moved to module

data/README.md

@@ -0,0 +1,76 @@
+Get the full scoop at [NaturalEarthData.com](http://naturalearthdata.com)
+
+_No, really! This readme is a poor substitute for the live site._
+
+# About Natural Earth Vector
+
+Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector (here) and raster data ([over there](https://github.com/nvkelso/natural-earth-raster)), with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
+
+Natural Earth was built through a collaboration of many [volunteers](http://www.naturalearthdata.com/about/contributors/) and is supported by [NACIS](http://www.nacis.org/) (North American Cartographic Information Society), and is free for use in any type of project (see our [Terms of Use](http://www.naturalearthdata.com/about/terms-of-use/) page for more information).
+
+[Get the Data »](http://www.naturalearthdata.com/downloads)
+
+![Convenience](http://www.naturalearthdata.com/wp-content/uploads/2009/08/home_image_11.png)
+
+# Convenience
+
+Natural Earth solves a problem: finding suitable data for making small-scale maps. In a time when the web is awash in geospatial data, cartographers are forced to waste time sifting through confusing tangles of poorly attributed data to make clean, legible maps. Because your time is valuable, Natural Earth data comes ready-to-use.
+
+![Neatness Counts](http://www.naturalearthdata.com/wp-content/uploads/2009/08/home_image_21.png)
+
+# Neatness Counts
+
+The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up so you will find that all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident.
+
+![GIS Atributes](http://www.naturalearthdata.com/wp-content/uploads/2009/08/home_image_32.png)
+
+# GIS Attributes
+
+Natural Earth, however, is more than just a collection of pretty lines. The data attributes are equally important for mapmaking. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers.
+
+# Versioning
+
+The 2.0 release in 2012 marked the project's shift from so-called marketing versions to [semantic versioning](http://semver.org/). 
+
+Natural Earth is a big project with hundreds of files that depend on each other and the total weighs in at several gigabytes. SemVer is a simple set of rules and requirements around version numbers. For our project, the data layout is the API. 
+
+* **Version format of X.Y.Z** (Major.Minor.Patch). 
+* **Backwards incompatible** changes, increment the major version X.
+* **Backwards compatible** additions/changes, increment the minor version Y
+* **Bug fixes** not affecting the file and field names, patch version Z will be incremented. 
+
+Major version increments:
+
+* Changing existing data **file names**
+* Changing existing data **column (field) names**
+* Removing **`FeatureCla` field attribute values**
+* Additions, deletions to **admin-0**
+* Introduce **significant new themes**
+
+Minor version increments:
+
+* Any shape or attribute change in **admin-0**
+* Additions, deletions, and any shape or attribute changes in **admin-1**
+* Additions, deletions to **any theme**
+* Major shape or attribute changes in **any theme**
+* Adding, changing **`FeatureCla` field attribute values**
+* Introduce **minor new themes**
+
+Patch version increments:
+
+* Minor shape or attribute changes in **any theme**
+* Bug fixes to shape, attributes in **any theme**
+
+Under this scheme, version numbers and the way they change convey meaning about the underlying code and what has been modified from one version to the next.
+
+When we introduce a new version of Natural Earth, you can tell by the version number how much effort you will need to extend to integrate the data with your map implementation.
+
+* **Bug fixes Z**: can simply use the new data files, replacing your old files.
+* **Minor version Y**: limited integration challenges.
+* **Major version X**: significatnt integration challenges, either around changed file strucutre, field layout, field values like `FeatureCla` used in symbolizing data, or significant new additions or significant changes to existing themes.
+
+# &etc
+
+Natural Earth is maintained by Nathaneiel V. KELSO ([@nvkelso](https://github.com/nvkelso/)) and Tom Patterson.
+
+The project transitioned to Github in 2012. Versioned files are here to collaborate around. The frontend still lives at [NaturalEarthData.com](http://naturalearthdata.com).

pyproject.toml

@@ -0,0 +1,13 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name="energy_data_project"
+version="0.1.0"
+requires-python = ">3.10"
+dependencies=[
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]

src/T19_geopandas.py

@@ -1,62 +0,0 @@
-# pip/conda install geopandas
-
-import geopandas as gpd
-import matplotlib.pyplot as plt
-
-
-world = gpd.read_file("../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
-
-print(list(world.columns))
-
-print(world.iloc[0, -1])
-print(world.iloc[0]["NAME"])
-
-print()
-print(world.crs)
-
-# EPSG:4326  (latitude/longitude)
-
-# epsg:3587  Mercator
-
-w2 = world.to_crs("epsg:3587")
-print(w2.iloc[0, -1])
-
-print(world[["NAME", "CONTINENT", "POP_EST", "GDP_MD", "geometry"]].head())
-
-# wie groß ist jedes Land?
-world_proj_cylinder = world.to_crs("epsg:3587")  # zylinder
-world_proj_cylinder["area_sq_km"] = world_proj_cylinder.geometry.area / 1_000_000
-print(world_proj_cylinder[["NAME", "area_sq_km"]].head(10))
-
-
-cities = gpd.read_file("../data/110m_cultural.zip",
-                      layer="ne_110m_populated_places")
-
-print(cities[["NAME", "geometry"]].head())
-print(len(cities))
-
-fig, ax = plt.subplots()
-world.plot(column="CONTINENT", legend=True, ax=ax)
-cities.plot(ax=ax, color="red")
-plt.show()
-
-# Nachbar von Land X suchen
-def find_neighbouring_countries(df, country_name):
-    target = df[df["NAME"] == country_name]
-
-    if len(target) == 0:
-        print(f"{country_name} not found")
-        return []
-
-    # sjoin
-    neighbours = []
-    for idx, country in df.iterrows():
-        if country["NAME"] == country_name:
-            continue
-        if target.geometry.iloc[0].touches(country.geometry):
-            neighbours.append(country["NAME"])
-
-    return neighbours
-
-res = find_neighbouring_countries(world, "Spain")
-print(res)

src/T20_Geopandas_joining.py

@@ -1,55 +0,0 @@
-import geopandas as gpd
-import matplotlib.pyplot as plt
-
-
-world = gpd.read_file("../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
-cities = gpd.read_file("../data/110m_cultural.zip",
-                      layer="ne_110m_populated_places")
-
-
-def find_neighbouring_countries(df, country_name):
-    target = df[df["NAME"] == country_name]
-
-    if len(target) == 0:
-        print(f"{country_name} not found")
-        return gpd.GeoDataFrame()
-
-    # sjoin  spatial-join
-    neighbours = gpd.sjoin(target, df, how="inner", predicate="touches")
-    return neighbours[["NAME_right", "CONTINENT_right", "geometry"]]
-
-def get_close_cities(df, city_name):
-    target = df[df["NAMEASCII"] == city_name]
-
-    if len(target) == 0:
-        print(f"{city_name} not found")
-        return gpd.GeoDataFrame()
-
-    # sjoin  spatial-join
-    neighbours = gpd.sjoin_nearest(target, df, max_distance=10e12)
-    return neighbours
-
-
-#intersects
-#contains
-#within
-#touches
-#crosses
-#overlaps
-german_neighbours = find_neighbouring_countries(world, "Germany")
-print(german_neighbours[["NAME_right", "CONTINENT_right"]])
-print(len(german_neighbours))
-print("-"*100)
-w2 = world.to_crs("EPSG:3857")
-c2 = cities.to_crs("EPSG:3857")
-print(list(c2.columns))
-german_neighbours = get_close_cities(c2, "Paris")
-print(len(german_neighbours))
-print(german_neighbours)
-
-w2.plot(column="CONTINENT", legend=True)
-plt.show()
-
-
-# 1) get_cities_in_country    (sjoin)
-# 2) get_cities_in_countries  (concat)

src/energy_data_project/T08_WorldCities.py

@@ -54,7 +54,7 @@ def get_larger_cities_north_of_city(df, city: str, country: Optional[str]=None):
 
 if __name__ == "__main__":
     print("Country Population")
-    world_cities = pd.read_excel("../data/worldcities.xlsx")
+    world_cities = pd.read_excel("../../data/worldcities.xlsx")
     print(world_cities.columns)
     german_population = get_population_of_country(world_cities, "Germany")
     print(f"Population of Germany: {german_population}")

src/energy_data_project/T09_GroupBy.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-beverages = pd.read_csv("../data/beverages.csv")#
+beverages = pd.read_csv("../../data/beverages.csv")#
 print(beverages)
 
 
@@ -24,7 +24,7 @@ for name, info in groups:
 
 print()
 # print(help(pd.read_csv))
-donations_df = pd.read_csv("../data/donations.csv")
+donations_df = pd.read_csv("../../data/donations.csv")
 print(donations_df)
 
 subset = donations_df[["city", "job", "income", "donations"]]

src/energy_data_project/T10_ex_Energy.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-energy_df = pd.read_csv("../data/germany_energy_mix_2019_2024.csv")
+energy_df = pd.read_csv("../../data/germany_energy_mix_2019_2024.csv")
 print(energy_df)
 print(energy_df.columns)
 

src/energy_data_project/T11_Pivotieren.py

@@ -24,7 +24,7 @@ pivoted_df = df.pivot(index="Product",
 
 print(pivoted_df)
 
-beverages = pd.read_csv("../data/beverages.csv")
+beverages = pd.read_csv("../../data/beverages.csv")
 beverages["Day"] = (["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"] * 35)[:103]
 print(beverages)
 
@@ -50,7 +50,7 @@ print(coffees)
 
 # df.index <- name der index-spalte
 print("\n"*3)
-energy_df = pd.read_csv("../data/germany_energy_mix_2019_2024.csv")
+energy_df = pd.read_csv("../../data/germany_energy_mix_2019_2024.csv")
 # 1) Neue spalte
 energy_df["Year Quarter"] = energy_df["Year"].astype(str) + " " + energy_df['Quarter']
 # drop: axis=0 die zeilen  namens [Year, quarter] gelöscht

src/energy_data_project/T12_Datetimes.py

@@ -3,7 +3,7 @@ import pandas as pd
 # jahr-monat-tag
 # monat/tag/jahr  (us-schreibweise)
 
-beverages_by_date = pd.read_csv("../data/beverages_by_date.csv",
+beverages_by_date = pd.read_csv("../../data/beverages_by_date.csv",
                                 index_col=0)
 
 # zum datum konvertiert
@@ -34,7 +34,7 @@ daily = beverages_by_date.resample("8h").bfill()
 print(daily.loc["2024-02-8":"2024-02-14"])
 
 # übung mit zeiten
-solar_df = pd.read_csv("../data/Balkonkraftwerk.csv",
+solar_df = pd.read_csv("../../data/Balkonkraftwerk.csv",
                        index_col=0)
 solar_df.index = pd.to_datetime(solar_df.index)
 print(solar_df)

src/energy_data_project/T15_EnergyPlots.py

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from pandas.core.config_init import max_cols
 
-energy_df = pd.read_csv("../data/germany_energy_mix_2019_2024.csv")
+energy_df = pd.read_csv("../../data/germany_energy_mix_2019_2024.csv")
 # 1) Neue spalte
 energy_df["Year Quarter"] = energy_df["Year"].astype(str) + " " + energy_df['Quarter']
 energy_df.drop(["Year", "Quarter"], axis=1, inplace=True)

src/energy_data_project/T16_plotly_expls.py

@@ -19,7 +19,7 @@ fig = cities_df.plot(x="Stadt", y=["Population", "Flaeche"], backend="plotly")
 fig.show()
 
 
-energy_df = pd.read_csv("../data/germany_energy_mix_2019_2024.csv")
+energy_df = pd.read_csv("../../data/germany_energy_mix_2019_2024.csv")
 # 1) Neue spalte
 energy_df["Year Quarter"] = energy_df["Year"].astype(str) + " " + energy_df['Quarter']
 energy_df.drop(["Year", "Quarter"], axis=1, inplace=True)

src/energy_data_project/T19_geopandas.py

@@ -0,0 +1,63 @@
+# pip/conda install geopandas
+
+import geopandas as gpd
+import matplotlib.pyplot as plt
+
+
+
+
+# Nachbar von Land X suchen
+def find_neighbouring_countries(df, country_name):
+    target = df[df["NAME"] == country_name]
+
+    if len(target) == 0:
+        print(f"{country_name} not found")
+        return []
+
+    # sjoin
+    neighbours = []
+    for idx, country in df.iterrows():
+        if country["NAME"] == country_name:
+            continue
+        if target.geometry.iloc[0].touches(country.geometry):
+            neighbours.append(country["NAME"])
+
+    return neighbours
+
+if __name__ == "__main__":
+    world = gpd.read_file("../../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
+
+    print(list(world.columns))
+
+    print(world.iloc[0, -1])
+    print(world.iloc[0]["NAME"])
+
+    print()
+    print(world.crs)
+
+    # EPSG:4326  (latitude/longitude)
+
+    # epsg:3587  Mercator
+
+    w2 = world.to_crs("epsg:3587")
+    print(w2.iloc[0, -1])
+
+    print(world[["NAME", "CONTINENT", "POP_EST", "GDP_MD", "geometry"]].head())
+
+    # wie groß ist jedes Land?
+    world_proj_cylinder = world.to_crs("epsg:3587")  # zylinder
+    world_proj_cylinder["area_sq_km"] = world_proj_cylinder.geometry.area / 1_000_000
+    print(world_proj_cylinder[["NAME", "area_sq_km"]].head(10))
+
+    cities = gpd.read_file("../../data/110m_cultural.zip",
+                           layer="ne_110m_populated_places")
+
+    print(cities[["NAME", "geometry"]].head())
+    print(len(cities))
+
+    fig, ax = plt.subplots()
+    world.plot(column="CONTINENT", legend=True, ax=ax)
+    cities.plot(ax=ax, color="red")
+    plt.show()
+    res = find_neighbouring_countries(world, "Germany")
+    print(res)

src/energy_data_project/T20_Geopandas_joining.py

@@ -0,0 +1,92 @@
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import pandas as pd
+
+world = gpd.read_file("../../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
+cities = gpd.read_file("../../data/110m_cultural.zip",
+                       layer="ne_110m_populated_places")
+
+
+def find_neighbouring_countries(df, country_name):
+    target = df[df["NAME"] == country_name]
+
+    if len(target) == 0:
+        print(f"{country_name} not found")
+        return gpd.GeoDataFrame()
+
+    # sjoin  spatial-join
+    neighbours = gpd.sjoin(target, df, how="inner", predicate="touches")
+    return neighbours[["NAME_right", "CONTINENT_right", "geometry"]]
+
+def get_close_cities(df, city_name):
+    target = df[df["NAMEASCII"] == city_name]
+
+    if len(target) == 0:
+        print(f"{city_name} not found")
+        return gpd.GeoDataFrame()
+
+    # sjoin  spatial-join
+    #combined = gpd.sjoin(target, df, how="inner")
+    #combined["distance"] = combined.distance(target.iloc[0])
+    neighbours = gpd.sjoin_nearest(target, df, max_distance=10e12)
+    return neighbours
+
+
+#intersects
+#contains
+#within
+#touches
+#crosses
+#overlaps
+german_neighbours = find_neighbouring_countries(world, "Germany")
+print(german_neighbours[["NAME_right", "CONTINENT_right"]])
+print(len(german_neighbours))
+print("-"*100)
+w2 = world.to_crs("EPSG:3857")
+c2 = cities.to_crs("EPSG:3857")
+print(list(c2.columns))
+german_neighbours = get_close_cities(c2, "Paris")
+print(len(german_neighbours))
+print(german_neighbours)
+
+w2.plot(column="CONTINENT", legend=True)
+plt.show()
+
+def get_cities_in_country(city_df, world_df, country):
+    country_match = world_df[world_df["NAME"] == country]
+
+    if len(country_match) == 0:
+        print(f"Returning empty for {country}")
+        return gpd.GeoDataFrame()
+
+    cities = gpd.sjoin(country_match, city_df,
+                       how="inner",
+                       predicate="contains")
+    cities = cities[["NAME_left", "NAMEASCII"]]
+    return cities
+
+def get_cities_in_countries(city_df, world_df, countries):
+    res = get_cities_in_country(city_df, world_df, countries[0])
+    for country in countries[1:]:
+        tmp = get_cities_in_country(city_df, world_df, country)
+        res = pd.concat((res, tmp))
+    return res
+
+
+# 1) get_cities_in_country    (sjoin)  # NAME_right, NAME_left
+# 2) get_cities_in_countries  (concat)
+us_cities = get_cities_in_country(cities, world, "United States of America")
+print(us_cities)
+french_cities = get_cities_in_country(cities, world, "Switzerland")
+print(french_cities)
+
+central_eurpean_cities = get_cities_in_countries(
+    cities, world,
+    ["France", "Switzerland", "Narnia", "Germany", "Denmark"]
+)
+print(central_eurpean_cities)
+
+# United States = 9
+# France = 4
+# Italy: 3
+# Germany: 1

src/energy_data_project/T21_OwnPoints.py

@@ -0,0 +1,49 @@
+import geopandas as gpd
+import pandas as pd
+import matplotlib.pyplot as plt
+from shapely.geometry import Point
+
+capitals = {
+    'Paris': Point(2.3522, 48.8566),
+    'Berlin': Point(13.4050, 52.5200),
+    'Madrid': Point(-3.7038, 40.4168),
+    'Rome': Point(12.4964, 41.9028),
+    'London': Point(-0.1278, 51.5074)
+}
+capitals_gdf = gpd.GeoDataFrame(
+    [{'city': city, 'geometry': point} for city, point in capitals.items()],
+    crs='EPSG:4326'
+)
+capitals_proj = capitals_gdf.to_crs('EPSG:3857')  # Web Mercator
+print([{'city': city, 'geometry': point} for city, point in capitals.items()])
+# Alle Distanzen zwischen Städten berechnen
+print(capitals_proj)
+
+print("Distances between cities")
+for i, city1 in capitals_proj.iterrows():
+    for j, city2 in capitals_proj.iterrows():
+        if i >= j:
+            continue
+        dist_m = city1["geometry"].distance(city2["geometry"])
+        dist_km = dist_m / 1_000
+        print(f"Distance between {city1['city']} and {city2['city']} = {dist_km:.0f}km")
+
+print()
+
+# buffer
+world = gpd.read_file(
+    "../../data/110m_cultural.zip",
+    layer="ne_110m_admin_0_countries")
+
+switzerland = world[world['NAME'] == 'Switzerland']
+if len(switzerland) > 0:
+    # Project to equal-area for accurate buffering
+    switzerland_proj = switzerland.to_crs('EPSG:3857')
+    buffer_200km = switzerland_proj.geometry.buffer(900_000)  # 200km in meters
+
+    buffer_gdf = gpd.GeoDataFrame(geometry=buffer_200km.to_crs('EPSG:4326'))
+
+    # Find countries that intersect with this buffer
+    countries_near_switzerland = gpd.sjoin(world, buffer_gdf, how='inner', predicate='intersects')
+    nearby_countries = countries_near_switzerland['NAME'].tolist()
+    print(f"Countries within 200km of Switzerland: {nearby_countries}")

src/energy_data_project/__init__.py

@@ -0,0 +1,6 @@
+# init wird immer durchlaufen, wenn das modul importiert wird
+
+from .T19_geopandas import find_neighbouring_countries
+from .T05_Klassen_properties import Circle
+
+from .T08_WorldCities import *

tests/test_T08_WorldCities.py

@@ -0,0 +1,39 @@
+import unittest
+
+import pandas as pd
+from energy_data_project import get_larger_cities_north_of_city, get_cities_beyond_latitude
+
+# main.py  # python main.py
+# pip install -e /pfad/zum/modul
+# datei1.py
+# test_datei1.py
+
+
+# ein modul erstellen und darin datei1.py auslagern
+# datei1.py
+# test_datei1.py
+
+class MyTestCase(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.world_cities = pd.read_excel("data/worldcities.xlsx")
+
+    def setUp(self):
+        print("Loading data")
+
+    def tearDown(self):
+        print("After test")
+
+    def test_get_larger_cities_north_of_city(self):
+        self.assertEqual(True, False)  # add assertion here
+        cities_north = get_larger_cities_north_of_city(self.world_cities, "Berlin")
+        assert cities_north.iloc[0, 0], f"Error {cities_north.iloc[0, 0]} "
+
+    def test_something(self):
+        self.assertEqual(True, False)  # add assertion here
+        cities_north = get_larger_cities_north_of_city(self.world_cities, "Berlin")
+        assert cities_north.iloc[0, 0] == "Moscow", f"Error {cities_north.iloc[0, 0]} "
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_T19_geopandas.py

@@ -0,0 +1,14 @@
+import energy_data_project
+import geopandas as gpd
+
+def test_find_neighbouring_countries():
+    world = gpd.read_file("data/110m_cultural.zip",
+                          layer="ne_110m_admin_0_countries")
+    german_neighbours = energy_data_project.find_neighbouring_countries(
+        world,
+        "Germany")
+
+    # mit pytest
+    assert 1 == 1, "Correct"
+    assert german_neighbours == ['France', 'Poland', 'Austria', 'Switzerland', 'Luxembourg', 'Belgium', 'Netherlands', 'Denmark', 'Czechia'], "Error german neighbours not found"
+