mikkohei13

File unique identifier;Prediction by Naturalis;Probability by Naturalis;True or false by Naturalis;Prediction by iNat;Probability by iNat;True or false by iNat
_test_set_3/(Acanthosomatidae) Elasmucha grisea Parent Bug_8.jpg;Elasmucha grisea;0.894348;1;Elasmucha grisea;0.314679;1
_test_set_3/(Aphrophoridae) Aphrophora alni_3.jpg;Aphrophora alni;1;1;Aphrophora alni;0.410643;1
_test_set_3/(Cercopidae) Cercopis vulnerata_2.jpg;Cercopis vulnerata;1;1;Cercopis vulnerata;0.460223;1
_test_set_3/(Cicadellidae) Adarrus ocellaris_3.jpg;Errastunus ocellaris;0.973848;0;Speudotettix subfusculus;0.303513;0
_test_set_3/(Cicadellidae) Aguriahana stellulata_4.jpg;Aguriahana stellulata;0.999827;1;Aguriahana;0.433531;1
_test_set_3/(Cicadellidae) Arthaldeus pascuellus_4.jpg;Arthaldeus pascuellus;1;1;Graminella nigrifrons;0.347305;0
_test_set_3/(Cicadellidae) Athysanus argentarius_5.jpg;Athysanus argentarius;0.999749;1;Athysanus argentarius;0.491078;1
_test_set_3/(Cicadellidae) Batracomorphus irroratus_5.jpg;Iassus lanio;0.699501

mikkohei13

import json
from statsmodels.tsa.holtwinters import Holt

# Load the JSON data
with open("pyjamalude-years.json", "r") as file:
    data = json.load(file)

'''
Estimate future observation counts based on laji.fi data

mikkohei13

import json

def process_geojson_with_properties(input_filename, output_filename):
    # Load the JSON file
    with open(input_filename, "r") as file:
        data = json.load(file)

    # Extract all items under "results"
    results = data.get("results", [])

mikkohei13

import pandas as pd
import numpy as np
import re

# Mikko Heikkinen 2020, 2022, 2023

# Defines which columns to use. Order is not significant.
usecols = [
  'gbifID',

mikkohei13

# Made with ChatGPT/GPT-4

import requests
import pandas as pd
import time
import csv

def fetch_citation_count(doi):
    """
    Function to fetch the citation count for a given DOI using the OpenCitations API.

mikkohei13

# Made with ChatGPT GPT-4 2023-11-23
# Finds temporal outliers from a simple occurrence data file of a single species from FinBIF, with headers in English.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from shapely.geometry import Point
import geopandas as gpd

# Load the data

mikkohei13

# Made wth ChatGPT / GPT-4
# Finds spatial ouliers by clcularing SD of locations
# Input: FinBIF data of a single species in simple data format 

import pandas as pd
import numpy as np

# Load the data (Replace 'file_path' with your actual file path)
file_path = 'your_data_file.csv'  # Change this to your file path
bird_data = pd.read_csv(file_path, sep='\t')

mikkohei13

'''
Converts "Luken aineistokooste 4. lintuatlakseen" tsv file into FinBIF Data Bank secondary data format.
Mikko Heikkinen 2023-12-29
'''


import pandas as pd

# Load the file into a Pandas dataframe using tab as the delimiter. Keep "NA" as a value.
file_path = 'luke_atlasdata.txt'

mikkohei13

'''
Tool to compare observations from Tiira to atlas data, to see which observations could increase breeding indices.
MIT License
'''

# Source data - Tiira export saved as UTF-8 CSV file
tiira_filename = "./import/tiira.csv"

# Atlas square to compare to
atlas_square = "667:337"

mikkohei13

"""
Script to make conversions to a CSV tabular file containing bird species observations (occurrence records)
Updated for 2024 file format 

Data format sample:
id;pvm;aika;laji;lkm;ykj_p;ykj_i;PV-indeksi
1593921;01.02.2024;07:16;teeri;1;6890000;3410000;2
1593922;01.02.2024;08:04;metso;1;7070000;3380000;2
1593925;01.02.2024;08:17;metso;1;6980000;3230000;2
1593941;01.02.2024;10:39;teeri;80;7090000;3470000;2