diff --git a/.gitignore b/.gitignore index 267d6b19884255674e73f9905b977ddb2e03e36e..203b2ce152a335058b03aa4796835afeeaf59d13 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .ipynb_checkpoints/ .python-version .venv/ +.DS_Store diff --git a/1_column_headers_are_values.ipynb b/1_encabezados_columnas_son_valores.ipynb similarity index 64% rename from 1_column_headers_are_values.ipynb rename to 1_encabezados_columnas_son_valores.ipynb index b01a4b58f54a31ccfe324fb368eda03324b5825c..4df9158b58adb662568892e71c0484be60be0a65 100644 --- a/1_column_headers_are_values.ipynb +++ b/1_encabezados_columnas_son_valores.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Column Headers are Values, not Variable Names\n", + "# Encabezados de las columnas son valores\n", "\n", - "This notebook shows two examples of how column headers display values. These type of messy datasets have practical use in two kinds of settings:\n", + "Este notebook muestra dos ejemplos de como los encabezados o nombres de las columnas muestran valores. Este tipo de \"messy datasets\" tienen uso práctico en dos tipos de situaciones:\n", "\n", - "1. Presentations\n", - "2. Recordings of regularly spaced observations over time" + "1. Presentaciones\n", + "2. Registro de observaciones espaciadas regularmente en el tiempo." ] }, { @@ -21,118 +21,351 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "import re\n", "\n", - "import pandas as pd\n", - "import savReaderWriter as spss" + "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Example 1: Religion vs. Income\n", + "## Ejemplo 1: Religion vs. Income\n", "\n", - "> A common type of messy dataset is tabular data designed for **presentation**, where variables\n", - "form both the rows and columns, and column headers are values, not variable names.\n", + "> Un tipo de dataset messy común son los datos tabulares diseñados para **presentación**, donde las variables forman tanto filas y columnas, y los encabezados de las columnas son valores, y no nombres de las variables.\n", "\n", - "The [Pew Research Center](http://www.pewresearch.org/) provides many studies on all kinds of aspects of life in the USA. The following examples uses data taken from its [Religious Landscape Study](http://www.pewforum.org/religious-landscape-study/)." + "El [Pew Research Center](http://www.pewresearch.org/) es un centro de estudios muy prolífico e influyente en investigación sobre todo tipo de aspectos de la vida en EEUU. Los siguientes ejemplos usan datos tomados del [Religious Landscape Study](http://www.pewforum.org/religious-landscape-study/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load the Data\n", + "### Cargando la data\n", "\n", - "The data are provided as a SPSS data file. This is a binary specification with a built-in header section describing the data, for example, what variables / columns are included and what the realizations categorical data can have." + "Los datos son entregados en un archivo de datos de SPSS. Esta es una especificación binaria con una sección de encabezado describiendo los datos, por ejemplo, que variables/columnas están incluidas y que instancias pueden tener los datos categóricos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load the dataset's meta data." + "Cargando la \"metadata\" del dataset." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weightpsraidint_datelangtypecregionstateusrusr1form...q63educincomeregistregicertpartypartylnideopvote04apvote04b
04.51282110000001.050807.0EnglishRDDNortheastConnecticutSuburbanSuburbanForm A...Yes, father born outside U.S.Technical, trade, or vocational school AFTER h...75 to under $100,000Yes, registeredAbsolutely certainRepublicanNaNModerateVotedBush
12.10256410000002.050807.0EnglishRDDNortheastMaineRuralRuralForm B...No, both parents born in U.S.High school graduate (Grade 12 or GED certific...20 to under $30,000No, not registeredNaNRepublicanNaNConservativeDid not vote (includes too young to vote)NaN
21.28205110000003.050807.0EnglishRDDNortheastMaineRuralRuralForm A...No, both parents born in U.S.College graduate (B.S., B.A., or other 4-year ...30 to under $40,000No, not registeredNaNIndependentDemocratConservativeDid not vote (includes too young to vote)NaN
31.35532310000004.050807.0EnglishRDDNortheastMaineRuralRuralForm B...No, both parents born in U.S.Some college, no 4-year degree (including asso...Less than $10,000No, not registeredNaNIndependentDemocratModerateDid not vote (includes too young to vote)NaN
41.58974410000005.050807.0EnglishRDDNortheastNew YorkUrbanUrbanForm A...Yes, father born outside U.S.Post-graduate training or professional schooli...50 to under $75,000Yes, registeredAbsolutely certainIndependentDemocratModerateVotedOther candidate
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" + ], + "text/plain": [ + " weight psraid int_date lang type cregion state \\\n", + "0 4.512821 10000001.0 50807.0 English RDD Northeast Connecticut \n", + "1 2.102564 10000002.0 50807.0 English RDD Northeast Maine \n", + "2 1.282051 10000003.0 50807.0 English RDD Northeast Maine \n", + "3 1.355323 10000004.0 50807.0 English RDD Northeast Maine \n", + "4 1.589744 10000005.0 50807.0 English RDD Northeast New York \n", + "\n", + " usr usr1 form ... q63 \\\n", + "0 Suburban Suburban Form A ... Yes, father born outside U.S. \n", + "1 Rural Rural Form B ... No, both parents born in U.S. \n", + "2 Rural Rural Form A ... No, both parents born in U.S. \n", + "3 Rural Rural Form B ... No, both parents born in U.S. \n", + "4 Urban Urban Form A ... Yes, father born outside U.S. \n", + "\n", + " educ income \\\n", + "0 Technical, trade, or vocational school AFTER h... 75 to under $100,000 \n", + "1 High school graduate (Grade 12 or GED certific... 20 to under $30,000 \n", + "2 College graduate (B.S., B.A., or other 4-year ... 30 to under $40,000 \n", + "3 Some college, no 4-year degree (including asso... Less than $10,000 \n", + "4 Post-graduate training or professional schooli... 50 to under $75,000 \n", + "\n", + " regist regicert party partyln \\\n", + "0 Yes, registered Absolutely certain Republican NaN \n", + "1 No, not registered NaN Republican NaN \n", + "2 No, not registered NaN Independent Democrat \n", + "3 No, not registered NaN Independent Democrat \n", + "4 Yes, registered Absolutely certain Independent Democrat \n", + "\n", + " ideo pvote04a pvote04b \n", + "0 Moderate Voted Bush \n", + "1 Conservative Did not vote (includes too young to vote) NaN \n", + "2 Conservative Did not vote (includes too young to vote) NaN \n", + "3 Moderate Did not vote (includes too young to vote) NaN \n", + "4 Moderate Voted Other candidate \n", + "\n", + "[5 rows x 135 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "columns = [\"q16\", \"reltrad\", \"income\"]\n", - "encodings = {}\n", - "\n", - "# For the sake of simplicity, all data cleaning operations\n", - "# are done within the for-loop for all columns.\n", - "with spss.SavHeaderReader(\"data/pew.sav\") as pew:\n", - " for column in columns:\n", - " encodings[column] = {\n", - " int(key): (\n", - " re.sub(\n", - " r\"\\(.*\\)\",\n", - " \"\",\n", - " (\n", - " value.decode(\"iso-8859-1\")\n", - " .replace(\"\\x92\", \"'\")\n", - " .replace(\" Churches\", \"\")\n", - " .replace(\"Less than $10,000\", \"<$10k\")\n", - " .replace(\"10 to under $20,000\", \"$10-20k\")\n", - " .replace(\"20 to under $30,000\", \"$20-30k\")\n", - " .replace(\"30 to under $40,000\", \"$30-40k\")\n", - " .replace(\"40 to under $50,000\", \"$40-50k\")\n", - " .replace(\"50 to under $75,000\", \"$50-75k\")\n", - " .replace(\"75 to under $100,000\", \"$75-100k\")\n", - " .replace(\"100 to under $150,000\", \"$100-150k\")\n", - " .replace(\"$150,000 or more\", \">150k\")\n", - " ),\n", - " ).strip()\n", - " )\n", - " for (key, value) in pew.all().valueLabels[column.encode()].items()\n", - " }" + "pew_data = pd.read_spss(\"data/pew.sav\")\n", + "pew_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load the actual data and prepare them as they are presented in the paper." + "Cargando la data y preparándola tal como está presentada en el *paper*." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Evangelical Protestant Churches' 'Mainline Protestant Churches'\n", + " 'Unaffiliated' 'Jewish' 'Don’t know/refused' 'Other Faiths'\n", + " 'Historically Black Protestant Churches' \"Jehovah's Witness\" 'Catholic'\n", + " 'Buddhist' 'Mormon' 'Muslim' 'Hindu' 'Other Christian' 'Orthodox'\n", + " 'Other World Religions']\n", + "['$75-100k' '$20-30k' '$30-40k' '<$10k' '$50-75k' '>150k' '$40-50k'\n", + " \"Don't know/Refused\" '$100-150k' '$10-20k']\n", + "['Protestant' 'Nothing in particular' 'Jewish' 'Not Interpretable'\n", + " 'Liberal faith' 'Jehovah’s Witness' 'Atheist' 'Christian' 'Agnostic'\n", + " 'Unitarian' 'Roman Catholic' 'Buddhist' 'Mormon' 'Muslim'\n", + " 'Don’t know/Refused' 'Pagan' 'Eclectic, a bit of everything, own beliefs'\n", + " 'New Age' 'Hindu' 'Spiritual but not religious'\n", + " 'Unity; Unity Church Christ Church Unity' 'Deist' 'Orthodox'\n", + " 'Mixed Christians' 'Mixed Christian and non-Christian' 'Pantheist'\n", + " 'Native American Religions' 'Shinto' 'Wica' 'Bahai' 'Religious science'\n", + " 'Armenian Catholic' 'Zoroastrianism' 'New Apostolic Church' 'Asatru'\n", + " 'Spiritualist' 'Messianic Jews' 'Christian Scientists' 'Shamanism'\n", + " 'Nihilist' 'Humanist' 'Eckankar' 'Metaphysical'\n", + " 'Transcendental meditation/Meditation' 'Scientology'\n", + " 'Mixed non-Christians' 'National Catholic; Polish National Catholic'\n", + " 'Indian Shaker Church' 'Hebrew Israelite/African Hebrew Israelites'\n", + " 'International Bible Students' 'Tao' 'Druid' 'Rastafarian' 'Sikh'\n", + " 'Siddhayoga' 'New Thought' 'Maronite Catholic' 'Self realization'\n", + " 'Theosophy' 'Satanism' 'Unification Church' 'Greek rite Catholic'\n", + " 'Old Catholic'\n", + " 'Lutheran Orthodox Church/The Catholic Church - Lutheran Rite' 'Animism']\n" + ] + } + ], "source": [ - "with spss.SavReader(\n", - " \"data/pew.sav\", selectVars=[column.encode() for column in columns]\n", - ") as pew:\n", - " pew = list(pew)\n", + "pew_data['reltrad'] = pew_data['reltrad'].replace( to_replace=\" Churches\", value=\"\").str.replace(\n", + " pat= \"\\(.*\\)\",repl=\"\",regex=True).str.strip()\n", + "\n", + "pew_data['income'] = pew_data['income'].replace(\"Less than $10,000\", \"<$10k\").replace(\n", + " \"10 to under $20,000\", \"$10-20k\").replace(\"20 to under $30,000\", \"$20-30k\").replace(\n", + " \"30 to under $40,000\", \"$30-40k\").replace(\"40 to under $50,000\", \"$40-50k\").replace(\n", + " \"50 to under $75,000\", \"$50-75k\").replace(\n", + " \"75 to under $100,000\", \"$75-100k\").replace(\n", + " \"100 to under $150,000\", \"$100-150k\").replace(\n", + " \"$150,000 or more\", \">150k\").str.replace(\n", + " pat= \"\\(.*\\)\",repl=\"\",regex=True).str.strip()\n", "\n", - "# Use the above encodings to map the numeric data\n", - "# to the actual labels.\n", - "pew = pd.DataFrame(pew, columns=columns, dtype=int)\n", - "for column in columns:\n", - " pew[column] = pew[column].map(encodings[column])\n", + "pew_data['q16'] = pew_data['q16'].str.replace(pat= \"\\(.*\\)\",repl=\"\",regex=True).str.strip()\n", "\n", + "\n", + "print(pew_data['reltrad'].unique())\n", + "\n", + "print(pew_data['income'].unique())\n", + "\n", + "print(pew_data['q16'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ "for value in (\"Atheist\", \"Agnostic\"):\n", - " pew.loc[(pew[\"q16\"] == value), \"reltrad\"] = value\n", + " pew_data.loc[(pew_data[\"q16\"] == value), \"reltrad\"] = value\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ "\n", "income_columns = [\n", " \"<$10k\",\n", @@ -146,8 +379,16 @@ " \">150k\",\n", " \"Don't know/Refused\",\n", "]\n", - "\n", - "pew = pew.groupby([\"reltrad\", \"income\"]).size().unstack(\"income\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "pew = pew_data.groupby([\"reltrad\", \"income\"]).size().unstack(\"income\")\n", "pew = pew[income_columns]\n", "pew.index.name = \"religion\"" ] @@ -158,12 +399,12 @@ "source": [ "### Messy Data\n", "\n", - "The next cell shows the data as they can actually be provided as \"raw\" data (i.e., the pre-processing as done above is assumed to be done by someone else and the data analyst is only presented with the below dataset)." + "La siguiente celda muestra la data \"sin procesar\" (\"cruda\", raw-data) (es decir, se supone que el preprocesamiento realizado anteriormente lo realiza otra persona y al analista de datos solo se le entrega el conjunto de datos a continuación)." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -172,7 +413,7 @@ "(18, 10)" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -183,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -286,7 +527,7 @@ " 1489\n", " \n", " \n", - " Don't know/refused\n", + " Don’t know/refused\n", " 15\n", " 14\n", " 15\n", @@ -299,7 +540,7 @@ " 116\n", " \n", " \n", - " Evangelical Protestant\n", + " Evangelical Protestant Churches\n", " 575\n", " 869\n", " 1064\n", @@ -325,7 +566,7 @@ " 37\n", " \n", " \n", - " Historically Black Protestant\n", + " Historically Black Protestant Churches\n", " 228\n", " 244\n", " 236\n", @@ -368,47 +609,47 @@ "" ], "text/plain": [ - "income <$10k $10-20k $20-30k $30-40k $40-50k \\\n", + "income <$10k $10-20k $20-30k $30-40k \\\n", "religion \n", - "Agnostic 27 34 60 81 76 \n", - "Atheist 12 27 37 52 35 \n", - "Buddhist 27 21 30 34 33 \n", - "Catholic 418 617 732 670 638 \n", - "Don't know/refused 15 14 15 11 10 \n", - "Evangelical Protestant 575 869 1064 982 881 \n", - "Hindu 1 9 7 9 11 \n", - "Historically Black Protestant 228 244 236 238 197 \n", - "Jehovah's Witness 20 27 24 24 21 \n", - "Jewish 19 19 25 25 30 \n", + "Agnostic 27 34 60 81 \n", + "Atheist 12 27 37 52 \n", + "Buddhist 27 21 30 34 \n", + "Catholic 418 617 732 670 \n", + "Don’t know/refused 15 14 15 11 \n", + "Evangelical Protestant Churches 575 869 1064 982 \n", + "Hindu 1 9 7 9 \n", + "Historically Black Protestant Churches 228 244 236 238 \n", + "Jehovah's Witness 20 27 24 24 \n", + "Jewish 19 19 25 25 \n", "\n", - "income $50-75k $75-100k $100-150k >150k \\\n", - "religion \n", - "Agnostic 137 122 109 84 \n", - "Atheist 70 73 59 74 \n", - "Buddhist 58 62 39 53 \n", - "Catholic 1116 949 792 633 \n", - "Don't know/refused 35 21 17 18 \n", - "Evangelical Protestant 1486 949 723 414 \n", - "Hindu 34 47 48 54 \n", - "Historically Black Protestant 223 131 81 78 \n", - "Jehovah's Witness 30 15 11 6 \n", - "Jewish 95 69 87 151 \n", + "income $40-50k $50-75k $75-100k $100-150k \\\n", + "religion \n", + "Agnostic 76 137 122 109 \n", + "Atheist 35 70 73 59 \n", + "Buddhist 33 58 62 39 \n", + "Catholic 638 1116 949 792 \n", + "Don’t know/refused 10 35 21 17 \n", + "Evangelical Protestant Churches 881 1486 949 723 \n", + "Hindu 11 34 47 48 \n", + "Historically Black Protestant Churches 197 223 131 81 \n", + "Jehovah's Witness 21 30 15 11 \n", + "Jewish 30 95 69 87 \n", "\n", - "income Don't know/Refused \n", - "religion \n", - "Agnostic 96 \n", - "Atheist 76 \n", - "Buddhist 54 \n", - "Catholic 1489 \n", - "Don't know/refused 116 \n", - "Evangelical Protestant 1529 \n", - "Hindu 37 \n", - "Historically Black Protestant 339 \n", - "Jehovah's Witness 37 \n", - "Jewish 162 " + "income >150k Don't know/Refused \n", + "religion \n", + "Agnostic 84 96 \n", + "Atheist 74 76 \n", + "Buddhist 53 54 \n", + "Catholic 633 1489 \n", + "Don’t know/refused 18 116 \n", + "Evangelical Protestant Churches 414 1529 \n", + "Hindu 54 37 \n", + "Historically Black Protestant Churches 78 339 \n", + "Jehovah's Witness 6 37 \n", + "Jewish 151 162 " ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -423,16 +664,16 @@ "source": [ "### Tidy Data\n", "\n", - "> This dataset has **three** variables, **religion**, **income** and **frequency**. To tidy it, we need to **melt**, or stack it. In other words, we need to turn columns into rows.\n", + "> Este dataset tiene **tres** variables, **religion**, **income** y **frequency**. Para hacerlo *tidy*, necesitamos *\"fundirlo\"* (hacer un **melt**), o apilarlo. En otras palabras, necesitamos convertir columnas en filas.\n", "\n", - "pandas provides a [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) function to un-pivot the dataset.\n", + "`pandas` provee un método [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) para *des-pivotear* el dataset.\n", "\n", - "**Notes:** `.reset_index()` transforms the religion index column into a data column (`pd.melt()` needs that). Further, the resulting table is sorted implicitly by the `\"religion\"` column. To get to the same ordering as in the paper, the molten table is explicitly sorted." + "**Notas:** `.reset_index()` transforma la columna de índice de religión en una columna de datos (`pd.melt()` lo necesita). Además, la tabla resultante se ordena implícitamente por la columna `\"religión\"`. Para llegar al mismo orden que en el *paper*, la tabla *fundida* se ordena explícitamente." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -453,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -462,7 +703,7 @@ "(180, 3)" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -473,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -581,7 +822,7 @@ "9 Agnostic Don't know/Refused 96" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -594,23 +835,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example 2: Billboard\n", + "## Ejemplo 2: Billboard\n", "\n", - "> Another common use of this data format is to record regularly spaced observations over time. For example, the Billboard dataset shown in Table 7 records the date a song first entered the Billboard Top 100. It has variables for **artist**, **track**, **date.entered**, **rank** and **week**. The rank in each week after it enters the top 100 is recorded in 75 columns, wk1 to wk75. If a song is in the Top 100 for less than 75 weeks the remaining columns are filled with missing values. This form of storage is not tidy, but it is useful for data entry. It reduces duplication since otherwise each song in each week would need its own row, and song metadata like title and artist would need to be repeated." + "> Otro uso común de este formato de datos es registrar observaciones espaciadas regularmente a lo largo del tiempo. Por ejemplo, el conjunto de datos de Billboard que se muestra en la Tabla 7, registra la fecha en que una canción ingresó por primera vez al Billboard Top 100. Tiene variables para **artist**, **track**, **date.entered**, **rank** y **week**. El rango en cada semana después de que ingresa al top 100 se registra en 75 columnas, `wk1` a `wk75`. Si una canción está en el Top 100 por menos de 75 semanas, las columnas restantes se llenan con valores faltantes. Esta forma de almacenamiento no es ordenada, pero es útil para la entrada de datos. Reduce la duplicación ya que, de lo contrario, cada canción de cada semana necesitaría su propia fila, y los metadatos de la canción, como el título y el artista, tendrían que repetirse.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load the Data\n", + "### Cargando la data\n", "\n", - "The data come in a CSV file with tediously named week columns." + "Los datos vienen en un archivo CSV con columnas con nombre de número de semana de una manera engorrosa." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -657,12 +898,12 @@ "source": [ "### Messy Data\n", "\n", - "Again, the next cell shows the data as they were actually provided as \"raw\" data." + "De nuevo, la siguiente celda muestra los datos como si fueran realmente entregados como datos \"crudos\"." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -671,7 +912,7 @@ "(267, 80)" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1015,7 +1256,7 @@ "[10 rows x 80 columns]" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1030,12 +1271,12 @@ "source": [ "### \"Tidy\" Data\n", "\n", - "As before the `pd.melt()` function is used to transform the data from \"wide\" to \"long\" form." + "Como antes, el método `pd.melt()` se usa para transformar los datos desde un formato \"wide\" (\"ancho\") a uno \"largo\" (\"long\")." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1051,12 +1292,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In contrast to R, pandas keeps (unneccesary) rows for weeks where the song was already out of the charts. These are discarded. Also, a new column`\"date\"` indicating when exactly a particular song was at a certain rank in the charts is added." + "A diferencia de `R`, `pandas` mantiene (innecesariamente según algunos) filas para semanas donde la canción ya estaba fuera del ranking. Estas observaciones son descartadas. También, una nueva columna `\"date\"` es añadida, indicando cuando exactamente una canción en particular estuvo en un cierto ranking." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1084,12 +1325,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that this dataset is not yet fully tidy as will be explained in notebook No. 4." + "Ten en cuenta que este conjunto de datos aún no está completamente `tidy`, como se explicará en el notebook 4." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1313,7 +1554,7 @@ "14 Kryptonite 2000-05-06 5 66 " ] }, - "execution_count": 16, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1326,14 +1567,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Save the Data\n", - "\n", - "The above \"tidy\" billboard dataset is saved as input for notebook No. 4." + "### Guardando los datos\n", + "El dataset de bilboard ya \"ordenado\" (\"tidy\") es guardado como input para el notebook 4." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1343,7 +1583,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.14 ('tidy')", "language": "python", "name": "python3" }, @@ -1357,7 +1597,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } } }, "nbformat": 4, diff --git a/2_multiple_variables_stored_in_one_column.ipynb b/2_multiples_variables_almacenadas_en_una_columna.ipynb similarity index 93% rename from 2_multiple_variables_stored_in_one_column.ipynb rename to 2_multiples_variables_almacenadas_en_una_columna.ipynb index 535b8c0da43f8a3ba9e9694c0e1265c2ccdd416f..61ee7cd0784fb2b189869e23bc81a4036f607bc6 100644 --- a/2_multiple_variables_stored_in_one_column.ipynb +++ b/2_multiples_variables_almacenadas_en_una_columna.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Multiple Variables stored in one Column\n", + "# Múltiples variables almacenadas en una columna\n", "\n", - "This notebook shows how multiple variables stored in the same column can be isolated." + "Este cuaderno muestra cómo se pueden aislar varias variables almacenadas en la misma columna." ] }, { @@ -21,15 +21,6 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], "source": [ "import pandas as pd" ] @@ -38,21 +29,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example: Tuberculosis" + "## Ejemplo: Tuberculosis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load the Data\n", + "### Cargando la Data\n", "\n", - "Select the same columns as in the paper and name them accordingly." + "Seleccionamos las mismas columnas que en el *paper* y le asignamos el nombre correspondiente." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -88,13 +79,14 @@ "metadata": {}, "source": [ "### Messy Data\n", + "Se asume que los datos se proporcionan de la siguiente manera. Excepto por las columnas `\"country\"` y `\"year\"`, las columnas restantes son en realidad realizaciones conjuntas de dos variables `\"sex\"` y `\"age\"`.\n", "\n", "The data are assumed to be provided as below. Except for the `\"country\"` and `\"year\"` columns, the remaining columns are actually joint realizations of two variables `\"sex\"` and `\"age\"`." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -379,7 +371,7 @@ "265 NaN NaN NaN NaN 1.0 NaN NaN NaN " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -392,14 +384,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Molten Data\n", + "### *Molten* Data\n", "\n", - "As in the previous notebook the [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) function can be used to un-pivot the columns. As before, pandas keeps rows for columns with missing data that are discarded. Then, without any more missing values, the column's data type is casted as `int`. Furthermore, the resulting *molten* dataset is sorted as in the paper." + "Al igual que en el notebook anterior, la función [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) se puede usar para despivotear las columnas. Como antes, pandas mantiene las filas de las columnas con *missing data*, los que se descartan. Luego, sin más *missing data*, el tipo de datos de la columna se convierte como `int`. Luego, el conjunto de datos *fundido* resultante es ordenado como en el paper.\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -413,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -532,7 +524,7 @@ "167 AE 2000 f2534 1" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -546,13 +538,12 @@ "metadata": {}, "source": [ "### Tidy Data\n", - "\n", - "Using the [pd.Series.str.extract()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html) method the two variables are isolated. The age labels are renamed as in the paper." + "Usando el método [pd.Series.str.extract()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html) las dos variables son aisladas/separadas (\"splitted\"). Las etiquetas de `age` se renombran como en el documento." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -575,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -705,7 +696,7 @@ "167 AE 2000 f 25-34 1" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -717,7 +708,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.14 ('tidy')", "language": "python", "name": "python3" }, @@ -731,7 +722,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } } }, "nbformat": 4, diff --git a/3_variables_are_stored_in_both_rows_and_columns.ipynb b/3_variables_almacenadas_tanto_en_filas_y_en_columnas.ipynb similarity index 90% rename from 3_variables_are_stored_in_both_rows_and_columns.ipynb rename to 3_variables_almacenadas_tanto_en_filas_y_en_columnas.ipynb index 1c1d3d54438bb6163f677b2ae2fea6f8404840a6..4b60f7a804f2ab2feaf593bd9595f49bcba0ace7 100644 --- a/3_variables_are_stored_in_both_rows_and_columns.ipynb +++ b/3_variables_almacenadas_tanto_en_filas_y_en_columnas.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Variables are stored in both Rows and Columns" + "# Variables son almacenadas tanto en filas como en columnas" ] }, { @@ -19,15 +19,6 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" @@ -35,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,23 +37,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example: Weather\n", + "## Ejemplo: Clima\n", "\n", - "The [Global Historical Climatology Network](https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn) collects daily weather. For this example, data for one weather station (MX17004) in Mexico are used." + "El [Global Historical Climatology Network](https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn) recolecta datos de clima de manera diaria. Para este ejemplo, se usan datos de una estación climática en Mexico (MX17004)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load the Data\n", + "### Cargando datos\n", "\n", - "The raw dataset comes in a format that is a mixture of a fixed-width style with occasional usage of characters as seperators. Some tedious cleaning work is necessary." + "El raw dataset viene en un formato que es una mezcla de datos de ancho fijo con uso ocasional de caracteres como separadores. Se necesita hacer una limpieza al respecto." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +61,7 @@ "# use string slicing to obtain groups of columns.\n", "weather = pd.read_csv(\"data/weather.txt\", header=None, sep=\"^\")\n", "\n", - "# First, remove the weird character seperators,\n", + "# First, remove the weird character separators,\n", "# then split the columns by whitespace, and\n", "# finally name them appropriately.\n", "days = (\n", @@ -118,15 +109,15 @@ "source": [ "### Messy Data\n", "\n", - "Below is a dataset assumed to have been provided like this as \"raw\", i.e., the data analyst did not do the above parsing work but some third party instead.\n", + "A continuación hay un dataset que asumimos que ha sido proporcionado \"crudo\" como está al analista de datos, es decir, el analista no realizó el trabajo de \"parsing\" anterior, sino un tercero anteriormente.\n", "\n", - "> The most complicated form of messy data occurs when variables are stored in both rows and columns. Table 11 shows daily weather data from the Global Historical Climatology Network for one weather station (MX17004) in Mexico for five months in 2010. It has variables in\n", - "individual columns (`\"id\"`, `\"year\"`, `\"month\"`), spread across columns (day, `\"d1\"`–`\"d31\"`) and across rows (`\"tmin\"` and `\"tmax\"` for the minimum and maximum temperatures). Months with less than 31 days have missing values for the last day(s) of the month. The `\"element\"` column is not a variable: it stores the *names* of variables." + "> La forma más complicada de *messy data* ocurre cuando las variables se almacenan tanto en filas como en columnas. La Tabla 11 muestra datos meteorológicos diarios del Global Historical Climatology Network para una estación meteorológica (MX17004) en México durante cinco meses en 2010. Tiene variables en\n", + "columnas individuales (`\"id\"`, `\"year\"`, `\"month\"`), distribuidas en columnas (`day`, `\"d1\"`–`\"d31\"`) y en filas (`\"tmin\"` y `\"tmax\"` para las temperaturas mínima y máxima). Los meses con menos de 31 días tienen valores faltantes para los últimos días del mes. La columna `\"element\"` no es una variable: almacena los *nombres* de las variables." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -610,7 +601,7 @@ "1108 NaN NaN NaN NaN 18.2 NaN NaN NaN NaN " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -630,12 +621,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> To tidy this dataset we first melt it with colvars `\"id\"`, `\"year\"`, `\"month\"`, and the column that contains the actual variable names, `\"element\"` [...]. For presentation, we have dropped the missing values, making them implicit rather than explicit. This is permissible because we know how many days are in each month and can easily reconstruct the explicit missing values." + "> Para hacer este dataset tidy, primero le hacemos un `melt` con colvars `\"id\"`, `\"year\"`, `\"month\"`, y la columna que contiene los nombres de las variables reales, `\"element\"` [...]. Para la presentación, hemos descartado los valores faltantes, haciéndolos implícitos en lugar de explícitos. Esto es permisible porque sabemos cuántos días hay en cada mes y podemos reconstruir fácilmente los valores faltantes explícitos." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -659,12 +650,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> This dataset is mostly tidy, but we have two variables stored in rows: `\"tmin\"` and `\"tmax\"`, the type of observation." + "> Este conjunto de datos está mayormente tidy, pero tenemos dos variables almacenadas en filas: `\"tmin\"` y `\"tmax\"`, el tipo de observación." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -783,7 +774,7 @@ "23192 MX000017004 2010-02-23 tmin 10.7" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -803,14 +794,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> Fixing this requires the cast, or unstack, operation. This performs the inverse of melting by rotating the element variable back out into the columns\n", + "> Arreglar esto requiere la operación `cast`(conversión) o `unstack`(desapilar). Esto realiza el inverso del \"fundido\" (melting), rotando la variable `element` de vuelta hacia las columnas.\n", "\n", - "Below, [pd.DataFrame.unstack()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.unstack.html) uses a DataFrame's index as columns to unstack over." + "A continuación, [pd.DataFrame.unstack()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.unstack.html) usa un DataFrame's index como columnas para poder desapilar.\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -826,12 +817,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> This form is tidy. There is one variable in each column, and each row represents a day’s observations." + "> Esta forma está tidy. Hay una variable en cada columna, y cada fila representa la observación de un día." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -950,7 +941,7 @@ "12096 MX000017004 2010-05-27 33.2 18.2" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -962,7 +953,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.14 ('tidy')", "language": "python", "name": "python3" }, @@ -976,7 +967,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } } }, "nbformat": 4, diff --git a/4_multiple_types_in_one_table.ipynb b/4_multiples_tipos_de_observaciones_en_una_tabla.ipynb similarity index 92% rename from 4_multiple_types_in_one_table.ipynb rename to 4_multiples_tipos_de_observaciones_en_una_tabla.ipynb index 11c23ab3be196322fc7c8b3747c38abdced28968..1b0c5ce84f5845f95e49f5b52acd963674c74ad4 100644 --- a/4_multiple_types_in_one_table.ipynb +++ b/4_multiples_tipos_de_observaciones_en_una_tabla.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Multiple Types in one Table\n", + "# Multiples Tipos en una Tabla\n", "\n", - "> Datasets often involve values collected at multiple levels, on different types of observational units. During tidying, each type of observational unit should be stored in its own table. This is closely related to the idea of database normalisation, where each fact is expressed in only one place. If this is not done, it’s possible for inconsistencies to occur." + ">Los datasets a menudo involucran valores recopilados en múltiples niveles, en diferentes tipos de unidades de observación. Durante el proceso de *tidying*, cada tipo de unidad de observación debe almacenarse en su propia tabla/dataframe. Esto está estrechamente relacionado con la idea de la normalización de la base de datos, donde cada hecho se expresa en un solo lugar. Si esto no se hace, es posible que ocurran inconsistencias." ] }, { @@ -21,15 +21,6 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], "source": [ "import pandas as pd" ] @@ -38,21 +29,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example: Billboard revisited" + "## Ejemplo: Billboard revisited" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load the Data\n", - "\n", - "Load the cleaned and almost tidy dataset from notebook No. 1." + "### Cargando la data\n", + "Cargando el dataset limpio y casi \"tidy\" del notebook 1." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,13 +55,13 @@ "source": [ "### Messy Data\n", "\n", - "> The Billboard dataset described in Table 8 actually contains observations on two types of\n", - "observational units: the **song** and its **rank** in each week. This manifests itself through the duplication of facts about the song: `\"artist\"` and `\"time\"` are repeated for every song in each `\"week\"`." + "> El conjunto de datos de Billboard descrito en la Tabla 8 en realidad contiene observaciones sobre dos tipos de\n", + "unidades de observación: la **song** y su **rank** en cada semana. Esto se manifiesta a través de la duplicación de \"hechos\" sobre la canción: `\"artist\"` y `\"time\"` se repiten para cada canción en cada `\"semana\"`." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -295,7 +285,7 @@ "14 Kryptonite 2000-05-06 5 66 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -310,14 +300,14 @@ "source": [ "### Tidy Data\n", "\n", - "> The billboard dataset needs to be broken down into two datasets: a **song** dataset which stores `\"artist\"`, `\"song name\"` and `\"time\"`, and a **ranking** dataset which gives the `\"rank\"` of the song in each `\"week\".\n", + "> El billboard dataset necesita dividirse en dos conjuntos de datos: un dataframe de **song**, que almacena `\"artist\"`, `\"song name\"` y `\"time\"`, y un conjunto de datos de **rank** que proporciona el `\"rank\"` de la canción en cada `\"week\"`.\n", "\n", - "Transforming data columns into index columns is enough in pandas to obtain unique `tuple`s from several columns. So, no real \"function\" is needed to tidy up the dataset." + "Transformar columnas de datos en columnas de índice es suficiente en pandas para obtener 'tuplas' únicas de varias columnas. Por lo tanto, no se necesita una \"función\" real para ordenar el conjunto de datos." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -346,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -514,7 +504,7 @@ "15 4:18 " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -658,7 +648,7 @@ "3 2000-05-06 66" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -670,7 +660,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.14 ('tidy')", "language": "python", "name": "python3" }, @@ -684,7 +674,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } } }, "nbformat": 4, diff --git a/5_one_type_in_multiple_tables.ipynb b/5_one_type_in_multiple_tables.ipynb deleted file mode 100644 index b6812ea8f7f24c6d9871e151101a4d42be9cf3f6..0000000000000000000000000000000000000000 --- a/5_one_type_in_multiple_tables.ipynb +++ /dev/null @@ -1,55 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# One Type in multiple Tables\n", - "\n", - "The repository with the original R code does not provide code for this case but only refers to other projects that cannot be replicated any more (because the source website is *not* available any more)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Messy Data\n", - "\n", - "> It’s also common to find data values about a single type of observational unit spread out over multiple tables or files. These tables and files are often split up by another variable, so that each represents a single year, person, or location." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tidy Data\n", - "\n", - "> As long as the format for individual records is consistent, this is an easy problem to fix:\n", - "1. Read the files into a list of tables.\n", - "2. For each table, add a new column that records the original file name (because the file name is often the value of an important variable).\n", - "3. Combine all tables into a single table" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/5_un_tipo_de_obs_en_multiples_tablas.ipynb b/5_un_tipo_de_obs_en_multiples_tablas.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0fa087fc33bdefb78567864113287bfd4a7f9d12 --- /dev/null +++ b/5_un_tipo_de_obs_en_multiples_tablas.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Un tipo en múltiples tablas\n", + "\n", + "El repositorio con el código original en `R` no incluye código para este caso, sino solo se refiere a otros proyectos que no pueden ser replicados ya que el sitio web donde estaban los datos no está disponible actualmente." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Messy Data\n", + "\n", + "> Es también bastante común encontrar datos sobre un solo tipo de unidad de observación, distribuidos en varias tablas o archivos. Estas tablas y archivos a menudo se dividen por otra variable, de modo que cada uno representa un solo año, persona o ubicación." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tidy Data\n", + "> Siempre que el formato de los registros individuales sea consistente, este es un problema fácil de solucionar:\n", + "1. Se leen los archivos en una lista de tablas.\n", + "2. Para cada tabla, se agregue una nueva columna que registre el nombre del archivo original (porque el nombre del archivo suele ser el valor de una variable importante).\n", + "3. Se combinan todas las tablas en una sola tabla, usualmente usando `pd.merge()`. \n", + "\n", + "**El dolor de cabeza viene cuando los registros no son consistentes, problema ampliamente extendido en muchos lugares, donde un caso clásico son los datos de observaciones sin un número único de identificación, recolectados por distintas entidades de gobierno, y donde el caso paradigmático que ha llevado a un sinfin de problemas son los registros de personas en EEUU, debido a la falta de un número único de identificación.**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.14 ('tidy')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/6_case_study.ipynb b/6_case_study.ipynb deleted file mode 100644 index fb2b3b9ddf80376adc62f232eb2bb1ca4b0d86a3..0000000000000000000000000000000000000000 --- a/6_case_study.ipynb +++ /dev/null @@ -1,1000 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Case Study: Unusual Deaths in Mexico\n", - "\n", - "> The following case study illustrates how tidy data and tidy tools make data analysis easier by easing the transitions between manipulation, visualisation and modelling. You will not see any code that exists solely to get the output of one function into the right format to input to another.\n", - "\n", - "> The case study uses individual-level mortality data from Mexico. The goal is to find causes of death with unusual temporal patterns within a day." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## \"Housekeeping\"" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/webartifex/repos/tidy-data/.venv/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py:11: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.\n", - " from pandas.core.index import Index as PandasIndex\n" - ] - } - ], - "source": [ - "import math\n", - "import textwrap\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from matplotlib import pyplot as plt\n", - "from rpy2 import robjects # leads to a FutureWarning that can be safely ignored\n", - "from rpy2.robjects import pandas2ri\n", - "from sklearn.linear_model import HuberRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sns.set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the Data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "deaths = pandas2ri.ri2py(robjects.r[\"readRDS\"](\"data/deaths.rds\"))\n", - "deaths = deaths[(deaths[\"yod\"] == 2008) & (deaths[\"mod\"] != 0) & (deaths[\"dod\"] != 0)]\n", - "deaths = deaths[~(deaths[\"hod\"] < 0)]\n", - "deaths = deaths.reset_index(drop=True)\n", - "\n", - "assert set(deaths[\"hod\"].unique()) <= set(range(24))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(502520, 5)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deaths.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yodmoddodhodcod
02008111B20
12008111B22
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" - ], - "text/plain": [ - " yod mod dod hod cod\n", - "0 2008 1 1 1 B20\n", - "1 2008 1 1 1 B22\n", - "2 2008 1 1 1 C18\n", - "3 2008 1 1 1 C34\n", - "4 2008 1 1 1 C50" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deaths.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# The file contains 7 duplicates that are discarded.\n", - "codes = pd.read_csv(\"data/icd-main.csv\")\n", - "codes = codes[(codes[\"code\"] != codes[\"code\"].shift())].set_index(\"code\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1851, 1)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "codes.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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disease
code
A00Cholera
A01Typhoid and paratyphoid fevers
A02Other salmonella infections
A03Shigellosis
A04Other bacterial intestinal infections
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" - ], - "text/plain": [ - " disease\n", - "code \n", - "A00 Cholera\n", - "A01 Typhoid and paratyphoid fevers\n", - "A02 Other salmonella infections\n", - "A03 Shigellosis\n", - "A04 Other bacterial intestinal infections" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "codes.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Counts\n", - "\n", - "Count the number of deaths by `\"hod\"` (=\"hour of the day\") and `\"cod\"` (=\"cause of death\"), and also join in the more descriptive labels for the various causes." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hodcodfreqdisease
01A013Typhoid and paratyphoid fevers
11A023Other salmonella infections
21A047Other bacterial intestinal infections
31A051Other bacterial foodborne intoxications, not e...
41A062Amebiasis
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" - ], - "text/plain": [ - " hod cod freq disease\n", - "0 1 A01 3 Typhoid and paratyphoid fevers\n", - "1 1 A02 3 Other salmonella infections\n", - "2 1 A04 7 Other bacterial intestinal infections\n", - "3 1 A05 1 Other bacterial foodborne intoxications, not e...\n", - "4 1 A06 2 Amebiasis" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "counts = (\n", - " pd.DataFrame(deaths.groupby([\"hod\", \"cod\"]).size(), columns=[\"freq\"])\n", - " .reset_index()\n", - " .join(codes, on=\"cod\")\n", - ")\n", - "# This is to ensure that no duplicates are created\n", - "# because of duplicate entries in the codes DataFrame.\n", - "assert counts[\"cod\"].value_counts().max() <= 24\n", - "\n", - "# Keep only causes where a death happened in every hour.\n", - "counts = counts[counts[\"cod\"].isin(list((counts[\"cod\"].value_counts() == 24).index))]\n", - "\n", - "counts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add a `\"prop\"` (=\"proportion\") column indicating the relative frequency of a given cause of death on an hourly basis." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hodcodfreqdiseaseprop
01A013Typhoid and paratyphoid fevers0.062500
11A023Other salmonella infections0.048387
21A047Other bacterial intestinal infections0.051095
31A051Other bacterial foodborne intoxications, not e...0.050000
41A062Amebiasis0.024390
\n", - "
" - ], - "text/plain": [ - " hod cod freq disease prop\n", - "0 1 A01 3 Typhoid and paratyphoid fevers 0.062500\n", - "1 1 A02 3 Other salmonella infections 0.048387\n", - "2 1 A04 7 Other bacterial intestinal infections 0.051095\n", - "3 1 A05 1 Other bacterial foodborne intoxications, not e... 0.050000\n", - "4 1 A06 2 Amebiasis 0.024390" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "counts = counts.set_index(\"cod\")\n", - "counts[\"prop\"] = counts[\"freq\"] / deaths.groupby([\"cod\"]).size().reindex(counts.index)\n", - "counts = counts.reset_index()\n", - "# Re-order the columns as in the paper.\n", - "counts = counts[[\"hod\", \"cod\", \"freq\", \"disease\", \"prop\"]]\n", - "\n", - "counts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add `\"freq_all\"` and `\"prop_all\"` columns that show the absolute number of deaths for a given hour of day (disregarding cause of death) and the proportion of deaths for a certain hour of day with respect to the whole day." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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hodcodfreqdiseasepropfreq_allprop_all
01A013Typhoid and paratyphoid fevers0.062500200380.039875
11A023Other salmonella infections0.048387200380.039875
21A047Other bacterial intestinal infections0.051095200380.039875
31A051Other bacterial foodborne intoxications, not e...0.050000200380.039875
41A062Amebiasis0.024390200380.039875
\n", - "
" - ], - "text/plain": [ - " hod cod freq disease \\\n", - "0 1 A01 3 Typhoid and paratyphoid fevers \n", - "1 1 A02 3 Other salmonella infections \n", - "2 1 A04 7 Other bacterial intestinal infections \n", - "3 1 A05 1 Other bacterial foodborne intoxications, not e... \n", - "4 1 A06 2 Amebiasis \n", - "\n", - " prop freq_all prop_all \n", - "0 0.062500 20038 0.039875 \n", - "1 0.048387 20038 0.039875 \n", - "2 0.051095 20038 0.039875 \n", - "3 0.050000 20038 0.039875 \n", - "4 0.024390 20038 0.039875 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "counts = counts.set_index(\"hod\")\n", - "counts[\"freq_all\"] = deaths.groupby(\"hod\").size()\n", - "counts[\"prop_all\"] = counts[\"freq_all\"] / deaths.shape[0]\n", - "counts = counts.reset_index()\n", - "\n", - "counts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Distance between temporal Patterns\n", - "\n", - "> Next we compute a distance between the temporal pattern of each cause of death and the overall temporal pattern. There are many ways to measure this distance, but I found a simple mean squared deviation to be revealing. We also record the sample size, the total number of deaths from that cause. To ensure that the diseases we consider are sufficiently representative we’ll only work with diseases with more than 50 total deaths (∼2/hour)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
diseasendist
A02Other salmonella infections620.000738
A04Other bacterial intestinal infections1370.000208
A06Amebiasis820.000405
A09Diarrhea and gastroenteritis of infectious origin30160.000028
A16Respiratory tuberculosis, not confirmed bacter...16420.000029
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" - ], - "text/plain": [ - " disease n dist\n", - "A02 Other salmonella infections 62 0.000738\n", - "A04 Other bacterial intestinal infections 137 0.000208\n", - "A06 Amebiasis 82 0.000405\n", - "A09 Diarrhea and gastroenteritis of infectious origin 3016 0.000028\n", - "A16 Respiratory tuberculosis, not confirmed bacter... 1642 0.000029" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "devi = (\n", - " codes.join(deaths.groupby(\"cod\").count()[\"yod\"].to_frame(), how=\"inner\")\n", - " .join(\n", - " counts.groupby(\"cod\")\n", - " .apply(lambda x: ((x[\"prop\"] - x[\"prop_all\"]) ** 2).mean())\n", - " .to_frame(),\n", - " how=\"inner\",\n", - " )\n", - " .rename(columns={\"yod\": \"n\", 0: \"dist\"})\n", - ")\n", - "devi = devi[(devi[\"n\"] > 50)]\n", - "\n", - "devi.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot `\"dist\"` vs. `\"n\"`. Not a whole lot can be seen here." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_, ax = plt.subplots(figsize=(4, 4))\n", - "ax.set_xlim(0, 50000)\n", - "ax.set_ylim(0, 0.006)\n", - "sns.regplot(x=\"n\", y=\"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relationship becomes more obvious if one plots the same points on a `\"log\"`-`\"log\"` scale." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_, ax = plt.subplots(figsize=(4, 4))\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_xlim(30, 150000)\n", - "ax.set_ylim(0.00001, 0.1)\n", - "sns.regplot(\"n\", \"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> We are interested in points that have high y-values, relative to their x-neighbours. Controlling for the number of deaths, these points represent the diseases which depart the most from the overall pattern. To find these unusual points, we fit a robust linear model and plot the residuals, Figure 3. The plot shows an empty region around a residual of 1.5. So somewhat arbitrarily, we’ll select those diseases with a residual greater than 1.5." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Note that the HuberRegressor is not the exact\n", - "# same method as in the paper but close.\n", - "X = np.log(devi[\"n\"]).values[:, np.newaxis]\n", - "y = np.log(devi[\"dist\"]).values\n", - "rlm = HuberRegressor()\n", - "rlm.fit(X, y)\n", - "devi[\"residuals\"] = y - rlm.predict(X)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the threshold for \"unusual\" deaths, set arbitrarily at 1.5." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_, ax = plt.subplots(figsize=(4, 4))\n", - "ax.set_xscale(\"log\")\n", - "ax.set_xlim(50, 200000)\n", - "ax.set_ylim(-1, 3)\n", - "sns.regplot(\"n\", \"residuals\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})\n", - "ax.hlines(1.5, 0, 200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Finally, we plot the temporal course for each unusual cause, Figure 4. We split the diseases into two plots because of differences in variability. The top plot shows diseases with over 350 deaths and the bottom with under 350. The causes of death fall into three main groups: murder, drowning, and transportation related. Murder is more common at night, drowning in the afternoon, and transportation related deaths during commute times. The pale gray line in the background shows the temporal course across all diseases." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UqVMJDQ0lJCQEW1tb4uLiOHr0KJcuXWLXrl3Y2dmhVquZNGkSX3/9NcOHD6d///4YjUb27t1Lenq6dfbg6zk4OFjXUH3hhRdo2rQparWafv36Vagn8kYnT55k+vTptG3blubNm+Pt7U1KSgpbtmzBaDQyZcqUco+xceNGAOu6qlXJzc2Nzz77jGnTpjFu3Di6du1KixYtUKlUxMXFcfDgQdLS0jh69CgAXl5eDB06lDVr1jBq1Ci6d+9OZmYmf/75J3q9npCQEE6cOFHlcQohhKhdJGkVQoh6ZsmSJUUSx6rSqlUrHnzwQSIjI9m+fTvp6eno9XoCAgKYPHkyDz74YLFe3AEDBpCVlcXJkyfZs2cPBoMBV1dXevXqxdixY0sddjtp0iQaNGjAd999x/Lly1EUhebNm/Pss88yevToSseemZnJ559/DoBer8fR0ZGAgAAmTpzI4MGDCQsLK3G/4OBgVqxYwbx58/j9999ZunQparUaLy8vWrVqxdNPP13kvttnnnkGd3d3Fi9ezKJFi3BycqJbt248++yzJc6WC/Dhhx/y/vvvs2vXLtasWYOiKPj6+t5U0tqmTRumTp3Kvn372LlzJ+np6bi7u9O6dWsmTZpUZJbj0mzevJnAwEAaN25c6fNXRNeuXVm5cqV1uaTIyEh0Oh3e3t506dKFgQMHFtn+3XffJSAggLVr1/Lzzz/j7u5Ov379mDFjBjNmzKiWGIUQQtQuKuX6ef6FEEIIccc6f/48gwYNYtq0aZIQCiGEqDXknlYhhBBCAJZZg6F6hgYLIYQQN0t6WoUQQgghhBBC1FrS0yqEEEIIIYQQotaSpFUIIYQQQgghRK0lSasQQgghhBBCiFpLklYhhBBCCCGEELWWJK1CCCGEEEIIIWotSVqFEEIIIYQQQtRakrQKIYQQQgghhKi1JGkVQgghhBBCCFFrSdIqhBBCCCGEEKLWkqRVCCGEEEIIIUStJUmrEEIIIYQQQohaS5JWIYQQQgghhBC1liStQgghhBBCCCFqLUlahRBCCCGEEELUWpK0CiGEEEIIIYSotSRpFUIIIYQQQghRa0nSKoQQQgghhBCi1pKkVQghhBBCCCFErSVJqxBCCCGEEEKIWkuSViGEEEIIIYQQtZYkrUIIIYQQQgghai1JWoUQQgghhBBC1FqStAohhBBCCCGEqLUkaRVCCCGEEEIIUWtJ0iqEEEIIIYQQotaSpFUIIYQQQgghRK0lSasQQgghhBBCiFpLklYhhBBCCCGEELWWJK1CCCGEEEIIIWotSVqFEEIIIYQQQtRakrQKIYQQQgghhKi1JGkVQgghhBBCCFFrSdIqhBBCCCGEEKLWkqRVCCGEEEIIIUStJUmrEEIIIYQQQohaS5JWIYQQQgghhBC1liStQgghhBBCCCFqLUlahRBCCCGEEELUWpK01gOTJk1i8eLFldpn4cKFvPvuuwBcvnyZoKAgCgoKqiO8IubMmcM///nPcrd74403+OKLL0p9PCgoiIsXL5Z7nKioKCZMmFDmNjfz/FWF2/m8V4V+/frx559/1nQYQgghxG0TExNDaGgoJpOppkMp0969e+nVq1eVH7ei7a3bbejQoezdu7fGzl9WmygyMpKBAwdWaNuy3Hic+mTp0qXcd999ldpHktZbMGnSJMLDwzEYDDUdilVF3gQGg4Evv/ySxx577DZFdc0TTzxhTZbL8tZbbzFt2rRbPl9wcDBOTk5s3br1lo91qyTpE0IIIUrWr18/2rVrR2hoKGFhYUyYMIEFCxZgNptrNC5/f38OHjyIRqOp0ThEUWvWrKFz5841HUaJwsLC2LBhQ605TmVV1wWQWyVJ6026fPkykZGRqFQqtmzZUtPhVMqWLVto1qwZPj4+NR3KbTF8+HAWLVpU02FUq7rSWyuEEEKUZs6cORw8eJBt27YxZcoU5s6dW+borNre+ylunrRr6q7qeu0kab1Jy5cvp3379owePZrly5cXeWz79u0MGTKE0NBQevbsybfffgtASkoKjz/+OGFhYURERDBx4kTrFcSvv/6aAQMGEBoaypAhQ9i0aZP1eLNnz+bFF1+0/l7asNKzZ88ya9YsDh06ZL1SWZIdO3YQHh5e7O+rVq2iT58+dO7cmS+//NL6d4PBwLvvvkuPHj3o0aMH7777rrV3ufBqzNy5c+natSs9evRg8+bNbN++nYEDBxIREcGcOXNKLUtkZCQTJkwgLCyM3r17s3TpUgBmzpzJJ598Yt3um2++sZ7/t99+KxK3wWDggw8+oE+fPnTr1o033niDvLw86+OdO3dm9+7dZfaIR0dHc++999KxY0eefPJJ0tLSAJg6dSo//fRTkW2HDx9e5PW53pYtWxg6dChhYWFMmjSJs2fPAvDSSy8RExPDE088QWhoKHPnzi33eTebzdb3RefOnXnmmWescRW+BxYvXkyfPn146KGHisWSnp7O448/TpcuXQgPD+fxxx8nLi7O+vikSZP49NNPmTBhAqGhoUyePJmUlBTr48uXL6dv377F4hJCCCGqk5OTE/379+fTTz9l2bJlnDp1CrC0DWbNmsWUKVPo0KEDe/fu5ezZs0yaNImwsDCGDh1q7Ui4dOkSYWFh1nbWa6+9RteuXa3neOmll/j++++Bsj8Pb2xzVeaz84svvihzlNXvv//OqFGj6NixI71792b27NnWxwrPu2zZshLbCHl5ecycOZPw8HCGDBnC0aNHS30+FUXhvffeo2vXrnTs2JHhw4dbn9Mbb5EqacTe9u3b6d+/P507d+aDDz6wPqdLly5lwoQJvPfee4SFhdG/f3/++usvli5dSu/evenatSvLli2zHqes9lphe/Lrr7+me/fu/OMf/yiz3Xz981qRdup3331nbacuWbKkxOdpz549DB8+3Pr7I488wj333GP9feLEiWzevNn6+4kTJxg+fDidOnXi2WefJT8/v8g5S1JW2+5GNx6nX79+fPvttyWe80aFr+MHH3xAeHg4/fr1Y/v27dbHlyxZwuDBgwkNDaV///4sXLgQgJycHKZMmUJCQgKhoaGEhoYSHx9frF1eUmxff/01w4cPp0OHDhQUFJSZ29wMSVpv0ooVKxg+fDjDhw9n165dJCUlWR/75z//yVtvvcXBgwdZvXo1Xbp0AWDevHn4+Piwe/du/vjjD55//nlUKhUAAQEB/Pzzzxw4cIDp06fz0ksvkZCQUKmYmjdvzptvvkmHDh04ePAgkZGRJW536tQpmjZtWuzvBw4cYP369fzwww988cUX1oTryy+/5PDhw6xYsYKVK1dy9OhR/ve//1n3S0pKIj8/nx07djBjxgxee+01Vq5cyZIlS/j555/53//+x6VLl4qd78qVK0yZMoUHHniA3bt3s3z5ckJCQoptt2PHDr777ju+++47Nm7cyO7du4s8/vHHH3P+/HmWL1/Oxo0bSUhIKHI/rI+PD1qtlnPnzpX63C1fvpz33nuPXbt2odVqeeeddwAYNWoUK1eutG4XFRVFQkICvXv3LnaM8+fP88ILL/Dqq6+ye/duevXqxRNPPIHBYOCjjz7C39/fehV5ypQp5T7vP/30E5s3b2b+/Pns3LkTFxcX3nrrrSLn3L9/P2vXrrVeGLme2WxmzJgxbNu2jW3btmFjY1Ns/9WrV/P++++ze/dujEYj3333HQBnzpzhzTff5MMPP2Tnzp2kpaUVSXiFEEKI6tauXTt8fX2LtGdWr17NE088wV9//UW7du144okn6N69O3/++SevvfYaL774IufOnSMgIABHR0eOHz8OWD4v7e3trZ+x+/fvJyIioshxS/o8LEl5n50fffQRO3fuJCsri/j4+FKPY2dnxwcffEBkZCRfffUVCxYsKJIUQelthM8//5zo6Gg2bdrEt99+W6wD5Xq7du0iMjKSDRs2cODAAT799FNcXV1L3f5GmzZtYsmSJSxbtoytW7cWSfqOHDlCUFAQe/fuZdiwYTz//PMcPXqUTZs28dFHH/HWW2+RnZ0NlN9eS0pKIj09nW3btvH222+X2W6+XkXaqZmZmezYsYN3332Xt956i/T09GLH6dChAxcuXCAlJQWj0cjJkydJSEggKyuLvLw8/v77bzp16mTdft26dXzzzTds2bKFkydPWjteylKRtl1ZKnPOI0eO0LRpU/bs2cNjjz3GP//5TxRFAcDDw4OvvvqKv/76i/fff5/333+fY8eOYW9vz9y5c/H29ubgwYMcPHiwwiMz16xZw9dff01kZCRarbZKcpvrSdJ6EyIjI4mJiWHw4MG0adOGgIAAVq9ebX1cq9Vy5swZsrKycHFxoXXr1ta/JyYmEhMTg06nIywszPrPN3jwYHx8fFCr1QwZMoTGjRtz5MiRaok/MzMTBweHYn+fPn06tra2BAcHExwcTFRUFGDpCZw2bRoeHh64u7szbdq0IomcVqvlySefRKfTMWTIEFJTU3nwwQdxdHSkZcuWtGjRgpMnTxY73+rVq+nWrRvDhg1Dp9Ph5uZWYtK6bt06xowZQ2BgIPb29kyfPt36mKIo/Prrr7z66qu4urri6OjI448/zpo1a4ocw8HBgczMzFKfk5EjR1qP/8wzz7B+/XpMJhP9+/fnwoULXLhwAbBcrBg8eDB6vb7YMdauXUvv3r3p3r07Op2ORx99lLy8PA4ePFjqect63hcuXMhzzz2Hr68ver2e6dOns2HDhiI97E8//TT29vbY2toWO66bmxsDBw7Ezs4OR0dHnnzySfbv319kmzFjxtC0aVNsbW0ZNGgQJ06cAGD9+vX06dOH8PBw9Ho9zzzzDGq1VBdCCCFuL29v7yIJRv/+/enUqRNqtZqoqChycnKYOnUqer2erl270rdvX2sbIDw8nP3795OYmAjAwIED2bdvH5cuXSIrK4vg4GDrcUv7PCxJWZ+dffv2JSwsDL1ez4wZM0pMsgp17tyZoKAg1Go1wcHBDB06lH379hXZprQ2wrp163jiiSdwdXXFz8+PSZMmlXoerVZLdnY2586dQ1EUmjdvjre3d6nb32jKlCm4urri7+/Pgw8+WKTN27BhQ+655x40Gg1DhgwhNjaWadOmodfr6dGjB3q9nujo6Aq119RqNTNmzECv12Nra1tmu/l6FWmnTps2DZ1OR+/evbG3t+f8+fPFjmNra0vbtm2JjIzk2LFjBAcH07FjR/766y8OHTpE48aNcXNzs24/adIkfHx8cHV1pW/fvmW+ZwpVpG1Xlsqc09/fn3HjxqHRaBg9ejSJiYnWTrY+ffrQqFEjVCoVERERdO/evdTOroqaNGkSfn5+1jZpVec22luK7g61fPlyunfvjru7OwDDhg1j2bJlPPzwwwB89tlnfPnll/znP/8hKCiIF154gdDQUB599FE+//xzJk+eDMD48eOZOnWq9Zjz5s3jypUrgKV7PjU1tVrid3Z2tl71up6np6f1Zzs7O3JycgBISEjA39/f+pi/v3+RKyWurq7WCQoK36geHh7Wx21sbEo8X2xsLI0aNSo33oSEBNq0aWP9vUGDBtafU1JSyM3NZcyYMda/KYpSbOKG7OxsnJycSj2Hn59fkfIZjUZSU1Px9PRk8ODBrFy5kunTp7N69Wo+++yzUuO8/nlSq9X4+fmVeZUVSn/eY2JimDZtWpFkUa1Wk5ycbP3d19e31OPm5uby/vvvs3PnTusHfnZ2NiaTyfp6eXl5lXjuhISEIse2t7ev1FVZIYQQoirEx8fj4uJi/f36z+vCz6rrPyf9/f2tn7sRERFs2bIFHx8fwsPD6dy5MytWrMDGxoawsLAi+5X2eViSin522tnZlfnZefjwYT7++GNOnz6N0WjEYDAwaNCgItuU1Ta7se1Smq5du3L//ffz1ltvceXKFe6++25eeeUVHB0dS93netefp0GDBkXagNe39wrbgNfHXNgGrEh7zc3NDRsbG+vvZbWbr1eRdqpWey3lKev1DQ8PZ9++fdb3jLOzM/v370ev1xfpmYfi74OK9CKW1barSI9mZc5543sHsJZ7+/btfPHFF1y4cAGz2UxeXh6BgYHlnr8s179PoOpzG0laKykvL49169ZhNpvp3r07YBlLn5GRQVRUFMHBwbRr144vv/wSo9HIzz//zLPPPsv27dtxdHRk5syZzJw5k1OnTvHQQw/Rtm1bGjVqxGuvvcb3339PaGgoGo2GkSNHWs9pZ2dX5B7N64ci36isK3qFgoKCrD2HFeHt7U1MTAwtW7YELMlmZa7QlcbPz69CV1y8vb2JjY21/h4TE2P92c3NDVtbW9asWVPqP3t8fDxGo5FmzZqVeo7rjx8bG2vt+QUYPXo0L7/8Mp06dcLOzo7Q0NBS4yy8RwQslXFsbOxNT3jl6+vLe++9V2QoSqHLly8DZb/e3333HefPn+fXX3/Fy8uLEydOMGrUKOvQkLJ4e3tbhyCBJQEu7Z4LIYQQojocOXKE+Pj4Ej8HwfJZFRcXh9lstiYBsbGxNGnSBLAkIB9++CG+vr6Eh4fTqVMnZs2ahY2NTYlze9wqb2/vIj14eXl5ZX52vvDCCzzwwAN888032NjY8O6771a4Ue/l5UVsbGyRtllZHnzwQR588EGSk5N59tln+eabb3j22Wexs7MjNzfXul1JbczrzxMTE3NTbcCKtNdubNOU1m6+/t5kqNp2akREBP/+97/x9/dnypQpuLi48Prrr6PT6bj//vtv6pjXK6ttd7sYDAZmzJjBBx98QP/+/dHpdDz11FPW9mFJbcuK5CLX73flypUyc5ubIeP9Kmnz5s1oNBrWrFnD8uXLWb58OWvXriUsLIzly5djMBhYuXIlmZmZ6HQ6HBwcrBXptm3buHjxIoqi4OTkhEajQaVSkZubi0qlsvbcLlmyhNOnT1vPGRISwv79+4mJiSEzM5Ovvvqq1Pg8PDyIj48vc9Kh3r17FxsmWpahQ4fy5ZdfkpKSQkpKCl988UWRG9Vv1vDhw/nzzz9Zu3YtBQUFpKamljjMYdCgQSxbtowzZ86Qm5vL559/bn1MrVYzduxY3nvvPWsPZHx8PDt37rRus2/fPrp06VLikN5CK1eutB7/v//9LwMHDrT2RoaGhqJWq/n3v//NiBEjSj3G4MGD2b59e5F7XPR6vTXJ9fT0LPHe3tLcd999fPrpp9YrVCkpKcXudSlLdnY2NjY2ODs7k5aWVuR5K8/AgQP5/fffiYyMxGAw8Nlnn9X4sgNCCCHuDFlZWWzbto3nn3+eESNGEBQUVOJ27dq1w9bWlm+++Qaj0cjevXvZunUrQ4YMAaBJkybY2NiwcuVKIiIicHR0xMPDgw0bNlRL0jpw4EC2bt3KX3/9hcFgYPbs2WVeKM7OzsbFxQUbGxuOHDlSZNhteQYPHszXX39Neno6cXFxxSaNvN6RI0c4fPgwRqMROzs79Hq9tW0aEhLCpk2byM3N5eLFi8UmuwT49ttvSU9PJzY2lh9//NH6/FZGRdprNyqt3XyjqmynhoaGcv78eY4cOUK7du1o2bIlV65c4ciRI1XynrnVtl1VMBgMGAwG3N3d0Wq1bN++nT/++MP6uIeHB2lpaUVuqwsJCWH79u2kpaWRmJjIDz/8UOY5ysttboYkrZW0bNkyxowZg7+/P15eXtav+++/n1WrVgGW+x779etHx44dWbhwIR999BEAFy9e5JFHHiE0NJTx48dz33330aVLF1q0aMHkyZOZMGEC3bp149SpU3Ts2NF6zu7duzNkyBBGjBjBmDFj6Nu3b6nxFR6vR48epa5f1bdvX86dO1fusNVCTz31FG3atGHEiBGMGDGC1q1b89RTT1X0KSuVv78/c+fOZd68eURERDBq1CjrvRrX6927Nw899BAPPfQQd911l3Viq0IvvfQSjRs3Zty4cXTs2JGHH364yJXOVatWMWHChDJjGTlyJDNnzqR79+4YDIZiU+yPHDmSU6dOlXmVqFmzZnz00Ue8/fbbdOnShW3btjFnzhxrsjx16lS+/PJLwsLCSpw46UYPPvgg/fr1Y/LkyYSGhjJu3LhK3Qvw0EMPkZ+fT5cuXRg/fjw9e/as8L4tW7bkjTfe4MUXX6Rnz544OzuXORRZCCGEuFWFM+z37t2bOXPm8Mgjj/D++++Xur1er2fOnDns2LGDLl26WCcQbN68uXWbiIgI632fhb8rimKdb6QqtWzZktdff53nn3+enj17Ym9vj7u7e6kXzWfNmsVnn31GaGgoX3zxBYMHD67wuaZPn46/vz/9+/dn8uTJZbZPsrOzee2114iIiKBv3764urry6KOPApa2gk6no1u3brzyyislJnv9+/dnzJgxjBo1ij59+nDvvfdWOM7rlddeu1Fp7eYbVWU71d7entatW9OiRQvr6xYaGoq/v3+RodA361bbdlXB0dGR1157jWeffZbw8HBWr15Nv379rI83b96coUOHMmDAAMLCwoiPj2fkyJEEBwdbYy/vwkV5uc3NUCkVGSso6p1FixZx5syZMtc/qw+ioqKYNWvWLa/Tunz5chYtWsSCBQuqKDIhhBBC1GfZ2dmEh4ezYcMGAgICajocIeo0SVqFKEdubi4PPfQQEydOZNSoUTUdjhBCCCFqqa1bt9K1a1cUReHf//43R44cYdmyZRWac0QIUToZHixEGXbu3EnXrl3x8PBg2LBhNR2OEEIIIWqxLVu20LNnT3r27MnFixf5v//7P0lYhagC0tMqhBBCCCGEEKLWkp5WIYQQQgghhBC1liStd7DHHnuMZcuWWX//5JNP6Ny5M927dycmJobQ0FBMJlOlj3v58mWCgoIoKCgo8fFz584xcuRIQkND+fHHH3njjTf44osvbrocNS0oKIiLFy8C3HJZli5dyn333VdVoQlRp0yaNInFixdXap+FCxfy7rvvAuXXPVVpzpw5FZrIrrw64fr6oyxRUVHlzoJ+M89fZd2Oc9ysfv368eeff5b4WGRkJAMHDrzNEQkhhKgq2poOQNScb775xvpzTEwM8+bNY9u2bdYpvQ8ePFht5+3cuTMrVqyoluPXpLfeequmQxCi0iZNmkRUVBR//PFHmesZ305Lly5l8eLFZc7YbTAY+PLLL/n1119vY2QWTzzxRIW2q6o6ITg4GCcnJ7Zu3VpkaQJRMWFhYWzYsOGWjxMUFMTGjRtp3LhxFUQlhACYPXs2Fy9e5OOPP67pUEQtJj2tArAkra6urlWyBlVFztWyZcsKbVvVPSY303NcV92O3iZR912+fJnIyEhUKhVbtmyp6XAqZcuWLTRr1gwfH5+aDuW2GD58+C0v31Wb3En1sRCi+kh7584gSWsVu3Go18yZM/nkk08A2Lt3L7169eK7776ja9eu9OjRgyVLlli33b59O0OGDCE0NJSePXvy7bffFtlvzpw5dO7cmX79+rFy5UrrfgaDgQ8++IA+ffrQrVs33njjDfLy8qyPb968mZEjR9KxY0cGDBjAjh07gGvDvP78808mT55MQkICoaGhzJw5s9gwu8zMTF599VV69OhBz549+eSTT6wNDpPJxAcffEDnzp3p378/27dvL/X5efDBB9m7dy9vvfUWoaGhnD9/vsTn6Ouvv6Z79+784x//wGw28/XXXzNgwAA6d+7MM888Q1pamvWYM2bMoHv37nTq1In777+f06dPF3n+Z82axZQpU+jQoQN79+6lX79+fPPNNwwfPpwOHTrw6quvkpSUxGOPPUZoaCgPP/ww6enppZbhm2++oUePHvTo0YPffvutyGPXlyUlJYXHH3+csLAwIiIimDhxImazGYDY2FimT59Oly5d6Ny5c7HemA8++IDw8HD69etX5PlcsmQJgwcPJjQ0lP79+7Nw4ULrYyU9d3l5ebzyyiuEh4czePBg5s6dS69evaz7xMfH8/TTT9OlSxf69evHjz/+WGq5Rf20fPly2rdvz+jRo1m+fHmRx0qrk8p6bxf+r4aGhjJkyBA2bdpkPd7s2bN58cUXrb+XNpz37NmzzJo1i0OHDhEaGkpYWFiJse/YsYPw8PBif1+1ahV9+vShc+fOfPnll9a/GwwG3n33Xev/77vvvovBYACu/f/MnTvXWj9v3ryZ7du3M3DgQCIiIpgzZ06pZYmMjGTChAmEhYXRu3dvli5dChStE6Ds+qO8urxz587s3r3bGnNJoqOjuffee+nYsSNPPvmkta6cOnUqP/30U5Fthw8fXuT1KZSfn8+LL75I586dCQsL45577iEpKcn6+JUrV5gwYQKhoaFMnjyZlJQU62OVrY9nzpzJG2+8wSOPPEJoaCgPPPAAV65cKbV8W7ZsYejQoYSFhTFp0iTOnj1b5PGjR48yZMgQwsPD+cc//kF+fj5w7fUtVFbdZzKZmDNnjvV9PGbMGGJjY7n//vsBrLe3rF27ttQ4hbhTLFmypMjIk7vvvpsZM2ZYf+/duzcnTpzgnXfeoXfv3nTs2JExY8YQGRkJWOrxr776inXr1hEaGsqIESOAstudS5cuZcKECbz33nt07tyZ2bNn38YSixqjiCoVGBioXLhwwfr7K6+8ovzf//2foiiKsmfPHiUkJET59NNPFYPBoPz+++9Ku3btlLS0NEVRFKV79+7K/v37FUVRlLS0NOXvv/8ust97772n5OfnK3v37lXat2+vnD17VlEURXn33XeVxx9/XElNTVUyMzOVxx9/XPn4448VRVGUw4cPKx07dlR27dqlmEwmJS4uTjlz5oyiKIrywAMPKL/++qv1HD179rTGfenSJSUwMFAxGo2KoijKU089pbz++utKdna2kpSUpNxzzz3KggULFEVRlF9++UUZOHCgEhMTo6SmpioPPPBAkX1vdP15S3uOPvzwQyU/P1/Jzc1Vvv/+e2Xs2LFKbGyskp+fr7z++uvKc889Z91/8eLFSmZmppKfn6+88847yogRI4ocu2PHjkpkZKRiMpmUvLw8pW/fvsrYsWOVxMREJS4uTunSpYsyatQo5dixY0peXp4yadIkZfbs2SXGvn37dqVr167KyZMnlezsbOX5558v8ppfX5aPP/5Yef311xWDwaAYDAZl//79itlsVgoKCpThw4cr7777rpKdna3k5eVZX/clS5YorVq1UhYtWqQUFBQoP//8s9K9e3fFbDYriqIo27ZtUy5evKiYzWZl7969Srt27Yq9T65/7j766CPl/vvvV9LS0pTY2Fhl2LBh1tfZZDIpo0ePVmbPnq3k5+cr0dHRSr9+/ZQdO3aUWHZRPw0YMECZP3++cvToUaVVq1ZKYmKi9bHS6qTS3tuKoihr165V4uLiFJPJpKxZs0Zp3769Eh8fryiKonz22WfKCy+8YD3+jfXM9XXDkiVLlAkTJpQZ+5gxY5S1a9cWO94///lPJTc3Vzlx4oTSunVra5336aefKmPHjlWSkpKU5ORkZfz48conn3yiKMq1/5/Zs2crBoNBWbRokdK5c2fl+eefVzIzM5VTp04pbdu2VaKjo4uV5fLly0qHDh2UVatWKQaDQUlJSVGOHz+uKErROqG8+qOsurxQaGiocuLEiRKfjwceeEDp0aOH9fjTp0+3xrhmzRrl3nvvtW574sQJJSIiQsnPzy92nAULFiiPP/64kpOToxQUFChHjx5VMjMzrefo37+/cu7cOSU3N1d54IEHlI8++si6b2Xr41deeUXp0KGDsm/fPiU/P195++23S33dz507p7Rv317ZtWuXYjAYlK+//loZMGCAtQx9+/ZVhg4dav0sGj9+fJHPlorWfXPnzlWGDRumnD17VjGbzcqJEyeUlJQURVGKf8YLcaeLjo5WOnXqZG1j9unTx/q/Fh0drYSFhSkmk0lZvny5kpKSohiNRuXbb79VunXrpuTl5SmKUvyzQVHKbncuWbJECQkJUX788UfFaDQqubm5t7fQokZIT+ttptVqmTZtGjqdjt69e2Nvb8/58+etj505c4asrCxcXFxo3bp1kX2feeYZ9Ho9ERER9O7dm3Xr1qEoCr/++iuvvvoqrq6uODo68vjjj7NmzRoAfvvtN+655x66d++OWq3Gx8eH5s2bVyrmpKQktm/fzquvvoq9vT0eHh48/PDD1nOsW7eOhx56CD8/P1xdXXn88cdv6TlSq9XMmDEDvV6Pra0tCxcu5LnnnsPX1xe9Xs/06dPZsGGDtXfm3nvvxdHREb1ez9NPP01UVBSZmZnW4/Xv359OnTqhVquxsbEB4IEHHsDT0xMfHx/CwsJo164drVq1wsbGhrvuuovjx4+XGNu6desYM2YMgYGB2NvbM3369FLLodVqSUxMJCYmBp1OR1hYGCqViiNHjpCQkMDLL7+Mvb09NjY2RXqS/P39GTduHBqNhtGjR5OYmGjt5ejTpw+NGjVCpVIRERFB9+7drVcrS3ru1q1bx+OPP46Liwu+vr48+OCD1m2PHj1KSkoK06dPR6/XExAQwLhx46T34A4SGRlJTEwMgwcPpk2bNgQEBLB69Wrr46XVSaW9twEGDx6Mj48ParWaIUOG0LhxY44cOVIt8WdmZuLg4FDs79OnT8fW1pbg4GCCg4OJiooCLD2w06ZNw8PDA3d3d6ZNm1Zk1IpWq+XJJ59Ep9MxZMgQUlNTefDBB3F0dKRly5a0aNGCkydPFjvf6tWr6datG8OGDUOn0+Hm5kZISEix7cqqP8qryws5ODgUqd9uNHLkSOvxn3nmGdavX4/JZKJ///5cuHCBCxcuALBixQoGDx5c4j3MWq2WtLQ0Ll68iEajoU2bNjg6OlofHzNmDE2bNsXW1pZBgwZx4sQJ62M3Ux/36dOH8PBw9Ho9zz33HIcOHSI2NrZYXGvXrqV37950794dnU7Ho48+Sl5eXpH5F+6//37rZ9GTTz5Z7PmD8uu+xYsX88wzz9CsWTNUKhXBwcG4ubmV+pwLcScLCAjAwcGBEydOEBkZSY8ePfD29ubs2bPs27fP+v8+cuRI3Nzc0Gq1TJ48GYPBYG3/3qi8dieAt7c3kyZNQqvVYmtre7uKK2qQTMR0m7m6uqLVXnva7ezsyMnJAeCzzz7jyy+/5D//+Q9BQUG88MILhIaGAuDs7Iy9vb11P39/fxISEkhJSSE3N5cxY8ZYH1MUpcgw1N69e99SzDExMRQUFNCjRw/r38xmM35+fgAkJCRYfy6M7Va4ublZGzOF5582bRpq9bVrLGq1muTkZDw9Pfnkk09Yv349KSkp1m1SU1NxcnICKBJbIU9PT+vPNjY2RX63tbW1viY3SkhIoE2bNtbfGzRoUGo5Hn30UT7//HMmT54MwPjx45k6dSqxsbH4+/sXeR+UFpudnR2ANZ7t27fzxRdfcOHCBcxmM3l5eQQGBlq3v/G5u/G18fX1tf585coVEhISiiTMJpOp1KGYov5Zvnw53bt3x93dHYBhw4axbNkyHn74YaD0Oqm093bhMefNm2cd4pmTk0Nqamq1xO/s7Ex2dnaxv9/4P1T4/5OQkFCkfiqsRwu5urqi0WgArI2g6+/zt7GxKfF8sbGxNGrUqNx4y6o/yqvLC2VnZ1vrtpLcWBcbjUZSU1Px9PRk8ODBrFy5kunTp7N69Wo+++yzEo8xcuRI4uLieP7558nIyGDEiBE899xz6HQ6ALy8vKzbXv/8mkymm6qPr6+XHBwccHFxKVZ3QfHXT61W4+fnR3x8fKnlv/71LVRe3RcXF1eh11MIYREeHs6+ffu4ePEi4eHhODk5sX//fg4dOkRERAQA3377Lb/99hsJCQmoVCqysrJK/Wwor90JResNcWeQpLWK2dnZkZuba/09MTGxwpOEtGvXji+//BKj0cjPP//Ms88+a72fMSMjg5ycHGviGhsbS8uWLXFzc8PW1pY1a9aUeB4/Pz+io6NvqUyFPZx79uwpMdHy8vIqclW8pCvklVHYY3P9+d977z06depUbNvly5ezZcsW5s2bR8OGDcnMzCQ8PBxFUW4phtJ4e3sXKV9MTEyp2zo6OjJz5kxmzpzJqVOneOihh2jbti1+fn7ExsZSUFBQauJaEoPBwIwZM/jggw/o378/Op2Op556qkhZb3zuvLy8iIuLo0WLFoClMVbIz8+Phg0bsnHjxgrHIOqPvLw81q1bh9lspnv37oDlPZaRkUFUVBTBwcGl1kmlvbcbNWrEa6+9xvfff09oaCgajYaRI0daz2lnZ1fkHs3r75O80Y3v5ZIEBQVZew4rwtvbu8hEcLGxsXh7e1d4/9L4+flVqDe5rPqjvLocLPdhGo1GmjVrVuo5bqyLC3t+AUaPHs3LL79Mp06dsLOzs14UvZFOp2P69OlMnz6dy5cvM3XqVJo2bcrYsWPLLN+qVatuqj6+vl7Kzs4mPT29xNfF29ubU6dOWX9XFIXY2Ngiz9eNz29Jxymv7vP19SU6OrrIBUEhROkiIiLYunUrV65c4YknnsDZ2ZlVq1Zx8OBB7r//fiIjI/nmm2/4/vvvadmyJWq1ukjdUFK7r6x2Z0n7iPpPhgdXseDgYFavXo3JZGLHjh3s37+/QvsZDAZWrlxJZmYmOp0OBweHIj2LYJn4w2AwEBkZye+//86gQYNQq9WMHTuW9957j+TkZMDSsNm5cydgGaq1dOlSdu/ejdlsJj4+vtjEFeXx9vame/fu/Pvf/yYrKwuz2Ux0dDT79u0DLMMBf/rpJ+Li4khPT+frr7+u1PHLc9999/Hpp59ae25SUlLYvHkzYGng6PV63NzcyM3N5f/+7/+q9Nw3GjRoEMuWLePMmTPk5uby+eefl7rttm3buHjxIoqi4OTkhEajQaVS0a5dO7y8vPjPf/5DTk4O+fn5HDhwoNxzGwwGDAYD7u7uaLVatm/fzh9//FHmPoMHD+arr74iPT2d+Ph45s+fb32sXbt2ODg48PXXX5OXl4fJZOLUqVPVNpRT1C6bN29Go9GwZs0ali9fzvLly1m7di1hYWEsX768zDqptPd2bm4uKpXK2nO7ZMmSIhPxhISEsH//fmJiYsjMzOSrr74qNT4PDw/i4+PLnHSod+/eFa5jAYYOHcqXX35JSkoKKSkpfPHFFwwfPrzC+5dm+PDh/Pnnn6xdu5aCggJSU1OLDJktVFb9UV5dDrBv3z66dOlS5rJEK1eutB7/v//9LwMHDrT2HoeGhqJWq/n3v/9tneykJHv27OHkyZOYTCYcHR3RarXFPo9KcrP18fbt24mMjMRgMPDf//6X9u3bl9gjO3jwYLZv387u3bsxGo1899136PX6Isn3L7/8QlxcHGlpacyZM4chQ4YUO055dd/YsWP573//y4ULF1AUhaioKGuPkKenJ5cuXapQuYS4U4SHh7N3717y8vLw9fUlLCyMnTt3kpaWRqtWrcjOzkaj0eDu7k5BQQGff/45WVlZ1v09PDy4cuWKdWRJee1OcWeSpLWK/fOf/2Tbtm2EhYWxatUqBgwYUOF9V6xYQb9+/ejYsSMLFy7ko48+sj7m6emJs7MzPXv25MUXX+Rf//qX9d7Ul156icaNGzNu3Dg6duzIww8/bL1PoF27drz//vvWnsoHHnigzN7B0nz44YcYjUbrrIwzZswgMTERgHHjxtGjRw9GjhzJ6NGjufvuuyt9/LI8+OCD9OvXj8mTJxMaGsq4ceOsjYtRo0bh7+9Pz549GTp0KB06dKjSc9+od+/ePPTQQzz00EPcdddddOnSpdRtL168aJ0Rc/z48dx333106dIFjUbDnDlzuHjxIn379qVXr16sW7eu3HM7Ojry2muv8eyzzxIeHs7q1avLXa9x2rRp+Pr60r9/fx5++GEGDhxobfAWxhEVFUX//v3p0qULr732WpEPElF/LVu2jDFjxuDv74+Xl5f16/7772fVqlVA6XVSae/tFi1aMHnyZCZMmEC3bt04deoUHTt2tJ6ze/fuDBkyhBEjRjBmzBj69u1banyFx+vRowedO3cucZu+ffty7ty5IsNDy/LUU0/Rpk0bRowYwYgRI2jdujVPPfVURZ+yUvn7+zN37lzmzZtHREQEo0aNst5He73y6o+y6nKw9GROmDChzFhGjhzJzJkz6d69OwaDgX/+85/FHj916lSRHvAbJSUlMWPGDDp16sSQIUOIiIgoc/tCN1sfDxs2jC+++ILOnTtz7NixIp9912vWrBkfffQRb7/9Nl26dGHbtm3MmTOnSBI/bNgwJk+ezIABA2jUqBFPPvlkseOUV/c98sgjDB48mMmTJ9OxY0f++c9/Wmchnj59OjNnziQsLEzu/xfiqqZNm+Lg4GAdYu/o6EjDhg3p2LEjGo3GOgPwwIED6devHzY2NkUuTA0aNAiwzJA+evRooOx2p7gzqZTqGkcpqszevXt56aWXrEvVCHGzfvnlF9auXVukx1WIumzRokWcOXOmWHJW30RFRTFr1qxbXqd1+fLlLFq0iAULFlRRZLdm5syZ+Pj48Nxzz1XreXbv3s1rr71W59YiFkIIYSH3tApRjyUkJHDp0iVCQ0O5cOEC8+bNs641KER9MH78+JoO4bYIDg6+5YQ1NzeXX375hYkTJ1ZRVHXH6dOnadiwYU2HIYQQ4iZJ0ipEPWY0Gpk1axaXL1/GycmJoUOH3pENViHudDt37uTpp5+ma9euDBs2rKbDua3eeecdtm7dygcffFDToQghhLhJMjxYCCGEEEIIIUStJRMxCSGEEEIIIYSotSRpFUIIIYQQQghRa9WZe1pTU7Mxm+vfSGYPD0eSk+vfEiP1tVwgZast1GoVbm4ONR1GlZO6ru6pr2Wrr+WCulU2qevqlrr03qqs+lq2+louqFtlK6+uqzNJq9ms1MvKDZBy1UFSNlFdpK6rm+pr2epruaB+l60ukLqubqqvZauv5YL6UzYZHiyEEEIIIYQQotaqUNJ6/vx5xo8fz8CBAxk/fjwXLlwots2uXbsYM2YMbdq0KTat/BdffMHQoUMZPnw4Y8aMYefOnVUSvBBCCCGEEEKI+q1Cw4NnzZrFxIkTGTlyJCtWrOCNN97gxx9/LLJNQEAA7777LuvXr8dgMBR5rF27dkyePBk7OzuioqJ44IEH2LVrF7a2tlVXEiGEEEIIIYQQ9U65Pa3JyckcP37cuhj5sGHDOH78OCkpKUW2a9y4MSEhIWi1xfPgnj17YmdnB0BQUBCKopCWllYF4QshhBBCCCFE7WEym0nPyq/pMOqVcpPW2NhYfHx80Gg0AGg0Gry9vYmNjb2pEy5fvpxGjRrh6+t7U/sLIYQQQgghRG2Um1/AfxYeYuZXe8jOM9Z0OPXGbZ09eN++ffz3v//lu+++q/S+Hh6O1RBR7eDl5VTTIVSL+loukLKJ6iN1Xd1UX8tWX8sF9btsdYHUdXVTfS1bVZUrLTOfd346wLkr6QDEp+fTOcC9So59s+rLa1Zu0urn50d8fDwmkwmNRoPJZCIhIQE/P79KnejgwYO89NJL/O9//6NZs2aVDjQ5OaveTNl8PS8vJxITM2s6jCpXX8sFUrbaQq1W1ctGj9R1dU99LVt9LRfUrbJJXVe31KX3VmXV17JVVbmS0nL5eNEh0jLzmT6mLV+tPMbeo7E086m5/9+69JqVV9eVOzzYw8ODkJAQVq9eDcDq1asJCQnB3b3iVw2OHDnCc889x2effUbr1q0rvJ8QQgghhBBC1GaXE7N4d/4BsnONvHhfKB0DvWjRwIWT0ak1HVq9UaElb/71r38xf/58Bg4cyPz583nzzTcBmDJlCkePHgUgMjKSXr16MW/ePBYuXEivXr2sS9u8+eab5OXl8cYbbzBy5EhGjhzJyZMnq6lIQgghhBBCCFH9zlxO59/z/0IFvHJ/R1o0cAEgqJErlxKyyMqV+1qrQoXuaW3evDmLFy8u9ve5c+dafw4LC2PHjh0l7r9kyZKbDE8IIYQQQgghap8jZ5P537KjuDnZ8ML4Dni62lkfC27kxnLOc+pSGh0DvWowyvqhQj2tQgghhBBCCCEs9hyLY/aSI/h62POPBzoVSVgBmvo5o9OqiZIhwlXits4eLIQQQgghhBB12ebIS/yy+TTBjVx5+p522NkUT6l0WvXV+1rTbn+A9ZD0tAohhBBCCCFEORRFYdmOc/yy+TShLT15blz7EhPWQsFyX2uVkaRVCCGEEEIIIcpgNivM33iKVX9eoGc7P54a3QadVlPmPkGN3ACkt7UKSNIqhBBCCCGEEGVYuPU02w5eYXCXRjw8OBiNuvw0qpm/M3qtWpa+qQJyT6sQQgghhBBClGLnkRg2R15mQFhDxvZpUeH9tBo1LRq6ECU9rbdMelqFEEIIIYQQogRnrqTz04aTtG7ixvh+FU9YCwU1cuNyYhaZOYZqiO7OIUmrEEIIIYQQQtwgJSOPz5cexd3JlsdHtqnQkOAbBTdyBeDUpbSqDe4OI0mrEEIIIYQQQlzHYDQxe+lRDEYTT9/bDkc73U0dp6mfM3qdWoYI3yJJWoUQQgghhBDiKkVR+H5dFNFxmUwd3poGng43fSytRk3LBi5EyWRMt0SSViGEEEIIIYS4av3eaPYcj2d0r2Z0aOl5y8cLauTGlcRsMuS+1psmSasQQgghhBBCAEfOJvHb72eJCPFmaNfGVXLM4MaW9VpPyRDhmyZJqxBCCCGEEOKOF5uczVcrjxHg48gjQ0JQqVRVctwmvk7odWpOStJ60yRpFUIIIYQQQtzRcvKMfLbkKDqNmqfHtMNGp6myY2s1alo2dJX7Wm+BJK1CCCGEEEKIO5bZrDBn5TGS0nJ5anRbPFxsq/wcwY1cuZKUTUa23Nd6MyRpFUIIIYQQQtyxfvv9LH+fS+GBuwMJDHCtlnMEN7p6X6us13pTJGkVQgghhBBC3JG2Rl5i/b5o+nVsQO8ODartPI19nbDRaTghQ4RviramAxBCCCGEEEKI28VsVjgXk8HBM4lsjrxMcCNXJvRvWa3ntNzX6iKTMd0kSVqFEEIIIYQQ9Vq+0cTxCykcPJ3EkTNJZOQY0ahVhAZ5M+mulmg11T8ANbixG7/9fpaMbAPODvpqP199IkmrEEIIIYQQot5JzzZw+EwSh04ncexCCsYCM3Y2Gto28yC0pRdtm7nTOMCdxMTM2xJPUCNXAKKiU4kI8bkt56wvJGkVQgghhBBC1Atms8LG/Zc4cCqBc1cyUAAPZ1t6tfcntKUngQGut6VXtSSNfZyw0Ws4GZ0mSWslSdIqhBBCCCGEqBf2Ho/n121naOzjxMieTenQwpMAb0dUKlVNh4ZWoyZQ1mu9KRW6zHD+/HnGjx/PwIEDGT9+PBcuXCi2za5duxgzZgxt2rThgw8+KPKYyWTizTffZMCAAdx1110sXry4SoIXQgghhBBCiEJ/n0/GyV7H6w+HMaJ7Uxr5ONWKhLVQcCNXYpNzSJf1WiulQknrrFmzmDhxIhs2bGDixIm88cYbxbYJCAjg3Xff5dFHHy322KpVq4iOjmbjxo0sWrSI2bNnc/ny5VuPXgghhBBCCCEARVE4diGVVk3cUdeiRPV6QVfXaz0pva2VUm7SmpyczPHjxxk2bBgAw4YN4/jx46SkpBTZrnHjxoSEhKDVFh9xvHbtWsaOHYtarcbd3Z0BAwawfv36KiqCEEIIIYQQ4k53OTGbjGwDrZu413QopWrs64itXkOULH1TKeXe0xobG4uPjw8ajQYAjUaDt7c3sbGxuLtX7A0RGxuLv7+/9Xc/Pz/i4uIqFaiHh2Oltq9LvLycajqEalFfywVSNlF9pK6rm+pr2epruaB+l60ukLqubqrtZdv5dzwAvcIC8HCxq/B+t7tcbZp7cuZK+m05b21/zSqqzkzElJychdms1HQYVc7Ly+m2TbN9O9XXcoGUrbZQq1X1stEjdV3dU1/LVl/LBXWrbFLX1S116b1VWXWhbPuOxeLv6YDZUFDhWGuiXE19HYk8Ec/p80m4OtpU23nqwmtWqLy6rtzhwX5+fsTHx2MymQDLpEoJCQn4+flVOAg/Pz9iYmKsv8fGxuLr61vh/YUQQgghhBCiNAajiVOX0mr10OBCwdb7WtNqNpA6pNyk1cPDg5CQEFavXg3A6tWrCQkJqfDQYIBBgwaxePFizGYzKSkpbN68mYEDB9581EIIIYQQQghx1enL6RgLzLRu6lbToZSrkY8jdjYamYypEio0e/C//vUv5s+fz8CBA5k/fz5vvvkmAFOmTOHo0aMAREZG0qtXL+bNm8fChQvp1asXO3fuBGDkyJE0bNiQu+++m3HjxjFt2jQCAgKqqUhCCCGEEEKIO8mxCylo1CqCAmp/0qpRq2nZ0FUmY6qECt3T2rx58xLXVp07d67157CwMHbs2FHi/hqNxproCiGEEEIIIURVOnY+hZYNXbDRa2o6lAoJbuTGkbPJpGbm4+ZUffe11hcV6mkVQgghhBBCiNooPdvApYQsWjet/fezFgpq5ArAyUsyRLgiJGkVQgghhBBC1FnHL6QA1KmktbGP09X7WtNqOpQ6QZJWIYQQQgghRJ117HwKjnY6GvnUnTVJ1WoVgQ1dibooPa0VIUmrEEIIIYQQok5SFIVjF1Jo1cQNtUpV0+FUSlAjN+JTc0nNzK/pUGo9SVqFEEIIIYQQddKVpGzSswx1Yn3WG4U0LlyvVXpbyyNJqxBCCCGEEKJOOna+7t3PWijA2xE7G60sfVMBkrQKIYQQQggh6qRj51Pw87DH3dm2pkOpNLVaRVCAK1HS01ouSVqFEEIIIYQQdY6xwMSpS2l1cmhwoeYNnElIzSXPUFDTodRqkrQKIYQQQggh6pzTl9MxFJhpVQeHBhfydbcHID4lt4Yjqd0kaRVCCCGEEELUOcfOp6BRqwhu5FrTodw0n8KkNTWnhiOp3SRpFUIIIYQQQtQ5xy6k0KKBC7Z6bU2HctO8Xe1QAXEpkrSWRZJWIYQQQgghRJ2SkW0gOj6rTg8NBtDrNLg72xIvSWuZJGkVQgghhBBC1CnHL1iWumlTx5NWAB93O+LkntYySdIqhBBCCCGEqFOOXUjBwVZLYx+nmg7llvm42xOfkoOiKDUdSq0lSasQQgghhBCizlAUhWPnUwhp4o5ararpcG6Zr5s9OfkFZOYaazqUWkuSViGEEEIIIUSdEZOcQ1qWoV4MDYbrZhCW+1pLJUmrEEIIIYQQ4rYqMJlZvvMcKRl5ld732HnL/aytmrhVdVg1wtfdDpAZhMsiSasQQtQxOXkFJKXJhA1CCCHqrvOxGaz84wKfLTlCvtFUqX2PnU/Bx90eTxe7aoru9vJwsUWjVhEvkzGVSpJWIYSoY9bvu8gb3+0jNTO/pkMRQgghbkphr2J0fBbz1p6o8CRExgIzJy+l0qZJ/RgaDKBRq/F2s5PhwWWQpFUIIeqY7m39KDAp/LrtTE2HIoQQQtyU+JRcNGoVY3o1Y9+JBNbuuVih/c5cScdgNNOqaf0YGlzIx82euFRJWksjSasQQtQxPm72DOnSiL3H4zlxdZ06IYQQoi6JT8nB282OoV0b06WVD0u3n+PQ6aRy9zt2PgWNWkVwo/qVtPq62xOfkotZlr0pkSStQghRBw3p0hhPF1vmbzpFgclc0+EIIYQQlRKXmoOPmz0qlYqHBwfTyNeJr1cd40pSdpn7HbuQQnN/Z+xstLcp0tvDx92OApP5piamuhNUKGk9f/4848ePZ+DAgYwfP54LFy4U28ZkMvHmm28yYMAA7rrrLhYvXmx9LDk5malTpzJ8+HAGDx7Mv/71LwoKCqqsEEIIcafR6zTcf1cgsck5bNx/qabDEUIIISrMrCjEp+Tie3WpF71Ow9Nj2qLXaZi95AjZeSWvV5qRYyA6LpNW9WSpm+v5Wpe9kcmYSlKhpHXWrFlMnDiRDRs2MHHiRN54441i26xatYro6Gg2btzIokWLmD17NpcvXwZgzpw5NG/enFWrVrFy5UqOHTvGxo0bq7YkQghxh2nfwpPQlp6s/OM8yelyZVYIIUTdkJKRR4HJjI/7tdl/3Z1tmT66LSkZecxZ/jcmc/FRRCcupKIAreth0lq4Vqsse1OycpPW5ORkjh8/zrBhwwAYNmwYx48fJyWl6H1Ua9euZezYsajVatzd3RkwYADr168HQKVSkZ2djdlsxmAwYDQa8fHxqYbiCCHEneW+AS1BgYVbTtd0KEIIIUSFFPYmFvYuFmrR0IVJdwdx7EIqi7edLbbfsQsp2NtoaerrfFvivJ1cHPTY6DUyg3Apyh0MHhsbi4+PDxqNBgCNRoO3tzexsbG4u7sX2c7f39/6u5+fH3FxcQA89dRTPP300/To0YPc3Fzuv/9+OnXqVKlAPTwcK7V9XeLl5VTTIVSL+loukLKJ6lPZus7Ly4kJdwfx49oTXEzKISyk9l4QrM/vrfpatvpaLqjfZasLpF1XN1VV2bJPJgLQqqU37s62RR4bMyCIpCwDq3aeo1VzT/qHNwJAURSiLqbSIcgLH5+qTVpry2vW0NuRlGxDlcZTW8p2q27LHczr168nKCiIH374gezsbKZMmcL69esZNGhQhY+RnJyF2Vz/ZtPy8nIiMTGzpsOocvW1XCBlqy3UalW9bPTcTF3Xo7UPG/dc5MvfDvP2YxHotJpqiu7m1aX3VmXV17LV13JB3Sqb1HV1S116b1VWVZbtTHQqNnoNBXkGEvOL3786omsjzkSn8vniQzjo1DRv4EJMUjZJ6XkM8Xeu0ue4Nr1mHk42nI/NqLJ4alPZylNeXVfu8GA/Pz/i4+MxmUyAZcKlhIQE/Pz8im0XExNj/T02NhZfX18A5s+fz4gRI1Cr1Tg5OdGvXz/27t17UwUSQghRlFaj5oG7A0lIy2XtnuiaDkcIIYQoU1xqDr5XZw4uiUat5slRbXBzsuHzpUdJzczn2NUl3lo3qX/3sxbydbcnKT0PY4GsCnCjcpNWDw8PQkJCWL16NQCrV68mJCSkyNBggEGDBrF48WLMZjMpKSls3ryZgQMHAtCwYUN27NgBgMFgYPfu3bRs2bKqyyKEEHesVk3ciQjxZs3uiyTI4uRCCCFqsfiUnCKTMJXE0U7HjHvakWc08fnSIxw6nYS3mx1ermXvV5f5uNujKJCYJjMI36hCswf/61//Yv78+QwcOJD58+fz5ptvAjBlyhSOHj0KwMiRI2nYsCF3330348aNY9q0aQQEBADw6quvcuDAAYYPH86oUaNo0qQJ48aNq6YiCSHEnWl8v5ZoNCp+2XwaRRYnF0IIUQsZC8wkpecVm4SpJA28HJk6rBXnYzM5cTG1Xs4afL1ry97IxecbVeie1ubNmxdZd7XQ3LlzrT9rNBprMnujRo0aMW/evJsMUQghREW4OdkwqkdTFm09w8HTSXQM9KrpkIQQQogiEtNyUZRrS7yUJzTQi1E9m7J853naNvOo5uhqlo+bpRc5TkZMFXNbJmISQghxe/Tv1JA/jsayYPMpWjdxx0Zf+yZlEkIIcecq7EWsSE9roeHdmtCuuQeNferHTLilsbfV4Wyvk57WElRoeLAQQoi6wTIpUxDJGfms+vNCTYcjhBBCFFHYi1jYq1gRKpWKJr7OpU7cVJ/4uNsTlyL3tN5IklYhhKhnAgNc6d7Glw37oolNzq7pcIQQQgir+JQcnO112NvqajqUWsnH3V56WksgSasQQtRDY/u2wEanYf7GUzIpkxBCiFojLiW3wvez3ol83e1JzzaQm19Q06HUKpK0CiFEPeTsoGdM72acuJjKvhMJNR2OEOI6qZn5GIymmg5DiBphWe5GktbS+LhdnUFYJmMqQpJWIYSop/p0aEBjXycWbT1NnkGu2ApRG/x9LplX5uzm94NXajoUIW673PwC0rMNlZqE6U7je3X92jgZIlyEJK1CiFpDhrFWLbVaxf0DAknLMrB2T3RNhyPEHe/v88l8tuQo/h72dGvrV9PhCHHbxVsnYZKktTTebnaogHiZjKkISVqFELXCmcvpPDd7F4fPJNV0KPVKi4YudG7lw4Z90SSlywegEDXl2IUUZi85ip+HPS/eF4qjnUxCI+48cdblbio+c/CdRqfV4OFiK5Mx3UCSViFEjTsZncp/Fh3CzkZLE9/6vQZbTRjbpzkqYPG2szUdihB3pOMXUvjstyP4uNnz4oQOkrCKO1Z8Si4qLL2JonSWZW8kab2eJK1CiBp1/EIKn/x6GHdnG165vyMujjY1HVK94+5sy6DOjdgflcCpS2k1HY4Qd5QTF1P57LcjeLvZ8eJ9HXCy19d0SELUmPiUHDxcbNFpNTUdSq3m62ZPfGqO3DZ1HUlahRA15ui5ZP57tTH3ysSOuErCWm0Gd2mMm5MNCzafxiwfgkLcFiejU/nv4sN4udrx0oRQnCVhFXe4OJk5uEJ83O3IzTeRkWOs6VBqDUlahRA14tDpJGYvOYKfhz0vT+yIs4M05qqTjU7D2D7NuRifyR9HYms6HCHqvZPRqXyy+DCerna8dF+o1HHijqcoCvGpOfjKJEzlKpxdWe5rvUaSViHEbRcZlcAXy44S4O3ISzIhyW3TuZUPzf2dWbLjnCxaLkQ1OnUpjU8XH8HD2VYSViGuysgxkptvwkcmYSpXYW+03Nd6jSStQojbas/xOOasOEZTP2deGB+Kg60krLeLSqXivgGBZGQbWL37Qk2HI0S9dPpyGp8sPoybkw0v3xeKiySsQgDXeg1ljdbyeTjbotWopKf1OpK0CiFumz+OxjJ31XFaNHThuXHtsbfV1nRId5xm/s50be3Lpv2XSEiVD0MhqtKZy+n836+HcXW04eWJoTKxnBDXKew1lHtay6dWq/B2kxmErydJqxDitthxOIbv1pwguJEbz41tj52NJKw15d4+zVGrVfwqS+AIUWXOXknn/349hKuDnpfvC5WJ5YS4QXxKDlqNCg9n25oOpU7wcbMjPlXWVy8kSasQotpt/esy36+LonUzd565tx02epnqvia5OdkwtEtj/jqVyImLqTUdjhB1XnpWPv/97QjO9npentgRNydJWIW4UVxKDt5u9qjVqpoOpU7wdbcnITUHs1lm/AdJWoUQ1cisKKzbe5H5G0/RoYUnT49ph14nCWttMDCiER7OtpYlcOQDUYibpigKP6w/SZ7BxNP3tpOEVYhSxKfm4uMmkzBVlI+7PQUmheSMvJoOpVaQpFUIUS2iLqby9veRLN52lk5BXjw1ug06rVQ5tYVep2Fs3+ZcTsxix5GYmg5HiDrrj6NxHDqTxD29m9HA06GmwxGiVjKbFRJSc2QSpkqQZW+KkpvKhBBVKjY5m8XbznLoTBIezjZMHd6KiFY+qFUyHKi2CQ/2ZuuByyzbcY6IYB+ZGEuISkpOz2PBllMEBrhyV3hATYcjRK2VnJFHgUmRSZgq4fplb9o086jhaGqetFCEEFUiI8fAil3n2X4wBhu9mnv7NGdAp4YyHLgWK1wC563v97Pqz/OM79eypkMSotrl5heQZ7j1dYrNisJ3a09gNsPkoSFyYU6IMshyN5XnbK/DzkZDfIpMxgQVTFrPnz/PzJkzSUtLw9XVlQ8++IAmTZoU2cZkMvHOO++wc+dOVCoVU6dOZezYsdbH165dy5dffomiKKhUKubNm4enp2eVFkYIcfsZjCY2RV5ize6LGIxm+oT6M6JHU5ztZW3CuqCxrxPd2/mxOfIyfTo0kKvgot6bveQIiel5vDShA95uN/9+3/bXFU5cTOXBQUF4u8p9ekKURZa7qTyVSoWPmz1xsjwdUMGkddasWUycOJGRI0eyYsUK3njjDX788cci26xatYro6Gg2btxIWloao0aNomvXrjRs2JCjR4/y+eef88MPP+Dl5UVmZiZ6fdU3aDdHXiIxLY/x/VrIzGRCVDOzorD3eDxLt58lOSOfDi08Gdu3OX4eck9XXXNPr2ZERiWwaOsZZtzbrqbDEaLaJKXlEhWdBsCHCw7y8sSON5VwxqfksHjbGdo0c6d3e/8qjlKI+ic+JRc7Gw3O9rqaDqVO8XW358yV9JoOo1Yod1aU5ORkjh8/zrBhwwAYNmwYx48fJyUlpch2a9euZezYsajVatzd3RkwYADr168H4Pvvv2fy5Ml4eXkB4OTkhI1N1c+up9dp2BR5iXlrT2BWZDZMIaqaoiikZ+Vz8HQi7/wQydxVx3G00/PSfaHMuLedJKx1lIujDcO6NeHQmSSOnU8pfwch6qj9UQkAvPpwOPkGEx/98heJaZUbemc2K3yz5jhajZpHBoegkmHBQpQrLjUHHzd7+X+pJB93e5LT8zAWmGo6lBpXbk9rbGwsPj4+aDSW+9I0Gg3e3t7Exsbi7u5eZDt//2tXG/38/IiLiwPg7NmzNGzYkPvvv5+cnBzuuusunnzyySp/4/Zq709aVj7Ld55Hq1Xz4MAg+ecQ4iYZjCZikrO5lJDF5YRsLidmcTkxi8wcI2BZ6/OxYSF0ae0r93LVA3eFBfD7wSss3XGW1k3dy99BiDpo74l4mvo50bWtPzrg44UH+fCXg7wyMRTPCva4btgXzdkrGUwZ3kqWtxGiguJTcmjRwKWmw6hzfNztUICE1FwaeDnWdDg16rZMxGQymTh58iTz5s3DYDDw2GOP4e/vz6hRoyp8DA+Pir1Qk0e2RafXsnjLaZydbJkysk2tT1y9vJxqOoRqUV/LBfWzbOeupLN100nOx2ZwISaD2KQsCpfv1Os0NPFzoksbP5r4O9PEz5ngxu4yyVI1qGhdVx1G92nB3BV/k2U009S/6hsX9fH/plB9LVt9KldMYhbR8Vk8OqI1AGFt/XnHzZ7X5vzJx78e5v0nu+Ndzv12F2IzWLbzPN3a+TG8d4ta376ozWqyrqtu9en/5kY3UzaD0URyRh53d25ca5+b2hpXSDNLD2uuSbnpGGtr2Sqr3KTVz8+P+Ph4TCYTGo0Gk8lEQkICfn5+xbaLiYmhXTvL/VDX97z6+/szaNAg9Ho9er2e/v37c+TIkUolrcnJWZjNFRvyOyisIekZeazaeQ6joYCxfZrX2g8WLy8nEhMzazqMKldfywX1s2yxydm8+f1+DEYz3q52NPR2pFOgJw29HAnwdsTL1a7YfeLpaTU7MYBaraqXjZ7K1HVVrW0TN7QaFSt+P8P9dwVW6bHr4/9NofpatvpWrvV/nAcgpKHlgkxiYiYuNhpeGN+ejxcc4pXPd/LKxI54uNiWuH+BycxHP0Vib6NhXJ/mJCVl3Za4pa6rW+rb/831brZsVxKzUBRwtNXUyuemNr9mepXlf+TUhRRa+FY++azNZbtReXVdufe0enh4EBISwurVqwFYvXo1ISEhRYYGAwwaNIjFixdjNptJSUlh8+bNDBw4ELDcB7tr1y4URcFoNLJnzx6Cg4NvpVxlUqlUjO/Xgr6hDVi/N5oVu85X27mEqOuMBWa+WnkMvVbDd6/dzb+f6Mr0MW0Z1bMZYcHe+Ljby8RmdwhHOx0dA73YcyxO7p8R9c6+qARaNHTB3bloUtrE15kXJnQgO6+ADxf8RUpGXon7r/rjAtHxWTw4KFhmRxeiEuKuLtkiy91Unp2NFhcHvXX25TtZuUkrwL/+9S/mz5/PwIEDmT9/Pm+++SYAU6ZM4ejRowCMHDmShg0bcvfddzNu3DimTZtGQIBloe2hQ4fi4eHBkCFDGDVqFC1atODee++tpiJZqFQq7r87kB7t/Fj5xwXW7L5QrecToq5auuMs0fFZTB4SgpebLNtwp+vZ3p/svAIOnEqs6VCEqDJXErO4kphN5xCfEh9v6ufMC+M7kJVr5MNfDhZLXM/HZrBm90W6tfGlY6DX7QhZiHoj/uqSLT63sMTUnczH3d66zu2drEL3tDZv3pzFixcX+/vcuXOtP2s0GmsyeyO1Ws0//vEP/vGPf9xkmDdHrVLx8KBgCgrMLNl+Dp1Gzd0RjW5rDELUZn+fT2bDvkv069iADi1l3WQBIY3d8HSxZefhWLq08q3pcISoEvtOJKBSQVhQ6QlnM39nnh/fgf8sPMSHCw7yysSOuDnZYDCa+Gb1cVwc9Uwc0PI2Ri1E/RCXkoOLgx47m9sylU694+tux6HTSTUdRo2rUE9rXaZWq3h0WAidgrxYuPUMW/+6XNMhCVErZGQb+Gb1CRp4OjCub4uaDkfUEmqVip7t/DhxMZUEWdBc1AOKorAvKoGgAFdcHMue7be5vwvPj+9ARraBDxccJDUzn6U7zhGbnMPkISHY28oak0JUVnxKDj4yNPim+bjbk5FjJCfPWNOh1Kh6n7QCaNRqHh/Rmg4tPJm/8RQ7D8fUdEhC1ChFUfhu7Qly8gp4fERrmQVYFNG9rR8qFew6GlvToQhxyy4lZBGfkkNEKUODb9SigQvPj+tAWlY+7/0Uyab9l+jbsYEsBSXETYpPycHXXW4/ulm+V4dVx6dWbk3p+uaOSFoBtBo1T45qTeum7ny/Loo9x+JqOiQhaszWv65w5Gwy4/u1oKF3/ZuVUtwad2db2jbzYNeRWExmc02HI8Qt2XsiHrVKRacyhgbfqEVDF54f156s3AK8XO0Y26d5NUYoRP2Vk2ckI8coPa23oPC5u9MnY7pjklYAnVbD9DFtCWrkyjerT3DgpEw0Iu48lxOyWLT1DO2ae9CvY4OaDkfUUj3b+ZOWZeDouZSaDkWIm6YoCvtPJNCqiRtOlZzxt2VDV95+NIJ/TOqErV7uxRPiZhT2DvrKJEw3zcvVDpWKO34ypjsqaQWw0WmYcW87Gvs68d3aE6Rl5dd0SEJUyoW4DE5cTL2pfQ1GE1+tPIaDrZbJQ0Nq7frFoua1b+GBs71ObqcQddr52EyS0vMID/G+qf09Xe1wcZDlbYS4WYW9g9LTevN0WjWeLrbS01rTAdQEW72WqcNbUWAy8/PGUzUdjhAVlpCWy0cLDvLRgoN88uthYpOzK7X/r9vOcCUpm0eHhcg6g6JMWo2abm39OHwmmXS5uCfqqH0n4tGoVbJMjRA1JD4lB5XK0lsobp5l2Ru5p/WO5ONuz8geTTlwKpHIqISaDkeIchkLzHy5/G9UqBjVoylnrqTzxrf7WLD5NNkVmFHu0Okktv51hYERAbRp6nEbIhZ1Xc92fpgVhT/+ljkARN1jVhT2RyXQtpkHDjLrrxA1Ii4lB08XW3TaOzblqBK+bvbEpeagKEpNh1Jj7uh30MCIABr5OPLzplMVavQLUZN+3XqGi3GZPDoshBE9mvL+1C70aOfH5shL/OOrPWw7eAWzueTKLDUzn+/WnqCRjyNjesmEIqJi/DwcCGzows7DMXf0B6Wom85cTic1M/+mhwYLIW5dfEquDA2uAj7u9uQbTKRnG2o6lBpzRyetGrWaRwaHkJljZNHWMzUdjhClioxKYMtfl7k7PIDQlpZhbs4Oeh4aFMysR8Jp4OnATxtO8q95+zhxoejEOWZF4ds1xzEUmHh8RGu52ikqpWd7f+JTczl1Ka2mQxGiUvafSECnVdOhhWdNhyLEHUlRFOJSc2QSpirgezXxv5MnY7rjW6+NfZ0Y2DmAXUdiOX5BZskUtU9Cag7z1p2gmb8z95aw7EIjHydenhjKU6PakJtv4qOFh/h86VES0iz3Pmzcd4njF1KZOCAQPw+H2x2+qOPCgr2xs9Gw47Cs2SrqDrNZYf/JBNo188DORmb+FaImpGcbyDeYpKe1Cvi4We4JvpMnY7rjk1aAkd2b4uNmxw/ro8g3mGo6HCGsjAUm/rf8b9QqFU+MbI1WU/K/rEqlIizYm3endGZ0r2b8fT6Z1+bu4ccNJ1my/Sydgrzo2c7vNkcv6gMbnYbOrXyJPJlAjtxGIeqIk9GpZGQbiGjlU9OhCHHHKuwV9JWk9Za5O9ui1ajv6MmYJGkF9DoNDw8OJjEtj2U7z9V0OEJYLdp6huj4LB4d2gpPl/Jn3tPrNAzv1oT3p3YlPNiH3w9esQ4jluVtxM3q1d4PY4GZPcfjazoUISpkX1QCNjoN7ZrLpHNC1JRry93IzMG3Sq1W4eNmd0f3tMqYmauCGrnRp4M/myIv0bmVD039nGs6JHGH2x+VYJ3tt0PLyt2T5eZkw5ThrRjUuRG2eg2OdjJzprh5jX2caOTtyM7DsfTr2LCmwxF3CJPZjNlMpe/DLzCZOXAykfYtPLDRaaopOiFEeeJTctFq1Lg729Z0KPWCj7t9pZc6rE8kab3OvX1acOhMEvPWnuCNh8NLHYopymaKO01i5H7yFRvUTl6onL0s3x3cUanlOa2I+NQc5q09QXN/Z+7p3RwlPxtT4nlMCecwJ10AnR264F5ofAPL7EEN8Ha8fUGLekulUtGzvT8/bzrFxbhMGvs61XRItYIp5RLG47+T4uqMQeOC2skLtbMXKkd3VGr5eL0ZiiEXU9JFzInnOHvkMKlZBhp3G4h/24gKf35EXUwlK9dIRIgMDRaiKpgzEjAc20Kygw0GraulrnPyQuXkgUpT+kXxuJQcfNztUMtIr2KUAgPmpIuWtl3iOTDmo23ZDW2T0FI/P3zc7Th8JgmT2YzmDmxPy6fqdexttUwaGMTsJUdZt+ciw7s3remQrJS8LNDboVLX3qvGpuRo8vcvwRR9GJXOBqXACIr52gZqDSpHT9TOXqidPFE5eV/92fI7Ng71ZgirYsjBnBqDkp+NytYRlY0jKltHy2uoKruiMeTlsnzpZnrqrzDYRyHvt8Uo6deGZapcfFFy0yk4/QdqV390Ib3RtexuOb4Q1aRLax8WbT3DjiMxTPINqrbzKPnZoLVBpam9H0/m9HjyDyyj4Mxe0GhJU8xgvm4+BJUalaP7tSTWyRu1kydqZ29Ujh6o7JzrT11nzMecFoOSm26p5wrrOhv7cus6xVSAOeUypsRzlgtyiecxp8YAluWV7MyOuKgKcNo7h7TDi7Bv0xddcC/U9q5lHnfviXjsbDS0beZeRaUUouophlxLu0irr+lQSmXOTsXw10qMUTtApSJDpUIxXT+3gQqVg9vVes7rujadFyonT+JTsvH3rB9tE6XAgDktFiU71VK/FbbtbBzLvaCmmE2YU2MwJZ7DnGBJUs0pl61tZJWDGwAFFw+isnNGF9QLXXBv1M5eRY7j62aPyayQnJ6H9x04I3PtbRXUkNCWXoQHe7Pqzwt0CvLG37PmZltVFAVT/GkMh9Ziij6E2tUPmy7j0QS0r1UNHnNaHPmRSyk4tw/09ujD78W/z2iSUnNRslIwZyRizkxEyUy0/lyQeAElP6vogbQ2qJ08LImtkycqRw9LQ8/RA5WT59WGXu26sqQYcjGnXsGUegVzagzmq9+V7FJmolapUdk4WCo7W6erPzuhsnVEMeRgSjhPQfIlJmIGPaiS3NB4NUUd2BONV1M0Xk1Q2TigFORTcHYfhhO/k797Afn7FqNtGo6uVV80Pi1r1ftD1A8OtjrCgr3Ycyye8X1boK/iYZempIsYDq+j4Nw+VA5u2ETci7Z551r1P2/OSrE04E7uALUWfYch6NsNxquBNwkXL2HOSEC5WseZr9Z3BRcPoeRmFD2QRo/a0R2VkydqR09UTh7Wek7t6InK3rXWjUopTE4L6zlLnXcFJTOp5B1UKlR6B7B1vO7CnRMqWwcwGTElnsecHA2mAsvmtk6ovZqibxaOxqsZf8TY8MPvV3j2nlZs3b6VkKyjBEYuxXBgOdrGoehC+qBp2LrY+8NYYOavU0mEtvRCp629F3nFncucFofhyHqMp3eh0tmh7zQKXUifWtUpYc7LxHBoDcZjW8BsRhfSG33ocLwbNyTh4mVLmy4jEXNGwtX2XRKmy39TkJNW5DgvKmry013JWeNbtI5z9LC09xzca1W54Wpymh5nac+lWOo5U1oMSkYClLheucqSxF69YKe6rs5DUTAnXcCUdAEKrq6vqrdH49UUffshqL2bWdp4Dm4oZjOmy0cxnvgdw+E1GA6tQdOwNbqQPmgbd0Cl1lpnYY5LyZWkVVhMvCuQ4xdS+H5dFDMf6HjbhzUoZjMFF//CcHgd5oSzqGwc0bUdSEH0IXLXf4qmQStsukxA49HotsZ1I3NWMoYDKzCe2gUaLfrQ4ejbDUJl44Bab4tKbUTl7I3aueSF3RVDztUkNgklK+nq92TMmckYE85C/g3j9jXaq405Lai1oNZc/VJbKr3Cv6mu/q7VXW0kXf2yu/Zdbetcak+AoihQkI+Sn42Sn2P5bsiGqz8nK9nkXDlfPDnV6FG7+aHxC0Lt3gCNWwNUtk6W/fOyLF/5V7/nZaLkZ1sq+8Tzlp50rZ4se3/+zG2NR9NgevXvhvrq1bcbqbQ26IJ6ogvqiSn5EsYTv2M8/ScFZ3ajdvNHF9wHXWB3VDayxI2oOj3b+bPnWDwHTibStY3vLR9PURRMV45hOLwO05VjoLNF16ofprjT5G39CvXRjdh0vQ+tb2AVRH/zzLkZlgbc8S2gKOha9UUfOtza46dSa1A7WhJP/EOK7a8Y8yz1W0Yi5qwkzFnJKJmW7wVJF1HyMovuoNKgcnC1DLtTa0GtttZ3Kmu9p7Fsp9aARofK1gGVrfO1eu76Oq+UngBFUcBkuFbP5V+t5wyWn5PJvVbXZSZR2AOKWova1ReNd3PUQb1QuzVA7eBaSl139efsZMzJ0ZayqtRovJqgaz0AzdVGm8rR03qxTVEUtmzaT2NfJ9q19KV5wFj+uziIX2Mv8VhIMn5xhyi4cACVkye64N7ognpaX4tj51PIzS8gIqTkzx0haoop/ozlwtyFv0CjQdeyO+b0OPL/+AnjsS3YdBlX450SiiEXw5H1GI5usA5Xtek00tqOU6nUqB3cLG2TEuplpcCAOctS16XHx7Djz7/p5KlGMeZQEH0EJTe96A4qNSp7V9Dqi9Ztxeo6taXtp9Feu9hv51S8zrN1LDUJVgoMKIYca9uOq3WeYsghhVxyYy5gSo1ByYi/lpyq1KhdfNC4B6Bu3sVS1zl5oBhyr9VveZlF67vsNMwply11naKg9myMLri3pePBuxkqZ+8S254qtRpto/ZoG7XHnJWC8eQOjFE7yNv0OSo7F3TBvfBt1BW4OivzHTjJnEpRSrxsUOskJ2dhNt++UP84Gsu3a05w/12B9O9UfROPeHk5kZhoabAoBQaMp/7AcHQ9Sno8Kicv9O0GogvqiUprg2IqwHhiG/kHlkN+DrqgHujDxpSa2FQXc046hkOrMR7fBmBpwHUYhtrepcRy3SzFkGtp3GUlYc5MtjT6ctIsw/DMJhSzCRRT0d+v+1IKDJZKw5hX8glUamuPJ1obFENhJZZjOW4pVBodKlc/S+XlZklO1e4NLI2um+wdURSFhJQc3vwhkgaeDrxyf8dK31OtGPMpOLsXQ9TvmBPOgUaHtlkEGt+WlgpYMRf7rlh/NwMK7oFtyXJoWid6atVqFR4e9WPY0fVud11XGWZF4dWv9uDmZMMr93es1L5F6jpzAQXn9lsuzCVHWz6Q296NPqSPZSSBYqbg9G7y9/+Gkp2KtkknbDqPQ+1ye+9RVAw5VxtwG6EgH23LHth0GoHaqeiQrVut7xRjfpG6TslKxpydCibjDXWdGcwFxes6k9FS1xlKWwpBda2u09uCIfdaw81cUHpgai1qFx9rXad280ft3gC1s88t9Y4oilJmHXM+NoO3f4hk0sAg+oY2ACDfaOLL5X9z5GwyY3o04m7fJAqitmOKOQEqDdomoWgatmHn4SvEJmVxb+9mqFVYnrMb6zpFwa1pS7LdQqSuq0G1ua67FUXqOsWM6eJhDEfWYYo7BTYO6Fv1Q9d6AGp7FxRFoeDiQfL3LkJJj6+xTgmlwIDx2BYMh9ag5GehbdIJfdgYNO4NSi1beY6cTebTxYf5xwMdadnQ1XoeJTvF0qbLSrJcvLuurivalius68zXPWa0XOS/sVPjejYOqG2dwMYejHnWi3KYyliyTaVG7extqefcG1yr81x8b+lWlfLqunL3N5sxXTqC4cQ2TJeOgAInC/zJ9W5D51a+gGJ5fqzfzShmBTBb/+7SoBG5Xu1qXY92Scqr6yRpLYWiKPzfr4c5cyWddx7tjIdL9cx85uXlRMKlWAzHt2I8thklN8MyRKrdYLRNO5X4JlPys8k/uArj35tArUHf3jJETaWzqZYYrz+v4fA6DH9vBFMBusAe6DuNtPQulFCuW01aq4piMqLkZlquhuVlouRmXP1e+LcslIJ8VHp7VDb2lt5JvYP1Z8uXPaqrf/Nq4E1SUvmzt+08HMP+qAQc7HQ42ulwstfhZKfDyV5v/d3RXo+jnRazWeHdHw+QnJHHvx6JuOX3mynpIsao7RhP/1l60l4KjV8wNhH3ovFpcUsxVDdpyNWMNbsvsGT7Od6f2qVSC8Z7eTmREJOIMWoHhqMbULKSUbv6o283CG3LriVO5qEU5FuSxkNrwVyArvUAbDqOqPYRBEpBPoa/t2A4vAbys9E2C0cfNhqNq3+pZasN9Z1iKrBc8c+9oa67vr4z5qHS212t7xwsI07019VzhXWe/mpdl3z7l1f4ccNJ/jway/9N74G97bUGY4HJzLy1J9h9LJ4BYQ2Z0L8lpMdjiPqdglN/FO+xLofas4mlrmvQulYnr1LX1S1eXk4kxKVgPP0nxiPrMafFonL0QN9ukKUTQlf8872mOiUUUwHGkzsw/LUSJScNTcM22ITfg8ar5DldKlPXbdp/iQVbTvPpjB4421ftfbuK2XStl9Nav2UUbesZclHpbFHZ2Bdt0+mLtu2wccC7gU+N1HWVYc5Kxhi1g+S/NuNE5WYQVrv4og8fg7ZpWK265eZGkrTegqS0XF7/dh+BAa48O7ZdlX+omTOT0JzZSsbBLVCQjyagHfr2g9H4VWxNTXNGAvl7f6XgfCQqe1dswu9BG9j9lt6QimJGyUm/em9WguV+hcKv1BhLb0Pzzth0Go3atfThgbWlEVcdKlK2jfuiWbj1DN5udqBAZq6R3PzSezT0OjUGo5kZ97ajQ4vKLW9TFqXAYLnCqFIBKstQ6qvfuf67Sg2KGZvLe0nZuRglNwNt41D04fegca+dS5xIQ65mpGbm89L//mRQ50bc26d5hfYx56SjO7+DtMj1kJ+NxjfQUtc1al+h+sqck4Zh/1KMJ3eCjT02HUeia9Xvlq+AK7kZxeu5zERLXWfIQRPQztKA82xc5rHqa31XE+XKN5h4/otdhLb04rFhrYo9blYUFm05w6bIS3Rp7cPkISFoNWoUUwGH/z7H9+tP8fiotgQ1drfUdaqr9ZtKfe1nwC7hEEnbFqBkJdf6C3VS19UdSn42+ug/Sd2zGiU3HbVHI/Tth6BtFl6hni4lP5v8v1ZiPLb5aqfEUMttV7fQKaEoiuViVkaC5Zasq3WdkpFgmVgoLxONT0v04feg9Q8u81iVqRN+2niSPcfi+fzZnrX6ohDUrTp87sqjXImO4fWHr76nVCrL52hJdZ0KHNJOkbhlPubUGNSejbEJvxdNwza18jUpr66Te1rL4Olqx5hezViw5TR7jsfTtfWt38MFV+8F/WuVpQGmAm3zLujbD0LjHlCp46idvbG7azoFcafJ372AvO3fov57EzZdJqB2b2gZClFgQCkwgMlY8veCfMswjYwElExLhVZkCIVKbblh3tkbXWB3y7j8chpwd7oN+6JZtPUMYUFeTB3R2jrMt8BkJivXSFaOkcwcA5m5RrJyjWTmWP7WzN+5ShNWAJVWX6mZCV3Ch5DfIBzD0Y2We29+ex1tiy7YhI0u9d5kcWdxc7KhXXMP/jgay6ieTcscxm7OzcBweC3GY1vBZETbpKMlWa1kcqC2d8W292R0be4if89C8nf/guHYFmw6j7MMfzcZocCIYjJY6rwSfzdYhuBe13CjIP+6sxTOgumNrlkY2pbd0fpV3yzJomSRJxPIzTfRq33JvdpqlYoJ/Vvg7KBjyfZzZOcW8NSoNtjotfx53oBi50JgywDU5dyq4dSuL7ne7S2TnhxcRc6Kd65eqBtT6c9iIRRDLoa/N2I4sh4MuWgatEbffkqle/FVNg7Ydr0Pfev+5O/9FcOBZRhPbLNcPGvcwTKZj8loWZ3BdLV9V2C01HHX/56dcjU5vVrXGYveOqCyd0Xt7I2mUXt0zcLRBFR9x0x8Sg6+7na1Mjmqy1o2cmf38USuZOto5FP+8nMOQZ3Jdg2m4Mxu8g8sI3fdf9D4BWETMbbWXqgrjSSt5ejfqSF7jsezaOsZOrTwxM7m5p8yc04ahoOrMZ74HVDQhfTGr/94UvNvbViv1rclmlGvUXB2H/n7FpO75sNKHkBvGcvv4osmoN3VZRosEyipnDxkvcFKWL83ml+3nSEs2Jupw1sVadBrNWpcHW1wdazeYdy3SqWzxabjCPSt+mE4vBbD35soOLvPMntgxxHlLjch6r+e7f04dCaJo2eTCQ30Kva4kp9tGdb79ybLZB4tuuA74D7Szc63dF6NRwB2Q17EdOkI+XsWkbdpdiUPoEXt5I3K2Qudf7C1nrPUdZ5lrjcobo8dh2PwcbenZUOXUrdRqVQM7doERzsdP244yceLDvLkyDYcOZNE93Z+FV6/UKXRoW9zF7qgnpYLdUfWUfDbG3KhTlSYYszHcOy6Wwkah+LTfyIZ2uL1YmVc65Q4Rf7uheRt/7aSB9BYZup19kbn0+JaPefsjdrZE5W2+tsh8Sk5tAxwrfbz3Gk6tvTipw0nOXAysUJJK1gmedIFdkfbPALjie0YDq4kZ8U7aBp1wCbinjpzoa5C2cj58+eZOXMmaWlpuLq68sEHH9CkSZMi25hMJt555x127tyJSqVi6tSpjB07tsg2586dY/To0UycOJFXXnmlygpRndRqFfffFcg7P0ayevcFxvap/FWJIr0N5gLLvQqhI1A7eaJ1doIqGJKgUqnRteiCtklHCs7uRTHmW2bP1epBo7c0xkr83aZerY9ak9btvcjibWeJCPFmyvBWdX7hZ5WtIzadx6Frc5dlmY8T2zGe3IW+zQD0HYZWyX2FiqKgpMdREHcKU9wpzMmXsOl2v/Rw1XLtmnvg4qhn0dYzeLjYWj84b+xt0DaLQN9pFBo3f/QeVVXXqdA2ao+mYRsKzu233MeoLazTSvquB63u6gy7jrX6fp47XWxyNqcvpzO2T/MKfSb17tAARzsdX608xhvf7sNQYCYiuPKJZvELdZur/EKdoigoWUmYYi11nSnxAvqOw9E1DbvlY4vbTykwYDyxzTJxUW4GmoC22HQajca7GTZeVVPXAWh9Ay2dEhcPoWQlW+oxrf7qd52lPWf93fIdrb5Ca4dWJ4PRRHJGPj3vwGVZqpuzg56gAFciTyYwulezSu1ruVA3wHKh7u+NGA6vJacaLtSZs1Mt9VzcKUzxZ9EF90bfqu8tH7dCSeusWbOYOHEiI0eOZMWKFbzxxhv8+OOPRbZZtWoV0dHRbNy4kbS0NEaNGkXXrl1p2NByP5zJZGLWrFkMGDDgloO+3Zr5O9O9jS+b9l+iV3t/fCr4T3ht4qJNUGBA27IrNh1HVusMmCqtHl1Qz2o7fl2hKAoGoxkb/e2ZLW3tnov89nv9SVivp3Zww7bnQ+jbDSL/wDLLe/rENrSN2lsXEFc7e1vW1i1nzTXFbMKcHH2t4RZ/2rqGpcrWCY1vIGpH99tVNHGTNGo1T45sw5wVf/POj5Hc17sx3fQnMBxea+1t0IeNrtYZMFVqDboWXart+OL223kkFo1aRbdKLKfUKcib58Zq+WzpUdycbG6pZ6fIhbqDq6wX6rSNr9Z1V+s5tbM3Kkf3MkchKYoZc8qVaw23uFMo2amWB/V2aHxaSk9uHWSduOjgKpTsVDT+Iejvehqtb8tqO6dKpUbXpHKztde0hFTLcOTKTNYnKq5TkDc/bzrFlaRsGnhWvgNBpbPBJnQ4+pC+RS7UaRu1Q+Xic3XEpZel3nP0KPM2syKdD1fbdkpmouVBrR6NTwvUrn43W9Qiyk1ak5OTOX78OPPmzQNg2LBhvP3226SkpODufq1xuXbtWsaOHYtarcbd3Z0BAwawfv16HnvsMQC+/vpr+vTpQ05ODjk51TNDV+GU9tUxrfM9fZoTeSqRX7ee4el72pUdhyHXMtToaPHeBlH9FEXhl82n2XrgMi0butAx0IuOgV54utpVy/kKZ1Pt3MqHx4aF1KuE9XpqFx/s+j2Bqf1QDAdXYoo/S8HZfVeXy7lKpUHl6H61svNE5eSN2tEdc0bi1ST1jPU+QpWTF5qGbdD4BqLxC0Tt4ic9/hVUnXVdRQUGuPLmQ6HsWf4rQX8twqDOA/822EeMQeNduau/QhSYzPx5NJb2LTxxqeQtFCFN3HlzcgQmk7lK1lVXO7hh2+NBy4W6v1ZiijttWVvTfN1SaCoVKgf3qxfuvFA7e6J29MSck3Y1ST0NBktbR2Xvaq3nNL6BqN0a1mgvWF2jmE01vlyHYjZRcOoP8g+uRMlMQu3TAts+U9A2KD5ZmIC4FMt731eS1mrRMdCLnzed4sDJBBp4ljzTc0VYL9S1vRvDX6swxRzHfPkomIpOHKpycLvWSXE1oVXysq9dkLs6c3th54OmdX9LXefZqEpvMSz3SLGxsfj4+KDRWCoMjUaDt7c3sbGxRZLW2NhY/P2vJWV+fn7ExcUBEBUVxa5du/jxxx/53//+d1OBVmTmvKT1c8n6eweO7fvh3HEgeo+qSxK9vJwYPyCQH9ee4HJKLqFB166QmvOyMaTEYkyJwZBwkcxDmzHnZmEfGI5brwnY+DQp99j1UU2Va/76E2w5cJmIVr4kpOawcOsZFm49Q7MGLnRt60fXtn408nG6pQSpsGyLNp9kyfZz9A5tyHP3haKp5NqqtVG5r5tXKwi2fFArZhMFGUkUpCVgTI2nIC0eY3oCBanxFFw6jCm7cCFxFXrvRji074ttQAi2ASFone+8hbEroiJ1XeofS0j7YwmOrXvi3GkQNr43/6FVGWZDLsaUWIwpsRiSLqM5tJkuuSlkuTVn9pVA0i4F8GLvhrQu5T1UX+s6qL9lu13l2n00howcI8N6Nrupc1bLPl5O0Pw5wFLXmbJSr9VzaQnXvsf8jeFUqnU3nUcD7Ft1s9R1jULQunjLBbkSVKSuyzy8lcS1X+EQ0gWXToOxaRh0W55LszGfgtQ4DCkxGJNjyDqyDWNKLHrf5rgPfQK7Zh3KjKO+1gdQsbJlHYkFoFVLL+xt68ZcAXXpNfPyciKkiTuHzybz6KiyO9IKty+bEzR+Cri6tnBWmqVdlxZ39fvV+i7+FIbTuwHLrN9aV28cWna62q4LRufRoFr/P6t9hh2j0cjrr7/O+++/b018b0ZFpkY3N++DOi2VjMh1ZOxbjaZBa3St+6Nt1OGWr2oqBQa6+hk55xrLwSU/4N5SCxkJmNPjrMMbLVRoAtpgf/Xehgwo896Gmp5mOy4lB29XO9Tqqn2T1VS5Nl6dubdnOz8eHmxZOighNYe/TiVx4FQCP6+P4uf1Ufi42Vl6YIO8aOrnXKmr84VlW/XHeZbtPE/X1j5MuqslKSmVWzerNrq5180eHJpYvhqCGtBf/VKMeZizklHbu6KycUABcoHcfG75np87eRkIc4NwtM0vk3l0B5mHNqP2aYG+dX/LGmy3OJmQYipAyUzEnB5n+UqLt/6s5KQV2VbjG4hdr8dwatCKiXEZzFlxjH/8bxcjezRlWNcmReqVmq7rqlN9LdvtLNfqnedwc7IhwMPutpzz5spmA/aNLF/+oOK6uq7AgDkrCZWNI2o7y4RjeUCeEUjKuqVY7+S6TvFoha5VX7JP7SL72C7UHgHoWvVH16LrLa9Nr5hNKFnJmNPirtV36Zb6TslKobBhDqD2aITt3TPQNg4lW6Uiu4zXtL7WB1Dxsp29lIqLo57szDyyMyu3VnxNqIuvWfvmHizccpq/T8aXOQz75sqmA9sG4NsAfC11ne7ql2IyWu6v1tpY1xHOB/IVqr2uKzdp9fPzIz4+HpPJhEajwWQykZCQgJ+fX7HtYmJiaNfOkvEX9rwmJiYSHR3N1KlTAcjIyEBRFLKysnj77bdvpWzFqF19sev/JOacNIxROzCe+J28jZ+hcvRAF9IHXXBv64dJWRRTgeW+u4RzmBLOYko8h5KeACiMVQMK5J5zxM7T35IQu/iidvVB7eJr6T6vxBIjNWn333HMXX2crq19eWxYSJ2/ErzzSAwLry4189Cga2vdervZM6hzIwZ1bkRqZj6HTify16lENu6/xLq90bg66mnX3IMGno74edrj5+6Au7NNmc/Hyl3nWb7rPF1b+/Lo0JAqT/rrC5XOFo1bg5oOo95RO7hh22syNp3HYzy5C8PxreRt/QqV3QJ0Qb3QteqL2rH8nmzFbMKcesVS18WfxZx4FnNaXJEh3ypbJ1QuPmgatrbUcdYv7yIzUDbxdWbWw+H8tPEky3eeJ+piKlOGt8bNqXbPli1qXkpGHkfPJTO0a5M6e3uFSqtH4yq3AFU1lY0Dtt3uxyb8XoxndmM8voX8nd+Tv3cRusAe6Fv1q9D9copitiSnCWct7bqEc5hTrxQd8q23s6yi4Bt4tY7zQe3qi9rZB5W+em4vqq/iU3LxlUmYqlWnQC8WbjlN5MkEhnZtctvOq9LoULlUzRKglVVu0urh4UFISAirV69m5MiRrF69mpCQkCJDgwEGDRrE4sWLufvuu0lLS2Pz5s38/PPP+Pv7s3fvXut2s2fPJicnp1pnD1bbu1pmAuwwlIKLhzAe34Jh/xIMB1agbRZuqeR8WqBSqa7N6He10WZKPIc56YJ1PLfK3hWNd3PUzbugdvVD5ezD55vjOZtg4P37umJvVzeGPdzoSmIWP2yIwsVBz+5jcbg66W9qZuTa4sDJBL5fF0Xrpu5MGd661CTSzcmGvh0b0rdjQ7LzjBw+k2TphT2ZyI7DsdbtbHQafD3s8fewx9fDAX8Pe/w8HPB2s+OXDVEs33We7m18eWSIJKyi5qhsHNC3G4iu7V2YrhzHeHXpBcPhNWgbdUDXuj+aBq2sM+b+P3v3HR5VsT5w/Lu76Y0UUgkEEgiEEkiB0GvoYABpIuhFRVHsvaJ4r/7sjatybViwIiiCgIjSpfdeAgmQbHpCerbN74/ISgikQEIK7+d5eEhyzpkzc3Z3dt4zc2YsBdmY0+KxWG/InSpd9w9Ke4h8Q7BrGVXaYDvfWHOoeg+Po70NM0a1p32QJwt+P8rzn23j9pFhdK7h9YdF47Jpvx6loHd4zUzWIRofja09dn93PphTT2A89AfGQ39iPPB76ai69gOxCepiffbVUpxXGqCmlgao5vSTYPh7rVJbR3Q+wdh1Glrarvs7QNU4XN1jQ+IfKVmFRF5iOTRRc7yaONDK342dR9OvadBal6o0PPiFF17gySef5IMPPsDNzY1XX30VgBkzZnD//ffTqVMn4uLi2Lt3L0OGDAFg1qxZNG9et+v+aLQ6bFtFYdsqCnNOMsZDazAe3YjpxGa0Xi3QunhhTov/Z3ivzhaddytsO8Si8wlB5xNcOhvqRZXY+MHevPDZdn7ecJKpQxre0hzFBhMf/HwABzsbZv+rK0v/SmDFltO4O9szuGvDWKvpQgcTsvjfLwcJDnDj3rGdsLWp2p16Zwdbenb0p2dHf5RS5BYaScksIDmzEH1GAfrMAo6czmHzwVTrMVqNBotS9Orkx/ThErCK+kGj0WIT2BGbwI5Y8jIwHl6L8cg6TIm70TTxQ+fRDHP6KVRBVukBWh1aryBs2/ZF5xuCzicEjat3jTTYNBoNvcP9CWnmxrwlB3n3x30M6dqcmeO7XHXaovGxKMWGfXrCgjzwqaXJ8kTjodFosPFrg41fGyzdb8J4ZF3pqLrf56Jx9kTnG4I5PeGf2Us1GrSezbENiSntgPAJQevuJ0tfVZPJbOG1b3eTk2/AYrFUuK9SkF9kxNdTPs+1LbqtNwvXxpORU1Rrk43WJxqlVMUPFNQTVXn2oSqUsRjjiS0YD69BGUuswanONwStZ2CVZ7n6etUx/tx9ljnTuxHoc+XPmlzrcfRKKT5aeohth1N5dHIEYUEeWCyK93/az57jGdwV14FuYVe/JM+1Kld80jne+G4P3u4OPHFzJM618MB/UYmJlKxC9JkF6DML8W3qQs/2PjUyS2V905Ce67ien/OqCmU2Yjq5HeOhNVgKc0rrOZ/g0oabV4tr8hiD0WTmhz/j+WPXWXw8nYiNbEaf8IBrthTVtdKQPjfVcS3KdTAhize/28NdN3Qgpn3tLQd3sYb0mkldVzFlMWM6vQfjwT+xnEtB17RlaZvOJwRd05ZX/exrdTWk91ZVJabkMefz7USEeuNsX3k72UanYVTPlni6OVyD3F29hvqapWUX8uT/tjBpYGuGdrv0EnMNqWxX/UxrY6OxdcAurD92Yf2vKp24Pq3YciiFb/84zqOTK55Frj5ZuzuJrYdSGds3mLCg0geotVoNd93QgTe/38Mnyw7h6mhLWMv6v1bm2bR83lm4lybOdjw8qUutBKxQOuSxlb8brfxLn4duSBWAuH5pdLbYtumJbZuedZYHWxsdNw8JpXNrL1ZsO8M3q4+zZOMpBkYGMigqEDfnhvH8f02xKNUob3ZdjQ17k3F2sCEyVIaQiyuj0eqwbRmFbcuous5Ko3VKXzoi8Z7xndFV0tMqrh0fDyda+Lqw42jaZYPWxkTGR1whF0dbxvQJ5nBiNruPZ9R1dqrklD6Xb/84TqdgL0b2CCqzzc5Wx/3jw/H1cGLu4v2cTr2yoEwpxeYDKTz5/kb2xdfedUnLKeLN7/dga6PlkcldcK/mun5CiGunY7AXr93Xh6enRhHa3J1lfyXw2Id/8eXKI6Rm1c663fVFYbGJ9XuTeWXBTu56fS3f/XEco0kafVA6hHDXsXR6dPDD1qZx9b4L0ZgkpOTi7GBT4Sy1om5EtfUhPimXrNz6P0vz1ZKg9Sr0jwigWVNnvv/zOEaTufID6lBBsZEPfz6Am7MdM0a3v+TdfmcHWx6a2BlHexve/mEvGTlF1TqHPrOA17/dzcfLDnEyKYd3Fu7jm9+P1fi1yc4r4Y1vd2MyW3hkUhe8r4Nx/EI0Bq0Dm3DfjeH8Z0YMPTr4sXF/Ck9/tIX3F+8nPvlc5Qk0EGaLhX3xGcxbcoCH/ruRz1ccIbfQSESbpqzafoaXF+wkNbtxB+tVsflACiazok9nmXVXiPosQZ9HS3+3BjOq8HoS3bZ0wqtdx9LrOCe177obHlyTdFotN8W24Y3v9rBq+5l6O3uXUopPlx0mO6+EJ2+OxKWCGY893Rx4eFIXXlmwkzd/2MvTUyNxdap4CJ/BaGbZ5kRWbEnE3lbHLUPbMrp/a/63aC+rd5zlyOls7ryhA4HeV/9MTn6Rkbe+30NekZHHJkfQrAbSFEJcW/5ezvxreDvG9mnF6p1nWbMriZ3H0gkNbMKwmCDCW3s1uGG0SinOpOXz14EUthxKJbfAgIujLX3DA+jZyY+WfqUzk+4+ls5nyw/zwvzt3DK0LT061M3SAXVNKcX6fcm08nej+VXMCyGEqF0Go5mkjAKGhVS+jJq49vy9nGnW1JkdR9OJjW54k6lWhwStV6l9S08i2jRl2V+J9OzoXy/XJFy57TR7TmRwU2wbQpo1qXT/Zk2duX98OG98t4d3f9zHY5MjLjtxyv6TmSxYdZT0nGJ6dPBj4sDWNHG2w8HOhimxoXRs5cVnvx7ixc93MGlgawZGNrviO3Xn8kt4b9F+UrMLeWhCZ4IDKl9zVwhRfzVxsefGfiGM7BHEhr16Vm0/zXuL9hEe4sXdYzpib1v/h4xm55Ww4UAqv29N4Gx6ATqthi6tm9Kzox+dQryw0ZUd0BQR6s0cP1f+98tBPl56iEMJWdw8OBQHu+vr6/iUPo+k9AJuGdbwZuAX4npyJi0fs0XR0k/aXPVVVFtvlm5K4FyBgSaNeK6I6+tbspZMGtiaZz/ZyqJ18dwxqn1dZ6eMY2dyWLT2JNHtfIiNCqzycW0C3bnrhg68/9N+PlxygPtu7FRm0ffsvBK+XX2MHUfT8fN04rHJXS45eVN4iBdzbo9h/vLDfP37MfafzOS2EWFVnoDFohSHTmWxbk8ye05koBTcM7Zjg5goSghRNQ52Ngzu2pwBkc1YsyuJ7/48zpvf7eH+8eEVjgypCyazhRNnz3HgVBYHT2WR+Pfz/yEBbkwbEkrXMN9K8+zp5sDjUyJYuimBpZsSiE/KZWZcB1r4ul5xni4Ojuu79XuTsbPVElMDs9ULIWpPQkppHdfK/8rqJ1H7otv68MumBHYdS2dARLO6zk6tkaC1Bvh4ODGkawuWb0lkQGQzQgIq7828Fs4VGPhwyQG83R2YPrxdtXs4I0O9mTakLV/+dpQvVh5l+vB2WJTiz51JLN5wEotFMbZvMMO6tahwbdQmznY8MD6cP3ae5Yc18cz+bBu3jwyjU/Dlh5pk55Wwcb+eDXuTyThXjIujLYOjm9O3SwB+MhGAEI2SjU7L4K7N8XC156OlB3n1m108PLFLjYxgMZktaDWaaq+trJQiNbuIAyczOXgqiyOncygxmtFpNYQEuDG2bzBDe7bCjuot3aHTahnTJ5i2LTz4eOlB/vPlziqPRik2mDhyOoeDfwfOqdmFdGjpSc+OfkSEetf7Hupig4mth1Pp1s4XxyosnyGEqDsJ+lzcnO3q5UhCUaqZtzO+nk7sPJomQauo3MgeQWw6oOfb1cd5elpUnT+PZbEoPvrlIIXFJh6e2OWKGwb9I5qRk1/CL5sS0Go0JOhzOZ2WT8dgT6YODsXHo2oBpEajITa6Oe1aePC/pQd5+4e9DI5uzvj+wdZZIy0WxYFTmazbk8zeE5lYlCIsyIPx/UOIaONdYWAshGg8otv54Oxgw3uL9/PyVzt5ZHKXK75ZZbEo/th1lsXrT2IwmnF1ssPNyY4mLpf439mOJs52ONnbcDI519qbmvn3rIw+7o707ORHx5aetAvysNar3t4uV7wMVliQBy/c1o3Pfi0djXIoIYvpI8LK9NZalOJMaj4HTpUGzsfPnsNsUdjZaGnbwoNOwV7sOpbOR0sPYW+no2tbH3p09KNtC/c6/y66lO1H0igxmOkrEzAJUe8lpORZn8kX9ZNGoyG6rTcrtpwmr9BQ6Vw0DZUErTXE0d6G8f1C+PTXw3y3+jj9ugQQ0NS5zj7kSzae4nBiNtOHt7vqSS7ierciJ9/A+r3JuLvYcc+YjkS19b6isgX6uPDcLdEsXBvP7zvOcDgxm5ti23D8TA7r9yWTlVuCm5MtQ2Oa07dzAL5VDIqFEI1LWEtPHr8pgrd/2Mv/LdjJQxM7V/uZqrPp+Xy+4ggnk3Pp2MqTlv5u5BYYyC0wcK7AQEpmAecKjJjMl16CxsFOR1iQByO6t6BDK88q36SrLjen0tEov28/w8K18bwwfxvThrQlv8hoDZzzi4wAtPBxYUjX5nRo5UmbQHfrzbxJg1pz/EwOmw6ksONIGhv36/Fys6d7Bz96dvTD38u5VvJ+JTbs1ePv5URIM3lGToj6rNhgIjmzgKi/Z6gV9Vd0Wx9+3ZzI7uMZjfaGoAStNahHRz/2xWfyx86zrN55Fl8PRyJDvYls600rf7drdsf7wMlMlv2VQO9O/jWylIBGo2Ha0FDCQ7wIu6B34UrZ2eq4eXAonYI9+ezXw7z+7W4AOrTyZPLANnRp07TBPZ8lhKh5rfzdeHpaFG9+t5vXvtnNfTeGExbkUelxRpOFZX8lsHxLIo72Ntw5uj0x7X0veaNNKUVRiYlzFwSz+UVGAr1dCA5wu2Z1kUajYUi3FoS2cGfezwd598d9ALg52dIp2JOOrbxo39KDJpdZk1qr0dC2hQdtW3hw8+BQ9hzP4K8DKSzfksivmxNp5e9Gz45+dAvzqdO78MkZBZxIOsfEAa2l50aIeu50aj5KldbFon5r4etC0yYO7DyaLkGrqJxWo+HuMR3JyS9h9/EMdh1LZ9X2M6zYehp3F7vSADbUm9Dm7rXSEErPKWLDvmT+2JlEM29nbh4SWmNp67RaIkNr9k5beEhT5twew57j6YS19MRH1lsVQlzEz9OJp6dF89b3e3j7hz3cdUMHotr6XHb/42dz+HzFEfSZhfTo4MvkQW0qDNI0Gg1ODrY4OdjWi97Iln5uPD+9K3uOZ9DM25lAH5dq3/C0t9UR096XmPa+5OSXsPVQKn8dSOHr34/x49p4HrsposZnX88vMpKTX2IN/C/3f16hAZ1WQ8+O1+dSP0I0JAn6XABa+skkTPVd6RBhH37fcYbCYiNODvVrEsOaIEFrLXB3sWdARDMGRDSjoNjIvhOZ7DqWzsZ9ev7clYSzgw1dWjclMtSbPm5XF6iZzBb2HM9g3d5kDp3KAg10CvZiyuDQej8ZB5RO0tSvS+N9aFwIcfU8XO154uZI3v1xLx/8fIBbhrYtV28UlZj4cV08a3Yl4eXmwEMTO1c42Vt95mhvQ48aCurcXewZ2q0FQ7u14HRqHv9dvJ+5i/bx3K3ReLo5XHX6Sim+/v0Yf+5KKrfNRqeliXPps8Jebg608nejibMdIc3cqjyDvBCi7iSk5OHhan/ZER6ifolq521d5rJnR/+6zk6Nk6C1ljk72NKjox89OvpRYjRz8FQWO4+ms/t4BpsOpPD+zwdo4eNCaHN32gS606Z5E9yqMHQrNbuQ9XuT2bRPT26hEQ9Xe27o3Yo+4f410hARQoj6xMXRlkcnRfDBzwf4YuVR8ouMjOgehEajYc+JDL767Sg5eSXERgcyrm/wdbfuaVW08HXlgfHhvPTVTt77cR9PTo286uu0ZOMp/tyVRN/O/rRv6WkNUps42+Nor5MhwEI0YKf0udLL2oAE+7vh6WbPjiPpErSKq2Nvq7MOETaZLRw7k8OZjEL2HE1jze4kVm0/A4C/lxNtAt0Jbd6E0EB3vJo4oNFoMJos7D6ezro9yRxOzEar0dC5tRf9ugTQsZVXtZdyEEKIhsTeTsd9N3bis18Ps2jdSXLyDOQVGdh2OI1m3s7cM7ZjvVlyrL5q5u3CzLiOvPvjXj5eeohZ4zpd8XwLf+46yy+bEugd7s+tw6q/rJoQov4qLDaSml1Er06NL/hprDQaDZGh3qzdnUxRianRLSnWuErTgNjotLRv6Um/rkEMjQ7EaLKQmJLHsbM5HDuTw44jaazfmwyUDo1r6efK8bPnyC8y4uXmwNi+wfTu5C/rZgkhris2Oi13jG6Pi6Mtq3eexUanYUyfVozoHiQTuFVReIgXkwe24ds/jrN43UnG9w+pdhrbDqfy9apjdGndlFuHtZWAVYhGJjGldBmvlv7S09qQRLf1YfWOs+yLzySmvW9dZ6dGSdBaT9jaaGkd2ITWgU0Y0T0Ii1IkpRdw7EwOx8/mcEqfS9vm7vTrEkD7Vp71cu09IYS4FrQaDTfFtiG0uTvNvJ3rxQRKDU1sdCDJmQUs35KIv5dTtXpTDiVk8fHSQ7QObMLMuA7otHKzQIjGJuF80FrNpcZE3Wod2IQmznbsOJomQau4NrQaDc19XGju48KgqMC6zo4QQtQrGo2G6HaXn0VYVEyj0XDz4FDSsov4fMURvN0dCW3uXulxiSl5zF28Hz8vJ+4fH45dA5jwTwhRfaf0uTRt4oCLY+ObhbYx02o0RLb1ZtN+PSUGc11np0bJ7VEhhBDiOmSj03L3mI40beLAfxfvJz2nqML9U7MLefuHPbg42PLwxC44N8IlFYQQpRJS8mR91gYqOtQbg9HC/pOZdZ2VGiVBqxBCCHGdcnG05YEJnVFK8d6P+ygqMV1yv3P5Jbz53R4sCh6e1FnmUxCiEcsrNJBxrlieZ22gQlu44+Joy85j6XWdlRolQasQQghxHfPzdOKeMR1JySpk3pKDmC2WMtsLi0289cNe8gqNPDihszxDLEQjlyjPszZoOq2WyFBv9pzIwGBsPEOEJWgVQgghrnNhLT25eXAo+09m8sOf8da/G01m5i7aR3JGAbPGdiQ4QBqxQjR2p/S5AAT5Sk9rQxXd1psSg5ndR9PqOis1RiZiEkIIIQT9I5qRnFnA7zvO4N/UiXGD2vLRL4c4eiaHGaPb0zHYq66zKMRlZeUWk5lbTJtA97rOSoOXkJKHn6cTTg4SJjRU7YI8cHawYcn6k4QHe1bpGHcXezq08sDBrn6+7lXK1alTp3jyySfJycnB3d2dV199lZYtW5bZx2w285///IcNGzag0Wi48847mTBhAgDvv/8+y5cvR6vVYmtry0MPPUSfPn1qvDBCCCGEuHKTB7YhNauIr1cd42BCNjuPpTN5UBt6dPCr66wJcVl5hQZe+XoXWbklPHdrNEF+0kN4NRJS8mjbwr2usyGugo1OS0x7X/7clcT++IxqHKchLMiTiDZN6dy6ab2av6BKQevzzz/PlClTiIuLY8mSJcyePZsvv/yyzD5Lly7l9OnTrFq1ipycHMaMGUOPHj0IDAwkPDyc2267DUdHR44cOcLUqVPZuHEjDg4OtVIoIYQQQlSfVqthZlwHXv5qJzuPpDGiexBDujav62wJcVkms4UPfz5ATr4BZ0cbPlt+mOdujcZGJ0/AXYmc/BKy80rkedZG4ObBodwW14nMzPxK91VAcnoBe05ksPt4Ol/+lgm/HaWVvytdWjelSxtvAr2d0Wg0tZ/xy6g0aM3MzOTQoUPMnz8fgFGjRvHvf/+brKwsPD3/6W5evnw5EyZMQKvV4unpSWxsLCtXruSOO+4o06vatm1blFLk5OTg5yd3boUQQoj6xNHehocndSE5p5j2gdJwFfXb93+c4MjpHO4YFYajnQ1zF+9nxZZERvdqVddZa5ASrJMwSW91Q6fRaGjiYo+hyFCl/d2C7GgX5MGkga1JzjgfwGbw04ZT/LThFE2bONCldVMi2jSlTXP3a35jqNKgVa/X4+vri05XuoC4TqfDx8cHvV5fJmjV6/UEBARYf/f39yclJaVcej///DMtWrSodsDq5eVSrf0bEm/vxlkxNNZygZRN1B6p6xqmxlY2b29XQus6E7Wssb1mDU1N1HW/bUnkj11nGdMvhLgBpe/YPSezWPpXIrHdW9KijnoLG/J7K21nEloNRHXwx8G+fJjQkMtWkcZaLriysvn4uNGlvT//ArJzi9l2KJWtB/Ws35vM6p1n8fdy5q5xnYhq51vj+b2ca/qk7bZt23j33Xf57LPPqn1sZmY+FouqhVzVLW9vV9LT8+o6GzWusZYLpGz1hVaraZQBntR1DU9jLVtjLRc0rLJJXXdpx87k8OGivXRs5cmomBbW1/PGvq3YfTSNN7/eydNTo9Bqr+1wxob03rqUQycz8G/qTF5uEReXoqGX7XIaa7mg5soWGeJJZIgnJQYz+09msnj9SV74eAvRbb25KTa0Rp59rayuq7Rf19/fn9TUVMzm0nV+zGYzaWlp+Pv7l9svOTnZ+rtery/Tm7p7924ee+wx3n//fYKDg6tdECGEEEIIITLPFfPBT/tp2sSBu+I6lAlM3ZzsmBLbhpPJuazecaYOc9nwKKVI0OfK0GBxWfZ2OqLb+TDntm6M7RvM3vhMnv54C6u2nS63xndNqzRo9fLyIiwsjGXLlgGwbNkywsLCygwNBhg2bBgLFy7EYrGQlZXF6tWrGTp0KAD79u3joYce4r333qNDhw61UAwhhBBCCNHYlRjNzF28D6PZwv3jw3F2sC23T0x7XzqHeLF4/UnSsguv+FxKKX7ffoYlG09hUY1vBMzFsvNKyC00yiRMolK2NlpG92zJv++IoW1zd7778wQvfr6DE0nnau2cVXqC9oUXXmDBggUMHTqUBQsWMGfOHABmzJjB/v37AYiLiyMwMJAhQ4YwceJEZs2aRfPmpTMOzpkzh+LiYmbPnk1cXBxxcXEcPXq0lookhBBCCCEaG6UU85cf5kxqPnfd0AF/L+dL7qfRaLhlWDt0Og2frzhyRQGnRSm+WX2cb/84zpKNp/jol4OYzLXbk1TXTulzAWjlL0GrqBofd0ceGB/OrLGdyC8y8vJXO/l8xWHyi4w1fq4qPdMaEhLCwoULy/39448/tv6s0+mswezFFi1adIXZE0IIIYQQApZvSWTb4TTG9w8hPKRphft6uNozcUBrvlh5lPV7kukf0azK5zGZLXz262G2HEplSNfmuDnb8ePaeAqLTcwa2wl7O93VFqVeSkjJQ6fV0Nzn0jcDhLgUjUZDVFtvOrTy4JeNCazafoZdxzKYMCCE3p38a2yZHFnESgghhBBC1Gt7jmeweN1Jurf3ZXhMiyod07dzAGFBHvyw5gRZucVVOqbEYOa9RfvYciiVG/sFM2lga0Z0D+Jfw9txMCGL17/bXSu9SDXl+NkccgurtsTJxRL0uTTzdsbWpnEG5aJ2OdjZMHFga16Y3hU/LyfmLz/CK1/vuqoh+heSoFUIIYQQQtRbSRkFfLT0IC38XPnX8HZV7rnRaDTcOrwdFqX48rejqEqGCecXGXnj+90cPJXFrcPaMrJHS+u5+nYO4J4xnTidms8rX++qchB8rWTkFPHej/v4vwW7+N+Sg5WW9WJKKRJS8uR5VnHVAn1cePLmSKYPb4c+s5B1e5MrP6gKJGgVQgghhBD1UkGxkbmL9mFnq+O+cZ2ws61eL6CPuyM39g1hX3wmWw6mXna/7LwSXv16F4kpedwzpiP9upQfThzV1puHJ3YmK7eY/1uwk5SsmulBuhoms4VfNyfw7CdbOZyYTecQLw4nZnPgVFa10knPKaKg2EQrf5k5WFw9rUZDn84BvH1fL27sG1IzadZIKkIIIYQQQtQgs8XCvJ8PkHmumFljO+Lp5nBF6QyKCiSkmRvfrD7GuYLyQ2dTswp5+audZOQW89CEzkS19blsWu2CPHhiSiQGk4WXv9pJQkruFeWpJhxJzOb5z7axaN1JOgZ78dKMGGaN64S3uwML18RXax3chJTStTylp1XUJJ1WW2NrJUvQKoQQQggh6p0tB1M5mJDNtKFtaRPofsXpaLUapg8Po8Ro5utVZVevSEzJ4+UFOykxmnn8pgjCWnpeJpV/BPm58tTUKOxtdbz6zW4OJ2Zfcd6uxLkCAx8vPchr3+7GaLLwwPhw7h3XCU83B2x0Wsb1DeFsej6bD6ZUOc0EfR42Oi3NvGUSJlE/SdAqhBBCCCHqnY7BXtx3Yyf6dg646rQCmjpzQ69W7Diazs6jaUBpT+Wr3+zCzkbLU1Mjq7XUi5+nE09Pi6KpmwNv/7DHmmZtslgUa3ad5ZmPtrDtcBqjegbx7zti6Ny67EzKXcN8CPJz5acNJzGazFVKOyEll+Y+LtjoJDQQ9ZO8M4UQQgghRL3TxNmOiDbeNZbesJgWtPBx4atVx9iwL5m3ftiLp5sDT02NuuyarxXxcLXniZsjCfJz5YOfD7BuT1KN5fViiSl5vPTVDr5adYwWvi68eHs3xvUNwf4Sz/hqNRomDmhNVm4Jq3eerTRty9+TMMnzrKI+k6BVCCGEEEI0ejY6LdNHhJFfaGT+8iO08C2d5fRKn5UFcHG05dFJEXRo5ckXK4/y5fJDGE2WGstzYbGJr38/xotfbCczt4QZo9vz2E0RlQbZYUEedAr24te/Eitdoic1q5Big1meZxX1mgStQgghhBDiuhDk58pNsW3o0cGPRyd3wcXR9qrTtLfTcf+N4fTu5M/CP44z+7NtHEqo3uy9F1NKseVQCs98vIU/d55lQEQzXp4RQ48OflVe8md8/xCKSkws35xY4X4J+r8nYZKeVlGP2dR1BoQQQgghhLhWBkUFMiiqZtO00Wm5bWQYg7u35P2Fe3jjuz3EtPdl0sDWuLvYVyutlKxCvvrtKIcTswnyc+X+8eHVet72vOY+LvTs6MfqnWcYGNWMpk0cL7nfqZRc7Gy1+Hs5VfscQlwrErQKIYQQQghRAyLb+fDvO7rx6+ZElm9JZF98BmP7BDMwMrDSpT8MRjO/bk5kxdZEbG10TB0SSv8uza5qyZCxfYPZejiNn9afYsbo9pfcJ0GfR5CvKzqtDMAU9ZcErUIIIYQQQtQQWxsdY/oE06ODHwtWHeWb1cfZtD+FW4a1vWyP6b74TL7+/SjpOcV07+DLpAGtaVLNHtpL8XRzYHB0ICu3nmZot+a08C07BNhssXA6NY9+XZpd9bmEqE1yS0UIIYQQQoga5uvpxMOTujAzrgM5BSX854sdfPXbUQqK/5kYKSu3mPd/2s87C/ei02p5bHIX7hzdoUYC1vNG9AjCycGGH9fGl9umzyjEYLLI86yi3pOeViGEEEIIIWqBRqOhW5gvnYK9+GnDSf7YeZadR9OYOLA1eYVGft54CotFMa5vMEO7tcDWpub7k5wdbBnZoyU/rDnBwYQsOrT0tG47lZILQEs/CVpF/SZBqxBCCCGEELXI0d6GKbGh9Oroz1erjvLJssMAhId4cfPgULzdLz1JUk0ZFNWMP3aeZeGaE4T9qyvav2cgTtDn4Wivw9dTJmES9ZsErUIIIYQQQlwDQX6uPD0tii0HU3Cyt6Vza68qL2FzNWxtdIzrG8zHyw6x7VAq3Tv4AZCQkkuQr6s1iBWivpJnWoUQQgghhLhGtBoNPTv606VN02sSsJ4X08GXFj4uLF5/EqPJgsls4UxaPi2vYDkdIa41CVqFEEIIIYRo5LQaDRMGtCbjXDFrdp0lKb0Ak1nJ86yiQZDhwUIIIYQQQlwHOrTypENLD5b+lYDZogCkp1U0CNLTKoQQQgghxHVifP/WFBSbWLLxFM4ONng3cajrLAlRKQlahRBCCCGEuE4E+bnSvYPv3+uzul3T52qFuFJVClpPnTrFpEmTGDp0KJMmTSIhIaHcPmazmTlz5hAbG8vgwYNZuHBhlbYJIYQQQgghrp1xfYKxtdHSJrBJXWdFiCqp0jOtzz//PFOmTCEuLo4lS5Ywe/ZsvvzyyzL7LF26lNOnT7Nq1SpycnIYM2YMPXr0IDAwsMJtQgghhBBCiGunqbsj/3dnd1yd7Oo6K0JUSaU9rZmZmRw6dIhRo0YBMGrUKA4dOkRWVlaZ/ZYvX86ECRPQarV4enoSGxvLypUrK90mhBBCCCGEuLY83RywtZEnBUXDUGlPq16vx9fXF51OB4BOp8PHxwe9Xo+np2eZ/QICAqy/+/v7k5KSUum2qtJqG+94+8ZatsZaLpCy1QcNJZ/V1VjLBVK2hqixlgsaTtkaSj6rq7GWC6RsDVFjLRc0nLJVls8Gs+SNh4dzXWeh1nh5udR1FmpFYy0XSNlE7ZG6rmFqrGVrrOWCxl22hkDquoapsZatsZYLGk/ZKh0T4O/vT2pqKmazGSidVCktLQ1/f/9y+yUnJ1t/1+v1+Pn5VbpNCCGEEEIIIYS4nEqDVi8vL8LCwli2bBkAy5YtIywsrMzQYIBhw4axcOFCLBYLWVlZrF69mqFDh1a6TQghhBBCCCGEuByNUkpVtlN8fDxPPvkkubm5uLm58eqrrxIcHMyMGTO4//776dSpE2azmRdffJFNmzYBMGPGDCZNmgRQ4TYhhBBCCCGEEOJyqhS0CiGEEEIIIYQQdUHmuRZCCCGEEEIIUW9J0CqEEEIIIYQQot6SoFUIIYQQQgghRL0lQasQQghxHZo7dy6PPvroZbePHDmSrVu3VppO27ZtSUxMrMmsXbGK8rx161b69u17jXMkrleVfb5q0uzZs3n//fdrPN0dO3ZUebWP6uxbEwYOHMhff/11yW21dT0uZdq0aSxcuPCanOtqXcv3ZG2QoFUIIRq5gQMH0rFjR7Kyssr8fcyYMbRt25azZ89WmsbixYu56aabaiuL142GFDj9+uuvxMTE1HU2quVa5bmiBrNo2AYOHEh4eDgRERH07NmTJ598koKCgjrJS1Xr3RdffJFZs2bV+Pmjo6P57bffanzf2lZb16OhB30NnQStQghxHWjWrBm//vqr9fejR49SVFR0zc5vMplqND2z2Vyj6TUUNX0dhRDlzZs3j927d/PTTz9x4MABPvzww7rO0mU1hrpQ6jVQSmGxWOo6G/WaBK1CCHEdiIuL4+eff7b+/vPPPzNmzJgy++Tl5fH444/TvXt3BgwYwAcffIDFYiE+Pp7nn3+ePXv2EBERQXR0dIX7Q2kPweTJk3n55ZeJiYlh7ty5leYxPj6eadOmER0dzciRI/njjz+s25588kmef/55ZsyYQZcuXS45BHTatGm88847TJ48mYiICG677bYyvct//PEHI0eOJDo6mmnTphEfH2/dNnDgQD799FNGjx5NVFQUDz74ICUlJcA/vaMff/wxPXr0oHfv3qxevZp169YxdOhQunXrxrx586xpGQwGXnrpJXr37k3v3r156aWXMBgMFBYWMmPGDNLS0oiIiCAiIoLU1NTL7n/huT/66CN69erFU089VabMzz//PK+++mqZv919993Mnz8fgNTUVO677z66d+/OwIED+fLLL8vsazQaefzxx4mIiGDkyJHs37+/zDU535toNpuZN28esbGxREREMG7cOPR6fbnXwGAw8Oqrr9K/f3969uzJ7NmzKS4uvtTLzenTp7nllluIiYkhJiaGRx55hNzcXOt2vV7PvffeS/fu3YmJieHFF1+0bvvhhx8YPnw4ERERjBgxgoMHD5bLc3FxMU8++SRdu3ZlxIgRZcpW2bWZO3cuDzzwwCWvzWOPPUZycjIzZ84kIiKCjz/++JLlEw2fr68vffr04fjx4wDs2bOHyZMnEx0dzQ033FCmHjpz5gxTp04lIiKC6dOnk52dXSatio5dvHgxgwYNIiIigoEDB/LLL79ctt69VF345JNP8vbbbwNw7tw57rrrLrp3707Xrl256667SElJsZ6rsnryQhePDKlKPXnexY8NXJjHS9VrFouFjz76iNjYWGJiYnjggQfIycmxHv/zzz8zYMAAYmJiKr2JcKlzffbZZ9b6e9GiRZc9NjU1lZkzZ9KtWzcGDx7MDz/8AMD69ev53//+x4oVK4iIiOCGG26wHpOUlHTZ61nR6z5t2jTefvttJk+eTOfOnTlz5ky5/Jy/Jufrut9//9267XxP/KuvvkrXrl0ZOHAg69ats26v7D3Z4CghhBCN2oABA9SmTZvUkCFD1IkTJ5TJZFJ9+vRRZ8+eVaGhoerMmTNKKaUee+wxNXPmTJWXl6fOnDmjhgwZon744QellFKLFi1SkydPLpNuZfuHhYWpL7/8UhmNRlVUVFRhHg0Gg4qNjVUffvihKikpUX/99Zfq0qWLio+PV0op9cQTT6jIyEi1Y8cOZTabVXFxcbk0pk6dqgYNGqROnjypioqK1NSpU9Xrr7+ulFLq5MmTqnPnzmrjxo3KYDCojz76SMXGxqqSkhLrNbrxxhtVSkqKys7OVsOGDVPffPONUkqpLVu2qLCwMDV37lxlMBjU999/r2JiYtTDDz+s8vLy1LFjx1SnTp3U6dOnlVJKvfPOO2rChAkqIyNDZWZmqkmTJqm3337bmlafPn3K5Luy/cPCwtRrr72mSkpKyl3Hbdu2qb59+yqLxaKUUionJ0d16tRJpaSkKLPZrMaOHavmzp2rSkpK1OnTp9XAgQPV+vXrlVJKvffee6pjx45q7dq1ymQyqTfeeENNmDCh3PtGKaU+/vhjNWrUKBUfH68sFos6fPiwysrKUkopFRoaqhISEpRSSr300kvqrrvuUtnZ2SovL0/ddddd6o033rjka56QkKA2btyoSkpKVGZmppoyZYr6z3/+o5RSymQyqdGjR6uXXnpJFRQUqOLiYrV9+3allFLLly9XvXv3Vnv37lUWi0UlJCSos2fPlsvz66+/rm666SaVnZ2tkpOT1ciRI63XviavjWhcLnxtk5OT1YgRI9Tbb7+tUlJSVLdu3dTatWuV2WxWGzduVN26dVOZmZlKKaUmTpyoXn75ZVVSUqK2bdumunTpoh555BGllKrw2IKCAhUREWGt61JTU9WxY8eUUpeudy9VFz7xxBPqrbfeUkoplZWVpVauXKkKCwtVXl6euu+++9Tdd99tPb6ievJiF9dXldWTF+57Yb1wPt/n83ipeu3zzz9XEyZMUHq9XpWUlKjnnntOPfTQQ0oppY4fP666dOmitm3bpkpKStTLL7+swsLCLvsZvNS53nnnHWUwGNTatWtVeHi4ysnJueSxU6ZMUc8//7wqLi5Whw4dUjExMeqvv/5SSpXWC+df06pcz8reM1OnTlX9+vVTx44dU0ajURkMhnL5Wb58ubU+//XXX1Xnzp1VamqqUqr0/dG+fXv1/fffK5PJpL7++mvVq1cv6/dBRe/Jhkh6WoUQ4jpxvrd106ZNhISE4Ovra91mNptZvnw5jzzyCC4uLgQGBjJ9+nR++eWXS6ZVlf19fHyYNm0aNjY2ODg4VJi3vXv3UlhYyJ133omdnR09evRgwIABZYY0Dxo0iKioKLRaLfb29pdMZ9y4cbRq1QoHBweGDRvG4cOHAVi+fDn9+vWjV69e2Nracvvtt1NcXMzu3butx06bNg1fX1/c3d0ZMGCA9VgAGxsb7r77bmxtbRkxYgTZ2dnccsstuLi40KZNG1q3bs3Ro0cBWLp0KbNmzcLLywtPT09mzZp12etYlf21Wi33338/dnZ25a5jdHQ0Go2GHTt2APDbb7/RpUsXfH192b9/P1lZWdx7773Y2dnRvHlzJk6cyPLly63HR0VF0a9fP3Q6HXFxcRw5cuSSeVy4cCEPPPAAwcHBaDQa2rVrh4eHR5l9lFL88MMPPP3007i7u+Pi4sJdd91V5jW8UFBQEL169cLOzg5PT0+mT5/O9u3bAdi3bx9paWk8/vjjODk5YW9vb+1p+vHHH7njjjsIDw9Ho9EQFBREs2bNyqW/YsUKZs6cibu7O/7+/kybNs26rSavjWh8Zs2aRXR0NFOmTKFr167MnDmTJUuW0LdvX/r164dWq6VXr1507NiRdevWkZyczP79+3nggQews7Oz9nqdV9GxUPoZP378OMXFxfj4+NCmTZsK81dRXejh4cHQoUNxdHTExcWFu+++2/q5Ou9y9WRVVFRPVsfF9dp3333HQw89hJ+fH3Z2dtx777389ttvmEwmVq5cSf/+/enatSt2dnY88MADaLVVD2FsbGyYNWsWtra29OvXDycnJ06dOlVuP71ez65du3j00Uext7cnLCyMCRMmsGTJkgrTv9z1rOx1Bxg7dixt2rTBxsYGW1vbcmkPHz4cX19ftFotI0aMICgoiH379lm3BwQEMHHiRHQ6HWPHjiU9PZ2MjIxK35MNkU1dZ0AIIcS1ERcXx9SpUzl79ixxcXFltmVnZ2M0GgkICLD+LSAggNTU1EumVZX9/fz8rD/Pnj2bpUuXAnDXXXcxc+bMMumlpaXh5+dXpiFycXr+/v6VltHb29v6s6OjI4WFhdb0L8yrVqvF39+/TPoXH5uWlmb93d3dHZ1OB2ANHL28vKzb7e3trZO1XHyugICAMmldrLL9PTw8rA3TefPm8b///Q+A0aNH8+KLLzJixAiWLVtG165dWbp0qXXYWlJSEmlpadZgD0pvNlz4e9OmTa0/Ozg4UFJSgslkwsambPMgJSWFFi1aXLYMAFlZWRQVFTFu3Djr31QFz2llZGTw0ksvsWPHDgoKClBK4ebmBpQ2HgMCAsrl4/y2yvICpdf1wvfMhde4Jq+NaHzef/99evbsWeZvycnJrFy5kjVr1lj/ZjKZiImJIS0tDTc3N5ycnKzbAgICrEPoKzrWycmJt99+m88++4xnnnmGyMhInnjiCUJCQi6bv4rqwqKiIv7v//6PDRs2cO7cOQAKCgowm83WOuxy9WRVVFRPVseF9RqUXqNZs2aV+Q7QarVkZmZavx/Oc3Jywt3dvcrncnd3L/O5vVyZ09LSaNKkCS4uLta/BQQEcODAgQrTv9z1rOh1P6+y77Wff/6Z+fPnk5SUBEBhYWGZYb4X1lOOjo5l9qnoPdkQSc0rhBDXiWbNmhEYGMi6det46aWXymzz8PDA1taW5ORkWrduDZQGB+d7YzUaTbX2v/iYF198scwziRfz8fEhJSUFi8VibbTo9Xpatmx55QW+KP1jx45Zf1dKlctvTfHx8SE5OdnaW6LX6/Hx8QHKX8fK9r/4mJkzZ5YL+EeNGsVtt93GnXfeyb59+6xLPfj7+xMYGMiqVauuukx+fn6cPn2a0NDQy+7j4eGBg4MDv/76a5Wu61tvvYVGo2Hp0qW4u7uzevVq63vE398fvV5/ySDR39+f06dPV5q+t7c3er2+zHW9MI2aujbi+uDv709cXBz/+c9/ym1LSkoiNzeXwsJCa5CQnJxs/exWdCxAnz596NOnD8XFxbzzzjs899xzfPPNN5esLyrz2WefcerUKX744Qe8vb05fPgwY8aMQSlV7bSuhqOjY5nJ/tLT0y/7/QCldczLL79MVFRUubR8fHzKzEFQVFRU5nnXmuLj48O5c+fIz8+3Bq4VfQ9WprLXvbI0k5KSePbZZ/n888+JiIiwjvqoCm9v7wrfkw2RDA8WQojryEsvvcQXX3xR5u4rgE6nY9iwYbz99tvk5+eTlJTE/Pnzrb12Xl5e1kmDqrJ/dYWHh+Pg4MAnn3yC0Whk69at/Pnnn4wYMeLqCvy34cOHs27dOjZv3ozRaOSzzz7Dzs6OiIiIGkn/QiNHjuTDDz8kKyuLrKws3n//fUaPHg2UXsecnBzy8vKqtH9VtG/fHg8PD5599ll69+5t7a0MDw/H2dmZjz76iOLiYsxmM8eOHSsztKyqJkyYwLvvvktCQgJKKY4cOVJuUg+tVsuECRN4+eWXyczMBEonNdmwYcMl0ywoKMDJyQlXV1dSU1P55JNPrNvCw8Px9vbmzTffpLCwkJKSEnbu3AnA+PHj+eyzzzhw4ABKKRITE629EBcaPnw4H330EefOnSMlJYWvvvqqTPpXc22aNm1abtKUgQMHsnjx4iodLxqeG264gTVr1rBhwwbMZjMlJSVs3bqVlJQUmjVrRseOHZk7dy4Gg4EdO3aU6V2r6NiMjAxWr15NYWEhdnZ2ODk5WW/cXVzvVkVBQQH29va4ubmRk5PDf//73xq/FlXRrl07li1bhtlsZv369eWGKF/spptu4p133rF+lrOysli9ejUAQ4cOZe3atezYsQODwcB7771XKzPt+vv7ExERwVtvvUVJSQlHjhzhxx9/LPM9mJSUVOVzV/S6V0VRUREajQZPT08AFi1aZJ0UrDKVvScbIglahRDiOtKiRQs6dep0yW3PPfccjo6OxMbGMmXKFEaNGsWNN94IQPfu3WndujW9e/e2Dm2qaP/qsrOzY968eaxfv57u3bszZ84cXnvttQqHyFVHcHAwr7/+Ov/+97/p3r07a9asYd68edjZ2dVI+he655576NixIzfccAM33HADHTp04J577gEgJCSEkSNHEhsbS3R0NKmpqRXuX1WjRo3ir7/+YtSoUda/6XQ65s2bx5EjRxg0aBDdu3fn2WefJT8/v9plmj59OsOHD+e2224jMjKSZ555xjpr6IUee+wxgoKCmDhxIpGRkfzrX/+65LNjAPfeey+HDh0iOjqaO++8kyFDhpTLe2JiIgMGDKBv376sWLECKA1GZ86cySOPPEJkZCSzZs2yDoO8OP2AgAAGDRrEbbfdVqaH4mqvzZ133smHH35IdHQ0n376KQaDgezsbDp37lyl40XD4+/vzwcffMD//vc/evToQb9+/fj000+tAcybb77J3r17iYmJ4f333y8zO3tFx1osFj7//HP69OlDt27d2L59Oy+88AJw6Xq3MrfeeislJSV0796dSZMm0adPn5q+FFXyzDPPsGbNGqKjo1m6dCmxsbEV7n/LLbcwcOBAbrvtNiIiIpg4caL1JlKbNm2YPXs2jz76KH369MHNza3McOGa9NZbb5GUlESfPn249957ue+++6xDxYcNGwZATEwMY8eOrTStyt4zlWndujW33XYbkydPpmfPnhw7dozIyMgql6Wi92RDpFHXeryAEEIIIUQjsmPHDr755hveeuutus6KEHVi8+bNPPvss2WWKhOiJskzrUIIIYQQVyE6OrrMJE5CXG+OHz9OYGBgXWdDNGIStAohhBBCCCGuyH/+8x/+/PNPXn311brOimjEZHiwEEIIIYQQQoh6SyZiEkIIIYQQQghRb0nQKhqMX375hdtuu836+86dOxkyZAgRERGsXr2aO+64g59++umK0p42bRoLFy6s9nFnz56lbdu2mEymKzpvdSQnJxMREYHZbK71cwnRmJ08eZK4uDgiIiL48ssvayzdrVu30rdv38tunz17tnUN1YpcaX3U0ERERFiXjSkuLmbmzJlERUVx//33l6vvq2Px4sXcdNNNNZlVIeqVi9s7b7/9NjExMfTq1euq2grXsk1zParsO6Bt27YkJiZewxw1LI3ymdaBAweSkZGBTqez/m3s2LHMnj27DnNVOxYvXszChQv59ttv6zorte78chDnvffee9x8883ceuutAJVOp16fDRw4kP/85z/WadUvJSAggN27d1/DXNW+adOmccMNNzBhwoS6zoqoBVV5X9eFTz75hJiYGJYsWXJNz/viiy9e0/PVdxfWZytXriQjI4OtW7diY1PaNLnSNX+FaOwuXNM4OTmZ+fPns2bNGry8vABqra1QX+v0K1EX7Y9r9R3w5JNP4uvry0MPPXRNznetNMqgFWDevHkN8kNlMpmsX9iiYsnJybRp06aus3FN1If3xaXyUB/yJUR1JScnM3LkyLrOhrhAcnIyLVu2lPpEiGpKTk7G3d3dGrDWpYbQJlBKIdP5NEzX3fDg559/nvvuu8/6++uvv86tt96KUso6tGvevHnExMQwcOBAfvnlF+u+eXl5PP7443Tv3p0BAwbwwQcfWBcITkxMZOrUqURFRRETE8ODDz4IXHqoxYVDvxYvXszkyZN5+eWXiYmJYe7cuRgMBl599VX69+9Pz549mT17NsXFxeXKEh8fz/PPP8+ePXuIiIiwTrdfUT4v9uSTT/L2229bf794eNvAgQP59NNPGT16NFFRUTz44IPWBeWzsrK46667iI6Oplu3bkyZMsV6noEDB/K///2PESNG0LVrV5566qkyC9GvWbOGuLg4oqOjmTx5MkeOHLFu0+v13HvvvXTv3p2YmBjrnakLh3zFxsZy5swZZs6cSUREBAaDodyQuh9//JHhw4fTtWtXbr/9dpKSkqzbNm3axLBhw4iKiuLFF1+ssAKzWCx89NFHxMbGEhMTwwMPPEBOTs4l983Ly+Ppp5+md+/e9OnTh7fffrvMEJ0ffviB4cOHExERwYgRIzh48CCPPfYYycnJ1rJ8/PHH1vfNwoUL6d+/P7feemu591JOTg5PPfUUvXv3pmvXrtxzzz2XLcOlzgvlh6Jc+H44/1746KOP6NWrF0899RRz587l/vvv59FHHyUyMpKffvqpwjKff81effVVunbtysCBA1m3bh1QOpxpx44dvPjii0REREgvVCNzqff1nXfeyVdffVVmv9GjR/P7778Dpe/HL7/8kkGDBhETE8Orr75apu6q6DN9sT/++IORI0cSHR3NtGnTiI+PB0oXsN+6dav1fXfq1Kkyxy1fvpxx48aV+dvnn3/OzJkzAapUP3/22Wf06NGD3r17s2jRIuvfL65vV69eTVxcHJGRkcTGxrJ+/fpLlqWq5b7U8OSBAwfy119/ATB37lweeOABHn/8cSIiIhg5ciT79++37vvRRx/Rp08fIiIiGDp0KJs3b7Yed//99/Pggw8SERHB2LFjy9TZqamp3HfffXTv3p2BAweWGXJtNpuZN28esbGxREREMG7cOPR6PfBP/fPee+/xwQcfsGLFCiIiIli4cGG5Ib7x8fFMnz6dbt26MXToUJYvX27dlp2dzcyZM4mMjGT8+PGcPn36ktdHiIpU5fvwcp/tdevWMWLECCIiIujTpw+ffvppmeMu166srD65XB1xvr3z119/cdttt5GWlkZERARPPvlkubZCRd/RZrOZV199lZiYGAYNGmT9fr6UqrZVAO6//3569epFVFQUN998M8ePHy9zXefMmcOdd95JREQEEyZMsH5mlVK8/PLL9OjRg8jISEaPHs2xY8esx82ePZvp06cTERHB1KlTy9SFu3bt4sYbbyQqKoobb7yRXbt2WbdNmzaNt99+m8mTJ9O5c2cee+yxKrU/KipHcXExr7zyCgMGDCAqKoqbbrrJ+trt2LGDyZMnEx0dTb9+/Vi8eHG59xSU9pj37t2b3r178+OPP5Y5d0XvjYrej99//z1Lly7l008/JSIiwvrddbn6vUFRjdCAAQPUpk2bLrmtsLBQDRkyRC1atEht375ddevWTen1eqWUUlu2bFFhYWHq5ZdfViUlJWrr1q2qc+fOKj4+Ximl1GOPPaZmzpyp8vLy1JkzZ9SQIUPUDz/8oJRS6qGHHlIffPCBMpvNqri4WG3fvl0ppdSZM2dUaGioMhqN1jxMnTrVetyiRYtUWFiY+vLLL5XRaFRFRUXqpZdeUnfddZfKzs5WeXl56q677lJvvPHGJcuzaNEiNXny5DJ/qyifF3viiSfUW2+9Zf19y5Ytqk+fPmWu5Y033qhSUlJUdna2GjZsmPrmm2+UUkq98cYb6rnnnlMGg0EZDAa1fft2ZbFYrMeNHDlSJScnq+zsbDVp0iTreQ4ePKi6d++u9uzZo0wmk1q8eLEaMGCAKikpUSaTSY0ePVq99NJLqqCgoMy1vLisF7/OF17X33//XcXGxqoTJ04oo9Go3n//fTVp0iSllFKZmZmqS5cuasWKFcpgMKj58+ersLCwy16jzz//XE2YMEHp9XpVUlKinnvuOfXQQw9d8vW955571HPPPacKCgpURkaGuvHGG9W3336rlFJq+fLlqnfv3mrv3r3KYrGohIQEdfbs2UuW5Xy6jz32mCooKFBFRUXlzjVjxgz1wAMPqJycHGUwGNTWrVsvmf+KzhsaGqoSEhIu+X44/3l47bXXVElJiSoqKlLvvfeeat++vfr999+V2WxWRUVFFZZ50aJFqn379ur7779XJpNJff3116pXr17W98mFr5lofC5+X//6669q/Pjx1t8PHz6sunXrpkpKSpRSpe/HqVOnquzsbJWUlFSm7qroM32xkydPqs6dO6uNGzcqg8GgPvroIxUbG2s9T0Xvu8LCQtWlSxd16tQp69/GjRunli1bppRSFdbP5z8z77zzjjIYDGrt2rUqPDxc5eTkKKXKfr727t2rIiMj1caNG5XZbFYpKSnqxIkT5fJXnXJfXH9f/Bq89957qmPHjmrt2rXKZDKpN954Q02YMEEppVR8fLzq27evSklJUUqV1kGJiYnW49q3b2+tMz/55BM1YMAAZTAYlNlsVmPHjlVz585VJSUl6vTp02rgwIFq/fr1SimlPv74YzVq1CgVHx+vLBaLOnz4sMrKyrK+3ufrn/fee0898sgj1nxfWN8XFBSovn37qh9//FEZjUZ18OBB1a1bN3X8+HGllFIPPviguv/++1VBQYE6evSo6t27d7nvRSEqU5Xvw8t9tnv16mVtq+Tk5KgDBw6UOe5y7cqK6pOq1hEXf+6r0y755ptv1NChQ61ttalTp5Zrs16oKm0VpZRauHChysvLUyUlJeo///mPuuGGG8pc127duqm9e/cqo9GoHn74YfXggw8qpZRav369Gjt2rDp37pyyWCzqxIkTKjU11Xpcly5d1LZt21RJSYn697//bf2cZ2dnq+joaPXTTz8po9Goli5dqqKjo611zdSpU1W/fv3UsWPHlNFoVAaDoUrtj4rK8cILL6ipU6eqlJQUZTKZ1M6dO1VJSYk6e/as6tKli1q6dKkyGAwqKytLHTp0qNx7at26dapHjx7q6NGjqqCgQD388MNl3oM19V2jVMX1e0PSaHtaZ82aZV3sOzo6mh9++AEAR0dHXnvtNV555RUee+wxnnvuOfz8/Moc+8ADD2BnZ0e3bt3o168fK1aswGw2s3z5ch555BFcXFwIDAxk+vTp1jtmNjY2JCcnk5aWhr29fbUWGffx8WHatGnY2Nhgb2/PDz/8wNNPP427uzsuLi7cdddd/Prrr1VKq7J8Xolp06bh6+uLu7s7AwYM4PDhw0BpmdPT00lOTsbW1pbo6Gg0Go31uJtvvhl/f3/c3d25++67rWX4/vvvmTRpEp07d0an0zF27FhsbW3Zs2cP+/btIy0tjccffxwnJ6dqX8vzvvvuO+68805CQkKwsbFh5syZHD58mKSkJNavX0+bNm0YNmwYtra23HrrrTRt2rTCtB566CH8/Pyws7Pj3nvv5bfffis3UUFGRgbr1q3j6aefxsnJCS8vL/71r39Zy/3jjz9yxx13EB4ejkajISgoiGbNmlVYjvvuuw8nJyccHBzK/D0tLY3169czZ84cmjRpgq2tLd26dbtkGldy3vO0Wi33338/dnZ21jx06dKF2NhYtFot+fn5FZYZSp/FnThxovW1Tk9PJyMjo0rnF43LoEGDSEhIICEhAYAlS5YwfPhw7OzsrPvMmDEDd3d3AgICuOWWW1i2bBlQ8Wf6YsuXL6dfv3706tULW1tbbr/9doqLi6v0nJejoyODBg2ynjchIYGTJ08ycOBAlFKV1s82NjbMmjULW1tb+vXrh5OTU7neXCj9XN5444306tULrVaLr68vISEh5farTrmrIioqin79+qHT6YiLi7P2mOp0OgwGA/Hx8RiNRgIDA2nRooX1uA4dOljrzOnTp2MwGNi7dy/79+8nKyuLe++9Fzs7O5o3b87EiROtPaELFy7kgQceIDg4GI1GQ7t27fDw8KhWnteuXUuzZs248cYbsbGxoX379gwdOpSVK1diNptZtWoV999/P05OToSGhjJ27NgrujZCVKSiz7aNjQ0nTpwgPz+fJk2a0KFDhzLHXqpdWVl9UtU6oiKVtUtWrFjBrbfeam2r3XXXXVd0bS5uq4wfPx4XFxfs7Oy47777OHLkCHl5edb9Y2NjCQ8Px8bGhhtuuKFMu7KgoICTJ0+ilCIkJAQfHx/rcf3796dr167Y2dnx0EMPsWfPHvR6PWvXriUoKIgxY8ZgY2PDqFGjCA4OZs2aNdZjx44dS5s2bbCxscHW1rZK5bpcOSwWC4sWLeKZZ57B19cXnU5HZGQkdnZ2LFu2jJ49ezJq1ChsbW3x8PAgLCysXNorVqxg3LhxhIaG4uTkxL333mvdVpPfNVB5/d5Q1O+B51fh/fffv+wzrZ07dyYwMJCsrCyGDx9eZpubmxtOTk7W3wMCAkhLSyM7Oxuj0UhAQECZbampqUDpsIl3332X8ePH06RJE6ZPn8748eOrlNcLg+asrCyKiorKDE9TSl12eO/FKsvnlfD29rb+7OjoSFpaGgC33347//3vf60zPE6aNIk777zTuq+/v3+ZPJw/Ljk5mZ9//pkFCxZYtxuNRtLS0tBqtQQEBFz1MxHJycm8/PLLZRa6VkqRmppKWlpamWuu0WjK5PVSac2aNQut9p97PFqtlszMzHL7mUwmevfubf2bxWKxpq3X66tdSVx8Q+W8lJQUmjRpQpMmTSpN40rOe56Hhwf29vaXzVNlZQbK3BBwdHQEoLCw8IryIxo2e3t7hg8fzi+//MK9997LsmXLeO+998rsc+F7p1mzZmXqjct9pi++CZOWllamDtRqtfj7+1e5Hhw9ejSvvPKKNY+xsbE4OjqSmZlZaf3s7u5epv5ydHS85Ptdr9fTr1+/SvNSnXJXxYWfRwcHB0pKSjCZTAQFBfH0008zd+5cTpw4Qe/eva2TeUDZz/35BvT51yYtLa3MzUWz2Wz9PSUl5aobR0lJSezbt6/cOW644QaysrIwmUzlvm+EqGkVfbbfe+89PvzwQ958803atm3LI488QkREBHD5dmVl7b2q1hEVqew7Oi0trUY+OxfWD2azmbfffpuVK1eSlZVlbTtlZ2fj6uoKlK+Hzl/HHj16cPPNN/Piiy+SlJTEkCFDeOKJJ3BxcSl3HmdnZ5o0aUJaWlq5Ov98WS6s8ytq511KReUwGAyUlJTQvHnzcsdVtc2VlpZGx44drb9fWJ9XJRao6ncNUGn93lA02qC1Il9//TVGoxEfHx8++eSTMneWcnNzKSwstFYwer2eNm3a4OHhga2tLcnJybRu3dq67fwL7u3tzX/+8x+gdCz79OnT6dq1q/UDWlxcbP3Qpaenl8nPhb2THh4eODg48Ouvv1bpzXThseePryifF3N0dCzz/ER1esBcXFx48sknefLJJzl27Bi33nornTp1okePHtbznpecnGy9W+bv78/MmTO5++67y6W5e/du9Hr9VT/Mf/4cl5p9MjExkZSUFOvvSqkyeb2Yn58fL7/8MlFRUeW2nT17tsx+dnZ2bNmy5ZJ59/f3r/azVhe/vhee69y5c+Tm5uLm5lZhGhWd19HRkaKiIuvv6enpZd4rlzr/hX+rrMxCXGzs2LE8/vjjREVF4ejoaG3YnXe+zoVL1xtVmVHWx8fH+hwU/PMZr+oXdM+ePcnKyuLw4cMsW7aMp556Cqh+/VyRqtYH1Sn3xfW52WwmKyurynkaPXo0o0ePJj8/n9mzZ/PGG2/w+uuvA5SpMy0WC6mpqfj4+KDT6QgMDGTVqlWXTNPPz4/Tp08TGhpa5XxczN/fn65duzJ//vxy28xmMzY2Nuj1emsvVEX1uRCXU9n3YUXCw8P58MMPMRqNfP311zz44IPW50MraldWVJ9cSZvhYpV9R3t7e5f5vFzpZ+fCdsHSpUv5448/mD9/PoGBgeTl5dG1a9cqT350yy23cMstt5CZmcmDDz7IJ598Yp0n5sJ6qKCggHPnzuHj44OPjw/Jycll0tHr9fTp0+eSeayKispx/ob+mTNnaNeuXZnj/P392bdvX6Xp+/j4lGsnn3e13zWXKmtF9XtD0WiHB1/OqVOneOedd3j99dd57bXX+OSTT6zDEs47PxnSjh07WLt2LcOGDUOn0zFs2DDefvtt8vPzSUpKYv78+daGxIoVK6wfpiZNmqDRaNBqtXh6euLr68uSJUswm838+OOP1nXpLkWr1TJhwgRefvlla09eamoqGzZsuOT+Xl5epKamYjAYACrN58XCwsJYt24dOTk5pKen88UXX1T5Wq5Zs4bExESUUri6uqLT6cp8UL755htSUlLIyclh3rx5jBgxAoAJEybw3XffsXfvXpRSFBYWsnbtWvLz8wkPD8fb25s333yTwsJCSkpK2LlzZ5XzdN7kyZP56KOPrA/N5+XlsWLFCgD69evH8ePHWbVqFSaTiS+//LLCYP2mm27inXfesQ7Hy8rKYvXq1eX28/HxoVevXrzyyivk5+djsVg4ffo027ZtA0qHmXz22WccOHAApRSJiYnWNJs2bVrh++JS5+rbty9z5szh3LlzGI1Gtm/ffsl9Kzpvu3btWLZsGWazmfXr1182jYryUVGZK1PdcouG5VKvb0REBFqtlldeeeWS9dKnn37KuXPn0Ov1fPnll9Z6o6LP9MWGDx/OunXr2Lx5M0ajkc8++ww7O7tyAfLl2NraMmzYMF577TXOnTtHr169gOrXzxUZP348ixcvZvPmzdYg8PxkUReqTrlbtWpFSUkJa9euxWg08uGHH1q/Gypz8uRJNm/ejMFgwM7ODnt7+zKjSw4ePGitM7/44gvs7Ozo3Lkz4eHhODs789FHH1FcXIzZbObYsWPWRtuECRN49913SUhIQCnFkSNHyM7Orta16t+/PwkJCfz8888YjUaMRiP79u0jPj4enU7H4MGD+e9//0tRUREnTpy44vW6xfXtSr8PDQYDv/zyC3l5edja2uLs7FzmswOXbldWVp9UtY6oSGXf0cOHD+err74iJSWFc+fO8dFHH1WYXlW+swsKCrCzs8PDw4OioiLeeuutKud337597N27F6PRiKOjI3Z2dmWu5bp169ixYwcGg4F3332Xzp074+/vT79+/UhISGDp0qWYTCaWL1/OiRMn6N+//xWXpaJyaLVabrzxRv7v//6P1NRUzGYzu3fvxmAwMHr0aP766y+WL1+OyWQiOzu7XJwBMGzYMH766SdOnDhBUVER//3vf8ukfzXfNV5eXmU6VSqr3xuKhpfjKjo/u9n5f7NmzcJkMvHYY48xY8YM2rVrR8uWLXnooYd4/PHHrV/sTZs2xc3NjT59+vDoo4/ywgsvWO/ePvfcczg6OhIbG8uUKVMYNWoUN954IwD79+9nwoQJREREcPfdd/PMM89Yhw38+9//5tNPPyUmJoYTJ05U2nB67LHHCAoKYuLEiURGRvKvf/3rsuPUu3fvTuvWrenduzcxMTGV5vNicXFxtGvXjoEDB3LbbbdZG4hVkZiYaJ3FbdKkSdx00010797dun3UqFHcdtttxMbG0qJFC2vPaqdOnfj3v//Niy++SNeuXRkyZIh1ZjWdTse8efNITExkwIAB9O3b97INtIoMHjyYO+64g4cffpjIyEhGjRplnXXP09OTd999lzfffJOYmBgSExOJjIy8bFq33HKL9fpEREQwceLEy95Fe+211zAajdZZk++//35rz/rw4cOZOXMmjzzyCJGRkcyaNYtz584BcOedd/Lhhx8SHR1tnXWwMq+99ho2NjYMHz6cnj17XvaGQ0XnfeaZZ1izZg3R0dEsXbr0ita6rajMlbnlllv47bff6Nq1q3Wkgmg8Lve+jouL49ixY8TFxZU7ZtCgQYwbN44xY8bQv39/62MWFX2mLxYcHMzrr7/Ov//9b7p3786aNWuYN29emWdnK3O+4TFs2LAyPRTVqZ8rEh4ezv/93/9ZR3FMnTq1XE9Bdcvt6urK888/z7PPPkvfvn1xdHS87CMGFzMYDNY6sXfv3mRlZfHwww9btw8aNIjly5fTtWtXlixZwty5c7G1tbXW2UeOHGHQoEF0796dZ599lvz8fACmT5/O8OHDue2224iMjOSZZ54pM5N8Vbi4uPDpp5+yfPly+vTpQ+/evXnjjTes39uzZ8+msLCQXr168eSTT5ab/VmIqria78MlS5YwcOBAIiMj+e6778r0YFXUrqyoPqlqHVGZir6jJ06cSO/evYmLi2Ps2LEMGTKkwrSq0lYZM2YMAQEB9OnTh5EjR9KlS5cq57WgoIBnn32Wbt26MWDAANzd3bn99tut20eNGsX7779PTEwMBw8etF5nDw8P5s2bx/z584mJieGTTz5h3rx5eHp6XvZclbU/KivHE088QWhoKOPHj6dbt2688cYbWCwWAgIC+Pjjj5k/fz7dunVjzJgxZWZbP69fv37ceuut3HrrrQwePLhM+xmu7rtm/PjxnDhxgujoaO65555K6/eGQqOq2l9/Hdi6dSuPPfbYZRsEouoa0wLUQoia9fPPP/P999/z7bfflvl727ZtWbVqFUFBQXWUM3Epc+fOJTExkTfeeKOusyJEgyLtyppz/hnMhx56qK6zIupIo+1pFUIIUf8UFRXxzTffMGnSpLrOihBCCCEaCAlahRBCXBMbNmygR48eeHl5MWrUqLrOjhBCCCEaCBkeLIQQQgghhBCi3pKeViGEEEIIIYQQ9ZYErUIIIYQQQggh6q3yKw3XU9nZBVgsjW8ks5eXC5mZ+XWdjRrXWMsFUrb6QqvV4OHhXNfZqHFS1zU8jbVsjbVc0LDKJnVdw9KQ3lvV1VjL1ljLBQ2rbJXVdQ0maLVYVKOs3AApVwMkZRO1Req6hqmxlq2xlgsad9kaAqnrGqbGWrbGWi5oPGWT4cFCCCGEEEIIIeotCVqFEEIIIYQQQtRbVQpaT506xaRJkxg6dCiTJk0iISGh3D4bN25k3LhxdOzYkVdffbXMtvfff5+RI0cyevRoxo0bx4YNG2ok80IIIYQQQgghGrcqPdP6/PPPM2XKFOLi4liyZAmzZ8/myy+/LLNP8+bNeemll1i5ciUGg6HMtvDwcG677TYcHR05cuQIU6dOZePGjTg4ONRcSYQQQgghhBBCNDqV9rRmZmZy6NAhRo0aBcCoUaM4dOgQWVlZZfYLCgoiLCwMG5vycXCfPn1wdHQEoG3btiilyMnJqYHsCyGEEEIIIYRozCrtadXr9fj6+qLT6QDQ6XT4+Pig1+vx9PSs9gl//vlnWrRogZ+fX7WO8/Jyqfa5Ggpvb9e6zkKtaKzlAimbqD1S1zVMjbVsjbVc0LjL1hBIXdcwNdayNdZyQeMp2zVd8mbbtm28++67fPbZZ9U+NjMzv9FM2Xwhb29X0tPz6jobNa6xlgukbPWFVqtplI0eqesansZatsZaLmhYZZO6rmFpSO+t6mqsZWus5YKGVbbK6rpKhwf7+/uTmpqK2WwGwGw2k5aWhr+/f7Uysnv3bh577DHef/99goODq3WsEEIIIYQQQojrU6VBq5eXF2FhYSxbtgyAZcuWERYWVq2hwfv27eOhhx7ivffeo0OHDleeWyGEEEIIIYQQ15UqLXnzwgsvsGDBAoYOHcqCBQuYM2cOADNmzGD//v0A7Nixg759+zJ//ny+++47+vbta13aZs6cORQXFzN79mzi4uKIi4vj6NGjtVQkIYQQQgghhBCNRZWeaQ0JCWHhwoXl/v7xxx9bf46Ojmb9+vWXPH7RokVXmD0hhBBCCCGEENezKvW0CiGEEEIIIYQQdUGCViGEEEIIIYQQ9ZYErUIIIYQQQggh6i0JWoUQQgghhBBC1FsStAohhBBCCCGEqLckaBVCCCGEEEIIUW9J0CqEEEIIIYQQot6SoFUIIYQQQgghRL0lQasQQgghhBBCiHpLglYhhBBCCCGEEPWWBK1CCCGEEEIIIeotCVqFEEIIIYQQQtRbErQKIYQQQgghhKi3JGgVQgghhBBCCFFvSdAqhBBCCCGEEKLekqBVCCGEEEIIIUS9JUGrEEIIIYQQQoh6S4JWIYQQQgghhBD1lgStQgghhBBCCCHqLQlahRBCCCGEEELUWxK0CiGEEEIIIYSot6oUtJ46dYpJkyYxdOhQJk2aREJCQrl9Nm7cyLhx4+jYsSOvvvpqmW1ms5k5c+YQGxvL4MGDWbhwYY1kXgghhBBCCCFE41aloPX5559nypQp/Pbbb0yZMoXZs2eX26d58+a89NJL3H777eW2LV26lNOnT7Nq1Sq+//575s6dy9mzZ68+90IIIYQQQgghGrVKg9bMzEwOHTrEqFGjABg1ahSHDh0iKyurzH5BQUGEhYVhY2NTLo3ly5czYcIEtFotnp6exMbGsnLlyhoqghBCCCGEEEKIxqrSoFWv1+Pr64tOpwNAp9Ph4+ODXq+v8kn0ej0BAQHW3/39/UlJSbmC7AohhBBCCCGEuJ6U7xatp7y8XOo6C7XG29u1rrNQKxpruUDKJmqP1HUNU2MtW2MtFzTusjUEUtc1TI21bI21XNB4ylZp0Orv709qaipmsxmdTofZbCYtLQ1/f/8qn8Tf35/k5GTCw8OB8j2vVZGZmY/Foqp1TEPg7e1KenpeXWejxjXWcoGUrb7QajWNstEjdV3D01jL1ljLBQ2rbFLXNSwN6b1VXY21bI21XNCwylZZXVfp8GAvLy/CwsJYtmwZAMuWLSMsLAxPT88qZ2LYsGEsXLgQi8VCVlYWq1evZujQoVU+XgghhBBCCCHE9alKswe/8MILLFiwgKFDh7JgwQLmzJkDwIwZM9i/fz8AO3bsoG/fvsyfP5/vvvuOvn37smHDBgDi4uIIDAxkyJAhTJw4kVmzZtG8efNaKpIQQgghhBBCiMaiSs+0hoSEXHJt1Y8//tj6c3R0NOvXr7/k8TqdzhroCiGEEEIIIYQQVVWlnlYhhBBCCCGEEKIuSNAqhBBCCCGEEKLekqBVCCGEEEIIIUS9JUGrEEIIIYQQQoh6S4JWIYQQQgghhBD1lgStQgghhBBCCCHqLQlahRBCCCGEEELUWxK0CiGEEEIIIYSotyRoFUIIIYQQQghRb0nQKoQQQgghhBCi3pKgVQjAZLbUdRaEEEIIIYQQlyBBq7juLVh1lCfmbabEaK7rrAghhBBCCCEuIkGruK6t3Z3En7uSyM4r4eCprLrOjhBCCCGEEOIiErSK69bxszl8/fsxOgZ74uxgw86jaXWdJSGEEEIIIcRFJGgVdSrzXDFfrDxCUYnpmp43O6+E9386gFcTB2be0IGINt7sOZEpz7YKIYQQQghRz0jQKurUL5tOsW5PMn8dSLlm5zSaLLz/035KjGbuG9cJJwdbItt6U1Ri4khi9jXLhxBCCCGEEKJyErSKOpOTX8Lmg6XB6ro9SSilav2cSim+WnWUk8m53DEyjGbeLgB0aOmBvZ2OncfSaz0PQgghhBBCiKqToFXUmT92nsVsVgyLacHZ9AJOJufW+jnX7E5i4z49o3q2JKqtj/XvtjY6Ood4sftYOhZL7QfPQgghhBBCiKqRoFXUiaISE3/uSiKyrTeje7bE3k7H2j1JtXrOY2dy+Hb1ccJDvBjTp1W57ZGh3uQWGjmRdK5W8yGEEEIIIYSoOglaRZ3YsDeZohITw2Ja4GhvQ/f2vmw/nEZhsbFWzpeVW8wHP+2naRMH7hzdHq1GU26fTsFe2Oi07DwqQ4SFEEIIIYSoLyRoFdecyWxh1Y4zhDZ3JySgCQD9uzTDYLKw+WBqjZ/PaDLz/k/7MZgs3HdjOE4Otpfcz9Heho6tPNl1LO2aPF8rhBBCCCGEqJwEreKa2344jazcEobFtLD+LcjPlSA/V9bW8IRMSim+/O0op/R5zBjVnoCmzhXuHxnqTWZuCQkpeTWWByGEEEIIIcSVq1LQeurUKSZNmsTQoUOZNGkSCQkJ5fYxm83MmTOH2NhYBg8ezMKFC63bMjMzufPOOxk9ejTDhw/nhRdewGS6tutyivpBKcWKracJaOpMeIhXmW39uwSQlF5AfA1OyPTnriQ27U/hhl4tiQj1rnT/Lm2aotVo2CWzCAshhBBCCFEvVCloff7555kyZQq//fYbU6ZMYfbs2eX2Wbp0KadPn2bVqlV8//33zJ07l7NnzwIwb948QkJCWLp0Kb/88gsHDx5k1apVNVsS0SAcTMjibHo+Q7s1L/dcaUx7X+ztdKzbXTMTMh09nc13fxynS+um3NC7/MRLl+LiaEvbFu7sOJouQ4SFEEIIIYSoByoNWjMzMzl06BCjRo0CYNSoURw6dIisrKwy+y1fvpwJEyag1Wrx9PQkNjaWlStXAqDRaCgoKMBisWAwGDAajfj6+tZCcUR9t2LLaZq42NG9vV+5bQ52NvRo78u2I2kUXOWETFm5xXzw8wG83R2ZcZmJly4nuq03qVmFJGcWXlUehBBCCCGEEFfPprId9Ho9vr6+6HQ6AHQ6HT4+Puj1ejw9PcvsFxAQYP3d39+flJQUAO655x7uu+8+evfuTVFRETfffDNRUVHVyqiXl0u19m9IvL1d6zoLteLicp04m8PhxGz+NbI9Af5NLnnMmAFtWLsnmf0JOYzuE3xF51VKMXfxfkxmC7Pv6E5z3+pd39gerVjw+zGOnj1Hl7DywTU03tcMGnfZGgKp6xqmxlq2xlouaNxlawikrmuYGmvZGmu5oPGUrdKgtSasXLmStm3b8sUXX1BQUMCMGTNYuXIlw4YNq3IamZn5WCyNb7imt7cr6emNb9KfS5Xru9+O4GCnI7pN08uW2c1eR0s/V37deJKYtk3RVKOH9LxN+/XsPpbOzYNDcdByRdc3JKAJ63efZWCXgHLbGutrBg2rbFqtplE2eqSua3gaa9kaa7mgYZVN6rqGpSG9t6qrsZatsZYLGlbZKqvrKh0e7O/vT2pqKmazGSidcCktLQ1/f/9y+yUnJ1t/1+v1+PmV9lItWLCAG264Aa1Wi6urKwMHDmTr1q1XVCDRMGXkFLH9cBr9ugTg5FDxvZL+Ec1IyijgRNK5ap8nt8DAd38cJ6SZGwMim11pdokM9eZ0aj7pOUVXnIYQQgghhBDi6lUatHp5eREWFsayZcsAWLZsGWFhYWWGBgMMGzaMhQsXYrFYyMrKYvXq1QwdOhSAwMBA1q9fD4DBYGDz5s20adOmpssi6rFV28+g0cDg6OaV7tstzAcHOx3r9iRXuu/Fvv3jOCVGM/8aHlat51gvFtm2dKZhmUVYCCGEEEKIulWl2YNfeOEFFixYwNChQ1mwYAFz5swBYMaMGezfvx+AuLg4AgMDGTJkCBMnTmTWrFk0b14aoDz99NPs3LmT0aNHM2bMGFq2bMnEiRNrqUiivskvMrJ+XzIx7X3xdHOodH8HOxu6d/BjezUnZNp7IoOth1IZ1aMlzSpZj7UyPu6OtPBxYedRCVqFEEIIIYSoS1V6pjUkJKTMuqvnffzxx9afdTqdNZi9WIsWLZg/f/4VZlE0dGt2J2EwWhjWrUWVj+nfJYC1u5P4a38Kg7tW3jtbVGLiq1VHadbUmRE9gq4mu1aRbb35ecMpcvJLcHexr5E0hRBCCCGEqG3L/krgRHIuw7s1p20Lj7rOzlWrUk+rEFfKaDLzx44zdAr2ItCn6hNJtPB1pZW/G+v2JldpvdTF606SnVvCrcPbYaOrmbd1VGjpEOHdMkRYCCGEEEI0EKf0ufy04SSHTmXy6je7ee/HfSRlFNR1tq6KBK2iVm06kEJuoZFhMVXvZT2vX5cAkjMKOH624gmZTpw9x5+7zjIwKpDWzS69lM6VCGjqjK+nEzslaBVCCCGEEA2AyWxh/vIjuLvY89mzQ7ixXzBHz2Qz+9OtfL7iMNl5JXWdxSsiQauoNRaL4rdtZwjyc6VdC/dqHx8T5lvphExGk4X5Kw7j6WbPuL5Xtq7r5Wg0GqJCvTmSmEN+UdWfrRVCCCGEEKIu/LbtNGfT85k6JBQPNwdG9mjJK3f1IDaqOZv2p/DU/zazeH08RSWmus5qtUjQKmrN7uMZpGYVMjymxRWtt2pvp6PH3xMyXS5o/HVzAvrMQqYNbYejfc0vOxzV1huLUuw9kVHjaQshhBBCCFFTUrIKWbIxgeh2PkS08bb+3dXJjpti2/DSnd2JCPVm2V+JPDFvM6t3nMFkttRhjqtOglZRa1ZuS6RpEwei2npXvvNl9OsSgMls4a8DKeW2JaXn8+vmRLq39yU8xOtqsnpZLf1c8XSzl1mEhRBCCCFEvWVRii9WHMHORsvNsZdeWtTH3ZG7bujA7H9F09zHhW9WH+fZj7ey7XBqleaQqUsStIpacehUJvFJuQzt1gKd9srfZi18XQkOcGPdnqQyHyaLRfH5iiM42tsw+TIfzJqg0WiIDPXmwKmsBjeMQgghhBBCXB827E3m6JkcJg5sTZNKVr1o6efGo5O78NDEztjZ6pi35CAf/HzgGuX0ykjQKmrF4jUncHG0pXcn/6tOq1/nAPSZhWUmZFqzO4n45FxuGtQGNye7qz5HRaJCvTGZLew/mVmr5xFCCCGEEKK6svNK+GFNPO1auNMnvGptb41GQ6dgL16Y3pVRPYPYeTSdQwlZtZzTKydBq6hxqdmFbD2YwsDIZtjb6a46vW5hvjja61i7JwmAzHPF/Lguno6tPOnewfeq069Mm0B3XJ1s2SWzCAshhBBCiHrmm9+PYTJbuHVYu2rPI6PVahjdsyVebvYsWhdfb4cJS9AqatzhxGwAenb0q5H0zk/ItONIOvlFRr5adRQU3DK07RVN8FRdWq2GiDbe7I3PxGgy1/r5hBBCCCGEqIqdR9PYeSyduN6t8PV0uqI0bG103NC7Faf0efW2k0aCVlHjElPycHG0xdvdscbS7NelGSazhQ9+2s+++EzG9Q2maQ2mX5mott6UGMwcTMi+ZucUQgghhBDicgqLjSz4/RgtfFwY0rX5VaXVs6Mf/l5OLF5/ErOl/s0oLEGrqHEJ+jxCApvUaC9ocx8XQgLcOHI6h1b+bgyKCqyxtKsiLMgDR3sdu2QWYSGEEEIIUQ8sXBtPboGBf41oh43u6sI6nVbLuL7B6DMLL7lqR12ToFXUKKPJwtn0fFoHutd42oO7NsfeVsf04e3Qamt/WPCFbHRaOrduyu7j6ZgbyHpWQgghhBCicTp6Opt1e5IZ2rUFLf3caiTNyFBvWvm78svGU/XukTgJWkWNSsrIx2xRtG7uXuNpdwvzZe6DfQj0canxtKsiKtSbgmITB2QWYSGEEEIIUUeMJjOfrzyKt7sDcX1a1Vi6Go2GG/uFkJlbwprdyTWWbk2QoFXUqAR9HkCt9LQCVz304Wp0DPbCzkbLX/vq14dYCCGEEEJcP5b+lUBqViG3DGuHve3Vr9RxofYtPWnf0oNlfyVQVGKq0bSvhgStokYlpOTh7GBzxbOX1Wf2tjo6BXuxYU8S+syCGku3xGDmv4v388j7m/hi5RH2xWdiNMkQZCGEEEIIUdaZtHxWbDlNr45+dGjpWSvnuLFfCPlFRlZtP1Mr6V8JCVpFjUpMySPIz/WaLEVTF+L6tEKr1fDK17tITMm76vQKio288f1udh9Pp7mPC1sOpfLOwr3c/94GPvj5AFsOplBYbKyBnAshhBBCiIbMYlF8vuIwTg42TBrUptbO08rfjai23qzcdprcQkOtnac6bOo6A6LxOD8J05BuVzfldn0W6O3CK7N688yHm3jt2108ML4zoVf4/O65/BLe/H4vKVkF3DOmI1FtfTCaLBxOzGb38XT2HM9gx5E0dFoN7Vq4ExHqTZfWTfF0c6jZQgkhRAO3attpElLymBzbBjcnu7rOjhBC1Io1u5M4pc/jrhs64OJoW6vnGtc3mF3H0lm+OZHJtRggV5UEraLGnE0vnYSpVQ3NYFZfBfq48tTNUbzx/R7e+n4Ps8Z1olOwV7XSyDhXxBvf7SEnv4QHxnemQ6vS4R22NlrCQ7wID/Fi2lDFqeRcdh1PZ/exDBasOsaCVcdo5e/K6J6t6NKmaW0UTwghGpQSo5klm05RVGLm8Ols7hzdgbAgj7rOlhBC1CiT2cLyLYm0be5OtzCfWj+fv5czvTr58+euswyObo5Xk7rtNJHhwaLGnB8uG+TnWsc5qX1eTRx46uZI/DydeO/HfWw/klblY5MzCvi/BbvILzTy6KQIa8B6Ma1GQ0izJkzo35qX7+zOSzNiGN8/hIIiE5+vPILFomqqOEII0WDtPJpGUYmZKbFtcLSz4Y1vd/PT+pOYLTI3gBCi8dh+OI3svBKGd29xzR7Di+vVCtCwZNOpa3K+ikjQKmrM+UmYmtbxnZhrxc3ZjsenRNAqwI15Sw6wfm/lswonpuTxyte7MFsUT9wcSevAJlU+n7+XMyO6BzGuXzC5BQZOJJ27muwLIUSjsHGfHh93RwZFBTL7X9H07OTH0r8SeP2b3WTlFtd19oQQ4qoppVi57TQBTZ3pWM3RfVfDq4kDAyObsWm/nuSMmpuE9EpI0CpqTEJKLi0b8SRMl+LkYMsjk7rQoZUnn684wsqtpy+779HT2bz27S7sbXU8dXMkza9wvdlOwV7Y6DTsOpZ+pdkWQohGIS27kCOnc+gd7o9Go8HBzobbR7Znxqj2JKbm8/xn29hzPKOusymEEFflUGI2Z9LyGdK1Odpr3M4e0SMIe1sdP60/eU3PezEJWkWNMJosJKUXENTIn2e9FHtbHfffGE50Ox9+WHOCxevjUars0N198Rm89cNe3F3seWpq5FUtCeRob0OHlp7sPJpe7jxCCHE92bBPj0YDvTr5l/l7j45+PD+9K15uDry3aB/frj4uS4kJIRqs37adxs3Zjh4dfK/5ud2c7BjarQU7j6VzSp97zc9/XpWC1lOnTjFp0iSGDh3KpEmTSEhIKLeP2Wxmzpw5xMbGMnjwYBYuXFhm+/Llyxk9ejSjRo1i9OjRZGTInc/G5PwkTC2vg+dZL8VGp2XmDR3o29mfZX8l8vXvx7D8HVBuPZTK3EX7CWjqzBM3R9bI7L+Rbb3JzC3mdGr+VaclhBANkcWi2LRfT6dgLzxc7ctt9/N04plbohkUFcjvO87w8oKdpGYX1kFOhRDiyp1Nz+fAySwGRTbD1kZXJ3kY0rU5Lo62/Lg2vk7OD1WcPfj5559nypQpxMXFsWTJEmbPns2XX35ZZp+lS5dy+vRpVq1aRU5ODmPGjKFHjx4EBgayf/9+/vvf//LFF1/g7e1NXl4ednYyJX1jkvD3JEzXa9AKoNVquHVYO5zsbVm57TRFJSZCmjXh61XHaNPcnQfGh+NoXzMTdndp3RStRsPOY2nXxcRXQghxsQOnMsnJN3DzYP/L7mNro+XmwaG0D/Lgs+WHmTN/O7cMa8voflJvCiEahlXbzmBno2VAZGCd5cHR3oZRPVvy3R/HOZiQRYeWl55EtDZV2tOamZnJoUOHGDVqFACjRo3i0KFDZGVlldlv+fLlTJgwAa1Wi6enJ7GxsaxcuRKAzz//nNtuuw1vb28AXF1dsbcvf1dUNFyJKbk4O9jU+XTYdU2j0TBhQAjj+gaz+WAqC1Ydo1OIFw9P7FxjASuAq5MdbVu4s/OoPNcqhLg+bdinx9XJls6tK1/+KyLUmxemdyPQx4WPfjnEGwt21tkkTWaLha9+O8qBU5l1cn4hRMORk1/C5oMp9A73r/V1WSszICIALzd7Fq8r/xjctVBpK1qv1+Pr64tOV9odrdPp8PHxQa/X4+npWWa/gIAA6+/+/v6kpKQAEB8fT2BgIDfffDOFhYUMHjyYu+++u1oT9nh5XdmkNQ2Bt3fDv+N7NqOQNi088PH555nWxlCuy6msbNPjOtHcvwlnUvOYNiIMG13NPz7eLzKQeT/tp9gCzX1r7lo35tetIZC6rmFqrGWrr+U6l1/C3hMZjOodjL9f1WZh9/Z25Y37+/Ld78f48c/jbD6gZ1z/1owb0LpGbypW5uuVR1izO4kenZvV2+t7LUhd1zA11rLV13Kt2H4Gi1JMGtoO76ZX9pmpybJNHR7Gu9/v4URKPj3DAyo/oAZdk1rabDZz9OhR5s+fj8Fg4I477iAgIIAxY8ZUOY3MzPxGuS6lt7cr6el5dZ2Nq2I0mUnU5zK0WwtrWRpDuS6nqmXr3MqDzq08yM6qnSnC2wSU3iD4fUsCo3u2rJE0G9LrptVqGmWjR+q6hqexlq0+l+u3bacxmRVRrb2qncchUc0Y1LU5H/+0j+9+P8qKzacY1yeYXp380Wprd1bOo6ez+X71UXp29KO1n0uV8i51XcNSnz83V6uxlq2+lqvYYGL5plNEtvHGVqkrymNNl61jkDsBTZ157/vdKJOZ0ObuNZZ2ZXVdpd0//v7+pKamYjabgdIANC0tDX9//3L7JSf/s06lXq/Hz88PgICAAIYNG4adnR0uLi4MGjSIffv2XVGBRP1zNr3gup6Eqa54uNoT0syNXTJEWAhxHVFKsWGfnuAAN5p5X1kw5+flzMy4jjw9LYqmbg7MX3GEOZ9v51BCVuUHX6H8IiMfLT2Ej7sjU4eE1tp5hBCNw8Z9egqKTQzt1qKus2Kl02q5f3w4rk52vPHdHrYdTr1m5640aPXy8iIsLIxly5YBsGzZMsLCwsoMDQYYNmwYCxcuxGKxkJWVxerVqxk6dChQ+hzsxo0bUUphNBrZsmUL7dq1q4XiiLogkzDVnahQHxJT88jIKarrrAghxDVxUp9LckYBfcIvPwFTVbVu1oSnp0UxM64DRSUm3vhuD+8u3Is+s2ZHyCil+OzXw+QWGJgZ1xEHu2s3HFkI0fBYLIpV288Q0syN1oFVewTiWvFxd+TpaVG08ndl3pKDrNiSeE2eca3Sg3YvvPACCxYsYOjQoSxYsIA5c+YAMGPGDPbv3w9AXFwcgYGBDBkyhIkTJzJr1iyaN28OwMiRI/Hy8mLEiBGMGTOG1q1bM378+FoqkrjWEvQyCVNdiQwtnYBk1zHpbRVCXB827NVjZ6ulW1jNrFeo0WjoFubLSzNimNA/hGNnc3juk20sWHWUvEJDjZzjj51n2XMig4kDWsuM70KISu06lk7GuWKGdq0/vawXcnG05dHJXegW5sPCtfF8teoYZkvtroVdpVt9ISEh5dZdBfj444+tP+t0OmswezGtVstTTz3FU089dYXZFPVZYkoeLf3dqjWxlqgZPh5ONPdxYeexdIbUo+EjQghRG0oMZrYdTqVrW58anzzJ1kbH8O5B9Ar3Z8nGU6zdnczmg6mM7x9C/y4BV/wdl5iSxw9rTtA5xIvY6LpbskII0TAopVi57TTe7g5EhnrXdXYuy9ZGx503dMCriQMrtpwmK7eYmXEdam0kSc1PaSquK0aTmaSMAhkaXIeiQr05cfYc5/JL6jorQghRq3YcTaPYYKZP59qbtdLNyY5pQ9ry4u3dCPZ35avfjjJ/xRGMJnO10yo2mJj3y0FcHG25bWSY3NwVQlTqRNI5TibnMqRri1qfHO5qaTUaJvRvzbShbdl/MpNXv95NTi21RyVoFVflTFrpJExBNbjkiqieyLbeKGD38Yy6zooQQtSqDfv0+Ho40uYaPOMV0NSZhyZ1YXTPlmzcp+fVb3aTnVe9xtjXvx8jLauQGaM74OpkV0s5FUI0Jr9tO4Ozgw29O139c/vXyoCIZjwwPpyUrEJe+nIHSen5NX4OCVrFVUlMyQWgpb8ErXWlWVNnfD0c2SnPtQohGrHUrEKOncmhd7j/Neux1Go0jO0bzKyxHUlKL+DFz7dzIulclY7dcjCFTftTGNmzJWFBHrWcUyFEY5CaVcjuY+kMiGyGvZ2urrNTLeEhTXny5khMZsXLC3ZxODG7RtOXoFVclYSUPFwcbfFyk0mY6opGoyGyrTdHErMpKDbWdXaEEKJWbNinR6vR0KsOeh+i2vrwzC1R2NlqefXrXazfm1zh/mnZhXz521FaBzYhrnfLa5NJIUSDt2rHGXQ6DYMiG+bz70F+rjxzSxQerva89f0e/jqgr7G0JWgVVyUhJY8gP1d5TqeORYX6YLYo9sgQYSFEI2S2WNh0QE+nYE/cXezrJA+B3i48d2tX2rVw5/MVR/hq1VFM5vKzZZrMFuYtOYhWo+HO0e3RaaWpJYSoXF6hgU379HTv4EeTOqrnakLTJo48PTWSNoFN+GTZYTbtr5nAVWpSccWMJjPJMglTvdDS3xUPV3tZ+kYI0SjtP5nFuXxDrU7AVBUujrY8OLEzw7q1YM2uJN74dje5BWWXxVm87iQJKXlMH9GOpk0c6yinQoiGZs3uJAwmC0O7Nq/rrFw1JwdbHp7UhRHdg7C1qZlwU4JWccXOT8IkQWvd02o0RIV6c+BUFsUGU11nRwghatSGvcm4OdkSHuJV11lBp9UycWBrZoxuz6mUPF78YjsJf8/vsP9kJiu3nWZARDOi2vrUcU6FEA2F0WTmz51n6RTsRTNvl7rOTo2w0WkZ3z+kxtbUlqBVXLHzX9KyUHr9ENXWG6PJwoGTWXWdFSGEqDHnCgzsi8+kZ0d/bHT1p9nSo4MfT0+NAuD/Fuxi9Y4zfLLsEIHezkwa2LqOcyeEaEg2H0wlt9DIsG4Nv5e1ttSf2l80ODIJU/3SJtAdVydbmUVYCNGobD6Qgtmi6B1e/5Z/CPJzZfatXWnl78Y3q49TYjBzV1xH7Gwb1qyfQoi6Y1GK37adpoWPC+1kpvHLsqnrDIiGKzElj5YyCVO9odVqiGjTlG2H0zCaLDX2DIEQQtQVpRQb9iUT0syNgKbOdZ2dS3JztuPRyV1YviWRFj6uNKun+RRC1E/7TmSizyxkxuj20qaugLRqxRUxGEsnYZKhwfVLZKgPxQYzhxOrN0TYbLHw84aT/LWv4mUcRP2wcZ+e937cR36RLHEkGrf45Fz0mYX0Ca/bCZgqY6PTckOvVnRp07SusyKEaEByCw18teooPu6OdG0nz8FXRIJWcUXOpOfLJEz1UFiQB472OnYerfoQYaPJwoc/H+SXTQm898MeWeu1AWjaxIEDpzJ57ZtdnLto5lIhGpMNe5Oxt9VJY04I0ehYLIqPlx4ir9DI3WM61qtn9usjuTriiiSm5AHQ0s+t3DZVUkDe/nWYTu/DkqNHmaRRXROUsRhT8hEMe1dgOLwWZSgst4+tjZbOIU3ZfTwDs6X8+oEXKzaYePfHvew6ls7g6OYUFhtZvjmxNrIvalC7IA8emNCZtJwiXlmwk8xzxXWdpTqRkVNUp7NlK1MJxhNbKDi+A3NWEspUUmd5aUyUyYA59QQFe1ZiG7+WXm1ccLSXp5mEqCvKYsJ4cjsFR7dizjyDMhTVdZYahWWb4sk9fYz7w7PwT92AJV8m0qyIfAuIK3J+EiZPt38WP1bGEgwHf8ewZzn5ZQIqDRpnd7Su3mjcvNG6+qB1bYrGzQetmzcaxyaNcgy/spjAZABbx2qXT5lNWLLOYk4/iTntFJb0k1hykkEp6z4lm7/BNqQ7tmH90Xq3sp4jMtSbLYdSOXbmHGEVPNBfUGzknR/2clKfy+0jw+jVyR+TUqzeeZbY6OZ4uDbcha2vBx1aevLopAjeXriXV77eyaM3ReDr4VTX2ap1hcUmth5OZeO+ZE7p80p74cJ86BseQEgzt2tSlyizCeORdRh2/YIqOseFtww0jk3+rue80f79v8bt7zrP2QONpvHdK1YWCxiLwM6x2uVTFjOW7GTM6SexpJ3CnH4KS9ZZUGYA4uxBpe2g6I9obMP6o/Nv1yi/L4Soj5TFgil+CyU7fkLlpZetCheBzQAAtKVJREFU6xxc0ZSp5/6p9zTOnmi0jW8yMqUsYCgqbddpq1nXKQuWcyl/13MnyT9znD7nzjKgiQUSoSQRSrYtRNe8M3bt+6MLDK/2ORo7CVrrOVVSgOHwWkzHNqH1Cca+641onet+ZrEE/T+TMCmzEePhtRh2L0UV5aJr0Rnf/hPJzilE5aZhyUvHkpeOyk3HnHQIU8GmsonpbNG6eKFxbXqJ/5uicXKv1x9cZTFjyU3FkpWEJTsZS/bf/5/Tg8UMGh0aB2c09i5oHP7+d+HPDq5o7F1QhsLSIDX9FJbM02Au7UHSOLii9QnGLrgrOu9gtN4tUXkZGI+sxXhiK8aj69F6tcA2rD+2rXvQKdgLWxstu46mXzZoPZdfwpvf7yElq5B7xnQiqq03AFOGtmP97iSWbDzFv4a3u2bXUFyZ1oFNePymCN78fg+vLNjFI5O7ENhA13dTphKMRzdgPLwOrZsP9jET0DbxK92mFMfO5LB+r56dR9MwmCw083ZmwoAQ9JmFbD+cxsZ9evy9nOgTHkDPjn64OdvVfB4tFkwnNlOy8ydUXgY6v1DsBt6Fh7cHWacTseSmofLSseSmY045hil+S5kbTWht0Lh4oj1fx7k0LQ1mXbzQunrV+4aeslhKy5edjDk76e+6LglLjh7MRtBoSus2e+fSes3BBcrUdaX/MBlL67n0U5gzEkpv7gHYOZHv1IxTjl3ZmelEfLEnob623BaagfHYJkzxW9E08cMurB82ob3ROsjjKaLhUWYTpvitGA6uRmPvjH23CeiaBtV1tspQSmFK3IVh+2Is2UlovVrgMPQBPAMCyDydaG3TWfLSMaedxHRyh/VGE1Da7nHxKNOWs/7s2rS0rtPV3xBEKYXKz7TWceYL2naYSgAN2Dtd0IZzLtOes9Z1SmHJSMCcdhJzekLpzT1A2diTVOJBui6cXv17YO8fAsqC8ch6jEfXU7RyDxpnT2zb9cO2Xd960e6vDzRKXfiNWn9lZuZjsTSIrFaLt7cr6el55f5uyc/CcGAVxsNrwViM1icYS8Zp0Gix6zwcu87D0djWzVIzBqOZe95az8jugYzyS6Fk58+o/Ex0/m2x7zoenV+by5YLSod+qfxMLLnpWPLSsORloPIysORnovIyUMUXHafRlTb0XLxKeyp0NqDV/f3PBjTa0obeBf80Wh3obEsrEEfXvxtQpY2o6jYKlcWEKimEkkKUoQBXXTHZiSdKg9ScZCw5KWA5P0RRU1opewSg82iGxtEVVVyAKslHFf/974Kf/znub7YO6JoGofUORufTCp13MBoXr8v2LChDEcYTmzEeXoMl8wzY2GPbujuL9c3YmeHM6/f0RHvRsRk5Rbzx3R7OFRi498ZOdGjpad3m7e3Ku9/s5M9dSfz7jm74e9XfWTC1Wg1eXg0zQKvIldR1SRkFvPndbowmCw9P6kIr//LD9uvaZeu6olyMB//AePAPVEk+2qZBWM6lgsmIpU0/tuiiWXPoHGnZRTja64gJ86VP54AyM5cXlZjYfiSNDfuSiU/KRafV0Ll1U/qE+9Mx2BPdVd70UkphOrUDw46fsOQko20ahH3XG9EFdkKj0Vy2bMpsKq3r/g5kVV46lgvruqJzZQ/QaNA4n6/rPNHY2JbWaZqL6rZydZ1NaUPJ0e2COs8FjbZ6jUJlsYChEFVSgCopwNXW8E9dl538d3D6z+MeGmdPtJ7N0Ho0Q+vkbj1OFeddVNflWW/CWels0TYNwuzeggSjF9vSnNh6VmG2QBNnOyLaNCUy1Jt2QR7Y6LQokwHTye0YD6/FnHoctDbYBEdjGzYAnV9otXtfK/qOqm+krmtYLlsfGIowHlmLYf8qVEE2Wo8AVGEuqqQAm9Be9aJTQimFOekgJdsXYUk/haaJH/bR47AJjkaj0V6+bBYzqiDr73bd3wFtfgYqL7O0vivIBi58rTVonJqUtpecPcHG/qK6TVtaf5X5XQda29JOgPNtuvN1XjUD4PM9pqV1ViFudgayE+L/CU5zksH4T7+yxskdrUdAaV3n4oUyFJVrz6niPFRJ/j834c7T6tB6tUDn3QqddyuUV0veWK7nTEYRs2+NLtfOUhYTpoTdpXVd0kHQaLFp0bm0rgvsWO1OnMZU10nQWscufjOZs85g2LsS04ktgMImuBt2nYejaxqEJTedkm0LMZ3chsbJvbQiCe19zXsh45Oy+fW7n7jZ5zB2RelovVuVNuCadbA2HK7mQ6JMJdZGnSUvs7TRl59RGtwWnivtvbSYUBbz3z///U9V/gzn+btj2gsrOwdXsLUvDUwNBdZKTJUUlD43arz084KlwWkzdB5/N9o8mqF190djW7VhtUopMBZbKz10dqXHX8HrqZTCkn4K4+E1GOO3gsnAGZMnTSIG49cuvLS3R1lIyy7k698OYzJZuGlQSOnSDMry9z+FT5t2JKSbeOJ/m+nYypNZYztVOy/XijTkykrLKeKNb3eTX2TkgfHhtG1R9cbP+Z7Mvw6k4OZsxw29WmJrU7M9fhfXCZbcNAz7VmI8uhHMBnQtumDXZQSqaQgHDydg3PkToSUHKVa27HKIoUnEEKLaB2BfyfqXyRkFbNyn568DenILjbi72NGrkz+9w/2rPXxaKYX57IHSBlxGAlp3f+yix2HTKrpMkHSl9Z0yGUobenmZfzfw/g5o8zOxFGSXBnpl6jrL33WdufLEAeycrDfttH8Hs9g6/tNYuyBAVSWF1l6Ai2mcPf6p4/6+Iaf1aIbGzrFq5VQKTIa/67o8MvMM7Eq1YefxLE4m5aIAHw9HIkO9iWzjTXAzt3I32y5kzjqL8fBajMc3gaEIrbs/tu36owvsCChrfffP/5bSPFxQ13kHtya7pOZ742uD1HUNS7m6riAb44HfMRxaA8YidAFh2IUPR9e8ExgKKdm9FOOB1aDVYhded50S5tQTlGz7EbP+CBoXL+yjxmDTpmeZG/1XXNeZTaV1nbVtd2FdlwUmo7UtV6ZtV9W6ztaxTAeF1tEV7Jz+bmNdWNeV/o+hiLJBdCmNo9sFdV0ztJ7N0LkHlPaaVrWsJsM/N+uUQusRgMbmn7rmuz+Os2r7GWbGdaBbmG+FaVly0zAeWYfx6AZUUS4aFy9s2/XDJigCNJoq1XVNg4LIMdXfDogLSdBaz3l7u5KWlotZXzrBjvnMPrCxw7ZdP+w6DUHr6l3uGHPqCYo3f4slLR6tZ3Psu0/GJrBDredVKYX5zD4y1n2Hc5Eei5s/TjHjsWkZWe4ud13c2Sn9oP5T2ZVWHHmooryy/xfnoYpy//m5OB9lLEFj74TGzrn0f3vn0qEfds6lwz7+/pvGzgnPAH/O0aTOerorowyFFBzaSNLmFQTosqt1rMbWAduOg1ld2J5Fm/U8e0s0wQH1r9cOpCF3Kdl5Jbzx3W4yzxUza1wnOgV7Vbr/Xwf0bNinJy27CHs7HSUGM4HeLtw9pkON9rSfrxPMaScx7FuB6dQO0GixbdMT2/Bh6DyaYbZYePHzHZxJy6eJix3DQnXEFG1Al3oIjZsP9t0mlAsYL8dktrD3RCYb9iWz/2QmSsFNg9owuGvzKuXXlHIMw/ZFmPVHSxtw0WOxad3jkiM1rnV9V76uM/5zp7/4wvou96J6Lw9lLEZj52it7zhft/1dv1l/tnfCw9+PXOVWWh/WgOy8Er767Sh7TmQA0MLXpTRQDfWmWVPn6j/7byrBdHI7hsNrsaSeqF5mtDbYhvXHLmI0Wqcm1Tv2GpO6rmGx1nXZSX93QvwFyoJNq/9n777Do6zSBg7/ZiaZ9N5DQgskhJ4QAkiHQOhVRBHrWte+qyvurii21V13dWUtn66NtaNYQJooXXqH0ElI771OO98fAyMhCUkgIYXnvq5cyeRt50w5c573tBhrsOrftcYxLdkoYc5LpmrXN5iTD6BxckcfNRX7yFFodPZ15u1qsQVg58s6s9F686vWet35v8+Ve4YKNPaOF9TnnC+qz1n/xsEF78AAivFoVHB6OXYfy+at7w4zNjqEm8eHN/g4ZTZhOrsX49GN1tbXxtBoses2BIeYGbXGFK2JBK2tmLKYcc47Qu7mZVhyk9A4uWPfKw59zzH1fnCUUpjO7KRq51Lr2KqO/XAYNBedV9OsZafMJuvdsOJs24856xSWnDOU6jxYXdGP2+6/Da2u9laPttQdobHaSt7+9cU+dEXJ3D82kIz8clZsS8ZBb8+MEV3xdHOy3qXTaM/9aMBiQZv0K2UJW0HvzOqyXqR4xvDYvNhWOfGJVORqV1xu4F9f7Cctt4x7p/Ui5qKlQkxmCwdP57H5QDqHzuRjUYrwUE+G9w0ipoc/x5ML+O+KoxhMZuaPi2Bon8Arfv2VUrgWnyRn0zLMGcdA74Q+cjT2vcdV6w637XAm761I4OZx4YyKCrZ16zWlHKRq+5dYCtLQBYbjMPjGWit+dSkoqWLJ6mMcTsznqfkDqt2IURbzueEKv5V1ltyzmNOPonHyQB89FfseI2utwJ3XVsqExmqqfCml+PVwJp+vO4nJbGHydZ0Z0jMAX8+GtdQ2hDk/FUtB2m/lmUZrfd9WK+fObQPsUvdQcuAX0Nmj7zMefd8JTRacNzUp69oOpRRulalkb/wac/IB0OmxjxiOvm88Wvf6l20yZ56kcvsX1kYJn1AcBjVdo4SyWFDlBbZyThVnY85LsTaW6J3R95uEvve4WnuLKaXYeyKHmN7BKGPLzdjeXK5GGZ6VX86ij3YR5OPCgpujsbe7vBsSlqIs61wAtrJNY50A78KyDg2c+/7U5x6laNcqUJZzN+qmtdobdRK0tkK2yTz2fo8qzkbjEYC+zwTsw4dW60LQoHOZDBiPrKNq73IwVVnfkANmoHWq3jqmlLKOnzQbrUvQmIwoswGM1q645wswW2FWll99AhGdHq1nAPaRo3npVyfc3Z34ww3960xXe63EQdvJ24b9aSxZfZzrR4Xx/ZZEfD0cefzGqEvOCuzn50bmUWt3SHPKQYosTph6TqLTsImNHh/X3KQiV7fySiOvLz3I6fQi7pxknRk6I6+MzQd+6zbr4apnWJ8ghvUJIsC7erfZgpIq3lt+hGPJhQzuFcAt4yMua8kR22Qeu7/Dkp+CxsUbfZ9x2PcYVaNrqUUpFr6/E40GFt0ZW6N7qLKYMR7fjGH3MlRFMXbdBuMQOweta/XWZGtZZ/6trDMbUCYjFeXlfPHDdnw0JYyLdEJblntu4qS86l3QdHZo3fyxCx+KvnccGrv6u/u3lTKhsZoiXwUlVXy8+hgHT+fRLcSD302KrPF+awl+fm5knTxJ1e5lmM7sBAcX9P0mo+89tkGv+dUkZV3bYEpLwLD7W8xZJ9E4uGLfOw77XmMbPWFYYxolrKsUGFFmo7ULvvlc/a4s/4IbcTnnyrrc6vNoaHVo3Hyx7zLQ2iW5jps2JrOFD1ceY9uRTJwc7JgzKowR/YMv2YW/rWnuMtxgNPPCkj0UlFTyzB0D8fVouht29fHzcyMr8SyGvT9gPL4JdHboe4+/5GveUiRobUWUsli7Mu35DkthBlqfjviOuoFyr55X3AXEUlGMYc/3GI+uBzs9GicP62yOFxRitfXfv5DG0c22DI3W3R+tu/+5x/62ZWnOT8I0aUhHZo0Iq/Nc7bUSB20nb0VlBv6weAsK6BToxh9u6Ieb86VvilyYt6rUY5z68UM6arKsXTNjZmIXNqjVLNkhFblLqzKYeeObgxw9W0DHAFeSs0rRaTX0DfNheL9g+tQzQZHFolixLYnvtyTi5+nE/dN70ymwYZUv61CCQ1Tt+fbcZB4B+I6YQ4V//zonzNh3IofFyw5xz9SeDO4VWPe5DRUY9v+I4dAaQKFx9bGWbxcEqdTztValccTJJ9BWzlnLOmu5dznL0rSVMqGxrmhugotaV2eNDCNuQAhabeuo6F6YN3PuWduNOo2zJ/roadj3GNFqbtRJWde6mTJPYti9zNo7w8UL76GzqAqJveKbHxc2SihTFRYXX+zUucaHc/W7eufysHe8qJw7/7cfGhefeuue5ZVG/rPsEMeSC5k4uCNpueUcPJVLj46e3D6xB/7tZJm15i7DP1h5lC0HM3h0Tj/6hl162E5TuzBvlqJMqnZ/i+n0jnM36iY1+Obs1dAkQWtiYiILFiygsLAQT09PXnnlFTp37lxtH7PZzAsvvMDmzZvRaDTcc889zJkzp9o+Z86cYebMmcybN48nn3yyURlpy4Xbb60N32LJT0XrFYx+wEzsugzA39+jST8o5sJ0jAdXo4xVoNNbZ5+001u7tunsrS251R47WGfmdfdv0KQap9OKePF/e3hwVh+iw+vuG99eK3HQtvL2zveHqagyc9/0Xg1qKbs4b9sOZbB17U/cFnAUx/IMtN4h1hmiO/Zrsi7DSiksRRmYM05gyUtB33scWs+6g5bzpCJXP6PJzH9XHCU9r4yhvYMY0jsQj0YuBXM8uYB3lydQUm5gzuhuxA0IueRrb0pLoGr3MixZp9C4+eIQPR277tfhH+BZ94ziSvHCkj2UVhh46Z7BDZrt11Kah2H/SutkF3Z6a9mms7eWbXbnyj7db//Hzh6tizdrj1WydGsGd0zqwfC+TTOcoi2VCY1xuflqra2rF6otb6aM4xh2fm1tKXPzOzeOeXCT3ahTSqFKrMshmXMSsQ8fhs6vS73HSVnXOpmzz1C151vMKYesY0H7T8E+chT+QT5NVh6czSzhm58O0DV/Gz52ZXQK9sbf1+O3epydPRqdvtpvdHq0zh5o3P2tjRGX+V2dW1TB60sPkpVfzp2TIhnSOxBfX1e+WXecr9afwmxWzBrRlbiY0FZzM+pyNWcZvvlAOh+uOsaU6zoza0TDh7U0ldrydjVu1FlK861lXfZp7DpFYdehZ73H1FfWNSh1zzzzDPPmzWP69Ol8//33LFy4kCVLllTbZ/ny5SQnJ7N27VoKCwuZMWMGQ4YMISQkBLAGtc888wxxcXENuWS7YGtt2H1uzKpHAI5j7sWu66BmG1yv8wxGN+LOZjk3QFKm9Y3fuYEtLqJl3Te99xUdP6h3IKt29uCfpd14dgyY9n5HxZrX0QZ0w77bkHMLip9bT7eBXduVxYQlN9lamGWewJx50rbMkcbJHfvw664ozeI39nY67p9xZe+BiI5ePHvHQD748SifrzvJ0aQC7pwciatT9TGeF7c2OAy7FfuIEQ1aiuDY2QISM4q5NT6iwcvTaF19cBx2S6PzE++jOJRSwac/naBbB49WvaxTW3Nx6+qNY7u3qtbV+tgFRaCb9mfMKQep2vU1levfRXtgJfYRI6wtU27+1jUmGzpDvMWCpSAVc8YJW3mnygutG/XO2IW03tnZRd3MeckYdn+L6ew+NA6u6GNvQN9rbIPfFw1RXGZg2abTbD6QgauzPf2G3cSvx7L58Hgh49xCmXNdGHa65uv1dDazhNeXHsBwbhm182u+azQaRvbvQJ+uPixZc5wvfjnFrmPZ3D4p0roagagmOauET346QWQnL2YMq/8G1dWi8+2E88Q/WG/U7fqGqi1LMBxYhb7naDQeAWjd/Kz1u0bMEG8pzLigXnfC2h0drK393iFNku56axN5eXkkJCTw4YcfAjBlyhSef/558vPz8fb+bX3HlStXMmfOHLRaLd7e3sTFxbF69WruuusuAN59911GjRpFeXk55eXlTZL41uzi1gbHkb+rMXV4a2BRqlHjEpIyi3Fztr/kuEjRfmg1Gq4f1ZXXlx7k19JwRt/wEsZjmzHs+4Gqrf+rtq/G2fNcEOt3rsDztXZFcvXGUpyDOeM45qyTmLNO2dYx07j5oevYF11gOHaBEWg8AlrlpE/XOjdnPQ9f35efdqeydP0pnvlgJ/dO60V4qCfmnESqdi+ztTY4DJlnnXmyEePzV2w7i4ernqF96m9hv1JarYa7p/bimQ928s73R/jrrQOafHmfa1FbaF1tCI1Gg13HfuhC+2A6vZOqPd9Ste2z6vs4uaM5V6mzBrO+tjLPUl74W5CadfLc0hrWNW11QT3QBYWjC+xuXTKolQy1EA1jLkjHsOc76xhovRP6mFnWiYsaWLFvCJPZwi97Uvl+axIGo5lxA0OZNrQLzo52jOwfzFfrT/HT7hRSsku4b0Zv3OsZ8nM5DpzK5Z3vj+DqZM/jN0XVGox6uzvyyPV92Z6QxefrTrLow51MHdqFiYM6Nmsw3VYYTRayCsp567vDODvacc+0Xq3y5p1dUAS6qU+du1H3DVU7vqq2XePgWr1Od67M07r5oarKMGceP1fenbSuUYu1fNQFdEfXexy6wAi0PqFNFvvUG7RmZGQQEBCA7twssTqdDn9/fzIyMqoFrRkZGQQH/9bVKigoiMzMTACOHTvGli1bWLJkCW+99VaTJLw2hoOrMaUetq5h1Dnqqo5HUcqCKs3Hkp+K4dCay2ptuNqyCyt49oOd9A3z4aax3fFwrT8QPZtZQudAdwksriF9uvoQHurJD1uTuK53II49R2MfOdK6bNC5hcQtJTlYinOsXd8yjmM6uY2aY6g1aH1CsY8Yji4wwlpxa+GF1Nsq44mtGI9vxj58qHWccSMncLscGo2G8QND6d7Bnc9+2MW3X63gptA0vAqPonFwxWHQDdj3bHxrw5n0Yo6eLeCG0d2uWvDo5ebA7yZH8u+vD/LV+tPcPK7hSw8Iq7JKI6nZpaTmlJGWU8rOo9ltsnW1LhqNFvtug7ELG2RdRuNcGWcpsZZzlpJczNmnrQFMLeMKtZ7B2HcdZAtSNa6+8r15GUwpBzHsW4FdWCz23Yc2aYBYH6UUqqIYS1EmxmMbMZ3aBnYO6KOmNsts04fO5PHFzyfJyCunT1cfbhzbrVpPEDudlnlx4XQOdOPj1cd57qNdPDirD50Dm25ZuvV7U/nkpxN0DHDj0ev7XrJeqNFoGNIrkF6dvfn0pxN8u+kMe45lc8ekyAbPf9BQFqUwGi0oFI761lGfNpkt5BVVkplfTlZBBVkF5WTnl5OZX0F+cSUK643/P82LavSwnKvptxt1faGqzLqGbkk2luJcVEm2tazLO4spaY91ksOLj3f3R9epP7rA7s3e+NDsr7zRaOTpp5/mb3/7my3wvRwNGc9R3qkruUd/pnLdm+hcvXGPGodb1Djs3JqmYqyUwlJejDE/HUNeOsb8dIz5GRjzMzAVZFonAQF0Lp74jLsDt+jxaBtQmfTza5mutj/uSMZgsrD3RC5HEvO5bXJP4gd3rrOyUWkwkZ5XztB+HRqU5pbK19VwreXt7hl9eGLxZn5NyGbuuIhz//UAal/zUpmNmIpyMRZmYyrKxs7NB8eQCLSO0n2oPg0p6yoNXck5vIrKje+j3fklbv3G4j4gHnvPSy9U3hjmihJr+ZZ3vpyz/vbLz+BhbSW4QUWBPZaoGXSNm43Wof5WtdreW++uSMDVyZ7ZceE4O9a9rExTi/NzIzG7lB82nWFw32AG9w66ovO11zLBw9OZlKwSzmYWczajhKTMYs5mFJNXVGnbx8XJnl5hPtw1rTfBfm1n7GXDXzN3oEOtW5TFjKk4F1NhNsbCLHRO7jiG9kDn3DrXt25NGlLWGTSdyNlvpmrrJxh2fY1bn1G4D4hH79exydJhqSyz1eV+K+vSMeRnoKqsPQM1dno8Bk3Dc8iMBr22jSkP0nNK+e8Ph9mVkEWwrwsLfzeIgT3r7nUyfbQbvbr58+JHO/nbJ3t5cE4/xsRc2fNhsSg+/jGBZRtOMbBnAE/Mj6lzHoyL8+bnBwvvHsK2Q+m8/c1Bnl+ym5kjw4jo5EWV0YLBaKbKYMZgtP5UnfsxnN9me2y+YF9Ltf8bTb/dGBrYM4A5Y8KJ7OJ9cdKuSENes91Hs/hxayJpOaVk5ZdXG5Pt4mhHkJ8rvcN8CfZzIdjXhfCOXq2iTGxcWVf7d6GymDGXFmAszMJUkIVG74hjSA/s3Jr2dbiUeidiysvLIz4+nh07dqDT6TCbzQwaNIi1a9dWa2m95557mDVrFhMmTADgueeeIzg4mEmTJjFz5kxcXKyV1eLiYpRSTJo0ieeff77BCW3ogH1lsWBOOYgh4WfMKYdAo8OuywDse45BFxTR4OhfWcxY8lMxZ5/GnH0GS0EalqJMW1cfALQ66yxsHoHWPuAegWg9AtD5d23wTFwtNYGHyWzhj29upXuIJ9ePCuN/a45z9GwBYR3cuS2+ByH+NT9kp9KKeKkBkzBB+52YBK7dvC3+5iDHkgt4+d4h9c5CfDVc65OTKKUwZxzDeORnTEl7QSl0oX3Q9xqLLrRPg7seKovFOhYl+xSW7DOYC9JQhZm2rj6Adc1LNz+058s5z0CqHH3426p8NHonnrl9IHr7S9+UrO29lZZTytPv72Ta0M7MGH71J6gwmiy89L895BZVsOjOWLzdHS/rPO2pTKioMpGQVMDhxDzOZBSTll2G5Vw1wU6nIcjHhRA/F0L8XOng50qInwtebg5trhWxLb1m13pZB9ZJjwwJP1tnPTWb0AVFYN9rLHadoxvcq04phSrK+q1el5+CpSgTVVF8wV4aNG4+tvqc7bdflwYvXdPQ91ZFlYkVvyaxdlcK9nZapg3tQlxMSIO71xaXG3jnu8McSy4kbkAIN4zpdlldc89P2LfrWDajozowb1z3OucWqC9vZZVGvvj5JFsPZda5j51Og72dDr29Fr2dFr2dDns7LXp7HXo7LfZ2Whzsz/3PTof9+f3sdZRVGtm0P52yShPhoZ5MHtKJ3l28r7j8qS9f+cWVfP7zSfYcz8HH3ZEuwe4EejsR4OVMgJcz/t5OuDnZt8pysD2VdQ2aPfiWW27h+uuvt03E9PXXX/O//1Ufz7Zs2TJ+/PFH3nvvPdtETJ9++imhodVbYhYvXkx5eflVmT3YUpSF4eh6jMc3Q1UZWq8Q7HuNwb7bkJprBJbmnyvITlsrbjlJ1qUTsPbP1nqH2ipr5wsyjavPFffTbqk3086jWbzz/REeu6Effbr6oJRi25FMvvj5FOWVJuJjreMoHPS/5e/nPal8+tMJXv39dfVW7trSh6SxrtW8peWWsfD9HYyLCeXGsd2vcspqkorcbyxlBRiPbsB4dAOqogiNmx/6nqOtQxMcqz9HlvIiaxl3rrwz5ySC8VyrmYMLOu/Qc+VcoK3CpnHzq3WIw5HEfP755X7iBoQwr54utrW9t95bfoS9J3L5x++vqzGx09WSlV/Osx/tolOAG3+6KeqyurW25TLBohQpWaUcTszj0Jl8TqcVYbYoHPU6+nTzJcDTiRA/Fzr4uRLg5dRuxqu1pddMyrrfWCpLMB7bjPHoL6iSXDTOntj3GIl95Kgaw01UZSnmnDOYbeXdGagqs260d0Tn0/Fco0MgWs8AtO6B1jHKVzjcoiHvrYy8Ml776gC5RZUM6xPE7JFdGzRE62Jmi4WvfjnNT7tTiAj15P4ZvXFvRFfU0gojb3xzkFOpRcwZHcaE2I6XDLwa+rnJzC/HYDRXCzwdzgWnVzp0oNJgYtP+dNbsSqGgpIqOAa5MGtyJmAj/yz53XfkyWyz8vCeNbzefwWJRTBvamfjYtjVutz2VdQ26PfXss8+yYMEC3nrrLdzd3XnllVcAuPvuu3n44Yfp06cP06dP58CBA4wfPx6ABx54oEbAerVpPQJwHHwjDjEzMZ3eieHIz1RtWULVjq+sYyNcfbCcq7jZZvTT2qH17YR95Ch0/l3RBYS1y7EoG/en4+vhSK9z3Ss0Gg3X9Q6ib5gvS9efYtWOZHYezeaW+HD6hvkC1kmY3GUSpmtWB18XhvYO4pe9qYyLCcXH4/JapUTT07p44RAzE330VEyJezEm/EzVjq+o2v0tdmGD0HmHWCtuOWd+m9FPo0PrE4J99+vQ+YdZe4h4BDRqcpheXbyJGxDCuj2p9OvmaytPGiKnsIIdCdnExYS0WMAKEODtzC3jw/nviqMs/zWJ6a1ohsfmUlJu4EhiPofP/RSXWW/QdgxwZcKgjvTu4k1YBw+CApt2STYhGuvQmTyWrj9NiL8LYcEedA12J7TPBPR9J2BOPYjhyC8Y9v6AYd9y7DpHowsMx5ybhDn7DKrofGufBq1XB+y7DEDrH4bOPwytZ3CzreJQn+PJBSz+5hB2dlr+PH8A3UI8LvtcOq2Wm+K60ynQ1TrO9eNdPDCzD12CrF2YzRYLRaUG8kuqKCipoqC4kvySKgpLq8gvqSIzr5xKg3VZvNjIphteEtiMk7A56u0YH9uR0dEhbD+Sycodybzz/RECvM4wcXAnhvQKxN7uyl/bM+nFLFl9jOTsUvp09eHm8eH4e169MdWipga1tLYGTbGel1IKS84ZDEd+xnR6J1hM1gHE/l3PVdrCrLNc6a5eBaol7oBk5Zfz1LvbmTWiK1Ou61zrPseTC1iy5jgZeeXE9PDnprHd+ddX+/F2c+SxG/rVe422dGensa7lvOUXV7Lg/7YzqKc/v5tc/5pbzUlaHy7NnJ+CMWE9xhNbwVRlnb303I04rX8YOt9OTbKguMFoZtFHu6ioMvHc7wbVGYBe/N7635rjbD6Yziv3XdcqboS9tzyB7QmZ/OmmKCI6Nm4ehNZcJiilyCmqJDG9mMSMYk6mFpKUUYICXJ3s6dXFm97nfi5u6WnN+bpSbSlv13JZl5lfztcbTnM6vYiiUuvNFXs7LZ0C3ega5E5YBw/C3KpwTtlq61WncXK31ufO3YzT+XW5qhM4Xeq9tT0hkw9+PIqfpxOPzemHbxMGQWczS/jPsoMUlRkJ9XehoKSKojIDF9fy7e20eLk54O3mgJebA2OiQwjr0LDAuTV+biwWxd4TOfy47Sxns0rwdNUTH9uRkf2DGzxp04X5Kq808s3GM2zYl4aHq555ceEMiPBrs41XrfE1q0uTdA9uDZp6EWpVVYZSlgaPUWguLfFm+uoX65Tp//j9dXheojuKyWxh1Y5klm9Nwt5OQ6XBzOQhDVscuS19SBrrWs/bl7+cZO2uFJ67M5YOLTjBwLVckWsMZaxEGSvROns22TkvlpRZzItL9jAgwq/OtYEvfG8VlVbxxNvbuK53ILdP7NFs6WqMiioTiz7ahdFkYdGdsY1q/W1NZUJxmYHEDGuAeiajmKSMEkorjIC1sto50I1eXbzp09WHTgFul+xO15ry1dTaUt6krLPefCkoqeJ0ejGn04o4k15MUmYJJrN1gh5PVz3dg5yJ6uRMbHQ42hZqRYXa31tKKVZuP8s3G88QEerJg7P74NIME88Vlxv4Yt1JSsoNeLk54uXmgJf7+QDV+tjF0e6yA7DW/LlRSnEkKZ+V285yLLkQJwcdnQPdCfV3JdTflRA/V4J9XWpthfXzcyM7u5gdCVl88cspSsoNxA0IZcbwLnVOSNVWtObX7GJN0j24PdI4uNA275lcGaPJwpZDGfTv5nvJgBWs06tPva4zsZH+/G/NcRKSCggPvfxuLKJ9mDykMxv3p7N2Vwp3TIps6eSIemjsHdHYN29X7s6B7kwb1oVvN52hf/dMBl9i5kuAtbtSMFssTBzcdDOAXiknBzvum96LF5fs4cOVR3lwVp82cWe9uMzAtiOZnDnXkpp7blZfjcbapT+quy9dgt3pGuROsK9LmxqLJcR5Go0Gb3dHvN0dGdjDH7DeWE/JLuVMejGn04s4lVrErpOFHM+F+ePD65xM6GozWyz8b80JNh1IZ3DPAO6YFNkk3Vdr4+6s555pvZrl3K2dRqOhdxcfenfx4XRaEZsPZpCSXcKGfWkYzs0+rNVoCPJxtgaxFwSzBjT8+4v9HD1bQJcgNx6b06/Jl+0RV+6aDVqvVXtP5FBaYWRk/+D6dz4nwMuZP87tT05hBf5ebW+xeNG0zncpPJKUj1KqTVTsRfObNLgjB0/n8smaE4SHeNY5WVtZpZFf9qURGxlAQCsrTzoHujNndDe++PkkX288zfUjw1rt+7u80sSancms3Z1ClcGMr4cjXYLcGRMdQtdgdzoGuLaa9QyFaA52Oi1dgtzpEuTO2AEhWJTi201n+HHbWfKLq7hveq8WbyWrqDLx9veHOXwmnynXdWLm8K6ttkxpT8I6eNi6PFssiqyCclKyS0nNKSUlq5STqYVsT8iqdoyTgx23jA9nZP8ObX6d6fZKvtGuMRv3p+Hr4UjPRq5vpdFoJGAVNj07e7PneA5ZBRXNOuGCaDt0Wi13TenJsx/s4v0fj/LHG/ujraVy9sueVKoMZiYN7tQCqazfuJgQMvLKWLU9mdJyI7dOiGg1LTZgHUP8895UVm47S1mliZge/swc3oUgH1kDWVzbtBoNs0eG4ePhyCdrTvDKZ3t55Pp+LTZmvqCkin8vPUBqThm3TYhgZP/a1/oVzUurtS7VFeTjUm2yqbJKI6nZpaRkl6K0WmLDfS9rBmdx9UjQeg3JzC/nWHIhs0d2rbUyKURD9epsnajmSGK+BK3CJsDLmRvHduPj1cf5eXcq4wZWn0G+ymDmp92p9AvzIbSWtaBbA41Gw63xEbg561nxaxKlFUbundar3nVom5vJbGHLwQx+2JpIYamB3l29mT0iTLqwCXGRUf074O3myNvfH+bF/+3m0Tn9CLnK8y+kZpfy2tIDlFeZeGROX/p09bmq1xf1c3G0J6KjFxEdvdrUuM9rWeu5fSya3cb9aei0Gob1CWrppIg2zs/TCV8PRxKS8ls6KaKVGdEvmH5hPizdcJq03LJq2zYeSKe0wsjkOmYtby00Gg2zRnRlXlx39p/M5V9fHaC80tgiabEoxfYjmfz1vR0sWXMcXw8nnpwXxR9u6C8BqxB16Bvmw4J50Zgtir99sueqflftP5HN3z7dg1KKp26OloBViCYiQes1wmiysPVQJv27S/cHceU0Gg09O3tzLLkAs8XS0skRrYhGo+H2SZE46nW8t/yIbXZPo8nCmp3JRIR60q2Byyu0tLiYUO6Z1ovTaUW8/Ok+Ckurrtq1lVLsP5XLsx/s4t3lCejtdTxyfV+emh/d6CV5hLgWdQp046+3xODt7shrXx1g66GMZr/mloMZPPvedrzdHfnrrTF0DJAbS0I0FekefI3YcyKb0gojo2RMhWgiPTt7selAOkkZJQ1e401cGzxc9Nw+sQf/WXaI77ckMntkGOv3pFBQUsUdk1rHEjcNNahnAC5Odry57DAv/W8Pf7yxf6MnkLIoxeEzeew6mo3R3LCbPDmFFSRmlODv6cQ903oSGxkgwzqEaCQfD0eeunkAb313iPd/PEpuUSXThnZu8smQlFJ8vyWRH7Ym0a+7L3dP7omzo1SxhWhK8om6Rmzcl46fpyORneUOvWgakZ280AAJSfkStIoaosP9GNY3iJXbz9K7izff/HKSToFu9OrcuEngWoPeXXx44qYoXl96gL/9bw+PNbBrbmmFkS0HM9iwL43swgpcnewbvP6r3k7LrfERDOsbJMvUCHEFnB3teHROPz5edYzvtySSW1TBbRN6NNnnymS28PGqY2w9nMnQPoH8cf5ACgvK6j9QCNEoErReAzLyyjieIhMwiabl5qynY4AbR5IKmDq0S0snR7RCN43tzrGzBby+9CBVRjO/n9G7zS730DXYnafmR/OvL/fzymd7eWhWHyLrCMCTs0r4ZW8q249kYTBZ6B7iwayRXYkO95MAVIgWYKfTcufkSHw9nfh+SyL5xVU8MLPPFbeGllcaefPbwxw9W8CMYV2YOrRzs63BKsS1ToLWa8DG/enWCZj6NnxtViEaomdnL9buSqHSYJI1IUUNTg523DWlJ698upcQf1eiI/xaOklXJMjHhafmD+C1rw7w2tID3DO1FzE9/AFra8ue4zn8vDeVU6lF6O20DO4VyJjoDjKuTYhWQKPRMH1YF3w9HPlo1TGe+2gX148KY0CE32XdTMsrquT1pQfIzC/nd5MjGSqTXArRrKSW2c4ZTWa2HsogqrsvHi76lk6OaGd6dvFm1Y5kTqQU0jfMt6WTI1qh8FBPHr6+L907+7SLnh7e7o48eXM0b3x9kLe/O8wNY7qhtdOxcmsiRWUG/DwdmTumG8P6BuHi2LCuwEKIq2donyB8PRz539oTvPXdYcKC3ZkzuhvhoZ4NPsfZzBJe//oABqOZx27oR882OOxBiLZGgtZ2bs/xHMoqTYyMkgmYRNPr3sEDO52WhKQCCVpFnfp1821X6+C5Otnzxxv78/Z3h/nyl1NoNNCnqw9jokPo3dW7XQTnQrRnER29WHTnQLYeyuS7zWd4+dO99O/my/Wjwgj2dbnksQdP5/L2d0dwcbLjqfkDrvoasEJcqyRobec27E/H39OJyE4yAZNoenp7HeGhHhyR9VrFNcbBXseDs/qwIyGL2L7B2CvV0kkSQjSCTqtlRL9gBvUM4KddKazcfpan39/B8L7BTB/WBS+3mssDbtiXxidrTxDi78Ij1/erdR8hRPOQ0eLtWHpuGSdSChnRP1ju/Itm07OzN2k5ZRRdxTUshWgN7HRahvYJIthXWlqEaKsc7HVMua4zL983hLHRIWw9lMFT/7eNZZtOU1FlAqzLVn294TRL1hynd1dvFtwcLQGrEFeZtLS2Y5sOnJuASSYHEM2oV2dvvuY0CUkFDOkd2NLJEUIIIRrN3VnPvHHhxMWEsGzTGVb8epYN+9KZNrQzp9KK2Hk0m1H9g7l5fDg6rbT5CHG1SdDaTp2fgCk63A93mYBJNKPQAFdcHO1ISMqXoFUIIUSb5u/lzH3TexMfW8zS9af4bN1JAK4fFcbEQR3b7LJdQrR17SpoNRjNgHWc3bVu9/kJmPrLMjeieWk1GiI7e5NwtgCllHyhCyGEaPO6BLnzxE1R1jkbFPTu6tPSSRLimtau+jd8tu4kiz7ahdFkbumktLiN+9Lw93Kih0zAJK6CXp29KCipIiOvvKWTIoQQQjQJjUZD7y4+ErAK0Qq0q6B1YA9/MvLKWf5rUksnpUWl5ZZxIrWIkTIBk7hKzq9RlyCzCAshhBBCiCbWroLWXl28Gdo7kFXbk0nJLm3p5LSYTfutEzAN7S0TMImrw8/TCX9PJxKSClo6KUIIIYQQop1pV0ErwNyx3XF2tOOjVUexWK6tdfMy8spYuf0sWw6lMyBCJmASV1fPzl4cSy7AZLZc1vEms4UPVh7lbGZJE6dMCCGEEEK0ZQ2aiCkxMZEFCxZQWFiIp6cnr7zyCp07d662j9ls5oUXXmDz5s1oNBruuece5syZA8Cbb77JypUr0Wq12Nvb89hjjzF8+PAmzwyAq5M98+LC+b8fjrBudwrjYzs2y3VaA4tSJGYUs+9ELvtO5tjGE3YOdGPa0C4tnDpxrenZ2ZsN+9NJzCime4hno4/fuD+dLQczGNIzoOkTJ4QQQggh2qwGBa3PPPMM8+bNY/r06Xz//fcsXLiQJUuWVNtn+fLlJCcns3btWgoLC5kxYwZDhgwhJCSEvn37cuedd+Lk5MSxY8eYP38+W7ZswdHRsVkyFRvpz7YjmSzbfIaocD/8PJ2a5TotwWiycCy5gH0ncth3KpeiUgM6rYaIjp6MiQ4hqrsv3u7N87wKcSk9OnmhARKSChodtFYaTCzfmkhEqKdMHiaEEEIIIaqpt3twXl4eCQkJTJkyBYApU6aQkJBAfn71CVdWrlzJnDlz0Gq1eHt7ExcXx+rVqwEYPnw4Tk7WwDEiIgKlFIWFhU2cld9oNBpujY9Ao9GwZM1xlGr73YQPns7jne8P88gbm3ntqwNsS8iie4gn90ztyb8fHsbjN0YxdkCIBKyixbg62dMp0M26PEAjrd2VQnG5ketHhcmSOUIIIYQQopp6W1ozMjIICAhAp7OufarT6fD39ycjIwNvb+9q+wUH/7YmaFBQEJmZmTXO991339GxY0cCAwMblVAfH9dG7e/n58Ydk3vyzreHOJxcxJiY0EYdfzX5+bldcvvJlAJeX3oAT1cHRkaHMLh3EH27+bb69Wjry1dbJnmr3cBegXyz/hQubo44O9o36Jii0irW7ExhSJ8gBvcPuexrtxeNLevaEvnctD3tNV/QvvPWFkhZ1za117y113xB+8lbg7oHN5WdO3fy73//mw8++KDRx+bllTZ6YqWYcF/COrjz7rcH6eTr3ConJvLzcyMn59ITz7z37SFcnex58e5BODlYX7Kiwta9HmZD8tVWSd7q1tnPBYtFsXVfKv27+TbomC9+PkmlwcTkQR0bdW2tVtMuKz2XU9a1BfK5aXvaa76gbeVNyrq2pS29txqrveatveYL2lbe6ivr6u0eHBQURFZWFmazGbBOuJSdnU1QUFCN/dLT022PMzIyqrWm7tu3jyeeeII333yTrl27Njojl0Or0XD7xEgqDWY+//nkVblmUzuSmM/RswVMva6zLWAVorXqFuKB3k5LQmLDugjnFlXwy95UhvUJItjXpZlTJ4QQQggh2qJ6g1YfHx8iIyNZsWIFACtWrCAyMrJa12CACRMmsHTpUiwWC/n5+axbt474+HgADh48yGOPPcYbb7xBr169miEbdevg68KU6zqzIyGLA6dyr+q1r5RFKb7ecBpfD0dGRXVo6eQIUS97Ox3dQz1JONuw9Vq/35wIaJg+TGa7FkIIIYQQtWvQOq3PPvssn3zyCfHx8XzyyScsWrQIgLvvvptDhw4BMH36dEJCQhg/fjw33HADDzzwAKGh1nGkixYtorKykoULFzJ9+nSmT5/O8ePHmylLNU0e0okOvi78b+1xKqpMV+26V2r3sWzOZpUwY3gX7O3a3ZK6op3q2dmL9NwyCkqqLrlfak4pvx7OJE4mEBNCCCGEEJfQoP6mYWFhLF26tMb/33vvPdvfOp3OFsxe7JtvvrnM5DUNO52W2yb24G//28OyTWe4eVx4i6anIUxmC8s2naGDnwuDezZu0iohWlKvzt4s5TQJSfkM7RNU537LNp7B0cGOSUM6XcXUCSGEEEKItuaaab7r1sGDMQNC+GVPKqfSilo6OfXafDCD7IIKZo8MQ6uVJUBE2xHi74qbsz0Jl1j65kRKIftP5TJpcEdcnRo2y7AQQgghhLg2XTNBK8CsEV3xcnfgw5VHMZosLZ2cOlUZzPywJZHuIR70C/Np6eQI0ShajYbITl4kJBXUukayUoqvN57Gw1VPXCteikoIIYQQQrQO11TQ6uRgx63xPcjIK2fl9rMtnZw6/bQ7haIyA3NGdUOjkVZW0fb07OxNUZmBtNyyGtsOnMrjVGoR04d2waGVrzUshBBCCCFa3jW3hkrfMB8G9wxgxa9JRIR64uXm0LQX0ICfh9Nld+ktrTCyasdZ+nfzpVuIR9OmTYirpFdn6+ziCUkFhPj9tuaWxaL4ZuNpArycGNa37vGuQgghhBBCnHfNBa0AN8Z153BiPn//fF+znL97iAePXN8XZ8fGj9X7cVsSlQYzs0denbVshWgOPh6OBHg5kZCUz/iBv3UB3nYkk7TcMu6b3gs73TXV0UMIIYQQQlymazJodXfW89fbYjjdDBMyFZUa+Gbjaf7+2T7+MLc/7i76Bh+bV1TJz3vSuK53IB0uaJ0Soi3q2dmbXw9nYjJbsNNpMZosfLc5kU6BbsT08G/p5AkhhBBCiDbimgxaAfw9nfD3dGqWc4f4ufCfZYf426d7eXxuf3w8GrYG5fdbEgHFjGHSyiravp6dvVm/L40z6cWEh3qyfl8aecWV3D6pB1oZqy2EEEIIIRpI+uc1g95dffjjjf0pLjPwt0/3kJFXczKai6XllrH1cAZjokMaHOQK0ZpFdvJEo4EjiflUVJlY8WsSPTt72ca7CiGEEEII0RAStDaT7iGePDkvCpPJwsuf7uVsZskl91+28TSOeh2Th3S6SikUonk5O9rTJcidhLP5rN6RTGmFketHhbV0soQQQgghRBsjQWsz6hjgxoL5A7C30/L3z/dyIqWw1v1OpRax72QuE2I74ubc8DGwQrR2PTt7kZhewtpdKQzs4U/nQPeWTpIQQgghhGhjJGhtZoHezvx5/gA8XBz415f7OXQmr9p2pRRfbziFu4ue8QM7tlAqhWgevTp7Y1EKk9nCrBEyVlsIIYQQQjSeBK1Xgbe7IwtujibQx5k3vj7IzqNZtm27j2ZxIrWIaUM746DXtWAqhWh6XYM9cHWyZ1T/DgR4O7d0coQQQgghRBskQetV4u6i5083RdM12J3/+/4Imw6kY1GKJSuP4u/pxIh+wS2dRCGanL2dlpfuGcxNcd1bOilCCCGEEKKNkqD1KnJ2tOMPc/vTu6sPH606xn++OURSRjEzR3TFTicvhWifXJ3s0WpliRshhBBCCHF5JFK6yhzsdTw0uw+xkf7sP5VL1w4eDIz0b+lkCSGEEEIIIUSrZNfSCbgW2em03DO1F2EdPBgWFYJWI61QQgghhBBCCFEbaWltIVqthnExoXSUJUCEEEIIIYQQok4StAohhBBCCCGEaLUkaBVCCCGEEEII0WpJ0CqEEEIIIYQQotWSoFUIIYQQQgghRKslQasQQgghhBBCiFarQUFrYmIic+fOJT4+nrlz55KUlFRjH7PZzKJFi4iLi2PcuHEsXbq0QduEEEIIIYQQQoi6NChofeaZZ5g3bx5r1qxh3rx5LFy4sMY+y5cvJzk5mbVr1/Lll1+yePFiUlNT690mhBBCCCGEEELUxa6+HfLy8khISODDDz8EYMqUKTz//PPk5+fj7e1t22/lypXMmTMHrVaLt7c3cXFxrF69mrvuuuuS2xpKq9VcRvbahvaat/aaL5C8tQZtJZ2N1V7zBZK3tqi95gvaTt7aSjobq73mCyRvbVF7zRe0nbzVl856g9aMjAwCAgLQ6XQA6HQ6/P39ycjIqBa0ZmRkEBwcbHscFBREZmZmvdsaysvLpVH7tyU+Pq4tnYRm0V7zBZI30XykrGub2mve2mu+oH3nrS2Qsq5taq95a6/5gvaTN5mISQghhBBCCCFEq1Vv0BoUFERWVhZmsxmwTqqUnZ1NUFBQjf3S09NtjzMyMggMDKx3mxBCCCGEEEIIUZd6g1YfHx8iIyNZsWIFACtWrCAyMrJa12CACRMmsHTpUiwWC/n5+axbt474+Ph6twkhhBBCCCGEEHXRKKVUfTudPn2aBQsWUFxcjLu7O6+88gpdu3bl7rvv5uGHH6ZPnz6YzWaee+45tm7dCsDdd9/N3LlzAS65TQghhBBCCCGEqEuDglYhhBBCCCGEEKIlyERMQgghhBBCCCFaLQlahRBCCCGEEEK0WhK0NpHc3FxuvvlmoqKiePnll1FK8dRTTzFw4ECuv/56du/e3Womn4qKiiIlJaXWbcuWLeOmm266yimq3U8//cTIkSOJiooiISGhRdOSnp5OVFSUbRbti1/vd955h7/85S+Xde7Fixfz+OOPN2Vym0RrTZcQQgghhLi22LV0AlpSVFSU7e+Kigr0ej06nQ6ARYsWMW3atAaf68svv8TLy4u9e/ei0WjYvXs3W7duZePGjTg7OwOwZs2aps3AZdq3b99VuU5ERARr166lU6dOl3X8K6+8wtNPP01cXFwTp6zxgoODqz1vF7/ezWXHjh088cQTbNq0qdmu0RzaarqFEEIIIUTrc00HrRcGIWPGjOGFF17guuuuq7GfyWTCzu7ST1V6ejphYWG2ACYtLY0OHTrYAlbReOnp6XTv3r2lk1Gri19vIYQQQgghRPOQ7sG12LFjByNGjODdd99l6NChPPXUUxQVFXHvvfcyePBgBg4cyL333ktmZiYACxYs4LvvvuP9998nKiqKL774gr/+9a/s37+fqKgo3njjDds5z8vIyODBBx9k8ODBDBo0iOeee67WtBw8eJC5c+cSExPDsGHDeO655zAYDLbtJ0+e5I477iA2NpbrrruOd955B7AuM/TOO+8QFxdHVFQUs2bNIiMjA7C2gJ49exaAgoIC7rvvPqKjo7n++utJTk6udv3Tp0/bzh8fH8/KlStt2xYsWMCiRYu45557iIqKYs6cObbjb775ZgCmT59OVFRUtePOs1gsvPXWW4wePZohQ4bwpz/9iZKSEgwGg60r7vTp02ttaVVK8dJLLzFkyBCio6OZOnUqJ06csKVr4cKF3HHHHURFRTF//nzS0tIalKfKykpefvllRo8ezYABA7jpppuorKwkNTWViIgITCZTjdf7119/rdGVdv/+/dx4443ExMQwbdo0duzYYduWkpLC/PnziYqK4o477qCgoKDW1768vJy7776b7OxsoqKiiIqKIisri379+lU75siRIwwePBij0ciyZcu48cYbee655xgwYAATJkxg27Zttn1LSkr485//zLBhwxg+fDivvfaarctzbQwGA48++ihRUVHMnDmTY8eO2bZd+D46/7y/9tprdaZbCCGEEEKIy6KEUkqp0aNHq61btyqllNq+fbuKjIxUf//731VVVZWqqKhQ+fn5avXq1aq8vFyVlJSohx56SN1///2245988kn1r3/9y/b4m2++UTfeeKPt8fbt29Xw4cOVUkqZTCY1depU9eKLL6qysjJVWVmpdu3aVWu6Dh06pPbt26eMRqNKSUlREyZMUB9++KFSSqmSkhI1dOhQ9f7776vKykpVUlKi9u/fr5RS6r333lNTpkxRp0+fVhaLRR09elTl5+crpZQKDw9XSUlJSimlHn30UfXwww+rsrIydfz4cTVs2DBbusvKytSIESPU119/rYxGozpy5IiKjY1VJ0+etOU5NjZWHThwQBmNRvWHP/xBPfroo7a0X3id2ixdulTFxcWp5ORkVVpaqh544AH1+OOPN+j4TZs2qZkzZ6qioiJlsVjUqVOnVFZWli1d/fv3Vzt37lRVVVXq+eefb3Cenn32WTV//nyVmZmpTCaT2rNnj6qqqlIpKSkqPDxcGY3GWl/vN954Q/3xj39USimVmZmpYmNj1YYNG5TZbFZbtmxRsbGxKi8vTyml1A033KBeeuklVVVVpXbu3Kn69+9vO/ZiF75vzrvrrrvUp59+anv84osvqueee04pZX3fRUZGqg8//FAZDAb1448/qujoaFVQUKCUUur3v/+9evrpp1VZWZnKzc1Vs2fPVp9//nmt137jjTdUz5491apVq5TBYFD//e9/1ejRo5XBYKj19bnwOakt3UIIIYQQQlwOaWmtg1ar5eGHH0av1+Po6IiXlxfx8fE4OTnh6urK/fffz65duy7r3AcPHiQ7O5s//elPODs74+DgQExMTK379u7dm/79+2NnZ0dISAhz5861XXfDhg34+vpy55134uDggKurK/369QNg6dKlPPLII3Tt2hWNRkOPHj3w8vKqdm6z2czatWt5+OGHcXZ2Jjw8nJkzZ9q2b9iwgQ4dOjB79mzs7Ozo2bMn8fHxrF692rZPXFwcffv2xc7OjmnTpnH06NEGPw/Lly/n9ttvJzQ0FBcXF/7whz+wcuVKTCZTvcfa2dlRVlbGmTNnUEoRFhaGv7+/bfuoUaMYOHAger2exx57jP3795ORkXHJPFksFr755hv+8pe/EBAQgE6nIzo6Gr1e3+A8AXz//feMGDGCkSNHotVqGTp0KL1792bjxo2kp6dz6NAhHnnkEfR6PQMHDmTMmDGNOv/MmTP54YcfAOtr+OOPPzJ9+nTbdm9vb2677Tbs7e2ZNGkSXbp0YcOGDeTm5rJx40b+/Oc/4+zsjI+PD7fffjs//vhjndfq1asXEyZMwN7enjvuuAODwcCBAwcalV4hhBBCCCGuxDU9pvVSvLy8cHBwsD2uqKjgb3/7G5s3b6aoqAiAsrIyzGazbfKmhsrIyCA4OLjecbIAiYmJvPzyyxw+fJiKigrMZjO9evWynadjx461HpeZmVnntvPy8/MxmUwEBQXZ/hccHGz7Oy0tjYMHD1YLqM1mc7UJqnx9fW1/Ozo6Ul5eXm+ezsvOzqZDhw62xx06dMBkMpGXl0dAQMAljx0yZAg333wzzz33HGlpaYwfP54nn3wSV1dXAAIDA237uri44OHhQXZ29iXzVFBQQFVVFaGhoQ3OQ23S09NZvXo169evt/3PZDIxaNAgsrOzcXd3rzbWOTg42NZ1uyHGjh3LM888Q0pKComJibi6utK3b1/b9oCAgGpjbYODg8nOziY9PR2TycSwYcNs2ywWS7XX/2IXPo9arZaAgACys7MbnFYhhBBCCCGulAStdbh4gp0PPviAxMREvvrqK/z8/Dh69CgzZsxAKdXocwcFBZGRkdGgCZ6effZZevbsyT//+U9cXV356KOPbLMQBwUF1TpWFKzBRnJyMuHh4XWe29vbGzs7OzIyMggLCwOoFjwFBQUxcOBAPvzww8ZmsUH8/f2rjTVNT0/Hzs4OHx+fBh1/6623cuutt5KXl8ejjz7Kf//7Xx599FEA23hjsN5cKCoqwt/f/5J5slgsODg4kJKSQo8ePS47X0FBQUyfPp0XXnihxra0tDSKi4spLy+3Ba7p6el1TuhU2/8dHByYOHEiP/zwA2fOnKnWygqQlZWFUsp2bEZGBmPGjCEwMBC9Xs/27dsbdMMEqj+PFouFrKwsW4u2k5MTFRUVtu05OTm2mw0yQZUQQgghhGgq0j24gcrKynBwcMDd3Z3CwkL+85//XPa5+vbti5+fH//85z8pLy+nqqqKPXv21HldFxcXXFxcOH36NJ9//rlt26hRo8jJyeGjjz7CYDBQWlpq67o5Z84c/v3vf5OUlIRSimPHjtWY8Een0zFu3Dj+85//UFFRwalTp/j222+rnT8pKYnvvvsOo9GI0Wjk4MGDnD59ukH59PX1rXM9WIApU6bw8ccfk5KSQllZGa+99hoTJ05sUEB18OBBDhw4gNFoxMnJCb1ej1b729t548aN7N69G4PBwL///W/69etHUFDQJfOk1WqZPXs2f/vb38jKysJsNrNv375qE181xLRp01i/fj2bN2/GbDZTVVXFjh07yMzMpEOHDvTu3ZvFixdjMBjYvXt3tRbZi/n4+FBYWEhJSUm1/0+fPp1vv/2WX375pUbQmp+fz5IlSzAajaxatYrTp08zcuRI/P39GTp0KC+//DKlpaVYLBaSk5PZuXNnndc/cuQIa9euxWQy8fHHH6PX621d0Hv06MGKFSswm81s2rSpWnf5utIthBBCCCFEY0nQ2kC33XYbVVVVDB48mLlz5zJ8+PDLPpdOp+Odd97h7NmzjB49mhEjRrBq1apa933yySdZsWIF0dHRPP3000yaNMm2zdXVlQ8++ID169czdOhQ4uPjbbPU3nHHHUycOJE777yT6Oho/vKXv1BVVVXj/AsXLqS8vJyhQ4eyYMECZs2aVe3877//PitXrmT48OEMGzaMV199tcFB3IMPPsiCBQuIiYmptUV49uzZTJs2jfnz5zN27Fj0ej1PP/10g85dVlbGX//6V2JjYxk9ejSenp787ne/s22fMmUKb775JoMGDeLIkSP84x//aFCennzyScLDw7n++uuJjY3l1VdfxWKxNChN5wUFBfHWW2/xf//3fwwZMoSRI0fy/vvv287zz3/+kwMHDjBo0CDefPNNZsyYUee5wsLCmDx5MnFxccTExNhm4R0wYABarZZevXpV62IN1psiZ8+eZfDgwbz++uu88cYbtvHMf//73zEajUyaNImBAwfy8MMPk5OTU+f1x44dy8qVKxk4cCDff/89ixcvxt7eHoC//OUvrF+/npiYGJYvX15tlue60i2EEEIIIURjadTl9G8VohVbsGABAQEBPPbYYy2dlGZ16623MnXqVObMmWP737Jly1i6dGm1FnkhhBBCCCHaMmlpFaINOnjwIAkJCUycOLGlkyKEEEIIIUSzkomYhGhjnnzySdatW8df/vIX22zJQgghhBBCtFfSPVgIIYQQQgghRKsl3YOFEEIIIYQQQrRaErRewl133VVtCZiGSk9PJyoqCrPZ3Aypuvp27NjBiBEjmuXcr732GoMGDWLo0KHNcv5rRXO+RkI0p4vL2QvLhCspS1NTU4mIiMBkMjVo/wULFvDaa681+jpN6XK/c662MWPG8OuvvzbpOVvD81+bd955h7/85S8tnQwhhLjmyZjWS/jvf//boP3GjBnDCy+8wHXXXQdAcHAw+/btu+zrRkREcPz48cs+vq1IT0/nww8/ZP369fj4+NTYvmPHDp544gk2bdrUAqlrWqmpqYwdO5YjR440aB1aIS5XREQEa9eupVOnTrb/LV68mLNnz/Lqq6+2YMpqd2E5W1uZcCVlaVvT0O8ccfXcd999LZ0EIYQQSEuraEHp6el4enrWGrA2VENbUVpSW0ijEK1BU5QJzUE+w+2XvLZCCNE2XNNB67vvvsvDDz9c7X8vvPACL7zwAgC33HILS5cutW376quvmDhxIlFRUUyaNIkjR47wxBNPkJ6ezn333UdUVBTvvfdejW5pt9xyC6+99ho33ngjUVFR3HfffRQUFPDHP/6R6OhoZs+eTWpqaq1pXLZsGWPHjiUqKooxY8bwww8/1Lrf4sWLefjhh3n88ceJiopi6tSpJCYm8n//938MGTKEkSNHsmXLFtv+WVlZ3HfffcTGxjJu3Di++uor27bKykoWLFjAwIEDmTRpEocOHap2raysLB566CEGDx7MmDFjWLJkSZ3PcUlJCX/6058YPHgwo0eP5q233sJisfDrr79y5513kp2dTVRUFAsWLKh2XHl5OXfffbdte1RUFFlZWdXyGR0dzbfffsvBgweZO3cuMTExDBs2jOeeew6DwWA7V0REBJ9//jnjx48nJiaGRYsWcX7+sbNnzzJ//nwGDBjAoEGDePTRR6sdt2TJEsaOHcugQYN45ZVXsFgsAFgsFt566y1Gjx7NkCFD+NOf/kRJSQnwW7fEpUuXMmrUKG677Tbmz58PwMCBA4mKiqq19eji7nEXd/kdM2YM77//PlOnTmXAgAE8+uijVFVV1fq8L1myhEmTJpGZmWk7zwcffMCQIUMYNmwY33zzTb2vEcDo0aM5fPgwAD/88AMRERGcPHkSgKVLl/L73/8esL7/HnnkEf70pz8RFRXF5MmTa7xvROtQ3/th48aNTJo0iaioKIYPH877779f7bh33nmHQYMG1SiPDAYDr7zyCqNGjeK6665j4cKFVFZW2ravW7eO6dOnEx0dTVxcnK0HxflytrYy4eKytKSkhD//+c8MGzaM4cOH89prr9m6DpvNZl555RUGDRrE2LFj2bhx4yWfh4SEBGbOnElUVFSNz9L5vL777rsMHTqUp556CoPBwIsvvsiwYcMYNmwYL774oq2cmT9/PmvWrAFgz549REREsGHDBgC2bdvG9OnTAWt5ftNNN/HKK68wcOBAxowZUy2dF37n1LdvSkoKN998M1FRUdx+++0sWrSIxx9/vNa8FhUVce+99zJ48GAGDhzIvffeS2ZmZrXrvv7667bvqDvvvJP8/Hzb9u+++47Ro0czaNAg3n777Us+r3W9f87n50IRERGcPXvW9rigoIA77riDqKgo5s+fT1paWrV9P/30U8aPH09UVBSvv/46ycnJ3HjjjURHR/PII49UK/fXr1/P9OnTiYmJ4cYbb+TYsWO2bWPGjOHdd99l6tSp9O/fH5PJxLvvvsvw4cOJiooiPj6ebdu2Aday7cLn9eeff2by5MnExMRwyy23cPr06WrnbWgZLYQQopHUNSw1NVX17dtXlZSUKKWUMplMaujQoWrfvn1KKaXmz5+vvvrqK6WUUitXrlTDhg1TBw4cUBaLRSUlJanU1FSllFKjR49WW7dutZ03JSVFhYeHK6PRaDtPXFycOnv2rCouLlYTJ05U48ePV1u3blVGo1E98cQTasGCBTXSV1ZWpqKiotTp06eVUkplZWWpEydO1JqXN954Q/Xu3Vtt2rTJds7Ro0ert956SxkMBvXll1+q0aNH2/afN2+eeuaZZ1RlZaVKSEhQgwYNUr/++qtSSql//OMf6qabblIFBQUqPT1dTZ48WQ0fPlwppZTZbFYzZ85UixcvVlVVVSo5OVmNGTNGbdq0qdZ0PfHEE+q+++5TJSUlKiUlRY0fP972nG7fvt123trUtv2NN95QPXv2VD/99JMym82qoqJCHTp0SO3bt08ZjUaVkpKiJkyYoD788EPbMeHh4eqee+5RRUVFKi0tTQ0aNEht3LhRKaXUY489pt566y1lNptVZWWl2rVrV7Xj5s+frwoKClRaWlq1tC9dulTFxcWp5ORkVVpaqh544AH1+OOPK6V+e/2feOIJVVZWpioqKmq8J2rz5JNPqn/961915n/06NFq9uzZKjMzUxUUFKgJEyaozz77rMa+ixcvVjNmzFB5eXm2bZGRker1119XBoNBbdiwQfXt21cVFhbW+xo98cQT6v3331dKKfXXv/5VjR07Vn366ae2beef5/Pvvw0bNiiTyaReffVVNWfOnDrzKppPeHi4SkpKqva/N954Q/3xj39UStX/fhg6dKjtc1BYWKgOHz5c7biXXnpJVVVVqR07dqh+/frZyqcXX3xR3XvvvaqgoECVlJSoe++9V7366qtKKaUOHDigoqOj1ZYtW5TZbFaZmZnq1KlTSqnq5ezF7/mLPze///3v1dNPP63KyspUbm6umj17tvr888+VUkp99tlnKj4+XqWnp6uCggI1f/78Oj9zVVVVatSoUerDDz9UBoNBrVq1SvXs2dP2+Tuf17///e+qqqpKVVRUqNdff13NmTNH5ebmqry8PDV37lz12muvKaWUev3119Vzzz2nlFLq7bffVmPHjlV///vfbduef/55pZRS33zzjerZs6f68ssvlclkUp9++qkaOnSoslgsNZ6L+va94YYb1Msvv6yqqqrUrl27VFRUlO01vlh+fr5avXq1Ki8vVyUlJeqhhx5S999/v237/Pnz1dixY9WZM2dURUWFmj9/vvrHP/6hlFLq5MmTqn///mrnzp2qqqpKvfTSSyoyMrLad96F6nr/fPPNN+rGG2+stu+F79Unn3yy2nWef/75avuHh4fbyqkTJ06oXr16qVtvvVUlJyfbvleXLVumlFLqyJEjavDgwWr//v3KZDKpZcuWqdGjR6uqqiqllLUsnTZtmkpPT1cVFRXq9OnTasSIESozM1MpZX3fnT17VilV/bNz5swZ1a9fP7VlyxZlMBjUu+++q+Li4qqdt64yWgghxJW5pltaO3ToQM+ePVm3bh0A27dvx9HRkf79+9fY9+uvv+auu+6ib9++aDQaOnXqRIcOHRp8rVmzZtGxY0fc3NwYMWIEoaGhXHfdddjZ2TFhwgQSEhJqPU6r1XLy5EkqKyvx9/ene/fudV4jJiaG4cOH285ZUFDAPffcg729PZMmTSItLY3i4mIyMjLYu3cvjz/+OA4ODkRGRjJnzhy+//57AFatWsV9992Hp6cnQUFB3HLLLbZrHDp0iPz8fB588EH0ej2hoaHccMMNrFy5skZ6zGYzK1eu5I9//COurq6EhIRwxx131Nla3FD9+/cnLi4OrVaLo6MjvXv3pn///tjZ2RESEsLcuXPZtWtXtWPuvvtu3N3dCQ4OZtCgQba77nZ2dqSnp5OdnY2DgwMxMTE1jvP09CQ4OJhbb72VFStWALB8+XJuv/12QkNDcXFx4Q9/+AMrV66s1tXsoYcewtnZGUdHxyvK74VuueUWAgIC8PT0ZPTo0Rw9etS2TSnF3/72N7Zu3cqSJUvw9va2bbOzs+OBBx7A3t6ekSNH4uzsTGJiYr2v0cCBA9m5cycAu3fv5t5777U9t7t27WLgwIG2awwYMICRI0ei0+mYPn16tZYN0brU9X44v+3UqVOUlpbi4eFBr169qh37yCOPoNfriY2NZeTIkaxatQqlFF999RV//vOf8fT0xNXVlXvvvZcff/wRsJafs2fPZujQoWi1WgICAggLC2tUmnNzc9m4cSN//vOfcXZ2xsfHh9tvv912jVWrVnHbbbcRFBSEp6cn9957b53nOnDgAEajkdtuuw17e3smTJhAnz59qu2j1Wp5+OGH0ev1ODo6snz5ch544AF8fHzw9vbmgQcesH1OYmNjbZ+TXbt21ficxMbG2s4bHBzMDTfcgE6nY+bMmeTk5JCbm1trOuvaNz09nUOHDtnSFxMTw5gxY+rMr5eXF/Hx8Tg5OeHq6sr9999fo4ycNWsWXbp0wdHRkQkTJtjKltWrVzNq1CgGDhyIXq/nkUceQautu+pQ3/vnUi68zmOPPcb+/fvJyMiwbb/rrrtwdXWle/fuhIeHM3ToUEJDQ23fq+e/R7/88kvmzp1Lv379bM+dvb09+/fvt53rlltuISgoCEdHR3Q6HQaDgdOnT2M0GgkJCaFjx4410rdy5UpGjhzJ0KFDsbe353e/+x2VlZXVes5cqowWQghx+a7poBVgypQptkBkxYoVTJkypdb9MjIyav0SayhfX1/b3w4ODtUeOzo6Ul5eXuMYZ2dnXnvtNb744guGDRvGPffcU60r0sUuHAfm6OiIl5cXOp3O9his3W6zs7Px8PDA1dXVtn9wcDBZWVkAZGdnExQUVG3beWlpaWRnZxMTE2P7eeedd2qtdBUUFGA0Gqsdf+F1LldgYGC1x4mJidx7770MHTqU6OhoXnvtNQoKCqrt4+fnZ/vbycmJsrIyAJ544gmUUlx//fVMnjyZr7/+utpxFz4PHTp0IDs7G7A+RxfetOjQoQMmk4m8vLw609kULs7Hhe+bkpISvvrqK+69917c3NyqHefp6VltAqjzx9b3GsXGxrJnzx6ys7OxWCxMnDiRvXv3kpqaSklJCZGRkbbjLn5PV1VVyXixFqDT6Wo87yaTCXt7e9vjut4PAG+88QYbN25k9OjRzJ8/v1qF3N3dHWdnZ9vj4OBgsrOzyc/Pp6KiglmzZtnKhbvuusv2ObzS8hOs411NJhPDhg2zXWPhwoW2bqyXKrculp2dTUBAABqNps79vby8cHBwqHbMxZ+T8+VB//79SUpKIjc3l2PHjjF9+nQyMjLIz8/n4MGD1W6GXfg5cXJyAqi1/L/UvufL8PP/g+pl1cUqKipYuHAho0ePJjo6mptvvpni4uJqszLXVbZkZ2dXK8ucnZ3x9PSs81qXev/U58LruLi44OHhYXuO4dLfow4ODrY0n5/Q68LvqczMzGrnuvD56tSpE3/+859ZvHgx1113HY899lit31MXvwe0Wi1BQUHV9r1UGS2EEOLyXfPTmE6cOJFXXnmFzMxMfvrpJ7788sta9wsKCiI5Ofkqpw6GDx/O8OHDqays5PXXX+fpp5/ms88+u6Jz+vv7U1RURGlpqS1wzcjIICAgALB+6WZkZNhadS+80x0UFERISAhr166t9zpeXl7Y29uTnp5Ot27dalynPhdWKC/1/2effZaePXvyz3/+E1dXVz766CPb+LL6+Pn52cYw7969mzvuuIOBAwfaZl698HlIT0/H398fsD6HF463Sk9Px87ODh8fH9tYsQvTWVdeLuTk5FRtDGBdrS91cXd35x//+AePPvoo//nPfxgwYEC9x9T3GnXq1AlHR0c++eQTYmJicHV1xdfXl6+++ooBAwZcssVFtIygoCBSU1OrtWSmpqbSuXPnBh3ft29f3n77bYxGI59++imPPvqobSxlcXEx5eXltsD1/OfDy8sLR0dHfvzxx1o/301RfgYGBqLX69m+fXutM3CfL7fOu/Dv2vbNyspCKWX7bKanpxMaGmrb5+LPrL+/P+np6dXKxfPlgZOTE7169WLJkiV0794dvV5PVFQUH330ER07dqzW66Ep+Pn5UVRUREVFhS1wvVR+P/jgAxITE/nqq6/w8/Pj6NGjzJgxwza2/1L8/f2r3SytqKigsLCwzv3rev9cXL7l5OTUOPbCcbZlZWUUFRXZnuPGCAoK4r777uP++++vc5+LX9+pU6cydepUSktLWbhwIa+++ir/+Mc/qu3j7+/PiRMnbI+VUo36ThNCCHH5rvkap7e3N7GxsTz11FOEhITU2WXt+uuv54MPPuDw4cMopTh79qwtaPH19SUlJaXJ05abm8u6desoLy9Hr9fj7OzcJEFCUFAQUVFR/Otf/6Kqqopjx47x9ddfM23aNMAayL/77rsUFRWRmZnJ//73P9uxffv2xcXFhXfffZfKykrMZjMnTpzg4MGDNa6j0+mYMGECr732GqWlpaSlpfHhhx/arlMfHx8fCgsLbRMc1aWsrAwXFxdcXFw4ffo0n3/+eYOfi1WrVtkqSh4eHmg0mmrP8fvvv09RUREZGRm2yY3A2kL/8ccfk5KSQllZGa+99hoTJ06sczkbb29vtFrtJd8nkZGRbNy4kcLCQnJycvj4448bnI/zBg0axKuvvspDDz1U62tysYa8RrGxsXzyySe2rsAXPxaty6RJk3j77bfJzMy0TXr2yy+/EB8fX++xBoOBH374gZKSEuzt7XFxcalR5ixevBiDwcDu3bvZsGEDEyZMQKvVMmfOHF566SVbb4OsrCw2b94MWMvPZcuWsW3bNiwWC1lZWZfsNVIbf39/hg4dyssvv0xpaSkWi4Xk5GRbt9yJEyfyv//9j8zMTIqKinj33XfrPNf54QRLlizBaDSydu3aeicOmzx5Mm+//Tb5+fnk5+fz5ptvMnXqVNv2iz8XgwYNarbPSYcOHejdu7fttdi3bx/r16+vc/+ysjIcHBxwd3ensLCQ//znPw2+Vnx8PBs2bGD37t0YDAbeeOMN20RtF7vU+6dHjx6cPHmSo0ePUlVVxeLFi2scv3HjRtt1/v3vf9OvX79LtiDXZc6cOXzxxRccOHAApRTl5eVs2LCB0tLSWvc/c+YM27Ztw2AwoNfrcXBwqPW7duLEiWzcuJFt27ZhNBr54IMPbDcohBBCNK9rPmgFawDy66+/1tk1GKxfVvfdd59txt8HHniAoqIiAO655x7efvttYmJibDMlNgWLxcJHH33E8OHDiY2NZdeuXTz77LNNcu5//etfpKWlMXz4cB588EEeeugh2zqzDz74IMHBwYwdO5Y777zTNvMlWIOcd955h2PHjjF27FgGDx7MX//61zorA08//TROTk7ExcUxb948pkyZwuzZsxuUxrCwMCZPnkxcXBwxMTF1dit+8sknWbFiBdHR0Tz99NO2wLIhDh06xJw5c4iKiuL+++/nL3/5S7XWlrFjxzJr1ixmzJjBqFGjuP766wGYPXs206ZNY/78+YwdOxa9Xs/TTz9d53WcnJy47777uOmmm4iJiak2tuq86dOn06NHD8aMGcOdd97ZqHxcaOjQobz00kvcd999HDlypN7963uNBg4cSFlZWbWg9cLHonV54IEHiIqKYt68eQwcOJB//OMfvPrqq4SHhzfo+O+//54xY8YQHR3NF198Ua21ydfXF3d3d4YPH87jjz/Os88+a7vR98QTT9CpUyduuOEGoqOjuf32223jZPv27cvf/vY3XnrpJQYMGMD8+fNJT09vdN7+/ve/YzQamTRpEgMHDuThhx+2tdjdcMMNDBs2jOnTpzNz5kzGjx9f53n0ej2LFy/m22+/JTY2lpUrVzJu3LhLXvv3v/89vXv3Ztq0aUybNo1evXrZZs+Gmp+Tix83tVdffZX9+/czaNAgXn/9dSZNmoRer69139tuu42qqioGDx7M3LlzGT58eIOv0717dxYuXMjjjz/O8OHDcXd3v+TQh7reP126dOGBBx7g9ttvZ/z48bX2BJkyZQpvvvkmgwYN4siRIzVaOhuqT58+PP/88zz33HMMHDiQ8ePHs2zZsjr3NxgM/POf/2TQoEEMGzaM/Px8/vCHP9TYr2vXrvzjH//g+eefZ/Dgwaxfv5533nmnzuddCCFE09GohvQPEuIaFBERwdq1a21dhYW4lu3YsYMnnnjCtlSNaF0effRRunbtWmMZNyGEEKI9kJZWIYQQoo05ePAgycnJWCwWNm3axM8//0xcXFxLJ0sIIYRoFtf8RExCCCFEW5Obm8tDDz1EYWEhgYGBtgnphBBCiPZIugcLIYQQQgghhGi1pHuwEEIIIYQQQohWS4JWIYQQQgghhBCtVpsZ01pQUIbF0v56Mvv4uJKXV/tyMW1Ze80XSN5aC61Wg5eXS0sno8lJWdf2tNe8tdd8QdvKW3st64QQojHaTNBqsah2WZEDJF9tkORNNBcp69qm9pq39povaN95E0KI9ka6BwshhBBCCCGEaLUkaBVCCCGEEEII0Wo1KGhNTExk7ty5xMfHM3fuXJKSkmrss2XLFmbNmkXv3r155ZVXqm178803mTx5MlOnTmXWrFls3ry5SRIvhBBCCCGEEKJ9a9CY1meeeYZ58+Yxffp0vv/+exYuXMiSJUuq7RMaGsqLL77I6tWrMRgM1bb17duXO++8EycnJ44dO8b8+fPZsmULjo6OTZcTIYQQQgghhBDtTr0trXl5eSQkJDBlyhQApkyZQkJCAvn5+dX269SpE5GRkdjZ1YyDhw8fjpOTEwAREREopSgsLGyC5AshhBBCCCGEaM/qbWnNyMggICAAnU4HgE6nw9/fn4yMDLy9vRt9we+++46OHTsSGBjYqON8fFwbfa22ws/PraWT0Czaa75A8iaaj5R1bVN7zVt7zRe077wJIUR7c1WXvNm5cyf//ve/+eCDDxp9bF5eabucnt7Pz42cnJKWTkaTa6/5Aslba6HVatplgCdlXdvTXvPWXvMFbStv7bWsE0KIxqi3e3BQUBBZWVmYzWYAzGYz2dnZBAUFNepC+/bt44knnuDNN9+ka9eul5daIYQQQgghhBDXlHqDVh8fHyIjI1mxYgUAK1asIDIyslFdgw8ePMhjjz3GG2+8Qa9evS4/tUIIIYQQQgghrikNWvLm2Wef5ZNPPiE+Pp5PPvmERYsWAXD33Xdz6NAhAHbv3s2IESP48MMP+eKLLxgxYoRtaZtFixZRWVnJwoULmT59OtOnT+f48ePNlCUhhBBCCCGEEO2FRinVJgZPyTivtqW95gskb61Fex3nJWVd29Ne89Ze8wVtK2/ttawTQojGaFBLqxBCCCGEEEII0RIkaBVCCCGEEEII0WpJ0CqEEEIIIYQQotWSoFUIIYQQQgghRKslQasQQgghhBBCiFZLglYhhBBCCCGEEK2WBK1CCCGEEEIIIVotCVqFEEIIIYQQQrRaErQKIYQQQgghhGi1JGgVQgghhBBCCNFqSdAqhBBCCCGEEKLVkqBVCCGEEEIIIUSrJUGrEEIIIYQQQohWS4JWIYQQQgghhBCtlgStQgghhBBCCCFaLQlahRBCCCGEEEK0WhK0CiGEEEIIIYRotSRoFUIIIYQQQgjRaknQKoQQQgghhBCi1ZKgVQghhBBCCCFEqyVBqxBCCCGEEEKIVqtBQWtiYiJz584lPj6euXPnkpSUVGOfLVu2MGvWLHr37s0rr7xSbZvZbGbRokXExcUxbtw4li5d2iSJF0IIIYQQQgjRvjUoaH3mmWeYN28ea9asYd68eSxcuLDGPqGhobz44ov87ne/q7Ft+fLlJCcns3btWr788ksWL15MamrqladeCCGEEEIIIUS7Vm/QmpeXR0JCAlOmTAFgypQpJCQkkJ+fX22/Tp06ERkZiZ2dXY1zrFy5kjlz5qDVavH29iYuLo7Vq1c3URaEEEIIIYQQQrRX9QatGRkZBAQEoNPpANDpdPj7+5ORkdHgi2RkZBAcHGx7HBQURGZm5mUkVwghhBBCCCHEtaRms2gr5ePj2tJJaDZ+fm4tnYRm0V7zBZI30XykrGub2mve2mu+oH3nTQgh2pt6g9agoCCysrIwm83odDrMZjPZ2dkEBQU1+CJBQUGkp6fTt29foGbLa0Pk5ZVisahGHdMW+Pm5kZNT0tLJaHLtNV8geWsttFpNuwzwpKxre9pb3swWC8s2nuHgmTzumtyTToHtL7hrS69Zey3rhBCiMertHuzj40NkZCQrVqwAYMWKFURGRuLt7d3gi0yYMIGlS5disVjIz89n3bp1xMfHX36qhRBCCNHkissM/POL/azakUxhSRUvf7aXI0n59R8ohBBCNKMGzR787LPP8sknnxAfH88nn3zCokWLALj77rs5dOgQALt372bEiBF8+OGHfPHFF4wYMYLNmzcDMH36dEJCQhg/fjw33HADDzzwAKGhoc2UJSGEEEI01pn0YhZ9tIvT6cX8bnIkb/5pDH4ejrz+1QG2H5F5KIQQQrQcjVKqTfRDky5zbUt7zRdI3lqL9tplTsq6tqc95G3j/jQ+/ekEnq4OPDCzD50C3fDzc+NsSj6LvznE8ZRC5o7pRnxsx5ZOapNoS69Zey3rhBCiMdrMRExCCCGEaFpGk5lPfzrBpgMZ9Orizb3TeuHqZG/b7uxozx/m9uPd5Ql8+cspikoNXD86DK1G04KpFkIIca2RoFUIIYS4BuUXV/Lmt4dIzChh8pBOzBzeFa22ZjBqb6fj/um9+XTdCVbvTKaorIo7JkVip2vQCCMhhBDiiknQKoQQQlxjjp4t4J3vD2M0WXhwVh+iw/0uub9Wq2H+uHA8XR34dtMZSsqN/H5mbxz1DatGmMwWjp4tYNfRbOx0Gm6d0KMpsiGEEOIaIUGrEEIIcY1QSrFmZwpLN5wi0NuZB2f1IcjHpUHHajQapl7XGU8XPR+vPs7fP9vHo3P64e6ir3V/s8XCsbOF7DqWxZ7jOZRVmtBqNFiUYmxMKB18G3ZdIYQQQoJWIYQQ4hpQZTTz/o9H2X0sm5gIP+6YFImTQ+OrAcP7BePmoued7w7z0id7+MPc/vh7OgFgsSiOJxew61g2u4/nUFphxEGvI6q7L7E9Agjxc+HJd7ax62gWHYZ3beosCiGEaKckaBVCCCGuAWt3JrP7WDZzRocxIbYjmiuYTKl/N18evymKfy89wEv/28ONY7txMrWIPcdzKC4z4GCvo183H2IjA+jdxRu9vc52bERHT3YczWb6sC5XlAYhhBDXDglahRBCiHbOohSbD2YQ2cmLiYM6Nck5u3Xw4Kn5A3jtq/28+0MCejstfbv5EtvDnz5hPjhcEKheKDYygCVrjpOSXUrHALcmSYsQQoj2TYJWIYQQop07draA3KJKZo5o2i65wb4uPH37QJIzS+ge4omDvvZA9UIDIvz4ZO0JdhzNkqBVCCFEg8h89UIIIUQ7t+VgBk4OdgyoZ5bgy+HurKd3V58GBawAbs56enb2YtfRbJRSTZ4eIYQQ7Y8ErUIIIUQ7VlZpZPfxHAb3Cqg2trQlxUYGkFtUyZmM4pZOihBCiDZAglYhhBCiHdt+JAuT2cKIvsEtnRSb6HBf7HQadh3NbumkCCGEaAMkaBVCCCHasc0H0+no70qnwNYzftTZ0Z7eXXzYdSwbi3QRFkIIUQ8JWoUQQoh26mxmCclZpQzv13paWc+LjfSnoKSKU6lFLZ0UIYQQrZwErUIIIUQ7tflgOnY6LYN7BbR0Umro390XvZ2WnUezWjopQgghWjkJWoUQQoh2yGA0s/1IFgMi/HBxtG/p5NTgqLejb5gPu49lY7ZYWjo5QgghWjEJWoUQQoh2aO/JHMqrTAzrG9TSSalTbGQAxeVGjicXtnRShBBCtGIStAohhBDt0OYDGfh6OBLZyaulk1KnvmHW9V2li7AQQohLkaBVCCGEaGdyCis4eraAYX2C0Go0LZ2cOuntdUR182XP8RxMZukiLIQQonYStAohhBDtzJaDGWiAoX1ab9fg82IjAyirNJGQlN/SSRFCCNFKSdAqhBBCtCMWi2LLoQx6dfHGx8OxpZNTr15dvHFysGPn0eyWTooQQohWSoJWIYQQoh05kpRPQUlVq1ybtTb2dloGhPux90QORpO5pZMjhBCiFZKgVQghhGhHNh/MwNXJnv7dfFs6KQ0WG+lPpcHMoTPSRVgIIURNDQpaExMTmTt3LvHx8cydO5ekpKQa+5jNZhYtWkRcXBzjxo1j6dKltm15eXncc889TJ06lYkTJ/Lss89iMpmaLBNCCCGEgJJyA/tO5DC4VwD2dm3nvnSPTl64OtnLLMJCCCFq1aBvtGeeeYZ58+axZs0a5s2bx8KFC2vss3z5cpKTk1m7di1ffvklixcvJjU1FYB33nmHsLAwli9fzg8//MCRI0dYu3Zt0+ZECCGEuMZtO5KF2aIY0bdtdA0+z06nJSbCj/2ncqkySBdhIYQQ1dUbtObl5ZGQkMCUKVMAmDJlCgkJCeTnV+/Cs3LlSubMmYNWq8Xb25u4uDhWr14NgEajoaysDIvFgsFgwGg0EhAQ0AzZEUIIIa5NSik2H0ynS5AbIf6uLZ2cRouNDMBgtHDgdG5LJ0UIIUQrU2/QmpGRQUBAADqdDgCdToe/vz8ZGRk19gsO/u3OblBQEJmZmQD8/ve/JzExkWHDhtl+BgwY0JT5EEIIIa5piRklpOWUMbyNtbKeFx7qiYeLXmYRFkIIUYPd1bjI6tWriYiI4OOPP6asrIy7776b1atXM2HChAafw8en7d01big/P7eWTkKzaK/5AsmbaD5S1rVNrSFvX244jd5ex6ThYbg42TfJOa92vkZEh7B6WxIubo44OzZNHurSGl4zIYQQDVNv0BoUFERWVhZmsxmdTofZbCY7O5ugoKAa+6Wnp9O3b1+gesvrJ598wksvvYRWq8XNzY0xY8awY8eORgWteXmlWCyqMXlrE/z83MjJKWnpZDS59povkLy1Flqtpl0GeFLWtT2tIW9VBjMb96YSE+FHeWkl5aWVV3zOlshXn05eLN98hp+2JXJd76D6D7hMreE1a6j2WtYJIURj1Ns92MfHh8jISFasWAHAihUriIyMxNvbu9p+EyZMYOnSpVgsFvLz81m3bh3x8fEAhISEsGnTJgAMBgPbtm2je/fuTZ0XIYQQ4pq0+3g2lQYzw/s2X6B3NXTt4I6Pu4N0ERZCCFFNg2YPfvbZZ/nkk0+Ij4/nk08+YdGiRQDcfffdHDp0CIDp06cTEhLC+PHjueGGG3jggQcIDQ0F4M9//jN79uxh6tSpzJgxg86dO3PDDTc0U5aEEEKIa8vmgxn4ezkRHurZ0km5IlqNhoE9AjiSmE9phbGlkyOEEKKVaNCY1rCwsGrrrp733nvv2f7W6XS2YPZiHTt25MMPP7zMJAohhBCiLln55ZxIKWT2yK5oNJqWTs4Vi+3pz+qdyew9kcOIfm1zUikhhBBNq+2sPC6EEEKIGjYfzECjoVnHgF5NnQLc8Pd0YufRrJZOihBCiFZCglYhhBCijTJbLGw9nEHfrj54uTm0dHKahEajIbanP0fPFlBcZmjp5AghhGgFJGgVQggh2qDC0io++PEYRaUGhrezbrSxPQJQyjrBlBBCCHFV1mkVQgghRNOoMppZuzOZlduTMZktTBjUkf7dfFs6WU2qg58Lwb4u7EzIYkx0SEsnRwghRAuToFUIIYRoAyxKsSMhi683nKagpIoB4X5cPzqMAC/nlk5ak9NoNAzpFcA3G8+QlltGB1+Xlk6SEEKIFiRBqxBCCNHKnUgp5MtfTpKYUUKnQDfumdqTiI5eLZ2sZjW8XzDfb0nil72p3DI+oqWTI4QQogVJ0CqEEEK0UtmFFXy9/hS7j+fg6arnd5MjGdI7EG07WNqmPu7OegZF+vProUxmjwjD2VGqLEIIca2SbwAhhBCilSmvNLLi17Os25OCVqthxrAuxMd2xEGva+mkXVVjY0LYejiTrYczGBcT2tLJEUII0UIkaBVCCCFakeyCcl5YsoeyCiND+wQxc0TXdrOcTWN1DnQnLNidX/amMXZAyDXRwiyEEKImWfJGCCGEaEW2HMqgrNLIX2+L4c7JkddswHremAEhZOWXk5CU39JJEUII0UIkaBVCCCFaCaUUu47l0KOjF12C3Fs6Oa1CTIQ/7s72/Lw7taWTIoQQooVI0CqEEEK0Emm5ZWTllxMT4dfSSWk17O20jOzfgYOn88gurGjp5AghhGgBErQKIYQQrcTuY9logOgI/5ZOSqsyKqoDGo2GDXvTWjopQgghWoAErUIIIUQrsft4DuGhnni46Fs6Ka2Kl5sD0RF+bD6YTpXR3NLJEUIIcZVJ0CqEEEK0Amm5ZaTnlhHTQ1pZaxM3IISyShM7ErJaOilCCCGuMglahWhjlFItnQQhRDPYc/xc1+BwGc9am+4hHoT4ufLznlQpB4UQ4hojQasQbcx/VyTw0id7KC4ztHRShBBNaPexbLqFeFzzS9zURaPRMHZAB1KySzmZWtTSyRFCCHEVSdAqRBuSnlvGtiNZnEot4qVP9pAjM2kK0S5k5JWRmlNGjEzAdEmDewbi7GDHL3tl+RshhLiWSNAqRBuydlcy9nZaHprdh7IKIy99sofU7NKWTpYQ4grtOZ4DwABZ6uaSHPQ6hvcLYs/xHApKqlo6OUIIIa4SCVqFaCOKygz8ejiLob0Dierux4Kbo9FqNLz86V5OpBS2dPKEEFdg9/FswoLd8XZ3bOmktHqjozpgsSg27pflb4QQ4lohQasQbcT6vamYzBbGDQwFoIOfK0/Nj8bdRc8/v9zP/pO5LZxCIcTlyC4oJzmrVGYNbiB/L2f6hPmwYX86JrOlpZMjhBDiKpCgVYg2wGA088veNPp38yXIx8X2f18PJ56aH02Inwv/WXaIrYcyrkp6jicXSNc8IZrIbuka3GhjB4RQXGZg9/Hslk6KEEKIq6BBQWtiYiJz584lPj6euXPnkpSUVGMfs9nMokWLiIuLY9y4cSxdurTa9pUrVzJ16lSmTJnC1KlTyc2VViEhGurXw5mUVhiJjw2tsc3NWc8TN0UR2cmT9388yuodyc2WDoPRzJLVx3jls30s/uYgFll2QogrtvtYNl2C3PD1cGrppLQZvbp4E+DlxM97ZEImIYS4FjQoaH3mmWeYN28ea9asYd68eSxcuLDGPsuXLyc5OZm1a9fy5ZdfsnjxYlJTrV8mhw4d4j//+Q8ffPABK1as4LPPPsPNza1pcyJEO2VRijW7UugU6EZ4qGet+zjq7Xj4+n7ERvrz1fpTfLX+VJOvY5iRV8YLS3azYX86vbt6k5RZwtaDV6dlV4j2KrewgqTMEpk1uJG0Gg1jokM4nVZMUmZxSydHCCFEM6s3aM3LyyMhIYEpU6YAMGXKFBISEsjPz6+238qVK5kzZw5arRZvb2/i4uJYvXo1AB999BF33nknfn7Wrk9ubm44OMg6dEI0xMFTeWTllxMfG4pGo6lzP3s7LfdM7cWY6A6s3pHMBz8exWxpmvFeWw9lsOijXRSWGnjshn48NqcfYR3c+WbjacorTU1yDSGuRbauwTKetdGG9gnCwV7HL3tkQiYhhGjv7OrbISMjg4CAAHQ6HQA6nQ5/f38yMjLw9vautl9wcLDtcVBQEJmZmQCcPn2akJAQbr75ZsrLyxk3bhz333//JSvgF/PxcW3wvm2Nn1/7bHVur/mCq5u3X5YewNfTiYnDwrDT1d854tF5Awj0c+OzNccwWBR/uiUGR329H3WbC/NWUWXinWUH+WV3Cr3DfHj85gH4nOvC+MCc/vzx35tYty+N303r3fiMiVpJWdc2XW7eDpzOIyzEg17dW2fQ2tpfszEDQ1m3M5n75/TH3UXfqGNbe96EEEL8puE12StgNps5fvw4H374IQaDgbvuuovg4GBmzJjR4HPk5ZVisbS/8XN+fm7k5JS0dDKaXHvNF1zdvCVlFnP4dB43jO5GQX5Zg4+LiwpGh+KTNce556V1xEb6ExsZQOdAt0veLLowbynZpbzz/WEy88qZNrQz04Z2wWIw2bZ7OtoxrE8QyzefYWB49QmirgatVtMuAzwp69qey81bXlElx5MLmD2ya6t8btrCa3ZdpD+rfk3i219OMGlwpwYf1xbydl57LeuEEKIx6m22CQoKIisrC7PZDFgD0OzsbIKCgmrsl56ebnuckZFBYGAgAMHBwUyYMAG9Xo+rqytjx47l4MGDTZkPIdqlNTtTcNTrGNEvuP6dLzI6qgOPze1HR39X1u1O5fmPd/PU/23nm42nSc0urXPMq1KKDfvTeGHJbsorTTx+Y39mDO+KVlsz2J09Mgy9vZbP151s8jG0QrR3e06cnzW4dbaytgUd/Fzp0dGT9XtT2+XNHiGEEFb1Bq0+Pj5ERkayYsUKAFasWEFkZGS1rsEAEyZMYOnSpVgsFvLz81m3bh3x8fGAdRzsli1bUEphNBrZvn07PXr0aIbsCNF+5BVVsutoNiP6BePseHmdInp38eGROf14/eFh3DGxB36ejqzanszCD3by1//u4IctiWTk/daCW15p5P9+OMKS1ccJD/Vk0Z2xRHb2rvP87i56pg/twuHEfA6cyrusNApxrdp9PJsQP1cCvZ1bOilt2tgBIeQVV3HglKxKIIQQ7VWDasLPPvssCxYs4K233sLd3Z1XXnkFgLvvvpuHH36YPn36MH36dA4cOMD48eMBeOCBBwgNtS7PMXnyZA4fPsykSZPQarUMGzaM66+/vpmyJET7sG5PCgDjYmouc9NYLo72DO8XzPB+wRSXGdhzPJsdR7P5fksi321JpKO/K1Hhfuw8mk1WfjmzR3Zl4uBOaBsw7nzMgBA2Hkjni59P0quLN/Z2l7/8c3ZBOT4ejui0soS0aN8KSqo4lVrEzOFdWjopbV7/7r54uzuwfl8aUeGy1q0QQrRHGtVG+vTJOK+2pb3mC65O3iqqTDz+1lb6dPXhvunNN8lRQUkVu45ls/NoFmfSi/H1dOLuKZF0D/Fs1HkOJ+bxry8PMHtkVyYP6XxZadmwP40lq49z3/RexEYG1Lt/ex3nJWVd23M5eVu3O4XP1p3kxbsHXfXx4A3Vll6zL385yc97UvnPoyPQ2+vq3b8t5a29lnVCCNEYV2UiJiFE42w6kE5FlZn42I7Neh0vNwfGDwxl/MBQCkurCO3gSUlRRaPP07uLD1HdfVnx61mu6x2El1vjlrT6aXcKn687Sd8w63mEaO92H8+hg69Lqw1Y25oeHb1YszOF02lFlxzSIIQQom2SPnhCtDJmi4V1u1MID/WkS5D7Vbuup6tDo5bGudjcMd0wWyx8veFUo45bteMsn687SXS4Hw/O6oO9Xf2tJEK0ZUWlVZxMKWRAhHRlbSrhoZ5oNRqOJhe2dFKEEEI0AwlahWhldh/LIa+4ivjYKx/LejX5ezkTH9uRbUeyOJVW1KBjlm9NZOn608RG+nPf9F4NWodWiLZu74kcFDCwh8wa3FScHOzoFOjGseSClk6KEEKIZiA1RCFaEaUUa3YmE+DtTL9uba+b7OQhnfB01fPpTyewXGK4vFKKZZtO8+3mRIb0CuSeqRKwimvH7uM5BPk4E+wrXYObUo9OniSmF1NlMLd0UoQQQjQxqSUK0YqcSCkkKbOE8QNDGzRzb2vjqLfjhtHdOJtZwpaDGbXuo5Ri6frTrPj1LCP6BfG7yZG1rgErRHtUXGbgWHIBAyL80bTBz3hrFtnRC7NFcTKtsKWTIoQQoolJ0CpEK7JmZwquTvZc1zuwpZNy2Qb1DKBbiAffbDxNeaWx2jalFJ+tO8nqncmMie7ArRN6SMAqril7T+agFMTIeNYm1y3EA51Ww7GzhS2dFCGEEE1MglYhWonM/HIOnMpldFQHHBqwZENrpdFouDkunNJyIz9sTbL936IUS9Yc5+c9qYwfGMrN48LbZGuyEFdiz7Fs/L2cCPWXJUyamqPeji5B7jKuVQgh2iEJWoVoJdbuSkGn0zJmQEhLJ+WKdQp0Y3i/YH7ek0p6bhkWi+LDH4+ycX86k4d0Yu6YbtI1UlxzSiuMHD1byMAe0jW4ufTo5ElSRgkVVaaWTooQQogmJEGrEK1ASbmBrYcyGNIrAA8XfUsnp0nMGtkVvb2Oz9ed4L0VCWw9nMmMYV2YNaKrVNjFNWnviRwsShETIbMGN5ceHb2wKMXJ1IbNYC6EEKJtaFdB676TOWw+kN7SyRCi0dbvTcNosjA+tmNLJ6XJuDvrmTGsC0eSCtiRkMX1o8KYNqyLBKzimrX7eDa+Ho50DJCuwc2lWwcP7HQa6SIshBDtjF1LJ6AppWSV8t2WRHKKKpg5XFpzRNuQW1TByh1nieruS4d2tgTG6OgOnE4vIqKjF6OjOrR0coRoMWWVRo4mFTBuYKh8NzUjvb2OrsEeHDsrQasQQrQn7SponTK0M/kllaz49SxGk4UbRsu4OdG6KaX4dO0JAG6K697CqWl6djot903v3dLJEKLFrduditmiGNwzoKWT0u716OjJ8l+TKK804ezYrqo5QghxzWpX3YO1Gg23TujB2OgQ1uxM4bOfTmJRqqWTdc1RSmEszEIZKlo6Ka3e3hM5HDidx4xhXfH1cKp3f2UyYM46hTk/7SqkTghRH0tpHuaK0kvuU1phZM3OZAaE+9ExwO0qpaxtU2YT5pxEzDlJqEZ+j0d28kIp67rXQggh2od2dwtSq9Ewb1x37Ow0rNmZgtFs4dYJEbK0xlViyjiOYdc3lGZaWw81Dq5o3P3QuvmhdfdD4+aH1t0frZsvGldvNNp29xZssIoqE5/+dIJQf1fGDaw5Y7CymLEUpGPOOYMlOxFzTiKW/FRQZgC0/l3RR47GLiwWjZ3D1U6+ENc0c+5ZqnYvw5x8gDIAvRNaN/8Lyrnfyr1Ve4qoMpiZMbxLSye7VVLKgqUo81w5dwZzdiKWvGSwWGcA1nqHYN9jFPbdh6BxqH8IRddgD+zttBxLLqB/d9/mTr4QQoiroF1GDBqNhhtGd8PeTsuKX89iMlu4c1IkWm3bC1yVUpizTmI6sQWtfxj24cPRaJumgTy3sIK1u1IYHxvaoFa+SzHnJFG1+xvMKYfQOHviPeYWSkurUCXZWIpzMOeexZS4xxZwAaDRonH1Qevmey6I9UXr5vPbbxdvNNq2sV6pUgpVUYSlIB1LQdq5n3RUVRkaR1dr8O7ogsbR7dzfrmw9WoxXZQm3jo9BayjHYijHnH3GGpzmJGLOTQKTwXoBvTM6vy7o+09C69cFVZKL8egGKje+D9s+w77bddj3HIXOO7RFnwchroQ59yzGYxvRuvtj32ssGp19SyepBkthBlW7v8V0Zic4uKAfMBNXT3dKMlKxlORgyU/Fcna/LeACGK9guK8b7tt3UOFqLe+0rj5ozpd9Lt5odG3n61hVlmI+X87lp2EpTEdVFFnLNgcXa5nn6AYXlnuOrrayELPxt3Iu+wzmnCQwnuuZY++IzrcT9r3HofPviqoqw3hsI1W/fkLVjq+wC4tFHzkKrX9YncN/7O20dOsg41qFEKI90ajG9rtpIXl5pVgsjU/qD1sT+W5zIrGR/tw1pSd2uoYHfKUVRtbvS+OXPalER/hxy/iIRl+/Pn5+buTklNT4v7JYMJ3dh+HASizZp0FrBxYTWu8QHAbfiF3I5Y8TVEqx+WAGn/98kiqDmajuvjw0u+9lnctckI5h9zJMibvBwQWH/pOx7zUW/yDfGvlSFguqLB9LSQ6qOMdawTv3W5XmocoLq59co0Hj7HUuoPU5V7nzsrbOanXVfjS1PdbZn6ssuTVZhVAphbezhZxTx6sFqOaCNKgq+21HBxd0Xh3QOLqhqkpRlWWoyhJUVSlYzHVfAEBnj9a3Ezq/Luj8u6Lz64LGPaBGBU0phTnzBMajGzAl7gKzCW1AN/SRo7DrOvCyWl/rej+2RlqtBh+f9jcL6+WWda1dnWWdUpjTjmA4sApz2hFbWadx88Nh0BzsugxsFXMTWEpyMez9HuOJLaDTo+8zHn3fCWgcXGrkTSkLqrwIS3E2W349SG5aKmMjHHGozMdSmocqKwAufI01aJw9rEGs7aadFxqdvmFlndbOFihq7JpmySylFD6uGrJPHa92I85SkIaqKP5tR3tHtF4d0Dp7ogzlqMrS38o6cz1rpWp1aH06Wss6vy5o/bqi9Qyq9casOTcJY8IGjKe3g7ESrXco9pHnWl/1zjX2X37uu//fjwzH1anmzQ8p64QQom1p90ErwKrtZ1m64TTR4X7cN71XvYHr+RbIzQczqDKa8XZ3oLDEwMv3DsbX88paJC9Wo7JjMmA8+SuGg6tQRVlo3PzQ943HPnw4ppSDVO34ClWSgy60Dw6DbkTn3bgZWQtLq/ho1TEOns6jR0dPOvi58vOeVBbcHE14qGeDz2MpzqFq73eYTv4Kdg7o+8Sj7xtvqzxcToVAmQzngto8LKW5qJJcawWvNA9LSa61oqcsjTqnjb0TGic3WxCrPf+3kxsaR3ew06MM5VBVjqoqs1a+qsrO/ZSjDGXntpVXby3WO6Pz6mCttHmf++0VjMbJo9aKtlIKc1U5b3y6DUtlCfdP7ILebK3oYae3Vty8OzS627SqLMV4YivGo+uxFGWC3hn77tdhHzn6ku8R68dfgbL++AV4kpt76fF5rUV7rchdK0GrspgxndmJ4cAqLHnJaJw8sO8zDn3kaMw5iVRt/wJLfiragG44Dr4RXUC3Fkm3pbwIw/4VGBPWgwbse45F338yWid32z51lXfZhRX85d3tjOgXzC3xv930VGaTtawrzbOVc5aS3AvKuvz6b27Vxd7xt5t1tZR56J3AUFGjfFPnyj4uKP+qpcHeEa1nMFqvDui8g8+VdR2srcR1lHWYDLYA1hrMWgNaNNpzZV1Io4NsZajAeHqHtazLPQt2euzDBmEfORqt329Lap1MLeRvn+zlgZl9GBDhdy5NlnNlnQW/AC8p64QQog25JoJWgJ92pfD5zyfpG+bDAzN7Y29Xs9vp2cwSVu04y+5jOWg0MKhnABNiO+LsaMeT72xjZP9g5jdxa+v5yo6qLMWQ8AvGI+tQFcVofTuj7zcJuy4DqnWRVWYjxiPrqNr7Axgrse8xEv2AmWidPeq91q5j2SxZfQyDycL1I8MYGxOC0WThqf/bho+7I3++ZUC9LRqW8kIMe5djPLYBNBrse8Wh7zepWgXuwnw1JWUxWys8FjNYzCiLyfa39bG5+uPzFabKElRFSa1/11ox1GitwbeDCxoHZ2t3N/253w7OuPn5U27vY62wOXs2uhVo9Y5kvlp/igdm9mZAhH8TPTtWv7W+rsd0Zre1i6Le2VpRQ4HFAljAoqy/L/r46wO6oIuaiS60T6to3bqU9lqRa+9BqzJWYjy2CcOhNajSPLSeQej7TsSu+5Bq3YGVxYLxxGYMu5ahKoqwCxuEQ+z1aN38rkp6VVUZhgOrMBxeC2YT9hHD0UdPQ+vqU2feLvbfFQnsOpbNy/cOwcut4T0flMVyrnwyXVTWWS4o634r/5TZ+FsLZ61lXXEdrZ4acHC+oHw7V+bprb9dff2osPe2lnWuPq2uTDDnJGI8ugHjqe1gqrIG5ArOl3VGkwmdBrQaVaOss/cORhc13dorRdO656Rsr2WdEEI0xjUTtAKs35fG/9Ycp1cXbx6c1QcHex1KKQ4n5rN6RzJHzxbgqNcxqn8H4mJC8HZ3tB37wcqj7EjI4u/3X4eHS9N0vwLw1FeQuWEZxmObwFSFLrQP+n6T0AX1uGQFQVWWUrX3e4xHfgE7e/T9p6DvM77Wu9alFUY+/ekEOxKy6BLkxl1TehLk89tkFpsPpPPhqmP8fkZvYnpUD6KUoQJLcTaW4mzMWaesrQ0WM/Y9hqOPmobW1bvW9LWFrldKKTjfnc1UZau4Ye94yef+SvKWW1TBX/+7g56dvHlodvMGhpbKEkwnf8VSkgtoQKOxBuTnfmP7fe5vZcFy+ldMhdnoAsPRx87BLrD1LsPTXity7TVo9XIyk7npOwwJv0BVmfU91m8iuo79Lhk0KGMlhgMrMRxYDVjQ9x6PPmpKrV1Cr4QyVmEpsZZ1lpwkDEfWgaECu7DBOMTMQOsRWOextZUJabllLPzvDuJjO3LDmJZpJT5PKQXGSmsAa6xEo3eyPn96p0s+922hHIdzra+ntmMpSKtWtu06nkOFwczI/iHVyzpAnd2NMScZrU9HHAZe36pv1LXXsk4IIRrjmgpaATYfTOejlceI6OjJdb2DWLsrmdScMjxd9YwbGMrIfh1qXdctI6+Mv763g0lDOjF7ZNgVp8NSmEnV3u8xnd4BaLDrNgh934nofBo3kY6lMJOqHV9iOrsPjasPDgNnY9dtsK0icuhMHh+uPEpJuZGpQzszeUgndBeMF1JKYS4t4L9fbMLNUsisKDcozcFSnI0qzrHe7bfRYNdtMA4xM9G6X7qFsK1Udi7H5eZNKcW/vz7I8eRCXrhrED4ejvUfdJX5ejuSvvlHDHt/QFUUoevYD4eBs9H5dGzppNXQXity7S1otZTmYdi3AtOJLSizCbvO0dZgtZHdfS2l+VTt+gbTya1oHN3QD5iBfeSoBk/WppSyBm3nbsJZis+Xc9bHqqKo2v66jv3PvffrL5NrKxPe/PYQRxLzeeW+Ibg5N92NzquprZfjP25L4puNZ3j94WG4X/Qa+Po4k7F9HVW7v7UOuWnFN+raa1knhBCNcc0FrQDbj2Ty3xVHsShFBz8XJsR2ZFDPgHrHur717SGOJOXzj/uHXvaC5daxoN9jOrkVdPZ4RI/H1G10rV3OGsOUftQ6Biz3LFrfziiPYFIyCigoLMFVDyHeehy0FjAbUSbDb79NhmqzXCo0aF29rcvSuPujOffb9ljfsDG9bb2ycymXm7fdx7J567vDzB3TjfjY1hcEwoVdOKswHPkJw/6VYCg/19o0E61HQEsn0aa9VuTaS9BqKS/EsP9Ha+8MFG59R2MJj0PrWXeLZUOYc5Ko2v455ozjaD2D0PqHgdloHT9Z7bcRZTZc8LiqRhdZjYu3dWma82Xd+SW53P3RODb8vXVxmZCYUczzH+9m+rAuTB/Wdpe5aevl+On0Il5csof7Z/Rm4EW9iGxlndmE8djG327Uhfa13qzw7dRCqa6pvZZ1QgjRGG1njv0mNLhXIF5uDhjNFnp1rn0SidpMHtKZ3cdzWL8vlclDOjfqmtbWhuUYj20GrQb73uPR95uET6eQJqkU2AVHopv5DKaT2yjd/QNlOQfQW3R0cnXEw8MVrZ0e7PTWMWO23/Zo7Bys66W6+fPBplyO52t58bZhODlck2+NGkxmC5l55aTllpGWW0paThmOjvaM6R9MWIf6xxGfV15p4tN1J+jo70pcTM01WVsbjb0DDv2noI8cbRvXZzqzE/uIEegHTP//9u48Purq3v/4a2aSDFlJZrJNEpKQsIUsgAEEBBGMQCWWpUUs1d4u4u293trWX2vp/bVQ1Ptrae+vtfVie3v7q15ar22pihIRcMGCqOxLMASBBAJksickJGSb+f7+GIxSxCxMyGR4Px8PHmHyXeacfOd7Zj5zzvkczKFRXn0+wzBwN5Tjrj1DQFJWrwIG8S3u1ibaD26i4/03wN1J4KjpBN30WWLShnulrbPEpBKcv8KTXX3fBlznijxtmSXo0s9ATybdrsdBcKm9M4faPhagRnst0+7fe3F7CWHBgcyZpCWoBlJqfDjWIAvFp+uvCFo/ZLIEEJR5O4GjptP+/uu0H3yFlhdWeeZQT1z0qcPC+8IwDIzGKlzVpVgco73eloqI+KseRSalpaWsWLGChoYGIiMjWbNmDampqZft43K5ePzxx9mxYwcmk4kHHniAJUuWXLZPSUkJixYtYtmyZXzve9/zWiX6YnRy798oUuLDyRpu47U9Z7hj4jCCArsfluZuaaD9QAEdR98CDAIzZhI04a5+eaMymcwctYzhV2fasUVY+dr8DBJ7Uc85tzey67/38uqu0yy+9dqHQA8mbrdBVcNFzlVf8ASo1c2cq2mmsq4F16VeL7PJRJwtmObWTt4tdJKTbmfRjDRS4sO7Pf+L20tobG7noc/lXDY829eZrKFYJ3+ewKy8rgRcHcd3Eph5O4Ejp2EOj+lx7/vHGe5O3DWncTk/wFXxAa6K454lMoDged8iIHm8l2si/c1oa6b98Gbaj7wGHW2eqQS5C/uld95kMhGYehOBqTd5/dzX6lhZPUdK67h71gh9+TfALGYzo5IiKS7rfr1Wzxd18wnKuO1jX9TtIXD0rQRmzPQkALOG9nreq+F24647c6md+wCX84OuoejWGV8mKOO2vlRNROSG06N31FWrVrFs2TIWLFjASy+9xMqVK1m3bt1l+2zcuJGysjK2bt1KQ0MDCxcuZOrUqSQleXqVXC4Xq1atIi8vz/u1uI7mT01hzf8cYMdhJ7fnXr3H7Gq9Debw6H4r2+mKJn694QhJMaE8suymXg9hHu6I4OaxcWzdfYZZE5J6le1yMNu6u4znt5fQ0fnRcjoxkUNIjA5jwshoEmNCSYoOI84WQmCAmbCIYP685Sibd5Wx+pk95I6KYcGM4STFfHLv4Mny87y5/yy35yYx3BHxifv4OnNIJEOm30dQzjza9r1Ix+EtdBze7NloDfUMqQyP9gSx4TGeIZfh0ZjCojFZAjA6WnFVnsBVcdzzwa3yJLjaATBFxGFJmUCAYxSW+JFe79mQ/mW0X6T9yGu0H37Vk7gobRJBuQuxRPVuOS5/YBgGL2wvITIsiNk33Xj190VjUiJZv62WhgttRIZ1/5522Rd1BzZ6shMXv+XZGBTsGUIeHoPpUhtnDo/FFOFZX9cUEITR2Y6ruhSX89iltu4EdLR6zh1mx5I4Fkv8SCzxozFHJfRjzUVE/Eu3UU1tbS1FRUU8/fTTAOTn5/PYY49RV1eHzfZR5thNmzaxZMkSzGYzNpuNvLw8Nm/ezP333w/Ab3/7W2677TZaWlpoaWnpl8p0lh3GVVNK4Kjp1zxH9GpGDYskPTGCzbvKmDk+4Yp5sEbrBdoLt1yX3oaPqzl/kSfWHyIsOIBvLhnX5zm3i29NY9+xKjbsKOErd2Z4uZS+528Hz/GnN0+QnWZn0phYEmNCSbCHYg26ei96sDWA+VNTmTUhia17yti65wz7P6hm8tg4FkwfTrzto6ymnS43//3qMSLDrSy6Ne16VKlfmSNiCJ71AO7chbhqTmM0VXsS2jRV46oto/PUgcvmSGMyYQoeinGx0bPGrsmE2ZZMYMZMLPGXgtSQyAGrz2DlqjhO55nDBI685ZrniPaV0dFKR9GbtB/chNF2gYCUCQRNXOSTSbuul8KSOo6fPc99c0f3aCSO9L8xl0YbFZfVM2Vsz+8Vc0gkQ265j6Bx83FVl3raug/buwYn7jOHPXOpP8YUPPTS+raeNtAclUjgiKmeINUxut8+l4iI3Ai6jWycTidxcXFYLJ43YIvFQmxsLE6n87Kg1el0kpDw0beGDoeDiooKAIqLi3n77bdZt24dTz31VJ8K2pMkBA0na6nbu4H2fS8RMmoSEblzCU7N8Xoa+2V3jOQ/173Jqf3vkGHvoKPWSUddOR115bia6gAIzZhK1IylBMX0LPPktbjQ0s6TT++m0+Xm3/55Binxfe/Ni4kJJ396Gi9vP8nSOWNIuYaewWutV3/bebicP2w5Ru6YWP73V24mMKDnw3Y/rNvyYVEsnZvBi2+dYOPbJew5WsmsicO4547RxNtDeWHbCc5WX+BfvzyJ5KTBMXepR9ctJhzSrxxCbhhuXE31dDRU0NlQRUdDFZ3nqwgItzNkWAZDkkZjtnp3qRJ/05O2rqnyAtWHXqX9wEaCh48jInceISNze5xJt6cMt4vO89V01JbTUe/0/KzztHed52sAg+C0CUTNvIchCd1nA/b1NuFa2O1hvPyHfcTZQlg0e1Sv2hNfNtivmc0eRuiQAE5XNXPXzMvr0uO2bviVSZk8mfcb6DxfSUd9ZVd7ZwkJv9TWjcESMrj/diIivqTfJ9x0dHTwwx/+kB//+MddgW9f9CijZvosQmOz6Dj6FheLt9NybBfmofFd8+9M1tBPP/7vGG437vpzuKpLcNedw32+Avf5SqKbqvnfkW44AHWAyRqGKTIesyODoNFxBKRMwGwfxnmAbhKPXGt2xo5ON7/4y0HKq5t5eOl4Qiyma052Mnt8AlvfO81vXzzMt5aM69M5fD3rZNGpOp5Yf4i0hKHcPz+DhvrmHh/7SXW7c/IwbsmM49X3TvPm/nO8te8sU7Pi2X20kvEjokmPC/Ppv8eHvHPdgiA42fPPASbABTQDzY0uwDt/B3/NqNmjti5uAqHL/p2O4u20Hn2Li39dgynMTmDGLALH3Io5uHdfNhmG29O+VZXgqjuLcb7S87ixCtyuj3YMDMYcGY85ZgRBI24hIDETS/xImoCmfm7rfFlMTDhbdpZQcu489+f3rj3xZf5yzUYmRXLgWNVldfFO3QLAmgjxiRDvaevcQAvQ0gw0q60TEfGWboNWh8NBZWUlLpcLi8WCy+WiqqoKh8NxxX7l5eXk5OQAH/W8VldXU1ZWxgMPPABAY2MjhmFw4cIFHnvsMa9XyBweg3XyEoJuWkBn6V7a33+DtneepW33egJHTCMw8/arrrvnbmnAVXUSd+VJXFUluGpOdc1FISAI89B4LNEpmNMnU3IhmL/sbWLhnTczLjPV6/XoCcMweHrTUYrLGlh+11gyUrzTkxcWHMj8aSms33aSo6frvXZeX1HqbOTJFwqJs4XwzSU5WL00jG9oaBD33D6SuZOTKXj3FNsPlhNgMfPFO0b57KL1MniZQyKx3vRZgsbPp/P0QTqK3qB9z19p37fBM6907GzMcSM+8bXnbm3yBKhVl9q6qhJovzRtwxKAOSIec2QCAak3YY6I83wpNzQe05BwvZY/gcvl5sUdJSREh/ZqCKpcH2OSIzl4ooa6xlZsEb63PraIiHSv26DVbreTkZFBQUEBCxYsoKCggIyMjMuGBgPMmzeP9evXM2fOHBoaGnj99dd59tlnSUhIYNeuXV37Pfnkk7S0tPR79mBTQBCBI6cROHIarppTdLz/Bh3Hd9JR/BaWuJEEZt6OKcyOu+pk1wc340LtpYMtmKOTCRx1C5bYdCyx6ZgiYi/7sDbK7aa5+D1e3ltDztiUAfkg98L2Et4rqmTxrWlMzfTuB6W83CTe3HeW9dtO8IN/mIjZTz6oOmub+cVfDhEeHMjDd48ndEig158jKtzKfXNGM39KCm0dLuxD9SFJ+o/JbCFweC6Bw3NxNZTTUbSNjmNv03niXcz2ZM8XdbakruDUVXUSo7Hq0sEmzLYkAtMmY4lNwxyXjnmoA9MgynDtC7btO4uztoUHF2VjNvtHW+lPxqR8NK91Wpajm71FRMQX9Wh48I9+9CNWrFjBU089RUREBGvWrAFg+fLlPPTQQ2RnZ7NgwQIOHTrEnDlzAHjwwQcZNsw31qizRKdimfk1rDcvpeODt2kv2kbrm7/p2m4Ks3uC06w5WOLSMduTu12/z2I285kpKfxhyzGKT9eTkWr71P297a0D53jl3dPcOi6B+VO9vwh6YICFRbem8buCo+w+WukXvQe151v59z8dxGw28b/uGd/v2ZH1jb5cb5bIBCzTvoh10ufoOP4uHUVv0Lb96a7tppBILLHpmMfM9LR5MamYAvU6vRYdnW6e21pManw4N43qv+zw0ndJsZ55rcWnGxS0iogMUibDMLqZPOUbejTPq4cMw42rvBg62jDHpmEOGdqn83R0unjk1++SGBPKd+6Z0Kdz9GVezeGTNfzyr4fJTrPzjc9l99u6n27DYPXTe7jY1sm/LZ/S60RFvjQXqrGlnZ/8cT/nm9v53rIJJMf1PUGGr9XNmwZT3fx1npd32zoDd+UJ3C0NWGLTMIXaBmx472B6bfXGG/vO8uxrH/Dw0nFkDfev7LD+dM3+44VCyiqb+Ok/TQMGV938ta0TEemNG3IMmMlkJiBxLAGpE/ocsIKnN3LOpGEUnaqn1NnoxRJe3amKRn694X2SY8P5+oLMfgtYAcwmE3fPGkHN+Va27T/bb8/T3y62dfLEXw5R29jKNz+fc00Bq8hgYjKZsMSPJDBtEuYwu+ajelnDhTYK3jlFVrqdzOs82kZ6Z0xyJDXnW6lpuDjQRRERkT64IYNWb7ptQiIh1gA2vXu635+rpuEiv1x/+NJarDkMCer35M9kDreROdzGxndO0dza0f0BPqaj033pG/YL/NPCLEYNixzoIomIHzh+tsEzEqW9k6/kZ+oLAR/34bzWo2X1A1wSERHpCwWt1yjYGsDs3ET2fVBNeU3/LXPQ3NrBL9Yfor3TzbfuHk9kWP/Ox/y4Jbel09LaeV0Cc29yuw1++/L7HD1dz1fnj2H8CM03E5FrYxgGb+w7y0//5wDWIAs/+NJERiX7V4Z1f5QYHUp4SCDFpxsGuigiItIH/d9VdwPImziMrbvP8Op7p/la/tgeH9fpclPX2IqztpmLbS4utnV+9K/98selzkaqGy7y8N3jSYzu3Xqz1yo5LpxpWfG8tvcs7R1ubBFWbBFDPD/DhxAZHtSvw5T7wjAM1m0pZt8H1Xzh9pFKviEi16ytw8W6zcd49/0KxqXbWX7XWEL6IQO5eJ/JZGJ0chTFZfUMklQeIiLyMQpavSAiJIhbxyWw7cA5Fs5Iu+oSJ4ZhUFl/kcKTtRSW1HLsTAMdne5PPbc10EKw1ULokEAeuCuza4jT9bZ4ZjqVDRd55/0KLrZ1XrbNZILIMGtXEGuLsJKaGEmiLZjE6NDrPmyuo9PFui3H2FlYQf60VO6Y5BtZrEVk8KpquMjaFwo5W3WBhdOHk39Lqt8sBXajyEiOZG9xFdUNF4mNjRjo4oiISC8oaPWSuZOT2XbgHJt3l/HFO0Z1/b6tw0Xx6XoKSzyBanVDKwBxthBmjk9gZIoNV3snQ6wBhFgDGBJkIcQaQPAQz/99pQczKtzKv96bC3gSG9U1tlLX1EZdYyu1jW3UX3pcVtnEwRM1dOw+A4Atwkp2mp3sNDsZKVEEW/v3JVfX2MraFwspdTbx2VtSWTB9eL8+n4j4v8KSWn778vsYBnxzSQ456ZpqMBh9tF5rA5mj4ga4NCIi0hsKWr3EPnQIUzLj2HGonJvHxlFS3ujpTS1roNPlJijAzJiUKOZMSiY73U5sZDAwuNLufyjYGkBiTBiJMZ+cgt8wDAgIYPu+MgpL6thVVMnfDpZjMZsYmTSU7HRPEOvtXthjZfX8esMR2jvd/MvibG4aFeO1c4vIjcdtGLzyzik27CglMSaMf1mcRWxUyEAXS/oo3hbC0NAgik8rGZOIyGCjoNWL7pySwjuFFfyfP+wDPG+QsyYkkp1uY/SwSAIDLANcwuvDZDIRYwth5vhEZo5PpNPl5sTZ8129zeu3nWT9tpPYIqxkDbczboSdnHR7n3uVDcPgzf3n+NMbx4mODOaRxdkkXOd5vyLiX1paO/ldQREHT9QwZWwc//CZMVgDb4w23F+ZTCbGpERxVPNaRUQGHQWtXuSwh/KVOzNo73SRnWYn5lJv6o0uwOLpZR6TEsWSWSOoa2zlSGkdhSdr2X20ku2HyrFHDGHOpGHMGOfo1VI+H5+/6kmMkknIEL2sReRybreBy92zQKWyroW1LxZS3dDKF/JGkpebpCVt/MSY5Eh2FVVyrvoCVi9fUsMw9DoREekn+nTvZdNzlKW2O7aIIdw6LoFbxyXQ6XJTeLKWzbvLeO6N47y8s5TbJiSSl5vE0G6W9alrbOU/XijkVIVn/upnpw9XYhQRucLFtk7+9bfvcb65vcfHRIQG8d0vjGe0lrPxKx/Oay08UcPEkT2fm+w2DJqa2y/lcmijrqmV+ks/6xrbqG9qpeFCO2NSolh+11giQoL6qwoiIjckBa0yoAIsZiaMimHCqBhOnjvP5l1lbHr3NFt2lzEtK565k5Nx2K8c6vvx+avfWJzNBM1fFZGr2HusivPN7cydPIyw4O6XqLGYzdw8No6o8Ou3HrZcH7GRwUSFW3l2SzGv7OzB9TU866Q3XGij03V5T32AxXwpa76VUcOiCBkSwPZD5Tz6zB4eXJTNcIcyFIuIeIuCVvEZ6YlDeXBxNpV1LWzZc4adhU62H3IyfkQ0825OZmTSUADe2HeWP795gpjIYL73uexPDGpFRD70TmEFcVHB3D1rhIZv3uBMJhMLZwynsLSe9vbO7g8A4u0h2MIvrU9+6WdUhJXw4MArXk/Tsx38xwuF/PiP+7h3zmhuHZfQH9UQEbnhKGgVnxNnC+FLc0ezcPpw3tx/ljf3n+Mnz+4nLSECW8QQ9hZXMX5ENPfnj9X8VRH5VNUNFzl2poFFM4YrYBUAZuQksPj20f2SuT8lPpxVX5nEf750hGdeLabU2ciyvFEEBvjG8nUiIoOVPvGLz4oIDWLhjDQ+MyWFtw872bqnjJLyRs1fFZEee/dIBQBTs+IHuCRyowgLDuTbd4/nxR0lvPLuacoqL/DgoixsEUMGumgiIoOWglbxedZAC7fnJjFrQiL1TW3Yh+qNX0S6ZxgG7xypYExyJNFDlc1drh+z2cTnZqaTGh/O7145yupn9vD1BVlkpCixl4hIX2i8igwaZrNJAauI9NiJc+eparjItCxldZeBkTs6lpX/MJGw4ED+758OsnlXmdaIFRHpAwWtIiLil3YWVhAUaCZ3tLKLy8Bx2EP5wZcmMmFkNH/ZdoLfvPQ+rT1MAiUiIh4KWkVExO+0d7jYU1xJ7qhYgq2aCSMDK9gawD8vyuLzt6Wz91gV/7ZuHzXnLw50sUREBg0FrSIi4ncOnqjhYpuLW7KVgEl8g8lk4s4pKTy8dDxNLe0Unqwd6CKJiAwa+vpZRET8zs7CCmwRVsYo8Y34mMxUG7/4xnQ0s1VEpOfU0yoiIn6l4UIbR0prmZoZr6WxxCeZTCa9NkVEeqFHQWtpaSlLly5l7ty5LF26lFOnTl2xj8vlYvXq1eTl5XHHHXewfv36rm1r165l/vz53HXXXSxevJgdO3Z4rQIiIiIf9977lRgGTNParCIiIn6hR8ODV61axbJly1iwYAEvvfQSK1euZN26dZfts3HjRsrKyti6dSsNDQ0sXLiQqVOnkpSURE5ODl/96lcJDg6muLiYe++9l7fffpshQ7R8iYiIeI9hGOw84iQtIQKHPXSgiyMiIiJe0G1Pa21tLUVFReTn5wOQn59PUVERdXV1l+23adMmlixZgtlsxmazkZeXx+bNmwGYMWMGwcGehd1Hjx6NYRg0NDR4uSoiInKjK6u8wLnqZm5RL6uIiIjf6DZodTqdxMXFYbFYALBYLMTGxuJ0Oq/YLyEhoeuxw+GgoqLiivNt2LCB5ORk4uP1gUJERLxr5xEnARYTkzLiBrooIiIi4iXXNXvw7t27+eUvf8nvf//7Xh9rt4f1Q4l8Q0xM+EAXoV/4a71AdZP+o7au7zpdbvYUVzE5M57hybZ+fa6/56/3jb/WC/y7biIi/qbboNXhcFBZWYnL5cJiseByuaiqqsLhcFyxX3l5OTk5OcCVPa8HDhzgu9/9Lk899RRpaWm9Lmht7QXcbv9LEB8TE051ddNAF8Pr/LVeoLr5CrPZ5JcBntq6vjt4vIbzF9qZODLmur6OB9N90xv+Wi8YXHXz17ZORKQ3uh0ebLfbycjIoKCgAICCggIyMjKw2S7/FnvevHmsX78et9tNXV0dr7/+OnPnzgXg8OHDfPvb3+ZXv/oVmZmZ/VANERG50e084iQ8JJCstOvbyyoiIiL9q0fDg3/0ox+xYsUKnnrqKSIiIlizZg0Ay5cv56GHHiI7O5sFCxZw6NAh5syZA8CDDz7IsGHDAFi9ejWtra2sXLmy65w//elPGT16tLfrIyIig5iztpnwkCDCggN7ddyFix0cOlHDbRMSCbBoCXIRERF/0qOgNT09/bJ1Vz/0X//1X13/t1gsrF69+hOPf/755/tYPBERuRYut5vmi5093j8sOBCz2dSPJbq6ssomHl+3l6GhVh5eOq5XS9bsPlpJp8vglixH9zuLiIjIoHJdEzGJiMj10dLawd8OlvP6vrPUN7X1+Lh4WwgPLs4mMfr6rnF6sa2TX284QmhwIB2dLn78x/18c0kO6QlDe3T8O0cqSIoJJTlOc/9ERET8jYJWERE/UtNwkdf2nmX74XLa2l1kpETxmZuTe9R72tnpZtOuMh7/7718bX4GE8fEXocSg2EY/GHrMaoaLvLIFyYQGW7lF38+xM+eO8A/L8wiJz36U4931jZTUt7I3bNGYDINTC+xiIiI9B8FrSIifqCkvJEtu8vYe6wKs8nE5IxY5kxKJiW+d8t6TMqI46kXC3lqwxHm3ZzM52amYTH37xzRtwudvPd+JQunD2d0chQA378vlyf+cohf/bWQL39mDNNzrj7s950jFZhMMCVTa7OKiIj4IwWtIiKDlNswOHS8hi27y/jg7HmCrRbmTk4mLzcJW8SQPp0zKtzK9754E8+9fpzNu8o4XdHEPy7IJCIkyMul9zhX08yzr33AmORI8qeldv1+aGgQjyybwNoXC/n9pqOcb27jzikpV/Skut0G7xypIGu4ncgwa7+UUURERAaWglYRkUGmo9PF24UVbN1zhsq6FuwRVu65fSQzchwEW6+9WQ+wmLlv7miGOyJYt+UYjz6zhwcXZTPcEeGF0n+kvcPFb146gjXQwgOfzbxiCHOwNYBvLRnH/3vlKM//rYSGC+18IW8k5o8FrsVl9dQ3tbF09givlk1ERER8h4JWEZFBZvPuM7y4vYThjnC+viCT3NEx/TKEd3qOg6TYUNa+cIQf/3E/980ZxYxxCV47/3NvHOdcdTMP3z3uqr2kARYzy+8ay9DQILbuOUNjczv3548lMMBT352FFQRbAxg/4tPnvYqIiMjgpaBVRGSQuW18AjlpdpLjwvo98VBqfAQrvzyR/3z5fZ5+tZgSZyPL8kZ1BY19tftoJX87WM5npiSTlWb/1H3NJhP33D6SyDArf9l2gqaWdv5lcQ4mE+z7oIopY+MJCrRcU3lERETEdyloFREZZMJDggjvpzmmV3u+h+8ez4s7Snjl3dOUVV7gwUVZfZ43W1XfwjOvFpOeGMGiGWk9Pm7ezckMDQ3i95uOsuZ/9jM5I5b2Dje3ZMf3qRwiIiIyOPRvSkgREfELZrOJz81M58FFWZTXNvPoM3s48EE1bsPo1Xk6Ot38+qX3sZhN/ONnMwmw9O5taGpWPN/8fA5V9Rd5/m8lxEYFMyKxZ2u5ioiIyOCkoFVERHosd3QsP/zSREKDA3nyhUJ++LtdbD9UTkenq0fH//Wtk5yuaOIrd2YQPTS4T2XISrPzyLIJ2COszJk0TGuzioiI+DkNDxYRkV5JiA5l9Vcns+doFVt2l/HMq8W88LeTzM5NYtaExKsOXT5wvJrX9p4hLzeJm0bFXFMZhjsi+Ok/TVPAKiIicgNQ0CoiIr0WYDEzNSueKZlxFJ+uZ/PuM2zYUcqmd09zS7aDOZOGEWcL6dq/qr6F379ylJS4cJbM8s7yNApYRUREbgwKWkVEpM9MJhMZqTYyUm2cq2lm6+4ydhwu560D5xg/Mpq5k5NJT4zg5388iMtt8PWFmdeceVhERERuLApaRUTEKxKjQ/nKnRksvjWNN/afY9v+sxw4XkNUuJX6pjYe+OxY4qJCuj+RiIiIyMcoaBUREa8aGmZl8a1pzJ+Sws4jTt7cf44Z4xOZMlZL04iIiEjvKWgVEZF+YQ2yMPumJGbflERMTDjV1U0DXSQREREZhDSxSERERERERHyWglYRERERERHxWQpaRURERERExGcpaBURERERERGfpaBVREREREREfJaCVhEREREREfFZPQpaS0tLWbp0KXPnzmXp0qWcOnXqin1cLherV68mLy+PO+64g/Xr1/dom4iIiIiIiMjV9ChoXbVqFcuWLWPLli0sW7aMlStXXrHPxo0bKSsrY+vWrfz5z3/mySef5OzZs91uExEREREREbmagO52qK2tpaioiKeffhqA/Px8HnvsMerq6rDZbF37bdq0iSVLlmA2m7HZbOTl5bF582buv//+T93WU2azqQ/VGxz8tW7+Wi9Q3XzBYClnb/lrvUB1G4z8tV4weOo2WMopItKfug1anU4ncXFxWCwWACwWC7GxsTidzsuCVqfTSUJCQtdjh8NBRUVFt9t6KioqtFf7DyZ2e9hAF6Ff+Gu9QHWT/qO2bnDy17r5a73Av+smIuJvlIhJREREREREfFa3QavD4aCyshKXywV4kipVVVXhcDiu2K+8vLzrsdPpJD4+vtttIiIiIiIiIlfTbdBqt9vJyMigoKAAgIKCAjIyMi4bGgwwb9481q9fj9vtpq6ujtdff525c+d2u01ERERERETkakyGYRjd7XTy5ElWrFhBY2MjERERrFmzhrS0NJYvX85DDz1EdnY2LpeLRx99lJ07dwKwfPlyli5dCvCp20RERERERESupkdBq4iIiIiIiMhAUCImERERERER8VkKWkVERERERMRnKWgVERERERERn6WgVURERERERHxWwEAX4NOUlpayYsUKGhoaiIyMZM2aNaSmpg50sbxi9uzZBAUFYbVaAfjOd77DjBkzBrhUfbNmzRq2bNnCuXPn2LhxI6NGjQIG//W7Wr384drV19fzyCOPUFZWRlBQECkpKTz66KPYbDYOHjzIypUraWtrIzExkZ/97GfY7faBLrJfG+z3yqfxh/vlQ/7a1oH/tndq60RE/IThw+677z5jw4YNhmEYxoYNG4z77rtvgEvkPbNmzTKOHTs20MXwij179hjl5eVX1GmwX7+r1csfrl19fb3x3nvvdT3+yU9+Ynz/+983XC6XkZeXZ+zZs8cwDMNYu3atsWLFioEq5g1jsN8rn8Yf7pcP+WtbZxj+296prRMR8Q8+Ozy4traWoqIi8vPzAcjPz6eoqIi6uroBLpn8vYkTJ+JwOC77nT9cv0+ql7+IjIzk5ptv7no8fvx4ysvLOXLkCFarlYkTJwJwzz33sHnz5oEq5g3BH+6VG4W/tnXgv+2d2joREf/gs8ODnU4ncXFxWCwWACwWC7GxsTidTmw22wCXzju+853vYBgGubm5PPzww0RERAx0kbzG36+fP107t9vNc889x+zZs3E6nSQkJHRts9lsuN3urmGP4n3+fq+Af90vf0/Xb/BQWyciMnj5bE+rv3v22Wd5+eWXef755zEMg0cffXSgiyQ95G/X7rHHHiMkJIR77713oIsifsjf7pcbjT9dP7V1IiKDl88GrQ6Hg8rKSlwuFwAul4uqqiq/Gb70YT2CgoJYtmwZ+/fvH+ASeZc/Xz9/unZr1qzh9OnTPPHEE5jNZhwOB+Xl5V3b6+rqMJvN6nnoR/58r4B/3S+fRNdvcFBbJyIyuPls0Gq328nIyKCgoACAgoICMjIy/GK4VUtLC01NTQAYhsGmTZvIyMgY4FJ5l79eP3+6dj//+c85cuQIa9euJSgoCICsrCxaW1vZu3cvAH/605+YN2/eQBbT7/nrvQL+db9cja6f71NbJyIy+JkMwzAGuhBXc/LkSVasWEFjYyMRERGsWbOGtLS0gS7WNTtz5gzf+MY3cLlcuN1u0tPT+cEPfkBsbOxAF61PHn/8cbZu3UpNTQ1RUVFERkbyyiuvDPrr90n1+s1vfuMX1+748ePk5+eTmprKkCFDAEhKSmLt2rXs37+fVatWXbYMRHR09ACX2L8N9nvlatTWDR7+2t6prRMR8Q8+HbSKiIiIiIjIjc1nhweLiIiIiIiIKGgVERERERERn6WgVURERERERHyWglYRERERERHxWQpaRURERERExGcpaBURERERERGfpaBVREREREREfJaCVhEREREREfFZ/x8zSe+M8Mfr7wAAAABJRU5ErkJggg==\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get all temporarily \"unusual\" deaths.\n", - "unusual = devi.loc[(devi[\"residuals\"] > 1.5), [\"disease\", \"n\"]].sort_values(\"disease\")\n", - "# Helper dataset for easy indexing / value retrieval.\n", - "plot_data = counts[[\"cod\", \"hod\", \"prop\", \"prop_all\"]].set_index(\"cod\")\n", - "# Divide the plots in two big categories.\n", - "for header, cond, ylim in [\n", - " (\"> 350 Deaths / Year\", (unusual[\"n\"] > 350), 0.125),\n", - " (\"< 350 Deaths / Year\", (unusual[\"n\"] <= 350), 0.3),\n", - "]:\n", - " nrows = math.ceil(len(unusual[cond]) / 3)\n", - " fig = plt.figure(figsize=(16, 12),)\n", - " for i, (cod, (disease, _)) in enumerate(unusual[cond].iterrows(), 1):\n", - " ax = fig.add_subplot(nrows, 3, i)\n", - " ax.set_title(\"\\n\".join(textwrap.wrap(disease, 40)))\n", - " ax.set_xlim(0, 24)\n", - " ax.set_ylim(0, ylim)\n", - " ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop\"])\n", - " ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop_all\"])\n", - " # Show only lower and left axes.\n", - " if i not in (3 * nrows - 2, 3 * nrows - 1, 3 * nrows):\n", - " plt.setp(ax.get_xticklabels(), visible=False)\n", - " if i % 3 != 1:\n", - " plt.setp(ax.get_yticklabels(), visible=False)\n", - " fig.suptitle(header, fontsize=20)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/6_estudio_de_caso.ipynb b/6_estudio_de_caso.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d052eaa26e9a2cebf41be9425bfaf333b818e4d5 --- /dev/null +++ b/6_estudio_de_caso.ipynb @@ -0,0 +1,1046 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estudio de caso: muertes inusuales en México\n", + "\n", + "> El siguiente estudio de caso ilustra cómo los datos *tidy* y las *tidy tools* facilitan el análisis de datos al facilitar las transiciones entre la manipulación, la visualización y el modelado. No verás ningún código que exista únicamente para obtener el output de una función en el formato correcto para que sea el input para otra.\n", + "\n", + "> El estudio de caso utiliza datos de mortalidad a nivel individual de México. El objetivo es encontrar causas de muerte con patrones temporales inusuales dentro de un día.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"Housekeeping\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import math\n", + "import textwrap" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.linear_model import HuberRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys([None])\n", + " yod mod dod hod cod\n", + "0 0 0 0 NaN E14\n", + "1 0 0 0 NaN E46\n", + "2 0 0 0 NaN I21\n", + "3 0 0 0 NaN K70\n", + "4 0 0 0 NaN P21\n" + ] + } + ], + "source": [ + "\n", + "import pyreadr\n", + "\n", + "deaths = pyreadr.read_r('data/deaths.rds')\n", + "\n", + "# done! let's see what we got\n", + "print(deaths.keys()) # let's check what objects we got: there is only None\n", + "deaths = deaths[None] # extract the pandas data frame for the only object available\n", + "print(deaths.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n", + " 20, 21, 22, 23, nan], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "deaths = deaths[(deaths[\"yod\"] == 2008) & (deaths[\"mod\"] != 0) & (deaths[\"dod\"] != 0)]\n", + "deaths = deaths[~(deaths[\"hod\"] < 0)]\n", + "deaths = deaths.reset_index(drop=True)\n", + "\n", + "deaths.hod.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(527429, 5)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deaths.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yodmoddodhodcod
02008111B20
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" + ], + "text/plain": [ + " yod mod dod hod cod\n", + "0 2008 1 1 1 B20\n", + "1 2008 1 1 1 B22\n", + "2 2008 1 1 1 C18\n", + "3 2008 1 1 1 C34\n", + "4 2008 1 1 1 C50" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deaths.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# The file contains 7 duplicates that are discarded.\n", + "codes = pd.read_csv(\"data/icd-main.csv\")\n", + "codes = codes[(codes[\"code\"] != codes[\"code\"].shift())].set_index(\"code\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1851, 1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "codes.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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disease
code
A00Cholera
A01Typhoid and paratyphoid fevers
A02Other salmonella infections
A03Shigellosis
A04Other bacterial intestinal infections
\n", + "
" + ], + "text/plain": [ + " disease\n", + "code \n", + "A00 Cholera\n", + "A01 Typhoid and paratyphoid fevers\n", + "A02 Other salmonella infections\n", + "A03 Shigellosis\n", + "A04 Other bacterial intestinal infections" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "codes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Counts\n", + "\n", + "Cuenta el número de muertos por `\"hod\"` (=\"hour of the day\") y `\"cod\"` (=\"cause of death\"), y también hace un `join` con las etiquetas que son más descriptivas para las varias causas." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hodcodfreqdisease
01A013Typhoid and paratyphoid fevers
11A023Other salmonella infections
21A047Other bacterial intestinal infections
31A051Other bacterial foodborne intoxications, not e...
41A062Amebiasis
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" + ], + "text/plain": [ + " hod cod freq disease\n", + "0 1 A01 3 Typhoid and paratyphoid fevers\n", + "1 1 A02 3 Other salmonella infections\n", + "2 1 A04 7 Other bacterial intestinal infections\n", + "3 1 A05 1 Other bacterial foodborne intoxications, not e...\n", + "4 1 A06 2 Amebiasis" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counts = (\n", + " pd.DataFrame(deaths.groupby([\"hod\", \"cod\"]).size(), columns=[\"freq\"])\n", + " .reset_index()\n", + " .join(codes, on=\"cod\")\n", + ")\n", + "# This is to ensure that no duplicates are created\n", + "# because of duplicate entries in the codes DataFrame.\n", + "assert counts[\"cod\"].value_counts().max() <= 24\n", + "\n", + "# Keep only causes where a death happened in every hour.\n", + "counts = counts[counts[\"cod\"].isin(list((counts[\"cod\"].value_counts() == 24).index))]\n", + "\n", + "counts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Añade una columna `\"prop\"` (=\"proportion\") indicando la frecuencia relativa de una determinada causa de muerte por horas." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hodcodfreqdiseaseprop
01A013Typhoid and paratyphoid fevers0.062500
11A023Other salmonella infections0.046875
21A047Other bacterial intestinal infections0.048951
31A051Other bacterial foodborne intoxications, not e...0.050000
41A062Amebiasis0.022989
\n", + "
" + ], + "text/plain": [ + " hod cod freq disease prop\n", + "0 1 A01 3 Typhoid and paratyphoid fevers 0.062500\n", + "1 1 A02 3 Other salmonella infections 0.046875\n", + "2 1 A04 7 Other bacterial intestinal infections 0.048951\n", + "3 1 A05 1 Other bacterial foodborne intoxications, not e... 0.050000\n", + "4 1 A06 2 Amebiasis 0.022989" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counts = counts.set_index(\"cod\")\n", + "counts[\"prop\"] = counts[\"freq\"] / deaths.groupby([\"cod\"]).size().reindex(counts.index)\n", + "counts = counts.reset_index()\n", + "# Re-order the columns as in the paper.\n", + "counts = counts[[\"hod\", \"cod\", \"freq\", \"disease\", \"prop\"]]\n", + "\n", + "counts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Añade las columnas `\"freq_all\"` y `\"prop_all\"`, que muestran el número absoluto de muertes para una hora determinada del día (sin tener en cuenta la causa de la muerte), y la proporción de muertes de una determinada hora del día con respecto al día completo." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hodcodfreqdiseasepropfreq_allprop_all
01A013Typhoid and paratyphoid fevers0.062500200380.037992
11A023Other salmonella infections0.046875200380.037992
21A047Other bacterial intestinal infections0.048951200380.037992
31A051Other bacterial foodborne intoxications, not e...0.050000200380.037992
41A062Amebiasis0.022989200380.037992
\n", + "
" + ], + "text/plain": [ + " hod cod freq disease \\\n", + "0 1 A01 3 Typhoid and paratyphoid fevers \n", + "1 1 A02 3 Other salmonella infections \n", + "2 1 A04 7 Other bacterial intestinal infections \n", + "3 1 A05 1 Other bacterial foodborne intoxications, not e... \n", + "4 1 A06 2 Amebiasis \n", + "\n", + " prop freq_all prop_all \n", + "0 0.062500 20038 0.037992 \n", + "1 0.046875 20038 0.037992 \n", + "2 0.048951 20038 0.037992 \n", + "3 0.050000 20038 0.037992 \n", + "4 0.022989 20038 0.037992 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counts = counts.set_index(\"hod\")\n", + "counts[\"freq_all\"] = deaths.groupby(\"hod\").size()\n", + "counts[\"prop_all\"] = counts[\"freq_all\"] / deaths.shape[0]\n", + "counts = counts.reset_index()\n", + "\n", + "counts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distancia entre patrones temporales\n", + "\n", + "> A continuación calculamos una distancia entre el patrón temporal de cada causa de muerte y el patrón temporal general. Hay muchas maneras de medir esta distancia, pero encontré que una simple desviación cuadrática media es reveladora. También registramos el tamaño de la muestra, el número total de muertes por esa causa. Para asegurarnos de que las enfermedades que consideramos sean suficientemente representativas, solo trabajaremos con enfermedades con más de 50 muertes totales (∼2/hora)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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diseasendist
A02Other salmonella infections640.000692
A04Other bacterial intestinal infections1430.000191
A06Amebiasis870.000360
A09Diarrhea and gastroenteritis of infectious origin31140.000027
A16Respiratory tuberculosis, not confirmed bacter...17090.000027
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" + ], + "text/plain": [ + " disease n dist\n", + "A02 Other salmonella infections 64 0.000692\n", + "A04 Other bacterial intestinal infections 143 0.000191\n", + "A06 Amebiasis 87 0.000360\n", + "A09 Diarrhea and gastroenteritis of infectious origin 3114 0.000027\n", + "A16 Respiratory tuberculosis, not confirmed bacter... 1709 0.000027" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "devi = (\n", + " codes.join(deaths.groupby(\"cod\").count()[\"yod\"].to_frame(), how=\"inner\")\n", + " .join(\n", + " counts.groupby(\"cod\")\n", + " .apply(lambda x: ((x[\"prop\"] - x[\"prop_all\"]) ** 2).mean())\n", + " .to_frame(),\n", + " how=\"inner\",\n", + " )\n", + " .rename(columns={\"yod\": \"n\", 0: \"dist\"})\n", + ")\n", + "devi = devi[(devi[\"n\"] > 50)]\n", + "\n", + "devi.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Grafica `\"dist\"` vs. `\"n\"`. No se aprecia mucho en este gráfico." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, ax = plt.subplots(figsize=(8, 8))\n", + "ax.set_xlim(0, 50000)\n", + "ax.set_ylim(0, 0.006)\n", + "sns.regplot(x=\"n\", y=\"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La relación se vuelve más obvia si uno grafica los mimos puntos en una escala `\"log\"`-`\"log\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, ax = plt.subplots(figsize=(4, 4))\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlim(30, 150000)\n", + "ax.set_ylim(0.00001, 0.1)\n", + "sns.regplot(x=\"n\", y=\"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Estamos interesados en puntos que tienen valores de `y` altos, en relación con sus vecinos de `x`. \n", + "> Controlando por el número de muertes, estos puntos representan las enfermedades que más se apartan del patrón general. \n", + "> Para encontrar estos puntos inusuales, ajustamos un modelo lineal robusto y graficamos los residuos, Figura 3. \n", + "> El gráfico muestra una región vacía alrededor de un residuo de 1,5. \n", + "> Entonces, de manera un tanto arbitraria, seleccionaremos aquellas enfermedades con un residuo mayor a 1.5.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Note that the HuberRegressor is not the exact\n", + "# same method as in the paper but close.\n", + "X = np.log(devi[\"n\"]).values[:, np.newaxis]\n", + "y = np.log(devi[\"dist\"]).values\n", + "rlm = HuberRegressor()\n", + "rlm.fit(X, y)\n", + "devi[\"residuals\"] = y - rlm.predict(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Graficamos el umbral de muertes \"inusuales\", establecido arbitrariamente en 1.5" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, ax = plt.subplots(figsize=(8, 8))\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xlim(50, 200000)\n", + "ax.set_ylim(-1, 3)\n", + "sns.regplot(x=\"n\", y = \"residuals\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})\n", + "ax.hlines(1.5, 0, 200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Finalmente, graficamos el curso temporal de cada causa inusual, Figura 4. \n", + "> Dividimos las enfermedades en dos gráficos debido a las diferencias en la variabilidad. \n", + "> La gráfica superior muestra enfermedades con más de 350 muertes y la inferior con menos de 350. \n", + "> Las causas de muerte se dividen en tres grupos principales: las relacionadas con asesinato, con ahogamiento y con transporte. \n", + "> Los asesinatos son más comunes por la noche, los ahogamientos por la tarde y las muertes relacionadas con el transporte durante los viajes diarios. \n", + "> La línea naranja en el fondo muestra el curso temporal de todas las enfermedades." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get all temporarily \"unusual\" deaths.\n", + "unusual = devi.loc[(devi[\"residuals\"] > 1.5), [\"disease\", \"n\"]].sort_values(\"disease\")\n", + "# Helper dataset for easy indexing / value retrieval.\n", + "plot_data = counts[[\"cod\", \"hod\", \"prop\", \"prop_all\"]].set_index(\"cod\")\n", + "# Divide the plots in two big categories.\n", + "for header, cond, ylim in [\n", + " (\"> 350 Deaths / Year\", (unusual[\"n\"] > 350), 0.125),\n", + " (\"< 350 Deaths / Year\", (unusual[\"n\"] <= 350), 0.3),\n", + "]:\n", + " nrows = math.ceil(len(unusual[cond]) / 3)\n", + " fig = plt.figure(figsize=(16, 12),)\n", + " for i, (cod, (disease, _)) in enumerate(unusual[cond].iterrows(), 1):\n", + " ax = fig.add_subplot(nrows, 3, i)\n", + " ax.set_title(\"\\n\".join(textwrap.wrap(disease, 40)))\n", + " ax.set_xlim(0, 24)\n", + " ax.set_ylim(0, ylim)\n", + " ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop\"])\n", + " ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop_all\"])\n", + " # Show only lower and left axes.\n", + " if i not in (3 * nrows - 2, 3 * nrows - 1, 3 * nrows):\n", + " plt.setp(ax.get_xticklabels(), visible=False)\n", + " if i % 3 != 1:\n", + " plt.setp(ax.get_yticklabels(), visible=False)\n", + " fig.suptitle(header, fontsize=20)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.14 ('tidy')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.14" + }, + "vscode": { + "interpreter": { + "hash": "af7127df06252d69ce1b5fcf0f303ad2193973c1f3767a585df649c7fbdfe99b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md index a336f9f5c33437aba3a4537cdf40ce10c6f686e5..d6835fca7dffa6e304524c1b37c2d9ed2090ae8a 100644 --- a/README.md +++ b/README.md @@ -1,70 +1,44 @@ # Tidy Data -The purpose of this repository is to illustrate how the data cleaning process described - in the paper "[Tidy Data](tidy-data.pdf)" by Hadley Wickham, a member of the - [RStudio](https://rstudio.com/) team, can be done in - [Python](https://www.python.org/). +El propósito de este repositorio es ilustrar cómo se puede realizar en Python (con pandas) el proceso de limpieza de datos descrito en el artículo "Tidy Data" de Hadley Wickham. Este repositorio fue traducido al español del siguiente repositorio: https://github.com/webartifex/tidy-data -The paper was published in 2014 in the [Journal of Statistical Software](https://www.jstatsoft.org/article/view/v059i10). -The author offers it for free [here](http://vita.had.co.nz/papers/tidy-data.html). -Furthermore, the original [R](https://www.r-project.org/) code is available [here](https://github.com/hadley/tidy-data). +## Resumen -After installing the dependencies for this project (cf., the [installation notes](https://github.com/webartifex/tidy-data#installation) - below), it is recommended to first read the paper to get the big picture and - then work through the six Jupyter notebooks listed below. +### Definición -## Summary +**Tidy data** se definen como datos que vienen en forma de tabla que se adhieren a los siguientes requisitos: +1. cada variable es una columna, +2. cada observación una fila, y +3. cada tipo de unidad de observación forma una tabla. - -### Definition - -**Tidy** data is defined as data that comes in a table form adhering to the - following requirements: -1. each variable is a column, -2. each observation a row, and -3. each type of observational unit forms a table. - -This is equivalent to [Codd's 3rd normal form](https://en.wikipedia.org/wiki/Third_normal_form), - a concept from the theory on relational databases. -A dataset that does *not* satisfy these properties is called **messy**. +Esto es equivalente a [la tercera forma normal de Codd] (https://en.wikipedia.org/wiki/Third_normal_form), un concepto de la teoría sobre bases de datos relacionales. Un conjunto de datos que *no* satisface estas propiedades se llama **messy data**. ### Tidying Data -The five most common problems with messy data are: - -- column headers are values, not variable names - (cf., [notebook 1](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/1_column_headers_are_values.ipynb)) -- multiple variables are stored in one column - (cf., [notebook 2](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/2_multiple_variables_stored_in_one_column.ipynb)) -- variables are stored in both rows and columns - (cf., [notebook 3](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/3_variables_are_stored_in_both_rows_and_columns.ipynb)) -- multiple types of observational units are stored in the same table - (cf., [notebook 4](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/4_multiple_types_in_one_table.ipynb)) -- a single observational unit is stored in multiple tables - (cf., [notebook 5](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/5_one_type_in_multiple_tables.ipynb)) - +Los cinco problemas más comunes con los datos desordenados son: -### Case Study +1. Los encabezados de las columnas son valores, no nombres de variables. +2. Múltiples variables se almacenan en una columna +3. Las variables se almacenan tanto en filas como en columnas +4. Múltiples tipos de unidades de observación se almacenan en la misma tabla +5. Una sola unidad de observación se almacena en varias tablas -A case study (cf., [notebook 6](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/6_case_study.ipynb)) - shows the advantages of tidy data as a standardized input to statistical functions. +### Estudio de caso -## Installation +Un estudio de caso muestra las ventajas de **tidy data** como un input estandarizado para funciones estadísticas y de visualización. -Get a local copy of this repository with [git](https://git-scm.com/). +## Instalación -`git clone https://github.com/webartifex/tidy-data.git` +Obten una copia local de este repositorio con [git](https://git-scm.com/). -If you are not familiar with [git](https://git-scm.com/), simply download the latest - version of the files in a zip archive [here](https://github.com/webartifex/tidy-data/archive/master.zip). +`https://github.com/vmlandae/tidy-data-mds-espanol.git` -This project uses [poetry](https://python-poetry.org/docs/) to manage its dependencies. -Install all third-party packages into a [virtual environment](https://docs.python.org/3/library/venv.html). +En esta versión actualizada, este proyecto usa [conda](https://docs.conda.io/en/latest/) para manejar sus dependencias. +El archivo `environment.yml` tiene las dependencias -`poetry install` +```python -Alternatively, use the [Anaconda Distribution](https://www.anaconda.com/products/individual) - that *should* also suffice to run the provided notebooks. +``` diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..e15e7f9883ee41fd75b1ab8684788ac2bff781b6 --- /dev/null +++ b/environment.yml @@ -0,0 +1,164 @@ +name: tidy +channels: + - defaults + - conda-forge +dependencies: + - _r-mutex=1.0.0=anacondar_1 + - appnope=0.1.2=py38hecd8cb5_1001 + - asttokens=2.0.5=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bottleneck=1.3.5=py38h67323c0_0 + - brotli=1.0.9=hca72f7f_7 + - brotli-bin=1.0.9=hca72f7f_7 + - bwidget=1.9.11=1 + - bzip2=1.0.8=h1de35cc_0 + - c-ares=1.18.1=hca72f7f_0 + - ca-certificates=2022.9.24=h033912b_0 + - cairo=1.16.0=h691a603_2 + - cctools_osx-64=949.0.1=hc7db93f_25 + - certifi=2022.9.24=pyhd8ed1ab_0 + - cffi=1.15.1=py38hc55c11b_0 + - clang=14.0.6=hecd8cb5_0 + - clang-14=14.0.6=default_h32c6d10_0 + - clang_osx-64=14.0.6=hb1e4b1b_0 + - clangxx=14.0.6=default_h32c6d10_0 + - clangxx_osx-64=14.0.6=hd8b9576_0 + - compiler-rt=14.0.6=hda8b6b8_0 + - compiler-rt_osx-64=14.0.6=h8d5cb93_0 + - curl=7.85.0=hca72f7f_0 + - cycler=0.11.0=pyhd3eb1b0_0 + - debugpy=1.5.1=py38he9d5cce_0 + - decorator=5.1.1=pyhd3eb1b0_0 + - entrypoints=0.4=py38hecd8cb5_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.4.9=he9d5cce_0 + - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0 + - font-ttf-inconsolata=2.001=hcb22688_0 + - font-ttf-source-code-pro=2.030=hd3eb1b0_0 + - font-ttf-ubuntu=0.83=h8b1ccd4_0 + - fontconfig=2.14.1=h5bb23bf_0 + - fonts-anaconda=1=h8fa9717_0 + - fonts-conda-ecosystem=1=hd3eb1b0_0 + - fonttools=4.25.0=pyhd3eb1b0_0 + - freetype=2.12.1=hd8bbffd_0 + - fribidi=1.0.10=haf1e3a3_0 + - gettext=0.21.1=h8a4c099_0 + - gfortran_impl_osx-64=11.3.0=h29cdc64_28 + - gfortran_osx-64=11.3.0=h96634ac_0 + - giflib=5.2.1=haf1e3a3_0 + - glib=2.74.1=hbc0c0cd_0 + - glib-tools=2.74.1=hbc0c0cd_0 + - gmp=6.2.1=he9d5cce_3 + - graphite2=1.3.14=he9d5cce_1 + - gsl=2.7.1=hdbe807d_1 + - harfbuzz=5.3.0=h08f8713_0 + - icu=70.1=h96cf925_0 + - ipykernel=6.15.2=py38hecd8cb5_0 + - ipython=8.6.0=py38hecd8cb5_0 + - isl=0.22.1=he9d5cce_3 + - jedi=0.18.1=py38hecd8cb5_1 + - jinja2=3.1.2=py38hecd8cb5_0 + - joblib=1.1.1=py38hecd8cb5_0 + - jpeg=9e=hca72f7f_0 + - jupyter_client=7.4.7=py38hecd8cb5_0 + - jupyter_core=4.11.2=py38hecd8cb5_0 + - kiwisolver=1.4.2=py38he9d5cce_0 + - krb5=1.19.3=hb49756b_0 + - lcms2=2.12=hf1fd2bf_0 + - ld64_osx-64=530=h70f3046_25 + - ldid=2.1.2=h2d21305_2 + - lerc=3.0=he9d5cce_0 + - libblas=3.9.0=16_osx64_openblas + - libbrotlicommon=1.0.9=hca72f7f_7 + - libbrotlidec=1.0.9=hca72f7f_7 + - libbrotlienc=1.0.9=hca72f7f_7 + - libcblas=3.9.0=16_osx64_openblas + - libclang-cpp14=14.0.6=default_h32c6d10_0 + - libcurl=7.85.0=h6dfd666_0 + - libcxx=14.0.6=h9765a3e_0 + - libdeflate=1.8=h9ed2024_5 + - libedit=3.1.20210910=hca72f7f_0 + - libev=4.33=h9ed2024_1 + - libffi=3.4.2=hecd8cb5_5 + - libgfortran=5.0.0=11_3_0_hecd8cb5_28 + - libgfortran-devel_osx-64=11.3.0=h9dfd629_28 + - libgfortran5=11.3.0=h9dfd629_28 + - libglib=2.74.1=h3ba3332_0 + - libiconv=1.17=hac89ed1_0 + - liblapack=3.9.0=16_osx64_openblas + - libllvm14=14.0.6=h5b596cc_1 + - libnghttp2=1.46.0=ha29bfda_0 + - libopenblas=0.3.21=h54e7dc3_0 + - libpng=1.6.39=ha978bb4_0 + - libsodium=1.0.18=h1de35cc_0 + - libsqlite=3.40.0=ha978bb4_0 + - libssh2=1.10.0=h0a4fc7d_0 + - libtiff=4.4.0=h2cd0358_2 + - libwebp=1.2.4=h56c3ce4_0 + - libwebp-base=1.2.4=hca72f7f_0 + - libxml2=2.10.3=hb9e07b5_0 + - libzlib=1.2.13=hfd90126_4 + - llvm-openmp=14.0.6=h0dcd299_0 + - llvm-tools=14.0.6=h5b596cc_1 + - lz4-c=1.9.3=h23ab428_1 + - make=4.2.1=h3efe00b_1 + - markupsafe=2.1.1=py38hca72f7f_0 + - matplotlib=3.5.3=py38hecd8cb5_0 + - matplotlib-base=3.5.3=py38hfb0c5b7_0 + - matplotlib-inline=0.1.6=py38hecd8cb5_0 + - mpc=1.1.0=h6ef4df4_1 + - mpfr=4.0.2=h9066e36_1 + - munkres=1.1.4=py_0 + - ncurses=6.3=hca72f7f_3 + - nest-asyncio=1.5.5=py38hecd8cb5_0 + - numexpr=2.8.3=py38hec72209_1 + - numpy=1.23.5=py38hc2f29e8_0 + - openssl=1.1.1s=hfd90126_0 + - packaging=21.3=pyhd3eb1b0_0 + - pandas=1.5.1=py38h07fba90_0 + - pango=1.50.12=h7fca291_0 + - parso=0.8.3=pyhd3eb1b0_0 + - pcre2=10.37=he7042d7_1 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - pillow=9.2.0=py38hde71d04_1 + - pip=22.2.2=py38hecd8cb5_0 + - pixman=0.40.0=h9ed2024_1 + - prompt-toolkit=3.0.20=pyhd3eb1b0_0 + - psutil=5.9.0=py38hca72f7f_0 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.21=pyhd3eb1b0_0 + - pygments=2.11.2=pyhd3eb1b0_0 + - pyparsing=3.0.9=py38hecd8cb5_0 + - pyreadr=0.4.7=py38h748759a_2 + - pyreadstat=1.2.0=py38h01a1b83_1 + - python=3.8.14=hc915b28_0_cpython + - python-dateutil=2.8.2=pyhd3eb1b0_0 + - python_abi=3.8=3_cp38 + - pytz=2022.1=py38hecd8cb5_0 + - pyzmq=23.2.0=py38he9d5cce_0 + - r-base=4.1.3=he54549f_3 + - readline=8.2=hca72f7f_0 + - rpy2=3.5.6=py38r41hbd87e4b_0 + - scikit-learn=1.1.3=py38he9d5cce_0 + - scipy=1.9.3=py38hfb8b963_2 + - seaborn=0.12.0=py38hecd8cb5_0 + - setuptools=65.5.0=py38hecd8cb5_0 + - simplegeneric=0.8.1=py38_2 + - six=1.16.0=pyhd3eb1b0_1 + - stack_data=0.2.0=pyhd3eb1b0_0 + - tapi=1000.10.8=ha1b3eb9_0 + - threadpoolctl=2.2.0=pyh0d69192_0 + - tk=8.6.12=h5d9f67b_0 + - tktable=2.10=h1de35cc_0 + - tornado=6.2=py38hca72f7f_0 + - traitlets=5.1.1=pyhd3eb1b0_0 + - tzlocal=2.1=py38hecd8cb5_1 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - wheel=0.37.1=pyhd3eb1b0_0 + - xz=5.2.6=hca72f7f_0 + - zeromq=4.3.4=h23ab428_0 + - zlib=1.2.13=hfd90126_4 + - zstd=1.5.2=hcb37349_0 +prefix: /Users/vmlandae/opt/anaconda3/envs/tidy diff --git a/environment_from_history.yml b/environment_from_history.yml new file mode 100644 index 0000000000000000000000000000000000000000..281f76e45a1b3e3a7597ebc55d8954d8afc88f43 --- /dev/null +++ b/environment_from_history.yml @@ -0,0 +1,63 @@ +name: tidy +channels: + - defaults + - conda-forge +dependencies: + - python=3.8 + - matplotlib + - seaborn + - pandas + - pyreadstat + - rpy2 + - scikit-learn + - readline + - ptyprocess + - executing + - pyparsing + - tornado + - zlib + - ca-certificates + - libsqlite + - pyzmq + - pip + - ncurses + - pure_eval + - jedi + - wheel + - entrypoints + - prompt-toolkit + - setuptools + - libcxx + - zeromq + - six + - traitlets + - openssl + - decorator + - ipython + - xz + - pexpect + - appnope + - backcall + - python-dateutil + - pygments + - stack_data + - jupyter_client + - libsodium + - libffi + - wcwidth + - jupyter_core + - matplotlib-inline + - nest-asyncio + - psutil + - asttokens + - packaging + - tk + - pickleshare + - certifi + - bzip2 + - parso + - debugpy + - libzlib + - ipykernel + - pyreadr +prefix: /Users/vmlandae/opt/anaconda3/envs/tidy diff --git a/tidy-data.pdf b/paper/tidy-data.pdf similarity index 100% rename from tidy-data.pdf rename to paper/tidy-data.pdf diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 1c09045d5dd99d96a3e29ef8092345948bf77947..0000000000000000000000000000000000000000 --- a/poetry.lock +++ /dev/null @@ -1,1558 +0,0 @@ -[[package]] -category = "main" -description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." -name = "appdirs" -optional = false -python-versions = "*" -version = "1.4.4" - 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