diff --git a/README.md b/README.md index f7685ee..48a969a 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ Washington, DC 20001, United States | 08:40-08:55 | Installation | | 08:55-09:45 | Tutorial 1 - First figure + Subplots / layout | | 09:45-10:15 | **Break** | -| 10:15-11:00 | Tutorial 2 - Integration with scientific Python ecosystem: Pandas / GeoPandas | +| 10:15-11:00 | Tutorial 2 - Integration with scientific Python ecosystem: pandas / GeoPandas | | 11:00-11:45 | Tutorial 3 - Integration with scientific Python ecosystem: Xarray (grids) | | 11:45-12:45 | **Lunch** | | 12:45-13:30 | Tutorial 4 - Geophysics (Seismology) | diff --git a/book/_toc.yml b/book/_toc.yml index e0c29bc..77f0df1 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -14,4 +14,4 @@ parts: - file: notebooks - file: markdown-notebooks - file: tut01_firstfigure - - file: tut02_spec_pd_gpd \ No newline at end of file + - file: tut02_spe_pd_gpd diff --git a/book/tut02_spe_pd_gpd.ipynb b/book/tut02_spe_pd_gpd.ipynb new file mode 100755 index 0000000..08ca165 --- /dev/null +++ b/book/tut02_spe_pd_gpd.ipynb @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "315a335b-1325-4319-aa06-cb1b239616b8", + "metadata": {}, + "source": [ + "# **Tutorial 2** - scientific Python ecosystem: `pandas` and `GeoPandas`\n", + "\n", + "In this tutorial we will learn using\n", + " 1. [**pandas**](https://pandas.pydata.org/docs/): **tabular data** stored in ``pandas.DataFrame``s\n", + " 2. [**GeoPandas**](https://geopandas.org/en/stable/docs.html): **spatial data** (points, lines, polygons) stored in ``geopandas.GeoDataFrame``s\n", + "\n", + "within [`PyGMT`](https://www.pygmt.org) to create histograms and different maps.\n", + "\n", + "-----\n", + "This tutorial is part of the AGU2024 annual meeting GMT/PyGMT pre-conference workshop (PREWS9) **Mastering Geospatial Visualizations with GMT/PyGMT**\n", + "- Conference: https://agu.confex.com/agu/agu24/meetingapp.cgi/Session/226736\n", + "- GitHub: https://github.com/GenericMappingTools/agu24workshop\n", + "- Website: https://www.generic-mapping-tools.org/agu24workshop\n", + "- Recommended version: PyGMT v0.13.0 with GMT 6.5.0" + ] + }, + { + "cell_type": "markdown", + "id": "f56cbd63-a1bd-4e53-837c-13473e0db0d0", + "metadata": {}, + "source": [ + "## 0. General stuff" + ] + }, + { + "cell_type": "markdown", + "id": "0541bce3-b928-4eff-9fde-1ad3da25f240", + "metadata": {}, + "source": [ + "Import the required packages, besides [`PyGMT`](https://www.pygmt.org) itself, we use [`pandas`](https://pandas.pydata.org/docs/) and [`GeoPandas`](https://geopandas.org/en/stable/docs.html):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60f6a029-c037-4c8c-af4d-6e5a8af22c27", + "metadata": {}, + "outputs": [], + "source": [ + "import pygmt\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "# Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small\n", + "img_dpi = 150" + ] + }, + { + "cell_type": "markdown", + "id": "22e12c23-d01b-4c9f-91ca-0d9d1ed07409", + "metadata": {}, + "source": [ + "## 1. `pandas`" + ] + }, + { + "cell_type": "markdown", + "id": "83b9b297-0fea-4e38-8745-17994d93d813", + "metadata": {}, + "source": [ + "### 1.1 Tabular data - `pandas.DataFrame`\n", + "\n", + "Use an example dataset with tabular data provided by `PyGMT` and load it into a `pandas.DataFrame`. This dataset contains earthquakes in the area of Japan." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58207730-2ca4-4b9a-867f-57d1bbd7c1ce", + "metadata": {}, + "outputs": [], + "source": [ + "df_jp_eqs = pygmt.datasets.load_sample_data(name=\"japan_quakes\")\n", + "df_jp_eqs.head()" + ] + }, + { + "cell_type": "markdown", + "id": "8bf74452-3e78-4bce-a8db-4d0473d15eee", + "metadata": {}, + "source": [ + "### 1.2 Create a Cartesian histogram\n", + "\n", + "First we create a Cartesian histogram for the moment magnitude distribution of all earthquakes in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2136295-4486-4845-ac7e-da2d2eb87941", + "metadata": {}, + "outputs": [], + "source": [ + "mag_min = df_jp_eqs[\"magnitude\"].min(); print(mag_min)\n", + "mag_max = df_jp_eqs[\"magnitude\"].max(); print(mag_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d5e868c-bf73-4b7f-8a46-d91751cc71c5", + "metadata": {}, + "outputs": [], + "source": [ + "# Choose the bin width of the bars; feel free to play around with this value\n", + "step_histo = 0.2\n", + "\n", + "fig = pygmt.Figure()\n", + "\n", + "fig.histogram(\n", + " data=df_jp_eqs[\"magnitude\"],\n", + " projection=\"X10c\",\n", + " # Determine y range automatically\n", + " region=[mag_min - step_histo, mag_max + step_histo * 2, 0, 0],\n", + " # Define the frame, add labels to the x-axis and y-axis\n", + " frame=[\"WSne\", \"x+lmoment magnitude\", \"y+lcounts\"],\n", + " # Generate evenly spaced bins\n", + " series=step_histo,\n", + " # Fill bars with color \"orange\"\n", + " fill=\"orange\",\n", + " # Draw gray outlines with a width of 1 point\n", + " pen=\"1p,gray\",\n", + " # Choose histogram type 0, i.e., counts [Default]\n", + " histtype=0,\n", + ")\n", + "\n", + "fig.show(dpi=img_dpi)" + ] + }, + { + "cell_type": "markdown", + "id": "0658236e-869c-417c-8f4a-0195025e9430", + "metadata": {}, + "source": [ + "Use code example above as orientation, and create a similar histogram showing the hypocentral depth distribution for all earthquakes in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "743d7a80-c5d4-46ca-820c-451eae825aa6", + "metadata": {}, + "outputs": [], + "source": [ + "# Your code (:" + ] + }, + { + "cell_type": "markdown", + "id": "a32dd7d1-eab4-4a6f-b4d8-0a455135e9b5", + "metadata": {}, + "source": [ + "For details on creating Cartesian histograms you may find the tutorial [**Cartesian histograms**](https://www.pygmt.org/v0.13.0/tutorials/advanced/cartesian_histograms.html) helpful." + ] + }, + { + "cell_type": "markdown", + "id": "b387494c-15ae-4019-83fc-bdf325f8a76d", + "metadata": {}, + "source": [ + "### 1.3 Create a geographical map of the earthquakes\n", + "\n", + "Now it's time to create a geographical map showing the earthquakes. You can start with using [`pygmt.Figure.coast`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.coast.html) and [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). For plotting the earthquakes as size (moment magnitude) and color (hypocentral depth) coded circles on top of the map please follow the tutorial [**Plotting data points**](https://www.pygmt.org/v0.13.0/tutorials/basics/plot.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "badc1a20-714f-49d9-955e-0cd730d158ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Your code (:" + ] + }, + { + "cell_type": "markdown", + "id": "0b238bef-3018-4bdc-92f8-9caa888a2c32", + "metadata": {}, + "source": [ + "## 2. `GeoPandas`" + ] + }, + { + "cell_type": "markdown", + "id": "21b4748d-3fe1-4791-b233-2125bb40dde2", + "metadata": {}, + "source": [ + "### 2.1 Line geometry\n", + "\n", + "Features which can be represented as a **line geometry** are for example rivers, roads, national boundaries, shorlines, and any kind of profiles." + ] + }, + { + "cell_type": "markdown", + "id": "8ee81847-a26c-4bd7-a8ae-39a482f202fa", + "metadata": {}, + "source": [ + "#### 2.1.1 Spatial Data - `geopandas.GeoDataFrame` with line geometry\n", + "\n", + "First we download some data into in a `geopandas.GeoDataFrame`. This dataset contains European rivers with its length and name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d22d8f77-f73b-4a2a-be9a-9393c6693d2d", + "metadata": {}, + "outputs": [], + "source": [ + "gpd_rivers_org = gpd.read_file(\n", + " \"https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/\" + \\\n", + " \"zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/\" + \\\n", + " \"at_download/file/wise_large_rivers.zip\"\n", + ")\n", + "gpd_rivers_org.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f8db1757-f80d-4cc3-b650-83109eecc390", + "metadata": {}, + "source": [ + "Have a look at the values in the geometry column. The coordinates are currently not given in the geographic coordinate reference system (longitude/latitude) and have to be converted. This can be done directly with `GeoPandas`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc67a58c-45f3-4819-9df4-04e5aa9d06f7", + "metadata": {}, + "outputs": [], + "source": [ + "gpd_rivers_org.crs\n", + "gpd_rivers = gpd_rivers_org.to_crs('EPSG:4326')\n", + "gpd_rivers.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c6ed1e0c-3b1c-485a-9640-9e8098e573f1", + "metadata": {}, + "source": [ + "#### 2.1.2 Create a geographical map of the rivers\n", + "\n", + "Let's plot the rivers represented on top of a map. The `geopandas.DataFrame` can be directly passed to the `data` parameter of [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). Use the `pen` parameter to adjust the lines. The string argument has the form \"*width*,*color*,*style*\". Different line styles are explained in the Gallery example [**Line styles**](https://www.pygmt.org/v0.13.0/gallery/lines/linestyles.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "250d19db-daaf-459d-b8b6-fba5752eec62", + "metadata": {}, + "outputs": [], + "source": [ + "fig = pygmt.Figure()\n", + "\n", + "fig.coast(\n", + " projection=\"M10c\", \n", + " region=[-10, 30, 35, 57],\n", + " land=\"gray99\",\n", + " shorelines=\"1/0.1p,gray50\",\n", + " frame=True,\n", + ")\n", + "\n", + "fig.plot(data=gpd_rivers, pen=\"0.5p,steelblue,solid\")\n", + "\n", + "fig.show(dpi=img_dpi)" + ] + }, + { + "cell_type": "markdown", + "id": "94058ab7-657d-49b4-a8f5-2c1b8533a4a3", + "metadata": {}, + "source": [ + "#### 2.1.3 Plot subsets of the rivers differently\n", + "\n", + "Now we want to filter the dataset based on the river length and plot the subsets differently.\n", + "\n", + "To indicate the different subsets adding a legend to the plot is helpful. Therefore, we specify the text for the legend entries via the `label` parameter of [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). Note, that for the first plotted element, additionally the heading (**+H**) and the font size of the heading (**+f**) are specified. Placing the legend on the figure is done via the `position` parameter of [`pygmt.Figure.legend`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.legend.html). A box can be added via the `box` parameter. For further explanation feel free to look at our gallery example [**Legend**](https://www.pygmt.org/v0.13.0/gallery/embellishments/legend.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9de1b7a9-0e87-4368-8df1-7064e7ac616c", + "metadata": {}, + "outputs": [], + "source": [ + "# Split the dataset into two subsets of shorter and longer rivers\n", + "# Feel free to play around with the limit\n", + "len_limit = 700000 # in meters\n", + "gpd_rivers_short = gpd_rivers[gpd_rivers[\"Shape_Leng\"] < len_limit]\n", + "gpd_rivers_long = gpd_rivers[gpd_rivers[\"Shape_Leng\"] > len_limit]\n", + "\n", + "\n", + "fig = pygmt.Figure()\n", + "\n", + "fig.coast(\n", + " projection=\"M10c\", \n", + " region=[-10, 35, 35, 58],\n", + " land=\"gray99\",\n", + " shorelines=\"1/0.1p,gray50\",\n", + " frame=True,\n", + ")\n", + "\n", + "# Plot the subsets differently and specify the text for the legend entries\n", + "fig.plot(data=gpd_rivers_short, pen=\"0.5p,orange\", label=f\"shorter {len_limit} m+Hriver length+f9p\")\n", + "fig.plot(data=gpd_rivers_long, pen=\"0.5p,darkred\", label=f\"longer {len_limit} m\")\n", + "\n", + "# Place the legend at the Top Left corner with an offset of 0.1 centimeters from the map frame\n", + "# Add a box with semi-transparent (@30) white fill (+g) and a 0.1-points thick gray outline (+p)\n", + "fig.legend(position=\"jTL+o0.1c\", box=\"+gwhite@30+p0.1p,gray\")\n", + "\n", + "fig.show(dpi=img_dpi)" + ] + }, + { + "cell_type": "markdown", + "id": "eb723411-a607-4db4-bb9f-e9067248b512", + "metadata": {}, + "source": [ + "#### 2.1.4 Plot the rivers with color-coding for the river length\n", + "\n", + "Use the gallery example [**Line colors with a custom CPT**](https://www.pygmt.org/v0.13.0/gallery/lines/line_custom_cpt.html) to plot the rivers with color-coding for the river length." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a61552d6-6c87-4fe3-ba5a-4fa9af6c23db", + "metadata": {}, + "outputs": [], + "source": [ + "# Your code (:" + ] + }, + { + "cell_type": "markdown", + "id": "8e833bc6-54df-430b-91dd-bc63159a65c7", + "metadata": {}, + "source": [ + "### 2.2 Polygon geometry\n", + "\n", + "Any kind of areas, like continents, countries, and states can be stored as **polygon geometry**." + ] + }, + { + "cell_type": "markdown", + "id": "4ea5bd4e-d26c-449a-8b08-2eaa05d85877", + "metadata": {}, + "source": [ + "#### 2.2.1 Spatial Data - `geopandas.GeoDataFrame` with polygon geometry\n", + "\n", + "Again we download some data into in a `geopandas.GeoDataFrame`. This dataset contains information regarding airbnb rentals, socioeconomics, and crime in Chicagos.\n", + "This time we are lucky and the data is directly provided in the geographic coordinate reference system (longitude/latitude) and no further coordinate transformation is needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81facdc5-a644-4327-9cb1-7542c4d0d73f", + "metadata": {}, + "outputs": [], + "source": [ + "gdf_airbnb = gpd.read_file(\"https://geodacenter.github.io/data-and-lab/data/airbnb.zip\")\n", + "gdf_airbnb.head()" + ] + }, + { + "cell_type": "markdown", + "id": "9f6f0cab-9588-4b32-bd8e-2fbd4f626ef6", + "metadata": {}, + "source": [ + "#### 2.2.2 Create a choropleth map\n", + "\n", + "Here we are going to create a so-called **choropleth map**. Such a visualization is helpful to show a quantity which vary between subregions of a study area, e.g., countries or states. The dataset contains several columns, here we will focus on the column ``\"population\"``, but feel free to modify the code below and use another quantity for the color-coding!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e07f091-7018-4609-9e75-d40400e18181", + "metadata": {}, + "outputs": [], + "source": [ + "popul_min = gdf_airbnb[\"population\"].min(); print(popul_min)\n", + "popul_max = gdf_airbnb[\"population\"].max(); print(popul_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f10895a2-67c6-41e1-b33b-627e391f2f9c", + "metadata": {}, + "outputs": [], + "source": [ + "fig = pygmt.Figure()\n", + "\n", + "# frame=True adds a frame using the default settings\n", + "# Use \"rltb\" (right, left, top, bottom) to plot no frame at all which\n", + "# can be useful when inserting the figure within a document\n", + "fig.basemap(region=[-88, -87.5, 41.62, 42.05], projection=\"M10c\", frame=True)\n", + "\n", + "# Set up colormap\n", + "pygmt.makecpt(cmap=\"SCM/bilbao\", series=[popul_min, popul_max, 10])\n", + "# Add vertical colorbar at left side\n", + "fig.colorbar(frame=\"x+lpopulation\", position=\"jLM+v\")\n", + "\n", + "# Plot the polygons with color-coding for the population\n", + "fig.plot(\n", + " data=gdf_airbnb, \n", + " pen=\"0.2p,gray10\", \n", + " fill=\"+z\", \n", + " cmap=True,\n", + " aspatial=\"Z=population\",\n", + ")\n", + "\n", + "fig.show(dpi=img_dpi)" + ] + }, + { + "cell_type": "markdown", + "id": "03516fe8-5013-4acb-89d1-a1ebdbc1d426", + "metadata": {}, + "source": [ + "## 3. Additional comments\n", + "\n", + "Some helpful and interesting aspects:\n", + "\n", + "- Use suitable colormaps for your data: [**Scientific colourmaps by Fabio Crameri**](https://www.fabiocrameri.ch/colourmaps/), see also the publications [Crameri et al. 2024](https://doi.org/10.1002/cpz1.1126) and [Crameri et al. 2020](https://doi.org/10.1038/s41467-020-19160-7)\n", + "- Datasets provided by `GeoPandas`: Checkout the [**geodatasets**](https://geodatasets.readthedocs.io) libaray\n", + "- Convert other objects to `pandas` or `GeoPandas` objects to make them usable in `PyGMT`: For example, convert [**OSMnx**](https://osmnx.readthedocs.io)'s `MultiDiGraph` to a `geopandas.DataFrame`\n", + "- Create more complex geometries: Combine `GeoPandas` with [**shapely**](https://shapely.readthedocs.io) (i.e., `from shapely.geometry import Polygon`)\n", + "- Support of OGR formats: Use [`geopandas.read_file`](https://geopandas.org/en/v1.0.1/docs/reference/api/geopandas.read_file.html) to load data provided as shapefile (.shp), GeoJSON (.geojson), geopackage (.gpkg), etc." + ] + }, + { + "cell_type": "markdown", + "id": "9e7abfa1-13ac-4bed-896d-4e55438f1f94", + "metadata": {}, + "source": [ + "## 4. Orientation / suggestion for \"2.1.4 Plot the rivers with color-coding for the river length\"\n", + "\n", + "Below you find a rough code shipset for plotting the rives with color-coding in section 2.1.4." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b11d21b-e46e-4b34-9319-5aafb036552f", + "metadata": {}, + "outputs": [], + "source": [ + "fig = pygmt.Figure()\n", + "\n", + "fig.coast(\n", + " projection=\"M10c\", \n", + " region=[-10, 35, 35, 58],\n", + " land=\"gray99\",\n", + " shorelines=\"1/0.1p,gray50\",\n", + " frame=True,\n", + ")\n", + "\n", + "pygmt.makecpt(cmap=\"SCM/oslo\",series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True)\n", + "fig.colorbar(frame=[\"x+lriver length\", \"y+lm\"], position=\"+ef0.2c\")\n", + "\n", + "for i_river in range(len(gpd_rivers)):\n", + " fig.plot(\n", + " data=gpd_rivers[gpd_rivers.index==i_river],\n", + " zvalue=gpd_rivers.loc[i_river, \"Shape_Leng\"],\n", + " pen=\"0.5p\",\n", + " cmap=True,\n", + " )\n", + "\n", + "fig.show(dpi=img_dpi)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/tut02_spec_pd_gpd.ipynb b/book/tut02_spec_pd_gpd.ipynb deleted file mode 100755 index bfa8e0b..0000000 --- a/book/tut02_spec_pd_gpd.ipynb +++ /dev/null @@ -1,1072 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "315a335b-1325-4319-aa06-cb1b239616b8", - "metadata": {}, - "source": [ - "# **Tutorial 2** - scientific Python ecosystem: `pandas` and `GeoPandas`\n", - "\n", - "In this tutorial we will learn using\n", - " 1. [**pandas**](https://pandas.pydata.org/docs/): **tabular data** stored in ``pandas.DataFrame``s\n", - " 2. [**GeoPandas**](https://geopandas.org/en/stable/docs.html): **aspatial data** stored in ``geopandas.GeoDataFrame``s\n", - "\n", - "within [`PyGMT`](https://www.pygmt.org) to create histograms and different maps.\n", - "\n", - "-----\n", - "This tutorial is part of the AGU2024 annual meeting GMT/PyGMT pre-conference workshop (PREWS9) **Mastering Geospatial Visualizations with GMT/PyGMT**\n", - "- Conference: https://agu.confex.com/agu/agu24/meetingapp.cgi/Session/226736\n", - "- GitHub: https://github.com/GenericMappingTools/agu24workshop\n", - "- Website: https://www.generic-mapping-tools.org/agu24workshop\n", - "- Recommended version: PyGMT v0.13.0 with GMT 6.5.0" - ] - }, - { - "cell_type": "markdown", - "id": "f56cbd63-a1bd-4e53-837c-13473e0db0d0", - "metadata": {}, - "source": [ - "## 0. General stuff" - ] - }, - { - "cell_type": "markdown", - "id": "0541bce3-b928-4eff-9fde-1ad3da25f240", - "metadata": {}, - "source": [ - "Import the required packages, besides [`PyGMT`](https://www.pygmt.org) itself, we use [`pandas`](https://pandas.pydata.org/docs/) and [`GeoPandas`](https://geopandas.org/en/stable/docs.html):" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "60f6a029-c037-4c8c-af4d-6e5a8af22c27", - "metadata": {}, - "outputs": [], - "source": [ - "import pygmt\n", - "\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "\n", - "# Use a resolution of only 150 dpi for the images within the Jupyter notebook, to keep the file small\n", - "img_dpi = 150" - ] - }, - { - "cell_type": "markdown", - "id": "22e12c23-d01b-4c9f-91ca-0d9d1ed07409", - "metadata": {}, - "source": [ - "## 1. `pandas`" - ] - }, - { - "cell_type": "markdown", - "id": "83b9b297-0fea-4e38-8745-17994d93d813", - "metadata": {}, - "source": [ - "### 1.1 Tabular data - `pandas.DataFrame`\n", - "\n", - "Use an example dataset with tabular data provided by `PyGMT` and load it into a `pandas.DataFrame`. This dataset contains earthquakes in the area of Japan." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "58207730-2ca4-4b9a-867f-57d1bbd7c1ce", - "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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearmonthdaylatitudelongitudedepth_kmmagnitude
019871449.77149.294894.1
119871939.90141.68676.8
219871939.82141.64844.0
3198711442.56142.851026.5
4198711642.79145.10545.1
\n", - "
" - ], - "text/plain": [ - " year month day latitude longitude depth_km magnitude\n", - "0 1987 1 4 49.77 149.29 489 4.1\n", - "1 1987 1 9 39.90 141.68 67 6.8\n", - "2 1987 1 9 39.82 141.64 84 4.0\n", - "3 1987 1 14 42.56 142.85 102 6.5\n", - "4 1987 1 16 42.79 145.10 54 5.1" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jp_eqs = pygmt.datasets.load_sample_data(name=\"japan_quakes\")\n", - "df_jp_eqs.head()" - ] - }, - { - "cell_type": "markdown", - "id": "8bf74452-3e78-4bce-a8db-4d0473d15eee", - "metadata": {}, - "source": [ - "### 1.2 Create a Cartesian histogram\n", - "\n", - "First we create a Cartesian histogram for the moment magnitude distribution of all earthquakes in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c2136295-4486-4845-ac7e-da2d2eb87941", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.0\n", - "6.8\n" - ] - } - ], - "source": [ - "mag_min = df_jp_eqs[\"magnitude\"].min(); print(mag_min)\n", - "mag_max = df_jp_eqs[\"magnitude\"].max(); print(mag_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1d5e868c-bf73-4b7f-8a46-d91751cc71c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAApYAAAKYCAIAAABGmi8tAAAACXBIWXMAABcSAAAXEgFnn9JSAAAAHXRFWHRTb2Z0d2FyZQBHUEwgR2hvc3RzY3JpcHQgOS4yMcb0+xQAABu8SURBVHic7d3LjuTYfeBhUlWjcgseSxggExh0+QWMeoFajUrPQPId5iUEWHvDu97PTiC57LWrvRBKD5C72XZBgBM21LaAuthtenEsIpJkRDKzkpd/xPetsiKj4zAuHb88vOZd12WcVJZlVVVlWW69IGypaZq6rpum2XpB2JhPAske0vCT1UZqmmbyqR67fT/atq3reuulYGN1Xbdtu/VSsD2fBJI9pOH5aiNVVTW4pWma/sY8z4ui8IctAMy00ix8PM9O/S6Koq7rruuKomjbdufTcQDYjzUSXpbleL1TWv/Qb0homiZV/EEPe2bJz/N8hVHKslxtoHXeoDzPz+mTsNobtNrrts5Aq71uq/EG7Xyg1Zx4OosnvGmatm2Lohjcnmo9OTtfepEA4AwsnvC0tnwyzOOuZ3+enQMA9+iWlCKdtnYPhsuyrCiKwf0nb5z09ddfb/iiAcCaJlO44B7paRN4XddfvpXlzDZsAMCDjDtYFMVSCe83gT/JXhLjVe7ffvvtx48fv/yRAWD/xh2sqmqphPebtAcJ7/c/H/8nJ2I/vv/r169///vfZ1n29ddfv379+guXFgB2qD9Qa7Kby57aZXyQ2L2HjY3PADPp5cuX6YfXr1/biR2As3R6O/KCK9Inl6Pfr+3YUeDndIAvACxnvXOkD6TZdh/sY4ePAwCT1jtH+kBZlnVdV1XVryVwjnQAmG+9hHejq5qWZdl1XZqIb37JNgCIZbNZeM/MGwAeYbNt4QDAl5BwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJCeb70Ae3dzc3N7e7vmiG/evFlzOACCMgsHgJAkHABCsiJ9rjd/+NWij/+7699mz36W/fR/LToKAGdDwvfi87PrLMuyz5+3XhAAYrAiHQBCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASCkNRJelmVZlsd+2zTNid8CAJOeL/roZVm2bZt+zvO8KIqmafrfNk1TVdWx3wIAJyw4C0/9Loqi67qu64qiaNu2n3CnfhdFUdf1+LcAwGkLJjz1u59YN02TOp3+Wdd1lmVVVaVsD34LAJy22e5sqdbjabd16QAwx4IJ77rudI+LohjfmGbnAMBpy+7OlqSpdpp2nyh00zR5np94hEPv3r17ugUEgF0bd7CqqjUS3m/hnpx2P+gRAOACTXZwjW3h3Z9lWdYfRfa4R+g9+q8BAAhn3MGmaVbdnS31+9iRY44oA4D5lkp4WZZ5ng+qPCfSj5umA8ClWSrhk/uip4SnSB87CtxcHADmWHBF+vh0bIfHgg9Wqqff2sINADMtuEd6inTbtv2hYkVR9OvJy7Ks67qqqsPfOq8LAMy0YMLTBcqaphmcS/XwDl3X9ZNyq9ABYL7Fjws/faXRzBlVAeBRNjtHOgDwJSQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQphNeluXgn3me53lelmXTNKssGABwyjDhqdZt2x7e0v+zbduqqlQcADb3/PAfTdMcxjtJtxRF0f+zqqqu69ZZPgBg0p1ZeF3XWZYVRdEXOq1RL4qi+bPU8sGadgBgZXcSnibc1pMDwP7ds0d6inpVVassDAAw16mE99Nxq80BYG/uJDxt5+7L3W8aX3+xAIDTno9vqqoqZXuwd3rTNHVd214OAHswPKgsHQXexzvti55+7reIm5cDwOaGs/BU8cN/Du5wGHUAYCsTK9KPFdrpXABgP1zmBABCupPwwVr0Sek+1qUDwLburEjvdzg/ob+Pg8UBYEPP06FihzdpMwDs3/PxzPveiTgAsLnH7M7muDIA2Nzzwxin87o4eAwA9u/O7myuSAYAUdxZke5oMQCIYuLsbHP2SFd6ANjWMOF5nm+yHADAg9xJeD//di0yANi5OwlPR4TXde3sLgCwcxPHhes3AOyfK5UBQEh3Em4TOABEcSfh6dQuVqQDwP4NT+2SrnriHC8AsHPDg8rSTult256+XpnzqAPAttbYna1pmhMr50//FgCYdGcW/uQrz/tpfZZleZ4XRVFVVR/spmn6C6uk31p7DwAzLTgLb5qmbduiKOq67rquKIq2bftmp34Pfms6DgAzLZjwuq6zg/XkTdOkg9bSP9Nv+0l5+u3pDfAAQG/BhKcp+OEth+vJU63H027r0gFgjuG28DQ5vtec0KYt3yf+q8kzyThDOwDMcSfh6aDwp3roceb7VevH7n/sUqcugQrAJRt3sCiKOwkfTJoHUt0fdxLWftf0mbP8gfGg7969e//+/SMeCgDCGXewqqrhqV1Or8ROJX5QhtPK+bRd/PCIsgcZT9wPD1cDgPM2uQL7Ybuzpf3GT0/WB/dPd67r+t5TuNgEDgDzPXKP9Dm57Y/8ftD51+b/fQAAl2zBg8r6fk/+9thR4ObiADDH8/vv8mXGSU5bxKuqOrwkWn8qt6WXBwDOw8MSnkI7556p3MfunPabq+u6qqp+R3nnSAeA+aYvNnqve1s7J8ZlWXZdl2L/6J3VAeAyPWZF+uOO7T7GzBsAHuEBp3bJzJUBYDcedmoXAGAn7jmoLEXdum4A2JuJbeH9KVH7W/qf7TQOADsxnIWnU6od2y89Hcm9/FIBAPe4k/D+lOZZlhVF0R2o6zqdd0XFAWAP7iQ8HS2W4j1YYZ62iKc7tG1rdToAbOtOwtP68xOHlpVlmebiT3toOADwUBN7pFtPDgD7t+CVygCA5Uwk3HZuANi/OwlP27mrqjpW8f46KDIPANu6c2qXpmnSpT+rqupz3p919fAEL6svJwBwx/DsbHVdp1OzpWCPz/HiBG0AsAfDbeHp+O+iKMZT7aIo6rrWbwDYg+nrhes0AOycg8oAIKSjCR9MxJumMTUHgP2YPjtbnueD06zWdV1VVZ7nQg4AezCxO9uxK40mJ44aBwBWcyfhfb/ruu667vBXTdN0XdcfLL7mIgIAYxNXKqvr+tiVTtLxZplLoQDA1lypDABCclAZAITkSmUAENLElcrquj5276ZpXKkMAPZg4kplbdvmeV4URbpMWfrV4cXKXKkMADZ36kplkweIu1IZAOyBK5UBQEiuVAYAITmoDABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJCeb70ADL19+3aFUa6url69erXCQAAsxCwcAEKScAAIyYr03Xnzh18t+vi/u/5t9uxnL168XHQUAJYm4Rfn87PrLMs+ffq09YIA8EWsSAeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkJ5vvQBs4/b29u3btysMdHV19erVqxUGArg0ZuEAEJJZ+IW6+vDd0kPcfvXLpYcAuGQSfrle/fE3yz34zS9+vdyDA5BZkQ4AQUk4AIQk4QAQkoQDQEgSDgAhSTgAhCThABCShANASBIOACFJOACE5ASrMMvNzc2aw11fX19fX685IhCOhMMst7e3K48o4cBpVqQDQEhm4fAAVx++W/QKb1mW/e76t9mzn7148XLRUYAzIOGwL5+fXWdZ9unTp60XBNg7K9IBICQJB4CQJBwAQpJwAAhppYQ3TdM0zbFflWW5zmIAwNlYKeFVVdV1PbixaZo8z6uqats2z3MhB4D51kj45Py7aZqqqoqiqOu667qiKNq2VXEAmGnZ48LLsmzbdvJXaVJeVVXKdlqdfuzOAMDA4rPwoiiKohjfnmo9nnYf22QOABxadhbe9zjP8/FvJ9Ne17XV6QBwrx0dVGb+DQDzxThH+uQkHgAuxLiDRVHESPh4lfu7d+/ev3+/ycIAwMrGHayqakcJP7EJfLyO3e7rAFyOyW3NO9oWnlRVtfUiAEAAmyU8nctlfLvd0QFgjs0SnmbbfbCbpmnbdvIwMwBgbLNt4WVZ1nVdVVW/l11RFI4rA4CZVkp413XjG8uy7LouTcT7M60CAHNsv0e6mTcAPMLu9kgHAOaQcAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJCeb70AnKcffvo36Yfb29u3b9+uMOKbN29WGAVgP8zCASAks3CWdfXhu6WHuP3ql0sPAbBDEs7iXv3xN8s9+M0vfr3cgwPsmRXpABCShANASBIOACFJOACEJOEAEJKEA0BIEg4AIUk4AIQk4QAQkoQDQEhOsAp79MMPP9zc3Cz3+J8+ffr48WOWZT//+c+XG+XQq1ev1hkILoeEwx59/vz59vZ2hYHWGQVYghXpABCSWTjs0dWH7xa9wtvvr/7fh+d/nWXZmz/8arlRsiz7///z//7TV/8n++rloqPAZZJwuEQ/5i/WGejTs6vPz66zz5/XGQ4uihXpABCShANASBIOACFJOACEJOEAEJKEA0BIEg4AIUk4AIQk4QAQkoQDQEgSDgAhSTgAhCThABCShANASBIOACFJOACEJOEAEJKEA0BIEg4AIUk4AIQk4QAQkoQDQEgSDgAhSTgAhCThABDS860XAL7IDz/9m/TD27dvt10SgJWZhQNASGbhnImrD98t+vi3X/1y0ccHeCgJ50y8+uNvFn38txIO7IwV6QAQkoQDQEgSDgAhSTgAhCThABCShANASBIOACFJOACEJOEAEJKEA0BIEg4AITlHOsDlurm5ub29XW24q6urV69erTbc2TMLB4CQzMIBcLnekCQcAJfrDcmKdAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJAkHgJAkHABCknAACEnCASAkJ1gFFvTpJ1fph5ubmxWGu76+vr6+XnSIdZ5Iz3W9OEHCgQV9fP7fCV/tipZLJ3zNS3PCaVakA0BIZuHAGs7sWpZLP53M1TmZQcKBNZzftSwXfUY3v/j1cg/O2bAiHQBCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAjJCVYBdme1i7T+8MMPiz7+eLizuezsHkg4wO6sf5HWdXz+/PlsLju7B1akA0BIZuEA+/XmD79a9PF/d/3bz8/Wm61effhu8WvW/e9/WPTxd8UsHABCknAACEnCASAkCQeAkCQcAEKScAAIScIBICQJB4CQJBwAQpJwAAhJwgEgJOdIB87H7e3t27dvt14KtrfaJ+HNmzcrjHKMWTgAhCThABCSFenA+XAtS5KlPwk3v/j17Ve/XO7xZzILB4CQJBwAQpJwAAhJwgEgJAkHgJC2T3jTNGVZbr0UABDMlgeVNU1TVVX6Oc/zoiiaptlweQAgkM1m4anfRVHUdd11XVEUbduajgPATJslvK7rLMuqqkrZbpomVXyr5QGAWDZLeKr1eNptXToAzLHltvCiKMY31nU9Z3X6999/n3549+7doqvf//SnP3368G9Z9x/ffFxukCzLstuv/jbv/j3PflxhoJ90H1/8ePuX/77gKP/y4u9+/MnPVhjon//i7/8z/4ssy775sOAo2Vqv22oD/cuLv/vP/H+s8Hlb8w3KsuzFj//0V5/PYaDVXjdv0OP860+bT8/+Mcuyb775ZsFh7pN3XbfNwFP7rx3bqW0c6W+//fbjx4W/ewBgH8aT3qqqYlzmxDZyAC7ZZAdjJHy8quDly5fv37/fZGEAYGWTq8y3P7VL70GbtL///vuiKIqi6JaXZdlqA60wSlobs85A5/QGrfm6rTPQmh9sb9AjeIN2PtDKaZhM4Y4SnvQnewEATthsRfqxo8Dnz8XPL/aTu+g/udVet9UGKorinD4M5/e6rTPQOX0GEm/Qzgdazak0rLASYFI6tUu/FmLwz13Z7YKxptXWzrFzPgkke0jDZrPwsizruq6qKs/zdItzpAPAfFvukV6WZdd1ac15f6ZVAGCO7Q8q2//M+8w2tfI4PgMkPgkke0jDZmdnAwC+xO4OKgMA5pBwAAhJwgEgpO13Z4uiaZq6rve/8x1PbvJYCZ+ES1aWpYNoLtaJ9339rwW7s82Vjl/3cl2g/tQFh3wSLlD6O74/raRTWVymyS+EZP2vBbPwWfy5feHWOfctO5f6ncpdlmXbtmVZqvilmfw2SB+M9RfGLPx+6f/V9LOX69Kkd7+ua3/GXbjxJ8FngyR9ADb5Y84s/B5N06Q/ryYvysKF8B1NcvhJMP8m+3Mj0mU+1meP9HtUVWWLF2nFaeLDcJkO15T6k45easRWHwkJP6U/f/vWC8LG+s9A27ZVVan4xSrLMs/ztm3zPPf3HBuuQv9v210kbe/SX9x1Xad/erkuU3rf+49BN/pgcCHSJ6EoivTW9zPyrZeLzezhGtlm4dP6TeDWmF249P/J4ccgzci32vTFttImlfRDqriviIuVvgS2XRNjd7Zp/Rf04P/P7VebsDVf2RdrcNRQ0zQnDhHm7G11INkhCT9lvBe6/dIvSjqVh/NwAQM72VNKwqeN59nOznaB0nk0s7sz7538r8vKxkeW+sOOzT8DtoXDKemLu/8f1U4SF6v/Yy79fd9/EmxWu0x7WIuemYXDaf2pNPutnr61L1NZlmmryuE50q2MuUz7+QZwglW4X9oonmWZ7eL0W1J8EtichANASLaFA0BIEg4AIUk4AIQk4QAQkoQDQEgSDgAhSTgAhCThABCShANASBIOnKeyLPM8X+2S3muOBYmEw6Uoy7K/0NZl8gpwZlypDC5Ff4mtC7k+x/gyYpf2CnD2JBw4T2nOvfVSwIKsSAeAkCSci9BPyJqmyQ/0m0X7XZ8Gt48f5/BukxtWn3Csw3se24h7ONzg/v190q8O/5N7twc/ybMYLFJaqmMz4/HCp/988Mhznu/h6Id3nnwFDu82+QiDBRiPe/rFnPk+wmN0cAHSp70oivH/AnVdH7v98BHquj72P9Hgnl8+Vtd1k3ebvGd/++T9jz1aURQbvmKD0SfvWRRFGmVw5znPd/CsT78Ch3ebfBcOF+DYuP3tj34f4REknIsw/uIefBf3X6n97YNy9N/Fh/cc37jQWMdunBzu2CNPjrXcK9bfOPksxs/38J6H5Zt82DnPdzzW6Vd7/CIMEn44yvjpH3teM99HeAQJ5yKc/u6+9zv9xNfuzFLOH6tPwrGp+bFS3nvnY/ec9IXPon+Eex/5WEGPjTX/+T55wo/d87Di4xtnvo/wCLaFc0EG2yD7g45mbpssimK8xTR9HfdHK335WOmrf3Ks9CDjsbKpA6iexKOfRdM0/ZrwOcb3PD3EQs/3tPTKjxe1LMvxjY97H+FBJJzLNf+IoxPftjPz/yRj9Q8yfrR1jp6aP0raaWv84oxveVzJNjxabOY7/rj3ER7EceEw1+mZ39Puady27XmcrbOv1OlUT750RVHsZ6r6uNyezfvIPkk4zGXO9CBlWQ4CfGyjA/A4VqTDXKcT/rRbZ0/v67T/A4ubpum3HPf7c02uWu/vv+4CPtjj3t/o7yM7J+HwRQ5PdfIkDzh/F7A963fmGpxT5fT9B3Y1X++fxfydH5dcHMgyCYc5TnwdnziByZNb+eqZX2J+fY+9tnuYoU5uCPjyvzYCvY/snITD/fqjgAYTysPVxU81VkrXOmMt59hCnjjCatDs1f42Onak3LGVB+NFPXZy1iz++8jO2Z0N7pcO/G3bdvCN3H8XP+2Usa7rqqqWG6ssy6qqVtg779hTSD/0JydPt1RV1VdtcM8nd/gK9Et4uABpGQYLkBZ4sKiT90yWfh/B2dm4CMc+7ZO333u+sEPj/ZWeZKxjpw2f/9ROnK3s2EM97bOYPC354PzqJ+7c33Py7GwPer73vgKTr/axBZh8XscWbP77CI+QdwfnHAbudTijWnou++RjHc44V5iFN03TN+xwxMldww7vnG5PU96nnbCeeAUG13Y78SDHnte9g868P8wk4cCWUg4nw5Z2+KrrWvNgkm3hwJbquk6bh4/NifUbjrFHOrClwz2308/9XmOZ3bbhJCvSgY01TTN57jO7bcNpEg7swvj46a2WBKKQcAAIybZwAAjpvwAALqOEX74CNQAAAABJRU5ErkJggg==", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Choose the bin width of the bars; feel free to play around with this value\n", - "step_histo = 0.2\n", - "\n", - "fig = pygmt.Figure()\n", - "\n", - "fig.histogram(\n", - " data=df_jp_eqs[\"magnitude\"],\n", - " projection=\"X10c\",\n", - " # Determine y range automatically\n", - " region=[mag_min - step_histo, mag_max + step_histo * 2, 0, 0],\n", - " # Define the frame, add labels to the x-axis and y-axis\n", - " frame=[\"WSne\", \"x+lmoment magnitude\", \"y+lcounts\"],\n", - " # Generate evenly spaced bins\n", - " series=step_histo,\n", - " # Fill bars with color \"orange\"\n", - " fill=\"orange\",\n", - " # Draw gray outlines with a width of 1 point\n", - " pen=\"1p,gray\",\n", - " # Choose histogram type 0, i.e., counts [Default]\n", - " histtype=0,\n", - ")\n", - "\n", - "fig.show(dpi=img_dpi)" - ] - }, - { - "cell_type": "markdown", - "id": "0658236e-869c-417c-8f4a-0195025e9430", - "metadata": {}, - "source": [ - "Use code example above as orientation, and create a similar histogram showing the hypocentral depth distribution for all earthquakes in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "743d7a80-c5d4-46ca-820c-451eae825aa6", - "metadata": {}, - "outputs": [], - "source": [ - "# Your code (:" - ] - }, - { - "cell_type": "markdown", - "id": "a32dd7d1-eab4-4a6f-b4d8-0a455135e9b5", - "metadata": {}, - "source": [ - "For details on creating Cartesian histograms you may find the tutorial [**Cartesian histograms**](https://www.pygmt.org/v0.13.0/tutorials/advanced/cartesian_histograms.html) helpful." - ] - }, - { - "cell_type": "markdown", - "id": "b387494c-15ae-4019-83fc-bdf325f8a76d", - "metadata": {}, - "source": [ - "### 1.3 Create a geographical map of the earthquakes\n", - "\n", - "Now it's time to create a geographical map showing the earthquakes. You can start with using [`pygmt.Figure.coast`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.coast.html) and [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). For plotting the earthquakes as size (moment magnitude) and color (hypocentral depth) coded circles on top of the map please follow the tutorial [**Plotting data points**](https://www.pygmt.org/v0.13.0/tutorials/basics/plot.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "badc1a20-714f-49d9-955e-0cd730d158ba", - "metadata": {}, - "outputs": [], - "source": [ - "# Your code (:" - ] - }, - { - "cell_type": "markdown", - "id": "0b238bef-3018-4bdc-92f8-9caa888a2c32", - "metadata": {}, - "source": [ - "## 2. `GeoPandas`" - ] - }, - { - "cell_type": "markdown", - "id": "21b4748d-3fe1-4791-b233-2125bb40dde2", - "metadata": {}, - "source": [ - "### 2.1 Line geometry\n", - "\n", - "Features which can be represented as a **line geometry** are for example rivers, roads, national boundaries, shorlines, and any kind of profiles." - ] - }, - { - "cell_type": "markdown", - "id": "8ee81847-a26c-4bd7-a8ae-39a482f202fa", - "metadata": {}, - "source": [ - "#### 2.1.1 Aspatial Data - `geopandas.GeoDataFrame` with line geometry\n", - "\n", - "First we download some data into in a `geopandas.GeoDataFrame`. This dataset contains European rivers with its length and name." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d22d8f77-f73b-4a2a-be9a-9393c6693d2d", - "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", - "
NAMEShape_Lenggeometry
0Danube2.770357e+06MULTILINESTRING ((4185683.29 2775788.04, 41861...
1Douro8.162452e+05MULTILINESTRING ((2764963.81 2199037.624, 2766...
2Ebro8.269909e+05MULTILINESTRING ((3178371.814 2315100.781, 317...
3Elbe1.087288e+06MULTILINESTRING ((4235352.373 3422319.986, 423...
4Guadalquivir5.997583e+05MULTILINESTRING ((2859329.283 1682737.074, 286...
\n", - "
" - ], - "text/plain": [ - " NAME Shape_Leng \\\n", - "0 Danube 2.770357e+06 \n", - "1 Douro 8.162452e+05 \n", - "2 Ebro 8.269909e+05 \n", - "3 Elbe 1.087288e+06 \n", - "4 Guadalquivir 5.997583e+05 \n", - "\n", - " geometry \n", - "0 MULTILINESTRING ((4185683.29 2775788.04, 41861... \n", - "1 MULTILINESTRING ((2764963.81 2199037.624, 2766... \n", - "2 MULTILINESTRING ((3178371.814 2315100.781, 317... \n", - "3 MULTILINESTRING ((4235352.373 3422319.986, 423... \n", - "4 MULTILINESTRING ((2859329.283 1682737.074, 286... " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpd_rivers_org = gpd.read_file(\n", - " \"https://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes/\" + \\\n", - " \"zipped-shapefile-with-wise-large-rivers-vector-line/zipped-shapefile-with-wise-large-rivers-vector-line/\" + \\\n", - " \"at_download/file/wise_large_rivers.zip\"\n", - ")\n", - "gpd_rivers_org.head()" - ] - }, - { - "cell_type": "markdown", - "id": "f8db1757-f80d-4cc3-b650-83109eecc390", - "metadata": {}, - "source": [ - "Have a look at the values in the geometry column. The coordinates are currently not given in the geographic coordinate reference system (longitude/latitude) and have to be converted. This can be done directly with `GeoPandas`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cc67a58c-45f3-4819-9df4-04e5aa9d06f7", - "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", - "
NAMEShape_Lenggeometry
0Danube2.770357e+06MULTILINESTRING ((8.1846 48.0807, 8.19049 48.0...
1Douro8.162452e+05MULTILINESTRING ((-8.67141 41.14934, -8.64362 ...
2Ebro8.269909e+05MULTILINESTRING ((-4.05971 42.97715, -4.06841 ...
3Elbe1.087288e+06MULTILINESTRING ((8.69715 53.90109, 8.72716 53...
4Guadalquivir5.997583e+05MULTILINESTRING ((-6.37899 36.80363, -6.34806 ...
\n", - "
" - ], - "text/plain": [ - " NAME Shape_Leng \\\n", - "0 Danube 2.770357e+06 \n", - "1 Douro 8.162452e+05 \n", - "2 Ebro 8.269909e+05 \n", - "3 Elbe 1.087288e+06 \n", - "4 Guadalquivir 5.997583e+05 \n", - "\n", - " geometry \n", - "0 MULTILINESTRING ((8.1846 48.0807, 8.19049 48.0... \n", - "1 MULTILINESTRING ((-8.67141 41.14934, -8.64362 ... \n", - "2 MULTILINESTRING ((-4.05971 42.97715, -4.06841 ... \n", - "3 MULTILINESTRING ((8.69715 53.90109, 8.72716 53... \n", - "4 MULTILINESTRING ((-6.37899 36.80363, -6.34806 ... " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpd_rivers_org.crs\n", - "gpd_rivers = gpd_rivers_org.to_crs('EPSG:4326')\n", - "gpd_rivers.head()" - ] - }, - { - "cell_type": "markdown", - "id": "c6ed1e0c-3b1c-485a-9640-9e8098e573f1", - "metadata": {}, - "source": [ - "#### 2.1.2 Create a geographical map of the rivers\n", - "\n", - "Let's plot the rivers represented on top of a map. The `geopandas.DataFrame` can be directly passed to the `data` parameter of [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). Use the `pen` parameter to adjust the lines. The string argument has the form \"*width*,*color*,*style*\". Different line styles are explained in the Gallery example [**Line styles**](https://www.pygmt.org/v0.13.0/gallery/lines/linestyles.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "250d19db-daaf-459d-b8b6-fba5752eec62", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = pygmt.Figure()\n", - "\n", - "fig.coast(\n", - " projection=\"M10c\", \n", - " region=[-10, 30, 35, 57],\n", - " land=\"gray99\",\n", - " shorelines=\"1/0.1p,gray50\",\n", - " frame=True,\n", - ")\n", - "\n", - "fig.plot(data=gpd_rivers, pen=\"0.5p,steelblue,solid\")\n", - "\n", - "fig.show(dpi=img_dpi)" - ] - }, - { - "cell_type": "markdown", - "id": "94058ab7-657d-49b4-a8f5-2c1b8533a4a3", - "metadata": {}, - "source": [ - "#### 2.1.3 Plot subsets of the rivers differently\n", - "\n", - "Now we want to filter the dataset based on the river length and plot the subsets differently.\n", - "\n", - "To indicate the different subsets adding a legend to the plot is helpful. Therefore, we specify the text for the legend entries via the `label` parameter of [`pygmt.Figure.plot`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.plot.html). Note, that for the first plotted element, additionally the heading (**+H**) and the font size of the heading (**+f**) are specified. Placing the legend on the figure is done via the `position` parameter of [`pygmt.Figure.legend`](https://www.pygmt.org/v0.13.0/api/generated/pygmt.Figure.legend.html). A box can be added via the `box` parameter. For further explanation feel free to look at our gallery example [**Legend**](https://www.pygmt.org/v0.13.0/gallery/embellishments/legend.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9de1b7a9-0e87-4368-8df1-7064e7ac616c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAApAAAAHlCAIAAADN0FvoAAAACXBIWXMAABcSAAAXEgFnn9JSAAAAHXRFWHRTb2Z0d2FyZQBHUEwgR2hvc3RzY3JpcHQgOS4yMcb0+xQAACAASURBVHic7L1/iCP5fff5qR4ttxO6exzWMelZTcg519pTKfdgnofnPLMsF+dI2LV7c2Ck0ugxOGDHPP7jAvbgGO6PNOjQ/ufHGe/hfxbv2oQQR6OScM47bXtDIOHObI8JHImxpFgNScjI3SZ47d3uZmfI9nTdH++pz3ynSipVleq3Pi+GQS2VSl+p1fX+fn5rlmVR5tE0LRfrLDyaphmG0ev10l7IqtNsNolIfhGp02w2TdOUq1PqrMgvYi3tBQiCIAiCsBgRbEEQBEHIAVl0NZumefPmzXK5zPfcvn37xRdfjOr80+n07t27UZ2NiK5duxbh2fb39yM8W6VSeeqpp6I62z/8wz/cvXs3whOuzkcX7bfurbfeunbtWoTLW52PjiL91r311lv7+/sRvtnV+ego0m/dW2+99aEPfUhVjSWJ9qN76qmnKpWKnxclIvVd7O/vq8soRbWgCDFN8+7du+qi7927F+H57969i88lm0S7Nj/fEv+8++679+7dy+ynl+WPLvJvXYSXdVqxjy5CnnrqqWj/Ilbno6Oov3URqjVF/dFduXLF54vS42/EuQYrexiGYRiGek+06zQMI6pfQxyfYbRre/PNNyNcm3x0oZGPLjTy0YVmpT66aNcW7UfX6/V8vqi39kkMWxAEQRBygAi2IAiCIOQAEWxBEARByANRev0jIvK4iyAIgiDkEVUcs5glPhMr2fKz4XA4HA5Ho1G9Xtd1vdPpEJGu66PRyDCMWq3m/Vwc0Ov1+Ax+XnQwGIxGo3a7HcU7EITZDIdD0zTxZd7d3Y3vhS5cuEBEDx488HNwiG6Gg8GgWq0OBgMiivavxjRNItrc3Nzf38cHFflLzKPdbvu8zoQArfHq9Xq0pw3B2tqapmm47fMbsgq0Wq1+v+9xQG4EO2FqtVqtVjNNczAYjMdjVlzc7/FE0zQh9vx1HAwG3oKNy4Gu6/V6fTQasd4LQhzUarXhcHjp0iWPY6JqBuz/JIFeDltbIhqNRj53w37ADrtWq2ExXCUch3bOIz61JqK4t2j+OT8/x34uYUss74hgzwZtadXLwUKpxl87EeEpeDoeGgwGHrva8Xg8Go1gKBARLHvLstC4O5K3Iwgqo9Foc3PTQ+oiuYyen58vfxI3o9GITd433ngDRvDyp4VVrev6cDjE7nk6nR4fH2ualuQGGl3i4wBulZhOHoKYvh7FRpLOZgATmYh2d3chtNh3ez8L3m/crtfr9Xp9d3dX13VsmVmP3fCRfB7HXkEQoqXdbj///PP4WvK2Mg7isJ+gpkTU6/WOj4+JaHlBxQa9Xq9Xq1Wynca/+7u/S/b+W4gcjsumvZA8kVcLG4IaH1evXiWi8XiMRjPPP//8wlfUdV3TNH4i34n/j4+P+c55T9d1fTqdlsvl6XSK88T9NgXwK7/yKx/4wAfSXkWiwGOEuGxMmhTftRgLHgwGtVpteS8UNugQafwZEtFoNBqPxwv9aokhkTKB8ivY//Zv/xbrFfbKlSv37t07PT19//vf77Or3Ac/+MH79+//6q/+6sxHNzc3F57h7Ozs/e9///3799/3vvdtbGw4Hr1///6FCxeeeOIJP4sRAhH31ykxoD0+3arwkWYhBSkErNlEtIxmIwVPzQzFOeFUSz0mxdmvFIUjQcg7eRVsiufre3h4qGna1tYWEZ2cnJyenuK2g6OjI8uyLl++rN55cnJycnJCRDOf4s3p6Slebn19HS/Kgo3XOj09/eVf/uXT01Mi2tjYWF9fVxUdz+UfVcvGcTZhJj/60Y/SXsJS4JoOf4x/cxnHQwwykosUCGSDDofDJQWbrWqAaFSSiWYzUdNo6vW6R0xNWB1yLNjRcnJycnR0tL6+blnWZDJZX1+/fPnyTJ07PDzEjaOjI1WbT09P5wm8N0dHR0SkaRrUGmIM+cePRLS9vc2vgqXiWQwOc7O1tYUjRbMLiSM7Utd1/1lL7Xab7cs41xgXy9ud7jIn1HTEl/m1EDap8QvljdTygq3ren5/1wIQwSay1Xprawuyd3Bw4D6G5fn09HR7e/v09BQCz0J4cnIyTzU9gMzjNj99MpngR14Ss76+HvRVRLOLh/uyjtzpQEoDtaZ85lXhvS9TSYH3nh0Bc5jUkf9SUKca7TmFhBHBfgQc2vMUESYvFJTV/eTkBCoItYb6Qmh9viikFE9ho9mt08uAd3R6eiqCXQxQ6++4rA8GgxDSZRjGcDj0LjvMJiiGpLABbDXRzAF3UEgMVITqul6tVuf9InRd7/V62UmCE1JBBJuIaGNjY2NjYzKZsLHrnkoLBT06OtrY2HCnv+KJOAbGt0/NZgc4zPpAYh8IKZ8oBlCaSELOuPSjjHD5syUP9yALgSPRLEVg9S78hSKMjYNFs1cWqcN+BJK5iOjixYvq/ScnJ/BRE9HW1pYjw+vo6Iij2ltbW1tbW9vb24FE9+joiPU+JrXe2tpS1yzkl3lhyNDqZRhGrKXYMYFWB4jLpr2WpRgOhyj+XgjeMvwrca9KyCYi2I9gJXYYHJy8TYqmwn394MEDONJDg10CiEmtyXYACHkHhVjz7MKcGspLEu5d67o+sy9CtVrlvkkJ4P0LdYM4iGj2yiKC/YhKpVIqlYgI/zO4IrBOr6+vsw393nvv4RgOYLvzt72BToOo3oiDQC56IbN4hF2JqFqthjOUeRAIJtzkjnAh53nPYkVcblG+wC/Up3nN8Arz7loQQiCC/YiTk5Ozs7ONjQ1HgTV+ZPubPdikSDvyuY6OjjRNUxO/U4fVWjLO8o53TU7ocCzU2rsNXzYZDAaOLHHOe1+IRwQhsY/CXQLuE13Xd3d3LcsSzV41RLAfgUxv7n+icvnyZWzJNzY2UAmN+5944gnWwo2NDXeqWuogj13UOu8E9Z0y0DAUHM8kC/28QuDucYZ36tNDDrF03Mk9/xOYpMnTe0OfoV6vh3CM5zFfQWBEsB+BtifQbPejW1tblUoFLnGyo8JQ96Ojo3/6p3/a2to6PDzk0i88yzTN69evX79+Pck3or5ouF4ukayh2Wym2ICiSHg7wxlHBpZpmu1220+8s1ar5es3haJzNVmabWufHvLRaOT2RY/HY03TkvkoZu4Ywp3H/8HzIvdCODRN0zRtbS05GZWyrocg8HxwcLCxseGhcKjpwgFc6IUbnEmullb3+31cR27duhX/m3gIv+hXvvKVCOu5PeCIGltyEmOLkHANqlRJ8ClCeSnIdo/lQB0zJkkvfHoW2sVEMpoab1mqvNICQ5D5/wReUSzsR0DbvO1RNUX81AY+Zzas1QyyRqNhGEaKLsdkukCYNgm81qoR2hmOb51/kxHX/bz0rHZcHwP1UZ/594hJo1mozA6Ef52AEz5ojpvgDdQ6MSNbBPsh3Md7YY43wtgHBwecEH5kc//+fT7b5z//eSIyDOPWrVshzGu4tb2PWXiAwx8ON7X3U5rNpofuhlZlkXP/8O6n1+v1ej0/znDgsC/h6Pa/WcS4i1zEOOv1urpOdgv7357qus5bk8FggAz51Ad+BMXxOXjgHkomREVi5jWJYDNqS/CFRyKeXalU0KcMovjMM8/8xm/8xjPPPPONb3xjbW0N+nT9+vW1tTXsv/i2Kl1rNnwPDrt+/bppmrjhPhgP+ZfAZrMJS8s0TYfJpdngIYg6bqhn4CcyeBY9rvFue059ovu0Qq/Xaz+OZYMDfKo1plYsuZh8yRXrdCB/OMBXl9XOMIxms5mvt88sDGOLWsfK+fn5+fl5Mq8lMexHoIsZ2ZnVM49xlEih4uvw8FANFX/uc5+b+dxbt25Brvr9vuPKwj9Cp/keNkwdNrrPLDasigVVPWez2XRkDjuEttlssmY4TPNAouvW7zzmJMcEz59Y/koaSK48gOmZ/Ug2BmtCYtV5Zf6fbpomC3ZOpZrsz4HmvwVR67hJsmGRWNgPQYL35cuXK5XKPLWeTCaWZR0dHU0mE/acoyxbfYph4z6D407WXdZjVtZbNjTLPe7xEipYFZ+zZ0Oz3OO9Xg+Gnfu0rNA4xqH0vV6PnzLzUfW0YmGroG+GnyuppuB+FOZ1JKoTOukhyZkZiDez3PLkLv9nQM5auOSATIGt1byQ0//1n//zd3/zN0WtC4MINpGSRObtD69UKpqmoXCLy7XdLUVfeumlr3zlKx5xa8eflmpe4wY/d54kq3LuwcbGBqsy6+i8c7rv5+ey5OOYQGl0Yk/PY2Y22UxV5qwW1VXOQL2ishGXaS3uUzKXl3asENXS3Kkt6DcNu6VUYva9Xi9CBUWPccuyMOCcbKv6T/7kT959910iKuUhL0Hwgwg2kd3FzE8FFBdq8yRspIirSq8OyXbAEgttnmduringnuUNU7eJJsZuujjaUl64cOHChQvqr55/U5ZlPXjwYKbnjfuHROvUDSFj/lNvIkmpZcELlB/O4Ms/Ho8Hg0HC/vA45nCjXylalqL43rKscrn8nz79aSI66PcjfC0hRSSG/ZAnn3zS0ZF0HpcvXz48PNzY2Dg8PNQ0zT2/y7swzDAM/MWyXroN5TisUrF0M4XDvF5bW4P1zGI205h2U61WB4NBtL0+arUa2mv4V8FAibJLZuhgM4G3jI+RglvtWK2maQk0NVOBlMaUIjDTw/8Pf/RHcbyWkAoi2ESzpl97A2lf2ETs9PT03r17Mx9itZ6po3F0WfFoTikkjGOmtaZpbg3zqX9qeVJUYE+J08YR+1wySWc8HqvmNQZFB9XdVJq7xarW86g0GpN+f1Iq7XS7lUYjyZcWIkdc4uFhtfZo1n12doYbbIJ7iHHD/nNyl3L5b27q2CXwhsBdyhX0gsWbDCmqXhJHW0oWsNDB3ciHLSJNYTAY+NwNpDLZE+a1qt/+ST4tPBW1JqKdbnen2yWivVaL/yW8BiEqRLBjZF4wW82XVvWbk7m4GTg7SH3a3JjN5XgtPicXQ+Mh/zY3H7lQ6b37rgj0+NQHh0Lj1x3UY+wxeGoZarVau932f+YkNVvTNLW/epIJ6qFJcVp5pdGAeT3p9/HvZqk0kcB2DhHBDs/R0dHh4eHh4eFkMpmXXs7GrprRxpa02x9+69YtRwU2Srz8rIcnaV68eFG9n8uu1HMG8pC708IdP0oL8aC4hRCqc35+HuLKHvQpgcZQZq1Z6Wg0siwLXgoUd0l+xkKg2TfOzm6cncExvtdqiWbnDolhB0PtqeLIDP/JT36C21BN3Pjyl7/85S9/mYgODg62t7dxgHdZ1K1btxqNRr/fJ7sVOT/kbXvBtt7Y2HCLca/XU9VaPaf7Wj9Ty9Uz9Ho9t5FtWRbuxMlnrsFj8asD2n3MdOQGVWsUdAVdQK/X899mJMm2i/6p1Wqmae7u7kbS3y0ZIpn2EQk73e52vw/f+EGjAYe5kAtEsAOASZos2FtbWygGg3KrieJkN1ThFuWBJlKHmxeytbXl0Qh9mRkkMP7cdvk8O1uYB7prodZ5MBg0Gg3kh1Mol2m9Xq9Wq+PxOJBsB8pdMAyD87mCxl9nij2+S6E3AXinnAQg5nU4Ko0GdbsH/f6k36dWy63Zk35fMtQyiLjE/YLxHjCdT05ODg8Pj46OLMv6xS9+Aane2tratlGFc3t7W52QHR+oDo/jzI4ouKPXqeAfCNVwOMRH1+/3Z87mOz8/9zkCCLoVawoVnzwS3/gyo42QBFcul5EEAPHOb1fRdFED22oyGiLcXLqNR8V5nhHEwvbF4eEhKdVfmIENF/fR0dGTTz6pdieFWmua5m6CFisLy8xCAx+4Gp/22RtVcMBjKsjOnJrpnQ5kgKLNWXxN2nFabknt/4kz3wU2KOFWMhqN2u02twkLlx8uqGw3GkTEejwpPVQEWN6qTovBnQVEsL1ARxTLss7Ozn7t137N8Sh83eyIhopj+Cb3PkPoGgfEKt7w1QdyvAdC3N1RAc1rt9toc+Ghzf5LsXGkWouM1uIZtD7RtS3EE93h6hz5w7nBS9aoNBqVRmOHiIgm/T4M651uF2nkLNLzPOdCwohgz+Xw8JCD0A6t3draevvttyHDEOmNjQ3Mwz49PX3f+9739ttvk93H9PT0ND7bFxwdHaHnWnwvIUQIukjyj0tmdfGp+MZoNBqPx3CHRKXZQcdhRY5DnsUfHjkQb9y+YTeQeEirNen3tyWwnTYSw54LrFVorcNy3djYeN/73kdEcIYTETzkTz755Pr6+ttvv41Hj2zW19cjjy6jiOvo6Ojg4EDUWlBBZ2kevLg8w+Ew3VxxtzyLPzxJtu1KMGm6ki5iYc+FRXqmnxl3Ig0NYgzlhsBDpG/fvv36669/85vfjEqteaqYeqd7PyEIZLcZX/Ik7GbXdV0dVZIw4/HYYUznyB9O9vjt7E8Zn0el0aicnT3MPhPfeHqIhe3FvKgw6rtYreH0RtAavnHc+Ou//uvbt29HpdYHBwe3b9/+7Gc/C2t+e3ubvfHhTqjNQZ2qic5ouNPdasP7gPgezTWJteVyi1wIEGjf3d1Nd6byTId8jvzhOVqqB2q7tLTXsqKIhR0GGNCY06VWcCGNPCbn4fb29h//8R/fvn1b9X5jlxDOHz7TQFGlUW2Egh+5aZqfA+J7NNcsWYjsH+RUL3kS5J+naxoi3UzVvNQD6ivLdqOB3DQJZqeCCHZ4WLaRE86e6tPTU25qpsLR7qgWgM4tJycnk8lkY2MjqGy7E7+bzSZ3R4FkqjKpaZpaO+R9QHyP5p0lh0vOA95vVtYlW4CZponmJLVaDd1J09Vst1uiGDZr7qg0GgeNxqTf30l7JauJuMTDsLGxoWlapVK5fPnyxsZGs9nc3Ny8fPmypmkf/ehHiejg4IAPxiSPtbW1zc3NT3ziE+p5+CHM43K7hQGO4UfVI9fX1z//+c8/88wzly9fdriO+enqwA8P8HQWRXdrFJ7k7eeA+B5143inUHe8o0I61R2gaRoPFOE7l5E0wzCg92jNFsEqwzKvW7gIdroUwCuei5kxDsTCDoNqy6o699577/3lX/7lF77whVdeeYUPuH79+osvvvjCCy+USqXbt2/X6/WvfvWrW1tbUGJS+og5hKpv/0m4jcv79+/DFf/0008T0YsvvoiBH6rrWNU/P7YpDnPIpPdTvA+I71EHeKfq+uEqIOWzDXrOHDEYDNRmZHAXO3zIIVD7dTt2A5Gjugfwctx2e2YkXtQ6RQrjFc9gk/yFFE6w/9Wkf43UlnpuQcMQ6ATcyJPJ5Ozs7Pbt22oi9yuvvPKRj3wECWKf+MQnvvWtb331q18lW4/ZQXrr1i3Y2aqu4OR8DJ7y2muvofD6s5/9LBH96Z/+6e///u/jAPQN5S8inu7ne8leaD9Heh/mfUBMj5qm6R5KxgcX2MKG6xsVXJ1Oh4hqtRq3Pl0GnAfO8AjVeubAEu7+xsFpvBfgeC9B57gL0YI+5HutlqSLJ48IdngODw9Vpwryv77+9a//4he/UA+DWqPW60xpR+A2fOH4xf1HR0cYzemerYns9I2Njddff90wDFZrPoMqbH40mF/U5xvPJrz+jL+ROBKm0NQMgWe8/XA2KIZMY4XNZjOqjDM1zw7+bffyePG1Wu21116zLOvSpUscR19yARmhMLlyEslOi8IJ9iKDOCq42yg0UtM0OL1ff/31s7Mzy7K40IuUSu5SqYRnwRnuoNFomKbZ7/dxzYW688QRsqdrOwqvP/7xj5+dnZVKpSeeeCLce3HYo0J86LoeeccP3jXC1KawWxZONIMbvN1uR5hxZlnWYDCoVqswpt0arIZjrl69Cm0bDod5DDTOJI5ffYps21NDxMhOksIJdlKcnJygiKvX633ta1/79re/ffv2bSKCcvd6vaOjI5ZnzgzH1cdhajOGYbCQq2Hyg4MDNbccag2VvXfvHtT6/v37uBFiJkcg8zovge0VxNsSxRzueQ+h2TVHjnlwJxGNRiMIbeiFQa3JjlUvPBV3QS+MbV08Ko3GXhTnyebA9cySUcEul8vXrl3jHzMYgOTJXUT0W7/1Wzs7O6enp3fu3PmLv/iL733ve9/97nc/9KEPQZVRqw0Bfu+99/CUj3zkI9/61rcc53SY3cgj4wZq9Hha43PPPYcbvV4Pcs4TuAO1UnFUPC9EBDvLYK7azIdGoxFUWdM0HsA1Go0sy0JOmUNHYRFqmlar1ZY0DaHTWFgG/5aFcFSi8IoXUq15jMq20p7dJ97al1HBvnbtmuqhzaxb7Pd+7/du3779wgsvvPzyy+vr61evXv2d3/mdp59+ut/vs6BCa1GoDZsbV8zPfe5zcHE74KA1Dt7e3mbvOr7ck8mE7HruixcvsvENT7tpmj/+8Y9PTk54c+AHtxa6K6kchrj3AfE9WgDW1taiqsZWu6NAgOd9VqjUGtrgTjW93D0Li0/ebrdDG9mOuqwi/R4FIsJQr71WC+LEN9JeV3h4Ljg6u/l/InTaUfAW9KPw1j6pww4DAtiHh4df/vKXiahUKn3ve9/b2NioVCrI3G48/ku6ePEiZBs2t2VZlmW98MILt2/f/tjHPoZjoLUzL2fIWdva2oLNjb6kHD5nu5xzx9C25f79+37ei8f1nZSMXLch7n1AfI8WAFSHR35ajvuqMz+Gw2G73cY9tVqt2WzqCjPzv4BqVcPIDrekwWDg8SqrQ2atjtAger3XamEWJ9846PcxJiSPtdpYNlbuGHaCN4h/7jfI92Do2Y2zs51udzvqjUtGLezMwr1I2ZPzyiuvfPazn719+/bnPvc53HPr1q2PfvSjnHFGRPfu3bt48SLb03CPf+c734FIr6093DYZhuHOCVdBVtra2hqOxMFcxaSeYWtrq1Ra/Mv1qJBBrZradMVdpe1xQHyP5hpE7M7Pz5fxBHqP9HBkdeGGaZoo02It59EgXGTlkcNsGEY4I1vUutjMNB9Zxib9/kGjkcestBtnZ9iCTPr9iXIhfWxAONGk39+z74RUq282DjeDCHYwTk5OMO2DiLa2tg4ODl588cUf//jHX/jCF2D+vvTSSzyt6+WXXyaiJ5988v79+/fu3dvY2Hj55ZfVrqUQXZjIjUbDoUksyQwO6Pf7bMF7nOHb3/72wcHBycmJR0jbOzMcNdweJdreB8T36MJ34ZDDrCXARxK34wYpjtO6W4izXiKGTUT1ep112rIsaLx3oBrunBBecVHrAjNTjBHVVgVPlS6Wc+fI7QzAJjIpA8r4UdXVv6MEqvGOKolsTUSwg4E+4Ujvwj2Qw9dffx0/TiYT7jGO9ia44RFU9raqHcy0NeedAXuLJYdvhtDLZB5dWVBkNRqNeEODrAhN01CX5X4KKqoxJxv3sJmOtmLuZiaO6ZxqH5U43pRQMFTBU13HbI9mrSQMnnCH7nqssGLrd5LF6CLYgVH7hEMRL1++jB+Pjo4wCAROb8zGJqKtrS3LsmaWcsUK9gqHh4e8QiFdQtvWXMTsfoizvpFWxhate1SXqrWwqrElmtktHBXY6j1ZmNyVXxBWWLVPD4K3bdugfD/8zJNs9De9qTi9I486R4sIdgC4Wcrp6an7yovq6nv37kGkYWefnp4eHR1hTAgRTSYTVF4ltmbR7EyhdskO+kQkcLkfUj0uqv+53W5Ds2eOsq5WqzgnyrfmvS6fhMTIFsLiVuVHc7UDZmJHDnwAWEP289tFsAOAVG2yu5Cqk7CJaH19/Re/+MX6+jr0+PT09OLFixcuXFhfX+c6bA5vh5tgHXrZR0dHk8nE0SJNSBg1eTsoPIQDNrQqzJwBPu+5M4PTiH/jhJ1Ox30Am90eHVcWout6wUrylqQw3UmXZKfb3baTyStpBLMdOd7Zl2oggh0GnmyNEDXuhDMct9Hm7MKFCxBLNsc3NjY2NjaOjo4ODg5mzsyOCXgFsMMQzU4LDjBDC4NeuDGEo9PpoJkJKa2/ye7RjfaibBO3223cM8+djjsdRj8S0HiRnHMO/3kg8xqB8CL1A1+G5VvQZJNw3cp4iEjywWw1uYw8A9VZQ+qwwwBT+/Lly1tbWxiQhftnZnhtbGyow7vo8bajicF5cMm/tADYZh2Px6HLmonIMIzhcNjr9VDOt7u7W6/XLctyDBQHQa1bON41TWPV58HYFFCtybbjl3EtFAnDMBCGSHshERM6MwN5W5N+H5XNidVtc3IZ/iXzopEggh0GGMq4rZZBk52Gdnp6+sEPfpANcfcZ4CdPZrVgbW1tc3PzmWee0R6HdxsY88V3ultIeh8Q36OFAddrrn72LqdWGSiQbTeTLdVkayoMbodA4kifVh33OYHMt9ttzOwiZQJmCIrXMyQcvV4vdAuaosKSya1XYhVvRy+U3CEu8chgfziXPquqrA7dSgVcgh1l2ao0OhqKYUanap95HxDfo4VBHYK5ublJRP4TuFgs1dIsB6qmcuQY5rjP0CkctjPd11h50BCs2t90xcGnhyQ+ydpTQQEY2flfZFdYHTQaEYaW1U1AMjXTcSCCHRmc/n16eorRIHCGW5bFmWgAfcWTjGGTXat9cHDAY0uazSZ6ipEtmapMapqmpgt5HxDfo8UDxVemaV66dGl/f99P+zAuvPY4Rh2uhZ6j6iuG63miMq/Cm3FEvvG+YK8v87qFQeICC2ER3babcqOVWNCe3ozbUs+vVIMsjjZz97dyJzX8zd/8zW//9m9H+7rQ0aB2MNx9lmUdHh4S0fe///0/+7M/w6hNwwZHsjn79a9//Xvf+x7N6kWKediQK7Qz6/f76jHoZornqq3N+OR4+rzZEizY8DyzTPK74CNxAN/jfUB8j7pRI7U8ydswDPalOz52P/zoRz/6zd/8Tf/HLw/sbEjpzLIrB4grE5FlWQvts06nozYExfRMP6/CL+T49LBaUpSYH3LoNGfD8T0e7W9Xh16vhw+kVqv5+Q0KgBU3RFs0R2YZ5SG5rNVqaZrmoX1iYT/GycmJZVlqiNo/p6enf/u3f4vhHy+++OLFixehKCyK1ENEEwAAIABJREFUkBPTNF988UX1HtZjTPJgscFt0zT5AHQd54spOpJyv1I+ibdWwSuOwxx2mPcb9D4gvkcd8MfI64ergM/Dsu3/nMmD3quc2u1HSi3L8tm6pF6vo3k4fvSv1njuYDBwfIvwXFw4OJSuPupoGM6yLcnhZG9f1M9Q8Ila/RVIblmtsy/SgSiaYE9M8x8Dpiw9eO89Inrv7Iw07czuHloqlZ4olS488cT/Nqc75uHhIbu+GTQoVf8mkUXF95im+cILL3zzm99kD7mqxxBd/rHRaKgTsnH71q1b6pUUos6nMgzDe24jcuL+4A/+gPw1/kQ4OfQBMT3q2Aaxnc1PzEXamprut/BgFHQ1m00/rUvgG+fZ2GiN4t8ljtdSg9mOym+wsP573kPYoKyOliOawDNGh8PhYe+5p6993rqSg8Lf1Kk0GgdBBm9zM9TiqTUVT7D/0TQniVysNU1zqHWlUrl9+7bDsIP9pNorr7322rxOZ+6B06pgu21iDO+6fv06a7yftuSvv/56ASLEvP68vxGfdDodsm1WP0a2o/2ZaZo+Z23Bxe1nSaEVl8vHV+F3h90Y3iw+sdH3/s8rW3fozZZ1pXH+bNEUJQ627dKvhRrMLvRCqjUVT7DnGcQqCDajgQmniRGRIwsMnUZwMN+jHoDJXfzjzFidKtgYrzkvRq5qs+PpCw8LBNwAMlcjX3AGONTXo7U4cDioIcPj8Xg6nW5ubiJHnR6vq+ZodK1Wiy+vG+Y1UuTYB1Bg2A/Bc06tK43z/7mu3e1rd/sXbpVEthcCI5uIJv3+tqv9uBqrLqphzaxKHTbPsWYBxo/zssyg1g5TeNuFQ8LdqDbEMsM/5nlNOTfNP243gAd5CWznFNM0/UsjX/QhwO5Y8szz4wbMO6481DRtOp0eHx/zGXA29EuJ9ZNHeIiL09rtdoHTp7kInj9SdJ2DSMMlDtnW7ibUMySncLn2weNZ3zdLJR5NjRsFVmsqnoU9j9PT05OTE9jE6NMJqYZs84hrPpjrp72TxlVFd1vDpJjdaFbqcSr4tx13qhdcjmF7nGQhf/iHf0hBtFAEOz6Qeu2nIQlkVY0CIMPcO56NzGR+lrsD+f7+/mg0wtMxCySBlCh+RSKq1+vValXNjysS8PzT4743NZ8AhvXamy3tbn/tzRb027rSkNj2PHa63b1Wiwdsq97vvVYrL/3Al2FVBJuI1tfXNzY2ILHr6+twgLNLnEd0QK2h5QtLvNRkcg+B6fV6C/uaOSLWfkCJl3eWmQNsGp577jn3q7u7j7lreecdEN+jAnqPQJ7V0ZmcezzTzl7YLGU4HE6nUz4APUQ7nU4Cqd1qBty89RcAqLXjm+yuUD1/tgv3OIxs7W6fBVu9TbaW85ErKO2qb5weHwJWbMOaWQmXOI/fcGd7QaHJbgPOtrXbHz4TxzHQHt5QI105kDmrNjp16Dce5TsduWw+MU3z4x//OLtG1ZOTYgo4Wo8tPCC+R4sKcgjQ+9P7SAzPME1zf3+/3W5zC3FYzHBun5ycaJp2cnIynU5hryNU7NETFA+5O6Ridkiv14vJU91utzVN63Q6RdVpwO4NP7sfeMgfXD97cP2MJRlqzbdhhV+4VYJFjh9xO4G3kx0gzGon8BWRalB8C5uD1vPMZajX1tYWjGxocLg2okiiMU2TL5SGYbz88ss+24bD3X39+nUusIZIHxwcENFLL710cnKiKrrh6rviDU5bKpXUiZ98KixeXblDsD0OiO/RosIjsBYWYeNReM7L5TLsZkSaYXZblnV8fAxRx1M0TVuYEM7fLm5y4gAPxZEUxi+N1Pfi+cOh1jNr4QzD8G5Nev5sF+5xh/XsNqxZuVctbW2lFNpB8TudId0MejzzgKOjI3aVh4Bd6Kp7nM1EwzBQsU32vKyFJ0SzM3Qxu379+snJyXe/+111tZ/+9KdLpdInP/nJ0KqGNTvK0tSVe5dHexwQ36Oxknyns3a7XavVoFt+Wl8hhRuZYrqub2xs/OAHP4ApTESbm5vHx8eapg2Hw6tXr965cwf9T5DRBunlZiZs9nEQHa3NZnaXK3wW9/KoOx58gNzlbV7POMc809CovnROYePbK6XixWBhp7PiCzb00qdYhmOmZjPqPGz3Go6OjpD+tr6+vra2xkYzsuSefvrpF154AYKNnYcaiV+Go6MjdZj3ipOwYLNS8j3egs2O63K5jMAzepsYSmc3yD8pfzhQC/yIMirY6NeuXeMZJPV6nRuLQvXxXIjNSrU3CQe2RA5/Rr1e53J2dfY5vCM+C+L9M0+2yTbHVy3UnV+kNWkSo/3UUdPzeppCGnGMQybZFc/pV6jYBq+99hr58BMEBWXoItgp4nPylRpmnk6nV65cIbuPCtn+ZKgvm9GQc7VgDDllkG0EpzExbDweI1V7MBhsbm4+//zzotD+wSc501zGB06Pi7f/BrGBmCnJavA7Mdl2W1ZCtBRcsCeTCdQ0Kp2bh4dmb21tHR4echhb07SDgwPHkiqVytHR0UsvvXTv3j01RN3r9S5fvkz2dM4I3wUGiIlmpwK6grN97H2NQ9IZXNyj0QhtZdW2oCjLxj3c7psnZMPC47HZyMrGd+z4+BhFVnx/gbt/c3s190P8uwiKzy0Xi3eSOELdZBvf8b6oqHXMFFawUXVN81ujRI6HmsKcxQ01u01dGO5/5ZVXsGBV9fFGIt9ziJGdFuqI6IVZSGzAcacRd3DUMAy4rzGYi+w0eyiK4+T1ep3Nvul0ikpuLh6L+K2mDUSaR425bVw4MLCFKupmhSSeXRSKKdhHR0fokRK3Ye140XljvlAwhlFg+HFjY0M1u/mwjY0NTNNS73f0QI0KMbLTAsqqWsk+bTW4r+HWdogrW9uQKPwIJ02n03ELFRdew87G4OrCKNZCnWbQyWRkE2jLwv6MLI/LdBRzC7mmgIINtY7JDc4RcYfzB63TKpUKLHv4sVW2trYsy1L7qV2+fJk3FuqRbsk/PT119DmPEPFipYJazRVomhb7V+dV4TvyujHgyzTN8XjM6U4cVUW12ObmZlD7EoqYQcMUIQZ2Lfj/VIkINXKBEuPhY69Wq2QPSaNFKYSCEJoCCjbsRXaJ852wcSlsjbU3sJUnkwnZSWTuV7l8+fJkMlEf8rOr8NnCJTSnp6eHh4fhRoAL4cAmCWnYUO6gl/jj4+OnnnrK58HQVIwMgWwjyI3dJ4zyoKLLG46MhL1VnaY5iWDzwAfSbreRu9dut302eJ9Op7VaTa2AJ1fGeLpw97S0FyJEQwEFm+w8L/7x9PQUvUcoHrVG2Rgp24J5Zqu79HmhTMJbEMk6Z4KVw9YX2U4GdNfhK7uaF+aTzc3N9+zZ7T5BnBvtx9U0K3bO+wRNyMleNhH1ej13wXEycCpZCJ1muD2qYc+r9vOsXq935coVwzD+/M//nGydzpRtjW5oJAHsAlFMwSYidkpz7XKEqVsOPeZgOYqqMUrEHYoOQUzpZgxWyIsX2U4MlrfRaPTOO+9Mp9NOp+O/SBeNU/y8kDp5Gqa2wybGPf5XjibkuM1LtSwr4VmZsIaxhuX7kMBKRqcjP58/KmWRIvDEE0/EVK+1DKzWEsAuEoUVbIY95JGki1uW5V3YzeIalWDH6g9XT86V4iLbSQL5/MxnPsMJ3gsv/ajv8mnRwpRHK1NOTHMcEzRqq04YQ+ltwhnm3B4uQpnkXfhCD79pmni/kO1MWdVuEqvpEhIgx4Lt3yy4d+/e2dlZqVR699131RmXkZf5379/n2///Oc//8UvfrHkCd9777379+///Oc/X/I8QTk8PDw7OxPBTgAOl0Kt1emTM4Fakz39Rdf1hXKLEwYNNqMhNm5zwBuNUVH5jS5pbOYmFsmGGzxatUYY28+RsOzRxZ0yrNZoMM6NU9JejhANeRXsD3zgA34Ou3v37vHx8ebm5ubm5pNPPhn3qn72s5/hBl7Uj+AdHx+/8847aF/l5oknnvjpT3/69ttvl8vlKBe6iCeffHI6nb799tvzFlYwfH6dYkLtVLpQAFBFjdvQGG4VPtPGRe0Wv5B/uxyvpfbXBOjHp9tw09PEss9M04zcBY1EfW+vPj4T7hNHGVZrgGFfJOZ1gcirYC+8NMDBGEfnXg9wXUNGrp9yFzZiXnjhhXnH1Go1NNZIOEhmWdZgMPBYmBAJ7FYN9/uFg3p3dxd5zg7Z5uzlK1eubG5u+nwJDg+zTtdqNfZFYW/BngCWbW5pHuJd+KfX68XUOIzHpXDPGXWSB+DuNJR5tQZiXheMPAk2X0fIjvPBTedhNCx0MEYL/ubVVlMe4Eq9u7vLBtA8arWa/zrdqIDNEWLkthAIKO54PA5dxYvjuacKcrbVGiciunr1qk8phcMZksz1ZjDl0RCNb+N4dtETEfapgRYfCLb7Yzo/PkPTNJ80zdJo9Gu3bpH9R42dEMyADKaYCSvCWtoL8Euv10Pgand3l5NCLctCeioRmaaJaYPcQArRwZndg+MAVy78MftxAPjfTBiGgTZMUSwzANVqNVD+sLAkQX/F+HPgH1lfYRru7u6y/9y/WkOQ8CP6gZDih4dQlctltTsYuqVSzCXIHLqO7yXIzjnn8AS/nGVZSBoQtRZSJDcWNq5lPNVA/bvtdDr8xwyjUC1yTQaYHWhMQT7Ma45Z8jvyhgd5DQYD9Ro9GAzi8/n7CewJS8JJZ4H2cB4scwYWJHh0Zn6vYHGWy2V8dbEVcFSWR4jaZDRhL/Req7XT7eJNIb1O1FpIl9wIdrlc3tzc5C2/CtpBkB1OU514lGDjIfSOGA6HC+UN16BAxaOInOFNwS2JwOFoNJr5mUSFGtjLQkOrAqOathGy0GrnGVaWZbFIe6yEN5pXr149Pj7mvSknTkcCdy6jRc3A42O78TD6i5dOMhsmElCHLRSJ3Aj25ubmvIsIpv86/pY4sJfE4ohQl+mzrzKb14HmDaAp9P7+Phvl0O+4LyIc2CvkNKfsEKijuE88KrbdGSH+7Xv++vGZ1Ty10F8Sh0hT2M5lS7LXak36/UqjUWk8StfKl1QDZIlrd/sXbpVQ5ZX2ioRlyY1gQ9vm/QHD3ExyPaovmpPD/TyRzesQI5KGw+Hx8THZ1vzCUcpR4b5AC5EDr3Kn04lEpfDt4kwOB/jmuF8o0AwSsnUa6WnIUwva8oyHiHCKXLpu50m/D7Xe6SYkb8icjeMP2brSeHD9jOdhCwUgN4JN8ze5mqalUmKhjlLwr2Qwr9n0CeRnbjabuP5y0+PE3vh4PBaXeNwYhoFquiV/rd7fLrXsWyXoi0KksVpux82DNLy/2xkxpt1UGo29ZF9xYfPEZWC1Fs0uBnkS7HlE3rDMGx5NSIpP27+YoUsUEYWLCjt2Bj5z1pZEHY0gxAQX5S+/CUNyA25z0ha+b8PhcHNzM5JXAY7zwKznNqvub/hrr7129+7dLBjTHkz6/Z0EXy7c5WvhdY/bnIkzvDBkVLCn06k6sY7srKuZV5nz8/Ok1kVEhIIWrmMJmkG95PzgZcY2hMZ/40YhNJi2GYmOsr+am5NjuAWU238HlUDwytl8VzVbNakp211HKo3GpJ8De9RDrSHVsKpFrfOFW/tUMirY+/v7GOHHQJmW9xYuD9pH1Gq1FIudOMcnmU9DmqgkgGEY7XZb9RIv88tlSeYbaKuiNimLHNQU8G3kKqoTTdQDhPhg21ranOUOt/apZFSwZ4JrjVrpkeJKEuucPBOodZJhv6ChesEnw+Hw9WazNBr9Xq/3X2q1iWn+2DT/lYiIbhLtdLtqrnIgHGY0h5xj9UJPp1Mu6FBrGchW683NzYQb468aiFtH5QmXidqZIjeCDXVEylVi2dHzQCFZupotnUqLwXA4/NXnn//ZaPS64rA503UiKo1Ge61W5ewswpeLdZs7GAxQxYBuu/wVRYk2NzFN3UnmTS784R6cP9u9cKu0fJaZ6le/cKuEM4vJni65aU0KnTBNE81Hk+/T6YC7taT16sm/qHQqDQpyr1599VXumOs+YDQaffJLX7pxdvZLV6/+0tWrv3br1vM//OEXf/jDL/7wh++/cYOI9lq5aX+BXp785ZxOpwjME1Gz2dQ0LabwebTApZFr2YZBvEzjFO1uX7XUodNrb7akSCxdcmNhc79iykByaaCGJ/ERd0RfDTdyx0oxsj0wTfP4+Pjpp58+Pj7m2E25XD45OcEB3Oief+Tf4Ge//33H2f7Tpz6198Ybk35/u98P7RhPGO42iGIKhxeqXC5n3LxmDuZ85pGXTWuatra2dn5+HuU5l56qyQlrOAkLtszrTJfcCDZlyZOG2pUU/eFckB20xelC1OFLDmA/SdKQA7XZNRFdvHjx3Xfffe+998rl8nQ6VcurkNXPXxs1G2smuq7/9NOf/oc/+qNoHePJlEGqrVR4knR2/oTnwW3O5jVOiaXDiWVFe9olZXVeztr5s11o9tqbLYlqp0KeBDsLKeIAuuVnKldMxJ10pub0oWcWRx9TD0ZkCmybdF3HpDjHo44xLSHGqPyvn//8T37yk5/dvPkXH/vYf/nOd5ZeL1E8kjMPOGZ4WnbyU2KDknCbM4pBrcluShquIyn0mObovWh2uuRGsGGdQC34OhhfVz9v3GWmCTOzU1UkuHuwc66vOvZ4xb3iSHvkxp/D4RBq7bGhVBMVMbIadVwLR0p88ktfunnz5k//6q/+7Itf/OSXvhT1W4kXdezm/93p/OyNNygbe+5iw7LKtrLPJ0KJYVvPe5aq2UQkxWNJkhvBnidRIdQ6EpdgipodYthXINyqwxMGeaxTTC+dC4bDoaOdpM+a+Hq9ji8MGu8gLcOP0bnT7f7NV77ys5s3b968SXZWFO5f5o0kia7r/2xZkzt3JvmJx+camL9IF9fu9hdaw6q6LzyYNRtPlGZqiZEbwZ6pT+GUA1fb/KoO91lLEh68iKYxCb966qhOBXf6gs/9EzfcJruvCPe49QaTo/Zarel0+u6dO48SmFutHGn2dqMx6ffnJXMJcQDfOBF5eLDV8i3/0suHsbUtpnYC5Eaw53HhwoUQCZaRqDXmYSSvXoZh9Hq9VCL63L6KUooFxIraERAeb5qTGsZ3BvoQOFcRRcn1et2nYAOHNt8s5eyP9yDzhVI5qqDzCUxhsn3dDjEOJ9UzX0K14x0GvdRwR0ie/uZnStSDBw80TcujxczZxbquI6Lp84loCk0ppc3Di5tu05gIUcc7kiLM8HjzpBZShnPQ4wER/w4PdIvDa+F3t0yX0Ictr1stGNwxpUpFmCaCdWbTJbBnf4xEtF0sBwDXZCMNzX3A8g7t82e72BBw5be6OYCVL2odCbkRbPRO4sscKddWXE1Qy5jkknD9DZ0rbpomkrGhfz7Pg+QmFMyklTafhUZvy8P92LFh4rnRPGmKVYrzJxwNAOBs8B+e4FQA9TyhoxvbSn8PiPekVNq6enWjXD6ZTluuqu7QrK2tPXjwIJJTZdMZzqVclNX9xPKwqe2+P5Lzq7O3odCSRh4HuRFsvq4hXZlDqkzCas1Z08vEdNWWy44KXQTaee41Z8irYyFS7KyOHUOuBRtOb4djAyqODxNdYvihebl4jsJ071+E43u7TC4CAts8BXK73z/o9yf9/hEREe1FFN62LCsStc6st3lh4XVhSEA+HYHtmQa9sAy5+UBrtRq7gjHQ1wGumwuLZKKCr8shCpzYhmNgrMPIMwwDXlMkEuMAlmR+m6zZqcxBye8sEN798GfL98Nc5vQxtWNXtVqdeTYECMj2+gD/v44If3HQb/REu1kqZa0/GjwBWfM2T/r9FVHrhGEnOUXhcheYLIZ+m82meyAor1PVMNxgKzN59RoMBoHCzwzLg6M/CW7jvcxMPMZh6Q4rA51OxzCMfBnZMKARfVBXzoZ1uN2eYyAVpd2Vby/mwHZQJv3+XquVkcUw/CktMxJNECKk1Wr1XbmZqkbnxsJmDMPY39+/dOkS18KyhPM44SQvl+F2PBAM0zQRh/6rv/qrH/zgB+Vy+cMf/rD3LGQekrjUoqMg9RmjIbAsy73JQABimbp29Zc1zxZPkp1ulz3k1GoR0XajkaIm7dlrSGsBM4Ftne4nIwiByJNg8+AE9YLLTabwUAYdBh4YhmGaJvLpyuXypz71KZ9PTN28ziMIqbibg0bb4zojrTfhIecc8km/f5CqgVvJmC5yTnimViUI3uRmvCbg8ZrqPXwbap2kmC3fW9swjHK5HEitswDy3XJkXiN0reu6I9qCwERGVDZyVGcv/NKI2ia5hmya1wf2Piaz2XCC4CZPFja7kfkeRxg4j5imubm5mbu3gIyzfAl2VK1t88VOt4tMclWt9xIPb2fNkHVHDSjtwIEgLCRPgg04wws2k3+pi6lQG0ZbuHxpvIX4uoLHROrTRUOgaRoi7nnMbI+EbcXUfvh/q0VxVh5PbEXMpgo6ogaEz0QS0IQMkz/BZmAz+XdmxleoHc5KQ6J4Hm1reryYTe1AQnZvLApV8BYfuq6/8847mVpSwnAUeUeRUqK4GpKztzlryeEO2AOBBe+1WqLZQmbJsWBz8Dh1zVMHN/mE1drdfCP1t+PNaDRSZY+dBKhXplkFeFmwa4fD4XQ6RTxlxU1tUhzUcJLHUbF9kLeKqUqjQd2uaLaQZXIs2O12G+OEFx4Za6dxdwsOP7B7gBu3xbC06EFCu5puxrFht7eDdx6DwaDX67nTs5MEXbuxpdB13fF9SLjrThZQHcJ7rdYe0Y2zs8hfJV+zuUSzhYyTsyxxFf/5vfGpNXqYBH0W7DxkmEPvm81ms9kcjUZZqOL1YHd3t16vW5bVbrfhCfczvgIHONL7kwf5iYDdM81mE8NjiGgwGGBHsjrsdLs3zs6gTDHlSyeclL4k7MDP/mwxYQXJsYXtzjhzDAVJhqD+cI74ksu9TJmp4vUAmyTMcvY/+gItwNKay+neKKipgmoao7vF3iqASdWRn5Nsl3u+rNVKo7GX9hoEYSZ5tbB7vZ7DvEbPzkADhpfHrbgLMQyj3W4bhoGepqp6wUOehS5mC1GtVZ9PwcHqfiUx3EGHarU6HA7hJ+A755V+FZ441BSmKtvuubOzpURbyCD5E2xcZ8mVnMVSnZjghfOHAyQ9OWzNdKO8IQj69rHHStcxzitBNR2/BZ/u/UISnzLtdLt59DBjzdxqJu3lCMJDcibYpmnCJOLJkkjagkhDyEejUafTSSYYGSI/3ANoRsbD2EuSyrtzJDHgCzNzlnku3BsxEVPxFUztSb+PGWJxvEQcILQPzb5ZKolyC1kgN4KNnuGWZcEkMgwDKUK4+EK8MfwDZlzc3UiwY4i2NKjYbTIBBpEl/KKObRBUWdM01aXBhV74UiW8wmLDJeD5kj01I4+rtHO0fqF45CbpjGON0DNom5plxiOik2E8HkeurIZhDIfDTqeTu95n2cfxy1I7tTlyzYq9YZpJAiK00+2ihCxfhV5kd1bhVjMYoyJNTIVUyI1gq7XO7XYbNbWhBxgvT+TmNZ92FQQj3bmc2OphAY4ONiuLyI83cBLsKC3Z051+JqwmuRFsskO8iEem6zqGZR+55ECtC5/3pOplAsysPePEN8uyRK0RrN1Jexm5AFNDHsp2qZTxxqtCwciNYMPCxoU1dY/xeDyOQ2+iHcycWcL1hguN41PlMAqKuFbhA/cmmYGbULhiyFul0aicnSG1HjNUHG9KDXXDdSEudCESciPYtVotC3qGfKU4/OHq/IwCk3A+lzuPbzwec9wh9a9T6nBBV9xystPtTkq5udr44aFI26a249GKazZaJYbOr8KqkZs/ISRkpb2KR3oTkz882nNmkDiS9eYxcwCr+uNgMFhlzWZDsNJobMcs2KxbBXO8I5nOcadqT6thbzGyhSXJjWAjj3cwGCQ/oUHNRUeLzchfIuhs7/wSU7LeTOYNYMXnHNOvMi+wJzyZgVrc73Ov1SqYf3ihkx/ehb1WS4xsYUnyVIed8CvyKAi14ylXeEfVdBr15SuVqJxMupmfzmWrsEOax4FtWyemndgZoD4qmVfMDhB16XUqLEluLOy0Es04aO24f3kz0TRNy7LgCU89jS4ZlmnmGpSVbQweiLg94SqVRmMFpRrw4E53hpog+Cc3gs2tRuPQtpmuUYg0pzRDX4kIQzuWeTnU/mYk4z1hom3mOg8eYJrAa+UUhFSL5JrOOJVG4wBT0USzhbDkRrDJnhAV+WmxFWBrjBXFocpRRV5XuVNHYgVdK5IQkDswxxOipd6vJmQVWMy43VvBMu+ExMiNYMdniSL5KNZJWcgpw1YA7dBXQa3VZD20fE8mer0KLdmXB+FktP5ILAuMrUx35TffgxKpYlRsC0K05EawI4cTzhMYoQ0J4ZlRq2P8caxhMBi4x4nGwerk2y/Jtj3Tgiu7knldtOYmO138oN/HdoETsrgAjAqXTy4IS6I5Jg9mARi7vV6P79E07SzqigjVE66OgogcvJFVkxAeoQaSUWselL68eV286qOZ3LQ7fiRT3OUTjMbC7RvFKoUqUsc3IXJarZamaQ7tUzU6lxa26mtdCBt5agE3zmAYRtxlwaup1kRUr9fxrjudTjJqDaIKnTw0PbMkY3Fw4+yMB0ceKM251HedvLSg9+fNUql4H37xOr4JSZKzrw4XWfkPUlarVVQToZy6Wq2iOWXNJr7V9no9tjJXSrO5gxg++cRC15ubm1E1L9vpdiFje3YjsOKJB5gZV37sdrJxblDgkuWK5IoLYcmNYKsu1kA+TxypBlNrtVoyucqcyIZ5oPMOi7B9Wyqd4Nwk3DCcy+Q2NzdHo1EkrcJROHtgx1OLbW0jexlR7Zml0gnHuYsN54qLZgtByY1gA13XQwgSMssQDEjSPUt2DhTNaRWu7kKWV1lsSpJsTjJzAVy/ruv68jXrC4Fa4xXr9fqHP/xheFDK5XKn0wn3hQFcprytOI2LeoXl9zXGay8HAAAgAElEQVRTlSelUirFSEVVNanvEsKRG8HmWdHwbEMVFgYsR6MRksATHumoLgBLHQwGkFL2FeMdRTXUhF8oRbXmTUmtVkumYTjb1vwhc+x8NBqxwT0YDJaJbatO420Z4ZAILGmoPYNmc0p52quLAJSk7xVxOyLER26yxL/2ta8R0XQ6xdXZ23Ji5RgMBqh+TmzghBu8Ed5wQD8Q2YWtb5oma3boDGdYk6PRKPXWaZ1OJ7G9EZvXXO3tOAD3RxImUIccFyx1eSF47xvl8v/y3/5bknqJxuOOou3CpFhLxrjgoDhZ4sfHx8fHx1evXr1z5w7umXkJZpMa7lmYsEmu002z2WQ9hhnqcMsbhsG/kvF47N9/wLAvOvXUtoTnX+Fj3N/fPz4+nk6nRLS5uXl8fBxHIzkepLiCoxK5aHuv1Uoylo+oxI6Sg8Y2dwH2TBLMFoKSGwvbsiyHJToej1mfHDoBwWu326Zppi7YDGzBmethkxRxX13X8U49BLjT6eBDUO9MUbATjj7w6BQiYqeLmhAQ00ehzpBetYssBDvdN168z79470gITXEsbLInbKpZ32rUFm/MoRbZUWvyXEytVnOXP6Hl+MzjkesO1zp3PE1FrR2/CE3TElBr1MtBlR1zymu1Wqx7ULY143uJzIKZ1un2INvpdreVtioFQPXcSA6a4E2eBBsmFOdqTafT/f19imJ8Vuq411+r1YbD4cwyLe76gmdhO1atVpNa7EMcvcxIKWOLm2azye4W/nDwCQyHw1g1e5VnRJJdnv4wKJCSUxr7hiKx0+3iI12R/npCaPIk2LVajQPAvV4Pxhwu0GkvLRYgS9zvRZ2iAamGaMXn/l2I2jmOq9t1XU9GuSHM2NNw4F/t2R4Tq9ysiuc6o/tHKtJSSPfGo+GbUu8uzCdP1x21MRlLQlHVGsCMhmxznJ63LBRR3+xwOBII2N6FcCJgD+3klIJIXlct5WKnCz+K27HuYHg6xWo6MEVaYgIbwZX9Xgl+yJNgryzs/VbJQgUXUEufkXfWbrfVbcTu7i5GrSwJutAgGZ6L8lm51TWMx+P49jEIoK6yVqljrSVPKiqKFJgXYkIEO5fAxEx7FU7Q4N1t3S7fqZSlWtd1dZuC11JfMYFJ2DfOzm6uqkscPNZ+PHHN5hwCVeHyvm/gmuztFd4ICgtZ6etOBmFPspp37TavM1iM58Ey9u48qU6RPcnptcdap7Jx2XaNKiEidEPLr9tDOqgIfhDBzgqcsYUfYTWOx2PLshwPUTZ6pMRNBqUacL8LyeklouTbtcK+J1vkYJLutVoHK9bNRlhBRLAzhFuWuMiYw8N8fwZd4jPh5m6BnsUJ8JmSakY18lZZJCp2Q+yEx6LgtVQPx4E9szKPW6hC5r0LcZCnTmfprSgJkKiVheGYUcHjQPzPHYdJnW6tmk+4QVUBemQuw0RpY5Lu4HC1ZVjuZPtmqSQu8cjJnXAs7HS2lsaqhIcgYs04zOhcg7RwwzCazaYftTZNs9froeEop51nGb62rnhyL2SmojQbT8te3Ol28UuZ5LAVWsWulBMiQdO0tbW1tbWiCVwWNyCrY2FzcHrh/LF8gcpsP+1TuCU4O/9jX1ykQJ9W3MhmONUZP6ZiL/KAr3wZrEjfky9StFy4cOHBgwdpryIAheolXjBQmoVi4hT7n0QIPAQQXXQ4n4eaUEZB5pJlE5lqDB6NnwJpfCwY8IUOJPkqE8+XD19IBRHs9EnerMSkrwi3CJBqCDCc4TP7mhVMp5kVL/FSwXAO2LiYg5lKtVVFKRPPRTwbC5ZvkeCNCPYqous6WpTzkCtmXtsTD3VHchn3EveAnQoFcCcI8+Ah1t3nnju6cyeVaisuE384VEO8zUIhEMFOjRSj8qgT4y7cm5ubZ2dnpVLp+PiYBZuNZvyIJqBEpAbaOQ8c7c19vnT07ydtUpyEkWU2yuWj4M+KsFhup9s9sKvvJA1bKAAi2OnAPbFjfRU1qKzCVnW5XJ5Op1euXHnnnXcuXbqE4VekWNWqEpum+e///u8YQ8Ka7V+qC4katRXBdoCPJWivzYet2qMwiNnWR0pX7qLawjJ4p9HkFBHsdECp8TKeYbUGbJ7ww0Se2cq7XC5/6lOfYuX+zGc+Q0q7kpkyzM1PEIouwBjySHik2SIGCmpxdqAnxlHgdOPsjLPHE+7LJqRFIQuLRLBTgCdj+jweM7AR+lU91ehJwo+SK95crVZHo5EaWn711Ven0ym3JcH/w+EQ0jtzLJgb/41QVgTWbEkaAstXY0ceYuDs8Qx2MHWUwwlRcX5+nvYSIkYEOzngBse+L5AzHDli7IuGacuSWavVMDC7Xq9Dy9luhnKbpsky/JnPfAZmtHr+qCZVe5PNCWPRIpddlXCzp9D2NSZZhfmeKSN7ksOq8bxQPCNbBDshQjfdhADXajXY0zOPMQzDNE1uLobD8Ip4ouNgHD8cDmu1mri1I2Hb7qodSfA177B5HUJ3K43GXszx5kwZ2YgdyFRNwQ9F69yWWdDSK9xzobjeXmj2ZquWN+6c+UTDMNrttqh1VFTsYt/cNcWMA+5UGs4xzh1GI17W4+dPnZulErLhKnmoFBeygFjYScD1xyHSwnVd99lgXELL6bLT7eaxwVYcsL046fehSYHap2CAJjwWcXySN0uldMuyHRl5K/5tEfwjFnYSDIfDarUa4omdTgdp3mIK5wI2DW+WSimOwUgdRGTVtx/009iOZ5oISyO2EakwsdvA4VMStRb8IxZ27CDDy51v5Wgxhg4kDhMc6WZxrzAZipcA4qbSaFTOzmA/SXE2W7EQ3UCRY/4kI/8Yb9inDWq+q4sJp7I5HUwiZIdYBNs0TWQmu+1CPKROIyk2KFlW1RrdwciWcFT3Q8zUgmkuu+LO20Je2Ol2JZKtsr1caXXk9XIhghdq5VWgBDGIND0ekhe1FsLxULBnNoVhk8i0ITu5SVVinqLIMqwe7DgnP7QKDIdD0zTdrbN1XZ85epIbkqDjGDcB9TOnUsgOK+sJn0e4xO9Y652wqfKzJHW6dtCVqM+VcLWwPI8sbI8oKQSDD2g2m71eDz+qWoL7+UfTNB33rBSqWqM0iz3e83rmcdYYnktE4/G4MOa1pmmI5RfmHc2j0miwUSWtVMDM8Vne7u6DOPc9nNfmfRgrbgifPBvluRgXJuSCEtmiO09Z+VEWbE3TuBeHWq3UbDZVhUaxr9q1o/Dw+Ej8yLY1p3l3Oh0/pc9Q7nleijyC7wlC8oUXbFJ6n8mobOAen8UceNrQcX96/v3bflbCDnCyu+jIb1+IkMUxbLdsQIkXPrHX66nSvgpAqtFxjE1JNTIdaFRGYT43qLXaDHUV4DznTPXVSpcbdkaeirdfOr7mcTiz98mR++YzpVx1gNMSuWmCMI9HX0Q1uuywtj2UwzAMWNV4uiMTuNfrOczuYoO3D3sa2d2c5r2yU61UtV4p2DGuZkfDAltlH6lDw7i5imqbqsTUlYxf1P92iluSsT/fsWCcM1DFuSAE4pFgq9FoTdNUH/hMIMOQZHS3dqsyd8H0Y2fzAvb3969duxbsfWSDWq2GIq5ms8lx6N3d3U6ngz6gaS8waRAj2N3dTXsh6aBmI5OSjDbp90ku60Sk5KPNCyfH1LOTY9g+NwTq7+6g0dhuNNyuAjGphThgZTQMo0S205sVGslizWbTZ+Gst/XsX7BVN/t0Os1jMrnaaww3/v7v/34wGPz6r//6CmZ6Y8uysmoNbpydIXCLHyv2tT5T7azTZafbZVMVgrdnJ6bFOkwF2yk/R3JBOTdyEWNaiAm39qk/lsjV0SKQWbwQPttCx7iavGaa5v7+/pIvnTqcO1aYaLRP8A0bDocr6Al3A0FS3eCw7SSBHLgDyWynxi2HG+VyoF+E2hhnleMaQnzs7+87tE8V6NkbTD/5Yv5FCG7zlarAVlkFteb0BbUnjLsAfTWZl9kkszjTZa/VOplOQzxR/N5CWswWbD/KGkiH2GT3/xQhF8DvzcK84g5wn8Q68lnwCbu1016IIPilRETulDGOatOsIq4QPl52jEewZCEbcNG5WNJBedS1Q6q042dmnzLu2SJ7JiFHrJEtyZwVBfc1S7Kjo5mj65l/FqadCxHiKNKLfKvE59zd3RW1Dor0Lk0MHo2lFknvtVoch051dYIQjBIpMWZumaka3LiN2i2+J5z0ipGdGJZlcb46xoe02+1arYY+qZZlcbA5RPo6S3W0a14ppA1WMnBjUW6MOlEy0sW8FvLFwxg2dz6hWWVahmFYluXdwVRl3jE4z1LrFfzhkOFqtVqtVsfjMceb0Y5tXldzbyT9e0lQfCwkxk63u62M1patkjATDF5KexVePDb8w9tuXpFuZcVD1/XxeIxIsyq0g8EghKdk3nhvIRAVqexKBLahK40G2QXfotbCTDKu1hTTPGwhFeY1U4t22+gY782svdnS7vbPn+1aV8TNKGQFVZslxUzwiaZpa2trlk3ay3nEWtoLEKJBnVnuwDAMdQAJo+s6zOVAaJqGaaGDweAfX/0P//jqf7hwqwS1JqK1N1v4McRbWEEk+0wQMohlWefn50S0trYWLm4YEyLYRaDX60F6PVzcbs2GezxoGiBCJ5BtIqptjIgIas1od/sXbpUu3BL/zVyQn7yn5EAJQuHJlPh5A80+Pz9fW1u7cOGCunLNJvlVySW1CCDJ3yOujDC2+4BqtYoB1ZBtXddRlO/xWlx+rev6/1j/4QMiIlp7s2VdaWh3+w7lFubxqBRbEFaGTLmX/QDZdnjILcuC5X1+fp7wO8piUhzkx3FnBteZL9rt9swOJ4PBAM1PyB4MyhVf6iwTIhoOh3/3d3/3L//yL47kNRXtbl/1hyOeff6s5PjMAFOWebCEIAiZBZpNRBBsWNjsOY+KVqvVd23iVe0TwV4VuIeoW2thLjvuGY/HamNwvu2noMvhDBfZdoM+HlJfJAh5YaYnPFojWwR7FTFNk7PJDMNQrWTU5gWqoh4MBkRUrVZDlHJxMhoRPbgupuQjYF5L7w5ByBdsajMRavZCwZaks2Ki2zjywJvN5syMcQ/q9bp0C48DqTIShNzhdoMnmX0mgl1AkMWNr9HMyuzEOH+2y5XZUuvFcFNrQRByh0Ozk8wYlyzxYpKdOSvWlYZ1pQHfOGLb1pWGxLNJ+m0JvkFcSVoSZQeHZq+trT148CCB1xULW0gCt0KvvdlaTZsbUyiy5gy/WSrdLJX2Wi38Lz6ATKHmgggZQa3ySuxFxcJeLUK0NouK82e7KNRmU5uUfHIY4oW3ISb9fgbbWXM5OG48/L9UkiT2LACpFsHOLG5/eHxDRESwVwt3BVdiQI9Zs6HNfBnCnYVvRc7THtNeyEPUVmssz1gkz6Mkom3Jj0sPOKKK/XeRayzLcvjD0VklDuNbBDtPcIux0WiUnSh1IFQz2m06sJAXElbrjJitM9Wa2PpvtR5qNhwDjQZlzDGwCkCtC7+RLR7oj8b9VaJSbhHsHMBV6XBo84DLdDPAl0fNR8M9q+D3y4jmsRjPqwXf6XYx/XNPUW5qtTKy/pVC1DqPsEijdDuScm0R7KyD3t3wY9frdW5ABhXPu2YT0fmzXc4+U73lRQKh6+RzzfgVOXbO9+OGn1rwnW6X7Bw0OMlFswXBJ9x7PBLNFsHOEKZpokutYRjQY8MwoNbcmwzKXa1Wx+PxvAHYDmCR40jM+QjXtiw+kEMO2S6kYLNaJyN1vD8gor05x1QaDf+RaV72pFQSzU4MOJ+K9+ewgnAN2JL5aCLYKQORVrt2W5aF3qK6rrfbbXJ1EsWPuq4PBgPTNB3BbFZ6/Njr9bi1GQ/z4Nlc8b43gYhsl3Jias2RaYcYb9s/LmPlVxoNuMe3F3kL4kuULTZqPiZuS9OCIgGTjMI22xbBTg2exkFEjt6fnU4HVnW1WqX5ysrDMdnOZnkeDoewzkej0e7u7mAwwG0isiwLYp81wS7kgE41VJzAy7FaxzQEDO5xTC7Z8zTTRa1DwMPu2KoW87p4qMO+gj5XBDs1HL5uld3dXdzw1lQ8appmrVZj+Yc8j0YjdUJ2vV4fDAbq9yPQ/I9k4LqvC7dKPieFsBWS2evaQZzyyag+8LhNeSSjqaVf3sot+ERVa25tltkvtrAM0OwQpV9ZdFsVfloX7GCfoyoXgmlamGnNCs311ng0g/I8E3WctoczUE1S44OzeXWDyRu3YGP2V/KqCVObf8xOxVru4G++VHCtFA47W8ZrZgIEqpvNJt+ONu1rMBhkLY9sGbyHcjraNKoNWDI4wRNSGpNgs5lLaYslK3dFyrVDgZZ/otYrzkLBFpd4EnCiAcPiCguYljOC82JA+wSFXtxWxdFZRT2Mr244fu3NVqYydKBhcaiXo+dJTK/in0qjUTk7e7SqsGnkqr2+OqrPnnBRa8EbEewkgG2twn5s/FgY4zgqWLMdA0JwRXNf2vj4DJbBRO6jXtjzJC04JS1c6dej9iz40fbzOw4rWLCca7cytdcUsokIdgpwe1H8T3YEOu11ZQtVd/1cyzjJPGuCHS1sxWZNrcFOt7vd7z9sjlYq8Z1+lspOdVSgHSiTSFQm/X4l5pyAZGA3kqi14BOJYadDr9cjxcImIl3XixSHToVMGSsI60YSWuY+Zaxe2VRrht+76rf3toznfVwoYd9rtfB0bAUy/vb9wGotcWuBkaSzjIIqLFRbkdK/TAR7SbKTvBNVfnjeM7Ed68cHcvNxd3egjQiem2vNztTOUsgOknSWUWq1mmmag8EA7U2ocIljaYFg9tqbrXQLvSbRdQ4/UNqW5TF8y/loRDTp92+WHl1zHHlzPt/dTre712rttVrqADFuJ0d5yFZj2zrthQg5QwQ7NQzDQIeyWq3WbrdFsCMBVgsnoKVV6AWV3Q4lrmyPbttzO3JnVbvhkZ34kT+Zg+Bt4CqNBnW7HMvnSPkjD3zmW50j3yJrRQ1C9hHBTgF0CbUsi9uFiic8QqwrjQfXz7KQNO62FzlSO9OaVIu1SDFAwwl/BnHraDiHAax2UqL7+Dx37M9wZ/m1xoNa9SBqLQRFBDsFRqMRz7TmLqRCtLARk2I3FbdXXM0dUx9y9BZVn5JHN3hizBsPyklqyS/JA7XnT+o5FkIeyahgl8vla9eu8Y/uHLRcYxgGCrokeh0f7BtP0fF44BJsbsSNqie1hClHIdgs81gteJYS02T6luAHb+3LaJY42YVPoMCj+hC9hn6Px+N6vc5twAsMezIpZn1C0njyRvbCFHFH7jSIu+X46sAfb3aSyWFeZ6F+QcgsrVZL0zQP7cuohb066Lo+Ho/xPzdRKbByq77fh/fYSUNxyFVa2T0LU8QrjcaBckAG/bd52SXPXCcnpu21Wpmys0WthWUQwU4ZXG5QjV2r1SzLMgwDbhBHW5XUlhgdrNbsClbba9wslVbKxHS7FtTPJ3XHeC7UmuavkzXbHZiYByf9RV5Ex4XXkZxNWFlEsFNG13VEsjVNMwyD7x+NRlyizco9GAxynaTmViPk+m7bDsxYZ1slhtvXHRREuLPjzs0pjwrA5rQlV1GlemLnBh5EsXOS6LUQFSLYKVOr1VCNPfNO/I8WK0SEUHdOZVsdWeF4yN1bIyoTUx0iks3LpRrOP5lOj+7c4YcOZiWNC4GAZmOn6G5L7jxSqRffbjSW33ips+ay+fUT8oUIdg7Qdf2dd965dOkSF4PR4zOwMx7tVuPWHtYzrpXq6IhITG0eCrL8qQIxr3h6T2nrMTP1jIHG7PloxC14ULFLtP2DX9BBo7FkSTdXXYszXIiEtbQXIPhiOp0ahmEYxmg0qlarg8EAuWmDwaDT6YzH47QXOJeJLcA+jeZKo3Hj7KxiR7iXX0DyDUq3lcXj7avOA/z/sLmmbUPfODvb6XaffOqpratXd7rdjXJ5o1zG2S5fu8afYZLvQlgGdTKstbEt5rUQCVlMBF2psq6gmKZpWZY6mpNt62zODgk9qiGQzHuT/KwFD31VZ1iBeR/Ow7ZohqER/dgux8x7gD9HhB7f4pjjnmLrHiGDsJxpmqZpGtmJk5ZlSVlX0dB13TTNWq2GZHJubgoGg4Gu6xnpxML28bx2VN5wB4zst4Z2o7YBdzyE3imkRK/nfTgo/Zo83jlhL4efRk4JPb7l4ZTrp65qb90R21ogIggzWFtbY4U+Pz/HPeoBHohg54xarYYEciLirHKuBKPo+qapIyjCGce4vYyJvN1oII67ZGvo5C+afuKmPidTkfJhbpTLotbJEC4A8ci2/qUy1Fqi1wJYW3ssAA2p5tuapjkOmIkIdv5Qq79manYgWJjdZS18/56nJ5YDtI77l/dmVxqNvUKnSfvUbO5m+pl/+Zf4FyUQhZ239jC38ZfK9O40xemuQgZRFdod4WVr2xsR7DyBALajYhsMh8PhcOjfvHZ3HKPHw6sPE2XtgtSZnliPkRWRGIKSZsXIR5EkPM486GYRJQn07lRsa8EBLt0eyVh+8rREsPMEAthENBwO2+22+lCtVjNNE11Occ9NZU4wPa6gXE3ED7Ee7zw+6ejhBcuOJavucbUtVEx+Wi5QDh1NLAbqL0tIgBBTugFHr0WtBRUOWi95HhHsPMEtVty9VnAnctCQQP6Bq1fL5TKxcWZLMgutmpysXpvc16mdbpfLow8Un3nc9cFqBLfAcqVmis4kI81KV4dwG0TpPyrEjQh2zpgp1QCF2vCNE9H7b9zYqdeJaIcI5b88ZoOC11mpHaNoltUeEzvdrrrsQuK974Z5HTSYKiRM8qWDQl6IsCy54JfCFQRtTTG1k++8YTf+BOGEljOfE3ZQV5ZuOCUIcQO1FvNacBBtExER7GKi63qn09F1nbupRGgNF9g7nUFQ2+Z/5JSwJMvk94lgCypcch3ZCSM8l5ARTNNsNpvtdlvTNEwNKQCRtCnNDj77JJCSeSckAzxJkpYvLAlsaxFsYQHIIVcnheQa9g0UrJ+2T82W6HXk+G8s5feEb7ZIzGvhceLoqC2CXUBQpY3ss2q1mvZyouHh6KqM2dmhr/v+t95i8EWLpmnn5+fRXkklgC24QeF1tOeUGHYB4Snauq4XwMImop1uV23hsq3WiKdKAjNpCuZXSJ3ojR4xrwsHZiEu3+Y58i+bCHYxQU122quIDO45NUF9Wr9PRAcrMCVaLZpPey3CbB42SxHBzhUOf7Vj8uHyA4sjt62BuMQLS0zfmHTBqGxueF74hCyUy/OAcCEoy/wV+HRsQKofdhEXcgKr9Wg06nQ6g8GARXowGKgjEEOfPw73mwh20ej1erCtDcMYjUbLf/Oyxk63i39kB7bTXlG8iG0dmtBqrTbSXwg6paBxSriXE1JEt8nIVGJvRLCLhqZpPLbLMIxIdosZBHYnFa7cS4iQ0FZO0C8VNFsEO4P42bSNRiM1ORe3s3nZjD7vfHmazSYR9Xo9vieO/PgVAQO+crF5DAGHeD2mfwpCUG6WSoGat8O8lgld2cfbgEEMezQa1Wo1KE7CV85Wq6Vpmof2SdJZwTEMo9frDQaDomo2YWz245PEBCE0oZu3i1pnGUi1ruuGYagTGUzTNAwDww+h5XwArpw4LCPXTxHs4oPJm9yjtEhgNIgaxo5Vs9UKq9Djs1Z8VKggpMJoNHJINUDXCpTV4AppmiZqYjVNGw6HeCgj10+JYRefWq2GYHZh2pSqcAZ1rG1V9lotTDy7fO3a1tWreLmbpVLQV9xrtfAvnmUKERAo40xFYtgZx3vUIR7l+q7RaGRZlq7ryOFdvtArEkSwVwLM79I0DQUMaS8nYjhpPCZgWF++do2IDvf3j+7cwf0Xn3oqUJo6G+iFz2xfNSTpLDuMRqNwlziY2rqu41IJe7per+/u7u7u7mbEJS6CvUJgYDYRFU+2K7EFsFllD/f3+YXw795bb/l5ItnWP7dAkRS54mFdaYhgZ4HBYDCdTjudDieXodLaw7xmms1ms9lEom42s8Qlhr1aYFo22oxj/ibuz8j+cXn2Wq1orW2oLHYDjry27X5/r9VyD75ULek95f7QYW8hScT/UQzG4zGub4PBYGb0eia9Xo+94hm8KopgrxzIp2g2m5Dt0WiUwe9l6uCq/f995Sv4cWYKeqXROGg0Jv3+tq3Zjqw09WBJYs8LlUZjb/FRj4GyLskSzwhXrlzRdd00zXq9PhgMdF33o9YwrKfTKdLNLMvqdDr1ej3JXLPpdHrlyhWPA0SwVw7LskzTHA6HaClQpAEhyBjfieJsB0pw2sPfjhc96Pcdx4s855egFrZ2tw+1RiRbSJfd3V2u4ELgz2fDO+SXHR8f47mapl29ehUtS/2YNEuWzvoMvYtgrxzoS6PGaQpjYWMM5ZJe8YmivpVG4+jOnYVnU93m4vT2Rh2VgTlXmdI5rjXwX4eNd5Spd7HiwLCG7o5GI9RYLwThQtzG5fHOnTtk5417XyTxcuoxPvVbbeTiZ2KTCPaKguwzspsDFEOzYe8ueRJWawzeWHg8HyNS7c28btsXbpWy0yPsIMivHogzPIPggoaeZSGezpfH4XC4v7+PoQxuTyRrLQfLUa7t1m9vyuXydDolouPj40uXLnkcKYK9orTbbdRnF6ntayT1zYHUmkSnF6HqtEPY2M5ee7OVBZcyUhACqbWQWarV6kyVDQSb3Uj3cWSPw3NOtsCbpol2F9zi1M+r8zF+BiJnVLCn0ylPsBBiYjgcWpbVbDbhMsq7kc0JXyGaSrqRS3YkOLKxZkry+bNdhIEv3CpZVxr4l+wyH0N+9QUAIWH/yeELUR3mpOg390cju24Wyby6rnOausci4QNAb1Rd1994442TkxMP7cuoYO/v7+/v76e9iiKD7vbZrDUMx3ajEUnXTwTCI1mSH4rk4ZjJQtMZCo14NpQ7LQ/5dqgcCFRgr73ZSt1DIIDI1dqNQ7/dD3F52LyeppxlZlmWOu3DW1X+r6sAACAASURBVPukccqKgmnZRNTr9bJZcRgU5HIjZcznU9Be9Obj3ciTJPTA5rxw/mzXp4zhSJ4tDf1OGP4KBfo+SAA7OwxsYlXrhcC5XavVvC0iCLmmacfHx0RUrVZxwwMR7NWFe+cWQK0Boo8eF1y0BEcPcPQ84fvVrmSJOUWLbVuHwLrSeHD9DDbrhVul5JV7266n9/8UEey0UOugBoNBp9Oh+UM+kmE4HPZ6Pfi04Rif5xXXdR2Dt4fDITLOpKxL8ALhFlq6gjBTbNtGtlt0WZVVpzcPDsFTkh/LEZNmx+dpH41GGITAvr7I+0ucP9tF8BvZahdulcj2nMetjpVGg7pd7OcknTDjsM8ZX0i0HPFZxBUt6GyBKjJd13d3d8lHVpA6UASbjIWZWyLYqw6+W4UBbao82qegj/eObUXhooyOK9sBfaGZZW1tLT7bfTweYzegJsTCOIhWs3GDZTux8PbCr5AbaSSeIuPxGMqdilQDTj0LdDmt1+tcLI6ZnuVy2duLLi7xlYabAa0g6oyviuIIzXvHb03T4jCvkSPD/Si4uIDs5Fi1BUSEIGdNDW8nII3sd/FzMHc6i3lRwgxGoxEcy2mp9XA4RIJ3vV4P4afc3d1Vq7/gG/dABHulQdczdbJNAZh3tfW4/qqlt3lX67W1tfPz82hPqw5TVzszt9vtZrNZs4lJswFnm6eSj+aBdDpLBXh3YHKkFbGGWRxOqhnM7oQjvVwuex8sLvFVp9lsotmZz5a52WdmGJuD0/NyytAlLddqTURra9FswWFPIy8GkTZvC4ZjeLH2pUcMOwGLFnu7vVarIoNQMwx2h6mkmA2HQ0xRiiqkiGvv5uam92FiYa866ACA28Vwj8/TY9zvUfR14+ws700z4Alf0h8OexpFKYPBQNM0eGJmApegZVlL2hk+gSGbWMDYTxKi2hddSAxYpR710PERiWEdDhHsVQcRFK4IRGlErpnp+kaKOOZoJb+kxDg/P1/GHz4ajRAfQQWBruuIT3s8BYclPIUQjUrik+0btmHtpyab7X7R7BAgIf/m4yMA5vUnQFsbLvcYjUYoo0pioURkV21he+r4wicTVRTBFohsh0wxyoI9bOhVaBMd+pcIe/rq1avlctk0zVqthvi097MWdoeIHDayY63SxvfEp9NFMs7CAenlAMS8w6DTaJ+A4/HjfxwOkbSV2IJRduVQa2xz1Rqt0EjjFGEBfH1n906uHeO8+857NDot7ty5M51OFxrWTK1W86414OZTUa0QzVXU/iqRm9p+VMQNV6AJPoHTC/4M1ZmhWtiQZxyJg3kXlVYRJhS60+nwdzsq97gItrAArosgIr6RX9TR1G5uLj18s8DAaIBOB4oLehvZo9EI19/IixG41itypYQ28Hh1jyPxfUt9YEkeUeUW22vV3c3H4PO/cXam1mGyq+zPvvjFJNfcbrc1TUPrU9wmoghzLSVLXFgMf9swYSanzUrVC6vbvEYSeOH94UuCeUELrxr+Qao5NgGvvvpq5MUIjrzxCIVT7agz75vzcHd4dkZ27xQpyPbPgTJbDw3mMIqeRbrSaKyXy5U5E1nwF/3TN94gf66gqFCdT7iBKptkrpki2MIMlp8jmzDqxtzDGR6ofdUKghHCQQXb+9uCXhDcMPn4+Djaq5vaUAV6CWsbwrlkefSNszM1zuoAWgK3TaXReOYyVa4s82orBD5SuDFwD27vPF6B+djxj7vHKo3Gf1epvHfpks/YTXw0m01Mwq7X6wj9NBqNmPKBRLCFx0C3vIWTXLOGGrfOuxntyH9W215ChFiQ/KuRT8sPLpbAK7Yn+7prUuEDN03Tsiz1GxW5RcJDtfmzwg2e2omPi3O5Hc1EPT7MnW7Xa5OnqMuE6MVnytvPRvF+VphHfm+i7nPPHd25Q0Qb5fLJdLp19eqGvZvEn7ymtPFJEcMwTNPsdDr4kvf7/ZgMbhHslQa1/447cU++jGye5+Gh1jzIK5uKztKCH1mbVV1x3GbtUcWYE6fdYV1vjQ/tzkUNmPsKxRHrd955ZzqdovoLhd3D4XDenODQ8OfAbwRbH3abY4gIUN8sPuQLt0ohnOqsLtv9/l6rdft//8rOU1ez+QXLFD5n4La+/31S/GdHd+78Rzt6jV1UdspQVVd5u92O/OsNRLAFJ9VqlXtM5kiziWjmkK4QxySMahpCLbw12KHlrD0Lm4rMDLI6NgrGlcadwyvt9qhWq/l3NsI052+L2seUiNjNDhNkOByWy+X4vDj8BtXdCe9v+E71c+Bfgfd8EfXzd1BpNA7mT4oTZuKzKcJOt7vd7x9weNvWbJ49kzVgcItgCxEzM9CCpMd8dRfPbykXO8A9zF9WYocCqWOs6HHr3H0qvNDamy3eE8wsYtbu9q8RXasQvUfH33l+82Pf8/MuMBnQYVWMRiMUfY1Go+Pj483NTb6QTafThV0YowXFYB6PcvOTtTdbM49UXRozf1MTJYtK8MPCzQ3Cc7gW7Xa723bjlAN0QNK0eS1W0sXPoMxwiGALs8nm1nUe6ojreWz7OCZu5rm4/QSkHYadaoWzGM88EnAEV1V3x5E8iBo/Dk/0a/7eFyq7HEYzmjyjEVW5XNY0DeHGN954I2G19on7c1O3U+oN+M8dv7KKWNgBWbi5QYvcWq02HA7JlUx+puv/x3CYyEqzggj2SpPTCq553CyVMt7LzCHYMx3gIXCMsZp3NliZ3pbi+bNdSO90On3++ef9r8EwDK5vGY1GPCyEkyHYwb65uXnp0qWZYe/UcUe71bi494jPnW6XEG21M9Hy6PJJhpkTetzgm6NmLCKZ/L8fjf7f//pfF7YZSYth2G3EwvGaItirTr4s6Xls252P9lqtvVkzrbm4M8VrKOdA4cfIBzL6PKG3c1hNXwiafwtPoCOYUqvVLMvCnTzjCJHsQCdPBvgY+EePvdTMT/uZ/2k66T+qR3Knl0/sWCzlf/L6Mhz4Dh9YloUsRb4H36VNw3h/Jv00FDBjV3X7L9yCiGCvOp1OJ+HJDXGAffe2rcrueuuDtIPcbFtnfHAyT/7wmNA1DxZ4VenRE8rxEriiZbMSwecvSPWKs8Y/8z/cqfw/jfNnu0hsVtt98DeTlIIFtGRZQdl2FGG7gYyRa6iGpmn4OpmmmXoF9jz8F2Fjf4zmQogreScPiWCvLti0ciPoDF46g8IhrnlXwFTii6oHNRdtsJYZWeh44nA4vHv3brlcnk6nw+EQj37rW986OzuD9y+P3zqO9Duc56REGRzucba5WZ53VAmf4/uBOY7bqybqDrV21yxkVq39gwuv96R5ByLYqwv+GPB/ZgskguK9bU8eVa096oWyg67rEdouaHAGRx+SxuE213Vd07T8TodzBLMdbVjAI80mwlxXxzfzoQbbx3AD1IddUR9vY769YrlsKC7FbVif6a4nEH5yg/DugvqxRLBXF+53UQyp9oBbSyZspqhu8OxLNc2fQ7zMCa9cuYLCa/6aIekXZncG8878A1Pb3bCFWdAlTTlGjW1Tq7XT7aopF6hl2psj/HkEQQGPDweV/djV5Uith8MhPJfeJhBc3yG2xTKta3Vhl3jaC4mdtALYbH7lQq3Jjr1FaPgahoEvmJr+iusU7vnGN74R1WulgroVW+a3DFc5Bk2S7S13lzxktk9fUHy+EQ6j5AjEpN1tehlM5AznNhDBXl2ymfITHwlf6TgnPONZZg5QlBXhCWu1Wrvdhqed72TbYjqd5nr+euTsdLuOwRg8uopWLJLNMZS0FxIAjrXP/FZjkDYt4eQXl/jqkt8IYlCS75eCXCSftvVoNIIDLdf+YW/UWJ2jayM0u8DvPRBocerwe8P+LtKguYUucXUwa77gRI3BYMC9/7h2a8l4vAi2sBIkY16rfVH8TJLgP2MUK/O0H5i5Kfo/YnJF4rSIxRiGcffu3Tt37hwfH4tmqxTejF44gyd0iDc7oAsvvN+4B66mJU8rgr26zJyHWFTim4Q9s++VH084DGvHnzEmUUK5oWHJyzYqpOMQbLRVwZlRdYqhINPpFH540Ww3D9vutloFSDdTc+s8uqaMx+PcOcPdqPO7okJi2MJKkMyVDib1+bNdP3FrJJ64q0ubzSbuNAxD3aEnSUzhkna7vbm5yV2rdF2/dOkS56MhwpevqTMJgNSziT33Iu3lhAdvAbb1jbMzbws71+Z1fIiFLRQftlHicDaGyynjqQYzH+WHEAxLuBudWgIbOc8//3y5XB4Oh6+++uq1a9ccs7TH43Fh2vhECJd0T/r9/BZkz0t9F/wjgr2ixDSuVfADNMmnDYHKKNM0x+NxMh5jlMD2er0Q3Un9wMMHTdNEVJvfGr6ThWnjEyFqt/yDrHYzVVuzuR+i7PU1yh0i2ELx2el2J6VSRqyTEAk1MLh7vR5npTkmT0dOvV4fDAaxavZwONR1/Z//+Z/v3btXLpc56axer1er1TheNNdA6tDQFE3Is2Oqsk57l2MEWnAeK7ATQGLYK8rq1HSR0qVh3vY/SUIn1CC2PRqNNE1LILYN+cQ068jhUQdPPPHE/9/e17VIcp1pnqguw9hMp0Cza9aQBWJ3u6EyYcbrG1WJxsYXHsn0lcmKdP2Ahrm0JHyxFy6IIe+Mxq3bgTZt8JotRVZgvNDglsBiQatq3Qj7IjNxlS8EnUYSi810tmEHU12xF0/nq7dOfGR8nIg4ceI8NE1VVGTEyfg4z3m/nheSZ4KVrlrzOgVEe80+zLD1725v393efrB215P8C/7dPj7mv2Zna6Q9Vjr+lkJHOd/xeHx6erq/v09bkDrb4JDMg+d5XRAl5bi7vS2EIDGpRlBMQDgWvu9DsrjSm4gBV5SyC5e4WKvkYqNNFE8HbxTbiIUtdQjFRrUu+iAIHMfpYN7ZRu7T1CW+v7/Pl/bKJY4tRMfsGB3Sa9VWl47HY2ovreSAsUBFOMqxlE+gruuiVTasbXB21d7+tqNZtn6Q2s5EFbqcJZ7OfdYlbmE4zk5O7m5vUzFJI2MI1lDbdwiHqtp5CAm2MAx93yc7WBXG4/FgMPjxj3/85MkT13URzLbu0CTEpm4RiVYNKsqCr7sitkbKoQ1gx0JTC9uiUiifdnXG+XqOqzmxllTMhBDoJlle5ygKZHRXbZIOBgMIuVRhapOr4PT09M6dOw8fPrSVXUm4eXDwgLXQFmsKPzs5eXBVbEDt084N66rfoy6b1xvRUcImR+J0Oo22RjcenWr7cQNF2HVB4umqW/nW2VV6NBohe9zzvCpcBdPpFK24Hz9+HATBYrEQ1kMewe3j4y8acV4laf6co7t2rNf6QU7RNDKsRV2yqda8TkLnCJvCfrPZDLyF8pJOMXdHMvgoPUfUMtGQL7cGnia4rov8wRrOBaDsCvliCou+cLng+9nf3//Vr37lOM7jx487tbjMAqrvigJFX+j3BVIny/vGOrE81iInRzc/mpRcpmfld9fQLcLGLIPEWi7UPJ1OR6PRYrHAAr/YkUVmKQyLesDrXh5UI3NGIC2ULhgH5CH3PE/hMpfru52eng4GA6SkKTl4R4CHnBdti3WzDb6PVDYNKUAY5bQbzwO3bK0JNC3rElcLQMv7/WazGRbv6baIVE4AK5z+Ko0Be9KRaUuZcdYA+BiMb/vBq18eHB5WbSJMJpOm2BpvSiPVUPD/i8rqvkQn6w+rAL0LSTtwdxTQlFU9mUyqyPZoBTZyX1csbLjBN05qFKIT6+L9lJkiDEPsSRMKfRZAz0TdWBwhgKZHUQnglMbtwOwDU+O8Mi1xdNzCz03Z1nWGsSXwZDRRzRWQsupoiWDLtXNh45N/8+Dg5sWFyB/hVg4EKLWaM/VBVwgbmEwmGy1LZNZg541Le2go0j70WSFEEASYRonXkSqc/iDCpocn0PM8UpZQ5XjE8c0zr0GcqAZZLBaLxeI//eM/fvbuu5inIE1axXmVV2oVACa4BgkMp66oIycJj4t1ARuXwbK0rRzW+60zOkTYebNXsrBaygH5VAL1icViAfIGc1OtISdpOuZgMCBX52KxmM1mkrEOzzZ+hmLlRkaH99488xqGNREnrttyuRSDwWQywT5/K8TZycnHk4lYFxaXPyl+aDxoDV1u0uJuChVZ+XC241Hf399/9dVXBXtlahNXt6gUmB4xT8JWaXpEmsLwGDYoynEcCjPrYFxirufRccw1fMLFPtgCQsI+sGPIqIK1AeMS+8RGE8lSV8JV+iBLDBXf/b+enn727rsXg8F/8bzZbIbHIAiCYhM9ch2EEJooPOiQl1BD6BGLXeJvLHxXq9X169drUGm1qAK8tiJlBusINnKfaYSN2RlvMmxWKRTddrriZb7SBM3/BBc61iv4+uaZIHjVswQLILv9N9Pp9nz+wzDEr2I9R+TlOYV64AoBzeEGH+969J/50hNbcCvRQcSw9ajxiE3y7XL0uluETT5kMkkNfntTPPySdodJF4EneSlZjHuel4uwySDQ0GvXbEJ1nesYWpeL9YvAy7hNeuANhp4L32bRlSxxmBeYSUdrND2oapEyLyN317wrALZWnuTF+1RmqSPY3d2turVlMdQjU5qE2i4LpWHOZrNeryfVZfi+33g432Ij1DbC6Q4MIWwst20Qy2wU7iSdAqTii3XOVEppAG9KYeREU7I8DPVds9ms0gAk2da4F6vVCoFt/NVxHMdxnjx5Yjlbc1TxLncBJhA2Jlz7fhoMMt2qYEp+TMq9Rx7f7u4uggtgiMYruDQHrlKll8h1XX6/fN9frVb4mS+n4Iyxc4KFYWg9YTco81QFqCtOU/1u9URtYWM0Zsb6wHEcEhwFWyOJr+aom2Szep6XdB1KVtiXT2eBV7yKRRV9ZSnH0HGc/f396BIBmeRZpBQsLFqE1hO2STIgd5m4x/m6PY4FUFuSF7fhXNe9d+/edDrF2euPuuG8EmHXOYBcgFe8iiNDNFByokKcgGsDS6WSw+HQduq0MAmtJ+zBWhzDANqG6vX1fv9re3toCnm+trabHlqTaLb9w507d+jnmtm6QNty4sumqvgq0pVMOmDSs4HvjgtoOdvCGLSesB3HwaLbgJAVutODrcVar18IUYUIdosAaZQOgmLnWTK5ZrMZT6BrMF28tkpRJIpHt/d6vdVqxbWGLGdbmIHWEzaW3iTSyQXCWgqpFx62iDV53zg4OD85aVad36JOpGdy8YJGiu/mLS5XiNFoRHKwNSA9n4CnlAdBsFgszJMPsugUtpoegBogLWgymcznczTkaCmIhvEDmk0971aLdvSHh/if0tOMR6uXX2Ww0VT1PC+2m8twOMS7UOXo0lDAmV8FXNf1PA8iskKI1WoVBIGeNfQWFlnQegubgHU0NQ5q6Tr69vHxbfqZ/+HwEBFu/EZWuLWzzQNKy/gDnMTcWKeiB4a0HbFkNFSodrgRDAaDqquxcwFZhFhDUM1eZ1eBjcMumMrAHMJG/yvBHoiWcnYsqE3k81/XBWBn29tvXFw0OjQLZYC5PJvN0KJNCIFC8KT9U6Qn8Cff9+svbYI2sG4tjXGh8P90Oi3c98WiDCBwhrug1ePRFphD2ADKQDXxyFWK28fHYu0e73JKmgGgHi1CiMFgAP82cUkSYfu+v7HZxng8jtrrUZTUOIsCkWxNWplF4bouOFuYtazXH8iFtFRdGEYR9nw+7/f7mPu64PK6QbHt7W34xk1NRquoWEgTxPZeIySlcWXUbyEpmJQ3ooq87tFoBP+ztpyNh0pYzq4F9Ayb+hbXA3MIGx0vyExpejh14ObBgVgHthHSPjs5ebAObJtkdlcnoaUDqq6Dou7plZ4lelKxjhnrydnkHkf2ODwZtq92ddBZ86ctMIewqb8eMs4MqO/KgpsHBzcvLr6o2Gb5aGfb28IUidN6Gks0gtg0b+WnoDeizsAtuofpfNco0s8zoRosYTcVJC2spCVul2FIWRdhPB7Dzi5T00J1IG3B7eNj+vfGxQUnaRSANTs8JUDusWHZCdPptDrbl8QJ+LuAuqbaKr7w1Xh/Xw0xHo+JQozsS9s4kJmBVSk9lhYFYBphC+Z/K0zbtUk1VYSbBwdgbtA2EtOaHlRZYBqdTqeaz/65AEtuo5QbFit5Dx6GISZHz/PGa6Auuc5yZLpx9ZyuGFCxbQ3rqoGHwbBld50wkLBparCxKKJtAzibshNqbpZVNSjxIh0Flp7j8Tg26o+65JJeqFwYjUat8I7g0ZpMJlx2pnX+Ns2R8YG3iIWBhA0L27I14cbazr67vd1e2l4sFo7jGMbW4LD6fbCII9Yp0o6kcVI71xZwQriuyz0Q165d29oycKpsBLu7u61YvekJ055CPAqWrTluHhyY4R5ve6giCVmeVeXfvWYjG1HM0WhEjnqdMRwOKU0vDMPLy0vHcayprQQo57GEXQwGEjYeiKYHoh2Qktb0KIpjNBpBBazpgagE+WA3EmcVbCHZkTVgMBjAwKrzpMWA+nXclzAMnz171vSIzEErUhEbwXK5TN/BnLIuQLlmk0lA+070/mojeaf3rWopxuOx7/sppUTg1Cpq0NGesmaRTtR6IcNro1JbgwBh8/tiJxaF0FO/tiTQOg8/0wo74xeEb3jjM6YpYS+XS/39Zs0CWuJ5hcTRQQScbaosWruA5zypxRyMvOomtfF4DJHOOltPjkYjCJWEYQgxDbigdVuKIXXcSo5XBEj4GUDYJC0c27AnKhdDu3G3GXzD/X4/nft0tEcxiUgbM44TyslNNQOuE7y9Zl5pM/rszYODFtF2EAQ622TFQNVEscQA87qGVDu0jm4k+QOGLNcohB+FpEOBRjQ3SInd0nYVmEwmrZY/w1sj8pTv42mPbscDdnh4eBJpmsy5T1MLuzCGw2FHTPPbx8c3Tk7O132yhRDi8FBkkxOnxiH44PnJif4e8qptzaYwHA7DMGxcUpe0tesfA5JOuGT60dER/KW0gCBSr5mwsUSAEyIIAsvcCoHr2fQoioCb1HnXuCVTrEwj7E7hJrj58PDGwcE50Xa2JtkSZ2se1YY4l+u6unlNywPN5XRoVwNmarCXPHeMwW1OI0Hkuyl3IHXURshfdKZbgYUERKmLUbUSmJYlbga4u3sjIERaIAmcfwTlXtlPWicwRXqeZx5bA7BrdVCTqLk4Ox18NoR/pdnCquFwCOVjfS5Rq9Giuo/ZbOb7vud5WDKiQLGRRZuBhE0FlG3BF4221v2t8f/d7W38y0il5+vjZOddlGiD8s/WDvYy30UtgiCAm9QwvRQJWIjQl20QiDjo9vrAHQ0zt+mxWOEzldC/IBtUjTDraI0Gx2Na0plYl/c17mDcCNAwmFL6E/m0+Z+yZJbRMQv4t+9ubwtt0tDgA+9Ubx942/hzW1vGmQSt3iBcBH0eg+htsigDnTNJkVNWZz+YziWdibpKsSVqfJCtSipqK+Mg0m5Et7eFoHbX1DQT++N0d7e3+RjK2Me3j48phe28OdomqjYyYp0EUuiTtjfiJ0dpeBAEOtDSfD7XJJHY933cDh0uizHQs9U9UbVuBUcGWtg1LIG5wCdMUv4r52CiUvogHYRbzLG0HT2p5LLm1jkpjxYzr2PPUnMv7Q5a1YTY8qGmLGxAEztbHwsM98jKHisHnnN97jK89I2UA2y0sA0k7BpKsVO82RKkfcDu9HPhs4ur3M+PryTZm1YktXG2bp7PRkDrekwWkJVo8IKAs5stZNLnwbCEXR1wl1G21yBtw4nSYE/0LhK2EMLzvOoIm2K93HqOFR2rWZ+EzqXkaLQokUbOv5TIo9aSgmZNSd1AtC00uCbkB26wBBmuFyFEs7O553mWrSsFVdvX72arP1wdi+4SdkWv1vGtW58+eiQym54ZY9t6AksTcXURQGxNP5Tk7Pl8HgSBJnFKTQC/nA5mJaFBKTSAa6c0QtuaxAi6gDqjY1qJ2XWUsKt4tXhw9xuvv/6tt95SeHA9kdKL842LCzLBRTZ5tSjID9ap/LKMAGHzHxoHZYfQfCpqJ7AGadu6xGtGPZ4VrdZhHSXsvHlnREtJxMMTsGtOxWoWSelyt4+PJTrPflmIp9GvScOuD42DVv2O4zx+/HhnZ0eHfByx7mSAWzafz0n1SaxvaD0TX1O+U60m946A32shRJYZA/SR8ZXR6p52lLBns9kv//mfX3rypNfrxTpsk/K2eAE0OXuJmTSpUW4EuCAP4hRMsxR/Sw0eNMkI1RPkfMbKptfrvfnmm00P6jmQQMunS3jvMY3Wb4DWXFlQaXKMRRIQNaPnitc6YjIRa4lf3jWLTIKUqabxQI+EjhK2EOJ/vPbaZw8fCmYOkhBYdOckoRIwEEK5nTKs8yKp1ycZ06Khbkt6AtZz7KWAc4iH0yB/1ooYf1O98uqh7aRaeYuqcf/+/eVyGX2ueOer2NRIyZnHP0uEotUN7aJwCvDf/uVfftnr/X0Ynp2cnG1f+ZpSkTTHbRarRtNoSq2ybJ0CaRnEvVhY4VqeJlCn+ug1ifXOaSIzngXD4RDtMWqeBCEYiQZfs9msotT6AiKa165de/bsWRWDMQZ8rqBqRombl8tlv9+PfpZ3vop95LARz0asyacVW2eBsYQ9n89vHhzcCEPBZEmy5DODm2+wFDNePG0hgccLsIVkEFphFNaPWPOagtbRGQQ9EtA2u/Eqr40Yj8ee5zWScEu07fu+Pr6ceoQXywNeiuq8IzwoJlhnF8ofhN8bFnNUzf7ll18uc/bWEXMSjCVsvCRg39v5P174g13D+VVNNOparcl0qRuQ8i0F1TamH8PIbrztRDSAHQvXdRvpqw2QqS0yDDUv8kqTXl5eEltvbW1dXl6qHU9egBSFEHxpSI5lWLcVFTgFQYAnH24Y9OkaDoe0rMdfp9MpdqAUM7F+ohaLhTG8WxjGErYODYa7g/OTk5sHB5gOLFunAFOVYCmsGdNe0Neo2Yz6jK2y0Ve7hvEkAb200dtbVW4jTCeIbQAAIABJREFU3bjd3d3sn+K2dRiGjVvb9HqCJqlCjxaR1PObkryUzKLE1vgV/o/YW4ON0T8Nh0N99O0bhLGELbraZF4qtartvJatM2IwGIRhSIYFbJ30Z5UmKdgfVY8wCdnJj1rckkla88uI6KZC9zgPshY7Qs1sHV1dkcirEMJ13dPTU9jTPHRF1woLlDAMoy1f87rN4XUrHyBDtEUY5N8uADMJW/Meq9VBath1tr0dzdy2aBzFuurCEqpiPApBvmjUeoHjm/JnknscC50y1nb54p/a/OEULYaVvLu7S1lddAXm8/lyuUzJikha5RQwcxeLharnltzp4qo91h0KN5OwM/rujIHUyAsRZZB3bOW0Qtw+PkYuPc6i1g9pwQHTvOlRbADWylFHC4L09Y8HayNwmOd5ZWi7LVMKAtKe54HbQNv8jqB08OjoCKuZXBckr58AZr2SCQGPFhVN8NIJ/NyFEnkzCRsCTB1Zdkm9PgVry3G2vY3itKo5+8tvv02JnW2Z11qH0Wg0mUz0USqNRaznk9ia/OQ1O8nhIYetWYC2mw3JR7HRxkWuVlKomFK6CsB1XXRqoC28GFowY5eK42HEk2UscvI3yaGI9dwCYuaZ5B2Z6oWphN14Pm2doKqqmLLy4+PnTvIqOXs+n/+fp0/FOpdKZzrRAQjpFVPxROpZu64w2I7ymBzHGQ6HXLiqNoC2se7JxRlQ0QqCoPH+EJTmLZJZamNtG5KuxVrGv8AwpMA2ZXQjcEAUzmcDpIWLnAt6aqIVtZ67Q9IcZhK26Iydl94y6+bBgVhzdnUlaovFAn4qy9YbMRwOsZos5twG2ageVIXwPA+UIOW3h2FYUcZvxsPmXfcgeioanVgoo3vjw4PnJCnpgdLKir2wuKFRIQFcHyntnDAYDK5fv/706VMhhO/7SYqhcNfDKIeOUBcc3dlhJmF3R0HwPE5plePmwcH5uhVmFbCtrHMBRg8VXuf9eFskzwjwkEdZoQp9FWrutFEAZDQa5S3Udl23wQQCCkVTjXKW6jI4BpI4e2P/DFqgSG93LFXTk8kVk3iX2Ol02u/3l8uluFooIcVQpOeB3FGCmdRllrxth4GE3fhCuDacrRPNsri7q8g+I5kUtYc1HoUrlRUm3DaO4XAI30z5Q0k+2I2ZzDhp9gRJ3CwsMhq0BCgYnGWhA6dXGIbFtFqp7n8+nyclpknNNnq93s7OjlhfLpqHkaKPJDhspI/gYYZVTXFM+mo8+I0P4uJj527KvlZC2KijcF03epvxJ/g6KkKZlIoWgWuCpqduUiK38kg2+MMYCtEcChNudQB19yp/qMViwV2stB5KITZeqL2RzzDUBotFB4PBYrHAULPIfuFRoeanWb6jBHKbJ6WtEVUj27zX6/X7fcxClKJBFx8DoLnC933sg+NgN5rBwBH8u0dzPsIw7CBbi1jCxqPPOXW6hhDCXYP+So8CfYTvLB1cuhlVoCP54ecser3ROwTOVjsAw/ijZgyHw2iTg3QY5sxQtbCOPofkgyVtaoJE4ZSflc5nw3Xz71wyZ8pBud9CCCkJTipNBsORmxq6JXkj1ilrcUjP4ulFKy1OqNL0i4ssub5HoxHWHNFnQPo4nCVdsMGyQCbs2AcXG+mVGI/Hvu/jV74/ttOv0+lU2lIDOuIPJ2d49q4kam1rVepFnQViotkTryiEWfXAaoMSsYSUoAzsCjIPsKiNZqeTuEr6Ygg2jCoffl5Ir1t0RQJuJqUUwWZmkCs826KEvjpvWoMLOxqNwNZZpPqiz3nGK9kF6ys7tvgvsY2oKWGPIFhhIvzbtJ3zN25qzSWMXfCHc2d4U00/oULayKm7id3dXYrkmQE8P5PJJNqaKTs2BmXIIzgejzE7RRP3dnd3s1xY2NllRlsYsbkLKIl2HGc+n/d6Pc/z8GsQBP1+3/d9ErEZj8cIARSbjafTqed5YRhKH8/I1km4du1abP1tp4py8+ILwqbAs7RH1LnN160pkKi9HuDxbeSlqgcPDg9TCq/rgXnWXiPIFbvBhGsSYQ+HQ0RkRVw7xSwoEJRBppu0MSPfIHPKcZySi4wCSKkOcF13MBisVivMt1idIBmbZwthtVHg1BS8x8fxM/5frVZ4LAscVgjx7NmzpFie5ewkfOESx8uDCjlpp5RXAss33/fB99INQHCoZsd4xpoH/QHBUf4r/UztLOuHhs5walSAX5NKPNuO1hVhZwHlCVM4FlyYJRG6QFA/KtQlchbLgSBr1lFJPwuRMYml4HU4OjqiB6bw8hqULH2cYuRV+Ku7Wa+VEc8JW4pSZwFoGJQczVMDKIyURbGW3Omnp6fZhyHhT3/603e/+91+v1/4CLEg7rxR2gt9fOvWv52f73z72+mH4oKjHDcPDpI++GCt6V0pl7/33nu60eHvf//7r371qy+99BJ+/eSTT375y19+73vfa3RQGzCbzV5++eW8n+r1elUMplkQZ//hD3/AFsdx3nvvvY1c+O1vf/vRo0d5qajf73/wwQe3bt2iLX/+859zzRgUQv7oo49ELRkz3/nOd955552U8WDOfPnll6l5q+/7P//5z/n32lh4/de//rXf7zuO881vfpNm7NhP7ezs7O7uFniALQqAmNF13S2xDl3HVmFlge/7YRhSGpoEateT65hIySFk/+Bnn31WBVuDCyWTtwCOb9369NGj//enP+FQsZSM09Gf3ri4eOPi4vbxMf5P4mMMr+qodhAEOzs79TvDZ7OZ7/tJZv3FxcXnn3/+0UcfrVarfr//4osv/u53v9Pcezyfz6H6lAur1aqKwTSO4bpDs1i7+vr9/ka38+eff15gBbO/v//+++/zLe+///7+/n7e47iu+8ILL0Qd7FWg1+u9+OKLKbMoxn///v2PPvoITuzxePzJJ59kvz6z2exLX/rSiy+++Jvf/AY100l7TqfT69evDwYD5TOthdjEfdtk/lbktSYje6NjnP5aWDYLeYzFPpsC3lojGjbGX7Nsf3B4+OmjR9tf/vKXvvKVv3nxRbi4z7a3QbEkMkqubxjT9DP9H4vznEnjBVC/MxxBlo0eS5KNRNIsHJWaa27je23U5OJoRH+7TnCnyMauUIVFe+AxRitoCF+Loh5jTJsVaaxK2N3dTckdocw7qpDma6CMQML8xmfMuqxrBufNbVq1STSJdW569Dr7+WDEV52AVsWTRGx9vd+X6BAE/HS5FEKIw0N4qs9OTj5+++2/PH789I9/FEI8vXXrer8vhLixZuJX798nhkZPzLNIoLpAQlk95nVtznASRRJrMeGUXEKeJ4xaF/2JDQNGTq8tXIkiNtjMUUa0h8ehRYn4rqhR/gzaKRuXoWWEjKi/lkhO+4I8nH1im8LzGLZEpfSrEsIWdZV45bJXMoLY9Oly+eDw8OO9PSHEX5bL5zwthBDia3t74N0HkY9/+ujRp+w43KFNPxQmaUKsa10tgiCoTdQMUtuSthFKYPGnWPUlgOZinc1rweQvcn2qU9mzUCKL5Ybyzh6KQ4dhmHcZCqGuwqcujIwl48Xgui7YGk230q+t/gtiU7EddVNLGWTRIq4CT4wkYlAFSvrDYaHyHzgLwkp+ulx++ugR3/i3/f71fv/28TGZy0KI7S9/+e//6Z/+slz+39/97j/+wz/Q/tFkMdjEqvpocb7P22c+HXU6wxGBixZ30mwFjaQUdaq2qKXidbDGShJAqJVen8LMh9VkEATL5fIHP/iB2lGlAJUCVRRV8rcm3U7TPDvEbGzWEpci0AXyyQFyjBcYZRYUEw4EK4NoyT7mhjI3fJ/3lo6zhtVSbzHQUkOojg7U1nMCFk+SFAPpJUE7uobxVA1rqaQAhB0riEba2o7j1C8JgNUwVrH7+/tg0NpKvNBqrBVLUgvl2Nq4B6zt6XTqOI7jOGXyyauLgPKmMdlBud+CqYbhh+cEfHwcJWZxtSRaB2CQ1TnG61GxRrnBRuEkY1qE4Wtmf2jN+Na5AGKO/RM1boI8SJ1mH+4CYjSnp6ekL1bP2VEYXbPkM6GixF6LjIixsKPGGRLNeVPxdCTt41bWU7YwW4urceV0Exm8eKN0cVe7UFsOF7nxqftvrDvUmBaTVaRcGIZ0rzi2U6xElEsfy4XYmhcl6uhZAMe4wkQN+CoKLAepsalg+jNJb65FeeRor9nUmi4LEPWM/ROPLkdRQGakTDJ2FU2pCRWliNdm2CH4glRVVGpF76mGOmvFYC2VLEjxinOMRqPd3d1p5v7WSoD8cCHEL37xi69//eto5VLbPYVjXNS4RgGwpOaKe5RVTpIblq2rQyX9sGsGHtykVyWWrcFt5WXLsuN///CHH7/9thBCeVPqGlDbpADORh/A2GZWxpjXtpg1I6D+vZEIc/W3VgU8rufn5+fn557n1VaTLTJXeQGIF6Tsmf2KoYo9tlMnKjgsW1eKdhM2VrgpHhjye79xcSGupmXVBgzgOVsLcXZyUjI3TfIZ8PKwZrPelIAsJFhXgslKm9SBO28afyN1RDpgY0E2R6WFT1GAtH7729+en5/XcDoCXoSMXxZFaKouCJXDkTwLjkxsbf1GlaLdhC2ESHlE7m4//3Zk0TbSMOPB4eHX9vb4lgLrhiRpccEy4Koz3Bspa8YZEc+mOJkZ5rUQAr0Os5sjTXVi1gEpBdlR1Jyax6uhokvMKoCo0GAwIL90Up48DGvHcZSEkKjFDkUosGJAoZdl63rQbsKGXyj2KaEelHX6vaMAm6J6+/bxMUb14PDw09df/9Zbb2U5Ai88+yI5rkanOtryNMKUmA2pgt8M2xpIisuS2lR00jejkq0A8nojGtG6F8z6hAVcEXuBI/EdPc/Dyi/2K0+n036/f+fOnfInJS0jsVYeBBaLRc0NlDuOdhN2Cs4UNdcqiXPmuL55cCCOjz9+++1PHz36+O23/7JcpvMut6qbXXk0G3CdzWYmUTWpPceajNiIMiH4FaB3bUaqXTHk8oo3BWJNXvRV9UlJRf/evXu9Xg/LBdC57/u9Xk9Ve7fZbEaRRxIJPjo6gsI5rRuElRYQQqzX1lVMm5vrsHXGfD6PFUuRSqubAtcJv7Eu8j784INvvP669NcoqGEXlYM39V2oQUIZoN1WsWJZw5bwYGvXdWFMR3eAhY3qXiHEZDLpsj8cgFe86VEkQtLKxc8VDRj54TB5ybLf398nBwyJQL/55puq8u+QaxasIdYrSzyWpBac9Eh3DXmbTGZHuwlbRBZ04DlQXaWtq6IgaxiW8b2XXsKWr+3tfeP11zndfuutt0j9VOLsB4eHd7e3725vI87dLFUTygg1gKoxgxQjbMMsy/F4TMHOlHYmQgjXddGDRxgUvC+GXOGARlZ40g0Cw4l1jAMblZAZ8uHB1mjJhRg2DGtKASOqhrBMyZPiRI7j4CtgDPRXHhHQeV1lAFQqTqtCVKElNohFMpa0RfIh1xbojfIu4Wt7e4cffBD7J8qJE2y02AiG1qr6KwiCAtIKUjbKZDIxjH0LA/MsjGn+DMOOsVcpCnjFN3oa9LmAmKDEVZ5WIipCWgVi3ac1pR2OclDTW9wL6k+KXBNpTu4aSjrDDw8PHcdJ4b4Wx7CjF4X7kFWdhedmU5MPIcSNgwOpwlvqviVS2VoI8cbFBTH92ckJNcYWmlE1UKzrAGdr9PuqbIAtAwrNMcft7u4uFguEHursYdoupGSYkt4WGsNo8pgh9C7WWdzUHKzkYUGQvV4PcWUhxNHRUfZiNlR5lVnQkBGPpHFsiXaf6zJtV4cWE3ZU2fFiMOj1eqrYTrKbbx4c8C0padvZi6FvHx/fXh/tnKmalx16NSgmroRJ1qQSaiWgS4GuzFSlEytJYSESZEpB1WTtYU2pz2PmeR6oEXFl+FQKMBn3M8cqgCL5K8t6Gh8vLy8T2xOPviyYu1Oc7azx7Nmz6s7SVsKORkODIPh31/3PKnJzyLVOudkIKtMWMrtVZW4jPw4q5Rqa1wACV+CVjKSCGk18RAcvpYYYrrFRjspgZLEOKfBPIM+wno8W4kGCdZkro10qxb/5AoWcWFm6eMG1M5/PK5KXAVt3U04c/nC1rY3lU7Q0hh0NlkwmE8x6JRsqpPTQJMAgbrxmrBFQZ45cK3Tf99vSptqifmQx+KRXXp9YdSyIrQEloiLkThBMLIVKBMmPtbHlKFnAaq8eRlJ+Bm4prl27Rj8/e/asGG0bG8Om+BCAh5iCOoVBpVbpudmNF4w1CIQSc+W7YuZqpFeBRSuQztaU5cQX6PrEqmOBuYhSw5QU5sHFRcb6dDrFHEi+B0iPoeUoPPCx5A3DWol5zTUQO97PJgxDqmXY2tqijWrP0lbCFmt3q1jXrZI+VJljnutRwK0zCkSjMduKhiROLdoL8q+KqwJb+gPLC1icKPFS6yLGwclo2d/f5+XRROpE3lJuhBK2JkVxOho5/1sBtb7ry8vLra0t8orjf+Xx7BYTNrlYPc8jnu7yEq9qwAlZoIBkPB5bqraABZYrWh+GYXujoUEQ9Pv95XK5Wq3wq3LOFmtJmdVqhYoDlBvwHWB2F1AXBh8T03OdFkoUHwwGWBNgt9qalSmBcvP38vLScRyY15eXl1bpLB5owIfFHR5NkuMpBm3TvhoELqnrusXW5patLeAGw88ZtU1IfiQWGubfEMBqd+7ccV13b29vb2+PFD05yujDS+ol8/l8Z2dHJKjxFLhWvMza930kEIxGozAMPc8jzwe2/OQnP8Ep2iWc4jjOtWvXrl27pkqoPwxDUHVFD2eLLWwCPSh4tigpo8ChUrRCOwsyrLVN8LFoBcglhh88z9vorYlmhrcIMEkRnh8Oh0+fPo22eFcyraOOXwhx/fr15XLZ7/ejOxTg0WgjMjSkWSwWOAV3Kfd6Pbjo4e/EeCrtWqYExKwK+bU6thZmEDZJ7WDdhzccG3P5Z6iUq5phthJYBtniYAvlQH1R+j7tzWOK1p3CZT2bzeAIFJHZiXMq/YkKt1LOxU3tyWSyWq34ocCaJQ1f8orjVzQaEUJQ0eZwOFwul4h80blKZhTVg8vLy6aHkAMmEDa3/OBDK1BaQNVc1h8OUPmWNawtqoDruhs1rpMYHZSgqrOFWkBKTKyNTl6xhpA8FV/B2savXA5FCAEWJBqmgq6N09pgMEDUnFp1lSn+TgEcJJRMh7NQwy79beuWwgTClkDZ4wVgzWsCClH0nBMtzID0dFHj0cFg8MILL6xWq1iKKiZrXw+m0ynJrq1Wq9VqxYOjSLgjl5Xv+zCg8VeKGZNYGzLFQIQwozdmdUkrAHyc2LTMRUPQGi7x5XKJLVRfvlgsdnd3W5Rx1lIYSNjtdaMVw9aHh0KIy1esY8CiJpAmtpL1HNcYGQwGR0dHZF/GCpFqy9aCuQSgvS/FkigqjK+Mqyc1yAHjUsNK+iB+IHdXiglLyeFwku3t7T169Ajp3IWLNbAQgWi54zivvvoqjCIsDoizhd51OpVqkNUDAwk77y3JIm2mIbY+PHQeP0+RC3cUOwYodK32sBZtBBzXxM3oM4afixltVCAEZQ+ebyxY0miUrYtVFdYPOIpTtPcRtqMvLuV7U8FLNC/P8zyslqgQJiXCTU13+P2iIeW6jNiZS7XzlQeoWijSh6kIqvLAm4WBhJ0LZ6zjVrNs7Tw+cR6fhDsHWdgXbB3uHDh//F/i8q9qRzKfz0v287EwBuTj5dxMzBoEAVgni6nNPd4kpRmttEb5cvTjkDbTnK0hOYCGLkIICutKnE3fOjb7DBiNRki3lmibX4GN7b/AoLAsSdWkWFM413WjommUjAZfAlLE9eRsKJG13cg2kLCzpGYQzpu2reHQJluZfhBCXL5yHCVv5/EJPhLuHIiv9MHWai1snZfJFrUBxhPXwuRdkAWzC4Mg4KlVxCKki8lNav5uRokqKUOqRYtI3kkzqYic5qgs6d8SbdO6B/vQEWK5X4qXY51U+AWPdadL1V+LxWKj6W9RGKYRdt50sxsHB43UXsOe5vQMhDsHtHHrw0Ni4stXjrEd/8MQp+i1cpe4Ge4ji+wgGTK+RWpZsVgsYpfCsBQpO5o+wkWtM3a/AMFQo2V+6nYVFibZr9wTnhGctn3fdxyHvBRibS7P53PKCec0SXrmjuP0+3040nF2jERtQgBK11Bei4ovrWh7a2urXUVcUWhK2NPptB7OOG+ilIuHn8WajEHVlDuGfWi3a+98cafCnYPLV46rY+u2mDIWCkEzO0xDzP6cV6Q4qwSkOEXdobkma+rojGFITaA1d4ZHAdLiBqjktMgFXH+SSyMtFNy1fr+PKrLlchmrzTK42t0LNXW8lkzVeogi3EiJj7YwbxCxznAQjT5+8nTuM0GalAOPXa5eUrVh68ND/CMr+dn3L559/wIEjP9p58tXjp99/+LylWP8ibMydqODqB2knpfOompQlNR1XcdxlstlXq9pOqNvBJzeRBtw5DprtFG6h5rUkduvfKNoxBSOjo5Qlu04jud5ruvu7+/j6r355puo8Mb+WEgJ5s2mG40QxmAtBl7iiyZCt1sWq0GGB6yR8RSAphZ2GSCOkmvueHB4WJ2RLUWpxdqhnYVrsQ/tGT2I4rG20PdooRxZJE2UAw9ebBFUS0Gp76jRElf7YJY8OETEsAIYroE/wS9NdnZKBTb25F3FCo+HmmHjW+tZehdrRodhuLW11RZvuWkWthACMbCMluKNKpVSyJ6m9G9Y1YX92Jykq3CGkx67RceR3nVDOfDCajjFlwFdw8FggAUQ8qi5BVwYlBaAUyByjKIvsXaTkM/8yZMnSSMk86bM7fZ9H5EUMpPgDG+FDjzM7rbY2QYSthACmZBZ9kQMWy1tO49Prr2zfe2dbQpCk3Nbf3mTXq+nTzjHoilgKq+t85KRfh04n2kRXKnTAmsdKpIWVzmbhMmiYyBJtVyrpel06vs+ZboQ1VEeXK/Xm81mqCUr8bVqAmY86matM8wk7Lzt4s9LJ4qj2go8DR+4WBvEz75/UfLgtWE0GvV6PbTvteg4MIPXwNk4hWHmNYB1j+u64/GYyt7gApxMJqquLcxrnIVvB2f3+31cW5TUU0AdZrEQYjwew/T3PM/3/fRCm9lshiRwSjjAqeE5gESd67o0h7SiQBQ9MSvqYK0WBsawc0mToqzr7OTkbHv7jYuCzCplfYt1Inexo9UGrP15gwGxFp9qdFwWumA8Hnuex52lVVTpmOcM54j9alT4tFEbnAPKrDwVn/fN5Cl70bNTWw7aB1XyfDfQsOM4Sd4OEpElqRxeTuK6LtdSffjwYd4v2CD0p2pAR9mX8XgcDX5kHyfcPtmfkrOTk/OTk7OTk9vHxwWaf/Csb/1JGgBVozEAal5J46LpoXUaFIBseiDxkKTKlBxT575bNcDzvIwV6uJqismAdcQKggAduiQlOCnXzPf92Dw+mNSkWdbr9Z48eRKbNYZKMCljn2vm0AGxA03FbdcXqw2Hh4cnEXcvv3QGWth5/eE3Dw5uHhycbW+fn5wU7tZVRQpYRcAUSUtjS9L6QFuqBmi6n06nJJtV/rBd9uggT1tkcx0jnZtb54vFAgqmIq4gU+JIGEIkI4qNVBpOW548eYJDRZ/G2HVVdCN87/zjlq1VwbQYNikgFvhsMcmziuqhK0LHDRoLJYDzk/pPWBQG5Wln3B81WsPhEK8wXOIkBYpUL9o5+pqDOPk+4GZaM4VhuFqtEJPO/i2w5qCPIKqNg2OEiG1blIdpFjYPyubCzUIapVFtUZ1h2bqNgBeaC1Pr0AODZLPaEqTUFuDgjNcQ/Ed3H/IpQgi4xMUmJw2mRz4D8MCzKDQ5QBnGdV0sF6RKeqjXIfTWigQ0zWEaYSv0vSDFI72VSIvMazSybXyit8gI3uMBMx2eQzTI0iHtgDREy7jHSSLbriOzAC3OwIuDwWC5XFJvjyw6M5UKxu3s7EiJb7Qa8H1/sVhIGXMWBWAaYedq1RXFv9669R/eeEOwgJAZj5fBlTNGAlaL1NtKXE074q01GmQ7dF1EU+RiEtmiluIxPVFAbXswGICwEZMGQWZ8ACgXLMv+2RcBJGpLMXJpHwy1TLzSAjCKsHlBF9nHJBCY8sH5fP7vrnsxn39lbw/hHGoUj/nRAKdfl1N72gXk1sauO3d3d/FIk6WCOpy6h3gVmNbRwBGjsoZURmBZlutajUYjCgkXWIKnB6cpA468OxT7yGKag5Kl1HQqSJnNZgZMpM3CKMKGP5ysEOj+IP6HqSQ6j9DOw+Hwv19VDKDaRPSJU5UT2whsA66SwGMgkgWueY+HwjHmjd2cpMkd/J1yQIyqnls/Ho8p3M7bOG6co6FL2E2vOGIKeWksRR48y2eTVnggV4qp85UBmfLpgJHN2R0uzyAI0r9mbDNviyiMImzO07w7DX6AIL7kfOv1eulxHd4nTorB8ArsKr6OKtDUaVEMxKNiTYGxtA2ihWsnozsxepZc60JENGP3pxVGmck9L6SvDHMtS1YauKFk/4k2AnMLXSLuF0y5aFLqWS7Qqog/FVwRJZbOM675IJ+Od4ROxAWa8AVpf0mTp8DX6RqMIuz0pwqxFhgBjx8/3tnZyf7EU34N9QEjtm5FBXbjXtOqQYIeVcz4nEdHoxHMBU7byAKDAACMYOyDSZAb3OBRnv2HffjHc42NIprSgKmTYxAETVXBUsLRRs7GMrprbA1wI5s6gqffMp56Vvi8eBQxZ4I4eRP0xWIRBIF0fC6xkg7QNldSo07e1M5LrJPgsFxANblFOnQUoCmpdFYpEF8cX3wfv+qvbob3xGB/OJffSvEP5+36JxVTRSPKZDeQXyc2cEvJ+VLSTTSrsbA/kFfrRZ3qjT8AGz0HxNbdJGwhhOd5eMAmkwlF8bIsccoEEZ5PZczg5uvde/fu7e/vCyYcSw9wesQHL47rur/+9a/n8/lqtUpJiqTHknce67Kp3UWls0oxHo9n/zoU15+NBp0BAAANj0lEQVS7wbVia+qmR/M1JutWJ4dzM4LmAvqVqBrz3cYgWRiGnuclzTi8kkqwfsaxMwiCfLAd06cYHJb6IdLHN331rCBB6VhqhDWWfoRKJVFJHC32KlltACAIAiz48ITzhtYAnMm0hQeYiyHaKYT/ulwuYfuGYchftI1LXkpEp/eIu/eilhj5gchVrrz6K+lCZcyx0ArWws6Nx/9zfyd8VJEnnMd40p9aKU1jMpngVaEMT3r522u4RK1SyYwuIGpNYULpypAbuXUZzmShxhqyWRgRhk6lVjglNImrD7YlbCGE53kkLUKMiCoVvvKmfch/nldZgR7y1Wq1v78fOzP4vi+V+GMkGV0gFA4XzBZPd4NxnxP9TD6t8so8k8kk9tmmOqAC04gqSPrwP/3pT999911pH859lrBzg/xXSsBvGMAr06Qt0Y00CUqvLt6QVrsZ4bLD5I5pXZroy7xjfLKj/KzWUTWQkqlELpb0x0ByXVSEqGQbPcMGh2yyAPlfYEqpJkqs0ybwJ9Ah959nf8F5IcPOzs7e3l40RP3w4UMophW+I5TewbM0yMOEBxJ/JRYfjUawUmjdhhUMRHWosLbYu7lxRcgXEzXTtjQ2tElN14S3hJ0beJiU3NqMa2R+NaI7a97iaSNoHhfrZcfG6qbyIJLDrw0usSuFzvJ2dN+1HaEOQDsvnoUAFgdyeSa4m5qORrMHKYyiZ3aW0sSkHDSpQo/b3IK1+MSJYHMPI33GyPaFR302mxUzkzK+Apy2a1i4Y/6JDuy11157+PChtLMl7FLgC8My97WbzkCeFE0boy6E2qreDZY47uYDZhJiq/8pU0FJLuFGgYEk0KmTrHwcOfbxw2fJPZ50BFIRyNsxmSPJHx6LemibuxmkP23kPpt0lhuoNRRCUIlXAcATbrYnkLhZJCRFJyVz1TXAK+MxDPP5vAYvt0Wl4O1eaCNywd577z0R6XhdAIWjZulFGUBSNSm8aDhvygBI9JROVCBhJdf+NBgk/RVT2904pCxRqiRYwi4CSuzKfjspU5eru1Q1vqZBUSvaojDqb7ERcLi1OoPBQqxFb1LWlHjLlN/lFOOYMBwO013NXHJKsibpyeQVBCnfgozsvChsU2Gt4HmeWju7JFsLS9jFACM7o5eeCnZJpbzawTUKnmBiqvGqM7jUrsErwo6A+EzaztOUqjgvrJF08z3jchBPI6Lj0b9Kgr7pxylm7JbRjEJoXyjyw5Vna2EJuzAyrr/gmTSbpEUk09JSdSOgnmzWsDYGsbyI9DGplbVCSJ5kTF/FloBJg5TE+DbOGODOAj7qMslPMMzKhD45cJyS76Yl7OLAM5R+L1XdbG0RVS+xaBCO41i2Nh543WaVSa9LBoZye4NKzAfr5qobvwiJQ6ONUyxtR0s/RGlVZpTblSki5ZKI5e+XJezikIT7Y2F24g+pYViq1gFd8OVYAFA6q62tS3nwLl5ceDVXayI0X5/NZhJt379/f7lcCiGGwyFeAcp+L399xuNx4WA2iRohKGAJu2GMx2O0ARaRtGc8UlWkhGiClLbNFvUjb0KsRXsxHA6hbNgWthZr3zhoW5o0chnBlMt2enp6//79Xq+HJ39vb++1117ju6kauWAO+STahnEvKTvxojVVd8oSdlnwpgtSiMVg8xq5GEaKjVioQpKwhkVJVBe9rhqu66JdOm9OU+C7ILr85MkTkZy0ofDBG66b8pHmudQOVaxF+6kaCKaacoPNCqeoBNUeRLV7TALXR7TQB7rpmpHElXXUWxC43iq2tEvbh1TY8CsoWRUrt1U4pd/vo7kbUFH1gnLgcodhaHCmLgktNT0Qixhota7FRHbv3r2oSrZFxwG2buNTQQ0IRDXeo3Tu09TCFlclcx1Hx3EmgXdpbLudndKQoLlBWSQib0OIGoAlrGjn7Kw/dI47xI4Nmaptnxgrwkbu22pgUOZiNpv5vo810dHR0WAwaNE6Iwo8N2EY+r6PVcjPfvYzYdlaYyCHtulRXAFpWFLiroVacPkRrTCdTk9PT6WNoGrL1sWgqUu8XSARAKnGqXVJZ7wpJ7ekgyAgxR/L1jqD94TQxNSm1kxmCxI0BR1ucRL6/X50o84D1h86uppb5xJHw00RMT11SwJKAeUHkT1kubnVaNGzZ2EkqF+nZegUSOo3G7nPWthqkMRtOq8zCHi1qI4cfeatPdRq7O7uBkFgu013ENSVssEx8K6dDQ5DZ1AgI9clsoRdFinqpFAjqnk8eRGrf2LZuu2AaE8QBEl9FyzMA7XGEldNt43tsKoYibDTSByQj0zXJ+9NsYRdFuk29GAw8H1f20JDm/JtNuAsKfzx+id6izKg5D6xtnEdx8GvYRjWeR8dx0FwrTrB89aBt10QRQuIbJZ4WaT3fcOfPM/TM42zcNM6iy6AC0RY6I8wDOFZoZcaW4QQZNWlQ1XCOWXg6jnv1YzpdOp5HqlDlum0Zi3sUsjyOI5Go93d3el0qjBpHL6vkgkdG1uNWXQc2nqGLGKB9Tdeav5qo3aAjF1SieALMkwmqnJu0FnL+G40XGMAW7hHirzf1HQY2qVl5m1L2KWQMTkL+ywWC1XucfJ6Fb7xs9msFQlxFhYW2ZGeT0P0SbuBSObzOWxrqQ8HjEK40/NONWZTNQAyFiwwOp1OuTYZUfV8Poe4W8nLYgm7FLJzHiUBeZ6XvsKi1EGKQkl2OZauR0dHxTLaeCjF+sONh+ZZFBa1IUX8nyyKMAw55ZA7PTpfWblZQJpCkxp1o+d3+ctlCbsUQJzZ9+fucSmdhwuairU7BVppqPPG4pccLNgtV0NcTtW2dUdHAHdoixonWxQG2m7i5xRTO3Y7LIrYHYiw4QFGIltno2lkUGUPKS4WC1VSrJawi6NYDJgWs7SSlbRKsAM60NErhJpaEem6Dc82OaxoaSw9HJaqLSyMB09lkppGkC9wMpkkEXMs4MjlsrJhGGJG6uASkASmaOrO8imIIii5XDoqiLVF6czzPIk+CyDa+TzXZ3n7VR5QpxUA/Wod4N0EnhDrErcgUIPIjHNCEATRnXlE3Hj4vk8WkWRTZTxCRuVBq3RWFWBel/cLlTkIfRasz01n0gDHr9aq7ibm83nrBO0tqgY9DzA50nfGHIJ5hqzz2Ki2wcB7JK46OHPZP8qM7FA/2PnFwsLCwsJCXOVoTYVTXNfloxRKFxbKFwQKxxaq9vx/+OGHCsdmL11h2EtXGPbSFUanLp3asam9dL7vZzxplPs4NCVsCwsLCwsLCw5L2BYWFhYWFi2AJWwLCwsLC4sWQNMs8dPTU6kQRWFdyunpqapDATrXzPzoRz/6u7/7O1VHs5euMOylKwx76QqjU5dO7djUXrq7d+9KlfEpJ035IjrWN4vIiK1OkyaYTqf9fn9/f7/pgXQdeLHtjWgcp6eny+XSzk6Nw+AbcaUsW0/ClqCncEoHgdp//gBZNIKowIJFIxiPx9SyyaJBdORG2Bi2hYWFhYVFC2AJ28LCwsLCogWwrmYLCwsLC4sWwFrYFhYWFhYWLYAlbAsLCwsLixbAEraFhYWFhUULoKlwykZMp1MUZ0uFd+PxOHajsDUwFp0B3o6kv9oXoTbYG6EJzLkRajucZIGINOMKw9D3fWJZVPryv7pr8C3R49ARomds5Ju2HXlvikVJKHk1wk2Nhir/Gq0Fv9TRe5F+I8LkacreiLyQrlv0UhfjCwNuRN0WdtIyB0YwXdbxeEy3hKueYbsQwvd9x3FSzkKHwhmNVMCpGnlvikUZqHo1CFbiJhem06l0qbGFrmHKjRCp98LeiFyABAqfsaVLLYq+FCbciNqWBtxTHWsQ8FUS34cPkq+bouMX61u40Ra32IhiN8WiAJS/GvaZLwBctOgWXPz0GxEm3At7IwogesWkqb7AS2HMjag76SzW0o0awa7rbpRKx/60Gx0k9hStX1jVjmI3xaIwFL4aFgUQbVjAZxh7IxqEdF+6fC/qc4kTZca6slNc1q7rwrOBxIFwveDCTaLXjO6ilF/QkRtZBQrcFIsCUP5qWBRAdK0vTR3pYTV7L1QheumS1lKxMPtGaJ0ljuvu+/54PI62nYjeM/J7cCKP3dOiMNJvikU9yHgXon36klxQFkmPcfrjzbNqUu6FvREFwC9almuV5aVo+43QmrAJSe8M94QQQ5PBjaVWbYPsGixP64CUu5DkW2rR9NQUKAEt10OetLO9EcVA1y0vpxp8IwwRTpGiGlJIw1KLRQcRm2Jj34V0gKoh5yBlJheGvRHFQBeK1k8lYcCN0JqwN74t7jqP39Zu1QZ7kXWAvQtVgBvWGdna3oiqAfM6XfmE9qxnSA3CBMKO/oxFU7Sez6I87PXUAfYuKAfYOq9hbW+EWiD8HMvNlrCFJoQdTcrPTrRScZe03aIwytwUC1Wwd6E2EFvH/tXeiHqQNJ+L5Iin6NK90CLpDDeA8i0lFZuNwM2LvmlJ2y2yoORNsVCCknchNvLXrrTYmpGURWxvRD3gDnAe8eTXqsy9aP2NUKXAkh0iQTCZjyo2OyAW+GDSAbMfxyKKwjfFohhUvRobK4arGX6LkV7Xi33sjagH0ZBE+ZfCmBvhhDrVldu2WhrC3hQdYO+CJrA3oh5QllmK+dvBe/H/AX075g+YdxoPAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Split the dataset into two subsets of shorter and longer rivers\n", - "# Feel free to play around with the limit\n", - "len_limit = 700000 # in meters\n", - "gpd_rivers_short = gpd_rivers[gpd_rivers[\"Shape_Leng\"] < len_limit]\n", - "gpd_rivers_long = gpd_rivers[gpd_rivers[\"Shape_Leng\"] > len_limit]\n", - "\n", - "\n", - "fig = pygmt.Figure()\n", - "\n", - "fig.coast(\n", - " projection=\"M10c\", \n", - " region=[-10, 35, 35, 58],\n", - " land=\"gray99\",\n", - " shorelines=\"1/0.1p,gray50\",\n", - " frame=True,\n", - ")\n", - "\n", - "# Plot the subsets differently and specify the text for the legend entries\n", - "fig.plot(data=gpd_rivers_short, pen=\"0.5p,orange\", label=f\"shorter {len_limit} m+Hriver length+f9p\")\n", - "fig.plot(data=gpd_rivers_long, pen=\"0.5p,darkred\", label=f\"longer {len_limit} m\")\n", - "\n", - "# Place the legend at the Top Left corner with an offset of 0.1 centimeters from the map frame\n", - "# Add a box with semi-transparent (@30) white fill (+g) and a 0.1-points thick gray outline (+p)\n", - "fig.legend(position=\"jTL+o0.1c\", box=\"+gwhite@30+p0.1p,gray\")\n", - "\n", - "fig.show(dpi=img_dpi)" - ] - }, - { - "cell_type": "markdown", - "id": "eb723411-a607-4db4-bb9f-e9067248b512", - "metadata": {}, - "source": [ - "#### 2.1.4 Plot the rivers with color-coding for the river length\n", - "\n", - "Use the gallery example [**Line colors with a custom CPT**](https://www.pygmt.org/v0.13.0/gallery/lines/line_custom_cpt.html) to plot the rivers with color-coding for the river length." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a61552d6-6c87-4fe3-ba5a-4fa9af6c23db", - "metadata": {}, - "outputs": [], - "source": [ - "# Your code (:" - ] - }, - { - "cell_type": "markdown", - "id": "8e833bc6-54df-430b-91dd-bc63159a65c7", - "metadata": {}, - "source": [ - "### 2.2 Polygon geometry\n", - "\n", - "Any kind of areas, like continents, countries, and states can be stored as **polygon geometry**." - ] - }, - { - "cell_type": "markdown", - "id": "4ea5bd4e-d26c-449a-8b08-2eaa05d85877", - "metadata": {}, - "source": [ - "#### 2.2.1 Aspatial Data - `geopandas.GeoDataFrame` with polygon geometry\n", - "\n", - "Again we download some data into in a `geopandas.GeoDataFrame`. This dataset contains information regarding airbnb rentals, socioeconomics, and crime in Chicagos.\n", - "This time we are lucky and the data is directly provided in the geographic coordinate reference system (longitude/latitude) and no further coordinate transformation is needed." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "81facdc5-a644-4327-9cb1-7542c4d0d73f", - "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", - " \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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
communityshape_areashape_lenAREAIDresponse_raccept_rrev_ratingprice_pproom_typenum_spots...crowdeddependencywithout_hsunemployedincome_pcharship_innum_crimesnum_theftpopulationgeometry
0DOUGLAS46004621.158131027.05450983598.77142994.51428687.77777878.1578951.78947438...1.830.714.318.223791475013124118238POLYGON ((-87.60914 41.84469, -87.60915 41.844...
1OAKLAND16913961.040819565.50615333699.20000090.10526388.81250053.7750001.85000020...1.340.418.428.7192527813063115918POLYGON ((-87.59215 41.81693, -87.59231 41.816...
2FULLER PARK19916704.8692None3768.000000NaN91.75000084.0000001.8333336...3.244.926.633.9104329717643832876POLYGON ((-87.6288 41.80189, -87.62879 41.8017...
3GRAND BOULEVARD48492503.155428196.83715733894.03703783.61538592.750000119.5333331.53333330...3.339.515.924.323472576416142821929POLYGON ((-87.60671 41.81681, -87.6067 41.8165...
4KENWOOD29071741.928323325.16790623992.54285788.14285790.65625077.9914531.61538539...2.435.411.315.73591126271365417841POLYGON ((-87.59215 41.81693, -87.59215 41.816...
\n", - "

5 rows × 21 columns

\n", - "
" - ], - "text/plain": [ - " community shape_area shape_len AREAID response_r \\\n", - "0 DOUGLAS 46004621.1581 31027.0545098 35 98.771429 \n", - "1 OAKLAND 16913961.0408 19565.5061533 36 99.200000 \n", - "2 FULLER PARK 19916704.8692 None 37 68.000000 \n", - "3 GRAND BOULEVARD 48492503.1554 28196.8371573 38 94.037037 \n", - "4 KENWOOD 29071741.9283 23325.1679062 39 92.542857 \n", - "\n", - " accept_r rev_rating price_pp room_type num_spots ... crowded \\\n", - "0 94.514286 87.777778 78.157895 1.789474 38 ... 1.8 \n", - "1 90.105263 88.812500 53.775000 1.850000 20 ... 1.3 \n", - "2 NaN 91.750000 84.000000 1.833333 6 ... 3.2 \n", - "3 83.615385 92.750000 119.533333 1.533333 30 ... 3.3 \n", - "4 88.142857 90.656250 77.991453 1.615385 39 ... 2.4 \n", - "\n", - " dependency without_hs unemployed income_pc harship_in num_crimes \\\n", - "0 30.7 14.3 18.2 23791 47 5013 \n", - "1 40.4 18.4 28.7 19252 78 1306 \n", - "2 44.9 26.6 33.9 10432 97 1764 \n", - "3 39.5 15.9 24.3 23472 57 6416 \n", - "4 35.4 11.3 15.7 35911 26 2713 \n", - "\n", - " num_theft population geometry \n", - "0 1241 18238 POLYGON ((-87.60914 41.84469, -87.60915 41.844... \n", - "1 311 5918 POLYGON ((-87.59215 41.81693, -87.59231 41.816... \n", - "2 383 2876 POLYGON ((-87.6288 41.80189, -87.62879 41.8017... \n", - "3 1428 21929 POLYGON ((-87.60671 41.81681, -87.6067 41.8165... \n", - "4 654 17841 POLYGON ((-87.59215 41.81693, -87.59215 41.816... \n", - "\n", - "[5 rows x 21 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf_airbnb = gpd.read_file(\"https://geodacenter.github.io/data-and-lab/data/airbnb.zip\")\n", - "gdf_airbnb.head()" - ] - }, - { - "cell_type": "markdown", - "id": "9f6f0cab-9588-4b32-bd8e-2fbd4f626ef6", - "metadata": {}, - "source": [ - "#### 2.2.2 Create a choropleth map\n", - "\n", - "Here we are going to create a so-called **choropleth map**. Such a visualization is helpful to show a quantity which vary between subregions of a study area, e.g., countries or states. The dataset contains several columns, here we will focus on the column ``\"population\"``, but feel free to modify the code below and use another quantity for the color-coding!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4e07f091-7018-4609-9e75-d40400e18181", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2876\n", - "98514\n" - ] - } - ], - "source": [ - "popul_min = gdf_airbnb[\"population\"].min(); print(popul_min)\n", - "popul_max = gdf_airbnb[\"population\"].max(); print(popul_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f10895a2-67c6-41e1-b33b-627e391f2f9c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = pygmt.Figure()\n", - "\n", - "# frame=True adds a frame using the default settings\n", - "# Use \"rltb\" (right, left, top, bottom) to plot no frame at all which\n", - "# can be useful when inserting the figure within a document\n", - "fig.basemap(region=[-88, -87.5, 41.62, 42.05], projection=\"M10c\", frame=True)\n", - "\n", - "# Set up colormap\n", - "pygmt.makecpt(cmap=\"SCM/bilbao\", series=[popul_min, popul_max, 10])\n", - "# Add vertical colorbar at left side\n", - "fig.colorbar(frame=\"x+lpopulation\", position=\"jLM+v\")\n", - "\n", - "# Plot the polygons with color-coding for the population\n", - "fig.plot(\n", - " data=gdf_airbnb, \n", - " pen=\"0.2p,gray10\", \n", - " fill=\"+z\", \n", - " cmap=True,\n", - " aspatial=\"Z=population\",\n", - ")\n", - "\n", - "fig.show(dpi=img_dpi)" - ] - }, - { - "cell_type": "markdown", - "id": "03516fe8-5013-4acb-89d1-a1ebdbc1d426", - "metadata": {}, - "source": [ - "## 3. Additional comments\n", - "\n", - "Some helpful and interesting aspects:\n", - "\n", - "- Use suitable colormaps for your data: [**Scientific colourmaps by Fabio Crameri**](https://www.fabiocrameri.ch/colourmaps/), see also the publications [Crameri et al. 2024](https://doi.org/10.1002/cpz1.1126) and [Crameri et al. 2020](https://doi.org/10.1038/s41467-020-19160-7)\n", - "- Datasets provided by `GeoPandas`: Checkout the [**geodatasets**](https://geodatasets.readthedocs.io) libaray\n", - "- Convert other objects to `pandas` or `GeoPandas` objects to make them usable in `PyGMT`: For example, convert [**OSMnx**](https://osmnx.readthedocs.io)'s `MultiDiGraph` to a `geopandas.DataFrame`\n", - "- Create more complex geometries: Combine `GeoPandas` with [**shapely**](https://shapely.readthedocs.io) (i.e., `from shapely.geometry import Polygon`)\n", - "- Support of OGR formats: Use [`geopandas.read_file`](https://geopandas.org/en/v1.0.1/docs/reference/api/geopandas.read_file.html) to load data provided as shapefile (.shp), GeoJSON (.geojson), geopackage (.gpkg), etc." - ] - }, - { - "cell_type": "markdown", - "id": "9e7abfa1-13ac-4bed-896d-4e55438f1f94", - "metadata": {}, - "source": [ - "## 4. Orientation / suggestion for \"2.1.4 Plot the rivers with color-coding for the river length\"\n", - "\n", - "Below you find a rough code shipset for plotting the rives with color-coding in section 2.1.4." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1b11d21b-e46e-4b34-9319-5aafb036552f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = pygmt.Figure()\n", - "\n", - "fig.coast(\n", - " projection=\"M10c\", \n", - " region=[-10, 35, 35, 58],\n", - " land=\"gray99\",\n", - " shorelines=\"1/0.1p,gray50\",\n", - " frame=True,\n", - ")\n", - "\n", - "pygmt.makecpt(cmap=\"SCM/oslo\",series=[gpd_rivers.Shape_Leng.min(), 1500000], reverse=True)\n", - "fig.colorbar(frame=[\"x+lriver length\", \"y+lm\"], position=\"+ef0.2c\")\n", - "\n", - "for i_river in range(len(gpd_rivers)):\n", - " fig.plot(\n", - " data=gpd_rivers[gpd_rivers.index==i_river],\n", - " zvalue=gpd_rivers.loc[i_river, \"Shape_Leng\"],\n", - " pen=\"0.5p\",\n", - " cmap=True,\n", - " )\n", - "\n", - "fig.show(dpi=img_dpi)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.12.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}