From 589dbf9e4a1ef4ffa02fe1c86fb7b8448c520edd Mon Sep 17 00:00:00 2001 From: MiguelonGonzalez Date: Tue, 1 Mar 2022 12:09:55 +0100 Subject: [PATCH] tutorials keep me busy --- dinsar/agregado.py | 2 +- .../Adding other database to the model.ipynb | 57 +++++ examples/Creating a dinsar model.ipynb | 6 +- examples/Studying an Aggregate.ipynb | 212 +++++++++--------- ...ualizing with plot and mapa methods..ipynb | 39 ++++ examples/Working with model parts.ipynb | 49 ++++ 6 files changed, 255 insertions(+), 110 deletions(-) create mode 100644 examples/Adding other database to the model.ipynb create mode 100644 examples/Visualizing with plot and mapa methods..ipynb create mode 100644 examples/Working with model parts.ipynb diff --git a/dinsar/agregado.py b/dinsar/agregado.py index 775ea4a..4d7e3d0 100644 --- a/dinsar/agregado.py +++ b/dinsar/agregado.py @@ -335,7 +335,7 @@ def set_estacion(self, estacion=None, bd=None, way='nearest', orden=1, self._info_estaciones = {'estacion':_estacion, 'bd':bd_object} if not _silent: - print(f"Fijada la estación: {self.estacion.name}" + print(f"Fijada la estación: {self.estacion.name} " f"según el método '{way}'.") return self diff --git a/examples/Adding other database to the model.ipynb b/examples/Adding other database to the model.ipynb new file mode 100644 index 0000000..7904604 --- /dev/null +++ b/examples/Adding other database to the model.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8159acc0", + "metadata": {}, + "outputs": [], + "source": [ + "from shapely.geometry import Point\n", + "import random\n", + "\n", + "x1, y1, x2, y2 = Doñana.get('Asc').gdf.total_bounds\n", + "\n", + "random_point = lambda x1, y1, x2, y2 : Point(random.uniform(x1, x2), random.uniform(y1, y2))\n", + "\n", + "# Definición de la base de datos (pandas.DataFrame):\n", + "columns, values = [i for i in 'ABCDE'], [np.random.random(60) for i in range(5)]\n", + "df_table = {i:j for i,j in zip(columns, values)}\n", + "df = pd.DataFrame(df_table, index=pd.date_range(start='2015', end='2020', freq='M'))\n", + "df = df.melt(var_name='Nombre', value_name='Valores', ignore_index=False)\n", + "df.index.name = 'Fechas'\n", + "df.reset_index(inplace=True)\n", + "\n", + "# Definición de un archivo espacial\n", + "table = {'Nombre':[i for i in 'ABCDE'],\n", + " 'geometry':[random_point(x1, y1, x2, y2) for i in range(5)]}\n", + "gdf = gpd.GeoDataFrame(table, crs=25830)\n", + "\n", + "gps = dinsar.DataBase(df, name='gps', units='cm', color='black')\n", + "gps.append_geometry(gdf)\n", + "Doñana.append(gps)" + ] + } + ], + "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": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Creating a dinsar model.ipynb b/examples/Creating a dinsar model.ipynb index 2393824..80fbd4e 100644 --- a/examples/Creating a dinsar model.ipynb +++ b/examples/Creating a dinsar model.ipynb @@ -15,6 +15,8 @@ "\n", "---------------------------------------------------------------------------------------------------------------------------\n", "\n", + "

Creating a dinsar Model

\n", + "\n", "## 1. Creation of a Model\n", "The dinsar package design holds a data **model** as the main object around which all of the program functionalities spin around. This model will include all the DInSAR datasets and databases that are consulted in the study area and is created through the `Model` class. Since the simplest way of analyzing DInSAR deformation points (also called persistent or permanent scatterers; PS) is through their **aggregation into polygons**, the initialization of this class requieres a polygonal spatial file, that should be introduced as a relative path to its ubication. This way, each of these polygons will define the different analysis areas of the model -analogous to [ADA's](https://open.igme.es/xmlui/bitstream/handle/20.500.12468/708/fast_detection_ground_2017.pdf?sequence=1) (Active Deformation Areas), called in the code as **agregados** and hereinafter as *aggregates*.\n", "\n", @@ -280,7 +282,7 @@ }, { "cell_type": "markdown", - "id": "836305ca", + "id": "2b334d20", "metadata": {}, "source": [ "## 5. Main functions of Model object:\n", @@ -293,7 +295,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "da3c0596", + "id": "dd519337", "metadata": {}, "outputs": [ { diff --git a/examples/Studying an Aggregate.ipynb b/examples/Studying an Aggregate.ipynb index 59f96b2..1b4a7de 100644 --- a/examples/Studying an Aggregate.ipynb +++ b/examples/Studying an Aggregate.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "22f6250e", + "id": "b80d6e89", "metadata": {}, "source": [ "---------------------------------------------------------------------------------------------------------------------------\n", @@ -13,16 +13,20 @@ "\n", "`>>> help(dinsar.parts)` or press the **tabulator** key in Jupyter Notebook to access an object's methods.\n", "\n", - "---------------------------------------------------------------------------------------------------------------------------" + "---------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "

Studying an Aggregate

\n", + "\n", + "Tal y como se explicó en el tutorial *Creating a dinsar Model*, el modelo contiene un capa con agregados, que representan las áreas donde se analizará la deformación del terreno en combinación con otras variables. El estudio de cada uno de ellos se realiza a través de la función `agregado`, la cual exige indicar el agregado en cuestión a estudiar y, de manera opcional, si se quiere manejar únicamente la componente vertical del movimiento del terreno (`vm=True`) en los datasets DInSAR o según la dirección del LOS (Line Of Sigh).\n", + "\n", + "En el siguiente ejemplo se analiza el agregado *2*, creándose un objeto de tipo `Agregado` con información relativa a la extensión que abarca." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "91a03f03", - "metadata": { - "scrolled": true - }, + "execution_count": 2, + "id": "67c75e58", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -31,154 +35,148 @@ "Geometría añadida correctamente.\n", "Geometría añadida correctamente.\n" ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "import dinsar\n", - "import pandas as pd\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "\n", "\n", "Doñana = dinsar.example.get_model()\n", "\n", - "# Para centrar las gráficas salientes en un notebook de Jupyter.\n", - "from IPython.core.display import HTML\n", - "HTML(\"\"\"\"\"\")" + "A2 = Doñana.agregado('2', vm=True)" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "98c4b06c", + "cell_type": "markdown", + "id": "54f58aa4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Geometría añadida correctamente.\n" - ] - } - ], "source": [ - "from shapely.geometry import Point\n", - "import random\n", + "## Sensors vinculation\n", "\n", - "x1, y1, x2, y2 = Doñana.get('Asc').gdf.total_bounds\n", + "Si el modelo contiene una base de piezometría y de precipitación con información espacial, la definición de un agregado supone la **vinculación automática** al mismo del piezómetro y estación pluviométrica más cercanas. Para el agregado analizado, estos sensores se pueden modificar a través del método `set_piezo` y `set_estación`, así como añadir otro de otra base de datos mediante `set_other_bd`. Estos métodos permiten vincular un sensor al agregado de **tres formas posibles**:\n", "\n", - "random_point = lambda x1, y1, x2, y2 : Point(random.uniform(x1, x2), random.uniform(y1, y2))\n", + "**1.** Vinculación del sensor **más cercano**. Way = `nearest`\n", "\n", - "# Definición de la base de datos (pandas.DataFrame):\n", - "columns, values = [i for i in 'ABCDE'], [np.random.random(60) for i in range(5)]\n", - "df_table = {i:j for i,j in zip(columns, values)}\n", - "df = pd.DataFrame(df_table, index=pd.date_range(start='2015', end='2020', freq='M'))\n", - "df = df.melt(var_name='Nombre', value_name='Valores', ignore_index=False)\n", - "df.index.name = 'Fechas'\n", - "df.reset_index(inplace=True)\n", + "**2.** Vinculación de los sensores más cercanos en un **radio** (`radius`, in Km) a la redonda (way=`radius`). Disponible para bases de datos de tipo `Piezometria` y `DataBase`.\n", "\n", - "# Definición de un archivo espacial\n", - "table = {'Nombre':[i for i in 'ABCDE'],\n", - " 'geometry':[random_point(x1, y1, x2, y2) for i in range(5)]}\n", - "gdf = gpd.GeoDataFrame(table, crs=25830)\n", + "**3.** Vinculación **manual** del sensor. Way = `manual`.\n", "\n", - "gps = dinsar.DataBase(df, name='gps', units='cm', color='black')\n", - "gps.append_geometry(gdf)\n", - "Doñana.append(gps)" + "En el caso de las estaciones de preciptación, los valores asociados se corresponden con la desviación acumulada. Esto se puede elegir a través del argumento `values`\n", + "Puesto que estos métodos devuleven al propio objeto, se pueden encadenar en la misma línea de código." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "397af870", + "execution_count": 12, + "id": "d66173e5", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'Doñana' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2232/1276700181.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mDoñana\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstudy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Plotear las series temporales del agregado 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'Doñana' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "1 piezo(s) fijado(s) según el método 'nearest'.\n", + "Fijada la estación: Almontesegún el método 'manual'.\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "Doñana.study(2).plot() # Plotear las series temporales del agregado 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3429085c", - "metadata": {}, - "outputs": [], - "source": [ - "Doñana.study(2).mapa() # Representar espacialmente el agregado 2 y sus PS." + "A2.set_piezo(way='nearest').set_estacion(way='manual', estacion='Almonte')" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "da42d889", + "cell_type": "markdown", + "id": "74ca8306", "metadata": {}, - "outputs": [], "source": [ - " # Cojo los cinco primeros PS del Dataset 'Ascending' (dessde fuera del modelo ) y calculo su deformación promedio\n", - "ps = Asc.ps[0:5] \n", - "Asc.subset(ps).plot(plot_average=True)" + "Se puede acceder a las **series temporales** vinculadas de las bases de datos de piezometría, precipitación u otro tipo a través de los métodos `piezos`, `estacion` y `other_sensors`, respectivamente.\n", + "\n", + "## Aggregate visualization\n", + "\n", + "Toda esta información vinculada al agregado, así como las curvas de deformación de los datasets del agreagdo se puede **visualizar** a través del método `plot`. La **representación espacial** de todas las entidades del agregado se puede realizar a través del método `mapa`." ] }, { "cell_type": "code", - "execution_count": null, - "id": "4f8e9432", - "metadata": {}, - "outputs": [], + "execution_count": 13, + "id": "df500690", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# Cojo el primer piezómetro de la base de datos de piezometría y ploteo su serie temporal\n", - "piezo = bbdd.piezos[0]\n", - "bbdd.plot(piezo)\n", - "\n", - "# A través del método 'take' accedo al array con su serie tempral: --> bbdd.take(piezo)" + "A2.plot()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2c5d7ad8", - "metadata": {}, - "outputs": [], + "execution_count": 14, + "id": "53d649c4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Selecciono la estación de Almonte y ploteo su serie temporal según su desviación acumulada\n", - "# Puedo ver qué estaciones hay en la base de datos así: --> precipi.estaciones\n", - "precipi.plot('Almonte', values='dsv')" + "A2.mapa()" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "00623a1c", + "cell_type": "markdown", + "id": "f5bf3e87", "metadata": {}, - "outputs": [], "source": [ - "precipi.estaciones" + "## Other `Agregado` object methods\n", + "\n", + "Este objeto también contiene otros métodos que permiten conocer los **datasets** que contiene el agregado (`datasets`), el **área** que abarca (in ha; `area`), las coordenadas de su centroide (`centroid`) o si los valores de deformación son los originales (según la dirección del LOS) o se corresponden únicamente con la **componente vertical** (`vm`). Asimismo, se puede imprmir en pantalla toda la información del mismo a través del método `info`.\n", + "\n", + "Entre las funcionalidades del agregado destacan las funciones `subset` y `wavelet`. La primer permite acceder a uno de los datasets presentes en el agregado,\n", + "la segunda proporciona un análisis espectral de las variables del mismo mediante herramientas wavelet." ] } ], diff --git a/examples/Visualizing with plot and mapa methods..ipynb b/examples/Visualizing with plot and mapa methods..ipynb new file mode 100644 index 0000000..cf739e9 --- /dev/null +++ b/examples/Visualizing with plot and mapa methods..ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0604ed54", + "metadata": {}, + "outputs": [], + "source": [ + "import dinsar\n", + "\n", + "# Para centrar las gráficas salientes en un notebook de Jupyter.\n", + "from IPython.core.display import HTML\n", + "HTML(\"\"\"\"\"\")" + ] + } + ], + "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": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Working with model parts.ipynb b/examples/Working with model parts.ipynb new file mode 100644 index 0000000..27e5639 --- /dev/null +++ b/examples/Working with model parts.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "37fef053", + "metadata": {}, + "outputs": [], + "source": [ + " # Cojo los cinco primeros PS del Dataset 'Ascending' (dessde fuera del modelo ) y calculo su deformación promedio\n", + "ps = Asc.ps[0:5] \n", + "Asc.subset(ps).plot(plot_average=True)\n", + "\n", + "# Cojo el primer piezómetro de la base de datos de piezometría y ploteo su serie temporal\n", + "piezo = bbdd.piezos[0]\n", + "bbdd.plot(piezo)\n", + "\n", + "# A través del método 'take' accedo al array con su serie tempral: --> bbdd.take(piezo)\n", + "\n", + "# Selecciono la estación de Almonte y ploteo su serie temporal según su desviación acumulada\n", + "# Puedo ver qué estaciones hay en la base de datos así: --> precipi.estaciones\n", + "precipi.plot('Almonte', values='dsv')\n", + "\n", + "precipi.estaciones" + ] + } + ], + "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": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}