Erster Commit

environment.yml

@@ -20,3 +20,6 @@ dependencies:
   - yellowbrick=1.3
   - pip:
     - streamlit==1.7.0
+
+streamlit~=1.7.0
+scipy~=1.5.0

first_commits/HalloDavidStein7.txt

@@ -0,0 +1 @@
+Hallo Welt

src/App2.py

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+import streamlit as st

src/app/App.py

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+import streamlit as st
+
+st.title('Vorhersage der deutschen Co2 Emissionen')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+st.subheader("Vorhersage")
+years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
+emissions_per_year = [10.3, 10.0, 10.1, 10.2, 9.7, 9.7, 9.7, 9.5, 9.1, 8.5, 7.7]
+
+regression_result = linregress(years, emissions_per_year)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value=2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage der Emissionen für das ausgewählte Jahr ist:"
+
+st.write(prediction_rounded)
+
+"Tonnen pro Kopf pro Jahr"
+
+st.subheader("Über das Modell und die Daten")
+
+"Das Modell ist ein lineares Regressionsmodell auf Grundlage von Daen von 2010 bis 2020."
+"Es steht ein Datenpunkt pro Jahr zur Verfügung"
+
+"Die Daten stammen aus den folgenden Quellen:"
+
+"- Global Carbon Project. (2021). Supplemental data of Global Carbon Project 2021 (1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2021"
+"- Andrew, Robbie M., & Peters, Glen P. (2021). The Global Carbon Project's fossil CO2 emissions dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5569235"
+#%%

src/app/OwnApp1.py

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+import pandas as pd
+import streamlit as st
+
+st.title('Vorhersage des Anteils der erneuerbaren Energien am gesamten Energieverbrauch in Deutschland')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+
+st.subheader("Vorhersage")
+years = [2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
+r_energy = [10.72, 11.61, 12.54, 13.64, 13.63, 14.02, 14.55, 14.24, 15.22, 16.12, 17.17]
+
+
+regression_result = linregress(years, r_energy)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value = 2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage des Anteils der erneuerbaren Energien für das ausgewählte Jahr ist"
+
+st.write(prediction_rounded,"%")
+
+"des gesamten Energieverbrauchs."
+
+st.subheader("Genutzte Datenpunkte:")
+chart_data=pd.DataFrame(r_energy,years)
+st.bar_chart(chart_data)
+
+clicked = st.button("Drück mich! :)",help="Balloons")
+if clicked:
+    st.balloons()
+
+st.subheader("Über das Modell und Daten")
+
+"Das Modell ist ein lineares Regressionsmodell auf Grundlage von Daten von 2009 bis 2019."
+"Es steht ein Datenpunkt pro Jahr zur Verfügung"
+
+"Die Daten stammen aus den folgenden Quellen:"
+"- IEA. (2021). World Energy Balances."
+"- UN Statistics Division. (2021). Energy Balances."
+"- https://unstats.un.org/sdgs/dataportal/countryprofiles/DEU#goal-7"

src/app/OwnApp2.py

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+import pandas as pd
+import streamlit as st
+
+st.title('Number of Nuclear Warheads - USA vs Russia')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+
+st.subheader("Vorhersage 1")
+years = [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
+warheadsR = [12188, 11152, 10114, 9076, 8038, 7000, 6643, 6286, 5929, 5527, 5215, 4858, 4750, 4650, 4600, 4500, 4490, 4300, 4350, 4330, 4310, 4495, 4477]
+warheadsU = [10577, 10526, 10457, 10027, 8570, 8360, 7853, 5709, 5273, 5113, 5066, 4897, 4881, 4804, 4717, 4571, 4018, 3822, 3785, 3805, 3750, 3708, 3708]
+
+regression_result = linregress(years, warheadsR)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value = 2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage"
+
+st.write(prediction_rounded)
+
+"Lineare Vorhersage"
+slope = (warheadsR[22]-warheadsR[0])/(years[22]-years[0])
+intercept = warheadsR[0]-(slope*years[0])
+
+def model(desired_year):
+    model_result2 = slope * desired_year + intercept
+    return model_result2
+
+st.write(model(desired_year))
+
+"Daten"
+
+st.write(warheadsR[22])
+
+st.success("nice!")
+
+st.subheader("Data points used:")
+chart_data=pd.DataFrame(warheadsR,years)
+st.bar_chart(chart_data)
+
+clicked = st.button("Click me",help="Balloons")
+if clicked:
+    st.balloons()
+

src/hello_world.py

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+print(5+5)
+#%%
+print(5*2)
+#%%
+print(5*3)
+#%%
+multiplier = 2
+
+result = multiplier * -3
+print(result)
+#%%
+recent_years = [2020, 2021, 2022]
+
+print(recent_years[1])
+#%%
+for year in recent_years:
+    print(year)
+#%%
+
+#%%
+
+#%%
+for year in recent_years:
+    print(year)
+#%%
+for number in range (10):
+    print(number)
+#%%
+years = [2020, 2021]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/years[1]-years[0])
+#%%
+years = [2020, 2021]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/years[1]-years[0]
+
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+print(slope)
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+intercept = emissions_per_year[0]-(slope*years[0])
+print(intercept)
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+intercept = emissions_per_year[0]-(slope*years[0])
+
+def model(desired_year):
+    model_result = slope * desired_year + intercept
+    return model_result
+
+emissions_in_2022 = model(2022)
+print(emissions_in_2022)
+#%%
+# Modell mit SciPy
+years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
+emissions_per_year = [10.3, 10.0, 10.1, 10.2, 9.7, 9.7, 9.7, 9.5, 9.1, 8.5, 7.7]
+from scipy.stats import linregress
+regression_result = linregress(years, emissions_per_year)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+emissions_in_2022_scipy = scipy_model(2022)
+print(emissions_in_2022_scipy)
+#%%
+model_results = []
+scipy_model_results = []
+
+for year in years:
+    model_result = model(year)
+    scipy_model_result = scipy_model(year)
+
+    model_results.append(model_result)
+    scipy_model_results.append(scipy_model_result)
+
+print(emissions_per_year)
+print(model_results)
+print(scipy_model_results)
+
+#%%
+
+#%%