Karsten Müller kmunve

## regobslib_obs_example.py
import datetime as dt
from regobslib import Connection, SnowRegion, AvalancheObs, AvalancheActivity, SnowRegistration, Incident
import json
from pathlib import Path
from dataclasses import is_dataclass, asdict


def _to_serializable(obj):
    """Best-effort conversion of regobslib objects to JSON-serializable types."""
    # Primitives pass through

## NAWS_regions.geojson

      
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                kmunve
                / NAWS_regions.geojson
            
            
              Created
              February 27, 2024 08:03
            
              
                The forecasting region used by the Norwegian Avalanche Warning Service.
              
          
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## NAWS_svalbard_regions.geojson

      
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                kmunve
                / NAWS_svalbard_regions.geojson
            
            
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              February 27, 2024 08:02
            
              
                The forecasting region on Svalbard used by the Norwegian Avalanche Warning Service.
              
          
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## make_simple_gif.py
import imageio
from pathlib import Path

# Define the directory containing the PNG files
# Adjust this path as necessary. Here, it's set to the current directory.
directory_path = Path(r'.')

# Step 1: Collect all PNG filenames using pathlib
filenames = list(directory_path.glob('*_event.png'))

## regobslib_weather_example.py
# check regobslib for more information: https://github.com/NVE/regobslib
from regobslib import *
import datetime as dt
import pandas as pd
import matplotlib.pylab as plt


def get_data():
    regions = [
        SnowRegion.HELGELAND,

## ElevationDataGeiteskred.csv
X;Y;Z;M
44797.83400349226;6818912.225566525;951.3221999999951;0
44796.98600351438;6818911.695566539;950.6389999999956;0.9980999999970663
44796.13800353557;6818911.165566552;949.9324000000051;1.9962999999988824
44795.29000355676;6818910.635566566;949.1913999999961;2.9943999999959487
44794.44200357795;6818910.10556658;948.5329999999958;3.992599999997765
44793.59400360007;6818909.575566594;947.899900000004;4.990699999994831
44792.74600362126;6818909.045566607;947.2617000000027;5.988899999996647
44791.89800364245;6818908.515566621;946.6616000000067;6.986999999993714
44791.05000366364;6818907.985566631;946.0629999999946;7.98519999999553

## app.py
from flask import Flask, render_template

app = Flask(__name__)

@app.route('/')
@app.route('/index')
def index(chartID = 'chart_ID', chart_type = 'bar', chart_height = 350):
	chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
	series = [{"name": 'Label1', "data": [1,2,3]}, {"name": 'Label2', "data": [4, 5, 6]}]
	title = {"text": 'My Title'}

## flask_unveil_server.py

# you also need to download this file: https://github.com/luis-almeida/unveil/blob/master/jquery.unveil.js
# and place it in the same directory as this python file with the name "jquery.unveil.js"

import os
from io import BytesIO

from flask import Flask, Response, request, abort, render_template_string, send_from_directory
from PIL import Image

## loopfill_dataframe.py
_df = pd.DataFrame(
        columns=['aval_type', 'trigger', 'DestructiveSizeName', 'date', 'elevation', 'TerrainStartZoneName', 'Latitude', 'Longitude', 'ForecastRegionName',
                 'ForecastRegionTID', 'nick', 'RegID'])
for av in avals:
    _df = _df.append({'aval_type': av.AvalancheName,
                        'trigger': av.AvalancheTriggerName,
                        'DestructiveSizeName': av.DestructiveSizeName,
                        'date': av.DtObsTime.strftime('%Y-%m-%d'),
                        'elevation': av.Height,
                        'TerrainStartZoneName': av.TerrainStartZoneName,

## rhk_training.json
{
      events: [
        {
          title: "MKH",
          start: "2019-06-19"
        },
        {
          title: "SBH",
          start: "2019-06-19"
        },
	import datetime as dt
	from regobslib import Connection, SnowRegion, AvalancheObs, AvalancheActivity, SnowRegistration, Incident
	import json
	from pathlib import Path
	from dataclasses import is_dataclass, asdict


	def _to_serializable(obj):
	"""Best-effort conversion of regobslib objects to JSON-serializable types."""
	# Primitives pass through
	import imageio
	from pathlib import Path

	# Define the directory containing the PNG files
	# Adjust this path as necessary. Here, it's set to the current directory.
	directory_path = Path(r'.')

	# Step 1: Collect all PNG filenames using pathlib
	filenames = list(directory_path.glob('*_event.png'))
	# check regobslib for more information: https://github.com/NVE/regobslib
	from regobslib import *
	import datetime as dt
	import pandas as pd
	import matplotlib.pylab as plt


	def get_data():
	regions = [
	SnowRegion.HELGELAND,
	X;Y;Z;M
	44797.83400349226;6818912.225566525;951.3221999999951;0
	44796.98600351438;6818911.695566539;950.6389999999956;0.9980999999970663
	44796.13800353557;6818911.165566552;949.9324000000051;1.9962999999988824
	44795.29000355676;6818910.635566566;949.1913999999961;2.9943999999959487
	44794.44200357795;6818910.10556658;948.5329999999958;3.992599999997765
	44793.59400360007;6818909.575566594;947.899900000004;4.990699999994831
	44792.74600362126;6818909.045566607;947.2617000000027;5.988899999996647
	44791.89800364245;6818908.515566621;946.6616000000067;6.986999999993714
	44791.05000366364;6818907.985566631;946.0629999999946;7.98519999999553
	from flask import Flask, render_template

	app = Flask(__name__)

	@app.route('/')
	@app.route('/index')
	def index(chartID = 'chart_ID', chart_type = 'bar', chart_height = 350):
	chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
	series = [{"name": 'Label1', "data": [1,2,3]}, {"name": 'Label2', "data": [4, 5, 6]}]
	title = {"text": 'My Title'}

	# you also need to download this file: https://github.com/luis-almeida/unveil/blob/master/jquery.unveil.js
	# and place it in the same directory as this python file with the name "jquery.unveil.js"

	import os
	from io import BytesIO

	from flask import Flask, Response, request, abort, render_template_string, send_from_directory
	from PIL import Image
	_df = pd.DataFrame(
	columns=['aval_type', 'trigger', 'DestructiveSizeName', 'date', 'elevation', 'TerrainStartZoneName', 'Latitude', 'Longitude', 'ForecastRegionName',
	'ForecastRegionTID', 'nick', 'RegID'])
	for av in avals:
	_df = _df.append({'aval_type': av.AvalancheName,
	'trigger': av.AvalancheTriggerName,
	'DestructiveSizeName': av.DestructiveSizeName,
	'date': av.DtObsTime.strftime('%Y-%m-%d'),
	'elevation': av.Height,
	'TerrainStartZoneName': av.TerrainStartZoneName,
	{
	events: [
	{
	title: "MKH",
	start: "2019-06-19"
	},
	{
	title: "SBH",
	start: "2019-06-19"
	},