diff --git a/.DS_Store b/.DS_Store
index abf9d3d..0597325 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.gitignore b/.gitignore
index 2cf8102..58888ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
__pycache__/
.DS_Store
environmental_insights_visulisations/
-ipynb_checkpoints/
\ No newline at end of file
+ipynb_checkpoints/
+dist/
\ No newline at end of file
diff --git a/.ipynb_checkpoints/LICENSE-checkpoint b/.ipynb_checkpoints/LICENSE-checkpoint
deleted file mode 100644
index f288702..0000000
--- a/.ipynb_checkpoints/LICENSE-checkpoint
+++ /dev/null
@@ -1,674 +0,0 @@
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-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
- 17. Interpretation of Sections 15 and 16.
-
- If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
- To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-
- Copyright (C)
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
- If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- Copyright (C)
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-.
-
- The GNU General Public License does not permit incorporating your program
-into proprietary programs. If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License. But first, please read
-.
diff --git a/.ipynb_checkpoints/data-checkpoint.py b/.ipynb_checkpoints/data-checkpoint.py
deleted file mode 100644
index 7fa64d5..0000000
--- a/.ipynb_checkpoints/data-checkpoint.py
+++ /dev/null
@@ -1,441 +0,0 @@
-import geopandas as gpd
-import pandas as pd
-import os
-import overpy
-from scipy.spatial import cKDTree
-from shapely.geometry import Point
-from shapely.geometry import LineString
-import numpy as np
-import itertools
-from operator import itemgetter
-import pandas as pd
-pd.options.mode.chained_assignment = None # default='warn'
-
-def download_file_data(filename):
- """
- Checks if a file that has been requested has been downloaded and if it has then it will download the file
-
- Parameters:
- filename (string): The dataset filename to be downloaded from the remote server
- """
- print("Checking existence of file: " + str(filename))
-
-
-def air_pollution_concentration_typical_day_real_time_united_kingdom(month, day_of_Week, hour):
- """
- Retrieve the typical day complete dataset for the UK for a given time
-
- Parameters:
- month (int): An int to represent the month of interest, 1 being january, and 12 being December.
- day_of_Week (string): A string to represent the day of week of interest in the form of "Friday".
- hour (int): An int to represent the hour of interest, 0 being midnight, and 23 being the final possible hour of the day
-
- Returns:
- geodataframe: A geodataframe of the typical dataset for the UK for a given tiem of interest
- """
- desired_filename = "environmental_insights_data/air_pollution/uk_typical_day/Month_"+str(month)+"-Day_"+day_of_Week+"-Hour_"+str(hour)+".feather"
- if not os.path.isfile(desired_filename):
- #download_file_data(time)
- pass
-
- air_pollution_data = pd.read_feather(desired_filename)
- air_pollution_data = air_pollution_data.rename(columns={"Grid ID":"UK Model Grid ID"})
- return air_pollution_data
-
-def air_pollution_concentration_nearest_point_typical_day_united_kingdom(month, day_of_Week, hour, latitude, longitude, uk_grids):
- """
- Retrieve a single air pollution concentration data point predicted based on the UK data, based on the cloest point given by the latitude and longitude.
-
- Parameters:
- Parameters:
- month (int): An int to represent the month of interest, 1 being january, and 12 being December.
- day_of_Week (string): A string to represent the day of week of interest in the form of "Friday".
- hour (int): An int to represent the hour of interest, 0 being midnight, and 23 being the final possible hour of the day
- latitude (float): A float denoting the desired latitude.
- longitude (float): A float denoting the desired longitude.
-
- Returns:
- geodataframe: A geodataframe of the nearest point in the data at the given timestamp.
- """
-
- desired_filename = "environmental_insights_data/air_pollution/uk_typical_day/Month_"+str(month)+"-Day_"+day_of_Week+"-Hour_"+str(hour)+".feather"
- if not os.path.isfile(desired_filename):
- #download_file_data(time)
- pass
-
- air_pollution_data = pd.read_feather(desired_filename)
- air_pollution_data = air_pollution_data.rename(columns={"Grid ID":"UK Model Grid ID"})
- air_pollution_data = uk_grids.merge(air_pollution_data, on="UK Model Grid ID")
- air_pollution_data["geometry"] = air_pollution_data["geometry"].centroid
- air_pollution_data = air_pollution_data.to_crs(4326)
- air_pollution_data["Latitude"] = air_pollution_data["geometry"].y
- air_pollution_data["Longitude"] = air_pollution_data["geometry"].x
-
-
- tree = cKDTree(air_pollution_data[["Latitude", "Longitude"]])
-
- # Query for the closest points
- distance, idx = tree.query([latitude, longitude], k=1)
-
- # Print the closest points
- closest_points = pd.DataFrame(air_pollution_data.iloc[idx]).T
- closest_points["Distance"] = distance
- closest_points = closest_points.rename(columns={"Latitude":"Predicition Latitude", "Longitude":"Predicition Longitude"})
- closest_points["Requested Latitude"] = latitude
- closest_points["Requested Longitude"] = longitude
- closest_points = closest_points.drop(columns=["UK Model Grid ID", "geometry"])
- return closest_points
-
-def air_pollution_concentration_complete_set_real_time_united_kingdom(time):
- """
- Retrieve the complete predicted dataset for a given timestamp in the UK dataset.
-
- Parameters:
- time (string): A string denoting the timestamp desired, of the form YYYY-MM-DD HHmmss
-
- Returns:
- geodataframe: A geodataframe of the dataset for the UK for a given timestamp.
- """
- desired_filename = "environmental_insights_data/air_pollution/uk_complete_set/"+time+".feather"
- if not os.path.isfile(desired_filename):
- download_file_data(time)
-
- air_pollution_data = pd.read_feather(desired_filename)
- air_pollution_data = air_pollution_data.rename(columns={"Grid ID":"UK Model Grid ID"})
- return air_pollution_data
-
-def air_pollution_concentration_nearest_point_real_time_united_kingdom(latitude, longitude, time, uk_grids):
- """
- Retrieve a single air pollution concentration data point predicted based on the UK data, based on the cloest point given by the latitude and longitude.
-
- Parameters:
- latitude (float): A float denoting the desired latitude.
- longitude (float): A float denoting the desired longitude.
- time (string): A string denoting the timestamp desired, of the form YYYY-MM-DD HHmmss.
- uk_grids (geodataframe): A Geodataframe that describes the estimation points for the uk model.
-
- Returns:
- geodataframe: A geodataframe of the nearest point in the data at the given timestamp.
- """
- print("Accessing air pollution concentration at: Latitude: " + str(latitude) + " Longitude: " + str(longitude) + " Time: " + str(time))
-
- desired_filename = "environmental_insights_data/air_pollution/uk_complete_set/"+time+".feather"
- if not os.path.isfile(desired_filename):
- download_file_data(time)
-
- air_pollution_data = pd.read_feather(desired_filename)
- air_pollution_data = air_pollution_data.rename(columns={"Grid ID":"UK Model Grid ID"})
- air_pollution_data = uk_grids.merge(air_pollution_data, on="UK Model Grid ID")
- air_pollution_data["geometry"] = air_pollution_data["geometry"].centroid
-
- air_pollution_data = air_pollution_data.to_crs(4326)
- air_pollution_data["Latitude"] = air_pollution_data["geometry"].y
- air_pollution_data["Longitude"] = air_pollution_data["geometry"].x
-
-
- tree = cKDTree(air_pollution_data[["Latitude", "Longitude"]])
-
- # Query for the closest points
- distance, idx = tree.query([latitude, longitude], k=1)
-
- # Print the closest points
- closest_points = pd.DataFrame(air_pollution_data.iloc[idx]).T
- closest_points["Distance"] = distance
- closest_points = closest_points.rename(columns={"Latitude":"Predicition Latitude", "Longitude":"Predicition Longitude"})
- closest_points["Requested Latitude"] = latitude
- closest_points["Requested Longitude"] = longitude
- closest_points = closest_points.drop(columns=["UK Model Grid ID", "geometry"])
- return closest_points
-
-def air_pollution_concentration_complete_set_real_time_global(time):
- """
- Retrieve the complete calculated dataset for a given timestamp in the global dataset.
-
- Parameters:
- time (string): A string denoting the timestamp desired, of the form DD-MM-YYYY HHmmss
-
- Returns:
- geodataframe: A geodataframe of the dataset for the UK for a given timestamp.
- """
- desired_filename = "environmental_insights_data/air_pollution/global_complete_set/"+time+".feather"
- if not os.path.isfile(desired_filename):
- download_file_data(time)
-
- air_pollution_data = pd.read_feather(desired_filename)
- air_pollution_data = air_pollution_data.rename(columns={"id":"Global Model Grid ID"})
- return air_pollution_data
-
-def get_amenities_as_geodataframe(amenity_type, min_lat, min_lon, max_lat, max_lon):
- """
- Fetch amenities of a given type within a bounding box and return as a GeoDataFrame.
-
-
- Parameters:
- amenity_type (string): Type of amenity, e.g., "hospital"
- min_lat (float): Minimum latitude
- min_lon (float): Minimum longitude
- max_lat (float): Maximum latitude
- max_lon (float): Maximum longitude
-
- Returns:
- geodataframe: GeoDataFrame containing the amenities with their names and coordinates
- """
-
- api = overpy.Overpass()
-
- # Define the Overpass query
- query = f"""
- [out:json];
- (
- node["amenity"="{amenity_type}"]({min_lat},{min_lon},{max_lat},{max_lon});
- way["amenity"="{amenity_type}"]({min_lat},{min_lon},{max_lat},{max_lon});
- relation["amenity"="{amenity_type}"]({min_lat},{min_lon},{max_lat},{max_lon});
- );
- out center;
- """
-
- result = api.query(query)
-
- # Extract results and store them in lists
- names = []
- lats = []
- lons = []
-
- for node in result.nodes:
- names.append(node.tags.get("name", "Unknown"))
- lats.append(node.lat)
- lons.append(node.lon)
-
- for way in result.ways:
- names.append(way.tags.get("name", "Unknown"))
- lats.append(way.center_lat)
- lons.append(way.center_lon)
-
- for relation in result.relations:
- names.append(relation.tags.get("name", "Unknown"))
- lats.append(relation.center_lat)
- lons.append(relation.center_lon)
-
- # Convert lists to a GeoDataFrame
- geometry = [Point(xy) for xy in zip(lons, lats)]
- gdf = gpd.GeoDataFrame({'name': names, 'geometry': geometry})
-
- return gdf
-
-def get_highways_as_geodataframe(highway_type, min_lat, min_lon, max_lat, max_lon):
- """
- Fetch highways of a specified type within a bounding box from OSM and return as a GeoDataFrame.
-
- Parameters:
- highway_type (string): Type of highway, e.g., "motorway", "residential"
- min_lat (float): Minimum latitude
- min_lon (float): Minimum longitude
- max_lat (float): Maximum latitude
- max_lon (float): Maximum longitude
-
- Returns:
- geodataframe: GeoDataFrame containing the highways with their names and coordinates
- """
-
- api = overpy.Overpass()
-
- # Modify the Overpass query to retrieve nodes of ways explicitly
- query = f"""
- [out:json];
- (
- way["highway"="{highway_type}"]({min_lat},{min_lon},{max_lat},{max_lon});
- >; // Fetches all nodes for the ways returned in the previous statement
- );
- out geom;
- """
-
- result = api.query(query)
-
- names = []
- geometries = []
-
- for way in result.ways:
- try:
- coords = [(node.lon, node.lat) for node in way.nodes]
- names.append(way.tags.get("name", "Unknown"))
- geometries.append(LineString(coords))
- except Exception as e:
- print(f"Error processing way: {way.id}. Error: {e}")
-
- # Convert lists to a GeoDataFrame
- gdf = gpd.GeoDataFrame({'name': names, 'geometry': geometries}, crs=4326)
- gdf["highway"] = highway_type
- gdf["source"] = "osm"
- return gdf
-
-def ckd_nearest_LineString(gdf_A, gdf_B, gdf_B_cols):
- """
- Calculate the nearest points between two GeoDataFrames containing LineString geometries.
-
- This function uses cKDTree to efficiently find the nearest points between two sets
- of LineStrings. For each point in `gdf_A`, the function finds the closest point
- in `gdf_B` and returns the distances along with selected columns from `gdf_B`.
-
- Parameters:
- gdf_A (GeoDataFrame): A GeoDataFrame containing LineString geometries.
- gdf_B (GeoDataFrame): A GeoDataFrame containing LineString geometries which will be
- used to find the closest points to `gdf_A`.
- gdf_B_cols (list or tuple): A list or tuple containing column names from `gdf_B`
- which will be included in the resulting DataFrame.
-
- Returns:
- GeoDataFrame: A GeoDataFrame with each row containing a geometry from `gdf_A`,
- corresponding closest geometry details from `gdf_B` (as specified by `gdf_B_cols`),
- and the distance to the closest point in `gdf_B`.
-
- Note:
- The resulting GeoDataFrame maintains the order of `gdf_A` and attaches the nearest
- details from `gdf_B`.
- This code was adapted from the code avaliable here: https://gis.stackexchange.com/questions/222315/finding-nearest-point-in-other-geodataframe-using-geopandas
- """
- gdf_A = gdf_A.reset_index(drop=True)
- gdf_B = gdf_B.reset_index(drop=True)
- A = np.concatenate(
- [np.array(geom.coords) for geom in gdf_A.geometry.to_list()])
- B = [np.array(geom.coords) for geom in gdf_B.geometry.to_list()]
- B_ix = tuple(itertools.chain.from_iterable(
- [itertools.repeat(i, x) for i, x in enumerate(list(map(len, B)))]))
- B = np.concatenate(B)
- ckd_tree = cKDTree(B)
- dist, idx = ckd_tree.query(A, k=1)
- idx = itemgetter(*idx)(B_ix)
- gdf = pd.concat(
- [gdf_A, gdf_B.loc[idx, gdf_B_cols].reset_index(drop=True),
- pd.Series(dist, name='dist')], axis=1)
- return gdf
-
-def get_even_spaced_points(points, number_of_points):
- """
- Generate a list of evenly spaced points between two given points.
-
- This function calculates the distance (or difference) between two input points
- and divides this distance into `number_of_points` equal segments. The resulting
- points, including the start and end points, are returned in a list.
-
- Parameters:
- points (list or tuple of float): A list or tuple containing two points
- (start and end) between which the evenly spaced points
- are to be calculated.
- number_of_points (int): The total number of points to generate,
- including the start and end points.
-
- Returns:
- list: A list of evenly spaced points between the provided start and end points.
-
- Example:
- >>> getEvenSpacedPoints([1, 10], 5)
- [1.0, 3.25, 5.5, 7.75, 10.0]
-
- Note:
- The function assumes that the points list is sorted in ascending order.
- """
- step = (points[1] - points[0]) / (number_of_points - 1)
-
- return [points[0] + step * i for i in range(number_of_points)]
-
-
-
-
-def calculate_new_metrics_distance_total(current_infrastructure, highway_type, start_point, end_point, land_grids_centroids, land_grids):
- """
- Simulate the addition of a proposed highway to current infrastructure and calculate new metrics.
-
- This function creates a new proposed highway segment based on given start and end points.
- The proposed highway is then added to the current infrastructure dataset. After adding the
- new highway, the function calculates distance metrics and total length of the specific highway type.
-
- Parameters:
- current_infrastructure (GeoDataFrame): The current infrastructure dataset with existing highways.
- highway_type (str): Type of the highway for which metrics are calculated (e.g., "motorway").
- start_point (tuple of float): Coordinates (x, y) for the starting point of the proposed highway.
- end_point (tuple of float): Coordinates (x, y) for the ending point of the proposed highway.
- land_grids_centroids (dataframe) : Dataframe of the grids for predicition to be made on, with the geometry being a set of points representing the centroid of such grids.
- land_grids (dataframe) : Dataframe of the grids for predicition to be made on, with the geometry being a set of polygons representing the grids themselves.
-
- Returns:
- tuple:
- - GeoDataFrame: Contains metrics such as road infrastructure distance and total road length for each grid.
- - GeoDataFrame: A merged dataset of current infrastructure and the proposed highway.
-
- Note:
- - The function assumes the use of EPSG:4326 and EPSG:3395 for coordinate reference systems.
- - It also assumes the existence of helper functions like `getEvenSpacedPoints` and a global variable `land_grids_centroids`.
- """
-
- xPoints = get_even_spaced_points([start_point[0], end_point[0]], 1000)
- yPoints = get_even_spaced_points([start_point[1], end_point[1]], 1000)
-
- inputCoordinates = list(map(lambda x,y:Point(x,y),xPoints,yPoints))
-
- prposed_highway = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[LineString(inputCoordinates)])
- prposed_highway["source"] = "User Added"
- prposed_highway["highway"] = "motorway"
- prposed_highway = prposed_highway.to_crs(3395)
-
-
- current_infrastructure_user_added = pd.concat([current_infrastructure, prposed_highway])
-
-
- current_infrastructure_highway_type = current_infrastructure_user_added[current_infrastructure_user_added["highway"] == highway_type]
- current_infrastructure_highway_type = current_infrastructure_highway_type.to_crs(3395)
- #Calculate the new distance
- current_infrastructure_highway_distance = ckd_nearest_LineString(land_grids_centroids,current_infrastructure_highway_type,gdf_B_cols=["source", "highway"])
- current_infrastructure_highway_distance = current_infrastructure_highway_distance.rename(columns={"dist":"Road Infrastructure Distance " + str(highway_type)})
-
- #Calculate the new motorway column
- roadGrids_intersection_OSM = gpd.overlay(current_infrastructure_highway_type, land_grids, how='intersection')
-
- roadGrids_intersection_OSM_Subset = roadGrids_intersection_OSM[["highway", "UK Model Grid ID", "geometry"]]
- roadGrids_intersection_OSM_Subset["Road Length"] = roadGrids_intersection_OSM_Subset["geometry"].length
- goupby_result = pd.DataFrame(roadGrids_intersection_OSM_Subset.groupby(["highway", "UK Model Grid ID"])["Road Length"].sum()).reset_index()
- current_infrastructure_new_grid_total = goupby_result.pivot_table(values='Road Length', index="UK Model Grid ID", columns='highway', aggfunc='sum')
- current_infrastructure_new_grid_total = current_infrastructure_new_grid_total.rename(columns={highway_type:"Total Length " + str(highway_type)})
- current_infrastructure_all_grids = pd.merge(land_grids, current_infrastructure_new_grid_total, left_on="UK Model Grid ID", right_index=True, how="left")
- current_infrastructure_all_grids = current_infrastructure_all_grids.fillna(0)
-
- return current_infrastructure_all_grids.merge(current_infrastructure_highway_distance.drop(columns="geometry"), on="UK Model Grid ID", how="left"), current_infrastructure_user_added
-
-def replace_feature_vector_column(feature_vector, new_feature_vector, feature_vector_name):
- """
- Replace the feature vector column name with the new feature vector column name, replacing the data within the dataframe with new environmental conditions.
- Parameters:
- feature_vector (dataframe) : dataframe of the original data.
- new_feature_vector (dataframe) : dataframe containing the new feature vector that is to be used to replace the data in feature_vector.
- feature_vector_name (string) : Name of the feature vector to be changed.
-
- Returns:
- dataframe : A dataframe of the original data that was added with the feature vector now replace by the new data.
- """
- feature_vector = feature_vector.drop(columns=[feature_vector_name])
- feature_vector = feature_vector.merge(new_feature_vector[["UK Model Grid ID", feature_vector_name]], on="UK Model Grid ID")
-
-
- return feature_vector
-
-def get_uk_grids():
- """
- Get the spatial grids that represent the locations at air pollution estimations are made on for the UK Model.
-
- Returns:
- geodataframe : A Geodataframe of the polygons for each of the grids in the UK Model alongside their centroid and unique ID.
- """
- uk_grids = gpd.read_file("environmental_insights_data/supporting_data/1000mLandGridsEngland.gpkg")
- uk_grids["geometry Centroid"] = uk_grids["geometry"].centroid
- uk_grids = uk_grids.rename(columns={"Grid ID": "UK Model Grid ID"})
- return uk_grids
-
-def get_global_grids():
- """
- Get the spatial grids that represent the locations at air pollution estimations are made on for the Global Model.
-
- Returns:
- geodataframe : A Geodataframe of the polygons for each of the grids in the Global Model and unique ID.
- """
- global_grids = gpd.read_file("environmental_insights_data/supporting_data/worldGrids_025_landGrids.gpkg")
- global_grids = global_grids.rename(columns={"id": "Global Model Grid ID"})
- return global_grids
diff --git a/.ipynb_checkpoints/development-checkpoint.ipynb b/.ipynb_checkpoints/development-checkpoint.ipynb
deleted file mode 100644
index e56c0ef..0000000
--- a/.ipynb_checkpoints/development-checkpoint.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "33ce8b5e-fb37-4bb9-8acb-7bf80539e54f",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.8.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/download-checkpoint.py b/.ipynb_checkpoints/download-checkpoint.py
deleted file mode 100644
index 173fdf4..0000000
--- a/.ipynb_checkpoints/download-checkpoint.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import os
-import requests
-import pandas as pd
-from rpy2 import robjects
-
-def import_monitoring_station_data(site, pollutant, years, source):
- """
- Import monitoring station data from specified source and years.
-
- Parameters:
- site (str): The monitoring site identifier.
- pollutant (str): The pollutant identifier.
- years (int or list of int): The year or list of years to import data for.
- source (str): The data source identifier (e.g., 'aurn', 'saqn', 'aqe', 'waqn', 'ni').
-
- Returns:
- DataFrame: A pandas DataFrame containing the combined data for all specified years,
- or None if any downloads failed.
- """
-
- # Convert site and pollutant identifiers to uppercase
- site = site.upper()
- pollutant = pollutant.upper()
-
- # Ensure years is a list
- if isinstance(years, int):
- years = [years]
-
- # Initialize variables to store downloaded data and track errors
- downloaded_data = []
- errors_raised = False
-
- # Dictionary mapping source identifiers to their base URLs
- source_dict = {
- "aurn": "https://uk-air.defra.gov.uk/openair/R_data/",
- "saqn": "https://www.scottishairquality.scot/openair/R_data/",
- "aqe": "https://airqualityengland.co.uk/assets/openair/R_data/",
- "waqn": "https://airquality.gov.wales/sites/default/files/openair/R_data/",
- "ni": "https://www.airqualityni.co.uk/openair/R_data/"
- }
-
- # Get the base URL for the specified source
- source_url = source_dict.get(source)
-
- for year in years:
- # Construct the URL for the RData file
- url = f"{source_url}{site}_{year}.RData"
- print(url)
-
- # Define request headers to simulate a browser request
- headers = {
- "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
- }
-
- # Download the RData file
- response = requests.get(url, headers=headers)
-
- if response.status_code == 200:
- # Save the downloaded RData file locally
- local_filename = f"temp_downloads/{site}_{year}.RData"
- with open(local_filename, "wb") as file:
- file.write(response.content)
- print(f"Download completed successfully for {site} in {year}.")
- else:
- # Handle download failure
- print(f"Failed to download the file. Status code: {response.status_code}")
- errors_raised = True
- continue
-
- # Load the RData file into R environment
- robjects.r['load'](local_filename)
-
- # Assuming the RData file has only one object and it is a data frame
- # Get the name of the loaded object
- r_data_frame = robjects.r[robjects.r.objects()[0]]
-
- # Convert the R data frame to a pandas data frame
- result = pd.DataFrame({col: list(r_data_frame.rx2(col)) for col in r_data_frame.names})
-
- # Save the pandas data frame to a CSV file
- result.to_csv(local_filename.replace(".RData", ".csv"), index=False)
-
- # Read the CSV file into a pandas data frame
- result = pd.read_csv(local_filename.replace(".RData", ".csv"))
-
- # Clear the R environment
- robjects.r('rm(list=ls())')
-
- # Append the data frame to the list of downloaded data
- downloaded_data.append(result)
-
- if errors_raised:
- print("Some files failed to download.")
- return None
- else:
- print("All files downloaded successfully.")
- # Concatenate all the downloaded data frames
- df = pd.concat(downloaded_data, ignore_index=True)
- return df
-
-def monitoring_station_meta_data(monitoring_network="aurn"):
- """
- Import metadata for a specified monitoring network.
-
- Parameters:
- monitoring_network (str): The monitoring network identifier. Default is "aurn".
-
- Returns:
- DataFrame: A pandas DataFrame containing the metadata for the specified monitoring network,
- or None if the download fails.
- """
-
- # Dictionary mapping monitoring networks to their metadata URLs
- source_dict = {
- "aurn": "http://uk-air.defra.gov.uk/openair/R_data/AURN_metadata.RData",
- "saqn": "https://www.scottishairquality.scot/openair/R_data/SCOT_metadata.RData",
- "aqe": "https://airqualityengland.co.uk/assets/openair/R_data/AQE_metadata.RData",
- "waqn": "https://airquality.gov.wales/sites/default/files/openair/R_data/WAQ_metadata.RData",
- "ni": "https://www.airqualityni.co.uk/openair/R_data/NI_metadata.RData"
- }
-
- # Get the URL for the specified monitoring network
- url = source_dict.get(monitoring_network)
- display(url)
-
- # Define request headers to simulate a browser request
- headers = {
- "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
- }
-
- # Send a GET request to the URL with headers
- response = requests.get(url, headers=headers)
-
- # Check if the request was successful
- if response.status_code == 200:
- # Create the temp_downloads directory if it doesn't exist
- os.makedirs("temp_downloads", exist_ok=True)
-
- # Save the content to a local file
- local_filename = f"temp_downloads/{monitoring_network}_metadata.RData"
- with open(local_filename, "wb") as file:
- file.write(response.content)
- print("Download completed successfully.")
- else:
- # Handle download failure
- print("Failed to download the file. Status code:", response.status_code)
- return None
-
- # Load the RData file into R environment
- robjects.r['load'](local_filename)
-
- # Assuming the RData file has only one object and it is a data frame
- # Get the name of the loaded object
- r_data_frame = robjects.r[robjects.r.objects()[0]]
-
- # Convert the R data frame to a pandas data frame
- result = pd.DataFrame({col: list(r_data_frame.rx2(col)) for col in r_data_frame.names})
-
- # Save the pandas data frame to a CSV file
- result.to_csv(local_filename.replace(".RData", ".csv"), index=False)
-
- # Clear the R environment
- robjects.r('rm(list=ls())')
-
- # Read the CSV file into a pandas data frame
- meta_data = pd.read_csv(local_filename.replace(".RData", ".csv"))
- return meta_data
diff --git a/.ipynb_checkpoints/models-checkpoint.py b/.ipynb_checkpoints/models-checkpoint.py
deleted file mode 100644
index f8cb933..0000000
--- a/.ipynb_checkpoints/models-checkpoint.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import pickle
-import os
-import pandas as pd
-import variables
-import numpy as np
-
-def download_file_model(filename):
- """
- Checks if a file that has been requested has been downloaded and if it has then it will download the file
-
- Parameters:
- filename (string): The dataset filename to be downloaded from the remote server
- """
- print("Checking existence of file: " + str(filename))
-
-def load_model_united_kingdom(model_type, model_dataset, air_pollutant):
- """
- Load in a pre trained air pollution machine learning model for the UK.
-
- Parameters:
- model_type (string): The model type to load in for the quantile regression, options: 0.95, 0.5, 0.05.
- model_dataset (string): The underpinning dataset that the model has been trained on
- air_pollutant (string): The air pollutant the model has been trained on.
-
- Return:
- lightGBMRegression models: A lightGBM instance of the model.
- """
- model_filepath = "environmental_insights_models/uk/dataset_"+model_dataset+"_quantile_regression_"+model_type+"_air_pollutant_"+air_pollutant+".pkl"
- with open(model_filepath, "rb") as f: # Python 3: open(..., 'rb')
- bootstrapModel = pickle.load(f)
- return bootstrapModel
-
-def load_model_global(model_type, model_dataset, air_pollutant):
- """
- Load in a pre trained air pollution machine learning model for the globe.
-
- Parameters:
- model_type (string): The model type to load in for the quantile regression, options: 0.95, 0.5, 0.05.
- model_dataset (string): The underpinning dataset that the model has been trained on
- air_pollutant (string): The air pollutant the model has been trained on.
-
- Return:
- lightGBMRegression models: A lightGBM instance of the model.
- """
- model_filepath = "environmental_insights_models/global/dataset_"+model_dataset+"_quantile_regression_"+model_type+"_air_pollutant_"+air_pollutant+".pkl"
- with open(model_filepath, "rb") as f: # Python 3: open(..., 'rb')
- bootstrapModel = pickle.load(f)
- return bootstrapModel
-
-def load_feature_vector_typical_day_united_kingdom(month, day_of_week, hour, uk_grids):
- """
- Load in a feature vector for the typical day in the United Kingdom.
-
- Parameters:
- month (int): An int to represent the month of interest, 1 being january, and 12 being December.
- day_of_week (string): A string to represent the day of week of interest in the form of "Friday".
- hour (int): An int to represent the hour of interest, 0 being midnight, and 23 being the final possible hour of the day
- uk_grids (geodataframe): A Geodataframe that describes the estimation points for the uk model
-
- Returns:
- geodataframe: A geodataframe of the typical dataset feature vector in the UK.
- """
- desire_filename = "environmental_insights_data/feature_vector/uk_typical_day/Month_"+str(month)+"-Day_"+day_of_week+"-Hour_"+str(hour)+".feather"
- if not os.path.isfile(desire_filename):
- download_file_model(desire_filename)
-
- feature_vector = pd.read_feather(desire_filename)
- feature_vector = feature_vector.rename(columns={"Grid ID":"UK Model Grid ID"})
- feature_vector = uk_grids.merge(feature_vector, on="UK Model Grid ID")
- return feature_vector
-
-def get_model_feature_vector(model_type):
- """
- Getter function to return the list of features that were used for a given model type.
-
- Parameters:
- model_type (string): Whether the model requested is the one with all of the features or just the transport infrastructure features.
-
- Returns:
- list: A list of the feature vector names that were used in the model request.
- """
- return variables.featureVectorSubsets[model_type]
-
-def make_concentration_predicitions_united_kingdom(estimating_model, observation_data, estimating_feature_vector_column_names):
- """
- Make predicition for a given environment conditions for air pollution concentrations.
-
- Parameters:
- estimating_model (string): Whether the model requested is the one with all of the features or just the transport infrastructure features.
- observation_data (dataframe): The observational data for the environmental conditions to make the predicitons on.
- estimating_feature_vector_column_names (list): A list of the feature vector names that were used in the model request.
-
- Returns:
- geoodataframe: Predictions for the given air pollutant for the given environmental conditions.
- """
- feature_vector_DF = observation_data.copy(deep=True)
-
- timestamps = feature_vector_DF.index
-
-
-
- in_seqs = list()
- for columnName in estimating_feature_vector_column_names:
- in_seq = feature_vector_DF[[columnName]].to_numpy()
- in_seq = in_seq.reshape((len(in_seq), 1))
- in_seqs.append(in_seq)
-
- feature_vector = np.hstack(tuple(in_seqs))
- feature_vector = feature_vector[:,:feature_vector.shape[1]]
-
-
-
- predcited_pollution_comparison = feature_vector_DF[["UK Model Grid ID"]].copy(deep=True)
-
- predicitionColumnNames = list()
-
- predcited_pollution = estimating_model.predict(feature_vector)
- predcited_pollution = np.exp(predcited_pollution) - 0.0000001
- predcited_pollution_comparison["Model Predicition"] = predcited_pollution
- return predcited_pollution_comparison
diff --git a/.ipynb_checkpoints/test_workbook-checkpoint.ipynb b/.ipynb_checkpoints/test_workbook-checkpoint.ipynb
deleted file mode 100644
index ae2e5de..0000000
--- a/.ipynb_checkpoints/test_workbook-checkpoint.ipynb
+++ /dev/null
@@ -1,127 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "a340b6d7-d432-4b97-92bd-4ac72eee1b50",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'RUNNING air_pollution_functions.py TESTS!'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accessing air pollution concentration at: Latitude: 51.5 Longitude: 0.12 Time: 2018-01-01 080000\n",
- "test_air_pollution_concentrations_to_UK_daily_air_quality_index_correct_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_air_pollution_concentrations_to_UK_daily_air_quality_index_incorrect_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_change_in_aqi_visulisation_correct_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_change_in_aqi_visulisation_incorrect_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_change_in_concentrations_visulisation_correct_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_change_in_concentrations_visulisation_incorrect_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_visualise_air_pollution_daily_air_quality_bands_correct_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_visualise_air_pollution_daily_air_quality_bands_incorrect_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_visualise_air_pollution_daily_air_quality_index_correct_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "test_visualise_air_pollution_daily_air_quality_index_incorrect_input (tests_air_pollution_functions.air_pollution_functions_arguement_defence) ... ok\n",
- "\n",
- "----------------------------------------------------------------------\n",
- "Ran 10 tests in 307.057s\n",
- "\n",
- "OK\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'RUNNING data.py TESTS!'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "test_placeholders (tests_data.data_arguement_defence) ... ok\n",
- "\n",
- "----------------------------------------------------------------------\n",
- "Ran 1 test in 0.000s\n",
- "\n",
- "OK\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'RUNNING models.py TESTS!'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "test_placeholders (tests_models.models_arguement_defence) ... ok\n",
- "\n",
- "----------------------------------------------------------------------\n",
- "Ran 1 test in 0.000s\n",
- "\n",
- "OK\n"
- ]
- }
- ],
- "source": [
- "#######\n",
- "####### Run Tests\n",
- "#######\n",
- "display(\"RUNNING air_pollution_functions.py TESTS!\")\n",
- "!python -m unittest discover -s tests -p tests_air_pollution_functions.py -v\n",
- "\n",
- "display(\"RUNNING data.py TESTS!\")\n",
- "!python -m unittest discover -s tests -p tests_data.py -v\n",
- "\n",
- "display(\"RUNNING models.py TESTS!\")\n",
- "!python -m unittest discover -s tests -p tests_models.py -v\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6bc3285c-d7ad-4b16-b8ba-1f84e8bcd6ae",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.10.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/tutorial_1-checkpoint.ipynb b/.ipynb_checkpoints/tutorial_1-checkpoint.ipynb
deleted file mode 100644
index 4d17c12..0000000
--- a/.ipynb_checkpoints/tutorial_1-checkpoint.ipynb
+++ /dev/null
@@ -1,2213 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "590878ba-46c1-4687-8bc4-8fede873f1d3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "fae83b0c-caee-44d4-af5f-172c8e05982d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fetching metadata for aurn network...\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'http://uk-air.defra.gov.uk/openair/R_data/AURN_metadata.RData'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Download completed successfully.\n",
- "Metadata for aurn network:\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "
"
- ],
- "text/plain": [
- " date NO NO2 NOXasNO2 PM10 wd ws temp \\\n",
- "0 1.420070e+09 1.2473 3.8250 5.7375 12.932 229.9 8.2 7.1 \n",
- "1 1.420074e+09 NaN NaN NaN 5.112 230.0 7.8 6.6 \n",
- "2 1.420078e+09 1.2473 3.8250 5.7375 6.136 234.6 9.1 6.7 \n",
- "3 1.420081e+09 1.2473 5.7375 7.6500 5.372 236.4 9.5 6.9 \n",
- "4 1.420085e+09 0.0000 3.8250 3.8250 8.645 226.1 8.9 7.0 \n",
- "\n",
- " site code \n",
- "0 Aberdeen Anderson Dr 1 \n",
- "1 Aberdeen Anderson Dr 1 \n",
- "2 Aberdeen Anderson Dr 1 \n",
- "3 Aberdeen Anderson Dr 1 \n",
- "4 Aberdeen Anderson Dr 1 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fetching data for station YK9 in 2015 from aqe network...\n",
- "https://airqualityengland.co.uk/assets/openair/R_data/YK9_2015.RData\n",
- "Download completed successfully for YK9 in 2015.\n",
- "All files downloaded successfully.\n",
- "Data for YK9 in 2015 from aqe network:\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "
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- "\n",
- "
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- " \n",
- "
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- "
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- "
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- "
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- "
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\n",
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- "
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- "
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- "
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- " \n",
- "
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- "
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- ],
- "text/plain": [
- " date NO NO2 NOXasNO2 site code\n",
- "0 1.420070e+09 6.2365 30.6000 40.1625 York Lawrence Street 1\n",
- "1 1.420074e+09 9.9784 24.8625 40.1625 York Lawrence Street 1\n",
- "2 1.420078e+09 6.2365 24.8625 34.4250 York Lawrence Street 1\n",
- "3 1.420081e+09 3.7419 17.2125 22.9500 York Lawrence Street 1\n",
- "4 1.420085e+09 2.4946 11.4750 15.3000 York Lawrence Street 1"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fetching data for station ANG5 in 2010 from waqn network...\n",
- "https://airquality.gov.wales/sites/default/files/openair/R_data/ANG5_2010.RData\n",
- "Download completed successfully for ANG5 in 2010.\n",
- "All files downloaded successfully.\n",
- "Data for ANG5 in 2010 from waqn network:\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "
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- "\n",
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- "Fetching data for station DCSMOB01 in 2019 from ni network...\n",
- "https://www.airqualityni.co.uk/openair/R_data/DCSMOB01_2019.RData\n",
- "Download completed successfully for DCSMOB01 in 2019.\n",
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- " date O3 NO NO2 NOXasNO2 PM10 NV10 V10 PM2.5 \\\n",
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- "\n",
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- "0 9.0 2.4 229.9 8.2 7.1 10.1 13.1 999435.0 Aberdeen 1 \n",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# This notebook demonstrates how to download and process air quality monitoring data from various networks.\n",
- "import download\n",
- "# Define the networks to get metadata for\n",
- "networks = [\"aurn\", \"saqn\", \"aqe\", \"waqn\", \"ni\"]\n",
- "\n",
- "# Dictionary to store metadata for each network\n",
- "metadata_dict = {}\n",
- "\n",
- "# Loop through each network, download metadata, and store it in the dictionary\n",
- "for network in networks:\n",
- " print(f\"Fetching metadata for {network} network...\")\n",
- " metadata = download.monitoring_station_meta_data(network)\n",
- " metadata_dict[network] = metadata\n",
- " print(f\"Metadata for {network} network:\")\n",
- " display(metadata)\n",
- "\n",
- "# Define stations and their respective data parameters\n",
- "stations_data = {\n",
- " \"saqn\": [\"ABD1\", 2015, \"NO\"],\n",
- " \"aqe\": [\"YK9\", 2015, \"PM10\"],\n",
- " \"waqn\": [\"ANG5\", 2010, \"PM10\"],\n",
- " \"ni\": [\"DCSMOB01\", 2019, \"PM2.5\"],\n",
- " \"aurn\": [\"ABD\", 2015, \"O3\"]\n",
- "}\n",
- "\n",
- "# Loop through each station data entry, download the data, and display the first few rows\n",
- "for network, station_data in stations_data.items():\n",
- " station, year, pollutant = station_data\n",
- " print(f\"Fetching data for station {station} in {year} from {network} network...\")\n",
- " data = download.import_monitoring_station_data(station, pollutant, year, network)\n",
- " print(f\"Data for {station} in {year} from {network} network:\")\n",
- " display(data.head())\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "20a2cbae-86bf-4506-8b25-dab365ef42a4",
- "metadata": {},
- "outputs": [],
- "source": []
- }
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- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
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- "name": "ipython",
- "version": 3
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- "pygments_lexer": "ipython3",
- "version": "3.8.19"
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- },
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-}
diff --git a/.ipynb_checkpoints/tutorial_environmental_insights-checkpoint.ipynb b/.ipynb_checkpoints/tutorial_environmental_insights-checkpoint.ipynb
deleted file mode 100644
index cf1dd50..0000000
--- a/.ipynb_checkpoints/tutorial_environmental_insights-checkpoint.ipynb
+++ /dev/null
@@ -1,3375 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "440a2f27-ed66-45ec-953f-c802c31b43cb",
- "metadata": {},
- "source": [
- "
Environmental Insights Tutorial
\n",
- "
Before starting the tutorial, please ensure that you read the README.md file for this python package.
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "air_pollution_DF_daily_air_quality_index_global = ei_air_pollution_functions.air_pollution_concentrations_to_UK_daily_air_quality_index(global_complete_dataset, \"no2\", \"no2\")\n",
- "air_pollution_DF_daily_air_quality_index_global = global_grids.merge(air_pollution_DF_daily_air_quality_index_global, on=\"Global Model Grid ID\")\n",
- "\n",
- "ei_air_pollution_functions.visualise_air_pollution_daily_air_quality_index(air_pollution_DF_daily_air_quality_index_global, \"no2 AQI\", \"global_2018_01_01_080000_air_quality_index\")\n",
- "ei_air_pollution_functions.visualise_air_pollution_daily_air_quality_bands(air_pollution_DF_daily_air_quality_index_global, \"no2 Air Quality Index AQI Band\", \"global_2018_01_01_080000_air_quality_bands\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b2dad3a0-3ed1-40cf-99c5-ab22596599b9",
- "metadata": {},
- "source": [
- "# Load the typical day data for the UK\n",
- "A core issue with the use of the data within this package is the amount of data that is avaliable (TBs of data). As such the use of the typical day, e.g. a typical monday in January at 8AM is provided to make conducting analysis more manageable. \n",
- "The dataset that is used in this tutorial is for Friday in January at midnight. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "60d93300-8f6c-4bad-9ecd-e93a4251f3ff",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ei_air_pollution_functions.change_in_concentrations_visulisation(air_pollution_DF_8am, air_pollution_DF_9am, \"no2 Prediction mean\", \"uk_concentration_change_between_8_9_am\")\n",
- "ei_air_pollution_functions.change_in_aqi_visulisation(air_pollution_DF_8am, air_pollution_DF_9am, \"no2 AQI\", \"uk_aqi_change_between_8_9_am\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f17c1246-a4c6-42b3-a091-5243034fb76e",
- "metadata": {},
- "source": [
- "# Visualising the changes in the air pollution concentrations across a number of timestamps. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "58682916-b4c1-49b9-9e33-4e918095519c",
- "metadata": {},
- "source": [
- "Alongside being able to visualise the changes in air pollution spatially, there is the ability to visualise them temporally, with an aggregate across all of the desired locations.\n",
- "The example below gives the simple hypothetical scenario of changing the values based on simply doubling, or halving the concerntations. However a model could be plugged into this process as will be seen later. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "5a5e1031-89e0-4f16-ab6e-7f8abee71371",
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8.094753
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3.514129
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1.274053
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1.991810
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...
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1.849057
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4
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Month_1-Day_Friday-Hour_0
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172555
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7.739506
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2.665616
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0.392314
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60.468751
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8.619557
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3.967909
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1.953122
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...
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8.240040
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1.388418
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9.098235
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0.744974
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77.554046
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21.148254
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14.299424
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1.835284
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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355822
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Month_1-Day_Friday-Hour_0
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641063
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16.445102
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4.175528
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0.131788
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55.687207
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11.830601
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3.314833
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0.234283
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2.278441
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...
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9.366966
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6.171990
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1.243870
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11.333313
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1.635394
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64.831919
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20.974808
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12.484915
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2.756452
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355823
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Month_1-Day_Friday-Hour_0
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641069
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17.981549
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7.638318
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3.409901
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9.000086
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71.721185
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22.511852
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11.298645
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1.840405
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355824
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Month_1-Day_Friday-Hour_0
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641078
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20.375480
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7.728428
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0.106840
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64.726175
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11.828555
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3.106486
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3.174422
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...
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9.921261
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11.235233
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2.307508
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355825
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Month_1-Day_Friday-Hour_0
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641079
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23.329093
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10.905817
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2.593097
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355826
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Month_1-Day_Friday-Hour_0
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641084
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355827 rows × 30 columns
\n",
- "
"
- ],
- "text/plain": [
- " Timestamp UK Model Grid ID Model Predicition \\\n",
- "0 Month_1-Day_Friday-Hour_0 172550 8.035604 \n",
- "1 Month_1-Day_Friday-Hour_0 172551 7.945086 \n",
- "2 Month_1-Day_Friday-Hour_0 172552 9.400360 \n",
- "3 Month_1-Day_Friday-Hour_0 172554 9.104724 \n",
- "4 Month_1-Day_Friday-Hour_0 172555 7.739506 \n",
- "... ... ... ... \n",
- "355822 Month_1-Day_Friday-Hour_0 641063 16.445102 \n",
- "355823 Month_1-Day_Friday-Hour_0 641069 17.981549 \n",
- "355824 Month_1-Day_Friday-Hour_0 641078 20.375480 \n",
- "355825 Month_1-Day_Friday-Hour_0 641079 12.391479 \n",
- "355826 Month_1-Day_Friday-Hour_0 641084 15.807502 \n",
- "\n",
- " no2 Prediction mean no Prediction mean o3 Prediction mean \\\n",
- "0 2.812367 0.349236 63.668754 \n",
- "1 2.694829 0.426442 65.739356 \n",
- "2 2.600203 0.374548 70.660628 \n",
- "3 3.045493 0.328104 72.515322 \n",
- "4 2.665616 0.392314 60.468751 \n",
- "... ... ... ... \n",
- "355822 4.175528 0.131788 55.687207 \n",
- "355823 7.638318 0.195003 57.273293 \n",
- "355824 7.728428 0.106840 64.726175 \n",
- "355825 8.987267 0.182255 59.214336 \n",
- "355826 5.733832 0.105847 64.517153 \n",
- "\n",
- " pm10 Prediction mean pm2.5 Prediction mean so2 Prediction mean \\\n",
- "0 7.188661 2.786551 0.463751 \n",
- "1 6.697701 2.970919 0.433914 \n",
- "2 6.503175 2.916194 0.460107 \n",
- "3 8.094753 3.514129 1.274053 \n",
- "4 8.619557 3.967909 1.277714 \n",
- "... ... ... ... \n",
- "355822 11.830601 3.314833 0.234283 \n",
- "355823 11.979711 3.241447 0.499922 \n",
- "355824 11.828555 3.106486 0.435926 \n",
- "355825 10.903590 4.318315 0.529724 \n",
- "355826 11.575245 2.917744 0.291282 \n",
- "\n",
- " nox Prediction 0.05 ... pm10 Prediction 0.5 pm2.5 Prediction 0.5 \\\n",
- "0 2.091108 ... 7.146571 4.126741 \n",
- "1 1.850913 ... 7.178484 5.057778 \n",
- "2 1.802128 ... 7.519809 4.406307 \n",
- "3 1.991810 ... 8.339492 4.901497 \n",
- "4 1.953122 ... 8.240040 5.150398 \n",
- "... ... ... ... ... \n",
- "355822 2.278441 ... 9.366966 6.171990 \n",
- "355823 3.409901 ... 9.000086 5.567575 \n",
- "355824 3.174422 ... 9.921261 4.754073 \n",
- "355825 2.110240 ... 9.824611 5.449190 \n",
- "355826 2.243736 ... 8.802457 5.420002 \n",
- "\n",
- " so2 Prediction 0.5 nox Prediction 0.95 no2 Prediction 0.95 \\\n",
- "0 0.960756 11.203108 8.686345 \n",
- "1 0.925270 11.545962 7.687859 \n",
- "2 0.851541 11.077235 7.759612 \n",
- "3 0.985152 11.500751 9.182634 \n",
- "4 1.388418 10.519783 9.098235 \n",
- "... ... ... ... \n",
- "355822 1.243870 16.093022 11.333313 \n",
- "355823 1.373519 11.579877 11.553397 \n",
- "355824 1.223787 14.418216 10.067225 \n",
- "355825 1.755116 12.815938 13.405798 \n",
- "355826 1.174142 12.961680 10.468029 \n",
- "\n",
- " no Prediction 0.95 o3 Prediction 0.95 pm10 Prediction 0.95 \\\n",
- "0 0.800055 78.310499 19.028497 \n",
- "1 0.859786 79.103834 19.570082 \n",
- "2 0.694030 80.454327 19.102461 \n",
- "3 0.605452 81.569765 20.842673 \n",
- "4 0.744974 77.554046 21.148254 \n",
- "... ... ... ... \n",
- "355822 1.635394 64.831919 20.974808 \n",
- "355823 2.295638 71.721185 22.511852 \n",
- "355824 1.343596 72.829683 21.487459 \n",
- "355825 1.823801 71.062067 23.329093 \n",
- "355826 1.273300 70.051322 20.975045 \n",
- "\n",
- " pm2.5 Prediction 0.95 so2 Prediction 0.95 \n",
- "0 13.972185 2.125544 \n",
- "1 13.931100 1.938894 \n",
- "2 14.097474 1.664532 \n",
- "3 13.550126 1.849057 \n",
- "4 14.299424 1.835284 \n",
- "... ... ... \n",
- "355822 12.484915 2.756452 \n",
- "355823 11.298645 1.840405 \n",
- "355824 11.235233 2.307508 \n",
- "355825 10.905817 2.593097 \n",
- "355826 11.945049 1.994389 \n",
- "\n",
- "[355827 rows x 30 columns]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Show the change in concentration line example \n",
- "\n",
- "#A single month should be used in the example code, with the list days being populated with the days to be analysed, out of [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
- "month = 1 \n",
- "days = [\"Friday\"]\n",
- "\n",
- "\n",
- "#The baseline_DFs represent the DFs that will create the black link in the graph, with change_* being the DFs that contain the concentrations with some change, in this case the change_positive_DFs being the doubling of the concentrations and \n",
- "#change_negative_DFs being the halving of the concentrations. \n",
- "baseline_DFs = dict()\n",
- "change_postive_DFs = dict()\n",
- "change_negative_DFs = dict()\n",
- "for day_of_week in days:\n",
- " \n",
- " #Create a nested list for each day\n",
- " baseline_DFs_single_day = dict()\n",
- " change_postive_DFs_single_day = dict()\n",
- " change_negative_DFs_single_day = dict()\n",
- " for hour in np.arange(0,24):\n",
- " \n",
- " #load in the typical day data of interest\n",
- " air_pollution_DF = ei_data.air_pollution_concentration_typical_day_real_time_united_kingdom(month, day_of_week, hour)\n",
- " \n",
- " #Standardise the column names\n",
- " air_pollution_DF = air_pollution_DF.rename(columns={\"nox Prediction mean\":\"Model Predicition\"})\n",
- " baseline_DFs_single_day[hour] = air_pollution_DF\n",
- " air_pollution_DF_change = air_pollution_DF.copy(deep=True)\n",
- " \n",
- " #Double all of the concentrations and add the DF to the corresponding list.\n",
- " air_pollution_DF_change[\"Model Predicition\"] = air_pollution_DF_change[\"Model Predicition\"] * 2\n",
- " change_postive_DFs_single_day[hour] = air_pollution_DF_change\n",
- "\n",
- " \n",
- " #Repeat the process but for the halving of the concentrations \n",
- " air_pollution_DF_change = air_pollution_DF.copy(deep=True)\n",
- " air_pollution_DF_change[\"Model Predicition\"] = air_pollution_DF_change[\"Model Predicition\"] * 0.5\n",
- " change_negative_DFs_single_day[hour] = air_pollution_DF_change\n",
- " \n",
- " \n",
- " baseline_DFs[day_of_week] = baseline_DFs_single_day\n",
- " change_postive_DFs[day_of_week] = change_postive_DFs_single_day\n",
- " change_negative_DFs[day_of_week] = change_negative_DFs_single_day\n",
- " \n",
- " \n",
- "display(change_postive_DFs[\"Friday\"][0])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7fbbee98-9e67-4c3a-9dc7-023785e0ce97",
- "metadata": {},
- "source": [
- "### Visualise the changes based on the list of dataframe."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "342ce1b3-fab1-4abc-bb01-00e74eaa0b03",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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