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ax = plt.axes(projection=ccrs.PlateCarree())

# get country centroids
ax.set_extent([lon[0] - 1, lon[1] + 1, lat[0] - 1, lat[1] + 1])

for key, cn in zip(c.keys(),c.values()):
    ax.add_geometries(cn.geometry,
                      crs=ccrs.PlateCarree(),
                      edgecolor="grey",
                      facecolor="whitesmoke",

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Dim i, j, lr2 As Integer
Dim rg1, rg2 As Range
Dim k As Long

'Loop through each row of ProjectTasksTracker sheet except the header row
For i = 2 To lr1
    
    'Check if the update column is not empty in ProjectTasksTracker sheet.
    'Proceed if not empty
    If ws1.Cells(i, 6).Value <> "" Then

hbshrestha

import os, json, requests

#all sky surface shortwave downward irradiance
radiation_parameter = "ALLSKY_SFC_SW_DWN"

#url for irradiance
base_url = r"https://power.larc.nasa.gov/api/temporal/hourly/point?parameters=ALLSKY_SFC_SW_DWN&community=RE&time-standard=UTC&longitude={longitude}&latitude={latitude}&format=JSON&start=2020&end=2020"

rad_data = []

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rescale = lambda y: (y - np.min(y)) / (np.max(y) - np.min(y))

fig, ax1 = plt.subplots()

x = df_temp.index.tolist()
y = df_temp["mean"].values.tolist()

ax1.bar(x, y, color = my_cmap(rescale(y)), width = 0.8)
ax1.set_ylabel("Temperature anomaly \n relative to 1951-80 mean (°C)", color = "red")
ax1.tick_params(axis='y', color='red', labelcolor='red')

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gases = ["CO$_2$", "CH$_4$", "N$_2$O", "F-gases"]
warming_potential = [1, 25, 300, 1000]
text_height = [i*1.2 for i in warming_potential]
text = ["1", "25", "300", "1000+"] 
volume = [74.4, 17.3, 6.2, 2.1]
colors = ["brown", "darkslategray", "darkgray", "purple"]


fig, (ax1, ax2) = plt.subplots(1, 2)

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fig, ax = plt.subplots()
df["Annual Mean"].plot(ax = ax, c = "black", marker = "s")
df["Lowess Smoothing"].plot(ax = ax, c = "red")

x = df.index.tolist()
y = df["Annual Mean"].tolist()

# Define the 95% confidence interval
ci = np.mean(y) + 1.96 * np.std(y) / np.sqrt(len(y))
plt.fill_between(x, y-ci, y+ci,

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anomaly = df["Annual Mean"]

fig = plt.figure(figsize = (10, 1.5))
ax = fig.add_axes([0, 0, 1, 1])

#turn the x and y-axis off
ax.set_axis_off()

#create a collection with a rectangle for each year
col = PatchCollection([

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import folium

folium_coordinates = []
for x,y in coordinates:
    folium_coordinates.append([y,x])
    
route = []
for stop in cycle:
    route.append(folium_coordinates[stop])
    

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import networkx as nx
import matplotlib.pyplot as plt

cities = ["Berlin","Dusseldorf","Hamburg","Munich","Berlin"]

def make_tsp_tree(cities):
    """
    Create all Hamilton paths from start to end city from a list of cities.
    Creates a directed prefix tree from a list of the created paths. 
    Remove the root node and nil node.

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fig = go.Figure()

fig.add_trace(go.Waterfall(x = [["Initial","Short-term measure","Short-term measure",
                                 "Short-term measure","Short-term measure","Short-term measure",
                                 "Intermediate", "Long-term measure","Long-term measure",
                                 "Long-term measure","Final"],
                                df["Values"]],  
                                y = df["kWh"],
                                measure = df["measure"].tolist(),
                                base = 0,   #by default