Monitor air quality - Monitoreá la calidad del aire
Project description
respyrAR
Español
Instalación
-
Para poder autenticar seguir las instrucciones de este link
-
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 y particularmente para Windows aquí. 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
- Una vez resueltos estos dos pasos, sólo queda instalar el paquete ejecutando en la terminal el siguiente comando
$ pip install respyrar
English
-
To enable authentication follow these instructions.
-
Before installing respyrar you'll need to enable some dependencies installation (particularly,
cartopy). You can find detailed instructions for Linux, Windows and iOS here and particularly for Windows here. 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
- 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.
- Extract the property values from the
ee.FeatureCollectionas a list of lists stored in anee.DictionaryusingreduceColumns(). - Extract the list of lists from the dictionary.
- Add names to each list by converting to an
ee.Dictionarywhere 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.
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)
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.
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)
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:
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.
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.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
