjoJF1nLqziWmMfJpHzP4w6tQhkzoHEuH+dzILV69WpycnJYvnw5R44cYf/+/Z59rVu3ZvXq1UyZMoXVq1f7paKC0JhNGdHJa6rzpp+ScYtWKUEIKiqVArUfFjZ2u2XSckooLCkjXKyfV4XF5mD9DxeXgVEoJGZP7I6ikaaD8DmQ2rp1KytWrGDGjBno9dXPdLj//vv5+eeffa6cIDQVUeE6Rva9OIM1M6+Uo2dyA1gjQRCqE3KFCxvLskxGnpmcgvKlXxrbeJ+r4X+7kyixXGz1mzCkPW1aGANYoyvjcyCVlZXF8OHDaz0mOjqanJycWo8RhOZi0rCOaFQX33Kb9yZf0a9eQRAaxpUsbHyhwEJWbinhRq3IWl6Ns2mF7Dt5wfO4RaSeScM6BrBGV87nQMpoNJKaWvtirAkJCYSFBfdig4JwtYSFaLhu4MUxAHkmG/t/vVDLGYIgBIKvCxvnFVlJu1BCWIhGZC2vht3p4rNtZ722zZrQHbWqcScn9bn2w4cPZ9GiRaSlpVW7v6SkhBdeeIFrr73W58oJQlMzbnB79NqLeWS27DuP3emq5QxBEAKhvgsbm8xlJGeaCBVZy2u0Zd958oqsnscjr4klrl1E4CrkJz5nBps/fz633XYbU6ZMYdiwYfTsWb4g66pVq8jIyGD79u3IsszLL7/st8oKQmNn0KkZP7g9G38qT8xpKrWz51gm4wa3D3DNBEG4VJhBQ3bh5Rc2NlvsnE0rwqBVN/rWlYaSkWvmh0MXG17CQjTcOLpLAGvkPz7f8a5du/Kvf/2LqKgo9u7dy5o1a5BlmdWrV/PNN99gNBp555136NSpkx+rKwiN35iBbQk1XEzMt+3n1AbLqCwIgu8UCokQbfnCxnZH9S3HFpuDs2lFaNVKtBqx9Et1XO7yZWAq5zqdMa4bel3TyPJ+Rc9i2LBhbNu2jW3btnH8+HHMZjOhoaH079+fcePGodGIaZ+CcCmtWsmkYR1Z/2MiAKU2Jz8eTmfKyE6BrZggCFXUtrBxmcNFYroJSZK8uuwFb7uOppOWXeJ53LdrjNeKD43dFd95jUbD1KlTmTp1arX733jjDebPn3+llxGEJmVk31h+PJxOQbENgB8PpzO6fxuR/VgQglC4QUNeodVrYWOH08259CKcLjdh4n1bozyTlU0/pXge6zRKZoyLC1yFGoDPXXsTJkyo03Fvvvmmr5cQLsMty35fF0q4OlRKBTeMuDjlt8zhYtvB6iduCIIQWJIkERqi8Sxs7HK5Sck0YbE5RBBVC1mW+XzbWa+ZjzeO6UK4sWllevc5kMrIyKh1v9lsZuHChb4WL1yGTqMk0qiiuNSOqbRMBFSN0JCerWgVZfA83n0sg8ISWwBrJAhCTSovbHz+QglF5jKx9MtlHDydQ0Jqoedx17bhjLgmtpYzGqcrml7w3HPPVbv9p59+4sYbb+Srr766kuKFWiiVClpFaOjTOZqoMB2m0jJKSu1i2ZFGRKGQmHZtJ89jp0tmy/7ac7MJghA4FQsb5xWJrOWXU2Kx89WORM9jlVJi1oTuTTJJ6RUFUomJiSxbtszz2Gq18uyzzzJv3jxyc3PF2KirQK9T0blNOH26RBMWqsFkLsNssXsWkBaCW9+uMbRvFep5fOCXLHL8uPq8IAj+I0kSkaFaIkNFEHU5X+04h8V2cTby5OEdaVmpBb4p8TmQatOmDf/617/45ZdfePHFF/n555+56aab+PTTT+nSpQvr1q1j/vz53HzzzX6srlATg05N17YR9OocTYheTVFJGWarQwRUQU6SJKZXapVyy/Dd3pSA1UcQhNpJkiSCqMs4lZzP4TMXl4eLjQlp0rnyfA6ktm/fjsFg4N///jdHjx7l7rvvJj09nXvuuYcNGzbQp08fAF588UW/VVa4PKNeTVz7CHp2jkKvVVFQYqPUJgKqYNa9QyRx7cI9j48k5JKRU7dsyoIgCMGkzO7i8+0Xl4GRgNkTuzfpbO9X/MyMRiPvvfcevXv3ZtKkSSxatMgrf1RdZ/cJ/iNJEqEGDd07RNCzYxRatYLCkjKsNpH0MRiVt0p19tq2aW9ygGojCILgu00/JVNYUuZ5PGZgWzq2btpr7tY5j9TPP/9c6/6HHnqIJUuW8OKLLzJx4kTP9szMTJ8rd/DgQVatWkViYiIKhYIRI0awYMECWrdufdlz9+/fz7x58wgPD6+yr0uXLqxdu9Zr28KFC9m6dSs6na7K8Q8//DB33nmnz88jUCRJItyoJSxEQ3GpnfRcMwXFVvRatUgeF2Q6/TbO7ZekfAB+TS4gKdNElzZV/34FQRCC0fmsYnYdvTijPzJUy7SRnWs5o2mo87fpnDlzLtsvLMsyH374IR999NEVV2zfvn3MmzePefPmsWbNGiwWC08++SSzZs3iyy+/pEWLFpctY+DAgVUCptosXryYW2+99UqqHZQqAqpQgwZTaRmZOWYKSmwYtCp0GhFQBYtpIzvxa1I+FZ2wG/ckM/+2/mI8hiAIQc/pcrMuPoHKg0hmju/WLJbNqde36MMPP1yvwmVZ5q233qrXOQBOp5Onn36aDh068Pjjj6NQKAgLC2P58uWMGzeO1157TYy98oFCIREZqiMsREtRiY2M3FIKim2E6NVo1U3/jz3YtWlhZGCPlp5BmkkZJk6fL6RXp6gA10wQBKF22w+mkZVf6nk8uGdLenWODmCNrp56BVK+pDPwJbP5/v37SU1N5cEHH0ShuDiMq1WrVgwYMICNGzeyZMkSQkJC6l22AEqFRHS4nohQHYXFNjJyzZRa7Rj1GjQioAqoKSM7cfRsrifB6sY9yfToGNkkc68IgtA0ZBdY2HLgvOdxiE7Fzdd1DWCNrq46Dzb/+OOPfbrAtm3b6n3OgQMHAOjRo0eVfT179qSsrIxjx475VB/hIqVCIiZCzzVdoukUG47d6aawxIbdWf0q50LDaxGhZ3ifi2MAM3LNHD+bF8AaCYIg1Mwty3y2LQGX62Kn3u/HxjWrdUPr3CI1ePBgny7wxhtv1LsbLjm5fMZSy5Ytq+yr2JaSksK1115bazn5+fksXLiQI0eOYDKZiI2NZfLkycydOxettmpq/z179rBhwwZSU1NxuVz06tWLu+++mzFjxtSr/pdyuVy4XP4NTirK81e50eFawo1q8k02svLMFLtkQvVqVKqmO2XVn9xut9d/r8TEoe34+ddsnK7ysjbtTaZ3l0iUCtEqVVf+vB/ClRP3I/j4657sPZFFUobJ87h7hwgGdo/G5b46P8jdbhdul7vBvmProsFHGn/11Vf1DqTM5vIcOtXNoKvYVlJSctly8vLyGDduHM8//zx2u53vv/+epUuX8uOPP/Lhhx9WKT83N5dnn32Wrl27kpWVxauvvsp9993HokWLuPfee+v1HCpLSEjw+dzLOXHihN/LdLpkSixOUkpcyMjoNQrxJV5HiYmJlz+oDnq313E8pTzDeW6hlY0/HqdnO71fym5O/HU/BP8Q9yP4XMk9KbW5+GZ3geexSgmDOik5e/ZsLWf5l83uJkSnpDBbfdWueakrCqR+/fVXXn/9dQ4fPkxxcbG/6uQXAwcOJD4+nrCw8vwVarWaW2+9laysLP75z3+ydu1a7r//fs/xixYtwmAwoFaX34zY2FhWrFjByZMnefXVV5kyZUqd0i5Up3v37hgM/k2N73K5OHHiBH379kWpbJhxTXaHi7wiKxcKLMgyGPUqlE04qdqVcLvdJCYmEhcX5zWuz1dt2zs488FByhzlv4qOp9iZdl1f0UJYR/6+H8KVEfcj+Pjjnnzw7a84nBe79KZd25khA9r6q4p1Ump1EGHU0jHWv7mqLBZLnRtBfA6kfv31V+644w6MRiPdunXj4MGDDB06FICysjISExOxWq0MGTKk3mUbjUYAbDZblX0V20JDQ6vsq0yj0XglBq0wceJE/vnPf7J9+3avQKq6fFNKpZJx48axZs0adu3axcyZM+v1PCqX01DBTkOWrVcqad9aQ8toIzmFFrLzLYCbUINGtFDVQKFQoFRc+f0IC1EybnA7vttXPoCzyFzGvl+yGTuw3RWX3Zz4634I/iHuR/Dx9Z4cT8zlZNLF1qgOrUIZO7A9iqv83aBQuFEoFX7/HqxPeT4HUm+++Sbjx4/n1VdfRalU0qtXL6+cTSaTiSeffNKnzOadO5cn8MrJyamyr2Jbp06dfKp3Rf6pgoKCyxzpfXx+fr5P12sKtGol7VuG0iJCT06BleyCUhQKiVC95qq/aZqTsYPasetYJqVWBwDxB1IZ0Se2WeRlEQQheFltTr7YfrFLUCHBrIndm+33gc9trIcPH+axxx6rMWoLDw9n8eLFfPbZZ/Uue9iwYQCcOXOmyr4zZ86g1Wrp379/rWV88MEH1QZLeXnlM6AiIyM929LT02usZ8XxUVEil49Oo6JD61Cu6RpDTLiOotIyikvtuMU6fg1Cp1ExcWgHz2Oz1cGOI+kBrJEgCAJ8szuJEovd83j8kPa0bWEMYI0Cy+dAqrS0lDZt2ngeazQaSktLvY5p3bq1ZwZefQwfPpwOHTqwZcsWrxkF2dnZHDlyhOnTp3vlkMrJycHp9F5H7qOPPqp2WZvt27cDMHbsWM+2jIwMVq5cid1u9zrW5XLx448/olKpLjtDsDnRa1V0jA3nmi7RRIRqMZnLKBEBVYMY1a8NEcaLM0x/OJRGqc0RwBoJgtCcJaYXse9kludxiwg9k4Z3DGCNAs/nQKp169akpaV5HsfGxnLw4EGvY/bs2ePTIGuVSsXSpUtJTU1l1apVuFwuSkpKWLx4MTExMTzxxBOeYzdt2sSYMWOqTRa6YsUKjhw5gizL2O12vv32W/71r3/Rs2dP7r77bq9ji4qKWLJkiacVq6CggCVLlpCUlMTDDz9Mu3ZibMqlDDo1XdqG07tzNKFGNUVmG2aLHVkEVH6jVim4YcTFDymb3cX2n9NqOUMQBKFhOJxuPov3HoA9a2J3NKrmPdzA5zFS11xzDcuXL2fFihXExMQwbNgwFi9ezEMPPUSHDh04c+YM77zzzmW74GoycuRIPvzwQ1atWsXo0aORJIkRI0awbt06r/xSkZGRGI1GYmNjvc5ftWoVX3/9NX//+98pKCjAarXSunVr7rnnHu6//36vFq2BAwfy2muvsXHjRmbOnElpaSlOp5PevXvzxhtvMGnSJN9epGYiRK8mrl0kZoudrLxSikrK0KiVGHQqsU6cHwzt3ZrtB9PILbICsOtYBtcNbEu4sWouNEEQhIayZf95z+cQwIhrWhPXLiJwFQoSkuxj80F8fDwLFiygZ8+e/Pe//yUtLY2bb74Zi6U8940sy6jVaj799FP69Onj10o3FhaLhVOnTtGrV68GSX9w9OhRBgwY0GCz9nwhyzJmq4OMXDPF5jK0GhUhusDl97haXG4XCQkJdO/evUFmJR05k8NHm095Ho/q14bbxnfz+3Waioa+H0L9iPsRfOp7TzJyzbz238Oe5atCDRoW3j0EQ4A/381WBxGhGjrFVp15fyXq8/3tc4vUxIkTOXLkiOdx+/bt+eyzz/jggw/IzMwkNjaWO++8k969e/t6CaERkiSJUIOGHh0iKS61k5VnpqDYhl6jQq9r8PyvTVb/7i1oezCNjNzyZLV7T2Zx/eB2xISLJJ2CIDQst1tmXXyCJ4gCmDEuLuBBVLDw6zdb165def755/1ZpNBISZJEuFFLWIgGk9lORp6ZgmIrBq0anVYEVPWlkCSmXduJf399Eij/YPtubwp3TekV4JoJgtDU7TyaQVr2xdVE+naNpl9cTABrFFz8lmI2Ly+P06dP+6s4oYmQJImIUC29OkbRrUMkSFBQYsNmd17+ZMFLr05RdG5zMXvv4dM5ZOWV1nKGIAjClSkw2dj808XZ9zqNklvHdRPjXyu5okDKZrPxj3/8g7FjxzJmzBhuvfVWAHbv3s29997boGvMCY2LQiERGaqjV+dourYNxy2XB1QVS6AIlydJEtNHdfY8loFNP9U/vYggCEJdyLLM59sTsDsvpiH63eguXilZhCsIpMxmM7fffjurV68mOzubsLAwz7T3uLg4dDodc+bM4fz5836rrND4KRUS0eF6+nSOokubMJwuNwUlNpGDqo66to2gV6eLyWFPJuVzPiu41rkUBKFpOHQ6h9PnCz2PO7cJY2Tf2FrOaJ58DqRWr15NTk4Oy5cv58iRI+zfv9+zr3Xr1qxevZopU6awevVqv1RUaFqUSgUxEQau6RJNq0gDhcU2r4GMQs2mXdvJ6/FG0SolCIKfmS12vtpxcRkYpVJi9sQeKESXXhU+B1Jbt25lxYoVzJgxA72++plD999/f7XZxQWhglKpoH2rUNq0MFJktuESwdRltWsZyoBuLTyPz6YVkZBaWMsZgiAI9fPVznOU2i6OZZ08rCOtovybxqep8DmQysrKYvjw4bUeEx0dXe3Cw4JQmUIh0a6lkbYtjJjMZbhc7suf1MxNHdmJyuuDbtyTLDLKC4LgF6dSCjh0+uJ3d2x0COOHtA9gjYKbz4GU0WgkNTW11mMSEhIICwur9RhBgPKB1G1aGGnXKpQicxlOEUzVqmWUgaG9W3sep2aXcPJcfgBrJAhCU1Bmd/H5tosTxSTKl4FRKf02yb/J8fmVGT58OIsWLfJab6+ykpISXnjhBbHYr1BnkiTRJiaETrFhFJfacTpFMFWbG4Z3RKm82Cy18adkMc5MEIQrsmlvMoUlZZ7Howe0pVOsaBCpjc+ZEefPn89tt93GlClTGDZsGD179gTK17jLyMhg+/btyLLMyy+/7LfKCs1Dq+gQJEki5YKJUL0WtUr8EqpOZJiOUf3asPNIBgDZBRYOnclhaK9WAa6ZIAiN0fkLxez67fMEIDJUW2Vyi1CVz99QXbt25V//+hdRUVHs3buXNWvWIMsyq1ev5ptvvsFoNPLOO+/QqVMnP1ZXaC5aRhno0iaCEosdu8g1VaOJQzugUV98G3+3N0V0iwqCUG8ul5t18QlUbtOeOb4bOo1YieJyrugVGjZsGNu2bWPbtm0cP34cs9lMaGgo/fv3Z9y4cWg0Gn/VU2iGYiL0KCRIzDARAmjVYrHTS4UaNFw/qB1b9pePVywotrHvZBaj+7cNcM0EQWhMth9K81opYVCPlvTqHB3AGjUeVxxqajQapk6dytSpU/1RH0HwEhWup7tCQWJaEcig1Yhg6lLXD2rP7mOZWH6bqrxlfypDe7cWgacgCHWSU2Bhy/6LybMNOhU3j+0awBo1Ln4ZfFJQUMDevXvZunUre/fupbBQ5LQR/CciVEu3DhFY7U5sZWKNvkvptSomDOngeVxisbP7aEYtZwiCIJRzyzKfbUvA6brYqXfzdV0JNYgepbq6ohaptLQ0li1bxq5du7xy2CgUCsaOHcuiRYto317knhCuXLhRS4+OkZxNLUSWQa8T/faVje7fhh1H0ikutQOw/WAa1/ZtI14nQRBqdeCXbM5lmDyPu3eIZIiYsFIvPrdIJScnM2vWLHbs2IHBYKBXr14MGjSIXr16odPp2L59O7fffrtYa0/wm1CDhu4do3C63ZTaHIGuTlDRqJVMHt7R89hS5uSHw9WnJhGEq0mWZX5JyufgWTMX8i2Bro5QSanNxbe7Ly4xpVYpmDmhG5JYBqZefP65+sorr6DRaFi9ejXXXXcdCsXFmMztdvPjjz/y3HPP8corr/DGG2/4pbKCYNSr6dExkoTUIkqtDkL06kBXKWgM79OaHw6lkW+yAbDjSDpj+rclNEQ00QuBUWCy8fn2BM/Ct8eSjzB9VGfGDmon1mwLAntOmbHZL86KnjqyEzHh1S/5JtTM5xap/fv38+qrr3L99dd7BVFQ3rU3fvx4XnnlFfbt23fFlRSEygw6Nd07RCBJ5QtrCuVUSgVTR3byPLY73Gz9ufbVBwShIbjdMj8eTufltT97gigAl1vmm11J/GvDcUzmslpKEBraicQ8UrIv3oN2LY1cN7BdAGvUePkcSLndbgYMGFDrMQMHDsTtFjltBP8rD6YiUaoUFItgymNg95bERod4Hv90IpOCYlsAayQ0Nxm5ZlatO8LXO89hr2F1goTUIl75z0FOnMu7yrUTAKw2Jxt2JHkeKyS4fWIPlArRSugLnwOpHj16XHb8U0pKCv369fPa9tVXX/l6SUHwotOq6NY+Aq1KgalU/LqF8gWgK2cidrlkvt8nxikKDc/udLFxTxKv/fcwqdklXvs6twljeA+j1yoFpTYn7//vFz7bliCS7l5FLpeb9TsSPRNTAMYNbk/blsYA1qpx8zmQeuihh3j66adrXGsvLS2N5cuX83//939e2xctWuTrJQWhCp1GRbcOkei1KtFV8Js+XaLp2DrU8/jnUxfILiit5QxBuDKJ6UW8+p9DxP+c5rXeo06jZOb4bjw0oy/9Oxt44o4BtLvkC3vviSxWfnKY9JySS4sV/Cw1u4TX/nuYg6eyPdtiwnVMHtGxlrOEy/F5sPl3331HQUEBU6ZMoU+fPsTGxmIwGLBYLGRlZfHLL7/Qt29fPvnkE3/WVxCq0KiVdGsfwbn0IopKbESE6gJdpYCSJInpozrz1pfHAZBl2Lw3hXun9wlwzRqe0+XmyJkc9hzPpKjYwuiSdK4b2A6NSiQnbQgWm4P/7U5i38kLVfb17RrNreO6EWHU4nKXtzi1jDTw2OyBbP4phR8OpXmWI8kptPCPT8VA9IZS5nDx3d4UdhxJR75kXfMZ4+PE++MK+RxIbdiwwfP/x48f5/jx41WOOXr0KEePHvXaJqZVCg1BrVLStV0kSRkmCktsRBi1zfpvrVv7SLp3iCAhtQiAY2fzSMsuoX2r0NpPbKTMVgd7T2Sy+1imV5fFxj0p7D6ayeThHRnepzVKpVgA2x9kWeZ4Yh5f/pBIySVjFEMNGm4bH0e/uBbVnqtSKrhxTBd6dork4+9OY/rtflUMRD99vpA7J/cg3Kht8OfRHJw+X8Dn285WGSup16oY2dNAt/YRgalYE3JF2fq2bdtWr+NlWWbSpElXcklBqJFapaBru3CSM00UlZQ1+2Bq2rWdSUg94nm86adk/nRLv1rOaHyyCyzsOJLOwV+zcdSwWLOp1M7n28/yw+F0po7oxIAeLUSLxxUoMpex/oeznDiXX2XfiGtiuWl0lzolgu3WPpIFdw1hXXyC16DzhNRCXvnPQWZP6kHfrjF+rXtzYrY6+HrnOa9uvAoDu7fgpus6k5WecvUr1gT5HEi1adOGtm3rvzBqbGysr5cUhMtSKRV0aRNOyoVi8ousRITqmu2XZsfWYfTtGuP5kjp9vpDE9CLi2kUEtmJXSJZlEtKK2HE4nVMpBdUeo9MokXBjtV/sx8grsrL2u1NsO5jKtGs707tzVLMOtOvLLcvsPZHFt7uTvHIPAbSI0DNrYvd6/22F6NX88Xe92f/LBTb8mOiZ5VcxEH1k31huvq4rGrFuZJ3JsszhMzls2HGOUqt34uIIo5bbxnejT5doXG4XWQGqY1PjcyC1ffv2q3qeINSVUqmgU2w4Ckkit8hKhFHbbIOpqdd24uS5PM9YlI17knl01oBGGUA4nW4Onclhx5F0r1XqK4sK03HdwLYM6dmCc+fOcaHUwI+HM7BWWqMxM6+Ud785Sec2YUy/tjNdG3lgeTVkF1hYF3+G5Mxir+0KhcT4we2ZPLyj14y8+pAkiRHXxNKlTThrvztFeo7Zs2/viSzOpZuYM7Un7Vo2zW5pfyootvH5tgSv3F0AEjCqfxumj+qMTiOWjfI3v7+iaWlp/PDDD+h0OqZMmUJYWJi/LyEIl6VUSHRoHYZCksgusJQHU80wR0psdAiDe7XyNO+nZBXza3IBfbpEB7hmdWe22NlzPJM9xzMpsVS/NFDnNmFcP6gd13SJQaGQcLldqFUSE4a2Z3T/tmw/mMbOoxk4KuU1Ss4s5o0vjtGzYyTTR3UWX9TVcLrcbD+YxpYD53G5vEcpd2gVyuyJ3WnTwj/T5ltGiYHovnK7ZXYdzWDT3mTsDu8u7lZRBm6f2J1ObcIDVLumz+dA6uDBg/z1r38F4D//+Q+xsbEcP36cu+++m7KyMmRZ5q233uLzzz+nRYvqBx0KQkNSKiTatwpFkiArv5QIo65ZJpybMqIjR87k4PptWvqmn5Lp1Tkq6L+QsvJL2XkknYOnsr1Wpq+gkKB/txaMHdSOjq1r/sFm0Kn53egujBnYlvgDqew9keV5LaC8y/P0+UIGdGvB1JGdaBllaJDn09ikZBWzLv5MlfXxNCoF067tzJgBbf3+40QMRK+/zFwz6+ITquTuUiolJg3twIShHVCJSRYNyudA6ptvvkGn07FkyRJatSpfKfqFF17AZrNx2223ERcXx0cffcS7774rckcJAaP4LZhSKCQycsxEhDa/YCo6XM/IvrHsPpYJlHdtHT2Ty6CeLQNcs6pkWebM+UJ2HEmv0j1RQadRMrJvLGMGtCWyHqkuwkO0zBjXjbGD2vH9vvMcOpVN5fDs6NlcjifmMrR3a24Y0bFeZTclNruTTT+lsPtoBpeGrz07RjJzfHeiwhv2tRED0S/P4XSz9cB5th30zt0F5S20syd2p1VUSA1nC/7kcyB15MgRXnjhBfr37w/AuXPnOHr0KGPGjGHZsmUA9O7dm7///e8ikBICSpIk2rYwolBIpGWXEBGibXbT4CcO68D+Xy54urY2702mf7eYoHkd7E4Xh0+Xj3+6tAWkQky4jusGtmNY79ZoNb4PPo4J1/OHG3oyfnB7Nv2UzMmki7PP3DLs/+UCh05nM6pfGyYO7YDR0HwWff4lOZ8vtp+lqMQ7uW2ITsUt18cxqEfLqza+TgxEr1liehGfbUsgt9DqtV2rUXLjqC6M7Bcb9C3OTYnPgVR6ejq9evXyPN6yZQuSJDF79mzPtn79+pGdXXXqZV0dPHiQVatWkZiYiEKhYMSIESxYsIDWrVtf9tz9+/czb948wsOr9gt36dKFtWvXVtl+4cIFz0LLbrebuLg4HnvsMYYMGeLzcxCCgyRJxEaHIEkSqReKCTdoUfk4OLYxCg/Rct2Atmw7WL4SQZ7Jxv5fL3Bt3zYBrVdJqZ3dv41/unSGUYWubcMZO7AdfbpE+7UrKTYmhHk3XUNKVjEb9ySTmF7k2ed0yew4ksG+kxe4flA7rh/UDp226Q7SLbHY+WrHOQ6fyamyb3DPltx8XdeABJRiILo3q83JN7uT2Hey6ny7a7pEM2NcNyJCRbfn1ebzJ4PRaCQ/P9+TzuCbb74hJCSE6667znNMWVkZer3ep/L37dvHvHnzmDdvHmvWrMFisfDkk08ya9YsvvzyyzqNuxo4cGC1AVN1cnNzmT17NnFxcWzevBmDwcAbb7zBPffcw3vvvceIESN8eh5C8KgIphSSRGpWMUaDxueZRo3R+CHt2XM80zN1fcu+8wzp1SogWY0z88zsOJzBoTPZVQYxQ3mX7MDuLRg7sF2DJxHtFBvGn2f0IyGtiI17kkmrNNakzOHi+/3n2X0sg4nDOjCqX9sm9TcjyzI/n8rm653nsNicXvsiQ7XMmtCdnp2iAlS7i8RAdDiemMuXP3ivkQflCVBnjIujX1xMo5yN2xT4HEhdc801rFy5kgcffJDNmzeTkpLCzJkz0Wgu/mrZsWMHbdrU/xev0+nk6aefpkOHDjz++OMoFArCwsJYvnw548aN47XXXuPFF1/0terVWrlyJbm5uXz66aeemYaPPfYYW7du5ZlnnmHTpk0olc2vCbkpahVlQKmApIxijAZ1s1kewaBTM35Iezb9lAKUJ6rccyyTcYPbX5Xru2WZ0ykF7DiS7sm4XqWOWhUj+8YyekBbIq7igGJJkujRIZLu7SM4cS6PTT+lkF1wsYux1Obk651J7DicwQ0jOjK0d+tGP9Yuz2Tl820JVe6FJMF1A9oydWTnK+pC9bfmOhC99gSorblxdBcMOnUAaiZU8DmQevDBB/nDH/7Axo0bkWUZo9HIAw88AEBGRgb//e9/+fjjj5k7d269y96/fz+pqak8+OCDKBQXf/21atWKAQMGsHHjRpYsWUJIiH8G0pnNZjZu3Ei/fv28EoZKksSkSZN4++232b9/P9dee61fricEXkyEAUmSSMowgY5mM87iugHt2Hk0A/NvaQS2/ZzKyGtiG7Tbyu5wcfBUNjuOZJBTWP34pxYReq4b2JahvVujDeC9kCSJfnEtuKZLDAdPZfPdvhQKK40XKjKXsS4+ge0H05g6shP9uze+LOkut8zOI+ls3pvilQ4CoE1MCLMndqdDLbMgA+1yA9Fvn9SDa5rAQPTLJkCd0J04sbxLUPD507N///6sW7eOb775BoVCwW233Ua7du0AMJlM5OfnM2XKFG6++eZ6l33gwAEAevToUWVfz549OXToEMeOHfNbYHPs2DHsdnuN1wNEINUERYfrUSgkEtNMyDJB9eu7oWg1SiYN68iGHxOB8paWHw+nM2VkJ79fy1Raxp5jmfx0PJPSS7qNKsS1i+D6Qe2CLh2DQiExrE9rBvVoyU8nMtl6IBVzpTFcuUVWPtp8irYH05h+bSd6dmocWdLTc0pYF5/gNdYIQKWUuGF4J8YNbhc0ExBqU9tA9Pf+9wvX9o3l9414IHrtCVDbMWl4x2bTkt4YXNHP0D59+tCnT9UV5Xv37l1r19vPP//M0KFDa9yfnJwMQMuWVadnV2xLSUm5bGCTn5/PwoULOXLkCCaTidjYWCZPnszcuXPRai82/6akpFz2eufPn6/1WrVxuf5/e3ceH1V9L/7/dWbfJwkh+8oWtKzKjqgslRYEFStc/Sm2VQu91W/Fiq0iaqloadU2entFsShw1eKKitYNXBBFBAFBIUhZAhEIkJBtMus5vz9CAiEhkMkyM8n7+Xj4iDnnzDnvyWFm3vNZ3p8QoVDo7Ac285yn/hThcdmMdE93smt/GUFVF3bVX1VV6/2MZkPPT+LjjfvrWlo+/voAw/um4LC1TvdA0ZFKPt30A5t3HqlXr6mWXqcwoFdXLh6YRvqJYo6aptLIUKmwtdb9UHQwsn8qg85LYs3mIj7+uqhe60DRkUqefqOmSvqEETnkpkVnS04gGOL9L/fzydcHOP2WdEt3cc2YnnSNtwIaIbX131Pa6vUx+PwkslMdPP9uAUVHTla7/3zrQXYdOM7/Nz6P9KTWKRjaHoIhlY83HuCD9fsbvHYykhxMHdujrgBqS+9TLL1nNUVVQ6ghtc0+Y89FRKahTJ8+ne3bt59xf2Vlzbcli6VhrZLabRUVFQ32ne7o0aOMHj2aP/3pT/j9ft577z3mzZvHxx9/zJIlSxqcq7GB8bXbzuV6Z7Jz586wH3s2W7dubbNzdyY+X4jdx/wY9QomY/jfyHft2tWKUbWdftkmPtlWk0j5AiFe/fAbhvcOf1C3pmkUHvGzda+HH0oan31nNiqcn2nlR1lWbBaoKv2BnY2Ximo1rXk/suJg6kXxbNnjYds+D6eukbznh3L+8co3ZHY1MbinnURX9IxZKTrmZ823FZR76n8wmAwKw/Ic5GWYKT2yn9IjbR9LW70+fjLQxobvNbbsOdl1XFxaTf7yzQzp5aBvjjXqWwwPHw/w6bZySivr3yeDHgb1dNAn20plG7xmYuU960y8fhW7RU/p4ci95iKSSGlaK371PIOBAwfy4Ycf1g0cNxqNTJkyhYMHD/L444+zbNkybrnlljaPA6BXr17YbK1bLTkUCrF161b69u0rg+BbSaXHz/cHjqPX6bCdw+r1p1JVlV27dtGjR4964/qiVfceGtuLvqb4RB2a7fu9XDG6T7OnTvsCITZsL+azzT9w5Hh1o8ckxVsZNSCNC3sntVtXS1vej359oKzSx4df7efLbw/XK4a4/4if/Uf8DOiVyPhh2XSNC2/WcmvweAOs/Gwv67873mBf3+5duOrS7rjs7VPSoD1eH+f1hmH7j/Pi+zvrZrapGqwrqKTEY2Daj3tG5UB0rz/Iu1/sY+2W4gYFUPOy4rh6TA8SXK1fADXW3rPOpKo6QJzDTHZq67YGezyec24EiUgidbZvBg5HTdOl1+ttsK92m9PZ9Ldnk8lUbwZhrXHjxvH444+zevXqukSq9lzV1Q0/CGq3ne16TdHr9W2W7LTluTsbt9PKeTkGdu4vxetTsVub/w1Hp9Oh10X//dDrYMKIXJ57+zugpm7Sh18dYNq4Xuf0+OOVPj7bXMQXWw/i8TU+/qlXVhyXDMygd07kxj+11f1IcNmYOjaP0Rdm8e4Xe9lUUP9DcPPOo3zz/VGG/iiVy4Zlt+sMRE3T2Pz9EV77eFfdpIJabruJq0f3pG+PyAzGbuvXR+/sLtzV2ED0/cd57IVNUTcQvakCqFde0oMLe7d9AdRYec86E51ORafXtfrnYHPOF5UV5nJzcwEoLm5YHK52W05OTljnrq0/VVJSUret9lxNXS87Ozus64nYYrcaycuK5/v9x6n0+Dt0Vet+PRLJSHLUDTxe/+1BRl+YQVL8mVtP9x+u4JNNB9i080iDZSmgZn2vC/OSuWRgeqstZhvNusZZueGn5zF2UCbvfLGXb0+rkv7FtoNs2H6YiwakMXZQVljJeXMcr/Dxykff14uj1oi+qVx+UTesHbiwKMTGQPRoLYAqwhOVr6ghQ4awcOFCCgoKmDBhQr19BQUFmM3muqVpzuS5555j8uTJJCTULyZ39GjNt5T4+Pi6bf3798dkMlFQUNDgPLXbhg4dGtZzEbHHZjHS60QyVeHx4+ygb2iKojBxZC5PvV4zzk7V4N0v9jJ9wvn1jlNVjW93H+OTTQf4T1FZo+eyW42M7JfGRf3ScLZTd1E0Sevq4ObJfdjzQxlvr91T7+8UCKl8tPEAX2w9yOgLM7l4YHrYkxrORNU0Pv/mB1au3YPvtKnySfFWpo7rRff0uFa9ZjRrqiJ6zUD0yFREj5UCqKJ5ojKRGjp0KFlZWbz//vv89re/reu/PXz4MJs2beKKK66oV0OquLiYhIQEDIaTT2fp0qWkpqYyfvz4eudevXo1AJdcckndNofDwcSJE3nzzTc5dOhQ3RI0mqbxwQcfkJ2dzZAhQ9rs+YroYzUb6JUVx3/2H6esyofbHn1jK1pDXlY83dPddR/8m3YeYeygStKTHPj8Ib787hBrNh3gaFnDbnaoKW566QUZXNA7SaZjA7lpbn7zs/4U7Cvl7c/31PsA9/pD/PuLvazZXMS4wVmM6JfWKlXSDx2rYvmHO9l7sOFU+XGDMxk3OLtDVWNvjmiqiB5rBVDFuYvKV5fBYGDevHkUFhaSn59PKBSioqKCOXPmkJiYyKxZs+qOfeeddxg1ahS33nprg/P85S9/YdOmTWiaht/vZ+XKlTz11FP07t2b6dOn1zv2jjvuIDExkTlz5lBeXk4oFCI/P589e/bwxz/+sV6SJjoHi8lAj6x4LCYDx6t8Z39ADKptlTrVG2v+w5tr/sMDz3zB6x/vajSJ6p0dz4wr+/L7GwYxrE+qJFGnUBSF3jkJzLr2Am6ccP6JsgInVVYHWPHpf3hoyXq+/PZgoyUizkUwqPLuur088vzGBklUdoqT3113AT8dnttpk6hatRXRf311P9yntJbWVkR/6vWtlFW23es7pGp8tHE/f1m2oUESlZZo5/ZpA7nykh6SRMWwqM0Ohg8fzpIlS8jPz+eiiy6qaaodNozly5fXq/cUHx+Pw+GoV5EcID8/nzfeeIP77ruPkpISqqurSUlJ4cYbb+SWW25pUBU9KSmJ5cuX89e//pWf/OQnaJpGjx49WLJkiSxa3ImZjXp6Zsbxn6LjHK/0Eudo/dkzkZab5ub83AS+21MzbvD7/cf5fv/xBscZ9AqDzkvm4oEZpHZpnVUFOjKdUlMvq2+PRL767hDvrdvH8VM+sI9X+PjXBztZveEAPx2RQ/9mrJW254cyln+4s94yNgAmo46JI7txUb+0Vl3guSOIREX0JgugDsth9AWxUQBVNE3R2qMWwWl69+7Njh072vuy7c7j8bB9+3bOO++8Nil/sHnzZgYMGCCz9tpBIBhid1E5FR4/brupwQdeSA2xc+dOevXqFZMzYH44Uslfn9/Y6D6HzchF/dIY0S8tZsaLReP9CARV1n7zAx9+VUhVdcNaWxlJDiaOzCUvK/6MCZXXF+Ttz/ewdssPDabKn5eTwDVjehLfBlPlWyqa7oemaazbdogVn5wciF6rtQai+wMh3lu3j4+/3t+gAGqPDDdTx/aiaxOTOtpDNN2TlqisDhDnNJGT6m7V8zbn8zsiLVJNVTUXIhoZDXq6Z7jZU1TG8UofcQ5z1Bf4a460rg4u7J3Exh0nZxGlJtq5ZGAGF+QldfruodZgNOi49IIMhvVJ4ZOvD/DR1wfqDQw/UFzJU69vpXu6m8tH5pKTVv+DYdvuo7y6ele9Vi0Ah9XIVZf0YGBe1w71b7KtKIrC8L6pdE9vm4HoOwtLeXnVzgZd4lazgcmjujH0RylynzqYsBOpu+++u8llYJqybNmycC8rRMQY9Dpy093sPVhOSbmX+A6WTP1sdE/MRj3+oMqg85LplRnXoZ5ftLCYDIwflsPI/ums+qqQz7YUETxlfZz/FJWR/9JmftStCxNG5OC0mnjt411s/r5h6fHB5yVzxcXd27ysQkfU2gPRq7wB3vx0N+u/O9RgX/+eXZlyaY92K4Aq2lfYidSKFSvCTqSEiFUGvY7cVBc6ReHIcQ/xTktULbbbEhazgWvGnltBTtFyDquRKy7uziUDM3jvy32s//ZgvW6gb3cf47vdxzAZ9fgC9UsaJLgsTB3bk7xsmSrfErUD0XvnxPP8uzsoO1ERvXYg+o59pVx3WV6TFdE1TWPzziO89kkjBVAdJwqgRlERUNH6wk6kNE1j7NixZz3OaDSSkpLC6NGjufbaaxutNi5ELNHrdWSnutDpoLikul2rVouOJ85pZtq4Xoy+MKOmSvrOky1PGtRLohQFLr0gg/HDcjBHsKBkRxPuQPTSCi+vrP6+bqLGqUb2S+PykblYOngB1EjSNA1fIITFHNkW2bDvcFpaGpWVlZSV1dSfcbvd2O12qqqq6rYlJiYSDAbZsGED69atY+XKlSxdurTRxYGFiCV6nUJWsgtFUTh0rAqXTbpWRMskxduYPuF8xg6q5O3P97B9b/0P5/SuDqaN60VmcvsWkewsaiuinz4QvbGK6KqqsfabH3h77Z4GrYVJ8TamjetFt/TWHfwsGiqv8pPgMpMUwTUtoQWJ1OLFi7n11lu58847mThxYr1R7R6Ph5UrV/LWW2+Rn59PXFwca9as4f777+epp57i9ttvb43YhYgonU4hM8mJTlEoKi5vdMkUIZorPcnBr67sy3+KjvPh+kKOlXsZ+qMULh0oU+Xb2rkMRJ84MoePNh5oULtLr1MYNziLcYOzMMjkjDZX5Q1gNOrJSXVFvNRH2InUo48+yl133cXFF1/cYJ/NZmPq1KkkJyezYMECFixYwCWXXMLcuXN55JFHJJESHYZOp5CR5EDTVHbvVlE1DelwEa2he3oc3a+Ki3QYnVJTA9GfXfldg+NzUl1MHddL6qu1E38whD8Y4vycLhijoBhw2Gnz+vXrGT58eJPHDB8+nDVr1tT9PnjwYIqKisK9pBBRSVEU0hLtxNn1VFY1rA8khIg9Z6qIfiqzUc/Vo3tw29QBkkS1E1XVqKjyk5vqjprZqmEnUl6vl0OHGk7zPNWhQ4eoqqqq+12v12M2y8Bc0fEoikKCy4hOR4MxE0KI2FU7EP30mXfn5ybw++mDuKh/eoeZuRsLjld6SU2008UdPYVnw+7ay8vL4/777+fxxx/H4XA02F9RUcEDDzxA796967atX7++3vIuQnQkRr1CZrKTvQcrMRl0UoNJiA6idiD65u+PsLOwlPNzutCnexd5jbezsiofboeF9K6OqPrbh51IzZw5k9/85jdceumljBw5ktzcXGw2Gx6Ph927d/P555/j8XhYuHAhAC+//DKPPvoo48ePb7XghYg2CS4LZVUByqv8uO3S+ipER6EoCgN7JTGwlzQGRILXF0SnU8hOdUbdpIuwE6kxY8awYMEC5s2bx3vvvVcvO9Q0DZfLxV//+te6wej79+9n9OjRXHPNNS2PWogopSg1M/m+23OMQFCVpVWEEKKFgkEVjz9I7+wELKboq8vVoogmT57MmDFjWLVqFdu3b6eyshKn08n555/PmDFjsNtPDr674447WhysELHAYjaQkeRk78FyEqJwAVkhhIgVqqZR7vGRneKK2iV2wk6kvvrqKwYPHozD4eCKK67giiuuaM24hIhpiXFWSsqrqfT4cdii88UvhBDRrqzSR1KCjaQE29kPjpCw+x2mT5/emnEI0aHodApZKS6CIY1gSI10OEIIEXMqPX7sViMZSc6oGlx+urATKU3TuPTSS/n73/9OYWFha8YkRIdgsxhJS3JQXuWLdChCCBFTfIEQqqaRm+bGEGWDy0/Xougeeugh9u/fz+TJk7n++ut5/fXXqa6ubq3YhIh5yfFW7FYTHq8U6hRCiHMRCqlUVfvplh6HNQYWfQ47kbrqqqsYMWIEjz76KGvWrGHChAm88MILjBw5knvuuYcNGza0ZpxCxCS9XkdWihOvP0RI1uITQogmaZpGWZWfjCQncc7YKCETdiL18MMP1/2/0+nkuuuu4+WXX2b58uW43W5uv/12xo8fz1NPPdUqgQoRq5w2E6mJduniE0KIsyiv8hPvMpMSQ0vutHrHYzAYxO/3EwgE2LdvH3//+99b+xJCxJyULnbMRj1eXzDSoQghRFSq8gYwGvXkpLrQ6aJ3cPnpwu58nD59OkuXLgWgvLycN998k9dee43t27ejaRoOh4OpU6cyZcqUVgtWiFhlNOjITnVRUFiKyaSXtbmEEOIU/mAIfzDE+TldMBr0kQ6nWcJOpNavX8/atWt55ZVXWL16NX6/H4ChQ4cyZcoULrvsMiwWKUYoRC23w0xSvJVjx724HbHR9y+EEG1NVTUqqvx0z4jDbjVGOpxma9Fw+JtvvhlN00hPT+eqq67iqquuIj09vbViE6LDSUt0UFruwxcIYTbG1rcuIYRoC8crvaR1dZAYZ410KGFpUSI1adIkpkyZwrBhw1orHiE6NJNRT1aKk/8cKMNk0EV1kTkhhGhrZVU+3A4LaYmxM7j8dC1KpP7yl7+0VhxCdBoJLgslLi8VngAuWT5GCNFJeX1BdDqF7FQn+igvutmUsCPfsWPHOR23YsWKcC8hRIekKAqZSU40VSMQlOVjhBCdTzCo4vEH6Z4eh8UU/UU3m9LmKeDdd9/d1pcQIuZYzAYykp1UePyRDkUIIdqVqmmUe3xkJztx2WO/Vb5FaWBVVRUvv/wyX3/9NWVlZWiaVG4W4lx1jbNSWl5NpcePQ7r4hBCdRFmlj6QEG0kJtkiH0irCTqRKSkq49tpr2bdvH1DTXdFYIiWDaYVonE6nkJXi4rvdJYRCakyPERBCiHNR6fFjtxrJSHJ2mPwg7Hfu/Px8rFYrb7zxBt999x2aprFjxw527NjBli1b+Oc//0lmZiaLFy9uzXiF6FBsFiNpSQ7KpItPCNHB+QIhVE0jN82NoQN9cQz7maxZs4b777+fvLw8dLr607jNZjMjR45k/vz5staeEGeRHG/Fbjbg8QYiHYoQQrSJUEilqtpPt/Q4rObYHlx+urCfTXFxMeeff37d7zqdjkAggNF4sippv3792LZtW9jBbdiwgfz8fHbt2oVOp2PYsGHMnj2blJSUZp9r27ZtTJ06lZSUFFavXt1g/xNPPMHixYux2Rr22V599dXccccdYT0HIc5Gr9eRlepi+54SzCYD+hhaY0oIIc5G0zTKqvxkJDuJc3a8VR3CTqS6dOlCeXk5Xbt2BSA5OZldu3Zx3nnn1R2zc+fOsAegr1u3jptuuombbrqJZ599Fo/Hwx133MHUqVN59dVX6657LgKBAPfccw+hUKjJ4375y19y2223hRWvEC3htJlITbRz6FgV8U5ZWkkI0XGUVfmId5lJSYjdoptNCbtrr0ePHvXGP+Xl5fHHP/6R77//Hr/fzzfffMPcuXPp1q1bs88dDAaZO3cuWVlZ3H777RgMBlwuF/Pnz+fo0aM89thjzTrf008/jc1mIzU1tdmxCNFeUrrYMRv1eH3BSIcihBCtosobwGQ0kJPqQtdBW9vDTqTGjRvHs88+y4wZMwC46aab2LJlC5MnT6Z///5MmzaNnTt38otf/KLZ5/7yyy8pLCzksssuQ6c7GWJycjIDBgzg7bffpqqq6pzOtWvXLhYvXsz8+fPrnUuIaGM06MhKceHxB1GllIgQIsb5gyH8wRDd090YDR13bdGwM4urrrqKV155hbvuuguAQYMGsWDBArKystDr9WRmZnLvvfcyYcKEZp97/fr1QE0r1+l69+6Nz+djy5YtZz2Pqqrcc8893HzzzXTv3r3ZcQjR3uKcZrrGWamokll8QojYpaoaFVV+clPd2K3Gsz8ghoU9RspisdCnT5962yZPnszkyZNbHNSePXsASEpKarCvdtvevXsZMWJEk+dZsmQJPp+PW2655Zyuu23bNm6++WZ2796N1+ule/fuTJs2jcsvv7yZz6C+UCh01vFZ4Zzz1J8islrzfqQkWCkp81Dt82MydtxvcW1JVdV6P0Vkyf2IPm19T0orfKQk2Ih3mmLyc6o5MUflHMTKykqgJlk7Xe22ioqKJs9RWFjIE088wdKlSzEYzu1pHjx4kAcffJB+/fpx9OhR/vnPf/K73/2OzZs3c++99zbzWZy0c+fOsB97Nlu3bm2zc4vma637Ue4JcrAkgMOq6zBF6yJh165dkQ5BnELuR/Rpi3vi8YawmHToqk0cO9jx37+iMpFqKU3TmDNnDtddd12DVrMz+cUvfsEtt9xSl6glJiby+9//nu+++45ly5YxadIk+vfvH1Y8vXr1arSsQkuEQiG2bt1K37590eul1SLSWvt+aJrG7qIyyqsCOO0du1m8Laiqyq5du+jRo4eMjYwCcj+iT1vdk2pfEFXV6J2dgNkUu59NHo/nnBtBojKRcjgcAHi93gb7arc5nc4zPn758uUUFxc3q5RB7TVPN27cONatW8fq1avDTqT0en2bJTtteW7RfK15P7JS3Hy7+xiqqmA0yIdPOHQ6HXqdvD6ihdyP6NOa9yQYVPEHNc7LScBmje31Q5vzPh6ViVRubi5QU/TzdLXbcnJyzvj4VatWUVpaypgxY+ptLykpAWDkyJEA/Pa3v2Xq1KlNxlJbr6r2sUK0F4vZQEayk30Hy0lwSW0pIUT0UjWNco+P7FQ3zk62CHtUJlJDhgxh4cKFFBQUNJj1V1BQgNlsbrJ1aNGiRY1ur02sTq9sXl5ezksvvcTNN9/c4DFHjx4FICEhoVnPQYjW0DXOSml5NZXVARwdfOaLECJ2lVX6SEqwkRRvjXQo7S4q+wuGDh1KVlYW77//fr0ZBYcPH2bTpk1MnDgRu/1khdTi4mKCwfCLGJaXl/PII4802upUm3RdfPHFYZ9fiHDpdApZKS4CQZVQSGY8CSGiT6XHj91qJCPJ2Sknx0RlImUwGJg3bx6FhYXk5+cTCoWoqKhgzpw5JCYmMmvWrLpj33nnHUaNGsWtt97aomtqmsbs2bMpKioCamYO/u1vf2Pt2rX87Gc/48ILL2zR+YUIl81iJD3JTplHaksJIaKLzx9C1TRy09wY9FGZUrS5qOzaAxg+fDhLliwhPz+fiy66CEVRGDZsGMuXL69XXyo+Ph6Hw3HG5V/8fj+jR48GGo6R+uijjzCZTKSmpvLkk0+ycuVKfvGLX1BZWYnX66Vnz548+OCD/OxnP2vjZytE05LjbZSWefF4A9gs0sUnhIi8UEilyuunV1YCVnPUphNtLqqf+aBBg1i2bFmTxwwfPpyNGzeecb/JZGLt2rVNnkOv1zNmzJgGg9OFiBZ6vY6sVBc79pZgMRk67JpVQojYoGkaZVV+MpKdxDnNkQ4nojpnO5wQMchpM5HSxU65dPEJISKsrMpHvMtMSoL97Ad3cJJICRFDUrrYMRp0eP3hT64QQoiWqPIGMBkN5KS6pHUcSaSEiClGg47sFBdV3gCqpkU6HCFEJ+MPhvAHQ3RPd2M0SHFVkERKiJjjdpjoGmejokq6+IQQ7UdVNSo8fnJT3dilrl0dSaSEiDGKopCe5ECnU/AHYm9VdSFEbDpe6SUt0UFiXOcrutkUSaSEiEFmo56sFCeV1X406eITQrSxsiofboeFtEQZXH46SaSEiFEJLgvxLgsV1YFIhyKE6MCqfUH0Oh05aS70nbToZlPkLyJEjFIUhcwkJ5qqEQzK8jFCiNYXDKpU+4N0z3BjNsrg8sZIIiVEDLOYDWQkO6W2lBCi1amaRrnHR3aKC6fNFOlwopYkUkLEuMQ4Ky67kUrp4hNCtKKySh9JCTaS4mVweVMkkRIixul1CpnJLgJBlVBIuviEEC1X6fFjtxrJSHKiKFJ0symSSAnRAditRtK72imTLj4hRAv5/CFUTSM3zY1BBpeflfyFhOggkhNs2M0Gqr2yfIwQIjyhkEqVz0+39DisZkOkw4kJkkgJ0UHo9TqyUl14A0FUVWpLCSGaR9M0yqr8ZCQ5iXOaIx1OzJBESogOxGkzkZxgk1l8QohmK6vyEe8yk5IgRTebQxIpITqY1EQHRoMOr1+6+IQQ56bKG8BkNJCT6kKnk8HlzSGJlBAdjNGgIzvFRZU3gCrLxwghzsIfDOEPhuie7sZokKKbzSWJlBAdkNthomucdPEJIZqmqhoVHj+5qW7sVmOkw4lJkkgJ0QEpikJ6kgO9ouAPhCIdjhAiSpVV+UlLdJAYJ0U3wyWJlBAdlNmoJzPZSWW1H026+IQQp/F4Q7jtJtK6OiIdSkyTREqIDqyL20K800JFJ1s+pmahVZWgVHoXogF/MER5lR9Fp5Cd6kIvg8tbRKptCdGBKYpCRrKT73YfIxhUMRg67nenkKrh8QYIhlR0CtjMOqqqAwQCGg5ZcFV0YiFVw+sP1nXzm4x6EuMs+LuYMBtlcHlLSSIlRAdnNRvISHay72A5CS5LpMNpVaqqUe0L4guG0OsUElwWElwWrCYdeIrolZNA0ZEqSsq9OG0mjB04kRSilqZp+AIhvIEQqqqhVxRcDhPpXe3YLEasZgOqqlJyUF4PrUESKSE6gcQ4K6Xl1VRWB3DE+MwcTdPw+kP4AkE0IM5uJivOieOURCkUqvnmbbMY6ZWVwJHj1Rw4XIGiU3BajbIIq+hwAkEVrz9Y151tsxhIS3TgtNUkTrJmXtuRREqITkCvU8hMdvHdnhJCIRV9DL6p+vwhqn0BNMBhNZKa6MJpN5+1a0KnU0hOsOG2m9h/uILSCi8OmwmT1MsRMUw90V3nO9FdZ9TriHeZcTvM2CxG6bJrR5JICdFJ2K1G0rvaKTpSSbwzNrr4/MEQHl8QTdWwmg1kJjtxOcxhLaZqMRvokRnHsTIv+w9XUO0L4rSZ0EnrlIgBmqbhP9HqpKoaOp2Cw2okJdGOw2rEYjJIRfIIkURKiE4kOcFGabmXam8QqyU6X/7BkIrHGySkqpiMetK62E98yza0uEtOURQS46w47SYOFFdw9Hg1DosJs0m+vYvoEwypeP0hAsEQoGA160lJsOGwmbBZjDLmL0pE5zupEKJN6PU6MlNc7Nh3DLNJHzXfYEOqRrU3gD+kYtTr6BJXU7bBbjW2ydRss1FPtzQ3CU4Lew+VU10ZwGU3S+uUiChV0/D5Q3gDQRSt5vXqdpqJc5ixmQ2YTXoZ3xeFJJESopNx2U0kJ9g5UlpNnMMcsThUTcPrqxnjoSgnZ9w5bMZ2GRirKArxrppk7YejlRSXVGMzG7CE0W0oRLj8gRBef4igqqJDwWE3khTvxG4zYTUbpMZTDJB3DCE6obREB8crfHj9QSym9nsb0E584672B4GapC4j2YnTZozYYqkmo56cVDfxTgv7DpZzvNKHy2aKmtY60bGEarvrQiE0raZ1tGu8FZfdhM1ikEWDY1BUJ1IbNmwgPz+fXbt2odPpGDZsGLNnzyYlJaXZ59q2bRtTp04lJSWF1atXN3pMeXk5jz76KKtXryYYDJKRkcGMGTMYN25cS5+KEFHFaNCRneKioLAEk1Hf5l1avsCJGXca2KxGclJdOO2mdk3izsbtMHNebhcOHq3kUEkVVqMxaseRidihahp+f01NJ02rGSQeZzcT53JgsxixSHddzIvad4l169Zx0003cdNNN/Hss8/i8Xi44447mDp1Kq+++ipdu3Y953MFAgHuueeeutoyjfF4PFx//fUoisIrr7xCcnIyy5cv5ze/+Q0LFizgyiuvbIVnJUT0cDtMdI2zUVLhJc7e+l18gaCKxxdAVTXMJgPpSU7c9pruimj94DAadGSluIhzWig8VE5phReXzRST5SKihapqVHj8VHpCHK/wodfr0dBQapd/VEADdCjodAo6peY/RUe932OphdAfDOHz1XTXoYDdYiQ93oHTWlPTSf49dSxRmUgFg0Hmzp1LVlYWt99+OzqdDpfLxfz58xk9ejSPPfYYDz/88Dmf7+mnn8Zms5GamnrGY/75z39SUFDAv/71L5KTkwGYNm0aH374IQ899BBjxozB5XK1+LkJES0URSE9yUFZpQ9/IISpFerOhE7MuAuoKia9juQEG3FOCzZzbE3NdtlN9M5J4PCxKn44WoXJoMce44VM21swqFLp9YOm4HaayUwy069HIig6VFVD1TRCoZqfqqoRCKkEgzXrIwaCKiFVq/kZCp04vua8isLJRbgVBQVAoybxUpQGyZdOAeXEtrbS2BIsXeIsuOw1s01b47UloldUJlJffvklhYWFzJw5E53uZOaenJzMgAEDePvtt7n33nux2+1nPdeuXbtYvHgxL730Erfcckujx2iaxssvv0xSUhIDBw6st++yyy7j008/5b333uOaa65p2RMTIsqYjXoyk53sLjpOvMESVkuRqmp4fDUfIgaDji5uM3FOCw6bKaYHyhr0uppWtBOtU8fKq3HbzVIh+ix8/hBVvgAGvY60RAcJLgtGg0J5sQ6TUY9e3/ykIqRqpyRfKqpW8+9OVTVCak3SFQyqhFQVf1AlFFIJBjX8wRAhVUVTa7rYoDYRAxRqErATG2uSr5OtYLXJV10ydsprQ9M0/AGVan8QVTuxBIv95BIsUtOpc4nKRGr9+vUA5OXlNdjXu3dvNm7cyJYtWxgxYkST51FVlXvuuYebb76Z7t27n/G4ffv2cfjwYUaNGtXo9aAmuZNESnREXdwWSsstVHoCOO3ntrivqmn4fCGqA0F0KMQ5TXRJdeK0mTpcouGwGsnLTqC41EPR4UoMekUWQT6NptUk075ACIvJQLc0F26HpcGSPeHS65STSXkzW3c07ZQkrDYhU0/8/4l9waBKsPZnUCUQqp+UqVrNeZQTyZcG2MwG0hLtJ2o6yRIsnVlUJlJ79uwBICkpqcG+2m179+49ayK1ZMkSfD7fGVuiau3du/es19u3b99Z4xYiFimKQkayk293HyMYVDGcochf3UKo/prZRk67kdSublx2U4fvutDrFFK72HHbTRQeKpdFkE8IqRqV1X5UVcNpN9VMIoiyGY+KoqDXK+iBcDpn1VMSrtoETK9XomqihIisqPyXUFlZCYDF0nAZi9ptFRUVTZ6jsLCQJ554gqVLl2IwNP00a89ltVob7KvdVl5efvbAzyAUCrX4G1lj5zz1p4isWL8fJoNCWqKNfYfKSXDVf935AyGqvSFUNGxmA+mJNlyO+jPuou15t9X9MBt1dE93c/R4NQeKK1EUBYctegfPt5VAUKXKG0BBITHOQqLbWjeGTNNUTv+zx/rrQwH0upqE2ljTGRizz6VWrN+Tttacv0tUJlItpWkac+bM4brrrqNPnz6RDoedO3e22bm3bt3aZucWzRfL90NVNQ6V+DlQqGE0KPj8KiENjHqFOJsem1WP36vwQ7nCD5EO9hy15f0IBlWOlAWoqFaxmXUY9B0/mfIHVXwBDYNOId6hx2k1UOpVKD10bo+P5ddHRyX3pOWiMpFyOBwAeL3eBvtqtzmdzjM+fvny5RQXF3Pbbbed0/Vqz1VdXd1gX+22lszY69WrFzabLezHNyYUCrF161b69u0b1uBN0bo6yv3Iqw6wc18pik6hi9tCvLNmJflo6qo5F+11PzRNo6TcS+HhCjQVHHZjh1tmRtU0PNVBAkEVm8VAShcbcQ5zs6bwd5TXR0ci96RpHo/nnBtBojKRys3NBaC4uLjBvtptOTk5Z3z8qlWrKC0tZcyYMfW2l5SUADBy5EgAfvvb3zJ16tS6czV1vezs7OY9iVPo9eHNVIn0uUXzxfr9cDn0nN89EbNR3yFq3bTH/UhKcOB2Wk8ugmw1Ye4AY8ZCIZUKbwBN1YhzmEnuYsdpM7aoGzPWXx8dkdyTxjXnbxKVidSQIUNYuHAhBQUFTJgwod6+goICzGYz/fv3P+PjFy1a1Oj22sTq9Mrm2dnZJCcnU1BQ0OAxtduGDh3arOcgRKyyWaReUnPVLoIc7zCz73AF1b7YXQTZHwjh8QVQFIXkeBtd3Bb5NyFEE6LyK+fQoUPJysri/fffR1XVuu2HDx9m06ZNTJw4sV4NqeLiYoLBYNjXUxSFa665huLiYjZt2lRv33vvvYfL5WL8+PFhn18I0fEpikKC28qPcruQGGetWcvQF/77UnvSNI1qb5CSCi+BkEpmsot+PRLJTHZKEiXEWURlImUwGJg3bx6FhYXk5+cTCoWoqKhgzpw5JCYmMmvWrLpj33nnHUaNGsWtt97aomvedNNN5OXl8cc//rGuO2/58uV8+umnzJkzR6qaCyHOSe0iyHnZ8aiaxvFKH2ptWe4oo6oaFVV+jlf4MBgUembG0ad7IskJNlk8V4hzFJVdewDDhw9nyZIl5Ofnc9FFF6EoCsOGDWP58uX16j3Fx8fjcDjOuPyL3+9n9OjRQMMxUh999BEmU01hPZvNxrJly3j00UeZMmUKoVCItLQ0/vGPf8iixUKIZovmRZBPXb4lwW0hKb6mfEFnK+MgRGuIjlf1GQwaNIhly5Y1eczw4cPZuHHjGfebTCbWrl17Ttdzu93MmzePefPmNStOIYRoTLQtgnzq8i2pXex0cVuxmKP6Y0CIqCevICGEaGORXAT5bMu3CCFaRhIpIYRoB6cvglxSXo2rDRdBjoXlW4ToCCSREkKIdtTWiyDXLt8C0DXOQmKcrd1av4TojCSREkKIdtYWiyB7fUE8/iBGvY60rg66uC0dojCoENFOEikhhIgQm8VIr6wEjhyv5sDhChSdgrMZs+dUTcPjDeIPBLFbjHRPd+N2tF13oRCiIUmkhBAignQ6heQEG267if2HKyit8OKwmTA1Ucfp9OVbuqW7cVhjb01EIToCSaSEECIKWMwGemTGcazMy/7DFVT7grhspnqtU7J8ixDRRxIpIYSIEoqikBhnxWk3nVwE2WJCVTWqA0HMRj2ZSU4S3BapPC5ElJBESgghosypiyDvL67EZNDRMyUOl92MXrrvhIgqkkgJIUQUql0E2e20oFOQ5VuEiFKSSAkhRBSTFighopvMkRVCCCGECJMkUkIIIYQQYZJESgghhBAiTJJICSGEEEKESRIpIYQQQogwSSIlhBBCCBEmSaSEEEIIIcIkiZQQQgghRJgkkRJCCCGECJMkUkIIIYQQYZJESgghhBAiTJJICSGEEEKESRIpIYQQQogwGSIdQEemqioA1dXVrX7uUCgEgMfjQa/Xt/r5RfPI/Ygucj+ii9yP6CP3pGm1n9u1n+NNUTRN09o6oM7q2LFj7N27N9JhCCGEECIMOTk5dOnSpcljJJFqQ8FgkLKyMsxmMzqd9KIKIYQQsUBVVXw+H263G4Oh6c47SaSEEEIIIcIkzSRCCCGEEGGSREoIIYQQIkySSAkhhBBChEkSKSGEEEKIMEkiJYQQQggRJkmkhBBCCCHCJImUEEIIIUSYJJESQgghhAiTJFJCCCGEEGGSRYtjzIYNG8jPz2fXrl3odDqGDRvG7NmzSUlJiXRonUplZSUrVqxg5cqV7Nmzh2AwSGJiIj/+8Y+ZOXMmDocj0iF2eqFQiKlTp7Jt2zaWLl3K0KFDIx1Sp1RdXc0zzzzDu+++S1lZGYFAgMzMTH784x8zY8aMSIfXqVRXV7N06VJWrlzJsWPH0Ov19OjRg1/96lcMHz480uHFLGmRiiHr1q3jxhtvZODAgaxZs4Z///vflJWVMXXqVI4cORLp8DqVWbNm8ec//5np06fz+eefs379eu644w7+7//+j2uvvRaPxxPpEDu9xYsXs23btkiH0amVlZUxbdo09u3bx9KlS/nss89488030el0vP7665EOr1PRNI1bbrmF/Px8/vu//5u1a9fy7rvvkpCQwM9//nNWrFgR6RBjliRSMSIYDDJ37lyysrK4/fbbMRgMuFwu5s+fz9GjR3nsscciHWKnoqoq1113HRMmTECv16PX6xk/fjw33HADO3fu5KWXXop0iJ3anj17WLhwIZdddlmkQ+nUHnzwQRRFYcGCBXTp0gWA5ORk7rrrLnr16hXh6DqXjRs38tVXXzFu3Dh++tOfoigKdrudBx54AJ1Ox5NPPhnpEGOWJFIx4ssvv6SwsJDLLrsMne7kbUtOTmbAgAG8/fbbVFVVRTDCzmXSpElMmTKlwfYBAwYAsHXr1naOSNTSNI17772XG2+8UT6sI+jgwYO89dZbXH311ej1+nr7Bg0axOOPPx6hyDqn4uJiADIzM+ttdzqdxMfHc/DgwUiE1SFIIhUj1q9fD0BeXl6Dfb1798bn87Fly5b2DqvTuvLKK+ndu3eD7cFgEAC3293eIYkTXnjhBcrKypg5c2akQ+nUVq1ahaZp9OnTJ9KhCKBnz54A7N27t97248ePU1paSvfu3SMQVccgiVSM2LNnDwBJSUkN9tVuO/0FItpfbTI7fvz4CEfSORUVFfG3v/2N+fPnYzKZIh1Op7Z9+3YA9Ho9DzzwAGPHjmXYsGFcc8010vUdAT179mTmzJl89NFHrFixAr/fT0lJCffddx8Wi4U//OEPkQ4xZkkiFSMqKysBsFgsDfbVbquoqGjXmER9FRUVvPHGG4wePVpmiEXIfffdx5QpU+jfv3+kQ+n0jh07BsDMmTNJTk7mjTfe4IMPPmDkyJHMnTuXBx98MMIRdj6zZs3ivvvuY/78+VxwwQUMHz6cwsJCli1bJu9ZLSCJlBCt5OGHH8ZsNjN//vxIh9Ipvfbaa+zbt49Zs2ZFOhQBeL1eoKYl5Ne//jUOhwOn08ntt9/OgAEDWLZsGQUFBRGOsvMIhULMmjWLBQsW8MADD7Bx40bWr1/PJZdcwvXXX89bb70V6RBjliRSMaK2LlHtm9Oparc5nc52jUmctGjRIlatWsWiRYvqZieJ9nPkyBEWLFjAn/70J6xWa6TDEZxsKR8xYkSDfaNGjQJgzZo17RpTZ/bqq6/yzjvv8Mtf/pKJEydiNptxu93MmjWLvn37cs8997B///5IhxmTpCBnjMjNzQVOzrw4Ve22nJyc9gxJnPDiiy+yaNEinn32WRmwGSFffPEFfr+fO++8s9722npet912G0ajkZ49e/Lcc89FIMLOJz09HYD4+PgG+xITEwEoKSlp15g6s7Vr1wIwePDgBvuGDh3K+vXrWbt2Lf/1X//V3qHFPGmRihFDhgwBaLQpvKCgALPZLONCIuCVV17hscce45lnnuFHP/oRUDOebffu3RGOrHOZPHkymzZtYu3atfX+++UvfwnAE088wdq1ayWJakcDBw4E4OjRow321SZQ0nrbfqqrqwFQFKXBvtpttWNxRfNIIhUjhg4dSlZWFu+//z6qqtZtP3z4MJs2bWLixInY7fYIRtj5vPHGG/z5z39m0aJF9OvXr277t99+y/333x/ByISIvNGjRxMXF8dnn33WYN/nn3+Ooihceuml7R9YJ9W3b18ANm3a1GDf119/DSClKsIkiVSMMBgMzJs3j8LCQvLz8wmFQlRUVDBnzhwSExNlgG07e+edd7j77rsZMGAAa9as4Yknnqj7T5a+EALsdjv3338/mzZt4qmnnsLn8+H3+3n66af56quvmDFjhnSFt6Prr7+e9PR0nnnmGT755BNUVcXv9/PMM8/w2WefcckllzBs2LBIhxmTFE3TtEgHIc7dqYsWK4pSt2hxampqpEPrVK644gp27Nhxxv1Dhgxh2bJl7RiRONV9993HqlWr8Hg8eDwe3G43RqOR2bNnc+WVV0Y6vE5lzZo1LFy4kJ07d6JpGt27d+f6669n0qRJkQ6t0zl27BhPPvkkH3/8MaWlpWiaRkZGBpdffjk///nPpfZamCSREkIIIYQIk3TtCSGEEEKESRIpIYQQQogwSSIlhBBCCBEmSaSEEEIIIcIkiZQQQgghRJgkkRJCCCGECJMkUkIIIYQQYZJESgghhBAiTJJICSGEEEKESRIpIUSn0rdvX/Ly8sjLy+OJJ56IdDh15syZUxfXmDFjIh2OEOIcSSIlhOhUtm7dytKlSyMdRgPz58+noKCA9PT0SIcihGgGSaSEEEIIIcIkiZQQQgghRJgkkRJCdBgrVqxg2rRpDBw4kEGDBjFp0iTmzp3Lxo0bz/iYTz75hClTptC3b18uuugiHnvsMVRVrdv/2muv1Y1dysvLq/fYyy+/vNHxVps3b673mHXr1rF48WLGjh1Lnz59+MlPfsLKlSvP6Tl9+eWX9c4VbWO7hOjsJJESQnQI8+bN4/e//z2XXnopH330EatXr2bGjBmsXLmSGTNmNPqYDRs2sGLFCvLz8/nkk0+YNGkSTz31FM8++2zdMVOmTKGgoIAhQ4Y0ePzKlSsbHW81YMAACgoKuPXWWwFYtGgRgUCAl19+mX//+9907dqVO++8k2+++easz2vo0KG88MILJCYm8uyzz1JQUMBtt912rn8WIUQbk0RKCBHzVq9ezfPPP8/ll1/Or3/9a+Li4nC5XFx++eV1yUxjCgoKWLBgAZmZmSQkJHDnnXfidDp58803WzU+o9HIjBkzSEhIIDMzk9mzZ6NpGm+99dZZH7t+/Xr+3//7fzz66KOMGDGiVeMSQrScIdIBCCFES7344otATVfb6X76059y8ODBRh83atQoTCZT3e96vZ6srCx2797dqvGNHTu23u/dunUDYN++fU0+7osvvuB3v/sd+fn5DB48uFVjEkK0DmmREkLEvK1btwKQm5vbYF9aWhr33ntvo49LSkpqsM1ms1FdXd2q8Z1+HbvdDtDkddauXcvMmTNxuVz069evVeMRQrQeSaSEEDGvoqICAKvV2qzHWSyWBtsURWmVmJq6ztmuUVJSwuzZsxk0aBB79uzh73//e6vHJIRoHZJICSFintPpBJpu4WkrXq+31c+pqir/+Mc/yM/PJz09neeee46vv/661a8jhGg5SaSEEDGvtuursbFNhw8f5rnnnuPYsWMtukZtq5LH42lw/taWmJjIwIEDcTgcPPTQQ2iaxh/+8IeIJIpCiKZJIiWEiHnXXnstAG+//XaDff/61794+umncbvdLbpGdnY2AHv27Km3/YMPPmjRec9m2LBh3HDDDezbt49HHnmkTa8lhGg+SaSEEDFv9OjRXH/99axcuZInn3ySsrIyysvLefnll1m0aBFz5szBYGjZJOVJkyahKAqPPPIIRUVFlJSUsHDhwnrFO9vKnXfeSbdu3Xj++ef54osv2vx6Qohzp2iapkU6CCGEaA0rVqzgxRdfpKCgAJPJRK9evfjVr37FxRdfXHfMmDFjKCoqqve4VatWsX79eu6+++5626+66ir+/Oc/1/3+5ptv8r//+78cOHCA5ORkbrjhBs477zymT59ed8wrr7xCfHx8g5IH6enprF69mj/84Q+8/vrr9fY9/PDDFBUV8T//8z/1tt96663cdtttDSqq18ackZFxjn8ZIURbkURKCCGEECJM0rUnhBBCCBEmSaSEEEIIIcIkiZQQQgghRJgkkRJCCCGECJMkUkIIIYQQYZJESgghhBAiTJJICSGEEEKESRIpIYQQQogwSSIlhBBCCBEmSaSEEEIIIcIkiZQQQgghRJgkkRJCCCGECNP/D6oHFKvzKP4GAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 5 bins for the x-axis.\n","    num_bins=10,\n",")"]},{"cell_type":"markdown","metadata":{},"source":["## 🤖 GPT Conversation Analysis\n","\n","Finally, we can also use `edu-convokit` to analyze the conversations using GPT models. Let's use GPT4 to summarize one of the conversations."]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import os\n","# Remember to never share your API key!\n","os.environ[\"OPENAI_API_KEY\"]=\"YOUR_API_KEY\""]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df = utils.load_data(os.path.join(DATA_DIR, \"3.csv\"))\n","\n","# We're going to use the GPTConversationAnalyzer to summarize the conversations. \n","# First, let's look at the prompt for summarization.\n","analyzer = GPTConversationAnalyzer()\n","\n","prompt = analyzer.preview_prompt(\n","            df=df,\n","            prompt_name=\"summarize\",\n","            text_column=TEXT_COLUMN,\n","            speaker_column=SPEAKER_COLUMN,\n","            model=\"gpt-4\",\n","            # I'm going to only use the first quarter of the transcript\n","            # Because the transcript is very long\n","            keep_transcript_fraction=0.25,\n","\n","        )"]},{"cell_type":"markdown","metadata":{},"source":["💡 We can see that the prompt is set up to summarize the transcript. If you are comfortable with the prompt, you can now prompt the model on that prompt with run_prompt"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["prompt, response = analyzer.run_prompt(\n","            df=df,\n","            prompt_name=\"summarize\",\n","            text_column=TEXT_COLUMN,\n","            speaker_column=SPEAKER_COLUMN,\n","            model=\"gpt-4\",\n","            keep_transcript_fraction=0.25,\n","        )\n","\n","print(response)"]},{"cell_type":"markdown","metadata":{"id":"T48x_SxUW4M_"},"source":["## 📚 Conclusions and Next Steps\n","\n","Great! From this tutorial, we learned how to use `edu-convokit` to preprocess, annotate and analyze the Amber dataset.\n","We saw how very simple principles built into `edu-convokit` can be used to analyze the dataset and gain insights into the data from various perspectives (qualitative, quantitative, lexical and temporal).\n","\n","Other resources you can check out include:\n","- [Tutorial on edu-convokit for the NCTE dataset](https://colab.research.google.com/drive/1k3fn6uY4QRMtPUZN6hpMd6o-0g7fYotg)\n","- [Tutorial on edu-convokit for the TalkMoves dataset](https://colab.research.google.com/drive/1qt_S3GjxIwXk6ONztbYAHeX8WHy1uxDd)\n","- [`edu-convokit` documentation](https://edu-convokit.readthedocs.io/en/latest/index.html)\n","- [`edu-convokit` GitHub repository](https://github.com/rosewang2008/edu-convokit/tree/main)\n","\n","If you have any questions, please feel free to reach out to us on [`edu-convokit`'s GitHub](https://github.com/rosewang2008/edu-convokit).\n","\n","👋 Happy exploring your data with `edu-convokit`!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I7VrV3iMpvwM"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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