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plot_MAI090_PercentilesHeatMap.py
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plot_MAI090_PercentilesHeatMap.py
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##############################
# plot_MAI090_PercentilesHeatMap.py
# Author: Michael Hemming
# Description: Plot MAI Percentiles (Using temp anomalies for now) Heatmap for every date, depth, and year
# %% --------------------------------------------------------------------
# Import packages
import xarray as xr
import numpy as np
import s3fs
import pandas as pd
import plotly.graph_objects as go
# %% --------------------------------------------------------------------
# Load data using AWS S3
s3 = s3fs.S3FileSystem(anon=True)
bucket_prefix = "imos-data/UNSW/NRS_extremes/Temperature_DataProducts_v2/"
MAI090 = xr.open_dataset(s3.open(bucket_prefix + "MAI090/MAI090_TEMP_EXTREMES_1944-2023_v2.nc"))
# %% --------------------------------------------------------------------
# Function to organise data into a day x month matrix for a selected year
def organize_temperature_into_dataframe(temp_dataarray):
"""
Organizes temperature data into a DataFrame where rows are days of the month and columns are months.
Parameters:
temp_dataarray (xarray.DataArray): The temperature dataarray with time as one of the coordinates.
empty_matrix (numpy.ndarray): The matrix to be filled with temperatures.
Returns:
pandas.DataFrame: The filled DataFrame with temperatures.
"""
# Create an empty matrix with 31 rows and 12 columns
empty_matrix = np.ones((31,12)) * np.nan
# Iterate over the temperature data
for i in range(temp_dataarray.shape[0]):
# Extract the date and corresponding day/month
date = temp_dataarray.TIME[i].values
day = np.datetime64(date, 'D').astype(object).day
month = np.datetime64(date, 'M').astype(object).month
# Fill the matrix at the corresponding day-1 (0-index) and month-1 (0-index) position
empty_matrix[day - 1, month - 1] = temp_dataarray[i].values
# Convert the matrix to a pandas DataFrame
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
days = list(range(1, 32)) # Days of the month (1 to 31)
# create dataframe with days on x-axis and months on y-axis
df = pd.DataFrame(empty_matrix.transpose(), index=months, columns=days)
df.index.name = 'Day' # Set the index name to 'Day'
return df
# %% ------------------------------------------------------------------
# Calculate TEMP anomalies (whole record)
# Make a copy of the original dataset
ds = MAI090['TEMP'].copy()
# Calculate TEMP anomalies
ds.values = MAI090['TEMP'].values - MAI090['TEMP_MEAN'].values
# !!! Note: to be updated to records or percentiles
# %% -------------------------------------------------------------------
# Function to split the data set into year and depth
# Have a heatmap for each year and depth, saved inside a dictionary called 'split'
def split_by_year_and_depth(temp_dataarray):
"""
Splits the temperature data into year and depth.
Parameters:
temp_dataarray (xarray.DataArray): The temperature dataarray with time as one of the coordinates.
Returns:
tuple: A tuple containing the year and depth dataarrays.
"""
# identify number of years in data set
years = pd.to_datetime(temp_dataarray['TIME']).year
nY = np.unique(years)
# identify number of depths
nD = temp_dataarray.shape[1]
depths = temp_dataarray['DEPTH'].values
# megaloop to split data into day x month format for each year and depth
split = {}
for yr in nY:
print(yr)
for dep in range(nD):
yr_selection = years == yr
split[str(yr) + '_' + str(int(depths[dep])) + 'm'] = \
organize_temperature_into_dataframe(
temp_dataarray[yr_selection,dep])
return split
split = split_by_year_and_depth(ds)
# create split with new data
split_new_data = split
split_new_data[list(split_new_data.keys())[-1]].at['Nov', 5] = 5
# %% -------------------------------------------------------------------
# Create a plotly json file for the web app
# Initialize variables to store the selected year and depth
# (This will be the default heatmap when first opened)
selected_year = '2012' # default year
selected_depth = '2m' # default depth
# Create the figure
fig = go.Figure()
# Add traces for all year and depth combinations
# traces = plot/graphical objects that makes up a figure
# Loop over each item in the dictionary called 'split'
for key, data in split.items():
# Determine if this particular heatmap should be initially visible
# It's visible only if the current key matches a predetermined year and depth
visible = key == f"{selected_year}_{selected_depth}"
# Split the key into year and depth components
# The key is expected to be in the format 'year_depthm', e.g., '2012_2m'
year, depth = key.split('_')
# Create a heatmap object using Plotly's go.Heatmap
heatmap = go.Heatmap(
z=data.values, # Matrix of values to be displayed in the heatmap
x=data.columns, # Labels or positions for the x-axis
y=data.index, # Labels or positions for the y-axis
colorscale='balance', # Color scale name for mapping values to colors
showscale=False, # Whether or not to show the color scale bar
zmin=-2, zmax=2, # Set the scale range for the heatmap colors
visible=visible, # Use the previously determined visibility
name=f"{year} - {depth}" # Name of the trace, used for legend entries
)
# Add the created heatmap to the existing figure
fig.add_trace(heatmap)
# Function to update the visibility of heatmaps based on selected year and depth
def create_visibility(selected_year, selected_depth):
# Returns a list of boolean values for each key in the 'split' dictionary
# True if the key matches the selected year and depth, False otherwise
return [k == f"{selected_year}_{selected_depth}" for k in split.keys()]
# Dropdown for Years
year_buttons = [{
"label": year, # Text to display on the dropdown button for each year
"method": "update", # The action to perform when a button is clicked
"args": [
{"visible": create_visibility(year, selected_depth)}, # Update the visibility of heatmaps
{"title": f"Heatmaps for Year: {year} and Depth: {selected_depth}"} # Update the chart title
]
} for year in sorted(set(k.split('_')[0] for k in split.keys()))] # List comprehension to generate a button for each unique year
# Dropdown for Depths
depth_buttons = [{
"label": depth, # Text to display on the dropdown button for each depth
"method": "update", # The action to perform when a button is clicked
"args": [
{"visible": create_visibility(selected_year, depth)}, # Update the visibility of heatmaps
{"title": f"Heatmaps for Year: {selected_year} and Depth: {depth}"} # Update the chart title
]
} for depth in sorted(set(k.split('_')[1] for k in split.keys()))] # List comprehension to generate a button for each unique depth
# Update layout with dual dropdowns
fig.update_layout(
plot_bgcolor='white', # Sets the plot background to white for better readability
paper_bgcolor='white', # Sets the overall figure background to white
updatemenus=[ # Configures the dropdown menus for user interactivity
{
"buttons": year_buttons, # Buttons created previously for selecting years
"direction": "down", # Dropdown expands downwards
"pad": {"r": 10, "t": 10}, # Padding around the dropdown
"showactive": True, # Highlights the active button
"x": 0.6, # X position of the dropdown (percentage of the total width)
"xanchor": "left", # Anchor the dropdown at this x position
"y": 1.09, # Y position of the dropdown (percentage above the plot area)
"yanchor": "top" # Anchor the dropdown at this y position
},
{
"buttons": depth_buttons, # Buttons created for selecting depths
"direction": "down",
"pad": {"r": 10, "t": 10},
"showactive": True,
"x": 0.7, # Slightly to the right of the year dropdown
"xanchor": "left",
"y": 1.09,
"yanchor": "top"
}
],
title=f"Temperature Anomalies for Year: {selected_year} and Depth: {selected_depth}",
xaxis=dict(tickangle=0), # Ensuring x-axis labels are horizontal
yaxis=dict(autorange='reversed') # Invert y-axis so higher values appear lower
)
# Additional updates to layout properties for axis settings
fig.update_layout(
xaxis=dict(
title="Day", # Label for the x-axis
tickmode='array', # Explicitly specify tick positions and labels
tickvals=list(range(1, len(data.columns) + 1)), # Positions for x-axis ticks
ticktext=data.columns, # Text labels for x-axis ticks
tickangle=0 # Keep x-axis labels horizontal
# range=[0, 31] # Optionally set the range of the x-axis
),
yaxis=dict(
title="Month", # Label for the y-axis
tickmode='array', # Explicitly specify tick positions and labels
tickvals=list(range(len(data.index))), # Positions for y-axis ticks
ticktext=data.index, # Text labels for y-axis ticks
# range=[0, 12] # Optionally set the range of the y-axis
),
font=dict(size=18) # Set the global font size for text elements
)
# Show figure
fig.show()
# Save the figure as an HTML file
# fig.write_html("MAI090_PercentilesHeatMap.html")
fig.write_json("MAI090_PercentilesHeatMap.json")
# %% -------------------------------------------------------------------
# Create a plotly json file for the web app
#
# (version with new data)
# Initialize variables to store the selected year and depth
# (This will be the default heatmap when first opened)
selected_year = '2012' # default year
selected_depth = '2m' # default depth
# Create the figure
fig = go.Figure()
# Add traces for all year and depth combinations
# traces = plot/graphical objects that makes up a figure
# Loop over each item in the dictionary called 'split'
for key, data in split_new_data.items():
# Determine if this particular heatmap should be initially visible
# It's visible only if the current key matches a predetermined year and depth
visible = key == f"{selected_year}_{selected_depth}"
# Split the key into year and depth components
# The key is expected to be in the format 'year_depthm', e.g., '2012_2m'
year, depth = key.split('_')
# Create a heatmap object using Plotly's go.Heatmap
heatmap = go.Heatmap(
z=data.values, # Matrix of values to be displayed in the heatmap
x=data.columns, # Labels or positions for the x-axis
y=data.index, # Labels or positions for the y-axis
colorscale='balance', # Color scale name for mapping values to colors
showscale=False, # Whether or not to show the color scale bar
zmin=-2, zmax=2, # Set the scale range for the heatmap colors
visible=visible, # Use the previously determined visibility
name=f"{year} - {depth}" # Name of the trace, used for legend entries
)
# Add the created heatmap to the existing figure
fig.add_trace(heatmap)
# Function to update the visibility of heatmaps based on selected year and depth
def create_visibility(selected_year, selected_depth):
# Returns a list of boolean values for each key in the 'split' dictionary
# True if the key matches the selected year and depth, False otherwise
return [k == f"{selected_year}_{selected_depth}" for k in split.keys()]
# Dropdown for Years
year_buttons = [{
"label": year, # Text to display on the dropdown button for each year
"method": "update", # The action to perform when a button is clicked
"args": [
{"visible": create_visibility(year, selected_depth)}, # Update the visibility of heatmaps
{"title": f"Heatmaps for Year: {year} and Depth: {selected_depth}"} # Update the chart title
]
} for year in sorted(set(k.split('_')[0] for k in split.keys()))] # List comprehension to generate a button for each unique year
# Dropdown for Depths
depth_buttons = [{
"label": depth, # Text to display on the dropdown button for each depth
"method": "update", # The action to perform when a button is clicked
"args": [
{"visible": create_visibility(selected_year, depth)}, # Update the visibility of heatmaps
{"title": f"Heatmaps for Year: {selected_year} and Depth: {depth}"} # Update the chart title
]
} for depth in sorted(set(k.split('_')[1] for k in split.keys()))] # List comprehension to generate a button for each unique depth
# Update layout with dual dropdowns
fig.update_layout(
plot_bgcolor='white', # Sets the plot background to white for better readability
paper_bgcolor='white', # Sets the overall figure background to white
updatemenus=[ # Configures the dropdown menus for user interactivity
{
"buttons": year_buttons, # Buttons created previously for selecting years
"direction": "down", # Dropdown expands downwards
"pad": {"r": 10, "t": 10}, # Padding around the dropdown
"showactive": True, # Highlights the active button
"x": 0.6, # X position of the dropdown (percentage of the total width)
"xanchor": "left", # Anchor the dropdown at this x position
"y": 1.09, # Y position of the dropdown (percentage above the plot area)
"yanchor": "top" # Anchor the dropdown at this y position
},
{
"buttons": depth_buttons, # Buttons created for selecting depths
"direction": "down",
"pad": {"r": 10, "t": 10},
"showactive": True,
"x": 0.7, # Slightly to the right of the year dropdown
"xanchor": "left",
"y": 1.09,
"yanchor": "top"
}
],
title=f"Temperature Anomalies for Year: {selected_year} and Depth: {selected_depth}",
xaxis=dict(tickangle=0), # Ensuring x-axis labels are horizontal
yaxis=dict(autorange='reversed') # Invert y-axis so higher values appear lower
)
# Additional updates to layout properties for axis settings
fig.update_layout(
xaxis=dict(
title="Day", # Label for the x-axis
tickmode='array', # Explicitly specify tick positions and labels
tickvals=list(range(1, len(data.columns) + 1)), # Positions for x-axis ticks
ticktext=data.columns, # Text labels for x-axis ticks
tickangle=0 # Keep x-axis labels horizontal
# range=[0, 31] # Optionally set the range of the x-axis
),
yaxis=dict(
title="Month", # Label for the y-axis
tickmode='array', # Explicitly specify tick positions and labels
tickvals=list(range(len(data.index))), # Positions for y-axis ticks
ticktext=data.index, # Text labels for y-axis ticks
# range=[0, 12] # Optionally set the range of the y-axis
),
font=dict(size=18) # Set the global font size for text elements
)
# Show figure
fig.show()
# Save the figure as an HTML file
fig.write_html("New_data/MAI090_PercentilesHeatMap.html")
fig.write_json("New_data/MAI090_PercentilesHeatMap.json")
# %%