respyrar

Name	respyrar JSON
Version	0.0.7 JSON
	download
home_page
Summary	Monitor air quality - Monitoreá la calidad del aire
upload_time	2023-09-04 20:18:08
maintainer
docs_url	None
author
requires_python	>=3.10
license	Copyright 2022 ObSA - Observatorio de Salud y Ambiente, CeNDIE Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords	air emissions no2 earth engine geoscience atmospheric
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# respyrAR

<!-- En la carpeta codigos_iniciales están los script iniciales que fui armando.

La notebook es la clase que preparamos para el curso de postgrado. En ella esta explicado paso a paso el proceso de descarga de datos.
El script tiff_generator.py toma un cuadrado de latitud y longitud y un periodo en meses. Y devuelve un mapa .tiff con la media mensual en esa región. Tiene dos versiones de funciones, una en la que traté de modularizar un poco las funciones metidas. Mi objetivo sería dejar de usar la libreria geemap. Pero no sé bien aun cómo.

El script timeseries.py tiene las funciones de reducción y tres funciones que generan series promediadas en el día, mes y año. Esto puede ampliarse un montón.

El script collection.py quería modularizar un par de pasos que repito mucho en varios scripts pero no me funciona del todo bien.

El script roi.py es un poco más "interactivo" es simplemente para elegir una región de interes (Region Of Interest) pero me parece que esto es un paso posterior en el diseñor del paquete.

El script date_selection.py también es un poco interactivo, pero la idea es que pueda armar una selección de días o meses a la hora de pedirle a google engine que te devuelva una serie o un mapa.

-->

# Español

## Instalación

1. Para poder autenticar seguir las instrucciones de este [link](https://developers.google.com/earth-engine/guides/python_install#expandable-2 "Authentication")

2. Para poder instalar dependencias del paquete *respyrar* (en particular la bilbioteca ```cartopy```), primero es necesario instalar algunas dependencias. Se puede encontrar información detallada para Linux, Windows y iOS [aquí](https://scitools.org.uk/cartopy/docs/latest/installing.html "Cartopy") y particularmente para Windows [aquí](https://stackoverflow.com/questions/70177062/cartopy-not-able-to-identify-geos-for-proj-install-on-windows "Cartopy on Windows"). Para sistemas de linux basados en Debian (como Ubuntu) alcanza con correr los siguientes comandos en la terminal:

```
$ apt-get install libproj-dev proj-data proj-bin
$ apt-get install libgeos-dev
$ apt-get -qq install python-cartopy python3-cartopy
```
3. Una vez resueltos estos dos pasos, sólo queda instalar el paquete ejecutando en la terminal el siguiente comando

```
$ pip install respyrar
```

# English

1. To enable authentication follow [these](https://developers.google.com/earth-engine/guides/python_install#expandable-2 "Authentication") instructions.

2. Before installing *respyrar* you'll need to enable some dependencies installation (particularly, ```cartopy```). You can find detailed instructions for Linux, Windows and iOS [here](https://scitools.org.uk/cartopy/docs/latest/installing.html "Cartopy") and particularly for Windows [here](https://stackoverflow.com/questions/70177062/cartopy-not-able-to-identify-geos-for-proj-install-on-windows "Cartopy on Windows"). For Debian-based linux systems (like Ubuntu) run the following commands in your terminal:

```
$ apt-get install libproj-dev proj-data proj-bin
$ apt-get install libgeos-dev
$ apt-get -qq install python-cartopy python3-cartopy
```

3. Once these two steps are completed, all you need to do is run the following command

```
$ pip install respyrar
```

## Functions

### get_collection(ini,fin, sat = 'COPERNICUS/S5P/OFFL/L3_NO2', column ='tropospheric_NO2_column_number_density')

Returns the image collection (as an Earth Engine object) of a column from the indicated satellite for a determined period of time.

**Parameters:**

ini:

The start date (inclusive), of Date/Number/String type.

fin:

The finalization date (exclusive), of Date/Number/String type.

sat:

Optional; A string indicating the desired satellite and variable. By default it is set to the L3_NO2 variable, of the S5P satellite of the Copernicus Programme, on offline mode.

column:

Optional; A string indicating the desired column. By default it is set to tropospheric NO2 column number density.

### create_reduce_region_function(geometry, reducer=ee.Reducer.mean(),scale=1000,crs='EPSG:4326', bestEffort=True,maxPixels=1e13,tileScale=4)

Creates a region reduction function.

Creates a region reduction function intended to be used as the input function
to `ee.ImageCollection.map()` for reducing pixels intersecting a provided region
to a statistic for each image in a collection. See ee.Image.reduceRegion()
documentation for more details.

**Parameters:**

geometry:

An ee.Geometry that defines the region over which to reduce data.

reducer:

Optional; An ee.Reducer that defines the reduction method. By default set to ee.Reducer.mean(), which calculates the mean of the specified region.

scale:

Optional; A number that defines the nominal scale in meters of the projection to work in. By default set to 1000.

crs:

Optional; An ee.Projection or EPSG string ('EPSG:5070') that defines the projection to work in. By default set to 'EPSG:4326'.

bestEffort:

Optional; A Boolean indicator for whether to use a larger scale if the geometry contains too many pixels at the given scale for the operation to succeed. By default set to True.

maxPixels:

Optional; A number specifying the maximum number of pixels to reduce. By default set to 1e13.

tileScale:

Optional; A number representing the scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. By default set to 4.

**Returns:**

A function that accepts an ee.Image and reduces it by region, according to the provided arguments.

This function was taken from the time series tutorial for python of the Google Engine developers group (for further information visit: https://developers.google.com/earth-engine/tutorials/community/time-series-visualization-with-altair)

### fc_to_dict(ee.FeatureCollection)

Transfers feature properties to a dictionary. The result of create_reduce_region_function applied to an `ee.ImageCollection` produces an `ee.FeatureCollection`. This data needs to be transferred to the Python kernel, but serialized feature collections are large and hard to deal with. This step defines a function to convert the feature collection to an `ee.Dictionary` where the keys are feature property names and values are corresponding lists of property values, which `pandas` can deal with handily.

1. Extract the property values from the `ee.FeatureCollection` as a list of lists stored in an `ee.Dictionary` using `reduceColumns()`.
2. Extract the list of lists from the dictionary.
3. Add names to each list by converting to an `ee.Dictionary` where keys are property names and values are the corresponding value lists.

The returned `ee.Dictionary` is essentially a table, where keys define columns and list elements define rows.

**Parameters :**

fc:
An ee.FeatureCollection object which is a result of applying create_reduce_region_function to an ‘ee.ImageCollection’.

**Returns:**

The correspondent `ee.Dictionary`.

### add_date_info(df)

Add date columns derived from the milliseconds from Unix epoch column. The pandas library provides functions and objects for timestamps and the DataFrame object allows for easy mutation.
Define a function to add date variables to the DataFrame: year, month, day, weekday, and day of year (DOY)

**Parameters:**

df:
Pandas dataframe.

**Returns:**

The modified Pandas dataframe.

### geometry_rectangle(lon_w,lat_s,lon_e,lat_n)
Returns an ee.Geometry that defines the region over which to reduce data in create_reduce_region_function . The region is a latitude-longitude rectangle.

**Parameters:**

lon_w:
West boundary of the rectangle. Must be a float between -180° and 180°.

lat_s:
South boundary of the rectangle. Must be a float between -90° and 90°.

lon_e:
East boundary of the rectangle. Must be a float between -180° and 180°.

lat_n:
North boundary of the rectangle. Must be a float between -90° and 90°.

**Returns:**

The `ee.Geometry.Rectangle` correspondent to those coordinates.

For further information visit https://developers.google.com/earth-engine/apidocs/ee-geometry-rectangle

### time_series_df(roi, start, end, filename = 'NO2trop_series.csv', reducers = [ee.Reducer.mean()], red_names = ['NO2_trop_mean'], collection = None)

Creates a pandas dataframe that includes the time series of the concentration of a gas measured from the Sentinel 5p TROPOMI sensor available in the Google Earth Engine api. By default, it calculates the average tropospheric NO2 series over a region of interest.

**Parameters:**

roi:
An ee.Geometry object. It can be a rectangle of latitude and longitude, a polygon, or other Geometries. You can create a rectangle with the function `geometry_rectangle`.

start:
A string indicating the start of the time series. The format should be 'YYYY-MM-DD'. In the case of NO2, the series begins on 2018-06-28.

end:
A string indicating the end of the time series. The format should be 'YYYY-MM-DD'.

filename:
Optional; A string indicating the name of the output file. By default set to 'NO2trop_series.csv'.

reducers:
Optional; An array of ee.Reducer objects. For each object, a column is created in the dataframe where that spatial statistic is applied. By default, the average value over a region is taken. For more reducers, visit https://developers.google.com/earth-engine/guides/reducers_intro

red_names:
Optional; An array of strings indicating the name of the reducers used. It must have the same length as the list of reducers and respect the same order as the one used in it. By default it is an array containing only 'NO2_trop_mean'.

collection:
Optional; An `ee.ImageCollection` object for the desired satellite, variable and column, and for a period containing the desired one. It can be created with `get_collection`. If the same collection is used several times, it is more efficient to get it just once and pass it as a parameter. By default it is set to None, so that it is obtained automatically.

**Returns:**

A Pandas dataframe with a column for each reducer in `reducers`, respectively named with the names in `red_names`. Also contains the columns Timestamp, Year, Month, Day and Weekday.

### ts_dailydf(df, filename='dailymean_df.csv', statistic = 'mean')

Returns a daily time series. In case of missing data in the series, it interleaves NaN values. In case of two daily data (this is possible due to overlapping of the satellite pass in some regions) returns the average

**Parameters:**
df:
Panda dataframe with the original time series. This is the one calculated in `time_series_df`.

filename:
Optional; A string indicating the name of the output file. By default set to 'dailymean_df.csv'.

statistic:
Optional; A string indicating the statistic reduction to be performed on each day of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'.

**Returns:**

A Pandas dataframe collapsed by day.

### ts_monthlydf(df, filename='monthlymean_df.csv', statistic = 'mean')

Returns a monthly series of the concentration of the chosen gas.

**Parameters:**
df:
Panda dataframe with the original time series. This time series is the one that is calculated in time_series_df

filename:
Optional; A string indicating the name of the output file. By default set to

statistic:
Optional; A string indicating the statistic reduction to be performed on each month of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'.

**Returns:**

A Pandas dataframe collapsed by month.

### ts_weeklydf(df, filename='weeklymean_df.csv', statistic = 'mean')

Returns a weekly series of the concentration of the chosen gas.

**Parameters:**
df:
Panda dataframe with the original time series. This time series is the one that is calculated in time_series_df

filename:
Optional; A string indicating the name of the output file.

statistic:
Optional; A string indicating the statistic reduction to be performed on each week of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'.

**Returns:**

A Pandas dataframe collapsed by week.

### space_data_meshgrid(roi, start, end, collection = None, statistic = 'mean', export = False)

Obtains a meshgrid with the no2 values in the indicated region for the specified period.

**Parameters:**

roi:

An ee.Geometry object. It can be a rectangle of latitude and longitude, a polygon, or other Geometries. You can create a rectangle with the function `geometry_rectangle`. (ROI stands for Region Of Interest)

start:

String indicating start date (inclusive).

end:

String indicating end date (exclusive).

collection:

Optional; An `ee.ImageCollection` object for the desired satellite, variable and column, and for a period containing the desired one. It can be created with `get_collection`. If the same collection is used several times, it is more efficient to get it just once and pass it as a parameter. By default it is set to None, so that it is obtained automatically.

statistic:

Optional; A string indicating the statistic reduction to be performed on each pixel of the spatial data for the specified period. It can be set to 'mean' or 'median'. By default it is set to 'mean'.

export:

Optional; Boolean value. If set to True, exports to the Google Drive of the user the calcullated meshgrid as a GeoTIFF file. It is saved in the folder "NO2", with the filename "NO2_"+_start_ (where _start_ is the homonym parameter). By default it is set to False.

**Returns:**

values:

NumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the value of NO2 correspondent to the latitude and longitude (these expressed in the following objects)

lats:

NumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent latitude value.

lons:

NumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent longitude value.

### interanual_variation(df_m, year1, year2, month_num, column = 'NO2_trop_mean')

Calculate the interanual variation of NO2 (or the specified column) for the indicated month of two different years.

**Parameters:**

df_m:
Pandas Dataframe, collpased by month, containing data for the specified month of the specified years.

year1:

Numeric value, the first year to be considered.

year2:

Numeric value, the second year to be considered.

month_num:

Numeric value, the number of the month to be considered (e.g. The number for January is 1, the one for April is 4, etc.).

column:

Optional; A string with the name of the column of the dataframe to evaluate the variation over. By default it is set to 'NO2_trop_mean'.

**Returns:**

A float number indicating the porcentual variation of the column, in terms of the first year, i.e. (NO2 of year2 - NO2 of year1) / (NO2 of year1) .

### plot_map(no2, lats, lons, shapefile, title = 'Concentración media de NO2 troposférico (mol/m2)', filename = 'map.png', width = 8, height = 6, font_size = 15, save = True, show = False)

Plots a map for the specified latitudes and longitudes, colored as indicated by `no2`. Designed to be used with the result of `space_data_meshgrid`. Can be saved, shown, and/or returned.

**Parameters:**

no2:

lats:

NumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent latitude value.

lons:

NumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent longitude value.

shapefile:

String with the path to a .shp file containing borders of the region to be drawn.

title:

Optional; A string with the title the figure will show. By default it is 'Concentración media de NO2 troposférico (mol/m2)' (spanish for Tropospheric NO2 mean concentration (mol/m2)) .

filename:

Optional; String with the name of the output file (in the case `save` is set to True). By default set to 'map.png'.

width:

Optional; Int, the width of the figure to be generated. By default set to 8.

height:

Optional; Int, the height of the figure to be generated. By default set to 6.

font_size:

Optional; Int, the font size of the figure to be generated. By default set to 15.

save:

Optional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.

show:

Optional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.

**Returns:**

raw_fig, raw_ax:

`matplotlib` figure and axes respectively, before adding any formatting (title, color scale, etc.)

### plot_series(df, start = pd.Timestamp.min, end = pd.Timestamp.max, column = 'NO2_trop_mean', filename = 'series.png', width = 15, height = 4, save = True, show = False)

Plot the time series passed as a Pandas dataframe.

**Parameters:**

df:

Pandas dataframe, with a 'Fecha_datetime' column containing timestamps in YYYY-MM-DD date format.

start:

Optional; String in YYYY-MM-DD date format indicating start time. Values previous to this date won't be plotted. By default set to Pandas minimum timestamp (no filter).

end:

Optional; String in YYYY-MM-DD date format indicating end time. Values posterior to this date won't be plotted. By default set to Pandas maximum timestamp (no filter).

column:

Optional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.

filename:

Optional; String with the name of the output file (in the case `save` is set to True). By default set to 'series.png'.

width:

Optional; Int, the width of the figure to be generated. By default set to 15.

height:

Optional; Int, the height of the figure to be generated. By default set to 4.

save:

Optional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.

show:

Optional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.

**Returns:**

fig, ax:

`matplotlib` figure and axes, respectively.

### plot_autocorr(df, lags, column = 'NO2_trop_mean', filename = 'autocorrelogram.png', width = 30, height=5, save = True, show = False)

Plot autocorrelogram of the indicated dataframe. Plots lags on the horizontal and the correlations on vertical axis.

df:

Pandas dataframe, with a 'Fecha_datetime' column containing timestamps in YYYY-MM-DD date format.

lags:

An int or array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int.

column:

Optional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.

filename:

Optional; String with the name of the output file (in the case `save` is set to True). By default set to 'autocorrelogram.png'.

width:

Optional; Int, the width of the figure to be generated. By default set to 30.

height:

Optional; Int, the height of the figure to be generated. By default set to 5.

save:

Optional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.

show:

Optional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.

**Returns:**

fig, ax:

`matplotlib` figure and axes, respectively.

### barplot_year_cmp(df_m, year1, year2, column = 'NO2_trop_mean', filename='compared_series.png', width = 10, height=4, save = True, show = False)

Draw a bar graph comparing the monthly values between two years.

**Parameters:**

df_m:

Pandas dataframe aggregated by month, and containing at least some values for the indicated years.

year1:

One of the years to be compared, as Int/String.

year2:

The other of the years to be compared, as Int/String.

column:

Optional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.

filename:

Optional; String with the name of the output file (in the case `save` is set to True). By default set to 'compared_series.png'.

width:

Optional; Int, the width of the figure to be generated. By default set to 10.

height:

Optional; Int, the height of the figure to be generated. By default set to 4.

save:

Optional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.

show:

Optional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.

**Returns:**

fig, ax:

`matplotlib` figure and axes, respectively.

<!--

## TO DO
. armar un script de example / test razonable
. minima documentación acá
. poner todo en español
. corregir typo interanual es interannual

. para dibujar el mapa estoy usando un shp que dibuja contornos. hacer que sea opcional
. polygon funciona bien pero space_date_meshgrid se rompe si no es rectangular
. bounds de polygon redondean un poco mal (o está desfasado respecto al otro shape)
. hacer algo que descargue los datos del mapa aun si hay algunos pixeles que tienen solo nans (geemap lo hizo)
. cuando uso una coleccion que me pasan, agregar un chequeo que vea que el período que quiero esté adentro (cómo?)
. agregar un chequeo de fecha bien pasada
. está buena la solucion a lo de los reductores? pasan un arreglo de reductores y uno de nombres, tienen que medir lo mismo...
. mascara
. arreglar el warning de pandas index en lo de isocalendar
. desarrollar la documentacion
. reducir cantidad de bibliotecas a usar
. crear poligonos a partir del nombre de la ciudad-prov-pais?

<!-- NECESARIO CORRER ANTES DE INSTALAR (QUÈ PASA SI TIENEN WINDOWS?)

apt-get install libproj-dev proj-data proj-bin
apt-get install libgeos-dev
apt-get -qq install python-cartopy python3-cartopy

#sugerir esto?:
pip uninstall -y shapely # cartopy and shapely aren't friends (early 2020)
pip install shapely --no-binary shapely

Raw data

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"keywords": "air,emissions,no2,earth,engine,geoscience,atmospheric",
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"author_email": "Mart\u00edn Amigo <martinamigo97@gmail.com>",
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"description": "# respyrAR\n\n\n\n# Espa\u00f1ol\n\n## Instalaci\u00f3n\n\n1. Para poder autenticar seguir las instrucciones de este [link](https://developers.google.com/earth-engine/guides/python_install#expandable-2 \"Authentication\")\n\n2. Para poder instalar dependencias del paquete *respyrar* (en particular la bilbioteca ```cartopy```), primero es necesario instalar algunas dependencias. Se puede encontrar informaci\u00f3n detallada para Linux, Windows y iOS [aqu\u00ed](https://scitools.org.uk/cartopy/docs/latest/installing.html \"Cartopy\") y particularmente para Windows [aqu\u00ed](https://stackoverflow.com/questions/70177062/cartopy-not-able-to-identify-geos-for-proj-install-on-windows \"Cartopy on Windows\"). Para sistemas de linux basados en Debian (como Ubuntu) alcanza con correr los siguientes comandos en la terminal:\n\n```\n$ apt-get install libproj-dev proj-data proj-bin\n$ apt-get install libgeos-dev\n$ apt-get -qq install python-cartopy python3-cartopy\n```\n3. Una vez resueltos estos dos pasos, s\u00f3lo queda instalar el paquete ejecutando en la terminal el siguiente comando\n\n```\n$ pip install respyrar\n```\n\n# English\n\n1. To enable authentication follow [these](https://developers.google.com/earth-engine/guides/python_install#expandable-2 \"Authentication\") instructions.\n\n2. Before installing *respyrar* you'll need to enable some dependencies installation (particularly, ```cartopy```). You can find detailed instructions for Linux, Windows and iOS [here](https://scitools.org.uk/cartopy/docs/latest/installing.html \"Cartopy\") and particularly for Windows [here](https://stackoverflow.com/questions/70177062/cartopy-not-able-to-identify-geos-for-proj-install-on-windows \"Cartopy on Windows\"). For Debian-based linux systems (like Ubuntu) run the following commands in your terminal:\n\n```\n$ apt-get install libproj-dev proj-data proj-bin\n$ apt-get install libgeos-dev\n$ apt-get -qq install python-cartopy python3-cartopy\n```\n\n3. Once these two steps are completed, all you need to do is run the following command\n\n```\n$ pip install respyrar\n```\n\n\n## Functions\n\n### get_collection(ini,fin, sat = 'COPERNICUS/S5P/OFFL/L3_NO2', column ='tropospheric_NO2_column_number_density')\n\nReturns the image collection (as an Earth Engine object) of a column from the indicated satellite for a determined period of time. \n\n**Parameters:**\n\n ini:\n\nThe start date (inclusive), of Date/Number/String type.\n\n fin:\n\nThe finalization date (exclusive), of Date/Number/String type.\n\n sat:\n\nOptional; A string indicating the desired satellite and variable. By default it is set to the L3_NO2 variable, of the S5P satellite of the Copernicus Programme, on offline mode.\n\n column:\n\nOptional; A string indicating the desired column. By default it is set to tropospheric NO2 column number density.\n\n\n### create_reduce_region_function(geometry, reducer=ee.Reducer.mean(),scale=1000,crs='EPSG:4326', bestEffort=True,maxPixels=1e13,tileScale=4)\n\nCreates a region reduction function.\n\n Creates a region reduction function intended to be used as the input function\n to `ee.ImageCollection.map()` for reducing pixels intersecting a provided region\n to a statistic for each image in a collection. See ee.Image.reduceRegion()\n documentation for more details.\n\n**Parameters:**\n\n geometry:\n \n An ee.Geometry that defines the region over which to reduce data.\n\n reducer:\n \n Optional; An ee.Reducer that defines the reduction method. By default set to ee.Reducer.mean(), which calculates the mean of the specified region.\n\n scale:\n \n Optional; A number that defines the nominal scale in meters of the projection to work in. By default set to 1000.\n\n crs:\n \n Optional; An ee.Projection or EPSG string ('EPSG:5070') that defines the projection to work in. By default set to 'EPSG:4326'.\n\n bestEffort:\n \n Optional; A Boolean indicator for whether to use a larger scale if the geometry contains too many pixels at the given scale for the operation to succeed. By default set to True.\n\n maxPixels:\n \n Optional; A number specifying the maximum number of pixels to reduce. By default set to 1e13.\n \n tileScale:\n \n Optional; A number representing the scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. By default set to 4.\n\n**Returns:**\n \nA function that accepts an ee.Image and reduces it by region, according to the provided arguments. \n\nThis function was taken from the time series tutorial for python of the Google Engine developers group (for further information visit: https://developers.google.com/earth-engine/tutorials/community/time-series-visualization-with-altair)\n\n\n### fc_to_dict(ee.FeatureCollection)\n\nTransfers feature properties to a dictionary. The result of create_reduce_region_function applied to an `ee.ImageCollection` produces an `ee.FeatureCollection`. This data needs to be transferred to the Python kernel, but serialized feature collections are large and hard to deal with. This step defines a function to convert the feature collection to an `ee.Dictionary` where the keys are feature property names and values are corresponding lists of property values, which `pandas` can deal with handily.\n\n1. Extract the property values from the `ee.FeatureCollection` as a list of lists stored in an `ee.Dictionary` using `reduceColumns()`.\n2. Extract the list of lists from the dictionary.\n3. Add names to each list by converting to an `ee.Dictionary` where keys are property names and values are the corresponding value lists.\n\nThe returned `ee.Dictionary` is essentially a table, where keys define columns and list elements define rows.\n\n**Parameters :**\n\n fc: \nAn ee.FeatureCollection object which is a result of applying create_reduce_region_function to an \u2018ee.ImageCollection\u2019.\n\n**Returns:**\n\nThe correspondent `ee.Dictionary`.\n\nThis function was taken from the time series tutorial for python of the Google Engine developers group (for further information visit: https://developers.google.com/earth-engine/tutorials/community/time-series-visualization-with-altair)\n\n\n### add_date_info(df)\n\nAdd date columns derived from the milliseconds from Unix epoch column. The pandas library provides functions and objects for timestamps and the DataFrame object allows for easy mutation.\nDefine a function to add date variables to the DataFrame: year, month, day, weekday, and day of year (DOY)\n\n**Parameters:**\n\n df:\nPandas dataframe.\n\n**Returns:**\n\nThe modified Pandas dataframe.\n\nThis function was taken from the time series tutorial for python of the Google Engine developers group (for further information visit: https://developers.google.com/earth-engine/tutorials/community/time-series-visualization-with-altair)\n\n### geometry_rectangle(lon_w,lat_s,lon_e,lat_n)\nReturns an ee.Geometry that defines the region over which to reduce data in create_reduce_region_function . The region is a latitude-longitude rectangle. \n\n**Parameters:**\n\n lon_w:\nWest boundary of the rectangle. Must be a float between -180\u00b0 and 180\u00b0.\n\n lat_s:\nSouth boundary of the rectangle. Must be a float between -90\u00b0 and 90\u00b0.\n\n lon_e:\nEast boundary of the rectangle. Must be a float between -180\u00b0 and 180\u00b0.\n\n lat_n:\nNorth boundary of the rectangle. Must be a float between -90\u00b0 and 90\u00b0.\n\n**Returns:**\n\nThe `ee.Geometry.Rectangle` correspondent to those coordinates.\n\nFor further information visit https://developers.google.com/earth-engine/apidocs/ee-geometry-rectangle\n\n### time_series_df(roi, start, end, filename = 'NO2trop_series.csv', reducers = [ee.Reducer.mean()], red_names = ['NO2_trop_mean'], collection = None)\n\nCreates a pandas dataframe that includes the time series of the concentration of a gas measured from the Sentinel 5p TROPOMI sensor available in the Google Earth Engine api. By default, it calculates the average tropospheric NO2 series over a region of interest. \n\n**Parameters:**\n\n roi:\nAn ee.Geometry object. It can be a rectangle of latitude and longitude, a polygon, or other Geometries. You can create a rectangle with the function `geometry_rectangle`.\n\n start: \nA string indicating the start of the time series. The format should be 'YYYY-MM-DD'. In the case of NO2, the series begins on 2018-06-28.\n\n end:\nA string indicating the end of the time series. The format should be 'YYYY-MM-DD'. \n\n filename:\nOptional; A string indicating the name of the output file. By default set to 'NO2trop_series.csv'.\n\n reducers:\nOptional; An array of ee.Reducer objects. For each object, a column is created in the dataframe where that spatial statistic is applied. By default, the average value over a region is taken. For more reducers, visit https://developers.google.com/earth-engine/guides/reducers_intro\n\n red_names:\nOptional; An array of strings indicating the name of the reducers used. It must have the same length as the list of reducers and respect the same order as the one used in it. By default it is an array containing only 'NO2_trop_mean'.\n\n collection:\nOptional; An `ee.ImageCollection` object for the desired satellite, variable and column, and for a period containing the desired one. It can be created with `get_collection`. If the same collection is used several times, it is more efficient to get it just once and pass it as a parameter. By default it is set to None, so that it is obtained automatically. \n\n**Returns:**\n\nA Pandas dataframe with a column for each reducer in `reducers`, respectively named with the names in `red_names`. Also contains the columns Timestamp, Year, Month, Day and Weekday.\n\n\n\n\n### ts_dailydf(df, filename='dailymean_df.csv', statistic = 'mean')\n\nReturns a daily time series. In case of missing data in the series, it interleaves NaN values. In case of two daily data (this is possible due to overlapping of the satellite pass in some regions) returns the average \n\n**Parameters:**\n df:\nPanda dataframe with the original time series. This is the one calculated in `time_series_df`.\n\n filename:\nOptional; A string indicating the name of the output file. By default set to 'dailymean_df.csv'.\n\n statistic:\nOptional; A string indicating the statistic reduction to be performed on each day of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'. \n\n**Returns:**\n\nA Pandas dataframe collapsed by day. \n\n\n\n### ts_monthlydf(df, filename='monthlymean_df.csv', statistic = 'mean')\n\nReturns a monthly series of the concentration of the chosen gas. \n\n**Parameters:**\n df:\nPanda dataframe with the original time series. This time series is the one that is calculated in time_series_df\n\n filename:\nOptional; A string indicating the name of the output file. By default set to \n\n statistic:\nOptional; A string indicating the statistic reduction to be performed on each month of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'. \n\n**Returns:**\n\nA Pandas dataframe collapsed by month. \n\n### ts_weeklydf(df, filename='weeklymean_df.csv', statistic = 'mean')\n\nReturns a weekly series of the concentration of the chosen gas. \n\n**Parameters:**\n df:\nPanda dataframe with the original time series. This time series is the one that is calculated in time_series_df\n\n filename:\nOptional; A string indicating the name of the output file.\n\n statistic:\nOptional; A string indicating the statistic reduction to be performed on each week of the time series. It can be set to 'mean' or 'median'. By default it is set to 'mean'. \n\n**Returns:**\n\nA Pandas dataframe collapsed by week. \n\n### space_data_meshgrid(roi, start, end, collection = None, statistic = 'mean', export = False)\n\nObtains a meshgrid with the no2 values in the indicated region for the specified period.\n\n**Parameters:**\n\n roi:\n\nAn ee.Geometry object. It can be a rectangle of latitude and longitude, a polygon, or other Geometries. You can create a rectangle with the function `geometry_rectangle`. (ROI stands for Region Of Interest)\n\n start:\n\nString indicating start date (inclusive).\n\n end:\n\nString indicating end date (exclusive).\n\n collection:\n\nOptional; An `ee.ImageCollection` object for the desired satellite, variable and column, and for a period containing the desired one. It can be created with `get_collection`. If the same collection is used several times, it is more efficient to get it just once and pass it as a parameter. By default it is set to None, so that it is obtained automatically. \n \n statistic:\n \nOptional; A string indicating the statistic reduction to be performed on each pixel of the spatial data for the specified period. It can be set to 'mean' or 'median'. By default it is set to 'mean'. \n\n export:\n\nOptional; Boolean value. If set to True, exports to the Google Drive of the user the calcullated meshgrid as a GeoTIFF file. It is saved in the folder \"NO2\", with the filename \"NO2_\"+_start_ (where _start_ is the homonym parameter). By default it is set to False.\n\n**Returns:**\n\n values:\n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the value of NO2 correspondent to the latitude and longitude (these expressed in the following objects) \n\n lats:\n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent latitude value.\n\n lons: \n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent longitude value.\n\n### interanual_variation(df_m, year1, year2, month_num, column = 'NO2_trop_mean')\n\nCalculate the interanual variation of NO2 (or the specified column) for the indicated month of two different years.\n\n**Parameters:**\n\n df_m:\nPandas Dataframe, collpased by month, containing data for the specified month of the specified years.\n \n year1:\n \nNumeric value, the first year to be considered.\n\n year2:\n \nNumeric value, the second year to be considered.\n \n month_num:\n\nNumeric value, the number of the month to be considered (e.g. The number for January is 1, the one for April is 4, etc.). \n \n column:\n\nOptional; A string with the name of the column of the dataframe to evaluate the variation over. By default it is set to 'NO2_trop_mean'.\n\n**Returns:**\n\nA float number indicating the porcentual variation of the column, in terms of the first year, i.e. (NO2 of year2 - NO2 of year1) / (NO2 of year1) .\n\n### plot_map(no2, lats, lons, shapefile, title = 'Concentraci\u00f3n media de NO2 troposf\u00e9rico (mol/m2)', filename = 'map.png', width = 8, height = 6, font_size = 15, save = True, show = False)\n\nPlots a map for the specified latitudes and longitudes, colored as indicated by `no2`. Designed to be used with the result of `space_data_meshgrid`. Can be saved, shown, and/or returned.\n\n**Parameters:**\n\n no2:\n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the value of NO2 correspondent to the latitude and longitude (these expressed in the following objects) \n\n lats:\n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent latitude value.\n\n lons: \n\nNumPy ndarray of with as many rows as different latitude values, and as many columns as different longitude values. Each cell contains the correspondent longitude value.\n\n shapefile:\n\nString with the path to a .shp file containing borders of the region to be drawn.\n\n title:\n\nOptional; A string with the title the figure will show. By default it is 'Concentraci\u00f3n media de NO2 troposf\u00e9rico (mol/m2)' (spanish for Tropospheric NO2 mean concentration (mol/m2)) .\n\n filename:\n\nOptional; String with the name of the output file (in the case `save` is set to True). By default set to 'map.png'.\n\n width:\n\nOptional; Int, the width of the figure to be generated. By default set to 8.\n\n height:\n\nOptional; Int, the height of the figure to be generated. By default set to 6.\n\n font_size:\n\nOptional; Int, the font size of the figure to be generated. By default set to 15.\n\n save:\n\nOptional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.\n\n show:\n\nOptional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.\n\n**Returns:**\n\n raw_fig, raw_ax:\n\n`matplotlib` figure and axes respectively, before adding any formatting (title, color scale, etc.)\n\n\n\n\n### plot_series(df, start = pd.Timestamp.min, end = pd.Timestamp.max, column = 'NO2_trop_mean', filename = 'series.png', width = 15, height = 4, save = True, show = False)\n\nPlot the time series passed as a Pandas dataframe.\n\n**Parameters:**\n\n df:\n\nPandas dataframe, with a 'Fecha_datetime' column containing timestamps in YYYY-MM-DD date format.\n\n start:\n\nOptional; String in YYYY-MM-DD date format indicating start time. Values previous to this date won't be plotted. By default set to Pandas minimum timestamp (no filter).\n\n end:\n\nOptional; String in YYYY-MM-DD date format indicating end time. Values posterior to this date won't be plotted. By default set to Pandas maximum timestamp (no filter).\n\n column:\n\nOptional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.\n\n filename:\n\nOptional; String with the name of the output file (in the case `save` is set to True). By default set to 'series.png'.\n\n width:\n\nOptional; Int, the width of the figure to be generated. By default set to 15.\n\n height:\n\nOptional; Int, the height of the figure to be generated. By default set to 4.\n\n save:\n\nOptional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.\n\n show:\n\nOptional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.\n\n**Returns:**\n\n fig, ax:\n\n`matplotlib` figure and axes, respectively.\n\n\n### plot_autocorr(df, lags, column = 'NO2_trop_mean', filename = 'autocorrelogram.png', width = 30, height=5, save = True, show = False)\n\nPlot autocorrelogram of the indicated dataframe. Plots lags on the horizontal and the correlations on vertical axis. \n\n df:\n\nPandas dataframe, with a 'Fecha_datetime' column containing timestamps in YYYY-MM-DD date format.\n\n lags:\n\nAn int or array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int.\n\n column:\n\nOptional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.\n\n filename:\n\nOptional; String with the name of the output file (in the case `save` is set to True). By default set to 'autocorrelogram.png'.\n\n width:\n\nOptional; Int, the width of the figure to be generated. By default set to 30.\n\n height:\n\nOptional; Int, the height of the figure to be generated. By default set to 5.\n\n save:\n\nOptional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.\n\n show:\n\nOptional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.\n\n**Returns:**\n\n fig, ax:\n\n`matplotlib` figure and axes, respectively.\n\n### barplot_year_cmp(df_m, year1, year2, column = 'NO2_trop_mean', filename='compared_series.png', width = 10, height=4, save = True, show = False)\n\nDraw a bar graph comparing the monthly values between two years.\n\n**Parameters:**\n\n df_m:\n \nPandas dataframe aggregated by month, and containing at least some values for the indicated years. \n \n year1:\n \nOne of the years to be compared, as Int/String.\n \n year2:\n\nThe other of the years to be compared, as Int/String. \n\n column:\n\nOptional; String indicating the dataframe column name from which to plot values. By default set to 'NO2_trop_mean'.\n\n filename:\n\nOptional; String with the name of the output file (in the case `save` is set to True). By default set to 'compared_series.png'.\n\n width:\n\nOptional; Int, the width of the figure to be generated. By default set to 10.\n\n height:\n\nOptional; Int, the height of the figure to be generated. By default set to 4.\n\n save:\n\nOptional; Boolean value, if set to True saves the figure in the location indicated by 'filename'. By default set to True.\n\n show:\n\nOptional; Boolean value, if set to True shows the figure with `matplotlib` interactive interface. By default set to False.\n\n**Returns:**\n\n fig, ax:\n\n`matplotlib` figure and axes, respectively.\n\n\n\n<!--\n\n## TO DO\n. armar un script de example / test razonable\n. minima documentaci\u00f3n ac\u00e1\n. poner todo en espa\u00f1ol\n. corregir typo interanual es interannual\n\n--\n\n. para dibujar el mapa estoy usando un shp que dibuja contornos. hacer que sea opcional \n. polygon funciona bien pero space_date_meshgrid se rompe si no es rectangular\n. bounds de polygon redondean un poco mal (o est\u00e1 desfasado respecto al otro shape)\n. hacer algo que descargue los datos del mapa aun si hay algunos pixeles que tienen solo nans (geemap lo hizo)\n. cuando uso una coleccion que me pasan, agregar un chequeo que vea que el per\u00edodo que quiero est\u00e9 adentro (c\u00f3mo?)\n. agregar un chequeo de fecha bien pasada\n. est\u00e1 buena la solucion a lo de los reductores? pasan un arreglo de reductores y uno de nombres, tienen que medir lo mismo...\n. mascara\n. arreglar el warning de pandas index en lo de isocalendar\n. desarrollar la documentacion\n. reducir cantidad de bibliotecas a usar\n. crear poligonos a partir del nombre de la ciudad-prov-pais?\n\n\n<!-- NECESARIO CORRER ANTES DE INSTALAR (QU\u00c8 PASA SI TIENEN WINDOWS?)\n\napt-get install libproj-dev proj-data proj-bin\napt-get install libgeos-dev\napt-get -qq install python-cartopy python3-cartopy\n\n#sugerir esto?:\npip uninstall -y shapely # cartopy and shapely aren't friends (early 2020)\npip install shapely --no-binary shapely\n",
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"license": "Copyright 2022 ObSA - Observatorio de Salud y Ambiente, CeNDIE Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
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