neon
library(neonUtilities)
library(ggplot2)
foliar <- loadByProduct(dpID="DP1.10026.001", site="all",
package="expanded", check.size=F,
token=my_token)Finding available files
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Unpacking zip files using 1 cores.
Stacking table cfc_elementsSummary
Stacking table lig_externalSummary
Stacking table cfc_chlorophyllParameters
Stacking table cfc_chlorophyllSummary
Stacking table bgc_CNiso_externalSummary
Stacking operation across a single core.
Stacking table cfc_chemistrySubsampling
Stacking table cfc_chlorophyll
Stacking table cfc_elements
Stacking table cfc_fieldData
Stacking table cfc_lignin
Stacking table cfc_LMA
Stacking table cfc_shapefile
Stacking table vst_mappingandtagging
Stacking table cfc_carbonNitrogen
Copied the most recent publication of validation file to /stackedFiles
Copied the most recent publication of categoricalCodes file to /stackedFiles
Copied the most recent publication of variable definition file to /stackedFiles
Finished: Stacked 14 data tables and 4 metadata tables!
Stacking took 6.924111 secsnames(foliar) [1] "bgc_CNiso_externalSummary" "categoricalCodes_10026"
[3] "cfc_carbonNitrogen" "cfc_chemistrySubsampling"
[5] "cfc_chlorophyll" "cfc_chlorophyllParameters"
[7] "cfc_chlorophyllSummary" "cfc_elements"
[9] "cfc_elementsSummary" "cfc_fieldData"
[11] "cfc_lignin" "cfc_LMA"
[13] "cfc_shapefile" "issueLog_10026"
[15] "lig_externalSummary" "readme_10026"
[17] "validation_10026" "variables_10026"
[19] "vst_mappingandtagging" ggplot(foliar$cfc_carbonNitrogen, aes(x = carbonPercent, y = CNratio)) +
geom_point() +
labs(title = "Carbon to Nitrogen Ratio vs Carbon Concentration",
x = "Carbon Concentration (percent)",
y = "Carbon to Nitrogen Ratio") +
theme_minimal()Warning: Removed 96 rows containing missing values (`geom_point()`).
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from neon_api_tools.api_tools import neon_api_tools
# Set your NEON API token
my_token = "your_neon_api_token_here"
# Instantiate the API tools object
neon_tools = neon_api_tools()
# Download the NEON foliar data
foliar = neon_tools.loadByProduct(dpID="DP1.10026.001", site="all",
package="expanded", check_size=False,
token=my_token)
# Get the foliar dataframe
foliar_df = foliar['cfc_carbonNitrogen']
# Plot the carbon to nitrogen ratio against the carbon concentration
fig, ax = plt.subplots()
ax.scatter(foliar_df['carbonPercent'], foliar_df['CNratio'])
ax.set_xlabel("Carbon Concentration (percent)")
ax.set_ylabel("Carbon to Nitrogen Ratio")
ax.set_title("Carbon to Nitrogen Ratio vs Carbon Concentration")
plt.show()