\
\
\
'
}
$('#legendSectionKeywords').html(keywordsHtml);
}
nbVisibleKeywords = 0;
for (kw=0; kw
');
}
nbVisibleKeywords++;
} else {
$('#keywordLineW'+ kw).css("height", "0px");
$('#keywordLineV'+ kw).css("height", "0px");
$('#keywordLine' + kw).css("height", "0px");
$('#keywordLine' + kw).css("padding-bottom", "0px");
$('#keywordLineV'+ kw).css("visibility", "hidden");
}
}
if (nbVisibleKeywords==0) {
$('#legendSectionKeywords').removeClass('active');
}
}
```
--------------------------------
### Map Interaction and Geolocation Handling (JavaScript)
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
Handles user interactions with the map, including focusing search input, registering autocomplete callbacks, handling GPS location found and error events, and controlling map zoom. It also initiates a GPS location watch.
```javascript
$("#searchInput").focusout(tryRetractSarch);
var autocomplete = new kt.OsmNamesAutocomplete('searchInput', 'apps/search.php?q=', '');
autocomplete.registerCallback(function(item) {
setPosition(item['lat'], item['lon'], true);
});
function gpsLocationFound(e) {
setPosition(e.latlng.lat, e.latlng.lng, true);
$("#mapControlButtonGpsIcon").removeClass('searching');
$("#mapControlButtonGpsIcon").attr('src','imgs/icons/gps.svg');
$("#mapControlButtonGps").removeClass('disabled');
}
function gpsLocationError(e) {
$("#mapControlButtonGpsIcon").removeClass('searching');
$("#mapControlButtonGpsIcon").attr('src','imgs/icons/gps-disabled.svg');
$("#mapControlButtonGps").addClass('disabled');
}
map.on('locationfound', gpsLocationFound)
.on('locationerror', gpsLocationError);
function mapZoom(direction) {
map.setZoom(Math.max(map.getZoom() + direction, 0));
}
function mapGps() {
$("#mapControlButtonGpsIcon").addClass('searching');
map.locate({ enableHighAccuracy: true, watch: false, setView: false });
}
```
--------------------------------
### Calculate and Visualize Population Probability (Python)
Source: https://github.com/antoinemeler/paraglidable/blob/master/neural_network/docs/documentation.ipynb
This Python code calculates the probability of at least one person going to fly based on flyability and population. It generates a 3D surface plot to visualize this relationship. Dependencies include numpy, matplotlib, and calendar.
```Python
r = 200
in_proba = np.array([[1.*(float(x)/float(r-1))**2. for y in range(r)] for x in range(r)], np.float)
population = np.array([[4.*(float(y)/float(r-1))**2. for y in range(r)] for x in range(r)], np.float)
out_proba = np.array([[in_proba[x,y]*population[x,y] if population[x,y]<=1. else 1. - (1. - in_proba[x,y])**population[x,y] for y in range(r)] for x in range(r)], np.float)
logging.getLogger().disabled = True
ax = plt.figure(figsize=(9,5)).add_subplot(111, projection='3d')
ax.view_init(35, 210)
ax.set_xlim3d(0., np.amax(in_proba))
ax.set_ylim3d(0., np.amax(population))
ax.set_xlabel('Input probability')
ax.set_ylabel('Population value')
ax.set_zlabel('Output probability')
ax.plot_surface(in_proba, population, out_proba, cmap=matplotlib.cm.jet)
for p in [0.5*p for p in range(0,2)]:
ax.plot([1.*(float(x)/float(r-1))**2. for x in range(r)], [p for x in range(r)], [p*(float(x)/float(r-1))**2. for x in range(r)], alpha=1, color='black', linewidth=1.5, zorder=10)
for p in [0.5*p for p in range(2,9)]:
ax.plot([1.*(float(x)/float(r-1))**2. for x in range(r)], [p for x in range(r)], [1. - (1. - 1.*(float(x)/float(r-1))**2.)**p for x in range(r)], alpha=1, color='black', linewidth=1.5, zorder=10)
logging.getLogger().disabled = False
```
--------------------------------
### JavaScript Mode Switching Function
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
This function, `switchMode`, handles transitions between different application modes ('main', 'api', 'analysis'). It updates navigation bars, map tiles, legend visibility, zoom levels, date pickers, and API markers based on the selected mode. It also includes logic for handling date changes and removing specific map layers.
```javascript
function switchMode(strMode) { idsNavigationBar = { 'main': 'mainNavigationBar', 'api': 'apiNavigationBar', 'analysis': 'analysisNavigationBar' }; oldMode = g_mode; if (strMode == 'main' || strMode == 'api' || strMode == 'analysis') { if ( (strMode != 'main' && switchNavigationBar(idsNavigationBar[strMode])) ) { g_mode = strMode; } else { switchNavigationBar(idsNavigationBar['main']); g_mode = 'main'; } // tiles if (g_mode == 'api' || showHideColorsVal == 0) { applyShowHideMapColors(false); } else { applyShowHideMapColors(true); } // legend hideShowLegend(g_mode=='main' || g_mode=='analysis', g_mode=='analysis', g_mode=='analysis'); // maxNativeZoom if (g_mode == 'analysis') { var maxAnlLevel = 6; paraglidableTiles.options.maxNativeZoom = L.Browser.retina ? maxAnlLevel-1 : maxAnlLevel; map.setZoom(L.Browser.retina ? maxAnlLevel-1 : maxAnlLevel); } else { paraglidableTiles.options.maxNativeZoom = g_switchZoom-1; } // prevNextDayContainer if (g_mode == 'main' || g_mode == 'analysis') { $("#prevNextDayContainer").css("visibility", "visible"); updatePrevNextButtons(); } else { $("#prevNextDayContainer").css("visibility", "hidden"); } // API markers if (g_mode != 'api') removeAllApiMarkers(); if (g_mode != 'analysis') { if (g_flightsLayer != null) { if (map.hasLayer(g_flightsLayer)) map.removeLayer(g_flightsLayer); } } // Calendar if (g_mode == 'analysis') fillAnlDatepickerIfNeeded(); // date if (g_mode == 'analysis') { changeDate('2013-05-13'); } else if (oldMode=='analysis') { changeDate(moment().format("YYYY-MM-DD")); } } }
```
--------------------------------
### JavaScript: Handle Map Click Events
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
This function, `mapClick`, determines the action to take when the map is clicked based on the current application mode ('main', 'api', or 'analysis'). It calls other functions like `setPosition` or `addApiMarker` accordingly. The map's click event listener is then set to use this function.
```javascript
function mapClick(lat, lon) {
hideAllModals();
if (g_mode == 'main') {
setPosition(lat, lon, false);
} else if (g_mode == 'api') {
addApiMarker(lat, lon);
} else if (g_mode == 'analysis') {
setPosition(lat, lon, false, false);
}
}
map.on('click', function(e) {mapClick(e.latlng.lat, e.latlng.lng);});
```
--------------------------------
### Desktop Mode Cookie Management (JavaScript)
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
This script checks for and sets a cookie to manage desktop mode preferences. It appends a style to hide the mobile version popup if desktop mode is enabled. It is designed to run client-side in a web browser.
```javascript
if (getQueryVariable('forceDesktopMode') == '1' || getCookie('forceDesktopMode') == '1') { $('html > head').append($('')); setCookie('forceDesktopMode', '1'); } function stopAskingMobileVersion() { setCookie('forceDesktopMode', '1'); $('#popupMobileVersion').css('display','none'); }
```
--------------------------------
### Count and Plot Flights by Time and Altitude (Python)
Source: https://github.com/antoinemeler/paraglidable/blob/master/neural_network/README.md
This script counts paragliding flights by year, month, day of the week, and altitude using collections.Counter. It then generates bar plots to visualize these counts. The script iterates through flight data, aggregates counts, and uses matplotlib for plotting. Dependencies include 'collections', 'matplotlib.pyplot', and 'parser'.
```python
counter_flights_by_year, counter_flights_by_month, counter_flights_by_alt, counter_flights_by_dow = collections.Counter(), collections.Counter(), collections.Counter(), collections.Counter()
for cell_day in flights_by_cell_day:
for flight in cell_day:
counter_flights_by_alt[FlightsData.kAltitude(flight[1][5])] += 1
try:
counter_flights_by_year [cell_day[0][0][0:4]] += len(cell_day) # counter[year] += nb_flights
counter_flights_by_month[cell_day[0][0][5:7]] += len(cell_day) # counter[month] += nb_flights
counter_flights_by_dow [parser.parse(cell_day[0][0]).strftime("%a")] += len(cell_day) # counter[dow] += nb_flights
except IndexError:
pass
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(14, 10))
years, months, dow = sorted(list(counter_days)), sorted(list(counter_months)), ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
axes[0,0].set_title("nb days with available weather data")
axes[0,0].bar(years, [counter_days[y] for y in years], color='#f07000')
axes[0,1].set_title("nb flights in available days, by year")
axes[0,1].bar(years, [counter_flights_by_year[y] for y in years])
axes[1,0].set_title("nb flights per days, by year")
axes[1,0].bar(years, [counter_flights_by_year[y]/counter_days[y] for y in years])
axes[1,1].set_title("nb flights per days, by month")
axes[1,1].bar(months, [counter_flights_by_month[m]/counter_months[m] for m in months])
axes[2,0].set_title("nb flights by day of week")
axes[2,0].bar(dow, [counter_flights_by_dow[d] for d in dow])
print("Nb flights by day of week:", [counter_flights_by_dow[d] for d in dow])
# Flights by altitude
axes[2,1].set_title("nb flights by altitude level")
nb_alts = max(counter_flights_by_alt.keys())+1
axes[2,1].barh(["%d"%alt for alt in range(nb_alts)], [counter_flights_by_alt[alt] for alt in range(nb_alts)])
for alt in range(nb_alts):
axes[2,1].text(counter_flights_by_alt[alt], alt-0.06, str(counter_flights_by_alt[alt]), fontsize=12)
axes[2,1].set_xlim(0, 1.125*max(counter_flights_by_alt.values()))
fig.tight_layout()
```
--------------------------------
### Plot Flights by XC Score per Month (Python)
Source: https://github.com/antoinemeler/paraglidable/blob/master/neural_network/README.md
This script generates a plot showing the number of flights for each XC score bin, broken down by month. It processes flight data to categorize flights by month and XC score, then uses numpy and matplotlib to create a line plot. Dependencies include 'numpy', 'calendar', and 'matplotlib.pyplot'.
```python
max_score = 75
pts = [[] for m in range(0, 12)]
for cell_day in flights_by_cell_day:
try:
pts[int(cell_day[0][0][5:7])-1] += [f[1][0] for f in cell_day]
except IndexError:
pass
plt.figure(figsize=(12, 5))
plt.title("Number of flights by XC score for each month")
for m in range(12):
plt.plot(np.histogram(pts[m], bins=max_score, range=(0, max_score))[0], label=calendar.month_name[m+1])
plt.xlim(0., max_score-1)
plt.ylim(ymin=0.)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
plt.show()
```
--------------------------------
### Navigate Through Dates - JavaScript
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
Provides functionality to move between dates, either forward or backward by a specified number of days. It checks if the target date is selectable before attempting to change the date. Dependencies include moment.js.
```javascript
function moveDay(deltaDay) { var newStrDate = moment(g_strDate, "YYYY-MM-DD").add(deltaDay, 'days').format("YYYY-MM-DD"); if (isSelectableDate(newStrDate)) changeDate(newStrDate); }
```
--------------------------------
### Contact Form Submission (JavaScript/jQuery)
Source: https://github.com/antoinemeler/paraglidable/blob/master/www/index.html
This script handles the submission of a contact form using an AJAX POST request. It sends the user's name, email, and message to a server-side script for processing. It provides visual feedback on success or failure.
```javascript
$('#send-message').click(function(){ var message_name = $("#message-name").val(); var message_email = $("#message-email").val(); var message_text = $("#message-text").val(); $.post( '/apps/sendMessage.php', { name: message_name, email: message_email, text: message_text }, function(data) { if(data == '1') { $("#send-message").val('✓'); } else { $("#send-message").val("Sorry there was en error, your e-mail could not be sent."); } }, 'text' ); });
```
--------------------------------
### Visualize Flight Score Distribution by Month (Python)
Source: https://github.com/antoinemeler/paraglidable/blob/master/neural_network/docs/documentation.ipynb
This script visualizes the distribution of flight scores for each month. It processes flight data to group flights by month and then plots the histogram of flight scores (up to a maximum score of 75) for each month using matplotlib and numpy. It assumes flight data is available in 'flights_by_cell_day' and uses the 'calendar' module for month names.
```python
import matplotlib.pyplot as plt
import numpy as np
import calendar
# Assuming flights_by_cell_day is a pre-defined list of flight data
max_score = 75
pts = [[] for m in range(0, 12)]
for cell_day in flights_by_cell_day:
try:
# Assuming cell_day[0][0] contains a date string and cell_day contains flight data
month_index = int(cell_day[0][0][5:7])-1
if 0 <= month_index < 12:
pts[month_index] += [f[1][0] for f in cell_day if len(f) > 1 and len(f[1]) > 0]
except (IndexError, ValueError):
pass
plt.figure(figsize=(12, 5))
plt.title("Number of flights by XC score for each month")
for m in range(12):
# np.histogram returns counts and bin edges, we only need counts
counts, _ = np.histogram(pts[m], bins=max_score, range=(0, max_score))
plt.plot(counts, label=calendar.month_name[m+1])
plt.xlim(0., max_score-1)
plt.ylim(ymin=0.)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
plt.show()
```