Add files via upload

ipynb/BikeCode.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Reading Data: `segments`, `places`, and `tiles`\n",
+    "# Reading Data: `segments` and `tiles`\n",
     "\n",
     "I picked some representative climbing segments ([`bikesegments.csv`](bikesegments.csv)) with the segment length in miles and climb in feet, along with several of my times on the segment. A line like\n",
     "\n",
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,31 +146,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "NameError",
-     "evalue": "name 'parse_segments' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m segments \u001b[38;5;241m=\u001b[39m \u001b[43mparse_segments\u001b[49m(\u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbikesegments.csv\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m      3\u001b[0m places   \u001b[38;5;241m=\u001b[39m drop_index(pd\u001b[38;5;241m.\u001b[39mread_table(\u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbikeplaceshort.csv\u001b[39m\u001b[38;5;124m'\u001b[39m), sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m, comment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m      5\u001b[0m tiles \u001b[38;5;241m=\u001b[39m drop_index(pd\u001b[38;5;241m.\u001b[39mDataFrame(columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate square cluster total comment\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39msplit(), data\u001b[38;5;241m=\u001b[39m[\n\u001b[1;32m      6\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m09/21/2024\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m1394\u001b[39m, \u001b[38;5;241m3496\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMichael J. Fox ride in Sonoma!12470434052\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      7\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m04/28/2024\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m1275\u001b[39m, \u001b[38;5;241m3382\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLivermore!11287081291\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m09/08/2022\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m11\u001b[39m,  \u001b[38;5;241m300\u001b[39m, \u001b[38;5;241m2487\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFirst started tracking tiles\u001b[39m\u001b[38;5;124m'\u001b[39m)])\n\u001b[1;32m     18\u001b[0m     )\u001b[38;5;241m.\u001b[39mstyle\u001b[38;5;241m.\u001b[39mformat({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcomment\u001b[39m\u001b[38;5;124m'\u001b[39m: make_clickable, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m: link_date})\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'parse_segments' is not defined"
-     ]
-    }
-   ],
    "source": [
     "segments = parse_segments(open('bikesegments.csv'))\n",
     "\n",
-    "places   = drop_index(pd.read_table(open('bikeplaceshort.csv'), sep=',', comment='#'))\n",
-    "\n",
     "tiles = drop_index(pd.DataFrame(columns='date square cluster total comment'.split(), data=[\n",
+    "    ('01/01/2025', 14, 1395, 3520, 'Start of 2025'),\n",
     "    ('09/21/2024', 14, 1394, 3496, 'Michael J. Fox ride in Sonoma!12470434052'),\n",
     "    ('04/28/2024', 14, 1275, 3382, 'Livermore!11287081291'),\n",
     "    ('02/25/2024', 14, 1196, 3279, 'Expanding through Santa Cruz and to the South!10838162005'),\n",
-    "    ('01/01/2024', 14, 1056, 3105, 'Start of this year'),\n",
+    "    ('01/01/2024', 14, 1056, 3105, 'Start of 2024'),\n",
     "    ('12/08/2023', 14, 1042, 3084, 'Benicia ride connects East Bay and Napa clusters!10350071201'),\n",
     "    ('11/05/2023', 14,  932, 2914, 'Alum Rock ride gets 14x14 max square!8850905872'),\n",
     "    ('06/30/2023', 13,  689, 2640, 'Rides in east Bay fill in holes!9298603815'),\n",
@@ -191,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,15 +223,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "def mapl(f, *values): return list(map(f, *values))\n",
     "\n",
-    "def wandering(places=places, by='pct'):\n",
+    "places   = drop_index(pd.read_table(open('bikeplaceshort.csv'), sep=',', comment='#'))\n",
-    "    \"All those who wander are not lost.\" # Also try by=['cat', 'pct']\n",
+    "\n",
-    "    F = drop_index(places.sort_values(by=by, ascending=False))\n",
+    "def wandrer(places=places, by=['pct', 'name'], ascending=[False, True], county=None):\n",
+    "    \"All those who wander are not lost.\" # Also try by=['county', 'pct']\n",
+    "    if county:\n",
+    "        places = places[places.county == county]\n",
+    "    F = drop_index(places.sort_values(by=by, ascending=ascending))\n",
     "    pd.set_option('display.max_rows', None)\n",
     "    return pd.DataFrame(\n",
     "        {'name':   F['name'],\n",
@@ -285,51 +276,64 @@
     "            else f'{round(x):,d}' if x > 10 \n",
     "            else f'{x:.1f}')\n",
     "\n",
-    "small_places = wandering(places[places['county'] != '---'])\n",
+    "other_places = places[~places.county.isin(['---', 'SMC', 'SCC', 'SFC', 'ALA'])]"
-    "big_places   = wandering(places[places['county'] == '---']).drop(columns='county')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Pareto Front  "
+    "# SMC / SCC Leaders "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def make_leaders(data):\n",
+    "def make_leaders(raw_data):\n",
     "    \"\"\"Make a dataframe of leaders in two counties.\"\"\"\n",
-    "    leaders = pd.DataFrame(data, columns=['Name', 'Initials', 'SMC %', 'SCC %'])\n",
+    "    data = [(name, SMp, SCp, *county_miles(SMp, SCp), round((SMp * SCp) ** 0.5, 2), round((SMp + SCp) / 2, 2), initials(name))\n",
-    "    leaders['SMC miles'] = [round(2814 * d[2] / 100) for d in data]\n",
+    "            for (name, SMp, SCp) in raw_data]\n",
-    "    leaders['SCC miles'] = [round(7569 * d[3] / 100) for d in data]\n",
+    "    leaders = pd.DataFrame(data, columns=[\n",
-    "    leaders['Total miles'] = leaders['SMC miles'] + leaders['SCC miles']\n",
+    "        'Name', 'SMC %', 'SCC %', 'SMC miles', 'SCC miles', 'Total miles', 'GeoMean %', 'Mean %', 'Initials'])\n",
-    "    leaders['Avg %'] = (leaders['SMC %'] + leaders['SCC %']) / 2\n",
+    "    return drop_index(leaders)\n",
-    "    return drop_index(leaders.sort_values('Avg %', ascending=False))\n",
     "\n",
-    "leaders = make_leaders([ # Data as of Aug 19, 2024 (Name, Initials, SMC, SCC)\n",
+    "def county_miles(SMp, SCp) -> list:\n",
-    "    ('Megan Gardner',  'MG', 99.31, 19.36),\n",
+    "    SMmiles = round(2827.3 * SMp / 100)\n",
-    "    #('Matthew Ring',  'MR', 82.51,  2.43), # dominated by MG\n",
+    "    SCmiles = round(7688.7 * SCp / 100)\n",
-    "    ('Barry Mann',     'BM', 77.91, 30.70), \n",
+    "    return [SMmiles, SCmiles, SMmiles + SCmiles] \n",
-    "    ('Peter Norvig',   'PN', 67.14, 35.45),\n",
-    "    ('Brian Feinberg', 'BF', 36.76, 45.30),\n",
-    "    ('Jason Molenda',  'JM',   7.6, 56.09)  \n",
-    "    #('Jim Brooks',    'JB',   6.2, 49.51), # dominated by JM\n",
-    "    ])\n",
     "\n",
-    "def pareto_front(leaders):\n",
+    "def initials(name: str) -> str:\n",
-    "    ax = leaders.plot('SMC %', 'SCC %',  kind='scatter')\n",
+    "    \"\"\"First and last initials.\"\"\"\n",
+    "    return name[0] + name.split()[-1][0]\n",
+    "\n",
+    "def plot_leaders(leaders, by='Mean %'):\n",
+    "    leaders = leaders.sort_values(by=by, ascending=False)\n",
+    "    ax = leaders.plot('SMC %', 'SCC %',  kind='scatter', marker='D')\n",
     "    front = sorted((x, y) for i, (_, _, x, y, *_) in leaders.iterrows())\n",
-    "    ax.plot(*zip(*front), ':'); \"ax.axis('square')\"; grid()\n",
+    "    \"ax.axis('square')\"; grid()\n",
     "    ax.set_xlabel('San Mateo County %')\n",
     "    ax.set_ylabel('Santa Clara County %')\n",
-    "    for i, (name, initials, x, y, *_) in leaders.iterrows():\n",
+    "    for i, (name, x, y, *_) in leaders.iterrows():\n",
-    "        ax.text(x - 2, y + 2, initials)\n",
+    "        ax.text(x + 0.7, y - 0.2, initials(name))\n",
-    "    return leaders"
+    "    return leaders\n",
+    "\n",
+    "\n",
+    "leaders = make_leaders([ # Data as of Mar 24, 2025 (Name, Initials, SMC, SCC)\n",
+    "    ('Megan Gardner',  99.94, 25.21),\n",
+    "    ('Matthew Ring',   83.16,  2.48),\n",
+    "    ('Peter Norvig',   78.80, 38.83),\n",
+    "    ('Barry Mann',     78.27, 31.09), \n",
+    "    ('Catherine Kircos', 54.47, 16.04),\n",
+    "    ('Elliot Hoff',    52.89,  6.13),\n",
+    "    ('Greogory P. Smith', 51.37, 23.27),\n",
+    "    ('Brian Feinberg', 36.76, 48.22),\n",
+    "    ('Chris Okeefe',   32.17, 48.30),\n",
+    "    ('Jason Molenda',   7.60, 56.15),\n",
+    "    ('Jim Brooks',      6.17, 53.51),\n",
+    "    ])"
    ]
   },
   {
@@ -341,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.13.1"
   },
   "toc-autonumbering": true
  },

ipynb/bikeplaceshort.csv

@@ -3,25 +3,25 @@ name,miles,county,pct
 #,,, San Mateo County = SMC
 #,,,
 Atherton,56.3,SMC,100
-Bay Area Ridge Trail,395.6,SMC,29.02
+Bay Area Ridge Trail,395.6,SMC,29.11
 Belmont,98.1,SMC,90.43
-Brisbane,40.9,SMC,49.62
+Brisbane,40.9,SMC,93.60
 Broadmoor,8.8,SMC,38.26
 Burleigh Murray Park,2.1,SMC,95.08
-Burlingame,88.4,SMC,56.50
+Burlingame,88.4,SMC,93.72
-Burlingame Hills,6,SMC,71.45
+Burlingame Hills,6,SMC,92.38
 Butano State Park,15.2,SMC,29.28
 Coal Creek Preserve,3.9,SMC,68.74
-Colma,13.7,SMC,64.79
+Colma,13.7,SMC,92.62
-Daly City,148.1,SMC,28.42
+Daly City,148.1,SMC,28.75
-East Palo Alto,48.3,SMC,99.97
+East Palo Alto,48.3,SMC,100
 El Corte de Madera OSP,34.54,SMC,26.85
 El Granada,49.2,SMC,53.71
 Emerald Lake Hills,24.6,SMC,100
 Foster City,150,SMC,99.14
 Half Moon Bay,68,SMC,50.56
 Half Moon Bay State Beach,4.4,SMC,65.89
-Hillsborough,85.3,SMC,53.12
+Hillsborough,85.3,SMC,90.52
 Kensington Square,0.6,SMC,100
 Ladera,8.1,SMC,100
 Long Ridge Preserve,11.0,SMC,45.10
@@ -29,7 +29,7 @@ Los Trancos OSP,0.3,SMC,100
 Los Trancos Woods,5.3,SMC,100
 Menlo Oaks,3.5,SMC,100
 Menlo Park,139.5,SMC,99.97
-Millbrae,67.8,SMC,52.05
+Millbrae,67.8,SMC,92.31
 Montara,27.8,SMC,61.15
 Moss Beach,19.7,SMC,59.66
 North Fair Oaks,26.7,SMC,100
@@ -40,52 +40,53 @@ Portola Valley,48.2,SMC,100
 Purisima Creek Preserve,16.5,SMC,39.09
 Redwood City,240.5,SMC,99.66
 Russian Ridge Preserve,12.2,SMC,59.66
-San Bruno,114,SMC,36.42
+San Bruno,114,SMC,90.19
-San Carlos,99,SMC,99.51
+San Carlos,99,SMC,99.52
-San Mateo,256,SMC,54.64
+San Mateo,256,SMC,92.27
-San Mateo Highlands,18,SMC,92.36
+San Mateo Highlands,18,SMC,92.43
 Sequoia Tract,11,SMC,100
-Sky Londa,10.42,SMC,98.49
+Sky Londa,10.42,SMC,100
 Skyline Ridge OSP,0.8,SMC,75.41
-South San Francisco,185.3,SMC,30.97
+South San Francisco,185.3,SMC,40.36
 West Menlo Park,11.2,SMC,100
 Windy Hill Preserve,4.1,SMC,100
-Woodside,75.2,SMC,99.08
+Woodside,75.2,SMC,100
 #,,,
 #,,, Santa Clara County = SCC
 #,,,
 Branham,44,SCC,33.21
 Campbell,119,SCC,30.48
 Communications Hill,27.8,SCC,35.31
-Cupertino,172,SCC,76.32
+Cupertino,172,SCC,90.72
-Edenvale,30,SCC,47.65
+Edenvale,30,SCC,47.80
 Foothills OS Preserve,1.1,SCC,100
 Gardner,23.4,SCC,46.93
 Gilroy,188.9,SCC,26.88
 Los Altos,138.2,SCC,99.87
-Los Altos Hills,91.3,SCC,99.46
+Los Altos Hills,91.3,SCC,99.38
-Los Gatos,148,SCC,52.05
+Los Gatos,148,SCC,59.19
 Loyola,18.3,SCC,99.94
 Milpitas,224,SCC,37.36
-Monte Sereno,20.4,SCC,53.04
+Monte Sereno,20.4,SCC,92.18
-Mountain View,208.1,SCC,99.49
+Morgan Hill,198,SCC,26.06
+Mountain View,208.1,SCC,99.19
 Palo Alto,297.2,SCC,99.52
 Parkview,42.5,SCC,34.07
-San Francisco Bay Trail,260.8,SCC,67.77
+San Francisco Bay Trail,260.8,SCC,70.71
 San Jose,2618.7,SCC,28.59
 San Martin,35.3,SCC,31.41
 Santa Clara,348,SCC,35.26
-Saratoga,180,SCC,53.84
+Saratoga,180,SCC,90.16
-Stanford,82.53,SCC,99.66
+Stanford,82.53,SCC,100
 Seven Trees,40.9,SCC,34.06
 Spartan Keyes,64.3,SCC,36.59
-Sunnyvale,357,SCC,58.34
+Sunnyvale,357,SCC,68.34
 Willow Glen,81.6,SCC,36.23
 Willow Glen South,63.3,SCC,31.02
 #,,,
 #,,, Alameda County = ALA
 #,,,
-Alameda,206.7,ALA,12.41
+Alameda,206.7,ALA,27.36
 Albany,42.7,ALA,6.96
 Ashland,35.1,ALA,36.49
 Berkeley,260.3,ALA,7.84
@@ -99,6 +100,7 @@ Hayward,444.5,ALA,33.24
 Hayward Acres,3.5,ALA,43.53
 Livermore,448.52,ALA,5.46
 Newark,147,ALA,68.00
+Oakland,272.74,ALA,25.94
 Pleasonton,344.91,ALA,7.52
 San Leandro,230.6,ALA,28.18
 San Lorenzo,55.5,ALA,40.95
@@ -107,32 +109,38 @@ Union City,208.8,ALA,33.36
 #,,,
 #,,, SF County = SFC
 #,,,
-Aquatic Park Fort Mason,6.4,SFC,15.4
+Aquatic Park Fort Mason,4.41,SFC,34.04
 Ashbury Heights,3.7,SFC,13
 Balboa Terrace,3.4,SFC,18.2
-Central Waterfront,10.2,SFC,6
+Bayview,25.63,SFC,30.44
+Bret Harte,9.03,SFC,30.49
+Candkestick Point Sra,12.39,SFC,34.10
+Central Waterfront,11.67,SFC,35.99
 Clarendon Heights,6,SFC,14.2
 Cole Valley,1.7,SFC,18
 Cow Hollow,12,SFC,11.9
-Dogpatch,5.1,SFC,12.3
+Dogpatch,5.1,SFC,61.04
-Financial District,9.4,SFC,10.2
+Financial District,11.43,SFC,30.57
-Fisherman's Wharf,6.2,SFC,13.8
+Fisherman's Wharf,6.27,SFC,43.2
 Forest Hill,6.1,SFC,15.9
 Golden Gate Heights,17.8,SFC,10.7
 Golden Gate Park,40.8,SFC,29.4
+Hunters Point,8.35,SFC,35.38
+India Basin,6,SFC,57.57
 Lake Street,3.9,SFC,36.8
 Lincoln Park,4.5,SFC,39.6
-Little Hollywood,3.7,SFC,15.2
+Little Hollywood,3.7,SFC,30.74
-Mission Bay,13.8,SFC,8.6
+Mission Bay,16,SFC,30
-Northern Waterfront,5.6,SFC,15.5
+Northern Waterfront,6.15,SFC,41.48
 Pacific Heights,18,SFC,10.7
 Panhandle,7.3,SFC,20.6
 Polk Gulch,4,SFC,18.2
 Presidio Heights,6.5,SFC,21.6
 Presidio National Park,43.5,SFC,26.7
 Presidio Terrace,2.8,SFC,43.9
+Rincon Hill,3.49,SFC,46.83
 Seacliff,4.1,SFC,29.3
-South Beach,4.8,SFC,37.4
+South Beach,5.53,SFC,59.0
 Sutro Heights,7.1,SFC,13.2
 #,,,
 #,,, Far Away Places
@@ -161,16 +169,15 @@ Stinson Beach,11.2,MAR,32.9
 #,,,
 #,,, Counties and Bigger
 #,,,
-San Mateo County,2826.05,---,68.30
+San Mateo County,2826.05,---,78.80
-Santa Clara County,7690.85,---,36.18
+Santa Clara County,7690.85,---,38.70
-Alameda County,5802.83,---,18.93
+Alameda County,5802.83,---,25.06
-Marin County,2453.18,---,11.79
+Marin County,2453.18,---,11.97
-San Francisco County,1234.85,---,10.04
+San Francisco County,1234.85,---,11.60
 Napa County,1677.57,---,8.90
-Sonoma County,4955.3,---,7.55
+Sonoma County,4955.3,---,7.4
 Santa Cruz County,2700.36,---,10.58
-Contra Costa County,5905.56,---,4.12
+Contra Costa County,5905.56,---,4.14
-#,,,
+California,389799,---,2.216
-California,387818.83,---,0.01995
+USA,6459275,---,0.1406
-USA,6447239.43,---,0.001269
+Earth,46047996.8,---,0.020385
-Earth,45515258.11,---,0.0001865

ipynb/bikerides.tsv

@@ -1,8 +1,14 @@
 date	title	hours	miles	feet
 #				
+# 2025				
+#	
+Fri, 3/21/2025	Saratoga	5:51:36	71.96	2,241
+Fri, 2/28/2025	Colma etc	6:26:23	74.23	5,607
+#				
 # 2024				
 #	
-Sun, 10/27/2024	Morgan Hill + Newark	5:13:14	72.04	1,591
+Sun, 12/15/2024	Oakland	4:53:54	63.24	3,041
+Sun, 10/27/2024	Morgan Hill + Newark	5:13:14	72.04	5,669
 Fri, 10/11/2024	Pacific to Tunitas +	5:32:47	72.17	5,351
 Fri, 9/20/2024	Santa Rosa + Michael J Fox	6:16:40	93.33	3,628
 Fri, 9/6/2024	Yet Another Wandrer.earth Update + Canada with David	6:03:23	73.08	2,332

ipynb/bikeyears.tsv

@@ -1,10 +1,12 @@
-date	year	title	hours	miles	feet
+date	hours	miles	feet	rides
-all	2022	Yearly	398:48:00	4382	292575
+2024	511:41:00	6344	264838	393
-all	2021	Yearly	490:32:00	6064	196634
+2023	541:41:00	6316	243100	372
-all	2020	Yearly	438:53:00	5341	94777
+2022	532:56:00	6028	362323	349
-all	2019	Yearly	476:19:00	6016	149797
+2021	490:32:00	6064	196634	319
-all	2018	Yearly	475:56:00	6101	158642
+2020	438:53:00	5341	 94777	266
-all	2017	Yearly	567:20:00	7356	202096
+2019	476:19:00	6016	149797	438
-all	2016	Yearly	486:23:00	6339	201453
+2018	475:56:00	6101	158642	440
-all	2015	Yearly	419:57:00	5452	209859
+2017	567:20:00	7356	202096	367
-all	2014	Yearly	191:02:00	2469	118481
+2016	486:23:00	6339	201453	327
+2015	419:57:00	5452	209859	244
+2014	191:02:00	2469	118481	100